def test_bildegree():
deg = Network.SmallDirectedTestNetwork().bildegree()
deg_ref = np.array([0, 0, 0, 0, 0, 0], dtype=np.int16)
assert (deg == deg_ref).all()
net = Network.SmallTestNetwork()
assert (net.bildegree() == net.degree()).all()
def test_nsi_outdegree():
net = Network.SmallDirectedTestNetwork()
deg_ref = np.array([5.3, 5.9, 1.9, 3.8, 5.7, 4.])
assert np.allclose(net.nsi_outdegree(), deg_ref)
deg_ref = np.array([5.3, 5.9, 1.9, 3.8, 5.7, 4., 4.])
assert np.allclose(net.splitted_copy().nsi_outdegree(), deg_ref)
def test_nsi_indegree():
net = Network.SmallDirectedTestNetwork()
deg_ref = np.array([6.3, 5.3, 5.9, 3.6, 4., 2.5])
assert np.allclose(net.nsi_indegree(), deg_ref)
deg_ref = np.array([6.3, 5.3, 5.9, 3.6, 4., 2.5, 2.5])
assert np.allclose(net.splitted_copy().nsi_indegree(), deg_ref)
def test_nsi_local_midmotif_clustering():
net = Network.SmallDirectedTestNetwork()
res = net.nsi_local_midmotif_clustering()
exp = np.array([0.45372866, 0.51646946, 1., 1., 0.88815789, 1.])
assert np.allclose(res, exp)
res = net.splitted_copy(node=4).local_midmotif_clustering()
exp = np.array([0., 0., 0., 1., 0.8, 0., 0.8])
assert np.allclose(res, exp)
def test_nsi_local_outmotif_clustering():
net = Network.SmallDirectedTestNetwork()
res = net.nsi_local_outmotif_clustering()
exp = np.array(
[0.66998932, 0.66934789, 1., 0.75277008, 0.58387196, 0.765625])
assert np.allclose(res, exp)
res = net.splitted_copy(node=1).nsi_local_outmotif_clustering()
exp = np.array([
0.66998932, 0.66934789, 1., 0.75277008, 0.58387196, 0.765625,
0.66934789
])
assert np.allclose(res, exp)
def test_nsi_local_inmotif_clustering():
net = Network.SmallDirectedTestNetwork()
res = net.nsi_local_inmotif_clustering()
exp = np.array(
[0.52884858, 0.66998932, 0.66934789, 0.75694444, 0.755625, 1.])
assert np.allclose(res, exp)
res = net.splitted_copy(node=1).nsi_local_inmotif_clustering()
exp = np.array([
0.52884858, 0.66998932, 0.66934789, 0.75694444, 0.755625, 1.,
0.66998932
])
assert np.allclose(res, exp)
def test_nsi_local_cyclemotif_clustering():
net = Network.SmallDirectedTestNetwork()
res = net.nsi_local_cyclemotif_clustering()
exp = np.array(
[0.18448637, 0.20275024, 0.3220339, 0.32236842, 0.34385965, 0.625])
assert np.allclose(res, exp)
res = net.splitted_copy(node=1).nsi_local_cyclemotif_clustering()
exp = np.array([
0.18448637, 0.20275024, 0.3220339, 0.32236842, 0.34385965, 0.625,
0.20275024
])
assert np.allclose(res, exp)
def test_nsi_twinness():
net = Network.SmallDirectedTestNetwork()
res = net.nsi_twinness()
exp = np.array([[0.12931034, 0.45689655, 0., 0.31034483, 0., 0.],
[0., 0.15178571, 0.52678571, 0., 0.35714286, 0.],
[0., 0., 0.24358974, 0., 0., 0.],
[0., 0.33928571, 0., 0.28378378, 0., 0.],
[0.32758621, 0., 0.43298969, 0., 0.2371134, 0.],
[0.34482759, 0., 0., 0., 0., 0.38461538]])
assert np.allclose(res, exp)
res = net.splitted_copy().nsi_twinness()
exp = np.array([[0.12931034, 0.45689655, 0., 0.31034483, 0., 0., 0.],
[0., 0.15178571, 0.52678571, 0., 0.35714286, 0., 0.],
[0., 0., 0.24358974, 0., 0., 0., 0.],
[0., 0.33928571, 0., 0.28378378, 0., 0., 0.],
[0.32758621, 0., 0.43298969, 0., 0.2371134, 0., 0.],
[0.34482759, 0., 0., 0., 0., 0.38461538, 0.38461538],
[0.34482759, 0., 0., 0., 0., 0.38461538, 0.38461538]])
assert np.allclose(res, exp)
def testNSI():
"""
Consistency of nsi measures with splitted network copies
"""
dnw = Network.SmallDirectedTestNetwork()
nw = Network.SmallTestNetwork()
nsi_measures = [
"nsi_degree", "nsi_indegree", "nsi_outdegree", "nsi_closeness",
"nsi_harmonic_closeness", "nsi_exponential_closeness",
"nsi_arenas_betweenness", "nsi_spreading",
"nsi_local_cyclemotif_clustering", "nsi_local_midmotif_clustering",
"nsi_local_inmotif_clustering", "nsi_local_outmotif_clustering",
("nsi_degree", {
"key": "link_weights"
}), ("nsi_indegree", {
"key": "link_weights"
}), ("nsi_outdegree", {
"key": "link_weights"
}), ("nsi_local_cyclemotif_clustering", {
"key": "link_weights"
}), ("nsi_local_midmotif_clustering", {
"key": "link_weights"
}), ("nsi_local_inmotif_clustering", {
"key": "link_weights"
}), ("nsi_local_outmotif_clustering", {
"key": "link_weights"
})
]
nsi_undirected_measures = [
"nsi_local_clustering", "nsi_average_neighbors_degree",
"nsi_max_neighbors_degree", "nsi_eigenvector_centrality",
"nsi_local_clustering", "nsi_local_soffer_clustering",
"nsi_newman_betweenness"
]
compareNSI(dnw, nsi_measures)
compareNSI(nw, nsi_measures + nsi_undirected_measures)
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_outdegree():
deg = Network.SmallDirectedTestNetwork().outdegree()
deg_ref = np.array([2, 2, 0, 1, 2, 1])
assert (deg == deg_ref).all()
def test_indegree():
deg = Network.SmallDirectedTestNetwork().indegree()
deg_ref = np.array([2, 2, 2, 1, 1, 0])
assert (deg == deg_ref).all()