def test_unweighted_consistency_er(self):
net = models.er(10, [0.9, 0.1])
net = models.er(10, [0.1, 0.3])
net = models.er(10, [0.4, 0.6, 0.5, 0.3])
for i in range(1):
net = models.er(10, list(map(lambda x: random.random(), range(5))))
self.test_unweighted_consistency(net)
self.test_symmetric_aw(net)
net = pymnet.net.MultiplexNetwork([('categorical', 1.0)])
net[1, 9, 1, 1] = 1
net[2, 9, 1, 1] = 1
net[2, 4, 1, 1] = 1
net[5, 8, 0, 0] = 1
self.test_unweighted_consistency(net)
net = pymnet.net.MultiplexNetwork([('categorical', 1.0)])
#net.add_node(1)
net.add_layer(1)
net.add_layer(2)
net.add_layer(3)
net.add_layer(4)
net[1, 2, 1, 1] = 1
net[1, 3, 1, 1] = 1
net[2, 3, 1, 1] = 1
self.test_unweighted_consistency(net)
def test_comparison_random_relabel_mplex_single_aspect_fast(
self, backend="nx"):
"""Test that multiplex networks are isomorphic to randomly relabeled ones.
Single aspect version.
"""
nnodes = [4]
nlayers = [4]
ps = [0.5]
repeats = 5
for nodes in nnodes:
for layers in nlayers:
for p in ps:
for r in range(repeats):
net = models.er(nodes, layers * [p])
#node isomorphism
self._test_comparison_random_relabel(net, [0], backend)
#layer isomorphism
self._test_comparison_random_relabel(net, [1], backend)
#node-layer isomorphism
self._test_comparison_random_relabel(
net, [0, 1], backend)
def test_multiplex_erdosrenyi(self):
net = models.er(10, 0.5)
net2 = models.er(10, [0.4, 0.6])
#test that there are some links but not all
self.assertTrue(1 < len(list(net.edges)) < 10 * 9 / 2.)
self.assertTrue(1 < len(list(net2.A[1].edges)) < 10 * 9 + 10)
net3 = models.er(10, edges=[30, 45])
self.assertEqual(len(net3.A[0].edges), 30)
self.assertEqual(len(net3.A[1].edges), 45)
net4 = models.er([range(10), range(5, 15)], edges=[30, 45])
self.assertEqual(len(net4.A[0].edges), 30)
self.assertEqual(len(net4.A[1].edges), 45)
self.assertEqual(set(net4.A[0]), set(range(10)))
self.assertEqual(set(net4.A[1]), set(range(5, 15)))
net5 = models.er([range(10), range(5, 15)], edges=30)
self.assertEqual(len(net5.A[0].edges), 30)
self.assertEqual(len(net5.A[1].edges), 30)
self.assertEqual(set(net5.A[0]), set(range(10)))
self.assertEqual(set(net5.A[1]), set(range(5, 15)))
def test_mplex_fr_layout(self):
from pymnet import models
mplex=models.er(10,2*[0.2])
fig=visuals.draw(mplex,layout="fr")
fig.savefig(os.path.join(self.figdirpath,"mplex_er100_fr.png"))
def test_dijkstra_monoplex_compare(self):
n=models.er(100,0.1)
d,f=diagnostics.dijkstra(n,[1])
self.assertEqual(d, nx.shortest_path_length(n,1))