def test_dijkstra_multilayer_two_aspect(self):
n=net.MultilayerNetwork(aspects=2,directed=True)
n[1,'a',1][2,'a',2]=1
n[2,'a',2][3,'a',3]=1
n[3,'a',3][4,'a',4]=1
n[1,'b',1.5][3,'b',2.5]=1
n[1,'a',1][1,'b',1.25]=0.25
n[1,'b',1.5][1,'a',1.75]=0.25
n[3,'b',2.5][3,'a',2.75]=0.25
n[1,'a',1.75][1,'a',2]=0.25
n[1,'b',1.25][1,'b',1.5]=0.25
n[3,'a',2.75][3,'a',3]=0.25
d,f=diagnostics.dijkstra(n,[(1,'a',1)])
#for nl,dist in d.iteritems():
for nl in d:
dist=d[nl]
self.assertEqual(d[nl],nl[2]-1)
d,f=diagnostics.dijkstra_mlayer_prune(n,[(1,None,None)],aaspects=[1,2])
self.assertEqual(d[(1,)],0)
self.assertEqual(d[(2,)],1)
self.assertEqual(d[(3,)],1)
self.assertEqual(d[(4,)],2.5)
ftrue=net.MultilayerNetwork(aspects=2,fullyInterconnected=False,directed=True,noEdge=-1)
ftrue[1,'a',1][1,'a',1]=0; ftrue[1,'b',1.5][1,'b',1.5]=0;ftrue[1,'b',1.25][1,'b',1.25]=0; ftrue[1,'a',1.75][1,'a',1.75];ftrue[1,'a',2][1,'a',2]
def test_subnet_mplex_to_mlayer(self):
mplex = net.MultiplexNetwork([('categorical', 1.0)],
fullyInterconnected=False)
mplex[0, 1, 0] = 1
mplex[0, 2, 1] = 1
mlayer = transforms.subnet(mplex, [0, 1, 2], [0, 1],
newNet=net.MultilayerNetwork(
aspects=1, fullyInterconnected=False))
self.assertEqual(
set(mlayer.edges),
set([(0, 0, 0, 1, 1.0), (0, 1, 0, 0, 1), (0, 2, 1, 1, 1)]))
self.assertTrue(isinstance(mlayer, net.MultilayerNetwork))
self.assertRaises(
TypeError, lambda: transforms.subnet(mlayer, [0, 1, 2], [0, 1],
newNet=net.MultiplexNetwork([(
'categorical', 1.0)])))
mplex = net.MultiplexNetwork([('categorical', 1.0)],
fullyInterconnected=True)
mplex[0, 1, 0] = 1
mplex[0, 2, 1] = 1
mlayer = transforms.subnet(mplex, [0, 1, 2], [0, 1],
newNet=net.MultilayerNetwork(aspects=1))
self.assertEqual(
set(mlayer.edges),
set([(0, 0, 0, 1, 1.0), (0, 1, 0, 0, 1), (0, 2, 1, 1, 1),
(1, 1, 0, 1, 1.0), (2, 2, 0, 1, 1.0)]))
self.assertTrue(isinstance(mlayer, net.MultilayerNetwork))
def test_subnet_mlayer_example(self):
#monoplex
copynet = transforms.subnet(self.mlayer_example_monoplex, [1, 2, 3, 4])
self.assertEqual(copynet, self.mlayer_example_monoplex)
copynet = transforms.subnet(self.mlayer_example_monoplex, None)
self.assertEqual(copynet, self.mlayer_example_monoplex)
import copy
copynet2 = copy.deepcopy(self.mlayer_example_monoplex)
self.assertEqual(copynet2, self.mlayer_example_monoplex)
n = net.MultilayerNetwork(aspects=0, fullyInterconnected=True)
n[2, 3] = 1
n[3, 4] = 2
self.assertEqual(
n, transforms.subnet(self.mlayer_example_monoplex, [2, 3, 4]))
#1-aspect
copynet = transforms.subnet(self.mlayer_example_1d, [1, 2, 3, 4],
['A', 'B'])
self.assertEqual(copynet, self.mlayer_example_1d)
copynet = transforms.subnet(self.mlayer_example_1d, None, ['A', 'B'])
self.assertEqual(copynet, self.mlayer_example_1d)
copynet = transforms.subnet(self.mlayer_example_1d, None, None)
self.assertEqual(copynet, self.mlayer_example_1d)
copynet = transforms.subnet(self.mlayer_example_1d, [1, 2, 3, 4], None)
self.assertEqual(copynet, self.mlayer_example_1d)
import copy
copynet2 = copy.deepcopy(self.mlayer_example_1d)
self.assertEqual(copynet2, self.mlayer_example_1d)
n = net.MultilayerNetwork(aspects=1, fullyInterconnected=False)
n[2, 3, 'A', 'A'] = 1
self.assertEqual(
n, transforms.subnet(self.mlayer_example_1d, [2, 3, 4], ['A']))
#2-aspect
copynet = transforms.subnet(self.mlayer_example_2d, [1, 2, 3, 4],
['A', 'B'], ['X', 'Y'])
self.assertEqual(copynet, self.mlayer_example_2d)
import copy
copynet2 = copy.deepcopy(self.mlayer_example_2d)
self.assertEqual(copynet2, self.mlayer_example_2d)
n = net.MultilayerNetwork(aspects=2, fullyInterconnected=False)
n[3, 4, 'B', 'B', 'X', 'X'] = 1
n.add_node(2, layer=('A', 'X'))
n.add_node(3, layer=('A', 'X'))
self.assertEqual(
n,
transforms.subnet(self.mlayer_example_2d, [2, 3, 4], None,
['X', 'dummy']))
def test_pickle(self):
import pickle
n=net.MultilayerNetwork(aspects=1)
n[1,2,3,4]=1
self.assertEqual(pickle.loads(pickle.dumps(n)),n)
n=net.MultilayerNetwork(aspects=1,directed=True)
n[1,2,3,4]=1
self.assertEqual(pickle.loads(pickle.dumps(n)),n)
n=net.MultiplexNetwork(couplings=[('categorical',1)])
n[1,2,3,3]=1
self.assertEqual(pickle.loads(pickle.dumps(n)),n)
n=net.MultiplexNetwork(couplings=[('categorical',1)],directed=True)
n[1,2,3,3]=1
self.assertEqual(pickle.loads(pickle.dumps(n)),n)
def test_autowrapping(self):
n=net.MultilayerNetwork(aspects=0)
n[1,2]=1
n[2,3]=1
n[4,5]=1
self.assertEqual(set(map(frozenset,nxwrap.connected_components(n))),set([frozenset([1, 2, 3]), frozenset([4, 5])]))
def test_monoplex_erdosrenyi(self):
size = 10
full = net.MultilayerNetwork(aspects=0)
models.single_layer_er(full,
range(10, 10 + size),
p=None,
edges=int((size * (size - 1)) / 2))
for i in full:
for j in full:
if i != j:
self.assertEqual(full[i, j], 1)
self.assertEqual(len(full.edges), int((size * (size - 1)) / 2))
net2 = net.MultilayerNetwork(aspects=0)
models.single_layer_er(net2, range(10), p=None, edges=30)
self.assertEqual(len(net2.edges), 30)
def test_monoplex_tuples_nxversion1(self):
n = net.MultilayerNetwork(aspects=0)
nnx = nxwrap.MonoplexGraphNetworkxView(n)
nnx.add_node((1, 'a'))
nnx.add_nodes_from([(2, 'a'), (3, 'a'), (4, 'a'), (5, 'a')])
nnx.add_edge((1, 'a'), (2, 'a'))
nnx[(2, 'a')][(3, 'a')] = 3
nnx.add_edge((4, 'a'), (5, 'a'))
nnx[(4, 'a')][(5, 'a')] = 1
nnx[(4, 'a')][(5, 'a')] = 4
self.assertEqual(nnx[(1, 'a')][(2, 'a')]['weight'], 1)
self.assertEqual(nnx[(2, 'a')][(3, 'a')]['weight'], 3)
self.assertEqual(nnx[(4, 'a')][(5, 'a')]['weight'], 4)
self.assertEqual(nnx[(2, 'a')][(1, 'a')]['weight'], 1)
self.assertEqual(nnx[(3, 'a')][(2, 'a')]['weight'], 3)
self.assertEqual(nnx[(5, 'a')][(4, 'a')]['weight'], 4)
self.assertEqual(
set(map(frozenset, networkx.connected_components(nnx))),
set([
frozenset([(1, 'a'), (2, 'a'), (3, 'a')]),
frozenset([(4, 'a'), (5, 'a')])
]))
def test_monoplex_basics_writing_pymnet(self):
n = net.MultilayerNetwork(aspects=0)
n[1, 2] = 1
nnx = nxwrap.MonoplexGraphNetworkxView(n)
n[2, 3] = 3
n[4, 5] = 4
self.test_monoplex_basics(nnx)
def test_unweighted_flat_triangle(self):
n = net.MultilayerNetwork(aspects=0)
n[1, 2] = 1
n[2, 3] = 1
n[3, 1] = 1
self.assertEqual(cc.lcc(n, 1), 1.0)
self.assertEqual(cc.cc_zhang(n, 1), 1.0)
self.assertEqual(cc.cc_onnela(n, 1), 1.0)
self.assertEqual(cc.cc_barrat(n, 1), 1.0)
def test_unweighted_mplex_simple(self):
n = net.MultiplexNetwork([('categorical', 1.0)])
n[1, 2, 1] = 1
n[1, 3, 1] = 1
n[2, 3, 1] = 1
n[1, 2, 2] = 1
n[1, 3, 2] = 1
n[1, 4, 2] = 1
n[3, 4, 2] = 1
n[1, 2, 3] = 1
n[1, 3, 3] = 1
n[1, 4, 3] = 1
n[2, 4, 3] = 1
an = net.MultilayerNetwork(aspects=0)
an[1, 2] = 3
an[1, 3] = 3
an[1, 4] = 2
an[2, 3] = 1
an[3, 4] = 1
an[2, 4] = 1
for i in range(4):
self.assertEqual(cc.cc_barrett(n, i, an),
cc.cc_barrett_explicit(n, i))
self.assertEqual(cc.lcc_brodka(n, 1, anet=None, threshold=1), 2. / 3.)
self.assertEqual(cc.lcc_brodka(n, 2, anet=None, threshold=1), 4. / 3.)
self.assertEqual(cc.lcc_brodka(n, 3, anet=None, threshold=1), 4. / 3.)
self.assertEqual(cc.lcc_brodka(n, 4, anet=None, threshold=1), 14. / 9.)
self.assertEqual(cc.lcc_brodka(n, 1, anet=an, threshold=1), 2. / 3.)
self.assertEqual(cc.lcc_brodka(n, 2, anet=an, threshold=1), 4. / 3.)
self.assertEqual(cc.lcc_brodka(n, 3, anet=an, threshold=1), 4. / 3.)
self.assertEqual(cc.lcc_brodka(n, 4, anet=an, threshold=1), 14. / 9.)
self.assertEqual(cc.lcc_brodka(n, 1, anet=None, threshold=2), 2. / 3.)
self.assertEqual(cc.lcc_brodka(n, 2, anet=None, threshold=2), 0.)
self.assertEqual(cc.lcc_brodka(n, 3, anet=None, threshold=2), 0.)
self.assertEqual(cc.lcc_brodka(n, 4, anet=None, threshold=2), 0.)
self.assertEqual(cc.lcc_brodka(n, 1, anet=an, threshold=2), 2. / 3.)
self.assertEqual(cc.lcc_brodka(n, 2, anet=an, threshold=2), 0.)
self.assertEqual(cc.lcc_brodka(n, 3, anet=an, threshold=2), 0.)
self.assertEqual(cc.lcc_brodka(n, 4, anet=an, threshold=2), 0.)
self.assertEqual(cc.lcc_brodka(n, 1, anet=None, threshold=3), 1. / 3.)
self.assertEqual(cc.lcc_brodka(n, 2, anet=None, threshold=3), 0.)
self.assertEqual(cc.lcc_brodka(n, 3, anet=None, threshold=3), 0.)
self.assertEqual(cc.lcc_brodka(n, 4, anet=None, threshold=3), 0.)
self.assertEqual(cc.lcc_brodka(n, 1, anet=an, threshold=3), 1. / 3.)
self.assertEqual(cc.lcc_brodka(n, 2, anet=an, threshold=3), 0.)
self.assertEqual(cc.lcc_brodka(n, 3, anet=an, threshold=3), 0.)
self.assertEqual(cc.lcc_brodka(n, 4, anet=an, threshold=3), 0.)
def test_mst(self):
n = net.MultilayerNetwork(aspects=0)
n[1, 2] = 1
n[2, 3] = 1
n[1, 3] = 10
mst = nxwrap.minimum_spanning_tree(n)
self.assertEqual(mst[1, 2], 1)
self.assertEqual(mst[2, 3], 1)
self.assertEqual(mst[1, 3], mst.noEdge)
def clique_net(nodes, levels):
n = net.MultiplexNetwork([('categorical', 1.0)], directed=False)
for level in range(levels):
for i, j in itertools.combinations(range(nodes), 2):
n[i, j, level] = 1
an = net.MultilayerNetwork(aspects=0, directed=False)
for i, j in itertools.combinations(range(nodes), 2):
an[i, j] = levels
return n, an
def test_dijkstra_monoplex(self):
n=net.MultilayerNetwork(aspects=0)
n[1,2]=1
n[1,3]=1
n[2,3]=1
n[2,4]=1
n[3,4]=1
n[3,5]=1
d,f=diagnostics.dijkstra(n,[1])
ftrue=net.MultilayerNetwork(aspects=0,fullyInterconnected=False,directed=True,noEdge=-1)
ftrue[1,1]=0;ftrue[1,2]=1;ftrue[1,3]=1;ftrue[2,4]=1;ftrue[3,4]=1;ftrue[3,5]=1
self.assertEqual(d,{1:0,2:1,3:1,4:2,5:2})
self.assertEqual(f,ftrue)
d,f=diagnostics.dijkstra(n,[1,2])
ftrue=net.MultilayerNetwork(aspects=0,fullyInterconnected=False,directed=True,noEdge=-1)
ftrue[1,1]=0;ftrue[2,2]=0;ftrue[1,3]=1;ftrue[2,3]=1;ftrue[2,4]=1;ftrue[3,5]=1
self.assertEqual(d,{1:0,2:0,3:1,4:1,5:2})
self.assertEqual(f,ftrue)
def test_monoplex_basics_writing_nx_nxversion1(self):
n = net.MultilayerNetwork(aspects=0)
nnx = nxwrap.MonoplexGraphNetworkxView(n)
nnx.add_node(1)
nnx.add_nodes_from([2, 3, 4, 5])
nnx.add_edge(1, 2)
nnx[2][3] = 3
nnx.add_edge(4, 5)
nnx[4][5] = 1
nnx[4][5] = 4
self.test_monoplex_basics(nnx)
def test_monoplex_basics_writing_nx(self):
n = net.MultilayerNetwork(aspects=0)
nnx = nxwrap.MonoplexGraphNetworkxView(n)
nnx.add_node(1)
nnx.add_nodes_from([2, 3, 4, 5])
nnx.add_edge(1, 2)
nnx.add_edge(2, 3, weight=3)
nnx.add_edge(4, 5)
nnx.add_edge(4, 5, weight=1)
nnx.add_edge(4, 5, weight=4)
self.test_monoplex_basics(nnx)
def test_subnet_different_interconnectivities(self):
fully_interc = net.MultilayerNetwork(aspects=1,
fullyInterconnected=True)
fully_interc[1, 'X'][1, 'Y'] = 1
fully_interc[1, 'X'][2, 'X'] = 1
other_fully_interc = transforms.subnet(
fully_interc,
fully_interc.get_layers(aspect=0),
fully_interc.get_layers(aspect=1),
newNet=net.MultilayerNetwork(aspects=1, fullyInterconnected=True))
self.assertEqual(set(other_fully_interc.iter_node_layers()),
set([(1, 'X'), (1, 'Y'), (2, 'X'), (2, 'Y')]))
fully_interc = net.MultilayerNetwork(aspects=1,
fullyInterconnected=True)
fully_interc[1, 'X'][1, 'Y'] = 1
fully_interc[1, 'X'][2, 'X'] = 1
non_fully_interc = transforms.subnet(fully_interc,
fully_interc.get_layers(aspect=0),
fully_interc.get_layers(aspect=1),
newNet=net.MultilayerNetwork(
aspects=1,
fullyInterconnected=False))
self.assertEqual(set(non_fully_interc.iter_node_layers()),
set([(1, 'X'), (1, 'Y'), (2, 'X'), (2, 'Y')]))
non_fully_interc = net.MultilayerNetwork(aspects=1,
fullyInterconnected=False)
non_fully_interc[1, 'X'][1, 'Y'] = 1
non_fully_interc[1, 'X'][2, 'X'] = 1
non_fully_interc.add_node(2, layer='Y')
other_non_fully_interc = transforms.subnet(
non_fully_interc,
non_fully_interc.get_layers(aspect=0),
non_fully_interc.get_layers(aspect=1),
newNet=net.MultilayerNetwork(aspects=1, fullyInterconnected=False))
self.assertEqual(set(other_non_fully_interc.iter_node_layers()),
set([(1, 'X'), (1, 'Y'), (2, 'X'), (2, 'Y')]))
non_fully_interc = net.MultilayerNetwork(aspects=1,
fullyInterconnected=False)
non_fully_interc[1, 'X'][1, 'Y'] = 1
non_fully_interc[1, 'X'][2, 'X'] = 1
self.assertRaises(
TypeError, lambda: transforms.subnet(
non_fully_interc,
non_fully_interc.get_layers(aspect=0),
non_fully_interc.get_layers(aspect=1),
newNet=net.MultilayerNetwork(aspects=1,
fullyInterconnected=True)))
def test_aggregate_2dim_mlayer_interlayeredges(self):
n = net.MultilayerNetwork(aspects=2, fullyInterconnected=False)
n[1, 'a', 'x'][2, 'b', 'y'] = 3
n[1, 'c', 'x'][2, 'd', 'y'] = 1
n[1, 'a', 'x'][2, 'b', 'x'] = 1
an1 = transforms.aggregate(n, 1)
self.assertEqual(set(an1.edges),
set([(1, 2, 'x', 'y', 4), (1, 2, 'x', 'x', 1)]))
an2 = transforms.aggregate(n, 2)
self.assertEqual(set(an2.edges),
set([(1, 2, 'a', 'b', 4), (1, 2, 'c', 'd', 1)]))
an3 = transforms.aggregate(n, (1, 2))
self.assertEqual(set(an3.edges), set([(1, 2, 5)]))
self.assertEqual(an3, transforms.aggregate(an1, 1))
self.assertEqual(an3, transforms.aggregate(an2, 1))
def test_aggregate_1dim_mlayer_nonglobal_nodes(self):
def test_net(n):
n[1, 2, 'a'] = 1
n.add_layer('b')
n.add_node(3, layer=('a'))
n.add_node(4, layer=('c'))
an = transforms.aggregate(n, 1)
self.assertEqual(set(an), set([1, 2, 3, 4]))
self.assertEqual(an[1, 2], 1)
self.assertEqual(an.aspects, 0)
mln = net.MultilayerNetwork(aspects=1, fullyInterconnected=False)
test_net(mln)
mpn = net.MultiplexNetwork([('categorical', 1.0)],
fullyInterconnected=False)
test_net(mpn)
def test_aggregate_2dim_mlayer_nonglobal_nodes(self):
def test_net(n):
n[1, 2, 'a', 'x'] = 3
n.add_layer('b', 1)
n.add_layer('y', 2)
n.add_node(3, layer=('a', 'x'))
n.add_node(4, layer=('c', 'z'))
an1 = transforms.aggregate(n, 1)
self.assertEqual(set(an1), set([1, 2, 3, 4]))
self.assertEqual(set(an1.iter_node_layers()),
set([(1, 'x'), (2, 'x'), (3, 'x'), (4, 'z')]))
an2 = transforms.aggregate(n, 2)
self.assertEqual(set(an2), set([1, 2, 3, 4]))
self.assertEqual(set(an2.iter_node_layers()),
set([(1, 'a'), (2, 'a'), (3, 'a'), (4, 'c')]))
mln = net.MultilayerNetwork(aspects=2, fullyInterconnected=False)
test_net(mln)
mpn = net.MultiplexNetwork([('categorical', 1.0),
('categorical', 1.0)],
fullyInterconnected=False)
test_net(mpn)
def test_comparison_simple_mlayer(self, backend="nx"):
"""This tests that the simple example given in the article M. Kivela & M.A. Porter
"Isomorphisms in Multilayer Networks", Figure 2, works.
"""
neta = net.MultilayerNetwork(aspects=1, fullyInterconnected=False)
neta.add_layer("X")
neta.add_layer("Y")
neta[1, "X"][2, "X"] = 1
neta[3, "X"][3, "Y"] = 1
neta[2, "Y"][3, "Y"] = 1
netb = net.MultilayerNetwork(aspects=1, fullyInterconnected=False)
netb.add_layer("X")
netb.add_layer("Y")
netb[2, "X"][3, "X"] = 1
netb[1, "X"][1, "Y"] = 1
netb[1, "Y"][3, "Y"] = 1
netc = net.MultilayerNetwork(aspects=1, fullyInterconnected=False)
netc.add_layer("X")
netc.add_layer("Y")
netc[1, "Y"][2, "Y"] = 1
netc[3, "Y"][3, "X"] = 1
netc[2, "X"][3, "X"] = 1
netd = net.MultilayerNetwork(aspects=1, fullyInterconnected=False)
netd.add_layer("X")
netd.add_layer("Y")
netd[2, "Y"][3, "Y"] = 1
netd[1, "Y"][1, "X"] = 1
netd[3, "X"][1, "X"] = 1
#Building an auxiliary graph, but no testing yet
isomorphisms.nxbackend.AuxiliaryGraphBuilderNX(neta, [0])
isomorphisms.nxbackend.AuxiliaryGraphBuilderNX(netc, [0])
#Network a Isomorphic to itself
self.assertTrue(
self.is_isomorphic_multimethod(neta,
neta,
allowed_aspects=[],
backend=backend))
self.assertTrue(
self.is_isomorphic_multimethod(neta,
neta,
allowed_aspects=[0],
backend=backend))
self.assertTrue(
self.is_isomorphic_multimethod(neta,
neta,
allowed_aspects=[1],
backend=backend))
self.assertTrue(
self.is_isomorphic_multimethod(neta,
neta,
allowed_aspects=[0, 1],
backend=backend))
#Network a vertex-isomorphic to network b
self.assertTrue(
self.is_isomorphic_multimethod(neta,
netb,
allowed_aspects=[0],
backend=backend))
#Network a layer-isomorphic to network c
self.assertTrue(
self.is_isomorphic_multimethod(neta,
netc,
allowed_aspects=[1],
backend=backend))
#Network a vertex-layer-isomorphic to network d
self.assertTrue(
self.is_isomorphic_multimethod(neta,
netd,
allowed_aspects=[0, 1],
backend=backend))
#Network a is not vertex-isomorphic to network c or d
self.assertFalse(
self.is_isomorphic_multimethod(neta,
netc,
allowed_aspects=[0],
backend=backend))
self.assertFalse(
self.is_isomorphic_multimethod(neta,
netd,
allowed_aspects=[0],
backend=backend))
#Network a is not layer-isomorphic to network b or d
self.assertFalse(
self.is_isomorphic_multimethod(neta,
netb,
allowed_aspects=[1],
backend=backend))
self.assertFalse(
self.is_isomorphic_multimethod(neta,
netd,
allowed_aspects=[1],
backend=backend))
#Network a is vertex-layer-isomorphic to network b or c
self.assertTrue(
self.is_isomorphic_multimethod(neta,
netb,
allowed_aspects=[0, 1],
backend=backend))
self.assertTrue(
self.is_isomorphic_multimethod(neta,
netc,
allowed_aspects=[0, 1],
backend=backend))
def test_directed_unweighted(self):
"""The 3-layer multiplex networks example from Brodka et al. 'Analysis of Neighborhoods of in Multi-layered
Dynamic Social Networks', 2012"""
n = net.MultiplexNetwork(['categorical'], directed=True)
n['x', 'z', 1] = 1
n['x', 'y', 1] = 1
n['y', 'x', 1] = 1
n['y', 'z', 1] = 1
n['z', 'x', 1] = 1
n['z', 't', 1] = 1
n['u', 'x', 1] = 1
n['u', 'z', 1] = 1
n['u', 'v', 1] = 1
n['v', 'u', 1] = 1
n['v', 't', 1] = 1
n['t', 'z', 1] = 1
n['t', 'v', 1] = 1
n['x', 'u', 2] = 1
n['x', 'z', 2] = 1
n['x', 'v', 2] = 1
n['x', 'y', 2] = 1
n['z', 'x', 2] = 1
n['u', 'v', 2] = 1
n['v', 'u', 2] = 1
n['v', 'x', 2] = 1
n['v', 'y', 2] = 1
n['x', 'u', 3] = 1
n['x', 'z', 3] = 1
n['x', 'v', 3] = 1
n['x', 'y', 3] = 1
n['y', 'z', 3] = 1
n['y', 'v', 3] = 1
n['z', 'x', 3] = 1
n['z', 'y', 3] = 1
n['z', 't', 3] = 1
n['u', 'x', 3] = 1
n['v', 'x', 3] = 1
n['v', 't', 3] = 1
n['t', 'z', 3] = 1
n['t', 'v', 3] = 1
an = net.MultilayerNetwork(aspects=0, directed=True)
an['x', 'z'] = 3
an['x', 'u'] = 2
an['x', 'v'] = 2
an['x', 'y'] = 3
an['y', 'x'] = 1
an['y', 'z'] = 2
an['y', 'v'] = 1
an['z', 'x'] = 3
an['z', 'y'] = 1
an['z', 't'] = 2
an['u', 'x'] = 2
an['u', 'z'] = 1
an['u', 'v'] = 2
an['v', 'x'] = 2
an['v', 'y'] = 1
an['v', 'u'] = 2
an['v', 't'] = 2
an['t', 'z'] = 2
an['t', 'v'] = 2
self.assertEqual(cc.lcc_brodka(n, 't', threshold=1), 0.0)
self.assertEqual(cc.lcc_brodka(n, 't', threshold=2), 0.0)
self.assertEqual(cc.lcc_brodka(n, 't', threshold=3), 0.0)
self.assertEqual(cc.lcc_brodka(n, 'z', threshold=1), 2. / 3.)
self.assertEqual(cc.lcc_brodka(n, 'z', threshold=2), 4. / 9.)
self.assertEqual(cc.lcc_brodka(n, 'z', threshold=3), 0.0)
self.assertEqual(cc.lcc_brodka(n, 't', anet=an, threshold=1), 0.0)
self.assertEqual(cc.lcc_brodka(n, 't', anet=an, threshold=2), 0.0)
self.assertEqual(cc.lcc_brodka(n, 't', anet=an, threshold=3), 0.0)
self.assertEqual(cc.lcc_brodka(n, 'z', anet=an, threshold=1), 2. / 3.)
self.assertEqual(cc.lcc_brodka(n, 'z', anet=an, threshold=2), 4. / 9.)
self.assertEqual(cc.lcc_brodka(n, 'z', anet=an, threshold=3), 0.0)
def test_monoplex_density_degs_mnet(self):
n=net.MultilayerNetwork(aspects=0,directed=False)
dn=net.MultilayerNetwork(aspects=0,directed=True)
self.test_monoplex_density_degs(n,dn)
def test_multilayer_degs_multilayernet(self):
n=net.MultilayerNetwork(aspects=1,fullyInterconnected=False)
n[1,2,3,4]=1
n[1,2,3,3]=1
self.assertEqual(diagnostics.degs(n,degstype="distribution"),{1:2,2:1})
self.assertEqual(diagnostics.degs(n,degstype="nodes"),{(1,3):2,(2,3):1,(2,4):1})
def setUp(self):
n = net.MultiplexNetwork([('categorical', 1.0)])
n[1, 2, 1] = 1
n[1, 3, 1] = 1
n[2, 3, 1] = 1
n[1, 2, 2] = 1
n[1, 3, 2] = 1
n[1, 4, 2] = 1
n[3, 4, 2] = 1
n[1, 2, 3] = 1
n[1, 3, 3] = 1
n[1, 4, 3] = 1
n[2, 4, 3] = 1
self.mplex_simple = n
n = net.MultiplexNetwork([('categorical', 1.0)],
fullyInterconnected=False)
n[1, 2, 1] = 1
n[1, 3, 1] = 1
n[2, 3, 1] = 1
n[1, 2, 2] = 1
n[1, 3, 2] = 1
n[1, 4, 2] = 1
n[3, 4, 2] = 1
n[1, 2, 3] = 1
n[1, 3, 3] = 1
n[1, 4, 3] = 1
n[2, 4, 3] = 1
self.mplex_nonaligned_simple = n
#The 2-aspect example network for the review article
n = net.MultilayerNetwork(aspects=2, fullyInterconnected=False)
n[1, 2, 'A', 'A', 'X', 'X'] = 1
n[2, 3, 'A', 'A', 'Y', 'Y'] = 1
n[1, 3, 'B', 'B', 'X', 'X'] = 1
n[1, 4, 'B', 'B', 'X', 'X'] = 1
n[3, 4, 'B', 'B', 'X', 'X'] = 1
n[1, 1, 'A', 'B', 'X', 'X'] = 1
n[1, 4, 'A', 'B', 'X', 'X'] = 1
n[1, 1, 'B', 'B', 'X', 'Y'] = 1
n[3, 3, 'A', 'A', 'X', 'Y'] = 1
n[3, 4, 'A', 'B', 'X', 'Y'] = 1
self.mlayer_example_2d = n
n = net.MultilayerNetwork(aspects=1, fullyInterconnected=False)
n[1, 2, 'A', 'A'] = 1
n[2, 3, 'A', 'A'] = 1
n[1, 3, 'B', 'B'] = 1
n[1, 4, 'B', 'B'] = 1
n[3, 4, 'B', 'B'] = 1
n[1, 1, 'A', 'B'] = 1
n[1, 4, 'A', 'B'] = 1
n[3, 4, 'A', 'B'] = 1
self.mlayer_example_1d = n
n = net.MultilayerNetwork(aspects=0, fullyInterconnected=True)
n[1, 2] = 1
n[2, 3] = 1
n[1, 3] = 1
n[1, 4] = 2
n[3, 4] = 2
self.mlayer_example_monoplex = n
def setUp(self):
#create directory for figs if it doesn't exist
self.figdirpath=os.path.join(os.path.dirname(os.path.realpath(__file__)),self.figdirname)
if not os.path.exists(self.figdirpath):
os.mkdir(self.figdirpath)
n=net.MultiplexNetwork([('categorical',1.0)])
n[1,2,1]=1
n[1,3,1]=1
n[2,3,1]=1
n[1,2,2]=1
n[1,3,2]=1
n[1,4,2]=1
n[3,4,2]=1
n[1,2,3]=1
n[1,3,3]=1
n[1,4,3]=1
n[2,4,3]=1
self.mplex_simple=n
n=net.MultiplexNetwork([('categorical',1.0)],fullyInterconnected=False)
n[1,2,1]=1
n[1,3,1]=1
n[2,3,1]=1
n[1,2,2]=1
n[1,3,2]=1
n[1,4,2]=1
n[3,4,2]=1
n[1,2,3]=1
n[1,3,3]=1
n[1,4,3]=1
n[2,4,3]=1
self.mplex_nonaligned_simple=n
#The 2-aspect example network for the review article
n=net.MultilayerNetwork(aspects=2,fullyInterconnected=False)
n[1,2,'A','A','X','X']=1
n[2,3,'A','A','Y','Y']=1
n[1,3,'B','B','X','X']=1
n[1,4,'B','B','X','X']=1
n[3,4,'B','B','X','X']=1
n[1,1,'A','B','X','X']=1
n[1,4,'A','B','X','X']=1
n[1,1,'B','B','X','Y']=1
n[3,3,'A','A','X','Y']=1
n[3,4,'A','B','X','Y']=1
self.mlayer_example_2d=n
n=net.MultilayerNetwork(aspects=1,fullyInterconnected=False)
n[1,2,'A','A']=1
n[2,3,'A','A']=1
n[1,3,'B','B']=1
n[1,4,'B','B']=1
n[3,4,'B','B']=1
n[1,1,'A','B']=1
n[1,4,'A','B']=1
n[3,4,'A','B']=1
self.mlayer_example_1d=n
#Non-aligned network for testing multilayer coordinates
n=net.MultilayerNetwork(aspects=1,fullyInterconnected=False)
n.add_node(0,'a')
n.add_node(1,'b')
n.add_node(2,'b')
n.add_node(3,'b')
n.add_node(4,'b')
n.add_node(5,'b')
n[1,2,'b','b']=1
n[2,3,'b','b']=1
n[3,4,'b','b']=1
n[4,1,'b','b']=1
n[0,5,'a','b']=1
n[1,5,'b','b']=1
n[2,5,'b','b']=1
n[3,5,'b','b']=1
n[4,5,'b','b']=1
self.mlayer_nonaligned_aligntest=n
#Second non-aligned network for testing multilayer coordinates
n=net.MultilayerNetwork(aspects=1,fullyInterconnected=False)
n.add_node(0,'a')
n.add_node(1,'b')
n.add_node(2,'b')
n.add_node(3,'b')
n.add_node(4,'b')
n[1,2,'b','b']=1
n[2,3,'b','b']=1
n[3,4,'b','b']=1
n[4,1,'b','b']=1
n[0,0,'a','b']=1
n[1,0,'b','b']=1
n[2,0,'b','b']=1
n[3,0,'b','b']=1
n[4,0,'b','b']=1
self.mlayer_nonaligned_aligntest2=n
n=net.MultilayerNetwork(aspects=0,fullyInterconnected=True)
n[1,2]=1
n[2,3]=1
n[1,3]=1
n[1,4]=2
n[3,4]=2
self.mlayer_example_monoplex=n
