def setUp(self):
deg = [3, 2, 2, 1, 0]
self.G = havel_hakimi_graph(deg)
self.OI = numpy.array([[-1, -1, -1, 0], [1, 0, 0, -1], [0, 1, 0, 1],
[0, 0, 1, 0], [0, 0, 0, 0]])
self.A = numpy.array([[0, 1, 1, 1, 0], [1, 0, 1, 0,
0], [1, 1, 0, 0, 0],
[1, 0, 0, 0, 0], [0, 0, 0, 0, 0]])
self.WG = havel_hakimi_graph(deg)
self.WG.add_edges_from((u, v, {
'weight': 0.5,
'other': 0.3
}) for (u, v) in self.G.edges())
self.WA = numpy.array([[0, 0.5, 0.5, 0.5, 0], [0.5, 0, 0.5, 0, 0],
[0.5, 0.5, 0, 0, 0], [0.5, 0, 0, 0, 0],
[0, 0, 0, 0, 0]])
self.MG = nx.MultiGraph(self.G)
self.MG2 = self.MG.copy()
self.MG2.add_edge(0, 1)
self.MG2A = numpy.array([[0, 2, 1, 1, 0], [2, 0, 1, 0, 0],
[1, 1, 0, 0, 0], [1, 0, 0, 0, 0],
[0, 0, 0, 0, 0]])
self.MGOI = numpy.array([[-1, -1, -1, -1, 0], [1, 1, 0, 0, -1],
[0, 0, 1, 0, 1], [0, 0, 0, 1, 0],
[0, 0, 0, 0, 0]])
self.no_edges_G = nx.Graph([(1, 2), (3, 2, {'weight': 8})])
self.no_edges_A = numpy.array([[0, 0], [0, 0]])
def setup_class(cls):
deg = [3, 2, 2, 1, 0]
cls.G = havel_hakimi_graph(deg)
# fmt: off
cls.OI = np.array([[-1, -1, -1, 0], [1, 0, 0, -1], [0, 1, 0, 1],
[0, 0, 1, 0], [0, 0, 0, 0]])
cls.A = np.array([[0, 1, 1, 1, 0], [1, 0, 1, 0, 0], [1, 1, 0, 0, 0],
[1, 0, 0, 0, 0], [0, 0, 0, 0, 0]])
# fmt: on
cls.WG = havel_hakimi_graph(deg)
cls.WG.add_edges_from((u, v, {
"weight": 0.5,
"other": 0.3
}) for (u, v) in cls.G.edges())
# fmt: off
cls.WA = np.array([[0, 0.5, 0.5, 0.5, 0], [0.5, 0, 0.5, 0, 0],
[0.5, 0.5, 0, 0, 0], [0.5, 0, 0, 0, 0],
[0, 0, 0, 0, 0]])
# fmt: on
cls.MG = nx.MultiGraph(cls.G)
cls.MG2 = cls.MG.copy()
cls.MG2.add_edge(0, 1)
# fmt: off
cls.MG2A = np.array([[0, 2, 1, 1, 0], [2, 0, 1, 0, 0], [1, 1, 0, 0, 0],
[1, 0, 0, 0, 0], [0, 0, 0, 0, 0]])
cls.MGOI = np.array([[-1, -1, -1, -1, 0], [1, 1, 0, 0, -1],
[0, 0, 1, 0, 1], [0, 0, 0, 1, 0], [0, 0, 0, 0,
0]])
# fmt: on
cls.no_edges_G = nx.Graph([(1, 2), (3, 2, {"weight": 8})])
cls.no_edges_A = np.array([[0, 0], [0, 0]])
def setUp(self):
deg=[3,2,2,1,0]
self.G=havel_hakimi_graph(deg)
self.OI=numpy.array([[-1, -1, -1, 0],
[1, 0, 0, -1],
[0, 1, 0, 1],
[0, 0, 1, 0],
[0, 0, 0, 0]])
self.A=numpy.array([[0, 1, 1, 1, 0],
[1, 0, 1, 0, 0],
[1, 1, 0, 0, 0],
[1, 0, 0, 0, 0],
[0, 0, 0, 0, 0]])
self.WG=nx.Graph( (u,v,{'weight':0.5,'other':0.3})
for (u,v) in self.G.edges_iter() )
self.WG.add_node(4)
self.WA=numpy.array([[0 , 0.5, 0.5, 0.5, 0],
[0.5, 0 , 0.5, 0 , 0],
[0.5, 0.5, 0 , 0 , 0],
[0.5, 0 , 0 , 0 , 0],
[0 , 0 , 0 , 0 , 0]])
self.MG=nx.MultiGraph(self.G)
self.MG2=self.MG.copy()
self.MG2.add_edge(0,1)
self.MG2A=numpy.array([[0, 2, 1, 1, 0],
[2, 0, 1, 0, 0],
[1, 1, 0, 0, 0],
[1, 0, 0, 0, 0],
[0, 0, 0, 0, 0]])
self.MGOI=numpy.array([[-1, -1, -1, -1, 0],
[1, 1, 0, 0, -1],
[0, 0, 1, 0, 1],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 0]])
def setUp(self):
deg=[3,2,2,1,0]
self.G=havel_hakimi_graph(deg)
self.P=nx.path_graph(3)
self.WG=nx.Graph( (u,v,{'weight':0.5,'other':0.3})
for (u,v) in self.G.edges_iter() )
self.WG.add_node(4)
def setUp(self):
deg = [3, 2, 2, 1, 0]
self.G = havel_hakimi_graph(deg)
self.P = nx.path_graph(3)
self.A = numpy.array([[0, 1, 1, 1, 0], [1, 0, 1, 0,
0], [1, 1, 0, 0, 0],
[1, 0, 0, 0, 0], [0, 0, 0, 0, 0]])
def setUp(self):
deg = [3, 2, 2, 1, 0]
self.G = havel_hakimi_graph(deg)
# Graph used as an example in Sec. 4.1 of Langville and Meyer,
# "Google's PageRank and Beyond". (Used for test_directed_laplacian)
self.DG = nx.DiGraph()
self.DG.add_edges_from(((1, 2), (1, 3), (3, 1), (3, 2), (3, 5), (4, 5),
(4, 6), (5, 4), (5, 6), (6, 4)))
def setUp(self):
deg = [3, 2, 2, 1, 0]
self.G = havel_hakimi_graph(deg)
# Graph used as an example in Sec. 4.1 of Langville and Meyer,
# "Google's PageRank and Beyond". (Used for test_directed_laplacian)
self.DG = nx.DiGraph()
self.DG.add_edges_from(((1,2), (1,3), (3,1), (3,2), (3,5), (4,5), (4,6),
(5,4), (5,6), (6,4)))
def setUp(self):
deg = [3, 2, 2, 1, 0]
self.G = havel_hakimi_graph(deg)
# Graph with selfloops
self.Gsl = self.G.copy()
for node in self.Gsl.nodes():
self.Gsl.add_edge(node, node)
def setUp(self):
deg = [3, 2, 2, 1, 0]
self.G = havel_hakimi_graph(deg)
# Graph with selfloops
self.Gsl = self.G.copy()
for node in self.Gsl.nodes():
self.Gsl.add_edge(node, node)
def setUp(self):
deg=[3,2,2,1,0]
self.G=havel_hakimi_graph(deg)
self.P=nx.path_graph(3)
self.A=numpy.array([[0, 1, 1, 1, 0],
[1, 0, 1, 0, 0],
[1, 1, 0, 0, 0],
[1, 0, 0, 0, 0],
[0, 0, 0, 0, 0]])
def setUp(self):
deg = [3, 2, 2, 1, 0]
self.G = havel_hakimi_graph(deg)
self.P = nx.path_graph(3)
self.WG = nx.Graph((u, v, {
'weight': 0.5,
'other': 0.3
}) for (u, v) in self.G.edges_iter())
self.WG.add_node(4)
def setup_class(cls):
deg = [3, 2, 2, 1, 0]
cls.G = havel_hakimi_graph(deg)
cls.P = nx.path_graph(3)
cls.WG = nx.Graph((u, v, {'weight': 0.5, 'other': 0.3})
for (u, v) in cls.G.edges())
cls.WG.add_node(4)
cls.DG = nx.DiGraph()
nx.add_path(cls.DG, [0, 1, 2])
def setUp(self):
deg = [3, 2, 2, 1, 0]
self.G = havel_hakimi_graph(deg)
self.WG = nx.Graph((u, v, {"weight": 0.5, "other": 0.3}) for (u, v) in self.G.edges_iter())
self.WG.add_node(4)
self.MG = nx.MultiGraph(self.G)
# Graph with selfloops
self.Gsl = self.G.copy()
for node in self.Gsl.nodes():
self.Gsl.add_edge(node, node)
def setUp(self):
deg=[3,2,2,1,0]
self.G=havel_hakimi_graph(deg)
self.WG=nx.Graph( (u,v,{'weight':0.5,'other':0.3})
for (u,v) in self.G.edges_iter() )
self.WG.add_node(4)
self.MG=nx.MultiGraph(self.G)
# Graph with selfloops
self.Gsl = self.G.copy()
for node in self.Gsl.nodes():
self.Gsl.add_edge(node, node)
def setup_class(cls):
deg = [3, 2, 2, 1, 0]
cls.G = havel_hakimi_graph(deg)
cls.WG = nx.Graph(
(u, v, {"weight": 0.5, "other": 0.3}) for (u, v) in cls.G.edges()
)
cls.WG.add_node(4)
cls.MG = nx.MultiGraph(cls.G)
# Graph with clsloops
cls.Gsl = cls.G.copy()
for node in cls.Gsl.nodes():
cls.Gsl.add_edge(node, node)
def test_incidence_matrix_simple():
deg = [3, 2, 2, 1, 0]
G = havel_hakimi_graph(deg)
deg = [(1, 0), (1, 0), (1, 0), (2, 0), (1, 0), (2, 1), (0, 1), (0, 1)]
MG = nx.random_clustered_graph(deg, seed=42)
I = nx.incidence_matrix(G).todense().astype(int)
expected = np.array([[1, 1, 1, 0], [0, 1, 0, 1], [1, 0, 0, 1],
[0, 0, 1, 0], [0, 0, 0, 0]])
npt.assert_equal(I, expected)
I = nx.incidence_matrix(MG).todense().astype(int)
expected = np.array([[1, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 1, 0, 1]])
npt.assert_equal(I, expected)
with pytest.raises(NetworkXError):
nx.incidence_matrix(G, nodelist=[0, 1])
def setUp(self):
deg = [3, 2, 2, 1, 0]
self.G = havel_hakimi_graph(deg)
self.P = nx.path_graph(3)
self.A = numpy.array([[0, 1, 1, 1, 0], [1, 0, 1, 0,
0], [1, 1, 0, 0, 0],
[1, 0, 0, 0, 0], [0, 0, 0, 0, 0]])
self.WG = nx.Graph((u, v, {
'weight': 0.5,
'other': 0.3
}) for (u, v) in self.G.edges_iter())
self.WG.add_node(4)
self.WA = numpy.array([[0, 0.5, 0.5, 0.5, 0], [0.5, 0, 0.5, 0, 0],
[0.5, 0.5, 0, 0, 0], [0.5, 0, 0, 0, 0],
[0, 0, 0, 0, 0]])
self.MG = nx.MultiGraph(self.G)
self.MG2 = self.MG.copy()
self.MG2.add_edge(0, 1)
self.MG2A = numpy.array([[0, 2, 1, 1, 0], [2, 0, 1, 0, 0],
[1, 1, 0, 0, 0], [1, 0, 0, 0, 0],
[0, 0, 0, 0, 0]])
def setup_class(cls):
deg = [3, 2, 2, 1, 0]
cls.G = havel_hakimi_graph(deg)
cls.P = nx.path_graph(3)
def setUp(self):
deg = [3, 2, 2, 1, 0]
self.G = havel_hakimi_graph(deg)
self.P = nx.path_graph(3)
