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)