164 lines
7.8 KiB
Python
164 lines
7.8 KiB
Python
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import pytest
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numpy = pytest.importorskip('numpy')
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npt = pytest.importorskip('numpy.testing')
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scipy = pytest.importorskip('scipy')
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import networkx as nx
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from networkx.generators.degree_seq import havel_hakimi_graph
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class TestGraphMatrix(object):
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@classmethod
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def setup_class(cls):
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deg = [3, 2, 2, 1, 0]
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cls.G = havel_hakimi_graph(deg)
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cls.OI = numpy.array([[-1, -1, -1, 0],
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[1, 0, 0, -1],
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[0, 1, 0, 1],
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[0, 0, 1, 0],
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[0, 0, 0, 0]])
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cls.A = numpy.array([[0, 1, 1, 1, 0],
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[1, 0, 1, 0, 0],
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[1, 1, 0, 0, 0],
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[1, 0, 0, 0, 0],
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[0, 0, 0, 0, 0]])
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cls.WG = havel_hakimi_graph(deg)
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cls.WG.add_edges_from((u, v, {'weight': 0.5, 'other': 0.3})
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for (u, v) in cls.G.edges())
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cls.WA = numpy.array([[0, 0.5, 0.5, 0.5, 0],
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[0.5, 0, 0.5, 0, 0],
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[0.5, 0.5, 0, 0, 0],
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[0.5, 0, 0, 0, 0],
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[0, 0, 0, 0, 0]])
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cls.MG = nx.MultiGraph(cls.G)
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cls.MG2 = cls.MG.copy()
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cls.MG2.add_edge(0, 1)
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cls.MG2A = numpy.array([[0, 2, 1, 1, 0],
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[2, 0, 1, 0, 0],
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[1, 1, 0, 0, 0],
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[1, 0, 0, 0, 0],
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[0, 0, 0, 0, 0]])
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cls.MGOI = numpy.array([[-1, -1, -1, -1, 0],
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[1, 1, 0, 0, -1],
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[0, 0, 1, 0, 1],
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[0, 0, 0, 1, 0],
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[0, 0, 0, 0, 0]])
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cls.no_edges_G = nx.Graph([(1, 2), (3, 2, {'weight': 8})])
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cls.no_edges_A = numpy.array([[0, 0], [0, 0]])
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def test_incidence_matrix(self):
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"Conversion to incidence matrix"
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I = nx.incidence_matrix(self.G,
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nodelist=sorted(self.G),
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edgelist=sorted(self.G.edges()),
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oriented=True).todense().astype(int)
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npt.assert_equal(I, self.OI)
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I = nx.incidence_matrix(self.G,
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nodelist=sorted(self.G),
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edgelist=sorted(self.G.edges()),
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oriented=False).todense().astype(int)
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npt.assert_equal(I, numpy.abs(self.OI))
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I = nx.incidence_matrix(self.MG,
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nodelist=sorted(self.MG),
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edgelist=sorted(self.MG.edges()),
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oriented=True).todense().astype(int)
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npt.assert_equal(I, self.OI)
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I = nx.incidence_matrix(self.MG,
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nodelist=sorted(self.MG),
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edgelist=sorted(self.MG.edges()),
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oriented=False).todense().astype(int)
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npt.assert_equal(I, numpy.abs(self.OI))
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I = nx.incidence_matrix(self.MG2,
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nodelist=sorted(self.MG2),
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edgelist=sorted(self.MG2.edges()),
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oriented=True).todense().astype(int)
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npt.assert_equal(I, self.MGOI)
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I = nx.incidence_matrix(self.MG2,
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nodelist=sorted(self.MG),
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edgelist=sorted(self.MG2.edges()),
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oriented=False).todense().astype(int)
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npt.assert_equal(I, numpy.abs(self.MGOI))
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def test_weighted_incidence_matrix(self):
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I = nx.incidence_matrix(self.WG,
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nodelist=sorted(self.WG),
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edgelist=sorted(self.WG.edges()),
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oriented=True).todense().astype(int)
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npt.assert_equal(I, self.OI)
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I = nx.incidence_matrix(self.WG,
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nodelist=sorted(self.WG),
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edgelist=sorted(self.WG.edges()),
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oriented=False).todense().astype(int)
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npt.assert_equal(I, numpy.abs(self.OI))
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# npt.assert_equal(nx.incidence_matrix(self.WG,oriented=True,
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# weight='weight').todense(),0.5*self.OI)
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# npt.assert_equal(nx.incidence_matrix(self.WG,weight='weight').todense(),
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# numpy.abs(0.5*self.OI))
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# npt.assert_equal(nx.incidence_matrix(self.WG,oriented=True,weight='other').todense(),
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# 0.3*self.OI)
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I = nx.incidence_matrix(self.WG,
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nodelist=sorted(self.WG),
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edgelist=sorted(self.WG.edges()),
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oriented=True,
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weight='weight').todense()
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npt.assert_equal(I, 0.5 * self.OI)
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I = nx.incidence_matrix(self.WG,
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nodelist=sorted(self.WG),
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edgelist=sorted(self.WG.edges()),
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oriented=False,
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weight='weight').todense()
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npt.assert_equal(I, numpy.abs(0.5 * self.OI))
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I = nx.incidence_matrix(self.WG,
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nodelist=sorted(self.WG),
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edgelist=sorted(self.WG.edges()),
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oriented=True,
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weight='other').todense()
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npt.assert_equal(I, 0.3 * self.OI)
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# WMG=nx.MultiGraph(self.WG)
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# WMG.add_edge(0,1,weight=0.5,other=0.3)
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# npt.assert_equal(nx.incidence_matrix(WMG,weight='weight').todense(),
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# numpy.abs(0.5*self.MGOI))
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# npt.assert_equal(nx.incidence_matrix(WMG,weight='weight',oriented=True).todense(),
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# 0.5*self.MGOI)
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# npt.assert_equal(nx.incidence_matrix(WMG,weight='other',oriented=True).todense(),
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# 0.3*self.MGOI)
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WMG = nx.MultiGraph(self.WG)
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WMG.add_edge(0, 1, weight=0.5, other=0.3)
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I = nx.incidence_matrix(WMG,
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nodelist=sorted(WMG),
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edgelist=sorted(WMG.edges(keys=True)),
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oriented=True,
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weight='weight').todense()
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npt.assert_equal(I, 0.5 * self.MGOI)
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I = nx.incidence_matrix(WMG,
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nodelist=sorted(WMG),
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edgelist=sorted(WMG.edges(keys=True)),
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oriented=False,
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weight='weight').todense()
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npt.assert_equal(I, numpy.abs(0.5 * self.MGOI))
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I = nx.incidence_matrix(WMG,
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nodelist=sorted(WMG),
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edgelist=sorted(WMG.edges(keys=True)),
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oriented=True,
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weight='other').todense()
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npt.assert_equal(I, 0.3 * self.MGOI)
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def test_adjacency_matrix(self):
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"Conversion to adjacency matrix"
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npt.assert_equal(nx.adj_matrix(self.G).todense(), self.A)
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npt.assert_equal(nx.adj_matrix(self.MG).todense(), self.A)
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npt.assert_equal(nx.adj_matrix(self.MG2).todense(), self.MG2A)
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npt.assert_equal(nx.adj_matrix(self.G, nodelist=[0, 1]).todense(), self.A[:2, :2])
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npt.assert_equal(nx.adj_matrix(self.WG).todense(), self.WA)
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npt.assert_equal(nx.adj_matrix(self.WG, weight=None).todense(), self.A)
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npt.assert_equal(nx.adj_matrix(self.MG2, weight=None).todense(), self.MG2A)
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npt.assert_equal(nx.adj_matrix(self.WG, weight='other').todense(), 0.6 * self.WA)
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npt.assert_equal(nx.adj_matrix(self.no_edges_G, nodelist=[1, 3]).todense(), self.no_edges_A)
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