Mercurial > repos > shellac > sam_consensus_v3
diff env/lib/python3.9/site-packages/networkx/tests/test_convert_numpy.py @ 0:4f3585e2f14b draft default tip
"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/env/lib/python3.9/site-packages/networkx/tests/test_convert_numpy.py Mon Mar 22 18:12:50 2021 +0000 @@ -0,0 +1,434 @@ +import pytest + +np = pytest.importorskip("numpy") +np_assert_equal = np.testing.assert_equal + +import networkx as nx +from networkx.generators.classic import barbell_graph, cycle_graph, path_graph +from networkx.testing.utils import assert_graphs_equal + + +class TestConvertNumpy: + def setup_method(self): + self.G1 = barbell_graph(10, 3) + self.G2 = cycle_graph(10, create_using=nx.DiGraph) + + self.G3 = self.create_weighted(nx.Graph()) + self.G4 = self.create_weighted(nx.DiGraph()) + + def test_exceptions(self): + G = np.array("a") + pytest.raises(nx.NetworkXError, nx.to_networkx_graph, G) + + def create_weighted(self, G): + g = cycle_graph(4) + G.add_nodes_from(g) + G.add_weighted_edges_from((u, v, 10 + u) for u, v in g.edges()) + return G + + def assert_equal(self, G1, G2): + assert sorted(G1.nodes()) == sorted(G2.nodes()) + assert sorted(G1.edges()) == sorted(G2.edges()) + + def identity_conversion(self, G, A, create_using): + assert A.sum() > 0 + GG = nx.from_numpy_matrix(A, create_using=create_using) + self.assert_equal(G, GG) + GW = nx.to_networkx_graph(A, create_using=create_using) + self.assert_equal(G, GW) + GI = nx.empty_graph(0, create_using).__class__(A) + self.assert_equal(G, GI) + + def test_shape(self): + "Conversion from non-square array." + A = np.array([[1, 2, 3], [4, 5, 6]]) + pytest.raises(nx.NetworkXError, nx.from_numpy_matrix, A) + + def test_identity_graph_matrix(self): + "Conversion from graph to matrix to graph." + A = nx.to_numpy_matrix(self.G1) + self.identity_conversion(self.G1, A, nx.Graph()) + + def test_identity_graph_array(self): + "Conversion from graph to array to graph." + A = nx.to_numpy_matrix(self.G1) + A = np.asarray(A) + self.identity_conversion(self.G1, A, nx.Graph()) + + def test_identity_digraph_matrix(self): + """Conversion from digraph to matrix to digraph.""" + A = nx.to_numpy_matrix(self.G2) + self.identity_conversion(self.G2, A, nx.DiGraph()) + + def test_identity_digraph_array(self): + """Conversion from digraph to array to digraph.""" + A = nx.to_numpy_matrix(self.G2) + A = np.asarray(A) + self.identity_conversion(self.G2, A, nx.DiGraph()) + + def test_identity_weighted_graph_matrix(self): + """Conversion from weighted graph to matrix to weighted graph.""" + A = nx.to_numpy_matrix(self.G3) + self.identity_conversion(self.G3, A, nx.Graph()) + + def test_identity_weighted_graph_array(self): + """Conversion from weighted graph to array to weighted graph.""" + A = nx.to_numpy_matrix(self.G3) + A = np.asarray(A) + self.identity_conversion(self.G3, A, nx.Graph()) + + def test_identity_weighted_digraph_matrix(self): + """Conversion from weighted digraph to matrix to weighted digraph.""" + A = nx.to_numpy_matrix(self.G4) + self.identity_conversion(self.G4, A, nx.DiGraph()) + + def test_identity_weighted_digraph_array(self): + """Conversion from weighted digraph to array to weighted digraph.""" + A = nx.to_numpy_matrix(self.G4) + A = np.asarray(A) + self.identity_conversion(self.G4, A, nx.DiGraph()) + + def test_nodelist(self): + """Conversion from graph to matrix to graph with nodelist.""" + P4 = path_graph(4) + P3 = path_graph(3) + nodelist = list(P3) + A = nx.to_numpy_matrix(P4, nodelist=nodelist) + GA = nx.Graph(A) + self.assert_equal(GA, P3) + + # Make nodelist ambiguous by containing duplicates. + nodelist += [nodelist[0]] + pytest.raises(nx.NetworkXError, nx.to_numpy_matrix, P3, nodelist=nodelist) + + def test_weight_keyword(self): + WP4 = nx.Graph() + WP4.add_edges_from((n, n + 1, dict(weight=0.5, other=0.3)) for n in range(3)) + P4 = path_graph(4) + A = nx.to_numpy_matrix(P4) + np_assert_equal(A, nx.to_numpy_matrix(WP4, weight=None)) + np_assert_equal(0.5 * A, nx.to_numpy_matrix(WP4)) + np_assert_equal(0.3 * A, nx.to_numpy_matrix(WP4, weight="other")) + + def test_from_numpy_matrix_type(self): + A = np.matrix([[1]]) + G = nx.from_numpy_matrix(A) + assert type(G[0][0]["weight"]) == int + + A = np.matrix([[1]]).astype(np.float) + G = nx.from_numpy_matrix(A) + assert type(G[0][0]["weight"]) == float + + A = np.matrix([[1]]).astype(np.str) + G = nx.from_numpy_matrix(A) + assert type(G[0][0]["weight"]) == str + + A = np.matrix([[1]]).astype(np.bool) + G = nx.from_numpy_matrix(A) + assert type(G[0][0]["weight"]) == bool + + A = np.matrix([[1]]).astype(np.complex) + G = nx.from_numpy_matrix(A) + assert type(G[0][0]["weight"]) == complex + + A = np.matrix([[1]]).astype(np.object) + pytest.raises(TypeError, nx.from_numpy_matrix, A) + + G = nx.cycle_graph(3) + A = nx.adj_matrix(G).todense() + H = nx.from_numpy_matrix(A) + assert all(type(m) == int and type(n) == int for m, n in H.edges()) + H = nx.from_numpy_array(A) + assert all(type(m) == int and type(n) == int for m, n in H.edges()) + + def test_from_numpy_matrix_dtype(self): + dt = [("weight", float), ("cost", int)] + A = np.matrix([[(1.0, 2)]], dtype=dt) + G = nx.from_numpy_matrix(A) + assert type(G[0][0]["weight"]) == float + assert type(G[0][0]["cost"]) == int + assert G[0][0]["cost"] == 2 + assert G[0][0]["weight"] == 1.0 + + def test_to_numpy_recarray(self): + G = nx.Graph() + G.add_edge(1, 2, weight=7.0, cost=5) + A = nx.to_numpy_recarray(G, dtype=[("weight", float), ("cost", int)]) + assert sorted(A.dtype.names) == ["cost", "weight"] + assert A.weight[0, 1] == 7.0 + assert A.weight[0, 0] == 0.0 + assert A.cost[0, 1] == 5 + assert A.cost[0, 0] == 0 + + def test_numpy_multigraph(self): + G = nx.MultiGraph() + G.add_edge(1, 2, weight=7) + G.add_edge(1, 2, weight=70) + A = nx.to_numpy_matrix(G) + assert A[1, 0] == 77 + A = nx.to_numpy_matrix(G, multigraph_weight=min) + assert A[1, 0] == 7 + A = nx.to_numpy_matrix(G, multigraph_weight=max) + assert A[1, 0] == 70 + + def test_from_numpy_matrix_parallel_edges(self): + """Tests that the :func:`networkx.from_numpy_matrix` function + interprets integer weights as the number of parallel edges when + creating a multigraph. + + """ + A = np.matrix([[1, 1], [1, 2]]) + # First, with a simple graph, each integer entry in the adjacency + # matrix is interpreted as the weight of a single edge in the graph. + expected = nx.DiGraph() + edges = [(0, 0), (0, 1), (1, 0)] + expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges]) + expected.add_edge(1, 1, weight=2) + actual = nx.from_numpy_matrix(A, parallel_edges=True, create_using=nx.DiGraph) + assert_graphs_equal(actual, expected) + actual = nx.from_numpy_matrix(A, parallel_edges=False, create_using=nx.DiGraph) + assert_graphs_equal(actual, expected) + # Now each integer entry in the adjacency matrix is interpreted as the + # number of parallel edges in the graph if the appropriate keyword + # argument is specified. + edges = [(0, 0), (0, 1), (1, 0), (1, 1), (1, 1)] + expected = nx.MultiDiGraph() + expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges]) + actual = nx.from_numpy_matrix( + A, parallel_edges=True, create_using=nx.MultiDiGraph + ) + assert_graphs_equal(actual, expected) + expected = nx.MultiDiGraph() + expected.add_edges_from(set(edges), weight=1) + # The sole self-loop (edge 0) on vertex 1 should have weight 2. + expected[1][1][0]["weight"] = 2 + actual = nx.from_numpy_matrix( + A, parallel_edges=False, create_using=nx.MultiDiGraph + ) + assert_graphs_equal(actual, expected) + + def test_symmetric(self): + """Tests that a symmetric matrix has edges added only once to an + undirected multigraph when using :func:`networkx.from_numpy_matrix`. + + """ + A = np.matrix([[0, 1], [1, 0]]) + G = nx.from_numpy_matrix(A, create_using=nx.MultiGraph) + expected = nx.MultiGraph() + expected.add_edge(0, 1, weight=1) + assert_graphs_equal(G, expected) + + def test_dtype_int_graph(self): + """Test that setting dtype int actually gives an integer matrix. + + For more information, see GitHub pull request #1363. + + """ + G = nx.complete_graph(3) + A = nx.to_numpy_matrix(G, dtype=int) + assert A.dtype == int + + def test_dtype_int_multigraph(self): + """Test that setting dtype int actually gives an integer matrix. + + For more information, see GitHub pull request #1363. + + """ + G = nx.MultiGraph(nx.complete_graph(3)) + A = nx.to_numpy_matrix(G, dtype=int) + assert A.dtype == int + + +class TestConvertNumpyArray: + def setup_method(self): + self.G1 = barbell_graph(10, 3) + self.G2 = cycle_graph(10, create_using=nx.DiGraph) + self.G3 = self.create_weighted(nx.Graph()) + self.G4 = self.create_weighted(nx.DiGraph()) + + def create_weighted(self, G): + g = cycle_graph(4) + G.add_nodes_from(g) + G.add_weighted_edges_from((u, v, 10 + u) for u, v in g.edges()) + return G + + def assert_equal(self, G1, G2): + assert sorted(G1.nodes()) == sorted(G2.nodes()) + assert sorted(G1.edges()) == sorted(G2.edges()) + + def identity_conversion(self, G, A, create_using): + assert A.sum() > 0 + GG = nx.from_numpy_array(A, create_using=create_using) + self.assert_equal(G, GG) + GW = nx.to_networkx_graph(A, create_using=create_using) + self.assert_equal(G, GW) + GI = nx.empty_graph(0, create_using).__class__(A) + self.assert_equal(G, GI) + + def test_shape(self): + "Conversion from non-square array." + A = np.array([[1, 2, 3], [4, 5, 6]]) + pytest.raises(nx.NetworkXError, nx.from_numpy_array, A) + + def test_identity_graph_array(self): + "Conversion from graph to array to graph." + A = nx.to_numpy_array(self.G1) + self.identity_conversion(self.G1, A, nx.Graph()) + + def test_identity_digraph_array(self): + """Conversion from digraph to array to digraph.""" + A = nx.to_numpy_array(self.G2) + self.identity_conversion(self.G2, A, nx.DiGraph()) + + def test_identity_weighted_graph_array(self): + """Conversion from weighted graph to array to weighted graph.""" + A = nx.to_numpy_array(self.G3) + self.identity_conversion(self.G3, A, nx.Graph()) + + def test_identity_weighted_digraph_array(self): + """Conversion from weighted digraph to array to weighted digraph.""" + A = nx.to_numpy_array(self.G4) + self.identity_conversion(self.G4, A, nx.DiGraph()) + + def test_nodelist(self): + """Conversion from graph to array to graph with nodelist.""" + P4 = path_graph(4) + P3 = path_graph(3) + nodelist = list(P3) + A = nx.to_numpy_array(P4, nodelist=nodelist) + GA = nx.Graph(A) + self.assert_equal(GA, P3) + + # Make nodelist ambiguous by containing duplicates. + nodelist += [nodelist[0]] + pytest.raises(nx.NetworkXError, nx.to_numpy_array, P3, nodelist=nodelist) + + def test_weight_keyword(self): + WP4 = nx.Graph() + WP4.add_edges_from((n, n + 1, dict(weight=0.5, other=0.3)) for n in range(3)) + P4 = path_graph(4) + A = nx.to_numpy_array(P4) + np_assert_equal(A, nx.to_numpy_array(WP4, weight=None)) + np_assert_equal(0.5 * A, nx.to_numpy_array(WP4)) + np_assert_equal(0.3 * A, nx.to_numpy_array(WP4, weight="other")) + + def test_from_numpy_array_type(self): + A = np.array([[1]]) + G = nx.from_numpy_array(A) + assert type(G[0][0]["weight"]) == int + + A = np.array([[1]]).astype(np.float) + G = nx.from_numpy_array(A) + assert type(G[0][0]["weight"]) == float + + A = np.array([[1]]).astype(np.str) + G = nx.from_numpy_array(A) + assert type(G[0][0]["weight"]) == str + + A = np.array([[1]]).astype(np.bool) + G = nx.from_numpy_array(A) + assert type(G[0][0]["weight"]) == bool + + A = np.array([[1]]).astype(np.complex) + G = nx.from_numpy_array(A) + assert type(G[0][0]["weight"]) == complex + + A = np.array([[1]]).astype(np.object) + pytest.raises(TypeError, nx.from_numpy_array, A) + + def test_from_numpy_array_dtype(self): + dt = [("weight", float), ("cost", int)] + A = np.array([[(1.0, 2)]], dtype=dt) + G = nx.from_numpy_array(A) + assert type(G[0][0]["weight"]) == float + assert type(G[0][0]["cost"]) == int + assert G[0][0]["cost"] == 2 + assert G[0][0]["weight"] == 1.0 + + def test_to_numpy_recarray(self): + G = nx.Graph() + G.add_edge(1, 2, weight=7.0, cost=5) + A = nx.to_numpy_recarray(G, dtype=[("weight", float), ("cost", int)]) + assert sorted(A.dtype.names) == ["cost", "weight"] + assert A.weight[0, 1] == 7.0 + assert A.weight[0, 0] == 0.0 + assert A.cost[0, 1] == 5 + assert A.cost[0, 0] == 0 + + def test_numpy_multigraph(self): + G = nx.MultiGraph() + G.add_edge(1, 2, weight=7) + G.add_edge(1, 2, weight=70) + A = nx.to_numpy_array(G) + assert A[1, 0] == 77 + A = nx.to_numpy_array(G, multigraph_weight=min) + assert A[1, 0] == 7 + A = nx.to_numpy_array(G, multigraph_weight=max) + assert A[1, 0] == 70 + + def test_from_numpy_array_parallel_edges(self): + """Tests that the :func:`networkx.from_numpy_array` function + interprets integer weights as the number of parallel edges when + creating a multigraph. + + """ + A = np.array([[1, 1], [1, 2]]) + # First, with a simple graph, each integer entry in the adjacency + # matrix is interpreted as the weight of a single edge in the graph. + expected = nx.DiGraph() + edges = [(0, 0), (0, 1), (1, 0)] + expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges]) + expected.add_edge(1, 1, weight=2) + actual = nx.from_numpy_array(A, parallel_edges=True, create_using=nx.DiGraph) + assert_graphs_equal(actual, expected) + actual = nx.from_numpy_array(A, parallel_edges=False, create_using=nx.DiGraph) + assert_graphs_equal(actual, expected) + # Now each integer entry in the adjacency matrix is interpreted as the + # number of parallel edges in the graph if the appropriate keyword + # argument is specified. + edges = [(0, 0), (0, 1), (1, 0), (1, 1), (1, 1)] + expected = nx.MultiDiGraph() + expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges]) + actual = nx.from_numpy_array( + A, parallel_edges=True, create_using=nx.MultiDiGraph + ) + assert_graphs_equal(actual, expected) + expected = nx.MultiDiGraph() + expected.add_edges_from(set(edges), weight=1) + # The sole self-loop (edge 0) on vertex 1 should have weight 2. + expected[1][1][0]["weight"] = 2 + actual = nx.from_numpy_array( + A, parallel_edges=False, create_using=nx.MultiDiGraph + ) + assert_graphs_equal(actual, expected) + + def test_symmetric(self): + """Tests that a symmetric array has edges added only once to an + undirected multigraph when using :func:`networkx.from_numpy_array`. + + """ + A = np.array([[0, 1], [1, 0]]) + G = nx.from_numpy_array(A, create_using=nx.MultiGraph) + expected = nx.MultiGraph() + expected.add_edge(0, 1, weight=1) + assert_graphs_equal(G, expected) + + def test_dtype_int_graph(self): + """Test that setting dtype int actually gives an integer array. + + For more information, see GitHub pull request #1363. + + """ + G = nx.complete_graph(3) + A = nx.to_numpy_array(G, dtype=int) + assert A.dtype == int + + def test_dtype_int_multigraph(self): + """Test that setting dtype int actually gives an integer array. + + For more information, see GitHub pull request #1363. + + """ + G = nx.MultiGraph(nx.complete_graph(3)) + A = nx.to_numpy_array(G, dtype=int) + assert A.dtype == int