531 lines
19 KiB
Python
531 lines
19 KiB
Python
import itertools
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import pytest
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import networkx as nx
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from networkx.utils import graphs_equal
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np = pytest.importorskip("numpy")
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npt = pytest.importorskip("numpy.testing")
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class TestConvertNumpyArray:
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def setup_method(self):
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self.G1 = nx.barbell_graph(10, 3)
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self.G2 = nx.cycle_graph(10, create_using=nx.DiGraph)
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self.G3 = self.create_weighted(nx.Graph())
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self.G4 = self.create_weighted(nx.DiGraph())
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def create_weighted(self, G):
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g = nx.cycle_graph(4)
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G.add_nodes_from(g)
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G.add_weighted_edges_from((u, v, 10 + u) for u, v in g.edges())
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return G
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def assert_equal(self, G1, G2):
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assert sorted(G1.nodes()) == sorted(G2.nodes())
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assert sorted(G1.edges()) == sorted(G2.edges())
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def identity_conversion(self, G, A, create_using):
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assert A.sum() > 0
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GG = nx.from_numpy_array(A, create_using=create_using)
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self.assert_equal(G, GG)
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GW = nx.to_networkx_graph(A, create_using=create_using)
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self.assert_equal(G, GW)
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GI = nx.empty_graph(0, create_using).__class__(A)
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self.assert_equal(G, GI)
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def test_shape(self):
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"Conversion from non-square array."
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A = np.array([[1, 2, 3], [4, 5, 6]])
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pytest.raises(nx.NetworkXError, nx.from_numpy_array, A)
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def test_identity_graph_array(self):
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"Conversion from graph to array to graph."
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A = nx.to_numpy_array(self.G1)
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self.identity_conversion(self.G1, A, nx.Graph())
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def test_identity_digraph_array(self):
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"""Conversion from digraph to array to digraph."""
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A = nx.to_numpy_array(self.G2)
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self.identity_conversion(self.G2, A, nx.DiGraph())
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def test_identity_weighted_graph_array(self):
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"""Conversion from weighted graph to array to weighted graph."""
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A = nx.to_numpy_array(self.G3)
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self.identity_conversion(self.G3, A, nx.Graph())
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def test_identity_weighted_digraph_array(self):
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"""Conversion from weighted digraph to array to weighted digraph."""
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A = nx.to_numpy_array(self.G4)
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self.identity_conversion(self.G4, A, nx.DiGraph())
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def test_nodelist(self):
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"""Conversion from graph to array to graph with nodelist."""
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P4 = nx.path_graph(4)
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P3 = nx.path_graph(3)
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nodelist = list(P3)
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A = nx.to_numpy_array(P4, nodelist=nodelist)
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GA = nx.Graph(A)
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self.assert_equal(GA, P3)
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# Make nodelist ambiguous by containing duplicates.
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nodelist += [nodelist[0]]
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pytest.raises(nx.NetworkXError, nx.to_numpy_array, P3, nodelist=nodelist)
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# Make nodelist invalid by including nonexistent nodes
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nodelist = [-1, 0, 1]
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with pytest.raises(
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nx.NetworkXError,
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match=f"Nodes {nodelist - P3.nodes} in nodelist is not in G",
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):
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nx.to_numpy_array(P3, nodelist=nodelist)
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def test_weight_keyword(self):
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WP4 = nx.Graph()
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WP4.add_edges_from((n, n + 1, {"weight": 0.5, "other": 0.3}) for n in range(3))
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P4 = nx.path_graph(4)
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A = nx.to_numpy_array(P4)
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np.testing.assert_equal(A, nx.to_numpy_array(WP4, weight=None))
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np.testing.assert_equal(0.5 * A, nx.to_numpy_array(WP4))
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np.testing.assert_equal(0.3 * A, nx.to_numpy_array(WP4, weight="other"))
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def test_from_numpy_array_type(self):
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A = np.array([[1]])
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G = nx.from_numpy_array(A)
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assert isinstance(G[0][0]["weight"], int)
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A = np.array([[1]]).astype(float)
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G = nx.from_numpy_array(A)
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assert isinstance(G[0][0]["weight"], float)
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A = np.array([[1]]).astype(str)
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G = nx.from_numpy_array(A)
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assert isinstance(G[0][0]["weight"], str)
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A = np.array([[1]]).astype(bool)
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G = nx.from_numpy_array(A)
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assert isinstance(G[0][0]["weight"], bool)
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A = np.array([[1]]).astype(complex)
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G = nx.from_numpy_array(A)
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assert isinstance(G[0][0]["weight"], complex)
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A = np.array([[1]]).astype(object)
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pytest.raises(TypeError, nx.from_numpy_array, A)
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A = np.array([[[1, 1, 1], [1, 1, 1]], [[1, 1, 1], [1, 1, 1]]])
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with pytest.raises(
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nx.NetworkXError, match=f"Input array must be 2D, not {A.ndim}"
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):
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g = nx.from_numpy_array(A)
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def test_from_numpy_array_dtype(self):
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dt = [("weight", float), ("cost", int)]
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A = np.array([[(1.0, 2)]], dtype=dt)
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G = nx.from_numpy_array(A)
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assert isinstance(G[0][0]["weight"], float)
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assert isinstance(G[0][0]["cost"], int)
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assert G[0][0]["cost"] == 2
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assert G[0][0]["weight"] == 1.0
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def test_from_numpy_array_parallel_edges(self):
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"""Tests that the :func:`networkx.from_numpy_array` function
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interprets integer weights as the number of parallel edges when
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creating a multigraph.
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"""
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A = np.array([[1, 1], [1, 2]])
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# First, with a simple graph, each integer entry in the adjacency
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# matrix is interpreted as the weight of a single edge in the graph.
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expected = nx.DiGraph()
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edges = [(0, 0), (0, 1), (1, 0)]
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expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges])
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expected.add_edge(1, 1, weight=2)
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actual = nx.from_numpy_array(A, parallel_edges=True, create_using=nx.DiGraph)
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assert graphs_equal(actual, expected)
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actual = nx.from_numpy_array(A, parallel_edges=False, create_using=nx.DiGraph)
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assert graphs_equal(actual, expected)
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# Now each integer entry in the adjacency matrix is interpreted as the
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# number of parallel edges in the graph if the appropriate keyword
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# argument is specified.
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edges = [(0, 0), (0, 1), (1, 0), (1, 1), (1, 1)]
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expected = nx.MultiDiGraph()
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expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges])
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actual = nx.from_numpy_array(
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A, parallel_edges=True, create_using=nx.MultiDiGraph
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)
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assert graphs_equal(actual, expected)
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expected = nx.MultiDiGraph()
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expected.add_edges_from(set(edges), weight=1)
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# The sole self-loop (edge 0) on vertex 1 should have weight 2.
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expected[1][1][0]["weight"] = 2
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actual = nx.from_numpy_array(
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A, parallel_edges=False, create_using=nx.MultiDiGraph
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)
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assert graphs_equal(actual, expected)
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@pytest.mark.parametrize(
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"dt",
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(
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None, # default
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int, # integer dtype
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np.dtype(
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[("weight", "f8"), ("color", "i1")]
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), # Structured dtype with named fields
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),
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)
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def test_from_numpy_array_no_edge_attr(self, dt):
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A = np.array([[0, 1], [1, 0]], dtype=dt)
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G = nx.from_numpy_array(A, edge_attr=None)
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assert "weight" not in G.edges[0, 1]
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assert len(G.edges[0, 1]) == 0
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def test_from_numpy_array_multiedge_no_edge_attr(self):
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A = np.array([[0, 2], [2, 0]])
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G = nx.from_numpy_array(A, create_using=nx.MultiDiGraph, edge_attr=None)
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assert all("weight" not in e for _, e in G[0][1].items())
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assert len(G[0][1][0]) == 0
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def test_from_numpy_array_custom_edge_attr(self):
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A = np.array([[0, 2], [3, 0]])
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G = nx.from_numpy_array(A, edge_attr="cost")
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assert "weight" not in G.edges[0, 1]
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assert G.edges[0, 1]["cost"] == 3
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def test_symmetric(self):
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"""Tests that a symmetric array has edges added only once to an
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undirected multigraph when using :func:`networkx.from_numpy_array`.
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"""
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A = np.array([[0, 1], [1, 0]])
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G = nx.from_numpy_array(A, create_using=nx.MultiGraph)
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expected = nx.MultiGraph()
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expected.add_edge(0, 1, weight=1)
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assert graphs_equal(G, expected)
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def test_dtype_int_graph(self):
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"""Test that setting dtype int actually gives an integer array.
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For more information, see GitHub pull request #1363.
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"""
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G = nx.complete_graph(3)
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A = nx.to_numpy_array(G, dtype=int)
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assert A.dtype == int
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def test_dtype_int_multigraph(self):
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"""Test that setting dtype int actually gives an integer array.
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For more information, see GitHub pull request #1363.
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"""
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G = nx.MultiGraph(nx.complete_graph(3))
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A = nx.to_numpy_array(G, dtype=int)
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assert A.dtype == int
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@pytest.fixture
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def multigraph_test_graph():
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G = nx.MultiGraph()
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G.add_edge(1, 2, weight=7)
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G.add_edge(1, 2, weight=70)
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return G
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@pytest.mark.parametrize(("operator", "expected"), ((sum, 77), (min, 7), (max, 70)))
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def test_numpy_multigraph(multigraph_test_graph, operator, expected):
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A = nx.to_numpy_array(multigraph_test_graph, multigraph_weight=operator)
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assert A[1, 0] == expected
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def test_to_numpy_array_multigraph_nodelist(multigraph_test_graph):
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G = multigraph_test_graph
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G.add_edge(0, 1, weight=3)
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A = nx.to_numpy_array(G, nodelist=[1, 2])
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assert A.shape == (2, 2)
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assert A[1, 0] == 77
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@pytest.mark.parametrize(
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"G, expected",
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[
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(nx.Graph(), np.array([[0, 1 + 2j], [1 + 2j, 0]], dtype=complex)),
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(nx.DiGraph(), np.array([[0, 1 + 2j], [0, 0]], dtype=complex)),
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],
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)
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def test_to_numpy_array_complex_weights(G, expected):
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G.add_edge(0, 1, weight=1 + 2j)
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A = nx.to_numpy_array(G, dtype=complex)
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npt.assert_array_equal(A, expected)
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def test_to_numpy_array_arbitrary_weights():
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G = nx.DiGraph()
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w = 922337203685477580102 # Out of range for int64
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G.add_edge(0, 1, weight=922337203685477580102) # val not representable by int64
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A = nx.to_numpy_array(G, dtype=object)
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expected = np.array([[0, w], [0, 0]], dtype=object)
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npt.assert_array_equal(A, expected)
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# Undirected
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A = nx.to_numpy_array(G.to_undirected(), dtype=object)
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expected = np.array([[0, w], [w, 0]], dtype=object)
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npt.assert_array_equal(A, expected)
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@pytest.mark.parametrize(
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"func, expected",
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((min, -1), (max, 10), (sum, 11), (np.mean, 11 / 3), (np.median, 2)),
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)
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def test_to_numpy_array_multiweight_reduction(func, expected):
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"""Test various functions for reducing multiedge weights."""
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G = nx.MultiDiGraph()
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weights = [-1, 2, 10.0]
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for w in weights:
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G.add_edge(0, 1, weight=w)
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A = nx.to_numpy_array(G, multigraph_weight=func, dtype=float)
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assert np.allclose(A, [[0, expected], [0, 0]])
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# Undirected case
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A = nx.to_numpy_array(G.to_undirected(), multigraph_weight=func, dtype=float)
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assert np.allclose(A, [[0, expected], [expected, 0]])
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@pytest.mark.parametrize(
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("G, expected"),
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[
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(nx.Graph(), [[(0, 0), (10, 5)], [(10, 5), (0, 0)]]),
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(nx.DiGraph(), [[(0, 0), (10, 5)], [(0, 0), (0, 0)]]),
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],
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)
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def test_to_numpy_array_structured_dtype_attrs_from_fields(G, expected):
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"""When `dtype` is structured (i.e. has names) and `weight` is None, use
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the named fields of the dtype to look up edge attributes."""
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G.add_edge(0, 1, weight=10, cost=5.0)
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dtype = np.dtype([("weight", int), ("cost", int)])
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A = nx.to_numpy_array(G, dtype=dtype, weight=None)
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expected = np.asarray(expected, dtype=dtype)
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npt.assert_array_equal(A, expected)
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def test_to_numpy_array_structured_dtype_single_attr_default():
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G = nx.path_graph(3)
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dtype = np.dtype([("weight", float)]) # A single named field
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A = nx.to_numpy_array(G, dtype=dtype, weight=None)
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expected = np.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]], dtype=float)
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npt.assert_array_equal(A["weight"], expected)
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@pytest.mark.parametrize(
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("field_name", "expected_attr_val"),
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[
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("weight", 1),
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("cost", 3),
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],
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)
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def test_to_numpy_array_structured_dtype_single_attr(field_name, expected_attr_val):
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G = nx.Graph()
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G.add_edge(0, 1, cost=3)
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dtype = np.dtype([(field_name, float)])
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A = nx.to_numpy_array(G, dtype=dtype, weight=None)
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expected = np.array([[0, expected_attr_val], [expected_attr_val, 0]], dtype=float)
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npt.assert_array_equal(A[field_name], expected)
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@pytest.mark.parametrize("graph_type", (nx.Graph, nx.DiGraph))
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@pytest.mark.parametrize(
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"edge",
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[
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(0, 1), # No edge attributes
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(0, 1, {"weight": 10}), # One edge attr
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(0, 1, {"weight": 5, "flow": -4}), # Multiple but not all edge attrs
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(0, 1, {"weight": 2.0, "cost": 10, "flow": -45}), # All attrs
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],
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)
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def test_to_numpy_array_structured_dtype_multiple_fields(graph_type, edge):
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G = graph_type([edge])
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dtype = np.dtype([("weight", float), ("cost", float), ("flow", float)])
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A = nx.to_numpy_array(G, dtype=dtype, weight=None)
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for attr in dtype.names:
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expected = nx.to_numpy_array(G, dtype=float, weight=attr)
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npt.assert_array_equal(A[attr], expected)
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@pytest.mark.parametrize("G", (nx.Graph(), nx.DiGraph()))
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def test_to_numpy_array_structured_dtype_scalar_nonedge(G):
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G.add_edge(0, 1, weight=10)
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dtype = np.dtype([("weight", float), ("cost", float)])
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A = nx.to_numpy_array(G, dtype=dtype, weight=None, nonedge=np.nan)
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for attr in dtype.names:
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expected = nx.to_numpy_array(G, dtype=float, weight=attr, nonedge=np.nan)
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npt.assert_array_equal(A[attr], expected)
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@pytest.mark.parametrize("G", (nx.Graph(), nx.DiGraph()))
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def test_to_numpy_array_structured_dtype_nonedge_ary(G):
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"""Similar to the scalar case, except has a different non-edge value for
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each named field."""
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G.add_edge(0, 1, weight=10)
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dtype = np.dtype([("weight", float), ("cost", float)])
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nonedges = np.array([(0, np.inf)], dtype=dtype)
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A = nx.to_numpy_array(G, dtype=dtype, weight=None, nonedge=nonedges)
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for attr in dtype.names:
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nonedge = nonedges[attr]
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expected = nx.to_numpy_array(G, dtype=float, weight=attr, nonedge=nonedge)
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npt.assert_array_equal(A[attr], expected)
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def test_to_numpy_array_structured_dtype_with_weight_raises():
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"""Using both a structured dtype (with named fields) and specifying a `weight`
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parameter is ambiguous."""
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G = nx.path_graph(3)
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dtype = np.dtype([("weight", int), ("cost", int)])
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exception_msg = "Specifying `weight` not supported for structured dtypes"
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with pytest.raises(ValueError, match=exception_msg):
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nx.to_numpy_array(G, dtype=dtype) # Default is weight="weight"
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with pytest.raises(ValueError, match=exception_msg):
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nx.to_numpy_array(G, dtype=dtype, weight="cost")
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@pytest.mark.parametrize("graph_type", (nx.MultiGraph, nx.MultiDiGraph))
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def test_to_numpy_array_structured_multigraph_raises(graph_type):
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G = nx.path_graph(3, create_using=graph_type)
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dtype = np.dtype([("weight", int), ("cost", int)])
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with pytest.raises(nx.NetworkXError, match="Structured arrays are not supported"):
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nx.to_numpy_array(G, dtype=dtype, weight=None)
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def test_from_numpy_array_nodelist_bad_size():
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"""An exception is raised when `len(nodelist) != A.shape[0]`."""
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n = 5 # Number of nodes
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A = np.diag(np.ones(n - 1), k=1) # Adj. matrix for P_n
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expected = nx.path_graph(n)
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assert graphs_equal(nx.from_numpy_array(A, edge_attr=None), expected)
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nodes = list(range(n))
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assert graphs_equal(
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nx.from_numpy_array(A, edge_attr=None, nodelist=nodes), expected
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)
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# Too many node labels
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nodes = list(range(n + 1))
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with pytest.raises(ValueError, match="nodelist must have the same length as A"):
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nx.from_numpy_array(A, nodelist=nodes)
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# Too few node labels
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nodes = list(range(n - 1))
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with pytest.raises(ValueError, match="nodelist must have the same length as A"):
|
|
nx.from_numpy_array(A, nodelist=nodes)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"nodes",
|
|
(
|
|
[4, 3, 2, 1, 0],
|
|
[9, 7, 1, 2, 8],
|
|
["a", "b", "c", "d", "e"],
|
|
[(0, 0), (1, 1), (2, 3), (0, 2), (3, 1)],
|
|
["A", 2, 7, "spam", (1, 3)],
|
|
),
|
|
)
|
|
def test_from_numpy_array_nodelist(nodes):
|
|
A = np.diag(np.ones(4), k=1)
|
|
# Without edge attributes
|
|
expected = nx.relabel_nodes(
|
|
nx.path_graph(5), mapping=dict(enumerate(nodes)), copy=True
|
|
)
|
|
G = nx.from_numpy_array(A, edge_attr=None, nodelist=nodes)
|
|
assert graphs_equal(G, expected)
|
|
|
|
# With edge attributes
|
|
nx.set_edge_attributes(expected, 1.0, name="weight")
|
|
G = nx.from_numpy_array(A, nodelist=nodes)
|
|
assert graphs_equal(G, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"nodes",
|
|
(
|
|
[4, 3, 2, 1, 0],
|
|
[9, 7, 1, 2, 8],
|
|
["a", "b", "c", "d", "e"],
|
|
[(0, 0), (1, 1), (2, 3), (0, 2), (3, 1)],
|
|
["A", 2, 7, "spam", (1, 3)],
|
|
),
|
|
)
|
|
def test_from_numpy_array_nodelist_directed(nodes):
|
|
A = np.diag(np.ones(4), k=1)
|
|
# Without edge attributes
|
|
H = nx.DiGraph([(0, 1), (1, 2), (2, 3), (3, 4)])
|
|
expected = nx.relabel_nodes(H, mapping=dict(enumerate(nodes)), copy=True)
|
|
G = nx.from_numpy_array(A, create_using=nx.DiGraph, edge_attr=None, nodelist=nodes)
|
|
assert graphs_equal(G, expected)
|
|
|
|
# With edge attributes
|
|
nx.set_edge_attributes(expected, 1.0, name="weight")
|
|
G = nx.from_numpy_array(A, create_using=nx.DiGraph, nodelist=nodes)
|
|
assert graphs_equal(G, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"nodes",
|
|
(
|
|
[4, 3, 2, 1, 0],
|
|
[9, 7, 1, 2, 8],
|
|
["a", "b", "c", "d", "e"],
|
|
[(0, 0), (1, 1), (2, 3), (0, 2), (3, 1)],
|
|
["A", 2, 7, "spam", (1, 3)],
|
|
),
|
|
)
|
|
def test_from_numpy_array_nodelist_multigraph(nodes):
|
|
A = np.array(
|
|
[
|
|
[0, 1, 0, 0, 0],
|
|
[1, 0, 2, 0, 0],
|
|
[0, 2, 0, 3, 0],
|
|
[0, 0, 3, 0, 4],
|
|
[0, 0, 0, 4, 0],
|
|
]
|
|
)
|
|
|
|
H = nx.MultiGraph()
|
|
for i, edge in enumerate(((0, 1), (1, 2), (2, 3), (3, 4))):
|
|
H.add_edges_from(itertools.repeat(edge, i + 1))
|
|
expected = nx.relabel_nodes(H, mapping=dict(enumerate(nodes)), copy=True)
|
|
|
|
G = nx.from_numpy_array(
|
|
A,
|
|
parallel_edges=True,
|
|
create_using=nx.MultiGraph,
|
|
edge_attr=None,
|
|
nodelist=nodes,
|
|
)
|
|
assert graphs_equal(G, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"nodes",
|
|
(
|
|
[4, 3, 2, 1, 0],
|
|
[9, 7, 1, 2, 8],
|
|
["a", "b", "c", "d", "e"],
|
|
[(0, 0), (1, 1), (2, 3), (0, 2), (3, 1)],
|
|
["A", 2, 7, "spam", (1, 3)],
|
|
),
|
|
)
|
|
@pytest.mark.parametrize("graph", (nx.complete_graph, nx.cycle_graph, nx.wheel_graph))
|
|
def test_from_numpy_array_nodelist_rountrip(graph, nodes):
|
|
G = graph(5)
|
|
A = nx.to_numpy_array(G)
|
|
expected = nx.relabel_nodes(G, mapping=dict(enumerate(nodes)), copy=True)
|
|
H = nx.from_numpy_array(A, edge_attr=None, nodelist=nodes)
|
|
assert graphs_equal(H, expected)
|
|
|
|
# With an isolated node
|
|
G = graph(4)
|
|
G.add_node("foo")
|
|
A = nx.to_numpy_array(G)
|
|
expected = nx.relabel_nodes(G, mapping=dict(zip(G.nodes, nodes)), copy=True)
|
|
H = nx.from_numpy_array(A, edge_attr=None, nodelist=nodes)
|
|
assert graphs_equal(H, expected)
|