two version of R2R are here HEAD master

1 files changed, 282 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/networkx/tests/test_convert_scipy.py b/.venv/lib/python3.12/site-packages/networkx/tests/test_convert_scipy.py
new file mode 100644
index 00000000..aa513b85
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/networkx/tests/test_convert_scipy.py
@@ -0,0 +1,282 @@
+import pytest
+
+np = pytest.importorskip("numpy")
+sp = pytest.importorskip("scipy")
+
+import networkx as nx
+from networkx.generators.classic import barbell_graph, cycle_graph, path_graph
+from networkx.utils import graphs_equal
+
+
+class TestConvertScipy:
+    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):
+        class G:
+            format = None
+
+        pytest.raises(nx.NetworkXError, nx.to_networkx_graph, G)
+
+    def create_weighted(self, G):
+        g = cycle_graph(4)
+        e = list(g.edges())
+        source = [u for u, v in e]
+        dest = [v for u, v in e]
+        weight = [s + 10 for s in source]
+        ex = zip(source, dest, weight)
+        G.add_weighted_edges_from(ex)
+        return G
+
+    def identity_conversion(self, G, A, create_using):
+        GG = nx.from_scipy_sparse_array(A, create_using=create_using)
+        assert nx.is_isomorphic(G, GG)
+
+        GW = nx.to_networkx_graph(A, create_using=create_using)
+        assert nx.is_isomorphic(G, GW)
+
+        GI = nx.empty_graph(0, create_using).__class__(A)
+        assert nx.is_isomorphic(G, GI)
+
+        ACSR = A.tocsr()
+        GI = nx.empty_graph(0, create_using).__class__(ACSR)
+        assert nx.is_isomorphic(G, GI)
+
+        ACOO = A.tocoo()
+        GI = nx.empty_graph(0, create_using).__class__(ACOO)
+        assert nx.is_isomorphic(G, GI)
+
+        ACSC = A.tocsc()
+        GI = nx.empty_graph(0, create_using).__class__(ACSC)
+        assert nx.is_isomorphic(G, GI)
+
+        AD = A.todense()
+        GI = nx.empty_graph(0, create_using).__class__(AD)
+        assert nx.is_isomorphic(G, GI)
+
+        AA = A.toarray()
+        GI = nx.empty_graph(0, create_using).__class__(AA)
+        assert nx.is_isomorphic(G, GI)
+
+    def test_shape(self):
+        "Conversion from non-square sparse array."
+        A = sp.sparse.lil_array([[1, 2, 3], [4, 5, 6]])
+        pytest.raises(nx.NetworkXError, nx.from_scipy_sparse_array, A)
+
+    def test_identity_graph_matrix(self):
+        "Conversion from graph to sparse matrix to graph."
+        A = nx.to_scipy_sparse_array(self.G1)
+        self.identity_conversion(self.G1, A, nx.Graph())
+
+    def test_identity_digraph_matrix(self):
+        "Conversion from digraph to sparse matrix to digraph."
+        A = nx.to_scipy_sparse_array(self.G2)
+        self.identity_conversion(self.G2, A, nx.DiGraph())
+
+    def test_identity_weighted_graph_matrix(self):
+        """Conversion from weighted graph to sparse matrix to weighted graph."""
+        A = nx.to_scipy_sparse_array(self.G3)
+        self.identity_conversion(self.G3, A, nx.Graph())
+
+    def test_identity_weighted_digraph_matrix(self):
+        """Conversion from weighted digraph to sparse matrix to weighted digraph."""
+        A = nx.to_scipy_sparse_array(self.G4)
+        self.identity_conversion(self.G4, A, nx.DiGraph())
+
+    def test_nodelist(self):
+        """Conversion from graph to sparse matrix to graph with nodelist."""
+        P4 = path_graph(4)
+        P3 = path_graph(3)
+        nodelist = list(P3.nodes())
+        A = nx.to_scipy_sparse_array(P4, nodelist=nodelist)
+        GA = nx.Graph(A)
+        assert nx.is_isomorphic(GA, P3)
+
+        pytest.raises(nx.NetworkXError, nx.to_scipy_sparse_array, P3, nodelist=[])
+        # Test nodelist duplicates.
+        long_nl = nodelist + [0]
+        pytest.raises(nx.NetworkXError, nx.to_scipy_sparse_array, P3, nodelist=long_nl)
+
+        # Test nodelist contains non-nodes
+        non_nl = [-1, 0, 1, 2]
+        pytest.raises(nx.NetworkXError, nx.to_scipy_sparse_array, P3, nodelist=non_nl)
+
+    def test_weight_keyword(self):
+        WP4 = nx.Graph()
+        WP4.add_edges_from((n, n + 1, {"weight": 0.5, "other": 0.3}) for n in range(3))
+        P4 = path_graph(4)
+        A = nx.to_scipy_sparse_array(P4)
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+        np.testing.assert_equal(
+            0.5 * A.todense(), nx.to_scipy_sparse_array(WP4).todense()
+        )
+        np.testing.assert_equal(
+            0.3 * A.todense(), nx.to_scipy_sparse_array(WP4, weight="other").todense()
+        )
+
+    def test_format_keyword(self):
+        WP4 = nx.Graph()
+        WP4.add_edges_from((n, n + 1, {"weight": 0.5, "other": 0.3}) for n in range(3))
+        P4 = path_graph(4)
+        A = nx.to_scipy_sparse_array(P4, format="csr")
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+
+        A = nx.to_scipy_sparse_array(P4, format="csc")
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+
+        A = nx.to_scipy_sparse_array(P4, format="coo")
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+
+        A = nx.to_scipy_sparse_array(P4, format="bsr")
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+
+        A = nx.to_scipy_sparse_array(P4, format="lil")
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+
+        A = nx.to_scipy_sparse_array(P4, format="dia")
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+
+        A = nx.to_scipy_sparse_array(P4, format="dok")
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+
+    def test_format_keyword_raise(self):
+        with pytest.raises(nx.NetworkXError):
+            WP4 = nx.Graph()
+            WP4.add_edges_from(
+                (n, n + 1, {"weight": 0.5, "other": 0.3}) for n in range(3)
+            )
+            P4 = path_graph(4)
+            nx.to_scipy_sparse_array(P4, format="any_other")
+
+    def test_null_raise(self):
+        with pytest.raises(nx.NetworkXError):
+            nx.to_scipy_sparse_array(nx.Graph())
+
+    def test_empty(self):
+        G = nx.Graph()
+        G.add_node(1)
+        M = nx.to_scipy_sparse_array(G)
+        np.testing.assert_equal(M.toarray(), np.array([[0]]))
+
+    def test_ordering(self):
+        G = nx.DiGraph()
+        G.add_edge(1, 2)
+        G.add_edge(2, 3)
+        G.add_edge(3, 1)
+        M = nx.to_scipy_sparse_array(G, nodelist=[3, 2, 1])
+        np.testing.assert_equal(
+            M.toarray(), np.array([[0, 0, 1], [1, 0, 0], [0, 1, 0]])
+        )
+
+    def test_selfloop_graph(self):
+        G = nx.Graph([(1, 1)])
+        M = nx.to_scipy_sparse_array(G)
+        np.testing.assert_equal(M.toarray(), np.array([[1]]))
+
+        G.add_edges_from([(2, 3), (3, 4)])
+        M = nx.to_scipy_sparse_array(G, nodelist=[2, 3, 4])
+        np.testing.assert_equal(
+            M.toarray(), np.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]])
+        )
+
+    def test_selfloop_digraph(self):
+        G = nx.DiGraph([(1, 1)])
+        M = nx.to_scipy_sparse_array(G)
+        np.testing.assert_equal(M.toarray(), np.array([[1]]))
+
+        G.add_edges_from([(2, 3), (3, 4)])
+        M = nx.to_scipy_sparse_array(G, nodelist=[2, 3, 4])
+        np.testing.assert_equal(
+            M.toarray(), np.array([[0, 1, 0], [0, 0, 1], [0, 0, 0]])
+        )
+
+    def test_from_scipy_sparse_array_parallel_edges(self):
+        """Tests that the :func:`networkx.from_scipy_sparse_array` function
+        interprets integer weights as the number of parallel edges when
+        creating a multigraph.
+
+        """
+        A = sp.sparse.csr_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_scipy_sparse_array(
+            A, parallel_edges=True, create_using=nx.DiGraph
+        )
+        assert graphs_equal(actual, expected)
+        actual = nx.from_scipy_sparse_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_scipy_sparse_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_scipy_sparse_array(
+            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_scipy_sparse_array`.
+
+        """
+        A = sp.sparse.csr_array([[0, 1], [1, 0]])
+        G = nx.from_scipy_sparse_array(A, create_using=nx.MultiGraph)
+        expected = nx.MultiGraph()
+        expected.add_edge(0, 1, weight=1)
+        assert graphs_equal(G, expected)
+
+
+@pytest.mark.parametrize("sparse_format", ("csr", "csc", "dok"))
+def test_from_scipy_sparse_array_formats(sparse_format):
+    """Test all formats supported by _generate_weighted_edges."""
+    # trinode complete graph with non-uniform edge weights
+    expected = nx.Graph()
+    expected.add_edges_from(
+        [
+            (0, 1, {"weight": 3}),
+            (0, 2, {"weight": 2}),
+            (1, 0, {"weight": 3}),
+            (1, 2, {"weight": 1}),
+            (2, 0, {"weight": 2}),
+            (2, 1, {"weight": 1}),
+        ]
+    )
+    A = sp.sparse.coo_array([[0, 3, 2], [3, 0, 1], [2, 1, 0]]).asformat(sparse_format)
+    assert graphs_equal(expected, nx.from_scipy_sparse_array(A))