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env-llmeval/lib/python3.10/site-packages/networkx/classes/tests/historical_tests.py

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+"""Original NetworkX graph tests"""
+import pytest
+
+import networkx as nx
+from networkx import convert_node_labels_to_integers as cnlti
+from networkx.utils import edges_equal, nodes_equal
+
+
+class HistoricalTests:
+    @classmethod
+    def setup_class(cls):
+        cls.null = nx.null_graph()
+        cls.P1 = cnlti(nx.path_graph(1), first_label=1)
+        cls.P3 = cnlti(nx.path_graph(3), first_label=1)
+        cls.P10 = cnlti(nx.path_graph(10), first_label=1)
+        cls.K1 = cnlti(nx.complete_graph(1), first_label=1)
+        cls.K3 = cnlti(nx.complete_graph(3), first_label=1)
+        cls.K4 = cnlti(nx.complete_graph(4), first_label=1)
+        cls.K5 = cnlti(nx.complete_graph(5), first_label=1)
+        cls.K10 = cnlti(nx.complete_graph(10), first_label=1)
+        cls.G = nx.Graph
+
+    def test_name(self):
+        G = self.G(name="test")
+        assert G.name == "test"
+        H = self.G()
+        assert H.name == ""
+
+    # Nodes
+
+    def test_add_remove_node(self):
+        G = self.G()
+        G.add_node("A")
+        assert G.has_node("A")
+        G.remove_node("A")
+        assert not G.has_node("A")
+
+    def test_nonhashable_node(self):
+        # Test if a non-hashable object is in the Graph.  A python dict will
+        # raise a TypeError, but for a Graph class a simple  False should be
+        # returned (see Graph __contains__). If it cannot be a node then it is
+        # not a node.
+        G = self.G()
+        assert not G.has_node(["A"])
+        assert not G.has_node({"A": 1})
+
+    def test_add_nodes_from(self):
+        G = self.G()
+        G.add_nodes_from(list("ABCDEFGHIJKL"))
+        assert G.has_node("L")
+        G.remove_nodes_from(["H", "I", "J", "K", "L"])
+        G.add_nodes_from([1, 2, 3, 4])
+        assert sorted(G.nodes(), key=str) == [
+            1,
+            2,
+            3,
+            4,
+            "A",
+            "B",
+            "C",
+            "D",
+            "E",
+            "F",
+            "G",
+        ]
+        # test __iter__
+        assert sorted(G, key=str) == [1, 2, 3, 4, "A", "B", "C", "D", "E", "F", "G"]
+
+    def test_contains(self):
+        G = self.G()
+        G.add_node("A")
+        assert "A" in G
+        assert [] not in G  # never raise a Key or TypeError in this test
+        assert {1: 1} not in G
+
+    def test_add_remove(self):
+        # Test add_node and remove_node acting for various nbunch
+        G = self.G()
+        G.add_node("m")
+        assert G.has_node("m")
+        G.add_node("m")  # no complaints
+        pytest.raises(nx.NetworkXError, G.remove_node, "j")
+        G.remove_node("m")
+        assert list(G) == []
+
+    def test_nbunch_is_list(self):
+        G = self.G()
+        G.add_nodes_from(list("ABCD"))
+        G.add_nodes_from(self.P3)  # add nbunch of nodes (nbunch=Graph)
+        assert sorted(G.nodes(), key=str) == [1, 2, 3, "A", "B", "C", "D"]
+        G.remove_nodes_from(self.P3)  # remove nbunch of nodes (nbunch=Graph)
+        assert sorted(G.nodes(), key=str) == ["A", "B", "C", "D"]
+
+    def test_nbunch_is_set(self):
+        G = self.G()
+        nbunch = set("ABCDEFGHIJKL")
+        G.add_nodes_from(nbunch)
+        assert G.has_node("L")
+
+    def test_nbunch_dict(self):
+        # nbunch is a dict with nodes as keys
+        G = self.G()
+        nbunch = set("ABCDEFGHIJKL")
+        G.add_nodes_from(nbunch)
+        nbunch = {"I": "foo", "J": 2, "K": True, "L": "spam"}
+        G.remove_nodes_from(nbunch)
+        assert sorted(G.nodes(), key=str), ["A", "B", "C", "D", "E", "F", "G", "H"]
+
+    def test_nbunch_iterator(self):
+        G = self.G()
+        G.add_nodes_from(["A", "B", "C", "D", "E", "F", "G", "H"])
+        n_iter = self.P3.nodes()
+        G.add_nodes_from(n_iter)
+        assert sorted(G.nodes(), key=str) == [
+            1,
+            2,
+            3,
+            "A",
+            "B",
+            "C",
+            "D",
+            "E",
+            "F",
+            "G",
+            "H",
+        ]
+        n_iter = self.P3.nodes()  # rebuild same iterator
+        G.remove_nodes_from(n_iter)  # remove nbunch of nodes (nbunch=iterator)
+        assert sorted(G.nodes(), key=str) == ["A", "B", "C", "D", "E", "F", "G", "H"]
+
+    def test_nbunch_graph(self):
+        G = self.G()
+        G.add_nodes_from(["A", "B", "C", "D", "E", "F", "G", "H"])
+        nbunch = self.K3
+        G.add_nodes_from(nbunch)
+        assert sorted(G.nodes(), key=str), [
+            1,
+            2,
+            3,
+            "A",
+            "B",
+            "C",
+            "D",
+            "E",
+            "F",
+            "G",
+            "H",
+        ]
+
+    # Edges
+
+    def test_add_edge(self):
+        G = self.G()
+        pytest.raises(TypeError, G.add_edge, "A")
+
+        G.add_edge("A", "B")  # testing add_edge()
+        G.add_edge("A", "B")  # should fail silently
+        assert G.has_edge("A", "B")
+        assert not G.has_edge("A", "C")
+        assert G.has_edge(*("A", "B"))
+        if G.is_directed():
+            assert not G.has_edge("B", "A")
+        else:
+            # G is undirected, so B->A is an edge
+            assert G.has_edge("B", "A")
+
+        G.add_edge("A", "C")  # test directedness
+        G.add_edge("C", "A")
+        G.remove_edge("C", "A")
+        if G.is_directed():
+            assert G.has_edge("A", "C")
+        else:
+            assert not G.has_edge("A", "C")
+        assert not G.has_edge("C", "A")
+
+    def test_self_loop(self):
+        G = self.G()
+        G.add_edge("A", "A")  # test self loops
+        assert G.has_edge("A", "A")
+        G.remove_edge("A", "A")
+        G.add_edge("X", "X")
+        assert G.has_node("X")
+        G.remove_node("X")
+        G.add_edge("A", "Z")  # should add the node silently
+        assert G.has_node("Z")
+
+    def test_add_edges_from(self):
+        G = self.G()
+        G.add_edges_from([("B", "C")])  # test add_edges_from()
+        assert G.has_edge("B", "C")
+        if G.is_directed():
+            assert not G.has_edge("C", "B")
+        else:
+            assert G.has_edge("C", "B")  # undirected
+
+        G.add_edges_from([("D", "F"), ("B", "D")])
+        assert G.has_edge("D", "F")
+        assert G.has_edge("B", "D")
+
+        if G.is_directed():
+            assert not G.has_edge("D", "B")
+        else:
+            assert G.has_edge("D", "B")  # undirected
+
+    def test_add_edges_from2(self):
+        G = self.G()
+        # after failing silently, should add 2nd edge
+        G.add_edges_from([tuple("IJ"), list("KK"), tuple("JK")])
+        assert G.has_edge(*("I", "J"))
+        assert G.has_edge(*("K", "K"))
+        assert G.has_edge(*("J", "K"))
+        if G.is_directed():
+            assert not G.has_edge(*("K", "J"))
+        else:
+            assert G.has_edge(*("K", "J"))
+
+    def test_add_edges_from3(self):
+        G = self.G()
+        G.add_edges_from(zip(list("ACD"), list("CDE")))
+        assert G.has_edge("D", "E")
+        assert not G.has_edge("E", "C")
+
+    def test_remove_edge(self):
+        G = self.G()
+        G.add_nodes_from([1, 2, 3, "A", "B", "C", "D", "E", "F", "G", "H"])
+
+        G.add_edges_from(zip(list("MNOP"), list("NOPM")))
+        assert G.has_edge("O", "P")
+        assert G.has_edge("P", "M")
+        G.remove_node("P")  # tests remove_node()'s handling of edges.
+        assert not G.has_edge("P", "M")
+        pytest.raises(TypeError, G.remove_edge, "M")
+
+        G.add_edge("N", "M")
+        assert G.has_edge("M", "N")
+        G.remove_edge("M", "N")
+        assert not G.has_edge("M", "N")
+
+        # self loop fails silently
+        G.remove_edges_from([list("HI"), list("DF"), tuple("KK"), tuple("JK")])
+        assert not G.has_edge("H", "I")
+        assert not G.has_edge("J", "K")
+        G.remove_edges_from([list("IJ"), list("KK"), list("JK")])
+        assert not G.has_edge("I", "J")
+        G.remove_nodes_from(set("ZEFHIMNO"))
+        G.add_edge("J", "K")
+
+    def test_edges_nbunch(self):
+        # Test G.edges(nbunch) with various forms of nbunch
+        G = self.G()
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "D"), ("C", "B"), ("C", "D")])
+        # node not in nbunch should be quietly ignored
+        pytest.raises(nx.NetworkXError, G.edges, 6)
+        assert list(G.edges("Z")) == []  # iterable non-node
+        # nbunch can be an empty list
+        assert list(G.edges([])) == []
+        if G.is_directed():
+            elist = [("A", "B"), ("A", "C"), ("B", "D")]
+        else:
+            elist = [("A", "B"), ("A", "C"), ("B", "C"), ("B", "D")]
+        # nbunch can be a list
+        assert edges_equal(list(G.edges(["A", "B"])), elist)
+        # nbunch can be a set
+        assert edges_equal(G.edges({"A", "B"}), elist)
+        # nbunch can be a graph
+        G1 = self.G()
+        G1.add_nodes_from("AB")
+        assert edges_equal(G.edges(G1), elist)
+        # nbunch can be a dict with nodes as keys
+        ndict = {"A": "thing1", "B": "thing2"}
+        assert edges_equal(G.edges(ndict), elist)
+        # nbunch can be a single node
+        assert edges_equal(list(G.edges("A")), [("A", "B"), ("A", "C")])
+        assert nodes_equal(sorted(G), ["A", "B", "C", "D"])
+
+        # nbunch can be nothing (whole graph)
+        assert edges_equal(
+            list(G.edges()),
+            [("A", "B"), ("A", "C"), ("B", "D"), ("C", "B"), ("C", "D")],
+        )
+
+    def test_degree(self):
+        G = self.G()
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "D"), ("C", "B"), ("C", "D")])
+        assert G.degree("A") == 2
+
+        # degree of single node in iterable container must return dict
+        assert list(G.degree(["A"])) == [("A", 2)]
+        assert sorted(d for n, d in G.degree(["A", "B"])) == [2, 3]
+        assert sorted(d for n, d in G.degree()) == [2, 2, 3, 3]
+
+    def test_degree2(self):
+        H = self.G()
+        H.add_edges_from([(1, 24), (1, 2)])
+        assert sorted(d for n, d in H.degree([1, 24])) == [1, 2]
+
+    def test_degree_graph(self):
+        P3 = nx.path_graph(3)
+        P5 = nx.path_graph(5)
+        # silently ignore nodes not in P3
+        assert dict(d for n, d in P3.degree(["A", "B"])) == {}
+        # nbunch can be a graph
+        assert sorted(d for n, d in P5.degree(P3)) == [1, 2, 2]
+        # nbunch can be a graph that's way too big
+        assert sorted(d for n, d in P3.degree(P5)) == [1, 1, 2]
+        assert list(P5.degree([])) == []
+        assert dict(P5.degree([])) == {}
+
+    def test_null(self):
+        null = nx.null_graph()
+        assert list(null.degree()) == []
+        assert dict(null.degree()) == {}
+
+    def test_order_size(self):
+        G = self.G()
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "D"), ("C", "B"), ("C", "D")])
+        assert G.order() == 4
+        assert G.size() == 5
+        assert G.number_of_edges() == 5
+        assert G.number_of_edges("A", "B") == 1
+        assert G.number_of_edges("A", "D") == 0
+
+    def test_copy(self):
+        G = self.G()
+        H = G.copy()  # copy
+        assert H.adj == G.adj
+        assert H.name == G.name
+        assert H is not G
+
+    def test_subgraph(self):
+        G = self.G()
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "D"), ("C", "B"), ("C", "D")])
+        SG = G.subgraph(["A", "B", "D"])
+        assert nodes_equal(list(SG), ["A", "B", "D"])
+        assert edges_equal(list(SG.edges()), [("A", "B"), ("B", "D")])
+
+    def test_to_directed(self):
+        G = self.G()
+        if not G.is_directed():
+            G.add_edges_from(
+                [("A", "B"), ("A", "C"), ("B", "D"), ("C", "B"), ("C", "D")]
+            )
+
+            DG = G.to_directed()
+            assert DG is not G  # directed copy or copy
+
+            assert DG.is_directed()
+            assert DG.name == G.name
+            assert DG.adj == G.adj
+            assert sorted(DG.out_edges(list("AB"))) == [
+                ("A", "B"),
+                ("A", "C"),
+                ("B", "A"),
+                ("B", "C"),
+                ("B", "D"),
+            ]
+            DG.remove_edge("A", "B")
+            assert DG.has_edge("B", "A")  # this removes B-A but not  A-B
+            assert not DG.has_edge("A", "B")
+
+    def test_to_undirected(self):
+        G = self.G()
+        if G.is_directed():
+            G.add_edges_from(
+                [("A", "B"), ("A", "C"), ("B", "D"), ("C", "B"), ("C", "D")]
+            )
+            UG = G.to_undirected()  # to_undirected
+            assert UG is not G
+            assert not UG.is_directed()
+            assert G.is_directed()
+            assert UG.name == G.name
+            assert UG.adj != G.adj
+            assert sorted(UG.edges(list("AB"))) == [
+                ("A", "B"),
+                ("A", "C"),
+                ("B", "C"),
+                ("B", "D"),
+            ]
+            assert sorted(UG.edges(["A", "B"])) == [
+                ("A", "B"),
+                ("A", "C"),
+                ("B", "C"),
+                ("B", "D"),
+            ]
+            UG.remove_edge("A", "B")
+            assert not UG.has_edge("B", "A")
+            assert not UG.has_edge("A", "B")
+
+    def test_neighbors(self):
+        G = self.G()
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "D"), ("C", "B"), ("C", "D")])
+        G.add_nodes_from("GJK")
+        assert sorted(G["A"]) == ["B", "C"]
+        assert sorted(G.neighbors("A")) == ["B", "C"]
+        assert sorted(G.neighbors("A")) == ["B", "C"]
+        assert sorted(G.neighbors("G")) == []
+        pytest.raises(nx.NetworkXError, G.neighbors, "j")
+
+    def test_iterators(self):
+        G = self.G()
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "D"), ("C", "B"), ("C", "D")])
+        G.add_nodes_from("GJK")
+        assert sorted(G.nodes()) == ["A", "B", "C", "D", "G", "J", "K"]
+        assert edges_equal(
+            G.edges(), [("A", "B"), ("A", "C"), ("B", "D"), ("C", "B"), ("C", "D")]
+        )
+
+        assert sorted(v for k, v in G.degree()) == [0, 0, 0, 2, 2, 3, 3]
+        assert sorted(G.degree(), key=str) == [
+            ("A", 2),
+            ("B", 3),
+            ("C", 3),
+            ("D", 2),
+            ("G", 0),
+            ("J", 0),
+            ("K", 0),
+        ]
+        assert sorted(G.neighbors("A")) == ["B", "C"]
+        pytest.raises(nx.NetworkXError, G.neighbors, "X")
+        G.clear()
+        assert nx.number_of_nodes(G) == 0
+        assert nx.number_of_edges(G) == 0
+
+    def test_null_subgraph(self):
+        # Subgraph of a null graph is a null graph
+        nullgraph = nx.null_graph()
+        G = nx.null_graph()
+        H = G.subgraph([])
+        assert nx.is_isomorphic(H, nullgraph)
+
+    def test_empty_subgraph(self):
+        # Subgraph of an empty graph is an empty graph. test 1
+        nullgraph = nx.null_graph()
+        E5 = nx.empty_graph(5)
+        E10 = nx.empty_graph(10)
+        H = E10.subgraph([])
+        assert nx.is_isomorphic(H, nullgraph)
+        H = E10.subgraph([1, 2, 3, 4, 5])
+        assert nx.is_isomorphic(H, E5)
+
+    def test_complete_subgraph(self):
+        # Subgraph of a complete graph is a complete graph
+        K1 = nx.complete_graph(1)
+        K3 = nx.complete_graph(3)
+        K5 = nx.complete_graph(5)
+        H = K5.subgraph([1, 2, 3])
+        assert nx.is_isomorphic(H, K3)
+
+    def test_subgraph_nbunch(self):
+        nullgraph = nx.null_graph()
+        K1 = nx.complete_graph(1)
+        K3 = nx.complete_graph(3)
+        K5 = nx.complete_graph(5)
+        # Test G.subgraph(nbunch), where nbunch is a single node
+        H = K5.subgraph(1)
+        assert nx.is_isomorphic(H, K1)
+        # Test G.subgraph(nbunch), where nbunch is a set
+        H = K5.subgraph({1})
+        assert nx.is_isomorphic(H, K1)
+        # Test G.subgraph(nbunch), where nbunch is an iterator
+        H = K5.subgraph(iter(K3))
+        assert nx.is_isomorphic(H, K3)
+        # Test G.subgraph(nbunch), where nbunch is another graph
+        H = K5.subgraph(K3)
+        assert nx.is_isomorphic(H, K3)
+        H = K5.subgraph([9])
+        assert nx.is_isomorphic(H, nullgraph)
+
+    def test_node_tuple_issue(self):
+        H = self.G()
+        # Test error handling of tuple as a node
+        pytest.raises(nx.NetworkXError, H.remove_node, (1, 2))
+        H.remove_nodes_from([(1, 2)])  # no error
+        pytest.raises(nx.NetworkXError, H.neighbors, (1, 2))

env-llmeval/lib/python3.10/site-packages/networkx/classes/tests/test_coreviews.py

@@ -0,0 +1,362 @@
+import pickle
+
+import pytest
+
+import networkx as nx
+
+
+class TestAtlasView:
+    # node->data
+    def setup_method(self):
+        self.d = {0: {"color": "blue", "weight": 1.2}, 1: {}, 2: {"color": 1}}
+        self.av = nx.classes.coreviews.AtlasView(self.d)
+
+    def test_pickle(self):
+        view = self.av
+        pview = pickle.loads(pickle.dumps(view, -1))
+        assert view == pview
+        assert view.__slots__ == pview.__slots__
+        pview = pickle.loads(pickle.dumps(view))
+        assert view == pview
+        assert view.__slots__ == pview.__slots__
+
+    def test_len(self):
+        assert len(self.av) == len(self.d)
+
+    def test_iter(self):
+        assert list(self.av) == list(self.d)
+
+    def test_getitem(self):
+        assert self.av[1] is self.d[1]
+        assert self.av[2]["color"] == 1
+        pytest.raises(KeyError, self.av.__getitem__, 3)
+
+    def test_copy(self):
+        avcopy = self.av.copy()
+        assert avcopy[0] == self.av[0]
+        assert avcopy == self.av
+        assert avcopy[0] is not self.av[0]
+        assert avcopy is not self.av
+        avcopy[5] = {}
+        assert avcopy != self.av
+
+        avcopy[0]["ht"] = 4
+        assert avcopy[0] != self.av[0]
+        self.av[0]["ht"] = 4
+        assert avcopy[0] == self.av[0]
+        del self.av[0]["ht"]
+
+        assert not hasattr(self.av, "__setitem__")
+
+    def test_items(self):
+        assert sorted(self.av.items()) == sorted(self.d.items())
+
+    def test_str(self):
+        out = str(self.d)
+        assert str(self.av) == out
+
+    def test_repr(self):
+        out = "AtlasView(" + str(self.d) + ")"
+        assert repr(self.av) == out
+
+
+class TestAdjacencyView:
+    # node->nbr->data
+    def setup_method(self):
+        dd = {"color": "blue", "weight": 1.2}
+        self.nd = {0: dd, 1: {}, 2: {"color": 1}}
+        self.adj = {3: self.nd, 0: {3: dd}, 1: {}, 2: {3: {"color": 1}}}
+        self.adjview = nx.classes.coreviews.AdjacencyView(self.adj)
+
+    def test_pickle(self):
+        view = self.adjview
+        pview = pickle.loads(pickle.dumps(view, -1))
+        assert view == pview
+        assert view.__slots__ == pview.__slots__
+
+    def test_len(self):
+        assert len(self.adjview) == len(self.adj)
+
+    def test_iter(self):
+        assert list(self.adjview) == list(self.adj)
+
+    def test_getitem(self):
+        assert self.adjview[1] is not self.adj[1]
+        assert self.adjview[3][0] is self.adjview[0][3]
+        assert self.adjview[2][3]["color"] == 1
+        pytest.raises(KeyError, self.adjview.__getitem__, 4)
+
+    def test_copy(self):
+        avcopy = self.adjview.copy()
+        assert avcopy[0] == self.adjview[0]
+        assert avcopy[0] is not self.adjview[0]
+
+        avcopy[2][3]["ht"] = 4
+        assert avcopy[2] != self.adjview[2]
+        self.adjview[2][3]["ht"] = 4
+        assert avcopy[2] == self.adjview[2]
+        del self.adjview[2][3]["ht"]
+
+        assert not hasattr(self.adjview, "__setitem__")
+
+    def test_items(self):
+        view_items = sorted((n, dict(d)) for n, d in self.adjview.items())
+        assert view_items == sorted(self.adj.items())
+
+    def test_str(self):
+        out = str(dict(self.adj))
+        assert str(self.adjview) == out
+
+    def test_repr(self):
+        out = self.adjview.__class__.__name__ + "(" + str(self.adj) + ")"
+        assert repr(self.adjview) == out
+
+
+class TestMultiAdjacencyView(TestAdjacencyView):
+    # node->nbr->key->data
+    def setup_method(self):
+        dd = {"color": "blue", "weight": 1.2}
+        self.kd = {0: dd, 1: {}, 2: {"color": 1}}
+        self.nd = {3: self.kd, 0: {3: dd}, 1: {0: {}}, 2: {3: {"color": 1}}}
+        self.adj = {3: self.nd, 0: {3: {3: dd}}, 1: {}, 2: {3: {8: {}}}}
+        self.adjview = nx.classes.coreviews.MultiAdjacencyView(self.adj)
+
+    def test_getitem(self):
+        assert self.adjview[1] is not self.adj[1]
+        assert self.adjview[3][0][3] is self.adjview[0][3][3]
+        assert self.adjview[3][2][3]["color"] == 1
+        pytest.raises(KeyError, self.adjview.__getitem__, 4)
+
+    def test_copy(self):
+        avcopy = self.adjview.copy()
+        assert avcopy[0] == self.adjview[0]
+        assert avcopy[0] is not self.adjview[0]
+
+        avcopy[2][3][8]["ht"] = 4
+        assert avcopy[2] != self.adjview[2]
+        self.adjview[2][3][8]["ht"] = 4
+        assert avcopy[2] == self.adjview[2]
+        del self.adjview[2][3][8]["ht"]
+
+        assert not hasattr(self.adjview, "__setitem__")
+
+
+class TestUnionAtlas:
+    # node->data
+    def setup_method(self):
+        self.s = {0: {"color": "blue", "weight": 1.2}, 1: {}, 2: {"color": 1}}
+        self.p = {3: {"color": "blue", "weight": 1.2}, 4: {}, 2: {"watch": 2}}
+        self.av = nx.classes.coreviews.UnionAtlas(self.s, self.p)
+
+    def test_pickle(self):
+        view = self.av
+        pview = pickle.loads(pickle.dumps(view, -1))
+        assert view == pview
+        assert view.__slots__ == pview.__slots__
+
+    def test_len(self):
+        assert len(self.av) == len(self.s.keys() | self.p.keys()) == 5
+
+    def test_iter(self):
+        assert set(self.av) == set(self.s) | set(self.p)
+
+    def test_getitem(self):
+        assert self.av[0] is self.s[0]
+        assert self.av[4] is self.p[4]
+        assert self.av[2]["color"] == 1
+        pytest.raises(KeyError, self.av[2].__getitem__, "watch")
+        pytest.raises(KeyError, self.av.__getitem__, 8)
+
+    def test_copy(self):
+        avcopy = self.av.copy()
+        assert avcopy[0] == self.av[0]
+        assert avcopy[0] is not self.av[0]
+        assert avcopy is not self.av
+        avcopy[5] = {}
+        assert avcopy != self.av
+
+        avcopy[0]["ht"] = 4
+        assert avcopy[0] != self.av[0]
+        self.av[0]["ht"] = 4
+        assert avcopy[0] == self.av[0]
+        del self.av[0]["ht"]
+
+        assert not hasattr(self.av, "__setitem__")
+
+    def test_items(self):
+        expected = dict(self.p.items())
+        expected.update(self.s)
+        assert sorted(self.av.items()) == sorted(expected.items())
+
+    def test_str(self):
+        out = str(dict(self.av))
+        assert str(self.av) == out
+
+    def test_repr(self):
+        out = f"{self.av.__class__.__name__}({self.s}, {self.p})"
+        assert repr(self.av) == out
+
+
+class TestUnionAdjacency:
+    # node->nbr->data
+    def setup_method(self):
+        dd = {"color": "blue", "weight": 1.2}
+        self.nd = {0: dd, 1: {}, 2: {"color": 1}}
+        self.s = {3: self.nd, 0: {}, 1: {}, 2: {3: {"color": 1}}}
+        self.p = {3: {}, 0: {3: dd}, 1: {0: {}}, 2: {1: {"color": 1}}}
+        self.adjview = nx.classes.coreviews.UnionAdjacency(self.s, self.p)
+
+    def test_pickle(self):
+        view = self.adjview
+        pview = pickle.loads(pickle.dumps(view, -1))
+        assert view == pview
+        assert view.__slots__ == pview.__slots__
+
+    def test_len(self):
+        assert len(self.adjview) == len(self.s)
+
+    def test_iter(self):
+        assert sorted(self.adjview) == sorted(self.s)
+
+    def test_getitem(self):
+        assert self.adjview[1] is not self.s[1]
+        assert self.adjview[3][0] is self.adjview[0][3]
+        assert self.adjview[2][3]["color"] == 1
+        pytest.raises(KeyError, self.adjview.__getitem__, 4)
+
+    def test_copy(self):
+        avcopy = self.adjview.copy()
+        assert avcopy[0] == self.adjview[0]
+        assert avcopy[0] is not self.adjview[0]
+
+        avcopy[2][3]["ht"] = 4
+        assert avcopy[2] != self.adjview[2]
+        self.adjview[2][3]["ht"] = 4
+        assert avcopy[2] == self.adjview[2]
+        del self.adjview[2][3]["ht"]
+
+        assert not hasattr(self.adjview, "__setitem__")
+
+    def test_str(self):
+        out = str(dict(self.adjview))
+        assert str(self.adjview) == out
+
+    def test_repr(self):
+        clsname = self.adjview.__class__.__name__
+        out = f"{clsname}({self.s}, {self.p})"
+        assert repr(self.adjview) == out
+
+
+class TestUnionMultiInner(TestUnionAdjacency):
+    # nbr->key->data
+    def setup_method(self):
+        dd = {"color": "blue", "weight": 1.2}
+        self.kd = {7: {}, "ekey": {}, 9: {"color": 1}}
+        self.s = {3: self.kd, 0: {7: dd}, 1: {}, 2: {"key": {"color": 1}}}
+        self.p = {3: {}, 0: {3: dd}, 1: {}, 2: {1: {"span": 2}}}
+        self.adjview = nx.classes.coreviews.UnionMultiInner(self.s, self.p)
+
+    def test_len(self):
+        assert len(self.adjview) == len(self.s.keys() | self.p.keys()) == 4
+
+    def test_getitem(self):
+        assert self.adjview[1] is not self.s[1]
+        assert self.adjview[0][7] is self.adjview[0][3]
+        assert self.adjview[2]["key"]["color"] == 1
+        assert self.adjview[2][1]["span"] == 2
+        pytest.raises(KeyError, self.adjview.__getitem__, 4)
+        pytest.raises(KeyError, self.adjview[1].__getitem__, "key")
+
+    def test_copy(self):
+        avcopy = self.adjview.copy()
+        assert avcopy[0] == self.adjview[0]
+        assert avcopy[0] is not self.adjview[0]
+
+        avcopy[2][1]["width"] = 8
+        assert avcopy[2] != self.adjview[2]
+        self.adjview[2][1]["width"] = 8
+        assert avcopy[2] == self.adjview[2]
+        del self.adjview[2][1]["width"]
+
+        assert not hasattr(self.adjview, "__setitem__")
+        assert hasattr(avcopy, "__setitem__")
+
+
+class TestUnionMultiAdjacency(TestUnionAdjacency):
+    # node->nbr->key->data
+    def setup_method(self):
+        dd = {"color": "blue", "weight": 1.2}
+        self.kd = {7: {}, 8: {}, 9: {"color": 1}}
+        self.nd = {3: self.kd, 0: {9: dd}, 1: {8: {}}, 2: {9: {"color": 1}}}
+        self.s = {3: self.nd, 0: {3: {7: dd}}, 1: {}, 2: {3: {8: {}}}}
+        self.p = {3: {}, 0: {3: {9: dd}}, 1: {}, 2: {1: {8: {}}}}
+        self.adjview = nx.classes.coreviews.UnionMultiAdjacency(self.s, self.p)
+
+    def test_getitem(self):
+        assert self.adjview[1] is not self.s[1]
+        assert self.adjview[3][0][9] is self.adjview[0][3][9]
+        assert self.adjview[3][2][9]["color"] == 1
+        pytest.raises(KeyError, self.adjview.__getitem__, 4)
+
+    def test_copy(self):
+        avcopy = self.adjview.copy()
+        assert avcopy[0] == self.adjview[0]
+        assert avcopy[0] is not self.adjview[0]
+
+        avcopy[2][3][8]["ht"] = 4
+        assert avcopy[2] != self.adjview[2]
+        self.adjview[2][3][8]["ht"] = 4
+        assert avcopy[2] == self.adjview[2]
+        del self.adjview[2][3][8]["ht"]
+
+        assert not hasattr(self.adjview, "__setitem__")
+        assert hasattr(avcopy, "__setitem__")
+
+
+class TestFilteredGraphs:
+    def setup_method(self):
+        self.Graphs = [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph]
+
+    def test_hide_show_nodes(self):
+        SubGraph = nx.subgraph_view
+        for Graph in self.Graphs:
+            G = nx.path_graph(4, Graph)
+            SG = G.subgraph([2, 3])
+            RG = SubGraph(G, filter_node=nx.filters.hide_nodes([0, 1]))
+            assert SG.nodes == RG.nodes
+            assert SG.edges == RG.edges
+            SGC = SG.copy()
+            RGC = RG.copy()
+            assert SGC.nodes == RGC.nodes
+            assert SGC.edges == RGC.edges
+
+    def test_str_repr(self):
+        SubGraph = nx.subgraph_view
+        for Graph in self.Graphs:
+            G = nx.path_graph(4, Graph)
+            SG = G.subgraph([2, 3])
+            RG = SubGraph(G, filter_node=nx.filters.hide_nodes([0, 1]))
+            str(SG.adj)
+            str(RG.adj)
+            repr(SG.adj)
+            repr(RG.adj)
+            str(SG.adj[2])
+            str(RG.adj[2])
+            repr(SG.adj[2])
+            repr(RG.adj[2])
+
+    def test_copy(self):
+        SubGraph = nx.subgraph_view
+        for Graph in self.Graphs:
+            G = nx.path_graph(4, Graph)
+            SG = G.subgraph([2, 3])
+            RG = SubGraph(G, filter_node=nx.filters.hide_nodes([0, 1]))
+            RsG = SubGraph(G, filter_node=nx.filters.show_nodes([2, 3]))
+            assert G.adj.copy() == G.adj
+            assert G.adj[2].copy() == G.adj[2]
+            assert SG.adj.copy() == SG.adj
+            assert SG.adj[2].copy() == SG.adj[2]
+            assert RG.adj.copy() == RG.adj
+            assert RG.adj[2].copy() == RG.adj[2]
+            assert RsG.adj.copy() == RsG.adj
+            assert RsG.adj[2].copy() == RsG.adj[2]

env-llmeval/lib/python3.10/site-packages/networkx/classes/tests/test_graph.py

@@ -0,0 +1,920 @@
+import gc
+import pickle
+import platform
+import weakref
+
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal, graphs_equal, nodes_equal
+
+
+class BaseGraphTester:
+    """Tests for data-structure independent graph class features."""
+
+    def test_contains(self):
+        G = self.K3
+        assert 1 in G
+        assert 4 not in G
+        assert "b" not in G
+        assert [] not in G  # no exception for nonhashable
+        assert {1: 1} not in G  # no exception for nonhashable
+
+    def test_order(self):
+        G = self.K3
+        assert len(G) == 3
+        assert G.order() == 3
+        assert G.number_of_nodes() == 3
+
+    def test_nodes(self):
+        G = self.K3
+        assert isinstance(G._node, G.node_dict_factory)
+        assert isinstance(G._adj, G.adjlist_outer_dict_factory)
+        assert all(
+            isinstance(adj, G.adjlist_inner_dict_factory) for adj in G._adj.values()
+        )
+        assert sorted(G.nodes()) == self.k3nodes
+        assert sorted(G.nodes(data=True)) == [(0, {}), (1, {}), (2, {})]
+
+    def test_none_node(self):
+        G = self.Graph()
+        with pytest.raises(ValueError):
+            G.add_node(None)
+        with pytest.raises(ValueError):
+            G.add_nodes_from([None])
+        with pytest.raises(ValueError):
+            G.add_edge(0, None)
+        with pytest.raises(ValueError):
+            G.add_edges_from([(0, None)])
+
+    def test_has_node(self):
+        G = self.K3
+        assert G.has_node(1)
+        assert not G.has_node(4)
+        assert not G.has_node([])  # no exception for nonhashable
+        assert not G.has_node({1: 1})  # no exception for nonhashable
+
+    def test_has_edge(self):
+        G = self.K3
+        assert G.has_edge(0, 1)
+        assert not G.has_edge(0, -1)
+
+    def test_neighbors(self):
+        G = self.K3
+        assert sorted(G.neighbors(0)) == [1, 2]
+        with pytest.raises(nx.NetworkXError):
+            G.neighbors(-1)
+
+    @pytest.mark.skipif(
+        platform.python_implementation() == "PyPy", reason="PyPy gc is different"
+    )
+    def test_memory_leak(self):
+        G = self.Graph()
+
+        def count_objects_of_type(_type):
+            # Iterating over all objects tracked by gc can include weak references
+            # whose weakly-referenced objects may no longer exist. Calling `isinstance`
+            # on such a weak reference will raise ReferenceError. There are at least
+            # three workarounds for this: one is to compare type names instead of using
+            # `isinstance` such as `type(obj).__name__ == typename`, another is to use
+            # `type(obj) == _type`, and the last is to ignore ProxyTypes as we do below.
+            # NOTE: even if this safeguard is deemed unnecessary to pass NetworkX tests,
+            # we should still keep it for maximum safety for other NetworkX backends.
+            return sum(
+                1
+                for obj in gc.get_objects()
+                if not isinstance(obj, weakref.ProxyTypes) and isinstance(obj, _type)
+            )
+
+        gc.collect()
+        before = count_objects_of_type(self.Graph)
+        G.copy()
+        gc.collect()
+        after = count_objects_of_type(self.Graph)
+        assert before == after
+
+        # test a subgraph of the base class
+        class MyGraph(self.Graph):
+            pass
+
+        gc.collect()
+        G = MyGraph()
+        before = count_objects_of_type(MyGraph)
+        G.copy()
+        gc.collect()
+        after = count_objects_of_type(MyGraph)
+        assert before == after
+
+    def test_edges(self):
+        G = self.K3
+        assert isinstance(G._adj, G.adjlist_outer_dict_factory)
+        assert edges_equal(G.edges(), [(0, 1), (0, 2), (1, 2)])
+        assert edges_equal(G.edges(0), [(0, 1), (0, 2)])
+        assert edges_equal(G.edges([0, 1]), [(0, 1), (0, 2), (1, 2)])
+        with pytest.raises(nx.NetworkXError):
+            G.edges(-1)
+
+    def test_degree(self):
+        G = self.K3
+        assert sorted(G.degree()) == [(0, 2), (1, 2), (2, 2)]
+        assert dict(G.degree()) == {0: 2, 1: 2, 2: 2}
+        assert G.degree(0) == 2
+        with pytest.raises(nx.NetworkXError):
+            G.degree(-1)  # node not in graph
+
+    def test_size(self):
+        G = self.K3
+        assert G.size() == 3
+        assert G.number_of_edges() == 3
+
+    def test_nbunch_iter(self):
+        G = self.K3
+        assert nodes_equal(G.nbunch_iter(), self.k3nodes)  # all nodes
+        assert nodes_equal(G.nbunch_iter(0), [0])  # single node
+        assert nodes_equal(G.nbunch_iter([0, 1]), [0, 1])  # sequence
+        # sequence with none in graph
+        assert nodes_equal(G.nbunch_iter([-1]), [])
+        # string sequence with none in graph
+        assert nodes_equal(G.nbunch_iter("foo"), [])
+        # node not in graph doesn't get caught upon creation of iterator
+        bunch = G.nbunch_iter(-1)
+        # but gets caught when iterator used
+        with pytest.raises(nx.NetworkXError, match="is not a node or a sequence"):
+            list(bunch)
+        # unhashable doesn't get caught upon creation of iterator
+        bunch = G.nbunch_iter([0, 1, 2, {}])
+        # but gets caught when iterator hits the unhashable
+        with pytest.raises(
+            nx.NetworkXError, match="in sequence nbunch is not a valid node"
+        ):
+            list(bunch)
+
+    def test_nbunch_iter_node_format_raise(self):
+        # Tests that a node that would have failed string formatting
+        # doesn't cause an error when attempting to raise a
+        # :exc:`nx.NetworkXError`.
+
+        # For more information, see pull request #1813.
+        G = self.Graph()
+        nbunch = [("x", set())]
+        with pytest.raises(nx.NetworkXError):
+            list(G.nbunch_iter(nbunch))
+
+    def test_selfloop_degree(self):
+        G = self.Graph()
+        G.add_edge(1, 1)
+        assert sorted(G.degree()) == [(1, 2)]
+        assert dict(G.degree()) == {1: 2}
+        assert G.degree(1) == 2
+        assert sorted(G.degree([1])) == [(1, 2)]
+        assert G.degree(1, weight="weight") == 2
+
+    def test_selfloops(self):
+        G = self.K3.copy()
+        G.add_edge(0, 0)
+        assert nodes_equal(nx.nodes_with_selfloops(G), [0])
+        assert edges_equal(nx.selfloop_edges(G), [(0, 0)])
+        assert nx.number_of_selfloops(G) == 1
+        G.remove_edge(0, 0)
+        G.add_edge(0, 0)
+        G.remove_edges_from([(0, 0)])
+        G.add_edge(1, 1)
+        G.remove_node(1)
+        G.add_edge(0, 0)
+        G.add_edge(1, 1)
+        G.remove_nodes_from([0, 1])
+
+    def test_cache_reset(self):
+        G = self.K3.copy()
+        old_adj = G.adj
+        assert id(G.adj) == id(old_adj)
+        G._adj = {}
+        assert id(G.adj) != id(old_adj)
+
+        old_nodes = G.nodes
+        assert id(G.nodes) == id(old_nodes)
+        G._node = {}
+        assert id(G.nodes) != id(old_nodes)
+
+    def test_attributes_cached(self):
+        G = self.K3.copy()
+        assert id(G.nodes) == id(G.nodes)
+        assert id(G.edges) == id(G.edges)
+        assert id(G.degree) == id(G.degree)
+        assert id(G.adj) == id(G.adj)
+
+
+class BaseAttrGraphTester(BaseGraphTester):
+    """Tests of graph class attribute features."""
+
+    def test_weighted_degree(self):
+        G = self.Graph()
+        G.add_edge(1, 2, weight=2, other=3)
+        G.add_edge(2, 3, weight=3, other=4)
+        assert sorted(d for n, d in G.degree(weight="weight")) == [2, 3, 5]
+        assert dict(G.degree(weight="weight")) == {1: 2, 2: 5, 3: 3}
+        assert G.degree(1, weight="weight") == 2
+        assert nodes_equal((G.degree([1], weight="weight")), [(1, 2)])
+
+        assert nodes_equal((d for n, d in G.degree(weight="other")), [3, 7, 4])
+        assert dict(G.degree(weight="other")) == {1: 3, 2: 7, 3: 4}
+        assert G.degree(1, weight="other") == 3
+        assert edges_equal((G.degree([1], weight="other")), [(1, 3)])
+
+    def add_attributes(self, G):
+        G.graph["foo"] = []
+        G.nodes[0]["foo"] = []
+        G.remove_edge(1, 2)
+        ll = []
+        G.add_edge(1, 2, foo=ll)
+        G.add_edge(2, 1, foo=ll)
+
+    def test_name(self):
+        G = self.Graph(name="")
+        assert G.name == ""
+        G = self.Graph(name="test")
+        assert G.name == "test"
+
+    def test_str_unnamed(self):
+        G = self.Graph()
+        G.add_edges_from([(1, 2), (2, 3)])
+        assert str(G) == f"{type(G).__name__} with 3 nodes and 2 edges"
+
+    def test_str_named(self):
+        G = self.Graph(name="foo")
+        G.add_edges_from([(1, 2), (2, 3)])
+        assert str(G) == f"{type(G).__name__} named 'foo' with 3 nodes and 2 edges"
+
+    def test_graph_chain(self):
+        G = self.Graph([(0, 1), (1, 2)])
+        DG = G.to_directed(as_view=True)
+        SDG = DG.subgraph([0, 1])
+        RSDG = SDG.reverse(copy=False)
+        assert G is DG._graph
+        assert DG is SDG._graph
+        assert SDG is RSDG._graph
+
+    def test_copy(self):
+        G = self.Graph()
+        G.add_node(0)
+        G.add_edge(1, 2)
+        self.add_attributes(G)
+        # copy edge datadict but any container attr are same
+        H = G.copy()
+        self.graphs_equal(H, G)
+        self.different_attrdict(H, G)
+        self.shallow_copy_attrdict(H, G)
+
+    def test_class_copy(self):
+        G = self.Graph()
+        G.add_node(0)
+        G.add_edge(1, 2)
+        self.add_attributes(G)
+        # copy edge datadict but any container attr are same
+        H = G.__class__(G)
+        self.graphs_equal(H, G)
+        self.different_attrdict(H, G)
+        self.shallow_copy_attrdict(H, G)
+
+    def test_fresh_copy(self):
+        G = self.Graph()
+        G.add_node(0)
+        G.add_edge(1, 2)
+        self.add_attributes(G)
+        # copy graph structure but use fresh datadict
+        H = G.__class__()
+        H.add_nodes_from(G)
+        H.add_edges_from(G.edges())
+        assert len(G.nodes[0]) == 1
+        ddict = G.adj[1][2][0] if G.is_multigraph() else G.adj[1][2]
+        assert len(ddict) == 1
+        assert len(H.nodes[0]) == 0
+        ddict = H.adj[1][2][0] if H.is_multigraph() else H.adj[1][2]
+        assert len(ddict) == 0
+
+    def is_deepcopy(self, H, G):
+        self.graphs_equal(H, G)
+        self.different_attrdict(H, G)
+        self.deep_copy_attrdict(H, G)
+
+    def deep_copy_attrdict(self, H, G):
+        self.deepcopy_graph_attr(H, G)
+        self.deepcopy_node_attr(H, G)
+        self.deepcopy_edge_attr(H, G)
+
+    def deepcopy_graph_attr(self, H, G):
+        assert G.graph["foo"] == H.graph["foo"]
+        G.graph["foo"].append(1)
+        assert G.graph["foo"] != H.graph["foo"]
+
+    def deepcopy_node_attr(self, H, G):
+        assert G.nodes[0]["foo"] == H.nodes[0]["foo"]
+        G.nodes[0]["foo"].append(1)
+        assert G.nodes[0]["foo"] != H.nodes[0]["foo"]
+
+    def deepcopy_edge_attr(self, H, G):
+        assert G[1][2]["foo"] == H[1][2]["foo"]
+        G[1][2]["foo"].append(1)
+        assert G[1][2]["foo"] != H[1][2]["foo"]
+
+    def is_shallow_copy(self, H, G):
+        self.graphs_equal(H, G)
+        self.shallow_copy_attrdict(H, G)
+
+    def shallow_copy_attrdict(self, H, G):
+        self.shallow_copy_graph_attr(H, G)
+        self.shallow_copy_node_attr(H, G)
+        self.shallow_copy_edge_attr(H, G)
+
+    def shallow_copy_graph_attr(self, H, G):
+        assert G.graph["foo"] == H.graph["foo"]
+        G.graph["foo"].append(1)
+        assert G.graph["foo"] == H.graph["foo"]
+
+    def shallow_copy_node_attr(self, H, G):
+        assert G.nodes[0]["foo"] == H.nodes[0]["foo"]
+        G.nodes[0]["foo"].append(1)
+        assert G.nodes[0]["foo"] == H.nodes[0]["foo"]
+
+    def shallow_copy_edge_attr(self, H, G):
+        assert G[1][2]["foo"] == H[1][2]["foo"]
+        G[1][2]["foo"].append(1)
+        assert G[1][2]["foo"] == H[1][2]["foo"]
+
+    def same_attrdict(self, H, G):
+        old_foo = H[1][2]["foo"]
+        H.adj[1][2]["foo"] = "baz"
+        assert G.edges == H.edges
+        H.adj[1][2]["foo"] = old_foo
+        assert G.edges == H.edges
+
+        old_foo = H.nodes[0]["foo"]
+        H.nodes[0]["foo"] = "baz"
+        assert G.nodes == H.nodes
+        H.nodes[0]["foo"] = old_foo
+        assert G.nodes == H.nodes
+
+    def different_attrdict(self, H, G):
+        old_foo = H[1][2]["foo"]
+        H.adj[1][2]["foo"] = "baz"
+        assert G._adj != H._adj
+        H.adj[1][2]["foo"] = old_foo
+        assert G._adj == H._adj
+
+        old_foo = H.nodes[0]["foo"]
+        H.nodes[0]["foo"] = "baz"
+        assert G._node != H._node
+        H.nodes[0]["foo"] = old_foo
+        assert G._node == H._node
+
+    def graphs_equal(self, H, G):
+        assert G._adj == H._adj
+        assert G._node == H._node
+        assert G.graph == H.graph
+        assert G.name == H.name
+        if not G.is_directed() and not H.is_directed():
+            assert H._adj[1][2] is H._adj[2][1]
+            assert G._adj[1][2] is G._adj[2][1]
+        else:  # at least one is directed
+            if not G.is_directed():
+                G._pred = G._adj
+                G._succ = G._adj
+            if not H.is_directed():
+                H._pred = H._adj
+                H._succ = H._adj
+            assert G._pred == H._pred
+            assert G._succ == H._succ
+            assert H._succ[1][2] is H._pred[2][1]
+            assert G._succ[1][2] is G._pred[2][1]
+
+    def test_graph_attr(self):
+        G = self.K3.copy()
+        G.graph["foo"] = "bar"
+        assert isinstance(G.graph, G.graph_attr_dict_factory)
+        assert G.graph["foo"] == "bar"
+        del G.graph["foo"]
+        assert G.graph == {}
+        H = self.Graph(foo="bar")
+        assert H.graph["foo"] == "bar"
+
+    def test_node_attr(self):
+        G = self.K3.copy()
+        G.add_node(1, foo="bar")
+        assert all(
+            isinstance(d, G.node_attr_dict_factory) for u, d in G.nodes(data=True)
+        )
+        assert nodes_equal(G.nodes(), [0, 1, 2])
+        assert nodes_equal(G.nodes(data=True), [(0, {}), (1, {"foo": "bar"}), (2, {})])
+        G.nodes[1]["foo"] = "baz"
+        assert nodes_equal(G.nodes(data=True), [(0, {}), (1, {"foo": "baz"}), (2, {})])
+        assert nodes_equal(G.nodes(data="foo"), [(0, None), (1, "baz"), (2, None)])
+        assert nodes_equal(
+            G.nodes(data="foo", default="bar"), [(0, "bar"), (1, "baz"), (2, "bar")]
+        )
+
+    def test_node_attr2(self):
+        G = self.K3.copy()
+        a = {"foo": "bar"}
+        G.add_node(3, **a)
+        assert nodes_equal(G.nodes(), [0, 1, 2, 3])
+        assert nodes_equal(
+            G.nodes(data=True), [(0, {}), (1, {}), (2, {}), (3, {"foo": "bar"})]
+        )
+
+    def test_edge_lookup(self):
+        G = self.Graph()
+        G.add_edge(1, 2, foo="bar")
+        assert edges_equal(G.edges[1, 2], {"foo": "bar"})
+
+    def test_edge_attr(self):
+        G = self.Graph()
+        G.add_edge(1, 2, foo="bar")
+        assert all(
+            isinstance(d, G.edge_attr_dict_factory) for u, v, d in G.edges(data=True)
+        )
+        assert edges_equal(G.edges(data=True), [(1, 2, {"foo": "bar"})])
+        assert edges_equal(G.edges(data="foo"), [(1, 2, "bar")])
+
+    def test_edge_attr2(self):
+        G = self.Graph()
+        G.add_edges_from([(1, 2), (3, 4)], foo="foo")
+        assert edges_equal(
+            G.edges(data=True), [(1, 2, {"foo": "foo"}), (3, 4, {"foo": "foo"})]
+        )
+        assert edges_equal(G.edges(data="foo"), [(1, 2, "foo"), (3, 4, "foo")])
+
+    def test_edge_attr3(self):
+        G = self.Graph()
+        G.add_edges_from([(1, 2, {"weight": 32}), (3, 4, {"weight": 64})], foo="foo")
+        assert edges_equal(
+            G.edges(data=True),
+            [
+                (1, 2, {"foo": "foo", "weight": 32}),
+                (3, 4, {"foo": "foo", "weight": 64}),
+            ],
+        )
+
+        G.remove_edges_from([(1, 2), (3, 4)])
+        G.add_edge(1, 2, data=7, spam="bar", bar="foo")
+        assert edges_equal(
+            G.edges(data=True), [(1, 2, {"data": 7, "spam": "bar", "bar": "foo"})]
+        )
+
+    def test_edge_attr4(self):
+        G = self.Graph()
+        G.add_edge(1, 2, data=7, spam="bar", bar="foo")
+        assert edges_equal(
+            G.edges(data=True), [(1, 2, {"data": 7, "spam": "bar", "bar": "foo"})]
+        )
+        G[1][2]["data"] = 10  # OK to set data like this
+        assert edges_equal(
+            G.edges(data=True), [(1, 2, {"data": 10, "spam": "bar", "bar": "foo"})]
+        )
+
+        G.adj[1][2]["data"] = 20
+        assert edges_equal(
+            G.edges(data=True), [(1, 2, {"data": 20, "spam": "bar", "bar": "foo"})]
+        )
+        G.edges[1, 2]["data"] = 21  # another spelling, "edge"
+        assert edges_equal(
+            G.edges(data=True), [(1, 2, {"data": 21, "spam": "bar", "bar": "foo"})]
+        )
+        G.adj[1][2]["listdata"] = [20, 200]
+        G.adj[1][2]["weight"] = 20
+        dd = {
+            "data": 21,
+            "spam": "bar",
+            "bar": "foo",
+            "listdata": [20, 200],
+            "weight": 20,
+        }
+        assert edges_equal(G.edges(data=True), [(1, 2, dd)])
+
+    def test_to_undirected(self):
+        G = self.K3
+        self.add_attributes(G)
+        H = nx.Graph(G)
+        self.is_shallow_copy(H, G)
+        self.different_attrdict(H, G)
+        H = G.to_undirected()
+        self.is_deepcopy(H, G)
+
+    def test_to_directed_as_view(self):
+        H = nx.path_graph(2, create_using=self.Graph)
+        H2 = H.to_directed(as_view=True)
+        assert H is H2._graph
+        assert H2.has_edge(0, 1)
+        assert H2.has_edge(1, 0) or H.is_directed()
+        pytest.raises(nx.NetworkXError, H2.add_node, -1)
+        pytest.raises(nx.NetworkXError, H2.add_edge, 1, 2)
+        H.add_edge(1, 2)
+        assert H2.has_edge(1, 2)
+        assert H2.has_edge(2, 1) or H.is_directed()
+
+    def test_to_undirected_as_view(self):
+        H = nx.path_graph(2, create_using=self.Graph)
+        H2 = H.to_undirected(as_view=True)
+        assert H is H2._graph
+        assert H2.has_edge(0, 1)
+        assert H2.has_edge(1, 0)
+        pytest.raises(nx.NetworkXError, H2.add_node, -1)
+        pytest.raises(nx.NetworkXError, H2.add_edge, 1, 2)
+        H.add_edge(1, 2)
+        assert H2.has_edge(1, 2)
+        assert H2.has_edge(2, 1)
+
+    def test_directed_class(self):
+        G = self.Graph()
+
+        class newGraph(G.to_undirected_class()):
+            def to_directed_class(self):
+                return newDiGraph
+
+            def to_undirected_class(self):
+                return newGraph
+
+        class newDiGraph(G.to_directed_class()):
+            def to_directed_class(self):
+                return newDiGraph
+
+            def to_undirected_class(self):
+                return newGraph
+
+        G = newDiGraph() if G.is_directed() else newGraph()
+        H = G.to_directed()
+        assert isinstance(H, newDiGraph)
+        H = G.to_undirected()
+        assert isinstance(H, newGraph)
+
+    def test_to_directed(self):
+        G = self.K3
+        self.add_attributes(G)
+        H = nx.DiGraph(G)
+        self.is_shallow_copy(H, G)
+        self.different_attrdict(H, G)
+        H = G.to_directed()
+        self.is_deepcopy(H, G)
+
+    def test_subgraph(self):
+        G = self.K3
+        self.add_attributes(G)
+        H = G.subgraph([0, 1, 2, 5])
+        self.graphs_equal(H, G)
+        self.same_attrdict(H, G)
+        self.shallow_copy_attrdict(H, G)
+
+        H = G.subgraph(0)
+        assert H.adj == {0: {}}
+        H = G.subgraph([])
+        assert H.adj == {}
+        assert G.adj != {}
+
+    def test_selfloops_attr(self):
+        G = self.K3.copy()
+        G.add_edge(0, 0)
+        G.add_edge(1, 1, weight=2)
+        assert edges_equal(
+            nx.selfloop_edges(G, data=True), [(0, 0, {}), (1, 1, {"weight": 2})]
+        )
+        assert edges_equal(
+            nx.selfloop_edges(G, data="weight"), [(0, 0, None), (1, 1, 2)]
+        )
+
+
+class TestGraph(BaseAttrGraphTester):
+    """Tests specific to dict-of-dict-of-dict graph data structure"""
+
+    def setup_method(self):
+        self.Graph = nx.Graph
+        # build dict-of-dict-of-dict K3
+        ed1, ed2, ed3 = ({}, {}, {})
+        self.k3adj = {0: {1: ed1, 2: ed2}, 1: {0: ed1, 2: ed3}, 2: {0: ed2, 1: ed3}}
+        self.k3edges = [(0, 1), (0, 2), (1, 2)]
+        self.k3nodes = [0, 1, 2]
+        self.K3 = self.Graph()
+        self.K3._adj = self.k3adj
+        self.K3._node = {}
+        self.K3._node[0] = {}
+        self.K3._node[1] = {}
+        self.K3._node[2] = {}
+
+    def test_pickle(self):
+        G = self.K3
+        pg = pickle.loads(pickle.dumps(G, -1))
+        self.graphs_equal(pg, G)
+        pg = pickle.loads(pickle.dumps(G))
+        self.graphs_equal(pg, G)
+
+    def test_data_input(self):
+        G = self.Graph({1: [2], 2: [1]}, name="test")
+        assert G.name == "test"
+        assert sorted(G.adj.items()) == [(1, {2: {}}), (2, {1: {}})]
+
+    def test_adjacency(self):
+        G = self.K3
+        assert dict(G.adjacency()) == {
+            0: {1: {}, 2: {}},
+            1: {0: {}, 2: {}},
+            2: {0: {}, 1: {}},
+        }
+
+    def test_getitem(self):
+        G = self.K3
+        assert G.adj[0] == {1: {}, 2: {}}
+        assert G[0] == {1: {}, 2: {}}
+        with pytest.raises(KeyError):
+            G.__getitem__("j")
+        with pytest.raises(TypeError):
+            G.__getitem__(["A"])
+
+    def test_add_node(self):
+        G = self.Graph()
+        G.add_node(0)
+        assert G.adj == {0: {}}
+        # test add attributes
+        G.add_node(1, c="red")
+        G.add_node(2, c="blue")
+        G.add_node(3, c="red")
+        assert G.nodes[1]["c"] == "red"
+        assert G.nodes[2]["c"] == "blue"
+        assert G.nodes[3]["c"] == "red"
+        # test updating attributes
+        G.add_node(1, c="blue")
+        G.add_node(2, c="red")
+        G.add_node(3, c="blue")
+        assert G.nodes[1]["c"] == "blue"
+        assert G.nodes[2]["c"] == "red"
+        assert G.nodes[3]["c"] == "blue"
+
+    def test_add_nodes_from(self):
+        G = self.Graph()
+        G.add_nodes_from([0, 1, 2])
+        assert G.adj == {0: {}, 1: {}, 2: {}}
+        # test add attributes
+        G.add_nodes_from([0, 1, 2], c="red")
+        assert G.nodes[0]["c"] == "red"
+        assert G.nodes[2]["c"] == "red"
+        # test that attribute dicts are not the same
+        assert G.nodes[0] is not G.nodes[1]
+        # test updating attributes
+        G.add_nodes_from([0, 1, 2], c="blue")
+        assert G.nodes[0]["c"] == "blue"
+        assert G.nodes[2]["c"] == "blue"
+        assert G.nodes[0] is not G.nodes[1]
+        # test tuple input
+        H = self.Graph()
+        H.add_nodes_from(G.nodes(data=True))
+        assert H.nodes[0]["c"] == "blue"
+        assert H.nodes[2]["c"] == "blue"
+        assert H.nodes[0] is not H.nodes[1]
+        # specific overrides general
+        H.add_nodes_from([0, (1, {"c": "green"}), (3, {"c": "cyan"})], c="red")
+        assert H.nodes[0]["c"] == "red"
+        assert H.nodes[1]["c"] == "green"
+        assert H.nodes[2]["c"] == "blue"
+        assert H.nodes[3]["c"] == "cyan"
+
+    def test_remove_node(self):
+        G = self.K3.copy()
+        G.remove_node(0)
+        assert G.adj == {1: {2: {}}, 2: {1: {}}}
+        with pytest.raises(nx.NetworkXError):
+            G.remove_node(-1)
+
+        # generator here to implement list,set,string...
+
+    def test_remove_nodes_from(self):
+        G = self.K3.copy()
+        G.remove_nodes_from([0, 1])
+        assert G.adj == {2: {}}
+        G.remove_nodes_from([-1])  # silent fail
+
+    def test_add_edge(self):
+        G = self.Graph()
+        G.add_edge(0, 1)
+        assert G.adj == {0: {1: {}}, 1: {0: {}}}
+        G = self.Graph()
+        G.add_edge(*(0, 1))
+        assert G.adj == {0: {1: {}}, 1: {0: {}}}
+        G = self.Graph()
+        with pytest.raises(ValueError):
+            G.add_edge(None, "anything")
+
+    def test_add_edges_from(self):
+        G = self.Graph()
+        G.add_edges_from([(0, 1), (0, 2, {"weight": 3})])
+        assert G.adj == {
+            0: {1: {}, 2: {"weight": 3}},
+            1: {0: {}},
+            2: {0: {"weight": 3}},
+        }
+        G = self.Graph()
+        G.add_edges_from([(0, 1), (0, 2, {"weight": 3}), (1, 2, {"data": 4})], data=2)
+        assert G.adj == {
+            0: {1: {"data": 2}, 2: {"weight": 3, "data": 2}},
+            1: {0: {"data": 2}, 2: {"data": 4}},
+            2: {0: {"weight": 3, "data": 2}, 1: {"data": 4}},
+        }
+
+        with pytest.raises(nx.NetworkXError):
+            G.add_edges_from([(0,)])  # too few in tuple
+        with pytest.raises(nx.NetworkXError):
+            G.add_edges_from([(0, 1, 2, 3)])  # too many in tuple
+        with pytest.raises(TypeError):
+            G.add_edges_from([0])  # not a tuple
+        with pytest.raises(ValueError):
+            G.add_edges_from([(None, 3), (3, 2)])  # None cannot be a node
+
+    def test_remove_edge(self):
+        G = self.K3.copy()
+        G.remove_edge(0, 1)
+        assert G.adj == {0: {2: {}}, 1: {2: {}}, 2: {0: {}, 1: {}}}
+        with pytest.raises(nx.NetworkXError):
+            G.remove_edge(-1, 0)
+
+    def test_remove_edges_from(self):
+        G = self.K3.copy()
+        G.remove_edges_from([(0, 1)])
+        assert G.adj == {0: {2: {}}, 1: {2: {}}, 2: {0: {}, 1: {}}}
+        G.remove_edges_from([(0, 0)])  # silent fail
+
+    def test_clear(self):
+        G = self.K3.copy()
+        G.graph["name"] = "K3"
+        G.clear()
+        assert list(G.nodes) == []
+        assert G.adj == {}
+        assert G.graph == {}
+
+    def test_clear_edges(self):
+        G = self.K3.copy()
+        G.graph["name"] = "K3"
+        nodes = list(G.nodes)
+        G.clear_edges()
+        assert list(G.nodes) == nodes
+        assert G.adj == {0: {}, 1: {}, 2: {}}
+        assert list(G.edges) == []
+        assert G.graph["name"] == "K3"
+
+    def test_edges_data(self):
+        G = self.K3
+        all_edges = [(0, 1, {}), (0, 2, {}), (1, 2, {})]
+        assert edges_equal(G.edges(data=True), all_edges)
+        assert edges_equal(G.edges(0, data=True), [(0, 1, {}), (0, 2, {})])
+        assert edges_equal(G.edges([0, 1], data=True), all_edges)
+        with pytest.raises(nx.NetworkXError):
+            G.edges(-1, True)
+
+    def test_get_edge_data(self):
+        G = self.K3.copy()
+        assert G.get_edge_data(0, 1) == {}
+        assert G[0][1] == {}
+        assert G.get_edge_data(10, 20) is None
+        assert G.get_edge_data(-1, 0) is None
+        assert G.get_edge_data(-1, 0, default=1) == 1
+
+    def test_update(self):
+        # specify both edges and nodes
+        G = self.K3.copy()
+        G.update(nodes=[3, (4, {"size": 2})], edges=[(4, 5), (6, 7, {"weight": 2})])
+        nlist = [
+            (0, {}),
+            (1, {}),
+            (2, {}),
+            (3, {}),
+            (4, {"size": 2}),
+            (5, {}),
+            (6, {}),
+            (7, {}),
+        ]
+        assert sorted(G.nodes.data()) == nlist
+        if G.is_directed():
+            elist = [
+                (0, 1, {}),
+                (0, 2, {}),
+                (1, 0, {}),
+                (1, 2, {}),
+                (2, 0, {}),
+                (2, 1, {}),
+                (4, 5, {}),
+                (6, 7, {"weight": 2}),
+            ]
+        else:
+            elist = [
+                (0, 1, {}),
+                (0, 2, {}),
+                (1, 2, {}),
+                (4, 5, {}),
+                (6, 7, {"weight": 2}),
+            ]
+        assert sorted(G.edges.data()) == elist
+        assert G.graph == {}
+
+        # no keywords -- order is edges, nodes
+        G = self.K3.copy()
+        G.update([(4, 5), (6, 7, {"weight": 2})], [3, (4, {"size": 2})])
+        assert sorted(G.nodes.data()) == nlist
+        assert sorted(G.edges.data()) == elist
+        assert G.graph == {}
+
+        # update using only a graph
+        G = self.Graph()
+        G.graph["foo"] = "bar"
+        G.add_node(2, data=4)
+        G.add_edge(0, 1, weight=0.5)
+        GG = G.copy()
+        H = self.Graph()
+        GG.update(H)
+        assert graphs_equal(G, GG)
+        H.update(G)
+        assert graphs_equal(H, G)
+
+        # update nodes only
+        H = self.Graph()
+        H.update(nodes=[3, 4])
+        assert H.nodes ^ {3, 4} == set()
+        assert H.size() == 0
+
+        # update edges only
+        H = self.Graph()
+        H.update(edges=[(3, 4)])
+        assert sorted(H.edges.data()) == [(3, 4, {})]
+        assert H.size() == 1
+
+        # No inputs -> exception
+        with pytest.raises(nx.NetworkXError):
+            nx.Graph().update()
+
+
+class TestEdgeSubgraph:
+    """Unit tests for the :meth:`Graph.edge_subgraph` method."""
+
+    def setup_method(self):
+        # Create a path graph on five nodes.
+        G = nx.path_graph(5)
+        # Add some node, edge, and graph attributes.
+        for i in range(5):
+            G.nodes[i]["name"] = f"node{i}"
+        G.edges[0, 1]["name"] = "edge01"
+        G.edges[3, 4]["name"] = "edge34"
+        G.graph["name"] = "graph"
+        # Get the subgraph induced by the first and last edges.
+        self.G = G
+        self.H = G.edge_subgraph([(0, 1), (3, 4)])
+
+    def test_correct_nodes(self):
+        """Tests that the subgraph has the correct nodes."""
+        assert [0, 1, 3, 4] == sorted(self.H.nodes())
+
+    def test_correct_edges(self):
+        """Tests that the subgraph has the correct edges."""
+        assert [(0, 1, "edge01"), (3, 4, "edge34")] == sorted(self.H.edges(data="name"))
+
+    def test_add_node(self):
+        """Tests that adding a node to the original graph does not
+        affect the nodes of the subgraph.
+
+        """
+        self.G.add_node(5)
+        assert [0, 1, 3, 4] == sorted(self.H.nodes())
+
+    def test_remove_node(self):
+        """Tests that removing a node in the original graph does
+        affect the nodes of the subgraph.
+
+        """
+        self.G.remove_node(0)
+        assert [1, 3, 4] == sorted(self.H.nodes())
+
+    def test_node_attr_dict(self):
+        """Tests that the node attribute dictionary of the two graphs is
+        the same object.
+
+        """
+        for v in self.H:
+            assert self.G.nodes[v] == self.H.nodes[v]
+        # Making a change to G should make a change in H and vice versa.
+        self.G.nodes[0]["name"] = "foo"
+        assert self.G.nodes[0] == self.H.nodes[0]
+        self.H.nodes[1]["name"] = "bar"
+        assert self.G.nodes[1] == self.H.nodes[1]
+
+    def test_edge_attr_dict(self):
+        """Tests that the edge attribute dictionary of the two graphs is
+        the same object.
+
+        """
+        for u, v in self.H.edges():
+            assert self.G.edges[u, v] == self.H.edges[u, v]
+        # Making a change to G should make a change in H and vice versa.
+        self.G.edges[0, 1]["name"] = "foo"
+        assert self.G.edges[0, 1]["name"] == self.H.edges[0, 1]["name"]
+        self.H.edges[3, 4]["name"] = "bar"
+        assert self.G.edges[3, 4]["name"] == self.H.edges[3, 4]["name"]
+
+    def test_graph_attr_dict(self):
+        """Tests that the graph attribute dictionary of the two graphs
+        is the same object.
+
+        """
+        assert self.G.graph is self.H.graph

env-llmeval/lib/python3.10/site-packages/networkx/classes/tests/test_graph_historical.py

@@ -0,0 +1,12 @@
+"""Original NetworkX graph tests"""
+import networkx
+import networkx as nx
+
+from .historical_tests import HistoricalTests
+
+
+class TestGraphHistorical(HistoricalTests):
+    @classmethod
+    def setup_class(cls):
+        HistoricalTests.setup_class()
+        cls.G = nx.Graph

env-llmeval/lib/python3.10/site-packages/networkx/classes/tests/test_graphviews.py

@@ -0,0 +1,350 @@
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal, nodes_equal
+
+# Note: SubGraph views are not tested here. They have their own testing file
+
+
+class TestReverseView:
+    def setup_method(self):
+        self.G = nx.path_graph(9, create_using=nx.DiGraph())
+        self.rv = nx.reverse_view(self.G)
+
+    def test_pickle(self):
+        import pickle
+
+        rv = self.rv
+        prv = pickle.loads(pickle.dumps(rv, -1))
+        assert rv._node == prv._node
+        assert rv._adj == prv._adj
+        assert rv.graph == prv.graph
+
+    def test_contains(self):
+        assert (2, 3) in self.G.edges
+        assert (3, 2) not in self.G.edges
+        assert (2, 3) not in self.rv.edges
+        assert (3, 2) in self.rv.edges
+
+    def test_iter(self):
+        expected = sorted(tuple(reversed(e)) for e in self.G.edges)
+        assert sorted(self.rv.edges) == expected
+
+    def test_exceptions(self):
+        G = nx.Graph()
+        pytest.raises(nx.NetworkXNotImplemented, nx.reverse_view, G)
+
+    def test_subclass(self):
+        class MyGraph(nx.DiGraph):
+            def my_method(self):
+                return "me"
+
+            def to_directed_class(self):
+                return MyGraph()
+
+        M = MyGraph()
+        M.add_edge(1, 2)
+        RM = nx.reverse_view(M)
+        print("RM class", RM.__class__)
+        RMC = RM.copy()
+        print("RMC class", RMC.__class__)
+        print(RMC.edges)
+        assert RMC.has_edge(2, 1)
+        assert RMC.my_method() == "me"
+
+
+class TestMultiReverseView:
+    def setup_method(self):
+        self.G = nx.path_graph(9, create_using=nx.MultiDiGraph())
+        self.G.add_edge(4, 5)
+        self.rv = nx.reverse_view(self.G)
+
+    def test_pickle(self):
+        import pickle
+
+        rv = self.rv
+        prv = pickle.loads(pickle.dumps(rv, -1))
+        assert rv._node == prv._node
+        assert rv._adj == prv._adj
+        assert rv.graph == prv.graph
+
+    def test_contains(self):
+        assert (2, 3, 0) in self.G.edges
+        assert (3, 2, 0) not in self.G.edges
+        assert (2, 3, 0) not in self.rv.edges
+        assert (3, 2, 0) in self.rv.edges
+        assert (5, 4, 1) in self.rv.edges
+        assert (4, 5, 1) not in self.rv.edges
+
+    def test_iter(self):
+        expected = sorted((v, u, k) for u, v, k in self.G.edges)
+        assert sorted(self.rv.edges) == expected
+
+    def test_exceptions(self):
+        MG = nx.MultiGraph(self.G)
+        pytest.raises(nx.NetworkXNotImplemented, nx.reverse_view, MG)
+
+
+def test_generic_multitype():
+    nxg = nx.graphviews
+    G = nx.DiGraph([(1, 2)])
+    with pytest.raises(nx.NetworkXError):
+        nxg.generic_graph_view(G, create_using=nx.MultiGraph)
+    G = nx.MultiDiGraph([(1, 2)])
+    with pytest.raises(nx.NetworkXError):
+        nxg.generic_graph_view(G, create_using=nx.DiGraph)
+
+
+class TestToDirected:
+    def setup_method(self):
+        self.G = nx.path_graph(9)
+        self.dv = nx.to_directed(self.G)
+        self.MG = nx.path_graph(9, create_using=nx.MultiGraph())
+        self.Mdv = nx.to_directed(self.MG)
+
+    def test_directed(self):
+        assert not self.G.is_directed()
+        assert self.dv.is_directed()
+
+    def test_already_directed(self):
+        dd = nx.to_directed(self.dv)
+        Mdd = nx.to_directed(self.Mdv)
+        assert edges_equal(dd.edges, self.dv.edges)
+        assert edges_equal(Mdd.edges, self.Mdv.edges)
+
+    def test_pickle(self):
+        import pickle
+
+        dv = self.dv
+        pdv = pickle.loads(pickle.dumps(dv, -1))
+        assert dv._node == pdv._node
+        assert dv._succ == pdv._succ
+        assert dv._pred == pdv._pred
+        assert dv.graph == pdv.graph
+
+    def test_contains(self):
+        assert (2, 3) in self.G.edges
+        assert (3, 2) in self.G.edges
+        assert (2, 3) in self.dv.edges
+        assert (3, 2) in self.dv.edges
+
+    def test_iter(self):
+        revd = [tuple(reversed(e)) for e in self.G.edges]
+        expected = sorted(list(self.G.edges) + revd)
+        assert sorted(self.dv.edges) == expected
+
+
+class TestToUndirected:
+    def setup_method(self):
+        self.DG = nx.path_graph(9, create_using=nx.DiGraph())
+        self.uv = nx.to_undirected(self.DG)
+        self.MDG = nx.path_graph(9, create_using=nx.MultiDiGraph())
+        self.Muv = nx.to_undirected(self.MDG)
+
+    def test_directed(self):
+        assert self.DG.is_directed()
+        assert not self.uv.is_directed()
+
+    def test_already_directed(self):
+        uu = nx.to_undirected(self.uv)
+        Muu = nx.to_undirected(self.Muv)
+        assert edges_equal(uu.edges, self.uv.edges)
+        assert edges_equal(Muu.edges, self.Muv.edges)
+
+    def test_pickle(self):
+        import pickle
+
+        uv = self.uv
+        puv = pickle.loads(pickle.dumps(uv, -1))
+        assert uv._node == puv._node
+        assert uv._adj == puv._adj
+        assert uv.graph == puv.graph
+        assert hasattr(uv, "_graph")
+
+    def test_contains(self):
+        assert (2, 3) in self.DG.edges
+        assert (3, 2) not in self.DG.edges
+        assert (2, 3) in self.uv.edges
+        assert (3, 2) in self.uv.edges
+
+    def test_iter(self):
+        expected = sorted(self.DG.edges)
+        assert sorted(self.uv.edges) == expected
+
+
+class TestChainsOfViews:
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(9)
+        cls.DG = nx.path_graph(9, create_using=nx.DiGraph())
+        cls.MG = nx.path_graph(9, create_using=nx.MultiGraph())
+        cls.MDG = nx.path_graph(9, create_using=nx.MultiDiGraph())
+        cls.Gv = nx.to_undirected(cls.DG)
+        cls.DGv = nx.to_directed(cls.G)
+        cls.MGv = nx.to_undirected(cls.MDG)
+        cls.MDGv = nx.to_directed(cls.MG)
+        cls.Rv = cls.DG.reverse()
+        cls.MRv = cls.MDG.reverse()
+        cls.graphs = [
+            cls.G,
+            cls.DG,
+            cls.MG,
+            cls.MDG,
+            cls.Gv,
+            cls.DGv,
+            cls.MGv,
+            cls.MDGv,
+            cls.Rv,
+            cls.MRv,
+        ]
+        for G in cls.graphs:
+            G.edges, G.nodes, G.degree
+
+    def test_pickle(self):
+        import pickle
+
+        for G in self.graphs:
+            H = pickle.loads(pickle.dumps(G, -1))
+            assert edges_equal(H.edges, G.edges)
+            assert nodes_equal(H.nodes, G.nodes)
+
+    def test_subgraph_of_subgraph(self):
+        SGv = nx.subgraph(self.G, range(3, 7))
+        SDGv = nx.subgraph(self.DG, range(3, 7))
+        SMGv = nx.subgraph(self.MG, range(3, 7))
+        SMDGv = nx.subgraph(self.MDG, range(3, 7))
+        for G in self.graphs + [SGv, SDGv, SMGv, SMDGv]:
+            SG = nx.induced_subgraph(G, [4, 5, 6])
+            assert list(SG) == [4, 5, 6]
+            SSG = SG.subgraph([6, 7])
+            assert list(SSG) == [6]
+            # subgraph-subgraph chain is short-cut in base class method
+            assert SSG._graph is G
+
+    def test_restricted_induced_subgraph_chains(self):
+        """Test subgraph chains that both restrict and show nodes/edges.
+
+        A restricted_view subgraph should allow induced subgraphs using
+        G.subgraph that automagically without a chain (meaning the result
+        is a subgraph view of the original graph not a subgraph-of-subgraph.
+        """
+        hide_nodes = [3, 4, 5]
+        hide_edges = [(6, 7)]
+        RG = nx.restricted_view(self.G, hide_nodes, hide_edges)
+        nodes = [4, 5, 6, 7, 8]
+        SG = nx.induced_subgraph(RG, nodes)
+        SSG = RG.subgraph(nodes)
+        assert RG._graph is self.G
+        assert SSG._graph is self.G
+        assert SG._graph is RG
+        assert edges_equal(SG.edges, SSG.edges)
+        # should be same as morphing the graph
+        CG = self.G.copy()
+        CG.remove_nodes_from(hide_nodes)
+        CG.remove_edges_from(hide_edges)
+        assert edges_equal(CG.edges(nodes), SSG.edges)
+        CG.remove_nodes_from([0, 1, 2, 3])
+        assert edges_equal(CG.edges, SSG.edges)
+        # switch order: subgraph first, then restricted view
+        SSSG = self.G.subgraph(nodes)
+        RSG = nx.restricted_view(SSSG, hide_nodes, hide_edges)
+        assert RSG._graph is not self.G
+        assert edges_equal(RSG.edges, CG.edges)
+
+    def test_subgraph_copy(self):
+        for origG in self.graphs:
+            G = nx.Graph(origG)
+            SG = G.subgraph([4, 5, 6])
+            H = SG.copy()
+            assert type(G) == type(H)
+
+    def test_subgraph_todirected(self):
+        SG = nx.induced_subgraph(self.G, [4, 5, 6])
+        SSG = SG.to_directed()
+        assert sorted(SSG) == [4, 5, 6]
+        assert sorted(SSG.edges) == [(4, 5), (5, 4), (5, 6), (6, 5)]
+
+    def test_subgraph_toundirected(self):
+        SG = nx.induced_subgraph(self.G, [4, 5, 6])
+        SSG = SG.to_undirected()
+        assert list(SSG) == [4, 5, 6]
+        assert sorted(SSG.edges) == [(4, 5), (5, 6)]
+
+    def test_reverse_subgraph_toundirected(self):
+        G = self.DG.reverse(copy=False)
+        SG = G.subgraph([4, 5, 6])
+        SSG = SG.to_undirected()
+        assert list(SSG) == [4, 5, 6]
+        assert sorted(SSG.edges) == [(4, 5), (5, 6)]
+
+    def test_reverse_reverse_copy(self):
+        G = self.DG.reverse(copy=False)
+        H = G.reverse(copy=True)
+        assert H.nodes == self.DG.nodes
+        assert H.edges == self.DG.edges
+        G = self.MDG.reverse(copy=False)
+        H = G.reverse(copy=True)
+        assert H.nodes == self.MDG.nodes
+        assert H.edges == self.MDG.edges
+
+    def test_subgraph_edgesubgraph_toundirected(self):
+        G = self.G.copy()
+        SG = G.subgraph([4, 5, 6])
+        SSG = SG.edge_subgraph([(4, 5), (5, 4)])
+        USSG = SSG.to_undirected()
+        assert list(USSG) == [4, 5]
+        assert sorted(USSG.edges) == [(4, 5)]
+
+    def test_copy_subgraph(self):
+        G = self.G.copy()
+        SG = G.subgraph([4, 5, 6])
+        CSG = SG.copy(as_view=True)
+        DCSG = SG.copy(as_view=False)
+        assert hasattr(CSG, "_graph")  # is a view
+        assert not hasattr(DCSG, "_graph")  # not a view
+
+    def test_copy_disubgraph(self):
+        G = self.DG.copy()
+        SG = G.subgraph([4, 5, 6])
+        CSG = SG.copy(as_view=True)
+        DCSG = SG.copy(as_view=False)
+        assert hasattr(CSG, "_graph")  # is a view
+        assert not hasattr(DCSG, "_graph")  # not a view
+
+    def test_copy_multidisubgraph(self):
+        G = self.MDG.copy()
+        SG = G.subgraph([4, 5, 6])
+        CSG = SG.copy(as_view=True)
+        DCSG = SG.copy(as_view=False)
+        assert hasattr(CSG, "_graph")  # is a view
+        assert not hasattr(DCSG, "_graph")  # not a view
+
+    def test_copy_multisubgraph(self):
+        G = self.MG.copy()
+        SG = G.subgraph([4, 5, 6])
+        CSG = SG.copy(as_view=True)
+        DCSG = SG.copy(as_view=False)
+        assert hasattr(CSG, "_graph")  # is a view
+        assert not hasattr(DCSG, "_graph")  # not a view
+
+    def test_copy_of_view(self):
+        G = nx.MultiGraph(self.MGv)
+        assert G.__class__.__name__ == "MultiGraph"
+        G = G.copy(as_view=True)
+        assert G.__class__.__name__ == "MultiGraph"
+
+    def test_subclass(self):
+        class MyGraph(nx.DiGraph):
+            def my_method(self):
+                return "me"
+
+            def to_directed_class(self):
+                return MyGraph()
+
+        for origG in self.graphs:
+            G = MyGraph(origG)
+            SG = G.subgraph([4, 5, 6])
+            H = SG.copy()
+            assert SG.my_method() == "me"
+            assert H.my_method() == "me"
+            assert 3 not in H or 3 in SG

env-llmeval/lib/python3.10/site-packages/networkx/classes/tests/test_multidigraph.py

@@ -0,0 +1,459 @@
+from collections import UserDict
+
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal
+
+from .test_multigraph import BaseMultiGraphTester
+from .test_multigraph import TestEdgeSubgraph as _TestMultiGraphEdgeSubgraph
+from .test_multigraph import TestMultiGraph as _TestMultiGraph
+
+
+class BaseMultiDiGraphTester(BaseMultiGraphTester):
+    def test_edges(self):
+        G = self.K3
+        edges = [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]
+        assert sorted(G.edges()) == edges
+        assert sorted(G.edges(0)) == [(0, 1), (0, 2)]
+        pytest.raises((KeyError, nx.NetworkXError), G.edges, -1)
+
+    def test_edges_data(self):
+        G = self.K3
+        edges = [(0, 1, {}), (0, 2, {}), (1, 0, {}), (1, 2, {}), (2, 0, {}), (2, 1, {})]
+        assert sorted(G.edges(data=True)) == edges
+        assert sorted(G.edges(0, data=True)) == [(0, 1, {}), (0, 2, {})]
+        pytest.raises((KeyError, nx.NetworkXError), G.neighbors, -1)
+
+    def test_edges_multi(self):
+        G = self.K3
+        assert sorted(G.edges()) == [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]
+        assert sorted(G.edges(0)) == [(0, 1), (0, 2)]
+        G.add_edge(0, 1)
+        assert sorted(G.edges()) == [
+            (0, 1),
+            (0, 1),
+            (0, 2),
+            (1, 0),
+            (1, 2),
+            (2, 0),
+            (2, 1),
+        ]
+
+    def test_out_edges(self):
+        G = self.K3
+        assert sorted(G.out_edges()) == [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]
+        assert sorted(G.out_edges(0)) == [(0, 1), (0, 2)]
+        pytest.raises((KeyError, nx.NetworkXError), G.out_edges, -1)
+        assert sorted(G.out_edges(0, keys=True)) == [(0, 1, 0), (0, 2, 0)]
+
+    def test_out_edges_multi(self):
+        G = self.K3
+        assert sorted(G.out_edges()) == [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]
+        assert sorted(G.out_edges(0)) == [(0, 1), (0, 2)]
+        G.add_edge(0, 1, 2)
+        assert sorted(G.out_edges()) == [
+            (0, 1),
+            (0, 1),
+            (0, 2),
+            (1, 0),
+            (1, 2),
+            (2, 0),
+            (2, 1),
+        ]
+
+    def test_out_edges_data(self):
+        G = self.K3
+        assert sorted(G.edges(0, data=True)) == [(0, 1, {}), (0, 2, {})]
+        G.remove_edge(0, 1)
+        G.add_edge(0, 1, data=1)
+        assert sorted(G.edges(0, data=True)) == [(0, 1, {"data": 1}), (0, 2, {})]
+        assert sorted(G.edges(0, data="data")) == [(0, 1, 1), (0, 2, None)]
+        assert sorted(G.edges(0, data="data", default=-1)) == [(0, 1, 1), (0, 2, -1)]
+
+    def test_in_edges(self):
+        G = self.K3
+        assert sorted(G.in_edges()) == [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]
+        assert sorted(G.in_edges(0)) == [(1, 0), (2, 0)]
+        pytest.raises((KeyError, nx.NetworkXError), G.in_edges, -1)
+        G.add_edge(0, 1, 2)
+        assert sorted(G.in_edges()) == [
+            (0, 1),
+            (0, 1),
+            (0, 2),
+            (1, 0),
+            (1, 2),
+            (2, 0),
+            (2, 1),
+        ]
+        assert sorted(G.in_edges(0, keys=True)) == [(1, 0, 0), (2, 0, 0)]
+
+    def test_in_edges_no_keys(self):
+        G = self.K3
+        assert sorted(G.in_edges()) == [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]
+        assert sorted(G.in_edges(0)) == [(1, 0), (2, 0)]
+        G.add_edge(0, 1, 2)
+        assert sorted(G.in_edges()) == [
+            (0, 1),
+            (0, 1),
+            (0, 2),
+            (1, 0),
+            (1, 2),
+            (2, 0),
+            (2, 1),
+        ]
+
+        assert sorted(G.in_edges(data=True, keys=False)) == [
+            (0, 1, {}),
+            (0, 1, {}),
+            (0, 2, {}),
+            (1, 0, {}),
+            (1, 2, {}),
+            (2, 0, {}),
+            (2, 1, {}),
+        ]
+
+    def test_in_edges_data(self):
+        G = self.K3
+        assert sorted(G.in_edges(0, data=True)) == [(1, 0, {}), (2, 0, {})]
+        G.remove_edge(1, 0)
+        G.add_edge(1, 0, data=1)
+        assert sorted(G.in_edges(0, data=True)) == [(1, 0, {"data": 1}), (2, 0, {})]
+        assert sorted(G.in_edges(0, data="data")) == [(1, 0, 1), (2, 0, None)]
+        assert sorted(G.in_edges(0, data="data", default=-1)) == [(1, 0, 1), (2, 0, -1)]
+
+    def is_shallow(self, H, G):
+        # graph
+        assert G.graph["foo"] == H.graph["foo"]
+        G.graph["foo"].append(1)
+        assert G.graph["foo"] == H.graph["foo"]
+        # node
+        assert G.nodes[0]["foo"] == H.nodes[0]["foo"]
+        G.nodes[0]["foo"].append(1)
+        assert G.nodes[0]["foo"] == H.nodes[0]["foo"]
+        # edge
+        assert G[1][2][0]["foo"] == H[1][2][0]["foo"]
+        G[1][2][0]["foo"].append(1)
+        assert G[1][2][0]["foo"] == H[1][2][0]["foo"]
+
+    def is_deep(self, H, G):
+        # graph
+        assert G.graph["foo"] == H.graph["foo"]
+        G.graph["foo"].append(1)
+        assert G.graph["foo"] != H.graph["foo"]
+        # node
+        assert G.nodes[0]["foo"] == H.nodes[0]["foo"]
+        G.nodes[0]["foo"].append(1)
+        assert G.nodes[0]["foo"] != H.nodes[0]["foo"]
+        # edge
+        assert G[1][2][0]["foo"] == H[1][2][0]["foo"]
+        G[1][2][0]["foo"].append(1)
+        assert G[1][2][0]["foo"] != H[1][2][0]["foo"]
+
+    def test_to_undirected(self):
+        # MultiDiGraph -> MultiGraph changes number of edges so it is
+        # not a copy operation... use is_shallow, not is_shallow_copy
+        G = self.K3
+        self.add_attributes(G)
+        H = nx.MultiGraph(G)
+        # self.is_shallow(H,G)
+        # the result is traversal order dependent so we
+        # can't use the is_shallow() test here.
+        try:
+            assert edges_equal(H.edges(), [(0, 1), (1, 2), (2, 0)])
+        except AssertionError:
+            assert edges_equal(H.edges(), [(0, 1), (1, 2), (1, 2), (2, 0)])
+        H = G.to_undirected()
+        self.is_deep(H, G)
+
+    def test_has_successor(self):
+        G = self.K3
+        assert G.has_successor(0, 1)
+        assert not G.has_successor(0, -1)
+
+    def test_successors(self):
+        G = self.K3
+        assert sorted(G.successors(0)) == [1, 2]
+        pytest.raises((KeyError, nx.NetworkXError), G.successors, -1)
+
+    def test_has_predecessor(self):
+        G = self.K3
+        assert G.has_predecessor(0, 1)
+        assert not G.has_predecessor(0, -1)
+
+    def test_predecessors(self):
+        G = self.K3
+        assert sorted(G.predecessors(0)) == [1, 2]
+        pytest.raises((KeyError, nx.NetworkXError), G.predecessors, -1)
+
+    def test_degree(self):
+        G = self.K3
+        assert sorted(G.degree()) == [(0, 4), (1, 4), (2, 4)]
+        assert dict(G.degree()) == {0: 4, 1: 4, 2: 4}
+        assert G.degree(0) == 4
+        assert list(G.degree(iter([0]))) == [(0, 4)]
+        G.add_edge(0, 1, weight=0.3, other=1.2)
+        assert sorted(G.degree(weight="weight")) == [(0, 4.3), (1, 4.3), (2, 4)]
+        assert sorted(G.degree(weight="other")) == [(0, 5.2), (1, 5.2), (2, 4)]
+
+    def test_in_degree(self):
+        G = self.K3
+        assert sorted(G.in_degree()) == [(0, 2), (1, 2), (2, 2)]
+        assert dict(G.in_degree()) == {0: 2, 1: 2, 2: 2}
+        assert G.in_degree(0) == 2
+        assert list(G.in_degree(iter([0]))) == [(0, 2)]
+        assert G.in_degree(0, weight="weight") == 2
+
+    def test_out_degree(self):
+        G = self.K3
+        assert sorted(G.out_degree()) == [(0, 2), (1, 2), (2, 2)]
+        assert dict(G.out_degree()) == {0: 2, 1: 2, 2: 2}
+        assert G.out_degree(0) == 2
+        assert list(G.out_degree(iter([0]))) == [(0, 2)]
+        assert G.out_degree(0, weight="weight") == 2
+
+    def test_size(self):
+        G = self.K3
+        assert G.size() == 6
+        assert G.number_of_edges() == 6
+        G.add_edge(0, 1, weight=0.3, other=1.2)
+        assert round(G.size(weight="weight"), 2) == 6.3
+        assert round(G.size(weight="other"), 2) == 7.2
+
+    def test_to_undirected_reciprocal(self):
+        G = self.Graph()
+        G.add_edge(1, 2)
+        assert G.to_undirected().has_edge(1, 2)
+        assert not G.to_undirected(reciprocal=True).has_edge(1, 2)
+        G.add_edge(2, 1)
+        assert G.to_undirected(reciprocal=True).has_edge(1, 2)
+
+    def test_reverse_copy(self):
+        G = nx.MultiDiGraph([(0, 1), (0, 1)])
+        R = G.reverse()
+        assert sorted(R.edges()) == [(1, 0), (1, 0)]
+        R.remove_edge(1, 0)
+        assert sorted(R.edges()) == [(1, 0)]
+        assert sorted(G.edges()) == [(0, 1), (0, 1)]
+
+    def test_reverse_nocopy(self):
+        G = nx.MultiDiGraph([(0, 1), (0, 1)])
+        R = G.reverse(copy=False)
+        assert sorted(R.edges()) == [(1, 0), (1, 0)]
+        pytest.raises(nx.NetworkXError, R.remove_edge, 1, 0)
+
+    def test_di_attributes_cached(self):
+        G = self.K3.copy()
+        assert id(G.in_edges) == id(G.in_edges)
+        assert id(G.out_edges) == id(G.out_edges)
+        assert id(G.in_degree) == id(G.in_degree)
+        assert id(G.out_degree) == id(G.out_degree)
+        assert id(G.succ) == id(G.succ)
+        assert id(G.pred) == id(G.pred)
+
+
+class TestMultiDiGraph(BaseMultiDiGraphTester, _TestMultiGraph):
+    def setup_method(self):
+        self.Graph = nx.MultiDiGraph
+        # build K3
+        self.k3edges = [(0, 1), (0, 2), (1, 2)]
+        self.k3nodes = [0, 1, 2]
+        self.K3 = self.Graph()
+        self.K3._succ = {0: {}, 1: {}, 2: {}}
+        # K3._adj is synced with K3._succ
+        self.K3._pred = {0: {}, 1: {}, 2: {}}
+        for u in self.k3nodes:
+            for v in self.k3nodes:
+                if u == v:
+                    continue
+                d = {0: {}}
+                self.K3._succ[u][v] = d
+                self.K3._pred[v][u] = d
+        self.K3._node = {}
+        self.K3._node[0] = {}
+        self.K3._node[1] = {}
+        self.K3._node[2] = {}
+
+    def test_add_edge(self):
+        G = self.Graph()
+        G.add_edge(0, 1)
+        assert G._adj == {0: {1: {0: {}}}, 1: {}}
+        assert G._succ == {0: {1: {0: {}}}, 1: {}}
+        assert G._pred == {0: {}, 1: {0: {0: {}}}}
+        G = self.Graph()
+        G.add_edge(*(0, 1))
+        assert G._adj == {0: {1: {0: {}}}, 1: {}}
+        assert G._succ == {0: {1: {0: {}}}, 1: {}}
+        assert G._pred == {0: {}, 1: {0: {0: {}}}}
+        with pytest.raises(ValueError, match="None cannot be a node"):
+            G.add_edge(None, 3)
+
+    def test_add_edges_from(self):
+        G = self.Graph()
+        G.add_edges_from([(0, 1), (0, 1, {"weight": 3})])
+        assert G._adj == {0: {1: {0: {}, 1: {"weight": 3}}}, 1: {}}
+        assert G._succ == {0: {1: {0: {}, 1: {"weight": 3}}}, 1: {}}
+        assert G._pred == {0: {}, 1: {0: {0: {}, 1: {"weight": 3}}}}
+
+        G.add_edges_from([(0, 1), (0, 1, {"weight": 3})], weight=2)
+        assert G._succ == {
+            0: {1: {0: {}, 1: {"weight": 3}, 2: {"weight": 2}, 3: {"weight": 3}}},
+            1: {},
+        }
+        assert G._pred == {
+            0: {},
+            1: {0: {0: {}, 1: {"weight": 3}, 2: {"weight": 2}, 3: {"weight": 3}}},
+        }
+
+        G = self.Graph()
+        edges = [
+            (0, 1, {"weight": 3}),
+            (0, 1, (("weight", 2),)),
+            (0, 1, 5),
+            (0, 1, "s"),
+        ]
+        G.add_edges_from(edges)
+        keydict = {0: {"weight": 3}, 1: {"weight": 2}, 5: {}, "s": {}}
+        assert G._succ == {0: {1: keydict}, 1: {}}
+        assert G._pred == {1: {0: keydict}, 0: {}}
+
+        # too few in tuple
+        pytest.raises(nx.NetworkXError, G.add_edges_from, [(0,)])
+        # too many in tuple
+        pytest.raises(nx.NetworkXError, G.add_edges_from, [(0, 1, 2, 3, 4)])
+        # not a tuple
+        pytest.raises(TypeError, G.add_edges_from, [0])
+        with pytest.raises(ValueError, match="None cannot be a node"):
+            G.add_edges_from([(None, 3), (3, 2)])
+
+    def test_remove_edge(self):
+        G = self.K3
+        G.remove_edge(0, 1)
+        assert G._succ == {
+            0: {2: {0: {}}},
+            1: {0: {0: {}}, 2: {0: {}}},
+            2: {0: {0: {}}, 1: {0: {}}},
+        }
+        assert G._pred == {
+            0: {1: {0: {}}, 2: {0: {}}},
+            1: {2: {0: {}}},
+            2: {0: {0: {}}, 1: {0: {}}},
+        }
+        pytest.raises((KeyError, nx.NetworkXError), G.remove_edge, -1, 0)
+        pytest.raises((KeyError, nx.NetworkXError), G.remove_edge, 0, 2, key=1)
+
+    def test_remove_multiedge(self):
+        G = self.K3
+        G.add_edge(0, 1, key="parallel edge")
+        G.remove_edge(0, 1, key="parallel edge")
+        assert G._adj == {
+            0: {1: {0: {}}, 2: {0: {}}},
+            1: {0: {0: {}}, 2: {0: {}}},
+            2: {0: {0: {}}, 1: {0: {}}},
+        }
+
+        assert G._succ == {
+            0: {1: {0: {}}, 2: {0: {}}},
+            1: {0: {0: {}}, 2: {0: {}}},
+            2: {0: {0: {}}, 1: {0: {}}},
+        }
+
+        assert G._pred == {
+            0: {1: {0: {}}, 2: {0: {}}},
+            1: {0: {0: {}}, 2: {0: {}}},
+            2: {0: {0: {}}, 1: {0: {}}},
+        }
+        G.remove_edge(0, 1)
+        assert G._succ == {
+            0: {2: {0: {}}},
+            1: {0: {0: {}}, 2: {0: {}}},
+            2: {0: {0: {}}, 1: {0: {}}},
+        }
+        assert G._pred == {
+            0: {1: {0: {}}, 2: {0: {}}},
+            1: {2: {0: {}}},
+            2: {0: {0: {}}, 1: {0: {}}},
+        }
+        pytest.raises((KeyError, nx.NetworkXError), G.remove_edge, -1, 0)
+
+    def test_remove_edges_from(self):
+        G = self.K3
+        G.remove_edges_from([(0, 1)])
+        assert G._succ == {
+            0: {2: {0: {}}},
+            1: {0: {0: {}}, 2: {0: {}}},
+            2: {0: {0: {}}, 1: {0: {}}},
+        }
+        assert G._pred == {
+            0: {1: {0: {}}, 2: {0: {}}},
+            1: {2: {0: {}}},
+            2: {0: {0: {}}, 1: {0: {}}},
+        }
+        G.remove_edges_from([(0, 0)])  # silent fail
+
+
+class TestEdgeSubgraph(_TestMultiGraphEdgeSubgraph):
+    """Unit tests for the :meth:`MultiDiGraph.edge_subgraph` method."""
+
+    def setup_method(self):
+        # Create a quadruply-linked path graph on five nodes.
+        G = nx.MultiDiGraph()
+        nx.add_path(G, range(5))
+        nx.add_path(G, range(5))
+        nx.add_path(G, reversed(range(5)))
+        nx.add_path(G, reversed(range(5)))
+        # Add some node, edge, and graph attributes.
+        for i in range(5):
+            G.nodes[i]["name"] = f"node{i}"
+        G.adj[0][1][0]["name"] = "edge010"
+        G.adj[0][1][1]["name"] = "edge011"
+        G.adj[3][4][0]["name"] = "edge340"
+        G.adj[3][4][1]["name"] = "edge341"
+        G.graph["name"] = "graph"
+        # Get the subgraph induced by one of the first edges and one of
+        # the last edges.
+        self.G = G
+        self.H = G.edge_subgraph([(0, 1, 0), (3, 4, 1)])
+
+
+class CustomDictClass(UserDict):
+    pass
+
+
+class MultiDiGraphSubClass(nx.MultiDiGraph):
+    node_dict_factory = CustomDictClass  # type: ignore[assignment]
+    node_attr_dict_factory = CustomDictClass  # type: ignore[assignment]
+    adjlist_outer_dict_factory = CustomDictClass  # type: ignore[assignment]
+    adjlist_inner_dict_factory = CustomDictClass  # type: ignore[assignment]
+    edge_key_dict_factory = CustomDictClass  # type: ignore[assignment]
+    edge_attr_dict_factory = CustomDictClass  # type: ignore[assignment]
+    graph_attr_dict_factory = CustomDictClass  # type: ignore[assignment]
+
+
+class TestMultiDiGraphSubclass(TestMultiDiGraph):
+    def setup_method(self):
+        self.Graph = MultiDiGraphSubClass
+        # build K3
+        self.k3edges = [(0, 1), (0, 2), (1, 2)]
+        self.k3nodes = [0, 1, 2]
+        self.K3 = self.Graph()
+        self.K3._succ = self.K3.adjlist_outer_dict_factory(
+            {
+                0: self.K3.adjlist_inner_dict_factory(),
+                1: self.K3.adjlist_inner_dict_factory(),
+                2: self.K3.adjlist_inner_dict_factory(),
+            }
+        )
+        # K3._adj is synced with K3._succ
+        self.K3._pred = {0: {}, 1: {}, 2: {}}
+        for u in self.k3nodes:
+            for v in self.k3nodes:
+                if u == v:
+                    continue
+                d = {0: {}}
+                self.K3._succ[u][v] = d
+                self.K3._pred[v][u] = d
+        self.K3._node = self.K3.node_dict_factory()
+        self.K3._node[0] = self.K3.node_attr_dict_factory()
+        self.K3._node[1] = self.K3.node_attr_dict_factory()
+        self.K3._node[2] = self.K3.node_attr_dict_factory()

env-llmeval/lib/python3.10/site-packages/networkx/classes/tests/test_special.py

@@ -0,0 +1,131 @@
+import networkx as nx
+
+from .test_digraph import BaseDiGraphTester
+from .test_digraph import TestDiGraph as _TestDiGraph
+from .test_graph import BaseGraphTester
+from .test_graph import TestGraph as _TestGraph
+from .test_multidigraph import TestMultiDiGraph as _TestMultiDiGraph
+from .test_multigraph import TestMultiGraph as _TestMultiGraph
+
+
+def test_factories():
+    class mydict1(dict):
+        pass
+
+    class mydict2(dict):
+        pass
+
+    class mydict3(dict):
+        pass
+
+    class mydict4(dict):
+        pass
+
+    class mydict5(dict):
+        pass
+
+    for Graph in (nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph):
+        # print("testing class: ", Graph.__name__)
+        class MyGraph(Graph):
+            node_dict_factory = mydict1
+            adjlist_outer_dict_factory = mydict2
+            adjlist_inner_dict_factory = mydict3
+            edge_key_dict_factory = mydict4
+            edge_attr_dict_factory = mydict5
+
+        G = MyGraph()
+        assert isinstance(G._node, mydict1)
+        assert isinstance(G._adj, mydict2)
+        G.add_node(1)
+        assert isinstance(G._adj[1], mydict3)
+        if G.is_directed():
+            assert isinstance(G._pred, mydict2)
+            assert isinstance(G._succ, mydict2)
+            assert isinstance(G._pred[1], mydict3)
+        G.add_edge(1, 2)
+        if G.is_multigraph():
+            assert isinstance(G._adj[1][2], mydict4)
+            assert isinstance(G._adj[1][2][0], mydict5)
+        else:
+            assert isinstance(G._adj[1][2], mydict5)
+
+
+class TestSpecialGraph(_TestGraph):
+    def setup_method(self):
+        _TestGraph.setup_method(self)
+        self.Graph = nx.Graph
+
+
+class TestThinGraph(BaseGraphTester):
+    def setup_method(self):
+        all_edge_dict = {"weight": 1}
+
+        class MyGraph(nx.Graph):
+            def edge_attr_dict_factory(self):
+                return all_edge_dict
+
+        self.Graph = MyGraph
+        # build dict-of-dict-of-dict K3
+        ed1, ed2, ed3 = (all_edge_dict, all_edge_dict, all_edge_dict)
+        self.k3adj = {0: {1: ed1, 2: ed2}, 1: {0: ed1, 2: ed3}, 2: {0: ed2, 1: ed3}}
+        self.k3edges = [(0, 1), (0, 2), (1, 2)]
+        self.k3nodes = [0, 1, 2]
+        self.K3 = self.Graph()
+        self.K3._adj = self.k3adj
+        self.K3._node = {}
+        self.K3._node[0] = {}
+        self.K3._node[1] = {}
+        self.K3._node[2] = {}
+
+
+class TestSpecialDiGraph(_TestDiGraph):
+    def setup_method(self):
+        _TestDiGraph.setup_method(self)
+        self.Graph = nx.DiGraph
+
+
+class TestThinDiGraph(BaseDiGraphTester):
+    def setup_method(self):
+        all_edge_dict = {"weight": 1}
+
+        class MyGraph(nx.DiGraph):
+            def edge_attr_dict_factory(self):
+                return all_edge_dict
+
+        self.Graph = MyGraph
+        # build dict-of-dict-of-dict K3
+        ed1, ed2, ed3 = (all_edge_dict, all_edge_dict, all_edge_dict)
+        ed4, ed5, ed6 = (all_edge_dict, all_edge_dict, all_edge_dict)
+        self.k3adj = {0: {1: ed1, 2: ed2}, 1: {0: ed3, 2: ed4}, 2: {0: ed5, 1: ed6}}
+        self.k3edges = [(0, 1), (0, 2), (1, 2)]
+        self.k3nodes = [0, 1, 2]
+        self.K3 = self.Graph()
+        self.K3._succ = self.k3adj
+        # K3._adj is synced with K3._succ
+        self.K3._pred = {0: {1: ed3, 2: ed5}, 1: {0: ed1, 2: ed6}, 2: {0: ed2, 1: ed4}}
+        self.K3._node = {}
+        self.K3._node[0] = {}
+        self.K3._node[1] = {}
+        self.K3._node[2] = {}
+
+        ed1, ed2 = (all_edge_dict, all_edge_dict)
+        self.P3 = self.Graph()
+        self.P3._succ = {0: {1: ed1}, 1: {2: ed2}, 2: {}}
+        # P3._adj is synced with P3._succ
+        self.P3._pred = {0: {}, 1: {0: ed1}, 2: {1: ed2}}
+        self.P3._node = {}
+        self.P3._node[0] = {}
+        self.P3._node[1] = {}
+        self.P3._node[2] = {}
+
+
+class TestSpecialMultiGraph(_TestMultiGraph):
+    def setup_method(self):
+        _TestMultiGraph.setup_method(self)
+        self.Graph = nx.MultiGraph
+
+
+class TestSpecialMultiDiGraph(_TestMultiDiGraph):
+    def setup_method(self):
+        _TestMultiDiGraph.setup_method(self)
+        self.Graph = nx.MultiDiGraph

env-llmeval/lib/python3.10/site-packages/networkx/classes/tests/test_subgraphviews.py

@@ -0,0 +1,362 @@
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal
+
+
+class TestSubGraphView:
+    gview = staticmethod(nx.subgraph_view)
+    graph = nx.Graph
+    hide_edges_filter = staticmethod(nx.filters.hide_edges)
+    show_edges_filter = staticmethod(nx.filters.show_edges)
+
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(9, create_using=cls.graph())
+        cls.hide_edges_w_hide_nodes = {(3, 4), (4, 5), (5, 6)}
+
+    def test_hidden_nodes(self):
+        hide_nodes = [4, 5, 111]
+        nodes_gone = nx.filters.hide_nodes(hide_nodes)
+        gview = self.gview
+        G = gview(self.G, filter_node=nodes_gone)
+        assert self.G.nodes - G.nodes == {4, 5}
+        assert self.G.edges - G.edges == self.hide_edges_w_hide_nodes
+        if G.is_directed():
+            assert list(G[3]) == []
+            assert list(G[2]) == [3]
+        else:
+            assert list(G[3]) == [2]
+            assert set(G[2]) == {1, 3}
+        pytest.raises(KeyError, G.__getitem__, 4)
+        pytest.raises(KeyError, G.__getitem__, 112)
+        pytest.raises(KeyError, G.__getitem__, 111)
+        assert G.degree(3) == (3 if G.is_multigraph() else 1)
+        assert G.size() == (7 if G.is_multigraph() else 5)
+
+    def test_hidden_edges(self):
+        hide_edges = [(2, 3), (8, 7), (222, 223)]
+        edges_gone = self.hide_edges_filter(hide_edges)
+        gview = self.gview
+        G = gview(self.G, filter_edge=edges_gone)
+        assert self.G.nodes == G.nodes
+        if G.is_directed():
+            assert self.G.edges - G.edges == {(2, 3)}
+            assert list(G[2]) == []
+            assert list(G.pred[3]) == []
+            assert list(G.pred[2]) == [1]
+            assert G.size() == 7
+        else:
+            assert self.G.edges - G.edges == {(2, 3), (7, 8)}
+            assert list(G[2]) == [1]
+            assert G.size() == 6
+        assert list(G[3]) == [4]
+        pytest.raises(KeyError, G.__getitem__, 221)
+        pytest.raises(KeyError, G.__getitem__, 222)
+        assert G.degree(3) == 1
+
+    def test_shown_node(self):
+        induced_subgraph = nx.filters.show_nodes([2, 3, 111])
+        gview = self.gview
+        G = gview(self.G, filter_node=induced_subgraph)
+        assert set(G.nodes) == {2, 3}
+        if G.is_directed():
+            assert list(G[3]) == []
+        else:
+            assert list(G[3]) == [2]
+        assert list(G[2]) == [3]
+        pytest.raises(KeyError, G.__getitem__, 4)
+        pytest.raises(KeyError, G.__getitem__, 112)
+        pytest.raises(KeyError, G.__getitem__, 111)
+        assert G.degree(3) == (3 if G.is_multigraph() else 1)
+        assert G.size() == (3 if G.is_multigraph() else 1)
+
+    def test_shown_edges(self):
+        show_edges = [(2, 3), (8, 7), (222, 223)]
+        edge_subgraph = self.show_edges_filter(show_edges)
+        G = self.gview(self.G, filter_edge=edge_subgraph)
+        assert self.G.nodes == G.nodes
+        if G.is_directed():
+            assert G.edges == {(2, 3)}
+            assert list(G[3]) == []
+            assert list(G[2]) == [3]
+            assert list(G.pred[3]) == [2]
+            assert list(G.pred[2]) == []
+            assert G.size() == 1
+        else:
+            assert G.edges == {(2, 3), (7, 8)}
+            assert list(G[3]) == [2]
+            assert list(G[2]) == [3]
+            assert G.size() == 2
+        pytest.raises(KeyError, G.__getitem__, 221)
+        pytest.raises(KeyError, G.__getitem__, 222)
+        assert G.degree(3) == 1
+
+
+class TestSubDiGraphView(TestSubGraphView):
+    gview = staticmethod(nx.subgraph_view)
+    graph = nx.DiGraph
+    hide_edges_filter = staticmethod(nx.filters.hide_diedges)
+    show_edges_filter = staticmethod(nx.filters.show_diedges)
+    hide_edges = [(2, 3), (8, 7), (222, 223)]
+    excluded = {(2, 3), (3, 4), (4, 5), (5, 6)}
+
+    def test_inoutedges(self):
+        edges_gone = self.hide_edges_filter(self.hide_edges)
+        hide_nodes = [4, 5, 111]
+        nodes_gone = nx.filters.hide_nodes(hide_nodes)
+        G = self.gview(self.G, filter_node=nodes_gone, filter_edge=edges_gone)
+
+        assert self.G.in_edges - G.in_edges == self.excluded
+        assert self.G.out_edges - G.out_edges == self.excluded
+
+    def test_pred(self):
+        edges_gone = self.hide_edges_filter(self.hide_edges)
+        hide_nodes = [4, 5, 111]
+        nodes_gone = nx.filters.hide_nodes(hide_nodes)
+        G = self.gview(self.G, filter_node=nodes_gone, filter_edge=edges_gone)
+
+        assert list(G.pred[2]) == [1]
+        assert list(G.pred[6]) == []
+
+    def test_inout_degree(self):
+        edges_gone = self.hide_edges_filter(self.hide_edges)
+        hide_nodes = [4, 5, 111]
+        nodes_gone = nx.filters.hide_nodes(hide_nodes)
+        G = self.gview(self.G, filter_node=nodes_gone, filter_edge=edges_gone)
+
+        assert G.degree(2) == 1
+        assert G.out_degree(2) == 0
+        assert G.in_degree(2) == 1
+        assert G.size() == 4
+
+
+# multigraph
+class TestMultiGraphView(TestSubGraphView):
+    gview = staticmethod(nx.subgraph_view)
+    graph = nx.MultiGraph
+    hide_edges_filter = staticmethod(nx.filters.hide_multiedges)
+    show_edges_filter = staticmethod(nx.filters.show_multiedges)
+
+    @classmethod
+    def setup_class(cls):
+        cls.G = nx.path_graph(9, create_using=cls.graph())
+        multiedges = {(2, 3, 4), (2, 3, 5)}
+        cls.G.add_edges_from(multiedges)
+        cls.hide_edges_w_hide_nodes = {(3, 4, 0), (4, 5, 0), (5, 6, 0)}
+
+    def test_hidden_edges(self):
+        hide_edges = [(2, 3, 4), (2, 3, 3), (8, 7, 0), (222, 223, 0)]
+        edges_gone = self.hide_edges_filter(hide_edges)
+        G = self.gview(self.G, filter_edge=edges_gone)
+        assert self.G.nodes == G.nodes
+        if G.is_directed():
+            assert self.G.edges - G.edges == {(2, 3, 4)}
+            assert list(G[3]) == [4]
+            assert list(G[2]) == [3]
+            assert list(G.pred[3]) == [2]  # only one 2 but two edges
+            assert list(G.pred[2]) == [1]
+            assert G.size() == 9
+        else:
+            assert self.G.edges - G.edges == {(2, 3, 4), (7, 8, 0)}
+            assert list(G[3]) == [2, 4]
+            assert list(G[2]) == [1, 3]
+            assert G.size() == 8
+        assert G.degree(3) == 3
+        pytest.raises(KeyError, G.__getitem__, 221)
+        pytest.raises(KeyError, G.__getitem__, 222)
+
+    def test_shown_edges(self):
+        show_edges = [(2, 3, 4), (2, 3, 3), (8, 7, 0), (222, 223, 0)]
+        edge_subgraph = self.show_edges_filter(show_edges)
+        G = self.gview(self.G, filter_edge=edge_subgraph)
+        assert self.G.nodes == G.nodes
+        if G.is_directed():
+            assert G.edges == {(2, 3, 4)}
+            assert list(G[3]) == []
+            assert list(G.pred[3]) == [2]
+            assert list(G.pred[2]) == []
+            assert G.size() == 1
+        else:
+            assert G.edges == {(2, 3, 4), (7, 8, 0)}
+            assert G.size() == 2
+            assert list(G[3]) == [2]
+        assert G.degree(3) == 1
+        assert list(G[2]) == [3]
+        pytest.raises(KeyError, G.__getitem__, 221)
+        pytest.raises(KeyError, G.__getitem__, 222)
+
+
+# multidigraph
+class TestMultiDiGraphView(TestMultiGraphView, TestSubDiGraphView):
+    gview = staticmethod(nx.subgraph_view)
+    graph = nx.MultiDiGraph
+    hide_edges_filter = staticmethod(nx.filters.hide_multidiedges)
+    show_edges_filter = staticmethod(nx.filters.show_multidiedges)
+    hide_edges = [(2, 3, 0), (8, 7, 0), (222, 223, 0)]
+    excluded = {(2, 3, 0), (3, 4, 0), (4, 5, 0), (5, 6, 0)}
+
+    def test_inout_degree(self):
+        edges_gone = self.hide_edges_filter(self.hide_edges)
+        hide_nodes = [4, 5, 111]
+        nodes_gone = nx.filters.hide_nodes(hide_nodes)
+        G = self.gview(self.G, filter_node=nodes_gone, filter_edge=edges_gone)
+
+        assert G.degree(2) == 3
+        assert G.out_degree(2) == 2
+        assert G.in_degree(2) == 1
+        assert G.size() == 6
+
+
+# induced_subgraph
+class TestInducedSubGraph:
+    @classmethod
+    def setup_class(cls):
+        cls.K3 = G = nx.complete_graph(3)
+        G.graph["foo"] = []
+        G.nodes[0]["foo"] = []
+        G.remove_edge(1, 2)
+        ll = []
+        G.add_edge(1, 2, foo=ll)
+        G.add_edge(2, 1, foo=ll)
+
+    def test_full_graph(self):
+        G = self.K3
+        H = nx.induced_subgraph(G, [0, 1, 2, 5])
+        assert H.name == G.name
+        self.graphs_equal(H, G)
+        self.same_attrdict(H, G)
+
+    def test_partial_subgraph(self):
+        G = self.K3
+        H = nx.induced_subgraph(G, 0)
+        assert dict(H.adj) == {0: {}}
+        assert dict(G.adj) != {0: {}}
+
+        H = nx.induced_subgraph(G, [0, 1])
+        assert dict(H.adj) == {0: {1: {}}, 1: {0: {}}}
+
+    def same_attrdict(self, H, G):
+        old_foo = H[1][2]["foo"]
+        H.edges[1, 2]["foo"] = "baz"
+        assert G.edges == H.edges
+        H.edges[1, 2]["foo"] = old_foo
+        assert G.edges == H.edges
+        old_foo = H.nodes[0]["foo"]
+        H.nodes[0]["foo"] = "baz"
+        assert G.nodes == H.nodes
+        H.nodes[0]["foo"] = old_foo
+        assert G.nodes == H.nodes
+
+    def graphs_equal(self, H, G):
+        assert G._adj == H._adj
+        assert G._node == H._node
+        assert G.graph == H.graph
+        assert G.name == H.name
+        if not G.is_directed() and not H.is_directed():
+            assert H._adj[1][2] is H._adj[2][1]
+            assert G._adj[1][2] is G._adj[2][1]
+        else:  # at least one is directed
+            if not G.is_directed():
+                G._pred = G._adj
+                G._succ = G._adj
+            if not H.is_directed():
+                H._pred = H._adj
+                H._succ = H._adj
+            assert G._pred == H._pred
+            assert G._succ == H._succ
+            assert H._succ[1][2] is H._pred[2][1]
+            assert G._succ[1][2] is G._pred[2][1]
+
+
+# edge_subgraph
+class TestEdgeSubGraph:
+    @classmethod
+    def setup_class(cls):
+        # Create a path graph on five nodes.
+        cls.G = G = nx.path_graph(5)
+        # Add some node, edge, and graph attributes.
+        for i in range(5):
+            G.nodes[i]["name"] = f"node{i}"
+        G.edges[0, 1]["name"] = "edge01"
+        G.edges[3, 4]["name"] = "edge34"
+        G.graph["name"] = "graph"
+        # Get the subgraph induced by the first and last edges.
+        cls.H = nx.edge_subgraph(G, [(0, 1), (3, 4)])
+
+    def test_correct_nodes(self):
+        """Tests that the subgraph has the correct nodes."""
+        assert [(0, "node0"), (1, "node1"), (3, "node3"), (4, "node4")] == sorted(
+            self.H.nodes.data("name")
+        )
+
+    def test_correct_edges(self):
+        """Tests that the subgraph has the correct edges."""
+        assert edges_equal(
+            [(0, 1, "edge01"), (3, 4, "edge34")], self.H.edges.data("name")
+        )
+
+    def test_add_node(self):
+        """Tests that adding a node to the original graph does not
+        affect the nodes of the subgraph.
+
+        """
+        self.G.add_node(5)
+        assert [0, 1, 3, 4] == sorted(self.H.nodes)
+        self.G.remove_node(5)
+
+    def test_remove_node(self):
+        """Tests that removing a node in the original graph
+        removes the nodes of the subgraph.
+
+        """
+        self.G.remove_node(0)
+        assert [1, 3, 4] == sorted(self.H.nodes)
+        self.G.add_node(0, name="node0")
+        self.G.add_edge(0, 1, name="edge01")
+
+    def test_node_attr_dict(self):
+        """Tests that the node attribute dictionary of the two graphs is
+        the same object.
+
+        """
+        for v in self.H:
+            assert self.G.nodes[v] == self.H.nodes[v]
+        # Making a change to G should make a change in H and vice versa.
+        self.G.nodes[0]["name"] = "foo"
+        assert self.G.nodes[0] == self.H.nodes[0]
+        self.H.nodes[1]["name"] = "bar"
+        assert self.G.nodes[1] == self.H.nodes[1]
+        # Revert the change, so tests pass with pytest-randomly
+        self.G.nodes[0]["name"] = "node0"
+        self.H.nodes[1]["name"] = "node1"
+
+    def test_edge_attr_dict(self):
+        """Tests that the edge attribute dictionary of the two graphs is
+        the same object.
+
+        """
+        for u, v in self.H.edges():
+            assert self.G.edges[u, v] == self.H.edges[u, v]
+        # Making a change to G should make a change in H and vice versa.
+        self.G.edges[0, 1]["name"] = "foo"
+        assert self.G.edges[0, 1]["name"] == self.H.edges[0, 1]["name"]
+        self.H.edges[3, 4]["name"] = "bar"
+        assert self.G.edges[3, 4]["name"] == self.H.edges[3, 4]["name"]
+        # Revert the change, so tests pass with pytest-randomly
+        self.G.edges[0, 1]["name"] = "edge01"
+        self.H.edges[3, 4]["name"] = "edge34"
+
+    def test_graph_attr_dict(self):
+        """Tests that the graph attribute dictionary of the two graphs
+        is the same object.
+
+        """
+        assert self.G.graph is self.H.graph
+
+    def test_readonly(self):
+        """Tests that the subgraph cannot change the graph structure"""
+        pytest.raises(nx.NetworkXError, self.H.add_node, 5)
+        pytest.raises(nx.NetworkXError, self.H.remove_node, 0)
+        pytest.raises(nx.NetworkXError, self.H.add_edge, 5, 6)
+        pytest.raises(nx.NetworkXError, self.H.remove_edge, 0, 1)

env-llmeval/lib/python3.10/site-packages/networkx/readwrite/__init__.py

@@ -0,0 +1,18 @@
+"""
+A package for reading and writing graphs in various formats.
+
+"""
+
+
+from networkx.readwrite.adjlist import *
+from networkx.readwrite.multiline_adjlist import *
+from networkx.readwrite.edgelist import *
+from networkx.readwrite.pajek import *
+from networkx.readwrite.leda import *
+from networkx.readwrite.sparse6 import *
+from networkx.readwrite.graph6 import *
+from networkx.readwrite.gml import *
+from networkx.readwrite.graphml import *
+from networkx.readwrite.gexf import *
+from networkx.readwrite.json_graph import *
+from networkx.readwrite.text import *

env-llmeval/lib/python3.10/site-packages/networkx/readwrite/gexf.py

@@ -0,0 +1,1065 @@
+"""Read and write graphs in GEXF format.
+
+.. warning::
+    This parser uses the standard xml library present in Python, which is
+    insecure - see :external+python:mod:`xml` for additional information.
+    Only parse GEFX files you trust.
+
+GEXF (Graph Exchange XML Format) is a language for describing complex
+network structures, their associated data and dynamics.
+
+This implementation does not support mixed graphs (directed and
+undirected edges together).
+
+Format
+------
+GEXF is an XML format.  See http://gexf.net/schema.html for the
+specification and http://gexf.net/basic.html for examples.
+"""
+import itertools
+import time
+from xml.etree.ElementTree import (
+    Element,
+    ElementTree,
+    SubElement,
+    register_namespace,
+    tostring,
+)
+
+import networkx as nx
+from networkx.utils import open_file
+
+__all__ = ["write_gexf", "read_gexf", "relabel_gexf_graph", "generate_gexf"]
+
+
+@open_file(1, mode="wb")
+def write_gexf(G, path, encoding="utf-8", prettyprint=True, version="1.2draft"):
+    """Write G in GEXF format to path.
+
+    "GEXF (Graph Exchange XML Format) is a language for describing
+    complex networks structures, their associated data and dynamics" [1]_.
+
+    Node attributes are checked according to the version of the GEXF
+    schemas used for parameters which are not user defined,
+    e.g. visualization 'viz' [2]_. See example for usage.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+    path : file or string
+       File or file name to write.
+       File names ending in .gz or .bz2 will be compressed.
+    encoding : string (optional, default: 'utf-8')
+       Encoding for text data.
+    prettyprint : bool (optional, default: True)
+       If True use line breaks and indenting in output XML.
+    version: string (optional, default: '1.2draft')
+       The version of GEXF to be used for nodes attributes checking
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_gexf(G, "test.gexf")
+
+    # visualization data
+    >>> G.nodes[0]["viz"] = {"size": 54}
+    >>> G.nodes[0]["viz"]["position"] = {"x": 0, "y": 1}
+    >>> G.nodes[0]["viz"]["color"] = {"r": 0, "g": 0, "b": 256}
+
+
+    Notes
+    -----
+    This implementation does not support mixed graphs (directed and undirected
+    edges together).
+
+    The node id attribute is set to be the string of the node label.
+    If you want to specify an id use set it as node data, e.g.
+    node['a']['id']=1 to set the id of node 'a' to 1.
+
+    References
+    ----------
+    .. [1] GEXF File Format, http://gexf.net/
+    .. [2] GEXF schema, http://gexf.net/schema.html
+    """
+    writer = GEXFWriter(encoding=encoding, prettyprint=prettyprint, version=version)
+    writer.add_graph(G)
+    writer.write(path)
+
+
+def generate_gexf(G, encoding="utf-8", prettyprint=True, version="1.2draft"):
+    """Generate lines of GEXF format representation of G.
+
+    "GEXF (Graph Exchange XML Format) is a language for describing
+    complex networks structures, their associated data and dynamics" [1]_.
+
+    Parameters
+    ----------
+    G : graph
+    A NetworkX graph
+    encoding : string (optional, default: 'utf-8')
+    Encoding for text data.
+    prettyprint : bool (optional, default: True)
+    If True use line breaks and indenting in output XML.
+    version : string (default: 1.2draft)
+    Version of GEFX File Format (see http://gexf.net/schema.html)
+    Supported values: "1.1draft", "1.2draft"
+
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> linefeed = chr(10)  # linefeed=\n
+    >>> s = linefeed.join(nx.generate_gexf(G))
+    >>> for line in nx.generate_gexf(G):  # doctest: +SKIP
+    ...     print(line)
+
+    Notes
+    -----
+    This implementation does not support mixed graphs (directed and undirected
+    edges together).
+
+    The node id attribute is set to be the string of the node label.
+    If you want to specify an id use set it as node data, e.g.
+    node['a']['id']=1 to set the id of node 'a' to 1.
+
+    References
+    ----------
+    .. [1] GEXF File Format, https://gephi.org/gexf/format/
+    """
+    writer = GEXFWriter(encoding=encoding, prettyprint=prettyprint, version=version)
+    writer.add_graph(G)
+    yield from str(writer).splitlines()
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_gexf(path, node_type=None, relabel=False, version="1.2draft"):
+    """Read graph in GEXF format from path.
+
+    "GEXF (Graph Exchange XML Format) is a language for describing
+    complex networks structures, their associated data and dynamics" [1]_.
+
+    Parameters
+    ----------
+    path : file or string
+       File or file name to read.
+       File names ending in .gz or .bz2 will be decompressed.
+    node_type: Python type (default: None)
+       Convert node ids to this type if not None.
+    relabel : bool (default: False)
+       If True relabel the nodes to use the GEXF node "label" attribute
+       instead of the node "id" attribute as the NetworkX node label.
+    version : string (default: 1.2draft)
+    Version of GEFX File Format (see http://gexf.net/schema.html)
+       Supported values: "1.1draft", "1.2draft"
+
+    Returns
+    -------
+    graph: NetworkX graph
+        If no parallel edges are found a Graph or DiGraph is returned.
+        Otherwise a MultiGraph or MultiDiGraph is returned.
+
+    Notes
+    -----
+    This implementation does not support mixed graphs (directed and undirected
+    edges together).
+
+    References
+    ----------
+    .. [1] GEXF File Format, http://gexf.net/
+    """
+    reader = GEXFReader(node_type=node_type, version=version)
+    if relabel:
+        G = relabel_gexf_graph(reader(path))
+    else:
+        G = reader(path)
+    return G
+
+
+class GEXF:
+    versions = {
+        "1.1draft": {
+            "NS_GEXF": "http://www.gexf.net/1.1draft",
+            "NS_VIZ": "http://www.gexf.net/1.1draft/viz",
+            "NS_XSI": "http://www.w3.org/2001/XMLSchema-instance",
+            "SCHEMALOCATION": " ".join(
+                [
+                    "http://www.gexf.net/1.1draft",
+                    "http://www.gexf.net/1.1draft/gexf.xsd",
+                ]
+            ),
+            "VERSION": "1.1",
+        },
+        "1.2draft": {
+            "NS_GEXF": "http://www.gexf.net/1.2draft",
+            "NS_VIZ": "http://www.gexf.net/1.2draft/viz",
+            "NS_XSI": "http://www.w3.org/2001/XMLSchema-instance",
+            "SCHEMALOCATION": " ".join(
+                [
+                    "http://www.gexf.net/1.2draft",
+                    "http://www.gexf.net/1.2draft/gexf.xsd",
+                ]
+            ),
+            "VERSION": "1.2",
+        },
+    }
+
+    def construct_types(self):
+        types = [
+            (int, "integer"),
+            (float, "float"),
+            (float, "double"),
+            (bool, "boolean"),
+            (list, "string"),
+            (dict, "string"),
+            (int, "long"),
+            (str, "liststring"),
+            (str, "anyURI"),
+            (str, "string"),
+        ]
+
+        # These additions to types allow writing numpy types
+        try:
+            import numpy as np
+        except ImportError:
+            pass
+        else:
+            # prepend so that python types are created upon read (last entry wins)
+            types = [
+                (np.float64, "float"),
+                (np.float32, "float"),
+                (np.float16, "float"),
+                (np.int_, "int"),
+                (np.int8, "int"),
+                (np.int16, "int"),
+                (np.int32, "int"),
+                (np.int64, "int"),
+                (np.uint8, "int"),
+                (np.uint16, "int"),
+                (np.uint32, "int"),
+                (np.uint64, "int"),
+                (np.int_, "int"),
+                (np.intc, "int"),
+                (np.intp, "int"),
+            ] + types
+
+        self.xml_type = dict(types)
+        self.python_type = dict(reversed(a) for a in types)
+
+    # http://www.w3.org/TR/xmlschema-2/#boolean
+    convert_bool = {
+        "true": True,
+        "false": False,
+        "True": True,
+        "False": False,
+        "0": False,
+        0: False,
+        "1": True,
+        1: True,
+    }
+
+    def set_version(self, version):
+        d = self.versions.get(version)
+        if d is None:
+            raise nx.NetworkXError(f"Unknown GEXF version {version}.")
+        self.NS_GEXF = d["NS_GEXF"]
+        self.NS_VIZ = d["NS_VIZ"]
+        self.NS_XSI = d["NS_XSI"]
+        self.SCHEMALOCATION = d["SCHEMALOCATION"]
+        self.VERSION = d["VERSION"]
+        self.version = version
+
+
+class GEXFWriter(GEXF):
+    # class for writing GEXF format files
+    # use write_gexf() function
+    def __init__(
+        self, graph=None, encoding="utf-8", prettyprint=True, version="1.2draft"
+    ):
+        self.construct_types()
+        self.prettyprint = prettyprint
+        self.encoding = encoding
+        self.set_version(version)
+        self.xml = Element(
+            "gexf",
+            {
+                "xmlns": self.NS_GEXF,
+                "xmlns:xsi": self.NS_XSI,
+                "xsi:schemaLocation": self.SCHEMALOCATION,
+                "version": self.VERSION,
+            },
+        )
+
+        # Make meta element a non-graph element
+        # Also add lastmodifieddate as attribute, not tag
+        meta_element = Element("meta")
+        subelement_text = f"NetworkX {nx.__version__}"
+        SubElement(meta_element, "creator").text = subelement_text
+        meta_element.set("lastmodifieddate", time.strftime("%Y-%m-%d"))
+        self.xml.append(meta_element)
+
+        register_namespace("viz", self.NS_VIZ)
+
+        # counters for edge and attribute identifiers
+        self.edge_id = itertools.count()
+        self.attr_id = itertools.count()
+        self.all_edge_ids = set()
+        # default attributes are stored in dictionaries
+        self.attr = {}
+        self.attr["node"] = {}
+        self.attr["edge"] = {}
+        self.attr["node"]["dynamic"] = {}
+        self.attr["node"]["static"] = {}
+        self.attr["edge"]["dynamic"] = {}
+        self.attr["edge"]["static"] = {}
+
+        if graph is not None:
+            self.add_graph(graph)
+
+    def __str__(self):
+        if self.prettyprint:
+            self.indent(self.xml)
+        s = tostring(self.xml).decode(self.encoding)
+        return s
+
+    def add_graph(self, G):
+        # first pass through G collecting edge ids
+        for u, v, dd in G.edges(data=True):
+            eid = dd.get("id")
+            if eid is not None:
+                self.all_edge_ids.add(str(eid))
+        # set graph attributes
+        if G.graph.get("mode") == "dynamic":
+            mode = "dynamic"
+        else:
+            mode = "static"
+        # Add a graph element to the XML
+        if G.is_directed():
+            default = "directed"
+        else:
+            default = "undirected"
+        name = G.graph.get("name", "")
+        graph_element = Element("graph", defaultedgetype=default, mode=mode, name=name)
+        self.graph_element = graph_element
+        self.add_nodes(G, graph_element)
+        self.add_edges(G, graph_element)
+        self.xml.append(graph_element)
+
+    def add_nodes(self, G, graph_element):
+        nodes_element = Element("nodes")
+        for node, data in G.nodes(data=True):
+            node_data = data.copy()
+            node_id = str(node_data.pop("id", node))
+            kw = {"id": node_id}
+            label = str(node_data.pop("label", node))
+            kw["label"] = label
+            try:
+                pid = node_data.pop("pid")
+                kw["pid"] = str(pid)
+            except KeyError:
+                pass
+            try:
+                start = node_data.pop("start")
+                kw["start"] = str(start)
+                self.alter_graph_mode_timeformat(start)
+            except KeyError:
+                pass
+            try:
+                end = node_data.pop("end")
+                kw["end"] = str(end)
+                self.alter_graph_mode_timeformat(end)
+            except KeyError:
+                pass
+            # add node element with attributes
+            node_element = Element("node", **kw)
+            # add node element and attr subelements
+            default = G.graph.get("node_default", {})
+            node_data = self.add_parents(node_element, node_data)
+            if self.VERSION == "1.1":
+                node_data = self.add_slices(node_element, node_data)
+            else:
+                node_data = self.add_spells(node_element, node_data)
+            node_data = self.add_viz(node_element, node_data)
+            node_data = self.add_attributes("node", node_element, node_data, default)
+            nodes_element.append(node_element)
+        graph_element.append(nodes_element)
+
+    def add_edges(self, G, graph_element):
+        def edge_key_data(G):
+            # helper function to unify multigraph and graph edge iterator
+            if G.is_multigraph():
+                for u, v, key, data in G.edges(data=True, keys=True):
+                    edge_data = data.copy()
+                    edge_data.update(key=key)
+                    edge_id = edge_data.pop("id", None)
+                    if edge_id is None:
+                        edge_id = next(self.edge_id)
+                        while str(edge_id) in self.all_edge_ids:
+                            edge_id = next(self.edge_id)
+                        self.all_edge_ids.add(str(edge_id))
+                    yield u, v, edge_id, edge_data
+            else:
+                for u, v, data in G.edges(data=True):
+                    edge_data = data.copy()
+                    edge_id = edge_data.pop("id", None)
+                    if edge_id is None:
+                        edge_id = next(self.edge_id)
+                        while str(edge_id) in self.all_edge_ids:
+                            edge_id = next(self.edge_id)
+                        self.all_edge_ids.add(str(edge_id))
+                    yield u, v, edge_id, edge_data
+
+        edges_element = Element("edges")
+        for u, v, key, edge_data in edge_key_data(G):
+            kw = {"id": str(key)}
+            try:
+                edge_label = edge_data.pop("label")
+                kw["label"] = str(edge_label)
+            except KeyError:
+                pass
+            try:
+                edge_weight = edge_data.pop("weight")
+                kw["weight"] = str(edge_weight)
+            except KeyError:
+                pass
+            try:
+                edge_type = edge_data.pop("type")
+                kw["type"] = str(edge_type)
+            except KeyError:
+                pass
+            try:
+                start = edge_data.pop("start")
+                kw["start"] = str(start)
+                self.alter_graph_mode_timeformat(start)
+            except KeyError:
+                pass
+            try:
+                end = edge_data.pop("end")
+                kw["end"] = str(end)
+                self.alter_graph_mode_timeformat(end)
+            except KeyError:
+                pass
+            source_id = str(G.nodes[u].get("id", u))
+            target_id = str(G.nodes[v].get("id", v))
+            edge_element = Element("edge", source=source_id, target=target_id, **kw)
+            default = G.graph.get("edge_default", {})
+            if self.VERSION == "1.1":
+                edge_data = self.add_slices(edge_element, edge_data)
+            else:
+                edge_data = self.add_spells(edge_element, edge_data)
+            edge_data = self.add_viz(edge_element, edge_data)
+            edge_data = self.add_attributes("edge", edge_element, edge_data, default)
+            edges_element.append(edge_element)
+        graph_element.append(edges_element)
+
+    def add_attributes(self, node_or_edge, xml_obj, data, default):
+        # Add attrvalues to node or edge
+        attvalues = Element("attvalues")
+        if len(data) == 0:
+            return data
+        mode = "static"
+        for k, v in data.items():
+            # rename generic multigraph key to avoid any name conflict
+            if k == "key":
+                k = "networkx_key"
+            val_type = type(v)
+            if val_type not in self.xml_type:
+                raise TypeError(f"attribute value type is not allowed: {val_type}")
+            if isinstance(v, list):
+                # dynamic data
+                for val, start, end in v:
+                    val_type = type(val)
+                    if start is not None or end is not None:
+                        mode = "dynamic"
+                        self.alter_graph_mode_timeformat(start)
+                        self.alter_graph_mode_timeformat(end)
+                        break
+                attr_id = self.get_attr_id(
+                    str(k), self.xml_type[val_type], node_or_edge, default, mode
+                )
+                for val, start, end in v:
+                    e = Element("attvalue")
+                    e.attrib["for"] = attr_id
+                    e.attrib["value"] = str(val)
+                    # Handle nan, inf, -inf differently
+                    if val_type == float:
+                        if e.attrib["value"] == "inf":
+                            e.attrib["value"] = "INF"
+                        elif e.attrib["value"] == "nan":
+                            e.attrib["value"] = "NaN"
+                        elif e.attrib["value"] == "-inf":
+                            e.attrib["value"] = "-INF"
+                    if start is not None:
+                        e.attrib["start"] = str(start)
+                    if end is not None:
+                        e.attrib["end"] = str(end)
+                    attvalues.append(e)
+            else:
+                # static data
+                mode = "static"
+                attr_id = self.get_attr_id(
+                    str(k), self.xml_type[val_type], node_or_edge, default, mode
+                )
+                e = Element("attvalue")
+                e.attrib["for"] = attr_id
+                if isinstance(v, bool):
+                    e.attrib["value"] = str(v).lower()
+                else:
+                    e.attrib["value"] = str(v)
+                    # Handle float nan, inf, -inf differently
+                    if val_type == float:
+                        if e.attrib["value"] == "inf":
+                            e.attrib["value"] = "INF"
+                        elif e.attrib["value"] == "nan":
+                            e.attrib["value"] = "NaN"
+                        elif e.attrib["value"] == "-inf":
+                            e.attrib["value"] = "-INF"
+                attvalues.append(e)
+        xml_obj.append(attvalues)
+        return data
+
+    def get_attr_id(self, title, attr_type, edge_or_node, default, mode):
+        # find the id of the attribute or generate a new id
+        try:
+            return self.attr[edge_or_node][mode][title]
+        except KeyError:
+            # generate new id
+            new_id = str(next(self.attr_id))
+            self.attr[edge_or_node][mode][title] = new_id
+            attr_kwargs = {"id": new_id, "title": title, "type": attr_type}
+            attribute = Element("attribute", **attr_kwargs)
+            # add subelement for data default value if present
+            default_title = default.get(title)
+            if default_title is not None:
+                default_element = Element("default")
+                default_element.text = str(default_title)
+                attribute.append(default_element)
+            # new insert it into the XML
+            attributes_element = None
+            for a in self.graph_element.findall("attributes"):
+                # find existing attributes element by class and mode
+                a_class = a.get("class")
+                a_mode = a.get("mode", "static")
+                if a_class == edge_or_node and a_mode == mode:
+                    attributes_element = a
+            if attributes_element is None:
+                # create new attributes element
+                attr_kwargs = {"mode": mode, "class": edge_or_node}
+                attributes_element = Element("attributes", **attr_kwargs)
+                self.graph_element.insert(0, attributes_element)
+            attributes_element.append(attribute)
+        return new_id
+
+    def add_viz(self, element, node_data):
+        viz = node_data.pop("viz", False)
+        if viz:
+            color = viz.get("color")
+            if color is not None:
+                if self.VERSION == "1.1":
+                    e = Element(
+                        f"{{{self.NS_VIZ}}}color",
+                        r=str(color.get("r")),
+                        g=str(color.get("g")),
+                        b=str(color.get("b")),
+                    )
+                else:
+                    e = Element(
+                        f"{{{self.NS_VIZ}}}color",
+                        r=str(color.get("r")),
+                        g=str(color.get("g")),
+                        b=str(color.get("b")),
+                        a=str(color.get("a", 1.0)),
+                    )
+                element.append(e)
+
+            size = viz.get("size")
+            if size is not None:
+                e = Element(f"{{{self.NS_VIZ}}}size", value=str(size))
+                element.append(e)
+
+            thickness = viz.get("thickness")
+            if thickness is not None:
+                e = Element(f"{{{self.NS_VIZ}}}thickness", value=str(thickness))
+                element.append(e)
+
+            shape = viz.get("shape")
+            if shape is not None:
+                if shape.startswith("http"):
+                    e = Element(
+                        f"{{{self.NS_VIZ}}}shape", value="image", uri=str(shape)
+                    )
+                else:
+                    e = Element(f"{{{self.NS_VIZ}}}shape", value=str(shape))
+                element.append(e)
+
+            position = viz.get("position")
+            if position is not None:
+                e = Element(
+                    f"{{{self.NS_VIZ}}}position",
+                    x=str(position.get("x")),
+                    y=str(position.get("y")),
+                    z=str(position.get("z")),
+                )
+                element.append(e)
+        return node_data
+
+    def add_parents(self, node_element, node_data):
+        parents = node_data.pop("parents", False)
+        if parents:
+            parents_element = Element("parents")
+            for p in parents:
+                e = Element("parent")
+                e.attrib["for"] = str(p)
+                parents_element.append(e)
+            node_element.append(parents_element)
+        return node_data
+
+    def add_slices(self, node_or_edge_element, node_or_edge_data):
+        slices = node_or_edge_data.pop("slices", False)
+        if slices:
+            slices_element = Element("slices")
+            for start, end in slices:
+                e = Element("slice", start=str(start), end=str(end))
+                slices_element.append(e)
+            node_or_edge_element.append(slices_element)
+        return node_or_edge_data
+
+    def add_spells(self, node_or_edge_element, node_or_edge_data):
+        spells = node_or_edge_data.pop("spells", False)
+        if spells:
+            spells_element = Element("spells")
+            for start, end in spells:
+                e = Element("spell")
+                if start is not None:
+                    e.attrib["start"] = str(start)
+                    self.alter_graph_mode_timeformat(start)
+                if end is not None:
+                    e.attrib["end"] = str(end)
+                    self.alter_graph_mode_timeformat(end)
+                spells_element.append(e)
+            node_or_edge_element.append(spells_element)
+        return node_or_edge_data
+
+    def alter_graph_mode_timeformat(self, start_or_end):
+        # If 'start' or 'end' appears, alter Graph mode to dynamic and
+        # set timeformat
+        if self.graph_element.get("mode") == "static":
+            if start_or_end is not None:
+                if isinstance(start_or_end, str):
+                    timeformat = "date"
+                elif isinstance(start_or_end, float):
+                    timeformat = "double"
+                elif isinstance(start_or_end, int):
+                    timeformat = "long"
+                else:
+                    raise nx.NetworkXError(
+                        "timeformat should be of the type int, float or str"
+                    )
+                self.graph_element.set("timeformat", timeformat)
+                self.graph_element.set("mode", "dynamic")
+
+    def write(self, fh):
+        # Serialize graph G in GEXF to the open fh
+        if self.prettyprint:
+            self.indent(self.xml)
+        document = ElementTree(self.xml)
+        document.write(fh, encoding=self.encoding, xml_declaration=True)
+
+    def indent(self, elem, level=0):
+        # in-place prettyprint formatter
+        i = "\n" + "  " * level
+        if len(elem):
+            if not elem.text or not elem.text.strip():
+                elem.text = i + "  "
+            if not elem.tail or not elem.tail.strip():
+                elem.tail = i
+            for elem in elem:
+                self.indent(elem, level + 1)
+            if not elem.tail or not elem.tail.strip():
+                elem.tail = i
+        else:
+            if level and (not elem.tail or not elem.tail.strip()):
+                elem.tail = i
+
+
+class GEXFReader(GEXF):
+    # Class to read GEXF format files
+    # use read_gexf() function
+    def __init__(self, node_type=None, version="1.2draft"):
+        self.construct_types()
+        self.node_type = node_type
+        # assume simple graph and test for multigraph on read
+        self.simple_graph = True
+        self.set_version(version)
+
+    def __call__(self, stream):
+        self.xml = ElementTree(file=stream)
+        g = self.xml.find(f"{{{self.NS_GEXF}}}graph")
+        if g is not None:
+            return self.make_graph(g)
+        # try all the versions
+        for version in self.versions:
+            self.set_version(version)
+            g = self.xml.find(f"{{{self.NS_GEXF}}}graph")
+            if g is not None:
+                return self.make_graph(g)
+        raise nx.NetworkXError("No <graph> element in GEXF file.")
+
+    def make_graph(self, graph_xml):
+        # start with empty DiGraph or MultiDiGraph
+        edgedefault = graph_xml.get("defaultedgetype", None)
+        if edgedefault == "directed":
+            G = nx.MultiDiGraph()
+        else:
+            G = nx.MultiGraph()
+
+        # graph attributes
+        graph_name = graph_xml.get("name", "")
+        if graph_name != "":
+            G.graph["name"] = graph_name
+        graph_start = graph_xml.get("start")
+        if graph_start is not None:
+            G.graph["start"] = graph_start
+        graph_end = graph_xml.get("end")
+        if graph_end is not None:
+            G.graph["end"] = graph_end
+        graph_mode = graph_xml.get("mode", "")
+        if graph_mode == "dynamic":
+            G.graph["mode"] = "dynamic"
+        else:
+            G.graph["mode"] = "static"
+
+        # timeformat
+        self.timeformat = graph_xml.get("timeformat")
+        if self.timeformat == "date":
+            self.timeformat = "string"
+
+        # node and edge attributes
+        attributes_elements = graph_xml.findall(f"{{{self.NS_GEXF}}}attributes")
+        # dictionaries to hold attributes and attribute defaults
+        node_attr = {}
+        node_default = {}
+        edge_attr = {}
+        edge_default = {}
+        for a in attributes_elements:
+            attr_class = a.get("class")
+            if attr_class == "node":
+                na, nd = self.find_gexf_attributes(a)
+                node_attr.update(na)
+                node_default.update(nd)
+                G.graph["node_default"] = node_default
+            elif attr_class == "edge":
+                ea, ed = self.find_gexf_attributes(a)
+                edge_attr.update(ea)
+                edge_default.update(ed)
+                G.graph["edge_default"] = edge_default
+            else:
+                raise  # unknown attribute class
+
+        # Hack to handle Gephi0.7beta bug
+        # add weight attribute
+        ea = {"weight": {"type": "double", "mode": "static", "title": "weight"}}
+        ed = {}
+        edge_attr.update(ea)
+        edge_default.update(ed)
+        G.graph["edge_default"] = edge_default
+
+        # add nodes
+        nodes_element = graph_xml.find(f"{{{self.NS_GEXF}}}nodes")
+        if nodes_element is not None:
+            for node_xml in nodes_element.findall(f"{{{self.NS_GEXF}}}node"):
+                self.add_node(G, node_xml, node_attr)
+
+        # add edges
+        edges_element = graph_xml.find(f"{{{self.NS_GEXF}}}edges")
+        if edges_element is not None:
+            for edge_xml in edges_element.findall(f"{{{self.NS_GEXF}}}edge"):
+                self.add_edge(G, edge_xml, edge_attr)
+
+        # switch to Graph or DiGraph if no parallel edges were found.
+        if self.simple_graph:
+            if G.is_directed():
+                G = nx.DiGraph(G)
+            else:
+                G = nx.Graph(G)
+        return G
+
+    def add_node(self, G, node_xml, node_attr, node_pid=None):
+        # add a single node with attributes to the graph
+
+        # get attributes and subattributues for node
+        data = self.decode_attr_elements(node_attr, node_xml)
+        data = self.add_parents(data, node_xml)  # add any parents
+        if self.VERSION == "1.1":
+            data = self.add_slices(data, node_xml)  # add slices
+        else:
+            data = self.add_spells(data, node_xml)  # add spells
+        data = self.add_viz(data, node_xml)  # add viz
+        data = self.add_start_end(data, node_xml)  # add start/end
+
+        # find the node id and cast it to the appropriate type
+        node_id = node_xml.get("id")
+        if self.node_type is not None:
+            node_id = self.node_type(node_id)
+
+        # every node should have a label
+        node_label = node_xml.get("label")
+        data["label"] = node_label
+
+        # parent node id
+        node_pid = node_xml.get("pid", node_pid)
+        if node_pid is not None:
+            data["pid"] = node_pid
+
+        # check for subnodes, recursive
+        subnodes = node_xml.find(f"{{{self.NS_GEXF}}}nodes")
+        if subnodes is not None:
+            for node_xml in subnodes.findall(f"{{{self.NS_GEXF}}}node"):
+                self.add_node(G, node_xml, node_attr, node_pid=node_id)
+
+        G.add_node(node_id, **data)
+
+    def add_start_end(self, data, xml):
+        # start and end times
+        ttype = self.timeformat
+        node_start = xml.get("start")
+        if node_start is not None:
+            data["start"] = self.python_type[ttype](node_start)
+        node_end = xml.get("end")
+        if node_end is not None:
+            data["end"] = self.python_type[ttype](node_end)
+        return data
+
+    def add_viz(self, data, node_xml):
+        # add viz element for node
+        viz = {}
+        color = node_xml.find(f"{{{self.NS_VIZ}}}color")
+        if color is not None:
+            if self.VERSION == "1.1":
+                viz["color"] = {
+                    "r": int(color.get("r")),
+                    "g": int(color.get("g")),
+                    "b": int(color.get("b")),
+                }
+            else:
+                viz["color"] = {
+                    "r": int(color.get("r")),
+                    "g": int(color.get("g")),
+                    "b": int(color.get("b")),
+                    "a": float(color.get("a", 1)),
+                }
+
+        size = node_xml.find(f"{{{self.NS_VIZ}}}size")
+        if size is not None:
+            viz["size"] = float(size.get("value"))
+
+        thickness = node_xml.find(f"{{{self.NS_VIZ}}}thickness")
+        if thickness is not None:
+            viz["thickness"] = float(thickness.get("value"))
+
+        shape = node_xml.find(f"{{{self.NS_VIZ}}}shape")
+        if shape is not None:
+            viz["shape"] = shape.get("shape")
+            if viz["shape"] == "image":
+                viz["shape"] = shape.get("uri")
+
+        position = node_xml.find(f"{{{self.NS_VIZ}}}position")
+        if position is not None:
+            viz["position"] = {
+                "x": float(position.get("x", 0)),
+                "y": float(position.get("y", 0)),
+                "z": float(position.get("z", 0)),
+            }
+
+        if len(viz) > 0:
+            data["viz"] = viz
+        return data
+
+    def add_parents(self, data, node_xml):
+        parents_element = node_xml.find(f"{{{self.NS_GEXF}}}parents")
+        if parents_element is not None:
+            data["parents"] = []
+            for p in parents_element.findall(f"{{{self.NS_GEXF}}}parent"):
+                parent = p.get("for")
+                data["parents"].append(parent)
+        return data
+
+    def add_slices(self, data, node_or_edge_xml):
+        slices_element = node_or_edge_xml.find(f"{{{self.NS_GEXF}}}slices")
+        if slices_element is not None:
+            data["slices"] = []
+            for s in slices_element.findall(f"{{{self.NS_GEXF}}}slice"):
+                start = s.get("start")
+                end = s.get("end")
+                data["slices"].append((start, end))
+        return data
+
+    def add_spells(self, data, node_or_edge_xml):
+        spells_element = node_or_edge_xml.find(f"{{{self.NS_GEXF}}}spells")
+        if spells_element is not None:
+            data["spells"] = []
+            ttype = self.timeformat
+            for s in spells_element.findall(f"{{{self.NS_GEXF}}}spell"):
+                start = self.python_type[ttype](s.get("start"))
+                end = self.python_type[ttype](s.get("end"))
+                data["spells"].append((start, end))
+        return data
+
+    def add_edge(self, G, edge_element, edge_attr):
+        # add an edge to the graph
+
+        # raise error if we find mixed directed and undirected edges
+        edge_direction = edge_element.get("type")
+        if G.is_directed() and edge_direction == "undirected":
+            raise nx.NetworkXError("Undirected edge found in directed graph.")
+        if (not G.is_directed()) and edge_direction == "directed":
+            raise nx.NetworkXError("Directed edge found in undirected graph.")
+
+        # Get source and target and recast type if required
+        source = edge_element.get("source")
+        target = edge_element.get("target")
+        if self.node_type is not None:
+            source = self.node_type(source)
+            target = self.node_type(target)
+
+        data = self.decode_attr_elements(edge_attr, edge_element)
+        data = self.add_start_end(data, edge_element)
+
+        if self.VERSION == "1.1":
+            data = self.add_slices(data, edge_element)  # add slices
+        else:
+            data = self.add_spells(data, edge_element)  # add spells
+
+        # GEXF stores edge ids as an attribute
+        # NetworkX uses them as keys in multigraphs
+        # if networkx_key is not specified as an attribute
+        edge_id = edge_element.get("id")
+        if edge_id is not None:
+            data["id"] = edge_id
+
+        # check if there is a 'multigraph_key' and use that as edge_id
+        multigraph_key = data.pop("networkx_key", None)
+        if multigraph_key is not None:
+            edge_id = multigraph_key
+
+        weight = edge_element.get("weight")
+        if weight is not None:
+            data["weight"] = float(weight)
+
+        edge_label = edge_element.get("label")
+        if edge_label is not None:
+            data["label"] = edge_label
+
+        if G.has_edge(source, target):
+            # seen this edge before - this is a multigraph
+            self.simple_graph = False
+        G.add_edge(source, target, key=edge_id, **data)
+        if edge_direction == "mutual":
+            G.add_edge(target, source, key=edge_id, **data)
+
+    def decode_attr_elements(self, gexf_keys, obj_xml):
+        # Use the key information to decode the attr XML
+        attr = {}
+        # look for outer '<attvalues>' element
+        attr_element = obj_xml.find(f"{{{self.NS_GEXF}}}attvalues")
+        if attr_element is not None:
+            # loop over <attvalue> elements
+            for a in attr_element.findall(f"{{{self.NS_GEXF}}}attvalue"):
+                key = a.get("for")  # for is required
+                try:  # should be in our gexf_keys dictionary
+                    title = gexf_keys[key]["title"]
+                except KeyError as err:
+                    raise nx.NetworkXError(f"No attribute defined for={key}.") from err
+                atype = gexf_keys[key]["type"]
+                value = a.get("value")
+                if atype == "boolean":
+                    value = self.convert_bool[value]
+                else:
+                    value = self.python_type[atype](value)
+                if gexf_keys[key]["mode"] == "dynamic":
+                    # for dynamic graphs use list of three-tuples
+                    # [(value1,start1,end1), (value2,start2,end2), etc]
+                    ttype = self.timeformat
+                    start = self.python_type[ttype](a.get("start"))
+                    end = self.python_type[ttype](a.get("end"))
+                    if title in attr:
+                        attr[title].append((value, start, end))
+                    else:
+                        attr[title] = [(value, start, end)]
+                else:
+                    # for static graphs just assign the value
+                    attr[title] = value
+        return attr
+
+    def find_gexf_attributes(self, attributes_element):
+        # Extract all the attributes and defaults
+        attrs = {}
+        defaults = {}
+        mode = attributes_element.get("mode")
+        for k in attributes_element.findall(f"{{{self.NS_GEXF}}}attribute"):
+            attr_id = k.get("id")
+            title = k.get("title")
+            atype = k.get("type")
+            attrs[attr_id] = {"title": title, "type": atype, "mode": mode}
+            # check for the 'default' subelement of key element and add
+            default = k.find(f"{{{self.NS_GEXF}}}default")
+            if default is not None:
+                if atype == "boolean":
+                    value = self.convert_bool[default.text]
+                else:
+                    value = self.python_type[atype](default.text)
+                defaults[title] = value
+        return attrs, defaults
+
+
+def relabel_gexf_graph(G):
+    """Relabel graph using "label" node keyword for node label.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph read from GEXF data
+
+    Returns
+    -------
+    H : graph
+      A NetworkX graph with relabeled nodes
+
+    Raises
+    ------
+    NetworkXError
+        If node labels are missing or not unique while relabel=True.
+
+    Notes
+    -----
+    This function relabels the nodes in a NetworkX graph with the
+    "label" attribute.  It also handles relabeling the specific GEXF
+    node attributes "parents", and "pid".
+    """
+    # build mapping of node labels, do some error checking
+    try:
+        mapping = [(u, G.nodes[u]["label"]) for u in G]
+    except KeyError as err:
+        raise nx.NetworkXError(
+            "Failed to relabel nodes: missing node labels found. Use relabel=False."
+        ) from err
+    x, y = zip(*mapping)
+    if len(set(y)) != len(G):
+        raise nx.NetworkXError(
+            "Failed to relabel nodes: "
+            "duplicate node labels found. "
+            "Use relabel=False."
+        )
+    mapping = dict(mapping)
+    H = nx.relabel_nodes(G, mapping)
+    # relabel attributes
+    for n in G:
+        m = mapping[n]
+        H.nodes[m]["id"] = n
+        H.nodes[m].pop("label")
+        if "pid" in H.nodes[m]:
+            H.nodes[m]["pid"] = mapping[G.nodes[n]["pid"]]
+        if "parents" in H.nodes[m]:
+            H.nodes[m]["parents"] = [mapping[p] for p in G.nodes[n]["parents"]]
+    return H

env-llmeval/lib/python3.10/site-packages/networkx/readwrite/graphml.py

@@ -0,0 +1,1052 @@
+"""
+*******
+GraphML
+*******
+Read and write graphs in GraphML format.
+
+.. warning::
+
+    This parser uses the standard xml library present in Python, which is
+    insecure - see :external+python:mod:`xml` for additional information.
+    Only parse GraphML files you trust.
+
+This implementation does not support mixed graphs (directed and unidirected
+edges together), hyperedges, nested graphs, or ports.
+
+"GraphML is a comprehensive and easy-to-use file format for graphs. It
+consists of a language core to describe the structural properties of a
+graph and a flexible extension mechanism to add application-specific
+data. Its main features include support of
+
+    * directed, undirected, and mixed graphs,
+    * hypergraphs,
+    * hierarchical graphs,
+    * graphical representations,
+    * references to external data,
+    * application-specific attribute data, and
+    * light-weight parsers.
+
+Unlike many other file formats for graphs, GraphML does not use a
+custom syntax. Instead, it is based on XML and hence ideally suited as
+a common denominator for all kinds of services generating, archiving,
+or processing graphs."
+
+http://graphml.graphdrawing.org/
+
+Format
+------
+GraphML is an XML format.  See
+http://graphml.graphdrawing.org/specification.html for the specification and
+http://graphml.graphdrawing.org/primer/graphml-primer.html
+for examples.
+"""
+import warnings
+from collections import defaultdict
+
+import networkx as nx
+from networkx.utils import open_file
+
+__all__ = [
+    "write_graphml",
+    "read_graphml",
+    "generate_graphml",
+    "write_graphml_xml",
+    "write_graphml_lxml",
+    "parse_graphml",
+    "GraphMLWriter",
+    "GraphMLReader",
+]
+
+
+@open_file(1, mode="wb")
+def write_graphml_xml(
+    G,
+    path,
+    encoding="utf-8",
+    prettyprint=True,
+    infer_numeric_types=False,
+    named_key_ids=False,
+    edge_id_from_attribute=None,
+):
+    """Write G in GraphML XML format to path
+
+    Parameters
+    ----------
+    G : graph
+       A networkx graph
+    path : file or string
+       File or filename to write.
+       Filenames ending in .gz or .bz2 will be compressed.
+    encoding : string (optional)
+       Encoding for text data.
+    prettyprint : bool (optional)
+       If True use line breaks and indenting in output XML.
+    infer_numeric_types : boolean
+       Determine if numeric types should be generalized.
+       For example, if edges have both int and float 'weight' attributes,
+       we infer in GraphML that both are floats.
+    named_key_ids : bool (optional)
+       If True use attr.name as value for key elements' id attribute.
+    edge_id_from_attribute : dict key (optional)
+        If provided, the graphml edge id is set by looking up the corresponding
+        edge data attribute keyed by this parameter. If `None` or the key does not exist in edge data,
+        the edge id is set by the edge key if `G` is a MultiGraph, else the edge id is left unset.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_graphml(G, "test.graphml")
+
+    Notes
+    -----
+    This implementation does not support mixed graphs (directed
+    and unidirected edges together) hyperedges, nested graphs, or ports.
+    """
+    writer = GraphMLWriter(
+        encoding=encoding,
+        prettyprint=prettyprint,
+        infer_numeric_types=infer_numeric_types,
+        named_key_ids=named_key_ids,
+        edge_id_from_attribute=edge_id_from_attribute,
+    )
+    writer.add_graph_element(G)
+    writer.dump(path)
+
+
+@open_file(1, mode="wb")
+def write_graphml_lxml(
+    G,
+    path,
+    encoding="utf-8",
+    prettyprint=True,
+    infer_numeric_types=False,
+    named_key_ids=False,
+    edge_id_from_attribute=None,
+):
+    """Write G in GraphML XML format to path
+
+    This function uses the LXML framework and should be faster than
+    the version using the xml library.
+
+    Parameters
+    ----------
+    G : graph
+       A networkx graph
+    path : file or string
+       File or filename to write.
+       Filenames ending in .gz or .bz2 will be compressed.
+    encoding : string (optional)
+       Encoding for text data.
+    prettyprint : bool (optional)
+       If True use line breaks and indenting in output XML.
+    infer_numeric_types : boolean
+       Determine if numeric types should be generalized.
+       For example, if edges have both int and float 'weight' attributes,
+       we infer in GraphML that both are floats.
+    named_key_ids : bool (optional)
+       If True use attr.name as value for key elements' id attribute.
+    edge_id_from_attribute : dict key (optional)
+        If provided, the graphml edge id is set by looking up the corresponding
+        edge data attribute keyed by this parameter. If `None` or the key does not exist in edge data,
+        the edge id is set by the edge key if `G` is a MultiGraph, else the edge id is left unset.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_graphml_lxml(G, "fourpath.graphml")
+
+    Notes
+    -----
+    This implementation does not support mixed graphs (directed
+    and unidirected edges together) hyperedges, nested graphs, or ports.
+    """
+    try:
+        import lxml.etree as lxmletree
+    except ImportError:
+        return write_graphml_xml(
+            G,
+            path,
+            encoding,
+            prettyprint,
+            infer_numeric_types,
+            named_key_ids,
+            edge_id_from_attribute,
+        )
+
+    writer = GraphMLWriterLxml(
+        path,
+        graph=G,
+        encoding=encoding,
+        prettyprint=prettyprint,
+        infer_numeric_types=infer_numeric_types,
+        named_key_ids=named_key_ids,
+        edge_id_from_attribute=edge_id_from_attribute,
+    )
+    writer.dump()
+
+
+def generate_graphml(
+    G,
+    encoding="utf-8",
+    prettyprint=True,
+    named_key_ids=False,
+    edge_id_from_attribute=None,
+):
+    """Generate GraphML lines for G
+
+    Parameters
+    ----------
+    G : graph
+       A networkx graph
+    encoding : string (optional)
+       Encoding for text data.
+    prettyprint : bool (optional)
+       If True use line breaks and indenting in output XML.
+    named_key_ids : bool (optional)
+       If True use attr.name as value for key elements' id attribute.
+    edge_id_from_attribute : dict key (optional)
+        If provided, the graphml edge id is set by looking up the corresponding
+        edge data attribute keyed by this parameter. If `None` or the key does not exist in edge data,
+        the edge id is set by the edge key if `G` is a MultiGraph, else the edge id is left unset.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> linefeed = chr(10)  # linefeed = \n
+    >>> s = linefeed.join(nx.generate_graphml(G))
+    >>> for line in nx.generate_graphml(G):  # doctest: +SKIP
+    ...     print(line)
+
+    Notes
+    -----
+    This implementation does not support mixed graphs (directed and unidirected
+    edges together) hyperedges, nested graphs, or ports.
+    """
+    writer = GraphMLWriter(
+        encoding=encoding,
+        prettyprint=prettyprint,
+        named_key_ids=named_key_ids,
+        edge_id_from_attribute=edge_id_from_attribute,
+    )
+    writer.add_graph_element(G)
+    yield from str(writer).splitlines()
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_graphml(path, node_type=str, edge_key_type=int, force_multigraph=False):
+    """Read graph in GraphML format from path.
+
+    Parameters
+    ----------
+    path : file or string
+       File or filename to write.
+       Filenames ending in .gz or .bz2 will be compressed.
+
+    node_type: Python type (default: str)
+       Convert node ids to this type
+
+    edge_key_type: Python type (default: int)
+       Convert graphml edge ids to this type. Multigraphs use id as edge key.
+       Non-multigraphs add to edge attribute dict with name "id".
+
+    force_multigraph : bool (default: False)
+       If True, return a multigraph with edge keys. If False (the default)
+       return a multigraph when multiedges are in the graph.
+
+    Returns
+    -------
+    graph: NetworkX graph
+        If parallel edges are present or `force_multigraph=True` then
+        a MultiGraph or MultiDiGraph is returned. Otherwise a Graph/DiGraph.
+        The returned graph is directed if the file indicates it should be.
+
+    Notes
+    -----
+    Default node and edge attributes are not propagated to each node and edge.
+    They can be obtained from `G.graph` and applied to node and edge attributes
+    if desired using something like this:
+
+    >>> default_color = G.graph["node_default"]["color"]  # doctest: +SKIP
+    >>> for node, data in G.nodes(data=True):  # doctest: +SKIP
+    ...     if "color" not in data:
+    ...         data["color"] = default_color
+    >>> default_color = G.graph["edge_default"]["color"]  # doctest: +SKIP
+    >>> for u, v, data in G.edges(data=True):  # doctest: +SKIP
+    ...     if "color" not in data:
+    ...         data["color"] = default_color
+
+    This implementation does not support mixed graphs (directed and unidirected
+    edges together), hypergraphs, nested graphs, or ports.
+
+    For multigraphs the GraphML edge "id" will be used as the edge
+    key.  If not specified then they "key" attribute will be used.  If
+    there is no "key" attribute a default NetworkX multigraph edge key
+    will be provided.
+
+    Files with the yEd "yfiles" extension can be read. The type of the node's
+    shape is preserved in the `shape_type` node attribute.
+
+    yEd compressed files ("file.graphmlz" extension) can be read by renaming
+    the file to "file.graphml.gz".
+
+    """
+    reader = GraphMLReader(node_type, edge_key_type, force_multigraph)
+    # need to check for multiple graphs
+    glist = list(reader(path=path))
+    if len(glist) == 0:
+        # If no graph comes back, try looking for an incomplete header
+        header = b'<graphml xmlns="http://graphml.graphdrawing.org/xmlns">'
+        path.seek(0)
+        old_bytes = path.read()
+        new_bytes = old_bytes.replace(b"<graphml>", header)
+        glist = list(reader(string=new_bytes))
+        if len(glist) == 0:
+            raise nx.NetworkXError("file not successfully read as graphml")
+    return glist[0]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def parse_graphml(
+    graphml_string, node_type=str, edge_key_type=int, force_multigraph=False
+):
+    """Read graph in GraphML format from string.
+
+    Parameters
+    ----------
+    graphml_string : string
+       String containing graphml information
+       (e.g., contents of a graphml file).
+
+    node_type: Python type (default: str)
+       Convert node ids to this type
+
+    edge_key_type: Python type (default: int)
+       Convert graphml edge ids to this type. Multigraphs use id as edge key.
+       Non-multigraphs add to edge attribute dict with name "id".
+
+    force_multigraph : bool (default: False)
+       If True, return a multigraph with edge keys. If False (the default)
+       return a multigraph when multiedges are in the graph.
+
+
+    Returns
+    -------
+    graph: NetworkX graph
+        If no parallel edges are found a Graph or DiGraph is returned.
+        Otherwise a MultiGraph or MultiDiGraph is returned.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> linefeed = chr(10)  # linefeed = \n
+    >>> s = linefeed.join(nx.generate_graphml(G))
+    >>> H = nx.parse_graphml(s)
+
+    Notes
+    -----
+    Default node and edge attributes are not propagated to each node and edge.
+    They can be obtained from `G.graph` and applied to node and edge attributes
+    if desired using something like this:
+
+    >>> default_color = G.graph["node_default"]["color"]  # doctest: +SKIP
+    >>> for node, data in G.nodes(data=True):  # doctest: +SKIP
+    ...     if "color" not in data:
+    ...         data["color"] = default_color
+    >>> default_color = G.graph["edge_default"]["color"]  # doctest: +SKIP
+    >>> for u, v, data in G.edges(data=True):  # doctest: +SKIP
+    ...     if "color" not in data:
+    ...         data["color"] = default_color
+
+    This implementation does not support mixed graphs (directed and unidirected
+    edges together), hypergraphs, nested graphs, or ports.
+
+    For multigraphs the GraphML edge "id" will be used as the edge
+    key.  If not specified then they "key" attribute will be used.  If
+    there is no "key" attribute a default NetworkX multigraph edge key
+    will be provided.
+
+    """
+    reader = GraphMLReader(node_type, edge_key_type, force_multigraph)
+    # need to check for multiple graphs
+    glist = list(reader(string=graphml_string))
+    if len(glist) == 0:
+        # If no graph comes back, try looking for an incomplete header
+        header = '<graphml xmlns="http://graphml.graphdrawing.org/xmlns">'
+        new_string = graphml_string.replace("<graphml>", header)
+        glist = list(reader(string=new_string))
+        if len(glist) == 0:
+            raise nx.NetworkXError("file not successfully read as graphml")
+    return glist[0]
+
+
+class GraphML:
+    NS_GRAPHML = "http://graphml.graphdrawing.org/xmlns"
+    NS_XSI = "http://www.w3.org/2001/XMLSchema-instance"
+    # xmlns:y="http://www.yworks.com/xml/graphml"
+    NS_Y = "http://www.yworks.com/xml/graphml"
+    SCHEMALOCATION = " ".join(
+        [
+            "http://graphml.graphdrawing.org/xmlns",
+            "http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd",
+        ]
+    )
+
+    def construct_types(self):
+        types = [
+            (int, "integer"),  # for Gephi GraphML bug
+            (str, "yfiles"),
+            (str, "string"),
+            (int, "int"),
+            (int, "long"),
+            (float, "float"),
+            (float, "double"),
+            (bool, "boolean"),
+        ]
+
+        # These additions to types allow writing numpy types
+        try:
+            import numpy as np
+        except:
+            pass
+        else:
+            # prepend so that python types are created upon read (last entry wins)
+            types = [
+                (np.float64, "float"),
+                (np.float32, "float"),
+                (np.float16, "float"),
+                (np.int_, "int"),
+                (np.int8, "int"),
+                (np.int16, "int"),
+                (np.int32, "int"),
+                (np.int64, "int"),
+                (np.uint8, "int"),
+                (np.uint16, "int"),
+                (np.uint32, "int"),
+                (np.uint64, "int"),
+                (np.int_, "int"),
+                (np.intc, "int"),
+                (np.intp, "int"),
+            ] + types
+
+        self.xml_type = dict(types)
+        self.python_type = dict(reversed(a) for a in types)
+
+    # This page says that data types in GraphML follow Java(TM).
+    #  http://graphml.graphdrawing.org/primer/graphml-primer.html#AttributesDefinition
+    # true and false are the only boolean literals:
+    #  http://en.wikibooks.org/wiki/Java_Programming/Literals#Boolean_Literals
+    convert_bool = {
+        # We use data.lower() in actual use.
+        "true": True,
+        "false": False,
+        # Include integer strings for convenience.
+        "0": False,
+        0: False,
+        "1": True,
+        1: True,
+    }
+
+    def get_xml_type(self, key):
+        """Wrapper around the xml_type dict that raises a more informative
+        exception message when a user attempts to use data of a type not
+        supported by GraphML."""
+        try:
+            return self.xml_type[key]
+        except KeyError as err:
+            raise TypeError(
+                f"GraphML does not support type {key} as data values."
+            ) from err
+
+
+class GraphMLWriter(GraphML):
+    def __init__(
+        self,
+        graph=None,
+        encoding="utf-8",
+        prettyprint=True,
+        infer_numeric_types=False,
+        named_key_ids=False,
+        edge_id_from_attribute=None,
+    ):
+        self.construct_types()
+        from xml.etree.ElementTree import Element
+
+        self.myElement = Element
+
+        self.infer_numeric_types = infer_numeric_types
+        self.prettyprint = prettyprint
+        self.named_key_ids = named_key_ids
+        self.edge_id_from_attribute = edge_id_from_attribute
+        self.encoding = encoding
+        self.xml = self.myElement(
+            "graphml",
+            {
+                "xmlns": self.NS_GRAPHML,
+                "xmlns:xsi": self.NS_XSI,
+                "xsi:schemaLocation": self.SCHEMALOCATION,
+            },
+        )
+        self.keys = {}
+        self.attributes = defaultdict(list)
+        self.attribute_types = defaultdict(set)
+
+        if graph is not None:
+            self.add_graph_element(graph)
+
+    def __str__(self):
+        from xml.etree.ElementTree import tostring
+
+        if self.prettyprint:
+            self.indent(self.xml)
+        s = tostring(self.xml).decode(self.encoding)
+        return s
+
+    def attr_type(self, name, scope, value):
+        """Infer the attribute type of data named name. Currently this only
+        supports inference of numeric types.
+
+        If self.infer_numeric_types is false, type is used. Otherwise, pick the
+        most general of types found across all values with name and scope. This
+        means edges with data named 'weight' are treated separately from nodes
+        with data named 'weight'.
+        """
+        if self.infer_numeric_types:
+            types = self.attribute_types[(name, scope)]
+
+            if len(types) > 1:
+                types = {self.get_xml_type(t) for t in types}
+                if "string" in types:
+                    return str
+                elif "float" in types or "double" in types:
+                    return float
+                else:
+                    return int
+            else:
+                return list(types)[0]
+        else:
+            return type(value)
+
+    def get_key(self, name, attr_type, scope, default):
+        keys_key = (name, attr_type, scope)
+        try:
+            return self.keys[keys_key]
+        except KeyError:
+            if self.named_key_ids:
+                new_id = name
+            else:
+                new_id = f"d{len(list(self.keys))}"
+
+            self.keys[keys_key] = new_id
+            key_kwargs = {
+                "id": new_id,
+                "for": scope,
+                "attr.name": name,
+                "attr.type": attr_type,
+            }
+            key_element = self.myElement("key", **key_kwargs)
+            # add subelement for data default value if present
+            if default is not None:
+                default_element = self.myElement("default")
+                default_element.text = str(default)
+                key_element.append(default_element)
+            self.xml.insert(0, key_element)
+        return new_id
+
+    def add_data(self, name, element_type, value, scope="all", default=None):
+        """
+        Make a data element for an edge or a node. Keep a log of the
+        type in the keys table.
+        """
+        if element_type not in self.xml_type:
+            raise nx.NetworkXError(
+                f"GraphML writer does not support {element_type} as data values."
+            )
+        keyid = self.get_key(name, self.get_xml_type(element_type), scope, default)
+        data_element = self.myElement("data", key=keyid)
+        data_element.text = str(value)
+        return data_element
+
+    def add_attributes(self, scope, xml_obj, data, default):
+        """Appends attribute data to edges or nodes, and stores type information
+        to be added later. See add_graph_element.
+        """
+        for k, v in data.items():
+            self.attribute_types[(str(k), scope)].add(type(v))
+            self.attributes[xml_obj].append([k, v, scope, default.get(k)])
+
+    def add_nodes(self, G, graph_element):
+        default = G.graph.get("node_default", {})
+        for node, data in G.nodes(data=True):
+            node_element = self.myElement("node", id=str(node))
+            self.add_attributes("node", node_element, data, default)
+            graph_element.append(node_element)
+
+    def add_edges(self, G, graph_element):
+        if G.is_multigraph():
+            for u, v, key, data in G.edges(data=True, keys=True):
+                edge_element = self.myElement(
+                    "edge",
+                    source=str(u),
+                    target=str(v),
+                    id=str(data.get(self.edge_id_from_attribute))
+                    if self.edge_id_from_attribute
+                    and self.edge_id_from_attribute in data
+                    else str(key),
+                )
+                default = G.graph.get("edge_default", {})
+                self.add_attributes("edge", edge_element, data, default)
+                graph_element.append(edge_element)
+        else:
+            for u, v, data in G.edges(data=True):
+                if self.edge_id_from_attribute and self.edge_id_from_attribute in data:
+                    # select attribute to be edge id
+                    edge_element = self.myElement(
+                        "edge",
+                        source=str(u),
+                        target=str(v),
+                        id=str(data.get(self.edge_id_from_attribute)),
+                    )
+                else:
+                    # default: no edge id
+                    edge_element = self.myElement("edge", source=str(u), target=str(v))
+                default = G.graph.get("edge_default", {})
+                self.add_attributes("edge", edge_element, data, default)
+                graph_element.append(edge_element)
+
+    def add_graph_element(self, G):
+        """
+        Serialize graph G in GraphML to the stream.
+        """
+        if G.is_directed():
+            default_edge_type = "directed"
+        else:
+            default_edge_type = "undirected"
+
+        graphid = G.graph.pop("id", None)
+        if graphid is None:
+            graph_element = self.myElement("graph", edgedefault=default_edge_type)
+        else:
+            graph_element = self.myElement(
+                "graph", edgedefault=default_edge_type, id=graphid
+            )
+        default = {}
+        data = {
+            k: v
+            for (k, v) in G.graph.items()
+            if k not in ["node_default", "edge_default"]
+        }
+        self.add_attributes("graph", graph_element, data, default)
+        self.add_nodes(G, graph_element)
+        self.add_edges(G, graph_element)
+
+        # self.attributes contains a mapping from XML Objects to a list of
+        # data that needs to be added to them.
+        # We postpone processing in order to do type inference/generalization.
+        # See self.attr_type
+        for xml_obj, data in self.attributes.items():
+            for k, v, scope, default in data:
+                xml_obj.append(
+                    self.add_data(
+                        str(k), self.attr_type(k, scope, v), str(v), scope, default
+                    )
+                )
+        self.xml.append(graph_element)
+
+    def add_graphs(self, graph_list):
+        """Add many graphs to this GraphML document."""
+        for G in graph_list:
+            self.add_graph_element(G)
+
+    def dump(self, stream):
+        from xml.etree.ElementTree import ElementTree
+
+        if self.prettyprint:
+            self.indent(self.xml)
+        document = ElementTree(self.xml)
+        document.write(stream, encoding=self.encoding, xml_declaration=True)
+
+    def indent(self, elem, level=0):
+        # in-place prettyprint formatter
+        i = "\n" + level * "  "
+        if len(elem):
+            if not elem.text or not elem.text.strip():
+                elem.text = i + "  "
+            if not elem.tail or not elem.tail.strip():
+                elem.tail = i
+            for elem in elem:
+                self.indent(elem, level + 1)
+            if not elem.tail or not elem.tail.strip():
+                elem.tail = i
+        else:
+            if level and (not elem.tail or not elem.tail.strip()):
+                elem.tail = i
+
+
+class IncrementalElement:
+    """Wrapper for _IncrementalWriter providing an Element like interface.
+
+    This wrapper does not intend to be a complete implementation but rather to
+    deal with those calls used in GraphMLWriter.
+    """
+
+    def __init__(self, xml, prettyprint):
+        self.xml = xml
+        self.prettyprint = prettyprint
+
+    def append(self, element):
+        self.xml.write(element, pretty_print=self.prettyprint)
+
+
+class GraphMLWriterLxml(GraphMLWriter):
+    def __init__(
+        self,
+        path,
+        graph=None,
+        encoding="utf-8",
+        prettyprint=True,
+        infer_numeric_types=False,
+        named_key_ids=False,
+        edge_id_from_attribute=None,
+    ):
+        self.construct_types()
+        import lxml.etree as lxmletree
+
+        self.myElement = lxmletree.Element
+
+        self._encoding = encoding
+        self._prettyprint = prettyprint
+        self.named_key_ids = named_key_ids
+        self.edge_id_from_attribute = edge_id_from_attribute
+        self.infer_numeric_types = infer_numeric_types
+
+        self._xml_base = lxmletree.xmlfile(path, encoding=encoding)
+        self._xml = self._xml_base.__enter__()
+        self._xml.write_declaration()
+
+        # We need to have a xml variable that support insertion. This call is
+        # used for adding the keys to the document.
+        # We will store those keys in a plain list, and then after the graph
+        # element is closed we will add them to the main graphml element.
+        self.xml = []
+        self._keys = self.xml
+        self._graphml = self._xml.element(
+            "graphml",
+            {
+                "xmlns": self.NS_GRAPHML,
+                "xmlns:xsi": self.NS_XSI,
+                "xsi:schemaLocation": self.SCHEMALOCATION,
+            },
+        )
+        self._graphml.__enter__()
+        self.keys = {}
+        self.attribute_types = defaultdict(set)
+
+        if graph is not None:
+            self.add_graph_element(graph)
+
+    def add_graph_element(self, G):
+        """
+        Serialize graph G in GraphML to the stream.
+        """
+        if G.is_directed():
+            default_edge_type = "directed"
+        else:
+            default_edge_type = "undirected"
+
+        graphid = G.graph.pop("id", None)
+        if graphid is None:
+            graph_element = self._xml.element("graph", edgedefault=default_edge_type)
+        else:
+            graph_element = self._xml.element(
+                "graph", edgedefault=default_edge_type, id=graphid
+            )
+
+        # gather attributes types for the whole graph
+        # to find the most general numeric format needed.
+        # Then pass through attributes to create key_id for each.
+        graphdata = {
+            k: v
+            for k, v in G.graph.items()
+            if k not in ("node_default", "edge_default")
+        }
+        node_default = G.graph.get("node_default", {})
+        edge_default = G.graph.get("edge_default", {})
+        # Graph attributes
+        for k, v in graphdata.items():
+            self.attribute_types[(str(k), "graph")].add(type(v))
+        for k, v in graphdata.items():
+            element_type = self.get_xml_type(self.attr_type(k, "graph", v))
+            self.get_key(str(k), element_type, "graph", None)
+        # Nodes and data
+        for node, d in G.nodes(data=True):
+            for k, v in d.items():
+                self.attribute_types[(str(k), "node")].add(type(v))
+        for node, d in G.nodes(data=True):
+            for k, v in d.items():
+                T = self.get_xml_type(self.attr_type(k, "node", v))
+                self.get_key(str(k), T, "node", node_default.get(k))
+        # Edges and data
+        if G.is_multigraph():
+            for u, v, ekey, d in G.edges(keys=True, data=True):
+                for k, v in d.items():
+                    self.attribute_types[(str(k), "edge")].add(type(v))
+            for u, v, ekey, d in G.edges(keys=True, data=True):
+                for k, v in d.items():
+                    T = self.get_xml_type(self.attr_type(k, "edge", v))
+                    self.get_key(str(k), T, "edge", edge_default.get(k))
+        else:
+            for u, v, d in G.edges(data=True):
+                for k, v in d.items():
+                    self.attribute_types[(str(k), "edge")].add(type(v))
+            for u, v, d in G.edges(data=True):
+                for k, v in d.items():
+                    T = self.get_xml_type(self.attr_type(k, "edge", v))
+                    self.get_key(str(k), T, "edge", edge_default.get(k))
+
+        # Now add attribute keys to the xml file
+        for key in self.xml:
+            self._xml.write(key, pretty_print=self._prettyprint)
+
+        # The incremental_writer writes each node/edge as it is created
+        incremental_writer = IncrementalElement(self._xml, self._prettyprint)
+        with graph_element:
+            self.add_attributes("graph", incremental_writer, graphdata, {})
+            self.add_nodes(G, incremental_writer)  # adds attributes too
+            self.add_edges(G, incremental_writer)  # adds attributes too
+
+    def add_attributes(self, scope, xml_obj, data, default):
+        """Appends attribute data."""
+        for k, v in data.items():
+            data_element = self.add_data(
+                str(k), self.attr_type(str(k), scope, v), str(v), scope, default.get(k)
+            )
+            xml_obj.append(data_element)
+
+    def __str__(self):
+        return object.__str__(self)
+
+    def dump(self, stream=None):
+        self._graphml.__exit__(None, None, None)
+        self._xml_base.__exit__(None, None, None)
+
+
+# default is lxml is present.
+write_graphml = write_graphml_lxml
+
+
+class GraphMLReader(GraphML):
+    """Read a GraphML document.  Produces NetworkX graph objects."""
+
+    def __init__(self, node_type=str, edge_key_type=int, force_multigraph=False):
+        self.construct_types()
+        self.node_type = node_type
+        self.edge_key_type = edge_key_type
+        self.multigraph = force_multigraph  # If False, test for multiedges
+        self.edge_ids = {}  # dict mapping (u,v) tuples to edge id attributes
+
+    def __call__(self, path=None, string=None):
+        from xml.etree.ElementTree import ElementTree, fromstring
+
+        if path is not None:
+            self.xml = ElementTree(file=path)
+        elif string is not None:
+            self.xml = fromstring(string)
+        else:
+            raise ValueError("Must specify either 'path' or 'string' as kwarg")
+        (keys, defaults) = self.find_graphml_keys(self.xml)
+        for g in self.xml.findall(f"{{{self.NS_GRAPHML}}}graph"):
+            yield self.make_graph(g, keys, defaults)
+
+    def make_graph(self, graph_xml, graphml_keys, defaults, G=None):
+        # set default graph type
+        edgedefault = graph_xml.get("edgedefault", None)
+        if G is None:
+            if edgedefault == "directed":
+                G = nx.MultiDiGraph()
+            else:
+                G = nx.MultiGraph()
+        # set defaults for graph attributes
+        G.graph["node_default"] = {}
+        G.graph["edge_default"] = {}
+        for key_id, value in defaults.items():
+            key_for = graphml_keys[key_id]["for"]
+            name = graphml_keys[key_id]["name"]
+            python_type = graphml_keys[key_id]["type"]
+            if key_for == "node":
+                G.graph["node_default"].update({name: python_type(value)})
+            if key_for == "edge":
+                G.graph["edge_default"].update({name: python_type(value)})
+        # hyperedges are not supported
+        hyperedge = graph_xml.find(f"{{{self.NS_GRAPHML}}}hyperedge")
+        if hyperedge is not None:
+            raise nx.NetworkXError("GraphML reader doesn't support hyperedges")
+        # add nodes
+        for node_xml in graph_xml.findall(f"{{{self.NS_GRAPHML}}}node"):
+            self.add_node(G, node_xml, graphml_keys, defaults)
+        # add edges
+        for edge_xml in graph_xml.findall(f"{{{self.NS_GRAPHML}}}edge"):
+            self.add_edge(G, edge_xml, graphml_keys)
+        # add graph data
+        data = self.decode_data_elements(graphml_keys, graph_xml)
+        G.graph.update(data)
+
+        # switch to Graph or DiGraph if no parallel edges were found
+        if self.multigraph:
+            return G
+
+        G = nx.DiGraph(G) if G.is_directed() else nx.Graph(G)
+        # add explicit edge "id" from file as attribute in NX graph.
+        nx.set_edge_attributes(G, values=self.edge_ids, name="id")
+        return G
+
+    def add_node(self, G, node_xml, graphml_keys, defaults):
+        """Add a node to the graph."""
+        # warn on finding unsupported ports tag
+        ports = node_xml.find(f"{{{self.NS_GRAPHML}}}port")
+        if ports is not None:
+            warnings.warn("GraphML port tag not supported.")
+        # find the node by id and cast it to the appropriate type
+        node_id = self.node_type(node_xml.get("id"))
+        # get data/attributes for node
+        data = self.decode_data_elements(graphml_keys, node_xml)
+        G.add_node(node_id, **data)
+        # get child nodes
+        if node_xml.attrib.get("yfiles.foldertype") == "group":
+            graph_xml = node_xml.find(f"{{{self.NS_GRAPHML}}}graph")
+            self.make_graph(graph_xml, graphml_keys, defaults, G)
+
+    def add_edge(self, G, edge_element, graphml_keys):
+        """Add an edge to the graph."""
+        # warn on finding unsupported ports tag
+        ports = edge_element.find(f"{{{self.NS_GRAPHML}}}port")
+        if ports is not None:
+            warnings.warn("GraphML port tag not supported.")
+
+        # raise error if we find mixed directed and undirected edges
+        directed = edge_element.get("directed")
+        if G.is_directed() and directed == "false":
+            msg = "directed=false edge found in directed graph."
+            raise nx.NetworkXError(msg)
+        if (not G.is_directed()) and directed == "true":
+            msg = "directed=true edge found in undirected graph."
+            raise nx.NetworkXError(msg)
+
+        source = self.node_type(edge_element.get("source"))
+        target = self.node_type(edge_element.get("target"))
+        data = self.decode_data_elements(graphml_keys, edge_element)
+        # GraphML stores edge ids as an attribute
+        # NetworkX uses them as keys in multigraphs too if no key
+        # attribute is specified
+        edge_id = edge_element.get("id")
+        if edge_id:
+            # self.edge_ids is used by `make_graph` method for non-multigraphs
+            self.edge_ids[source, target] = edge_id
+            try:
+                edge_id = self.edge_key_type(edge_id)
+            except ValueError:  # Could not convert.
+                pass
+        else:
+            edge_id = data.get("key")
+
+        if G.has_edge(source, target):
+            # mark this as a multigraph
+            self.multigraph = True
+
+        # Use add_edges_from to avoid error with add_edge when `'key' in data`
+        # Note there is only one edge here...
+        G.add_edges_from([(source, target, edge_id, data)])
+
+    def decode_data_elements(self, graphml_keys, obj_xml):
+        """Use the key information to decode the data XML if present."""
+        data = {}
+        for data_element in obj_xml.findall(f"{{{self.NS_GRAPHML}}}data"):
+            key = data_element.get("key")
+            try:
+                data_name = graphml_keys[key]["name"]
+                data_type = graphml_keys[key]["type"]
+            except KeyError as err:
+                raise nx.NetworkXError(f"Bad GraphML data: no key {key}") from err
+            text = data_element.text
+            # assume anything with subelements is a yfiles extension
+            if text is not None and len(list(data_element)) == 0:
+                if data_type == bool:
+                    # Ignore cases.
+                    # http://docs.oracle.com/javase/6/docs/api/java/lang/
+                    # Boolean.html#parseBoolean%28java.lang.String%29
+                    data[data_name] = self.convert_bool[text.lower()]
+                else:
+                    data[data_name] = data_type(text)
+            elif len(list(data_element)) > 0:
+                # Assume yfiles as subelements, try to extract node_label
+                node_label = None
+                # set GenericNode's configuration as shape type
+                gn = data_element.find(f"{{{self.NS_Y}}}GenericNode")
+                if gn is not None:
+                    data["shape_type"] = gn.get("configuration")
+                for node_type in ["GenericNode", "ShapeNode", "SVGNode", "ImageNode"]:
+                    pref = f"{{{self.NS_Y}}}{node_type}/{{{self.NS_Y}}}"
+                    geometry = data_element.find(f"{pref}Geometry")
+                    if geometry is not None:
+                        data["x"] = geometry.get("x")
+                        data["y"] = geometry.get("y")
+                    if node_label is None:
+                        node_label = data_element.find(f"{pref}NodeLabel")
+                    shape = data_element.find(f"{pref}Shape")
+                    if shape is not None:
+                        data["shape_type"] = shape.get("type")
+                if node_label is not None:
+                    data["label"] = node_label.text
+
+                # check all the different types of edges available in yEd.
+                for edge_type in [
+                    "PolyLineEdge",
+                    "SplineEdge",
+                    "QuadCurveEdge",
+                    "BezierEdge",
+                    "ArcEdge",
+                ]:
+                    pref = f"{{{self.NS_Y}}}{edge_type}/{{{self.NS_Y}}}"
+                    edge_label = data_element.find(f"{pref}EdgeLabel")
+                    if edge_label is not None:
+                        break
+                if edge_label is not None:
+                    data["label"] = edge_label.text
+            elif text is None:
+                data[data_name] = ""
+        return data
+
+    def find_graphml_keys(self, graph_element):
+        """Extracts all the keys and key defaults from the xml."""
+        graphml_keys = {}
+        graphml_key_defaults = {}
+        for k in graph_element.findall(f"{{{self.NS_GRAPHML}}}key"):
+            attr_id = k.get("id")
+            attr_type = k.get("attr.type")
+            attr_name = k.get("attr.name")
+            yfiles_type = k.get("yfiles.type")
+            if yfiles_type is not None:
+                attr_name = yfiles_type
+                attr_type = "yfiles"
+            if attr_type is None:
+                attr_type = "string"
+                warnings.warn(f"No key type for id {attr_id}. Using string")
+            if attr_name is None:
+                raise nx.NetworkXError(f"Unknown key for id {attr_id}.")
+            graphml_keys[attr_id] = {
+                "name": attr_name,
+                "type": self.python_type[attr_type],
+                "for": k.get("for"),
+            }
+            # check for "default" sub-element of key element
+            default = k.find(f"{{{self.NS_GRAPHML}}}default")
+            if default is not None:
+                # Handle default values identically to data element values
+                python_type = graphml_keys[attr_id]["type"]
+                if python_type == bool:
+                    graphml_key_defaults[attr_id] = self.convert_bool[
+                        default.text.lower()
+                    ]
+                else:
+                    graphml_key_defaults[attr_id] = python_type(default.text)
+        return graphml_keys, graphml_key_defaults

env-llmeval/lib/python3.10/site-packages/networkx/readwrite/json_graph/__init__.py

@@ -0,0 +1,18 @@
+"""
+*********
+JSON data
+*********
+Generate and parse JSON serializable data for NetworkX graphs.
+
+These formats are suitable for use with the d3.js examples https://d3js.org/
+
+The three formats that you can generate with NetworkX are:
+
+ - node-link like in the d3.js example https://bl.ocks.org/mbostock/4062045
+ - tree like in the d3.js example https://bl.ocks.org/mbostock/4063550
+ - adjacency like in the d3.js example https://bost.ocks.org/mike/miserables/
+"""
+from networkx.readwrite.json_graph.node_link import *
+from networkx.readwrite.json_graph.adjacency import *
+from networkx.readwrite.json_graph.tree import *
+from networkx.readwrite.json_graph.cytoscape import *

env-llmeval/lib/python3.10/site-packages/networkx/readwrite/json_graph/adjacency.py

@@ -0,0 +1,156 @@
+import networkx as nx
+
+__all__ = ["adjacency_data", "adjacency_graph"]
+
+_attrs = {"id": "id", "key": "key"}
+
+
+def adjacency_data(G, attrs=_attrs):
+    """Returns data in adjacency format that is suitable for JSON serialization
+    and use in JavaScript documents.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    attrs : dict
+        A dictionary that contains two keys 'id' and 'key'. The corresponding
+        values provide the attribute names for storing NetworkX-internal graph
+        data. The values should be unique. Default value:
+        :samp:`dict(id='id', key='key')`.
+
+        If some user-defined graph data use these attribute names as data keys,
+        they may be silently dropped.
+
+    Returns
+    -------
+    data : dict
+       A dictionary with adjacency formatted data.
+
+    Raises
+    ------
+    NetworkXError
+        If values in attrs are not unique.
+
+    Examples
+    --------
+    >>> from networkx.readwrite import json_graph
+    >>> G = nx.Graph([(1, 2)])
+    >>> data = json_graph.adjacency_data(G)
+
+    To serialize with json
+
+    >>> import json
+    >>> s = json.dumps(data)
+
+    Notes
+    -----
+    Graph, node, and link attributes will be written when using this format
+    but attribute keys must be strings if you want to serialize the resulting
+    data with JSON.
+
+    The default value of attrs will be changed in a future release of NetworkX.
+
+    See Also
+    --------
+    adjacency_graph, node_link_data, tree_data
+    """
+    multigraph = G.is_multigraph()
+    id_ = attrs["id"]
+    # Allow 'key' to be omitted from attrs if the graph is not a multigraph.
+    key = None if not multigraph else attrs["key"]
+    if id_ == key:
+        raise nx.NetworkXError("Attribute names are not unique.")
+    data = {}
+    data["directed"] = G.is_directed()
+    data["multigraph"] = multigraph
+    data["graph"] = list(G.graph.items())
+    data["nodes"] = []
+    data["adjacency"] = []
+    for n, nbrdict in G.adjacency():
+        data["nodes"].append({**G.nodes[n], id_: n})
+        adj = []
+        if multigraph:
+            for nbr, keys in nbrdict.items():
+                for k, d in keys.items():
+                    adj.append({**d, id_: nbr, key: k})
+        else:
+            for nbr, d in nbrdict.items():
+                adj.append({**d, id_: nbr})
+        data["adjacency"].append(adj)
+    return data
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def adjacency_graph(data, directed=False, multigraph=True, attrs=_attrs):
+    """Returns graph from adjacency data format.
+
+    Parameters
+    ----------
+    data : dict
+        Adjacency list formatted graph data
+
+    directed : bool
+        If True, and direction not specified in data, return a directed graph.
+
+    multigraph : bool
+        If True, and multigraph not specified in data, return a multigraph.
+
+    attrs : dict
+        A dictionary that contains two keys 'id' and 'key'. The corresponding
+        values provide the attribute names for storing NetworkX-internal graph
+        data. The values should be unique. Default value:
+        :samp:`dict(id='id', key='key')`.
+
+    Returns
+    -------
+    G : NetworkX graph
+       A NetworkX graph object
+
+    Examples
+    --------
+    >>> from networkx.readwrite import json_graph
+    >>> G = nx.Graph([(1, 2)])
+    >>> data = json_graph.adjacency_data(G)
+    >>> H = json_graph.adjacency_graph(data)
+
+    Notes
+    -----
+    The default value of attrs will be changed in a future release of NetworkX.
+
+    See Also
+    --------
+    adjacency_graph, node_link_data, tree_data
+    """
+    multigraph = data.get("multigraph", multigraph)
+    directed = data.get("directed", directed)
+    if multigraph:
+        graph = nx.MultiGraph()
+    else:
+        graph = nx.Graph()
+    if directed:
+        graph = graph.to_directed()
+    id_ = attrs["id"]
+    # Allow 'key' to be omitted from attrs if the graph is not a multigraph.
+    key = None if not multigraph else attrs["key"]
+    graph.graph = dict(data.get("graph", []))
+    mapping = []
+    for d in data["nodes"]:
+        node_data = d.copy()
+        node = node_data.pop(id_)
+        mapping.append(node)
+        graph.add_node(node)
+        graph.nodes[node].update(node_data)
+    for i, d in enumerate(data["adjacency"]):
+        source = mapping[i]
+        for tdata in d:
+            target_data = tdata.copy()
+            target = target_data.pop(id_)
+            if not multigraph:
+                graph.add_edge(source, target)
+                graph[source][target].update(target_data)
+            else:
+                ky = target_data.pop(key, None)
+                graph.add_edge(source, target, key=ky)
+                graph[source][target][ky].update(target_data)
+    return graph

env-llmeval/lib/python3.10/site-packages/networkx/readwrite/json_graph/cytoscape.py

@@ -0,0 +1,178 @@
+import networkx as nx
+
+__all__ = ["cytoscape_data", "cytoscape_graph"]
+
+
+def cytoscape_data(G, name="name", ident="id"):
+    """Returns data in Cytoscape JSON format (cyjs).
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        The graph to convert to cytoscape format
+    name : string
+        A string which is mapped to the 'name' node element in cyjs format.
+        Must not have the same value as `ident`.
+    ident : string
+        A string which is mapped to the 'id' node element in cyjs format.
+        Must not have the same value as `name`.
+
+    Returns
+    -------
+    data: dict
+        A dictionary with cyjs formatted data.
+
+    Raises
+    ------
+    NetworkXError
+        If the values for `name` and `ident` are identical.
+
+    See Also
+    --------
+    cytoscape_graph: convert a dictionary in cyjs format to a graph
+
+    References
+    ----------
+    .. [1] Cytoscape user's manual:
+       http://manual.cytoscape.org/en/stable/index.html
+
+    Examples
+    --------
+    >>> G = nx.path_graph(2)
+    >>> nx.cytoscape_data(G)  # doctest: +SKIP
+    {'data': [],
+     'directed': False,
+     'multigraph': False,
+     'elements': {'nodes': [{'data': {'id': '0', 'value': 0, 'name': '0'}},
+       {'data': {'id': '1', 'value': 1, 'name': '1'}}],
+      'edges': [{'data': {'source': 0, 'target': 1}}]}}
+    """
+    if name == ident:
+        raise nx.NetworkXError("name and ident must be different.")
+
+    jsondata = {"data": list(G.graph.items())}
+    jsondata["directed"] = G.is_directed()
+    jsondata["multigraph"] = G.is_multigraph()
+    jsondata["elements"] = {"nodes": [], "edges": []}
+    nodes = jsondata["elements"]["nodes"]
+    edges = jsondata["elements"]["edges"]
+
+    for i, j in G.nodes.items():
+        n = {"data": j.copy()}
+        n["data"]["id"] = j.get(ident) or str(i)
+        n["data"]["value"] = i
+        n["data"]["name"] = j.get(name) or str(i)
+        nodes.append(n)
+
+    if G.is_multigraph():
+        for e in G.edges(keys=True):
+            n = {"data": G.adj[e[0]][e[1]][e[2]].copy()}
+            n["data"]["source"] = e[0]
+            n["data"]["target"] = e[1]
+            n["data"]["key"] = e[2]
+            edges.append(n)
+    else:
+        for e in G.edges():
+            n = {"data": G.adj[e[0]][e[1]].copy()}
+            n["data"]["source"] = e[0]
+            n["data"]["target"] = e[1]
+            edges.append(n)
+    return jsondata
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def cytoscape_graph(data, name="name", ident="id"):
+    """
+    Create a NetworkX graph from a dictionary in cytoscape JSON format.
+
+    Parameters
+    ----------
+    data : dict
+        A dictionary of data conforming to cytoscape JSON format.
+    name : string
+        A string which is mapped to the 'name' node element in cyjs format.
+        Must not have the same value as `ident`.
+    ident : string
+        A string which is mapped to the 'id' node element in cyjs format.
+        Must not have the same value as `name`.
+
+    Returns
+    -------
+    graph : a NetworkX graph instance
+        The `graph` can be an instance of `Graph`, `DiGraph`, `MultiGraph`, or
+        `MultiDiGraph` depending on the input data.
+
+    Raises
+    ------
+    NetworkXError
+        If the `name` and `ident` attributes are identical.
+
+    See Also
+    --------
+    cytoscape_data: convert a NetworkX graph to a dict in cyjs format
+
+    References
+    ----------
+    .. [1] Cytoscape user's manual:
+       http://manual.cytoscape.org/en/stable/index.html
+
+    Examples
+    --------
+    >>> data_dict = {
+    ...     "data": [],
+    ...     "directed": False,
+    ...     "multigraph": False,
+    ...     "elements": {
+    ...         "nodes": [
+    ...             {"data": {"id": "0", "value": 0, "name": "0"}},
+    ...             {"data": {"id": "1", "value": 1, "name": "1"}},
+    ...         ],
+    ...         "edges": [{"data": {"source": 0, "target": 1}}],
+    ...     },
+    ... }
+    >>> G = nx.cytoscape_graph(data_dict)
+    >>> G.name
+    ''
+    >>> G.nodes()
+    NodeView((0, 1))
+    >>> G.nodes(data=True)[0]
+    {'id': '0', 'value': 0, 'name': '0'}
+    >>> G.edges(data=True)
+    EdgeDataView([(0, 1, {'source': 0, 'target': 1})])
+    """
+    if name == ident:
+        raise nx.NetworkXError("name and ident must be different.")
+
+    multigraph = data.get("multigraph")
+    directed = data.get("directed")
+    if multigraph:
+        graph = nx.MultiGraph()
+    else:
+        graph = nx.Graph()
+    if directed:
+        graph = graph.to_directed()
+    graph.graph = dict(data.get("data"))
+    for d in data["elements"]["nodes"]:
+        node_data = d["data"].copy()
+        node = d["data"]["value"]
+
+        if d["data"].get(name):
+            node_data[name] = d["data"].get(name)
+        if d["data"].get(ident):
+            node_data[ident] = d["data"].get(ident)
+
+        graph.add_node(node)
+        graph.nodes[node].update(node_data)
+
+    for d in data["elements"]["edges"]:
+        edge_data = d["data"].copy()
+        sour = d["data"]["source"]
+        targ = d["data"]["target"]
+        if multigraph:
+            key = d["data"].get("key", 0)
+            graph.add_edge(sour, targ, key=key)
+            graph.edges[sour, targ, key].update(edge_data)
+        else:
+            graph.add_edge(sour, targ)
+            graph.edges[sour, targ].update(edge_data)
+    return graph

env-llmeval/lib/python3.10/site-packages/networkx/readwrite/json_graph/node_link.py

@@ -0,0 +1,244 @@
+from itertools import chain, count
+
+import networkx as nx
+
+__all__ = ["node_link_data", "node_link_graph"]
+
+
+_attrs = {
+    "source": "source",
+    "target": "target",
+    "name": "id",
+    "key": "key",
+    "link": "links",
+}
+
+
+def _to_tuple(x):
+    """Converts lists to tuples, including nested lists.
+
+    All other non-list inputs are passed through unmodified. This function is
+    intended to be used to convert potentially nested lists from json files
+    into valid nodes.
+
+    Examples
+    --------
+    >>> _to_tuple([1, 2, [3, 4]])
+    (1, 2, (3, 4))
+    """
+    if not isinstance(x, tuple | list):
+        return x
+    return tuple(map(_to_tuple, x))
+
+
+def node_link_data(
+    G,
+    *,
+    source="source",
+    target="target",
+    name="id",
+    key="key",
+    link="links",
+):
+    """Returns data in node-link format that is suitable for JSON serialization
+    and use in JavaScript documents.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+    source : string
+        A string that provides the 'source' attribute name for storing NetworkX-internal graph data.
+    target : string
+        A string that provides the 'target' attribute name for storing NetworkX-internal graph data.
+    name : string
+        A string that provides the 'name' attribute name for storing NetworkX-internal graph data.
+    key : string
+        A string that provides the 'key' attribute name for storing NetworkX-internal graph data.
+    link : string
+        A string that provides the 'link' attribute name for storing NetworkX-internal graph data.
+
+    Returns
+    -------
+    data : dict
+       A dictionary with node-link formatted data.
+
+    Raises
+    ------
+    NetworkXError
+        If the values of 'source', 'target' and 'key' are not unique.
+
+    Examples
+    --------
+    >>> G = nx.Graph([("A", "B")])
+    >>> data1 = nx.node_link_data(G)
+    >>> data1
+    {'directed': False, 'multigraph': False, 'graph': {}, 'nodes': [{'id': 'A'}, {'id': 'B'}], 'links': [{'source': 'A', 'target': 'B'}]}
+
+    To serialize with JSON
+
+    >>> import json
+    >>> s1 = json.dumps(data1)
+    >>> s1
+    '{"directed": false, "multigraph": false, "graph": {}, "nodes": [{"id": "A"}, {"id": "B"}], "links": [{"source": "A", "target": "B"}]}'
+
+    A graph can also be serialized by passing `node_link_data` as an encoder function. The two methods are equivalent.
+
+    >>> s1 = json.dumps(G, default=nx.node_link_data)
+    >>> s1
+    '{"directed": false, "multigraph": false, "graph": {}, "nodes": [{"id": "A"}, {"id": "B"}], "links": [{"source": "A", "target": "B"}]}'
+
+    The attribute names for storing NetworkX-internal graph data can
+    be specified as keyword options.
+
+    >>> H = nx.gn_graph(2)
+    >>> data2 = nx.node_link_data(H, link="edges", source="from", target="to")
+    >>> data2
+    {'directed': True, 'multigraph': False, 'graph': {}, 'nodes': [{'id': 0}, {'id': 1}], 'edges': [{'from': 1, 'to': 0}]}
+
+    Notes
+    -----
+    Graph, node, and link attributes are stored in this format.  Note that
+    attribute keys will be converted to strings in order to comply with JSON.
+
+    Attribute 'key' is only used for multigraphs.
+
+    To use `node_link_data` in conjunction with `node_link_graph`,
+    the keyword names for the attributes must match.
+
+
+    See Also
+    --------
+    node_link_graph, adjacency_data, tree_data
+    """
+    multigraph = G.is_multigraph()
+
+    # Allow 'key' to be omitted from attrs if the graph is not a multigraph.
+    key = None if not multigraph else key
+    if len({source, target, key}) < 3:
+        raise nx.NetworkXError("Attribute names are not unique.")
+    data = {
+        "directed": G.is_directed(),
+        "multigraph": multigraph,
+        "graph": G.graph,
+        "nodes": [{**G.nodes[n], name: n} for n in G],
+    }
+    if multigraph:
+        data[link] = [
+            {**d, source: u, target: v, key: k}
+            for u, v, k, d in G.edges(keys=True, data=True)
+        ]
+    else:
+        data[link] = [{**d, source: u, target: v} for u, v, d in G.edges(data=True)]
+    return data
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def node_link_graph(
+    data,
+    directed=False,
+    multigraph=True,
+    *,
+    source="source",
+    target="target",
+    name="id",
+    key="key",
+    link="links",
+):
+    """Returns graph from node-link data format.
+    Useful for de-serialization from JSON.
+
+    Parameters
+    ----------
+    data : dict
+        node-link formatted graph data
+
+    directed : bool
+        If True, and direction not specified in data, return a directed graph.
+
+    multigraph : bool
+        If True, and multigraph not specified in data, return a multigraph.
+
+    source : string
+        A string that provides the 'source' attribute name for storing NetworkX-internal graph data.
+    target : string
+        A string that provides the 'target' attribute name for storing NetworkX-internal graph data.
+    name : string
+        A string that provides the 'name' attribute name for storing NetworkX-internal graph data.
+    key : string
+        A string that provides the 'key' attribute name for storing NetworkX-internal graph data.
+    link : string
+        A string that provides the 'link' attribute name for storing NetworkX-internal graph data.
+
+    Returns
+    -------
+    G : NetworkX graph
+        A NetworkX graph object
+
+    Examples
+    --------
+
+    Create data in node-link format by converting a graph.
+
+    >>> G = nx.Graph([("A", "B")])
+    >>> data = nx.node_link_data(G)
+    >>> data
+    {'directed': False, 'multigraph': False, 'graph': {}, 'nodes': [{'id': 'A'}, {'id': 'B'}], 'links': [{'source': 'A', 'target': 'B'}]}
+
+    Revert data in node-link format to a graph.
+
+    >>> H = nx.node_link_graph(data)
+    >>> print(H.edges)
+    [('A', 'B')]
+
+    To serialize and deserialize a graph with JSON,
+
+    >>> import json
+    >>> d = json.dumps(node_link_data(G))
+    >>> H = node_link_graph(json.loads(d))
+    >>> print(G.edges, H.edges)
+    [('A', 'B')] [('A', 'B')]
+
+
+    Notes
+    -----
+    Attribute 'key' is only used for multigraphs.
+
+    To use `node_link_data` in conjunction with `node_link_graph`,
+    the keyword names for the attributes must match.
+
+    See Also
+    --------
+    node_link_data, adjacency_data, tree_data
+    """
+    multigraph = data.get("multigraph", multigraph)
+    directed = data.get("directed", directed)
+    if multigraph:
+        graph = nx.MultiGraph()
+    else:
+        graph = nx.Graph()
+    if directed:
+        graph = graph.to_directed()
+
+    # Allow 'key' to be omitted from attrs if the graph is not a multigraph.
+    key = None if not multigraph else key
+    graph.graph = data.get("graph", {})
+    c = count()
+    for d in data["nodes"]:
+        node = _to_tuple(d.get(name, next(c)))
+        nodedata = {str(k): v for k, v in d.items() if k != name}
+        graph.add_node(node, **nodedata)
+    for d in data[link]:
+        src = tuple(d[source]) if isinstance(d[source], list) else d[source]
+        tgt = tuple(d[target]) if isinstance(d[target], list) else d[target]
+        if not multigraph:
+            edgedata = {str(k): v for k, v in d.items() if k != source and k != target}
+            graph.add_edge(src, tgt, **edgedata)
+        else:
+            ky = d.get(key, None)
+            edgedata = {
+                str(k): v
+                for k, v in d.items()
+                if k != source and k != target and k != key
+            }
+            graph.add_edge(src, tgt, ky, **edgedata)
+    return graph

env-llmeval/lib/python3.10/site-packages/networkx/readwrite/json_graph/tree.py

@@ -0,0 +1,137 @@
+from itertools import chain
+
+import networkx as nx
+
+__all__ = ["tree_data", "tree_graph"]
+
+
+def tree_data(G, root, ident="id", children="children"):
+    """Returns data in tree format that is suitable for JSON serialization
+    and use in JavaScript documents.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+       G must be an oriented tree
+
+    root : node
+       The root of the tree
+
+    ident : string
+        Attribute name for storing NetworkX-internal graph data. `ident` must
+        have a different value than `children`. The default is 'id'.
+
+    children : string
+        Attribute name for storing NetworkX-internal graph data. `children`
+        must have a different value than `ident`. The default is 'children'.
+
+    Returns
+    -------
+    data : dict
+       A dictionary with node-link formatted data.
+
+    Raises
+    ------
+    NetworkXError
+        If `children` and `ident` attributes are identical.
+
+    Examples
+    --------
+    >>> from networkx.readwrite import json_graph
+    >>> G = nx.DiGraph([(1, 2)])
+    >>> data = json_graph.tree_data(G, root=1)
+
+    To serialize with json
+
+    >>> import json
+    >>> s = json.dumps(data)
+
+    Notes
+    -----
+    Node attributes are stored in this format but keys
+    for attributes must be strings if you want to serialize with JSON.
+
+    Graph and edge attributes are not stored.
+
+    See Also
+    --------
+    tree_graph, node_link_data, adjacency_data
+    """
+    if G.number_of_nodes() != G.number_of_edges() + 1:
+        raise TypeError("G is not a tree.")
+    if not G.is_directed():
+        raise TypeError("G is not directed.")
+    if not nx.is_weakly_connected(G):
+        raise TypeError("G is not weakly connected.")
+
+    if ident == children:
+        raise nx.NetworkXError("The values for `id` and `children` must be different.")
+
+    def add_children(n, G):
+        nbrs = G[n]
+        if len(nbrs) == 0:
+            return []
+        children_ = []
+        for child in nbrs:
+            d = {**G.nodes[child], ident: child}
+            c = add_children(child, G)
+            if c:
+                d[children] = c
+            children_.append(d)
+        return children_
+
+    return {**G.nodes[root], ident: root, children: add_children(root, G)}
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def tree_graph(data, ident="id", children="children"):
+    """Returns graph from tree data format.
+
+    Parameters
+    ----------
+    data : dict
+        Tree formatted graph data
+
+    ident : string
+        Attribute name for storing NetworkX-internal graph data. `ident` must
+        have a different value than `children`. The default is 'id'.
+
+    children : string
+        Attribute name for storing NetworkX-internal graph data. `children`
+        must have a different value than `ident`. The default is 'children'.
+
+    Returns
+    -------
+    G : NetworkX DiGraph
+
+    Examples
+    --------
+    >>> from networkx.readwrite import json_graph
+    >>> G = nx.DiGraph([(1, 2)])
+    >>> data = json_graph.tree_data(G, root=1)
+    >>> H = json_graph.tree_graph(data)
+
+    See Also
+    --------
+    tree_data, node_link_data, adjacency_data
+    """
+    graph = nx.DiGraph()
+
+    def add_children(parent, children_):
+        for data in children_:
+            child = data[ident]
+            graph.add_edge(parent, child)
+            grandchildren = data.get(children, [])
+            if grandchildren:
+                add_children(child, grandchildren)
+            nodedata = {
+                str(k): v for k, v in data.items() if k != ident and k != children
+            }
+            graph.add_node(child, **nodedata)
+
+    root = data[ident]
+    children_ = data.get(children, [])
+    nodedata = {str(k): v for k, v in data.items() if k != ident and k != children}
+    graph.add_node(root, **nodedata)
+    add_children(root, children_)
+    return graph

env-llmeval/lib/python3.10/site-packages/networkx/readwrite/multiline_adjlist.py

@@ -0,0 +1,393 @@
+"""
+*************************
+Multi-line Adjacency List
+*************************
+Read and write NetworkX graphs as multi-line adjacency lists.
+
+The multi-line adjacency list format is useful for graphs with
+nodes that can be meaningfully represented as strings.  With this format
+simple edge data can be stored but node or graph data is not.
+
+Format
+------
+The first label in a line is the source node label followed by the node degree
+d.  The next d lines are target node labels and optional edge data.
+That pattern repeats for all nodes in the graph.
+
+The graph with edges a-b, a-c, d-e can be represented as the following
+adjacency list (anything following the # in a line is a comment)::
+
+     # example.multiline-adjlist
+     a 2
+     b
+     c
+     d 1
+     e
+"""
+
+__all__ = [
+    "generate_multiline_adjlist",
+    "write_multiline_adjlist",
+    "parse_multiline_adjlist",
+    "read_multiline_adjlist",
+]
+
+import networkx as nx
+from networkx.utils import open_file
+
+
+def generate_multiline_adjlist(G, delimiter=" "):
+    """Generate a single line of the graph G in multiline adjacency list format.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    delimiter : string, optional
+       Separator for node labels
+
+    Returns
+    -------
+    lines : string
+        Lines of data in multiline adjlist format.
+
+    Examples
+    --------
+    >>> G = nx.lollipop_graph(4, 3)
+    >>> for line in nx.generate_multiline_adjlist(G):
+    ...     print(line)
+    0 3
+    1 {}
+    2 {}
+    3 {}
+    1 2
+    2 {}
+    3 {}
+    2 1
+    3 {}
+    3 1
+    4 {}
+    4 1
+    5 {}
+    5 1
+    6 {}
+    6 0
+
+    See Also
+    --------
+    write_multiline_adjlist, read_multiline_adjlist
+    """
+    if G.is_directed():
+        if G.is_multigraph():
+            for s, nbrs in G.adjacency():
+                nbr_edges = [
+                    (u, data)
+                    for u, datadict in nbrs.items()
+                    for key, data in datadict.items()
+                ]
+                deg = len(nbr_edges)
+                yield str(s) + delimiter + str(deg)
+                for u, d in nbr_edges:
+                    if d is None:
+                        yield str(u)
+                    else:
+                        yield str(u) + delimiter + str(d)
+        else:  # directed single edges
+            for s, nbrs in G.adjacency():
+                deg = len(nbrs)
+                yield str(s) + delimiter + str(deg)
+                for u, d in nbrs.items():
+                    if d is None:
+                        yield str(u)
+                    else:
+                        yield str(u) + delimiter + str(d)
+    else:  # undirected
+        if G.is_multigraph():
+            seen = set()  # helper dict used to avoid duplicate edges
+            for s, nbrs in G.adjacency():
+                nbr_edges = [
+                    (u, data)
+                    for u, datadict in nbrs.items()
+                    if u not in seen
+                    for key, data in datadict.items()
+                ]
+                deg = len(nbr_edges)
+                yield str(s) + delimiter + str(deg)
+                for u, d in nbr_edges:
+                    if d is None:
+                        yield str(u)
+                    else:
+                        yield str(u) + delimiter + str(d)
+                seen.add(s)
+        else:  # undirected single edges
+            seen = set()  # helper dict used to avoid duplicate edges
+            for s, nbrs in G.adjacency():
+                nbr_edges = [(u, d) for u, d in nbrs.items() if u not in seen]
+                deg = len(nbr_edges)
+                yield str(s) + delimiter + str(deg)
+                for u, d in nbr_edges:
+                    if d is None:
+                        yield str(u)
+                    else:
+                        yield str(u) + delimiter + str(d)
+                seen.add(s)
+
+
+@open_file(1, mode="wb")
+def write_multiline_adjlist(G, path, delimiter=" ", comments="#", encoding="utf-8"):
+    """Write the graph G in multiline adjacency list format to path
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    path : string or file
+       Filename or file handle to write to.
+       Filenames ending in .gz or .bz2 will be compressed.
+
+    comments : string, optional
+       Marker for comment lines
+
+    delimiter : string, optional
+       Separator for node labels
+
+    encoding : string, optional
+       Text encoding.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_multiline_adjlist(G, "test.adjlist")
+
+    The path can be a file handle or a string with the name of the file. If a
+    file handle is provided, it has to be opened in 'wb' mode.
+
+    >>> fh = open("test.adjlist", "wb")
+    >>> nx.write_multiline_adjlist(G, fh)
+
+    Filenames ending in .gz or .bz2 will be compressed.
+
+    >>> nx.write_multiline_adjlist(G, "test.adjlist.gz")
+
+    See Also
+    --------
+    read_multiline_adjlist
+    """
+    import sys
+    import time
+
+    pargs = comments + " ".join(sys.argv)
+    header = (
+        f"{pargs}\n"
+        + comments
+        + f" GMT {time.asctime(time.gmtime())}\n"
+        + comments
+        + f" {G.name}\n"
+    )
+    path.write(header.encode(encoding))
+
+    for multiline in generate_multiline_adjlist(G, delimiter):
+        multiline += "\n"
+        path.write(multiline.encode(encoding))
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def parse_multiline_adjlist(
+    lines, comments="#", delimiter=None, create_using=None, nodetype=None, edgetype=None
+):
+    """Parse lines of a multiline adjacency list representation of a graph.
+
+    Parameters
+    ----------
+    lines : list or iterator of strings
+        Input data in multiline adjlist format
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    nodetype : Python type, optional
+       Convert nodes to this type.
+
+    edgetype : Python type, optional
+       Convert edges to this type.
+
+    comments : string, optional
+       Marker for comment lines
+
+    delimiter : string, optional
+       Separator for node labels.  The default is whitespace.
+
+    Returns
+    -------
+    G: NetworkX graph
+        The graph corresponding to the lines in multiline adjacency list format.
+
+    Examples
+    --------
+    >>> lines = [
+    ...     "1 2",
+    ...     "2 {'weight':3, 'name': 'Frodo'}",
+    ...     "3 {}",
+    ...     "2 1",
+    ...     "5 {'weight':6, 'name': 'Saruman'}",
+    ... ]
+    >>> G = nx.parse_multiline_adjlist(iter(lines), nodetype=int)
+    >>> list(G)
+    [1, 2, 3, 5]
+
+    """
+    from ast import literal_eval
+
+    G = nx.empty_graph(0, create_using)
+    for line in lines:
+        p = line.find(comments)
+        if p >= 0:
+            line = line[:p]
+        if not line:
+            continue
+        try:
+            (u, deg) = line.strip().split(delimiter)
+            deg = int(deg)
+        except BaseException as err:
+            raise TypeError(f"Failed to read node and degree on line ({line})") from err
+        if nodetype is not None:
+            try:
+                u = nodetype(u)
+            except BaseException as err:
+                raise TypeError(
+                    f"Failed to convert node ({u}) to type {nodetype}"
+                ) from err
+        G.add_node(u)
+        for i in range(deg):
+            while True:
+                try:
+                    line = next(lines)
+                except StopIteration as err:
+                    msg = f"Failed to find neighbor for node ({u})"
+                    raise TypeError(msg) from err
+                p = line.find(comments)
+                if p >= 0:
+                    line = line[:p]
+                if line:
+                    break
+            vlist = line.strip().split(delimiter)
+            numb = len(vlist)
+            if numb < 1:
+                continue  # isolated node
+            v = vlist.pop(0)
+            data = "".join(vlist)
+            if nodetype is not None:
+                try:
+                    v = nodetype(v)
+                except BaseException as err:
+                    raise TypeError(
+                        f"Failed to convert node ({v}) to type {nodetype}"
+                    ) from err
+            if edgetype is not None:
+                try:
+                    edgedata = {"weight": edgetype(data)}
+                except BaseException as err:
+                    raise TypeError(
+                        f"Failed to convert edge data ({data}) to type {edgetype}"
+                    ) from err
+            else:
+                try:  # try to evaluate
+                    edgedata = literal_eval(data)
+                except:
+                    edgedata = {}
+            G.add_edge(u, v, **edgedata)
+
+    return G
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_multiline_adjlist(
+    path,
+    comments="#",
+    delimiter=None,
+    create_using=None,
+    nodetype=None,
+    edgetype=None,
+    encoding="utf-8",
+):
+    """Read graph in multi-line adjacency list format from path.
+
+    Parameters
+    ----------
+    path : string or file
+       Filename or file handle to read.
+       Filenames ending in .gz or .bz2 will be uncompressed.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    nodetype : Python type, optional
+       Convert nodes to this type.
+
+    edgetype : Python type, optional
+       Convert edge data to this type.
+
+    comments : string, optional
+       Marker for comment lines
+
+    delimiter : string, optional
+       Separator for node labels.  The default is whitespace.
+
+    Returns
+    -------
+    G: NetworkX graph
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_multiline_adjlist(G, "test.adjlist")
+    >>> G = nx.read_multiline_adjlist("test.adjlist")
+
+    The path can be a file or a string with the name of the file. If a
+    file s provided, it has to be opened in 'rb' mode.
+
+    >>> fh = open("test.adjlist", "rb")
+    >>> G = nx.read_multiline_adjlist(fh)
+
+    Filenames ending in .gz or .bz2 will be compressed.
+
+    >>> nx.write_multiline_adjlist(G, "test.adjlist.gz")
+    >>> G = nx.read_multiline_adjlist("test.adjlist.gz")
+
+    The optional nodetype is a function to convert node strings to nodetype.
+
+    For example
+
+    >>> G = nx.read_multiline_adjlist("test.adjlist", nodetype=int)
+
+    will attempt to convert all nodes to integer type.
+
+    The optional edgetype is a function to convert edge data strings to
+    edgetype.
+
+    >>> G = nx.read_multiline_adjlist("test.adjlist")
+
+    The optional create_using parameter is a NetworkX graph container.
+    The default is Graph(), an undirected graph.  To read the data as
+    a directed graph use
+
+    >>> G = nx.read_multiline_adjlist("test.adjlist", create_using=nx.DiGraph)
+
+    Notes
+    -----
+    This format does not store graph, node, or edge data.
+
+    See Also
+    --------
+    write_multiline_adjlist
+    """
+    lines = (line.decode(encoding) for line in path)
+    return parse_multiline_adjlist(
+        lines,
+        comments=comments,
+        delimiter=delimiter,
+        create_using=create_using,
+        nodetype=nodetype,
+        edgetype=edgetype,
+    )

env-llmeval/lib/python3.10/site-packages/networkx/readwrite/pajek.py

@@ -0,0 +1,286 @@
+"""
+*****
+Pajek
+*****
+Read graphs in Pajek format.
+
+This implementation handles directed and undirected graphs including
+those with self loops and parallel edges.
+
+Format
+------
+See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
+for format information.
+
+"""
+
+import warnings
+
+import networkx as nx
+from networkx.utils import open_file
+
+__all__ = ["read_pajek", "parse_pajek", "generate_pajek", "write_pajek"]
+
+
+def generate_pajek(G):
+    """Generate lines in Pajek graph format.
+
+    Parameters
+    ----------
+    G : graph
+       A Networkx graph
+
+    References
+    ----------
+    See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
+    for format information.
+    """
+    if G.name == "":
+        name = "NetworkX"
+    else:
+        name = G.name
+    # Apparently many Pajek format readers can't process this line
+    # So we'll leave it out for now.
+    # yield '*network %s'%name
+
+    # write nodes with attributes
+    yield f"*vertices {G.order()}"
+    nodes = list(G)
+    # make dictionary mapping nodes to integers
+    nodenumber = dict(zip(nodes, range(1, len(nodes) + 1)))
+    for n in nodes:
+        # copy node attributes and pop mandatory attributes
+        # to avoid duplication.
+        na = G.nodes.get(n, {}).copy()
+        x = na.pop("x", 0.0)
+        y = na.pop("y", 0.0)
+        try:
+            id = int(na.pop("id", nodenumber[n]))
+        except ValueError as err:
+            err.args += (
+                (
+                    "Pajek format requires 'id' to be an int()."
+                    " Refer to the 'Relabeling nodes' section."
+                ),
+            )
+            raise
+        nodenumber[n] = id
+        shape = na.pop("shape", "ellipse")
+        s = " ".join(map(make_qstr, (id, n, x, y, shape)))
+        # only optional attributes are left in na.
+        for k, v in na.items():
+            if isinstance(v, str) and v.strip() != "":
+                s += f" {make_qstr(k)} {make_qstr(v)}"
+            else:
+                warnings.warn(
+                    f"Node attribute {k} is not processed. {('Empty attribute' if isinstance(v, str) else 'Non-string attribute')}."
+                )
+        yield s
+
+    # write edges with attributes
+    if G.is_directed():
+        yield "*arcs"
+    else:
+        yield "*edges"
+    for u, v, edgedata in G.edges(data=True):
+        d = edgedata.copy()
+        value = d.pop("weight", 1.0)  # use 1 as default edge value
+        s = " ".join(map(make_qstr, (nodenumber[u], nodenumber[v], value)))
+        for k, v in d.items():
+            if isinstance(v, str) and v.strip() != "":
+                s += f" {make_qstr(k)} {make_qstr(v)}"
+            else:
+                warnings.warn(
+                    f"Edge attribute {k} is not processed. {('Empty attribute' if isinstance(v, str) else 'Non-string attribute')}."
+                )
+        yield s
+
+
+@open_file(1, mode="wb")
+def write_pajek(G, path, encoding="UTF-8"):
+    """Write graph in Pajek format to path.
+
+    Parameters
+    ----------
+    G : graph
+       A Networkx graph
+    path : file or string
+       File or filename to write.
+       Filenames ending in .gz or .bz2 will be compressed.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_pajek(G, "test.net")
+
+    Warnings
+    --------
+    Optional node attributes and edge attributes must be non-empty strings.
+    Otherwise it will not be written into the file. You will need to
+    convert those attributes to strings if you want to keep them.
+
+    References
+    ----------
+    See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
+    for format information.
+    """
+    for line in generate_pajek(G):
+        line += "\n"
+        path.write(line.encode(encoding))
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_pajek(path, encoding="UTF-8"):
+    """Read graph in Pajek format from path.
+
+    Parameters
+    ----------
+    path : file or string
+       File or filename to write.
+       Filenames ending in .gz or .bz2 will be uncompressed.
+
+    Returns
+    -------
+    G : NetworkX MultiGraph or MultiDiGraph.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_pajek(G, "test.net")
+    >>> G = nx.read_pajek("test.net")
+
+    To create a Graph instead of a MultiGraph use
+
+    >>> G1 = nx.Graph(G)
+
+    References
+    ----------
+    See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
+    for format information.
+    """
+    lines = (line.decode(encoding) for line in path)
+    return parse_pajek(lines)
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def parse_pajek(lines):
+    """Parse Pajek format graph from string or iterable.
+
+    Parameters
+    ----------
+    lines : string or iterable
+       Data in Pajek format.
+
+    Returns
+    -------
+    G : NetworkX graph
+
+    See Also
+    --------
+    read_pajek
+
+    """
+    import shlex
+
+    # multigraph=False
+    if isinstance(lines, str):
+        lines = iter(lines.split("\n"))
+    lines = iter([line.rstrip("\n") for line in lines])
+    G = nx.MultiDiGraph()  # are multiedges allowed in Pajek? assume yes
+    labels = []  # in the order of the file, needed for matrix
+    while lines:
+        try:
+            l = next(lines)
+        except:  # EOF
+            break
+        if l.lower().startswith("*network"):
+            try:
+                label, name = l.split(None, 1)
+            except ValueError:
+                # Line was not of the form:  *network NAME
+                pass
+            else:
+                G.graph["name"] = name
+        elif l.lower().startswith("*vertices"):
+            nodelabels = {}
+            l, nnodes = l.split()
+            for i in range(int(nnodes)):
+                l = next(lines)
+                try:
+                    splitline = [
+                        x.decode("utf-8") for x in shlex.split(str(l).encode("utf-8"))
+                    ]
+                except AttributeError:
+                    splitline = shlex.split(str(l))
+                id, label = splitline[0:2]
+                labels.append(label)
+                G.add_node(label)
+                nodelabels[id] = label
+                G.nodes[label]["id"] = id
+                try:
+                    x, y, shape = splitline[2:5]
+                    G.nodes[label].update(
+                        {"x": float(x), "y": float(y), "shape": shape}
+                    )
+                except:
+                    pass
+                extra_attr = zip(splitline[5::2], splitline[6::2])
+                G.nodes[label].update(extra_attr)
+        elif l.lower().startswith("*edges") or l.lower().startswith("*arcs"):
+            if l.lower().startswith("*edge"):
+                # switch from multidigraph to multigraph
+                G = nx.MultiGraph(G)
+            if l.lower().startswith("*arcs"):
+                # switch to directed with multiple arcs for each existing edge
+                G = G.to_directed()
+            for l in lines:
+                try:
+                    splitline = [
+                        x.decode("utf-8") for x in shlex.split(str(l).encode("utf-8"))
+                    ]
+                except AttributeError:
+                    splitline = shlex.split(str(l))
+
+                if len(splitline) < 2:
+                    continue
+                ui, vi = splitline[0:2]
+                u = nodelabels.get(ui, ui)
+                v = nodelabels.get(vi, vi)
+                # parse the data attached to this edge and put in a dictionary
+                edge_data = {}
+                try:
+                    # there should always be a single value on the edge?
+                    w = splitline[2:3]
+                    edge_data.update({"weight": float(w[0])})
+                except:
+                    pass
+                    # if there isn't, just assign a 1
+                #                    edge_data.update({'value':1})
+                extra_attr = zip(splitline[3::2], splitline[4::2])
+                edge_data.update(extra_attr)
+                # if G.has_edge(u,v):
+                #     multigraph=True
+                G.add_edge(u, v, **edge_data)
+        elif l.lower().startswith("*matrix"):
+            G = nx.DiGraph(G)
+            adj_list = (
+                (labels[row], labels[col], {"weight": int(data)})
+                for (row, line) in enumerate(lines)
+                for (col, data) in enumerate(line.split())
+                if int(data) != 0
+            )
+            G.add_edges_from(adj_list)
+
+    return G
+
+
+def make_qstr(t):
+    """Returns the string representation of t.
+    Add outer double-quotes if the string has a space.
+    """
+    if not isinstance(t, str):
+        t = str(t)
+    if " " in t:
+        t = f'"{t}"'
+    return t

env-llmeval/lib/python3.10/site-packages/networkx/readwrite/sparse6.py

@@ -0,0 +1,376 @@
+# Original author: D. Eppstein, UC Irvine, August 12, 2003.
+# The original code at https://www.ics.uci.edu/~eppstein/PADS/ is public domain.
+"""Functions for reading and writing graphs in the *sparse6* format.
+
+The *sparse6* file format is a space-efficient format for large sparse
+graphs. For small graphs or large dense graphs, use the *graph6* file
+format.
+
+For more information, see the `sparse6`_ homepage.
+
+.. _sparse6: https://users.cecs.anu.edu.au/~bdm/data/formats.html
+
+"""
+import networkx as nx
+from networkx.exception import NetworkXError
+from networkx.readwrite.graph6 import data_to_n, n_to_data
+from networkx.utils import not_implemented_for, open_file
+
+__all__ = ["from_sparse6_bytes", "read_sparse6", "to_sparse6_bytes", "write_sparse6"]
+
+
+def _generate_sparse6_bytes(G, nodes, header):
+    """Yield bytes in the sparse6 encoding of a graph.
+
+    `G` is an undirected simple graph. `nodes` is the list of nodes for
+    which the node-induced subgraph will be encoded; if `nodes` is the
+    list of all nodes in the graph, the entire graph will be
+    encoded. `header` is a Boolean that specifies whether to generate
+    the header ``b'>>sparse6<<'`` before the remaining data.
+
+    This function generates `bytes` objects in the following order:
+
+    1. the header (if requested),
+    2. the encoding of the number of nodes,
+    3. each character, one-at-a-time, in the encoding of the requested
+       node-induced subgraph,
+    4. a newline character.
+
+    This function raises :exc:`ValueError` if the graph is too large for
+    the graph6 format (that is, greater than ``2 ** 36`` nodes).
+
+    """
+    n = len(G)
+    if n >= 2**36:
+        raise ValueError(
+            "sparse6 is only defined if number of nodes is less than 2 ** 36"
+        )
+    if header:
+        yield b">>sparse6<<"
+    yield b":"
+    for d in n_to_data(n):
+        yield str.encode(chr(d + 63))
+
+    k = 1
+    while 1 << k < n:
+        k += 1
+
+    def enc(x):
+        """Big endian k-bit encoding of x"""
+        return [1 if (x & 1 << (k - 1 - i)) else 0 for i in range(k)]
+
+    edges = sorted((max(u, v), min(u, v)) for u, v in G.edges())
+    bits = []
+    curv = 0
+    for v, u in edges:
+        if v == curv:  # current vertex edge
+            bits.append(0)
+            bits.extend(enc(u))
+        elif v == curv + 1:  # next vertex edge
+            curv += 1
+            bits.append(1)
+            bits.extend(enc(u))
+        else:  # skip to vertex v and then add edge to u
+            curv = v
+            bits.append(1)
+            bits.extend(enc(v))
+            bits.append(0)
+            bits.extend(enc(u))
+    if k < 6 and n == (1 << k) and ((-len(bits)) % 6) >= k and curv < (n - 1):
+        # Padding special case: small k, n=2^k,
+        # more than k bits of padding needed,
+        # current vertex is not (n-1) --
+        # appending 1111... would add a loop on (n-1)
+        bits.append(0)
+        bits.extend([1] * ((-len(bits)) % 6))
+    else:
+        bits.extend([1] * ((-len(bits)) % 6))
+
+    data = [
+        (bits[i + 0] << 5)
+        + (bits[i + 1] << 4)
+        + (bits[i + 2] << 3)
+        + (bits[i + 3] << 2)
+        + (bits[i + 4] << 1)
+        + (bits[i + 5] << 0)
+        for i in range(0, len(bits), 6)
+    ]
+
+    for d in data:
+        yield str.encode(chr(d + 63))
+    yield b"\n"
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_sparse6_bytes(string):
+    """Read an undirected graph in sparse6 format from string.
+
+    Parameters
+    ----------
+    string : string
+       Data in sparse6 format
+
+    Returns
+    -------
+    G : Graph
+
+    Raises
+    ------
+    NetworkXError
+        If the string is unable to be parsed in sparse6 format
+
+    Examples
+    --------
+    >>> G = nx.from_sparse6_bytes(b":A_")
+    >>> sorted(G.edges())
+    [(0, 1), (0, 1), (0, 1)]
+
+    See Also
+    --------
+    read_sparse6, write_sparse6
+
+    References
+    ----------
+    .. [1] Sparse6 specification
+           <https://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    if string.startswith(b">>sparse6<<"):
+        string = string[11:]
+    if not string.startswith(b":"):
+        raise NetworkXError("Expected leading colon in sparse6")
+
+    chars = [c - 63 for c in string[1:]]
+    n, data = data_to_n(chars)
+    k = 1
+    while 1 << k < n:
+        k += 1
+
+    def parseData():
+        """Returns stream of pairs b[i], x[i] for sparse6 format."""
+        chunks = iter(data)
+        d = None  # partial data word
+        dLen = 0  # how many unparsed bits are left in d
+
+        while 1:
+            if dLen < 1:
+                try:
+                    d = next(chunks)
+                except StopIteration:
+                    return
+                dLen = 6
+            dLen -= 1
+            b = (d >> dLen) & 1  # grab top remaining bit
+
+            x = d & ((1 << dLen) - 1)  # partially built up value of x
+            xLen = dLen  # how many bits included so far in x
+            while xLen < k:  # now grab full chunks until we have enough
+                try:
+                    d = next(chunks)
+                except StopIteration:
+                    return
+                dLen = 6
+                x = (x << 6) + d
+                xLen += 6
+            x = x >> (xLen - k)  # shift back the extra bits
+            dLen = xLen - k
+            yield b, x
+
+    v = 0
+
+    G = nx.MultiGraph()
+    G.add_nodes_from(range(n))
+
+    multigraph = False
+    for b, x in parseData():
+        if b == 1:
+            v += 1
+        # padding with ones can cause overlarge number here
+        if x >= n or v >= n:
+            break
+        elif x > v:
+            v = x
+        else:
+            if G.has_edge(x, v):
+                multigraph = True
+            G.add_edge(x, v)
+    if not multigraph:
+        G = nx.Graph(G)
+    return G
+
+
+def to_sparse6_bytes(G, nodes=None, header=True):
+    """Convert an undirected graph to bytes in sparse6 format.
+
+    Parameters
+    ----------
+    G : Graph (undirected)
+
+    nodes: list or iterable
+       Nodes are labeled 0...n-1 in the order provided.  If None the ordering
+       given by ``G.nodes()`` is used.
+
+    header: bool
+       If True add '>>sparse6<<' bytes to head of data.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the graph is directed.
+
+    ValueError
+        If the graph has at least ``2 ** 36`` nodes; the sparse6 format
+        is only defined for graphs of order less than ``2 ** 36``.
+
+    Examples
+    --------
+    >>> nx.to_sparse6_bytes(nx.path_graph(2))
+    b'>>sparse6<<:An\\n'
+
+    See Also
+    --------
+    to_sparse6_bytes, read_sparse6, write_sparse6_bytes
+
+    Notes
+    -----
+    The returned bytes end with a newline character.
+
+    The format does not support edge or node labels.
+
+    References
+    ----------
+    .. [1] Graph6 specification
+           <https://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    if nodes is not None:
+        G = G.subgraph(nodes)
+    G = nx.convert_node_labels_to_integers(G, ordering="sorted")
+    return b"".join(_generate_sparse6_bytes(G, nodes, header))
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_sparse6(path):
+    """Read an undirected graph in sparse6 format from path.
+
+    Parameters
+    ----------
+    path : file or string
+       File or filename to write.
+
+    Returns
+    -------
+    G : Graph/Multigraph or list of Graphs/MultiGraphs
+       If the file contains multiple lines then a list of graphs is returned
+
+    Raises
+    ------
+    NetworkXError
+        If the string is unable to be parsed in sparse6 format
+
+    Examples
+    --------
+    You can read a sparse6 file by giving the path to the file::
+
+        >>> import tempfile
+        >>> with tempfile.NamedTemporaryFile(delete=False) as f:
+        ...     _ = f.write(b">>sparse6<<:An\\n")
+        ...     _ = f.seek(0)
+        ...     G = nx.read_sparse6(f.name)
+        >>> list(G.edges())
+        [(0, 1)]
+
+    You can also read a sparse6 file by giving an open file-like object::
+
+        >>> import tempfile
+        >>> with tempfile.NamedTemporaryFile() as f:
+        ...     _ = f.write(b">>sparse6<<:An\\n")
+        ...     _ = f.seek(0)
+        ...     G = nx.read_sparse6(f)
+        >>> list(G.edges())
+        [(0, 1)]
+
+    See Also
+    --------
+    read_sparse6, from_sparse6_bytes
+
+    References
+    ----------
+    .. [1] Sparse6 specification
+           <https://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    glist = []
+    for line in path:
+        line = line.strip()
+        if not len(line):
+            continue
+        glist.append(from_sparse6_bytes(line))
+    if len(glist) == 1:
+        return glist[0]
+    else:
+        return glist
+
+
+@not_implemented_for("directed")
+@open_file(1, mode="wb")
+def write_sparse6(G, path, nodes=None, header=True):
+    """Write graph G to given path in sparse6 format.
+
+    Parameters
+    ----------
+    G : Graph (undirected)
+
+    path : file or string
+       File or filename to write
+
+    nodes: list or iterable
+       Nodes are labeled 0...n-1 in the order provided.  If None the ordering
+       given by G.nodes() is used.
+
+    header: bool
+       If True add '>>sparse6<<' string to head of data
+
+    Raises
+    ------
+    NetworkXError
+        If the graph is directed
+
+    Examples
+    --------
+    You can write a sparse6 file by giving the path to the file::
+
+        >>> import tempfile
+        >>> with tempfile.NamedTemporaryFile(delete=False) as f:
+        ...     nx.write_sparse6(nx.path_graph(2), f.name)
+        ...     print(f.read())
+        b'>>sparse6<<:An\\n'
+
+    You can also write a sparse6 file by giving an open file-like object::
+
+        >>> with tempfile.NamedTemporaryFile() as f:
+        ...     nx.write_sparse6(nx.path_graph(2), f)
+        ...     _ = f.seek(0)
+        ...     print(f.read())
+        b'>>sparse6<<:An\\n'
+
+    See Also
+    --------
+    read_sparse6, from_sparse6_bytes
+
+    Notes
+    -----
+    The format does not support edge or node labels.
+
+    References
+    ----------
+    .. [1] Sparse6 specification
+           <https://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    if nodes is not None:
+        G = G.subgraph(nodes)
+    G = nx.convert_node_labels_to_integers(G, ordering="sorted")
+    for b in _generate_sparse6_bytes(G, nodes, header):
+        path.write(b)