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valley/lib/python3.10/site-packages/networkx/algorithms/tests/test_chains.py

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+"""Unit tests for the chain decomposition functions."""
+from itertools import cycle, islice
+
+import pytest
+
+import networkx as nx
+
+
+def cycles(seq):
+    """Yields cyclic permutations of the given sequence.
+
+    For example::
+
+        >>> list(cycles("abc"))
+        [('a', 'b', 'c'), ('b', 'c', 'a'), ('c', 'a', 'b')]
+
+    """
+    n = len(seq)
+    cycled_seq = cycle(seq)
+    for x in seq:
+        yield tuple(islice(cycled_seq, n))
+        next(cycled_seq)
+
+
+def cyclic_equals(seq1, seq2):
+    """Decide whether two sequences are equal up to cyclic permutations.
+
+    For example::
+
+        >>> cyclic_equals("xyz", "zxy")
+        True
+        >>> cyclic_equals("xyz", "zyx")
+        False
+
+    """
+    # Cast seq2 to a tuple since `cycles()` yields tuples.
+    seq2 = tuple(seq2)
+    return any(x == tuple(seq2) for x in cycles(seq1))
+
+
+class TestChainDecomposition:
+    """Unit tests for the chain decomposition function."""
+
+    def assertContainsChain(self, chain, expected):
+        # A cycle could be expressed in two different orientations, one
+        # forward and one backward, so we need to check for cyclic
+        # equality in both orientations.
+        reversed_chain = list(reversed([tuple(reversed(e)) for e in chain]))
+        for candidate in expected:
+            if cyclic_equals(chain, candidate):
+                break
+            if cyclic_equals(reversed_chain, candidate):
+                break
+        else:
+            self.fail("chain not found")
+
+    def test_decomposition(self):
+        edges = [
+            # DFS tree edges.
+            (1, 2),
+            (2, 3),
+            (3, 4),
+            (3, 5),
+            (5, 6),
+            (6, 7),
+            (7, 8),
+            (5, 9),
+            (9, 10),
+            # Nontree edges.
+            (1, 3),
+            (1, 4),
+            (2, 5),
+            (5, 10),
+            (6, 8),
+        ]
+        G = nx.Graph(edges)
+        expected = [
+            [(1, 3), (3, 2), (2, 1)],
+            [(1, 4), (4, 3)],
+            [(2, 5), (5, 3)],
+            [(5, 10), (10, 9), (9, 5)],
+            [(6, 8), (8, 7), (7, 6)],
+        ]
+        chains = list(nx.chain_decomposition(G, root=1))
+        assert len(chains) == len(expected)
+
+    # This chain decomposition isn't unique
+    #        for chain in chains:
+    #            print(chain)
+    #            self.assertContainsChain(chain, expected)
+
+    def test_barbell_graph(self):
+        # The (3, 0) barbell graph has two triangles joined by a single edge.
+        G = nx.barbell_graph(3, 0)
+        chains = list(nx.chain_decomposition(G, root=0))
+        expected = [[(0, 1), (1, 2), (2, 0)], [(3, 4), (4, 5), (5, 3)]]
+        assert len(chains) == len(expected)
+        for chain in chains:
+            self.assertContainsChain(chain, expected)
+
+    def test_disconnected_graph(self):
+        """Test for a graph with multiple connected components."""
+        G = nx.barbell_graph(3, 0)
+        H = nx.barbell_graph(3, 0)
+        mapping = dict(zip(range(6), "abcdef"))
+        nx.relabel_nodes(H, mapping, copy=False)
+        G = nx.union(G, H)
+        chains = list(nx.chain_decomposition(G))
+        expected = [
+            [(0, 1), (1, 2), (2, 0)],
+            [(3, 4), (4, 5), (5, 3)],
+            [("a", "b"), ("b", "c"), ("c", "a")],
+            [("d", "e"), ("e", "f"), ("f", "d")],
+        ]
+        assert len(chains) == len(expected)
+        for chain in chains:
+            self.assertContainsChain(chain, expected)
+
+    def test_disconnected_graph_root_node(self):
+        """Test for a single component of a disconnected graph."""
+        G = nx.barbell_graph(3, 0)
+        H = nx.barbell_graph(3, 0)
+        mapping = dict(zip(range(6), "abcdef"))
+        nx.relabel_nodes(H, mapping, copy=False)
+        G = nx.union(G, H)
+        chains = list(nx.chain_decomposition(G, root="a"))
+        expected = [
+            [("a", "b"), ("b", "c"), ("c", "a")],
+            [("d", "e"), ("e", "f"), ("f", "d")],
+        ]
+        assert len(chains) == len(expected)
+        for chain in chains:
+            self.assertContainsChain(chain, expected)
+
+    def test_chain_decomposition_root_not_in_G(self):
+        """Test chain decomposition when root is not in graph"""
+        G = nx.Graph()
+        G.add_nodes_from([1, 2, 3])
+        with pytest.raises(nx.NodeNotFound):
+            nx.has_bridges(G, root=6)

valley/lib/python3.10/site-packages/networkx/algorithms/tests/test_core.py

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+import pytest
+
+import networkx as nx
+from networkx.utils import nodes_equal
+
+
+class TestCore:
+    @classmethod
+    def setup_class(cls):
+        # G is the example graph in Figure 1 from Batagelj and
+        # Zaversnik's paper titled An O(m) Algorithm for Cores
+        # Decomposition of Networks, 2003,
+        # http://arXiv.org/abs/cs/0310049.  With nodes labeled as
+        # shown, the 3-core is given by nodes 1-8, the 2-core by nodes
+        # 9-16, the 1-core by nodes 17-20 and node 21 is in the
+        # 0-core.
+        t1 = nx.convert_node_labels_to_integers(nx.tetrahedral_graph(), 1)
+        t2 = nx.convert_node_labels_to_integers(t1, 5)
+        G = nx.union(t1, t2)
+        G.add_edges_from(
+            [
+                (3, 7),
+                (2, 11),
+                (11, 5),
+                (11, 12),
+                (5, 12),
+                (12, 19),
+                (12, 18),
+                (3, 9),
+                (7, 9),
+                (7, 10),
+                (9, 10),
+                (9, 20),
+                (17, 13),
+                (13, 14),
+                (14, 15),
+                (15, 16),
+                (16, 13),
+            ]
+        )
+        G.add_node(21)
+        cls.G = G
+
+        # Create the graph H resulting from the degree sequence
+        # [0, 1, 2, 2, 2, 2, 3] when using the Havel-Hakimi algorithm.
+
+        degseq = [0, 1, 2, 2, 2, 2, 3]
+        H = nx.havel_hakimi_graph(degseq)
+        mapping = {6: 0, 0: 1, 4: 3, 5: 6, 3: 4, 1: 2, 2: 5}
+        cls.H = nx.relabel_nodes(H, mapping)
+
+    def test_trivial(self):
+        """Empty graph"""
+        G = nx.Graph()
+        assert nx.core_number(G) == {}
+
+    def test_core_number(self):
+        core = nx.core_number(self.G)
+        nodes_by_core = [sorted(n for n in core if core[n] == val) for val in range(4)]
+        assert nodes_equal(nodes_by_core[0], [21])
+        assert nodes_equal(nodes_by_core[1], [17, 18, 19, 20])
+        assert nodes_equal(nodes_by_core[2], [9, 10, 11, 12, 13, 14, 15, 16])
+        assert nodes_equal(nodes_by_core[3], [1, 2, 3, 4, 5, 6, 7, 8])
+
+    def test_core_number2(self):
+        core = nx.core_number(self.H)
+        nodes_by_core = [sorted(n for n in core if core[n] == val) for val in range(3)]
+        assert nodes_equal(nodes_by_core[0], [0])
+        assert nodes_equal(nodes_by_core[1], [1, 3])
+        assert nodes_equal(nodes_by_core[2], [2, 4, 5, 6])
+
+    def test_core_number_multigraph(self):
+        G = nx.complete_graph(3)
+        G = nx.MultiGraph(G)
+        G.add_edge(1, 2)
+        with pytest.raises(
+            nx.NetworkXNotImplemented, match="not implemented for multigraph type"
+        ):
+            nx.core_number(G)
+
+    def test_core_number_self_loop(self):
+        G = nx.cycle_graph(3)
+        G.add_edge(0, 0)
+        with pytest.raises(
+            nx.NetworkXNotImplemented, match="Input graph has self loops"
+        ):
+            nx.core_number(G)
+
+    def test_directed_core_number(self):
+        """core number had a bug for directed graphs found in issue #1959"""
+        # small example where too timid edge removal can make cn[2] = 3
+        G = nx.DiGraph()
+        edges = [(1, 2), (2, 1), (2, 3), (2, 4), (3, 4), (4, 3)]
+        G.add_edges_from(edges)
+        assert nx.core_number(G) == {1: 2, 2: 2, 3: 2, 4: 2}
+        # small example where too aggressive edge removal can make cn[2] = 2
+        more_edges = [(1, 5), (3, 5), (4, 5), (3, 6), (4, 6), (5, 6)]
+        G.add_edges_from(more_edges)
+        assert nx.core_number(G) == {1: 3, 2: 3, 3: 3, 4: 3, 5: 3, 6: 3}
+
+    def test_main_core(self):
+        main_core_subgraph = nx.k_core(self.H)
+        assert sorted(main_core_subgraph.nodes()) == [2, 4, 5, 6]
+
+    def test_k_core(self):
+        # k=0
+        k_core_subgraph = nx.k_core(self.H, k=0)
+        assert sorted(k_core_subgraph.nodes()) == sorted(self.H.nodes())
+        # k=1
+        k_core_subgraph = nx.k_core(self.H, k=1)
+        assert sorted(k_core_subgraph.nodes()) == [1, 2, 3, 4, 5, 6]
+        # k = 2
+        k_core_subgraph = nx.k_core(self.H, k=2)
+        assert sorted(k_core_subgraph.nodes()) == [2, 4, 5, 6]
+
+    def test_k_core_multigraph(self):
+        core_number = nx.core_number(self.H)
+        H = nx.MultiGraph(self.H)
+        with pytest.deprecated_call():
+            nx.k_core(H, k=0, core_number=core_number)
+
+    def test_main_crust(self):
+        main_crust_subgraph = nx.k_crust(self.H)
+        assert sorted(main_crust_subgraph.nodes()) == [0, 1, 3]
+
+    def test_k_crust(self):
+        # k = 0
+        k_crust_subgraph = nx.k_crust(self.H, k=2)
+        assert sorted(k_crust_subgraph.nodes()) == sorted(self.H.nodes())
+        # k=1
+        k_crust_subgraph = nx.k_crust(self.H, k=1)
+        assert sorted(k_crust_subgraph.nodes()) == [0, 1, 3]
+        # k=2
+        k_crust_subgraph = nx.k_crust(self.H, k=0)
+        assert sorted(k_crust_subgraph.nodes()) == [0]
+
+    def test_k_crust_multigraph(self):
+        core_number = nx.core_number(self.H)
+        H = nx.MultiGraph(self.H)
+        with pytest.deprecated_call():
+            nx.k_crust(H, k=0, core_number=core_number)
+
+    def test_main_shell(self):
+        main_shell_subgraph = nx.k_shell(self.H)
+        assert sorted(main_shell_subgraph.nodes()) == [2, 4, 5, 6]
+
+    def test_k_shell(self):
+        # k=0
+        k_shell_subgraph = nx.k_shell(self.H, k=2)
+        assert sorted(k_shell_subgraph.nodes()) == [2, 4, 5, 6]
+        # k=1
+        k_shell_subgraph = nx.k_shell(self.H, k=1)
+        assert sorted(k_shell_subgraph.nodes()) == [1, 3]
+        # k=2
+        k_shell_subgraph = nx.k_shell(self.H, k=0)
+        assert sorted(k_shell_subgraph.nodes()) == [0]
+
+    def test_k_shell_multigraph(self):
+        core_number = nx.core_number(self.H)
+        H = nx.MultiGraph(self.H)
+        with pytest.deprecated_call():
+            nx.k_shell(H, k=0, core_number=core_number)
+
+    def test_k_corona(self):
+        # k=0
+        k_corona_subgraph = nx.k_corona(self.H, k=2)
+        assert sorted(k_corona_subgraph.nodes()) == [2, 4, 5, 6]
+        # k=1
+        k_corona_subgraph = nx.k_corona(self.H, k=1)
+        assert sorted(k_corona_subgraph.nodes()) == [1]
+        # k=2
+        k_corona_subgraph = nx.k_corona(self.H, k=0)
+        assert sorted(k_corona_subgraph.nodes()) == [0]
+
+    def test_k_corona_multigraph(self):
+        core_number = nx.core_number(self.H)
+        H = nx.MultiGraph(self.H)
+        with pytest.deprecated_call():
+            nx.k_corona(H, k=0, core_number=core_number)
+
+    def test_k_truss(self):
+        # k=-1
+        k_truss_subgraph = nx.k_truss(self.G, -1)
+        assert sorted(k_truss_subgraph.nodes()) == list(range(1, 21))
+        # k=0
+        k_truss_subgraph = nx.k_truss(self.G, 0)
+        assert sorted(k_truss_subgraph.nodes()) == list(range(1, 21))
+        # k=1
+        k_truss_subgraph = nx.k_truss(self.G, 1)
+        assert sorted(k_truss_subgraph.nodes()) == list(range(1, 21))
+        # k=2
+        k_truss_subgraph = nx.k_truss(self.G, 2)
+        assert sorted(k_truss_subgraph.nodes()) == list(range(1, 21))
+        # k=3
+        k_truss_subgraph = nx.k_truss(self.G, 3)
+        assert sorted(k_truss_subgraph.nodes()) == list(range(1, 13))
+
+        k_truss_subgraph = nx.k_truss(self.G, 4)
+        assert sorted(k_truss_subgraph.nodes()) == list(range(1, 9))
+
+        k_truss_subgraph = nx.k_truss(self.G, 5)
+        assert sorted(k_truss_subgraph.nodes()) == []
+
+    def test_k_truss_digraph(self):
+        G = nx.complete_graph(3)
+        G = nx.DiGraph(G)
+        G.add_edge(2, 1)
+        with pytest.raises(
+            nx.NetworkXNotImplemented, match="not implemented for directed type"
+        ):
+            nx.k_truss(G, k=1)
+
+    def test_k_truss_multigraph(self):
+        G = nx.complete_graph(3)
+        G = nx.MultiGraph(G)
+        G.add_edge(1, 2)
+        with pytest.raises(
+            nx.NetworkXNotImplemented, match="not implemented for multigraph type"
+        ):
+            nx.k_truss(G, k=1)
+
+    def test_k_truss_self_loop(self):
+        G = nx.cycle_graph(3)
+        G.add_edge(0, 0)
+        with pytest.raises(
+            nx.NetworkXNotImplemented, match="Input graph has self loops"
+        ):
+            nx.k_truss(G, k=1)
+
+    def test_onion_layers(self):
+        layers = nx.onion_layers(self.G)
+        nodes_by_layer = [
+            sorted(n for n in layers if layers[n] == val) for val in range(1, 7)
+        ]
+        assert nodes_equal(nodes_by_layer[0], [21])
+        assert nodes_equal(nodes_by_layer[1], [17, 18, 19, 20])
+        assert nodes_equal(nodes_by_layer[2], [10, 12, 13, 14, 15, 16])
+        assert nodes_equal(nodes_by_layer[3], [9, 11])
+        assert nodes_equal(nodes_by_layer[4], [1, 2, 4, 5, 6, 8])
+        assert nodes_equal(nodes_by_layer[5], [3, 7])
+
+    def test_onion_digraph(self):
+        G = nx.complete_graph(3)
+        G = nx.DiGraph(G)
+        G.add_edge(2, 1)
+        with pytest.raises(
+            nx.NetworkXNotImplemented, match="not implemented for directed type"
+        ):
+            nx.onion_layers(G)
+
+    def test_onion_multigraph(self):
+        G = nx.complete_graph(3)
+        G = nx.MultiGraph(G)
+        G.add_edge(1, 2)
+        with pytest.raises(
+            nx.NetworkXNotImplemented, match="not implemented for multigraph type"
+        ):
+            nx.onion_layers(G)
+
+    def test_onion_self_loop(self):
+        G = nx.cycle_graph(3)
+        G.add_edge(0, 0)
+        with pytest.raises(
+            nx.NetworkXNotImplemented, match="Input graph contains self loops"
+        ):
+            nx.onion_layers(G)

valley/lib/python3.10/site-packages/networkx/algorithms/tests/test_cycles.py

@@ -0,0 +1,974 @@
+from itertools import chain, islice, tee
+from math import inf
+from random import shuffle
+
+import pytest
+
+import networkx as nx
+from networkx.algorithms.traversal.edgedfs import FORWARD, REVERSE
+
+
+def check_independent(basis):
+    if len(basis) == 0:
+        return
+
+    np = pytest.importorskip("numpy")
+    sp = pytest.importorskip("scipy")  # Required by incidence_matrix
+
+    H = nx.Graph()
+    for b in basis:
+        nx.add_cycle(H, b)
+    inc = nx.incidence_matrix(H, oriented=True)
+    rank = np.linalg.matrix_rank(inc.toarray(), tol=None, hermitian=False)
+    assert inc.shape[1] - rank == len(basis)
+
+
+class TestCycles:
+    @classmethod
+    def setup_class(cls):
+        G = nx.Graph()
+        nx.add_cycle(G, [0, 1, 2, 3])
+        nx.add_cycle(G, [0, 3, 4, 5])
+        nx.add_cycle(G, [0, 1, 6, 7, 8])
+        G.add_edge(8, 9)
+        cls.G = G
+
+    def is_cyclic_permutation(self, a, b):
+        n = len(a)
+        if len(b) != n:
+            return False
+        l = a + a
+        return any(l[i : i + n] == b for i in range(n))
+
+    def test_cycle_basis(self):
+        G = self.G
+        cy = nx.cycle_basis(G, 0)
+        sort_cy = sorted(sorted(c) for c in cy)
+        assert sort_cy == [[0, 1, 2, 3], [0, 1, 6, 7, 8], [0, 3, 4, 5]]
+        cy = nx.cycle_basis(G, 1)
+        sort_cy = sorted(sorted(c) for c in cy)
+        assert sort_cy == [[0, 1, 2, 3], [0, 1, 6, 7, 8], [0, 3, 4, 5]]
+        cy = nx.cycle_basis(G, 9)
+        sort_cy = sorted(sorted(c) for c in cy)
+        assert sort_cy == [[0, 1, 2, 3], [0, 1, 6, 7, 8], [0, 3, 4, 5]]
+        # test disconnected graphs
+        nx.add_cycle(G, "ABC")
+        cy = nx.cycle_basis(G, 9)
+        sort_cy = sorted(sorted(c) for c in cy[:-1]) + [sorted(cy[-1])]
+        assert sort_cy == [[0, 1, 2, 3], [0, 1, 6, 7, 8], [0, 3, 4, 5], ["A", "B", "C"]]
+
+    def test_cycle_basis2(self):
+        with pytest.raises(nx.NetworkXNotImplemented):
+            G = nx.DiGraph()
+            cy = nx.cycle_basis(G, 0)
+
+    def test_cycle_basis3(self):
+        with pytest.raises(nx.NetworkXNotImplemented):
+            G = nx.MultiGraph()
+            cy = nx.cycle_basis(G, 0)
+
+    def test_cycle_basis_ordered(self):
+        # see gh-6654 replace sets with (ordered) dicts
+        G = nx.cycle_graph(5)
+        G.update(nx.cycle_graph(range(3, 8)))
+        cbG = nx.cycle_basis(G)
+
+        perm = {1: 0, 0: 1}  # switch 0 and 1
+        H = nx.relabel_nodes(G, perm)
+        cbH = [[perm.get(n, n) for n in cyc] for cyc in nx.cycle_basis(H)]
+        assert cbG == cbH
+
+    def test_cycle_basis_self_loop(self):
+        """Tests the function for graphs with self loops"""
+        G = nx.Graph()
+        nx.add_cycle(G, [0, 1, 2, 3])
+        nx.add_cycle(G, [0, 0, 6, 2])
+        cy = nx.cycle_basis(G)
+        sort_cy = sorted(sorted(c) for c in cy)
+        assert sort_cy == [[0], [0, 1, 2], [0, 2, 3], [0, 2, 6]]
+
+    def test_simple_cycles(self):
+        edges = [(0, 0), (0, 1), (0, 2), (1, 2), (2, 0), (2, 1), (2, 2)]
+        G = nx.DiGraph(edges)
+        cc = sorted(nx.simple_cycles(G))
+        ca = [[0], [0, 1, 2], [0, 2], [1, 2], [2]]
+        assert len(cc) == len(ca)
+        for c in cc:
+            assert any(self.is_cyclic_permutation(c, rc) for rc in ca)
+
+    def test_simple_cycles_singleton(self):
+        G = nx.Graph([(0, 0)])  # self-loop
+        assert list(nx.simple_cycles(G)) == [[0]]
+
+    def test_unsortable(self):
+        # this test ensures that graphs whose nodes without an intrinsic
+        # ordering do not cause issues
+        G = nx.DiGraph()
+        nx.add_cycle(G, ["a", 1])
+        c = list(nx.simple_cycles(G))
+        assert len(c) == 1
+
+    def test_simple_cycles_small(self):
+        G = nx.DiGraph()
+        nx.add_cycle(G, [1, 2, 3])
+        c = sorted(nx.simple_cycles(G))
+        assert len(c) == 1
+        assert self.is_cyclic_permutation(c[0], [1, 2, 3])
+        nx.add_cycle(G, [10, 20, 30])
+        cc = sorted(nx.simple_cycles(G))
+        assert len(cc) == 2
+        ca = [[1, 2, 3], [10, 20, 30]]
+        for c in cc:
+            assert any(self.is_cyclic_permutation(c, rc) for rc in ca)
+
+    def test_simple_cycles_empty(self):
+        G = nx.DiGraph()
+        assert list(nx.simple_cycles(G)) == []
+
+    def worst_case_graph(self, k):
+        # see figure 1 in Johnson's paper
+        # this graph has exactly 3k simple cycles
+        G = nx.DiGraph()
+        for n in range(2, k + 2):
+            G.add_edge(1, n)
+            G.add_edge(n, k + 2)
+        G.add_edge(2 * k + 1, 1)
+        for n in range(k + 2, 2 * k + 2):
+            G.add_edge(n, 2 * k + 2)
+            G.add_edge(n, n + 1)
+        G.add_edge(2 * k + 3, k + 2)
+        for n in range(2 * k + 3, 3 * k + 3):
+            G.add_edge(2 * k + 2, n)
+            G.add_edge(n, 3 * k + 3)
+        G.add_edge(3 * k + 3, 2 * k + 2)
+        return G
+
+    def test_worst_case_graph(self):
+        # see figure 1 in Johnson's paper
+        for k in range(3, 10):
+            G = self.worst_case_graph(k)
+            l = len(list(nx.simple_cycles(G)))
+            assert l == 3 * k
+
+    def test_recursive_simple_and_not(self):
+        for k in range(2, 10):
+            G = self.worst_case_graph(k)
+            cc = sorted(nx.simple_cycles(G))
+            rcc = sorted(nx.recursive_simple_cycles(G))
+            assert len(cc) == len(rcc)
+            for c in cc:
+                assert any(self.is_cyclic_permutation(c, r) for r in rcc)
+            for rc in rcc:
+                assert any(self.is_cyclic_permutation(rc, c) for c in cc)
+
+    def test_simple_graph_with_reported_bug(self):
+        G = nx.DiGraph()
+        edges = [
+            (0, 2),
+            (0, 3),
+            (1, 0),
+            (1, 3),
+            (2, 1),
+            (2, 4),
+            (3, 2),
+            (3, 4),
+            (4, 0),
+            (4, 1),
+            (4, 5),
+            (5, 0),
+            (5, 1),
+            (5, 2),
+            (5, 3),
+        ]
+        G.add_edges_from(edges)
+        cc = sorted(nx.simple_cycles(G))
+        assert len(cc) == 26
+        rcc = sorted(nx.recursive_simple_cycles(G))
+        assert len(cc) == len(rcc)
+        for c in cc:
+            assert any(self.is_cyclic_permutation(c, rc) for rc in rcc)
+        for rc in rcc:
+            assert any(self.is_cyclic_permutation(rc, c) for c in cc)
+
+
+def pairwise(iterable):
+    a, b = tee(iterable)
+    next(b, None)
+    return zip(a, b)
+
+
+def cycle_edges(c):
+    return pairwise(chain(c, islice(c, 1)))
+
+
+def directed_cycle_edgeset(c):
+    return frozenset(cycle_edges(c))
+
+
+def undirected_cycle_edgeset(c):
+    if len(c) == 1:
+        return frozenset(cycle_edges(c))
+    return frozenset(map(frozenset, cycle_edges(c)))
+
+
+def multigraph_cycle_edgeset(c):
+    if len(c) <= 2:
+        return frozenset(cycle_edges(c))
+    else:
+        return frozenset(map(frozenset, cycle_edges(c)))
+
+
+class TestCycleEnumeration:
+    @staticmethod
+    def K(n):
+        return nx.complete_graph(n)
+
+    @staticmethod
+    def D(n):
+        return nx.complete_graph(n).to_directed()
+
+    @staticmethod
+    def edgeset_function(g):
+        if g.is_directed():
+            return directed_cycle_edgeset
+        elif g.is_multigraph():
+            return multigraph_cycle_edgeset
+        else:
+            return undirected_cycle_edgeset
+
+    def check_cycle(self, g, c, es, cache, source, original_c, length_bound, chordless):
+        if length_bound is not None and len(c) > length_bound:
+            raise RuntimeError(
+                f"computed cycle {original_c} exceeds length bound {length_bound}"
+            )
+        if source == "computed":
+            if es in cache:
+                raise RuntimeError(
+                    f"computed cycle {original_c} has already been found!"
+                )
+            else:
+                cache[es] = tuple(original_c)
+        else:
+            if es in cache:
+                cache.pop(es)
+            else:
+                raise RuntimeError(f"expected cycle {original_c} was not computed")
+
+        if not all(g.has_edge(*e) for e in es):
+            raise RuntimeError(
+                f"{source} claimed cycle {original_c} is not a cycle of g"
+            )
+        if chordless and len(g.subgraph(c).edges) > len(c):
+            raise RuntimeError(f"{source} cycle {original_c} is not chordless")
+
+    def check_cycle_algorithm(
+        self,
+        g,
+        expected_cycles,
+        length_bound=None,
+        chordless=False,
+        algorithm=None,
+    ):
+        if algorithm is None:
+            algorithm = nx.chordless_cycles if chordless else nx.simple_cycles
+
+        # note: we shuffle the labels of g to rule out accidentally-correct
+        # behavior which occurred during the development of chordless cycle
+        # enumeration algorithms
+
+        relabel = list(range(len(g)))
+        shuffle(relabel)
+        label = dict(zip(g, relabel))
+        unlabel = dict(zip(relabel, g))
+        h = nx.relabel_nodes(g, label, copy=True)
+
+        edgeset = self.edgeset_function(h)
+
+        params = {}
+        if length_bound is not None:
+            params["length_bound"] = length_bound
+
+        cycle_cache = {}
+        for c in algorithm(h, **params):
+            original_c = [unlabel[x] for x in c]
+            es = edgeset(c)
+            self.check_cycle(
+                h, c, es, cycle_cache, "computed", original_c, length_bound, chordless
+            )
+
+        if isinstance(expected_cycles, int):
+            if len(cycle_cache) != expected_cycles:
+                raise RuntimeError(
+                    f"expected {expected_cycles} cycles, got {len(cycle_cache)}"
+                )
+            return
+        for original_c in expected_cycles:
+            c = [label[x] for x in original_c]
+            es = edgeset(c)
+            self.check_cycle(
+                h, c, es, cycle_cache, "expected", original_c, length_bound, chordless
+            )
+
+        if len(cycle_cache):
+            for c in cycle_cache.values():
+                raise RuntimeError(
+                    f"computed cycle {c} is valid but not in the expected cycle set!"
+                )
+
+    def check_cycle_enumeration_integer_sequence(
+        self,
+        g_family,
+        cycle_counts,
+        length_bound=None,
+        chordless=False,
+        algorithm=None,
+    ):
+        for g, num_cycles in zip(g_family, cycle_counts):
+            self.check_cycle_algorithm(
+                g,
+                num_cycles,
+                length_bound=length_bound,
+                chordless=chordless,
+                algorithm=algorithm,
+            )
+
+    def test_directed_chordless_cycle_digons(self):
+        g = nx.DiGraph()
+        nx.add_cycle(g, range(5))
+        nx.add_cycle(g, range(5)[::-1])
+        g.add_edge(0, 0)
+        expected_cycles = [(0,), (1, 2), (2, 3), (3, 4)]
+        self.check_cycle_algorithm(g, expected_cycles, chordless=True)
+
+        self.check_cycle_algorithm(g, expected_cycles, chordless=True, length_bound=2)
+
+        expected_cycles = [c for c in expected_cycles if len(c) < 2]
+        self.check_cycle_algorithm(g, expected_cycles, chordless=True, length_bound=1)
+
+    def test_directed_chordless_cycle_undirected(self):
+        g = nx.DiGraph([(1, 2), (2, 3), (3, 4), (4, 5), (5, 0), (5, 1), (0, 2)])
+        expected_cycles = [(0, 2, 3, 4, 5), (1, 2, 3, 4, 5)]
+        self.check_cycle_algorithm(g, expected_cycles, chordless=True)
+
+        g = nx.DiGraph()
+        nx.add_cycle(g, range(5))
+        nx.add_cycle(g, range(4, 9))
+        g.add_edge(7, 3)
+        expected_cycles = [(0, 1, 2, 3, 4), (3, 4, 5, 6, 7), (4, 5, 6, 7, 8)]
+        self.check_cycle_algorithm(g, expected_cycles, chordless=True)
+
+        g.add_edge(3, 7)
+        expected_cycles = [(0, 1, 2, 3, 4), (3, 7), (4, 5, 6, 7, 8)]
+        self.check_cycle_algorithm(g, expected_cycles, chordless=True)
+
+        expected_cycles = [(3, 7)]
+        self.check_cycle_algorithm(g, expected_cycles, chordless=True, length_bound=4)
+
+        g.remove_edge(7, 3)
+        expected_cycles = [(0, 1, 2, 3, 4), (4, 5, 6, 7, 8)]
+        self.check_cycle_algorithm(g, expected_cycles, chordless=True)
+
+        g = nx.DiGraph((i, j) for i in range(10) for j in range(i))
+        expected_cycles = []
+        self.check_cycle_algorithm(g, expected_cycles, chordless=True)
+
+    def test_chordless_cycles_directed(self):
+        G = nx.DiGraph()
+        nx.add_cycle(G, range(5))
+        nx.add_cycle(G, range(4, 12))
+        expected = [[*range(5)], [*range(4, 12)]]
+        self.check_cycle_algorithm(G, expected, chordless=True)
+        self.check_cycle_algorithm(
+            G, [c for c in expected if len(c) <= 5], length_bound=5, chordless=True
+        )
+
+        G.add_edge(7, 3)
+        expected.append([*range(3, 8)])
+        self.check_cycle_algorithm(G, expected, chordless=True)
+        self.check_cycle_algorithm(
+            G, [c for c in expected if len(c) <= 5], length_bound=5, chordless=True
+        )
+
+        G.add_edge(3, 7)
+        expected[-1] = [7, 3]
+        self.check_cycle_algorithm(G, expected, chordless=True)
+        self.check_cycle_algorithm(
+            G, [c for c in expected if len(c) <= 5], length_bound=5, chordless=True
+        )
+
+        expected.pop()
+        G.remove_edge(7, 3)
+        self.check_cycle_algorithm(G, expected, chordless=True)
+        self.check_cycle_algorithm(
+            G, [c for c in expected if len(c) <= 5], length_bound=5, chordless=True
+        )
+
+    def test_directed_chordless_cycle_diclique(self):
+        g_family = [self.D(n) for n in range(10)]
+        expected_cycles = [(n * n - n) // 2 for n in range(10)]
+        self.check_cycle_enumeration_integer_sequence(
+            g_family, expected_cycles, chordless=True
+        )
+
+        expected_cycles = [(n * n - n) // 2 for n in range(10)]
+        self.check_cycle_enumeration_integer_sequence(
+            g_family, expected_cycles, length_bound=2
+        )
+
+    def test_directed_chordless_loop_blockade(self):
+        g = nx.DiGraph((i, i) for i in range(10))
+        nx.add_cycle(g, range(10))
+        expected_cycles = [(i,) for i in range(10)]
+        self.check_cycle_algorithm(g, expected_cycles, chordless=True)
+
+        self.check_cycle_algorithm(g, expected_cycles, length_bound=1)
+
+        g = nx.MultiDiGraph(g)
+        g.add_edges_from((i, i) for i in range(0, 10, 2))
+        expected_cycles = [(i,) for i in range(1, 10, 2)]
+        self.check_cycle_algorithm(g, expected_cycles, chordless=True)
+
+    def test_simple_cycles_notable_clique_sequences(self):
+        # A000292: Number of labeled graphs on n+3 nodes that are triangles.
+        g_family = [self.K(n) for n in range(2, 12)]
+        expected = [0, 1, 4, 10, 20, 35, 56, 84, 120, 165, 220]
+        self.check_cycle_enumeration_integer_sequence(
+            g_family, expected, length_bound=3
+        )
+
+        def triangles(g, **kwargs):
+            yield from (c for c in nx.simple_cycles(g, **kwargs) if len(c) == 3)
+
+        # directed complete graphs have twice as many triangles thanks to reversal
+        g_family = [self.D(n) for n in range(2, 12)]
+        expected = [2 * e for e in expected]
+        self.check_cycle_enumeration_integer_sequence(
+            g_family, expected, length_bound=3, algorithm=triangles
+        )
+
+        def four_cycles(g, **kwargs):
+            yield from (c for c in nx.simple_cycles(g, **kwargs) if len(c) == 4)
+
+        # A050534: the number of 4-cycles in the complete graph K_{n+1}
+        expected = [0, 0, 0, 3, 15, 45, 105, 210, 378, 630, 990]
+        g_family = [self.K(n) for n in range(1, 12)]
+        self.check_cycle_enumeration_integer_sequence(
+            g_family, expected, length_bound=4, algorithm=four_cycles
+        )
+
+        # directed complete graphs have twice as many 4-cycles thanks to reversal
+        expected = [2 * e for e in expected]
+        g_family = [self.D(n) for n in range(1, 15)]
+        self.check_cycle_enumeration_integer_sequence(
+            g_family, expected, length_bound=4, algorithm=four_cycles
+        )
+
+        # A006231: the number of elementary circuits in a complete directed graph with n nodes
+        expected = [0, 1, 5, 20, 84, 409, 2365]
+        g_family = [self.D(n) for n in range(1, 8)]
+        self.check_cycle_enumeration_integer_sequence(g_family, expected)
+
+        # A002807: Number of cycles in the complete graph on n nodes K_{n}.
+        expected = [0, 0, 0, 1, 7, 37, 197, 1172]
+        g_family = [self.K(n) for n in range(8)]
+        self.check_cycle_enumeration_integer_sequence(g_family, expected)
+
+    def test_directed_chordless_cycle_parallel_multiedges(self):
+        g = nx.MultiGraph()
+
+        nx.add_cycle(g, range(5))
+        expected = [[*range(5)]]
+        self.check_cycle_algorithm(g, expected, chordless=True)
+
+        nx.add_cycle(g, range(5))
+        expected = [*cycle_edges(range(5))]
+        self.check_cycle_algorithm(g, expected, chordless=True)
+
+        nx.add_cycle(g, range(5))
+        expected = []
+        self.check_cycle_algorithm(g, expected, chordless=True)
+
+        g = nx.MultiDiGraph()
+
+        nx.add_cycle(g, range(5))
+        expected = [[*range(5)]]
+        self.check_cycle_algorithm(g, expected, chordless=True)
+
+        nx.add_cycle(g, range(5))
+        self.check_cycle_algorithm(g, [], chordless=True)
+
+        nx.add_cycle(g, range(5))
+        self.check_cycle_algorithm(g, [], chordless=True)
+
+        g = nx.MultiDiGraph()
+
+        nx.add_cycle(g, range(5))
+        nx.add_cycle(g, range(5)[::-1])
+        expected = [*cycle_edges(range(5))]
+        self.check_cycle_algorithm(g, expected, chordless=True)
+
+        nx.add_cycle(g, range(5))
+        self.check_cycle_algorithm(g, [], chordless=True)
+
+    def test_chordless_cycles_graph(self):
+        G = nx.Graph()
+        nx.add_cycle(G, range(5))
+        nx.add_cycle(G, range(4, 12))
+        expected = [[*range(5)], [*range(4, 12)]]
+        self.check_cycle_algorithm(G, expected, chordless=True)
+        self.check_cycle_algorithm(
+            G, [c for c in expected if len(c) <= 5], length_bound=5, chordless=True
+        )
+
+        G.add_edge(7, 3)
+        expected.append([*range(3, 8)])
+        expected.append([4, 3, 7, 8, 9, 10, 11])
+        self.check_cycle_algorithm(G, expected, chordless=True)
+        self.check_cycle_algorithm(
+            G, [c for c in expected if len(c) <= 5], length_bound=5, chordless=True
+        )
+
+    def test_chordless_cycles_giant_hamiltonian(self):
+        # ... o - e - o - e - o ... # o = odd, e = even
+        # ... ---/ \-----/ \--- ... # <-- "long" edges
+        #
+        # each long edge belongs to exactly one triangle, and one giant cycle
+        # of length n/2.  The remaining edges each belong to a triangle
+
+        n = 1000
+        assert n % 2 == 0
+        G = nx.Graph()
+        for v in range(n):
+            if not v % 2:
+                G.add_edge(v, (v + 2) % n)
+            G.add_edge(v, (v + 1) % n)
+
+        expected = [[*range(0, n, 2)]] + [
+            [x % n for x in range(i, i + 3)] for i in range(0, n, 2)
+        ]
+        self.check_cycle_algorithm(G, expected, chordless=True)
+        self.check_cycle_algorithm(
+            G, [c for c in expected if len(c) <= 3], length_bound=3, chordless=True
+        )
+
+        # ... o -> e -> o -> e -> o ... # o = odd, e = even
+        # ... <---/ \---<---/ \---< ... # <-- "long" edges
+        #
+        # this time, we orient the short and long edges in opposition
+        # the cycle structure of this graph is the same, but we need to reverse
+        # the long one in our representation.  Also, we need to drop the size
+        # because our partitioning algorithm uses strongly connected components
+        # instead of separating graphs by their strong articulation points
+
+        n = 100
+        assert n % 2 == 0
+        G = nx.DiGraph()
+        for v in range(n):
+            G.add_edge(v, (v + 1) % n)
+            if not v % 2:
+                G.add_edge((v + 2) % n, v)
+
+        expected = [[*range(n - 2, -2, -2)]] + [
+            [x % n for x in range(i, i + 3)] for i in range(0, n, 2)
+        ]
+        self.check_cycle_algorithm(G, expected, chordless=True)
+        self.check_cycle_algorithm(
+            G, [c for c in expected if len(c) <= 3], length_bound=3, chordless=True
+        )
+
+    def test_simple_cycles_acyclic_tournament(self):
+        n = 10
+        G = nx.DiGraph((x, y) for x in range(n) for y in range(x))
+        self.check_cycle_algorithm(G, [])
+        self.check_cycle_algorithm(G, [], chordless=True)
+
+        for k in range(n + 1):
+            self.check_cycle_algorithm(G, [], length_bound=k)
+            self.check_cycle_algorithm(G, [], length_bound=k, chordless=True)
+
+    def test_simple_cycles_graph(self):
+        testG = nx.cycle_graph(8)
+        cyc1 = tuple(range(8))
+        self.check_cycle_algorithm(testG, [cyc1])
+
+        testG.add_edge(4, -1)
+        nx.add_path(testG, [3, -2, -3, -4])
+        self.check_cycle_algorithm(testG, [cyc1])
+
+        testG.update(nx.cycle_graph(range(8, 16)))
+        cyc2 = tuple(range(8, 16))
+        self.check_cycle_algorithm(testG, [cyc1, cyc2])
+
+        testG.update(nx.cycle_graph(range(4, 12)))
+        cyc3 = tuple(range(4, 12))
+        expected = {
+            (0, 1, 2, 3, 4, 5, 6, 7),  # cyc1
+            (8, 9, 10, 11, 12, 13, 14, 15),  # cyc2
+            (4, 5, 6, 7, 8, 9, 10, 11),  # cyc3
+            (4, 5, 6, 7, 8, 15, 14, 13, 12, 11),  # cyc2 + cyc3
+            (0, 1, 2, 3, 4, 11, 10, 9, 8, 7),  # cyc1 + cyc3
+            (0, 1, 2, 3, 4, 11, 12, 13, 14, 15, 8, 7),  # cyc1 + cyc2 + cyc3
+        }
+        self.check_cycle_algorithm(testG, expected)
+        assert len(expected) == (2**3 - 1) - 1  # 1 disjoint comb: cyc1 + cyc2
+
+        # Basis size = 5 (2 loops overlapping gives 5 small loops
+        #        E
+        #       / \         Note: A-F = 10-15
+        #    1-2-3-4-5
+        #    / |   |  \   cyc1=012DAB -- left
+        #   0  D   F  6   cyc2=234E   -- top
+        #   \  |   |  /   cyc3=45678F -- right
+        #    B-A-9-8-7    cyc4=89AC   -- bottom
+        #       \ /       cyc5=234F89AD -- middle
+        #        C
+        #
+        # combinations of 5 basis elements: 2^5 - 1  (one includes no cycles)
+        #
+        # disjoint combs: (11 total) not simple cycles
+        #   Any pair not including cyc5 => choose(4, 2) = 6
+        #   Any triple not including cyc5 => choose(4, 3) = 4
+        #   Any quad not including cyc5 => choose(4, 4) = 1
+        #
+        # we expect 31 - 11 = 20 simple cycles
+        #
+        testG = nx.cycle_graph(12)
+        testG.update(nx.cycle_graph([12, 10, 13, 2, 14, 4, 15, 8]).edges)
+        expected = (2**5 - 1) - 11  # 11 disjoint combinations
+        self.check_cycle_algorithm(testG, expected)
+
+    def test_simple_cycles_bounded(self):
+        # iteratively construct a cluster of nested cycles running in the same direction
+        # there should be one cycle of every length
+        d = nx.DiGraph()
+        expected = []
+        for n in range(10):
+            nx.add_cycle(d, range(n))
+            expected.append(n)
+            for k, e in enumerate(expected):
+                self.check_cycle_algorithm(d, e, length_bound=k)
+
+        # iteratively construct a path of undirected cycles, connected at articulation
+        # points.  there should be one cycle of every length except 2: no digons
+        g = nx.Graph()
+        top = 0
+        expected = []
+        for n in range(10):
+            expected.append(n if n < 2 else n - 1)
+            if n == 2:
+                # no digons in undirected graphs
+                continue
+            nx.add_cycle(g, range(top, top + n))
+            top += n
+            for k, e in enumerate(expected):
+                self.check_cycle_algorithm(g, e, length_bound=k)
+
+    def test_simple_cycles_bound_corner_cases(self):
+        G = nx.cycle_graph(4)
+        DG = nx.cycle_graph(4, create_using=nx.DiGraph)
+        assert list(nx.simple_cycles(G, length_bound=0)) == []
+        assert list(nx.simple_cycles(DG, length_bound=0)) == []
+        assert list(nx.chordless_cycles(G, length_bound=0)) == []
+        assert list(nx.chordless_cycles(DG, length_bound=0)) == []
+
+    def test_simple_cycles_bound_error(self):
+        with pytest.raises(ValueError):
+            G = nx.DiGraph()
+            for c in nx.simple_cycles(G, -1):
+                assert False
+
+        with pytest.raises(ValueError):
+            G = nx.Graph()
+            for c in nx.simple_cycles(G, -1):
+                assert False
+
+        with pytest.raises(ValueError):
+            G = nx.Graph()
+            for c in nx.chordless_cycles(G, -1):
+                assert False
+
+        with pytest.raises(ValueError):
+            G = nx.DiGraph()
+            for c in nx.chordless_cycles(G, -1):
+                assert False
+
+    def test_chordless_cycles_clique(self):
+        g_family = [self.K(n) for n in range(2, 15)]
+        expected = [0, 1, 4, 10, 20, 35, 56, 84, 120, 165, 220, 286, 364]
+        self.check_cycle_enumeration_integer_sequence(
+            g_family, expected, chordless=True
+        )
+
+        # directed cliques have as many digons as undirected graphs have edges
+        expected = [(n * n - n) // 2 for n in range(15)]
+        g_family = [self.D(n) for n in range(15)]
+        self.check_cycle_enumeration_integer_sequence(
+            g_family, expected, chordless=True
+        )
+
+
+# These tests might fail with hash randomization since they depend on
+# edge_dfs. For more information, see the comments in:
+#    networkx/algorithms/traversal/tests/test_edgedfs.py
+
+
+class TestFindCycle:
+    @classmethod
+    def setup_class(cls):
+        cls.nodes = [0, 1, 2, 3]
+        cls.edges = [(-1, 0), (0, 1), (1, 0), (1, 0), (2, 1), (3, 1)]
+
+    def test_graph_nocycle(self):
+        G = nx.Graph(self.edges)
+        pytest.raises(nx.exception.NetworkXNoCycle, nx.find_cycle, G, self.nodes)
+
+    def test_graph_cycle(self):
+        G = nx.Graph(self.edges)
+        G.add_edge(2, 0)
+        x = list(nx.find_cycle(G, self.nodes))
+        x_ = [(0, 1), (1, 2), (2, 0)]
+        assert x == x_
+
+    def test_graph_orientation_none(self):
+        G = nx.Graph(self.edges)
+        G.add_edge(2, 0)
+        x = list(nx.find_cycle(G, self.nodes, orientation=None))
+        x_ = [(0, 1), (1, 2), (2, 0)]
+        assert x == x_
+
+    def test_graph_orientation_original(self):
+        G = nx.Graph(self.edges)
+        G.add_edge(2, 0)
+        x = list(nx.find_cycle(G, self.nodes, orientation="original"))
+        x_ = [(0, 1, FORWARD), (1, 2, FORWARD), (2, 0, FORWARD)]
+        assert x == x_
+
+    def test_digraph(self):
+        G = nx.DiGraph(self.edges)
+        x = list(nx.find_cycle(G, self.nodes))
+        x_ = [(0, 1), (1, 0)]
+        assert x == x_
+
+    def test_digraph_orientation_none(self):
+        G = nx.DiGraph(self.edges)
+        x = list(nx.find_cycle(G, self.nodes, orientation=None))
+        x_ = [(0, 1), (1, 0)]
+        assert x == x_
+
+    def test_digraph_orientation_original(self):
+        G = nx.DiGraph(self.edges)
+        x = list(nx.find_cycle(G, self.nodes, orientation="original"))
+        x_ = [(0, 1, FORWARD), (1, 0, FORWARD)]
+        assert x == x_
+
+    def test_multigraph(self):
+        G = nx.MultiGraph(self.edges)
+        x = list(nx.find_cycle(G, self.nodes))
+        x_ = [(0, 1, 0), (1, 0, 1)]  # or (1, 0, 2)
+        # Hash randomization...could be any edge.
+        assert x[0] == x_[0]
+        assert x[1][:2] == x_[1][:2]
+
+    def test_multidigraph(self):
+        G = nx.MultiDiGraph(self.edges)
+        x = list(nx.find_cycle(G, self.nodes))
+        x_ = [(0, 1, 0), (1, 0, 0)]  # (1, 0, 1)
+        assert x[0] == x_[0]
+        assert x[1][:2] == x_[1][:2]
+
+    def test_digraph_ignore(self):
+        G = nx.DiGraph(self.edges)
+        x = list(nx.find_cycle(G, self.nodes, orientation="ignore"))
+        x_ = [(0, 1, FORWARD), (1, 0, FORWARD)]
+        assert x == x_
+
+    def test_digraph_reverse(self):
+        G = nx.DiGraph(self.edges)
+        x = list(nx.find_cycle(G, self.nodes, orientation="reverse"))
+        x_ = [(1, 0, REVERSE), (0, 1, REVERSE)]
+        assert x == x_
+
+    def test_multidigraph_ignore(self):
+        G = nx.MultiDiGraph(self.edges)
+        x = list(nx.find_cycle(G, self.nodes, orientation="ignore"))
+        x_ = [(0, 1, 0, FORWARD), (1, 0, 0, FORWARD)]  # or (1, 0, 1, 1)
+        assert x[0] == x_[0]
+        assert x[1][:2] == x_[1][:2]
+        assert x[1][3] == x_[1][3]
+
+    def test_multidigraph_ignore2(self):
+        # Loop traversed an edge while ignoring its orientation.
+        G = nx.MultiDiGraph([(0, 1), (1, 2), (1, 2)])
+        x = list(nx.find_cycle(G, [0, 1, 2], orientation="ignore"))
+        x_ = [(1, 2, 0, FORWARD), (1, 2, 1, REVERSE)]
+        assert x == x_
+
+    def test_multidigraph_original(self):
+        # Node 2 doesn't need to be searched again from visited from 4.
+        # The goal here is to cover the case when 2 to be researched from 4,
+        # when 4 is visited from the first time (so we must make sure that 4
+        # is not visited from 2, and hence, we respect the edge orientation).
+        G = nx.MultiDiGraph([(0, 1), (1, 2), (2, 3), (4, 2)])
+        pytest.raises(
+            nx.exception.NetworkXNoCycle,
+            nx.find_cycle,
+            G,
+            [0, 1, 2, 3, 4],
+            orientation="original",
+        )
+
+    def test_dag(self):
+        G = nx.DiGraph([(0, 1), (0, 2), (1, 2)])
+        pytest.raises(
+            nx.exception.NetworkXNoCycle, nx.find_cycle, G, orientation="original"
+        )
+        x = list(nx.find_cycle(G, orientation="ignore"))
+        assert x == [(0, 1, FORWARD), (1, 2, FORWARD), (0, 2, REVERSE)]
+
+    def test_prev_explored(self):
+        # https://github.com/networkx/networkx/issues/2323
+
+        G = nx.DiGraph()
+        G.add_edges_from([(1, 0), (2, 0), (1, 2), (2, 1)])
+        pytest.raises(nx.NetworkXNoCycle, nx.find_cycle, G, source=0)
+        x = list(nx.find_cycle(G, 1))
+        x_ = [(1, 2), (2, 1)]
+        assert x == x_
+
+        x = list(nx.find_cycle(G, 2))
+        x_ = [(2, 1), (1, 2)]
+        assert x == x_
+
+        x = list(nx.find_cycle(G))
+        x_ = [(1, 2), (2, 1)]
+        assert x == x_
+
+    def test_no_cycle(self):
+        # https://github.com/networkx/networkx/issues/2439
+
+        G = nx.DiGraph()
+        G.add_edges_from([(1, 2), (2, 0), (3, 1), (3, 2)])
+        pytest.raises(nx.NetworkXNoCycle, nx.find_cycle, G, source=0)
+        pytest.raises(nx.NetworkXNoCycle, nx.find_cycle, G)
+
+
+def assert_basis_equal(a, b):
+    assert sorted(a) == sorted(b)
+
+
+class TestMinimumCycleBasis:
+    @classmethod
+    def setup_class(cls):
+        T = nx.Graph()
+        nx.add_cycle(T, [1, 2, 3, 4], weight=1)
+        T.add_edge(2, 4, weight=5)
+        cls.diamond_graph = T
+
+    def test_unweighted_diamond(self):
+        mcb = nx.minimum_cycle_basis(self.diamond_graph)
+        assert_basis_equal(mcb, [[2, 4, 1], [3, 4, 2]])
+
+    def test_weighted_diamond(self):
+        mcb = nx.minimum_cycle_basis(self.diamond_graph, weight="weight")
+        assert_basis_equal(mcb, [[2, 4, 1], [4, 3, 2, 1]])
+
+    def test_dimensionality(self):
+        # checks |MCB|=|E|-|V|+|NC|
+        ntrial = 10
+        for seed in range(1234, 1234 + ntrial):
+            rg = nx.erdos_renyi_graph(10, 0.3, seed=seed)
+            nnodes = rg.number_of_nodes()
+            nedges = rg.number_of_edges()
+            ncomp = nx.number_connected_components(rg)
+
+            mcb = nx.minimum_cycle_basis(rg)
+            assert len(mcb) == nedges - nnodes + ncomp
+            check_independent(mcb)
+
+    def test_complete_graph(self):
+        cg = nx.complete_graph(5)
+        mcb = nx.minimum_cycle_basis(cg)
+        assert all(len(cycle) == 3 for cycle in mcb)
+        check_independent(mcb)
+
+    def test_tree_graph(self):
+        tg = nx.balanced_tree(3, 3)
+        assert not nx.minimum_cycle_basis(tg)
+
+    def test_petersen_graph(self):
+        G = nx.petersen_graph()
+        mcb = list(nx.minimum_cycle_basis(G))
+        expected = [
+            [4, 9, 7, 5, 0],
+            [1, 2, 3, 4, 0],
+            [1, 6, 8, 5, 0],
+            [4, 3, 8, 5, 0],
+            [1, 6, 9, 4, 0],
+            [1, 2, 7, 5, 0],
+        ]
+        assert len(mcb) == len(expected)
+        assert all(c in expected for c in mcb)
+
+        # check that order of the nodes is a path
+        for c in mcb:
+            assert all(G.has_edge(u, v) for u, v in nx.utils.pairwise(c, cyclic=True))
+        # check independence of the basis
+        check_independent(mcb)
+
+    def test_gh6787_variable_weighted_complete_graph(self):
+        N = 8
+        cg = nx.complete_graph(N)
+        cg.add_weighted_edges_from([(u, v, 9) for u, v in cg.edges])
+        cg.add_weighted_edges_from([(u, v, 1) for u, v in nx.cycle_graph(N).edges])
+        mcb = nx.minimum_cycle_basis(cg, weight="weight")
+        check_independent(mcb)
+
+    def test_gh6787_and_edge_attribute_names(self):
+        G = nx.cycle_graph(4)
+        G.add_weighted_edges_from([(0, 2, 10), (1, 3, 10)], weight="dist")
+        expected = [[1, 3, 0], [3, 2, 1, 0], [1, 2, 0]]
+        mcb = list(nx.minimum_cycle_basis(G, weight="dist"))
+        assert len(mcb) == len(expected)
+        assert all(c in expected for c in mcb)
+
+        # test not using a weight with weight attributes
+        expected = [[1, 3, 0], [1, 2, 0], [3, 2, 0]]
+        mcb = list(nx.minimum_cycle_basis(G))
+        assert len(mcb) == len(expected)
+        assert all(c in expected for c in mcb)
+
+
+class TestGirth:
+    @pytest.mark.parametrize(
+        ("G", "expected"),
+        (
+            (nx.chvatal_graph(), 4),
+            (nx.tutte_graph(), 4),
+            (nx.petersen_graph(), 5),
+            (nx.heawood_graph(), 6),
+            (nx.pappus_graph(), 6),
+            (nx.random_tree(10, seed=42), inf),
+            (nx.empty_graph(10), inf),
+            (nx.Graph(chain(cycle_edges(range(5)), cycle_edges(range(6, 10)))), 4),
+            (
+                nx.Graph(
+                    [
+                        (0, 6),
+                        (0, 8),
+                        (0, 9),
+                        (1, 8),
+                        (2, 8),
+                        (2, 9),
+                        (4, 9),
+                        (5, 9),
+                        (6, 8),
+                        (6, 9),
+                        (7, 8),
+                    ]
+                ),
+                3,
+            ),
+        ),
+    )
+    def test_girth(self, G, expected):
+        assert nx.girth(G) == expected

valley/lib/python3.10/site-packages/networkx/algorithms/tests/test_distance_measures.py

@@ -0,0 +1,756 @@
+from random import Random
+
+import pytest
+
+import networkx as nx
+from networkx import convert_node_labels_to_integers as cnlti
+from networkx.algorithms.distance_measures import _extrema_bounding
+
+
+def test__extrema_bounding_invalid_compute_kwarg():
+    G = nx.path_graph(3)
+    with pytest.raises(ValueError, match="compute must be one of"):
+        _extrema_bounding(G, compute="spam")
+
+
+class TestDistance:
+    def setup_method(self):
+        G = cnlti(nx.grid_2d_graph(4, 4), first_label=1, ordering="sorted")
+        self.G = G
+
+    def test_eccentricity(self):
+        assert nx.eccentricity(self.G, 1) == 6
+        e = nx.eccentricity(self.G)
+        assert e[1] == 6
+
+        sp = dict(nx.shortest_path_length(self.G))
+        e = nx.eccentricity(self.G, sp=sp)
+        assert e[1] == 6
+
+        e = nx.eccentricity(self.G, v=1)
+        assert e == 6
+
+        # This behavior changed in version 1.8 (ticket #739)
+        e = nx.eccentricity(self.G, v=[1, 1])
+        assert e[1] == 6
+        e = nx.eccentricity(self.G, v=[1, 2])
+        assert e[1] == 6
+
+        # test against graph with one node
+        G = nx.path_graph(1)
+        e = nx.eccentricity(G)
+        assert e[0] == 0
+        e = nx.eccentricity(G, v=0)
+        assert e == 0
+        pytest.raises(nx.NetworkXError, nx.eccentricity, G, 1)
+
+        # test against empty graph
+        G = nx.empty_graph()
+        e = nx.eccentricity(G)
+        assert e == {}
+
+    def test_diameter(self):
+        assert nx.diameter(self.G) == 6
+
+    def test_radius(self):
+        assert nx.radius(self.G) == 4
+
+    def test_periphery(self):
+        assert set(nx.periphery(self.G)) == {1, 4, 13, 16}
+
+    def test_center(self):
+        assert set(nx.center(self.G)) == {6, 7, 10, 11}
+
+    def test_bound_diameter(self):
+        assert nx.diameter(self.G, usebounds=True) == 6
+
+    def test_bound_radius(self):
+        assert nx.radius(self.G, usebounds=True) == 4
+
+    def test_bound_periphery(self):
+        result = {1, 4, 13, 16}
+        assert set(nx.periphery(self.G, usebounds=True)) == result
+
+    def test_bound_center(self):
+        result = {6, 7, 10, 11}
+        assert set(nx.center(self.G, usebounds=True)) == result
+
+    def test_radius_exception(self):
+        G = nx.Graph()
+        G.add_edge(1, 2)
+        G.add_edge(3, 4)
+        pytest.raises(nx.NetworkXError, nx.diameter, G)
+
+    def test_eccentricity_infinite(self):
+        with pytest.raises(nx.NetworkXError):
+            G = nx.Graph([(1, 2), (3, 4)])
+            e = nx.eccentricity(G)
+
+    def test_eccentricity_undirected_not_connected(self):
+        with pytest.raises(nx.NetworkXError):
+            G = nx.Graph([(1, 2), (3, 4)])
+            e = nx.eccentricity(G, sp=1)
+
+    def test_eccentricity_directed_weakly_connected(self):
+        with pytest.raises(nx.NetworkXError):
+            DG = nx.DiGraph([(1, 2), (1, 3)])
+            nx.eccentricity(DG)
+
+
+class TestWeightedDistance:
+    def setup_method(self):
+        G = nx.Graph()
+        G.add_edge(0, 1, weight=0.6, cost=0.6, high_cost=6)
+        G.add_edge(0, 2, weight=0.2, cost=0.2, high_cost=2)
+        G.add_edge(2, 3, weight=0.1, cost=0.1, high_cost=1)
+        G.add_edge(2, 4, weight=0.7, cost=0.7, high_cost=7)
+        G.add_edge(2, 5, weight=0.9, cost=0.9, high_cost=9)
+        G.add_edge(1, 5, weight=0.3, cost=0.3, high_cost=3)
+        self.G = G
+        self.weight_fn = lambda v, u, e: 2
+
+    def test_eccentricity_weight_None(self):
+        assert nx.eccentricity(self.G, 1, weight=None) == 3
+        e = nx.eccentricity(self.G, weight=None)
+        assert e[1] == 3
+
+        e = nx.eccentricity(self.G, v=1, weight=None)
+        assert e == 3
+
+        # This behavior changed in version 1.8 (ticket #739)
+        e = nx.eccentricity(self.G, v=[1, 1], weight=None)
+        assert e[1] == 3
+        e = nx.eccentricity(self.G, v=[1, 2], weight=None)
+        assert e[1] == 3
+
+    def test_eccentricity_weight_attr(self):
+        assert nx.eccentricity(self.G, 1, weight="weight") == 1.5
+        e = nx.eccentricity(self.G, weight="weight")
+        assert (
+            e
+            == nx.eccentricity(self.G, weight="cost")
+            != nx.eccentricity(self.G, weight="high_cost")
+        )
+        assert e[1] == 1.5
+
+        e = nx.eccentricity(self.G, v=1, weight="weight")
+        assert e == 1.5
+
+        # This behavior changed in version 1.8 (ticket #739)
+        e = nx.eccentricity(self.G, v=[1, 1], weight="weight")
+        assert e[1] == 1.5
+        e = nx.eccentricity(self.G, v=[1, 2], weight="weight")
+        assert e[1] == 1.5
+
+    def test_eccentricity_weight_fn(self):
+        assert nx.eccentricity(self.G, 1, weight=self.weight_fn) == 6
+        e = nx.eccentricity(self.G, weight=self.weight_fn)
+        assert e[1] == 6
+
+        e = nx.eccentricity(self.G, v=1, weight=self.weight_fn)
+        assert e == 6
+
+        # This behavior changed in version 1.8 (ticket #739)
+        e = nx.eccentricity(self.G, v=[1, 1], weight=self.weight_fn)
+        assert e[1] == 6
+        e = nx.eccentricity(self.G, v=[1, 2], weight=self.weight_fn)
+        assert e[1] == 6
+
+    def test_diameter_weight_None(self):
+        assert nx.diameter(self.G, weight=None) == 3
+
+    def test_diameter_weight_attr(self):
+        assert (
+            nx.diameter(self.G, weight="weight")
+            == nx.diameter(self.G, weight="cost")
+            == 1.6
+            != nx.diameter(self.G, weight="high_cost")
+        )
+
+    def test_diameter_weight_fn(self):
+        assert nx.diameter(self.G, weight=self.weight_fn) == 6
+
+    def test_radius_weight_None(self):
+        assert pytest.approx(nx.radius(self.G, weight=None)) == 2
+
+    def test_radius_weight_attr(self):
+        assert (
+            pytest.approx(nx.radius(self.G, weight="weight"))
+            == pytest.approx(nx.radius(self.G, weight="cost"))
+            == 0.9
+            != nx.radius(self.G, weight="high_cost")
+        )
+
+    def test_radius_weight_fn(self):
+        assert nx.radius(self.G, weight=self.weight_fn) == 4
+
+    def test_periphery_weight_None(self):
+        for v in set(nx.periphery(self.G, weight=None)):
+            assert nx.eccentricity(self.G, v, weight=None) == nx.diameter(
+                self.G, weight=None
+            )
+
+    def test_periphery_weight_attr(self):
+        periphery = set(nx.periphery(self.G, weight="weight"))
+        assert (
+            periphery
+            == set(nx.periphery(self.G, weight="cost"))
+            == set(nx.periphery(self.G, weight="high_cost"))
+        )
+        for v in periphery:
+            assert (
+                nx.eccentricity(self.G, v, weight="high_cost")
+                != nx.eccentricity(self.G, v, weight="weight")
+                == nx.eccentricity(self.G, v, weight="cost")
+                == nx.diameter(self.G, weight="weight")
+                == nx.diameter(self.G, weight="cost")
+                != nx.diameter(self.G, weight="high_cost")
+            )
+            assert nx.eccentricity(self.G, v, weight="high_cost") == nx.diameter(
+                self.G, weight="high_cost"
+            )
+
+    def test_periphery_weight_fn(self):
+        for v in set(nx.periphery(self.G, weight=self.weight_fn)):
+            assert nx.eccentricity(self.G, v, weight=self.weight_fn) == nx.diameter(
+                self.G, weight=self.weight_fn
+            )
+
+    def test_center_weight_None(self):
+        for v in set(nx.center(self.G, weight=None)):
+            assert pytest.approx(nx.eccentricity(self.G, v, weight=None)) == nx.radius(
+                self.G, weight=None
+            )
+
+    def test_center_weight_attr(self):
+        center = set(nx.center(self.G, weight="weight"))
+        assert (
+            center
+            == set(nx.center(self.G, weight="cost"))
+            != set(nx.center(self.G, weight="high_cost"))
+        )
+        for v in center:
+            assert (
+                nx.eccentricity(self.G, v, weight="high_cost")
+                != pytest.approx(nx.eccentricity(self.G, v, weight="weight"))
+                == pytest.approx(nx.eccentricity(self.G, v, weight="cost"))
+                == nx.radius(self.G, weight="weight")
+                == nx.radius(self.G, weight="cost")
+                != nx.radius(self.G, weight="high_cost")
+            )
+            assert nx.eccentricity(self.G, v, weight="high_cost") == nx.radius(
+                self.G, weight="high_cost"
+            )
+
+    def test_center_weight_fn(self):
+        for v in set(nx.center(self.G, weight=self.weight_fn)):
+            assert nx.eccentricity(self.G, v, weight=self.weight_fn) == nx.radius(
+                self.G, weight=self.weight_fn
+            )
+
+    def test_bound_diameter_weight_None(self):
+        assert nx.diameter(self.G, usebounds=True, weight=None) == 3
+
+    def test_bound_diameter_weight_attr(self):
+        assert (
+            nx.diameter(self.G, usebounds=True, weight="high_cost")
+            != nx.diameter(self.G, usebounds=True, weight="weight")
+            == nx.diameter(self.G, usebounds=True, weight="cost")
+            == 1.6
+            != nx.diameter(self.G, usebounds=True, weight="high_cost")
+        )
+        assert nx.diameter(self.G, usebounds=True, weight="high_cost") == nx.diameter(
+            self.G, usebounds=True, weight="high_cost"
+        )
+
+    def test_bound_diameter_weight_fn(self):
+        assert nx.diameter(self.G, usebounds=True, weight=self.weight_fn) == 6
+
+    def test_bound_radius_weight_None(self):
+        assert pytest.approx(nx.radius(self.G, usebounds=True, weight=None)) == 2
+
+    def test_bound_radius_weight_attr(self):
+        assert (
+            nx.radius(self.G, usebounds=True, weight="high_cost")
+            != pytest.approx(nx.radius(self.G, usebounds=True, weight="weight"))
+            == pytest.approx(nx.radius(self.G, usebounds=True, weight="cost"))
+            == 0.9
+            != nx.radius(self.G, usebounds=True, weight="high_cost")
+        )
+        assert nx.radius(self.G, usebounds=True, weight="high_cost") == nx.radius(
+            self.G, usebounds=True, weight="high_cost"
+        )
+
+    def test_bound_radius_weight_fn(self):
+        assert nx.radius(self.G, usebounds=True, weight=self.weight_fn) == 4
+
+    def test_bound_periphery_weight_None(self):
+        result = {1, 3, 4}
+        assert set(nx.periphery(self.G, usebounds=True, weight=None)) == result
+
+    def test_bound_periphery_weight_attr(self):
+        result = {4, 5}
+        assert (
+            set(nx.periphery(self.G, usebounds=True, weight="weight"))
+            == set(nx.periphery(self.G, usebounds=True, weight="cost"))
+            == result
+        )
+
+    def test_bound_periphery_weight_fn(self):
+        result = {1, 3, 4}
+        assert (
+            set(nx.periphery(self.G, usebounds=True, weight=self.weight_fn)) == result
+        )
+
+    def test_bound_center_weight_None(self):
+        result = {0, 2, 5}
+        assert set(nx.center(self.G, usebounds=True, weight=None)) == result
+
+    def test_bound_center_weight_attr(self):
+        result = {0}
+        assert (
+            set(nx.center(self.G, usebounds=True, weight="weight"))
+            == set(nx.center(self.G, usebounds=True, weight="cost"))
+            == result
+        )
+
+    def test_bound_center_weight_fn(self):
+        result = {0, 2, 5}
+        assert set(nx.center(self.G, usebounds=True, weight=self.weight_fn)) == result
+
+
+class TestResistanceDistance:
+    @classmethod
+    def setup_class(cls):
+        global np
+        np = pytest.importorskip("numpy")
+        sp = pytest.importorskip("scipy")
+
+    def setup_method(self):
+        G = nx.Graph()
+        G.add_edge(1, 2, weight=2)
+        G.add_edge(2, 3, weight=4)
+        G.add_edge(3, 4, weight=1)
+        G.add_edge(1, 4, weight=3)
+        self.G = G
+
+    def test_resistance_distance_directed_graph(self):
+        G = nx.DiGraph()
+        with pytest.raises(nx.NetworkXNotImplemented):
+            nx.resistance_distance(G)
+
+    def test_resistance_distance_empty(self):
+        G = nx.Graph()
+        with pytest.raises(nx.NetworkXError):
+            nx.resistance_distance(G)
+
+    def test_resistance_distance_not_connected(self):
+        with pytest.raises(nx.NetworkXError):
+            self.G.add_node(5)
+            nx.resistance_distance(self.G, 1, 5)
+
+    def test_resistance_distance_nodeA_not_in_graph(self):
+        with pytest.raises(nx.NetworkXError):
+            nx.resistance_distance(self.G, 9, 1)
+
+    def test_resistance_distance_nodeB_not_in_graph(self):
+        with pytest.raises(nx.NetworkXError):
+            nx.resistance_distance(self.G, 1, 9)
+
+    def test_resistance_distance(self):
+        rd = nx.resistance_distance(self.G, 1, 3, "weight", True)
+        test_data = 1 / (1 / (2 + 4) + 1 / (1 + 3))
+        assert round(rd, 5) == round(test_data, 5)
+
+    def test_resistance_distance_noinv(self):
+        rd = nx.resistance_distance(self.G, 1, 3, "weight", False)
+        test_data = 1 / (1 / (1 / 2 + 1 / 4) + 1 / (1 / 1 + 1 / 3))
+        assert round(rd, 5) == round(test_data, 5)
+
+    def test_resistance_distance_no_weight(self):
+        rd = nx.resistance_distance(self.G, 1, 3)
+        assert round(rd, 5) == 1
+
+    def test_resistance_distance_neg_weight(self):
+        self.G[2][3]["weight"] = -4
+        rd = nx.resistance_distance(self.G, 1, 3, "weight", True)
+        test_data = 1 / (1 / (2 + -4) + 1 / (1 + 3))
+        assert round(rd, 5) == round(test_data, 5)
+
+    def test_multigraph(self):
+        G = nx.MultiGraph()
+        G.add_edge(1, 2, weight=2)
+        G.add_edge(2, 3, weight=4)
+        G.add_edge(3, 4, weight=1)
+        G.add_edge(1, 4, weight=3)
+        rd = nx.resistance_distance(G, 1, 3, "weight", True)
+        assert np.isclose(rd, 1 / (1 / (2 + 4) + 1 / (1 + 3)))
+
+    def test_resistance_distance_div0(self):
+        with pytest.raises(ZeroDivisionError):
+            self.G[1][2]["weight"] = 0
+            nx.resistance_distance(self.G, 1, 3, "weight")
+
+    def test_resistance_distance_same_node(self):
+        assert nx.resistance_distance(self.G, 1, 1) == 0
+
+    def test_resistance_distance_only_nodeA(self):
+        rd = nx.resistance_distance(self.G, nodeA=1)
+        test_data = {}
+        test_data[1] = 0
+        test_data[2] = 0.75
+        test_data[3] = 1
+        test_data[4] = 0.75
+        assert type(rd) == dict
+        assert sorted(rd.keys()) == sorted(test_data.keys())
+        for key in rd:
+            assert np.isclose(rd[key], test_data[key])
+
+    def test_resistance_distance_only_nodeB(self):
+        rd = nx.resistance_distance(self.G, nodeB=1)
+        test_data = {}
+        test_data[1] = 0
+        test_data[2] = 0.75
+        test_data[3] = 1
+        test_data[4] = 0.75
+        assert type(rd) == dict
+        assert sorted(rd.keys()) == sorted(test_data.keys())
+        for key in rd:
+            assert np.isclose(rd[key], test_data[key])
+
+    def test_resistance_distance_all(self):
+        rd = nx.resistance_distance(self.G)
+        assert type(rd) == dict
+        assert round(rd[1][3], 5) == 1
+
+
+class TestEffectiveGraphResistance:
+    @classmethod
+    def setup_class(cls):
+        global np
+        np = pytest.importorskip("numpy")
+        sp = pytest.importorskip("scipy")
+
+    def setup_method(self):
+        G = nx.Graph()
+        G.add_edge(1, 2, weight=2)
+        G.add_edge(1, 3, weight=1)
+        G.add_edge(2, 3, weight=4)
+        self.G = G
+
+    def test_effective_graph_resistance_directed_graph(self):
+        G = nx.DiGraph()
+        with pytest.raises(nx.NetworkXNotImplemented):
+            nx.effective_graph_resistance(G)
+
+    def test_effective_graph_resistance_empty(self):
+        G = nx.Graph()
+        with pytest.raises(nx.NetworkXError):
+            nx.effective_graph_resistance(G)
+
+    def test_effective_graph_resistance_not_connected(self):
+        G = nx.Graph([(1, 2), (3, 4)])
+        RG = nx.effective_graph_resistance(G)
+        assert np.isinf(RG)
+
+    def test_effective_graph_resistance(self):
+        RG = nx.effective_graph_resistance(self.G, "weight", True)
+        rd12 = 1 / (1 / (1 + 4) + 1 / 2)
+        rd13 = 1 / (1 / (1 + 2) + 1 / 4)
+        rd23 = 1 / (1 / (2 + 4) + 1 / 1)
+        assert np.isclose(RG, rd12 + rd13 + rd23)
+
+    def test_effective_graph_resistance_noinv(self):
+        RG = nx.effective_graph_resistance(self.G, "weight", False)
+        rd12 = 1 / (1 / (1 / 1 + 1 / 4) + 1 / (1 / 2))
+        rd13 = 1 / (1 / (1 / 1 + 1 / 2) + 1 / (1 / 4))
+        rd23 = 1 / (1 / (1 / 2 + 1 / 4) + 1 / (1 / 1))
+        assert np.isclose(RG, rd12 + rd13 + rd23)
+
+    def test_effective_graph_resistance_no_weight(self):
+        RG = nx.effective_graph_resistance(self.G)
+        assert np.isclose(RG, 2)
+
+    def test_effective_graph_resistance_neg_weight(self):
+        self.G[2][3]["weight"] = -4
+        RG = nx.effective_graph_resistance(self.G, "weight", True)
+        rd12 = 1 / (1 / (1 + -4) + 1 / 2)
+        rd13 = 1 / (1 / (1 + 2) + 1 / (-4))
+        rd23 = 1 / (1 / (2 + -4) + 1 / 1)
+        assert np.isclose(RG, rd12 + rd13 + rd23)
+
+    def test_effective_graph_resistance_multigraph(self):
+        G = nx.MultiGraph()
+        G.add_edge(1, 2, weight=2)
+        G.add_edge(1, 3, weight=1)
+        G.add_edge(2, 3, weight=1)
+        G.add_edge(2, 3, weight=3)
+        RG = nx.effective_graph_resistance(G, "weight", True)
+        edge23 = 1 / (1 / 1 + 1 / 3)
+        rd12 = 1 / (1 / (1 + edge23) + 1 / 2)
+        rd13 = 1 / (1 / (1 + 2) + 1 / edge23)
+        rd23 = 1 / (1 / (2 + edge23) + 1 / 1)
+        assert np.isclose(RG, rd12 + rd13 + rd23)
+
+    def test_effective_graph_resistance_div0(self):
+        with pytest.raises(ZeroDivisionError):
+            self.G[1][2]["weight"] = 0
+            nx.effective_graph_resistance(self.G, "weight")
+
+    def test_effective_graph_resistance_complete_graph(self):
+        N = 10
+        G = nx.complete_graph(N)
+        RG = nx.effective_graph_resistance(G)
+        assert np.isclose(RG, N - 1)
+
+    def test_effective_graph_resistance_path_graph(self):
+        N = 10
+        G = nx.path_graph(N)
+        RG = nx.effective_graph_resistance(G)
+        assert np.isclose(RG, (N - 1) * N * (N + 1) // 6)
+
+
+class TestBarycenter:
+    """Test :func:`networkx.algorithms.distance_measures.barycenter`."""
+
+    def barycenter_as_subgraph(self, g, **kwargs):
+        """Return the subgraph induced on the barycenter of g"""
+        b = nx.barycenter(g, **kwargs)
+        assert isinstance(b, list)
+        assert set(b) <= set(g)
+        return g.subgraph(b)
+
+    def test_must_be_connected(self):
+        pytest.raises(nx.NetworkXNoPath, nx.barycenter, nx.empty_graph(5))
+
+    def test_sp_kwarg(self):
+        # Complete graph K_5. Normally it works...
+        K_5 = nx.complete_graph(5)
+        sp = dict(nx.shortest_path_length(K_5))
+        assert nx.barycenter(K_5, sp=sp) == list(K_5)
+
+        # ...but not with the weight argument
+        for u, v, data in K_5.edges.data():
+            data["weight"] = 1
+        pytest.raises(ValueError, nx.barycenter, K_5, sp=sp, weight="weight")
+
+        # ...and a corrupted sp can make it seem like K_5 is disconnected
+        del sp[0][1]
+        pytest.raises(nx.NetworkXNoPath, nx.barycenter, K_5, sp=sp)
+
+    def test_trees(self):
+        """The barycenter of a tree is a single vertex or an edge.
+
+        See [West01]_, p. 78.
+        """
+        prng = Random(0xDEADBEEF)
+        for i in range(50):
+            RT = nx.random_labeled_tree(prng.randint(1, 75), seed=prng)
+            b = self.barycenter_as_subgraph(RT)
+            if len(b) == 2:
+                assert b.size() == 1
+            else:
+                assert len(b) == 1
+                assert b.size() == 0
+
+    def test_this_one_specific_tree(self):
+        """Test the tree pictured at the bottom of [West01]_, p. 78."""
+        g = nx.Graph(
+            {
+                "a": ["b"],
+                "b": ["a", "x"],
+                "x": ["b", "y"],
+                "y": ["x", "z"],
+                "z": ["y", 0, 1, 2, 3, 4],
+                0: ["z"],
+                1: ["z"],
+                2: ["z"],
+                3: ["z"],
+                4: ["z"],
+            }
+        )
+        b = self.barycenter_as_subgraph(g, attr="barycentricity")
+        assert list(b) == ["z"]
+        assert not b.edges
+        expected_barycentricity = {
+            0: 23,
+            1: 23,
+            2: 23,
+            3: 23,
+            4: 23,
+            "a": 35,
+            "b": 27,
+            "x": 21,
+            "y": 17,
+            "z": 15,
+        }
+        for node, barycentricity in expected_barycentricity.items():
+            assert g.nodes[node]["barycentricity"] == barycentricity
+
+        # Doubling weights should do nothing but double the barycentricities
+        for edge in g.edges:
+            g.edges[edge]["weight"] = 2
+        b = self.barycenter_as_subgraph(g, weight="weight", attr="barycentricity2")
+        assert list(b) == ["z"]
+        assert not b.edges
+        for node, barycentricity in expected_barycentricity.items():
+            assert g.nodes[node]["barycentricity2"] == barycentricity * 2
+
+
+class TestKemenyConstant:
+    @classmethod
+    def setup_class(cls):
+        global np
+        np = pytest.importorskip("numpy")
+        sp = pytest.importorskip("scipy")
+
+    def setup_method(self):
+        G = nx.Graph()
+        w12 = 2
+        w13 = 3
+        w23 = 4
+        G.add_edge(1, 2, weight=w12)
+        G.add_edge(1, 3, weight=w13)
+        G.add_edge(2, 3, weight=w23)
+        self.G = G
+
+    def test_kemeny_constant_directed(self):
+        G = nx.DiGraph()
+        G.add_edge(1, 2)
+        G.add_edge(1, 3)
+        G.add_edge(2, 3)
+        with pytest.raises(nx.NetworkXNotImplemented):
+            nx.kemeny_constant(G)
+
+    def test_kemeny_constant_not_connected(self):
+        self.G.add_node(5)
+        with pytest.raises(nx.NetworkXError):
+            nx.kemeny_constant(self.G)
+
+    def test_kemeny_constant_no_nodes(self):
+        G = nx.Graph()
+        with pytest.raises(nx.NetworkXError):
+            nx.kemeny_constant(G)
+
+    def test_kemeny_constant_negative_weight(self):
+        G = nx.Graph()
+        w12 = 2
+        w13 = 3
+        w23 = -10
+        G.add_edge(1, 2, weight=w12)
+        G.add_edge(1, 3, weight=w13)
+        G.add_edge(2, 3, weight=w23)
+        with pytest.raises(nx.NetworkXError):
+            nx.kemeny_constant(G, weight="weight")
+
+    def test_kemeny_constant(self):
+        K = nx.kemeny_constant(self.G, weight="weight")
+        w12 = 2
+        w13 = 3
+        w23 = 4
+        test_data = (
+            3
+            / 2
+            * (w12 + w13)
+            * (w12 + w23)
+            * (w13 + w23)
+            / (
+                w12**2 * (w13 + w23)
+                + w13**2 * (w12 + w23)
+                + w23**2 * (w12 + w13)
+                + 3 * w12 * w13 * w23
+            )
+        )
+        assert np.isclose(K, test_data)
+
+    def test_kemeny_constant_no_weight(self):
+        K = nx.kemeny_constant(self.G)
+        assert np.isclose(K, 4 / 3)
+
+    def test_kemeny_constant_multigraph(self):
+        G = nx.MultiGraph()
+        w12_1 = 2
+        w12_2 = 1
+        w13 = 3
+        w23 = 4
+        G.add_edge(1, 2, weight=w12_1)
+        G.add_edge(1, 2, weight=w12_2)
+        G.add_edge(1, 3, weight=w13)
+        G.add_edge(2, 3, weight=w23)
+        K = nx.kemeny_constant(G, weight="weight")
+        w12 = w12_1 + w12_2
+        test_data = (
+            3
+            / 2
+            * (w12 + w13)
+            * (w12 + w23)
+            * (w13 + w23)
+            / (
+                w12**2 * (w13 + w23)
+                + w13**2 * (w12 + w23)
+                + w23**2 * (w12 + w13)
+                + 3 * w12 * w13 * w23
+            )
+        )
+        assert np.isclose(K, test_data)
+
+    def test_kemeny_constant_weight0(self):
+        G = nx.Graph()
+        w12 = 0
+        w13 = 3
+        w23 = 4
+        G.add_edge(1, 2, weight=w12)
+        G.add_edge(1, 3, weight=w13)
+        G.add_edge(2, 3, weight=w23)
+        K = nx.kemeny_constant(G, weight="weight")
+        test_data = (
+            3
+            / 2
+            * (w12 + w13)
+            * (w12 + w23)
+            * (w13 + w23)
+            / (
+                w12**2 * (w13 + w23)
+                + w13**2 * (w12 + w23)
+                + w23**2 * (w12 + w13)
+                + 3 * w12 * w13 * w23
+            )
+        )
+        assert np.isclose(K, test_data)
+
+    def test_kemeny_constant_selfloop(self):
+        G = nx.Graph()
+        w11 = 1
+        w12 = 2
+        w13 = 3
+        w23 = 4
+        G.add_edge(1, 1, weight=w11)
+        G.add_edge(1, 2, weight=w12)
+        G.add_edge(1, 3, weight=w13)
+        G.add_edge(2, 3, weight=w23)
+        K = nx.kemeny_constant(G, weight="weight")
+        test_data = (
+            (2 * w11 + 3 * w12 + 3 * w13)
+            * (w12 + w23)
+            * (w13 + w23)
+            / (
+                (w12 * w13 + w12 * w23 + w13 * w23)
+                * (w11 + 2 * w12 + 2 * w13 + 2 * w23)
+            )
+        )
+        assert np.isclose(K, test_data)
+
+    def test_kemeny_constant_complete_bipartite_graph(self):
+        # Theorem 1 in https://www.sciencedirect.com/science/article/pii/S0166218X20302912
+        n1 = 5
+        n2 = 4
+        G = nx.complete_bipartite_graph(n1, n2)
+        K = nx.kemeny_constant(G)
+        assert np.isclose(K, n1 + n2 - 3 / 2)
+
+    def test_kemeny_constant_path_graph(self):
+        # Theorem 2 in https://www.sciencedirect.com/science/article/pii/S0166218X20302912
+        n = 10
+        G = nx.path_graph(n)
+        K = nx.kemeny_constant(G)
+        assert np.isclose(K, n**2 / 3 - 2 * n / 3 + 1 / 2)

valley/lib/python3.10/site-packages/networkx/algorithms/tests/test_distance_regular.py

@@ -0,0 +1,85 @@
+import pytest
+
+import networkx as nx
+from networkx import is_strongly_regular
+
+
+@pytest.mark.parametrize(
+    "f", (nx.is_distance_regular, nx.intersection_array, nx.is_strongly_regular)
+)
+@pytest.mark.parametrize("graph_constructor", (nx.DiGraph, nx.MultiGraph))
+def test_raises_on_directed_and_multigraphs(f, graph_constructor):
+    G = graph_constructor([(0, 1), (1, 2)])
+    with pytest.raises(nx.NetworkXNotImplemented):
+        f(G)
+
+
+class TestDistanceRegular:
+    def test_is_distance_regular(self):
+        assert nx.is_distance_regular(nx.icosahedral_graph())
+        assert nx.is_distance_regular(nx.petersen_graph())
+        assert nx.is_distance_regular(nx.cubical_graph())
+        assert nx.is_distance_regular(nx.complete_bipartite_graph(3, 3))
+        assert nx.is_distance_regular(nx.tetrahedral_graph())
+        assert nx.is_distance_regular(nx.dodecahedral_graph())
+        assert nx.is_distance_regular(nx.pappus_graph())
+        assert nx.is_distance_regular(nx.heawood_graph())
+        assert nx.is_distance_regular(nx.cycle_graph(3))
+        # no distance regular
+        assert not nx.is_distance_regular(nx.path_graph(4))
+
+    def test_not_connected(self):
+        G = nx.cycle_graph(4)
+        nx.add_cycle(G, [5, 6, 7])
+        assert not nx.is_distance_regular(G)
+
+    def test_global_parameters(self):
+        b, c = nx.intersection_array(nx.cycle_graph(5))
+        g = nx.global_parameters(b, c)
+        assert list(g) == [(0, 0, 2), (1, 0, 1), (1, 1, 0)]
+        b, c = nx.intersection_array(nx.cycle_graph(3))
+        g = nx.global_parameters(b, c)
+        assert list(g) == [(0, 0, 2), (1, 1, 0)]
+
+    def test_intersection_array(self):
+        b, c = nx.intersection_array(nx.cycle_graph(5))
+        assert b == [2, 1]
+        assert c == [1, 1]
+        b, c = nx.intersection_array(nx.dodecahedral_graph())
+        assert b == [3, 2, 1, 1, 1]
+        assert c == [1, 1, 1, 2, 3]
+        b, c = nx.intersection_array(nx.icosahedral_graph())
+        assert b == [5, 2, 1]
+        assert c == [1, 2, 5]
+
+
+@pytest.mark.parametrize("f", (nx.is_distance_regular, nx.is_strongly_regular))
+def test_empty_graph_raises(f):
+    G = nx.Graph()
+    with pytest.raises(nx.NetworkXPointlessConcept, match="Graph has no nodes"):
+        f(G)
+
+
+class TestStronglyRegular:
+    """Unit tests for the :func:`~networkx.is_strongly_regular`
+    function.
+
+    """
+
+    def test_cycle_graph(self):
+        """Tests that the cycle graph on five vertices is strongly
+        regular.
+
+        """
+        G = nx.cycle_graph(5)
+        assert is_strongly_regular(G)
+
+    def test_petersen_graph(self):
+        """Tests that the Petersen graph is strongly regular."""
+        G = nx.petersen_graph()
+        assert is_strongly_regular(G)
+
+    def test_path_graph(self):
+        """Tests that the path graph is not strongly regular."""
+        G = nx.path_graph(4)
+        assert not is_strongly_regular(G)

valley/lib/python3.10/site-packages/networkx/algorithms/tests/test_dominance.py

@@ -0,0 +1,285 @@
+import pytest
+
+import networkx as nx
+
+
+class TestImmediateDominators:
+    def test_exceptions(self):
+        G = nx.Graph()
+        G.add_node(0)
+        pytest.raises(nx.NetworkXNotImplemented, nx.immediate_dominators, G, 0)
+        G = nx.MultiGraph(G)
+        pytest.raises(nx.NetworkXNotImplemented, nx.immediate_dominators, G, 0)
+        G = nx.DiGraph([[0, 0]])
+        pytest.raises(nx.NetworkXError, nx.immediate_dominators, G, 1)
+
+    def test_singleton(self):
+        G = nx.DiGraph()
+        G.add_node(0)
+        assert nx.immediate_dominators(G, 0) == {0: 0}
+        G.add_edge(0, 0)
+        assert nx.immediate_dominators(G, 0) == {0: 0}
+
+    def test_path(self):
+        n = 5
+        G = nx.path_graph(n, create_using=nx.DiGraph())
+        assert nx.immediate_dominators(G, 0) == {i: max(i - 1, 0) for i in range(n)}
+
+    def test_cycle(self):
+        n = 5
+        G = nx.cycle_graph(n, create_using=nx.DiGraph())
+        assert nx.immediate_dominators(G, 0) == {i: max(i - 1, 0) for i in range(n)}
+
+    def test_unreachable(self):
+        n = 5
+        assert n > 1
+        G = nx.path_graph(n, create_using=nx.DiGraph())
+        assert nx.immediate_dominators(G, n // 2) == {
+            i: max(i - 1, n // 2) for i in range(n // 2, n)
+        }
+
+    def test_irreducible1(self):
+        # Graph taken from Figure 2 of
+        # K. D. Cooper, T. J. Harvey, and K. Kennedy.
+        # A simple, fast dominance algorithm.
+        # Software Practice & Experience, 4:110, 2001.
+        edges = [(1, 2), (2, 1), (3, 2), (4, 1), (5, 3), (5, 4)]
+        G = nx.DiGraph(edges)
+        assert nx.immediate_dominators(G, 5) == {i: 5 for i in range(1, 6)}
+
+    def test_irreducible2(self):
+        # Graph taken from Figure 4 of
+        # K. D. Cooper, T. J. Harvey, and K. Kennedy.
+        # A simple, fast dominance algorithm.
+        # Software Practice & Experience, 4:110, 2001.
+        edges = [(1, 2), (2, 1), (2, 3), (3, 2), (4, 2), (4, 3), (5, 1), (6, 4), (6, 5)]
+        G = nx.DiGraph(edges)
+        result = nx.immediate_dominators(G, 6)
+        assert result == {i: 6 for i in range(1, 7)}
+
+    def test_domrel_png(self):
+        # Graph taken from https://commons.wikipedia.org/wiki/File:Domrel.png
+        edges = [(1, 2), (2, 3), (2, 4), (2, 6), (3, 5), (4, 5), (5, 2)]
+        G = nx.DiGraph(edges)
+        result = nx.immediate_dominators(G, 1)
+        assert result == {1: 1, 2: 1, 3: 2, 4: 2, 5: 2, 6: 2}
+        # Test postdominance.
+        result = nx.immediate_dominators(G.reverse(copy=False), 6)
+        assert result == {1: 2, 2: 6, 3: 5, 4: 5, 5: 2, 6: 6}
+
+    def test_boost_example(self):
+        # Graph taken from Figure 1 of
+        # http://www.boost.org/doc/libs/1_56_0/libs/graph/doc/lengauer_tarjan_dominator.htm
+        edges = [(0, 1), (1, 2), (1, 3), (2, 7), (3, 4), (4, 5), (4, 6), (5, 7), (6, 4)]
+        G = nx.DiGraph(edges)
+        result = nx.immediate_dominators(G, 0)
+        assert result == {0: 0, 1: 0, 2: 1, 3: 1, 4: 3, 5: 4, 6: 4, 7: 1}
+        # Test postdominance.
+        result = nx.immediate_dominators(G.reverse(copy=False), 7)
+        assert result == {0: 1, 1: 7, 2: 7, 3: 4, 4: 5, 5: 7, 6: 4, 7: 7}
+
+
+class TestDominanceFrontiers:
+    def test_exceptions(self):
+        G = nx.Graph()
+        G.add_node(0)
+        pytest.raises(nx.NetworkXNotImplemented, nx.dominance_frontiers, G, 0)
+        G = nx.MultiGraph(G)
+        pytest.raises(nx.NetworkXNotImplemented, nx.dominance_frontiers, G, 0)
+        G = nx.DiGraph([[0, 0]])
+        pytest.raises(nx.NetworkXError, nx.dominance_frontiers, G, 1)
+
+    def test_singleton(self):
+        G = nx.DiGraph()
+        G.add_node(0)
+        assert nx.dominance_frontiers(G, 0) == {0: set()}
+        G.add_edge(0, 0)
+        assert nx.dominance_frontiers(G, 0) == {0: set()}
+
+    def test_path(self):
+        n = 5
+        G = nx.path_graph(n, create_using=nx.DiGraph())
+        assert nx.dominance_frontiers(G, 0) == {i: set() for i in range(n)}
+
+    def test_cycle(self):
+        n = 5
+        G = nx.cycle_graph(n, create_using=nx.DiGraph())
+        assert nx.dominance_frontiers(G, 0) == {i: set() for i in range(n)}
+
+    def test_unreachable(self):
+        n = 5
+        assert n > 1
+        G = nx.path_graph(n, create_using=nx.DiGraph())
+        assert nx.dominance_frontiers(G, n // 2) == {i: set() for i in range(n // 2, n)}
+
+    def test_irreducible1(self):
+        # Graph taken from Figure 2 of
+        # K. D. Cooper, T. J. Harvey, and K. Kennedy.
+        # A simple, fast dominance algorithm.
+        # Software Practice & Experience, 4:110, 2001.
+        edges = [(1, 2), (2, 1), (3, 2), (4, 1), (5, 3), (5, 4)]
+        G = nx.DiGraph(edges)
+        assert dict(nx.dominance_frontiers(G, 5).items()) == {
+            1: {2},
+            2: {1},
+            3: {2},
+            4: {1},
+            5: set(),
+        }
+
+    def test_irreducible2(self):
+        # Graph taken from Figure 4 of
+        # K. D. Cooper, T. J. Harvey, and K. Kennedy.
+        # A simple, fast dominance algorithm.
+        # Software Practice & Experience, 4:110, 2001.
+        edges = [(1, 2), (2, 1), (2, 3), (3, 2), (4, 2), (4, 3), (5, 1), (6, 4), (6, 5)]
+        G = nx.DiGraph(edges)
+        assert nx.dominance_frontiers(G, 6) == {
+            1: {2},
+            2: {1, 3},
+            3: {2},
+            4: {2, 3},
+            5: {1},
+            6: set(),
+        }
+
+    def test_domrel_png(self):
+        # Graph taken from https://commons.wikipedia.org/wiki/File:Domrel.png
+        edges = [(1, 2), (2, 3), (2, 4), (2, 6), (3, 5), (4, 5), (5, 2)]
+        G = nx.DiGraph(edges)
+        assert nx.dominance_frontiers(G, 1) == {
+            1: set(),
+            2: {2},
+            3: {5},
+            4: {5},
+            5: {2},
+            6: set(),
+        }
+        # Test postdominance.
+        result = nx.dominance_frontiers(G.reverse(copy=False), 6)
+        assert result == {1: set(), 2: {2}, 3: {2}, 4: {2}, 5: {2}, 6: set()}
+
+    def test_boost_example(self):
+        # Graph taken from Figure 1 of
+        # http://www.boost.org/doc/libs/1_56_0/libs/graph/doc/lengauer_tarjan_dominator.htm
+        edges = [(0, 1), (1, 2), (1, 3), (2, 7), (3, 4), (4, 5), (4, 6), (5, 7), (6, 4)]
+        G = nx.DiGraph(edges)
+        assert nx.dominance_frontiers(G, 0) == {
+            0: set(),
+            1: set(),
+            2: {7},
+            3: {7},
+            4: {4, 7},
+            5: {7},
+            6: {4},
+            7: set(),
+        }
+        # Test postdominance.
+        result = nx.dominance_frontiers(G.reverse(copy=False), 7)
+        expected = {
+            0: set(),
+            1: set(),
+            2: {1},
+            3: {1},
+            4: {1, 4},
+            5: {1},
+            6: {4},
+            7: set(),
+        }
+        assert result == expected
+
+    def test_discard_issue(self):
+        # https://github.com/networkx/networkx/issues/2071
+        g = nx.DiGraph()
+        g.add_edges_from(
+            [
+                ("b0", "b1"),
+                ("b1", "b2"),
+                ("b2", "b3"),
+                ("b3", "b1"),
+                ("b1", "b5"),
+                ("b5", "b6"),
+                ("b5", "b8"),
+                ("b6", "b7"),
+                ("b8", "b7"),
+                ("b7", "b3"),
+                ("b3", "b4"),
+            ]
+        )
+        df = nx.dominance_frontiers(g, "b0")
+        assert df == {
+            "b4": set(),
+            "b5": {"b3"},
+            "b6": {"b7"},
+            "b7": {"b3"},
+            "b0": set(),
+            "b1": {"b1"},
+            "b2": {"b3"},
+            "b3": {"b1"},
+            "b8": {"b7"},
+        }
+
+    def test_loop(self):
+        g = nx.DiGraph()
+        g.add_edges_from([("a", "b"), ("b", "c"), ("b", "a")])
+        df = nx.dominance_frontiers(g, "a")
+        assert df == {"a": set(), "b": set(), "c": set()}
+
+    def test_missing_immediate_doms(self):
+        # see https://github.com/networkx/networkx/issues/2070
+        g = nx.DiGraph()
+        edges = [
+            ("entry_1", "b1"),
+            ("b1", "b2"),
+            ("b2", "b3"),
+            ("b3", "exit"),
+            ("entry_2", "b3"),
+        ]
+
+        # entry_1
+        #   |
+        #   b1
+        #   |
+        #   b2  entry_2
+        #    |  /
+        #    b3
+        #    |
+        #   exit
+
+        g.add_edges_from(edges)
+        # formerly raised KeyError on entry_2 when parsing b3
+        # because entry_2 does not have immediate doms (no path)
+        nx.dominance_frontiers(g, "entry_1")
+
+    def test_loops_larger(self):
+        # from
+        # http://ecee.colorado.edu/~waite/Darmstadt/motion.html
+        g = nx.DiGraph()
+        edges = [
+            ("entry", "exit"),
+            ("entry", "1"),
+            ("1", "2"),
+            ("2", "3"),
+            ("3", "4"),
+            ("4", "5"),
+            ("5", "6"),
+            ("6", "exit"),
+            ("6", "2"),
+            ("5", "3"),
+            ("4", "4"),
+        ]
+
+        g.add_edges_from(edges)
+        df = nx.dominance_frontiers(g, "entry")
+        answer = {
+            "entry": set(),
+            "1": {"exit"},
+            "2": {"exit", "2"},
+            "3": {"exit", "3", "2"},
+            "4": {"exit", "4", "3", "2"},
+            "5": {"exit", "3", "2"},
+            "6": {"exit", "2"},
+            "exit": set(),
+        }
+        for n in df:
+            assert set(df[n]) == set(answer[n])

valley/lib/python3.10/site-packages/networkx/algorithms/tests/test_dominating.py

@@ -0,0 +1,46 @@
+import pytest
+
+import networkx as nx
+
+
+def test_dominating_set():
+    G = nx.gnp_random_graph(100, 0.1)
+    D = nx.dominating_set(G)
+    assert nx.is_dominating_set(G, D)
+    D = nx.dominating_set(G, start_with=0)
+    assert nx.is_dominating_set(G, D)
+
+
+def test_complete():
+    """In complete graphs each node is a dominating set.
+    Thus the dominating set has to be of cardinality 1.
+    """
+    K4 = nx.complete_graph(4)
+    assert len(nx.dominating_set(K4)) == 1
+    K5 = nx.complete_graph(5)
+    assert len(nx.dominating_set(K5)) == 1
+
+
+def test_raise_dominating_set():
+    with pytest.raises(nx.NetworkXError):
+        G = nx.path_graph(4)
+        D = nx.dominating_set(G, start_with=10)
+
+
+def test_is_dominating_set():
+    G = nx.path_graph(4)
+    d = {1, 3}
+    assert nx.is_dominating_set(G, d)
+    d = {0, 2}
+    assert nx.is_dominating_set(G, d)
+    d = {1}
+    assert not nx.is_dominating_set(G, d)
+
+
+def test_wikipedia_is_dominating_set():
+    """Example from https://en.wikipedia.org/wiki/Dominating_set"""
+    G = nx.cycle_graph(4)
+    G.add_edges_from([(0, 4), (1, 4), (2, 5)])
+    assert nx.is_dominating_set(G, {4, 3, 5})
+    assert nx.is_dominating_set(G, {0, 2})
+    assert nx.is_dominating_set(G, {1, 2})

valley/lib/python3.10/site-packages/networkx/algorithms/tests/test_efficiency.py

@@ -0,0 +1,58 @@
+"""Unit tests for the :mod:`networkx.algorithms.efficiency` module."""
+
+import networkx as nx
+
+
+class TestEfficiency:
+    def setup_method(self):
+        # G1 is a disconnected graph
+        self.G1 = nx.Graph()
+        self.G1.add_nodes_from([1, 2, 3])
+        # G2 is a cycle graph
+        self.G2 = nx.cycle_graph(4)
+        # G3 is the triangle graph with one additional edge
+        self.G3 = nx.lollipop_graph(3, 1)
+
+    def test_efficiency_disconnected_nodes(self):
+        """
+        When nodes are disconnected, efficiency is 0
+        """
+        assert nx.efficiency(self.G1, 1, 2) == 0
+
+    def test_local_efficiency_disconnected_graph(self):
+        """
+        In a disconnected graph the efficiency is 0
+        """
+        assert nx.local_efficiency(self.G1) == 0
+
+    def test_efficiency(self):
+        assert nx.efficiency(self.G2, 0, 1) == 1
+        assert nx.efficiency(self.G2, 0, 2) == 1 / 2
+
+    def test_global_efficiency(self):
+        assert nx.global_efficiency(self.G2) == 5 / 6
+
+    def test_global_efficiency_complete_graph(self):
+        """
+        Tests that the average global efficiency of the complete graph is one.
+        """
+        for n in range(2, 10):
+            G = nx.complete_graph(n)
+            assert nx.global_efficiency(G) == 1
+
+    def test_local_efficiency_complete_graph(self):
+        """
+        Test that the local efficiency for a complete graph with at least 3
+        nodes should be one. For a graph with only 2 nodes, the induced
+        subgraph has no edges.
+        """
+        for n in range(3, 10):
+            G = nx.complete_graph(n)
+            assert nx.local_efficiency(G) == 1
+
+    def test_using_ego_graph(self):
+        """
+        Test that the ego graph is used when computing local efficiency.
+        For more information, see GitHub issue #2710.
+        """
+        assert nx.local_efficiency(self.G3) == 7 / 12

valley/lib/python3.10/site-packages/networkx/algorithms/tests/test_graphical.py

@@ -0,0 +1,163 @@
+import pytest
+
+import networkx as nx
+
+
+def test_valid_degree_sequence1():
+    n = 100
+    p = 0.3
+    for i in range(10):
+        G = nx.erdos_renyi_graph(n, p)
+        deg = (d for n, d in G.degree())
+        assert nx.is_graphical(deg, method="eg")
+        assert nx.is_graphical(deg, method="hh")
+
+
+def test_valid_degree_sequence2():
+    n = 100
+    for i in range(10):
+        G = nx.barabasi_albert_graph(n, 1)
+        deg = (d for n, d in G.degree())
+        assert nx.is_graphical(deg, method="eg")
+        assert nx.is_graphical(deg, method="hh")
+
+
+def test_string_input():
+    pytest.raises(nx.NetworkXException, nx.is_graphical, [], "foo")
+    pytest.raises(nx.NetworkXException, nx.is_graphical, ["red"], "hh")
+    pytest.raises(nx.NetworkXException, nx.is_graphical, ["red"], "eg")
+
+
+def test_non_integer_input():
+    pytest.raises(nx.NetworkXException, nx.is_graphical, [72.5], "eg")
+    pytest.raises(nx.NetworkXException, nx.is_graphical, [72.5], "hh")
+
+
+def test_negative_input():
+    assert not nx.is_graphical([-1], "hh")
+    assert not nx.is_graphical([-1], "eg")
+
+
+class TestAtlas:
+    @classmethod
+    def setup_class(cls):
+        global atlas
+        from networkx.generators import atlas
+
+        cls.GAG = atlas.graph_atlas_g()
+
+    def test_atlas(self):
+        for graph in self.GAG:
+            deg = (d for n, d in graph.degree())
+            assert nx.is_graphical(deg, method="eg")
+            assert nx.is_graphical(deg, method="hh")
+
+
+def test_small_graph_true():
+    z = [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
+    assert nx.is_graphical(z, method="hh")
+    assert nx.is_graphical(z, method="eg")
+    z = [10, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2]
+    assert nx.is_graphical(z, method="hh")
+    assert nx.is_graphical(z, method="eg")
+    z = [1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
+    assert nx.is_graphical(z, method="hh")
+    assert nx.is_graphical(z, method="eg")
+
+
+def test_small_graph_false():
+    z = [1000, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
+    assert not nx.is_graphical(z, method="hh")
+    assert not nx.is_graphical(z, method="eg")
+    z = [6, 5, 4, 4, 2, 1, 1, 1]
+    assert not nx.is_graphical(z, method="hh")
+    assert not nx.is_graphical(z, method="eg")
+    z = [1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
+    assert not nx.is_graphical(z, method="hh")
+    assert not nx.is_graphical(z, method="eg")
+
+
+def test_directed_degree_sequence():
+    # Test a range of valid directed degree sequences
+    n, r = 100, 10
+    p = 1.0 / r
+    for i in range(r):
+        G = nx.erdos_renyi_graph(n, p * (i + 1), None, True)
+        din = (d for n, d in G.in_degree())
+        dout = (d for n, d in G.out_degree())
+        assert nx.is_digraphical(din, dout)
+
+
+def test_small_directed_sequences():
+    dout = [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
+    din = [3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 1]
+    assert nx.is_digraphical(din, dout)
+    # Test nongraphical directed sequence
+    dout = [1000, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
+    din = [103, 102, 102, 102, 102, 102, 102, 102, 102, 102]
+    assert not nx.is_digraphical(din, dout)
+    # Test digraphical small sequence
+    dout = [1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
+    din = [2, 2, 2, 2, 2, 2, 2, 2, 1, 1]
+    assert nx.is_digraphical(din, dout)
+    # Test nonmatching sum
+    din = [2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1]
+    assert not nx.is_digraphical(din, dout)
+    # Test for negative integer in sequence
+    din = [2, 2, 2, -2, 2, 2, 2, 2, 1, 1, 4]
+    assert not nx.is_digraphical(din, dout)
+    # Test for noninteger
+    din = dout = [1, 1, 1.1, 1]
+    assert not nx.is_digraphical(din, dout)
+    din = dout = [1, 1, "rer", 1]
+    assert not nx.is_digraphical(din, dout)
+
+
+def test_multi_sequence():
+    # Test nongraphical multi sequence
+    seq = [1000, 3, 3, 3, 3, 2, 2, 2, 1, 1]
+    assert not nx.is_multigraphical(seq)
+    # Test small graphical multi sequence
+    seq = [6, 5, 4, 4, 2, 1, 1, 1]
+    assert nx.is_multigraphical(seq)
+    # Test for negative integer in sequence
+    seq = [6, 5, 4, -4, 2, 1, 1, 1]
+    assert not nx.is_multigraphical(seq)
+    # Test for sequence with odd sum
+    seq = [1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
+    assert not nx.is_multigraphical(seq)
+    # Test for noninteger
+    seq = [1, 1, 1.1, 1]
+    assert not nx.is_multigraphical(seq)
+    seq = [1, 1, "rer", 1]
+    assert not nx.is_multigraphical(seq)
+
+
+def test_pseudo_sequence():
+    # Test small valid pseudo sequence
+    seq = [1000, 3, 3, 3, 3, 2, 2, 2, 1, 1]
+    assert nx.is_pseudographical(seq)
+    # Test for sequence with odd sum
+    seq = [1000, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
+    assert not nx.is_pseudographical(seq)
+    # Test for negative integer in sequence
+    seq = [1000, 3, 3, 3, 3, 2, 2, -2, 1, 1]
+    assert not nx.is_pseudographical(seq)
+    # Test for noninteger
+    seq = [1, 1, 1.1, 1]
+    assert not nx.is_pseudographical(seq)
+    seq = [1, 1, "rer", 1]
+    assert not nx.is_pseudographical(seq)
+
+
+def test_numpy_degree_sequence():
+    np = pytest.importorskip("numpy")
+    ds = np.array([1, 2, 2, 2, 1], dtype=np.int64)
+    assert nx.is_graphical(ds, "eg")
+    assert nx.is_graphical(ds, "hh")
+    ds = np.array([1, 2, 2, 2, 1], dtype=np.float64)
+    assert nx.is_graphical(ds, "eg")
+    assert nx.is_graphical(ds, "hh")
+    ds = np.array([1.1, 2, 2, 2, 1], dtype=np.float64)
+    pytest.raises(nx.NetworkXException, nx.is_graphical, ds, "eg")
+    pytest.raises(nx.NetworkXException, nx.is_graphical, ds, "hh")

valley/lib/python3.10/site-packages/networkx/algorithms/tests/test_matching.py

@@ -0,0 +1,605 @@
+import math
+from itertools import permutations
+
+from pytest import raises
+
+import networkx as nx
+from networkx.algorithms.matching import matching_dict_to_set
+from networkx.utils import edges_equal
+
+
+class TestMaxWeightMatching:
+    """Unit tests for the
+    :func:`~networkx.algorithms.matching.max_weight_matching` function.
+
+    """
+
+    def test_trivial1(self):
+        """Empty graph"""
+        G = nx.Graph()
+        assert nx.max_weight_matching(G) == set()
+        assert nx.min_weight_matching(G) == set()
+
+    def test_selfloop(self):
+        G = nx.Graph()
+        G.add_edge(0, 0, weight=100)
+        assert nx.max_weight_matching(G) == set()
+        assert nx.min_weight_matching(G) == set()
+
+    def test_single_edge(self):
+        G = nx.Graph()
+        G.add_edge(0, 1)
+        assert edges_equal(
+            nx.max_weight_matching(G), matching_dict_to_set({0: 1, 1: 0})
+        )
+        assert edges_equal(
+            nx.min_weight_matching(G), matching_dict_to_set({0: 1, 1: 0})
+        )
+
+    def test_two_path(self):
+        G = nx.Graph()
+        G.add_edge("one", "two", weight=10)
+        G.add_edge("two", "three", weight=11)
+        assert edges_equal(
+            nx.max_weight_matching(G),
+            matching_dict_to_set({"three": "two", "two": "three"}),
+        )
+        assert edges_equal(
+            nx.min_weight_matching(G),
+            matching_dict_to_set({"one": "two", "two": "one"}),
+        )
+
+    def test_path(self):
+        G = nx.Graph()
+        G.add_edge(1, 2, weight=5)
+        G.add_edge(2, 3, weight=11)
+        G.add_edge(3, 4, weight=5)
+        assert edges_equal(
+            nx.max_weight_matching(G), matching_dict_to_set({2: 3, 3: 2})
+        )
+        assert edges_equal(
+            nx.max_weight_matching(G, 1), matching_dict_to_set({1: 2, 2: 1, 3: 4, 4: 3})
+        )
+        assert edges_equal(
+            nx.min_weight_matching(G), matching_dict_to_set({1: 2, 3: 4})
+        )
+        assert edges_equal(
+            nx.min_weight_matching(G, 1), matching_dict_to_set({1: 2, 3: 4})
+        )
+
+    def test_square(self):
+        G = nx.Graph()
+        G.add_edge(1, 4, weight=2)
+        G.add_edge(2, 3, weight=2)
+        G.add_edge(1, 2, weight=1)
+        G.add_edge(3, 4, weight=4)
+        assert edges_equal(
+            nx.max_weight_matching(G), matching_dict_to_set({1: 2, 3: 4})
+        )
+        assert edges_equal(
+            nx.min_weight_matching(G), matching_dict_to_set({1: 4, 2: 3})
+        )
+
+    def test_edge_attribute_name(self):
+        G = nx.Graph()
+        G.add_edge("one", "two", weight=10, abcd=11)
+        G.add_edge("two", "three", weight=11, abcd=10)
+        assert edges_equal(
+            nx.max_weight_matching(G, weight="abcd"),
+            matching_dict_to_set({"one": "two", "two": "one"}),
+        )
+        assert edges_equal(
+            nx.min_weight_matching(G, weight="abcd"),
+            matching_dict_to_set({"three": "two"}),
+        )
+
+    def test_floating_point_weights(self):
+        G = nx.Graph()
+        G.add_edge(1, 2, weight=math.pi)
+        G.add_edge(2, 3, weight=math.exp(1))
+        G.add_edge(1, 3, weight=3.0)
+        G.add_edge(1, 4, weight=math.sqrt(2.0))
+        assert edges_equal(
+            nx.max_weight_matching(G), matching_dict_to_set({1: 4, 2: 3, 3: 2, 4: 1})
+        )
+        assert edges_equal(
+            nx.min_weight_matching(G), matching_dict_to_set({1: 4, 2: 3, 3: 2, 4: 1})
+        )
+
+    def test_negative_weights(self):
+        G = nx.Graph()
+        G.add_edge(1, 2, weight=2)
+        G.add_edge(1, 3, weight=-2)
+        G.add_edge(2, 3, weight=1)
+        G.add_edge(2, 4, weight=-1)
+        G.add_edge(3, 4, weight=-6)
+        assert edges_equal(
+            nx.max_weight_matching(G), matching_dict_to_set({1: 2, 2: 1})
+        )
+        assert edges_equal(
+            nx.max_weight_matching(G, maxcardinality=True),
+            matching_dict_to_set({1: 3, 2: 4, 3: 1, 4: 2}),
+        )
+        assert edges_equal(
+            nx.min_weight_matching(G), matching_dict_to_set({1: 2, 3: 4})
+        )
+
+    def test_s_blossom(self):
+        """Create S-blossom and use it for augmentation:"""
+        G = nx.Graph()
+        G.add_weighted_edges_from([(1, 2, 8), (1, 3, 9), (2, 3, 10), (3, 4, 7)])
+        answer = matching_dict_to_set({1: 2, 2: 1, 3: 4, 4: 3})
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+        G.add_weighted_edges_from([(1, 6, 5), (4, 5, 6)])
+        answer = matching_dict_to_set({1: 6, 2: 3, 3: 2, 4: 5, 5: 4, 6: 1})
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+    def test_s_t_blossom(self):
+        """Create S-blossom, relabel as T-blossom, use for augmentation:"""
+        G = nx.Graph()
+        G.add_weighted_edges_from(
+            [(1, 2, 9), (1, 3, 8), (2, 3, 10), (1, 4, 5), (4, 5, 4), (1, 6, 3)]
+        )
+        answer = matching_dict_to_set({1: 6, 2: 3, 3: 2, 4: 5, 5: 4, 6: 1})
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+        G.add_edge(4, 5, weight=3)
+        G.add_edge(1, 6, weight=4)
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+        G.remove_edge(1, 6)
+        G.add_edge(3, 6, weight=4)
+        answer = matching_dict_to_set({1: 2, 2: 1, 3: 6, 4: 5, 5: 4, 6: 3})
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+    def test_nested_s_blossom(self):
+        """Create nested S-blossom, use for augmentation:"""
+
+        G = nx.Graph()
+        G.add_weighted_edges_from(
+            [
+                (1, 2, 9),
+                (1, 3, 9),
+                (2, 3, 10),
+                (2, 4, 8),
+                (3, 5, 8),
+                (4, 5, 10),
+                (5, 6, 6),
+            ]
+        )
+        dict_format = {1: 3, 2: 4, 3: 1, 4: 2, 5: 6, 6: 5}
+        expected = {frozenset(e) for e in matching_dict_to_set(dict_format)}
+        answer = {frozenset(e) for e in nx.max_weight_matching(G)}
+        assert answer == expected
+        answer = {frozenset(e) for e in nx.min_weight_matching(G)}
+        assert answer == expected
+
+    def test_nested_s_blossom_relabel(self):
+        """Create S-blossom, relabel as S, include in nested S-blossom:"""
+        G = nx.Graph()
+        G.add_weighted_edges_from(
+            [
+                (1, 2, 10),
+                (1, 7, 10),
+                (2, 3, 12),
+                (3, 4, 20),
+                (3, 5, 20),
+                (4, 5, 25),
+                (5, 6, 10),
+                (6, 7, 10),
+                (7, 8, 8),
+            ]
+        )
+        answer = matching_dict_to_set({1: 2, 2: 1, 3: 4, 4: 3, 5: 6, 6: 5, 7: 8, 8: 7})
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+    def test_nested_s_blossom_expand(self):
+        """Create nested S-blossom, augment, expand recursively:"""
+        G = nx.Graph()
+        G.add_weighted_edges_from(
+            [
+                (1, 2, 8),
+                (1, 3, 8),
+                (2, 3, 10),
+                (2, 4, 12),
+                (3, 5, 12),
+                (4, 5, 14),
+                (4, 6, 12),
+                (5, 7, 12),
+                (6, 7, 14),
+                (7, 8, 12),
+            ]
+        )
+        answer = matching_dict_to_set({1: 2, 2: 1, 3: 5, 4: 6, 5: 3, 6: 4, 7: 8, 8: 7})
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+    def test_s_blossom_relabel_expand(self):
+        """Create S-blossom, relabel as T, expand:"""
+        G = nx.Graph()
+        G.add_weighted_edges_from(
+            [
+                (1, 2, 23),
+                (1, 5, 22),
+                (1, 6, 15),
+                (2, 3, 25),
+                (3, 4, 22),
+                (4, 5, 25),
+                (4, 8, 14),
+                (5, 7, 13),
+            ]
+        )
+        answer = matching_dict_to_set({1: 6, 2: 3, 3: 2, 4: 8, 5: 7, 6: 1, 7: 5, 8: 4})
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+    def test_nested_s_blossom_relabel_expand(self):
+        """Create nested S-blossom, relabel as T, expand:"""
+        G = nx.Graph()
+        G.add_weighted_edges_from(
+            [
+                (1, 2, 19),
+                (1, 3, 20),
+                (1, 8, 8),
+                (2, 3, 25),
+                (2, 4, 18),
+                (3, 5, 18),
+                (4, 5, 13),
+                (4, 7, 7),
+                (5, 6, 7),
+            ]
+        )
+        answer = matching_dict_to_set({1: 8, 2: 3, 3: 2, 4: 7, 5: 6, 6: 5, 7: 4, 8: 1})
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+    def test_nasty_blossom1(self):
+        """Create blossom, relabel as T in more than one way, expand,
+        augment:
+        """
+        G = nx.Graph()
+        G.add_weighted_edges_from(
+            [
+                (1, 2, 45),
+                (1, 5, 45),
+                (2, 3, 50),
+                (3, 4, 45),
+                (4, 5, 50),
+                (1, 6, 30),
+                (3, 9, 35),
+                (4, 8, 35),
+                (5, 7, 26),
+                (9, 10, 5),
+            ]
+        )
+        ansdict = {1: 6, 2: 3, 3: 2, 4: 8, 5: 7, 6: 1, 7: 5, 8: 4, 9: 10, 10: 9}
+        answer = matching_dict_to_set(ansdict)
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+    def test_nasty_blossom2(self):
+        """Again but slightly different:"""
+        G = nx.Graph()
+        G.add_weighted_edges_from(
+            [
+                (1, 2, 45),
+                (1, 5, 45),
+                (2, 3, 50),
+                (3, 4, 45),
+                (4, 5, 50),
+                (1, 6, 30),
+                (3, 9, 35),
+                (4, 8, 26),
+                (5, 7, 40),
+                (9, 10, 5),
+            ]
+        )
+        ans = {1: 6, 2: 3, 3: 2, 4: 8, 5: 7, 6: 1, 7: 5, 8: 4, 9: 10, 10: 9}
+        answer = matching_dict_to_set(ans)
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+    def test_nasty_blossom_least_slack(self):
+        """Create blossom, relabel as T, expand such that a new
+        least-slack S-to-free dge is produced, augment:
+        """
+        G = nx.Graph()
+        G.add_weighted_edges_from(
+            [
+                (1, 2, 45),
+                (1, 5, 45),
+                (2, 3, 50),
+                (3, 4, 45),
+                (4, 5, 50),
+                (1, 6, 30),
+                (3, 9, 35),
+                (4, 8, 28),
+                (5, 7, 26),
+                (9, 10, 5),
+            ]
+        )
+        ans = {1: 6, 2: 3, 3: 2, 4: 8, 5: 7, 6: 1, 7: 5, 8: 4, 9: 10, 10: 9}
+        answer = matching_dict_to_set(ans)
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+    def test_nasty_blossom_augmenting(self):
+        """Create nested blossom, relabel as T in more than one way"""
+        # expand outer blossom such that inner blossom ends up on an
+        # augmenting path:
+        G = nx.Graph()
+        G.add_weighted_edges_from(
+            [
+                (1, 2, 45),
+                (1, 7, 45),
+                (2, 3, 50),
+                (3, 4, 45),
+                (4, 5, 95),
+                (4, 6, 94),
+                (5, 6, 94),
+                (6, 7, 50),
+                (1, 8, 30),
+                (3, 11, 35),
+                (5, 9, 36),
+                (7, 10, 26),
+                (11, 12, 5),
+            ]
+        )
+        ans = {
+            1: 8,
+            2: 3,
+            3: 2,
+            4: 6,
+            5: 9,
+            6: 4,
+            7: 10,
+            8: 1,
+            9: 5,
+            10: 7,
+            11: 12,
+            12: 11,
+        }
+        answer = matching_dict_to_set(ans)
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+    def test_nasty_blossom_expand_recursively(self):
+        """Create nested S-blossom, relabel as S, expand recursively:"""
+        G = nx.Graph()
+        G.add_weighted_edges_from(
+            [
+                (1, 2, 40),
+                (1, 3, 40),
+                (2, 3, 60),
+                (2, 4, 55),
+                (3, 5, 55),
+                (4, 5, 50),
+                (1, 8, 15),
+                (5, 7, 30),
+                (7, 6, 10),
+                (8, 10, 10),
+                (4, 9, 30),
+            ]
+        )
+        ans = {1: 2, 2: 1, 3: 5, 4: 9, 5: 3, 6: 7, 7: 6, 8: 10, 9: 4, 10: 8}
+        answer = matching_dict_to_set(ans)
+        assert edges_equal(nx.max_weight_matching(G), answer)
+        assert edges_equal(nx.min_weight_matching(G), answer)
+
+    def test_wrong_graph_type(self):
+        error = nx.NetworkXNotImplemented
+        raises(error, nx.max_weight_matching, nx.MultiGraph())
+        raises(error, nx.max_weight_matching, nx.MultiDiGraph())
+        raises(error, nx.max_weight_matching, nx.DiGraph())
+        raises(error, nx.min_weight_matching, nx.DiGraph())
+
+
+class TestIsMatching:
+    """Unit tests for the
+    :func:`~networkx.algorithms.matching.is_matching` function.
+
+    """
+
+    def test_dict(self):
+        G = nx.path_graph(4)
+        assert nx.is_matching(G, {0: 1, 1: 0, 2: 3, 3: 2})
+
+    def test_empty_matching(self):
+        G = nx.path_graph(4)
+        assert nx.is_matching(G, set())
+
+    def test_single_edge(self):
+        G = nx.path_graph(4)
+        assert nx.is_matching(G, {(1, 2)})
+
+    def test_edge_order(self):
+        G = nx.path_graph(4)
+        assert nx.is_matching(G, {(0, 1), (2, 3)})
+        assert nx.is_matching(G, {(1, 0), (2, 3)})
+        assert nx.is_matching(G, {(0, 1), (3, 2)})
+        assert nx.is_matching(G, {(1, 0), (3, 2)})
+
+    def test_valid_matching(self):
+        G = nx.path_graph(4)
+        assert nx.is_matching(G, {(0, 1), (2, 3)})
+
+    def test_invalid_input(self):
+        error = nx.NetworkXError
+        G = nx.path_graph(4)
+        # edge to node not in G
+        raises(error, nx.is_matching, G, {(0, 5), (2, 3)})
+        # edge not a 2-tuple
+        raises(error, nx.is_matching, G, {(0, 1, 2), (2, 3)})
+        raises(error, nx.is_matching, G, {(0,), (2, 3)})
+
+    def test_selfloops(self):
+        error = nx.NetworkXError
+        G = nx.path_graph(4)
+        # selfloop for node not in G
+        raises(error, nx.is_matching, G, {(5, 5), (2, 3)})
+        # selfloop edge not in G
+        assert not nx.is_matching(G, {(0, 0), (1, 2), (2, 3)})
+        # selfloop edge in G
+        G.add_edge(0, 0)
+        assert not nx.is_matching(G, {(0, 0), (1, 2)})
+
+    def test_invalid_matching(self):
+        G = nx.path_graph(4)
+        assert not nx.is_matching(G, {(0, 1), (1, 2), (2, 3)})
+
+    def test_invalid_edge(self):
+        G = nx.path_graph(4)
+        assert not nx.is_matching(G, {(0, 3), (1, 2)})
+        raises(nx.NetworkXError, nx.is_matching, G, {(0, 55)})
+
+        G = nx.DiGraph(G.edges)
+        assert nx.is_matching(G, {(0, 1)})
+        assert not nx.is_matching(G, {(1, 0)})
+
+
+class TestIsMaximalMatching:
+    """Unit tests for the
+    :func:`~networkx.algorithms.matching.is_maximal_matching` function.
+
+    """
+
+    def test_dict(self):
+        G = nx.path_graph(4)
+        assert nx.is_maximal_matching(G, {0: 1, 1: 0, 2: 3, 3: 2})
+
+    def test_invalid_input(self):
+        error = nx.NetworkXError
+        G = nx.path_graph(4)
+        # edge to node not in G
+        raises(error, nx.is_maximal_matching, G, {(0, 5)})
+        raises(error, nx.is_maximal_matching, G, {(5, 0)})
+        # edge not a 2-tuple
+        raises(error, nx.is_maximal_matching, G, {(0, 1, 2), (2, 3)})
+        raises(error, nx.is_maximal_matching, G, {(0,), (2, 3)})
+
+    def test_valid(self):
+        G = nx.path_graph(4)
+        assert nx.is_maximal_matching(G, {(0, 1), (2, 3)})
+
+    def test_not_matching(self):
+        G = nx.path_graph(4)
+        assert not nx.is_maximal_matching(G, {(0, 1), (1, 2), (2, 3)})
+        assert not nx.is_maximal_matching(G, {(0, 3)})
+        G.add_edge(0, 0)
+        assert not nx.is_maximal_matching(G, {(0, 0)})
+
+    def test_not_maximal(self):
+        G = nx.path_graph(4)
+        assert not nx.is_maximal_matching(G, {(0, 1)})
+
+
+class TestIsPerfectMatching:
+    """Unit tests for the
+    :func:`~networkx.algorithms.matching.is_perfect_matching` function.
+
+    """
+
+    def test_dict(self):
+        G = nx.path_graph(4)
+        assert nx.is_perfect_matching(G, {0: 1, 1: 0, 2: 3, 3: 2})
+
+    def test_valid(self):
+        G = nx.path_graph(4)
+        assert nx.is_perfect_matching(G, {(0, 1), (2, 3)})
+
+    def test_valid_not_path(self):
+        G = nx.cycle_graph(4)
+        G.add_edge(0, 4)
+        G.add_edge(1, 4)
+        G.add_edge(5, 2)
+
+        assert nx.is_perfect_matching(G, {(1, 4), (0, 3), (5, 2)})
+
+    def test_invalid_input(self):
+        error = nx.NetworkXError
+        G = nx.path_graph(4)
+        # edge to node not in G
+        raises(error, nx.is_perfect_matching, G, {(0, 5)})
+        raises(error, nx.is_perfect_matching, G, {(5, 0)})
+        # edge not a 2-tuple
+        raises(error, nx.is_perfect_matching, G, {(0, 1, 2), (2, 3)})
+        raises(error, nx.is_perfect_matching, G, {(0,), (2, 3)})
+
+    def test_selfloops(self):
+        error = nx.NetworkXError
+        G = nx.path_graph(4)
+        # selfloop for node not in G
+        raises(error, nx.is_perfect_matching, G, {(5, 5), (2, 3)})
+        # selfloop edge not in G
+        assert not nx.is_perfect_matching(G, {(0, 0), (1, 2), (2, 3)})
+        # selfloop edge in G
+        G.add_edge(0, 0)
+        assert not nx.is_perfect_matching(G, {(0, 0), (1, 2)})
+
+    def test_not_matching(self):
+        G = nx.path_graph(4)
+        assert not nx.is_perfect_matching(G, {(0, 3)})
+        assert not nx.is_perfect_matching(G, {(0, 1), (1, 2), (2, 3)})
+
+    def test_maximal_but_not_perfect(self):
+        G = nx.cycle_graph(4)
+        G.add_edge(0, 4)
+        G.add_edge(1, 4)
+
+        assert not nx.is_perfect_matching(G, {(1, 4), (0, 3)})
+
+
+class TestMaximalMatching:
+    """Unit tests for the
+    :func:`~networkx.algorithms.matching.maximal_matching`.
+
+    """
+
+    def test_valid_matching(self):
+        edges = [(1, 2), (1, 5), (2, 3), (2, 5), (3, 4), (3, 6), (5, 6)]
+        G = nx.Graph(edges)
+        matching = nx.maximal_matching(G)
+        assert nx.is_maximal_matching(G, matching)
+
+    def test_single_edge_matching(self):
+        # In the star graph, any maximal matching has just one edge.
+        G = nx.star_graph(5)
+        matching = nx.maximal_matching(G)
+        assert 1 == len(matching)
+        assert nx.is_maximal_matching(G, matching)
+
+    def test_self_loops(self):
+        # Create the path graph with two self-loops.
+        G = nx.path_graph(3)
+        G.add_edges_from([(0, 0), (1, 1)])
+        matching = nx.maximal_matching(G)
+        assert len(matching) == 1
+        # The matching should never include self-loops.
+        assert not any(u == v for u, v in matching)
+        assert nx.is_maximal_matching(G, matching)
+
+    def test_ordering(self):
+        """Tests that a maximal matching is computed correctly
+        regardless of the order in which nodes are added to the graph.
+
+        """
+        for nodes in permutations(range(3)):
+            G = nx.Graph()
+            G.add_nodes_from(nodes)
+            G.add_edges_from([(0, 1), (0, 2)])
+            matching = nx.maximal_matching(G)
+            assert len(matching) == 1
+            assert nx.is_maximal_matching(G, matching)
+
+    def test_wrong_graph_type(self):
+        error = nx.NetworkXNotImplemented
+        raises(error, nx.maximal_matching, nx.MultiGraph())
+        raises(error, nx.maximal_matching, nx.MultiDiGraph())
+        raises(error, nx.maximal_matching, nx.DiGraph())

valley/lib/python3.10/site-packages/networkx/algorithms/tests/test_polynomials.py

@@ -0,0 +1,57 @@
+"""Unit tests for the :mod:`networkx.algorithms.polynomials` module."""
+
+import pytest
+
+import networkx as nx
+
+sympy = pytest.importorskip("sympy")
+
+
+# Mapping of input graphs to a string representation of their tutte polynomials
+_test_tutte_graphs = {
+    nx.complete_graph(1): "1",
+    nx.complete_graph(4): "x**3 + 3*x**2 + 4*x*y + 2*x + y**3 + 3*y**2 + 2*y",
+    nx.cycle_graph(5): "x**4 + x**3 + x**2 + x + y",
+    nx.diamond_graph(): "x**3 + 2*x**2 + 2*x*y + x + y**2 + y",
+}
+
+_test_chromatic_graphs = {
+    nx.complete_graph(1): "x",
+    nx.complete_graph(4): "x**4 - 6*x**3 + 11*x**2 - 6*x",
+    nx.cycle_graph(5): "x**5 - 5*x**4 + 10*x**3 - 10*x**2 + 4*x",
+    nx.diamond_graph(): "x**4 - 5*x**3 + 8*x**2 - 4*x",
+    nx.path_graph(5): "x**5 - 4*x**4 + 6*x**3 - 4*x**2 + x",
+}
+
+
+@pytest.mark.parametrize(("G", "expected"), _test_tutte_graphs.items())
+def test_tutte_polynomial(G, expected):
+    assert nx.tutte_polynomial(G).equals(expected)
+
+
+@pytest.mark.parametrize("G", _test_tutte_graphs.keys())
+def test_tutte_polynomial_disjoint(G):
+    """Tutte polynomial factors into the Tutte polynomials of its components.
+    Verify this property with the disjoint union of two copies of the input graph.
+    """
+    t_g = nx.tutte_polynomial(G)
+    H = nx.disjoint_union(G, G)
+    t_h = nx.tutte_polynomial(H)
+    assert sympy.simplify(t_g * t_g).equals(t_h)
+
+
+@pytest.mark.parametrize(("G", "expected"), _test_chromatic_graphs.items())
+def test_chromatic_polynomial(G, expected):
+    assert nx.chromatic_polynomial(G).equals(expected)
+
+
+@pytest.mark.parametrize("G", _test_chromatic_graphs.keys())
+def test_chromatic_polynomial_disjoint(G):
+    """Chromatic polynomial factors into the Chromatic polynomials of its
+    components. Verify this property with the disjoint union of two copies of
+    the input graph.
+    """
+    x_g = nx.chromatic_polynomial(G)
+    H = nx.disjoint_union(G, G)
+    x_h = nx.chromatic_polynomial(H)
+    assert sympy.simplify(x_g * x_g).equals(x_h)

valley/lib/python3.10/site-packages/networkx/algorithms/tests/test_reciprocity.py

@@ -0,0 +1,37 @@
+import pytest
+
+import networkx as nx
+
+
+class TestReciprocity:
+    # test overall reciprocity by passing whole graph
+    def test_reciprocity_digraph(self):
+        DG = nx.DiGraph([(1, 2), (2, 1)])
+        reciprocity = nx.reciprocity(DG)
+        assert reciprocity == 1.0
+
+    # test empty graph's overall reciprocity which will throw an error
+    def test_overall_reciprocity_empty_graph(self):
+        with pytest.raises(nx.NetworkXError):
+            DG = nx.DiGraph()
+            nx.overall_reciprocity(DG)
+
+    # test for reciprocity for a list of nodes
+    def test_reciprocity_graph_nodes(self):
+        DG = nx.DiGraph([(1, 2), (2, 3), (3, 2)])
+        reciprocity = nx.reciprocity(DG, [1, 2])
+        expected_reciprocity = {1: 0.0, 2: 0.6666666666666666}
+        assert reciprocity == expected_reciprocity
+
+    # test for reciprocity for a single node
+    def test_reciprocity_graph_node(self):
+        DG = nx.DiGraph([(1, 2), (2, 3), (3, 2)])
+        reciprocity = nx.reciprocity(DG, 2)
+        assert reciprocity == 0.6666666666666666
+
+    # test for reciprocity for an isolated node
+    def test_reciprocity_graph_isolated_nodes(self):
+        with pytest.raises(nx.NetworkXError):
+            DG = nx.DiGraph([(1, 2)])
+            DG.add_node(4)
+            nx.reciprocity(DG, 4)

valley/lib/python3.10/site-packages/networkx/algorithms/tests/test_simple_paths.py

@@ -0,0 +1,792 @@
+import random
+
+import pytest
+
+import networkx as nx
+from networkx import convert_node_labels_to_integers as cnlti
+from networkx.algorithms.simple_paths import (
+    _bidirectional_dijkstra,
+    _bidirectional_shortest_path,
+)
+from networkx.utils import arbitrary_element, pairwise
+
+
+class TestIsSimplePath:
+    """Unit tests for the
+    :func:`networkx.algorithms.simple_paths.is_simple_path` function.
+
+    """
+
+    def test_empty_list(self):
+        """Tests that the empty list is not a valid path, since there
+        should be a one-to-one correspondence between paths as lists of
+        nodes and paths as lists of edges.
+
+        """
+        G = nx.trivial_graph()
+        assert not nx.is_simple_path(G, [])
+
+    def test_trivial_path(self):
+        """Tests that the trivial path, a path of length one, is
+        considered a simple path in a graph.
+
+        """
+        G = nx.trivial_graph()
+        assert nx.is_simple_path(G, [0])
+
+    def test_trivial_nonpath(self):
+        """Tests that a list whose sole element is an object not in the
+        graph is not considered a simple path.
+
+        """
+        G = nx.trivial_graph()
+        assert not nx.is_simple_path(G, ["not a node"])
+
+    def test_simple_path(self):
+        G = nx.path_graph(2)
+        assert nx.is_simple_path(G, [0, 1])
+
+    def test_non_simple_path(self):
+        G = nx.path_graph(2)
+        assert not nx.is_simple_path(G, [0, 1, 0])
+
+    def test_cycle(self):
+        G = nx.cycle_graph(3)
+        assert not nx.is_simple_path(G, [0, 1, 2, 0])
+
+    def test_missing_node(self):
+        G = nx.path_graph(2)
+        assert not nx.is_simple_path(G, [0, 2])
+
+    def test_missing_starting_node(self):
+        G = nx.path_graph(2)
+        assert not nx.is_simple_path(G, [2, 0])
+
+    def test_directed_path(self):
+        G = nx.DiGraph([(0, 1), (1, 2)])
+        assert nx.is_simple_path(G, [0, 1, 2])
+
+    def test_directed_non_path(self):
+        G = nx.DiGraph([(0, 1), (1, 2)])
+        assert not nx.is_simple_path(G, [2, 1, 0])
+
+    def test_directed_cycle(self):
+        G = nx.DiGraph([(0, 1), (1, 2), (2, 0)])
+        assert not nx.is_simple_path(G, [0, 1, 2, 0])
+
+    def test_multigraph(self):
+        G = nx.MultiGraph([(0, 1), (0, 1)])
+        assert nx.is_simple_path(G, [0, 1])
+
+    def test_multidigraph(self):
+        G = nx.MultiDiGraph([(0, 1), (0, 1), (1, 0), (1, 0)])
+        assert nx.is_simple_path(G, [0, 1])
+
+
+# Tests for all_simple_paths
+def test_all_simple_paths():
+    G = nx.path_graph(4)
+    paths = nx.all_simple_paths(G, 0, 3)
+    assert {tuple(p) for p in paths} == {(0, 1, 2, 3)}
+
+
+def test_all_simple_paths_with_two_targets_emits_two_paths():
+    G = nx.path_graph(4)
+    G.add_edge(2, 4)
+    paths = nx.all_simple_paths(G, 0, [3, 4])
+    assert {tuple(p) for p in paths} == {(0, 1, 2, 3), (0, 1, 2, 4)}
+
+
+def test_digraph_all_simple_paths_with_two_targets_emits_two_paths():
+    G = nx.path_graph(4, create_using=nx.DiGraph())
+    G.add_edge(2, 4)
+    paths = nx.all_simple_paths(G, 0, [3, 4])
+    assert {tuple(p) for p in paths} == {(0, 1, 2, 3), (0, 1, 2, 4)}
+
+
+def test_all_simple_paths_with_two_targets_cutoff():
+    G = nx.path_graph(4)
+    G.add_edge(2, 4)
+    paths = nx.all_simple_paths(G, 0, [3, 4], cutoff=3)
+    assert {tuple(p) for p in paths} == {(0, 1, 2, 3), (0, 1, 2, 4)}
+
+
+def test_digraph_all_simple_paths_with_two_targets_cutoff():
+    G = nx.path_graph(4, create_using=nx.DiGraph())
+    G.add_edge(2, 4)
+    paths = nx.all_simple_paths(G, 0, [3, 4], cutoff=3)
+    assert {tuple(p) for p in paths} == {(0, 1, 2, 3), (0, 1, 2, 4)}
+
+
+def test_all_simple_paths_with_two_targets_in_line_emits_two_paths():
+    G = nx.path_graph(4)
+    paths = nx.all_simple_paths(G, 0, [2, 3])
+    assert {tuple(p) for p in paths} == {(0, 1, 2), (0, 1, 2, 3)}
+
+
+def test_all_simple_paths_ignores_cycle():
+    G = nx.cycle_graph(3, create_using=nx.DiGraph())
+    G.add_edge(1, 3)
+    paths = nx.all_simple_paths(G, 0, 3)
+    assert {tuple(p) for p in paths} == {(0, 1, 3)}
+
+
+def test_all_simple_paths_with_two_targets_inside_cycle_emits_two_paths():
+    G = nx.cycle_graph(3, create_using=nx.DiGraph())
+    G.add_edge(1, 3)
+    paths = nx.all_simple_paths(G, 0, [2, 3])
+    assert {tuple(p) for p in paths} == {(0, 1, 2), (0, 1, 3)}
+
+
+def test_all_simple_paths_source_target():
+    G = nx.path_graph(4)
+    assert list(nx.all_simple_paths(G, 1, 1)) == [[1]]
+
+
+def test_all_simple_paths_cutoff():
+    G = nx.complete_graph(4)
+    paths = nx.all_simple_paths(G, 0, 1, cutoff=1)
+    assert {tuple(p) for p in paths} == {(0, 1)}
+    paths = nx.all_simple_paths(G, 0, 1, cutoff=2)
+    assert {tuple(p) for p in paths} == {(0, 1), (0, 2, 1), (0, 3, 1)}
+
+
+def test_all_simple_paths_on_non_trivial_graph():
+    """you may need to draw this graph to make sure it is reasonable"""
+    G = nx.path_graph(5, create_using=nx.DiGraph())
+    G.add_edges_from([(0, 5), (1, 5), (1, 3), (5, 4), (4, 2), (4, 3)])
+    paths = nx.all_simple_paths(G, 1, [2, 3])
+    assert {tuple(p) for p in paths} == {
+        (1, 2),
+        (1, 3, 4, 2),
+        (1, 5, 4, 2),
+        (1, 3),
+        (1, 2, 3),
+        (1, 5, 4, 3),
+        (1, 5, 4, 2, 3),
+    }
+    paths = nx.all_simple_paths(G, 1, [2, 3], cutoff=3)
+    assert {tuple(p) for p in paths} == {
+        (1, 2),
+        (1, 3, 4, 2),
+        (1, 5, 4, 2),
+        (1, 3),
+        (1, 2, 3),
+        (1, 5, 4, 3),
+    }
+    paths = nx.all_simple_paths(G, 1, [2, 3], cutoff=2)
+    assert {tuple(p) for p in paths} == {(1, 2), (1, 3), (1, 2, 3)}
+
+
+def test_all_simple_paths_multigraph():
+    G = nx.MultiGraph([(1, 2), (1, 2)])
+    assert list(nx.all_simple_paths(G, 1, 1)) == [[1]]
+    nx.add_path(G, [3, 1, 10, 2])
+    paths = list(nx.all_simple_paths(G, 1, 2))
+    assert len(paths) == 3
+    assert {tuple(p) for p in paths} == {(1, 2), (1, 2), (1, 10, 2)}
+
+
+def test_all_simple_paths_multigraph_with_cutoff():
+    G = nx.MultiGraph([(1, 2), (1, 2), (1, 10), (10, 2)])
+    paths = list(nx.all_simple_paths(G, 1, 2, cutoff=1))
+    assert len(paths) == 2
+    assert {tuple(p) for p in paths} == {(1, 2), (1, 2)}
+
+    # See GitHub issue #6732.
+    G = nx.MultiGraph([(0, 1), (0, 2)])
+    assert list(nx.all_simple_paths(G, 0, {1, 2}, cutoff=1)) == [[0, 1], [0, 2]]
+
+
+def test_all_simple_paths_directed():
+    G = nx.DiGraph()
+    nx.add_path(G, [1, 2, 3])
+    nx.add_path(G, [3, 2, 1])
+    paths = nx.all_simple_paths(G, 1, 3)
+    assert {tuple(p) for p in paths} == {(1, 2, 3)}
+
+
+def test_all_simple_paths_empty():
+    G = nx.path_graph(4)
+    paths = nx.all_simple_paths(G, 0, 3, cutoff=2)
+    assert list(paths) == []
+
+
+def test_all_simple_paths_corner_cases():
+    assert list(nx.all_simple_paths(nx.empty_graph(2), 0, 0)) == [[0]]
+    assert list(nx.all_simple_paths(nx.empty_graph(2), 0, 1)) == []
+    assert list(nx.all_simple_paths(nx.path_graph(9), 0, 8, 0)) == []
+
+
+def test_all_simple_paths_source_in_targets():
+    # See GitHub issue #6690.
+    G = nx.path_graph(3)
+    assert list(nx.all_simple_paths(G, 0, {0, 1, 2})) == [[0], [0, 1], [0, 1, 2]]
+
+
+def hamiltonian_path(G, source):
+    source = arbitrary_element(G)
+    neighbors = set(G[source]) - {source}
+    n = len(G)
+    for target in neighbors:
+        for path in nx.all_simple_paths(G, source, target):
+            if len(path) == n:
+                yield path
+
+
+def test_hamiltonian_path():
+    from itertools import permutations
+
+    G = nx.complete_graph(4)
+    paths = [list(p) for p in hamiltonian_path(G, 0)]
+    exact = [[0] + list(p) for p in permutations([1, 2, 3], 3)]
+    assert sorted(paths) == sorted(exact)
+
+
+def test_cutoff_zero():
+    G = nx.complete_graph(4)
+    paths = nx.all_simple_paths(G, 0, 3, cutoff=0)
+    assert [list(p) for p in paths] == []
+    paths = nx.all_simple_paths(nx.MultiGraph(G), 0, 3, cutoff=0)
+    assert [list(p) for p in paths] == []
+
+
+def test_source_missing():
+    with pytest.raises(nx.NodeNotFound):
+        G = nx.Graph()
+        nx.add_path(G, [1, 2, 3])
+        list(nx.all_simple_paths(nx.MultiGraph(G), 0, 3))
+
+
+def test_target_missing():
+    with pytest.raises(nx.NodeNotFound):
+        G = nx.Graph()
+        nx.add_path(G, [1, 2, 3])
+        list(nx.all_simple_paths(nx.MultiGraph(G), 1, 4))
+
+
+# Tests for all_simple_edge_paths
+def test_all_simple_edge_paths():
+    G = nx.path_graph(4)
+    paths = nx.all_simple_edge_paths(G, 0, 3)
+    assert {tuple(p) for p in paths} == {((0, 1), (1, 2), (2, 3))}
+
+
+def test_all_simple_edge_paths_empty_path():
+    G = nx.empty_graph(1)
+    assert list(nx.all_simple_edge_paths(G, 0, 0)) == [[]]
+
+
+def test_all_simple_edge_paths_with_two_targets_emits_two_paths():
+    G = nx.path_graph(4)
+    G.add_edge(2, 4)
+    paths = nx.all_simple_edge_paths(G, 0, [3, 4])
+    assert {tuple(p) for p in paths} == {
+        ((0, 1), (1, 2), (2, 3)),
+        ((0, 1), (1, 2), (2, 4)),
+    }
+
+
+def test_digraph_all_simple_edge_paths_with_two_targets_emits_two_paths():
+    G = nx.path_graph(4, create_using=nx.DiGraph())
+    G.add_edge(2, 4)
+    paths = nx.all_simple_edge_paths(G, 0, [3, 4])
+    assert {tuple(p) for p in paths} == {
+        ((0, 1), (1, 2), (2, 3)),
+        ((0, 1), (1, 2), (2, 4)),
+    }
+
+
+def test_all_simple_edge_paths_with_two_targets_cutoff():
+    G = nx.path_graph(4)
+    G.add_edge(2, 4)
+    paths = nx.all_simple_edge_paths(G, 0, [3, 4], cutoff=3)
+    assert {tuple(p) for p in paths} == {
+        ((0, 1), (1, 2), (2, 3)),
+        ((0, 1), (1, 2), (2, 4)),
+    }
+
+
+def test_digraph_all_simple_edge_paths_with_two_targets_cutoff():
+    G = nx.path_graph(4, create_using=nx.DiGraph())
+    G.add_edge(2, 4)
+    paths = nx.all_simple_edge_paths(G, 0, [3, 4], cutoff=3)
+    assert {tuple(p) for p in paths} == {
+        ((0, 1), (1, 2), (2, 3)),
+        ((0, 1), (1, 2), (2, 4)),
+    }
+
+
+def test_all_simple_edge_paths_with_two_targets_in_line_emits_two_paths():
+    G = nx.path_graph(4)
+    paths = nx.all_simple_edge_paths(G, 0, [2, 3])
+    assert {tuple(p) for p in paths} == {((0, 1), (1, 2)), ((0, 1), (1, 2), (2, 3))}
+
+
+def test_all_simple_edge_paths_ignores_cycle():
+    G = nx.cycle_graph(3, create_using=nx.DiGraph())
+    G.add_edge(1, 3)
+    paths = nx.all_simple_edge_paths(G, 0, 3)
+    assert {tuple(p) for p in paths} == {((0, 1), (1, 3))}
+
+
+def test_all_simple_edge_paths_with_two_targets_inside_cycle_emits_two_paths():
+    G = nx.cycle_graph(3, create_using=nx.DiGraph())
+    G.add_edge(1, 3)
+    paths = nx.all_simple_edge_paths(G, 0, [2, 3])
+    assert {tuple(p) for p in paths} == {((0, 1), (1, 2)), ((0, 1), (1, 3))}
+
+
+def test_all_simple_edge_paths_source_target():
+    G = nx.path_graph(4)
+    paths = nx.all_simple_edge_paths(G, 1, 1)
+    assert list(paths) == [[]]
+
+
+def test_all_simple_edge_paths_cutoff():
+    G = nx.complete_graph(4)
+    paths = nx.all_simple_edge_paths(G, 0, 1, cutoff=1)
+    assert {tuple(p) for p in paths} == {((0, 1),)}
+    paths = nx.all_simple_edge_paths(G, 0, 1, cutoff=2)
+    assert {tuple(p) for p in paths} == {((0, 1),), ((0, 2), (2, 1)), ((0, 3), (3, 1))}
+
+
+def test_all_simple_edge_paths_on_non_trivial_graph():
+    """you may need to draw this graph to make sure it is reasonable"""
+    G = nx.path_graph(5, create_using=nx.DiGraph())
+    G.add_edges_from([(0, 5), (1, 5), (1, 3), (5, 4), (4, 2), (4, 3)])
+    paths = nx.all_simple_edge_paths(G, 1, [2, 3])
+    assert {tuple(p) for p in paths} == {
+        ((1, 2),),
+        ((1, 3), (3, 4), (4, 2)),
+        ((1, 5), (5, 4), (4, 2)),
+        ((1, 3),),
+        ((1, 2), (2, 3)),
+        ((1, 5), (5, 4), (4, 3)),
+        ((1, 5), (5, 4), (4, 2), (2, 3)),
+    }
+    paths = nx.all_simple_edge_paths(G, 1, [2, 3], cutoff=3)
+    assert {tuple(p) for p in paths} == {
+        ((1, 2),),
+        ((1, 3), (3, 4), (4, 2)),
+        ((1, 5), (5, 4), (4, 2)),
+        ((1, 3),),
+        ((1, 2), (2, 3)),
+        ((1, 5), (5, 4), (4, 3)),
+    }
+    paths = nx.all_simple_edge_paths(G, 1, [2, 3], cutoff=2)
+    assert {tuple(p) for p in paths} == {((1, 2),), ((1, 3),), ((1, 2), (2, 3))}
+
+
+def test_all_simple_edge_paths_multigraph():
+    G = nx.MultiGraph([(1, 2), (1, 2)])
+    paths = nx.all_simple_edge_paths(G, 1, 1)
+    assert list(paths) == [[]]
+    nx.add_path(G, [3, 1, 10, 2])
+    paths = list(nx.all_simple_edge_paths(G, 1, 2))
+    assert len(paths) == 3
+    assert {tuple(p) for p in paths} == {
+        ((1, 2, 0),),
+        ((1, 2, 1),),
+        ((1, 10, 0), (10, 2, 0)),
+    }
+
+
+def test_all_simple_edge_paths_multigraph_with_cutoff():
+    G = nx.MultiGraph([(1, 2), (1, 2), (1, 10), (10, 2)])
+    paths = list(nx.all_simple_edge_paths(G, 1, 2, cutoff=1))
+    assert len(paths) == 2
+    assert {tuple(p) for p in paths} == {((1, 2, 0),), ((1, 2, 1),)}
+
+
+def test_all_simple_edge_paths_directed():
+    G = nx.DiGraph()
+    nx.add_path(G, [1, 2, 3])
+    nx.add_path(G, [3, 2, 1])
+    paths = nx.all_simple_edge_paths(G, 1, 3)
+    assert {tuple(p) for p in paths} == {((1, 2), (2, 3))}
+
+
+def test_all_simple_edge_paths_empty():
+    G = nx.path_graph(4)
+    paths = nx.all_simple_edge_paths(G, 0, 3, cutoff=2)
+    assert list(paths) == []
+
+
+def test_all_simple_edge_paths_corner_cases():
+    assert list(nx.all_simple_edge_paths(nx.empty_graph(2), 0, 0)) == [[]]
+    assert list(nx.all_simple_edge_paths(nx.empty_graph(2), 0, 1)) == []
+    assert list(nx.all_simple_edge_paths(nx.path_graph(9), 0, 8, 0)) == []
+
+
+def test_all_simple_edge_paths_ignores_self_loop():
+    G = nx.Graph([(0, 0), (0, 1), (1, 1), (1, 2)])
+    assert list(nx.all_simple_edge_paths(G, 0, 2)) == [[(0, 1), (1, 2)]]
+
+
+def hamiltonian_edge_path(G, source):
+    source = arbitrary_element(G)
+    neighbors = set(G[source]) - {source}
+    n = len(G)
+    for target in neighbors:
+        for path in nx.all_simple_edge_paths(G, source, target):
+            if len(path) == n - 1:
+                yield path
+
+
+def test_hamiltonian__edge_path():
+    from itertools import permutations
+
+    G = nx.complete_graph(4)
+    paths = hamiltonian_edge_path(G, 0)
+    exact = [list(pairwise([0] + list(p))) for p in permutations([1, 2, 3], 3)]
+    assert sorted(exact) == sorted(paths)
+
+
+def test_edge_cutoff_zero():
+    G = nx.complete_graph(4)
+    paths = nx.all_simple_edge_paths(G, 0, 3, cutoff=0)
+    assert [list(p) for p in paths] == []
+    paths = nx.all_simple_edge_paths(nx.MultiGraph(G), 0, 3, cutoff=0)
+    assert [list(p) for p in paths] == []
+
+
+def test_edge_source_missing():
+    with pytest.raises(nx.NodeNotFound):
+        G = nx.Graph()
+        nx.add_path(G, [1, 2, 3])
+        list(nx.all_simple_edge_paths(nx.MultiGraph(G), 0, 3))
+
+
+def test_edge_target_missing():
+    with pytest.raises(nx.NodeNotFound):
+        G = nx.Graph()
+        nx.add_path(G, [1, 2, 3])
+        list(nx.all_simple_edge_paths(nx.MultiGraph(G), 1, 4))
+
+
+# Tests for shortest_simple_paths
+def test_shortest_simple_paths():
+    G = cnlti(nx.grid_2d_graph(4, 4), first_label=1, ordering="sorted")
+    paths = nx.shortest_simple_paths(G, 1, 12)
+    assert next(paths) == [1, 2, 3, 4, 8, 12]
+    assert next(paths) == [1, 5, 6, 7, 8, 12]
+    assert [len(path) for path in nx.shortest_simple_paths(G, 1, 12)] == sorted(
+        len(path) for path in nx.all_simple_paths(G, 1, 12)
+    )
+
+
+def test_shortest_simple_paths_singleton_path():
+    G = nx.empty_graph(3)
+    assert list(nx.shortest_simple_paths(G, 0, 0)) == [[0]]
+
+
+def test_shortest_simple_paths_directed():
+    G = nx.cycle_graph(7, create_using=nx.DiGraph())
+    paths = nx.shortest_simple_paths(G, 0, 3)
+    assert list(paths) == [[0, 1, 2, 3]]
+
+
+def test_shortest_simple_paths_directed_with_weight_function():
+    def cost(u, v, x):
+        return 1
+
+    G = cnlti(nx.grid_2d_graph(4, 4), first_label=1, ordering="sorted")
+    paths = nx.shortest_simple_paths(G, 1, 12)
+    assert next(paths) == [1, 2, 3, 4, 8, 12]
+    assert next(paths) == [1, 5, 6, 7, 8, 12]
+    assert [
+        len(path) for path in nx.shortest_simple_paths(G, 1, 12, weight=cost)
+    ] == sorted(len(path) for path in nx.all_simple_paths(G, 1, 12))
+
+
+def test_shortest_simple_paths_with_weight_function():
+    def cost(u, v, x):
+        return 1
+
+    G = nx.cycle_graph(7, create_using=nx.DiGraph())
+    paths = nx.shortest_simple_paths(G, 0, 3, weight=cost)
+    assert list(paths) == [[0, 1, 2, 3]]
+
+
+def test_Greg_Bernstein():
+    g1 = nx.Graph()
+    g1.add_nodes_from(["N0", "N1", "N2", "N3", "N4"])
+    g1.add_edge("N4", "N1", weight=10.0, capacity=50, name="L5")
+    g1.add_edge("N4", "N0", weight=7.0, capacity=40, name="L4")
+    g1.add_edge("N0", "N1", weight=10.0, capacity=45, name="L1")
+    g1.add_edge("N3", "N0", weight=10.0, capacity=50, name="L0")
+    g1.add_edge("N2", "N3", weight=12.0, capacity=30, name="L2")
+    g1.add_edge("N1", "N2", weight=15.0, capacity=42, name="L3")
+    solution = [["N1", "N0", "N3"], ["N1", "N2", "N3"], ["N1", "N4", "N0", "N3"]]
+    result = list(nx.shortest_simple_paths(g1, "N1", "N3", weight="weight"))
+    assert result == solution
+
+
+def test_weighted_shortest_simple_path():
+    def cost_func(path):
+        return sum(G.adj[u][v]["weight"] for (u, v) in zip(path, path[1:]))
+
+    G = nx.complete_graph(5)
+    weight = {(u, v): random.randint(1, 100) for (u, v) in G.edges()}
+    nx.set_edge_attributes(G, weight, "weight")
+    cost = 0
+    for path in nx.shortest_simple_paths(G, 0, 3, weight="weight"):
+        this_cost = cost_func(path)
+        assert cost <= this_cost
+        cost = this_cost
+
+
+def test_directed_weighted_shortest_simple_path():
+    def cost_func(path):
+        return sum(G.adj[u][v]["weight"] for (u, v) in zip(path, path[1:]))
+
+    G = nx.complete_graph(5)
+    G = G.to_directed()
+    weight = {(u, v): random.randint(1, 100) for (u, v) in G.edges()}
+    nx.set_edge_attributes(G, weight, "weight")
+    cost = 0
+    for path in nx.shortest_simple_paths(G, 0, 3, weight="weight"):
+        this_cost = cost_func(path)
+        assert cost <= this_cost
+        cost = this_cost
+
+
+def test_weighted_shortest_simple_path_issue2427():
+    G = nx.Graph()
+    G.add_edge("IN", "OUT", weight=2)
+    G.add_edge("IN", "A", weight=1)
+    G.add_edge("IN", "B", weight=2)
+    G.add_edge("B", "OUT", weight=2)
+    assert list(nx.shortest_simple_paths(G, "IN", "OUT", weight="weight")) == [
+        ["IN", "OUT"],
+        ["IN", "B", "OUT"],
+    ]
+    G = nx.Graph()
+    G.add_edge("IN", "OUT", weight=10)
+    G.add_edge("IN", "A", weight=1)
+    G.add_edge("IN", "B", weight=1)
+    G.add_edge("B", "OUT", weight=1)
+    assert list(nx.shortest_simple_paths(G, "IN", "OUT", weight="weight")) == [
+        ["IN", "B", "OUT"],
+        ["IN", "OUT"],
+    ]
+
+
+def test_directed_weighted_shortest_simple_path_issue2427():
+    G = nx.DiGraph()
+    G.add_edge("IN", "OUT", weight=2)
+    G.add_edge("IN", "A", weight=1)
+    G.add_edge("IN", "B", weight=2)
+    G.add_edge("B", "OUT", weight=2)
+    assert list(nx.shortest_simple_paths(G, "IN", "OUT", weight="weight")) == [
+        ["IN", "OUT"],
+        ["IN", "B", "OUT"],
+    ]
+    G = nx.DiGraph()
+    G.add_edge("IN", "OUT", weight=10)
+    G.add_edge("IN", "A", weight=1)
+    G.add_edge("IN", "B", weight=1)
+    G.add_edge("B", "OUT", weight=1)
+    assert list(nx.shortest_simple_paths(G, "IN", "OUT", weight="weight")) == [
+        ["IN", "B", "OUT"],
+        ["IN", "OUT"],
+    ]
+
+
+def test_weight_name():
+    G = nx.cycle_graph(7)
+    nx.set_edge_attributes(G, 1, "weight")
+    nx.set_edge_attributes(G, 1, "foo")
+    G.adj[1][2]["foo"] = 7
+    paths = list(nx.shortest_simple_paths(G, 0, 3, weight="foo"))
+    solution = [[0, 6, 5, 4, 3], [0, 1, 2, 3]]
+    assert paths == solution
+
+
+def test_ssp_source_missing():
+    with pytest.raises(nx.NodeNotFound):
+        G = nx.Graph()
+        nx.add_path(G, [1, 2, 3])
+        list(nx.shortest_simple_paths(G, 0, 3))
+
+
+def test_ssp_target_missing():
+    with pytest.raises(nx.NodeNotFound):
+        G = nx.Graph()
+        nx.add_path(G, [1, 2, 3])
+        list(nx.shortest_simple_paths(G, 1, 4))
+
+
+def test_ssp_multigraph():
+    with pytest.raises(nx.NetworkXNotImplemented):
+        G = nx.MultiGraph()
+        nx.add_path(G, [1, 2, 3])
+        list(nx.shortest_simple_paths(G, 1, 4))
+
+
+def test_ssp_source_missing2():
+    with pytest.raises(nx.NetworkXNoPath):
+        G = nx.Graph()
+        nx.add_path(G, [0, 1, 2])
+        nx.add_path(G, [3, 4, 5])
+        list(nx.shortest_simple_paths(G, 0, 3))
+
+
+def test_bidirectional_shortest_path_restricted_cycle():
+    cycle = nx.cycle_graph(7)
+    length, path = _bidirectional_shortest_path(cycle, 0, 3)
+    assert path == [0, 1, 2, 3]
+    length, path = _bidirectional_shortest_path(cycle, 0, 3, ignore_nodes=[1])
+    assert path == [0, 6, 5, 4, 3]
+
+
+def test_bidirectional_shortest_path_restricted_wheel():
+    wheel = nx.wheel_graph(6)
+    length, path = _bidirectional_shortest_path(wheel, 1, 3)
+    assert path in [[1, 0, 3], [1, 2, 3]]
+    length, path = _bidirectional_shortest_path(wheel, 1, 3, ignore_nodes=[0])
+    assert path == [1, 2, 3]
+    length, path = _bidirectional_shortest_path(wheel, 1, 3, ignore_nodes=[0, 2])
+    assert path == [1, 5, 4, 3]
+    length, path = _bidirectional_shortest_path(
+        wheel, 1, 3, ignore_edges=[(1, 0), (5, 0), (2, 3)]
+    )
+    assert path in [[1, 2, 0, 3], [1, 5, 4, 3]]
+
+
+def test_bidirectional_shortest_path_restricted_directed_cycle():
+    directed_cycle = nx.cycle_graph(7, create_using=nx.DiGraph())
+    length, path = _bidirectional_shortest_path(directed_cycle, 0, 3)
+    assert path == [0, 1, 2, 3]
+    pytest.raises(
+        nx.NetworkXNoPath,
+        _bidirectional_shortest_path,
+        directed_cycle,
+        0,
+        3,
+        ignore_nodes=[1],
+    )
+    length, path = _bidirectional_shortest_path(
+        directed_cycle, 0, 3, ignore_edges=[(2, 1)]
+    )
+    assert path == [0, 1, 2, 3]
+    pytest.raises(
+        nx.NetworkXNoPath,
+        _bidirectional_shortest_path,
+        directed_cycle,
+        0,
+        3,
+        ignore_edges=[(1, 2)],
+    )
+
+
+def test_bidirectional_shortest_path_ignore():
+    G = nx.Graph()
+    nx.add_path(G, [1, 2])
+    nx.add_path(G, [1, 3])
+    nx.add_path(G, [1, 4])
+    pytest.raises(
+        nx.NetworkXNoPath, _bidirectional_shortest_path, G, 1, 2, ignore_nodes=[1]
+    )
+    pytest.raises(
+        nx.NetworkXNoPath, _bidirectional_shortest_path, G, 1, 2, ignore_nodes=[2]
+    )
+    G = nx.Graph()
+    nx.add_path(G, [1, 3])
+    nx.add_path(G, [1, 4])
+    nx.add_path(G, [3, 2])
+    pytest.raises(
+        nx.NetworkXNoPath, _bidirectional_shortest_path, G, 1, 2, ignore_nodes=[1, 2]
+    )
+
+
+def validate_path(G, s, t, soln_len, path):
+    assert path[0] == s
+    assert path[-1] == t
+    assert soln_len == sum(
+        G[u][v].get("weight", 1) for u, v in zip(path[:-1], path[1:])
+    )
+
+
+def validate_length_path(G, s, t, soln_len, length, path):
+    assert soln_len == length
+    validate_path(G, s, t, length, path)
+
+
+def test_bidirectional_dijkstra_restricted():
+    XG = nx.DiGraph()
+    XG.add_weighted_edges_from(
+        [
+            ("s", "u", 10),
+            ("s", "x", 5),
+            ("u", "v", 1),
+            ("u", "x", 2),
+            ("v", "y", 1),
+            ("x", "u", 3),
+            ("x", "v", 5),
+            ("x", "y", 2),
+            ("y", "s", 7),
+            ("y", "v", 6),
+        ]
+    )
+
+    XG3 = nx.Graph()
+    XG3.add_weighted_edges_from(
+        [[0, 1, 2], [1, 2, 12], [2, 3, 1], [3, 4, 5], [4, 5, 1], [5, 0, 10]]
+    )
+    validate_length_path(XG, "s", "v", 9, *_bidirectional_dijkstra(XG, "s", "v"))
+    validate_length_path(
+        XG, "s", "v", 10, *_bidirectional_dijkstra(XG, "s", "v", ignore_nodes=["u"])
+    )
+    validate_length_path(
+        XG,
+        "s",
+        "v",
+        11,
+        *_bidirectional_dijkstra(XG, "s", "v", ignore_edges=[("s", "x")]),
+    )
+    pytest.raises(
+        nx.NetworkXNoPath,
+        _bidirectional_dijkstra,
+        XG,
+        "s",
+        "v",
+        ignore_nodes=["u"],
+        ignore_edges=[("s", "x")],
+    )
+    validate_length_path(XG3, 0, 3, 15, *_bidirectional_dijkstra(XG3, 0, 3))
+    validate_length_path(
+        XG3, 0, 3, 16, *_bidirectional_dijkstra(XG3, 0, 3, ignore_nodes=[1])
+    )
+    validate_length_path(
+        XG3, 0, 3, 16, *_bidirectional_dijkstra(XG3, 0, 3, ignore_edges=[(2, 3)])
+    )
+    pytest.raises(
+        nx.NetworkXNoPath,
+        _bidirectional_dijkstra,
+        XG3,
+        0,
+        3,
+        ignore_nodes=[1],
+        ignore_edges=[(5, 4)],
+    )
+
+
+def test_bidirectional_dijkstra_no_path():
+    with pytest.raises(nx.NetworkXNoPath):
+        G = nx.Graph()
+        nx.add_path(G, [1, 2, 3])
+        nx.add_path(G, [4, 5, 6])
+        _bidirectional_dijkstra(G, 1, 6)
+
+
+def test_bidirectional_dijkstra_ignore():
+    G = nx.Graph()
+    nx.add_path(G, [1, 2, 10])
+    nx.add_path(G, [1, 3, 10])
+    pytest.raises(nx.NetworkXNoPath, _bidirectional_dijkstra, G, 1, 2, ignore_nodes=[1])
+    pytest.raises(nx.NetworkXNoPath, _bidirectional_dijkstra, G, 1, 2, ignore_nodes=[2])
+    pytest.raises(
+        nx.NetworkXNoPath, _bidirectional_dijkstra, G, 1, 2, ignore_nodes=[1, 2]
+    )

valley/lib/python3.10/site-packages/networkx/algorithms/tests/test_threshold.py

@@ -0,0 +1,269 @@
+"""
+Threshold Graphs
+================
+"""
+
+import pytest
+
+import networkx as nx
+import networkx.algorithms.threshold as nxt
+from networkx.algorithms.isomorphism.isomorph import graph_could_be_isomorphic
+
+cnlti = nx.convert_node_labels_to_integers
+
+
+class TestGeneratorThreshold:
+    def test_threshold_sequence_graph_test(self):
+        G = nx.star_graph(10)
+        assert nxt.is_threshold_graph(G)
+        assert nxt.is_threshold_sequence([d for n, d in G.degree()])
+
+        G = nx.complete_graph(10)
+        assert nxt.is_threshold_graph(G)
+        assert nxt.is_threshold_sequence([d for n, d in G.degree()])
+
+        deg = [3, 2, 2, 1, 1, 1]
+        assert not nxt.is_threshold_sequence(deg)
+
+        deg = [3, 2, 2, 1]
+        assert nxt.is_threshold_sequence(deg)
+
+        G = nx.generators.havel_hakimi_graph(deg)
+        assert nxt.is_threshold_graph(G)
+
+    def test_creation_sequences(self):
+        deg = [3, 2, 2, 1]
+        G = nx.generators.havel_hakimi_graph(deg)
+
+        with pytest.raises(ValueError):
+            nxt.creation_sequence(deg, with_labels=True, compact=True)
+
+        cs0 = nxt.creation_sequence(deg)
+        H0 = nxt.threshold_graph(cs0)
+        assert "".join(cs0) == "ddid"
+
+        cs1 = nxt.creation_sequence(deg, with_labels=True)
+        H1 = nxt.threshold_graph(cs1)
+        assert cs1 == [(1, "d"), (2, "d"), (3, "i"), (0, "d")]
+
+        cs2 = nxt.creation_sequence(deg, compact=True)
+        H2 = nxt.threshold_graph(cs2)
+        assert cs2 == [2, 1, 1]
+        assert "".join(nxt.uncompact(cs2)) == "ddid"
+        assert graph_could_be_isomorphic(H0, G)
+        assert graph_could_be_isomorphic(H0, H1)
+        assert graph_could_be_isomorphic(H0, H2)
+
+    def test_make_compact(self):
+        assert nxt.make_compact(["d", "d", "d", "i", "d", "d"]) == [3, 1, 2]
+        assert nxt.make_compact([3, 1, 2]) == [3, 1, 2]
+        assert pytest.raises(TypeError, nxt.make_compact, [3.0, 1.0, 2.0])
+
+    def test_uncompact(self):
+        assert nxt.uncompact([3, 1, 2]) == ["d", "d", "d", "i", "d", "d"]
+        assert nxt.uncompact(["d", "d", "i", "d"]) == ["d", "d", "i", "d"]
+        assert nxt.uncompact(
+            nxt.uncompact([(1, "d"), (2, "d"), (3, "i"), (0, "d")])
+        ) == nxt.uncompact([(1, "d"), (2, "d"), (3, "i"), (0, "d")])
+        assert pytest.raises(TypeError, nxt.uncompact, [3.0, 1.0, 2.0])
+
+    def test_creation_sequence_to_weights(self):
+        assert nxt.creation_sequence_to_weights([3, 1, 2]) == [
+            0.5,
+            0.5,
+            0.5,
+            0.25,
+            0.75,
+            0.75,
+        ]
+        assert pytest.raises(
+            TypeError, nxt.creation_sequence_to_weights, [3.0, 1.0, 2.0]
+        )
+
+    def test_weights_to_creation_sequence(self):
+        deg = [3, 2, 2, 1]
+        with pytest.raises(ValueError):
+            nxt.weights_to_creation_sequence(deg, with_labels=True, compact=True)
+        assert nxt.weights_to_creation_sequence(deg, with_labels=True) == [
+            (3, "d"),
+            (1, "d"),
+            (2, "d"),
+            (0, "d"),
+        ]
+        assert nxt.weights_to_creation_sequence(deg, compact=True) == [4]
+
+    def test_find_alternating_4_cycle(self):
+        G = nx.Graph()
+        G.add_edge(1, 2)
+        assert not nxt.find_alternating_4_cycle(G)
+
+    def test_shortest_path(self):
+        deg = [3, 2, 2, 1]
+        G = nx.generators.havel_hakimi_graph(deg)
+        cs1 = nxt.creation_sequence(deg, with_labels=True)
+        for n, m in [(3, 0), (0, 3), (0, 2), (0, 1), (1, 3), (3, 1), (1, 2), (2, 3)]:
+            assert nxt.shortest_path(cs1, n, m) == nx.shortest_path(G, n, m)
+
+        spl = nxt.shortest_path_length(cs1, 3)
+        spl2 = nxt.shortest_path_length([t for v, t in cs1], 2)
+        assert spl == spl2
+
+        spld = {}
+        for j, pl in enumerate(spl):
+            n = cs1[j][0]
+            spld[n] = pl
+        assert spld == nx.single_source_shortest_path_length(G, 3)
+
+        assert nxt.shortest_path(["d", "d", "d", "i", "d", "d"], 1, 2) == [1, 2]
+        assert nxt.shortest_path([3, 1, 2], 1, 2) == [1, 2]
+        assert pytest.raises(TypeError, nxt.shortest_path, [3.0, 1.0, 2.0], 1, 2)
+        assert pytest.raises(ValueError, nxt.shortest_path, [3, 1, 2], "a", 2)
+        assert pytest.raises(ValueError, nxt.shortest_path, [3, 1, 2], 1, "b")
+        assert nxt.shortest_path([3, 1, 2], 1, 1) == [1]
+
+    def test_shortest_path_length(self):
+        assert nxt.shortest_path_length([3, 1, 2], 1) == [1, 0, 1, 2, 1, 1]
+        assert nxt.shortest_path_length(["d", "d", "d", "i", "d", "d"], 1) == [
+            1,
+            0,
+            1,
+            2,
+            1,
+            1,
+        ]
+        assert nxt.shortest_path_length(("d", "d", "d", "i", "d", "d"), 1) == [
+            1,
+            0,
+            1,
+            2,
+            1,
+            1,
+        ]
+        assert pytest.raises(TypeError, nxt.shortest_path, [3.0, 1.0, 2.0], 1)
+
+    def test_random_threshold_sequence(self):
+        assert len(nxt.random_threshold_sequence(10, 0.5)) == 10
+        assert nxt.random_threshold_sequence(10, 0.5, seed=42) == [
+            "d",
+            "i",
+            "d",
+            "d",
+            "d",
+            "i",
+            "i",
+            "i",
+            "d",
+            "d",
+        ]
+        assert pytest.raises(ValueError, nxt.random_threshold_sequence, 10, 1.5)
+
+    def test_right_d_threshold_sequence(self):
+        assert nxt.right_d_threshold_sequence(3, 2) == ["d", "i", "d"]
+        assert pytest.raises(ValueError, nxt.right_d_threshold_sequence, 2, 3)
+
+    def test_left_d_threshold_sequence(self):
+        assert nxt.left_d_threshold_sequence(3, 2) == ["d", "i", "d"]
+        assert pytest.raises(ValueError, nxt.left_d_threshold_sequence, 2, 3)
+
+    def test_weights_thresholds(self):
+        wseq = [3, 4, 3, 3, 5, 6, 5, 4, 5, 6]
+        cs = nxt.weights_to_creation_sequence(wseq, threshold=10)
+        wseq = nxt.creation_sequence_to_weights(cs)
+        cs2 = nxt.weights_to_creation_sequence(wseq)
+        assert cs == cs2
+
+        wseq = nxt.creation_sequence_to_weights(nxt.uncompact([3, 1, 2, 3, 3, 2, 3]))
+        assert wseq == [
+            s * 0.125 for s in [4, 4, 4, 3, 5, 5, 2, 2, 2, 6, 6, 6, 1, 1, 7, 7, 7]
+        ]
+
+        wseq = nxt.creation_sequence_to_weights([3, 1, 2, 3, 3, 2, 3])
+        assert wseq == [
+            s * 0.125 for s in [4, 4, 4, 3, 5, 5, 2, 2, 2, 6, 6, 6, 1, 1, 7, 7, 7]
+        ]
+
+        wseq = nxt.creation_sequence_to_weights(list(enumerate("ddidiiidididi")))
+        assert wseq == [s * 0.1 for s in [5, 5, 4, 6, 3, 3, 3, 7, 2, 8, 1, 9, 0]]
+
+        wseq = nxt.creation_sequence_to_weights("ddidiiidididi")
+        assert wseq == [s * 0.1 for s in [5, 5, 4, 6, 3, 3, 3, 7, 2, 8, 1, 9, 0]]
+
+        wseq = nxt.creation_sequence_to_weights("ddidiiidididid")
+        ws = [s / 12 for s in [6, 6, 5, 7, 4, 4, 4, 8, 3, 9, 2, 10, 1, 11]]
+        assert sum(abs(c - d) for c, d in zip(wseq, ws)) < 1e-14
+
+    def test_finding_routines(self):
+        G = nx.Graph({1: [2], 2: [3], 3: [4], 4: [5], 5: [6]})
+        G.add_edge(2, 4)
+        G.add_edge(2, 5)
+        G.add_edge(2, 7)
+        G.add_edge(3, 6)
+        G.add_edge(4, 6)
+
+        # Alternating 4 cycle
+        assert nxt.find_alternating_4_cycle(G) == [1, 2, 3, 6]
+
+        # Threshold graph
+        TG = nxt.find_threshold_graph(G)
+        assert nxt.is_threshold_graph(TG)
+        assert sorted(TG.nodes()) == [1, 2, 3, 4, 5, 7]
+
+        cs = nxt.creation_sequence(dict(TG.degree()), with_labels=True)
+        assert nxt.find_creation_sequence(G) == cs
+
+    def test_fast_versions_properties_threshold_graphs(self):
+        cs = "ddiiddid"
+        G = nxt.threshold_graph(cs)
+        assert nxt.density("ddiiddid") == nx.density(G)
+        assert sorted(nxt.degree_sequence(cs)) == sorted(d for n, d in G.degree())
+
+        ts = nxt.triangle_sequence(cs)
+        assert ts == list(nx.triangles(G).values())
+        assert sum(ts) // 3 == nxt.triangles(cs)
+
+        c1 = nxt.cluster_sequence(cs)
+        c2 = list(nx.clustering(G).values())
+        assert sum(abs(c - d) for c, d in zip(c1, c2)) == pytest.approx(0, abs=1e-7)
+
+        b1 = nx.betweenness_centrality(G).values()
+        b2 = nxt.betweenness_sequence(cs)
+        assert sum(abs(c - d) for c, d in zip(b1, b2)) < 1e-7
+
+        assert nxt.eigenvalues(cs) == [0, 1, 3, 3, 5, 7, 7, 8]
+
+        # Degree Correlation
+        assert abs(nxt.degree_correlation(cs) + 0.593038821954) < 1e-12
+        assert nxt.degree_correlation("diiiddi") == -0.8
+        assert nxt.degree_correlation("did") == -1.0
+        assert nxt.degree_correlation("ddd") == 1.0
+        assert nxt.eigenvalues("dddiii") == [0, 0, 0, 0, 3, 3]
+        assert nxt.eigenvalues("dddiiid") == [0, 1, 1, 1, 4, 4, 7]
+
+    def test_tg_creation_routines(self):
+        s = nxt.left_d_threshold_sequence(5, 7)
+        s = nxt.right_d_threshold_sequence(5, 7)
+        s1 = nxt.swap_d(s, 1.0, 1.0)
+        s1 = nxt.swap_d(s, 1.0, 1.0, seed=1)
+
+    def test_eigenvectors(self):
+        np = pytest.importorskip("numpy")
+        eigenval = np.linalg.eigvals
+        pytest.importorskip("scipy")
+
+        cs = "ddiiddid"
+        G = nxt.threshold_graph(cs)
+        (tgeval, tgevec) = nxt.eigenvectors(cs)
+        np.testing.assert_allclose([np.dot(lv, lv) for lv in tgevec], 1.0, rtol=1e-9)
+        lapl = nx.laplacian_matrix(G)
+
+    def test_create_using(self):
+        cs = "ddiiddid"
+        G = nxt.threshold_graph(cs)
+        assert pytest.raises(
+            nx.exception.NetworkXError,
+            nxt.threshold_graph,
+            cs,
+            create_using=nx.DiGraph(),
+        )
+        MG = nxt.threshold_graph(cs, create_using=nx.MultiGraph())
+        assert sorted(MG.edges()) == sorted(G.edges())

valley/lib/python3.10/site-packages/networkx/algorithms/tests/test_time_dependent.py

@@ -0,0 +1,431 @@
+"""Unit testing for time dependent algorithms."""
+
+from datetime import datetime, timedelta
+
+import pytest
+
+import networkx as nx
+
+_delta = timedelta(days=5 * 365)
+
+
+class TestCdIndex:
+    """Unit testing for the cd index function."""
+
+    def test_common_graph(self):
+        G = nx.DiGraph()
+        G.add_nodes_from([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+        G.add_edge(4, 2)
+        G.add_edge(4, 0)
+        G.add_edge(4, 1)
+        G.add_edge(4, 3)
+        G.add_edge(5, 2)
+        G.add_edge(6, 2)
+        G.add_edge(6, 4)
+        G.add_edge(7, 4)
+        G.add_edge(8, 4)
+        G.add_edge(9, 4)
+        G.add_edge(9, 1)
+        G.add_edge(9, 3)
+        G.add_edge(10, 4)
+
+        node_attrs = {
+            0: {"time": datetime(1992, 1, 1)},
+            1: {"time": datetime(1992, 1, 1)},
+            2: {"time": datetime(1993, 1, 1)},
+            3: {"time": datetime(1993, 1, 1)},
+            4: {"time": datetime(1995, 1, 1)},
+            5: {"time": datetime(1997, 1, 1)},
+            6: {"time": datetime(1998, 1, 1)},
+            7: {"time": datetime(1999, 1, 1)},
+            8: {"time": datetime(1999, 1, 1)},
+            9: {"time": datetime(1998, 1, 1)},
+            10: {"time": datetime(1997, 4, 1)},
+        }
+
+        nx.set_node_attributes(G, node_attrs)
+
+        assert nx.cd_index(G, 4, time_delta=_delta) == 0.17
+
+    def test_common_graph_with_given_attributes(self):
+        G = nx.DiGraph()
+        G.add_nodes_from([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+        G.add_edge(4, 2)
+        G.add_edge(4, 0)
+        G.add_edge(4, 1)
+        G.add_edge(4, 3)
+        G.add_edge(5, 2)
+        G.add_edge(6, 2)
+        G.add_edge(6, 4)
+        G.add_edge(7, 4)
+        G.add_edge(8, 4)
+        G.add_edge(9, 4)
+        G.add_edge(9, 1)
+        G.add_edge(9, 3)
+        G.add_edge(10, 4)
+
+        node_attrs = {
+            0: {"date": datetime(1992, 1, 1)},
+            1: {"date": datetime(1992, 1, 1)},
+            2: {"date": datetime(1993, 1, 1)},
+            3: {"date": datetime(1993, 1, 1)},
+            4: {"date": datetime(1995, 1, 1)},
+            5: {"date": datetime(1997, 1, 1)},
+            6: {"date": datetime(1998, 1, 1)},
+            7: {"date": datetime(1999, 1, 1)},
+            8: {"date": datetime(1999, 1, 1)},
+            9: {"date": datetime(1998, 1, 1)},
+            10: {"date": datetime(1997, 4, 1)},
+        }
+
+        nx.set_node_attributes(G, node_attrs)
+
+        assert nx.cd_index(G, 4, time_delta=_delta, time="date") == 0.17
+
+    def test_common_graph_with_int_attributes(self):
+        G = nx.DiGraph()
+        G.add_nodes_from([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+        G.add_edge(4, 2)
+        G.add_edge(4, 0)
+        G.add_edge(4, 1)
+        G.add_edge(4, 3)
+        G.add_edge(5, 2)
+        G.add_edge(6, 2)
+        G.add_edge(6, 4)
+        G.add_edge(7, 4)
+        G.add_edge(8, 4)
+        G.add_edge(9, 4)
+        G.add_edge(9, 1)
+        G.add_edge(9, 3)
+        G.add_edge(10, 4)
+
+        node_attrs = {
+            0: {"time": 20},
+            1: {"time": 20},
+            2: {"time": 30},
+            3: {"time": 30},
+            4: {"time": 50},
+            5: {"time": 70},
+            6: {"time": 80},
+            7: {"time": 90},
+            8: {"time": 90},
+            9: {"time": 80},
+            10: {"time": 74},
+        }
+
+        nx.set_node_attributes(G, node_attrs)
+
+        assert nx.cd_index(G, 4, time_delta=50) == 0.17
+
+    def test_common_graph_with_float_attributes(self):
+        G = nx.DiGraph()
+        G.add_nodes_from([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+        G.add_edge(4, 2)
+        G.add_edge(4, 0)
+        G.add_edge(4, 1)
+        G.add_edge(4, 3)
+        G.add_edge(5, 2)
+        G.add_edge(6, 2)
+        G.add_edge(6, 4)
+        G.add_edge(7, 4)
+        G.add_edge(8, 4)
+        G.add_edge(9, 4)
+        G.add_edge(9, 1)
+        G.add_edge(9, 3)
+        G.add_edge(10, 4)
+
+        node_attrs = {
+            0: {"time": 20.2},
+            1: {"time": 20.2},
+            2: {"time": 30.7},
+            3: {"time": 30.7},
+            4: {"time": 50.9},
+            5: {"time": 70.1},
+            6: {"time": 80.6},
+            7: {"time": 90.7},
+            8: {"time": 90.7},
+            9: {"time": 80.6},
+            10: {"time": 74.2},
+        }
+
+        nx.set_node_attributes(G, node_attrs)
+
+        assert nx.cd_index(G, 4, time_delta=50) == 0.17
+
+    def test_common_graph_with_weights(self):
+        G = nx.DiGraph()
+        G.add_nodes_from([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+        G.add_edge(4, 2)
+        G.add_edge(4, 0)
+        G.add_edge(4, 1)
+        G.add_edge(4, 3)
+        G.add_edge(5, 2)
+        G.add_edge(6, 2)
+        G.add_edge(6, 4)
+        G.add_edge(7, 4)
+        G.add_edge(8, 4)
+        G.add_edge(9, 4)
+        G.add_edge(9, 1)
+        G.add_edge(9, 3)
+        G.add_edge(10, 4)
+
+        node_attrs = {
+            0: {"time": datetime(1992, 1, 1)},
+            1: {"time": datetime(1992, 1, 1)},
+            2: {"time": datetime(1993, 1, 1)},
+            3: {"time": datetime(1993, 1, 1)},
+            4: {"time": datetime(1995, 1, 1)},
+            5: {"time": datetime(1997, 1, 1)},
+            6: {"time": datetime(1998, 1, 1), "weight": 5},
+            7: {"time": datetime(1999, 1, 1), "weight": 2},
+            8: {"time": datetime(1999, 1, 1), "weight": 6},
+            9: {"time": datetime(1998, 1, 1), "weight": 3},
+            10: {"time": datetime(1997, 4, 1), "weight": 10},
+        }
+
+        nx.set_node_attributes(G, node_attrs)
+        assert nx.cd_index(G, 4, time_delta=_delta, weight="weight") == 0.04
+
+    def test_node_with_no_predecessors(self):
+        G = nx.DiGraph()
+        G.add_nodes_from([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+        G.add_edge(4, 2)
+        G.add_edge(4, 0)
+        G.add_edge(4, 3)
+        G.add_edge(5, 2)
+        G.add_edge(6, 2)
+        G.add_edge(6, 4)
+        G.add_edge(7, 4)
+        G.add_edge(8, 4)
+        G.add_edge(9, 4)
+        G.add_edge(9, 1)
+        G.add_edge(9, 3)
+        G.add_edge(10, 4)
+
+        node_attrs = {
+            0: {"time": datetime(1992, 1, 1)},
+            1: {"time": datetime(1992, 1, 1)},
+            2: {"time": datetime(1993, 1, 1)},
+            3: {"time": datetime(1993, 1, 1)},
+            4: {"time": datetime(1995, 1, 1)},
+            5: {"time": datetime(2005, 1, 1)},
+            6: {"time": datetime(2010, 1, 1)},
+            7: {"time": datetime(2001, 1, 1)},
+            8: {"time": datetime(2020, 1, 1)},
+            9: {"time": datetime(2017, 1, 1)},
+            10: {"time": datetime(2004, 4, 1)},
+        }
+
+        nx.set_node_attributes(G, node_attrs)
+        assert nx.cd_index(G, 4, time_delta=_delta) == 0.0
+
+    def test_node_with_no_successors(self):
+        G = nx.DiGraph()
+        G.add_nodes_from([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+        G.add_edge(8, 2)
+        G.add_edge(6, 0)
+        G.add_edge(6, 3)
+        G.add_edge(5, 2)
+        G.add_edge(6, 2)
+        G.add_edge(6, 4)
+        G.add_edge(7, 4)
+        G.add_edge(8, 4)
+        G.add_edge(9, 4)
+        G.add_edge(9, 1)
+        G.add_edge(9, 3)
+        G.add_edge(10, 4)
+
+        node_attrs = {
+            0: {"time": datetime(1992, 1, 1)},
+            1: {"time": datetime(1992, 1, 1)},
+            2: {"time": datetime(1993, 1, 1)},
+            3: {"time": datetime(1993, 1, 1)},
+            4: {"time": datetime(1995, 1, 1)},
+            5: {"time": datetime(1997, 1, 1)},
+            6: {"time": datetime(1998, 1, 1)},
+            7: {"time": datetime(1999, 1, 1)},
+            8: {"time": datetime(1999, 1, 1)},
+            9: {"time": datetime(1998, 1, 1)},
+            10: {"time": datetime(1997, 4, 1)},
+        }
+
+        nx.set_node_attributes(G, node_attrs)
+        assert nx.cd_index(G, 4, time_delta=_delta) == 1.0
+
+    def test_n_equals_zero(self):
+        G = nx.DiGraph()
+        G.add_nodes_from([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+        G.add_edge(4, 2)
+        G.add_edge(4, 0)
+        G.add_edge(4, 3)
+        G.add_edge(6, 4)
+        G.add_edge(7, 4)
+        G.add_edge(8, 4)
+        G.add_edge(9, 4)
+        G.add_edge(9, 1)
+        G.add_edge(10, 4)
+
+        node_attrs = {
+            0: {"time": datetime(1992, 1, 1)},
+            1: {"time": datetime(1992, 1, 1)},
+            2: {"time": datetime(1993, 1, 1)},
+            3: {"time": datetime(1993, 1, 1)},
+            4: {"time": datetime(1995, 1, 1)},
+            5: {"time": datetime(2005, 1, 1)},
+            6: {"time": datetime(2010, 1, 1)},
+            7: {"time": datetime(2001, 1, 1)},
+            8: {"time": datetime(2020, 1, 1)},
+            9: {"time": datetime(2017, 1, 1)},
+            10: {"time": datetime(2004, 4, 1)},
+        }
+
+        nx.set_node_attributes(G, node_attrs)
+
+        with pytest.raises(
+            nx.NetworkXError, match="The cd index cannot be defined."
+        ) as ve:
+            nx.cd_index(G, 4, time_delta=_delta)
+
+    def test_time_timedelta_compatibility(self):
+        G = nx.DiGraph()
+        G.add_nodes_from([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+        G.add_edge(4, 2)
+        G.add_edge(4, 0)
+        G.add_edge(4, 3)
+        G.add_edge(6, 4)
+        G.add_edge(7, 4)
+        G.add_edge(8, 4)
+        G.add_edge(9, 4)
+        G.add_edge(9, 1)
+        G.add_edge(10, 4)
+
+        node_attrs = {
+            0: {"time": 20.2},
+            1: {"time": 20.2},
+            2: {"time": 30.7},
+            3: {"time": 30.7},
+            4: {"time": 50.9},
+            5: {"time": 70.1},
+            6: {"time": 80.6},
+            7: {"time": 90.7},
+            8: {"time": 90.7},
+            9: {"time": 80.6},
+            10: {"time": 74.2},
+        }
+
+        nx.set_node_attributes(G, node_attrs)
+
+        with pytest.raises(
+            nx.NetworkXError,
+            match="Addition and comparison are not supported between",
+        ) as ve:
+            nx.cd_index(G, 4, time_delta=_delta)
+
+    def test_node_with_no_time(self):
+        G = nx.DiGraph()
+        G.add_nodes_from([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+        G.add_edge(8, 2)
+        G.add_edge(6, 0)
+        G.add_edge(6, 3)
+        G.add_edge(5, 2)
+        G.add_edge(6, 2)
+        G.add_edge(6, 4)
+        G.add_edge(7, 4)
+        G.add_edge(8, 4)
+        G.add_edge(9, 4)
+        G.add_edge(9, 1)
+        G.add_edge(9, 3)
+        G.add_edge(10, 4)
+
+        node_attrs = {
+            0: {"time": datetime(1992, 1, 1)},
+            1: {"time": datetime(1992, 1, 1)},
+            2: {"time": datetime(1993, 1, 1)},
+            3: {"time": datetime(1993, 1, 1)},
+            4: {"time": datetime(1995, 1, 1)},
+            6: {"time": datetime(1998, 1, 1)},
+            7: {"time": datetime(1999, 1, 1)},
+            8: {"time": datetime(1999, 1, 1)},
+            9: {"time": datetime(1998, 1, 1)},
+            10: {"time": datetime(1997, 4, 1)},
+        }
+
+        nx.set_node_attributes(G, node_attrs)
+
+        with pytest.raises(
+            nx.NetworkXError, match="Not all nodes have a 'time' attribute."
+        ) as ve:
+            nx.cd_index(G, 4, time_delta=_delta)
+
+    def test_maximally_consolidating(self):
+        G = nx.DiGraph()
+        G.add_nodes_from([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
+        G.add_edge(5, 1)
+        G.add_edge(5, 2)
+        G.add_edge(5, 3)
+        G.add_edge(5, 4)
+        G.add_edge(6, 1)
+        G.add_edge(6, 5)
+        G.add_edge(7, 1)
+        G.add_edge(7, 5)
+        G.add_edge(8, 2)
+        G.add_edge(8, 5)
+        G.add_edge(9, 5)
+        G.add_edge(9, 3)
+        G.add_edge(10, 5)
+        G.add_edge(10, 3)
+        G.add_edge(10, 4)
+        G.add_edge(11, 5)
+        G.add_edge(11, 4)
+
+        node_attrs = {
+            0: {"time": datetime(1992, 1, 1)},
+            1: {"time": datetime(1992, 1, 1)},
+            2: {"time": datetime(1993, 1, 1)},
+            3: {"time": datetime(1993, 1, 1)},
+            4: {"time": datetime(1995, 1, 1)},
+            5: {"time": datetime(1997, 1, 1)},
+            6: {"time": datetime(1998, 1, 1)},
+            7: {"time": datetime(1999, 1, 1)},
+            8: {"time": datetime(1999, 1, 1)},
+            9: {"time": datetime(1998, 1, 1)},
+            10: {"time": datetime(1997, 4, 1)},
+            11: {"time": datetime(1998, 5, 1)},
+        }
+
+        nx.set_node_attributes(G, node_attrs)
+
+        assert nx.cd_index(G, 5, time_delta=_delta) == -1
+
+    def test_maximally_destabilizing(self):
+        G = nx.DiGraph()
+        G.add_nodes_from([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
+        G.add_edge(5, 1)
+        G.add_edge(5, 2)
+        G.add_edge(5, 3)
+        G.add_edge(5, 4)
+        G.add_edge(6, 5)
+        G.add_edge(7, 5)
+        G.add_edge(8, 5)
+        G.add_edge(9, 5)
+        G.add_edge(10, 5)
+        G.add_edge(11, 5)
+
+        node_attrs = {
+            0: {"time": datetime(1992, 1, 1)},
+            1: {"time": datetime(1992, 1, 1)},
+            2: {"time": datetime(1993, 1, 1)},
+            3: {"time": datetime(1993, 1, 1)},
+            4: {"time": datetime(1995, 1, 1)},
+            5: {"time": datetime(1997, 1, 1)},
+            6: {"time": datetime(1998, 1, 1)},
+            7: {"time": datetime(1999, 1, 1)},
+            8: {"time": datetime(1999, 1, 1)},
+            9: {"time": datetime(1998, 1, 1)},
+            10: {"time": datetime(1997, 4, 1)},
+            11: {"time": datetime(1998, 5, 1)},
+        }
+
+        nx.set_node_attributes(G, node_attrs)
+
+        assert nx.cd_index(G, 5, time_delta=_delta) == 1

valley/lib/python3.10/site-packages/networkx/algorithms/tests/test_triads.py

@@ -0,0 +1,289 @@
+"""Tests for the :mod:`networkx.algorithms.triads` module."""
+
+import itertools
+from collections import defaultdict
+from random import sample
+
+import pytest
+
+import networkx as nx
+
+
+def test_all_triplets_deprecated():
+    G = nx.DiGraph([(1, 2), (2, 3), (3, 4)])
+    with pytest.deprecated_call():
+        nx.all_triplets(G)
+
+
+def test_random_triad_deprecated():
+    G = nx.path_graph(3, create_using=nx.DiGraph)
+    with pytest.deprecated_call():
+        nx.random_triad(G)
+
+
+def test_triadic_census():
+    """Tests the triadic_census function."""
+    G = nx.DiGraph()
+    G.add_edges_from(["01", "02", "03", "04", "05", "12", "16", "51", "56", "65"])
+    expected = {
+        "030T": 2,
+        "120C": 1,
+        "210": 0,
+        "120U": 0,
+        "012": 9,
+        "102": 3,
+        "021U": 0,
+        "111U": 0,
+        "003": 8,
+        "030C": 0,
+        "021D": 9,
+        "201": 0,
+        "111D": 1,
+        "300": 0,
+        "120D": 0,
+        "021C": 2,
+    }
+    actual = nx.triadic_census(G)
+    assert expected == actual
+
+
+def test_is_triad():
+    """Tests the is_triad function"""
+    G = nx.karate_club_graph()
+    G = G.to_directed()
+    for i in range(100):
+        nodes = sample(sorted(G.nodes()), 3)
+        G2 = G.subgraph(nodes)
+        assert nx.is_triad(G2)
+
+
+def test_all_triplets():
+    """Tests the all_triplets function."""
+    G = nx.DiGraph()
+    G.add_edges_from(["01", "02", "03", "04", "05", "12", "16", "51", "56", "65"])
+    expected = [
+        f"{i},{j},{k}"
+        for i in range(7)
+        for j in range(i + 1, 7)
+        for k in range(j + 1, 7)
+    ]
+    expected = [set(x.split(",")) for x in expected]
+    actual = [set(x) for x in nx.all_triplets(G)]
+    assert all(any(s1 == s2 for s1 in expected) for s2 in actual)
+
+
+def test_all_triads():
+    """Tests the all_triplets function."""
+    G = nx.DiGraph()
+    G.add_edges_from(["01", "02", "03", "04", "05", "12", "16", "51", "56", "65"])
+    expected = [
+        f"{i},{j},{k}"
+        for i in range(7)
+        for j in range(i + 1, 7)
+        for k in range(j + 1, 7)
+    ]
+    expected = [G.subgraph(x.split(",")) for x in expected]
+    actual = list(nx.all_triads(G))
+    assert all(any(nx.is_isomorphic(G1, G2) for G1 in expected) for G2 in actual)
+
+
+def test_triad_type():
+    """Tests the triad_type function."""
+    # 0 edges (1 type)
+    G = nx.DiGraph({0: [], 1: [], 2: []})
+    assert nx.triad_type(G) == "003"
+    # 1 edge (1 type)
+    G = nx.DiGraph({0: [1], 1: [], 2: []})
+    assert nx.triad_type(G) == "012"
+    # 2 edges (4 types)
+    G = nx.DiGraph([(0, 1), (0, 2)])
+    assert nx.triad_type(G) == "021D"
+    G = nx.DiGraph({0: [1], 1: [0], 2: []})
+    assert nx.triad_type(G) == "102"
+    G = nx.DiGraph([(0, 1), (2, 1)])
+    assert nx.triad_type(G) == "021U"
+    G = nx.DiGraph([(0, 1), (1, 2)])
+    assert nx.triad_type(G) == "021C"
+    # 3 edges (4 types)
+    G = nx.DiGraph([(0, 1), (1, 0), (2, 1)])
+    assert nx.triad_type(G) == "111D"
+    G = nx.DiGraph([(0, 1), (1, 0), (1, 2)])
+    assert nx.triad_type(G) == "111U"
+    G = nx.DiGraph([(0, 1), (1, 2), (0, 2)])
+    assert nx.triad_type(G) == "030T"
+    G = nx.DiGraph([(0, 1), (1, 2), (2, 0)])
+    assert nx.triad_type(G) == "030C"
+    # 4 edges (4 types)
+    G = nx.DiGraph([(0, 1), (1, 0), (2, 0), (0, 2)])
+    assert nx.triad_type(G) == "201"
+    G = nx.DiGraph([(0, 1), (1, 0), (2, 0), (2, 1)])
+    assert nx.triad_type(G) == "120D"
+    G = nx.DiGraph([(0, 1), (1, 0), (0, 2), (1, 2)])
+    assert nx.triad_type(G) == "120U"
+    G = nx.DiGraph([(0, 1), (1, 0), (0, 2), (2, 1)])
+    assert nx.triad_type(G) == "120C"
+    # 5 edges (1 type)
+    G = nx.DiGraph([(0, 1), (1, 0), (2, 1), (1, 2), (0, 2)])
+    assert nx.triad_type(G) == "210"
+    # 6 edges (1 type)
+    G = nx.DiGraph([(0, 1), (1, 0), (1, 2), (2, 1), (0, 2), (2, 0)])
+    assert nx.triad_type(G) == "300"
+
+
+def test_triads_by_type():
+    """Tests the all_triplets function."""
+    G = nx.DiGraph()
+    G.add_edges_from(["01", "02", "03", "04", "05", "12", "16", "51", "56", "65"])
+    all_triads = nx.all_triads(G)
+    expected = defaultdict(list)
+    for triad in all_triads:
+        name = nx.triad_type(triad)
+        expected[name].append(triad)
+    actual = nx.triads_by_type(G)
+    assert set(actual.keys()) == set(expected.keys())
+    for tri_type, actual_Gs in actual.items():
+        expected_Gs = expected[tri_type]
+        for a in actual_Gs:
+            assert any(nx.is_isomorphic(a, e) for e in expected_Gs)
+
+
+def test_random_triad():
+    """Tests the random_triad function"""
+    G = nx.karate_club_graph()
+    G = G.to_directed()
+    for i in range(100):
+        assert nx.is_triad(nx.random_triad(G))
+
+    G = nx.DiGraph()
+    msg = "at least 3 nodes to form a triad"
+    with pytest.raises(nx.NetworkXError, match=msg):
+        nx.random_triad(G)
+
+
+def test_triadic_census_short_path_nodelist():
+    G = nx.path_graph("abc", create_using=nx.DiGraph)
+    expected = {"021C": 1}
+    for nl in ["a", "b", "c", "ab", "ac", "bc", "abc"]:
+        triad_census = nx.triadic_census(G, nodelist=nl)
+        assert expected == {typ: cnt for typ, cnt in triad_census.items() if cnt > 0}
+
+
+def test_triadic_census_correct_nodelist_values():
+    G = nx.path_graph(5, create_using=nx.DiGraph)
+    msg = r"nodelist includes duplicate nodes or nodes not in G"
+    with pytest.raises(ValueError, match=msg):
+        nx.triadic_census(G, [1, 2, 2, 3])
+    with pytest.raises(ValueError, match=msg):
+        nx.triadic_census(G, [1, 2, "a", 3])
+
+
+def test_triadic_census_tiny_graphs():
+    tc = nx.triadic_census(nx.empty_graph(0, create_using=nx.DiGraph))
+    assert {} == {typ: cnt for typ, cnt in tc.items() if cnt > 0}
+    tc = nx.triadic_census(nx.empty_graph(1, create_using=nx.DiGraph))
+    assert {} == {typ: cnt for typ, cnt in tc.items() if cnt > 0}
+    tc = nx.triadic_census(nx.empty_graph(2, create_using=nx.DiGraph))
+    assert {} == {typ: cnt for typ, cnt in tc.items() if cnt > 0}
+    tc = nx.triadic_census(nx.DiGraph([(1, 2)]))
+    assert {} == {typ: cnt for typ, cnt in tc.items() if cnt > 0}
+
+
+def test_triadic_census_selfloops():
+    GG = nx.path_graph("abc", create_using=nx.DiGraph)
+    expected = {"021C": 1}
+    for n in GG:
+        G = GG.copy()
+        G.add_edge(n, n)
+        tc = nx.triadic_census(G)
+        assert expected == {typ: cnt for typ, cnt in tc.items() if cnt > 0}
+
+    GG = nx.path_graph("abcde", create_using=nx.DiGraph)
+    tbt = nx.triads_by_type(GG)
+    for n in GG:
+        GG.add_edge(n, n)
+    tc = nx.triadic_census(GG)
+    assert tc == {tt: len(tbt[tt]) for tt in tc}
+
+
+def test_triadic_census_four_path():
+    G = nx.path_graph("abcd", create_using=nx.DiGraph)
+    expected = {"012": 2, "021C": 2}
+    triad_census = nx.triadic_census(G)
+    assert expected == {typ: cnt for typ, cnt in triad_census.items() if cnt > 0}
+
+
+def test_triadic_census_four_path_nodelist():
+    G = nx.path_graph("abcd", create_using=nx.DiGraph)
+    expected_end = {"012": 2, "021C": 1}
+    expected_mid = {"012": 1, "021C": 2}
+    a_triad_census = nx.triadic_census(G, nodelist=["a"])
+    assert expected_end == {typ: cnt for typ, cnt in a_triad_census.items() if cnt > 0}
+    b_triad_census = nx.triadic_census(G, nodelist=["b"])
+    assert expected_mid == {typ: cnt for typ, cnt in b_triad_census.items() if cnt > 0}
+    c_triad_census = nx.triadic_census(G, nodelist=["c"])
+    assert expected_mid == {typ: cnt for typ, cnt in c_triad_census.items() if cnt > 0}
+    d_triad_census = nx.triadic_census(G, nodelist=["d"])
+    assert expected_end == {typ: cnt for typ, cnt in d_triad_census.items() if cnt > 0}
+
+
+def test_triadic_census_nodelist():
+    """Tests the triadic_census function."""
+    G = nx.DiGraph()
+    G.add_edges_from(["01", "02", "03", "04", "05", "12", "16", "51", "56", "65"])
+    expected = {
+        "030T": 2,
+        "120C": 1,
+        "210": 0,
+        "120U": 0,
+        "012": 9,
+        "102": 3,
+        "021U": 0,
+        "111U": 0,
+        "003": 8,
+        "030C": 0,
+        "021D": 9,
+        "201": 0,
+        "111D": 1,
+        "300": 0,
+        "120D": 0,
+        "021C": 2,
+    }
+    actual = {k: 0 for k in expected}
+    for node in G.nodes():
+        node_triad_census = nx.triadic_census(G, nodelist=[node])
+        for triad_key in expected:
+            actual[triad_key] += node_triad_census[triad_key]
+    # Divide all counts by 3
+    for k, v in actual.items():
+        actual[k] //= 3
+    assert expected == actual
+
+
+@pytest.mark.parametrize("N", [5, 10])
+def test_triadic_census_on_random_graph(N):
+    G = nx.binomial_graph(N, 0.3, directed=True, seed=42)
+    tc1 = nx.triadic_census(G)
+    tbt = nx.triads_by_type(G)
+    tc2 = {tt: len(tbt[tt]) for tt in tc1}
+    assert tc1 == tc2
+
+    for n in G:
+        tc1 = nx.triadic_census(G, nodelist={n})
+        tc2 = {tt: sum(1 for t in tbt.get(tt, []) if n in t) for tt in tc1}
+        assert tc1 == tc2
+
+    for ns in itertools.combinations(G, 2):
+        ns = set(ns)
+        tc1 = nx.triadic_census(G, nodelist=ns)
+        tc2 = {
+            tt: sum(1 for t in tbt.get(tt, []) if any(n in ns for n in t)) for tt in tc1
+        }
+        assert tc1 == tc2
+
+    for ns in itertools.combinations(G, 3):
+        ns = set(ns)
+        tc1 = nx.triadic_census(G, nodelist=ns)
+        tc2 = {
+            tt: sum(1 for t in tbt.get(tt, []) if any(n in ns for n in t)) for tt in tc1
+        }
+        assert tc1 == tc2

valley/lib/python3.10/site-packages/networkx/algorithms/tests/test_vitality.py

@@ -0,0 +1,41 @@
+import networkx as nx
+
+
+class TestClosenessVitality:
+    def test_unweighted(self):
+        G = nx.cycle_graph(3)
+        vitality = nx.closeness_vitality(G)
+        assert vitality == {0: 2, 1: 2, 2: 2}
+
+    def test_weighted(self):
+        G = nx.Graph()
+        nx.add_cycle(G, [0, 1, 2], weight=2)
+        vitality = nx.closeness_vitality(G, weight="weight")
+        assert vitality == {0: 4, 1: 4, 2: 4}
+
+    def test_unweighted_digraph(self):
+        G = nx.DiGraph(nx.cycle_graph(3))
+        vitality = nx.closeness_vitality(G)
+        assert vitality == {0: 4, 1: 4, 2: 4}
+
+    def test_weighted_digraph(self):
+        G = nx.DiGraph()
+        nx.add_cycle(G, [0, 1, 2], weight=2)
+        nx.add_cycle(G, [2, 1, 0], weight=2)
+        vitality = nx.closeness_vitality(G, weight="weight")
+        assert vitality == {0: 8, 1: 8, 2: 8}
+
+    def test_weighted_multidigraph(self):
+        G = nx.MultiDiGraph()
+        nx.add_cycle(G, [0, 1, 2], weight=2)
+        nx.add_cycle(G, [2, 1, 0], weight=2)
+        vitality = nx.closeness_vitality(G, weight="weight")
+        assert vitality == {0: 8, 1: 8, 2: 8}
+
+    def test_disconnecting_graph(self):
+        """Tests that the closeness vitality of a node whose removal
+        disconnects the graph is negative infinity.
+
+        """
+        G = nx.path_graph(3)
+        assert nx.closeness_vitality(G, node=1) == -float("inf")

valley/lib/python3.10/site-packages/networkx/algorithms/tests/test_voronoi.py

@@ -0,0 +1,103 @@
+import networkx as nx
+from networkx.utils import pairwise
+
+
+class TestVoronoiCells:
+    """Unit tests for the Voronoi cells function."""
+
+    def test_isolates(self):
+        """Tests that a graph with isolated nodes has all isolates in
+        one block of the partition.
+
+        """
+        G = nx.empty_graph(5)
+        cells = nx.voronoi_cells(G, {0, 2, 4})
+        expected = {0: {0}, 2: {2}, 4: {4}, "unreachable": {1, 3}}
+        assert expected == cells
+
+    def test_undirected_unweighted(self):
+        G = nx.cycle_graph(6)
+        cells = nx.voronoi_cells(G, {0, 3})
+        expected = {0: {0, 1, 5}, 3: {2, 3, 4}}
+        assert expected == cells
+
+    def test_directed_unweighted(self):
+        # This is the singly-linked directed cycle graph on six nodes.
+        G = nx.DiGraph(pairwise(range(6), cyclic=True))
+        cells = nx.voronoi_cells(G, {0, 3})
+        expected = {0: {0, 1, 2}, 3: {3, 4, 5}}
+        assert expected == cells
+
+    def test_directed_inward(self):
+        """Tests that reversing the graph gives the "inward" Voronoi
+        partition.
+
+        """
+        # This is the singly-linked reverse directed cycle graph on six nodes.
+        G = nx.DiGraph(pairwise(range(6), cyclic=True))
+        G = G.reverse(copy=False)
+        cells = nx.voronoi_cells(G, {0, 3})
+        expected = {0: {0, 4, 5}, 3: {1, 2, 3}}
+        assert expected == cells
+
+    def test_undirected_weighted(self):
+        edges = [(0, 1, 10), (1, 2, 1), (2, 3, 1)]
+        G = nx.Graph()
+        G.add_weighted_edges_from(edges)
+        cells = nx.voronoi_cells(G, {0, 3})
+        expected = {0: {0}, 3: {1, 2, 3}}
+        assert expected == cells
+
+    def test_directed_weighted(self):
+        edges = [(0, 1, 10), (1, 2, 1), (2, 3, 1), (3, 2, 1), (2, 1, 1)]
+        G = nx.DiGraph()
+        G.add_weighted_edges_from(edges)
+        cells = nx.voronoi_cells(G, {0, 3})
+        expected = {0: {0}, 3: {1, 2, 3}}
+        assert expected == cells
+
+    def test_multigraph_unweighted(self):
+        """Tests that the Voronoi cells for a multigraph are the same as
+        for a simple graph.
+
+        """
+        edges = [(0, 1), (1, 2), (2, 3)]
+        G = nx.MultiGraph(2 * edges)
+        H = nx.Graph(G)
+        G_cells = nx.voronoi_cells(G, {0, 3})
+        H_cells = nx.voronoi_cells(H, {0, 3})
+        assert G_cells == H_cells
+
+    def test_multidigraph_unweighted(self):
+        # This is the twice-singly-linked directed cycle graph on six nodes.
+        edges = list(pairwise(range(6), cyclic=True))
+        G = nx.MultiDiGraph(2 * edges)
+        H = nx.DiGraph(G)
+        G_cells = nx.voronoi_cells(G, {0, 3})
+        H_cells = nx.voronoi_cells(H, {0, 3})
+        assert G_cells == H_cells
+
+    def test_multigraph_weighted(self):
+        edges = [(0, 1, 10), (0, 1, 10), (1, 2, 1), (1, 2, 100), (2, 3, 1), (2, 3, 100)]
+        G = nx.MultiGraph()
+        G.add_weighted_edges_from(edges)
+        cells = nx.voronoi_cells(G, {0, 3})
+        expected = {0: {0}, 3: {1, 2, 3}}
+        assert expected == cells
+
+    def test_multidigraph_weighted(self):
+        edges = [
+            (0, 1, 10),
+            (0, 1, 10),
+            (1, 2, 1),
+            (2, 3, 1),
+            (3, 2, 10),
+            (3, 2, 1),
+            (2, 1, 10),
+            (2, 1, 1),
+        ]
+        G = nx.MultiDiGraph()
+        G.add_weighted_edges_from(edges)
+        cells = nx.voronoi_cells(G, {0, 3})
+        expected = {0: {0}, 3: {1, 2, 3}}
+        assert expected == cells

wemm/lib/python3.10/lib2to3/Grammar.txt

@@ -0,0 +1,196 @@
+# Grammar for 2to3. This grammar supports Python 2.x and 3.x.
+
+# NOTE WELL: You should also follow all the steps listed at
+# https://devguide.python.org/grammar/
+
+# Start symbols for the grammar:
+#	file_input is a module or sequence of commands read from an input file;
+#	single_input is a single interactive statement;
+#	eval_input is the input for the eval() and input() functions.
+# NB: compound_stmt in single_input is followed by extra NEWLINE!
+file_input: (NEWLINE | stmt)* ENDMARKER
+single_input: NEWLINE | simple_stmt | compound_stmt NEWLINE
+eval_input: testlist NEWLINE* ENDMARKER
+
+decorator: '@' dotted_name [ '(' [arglist] ')' ] NEWLINE
+decorators: decorator+
+decorated: decorators (classdef | funcdef | async_funcdef)
+async_funcdef: ASYNC funcdef
+funcdef: 'def' NAME parameters ['->' test] ':' suite
+parameters: '(' [typedargslist] ')'
+
+# The following definition for typedarglist is equivalent to this set of rules:
+#
+#     arguments = argument (',' argument)*
+#     argument = tfpdef ['=' test]
+#     kwargs = '**' tname [',']
+#     args = '*' [tname]
+#     kwonly_kwargs = (',' argument)* [',' [kwargs]]
+#     args_kwonly_kwargs = args kwonly_kwargs | kwargs
+#     poskeyword_args_kwonly_kwargs = arguments [',' [args_kwonly_kwargs]]
+#     typedargslist_no_posonly  = poskeyword_args_kwonly_kwargs | args_kwonly_kwargs
+#     typedarglist = arguments ',' '/' [',' [typedargslist_no_posonly]])|(typedargslist_no_posonly)"
+#
+# It needs to be fully expanded to allow our LL(1) parser to work on it.
+
+typedargslist: tfpdef ['=' test] (',' tfpdef ['=' test])* ',' '/' [
+                     ',' [((tfpdef ['=' test] ',')* ('*' [tname] (',' tname ['=' test])*
+                            [',' ['**' tname [',']]] | '**' tname [','])
+                     | tfpdef ['=' test] (',' tfpdef ['=' test])* [','])]
+                ] | ((tfpdef ['=' test] ',')* ('*' [tname] (',' tname ['=' test])*
+                     [',' ['**' tname [',']]] | '**' tname [','])
+                     | tfpdef ['=' test] (',' tfpdef ['=' test])* [','])
+
+tname: NAME [':' test]
+tfpdef: tname | '(' tfplist ')'
+tfplist: tfpdef (',' tfpdef)* [',']
+
+# The following definition for varargslist is equivalent to this set of rules:
+#
+#     arguments = argument (',' argument )*
+#     argument = vfpdef ['=' test]
+#     kwargs = '**' vname [',']
+#     args = '*' [vname]
+#     kwonly_kwargs = (',' argument )* [',' [kwargs]]
+#     args_kwonly_kwargs = args kwonly_kwargs | kwargs
+#     poskeyword_args_kwonly_kwargs = arguments [',' [args_kwonly_kwargs]]
+#     vararglist_no_posonly = poskeyword_args_kwonly_kwargs | args_kwonly_kwargs
+#     varargslist = arguments ',' '/' [','[(vararglist_no_posonly)]] | (vararglist_no_posonly)
+#
+# It needs to be fully expanded to allow our LL(1) parser to work on it.
+
+varargslist: vfpdef ['=' test ](',' vfpdef ['=' test])* ',' '/' [',' [
+                     ((vfpdef ['=' test] ',')* ('*' [vname] (',' vname ['=' test])*
+                            [',' ['**' vname [',']]] | '**' vname [','])
+                            | vfpdef ['=' test] (',' vfpdef ['=' test])* [','])
+                     ]] | ((vfpdef ['=' test] ',')*
+                     ('*' [vname] (',' vname ['=' test])*  [',' ['**' vname [',']]]| '**' vname [','])
+                     | vfpdef ['=' test] (',' vfpdef ['=' test])* [','])
+
+vname: NAME
+vfpdef: vname | '(' vfplist ')'
+vfplist: vfpdef (',' vfpdef)* [',']
+
+stmt: simple_stmt | compound_stmt
+simple_stmt: small_stmt (';' small_stmt)* [';'] NEWLINE
+small_stmt: (expr_stmt | print_stmt  | del_stmt | pass_stmt | flow_stmt |
+             import_stmt | global_stmt | exec_stmt | assert_stmt)
+expr_stmt: testlist_star_expr (annassign | augassign (yield_expr|testlist) |
+                     ('=' (yield_expr|testlist_star_expr))*)
+annassign: ':' test ['=' test]
+testlist_star_expr: (test|star_expr) (',' (test|star_expr))* [',']
+augassign: ('+=' | '-=' | '*=' | '@=' | '/=' | '%=' | '&=' | '|=' | '^=' |
+            '<<=' | '>>=' | '**=' | '//=')
+# For normal and annotated assignments, additional restrictions enforced by the interpreter
+print_stmt: 'print' ( [ test (',' test)* [','] ] |
+                      '>>' test [ (',' test)+ [','] ] )
+del_stmt: 'del' exprlist
+pass_stmt: 'pass'
+flow_stmt: break_stmt | continue_stmt | return_stmt | raise_stmt | yield_stmt
+break_stmt: 'break'
+continue_stmt: 'continue'
+return_stmt: 'return' [testlist_star_expr]
+yield_stmt: yield_expr
+raise_stmt: 'raise' [test ['from' test | ',' test [',' test]]]
+import_stmt: import_name | import_from
+import_name: 'import' dotted_as_names
+import_from: ('from' ('.'* dotted_name | '.'+)
+              'import' ('*' | '(' import_as_names ')' | import_as_names))
+import_as_name: NAME ['as' NAME]
+dotted_as_name: dotted_name ['as' NAME]
+import_as_names: import_as_name (',' import_as_name)* [',']
+dotted_as_names: dotted_as_name (',' dotted_as_name)*
+dotted_name: NAME ('.' NAME)*
+global_stmt: ('global' | 'nonlocal') NAME (',' NAME)*
+exec_stmt: 'exec' expr ['in' test [',' test]]
+assert_stmt: 'assert' test [',' test]
+
+compound_stmt: if_stmt | while_stmt | for_stmt | try_stmt | with_stmt | funcdef | classdef | decorated | async_stmt
+async_stmt: ASYNC (funcdef | with_stmt | for_stmt)
+if_stmt: 'if' namedexpr_test ':' suite ('elif' namedexpr_test ':' suite)* ['else' ':' suite]
+while_stmt: 'while' namedexpr_test ':' suite ['else' ':' suite]
+for_stmt: 'for' exprlist 'in' testlist ':' suite ['else' ':' suite]
+try_stmt: ('try' ':' suite
+           ((except_clause ':' suite)+
+	    ['else' ':' suite]
+	    ['finally' ':' suite] |
+	   'finally' ':' suite))
+with_stmt: 'with' with_item (',' with_item)*  ':' suite
+with_item: test ['as' expr]
+with_var: 'as' expr
+# NB compile.c makes sure that the default except clause is last
+except_clause: 'except' [test [(',' | 'as') test]]
+suite: simple_stmt | NEWLINE INDENT stmt+ DEDENT
+
+# Backward compatibility cruft to support:
+# [ x for x in lambda: True, lambda: False if x() ]
+# even while also allowing:
+# lambda x: 5 if x else 2
+# (But not a mix of the two)
+testlist_safe: old_test [(',' old_test)+ [',']]
+old_test: or_test | old_lambdef
+old_lambdef: 'lambda' [varargslist] ':' old_test
+
+namedexpr_test: test [':=' test]
+test: or_test ['if' or_test 'else' test] | lambdef
+or_test: and_test ('or' and_test)*
+and_test: not_test ('and' not_test)*
+not_test: 'not' not_test | comparison
+comparison: expr (comp_op expr)*
+comp_op: '<'|'>'|'=='|'>='|'<='|'<>'|'!='|'in'|'not' 'in'|'is'|'is' 'not'
+star_expr: '*' expr
+expr: xor_expr ('|' xor_expr)*
+xor_expr: and_expr ('^' and_expr)*
+and_expr: shift_expr ('&' shift_expr)*
+shift_expr: arith_expr (('<<'|'>>') arith_expr)*
+arith_expr: term (('+'|'-') term)*
+term: factor (('*'|'@'|'/'|'%'|'//') factor)*
+factor: ('+'|'-'|'~') factor | power
+power: [AWAIT] atom trailer* ['**' factor]
+atom: ('(' [yield_expr|testlist_gexp] ')' |
+       '[' [listmaker] ']' |
+       '{' [dictsetmaker] '}' |
+       '`' testlist1 '`' |
+       NAME | NUMBER | STRING+ | '.' '.' '.')
+listmaker: (namedexpr_test|star_expr) ( comp_for | (',' (namedexpr_test|star_expr))* [','] )
+testlist_gexp: (namedexpr_test|star_expr) ( comp_for | (',' (namedexpr_test|star_expr))* [','] )
+lambdef: 'lambda' [varargslist] ':' test
+trailer: '(' [arglist] ')' | '[' subscriptlist ']' | '.' NAME
+subscriptlist: subscript (',' subscript)* [',']
+subscript: test | [test] ':' [test] [sliceop]
+sliceop: ':' [test]
+exprlist: (expr|star_expr) (',' (expr|star_expr))* [',']
+testlist: test (',' test)* [',']
+dictsetmaker: ( ((test ':' test | '**' expr)
+                 (comp_for | (',' (test ':' test | '**' expr))* [','])) |
+                ((test | star_expr)
+		 (comp_for | (',' (test | star_expr))* [','])) )
+
+classdef: 'class' NAME ['(' [arglist] ')'] ':' suite
+
+arglist: argument (',' argument)* [',']
+
+# "test '=' test" is really "keyword '=' test", but we have no such token.
+# These need to be in a single rule to avoid grammar that is ambiguous
+# to our LL(1) parser. Even though 'test' includes '*expr' in star_expr,
+# we explicitly match '*' here, too, to give it proper precedence.
+# Illegal combinations and orderings are blocked in ast.c:
+# multiple (test comp_for) arguments are blocked; keyword unpackings
+# that precede iterable unpackings are blocked; etc.
+argument: ( test [comp_for] |
+            test ':=' test |
+            test '=' test |
+            '**' test |
+	        '*' test )
+
+comp_iter: comp_for | comp_if
+comp_for: [ASYNC] 'for' exprlist 'in' testlist_safe [comp_iter]
+comp_if: 'if' old_test [comp_iter]
+
+testlist1: test (',' test)*
+
+# not used in grammar, but may appear in "node" passed from Parser to Compiler
+encoding_decl: NAME
+
+yield_expr: 'yield' [yield_arg]
+yield_arg: 'from' test | testlist_star_expr

wemm/lib/python3.10/lib2to3/__main__.py

@@ -0,0 +1,4 @@
+import sys
+from .main import main
+
+sys.exit(main("lib2to3.fixes"))

wemm/lib/python3.10/lib2to3/btm_utils.py

@@ -0,0 +1,281 @@
+"Utility functions used by the btm_matcher module"
+
+from . import pytree
+from .pgen2 import grammar, token
+from .pygram import pattern_symbols, python_symbols
+
+syms = pattern_symbols
+pysyms = python_symbols
+tokens = grammar.opmap
+token_labels = token
+
+TYPE_ANY = -1
+TYPE_ALTERNATIVES = -2
+TYPE_GROUP = -3
+
+class MinNode(object):
+    """This class serves as an intermediate representation of the
+    pattern tree during the conversion to sets of leaf-to-root
+    subpatterns"""
+
+    def __init__(self, type=None, name=None):
+        self.type = type
+        self.name = name
+        self.children = []
+        self.leaf = False
+        self.parent = None
+        self.alternatives = []
+        self.group = []
+
+    def __repr__(self):
+        return str(self.type) + ' ' + str(self.name)
+
+    def leaf_to_root(self):
+        """Internal method. Returns a characteristic path of the
+        pattern tree. This method must be run for all leaves until the
+        linear subpatterns are merged into a single"""
+        node = self
+        subp = []
+        while node:
+            if node.type == TYPE_ALTERNATIVES:
+                node.alternatives.append(subp)
+                if len(node.alternatives) == len(node.children):
+                    #last alternative
+                    subp = [tuple(node.alternatives)]
+                    node.alternatives = []
+                    node = node.parent
+                    continue
+                else:
+                    node = node.parent
+                    subp = None
+                    break
+
+            if node.type == TYPE_GROUP:
+                node.group.append(subp)
+                #probably should check the number of leaves
+                if len(node.group) == len(node.children):
+                    subp = get_characteristic_subpattern(node.group)
+                    node.group = []
+                    node = node.parent
+                    continue
+                else:
+                    node = node.parent
+                    subp = None
+                    break
+
+            if node.type == token_labels.NAME and node.name:
+                #in case of type=name, use the name instead
+                subp.append(node.name)
+            else:
+                subp.append(node.type)
+
+            node = node.parent
+        return subp
+
+    def get_linear_subpattern(self):
+        """Drives the leaf_to_root method. The reason that
+        leaf_to_root must be run multiple times is because we need to
+        reject 'group' matches; for example the alternative form
+        (a | b c) creates a group [b c] that needs to be matched. Since
+        matching multiple linear patterns overcomes the automaton's
+        capabilities, leaf_to_root merges each group into a single
+        choice based on 'characteristic'ity,
+
+        i.e. (a|b c) -> (a|b) if b more characteristic than c
+
+        Returns: The most 'characteristic'(as defined by
+          get_characteristic_subpattern) path for the compiled pattern
+          tree.
+        """
+
+        for l in self.leaves():
+            subp = l.leaf_to_root()
+            if subp:
+                return subp
+
+    def leaves(self):
+        "Generator that returns the leaves of the tree"
+        for child in self.children:
+            yield from child.leaves()
+        if not self.children:
+            yield self
+
+def reduce_tree(node, parent=None):
+    """
+    Internal function. Reduces a compiled pattern tree to an
+    intermediate representation suitable for feeding the
+    automaton. This also trims off any optional pattern elements(like
+    [a], a*).
+    """
+
+    new_node = None
+    #switch on the node type
+    if node.type == syms.Matcher:
+        #skip
+        node = node.children[0]
+
+    if node.type == syms.Alternatives  :
+        #2 cases
+        if len(node.children) <= 2:
+            #just a single 'Alternative', skip this node
+            new_node = reduce_tree(node.children[0], parent)
+        else:
+            #real alternatives
+            new_node = MinNode(type=TYPE_ALTERNATIVES)
+            #skip odd children('|' tokens)
+            for child in node.children:
+                if node.children.index(child)%2:
+                    continue
+                reduced = reduce_tree(child, new_node)
+                if reduced is not None:
+                    new_node.children.append(reduced)
+    elif node.type == syms.Alternative:
+        if len(node.children) > 1:
+
+            new_node = MinNode(type=TYPE_GROUP)
+            for child in node.children:
+                reduced = reduce_tree(child, new_node)
+                if reduced:
+                    new_node.children.append(reduced)
+            if not new_node.children:
+                # delete the group if all of the children were reduced to None
+                new_node = None
+
+        else:
+            new_node = reduce_tree(node.children[0], parent)
+
+    elif node.type == syms.Unit:
+        if (isinstance(node.children[0], pytree.Leaf) and
+            node.children[0].value == '('):
+            #skip parentheses
+            return reduce_tree(node.children[1], parent)
+        if ((isinstance(node.children[0], pytree.Leaf) and
+               node.children[0].value == '[')
+               or
+               (len(node.children)>1 and
+               hasattr(node.children[1], "value") and
+               node.children[1].value == '[')):
+            #skip whole unit if its optional
+            return None
+
+        leaf = True
+        details_node = None
+        alternatives_node = None
+        has_repeater = False
+        repeater_node = None
+        has_variable_name = False
+
+        for child in node.children:
+            if child.type == syms.Details:
+                leaf = False
+                details_node = child
+            elif child.type == syms.Repeater:
+                has_repeater = True
+                repeater_node = child
+            elif child.type == syms.Alternatives:
+                alternatives_node = child
+            if hasattr(child, 'value') and child.value == '=': # variable name
+                has_variable_name = True
+
+        #skip variable name
+        if has_variable_name:
+            #skip variable name, '='
+            name_leaf = node.children[2]
+            if hasattr(name_leaf, 'value') and name_leaf.value == '(':
+                # skip parenthesis
+                name_leaf = node.children[3]
+        else:
+            name_leaf = node.children[0]
+
+        #set node type
+        if name_leaf.type == token_labels.NAME:
+            #(python) non-name or wildcard
+            if name_leaf.value == 'any':
+                new_node = MinNode(type=TYPE_ANY)
+            else:
+                if hasattr(token_labels, name_leaf.value):
+                    new_node = MinNode(type=getattr(token_labels, name_leaf.value))
+                else:
+                    new_node = MinNode(type=getattr(pysyms, name_leaf.value))
+
+        elif name_leaf.type == token_labels.STRING:
+            #(python) name or character; remove the apostrophes from
+            #the string value
+            name = name_leaf.value.strip("'")
+            if name in tokens:
+                new_node = MinNode(type=tokens[name])
+            else:
+                new_node = MinNode(type=token_labels.NAME, name=name)
+        elif name_leaf.type == syms.Alternatives:
+            new_node = reduce_tree(alternatives_node, parent)
+
+        #handle repeaters
+        if has_repeater:
+            if repeater_node.children[0].value == '*':
+                #reduce to None
+                new_node = None
+            elif repeater_node.children[0].value == '+':
+                #reduce to a single occurrence i.e. do nothing
+                pass
+            else:
+                #TODO: handle {min, max} repeaters
+                raise NotImplementedError
+                pass
+
+        #add children
+        if details_node and new_node is not None:
+            for child in details_node.children[1:-1]:
+                #skip '<', '>' markers
+                reduced = reduce_tree(child, new_node)
+                if reduced is not None:
+                    new_node.children.append(reduced)
+    if new_node:
+        new_node.parent = parent
+    return new_node
+
+
+def get_characteristic_subpattern(subpatterns):
+    """Picks the most characteristic from a list of linear patterns
+    Current order used is:
+    names > common_names > common_chars
+    """
+    if not isinstance(subpatterns, list):
+        return subpatterns
+    if len(subpatterns)==1:
+        return subpatterns[0]
+
+    # first pick out the ones containing variable names
+    subpatterns_with_names = []
+    subpatterns_with_common_names = []
+    common_names = ['in', 'for', 'if' , 'not', 'None']
+    subpatterns_with_common_chars = []
+    common_chars = "[]().,:"
+    for subpattern in subpatterns:
+        if any(rec_test(subpattern, lambda x: type(x) is str)):
+            if any(rec_test(subpattern,
+                            lambda x: isinstance(x, str) and x in common_chars)):
+                subpatterns_with_common_chars.append(subpattern)
+            elif any(rec_test(subpattern,
+                              lambda x: isinstance(x, str) and x in common_names)):
+                subpatterns_with_common_names.append(subpattern)
+
+            else:
+                subpatterns_with_names.append(subpattern)
+
+    if subpatterns_with_names:
+        subpatterns = subpatterns_with_names
+    elif subpatterns_with_common_names:
+        subpatterns = subpatterns_with_common_names
+    elif subpatterns_with_common_chars:
+        subpatterns = subpatterns_with_common_chars
+    # of the remaining subpatterns pick out the longest one
+    return max(subpatterns, key=len)
+
+def rec_test(sequence, test_func):
+    """Tests test_func on all items of sequence and items of included
+    sub-iterables"""
+    for x in sequence:
+        if isinstance(x, (list, tuple)):
+            yield from rec_test(x, test_func)
+        else:
+            yield test_func(x)

wemm/lib/python3.10/lib2to3/fixer_base.py

@@ -0,0 +1,186 @@
+# Copyright 2006 Google, Inc. All Rights Reserved.
+# Licensed to PSF under a Contributor Agreement.
+
+"""Base class for fixers (optional, but recommended)."""
+
+# Python imports
+import itertools
+
+# Local imports
+from .patcomp import PatternCompiler
+from . import pygram
+from .fixer_util import does_tree_import
+
+class BaseFix(object):
+
+    """Optional base class for fixers.
+
+    The subclass name must be FixFooBar where FooBar is the result of
+    removing underscores and capitalizing the words of the fix name.
+    For example, the class name for a fixer named 'has_key' should be
+    FixHasKey.
+    """
+
+    PATTERN = None  # Most subclasses should override with a string literal
+    pattern = None  # Compiled pattern, set by compile_pattern()
+    pattern_tree = None # Tree representation of the pattern
+    options = None  # Options object passed to initializer
+    filename = None # The filename (set by set_filename)
+    numbers = itertools.count(1) # For new_name()
+    used_names = set() # A set of all used NAMEs
+    order = "post" # Does the fixer prefer pre- or post-order traversal
+    explicit = False # Is this ignored by refactor.py -f all?
+    run_order = 5   # Fixers will be sorted by run order before execution
+                    # Lower numbers will be run first.
+    _accept_type = None # [Advanced and not public] This tells RefactoringTool
+                        # which node type to accept when there's not a pattern.
+
+    keep_line_order = False # For the bottom matcher: match with the
+                            # original line order
+    BM_compatible = False # Compatibility with the bottom matching
+                          # module; every fixer should set this
+                          # manually
+
+    # Shortcut for access to Python grammar symbols
+    syms = pygram.python_symbols
+
+    def __init__(self, options, log):
+        """Initializer.  Subclass may override.
+
+        Args:
+            options: a dict containing the options passed to RefactoringTool
+            that could be used to customize the fixer through the command line.
+            log: a list to append warnings and other messages to.
+        """
+        self.options = options
+        self.log = log
+        self.compile_pattern()
+
+    def compile_pattern(self):
+        """Compiles self.PATTERN into self.pattern.
+
+        Subclass may override if it doesn't want to use
+        self.{pattern,PATTERN} in .match().
+        """
+        if self.PATTERN is not None:
+            PC = PatternCompiler()
+            self.pattern, self.pattern_tree = PC.compile_pattern(self.PATTERN,
+                                                                 with_tree=True)
+
+    def set_filename(self, filename):
+        """Set the filename.
+
+        The main refactoring tool should call this.
+        """
+        self.filename = filename
+
+    def match(self, node):
+        """Returns match for a given parse tree node.
+
+        Should return a true or false object (not necessarily a bool).
+        It may return a non-empty dict of matching sub-nodes as
+        returned by a matching pattern.
+
+        Subclass may override.
+        """
+        results = {"node": node}
+        return self.pattern.match(node, results) and results
+
+    def transform(self, node, results):
+        """Returns the transformation for a given parse tree node.
+
+        Args:
+          node: the root of the parse tree that matched the fixer.
+          results: a dict mapping symbolic names to part of the match.
+
+        Returns:
+          None, or a node that is a modified copy of the
+          argument node.  The node argument may also be modified in-place to
+          effect the same change.
+
+        Subclass *must* override.
+        """
+        raise NotImplementedError()
+
+    def new_name(self, template="xxx_todo_changeme"):
+        """Return a string suitable for use as an identifier
+
+        The new name is guaranteed not to conflict with other identifiers.
+        """
+        name = template
+        while name in self.used_names:
+            name = template + str(next(self.numbers))
+        self.used_names.add(name)
+        return name
+
+    def log_message(self, message):
+        if self.first_log:
+            self.first_log = False
+            self.log.append("### In file %s ###" % self.filename)
+        self.log.append(message)
+
+    def cannot_convert(self, node, reason=None):
+        """Warn the user that a given chunk of code is not valid Python 3,
+        but that it cannot be converted automatically.
+
+        First argument is the top-level node for the code in question.
+        Optional second argument is why it can't be converted.
+        """
+        lineno = node.get_lineno()
+        for_output = node.clone()
+        for_output.prefix = ""
+        msg = "Line %d: could not convert: %s"
+        self.log_message(msg % (lineno, for_output))
+        if reason:
+            self.log_message(reason)
+
+    def warning(self, node, reason):
+        """Used for warning the user about possible uncertainty in the
+        translation.
+
+        First argument is the top-level node for the code in question.
+        Optional second argument is why it can't be converted.
+        """
+        lineno = node.get_lineno()
+        self.log_message("Line %d: %s" % (lineno, reason))
+
+    def start_tree(self, tree, filename):
+        """Some fixers need to maintain tree-wide state.
+        This method is called once, at the start of tree fix-up.
+
+        tree - the root node of the tree to be processed.
+        filename - the name of the file the tree came from.
+        """
+        self.used_names = tree.used_names
+        self.set_filename(filename)
+        self.numbers = itertools.count(1)
+        self.first_log = True
+
+    def finish_tree(self, tree, filename):
+        """Some fixers need to maintain tree-wide state.
+        This method is called once, at the conclusion of tree fix-up.
+
+        tree - the root node of the tree to be processed.
+        filename - the name of the file the tree came from.
+        """
+        pass
+
+
+class ConditionalFix(BaseFix):
+    """ Base class for fixers which not execute if an import is found. """
+
+    # This is the name of the import which, if found, will cause the test to be skipped
+    skip_on = None
+
+    def start_tree(self, *args):
+        super(ConditionalFix, self).start_tree(*args)
+        self._should_skip = None
+
+    def should_skip(self, node):
+        if self._should_skip is not None:
+            return self._should_skip
+        pkg = self.skip_on.split(".")
+        name = pkg[-1]
+        pkg = ".".join(pkg[:-1])
+        self._should_skip = does_tree_import(pkg, name, node)
+        return self._should_skip