Fixed database typo and removed unnecessary class identifier.

venv/Lib/site-packages/networkx/algorithms/traversal/tests/test_beamsearch.py

@@ -0,0 +1,27 @@
+"""Unit tests for the beam search functions."""
+
+import networkx as nx
+
+
+def identity(x):
+    return x
+
+
+class TestBeamSearch:
+    """Unit tests for the beam search function."""
+
+    def test_narrow(self):
+        """Tests that a narrow beam width may cause an incomplete search."""
+        # In this search, we enqueue only the neighbor 3 at the first
+        # step, then only the neighbor 2 at the second step. Once at
+        # node 2, the search chooses node 3, since it has a higher value
+        # that node 1, but node 3 has already been visited, so the
+        # search terminates.
+        G = nx.cycle_graph(4)
+        edges = nx.bfs_beam_edges(G, 0, identity, width=1)
+        assert list(edges) == [(0, 3), (3, 2)]
+
+    def test_wide(self):
+        G = nx.cycle_graph(4)
+        edges = nx.bfs_beam_edges(G, 0, identity, width=2)
+        assert list(edges) == [(0, 3), (0, 1), (3, 2)]

venv/Lib/site-packages/networkx/algorithms/traversal/tests/test_bfs.py

@@ -0,0 +1,100 @@
+from functools import partial
+import networkx as nx
+
+
+class TestBFS:
+    @classmethod
+    def setup_class(cls):
+        # simple graph
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (1, 2), (1, 3), (2, 4), (3, 4)])
+        cls.G = G
+
+    def test_successor(self):
+        assert dict(nx.bfs_successors(self.G, source=0)) == {0: [1], 1: [2, 3], 2: [4]}
+
+    def test_predecessor(self):
+        assert dict(nx.bfs_predecessors(self.G, source=0)) == {1: 0, 2: 1, 3: 1, 4: 2}
+
+    def test_bfs_tree(self):
+        T = nx.bfs_tree(self.G, source=0)
+        assert sorted(T.nodes()) == sorted(self.G.nodes())
+        assert sorted(T.edges()) == [(0, 1), (1, 2), (1, 3), (2, 4)]
+
+    def test_bfs_edges(self):
+        edges = nx.bfs_edges(self.G, source=0)
+        assert list(edges) == [(0, 1), (1, 2), (1, 3), (2, 4)]
+
+    def test_bfs_edges_reverse(self):
+        D = nx.DiGraph()
+        D.add_edges_from([(0, 1), (1, 2), (1, 3), (2, 4), (3, 4)])
+        edges = nx.bfs_edges(D, source=4, reverse=True)
+        assert list(edges) == [(4, 2), (4, 3), (2, 1), (1, 0)]
+
+    def test_bfs_edges_sorting(self):
+        D = nx.DiGraph()
+        D.add_edges_from([(0, 1), (0, 2), (1, 4), (1, 3), (2, 5)])
+        sort_desc = partial(sorted, reverse=True)
+        edges_asc = nx.bfs_edges(D, source=0, sort_neighbors=sorted)
+        edges_desc = nx.bfs_edges(D, source=0, sort_neighbors=sort_desc)
+        assert list(edges_asc) == [(0, 1), (0, 2), (1, 3), (1, 4), (2, 5)]
+        assert list(edges_desc) == [(0, 2), (0, 1), (2, 5), (1, 4), (1, 3)]
+
+    def test_bfs_tree_isolates(self):
+        G = nx.Graph()
+        G.add_node(1)
+        G.add_node(2)
+        T = nx.bfs_tree(G, source=1)
+        assert sorted(T.nodes()) == [1]
+        assert sorted(T.edges()) == []
+
+
+class TestBreadthLimitedSearch:
+    @classmethod
+    def setup_class(cls):
+        # a tree
+        G = nx.Graph()
+        nx.add_path(G, [0, 1, 2, 3, 4, 5, 6])
+        nx.add_path(G, [2, 7, 8, 9, 10])
+        cls.G = G
+        # a disconnected graph
+        D = nx.Graph()
+        D.add_edges_from([(0, 1), (2, 3)])
+        nx.add_path(D, [2, 7, 8, 9, 10])
+        cls.D = D
+
+    def test_limited_bfs_successor(self):
+        assert dict(nx.bfs_successors(self.G, source=1, depth_limit=3)) == {
+            1: [0, 2],
+            2: [3, 7],
+            3: [4],
+            7: [8],
+        }
+        result = {
+            n: sorted(s) for n, s in nx.bfs_successors(self.D, source=7, depth_limit=2)
+        }
+        assert result == {8: [9], 2: [3], 7: [2, 8]}
+
+    def test_limited_bfs_predecessor(self):
+        assert dict(nx.bfs_predecessors(self.G, source=1, depth_limit=3)) == {
+            0: 1,
+            2: 1,
+            3: 2,
+            4: 3,
+            7: 2,
+            8: 7,
+        }
+        assert dict(nx.bfs_predecessors(self.D, source=7, depth_limit=2)) == {
+            2: 7,
+            3: 2,
+            8: 7,
+            9: 8,
+        }
+
+    def test_limited_bfs_tree(self):
+        T = nx.bfs_tree(self.G, source=3, depth_limit=1)
+        assert sorted(T.edges()) == [(3, 2), (3, 4)]
+
+    def test_limited_bfs_edges(self):
+        edges = nx.bfs_edges(self.G, source=9, depth_limit=4)
+        assert list(edges) == [(9, 8), (9, 10), (8, 7), (7, 2), (2, 1), (2, 3)]

venv/Lib/site-packages/networkx/algorithms/traversal/tests/test_dfs.py

@@ -0,0 +1,148 @@
+import networkx as nx
+
+
+class TestDFS:
+    @classmethod
+    def setup_class(cls):
+        # simple graph
+        G = nx.Graph()
+        G.add_edges_from([(0, 1), (1, 2), (1, 3), (2, 4), (3, 4)])
+        cls.G = G
+        # simple graph, disconnected
+        D = nx.Graph()
+        D.add_edges_from([(0, 1), (2, 3)])
+        cls.D = D
+
+    def test_preorder_nodes(self):
+        assert list(nx.dfs_preorder_nodes(self.G, source=0)) == [0, 1, 2, 4, 3]
+        assert list(nx.dfs_preorder_nodes(self.D)) == [0, 1, 2, 3]
+
+    def test_postorder_nodes(self):
+        assert list(nx.dfs_postorder_nodes(self.G, source=0)) == [3, 4, 2, 1, 0]
+        assert list(nx.dfs_postorder_nodes(self.D)) == [1, 0, 3, 2]
+
+    def test_successor(self):
+        assert nx.dfs_successors(self.G, source=0) == {0: [1], 1: [2], 2: [4], 4: [3]}
+        assert nx.dfs_successors(self.D) == {0: [1], 2: [3]}
+
+    def test_predecessor(self):
+        assert nx.dfs_predecessors(self.G, source=0) == {1: 0, 2: 1, 3: 4, 4: 2}
+        assert nx.dfs_predecessors(self.D) == {1: 0, 3: 2}
+
+    def test_dfs_tree(self):
+        exp_nodes = sorted(self.G.nodes())
+        exp_edges = [(0, 1), (1, 2), (2, 4), (4, 3)]
+        # Search from first node
+        T = nx.dfs_tree(self.G, source=0)
+        assert sorted(T.nodes()) == exp_nodes
+        assert sorted(T.edges()) == exp_edges
+        # Check source=None
+        T = nx.dfs_tree(self.G, source=None)
+        assert sorted(T.nodes()) == exp_nodes
+        assert sorted(T.edges()) == exp_edges
+        # Check source=None is the default
+        T = nx.dfs_tree(self.G)
+        assert sorted(T.nodes()) == exp_nodes
+        assert sorted(T.edges()) == exp_edges
+
+    def test_dfs_edges(self):
+        edges = nx.dfs_edges(self.G, source=0)
+        assert list(edges) == [(0, 1), (1, 2), (2, 4), (4, 3)]
+        edges = nx.dfs_edges(self.D)
+        assert list(edges) == [(0, 1), (2, 3)]
+
+    def test_dfs_labeled_edges(self):
+        edges = list(nx.dfs_labeled_edges(self.G, source=0))
+        forward = [(u, v) for (u, v, d) in edges if d == "forward"]
+        assert forward == [(0, 0), (0, 1), (1, 2), (2, 4), (4, 3)]
+
+    def test_dfs_labeled_disconnected_edges(self):
+        edges = list(nx.dfs_labeled_edges(self.D))
+        forward = [(u, v) for (u, v, d) in edges if d == "forward"]
+        assert forward == [(0, 0), (0, 1), (2, 2), (2, 3)]
+
+    def test_dfs_tree_isolates(self):
+        G = nx.Graph()
+        G.add_node(1)
+        G.add_node(2)
+        T = nx.dfs_tree(G, source=1)
+        assert sorted(T.nodes()) == [1]
+        assert sorted(T.edges()) == []
+        T = nx.dfs_tree(G, source=None)
+        assert sorted(T.nodes()) == [1, 2]
+        assert sorted(T.edges()) == []
+
+
+class TestDepthLimitedSearch:
+    @classmethod
+    def setup_class(cls):
+        # a tree
+        G = nx.Graph()
+        nx.add_path(G, [0, 1, 2, 3, 4, 5, 6])
+        nx.add_path(G, [2, 7, 8, 9, 10])
+        cls.G = G
+        # a disconnected graph
+        D = nx.Graph()
+        D.add_edges_from([(0, 1), (2, 3)])
+        nx.add_path(D, [2, 7, 8, 9, 10])
+        cls.D = D
+
+    def test_dls_preorder_nodes(self):
+        assert list(nx.dfs_preorder_nodes(self.G, source=0, depth_limit=2)) == [0, 1, 2]
+        assert list(nx.dfs_preorder_nodes(self.D, source=1, depth_limit=2)) == ([1, 0])
+
+    def test_dls_postorder_nodes(self):
+        assert list(nx.dfs_postorder_nodes(self.G, source=3, depth_limit=3)) == [
+            1,
+            7,
+            2,
+            5,
+            4,
+            3,
+        ]
+        assert list(nx.dfs_postorder_nodes(self.D, source=2, depth_limit=2)) == (
+            [3, 7, 2]
+        )
+
+    def test_dls_successor(self):
+        result = nx.dfs_successors(self.G, source=4, depth_limit=3)
+        assert {n: set(v) for n, v in result.items()} == {
+            2: {1, 7},
+            3: {2},
+            4: {3, 5},
+            5: {6},
+        }
+        result = nx.dfs_successors(self.D, source=7, depth_limit=2)
+        assert {n: set(v) for n, v in result.items()} == {8: {9}, 2: {3}, 7: {8, 2}}
+
+    def test_dls_predecessor(self):
+        assert nx.dfs_predecessors(self.G, source=0, depth_limit=3) == {
+            1: 0,
+            2: 1,
+            3: 2,
+            7: 2,
+        }
+        assert nx.dfs_predecessors(self.D, source=2, depth_limit=3) == {
+            8: 7,
+            9: 8,
+            3: 2,
+            7: 2,
+        }
+
+    def test_dls_tree(self):
+        T = nx.dfs_tree(self.G, source=3, depth_limit=1)
+        assert sorted(T.edges()) == [(3, 2), (3, 4)]
+
+    def test_dls_edges(self):
+        edges = nx.dfs_edges(self.G, source=9, depth_limit=4)
+        assert list(edges) == [(9, 8), (8, 7), (7, 2), (2, 1), (2, 3), (9, 10)]
+
+    def test_dls_labeled_edges(self):
+        edges = list(nx.dfs_labeled_edges(self.G, source=5, depth_limit=1))
+        forward = [(u, v) for (u, v, d) in edges if d == "forward"]
+        assert forward == [(5, 5), (5, 4), (5, 6)]
+
+    def test_dls_labeled_disconnected_edges(self):
+        edges = list(nx.dfs_labeled_edges(self.G, source=6, depth_limit=2))
+        forward = [(u, v) for (u, v, d) in edges if d == "forward"]
+        assert forward == [(6, 6), (6, 5), (5, 4)]

venv/Lib/site-packages/networkx/algorithms/traversal/tests/test_edgebfs.py

@@ -0,0 +1,151 @@
+import pytest
+
+import networkx as nx
+
+edge_bfs = nx.edge_bfs
+
+FORWARD = nx.algorithms.edgedfs.FORWARD
+REVERSE = nx.algorithms.edgedfs.REVERSE
+
+
+class TestEdgeBFS:
+    @classmethod
+    def setup_class(cls):
+        cls.nodes = [0, 1, 2, 3]
+        cls.edges = [(0, 1), (1, 0), (1, 0), (2, 0), (2, 1), (3, 1)]
+
+    def test_empty(self):
+        G = nx.Graph()
+        edges = list(edge_bfs(G))
+        assert edges == []
+
+    def test_graph_single_source(self):
+        G = nx.Graph(self.edges)
+        G.add_edge(4, 5)
+        x = list(edge_bfs(G, [0]))
+        x_ = [(0, 1), (0, 2), (1, 2), (1, 3)]
+        assert x == x_
+
+    def test_graph(self):
+        G = nx.Graph(self.edges)
+        x = list(edge_bfs(G, self.nodes))
+        x_ = [(0, 1), (0, 2), (1, 2), (1, 3)]
+        assert x == x_
+
+    def test_digraph(self):
+        G = nx.DiGraph(self.edges)
+        x = list(edge_bfs(G, self.nodes))
+        x_ = [(0, 1), (1, 0), (2, 0), (2, 1), (3, 1)]
+        assert x == x_
+
+    def test_digraph_orientation_invalid(self):
+        G = nx.DiGraph(self.edges)
+        edge_iterator = edge_bfs(G, self.nodes, orientation="hello")
+        pytest.raises(nx.NetworkXError, list, edge_iterator)
+
+    def test_digraph_orientation_none(self):
+        G = nx.DiGraph(self.edges)
+        x = list(edge_bfs(G, self.nodes, orientation=None))
+        x_ = [(0, 1), (1, 0), (2, 0), (2, 1), (3, 1)]
+        assert x == x_
+
+    def test_digraph_orientation_original(self):
+        G = nx.DiGraph(self.edges)
+        x = list(edge_bfs(G, self.nodes, orientation="original"))
+        x_ = [
+            (0, 1, FORWARD),
+            (1, 0, FORWARD),
+            (2, 0, FORWARD),
+            (2, 1, FORWARD),
+            (3, 1, FORWARD),
+        ]
+        assert x == x_
+
+    def test_digraph2(self):
+        G = nx.DiGraph()
+        nx.add_path(G, range(4))
+        x = list(edge_bfs(G, [0]))
+        x_ = [(0, 1), (1, 2), (2, 3)]
+        assert x == x_
+
+    def test_digraph_rev(self):
+        G = nx.DiGraph(self.edges)
+        x = list(edge_bfs(G, self.nodes, orientation="reverse"))
+        x_ = [
+            (1, 0, REVERSE),
+            (2, 0, REVERSE),
+            (0, 1, REVERSE),
+            (2, 1, REVERSE),
+            (3, 1, REVERSE),
+        ]
+        assert x == x_
+
+    def test_digraph_rev2(self):
+        G = nx.DiGraph()
+        nx.add_path(G, range(4))
+        x = list(edge_bfs(G, [3], orientation="reverse"))
+        x_ = [(2, 3, REVERSE), (1, 2, REVERSE), (0, 1, REVERSE)]
+        assert x == x_
+
+    def test_multigraph(self):
+        G = nx.MultiGraph(self.edges)
+        x = list(edge_bfs(G, self.nodes))
+        x_ = [(0, 1, 0), (0, 1, 1), (0, 1, 2), (0, 2, 0), (1, 2, 0), (1, 3, 0)]
+        # This is an example of where hash randomization can break.
+        # There are 3! * 2 alternative outputs, such as:
+        #    [(0, 1, 1), (1, 0, 0), (0, 1, 2), (1, 3, 0), (1, 2, 0)]
+        # But note, the edges (1,2,0) and (1,3,0) always follow the (0,1,k)
+        # edges. So the algorithm only guarantees a partial order. A total
+        # order is guaranteed only if the graph data structures are ordered.
+        assert x == x_
+
+    def test_multidigraph(self):
+        G = nx.MultiDiGraph(self.edges)
+        x = list(edge_bfs(G, self.nodes))
+        x_ = [(0, 1, 0), (1, 0, 0), (1, 0, 1), (2, 0, 0), (2, 1, 0), (3, 1, 0)]
+        assert x == x_
+
+    def test_multidigraph_rev(self):
+        G = nx.MultiDiGraph(self.edges)
+        x = list(edge_bfs(G, self.nodes, orientation="reverse"))
+        x_ = [
+            (1, 0, 0, REVERSE),
+            (1, 0, 1, REVERSE),
+            (2, 0, 0, REVERSE),
+            (0, 1, 0, REVERSE),
+            (2, 1, 0, REVERSE),
+            (3, 1, 0, REVERSE),
+        ]
+        assert x == x_
+
+    def test_digraph_ignore(self):
+        G = nx.DiGraph(self.edges)
+        x = list(edge_bfs(G, self.nodes, orientation="ignore"))
+        x_ = [
+            (0, 1, FORWARD),
+            (1, 0, REVERSE),
+            (2, 0, REVERSE),
+            (2, 1, REVERSE),
+            (3, 1, REVERSE),
+        ]
+        assert x == x_
+
+    def test_digraph_ignore2(self):
+        G = nx.DiGraph()
+        nx.add_path(G, range(4))
+        x = list(edge_bfs(G, [0], orientation="ignore"))
+        x_ = [(0, 1, FORWARD), (1, 2, FORWARD), (2, 3, FORWARD)]
+        assert x == x_
+
+    def test_multidigraph_ignore(self):
+        G = nx.MultiDiGraph(self.edges)
+        x = list(edge_bfs(G, self.nodes, orientation="ignore"))
+        x_ = [
+            (0, 1, 0, FORWARD),
+            (1, 0, 0, REVERSE),
+            (1, 0, 1, REVERSE),
+            (2, 0, 0, REVERSE),
+            (2, 1, 0, REVERSE),
+            (3, 1, 0, REVERSE),
+        ]
+        assert x == x_

venv/Lib/site-packages/networkx/algorithms/traversal/tests/test_edgedfs.py

@@ -0,0 +1,134 @@
+import pytest
+
+import networkx as nx
+
+edge_dfs = nx.algorithms.edge_dfs
+
+FORWARD = nx.algorithms.edgedfs.FORWARD
+REVERSE = nx.algorithms.edgedfs.REVERSE
+
+# These tests can fail with hash randomization. The easiest and clearest way
+# to write these unit tests is for the edges to be output in an expected total
+# order, but we cannot guarantee the order amongst outgoing edges from a node,
+# unless each class uses an ordered data structure for neighbors. This is
+# painful to do with the current API. The alternative is that the tests are
+# written (IMO confusingly) so that there is not a total order over the edges,
+# but only a partial order. Due to the small size of the graphs, hopefully
+# failures due to hash randomization will not occur. For an example of how
+# this can fail, see TestEdgeDFS.test_multigraph.
+
+
+class TestEdgeDFS:
+    @classmethod
+    def setup_class(cls):
+        cls.nodes = [0, 1, 2, 3]
+        cls.edges = [(0, 1), (1, 0), (1, 0), (2, 1), (3, 1)]
+
+    def test_empty(self):
+        G = nx.Graph()
+        edges = list(edge_dfs(G))
+        assert edges == []
+
+    def test_graph(self):
+        G = nx.Graph(self.edges)
+        x = list(edge_dfs(G, self.nodes))
+        x_ = [(0, 1), (1, 2), (1, 3)]
+        assert x == x_
+
+    def test_digraph(self):
+        G = nx.DiGraph(self.edges)
+        x = list(edge_dfs(G, self.nodes))
+        x_ = [(0, 1), (1, 0), (2, 1), (3, 1)]
+        assert x == x_
+
+    def test_digraph_orientation_invalid(self):
+        G = nx.DiGraph(self.edges)
+        edge_iterator = edge_dfs(G, self.nodes, orientation="hello")
+        pytest.raises(nx.NetworkXError, list, edge_iterator)
+
+    def test_digraph_orientation_none(self):
+        G = nx.DiGraph(self.edges)
+        x = list(edge_dfs(G, self.nodes, orientation=None))
+        x_ = [(0, 1), (1, 0), (2, 1), (3, 1)]
+        assert x == x_
+
+    def test_digraph_orientation_original(self):
+        G = nx.DiGraph(self.edges)
+        x = list(edge_dfs(G, self.nodes, orientation="original"))
+        x_ = [(0, 1, FORWARD), (1, 0, FORWARD), (2, 1, FORWARD), (3, 1, FORWARD)]
+        assert x == x_
+
+    def test_digraph2(self):
+        G = nx.DiGraph()
+        nx.add_path(G, range(4))
+        x = list(edge_dfs(G, [0]))
+        x_ = [(0, 1), (1, 2), (2, 3)]
+        assert x == x_
+
+    def test_digraph_rev(self):
+        G = nx.DiGraph(self.edges)
+        x = list(edge_dfs(G, self.nodes, orientation="reverse"))
+        x_ = [(1, 0, REVERSE), (0, 1, REVERSE), (2, 1, REVERSE), (3, 1, REVERSE)]
+        assert x == x_
+
+    def test_digraph_rev2(self):
+        G = nx.DiGraph()
+        nx.add_path(G, range(4))
+        x = list(edge_dfs(G, [3], orientation="reverse"))
+        x_ = [(2, 3, REVERSE), (1, 2, REVERSE), (0, 1, REVERSE)]
+        assert x == x_
+
+    def test_multigraph(self):
+        G = nx.MultiGraph(self.edges)
+        x = list(edge_dfs(G, self.nodes))
+        x_ = [(0, 1, 0), (1, 0, 1), (0, 1, 2), (1, 2, 0), (1, 3, 0)]
+        # This is an example of where hash randomization can break.
+        # There are 3! * 2 alternative outputs, such as:
+        #    [(0, 1, 1), (1, 0, 0), (0, 1, 2), (1, 3, 0), (1, 2, 0)]
+        # But note, the edges (1,2,0) and (1,3,0) always follow the (0,1,k)
+        # edges. So the algorithm only guarantees a partial order. A total
+        # order is guaranteed only if the graph data structures are ordered.
+        assert x == x_
+
+    def test_multidigraph(self):
+        G = nx.MultiDiGraph(self.edges)
+        x = list(edge_dfs(G, self.nodes))
+        x_ = [(0, 1, 0), (1, 0, 0), (1, 0, 1), (2, 1, 0), (3, 1, 0)]
+        assert x == x_
+
+    def test_multidigraph_rev(self):
+        G = nx.MultiDiGraph(self.edges)
+        x = list(edge_dfs(G, self.nodes, orientation="reverse"))
+        x_ = [
+            (1, 0, 0, REVERSE),
+            (0, 1, 0, REVERSE),
+            (1, 0, 1, REVERSE),
+            (2, 1, 0, REVERSE),
+            (3, 1, 0, REVERSE),
+        ]
+        assert x == x_
+
+    def test_digraph_ignore(self):
+        G = nx.DiGraph(self.edges)
+        x = list(edge_dfs(G, self.nodes, orientation="ignore"))
+        x_ = [(0, 1, FORWARD), (1, 0, FORWARD), (2, 1, REVERSE), (3, 1, REVERSE)]
+        assert x == x_
+
+    def test_digraph_ignore2(self):
+        G = nx.DiGraph()
+        nx.add_path(G, range(4))
+        x = list(edge_dfs(G, [0], orientation="ignore"))
+        x_ = [(0, 1, FORWARD), (1, 2, FORWARD), (2, 3, FORWARD)]
+        assert x == x_
+
+    def test_multidigraph_ignore(self):
+        G = nx.MultiDiGraph(self.edges)
+        x = list(edge_dfs(G, self.nodes, orientation="ignore"))
+        x_ = [
+            (0, 1, 0, FORWARD),
+            (1, 0, 0, FORWARD),
+            (1, 0, 1, REVERSE),
+            (2, 1, 0, REVERSE),
+            (3, 1, 0, REVERSE),
+        ]
+        assert x == x_