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(object): @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_