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import pytest
import networkx as nx
from networkx.algorithms import edge_dfs
from networkx.algorithms.traversal.edgedfs import FORWARD, 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_
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