def test_edge_attribute_discard():
# Test that edge attributes are discarded if we do not specify to keep them
G = nx.Graph()
edgelist = [(0, 1, [('weight', 5), ('otherattr', 1), ('otherattr2', 3)]),
(0, 2, [('weight', 5), ('otherattr', 2), ('otherattr2', 2)]),
(1, 2, [('weight', 6), ('otherattr', 3), ('otherattr2', 1)])]
G.add_edges_from(edgelist)
ed = branchings.Edmonds(G)
B = ed.find_optimum('weight', preserve_attrs=False)
edge_dict = B[0][1]
with pytest.raises(KeyError):
_ = edge_dict['otherattr']
def test_edge_attribute_preservation_normal_graph():
# Test that edge attributes are preserved when finding an optimum graph
# using the Edmonds class for normal graphs.
G = nx.Graph()
edgelist = [(0, 1, [('weight', 5), ('otherattr', 1), ('otherattr2', 3)]),
(0, 2, [('weight', 5), ('otherattr', 2), ('otherattr2', 2)]),
(1, 2, [('weight', 6), ('otherattr', 3), ('otherattr2', 1)])]
G.add_edges_from(edgelist)
ed = branchings.Edmonds(G)
B = ed.find_optimum('weight', preserve_attrs=True, seed=1)
assert B[0][1]['otherattr'] == 1
assert B[0][1]['otherattr2'] == 3
def test_edge_attribute_preservation_multigraph():
# Test that edge attributes are preserved when finding an optimum graph
# using the Edmonds class for multigraphs.
G = nx.MultiGraph()
edgelist = [(0, 1, [('weight', 5), ('otherattr', 1), ('otherattr2', 3)]),
(0, 2, [('weight', 5), ('otherattr', 2), ('otherattr2', 2)]),
(1, 2, [('weight', 6), ('otherattr', 3), ('otherattr2', 1)])]
G.add_edges_from(edgelist * 2) # Make sure we have duplicate edge paths
ed = branchings.Edmonds(G)
B = ed.find_optimum('weight', preserve_attrs=True)
assert B[0][1][0]['otherattr'] == 1
assert B[0][1][0]['otherattr2'] == 3
