def test_project_weighted_jaccard(self):
edges = [
("A", "B", 2 / 5.0),
("A", "C", 1 / 2.0),
("B", "C", 1 / 5.0),
("B", "D", 1 / 5.0),
("B", "E", 2 / 6.0),
("E", "F", 1 / 3.0),
]
Panswer = nx.Graph()
Panswer.add_weighted_edges_from(edges)
P = bipartite.overlap_weighted_projected_graph(self.G, "ABCDEF")
assert edges_equal(list(P.edges()), Panswer.edges())
for u, v in list(P.edges()):
assert P[u][v]["weight"] == Panswer[u][v]["weight"]
edges = [
("A", "B", 3 / 3.0),
("A", "E", 1 / 3.0),
("A", "C", 1 / 3.0),
("A", "D", 1 / 3.0),
("B", "E", 1 / 3.0),
("B", "C", 1 / 3.0),
("B", "D", 1 / 3.0),
("C", "D", 1 / 1.0),
]
Panswer = nx.Graph()
Panswer.add_weighted_edges_from(edges)
P = bipartite.overlap_weighted_projected_graph(self.N, "ABCDE")
assert edges_equal(list(P.edges()), Panswer.edges())
for u, v in P.edges():
assert P[u][v]["weight"] == Panswer[u][v]["weight"]
def test_project_weighted_ratio(self):
edges = [
("A", "B", 2 / 6.0),
("A", "C", 1 / 6.0),
("B", "C", 1 / 6.0),
("B", "D", 1 / 6.0),
("B", "E", 2 / 6.0),
("E", "F", 1 / 6.0),
]
Panswer = nx.Graph()
Panswer.add_weighted_edges_from(edges)
P = bipartite.weighted_projected_graph(self.G, "ABCDEF", ratio=True)
assert edges_equal(list(P.edges()), Panswer.edges())
for u, v in list(P.edges()):
assert P[u][v]["weight"] == Panswer[u][v]["weight"]
edges = [
("A", "B", 3 / 3.0),
("A", "E", 1 / 3.0),
("A", "C", 1 / 3.0),
("A", "D", 1 / 3.0),
("B", "E", 1 / 3.0),
("B", "C", 1 / 3.0),
("B", "D", 1 / 3.0),
("C", "D", 1 / 3.0),
]
Panswer = nx.Graph()
Panswer.add_weighted_edges_from(edges)
P = bipartite.weighted_projected_graph(self.N, "ABCDE", ratio=True)
assert edges_equal(list(P.edges()), Panswer.edges())
for u, v in list(P.edges()):
assert P[u][v]["weight"] == Panswer[u][v]["weight"]
def test_graph(self):
g = nx.cycle_graph(10)
G = nx.Graph()
G.add_nodes_from(g)
G.add_weighted_edges_from((u, v, u) for u, v in g.edges())
# Dict of dicts
dod = to_dict_of_dicts(G)
GG = from_dict_of_dicts(dod, create_using=nx.Graph)
assert nodes_equal(sorted(G.nodes()), sorted(GG.nodes()))
assert edges_equal(sorted(G.edges()), sorted(GG.edges()))
GW = to_networkx_graph(dod, create_using=nx.Graph)
assert nodes_equal(sorted(G.nodes()), sorted(GW.nodes()))
assert edges_equal(sorted(G.edges()), sorted(GW.edges()))
GI = nx.Graph(dod)
assert sorted(G.nodes()) == sorted(GI.nodes())
assert sorted(G.edges()) == sorted(GI.edges())
# Dict of lists
dol = to_dict_of_lists(G)
GG = from_dict_of_lists(dol, create_using=nx.Graph)
# dict of lists throws away edge data so set it to none
enone = [(u, v, {}) for (u, v, d) in G.edges(data=True)]
assert nodes_equal(sorted(G.nodes()), sorted(GG.nodes()))
assert edges_equal(enone, sorted(GG.edges(data=True)))
GW = to_networkx_graph(dol, create_using=nx.Graph)
assert nodes_equal(sorted(G.nodes()), sorted(GW.nodes()))
assert edges_equal(enone, sorted(GW.edges(data=True)))
GI = nx.Graph(dol)
assert nodes_equal(sorted(G.nodes()), sorted(GI.nodes()))
assert edges_equal(enone, sorted(GI.edges(data=True)))
def test_selfloops(graph_type):
G = nx.complete_graph(3, create_using=graph_type)
G.add_edge(0, 0)
assert nodes_equal(nx.nodes_with_selfloops(G), [0])
assert edges_equal(nx.selfloop_edges(G), [(0, 0)])
assert edges_equal(nx.selfloop_edges(G, data=True), [(0, 0, {})])
assert nx.number_of_selfloops(G) == 1
def test_from_biadjacency_weight(self):
M = sparse.csc_matrix([[1, 2], [0, 3]])
B = bipartite.from_biadjacency_matrix(M)
assert edges_equal(B.edges(), [(0, 2), (0, 3), (1, 3)])
B = bipartite.from_biadjacency_matrix(M, edge_attribute="weight")
e = [(0, 2, {"weight": 1}), (0, 3, {"weight": 2}), (1, 3, {"weight": 3})]
assert edges_equal(B.edges(data=True), e)
def test_dorogovtsev_goltsev_mendes_graph(self):
G = nx.dorogovtsev_goltsev_mendes_graph(0)
assert edges_equal(G.edges(), [(0, 1)])
assert nodes_equal(list(G), [0, 1])
G = nx.dorogovtsev_goltsev_mendes_graph(1)
assert edges_equal(G.edges(), [(0, 1), (0, 2), (1, 2)])
assert nx.average_clustering(G) == 1.0
assert sorted(nx.triangles(G).values()) == [1, 1, 1]
G = nx.dorogovtsev_goltsev_mendes_graph(10)
assert nx.number_of_nodes(G) == 29526
assert nx.number_of_edges(G) == 59049
assert G.degree(0) == 1024
assert G.degree(1) == 1024
assert G.degree(2) == 1024
pytest.raises(
nx.NetworkXError,
nx.dorogovtsev_goltsev_mendes_graph,
7,
create_using=nx.DiGraph,
)
pytest.raises(
nx.NetworkXError,
nx.dorogovtsev_goltsev_mendes_graph,
7,
create_using=nx.MultiGraph,
)
def test_negative_weights(self):
"""Negative weights"""
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, 1),
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
}))
assert edges_equal(nx.min_weight_matching(G, 1),
matching_dict_to_set({
1: 2,
3: 4
}))
def test_from_edgelist(self):
# Pandas DataFrame
G = nx.cycle_graph(10)
G.add_weighted_edges_from((u, v, u) for u, v in list(G.edges))
edgelist = nx.to_edgelist(G)
source = []
target = []
weight = []
# N.B the iterate order of edgelist may not all the same
for s, t, d in edgelist:
source.append(s)
target.append(t)
weight.append(d["weight"])
edges = pd.DataFrame({
"source": source,
"target": target,
"weight": weight
})
GG = nx.from_pandas_edgelist(edges, edge_attr="weight")
assert nodes_equal(G.nodes(), GG.nodes())
assert edges_equal(G.edges(), GG.edges())
GW = nx.to_networkx_graph(edges, create_using=nx.Graph)
assert nodes_equal(G.nodes(), GW.nodes())
assert edges_equal(G.edges(), GW.edges())
def test_add_star(self):
G = self.G.copy()
nlist = [12, 13, 14, 15]
nx.add_star(G, nlist)
assert edges_equal(G.edges(nlist), [(12, 13), (12, 14), (12, 15)])
G = self.G.copy()
nx.add_star(G, nlist, weight=2.0)
assert edges_equal(
G.edges(nlist, data=True),
[
(12, 13, {
"weight": 2.0
}),
(12, 14, {
"weight": 2.0
}),
(12, 15, {
"weight": 2.0
}),
],
)
G = self.G.copy()
nlist = [12]
nx.add_star(G, nlist)
assert nodes_equal(G, list(self.G) + nlist)
G = self.G.copy()
nlist = []
nx.add_star(G, nlist)
assert nodes_equal(G.nodes, self.Gnodes)
assert edges_equal(G.edges, self.G.edges)
def test_transitive_closure_dag(self):
G = nx.DiGraph([(1, 2), (2, 3), (3, 4)])
transitive_closure = nx.algorithms.dag.transitive_closure_dag
solution = [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]
assert edges_equal(transitive_closure(G).edges(), solution)
G = nx.DiGraph([(1, 2), (2, 3), (2, 4)])
solution = [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4)]
assert edges_equal(transitive_closure(G).edges(), solution)
G = nx.Graph([(1, 2), (2, 3), (3, 4)])
pytest.raises(nx.NetworkXNotImplemented, transitive_closure, G)
# test if edge data is copied
G = nx.DiGraph([(1, 2, {"a": 3}), (2, 3, {"b": 0}), (3, 4)])
H = transitive_closure(G)
for u, v in G.edges():
assert G.get_edge_data(u, v) == H.get_edge_data(u, v)
k = 10
G = nx.DiGraph((i, i + 1, {
"foo": "bar",
"weight": i
}) for i in range(k))
H = transitive_closure(G)
for u, v in G.edges():
assert G.get_edge_data(u, v) == H.get_edge_data(u, v)
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_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_transitive_closure(self):
G = nx.DiGraph([(1, 2), (2, 3), (3, 4)])
solution = [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]
assert edges_equal(nx.transitive_closure(G).edges(), solution)
G = nx.DiGraph([(1, 2), (2, 3), (2, 4)])
solution = [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4)]
assert edges_equal(nx.transitive_closure(G).edges(), solution)
G = nx.DiGraph([(1, 2), (2, 3), (3, 1)])
solution = [(1, 2), (2, 1), (2, 3), (3, 2), (1, 3), (3, 1)]
soln = sorted(solution + [(n, n) for n in G])
assert edges_equal(sorted(nx.transitive_closure(G).edges()), soln)
G = nx.Graph([(1, 2), (2, 3), (3, 4)])
pytest.raises(nx.NetworkXNotImplemented, nx.transitive_closure, G)
# test if edge data is copied
G = nx.DiGraph([(1, 2, {"a": 3}), (2, 3, {"b": 0}), (3, 4)])
H = nx.transitive_closure(G)
for u, v in G.edges():
assert G.get_edge_data(u, v) == H.get_edge_data(u, v)
k = 10
G = nx.DiGraph((i, i + 1, {"f": "b", "weight": i}) for i in range(k))
H = nx.transitive_closure(G)
for u, v in G.edges():
assert G.get_edge_data(u, v) == H.get_edge_data(u, v)
def test_trivial5(self):
"""Path"""
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_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_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_edge_attr3(self):
G = self.Graph()
G.add_edges_from([(1, 2, {
"weight": 32
}), (3, 4, {
"weight": 64
})],
foo="foo")
assert edges_equal(
G.edges(data=True),
[
(1, 2, {
"foo": "foo",
"weight": 32
}),
(3, 4, {
"foo": "foo",
"weight": 64
}),
],
)
G.remove_edges_from([(1, 2), (3, 4)])
G.add_edge(1, 2, data=7, spam="bar", bar="foo")
assert edges_equal(G.edges(data=True), [(1, 2, {
"data": 7,
"spam": "bar",
"bar": "foo"
})])
def test_digraphs(self):
for dest, source in [
(to_dict_of_dicts, from_dict_of_dicts),
(to_dict_of_lists, from_dict_of_lists),
]:
G = cycle_graph(10)
# Dict of [dicts, lists]
dod = dest(G)
GG = source(dod)
assert nodes_equal(sorted(G.nodes()), sorted(GG.nodes()))
assert edges_equal(sorted(G.edges()), sorted(GG.edges()))
GW = to_networkx_graph(dod)
assert nodes_equal(sorted(G.nodes()), sorted(GW.nodes()))
assert edges_equal(sorted(G.edges()), sorted(GW.edges()))
GI = nx.Graph(dod)
assert nodes_equal(sorted(G.nodes()), sorted(GI.nodes()))
assert edges_equal(sorted(G.edges()), sorted(GI.edges()))
G = cycle_graph(10, create_using=nx.DiGraph)
dod = dest(G)
GG = source(dod, create_using=nx.DiGraph)
assert sorted(G.nodes()) == sorted(GG.nodes())
assert sorted(G.edges()) == sorted(GG.edges())
GW = to_networkx_graph(dod, create_using=nx.DiGraph)
assert sorted(G.nodes()) == sorted(GW.nodes())
assert sorted(G.edges()) == sorted(GW.edges())
GI = nx.DiGraph(dod)
assert sorted(G.nodes()) == sorted(GI.nodes())
assert sorted(G.edges()) == sorted(GI.edges())
def test_edges(self):
assert edges_equal(self.G.edges(), list(nx.edges(self.G)))
assert sorted(self.DG.edges()) == sorted(nx.edges(self.DG))
assert edges_equal(self.G.edges(nbunch=[0, 1, 3]),
list(nx.edges(self.G, nbunch=[0, 1, 3])))
assert sorted(self.DG.edges(nbunch=[0, 1, 3])) == sorted(
nx.edges(self.DG, nbunch=[0, 1, 3]))
def test_project_weighted_newman(self):
edges = [
("A", "B", 1.5),
("A", "C", 0.5),
("B", "C", 0.5),
("B", "D", 1),
("B", "E", 2),
("E", "F", 1),
]
Panswer = nx.Graph()
Panswer.add_weighted_edges_from(edges)
P = bipartite.collaboration_weighted_projected_graph(self.G, "ABCDEF")
assert edges_equal(list(P.edges()), Panswer.edges())
for u, v in list(P.edges()):
assert P[u][v]["weight"] == Panswer[u][v]["weight"]
edges = [
("A", "B", 11 / 6.0),
("A", "E", 1 / 2.0),
("A", "C", 1 / 3.0),
("A", "D", 1 / 3.0),
("B", "E", 1 / 2.0),
("B", "C", 1 / 3.0),
("B", "D", 1 / 3.0),
("C", "D", 1 / 3.0),
]
Panswer = nx.Graph()
Panswer.add_weighted_edges_from(edges)
P = bipartite.collaboration_weighted_projected_graph(self.N, "ABCDE")
assert edges_equal(list(P.edges()), Panswer.edges())
for u, v in list(P.edges()):
assert P[u][v]["weight"] == Panswer[u][v]["weight"]
def test_nonisomorphic_trees(self):
def f(x):
return list(nx.nonisomorphic_trees(x))
assert edges_equal(f(3)[0].edges(), [(0, 1), (0, 2)])
assert edges_equal(f(4)[0].edges(), [(0, 1), (0, 3), (1, 2)])
assert edges_equal(f(4)[1].edges(), [(0, 1), (0, 2), (0, 3)])
def test_restricted_induced_subgraph_chains(self):
"""Test subgraph chains that both restrict and show nodes/edges.
A restricted_view subgraph should allow induced subgraphs using
G.subgraph that automagically without a chain (meaning the result
is a subgraph view of the original graph not a subgraph-of-subgraph.
"""
hide_nodes = [3, 4, 5]
hide_edges = [(6, 7)]
RG = nx.restricted_view(self.G, hide_nodes, hide_edges)
nodes = [4, 5, 6, 7, 8]
SG = nx.induced_subgraph(RG, nodes)
SSG = RG.subgraph(nodes)
assert RG._graph is self.G
assert SSG._graph is self.G
assert SG._graph is RG
assert edges_equal(SG.edges, SSG.edges)
# should be same as morphing the graph
CG = self.G.copy()
CG.remove_nodes_from(hide_nodes)
CG.remove_edges_from(hide_edges)
assert edges_equal(CG.edges(nodes), SSG.edges)
CG.remove_nodes_from([0, 1, 2, 3])
assert edges_equal(CG.edges, SSG.edges)
# switch order: subgraph first, then restricted view
SSSG = self.G.subgraph(nodes)
RSG = nx.restricted_view(SSSG, hide_nodes, hide_edges)
assert RSG._graph is not self.G
assert edges_equal(RSG.edges, CG.edges)
def test_edges(self):
G = self.K3
assert edges_equal(G.edges(), [(0, 1), (0, 2), (1, 2)])
assert edges_equal(G.edges(0), [(0, 1), (0, 2)])
assert edges_equal(G.edges([0, 1]), [(0, 1), (0, 2), (1, 2)])
with pytest.raises(nx.NetworkXError):
G.edges(-1)
def test_path_projected_graph(self):
G = nx.path_graph(4)
P = bipartite.projected_graph(G, [1, 3])
assert nodes_equal(list(P), [1, 3])
assert edges_equal(list(P.edges()), [(1, 3)])
P = bipartite.projected_graph(G, [0, 2])
assert nodes_equal(list(P), [0, 2])
assert edges_equal(list(P.edges()), [(0, 2)])
def test_selfloop_edges_attr(graph_type):
G = nx.complete_graph(3, create_using=graph_type)
G.add_edge(0, 0)
G.add_edge(1, 1, weight=2)
assert edges_equal(
nx.selfloop_edges(G, data=True), [(0, 0, {}), (1, 1, {"weight": 2})]
)
assert edges_equal(nx.selfloop_edges(G, data="weight"), [(0, 0, None), (1, 1, 2)])
def test_edges_data(self):
G = self.K3
all_edges = [(0, 1, {}), (0, 2, {}), (1, 2, {})]
assert edges_equal(G.edges(data=True), all_edges)
assert edges_equal(G.edges(0, data=True), [(0, 1, {}), (0, 2, {})])
assert edges_equal(G.edges([0, 1], data=True), all_edges)
with pytest.raises(nx.NetworkXError):
G.edges(-1, True)
def test_edge_attr2(self):
G = self.Graph()
G.add_edges_from([(1, 2), (3, 4)], foo="foo")
assert edges_equal(G.edges(data=True), [(1, 2, {
"foo": "foo"
}), (3, 4, {
"foo": "foo"
})])
assert edges_equal(G.edges(data="foo"), [(1, 2, "foo"), (3, 4, "foo")])
def test_path_weighted_projected_graph(self):
G = nx.path_graph(4)
P = bipartite.weighted_projected_graph(G, [1, 3])
assert nodes_equal(list(P), [1, 3])
assert edges_equal(list(P.edges()), [(1, 3)])
P[1][3]["weight"] = 1
P = bipartite.weighted_projected_graph(G, [0, 2])
assert nodes_equal(list(P), [0, 2])
assert edges_equal(list(P.edges()), [(0, 2)])
P[0][2]["weight"] = 1
def test_selfloops_attr(self):
G = self.K3.copy()
G.add_edge(0, 0)
G.add_edge(1, 1, weight=2)
assert edges_equal(nx.selfloop_edges(G, data=True), [(0, 0, {}),
(1, 1, {
"weight": 2
})])
assert edges_equal(nx.selfloop_edges(G, data="weight"), [(0, 0, None),
(1, 1, 2)])
