ai-content-maker/.venv/Lib/site-packages/networkx/classes/tests/test_function.py

802 lines
26 KiB
Python

import random
import pytest
import networkx as nx
from networkx.utils import edges_equal, nodes_equal
class TestFunction:
def setup_method(self):
self.G = nx.Graph({0: [1, 2, 3], 1: [1, 2, 0], 4: []}, name="Test")
self.Gdegree = {0: 3, 1: 2, 2: 2, 3: 1, 4: 0}
self.Gnodes = list(range(5))
self.Gedges = [(0, 1), (0, 2), (0, 3), (1, 0), (1, 1), (1, 2)]
self.DG = nx.DiGraph({0: [1, 2, 3], 1: [1, 2, 0], 4: []})
self.DGin_degree = {0: 1, 1: 2, 2: 2, 3: 1, 4: 0}
self.DGout_degree = {0: 3, 1: 3, 2: 0, 3: 0, 4: 0}
self.DGnodes = list(range(5))
self.DGedges = [(0, 1), (0, 2), (0, 3), (1, 0), (1, 1), (1, 2)]
def test_nodes(self):
assert nodes_equal(self.G.nodes(), list(nx.nodes(self.G)))
assert nodes_equal(self.DG.nodes(), list(nx.nodes(self.DG)))
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_degree(self):
assert edges_equal(self.G.degree(), list(nx.degree(self.G)))
assert sorted(self.DG.degree()) == sorted(nx.degree(self.DG))
assert edges_equal(
self.G.degree(nbunch=[0, 1]), list(nx.degree(self.G, nbunch=[0, 1]))
)
assert sorted(self.DG.degree(nbunch=[0, 1])) == sorted(
nx.degree(self.DG, nbunch=[0, 1])
)
assert edges_equal(
self.G.degree(weight="weight"), list(nx.degree(self.G, weight="weight"))
)
assert sorted(self.DG.degree(weight="weight")) == sorted(
nx.degree(self.DG, weight="weight")
)
def test_neighbors(self):
assert list(self.G.neighbors(1)) == list(nx.neighbors(self.G, 1))
assert list(self.DG.neighbors(1)) == list(nx.neighbors(self.DG, 1))
def test_number_of_nodes(self):
assert self.G.number_of_nodes() == nx.number_of_nodes(self.G)
assert self.DG.number_of_nodes() == nx.number_of_nodes(self.DG)
def test_number_of_edges(self):
assert self.G.number_of_edges() == nx.number_of_edges(self.G)
assert self.DG.number_of_edges() == nx.number_of_edges(self.DG)
def test_is_directed(self):
assert self.G.is_directed() == nx.is_directed(self.G)
assert self.DG.is_directed() == nx.is_directed(self.DG)
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_add_path(self):
G = self.G.copy()
nlist = [12, 13, 14, 15]
nx.add_path(G, nlist)
assert edges_equal(G.edges(nlist), [(12, 13), (13, 14), (14, 15)])
G = self.G.copy()
nx.add_path(G, nlist, weight=2.0)
assert edges_equal(
G.edges(nlist, data=True),
[
(12, 13, {"weight": 2.0}),
(13, 14, {"weight": 2.0}),
(14, 15, {"weight": 2.0}),
],
)
G = self.G.copy()
nlist = ["node"]
nx.add_path(G, nlist)
assert edges_equal(G.edges(nlist), [])
assert nodes_equal(G, list(self.G) + ["node"])
G = self.G.copy()
nlist = iter(["node"])
nx.add_path(G, nlist)
assert edges_equal(G.edges(["node"]), [])
assert nodes_equal(G, list(self.G) + ["node"])
G = self.G.copy()
nlist = [12]
nx.add_path(G, nlist)
assert edges_equal(G.edges(nlist), [])
assert nodes_equal(G, list(self.G) + [12])
G = self.G.copy()
nlist = iter([12])
nx.add_path(G, nlist)
assert edges_equal(G.edges([12]), [])
assert nodes_equal(G, list(self.G) + [12])
G = self.G.copy()
nlist = []
nx.add_path(G, nlist)
assert edges_equal(G.edges, self.G.edges)
assert nodes_equal(G, list(self.G))
G = self.G.copy()
nlist = iter([])
nx.add_path(G, nlist)
assert edges_equal(G.edges, self.G.edges)
assert nodes_equal(G, list(self.G))
def test_add_cycle(self):
G = self.G.copy()
nlist = [12, 13, 14, 15]
oklists = [
[(12, 13), (12, 15), (13, 14), (14, 15)],
[(12, 13), (13, 14), (14, 15), (15, 12)],
]
nx.add_cycle(G, nlist)
assert sorted(G.edges(nlist)) in oklists
G = self.G.copy()
oklists = [
[
(12, 13, {"weight": 1.0}),
(12, 15, {"weight": 1.0}),
(13, 14, {"weight": 1.0}),
(14, 15, {"weight": 1.0}),
],
[
(12, 13, {"weight": 1.0}),
(13, 14, {"weight": 1.0}),
(14, 15, {"weight": 1.0}),
(15, 12, {"weight": 1.0}),
],
]
nx.add_cycle(G, nlist, weight=1.0)
assert sorted(G.edges(nlist, data=True)) in oklists
G = self.G.copy()
nlist = [12]
nx.add_cycle(G, nlist)
assert nodes_equal(G, list(self.G) + nlist)
G = self.G.copy()
nlist = []
nx.add_cycle(G, nlist)
assert nodes_equal(G.nodes, self.Gnodes)
assert edges_equal(G.edges, self.G.edges)
def test_subgraph(self):
assert (
self.G.subgraph([0, 1, 2, 4]).adj == nx.subgraph(self.G, [0, 1, 2, 4]).adj
)
assert (
self.DG.subgraph([0, 1, 2, 4]).adj == nx.subgraph(self.DG, [0, 1, 2, 4]).adj
)
assert (
self.G.subgraph([0, 1, 2, 4]).adj
== nx.induced_subgraph(self.G, [0, 1, 2, 4]).adj
)
assert (
self.DG.subgraph([0, 1, 2, 4]).adj
== nx.induced_subgraph(self.DG, [0, 1, 2, 4]).adj
)
# subgraph-subgraph chain is allowed in function interface
H = nx.induced_subgraph(self.G.subgraph([0, 1, 2, 4]), [0, 1, 4])
assert H._graph is not self.G
assert H.adj == self.G.subgraph([0, 1, 4]).adj
def test_edge_subgraph(self):
assert (
self.G.edge_subgraph([(1, 2), (0, 3)]).adj
== nx.edge_subgraph(self.G, [(1, 2), (0, 3)]).adj
)
assert (
self.DG.edge_subgraph([(1, 2), (0, 3)]).adj
== nx.edge_subgraph(self.DG, [(1, 2), (0, 3)]).adj
)
def test_create_empty_copy(self):
G = nx.create_empty_copy(self.G, with_data=False)
assert nodes_equal(G, list(self.G))
assert G.graph == {}
assert G._node == {}.fromkeys(self.G.nodes(), {})
assert G._adj == {}.fromkeys(self.G.nodes(), {})
G = nx.create_empty_copy(self.G)
assert nodes_equal(G, list(self.G))
assert G.graph == self.G.graph
assert G._node == self.G._node
assert G._adj == {}.fromkeys(self.G.nodes(), {})
def test_degree_histogram(self):
assert nx.degree_histogram(self.G) == [1, 1, 1, 1, 1]
def test_density(self):
assert nx.density(self.G) == 0.5
assert nx.density(self.DG) == 0.3
G = nx.Graph()
G.add_node(1)
assert nx.density(G) == 0.0
def test_density_selfloop(self):
G = nx.Graph()
G.add_edge(1, 1)
assert nx.density(G) == 0.0
G.add_edge(1, 2)
assert nx.density(G) == 2.0
def test_freeze(self):
G = nx.freeze(self.G)
assert G.frozen
pytest.raises(nx.NetworkXError, G.add_node, 1)
pytest.raises(nx.NetworkXError, G.add_nodes_from, [1])
pytest.raises(nx.NetworkXError, G.remove_node, 1)
pytest.raises(nx.NetworkXError, G.remove_nodes_from, [1])
pytest.raises(nx.NetworkXError, G.add_edge, 1, 2)
pytest.raises(nx.NetworkXError, G.add_edges_from, [(1, 2)])
pytest.raises(nx.NetworkXError, G.remove_edge, 1, 2)
pytest.raises(nx.NetworkXError, G.remove_edges_from, [(1, 2)])
pytest.raises(nx.NetworkXError, G.clear)
def test_is_frozen(self):
assert not nx.is_frozen(self.G)
G = nx.freeze(self.G)
assert G.frozen == nx.is_frozen(self.G)
assert G.frozen
def test_info(self):
G = nx.path_graph(5)
G.name = "path_graph(5)"
info = nx.info(G)
expected_graph_info = "Graph named 'path_graph(5)' with 5 nodes and 4 edges"
assert info == expected_graph_info
info = nx.info(G, n=1)
assert type(info) == str
expected_node_info = "\n".join(
["Node 1 has the following properties:", "Degree: 2", "Neighbors: 0 2"]
)
assert info == expected_node_info
# must raise an error for a non-existent node
pytest.raises(nx.NetworkXError, nx.info, G, 1248)
def test_info_digraph(self):
G = nx.DiGraph(name="path_graph(5)")
nx.add_path(G, [0, 1, 2, 3, 4])
info = nx.info(G)
expected_graph_info = "DiGraph named 'path_graph(5)' with 5 nodes and 4 edges"
assert info == expected_graph_info
info = nx.info(G, n=1)
expected_node_info = "\n".join(
["Node 1 has the following properties:", "Degree: 2", "Neighbors: 2"]
)
assert info == expected_node_info
pytest.raises(nx.NetworkXError, nx.info, G, n=-1)
def test_neighbors_complete_graph(self):
graph = nx.complete_graph(100)
pop = random.sample(list(graph), 1)
nbors = list(nx.neighbors(graph, pop[0]))
# should be all the other vertices in the graph
assert len(nbors) == len(graph) - 1
graph = nx.path_graph(100)
node = random.sample(list(graph), 1)[0]
nbors = list(nx.neighbors(graph, node))
# should be all the other vertices in the graph
if node != 0 and node != 99:
assert len(nbors) == 2
else:
assert len(nbors) == 1
# create a star graph with 99 outer nodes
graph = nx.star_graph(99)
nbors = list(nx.neighbors(graph, 0))
assert len(nbors) == 99
def test_non_neighbors(self):
graph = nx.complete_graph(100)
pop = random.sample(list(graph), 1)
nbors = list(nx.non_neighbors(graph, pop[0]))
# should be all the other vertices in the graph
assert len(nbors) == 0
graph = nx.path_graph(100)
node = random.sample(list(graph), 1)[0]
nbors = list(nx.non_neighbors(graph, node))
# should be all the other vertices in the graph
if node != 0 and node != 99:
assert len(nbors) == 97
else:
assert len(nbors) == 98
# create a star graph with 99 outer nodes
graph = nx.star_graph(99)
nbors = list(nx.non_neighbors(graph, 0))
assert len(nbors) == 0
# disconnected graph
graph = nx.Graph()
graph.add_nodes_from(range(10))
nbors = list(nx.non_neighbors(graph, 0))
assert len(nbors) == 9
def test_non_edges(self):
# All possible edges exist
graph = nx.complete_graph(5)
nedges = list(nx.non_edges(graph))
assert len(nedges) == 0
graph = nx.path_graph(4)
expected = [(0, 2), (0, 3), (1, 3)]
nedges = list(nx.non_edges(graph))
for (u, v) in expected:
assert (u, v) in nedges or (v, u) in nedges
graph = nx.star_graph(4)
expected = [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]
nedges = list(nx.non_edges(graph))
for (u, v) in expected:
assert (u, v) in nedges or (v, u) in nedges
# Directed graphs
graph = nx.DiGraph()
graph.add_edges_from([(0, 2), (2, 0), (2, 1)])
expected = [(0, 1), (1, 0), (1, 2)]
nedges = list(nx.non_edges(graph))
for e in expected:
assert e in nedges
def test_is_weighted(self):
G = nx.Graph()
assert not nx.is_weighted(G)
G = nx.path_graph(4)
assert not nx.is_weighted(G)
assert not nx.is_weighted(G, (2, 3))
G.add_node(4)
G.add_edge(3, 4, weight=4)
assert not nx.is_weighted(G)
assert nx.is_weighted(G, (3, 4))
G = nx.DiGraph()
G.add_weighted_edges_from(
[
("0", "3", 3),
("0", "1", -5),
("1", "0", -5),
("0", "2", 2),
("1", "2", 4),
("2", "3", 1),
]
)
assert nx.is_weighted(G)
assert nx.is_weighted(G, ("1", "0"))
G = G.to_undirected()
assert nx.is_weighted(G)
assert nx.is_weighted(G, ("1", "0"))
pytest.raises(nx.NetworkXError, nx.is_weighted, G, (1, 2))
def test_is_negatively_weighted(self):
G = nx.Graph()
assert not nx.is_negatively_weighted(G)
G.add_node(1)
G.add_nodes_from([2, 3, 4, 5])
assert not nx.is_negatively_weighted(G)
G.add_edge(1, 2, weight=4)
assert not nx.is_negatively_weighted(G, (1, 2))
G.add_edges_from([(1, 3), (2, 4), (2, 6)])
G[1][3]["color"] = "blue"
assert not nx.is_negatively_weighted(G)
assert not nx.is_negatively_weighted(G, (1, 3))
G[2][4]["weight"] = -2
assert nx.is_negatively_weighted(G, (2, 4))
assert nx.is_negatively_weighted(G)
G = nx.DiGraph()
G.add_weighted_edges_from(
[
("0", "3", 3),
("0", "1", -5),
("1", "0", -2),
("0", "2", 2),
("1", "2", -3),
("2", "3", 1),
]
)
assert nx.is_negatively_weighted(G)
assert not nx.is_negatively_weighted(G, ("0", "3"))
assert nx.is_negatively_weighted(G, ("1", "0"))
pytest.raises(nx.NetworkXError, nx.is_negatively_weighted, G, (1, 4))
class TestCommonNeighbors:
@classmethod
def setup_class(cls):
cls.func = staticmethod(nx.common_neighbors)
def test_func(G, u, v, expected):
result = sorted(cls.func(G, u, v))
assert result == expected
cls.test = staticmethod(test_func)
def test_K5(self):
G = nx.complete_graph(5)
self.test(G, 0, 1, [2, 3, 4])
def test_P3(self):
G = nx.path_graph(3)
self.test(G, 0, 2, [1])
def test_S4(self):
G = nx.star_graph(4)
self.test(G, 1, 2, [0])
def test_digraph(self):
with pytest.raises(nx.NetworkXNotImplemented):
G = nx.DiGraph()
G.add_edges_from([(0, 1), (1, 2)])
self.func(G, 0, 2)
def test_nonexistent_nodes(self):
G = nx.complete_graph(5)
pytest.raises(nx.NetworkXError, nx.common_neighbors, G, 5, 4)
pytest.raises(nx.NetworkXError, nx.common_neighbors, G, 4, 5)
pytest.raises(nx.NetworkXError, nx.common_neighbors, G, 5, 6)
def test_custom1(self):
"""Case of no common neighbors."""
G = nx.Graph()
G.add_nodes_from([0, 1])
self.test(G, 0, 1, [])
def test_custom2(self):
"""Case of equal nodes."""
G = nx.complete_graph(4)
self.test(G, 0, 0, [1, 2, 3])
@pytest.mark.parametrize(
"graph_type", (nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph)
)
def test_set_node_attributes(graph_type):
# Test single value
G = nx.path_graph(3, create_using=graph_type)
vals = 100
attr = "hello"
nx.set_node_attributes(G, vals, attr)
assert G.nodes[0][attr] == vals
assert G.nodes[1][attr] == vals
assert G.nodes[2][attr] == vals
# Test dictionary
G = nx.path_graph(3, create_using=graph_type)
vals = dict(zip(sorted(G.nodes()), range(len(G))))
attr = "hi"
nx.set_node_attributes(G, vals, attr)
assert G.nodes[0][attr] == 0
assert G.nodes[1][attr] == 1
assert G.nodes[2][attr] == 2
# Test dictionary of dictionaries
G = nx.path_graph(3, create_using=graph_type)
d = {"hi": 0, "hello": 200}
vals = dict.fromkeys(G.nodes(), d)
vals.pop(0)
nx.set_node_attributes(G, vals)
assert G.nodes[0] == {}
assert G.nodes[1]["hi"] == 0
assert G.nodes[2]["hello"] == 200
@pytest.mark.parametrize(
("values", "name"),
(
({0: "red", 1: "blue"}, "color"), # values dictionary
({0: {"color": "red"}, 1: {"color": "blue"}}, None), # dict-of-dict
),
)
def test_set_node_attributes_ignores_extra_nodes(values, name):
"""
When `values` is a dict or dict-of-dict keyed by nodes, ensure that keys
that correspond to nodes not in G are ignored.
"""
G = nx.Graph()
G.add_node(0)
nx.set_node_attributes(G, values, name)
assert G.nodes[0]["color"] == "red"
assert 1 not in G.nodes
@pytest.mark.parametrize("graph_type", (nx.Graph, nx.DiGraph))
def test_set_edge_attributes(graph_type):
# Test single value
G = nx.path_graph(3, create_using=graph_type)
attr = "hello"
vals = 3
nx.set_edge_attributes(G, vals, attr)
assert G[0][1][attr] == vals
assert G[1][2][attr] == vals
# Test multiple values
G = nx.path_graph(3, create_using=graph_type)
attr = "hi"
edges = [(0, 1), (1, 2)]
vals = dict(zip(edges, range(len(edges))))
nx.set_edge_attributes(G, vals, attr)
assert G[0][1][attr] == 0
assert G[1][2][attr] == 1
# Test dictionary of dictionaries
G = nx.path_graph(3, create_using=graph_type)
d = {"hi": 0, "hello": 200}
edges = [(0, 1)]
vals = dict.fromkeys(edges, d)
nx.set_edge_attributes(G, vals)
assert G[0][1]["hi"] == 0
assert G[0][1]["hello"] == 200
assert G[1][2] == {}
@pytest.mark.parametrize(
("values", "name"),
(
({(0, 1): 1.0, (0, 2): 2.0}, "weight"), # values dict
({(0, 1): {"weight": 1.0}, (0, 2): {"weight": 2.0}}, None), # values dod
),
)
def test_set_edge_attributes_ignores_extra_edges(values, name):
"""If `values` is a dict or dict-of-dicts containing edges that are not in
G, data associate with these edges should be ignored.
"""
G = nx.Graph([(0, 1)])
nx.set_edge_attributes(G, values, name)
assert G[0][1]["weight"] == 1.0
assert (0, 2) not in G.edges
@pytest.mark.parametrize("graph_type", (nx.MultiGraph, nx.MultiDiGraph))
def test_set_edge_attributes_multi(graph_type):
# Test single value
G = nx.path_graph(3, create_using=graph_type)
attr = "hello"
vals = 3
nx.set_edge_attributes(G, vals, attr)
assert G[0][1][0][attr] == vals
assert G[1][2][0][attr] == vals
# Test multiple values
G = nx.path_graph(3, create_using=graph_type)
attr = "hi"
edges = [(0, 1, 0), (1, 2, 0)]
vals = dict(zip(edges, range(len(edges))))
nx.set_edge_attributes(G, vals, attr)
assert G[0][1][0][attr] == 0
assert G[1][2][0][attr] == 1
# Test dictionary of dictionaries
G = nx.path_graph(3, create_using=graph_type)
d = {"hi": 0, "hello": 200}
edges = [(0, 1, 0)]
vals = dict.fromkeys(edges, d)
nx.set_edge_attributes(G, vals)
assert G[0][1][0]["hi"] == 0
assert G[0][1][0]["hello"] == 200
assert G[1][2][0] == {}
@pytest.mark.parametrize(
("values", "name"),
(
({(0, 1, 0): 1.0, (0, 2, 0): 2.0}, "weight"), # values dict
({(0, 1, 0): {"weight": 1.0}, (0, 2, 0): {"weight": 2.0}}, None), # values dod
),
)
def test_set_edge_attributes_multi_ignores_extra_edges(values, name):
"""If `values` is a dict or dict-of-dicts containing edges that are not in
G, data associate with these edges should be ignored.
"""
G = nx.MultiGraph([(0, 1, 0), (0, 1, 1)])
nx.set_edge_attributes(G, values, name)
assert G[0][1][0]["weight"] == 1.0
assert G[0][1][1] == {}
assert (0, 2) not in G.edges()
def test_get_node_attributes():
graphs = [nx.Graph(), nx.DiGraph(), nx.MultiGraph(), nx.MultiDiGraph()]
for G in graphs:
G = nx.path_graph(3, create_using=G)
attr = "hello"
vals = 100
nx.set_node_attributes(G, vals, attr)
attrs = nx.get_node_attributes(G, attr)
assert attrs[0] == vals
assert attrs[1] == vals
assert attrs[2] == vals
def test_get_edge_attributes():
graphs = [nx.Graph(), nx.DiGraph(), nx.MultiGraph(), nx.MultiDiGraph()]
for G in graphs:
G = nx.path_graph(3, create_using=G)
attr = "hello"
vals = 100
nx.set_edge_attributes(G, vals, attr)
attrs = nx.get_edge_attributes(G, attr)
assert len(attrs) == 2
if G.is_multigraph():
keys = [(0, 1, 0), (1, 2, 0)]
for u, v, k in keys:
try:
assert attrs[(u, v, k)] == 100
except KeyError:
assert attrs[(v, u, k)] == 100
else:
keys = [(0, 1), (1, 2)]
for u, v in keys:
try:
assert attrs[(u, v)] == 100
except KeyError:
assert attrs[(v, u)] == 100
def test_is_empty():
graphs = [nx.Graph(), nx.DiGraph(), nx.MultiGraph(), nx.MultiDiGraph()]
for G in graphs:
assert nx.is_empty(G)
G.add_nodes_from(range(5))
assert nx.is_empty(G)
G.add_edges_from([(1, 2), (3, 4)])
assert not nx.is_empty(G)
@pytest.mark.parametrize(
"graph_type", [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph]
)
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
@pytest.mark.parametrize(
"graph_type", [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph]
)
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_selfloop_edges_multi_with_data_and_keys():
G = nx.complete_graph(3, create_using=nx.MultiGraph)
G.add_edge(0, 0, weight=10)
G.add_edge(0, 0, weight=100)
assert edges_equal(
nx.selfloop_edges(G, data="weight", keys=True), [(0, 0, 0, 10), (0, 0, 1, 100)]
)
@pytest.mark.parametrize("graph_type", [nx.Graph, nx.DiGraph])
def test_selfloops_removal(graph_type):
G = nx.complete_graph(3, create_using=graph_type)
G.add_edge(0, 0)
G.remove_edges_from(nx.selfloop_edges(G, keys=True))
G.add_edge(0, 0)
G.remove_edges_from(nx.selfloop_edges(G, data=True))
G.add_edge(0, 0)
G.remove_edges_from(nx.selfloop_edges(G, keys=True, data=True))
@pytest.mark.parametrize("graph_type", [nx.MultiGraph, nx.MultiDiGraph])
def test_selfloops_removal_multi(graph_type):
"""test removing selfloops behavior vis-a-vis altering a dict while iterating.
cf. gh-4068"""
G = nx.complete_graph(3, create_using=graph_type)
# Defaults - see gh-4080
G.add_edge(0, 0)
G.add_edge(0, 0)
G.remove_edges_from(nx.selfloop_edges(G))
assert (0, 0) not in G.edges()
# With keys
G.add_edge(0, 0)
G.add_edge(0, 0)
with pytest.raises(RuntimeError):
G.remove_edges_from(nx.selfloop_edges(G, keys=True))
# With data
G.add_edge(0, 0)
G.add_edge(0, 0)
with pytest.raises(TypeError):
G.remove_edges_from(nx.selfloop_edges(G, data=True))
# With keys and data
G.add_edge(0, 0)
G.add_edge(0, 0)
with pytest.raises(RuntimeError):
G.remove_edges_from(nx.selfloop_edges(G, data=True, keys=True))
def test_pathweight():
valid_path = [1, 2, 3]
invalid_path = [1, 3, 2]
graphs = [nx.Graph(), nx.DiGraph(), nx.MultiGraph(), nx.MultiDiGraph()]
edges = [
(1, 2, dict(cost=5, dist=6)),
(2, 3, dict(cost=3, dist=4)),
(1, 2, dict(cost=1, dist=2)),
]
for graph in graphs:
graph.add_edges_from(edges)
assert nx.path_weight(graph, valid_path, "cost") == 4
assert nx.path_weight(graph, valid_path, "dist") == 6
pytest.raises(nx.NetworkXNoPath, nx.path_weight, graph, invalid_path, "cost")
@pytest.mark.parametrize(
"G", (nx.Graph(), nx.DiGraph(), nx.MultiGraph(), nx.MultiDiGraph())
)
def test_ispath(G):
G.add_edges_from([(1, 2), (2, 3), (1, 2), (3, 4)])
valid_path = [1, 2, 3, 4]
invalid_path = [1, 2, 4, 3] # wrong node order
another_invalid_path = [1, 2, 3, 4, 5] # contains node not in G
assert nx.is_path(G, valid_path)
assert not nx.is_path(G, invalid_path)
assert not nx.is_path(G, another_invalid_path)
@pytest.mark.parametrize("G", (nx.Graph(), nx.DiGraph()))
def test_restricted_view(G):
G.add_edges_from([(0, 1), (0, 2), (0, 3), (1, 0), (1, 1), (1, 2)])
G.add_node(4)
H = nx.restricted_view(G, [0, 2, 5], [(1, 2), (3, 4)])
assert set(H.nodes()) == {1, 3, 4}
assert set(H.edges()) == {(1, 1)}
@pytest.mark.parametrize("G", (nx.MultiGraph(), nx.MultiDiGraph()))
def test_restricted_view_multi(G):
G.add_edges_from(
[(0, 1, 0), (0, 2, 0), (0, 3, 0), (0, 1, 1), (1, 0, 0), (1, 1, 0), (1, 2, 0)]
)
G.add_node(4)
H = nx.restricted_view(G, [0, 2, 5], [(1, 2, 0), (3, 4, 0)])
assert set(H.nodes()) == {1, 3, 4}
assert set(H.edges()) == {(1, 1)}