def test_null_graph(self):
G = nx.DiGraph()
assert_equal(list(nx.strongly_connected_components(G)), [])
assert_equal(list(nx.kosaraju_strongly_connected_components(G)), [])
assert_equal(list(nx.strongly_connected_components_recursive(G)), [])
assert_equal(len(nx.condensation(G)), 0)
assert_raises(nx.NetworkXPointlessConcept, nx.is_strongly_connected, nx.DiGraph())
def test_null_graph(self):
G = nx.DiGraph()
assert list(nx.strongly_connected_components(G)) == []
assert list(nx.kosaraju_strongly_connected_components(G)) == []
assert list(nx.strongly_connected_components_recursive(G)) == []
assert len(nx.condensation(G)) == 0
pytest.raises(nx.NetworkXPointlessConcept, nx.is_strongly_connected, nx.DiGraph())
def test_connected_raise(self):
G = nx.Graph()
with pytest.raises(NetworkXNotImplemented):
next(nx.strongly_connected_components(G))
with pytest.raises(NetworkXNotImplemented):
next(nx.kosaraju_strongly_connected_components(G))
with pytest.raises(NetworkXNotImplemented):
next(nx.strongly_connected_components_recursive(G))
pytest.raises(NetworkXNotImplemented, nx.is_strongly_connected, G)
pytest.raises(nx.NetworkXPointlessConcept, nx.is_strongly_connected,
nx.DiGraph())
pytest.raises(NetworkXNotImplemented, nx.condensation, G)
nx.betweenness_centrality(G) nx.closeness_centrality(G) nx.eigenvector_centrality(G) # clique #list(nx.find_cliques(G)) #list(nx.make_max_clique_graph(G)) #list(nx.make_clique_bipartite(G)) #nx.graph_clique_number(G) #nx.graph_number_of_cliques(G) # components nx.is_strongly_connected(G) nx.number_strongly_connected_components(G) scc = nx.strongly_connected_components(G) nx.strongly_connected_components_recursive(G) nx.condensation(G, scc) # attracting components nx.is_attracting_component(G) nx.number_attracting_components(G) nx.attracting_components(G) # directed acyclic graphs nx.is_directed_acyclic_graph(G) nx.is_aperiodic(G) # distance measure (all for connected graph) nx.center(Gcc) nx.diameter(Gcc) nx.eccentricity(Gcc)
cnt = 0
stack = []
for source in G:
print("_________________")
print("source:", source)
print("Visited:", visited)
print("Component:", component)
print("root", root)
print("cnt:", cnt)
print("stack:", stack)
print("_________________")
if source not in visited:
print("not")
yield from visit(source, cnt)
G = nx.DiGraph()
G.add_edges_from([(1, 3), (2, 1), (3, 2), (3, 4), (4, 5), (5, 6), (6, 7),
(7, 8), (8, 5)])
lensccs = [
len(c) for c in sorted(
strongly_connected_components_recursive(G), key=len, reverse=True)
]
sccs = [
tuple(sorted(c)) for c in nx.strongly_connected_components_recursive(G)
]
print(lensccs)
print(sccs)
print(strongly_connected_components_recursive(G))
nx.betweenness_centrality(G) nx.closeness_centrality(G) nx.eigenvector_centrality(G) # clique #list(nx.find_cliques(G)) #list(nx.make_max_clique_graph(G)) #list(nx.make_clique_bipartite(G)) #nx.graph_clique_number(G) #nx.graph_number_of_cliques(G) # components nx.is_strongly_connected(G) nx.number_strongly_connected_components(G) scc = nx.strongly_connected_components(G) nx.strongly_connected_components_recursive(G) nx.condensation(G, scc) # attracting components nx.is_attracting_component(G) nx.number_attracting_components(G) nx.attracting_components(G) # directed acyclic graphs nx.is_directed_acyclic_graph(G) nx.is_aperiodic(G) # distance measure (all for connected graph) nx.center(Gcc) nx.diameter(Gcc) nx.eccentricity(Gcc)
