# -*- coding: utf-8 -*-
# Copyright (C) 2004-2017 by
# Aric Hagberg <hagberg@lanl.gov>
# Dan Schult <dschult@colgate.edu>
# Pieter Swart <swart@lanl.gov>
# All rights reserved.
# BSD license.
#
# Authors: Aric Hagberg (hagberg@lanl.gov)
# Christopher Ellison
"""Weakly connected components."""
import networkx as nx
from networkx.utils.decorators import not_implemented_for
__all__ = [
'number_weakly_connected_components',
'weakly_connected_components',
'weakly_connected_component_subgraphs',
'is_weakly_connected',
]
[docs]@not_implemented_for('undirected')
def weakly_connected_components(G):
"""Generate weakly connected components of G.
Parameters
----------
G : NetworkX graph
A directed graph
Returns
-------
comp : generator of sets
A generator of sets of nodes, one for each weakly connected
component of G.
Raises
------
NetworkXNotImplemented:
If G is undirected.
Examples
--------
Generate a sorted list of weakly connected components, largest first.
>>> G = nx.path_graph(4, create_using=nx.DiGraph())
>>> nx.add_path(G, [10, 11, 12])
>>> [len(c) for c in sorted(nx.weakly_connected_components(G),
... key=len, reverse=True)]
[4, 3]
If you only want the largest component, it's more efficient to
use max instead of sort:
>>> largest_cc = max(nx.weakly_connected_components(G), key=len)
See Also
--------
connected_components
strongly_connected_components
Notes
-----
For directed graphs only.
"""
seen = set()
for v in G:
if v not in seen:
c = set(_plain_bfs(G, v))
yield c
seen.update(c)
[docs]@not_implemented_for('undirected')
def number_weakly_connected_components(G):
"""Return the number of weakly connected components in G.
Parameters
----------
G : NetworkX graph
A directed graph.
Returns
-------
n : integer
Number of weakly connected components
Raises
------
NetworkXNotImplemented:
If G is undirected.
See Also
--------
weakly_connected_components
number_connected_components
number_strongly_connected_components
Notes
-----
For directed graphs only.
"""
return len(list(weakly_connected_components(G)))
[docs]@not_implemented_for('undirected')
def weakly_connected_component_subgraphs(G, copy=True):
"""Generate weakly connected components as subgraphs.
Parameters
----------
G : NetworkX graph
A directed graph.
copy: bool (default=True)
If True make a copy of the graph attributes
Returns
-------
comp : generator
A generator of graphs, one for each weakly connected component of G.
Raises
------
NetworkXNotImplemented:
If G is undirected.
Examples
--------
Generate a sorted list of weakly connected components, largest first.
>>> G = nx.path_graph(4, create_using=nx.DiGraph())
>>> nx.add_path(G, [10, 11, 12])
>>> [len(c) for c in sorted(nx.weakly_connected_component_subgraphs(G),
... key=len, reverse=True)]
[4, 3]
If you only want the largest component, it's more efficient to
use max instead of sort:
>>> Gc = max(nx.weakly_connected_component_subgraphs(G), key=len)
See Also
--------
weakly_connected_components
strongly_connected_component_subgraphs
connected_component_subgraphs
Notes
-----
For directed graphs only.
Graph, node, and edge attributes are copied to the subgraphs by default.
"""
for comp in weakly_connected_components(G):
if copy:
yield G.subgraph(comp).copy()
else:
yield G.subgraph(comp)
[docs]@not_implemented_for('undirected')
def is_weakly_connected(G):
"""Test directed graph for weak connectivity.
A directed graph is weakly connected if, and only if, the graph
is connected when the direction of the edge between nodes is ignored.
Parameters
----------
G : NetworkX Graph
A directed graph.
Returns
-------
connected : bool
True if the graph is weakly connected, False otherwise.
Raises
------
NetworkXNotImplemented:
If G is undirected.
See Also
--------
is_strongly_connected
is_semiconnected
is_connected
is_biconnected
weakly_connected_components
Notes
-----
For directed graphs only.
"""
if len(G) == 0:
raise nx.NetworkXPointlessConcept(
"""Connectivity is undefined for the null graph.""")
return len(list(weakly_connected_components(G))[0]) == len(G)
def _plain_bfs(G, source):
"""A fast BFS node generator
The direction of the edge between nodes is ignored.
For directed graphs only.
"""
Gsucc = G.succ
Gpred = G.pred
seen = set()
nextlevel = {source}
while nextlevel:
thislevel = nextlevel
nextlevel = set()
for v in thislevel:
if v not in seen:
yield v
seen.add(v)
nextlevel.update(Gsucc[v])
nextlevel.update(Gpred[v])