177 lines
3.9 KiB
Python
177 lines
3.9 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (C) 2004-2019 by
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# Aric Hagberg <hagberg@lanl.gov>
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# Dan Schult <dschult@colgate.edu>
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# Pieter Swart <swart@lanl.gov>
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# All rights reserved.
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# BSD license.
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#
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# Authors: Aric Hagberg (hagberg@lanl.gov)
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# Christopher Ellison
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"""Weakly connected components."""
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import warnings as _warnings
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import networkx as nx
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from networkx.utils.decorators import not_implemented_for
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__all__ = [
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'number_weakly_connected_components',
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'weakly_connected_components',
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'is_weakly_connected',
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]
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@not_implemented_for('undirected')
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def weakly_connected_components(G):
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"""Generate weakly connected components of G.
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Parameters
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----------
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G : NetworkX graph
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A directed graph
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Returns
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-------
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comp : generator of sets
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A generator of sets of nodes, one for each weakly connected
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component of G.
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Raises
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------
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NetworkXNotImplemented:
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If G is undirected.
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Examples
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--------
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Generate a sorted list of weakly connected components, largest first.
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>>> G = nx.path_graph(4, create_using=nx.DiGraph())
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>>> nx.add_path(G, [10, 11, 12])
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>>> [len(c) for c in sorted(nx.weakly_connected_components(G),
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... key=len, reverse=True)]
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[4, 3]
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If you only want the largest component, it's more efficient to
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use max instead of sort:
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>>> largest_cc = max(nx.weakly_connected_components(G), key=len)
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See Also
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--------
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connected_components
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strongly_connected_components
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Notes
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-----
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For directed graphs only.
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"""
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seen = set()
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for v in G:
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if v not in seen:
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c = set(_plain_bfs(G, v))
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yield c
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seen.update(c)
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@not_implemented_for('undirected')
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def number_weakly_connected_components(G):
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"""Returns the number of weakly connected components in G.
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Parameters
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----------
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G : NetworkX graph
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A directed graph.
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Returns
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-------
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n : integer
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Number of weakly connected components
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Raises
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------
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NetworkXNotImplemented:
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If G is undirected.
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See Also
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--------
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weakly_connected_components
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number_connected_components
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number_strongly_connected_components
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Notes
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-----
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For directed graphs only.
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"""
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return sum(1 for wcc in weakly_connected_components(G))
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@not_implemented_for('undirected')
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def is_weakly_connected(G):
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"""Test directed graph for weak connectivity.
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A directed graph is weakly connected if and only if the graph
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is connected when the direction of the edge between nodes is ignored.
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Note that if a graph is strongly connected (i.e. the graph is connected
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even when we account for directionality), it is by definition weakly
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connected as well.
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Parameters
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----------
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G : NetworkX Graph
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A directed graph.
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Returns
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-------
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connected : bool
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True if the graph is weakly connected, False otherwise.
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Raises
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------
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NetworkXNotImplemented:
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If G is undirected.
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See Also
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--------
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is_strongly_connected
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is_semiconnected
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is_connected
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is_biconnected
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weakly_connected_components
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Notes
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-----
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For directed graphs only.
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"""
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if len(G) == 0:
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raise nx.NetworkXPointlessConcept(
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"""Connectivity is undefined for the null graph.""")
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return len(list(weakly_connected_components(G))[0]) == len(G)
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def _plain_bfs(G, source):
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"""A fast BFS node generator
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The direction of the edge between nodes is ignored.
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For directed graphs only.
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"""
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Gsucc = G.succ
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Gpred = G.pred
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seen = set()
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nextlevel = {source}
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while nextlevel:
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thislevel = nextlevel
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nextlevel = set()
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for v in thislevel:
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if v not in seen:
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yield v
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seen.add(v)
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nextlevel.update(Gsucc[v])
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nextlevel.update(Gpred[v])
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