U ?h@sjdZddlZddlmZdddgZejedddZedd dZejedd dZ d d Z dS) zWeakly connected components.N)not_implemented_for"number_weakly_connected_componentsweakly_connected_componentsis_weakly_connectedZ undirectedccs:t}|D]*}||kr tt||}|||Vq dS)aGenerate 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. N)set _plain_bfsupdate)Gseenvcr a/var/www/html/venv/lib/python3.8/site-packages/networkx/algorithms/components/weakly_connected.pyr s 0 cCstddt|DS)alReturns 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. Examples -------- >>> G = nx.DiGraph([(0, 1), (2, 1), (3, 4)]) >>> nx.number_weakly_connected_components(G) 2 See Also -------- weakly_connected_components number_connected_components number_strongly_connected_components Notes ----- For directed graphs only. css|] }dVqdS)Nr ).0Zwccr r r hsz5number_weakly_connected_components..)sumrr r r rrDs$cCs.t|dkrtdttt|t|kS)a)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. Note that if a graph is strongly connected (i.e. the graph is connected even when we account for directionality), it is by definition weakly connected as well. 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. Examples -------- >>> G = nx.DiGraph([(0, 1), (2, 1)]) >>> G.add_node(3) >>> nx.is_weakly_connected(G) # node 3 is not connected to the graph False >>> G.add_edge(2, 3) >>> nx.is_weakly_connected(G) True See Also -------- is_strongly_connected is_semiconnected is_connected is_biconnected weakly_connected_components Notes ----- For directed graphs only. rz-Connectivity is undefined for the null graph.)lennxZNetworkXPointlessConceptnextrrr r rrks 2 ccsj|j}|j}t}|h}|rf|}t}|D]8}||kr*|||||||||Vq*qdS)zwA fast BFS node generator The direction of the edge between nodes is ignored. For directed graphs only. N)succpredraddr)r sourceZGsuccZGpredr Z nextlevelZ thislevelr r r rrs r) __doc__ZnetworkxrZnetworkx.utils.decoratorsr__all__Z _dispatchrrrrr r r rs  6 & 8