Pi=dZddlZddlmZgdZedejdZedejdZedejdZ d Z dS) zWeakly connected components.N)not_implemented_for)"number_weakly_connected_componentsweakly_connected_componentsis_weakly_connected undirectedc#Kt}t|}|D]@}||vr:t||t|z |}|||VAdS)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)setlen _plain_bfsupdate)Gseennvcs /home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/networkx/algorithms/components/weakly_connected.pyrr ss` 55D AA  D==1a#d))mQ//A KKNNNGGG cNtdt|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. c3K|]}dVdS)N).0_s r z5number_weakly_connected_components..ks"99Qq999999r)sumr)r s rrrFs+J 995a88999 9 99rct|}|dkrtjdttt ||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.)r nxNetworkXPointlessConceptnextr)r rs rrrnsWd AAAvv) ?    t/2233 4 4 99rcZ|j}|j}|h}|g}|r|}g}|D]}||D]0} | |vr*|| || 1||D]0} | |vr*|| || 1t ||kr|cS||S)zwA fast BFS node generator The direction of the edge between nodes is ignored. For directed graphs only. )_succ_predaddappendr ) r rsourceGsuccGpredr nextlevel thislevelrws rr r s GE GE 8DI      A1X ( (D==HHQKKK$$Q'''1X ( (D==HHQKKK$$Q'''4yyA~~    Kr) __doc__networkxrnetworkx.utils.decoratorsr__all__ _dispatchablerrrr rrrr0s""999999   \""44#"4n\""#:#:#"#:L\""6:6:#"6:rr