9j=SrSSKrSSKJr /SQr\"S5\R S55r\"S5\R S55r\"S5\R S55r S r g) zWeakly connected components.N)not_implemented_for)"number_weakly_connected_componentsweakly_connected_componentsis_weakly_connected undirectedc## [5n[U5nUH7nX1;dM [X[U5- U5nURU5 Uv M9 g7f)aRGenerate 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 ڀ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/networkx/algorithms/components/weakly_connected.pyrr sK` 5D AA  =1#d)mQ/A KKNG s A2Ac8[S[U555$)aReturns the number of weakly connected components in G. Parameters ---------- G : NetworkX graph A directed graph. Returns ------- n : integer Number of weakly connected components Raises ------ NetworkXNotImplemented If G is undirected. Examples -------- >>> G = nx.DiGraph([(0, 1), (2, 1), (3, 4)]) >>> nx.number_weakly_connected_components(G) 2 See Also -------- weakly_connected_components number_connected_components number_strongly_connected_components Notes ----- For directed graphs only. c3&# UHnSv M g7f)N).0_s r 5number_weakly_connected_components..ks98Qq8s)sumr)r s rrrFsJ 95a89 99c[U5nUS:Xa[R"S5e[[[ U555U:H$)aTest 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 rrrnsHd AAAv)) ?   t/23 4 99rcrURnURnU1nU/nU(aUn/nUH~nX8H,n X;dM URU 5 URU 5 M. XHH,n X;dM URU 5 URU 5 M. [ U5U:XdM|Us $ U(aMU$)zkA 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 GGE GGE 8DI   AX=HHQK$$Q'X=HHQK$$Q'4yA~  ) Kr) __doc__networkxrnetworkx.utils.decoratorsr__all__ _dispatchablerrrr rrrr1s"9 \"4#4n\"#:##:L\"6:#6:rr