+ j=Rt^RIt^RIHt.ROt]!R4]P R44t]!R4]P R44t]!R4]P R44t Rt R#) zWeakly connected components.N)not_implemented_for undirectedc#"\4p\V4pVF<pW19gK \W\V4, V4pVPV4VxK> R#5i)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& p/home/luca/screen-checking/.venv/lib/python3.14/site-packages/networkx/algorithms/components/weakly_connected.pyweakly_connected_componentsr sK` 5D AA  =1#d)mQ/A KKNG s A7Ac8\R\V444#)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&"TFp^xK R#5i)N).0_s& r 5number_weakly_connected_components..ks98Qq8s)sumr)r s&r"number_weakly_connected_componentsrFsJ 95a89 99c\V4pV^8Xd\P!R4h\\\ V444V8H#)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. z-Connectivity is undefined for the null graph.)rnxNetworkXPointlessConceptnextr)r r s& ris_weakly_connectedrnsHd AAAv)) ?   t/23 4 99rc|VPpVPpV0pV.pV'dTp.pVFpW8,F-p W9gK VPV 4VPV 4K/ WH,F-p W9gK VPV 4VPV 4K/ \ V4V8XgKVu# KV#)zkA fast BFS node generator The direction of the edge between nodes is ignored. For directed graphs only. )_succ_predaddappendr) r r sourceGsuccGpredr nextlevel thislevelr ws &&& rrrs GGE GGE 8DI   AXX=HHQK$$Q'XX=HHQK$$Q'4yA~  Kr)rrr) __doc__networkxrnetworkx.utils.decoratorsr__all__ _dispatchablerrrrrrrr0s"9 \"4#4n\"#:##:L\"6:#6:rr