Pi&dZddlZddlmZgdZedejdZedejd dZedejdZ edejd Z edejd d d Z dS)zStrongly connected components.N)not_implemented_for)$number_strongly_connected_componentsstrongly_connected_componentsis_strongly_connected&kosaraju_strongly_connected_components condensation undirectedc#RKi}i}t}g}d}fdD}D]{}||vrs|g}|rm|d} | |vr |dz}||| <d} || D]} | |vr|| d} n | r'|| || <j| D]Y} | |vrS|| || kr!t|| || g|| <9t|| || g|| <Z||| || krz| h} |r[||d|| krC|} | | |r||d|| kC|| | Vn|| |m}dS)aGenerate nodes in strongly connected components of graph. Parameters ---------- G : NetworkX Graph A directed graph. Returns ------- comp : generator of sets A generator of sets of nodes, one for each strongly connected component of G. Raises ------ NetworkXNotImplemented If G is undirected. Examples -------- Generate a sorted list of strongly connected components, largest first. >>> G = nx.cycle_graph(4, create_using=nx.DiGraph()) >>> nx.add_cycle(G, [10, 11, 12]) >>> [ ... len(c) ... for c in sorted(nx.strongly_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 = max(nx.strongly_connected_components(G), key=len) See Also -------- connected_components weakly_connected_components kosaraju_strongly_connected_components Notes ----- Uses Tarjan's algorithm[1]_ with Nuutila's modifications[2]_. Nonrecursive version of algorithm. References ---------- .. [1] Depth-first search and linear graph algorithms, R. Tarjan SIAM Journal of Computing 1(2):146-160, (1972). .. [2] On finding the strongly connected components in a directed graph. E. Nuutila and E. Soisalon-Soinen Information Processing Letters 49(1): 9-14, (1994).. rcFi|]}|tj|S)iter_adj).0vGs /home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/networkx/algorithms/components/strongly_connected.py z1strongly_connected_components..Os'///DOO///TFN)setappendrminpopaddupdate)rpreorderlowlink scc_found scc_queuei neighborssourcequeuerdonewsccks` rrrs(vHGII A////Q///I,,  " "HE ,"IH$$AA"#HQK"1A(( Q$),!)!GAJVAYLLI--'{Xa[88-0'!*gaj1I-J-J -0'!*hqk1J-K-K IIKKKqzXa[00 c''HYr],Chqk,Q,Q ) AGGAJJJ('HYr],Chqk,Q,Q"((---! !((+++9 ,,,rc#Kttj|d|}t |}t }|rt ||kr|}||vr.|h}|||g}|rt ||kr||}|j|D]E} | |vr?|| || | | F|rt ||k||V|rt ||kdSdSdSdS)aGenerate nodes in strongly connected components of graph. Parameters ---------- G : NetworkX Graph A directed graph. source : node, optional (default=None) Specify a node from which to start the depth-first search. If not provided, the algorithm will start from an arbitrary node. Yields ------ set A set of all nodes in a strongly connected component of `G`. Raises ------ NetworkXNotImplemented If `G` is undirected. NetworkXError If `source` is not a node in `G`. Examples -------- Generate a list of strongly connected components of a graph: >>> G = nx.cycle_graph(4, create_using=nx.DiGraph()) >>> nx.add_cycle(G, [10, 11, 12]) >>> sorted(nx.kosaraju_strongly_connected_components(G), key=len, reverse=True) [{0, 1, 2, 3}, {10, 11, 12}] If you only want the largest component, it's more efficient to use `max()` instead of `sorted()`. >>> max(nx.kosaraju_strongly_connected_components(G), key=len) {0, 1, 2, 3} See Also -------- strongly_connected_components Notes ----- Uses Kosaraju's algorithm. F)copy)r#N) listnxdfs_postorder_nodesreverselenrrrrr) rr#postnseenrnewstackrr&s rrrrsZd &qyyey'<'>> G = nx.DiGraph( ... [(0, 1), (1, 2), (2, 0), (2, 3), (4, 5), (3, 4), (5, 6), (6, 3), (6, 7)] ... ) >>> nx.number_strongly_connected_components(G) 3 See Also -------- strongly_connected_components number_connected_components number_weakly_connected_components Notes ----- For directed graphs only. c3K|]}dVdS)rNr )rr's r z7number_strongly_connected_components..s"==Sq======r)sumrrs rrrs+L ==9!<<=== = ==rct|dkrtjdttt |t|kS)aWTest directed graph for strong connectivity. A directed graph is strongly connected if and only if every vertex in the graph is reachable from every other vertex. Parameters ---------- G : NetworkX Graph A directed graph. Returns ------- connected : bool True if the graph is strongly connected, False otherwise. Examples -------- >>> G = nx.DiGraph([(0, 1), (1, 2), (2, 3), (3, 0), (2, 4), (4, 2)]) >>> nx.is_strongly_connected(G) True >>> G.remove_edge(2, 3) >>> nx.is_strongly_connected(G) False Raises ------ NetworkXNotImplemented If G is undirected. See Also -------- is_weakly_connected is_semiconnected is_connected is_biconnected strongly_connected_components Notes ----- For directed graphs only. rz-Connectivity is undefined for the null graph.)r/r,NetworkXPointlessConceptnextrr:s rrrsXX 1vv{{) ?    t1!4455 6 6#a&& @@rT) returns_graphc|tj|}ii}tj}|jd<t |dkr|St |D]+\}||<fd|D,dz}|t|| fd| Dtj ||d|S)aReturns the condensation of G. The condensation of G is the graph with each of the strongly connected components contracted into a single node. Parameters ---------- G : NetworkX DiGraph A directed graph. scc: list or generator (optional, default=None) Strongly connected components. If provided, the elements in `scc` must partition the nodes in `G`. If not provided, it will be calculated as scc=nx.strongly_connected_components(G). Returns ------- C : NetworkX DiGraph The condensation graph C of G. The node labels are integers corresponding to the index of the component in the list of strongly connected components of G. C has a graph attribute named 'mapping' with a dictionary mapping the original nodes to the nodes in C to which they belong. Each node in C also has a node attribute 'members' with the set of original nodes in G that form the SCC that the node in C represents. Raises ------ NetworkXNotImplemented If G is undirected. Examples -------- Contracting two sets of strongly connected nodes into two distinct SCC using the barbell graph. >>> G = nx.barbell_graph(4, 0) >>> G.remove_edge(3, 4) >>> G = nx.DiGraph(G) >>> H = nx.condensation(G) >>> H.nodes.data() NodeDataView({0: {'members': {0, 1, 2, 3}}, 1: {'members': {4, 5, 6, 7}}}) >>> H.graph["mapping"] {0: 0, 1: 0, 2: 0, 3: 0, 4: 1, 5: 1, 6: 1, 7: 1} Contracting a complete graph into one single SCC. >>> G = nx.complete_graph(7, create_using=nx.DiGraph) >>> H = nx.condensation(G) >>> H.nodes NodeView((0,)) >>> H.nodes.data() NodeDataView({0: {'members': {0, 1, 2, 3, 4, 5, 6}}}) Notes ----- After contracting all strongly connected components to a single node, the resulting graph is a directed acyclic graph. Nmappingrc3 K|]}|fV dSNr )rr1r!s rr8zcondensation.._s'11!1v111111rrc3bK|])\}}||k||fV*dSrBr )rurr@s rr8zcondensation..bsP%)Q'!*PQ :R:RWQZ :R:R:R:Rrmembers) r,rDiGraphgraphr/ enumerateradd_nodes_fromrangeadd_edges_fromedgesset_node_attributes)rr'rEC componentnumber_of_componentsr!r@s @@rrrs&~ {.q11GG A AGI 1vv{{!#22 9 1111y1111111q5U/00111-.WWYY1gy111 HrrB) __doc__networkxr,networkx.utils.decoratorsr__all__ _dispatchablerrrrrr rrrVsx$$999999   \""^,^,#"^,B\""AAA#"AH\""$>$>#"$>N\""/A/A#"/Ad\""%%%P P P &%#"P P P r