ó |£*^c@sõdZddlZddlmZdjdddgƒZdd d d d d dgZedƒd„ƒZedƒdd„ƒZ edƒd„ƒZ edƒe d„ƒZ edƒd„ƒZedƒd„ƒZedƒdd„ƒZdS(sStrongly connected components. iÿÿÿÿN(tnot_implemented_fors s Eben Kenahs2Aric Hagberg (hagberg@lanl.gov)Christopher Ellisons!Ben Edwards (bedwards@cs.unm.edu)t$number_strongly_connected_componentststrongly_connected_componentst&strongly_connected_component_subgraphstis_strongly_connectedt'strongly_connected_components_recursivet&kosaraju_strongly_connected_componentst condensationt undirectedccsçi}i}i}g}d}xÂ|D]º}||kr%|g}xœ|rÛ|d}||krv|d}|||>> G = nx.cycle_graph(4, create_using=nx.DiGraph()) >>> G.add_cycle([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 Notes ----- Uses Tarjan's algorithm with Nuutila's modifications. 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).. iiÿÿÿÿiN(tappendtmintpoptTruetadd(tGtpreordertlowlinkt scc_foundt scc_queuetitsourcetqueuetvtdonetv_nbrstwtscctk((s…/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/algorithms/components/strongly_connected.pyRsJ7               !%   !  c #s¦tjj|ƒ ttj|d|ƒƒ}WdQXtƒ‰xb|r¡|jƒ}|ˆkrdq@ntj||ƒ}‡fd†|Dƒ}|Vˆj|ƒq@WdS(sýGenerate nodes in strongly connected components of graph. Parameters ---------- G : NetworkX Graph An directed graph. Returns ------- comp : generator of sets A genrator 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()) >>> G.add_cycle([10, 11, 12]) >>> [len(c) for c in sorted(nx.kosaraju_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.kosaraju_strongly_connected_components(G), key=len) See Also -------- connected_components weakly_connected_components Notes ----- Uses Kosaraju's algorithm. RNcs"h|]}|ˆkr|’qS(((t.0R(tseen(s…/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/algorithms/components/strongly_connected.pys ±s ( tnxtutilstreversedtlisttdfs_postorder_nodestsetR tdfs_preorder_nodestupdate(RRtposttrtctnew((Rs…/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/algorithms/components/strongly_connected.pyR{s-!    c#s‡‡‡‡‡‡fd†‰i‰i‰i‰d}g‰x<ˆD]4}|ˆkrCxˆ||ƒD] }|VqeWqCqCWdS(smGenerate nodes in strongly connected components of graph. Recursive version of algorithm. Parameters ---------- G : NetworkX Graph An 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()) >>> G.add_cycle([10, 11, 12]) >>> [len(c) for c in sorted(nx.strongly_connected_components_recursive(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_recursive(G), key=len) See Also -------- connected_components Notes ----- Uses Tarjan's algorithm with Nuutila's modifications. 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).. c3s|ˆ|<|ˆ|<|d7}ˆj|ƒxjˆ|D]^}|ˆkrjxˆ||ƒD] }|VqXWn|ˆkr6tˆ|ˆ|ƒˆ|>> G = nx.cycle_graph(4, create_using=nx.DiGraph()) >>> G.add_cycle([10, 11, 12]) >>> [len(Gc) for Gc in sorted(nx.strongly_connected_component_subgraphs(G), ... key=len, reverse=True)] [4, 3] If you only want the largest component, it's more efficient to use max instead of sort. >>> Gc = max(nx.strongly_connected_component_subgraphs(G), key=len) See Also -------- connected_component_subgraphs weakly_connected_component_subgraphs N(Rtsubgraphtcopy(RR3tcomp((s…/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/algorithms/components/strongly_connected.pyR s'cCsttt|ƒƒƒS(sKReturn number of strongly connected components in graph. Parameters ---------- G : NetworkX graph A directed graph. Returns ------- n : integer Number of strongly connected components See Also -------- connected_components Notes ----- For directed graphs only. (tlenR!R(R((s…/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/algorithms/components/strongly_connected.pyR;scCsJt|ƒdkr$tjdƒ‚nttt|ƒƒdƒt|ƒkS(s^Test directed graph for strong connectivity. Parameters ---------- G : NetworkX Graph A directed graph. Returns ------- connected : bool True if the graph is strongly connected, False otherwise. See Also -------- strongly_connected_components Notes ----- For directed graphs only. is-Connectivity is undefined for the null graph.(R5RtNetworkXPointlessConceptR!R(R((s…/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/algorithms/components/strongly_connected.pyRTs csÞ|dkrtj|ƒ}ni‰i}tjƒ}xAt|ƒD]3\‰}||ˆ<ˆj‡fd†|DƒƒqCWˆd}|jt|ƒƒ|j‡fd†|j ƒDƒƒtj |d|ƒˆ|j d<|S(s×Returns 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 not directed Notes ----- After contracting all strongly connected components to a single node, the resulting graph is a directed acyclic graph. c3s|]}|ˆfVqdS(N((Rtn(R(s…/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/algorithms/components/strongly_connected.pys Ÿsic3s=|]3\}}ˆ|ˆ|krˆ|ˆ|fVqdS(N((RtuR(tmapping(s…/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/algorithms/components/strongly_connected.pys ¢s tmembersR9N( tNoneRRtDiGrapht enumerateR%tadd_nodes_fromtrangetadd_edges_fromt edges_itertset_node_attributestgraph(RRR:tCR-tnumber_of_components((RR9s…/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/algorithms/components/strongly_connected.pyRqs'   ! # (t__doc__tnetworkxRtnetworkx.utils.decoratorsRtjoint __authors__t__all__RR;RRR RRRR(((s…/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/algorithms/components/strongly_connected.pyts,    ` :W -