,[c@sIdZddlZddlZddgZeedZedZdS(sT Provides functions for finding and testing for locally `(k, l)`-connected graphs. iNtkl_connected_subgraphtis_kl_connectedcCstj|}t}t}x|rt}xt|jD]{}|\} } |rt| | g} x<t|D].} x%| jD]} | j|| qWqtW|j | j}ntj|}| | g}d}d}x|r|d7}||krd}Pn| }x3|D]+} || kr|j || | }qqWyt j || | }Wqt j k rt}qXqW|dkr=|j | | t}|rt}qq=q=WqW|r||fS|S(sPReturns the maximum locally `(k, l)`-connected subgraph of `G`. A graph is locally `(k, l)`-connected if for each edge `(u, v)` in the graph there are at least `l` edge-disjoint paths of length at most `k` joining `u` to `v`. Parameters ---------- G : NetworkX graph The graph in which to find a maximum locally `(k, l)`-connected subgraph. k : integer The maximum length of paths to consider. A higher number means a looser connectivity requirement. l : integer The number of edge-disjoint paths. A higher number means a stricter connectivity requirement. low_memory : bool If this is True, this function uses an algorithm that uses slightly more time but less memory. same_as_graph : bool If True then return a tuple of the form `(H, is_same)`, where `H` is the maximum locally `(k, l)`-connected subgraph and `is_same` is a Boolean representing whether `G` is locally `(k, l)`-connected (and hence, whether `H` is simply a copy of the input graph `G`). Returns ------- NetworkX graph or two-tuple If `same_as_graph` is True, then this function returns a two-tuple as described above. Otherwise, it returns only the maximum locally `(k, l)`-connected subgraph. See also -------- is_kl_connected References ---------- .. [1]: Chung, Fan and Linyuan Lu. "The Small World Phenomenon in Hybrid Power Law Graphs." *Complex Networks*. Springer Berlin Heidelberg, 2004. 89--104. ii(tcopytdeepcopytTruetFalsetlisttedgestsettrangetupdatetsubgrapht remove_edgetnxt shortest_pathtNetworkXNoPath(tGtktlt low_memoryt same_as_graphtHtgraphOKt deleted_sometedgetutvtvertstitwtG2tpathtcnttaccepttprev((s9lib/python2.7/site-packages/networkx/algorithms/hybrid.pyRsN2           cCsyt}xl|jD]^}|\}}|rt||g}xCt|D]5} g|jD]} |j|j| ^q]qJW|j|} ntj|} ||g} d} d}x| rZ| d7} | |krd}Pn|}x3| D]+} | |kr| j || | }qqWyt j | ||} Wqt j k rVt } qXqW|dkrt }PqqW|S(s\Returns True if and only if `G` is locally `(k, l)`-connected. A graph is locally `(k, l)`-connected if for each edge `(u, v)` in the graph there are at least `l` edge-disjoint paths of length at most `k` joining `u` to `v`. Parameters ---------- G : NetworkX graph The graph to test for local `(k, l)`-connectedness. k : integer The maximum length of paths to consider. A higher number means a looser connectivity requirement. l : integer The number of edge-disjoint paths. A higher number means a stricter connectivity requirement. low_memory : bool If this is True, this function uses an algorithm that uses slightly more time but less memory. Returns ------- bool Whether the graph is locally `(k, l)`-connected subgraph. See also -------- kl_connected_subgraph References ---------- .. [1]: Chung, Fan and Linyuan Lu. "The Small World Phenomenon in Hybrid Power Law Graphs." *Complex Networks*. Springer Berlin Heidelberg, 2004. 89--104. ii(RRRR RR t neighborsR RR R RRR(RRRRRRRRRRRRRR R!R"((s9lib/python2.7/site-packages/networkx/algorithms/hybrid.pyR~s<( 3        (t__doc__RtnetworkxR t__all__RRR(((s9lib/python2.7/site-packages/networkx/algorithms/hybrid.pyts    h