phPfSrSSKrSSKJr SrSrSrSrSr S r S r S r SS jr SS jrSrg)z< Functions for determining network connectivity properties. N)connected_componentscHUR5nSXSS24'SUSS2U4'U$)z-Remove connections to or from external nodes.rN)copy)external_indicescms L/home/james-whalen/.local/lib/python3.13/site-packages/pyphi/connectivity.pyapply_boundary_conditions_to_cmr s/ BBBq  Ic^^^[UU4Sj[TRS555$)z:Return indices of inputs to the node with the given index.c3F># UHnTUT(dMUv M g7fN.0irindexs r %get_inputs_from_cm..s?.q"Q%,.! !rtuplerangeshaperrs``rget_inputs_from_cmr! ?E"((1+.? ??r c^^^[UU4Sj[TRS555$)z;Return indices of the outputs of node with the given index.c3F># UHnTTU(dMUv M g7fr rrs rr&get_outputs_from_cm..s?.q"U)A,.rrrrs``rget_outputs_from_cmr rr cURS5nURS5n[R"US:US:5n[[R"U5S5$)z:Return indices of nodes that have both inputs and outputs.r)sumnp logical_andrwhere)rinputsoutputsnodes_with_inputs_and_outputss rcausally_significant_nodesr*sO VVAYFffQiG$&NN6A:w{$K! 78; <[R"TUSS24RS55S$)z5Return all nodes that `nodes` connect to (output to).Nrr$r&r#nodesrs r outputs_ofblock_cm..outputs_offs*xx5!8 ((+,Q//r cb>[R"TSS2U4RS55S$)z5Return all nodes which connect to (input to) `nodes`.Nr"rr4r5s r inputs_toblock_cm..inputs_tojs*xx1e8 ((+,Q//r F) r$anyr#alllistrrargmax array_equal)rr(r7r:sourcessinks sink_inputss` rblock_cmrD?s> vvbffQi1n vvbffQi1n5!%&G00 yy#$G w EE"K  >>+ / / 7#&  >>% ) ) r c8U(aU(dgU[R"X5nURS5R5(a$URS5R5(dg[ U5S:a[ U5S:a [ U5$g)zReturn whether connections from ``nodes1`` to ``nodes2`` are reducible. Args: cm (np.ndarray): The network's connectivity matrix. nodes1 (tuple[int]): Source nodes nodes2 (tuple[int]): Sink nodes Trr"F)r$r-r#r=lenrDrnodes1nodes2s rblock_reduciblerJsk  BFF6 " #B 66!9==??"&&)--// 6{Q3v;?| r cZUbU[R"X5n[XS9up4US:$)z.Test connectivity for the connectivity matrix.) connection)r$r-r)rr6rLnum_components_s r _connectedrPs3  u$ %,RGN A r c[XS5$)zReturn whether the connectivity matrix is strongly connected. Remember that a singleton graph is strongly connected. Args: cm (np.ndarray): A square connectivity matrix. Keyword Args: nodes (tuple[int]): A subset of nodes to consider. strongrPrr6s r is_strongrUs b **r c[XS5$)zReturn whether the connectivity matrix is weakly connected. Args: cm (np.ndarray): A square connectivity matrix. Keyword Args: nodes (tuple[int]): A subset of nodes to consider. weakrSrTs ris_weakrXs b ((r cU(aU(dgU[R"X5nURS5R5=(a URS5R5$)a Test connectivity of one set of nodes to another. Args: cm (``np.ndarrray``): The connectivity matrix nodes1 (tuple[int]): The nodes whose outputs to ``nodes2`` will be tested. nodes2 (tuple[int]): The nodes whose inputs from ``nodes1`` will be tested. Returns: bool: ``True`` if all elements in ``nodes1`` output to some element in ``nodes2`` and all elements in ``nodes2`` have an input from some element in ``nodes1``, or if either set of nodes is empty; ``False`` otherwise. Trr")r$r-r#r=rGs ris_fullrZsH  BFF6 " #B 66!9==? .rvvay}}.r r )__doc__numpyr$scipy.sparse.csgraphrr rr r*r1rDrJrPrUrXrZrr rr^sM 5@ @ =.CP, + )/r