params_nln_cost

PARAMS_NLN_COST  Returns cost parameters for general nonlinear costs.
   [N, FCN] = OM.PARAMS_NLN_COST(NAME)
   [N, FCN] = OM.PARAMS_NLN_COST(NAME, IDX_LIST)
   [N, FCN, VS] = OM.PARAMS_NLN_COST(...)

   Returns the parameters N and FCN provided when the corresponding
   named general nonlinear cost set was added to the model. Likewise
   for indexed named sets specified by NAME and IDX_LIST.

   An optional 3rd output argument VS indicates the variable sets used by
   this cost set.

   See also OPT_MODEL, ADD_NLN_COST, EVAL_NLN_COST.

0001 function [N, fcn, vs] = params_nln_cost(om, name, idx)
0002 %PARAMS_NLN_COST  Returns cost parameters for general nonlinear costs.
0003 %   [N, FCN] = OM.PARAMS_NLN_COST(NAME)
0004 %   [N, FCN] = OM.PARAMS_NLN_COST(NAME, IDX_LIST)
0005 %   [N, FCN, VS] = OM.PARAMS_NLN_COST(...)
0006 %
0007 %   Returns the parameters N and FCN provided when the corresponding
0008 %   named general nonlinear cost set was added to the model. Likewise
0009 %   for indexed named sets specified by NAME and IDX_LIST.
0010 %
0011 %   An optional 3rd output argument VS indicates the variable sets used by
0012 %   this cost set.
0013 %
0014 %   See also OPT_MODEL, ADD_NLN_COST, EVAL_NLN_COST.
0015 
0016 %   MP-Opt-Model
0017 %   Copyright (c) 2017-2020, Power Systems Engineering Research Center (PSERC)
0018 %   by Ray Zimmerman, PSERC Cornell
0019 %
0020 %   This file is part of MP-Opt-Model.
0021 %   Covered by the 3-clause BSD License (see LICENSE file for details).
0022 %   See https://github.com/MATPOWER/mp-opt-model for more info.
0023 
0024 nlc = om.nlc;
0025 if nargin < 3
0026     idx = {};
0027 end
0028 
0029 if isempty(idx)
0030     dims = size(nlc.idx.i1.(name));
0031     if prod(dims) ~= 1
0032         error('@opt_model/params_nln_cost: general nonlinear cost set ''%s'' requires an IDX_LIST arg', name)
0033     end
0034     N = nlc.idx.N.(name);
0035     fcn = nlc.data.fcn.(name);
0036     vs = nlc.data.vs.(name);
0037 else
0038     %% calls to substruct() are relatively expensive, so we pre-build the
0039     %% structs for addressing cell and numeric array fields
0040     %% sn = substruct('.', name, '()', idx);
0041     %% sc = substruct('.', name, '{}', idx);
0042     sc = struct('type', {'.', '{}'}, 'subs', {name, idx});  %% cell array field
0043     sn = sc; sn(2).type = '()';                             %% num array field
0044 
0045     N = subsref(nlc.idx.N, sn);
0046     fcn = subsref(nlc.data.fcn, sc);
0047     vs = subsref(nlc.data.vs, sc);
0048 end