varsets_len

VARSETS_LEN  Returns the total number of variables in VARSETS
   NV = OM.VARSETS_LEN(VARSETS)

   Returns the total number of elements in the optimization sub-vector
   specified by VARSETS.

   See also VARSETS_CELL2STRUCT

0001 function nv = varsets_len(om, vs)
0002 %VARSETS_LEN  Returns the total number of variables in VARSETS
0003 %   NV = OM.VARSETS_LEN(VARSETS)
0004 %
0005 %   Returns the total number of elements in the optimization sub-vector
0006 %   specified by VARSETS.
0007 %
0008 %   See also VARSETS_CELL2STRUCT
0009 
0010 %   MP-Opt-Model
0011 %   Copyright (c) 2017-2020, Power Systems Engineering Research Center (PSERC)
0012 %   by Ray Zimmerman, PSERC Cornell
0013 %
0014 %   This file is part of MP-Opt-Model.
0015 %   Covered by the 3-clause BSD License (see LICENSE file for details).
0016 %   See https://github.com/MATPOWER/mp-opt-model for more info.
0017 
0018 persistent sn;
0019 if isempty(vs)
0020     nv = om.var.N;
0021 else
0022     nv = 0;
0023 
0024     %% calls to substruct() are relatively expensive, so we pre-build the
0025     %% struct for addressing numeric array fields, updating only
0026     %% the subscripts before use
0027     if isempty(sn)
0028         sn = struct('type', {'.', '()'}, 'subs', {'', 1});
0029     end
0030 
0031     for v = 1:length(vs)
0032         idx = vs(v).idx;
0033         if isempty(idx)
0034             N = om.var.idx.N.(vs(v).name);
0035         else
0036             % (calls to substruct() are relatively expensive ...
0037             % sn = substruct('.', vs(v).name, '()', vs(v).idx);
0038             % ... so replace it with these more efficient lines)
0039             sn(1).subs = vs(v).name;
0040             sn(2).subs = idx;
0041             N = subsref(om.var.idx.N, sn);
0042         end
0043         nv = nv + sum(N(:));
0044     end
0045 end