% test for matrix/linearize
% 
% 15-01-2010 L Ferraioli
%       CREATION
% 
% 
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% $Id: mdc3_exp3_loop_v3.m,v 1.2 2009/09/24 09:48:12 luigi Exp $
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%% Loading data

fprintf('===== loading data... =====\n')

% laod parnames and values
load C:\Users\Luigi\ltp_data_analysis\MDCs\MDC3\lf_analysis\Models\parnames_10perc.mat
load C:\Users\Luigi\ltp_data_analysis\MDCs\MDC3\lf_analysis\Models\exp3_2_10perc_nomvalues.mat
load C:\Users\Luigi\ltp_data_analysis\MDCs\MDC3\lf_analysis\Models\exp3_2_10perc_truevalues.mat
% load C:\Users\Luigi\ltp_data_analysis\MDCs\MDC3\lf_analysis\Models\usedparams_10perc.mat


% set ordered used parameters
usedparams = {'dH','dsH','dS11','dS1D','dSD1','dSDD',...
  'dh2','dsh2','dx1','dx2'};

[nonfitparnms,ia,ib] = setxor(parnames,usedparams);
nonfitnomvals = exp3_nomvalues(ia);
nonfittruevals = exp3_truevalues(ia);

[fitparnms,ia,ib] = setxor(parnames,nonfitparnms);
fitnomvals = exp3_nomvalues(ia);
fittruevals = exp3_truevalues(ia);

%% get non-linear response model
% the model is non-linear in the parameter dependence
fprintf('===== Get TF Model... =====\n')

H = matrix(plist('built-in','mdc3_ifo2ifo_v2'));

% substitute for non fit parameters
% subs for non fit parameters
for ii = 1:numel(H.objs)
  H.objs(ii).subs(plist('Params', nonfitparnms, 'Values',nonfittruevals));
end


%% Linearize model

Hl = linearize(H);

%% Random test

% get random index
i1 = round(numel(Hl.objs)*rand(1)); % choose the parameter
i2 = round(numel(H.objs)*rand(1)); % choose the transfer function

% extract element to be tested
dH = Hl.objs{i1}.objs(i2);

% get the parameter name
pars = sort(usedparams);
prm = pars{i1};

% differentiate
tdH = diff(H.objs(i2),prm);

% evaluate response
dHr = eval(dH);
tdHr = eval(tdH);

% plot
iplot(dHr./tdHr)