%============= Fitting a single 1d cut with a generic peak =================
%====
%Simplest case allows all parameters to be free
pars_in=[0.4,-0.7,0.1,0.5,-0.2,0.1,0.5,0.2,0.1,0.4,0.6,0.1,0.4,1.3,0.1];%vector of input parameters, in this case characterising some gaussian peaks
[wfit,fitdata]=fit_func(my_cut-0.3,@mgauss,pars_in);%note subtraction of 0.3 to account for background (see later for background fitting)
%The output wfit is an object that covers the same range as the data and is the resultant best fit.
%The output fitdata is a structure array giving information about the fit (parameters, errors, chi^2, correlation matrix, etc)
%Plot the result in a nice way (data is black circles + errorbars, fit is red line)
acolor black
plot(my_cut-0.3);%crude background subtraction
acolor red
pl(wfit)
%====
%Keep the widths of all the peaks fixed, but allow heights and centres to vary;
pars_in=[0.4,-0.8,0.1,0.5,-0.22,0.1,0.5,0.22,0.1,0.4,0.8,0.1,0.4,1.2,0.1];
pars_free=[1,1,0,1,1,0,1,1,0,1,1,0,1,1,0];%vector same size as that giving inputs, with 0 for parameter to be kept fixed, 1 for allowed to vary
[wfit,fitdata]=fit_func(my_cut-0.3,@mgauss,pars_in,pars_free);
%====
%Now bind some of the positions to follow symmetry (i.e. position of peaks for Q<0 are reflection of those at Q>0)
pars_in=[0.4,-0.8,0.07,0.5,-0.22,0.07,0.5,0.22,0.07,0.4,0.8,0.07,0.4,1.2,0.07];
pars_free=[1,1,0,1,1,0,1,1,0,1,1,0,1,1,0];
pars_bind={{2,11,0,-1},{5,8,0,-1}};%ensures symmetry about x=0; 2nd parameter is bound to 11th parameter in ratio -1, ditto the 5th and 8th parameters
[wfit,fitdata]=fit_func(my_cut-0.3,@mgauss,pars_in,pars_free,pars_bind);
%====
%Repeat the above, but using some of the options
pars_in=[0.4,-0.8,0.07,0.5,-0.22,0.07,0.5,0.22,0.07,0.4,0.8,0.07,0.4,1.2,0.07];
pars_free=[1,1,0,1,1,0,1,1,0,1,1,0,1,1,0];
pars_bind={{2,11,0,-1},{5,8,0,-1}};%ensures symmetry about x=0
[wfit,fitdata]=fit_func(my_cut(1)-0.35,@multigauss,pars_in,pars_free,pars_bind,'list',2,'fit',[0.001 50 0.001]);
%This example of setting 'list' to 2 gives a very verbose output to the Matlab command window as the fit progresses.
%Setting 'fit' to [0.001 50 0.001] (from the default setting of fcp=[0.0001 30 0.0001]) changes respectively:
% - The relative step length for calculation of partial derivatives
% - The maximum number of iterations
% - The stopping criterion, that is the relative change in chi-squared (i.e. stops if chisqr_new-chisqr_old < fcp(3)chisqr_old)
%============== Fitting a single cut with an S(Q,w) model ======================
%The syntax, in terms of options, is the same as for fit_func. But this time the routine we use is called fit_sqw
pars=[1,2,3,4]
pfree=[1,1,1,1]
[wfit,fitdata]=fit_sqw(my_cut-0.3,@my_sqw_model,pars,pfree,'list',1);%this time we choose a medium level of verbosity during the fit
%============= Fitting a cut with a foreground and background ==================
%We will use fit_sqw (S(Q,w) model for the cross-section) here, i.e. use fit_sqw. The same syntax applies for fit_func.
%We will fit a linear background model (in the format used for func_eval and fit_func; the function needs to be on the Matlab path like the model function)
bgpars=[1,1];
bgfree=[1,1];
[wfit,fitdata]=fit_sqw(my_cut,@my_sqw_model,pars,pfree,@linear_bg,bgpars,bgfree,'list',1);
%============= Fitting multiple cuts simultaneously with the same foreground but different backgrounds ===========
%Here we have an array of cuts (or slices). They are all fitted with the same foreground function and parameters, but the background
%for each cut is allowed to be different. This corresponds to the most realistic situation you will encounter in your data analysis
%Make an array of cuts:
my_en=[2:2:10];
for i=1:numel(my_en)
my_cut(i)=cut_sqw(data_source,proj,[0,0.1:8],[-1,1],[-1,1],[my_en(i)-25,my_en(i)+25]);
end
%If all of the backgrounds have the same form (e.g. they are all linear) but have different parameters, then this is easy,
%since we can just re-use the code from above:
pars=[1,2,3,4]
pfree=[1,1,1,1]
bgpars=[1,1];
bgfree=[1,1];
[wfit,fitdata]=multifit_sqw(my_cut,@my_sqw_model,pars,pfree,@linear_bg,bgpars,bgfree,'list',1);
%But suppose the backgrounds have different functional forms. Now we need to use cell arrays for the background function, parameters and pfree.
%In the example here we have a mixture of linear and quadratic backgrounds
bgfunc={@linear_bg,@linear_bg,@linear_bg,@quadratic_bg,@quadratic_bg};
bgpars={[1,0],[2,0],[2,1],[3,2,2],[3,0,1]};%use different initial guesses and different free/fixed parameters for the background
bgfree={[1,1],[1,1],[1,1],[1,1,1],[1,1,1]};
[wfit,fitdata]=fit_sqw(my_cut,@my_sqw_model,pars,pfree,bgfunc,bgpars,bgfree,'list',1);