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rINSTCONTROL/Fit classes/@MyLorentzianFit/MyLorentzianFit.m
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MyLorentzianFit.m
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classdef MyLorentzianFit < MyFitParamScaling
methods (Access = public)
function this = MyLorentzianFit(varargin)
this@MyFitParamScaling( ...
'fit_name', 'Lorentzian', ...
'fit_function', '1/pi*a*b/2/((x-c)^2+(b/2)^2)+d', ...
'fit_tex', '$$\frac{a}{\pi}\frac{b/2}{(x-c)^2+(b/2)^2}+d$$', ...
'fit_params', {'a','b','c','d'}, ...
'fit_param_names', {'Amplitude','Width','Center','Offset'}, ...
varargin{:});
end
end
methods (Access = protected)
function calcInitParams(this)
ind = this.data_selection;
x = this.Data.x(ind);
y = this.Data.y(ind);
this.lim_upper=[Inf,Inf,Inf,Inf];
this.lim_lower=[-Inf,0,-Inf,-Inf];
%Finds peaks on the positive signal (max 1 peak)
try
[~,locs(1),widths(1),proms(1)]=findpeaks(y,x,...
'MinPeakDistance',range(x)/2,'SortStr','descend',...
'NPeaks',1);
catch
proms(1)=0;
end
%Finds peaks on the negative signal (max 1 peak)
try
[~,locs(2),widths(2),proms(2)]=findpeaks(-y,x,...
'MinPeakDistance',range(x)/2,'SortStr','descend',...
'NPeaks',1);
catch
proms(2)=0;
end
if proms(1)==0 && proms(2)==0
warning(['No peaks were found in the data, giving ' ...
'default initial parameters to fit function'])
this.param_vals=[1,1,1,1];
this.lim_lower=-[Inf,0,Inf,Inf];
this.lim_upper=[Inf,Inf,Inf,Inf];
return
end
%If the prominence of the peak in the positive signal is
%greater, we adapt our limits and parameters accordingly,
%if negative signal has a greater prominence, we use this
%for fitting.
if proms(1)>proms(2)
ind=1;
p_in(4)=min(y);
else
ind=2;
p_in(4)=max(y);
proms(2)=-proms(2);
end
p_in(2)=widths(ind);
%Calculates the amplitude, as when x=c, the amplitude
%is 2a/(pi*b)
p_in(1)=proms(ind)*pi*p_in(2)/2;
p_in(3)=locs(ind);
this.param_vals = p_in;
this.lim_lower(2)=0.01*p_in(2);
this.lim_upper(2)=100*p_in(2);
end
function genSliderVecs(this)
genSliderVecs@MyFit(this);
try
%We choose to have the slider go over the range of
%the x-values of the plot for the center of the
%Lorentzian.
this.slider_vecs{3}=...
linspace(this.Fit.x(1),this.Fit.x(end),101);
%Find the index closest to the init parameter
[~,ind]=...
min(abs(this.param_vals(3)-this.slider_vecs{3}));
%Set to ind-1 as the slider goes from 0 to 100
set(this.Gui.(sprintf('Slider_%s',...
this.fit_params{3})),'Value',ind-1);
catch
end
end
end
methods (Access = protected)
function sc_vals = scaleFitParams(~, vals, scaling_coeffs)
[mean_x,std_x,mean_y,std_y]=scaling_coeffs{:};
sc_vals(1)=vals(1)/(std_y*std_x);
sc_vals(2)=vals(2)/std_x;
sc_vals(3)=(vals(3)-mean_x)/std_x;
sc_vals(4)=(vals(4)-mean_y)/std_y;
end
%Converts scaled coefficients to real coefficients
function vals = unscaleFitParams(~, sc_vals, scaling_coeffs)
[mean_x,std_x,mean_y,std_y]=scaling_coeffs{:};
vals(1)=sc_vals(1)*std_y*std_x;
vals(2)=sc_vals(2)*std_x;
vals(3)=sc_vals(3)*std_x+mean_x;
vals(4)=sc_vals(4)*std_y+mean_y;
end
end
end
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