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rINSTCONTROL/Fit classes/@MyLorentzianGradFit/MyLorentzianGradFit.m
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MyLorentzianGradFit.m
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classdef MyLorentzianGradFit < MyFit
%Public methods
methods (Access=public)
%Constructor function
function this=MyLorentzianGradFit(varargin)
this@MyFit(...
'fit_name','LorentzianGrad',...
'fit_function','1/pi*a*b/2/((x-c)^2+(b/2)^2)+d*(x-c)+e',...
'fit_tex','$$\frac{a}{\pi}\frac{b/2}{(x-c)^2+(b/2)^2}+d(x-c)+e$$',...
'fit_params', {'a','b','c','d','e'},...
'fit_param_names',{'Amplitude','Width','Center','Gradient','Offset'},...
varargin{:});
end
end
methods (Access=protected)
function [p_in,lim_lower,lim_upper]=calcInitParams(this)
x = this.Data.x;
y = this.Data.y;
%Assumes form a/pi*b/2/((x-c)^2+(b/2)^2)+d
lim_upper=[Inf,Inf,Inf,Inf,Inf];
lim_lower=[-Inf,0,-Inf,-Inf,-Inf];
%Finds peaks on the positive signal (max 1 peak)
rng_x = max(x)-min(x);
try
[~,locs(1),widths(1),proms(1)]=findpeaks(y,x,...
'MinPeakDistance',rng_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',rng_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')
p_in=[1,1,1,1,1];
lim_lower=-[Inf,0,Inf,Inf];
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(5)=min(y);
else
ind=2;
p_in(5)=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);
p_in(4)=(y(end)-y(1))/(x(end)-x(1));
lim_lower(2)=0.01*p_in(2);
lim_upper(2)=100*p_in(2);
end
function genSliderVecs(this)
genSliderVecs@MyFit(this);
if validateData(this)
%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.x_vec(1),this.x_vec(end),101);
%Find the index closest to the init parameter
[~,ind]=...
min(abs(this.init_params(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);
end
end
end
end
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