initParamLorentzianGrad.m
48802f884580master

initParamLorentzianGrad.m
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			function [p_in,lim_lower,lim_upper]=initParamLorentzianGrad(x,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)
			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')
			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)pip_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