ifp.m

Basic function to locate and measure the positive peaks in a noisy data sets. Detects peaks by loo

function P=ipf(x,y)
% Interactive Peek Find function, P=ipf(x,y,WidthPoints),
% where x and y are the data (x=independent variable, 
% y=dependent variable).  Peak number and position, height, 
% and width of each peak is returned in the matrix P. 
% Adjust the sliders to determine what values of the findpeaks
% parameters give the most reliable peak detection. The function
% returns a matrix of position, height, and width of each peak.
% The 4 slider-controlled parameters are:
% SlopeThreshold (SlopeT) - Slope of the smoothed third-derivative that is  
%   taken to indicate a peak. Larger values will neglect small features.
% AmpThreshold (AmpT) - Any peaks with height less than this value are ignored.
% SmoothWidth (Smooth) - Width of smooth functions applied to data before slope is
%   measured. Larger values will neglect small features. The best value is 
%   about equal to the half-width of the peaks.
% FitWidth (Fit) - The number of points around the "top part" of the (unsmoothed)
%   peak that are taken to determine the peak height, positions, and width.
%   The best value is about equal to the half-width of the peaks.
% The BG button is used to remove background (offset). Click on this
%    button, then click on the background at 10 points. (To change the
%    number of points, edit BackgroundPoints in findpeaksliders6.
% Note: If the slider ranges are not appropriate for your data, change 
%    them in lines 64-69. Set WidthPoints in line 48 to the 
%    average number of points in half-width of peaks 
%    to set the initial values of the sliders.
% Tom O'Haver ([e-mail]). Version 1.6 October 26, 2006

warning off MATLAB:polyfit:RepeatedPointsOrRescale
format compact

global SlopeThreshold 
global AmpThreshold  
global SmoothWidth
global FitWidth
global P
global PeakNumber
close
figure(1)

% Graph the signal in magenta
h=figure(1);
plot(x,y,'m') 
h2=gca;axis([x(1) x(length(x)) min(y) max(y)]);
title('Vary the sliders to optimize peak finding performance')

% Initial values of variable parameters
WidthPoints=length(y)/40;  % Change to match approx. # of points in your peaks
SlopeThreshold=WidthPoints^-2;
AmpThreshold=min(y)+0.05*(max(y)-min(y));
SmoothWidth=round(WidthPoints/2); 
if SmoothWidth<1,SmoothWidth=1;end
FitWidth=round(WidthPoints/2); 
if FitWidth<3,FitWidth=3;end
PeakNumber=0;

% Find and number the peaks on the graph
warning off MATLAB:polyfit:RepeatedPointsOrRescale
P=findpeaks(x,y,SlopeThreshold,AmpThreshold,SmoothWidth,FitWidth);
xlabel(['SlopeT = ' num2str(SlopeThreshold) '    AmpT = ' num2str(AmpThreshold) '    SmoothWidth = ' num2str(SmoothWidth) '    FitWidth = ' num2str(FitWidth) ])
text(P(:, 2),P(:, 3),num2str(P(:,1)))  % Number the peaks found on the graph

% Maximum ranges of the sliders (change as needed)
SlopeMax=100;
SlopeMin=10^-6;
AmpMax=max(y);
AmpMin=min(y);
SmoothWidthMax=100;
FitWidthMax=100;

if SmoothWidth>SmoothWidthMax,SmoothWidth=SmoothWidthMax;end
if FitWidth>FitWidthMax,FitWidth=FitWidthMax;end

% Draw the sliders
rtslid(h,@SlopeT,h2,1,'Scale',[log10(SlopeMin) log10(SlopeMax)],'Def',log10(SlopeThreshold),'Back',[0.9 0.9 0.9],'Label','SlopeT','Position',[0.03 0.5 0.03 0.35]);
rtslid(h,@AmpT,h2,0,'Scale',[AmpMin AmpMax],'Def',AmpThreshold,'Back',[0.9 0.9 0.9],'Label','AmpT','Position',[0.03 0.04 0.03 0.35]);
rtslid(h,@BG,h2,0,'Scale',[0 1],'Def',0,'Back',[0.9 0.9 0.9],'Label','BG','Position',[0.94 0.8 0.03 0.04]);
rtslid(h,@Smooth,h2,0,'Scale',[0 2],'Def',log10(SmoothWidth),'Back',[0.9 0.9 0.9],'Label','Smooth','Position',[0.94 0.42 0.03 0.3]);
rtslid(h,@Fit,h2,0,'Scale',[log10(3) log10(FitWidthMax)],'Def',log10(FitWidth),'Back',[0.9 0.9 0.9],'Label','Fit','Position',[0.95 0.04 0.03 0.3]);