
1D Nonderivative Peak Finder 1.0 File ID: 79097 


 1D Nonderivative Peak Finder 1.0 License: Freeware File Size: 10.0 KB Downloads: 31
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1D Nonderivative Peak Finder 1.0 Description 

Description: PEAKFIND general 1D peak finding algorithm peakfind(x_data,y_data) peakfind(x_data,y_data,upsam) peakfind(x_data,y_data,upsam,gsize,gstd) peakfind(x_data,y_data,upsam,htcut,'cuttype') peakfind(x_data,y_data,upsam,gsize,gstd,htcut,'cuttype') [xpeaks]=peakfind() [xout,yout,peakspos]=peakfind() This function finds peaks without taking first or second derivatives, rather it uses local slope features in a given data set. The function has four basic modes. Mode 1: peakfind(x_data,y_data) simply finds all peaks in the data given by 'xdata' and 'ydata'. Mode 2: peakfind(x_data,y_data,upsam) finds peaks after upsampling the data by the integer factor 'upsam'  this allows for higher resolution peak finding. The interpolation uses a cubic spline that does not introduce fictitious peaks. Mode 3: peakfind(x_data,y_data,upsam,gsize,gstd) upsamples and then convolves the data with a Gaussian point spread vector of length gsize (>=3) and standard deviation gstd (>0). The convolution option only works with upsam>=2. Mode 4: peakfind(x_data,y_data,upsam,htcut,'cuttype') upsamples the data, however upsam>=1 in this case, i.e. upsam=1 analyzes the data unmodified. The string 'cuttype' can either be 'abs' (absolute) or 'rel' (relative), which specifies a peak height cutoff which is either: 'abs'  (htcut > 0) peaks are found if peakheights > min(yout) + htcut 'rel'  (0 < htcut < 1) peaks are found if (peakheightsmin(yout))/(max(yout)min(yout)) > htcut Upsampling and convolution allows one to find significant peaks in noisy data with subpixel resolution. The algorithm also finds peaks in data where the peak is surrounded by zero first derivatives, i.e. the peak is actually a large plateau. The function outputs the xposition of the peaks in 'xpeaks' or the processed input data in 'xout' and 'yout' with 'peakspos' as the indices of the peaks, i.e. xpeaks = xout(peakspos). If you want the algorithm to find the position of minima, simply input 'y_data'. Peaks within half the convolion box size of the boundary will be ignored (to avoid this, pad the data before processing). Example: x_data = 50:50; y_data =(sin(x_data)+0.000001)./(x_data+0.000001)+1+0.025*(2*rand(1,length(x_data))1); [xout,yout,peakspos]=peakfind(x_data,y_data,4,6,2,0.2,'rel'); plot(xout,yout,'r','linewidth',2) hold on plot(xout,yout,'b','linewidth',2) plot(xout(peakspos),yout(peakspos),'g.','Markersize',30) xlabel('x') ylabel('y') title(['Found ' num2str(length(peakspos)) ' peaks.']) box on
License: Freeware Related: subpixel, noisy, significant, peakheightsminyoutmaxyoutminyout, upsampling, surrounded, xpeaks, processed, xposition, outputs, Large, plateau, minyout, peakheights, Absolute, rel, abs O/S:BSD, Linux, Solaris, Mac OS X File Size: 10.0 KB Downloads: 31


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