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1D Non-derivative Peak Finder 1.0
File ID: 79097

1D Non-derivative Peak Finder 1.0
Download 1D Non-derivative Peak Finder 1.0http://www.mathworks.comReport Error Link
License: Freeware
File Size: 10.0 KB
Downloads: 31
Submit Rating:
1D Non-derivative Peak Finder 1.0 Description
Description: PEAKFIND general 1D peak finding algorithm



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 up-sampling
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) up-samples 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') up-samples 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
'abs' - (htcut > 0) peaks are found if

peakheights > min(yout) + htcut

'rel' - (0 < htcut < 1) peaks are found if

(peakheights-min(yout))/(max(yout)-min(yout)) > htcut

Upsampling and convolution allows one to find significant peaks in noisy
data with sub-pixel 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 x-position 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).


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);


hold on
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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