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I have this data:

  data = [1.0, 0.35671858559485703, 0.44709399319470694, 0.29438948200831194, 0.5163825635166547, 0.3036363865322419, 0.34031782308777747, 0.2869558046065574, 0.28190537831716, 0.2807516154537239, 0.34320479518313507, 0.21117275536958913, 0.30304626765388043, 0.4972542099530442, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.18200891715227194, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.28830608331168983, 0.057156776746163526, 0.043418555819326035, 0.022527521866967784, 0.035414574439784685, 0.062273775107322626, 0.04569227783752021, 0.04978915781132807, 0.0599089458581528, 0.05692515997545401, 0.05884619933405206, 0.0809943356922021, 0.07466587894671428, 0.08548458657792352, 0.049216679971411645, 0.04742180324984401, 0.05822208549398862, 0.03465282733964001, 0.014005094192867372, 0.052004161876744344, 0.061297263734617496, 0.01867087951563289, 0.01390993522118277, 0.021515814095838564, 0.025260618727204275, 0.0157022555745128, 0.041999490119172936, 0.0441231248537558, 0.03079711140612242, 0.04177946154195037, 0.047476050325192885, 0.05087930020034335, 0.03889899267688956, 0.02114033158686702, 0.026726959895528927, 0.04623461918879543, 0.05426474524591766, 0.04421866212189775, 0.041911901968304605, 0.019982199103543322, 0.026520396430805435, 0.03952286472888431, 0.03842652984978244, 0.02779682035551695, 0.02043518392128019, 0.07706934170969436]

Now the question is: How can I remove the noise (colored) intelligently without removing the peaks ?

plt.plot(data)
plt.show()

enter image description here

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  • $\begingroup$ Have you considered to use a bandpass filter for the region of interest? $\endgroup$ – Irreducible Apr 11 at 6:00
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Here is a rough cut. You could definitely get more elaborate on your peak testing, e.g. comparing it to neighbor peaks. This simply makes a metric for how far the peak "sticks out" and uses a cutoff value on that and the height to select a candidate peak.

In the case of two or more isolated peaks in your field of zeroes, it would select all of them. Not sure that is what you want, but this works for your given example.

Once your peaks are selected, then you can zero all other data values.

Ced


        thePeak_Spot       = np.zeros(100, dtype=int )
        thePeak_Height     = np.zeros(100)
        thePeak_Protrusion = np.zeros(100)

        thePeakCount = 0

        for n in range( 1, len( data ) - 1 ) :

            theLeftDrop  = data[n] - data[n-1]
            theRightDrop = data[n] - data[n+1]

            if theLeftDrop > 0 and theRightDrop > 0 :
                thePeak_Spot[thePeakCount] = n
                thePeak_Height[thePeakCount] = data[n]
                thePeak_Protrusion[thePeakCount] = (theLeftDrop + theRightDrop) / ( 2.0 * data[n] )
                thePeakCount += 1

        for p in range( thePeakCount ) :
            if thePeak_Protrusion[p] > 0.5 and thePeak_Height[p] > 0.1 :
               print p, thePeak_Spot[p], thePeak_Height[p], thePeak_Protrusion[p]  

        plt.plot(data)
        plt.show()

output:

4 13 0.497254209953 0.695280339927
5 58 0.182008917152 1.0
6 122 0.288306083312 0.900874833979
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