[Question] period-finding packages in python

Tom asked:

"Hi, I have a question about period-finding packages in python. I know that in IDL the Lomb-Scargle periodogram is commonly used, and I am looking for an equivalent function from some package somewhere.

I've googled "
Lomb Scargle python" (noquotes) and found a couple of possibly useful results, but nothing that was included in any of the modules recommended by this website (such as scipy).

Do you know of a good way to calculate Lomb-Scargle periodograms for unevenly-sampled data in python?" 
Posted by virtualastronomer

Category: questions

Add a comment...


  1. Lomb Scargle by Morgan (2010-09-27)

    I did wrote a lomb scargle python version a couple of years ago based on:
        Press, W. H. & Rybicki, G. B. 1989
        ApJ vol. 338, p. 277-280.
        Fast algorithm for spectral analysis of unevenly sampled data
        bib code: 1989ApJ...338..277P

    #!/usr/bin/env python
    """ Fast algorithm for spectral analysis of unevenly sampled data

    The Lomb-Scargle method performs spectral analysis on unevenly sampled
    data and is known to be a powerful way to find, and test the
    significance of, weak periodic signals. The method has previously been
    thought to be 'slow', requiring of order 10(2)N(2) operations to analyze
    N data points. We show that Fast Fourier Transforms (FFTs) can be used
    in a novel way to make the computation of order 10(2)N log N. Despite
    its use of the FFT, the algorithm is in no way equivalent to
    conventional FFT periodogram analysis.


        > import numpy
        > import lomb
        > x = numpy.arange(10)
        > y = numpy.sin(x)
        > fx,fy, nout, jmax, prob = lomb.fasper(x,y, 6., 6.)

        Press, W. H. & Rybicki, G. B. 1989
        ApJ vol. 338, p. 277-280.
        Fast algorithm for spectral analysis of unevenly sampled data
        bib code: 1989ApJ...338..277P

    from numpy import *
    from numpy.fft import *

    def __spread__(y, yy, n, x, m):
        Given an array yy(0:n-1), extirpolate (spread) a value y into
        m actual array elements that best approximate the "fictional"
        (i.e., possible noninteger) array element number x.  The weights
        used are coefficients of the Lagrange interpolating polynomial
            y  :
            yy :
            n  :
            x  :
            m  :
        if m > 10. :
            print 'factorial table too small in spread'

        if x == float(ix):
            ilo  = long(x-0.5*float(m)+1.0)
            ilo  = min( max( ilo , 1 ), n-m+1 )
            ihi  = ilo+m-1
            nden = nfac[m]
            for j in range(ilo+1,ihi+1): fac = fac*(x-j)
            yy[ihi] = yy[ihi] + y*fac/(nden*(x-ihi))
            for j in range(ihi-1,ilo-1,-1):
                yy[j] = yy[j] + y*fac/(nden*(x-j))

    def fasper(x,y,ofac,hifac, MACC=4):
        """ function fasper
            Given abscissas x (which need not be equally spaced) and ordinates
            y, and given a desired oversampling factor ofac (a typical value
            being 4 or larger). this routine creates an array wk1 with a
            sequence of nout increasing frequencies (not angular frequencies)
            up to hifac times the "average" Nyquist frequency, and creates
            an array wk2 with the values of the Lomb normalized periodogram at
            those frequencies.  The arrays x and y are not altered.  This
            routine also returns jmax such that wk2(jmax) is the maximum
            element in wk2, and prob, an estimate of the significance of that
            maximum against the hypothesis of random noise. A small value of prob
            indicates that a significant periodic signal is present.
            Press, W. H. & Rybicki, G. B. 1989
            ApJ vol. 338, p. 277-280.
            Fast algorithm for spectral analysis of unevenly sampled data

                X     : Abscissas array, (e.g. an array of times).
                Y     : Ordinates array, (e.g. corresponding counts).
                Ofac  : Oversampling factor.
                Hifac : Hifac * "average" Nyquist frequency = highest frequency
                          for which values of the Lomb normalized periodogram will
                          be calculated.
                Wk1  : An array of Lomb periodogram frequencies.
                Wk2  : An array of corresponding values of the Lomb periodogram.
                Nout : Wk1 & Wk2 dimensions (number of calculated frequencies)
                Jmax : The array index corresponding to the MAX( Wk2 ).
                Prob : False Alarm Probability of the largest Periodogram value
                MACC : Number of interpolation points per 1/4 cycle
                           of highest frequency

            02/23/2009, v1.0, MF
                Translation of IDL code (orig. Numerical recipies)
        #Check dimensions of input arrays
        n  = long(len(x))
        if n != len(y):
            print 'Incompatible arrays.'

        nout   = 0.5*ofac*hifac*n
        nfreqt = long(ofac*hifac*n*MACC)     #Size the FFT as next power
        nfreq  = 64L                         # of 2 above nfreqt.

        while nfreq < nfreqt:
            nfreq = 2*nfreq

        ndim = long(2*nfreq)
        #Compute the mean, variance
        ave = y.mean()
        ##sample variance because the divisor is N-1
        var = ((y-y.mean())**2).sum()/(len(y)-1)
        # and range of the data.
        xmin = x.min()
        xmax = x.max()
        xdif = xmax-xmin

        #extirpolate the data into the workspaces
        wk1 = zeros(ndim, dtype='complex')
        wk2 = zeros(ndim, dtype='complex')

        fac   = ndim/(xdif*ofac)
        fndim = ndim
        ck    = ((x-xmin)*fac) % fndim
        ckk   = (2.0*ck) % fndim

        for j in range(0L, n):

        #Take the Fast Fourier Transforms
        wk1  = ifft( wk1 )*len(wk1)
        wk2  = ifft( wk2 )*len(wk1)

        wk1  = wk1[1:nout+1]
        wk2  = wk2[1:nout+1]
        rwk1 = wk1.real
        iwk1 = wk1.imag
        rwk2 = wk2.real
        iwk2 = wk2.imag
        df   = 1.0/(xdif*ofac)
        #Compute the Lomb value for each frequency
        hypo2 = 2.0 * abs( wk2 )
        hc2wt = rwk2/hypo2
        hs2wt = iwk2/hypo2

        cwt   = sqrt(0.5+hc2wt)
        swt   = sign(hs2wt)*(sqrt(0.5-hc2wt))
        den   = 0.5*n+hc2wt*rwk2+hs2wt*iwk2
        cterm = (cwt*rwk1+swt*iwk1)**2./den
        sterm = (cwt*iwk1-swt*rwk1)**2./(n-den)

        wk1  = df*(arange(nout, dtype='float')+1.)
        wk2  = (cterm+sterm)/(2.0*var)
        pmax = wk2.max()
        jmax = wk2.argmax()

        #Significance estimation
        #expy = exp(-wk2)                  
        #effm = 2.0*(nout)/ofac             
        #sig = effm*expy
        #ind = (sig > 0.01).nonzero()
        #sig[ind] = 1.0-(1.0-expy[ind])**effm

        #Estimate significance of largest peak value
        expy = exp(-pmax)                  
        effm = 2.0*(nout)/ofac             
        prob = effm*expy

        if prob > 0.01:
            prob = 1.0-(1.0-expy)**effm

        return wk1,wk2,nout,jmax,prob

    def getSignificance(wk1, wk2, nout, ofac):
        """ returns the peak false alarm probabilities
        Hence the lower is the probability and the more significant is the peak
        expy = exp(-wk2)                  
        effm = 2.0*(nout)/ofac             
        sig = effm*expy
        ind = (sig > 0.01).nonzero()
        sig[ind] = 1.0-(1.0-expy[ind])**effm
        return sig

Enter Your Comment