visitor "Morgan" contributed a Python implementation of Lomb-Scargle via the comments to [Question] period-finding packages in python.  This script is 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

In order to make the script easier to access via cut and paste we are providing it here as a code snippet.  This version repairs a couple of apparent issues with indentation in the original comment posting (near the top of the __spread__ function).

#!/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

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