root/MGET/Branches/Jason/PythonPackage/src/GeoEco/AssimilatedModules/lomb_scargle.py @ 974

#!/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. 
 
Keywords: 
  DATA SAMPLING, FAST FOURIER TRANSFORMATIONS,  
  SPECTRUM ANALYSIS, SIGNAL  PROCESSING 
 
Example: 
  > import numpy 
  > import lomb 
  > x = numpy.arange(10) 
  > y = numpy.sin(x) 
  > fx,fy, nout, jmax, prob = lomb.fasper(x,y, 6., 6.) 
 
Reference:  
  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 
  Arguments: 
    y :  
    yy :  
    n :  
    x :  
    m :  
  Returns: 
     
  """  
  nfac=[0,1,1,2,6,24,120,720,5040,40320,362880]  
  if m > 10. :  
    print 'factorial table too small in spread'  
    return  
  
  ix=long(x)  
  if x == float(ix):   
    yy[ix]=yy[ix]+y  
  else:  
    ilo = long(x-0.5*float(m)+1.0)  
    ilo = min( max( ilo , 1 ), n-m+1 )   
    ihi = ilo+m-1  
    nden = nfac[m]  
    fac=x-ilo  
    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):  
      nden=(nden/(j+1-ilo))*(j-ihi)  
      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. 
   
  Reference:  
    Press, W. H. & Rybicki, G. B. 1989 
    ApJ vol. 338, p. 277-280. 
    Fast algorithm for spectral analysis of unevenly sampled data 
    (1989ApJ...338..277P) 
 
  Arguments: 
      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. 
       
   Returns: 
      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 
 
  History: 
    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.'  
    return  
  
  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):  
    __spread__(y[j]-ave,wk1,ndim,ck[j],MACC)  
    __spread__(1.0,wk2,ndim,ckk[j],MACC)  
  
  #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