source: trunk/STATISTICS/c2_correlate.pro @ 2

; $Id$
;
; Copyright (c) 1995-1997, Research Systems, Inc.  All rights reserved.
;       Unauthorized reproduction prohibited.
;+
; NAME:
;       c2_correlate
;
; PURPOSE:
;       This function computes the cross correlation Pxy(L) or cross
;       covariance Rxy(L) of two sample populations X and Y as a function
;       of the lag (L).
;
; CATEGORY:
;       Statistics.
;
; CALLING SEQUENCE:
;       Result = c2_correlate(X, Y, Lag)
;
; INPUTS:
;       X:    An n-element vector of type integer, float or double.
;
;       Y:    An n-element vector of type integer, float or double.
;
;     LAG:    A scalar or n-element vector, in the interval [-(n-2), (n-2)],
;             of type integer that specifies the absolute distance(s) between
;             indexed elements of X.
;
; KEYWORD PARAMETERS:
;       COVARIANCE:    If set to a non-zero value, the sample cross
;                      covariance is computed.
;
;       DOUBLE:        If set to a non-zero value, computations are done in
;                      double precision arithmetic.
;
; EXAMPLE
;       Define two n-element sample populations.
;         x = [3.73, 3.67, 3.77, 3.83, 4.67, 5.87, 6.70, 6.97, 6.40, 5.57]
;         y = [2.31, 2.76, 3.02, 3.13, 3.72, 3.88, 3.97, 4.39, 4.34, 3.95]
;
;       Compute the cross correlation of X and Y for LAG = -5, 0, 1, 5, 6, 7
;         lag = [-5, 0, 1, 5, 6, 7]
;         result = c2_correlate(x, y, lag)
;
;       The result should be:
;         [-0.428246, 0.914755, 0.674547, -0.405140, -0.403100, -0.339685]
;
; PROCEDURE:
;       See computational formula published in IDL manual.
;
; REFERENCE:
;       INTRODUCTION TO STATISTICAL TIME SERIES
;       Wayne A. Fuller
;       ISBN 0-471-28715-6
;
; MODIFICATION HISTORY:
;       Written by:  GGS, RSI, October 1994
;       Modified:    GGS, RSI, August 1995
;                    Corrected a condition which excluded the last term of the
;                    time-series.
;       Modified:    GGS, RSI, April 1996
;                    Simplified CROSS_COV function. Added DOUBLE keyword.
;                    Modified keyword checking and use of double precision.
;       Modified:    W. Biagiotti,  Advanced Testing Technologies Inc., Hauppauge, NY, July 1997
;                    Moved all constant calculations out of main loop for greatly
;                    reduced processing time.
;
;
; DISCLAIMER:  This routine has been modified from its original form as it was
;              supplied by Research Systems, Inc (RSI).  As such, RSI is not responsible
;              for any errors existing in this code.
;-

FUNCTION Cross_Cov, X, Y, M, nX, Double = Double
  ;Sample cross covariance function.

  Xmean = TOTAL(X, Double = Double) / nX
  Ymean = TOTAL(Y, Double = Double) / nX
  RETURN, TOTAL((X[0:nX - M - 1L] - Xmean) * (Y[M:nX - 1L] - Ymean), $
                 Double = Double)

END

FUNCTION c2_correlate, X, Y, Lag, Covariance = Covariance, Double = Double

  ;Compute the sample cross correlation or cross covariance of
  ;(Xt, Xt+l) and (Yt, Yt+l) as a function of the lag (l).

  ON_ERROR, 2

  TypeX = SIZE(X)
  TypeY = SIZE(Y)
  nX = TypeX[TypeX[0]+2]
  nY = TypeY[TypeY[0]+2]

  if nX ne nY then $
    MESSAGE, "X and Y arrays must have the same number of elements."

  ;Check length.
  if nX lt 2 then $
    MESSAGE, "X and Y arrays must contain 2 or more elements."

  ;If the DOUBLE keyword is not set then the internal precision and
  ;result are identical to the type of input.
  if N_ELEMENTS(Double) eq 0 then $
    Double = (TypeX[TypeX[0]+1] eq 5 or TypeY[TypeY[0]+1] eq 5)

  nLag = N_ELEMENTS(Lag)

  if nLag eq 1 then Lag = [Lag] ;Create a 1-element vector.

  if Double eq 0 then Cross = FLTARR(nLag) else Cross = DBLARR(nLag)

  if KEYWORD_SET(Covariance) eq 0 then begin ;Compute Cross Correlation.

    denom = SQRT(Cross_Cov(X, X, 0L, nX, Double = Double) * $
                   Cross_Cov(Y, Y, 0L, nY, Double = Double))

    for k = 0, nLag-1 do begin
      if Lag[k] ge 0 $
        then Cross[k] = Cross_Cov(x, y, Lag[k], nX, Double = Double) / denom  $
        else Cross[k] = Cross_Cov(Y, X, ABS(Lag[k]), nY, Double = Double) / denom

    endfor
  endif else begin ;Compute Cross Covariance.
    for k = 0, nLag-1 do begin
      if Lag[k] ge 0 then $
        Cross[k] = Cross_Cov(X, Y, Lag[k], nX, Double = Double) / nX $
      else Cross[k] = Cross_Cov(Y, X, ABS(Lag[k]), nX, Double = Double) / nX
    endfor
  endelse

  if Double eq 0 then RETURN, FLOAT(Cross) else $
  RETURN, Cross

END