;+
; NAME:
;       INTERPOLATE_BAD_POINTS
;
; PURPOSE:
;
;       The purpose of this function is to provide
;	a simple method of finding and replacing
;	bad data points in a time series using
;	linear interpolation.
;
; AUTHOR:
;
;       Nathan Johnson
;       University of Arizona
;       Department of Atmospheric Sciences
;       johnson AT atmo DOT arizona DOT edu
;
; CATEGORY:
;
;       Data processing
;
; CALLING SEQUENCE (WITH REQUIRED INPUT PARAMETERS):
;
;       new_data = INTERPOLATE_BAD_POINTS(data, -9999) 
;
;
; OPTIONAL INPUT PARAMETERS:
;       None.
;
; INPUT KEYWORD PARAMETERS:
;	None
;
; OUTPUT KEYWORD PARAMETERS:
;       None.
;
; COMMON BLOCKS:
;       None.
;
; SIDE EFFECTS:
;       None known.
;
; RESTRICTIONS:
;	None.
;
; EXAMPLE:
;
;       new_data = INTERPOLATE_BAD_POINTS(data, -9999) 
;
;	This command will find the bad data points, those
;	values equal to -9999, and do a linear interpolation
;	to fill those values with "good" values.
;
;
; MODIFICATION HISTORY:
;
;       Written by: Nathan Johnson, 16 January 2008.
;-


Function INTERPOLATE_BAD_POINTS, array, bad_val

  new_array=array

  n = 0
  WHILE new_array[0] eq bad_val DO BEGIN
    new_array = new_array[1:*]
    n += 1
  ENDWHILE

  m = 0
  WHILE new_array[N_ELEMENTS(new_array)-1] eq bad_val DO BEGIN
    new_array = new_array[0:N_ELEMENTS(new_array)-2]
    m += 1
  ENDWHILE

  bad = Where(new_array eq bad_val, nbad, COMPLEMENT=good, NCOMPLEMENT=ngood)  
  IF nbad GT 0 && ngood GT 1 THEN new_array[bad] = INTERPOL(new_array[good], good, bad)

return, new_array
END