NAME: high_pass_filter PURPOSE: subtracts running average from a data array CALLING SEQUENCE: y = high_pass_filter(array, time_array, no_time_interp=no_time_interp) INPUT: array = a data array time_array = a time array (in units of seconds) dt = the averaging time (in seconds) no_time_interp = if set, do *not* interpolate the data to the minimum time resolution. The default procedure is to interpolate the data to a regularly spaced grid, and then use ts_smooth to get the running average. This alternative can be slow, but it may save a lot of memory. double = if set, do calculation in double precision regardless of input type. (If input data is double calculation is always done in double precision) interp_resolution = If time interpolation is being used, set this option to control the number of seconds between interpolated samples. The default is to use the value of the smallest separation between samples. Any number higher than this will sacrifice output resolution to save memory. (NOTE: This option will not be applied if no interpolation is being performed because either (1) no_time_interp is set or (2) the sample rate of the data is constant) interactive_warning = if keyword is set pops up a message box if there are memory problems and asks the user if they would like to continue interactive_varname = set this to a string indicating the name of the quantity to be used in the warning message. warning_result = assign a named variable to this keyword to determine the result of the computation OUTPUT: y = the data array where at each point an average of the data for the previous dt has been subtracted. HISTORY: 14-jan-2008, jmm, jimm@ssl.berkeley.edu 06-feb-2008, teq, teq@ssl.berkeley.edu 13-mar-2008, jmm, added the default behavior using interpolation 17-mar-2008, jmm, Gutted and rewritten to use smooth_in_time program 23-apr-2008, pcruce, Added padding for no_time_interp option, added _extra keyword 28-apr-2008, pcruce, Added interp_resolution option, added memory warning, mod to guarantee that precision of output is at least as large as precision of input $LastChangedBy$ $LastChangedDate$ $LastChangedRevision$ $URL$