./guide
gconvol.pro

Last modification date:

Wed Sep 28 13:27:08 2016

gconvol

procedure gconvol, kernel[, scale_factor], [buffer=integer], [ok=variable], [/normalize], [_extra=extra keywords]

A GUIDE front-end to the standard IDL CONVOL function.

This allows GUIDE users to convolve a data container with an arbitrary kernel using the IDL CONVOL function. All of the arguments except the dc keyword have the same meaning and use as in the CONVOL function. Users should consult the IDL documentation for CONVOL for a more detailed explanation than is given here. All of the CONVOL keywords are passed in through the _EXTRA keyword are so are not shown explicitly here. These keywords are /CENTER, /EDGE_WRAP, /EDGE_TRUNCATE, MISSING, /NAN, and /NORMALIZE

This procedure follows the GUIDE model and the result of the convolution replaces the original data values in the indicated data container.

NORMALIZE was added in IDL 6.2. It is essential when working with blanked data (NAN). It is implemented here so that it works for earlier version of IDL. For IDL 6.2 and later, /NORMALIZE is passed directly to CONVOL. For earlier versions of IDL, a second convolution is done using a vector of 1.0s having the same length as the data and blanked in the same locations as the data. The convolution of the data is then divided by this convolution and the result is put back into the data container. BIAS was also added in IDL 6.2 but that functionality isn't necessary for the rest of GBTIDL and so it has not been implemented here apart from what CONVOL provides in IDL 6.2

Examples

    ; hanning smoothing kernel
    kernel = [0.25,0.5,0.25]
    ; convolve with the data in the primary data container
    gconvol, kernel
    ; same kernel, using DC 1, ignore NAN (missing) values, 
    ; truncate the data at the edges, normalize the result.
    ; This is how the hanning procedure is implemented.
    gconvol, kernel, buffer=1, /nan, /edge_truncate, /normalize
 

Uses

getdata setdata

Version

$Id$

Parameters
kernel in, required array	The kernel to use in the convolution. Must have fewer elements than the data container being convolved.
scale_factor in, optional real (def. 1)	The scale factor.

Keywords
buffer in, optional integer (def. 0)	The data container buffer to use in the convolution.
ok out, optional variable	Returns 1 if everything went ok, 0 if it did not (empty or invalid buffer, bad kernel).
normalize in, optional boolean	Set this keyword to automatically compute a scale factor and bias and apply it to the result values. If this keyword is set, the scale_factor argument and the BIAS keyword are ignored. For all input types, the scale factor is defined as the sum of the absolute values of Kernel. If blanked (NAN) values are present, the scale factor and bias are calculated without using those values so that all result values are comparable in magnitude.
_extra in, optional extra keywords	Keyword arguments to CONVOL. See the IDL documentation on CONVOL for more information.