./toolbox
dcconvol.pro

Last modification date:

Wed Sep 28 13:27:10 2016

dcconvol

procedure dcconvol, dc, kernel[, scale_factor], [ok=variable], [/normalize], [_extra=extra keywords]

A front-end to the standard IDL CONVOL function.

This allows users to convolve a data container with an arbitrary kernel using the IDL CONVOL function. All of the arguments except the dc parameter 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 replaces the data values in dc with the results of the convolution.

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 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 appart from what CONVOL provides in IDL 6.2

Examples

    ; hanning smoothing kernel
    kernel = [0.25,0.5,0.25]
    ; dc is some data container that already exists and is valid
    dcconvol, dc, kernel
    ; same kernel,ignore NAN (missing) values, truncate the 
    ; data at the edges and normalize.  This is how the dchanning
    ; procedure is implemented.
    dcconvol, dc, kernel, /nan, /edge_truncate, /normalize
 

Uses

getdcdata.html setdcdata.html

Version

$Id$

Parameters
dc in, required data container	The data container to use in the convolution.
kernel in, required array	The kernel to use in the convolution. Must have fewer elements than the data container being convolved. 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. The scale factor.

Keywords
ok out, optional variable	Returns 1 if everything went ok, 0 if it did not (missing parameter, empty or invalid dc, bad kernel). 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