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ATNF Visualisation Software

Richard Gooch ( Tom Oosterloo (

Last update: November 6, 2000

This document describes the visualisation software available at the ATNF. The programs available at the moment are:

Much of this software is still experimental, and some parts may seem a bit cumbersome to use. Any suggestions for improvements, additions etc. are welcome (mail to or

This document is also available as gzipped postscript (170 Kb).

Most of the visualisation tools (except kubes, ktrek, ksnow or krot) are available from the Karma home page or you can go directly to the Karma ftp site.

More detailed information on certain aspects of visualisation can be found in the following documents:

The Challenge of Visualizing Astronomical Data, Ray Norris, ADASS '93, (compressed postscript, 20K)

Space and the Spaceball, Richard Gooch, ADASS '94

Visualisation of Radio Data, Tom Oosterloo, 1995, PASA, 12, 215

Visualisation: from Data to Understanding, Richard Gooch, WARS '95

Astronomers and their Shady Algorithms, Richard Gooch, VIS '95

Adaptive Filtering and Masking of HI Data Cubes, Tom Oosterloo, ESF workshop on Vision Modeling and Information Coding', Nice, 4-6 October 1995
(See also ESF Network on Converging Computing Methodologies in Astronomy)

Some examples of movies made with our software:  

To see an example of a movie made the rendering program krot, click on the picture below. Left is the rendered HI cube from an observation of NGC 253 with ATCA (Koribalski, Whiteoak & Houghton, Publ. ASA, 1995, in press) and on the right a rendered model cube (made with modeling software we are writing at the moment).


The   movie below shows HI data of NGC 2502, taken with the ATCA (Oosterloo, Schiminovich & van Gorkom). The cube on the left is the original data cube, rendered with krot. The settings for the rendering are such that low level emission should be relatively bright. But as a consequence, the noise is also quite bright and the object is only visible through a fog of noise. On the right is the same data after applying wavelet adaptive filtering, rendered with exactly the same settings as the left cube. The noise is much less a problem now. This adaptive filtering is available in xray and krot.


This   example is a movie made with ksnow. The input table is a list of all galaxies within 200 Mpc for which the redshift is known. (This data was collected by Georges Paturel and Helene Di Nella, Observatoire of Lyon, France, using the the LEDA database).


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Richard Gooch