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Astronomical Data Analysis Software and Systems VII
ASP Conference Series, Vol. 145, 1998
R. Albrecht, R. N. Hook and H. A. Bushouse, e
Ö Copyright 1998 Astronomical Society of the Pacific. All rights reserved.
ds.
The IDL Wavelet Workbench
M. Werger
Astrophysics Division, Space Science Department of ESA, ESTEC, 2200
AG Noordwijk, The Netherlands, EMail: mwerger@astro.estec.esa.nl
A. Graps
Stanford University, Center for Space Science and Astrophysics, HEPL
Annex A210, Stanford, California, 94305­4085 EMail:
amara@quake.stanford.edu
Abstract. Progress in the development of the 1996 release of the IDL
Wavelet Workbench (WWB) is shown. The WWB is now improved in
several ways, among them are: (1) a smarter GUI which easily directs the
user to the possibilities of the WWB, (2) the inclusion of more wavelets,
(3) the enhancement of the input and output modules to provide a better
interface to the input and output data and (4) the addition of more
analysis methods based on the wavelet transform.
1. Introduction
One of the most advanced packages for wavelet analysis is probably Wavelab 1
written for MATLAB. New insights have been gained in many other fields by
applying wavelet data analysis, thus it was a reasonable task for us in astro­
nomical research to translate most of the code from the Wavelab package into
IDL (Interactive Data Language, by Research Systems, Inc.). IDL was chosen
because of its wide­spread availability in the astronomical community and be­
cause of its development environment. The last o#cial version of the so­called
IDL Wavelet Workbench (WWB) was in the Spring of 1996. It has been made
publicly available at the ftp site of Research Systems, Inc. 2 .
2. The 1996 version of IDL
The 1996 version of the WWB consists of 111 di#erent modules with approx­
imately 10,000 lines of code in total. Approximately all modules have been
written or translated from MATLAB code into IDL by AG. The 1996 version
can be run either from the IDL command line or from a graphical user interface
(GUI).
1 http://stat.stanford.edu/ # wavelab/
2 ftp://ftp.rsinc.com/
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130 Werger and Graps
The WWB is written in a highly modularized way to be easily maintained
and improved. In the 1996 version, COMMON blocks are used to store impor­
tant variables for the di#erent routines. These COMMON blocks can be set
also from the command line. Therefore, it is possible to use the WWB as a
stand­alone package and also as a library to supplement ones own IDL routines.
The 1996 WWB provides simple input and output routines. Its analysis
and plotting libraries are sophisticated and employ most of the typical methods
used in wavelet analysis like the Discrete Wavelet Transform, Multiresolution
Analysis, Wavelet Packet Analysis, Scalegram, and Scalogram. In addition, the
1996 WWB o#ers typical routines for de­noising and compression of one­ and
two­dimensional data. The available set of wavelets is restricted up to four im­
portant families: the Haar­wavelet and the families of the Daubechies­wavelets,
Coiflets, and Symmlets.
3. Current Developments
The 1996 release the IDL WWB has been widely used for di#erent tasks such
as pattern detection, time­series analysis and de­noising of data. A lot of useful
routines have been added to the WWB since 1996, or they are foreseen to be
included.
. The current version makes use of the most recent changes to IDL (version
5.0.2); now WWB uses pointers to handle arbitrary data arrays. Also, the
WWB command line interface and the GUI may be used at the same time.
. The GUI has been simplified; now it includes more possibilities, but with
an easier interface and a less complicated dialog structure.
. All necessary variables are now kept in two IDL data structures, those
variables also may be set from the command line.
. The data input portion of the WWB has been upgraded to handle FITS­
files; the output portion of WWB has been upgraded so that one can use
the GUI to set PostScript output.
. More analysis routines are now available. In additional to the forward
DWT, now the backward DWT (IWT ) has been included to show possi­
ble di#erences between the original and transformed data. A continuous
wavelet transform using the Gauss, Sombrero, and Morlet wavelets has
been added also.
. The capabilities for time­series analysis has been greatly enhanced by
adding wavelets and routines which improve period detection. For exam­
ple, a routine has been added for detecting periods in unevenly­sampled
time­series, and eleven new wavelet filters are provided.
. The computations can now allow datasets more than 32767 points long.
. Plotting capabilities of the Scalogram have been improved.

The IDL Wavelet Workbench 131
. For a better understanding of the wavelet transform, a GUI for manipulat­
ing specific wavelet coe#cients has been included. This greatly improves
the learning and analyzing process.
4. Future Plans
There are some future plans for integrating capabilities to analyze multidimen­
sional data and adding additional routines. Suggestions and contributions from
the user community are greatly welcome.
Acknowledgments. The 1996 WWB has been partly funded by RSI, Inc.