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Astronomical Data Analysis Software and Systems VII
ASP Conference Series, Vol. 145, 1998
Editors: R. Albrecht, R. N. Hook and H. A. Bushouse
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, 94305-4085
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.
One of the most advanced packages for wavelet analysis is
probably
Wavelab
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
astronomical 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 wide-spread availability in the astronomical
community and because of its development environment.
The last official version of the so-called
IDL Wavelet Workbench (WWB) was in the Spring of 1996. It
has been made publicly available at the
ftp site of Research Systems, Inc.
.
The 1996 version of the WWB consists of 111 different modules with
approximately 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).
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
important variables for the different routines. These COMMON blocks
can be set also from the command line. Therefore, it is possible to
use the WWB as a stand-alone 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 offers typical routines for de-noising and compression
of one- and two-dimensional data. The available set of wavelets is
restricted up to four important families: the Haar-wavelet and the
families of the Daubechies-wavelets, Coiflets, and Symmlets.
The 1996 release the IDL WWB has been widely used for different
tasks such as pattern detection, time-series analysis and
de-noising 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 possible differences between the original and transformed
data. A continuous wavelet transform using the Gauss, Sombrero, and
Morlet wavelets has been added also.
- The capabilities for time-series analysis has been greatly enhanced
by adding wavelets and routines which improve period detection.
For example, a routine has been added for detecting periods
in unevenly-sampled time-series, 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.
- For a better understanding of the wavelet transform, a
GUI for manipulating specific wavelet coefficients has been included.
This greatly improves the learning and analyzing process.
There are some future plans for integrating capabilities to analyze
multidimensional 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.
© Copyright 1998 Astronomical Society of the Pacific, 390 Ashton Avenue, San Francisco, California 94112, USA
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