Python and XML for Agile Scientific Computing

• Published in: Computing in science & engineering Vol. 10; no. 1; pp. 80 - 87
• Main Authors: Vallisneri, Michele, Babak, Stanislav
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2008; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: agile computing; Algorithm design and analysis; Algorithms; Antennas; Blinds; Computation; Computational modeling; Computer simulation; Data analysis; Extensible Markup Language; Instruments; Interferometers; Laser noise; NASA; Observatories; Python; Scientific computing; scientific programming; Space environment; Testing; XML
• ISSN: 1521-9615; 1558-366X
• DOI: 10.1109/MCSE.2008.20

To set up a mock data challenge, the authors needed to string together a lot of existing and new code. In the mock LISA data challenges (MLDCs; http:// astrogravs.nasa.gov/docs/mldc), the challengers distribute several simulated LISA data sets to challenge participants; the data include GW signals from sources of undisclosed parameters as well as realistic instrument noise. Participants must analyze the data and report their estimates for the GW source parameters. These challenges are meant to be blind tests but not really contests: their greatest benefit comes from the quantitative comparison of results, analysis methods, and algorithm implementations. The laser interferometer space antenna (LISA), a space-borne GW observatory planned jointly by NASA and the European Space Agency, will assuredly detect a wide variety of GW sources throughout the universe, thanks mostly to the quietness of the space environment and to LISA's very long 5-million-km baseline.