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Final Published Version
Author
Koch, Eric W.Rosolowsky, Erik W.
Boyden, Ryan D.
Burkhart, Blakesley
Ginsburg, Adam
Loeppky, Jason L.
Offner, Stella S. R.
Affiliation
Univ Arizona, Dept AstronUniv Arizona, Steward Observ
Issue Date
2019-07
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IOP PUBLISHING LTDCitation
Koch, E. W., Rosolowsky, E. W., Boyden, R. D., Burkhart, B., Ginsburg, A., Loeppky, J. L., & Offner, S. S. (2019). TurbuStat: Turbulence Statistics in Python. The Astronomical Journal, 158(1), 1.Journal
ASTRONOMICAL JOURNALRights
© 2019. The American Astronomical Society. All rights reserved.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
We present TURBUSTAT (v1.0): a PYTHON package for computing turbulence statistics in spectral-line data cubes. TURBUSTAT includes implementations of 14 methods for recovering turbulent properties from observational data. Additional features of the software include: distance metrics for comparing two data sets; a segmented linear model for fitting lines with a break point; a two-dimensional elliptical power-law model; multicore fast-Fourier-transform support; a suite for producing simulated observations of fractional Brownian Motion fields, including two-dimensional images and optically thin H I data cubes; and functions for creating realistic world coordinate system information for synthetic observations. This paper summarizes the TURBUSTAT package and provides representative examples using several different methods. TURBUSTAT is an open-source package and we welcome community feedback and contributions.ISSN
0004-6256EISSN
1538-3881Version
Final published versionSponsors
Natural Sciences and Engineering Research Council of Canada (NSERC); NSERC [RGPIN-2012-355247, RGPIN-2017-03987]; WestGrid; Compute Canada; CANFARae974a485f413a2113503eed53cd6c53
10.3847/1538-3881/ab1cc0