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    TurbuStat: Turbulence Statistics in Python

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    Name:
    Koch_2019_AJ_158_1.pdf
    Size:
    1.085Mb
    Format:
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    Description:
    Final Published Version
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    Author
    Koch, Eric W.
    Rosolowsky, Erik W.
    Boyden, Ryan D.
    Burkhart, Blakesley
    Ginsburg, Adam cc
    Loeppky, Jason L.
    Offner, Stella S. R.
    Affiliation
    Univ Arizona, Dept Astron
    Univ Arizona, Steward Observ
    Issue Date
    2019-07
    Keywords
    methods: data analysis
    methods: statistical
    turbulence
    
    Metadata
    Show full item record
    Publisher
    IOP PUBLISHING LTD
    Citation
    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 JOURNAL
    Rights
    © 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
    0004-6256
    EISSN
    1538-3881
    DOI
    10.3847/1538-3881/ab1cc0
    Version
    Final published version
    Sponsors
    Natural Sciences and Engineering Research Council of Canada (NSERC); NSERC [RGPIN-2012-355247, RGPIN-2017-03987]; WestGrid; Compute Canada; CANFAR
    ae974a485f413a2113503eed53cd6c53
    10.3847/1538-3881/ab1cc0
    Scopus Count
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    UA Faculty Publications

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