AuthorKoch, Eric W.
Rosolowsky, Erik W.
Boyden, Ryan D.
Loeppky, Jason L.
Offner, Stella S. R.
AffiliationUniv Arizona, Dept Astron
Univ Arizona, Steward Observ
MetadataShow full item record
PublisherIOP PUBLISHING LTD
CitationKoch, 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.
Rights© 2019. The American Astronomical Society. All rights reserved.
Collection InformationThis 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 firstname.lastname@example.org.
AbstractWe 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.
VersionFinal published version
SponsorsNatural Sciences and Engineering Research Council of Canada (NSERC); NSERC [RGPIN-2012-355247, RGPIN-2017-03987]; WestGrid; Compute Canada; CANFAR