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arXiv · 1904.10484

TurbuStat: Turbulence Statistics in Python

Eric W. Koch·Erik W. Rosolowsky·Ryan D. Boyden·Blakesley Burkhart·Adam Ginsburg·Jason L. Loeppky·Stella S. R. Offner
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Abstract

We present TurbuStat (v1.0): a Python package for computing turbulence statistics in spectral-line data cubes. TurbuStat includes implementations of fourteen 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; multi-core fast-fourier-transform support; a suite for producing simulated observations of fractional Brownian Motion fields, including two-dimensional images and optically-thin HI 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.

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Eric W. Koch, Erik W. Rosolowsky, Ryan D. Boyden, Blakesley Burkhart, Adam Ginsburg, Jason L. Loeppky, Stella S. R. Offner. 2019-04-23. TurbuStat: Turbulence Statistics in Python. https://doi.org/10.3847/1538-3881%2Fab1cc0

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