TurbuStat: Turbulence Statistics in Python
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.
Cite
@article{arxiv.1904.10484,
title = {TurbuStat: Turbulence Statistics in Python},
author = {Eric W. Koch and Erik W. Rosolowsky and Ryan D. Boyden and Blakesley Burkhart and Adam Ginsburg and Jason L. Loeppky and Stella S. R. Offner},
journal= {arXiv preprint arXiv:1904.10484},
year = {2019}
}
Comments
Accepted in AJ. 21 pages, 8 figures