发表机构
Dominion Radio Astrophysical Observatory, Herzberg Astronomy & Astrophysics, National Research Council Canada; Department of Astronomy, University of Florida; Aperio Software; Dept. of Physics, University of Alberta; Department of Physics and Astronomy, Haverford College; Max-Planck-Institut für Astronomie; National Radio Astronomy Observatory; Department of Astronomy, The Ohio State University; Center for Cosmology and Astroparticle Physics (CCAPP); Univ. Grenoble Alpes, CNRS, IPAG（加拿大国家研究委员会赫茨伯格天文学与天体物理学多明尼奥射电天文台; 佛罗里达大学天文学系; 阿佩里奥软件公司; 阿尔伯塔大学物理系; 哈弗福德学院物理与天文学系; 马克斯·普朗克天文学研究所; 美国国家射电天文台; 俄亥俄州立大学天文学系; 宇宙学与基本粒子物理中心; 格勒诺布尔阿尔卑斯大学，法国国家科学研究中心，IPAG）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
radio-astro-tools代码套件依托Astropy生态系统，含多个Python包，可分析射电等波段数据，实现并行化、数据组合等功能，还配套交互式教程，提升射电谱线数据分析的便捷性。
AI 中文摘要
我们推出了radio-astro-tools代码套件，它由多个Python包组成，可在Astropy软件生态系统的框架下对射电数据，尤其是干涉光谱立方体进行分析。这些工具虽针对射电数据设计，但具备通用性，也可应用于其他波段的数据集。核心包spectral-cube负责立方体数据的读写与分析，支持通过dask和joblib后端轻松实现并行化；配套包包括casa-formats-io（处理CASA表与图像的读取）和radio-beam（处理点扩散函数的读取与操作）；pvextractor包用于创建位置-速度图；uvcombine包实现了用于组合单天线与干涉数据的“feather”算法。radio-astro-tools的开发还包含对其他代码库的多项贡献，涉及matplotlib、astropy和regions，以支持CASA与天文软件生态系统其他部分的交互。我们同时推出了一套基于Jupyter笔记本的详细教程，可在浏览器中交互式运行，为射电谱线数据立方体的数据分析提供更便捷的途径。
英文摘要
We present the radio-astro-tools code suite, which consists of several Python packages that enable analysis of radio data, especially interferometric spectral cubes, in the context of the Astropy software ecosystem. While these tools were designed with radio data in mind, they are built to be general and have applications on data sets at other wavelengths. The core package, spectral-cube, handles reading, writing and analysis of cube data, and it enables straightforward parallelization via dask and joblib backends. Support packages include casa-formats-io and radio-beam, which handle reading of CASA tables & images and reading and manipulation of point spread functions, respectively. The pvextractor package facilitates creation of position-velocity diagrams. The uvcombine package implements the "feather" algorithm for combining single-dish and interferometric data. The development of radio-astro-tools included several contributions to other repositories, including matplotlib, astropy, and regions to support interaction between CASA and other parts of the astronomy software ecosystem. We also present a detailed set of tutorials written in Jupyter notebooks that can be run interactively in a browser, providing more accessible access to data analysis for radio spectral-line data cubes.
CommentsTo be submitted to Open Journal of Astrophysics. For more information, visit https://radio-astro-tools.github.io/
