radio-astro-tools: linking radio astronomical data to the astronomical Python ecosystem

@misc{koch2026radioastrotools,
  title = {{radio-astro-tools: linking radio astronomical data to the astronomical Python ecosystem}},
  author = {Eric W. Koch and Adam Ginsburg and Thomas P. Robitaille and Erik W. Rosolowsky and Alyssa Bulatek and Savannah Gramze and Jonathan D. Henshaw and Derek Homeier and Preshanth Jagannathan and Amanda A. Kepley and Adam K. Leroy and Sébastien Maret and Stuart Mumford and George Privon and Álvaro Sánchez-Monge and Srikrishna Sekhar and Brigitta Sipőcz and A. J. M. Thomson},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.11429},
  eprint = {2610.11429},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.11429},
  note = {CC BY 4.0},
}

TY  - UNPB
TI  - radio-astro-tools: linking radio astronomical data to the astronomical Python ecosystem
AU  - Eric W. Koch
AU  - Adam Ginsburg
AU  - Thomas P. Robitaille
AU  - Erik W. Rosolowsky
AU  - Alyssa Bulatek
AU  - Savannah Gramze
AU  - Jonathan D. Henshaw
AU  - Derek Homeier
AU  - Preshanth Jagannathan
AU  - Amanda A. Kepley
AU  - Adam K. Leroy
AU  - Sébastien Maret
AU  - Stuart Mumford
AU  - George Privon
AU  - Álvaro Sánchez-Monge
AU  - Srikrishna Sekhar
AU  - Brigitta Sipőcz
AU  - A. J. M. Thomson
PY  - 2026
DA  - 2026/10/08
PB  - arXiv
DO  - 10.48550/arxiv.2610.11429
UR  - https://arxiv.org/abs/2610.11429
AB  - 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.
ER  -