{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/kuibit-analyzing-einstein-toolkit-simulations","title":"kuibit: Analyzing Einstein Toolkit simulations with Python","arxiv_id":"2104.06376","date":"2021-04-13","proceeding":null,"authors":["Gabriele Bozzola"],"abstract":"In the era of gravitational-wave astronomy, general-relativistic simulations of compact objects play a role of paramount importance. These calculations can be performed with the Einstein Toolkit, an open-source and community-supported software for numerical-relativity and relativistic astrophysics. The code comes with multiple solvers for Einstein's equations and for the equations of general-relativistic magneto-hydrodynamics, along with a series of useful diagnostics. However, analyzing the output of the Einstein Toolkit can be a challenging task. Usually, the process involves a series of technical obstacles, like combining data from different restarts or working with HDF5 files. Here, we present kuibit, a Python library that takes care of all these low-level details (and many other more) and that provides high-level, intuitive, representations of the data. Kuibit ships with a wide range of features that include full support for 1-3D ASCII and HDF5 grid data, time and frequency series, gravitational waves, and apparent horizons. With kuibit, users can inspect most of the content of a simulation with just a few lines of code. Importantly, kuibit is designed to be a code for the community: it is user-friendly and does not require any proprietary software to run, it has documentation and examples, and it is openly developed with emphasis on extensibility and maintainability.","url_abs":"https://arxiv.org/abs/2104.06376v1","url_pdf":"https://arxiv.org/pdf/2104.06376v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"kuibit-analyzing-einstein-toolkit-simulations","repo_url":"https://github.com/Sbozzolo/kuibit","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}