{"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/paicos-a-python-package-for-analysis-of","title":"Paicos: A Python package for analysis of (cosmological) simulations performed with Arepo","arxiv_id":"2404.14484","date":"2024-04-22","proceeding":null,"authors":["Thomas Berlok","Léna Jlassi","Ewald Puchwein","Troels Haugbølle"],"abstract":"We present Paicos, a new object-oriented Python package for analyzing simulations performed with Arepo. Paicos strives to reduce the learning curve for students and researchers getting started with Arepo simulations. As such, Paicos includes many examples in the form of Python scripts and Jupyter notebooks as well as an online documentation describing the installation procedure and recommended first steps. Paicos' main features are automatic handling of cosmological and physical units, computation of derived variables, 2D visualization (slices and projections), 1D and 2D histograms, and easy saving and loading of derived data including units and all the relevant metadata.","url_abs":"https://arxiv.org/abs/2404.14484v1","url_pdf":"https://arxiv.org/pdf/2404.14484v1.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":"paicos-a-python-package-for-analysis-of","repo_url":"https://github.com/tberlok/paicos","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}