{"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/pytransit-fast-and-easy-exoplanet-transit","title":"PyTransit: Fast and Easy Exoplanet Transit Modelling in Python","arxiv_id":"1504.07433","date":"2015-04-28","proceeding":null,"authors":["Hannu Parviainen"],"abstract":"We present a fast and user friendly exoplanet transit light curve modelling package PyTransit, implementing optimised versions of the Gimen\\'ez and the Mandel & Agol transit models. The package offers an object-oriented Python interface to access the two models implemented natively in Fortran with OpenMP parallelisation. A partial OpenCL version of the quadratic Mandel-Agol model is also included for GPU-accelerated computations. The aim of PyTransit is to facilitate the analysis of photometric time series of exoplanet transits consisting of hundreds of thousands of datapoints, and of multi-passband transit light curves from spectrophotometric observations, as a part of a researcher's programming toolkit for building complex, problem-specific, analyses.","url_abs":"http://arxiv.org/abs/1504.07433v1","url_pdf":"http://arxiv.org/pdf/1504.07433v1.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":"pytransit-fast-and-easy-exoplanet-transit","repo_url":"https://github.com/hpparvi/PyTransit","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}