{"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/ggchempy-a-pure-python-based-gas-grain","title":"GGCHEMPY: A pure Python-based gas-grain chemical code for efficient simulation of interstellar chemistry","arxiv_id":"2110.11117","date":"2021-10-21","proceeding":null,"authors":["Jixing Ge"],"abstract":"In this paper, we present a new gas-grain chemical code for interstellar clouds written in pure Python (GGCHEMPY). By combining with the high-performance Python compiler Numba, GGCHEMPY is as efficient as the Fortran-based version. With the Python features, flexible computational workflows and extensions become possible. As a showcase, GGCHEMPY is applied to study the general effects of three-dimensional projection on molecular distributions using a two-core system which can be easily extended for more complex cases. By comparing the molecular distribution differences between two overlapping cores and two merging cores, we summarized the typical chemical differences such as, N2H+, HC3N, C2S, H2CO, HCN and C2H, which can be used to interpret 3-D structures in molecular clouds.","url_abs":"https://arxiv.org/abs/2110.11117v1","url_pdf":"https://arxiv.org/pdf/2110.11117v1.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":"ggchempy-a-pure-python-based-gas-grain","repo_url":"https://github.com/JixingGE/GGCHEMPY","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"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}