{"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/partycls-a-python-package-for-structural","title":"partycls: A Python package for structural clustering","arxiv_id":"2111.10133","date":"2021-11-19","proceeding":null,"authors":["Joris Paret","Daniele Coslovich"],"abstract":"partycls is a Python framework for cluster analysis of systems of interacting particles. By grouping particles that share similar structural or dynamical properties, partycls enables rapid and unsupervised exploration of the system's relevant features. It provides descriptors suitable for applications in condensed matter physics and integrates the necessary tools of unsupervised learning, such as dimensionality reduction, into a streamlined workflow. Through a simple and expressive interface, partycls allows one to open a trajectory file, perform a clustering based on the selected structural descriptor, and analyze and save the results with only a few lines of code.","url_abs":"https://arxiv.org/abs/2111.10133v1","url_pdf":"https://arxiv.org/pdf/2111.10133v1.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":"partycls-a-python-package-for-structural","repo_url":"https://github.com/jorisparet/partycls","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}