opseestools: A Python library to streamline OpenSeesPy workflows

Autor: Orlando Arroyo, Dirsa Feliciano, Daniela Novoa, Jairo Valcárcel
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: SoftwareX, Vol 27, Iss , Pp 101832- (2024)
Druh dokumentu: article
ISSN: 2352-7110
DOI: 10.1016/j.softx.2024.101832
Popis: OpenSees, a framework for nonlinear structural analysis, has significantly advanced seismic research and practice. The introduction of OpenSeesPy in 2018, which integrated Python as an interpreter alongside TCL, greatly enhanced the framework's utility by leveraging Python's robust data management tools. However, the legacy practice of exporting results for external processing, inherited from the TCL era, remains a challenge. This paper addresses this need with opseestools, a Python library designed to streamline OpenSeesPy workflows. It provides tools for model setup, analysis, and results post-processing, seamlessly integrating with popular scientific libraries like Pandas, Numpy, and joblib. As an open-source project on PyPI, opseestools is backed by a GitHub repository featuring practical examples, making it easily accessible for the community.
Databáze: Directory of Open Access Journals