| Autor: |
Dimitrios Tsapetis, Michael D. Shields, Dimitris G. Giovanis, Audrey Olivier, Lukas Novak, Promit Chakroborty, Himanshu Sharma, Mohit Chauhan, Katiana Kontolati, Lohit Vandanapu, Dimitrios Loukrezis, Michael Gardner |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
SoftwareX, Vol 24, Iss , Pp 101561- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2352-7110 |
| DOI: |
10.1016/j.softx.2023.101561 |
| Popis: |
This paper presents the latest improvements introduced in Version 4 of the UQpy, Uncertainty Quantification with Python, library. In the latest version, the code was restructured to conform with the latest Python coding conventions, refactored to simplify previous tightly coupled features, and improve its extensibility and modularity. To improve the robustness of UQpy, software engineering best practices were adopted. A new software development workflow significantly improved collaboration between team members, and continuous integration and automated testing ensured the robustness and reliability of software performance. Continuous deployment of UQpy allowed its automated packaging and distribution in system agnostic format via multiple channels, while a Docker image enables the use of the toolbox regardless of operating system limitations. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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