AsaPy: A Python Library for Aerospace Simulation Analysis
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929330897027072 |
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| author | Dantas, Joao P. A. Silva, Samara R. Gomes, Vitor C. F. Costa, Andre N. Samersla, Adrisson R. Geraldo, Diego Maximo, Marcos R. O. A. Yoneyama, Takashi |
| author_facet | Dantas, Joao P. A. Silva, Samara R. Gomes, Vitor C. F. Costa, Andre N. Samersla, Adrisson R. Geraldo, Diego Maximo, Marcos R. O. A. Yoneyama, Takashi |
| contents | AsaPy is a custom-made Python library designed to simplify and optimize the analysis of aerospace simulation data. Instead of introducing new methodologies, it excels in combining various established techniques, creating a unified, specialized platform. It offers a range of features, including the design of experiment methods, statistical analysis techniques, machine learning algorithms, and data visualization tools. AsaPy's flexibility and customizability make it a viable solution for engineers and researchers who need to quickly gain insights into aerospace simulations. AsaPy is built on top of popular scientific computing libraries, ensuring high performance and scalability. In this work, we provide an overview of the key features and capabilities of AsaPy, followed by an exposition of its architecture and demonstrations of its effectiveness through some use cases applied in military operational simulations. We also evaluate how other simulation tools deal with data science, highlighting AsaPy's strengths and advantages. Finally, we discuss potential use cases and applications of AsaPy and outline future directions for the development and improvement of the library. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_00001 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | AsaPy: A Python Library for Aerospace Simulation Analysis Dantas, Joao P. A. Silva, Samara R. Gomes, Vitor C. F. Costa, Andre N. Samersla, Adrisson R. Geraldo, Diego Maximo, Marcos R. O. A. Yoneyama, Takashi Mathematical Software AsaPy is a custom-made Python library designed to simplify and optimize the analysis of aerospace simulation data. Instead of introducing new methodologies, it excels in combining various established techniques, creating a unified, specialized platform. It offers a range of features, including the design of experiment methods, statistical analysis techniques, machine learning algorithms, and data visualization tools. AsaPy's flexibility and customizability make it a viable solution for engineers and researchers who need to quickly gain insights into aerospace simulations. AsaPy is built on top of popular scientific computing libraries, ensuring high performance and scalability. In this work, we provide an overview of the key features and capabilities of AsaPy, followed by an exposition of its architecture and demonstrations of its effectiveness through some use cases applied in military operational simulations. We also evaluate how other simulation tools deal with data science, highlighting AsaPy's strengths and advantages. Finally, we discuss potential use cases and applications of AsaPy and outline future directions for the development and improvement of the library. |
| title | AsaPy: A Python Library for Aerospace Simulation Analysis |
| topic | Mathematical Software |
| url | https://arxiv.org/abs/2310.00001 |