AsaPy: A Python Library for Aerospace Simulation Analysis

TL;DR

AsaPy is a Python library that streamlines aerospace simulation data analysis by integrating established techniques into a flexible, high-performance platform suitable for engineers and researchers.

Contribution

It introduces a unified, customizable platform for aerospace simulation analysis that combines existing methods with high scalability and ease of use.

Findings

Effective in military simulation case studies

Outperforms traditional tools in data handling and analysis

Demonstrates high performance and scalability

Abstract

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.