GMV has developed maori-tools, a new Python framework built on top of the maori Flight Dynamics and Geodesy library. The objective of maori-tools is to streamline the development of Flight Dynamics (FD) applications by providing a unified, Python-only environment for tool creation, configuration, and execution. While maori delivers state-of-the-art accuracy and performance in the flight dynamics algorithmic layer through its modern C++ core and pymaori bindings, maori-tools focuses on simplifying how FD engineers design and deploy applications around it.
The framework is based entirely on Python dependencies, primarily pymaori for the algorithmic layer, and click and typer as external open-source dependencies. Typer provides a structured, type-safe command-line interface layer that automatically generates well-documented and consistent command-line tools from Python functions, enabling both command-line and scripting integration. Combined with click, it allows developers to define complex workflows with minimal boilerplate while maintaining flexibility for user interaction, configuration management (via YAML, in the maori style), and logging. This approach promotes modularity, consistency, and rapid prototyping of new FD tools, all within a reproducible Python ecosystem.
A key example developed within this framework is the Monte Carlo Orbit Determination (MCOD) tool, which shall serve as an example use case of the capabilities of maori-tools. MCOD performs large-scale sensitivity analyses by simulating noisy observations, perturbing initial conditions, and running multiple orbit determinations in parallel to evaluate estimation robustness. It fully exploits the maori-tools parallelization, configuration, and plotting capabilities.
This contribution will describe the design principles and architecture of maori-tools, its seamless integration with maori, and its role in enabling efficient FD software development. Results obtained with the MCOD tool will be presented to demonstrate the framework’s functionality in an operational-like scenario.

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