FastAPI framework, high performance, easy to learn, fast to code, ready for production
Documentation: https://duncaneddy.github.io/rastro
Rust Library Reference: https://docs.rs/crate/rastro/latest
Source Code: https://github.com/duncaneddy/rastro
RAstro is a modern satellite dynamics library for research and engineering applications. It is designed to be high performance, easy to learn, and fast to code, and ready for flight.
The key features are:
- Intuitive: API designed to be easily composable, making it easy to solve complex problems correctly by building on core functionality.
- Easy to Learn: Designed to be easy to use and learn. Less time reading papers, more time building.
- Fast to code: Rastro provides a Python 3.6+ wrapper that is auto-generated from the core rust libraries. Making it easy to use without compromising performance.
- Fast to run: Very high performance, on par with C++ libraries, thanks to core library being written in Rust.
RAstro gets its name from the combination of Rust and astrodynamics (Rust + astrodynamics = RAstro). The library specifically focuses on satellite astrodynamics and space mission analysis. While the underlying concepts have been studied and known since Kepler wrote down his three laws, there are few modern software libraries that make these concepts easily accessible. While extremely well tested, other astrodynamics and mission analysis software can have an extremely steep learning curve, making it difficult to quickly run simple analysis that is known to be correct.
Because of this, students, researchers, and engineers frequently end up reimplementing common astrodynamics and mission analysis tools with unfortunately frequent regularity. While reimplementation of common code can be a good learning mechanisms, in most cases it is both error-prone and costs time better spent on other endeavours. This project seeks to providing an easy-to-use, well-tested library, to enable everyone to more easily, and quickly perform astrodynamics and space mission analysis without sacrificing performance or correctness. The software built in Rust for performance with bindings to Python for ease of use.
The implementation approach is opinionated, RAstro is not intended to implement every astrodynamics model and function that exists, but instead provide accurate, contemporary and commonly used functions that will address most use-cases. One example of this is that the built-in Earth reference frame transformation utilize the IAU 2006/2000A precession nutation model. However, if a desired model isn't implemented because RAstro is open source users are free to extend the software to address and functionality or modeling gaps that exist to address their specific application.
This project builds on experience from past projects in building space dynamics software:
- brahe A pure-python astrodynamics library
- SatelliteDynamics.jl A Julia astrodynamics library
You can find the package documentation here. This documentation is meant to provide a human-friendly walk through of the software and package. RAstro is currently in the early stages of development so the documentation will likely not be complete. Sections marked [WIP] will have some software functionality implemented but not be considered documented.
The most complete API reference guide will always be the Rust crate API reference, found on crates.io. This is always up-to-date with the latest release since it is autogenerated at build time during the release process.
The RAstro package is licensed and distributed under an MIT License to encourage adoption and to make it easy to integrate with other tools.
The only thing asked is that if you do use the package in your work, or appreciate the project, either send a message or star the project. Knowing that the project is being actively used is a large motivator for continued development.
RAstro is currently being developed primarily for my own enjoyment and because I find having these tools helpful in professional and hobby work. I plan to continue developing it for the time being regardless of greater adoption as time permitting.
That being said, it's incredibly encouraging and useful to know if the software is being adopted or found useful in wider practice. If you're using RAstro for school, research, or a commercial endeavour, I'd love to know about it! Tweet me @duncaneddy or email me at duncan.eddy (at) gmail.com.
I'd love to hear your feedback or thoughts!