.. admonition:: Join the Discussion Feel free to share ideas and ask questions over at `MaDDG's discussion page`_. .. _MaDDG's discussion page: https://github.com/mit-ll/MaDDG/discussions ================================= Welcome to MaDDG's documentation! ================================= MaDDG (Maneuver Detection Data Generation) is a library for simulating high-fidelity observations of satellite trajectories with configurable maneuvers and custom sensor networks. MaDDG provides a simple interface for modeling complex observation scenarios. It allows you to create a satellite in any geocentric orbit, propagate its motion with a robust physical model, and track its position through optical sensors with customizable locations, observing limits, and noise parameters. Through its use of `hydra-zen`_ and the `submitit plugin`_, MaDDG` can easily configure an array of simulation scenarios and distribute them in a SLURM cluster, empowering users to create large-scale, realistic datasets for training reliable maneuver detection and characterization models. .. _hydra-zen: https://github.com/mit-ll-responsible-ai/hydra-zen .. _submitit plugin: https://hydra.cc/docs/plugins/submitit_launcher/ Installation ============ MaDDG is available on PyPI: .. code:: console $ pip install MaDDG To install from source, clone the `MaDDG repository`_ and run the following command from its top-level directory: .. _MaDDG repository: https://github.com/mit-ll/MaDDG .. code:: console $ pip install -e . If you want to modify the orbit propagation physics behind MaDDG, you will likely need to edit the `AstroForge`_ library, as well. AstroForge is an open-source astrodynamics library and a key requirement of MaDDG. See the `AstroForge documentation`_ for installation instructions. .. _AstroForge: https://github.com/mit-ll/AstroForge .. _AstroForge documentation: https://astroforge.readthedocs.io/en/latest/ Documentation Contents ====================== .. toctree:: :maxdepth: 2 How-To Guides Reference