/thermospheric-density-reduced-order-modeling

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Thermospheric Density Reduced Order Modeling

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Python package for Thermospheric Density Reduced Order Modeling.

Credits 🚀 Your next Python package needs a bleeding-edge project structure.

This code has been developed by Vahid Nateghi and Matteo Manzi. It is associated with the paper submitted to "Advances in Space Research", whose preprint is available here.

The SET HASDM density data are provided for scientific use courtesy of Space Environment Technologies.

This project was generated with python-package-template

Description

In the context of the commercial activities performed in Low-Earth Orbit, a region of space of a few hundred kilometers of altitude, because the space traffic is increasing, it is important for us to obtain a model of the density field of the atmosphere, as the motion of the satellites in this orbital regime is strongly influenced by atmospheric drag, which is a function of the atmospheric density.

While such models, based on first principles, already exist, they are complex and require a lot of computations; at the same time, more empirical models are less accurate.

The trade-off proposed in this work, called reduced-order modeling, enables us to obtain a compressed representation of the density field, which can be used to construct predictive models, to perform uncertainty quantification and estimate the position of spacecraft in the future taking into account our knowledge of the environment and our availability of observation data.

We here focus on non-linear methods, to perform the compression, using Machine Learning Methods. In particular, the use of Neural Networks is compared with the use of Support Vector Machine Methods. Interestingly, for the datasets investigated, the latter technique is not only much more efficient, but also more accurate.

Clone repository with Large Files (LFS)

Please install git-lfs and use this command to clone our repository:

git lfs clone https://github.com/crunchdao/thermospheric-density-reduced-order-modeling.git

Installation

Makefile contains functions for faster development.

1. Install all dependencies and pre-commit hooks

Install requirements:

make install

Pre-commit hooks could be installed after git init via

make pre-commit-install

2. Codestyle

Automatic formatting uses pyupgrade, isort and black.

make codestyle

# or use synonym
make formatting

Codestyle checks only, without rewriting files:

make check-codestyle

Note: check-codestyle uses isort, black and darglint library

Update all dev libraries to the latest version using one comand

make update-dev-deps
3. Cleanup

Delete pycache files 

make pycache-remove

Remove package build

make build-remove

Delete .DS_STORE files

make dsstore-remove

Remove .mypycache

make mypycache-remove

Or to remove all above run:

make cleanup

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