T-FORS

Traveling Ionospheric Disturbances Forecasting System (funded by the European Community, Horizon Europe)

What is that?

We aim at the development of a machine-learning-based algorithm to forecast Large Scale Traveling Ionospheric Disturbances. The work is carried out within the "T-FORS - Traveling Ionospheric Disturbances Forecasting System" project.

How can I run it?

• First, you need to clone the repo and install dependencies via poetry with poetry install

• To launch a web server and execute jupyter notebooks, (on Windows) you can run the scripts/run-jupyter.ps1 script; otherwise, you can activate the virtual environment manually (via poetry shell) and then execute the poetry run jupyter notebook command

• To start an MLflow tracking server, (on Windows) you can run the scripts/run-mlflow-ui.ps1 script; the tracking UI can be accessed locally by navigating to http://localhost:5000/

• Launch the web app via streamlit run ./app/0_🏠_Home.py

How can I help?

Contributions are what make the open source community an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature_amazing_feature)
3. Commit your Changes (git commit -m 'Add some amazing stuff')
4. Push to the Branch (git push origin feature_amazing_feature)
5. Open a Pull Request

An (hopefully) up-to-date list of things to do can be found here.