- 01: THEMIS Image ML Data set (2019)
- In this notebook, we demonstrate visualizing a small amount of the THEMIS All-Sky Imager data sets.
The following notebook is currently under development:
- 02: THEMIS Image Classification (2019)
- Unsupervised classification of images
Each notebook is contained within its own folder:
.
└── notebooks
└── ##_<project>_<year> # Each project has its own folder named sequentially, with the project name, and year of the project
├── README.md
├── <project>_colab.ipynb # A Jupyter notebook designed to be executed on Google Colab.
├── <project>.ipynb # The corresponding local development version of the colab notebook.
├── environment.yml # Conda environment file
└── requirements.txt # Requirements file
For local development, the necessary environment can be created as follows (under the assumption that an anaconda installation exists).
cd notebooks/<project>
conda env create -f environment.yml
conda activate <environment>
# start the jupyter notebook app
jupyter notebook
@inproceedings{prediction,
title={Prediction of GNSS phase scintillations: A machine learning approach},
author={Lamb, Kara and Malhotra, Garima and Vlontzos, Athanasios and Wagstaff, Edward and Baydin, At{\i}l{\i}m G{\"u}nes and Bhiwandiwalla, Anahita and Gal, Yarin and Kalaitzis, Alfredo and Reina, Anthony and Bhatt, Asti},
booktitle={33rd Conference on Neural Information Processing Systems},
series = {The Machine Learning and the Physical Sciences 2019 workshop},
url={https://ml4physicalsciences.github.io/2019/files/NeurIPS_ML4PS_2019_136.pdf},
year={2019}
}
@inproceedings{correlation,
title={Correlation of auroral dynamics and GNSS scintillation with an autoencoder},
author={Lamb, Kara and Malhotra, Garima and Vlontzos, Athanasios and Wagstaff, Edward and Baydin, At{\i}l{\i}m G{\"u}nes and Bhiwandiwalla, Anahita and Gal, Yarin and Kalaitzis, Alfredo and Reina, Anthony and Bhatt, Asti},
booktitle={33rd Conference on Neural Information Processing Systems},
series = {The Machine Learning and the Physical Sciences 2019 workshop},
url={https://ml4physicalsciences.github.io/2019/files/NeurIPS_ML4PS_2019_76.pdf},
year={2019}
}