Citing: If you use this framework please cite as follows:
@inproceedings{2017AGUFMSM23A2591C,
author = {{Cheung}, C.~M.~M. and {Handmer}, C. and {Kosar}, B. and {Gerules}, G. and
{Poduval}, B. and {Mackintosh}, G. and {Munoz-Jaramillo}, A. and
{Bobra}, M. and {Hernandez}, T. and {McGranaghan}, R.~M.},
booktitle = {AGU Fall Meeting},
title = {Modeling Geomagnetic Variations using a Machine Learning Framework},
address = {Long Beach},
year = {2017}
}
This was tested on P100 nVidia GPU and nVidia GTX 1060 GPU. Should work if enough RAM on computer and fast enough GPU.
python, anaconda, keras-gp, scikit-learn, pandas
For some of the csv files c++ programs were used for conversion. They can be found in cpp_files subdirectory. These are placeholder programs and probably should be converted to python for c++ phobic people. Check the README in that subdirectory for further details.
For either Project 1 or Project 2 edit the config.cfg file to point to the appropriate directory and import data.
Project 1: geomag with omni solar wind data Two files to run to explore LSTM with geo magnetic and solar wind data. Each one is a different take on LSTMs.
python LSTMarrayprediction.py
or
python lstm_multi_channel.py
Project 2: geomag, omni solar wind data and kp index
python kp_regress.py
Contact Information:
Mark Cheung [email protected]
George Gerules [email protected] or [email protected]