A Bayesian framework to estimate Chorus waves in from low-Earth-orbit, Van Allen Probe and other datasets

This notebook is a proof-of-concept that describes how we can use hierarchical Bayesian framework to estimate event specific Chorus wave maps during quiet and extreme space weather conditions.

Follow through the notebook to generate the plots for data analysis, describes different models and does a small POC. You need to have Matplotlib, Numpy, h5py and Pandas installed. If you are not familiar with the IPython notebook format, start here: http://ipython.org/index.html

Your files and directories should be organized as follow, in your working directory:

• code: contains this IPython notebook

• figures: where all figures will be saved

• data: where the CSV and hdf5 files should be. There are two sets of data:

  1. The data compiled from the literature review on chorus waves: -Statis_wave_crres_chorus_model_intensity_fband_0P1-0P5fce.h5
  2. The data generated by the analysis and Bayesian model.

  You can download these project from GitHub here: http://dx.doi.org/10.6084/m9.figshare.1412626

-Jan, 2021. S. Chakraborty, G. S. Cunninghum

Email to shibaji7@vt.edu if you have any questions or comments!