Storm intensity forecasting using machine learning and RAMP distributed high computing environments
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README.md

Ramp kit storm forecast

Build Status

Authors: Sophie Giffard-Roisin, Alexandre Boucaud, Mo Yang, Balazs Kegl, Claire Monteleoni (AppStat-CDS)

The goal is to predict the hurricane evolution (24h forecast) using collected data from all past hurricanes (since 1979). New version.

Set up

  1. clone this repository
git clone https://github.com/ramp-kits/storm_forecast.git
cd storm_forecast
  1. install the dependancies
conda install -y -c conda conda-env     # First install conda-env
conda env create                        # Use environment.yml to create the 'storm_forecast' env
source activate storm_forecast       # Activates the virtual env
  • without conda (best to use a virtual environment)
python -m pip install -r requirements.txt
  1. download the data
python download_data.py        # quick-test data for testing ~200Mb
  1. get started with the storm_forecast_starting_kit.ipynb

New submissions

  1. create a new submission <new_sub> by building on the existing ones
cp -r submissions/starting_kit submissions/<new_sub>
  1. modify the *.py files in submissions/<new_sub> with your favorite editor

  2. test the submission with

ramp_test_submission --quick-test --submission <new_sub>
  1. if the job complete, you can submit the code in the sandbox of ramp.studio

License

BSD license : see LICENSE file

Credits

This package was created with Cookiecutter and the ramp-kits/cookiecutter-ramp-kit project template issued by the Paris-Saclay Center for Data Science.