/EMS2019_python_workshop

Python: Machine Learning applications for weather-related problems

Primary LanguageJupyter NotebookMIT LicenseMIT

https://www.ems2019.eu/plenaries_and_events/python_workshop.html

Python: Machine Learning applications for weather-related problems

Training Workshop, 8 September 2019, Copenhagen

  • Welcome to the workshop repository. We recommend you clone and follow the instructions in this README file before the workshop day.

  • It is also recommended that you configure a virtual environment for this project. The most efficient way to install is to use to conda tool:

    • Install anaconda Python 3.7 version for your system (if you don't have it already): https://www.anaconda.com/distribution/#download-section

    • create a new environment:

      • conda create -n ml_workshop python=3.7 anaconda

    • activate the env you just created:

      • conda activate ml_workshop

    • install the necessary packages for this course using the prepare.sh script:

      • chmod +x prepare.sh; ./prepare.sh

    • clone this repository to your local machine: git clone git@github.com:igorol/EMS2019_python_workshop.git

    • After installing all libraries, make an account on Climate Data Store. You will also need to install the CDS API key (instructions are here)[optional]

  • The content of this repo is organized in the following directories:

  Data\
     Weather\ - weather data (see link below)
     Energy\ - csv used in renewable energy exercises
     Tourism\ - csv used for the tourism exercises

  CDSAPI_examples.ipynb
  Manipulating meteorological data.ipynb   
  ML – Part 1.ipynb
  ML – Part 2.ipynb

  README
  LICENSE
  requirements.txt

  • Some of the weather data files that will be used in the exercises are bigger than GitHub's filesize limit, you can download them in a separate link. Please save these files in the data\weather directory:

    https://meteogroup.box.com/v/workshop-input-data

    • era5_EU_monthly.nc : Weather data for the Machine Learning 1st exercise
    • era5_DE_hourly_YYYY.nc : Weather data for the Machine Learning 2nd exercise