/Regridding_scripts

A series of Jupyter Notebooks that can be used to regrid CESM data between different grids

Primary LanguageJupyter Notebook

Regridding Scripts

This repository contains Jupyter Notebooks that are used to regrid CESM data between different grids.

Instructions

TBD

Python environment

To be able to run the different Python scripts in this repository, you'll need to install a few packages. I recommend creating a new, clean environment to avoid any dependency issues.

To do so using Conda:

  • Create a new envrionment (in this case named regrid_env) 
    conda create -n regrid_env python=3.7
  • Activate your new environment 
    conda activate regrid_env
  • Install the xESMF package 
    conda install -c conda-forge xesmf
  • Install dask and netCDF4 to support all features in xESMF 
    conda install -c conda-forge dask netCDF4
  • Install plotting and notebook dependencies (optional) 
    conda install -c conda-forge matplotlib cartopy jupyterlab

For more information, check out the xESMF installation webpage: https://xesmf.readthedocs.io/en/latest/installation.html

Alternatively, you can download the environment file regrid_env.yml from this repository and create a new environment from that file directly.

conda env create --name regrid_env --file=regrid_env.yml

Installing Python on your local machine

Python is usually already installed on your machine. To check, type python --version in a terminal.

Here are 3 different methods to install Python:

  1. Instally Python manually (https://www.python.org/downloads/):

    • This will get you the latest version of Python3
    • You will now need to use the command python3 instead of python (unless you create an alias by adding the line alias python=python3 in your .bash_profile)

  2. Installing Python using Anaconda (https://www.anaconda.com/products/individual):

    • Anaconda is a data science platform by Continuum Analytics that comes with a lot of useful features right out of the box
    • Many people find that installing Python through Anaconda is much easier than doing so manually (see method #1 above)
    • Anaconda comes with Conda, which is Continuum's package, dependency and environment manager (analogous to pip for Python)
    • The libraries and packages included in Anaconda are usually related to data science (numpy, scipy, jupyter notebooks, etc.)
    • Benefits:
      • Simplify common problems
      • Great for use in the classroom as it provides each student with the same setup

  3. Installing Python using Miniconda (https://docs.conda.io/en/latest/miniconda.html):

    • Miniconda is a free minimal installer for conda
    • Good if hard drive space is an issue for you
    • It is a small bootstrapped version of Anaconda that comes with the Python distribution, essential packages, and conda.