PyEarthTools: Machine learning for Earth system science

PyEarthTools: Machine learning for Earth system science#

DOI

  • An approachable way for researchers to get started with ML research for Earth system science

  • Provides a software framework for research and experimentation

  • Also suitable for students and newcomers

  • Still under early-stage development - things are likely to change a lot. If you notice an issue, please feel free to raise it on GitHub

A prediction of the weather
A weather prediction from a model trained with PyEarthTools.
A data processing pipeline
A data processing flow composed for working with climate data.

Source Code: github.com/ACCESS-Community-Hub/PyEarthTools
Documentation: pyearthtools.readthedocs.io
Tutorial Gallery: available here

Installation#

Here is the quickest way to install the complete framework and get started:

We strongly recommend using either a Conda or Python virtual environment.

Run the following commands to install PyEarthTools in a Conda environment:

git clone git@github.com:ACCESS-Community-Hub/PyEarthTools.git
conda create -y -p ./venv python graphviz
conda activate ./venv
pip install -r requirements.txt
cd notebooks
jupyter lab

Run the following commands to install PyEarthTools in a Python virtual environment:

git clone git@github.com:ACCESS-Community-Hub/PyEarthTools.git
python3 -m venv ./venv
source venv/bin/activate
pip install -r requirements.txt
cd notebooks
jupyter lab

Optional dependencies

Install Graphviz (not installable via pip) to display pipelines.

PyEarthTools comprises multiple sub-packages which may be installed and used separately. See the installation guide for more details.

Overview of PyEarthTools#

PyEarthTools is a Python framework containing modules for:

  • loading and fetching data;

  • pre-processing, normalising and standardising data into a normal form suitable for machine learning;

  • defining machine learning (ML) models;

  • training ML models and managing experiments;

  • performing inference with ML models;

  • and evaluating ML models.

Overview of the Packages within PyEarthTools#

PyEarthTools comprises multiple sub-packages which can be used individually or together.

Sub-Package

Purpose

Data

Loading and indexing Earth system data into xarray

Utils

Code for common functionality across the sub-packages

Pipeline

Process and normalise Earth system data ready for machine learning

Training

Training processes for machine learning modelsl

Tutorial

Contains helper code for data sets used in tutorials

Bundled Models

Maintained versions of specific, bundled models which can be easily trained and run

Zoo

Contains code for managing registered models (such as the bundled models)

Evaluation

(Coming soon) Contains code for producing standard evaluations (such as benchmarks and scorecards)

Acknowleging or Citing PyEarthTools#

If you use PyEarthTools for your work, we would appreciate you citing our software as below:

Cook, H., Leeuwenburg, T., Rio, M., Miller, J., Mason, G., Ramanathan, N., Pill, J., Haddad, S., & de Burgh-Day, C. (2025). PyEarthTools: Machine learning for Earth system science (0.1.1). Zenodo. https://doi.org/10.5281/zenodo.15760769

@software{cook_2025_15760769,
  author       = {Cook, Harrison and
                  Leeuwenburg, Tennessee and
                  Rio, Maxime and
                  Miller, Joel and
                  Mason, Gemma and
                  Ramanathan, Nikeeth and
                  Pill, John and
                  Haddad, Stephen and
                  de Burgh-Day, Catherine},
  title        = {{PyEarthTools: Machine learning for Earth system science}},
  month        = jun,
  year         = 2025,
  publisher    = {Zenodo},
  version      = {0.1.1},
  doi          = {10.5281/zenodo.15760769},
  url          = {https://doi.org/10.5281/zenodo.15760769}
}
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