PyEarthTools: Machine learning for Earth system science

• 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 weather prediction from a model trained with PyEarthTools.	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
New Users Guide: available here

If you use PyEarthTools for your work or a publication, please cite our work.

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

Alternatively, 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

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 (coming soon).

Overview of the Packages within PyEarthTools

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

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., Sullivan, B., Hobeichi, S., Holmes, R., Potokina, M., Bogacheva, J., James, M., & Stassen, C. (2025). PyEarthTools: Machine learning for Earth system science (0.4.0). Zenodo. https://doi.org/10.5281/zenodo.17429589

BibTeX:

@software{cook_2025_17429589,
  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 and
                  Sullivan, Ben and
                  Hobeichi, Sanaa and
                  Holmes, Ryan and
                  Potokina, Margarita and
                  Bogacheva, Jenya and
                  James, Matthew and
                  Stassen, Christian},
  title        = {PyEarthTools: Machine learning for Earth system
                   science
                  },
  month        = oct,
  year         = 2025,
  publisher    = {Zenodo},
  version      = {0.4.0},
  doi          = {10.5281/zenodo.17429589},
  url          = {https://doi.org/10.5281/zenodo.17429589},
}