SkillAgentSearch skills...

PyTMD

Python-based tidal prediction software

Install / Use

/learn @pyTMD/PyTMD
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

pyTMD

Python-based tidal prediction software for estimating ocean, load, solid Earth and pole tides

About

<table> <tr> <td><b>Version:</b></td> <td> <a href="https://pypi.python.org/pypi/pyTMD/" alt="PyPI"><img src="https://img.shields.io/pypi/v/pyTMD.svg"></a> <a href="https://anaconda.org/conda-forge/pytmd" alt="conda-forge"><img src="https://img.shields.io/conda/vn/conda-forge/pytmd"></a> <a href="https://github.com/pyTMD/pyTMD/releases/latest" alt="commits-since"><img src="https://img.shields.io/github/commits-since/pyTMD/pyTMD/latest"></a> </td> </tr> <tr> <td><b>Citation:</b></td> <td> <a href="https://doi.org/10.21105/joss.08566" alt="JOSS"><img src="https://joss.theoj.org/papers/10.21105/joss.08566/status.svg"></a> <a href="https://doi.org/10.5281/zenodo.5555395" alt="zenodo"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.5555395.svg"></a> </td> </tr> <tr> <td><b>Tests:</b></td> <td> <a href="https://pytmd.readthedocs.io/en/latest/?badge=latest" alt="Documentation Status"><img src="https://readthedocs.org/projects/pytmd/badge/?version=latest"></a> <a href="https://github.com/pyTMD/pyTMD/actions/workflows/python-request.yml" alt="Build"><img src="https://github.com/pyTMD/pyTMD/actions/workflows/python-request.yml/badge.svg"></a> <a href="https://github.com/pyTMD/pyTMD/actions/workflows/ruff-format.yml" alt="Ruff"><img src="https://github.com/pyTMD/pyTMD/actions/workflows/ruff-format.yml/badge.svg"></a> </td> </tr> <tr> <td><b>Data:</b></td> <td> <a href="https://doi.org/10.5281/zenodo.18091740" alt="zenodo"><img src="https://img.shields.io/badge/zenodo-pyTMD_test_data-2f6fa7.svg?logo=data:image/svg%2bxml;base64,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"></a> <a href="https://doi.org/10.6084/m9.figshare.30260326" alt="figshare"><img src="https://img.shields.io/badge/figshare-pyTMD_test_data-a60845?logo=figshare"></a> </td> </tr> <tr> <td><b>License:</b></td> <td> <a href="https://github.com/pyTMD/pyTMD/blob/main/LICENSE" alt="License"><img src="https://img.shields.io/github/license/pyTMD/pyTMD"></a> </td> </tr> </table>

For more information: see the documentation at pytmd.readthedocs.io

Installation

From PyPI:

python3 -m pip install pyTMD

To include all optional dependencies:

python3 -m pip install pyTMD[all]

Using conda or mamba from conda-forge:

conda install -c conda-forge pytmd
mamba install -c conda-forge pytmd

Development version from GitHub:

python3 -m pip install git+https://github.com/pyTMD/pyTMD.git

Running with Pixi

Alternatively, you can use Pixi for a streamlined workspace environment:

  1. Install Pixi following the installation instructions
  2. Clone the project repository:
git clone https://github.com/pyTMD/pyTMD.git
  1. Move into the pyTMD directory
cd pyTMD
  1. Install dependencies and start JupyterLab:
pixi run start

This will automatically create the environment, install all dependencies, and launch JupyterLab in the notebooks directory.

Dependencies

References

T. C. Sutterley, S. L. Howard, L. Padman, and M. R. Siegfried, "pyTMD: Python-based tidal prediction software". Journal of Open Source Software, 10(116), 8566, (2025). doi: 10.21105/joss.08566

T. C. Sutterley, T. Markus, T. A. Neumann, M. R. van den Broeke, J. M. van Wessem, and S. R. M. Ligtenberg, "Antarctic ice shelf thickness change from multimission lidar mapping", The Cryosphere, 13, 1801-1817, (2019). doi: 10.5194/tc-13-1801-2019

L. Padman, M. R. Siegfried, and H. A. Fricker, "Ocean Tide Influences on the Antarctic and Greenland Ice Sheets", Reviews of Geophysics, 56, 142-184, (2018). doi: 10.1002/2016RG000546

Download

The program homepage is:
https://github.com/pyTMD/pyTMD

A zip archive of the latest version is available directly at:
https://github.com/pyTMD/pyTMD/archive/main.zip

Alternative Software

perth5 from NASA Goddard Space Flight Center:
https://codeberg.org/rray/perth5

Matlab Tide Model Driver from Earth & Space Research:
https://github.com/EarthAndSpaceResearch/TMD_Matlab_Toolbox_v2.5

Fortran OSU Tidal Prediction Software:
https://www.tpxo.net/otps

Disclaimer

This package includes software developed at NASA Goddard Space Flight Center (GSFC) and the University of Washington Applied Physics Laboratory (UW-APL). It is not sponsored or maintained by the Universities Space Research Association (USRA), AVISO or NASA. The software is provided here for your convenience but with no guarantees whatsoever. It should not be used for coastal navigation or any application that may risk life or property.

Contributing

This project contains work and contributions from the scientific community. If you would like to contribute to the project, please have a look at the contribution guidelines, open issues and discussions board.

Credits

The Tidal Model Driver (TMD) Matlab Toolbox was developed by Laurie Padman, Lana Erofeeva and Susan Howard. An updated version of the TMD Matlab Toolbox (TMD3) was developed by Chad Greene. The OSU Tidal Inversion Software (OTIS) and OSU Tidal Prediction Software (OTPS) were developed by Lana Erofeeva and Gary Egbert (copyright OSU, licensed for non-commercial use). The NASA Goddard Space Flight Center (GSFC) PREdict Tidal Heights (PERTH3) software was developed by Richard Ray and Remko Scharroo. An updated and more versatile version of the NASA GSFC tidal prediction

View on GitHub
GitHub Stars198
CategoryDevelopment
Updated19h ago
Forks59

Languages

Python

Security Score

100/100

Audited on Apr 8, 2026

No findings