Skip to main content

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

Project description

License Documentation Status PyPI conda-forge commits-since zenodo

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

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

Dependencies

References

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, 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:
A zip archive of the latest version is available directly at:

Alternative Software

perth5 from NASA Goddard Space Flight Center:
Matlab Tide Model Driver from Earth & Space Research:
Fortran OSU Tidal Prediction Software:

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 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 software (PERTH5) was developed by Richard Ray.

License

The content of this project is licensed under the Creative Commons Attribution 4.0 Attribution license and the source code is licensed under the MIT license.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pytmd-2.2.2.tar.gz (6.3 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pyTMD-2.2.2-py3-none-any.whl (6.4 MB view details)

Uploaded Python 3

File details

Details for the file pytmd-2.2.2.tar.gz.

File metadata

  • Download URL: pytmd-2.2.2.tar.gz
  • Upload date:
  • Size: 6.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.2

File hashes

Hashes for pytmd-2.2.2.tar.gz
Algorithm Hash digest
SHA256 075407402c6a81f8df6f9e23f60dfb35b68495ac27460fd887b27ab4b3944d3d
MD5 3605dd47774c5c6a8c4fb986da425d41
BLAKE2b-256 9f33e243caa6fa531d695cdf3fd7f7088c5e11006268581019e59a161d8cc03a

See more details on using hashes here.

File details

Details for the file pyTMD-2.2.2-py3-none-any.whl.

File metadata

  • Download URL: pyTMD-2.2.2-py3-none-any.whl
  • Upload date:
  • Size: 6.4 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.2

File hashes

Hashes for pyTMD-2.2.2-py3-none-any.whl
Algorithm Hash digest
SHA256 a37c6529bb6fc589d17ac462ef4cb7226b5776411feba96fdcf4e6da34927443
MD5 ac02aa78edecd6cf06ce1b6c1e36e159
BLAKE2b-256 5ef3b5dbb81ad02c3b3375358a3ed3fb709f6e150bc6c2879bfcae3288db5ed1

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page