Skip to main content

Tide Model Driver to read OTIS, ATLAS, GOT and FES formatted tidal solutions and make tidal predictions

Project description

Language License PyPI Version Anaconda-Server Documentation Status codecov 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/tsutterley/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 the project code of conduct.

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-2.1.7.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-2.1.7-py3-none-any.whl (6.4 MB view details)

Uploaded Python 3

File details

Details for the file pytmd-2.1.7.tar.gz.

File metadata

  • Download URL: pytmd-2.1.7.tar.gz
  • Upload date:
  • Size: 6.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for pytmd-2.1.7.tar.gz
Algorithm Hash digest
SHA256 8455dc07b5600ab4eff9dc246371f7b923239cc7990beb08529d6011cec46b6d
MD5 174c505192462846770bfb7a0e06e584
BLAKE2b-256 7f0c79898afb48bd3af2a4c877b9bc1bdf4ac2490e4c0cf7a8f408c755651ec5

See more details on using hashes here.

File details

Details for the file pyTMD-2.1.7-py3-none-any.whl.

File metadata

  • Download URL: pyTMD-2.1.7-py3-none-any.whl
  • Upload date:
  • Size: 6.4 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for pyTMD-2.1.7-py3-none-any.whl
Algorithm Hash digest
SHA256 8d4bc46ee925ca5a9760ead54a8e65796dd79f89177cdf6d8fd26debb3fb5baf
MD5 7d89580f6baa77400124a793be8a8669
BLAKE2b-256 929502d28427e62b0f8691618a85c9df42fcc0d25ee8a320b590afc376786249

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