Skip to main content

Gibbs Seawater Oceanographic Package of TEOS-10

Project description

gsw Python package

https://travis-ci.org/TEOS-10/GSW-Python https://conda.anaconda.org/conda-forge

This Python implementation of the Thermodynamic Equation of Seawater 2010 (TEOS-10) is based primarily on numpy ufunc wrappers of the GSW-C implementation. We expect it to replace the original python-gsw pure-python implementation after a brief overlap period. The primary reasons for this change are that by building on the C implementation we reduce code duplication and we gain an immediate update to the 75-term equation. Additional benefits include a major increase in speed, a reduction in memory usage, and the inclusion of more functions. The penalty is that a C (or MSVC C++ for Windows) compiler is required to build the package from source.

Warning: this is for Python >=3.5 only.

Documentation is provided at https://teos-10.github.io/GSW-Python/.

For the core functionality, we use an auto-generated C extension module to wrap the C functions as numpy ufuncs, and then use an autogenerated Python module to add docstrings and handle masked arrays. 165 scalar C functions with only double-precision arguments and return values are wrapped as ufuncs, and 158 of these are exposed in the gsw namespace with an additional wrapper in Python.

A hand-written wrapper is used for one C function, and others are re-implemented directly in Python instead of being wrapped. Additional functions present in GSW-Matlab but not in GSW-C may be re-implemented in Python, but there is no expectation that all such functions will be provided.

The package can be installed from a clone of the repo using pip install .. It is neither necessary nor recommended to run the code generators, and no instructions are provided for them; their output is included in the repo. You will need a suitable compiler: gcc or clang for unix-like systems, or the MSVC compiler set used for Python itself on Windows. For Windows, some of the source code has been modified to C++ because the MSVC C compiler does not support the C99 complex data type used in original GSW-C.

To test, after installation, run "pytest" from the source directory.

Project details


Download files

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

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

gsw-3.3.1.post1-cp38-cp38-win_amd64.whl (2.1 MB view details)

Uploaded CPython 3.8 Windows x86-64

gsw-3.3.1.post1-cp38-cp38-manylinux2010_x86_64.whl (2.4 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.12+ x86-64

gsw-3.3.1.post1-cp38-cp38-macosx_10_14_x86_64.whl (2.2 MB view details)

Uploaded CPython 3.8 macOS 10.14+ x86-64

gsw-3.3.1.post1-cp37-cp37m-win_amd64.whl (2.1 MB view details)

Uploaded CPython 3.7m Windows x86-64

gsw-3.3.1.post1-cp37-cp37m-manylinux2010_x86_64.whl (2.4 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.12+ x86-64

gsw-3.3.1.post1-cp37-cp37m-macosx_10_14_x86_64.whl (2.2 MB view details)

Uploaded CPython 3.7m macOS 10.14+ x86-64

gsw-3.3.1.post1-cp36-cp36m-win_amd64.whl (2.1 MB view details)

Uploaded CPython 3.6m Windows x86-64

gsw-3.3.1.post1-cp36-cp36m-manylinux2010_x86_64.whl (2.4 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.12+ x86-64

gsw-3.3.1.post1-cp36-cp36m-macosx_10_14_x86_64.whl (2.2 MB view details)

Uploaded CPython 3.6m macOS 10.14+ x86-64

File details

Details for the file gsw-3.3.1.post1-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: gsw-3.3.1.post1-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 2.1 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/47.1.1.post20200529 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.8.3

File hashes

Hashes for gsw-3.3.1.post1-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 b65cdc304131e4833519598eeea9297b7b4e12deecd85b098c4d3f791bb944e8
MD5 66aead99b8979f78caeb519eaeea8079
BLAKE2b-256 3b83a53c94dd1558e30364e7b0d1744d1e5274b9fd10344f795f2c73a691af99

See more details on using hashes here.

File details

Details for the file gsw-3.3.1.post1-cp38-cp38-manylinux2010_x86_64.whl.

File metadata

  • Download URL: gsw-3.3.1.post1-cp38-cp38-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 2.4 MB
  • Tags: CPython 3.8, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.3.1 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.8.5

File hashes

Hashes for gsw-3.3.1.post1-cp38-cp38-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 d21a42a63ba061710de6dcef7082ffb916429ac9dd4f8343ee4acfa915517e8f
MD5 48bd61f37cbaf9d8a79d123349153402
BLAKE2b-256 9ba01b5b13d489932428ddd93b484f36c3d3ed475e5322e0bbad5c8919242dae

See more details on using hashes here.

File details

Details for the file gsw-3.3.1.post1-cp38-cp38-macosx_10_14_x86_64.whl.

File metadata

  • Download URL: gsw-3.3.1.post1-cp38-cp38-macosx_10_14_x86_64.whl
  • Upload date:
  • Size: 2.2 MB
  • Tags: CPython 3.8, macOS 10.14+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/47.1.1.post20200529 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.8.3

File hashes

Hashes for gsw-3.3.1.post1-cp38-cp38-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 8a349ee16030d294f82c64279125b1362207ddfd9dce0c7f924a2b77ebc3e1ed
MD5 1cf41466cf15cecf72aa2640d9e147f3
BLAKE2b-256 4ade869433eaeb794d079b7bda2f3c5c727700f1f8f9fa5b7f260fcc91e8635d

See more details on using hashes here.

File details

Details for the file gsw-3.3.1.post1-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: gsw-3.3.1.post1-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 2.1 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/47.1.1.post20200529 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.8.3

File hashes

Hashes for gsw-3.3.1.post1-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 0061756d626b5d18a8995b732a7ac71204502dee29611342a1d1db3db50565d5
MD5 2757f2fcc739ac63f16aad5a089d1db8
BLAKE2b-256 1379756c748d046104492879fc03dfafa1bba98f196fc5e933dbf74c8dbed9b5

See more details on using hashes here.

File details

Details for the file gsw-3.3.1.post1-cp37-cp37m-manylinux2010_x86_64.whl.

File metadata

  • Download URL: gsw-3.3.1.post1-cp37-cp37m-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 2.4 MB
  • Tags: CPython 3.7m, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.3.1 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.8.5

File hashes

Hashes for gsw-3.3.1.post1-cp37-cp37m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 bd339068b95ee20009dc2542429160c5cccfa3b6fef10fbb1c2a1946f33b3c1d
MD5 a28275ab7164734df2df7d28245ff372
BLAKE2b-256 4140ff78278f041007dd71fc4b52063ec85559f7163a05fe6c7d69063196aca8

See more details on using hashes here.

File details

Details for the file gsw-3.3.1.post1-cp37-cp37m-macosx_10_14_x86_64.whl.

File metadata

  • Download URL: gsw-3.3.1.post1-cp37-cp37m-macosx_10_14_x86_64.whl
  • Upload date:
  • Size: 2.2 MB
  • Tags: CPython 3.7m, macOS 10.14+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/47.1.1.post20200529 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.8.3

File hashes

Hashes for gsw-3.3.1.post1-cp37-cp37m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 3f99d4a800e3268153c16a7453f60c151f8831d129cba472c38ecb9ef4713cff
MD5 dcbcd2694ef370ff869602e996ae0424
BLAKE2b-256 9db42e85b3beabb9e3b798c5c0a80ffabb841f2a8de1588bec91b1febb389bc9

See more details on using hashes here.

File details

Details for the file gsw-3.3.1.post1-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: gsw-3.3.1.post1-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 2.1 MB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/47.1.1.post20200529 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.8.3

File hashes

Hashes for gsw-3.3.1.post1-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 77a8c97e1fa9c6159b539d042404dae86ce31403094ae920a99eefb1e10cc78f
MD5 e6c2239d7582570f80938b5591d6a24c
BLAKE2b-256 94adf2173f3e8692aa3712975bff32b80e03b0ea1c3a0094832bd60ec9642d7d

See more details on using hashes here.

File details

Details for the file gsw-3.3.1.post1-cp36-cp36m-manylinux2010_x86_64.whl.

File metadata

  • Download URL: gsw-3.3.1.post1-cp36-cp36m-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 2.4 MB
  • Tags: CPython 3.6m, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.3.1 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.8.5

File hashes

Hashes for gsw-3.3.1.post1-cp36-cp36m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 995d825e925a727dc277a293696285f2218eb562b11f90aadb7284899ccdc84d
MD5 25fd97ac6a39dc848324028be67bb8e1
BLAKE2b-256 724642d3b297108f88a1e152515323af6dea379c4e0d31c4b9c9adf722111a3a

See more details on using hashes here.

File details

Details for the file gsw-3.3.1.post1-cp36-cp36m-macosx_10_14_x86_64.whl.

File metadata

  • Download URL: gsw-3.3.1.post1-cp36-cp36m-macosx_10_14_x86_64.whl
  • Upload date:
  • Size: 2.2 MB
  • Tags: CPython 3.6m, macOS 10.14+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/47.1.1.post20200529 requests-toolbelt/0.9.1 tqdm/4.47.0 CPython/3.8.3

File hashes

Hashes for gsw-3.3.1.post1-cp36-cp36m-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 40ede3030aea043a2c5e05d23015f18e41ee81716fdda836f9f408de78c2a84a
MD5 f81c6ff54d4f09aadd8331c6e2affe35
BLAKE2b-256 51a025377d432b322a381fd2e9706bedfd10cfec0276354fb4d4f2974a556dd5

See more details on using hashes here.

Supported by

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