Skip to main content

An analysis and visualization toolkit for volumetric data

Project description

The yt Project

PyPI Supported Python Versions Latest Documentation Users' Mailing List Devel Mailing List Data Hub Powered by NumFOCUS Sponsor our Project

Build and Test CI (bleeding edge) pre-commit.ci status Code style: black Ruff

yt is an open-source, permissively-licensed Python library for analyzing and visualizing volumetric data.

yt supports structured, variable-resolution meshes, unstructured meshes, and discrete or sampled data such as particles. Focused on driving physically-meaningful inquiry, yt has been applied in domains such as astrophysics, seismology, nuclear engineering, molecular dynamics, and oceanography. Composed of a friendly community of users and developers, we want to make it easy to use and develop - we'd love it if you got involved!

We've written a method paper you may be interested in; if you use yt in the preparation of a publication, please consider citing it.

Code of Conduct

yt abides by a code of conduct partially modified from the PSF code of conduct, and is found in our contributing guide.

Installation

You can install the most recent stable version of yt either with conda from conda-forge:

conda install -c conda-forge yt

or with pip:

python -m pip install yt

More information on the various ways to install yt, and in particular to install from source, can be found on the project's website.

Getting Started

yt is designed to provide meaningful analysis of data. We have some Quickstart example notebooks in the repository:

If you'd like to try these online, you can visit our yt Hub and run a notebook next to some of our example data.

Contributing

We love contributions! yt is open source, built on open source, and we'd love to have you hang out in our community.

We have developed some guidelines for contributing to yt.

Imposter syndrome disclaimer: We want your help. No, really.

There may be a little voice inside your head that is telling you that you're not ready to be an open source contributor; that your skills aren't nearly good enough to contribute. What could you possibly offer a project like this one?

We assure you - the little voice in your head is wrong. If you can write code at all, you can contribute code to open source. Contributing to open source projects is a fantastic way to advance one's coding skills. Writing perfect code isn't the measure of a good developer (that would disqualify all of us!); it's trying to create something, making mistakes, and learning from those mistakes. That's how we all improve, and we are happy to help others learn.

Being an open source contributor doesn't just mean writing code, either. You can help out by writing documentation, tests, or even giving feedback about the project (and yes - that includes giving feedback about the contribution process). Some of these contributions may be the most valuable to the project as a whole, because you're coming to the project with fresh eyes, so you can see the errors and assumptions that seasoned contributors have glossed over.

(This disclaimer was originally written by Adrienne Lowe for a PyCon talk, and was adapted by yt based on its use in the README file for the MetPy project)

Resources

We have some community and documentation resources available.

Is your code compatible with yt ? Great ! Please consider giving us a shoutout as a shiny badge in your README

  • markdown
[![yt-project](https://img.shields.io/static/v1?label="works%20with"&message="yt"&color="blueviolet")](https://yt-project.org)
  • rst
|yt-project|

.. |yt-project| image:: https://img.shields.io/static/v1?label="works%20with"&message="yt"&color="blueviolet"
   :target: https://yt-project.org

Powered by NumFOCUS

yt is a fiscally sponsored project of NumFOCUS. If you're interested in supporting the active maintenance and development of this project, consider donating to the project.

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

yt-4.3.0.tar.gz (13.7 MB view details)

Uploaded Source

Built Distributions

yt-4.3.0-cp312-cp312-win_amd64.whl (15.6 MB view details)

Uploaded CPython 3.12 Windows x86-64

yt-4.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (46.4 MB view details)

Uploaded CPython 3.12 manylinux: glibc 2.17+ x86-64

yt-4.3.0-cp312-cp312-macosx_11_0_arm64.whl (16.0 MB view details)

Uploaded CPython 3.12 macOS 11.0+ ARM64

yt-4.3.0-cp312-cp312-macosx_10_9_x86_64.whl (16.5 MB view details)

Uploaded CPython 3.12 macOS 10.9+ x86-64

yt-4.3.0-cp311-cp311-win_amd64.whl (15.6 MB view details)

Uploaded CPython 3.11 Windows x86-64

yt-4.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (46.6 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64

yt-4.3.0-cp311-cp311-macosx_11_0_arm64.whl (16.0 MB view details)

Uploaded CPython 3.11 macOS 11.0+ ARM64

yt-4.3.0-cp311-cp311-macosx_10_9_x86_64.whl (16.5 MB view details)

Uploaded CPython 3.11 macOS 10.9+ x86-64

yt-4.3.0-cp310-cp310-win_amd64.whl (15.6 MB view details)

Uploaded CPython 3.10 Windows x86-64

yt-4.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (44.4 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

yt-4.3.0-cp310-cp310-macosx_11_0_arm64.whl (16.0 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

yt-4.3.0-cp310-cp310-macosx_10_9_x86_64.whl (16.5 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

yt-4.3.0-cp39-cp39-win_amd64.whl (15.6 MB view details)

Uploaded CPython 3.9 Windows x86-64

yt-4.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (44.4 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

yt-4.3.0-cp39-cp39-macosx_11_0_arm64.whl (16.0 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

yt-4.3.0-cp39-cp39-macosx_10_9_x86_64.whl (16.5 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

File details

Details for the file yt-4.3.0.tar.gz.

File metadata

  • Download URL: yt-4.3.0.tar.gz
  • Upload date:
  • Size: 13.7 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.6

File hashes

Hashes for yt-4.3.0.tar.gz
Algorithm Hash digest
SHA256 cdcab7c07d8c46fe87d94fcec4bbac750394aa40b88cb7381abc843dcdce30ee
MD5 69eeebfbd111ec3733302b516d09947e
BLAKE2b-256 08b9ba6c7e1f2c9790100ac771d0108513c75a455a63cac76a7be2e3fd8dac73

See more details on using hashes here.

File details

Details for the file yt-4.3.0-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: yt-4.3.0-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 15.6 MB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.6

File hashes

Hashes for yt-4.3.0-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 542869098898153dd04debb6903e0118fb37e46405750ba7df350dc8bea3cf1e
MD5 82aae1cf711ec4b30edf55928d47df33
BLAKE2b-256 ed743e2fd7af802d4eac9e8f447266aa62ac6a2549b1809654221ed9564d3190

See more details on using hashes here.

File details

Details for the file yt-4.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yt-4.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 5f5bb5db9bc756e64823a2f75c20539e01fc0d41dea31d315c83a05338b3fdde
MD5 49899c5ff962d4193c9d38ae1380a67a
BLAKE2b-256 d584a7e0e866b8e9b98d7bb9908ad33a1e79d9b06e573f5a5a955673074e98f9

See more details on using hashes here.

File details

Details for the file yt-4.3.0-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for yt-4.3.0-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 9b86c427ec07b04859caee1c702c6695de6d2671205aab7fd5701ef9d50c8bc0
MD5 1f0c7d30acd093567d782e9f624e460e
BLAKE2b-256 757e080807b0f0610d51f05847bdcb4dea50218c6944f9c4d3c82451ddd6fbc5

See more details on using hashes here.

File details

Details for the file yt-4.3.0-cp312-cp312-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for yt-4.3.0-cp312-cp312-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 df8bb4405f64ea21cb155c42584c1fe84d077100ff69c48e3ff259c30e2f4821
MD5 9a3d05770b5bfea5db016037b6aecc6a
BLAKE2b-256 f31601f128b7d1723d61f1c7916a03d43f724fd83063fb8d573400e44b651628

See more details on using hashes here.

File details

Details for the file yt-4.3.0-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: yt-4.3.0-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 15.6 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.6

File hashes

Hashes for yt-4.3.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 d11bdcae70fc83c1dfd49624a6a455f4a6498d286b32b16565457bc584c877a3
MD5 67a285384fc85565891b3d13d38e9086
BLAKE2b-256 76e92b475cf35a94b3b6824f0d3fa9d0f032c0c9c97ca353cf1753b414acad4d

See more details on using hashes here.

File details

Details for the file yt-4.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yt-4.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 b0a5096068490d2db63b016fcec1a6b9860dd3f5718cd9fc5dc2ee73fd9260c5
MD5 d945a8ba6486580a42d5e448c8175964
BLAKE2b-256 992c02103dcdf83bab38702e3e3efb054c241f6978038b6d6b44c3e035c16619

See more details on using hashes here.

File details

Details for the file yt-4.3.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for yt-4.3.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 7e573a0ff34c5b975854a00f9c51e9fcef9b98e612100eb4789e7c9c978bb3c7
MD5 904897ef97d2cf4744e9f0006f24aa4e
BLAKE2b-256 3806e346f3f2949ecc56744484ad2218ae7e16c8751e8d9e1ba01e3296277cf8

See more details on using hashes here.

File details

Details for the file yt-4.3.0-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for yt-4.3.0-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 3f51d741210d74d4281e9d3610504f76511b32db82a4a188fc5ecb4f1dc48488
MD5 8d689b03d81724bf06ca5396ffc202a7
BLAKE2b-256 b7597dc557404752a30dcb536f151d4709364cf58e4b3c47d650042912e3f263

See more details on using hashes here.

File details

Details for the file yt-4.3.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: yt-4.3.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 15.6 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.6

File hashes

Hashes for yt-4.3.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 ab78e81de1dd69dd39ab1f582a571b2dea62da7d522f85681728552ad4ec6276
MD5 b84263507402e2bf519e5091a40bd6bb
BLAKE2b-256 93534c333a53b17027dac6dc2fab8a3fca6c28b15c66cfb31f9b1ebb46e14a06

See more details on using hashes here.

File details

Details for the file yt-4.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yt-4.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 e0de9814a739b879fd413909ebfa72693c36cb068a54275e64f50dad9dbdd332
MD5 e418354f80341e927ca857b1bc70b7d3
BLAKE2b-256 3d4b4490ac0b90e0809c732419524b5e0ad86ce2ed6a16aacc66ecc3c531b50f

See more details on using hashes here.

File details

Details for the file yt-4.3.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for yt-4.3.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 327d591ba5fd5fcd5d02dd5f92b80db69ff621379d009379237d7051af6ad795
MD5 4f5c29a4d2aca086fdb1979488b6996a
BLAKE2b-256 1f8e070ab83e95b0bbf2854a94978d3bd25620b55a4b4a22907611cd31cb9206

See more details on using hashes here.

File details

Details for the file yt-4.3.0-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for yt-4.3.0-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 85bdf8021a53dab22604821d94489ed4e93363f3584f9e83c0df34f30a05dda7
MD5 82ca7e65fbbed9e24539e78be16ca2a5
BLAKE2b-256 779419c1db16682829491e66a24fcac2c23b02990996186b569790827a139e99

See more details on using hashes here.

File details

Details for the file yt-4.3.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: yt-4.3.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 15.6 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.6

File hashes

Hashes for yt-4.3.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 68bb581e9030d3984613ceefeff191796831ffea10ddcf205fa46fba0ed1bc36
MD5 2916be013a73686dae55fa699b88f17d
BLAKE2b-256 7456a41ba2df94df404a57e6d0778b32c8655710b926b5f0c9fb25c0148c2c86

See more details on using hashes here.

File details

Details for the file yt-4.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yt-4.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 87070a6234ac8e845afb4b92ebe8a0d8a63e5bdd155c63f1e54f79f3a1320311
MD5 19e839220e3b985351fdad45a41c7e84
BLAKE2b-256 827e62039d4d436b7aa7a0b55b4ee2e8accc3841481e293024dceb48a822a9de

See more details on using hashes here.

File details

Details for the file yt-4.3.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

  • Download URL: yt-4.3.0-cp39-cp39-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 16.0 MB
  • Tags: CPython 3.9, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.6

File hashes

Hashes for yt-4.3.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 db311a58df1fcf385508f27a023fe3b5c851bba648cde3f24265302d0dc9b14f
MD5 412ce18c961a636e2548abffa078b79d
BLAKE2b-256 50c7218943f5bbeca294bab69dac0fd362213b02d0a775d1a4f75651b15f152a

See more details on using hashes here.

File details

Details for the file yt-4.3.0-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for yt-4.3.0-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 510549301c8948a74dbd57808f2c207ee848df5c9f5e7634d455e8a849fc245d
MD5 16da0c05d96b47d6fa347a3124af81a9
BLAKE2b-256 3e7416602229440c1478f9c24deec65330fca16ae96201fb07dc75f911acd3a6

See more details on using hashes here.

Supported by

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