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-4.2.0.tar.gz (11.7 MB view details)

Uploaded Source

Built Distributions

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

yt-4.2.0-cp311-cp311-win_amd64.whl (12.7 MB view details)

Uploaded CPython 3.11Windows x86-64

yt-4.2.0-cp311-cp311-win32.whl (12.2 MB view details)

Uploaded CPython 3.11Windows x86

yt-4.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (39.6 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

yt-4.2.0-cp311-cp311-macosx_11_0_arm64.whl (13.0 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

yt-4.2.0-cp311-cp311-macosx_10_9_x86_64.whl (13.6 MB view details)

Uploaded CPython 3.11macOS 10.9+ x86-64

yt-4.2.0-cp310-cp310-win_amd64.whl (12.8 MB view details)

Uploaded CPython 3.10Windows x86-64

yt-4.2.0-cp310-cp310-win32.whl (12.2 MB view details)

Uploaded CPython 3.10Windows x86

yt-4.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.2 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

yt-4.2.0-cp310-cp310-macosx_11_0_arm64.whl (13.1 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

yt-4.2.0-cp310-cp310-macosx_10_9_x86_64.whl (13.7 MB view details)

Uploaded CPython 3.10macOS 10.9+ x86-64

yt-4.2.0-cp39-cp39-win_amd64.whl (12.8 MB view details)

Uploaded CPython 3.9Windows x86-64

yt-4.2.0-cp39-cp39-win32.whl (12.2 MB view details)

Uploaded CPython 3.9Windows x86

yt-4.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.6 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

yt-4.2.0-cp39-cp39-macosx_11_0_arm64.whl (13.1 MB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

yt-4.2.0-cp39-cp39-macosx_10_9_x86_64.whl (13.6 MB view details)

Uploaded CPython 3.9macOS 10.9+ x86-64

yt-4.2.0-cp38-cp38-win_amd64.whl (13.3 MB view details)

Uploaded CPython 3.8Windows x86-64

yt-4.2.0-cp38-cp38-win32.whl (12.7 MB view details)

Uploaded CPython 3.8Windows x86

yt-4.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (39.3 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

yt-4.2.0-cp38-cp38-macosx_11_0_arm64.whl (13.5 MB view details)

Uploaded CPython 3.8macOS 11.0+ ARM64

yt-4.2.0-cp38-cp38-macosx_10_9_x86_64.whl (14.1 MB view details)

Uploaded CPython 3.8macOS 10.9+ x86-64

File details

Details for the file yt-4.2.0.tar.gz.

File metadata

  • Download URL: yt-4.2.0.tar.gz
  • Upload date:
  • Size: 11.7 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.3

File hashes

Hashes for yt-4.2.0.tar.gz
Algorithm Hash digest
SHA256 ed518bab672a84ff4c145a27654f6c7b1c91632062a223592e15ee558779db26
MD5 0a78e222b8291ce4a80189bb659fd934
BLAKE2b-256 e930876b37ef8fd228006f9d553e1c00c6a9c72b0a07c43e7fee445221ade707

See more details on using hashes here.

File details

Details for the file yt-4.2.0-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: yt-4.2.0-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 12.7 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.3

File hashes

Hashes for yt-4.2.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 bb9f9b68cbef02b716d5d08202c4e47aa01ee44945c36c29170f65b9e57ec022
MD5 fa1c5f9adfd4e1117db877e375021daa
BLAKE2b-256 b45b4f8fb8b03567c0773271d5f16987fdbe1067ecc8ab759ac348a9443340e2

See more details on using hashes here.

File details

Details for the file yt-4.2.0-cp311-cp311-win32.whl.

File metadata

  • Download URL: yt-4.2.0-cp311-cp311-win32.whl
  • Upload date:
  • Size: 12.2 MB
  • Tags: CPython 3.11, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.3

File hashes

Hashes for yt-4.2.0-cp311-cp311-win32.whl
Algorithm Hash digest
SHA256 24ceb312e53b3cc65f81413d167fec02e33259c09af6151334a4cdacccf2daae
MD5 d0fa0b653a212cd6389fe79960b40ecf
BLAKE2b-256 37d2ea80f76a5b0e2ba2ec9bae189a98fdcb904002c1a3e90dbc717f800c0adb

See more details on using hashes here.

File details

Details for the file yt-4.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yt-4.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 366cb692acb2f602ca1077415a726a08c3a33d7ab2737e62aaa256248ea673cb
MD5 ed4803f22c3b3023a020433a4c3823e5
BLAKE2b-256 2d240986fea8f1be07564ceef88448046d449bd7219132bdbbb4b15d76fd162d

See more details on using hashes here.

File details

Details for the file yt-4.2.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

  • Download URL: yt-4.2.0-cp311-cp311-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 13.0 MB
  • Tags: CPython 3.11, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.3

File hashes

Hashes for yt-4.2.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 0e02d0522904823591e53f902b31dfa5ff008690b271ed69072e6c10e1fbf559
MD5 c141121d44efab343468bb48a3260831
BLAKE2b-256 6807aad0070464a159c3b33aebc7cfd96978258a1f929a362ada370d1cf5e13d

See more details on using hashes here.

File details

Details for the file yt-4.2.0-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: yt-4.2.0-cp311-cp311-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 13.6 MB
  • Tags: CPython 3.11, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.3

File hashes

Hashes for yt-4.2.0-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 779706b7a42a2235556fe0b5af2d06ccc0618f637e456da66894fcb80b09d07b
MD5 2134329936e2da280ea7e288913cf9a8
BLAKE2b-256 9a50ce0951c8bf73a4eb5a895a60fb6a250612042c8b27a067580b294c2a357f

See more details on using hashes here.

File details

Details for the file yt-4.2.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: yt-4.2.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 12.8 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.3

File hashes

Hashes for yt-4.2.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 756b61c87dd033765df1c7fdba3358d30efeb78f3bce41ad0ca00af7afc7d49b
MD5 1aa07fff700389cae96d2807f748a502
BLAKE2b-256 c083af4eab9e54656b89b529abd8b196af1eba9796eb04c2f490604731f15ebb

See more details on using hashes here.

File details

Details for the file yt-4.2.0-cp310-cp310-win32.whl.

File metadata

  • Download URL: yt-4.2.0-cp310-cp310-win32.whl
  • Upload date:
  • Size: 12.2 MB
  • Tags: CPython 3.10, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.3

File hashes

Hashes for yt-4.2.0-cp310-cp310-win32.whl
Algorithm Hash digest
SHA256 dbe1b0f9736a973b8d8f2ce87f9da3bc4fd75bdf1f332791626c8a63d3cbc5e9
MD5 3c0cc360bcc9cd345345edc1f857343f
BLAKE2b-256 3839d210d9dbb05c7859b22902d88222b47ffe8f6957bab09222959f49a40894

See more details on using hashes here.

File details

Details for the file yt-4.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yt-4.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 c3e86baab7ca6b891daf761344ecc1f7eee233fa1c7639bb5357cdef7fdac592
MD5 f2d7d642429555e083b53fd4ebe389be
BLAKE2b-256 473a059303f1398e30780ba85ff6e099df32a7484f504e43171651cbed6674a0

See more details on using hashes here.

File details

Details for the file yt-4.2.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

  • Download URL: yt-4.2.0-cp310-cp310-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 13.1 MB
  • Tags: CPython 3.10, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.3

File hashes

Hashes for yt-4.2.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 93b65cce59b4cccd0ad4859ab41e998a884793e4aa7ce870d5a4feaacf3d120b
MD5 d82e34874e8486030010c55df9b390a0
BLAKE2b-256 5f2e4b2cc70fcb9a5ed6893653e9687ce84df89dbccd171cdfd8a6892795ff51

See more details on using hashes here.

File details

Details for the file yt-4.2.0-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: yt-4.2.0-cp310-cp310-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 13.7 MB
  • Tags: CPython 3.10, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.3

File hashes

Hashes for yt-4.2.0-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 25fc35533e0bf25e8be0642b33290c6c329b617a5ad0d1de631509d7a1b779f7
MD5 6ac6e891f46787b2882cd37f444bcc2b
BLAKE2b-256 da29e6e4d1854d213e714a052c6a8bb69c3a2fdeb22804b6926ba1c67fd3a0af

See more details on using hashes here.

File details

Details for the file yt-4.2.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: yt-4.2.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 12.8 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.3

File hashes

Hashes for yt-4.2.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 6727538d2d6c39f78435f8454c9d76bf1d815162cb10161d508267cbf7dd5615
MD5 5bd074d49e6cc36aaa0bfecfc29ea15a
BLAKE2b-256 7fcd524f63a66cafb15064a2325d7d27bdf556d80c8c31c9aa4ad48abe79ff9c

See more details on using hashes here.

File details

Details for the file yt-4.2.0-cp39-cp39-win32.whl.

File metadata

  • Download URL: yt-4.2.0-cp39-cp39-win32.whl
  • Upload date:
  • Size: 12.2 MB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.3

File hashes

Hashes for yt-4.2.0-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 df7706a4e34948f1ff707b5c1db5fda8af45d53c97fabcc1b8bcc30102e01039
MD5 cfca9a2f3f73ee3a18d170d2494eb4a2
BLAKE2b-256 e75731cf601ff30f2092346d6c5b81db06cc3d0ee37967baf56dafeeff2c34ce

See more details on using hashes here.

File details

Details for the file yt-4.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yt-4.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 e4a499a4cf25513bcf30e2136b2ea533b84837d068801fc57dacffedce2f8b54
MD5 9af2ea919059e28f6585cbe7b0238369
BLAKE2b-256 6d682f74446d0af58bcd14f44c19329a4a5450107803599cfec3d590d03981d4

See more details on using hashes here.

File details

Details for the file yt-4.2.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

  • Download URL: yt-4.2.0-cp39-cp39-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 13.1 MB
  • Tags: CPython 3.9, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.3

File hashes

Hashes for yt-4.2.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 54b1a1a687abb1482c914585879aef5adda8e252a226f61a10f2c8a884aaef93
MD5 6703d071e1f0c3cb70ed16c06e1b123e
BLAKE2b-256 64c7e43ac322a2723a46bebd91ec8459a167aeffb54f837b3712c7dc31118aca

See more details on using hashes here.

File details

Details for the file yt-4.2.0-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: yt-4.2.0-cp39-cp39-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 13.6 MB
  • Tags: CPython 3.9, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.3

File hashes

Hashes for yt-4.2.0-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 53aa5f03f981b311dcb6c48b9230900634094e2565107a8ca1faefde4a90f123
MD5 2498b7b1aed83bcc4ac83fdc90d90acf
BLAKE2b-256 0c3d3c3ef01874eaf8faa4eb64ce57710d29a28b96b2dd7dd58c72fbb98111d7

See more details on using hashes here.

File details

Details for the file yt-4.2.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: yt-4.2.0-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 13.3 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.3

File hashes

Hashes for yt-4.2.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 1ed3c1d5b719ab5e04e8d35ccf295020d7f7ced9e74b3239cb0cd00c21a99cd3
MD5 6ba64cdfe911aef59c144b58de0fe0bb
BLAKE2b-256 c69b4f74992fa17ade3cf1cec20c1f0af2371daac1c758ed0d984333f8bc47f7

See more details on using hashes here.

File details

Details for the file yt-4.2.0-cp38-cp38-win32.whl.

File metadata

  • Download URL: yt-4.2.0-cp38-cp38-win32.whl
  • Upload date:
  • Size: 12.7 MB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.3

File hashes

Hashes for yt-4.2.0-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 645cb96f3f885125a3d801af3a4e0883f83ee51bf9c156cc7e5bf75cb6ec4a69
MD5 7c5d12a377ce53dc4a672e9262ecc947
BLAKE2b-256 a39eaaff0dde693897d08f3c999fd9a32c2d0b141ba00a56aa789e68a1066b91

See more details on using hashes here.

File details

Details for the file yt-4.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yt-4.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 4df0a454d86285ded026a1c3b6be4d0893deaaaafb5eccd3347fe721b5f18e2b
MD5 e29f2b54a789dd82618a3c9a6ec62fe0
BLAKE2b-256 67a5a5f3b637d52e0cbd6b885cb4dbfb4efd2c9db7ceab815e864a7217d0df62

See more details on using hashes here.

File details

Details for the file yt-4.2.0-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

  • Download URL: yt-4.2.0-cp38-cp38-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 13.5 MB
  • Tags: CPython 3.8, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.3

File hashes

Hashes for yt-4.2.0-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 9281a470c4f7f7170f7c04658f96e237eb276df3c148f707557efd60a14bfd11
MD5 ec68843136416a8c1d3b74a0af2607f8
BLAKE2b-256 441e2011e275f1e05f982d2d46f4a0ed282fcd6fa9357ef0c5bbbd6734dd2374

See more details on using hashes here.

File details

Details for the file yt-4.2.0-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: yt-4.2.0-cp38-cp38-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 14.1 MB
  • Tags: CPython 3.8, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.3

File hashes

Hashes for yt-4.2.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 c9c22fd6c08af950a475944e482144628998e7a960004cad7582153f4797181c
MD5 64e673a2c6a3ebc7a6534a006c2df95f
BLAKE2b-256 9b6036988bee2635686c515a1c30313aedb60161c3eda98d07b0c14869251a16

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