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 Imports: isort

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-4.1.2.tar.gz (12.4 MB view details)

Uploaded Source

Built Distributions

yt-4.1.2-cp311-cp311-win_amd64.whl (13.1 MB view details)

Uploaded CPython 3.11 Windows x86-64

yt-4.1.2-cp311-cp311-win32.whl (12.5 MB view details)

Uploaded CPython 3.11 Windows x86

yt-4.1.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (41.5 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64

yt-4.1.2-cp311-cp311-macosx_11_0_arm64.whl (13.4 MB view details)

Uploaded CPython 3.11 macOS 11.0+ ARM64

yt-4.1.2-cp311-cp311-macosx_10_9_x86_64.whl (14.0 MB view details)

Uploaded CPython 3.11 macOS 10.9+ x86-64

yt-4.1.2-cp310-cp310-win_amd64.whl (13.1 MB view details)

Uploaded CPython 3.10 Windows x86-64

yt-4.1.2-cp310-cp310-win32.whl (12.5 MB view details)

Uploaded CPython 3.10 Windows x86

yt-4.1.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (40.1 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

yt-4.1.2-cp310-cp310-macosx_11_0_arm64.whl (13.5 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

yt-4.1.2-cp310-cp310-macosx_10_9_x86_64.whl (14.1 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

yt-4.1.2-cp39-cp39-win_amd64.whl (13.1 MB view details)

Uploaded CPython 3.9 Windows x86-64

yt-4.1.2-cp39-cp39-win32.whl (12.5 MB view details)

Uploaded CPython 3.9 Windows x86

yt-4.1.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (40.5 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

yt-4.1.2-cp39-cp39-macosx_11_0_arm64.whl (13.4 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

yt-4.1.2-cp39-cp39-macosx_10_9_x86_64.whl (14.1 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

yt-4.1.2-cp38-cp38-win_amd64.whl (13.6 MB view details)

Uploaded CPython 3.8 Windows x86-64

yt-4.1.2-cp38-cp38-win32.whl (13.0 MB view details)

Uploaded CPython 3.8 Windows x86

yt-4.1.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (41.2 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

yt-4.1.2-cp38-cp38-macosx_11_0_arm64.whl (13.9 MB view details)

Uploaded CPython 3.8 macOS 11.0+ ARM64

yt-4.1.2-cp38-cp38-macosx_10_9_x86_64.whl (14.5 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

yt-4.1.2-cp37-cp37m-win_amd64.whl (13.6 MB view details)

Uploaded CPython 3.7m Windows x86-64

yt-4.1.2-cp37-cp37m-win32.whl (13.0 MB view details)

Uploaded CPython 3.7m Windows x86

yt-4.1.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (39.1 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ x86-64

yt-4.1.2-cp37-cp37m-macosx_10_9_x86_64.whl (14.4 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

File details

Details for the file yt-4.1.2.tar.gz.

File metadata

  • Download URL: yt-4.1.2.tar.gz
  • Upload date:
  • Size: 12.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.15

File hashes

Hashes for yt-4.1.2.tar.gz
Algorithm Hash digest
SHA256 0ae03288b067721baad14c016f253dc791cd444a1f2dd5d804cf91da622a0c76
MD5 5275ea1542fa3ce55735103bc091d2bb
BLAKE2b-256 e2575c519b90ce1c0d254f21b76f04c02522277a4aeabea0a9c92ff2620e8bac

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: yt-4.1.2-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 13.1 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.15

File hashes

Hashes for yt-4.1.2-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 9c08cbdd133ed578929bf60c9139688f59325c51b201fa7f7adf77cc2e5f1d69
MD5 f969fab1d6501ca98affe6363e1c1c6f
BLAKE2b-256 fda573c2bee473d6cb5b3b102d1b33ead47df2da33456db010b62139aad9b676

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp311-cp311-win32.whl.

File metadata

  • Download URL: yt-4.1.2-cp311-cp311-win32.whl
  • Upload date:
  • Size: 12.5 MB
  • Tags: CPython 3.11, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.15

File hashes

Hashes for yt-4.1.2-cp311-cp311-win32.whl
Algorithm Hash digest
SHA256 fa7e9ab24979ed5da14aa52e666a031ad4a0449ceb89bf81c523a787b9ad989f
MD5 edacda245f9bd367a26419b0b809dc7a
BLAKE2b-256 b4370fb12a8354c686948c9a7caa7fbf27076f4faae9626d0262058bf1dbeadf

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yt-4.1.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 79fbcd37f68da939a9b6d3500e3a0bc686e61fb78552ffd9e7a31f54acfda269
MD5 46c93e704a7c09dc19ef407f076c34f9
BLAKE2b-256 ad61f1481e9f4e3854fcd009ed51eb6b8f60a29c7618df006c7c8f6739721f2b

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for yt-4.1.2-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 349b8a20697c1c22aa3ab1ce90b1527ac797a3ec394bef132265335c21225951
MD5 37151c5a5ac3df7649f14914f98706c3
BLAKE2b-256 2b610c3b57d7e7afcd5c8a07a1b0057eb08564c1bf9ead908d5f9ea6eec0bc6a

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for yt-4.1.2-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 319cff0237f57d38089d4386969f1d54c9587e0c13e902b41f505c252da78f48
MD5 f550b4ee7f6ba61deca003051b54d45b
BLAKE2b-256 ea193e1b1e79aa125df2fde9af6541b5978660787b07e180aceb12bb668c178f

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: yt-4.1.2-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 13.1 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.15

File hashes

Hashes for yt-4.1.2-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 25e0f738b961a86eb88b256b3d5be27055e30c53f4c2719be39d2a287c9583b6
MD5 6deb81d932ef1d8e30bbe8ad90d0deb6
BLAKE2b-256 9253550782c81a4aa3548c0c19919d25d86f36982a183938512898bcac9d6b09

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp310-cp310-win32.whl.

File metadata

  • Download URL: yt-4.1.2-cp310-cp310-win32.whl
  • Upload date:
  • Size: 12.5 MB
  • Tags: CPython 3.10, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.15

File hashes

Hashes for yt-4.1.2-cp310-cp310-win32.whl
Algorithm Hash digest
SHA256 a6c04a2918988bde170e5360fa97ef4d1411538ae0ba4bc91497847b16ee4dfb
MD5 b65fecab7d029eba35b8cb46cc08e836
BLAKE2b-256 9243dc58a500e817355b9b9059954d1478c0a2da45a945474bcbecfdeecab475

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yt-4.1.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 ba4a0e7662df0a3875818d289f15257b2727a51b167e9ecc3e0d9a7d03793295
MD5 cb6160bcea38703d9315deaf8cfd78bc
BLAKE2b-256 23f071786b47580f1236a4562b8fe8c8d89ae72cfd098f672026c1efe770685b

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for yt-4.1.2-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 c3da741e4d78fa2e67081f50202257a6b1aec0b18601a14e3857c2dda4f35d0d
MD5 9ac601fe25618ec741e6381b38ebdbec
BLAKE2b-256 decefb2a9a43c2d567b2fa1f7937155f38602f512ac6ca171cadb652d1662f78

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for yt-4.1.2-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 5874205f1af4dbb32ab07e7935c3a4f0fcc73360b84d6bb5aa0a1127e68ce7cd
MD5 83a58ff122983785da05f678d4ba8314
BLAKE2b-256 80f13f563e450eb60f6809dd37f8c3cec23b5a4d6f7cdc2e0a27d66c131bf7f2

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: yt-4.1.2-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 13.1 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.15

File hashes

Hashes for yt-4.1.2-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 64ad890626fa6e0a939ccff87c8ebc5ab7140e686ac1f7349891bf0ddb2b4487
MD5 a0c2021c21685c781fff4186d107f4d7
BLAKE2b-256 b9bd83b2775fd0abbe4f3aef7635b1d13e3bbdfe4b406189f140ec11d45817af

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp39-cp39-win32.whl.

File metadata

  • Download URL: yt-4.1.2-cp39-cp39-win32.whl
  • Upload date:
  • Size: 12.5 MB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.15

File hashes

Hashes for yt-4.1.2-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 11e4fda846299860ef5dde780a8d5ebac90b439e5ae94685870a84670229ca63
MD5 33da7711731fba0416d1f991c4f49cba
BLAKE2b-256 da0c8f43dc72b8de03fa0aa42f1d27fa9798139cbf2e2df03447b90341833b6a

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yt-4.1.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 31fcf3903a0f690fa7cd3135e201611822c66e7aabb2e51040348fee6624d55f
MD5 8e27dffc6f4ccc27ecd23d2777e50dec
BLAKE2b-256 cb28b7254530522cd0a6979a27cf0d80e74540417f945c0a876852e6b92fd479

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

  • Download URL: yt-4.1.2-cp39-cp39-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 13.4 MB
  • Tags: CPython 3.9, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.15

File hashes

Hashes for yt-4.1.2-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 f7884a20502a8325221c9d9b3dd43e0a36c821677c1c4ddb81d82d5a54f571d2
MD5 26a877c791ff169fe68889b0eef57e66
BLAKE2b-256 0aeb6ca078dc67edea5b8a6437baeb9bdaf1fbe2899c343c70de4eec963559bd

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for yt-4.1.2-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 73acae02b49fc4c3d3d98dd5908986daa39024a39b70882ca998c7acc306d047
MD5 39508e384b886f7c84e0fbbfaeacfa05
BLAKE2b-256 6ab1ce70e18abf74b33dbcd4b9aa5d3ee5a6ded4f7101401dfd08f3c2695d342

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: yt-4.1.2-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 13.6 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.15

File hashes

Hashes for yt-4.1.2-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 f881c3f68149fa9350eef2ab46849b59bfad81f7a92a66241d0bde6b698f533e
MD5 91fe36b4e0d2fd2dcd1aff0709decf90
BLAKE2b-256 b06ae851e696a3fd1530a3cf2a9a1d99609f629ec90d243a7424d5caee2edbe9

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp38-cp38-win32.whl.

File metadata

  • Download URL: yt-4.1.2-cp38-cp38-win32.whl
  • Upload date:
  • Size: 13.0 MB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.15

File hashes

Hashes for yt-4.1.2-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 b40d986c5e224907cb2639b128c2423b03da480de882e7d31ee4c7c4bc7a79fc
MD5 32d2f642377d4e0c990cc2220e928a41
BLAKE2b-256 cae44319f888044a6ccac44533590cc4511542b0b1f2c0da8bece93fbfe3ba10

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yt-4.1.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 17bfb7ac59f85c2431ac28fd4a96cb865db76b91d0d0b89de0792bf02406fafe
MD5 ee4504f001d861aa3320d619bfecc32a
BLAKE2b-256 8e8f3f4b42a5120137af8042bfcb483061d4f04f50fd87960dff1890f1905390

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

  • Download URL: yt-4.1.2-cp38-cp38-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 13.9 MB
  • Tags: CPython 3.8, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.15

File hashes

Hashes for yt-4.1.2-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 1ae6fe04215b99cecdf6af7e88daecfa81f6dbd82d058eac7b0fd1c108a88138
MD5 bc9e5c204f1ebb6f34634be76d64a8f1
BLAKE2b-256 efcdc53f3907bb96383ca37c40ec06d64868858b92469de6e5410f301491373b

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for yt-4.1.2-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 ee76642af64697a3a069a65a9614896787467cc718b1a64b5d523a64bf717546
MD5 451a84803dd87c7371fab01fb7095dba
BLAKE2b-256 98c584ad9c4704aa5f277bd8ee17f67f968d02d9b2750249f61d387392cd543e

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: yt-4.1.2-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 13.6 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.15

File hashes

Hashes for yt-4.1.2-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 6d00a02483c56edaf8a96624c6357d8f9770eccf301d8e9530e3fcb8c0aa642d
MD5 19e0a1cb76414dfb95e4dcaf1349b64c
BLAKE2b-256 4334e6806e6e072336e4065d3d5a71b24f2e32f6926884da562033542324c365

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp37-cp37m-win32.whl.

File metadata

  • Download URL: yt-4.1.2-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 13.0 MB
  • Tags: CPython 3.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.15

File hashes

Hashes for yt-4.1.2-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 d0e5ae5c17b8ba23c677989b5c1cee50db96ce3ea7c4c58d2e3b6cc5a25db131
MD5 22c6e1b094a18714c0f75d9584f41017
BLAKE2b-256 13ca8f9c075053cec981c067e30b438977d74b5707895c4354fa004281ecb162

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for yt-4.1.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 6ddc0c82ed549e3a3cdabd9058ea1f18347d2354c113e5b1880ba8ec2ad3668a
MD5 259b320b95f96c30cd1e89ea74d6dff4
BLAKE2b-256 acd366314dfca6348548c085c3eab0f8049260467203a7c822a3ec173d34ca55

See more details on using hashes here.

File details

Details for the file yt-4.1.2-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for yt-4.1.2-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 e54a3dfbfe7f422f107c6a819ca7b7ac6a41c8e77b11a2c9e2e868eeda1a789f
MD5 dc3fcd47afa715497c8852cabc9fec8a
BLAKE2b-256 0a7bbd983f375e2a1d8b3b7636d841b28bcbf1b4ef95110391707e9a25b7ceed

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