Skip to main content

VisPy: interactive scientific visualization in Python

Main website: http://vispy.org

Build Status Coverage Status Zenodo Link Contributor Covenant


VisPy is a high-performance interactive 2D/3D data visualization library. VisPy leverages the computational power of modern Graphics Processing Units (GPUs) through the OpenGL library to display very large datasets. Applications of VisPy include:

  • High-quality interactive scientific plots with millions of points.

  • Direct visualization of real-time data.

  • Fast interactive visualization of 3D models (meshes, volume rendering).

  • OpenGL visualization demos.

  • Scientific GUIs with fast, scalable visualization widgets (Qt or IPython notebook with WebGL).

Releases

See CHANGELOG.md.

Announcements

See the VisPy Website.

Using VisPy

VisPy is a young library under heavy development at this time. It targets two categories of users:

  1. Users knowing OpenGL, or willing to learn OpenGL, who want to create beautiful and fast interactive 2D/3D visualizations in Python as easily as possible.

  2. Scientists without any knowledge of OpenGL, who are seeking a high-level, high-performance plotting toolkit.

If you’re in the first category, you can already start using VisPy. VisPy offers a Pythonic, NumPy-aware, user-friendly interface for OpenGL ES 2.0 called gloo. You can focus on writing your GLSL code instead of dealing with the complicated OpenGL API - VisPy takes care of that automatically for you.

If you’re in the second category, we’re starting to build experimental high-level plotting interfaces. Notably, VisPy now ships a very basic and experimental OpenGL backend for matplotlib.

Installation

Please follow the detailed installation instructions on the VisPy website.

Structure of VisPy

Currently, the main subpackages are:

  • app: integrates an event system and offers a unified interface on top of many window backends (Qt4, wx, glfw, jupyter notebook, and others). Relatively stable API.

  • gloo: a Pythonic, object-oriented interface to OpenGL. Relatively stable API.

  • scene: this is the system underlying our upcoming high level visualization interfaces. Under heavy development and still experimental, it contains several modules.

    • Visuals are graphical abstractions representing 2D shapes, 3D meshes, text, etc.

    • Transforms implement 2D/3D transformations implemented on both CPU and GPU.

    • Shaders implements a shader composition system for plumbing together snippets of GLSL code.

    • The scene graph tracks all objects within a transformation graph.

  • plot: high-level plotting interfaces.

The API of all public interfaces are subject to change in the future, although app and gloo are relatively stable at this point.

Code of Conduct

The VisPy community requires its members to abide by the Code of Conduct. In this CoC you will find the expectations of members, the penalties for violating these expectations, and how violations can be reported to the members of the community in charge of enforcing this Code of Conduct.

Governance

The VisPy project maintainers make decisions about the project based on a simple consensus model. This is described in more detail on the governance page of the vispy website as well as the list of maintainers.

In addition to decisions about the VisPy project, there is also a steering committee for the overall VisPy organization. More information about this committee can also be found on the steering committee page of the vispy website, along with the organization’s charter and other related documents (linked in the charter).

Genesis

VisPy began when four developers with their own visualization libraries decided to team up: Luke Campagnola with PyQtGraph, Almar Klein with Visvis, Cyrille Rossant with Galry, Nicolas Rougier with Glumpy.

Now VisPy looks to build on the expertise of these developers and the broader open-source community to build a high-performance OpenGL library.


Download files

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

Source Distribution

vispy-0.14.1.tar.gz (2.5 MB view details)

Uploaded Source

Built Distributions

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

vispy-0.14.1-cp312-cp312-win_amd64.whl (1.5 MB view details)

Uploaded CPython 3.12Windows x86-64

vispy-0.14.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.9 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ x86-64

vispy-0.14.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.9 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ ARM64

vispy-0.14.1-cp312-cp312-macosx_11_0_arm64.whl (1.5 MB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

vispy-0.14.1-cp312-cp312-macosx_10_9_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.12macOS 10.9+ x86-64

vispy-0.14.1-cp311-cp311-win_amd64.whl (1.5 MB view details)

Uploaded CPython 3.11Windows x86-64

vispy-0.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.9 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

vispy-0.14.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.9 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ ARM64

vispy-0.14.1-cp311-cp311-macosx_11_0_arm64.whl (1.5 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

vispy-0.14.1-cp311-cp311-macosx_10_9_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.11macOS 10.9+ x86-64

vispy-0.14.1-cp310-cp310-win_amd64.whl (1.5 MB view details)

Uploaded CPython 3.10Windows x86-64

vispy-0.14.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.8 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

vispy-0.14.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.8 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ ARM64

vispy-0.14.1-cp310-cp310-macosx_11_0_arm64.whl (1.5 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

vispy-0.14.1-cp310-cp310-macosx_10_9_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.10macOS 10.9+ x86-64

vispy-0.14.1-cp39-cp39-win_amd64.whl (1.5 MB view details)

Uploaded CPython 3.9Windows x86-64

vispy-0.14.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.8 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

vispy-0.14.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.8 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ ARM64

vispy-0.14.1-cp39-cp39-macosx_11_0_arm64.whl (1.5 MB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

vispy-0.14.1-cp39-cp39-macosx_10_9_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.9macOS 10.9+ x86-64

vispy-0.14.1-cp38-cp38-win_amd64.whl (1.5 MB view details)

Uploaded CPython 3.8Windows x86-64

vispy-0.14.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.8 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

vispy-0.14.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.8 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ ARM64

vispy-0.14.1-cp38-cp38-macosx_11_0_arm64.whl (1.5 MB view details)

Uploaded CPython 3.8macOS 11.0+ ARM64

vispy-0.14.1-cp38-cp38-macosx_10_9_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.8macOS 10.9+ x86-64

File details

Details for the file vispy-0.14.1.tar.gz.

File metadata

  • Download URL: vispy-0.14.1.tar.gz
  • Upload date:
  • Size: 2.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.18

File hashes

Hashes for vispy-0.14.1.tar.gz
Algorithm Hash digest
SHA256 249a50979fc00a8b65109283354dcf12cf415c1a5dcf9821e113f6e590b9b93c
MD5 94e53acd344f13041810274f6776bd57
BLAKE2b-256 9ac1b072e7910a2da014980452446ca27db7e9a4ac59ce97c25d5316dcccfa2b

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: vispy-0.14.1-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.18

File hashes

Hashes for vispy-0.14.1-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 ac9bd77cc8ca01dd7b72280677f4b6e12623e901dffd71be776110dc34cb2a30
MD5 893b42fc27325f1cd6c0bd243a1bb71d
BLAKE2b-256 f78c4eb280a20cf3ebbddfce1fe2dcf6f2bcd57365203b84350486ab52c41339

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for vispy-0.14.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 4537a927365392470ab337eaf7e36511a748c24460fb7ae3c56a86512dca7feb
MD5 d697594eae563b3753ab8eb7406985f3
BLAKE2b-256 acccb51ffb0a57f0a2d4dfe3d539fda59299e0dad72c9d59d32459ec7537fa95

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for vispy-0.14.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 fcb84178ed7a3e24aba49c9a054405eddd87a7f6bdfaac90f57366213f3f5898
MD5 5cb288a3f11096152035d4587d5bd50e
BLAKE2b-256 d2d698abd7fbf2a17ca17fba3893c751ccd6a953d5fca0f0a70d076f3794ab88

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for vispy-0.14.1-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 15a4e155d27bbcffeb00f2ddff2c38d27d6bf6e0f1727361cc94a2726cc01d7f
MD5 8ec7df6df1ab12429efe853673fdb8b0
BLAKE2b-256 a344e8e0be8810459af66321fae2eb3dca9d4e23fa3e14c9c1457e16a2790ff3

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp312-cp312-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for vispy-0.14.1-cp312-cp312-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 d4850ab3f441e5d242338a1ceb778b76cbb0ccef004f2c44c5831bea3b68ae5a
MD5 bd14cab59c35f3d7df0a0258a3f759f5
BLAKE2b-256 c0dc0e76e8aebf5ac1d3e9aa251bb935399a49b7dd10511092285b3859577bdf

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: vispy-0.14.1-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.18

File hashes

Hashes for vispy-0.14.1-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 15b32d62ae648f439977f72173a580ba9ff922adfe156ecccccc77cc508c04c5
MD5 a41e220eb9a6f008a2aa964762860585
BLAKE2b-256 fa937a861172b958e8cfaac0c4f6d4a98d0be60eb904caa40c372f084f7b5e54

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for vispy-0.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 62f6cdf1e318f1d64000d97727cd8f291f846cb7b4469f9302d42ecab451aa03
MD5 bef4345186c053a6ca84706a42ce1841
BLAKE2b-256 39fb882b2f14fe268db39c7d49f8196db830fa6b59107391a0a4acdc69e33a1e

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for vispy-0.14.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 d6ff102fbb6578aa94eac893dcec95dc9d962d7fca4ec0e3e3b05a63ca8860e0
MD5 1490f307387d7d1d8882b62e735f7794
BLAKE2b-256 1ab2cb2dc2da19dc924441e71ea8bc4400afeaafaf113a83b2332f5eb5cdeda6

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for vispy-0.14.1-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 e15f6448bd66ea62407e362d205a4b8cf81ecf201c626742cfd06c81d59703c1
MD5 4539dff56fccf39833460793f2f9255a
BLAKE2b-256 2309b778ef0388688ebc266c9e824134090be4d76c5c8a18b34cd3a88edec42c

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for vispy-0.14.1-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 a0ffcf972ca5d7578db8d065b00a223bfff734c6f04716ac24e8c37ee6508187
MD5 91c891f8df39cd75dfbf6b2b0eb9c0ee
BLAKE2b-256 53f19ecc55fb1a842b5b07db536efb4de7b8fc941ec28f31b7a073aaed56c39f

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: vispy-0.14.1-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.18

File hashes

Hashes for vispy-0.14.1-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 a4cc6b9e161051673909178892e4a30bbce1ef09782e03f90f39758d83b8694f
MD5 544b31c24fa73a54b6ef76c820662b18
BLAKE2b-256 75219d1d5b9cf569b41c2f61ae158d6ada85c42eadbcb226c514f26f38e87bad

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for vispy-0.14.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 4b828784549dfb5cbc6df02f3bc96d4d8cef49e752676ba12939f4400e0d6570
MD5 8ca3d8bc7c7a57fa7a3888874f5bba6e
BLAKE2b-256 fe8717a68f885a2f99734f06e83bfeeaeeff85b4243876f60c4517532090a032

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for vispy-0.14.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 6f8872ec2d33e2f8579055aa37cd4427e2334ee9e6a2b6a1abc1d82c3f741f1c
MD5 7e9034fdac5ff49b8f013b3530e184eb
BLAKE2b-256 37518606a54522afeb1374209a6edec284c938c2d7195eb9b666e481114762f1

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for vispy-0.14.1-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 27c100b647461e171b36090a6c68d852328d6b4e1fcc3a326a7c8119fed9b4b1
MD5 7c6015eefe6fd78d7882d915b3f0c358
BLAKE2b-256 6e7ab4be523d329a7107e30c4b911056e3321f3b833502587bc23553692869ef

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for vispy-0.14.1-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 629274e84fd823ce81abe3024c2675323ccb8aab5a696556a0cce656d96397bb
MD5 41ce548ae4e098dd58e6c761a26b4dc8
BLAKE2b-256 3f1f4c1e42c69fcaf951d1f640ddf7945608d8e14c9996c20941986b90186091

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: vispy-0.14.1-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.18

File hashes

Hashes for vispy-0.14.1-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 4f4f3f48ed18deed51578e5f05f9ecbe7a37f0e563d4b98fa159d5900bb83504
MD5 5a9ff990536934129835a8fb54b4f8e2
BLAKE2b-256 3ca7b5446bc67ae2fd1ae52a98d56d44dde3343af00bb25a1006f00c03a2524b

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for vispy-0.14.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 23ccbf235ef9bc654c49774062b9d7478b7c7fb9d21abbc64c9210ccf4821676
MD5 40c5ccd3eb636e734477d0f2c3c6153a
BLAKE2b-256 ae941e198bee0c08dfdfbc9a3c9278a515d4edb22edd4501dab919592386e248

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for vispy-0.14.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 c430e2be26006a3ea30e775db88c1e6c02885d141f3243b2107c92793e4fc5b0
MD5 13089a1e7b7360959b500e4aeada6375
BLAKE2b-256 e5764cc1c0e77e3be8c68bc383db2125cdf76a9f2393f2ff66a2317cd4e84f3b

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for vispy-0.14.1-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 364f6df46008bcea8bba8b7bc98be7c4d6803c32838bdea5d6ee46974ebd2436
MD5 bdfeb476cb48288c35f1e98631bccb18
BLAKE2b-256 a4b2722638d82b51ff123dfedf0791de64e8f12bf58ce784e594fc3cfda79d54

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for vispy-0.14.1-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 60e1cd308f0c894faec68ba8c209f1e967f3c67e103c26768c47ea8f0d1b046b
MD5 d8601cc1d8f6974449cd2d0117cfb79e
BLAKE2b-256 642bcf2c1410c5dc2fac8474e62a3001eb576fc0a58ac06a20cd604181b7ae31

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: vispy-0.14.1-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.18

File hashes

Hashes for vispy-0.14.1-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 a009cdcf2b6aa5bf3a4891988b3f4d92c05f0e68526b1fde727c358403a0aa12
MD5 79bc138874d3a588c87853f6c23f9e00
BLAKE2b-256 0033014124e4e51e75305612f92047935bf45fff4bd7c8c0e314a74ee6ac9664

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for vispy-0.14.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 9700e6b7e610276f41bfe2aaf34a856049ee5b7c43e03347b068f78042659451
MD5 39b7ebefffc3dd8004d7d1101333bd89
BLAKE2b-256 1f803a2a3c51744e3301271bffb4bf60c0070edcf7e59c02b88182970dbd6806

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for vispy-0.14.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 aa1234671e2a0232ee7b2a4e0d23c2c38eba7f1f92d6466f54f94ca94a7449ca
MD5 16bceda6c2664e41df8197f20ca0c16a
BLAKE2b-256 c1d5f07102d5f193a1fdc147efcf211c4fb2a170055780b9a16d8fc6ab2af409

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for vispy-0.14.1-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 1f3fcae30a8b4b03c190e627f7af7f43e3ab9be43370d9697eed09c988d2ca4d
MD5 5cc1c2e22ea0124c17c49b69d7e34a27
BLAKE2b-256 c7adca204f79c0d5b3f31cd8894115f722afec102a6d8b215e1212b6d4b1f666

See more details on using hashes here.

File details

Details for the file vispy-0.14.1-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for vispy-0.14.1-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 985718f1f17b07fd9848e31269465c81fb21cc2552e1ce965c50dbf5e466d6d7
MD5 a866a431548dff52a1407f33ebac1195
BLAKE2b-256 fed75d1a3acdf3f09741bdc3fbc87e9917c95aca207c8c87c97775766db9f90b

See more details on using hashes here.

Release history Release notifications | RSS feed

0.17.0

41 files

0.16.2

36 files

0.16.1

36 files

0.16.0

36 files

0.15.2

26 files

0.15.1

26 files

0.15.0

25 files

0.14.3

21 files

0.14.2

26 files

This release

0.14.1 This release

26 files

0.14.0

26 files

0.13.0

25 files

0.12.2

21 files

0.12.1

17 files

0.12.0

17 files

0.11.0

17 files

0.10.0

17 files

0.9.6

17 files

0.9.5

17 files

0.9.4

16 files

0.9.3

16 files

0.9.2

13 files

0.9.1

13 files

0.9.0

13 files

0.8.1

13 files

0.8.0

13 files

0.7.3

13 files

0.7.2

13 files

0.7.1

19 files

0.7.0

19 files

0.6.6

29 files

0.6.5

22 files

0.6.4

22 files

0.6.3

22 files

0.6.2

16 files

0.6.1

16 files

0.6.0

16 files

0.5.3

1 file

0.5.2

1 file

0.5.1

1 file

0.5.0

1 file

0.4.0

1 file

0.3.0

1 file

0.2.1

1 file

0.2

1 file

0.1.0

1 file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page