Skip to main content

Interactive visualization in Python

Project description

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](./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.

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.


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

vispy-0.10.0.tar.gz (2.4 MB view details)

Uploaded Source

Built Distributions

vispy-0.10.0-cp310-cp310-win_amd64.whl (1.4 MB view details)

Uploaded CPython 3.10 Windows x86-64

vispy-0.10.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.5 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ARM64

vispy-0.10.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64 manylinux: glibc 2.5+ x86-64

vispy-0.10.0-cp310-cp310-macosx_10_9_x86_64.whl (1.4 MB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

vispy-0.10.0-cp39-cp39-win_amd64.whl (1.4 MB view details)

Uploaded CPython 3.9 Windows x86-64

vispy-0.10.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.5 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

vispy-0.10.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64 manylinux: glibc 2.5+ x86-64

vispy-0.10.0-cp39-cp39-macosx_10_9_x86_64.whl (1.4 MB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

vispy-0.10.0-cp38-cp38-win_amd64.whl (1.4 MB view details)

Uploaded CPython 3.8 Windows x86-64

vispy-0.10.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.5 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

vispy-0.10.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64 manylinux: glibc 2.5+ x86-64

vispy-0.10.0-cp38-cp38-macosx_10_9_x86_64.whl (1.4 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

vispy-0.10.0-cp37-cp37m-win_amd64.whl (1.4 MB view details)

Uploaded CPython 3.7m Windows x86-64

vispy-0.10.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.5 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ ARM64

vispy-0.10.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ x86-64 manylinux: glibc 2.5+ x86-64

vispy-0.10.0-cp37-cp37m-macosx_10_9_x86_64.whl (1.4 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

File details

Details for the file vispy-0.10.0.tar.gz.

File metadata

  • Download URL: vispy-0.10.0.tar.gz
  • Upload date:
  • Size: 2.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.12

File hashes

Hashes for vispy-0.10.0.tar.gz
Algorithm Hash digest
SHA256 b76ad6f3eacadbfc4944cfb8211eadb6ea8417a590993ece5aa7caac082cffc2
MD5 e767ca7afebdcb2090da7c91aab07749
BLAKE2b-256 b24463cc2864fdf88ac8d3d0abaaa1799f19ee9327322a7c16b0525a7d956c40

See more details on using hashes here.

File details

Details for the file vispy-0.10.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: vispy-0.10.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 1.4 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.12

File hashes

Hashes for vispy-0.10.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 64dc5340f2a6a1513d048723515d6c59dbeb019148bfd7f543146593074dfdb0
MD5 c407b7c5868e0dd87ca5e0105f6e27b1
BLAKE2b-256 7f657af5ae59be2a54a40d47d0d1656edae78bef9d48b4d10f8a6abf98ed2aa1

See more details on using hashes here.

File details

Details for the file vispy-0.10.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for vispy-0.10.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 1980bc30b6f5c41a3836f9613cbff48be1c9162f7e5f421587832abfda6a7b37
MD5 b2bb4f12a37aa67163c6ae26af2e0ae5
BLAKE2b-256 3e3d11837c713881f902a15e18c0ab7c870b1829526b97523d09559e0581207e

See more details on using hashes here.

File details

Details for the file vispy-0.10.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for vispy-0.10.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 32d9522246cff28261c3075b5befee1db1c084b717448de4fdda5268e4cd391f
MD5 39456e47e4f5dac42be14f27cf988618
BLAKE2b-256 2f9394bd7bd25783b838d9161c2c6e127dbb626d532e3ab66b11b97012e83bd5

See more details on using hashes here.

File details

Details for the file vispy-0.10.0-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for vispy-0.10.0-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 58057de0a8995a384b8b4163e912cbe2b80a2034afdb6d437739a9fa5c70d921
MD5 50f5f0d12623bbebe87b6125ee4bfb76
BLAKE2b-256 38055b281e0e4eb4c036f21efd2180a369b1379e9a20bdc4e574c35eec4587b9

See more details on using hashes here.

File details

Details for the file vispy-0.10.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: vispy-0.10.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 1.4 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.12

File hashes

Hashes for vispy-0.10.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 910d883df109c92cc779243fe814f4a2d5e1b18ba7fe8eaa59d61bdd8abb8833
MD5 a7a5e41942a2939ea830284da5866ca6
BLAKE2b-256 579ce633f50425728a6f2d51237473553f29afda3d487d619fc3202f9c18f201

See more details on using hashes here.

File details

Details for the file vispy-0.10.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for vispy-0.10.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 f212733c3a4ff9522eb59bb12375e2aec38c4c2723c969f35627530796ed0229
MD5 79beee7a6e38eb3525b4e6fdd494795e
BLAKE2b-256 fd42885d2c99061704b04fed7313f87e23121aee467d943d461272a3858592a6

See more details on using hashes here.

File details

Details for the file vispy-0.10.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for vispy-0.10.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 626ea19cda7caa027c29381b8a5e29f052f58cd23c9357b856791341c4372f7e
MD5 2d61593d517f78c7f6a53329f3d6e099
BLAKE2b-256 24c31e7c80fd7f935aad6ac7d393a11daac9358d481faedee8923a26b7a7e2e8

See more details on using hashes here.

File details

Details for the file vispy-0.10.0-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for vispy-0.10.0-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 b471b0c9dc0d3623421acbf3ce75c1fd45b41467d830ab32c28af54d56d25cb5
MD5 cec530847fd7aafa72d55f940dc74fd6
BLAKE2b-256 63a623c0ede379e1c2bbd149e5cb72b21f844c5bef6f302b5bfb66d7e60ea0f7

See more details on using hashes here.

File details

Details for the file vispy-0.10.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: vispy-0.10.0-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 1.4 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.12

File hashes

Hashes for vispy-0.10.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 e467ae5d041e2af056e6b57d8ad6b31dcf881ae8944400008834191483b78d5a
MD5 0c958d7b221ab1da1320ac120dee7591
BLAKE2b-256 23d30226d286443a314d4e354ad1e45b2197cad56ba105796a7045033f4ed745

See more details on using hashes here.

File details

Details for the file vispy-0.10.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for vispy-0.10.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 5a68a406f59f9d5be1b97af7e7fa4e60c676a02c231254a693268a7723d544fb
MD5 a0f9dc08b179a1b09f4f01c09bb93eca
BLAKE2b-256 47455627d7b1a6bd7884557d54968c07ea610efee3603a9b205567a87744c102

See more details on using hashes here.

File details

Details for the file vispy-0.10.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for vispy-0.10.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 44493bcfd4aaf6eec96ce6ed911cc8b6fc48d9c2b5bd082e900d17416ed2de8d
MD5 2533479022352bdfa15dc35fdf58cbf7
BLAKE2b-256 a2879d329789cb4aee75be81f36d36cbc91142bb24e36078f4899137a2293ac2

See more details on using hashes here.

File details

Details for the file vispy-0.10.0-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for vispy-0.10.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 633034d5621834c6852965604e43dc94c7623f787d356f0fd41fd4f4771a8654
MD5 7aff3df663e49ab1a926d8e3b7d8effb
BLAKE2b-256 454c6615512e479c6c3b3b061ae6226097aca658e90d80eff663841bd3f08eca

See more details on using hashes here.

File details

Details for the file vispy-0.10.0-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: vispy-0.10.0-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 1.4 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.12

File hashes

Hashes for vispy-0.10.0-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 a5663b1a16f4de0034e8e546122ead82c78b588894f514f0ab92188030f4bafc
MD5 00b803ebd3aef09aa63930898ee92a49
BLAKE2b-256 29948d9afe73981a0d20deb03585ac083d4cac54877c86f352593b62c1b22200

See more details on using hashes here.

File details

Details for the file vispy-0.10.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for vispy-0.10.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 0635a411dacca404d6ba5544afd9badd833c894196b0bdb611f18ff7c206b3cc
MD5 dde7a4b58321452671a8fc617fd82611
BLAKE2b-256 4feab3fb53f1f13f1ec76d8410754bae37f08b8ee4535368b1d7155889f1c694

See more details on using hashes here.

File details

Details for the file vispy-0.10.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for vispy-0.10.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 2da21a10647d98feb0242304264b51be618f5d66d0d800989617310abb1834ec
MD5 e9d90dd0763ca25ee870ccb89f5643dc
BLAKE2b-256 828a6cec90202f9cc959f3e7bf5329ee1adcb0288431a10225402d3e2407f011

See more details on using hashes here.

File details

Details for the file vispy-0.10.0-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for vispy-0.10.0-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 294f747002773480e72d01fa005807992f8f946944d6cc597ff26addb5b22ac6
MD5 43f2e27cdae0abd9c745e605f5c84922
BLAKE2b-256 55e20c45c7b061986f8788f10eeea0e0fb4548047aa8bd686243aae482472c53

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