vispy

Interactive visualization in Python

These details have not been verified by PyPI

Project links

Project description

VisPy: interactive scientific visualization in Python

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:

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.
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.

External links

Project details

These details have not been verified by PyPI

Project links

Release history Release notifications | RSS feed

0.14.3

Jun 17, 2024

This version

0.14.2

Mar 14, 2024

0.14.1

Oct 3, 2023

0.14.0

Sep 8, 2023

0.13.0

May 12, 2023

0.12.2

Mar 20, 2023

0.12.1

Nov 14, 2022

0.12.0

Nov 8, 2022

0.11.0

Jul 4, 2022

0.10.0

Apr 18, 2022

0.9.6

Feb 4, 2022

0.9.5

Feb 4, 2022

0.9.4

Nov 24, 2021

0.9.3

Oct 27, 2021

0.9.2

Oct 21, 2021

0.9.1

Oct 20, 2021

0.9.0

Sep 29, 2021

0.8.1

Aug 27, 2021

0.8.0

Aug 20, 2021

0.7.3

Jul 21, 2021

0.7.2

Jul 20, 2021

0.7.1

Jul 13, 2021

0.7.0

Jun 30, 2021

0.6.6

Nov 28, 2020

0.6.5

Sep 24, 2020

0.6.4

Dec 13, 2019

0.6.3

Nov 27, 2019

0.6.2

Nov 5, 2019

0.6.1

Jul 28, 2019

0.6.0

Jul 11, 2019

0.5.3

Mar 28, 2018

0.5.2

Dec 12, 2017

0.5.1

Nov 4, 2017

0.5.0

Oct 24, 2017

0.4.0

May 22, 2015

0.3.0

Aug 29, 2014

0.2.1

Nov 4, 2013

0.2

Nov 4, 2013

0.1.0

Aug 14, 2013

Download files

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

Source Distribution

vispy-0.14.2.tar.gz (2.5 MB view details)

Uploaded Mar 14, 2024 Source

Built Distributions

vispy-0.14.2-cp312-cp312-win_amd64.whl (1.5 MB view details)

Uploaded Mar 14, 2024 CPython 3.12 Windows x86-64

vispy-0.14.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.9 MB view details)

Uploaded Mar 14, 2024 CPython 3.12 manylinux: glibc 2.17+ x86-64

vispy-0.14.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.9 MB view details)

Uploaded Mar 14, 2024 CPython 3.12 manylinux: glibc 2.17+ ARM64

vispy-0.14.2-cp312-cp312-macosx_11_0_arm64.whl (1.5 MB view details)

Uploaded Mar 14, 2024 CPython 3.12 macOS 11.0+ ARM64

vispy-0.14.2-cp312-cp312-macosx_10_9_x86_64.whl (1.5 MB view details)

Uploaded Mar 14, 2024 CPython 3.12 macOS 10.9+ x86-64

vispy-0.14.2-cp311-cp311-win_amd64.whl (1.5 MB view details)

Uploaded Mar 14, 2024 CPython 3.11 Windows x86-64

vispy-0.14.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.9 MB view details)

Uploaded Mar 14, 2024 CPython 3.11 manylinux: glibc 2.17+ x86-64

vispy-0.14.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.9 MB view details)

Uploaded Mar 14, 2024 CPython 3.11 manylinux: glibc 2.17+ ARM64

vispy-0.14.2-cp311-cp311-macosx_11_0_arm64.whl (1.5 MB view details)

Uploaded Mar 14, 2024 CPython 3.11 macOS 11.0+ ARM64

vispy-0.14.2-cp311-cp311-macosx_10_9_x86_64.whl (1.5 MB view details)

Uploaded Mar 14, 2024 CPython 3.11 macOS 10.9+ x86-64

vispy-0.14.2-cp310-cp310-win_amd64.whl (1.5 MB view details)

Uploaded Mar 14, 2024 CPython 3.10 Windows x86-64

vispy-0.14.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.8 MB view details)

Uploaded Mar 14, 2024 CPython 3.10 manylinux: glibc 2.17+ x86-64

vispy-0.14.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.8 MB view details)

Uploaded Mar 14, 2024 CPython 3.10 manylinux: glibc 2.17+ ARM64

vispy-0.14.2-cp310-cp310-macosx_11_0_arm64.whl (1.5 MB view details)

Uploaded Mar 14, 2024 CPython 3.10 macOS 11.0+ ARM64

vispy-0.14.2-cp310-cp310-macosx_10_9_x86_64.whl (1.5 MB view details)

Uploaded Mar 14, 2024 CPython 3.10 macOS 10.9+ x86-64

vispy-0.14.2-cp39-cp39-win_amd64.whl (1.5 MB view details)

Uploaded Mar 14, 2024 CPython 3.9 Windows x86-64

vispy-0.14.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.8 MB view details)

Uploaded Mar 14, 2024 CPython 3.9 manylinux: glibc 2.17+ x86-64

vispy-0.14.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.8 MB view details)

Uploaded Mar 14, 2024 CPython 3.9 manylinux: glibc 2.17+ ARM64

vispy-0.14.2-cp39-cp39-macosx_11_0_arm64.whl (1.5 MB view details)

Uploaded Mar 14, 2024 CPython 3.9 macOS 11.0+ ARM64

vispy-0.14.2-cp39-cp39-macosx_10_9_x86_64.whl (1.5 MB view details)

Uploaded Mar 14, 2024 CPython 3.9 macOS 10.9+ x86-64

vispy-0.14.2-cp38-cp38-win_amd64.whl (1.5 MB view details)

Uploaded Mar 14, 2024 CPython 3.8 Windows x86-64

vispy-0.14.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.8 MB view details)

Uploaded Mar 14, 2024 CPython 3.8 manylinux: glibc 2.17+ x86-64

vispy-0.14.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.8 MB view details)

Uploaded Mar 14, 2024 CPython 3.8 manylinux: glibc 2.17+ ARM64

vispy-0.14.2-cp38-cp38-macosx_11_0_arm64.whl (1.5 MB view details)

Uploaded Mar 14, 2024 CPython 3.8 macOS 11.0+ ARM64

vispy-0.14.2-cp38-cp38-macosx_10_9_x86_64.whl (1.5 MB view details)

Uploaded Mar 14, 2024 CPython 3.8 macOS 10.9+ x86-64

File details

Details for the file vispy-0.14.2.tar.gz.

File metadata

Download URL: vispy-0.14.2.tar.gz
Upload date: Mar 14, 2024
Size: 2.5 MB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/5.0.0 CPython/3.9.18

File hashes

Hashes for vispy-0.14.2.tar.gz
Algorithm	Hash digest
SHA256	`eed8b44d6f5c87bd295b24aa9a2e0e4e6dc6a905ccee01b2d41c8fbf0a767b3d`
MD5	`0b8c1c3b06bef3cfb7ebe8615b313cdb`
BLAKE2b-256	`b31444e7def7e4415083fafd8b9fd1d57774007776d2be203442ac332b75f27b`

Algorithm	Hash digest
SHA256	`6f493f37db6130ae2a60fb97ad714429dd4b4fa8551883a3a6aa374efab7e04f`
MD5	`84b2232b3275290dc62a9c2189a9a487`
BLAKE2b-256	`73c19c8ab1168099e0ea1ff1e678ff3bf7bc2e3ab84688bf2e7f1fbf109fe7b2`

Algorithm	Hash digest
SHA256	`0b151f038b09829eddeffd1138e10a5cf98cdd3ef5f76427abd04718c33e0492`
MD5	`82f8466a7f9531c38309a60f29f161fb`
BLAKE2b-256	`79dec251f69797ac77bf422b9ea702435e7ff2c067f9788c07cc96cef4cb9c66`

Algorithm	Hash digest
SHA256	`d50eeca053fdcd7f2f1f93204f327116018269453ef99a092f79308eab76ddd3`
MD5	`f3dad01aeac5e99f2c3e93a7a3f90fbb`
BLAKE2b-256	`e64b911c9735cfbadadafb3d72f78f726fa338f4acbb7486b0868950ef4533f2`

Algorithm	Hash digest
SHA256	`f6af62ff8f5c3723bf15fddd8deb0057eb750e665af5698920c46e2351997df8`
MD5	`a99cb76ec5869384b5166cca528049b0`
BLAKE2b-256	`a04776f96526daa72e83b18735f426df5706de31ec6972d8d54ea83b1ba4ae50`

Algorithm	Hash digest
SHA256	`3b93616278cbcfb5a529c06d278085cf57d93ce57dc063078998564fbcca3a57`
MD5	`da03fb43bf8f89b5b4b894a4be74bb30`
BLAKE2b-256	`6ed5a4da1dc7b3d7b1e4731b6e6b54db34b10dfebc1760d2658286ff0cd85736`

Algorithm	Hash digest
SHA256	`8bf14394d0cd612751e8df29ac530538185ae963b0969a19fc648da04413c71a`
MD5	`bf75be93322a2abb850c01f3b6b50a01`
BLAKE2b-256	`050a542d26c93ed9422301e6f57ff01f4e176357297b4eaaf24cc713efcd6c8e`

Algorithm	Hash digest
SHA256	`4f2a0b26219e6b307b039a925da00def4926b9255adf88fd24beeb3301e120e6`
MD5	`c27996930b590bb107b9d0bbaf62644b`
BLAKE2b-256	`49085f0264c7de9ccc278e5b93b5130cfc114d054a43f28c6234620ed1853677`

Algorithm	Hash digest
SHA256	`492eba503d1831fd55f16c8d17a543a3f811c2acee22fb212b9402622685a165`
MD5	`8d332f19faee8528a21ac4871d1cb411`
BLAKE2b-256	`cffd30cc965594f4bb8911509cb5e5d0aaaf70db393514327418ad7fa9e66676`

Algorithm	Hash digest
SHA256	`f3230929b11e2515f59396f8972dd0864e6d4133297a29b30581173a59b8fc30`
MD5	`87f1f9bb81dd5e15f8a8437f25ee36f2`
BLAKE2b-256	`273bd361ce2df7d46f60f4af04e100ed72612d50788c4c012271aa1a609ea5b7`

Algorithm	Hash digest
SHA256	`15bb4e80e533651b9f9132839a62cabf053988f2a87f066d492bdba6a807a3f0`
MD5	`a5ac6802d13da9a7fc1ec0bce3384a40`
BLAKE2b-256	`f2ceffb3f48acc4356307f5fc1cded64aab62f60d197c7380d16083bc36aad35`

Algorithm	Hash digest
SHA256	`0738c0b606939978309280c5528dc08db368d6b3a88e8eee532e5df9050d98cb`
MD5	`ef04f4345aebbe1df12e8ef17b588676`
BLAKE2b-256	`0a42138f876bb65e66e9534223e877239dbe85bd2d6cf0e2bdda5ac35f8e32ca`

Algorithm	Hash digest
SHA256	`2db2a4487ede8356eea4c29a189b345ca7f904bb507d0193356e8c3924a85ac8`
MD5	`74b1a72fd2e76c7a0aaa5ecfa7ab5adc`
BLAKE2b-256	`877b83777e05c4ceffa6df974f03756506dd69466d633c60fe8f3767b7ce0ea5`

Algorithm	Hash digest
SHA256	`5b178cd8921c02de0405a5c4af7a4d48185849810bccc30d404ac4bac0f36846`
MD5	`e5bc0ac1dc1d3287813faa16c6162e06`
BLAKE2b-256	`7319271b58e7cc27d23f77838073106fd432554e3abd3b6e33fe0c54f2f0e534`

Algorithm	Hash digest
SHA256	`bed0fefc54c0b63cbc326b4fca043cbb490a329a14edb2e24f5d714afc56d8e2`
MD5	`9384469e0d3e0ba7327362f1a2dcc173`
BLAKE2b-256	`485a3197b38a9cfa5149f33b745786f7f1b4013acf5b2ce0a875c8e1bce51ce9`

Algorithm	Hash digest
SHA256	`be9cafc28da544692e4377168448b0901c06eef62057deeab861e7c38fe5f042`
MD5	`5e373c684c5c5ae20ecca87eb0cacf60`
BLAKE2b-256	`aed85ce66b11efa7f208a2b6bf457e43d4bd4df95ebe2553872d9816ea1fd469`

Algorithm	Hash digest
SHA256	`cf5882d996e31c94d67a678ffa41575c14c23cba856baf2f048a4bf5c2bbaa37`
MD5	`ef7dc87282ca1ed002725f7beb662d55`
BLAKE2b-256	`88cdad9dabbf8b0be9d997f0758389d2d71fe48eb99f4d931bc90bd5a021d937`

Algorithm	Hash digest
SHA256	`679d151bd767f9b04d5d8cb73caf46f5ffbd73437ac707e1ed703172e7496fcd`
MD5	`61c5d1efce92e63100be94f436f7ba80`
BLAKE2b-256	`8dca4668be0a30f652966d02ca4c0a1bcf057c6ada7839948022df7d7134ee3e`

Algorithm	Hash digest
SHA256	`c4ab02e56e655a0e53c60f2b3b4fbc87361fbd6126d28fc9ad11e32313eab9a3`
MD5	`bff958320cda45cf13de2df4c67e780e`
BLAKE2b-256	`116eabcfc81f15b1a4ccbda786c635b3f1f571b807d5268d1766b4ce307a79c6`

Algorithm	Hash digest
SHA256	`98405adc58b9fb119dceb7c6606b05304cf1e21826f7877e19c43c750b03386b`
MD5	`ed9d5f3f47fdb125a1f70444928dbc7d`
BLAKE2b-256	`490ae6276b383d661d7ce13c7551065b256458ca8b647c5e093fa978ee3cdb4f`

Algorithm	Hash digest
SHA256	`deb724e8af31d3d6bd135b88cf7a17fc457af02a27796fcade9a14b9747c36c0`
MD5	`d6a03faac5346a9cb3d858e463722c2e`
BLAKE2b-256	`912d796967650e7d4a23c5849358bcac9534efcf1a4b332084fe04b091a8fee0`

vispy 0.14.2

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Project description

VisPy: interactive scientific visualization in Python

Releases

Announcements

Using VisPy

Installation

Structure of VisPy

Code of Conduct

Governance

Genesis

External links

Project details

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Release history Release notifications | RSS feed

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

Algorithm	Hash digest
SHA256	`6d944ccd0d7fb1b8fa694781cb036cb1011853e6d3e1038f5b4da4d0094ed9a1`
MD5	`618e2486e6024185a5b5382e748bbc67`
BLAKE2b-256	`1f2b208448d84838b4ded8240dc979f94bbc6a73f64ebd94e667b2a7e05f01fe`

Algorithm	Hash digest
SHA256	`9951832b2bc1f964d9fc916c207f7771357ca34747863cfbd4a7a34cbed76550`
MD5	`c07ed71b01035545009feec451660b5f`
BLAKE2b-256	`8152a5279c60b6cfd97e2a8ff250e7d56efc0aa5cd31b08dd2d5f4e6a1a46afc`

Algorithm	Hash digest
SHA256	`95f1e6463ffc8aca6fdb4101cb65196b2725ca9f677a267acf2c675c660d12dd`
MD5	`d4d98a468b8260db4bbf2cde76c577c4`
BLAKE2b-256	`b19549d44fe2fc0c476064f29c555ed6470dfb5066c0ccfea57506d90e6fbb42`

Algorithm	Hash digest
SHA256	`721e076169075af80ae000b691a7d8b568a297deb9c3b781f6840b8e60c9514e`
MD5	`f7e20f56a37ab5156c5c587989734b15`
BLAKE2b-256	`a0a923c926bc851fb33fd13757f11fa6bf1d686928fc9ff4108e782195666992`

Algorithm	Hash digest
SHA256	`e389673720aaff3ef647c9bbf15ebb0d50cfb7d959b59a321056087eec8ab7de`
MD5	`9a3e8a9510fbb97cdce9078890875d40`
BLAKE2b-256	`83937da10cac917411386494dfb073dc42a3c28fe14e4f38b75e8351c6788445`