Skip to main content

Python visualization framework

Project description

GR is a universal framework for cross-platform visualization applications. It offers developers a compact, portable and consistent graphics library for their programs. Applications range from publication quality 2D graphs to the representation of complex 3D scenes.

GR is essentially based on an implementation of a Graphical Kernel System (GKS) and OpenGL. As a self-contained system it can quickly and easily be integrated into existing applications (i.e. using the ctypes mechanism in Python or ccall in Julia).

The GR framework can be used in imperative programming systems or integrated into modern object-oriented systems, in particular those based on GUI toolkits. GR is characterized by its high interoperability and can be used with modern web technologies. The GR framework is especially suitable for real-time or signal processing environments.

GR was developed by the Scientific IT-Systems group at the Peter Gruenberg Institute at Forschunsgzentrum Juelich. The main development has been done by Josef Heinen who currently maintains the software, but there are other developers who currently make valuable contributions. Special thanks to Florian Rhiem (GR3] and Christian Felder (qtgr, setup.py).

Starting with release 0.6 GR can be used as a backend for Matplotlib and significantly improve the performance of existing Matplotlib or PyPlot applications written in Python or Julia, respectively. In this tutorial section you can find some examples.

Beginning with version 0.10.0 GR supports inline graphics which shows up in IPython’s Qt Console or interactive computing environments for Python and Julia, such as IPython and Jupyter. An interesting example can be found here.

For further information please refer to the GR home page.

Project details


Release history Release notifications | RSS feed

Download files

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

Source Distribution

gr-0.21.0.tar.gz (13.6 MB view details)

Uploaded Source

Built Distributions

gr-0.21.0-cp27-none-win_amd64.whl (4.9 MB view details)

Uploaded CPython 2.7 Windows x86-64

gr-0.21.0-cp27-none-macosx_10_4_x86_64.whl (10.7 MB view details)

Uploaded CPython 2.7 macOS 10.4+ x86-64

File details

Details for the file gr-0.21.0.tar.gz.

File metadata

  • Download URL: gr-0.21.0.tar.gz
  • Upload date:
  • Size: 13.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for gr-0.21.0.tar.gz
Algorithm Hash digest
SHA256 08e568620ace9843e10ea905cd80e26f5af11eb079005bb8867cdd62dd059867
MD5 574eeb7f3e65346de464667e23f05201
BLAKE2b-256 d540c02beb82a7b311e256e0e7f1144e4075e9f4c5ce33762afb3f4ee30dfcc0

See more details on using hashes here.

File details

Details for the file gr-0.21.0-cp27-none-win_amd64.whl.

File metadata

File hashes

Hashes for gr-0.21.0-cp27-none-win_amd64.whl
Algorithm Hash digest
SHA256 5cd21fb86b66de03dee60c64fc9130c5d295ce14b1dd9fc108f58a3062d64988
MD5 ed02e4d0964d66f444897759a42fe245
BLAKE2b-256 b2ff742d3b2746f6c4c69489964c156d0c0fb6216178664b65fbddfa3d651eb1

See more details on using hashes here.

File details

Details for the file gr-0.21.0-cp27-none-macosx_10_4_x86_64.whl.

File metadata

File hashes

Hashes for gr-0.21.0-cp27-none-macosx_10_4_x86_64.whl
Algorithm Hash digest
SHA256 ccbfa132822c5d1f768bb20b38f3df1b02378639280d03b1a816fc2b4a052bda
MD5 ae5cc546a775f3c6ef5b332238a67f93
BLAKE2b-256 6353dbc968aa5dfbdfa0ee5a3929ac57d04feac27da98b377b2adcc6e40fc027

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