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-0.17.3.tar.gz (13.3 MB view details)

Uploaded Source

Built Distributions

gr-0.17.3-cp27-none-win_amd64.whl (4.8 MB view details)

Uploaded CPython 2.7 Windows x86-64

gr-0.17.3-cp27-none-macosx_10_4_x86_64.whl (10.5 MB view details)

Uploaded CPython 2.7 macOS 10.4+ x86-64

File details

Details for the file gr-0.17.3.tar.gz.

File metadata

  • Download URL: gr-0.17.3.tar.gz
  • Upload date:
  • Size: 13.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for gr-0.17.3.tar.gz
Algorithm Hash digest
SHA256 cf8485da1444168f2e2e219468d5d28fd9a8fb91349e122f26976f4503706872
MD5 96e491648625ae179d54b87523326569
BLAKE2b-256 18edb91a6656d02b2f2769d52eb15b0926dc7fd0c802c02ba4b0525c7e145060

See more details on using hashes here.

File details

Details for the file gr-0.17.3-cp27-none-win_amd64.whl.

File metadata

File hashes

Hashes for gr-0.17.3-cp27-none-win_amd64.whl
Algorithm Hash digest
SHA256 079f3322d42bb9ba7734a9b2e1cc95f02e6e65cb6bca6bd525a849fc00cc9c8e
MD5 4ba1420991ee72bd2c02a02b1050f049
BLAKE2b-256 43b3fa2e60c49496c499d22e9fe5c6973638366e79cfe47df30b2737866850cb

See more details on using hashes here.

File details

Details for the file gr-0.17.3-cp27-none-macosx_10_4_x86_64.whl.

File metadata

File hashes

Hashes for gr-0.17.3-cp27-none-macosx_10_4_x86_64.whl
Algorithm Hash digest
SHA256 ff0ccce7a186df161e7fb08ba7f836636ddc13dce49704799281d50d79a848d1
MD5 cd13c438fe16dd784765fefcef04da55
BLAKE2b-256 cf6aee010b37f0ac5d7aeb6abd9a116fa54bba634acf8f11e058e6d7b15afcc1

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