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-0.17.2.tar.gz (13.3 MB view details)

Uploaded Source

Built Distributions

gr-0.17.2-cp27-none-win_amd64.whl (4.7 MB view details)

Uploaded CPython 2.7 Windows x86-64

gr-0.17.2-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-0.17.2.tar.gz.

File metadata

  • Download URL: gr-0.17.2.tar.gz
  • Upload date:
  • Size: 13.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for gr-0.17.2.tar.gz
Algorithm Hash digest
SHA256 d198fc8f98354f8a795f30ead6b8face4273e98af502ac687c19e1836108418e
MD5 6eb9f90ab94bd9af7d4fd178b20a2ee2
BLAKE2b-256 8b5a7f516a812d74c4c77ef9b03ee50ede94d193a664324940604739e5d77c91

See more details on using hashes here.

File details

Details for the file gr-0.17.2-cp27-none-win_amd64.whl.

File metadata

File hashes

Hashes for gr-0.17.2-cp27-none-win_amd64.whl
Algorithm Hash digest
SHA256 dd40e6b66f38139ce7ed34c4bba92eed8ae64b9b8bd244d19717b19b3d98c421
MD5 e0cbff900e9fe63ed9ef89233e2546cf
BLAKE2b-256 b2d4316d5a10cdb81195702195fd43a1945233584973ae1a381f38db33884d6e

See more details on using hashes here.

File details

Details for the file gr-0.17.2-cp27-none-macosx_10_4_x86_64.whl.

File metadata

File hashes

Hashes for gr-0.17.2-cp27-none-macosx_10_4_x86_64.whl
Algorithm Hash digest
SHA256 8aa36d3ded7e695b34a9246e0ef5cd7557fcf62b0171d7367eabc3efdf213b7e
MD5 5a3f0590e8a6330d85689bd6eababe8d
BLAKE2b-256 8c7c28e926e578374cd1e1c357e8db07aed8ceca3f68888d092ade8b5894f026

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