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-0.18.0.tar.gz (13.3 MB view details)

Uploaded Source

Built Distributions

gr-0.18.0-cp27-none-win_amd64.whl (4.8 MB view details)

Uploaded CPython 2.7 Windows x86-64

gr-0.18.0-cp27-none-macosx_10_4_x86_64.whl (10.6 MB view details)

Uploaded CPython 2.7 macOS 10.4+ x86-64

File details

Details for the file gr-0.18.0.tar.gz.

File metadata

  • Download URL: gr-0.18.0.tar.gz
  • Upload date:
  • Size: 13.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for gr-0.18.0.tar.gz
Algorithm Hash digest
SHA256 9ac7b967ab43007869474a93c106c326f65c0bae0171089e5891532b93ba0706
MD5 c1012b2bea710026cd0323249eb91bca
BLAKE2b-256 0a215503024133949217b7a1ea0676738c1a7461863d5111ad873b4daaf1319f

See more details on using hashes here.

File details

Details for the file gr-0.18.0-cp27-none-win_amd64.whl.

File metadata

File hashes

Hashes for gr-0.18.0-cp27-none-win_amd64.whl
Algorithm Hash digest
SHA256 bc5681bd232a2153d88f8e6f39a53237473f7c901f46795b823dee6f756dac95
MD5 4f0bfc575b1ea2354d43a64635e3c953
BLAKE2b-256 e519a9ac0e2fb876f8a0d7218ebcdc4f14db739610418127951700e156e04e0d

See more details on using hashes here.

File details

Details for the file gr-0.18.0-cp27-none-macosx_10_4_x86_64.whl.

File metadata

File hashes

Hashes for gr-0.18.0-cp27-none-macosx_10_4_x86_64.whl
Algorithm Hash digest
SHA256 7e0bd1d8dc2e52971488833125fa04ef6a1c0c90402fc23a2cf6b0e695ea120b
MD5 d5ffe117dcdda574963ea3ef30101275
BLAKE2b-256 afee74c17db6a41f6070c9a376ddb7c7c3b58822dbc35c684de700aa2a87dfb3

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