Bokeh 0.6 release by Bryan Van de Ven.
From the post:
Bokeh is a Python library for visualizing large and realtime datasets on the web. Its goal is to provide to developers (and domain experts) with capabilities to easily create novel and powerful visualizations that extract insight from local or remote (possibly large) data sets, and to easily publish those visualization to the web for others to explore and interact with.
This release includes many bug fixes and improvements over our most recent 0.5.2 release:
- Abstract Rendering recipes for large data sets: isocontour, heatmap
- New charts in bokeh.charts: Time Series and Categorical Heatmap
- Full Python 3 support for bokeh-server
- Much expanded User and Dev Guides
- Multiple axes and ranges capability
- Plot object graph query interface
- Hit-testing (hover tool support) for patch glyphs
See the CHANGELOG for full details.
I’d also like to announce a new Github Organization for Bokeh: https://github.com/bokeh. Currently it is home to Scala and and Julia language bindings for Bokeh, but the Bokeh project itself will be moved there before the next 0.7 release. Any implementors of new language bindings who are interested in hosting your project under this organization are encouraged to contact us.
In upcoming releases, you should expect to see more new layout capabilities (colorbar axes, better grid plots and improved annotations), additional tools, even more widgets and more charts, R language bindings, Blaze integration and cloud hosting for Bokeh apps.
Don’t forget to check out the full documentation, interactive gallery, and tutorial at
as well as the Bokeh IPython notebook nbviewer index (including all the tutorials) at:
http://nbviewer.ipython.org/github/ContinuumIO/bokeh-notebooks/blob/master/index.ipynb
One of the examples from the gallery:
reminds me of U.S. foreign policy. The unseen attractors are defense contractors and other special interests.