[Matplotlib-users] ANN: Bokeh 0.4.2

[Bryan V. de V. <br...@co...>]

I am happy to announce the release of Bokeh version 0.4.2!

Bokeh is a Python library for visualizing large and realtime datasets on the web.  Its goal is to provide elegant, concise construction of novel graphics in the style of Protovis/D3, while delivering high-performance interactivity to thin clients.  Bokeh includes its own Javascript library (BokehJS) that implements a reactive scenegraph representation of the plot, and renders efficiently to HTML5 Canvas. Bokeh works well with IPython Notebook, but can generate standalone graphics that embed into regular HTML. We are also building matplotlib compatibility so that users can drive Bokeh visualizations directly from MPL code. 

Check out the full documentation, interactive gallery, and tutorial at

     http://bokeh.pydata.org

If you are using Anaconda, you can install with conda:

     conda install bokeh

Alternatively, you can install with pip:

     pip install bokeh

Some of the new features in this release include:

* Additional Matplotlib and Seaborn compatibility (PolyCollection)
* Extensive tutorial with exercises and solutions added to docs
* new %bokeh magic for improved IPython notebook integration
* Windows support for bokeh-server with two new storage backends (in-memory and shelve)

Also, we've fixed lots of little bugs - see the CHANGELOG for full details.

BokehJS is also available by CDN for use in standalone javascript applications:

     http://cdn.pydata.org/bokeh-0.4.2.js
     http://cdn.pydata.org/bokeh-0.4.2.css
     http://cdn.pydata.org/bokeh-0.4.2.min.js
     http://cdn.pydata.org/bokeh-0.4.2.min.css

Some examples of BokehJS use can be found on the Bokeh JSFiddle page:

     http://jsfiddle.net/user/bokeh/fiddles/

The release of Bokeh 0.5 is planned for late March. Some notable features we plan to include are:

* Abstract Rendering for semantically meaningful downsampling of large datasets
* Better grid-based layout system, using Cassowary.js
* More MPL/Seaborn/ggplot.py compatibility and examples
* Additional tools, improved interactions, and better plot frame
* Touch support

Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/continuumio/bokeh

Questions can be directed to the Bokeh mailing list: bo...@co...

Special thanks to recent contributors: Melissa Gymrek, Amy Troschinetz, Ben Zaitlen, Damian Avila, and Terry Jones

Regards,

Bryan Van de Ven
Continuum Analytics
http://continuum.io