[SciPy-User] ANN: Bokeh 0.3 released

[Bryan Van de Ven]

I am pleased to announce the release of Bokeh 0.3! Bokeh is a Python interactive visualization library for large datasets that natively uses the latest web technologies. Its goal is to provide elegant, concise construction of novel graphics in the style of Protovis/D3, while delivering high-performance interactivity over large data to thin clients.

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

	conda install bokeh

Alternatively you can install from PyPI using pip:

	pip install bokeh

This release was largely an internal refactor to merge the BokehJS and Bokeh projects into one repository, and to greatly improve and simplify the BokehJS coffee script build process. Additionally, this release also includes a number of bug and stability fixes, and some enhancements. See the CHANGELOG for full details. 

Many new examples were added including a reproduction of Burtin's Antibiotics, and examples of animation using the Bokeh plot server inside IPython notebooks. ColorBrewer palettes were also added on the python side. Finally, the user guide has been flushed out and will continually be updated as features and API changes are made. Check out the full documentation and interactive gallery at

	http://bokeh.pydata.org

The release of Bokeh 0.4 is planned for early January. Some notable features to be included are:

* Integrate Abstract Rendering into bokeh server
* Better grid-based layout system; use Cassowary.js for layout solver
* Tool Improvements (pan always on, box zoom always on, passive resize with hot corners)
* Basic MPL compatibility interface (enough to make ggplot.py work)
* Expose image plot in Python interface: Add BSON for sending large data

Issues or enhancement requests can be logged on the Bokeh Github page: https://github.com/continuumio/bokeh

Questions can be directed to the Bokeh mailing list: bokeh at continuum.io

Regards, 

Bryan Van de Ven