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
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: bo...@continuum.io
Regards,
Bryan Van de Ven
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