I am pleased to announce the release of Bokeh version 0.4!
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.
Check out the full documentation and interactive gallery at
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:
* Preliminary work on Matplotlib support: convert MPL figures to Bokeh plots
* Free public beta of Bokeh plot hosting at http://bokehplots.com
* Tool improvements:
- "always on" pan tool and wheel zoom tool (with shift key)
- box zoom tool
- viewport reset tool
* Enhanced datetime axis, with better performance and nicer ticking
* Expanded testing, including TravisCI integrations and static image output using PhantomJS
* RGBA and color mapped image plots now available from Python
* Python 3 supported
* Vastly improved documentation for glyphs, with inline examples and JSFiddle integration
Also, we've fixed lots of little bugs - see the CHANGELOG for full details.
Bokeh will be having a free "Office Hours" later this week! Join us this Thursday at 2pm CST on EngineHere at https://www.enginehere.com/stream/437/bokeh-04-release/ for a live informational session about the latest release. We'll be covering all the newest features and updates through a combination of live lecture, Q&A, and pair programming. It's all free, just sign up to the EngineHere learning platform.
BokehJS is also available by CDN for use in standalone javascript applications:
http://cdn.pydata.org/bokeh-0.4.js
http://cdn.pydata.org/bokeh-0.4.css
http://cdn.pydata.org/bokeh-0.4.min.js
http://cdn.pydata.org/bokeh-0.4.min.css
Some examples of BokehJS use can be found on the Bokeh JSFiddle page:
Settings - JSFiddle - Code Playground
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
* Selection tools, tooltips, etc.
Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: GitHub - bokeh/bokeh: Interactive Data Visualization in the browser, from Python
Questions can be directed to the Bokeh mailing list: [email protected]
Special thanks to recent contributors: Janek Klawe, Samantha Hughes, Rebecca Paz, and Benedikt Sauer.
Regards,
Bryan Van de Ven
Continuum Analytics