ANN: Bokeh 0.7.1 released

[Damian Avila]

On behalf of the Bokeh team, I am very happy to announce the release of
Bokeh version 0.7.1!

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 was focused on stability, bug fixes, improved documentation
and better examples.

* Several bokeh.charts bug fixes and enhancements, such as configurable
tools
* Docs improvements, in particular, documenting json for bokeh.models
* Mpl compatility improved, now returning the plot object
* A lot of encoding fixes, including fixes in some of our sample data
* Faster runs in TravisCI using the new docker-based containerized
infrastructure
* New and improved examples, such as the Interactive Image Processing with
Numba and Bokeh notebook

You can also see the CHANGELOG for full details.

We would like to mention that we are working hard to provide new language
binding for Bokeh. Anyone interested in developing new language bindings is
encouraged to contact us to request any help to make this happen quickly
and to host your project under the Bokeh organization umbrella.

Also, the release of Bokeh 0.8 should happen in early 2015. Some notable
features we intend to work on are:

* R language binding
* Simplifying production and multi-user Bokeh server deployments
* CLI interface
* Colorbar axis and axis location inspectors
* Better support for maps and projections

As usual, don't forget to check out the full documentation, interactive
gallery, and tutorial at

    http://bokeh.pydata.org

as well as the Bokeh IPython notebook nbviewer index (including all the
tutorials) at:


http://nbviewer.ipython.org/github/bokeh/bokeh-notebooks/blob/master/index.ipynb

To install the latest release, if you are using Anaconda, you can install
it with conda:

    conda install bokeh

Alternatively, you can install it with pip:

    pip install bokeh

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

    http://cdn.pydata.org/bokeh-0.7.1.min.js
    http://cdn.pydata.org/bokeh-0.7.1.min.css

Finally, BokehJS is also installable with the Node Package Manager.

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

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

Thank you for your attention!

Damián