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ANN: Bokeh 0.7 released

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On behalf of the Bokeh team, I am very happy to announce the release of
Bokeh version 0.7!

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 includes many major new features:

* IPython widgets and animations without a Bokeh server
* Touch UI working for tools on mobile devices
* Vastly improved linked data table
* More new (and improving) bokeh.charts (high level charting interface)
* Color mappers on the python side
* Improved toolbar
* Many new tools: lasso, poly, and point selection, crosshair inspector

Check our blog post: http://continuum.io/blog/bokeh-0.7, to watch some of
these tools in action! And you can also see the CHANGELOG for full details.

We would like to mention that the Github Organization for Bokeh is growing!
This organization was already home to bokeh-scala and bokeh.jl, and now the
Bokeh project itself has a new home there as well, located at
https://github.com/bokeh/bokeh. Anyone interested in developing new
language bindings for Bokeh is encouraged to contact us about hosting your
project under this organization.

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

* Simplifying production and multi-user Bokeh server deployments
* 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-0.7.0.min.js
    http://cdn.pydata.org/bokeh-0.7.0.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@continuum.io

Thank you for your attention!

Damián

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ANN: Bokeh 0.7 released Damian Avila <damian.avila@continuum.io> - 2014-12-08 12:27 -0300

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