Path: csiph.com!usenet.pasdenom.info!weretis.net!feeder4.news.weretis.net!rt.uk.eu.org!newsfeed.xs4all.nl!newsfeed4.news.xs4all.nl!xs4all!newsgate.cistron.nl!newsgate.news.xs4all.nl!post.news.xs4all.nl!not-for-mail
From: Bryan Van de Ven <bryanv@continuum.io>
Content-Type: text/plain; charset=us-ascii
Content-Transfer-Encoding: quoted-printable
Subject: ANN: Bokeh 0.4.4 released
Date: Thu, 17 Apr 2014 10:39:17 -0500
To: python-announce-list@python.org
Mime-Version: 1.0 (Mac OS X Mail 7.2 \(1874\))
Precedence: list
Reply-To: python-list@python.org
Approved: python-announce-list@python.org
Newsgroups: comp.lang.python.announce
Message-ID: <mailman.9350.1397890479.18130.python-announce-list@python.org>
Lines: 75
NNTP-Posting-Host: 2001:888:2000:d::a6
Xref: csiph.com comp.lang.python.announce:1264

I am happy to announce the release of Bokeh version 0.4.4!

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. If you are a Matplotlib user, you can just use %bokeh =
magic to start interacting with your plots in the notebook immediately!

Check out the full documentation, interactive gallery, and tutorial at

	http://bokeh.pydata.org

If you are using Anaconda, you can install with conda:

	conda install bokeh

Alternatively, you can install with pip:

	pip install bokeh

We are still working on some bigger features but want to get new fixes =
and functionality out to users as soon as we can. Some notable features =
of this release are:

	* Additional Matplotlib, ggplot, and Seaborn compatibility =
(styling, more examples)
 	* TravisCI testing integration at =
https://travis-ci.org/ContinuumIO/bokeh
	* Tool enhancements, constrained pan/zoom, more hover glyphs=20
	* Server remote data and downsampling examples=20
	* Initial work for Bokeh "app" concept=20

Also, we've also made lots of little bug fixes and enhancements - see =
the CHANGELOG for full details.

BokehJS is also available by CDN for use in standalone javascript =
applications:

	http://cdn.pydata.org/bokeh-0.4.4.js
	http://cdn.pydata.org/bokeh-0.4.4.css
	http://cdn.pydata.org/bokeh-0.4.4.min.js
	http://cdn.pydata.org/bokeh-0.4.4.min.css

Some examples of BokehJS use can be found on the Bokeh JSFiddle page:

	http://jsfiddle.net/user/bokeh/fiddles/

The release of Bokeh 0.5 is planned for early May. 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
	* More MPL/Seaborn/ggplot.py compatibility and examples, using =
MPLExporter
	* Additional tools, improved interactions, and better plot frame
	* Touch support

Issues, enhancement requests, and pull requests can be made on the Bokeh =
Github page: https://github.com/continuumio/bokeh

Questions can be directed to the Bokeh mailing list: bokeh@continuum.io

If you have interest in helping to develop Bokeh, please get involved! =
Special thanks to recent contributors: Amy Troschinetz and Gerald Dalley

Bryan Van de Ven
Continuum Analytics
http://continuum.io