matplotlib-users Mailing List for matplotlib

> A build of the docs is available at http://matplotlib.org/2.0.0b1
Thanks. Is there a "What's New" page in the docs? Or maybe an upgrade
guide which identifies incompatibilities with 1.5?
Skip Montanaro
who still remembers John Hunter's interview presentation at TradeLink,
lo these many years ago...

On Mon, May 30, 2016 at 9:01 PM, Thomas Caswell <tca...@gm...> wrote:
> Folks,
>
> We tagged the first beta for v2.0.0 tonight. Please check out the new
> defaults!
>
> This is tagged as a beta because we anticipate a longer than normal release
> cycle. The style changes are substantial and we want to make sure that we
> have not crippled any common use cases. The target for the final release
> around scipy.
You should now be able to get this release on OSX or Linux with:
python -m pip install --upgrade pip # upgrade pip to latest
pip install --pre matplotlib
Cheers,
Matthew

Folks,
We tagged the first beta for v2.0.0 tonight. Please check out the new
defaults!
This is tagged as a beta because we anticipate a longer than normal release
cycle. The style changes are substantial and we want to make sure that we
have not crippled any common use cases. The target for the final release
around scipy.
A build of the docs is available at http://matplotlib.org/2.0.0b1
Preview conda packages are available via
conda install -c conda-forge/label/rc -c conda-forge matplotlib
pre-release packages should be on pypi tomorrow.
Brief release notes:
This previews the new default style and many bug-fixes. A full list of
the style changes will be collected for the final release.
In addition to the style change this release includes:
 - overhaul of font handling/text rendering to be faster and clearer
 - many new rcParams
 - Agg based OSX backend
 - optionally deterministic SVGs
 - complete re-write of image handling code
 - simplified color conversion
 - specify colors in the global property cycle via `'C0'`,
 `'C1'`... `'C9'`
 - use the global property cycle more places (bar, stem, scatter)
There is a 'classic' style sheet which reproduces the 1.Y defaults:
 import matplotlib.style as mstyle
 mstyle.use('classic')
A big thank you to everyone who worked on this release.
Tom