on behalf of the IPython development team, and just in time for the
imminent Debian freeze and SciPy 2012, I'm thrilled to announce, after
an intense 6 months of work, the official release of IPython 0.13.
This version contains several major new features, as well as a large
amount of bug and regression fixes. The previous version (0.12) was
released on December 19 2011, so in this development cycle we had:
- ~6 months of work.
- 373 pull requests merged.
- 742 issues closed (non-pull requests).
- contributions from 62 authors.
- 1760 commits.
- a diff of 114226 lines.
This means that we closed a total of 1115 issues over 6 months, for a
rate of almost 200 issues closed per month and almost 300 commits per
month. We are very grateful to all of you who have contributed so
enthusiastically to the project and have had the patience of pushing
your contributions through our often lengthy review process.
We've also welcomed several new members to the core IPython
development group: Jörgen Stenarson (@jstenar - this really was an
omission as Jörgen has been our Windows expert for a long time) and
Matthias Bussonier (@Carreau), who has been very active on all fronts
of the project.
There is too much new work to write up here, so we refer you to our
full What's New document
for the full details. But the main highlights of this release are:
* Brand new UI for the notebook, with major usability improvements
(real menus, toolbar, and much more)
* Manage all your parallel cluster configurations from the notebook
with push-button simplicity (cluster start/stop with one button).
* Cell magics: commands prefixed with %% apply to an entire cell. We
ship with many cell magics by default, including timing, profiling,
running cells under bash, Perl and Ruby as well as magics to interface
seamlessly with Cython, R and Octave.
* The IPython.parallel tools have received many fixes, optimizations,
and a number of API improvements to make writing, profiling and
debugging parallel codes with IPython much easier.
* We have unified our interactive kernels (the basic ipython object
you know and love) with the engines running in parallel, so that you
can now use all IPython special tricks in parallel too. And you can
connect a console or qtconsole to any parallel engine for direct,
interactive execution, plotting and debugging in a cluster.
Those contain a built version of the HTML docs; if you want pure
source downloads with no docs, those are available on github:
Please see our release notes for the full details on everything about
As usual, if you find any other problem, please file a ticket --or
even better, a pull request fixing it-- on our github issues site
Many thanks to all who contributed!
Fernando, on behalf of the IPython development team.