Wednesday, December 30, 2009

Ironclad 2.6 and Spare Batteries for IronPython

Ironclad is a compatibility layer that allows you to use Python C extensions with IronPython. Ironclad is open source and development has been funded by Resolver Systems and it is integrated into Resolver One to allow you to use Numpy within Resolver One spreadsheets.

Ironclad works by implementing the Python C API in a combination of C#, C and Python. Although Ironclad only works on 32 bit Windows at the moment the implementation has been done in such a way that porting it to run on other platforms (with Mono) and 64 bit would be relatively easy. Patches welcomed!

Recent development has changed the implementation to use gcc-xml to access and transform the Python C source code. By reusing as much of the original implementation as possible it minimizes the amount that needs to be 'hand-coded'. It leaves only a (moderately) small core that would need to be reimplemented if Jython, PyPy (or other implementations) wanted to reuse Ironclad. The C# would need to be re-coded in Java or RPython, using the platform's native FFI instead of PInvoke on .NET. The advantage of reusing Ironclad is that difficult problems, like handling the Global Interpreter Lock and matching reference counting to different garbage collection strategies, are already solved (well, for some value of solved...).

Anyway, that is all by way of introduction. William Reade, core developer of Ironclad, has just announced Ironclad 2.6 RC. This is a release candidate of Ironclad targeting IronPython 2.6.
I'm very happy to announce the latest release (candidate) of Ironclad, the 120-proof home-brewed CPython compatibility layer, now available for IronPython 2.6!

No longer need .NET pythonistas toil thanklessly without the benefits of bz2, csv, numpy and scipy: with a simple 'import ironclad', (most parts of) the above packages -- and many more -- will transparently Just Work.

Get the package from:

...and get support from:

...or just ask me directly.

I'm very keen to hear your experiences, both positive and negative; I haven't been able to test it on as many machines as I have in the past, so your feedback is especially important this time round*.


* I'd be especially grateful if someone with a newish multicore machine would run the numpy and scipy test scripts (included in the source distrbution) a few times to check for consistent results and absence of weird crashes; if someone volunteers, I'll help however I can.
William has recently started blogging. I recommend browsing the few entries he has already posted, particularly this rant on static typing and this post on .NET marshalling, but his latest is of particular interest:
As we all know, Python comes with batteries included in the form of a rich standard library; and, on top of this, there are many awesome and liberally-licensed packages just an easy_install away.

IronPython, of course, includes *most* of the CPython standard library, but if you're a heavy user you might have noticed a few minor holes: in the course of my work on Ironclad, I certainly have. Happily for you I can vaguely remember what I did in the course of bodging them closed with cow manure and chewing gum; here then, for your edification and delectation, is my personal recipe for a delicious reduced-hassle IronPython install, with access to the best and brightest offered by CPython, on win32.

No comments:

Post a Comment

Note: only a member of this blog may post a comment.