Using NumPy and Cpython with Jython

Question

I must use a commercial Java library, and would like to do it from Python. Jython is robust and I am fine with it being a few dot releases behind. However, I would like to use NumPy as well, which obviously does not work with Jython. Options like CPype and Java numeric libraries are unappealing. The former is essentially dead. The latter are mostly immature and lack the ease of use and wide acceptance of NumPy. My question is: How can one have Jython and Python code interoperate? It would be acceptable for me to call Jython from Cpython or the other way around.

Answer 1

It's ironic, considering that Jython and Numeric (NumPy's ancestor) were initiated by the same developer (Jim Hugunin, who then moved on to also initiate IronPython and now holds some kind of senior architect position at Microsoft, working on all kind of dynamic languages support for .NET and Silverlight), that there's no really good way to use numpy in Jython. The closest thing to that, which I know of, is the "jnumerical" project -- the (scarce) docs are on sourceforge, but the updated sources are on bitbucket.

"Numeric Python", what jnumerical implements, is not as slick and streamlined as its numpy descendant, but it has about the same functionality and shares a lot of the concepts and philosophy, so maybe you could find it usable -- worth checking out, at least.

Answer 2

Consider using execnet, which allows you to combine the strengths of both Jython and CPython, including current NumPy. The disadvantage here is that you will have to pay for the cost of serializing/deserializing objects between the two interpreters in two different process spaces. (You can avoid network overhead by using its support for subprocess.) But such a combination may work well, given that you're considering JPype, which would have similar (and probably higher) overhead. Just ensure you've partitioned the work appropriately.

The Jython developers (and I'm one of them) are looking at supporting NumPy in the future, via support of the C Extension API, but this is very much preliminary planning indeed.

Answer 3

Disclaimer: Have not had persnal experience with it yet

Seems like JyNI – Jython Native Interface is the way to go.

There's also a newer question posted which may have newer alternatives.

Answer 4

I look very much formard to the Jython C Extension API! That would be awesome!

Until, that point, I think you have two alternatives:

• http://jepp.sourceforge.net/ for embedding python in java, it has a nice console. The disadvatage, for me a too big disadvatage, is that it needs to be compiled against your own python. And with the python upgrade, you have to recompile (I don't want to compile python, in order to compile and use the extension - it is also not possible, especially if the code should be executed on different machines, on grid for example)

• http://lucene.apache.org/pylucene/jcc/ - this is used for lucene, and for many other projects. I personally use it to wrap GATE NLP engine and also solr. To make that available to Python. Jcc is much faster than the (dead) JPype, probably because some data structures (like lists) are optimized and also because it is interfacing python<->java via C++ extension (according to this: http://www.slideshare.net/onyame/mixing-python-and-java page 30) I have tried moving 6mil of integers in the list between python and java, JPype was orders of magnitude slower (but i don't remember the numbers)

However, using Jcc, you can wrap only public methods, and sometimes it is tricky, especially if that method is receiving or returning certain java objects (in short, JCC must compile wrappers also for the passed-in objects, otherwise all the methods using/returning such methods are not accessible). So unless you need to distribute your code, you are better of with JEPP.

Answer 5

If you stick to vector and matrix maths, I suggest to have a look onto vectorz. It is a pure Java implementation and shall be 100% usable from within jython. I still didn't try it, but will soon, since I have the same necessity in finding a numpy alternative.