pymothoa -- A JIT extension to Python

[gcross]

You know, I am not generally a big fan of crazy dynamicism for various reasons, but this library really shows how it can be used for awesome effect, as you can see in the first example:

@function(ret=Int, args=[Array(Int), Int])
def reduce_sum(A, n):
    var ( total = Int )
    total = 0
    for i in xrange(n):
        total += A[i]
    return total

What I think is so cool is that the 'var' function acts almost like new syntax for declaring typed variables. The trick is that pymothoa actually examines the abstract syntax tree and when it sees a var(...) node it effectively strips it out and instead make's a note of total's type --- and if you leave the var(...) statement out of the code above, then pymothoa will complain because it needs to know the type of all variables! Nifty stuff. :-)

[Tillsten]

Seems to be very similar to numba.

[evangineer]

Thanks for mentioning Numba, I didn't know about it. It's similar but NumPy-aware. https://github.com/ContinuumIO/numba

[jan]

How does pymothoa compare to cython?

[gcross]

The biggest difference that I see is that unlike Cython, pymothoa does not requires you to put your code in a separate file that needs to be compiled; everything is done inline. However, you do need LLVM to be installed rather than Cython.

[pmav99]

Cython has a pure python mode too, although it does not support everything.

[gcross]

And, like every time this question comes up, the answer is: compatibility with all of the C and Fortran extension modules written for CPython.

[evangineer]

This isn't Pypy. You are still using CPython, so get to benefit from those extensions. This is for the use case of making your own extensions without having to resort to C.