--On 13 décembre 2012 10:45:42 -0500 Raul Miller <[email protected]>
wrote:
Copying is an easy operation, and if you are doing any significant
work the cost of a copy is often trivial when compared with everything
else you are doing.
Except for arrays that take up half of my machine's RAM.
The result of 3!:1 is very close to the internal representation. That
said, if you look in jtype.j in the j source, you'll see this:
:typedef struct {I k,flag,m,t,c,n,r,s[1];}* A;
along other supporting declarations (note especially the section
marked "Fields of type A") -- that's the internal representation of
J's arrays.
OK, that looks quite close in spirit (though not in layout) to NumPy
arrays. It should be possible to wrap a J array in a Python buffer object
and create a NumPy array on top of that. Then we would have a J array and a
NumPy array with shared data space, readable and writable (with care!) from
both sides.
That leaves the question of how to make Python and J live in the same
address space. I am sure it can be done, but I suspect it's not trivial.
Python embedded into the J interpreter is one option, but if the inverse is
doable, it's probably easier.
Konrad.
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