That is rather unrelated, you better ask this again on the cython-users list
(be warned that top-posting is strongly discouraged in that place).
Dag
--
Sent from my Android phone with K-9 Mail. Please excuse my brevity.
Nadav Horesh <nad...@visionsense.com> wrote:
Sorry for being slow.
There is (I think) a related question I raised on the skimage list:
I have a cython function that calls a C callback function in a loop (one call
for each pixel in an image). The C function in compiled in a different shared
library (a simple C library, not a python module). I would like a python script
to get the address of the C function and pass it on to the cython function as
the pointer for the callback function.
As I understand Travis' isue starts ones the callback address is obtained, but,
is there a direct method to retrieve the address from the shared library?
Nadav.
_____________________________________________
From: numpy-discussion-boun...@scipy.org [numpy-discussion-boun...@scipy.org]
On Behalf Of Travis Oliphant [teoliph...@gmail.com]
Sent: 10 April 2012 03:11
To: Discussion of Numerical Python
Subject: [Numpy-discussion] Getting C-function pointers from Python to C
Hi all,
Some of you are aware of Numba. Numba allows you to create the equivalent of
C-function's dynamically from Python. One purpose of this system is to allow
NumPy to take these functions and use them in operations like ufuncs,
generalized ufuncs, file-reading, fancy-indexing, and so forth. There are
actually many use-cases that one can imagine for such things.
One question is how do you pass this function pointer to the C-side. On the
Python side, Numba allows you to get the raw integer address of the equivalent
C-function pointer that it just created out of the Python code. One can think
of this as a 32- or 64-bit integer that you can cast to a C-function pointer.
Now, how should this C-function pointer be passed from Python to NumPy? One
approach is just to pass it as an integer --- in other words have an API in C
that accepts an integer as the first argument that the internal function
interprets as a C-function pointer.
This is essentially what ctypes does when creating a ctypes function pointer
out of:
func = ctypes.CFUNCTYPE(restype, *argtypes)(integer)
Of course the problem with this is that you can easily hand it integers which
don't make sense and which will cause a segfault when control is passed to this
"function"
We could also piggy-back on-top of Ctypes and assume that a ctypes
function-pointer object is passed in. This allows some error-checking at least
and also has the benefit that one could use ctypes to access a c-function
library where these functions were defined. I'm leaning towards this approach.
Now, the issue is how to get the C-function pointer (that npy_intp integer)
back and hand it off internally. Unfortunately, ctypes does not make it very
easy to get this address (that I can see). There is no ctypes C-API, for
example. There are two potential options:
1) Create an API for such Ctypes function pointers in NumPy and use the ctypes
object structure. If ctypes were to ever change it's object structure we would
have to adapt this API.
Something like this is what is envisioned here:
typedef struct {
PyObject_HEAD
char *b_ptr;
} _cfuncptr_object;
then the function pointer is:
(*((void **)(((_sp_cfuncptr_object *)(obj))->b_ptr)))
which could be wrapped-up into a nice little NumPy C-API call like
void * Npy_ctypes_funcptr(obj)
2) Use the Python API of ctypes to do the same thing. This has the advantage of
not needing to mirror the simple _cfuncptr_object structure in NumPy but it is
*much* slower to get the address. It basically does the equivalent of
ctypes.cast(obj, ctypes.c_void_p).value
There is working code for this in the ctypes_callback branch of my scipy fork
on github.
I would like to propose two things:
* creating a Npy_ctypes_funcptr(obj) function in the C-API of NumPy and
* implement it with the simple pointer dereference above (option #1)
Thoughts?
-Travis
_____________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
_____________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
_______________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
