Oh my word I'm a fool. I was simply omitting the second and third arguments when calling the function. Like a fool. Because that's what I am. Original silly question follows:
This seems like it must be a very common thing to do, but I can't find a relevant tutorial, and I'm too ignorant about Numpy
and ctypes
to figure it out myself.
I have a C function in file ctest.c
.
#include <stdio.h> void cfun(const void * indatav, int rowcount, int colcount, void * outdatav) { //void cfun(const double * indata, int rowcount, int colcount, double * outdata) { const double * indata = (double *) indatav; double * outdata = (double *) outdatav; int i; puts("Here we go!"); for (i = 0; i < rowcount * colcount; ++i) { outdata[i] = indata[i] * 2; } puts("Done!"); }
(As you may guess, I originally had the arguments as double * rather than void *, but couldn't figure out what to do on the Python side. I'd certainly love to change them back, but I'm not picky as long as it works.)
I make a shared library out of it. gcc -fPIC -shared -o ctest.so ctest.c
Then in Python, I have a couple numpy arrays, and I'd like to pass them to the C function, one as input and one as output.
indata = numpy.ones((5,6), dtype=numpy.double) outdata = numpy.zeros((5,6), dtype=numpy.double) lib = ctypes.cdll.LoadLibrary('./ctest.so') fun = lib.cfun # Here comes the fool part. fun(ctypes.c_void_p(indata.ctypes.data), ctypes.c_void_p(outdata.ctypes.data)) print 'indata: %s' % indata print 'outdata: %s' % outdata
This doesn't report any errors, but prints out
>>> Here we go! Done! indata: [[ 1. 1. 1. 1. 1. 1.] [ 1. 1. 1. 1. 1. 1.] [ 1. 1. 1. 1. 1. 1.] [ 1. 1. 1. 1. 1. 1.] [ 1. 1. 1. 1. 1. 1.]] outdata: [[ 0. 0. 0. 0. 0. 0.] [ 0. 0. 0. 0. 0. 0.] [ 0. 0. 0. 0. 0. 0.] [ 0. 0. 0. 0. 0. 0.] [ 0. 0. 0. 0. 0. 0.]]
The outdata array is not modified. And in fact if I call the function again I get a segfault. Which doesn't surprise me -- I really don't know what I'm doing here. Can anyone point me in the right direction?
Python does not pass-by-reference nor pass-by-value.
It is almost 7 times faster than straightforward C implementation. But still we are about 4 times slower than NumPy.
While not a direct answer to your original question, here's a much more convenient way to call your function. First, make the prototype of your C function exactly as you would do it in plain C. Since you don't need rowcount
and colcount
separately, I'll collapse them into a single size
parameter:
void cfun(const double *indatav, size_t size, double *outdatav) { size_t i; for (i = 0; i < size; ++i) outdatav[i] = indatav[i] * 2.0; }
Now define the ctypes prototype in the following way:
import ctypes from numpy.ctypeslib import ndpointer lib = ctypes.cdll.LoadLibrary("./ctest.so") fun = lib.cfun fun.restype = None fun.argtypes = [ndpointer(ctypes.c_double, flags="C_CONTIGUOUS"), ctypes.c_size_t, ndpointer(ctypes.c_double, flags="C_CONTIGUOUS")]
Now, calls to your function will be really convenient:
indata = numpy.ones((5,6)) outdata = numpy.empty((5,6)) fun(indata, indata.size, outdata)
You could also define a wrapper to make this even more convenient:
def wrap_fun(indata, outdata): assert indata.size == outdata.size fun(indata, indata.size, outdata)
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With