Question

I am trying to get some data from an module which is a shared object wrapped with ctypes.
The data is a numeric array so I used numpy array to store the data. But I learned that I
don’t understand how numpy organize the array in memory.

If I had a C function that would fill a array like below:

int filler(int* a,int length){
        int i=0;
        for(i=0;i<length;i++){
                a[i]=i;
        }
        return 0;
}

Then I would call this function in python using ctypes

import ctypes
import numpy
lib = ctypes.cdll.LoadLibrary("libname")    
data = numpy.zeros((1,10),dtype=numpy.int16)
lib.filler(data.ctypes.data,ctypes.c_int(10))
print data

But my output comes out this way.

dtype=numpy.int16
[[0 0 1 0 2 0 3 0 4 0]]

This would make sense if int was 32 bit, but I suppose C int are 16 bits (GCC in openSUSE in a x86 intel machine).
I tried running with dtypes being 32 bits and strangely I get the result I want:

dtype=numpy.int32
[[0 1 2 3 4 5 6 7 8 9]]

Trying to make sense of what is happening I ran with int8 and I got the following:

dtype=numpy.int8
[[0 0 0 0 1 0 0 0 2 0]]

I did give a look give a look at numpy docs, but so far I have not found what the answer.

Answer 1

This would make sense if int was 32
bit, but I suppose C int are 16 bits
(GCC in openSUSE in a x86 intel
machine). I tried running with dtypes
being 32 bits and strangely I get the
result I want:

Not strange at all: your supposition is wrong, and your machine is 32 bit with a 32 bit int and a 16 bit short int.. unless you’re doing some (rather admirable) retrocomputing!

Check sizeof(int) and multiply by 8, or simply store numbers in an int and print them out, to convince yourself.