Dag Sverre Seljebotn wrote:
> Ondrej Certik wrote:
>>
>> cdef inline ndarray array_d(int size, double *data):
>> #cdef ndarray ary2 = PyArray_ZEROS(1, &size, 12, 0)
>> cdef ndarray ary = zeros(size, dtype=float64)
>> if data != NULL: memcpy(ary.data, data, size*sizeof(double))
>> return ary
>>
>> cdef inline int iarray_d(ndarray a, int *size, double **data) except -1:
>> if a.dtype != float64:
>> raise TypeError("The array must have the dtype=float64.")
>> if size!=NULL: size[0] = a.dimensions[0]
>> if data!=NULL: data[0] = <double *> (a.data)
>>
> HOWEVER, note that your iarray_d will only work if the array that is
> passed in is contiguous. So you should either a) insert an assertion of
> a.flags['C_CONTIGUOUS'] at the beginning, or b) fix it so that it works
> with all arrays (as by my example).
>
> Any non-trivial NumPy usage is likely to end up with non-contiguous
> arrays, so if the "a" argument could come from the end-user somehow then
> choking on non-contiguous arrays is not going to work well.
>
>
Note that this is non-trivial since any copy made must also not be
deallocated, so that iarray_d would have to return a reference which
must be held on to for the duration of using the buffer data.
If you need to accept ndarrays coming from the user, a better idiom is
likely to take a callback function which will be called with the buffer
as an argument, or similar -- returning the buffer through a double**
(or np.float64_t**) is rather error-prone when it comes to reference
counting.
--
Dag Sverre
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