Hi Monte,
This strikes me as a slightly strange request; ctypes is intended to
interface with the C memory model, which has no native representation of
fortran arrays.
The underlying workings of `as_array` is to cast your ctype pointer into a
ctypes array object, and then pass that into numpy.
That approach doesn't work when there is no ctypes representation to begin
with
If you were writing C code to work with fortran arrays, probably you would
flatten your data into a single 1D array. You can use the same approach
here:
>>> np.ctypeslib.as_array(a_ptr, shape=(a.size,)).reshape(a.shape,
order='F')
array([[0, 1, 2],
[3, 4, 5]])
Eric
On Thu, 23 Mar 2023 at 17:04, <[email protected]> wrote:
> Would it be okay to add an argument to ctypeslib.as_array() that allowed
> specifying that a pointer references column-major memory layout?
>
> Currently if we use ndarray.ctypes.data_as() to get a pointer to a
> Fortran-ordered array and then we use ctypeslib.as_array() to read that
> same array back in, we don't have a way of doing the round trip correctly.
>
> For example:
> >>> import ctypes as ct
> >>> a = np.arange(6).reshape(2,3)
> >>> a = np.asfortranarray(a)
> >>> a
> array([[0, 1, 2],
> [3, 4, 5]])
> >>> a_ptr = a.ctypes.data_as(ct.POINTER(ct.c_int))
> >>> b = np.ctypeslib.as_array(a_ptr, shape=a.shape)
> >>> b
> array([[0, 3, 1],
> [4, 2, 5]])
>
> The proposed function signature would be something like:
> numpy.ctypeslib.as_array(obj, shape=None, order='None'), with order{‘C’,
> ‘F’}, optional
>
> Thanks,
> Monte
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