[Numpy-discussion] Type declaration to include all valid numerical NumPy types for Cython

Ilhan Polat ilhanpolat at gmail.com
Mon Aug 10 05:24:19 EDT 2020

Yes it seems like I don't have any other option anyways. There is a bit of
a penalty but I guess this should do the trick.

Thanks Eric (again! :D)

On Mon, Aug 10, 2020 at 2:51 AM Eric Moore <ewm at redtetrahedron.org> wrote:

> If that is really all you need, then the version in python is:
> def convert_one(a):
>     """
>     Converts input with arbitrary layout and dtype to a blas/lapack
>     compatible dtype with either C or F order.  Acceptable objects are
> passed
>     through without making copies.
>     """
>     a_arr = np.asarray(a)
>     dtype = np.result_type(a_arr, 1.0)
>     # need to handle these separately
>     if dtype == np.longdouble:
>         dtype = np.dtype('d')
>     elif dtype == np.clongdouble:
>         dtype = np.dtype('D')
>     elif dtype == np.float16:
>         dtype = np.dtype('f')
>     # explicitly force a copy if a_arr isn't one segment
>     return np.array(a_arr, dtype, copy=not a_arr.flags.forc, order='K')
> In Cython, you could just run exactly this code and it's probably fine.
> The could also be rewritten using the C calls if you really wanted.
> You need to either provide your own or use a casting table and the copy /
> conversion routines from somewhere.  Cython, to my knowledge, doesn't
> provide these things, but Numpy does.
> Eric
> On Sun, Aug 9, 2020 at 6:16 PM Ilhan Polat <ilhanpolat at gmail.com> wrote:
>> Hi all,
>> As you might have seen my recent mails in Cython list, I'm trying to cook
>> up an input validator for the linalg.solve() function. The machinery of
>> SciPy linalg is as follows:
>> Some input comes in passes through np.asarray() then depending on the
>> resulting dtype of the numpy array we choose a LAPACK flavor (s,d,c,z) and
>> off it goes through f2py to lalaland and comes back with some result.
>> For the backslash polyalgorithm I need the arrays to be contiguous (C- or
>> F- doesn't matter) and any of the four (possibly via making new copies)
>> float, double, float complex, double complex after the intake because we
>> are using wrapped fortran code (LAPACK) in SciPy. So my difficulty is how
>> to type such function input, say,
>> ctypedef fused numeric_numpy_t:
>>     bint
>>     cnp.npy_bool
>>     cnp.int_t
>>     cnp.intp_t
>>     cnp.int8_t
>>     cnp.int16_t
>>     cnp.int32_t
>>     cnp.int64_t
>>     cnp.uint8_t
>>     cnp.uint16_t
>>     cnp.uint32_t
>>     cnp.uint64_t
>>     cnp.float32_t
>>     cnp.float64_t
>>     cnp.complex64_t
>>     cnp.complex128_t
>> Is this acceptable or something else needs to be used? Then there is the
>> storyof np.complex256 and mysterious np.float16. Then there is the Linux vs
>> Windows platform dependence issue and possibly some more that I can't
>> comprehend. Then there are datetime, str, unicode etc. that need to be
>> rejected. So this is quickly getting out of hand for my small brain.
>> To be honest, I am a bit running out of steam working with this issue
>> even though I managed to finish the actual difficult algorithmic part but
>> got stuck here. I am quite surprised how fantastically complicated and
>> confusing both NumPy and Cython docs about this stuff. Shouldn't we keep a
>> generic fused type for such usage? Or maybe there already exists but I
>> don't know and would be really grateful for pointers.
>> Here I wrote a dummy typed Cython function just for type checking:
>> cpdef inline bint ncc( numeric_numpy_t[:, :] a):
>>     print(a.is_f_contig())
>>     print(a.is_c_contig())
>>     return a.is_f_contig() or a.is_c_contig()
>> And this is a dummy loop (with aliases) just to check whether fused type
>> is working or not (on windows I couldn't make it work for float16).
>> for x in (np.uint, np.uintc, np.uintp, np.uint0, np.uint8, np.uint16,
>> np.uint32,
>>           np.uint64, np.int, np.intc, np.intp, np.int0, np.int8,
>> np.int16,
>>           np.int32,np.int64, np.float, np.float32, np.float64, np.float_,
>>           np.complex, np.complex64, np.complex128, np.complex_):
>>     print(x)
>>     C = np.arange(25., dtype=x).reshape(5, 5)
>>     ncc(C)
>> Thanks in advance,
>> ilhan
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