[Numpy-discussion] PyInt and Numpy's int64 conversion

Wes McKinney wesmckinn@gmail....
Fri Jan 6 15:00:00 CST 2012


On Wed, Jan 4, 2012 at 5:22 AM, xantares 09 <xantares09@hotmail.com> wrote:
>
>
>> From: wesmckinn@gmail.com
>> Date: Sat, 24 Dec 2011 19:51:06 -0500
>
>> To: numpy-discussion@scipy.org
>> Subject: Re: [Numpy-discussion] PyInt and Numpy's int64 conversion
>>
>> On Sat, Dec 24, 2011 at 3:11 AM, xantares 09 <xantares09@hotmail.com>
>> wrote:
>> >
>> >
>> >> From: wesmckinn@gmail.com
>> >> Date: Fri, 23 Dec 2011 12:31:45 -0500
>> >> To: numpy-discussion@scipy.org
>> >> Subject: Re: [Numpy-discussion] PyInt and Numpy's int64 conversion
>> >
>> >>
>> >> On Fri, Dec 23, 2011 at 4:37 AM, xantares 09 <xantares09@hotmail.com>
>> >> wrote:
>> >> > Hi,
>> >> >
>> >> > I'm using Numpy from the C python api side while tweaking my SWIG
>> >> > interface
>> >> > to work with numpy array types.
>> >> > I want to convert a numpy array of integers (whose elements are
>> >> > numpy's
>> >> > 'int64')
>> >> > The problem is that it this int64 type is not compatible with the
>> >> > standard
>> >> > python integer type:
>> >> > I cannot use PyInt_Check, and PyInt_AsUnsignedLongMask to check and
>> >> > convert
>> >> > from int64: basically PyInt_Check returns false.
>> >> > I checked the numpy config header and npy_int64 does have a size of
>> >> > 8o,
>> >> > which should be the same as int on my x86_64.
>> >> > What is the correct way to do that ?
>> >> > I checked for a Int64_Check function and didn't find any in numpy
>> >> > headers.
>> >> >
>> >> > Regards,
>> >> >
>> >> > x.
>> >> >
>> >> > _______________________________________________
>> >> > NumPy-Discussion mailing list
>> >> > NumPy-Discussion@scipy.org
>> >> > http://mail.scipy.org/mailman/listinfo/numpy-discussion
>> >> >
>> >>
>> >> hello,
>> >>
>> >> I think you'll want to use the C macro PyArray_IsIntegerScalar, e.g.
>> >> in pandas I have the following function exposed to my Cython code:
>> >>
>> >> PANDAS_INLINE int
>> >> is_integer_object(PyObject* obj) {
>> >> return PyArray_IsIntegerScalar(obj);
>> >> }
>> >>
>> >> last time I checked that macro detects Python int, long, and all of
>> >> the NumPy integer hierarchy (int8, 16, 32, 64). If you ONLY want to
>> >> check for int64 I am not 100% sure the best way.
>> >>
>> >> - Wes
>> >
>> > Hi,
>> >
>> > Thank you for your reply !
>> >
>> > That's the thing : I want to check/convert every type of integer,
>> > numpy's
>> > int64 and also python standard ints.
>> > Is there a way to avoid to use only the python api ? ( and avoid to
>> > depend
>> > on numpy's PyArray_* functions )
>> >
>> > Regards.
>> >
>> > x.
>> >
>> >
>> >
>> >
>> >
>> >
>> > _______________________________________________
>> > NumPy-Discussion mailing list
>> > NumPy-Discussion@scipy.org
>> > http://mail.scipy.org/mailman/listinfo/numpy-discussion
>> >
>>
>> No. All of the PyTypeObject objects for the NumPy array scalars are
>> explicitly part of the NumPy C API so you have no choice but to depend
>> on that (to get the best performance). If you want to ONLY check for
>> int64 at the C API level, I did a bit of digging and the relevant type
>> definitions are in
>>
>>
>> https://github.com/numpy/numpy/blob/master/numpy/core/include/numpy/npy_common.h
>>
>> so you'll want to do:
>>
>> int is_int64(PyObject* obj){
>> return PyObject_TypeCheck(obj, &PyInt64ArrType_Type);
>> }
>>
>> and that will *only* detect np.int64
>>
>> - Wes
>
> Ok many thanks !
>
> One last thing, do you happen to know how to actually convert an np int64 to
> a C int ?
>
> - x.
>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>

Not sure off-hand. You'll have to look at the NumPy scalar API in the C code


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