[Numpy-discussion] PyInt and Numpy's int64 conversion
xantares 09
xantares09@hotmail....
Wed Jan 4 04:22:39 CST 2012
> 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.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://mail.scipy.org/pipermail/numpy-discussion/attachments/20120104/88522622/attachment.html
More information about the NumPy-Discussion
mailing list