On Sat, Dec 24, 2011 at 3:11 AM, xantares 09 <xantare...@hotmail.com> wrote: > > >> From: wesmck...@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 <xantare...@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 _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
