On Wed, Jan 4, 2012 at 5:22 AM, xantares 09 <xantare...@hotmail.com> wrote: > > >> From: wesmck...@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 <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 > > 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 _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
