On Wed, Feb 29, 2012 at 10:22 AM, Paweł Biernat <pw...@wp.pl> wrote: > I am completely new to Numpy and I know only the basics of Python, to > this point I was using Fortran 03/08 to write numerical code. However, > I am starting a new large project of mine and I am looking forward to > using Python to call some low level Fortran code responsible for most > of the intensive number crunching. In this context I stumbled into > f2py and it looks just like what I need, but before I start writing an > app in mixture of Python and Fortran I have a question about numerical > precision of variables used in numpy and f2py. > > Is there any way to interact with Fortran's real(16) (supported by gcc > and Intel's ifort) data type from numpy? By real(16) I mean the > binary128 type as in IEEE 754. (In C this data type is experimentally > supported as __float128 (gcc) and _Quad (Intel's icc).) I have > investigated the float128 data type, but it seems to work as binary64 > or binary80 depending on the architecture. If there is currently no > way to interact with binary128, how hard would it be to patch the > sources of numpy to add such data type? I am interested only in > basic stuff, comparable in functionality to libmath. > > As said before, I have little knowledge of Python, Numpy and f2py, I > am however, interested in investing some time in learing it and > implementing the mentioned features, but only if there is any hope of > succeeding.
Numpy does not have proper support for the quadruple precision float numbers, because very few implementation do (no common CPU handle it in hw, for example). The dtype128 is a bit confusingly named: the 128 refers to the padding in memory, but not its "real" precision. It often (but not always) refer to the long double in the underlying C implementation. The latter depends on the OS, CPU and compilers. cheers, David _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
