Hi, On Thu, Feb 23, 2012 at 4:23 AM, Francesc Alted <franc...@continuum.io> wrote: > On Feb 23, 2012, at 6:06 AM, Francesc Alted wrote: >> On Feb 23, 2012, at 5:43 AM, Nathaniel Smith wrote: >> >>> On Thu, Feb 23, 2012 at 11:40 AM, Francesc Alted <franc...@continuum.io> >>> wrote: >>>> Exactly. I'd update this to read: >>>> >>>> float96 96 bits. Only available on 32-bit (i386) platforms. >>>> float128 128 bits. Only available on 64-bit (AMD64) platforms. >>> >>> Except float96 is actually 80 bits. (Usually?) Plus some padding… >> >> Good point. The thing is that they actually use 96 bit for storage purposes >> (this is due to alignment requirements). >> >> Another quirk related with this is that MSVC automatically maps long double >> to 64-bit doubles: >> >> http://msdn.microsoft.com/en-us/library/9cx8xs15.aspx >> >> Not sure on why they did that (portability issues?). > > Hmm, yet another quirk (this time in NumPy itself). On 32-bit platforms: > > In [16]: np.longdouble > Out[16]: numpy.float96 > > In [17]: np.finfo(np.longdouble).eps > Out[17]: 1.084202172485504434e-19 > > while on 64-bit ones: > > In [8]: np.longdouble > Out[8]: numpy.float128 > > In [9]: np.finfo(np.longdouble).eps > Out[9]: 1.084202172485504434e-19 > > i.e. NumPy is saying that the eps (machine epsilon) is the same on both > platforms, despite the fact that one uses 80-bit precision and the other > 128-bit precision. For the 80-bit, the eps should be (): > > In [5]: 1 / 2**63. > Out[5]: 1.0842021724855044e-19 > > [http://en.wikipedia.org/wiki/Extended_precision] > > which is correctly stated by NumPy, while for 128-bit (quad precision), eps > should be: > > In [6]: 1 / 2**113. > Out[6]: 9.62964972193618e-35 > > [http://en.wikipedia.org/wiki/Quadruple-precision_floating-point_format] > > If nobody objects, I'll file a bug about this.
There was half a proposal for renaming these guys in the interests of clarity: http://mail.scipy.org/pipermail/numpy-discussion/2011-October/058820.html I'd be happy to write this up as a NEP. Best, Matthew _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion