[Numpy-discussion] np.longlong casts to int

Matthew Brett matthew.brett@gmail....
Thu Feb 23 10:26:52 CST 2012


Hi,

On Thu, Feb 23, 2012 at 4:23 AM, Francesc Alted <francesc@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 <francesc@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


More information about the NumPy-Discussion mailing list