[Numpy-discussion] float32 to float64 casting
Olivier Delalleau
shish at keba.be
Sat Nov 17 15:00:14 EST 2012
2012/11/17 Gökhan Sever <gokhansever at gmail.com>
>
>
> On Sat, Nov 17, 2012 at 9:47 AM, Nathaniel Smith <njs at pobox.com> wrote:
>
>> On Fri, Nov 16, 2012 at 9:53 PM, Gökhan Sever <gokhansever at gmail.com>
>> wrote:
>> > Thanks for the explanations.
>> >
>> > For either case, I was expecting to get float32 as a resulting data
>> type.
>> > Since, float32 is large enough to contain the result. I am wondering if
>> > changing casting rule this way, requires a lot of modification in the
>> NumPy
>> > code. Maybe as an alternative to the current casting mechanism?
>> >
>> > I like the way that NumPy can convert to float64. As if these
>> data-types are
>> > continuation of each other. But just the conversation might happen too
>> early
>> > --at least in my opinion, as demonstrated in my example.
>> >
>> > For instance comparing this example to IDL surprises me:
>> >
>> > I16 np.float32(5555)*5e38
>> > O16 2.7774999999999998e+42
>> >
>> > I17 (np.float32(5555)*5e38).dtype
>> > O17 dtype('float64')
>>
>> In this case, what's going on is that 5e38 is a Python float object,
>> and Python float objects have double-precision, i.e., they're
>> equivalent to np.float64's. So you're multiplying a float32 and a
>> float64. I think most people will agree that in this situation it's
>> better to use float64 for the output?
>>
>> -n
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>>
>
> OK, I see your point. Python numeric data objects and NumPy data objects
> mixed operations require more attention.
>
> The following causes float32 overflow --rather than casting to float64 as
> in the case for Python float multiplication, and behaves like in IDL.
>
> I3 (np.float32(5555)*np.float32(5e38))
> O3 inf
>
> However, these two still surprises me:
>
> I5 (np.float32(5555)*1).dtype
> O5 dtype('float64')
>
> I6 (np.float32(5555)*np.int32(1)).dtype
> O6 dtype('float64')
>
That's because the current way of finding out the result's dtype is based
on input dtypes only (not on numeric values), and numpy.can_cast('int32',
'float32') is False, while numpy.can_cast('int32', 'float64') is True (and
same for int64).
Thus it decides to cast to float64.
-=- Olivier
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