On Wed, Oct 26, 2016 at 12:23 PM, Charles R Harris
<charlesr.har...@gmail.com> wrote:
[...]
> What I have been concerned about are the follow combinations that currently
> return floats
>
> num: <type 'numpy.int8'>, exp: <type 'numpy.int8'>, res: <type
> 'numpy.float32'>
> num: <type 'numpy.int16'>, exp: <type 'numpy.int8'>, res: <type
> 'numpy.float32'>
> num: <type 'numpy.int16'>, exp: <type 'numpy.int16'>, res: <type
> 'numpy.float32'>
> num: <type 'numpy.int32'>, exp: <type 'numpy.int8'>, res: <type
> 'numpy.float64'>
> num: <type 'numpy.int32'>, exp: <type 'numpy.int16'>, res: <type
> 'numpy.float64'>
> num: <type 'numpy.int32'>, exp: <type 'numpy.int32'>, res: <type
> 'numpy.float64'>
> num: <type 'numpy.int64'>, exp: <type 'numpy.int8'>, res: <type
> 'numpy.float64'>
> num: <type 'numpy.int64'>, exp: <type 'numpy.int16'>, res: <type
> 'numpy.float64'>
> num: <type 'numpy.int64'>, exp: <type 'numpy.int32'>, res: <type
> 'numpy.float64'>
> num: <type 'numpy.int64'>, exp: <type 'numpy.int64'>, res: <type
> 'numpy.float64'>
> num: <type 'numpy.int64'>, exp: <type 'numpy.int64'>, res: <type
> 'numpy.float64'>
> num: <type 'numpy.uint64'>, exp: <type 'numpy.int8'>, res: <type
> 'numpy.float64'>
> num: <type 'numpy.uint64'>, exp: <type 'numpy.int16'>, res: <type
> 'numpy.float64'>
> num: <type 'numpy.uint64'>, exp: <type 'numpy.int32'>, res: <type
> 'numpy.float64'>
> num: <type 'numpy.uint64'>, exp: <type 'numpy.int64'>, res: <type
> 'numpy.float64'>
> num: <type 'numpy.uint64'>, exp: <type 'numpy.int64'>, res: <type
> 'numpy.float64'>
What's this referring to? For both arrays and scalars I get:
In [8]: (np.array(2, dtype=np.int8) ** np.array(2, dtype=np.int8)).dtype
Out[8]: dtype('int8')
In [9]: (np.int8(2) ** np.int8(2)).dtype
Out[9]: dtype('int8')
-n
--
Nathaniel J. Smith -- https://vorpus.org
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