[Numpy-discussion] Unexpected behavior with np.min_scalar_type

[Kathleen M Tacina]

Thanks!

It was interesting to see why that happened.  

Kathy

On Tue, 2012-01-24 at 18:56 -0600, Mark Wiebe wrote:
> On Tue, Jan 24, 2012 at 7:29 AM, Kathleen M Tacina
> <Kathleen.M.Tacina at nasa.gov> wrote:
> 
>         I was experimenting with np.min_scalar_type to make sure it
>         worked as expected, and found some unexpected results for
>         integers between 2**63 and 2**64-1.  I would have expected
>         np.min_scalar_type(2**64-1) to return uint64.  Instead, I get
>         object.  Further experimenting showed that the largest integer
>         for which np.min_scalar_type will return uint64 is 2**63-1.
>         Is this expected behavior?
>         
> 
> 
> This is a bug in how numpy detects the dtype of python objects.
> 
> 
> https://github.com/numpy/numpy/blob/master/numpy/core/src/multiarray/common.c#L18
> 
> 
> You can see there it's only checking for a signed long long, not
> accounting for the unsigned case. I created a ticket for you here:
> 
> 
> http://projects.scipy.org/numpy/ticket/2028
> 
> 
> -Mark
>  
>         
>         On python 2.7.2 on a 64-bit linux machine:
>         >>> import numpy as np
>         >>> np.version.full_version
>         '2.0.0.dev-55472ca'
>         >>> np.min_scalar_type(2**8-1)
>         dtype('uint8')
>         >>> np.min_scalar_type(2**16-1)
>         dtype('uint16')
>         >>> np.min_scalar_type(2**32-1)
>         dtype('uint32')
>         >>> np.min_scalar_type(2**64-1)
>         dtype('O')
>         >>> np.min_scalar_type(2**63-1)
>         dtype('uint64')
>         >>> np.min_scalar_type(2**63)
>         dtype('O')
>         
>         I get the same results on a Windows XP  machine running python
>         2.7.2 and numpy 1.6.1. 
>         
>         Kathy          
>         
>         
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