[Numpy-discussion] Unexpected behavior with np.min_scalar_type
Mark Wiebe
mwwiebe@gmail....
Tue Jan 24 18:56:34 CST 2012
On Tue, Jan 24, 2012 at 7:29 AM, Kathleen M Tacina <
Kathleen.M.Tacina@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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>
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