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         

_______________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion