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@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....
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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