On Wed, Jul 2, 2014 at 7:57 AM, Mark Szepieniec <mszepien@gmail.com> wrote:
Looks this could be a float32 vs float64 problem:

that would explain it.
 
I guess users always be very careful when mixing floating point types, but should numpy prevent (or warn) the user from doing so in this case?

I don't think so -- this "uncertainty" is very much the nature of histogramming, particularly with floating point values -- you should expect to get different results with different data precisions. As you should for ANY floating point computation.

-Chris
 


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