Hi Robert,
On Fri, 30 Dec 2011 20:05:14 +0000, Robert Kern
On Fri, Dec 30, 2011 at 18:57, Andreas Kloeckner
wrote: Hi Robert,
On Tue, 27 Dec 2011 10:17:41 +0000, Robert Kern
wrote: On Tue, Dec 27, 2011 at 01:22, Andreas Kloeckner
wrote: Hi all,
Two questions:
- Are dtypes supposed to be comparable (i.e. implement '==', '!=')?
Yes.
- Are dtypes supposed to be hashable?
Yes, with caveats. Strictly speaking, we violate the condition that objects that equal each other should hash equal since we define == to be rather free. Namely,
np.dtype(x) == x
for all objects x that can be converted to a dtype.
np.dtype(float) == np.dtype('float') np.dtype(float) == float np.dtype(float) == 'float'
Since hash(float) != hash('float') we cannot implement np.dtype.__hash__() to follow the stricture that objects that compare equal should hash equal.
However, if you restrict the domain of objects to just dtypes (i.e. only consider dicts that use only actual dtype objects as keys instead of arbitrary mixtures of objects), then the stricture is obeyed. This is a useful domain that is used internally in numpy.
Is this the problem that you found?
Thanks for the reply.
It doesn't seem like this is our issue--instead, we're encountering two different dtype objects that claim to be float64, compare as equal, but don't hash to the same value.
I've asked the user who encountered the user to investigate, and I'll be back with more detail in a bit.
I think we've run into this before and tried to fix it. Try to find the version of numpy the user has and a minimal example, if you can.
This is what Thomas found: http://projects.scipy.org/numpy/ticket/2017 Hope this helps, Andreas