Hi Robert,

On Tue, 27 Dec 2011 10:17:41 +0000, Robert Kern robert.kern@gmail.com wrote:

On Tue, Dec 27, 2011 at 01:22, Andreas Kloeckner lists@informa.tiker.net 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.

Andreas