issue_comments

The problem with generic scalar types is that it wouldn't work after serialization and deserialization (which would presumably go to strings). My suggestion has the advantage of being able to create a __eq__ method in the base class that would match both the object itself or its string equivalent, so that one could use the scalar type even after ser/deser. I disagree that base classes aren't very pythonic.

However, I think (1)/(2) are both reasonable solution (in fact, they seem to be identical except for when you call str). It has its warts, as even a mutable sequence would pass muster. :)

If that's the direction you'd like to see the project go towards, I'd be happy to take a stab at it.

We're using xarray in a project that is encouraging use of python typing, and we too would like to use enums as data dimension names. How do you feel about using a base class that data dimension classes need to subclass?

Here's a really simple proof-of-concept (though not very thorough, as it would certainly fail serialization): https://github.com/ttung/xarray/commit/8e623ebebc8f5c1e5615e6d07a82451c0dbe763d

``` In [1]: import xarray as xr

In [2]: import numpy as np

In [5]: from enum import Enum

In [6]: class A(xr.core.dataarray.DimensionBase, Enum): ...: X = "abc" ...: Y = "def" ...: Z = "ghi" ...:

In [7]: a = xr.DataArray(np.random.randint(0, 255, size=(4, 3, 5)), dims=[A.X, A.Y, A.Z])

In [8]: a[A.X] Out[8]: <xarray.DataArray \<A.X: 'abc'> (A.X: 4)> array([0, 1, 2, 3]) Dimensions without coordinates: A.X

In [9]: a.max(A.X) Out[9]: <xarray.DataArray (A.Y: 3, A.Z: 5)> array([[254, 226, 181, 191, 233], [139, 195, 212, 167, 169], [191, 241, 199, 174, 208]]) Dimensions without coordinates: A.Y, A.Z

In [10]: ```