On 6/13/2016 1:54 PM, Marten van Kerkwijk wrote:
1. What in principle is the best return type for int ** int (which Josef I think most properly rephrased as whether `**` should be thought of as a float operator, like `/` in python3 and `sqrt` etc.);
Perhaps the question is somewhat different. Maybe it is: what type should a user expect when the exponent is a Python int? The obvious choices seem to be an object array of Python ints, or an array of floats. So far, nobody has proposed the former, and concerns have been expressed about the latter. More important, either would break the rule that the scalar type is not important in array operations, which seems like a good general rule (useful and easy to remember). How much commitment is there to such a rule? E.g., np.int64(2**7)*np.arange(5,dtype=np.int8) violates this. One thing that has come out of this discussion for me is that the actual rules in play are hard to keep track of. Are they all written down in one place? I suspect there is general support for the idea that if someone explicitly specifies the same dtype for the base and the exponent then the result should also have that dtype. I think this is already true for array exponentiation and for scalar exponentiation. One other thing that a user might expect, I believe, is that any type promotion rules for scalars and arrays will be the same. This is not currently the case, and that feels like an inconsistency. But is it an inconsistency? If the rule is that that array type dominates the scalar type, that may be understandable, but then it should be a firm rule. In this case, an exponent that is a Python int should not affect the dtype of the (array) result. In sum, as a user, I've come around to Chuck's original proposal: integers raised to negative integer powers raise an error. My reason for coming around is that I believe it meshes well with a general rule that in binary operations the scalar dtypes should not influence the dtype of an array result. Otoh, it is unclear to me how much commitment there is to that rule. Thanks in advance to anyone who can help me understand better the issues in play. Cheers, Alan Isaac _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
