On 01/04/2013 12:39 AM, Andrew Collette wrote: >> Consensus in that bug report seems to be that for array/scalar operations >> like: >> np.array([1], dtype=np.int8) + 1000 # can't be represented as an int8! >> we should raise an error, rather than either silently upcasting the >> result (as in 1.6 and 1.7) or silently downcasting the scalar (as in >> 1.5 and earlier). > > I have run into this a few times as a NumPy user, and I just wanted to > comment that (in my opinion), having this case generate an error is > the worst of both worlds. The reason people can't decide between > rollover and promotion is because neither is objectively better. One
If neither is objectively better, I think that is a very good reason to kick it down to the user. "Explicit is better than implicit". > avoids memory inflation, and the other avoids losing precision. You > just need to pick one and document it. Kicking the can down the road > to the user, and making him/her explicitly test for this condition, is > not a very good solution. It's a good solution to encourage bug-free code. It may not be a good solution to avoid typing. > What does this mean in practical terms for NumPy users? I personally > don't relish the choice of always using numpy.add, or always wrapping > my additions in checks for ValueError. I think you usually have a bug in your program when this happens, since either the dtype is wrong, or the value one is trying to store is wrong. I know that's true for myself, though I don't claim to know everybody elses usecases. Dag Sverre _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
