Hi, On Thu, Oct 16, 2014 at 6:38 PM, Benjamin Root <ben.r...@ou.edu> wrote: > That isn't what I meant. Higher order doesn't "necessarily" mean more > accurate. The results simply have different properties. The user needs to > choose the differentiation order that they need. One interesting effect in > data assimilation/modeling is that even-order differentiation can often have > detrimental effects while higher odd order differentiation are better, but > it is highly dependent upon the model. > > This change in gradient broke a unit test in matplotlib (for a new feature, > so it isn't *that* critical). We didn't notice it at first because we > weren't testing numpy 1.9 at the time. I want the feature (I have need for > it elsewhere), but I don't want the change in default behavior.
I think it would be a bad idea to revert now. I suspect, if you revert, then a lot of other code will assume the < 1.9.0, >= 1.9.1 behavior. In that case, the code will work as expected most of the time, except when combined with 1.9.0, which could be seriously surprising, and often missed. If you keep the new behavior, then it will be clearer that other code will have to adapt to this change >= 1.9.0 - surprise, but predictable surprise, if you see what I mean... Matthew _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
