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. Cheers! Ben Root On Thu, Oct 16, 2014 at 9:31 PM, Nathaniel Smith <n...@pobox.com> wrote: > On Fri, Oct 17, 2014 at 2:23 AM, Benjamin Root <ben.r...@ou.edu> wrote: > > It isn't really a question of accuracy. It breaks unit tests and > > reproducibility elsewhere. My vote is to revert to the old behavior in > > 1.9.1. > > Why would one want the 2nd order differences at all, if they're not > more accurate? Should we just revert the patch entirely? I assumed the > change had some benefit... > > -- > Nathaniel J. Smith > Postdoctoral researcher - Informatics - University of Edinburgh > http://vorpus.org > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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