I could get behind the context manager approach. It would help keep backwards compatibility, while providing a very easy (and clean) way of consistently using the same reduction operation. Adding kwargs is just a road to hell.
Cheers! Ben Root On Sat, Jul 26, 2014 at 9:53 AM, Julian Taylor < jtaylor.deb...@googlemail.com> wrote: > On 26.07.2014 15:38, Eelco Hoogendoorn wrote: > > > > Why is it not always used? > > for 1d reduction the iterator blocks by 8192 elements even when no > buffering is required. There is a TODO in the source to fix that by > adding additional checks. Unfortunately nobody knows hat these > additional tests would need to be and Mark Wiebe who wrote it did not > reply to a ping yet. > > Also along the non-fast axes the iterator optimizes the reduction to > remove the strided access, see: > https://github.com/numpy/numpy/pull/4697#issuecomment-42752599 > > > Instead of having a keyword argument to mean I would prefer a context > manager that changes algorithms for different requirements. > This would easily allow changing the accuracy and performance of third > party functions using numpy without changing the third party library as > long as they are using numpy as the base. > E.g. > with np.precisionstate(sum="kahan"): > scipy.stats.nanmean(d) > > We also have case where numpy uses algorithms that are far more precise > than most people needs them. E.g. np.hypot and the related complex > absolute value and division. > These are very slow with glibc as it provides 1ulp accuracy, this is > hardly ever needed. > Another case that could use dynamic changing is flushing subnormals to > zero. > > But this api is like Nathaniels parameterizable dtypes just an idea > floating in my head which needs proper design and implementation written > down. The issue is as usual ENOTIME. > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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