On Sat, Nov 20, 2010 at 6:54 PM, Wes McKinney <wesmck...@gmail.com> wrote: > On Sat, Nov 20, 2010 at 6:39 PM, Keith Goodman <kwgood...@gmail.com> wrote: >> On Fri, Nov 19, 2010 at 7:42 PM, Keith Goodman <kwgood...@gmail.com> wrote: >>> I should make a benchmark suite. >> >>>> ny.benchit(verbose=False) >> Nanny performance benchmark >> Nanny 0.0.1dev >> Numpy 1.4.1 >> Speed is numpy time divided by nanny time >> NaN means all NaNs >> Speed Test Shape dtype NaN? >> 6.6770 nansum(a, axis=-1) (500,500) int64 >> 4.6612 nansum(a, axis=-1) (10000,) float64 >> 9.0351 nansum(a, axis=-1) (500,500) int32 >> 3.0746 nansum(a, axis=-1) (500,500) float64 >> 11.5740 nansum(a, axis=-1) (10000,) int32 >> 6.4484 nansum(a, axis=-1) (10000,) int64 >> 51.3917 nansum(a, axis=-1) (500,500) float64 NaN >> 13.8692 nansum(a, axis=-1) (10000,) float64 NaN >> 6.5327 nanmax(a, axis=-1) (500,500) int64 >> 8.8222 nanmax(a, axis=-1) (10000,) float64 >> 0.2059 nanmax(a, axis=-1) (500,500) int32 >> 6.9262 nanmax(a, axis=-1) (500,500) float64 >> 5.0688 nanmax(a, axis=-1) (10000,) int32 >> 6.5605 nanmax(a, axis=-1) (10000,) int64 >> 48.4850 nanmax(a, axis=-1) (500,500) float64 NaN >> 14.6289 nanmax(a, axis=-1) (10000,) float64 NaN >> >> You can also use the makefile to run the benchmark: make bench >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> > > Keith (and others), > > What would you think about creating a library of mostly Cython-based > "domain specific functions"? So stuff like rolling statistical > moments, nan* functions like you have here, and all that-- NumPy-array > only functions that don't necessarily belong in NumPy or SciPy (but > could be included on down the road). You were already talking about > this on the statsmodels mailing list for larry. I spent a lot of time > writing a bunch of these for pandas over the last couple of years, and > I would have relatively few qualms about moving these outside of > pandas and introducing a dependency. You could do the same for larry-- > then we'd all be relying on the same well-vetted and tested codebase. > > - Wes >
By the way I wouldn't mind pushing all of my datetime-related code (date range generation, date offsets, etc.) into this new library, too. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
