On Fri, Nov 19, 2010 at 2:35 PM, Keith Goodman <[email protected]> wrote: > On Fri, Nov 19, 2010 at 11:12 AM, Benjamin Root <[email protected]> wrote: > >> That's why I use masked arrays. It is dtype agnostic. >> >> I am curious if there are any lessons that were learned in making Nanny that >> could be applied to the masked array functions? > > I suppose you could write a cython function that operates on masked > arrays. But other than that, I can't think of any lessons. All I can > think about is speed: > >>> x = np.ma.array([[1, 2], [3, 4]], mask=[[0, 1], [1, 0]]) >>> timeit np.sum(x) > 10000 loops, best of 3: 25.1 us per loop >>> a = np.array([[1, np.nan], [np.nan, 4]]) >>> timeit ny.nansum(a) > 100000 loops, best of 3: 3.11 us per loop >>> from nansum import nansum_2d_float64_axisNone >>> timeit nansum_2d_float64_axisNone(a) > 1000000 loops, best of 3: 395 ns per loop
What's the speed advantage of nanny compared to np.nansum that you have if the arrays are larger, say (1000,10) or (10000,100) axis=0 ? Josef > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
