On Fri, Nov 19, 2010 at 11:12 AM, Benjamin Root <ben.r...@ou.edu> 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 _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion