On Fri, Nov 19, 2010 at 2:35 PM, Keith Goodman
On Fri, Nov 19, 2010 at 11:12 AM, Benjamin Root
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
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