Yes, clearing is not the proper word but the "trick" works only work for 0 (I'll get the same result in both cases). Nicolas
On 27 Dec 2016, at 20:52, Chris Barker <chris.barker@noaa.gov> wrote:
On Mon, Dec 26, 2016 at 1:34 AM, Nicolas P. Rougier <Nicolas.Rougier@inria.fr> wrote:
I'm trying to understand why viewing an array as bytes before clearing makes the whole operation faster. I imagine there is some kind of special treatment for byte arrays but I've no clue.
I notice that the code is simply setting a value using broadcasting -- I don't think there is anything special about zero in that case. But your subject refers to "clearing" an array.
So I wonder if you have a use case where the performance difference matters, in which case _maybe_ it would be worth having a ndarray.zero() method that efficiently zeros out an array.
Actually, there is ndarray.fill():
In [7]: %timeit Z_float[...] = 0
1000 loops, best of 3: 380 µs per loop
In [8]: %timeit Z_float.view(np.byte)[...] = 0
1000 loops, best of 3: 271 µs per loop
In [9]: %timeit Z_float.fill(0)
1000 loops, best of 3: 363 µs per loop
which seems to take an insignificantly shorter time than assignment. Probably because it's doing exactly the same loop.
whereas a .zero() could use a memset, like it does with bytes.
can't say I have a use-case that would justify this, though.
-CHB
# Native float Z_float = np.ones(1000000, float) Z_int = np.ones(1000000, int)
%timeit Z_float[...] = 0 1000 loops, best of 3: 361 µs per loop
%timeit Z_int[...] = 0 1000 loops, best of 3: 366 µs per loop
%timeit Z_float.view(np.byte)[...] = 0 1000 loops, best of 3: 267 µs per loop
%timeit Z_int.view(np.byte)[...] = 0 1000 loops, best of 3: 266 µs per loop
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