[Numpy-discussion] load from text files Pull Request Review
Chris.Barker
Chris.Barker@noaa....
Thu Sep 8 15:57:59 CDT 2011
On 9/8/11 1:43 PM, Christopher Jordan-Squire wrote:
> I just ran a quick test on my machine of this idea. With
>
> dt = np.dtype([('x',np.float32),('y', np.int32),('z', np.float64)])
> temp = np.empty((), dtype=dt)
> temp2 = np.zeros(1,dtype=dt)
>
> In [96]: def f():
> ...: l=[0]*3
> ...: l[0] = 2.54
> ...: l[1] = 4
> ...: l[2] = 2.3645
> ...: j = tuple(l)
> ...: temp2[0] = j
>
> vs
>
>
> In [97]: def g():
> ...: temp['x'] = 2.54
> ...: temp['y'] = 4
> ...: temp['z'] = 2.3645
> ...: temp2[0] = temp
> ...:
>
> The timing results were 2.73 us for f and 3.43 us for g. So good idea,
> but it doesn't appear to be faster. (Though the difference wasn't
> nearly as dramatic as I thought it would be, based on Pauli's
> comment.)
my guess is that the lines like: temp['x'] = 2.54 are slower (it
requires a dict lookup, and a conversion from a python type to a "raw" type)
and
temp2[0] = temp
is faster, as that doesn't require any conversion.
Which means that if you has a larger struct dtype, it would be even
slower, so clearly not the way to go for performance.
It would be nice to have a higher performing struct dtype scalar -- as
it is ordered, it might be nice to be able to index it with either the
name or an numeric index.
-Chris
--
Christopher Barker, Ph.D.
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