[SciPy-User] scipy interpolate.interp1d spline slowness
Wed Dec 12 17:26:49 CST 2012
Sorry again, i should include that interp came from: interp = interpolate.interp1d(x, y, kind='cubic')
On 2012-12-12, at 6:25 PM, Wolfgang Kerzendorf <email@example.com> wrote:
> Hi Josef,
> Your'e absolutely right - I just omitted the other timings as they seemed small compared to the interp1d:
> %timeit interp(x_new)
> 100 loops, best of 3: 3.89 ms per loop
> %timeit tck = interpolate.splrep(x, y, s=0)
> 1000 loops, best of 3: 204 us per loop
> It would be great if you look into this.
> On 2012-12-12, at 5:25 PM, firstname.lastname@example.org wrote:
>> On Wed, Dec 12, 2012 at 5:08 PM, Wolfgang Kerzendorf
>> <email@example.com> wrote:
>>> Hello Scipyers,
>>> I've just stumbled across a problem with interpolate.interp1d:
>>> import numpy as np
>>> from scipy import interpolate
>>> x = arange(1000)
>>> y = y = np.random.random_integers(0, 900, 1000)
>>> %timeit interp = interpolate.interp1d(x, y, kind='cubic')
>>> 1 loops, best of 3: 3.63 s per loop
>>> #the call for the interpolation is really quick afterwards (a couple ms)
>>> tck = interpolate.splrep(x, y, s=0)
>>> %timeit interpolate.splev(x_new, tck, der=0)
>>> 100 loops, best of 3: 5.51 ms per loop
>> It looks to me, you are timing two different things here, with
>> interp1d you time the spline creation with splev you time the
>> for "cubic", interp1d uses _fitpack._bspleval so I wouldn't expect
>> much difference in timing.
>> But I didn't check whether there is a difference in what the wrappers are doing
>>> I do understand that these are different spline interpolations (but that's as far as my knowledge goes). I was just annoyed at the person saying: Ah, you see python is slow - which it is not as shown by the second scipy command.
>>> Would it be possible to switch the spline interpolator used in interpolate.interp1d to the B-Splines, or to give an option to switch between different spline interpolators (maybe with a warning: slow).
>>> Ah - a last question: Why don't you use the issues tab on the github page?
>>> Thanks in advance,
>>> SciPy-User mailing list
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