On 21. des. 2012, at 16:53, David Cournapeau wrote:

> On Fri, Dec 21, 2012 at 3:16 PM, Paul Anton Letnes
> <paul.anton.letnes@gmail.com> wrote:
>> Dear Scipy devs,
>>
>> 1) thanks for doing a great job in general!
>> 2) Why is there a huge performance difference between scipy.fft and scipy.fftpack.fft? It apperars that scipy.fft == numpy.fft:
>>
>> In [4]: import numpy as np
>>
>> In [5]: from scipy import fft
>>
>> In [6]: from scipy import fftpack
>>
>> In [7]: d = np.linspace(0, 1e3, 1e7)
>>
>> In [8]: %timeit np.fft.fft(d)
>> 1 loops, best of 3: 1.29 s per loop
>>
>> In [9]: %timeit fft(d)
>> 1 loops, best of 3: 1.3 s per loop
>>
>> In [10]: %timeit fftpack.fft(d)
>> 1 loops, best of 3: 651 ms per loop
>>
>> 3) On a related note - what's the best performing python fft library/wrapper out there? I take it from some google research I did that e.g. fftw cannot be used due to the GPL licence.
>
> But you could use e.g. pyfftw that will give you a better wrapper that
> scipy ever had for FFTW.
>
> The difference in speed is not unexpected: the fft in numpy is there
> for historical reasons and backward compatibility. Unless you have a
> very good reason not to use it, you should be using scipy.fftpack
> instead of numpy.fft when you can depend on scipy.

I understand that this must be something like a backwards compatibility thing. But my point was (although I was perhaps not all that clear): Why does scipy.fft link to numpy.fft,

and not scipy.fftpack.fft? The interface seems similar enough:
fft(a[, n, axis])
fft(x[, n, axis, overwrite_x])
The extra overwrite_x in fftpack shouldn't be much of an issue.

Cheers
Paul

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