My point isn't about speed; its about the scope of numpy. typing np.fft.fft isn't more or less convenient than using some other symbol from the scientific python stack. Numerical algorithms should be part of the stack, for sure; but should they be part of numpy? I think its cleaner to have them in a separate package. Id rather have us discuss how to facilitate the integration of as many possible fft libraries with numpy behind a maximally uniform interface, rather than having us debate which fft library is 'best'. On Tue, Oct 28, 2014 at 6:21 PM, Sturla Molden <sturla.molden@gmail.com> wrote:

Eelco Hoogendoorn <hoogendoorn.eelco@gmail.com> wrote:

Perhaps the 'batteries included' philosophy made sense in the early days of numpy; but given that there are several fft libraries with their own pros and cons, and that most numpy projects will use none of them at all, why should numpy bundle any of them?

Because sometimes we just need to compute a DFT, just like we sometimes need to compute a sine or an exponential. It does that job perfectly well. It is not always about speed. Just typing np.fft.fft(x) is convinient.

Sturla

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