On 29.10.2014 18:03, Pierre-Andre Noel wrote:

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'.

I agree with the above.

Absolutely. I think the NumPy/Scipy interfaces are easy and convenient enough to serve as a front-end to different FFT codes.

I would agree if it were not already there, but removing it (like Blas/Lapack) is out of the question for backward compatibility reason. Too much code depends on it.

And I definitely agree with that too.

I think that numpy.fft should be left there in its current state (although perhaps as deprecated). Now scipy.fft should have a good generic algorithm as default, and easily allow for different implementations to be accessed through the same interface.

Definitely. My attempt at streamlining the use of pyfftw even further can be found here: https://github.com/aeberspaecher/transparent_pyfftw This small package does nothing more than to automatically load fftw wisdom on export and add a keyword that gives the number of threads to use to NumPy/Scipy style FFT calls. I think similar attempts could be made with other FFT libraries. The mission statement would be to map each library's interface to the simple and convenient SciPy/NumPy-style interface, and also to wisely choose default parameters (such as e.g. pyfftw's planner_effort). Also, I think it's obvious that a generic and easy-to-use implementation cannot deliver exactly the same performance as hand-tuned code, but anyway I see plenty room for improvement for SciPy/NumPy's FFT modules. Alex