On 29/10/14 10:48, Eelco Hoogendoorn 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 am happy with the NumPy interface. There are minor differences between the SciPy and NumPy FFT interfaces (e.g. for rfft, see below). Personally I prefer the NumPy interface because it makes it easier to map Fourier coeffs to frequencies. One thing we could do, without too much hassle, is to use FFTs from MKL or Accelerate (vDSP) if we link with these libararies for BLAS/LAPACK. MKL has an API compatible with FFTW, so FFTW and MKL can be supported with the same C code. FFTW and MKL also have a Fortran 77 API which we could wrap with f2py (no Fortran compiler are needed). It is actually possible to use the FFTs in FFTW and MKL from Python without any C coding at all. We just need to add a Python interface on top of the f2py wrappers, which is similar to what we do for scipy.linalg. The FFTs in Accelerate have a very simple C interface, but only support power-of-two array sizes, so we would need to use them with Bluestein's algorithm. Again, because of their simplicity, it is possible to wrap these FFT functions with f2py. We cannot bundle NumPy or SciPy binaries with FFTW due to GPL [*], but as I understand it we already have permission from Intel to bundle binary wheels linked with MKL. Accelerate is a system library, so that does not pose a license problem. [*] Actually, we could, but the binaries would be tainted with a viral license.

a = np.random.rand(8)

scipy.fftpack.rfft(a)[:,None]

array([[ 3.47756851], [-0.45869926], [-0.21730867], [-0.43763425], [-0.67338213], [-0.28799 ], [ 0.17321793], [-0.31514119]])

np.fft.rfft(a)[:,None]

array([[ 3.47756851+0.j ], [-0.45869926-0.21730867j], [-0.43763425-0.67338213j], [-0.28799000+0.17321793j], [-0.31514119+0.j ]]) Sturla