`Ahhhhh, looking back I see what Pierre-André did, the penny drops. Sorry I miss-read.`
`I'm only interested in this part;`
`>zeropadded_fft_A = fft(seq_A, n=2**(ceil(log(len(seq_A),2))+1))>zeropadded_fft_B = fft(seq_B, n=2**(ceil(log(len(seq_B),2))+1))`
`>You could remove the "+1" above to get faster results, but then that may >lead to unwanted frequencies (coming from the fact that fft assumes >periodic signals, read online about zero-padding).`
`I did it in the following way and it seems to work. I've seen an increase in performance for sure.My Python sucks by the way :)`
`def pad_to_power_of_two(audio):    '''    Calculates next power of two and pads the audio sample array with zeros    '''    # RIFF spec specifies: ckSize    A 32-bit unsigned value identifying the size of ckData.    max_shift = 32    shift = 1    power = len(audio) - 1    print "{0:b}".format(power)    # Test if a power of two. Turn off the right-most bit (x & (x - 1)) and zero test    while ((power + 1) & power) and (shift <= max_shift):        power |= power >> shift        shift *= 2        print  "new power: " + "{0:b}".format(power)    power = power + 1    print  "{0:b}".format(power)    ext = np.array([0x00] * (power - len(audio)))    new_array = np.concatenate((audio, ext), axis=0)    print  "Next power of 2 greater than actual array length is " + str(power)    print  "Extending current array length (" + str(len(audio)) + ") by " + str(power - len(audio)) + " null bytes"    print  "New array length is " + str(len(new_array))    return new_arrayI will try using the suggested methods and see if I can boost the performance further.`
`Thanks.`

> From: oscar.j.benjamin@gmail.com
> Date: Tue, 1 Sep 2015 16:14:41 +0100
> To: numpy-discussion@scipy.org
> Subject: Re: [Numpy-discussion] Numpy FFT.FFT slow with certain samples
>
> On 1 September 2015 at 11:38, Joseph Codadeen <jdmc80@hotmail.com> wrote:
> >
> >> And while you zero-pad, you can zero-pad to a sequence that is a power of
> >> two, thus preventing awkward factorizations.
> >
> > Does numpy have an easy way to do this, i.e. for a given number, find the
> > next highest number (within a range) that could be factored into small,
> > prime numbers as Phil explained? It would help if it gave a list,
> > prioritised by number of factors.
>
> Just use the next power of 2. Pure powers of 2 are the most efficient
> for FFT algorithms so it potentially works out better than finding a
> smaller but similarly composite size to pad to. Finding the next power
> of 2 is easy to code and never a bad choice.
>
> you can use the Bluestein transform as below. This pads up to a power
> of two (greater than 2N-1) but does it in a special way that ensures
> that it is still calculating the exact (up to floating point error)
> same DFT:
>
> from numpy import array, exp, pi, arange, concatenate
> from numpy.fft import fft, ifft
>
> def ceilpow2(N):
> '''
> >>> ceilpow2(15)
> 16
> >>> ceilpow2(16)
> 16
> '''
> p = 1
> while p < N:
> p *= 2
> return p
>
> def fftbs(x):
> '''
> >>> data = [1, 2, 5, 2, 5, 2, 3]
> >>> from numpy.fft import fft
> >>> from numpy import allclose
> >>> from numpy.random import randn
> >>> for n in range(1, 1000):
> ... data = randn(n)
> ... assert allclose(fft(data), fftbs(data))
> '''
> N = len(x)
> x = array(x)
>
> n = arange(N)
> b = exp((1j*pi*n**2)/N)
> a = x * b.conjugate()
>
> M = ceilpow2(N) * 2
> A = concatenate((a, [0] * (M - N)))
> B = concatenate((b, [0] * (M - 2*N + 1), b[:0:-1]))
> C = ifft(fft(A) * fft(B))
> c = C[:N]
> return b.conjugate() * c
>
> if __name__ == "__main__":
> import doctest
> doctest.testmod()
>
>
> --
> Oscar
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