scipy.fft.next_fast_len¶

scipy.fft.
next_fast_len
()[source]¶ Find the next fast size of input data to
fft
, for zeropadding, etc.SciPy’s FFT algorithms gain their speed by a recursive divide and conquer strategy. This relies on efficient functions for small prime factors of the input length. Thus, the transforms are fastest when using composites of the prime factors handled by the fft implementation. If there are efficient functions for all radices <= n, then the result will be a number x >=
target
with only prime factors < n. (Also known as nsmooth numbers) Parameters
 Returns
 outint
The smallest fast length greater than or equal to
target
.
Notes
The result of this function may change in future as performance considerations change, for example, if new prime factors are added.
Calling
fft
orifft
with real input data performs an'R2C'
transform internally.Examples
On a particular machine, an FFT of prime length takes 11.4 ms:
>>> from scipy import fft >>> min_len = 93059 # prime length is worst case for speed >>> a = np.random.randn(min_len) >>> b = fft.fft(a)
Zeropadding to the next regular length reduces computation time to 1.6 ms, a speedup of 7.3 times:
>>> fft.next_fast_len(min_len, real=True) 93312 >>> b = fft.fft(a, 93312)
Rounding up to the next power of 2 is not optimal, taking 3.0 ms to compute; 1.9 times longer than the size given by
next_fast_len
:>>> b = fft.fft(a, 131072)