prev_fast_len#
- scipy.fft.prev_fast_len(target, real=False)#
Find the previous fast size of input data to
fft
. Useful for discarding a minimal number of samples before FFT.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 n-smooth numbers)- Parameters:
- Returns:
- outint
The largest fast length less 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.In the current implementation, prev_fast_len assumes radices of 2,3,5,7,11 for complex FFT and 2,3,5 for real FFT.
Examples
On a particular machine, an FFT of prime length takes 16.2 ms:
>>> from scipy import fft >>> import numpy as np >>> rng = np.random.default_rng() >>> max_len = 93059 # prime length is worst case for speed >>> a = rng.standard_normal(max_len) >>> b = fft.fft(a)
Performing FFT on the maximum fast length less than max_len reduces the computation time to 1.5 ms, a speedup of 10.5 times:
>>> fft.prev_fast_len(max_len, real=True) 92160 >>> c = fft.fft(a[:92160]) # discard last 899 samples