Oops, I thought we were posting to comp.dsp. Nevertheless, I think
numpy.fft does mixed-radix (can't check it now)
Am 20.05.13 19:50, schrieb Christian Gollwitzer:
Am 20.05.13 19:23, schrieb jmfauth:
Non sense.
Dito.
The discrete fft algorithm is valid only if the number of data
points you transform does correspond to a power of 2 (2**n).
Where did you get this? The DFT is defined for any integer point number
the same way.
Just if you want to get it fast, you need to worry about the length. For
powers of two, there is the classic Cooley-Tukey. But there do exist FFT
algorithms for any other length. For example, there is the Winograd
transform for a set of small numbers, there is "mixed-radix" to reduce
any length which can be factored, and there is finally Bluestein which
works for any size, even for a prime. All of the aforementioned
algorithms are O(log n) and are implemented in typical FFT packages. All
of them should result (up to rounding differences) in the same thing as
the naive DFT sum. Therefore, today
Keywords to the problem: apodization, zero filling, convolution
product, ...
Not for a periodic signal of integer length.
eg. http://en.wikipedia.org/wiki/Convolution
How long do you read this group?
Christian
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