numpy.fft.fft(a, n=None, axis=-1)

Compute the one dimensional fft on a given axis.

Return the n point discrete Fourier transform of a. n defaults to the length of a. If n is larger than the length of a, then a will be zero-padded to make up the difference. If n is smaller than the length of a, only the first n items in a will be used.


a : array

input array

n : int

length of the fft

axis : int

axis over which to compute the fft


The packing of the result is “standard”: If A = fft(a, n), then A[0] contains the zero-frequency term, A[1:n/2+1] contains the positive-frequency terms, and A[n/2+1:] contains the negative-frequency terms, in order of decreasingly negative frequency. So for an 8-point transform, the frequencies of the result are [ 0, 1, 2, 3, 4, -3, -2, -1].

This is most efficient for n a power of two. This also stores a cache of working memory for different sizes of fft’s, so you could theoretically run into memory problems if you call this too many times with too many different n’s.

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