# aesara.tensor.fourier.fft#

aesara.tensor.fourier.fft = <aesara.tensor.fourier.Fourier object>[source]#

for officially supported FFTs, use aesara.tensor.fft, which provides real-input FFTs. Gradients are supported.

An instance of this class returns a finite fourier transform calculated along one dimension of an input array.

inputs:

a : Array of at least one dimension. Can be complex. n : Integer, optional. Length of the transformed axis of the output. If n is smaller than the length of the input, the input is cropped. If it is larger, the input is padded with zeros. If n is not given, the length of the input (along the axis specified by axis) is used. axis : Integer, optional. Axis over which to compute the FFT. If not supplied, the last axis is used.

output:

Complex array. The input array, transformed along the axis indicated by ‘axis’ or along the last axis if ‘axis’ is not specified. It is truncated or zero-padded as required if ‘n’ is specified. (From numpy.fft.fft’s documentation:) The values in the output follow so-called standard order. If A = fft(a, n), then A[0] contains the zero-frequency term (the mean of the signal), which is always purely real for real inputs. Then A[1:n/2] contains the positive-frequency terms, and A[n/2+1:] contains the negative-frequency terms, in order of decreasingly negative frequency. For an even number of input points, A[n/2] represents both positive and negative Nyquist frequency, and is also purely real for real input. For an odd number of input points, A[(n-1)/2] contains the largest positive frequency, while A[(n+1)/2] contains the largest negative frequency.

Type:

WARNING