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# scipy.fftpack.fft

`scipy.fftpack.``fft`(x, n=None, axis=-1, overwrite_x=False)

  Return discrete Fourier transform of real or complex sequence.

  The returned complex array contains `y(0), y(1),..., y(n-1)` where

  `y(j) = (x * exp(-2*pi*sqrt(-1)*j*np.arange(n)/n)).sum()`.

  Parameters: | x : array_like Array to Fourier transform. n : int, optional Length of the Fourier transform. If `n < x.shape[axis]`, x is truncated. If `n > x.shape[axis]`, x is zero-padded. The default results in `n = x.shape[axis]`. axis : int, optional Axis along which the fft’s are computed; the default is over the last axis (i.e., `axis=-1`). overwrite_x : bool, optional If True, the contents of x can be destroyed; the default is False.
  > Returns: | z : complex ndarray with the elements:
  >     [y(0),y(1),..,y(n/2),y(1-n/2),...,y(-1)]        if n is even
  >     [y(0),y(1),..,y((n-1)/2),y(-(n-1)/2),...,y(-1)]  if n is odd
  > where:
  >     y(j) = sum[k=0..n-1] x[k] * exp(-sqrt(-1)*j*k* 2*pi/n), j = 0..n-1
  Note that `y(-j) = y(n-j).conjugate()`.

  See also

  `ifft`
    Inverse FFT
  `rfft`
    FFT of a real sequence

  Notes

  The packing of the result is “standard”: If `A = fft(a, n)`, then `A[0]` contains the zero-frequency term, `A[1:n/2]` contains the positive-frequency terms, and `A[n/2:]` 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]. To rearrange the fft output so that the zero-frequency component is centered, like [-4, -3, -2, -1, 0, 1, 2, 3], use `fftshift`.

  For n even, `A[n/2]` contains the sum of the positive and negative-frequency terms. For n even and x real, `A[n/2]` will always be real.

  This function is most efficient when n is a power of two, and least efficient when n is prime.

  If the data type of x is real, a “real FFT” algorithm is automatically used, which roughly halves the computation time. To increase efficiency a little further, use `rfft`, which does the same calculation, but only outputs half of the symmetrical spectrum. If the data is both real and symmetrical, the `dct` can again double the efficiency, by generating half of the spectrum from half of the signal.

  Examples

      >>> from scipy.fftpack import fft, ifft
      >>> x = np.arange(5)
      >>> np.allclose(fft(ifft(x)), x, atol=1e-15)  # within numerical accuracy.
      True