 The two-dimensional FFT.
    fftn : The *n*-dimensional FFT.
    rfftn : The *n*-dimensional FFT of real input.
    fftfreq : Frequency bins for given FFT parameters.

    Notes
    -----
    FFT (Fast Fourier Transform) refers to a way the discrete Fourier
    Transform (DFT) can be calculated efficiently, by using symmetries in the
    calculated terms.  The symmetry is highest when `n` is a power of 2, and
    the transform is therefore most efficient for these sizes.

    The DFT is defined, with the conventions used in this implementation, in
    the documentation for the `numpy.fft` module.

    References
    ----------
    .. [CT] Cooley, James W., and John W. Tukey, 1965, "An algorithm for the
            machine calculation of complex Fourier series," *Math. Comput.*
            19: 297-301.

    Examples
    --------
    >>> np.fft.fft(np.exp(2j * np.pi * np.arange(8) / 8))
    array([ -3.44505240e-16 +1.14383329e-17j,
             8.00000000e+00 -5.71092652e-15j,
             2.33482938e-16 +1.22460635e-16j,
             1.64863782e-15 +1.77635684e-15j,
             9.95839695e-17 +2.33482938e-16j,
             0.00000000e+00 +1.66837030e-15j,
             1.14383329e-17 +1.22460635e-16j,
             -1.64863782e-15 +1.77635684e-15j])

    >>> import matplotlib.pyplot as plt
    >>> t = np.arange(256)
    >>> sp = np.fft.fft(np.sin(t))
    >>> freq = np.fft.fftfreq(t.shape[-1])
    >>> plt.plot(freq, sp.real, freq, sp.imag)
    [<matplotlib.lines.Line2D object at 0x...>, <matplotlib.lines.Line2D object at 0x...>]
    >>> plt.show()

    In this example, real input has an FFT which is Hermitian, i.e., symmetric
    in the real part and anti-symmetric in the imaginary part, as described in
    the `numpy.fft` documentation.

    ©