axes, the positive frequency terms in the
    first half of all axes, the term for the Nyquist frequency in the middle
    of all axes and the negative frequency terms in the second half of all
    axes, in order of decreasingly negative frequency.

    See `numpy.fft` for details, definitions and conventions used.

    Examples
    --------
    >>> a = np.mgrid[:3, :3, :3][0]
    >>> np.fft.fftn(a, axes=(1, 2))
    array([[[  0.+0.j,   0.+0.j,   0.+0.j],
            [  0.+0.j,   0.+0.j,   0.+0.j],
            [  0.+0.j,   0.+0.j,   0.+0.j]],
           [[  9.+0.j,   0.+0.j,   0.+0.j],
            [  0.+0.j,   0.+0.j,   0.+0.j],
            [  0.+0.j,   0.+0.j,   0.+0.j]],
           [[ 18.+0.j,   0.+0.j,   0.+0.j],
            [  0.+0.j,   0.+0.j,   0.+0.j],
            [  0.+0.j,   0.+0.j,   0.+0.j]]])
    >>> np.fft.fftn(a, (2, 2), axes=(0, 1))
    array([[[ 2.+0.j,  2.+0.j,  2.+0.j],
            [ 0.+0.j,  0.+0.j,  0.+0.j]],
           [[-2.+0.j, -2.+0.j, -2.+0.j],
            [ 0.+0.j,  0.+0.j,  0.+0.j]]])

    >>> import matplotlib.pyplot as plt
    >>> [X, Y] = np.meshgrid(2 * np.pi * np.arange(200) / 12,
    ...                      2 * np.pi * np.arange(200) / 34)
    >>> S = np.sin(X) + np.cos(Y) + np.random.uniform(0, 1, X.shape)
    >>> FS = np.fft.fftn(S)
    >>> plt.imshow(np.log(np.abs(np.fft.fftshift(FS))**2))
    <matplotlib.image.AxesImage object at 0x...>
    >>> plt.show()

    r@