def METHOD_NAME(self, inplace=False, **kwargs):
    """Returns the power spectrum of the angular auto-correlation function
    in the form of a Signal2D class.
    This gives the fourier decomposition of the radial correlation. Due to
    nyquist sampling the number of fourier coefficients will be equal to the
    angular range.
    Parameters
    ----------
    mask: Numpy array or Signal2D
        A bool mask of values to ignore of shape equal to the signal shape.  If the mask
        is a BaseSignal than it is iterated with the polar signal
     normalize: bool
         Normalize the radial correlation by the average value at some radius.
    inplace: bool
        From hyperspy.signal.map(). inplace=True means the signal is
        overwritten.
    Returns
    -------
    power: Signal2D
        The power spectrum of the Signal2D
    """
    power = self.map(corr_to_power, inplace=inplace, **kwargs)
    s = self if inplace else power
    s.set_signal_type("power")
    fourier_axis = s.axes_manager.signal_axes[0]
    fourier_axis.name = "Fourier Coefficient"
    fourier_axis.units = "a.u"
    fourier_axis.scale = 1
    return power