Perform a real Discrete Fourier Transform (DFT; implemented
        using a Fast Fourier Transform algorithm, FFT) of the current
        sample from the signal multiplied by the smoothing window.

        See numpy.rfft for information about the Fourier transform.

def _get_row_amplitudes(self):
        """
        Perform a real Discrete Fourier Transform (DFT; implemented
        using a Fast Fourier Transform algorithm, FFT) of the current
        sample from the signal multiplied by the smoothing window.

        See numpy.rfft for information about the Fourier transform.
        """

        signal_interval = self.signal()
        sample_rate = self.signal.sample_rate

        # A signal window *must* span one sample rate
        signal_window = np.tile(signal_interval, int(np.ceil(1.0/self.signal.interval_length)))

        if self.windowing_function:
            smoothed_window = signal_window[0:sample_rate] * self.windowing_function(sample_rate)
        else:
            smoothed_window = signal_window[0:sample_rate]

        amplitudes = (np.abs(np.fft.rfft(smoothed_window))[0:sample_rate/2] + self.offset) * self.scale

        for index in range(0, self._sheet_dimensions[0]-2):
            start_frequency = self.frequency_spacing[index]
            end_frequency = self.frequency_spacing[index+1]

            normalisation_factor =  end_frequency - start_frequency
            if normalisation_factor == 0:
                amplitudes[index] = amplitudes[start_frequency]
            else:
                amplitudes[index] = np.sum(amplitudes[int(start_frequency):int(end_frequency)]) / normalisation_factor

        return np.flipud(amplitudes[0:self._sheet_dimensions[0]].reshape(-1,1))