How to use the sunpy.util.to_signed function in sunpy

def randomized_auto_const_bg(self, amount):
        """ Automatically determine background. Only consider a randomly
        chosen subset of the image.

        Parameters
        ----------
        amount : int
            Size of random sample that is considered for calculation of
            the background.
        """
        cols = [randint(0, self.shape[1] - 1) for _ in xrange(amount)]

        # pylint: disable=E1101,E1103
        data = self.data.astype(to_signed(self.dtype))
        # Subtract average value from every frequency channel.
        tmp = (data - np.average(self.data, 1).reshape(self.shape[0], 1))
        # Get standard deviation at every point of time.
        # Need to convert because otherwise this class's __getitem__
        # is used which assumes two-dimensionality.
        tmp = tmp[:, cols]
        sdevs = np.asarray(np.std(tmp, 0))

        # Get indices of values with lowest standard deviation.
        cand = sorted(xrange(amount), key=lambda y: sdevs[y])
        # Only consider the best 5 %.
        realcand = cand[:max(1, int(0.05 * len(cand)))]

        # Average the best 5 %
        bg = np.average(self[:, [cols[r] for r in realcand]], 1)

def auto_find_background(self, amount=0.05):
        # pylint: disable=E1101,E1103
        data = self.data.astype(to_signed(self.dtype))
        # Subtract average value from every frequency channel.
        tmp = (data - np.average(self.data, 1).reshape(self.shape[0], 1))
        # Get standard deviation at every point of time.
        # Need to convert because otherwise this class's __getitem__
        # is used which assumes two-dimensionality.
        sdevs = np.asarray(np.std(tmp, 0))

        # Get indices of values with lowest standard deviation.
        cand = sorted(xrange(self.shape[1]), key=lambda y: sdevs[y])
        # Only consider the best 5 %.
        return cand[:max(1, int(amount * len(cand)))]