def covariance_eigvals(self):
        """
        The two eigenvalues of the `covariance` matrix in decreasing
        order.
        """

        if not np.isnan(np.sum(self.covariance)):
            eigvals = np.linalg.eigvals(self.covariance)
            if np.any(eigvals < 0):    # negative variance
                return (np.nan, np.nan) * u.pix**2  # pragma: no cover
            return (np.max(eigvals), np.min(eigvals)) * u.pix**2
        else:
            return (np.nan, np.nan) * u.pix**2
The two eigenvalues of the `covariance` matrix in decreasing
        order.