A few rough approximations of the fit uncertainty. These
        values shouldn't be trusted for anything real (use MCMC instead).

def err(self):
        """
        A few rough approximations of the fit uncertainty. These
        values shouldn't be trusted for anything real (use MCMC instead).
        """
        # Initiallize error to nan
        err = odict(self.mle())
        err.update([(k,np.nan*np.ones(2)) for k in err.keys()])

        # Find the maximum likelihood
        a = self.log_likelihood_sparse_array
        j,k = np.unravel_index(a.argmax(),a.shape)

        self.loglike.set_params(distance_modulus=self.distance_modulus_array[j],
                                lon=self.roi.pixels_target.lon[k],
                                lat=self.roi.pixels_target.lat[k])
        self.loglike.sync_params()

        # Find the error in richness, starting at maximum
        lo,hi = np.array(self.loglike.richness_interval())
        err['richness'] = np.array([lo,hi])

        # ADW: This is a rough estimate of the distance uncertainty 
        # hacked to keep the maximum distance modulus on a grid index

        # This is a hack to get the confidence interval to play nice...
        # Require at least three points.
        if (a[:,k]>0).sum() >= 3:
            parabola = Parabola(np.insert(self.distance_modulus_array,0,0.), 
                                np.insert(a[:,k],0,0.) )
            lo,hi = np.array(parabola.confidenceInterval())
            err['distance_modulus'] = self.distance_modulus_array[j] + (hi-lo)/2.*np.array([-1.,1.])

        # ADW: Could estimate lon and lat from the grid.
        # This is just concept right now...
        if (a[j,:]>0).sum() >= 10:
            delta_ts = 2*(a[j,k] - a[j,:])
            pix = np.where(a[j,:][delta_ts < 2.71])[0]
            lons = self.roi.pixels_target.lon[pix]
            lats = self.roi.pixels_target.lat[pix]
            err['lon'] = np.array([ np.min(lons),np.max(lons)])
            err['lat'] = np.array([ np.min(lats),np.max(lats)])

        return err