def METHOD_NAME(self):
    if self.get_install_options() and self.packages_path:
        if self.lmin < 2 or self.lmax > 200 or self.lmin >= self.lmax:
            raise LoggedError(
                self.log, "lmin must be >= 2, lmax must be <= 200,\n"
                          "and lmin must be less than lmax.")
        path = self.get_path(self.packages_path)
        # The txt files start at l=2, hence the index gymnastics
        cov = np.loadtxt(
            os.path.join(path, 'cov.txt'))[
              self.lmin - 2:self.lmax + 1 - 2,
              self.lmin - 2:self.lmax + 1 - 2]
        # The inverse covariance matrix for the gaussian likelihood calculation
        self._covinv = np.linalg.inv(cov)
        # The average cl's for the gaussian likelihood calculation
        self._mu = np.ascontiguousarray(np.loadtxt(
            os.path.join(path, 'mu.txt'))[self.lmin - 2:self.lmax + 1 - 2])
        # The cl's used for offset calculation - hence the full range of ells
        mu_sigma = np.zeros(self.lmax + 1)
        mu_sigma[self.lmin:] = np.loadtxt(
            os.path.join(path, 'mu_sigma.txt'))[self.lmin - 2:self.lmax + 1 - 2]
        # Spline info
        nbins = 1000
        spline_cl = np.loadtxt(
            os.path.join(path, 'cl2x_1.txt'))[:, self.lmin - 2:self.lmax + 1 - 2]
        spline_val = np.loadtxt(
            os.path.join(path, 'cl2x_2.txt'))[:, self.lmin - 2:self.lmax + 1 - 2]
        self._spline = []
        self._spline_derivative = []
        # Set up prior and spline
        self._prior_bounds = np.zeros((self.lmax + 1 - self.lmin, 2))
        for i in range(self.lmax - self.lmin + 1):
            j = 0
            while abs(spline_val[j, i] + 5) < 1e-4:
                j += 1
            self._prior_bounds[i, 0] = spline_cl[j + 2, i]
            j = nbins - 1
            while abs(spline_val[j, i] - 5) < 1e-4:
                j -= 1
            self._prior_bounds[i, 1] = spline_cl[j - 2, i]
            self._spline.append(
                InterpolatedUnivariateSpline(spline_cl[:, i], spline_val[:, i]))
            self._spline_derivative.append(self._spline[-1].derivative())
        # initialize offset to normalize like a chi-squared
        self._offset = 0
        self._offset = self.log_likelihood(mu_sigma)