def image_linear_solve(self, kwargs_lens, kwargs_source, kwargs_lens_light, kwargs_else, inv_bool=False):
        """
        computes the image (lens and source surface brightness with a given lens model).
        The linear parameters are computed with a weighted linear least square optimization (i.e. flux normalization of the brightness profiles)
        :param kwargs_lens: list of keyword arguments corresponding to the superposition of different lens profiles
        :param kwargs_source: list of keyword arguments corresponding to the superposition of different source light profiles
        :param kwargs_lens_light: list of keyword arguments corresponding to different lens light surface brightness profiles
        :param kwargs_else: keyword arguments corresponding to "other" parameters, such as external shear and point source image positions
        :param inv_bool: if True, invert the full linear solver Matrix Ax = y for the purpose of the covariance matrix.
        :return: 1d array of surface brightness pixels of the optimal solution of the linear parameters to match the data
        """
        wls_list, error_map_list, cov_param_list, param_list = [], [], [], []
        for i in range(self._num_bands):
            wls_model, error_map, cov_param, param = self._imageModel_list[i].image_linear_solve(kwargs_lens,
                                                                                                 kwargs_source,
                                                                                                 kwargs_lens_light,
                                                                                                 kwargs_else,
                                                                                                 inv_bool=inv_bool)
            wls_list.append(wls_model)
            error_map_list.append(error_map)
            cov_param_list.append(cov_param)
            param_list.append(param)
        return wls_list, error_map_list, cov_param_list, param_list
def image_linear_solve ( self , kwargs_lens , kwargs_source , kwargs_lens_light , kwargs_else , inv_bool = False ) : wls_list , error_map_list , cov_param_list , param_list = [ ] , [ ] , [ ] , [ ] for i in range ( self . _num_bands ) : wls_model , error_map , cov_param , param = self . _imageModel_list [ i ] . image_linear_solve ( kwargs_lens , kwargs_source , kwargs_lens_light , kwargs_else , inv_bool = inv_bool ) wls_list . append ( wls_model ) error_map_list . append ( error_map ) cov_param_list . append ( cov_param ) param_list . append ( param ) return wls_list , error_map_list , cov_param_list , param_list
computes the image (lens and source surface brightness with a given lens model).
        The linear parameters are computed with a weighted linear least square optimization (i.e. flux normalization of the brightness profiles)
        :param kwargs_lens: list of keyword arguments corresponding to the superposition of different lens profiles
        :param kwargs_source: list of keyword arguments corresponding to the superposition of different source light profiles
        :param kwargs_lens_light: list of keyword arguments corresponding to different lens light surface brightness profiles
        :param kwargs_else: keyword arguments corresponding to "other" parameters, such as external shear and point source image positions
        :param inv_bool: if True, invert the full linear solver Matrix Ax = y for the purpose of the covariance matrix.
        :return: 1d array of surface brightness pixels of the optimal solution of the linear parameters to match the data