Source code for lenstronomy.LensModel.lens_param

from lenstronomy.LensModel.single_plane import SinglePlane
import numpy as np

__all__ = ['LensParam']


[docs]class LensParam(object): """ class to handle the lens model parameter """ def __init__(self, lens_model_list, kwargs_fixed, kwargs_lower=None, kwargs_upper=None, kwargs_logsampling=None, num_images=0, solver_type='NONE', num_shapelet_lens=0): """ :param lens_model_list: list of strings of lens model names :param kwargs_fixed: list of keyword arguments for model parameters to be held fixed :param kwargs_lower: list of keyword arguments of the lower bounds of the model parameters :param kwargs_upper: list of keyword arguments of the upper bounds of the model parameters :param kwargs_logsampling: list of keyword arguments of parameters to be sampled in log10 space :param num_images: number of images to be constrained by a non-linear solver (only relevant when shapelet potential functions are used) :param solver_type: string, type of non-linear solver (only relevant in this class when 'SHAPELETS' is the solver type) :param num_shapelet_lens: integer, number of shapelets in the lensing potential (only relevant when 'SHAPELET' lens model is used) """ self.model_list = lens_model_list self.kwargs_fixed = kwargs_fixed self._num_images = num_images self._solver_type = solver_type self._num_shapelet_lens = num_shapelet_lens lens_model = SinglePlane(lens_model_list=lens_model_list) name_list = [] for func in lens_model.func_list: name_list.append(func.param_names) self._param_name_list = name_list if kwargs_lower is None: kwargs_lower = [] for func in lens_model.func_list: kwargs_lower.append(func.lower_limit_default) if kwargs_upper is None: kwargs_upper = [] for func in lens_model.func_list: kwargs_upper.append(func.upper_limit_default) self.lower_limit = kwargs_lower self.upper_limit = kwargs_upper if kwargs_logsampling is None : kwargs_logsampling = [[] for i in range(len(self.model_list))] self.kwargs_logsampling = kwargs_logsampling
[docs] def get_params(self, args, i): """ :param args: tuple of individual floats of sampling argument :param i: integer, index at the beginning of the tuple for read out to keyword argument convention :return: kwargs_list, index at the end of read out of this model component """ kwargs_list = [] for k, model in enumerate(self.model_list): kwargs = {} kwargs_fixed = self.kwargs_fixed[k] kwargs_logsampling = self.kwargs_logsampling[k] param_names = self._param_name_list[k] for name in param_names: if not name in kwargs_fixed: if model in ['SHAPELETS_POLAR', 'SHAPELETS_CART'] and name == 'coeffs': num_coeffs = self._num_shapelet_lens if self._solver_type == 'SHAPELETS' and k == 0: if self._num_images == 4: num_coeffs -= 6 coeffs = args[i:i + num_coeffs] coeffs = [0, 0, 0, 0, 0, 0] + list(coeffs[0:]) elif self._num_images == 2: num_coeffs -= 3 coeffs = args[i:i + num_coeffs] coeffs = [0, 0, 0] + list(coeffs[0:]) else: raise ValueError("Option for solver_type not valid!") kwargs['coeffs'] = coeffs else: kwargs['coeffs'] = args[i:i + num_coeffs] i += num_coeffs elif model in ['MULTI_GAUSSIAN_KAPPA', 'MULTI_GAUSSIAN_KAPPA_ELLIPSE'] and name == 'amp': if 'sigma' in kwargs_fixed: num_param = len(kwargs_fixed['sigma']) else: num_param = len(kwargs['sigma']) kwargs['amp'] = args[i:i + num_param] i += num_param elif model in ['MULTI_GAUSSIAN_KAPPA', 'MULTI_GAUSSIAN_KAPPA_ELLIPSE'] and name == 'sigma': raise ValueError("%s must have fixed 'sigma' list!" % model) elif model in ['INTERPOL', 'INTERPOL_SCALED'] and name in ['f_', 'f_xx', 'f_xy', 'f_yy']: pass else: kwargs[name] = args[i] i += 1 else: kwargs[name] = kwargs_fixed[name] if name in kwargs_logsampling and not name in kwargs_fixed: kwargs[name] = 10**(kwargs[name]) kwargs_list.append(kwargs) return kwargs_list, i
[docs] def set_params(self, kwargs_list): """ :param kwargs_list: keyword argument list of lens model components :return: tuple of arguments (floats) that are being sampled """ args = [] for k, model in enumerate(self.model_list): kwargs = kwargs_list[k] kwargs_fixed = self.kwargs_fixed[k] kwargs_logsampling = self.kwargs_logsampling[k] param_names = self._param_name_list[k] for name in param_names: if not name in kwargs_fixed: if model in ['SHAPELETS_POLAR', 'SHAPELETS_CART'] and name == 'coeffs': coeffs = kwargs['coeffs'] if self._solver_type == 'SHAPELETS' and k == 0: if self._num_images == 4: coeffs = coeffs[6:] elif self._num_images == 2: coeffs = coeffs[3:] args += list(coeffs) elif model in ['MULTI_GAUSSIAN_KAPPA', 'MULTI_GAUSSIAN_KAPPA_ELLIPSE'] and name == 'amp': amp = kwargs['amp'] args += list(amp) elif model in ['MULTI_GAUSSIAN_KAPPA', 'MULTI_GAUSSIAN_KAPPA_ELLIPSE'] and name == 'sigma': raise ValueError("%s must have fixed 'sigma' list!" % model) elif model in ['INTERPOL', 'INTERPOL_SCALED'] and name in ['f_', 'f_xx', 'f_xy', 'f_yy']: pass #elif self._solver_type == 'PROFILE_SHEAR' and k == 1: # if name == 'e1': # _, gamma_ext = param_util.ellipticity2phi_gamma(kwargs['e1'], kwargs['e2']) # args.append(gamma_ext) # else: # pass else: if name in kwargs_logsampling: args.append(np.log10(kwargs[name])) else: args.append(kwargs[name]) return args
[docs] def num_param(self): """ :return: integer, number of free parameters being sampled from the lens model components """ num = 0 list = [] type = 'lens' for k, model in enumerate(self.model_list): kwargs_fixed = self.kwargs_fixed[k] param_names = self._param_name_list[k] for name in param_names: if not name in kwargs_fixed: if model in ['SHAPELETS_POLAR', 'SHAPELETS_CART'] and name == 'coeffs': num_coeffs = self._num_shapelet_lens if self._solver_type == 'SHAPELETS' and k == 0: if self._num_images == 4: num_coeffs -= 6 elif self._num_images == 2: num_coeffs -= 3 num += num_coeffs list += [str(name + '_' + type + str(k))] * num_coeffs elif model in ['MULTI_GAUSSIAN_KAPPA', 'MULTI_GAUSSIAN_KAPPA_ELLIPSE'] and name == 'amp': num_param = len(kwargs_fixed['sigma']) num += num_param for i in range(num_param): list.append(str(name + '_' + type + str(k))) elif model in ['MULTI_GAUSSIAN_KAPPA', 'MULTI_GAUSSIAN_KAPPA_ELLIPSE'] and name == 'sigma': raise ValueError("'sigma' must be a fixed keyword argument for MULTI_GAUSSIAN") elif model in ['INTERPOL', 'INTERPOL_SCALED'] and name in ['f_', 'f_xx', 'f_xy', 'f_yy']: pass else: num += 1 list.append(str(name + '_' + type + str(k))) return num, list