Source code for hierarc.Likelihood.cosmo_likelihood

from hierarc.Likelihood.lens_sample_likelihood import LensSampleLikelihood
from hierarc.Sampling.ParamManager.param_manager import ParamManager
# from hierarc.Likelihood.cosmo_interp import CosmoInterp
from lenstronomy.Cosmo.cosmo_interp import CosmoInterp
from hierarc.Likelihood.SneLikelihood.sne_likelihood import SneLikelihood
from hierarc.Likelihood.KDELikelihood.kde_likelihood import KDELikelihood
from hierarc.Likelihood.KDELikelihood.chain import rescale_vector_from_unity, rescale_vector_to_unity
import numpy as np

[docs]class CosmoLikelihood(object): """ this class contains the likelihood function of the Strong lensing analysis """ def __init__(self, kwargs_likelihood_list, cosmology, kwargs_bounds, sne_likelihood=None, kwargs_sne_likelihood=None, KDE_likelihood_chain=None, kwargs_kde_likelihood=None, ppn_sampling=False, lambda_mst_sampling=False, lambda_mst_distribution='delta', anisotropy_sampling=False, kappa_ext_sampling=False, kappa_ext_distribution='NONE', alpha_lambda_sampling=False, beta_lambda_sampling=False, lambda_ifu_sampling=False, lambda_ifu_distribution='NONE', sigma_v_systematics=False, sne_apparent_m_sampling=False, sne_distribution='GAUSSIAN', z_apparent_m_anchor=0.1, log_scatter=False, anisotropy_model='OM', anisotropy_distribution='NONE', custom_prior=None, interpolate_cosmo=True, num_redshift_interp=100, cosmo_fixed=None): """ :param kwargs_likelihood_list: keyword argument list specifying the arguments of the LensLikelihood class :param cosmology: string describing cosmological model :param kwargs_bounds: keyword arguments of the lower and upper bounds and parameters that are held fixed. Includes: 'kwargs_lower_lens', 'kwargs_upper_lens', 'kwargs_fixed_lens', 'kwargs_lower_kin', 'kwargs_upper_kin', 'kwargs_fixed_kin' 'kwargs_lower_cosmo', 'kwargs_upper_cosmo', 'kwargs_fixed_cosmo' :param KDE_likelihood_chain: (Likelihood.chain.Chain). Chain object to be evaluated with a kernel density estimator :param kwargs_kde_likelihood: keyword argument for the KDE likelihood, see KDELikelihood module for options :param sne_likelihood: (string), optional. Sampling supernovae relative expansion history likelihood, see SneLikelihood module for options :param kwargs_sne_likelihood: keyword argument for the SNe likelihood, see SneLikelihood module for options :param ppn_sampling:post-newtonian parameter sampling :param lambda_mst_sampling: bool, if True adds a global mass-sheet transform parameter in the sampling :param lambda_mst_distribution: string, defines the distribution function of lambda_mst :param lambda_ifu_sampling: bool, if True samples a separate lambda_mst for a second (e.g. IFU) data set independently :param lambda_ifu_distribution: string, distribution function of the lambda_ifu parameter :param alpha_lambda_sampling: bool, if True samples a parameter alpha_lambda, which scales lambda_mst linearly according to the lens posterior kwargs 'lambda_scaling_property' :param beta_lambda_sampling: bool, if True samples a parameter beta_lambda, which scales lambda_mst linearly according to the lens posterior kwargs 'lambda_scaling_property_beta' :param kappa_ext_sampling: bool, if True samples a global external convergence parameter :param kappa_ext_distribution: string, distribution function of the kappa_ext parameter :param anisotropy_sampling: bool, if True adds a global stellar anisotropy parameter that alters the single lens kinematic prediction :param anisotropy_model: string, specifies the stellar anisotropy model :param anisotropy_distribution: string, distribution of the anisotropy parameters :param sigma_v_systematics: bool, if True samples paramaters relative to systematics in the velocity dispersion measurement :param sne_apparent_m_sampling: boolean, if True, samples/queries SNe unlensed magnitude distribution (not intrinsic magnitudes but apparent!) :param sne_distribution: string, apparent non-lensed brightness distribution (in linear space). Currently supports: 'GAUSSIAN': Gaussian distribution :param z_apparent_m_anchor: redshift of pivot/anchor at which the apparent SNe brightness is defined relative to :param log_scatter: boolean, if True, samples the Gaussian scatter amplitude in log space (and thus flat prior in log) :param custom_prior: None or a definition that takes the keywords from the CosmoParam conventions and returns a log likelihood value (e.g. prior) :param interpolate_cosmo: bool, if True, uses interpolated comoving distance in the calculation for speed-up :param num_redshift_interp: int, number of redshift interpolation steps :param cosmo_fixed: astropy.cosmology instance to be used and held fixed throughout the sampling """ self._cosmology = cosmology self._kwargs_lens_list = kwargs_likelihood_list self._likelihoodLensSample = LensSampleLikelihood(kwargs_likelihood_list) self.param = ParamManager(cosmology, ppn_sampling=ppn_sampling, lambda_mst_sampling=lambda_mst_sampling, lambda_mst_distribution=lambda_mst_distribution, lambda_ifu_sampling=lambda_ifu_sampling, lambda_ifu_distribution=lambda_ifu_distribution, alpha_lambda_sampling=alpha_lambda_sampling, beta_lambda_sampling=beta_lambda_sampling, sne_apparent_m_sampling=sne_apparent_m_sampling, sne_distribution=sne_distribution, z_apparent_m_anchor=z_apparent_m_anchor, sigma_v_systematics=sigma_v_systematics, kappa_ext_sampling=kappa_ext_sampling, kappa_ext_distribution=kappa_ext_distribution, anisotropy_sampling=anisotropy_sampling, anisotropy_model=anisotropy_model, anisotropy_distribution=anisotropy_distribution, log_scatter=log_scatter, **kwargs_bounds) self._lower_limit, self._upper_limit = self.param.param_bounds self._prior_add = False if custom_prior is not None: self._prior_add = True self._custom_prior = custom_prior self._interpolate_cosmo = interpolate_cosmo self._num_redshift_interp = num_redshift_interp self._cosmo_fixed = cosmo_fixed z_max = 0 if sne_likelihood is not None: if kwargs_sne_likelihood is None: kwargs_sne_likelihood = {} self._sne_likelihood = SneLikelihood(sample_name=sne_likelihood, **kwargs_sne_likelihood) z_max = np.max(self._sne_likelihood.zcmb) self._sne_evaluate = True else: self._sne_evaluate = False if KDE_likelihood_chain is not None: if kwargs_kde_likelihood is None: kwargs_kde_likelihood = {} self._kde_likelihood = KDELikelihood(KDE_likelihood_chain, **kwargs_kde_likelihood) self._kde_evaluate = True self._chain_params = self._kde_likelihood.chain.list_params() else: self._kde_evaluate = False for kwargs_lens in kwargs_likelihood_list: if kwargs_lens['z_source'] > z_max: z_max = kwargs_lens['z_source'] self._z_max = z_max
[docs] def likelihood(self, args): """ :param args: list of sampled parameters :return: log likelihood of the combined lenses """ for i in range(0, len(args)): if args[i] < self._lower_limit[i] or args[i] > self._upper_limit[i]: return -np.inf kwargs_cosmo, kwargs_lens, kwargs_kin, kwargs_source = self.param.args2kwargs(args) if self._cosmology == "oLCDM": # assert we are not in a crazy cosmological situation that prevents computing the angular distance integral h0, ok, om = kwargs_cosmo['h0'], kwargs_cosmo['ok'], kwargs_cosmo['om'] if np.any( [ok * (1.0 + lens['z_source']) ** 2 + om * (1.0 + lens['z_source']) ** 3 + (1.0 - om - ok) <= 0 for lens in self._kwargs_lens_list]): return -np.inf # make sure that Omega_DE is not negative... if 1.0 - om - ok <= 0: return -np.inf cosmo = self.cosmo_instance(kwargs_cosmo) logL = self._likelihoodLensSample.log_likelihood(cosmo=cosmo, kwargs_lens=kwargs_lens, kwargs_kin=kwargs_kin, kwargs_source=kwargs_source) if self._sne_evaluate is True: apparent_m_z = kwargs_source.get('mu_sne', None) z_apparent_m_anchor = kwargs_source['z_apparent_m_anchor'] sigma_m_z = kwargs_source.get('sigma_sne', None) logL += self._sne_likelihood.log_likelihood(cosmo=cosmo, apparent_m_z=apparent_m_z, z_anchor=z_apparent_m_anchor, sigma_m_z=sigma_m_z) if self._kde_evaluate is True: cosmo_params = np.array([[kwargs_cosmo[k] for k in self._chain_params]]) #all chain_params must be in the kwargs_cosmo cosmo_params = rescale_vector_to_unity(cosmo_params, self._kde_likelihood.chain.rescale_dic, self._chain_params) logL += self._kde_likelihood.kdelikelihood_samples(cosmo_params)[0] if self._prior_add is True: logL += self._custom_prior(kwargs_cosmo, kwargs_lens, kwargs_kin, kwargs_source) return logL
[docs] def cosmo_instance(self, kwargs_cosmo): """ :param kwargs_cosmo: cosmology parameter keyword argument list :return: astropy.cosmology (or equivalent interpolation scheme class) """ if self._cosmo_fixed is None: cosmo = self.param.cosmo(kwargs_cosmo) if self._interpolate_cosmo is True: cosmo = CosmoInterp(cosmo=cosmo, z_stop=self._z_max, num_interp=self._num_redshift_interp) else: if self._interpolate_cosmo is True: if not hasattr(self, '_cosmo_fixed_interp'): self._cosmo_fixed_interp = CosmoInterp(cosmo=self._cosmo_fixed, z_stop=self._z_max, num_interp=self._num_redshift_interp) cosmo = self._cosmo_fixed_interp else: cosmo = self._cosmo_fixed return cosmo