Source code for hierarc.Likelihood.lens_sample_likelihood

from hierarc.Likelihood.hierarchy_likelihood import LensLikelihood

[docs]class LensSampleLikelihood(object): """ class to evaluate the likelihood of a cosmology given a sample of angular diameter posteriors Currently this class does not include possible covariances between the lens samples """ def __init__(self, kwargs_lens_list, normalized=False): """ :param kwargs_lens_list: keyword argument list specifying the arguments of the LensLikelihood class :param normalized: bool, if True, returns the normalized likelihood, if False, separates the constant prefactor (in case of a Gaussian 1/(sigma sqrt(2 pi)) ) to compute the reduced chi2 statistics """ self._lens_list = [] for kwargs_lens in kwargs_lens_list: self._lens_list.append(LensLikelihood(normalized=normalized, **kwargs_lens))
[docs] def log_likelihood(self, cosmo, kwargs_lens=None, kwargs_kin=None, kwargs_source=None): """ :param cosmo: astropy.cosmology instance :param kwargs_lens: keywords of the parameters of the lens model :param kwargs_kin: keyword arguments of the kinematic model :param kwargs_source: keyword argument of the source model (such as SNe) :return: log likelihood of the combined lenses """ log_likelihood = 0 for lens in self._lens_list: log_likelihood += lens.lens_log_likelihood(cosmo=cosmo, kwargs_lens=kwargs_lens, kwargs_kin=kwargs_kin, kwargs_source=kwargs_source) return log_likelihood
[docs] def num_data(self): """ number of data points across the lens sample :return: integer """ num = 0 for lens in self._lens_list: num += lens.num_data() return num