Source code for lenstronomy.Sampling.Likelihoods.time_delay_likelihood

import numpy as np
import lenstronomy.Util.constants as const

__all__ = ['TimeDelayLikelihood']


[docs]class TimeDelayLikelihood(object): """ class to compute the likelihood of a model given a measurement of time delays """ def __init__(self, time_delays_measured, time_delays_uncertainties, lens_model_class, point_source_class): """ :param time_delays_measured: relative time delays (in days) in respect to the first image of the point source :param time_delays_uncertainties: time-delay uncertainties in same order as time_delay_measured :param lens_model_class: instance of the LensModel() class :param point_source_class: instance of the PointSource() class, note: the first point source type is the one the time delays are imposed on """ if time_delays_measured is None: raise ValueError("time_delay_measured need to be specified to evaluate the time-delay likelihood.") if time_delays_uncertainties is None: raise ValueError("time_delay_uncertainties need to be specified to evaluate the time-delay likelihood.") self._delays_measured = np.array(time_delays_measured) self._delays_errors = np.array(time_delays_uncertainties) self._lensModel = lens_model_class self._pointSource = point_source_class
[docs] def logL(self, kwargs_lens, kwargs_ps, kwargs_cosmo): """ routine to compute the log likelihood of the time delay distance :param kwargs_lens: lens model kwargs list :param kwargs_ps: point source kwargs list :param kwargs_cosmo: cosmology and other kwargs :return: log likelihood of the model given the time delay data """ x_pos, y_pos = self._pointSource.image_position(kwargs_ps=kwargs_ps, kwargs_lens=kwargs_lens, original_position=True) x_pos, y_pos = x_pos[0], y_pos[0] delay_arcsec = self._lensModel.fermat_potential(x_pos, y_pos, kwargs_lens) D_dt_model = kwargs_cosmo['D_dt'] delay_days = const.delay_arcsec2days(delay_arcsec, D_dt_model) logL = self._logL_delays(delay_days, self._delays_measured, self._delays_errors) return logL
def _logL_delays(self, delays_model, delays_measured, delays_errors): """ log likelihood of modeled delays vs measured time delays under considerations of errors :param delays_model: n delays of the model (not relative delays) :param delays_measured: relative delays (1-2,1-3,1-4) relative to the first in the list :param delays_errors: gaussian errors on the measured delays :return: log likelihood of data given model """ delta_t_model = np.array(delays_model[1:]) - delays_model[0] logL = np.sum(-(delta_t_model - delays_measured) ** 2 / (2 * delays_errors ** 2)) return logL @property def num_data(self): """ :return: number of time delay measurements """ return len(self._delays_measured)