Source code for hierarc.Likelihood.LensLikelihood.ddt_dd_gauss_likelihood
from hierarc.Likelihood.LensLikelihood.ddt_gauss_likelihood import DdtGaussianLikelihood
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class DdtDdGaussian(object):
"""Class for joint kinematics and time delay likelihood assuming independent
Gaussian likelihoods in Ddt and Dd.
Attention: Gaussian errors in the velocity dispersion do not translate into Gaussian uncertainties in Dd.
"""
def __init__(self, z_lens, z_source, ddt_mean, ddt_sigma, dd_mean, dd_sigma):
"""
:param z_lens: lens redshift
:param z_source: source redshift
:param ddt_mean: mean of Ddt distance
:param ddt_sigma: 1-sigma uncertainty in the Ddt distance
:param dd_mean: mean of Dd distance ratio
:param dd_sigma: 1-sigma uncertainty in the Dd distance
"""
self._dd_mean = dd_mean
self._dd_sigma2 = dd_sigma**2
self._tdLikelihood = DdtGaussianLikelihood(
z_lens, z_source, ddt_mean=ddt_mean, ddt_sigma=ddt_sigma
)
self.num_data = 2
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def log_likelihood(self, ddt, dd, kin_scaling=None):
"""
:param ddt: time-delay distance
:param dd: angular diameter distance to the deflector
:param kin_scaling: array of size of the velocity dispersion measurement or None, scaling of the predicted
dimensionless quantity J (proportional to sigma_v^2) of the anisotropy model in the sampling relative to the
anisotropy model used to derive the prediction and covariance matrix in the init of this class.
:return: log likelihood given the single lens analysis
"""
if kin_scaling is not None:
dd_ = dd * kin_scaling[0]
else:
dd_ = dd
lnlikelihood = (
self._tdLikelihood.log_likelihood(ddt, dd_)
- (dd_ - self._dd_mean) ** 2 / self._dd_sigma2 / 2
)
return lnlikelihood
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def ddt_measurement(self):
"""
:return: mean, 1-sigma of the ddt inference/model measurement
"""
return self._tdLikelihood.ddt_measurement()