import copy
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, kwargs_global_model=None):
"""
: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
:param kwargs_global_model: arguments of global distribution parameters for initialization of
ParamManager() class
"""
if kwargs_global_model is None:
kwargs_global_model = {}
self._lens_list = []
self._gamma_pl_num = 0
gamma_pl_index = 0
gamma_pl_global_sampling = kwargs_global_model.get(
"gamma_pl_global_sampling", False
)
for kwargs_lens in kwargs_lens_list:
gamma_pl_index_ = None
if "kin_scaling_param_list" in kwargs_lens and not gamma_pl_global_sampling:
kin_scaling_param_list = kwargs_lens["kin_scaling_param_list"]
if "gamma_pl" in kin_scaling_param_list:
self._gamma_pl_num += 1
gamma_pl_index_ = copy.deepcopy(gamma_pl_index)
gamma_pl_index += 1
kwargs_lens_ = self._merge_global2local_settings(
kwargs_global_model=kwargs_global_model, kwargs_lens=kwargs_lens
)
self._lens_list.append(
LensLikelihood(
gamma_pl_index=gamma_pl_index_,
normalized=normalized,
**kwargs_lens_
)
)
[docs]
def log_likelihood(
self,
cosmo,
kwargs_lens=None,
kwargs_kin=None,
kwargs_source=None,
kwargs_los=None,
verbose=False,
):
"""
: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)
:param kwargs_los: line of sight keyword argument list
:param verbose: If true, prints intermediate outputs of likelihood calculation
:type verbose: bool
: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,
kwargs_los=kwargs_los,
verbose=verbose,
)
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
[docs]
def info(self):
"""
:return:
"""
print("number of lenses: ", len(self._lens_list))
print("number of data points: ", self.num_data())
for lens in self._lens_list:
lens.info()
@property
def gamma_pl_num(self):
"""Number of power-law density slope parameters being sampled on individual
lenses.
:return: number of power-law density slope parameters being sampled on
individual lenses
"""
return self._gamma_pl_num
@staticmethod
def _merge_global2local_settings(kwargs_global_model, kwargs_lens):
"""
:param kwargs_global_model: dictionary of global model settings and distribution functions
:param kwargs_lens: specific settings of an individual lens
:return: joint dictionary that overwrites global with local parameters (if needed) and only keeps the relevant
arguments that an individual lens likelihood needs
:rtype: dict
"""
kwargs_global_model_ = {}
for key in _input_param_list:
if key in kwargs_global_model:
kwargs_global_model_[key] = kwargs_global_model[key]
kwargs_global_model_subset = copy.deepcopy(kwargs_global_model_)
return {**kwargs_global_model_subset, **kwargs_lens}
_input_param_list = [
"anisotropy_model",
"anisotropy_sampling",
"anisotropy_distribution",
"anisotropy_parameterization",
"los_distributions",
"lambda_mst_distribution",
"gamma_in_sampling",
"gamma_in_distribution",
"log_m2l_sampling",
"log_m2l_distribution",
"alpha_lambda_sampling",
"beta_lambda_sampling",
"alpha_gamma_in_sampling",
"alpha_log_m2l_sampling",
"gamma_pl_global_sampling",
"gamma_pl_global_dist",
"log_scatter",
]