from hierarc.Sampling.ParamManager.kin_param import KinParam
from hierarc.Sampling.ParamManager.cosmo_param import CosmoParam
from hierarc.Sampling.ParamManager.lens_param import LensParam
from hierarc.Sampling.ParamManager.source_param import SourceParam
from hierarc.Sampling.ParamManager.los_param import LOSParam
from hierarc.Sampling.ParamManager.deprojection_param import DeprojectionParam
[docs]
class ParamManager(object):
"""Class for managing the parameters involved."""
def __init__(
self,
cosmology,
ppn_sampling=False,
rd_sampling=False,
lambda_mst_sampling=False,
lambda_mst_distribution="NONE",
anisotropy_sampling=False,
anisotropy_model="OM",
anisotropy_distribution="NONE",
anisotropy_parameterization="beta",
gamma_in_sampling=False,
gamma_in_distribution="NONE",
log_m2l_sampling=False,
log_m2l_distribution="NONE",
lambda_ifu_sampling=False,
lambda_ifu_distribution="NONE",
alpha_lambda_sampling=False,
beta_lambda_sampling=False,
alpha_gamma_in_sampling=False,
alpha_log_m2l_sampling=False,
q_intrinsic_sampling=False,
q_intrinsic_distribution="NONE",
gamma_pl_num=0,
gamma_pl_global_sampling=False,
gamma_pl_global_dist="NONE",
sigma_v_systematics=False,
sne_apparent_m_sampling=False,
sne_distribution="GAUSSIAN",
z_apparent_m_anchor=0.1,
log_scatter=False,
los_sampling=False,
los_distributions=None,
kwargs_lower_cosmo=None,
kwargs_upper_cosmo=None,
kwargs_fixed_cosmo=None,
kwargs_lower_lens=None,
kwargs_upper_lens=None,
kwargs_fixed_lens=None,
kwargs_lower_kin=None,
kwargs_upper_kin=None,
kwargs_fixed_kin=None,
kwargs_lower_source=None,
kwargs_upper_source=None,
kwargs_fixed_source=None,
kwargs_lower_los=None,
kwargs_upper_los=None,
kwargs_fixed_los=None,
):
"""
:param cosmology: string describing cosmological model
:param ppn_sampling: post-newtonian parameter sampling
:param rd_sampling: sound horizon at drag epoch sampling (used only if the BAOLIkelihood is used)
:param lambda_mst_sampling: bool, if True adds a global mass-sheet transform parameter in the sampling
:param lambda_mst_distribution: string, distribution function of the MST transform
:param lambda_ifu_sampling: bool, if True samples a separate lambda_mst for a second (e.g. IFU) data set
independently
:param alpha_lambda_sampling: bool, if True samples a parameter alpha_lambda, which scales lambda_mst linearly
according to a predefined quantity of the lens
:param beta_lambda_sampling: bool, if True samples a parameter beta_lambda, which scales lambda_mst linearly
according to a predefined quantity of the lens
:param lambda_ifu_distribution: string, distribution function of the lambda_ifu parameter
:param anisotropy_sampling: bool, if True adds a global stellar anisotropy parameter that alters the single lens
kinematic prediction
:param anisotropy_distribution: string, indicating the distribution function of the anisotropy model
:param gamma_in_sampling: bool, if True samples gNFW inner slope parameter
:param gamma_in_distribution: string, distribution function of the gamma_in parameter
:param log_m2l_sampling: bool, if True samples the mass-to-light ratio of the stars in logarithmic scale
:param log_m2l_distribution: string, distribution function of the log_m2l parameter
:param alpha_gamma_in_sampling: bool, if True samples a parameter alpha_gamma_in, which scales gamma_in linearly
according to a predefined quantity of the lens
:param alpha_log_m2l_sampling: bool, if True samples a parameter alpha_log_m2l, which scales log_m2l linearly
according to a predefined quantity of the lens
:param q_intrinsic_sampling: bool, if True samples q_intrinsic parameter for axisymmetric deprojection
:param q_intrinsic_distribution: string, distribution function of the q_intrinsic parameter
("GAUSSIAN", "GAUSSIAN_SCALED" or "NONE")
:param gamma_pl_num: int, number of power-law density slopes being sampled (to be assigned to individual lenses)
:param gamma_pl_global_sampling: if sampling a global power-law density slope distribution
:type gamma_pl_global_sampling: bool
:param gamma_pl_global_dist: distribution of global gamma_pl distribution ("GAUSSIAN" or "NONE")
:type gamma_pl_global_dist: str
: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 sigma_v_systematics: bool, if True samples paramaters relative to systematics in the velocity dispersion
measurement
:param log_scatter: boolean, if True, samples the Gaussian scatter amplitude in log space (and thus flat prior in log)
:param los_sampling: if sampling of the parameters should be done
:type los_sampling: bool
:param los_distributions: list of line of sight distributions to be sampled
:type los_distributions: list of str
:param kwargs_fixed_los: fixed arguments in sampling
:type kwargs_fixed_los: list of dictionaries for each los distribution
:param anisotropy_parameterization: model of parameterization (currently for constant anisotropy),
["beta" or "TAN_RAD"] supported
:type anisotropy_parameterization: str
"""
if kwargs_fixed_kin is not None:
kwargs_fixed_deprojection = {}
for param in DeprojectionParam.param_names:
if param in kwargs_fixed_kin:
kwargs_fixed_deprojection[param] = kwargs_fixed_kin.pop(param)
else:
kwargs_fixed_deprojection = None
self._kin_param = KinParam(
anisotropy_sampling=anisotropy_sampling,
anisotropy_model=anisotropy_model,
distribution_function=anisotropy_distribution,
log_scatter=log_scatter,
sigma_v_systematics=sigma_v_systematics,
kwargs_fixed=kwargs_fixed_kin,
anisotropy_parameterization=anisotropy_parameterization,
)
self._cosmo_param = CosmoParam(
cosmology=cosmology,
ppn_sampling=ppn_sampling,
rd_sampling=rd_sampling,
kwargs_fixed=kwargs_fixed_cosmo,
)
self._lens_param = LensParam(
lambda_mst_sampling=lambda_mst_sampling,
lambda_mst_distribution=lambda_mst_distribution,
lambda_ifu_sampling=lambda_ifu_sampling,
lambda_ifu_distribution=lambda_ifu_distribution,
gamma_in_sampling=gamma_in_sampling,
gamma_in_distribution=gamma_in_distribution,
log_m2l_sampling=log_m2l_sampling,
log_m2l_distribution=log_m2l_distribution,
alpha_lambda_sampling=alpha_lambda_sampling,
beta_lambda_sampling=beta_lambda_sampling,
alpha_gamma_in_sampling=alpha_gamma_in_sampling,
alpha_log_m2l_sampling=alpha_log_m2l_sampling,
gamma_pl_num=gamma_pl_num,
gamma_pl_global_sampling=gamma_pl_global_sampling,
gamma_pl_global_dist=gamma_pl_global_dist,
log_scatter=log_scatter,
kwargs_fixed=kwargs_fixed_lens,
)
self._source_param = SourceParam(
sne_apparent_m_sampling=sne_apparent_m_sampling,
sne_distribution=sne_distribution,
z_apparent_m_anchor=z_apparent_m_anchor,
kwargs_fixed=kwargs_fixed_source,
)
self._los_param = LOSParam(
los_sampling=los_sampling,
los_distributions=los_distributions,
kwargs_fixed=kwargs_fixed_los,
)
self._deprojection_param = DeprojectionParam(
deprojection_sampling=q_intrinsic_sampling,
distribution_function=q_intrinsic_distribution,
kwargs_fixed=kwargs_fixed_deprojection,
log_scatter=log_scatter,
)
self._kwargs_upper_cosmo, self._kwargs_lower_cosmo = (
kwargs_upper_cosmo,
kwargs_lower_cosmo,
)
self._kwargs_upper_lens, self._kwargs_lower_lens = (
kwargs_upper_lens,
kwargs_lower_lens,
)
self._kwargs_upper_kin, self._kwargs_lower_kin = (
kwargs_upper_kin,
kwargs_lower_kin,
)
self._kwargs_upper_source, self._kwargs_lower_source = (
kwargs_upper_source,
kwargs_lower_source,
)
self._kwargs_upper_los, self._kwargs_lower_los = (
kwargs_upper_los,
kwargs_lower_los,
)
@property
def num_param(self):
"""Number of parameters being sampled.
:return: integer
"""
return len(self.param_list())
[docs]
def param_list(self, latex_style=False):
"""
:param latex_style: bool, if True returns strings in latex symbols, else in the convention of the sampler
:return: list of the free parameters being sampled in the same order as the sampling
"""
list_param = []
list_param += self._cosmo_param.param_list(latex_style=latex_style)
list_param += self._lens_param.param_list(latex_style=latex_style)
list_param += self._kin_param.param_list(latex_style=latex_style)
list_param += self._deprojection_param.param_list(latex_style=latex_style)
list_param += self._source_param.param_list(latex_style=latex_style)
list_param += self._los_param.param_list(latex_style=latex_style)
return list_param
[docs]
def args2kwargs(self, args):
"""
:param args: sampling argument list
:return: keyword argument list with parameter names
"""
i = 0
kwargs_cosmo, i = self._cosmo_param.args2kwargs(args, i=i)
kwargs_lens, i = self._lens_param.args2kwargs(args, i=i)
kwargs_kin, i = self._kin_param.args2kwargs(args, i=i)
kwargs_deprojection, i = self._deprojection_param.args2kwargs(args, i=i)
# kwargs_kin includes kwargs_deprojection at the high level
kwargs_kin.update(kwargs_deprojection)
kwargs_source, i = self._source_param.args2kwargs(args, i=i)
kwargs_los, i = self._los_param.args2kwargs(args, i=i)
return (
kwargs_cosmo,
kwargs_lens,
kwargs_kin,
kwargs_source,
kwargs_los,
)
[docs]
def kwargs2args(
self,
kwargs_cosmo=None,
kwargs_lens=None,
kwargs_kin=None,
kwargs_source=None,
kwargs_los=None,
):
"""
:param kwargs_cosmo: keyword argument list of parameters for cosmology sampling
:param kwargs_lens: keyword argument list of parameters for lens model sampling
:param kwargs_kin: keyword argument list of parameters for kinematic sampling
:param kwargs_source: keyword arguments of parameters of source brightness
:param kwargs_los: keyword arguments of parameters of the line of sight
:return: sampling argument list in specified order
"""
args = []
args += self._cosmo_param.kwargs2args(kwargs_cosmo)
args += self._lens_param.kwargs2args(kwargs_lens)
args += self._kin_param.kwargs2args(kwargs_kin)
args += self._deprojection_param.kwargs2args(kwargs_kin)
args += self._source_param.kwargs2args(kwargs_source)
args += self._los_param.kwargs2args(kwargs_los)
return args
[docs]
def cosmo(self, kwargs_cosmo):
"""
:param kwargs_cosmo: keyword arguments of parameters (can include others not used for the cosmology)
:return: cosmology
:rtype: ~astropy.cosmology instance
"""
return self._cosmo_param.cosmo(kwargs_cosmo)
@property
def param_bounds(self):
"""
:return: argument list of the hard bounds in the order of the sampling
"""
lower_limit = self.kwargs2args(
kwargs_cosmo=self._kwargs_lower_cosmo,
kwargs_lens=self._kwargs_lower_lens,
kwargs_kin=self._kwargs_lower_kin,
kwargs_source=self._kwargs_lower_source,
kwargs_los=self._kwargs_lower_los,
)
upper_limit = self.kwargs2args(
kwargs_cosmo=self._kwargs_upper_cosmo,
kwargs_lens=self._kwargs_upper_lens,
kwargs_kin=self._kwargs_upper_kin,
kwargs_source=self._kwargs_upper_source,
kwargs_los=self._kwargs_upper_los,
)
return lower_limit, upper_limit