hierarc.LensPosterior package

Submodules

hierarc.LensPosterior.anisotropy_config module

class hierarc.LensPosterior.anisotropy_config.AnisotropyConfig(anisotropy_model, r_eff)

Bases: object

class to manage the anisotropy model and parameters for the Posterior processing

property ani_param_array

Returns:

numpy array of anisotropy parameter values to be explored

anisotropy_kwargs(a_ani, beta_inf=None)

Parameters:

• a_ani – anisotropy parameter

• beta_inf – anisotropy at infinity (only used for ‘GOM’ model)

Returns:

list of anisotropy keyword arguments, value of anisotropy parameter list

property kwargs_anisotropy_base

Returns:

keyword arguments of base anisotropy model configuration

hierarc.LensPosterior.base_config module

class hierarc.LensPosterior.base_config.BaseLensConfig(z_lens, z_source, theta_E, theta_E_error, gamma, gamma_error, r_eff, r_eff_error, kwargs_aperture, kwargs_seeing, kwargs_numerics_galkin, anisotropy_model, kwargs_lens_light=None, lens_light_model_list=['HERNQUIST'], MGE_light=False, kwargs_mge_light=None, hernquist_approx=True, sampling_number=1000, num_psf_sampling=100, num_kin_sampling=1000, multi_observations=False)

Bases: TDCosmography, ImageModelPosterior, AnisotropyConfig

this class contains and manages the base configurations of the lens posteriors and makes sure that they are universally applied consistently through the different likelihood definitions

hierarc.LensPosterior.ddt_kin_constraints module

class hierarc.LensPosterior.ddt_kin_constraints.DdtKinConstraints(z_lens, z_source, ddt_samples, ddt_weights, theta_E, theta_E_error, gamma, gamma_error, r_eff, r_eff_error, sigma_v_measured, kwargs_aperture, kwargs_seeing, kwargs_numerics_galkin, anisotropy_model, sigma_v_error_independent=None, sigma_v_error_covariant=None, sigma_v_error_cov_matrix=None, kwargs_lens_light=None, lens_light_model_list=['HERNQUIST'], MGE_light=False, kwargs_mge_light=None, hernquist_approx=False, kappa_ext=0, kappa_ext_sigma=0, sampling_number=1000, num_psf_sampling=100, num_kin_sampling=1000, multi_observations=False)

Bases: KinConstraints

class for sampling Ds/Dds posteriors from imaging data and kinematic constraints with additional constraints on the time-delay distance Ddt

hierarchy_configuration(num_sample_model=20)

routine to configure the likelihood to be used in the hierarchical sampling. In particular, a default configuration is set to compute the Gaussian approximation of Ds/Dds by sampling the posterior and the estimate of the variance of the sample. The anisotropy scaling is then performed. Different anisotropy models are supported.

Parameters:

num_sample_model – number of samples drawn from the lens and light model posterior to compute the dimensionless kinematic component J()

Returns:

keyword arguments

hierarc.LensPosterior.ddt_kin_gauss_constraints module

class hierarc.LensPosterior.ddt_kin_gauss_constraints.DdtGaussKinConstraints(z_lens, z_source, ddt_mean, ddt_sigma, theta_E, theta_E_error, gamma, gamma_error, r_eff, r_eff_error, sigma_v_measured, kwargs_aperture, kwargs_seeing, kwargs_numerics_galkin, anisotropy_model, sigma_v_error_independent=None, sigma_v_error_covariant=None, sigma_v_error_cov_matrix=None, kwargs_lens_light=None, lens_light_model_list=['HERNQUIST'], MGE_light=False, kwargs_mge_light=None, hernquist_approx=True, kappa_ext=0, kappa_ext_sigma=0, sampling_number=1000, num_psf_sampling=100, num_kin_sampling=1000, multi_observations=False)

Bases: KinConstraints

class for sampling Ds/Dds posteriors from imaging data and kinematic constraints with additional constraints on the time-delay distance Ddt

hierarchy_configuration(num_sample_model=20)

routine to configure the likelihood to be used in the hierarchical sampling. In particular, a default configuration is set to compute the Gaussian approximation of Ds/Dds by sampling the posterior and the estimate of the variance of the sample. The anisotropy scaling is then performed. Different anisotropy models are supported.

Parameters:

num_sample_model – number of samples drawn from the lens and light model posterior to compute the dimensionless kinematic component J()

Returns:

keyword arguments

hierarc.LensPosterior.imaging_constraints module

class hierarc.LensPosterior.imaging_constraints.ImageModelPosterior(theta_E, theta_E_error, gamma, gamma_error, r_eff, r_eff_error)

Bases: object

class to manage lens and light model posteriors inferred from imaging data

draw_lens(no_error=False)

Parameters:

no_error – bool, if True, does not render from the uncertainty but uses the mean values instead

Returns:

theta_E, gamma, r_eff, delta_r_eff

hierarc.LensPosterior.kin_constraints module

class hierarc.LensPosterior.kin_constraints.KinConstraints(z_lens, z_source, theta_E, theta_E_error, gamma, gamma_error, r_eff, r_eff_error, sigma_v_measured, kwargs_aperture, kwargs_seeing, kwargs_numerics_galkin, anisotropy_model, sigma_v_error_independent=None, sigma_v_error_covariant=None, sigma_v_error_cov_matrix=None, kwargs_lens_light=None, lens_light_model_list=['HERNQUIST'], MGE_light=False, kwargs_mge_light=None, hernquist_approx=True, sampling_number=1000, num_psf_sampling=100, num_kin_sampling=1000, multi_observations=False)

Bases: BaseLensConfig

class that manages constraints from Integral Field Unit spectral observations.

anisotropy_scaling()

Returns:

anisotropy scaling grid along the axes defined by ani_param_array

property error_cov_measurement

error covariance matrix of the measured velocity dispersion data points This is either calculated from the diagonal ‘sigma_v_error_independent’ and the off-diagonal ‘sigma_v_error_covariant’ terms, or directly from the ‘sigma_v_error_cov_matrix’ if provided.

Returns:

nxn matrix of the error covariances in the velocity dispersion measurements (km/s)^2

hierarchy_configuration(num_sample_model=20)

routine to configure the likelihood to be used in the hierarchical sampling. In particular, a default configuration is set to compute the Gaussian approximation of Ds/Dds by sampling the posterior and the estimate of the variance of the sample. The anisotropy scaling is then performed. Different anisotropy models are supported.

Parameters:

num_sample_model – number of samples drawn from the lens and light model posterior to compute the dimensionless kinematic component J()

Returns:

keyword arguments

j_kin_draw(kwargs_anisotropy, no_error=False)

one simple sampling realization of the dimensionless kinematics of the model

Parameters:

• kwargs_anisotropy – keyword argument of anisotropy setting

• no_error – bool, if True, does not render from the uncertainty but uses the mean values instead

Returns:

dimensionless kinematic component J() Birrer et al. 2016, 2019

model_marginalization(num_sample_model=20)

Parameters:

num_sample_model – number of samples drawn from the lens and light model posterior to compute the dimensionless kinematic component J()

Returns:

J() as array for each measurement prediction, covariance matrix in sqrt(J)

Module contents