hierarc.LensPosterior package

Submodules

hierarc.LensPosterior.anisotropy_config module

class hierarc.LensPosterior.anisotropy_config.AnisotropyConfig(anisotropy_model, r_eff)[source]

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)[source]
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)[source]

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=True, kappa_ext=0, kappa_ext_sigma=0, sampling_number=1000, num_psf_sampling=100, num_kin_sampling=1000, multi_observations=False)[source]

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)[source]

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)[source]

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)[source]

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)[source]

Bases: object

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

draw_lens(no_error=False)[source]
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)[source]

Bases: BaseLensConfig

class that manages constraints from Integral Field Unit spectral observations.

anisotropy_scaling()[source]
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)[source]

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)[source]

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)[source]
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