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=False, 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¶
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