jaxtronomy.Sampling package

Subpackages

• jaxtronomy.Sampling.Likelihoods package
  • Submodules
  • jaxtronomy.Sampling.Likelihoods.image_likelihood module
    • ImageLikelihood
      • ImageLikelihood.__init__()
      • ImageLikelihood.logL()
      • ImageLikelihood.num_data
      • ImageLikelihood.num_param_linear()
  • jaxtronomy.Sampling.Likelihoods.position_likelihood module
    • PositionLikelihood
      • PositionLikelihood.__init__()
      • PositionLikelihood.logL()
      • PositionLikelihood.astrometric_likelihood()
      • PositionLikelihood.image_position_likelihood()
      • PositionLikelihood.source_position_likelihood()
      • PositionLikelihood.num_data
  • Module contents
• jaxtronomy.Sampling.Samplers package
  • Submodules
  • jaxtronomy.Sampling.Samplers.optax module
    • OptaxMinimizer
      • OptaxMinimizer.__init__()
      • OptaxMinimizer.run()
      • OptaxMinimizer.run_single()
  • Module contents

Submodules

jaxtronomy.Sampling.likelihood module

class Likelihood(kwargs_data_joint, kwargs_model, param_class, image_likelihood=True, check_bounds=True, astrometric_likelihood=False, image_position_likelihood=False, source_position_likelihood=None, image_position_uncertainty=0.004, check_positive_flux=False, source_position_tolerance=None, source_position_sigma=0.001, force_no_add_image=False, source_marg=False, linear_prior=None, restrict_image_number=False, max_num_images=None, bands_compute=None, time_delay_likelihood=False, image_likelihood_mask_list=None, flux_ratio_likelihood=False, kwargs_flux_compute=None, prior_lens=None, prior_source=None, prior_extinction=None, prior_lens_light=None, prior_ps=None, prior_special=None, prior_lens_kde=None, prior_source_kde=None, prior_lens_light_kde=None, prior_ps_kde=None, prior_special_kde=None, prior_extinction_kde=None, prior_lens_lognormal=None, prior_source_lognormal=None, prior_extinction_lognormal=None, prior_lens_light_lognormal=None, prior_ps_lognormal=None, prior_special_lognormal=None, custom_logL_addition=None, kwargs_pixelbased=None, kinematic_2d_likelihood=False, kin_lens_idx=0, kin_lens_light_idx=0, tracer_likelihood=False, tracer_likelihood_mask=None)

Bases: object

This class contains the routines to run a MCMC process.

the key components are: - im_sim_class: an instance of a class that simulates one (or more) images and returns the likelihood, such as ImageModel(), Multiband(), MultiExposure() - param_class: instance of a Param() class that can cast the sorted list of parameters that are sampled into the conventions of the ImSim class

Additional arguments are supported for adding a time-delay likelihood etc (see __init__ definition)

__init__(kwargs_data_joint, kwargs_model, param_class, image_likelihood=True, check_bounds=True, astrometric_likelihood=False, image_position_likelihood=False, source_position_likelihood=None, image_position_uncertainty=0.004, check_positive_flux=False, source_position_tolerance=None, source_position_sigma=0.001, force_no_add_image=False, source_marg=False, linear_prior=None, restrict_image_number=False, max_num_images=None, bands_compute=None, time_delay_likelihood=False, image_likelihood_mask_list=None, flux_ratio_likelihood=False, kwargs_flux_compute=None, prior_lens=None, prior_source=None, prior_extinction=None, prior_lens_light=None, prior_ps=None, prior_special=None, prior_lens_kde=None, prior_source_kde=None, prior_lens_light_kde=None, prior_ps_kde=None, prior_special_kde=None, prior_extinction_kde=None, prior_lens_lognormal=None, prior_source_lognormal=None, prior_extinction_lognormal=None, prior_lens_light_lognormal=None, prior_ps_lognormal=None, prior_special_lognormal=None, custom_logL_addition=None, kwargs_pixelbased=None, kinematic_2d_likelihood=False, kin_lens_idx=0, kin_lens_light_idx=0, tracer_likelihood=False, tracer_likelihood_mask=None)

Initializing class.

Parameters:

• param_class – instance of a Param() class that can cast the sorted list of parameters that are sampled into the conventions of the ImSim class

• image_likelihood – bool, option to compute the imaging likelihood

• source_position_likelihood – bool, if True, ray-traces image positions back to source plane and evaluates relative errors in respect ot the position_uncertainties in the image plane

• check_bounds – bool, option to punish the hard bounds in parameter space

• astrometric_likelihood – bool, additional likelihood term of the predicted vs modelled point source position

• image_position_uncertainty – float, 1-sigma Gaussian uncertainty on the point source position (only used if point_source_likelihood=True)

• check_positive_flux – bool, option to punish models that do not have all positive linear amplitude parameters

• source_position_tolerance – float, punishment of check_solver occurs when image positions are predicted further away than this number

• image_likelihood_mask_list – list of boolean 2d arrays of size of images marking the pixels to be evaluated in the likelihood

• force_no_add_image – bool, if True: computes ALL image positions of the point source. If there are more images predicted than modelled, a punishment occurs

• source_marg – marginalization addition on the imaging likelihood based on the covariance of the inferred linear coefficients

• linear_prior – float or list of floats (when multi-linear setting is chosen) indicating the range of linear amplitude priors when computing the marginalization term.

• restrict_image_number – bool, if True: computes ALL image positions of the point source. If there are more images predicted than indicated in max_num_images, a punishment occurs

• max_num_images – int, see restrict_image_number

• bands_compute – list of bools with same length as data objects, indicates which “band” to include in the fitting

• time_delay_likelihood – bool, if True computes the time-delay likelihood of the FIRST point source

• kwargs_flux_compute – keyword arguments of how to compute the image position fluxes (see FluxRatioLikeliood)

• custom_logL_addition – a definition taking as arguments (kwargs_lens, kwargs_source, kwargs_lens_light, kwargs_ps, kwargs_special, kwargs_extinction) and returns a logL (punishing) value.

• kwargs_pixelbased – keyword arguments with various settings related to the pixel-based solver (see SLITronomy documentation)

• kinematic_2d_likelihood – bool, option to compute the kinematic likelihood

• tracer_likelihood – option to perform likelihood on tracer quantity derived from imaging or spectroscopy

property kwargs_imaging

Dictionary of imaging model keyword arguments.

Returns:

kwargs_imaging

logL(args, verbose=False)

Routine to compute X2 given variable parameters for a MCMC/PSO chain.

Parameters:

• args (tuple or list of floats) – ordered parameter values that are being sampled

• verbose (boolean) – if True, makes print statements about individual likelihood components

Returns:

log likelihood of the data given the model (natural logarithm)

log_likelihood(kwargs_return, verbose=False)

Parameters:

• kwargs_return (keyword arguments) – need to contain ‘kwargs_lens’, ‘kwargs_source’, ‘kwargs_lens_light’, ‘kwargs_ps’, ‘kwargs_special’. These entries themselves are lists of keyword argument of the parameters entering the model to be evaluated

• verbose (boolean) – if True, makes print statements about individual likelihood components

Returns:

• logL (float) log likelihood of the data given the model (natural logarithm)

static check_bounds(args, lowerLimit, upperLimit, verbose=False)

Checks whether the parameter vector has left its bound, if so, adds a big number.

property num_data

Returns:

number of independent data points in the combined fitting

property param_limits

effective_num_data_points(**kwargs)

Returns the effective number of data points considered in the X2 estimation to compute the reduced X2 value.

likelihood(a)

negativelogL(a)

For minimizer function, the negative value of the logl value is requested.

Parameters:

a – array of parameters

Returns:

-logL

Module contents