#include <shapeprior.h>

Inheritance diagram for lsst::gauss2d::fit::ShapePrior:

Public Member Functions
	ShapePrior (std::shared_ptr< const ParametricEllipse > ellipse, std::shared_ptr< ParametricGaussian1D > prior_size=nullptr, std::shared_ptr< ParametricGaussian1D > prior_axrat=nullptr, std::shared_ptr< ShapePriorOptions > options=nullptr)

PriorEvaluation	evaluate (bool calc_jacobians=false, bool normalize_loglike=false) const override

std::shared_ptr< ParametricGaussian1D >	get_prior_size () const

std::shared_ptr< ParametricGaussian1D >	get_prior_axrat () const

std::vector< double >	get_loglike_const_terms () const override
	Return the constant terms of the log likelihood (dependent on stddevs only)

void	set_prior_size (std::shared_ptr< ParametricGaussian1D > prior_size)

void	set_prior_axrat (std::shared_ptr< ParametricGaussian1D > prior_axrat)

size_t	size () const override

std::string	repr (bool name_keywords=false, std::string_view namespace_separator=Object::CC_NAMESPACE_SEPARATOR) const override

std::string	str () const override

Detailed Description

A two-part prior on the shape of a parametric ellipse.

Note: The size and axis ratio priors are separate and optional.; The size prior applies to the major-axis size.

Constructor & Destructor Documentation

lsst::gauss2d::fit::ShapePrior::ShapePrior	(	std::shared_ptr< const ParametricEllipse >	ellipse,
		std::shared_ptr< ParametricGaussian1D >	prior_size = nullptr,
		std::shared_ptr< ParametricGaussian1D >	prior_axrat = nullptr,
		std::shared_ptr< ShapePriorOptions >	options = nullptr )

explicit

Construct a ShapePrior from a Parameter and mean_size/std. deviation.

Parameters

ellipse	The ParametricEllipse to compute a prior for.
mean_size	The mean value of the size prior.
stddev_size	The standard deviation of the size prior.

PriorEvaluation lsst::gauss2d::fit::ShapePrior::evaluate	(	bool	calc_jacobians = false,
		bool	normalize_loglike = false ) const

overridevirtual

Evaluate the log likelihood and residual-dependent terms.

Parameters

calc_jacobians	Whether to compute the Jacobian and residual terms.
normalize_loglike	Whether to add the constant (sigma-dependent) factors to the log likehood.

std::vector< double > lsst::gauss2d::fit::ShapePrior::get_loglike_const_terms ( ) const

overridevirtual

Return the constant terms of the log likelihood (dependent on stddevs only)

size_t lsst::gauss2d::fit::ShapePrior::size ( ) const

overridevirtual

The documentation for this class was generated from the following files: