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LSSTDataManagementBasePackage
Public Member Functions | Protected Attributes | List of all members
lsst::meas::modelfit::MultiShapeletPsfLikelihood Class Reference

Likelihood object used to fit multishapelet models to PSF model images; mostly for internal use by GeneralPsfFitter. More...

#include <GeneralPsfFitter.h>

Inheritance diagram for lsst::meas::modelfit::MultiShapeletPsfLikelihood:
lsst::meas::modelfit::Likelihood

Public Member Functions

 MultiShapeletPsfLikelihood (ndarray::Array< Pixel const, 2, 1 > const &image, geom::Point2I const &xy0, boost::shared_ptr< Model > model, Scalar sigma, ndarray::Array< Scalar const, 1, 1 > const &fixed)
 
void computeModelMatrix (ndarray::Array< Pixel, 2,-1 > const &modelMatrix, ndarray::Array< Scalar const, 1, 1 > const &nonlinear, bool doApplyWeights=true) const override
 Evaluate the model for the given vector of nonlinear parameters. More...
 
virtual ~MultiShapeletPsfLikelihood ()
 
int getDataDim () const
 Return the number of data points. More...
 
int getAmplitudeDim () const
 Return the number of linear parameters (columns of the model matrix) More...
 
int getNonlinearDim () const
 Return the number of nonlinear parameters (which parameterize the model matrix) More...
 
int getFixedDim () const
 Return the number of fixed nonlinear parameters (set on Likelihood construction) More...
 
ndarray::Array< Scalar const, 1, 1 > getFixed () const
 Return the vector of fixed nonlinear parameters. More...
 
ndarray::Array< Pixel const, 1, 1 > getData () const
 Return the vector of weighted, scaled data points \(z\). More...
 
ndarray::Array< Pixel const, 1, 1 > getUnweightedData () const
 Return the vector of unweighted data points \(y\). More...
 
ndarray::Array< Pixel const, 1, 1 > getWeights () const
 Return the vector of weights \(w\) applied to data points and model matrix rows. More...
 
ndarray::Array< Pixel const, 1, 1 > getVariance () const
 Return the vector of per-data-point variances. More...
 
boost::shared_ptr< ModelgetModel () const
 Return an object that defines the model and its parameters. More...
 

Protected Attributes

boost::shared_ptr< Model_model
 
ndarray::Array< Scalar const, 1, 1 > _fixed
 
ndarray::Array< Pixel, 1, 1 > _data
 
ndarray::Array< Pixel, 1, 1 > _unweightedData
 
ndarray::Array< Pixel, 1, 1 > _variance
 
ndarray::Array< Pixel, 1, 1 > _weights
 

Detailed Description

Likelihood object used to fit multishapelet models to PSF model images; mostly for internal use by GeneralPsfFitter.

Definition at line 315 of file GeneralPsfFitter.h.

Constructor & Destructor Documentation

◆ MultiShapeletPsfLikelihood()

lsst::meas::modelfit::MultiShapeletPsfLikelihood::MultiShapeletPsfLikelihood ( ndarray::Array< Pixel const, 2, 1 > const &  image,
geom::Point2I const &  xy0,
boost::shared_ptr< Model model,
Scalar  sigma,
ndarray::Array< Scalar const, 1, 1 > const &  fixed 
)

◆ ~MultiShapeletPsfLikelihood()

virtual lsst::meas::modelfit::MultiShapeletPsfLikelihood::~MultiShapeletPsfLikelihood ( )
virtual

Member Function Documentation

◆ computeModelMatrix()

void lsst::meas::modelfit::MultiShapeletPsfLikelihood::computeModelMatrix ( ndarray::Array< Pixel, 2,-1 > const &  modelMatrix,
ndarray::Array< Scalar const, 1, 1 > const &  nonlinear,
bool  doApplyWeights = true 
) const
overridevirtual

Evaluate the model for the given vector of nonlinear parameters.

Parameters
[out]modelMatrixThe dataDim x amplitudeDim matrix \(B\) that expresses the model projected in such a way that it can be compared to the data when multiplied by an amplitude vector \(\alpha\). It should be weighted if the data vector is. The caller is responsible for guaranteeing that the shape of the matrix correct, but implementations should not assume anything about the initial values of the matrix elements.
[in]nonlinearVector of nonlinear parameters at which to evaluate the model.
[in]doApplyWeightsIf False, do not apply the weights to the modelMatrix.

Implements lsst::meas::modelfit::Likelihood.

◆ getAmplitudeDim()

int lsst::meas::modelfit::Likelihood::getAmplitudeDim ( ) const
inlineinherited

Return the number of linear parameters (columns of the model matrix)

Definition at line 77 of file Likelihood.h.

77 { return _model->getAmplitudeDim(); }

◆ getData()

ndarray::Array<Pixel const,1,1> lsst::meas::modelfit::Likelihood::getData ( ) const
inlineinherited

Return the vector of weighted, scaled data points \(z\).

Definition at line 89 of file Likelihood.h.

89 { return _data; }

◆ getDataDim()

int lsst::meas::modelfit::Likelihood::getDataDim ( ) const
inlineinherited

Return the number of data points.

Definition at line 74 of file Likelihood.h.

74 { return _data.getSize<0>(); }

◆ getFixed()

ndarray::Array<Scalar const,1,1> lsst::meas::modelfit::Likelihood::getFixed ( ) const
inlineinherited

Return the vector of fixed nonlinear parameters.

Definition at line 86 of file Likelihood.h.

86 { return _fixed; }

◆ getFixedDim()

int lsst::meas::modelfit::Likelihood::getFixedDim ( ) const
inlineinherited

Return the number of fixed nonlinear parameters (set on Likelihood construction)

Definition at line 83 of file Likelihood.h.

83 { return _model->getFixedDim(); }

◆ getModel()

boost::shared_ptr< Model > lsst::meas::modelfit::Likelihood::getModel ( ) const
inlineinherited

Return an object that defines the model and its parameters.

Definition at line 105 of file Likelihood.h.

105 { return _model; }

◆ getNonlinearDim()

int lsst::meas::modelfit::Likelihood::getNonlinearDim ( ) const
inlineinherited

Return the number of nonlinear parameters (which parameterize the model matrix)

Definition at line 80 of file Likelihood.h.

80 { return _model->getNonlinearDim(); }

◆ getUnweightedData()

ndarray::Array<Pixel const,1,1> lsst::meas::modelfit::Likelihood::getUnweightedData ( ) const
inlineinherited

Return the vector of unweighted data points \(y\).

Definition at line 92 of file Likelihood.h.

92 { return _unweightedData; }

◆ getVariance()

ndarray::Array<Pixel const,1,1> lsst::meas::modelfit::Likelihood::getVariance ( ) const
inlineinherited

Return the vector of per-data-point variances.

Definition at line 102 of file Likelihood.h.

102 { return _variance; }

◆ getWeights()

ndarray::Array<Pixel const,1,1> lsst::meas::modelfit::Likelihood::getWeights ( ) const
inlineinherited

Return the vector of weights \(w\) applied to data points and model matrix rows.

Will be an empty array if no weights are applied.

Definition at line 99 of file Likelihood.h.

99 { return _weights; }

Member Data Documentation

◆ _data

ndarray::Array<Pixel,1,1> lsst::meas::modelfit::Likelihood::_data
protectedinherited

Definition at line 149 of file Likelihood.h.

◆ _fixed

ndarray::Array<Scalar const,1,1> lsst::meas::modelfit::Likelihood::_fixed
protectedinherited

Definition at line 148 of file Likelihood.h.

◆ _model

boost::shared_ptr< Model > lsst::meas::modelfit::Likelihood::_model
protectedinherited

Definition at line 147 of file Likelihood.h.

◆ _unweightedData

ndarray::Array<Pixel,1,1> lsst::meas::modelfit::Likelihood::_unweightedData
protectedinherited

Definition at line 150 of file Likelihood.h.

◆ _variance

ndarray::Array<Pixel,1,1> lsst::meas::modelfit::Likelihood::_variance
protectedinherited

Definition at line 151 of file Likelihood.h.

◆ _weights

ndarray::Array<Pixel,1,1> lsst::meas::modelfit::Likelihood::_weights
protectedinherited

Definition at line 152 of file Likelihood.h.


The documentation for this class was generated from the following file:
lsst::meas::modelfit::Likelihood::_variance
ndarray::Array< Pixel, 1, 1 > _variance
Definition: Likelihood.h:151
lsst::meas::modelfit::Likelihood::_unweightedData
ndarray::Array< Pixel, 1, 1 > _unweightedData
Definition: Likelihood.h:150
lsst::meas::modelfit::Likelihood::_fixed
ndarray::Array< Scalar const, 1, 1 > _fixed
Definition: Likelihood.h:148
lsst::meas::modelfit::Likelihood::_weights
ndarray::Array< Pixel, 1, 1 > _weights
Definition: Likelihood.h:152
lsst::meas::modelfit::Likelihood::_data
ndarray::Array< Pixel, 1, 1 > _data
Definition: Likelihood.h:149
lsst::meas::modelfit::Likelihood::_model
boost::shared_ptr< Model > _model
Definition: Likelihood.h:147