File KernelCandidate.h

Class used by SpatialModelCell for spatial Kernel fitting.

Author

Andrew Becker, University of Washington

namespace lsst

Class for a simple mapping implementing a generic AstrometryTransform.

Remove all non-astronomical counts from the Chunk Exposure’s pixels.

Forward declarations for lsst::utils::Cache

For details on the Cache class, see the Cache.h file.

It uses a template rather than a pointer so that the derived classes can use the specifics of the transform. The class simplePolyMapping overloads a few routines.

A base class for image defects

Numeric constants used by the Integrate.h integrator routines.

Compute Image Statistics

Note

Gauss-Kronrod-Patterson quadrature coefficients for use in quadpack routine qng. These coefficients were calculated with 101 decimal digit arithmetic by L. W. Fullerton, Bell Labs, Nov 1981.

Note

The Statistics class itself can only handle lsst::afw::image::MaskedImage() types. The philosophy has been to handle other types by making them look like lsst::afw::image::MaskedImage() and reusing that code. Users should have no need to instantiate a Statistics object directly, but should use the overloaded makeStatistics() factory functions.

namespace ip
namespace diffim

Functions

template<typename PixelT>
std::shared_ptr<KernelCandidate<PixelT>> makeKernelCandidate(float const xCenter, float const yCenter, std::shared_ptr<afw::image::MaskedImage<PixelT>> const &templateMaskedImage, std::shared_ptr<afw::image::MaskedImage<PixelT>> const &scienceMaskedImage, daf::base::PropertySet const &ps)

Return a KernelCandidate pointer of the right sort.

Parameters

  • xCenter: X-center of candidate

  • yCenter: Y-center of candidate

  • templateMaskedImage: Template subimage

  • scienceMaskedImage: Science image subimage

  • ps: PropertySet for creation of rating

template<typename PixelT>
std::shared_ptr<KernelCandidate<PixelT>> makeKernelCandidate(std::shared_ptr<afw::table::SourceRecord> const &source, std::shared_ptr<afw::image::MaskedImage<PixelT>> const &templateMaskedImage, std::shared_ptr<afw::image::MaskedImage<PixelT>> const &scienceMaskedImage, daf::base::PropertySet const &ps)

Return a KernelCandidate pointer of the right sort.

Parameters

template<typename _PixelT>
class KernelCandidate : public lsst::afw::math::SpatialCellImageCandidate
#include <KernelCandidate.h>

Class stored in SpatialCells for spatial Kernel fitting.

Note

KernelCandidate is a single Kernel derived around a source. We’ll assign them to sets of SpatialCells; these sets will then be used to fit a spatial model to the Kernel.

Public Types

enum CandidateSwitch

Values:

ORIG = 0
PCA = 1
RECENT = 2
typedef afw::image::Image<afw::math::Kernel::Pixel> ImageT
typedef _PixelT PixelT
typedef std::shared_ptr<KernelCandidate> Ptr
typedef std::shared_ptr<afw::image::MaskedImage<PixelT>> MaskedImagePtr
typedef std::shared_ptr<afw::image::Image<afw::image::VariancePixel>> VariancePtr
typedef std::shared_ptr<afw::table::SourceRecord> SourcePtr

Public Functions

KernelCandidate(float const xCenter, float const yCenter, MaskedImagePtr const &templateMaskedImage, MaskedImagePtr const &scienceMaskedImage, daf::base::PropertySet const &ps)

Constructor.

Parameters

  • xCenter: Col position of object

  • yCenter: Row position of object

  • templateMaskedImage: Pointer to template image

  • scienceMaskedImage: Pointer to science image

  • ps: PropertySet

KernelCandidate(SourcePtr const &source, MaskedImagePtr const &templateMaskedImage, MaskedImagePtr const &scienceMaskedImage, daf::base::PropertySet const &ps)

Constructor.

Parameters

  • source: Pointer to a source to use in constructing the candidate

  • templateMaskedImage: Pointer to template image

  • scienceMaskedImage: Pointer to science image

  • ps: PropertySet

virtual ~KernelCandidate()

Destructor.

double getCandidateRating() const

Return Candidate rating.

Note

Required method for use by SpatialCell; e.g. total flux

SourcePtr getSource() const

Return the original source.

MaskedImagePtr getTemplateMaskedImage()

Return pointers to the image pixels used in kernel determination.

MaskedImagePtr getScienceMaskedImage()
std::shared_ptr<afw::math::Kernel> getKernel(CandidateSwitch cand) const

Return results of kernel solution.

double getBackground(CandidateSwitch cand) const
double getKsum(CandidateSwitch cand) const
PTR(ImageT) const
CONST_PTR(ImageT) const
std::shared_ptr<StaticKernelSolution<PixelT>> getKernelSolution(CandidateSwitch cand) const
afw::image::MaskedImage<PixelT> getDifferenceImage(CandidateSwitch cand)

Calculate associated difference image using internal solutions.

afw::image::MaskedImage<PixelT> getDifferenceImage(std::shared_ptr<afw::math::Kernel> kernel, double background)

Calculate associated difference image using input kernel and background.

Note

Useful for spatial modeling

bool isInitialized() const
void build(afw::math::KernelList const &basisList)

Core functionality of KernelCandidate, to build and fill a KernelSolution.

o _isInitialized = false. This is a constructed but not initialized

KernelCandidate. When build() is called, M and B are derived from the MaskedImages and the basisList. KernelCandidate owns the knowledge of how to fill this KernelSolution; the solution knows how to solve itself and how to turn that into an output kernel. This solution ends up being _kernelSolution0.
Note

This is an expensive step involving matrix math, and one that may be called multiple times per candidate. Use cases are:

o _isInitialized = true. This is for when build() is re-called using a different basis list, e.g. one based on Pca. We need to use M and B for the spatial modeling, but do not want to override the original KernelSolution. This solution ends up as _kernelSolutionCurrent. Build KernelSolution matrices for M x = B with regularization matrix H

Note

Modified equation is (Mt.M + lambda H) x = Mt.B with lambda a degree of freedom describing the “strength” of the regularization. The larger the value of lambda, the smoother the kernel, but the larger the residuals in the difference image.

Note

A value of lambda = Trace(Mt.M) / Tr(H) will yield essentially equivalent power in the kernel smoothness and in the diffim quality. We scale this estimate by lambdaScaling to give more/less consideration to the smoothness of the kernel.

void build(afw::math::KernelList const &basisList, Eigen::MatrixXd const &hMat)

Private Functions

void _buildKernelSolution(afw::math::KernelList const &basisList, Eigen::MatrixXd const &hMat)

Private Members

MaskedImagePtr _templateMaskedImage

Subimage around which you build kernel.

MaskedImagePtr _scienceMaskedImage

Subimage around which you build kernel.

VariancePtr _varianceEstimate

Estimate of the local variance.

daf::base::PropertySet::Ptr _ps

Parameters.

SourcePtr _source
double _coreFlux

Mean S/N in the science image.

bool _isInitialized

Has the kernel been built.

bool _useRegularization

Use regularization?

bool _fitForBackground
std::shared_ptr<StaticKernelSolution<PixelT>> _kernelSolutionOrig

Original basis solution.

std::shared_ptr<StaticKernelSolution<PixelT>> _kernelSolutionPca

Most recent solution.