lsst::ip::diffim Namespace Reference

Namespaces
namespace	dcrModel
namespace	detail
namespace	detectAndMeasure
namespace	diaCatalogSourceSelector
namespace	diffimLib
namespace	diffimTools
namespace	dipoleFitTask
namespace	dipoleMeasurement
namespace	getTemplate
namespace	imageDecorrelation
namespace	imageMapReduce
namespace	imagePsfMatch
namespace	kernelCandidateQa
namespace	makeKernel
namespace	makeKernelBasisList
namespace	metrics
namespace	modelPsfMatch
namespace	psfMatch
namespace	snapPsfMatch
namespace	subtractImages
namespace	utils
namespace	version
namespace	zogy

Classes
class	DipoleCentroidAlgorithm
	Intermediate base class for algorithms that compute a centroid. More...
class	DipoleCentroidControl
class	DipoleFluxAlgorithm
	Intermediate base class for algorithms that compute a flux. More...
class	DipoleFluxControl
class	FindSetBits
	Class to accumulate Mask bits. More...
class	ImageStatistics
	Class to calculate difference image statistics. More...
class	KernelCandidate
	Class stored in SpatialCells for spatial Kernel fitting. More...
class	KernelCandidateDetection
	Search through images for Footprints with no masked pixels. More...
class	KernelSolution
class	MaskedKernelSolution
class	MinimizeDipoleChi2
	Class to minimize PsfDipoleFlux; this is the object that Minuit minimizes. More...
class	NaiveDipoleCentroid
	Intermediate base class for algorithms that compute a centroid. More...
class	NaiveDipoleFlux
class	PsfDipoleFlux
	Implementation of Psf dipole flux. More...
class	PsfDipoleFluxControl
	C++ control object for PSF dipole fluxes. More...
class	RegularizedKernelSolution
class	SpatialKernelSolution
class	StaticKernelSolution

Typedefs
typedef float	PixelT
typedef float	InputT

Functions
lsst::afw::math::KernelList	makeDeltaFunctionBasisList (int width, int height)
	Build a set of Delta Function basis kernels. More...
Eigen::MatrixXd	makeRegularizationMatrix (lsst::daf::base::PropertySet const &ps)
	Build a regularization matrix for Delta function kernels. More...
Eigen::MatrixXd	makeForwardDifferenceMatrix (int width, int height, std::vector< int > const &orders, float borderPenalty, bool fitForBackground)
	Build a forward difference regularization matrix for Delta function kernels. More...
Eigen::MatrixXd	makeCentralDifferenceMatrix (int width, int height, int stencil, float borderPenalty, bool fitForBackground)
	Build a central difference Laplacian regularization matrix for Delta function kernels. More...
lsst::afw::math::KernelList	renormalizeKernelList (lsst::afw::math::KernelList const &kernelListIn)
	Renormalize a list of basis kernels. More...
lsst::afw::math::KernelList	makeAlardLuptonBasisList (int halfWidth, int nGauss, std::vector< double > const &sigGauss, std::vector< int > const &degGauss)
	Build a set of Alard/Lupton basis kernels. More...
template<typename PixelT , typename BackgroundT >
lsst::afw::image::MaskedImage< PixelT >	convolveAndSubtract (lsst::afw::image::MaskedImage< PixelT > const &templateImage, lsst::afw::image::MaskedImage< PixelT > const &scienceMaskedImage, lsst::afw::math::Kernel const &convolutionKernel, BackgroundT background, bool invert=true)
	Execute fundamental task of convolving template and subtracting it from science image. More...
template<typename PixelT , typename BackgroundT >
lsst::afw::image::MaskedImage< PixelT >	convolveAndSubtract (lsst::afw::image::Image< PixelT > const &templateImage, lsst::afw::image::MaskedImage< PixelT > const &scienceMaskedImage, lsst::afw::math::Kernel const &convolutionKernel, BackgroundT background, bool invert=true)
	Execute fundamental task of convolving template and subtracting it from science image. More...
template<typename PixelT >
Eigen::MatrixXd	imageToEigenMatrix (lsst::afw::image::Image< PixelT > const &img)
	Turns a 2-d Image into a 2-d Eigen Matrix. More...
Eigen::MatrixXi	maskToEigenMatrix (lsst::afw::image::Mask< lsst::afw::image::MaskPixel > const &mask)
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. More...
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. More...
	PYBIND11_MODULE (basisLists, mod)
	PYBIND11_MODULE (_dipoleAlgorithms, mod)
	PYBIND11_MODULE (findSetBits, mod)
	PYBIND11_MODULE (imageStatistics, mod)
	PYBIND11_MODULE (imageSubtract, mod)
	PYBIND11_MODULE (kernelCandidate, mod)
	PYBIND11_MODULE (kernelCandidateDetection, mod)
	PYBIND11_MODULE (kernelSolution, mod)
Eigen::MatrixXd	makeFiniteDifferenceRegularizationDeprecated (unsigned int width, unsigned int height, unsigned int order, unsigned int boundary_style, unsigned int difference_style, bool printB)
	Generate regularization matrix for delta function kernels. More...
int const	NEGCENTXPAR (0)
int const	NEGCENTYPAR (1)
int const	NEGFLUXPAR (2)
int const	POSCENTXPAR (3)
int const	POSCENTYPAR (4)
int const	POSFLUXPAR (5)
template<typename PixelT , typename BackgroundT >
afwImage::MaskedImage< PixelT >	convolveAndSubtract (lsst::afw::image::MaskedImage< PixelT > const &templateImage, lsst::afw::image::MaskedImage< PixelT > const &scienceMaskedImage, lsst::afw::math::Kernel const &convolutionKernel, BackgroundT background, bool invert)
	Implement fundamental difference imaging step of convolution and subtraction : D = I - (K*T + bg) where * denotes convolution. More...
template<typename PixelT , typename BackgroundT >
afwImage::MaskedImage< PixelT >	convolveAndSubtract (lsst::afw::image::Image< PixelT > const &templateImage, lsst::afw::image::MaskedImage< PixelT > const &scienceMaskedImage, lsst::afw::math::Kernel const &convolutionKernel, BackgroundT background, bool invert)
	Implement fundamental difference imaging step of convolution and subtraction : D = I - (K.x.T + bg) More...
template Eigen::MatrixXd	imageToEigenMatrix (lsst::afw::image::Image< double > const &)
template lsst::afw::image::MaskedImage< float >	convolveAndSubtract (lsst::afw::image::Image< float > const &templateImage, lsst::afw::image::MaskedImage< float > const &scienceMaskedImage, lsst::afw::math::Kernel const &convolutionKernel, double background, bool invert)
template afwImage::MaskedImage< float >	convolveAndSubtract (lsst::afw::image::Image< float > const &templateImage, lsst::afw::image::MaskedImage< float > const &scienceMaskedImage, lsst::afw::math::Kernel const &convolutionKernel, lsst::afw::math::Function2< double > const &backgroundFunction, bool invert)
template lsst::afw::image::MaskedImage< float >	convolveAndSubtract (lsst::afw::image::MaskedImage< float > const &templateImage, lsst::afw::image::MaskedImage< float > const &scienceMaskedImage, lsst::afw::math::Kernel const &convolutionKernel, double background, bool invert)
template afwImage::MaskedImage< float >	convolveAndSubtract (lsst::afw::image::MaskedImage< float > const &templateImage, lsst::afw::image::MaskedImage< float > const &scienceMaskedImage, lsst::afw::math::Kernel const &convolutionKernel, lsst::afw::math::Function2< double > const &backgroundFunction, bool invert)
template lsst::afw::image::MaskedImage< double >	convolveAndSubtract (lsst::afw::image::Image< double > const &templateImage, lsst::afw::image::MaskedImage< double > const &scienceMaskedImage, lsst::afw::math::Kernel const &convolutionKernel, double background, bool invert)
template afwImage::MaskedImage< double >	convolveAndSubtract (lsst::afw::image::Image< double > const &templateImage, lsst::afw::image::MaskedImage< double > const &scienceMaskedImage, lsst::afw::math::Kernel const &convolutionKernel, lsst::afw::math::Function2< double > const &backgroundFunction, bool invert)
template lsst::afw::image::MaskedImage< double >	convolveAndSubtract (lsst::afw::image::MaskedImage< double > const &templateImage, lsst::afw::image::MaskedImage< double > const &scienceMaskedImage, lsst::afw::math::Kernel const &convolutionKernel, double background, bool invert)
template afwImage::MaskedImage< double >	convolveAndSubtract (lsst::afw::image::MaskedImage< double > const &templateImage, lsst::afw::image::MaskedImage< double > const &scienceMaskedImage, lsst::afw::math::Kernel const &convolutionKernel, lsst::afw::math::Function2< double > const &backgroundFunction, bool invert)

Variables
	NaiveDipoleCentroid
	Control
	DipoleCentroidControl
	executionOrder
	NaiveDipoleFlux
	DipoleFluxControl
	PsfDipoleFlux
	PsfDipoleFluxControl

Typedef Documentation

InputT

typedef float lsst::ip::diffim::InputT

Definition at line 1580 of file KernelSolution.cc.

PixelT

typedef float lsst::ip::diffim::PixelT

Definition at line 383 of file KernelCandidate.cc.

Function Documentation

convolveAndSubtract() [1/12]

template lsst::afw::image::MaskedImage< double > lsst::ip::diffim::convolveAndSubtract	(	lsst::afw::image::Image< double > const &	templateImage,
		lsst::afw::image::MaskedImage< double > const &	scienceMaskedImage,
		lsst::afw::math::Kernel const &	convolutionKernel,
		double	background,
		bool	invert
	)

convolveAndSubtract() [2/12]

template afwImage::MaskedImage< double > lsst::ip::diffim::convolveAndSubtract	(	lsst::afw::image::Image< double > const &	templateImage,
		lsst::afw::image::MaskedImage< double > const &	scienceMaskedImage,
		lsst::afw::math::Kernel const &	convolutionKernel,
		lsst::afw::math::Function2< double > const &	backgroundFunction,
		bool	invert
	)

convolveAndSubtract() [3/12]

template lsst::afw::image::MaskedImage< float > lsst::ip::diffim::convolveAndSubtract	(	lsst::afw::image::Image< float > const &	templateImage,
		lsst::afw::image::MaskedImage< float > const &	scienceMaskedImage,
		lsst::afw::math::Kernel const &	convolutionKernel,
		double	background,
		bool	invert
	)

convolveAndSubtract() [4/12]

template afwImage::MaskedImage< float > lsst::ip::diffim::convolveAndSubtract	(	lsst::afw::image::Image< float > const &	templateImage,
		lsst::afw::image::MaskedImage< float > const &	scienceMaskedImage,
		lsst::afw::math::Kernel const &	convolutionKernel,
		lsst::afw::math::Function2< double > const &	backgroundFunction,
		bool	invert
	)

convolveAndSubtract() [5/12]

template<typename PixelT , typename BackgroundT >

afwImage::MaskedImage< PixelT > lsst::ip::diffim::convolveAndSubtract	(	lsst::afw::image::Image< PixelT > const &	templateImage,
		lsst::afw::image::MaskedImage< PixelT > const &	scienceMaskedImage,
		lsst::afw::math::Kernel const &	convolutionKernel,
		BackgroundT	background,
		bool	invert
	)

Implement fundamental difference imaging step of convolution and subtraction : D = I - (K.x.T + bg)

Execute fundamental task of convolving template and subtracting it from science image.

Note

The template is taken to be an Image, not a MaskedImage; it therefore has neither variance nor bad pixels

If you convolve the science image, D = (K*I + bg) - T, set invert=False

Instantiated such that background can be a double or Function2D

Returns

Difference image

Parameters

templateImage	Image T to convolve with Kernel
scienceMaskedImage	Image I to subtract T from
convolutionKernel	PSF-matching Kernel used
background	Differential background
invert	Invert the output difference image

Definition at line 166 of file ImageSubtract.cc.

      {
 
    boost::timer t;
    t.restart();
 
    afwImage::MaskedImage<PixelT> convolvedMaskedImage(templateImage.getDimensions());
    afwMath::ConvolutionControl convolutionControl = afwMath::ConvolutionControl();
    convolutionControl.setDoNormalize(false);
    afwMath::convolve(*convolvedMaskedImage.getImage(), templateImage,
                      convolutionKernel, convolutionControl);
 
    /* Add in background */
    *(convolvedMaskedImage.getImage()) += background;
 
    /* Do actual subtraction */
    *convolvedMaskedImage.getImage() -= *scienceMaskedImage.getImage();
 
    /* Invert */
    if (invert) {
        *convolvedMaskedImage.getImage() *= -1.0;
    }
    convolvedMaskedImage.getMask()->assign(*scienceMaskedImage.getMask());
    convolvedMaskedImage.getVariance()->assign(*scienceMaskedImage.getVariance());
 
    double time = t.elapsed();
    LOGL_DEBUG("TRACE4.ip.diffim.convolveAndSubtract",
               "Total compute time to convolve and subtract : %.2f s", time);
 
    return convolvedMaskedImage;
}

convolveAndSubtract() [6/12]

template<typename PixelT , typename BackgroundT >

lsst::afw::image::MaskedImage< PixelT > lsst::ip::diffim::convolveAndSubtract	(	lsst::afw::image::Image< PixelT > const &	templateImage,
		lsst::afw::image::MaskedImage< PixelT > const &	scienceMaskedImage,
		lsst::afw::math::Kernel const &	convolutionKernel,
		BackgroundT	background,
		bool	invert
	)

Execute fundamental task of convolving template and subtracting it from science image.

Note

This version accepts an Image for the template, and is thus faster during convolution

Parameters

templateImage	Image to apply convolutionKernel to
scienceMaskedImage	MaskedImage from which convolved templateImage is subtracted
convolutionKernel	Kernel to apply to templateImage
background	Background scalar or function to subtract after convolution
invert	Invert the output difference image

Execute fundamental task of convolving template and subtracting it from science image.

Note

The template is taken to be an Image, not a MaskedImage; it therefore has neither variance nor bad pixels

If you convolve the science image, D = (K*I + bg) - T, set invert=False

Instantiated such that background can be a double or Function2D

Returns

Difference image

Parameters

templateImage	Image T to convolve with Kernel
scienceMaskedImage	Image I to subtract T from
convolutionKernel	PSF-matching Kernel used
background	Differential background
invert	Invert the output difference image

Definition at line 166 of file ImageSubtract.cc.

      {
 
    boost::timer t;
    t.restart();
 
    afwImage::MaskedImage<PixelT> convolvedMaskedImage(templateImage.getDimensions());
    afwMath::ConvolutionControl convolutionControl = afwMath::ConvolutionControl();
    convolutionControl.setDoNormalize(false);
    afwMath::convolve(*convolvedMaskedImage.getImage(), templateImage,
                      convolutionKernel, convolutionControl);
 
    /* Add in background */
    *(convolvedMaskedImage.getImage()) += background;
 
    /* Do actual subtraction */
    *convolvedMaskedImage.getImage() -= *scienceMaskedImage.getImage();
 
    /* Invert */
    if (invert) {
        *convolvedMaskedImage.getImage() *= -1.0;
    }
    convolvedMaskedImage.getMask()->assign(*scienceMaskedImage.getMask());
    convolvedMaskedImage.getVariance()->assign(*scienceMaskedImage.getVariance());
 
    double time = t.elapsed();
    LOGL_DEBUG("TRACE4.ip.diffim.convolveAndSubtract",
               "Total compute time to convolve and subtract : %.2f s", time);
 
    return convolvedMaskedImage;
}

convolveAndSubtract() [7/12]

template lsst::afw::image::MaskedImage< double > lsst::ip::diffim::convolveAndSubtract	(	lsst::afw::image::MaskedImage< double > const &	templateImage,
		lsst::afw::image::MaskedImage< double > const &	scienceMaskedImage,
		lsst::afw::math::Kernel const &	convolutionKernel,
		double	background,
		bool	invert
	)

convolveAndSubtract() [8/12]

template afwImage::MaskedImage< double > lsst::ip::diffim::convolveAndSubtract	(	lsst::afw::image::MaskedImage< double > const &	templateImage,
		lsst::afw::image::MaskedImage< double > const &	scienceMaskedImage,
		lsst::afw::math::Kernel const &	convolutionKernel,
		lsst::afw::math::Function2< double > const &	backgroundFunction,
		bool	invert
	)

convolveAndSubtract() [9/12]

template lsst::afw::image::MaskedImage< float > lsst::ip::diffim::convolveAndSubtract	(	lsst::afw::image::MaskedImage< float > const &	templateImage,
		lsst::afw::image::MaskedImage< float > const &	scienceMaskedImage,
		lsst::afw::math::Kernel const &	convolutionKernel,
		double	background,
		bool	invert
	)

convolveAndSubtract() [10/12]

template afwImage::MaskedImage< float > lsst::ip::diffim::convolveAndSubtract	(	lsst::afw::image::MaskedImage< float > const &	templateImage,
		lsst::afw::image::MaskedImage< float > const &	scienceMaskedImage,
		lsst::afw::math::Kernel const &	convolutionKernel,
		lsst::afw::math::Function2< double > const &	backgroundFunction,
		bool	invert
	)

convolveAndSubtract() [11/12]

template<typename PixelT , typename BackgroundT >

afwImage::MaskedImage< PixelT > lsst::ip::diffim::convolveAndSubtract	(	lsst::afw::image::MaskedImage< PixelT > const &	templateImage,
		lsst::afw::image::MaskedImage< PixelT > const &	scienceMaskedImage,
		lsst::afw::math::Kernel const &	convolutionKernel,
		BackgroundT	background,
		bool	invert
	)

Implement fundamental difference imaging step of convolution and subtraction : D = I - (K*T + bg) where * denotes convolution.

Execute fundamental task of convolving template and subtracting it from science image.

Note

If you convolve the science image, D = (K*I + bg) - T, set invert=False

The template is taken to be an MaskedImage; this takes c 1.6 times as long as using an Image.

Instantiated such that background can be a double or Function2D

Returns

Difference image

Parameters

templateImage	Image T to convolve with Kernel
scienceMaskedImage	Image I to subtract T from
convolutionKernel	PSF-matching Kernel used
background	Differential background
invert	Invert the output difference image

Definition at line 115 of file ImageSubtract.cc.

      {
 
    boost::timer t;
    t.restart();
 
    afwImage::MaskedImage<PixelT> convolvedMaskedImage(templateImage.getDimensions());
    afwMath::ConvolutionControl convolutionControl = afwMath::ConvolutionControl();
    convolutionControl.setDoNormalize(false);
    afwMath::convolve(convolvedMaskedImage, templateImage,
                      convolutionKernel, convolutionControl);
 
    /* Add in background */
    *(convolvedMaskedImage.getImage()) += background;
 
    /* Do actual subtraction */
    convolvedMaskedImage -= scienceMaskedImage;
 
    /* Invert */
    if (invert) {
        convolvedMaskedImage *= -1.0;
    }
 
    double time = t.elapsed();
    LOGL_DEBUG("TRACE4.ip.diffim.convolveAndSubtract",
               "Total compute time to convolve and subtract : %.2f s", time);
 
    return convolvedMaskedImage;
}

convolveAndSubtract() [12/12]

template<typename PixelT , typename BackgroundT >

lsst::afw::image::MaskedImage< PixelT > lsst::ip::diffim::convolveAndSubtract	(	lsst::afw::image::MaskedImage< PixelT > const &	templateImage,
		lsst::afw::image::MaskedImage< PixelT > const &	scienceMaskedImage,
		lsst::afw::math::Kernel const &	convolutionKernel,
		BackgroundT	background,
		bool	invert
	)

Execute fundamental task of convolving template and subtracting it from science image.

Note

This version accepts a MaskedImage for the template

Parameters

templateImage	MaskedImage to apply convolutionKernel to
scienceMaskedImage	MaskedImage from which convolved templateImage is subtracted
convolutionKernel	Kernel to apply to templateImage
background	Background scalar or function to subtract after convolution
invert	Invert the output difference image

Execute fundamental task of convolving template and subtracting it from science image.

Note

If you convolve the science image, D = (K*I + bg) - T, set invert=False

The template is taken to be an MaskedImage; this takes c 1.6 times as long as using an Image.

Instantiated such that background can be a double or Function2D

Returns

Difference image

Parameters

templateImage	Image T to convolve with Kernel
scienceMaskedImage	Image I to subtract T from
convolutionKernel	PSF-matching Kernel used
background	Differential background
invert	Invert the output difference image

Definition at line 115 of file ImageSubtract.cc.

      {
 
    boost::timer t;
    t.restart();
 
    afwImage::MaskedImage<PixelT> convolvedMaskedImage(templateImage.getDimensions());
    afwMath::ConvolutionControl convolutionControl = afwMath::ConvolutionControl();
    convolutionControl.setDoNormalize(false);
    afwMath::convolve(convolvedMaskedImage, templateImage,
                      convolutionKernel, convolutionControl);
 
    /* Add in background */
    *(convolvedMaskedImage.getImage()) += background;
 
    /* Do actual subtraction */
    convolvedMaskedImage -= scienceMaskedImage;
 
    /* Invert */
    if (invert) {
        convolvedMaskedImage *= -1.0;
    }
 
    double time = t.elapsed();
    LOGL_DEBUG("TRACE4.ip.diffim.convolveAndSubtract",
               "Total compute time to convolve and subtract : %.2f s", time);
 
    return convolvedMaskedImage;
}

imageToEigenMatrix() [1/2]

template Eigen::MatrixXd lsst::ip::diffim::imageToEigenMatrix

(

lsst::afw::image::Image< double > const &

)

imageToEigenMatrix() [2/2]

template<typename PixelT >

template Eigen::MatrixXd lsst::ip::diffim::imageToEigenMatrix

(

lsst::afw::image::Image< PixelT > const &

img

)

Turns a 2-d Image into a 2-d Eigen Matrix.

Turns Image into a 2-D Eigen Matrix.

Parameters

img	Image whose pixel values are read into an Eigen::MatrixXd

Definition at line 62 of file ImageSubtract.cc.

      {
    unsigned int rows = img.getHeight();
    unsigned int cols = img.getWidth();
    Eigen::MatrixXd M = Eigen::MatrixXd::Zero(rows, cols);
    for (int y = 0; y != img.getHeight(); ++y) {
        int x = 0;
        for (typename afwImage::Image<PixelT>::x_iterator ptr = img.row_begin(y);
             ptr != img.row_end(y); ++ptr, ++x) {
            // M is addressed row, col.  Need to invert y-axis.
            // WARNING : CHECK UNIT TESTS BEFORE YOU COMMIT THIS (-y-1) INVERSION
            M(rows-y-1,x) = *ptr;
        }
    }
    return M;
}

makeAlardLuptonBasisList()

lsst::afw::math::KernelList lsst::ip::diffim::makeAlardLuptonBasisList	(	int	halfWidth,
		int	nGauss,
		std::vector< double > const &	sigGauss,
		std::vector< int > const &	degGauss
	)

Build a set of Alard/Lupton basis kernels.

Generate an Alard-Lupton basis set of Kernels.

Note

Should consider implementing as SeparableKernels for additional speed, but this will make the normalization a bit more complicated

Parameters

halfWidth	size is 2*N + 1
nGauss	number of gaussians
sigGauss	Widths of the Gaussian Kernels
degGauss	Local spatial variation of bases

Note

Should consider implementing as SeparableKernels for additional speed, but this will make the normalization a bit more complicated

Returns

Vector of Alard-Lupton Kernels.

Parameters

halfWidth	size is 2*N + 1
nGauss	number of gaussians
sigGauss	width of the gaussians
degGauss	local spatial variation of gaussians

Definition at line 78 of file BasisLists.cc.

          {
        typedef afwMath::Kernel::Pixel Pixel;
        typedef afwImage::Image<Pixel> Image;
 
        if (halfWidth < 1) {
            throw LSST_EXCEPT(pexExcept::Exception, "halfWidth must be positive");
        }
        if (nGauss != static_cast<int>(sigGauss.size())) {
            throw LSST_EXCEPT(pexExcept::Exception, "sigGauss does not have enough entries");
        }
        if (nGauss != static_cast<int>(degGauss.size())) {
            throw LSST_EXCEPT(pexExcept::Exception, "degGauss does not have enough entries");
        }
        int fullWidth = 2 * halfWidth + 1;
        Image image(geom::Extent2I(fullWidth, fullWidth));
 
        afwMath::KernelList kernelBasisList;
        for (int i = 0; i < nGauss; i++) {
            /*
               sigma = FWHM / ( 2 * sqrt(2 * ln(2)) )
            */
            double sig  = sigGauss[i];
            int deg     = degGauss[i];
 
            LOGL_DEBUG("TRACE1.ip.diffim.BasisLists.makeAlardLuptonBasisList",
                       "Gaussian %d : sigma %.2f degree %d", i, sig, deg);
 
            afwMath::GaussianFunction2<Pixel> gaussian(sig, sig);
            afwMath::AnalyticKernel kernel(fullWidth, fullWidth, gaussian);
            afwMath::PolynomialFunction2<Pixel> polynomial(deg);
 
            for (int j = 0, n = 0; j <= deg; j++) {
                for (int k = 0; k <= (deg - j); k++, n++) {
                    /* for 0th order term, skip polynomial */
                    (void)kernel.computeImage(image, true);
                    if (n == 0) {
                        std::shared_ptr<afwMath::Kernel>
                            kernelPtr(new afwMath::FixedKernel(image));
                        kernelBasisList.push_back(kernelPtr);
                        continue;
                    }
 
                    /* gaussian to be modified by this term in the polynomial */
                    polynomial.setParameter(n, 1.);
                    (void)kernel.computeImage(image, true);
                    for (int y = 0, v = -halfWidth; y < image.getHeight(); y++, v++) {
                        int u = -halfWidth;
                        for (auto ptr = image.row_begin(y), end = image.row_end(y); ptr != end; ++ptr, ++u) {
                            /* Evaluate from -1 to 1 */
                            *ptr  = *ptr * polynomial(u/static_cast<double>(halfWidth),
                                                      v/static_cast<double>(halfWidth));
                        }
                    }
                    std::shared_ptr<afwMath::Kernel>
                        kernelPtr(new afwMath::FixedKernel(image));
                    kernelBasisList.push_back(kernelPtr);
                    polynomial.setParameter(n, 0.);
                }
            }
        }
        return renormalizeKernelList(kernelBasisList);
    }

makeCentralDifferenceMatrix()

Eigen::MatrixXd lsst::ip::diffim::makeCentralDifferenceMatrix	(	int	width,
		int	height,
		int	stencil,
		float	borderPenalty,
		bool	fitForBackground
	)

Build a central difference Laplacian regularization matrix for Delta function kernels.

Generate regularization matrix for delta function kernels.

Parameters

width	Width of basis set you want to regularize
height	Height of basis set you want to regularize
stencil	Which type of Laplacian approximation to use
borderPenalty	Amount of penalty (if any) to apply to border pixels; > 0
fitForBackground	Fit for differential background?

Definition at line 207 of file BasisLists.cc.

          {
 
        /* 5- or 9-point stencil to approximate the Laplacian; i.e. this is a second
         * order central finite difference.
         *
         * 5-point approximation ignores off-diagonal elements, and is
         *
         *  f(x-1,y) + f(x+1,y) + f(x,y-1) + f(x,y+1) - 4 f(x,y)
         *
         *   0   1   0
         *   1  -4   1
         *   0   1   0
         *
         *
         * 9-point approximation includes diagonals and is
         *
         *   1   4   1
         *   4  -20  4  / 6
         *   1   4   1
         *
         */
 
        if (borderPenalty < 0)
            throw LSST_EXCEPT(pexExcept::Exception, "Only border penalty of >= 0 allowed");
 
        std::vector<std::vector<float> >
            coeffs(3, std::vector<float>(3,0));
 
        if (stencil == 5) {
            /* http://www.physics.arizona.edu/~restrepo/475B/Notes/source/node51.html
             *
             * This is equivalent to a second order central difference summed along
             * the x-axis, and then summed along the y-axis.
             *
             * http://www.holoborodko.com/pavel/?page_id=239
             *
             */
            coeffs[0][1] =  1.;
            coeffs[1][0] =  1.;
            coeffs[1][1] = -4.;
            coeffs[1][2] =  1.;
            coeffs[2][1] =  1.;
        }
        else if (stencil == 9) {
            /* http://www.physics.arizona.edu/~restrepo/475B/Notes/source/node52.html */
            coeffs[0][0] =   1. / 6.;
            coeffs[0][1] =   4. / 6.;
            coeffs[0][2] =   1. / 6.;
            coeffs[1][0] =   4. / 6.;
            coeffs[1][1] = -20. / 6.;
            coeffs[1][2] =   4. / 6.;
            coeffs[2][0] =   1. / 6.;
            coeffs[2][1] =   4. / 6.;
            coeffs[2][2] =   1. / 6.;
        }
        else {
            throw LSST_EXCEPT(pexExcept::Exception, "Only 5- or 9-point Laplacian stencils allowed");
        }
 
        int nBgTerms = fitForBackground ? 1 : 0;
        Eigen::MatrixXd bMat = Eigen::MatrixXd::Zero(width * height + nBgTerms, width * height + nBgTerms);
 
        for (int i = 0; i < width*height; i++) {
            int const x0    = i % width;       // the x coord in the kernel image
            int const y0    = i / width;       // the y coord in the kernel image
            int const distX = width - x0 - 1;  // distance from edge of image
            int const distY = height - y0 - 1; // distance from edge of image
 
            if ( (x0 > 0) && (y0 > 0) && (distX > 0) && (distY > 0) ) {
                for (int dx = -1; dx < 2; dx += 1) {
                    for (int dy = -1; dy < 2; dy += 1) {
                        bMat(i, i + dx + dy * width) += coeffs[dx+1][dy+1];
                    }
                }
            }
            else {
                bMat(i, i) = borderPenalty;
            }
        }
 
        if (fitForBackground) {
            /* Last row / col should have no regularization since its the background term */
            if (bMat.col(width*height).sum() != 0.) {
                throw LSST_EXCEPT(pexExcept::Exception, "Error 1 in regularization matrix");
            }
            if (bMat.row(width*height).sum() != 0.) {
                throw LSST_EXCEPT(pexExcept::Exception, "Error 2 in regularization matrix");
            }
        }
 
        return bMat;
    }

makeDeltaFunctionBasisList()

lsst::afw::math::KernelList lsst::ip::diffim::makeDeltaFunctionBasisList	(	int	width,
		int	height
	)

Build a set of Delta Function basis kernels.

Generate a basis set of delta function Kernels.

Note

Total number of basis functions is width*height

Parameters

width	Width of basis set (cols)
height	Height of basis set (rows)

Generates a vector of Kernels sized nCols * nRows, where each Kernel has a unique pixel set to value 1.0 with the other pixels valued 0.0. This is the "delta function" basis set.

Returns

Vector of orthonormal delta function Kernels.

Exceptions

lsst::pex::exceptions::DomainError

if nRows or nCols not positive

Parameters

width	number of columns in the set
height	number of rows in the set

Definition at line 47 of file BasisLists.cc.

          {
        if ((width < 1) || (height < 1)) {
            throw LSST_EXCEPT(pexExcept::Exception, "nRows and nCols must be positive");
        }
        const int signedWidth = static_cast<int>(width);
        const int signedHeight = static_cast<int>(height);
        afwMath::KernelList kernelBasisList;
        for (int row = 0; row < signedHeight; ++row) {
            for (int col = 0; col < signedWidth; ++col) {
                std::shared_ptr<afwMath::Kernel>
                    kernelPtr(new afwMath::DeltaFunctionKernel(width, height, geom::Point2I(col,row)));
                kernelBasisList.push_back(kernelPtr);
            }
        }
        return kernelBasisList;
    }

makeFiniteDifferenceRegularizationDeprecated()

Eigen::MatrixXd lsst::ip::diffim::makeFiniteDifferenceRegularizationDeprecated	(	unsigned int	width,
		unsigned int	height,
		unsigned int	order,
		unsigned int	boundary_style,
		unsigned int	difference_style,
		bool	printB
	)

Generate regularization matrix for delta function kernels.

Definition at line 505 of file BasisLists.cc.

          {
 
        if (order > 2) throw LSST_EXCEPT(pexExcept::Exception, "Only orders 0..2 allowed");
 
        if (boundary_style > 2) {
            throw LSST_EXCEPT(pexExcept::Exception, "Boundary styles 0..2 defined");
        }
        if (difference_style > 1) {
            throw LSST_EXCEPT(pexExcept::Exception, "Only forward(0), and central(1) difference types defined");
        }
 
        /* what works, and what doesn't */
        // == good job ==
        // - order 0, wrapped, forward
        // - order 1, wrapped or tapered, central or forward
        // - order 2, wrapped or tapered, forward
        // == bad job (usually diagongal stripes) ==
        // - all others
 
 
        /*
           Instead of Taylor expanding the forward difference approximation of
           derivatives (N.R. section 18.5) lets just hard code in the expansion of
           the 1st through 3rd derivatives, which will try and enforce smoothness of
           0 through 2nd derivatives.
 
           A property of the basic "finite difference regularization" is that their
           rows (column multipliers) sum to 0.
 
           Another consideration is to use *multiple* finite difference operators as
           a constraint.
 
        */
 
 
        // ===========================================================================
        // Get the coeffs for the finite differencing
        // note: The coeffs are stored 2D although they are essentially 1D entities.
        //       The 2d design was chosen to allow cross-terms to be included,
        //         though they are not yet implemented.
        //
        std::vector<std::vector<std::vector<float> > >
            coeffs(3, std::vector<std::vector<float> >(5, std::vector<float>(5,0)));
        unsigned int x_cen = 0,  y_cen = 0;  // center of reqested order coeffs
        unsigned int x_cen1 = 0, y_cen1 = 0; // center of order 1 coeffs
        unsigned int x_cen2 = 0, y_cen2 = 0; // center of order 2 coeffs
        unsigned int x_size = 0, y_size = 0;
 
        // forward difference coefficients
        if (difference_style == 0) {
 
            y_cen  = x_cen  = 0;
            x_cen1 = y_cen1 = 0;
            x_cen2 = y_cen2 = 0;
 
            x_size = y_size = order + 2;
 
            // default forward difference suggested in NR chap 18
            // 0th order
            coeffs[0][0][0] = -2;
            coeffs[0][0][1] =  1;
            coeffs[0][1][0] =  1;
            coeffs[0][1][1] =  0;
 
            // 1st 2
            coeffs[1][0][0] = -2;
            coeffs[1][0][1] =  2;
            coeffs[1][0][2] = -1;
            coeffs[1][1][0] =  2;
            coeffs[1][1][1] =  0;
            coeffs[1][1][2] =  0;
            coeffs[1][2][0] = -1;
            coeffs[1][2][1] =  0;
            coeffs[1][2][2] =  0;
 
            // 2nd 2
            coeffs[2][0][0] = -2;
            coeffs[2][0][1] =  3;
            coeffs[2][0][2] = -3;
            coeffs[2][0][3] =  1;
            coeffs[2][1][0] =  3;
            coeffs[2][1][1] =  0;
            coeffs[2][1][2] =  0;
            coeffs[2][1][3] =  0;
            coeffs[2][2][0] = -3;
            coeffs[2][2][1] =  0;
            coeffs[2][2][2] =  0;
            coeffs[2][2][3] =  0;
            coeffs[2][3][0] =  1;
            coeffs[2][3][1] =  0;
            coeffs[2][3][2] =  0;
            coeffs[2][3][3] =  0;
        }
 
        // central difference coefficients
        if (difference_style == 1) {
 
            // this is asymmetric and produces diagonal banding in the kernel
            // from: http://www.holoborodko.com/pavel/?page_id=239
            if (order == 0) {
                y_cen = x_cen = 1;
                x_size = y_size = 3;
            }
            coeffs[0][0][0] =  0;
            coeffs[0][0][1] = -1;
            coeffs[0][0][2] =  0;
 
            coeffs[0][1][0] = -1;
            coeffs[0][1][1] =  0;
            coeffs[0][1][2] =  1;
 
            coeffs[0][2][0] =  0;
            coeffs[0][2][1] =  1;
            coeffs[0][2][2] =  0;
 
 
            // this works well and is largely the same as order=1 forward-diff.
            // from: http://www.holoborodko.com/pavel/?page_id=239
            if (order == 1) {
                y_cen = x_cen = 1;
                x_size = y_size = 3;
            }
            y_cen1 = x_cen1 = 1;
            coeffs[1][0][0] =  0;
            coeffs[1][0][1] =  1;
            coeffs[1][0][2] =  0;
 
            coeffs[1][1][0] =  1;
            coeffs[1][1][1] = -4;
            coeffs[1][1][2] =  1;
 
            coeffs[1][2][0] =  0;
            coeffs[1][2][1] =  1;
            coeffs[1][2][2] =  0;
 
 
            // asymmetric and produces diagonal banding in the kernel
            // from http://www.holoborodko.com/pavel/?page_id=239
            if (order == 2) {
                y_cen = x_cen = 2;
                x_size = y_size = 5;
            }
            y_cen2 = x_cen2 = 2;
            coeffs[2][0][0] =  0;
            coeffs[2][0][1] =  0;
            coeffs[2][0][2] = -1;
            coeffs[2][0][3] =  0;
            coeffs[2][0][4] =  0;
 
            coeffs[2][1][0] =  0;
            coeffs[2][1][1] =  0;
            coeffs[2][1][2] =  2;
            coeffs[2][1][3] =  0;
            coeffs[2][1][4] =  0;
 
            coeffs[2][2][0] = -1;
            coeffs[2][2][1] =  2;
            coeffs[2][2][2] =  0;
            coeffs[2][2][3] = -2;
            coeffs[2][2][4] =  1;
 
            coeffs[2][3][0] =  0;
            coeffs[2][3][1] =  0;
            coeffs[2][3][2] = -2;
            coeffs[2][3][3] =  0;
            coeffs[2][3][4] =  0;
 
            coeffs[2][4][0] =  0;
            coeffs[2][4][1] =  0;
            coeffs[2][4][2] =  1;
            coeffs[2][4][3] =  0;
            coeffs[2][4][4] =  0;
        }
 
 
        /* Note we have to add 1 extra (empty) term here because of the differential
         * background fitting */
        Eigen::MatrixXd bMat = Eigen::MatrixXd::Zero(width*height+1, width*height+1);
 
        /* Forward difference approximation */
        for (unsigned int i = 0; i < width*height; i++) {
 
            unsigned int const x0 = i % width;  // the x coord in the kernel image
            unsigned int const y0 = i / width;  // the y coord in the kernel image
 
            unsigned int x_edge_distance = (x0 > (width - x0 - 1))  ? width - x0 - 1  : x0;
            unsigned int y_edge_distance = (y0 > (height - y0 - 1)) ? height - y0 - 1 : y0;
            unsigned int edge_distance = (x_edge_distance < y_edge_distance) ? x_edge_distance :
                y_edge_distance;
 
            for (unsigned int dx = 0; dx < x_size; dx++) {
                for (unsigned int dy = 0; dy < y_size; dy++) {
 
                    // determine where to put this coeff
 
                    // handle the boundary condition
                    // note: adding width and height in the sum prevents negatives
                    unsigned int x = 0;
                    unsigned int y = 0;
                    double this_coeff = 0;
 
                    // no-wrapping at edges
                    if (boundary_style == 0) {
                        x = x0 + dx - x_cen;
                        y = y0 + dy - y_cen;
                        if (y > height - 1 || x > width - 1) {
                            continue;
                        }
                        this_coeff = coeffs[order][dx][dy];
 
                        // wrapping at edges
                    } else if (boundary_style == 1) {
                        x = (width  + x0 + dx - x_cen) % width;
                        y = (height + y0 + dy - y_cen) % height;
                        this_coeff = coeffs[order][dx][dy];
 
                        // order tapering to the edge (just clone wrapping for now)
                        // - use the lowest order possible
                    } else if (boundary_style == 2) {
 
                        // edge rows and columns ... set to constant
                        if (edge_distance == 0) {
                            x = x0;
                            y = y0;
                            this_coeff = 1;
                        }
                        // in one from edge, use 1st order
                        else if (edge_distance == 1 && order > 0) {
                            x = (width  + x0 + dx - x_cen1) % width;
                            y = (height + y0 + dy - y_cen1) % height;
                            if ((dx < 3) && (dy < 3)) { this_coeff = coeffs[1][dx][dy]; }
                        }
                        // in two from edge, use 2st order if order > 1
                        else if (edge_distance == 2 && order > 1){
                            x = (width  + x0 + dx - x_cen2) % width;
                            y = (height + y0 + dy - y_cen2) % height;
                            if ((dx < 5) && (dy < 5)) { this_coeff = coeffs[2][dx][dy]; }
                        }
                        // if we're somewhere in the middle
                        else if (edge_distance > order) {
                            x = (width  + x0 + dx - x_cen) % width;
                            y = (height + y0 + dy - y_cen) % height;
                            this_coeff = coeffs[order][dx][dy];
                        }
 
                    }
 
                    bMat(i, y*width + x) = this_coeff;
 
                }
 
            }
 
        }
 
        if (printB)  {
            std::cout << bMat << std::endl;
        }
 
        Eigen::MatrixXd hMat = bMat.transpose() * bMat;
        return hMat;
    }

makeForwardDifferenceMatrix()

Eigen::MatrixXd lsst::ip::diffim::makeForwardDifferenceMatrix	(	int	width,
		int	height,
		std::vector< int > const &	orders,
		float	borderPenalty,
		bool	fitForBackground
	)

Build a forward difference regularization matrix for Delta function kernels.

Generate regularization matrix for delta function kernels.

Parameters

width	Width of basis set you want to regularize
height	Height of basis set you want to regularize
orders	Which derivatives to penalize (1,2,3)
borderPenalty	Amount of penalty (if any) to apply to border pixels; > 0
fitForBackground	Fit for differential background?

Definition at line 309 of file BasisLists.cc.

          {
 
        /*
           Instead of Taylor expanding the forward difference approximation of
           derivatives (N.R. section 18.5) lets just hard code in the expansion of
           the 1st through 3rd derivatives, which will try and enforce smoothness of
           0 through 2nd derivatives.
 
           A property of the basic "finite difference regularization" is that their
           rows (column multipliers) sum to 0.
 
           We taper the order of the constraint as you get close to the boundary.
           The actual perimeter values are left unconstrained but we might want to
           consider giving these the same penalties as the other border pixels,
           which is +1 (since the value gets squared).
 
        */
 
        if (borderPenalty < 0)
            throw LSST_EXCEPT(pexExcept::Exception, "Only border penalty of >= 0 allowed");
 
        std::vector<std::vector<float> >
            coeffs(4, std::vector<float>(4,0));
 
        // penalize border?  this is along the diagonal and gets squared, so sign does not matter
        coeffs[0][0] = borderPenalty;
 
        // penalize zeroth derivative
        coeffs[1][0] = -1.;
        coeffs[1][1] = +1.;
 
        // penalize first derivative
        coeffs[2][0] = -1.;
        coeffs[2][1] = +2.;
        coeffs[2][2] = -1.;
 
        // penalize second derivative
        coeffs[3][0] = -1.;
        coeffs[3][1] = +3.;
        coeffs[3][2] = -3.;
        coeffs[3][3] = +1.;
 
        int nBgTerms = fitForBackground ? 1 : 0;
        Eigen::MatrixXd bTot  = Eigen::MatrixXd::Zero(width * height + nBgTerms, width * height + nBgTerms);
 
        std::vector<int>::const_iterator order;
        for (order = orders.begin(); order != orders.end(); order++) {
            if ((*order < 1) || (*order > 3))
                throw LSST_EXCEPT(pexExcept::Exception, "Only orders 1..3 allowed");
 
            Eigen::MatrixXd bMatX = Eigen::MatrixXd::Zero(width * height + nBgTerms,
                                                          width * height + nBgTerms);
            Eigen::MatrixXd bMatY = Eigen::MatrixXd::Zero(width * height + nBgTerms,
                                                          width * height + nBgTerms);
 
            for (int i = 0; i < width*height; i++) {
                int const x0 = i % width;         // the x coord in the kernel image
                int const y0 = i / width;         // the y coord in the kernel image
 
                int distX       = width - x0 - 1; // distance from edge of image
                int orderToUseX = std::min(distX, *order);
                for (int j = 0; j < orderToUseX+1; j++) {
                    bMatX(i, i + j) = coeffs[orderToUseX][j];
                }
 
                int distY       = height - y0 - 1; // distance from edge of image
                int orderToUseY = std::min(distY, *order);
                for (int j = 0; j < orderToUseY+1; j++) {
                    bMatY(i, i + j * width) = coeffs[orderToUseY][j];
                }
            }
            bTot += bMatX;
            bTot += bMatY;
        }
 
        if (fitForBackground) {
            /* Last row / col should have no regularization since its the background term */
            if (bTot.col(width*height).sum() != 0.) {
                throw LSST_EXCEPT(pexExcept::Exception, "Error in regularization matrix");
            }
            if (bTot.row(width*height).sum() != 0.) {
                throw LSST_EXCEPT(pexExcept::Exception, "Error in regularization matrix");
            }
        }
 
        return bTot;
    }

makeKernelCandidate() [1/2]

template<typename PixelT >

std::shared_ptr< KernelCandidate< PixelT > > lsst::ip::diffim::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

Definition at line 220 of file KernelCandidate.h.

                                                     {
 
        return std::shared_ptr<KernelCandidate<PixelT>>(new KernelCandidate<PixelT>(xCenter, yCenter,
                                                                                 templateMaskedImage,
                                                                                 scienceMaskedImage,
                                                                                 ps));
    }

makeKernelCandidate() [2/2]

template<typename PixelT >

std::shared_ptr< KernelCandidate< PixelT > > lsst::ip::diffim::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

source	afw::table::SourceRecord used to construct the KernelCandidate
templateMaskedImage	Template subimage
scienceMaskedImage	Science image subimage
ps	PropertySet for creation of rating

Definition at line 245 of file KernelCandidate.h.

                                                     {
 
        return std::shared_ptr<KernelCandidate<PixelT>>(new KernelCandidate<PixelT>(source,
                                                                                    templateMaskedImage,
                                                                                    scienceMaskedImage,
                                                                                    ps));
    }

makeRegularizationMatrix()

Eigen::MatrixXd lsst::ip::diffim::makeRegularizationMatrix

(

lsst::daf::base::PropertySet const &

ps

)

Build a regularization matrix for Delta function kernels.

Parameters

ps	PropertySet dictating which type of matrix to make

Note

Calls either makeForwardDifferenceMatrix or makeCentralDifferenceMatrix based on the PropertySet config.

Definition at line 147 of file BasisLists.cc.

          {
 
        /* NOTES
         *
         * The 3-point first derivative central difference (Laplacian) yields
         * diagonal stripes and should not be used.
 
         coeffs[0][1] = -1. / 2.;
         coeffs[1][0] = -1. / 2.;
         coeffs[1][2] =  1. / 2.;
         coeffs[2][1] =  1. / 2.;
 
         * The 5-point second derivative central difference (Laplacian) looks great.
         * For smaller lambdas you need a higher border penalty.  In general, if you
         * decrease the regularization strength you should increase the border
         * penalty to avoid noise in the border pixels.  This has been used to value
         * 10 and things still look OK.
 
         * The 9-point second derivative central difference (Laplacian with off
         * diagonal terms) also looks great.  Seems to have slightly higher-valued
         * border pixels so make boundary larger if using this.  E.g. 1.5.
 
         * The forward finite difference, first derivative term works good
         *
         * The forward finite difference, second derivative term is slightly banded
         * in LLC
         *
         * The forward finite difference, third derivative term is highly banded in
         * LLC and should not be used.
         *
         */
 
        std::string regularizationType = ps.getAsString("regularizationType");
        int width   = ps.getAsInt("kernelSize");
        int height  = ps.getAsInt("kernelSize");
        float borderPenalty  = ps.getAsDouble("regularizationBorderPenalty");
        bool fitForBackground = ps.getAsBool("fitForBackground");
 
        Eigen::MatrixXd bMat;
        if (regularizationType == "centralDifference") {
            int stencil = ps.getAsInt("centralRegularizationStencil");
            bMat = makeCentralDifferenceMatrix(width, height, stencil, borderPenalty, fitForBackground);
        }
        else if (regularizationType == "forwardDifference") {
            std::vector<int> orders = ps.getArray<int>("forwardRegularizationOrders");
            bMat = makeForwardDifferenceMatrix(width, height, orders, borderPenalty, fitForBackground);
        }
        else {
            throw LSST_EXCEPT(pexExcept::Exception, "regularizationType not recognized");
        }
 
        Eigen::MatrixXd hMat = bMat.transpose() * bMat;
        return hMat;
    }

maskToEigenMatrix()

Eigen::MatrixXi lsst::ip::diffim::maskToEigenMatrix

(

lsst::afw::image::Mask< lsst::afw::image::MaskPixel > const &

mask

)

Definition at line 80 of file ImageSubtract.cc.

      {
    unsigned int rows = mask.getHeight();
    unsigned int cols = mask.getWidth();
    Eigen::MatrixXi M = Eigen::MatrixXi::Zero(rows, cols);
    for (int y = 0; y != mask.getHeight(); ++y) {
        int x = 0;
        for (afwImage::Mask<lsst::afw::image::MaskPixel>::x_iterator ptr = mask.row_begin(y);
             ptr != mask.row_end(y); ++ptr, ++x) {
            // M is addressed row, col.  Need to invert y-axis.
            // WARNING : CHECK UNIT TESTS BEFORE YOU COMMIT THIS (-y-1) INVERSION
            M(rows-y-1,x) = *ptr;
        }
    }
    return M;
}

NEGCENTXPAR()

int const lsst::ip::diffim::NEGCENTXPAR

(

0

)

NEGCENTYPAR()

int const lsst::ip::diffim::NEGCENTYPAR

(

1

)

NEGFLUXPAR()

int const lsst::ip::diffim::NEGFLUXPAR

(

2

)

POSCENTXPAR()

int const lsst::ip::diffim::POSCENTXPAR

(

3

)

POSCENTYPAR()

int const lsst::ip::diffim::POSCENTYPAR

(

4

)

POSFLUXPAR()

int const lsst::ip::diffim::POSFLUXPAR

(

5

)

PYBIND11_MODULE() [1/8]

lsst::ip::diffim::PYBIND11_MODULE	(	_dipoleAlgorithms	,
		mod
	)

Definition at line 140 of file dipoleAlgorithms.cc.

                                        {
    py::module::import("lsst.afw.table");
    py::module::import("lsst.meas.base");
    py::module::import("lsst.pex.config");
 
    declareDipoleCentroidControl(mod);
    declareDipoleFluxControl(mod);
    declareDipolePsfFluxControl(mod);
    declareDipoleCentroidAlgorithm(mod);
    declareDipoleFluxAlgorithm(mod);
    declareNaiveDipoleFlux(mod);
    declareNaiveDipoleCentroid(mod);
    declarePsfDipoleFlux(mod);
}

PYBIND11_MODULE() [2/8]

lsst::ip::diffim::PYBIND11_MODULE	(	basisLists	,
		mod
	)

Definition at line 39 of file basisLists.cc.

                                 {
    py::module::import("lsst.afw.math");
    py::module::import("lsst.daf.base");
 
    mod.def("makeDeltaFunctionBasisList", &makeDeltaFunctionBasisList, "width"_a, "height"_a);
    mod.def("makeRegularizationMatrix", &makeRegularizationMatrix, "ps"_a);
    mod.def("makeForwardDifferenceMatrix", &makeForwardDifferenceMatrix, "width"_a, "height"_a, "orders"_a,
            "borderPenalty"_a, "fitForBackground"_a);
    mod.def("makeCentralDifferenceMatrix", &makeCentralDifferenceMatrix, "width"_a, "height"_a, "stencil"_a,
            "borderPenalty"_a, "fitForBackground"_a);
    mod.def("renormalizeKernelList", &renormalizeKernelList, "kernelListIn"_a);
    mod.def("makeAlardLuptonBasisList", &makeAlardLuptonBasisList, "halfWidth"_a, "nGauss"_a, "sigGauss"_a,
            "degGauss"_a);
}

PYBIND11_MODULE() [3/8]

lsst::ip::diffim::PYBIND11_MODULE	(	findSetBits	,
		mod
	)

Definition at line 58 of file findSetBits.cc.

                                  {
    py::module::import("lsst.afw.image");
 
    declareFindSetBits<afw::image::Mask<afw::image::MaskPixel>>(mod, "U");
}

PYBIND11_MODULE() [4/8]

lsst::ip::diffim::PYBIND11_MODULE	(	imageStatistics	,
		mod
	)

Definition at line 73 of file imageStatistics.cc.

                                      {
    py::module::import("lsst.daf.base");
    py::module::import("lsst.afw.image");
 
    declareImageStatistics<int>(mod, "I");
    declareImageStatistics<float>(mod, "F");
    declareImageStatistics<double>(mod, "D");
}

PYBIND11_MODULE() [5/8]

lsst::ip::diffim::PYBIND11_MODULE	(	imageSubtract	,
		mod
	)

Definition at line 72 of file imageSubtract.cc.

                                    {
    py::module::import("lsst.afw.image");
    py::module::import("lsst.afw.math");
 
    declareConvolveAndSubtract<float, double>(mod);
    declareConvolveAndSubtract<float, afw::math::Function2<double> const &>(mod);
}

PYBIND11_MODULE() [6/8]

lsst::ip::diffim::PYBIND11_MODULE	(	kernelCandidate	,
		mod
	)

Definition at line 116 of file kernelCandidate.cc.

                                      {
    py::module::import("lsst.afw.image");
    py::module::import("lsst.afw.math");
    py::module::import("lsst.afw.table");
    py::module::import("lsst.daf.base");
 
    declareKernelCandidate<float>(mod, "F");
}

PYBIND11_MODULE() [7/8]

lsst::ip::diffim::PYBIND11_MODULE	(	kernelCandidateDetection	,
		mod
	)

Definition at line 62 of file kernelCandidateDetection.cc.

                                               {
    py::module::import("lsst.afw.image");
    py::module::import("lsst.afw.detection");
    py::module::import("lsst.daf.base");
 
    declareKernelCandidateDetection<float>(mod, "F");
}

PYBIND11_MODULE() [8/8]

lsst::ip::diffim::PYBIND11_MODULE	(	kernelSolution	,
		mod
	)

Definition at line 176 of file kernelSolution.cc.

                                     {
    py::module::import("lsst.afw.geom");
    py::module::import("lsst.afw.image");
    py::module::import("lsst.afw.math");
    py::module::import("lsst.daf.base");
 
    declareKernelSolution(mod);
    declareStaticKernelSolution<float>(mod, "F");
    declareMaskedKernelSolution<float>(mod, "F");
    declareRegularizedKernelSolution<float>(mod, "F");
    declareSpatialKernelSolution(mod);
}

renormalizeKernelList()

lsst::afw::math::KernelList lsst::ip::diffim::renormalizeKernelList

(

lsst::afw::math::KernelList const &

kernelListIn

)

Renormalize a list of basis kernels.

Rescale an input set of kernels.

Note

Renormalization means make Ksum_0 = 1.0, Ksum_i = 0.0, K_i.dot.K_i = 1.0

Output list of shared pointers to FixedKernels

Parameters

kernelListIn

input list of basis kernels

Note

Images are checked for their current kernel sum. If it is larger than std::numeric_limits<double>::epsilon(), the kernel is first divided by the kernel sum, giving it a kSum of 1.0, and then the first (normalized) component is subtracted from it, giving it a kSum of 0.0.

Returns

Vector of renormalized kernels

Parameters

kernelListIn

Input list to be renormalized

Definition at line 412 of file BasisLists.cc.

          {
        typedef afwMath::Kernel::Pixel Pixel;
        typedef afwImage::Image<Pixel> Image;
        double kSum;
 
        /*
 
        We want all the bases except for the first to sum to 0.0.  This allows
        us to achieve kernel flux conservation (Ksum) across the image since all
        the power will be in the first term, which will not vary spatially.
 
        K(x,y) = Ksum * B_0 + Sum_i : a(x,y) * B_i
 
        To do this, normalize all Kernels to sum = 1. and subtract B_0 from all
        subsequent kenrels.
 
        To get an idea of the relative contribution of each of these basis
        functions later on down the line, lets also normalize them such that
 
        Sum(B_i)  == 0.0   *and*
        B_i * B_i == 1.0
 
        For completeness
 
        Sum(B_0)  == 1.0
        B_0 * B_0 != 1.0
 
        */
        afwMath::KernelList kernelListOut;
        if (kernelListIn.size() == 0) {
            return kernelListOut;
        }
 
        Image image0(kernelListIn[0]->getDimensions());
        for (unsigned int i = 0; i < kernelListIn.size(); i++) {
            if (i == 0) {
                /* Make sure that it is normalized to kSum 1. */
                (void)kernelListIn[i]->computeImage(image0, true);
                std::shared_ptr<afwMath::Kernel>
                    kernelPtr(new afwMath::FixedKernel(image0));
                kernelListOut.push_back(kernelPtr);
 
                continue;
            }
 
            /* Don't normalize here */
            Image image(kernelListIn[i]->getDimensions());
            (void)kernelListIn[i]->computeImage(image, false);
            /* image.writeFits(str(boost::format("in_k%d.fits") % i)); */
 
            /* Check the kernel sum; if its close to zero don't do anything */
            kSum = 0.;
            for (int y = 0; y < image.getHeight(); y++) {
                for (auto ptr = image.row_begin(y), end = image.row_end(y); ptr != end; ++ptr) {
                    kSum += *ptr;
                }
            }
 
            /* std::numeric_limits<float>::epsilon() ~ e-7
               std::numeric_limits<double>::epsilon() ~ e-16
 
               If we end up with 2e-16 kernel sum, this still blows up the kernel values.
               Even though the kernels are double, use the float limits instead
            */
            if (fabs(kSum) > std::numeric_limits<float>::epsilon()) {
                image /= kSum;
                image -= image0;
            }
 
            /* Finally, rescale such that the inner product is 1 */
            kSum = 0.;
            for (int y = 0; y < image.getHeight(); y++) {
                for (auto ptr = image.row_begin(y), end = image.row_end(y); ptr != end; ++ptr) {
                    kSum += *ptr * *ptr;
                }
            }
            image /= std::sqrt(kSum);
            /* image.writeFits(str(boost::format("out_k%d.fits") % i));  */
 
            std::shared_ptr<afwMath::Kernel>
                kernelPtr(new afwMath::FixedKernel(image));
            kernelListOut.push_back(kernelPtr);
        }
        return kernelListOut;
    }

Variable Documentation

Control

lsst.ip.diffim.Control

Definition at line 51 of file __init__.py.

DipoleCentroidControl

lsst.ip.diffim.DipoleCentroidControl

Definition at line 51 of file __init__.py.

DipoleFluxControl

lsst.ip.diffim.DipoleFluxControl

Definition at line 52 of file __init__.py.

executionOrder

lsst.ip.diffim.executionOrder

Definition at line 51 of file __init__.py.

NaiveDipoleCentroid

lsst.ip.diffim.NaiveDipoleCentroid

Definition at line 51 of file __init__.py.

NaiveDipoleFlux

lsst.ip.diffim.NaiveDipoleFlux

Definition at line 52 of file __init__.py.

PsfDipoleFlux

lsst.ip.diffim.PsfDipoleFlux

Definition at line 53 of file __init__.py.

PsfDipoleFluxControl

lsst.ip.diffim.PsfDipoleFluxControl

Definition at line 53 of file __init__.py.