lsst::ip::diffim Namespace Reference

Namespaces
	detail
	diaCatalogSourceSelector
	diaSourceAnalysis
	diffimTools
	dipoleFitTask
	dipoleMeasurement
	getTemplate
	imageDecorrelation
	imagePsfMatch
	kernelCandidateQa
	makeKernelBasisList
	makeRatingVector
	modelPsfMatch
	psfMatch
	snapPsfMatch
	utils
	version

Classes
class	MinimizeDipoleChi2
	Class to minimize PsfDipoleFlux; this is the object that Minuit minimizes. More...
class	DipoleCentroidControl
class	DipoleFluxControl
class	PsfDipoleFluxControl
	C++ control object for PSF dipole fluxes. More...
class	DipoleCentroidAlgorithm
	Intermediate base class for algorithms that compute a centroid. More...
class	DipoleFluxAlgorithm
	Intermediate base class for algorithms that compute a flux. More...
class	NaiveDipoleFlux
class	NaiveDipoleCentroid
	Intermediate base class for algorithms that compute a centroid. More...
class	PsfDipoleFlux
	Implementation of Psf dipole flux. More...
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	StaticKernelSolution
class	MaskedKernelSolution
class	RegularizedKernelSolution
class	SpatialKernelSolution

Typedefs
typedef float	PixelT
typedef float	InputT

Functions
lsst::afw::math::KernelList	makeDeltaFunctionBasisList (int width, int height)
	Generate a basis set of delta function Kernels. More...
lsst::afw::math::KernelList	makeAlardLuptonBasisList (int halfWidth, int nGauss, std::vector< double > const &sigGauss, std::vector< int > const &degGauss)
	Generate an Alard-Lupton basis set of Kernels. More...
std::shared_ptr< Eigen::MatrixXd >	makeRegularizationMatrix (lsst::pex::policy::Policy policy)
	Build a regularization matrix for Delta function kernels. More...
std::shared_ptr< Eigen::MatrixXd >	makeCentralDifferenceMatrix (int width, int height, int stencil, float borderPenalty, bool fitForBackground)
	Generate regularization matrix for delta function kernels. More...
std::shared_ptr< Eigen::MatrixXd >	makeForwardDifferenceMatrix (int width, int height, std::vector< int > const &orders, float borderPenalty, bool fitForBackground)
	Generate regularization matrix for delta function kernels. More...
lsst::afw::math::KernelList	renormalizeKernelList (lsst::afw::math::KernelList const &kernelListIn)
	Rescale an input set of kernels. More...
std::shared_ptr< 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 >
Eigen::MatrixXd	imageToEigenMatrix (lsst::afw::image::Image< PixelT > const &img)
	Turns Image into a 2-D Eigen Matrix. More...
Eigen::MatrixXi	maskToEigenMatrix (lsst::afw::image::Mask< lsst::afw::image::MaskPixel > const &mask)
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)
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 >
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, pex::policy::Policy const &policy)
	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, pex::policy::Policy const &policy)
	Return a KernelCandidate pointer of the right sort. More...

Typedef Documentation

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

Definition at line 1622 of file KernelSolution.cc.

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

Definition at line 448 of file KernelCandidate.cc.

Function Documentation

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 118 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;
}

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 169 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;
}

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 118 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;
}

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 169 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;
}

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
	)

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
	)

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
	)

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
	)

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
	)

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
	)

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
	)

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
	)

template<typename PixelT >

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

(

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

img

)

Turns Image into a 2-D Eigen Matrix.

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

Parameters

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

Definition at line 65 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;
}

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

(

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

)

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

Generate an Alard-Lupton basis set of Kernels.

Build a set of Alard/Lupton basis kernels.

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.

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

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(afwGeom::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 (Image::xy_locator ptr = image.xy_at(0, y), 
                                 end = image.xy_at(image.getWidth(), y); 
                             ptr != end; ++ptr.x(), 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);
    }

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

Generate regularization matrix for delta function kernels.

Build a central difference Laplacian 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 211 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");
            }
        }
        
        std::shared_ptr<Eigen::MatrixXd> bMatPtr (new Eigen::MatrixXd(bMat));
        return bMatPtr;
    }

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

Generate a basis set of delta function Kernels.

Build a set of Delta Function basis kernels.

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

Note

Total number of basis functions is width*height

Parameters

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

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, afwGeom::Point2I(col,row)));
                kernelBasisList.push_back(kernelPtr);
            }
        }
        return kernelBasisList;
    }

std::shared_ptr<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 515 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;
        }
        
        std::shared_ptr<Eigen::MatrixXd> hMat (new Eigen::MatrixXd(bMat.transpose() * bMat));
        return hMat;
    }

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

Generate regularization matrix for delta function kernels.

Build a forward difference 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 315 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");
            }
        }
        
        std::shared_ptr<Eigen::MatrixXd> bMatPtr (new Eigen::MatrixXd(bTot));
        return bMatPtr;
    }

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,
		pex::policy::Policy const &	policy
	)

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
policy	Policy file for creation of rating

Definition at line 219 of file KernelCandidate.h.

                                                      {

        return typename KernelCandidate<PixelT>::Ptr(new KernelCandidate<PixelT>(xCenter, yCenter,
                                                                                 templateMaskedImage,
                                                                                 scienceMaskedImage,
                                                                                 policy));
    }

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,
		pex::policy::Policy const &	policy
	)

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
policy	Policy file for creation of rating

Definition at line 244 of file KernelCandidate.h.

                                                      {

        return typename KernelCandidate<PixelT>::Ptr(new KernelCandidate<PixelT>(source,
                                                                                 templateMaskedImage,
                                                                                 scienceMaskedImage,
                                                                                 policy));
    }

std::shared_ptr< Eigen::MatrixXd > lsst::ip::diffim::makeRegularizationMatrix

(

lsst::pex::policy::Policy

policy

)

Build a regularization matrix for Delta function kernels.

Parameters

policy

Policy file dictating which type of matrix to make

Note

Calls either makeForwardDifferenceMatrix or makeCentralDifferenceMatrix based on the policy file.

Definition at line 150 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 = policy.getString("regularizationType");
        int width   = policy.getInt("kernelSize");
        int height  = policy.getInt("kernelSize");
        float borderPenalty  = policy.getDouble("regularizationBorderPenalty");
        bool fitForBackground = policy.getBool("fitForBackground");
        
        std::shared_ptr<Eigen::MatrixXd> bMat;
        if (regularizationType == "centralDifference") {
            int stencil = policy.getInt("centralRegularizationStencil");
            bMat = makeCentralDifferenceMatrix(width, height, stencil, borderPenalty, fitForBackground);
        }
        else if (regularizationType == "forwardDifference") {
            std::vector<int> orders = policy.getIntArray("forwardRegularizationOrders");
            bMat = makeForwardDifferenceMatrix(width, height, orders, borderPenalty, fitForBackground);
        }
        else {
            throw LSST_EXCEPT(pexExcept::Exception, "regularizationType not recognized");
        }
        
        std::shared_ptr<Eigen::MatrixXd> hMat (new Eigen::MatrixXd((*bMat).transpose() * (*bMat)));
        return hMat;
    }

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

(

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

mask

)

Definition at line 83 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;
}

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

(

0

)

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

(

1

)

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

(

2

)

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

(

3

)

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

(

4

)

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

(

5

)

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

(

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

kernelListIn

)

Rescale an input set of kernels.

Renormalize a list of basis kernels.

Returns

Vector of renormalized 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.

Parameters

kernelListIn

Input list to be renormalized

Definition at line 419 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 (Image::xy_locator ptr = image.xy_at(0, y), end = image.xy_at(image.getWidth(), y); 
                     ptr != end; ++ptr.x()) {
                    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 (Image::xy_locator ptr = image.xy_at(0, y), end = image.xy_at(image.getWidth(), y); 
                     ptr != end; ++ptr.x()) {
                    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;
    }