LSSTApplications  1.1.2+25,10.0+13,10.0+132,10.0+133,10.0+224,10.0+41,10.0+8,10.0-1-g0f53050+14,10.0-1-g4b7b172+19,10.0-1-g61a5bae+98,10.0-1-g7408a83+3,10.0-1-gc1e0f5a+19,10.0-1-gdb4482e+14,10.0-11-g3947115+2,10.0-12-g8719d8b+2,10.0-15-ga3f480f+1,10.0-2-g4f67435,10.0-2-gcb4bc6c+26,10.0-28-gf7f57a9+1,10.0-3-g1bbe32c+14,10.0-3-g5b46d21,10.0-4-g027f45f+5,10.0-4-g86f66b5+2,10.0-4-gc4fccf3+24,10.0-40-g4349866+2,10.0-5-g766159b,10.0-5-gca2295e+25,10.0-6-g462a451+1
LSSTDataManagementBasePackage
KernelFunctions.cc
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1 // -*- LSST-C++ -*-
2 
3 /*
4  * LSST Data Management System
5  * Copyright 2008, 2009, 2010 LSST Corporation.
6  *
7  * This product includes software developed by the
8  * LSST Project (http://www.lsst.org/).
9  *
10  * This program is free software: you can redistribute it and/or modify
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24 
32 #include <iostream>
33 
34 #include "boost/format.hpp"
35 
37 
45 void
47  lsst::afw::math::Kernel const &kernel,
48  bool doNormalize,
49  double xPos,
50  double yPos,
51  std::string pixelFmt
52 ) {
53  typedef lsst::afw::math::Kernel::Pixel Pixel;
54 
56  double kSum = kernel.computeImage(kImage, doNormalize, xPos, yPos);
57 
58  for (int y = kImage.getHeight() - 1; y >= 0; --y) {
59  for (lsst::afw::image::Image<Pixel>::const_x_iterator ptr = kImage.row_begin(y);
60  ptr != kImage.row_end(y); ++ptr) {
61  std::cout << boost::format(pixelFmt) % *ptr << " ";
62  }
63  std::cout << std::endl;
64  }
65 
66  if (doNormalize && std::abs(static_cast<double>(kSum) - 1.0) > 1.0e-5) {
67  std::cout << boost::format("Warning! Sum of all pixels = %9.5f != 1.0\n") % kSum;
68  }
69  std::cout << std::endl;
70 }
int y
void printKernel(lsst::afw::math::Kernel const &kernel, bool doNormalize, double x=0, double y=0, std::string pixelFmt="%7.3f")
Print the pixel values of a Kernel to std::cout.
_const_view_t::x_iterator const_x_iterator
A const iterator for traversing the pixels in a row.
Definition: Image.h:158
geom::Extent2I const getDimensions() const
Return the Kernel&#39;s dimensions (width, height)
Definition: Kernel.h:226
Kernels are used for convolution with MaskedImages and (eventually) Images.
Definition: Kernel.h:134
Utility functions for kernels.
double computeImage(lsst::afw::image::Image< Pixel > &image, bool doNormalize, double x=0.0, double y=0.0) const
Compute an image (pixellized representation of the kernel) in place.
Definition: Kernel.cc:94