LSSTApplications  21.0.0+1b62c9342b,21.0.0+45a059f35e,21.0.0-1-ga51b5d4+ceb9cf20a3,21.0.0-10-g68cce58c5+c7d3cce47e,21.0.0-2-g103fe59+c1ca725317,21.0.0-2-g1367e85+a1c2f7fe71,21.0.0-2-g2909d54+45a059f35e,21.0.0-2-g45278ab+1b62c9342b,21.0.0-2-g4bc9b9f+b2e40a4e47,21.0.0-2-g5242d73+a1c2f7fe71,21.0.0-2-g54e2caa+c00cf99ed0,21.0.0-2-g66bcc37+27b9d7859a,21.0.0-2-g7f82c8f+203cf74700,21.0.0-2-g8dde007+b0df52bfdd,21.0.0-2-g8f08a60+73884b2cf5,21.0.0-2-ga326454+203cf74700,21.0.0-2-ga63a54e+eec04437aa,21.0.0-2-gc738bc1+59028256f4,21.0.0-2-gde069b7+5a8f2956b8,21.0.0-2-ge17e5af+a1c2f7fe71,21.0.0-2-ge712728+9ad031c87e,21.0.0-2-gecfae73+d3766aec80,21.0.0-2-gfc62afb+a1c2f7fe71,21.0.0-20-g4449a12+38dfb87bce,21.0.0-22-gf0532904+afb8e7912b,21.0.0-3-g4c5b185+a403cb96fd,21.0.0-3-g6d51c4a+27b9d7859a,21.0.0-3-g8076721+e873df194c,21.0.0-3-gaa929c8+df5d87f43a,21.0.0-3-gd222c45+afc8332dbe,21.0.0-4-g1383c07+27b9d7859a,21.0.0-4-g3300ddd+1b62c9342b,21.0.0-4-g5873dc9+9a92674037,21.0.0-4-g8a80011+f67daf2f53,21.0.0-5-gcff38f6+bce43c5818,21.0.0-6-g463d161+44134145d4,21.0.0-6-gd3283ba+df5d87f43a,21.0.0-8-g19111d86+d6551531e4,w.2021.04
LSSTDataManagementBasePackage
ImagePca.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
11  * it under the terms of the GNU General Public License as published by
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18  * GNU General Public License for more details.
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21  * the GNU General Public License along with this program. If not,
22  * see <http://www.lsstcorp.org/LegalNotices/>.
23  */
24 
33 #include "lsst/afw.h"
35 
36 namespace lsst {
37 namespace meas {
38 namespace algorithms {
39 
40 template <typename ImageT>
42  Super::analyze();
43 
44  typename Super::ImageList const &eImageList = this->getEigenImages();
45  typename Super::ImageList::const_iterator iter = eImageList.begin(), end = eImageList.end();
46  for (size_t i = 0; iter != end; ++i, ++iter) {
47  PTR(ImageT) eImage = *iter;
48 
49  /*
50  * Normalise eigenImages to have a maximum of 1.0. For n > 0 they
51  * (should) have mean == 0, so we can't use that to normalize
52  */
54  double const min = stats.getValue(afw::math::MIN);
55  double const max = stats.getValue(afw::math::MAX);
56 
57  double const extreme = (fabs(min) > max) ? min : max;
58  if (extreme != 0.0) {
59  *eImage /= extreme;
60  }
61 
62  /*
63  * Estimate and subtract the mean background level from the i > 0
64  * eigen images; if we don't do that then PSF variation can get mixed
65  * with subtle variations in the background and potentially amplify
66  * them disasterously.
67  *
68  * It is not at all clear that doing this is a good idea; it'd be
69  * better to get the sky level right in the first place.
70  */
71  if (i > 0 && _border > 0) { /* not the zeroth KL component */
72  int const height = eImage->getHeight();
73  int const width = eImage->getWidth();
74  double background;
75  if (2 * _border >= std::min(height, width)) {
76  // _Border consumes the entire image
79  .getValue();
80  } else {
81  // Use the median of the edge pixels
82 
83  // If ImageT is a MaskedImage, unpack the Image
86 
87  int const nEdge = width * height - (width - 2 * _border) * (height - 2 * _border);
88  std::vector<double> edgePixels(nEdge);
89 
90  std::vector<double>::iterator bi = edgePixels.begin();
91 
92  typedef typename afw::image::GetImage<ImageT>::type::x_iterator imIter;
93  int y = 0;
94  for (; y != _border; ++y) { // Bottom border of eImage
95  for (imIter ptr = eImageIm->row_begin(y), end = eImageIm->row_end(y); ptr != end;
96  ++ptr, ++bi) {
97  *bi = *ptr;
98  }
99  }
100  for (; y != height - _border; ++y) { // Left and right borders of eImage
101  for (imIter ptr = eImageIm->row_begin(y), end = eImageIm->x_at(_border, y); ptr != end;
102  ++ptr, ++bi) {
103  *bi = *ptr;
104  }
105  for (imIter ptr = eImageIm->x_at(width - _border, y), end = eImageIm->row_end(y);
106  ptr != end; ++ptr, ++bi) {
107  *bi = *ptr;
108  }
109  }
110  for (; y != height; ++y) { // Top border of eImage
111  for (imIter ptr = eImageIm->row_begin(y), end = eImageIm->row_end(y); ptr != end;
112  ++ptr, ++bi) {
113  *bi = *ptr;
114  }
115  }
116  assert(std::distance(edgePixels.begin(), bi) == nEdge);
117 
119  }
120  *eImage -= background;
121  }
122  }
123 }
124 
125 #define INSTANTIATE_IMAGE(IMAGE) template class PsfImagePca<IMAGE>;
126 
127 #define INSTANTIATE(TYPE) \
128  INSTANTIATE_IMAGE(afw::image::Image<TYPE>); \
129  INSTANTIATE_IMAGE(afw::image::MaskedImage<TYPE>);
130 
131 INSTANTIATE(float);
132 
133 } // namespace algorithms
134 } // namespace meas
135 } // namespace lsst
y
int y
Definition: SpanSet.cc:49
std::shared_ptr
STL class.
PTR
#define PTR(...)
Definition: base.h:41
afw.h
INSTANTIATE
#define INSTANTIATE(TYPE)
Definition: ImagePca.cc:127
std::vector< std::shared_ptr< ImageT > >
std::distance
T distance(T... args)
lsst::meas::algorithms::PsfImagePca::analyze
virtual void analyze()
Generate eigenimages that are normalised and background-subtracted.
Definition: ImagePca.cc:41
lsst::afw::math::Statistics::getValue
double getValue(Property const prop=NOTHING) const
Return the value of the desired property (if specified in the constructor)
Definition: Statistics.cc:1056
end
int end
Definition: BoundedField.cc:105
lsst::afw::math::makeStatistics
Statistics makeStatistics(lsst::afw::image::Image< Pixel > const &img, lsst::afw::image::Mask< image::MaskPixel > const &msk, int const flags, StatisticsControl const &sctrl=StatisticsControl())
Handle a watered-down front-end to the constructor (no variance)
Definition: Statistics.h:354
lsst.pipe.drivers.visualizeVisit.background
background
Definition: visualizeVisit.py:37
ptr
uint64_t * ptr
Definition: RangeSet.cc:88
max
int max
Definition: BoundedField.cc:104
lsst
A base class for image defects.
Definition: imageAlgorithm.dox:1
ImagePca.h
Class for doing PCA on PSF stars.
std::min
T min(T... args)
std::vector::begin
T begin(T... args)
lsst::afw::math::MIN
@ MIN
estimate sample minimum
Definition: Statistics.h:76
min
int min
Definition: BoundedField.cc:103
lsst::afw::math::Statistics
A class to evaluate image statistics.
Definition: Statistics.h:215
lsst::afw::math::MAX
@ MAX
estimate sample maximum
Definition: Statistics.h:77
lsst::afw::image::GetImage
Definition: MaskedImage.h:1315
std::vector::end
T end(T... args)
astshim.fitsChanContinued.iter
def iter(self)
Definition: fitsChanContinued.py:88
lsst::afw::math::MEDIAN
@ MEDIAN
estimate sample median
Definition: Statistics.h:70