imageStatistics.cc

// -*- LSST-C++ -*-

/* 
 * LSST Data Management System
 * Copyright 2008, 2009, 2010 LSST Corporation.
 * 
 * This product includes software developed by the
 * LSST Project (http://www.lsst.org/).
 *
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 * 
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 * 
 * You should have received a copy of the LSST License Statement and 
 * the GNU General Public License along with this program.  If not, 
 * see <http://www.lsstcorp.org/LegalNotices/>.
 */
 
#include <iostream>
#include <limits>
#include <cmath>
#include "lsst/afw/image/Image.h"
#include "lsst/afw/image/MaskedImage.h"
#include "lsst/afw/math/Statistics.h"

#include "lsst/afw/math/MaskedVector.h"

#include "boost/shared_ptr.hpp"

namespace image = lsst::afw::image;
namespace math = lsst::afw::math;
namespace geom = lsst::afw::geom;

typedef image::Image<float> ImageF;
typedef image::MaskedImage<float> MaskedImageF;
typedef math::Statistics ImgStat;
typedef math::MaskedVector<float> MaskedVectorF;


/*
 *
 */
template<typename Image>
void printStats(Image &img, math::StatisticsControl const &sctrl) {
    
    // initialize a Statistics object with any stats we might want
    ImgStat stats = math::makeStatistics(img, math::NPOINT | math::STDEV | math::MEAN | math::VARIANCE |
                                         math::ERRORS | math::MIN | math::MAX | math::VARIANCECLIP |
                                         math::MEANCLIP | math::MEDIAN | math::IQRANGE | math::STDEVCLIP,
                                         sctrl);
    
    // get various stats with getValue() and their errors with getError()
    double const npoint    = stats.getValue(math::NPOINT);
    double const mean      = stats.getValue(math::MEAN);
    double const var       = stats.getValue(math::VARIANCE);
    double const dmean     = stats.getError(math::MEAN);
    double const sd        = stats.getValue(math::STDEV);
    double const min       = stats.getValue(math::MIN);
    double const max       = stats.getValue(math::MAX);
    double const meanclip  = stats.getValue(math::MEANCLIP);
    double const varclip   = stats.getValue(math::VARIANCECLIP);
    double const stdevclip = stats.getValue(math::STDEVCLIP);
    double const median    = stats.getValue(math::MEDIAN);
    double const iqrange   = stats.getValue(math::IQRANGE);

    // output
    std::cout << "N          " << npoint << std::endl;
    std::cout << "dmean      " << dmean << std::endl;

    std::cout << "mean:      " << mean << std::endl;
    std::cout << "meanclip:  " << meanclip << std::endl;

    std::cout << "var:       " << var << std::endl;
    std::cout << "varclip:   " << varclip << std::endl;

    std::cout << "stdev:     " << sd << std::endl;
    std::cout << "stdevclip: " << stdevclip << std::endl;

    std::cout << "min:       " << min << std::endl;
    std::cout << "max:       " << max <<  std::endl;
    std::cout << "median:    " << median << std::endl;
    std::cout << "iqrange:   " << iqrange << std::endl;
    std::cout << std::endl;
    
}


int main() {

    // declare an image and a masked image
    int const wid = 1024;
    ImageF img(geom::Extent2I(wid, wid));
    MaskedImageF mimg(img.getDimensions());
    std::vector<double> v(0);
    MaskedVectorF mv(wid*wid);
    
    // fill it with some noise (Cauchy noise in this case)
    for (int j = 0; j != img.getHeight(); ++j) {
        
        int k = 0;
        MaskedImageF::x_iterator mip = mimg.row_begin(j);
        for (ImageF::x_iterator ip = img.row_begin(j); ip != img.row_end(j); ++ip) {
            double const xUniform = M_PI*static_cast<ImageF::Pixel>(std::rand())/RAND_MAX;
            double xLorentz = xUniform; //tan(xUniform - M_PI/2.0);

            // throw in the occassional nan ... 1% of the time
            if ( static_cast<double>(std::rand())/RAND_MAX < 0.01 ) { xLorentz = NAN; }
            
            *ip = xLorentz;
            
            // mask the odd rows
            // variance actually diverges for Cauchy noise ... but stats doesn't access this.
            *mip = MaskedImageF::Pixel(xLorentz, (k%2) ? 0x1 : 0x0, (k%2) ? 1.0e99 : 1.0);

            v.push_back(xLorentz);
            ++k;
            ++mip;
        }
    }

    int j = 0;
    for (MaskedVectorF::iterator mvp = mv.begin(); mvp != mv.end(); ++mvp) {
        *mvp = MaskedVectorF::Pixel(v[j], (j%2) ? 0x1 : 0x0, 10.0);
        ++j;
    }

    boost::shared_ptr<std::vector<float> > vF = mv.getVector();
    
    // make a statistics control object and override some of the default properties
    math::StatisticsControl sctrl;
    sctrl.setNumIter(3);
    sctrl.setNumSigmaClip(5.0);
    sctrl.setAndMask(0x1);        // pixels with this mask bit set will be ignored.
    sctrl.setNanSafe(true);


    // ==================================================================
    // Get stats for the Image, MaskedImage, and vector
    std::cout << "image::Image" << std::endl;
    printStats(img, sctrl);
    std::cout << "image::MaskedImage" << std::endl;
    printStats(mimg, sctrl);
    std::cout << "std::vector" << std::endl;
    printStats(v, sctrl);
    std::cout << "image::MaskedVector" << std::endl;
    printStats(mv, sctrl);
    std::cout << "image::MaskedVector::getVector()" << std::endl;
    printStats(*vF, sctrl);


    // Now try the weighted statistics
    sctrl.setWeighted(true);
    sctrl.setAndMask(0x0);
    std::cout << "image::MaskedImage (weighted)" << std::endl;
    printStats(mimg, sctrl);
    
    // Now try the specialization to get NPOINT and SUM (bitwise OR) for an image::Mask
    math::Statistics mskstat = makeStatistics(*mimg.getMask(), (math::NPOINT | math::SUM), sctrl);
    std::cout << "image::Mask" << std::endl;
    std::cout << mskstat.getValue(math::NPOINT) << " " << mskstat.getValue(math::SUM) << std::endl;
    
    return 0;

}