source: tags/release-1.1.5/src/Cubes/cubes.cc @ 1441

// -----------------------------------------------------------------------
// cubes.cc: Member functions for the DataArray, Cube and Image classes.
// -----------------------------------------------------------------------
// Copyright (C) 2006, Matthew Whiting, ATNF
//
// 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 2 of the License, or (at your
// option) any later version.
//
// Duchamp 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 GNU General Public License
// along with Duchamp; if not, write to the Free Software Foundation,
// Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA
//
// Correspondence concerning Duchamp may be directed to:
//    Internet email: Matthew.Whiting [at] atnf.csiro.au
//    Postal address: Dr. Matthew Whiting
//                    Australia Telescope National Facility, CSIRO
//                    PO Box 76
//                    Epping NSW 1710
//                    AUSTRALIA
// -----------------------------------------------------------------------
#include <unistd.h>
#include <iostream>
#include <iomanip>
#include <vector>
#include <algorithm>
#include <string>
#include <math.h>

#include <wcslib/wcs.h>

#include <duchamp/pgheader.hh>

#include <duchamp/duchamp.hh>
#include <duchamp/param.hh>
#include <duchamp/fitsHeader.hh>
#include <duchamp/Cubes/cubes.hh>
#include <duchamp/PixelMap/Voxel.hh>
#include <duchamp/PixelMap/Object3D.hh>
#include <duchamp/Detection/detection.hh>
#include <duchamp/Detection/columns.hh>
#include <duchamp/Utils/utils.hh>
#include <duchamp/Utils/mycpgplot.hh>
#include <duchamp/Utils/Statistics.hh>

using namespace mycpgplot;
using namespace Statistics;
using namespace PixelInfo;

#ifdef TEST_DEBUG
const bool TESTING=true;
#else
const bool TESTING=false;
#endif

namespace duchamp
{

  using namespace Column;

  /****************************************************************/
  ///////////////////////////////////////////////////
  //// Functions for DataArray class:
  ///////////////////////////////////////////////////

  DataArray::DataArray(){
    /**
     * Fundamental constructor for DataArray.
     * Number of dimensions and pixels are set to 0. Nothing else allocated.
     */
    this->numDim=0; 
    this->numPixels=0;
    this->objectList = new std::vector<Detection>;
    this->axisDimAllocated = false;
    this->arrayAllocated = false;
  }
  //--------------------------------------------------------------------

  DataArray::DataArray(short int nDim){
    /**
     * N-dimensional constructor for DataArray.
     * Number of dimensions defined, and dimension array allocated.
     * Number of pixels are set to 0. 
     * \param nDim Number of dimensions.
     */
    this->axisDimAllocated = false;
    this->arrayAllocated = false;
    if(nDim>0){
      this->axisDim = new long[nDim];
      this->axisDimAllocated = true;
    }
    this->numDim=nDim; 
    this->numPixels=0;
    this->objectList = new std::vector<Detection>;
  } 
  //--------------------------------------------------------------------

  DataArray::DataArray(short int nDim, long size){
    /**
     * N-dimensional constructor for DataArray.
     * Number of dimensions and number of pixels defined. 
     * Arrays allocated based on these values.
     * \param nDim Number of dimensions. 
     * \param size Number of pixels. 
     *
     * Note that we can assign values to the dimension array.
     */

    this->axisDimAllocated = false;
    this->arrayAllocated = false;
    if(size<0)
      duchampError("DataArray(nDim,size)",
                   "Negative size -- could not define DataArray");
    else if(nDim<0)
      duchampError("DataArray(nDim,size)",
                   "Negative number of dimensions: could not define DataArray");
    else {
      if(size>0){
        this->array = new float[size];
        this->arrayAllocated = true;
      }
      this->numPixels = size;
      if(nDim>0){
        this->axisDim = new long[nDim];
        this->axisDimAllocated = true;
      }
      this->numDim = nDim;
    }
    this->objectList = new std::vector<Detection>;
  }
  //--------------------------------------------------------------------

  DataArray::DataArray(short int nDim, long *dimensions)
  {
    /**
     * Most robust constructor for DataArray.
     * Number and sizes of dimensions are defined, and hence the number of 
     * pixels. Arrays allocated based on these values.
     * \param nDim Number of dimensions. 
     * \param dimensions Array giving sizes of dimensions.
     */
    this->axisDimAllocated = false;
    this->arrayAllocated = false;
    if(nDim<0)
      duchampError("DataArray(nDim,dimArray)",
                   "Negative number of dimensions: could not define DataArray");
    else {
      int size = dimensions[0];
      for(int i=1;i<nDim;i++) size *= dimensions[i];
      if(size<0)
        duchampError("DataArray(nDim,dimArray)",
                     "Negative size: could not define DataArray");
      else{
        this->numPixels = size;
        if(size>0){
          this->array = new float[size];
          this->arrayAllocated = true;
        }
        this->numDim=nDim;
        if(nDim>0){
          this->axisDim = new long[nDim];
          this->axisDimAllocated = true;
        }
        for(int i=0;i<nDim;i++) this->axisDim[i] = dimensions[i];
      }
    }
  }
  //--------------------------------------------------------------------

  DataArray::~DataArray()
  {
    /** 
     *  Destructor -- arrays deleted if they have been allocated, and the 
     *   object list is deleted.
     */
    if(this->numPixels>0 && this->arrayAllocated){
      delete [] this->array;
      this->arrayAllocated = false;
    }
    if(this->numDim>0 && this->axisDimAllocated){
      delete [] this->axisDim;
    this->axisDimAllocated = false;
    }
    delete this->objectList;
  }
  //--------------------------------------------------------------------
  //--------------------------------------------------------------------

  void DataArray::getDim(long &x, long &y, long &z){
    /**
     * The sizes of the first three dimensions (if they exist) are returned.
     * \param x The first dimension. Defaults to 0 if numDim \f$\le\f$ 0.
     * \param y The second dimension. Defaults to 1 if numDim \f$\le\f$ 1.
     * \param z The third dimension. Defaults to 1 if numDim \f$\le\f$ 2.
     */
    if(this->numDim>0) x=this->axisDim[0]; 
    else x=0;
    if(this->numDim>1) y=this->axisDim[1]; 
    else y=1;
    if(this->numDim>2) z=this->axisDim[2]; 
    else z=1;
  }
  //--------------------------------------------------------------------

  void DataArray::getDimArray(long *output){
    /**
     * The axisDim array is written to output. This needs to be defined 
     *  beforehand: no checking is done on the memory.
     * \param output The array that is written to.
     */
    for(int i=0;i<this->numDim;i++) output[i] = this->axisDim[i];
  }
  //--------------------------------------------------------------------

  void DataArray::getArray(float *output){
    /**
     * The pixel value array is written to output. This needs to be defined 
     *  beforehand: no checking is done on the memory.
     * \param output The array that is written to.
     */
    for(int i=0;i<this->numPixels;i++) output[i] = this->array[i];
  }
  //--------------------------------------------------------------------

  void DataArray::saveArray(float *input, long size){
    /**
     * Saves the array in input to the pixel array DataArray::array.
     * The size of the array given must be the same as the current number of
     * pixels, else an error message is returned and nothing is done.
     * \param input The array of values to be saved.
     * \param size The size of input.
     */
    if(size != this->numPixels)
      duchampError("DataArray::saveArray",
                   "Input array different size to existing array. Cannot save.");
    else {
      if(this->numPixels>0 && this->arrayAllocated) delete [] this->array;
      this->numPixels = size;
      this->array = new float[size];
      this->arrayAllocated = true;
      for(int i=0;i<size;i++) this->array[i] = input[i];
    }
  }
  //--------------------------------------------------------------------

  void DataArray::addObject(Detection object){ 
    /**
     * \param object The object to be added to the object list.
     */
    // objectList is a vector, so just use push_back()
    this->objectList->push_back(object);
  }
  //--------------------------------------------------------------------

  void DataArray::addObjectList(std::vector <Detection> newlist) {
    /**
     * \param newlist The list of objects to be added to the object list.
     */
    for(int i=0;i<newlist.size();i++) this->objectList->push_back(newlist[i]);
  }
  //--------------------------------------------------------------------

  bool DataArray::isDetection(float value){
    /** 
     * Is a given value a detection, based on the statistics in the 
     * DataArray's StatsContainer? 
     * \param value The pixel value to test.
     */
    if(par.isBlank(value)) return false;
    else return Stats.isDetection(value);
  }
  //--------------------------------------------------------------------

  bool DataArray::isDetection(long voxel){
    /** 
     * Is a given pixel a detection, based on the statistics in the 
     * DataArray's StatsContainer? 
     * If the pixel lies outside the valid range for the data array, return false.
     * \param voxel Location of the DataArray's pixel to be tested.
     */
    if((voxel<0)||(voxel>this->numPixels)) return false;
    else if(par.isBlank(this->array[voxel])) return false;
    else return Stats.isDetection(this->array[voxel]);
  }  
  //--------------------------------------------------------------------

  std::ostream& operator<< ( std::ostream& theStream, DataArray &array)
  {
    /**
     * A way to print out the pixel coordinates & flux values of the 
     * list of detected objects belonging to the DataArray.
     * These are formatted nicely according to the << operator for Detection,
     *  with a line indicating the number of detections at the start.
     * \param theStream The ostream object to which the output should be sent.
     * \param array The DataArray containing the list of objects.
     */
    for(int i=0;i<array.numDim;i++){
      if(i>0) theStream<<"x";
      theStream<<array.axisDim[i];
    }
    theStream<<std::endl;
    theStream<<array.objectList->size()<<" detections:\n--------------\n";
    for(int i=0;i<array.objectList->size();i++){
      theStream << "Detection #" << array.objectList->at(i).getID()<<std::endl;
      Detection *obj = new Detection;
      *obj = array.objectList->at(i);
      obj->addOffsets();
      theStream<<*obj;
      delete obj;
    }
    theStream<<"--------------\n";
    return theStream;
  }

  /****************************************************************/
  /////////////////////////////////////////////////////////////
  //// Functions for Cube class
  /////////////////////////////////////////////////////////////

  Cube::Cube(){
    /**
     * Basic Constructor for Cube class.
     * numDim set to 3, but numPixels to 0 and all bool flags to false.
     * No allocation done.
     */
    numPixels=0; numDim=3; 
    reconExists = false; reconAllocated = false; baselineAllocated = false;
  }
  //--------------------------------------------------------------------

  Cube::Cube(long size){
    /**
     * Alternative Cube constructor, where size is given but not individual
     *  dimensions. Arrays are allocated as appropriate (according to the 
     *  relevant flags in Param set), but the Cube::axisDim array is not.
     */
    this->reconAllocated = false;
    this->baselineAllocated = false;
    this->axisDimAllocated = false;
    this->arrayAllocated = false;
    this->numPixels = this->numDim = 0;
    if(size<0)
      duchampError("Cube(size)","Negative size -- could not define Cube");
    else{
      if(size>0){
        this->array = new float[size];
        this->arrayAllocated = true;
        if(this->par.getFlagATrous()||this->par.getFlagSmooth()){
          this->recon = new float[size];
          this->reconAllocated = true;
        }
        if(this->par.getFlagBaseline()){
          this->baseline = new float[size];
          this->baselineAllocated = true;
        }
      }
      this->numPixels = size;
      this->axisDim = new long[3];
      this->axisDimAllocated = true;
      this->numDim = 3;
      this->reconExists = false;
    }
  }
  //--------------------------------------------------------------------

  Cube::Cube(long *dimensions){
    /**
     * Alternative Cube constructor, where sizes of dimensions are given. 
     * Arrays are allocated as appropriate (according to the 
     *  relevant flags in Param set), as is the Cube::axisDim array.
     */
    int size   = dimensions[0] * dimensions[1] * dimensions[2];
    int imsize = dimensions[0] * dimensions[1];
    this->reconAllocated = false;
    this->baselineAllocated = false;
    this->axisDimAllocated = false;
    this->arrayAllocated = false;
    this->numPixels = this->numDim = 0;
    if((size<0) || (imsize<0) )
      duchampError("Cube(dimArray)","Negative size -- could not define Cube");
    else{
      this->numPixels = size;
      if(size>0){
        this->array      = new float[size];
        this->arrayAllocated = false;
        this->detectMap  = new short[imsize];
        if(this->par.getFlagATrous()||this->par.getFlagSmooth()){
          this->recon    = new float[size];
          this->reconAllocated = true;
        }
        if(this->par.getFlagBaseline()){
          this->baseline = new float[size];
          this->baselineAllocated = true;
        }
      }
      this->numDim  = 3;
      this->axisDim = new long[3];
      this->axisDimAllocated = true;
      for(int i=0;i<3     ;i++) this->axisDim[i]   = dimensions[i];
      for(int i=0;i<imsize;i++) this->detectMap[i] = 0;
      this->reconExists = false;
    }
  }
  //--------------------------------------------------------------------

  Cube::~Cube()
  {
    /**
     *  The destructor deletes the memory allocated for Cube::detectMap, and,
     *  if these have been allocated, Cube::recon and Cube::baseline.
     */
    delete [] this->detectMap;
    if(this->reconAllocated)    delete [] this->recon;
    if(this->baselineAllocated) delete [] this->baseline;
  }
  //--------------------------------------------------------------------

  void Cube::initialiseCube(long *dimensions)
  {
    /**
     *  This function will set the sizes of all arrays that will be used by Cube.
     *  It will also define the values of the axis dimensions: this will be done 
     *   using the WCS in the FitsHeader class, so the WCS needs to be good and 
     *   have three axes. If this is not the case, the axes are assumed to be 
     *   ordered in the sense of lng,lat,spc.
     *
     *  \param dimensions An array of values giving the dimensions (sizes) for 
     *  all axes.  
     */ 

    int lng,lat,spc,size,imsize;
  
    if(this->head.isWCS() && (this->head.getNumAxes()>=3)){
      // if there is a WCS and there is at least 3 axes
      lng = this->head.WCS().lng;
      lat = this->head.WCS().lat;
      spc = this->head.WCS().spec;
    }
    else{
      // just take dimensions[] at face value
      lng = 0;
      lat = 1;
      spc = 2;
    }

    size   = dimensions[lng];
    if(this->head.getNumAxes()>1) size *= dimensions[lat];
    //   if(this->head.isSpecOK()) size *= dimensions[spc];
    if(this->head.canUseThirdAxis()) size *= dimensions[spc];
    imsize = dimensions[lng];
    if(this->head.getNumAxes()>1) imsize *= dimensions[lat];

    this->reconAllocated = false;
    this->baselineAllocated = false;

    if(this->axisDimAllocated){
      delete [] this->axisDim;
      this->axisDimAllocated = false;
    }

    if(this->arrayAllocated){
      delete [] this->array;
      this->arrayAllocated = false;
    }

    if((size<0) || (imsize<0) )
      duchampError("Cube::initialiseCube(dimArray)",
                   "Negative size -- could not define Cube.\n");
    else{
      this->numPixels = size;
      if(size>0){
        this->array      = new float[size];
        this->arrayAllocated = true;
        this->detectMap  = new short[imsize];
        if(this->par.getFlagATrous() || this->par.getFlagSmooth()){
          this->recon    = new float[size];
          this->reconAllocated = true;
        }
        if(this->par.getFlagBaseline()){
          this->baseline = new float[size];
          this->baselineAllocated = true;
        }
      }
      this->numDim  = 3;
      this->axisDim = new long[this->numDim];
      this->axisDimAllocated = true;
      this->axisDim[0] = dimensions[lng];
      if(this->head.getNumAxes()>1) this->axisDim[1] = dimensions[lat];
      else this->axisDim[1] = 1;
      //     if(this->head.isSpecOK()) this->axisDim[2] = dimensions[spc];
      if(this->head.canUseThirdAxis()) this->axisDim[2] = dimensions[spc];
      else this->axisDim[2] = 1;
      for(int i=0;i<imsize;i++) this->detectMap[i] = 0;
      this->reconExists = false;
    }
  }
  //--------------------------------------------------------------------

  int Cube::getCube(){  
    /** 
     * A front-end to the Cube::getCube() function, that does 
     *  subsection checks.
     * Assumes the Param is set up properly.
     */
    std::string fname = par.getImageFile();
    if(par.getFlagSubsection()) fname+=par.getSubsection();
    return getCube(fname);
  }
  //--------------------------------------------------------------------

  void Cube::saveArray(float *input, long size){
    if(size != this->numPixels){
      std::stringstream errmsg;
      errmsg << "Input array different size to existing array ("
             << size << " cf. " << this->numPixels << "). Cannot save.\n";
      duchampError("Cube::saveArray",errmsg.str());
    }
    else {
      if(this->numPixels>0 && this->arrayAllocated) delete [] array;
      this->numPixels = size;
      this->array = new float[size];
      this->arrayAllocated = true;
      for(int i=0;i<size;i++) this->array[i] = input[i];
    }
  }
  //--------------------------------------------------------------------

  void Cube::saveRecon(float *input, long size){
    /**
     * Saves the array in input to the reconstructed array Cube::recon
     * The size of the array given must be the same as the current number of
     * pixels, else an error message is returned and nothing is done.
     * If the recon array has already been allocated, it is deleted first, and 
     * then the space is allocated.
     * Afterwards, the appropriate flags are set.
     * \param input The array of values to be saved.
     * \param size The size of input.
     */
    if(size != this->numPixels){
      std::stringstream errmsg;
      errmsg << "Input array different size to existing array ("
             << size << " cf. " << this->numPixels << "). Cannot save.\n";
      duchampError("Cube::saveRecon",errmsg.str());
    }
    else {
      if(this->numPixels>0){
        if(this->reconAllocated) delete [] this->recon;
        this->numPixels = size;
        this->recon = new float[size];
        this->reconAllocated = true;
        for(int i=0;i<size;i++) this->recon[i] = input[i];
        this->reconExists = true;
      }
    }
  }
  //--------------------------------------------------------------------

  void Cube::getRecon(float *output){
    /**
     * The reconstructed array is written to output. The output array needs to 
     *  be defined beforehand: no checking is done on the memory.
     * \param output The array that is written to.
     */
    // Need check for change in number of pixels!
    for(int i=0;i<this->numPixels;i++){
      if(this->reconExists) output[i] = this->recon[i];
      else output[i] = 0.;
    }
  }
  //--------------------------------------------------------------------

  void Cube::removeMW()
  {
    /**
     * The channels corresponding to the Milky Way range (as given by the Param
     *  set) are all set to 0 in the pixel array.
     * Only done if the appropriate flag is set, and the pixels are not BLANK.
     * \deprecated
     */ 
    if(this->par.getFlagMW()){
      for(int pix=0;pix<this->axisDim[0]*this->axisDim[1];pix++){
        for(int z=0;z<this->axisDim[2];z++){
          int pos = z*this->axisDim[0]*this->axisDim[1] + pix;
          if(!this->isBlank(pos) && this->par.isInMW(z)) this->array[pos]=0.;
        }
      }
    }
  }
  //--------------------------------------------------------------------

  void Cube::setCubeStatsOld()
  {
    /**  
     *   \deprecated
     *
     *   Calculates the full statistics for the cube:  mean, rms, median, madfm.
     *   Only do this if the threshold has not been defined (ie. is still 0.,
     *    its default). 
     *   Also work out the threshold and store it in the Param set.
     *   
     *   For the stats calculations, we ignore BLANKs and MW channels.
     */

    if(!this->par.getFlagFDR() && this->par.getFlagUserThreshold() ){
      // if the user has defined a threshold, set this in the StatsContainer
      this->Stats.setThreshold( this->par.getThreshold() );
    }
    else{
      // only work out the mean etc if we need to.
      // the only reason we don't is if the user has specified a threshold.
    
      std::cout << "Calculating the cube statistics... " << std::flush;
    
      // get number of good pixels;
      int goodSize = 0;
      for(int p=0;p<this->axisDim[0]*this->axisDim[1];p++){
        for(int z=0;z<this->axisDim[2];z++){
          int vox = z * this->axisDim[0] * this->axisDim[1] + p;
          if(!this->isBlank(vox) && !this->par.isInMW(z)) goodSize++;
        }
      }

      float *tempArray = new float[goodSize];

      goodSize=0;
      for(int p=0;p<this->axisDim[0]*this->axisDim[1];p++){
        for(int z=0;z<this->axisDim[2];z++){
          int vox = z * this->axisDim[0] * this->axisDim[1] + p;
          if(!this->isBlank(vox) && !this->par.isInMW(z))
            tempArray[goodSize++] = this->array[vox];
        }
      }
      if(!this->reconExists){
        // if there's no recon array, calculate everything from orig array
        this->Stats.calculate(tempArray,goodSize);
      }
      else{
        // just get mean & median from orig array, and rms & madfm from recon
        StatsContainer<float> origStats,reconStats;
        origStats.calculate(tempArray,goodSize);
        goodSize=0;
        for(int p=0;p<this->axisDim[0]*this->axisDim[1];p++){
          for(int z=0;z<this->axisDim[2];z++){
            int vox = z * this->axisDim[0] * this->axisDim[1] + p;
            if(!this->isBlank(vox) && !this->par.isInMW(z))
              tempArray[goodSize++] = this->array[vox] - this->recon[vox];
          }
        }
        reconStats.calculate(tempArray,goodSize);

        // Get the "middle" estimators from the original array.
        this->Stats.setMean(origStats.getMean());
        this->Stats.setMedian(origStats.getMedian());
        // Get the "spread" estimators from the residual (orig-recon) array
        this->Stats.setStddev(reconStats.getStddev());
        this->Stats.setMadfm(reconStats.getMadfm());
      }

      delete [] tempArray;

      this->Stats.setUseFDR( this->par.getFlagFDR() );
      // If the FDR method has been requested
      if(this->par.getFlagFDR())  this->setupFDR();
      else{
        // otherwise, calculate one based on the requested SNR cut level, and 
        //   then set the threshold parameter in the Par set.
        this->Stats.setThresholdSNR( this->par.getCut() );
        this->par.setThreshold( this->Stats.getThreshold() );
      }
    
    
    }
    std::cout << "Using ";
    if(this->par.getFlagFDR()) std::cout << "effective ";
    std::cout << "flux threshold of: ";
    float thresh = this->Stats.getThreshold();
    if(this->par.getFlagNegative()) thresh *= -1.;
    std::cout << thresh << std::endl;

  }
  //--------------------------------------------------------------------

  void Cube::setCubeStats()
  {
    /**  
     *   Calculates the full statistics for the cube:
     *     mean, rms, median, madfm
     *   Only do this if the threshold has not been defined (ie. is still 0.,
     *    its default). 
     *   Also work out the threshold and store it in the par set.
     *   
     *   Different from Cube::setCubeStatsOld() as it doesn't use the 
     *    getStats functions but has own versions of them hardcoded to 
     *    ignore BLANKs and MW channels. This saves on memory usage -- necessary
     *    for dealing with very big files.
     *
     *   Three cases exist:
     *  <ul><li>Simple case, with no reconstruction/smoothing: all stats 
     *          calculated from the original array.
     *      <li>Wavelet reconstruction: mean & median calculated from the 
     *          original array, and stddev & madfm from the residual.
     *      <li>Smoothing: all four stats calculated from the recon array 
     *          (which holds the smoothed data).
     *  </ul>
     */

    this->Stats.setRobust(this->par.getFlagRobustStats());

    if(!this->par.getFlagFDR() && this->par.getFlagUserThreshold() ){
      // if the user has defined a threshold, set this in the StatsContainer
      this->Stats.setThreshold( this->par.getThreshold() );
    }
    else{
      // only work out the stats if we need to.
      // the only reason we don't is if the user has specified a threshold.
    
      if(this->par.isVerbose())
        std::cout << "Calculating the cube statistics... " << std::flush;
    
      long xysize = this->axisDim[0]*this->axisDim[1];

      bool *mask = new bool[this->numPixels];
      int vox,goodSize = 0;
      for(int x=0;x<this->axisDim[0];x++){
        for(int y=0;y<this->axisDim[1];y++){
          for(int z=0;z<this->axisDim[2];z++){
            vox = z * xysize + y*this->axisDim[0] + x;
            mask[vox] = (!this->isBlank(vox) && 
                         !this->par.isInMW(z) && 
                         this->par.isStatOK(x,y,z) );
            if(mask[vox]) goodSize++;
          }
        }
      }

      float mean,median,stddev,madfm;
      if( this->par.getFlagATrous() ){
        // Case #2 -- wavelet reconstruction
        // just get mean & median from orig array, and rms & madfm from
        // residual recompute array values to be residuals & then find
        // stddev & madfm
        if(!this->reconExists)
          duchampError("setCubeStats",
                       "Reconstruction not yet done!\nCannot calculate stats!\n");
        else{
          float *tempArray = new float[goodSize];

          goodSize=0;
          for(int x=0;x<this->axisDim[0];x++){
            for(int y=0;y<this->axisDim[1];y++){
              for(int z=0;z<this->axisDim[2];z++){
                vox = z * xysize + y*this->axisDim[0] + x;
                if(mask[vox]) tempArray[goodSize++] = this->array[vox];
              }
            }
          }

          // First, find the mean of the original array. Store it.
          mean = tempArray[0];
          for(int i=1;i<goodSize;i++) mean += tempArray[i];
          mean /= float(goodSize);
          mean = findMean(tempArray,goodSize);
          this->Stats.setMean(mean);
        
          // Now sort it and find the median. Store it.
          std::sort(tempArray,tempArray+goodSize);
          if((goodSize%2)==0) 
            median = (tempArray[goodSize/2-1] + tempArray[goodSize/2])/2;
          else median = tempArray[goodSize/2];
          this->Stats.setMedian(median);

          // Now calculate the residuals and find the mean & median of
          // them. We don't store these, but they are necessary to find
          // the sttdev & madfm.
          goodSize = 0;
          for(int p=0;p<xysize;p++){
            for(int z=0;z<this->axisDim[2];z++){
              vox = z * xysize + p;
              if(mask[vox])
                tempArray[goodSize++] = this->array[vox] - this->recon[vox];
            }
          }
          mean = tempArray[0];
          for(int i=1;i<goodSize;i++) mean += tempArray[i];
          mean /= float(goodSize);
          std::sort(tempArray,tempArray+goodSize);
          if((goodSize%2)==0) 
            median = (tempArray[goodSize/2-1] + tempArray[goodSize/2])/2;
          else median = tempArray[goodSize/2];

          // Now find the standard deviation of the residuals. Store it.
          stddev = (tempArray[0]-mean) * (tempArray[0]-mean);
          for(int i=1;i<goodSize;i++) 
            stddev += (tempArray[i]-mean)*(tempArray[i]-mean);
          stddev = sqrt(stddev/float(goodSize-1));
          this->Stats.setStddev(stddev);

          // Now find the madfm of the residuals. Store it.
          for(int i=0;i<goodSize;i++){
            if(tempArray[i]>median) tempArray[i] = tempArray[i]-median;
            else tempArray[i] = median - tempArray[i];
          }
          std::sort(tempArray,tempArray+goodSize);
          if((goodSize%2)==0) 
            madfm = (tempArray[goodSize/2-1] + tempArray[goodSize/2])/2;
          else madfm = tempArray[goodSize/2];
          this->Stats.setMadfm(madfm);

          delete [] tempArray;
        }
      }
      else if(this->par.getFlagSmooth()) {
        // Case #3 -- smoothing
        // get all four stats from the recon array, which holds the
        // smoothed data. This can just be done with the
        // StatsContainer::calculate function, using the mask generated
        // earlier.
        if(!this->reconExists)
          duchampError("setCubeStats","Smoothing not yet done!\nCannot calculate stats!\n");
        else this->Stats.calculate(this->recon,this->numPixels,mask);
      }
      else{
        // Case #1 -- default case, with no smoothing or reconstruction.
        // get all four stats from the original array. This can just be
        // done with the StatsContainer::calculate function, using the
        // mask generated earlier.
        this->Stats.calculate(this->array,this->numPixels,mask);
      }

      this->Stats.setUseFDR( this->par.getFlagFDR() );
      // If the FDR method has been requested, define the P-value
      // threshold
      if(this->par.getFlagFDR())  this->setupFDR();
      else{
        // otherwise, calculate threshold based on the requested SNR cut
        // level, and then set the threshold parameter in the Par set.
        this->Stats.setThresholdSNR( this->par.getCut() );
        this->par.setThreshold( this->Stats.getThreshold() );
      }
    
      delete [] mask;

    }

    if(this->par.isVerbose()){
      std::cout << "Using ";
      if(this->par.getFlagFDR()) std::cout << "effective ";
      std::cout << "flux threshold of: ";
      float thresh = this->Stats.getThreshold();
      if(this->par.getFlagNegative()) thresh *= -1.;
      std::cout << thresh << std::endl;
    }

  }
  //--------------------------------------------------------------------

  void Cube::setupFDR()
  {
    /**
     *  Call the setupFDR(float *) function on the pixel array of the
     *  cube. This is the usual way of running it.
     *
     *  However, if we are in smoothing mode, we calculate the FDR
     *  parameters using the recon array, which holds the smoothed
     *  data. Gives an error message if the reconExists flag is not set.
     *
     */
    if(this->par.getFlagSmooth()) 
      if(this->reconExists) this->setupFDR(this->recon);
      else{
        duchampError("setupFDR",
                     "Smoothing not done properly! Using original array for defining threshold.\n");
        this->setupFDR(this->array);
      }
    else if( this->par.getFlagATrous() ){
      this->setupFDR(this->recon);
    }
    else{
      this->setupFDR(this->array);
    }
  }
  //--------------------------------------------------------------------

  void Cube::setupFDR(float *input)
  {
    /**  
     *   Determines the critical Probability value for the False
     *   Discovery Rate detection routine. All pixels in the given arry
     *   with Prob less than this value will be considered detections.
     *
     *   Note that the Stats of the cube need to be calculated first.
     *
     *   The Prob here is the probability, assuming a Normal
     *   distribution, of obtaining a value as high or higher than the
     *   pixel value (ie. only the positive tail of the PDF).
     *
     *   The probabilities are calculated using the
     *   StatsContainer::getPValue(), which calculates the z-statistic,
     *   and then the probability via
     *   \f$0.5\operatorname{erfc}(z/\sqrt{2})\f$ -- giving the positive
     *   tail probability.
     */

    // first calculate p-value for each pixel -- assume Gaussian for now.

    float *orderedP = new float[this->numPixels];
    int count = 0;
    for(int x=0;x<this->axisDim[0];x++){
      for(int y=0;y<this->axisDim[1];y++){
        for(int z=0;z<this->axisDim[2];z++){
          int pix = z * this->axisDim[0]*this->axisDim[1] + 
            y*this->axisDim[0] + x;

          if(!(this->par.isBlank(this->array[pix])) && !this->par.isInMW(z)){ 
            // only look at non-blank, valid pixels 
            //            orderedP[count++] = this->Stats.getPValue(this->array[pix]);
            orderedP[count++] = this->Stats.getPValue(input[pix]);
          }
        }
      }
    }

    // now order them 
    std::stable_sort(orderedP,orderedP+count);
  
    // now find the maximum P value.
    int max = 0;
    float cN = 0.;
    int numVox = int(ceil(this->par.getBeamSize()));
    //  if(this->head.isSpecOK()) numVox *= 2;
    if(this->head.canUseThirdAxis()) numVox *= 2;
    // why beamSize*2? we are doing this in 3D, so spectrally assume just the
    //  neighbouring channels are correlated, but spatially all those within
    //  the beam, so total number of voxels is 2*beamSize
    for(int psfCtr=1;psfCtr<=numVox;psfCtr++) cN += 1./float(psfCtr);

    double slope = this->par.getAlpha()/cN;
    for(int loopCtr=0;loopCtr<count;loopCtr++) {
      if( orderedP[loopCtr] < (slope * double(loopCtr+1)/ double(count)) ){
        max = loopCtr;
      }
    }

    this->Stats.setPThreshold( orderedP[max] );


    // Find real value of the P threshold by finding the inverse of the 
    //  error function -- root finding with brute force technique 
    //  (relatively slow, but we only do it once).
    double zStat     = 0.;
    double deltaZ    = 0.1;
    double tolerance = 1.e-6;
    double initial   = 0.5 * erfc(zStat/M_SQRT2) - this->Stats.getPThreshold();
    do{
      zStat+=deltaZ;
      double current = 0.5 * erfc(zStat/M_SQRT2) - this->Stats.getPThreshold();
      if((initial*current)<0.){
        zStat-=deltaZ;
        deltaZ/=2.;
      }
    }while(deltaZ>tolerance);
    this->Stats.setThreshold( zStat*this->Stats.getSpread() + 
                              this->Stats.getMiddle() );

    ///////////////////////////
    //   if(TESTING){
    //     std::stringstream ss;
    //     float *xplot = new float[2*max];
    //     for(int i=0;i<2*max;i++) xplot[i]=float(i)/float(count);
    //     cpgopen("latestFDR.ps/vcps");
    //     cpgpap(8.,1.);
    //     cpgslw(3);
    //     cpgenv(0,float(2*max)/float(count),0,orderedP[2*max],0,0);
    //     cpglab("i/N (index)", "p-value","");
    //     cpgpt(2*max,xplot,orderedP,DOT);

    //     ss.str("");
    //     ss << "\\gm = " << this->Stats.getMiddle();
    //     cpgtext(max/(4.*count),0.9*orderedP[2*max],ss.str().c_str());
    //     ss.str("");
    //     ss << "\\gs = " << this->Stats.getSpread();
    //     cpgtext(max/(4.*count),0.85*orderedP[2*max],ss.str().c_str());
    //     ss.str("");
    //     ss << "Slope = " << slope;
    //     cpgtext(max/(4.*count),0.8*orderedP[2*max],ss.str().c_str());
    //     ss.str("");
    //     ss << "Alpha = " << this->par.getAlpha();
    //     cpgtext(max/(4.*count),0.75*orderedP[2*max],ss.str().c_str());
    //     ss.str("");
    //     ss << "c\\dN\\u = " << cN;
    //     cpgtext(max/(4.*count),0.7*orderedP[2*max],ss.str().c_str());
    //     ss.str("");
    //     ss << "max = "<<max << " (out of " << count << ")";
    //     cpgtext(max/(4.*count),0.65*orderedP[2*max],ss.str().c_str());
    //     ss.str("");
    //     ss << "Threshold = "<<zStat*this->Stats.getSpread()+this->Stats.getMiddle();
    //     cpgtext(max/(4.*count),0.6*orderedP[2*max],ss.str().c_str());
  
    //     cpgslw(1);
    //     cpgsci(RED);
    //     cpgmove(0,0);
    //     cpgdraw(1,slope);
    //     cpgsci(BLUE);
    //     cpgsls(DOTTED);
    //     cpgmove(0,orderedP[max]);
    //     cpgdraw(2*max/float(count),orderedP[max]);
    //     cpgmove(max/float(count),0);
    //     cpgdraw(max/float(count),orderedP[2*max]);
    //     cpgsci(GREEN);
    //     cpgsls(SOLID);
    //     for(int i=1;i<=10;i++) {
    //       ss.str("");
    //       ss << float(i)/2. << "\\gs";
    //       float prob = 0.5*erfc((float(i)/2.)/M_SQRT2);
    //       cpgtick(0, 0, 0, orderedP[2*max],
    //        prob/orderedP[2*max],
    //        0, 1, 1.5, 90., ss.str().c_str());
    //     }
    //     cpgend();
    //     delete [] xplot;
    //   }
    delete [] orderedP;

  }
  //--------------------------------------------------------------------

  bool Cube::isDetection(long x, long y, long z)
  {
    /** 
     * Is a given voxel at position (x,y,z) a detection, based on the statistics
     *  in the Cube's StatsContainer? 
     * If the pixel lies outside the valid range for the data array,
     * return false.
     * \param x X-value of the Cube's voxel to be tested.
     * \param y Y-value of the Cube's voxel to be tested.
     * \param z Z-value of the Cube's voxel to be tested.
     */
    long voxel = z*axisDim[0]*axisDim[1] + y*axisDim[0] + x;
    return DataArray::isDetection(array[voxel]);
  }
  //--------------------------------------------------------------------

  void Cube::calcObjectFluxes()
  {
    /**
     *  A function to calculate the fluxes and centroids for each
     *  object in the Cube's list of detections. Uses
     *  Detection::calcFluxes() for each object.
     */
    std::vector<Detection>::iterator obj;
    for(obj=this->objectList->begin();obj<this->objectList->end();obj++){
      obj->calcFluxes(this->array, this->axisDim);
      if(this->par.getFlagUserThreshold())
        obj->setPeakSNR( obj->getPeakFlux() / this->Stats.getThreshold() );
      else
        obj->setPeakSNR( (obj->getPeakFlux() - this->Stats.getMiddle()) / this->Stats.getSpread() );
    }
  }
  //--------------------------------------------------------------------

  void Cube::calcObjectWCSparams()
  {
    /** 
     *  A function that calculates the WCS parameters for each object in the 
     *  Cube's list of detections.
     *  Each object gets an ID number assigned to it (which is simply its order 
     *   in the list), and if the WCS is good, the WCS paramters are calculated.
     */
  
    for(int i=0;i<this->objectList->size();i++){
      this->objectList->at(i).setID(i+1);
      this->objectList->at(i).setCentreType(this->par.getPixelCentre());
      this->objectList->at(i).calcFluxes(this->array,this->axisDim);
      //      this->objectList->at(i).calcWCSparams(this->array,this->axisDim,this->head);
      this->objectList->at(i).calcWCSparams(this->head);
      this->objectList->at(i).calcIntegFlux(this->array,this->axisDim,this->head);
    
      if(this->par.getFlagUserThreshold())
        this->objectList->at(i).setPeakSNR( this->objectList->at(i).getPeakFlux() / this->Stats.getThreshold() );
      else
        this->objectList->at(i).setPeakSNR( (this->objectList->at(i).getPeakFlux() - this->Stats.getMiddle()) / this->Stats.getSpread() );

    }  

    if(!this->head.isWCS()){ 
      // if the WCS is bad, set the object names to Obj01 etc
      int numspaces = int(log10(this->objectList->size())) + 1;
      std::stringstream ss;
      for(int i=0;i<this->objectList->size();i++){
        ss.str("");
        ss << "Obj" << std::setfill('0') << std::setw(numspaces) << i+1;
        this->objectList->at(i).setName(ss.str());
      }
    }
  
  }
  //--------------------------------------------------------------------

  void Cube::calcObjectWCSparams(std::vector< std::vector<PixelInfo::Voxel> > bigVoxList)
  {
    /** 
     *  A function that calculates the WCS parameters for each object in the 
     *  Cube's list of detections.
     *  Each object gets an ID number assigned to it (which is simply its order 
     *   in the list), and if the WCS is good, the WCS paramters are calculated.
     *
     *  This version uses vectors of Voxels to define the fluxes.
     *
     * \param bigVoxList A vector of vectors of Voxels, with the same
     * number of elements as this->objectList, where each element is a
     * vector of Voxels corresponding to the same voxels in each
     * detection and indicating the flux of each voxel.
     */
  
    for(int i=0;i<this->objectList->size();i++){
      this->objectList->at(i).setID(i+1);
      this->objectList->at(i).setCentreType(this->par.getPixelCentre());
      this->objectList->at(i).calcFluxes(bigVoxList[i]);
      this->objectList->at(i).calcWCSparams(this->head);
      this->objectList->at(i).calcIntegFlux(bigVoxList[i],this->head);
    
      if(this->par.getFlagUserThreshold())
        this->objectList->at(i).setPeakSNR( this->objectList->at(i).getPeakFlux() / this->Stats.getThreshold() );
      else
        this->objectList->at(i).setPeakSNR( (this->objectList->at(i).getPeakFlux() - this->Stats.getMiddle()) / this->Stats.getSpread() );
    }  

    if(!this->head.isWCS()){ 
      // if the WCS is bad, set the object names to Obj01 etc
      int numspaces = int(log10(this->objectList->size())) + 1;
      std::stringstream ss;
      for(int i=0;i<this->objectList->size();i++){
        ss.str("");
        ss << "Obj" << std::setfill('0') << std::setw(numspaces) << i+1;
        this->objectList->at(i).setName(ss.str());
      }
    }
  
  }
  //--------------------------------------------------------------------

  void Cube::updateDetectMap()
  {
    /**
     *  A function that, for each detected object in the cube's list, increments 
     *   the cube's detection map by the required amount at each pixel.
     */

    Scan temp;
    for(int obj=0;obj<this->objectList->size();obj++){
      long numZ=this->objectList->at(obj).pixels().getNumChanMap();
      for(int iz=0;iz<numZ;iz++){ // for each channel map
        Object2D *chanmap = new Object2D;
        *chanmap = this->objectList->at(obj).pixels().getChanMap(iz).getObject();
        for(int iscan=0;iscan<chanmap->getNumScan();iscan++){
          temp = chanmap->getScan(iscan);
          for(int x=temp.getX(); x <= temp.getXmax(); x++)
            this->detectMap[temp.getY()*this->axisDim[0] + x]++;
        } // end of loop over scans
        delete chanmap;
      } // end of loop over channel maps
    } // end of loop over objects.

  }
  //--------------------------------------------------------------------

  void Cube::updateDetectMap(Detection obj)
  {
    /** 
     *  A function that, for the given object, increments the cube's
     *  detection map by the required amount at each pixel.
     * 
     *  \param obj A Detection object that is being incorporated into the map.
     */

    Scan temp;
    long numZ=obj.pixels().getNumChanMap();
    for(int iz=0;iz<numZ;iz++){ // for each channel map
      Object2D chanmap = obj.pixels().getChanMap(iz).getObject();
      for(int iscan=0;iscan<chanmap.getNumScan();iscan++){
        temp = chanmap.getScan(iscan);
        for(int x=temp.getX(); x <= temp.getXmax(); x++)
          this->detectMap[temp.getY()*this->axisDim[0] + x]++;
      } // end of loop over scans
    } // end of loop over channel maps

  }
  //--------------------------------------------------------------------

  float Cube::enclosedFlux(Detection obj)
  {
    /** 
     *   A function to calculate the flux enclosed by the range
     *    of pixels detected in the object obj (not necessarily all
     *    pixels will have been detected).
     *
     *   \param obj The Detection under consideration.
     */
    obj.calcFluxes(this->array, this->axisDim);
    int xsize = obj.getXmax()-obj.getXmin()+1;
    int ysize = obj.getYmax()-obj.getYmin()+1;
    int zsize = obj.getZmax()-obj.getZmin()+1; 
    std::vector <float> fluxArray(xsize*ysize*zsize,0.);
    for(int x=0;x<xsize;x++){
      for(int y=0;y<ysize;y++){
        for(int z=0;z<zsize;z++){
          fluxArray[x+y*xsize+z*ysize*xsize] = 
            this->getPixValue(x+obj.getXmin(),
                              y+obj.getYmin(),
                              z+obj.getZmin());
          if(this->par.getFlagNegative()) 
            fluxArray[x+y*xsize+z*ysize*xsize] *= -1.;
        }
      }
    }
    float sum = 0.;
    for(int i=0;i<fluxArray.size();i++) 
      if(!this->par.isBlank(fluxArray[i])) sum+=fluxArray[i];
    return sum;
  }
  //--------------------------------------------------------------------

  void Cube::setupColumns()
  {
    /** 
     *   A front-end to the two setup routines in columns.cc.  
     *
     *   This first gets the starting precisions, which may be from
     *   the input parameters. It then sets up the columns (calculates
     *   their widths and precisions and so on based on the values
     *   within). The precisions are also stored in each Detection
     *   object.
     *
     *   Need to have called calcObjectWCSparams() somewhere
     *   beforehand.
     */ 

    std::vector<Detection>::iterator obj;
    for(obj=this->objectList->begin();obj<this->objectList->end();obj++){
      obj->setVelPrec( this->par.getPrecVel() );
      obj->setFpeakPrec( this->par.getPrecFlux() );
      obj->setXYZPrec( Column::prXYZ );
      obj->setPosPrec( Column::prWPOS );
      obj->setFintPrec( this->par.getPrecFlux() );
      obj->setSNRPrec( this->par.getPrecSNR() );
    }
  
    this->fullCols.clear();
    this->fullCols = getFullColSet(*(this->objectList), this->head);

    this->logCols.clear();
    this->logCols = getLogColSet(*(this->objectList), this->head);

    int vel,fpeak,fint,pos,xyz,snr;
    vel = fullCols[VEL].getPrecision();
    fpeak = fullCols[FPEAK].getPrecision();
    snr = fullCols[SNRPEAK].getPrecision();
    xyz = fullCols[X].getPrecision();
    xyz = std::max(xyz, fullCols[Y].getPrecision());
    xyz = std::max(xyz, fullCols[Z].getPrecision());
    if(this->head.isWCS()) fint = fullCols[FINT].getPrecision();
    else fint = fullCols[FTOT].getPrecision();
    pos = fullCols[WRA].getPrecision();
    pos = std::max(pos, fullCols[WDEC].getPrecision());
  
    for(obj=this->objectList->begin();obj<this->objectList->end();obj++){
      obj->setVelPrec(vel);
      obj->setFpeakPrec(fpeak);
      obj->setXYZPrec(xyz);
      obj->setPosPrec(pos);
      obj->setFintPrec(fint);
      obj->setSNRPrec(snr);
    }

  }
  //--------------------------------------------------------------------

  bool Cube::objAtSpatialEdge(Detection obj)
  {
    /** 
     *   A function to test whether the object obj
     *    lies at the edge of the cube's spatial field --
     *    either at the boundary, or next to BLANKs.
     *
     *   \param obj The Detection under consideration.
     */

    bool atEdge = false;

    int pix = 0;
    std::vector<Voxel> voxlist = obj.pixels().getPixelSet();
    while(!atEdge && pix<voxlist.size()){
      // loop over each pixel in the object, until we find an edge pixel.
      for(int dx=-1;dx<=1;dx+=2){
        if( ((voxlist[pix].getX()+dx)<0) || 
            ((voxlist[pix].getX()+dx)>=this->axisDim[0]) ) 
          atEdge = true;
        else if(this->isBlank(voxlist[pix].getX()+dx,
                              voxlist[pix].getY(),
                              voxlist[pix].getZ())) 
          atEdge = true;
      }
      for(int dy=-1;dy<=1;dy+=2){
        if( ((voxlist[pix].getY()+dy)<0) || 
            ((voxlist[pix].getY()+dy)>=this->axisDim[1]) ) 
          atEdge = true;
        else if(this->isBlank(voxlist[pix].getX(),
                              voxlist[pix].getY()+dy,
                              voxlist[pix].getZ())) 
          atEdge = true;
      }
      pix++;
    }

    return atEdge;
  }
  //--------------------------------------------------------------------

  bool Cube::objAtSpectralEdge(Detection obj)
  {
    /**  
     *   A function to test whether the object obj
     *    lies at the edge of the cube's spectral extent --
     *    either at the boundary, or next to BLANKs.
     *
     *   /param obj The Detection under consideration.
     */

    bool atEdge = false;

    int pix = 0;
    std::vector<Voxel> voxlist = obj.pixels().getPixelSet();
    while(!atEdge && pix<voxlist.size()){
      // loop over each pixel in the object, until we find an edge pixel.
      for(int dz=-1;dz<=1;dz+=2){
        if( ((voxlist[pix].getZ()+dz)<0) || 
            ((voxlist[pix].getZ()+dz)>=this->axisDim[2])) 
          atEdge = true;
        else if(this->isBlank(voxlist[pix].getX(),
                              voxlist[pix].getY(),
                              voxlist[pix].getZ()+dz)) 
          atEdge = true;
      }
      pix++;
    }

    return atEdge;
  }
  //--------------------------------------------------------------------

  void Cube::setObjectFlags()
  {
    /**    
     *   A function to set any warning flags for all the detected objects
     *    associated with the cube.
     *   Flags to be looked for:
     *    <ul><li> Negative enclosed flux (N)
     *        <li> Detection at edge of field (spatially) (E)
     *        <li> Detection at edge of spectral region (S)
     *    </ul>
     */

    for(int i=0;i<this->objectList->size();i++){

      if( this->enclosedFlux(this->objectList->at(i)) < 0. )  
        this->objectList->at(i).addToFlagText("N");

      if( this->objAtSpatialEdge(this->objectList->at(i)) ) 
        this->objectList->at(i).addToFlagText("E");

      if( this->objAtSpectralEdge(this->objectList->at(i)) &&
          (this->axisDim[2] > 2)) 
        this->objectList->at(i).addToFlagText("S");

    }

  }
  //--------------------------------------------------------------------



  /****************************************************************/
  /////////////////////////////////////////////////////////////
  //// Functions for Image class
  /////////////////////////////////////////////////////////////

  Image::Image(long size){
    // need error handling in case size<0 !!!
    this->numPixels = this->numDim = 0;
    if(size<0)
      duchampError("Image(size)","Negative size -- could not define Image");
    else{
      if(size>0 && !this->arrayAllocated){
        this->array = new float[size];
        this->arrayAllocated = true;
      }
      this->numPixels = size;
      this->axisDim = new long[2];
      this->axisDimAllocated = true;
      this->numDim = 2;
    }
  }
  //--------------------------------------------------------------------

  Image::Image(long *dimensions){
    this->numPixels = this->numDim = 0;
    int size = dimensions[0] * dimensions[1];
    if(size<0)
      duchampError("Image(dimArray)","Negative size -- could not define Image");
    else{
      this->numPixels = size;
      if(size>0){
        this->array = new float[size];
        this->arrayAllocated = true;
      }
      this->numDim=2;
      this->axisDim = new long[2];
      for(int i=0;i<2;i++) this->axisDim[i] = dimensions[i];
    }
  }
  //--------------------------------------------------------------------
  //--------------------------------------------------------------------

  void Image::saveArray(float *input, long size)
  {
    /**
     * Saves the array in input to the pixel array Image::array.
     * The size of the array given must be the same as the current number of
     * pixels, else an error message is returned and nothing is done.
     * \param input The array of values to be saved.
     * \param size The size of input.
     */
    if(size != this->numPixels)
      duchampError("Image::saveArray",
                   "Input array different size to existing array. Cannot save.");
    else {
      if(this->numPixels>0 && this->arrayAllocated) delete [] array;
      this->numPixels = size;
      if(this->numPixels>0){
        this->array = new float[size];
        this->arrayAllocated = true;
        for(int i=0;i<size;i++) this->array[i] = input[i];
      }
    }
  }
  //--------------------------------------------------------------------

  void Image::extractSpectrum(float *Array, long *dim, long pixel)
  {
    /**
     *  A function to extract a 1-D spectrum from a 3-D array.
     *  The array is assumed to be 3-D with the third dimension the spectral one.
     *  The spectrum extracted is the one lying in the spatial pixel referenced
     *    by the third argument.
     *  The extracted spectrum is stored in the pixel array Image::array.
     * \param Array The array containing the pixel values, from which
     *               the spectrum is extracted.
     * \param dim The array of dimension values.
     * \param pixel The spatial pixel that contains the desired spectrum.
     */ 
    if((pixel<0)||(pixel>=dim[0]*dim[1]))
      duchampError("Image::extractSpectrum",
                   "Requested spatial pixel outside allowed range. Cannot save.");
    else if(dim[2] != this->numPixels)
      duchampError("Image::extractSpectrum",
                   "Input array different size to existing array. Cannot save.");
    else {
      if(this->numPixels>0 && this->arrayAllocated) delete [] array;
      this->numPixels = dim[2];
      if(this->numPixels>0){
        this->array = new float[dim[2]];
        this->arrayAllocated = true;
        for(int z=0;z<dim[2];z++) this->array[z] = Array[z*dim[0]*dim[1] + pixel];
      }
    }
  }
  //--------------------------------------------------------------------

  void Image::extractSpectrum(Cube &cube, long pixel)
  {
    /**
     *  A function to extract a 1-D spectrum from a Cube class
     *  The spectrum extracted is the one lying in the spatial pixel referenced
     *    by the second argument.
     *  The extracted spectrum is stored in the pixel array Image::array.
     * \param cube The Cube containing the pixel values, from which the spectrum is extracted.
     * \param pixel The spatial pixel that contains the desired spectrum.
     */ 
    long zdim = cube.getDimZ();
    long spatSize = cube.getDimX()*cube.getDimY();
    if((pixel<0)||(pixel>=spatSize))
      duchampError("Image::extractSpectrum",
                   "Requested spatial pixel outside allowed range. Cannot save.");
    else if(zdim != this->numPixels)
      duchampError("Image::extractSpectrum",
                   "Input array different size to existing array. Cannot save.");
    else {
      if(this->numPixels>0 && this->arrayAllocated) delete [] array;
      this->numPixels = zdim;
      if(this->numPixels>0){
        this->array = new float[zdim];
        this->arrayAllocated = true;
        for(int z=0;z<zdim;z++) 
          this->array[z] = cube.getPixValue(z*spatSize + pixel);
      }
    }
  }
  //--------------------------------------------------------------------

  void Image::extractImage(float *Array, long *dim, long channel)
  {
    /**
     *  A function to extract a 2-D image from a 3-D array.
     *  The array is assumed to be 3-D with the third dimension the spectral one.
     *  The dimensions of the array are in the dim[] array.
     *  The image extracted is the one lying in the channel referenced
     *    by the third argument.
     *  The extracted image is stored in the pixel array Image::array.
     * \param Array The array containing the pixel values, from which the image is extracted.
     * \param dim The array of dimension values.
     * \param channel The spectral channel that contains the desired image.
     */ 

    long spatSize = dim[0]*dim[1];
    if((channel<0)||(channel>=dim[2]))
      duchampError("Image::extractImage",
                   "Requested channel outside allowed range. Cannot save.");
    else if(spatSize != this->numPixels)
      duchampError("Image::extractImage",
                   "Input array different size to existing array. Cannot save.");
    else {
      if(this->numPixels>0 && this->arrayAllocated) delete [] array;
      this->numPixels = spatSize;
      if(this->numPixels>0){
        this->array = new float[spatSize];
        this->arrayAllocated = true;
        for(int npix=0; npix<spatSize; npix++)
          this->array[npix] = Array[channel*spatSize + npix];
      }
    }
  }
  //--------------------------------------------------------------------

  void Image::extractImage(Cube &cube, long channel)
  {
    /**
     *  A function to extract a 2-D image from Cube class.
     *  The image extracted is the one lying in the channel referenced
     *    by the second argument.
     *  The extracted image is stored in the pixel array Image::array.
     * \param cube The Cube containing the pixel values, from which the image is extracted.
     * \param channel The spectral channel that contains the desired image.
     */ 
    long spatSize = cube.getDimX()*cube.getDimY();
    if((channel<0)||(channel>=cube.getDimZ()))
      duchampError("Image::extractImage",
                   "Requested channel outside allowed range. Cannot save.");
    else if(spatSize != this->numPixels)
      duchampError("Image::extractImage",
                   "Input array different size to existing array. Cannot save.");
    else {
      if(this->numPixels>0 && this->arrayAllocated) delete [] array;
      this->numPixels = spatSize;
      if(this->numPixels>0){
        this->array = new float[spatSize];
        this->arrayAllocated = true;
        for(int npix=0; npix<spatSize; npix++) 
          this->array[npix] = cube.getPixValue(channel*spatSize + npix);
      }
    }
  }
  //--------------------------------------------------------------------

  void Image::removeMW()
  {
    /**
     *  A function to remove the Milky Way range of channels from a 1-D spectrum.
     *  The array in this Image is assumed to be 1-D, with only the first axisDim
     *    equal to 1.
     *  The values of the MW channels are set to 0, unless they are BLANK.
     */ 
    if(this->par.getFlagMW() && (this->axisDim[1]==1) ){
      for(int z=0;z<this->axisDim[0];z++){
        if(!this->isBlank(z) && this->par.isInMW(z)) this->array[z]=0.;
      }
    }
  }

}