#include <iostream>
#include <iomanip>
#include <math.h>
#include <duchamp.hh>
#include <ATrous/atrous.hh>
#include <ATrous/filter.hh>
#include <Utils/utils.hh>
#include <Utils/feedback.hh>
#include <Utils/Statistics.hh>
using Statistics::madfmToSigma;

using std::endl;
using std::setw;

void atrous3DReconstruct(long &xdim, long &ydim, long &zdim, float *&input, 
			 float *&output, Param &par)
{
  /**
   *  A routine that uses the a trous wavelet method to reconstruct a 
   *   3-dimensional image cube.
   *  The Param object "par" contains all necessary info about the filter and 
   *   reconstruction parameters.
   *
   *  If there are no non-BLANK pixels (and we are testing for
   *  BLANKs), the reconstruction cannot be done, so we return the
   *  input array as the output array and give a warning message.
   *
   *  \param xdim The length of the x-axis.
   *  \param ydim The length of the y-axis.
   *  \param zdim The length of the z-axis.
   *  \param input The input spectrum.
   *  \param output The returned reconstructed spectrum. This array needs to be declared beforehand.
   *  \param par The Param set.
   */

  long size = xdim * ydim * zdim;
  long spatialSize = xdim * ydim;
  long mindim = xdim;
  if (ydim<mindim) mindim = ydim;
  if (zdim<mindim) mindim = zdim;
  int numScales = par.filter().getNumScales(mindim);

  double *sigmaFactors = new double[numScales+1];
  for(int i=0;i<=numScales;i++){
    if(i<=par.filter().maxFactor(3)) 
      sigmaFactors[i] = par.filter().sigmaFactor(3,i);
    else sigmaFactors[i] = sigmaFactors[i-1] / sqrt(8.);
  }

  float mean,sigma,originalSigma,originalMean,oldsigma,newsigma;
  bool *isGood = new bool[size];
  int goodSize=0;
  float blankPixValue = par.getBlankPixVal();
  for(int pos=0;pos<size;pos++){
    isGood[pos] = !par.isBlank(input[pos]);
    if(isGood[pos]) goodSize++;
  }

  if(goodSize == 0){
    // There are no good pixels -- everything is BLANK for some reason.
    // Return the input array as the output, and give a warning message.

    for(int pos=0;pos<xdim; pos++) output[pos] = input[pos];

    duchampWarning("atrous1DReconstruct",
		   "There are no good pixels to be reconstructed -- all are BLANK.\nPerhaps you need to try this with flagBlankPix=false.\nReturning input array.\n");
  }
  else{
    // Otherwise, all is good, and we continue.


    float *array = new float[size];
    goodSize=0;
    for(int i=0;i<size;i++) if(isGood[i]) array[goodSize++] = input[i];
    findMedianStats(array,goodSize,originalMean,originalSigma);
    originalSigma = madfmToSigma(originalSigma);
    delete [] array;

    float *coeffs = new float[size];
    float *wavelet = new float[size];

    for(int pos=0;pos<size;pos++) output[pos]=0.;

    // Define the 3-D (separable) filter, using info from par.filter()
    int filterwidth = par.filter().width();
    int filterHW = filterwidth/2;
    int fsize = filterwidth*filterwidth*filterwidth;
    double *filter = new double[fsize];
    for(int i=0;i<filterwidth;i++){
      for(int j=0;j<filterwidth;j++){
	for(int k=0;k<filterwidth;k++){
	  filter[i +j*filterwidth + k*filterwidth*filterwidth] = 
	    par.filter().coeff(i) * par.filter().coeff(j) * par.filter().coeff(k);
	}
      }
    }

    // Locating the borders of the image -- ignoring BLANK pixels
    //  Only do this if flagBlankPix is true. 
    //  Otherwise use the full range of x and y.
    //  No trimming is done in the z-direction at this point.
    int *xLim1 = new int[ydim];
    for(int i=0;i<ydim;i++) xLim1[i] = 0;
    int *xLim2 = new int[ydim];
    for(int i=0;i<ydim;i++) xLim2[i] = xdim-1;
    int *yLim1 = new int[xdim];
    for(int i=0;i<xdim;i++) yLim1[i] = 0;
    int *yLim2 = new int[xdim];
    for(int i=0;i<xdim;i++) yLim2[i] = ydim-1;

    if(par.getFlagBlankPix()){
      float avGapX = 0, avGapY = 0;
      for(int row=0;row<ydim;row++){
	int ct1 = 0;
	int ct2 = xdim - 1;
	while((ct1<ct2)&&(par.isBlank(input[row*xdim+ct1]))) ct1++;
	while((ct2>ct1)&&(par.isBlank(input[row*xdim+ct2]))) ct2--;
	xLim1[row] = ct1;
	xLim2[row] = ct2;
	avGapX += ct2 - ct1 + 1;
      }
      avGapX /= float(ydim);

      for(int col=0;col<xdim;col++){
	int ct1=0;
	int ct2=ydim-1;
	while((ct1<ct2)&&(par.isBlank(input[col+xdim*ct1]))) ct1++;
	while((ct2>ct1)&&(par.isBlank(input[col+xdim*ct2]))) ct2--;
	yLim1[col] = ct1;
	yLim2[col] = ct2;
	avGapY += ct2 - ct1 + 1;
      }
      avGapY /= float(xdim);
 
      mindim = int(avGapX);
      if(avGapY < avGapX) mindim = int(avGapY);
      numScales = par.filter().getNumScales(mindim);
    }

    float threshold;
    int iteration=0;
    newsigma = 1.e9;
    for(int i=0;i<size;i++) output[i] = 0;
    do{
      if(par.isVerbose()) std::cout << "Iteration #"<<setw(2)<<++iteration<<": ";
      // first, get the value of oldsigma, set it to the previous newsigma value
      oldsigma = newsigma;
      // we are transforming the residual array (input array first time around)
      for(int i=0;i<size;i++)  coeffs[i] = input[i] - output[i];

      int spacing = 1;
      for(int scale = 1; scale<=numScales; scale++){

	if(par.isVerbose()){
	  std::cout << "Scale ";
	  std::cout << setw(2)<<scale<<" / "<<setw(2)<<numScales;
	  printBackSpace(13);
	  std::cout << std::flush;
	}

	int pos = -1;
	for(int zpos = 0; zpos<zdim; zpos++){
	  for(int ypos = 0; ypos<ydim; ypos++){
	    for(int xpos = 0; xpos<xdim; xpos++){
	      // loops over each pixel in the image
	      pos++;

	      wavelet[pos] = coeffs[pos];
 	    
	      if(!isGood[pos] )  wavelet[pos] = 0.;
	      else{

		int filterpos = -1;
		for(int zoffset=-filterHW; zoffset<=filterHW; zoffset++){
		  int z = zpos + spacing*zoffset;
		  if(z<0) z = -z;                 // boundary conditions are 
		  if(z>=zdim) z = 2*(zdim-1) - z; //    reflection.
		
		  int oldchan = z * spatialSize;
		
		  for(int yoffset=-filterHW; yoffset<=filterHW; yoffset++){
		    int y = ypos + spacing*yoffset;

		    // Boundary conditions -- assume reflection at boundaries.
		    // Use limits as calculated above
		    if(yLim1[xpos]!=yLim2[xpos]){ 
		      // if these are equal we will get into an infinite loop
		      while((y<yLim1[xpos])||(y>yLim2[xpos])){
			if(y<yLim1[xpos]) y = 2*yLim1[xpos] - y;      
			else if(y>yLim2[xpos]) y = 2*yLim2[xpos] - y;      
		      }
		    }
		    int oldrow = y * xdim;
	  
		    for(int xoffset=-filterHW; xoffset<=filterHW; xoffset++){
		      int x = xpos + spacing*xoffset;

		      // Boundary conditions -- assume reflection at boundaries.
		      // Use limits as calculated above
		      if(xLim1[ypos]!=xLim2[ypos]){
			// if these are equal we will get into an infinite loop
			while((x<xLim1[ypos])||(x>xLim2[ypos])){
			  if(x<xLim1[ypos]) x = 2*xLim1[ypos] - x;      
			  else if(x>xLim2[ypos]) x = 2*xLim2[ypos] - x;      
			}
		      }

		      int oldpos = oldchan + oldrow + x;

		      filterpos++;
		   
		      if(isGood[oldpos]) 
			wavelet[pos] -= filter[filterpos]*coeffs[oldpos];
		      
		    } //-> end of xoffset loop
		  } //-> end of yoffset loop
		} //-> end of zoffset loop
	      } //-> end of else{ ( from if(!isGood[pos])  )
	    
	    } //-> end of xpos loop
	  } //-> end of ypos loop
	} //-> end of zpos loop

	// Need to do this after we've done *all* the convolving
	for(int pos=0;pos<size;pos++) coeffs[pos] = coeffs[pos] - wavelet[pos];

	// Have found wavelet coeffs for this scale -- now threshold
	if(scale>=par.getMinScale()){
	  array = new float[size];
	  goodSize=0;
	  for(int pos=0;pos<size;pos++) 
	    if(isGood[pos]) array[goodSize++] = wavelet[pos];
	  findMedianStats(array,goodSize,mean,sigma);
	  delete [] array;
	
	  threshold = mean + 
	    par.getAtrousCut()*originalSigma*sigmaFactors[scale];
	  for(int pos=0;pos<size;pos++){
	    if(!isGood[pos]){
	      output[pos] = blankPixValue; 
	      // this preserves the Blank pixel values in the output.
	    }
	    else if( fabs(wavelet[pos]) > threshold ){
	      output[pos] += wavelet[pos];
	      // only add to the output if the wavelet coefficient is significant
	    }
	  }
	}
 
	spacing *= 2;  // double the scale of the filter.

      } //-> end of scale loop 

      for(int pos=0;pos<size;pos++) if(isGood[pos]) output[pos] += coeffs[pos];

      array = new float[size];
      goodSize=0;
      for(int i=0;i<size;i++) {
	if(isGood[i]) array[goodSize++] = input[i] - output[i];
      }
      findMedianStats(array,goodSize,mean,newsigma);
      newsigma = madfmToSigma(newsigma);
      delete [] array;

      if(par.isVerbose()) printBackSpace(15);

    } while( (iteration==1) || 
	     (fabs(oldsigma-newsigma)/newsigma > reconTolerance) );

    if(par.isVerbose()) std::cout << "Completed "<<iteration<<" iterations. ";

    delete [] xLim1;
    delete [] xLim2;
    delete [] yLim1;
    delete [] yLim2;
    delete [] filter;
    delete [] coeffs;
    delete [] wavelet;

  }

  delete [] isGood;
  delete [] sigmaFactors;
}