/*  fslmeants.cc

    Mark Jenkinson and Matthew Webster, FMRIB Image Analysis Group

    Copyright (C) 2007 University of Oxford  */

/*  Part of FSL - FMRIB's Software Library
    http://www.fmrib.ox.ac.uk/fsl
    fsl@fmrib.ox.ac.uk
    
    Developed at FMRIB (Oxford Centre for Functional Magnetic Resonance
    Imaging of the Brain), Department of Clinical Neurology, Oxford
    University, Oxford, UK
    
    
    LICENCE
    
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    University").
    
    The Software is distributed "AS IS" under this Licence solely for
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    makes clear that no condition is made or to be implied, nor is any
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// Creates a mean time series (ignoring zeros) from the input 4D volume
// Saves the results as a column in a text file

#include "newimage/newimageall.h"
#include "miscmaths/miscmaths.h"
#include "utils/options.h"

using namespace NEWIMAGE;
using namespace MISCMATHS;


using namespace Utilities;

// The two strings below specify the title and example usage that is
//  printed out as the help or usage message

string title="fslmeants \nCopyright(c) 2004-2009, University of Oxford (Mark Jenkinson, Christian F. Beckmann)\nPrints average timeseries (intensities) to the screen (or saves to a file).\nThe average is taken over all voxels in the mask (or all voxels in the image if no mask is specified).\n";
string examples="fslmeants -i filtered_func_data -o meants.txt -m my_mask\nfslmeants -i filtered_func_data -m my_mask\nfslmeants -i filtered_func_data -c 24 19 10";

// Each (global) object below specificies as option and can be accessed
//  anywhere in this file (since they are global).  The order of the
//  arguments needed is: name(s) of option, default value, help message,
//       whether it is compulsory, whether it requires arguments
// Note that they must also be included in the main() function or they
//  will not be active.

Option<bool> verbose(string("-v,--verbose"), false, 
		     string("switch on diagnostic messages"), 
		     false, no_argument);
Option<bool> help(string("-h,--help"), false,
		  string("display this message"),
		  false, no_argument);
Option<bool> usemm(string("--usemm"), false,
		  string("\tuse mm instead of voxel coordinates (for -c option)"),
		  false, no_argument);
Option<bool> showall(string("--showall"), false,
		  string("show all voxel time series (within mask) instead of averaging"),
		  false, no_argument);
Option<string> inname(string("-i"), string(""),
		  string("~<filename>\tinput 4D image"),
		  true, requires_argument);
Option<string> maskname(string("-m"), string(""),
		  string("~<filename>\tinput 3D mask"),
		  false, requires_argument);
Option<string> labelname(string("--label"), string(""),
		  string("input 3D label image (generate separate mean for each integer label value - cannot be used with showall)"),
		  false, requires_argument);
Option<string> outmat(string("-o"), string(""),
		  string("~<filename>\toutput text matrix"),
		  false, requires_argument);
Option<float> coordval(string("-c"), 0.0,
		  string("~<x y z>\trequested spatial coordinate (instead of mask)"), 
		  false, requires_3_arguments);
Option<bool> eig(string("--eig"), false,
		  string("        calculate Eigenvariate(s) instead of mean (output will have 0 mean)"),
		  false, no_argument);
Option<bool> bin_mask(string("--no_bin"), true,
			  string("        do not binarise the mask for calculation of Eigenvariates"),
			  false, no_argument);
Option<int> order(string("--order"), 1,
		  string("        select number of Eigenvariates (default 1)"),
		  false, requires_argument);
Option<bool> transpose(string("--transpose"), false,
		  string("        output results in transpose format (one row per voxel/mean)"),
		  false, no_argument);
Option<bool> weightedMask(string("-w"), false,
		  string("output weighted mean, using mask values as weights, and exit."),
		  false, no_argument);


int nonoptarg;



int main(int argc,char *argv[])
{

  Tracer tr("main");
  OptionParser options(title, examples);

  options.add(inname);
  options.add(outmat);
  options.add(maskname);
  options.add(coordval);
  options.add(usemm);
  options.add(showall);
  options.add(eig);
  options.add(order);
  options.add(bin_mask);
  options.add(labelname);
  options.add(transpose);
  options.add(verbose);
  options.add(help);
  options.add(weightedMask);
  
  nonoptarg = options.parse_command_line(argc, argv);
  
  // line below stops the program if the help was requested or 
  //  a compulsory option was not set
  if ( (help.value()) || (!options.check_compulsory_arguments(true)) )
    {
      options.usage();
      exit(EXIT_FAILURE);
    }
  

  // OK, now do the job ...

  volume4D<float> vin;
  read_volume4D(vin,inname.value());

  volume<float> mask, label;
  volume4D<float> mask_nonbin;
  if (maskname.set()) {
    read_volume(mask,maskname.value());
  } else {
    mask = vin[0];
    mask = 1.0;
  }



  if ( weightedMask.value() ) {
    Matrix meanwts(vin.tsize(),1);
    for(int t=0;t<vin.tsize();t++) {    
      double mean(0);
      double meanWeight(0);
      for (int z=mask.minz(); z<=mask.maxz(); z++) 
	for (int y=mask.miny(); y<=mask.maxy(); y++) 
	  for (int x=mask.minx(); x<=mask.maxx(); x++) {
	    mean+=mask(x,y,z)*vin(x,y,z,t);	
	    meanWeight+=mask(x,y,z);
	  }
      meanwts(t+1,1)=(mean/meanWeight);
    }

    if ( transpose.value() ) 
      meanwts=meanwts.t();
    if ( outmat.set() ) 
      write_ascii_matrix(meanwts,outmat.value());
    else 
      cout << meanwts << endl;
    return(0);
  }


  int nlabs=1;
  if (labelname.set() && showall.unset()) {
    read_volume(label,labelname.value());
    if (verbose.value()) { print_volume_info(label,"label"); }
    float labmax=label.max();
    if (labmax>=0.99) {
      mask *= label;
    }
    nlabs=MISCMATHS::round(labmax);
    if (nlabs<1) nlabs=1;
  }

  // override the mask (and label) if a coordinate is specified
  if (coordval.set()) {
    mask = vin[0];
    mask = 0.0;
    float x, y, z;
    x = coordval.value(0);
    y = coordval.value(1);
    z = coordval.value(2);
    ColumnVector v(4);
    v << x << y << z << 1.0;
    if (usemm.value()) {
      // convert from mm to newimage voxels
      v = (vin[0].newimagevox2mm_mat()).i() * v;
    } else {
      // convert from nifti voxels (input) to newimage voxels (internal)
      v = vin[0].niftivox2newimagevox_mat() * v;
    }
    x = v(1);  y = v(2);  z = v(3);
    mask(MISCMATHS::round(x),MISCMATHS::round(y),MISCMATHS::round(z)) = 1.0;
  }
  
  if (!samesize(vin[0],mask)) {
    cerr << "ERROR: Mask and Input volumes have different (x,y,z) size." 
	 << endl;
    return -1;
  }
  
  mask_nonbin.addvolume(mask);
  if (labelname.unset()) { mask.binarise(1e-8); }  // arbitrary "0" threshold
  
  Matrix meants;
  int nt = vin.tsize();
  int nvox = nlabs;
  if (showall.value()) {
    nvox = no_mask_voxels(mask);
    nt += 3;
  }
  meants.ReSize(nt,nvox);
  meants = 0;

  for (int iter=1; iter<=nlabs; iter++) {

    if (eig.value()) {
      Matrix dat, evecs, scales;
      scales = mask_nonbin.matrix(mask);
      dat = vin.matrix(mask);
      if(!bin_mask.value())
	dat = SP (dat, ones(dat.Nrows(),1) * scales);
      dat = remmean(dat,1);
    
      if (verbose.value()) {
	cout << "Number of voxels used = " << dat.Ncols() << endl;
      }
    
      SymmetricMatrix Corr;
      Corr << dat * dat.t() / dat.Ncols();
      DiagonalMatrix tmpD;
      EigenValues(Corr,tmpD,evecs);	
      evecs = fliplr(evecs.Columns(evecs.Ncols()-order.value()+1 , evecs.Ncols())) * std::sqrt(dat.Nrows());
    
      Matrix res2 = mean(dat,2);
      res2 = res2.Column(1).t() * evecs.Column(1);
    
      if((float)res2.AsScalar()<0)  
	evecs = -1.0 * evecs;
    
      if (transpose.value()) { evecs=evecs.t(); }
      if (outmat.set()) {
	write_ascii_matrix(evecs,outmat.value());
      } else {
	cout << evecs << endl;
      }
      if (transpose.value()) { evecs=evecs.t(); }

    } else {	
      // NOT EIG
      long int num=0;
    
      for (int z=mask.minz(); z<=mask.maxz(); z++) {
	for (int y=mask.miny(); y<=mask.maxy(); y++) {
	  for (int x=mask.minx(); x<=mask.maxx(); x++) {
	    if (MISCMATHS::round(mask(x,y,z))==iter) {
	      num++;
	      if (showall.value()) {
		ColumnVector v(4);
		v << x << y << z << 1.0;
		v = (vin[0].niftivox2newimagevox_mat()).i() * v;
		meants(1,num) = v(1);
		meants(2,num) = v(2);
		meants(3,num) = v(3);
		meants.SubMatrix(4,nt,num,num) = vin.voxelts(x,y,z);
	      } else {
		meants.SubMatrix(1,nt,iter,iter) = meants.SubMatrix(1,nt,iter,iter) + vin.voxelts(x,y,z);
	      }
	    }
	  }
	}
      }
    
      if (verbose.value()) {
	cout << "Number of voxels used = " << num << endl;
      }
    
      // normalise for number of valid entries if averaging
      if (!showall.value()) {
	if (num>0) meants.SubMatrix(1,nt,iter,iter) = meants.SubMatrix(1,nt,iter,iter) / ((float) num);
      }

      // save the result
      if (transpose.value()) { meants=meants.t(); }
      if (outmat.set()) {
	write_ascii_matrix(meants,outmat.value());
      } else {
	cout << meants << endl;
      }
      if (transpose.value()) { meants=meants.t(); }
    }
  }
  return 0;
}