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/* film_gls.cc
Mark Woolrich, FMRIB Image Analysis Group
Copyright (C) 1999-2000 University of Oxford */
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#include <iostream>
#include <fstream>
#include <strstream>
#define WANT_STREAM
#define WANT_MATH
#include "newmatap.h"
#include "newmatio.h"
#include "VolumeSeries.h"
#include "Volume.h"
#include "glim.h"
#include "sigproc.h"
#include "miscmaths.h"
#include "gaussComparer.h"
#include "Log.h"
#include "AutoCorrEstimator.h"
#include "paradigm.h"
#include "FilmGlsOptions.h"
#include "glimGls.h"
#include <string>
#ifndef NO_NAMESPACE
using namespace NEWMAT;
using namespace SIGPROC;
using namespace TACO;
using namespace UTILS;
#endif
int main(int argc, char *argv[])
{
try{
rand();
// parse command line to find out directory name for logging:
ofstream out2;
FilmGlsOptions& globalopts = FilmGlsOptions::getInstance();
globalopts.parse_command_line(argc, argv, out2);
// Setup logging:
Log& logger = Log::getInstance();
logger.setLogFile("glslogfile");
logger.establishDir(globalopts.datadir);
// parse command line again to output arguments to logfile
globalopts.parse_command_line(argc, argv, logger.str());
// load non-temporally filtered data
VolumeSeries x;
x.read(globalopts.inputfname);
// if needed output the 12th volume for use later
Volume epivol;
if(globalopts.smoothACEst)
{
epivol = x.getVolume(12).AsColumn();
epivol.setDims(x.getDims());
epivol.writeAsInt(logger.getDir() + "/" + globalopts.epifname);
}
// This also removes the mean from each of the time series:
x.thresholdSeries(globalopts.thresh, true);
// if needed later also threshold the epi volume
if(globalopts.smoothACEst)
{
epivol.setPreThresholdPositions(x.getPreThresholdPositions());
epivol.threshold();
}
int sizeTS = x.getNumVolumes();
int numTS = x.getNumSeries();
// Load paradigm:
Paradigm parad;
parad.load(globalopts.paradigmfname, "", false, sizeTS);
// Sort out detrending:
if(globalopts.detrend)
{
SIGPROC::Detrend(x, false);
}
if(globalopts.verbose)
{
logger.out("Gc", parad.getDesignMatrix());
}
// Set up OLS GLM for non-whitened data
Glim glim(x, parad.getDesignMatrix());
cerr << "Computing residuals for non-whitened data... ";
const VolumeSeries& rnotw = glim.ComputeResids();
cerr << "Completed" << endl;
if(globalopts.verbose)
{
logger.out("rnotw", rnotw);
}
// Estimate Autocorrelations:
AutoCorrEstimator acEst(rnotw);
if(globalopts.fitAutoRegressiveModel)
{
acEst.fitAutoRegressiveModel();
if(globalopts.verbose)
{
AutoCorrEstimator acEstForLogging(rnotw);
acEstForLogging.calcRaw();
logger.out("rawac", acEstForLogging.getEstimates());
logger.out("autoregac", acEst.getEstimates());
}
}
else
{
acEst.calcRaw();
if(globalopts.verbose)
{
logger.out("rawac", acEst.getEstimates());
}
// Smooth raw estimates:
if(globalopts.smoothACEst)
{
acEst.spatiallySmooth(logger.getDir() + "/" + globalopts.epifname, epivol, globalopts.ms, globalopts.epifname, globalopts.susanpath);
}
// Apply constraints to estimate autocorr:
acEst.pava();
if(globalopts.verbose)
{
logger.out("threshac", acEst.getEstimates());
}
}
ColumnVector I(sizeTS);
I = 0;
I(1) = 1;
// Setup OLS GLM for temporally filtered data:
int numParams = parad.getDesignMatrix().Ncols();
GlimGls glimGls(numTS, sizeTS, numParams);
ColumnVector xw(sizeTS);
ColumnVector xprew(sizeTS);
acEst.setDesignMatrix(parad.getDesignMatrix());
int co = 1;
// Loop through voxels:
cerr << "Calculating params..." << endl;
for(int i = 1; i <= numTS; i++)
{
// Use autocorr estimate to prewhiten data:
xprew = x.getSeries(i);
Matrix designmattw;
acEst.preWhiten(xprew, xw, i, designmattw);
if(co > 1000 || i == 5618 || i == 5582)
{
co = 1;
cerr << (float)i/(float)numTS << ",";
}
else
co++;
glimGls.setData(xw, designmattw, i);
}
cerr << "Completed" << endl;
// Write out necessary data:
cerr << "Saving results... ";
glimGls.Save(x.getDims(), x.getPreThresholdPositions());
cerr << "Completed" << endl;
}
catch(Exception p_excp)
{
cerr << p_excp.what() << endl;
}
catch(...)
{
cerr << "Image error" << endl;
}
return 0;
}