/* {{{ Copyright etc. */

/*  prewhiten - apply prehitening

    Stephen Smith and Mark Woolrich, FMRIB Image Analysis Group

    Copyright (C) 1999-2002 University of Oxford  */

/*  Part of FSL - FMRIB's Software Library
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    Developed at FMRIB (Oxford Centre for Functional Magnetic Resonance
    Imaging of the Brain), Department of Clinical Neurology, Oxford
    University, Oxford, UK
    
    
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/* }}} */
/* {{{ background theory */

/*

consider the GLM:

Y = Xb + E

consider a single voxel, in which case Y (data) and X (model) and E
(residuals) are column vectors (ie time series)

we generate a prewhitening matrix S which makes the residuals white:

SY = SXb + SE

(SPM also uses this form, but in the case of SPM, S is not a whitening
matrix but a colouring matrix, so that instead of the resulting
residuals being white, they contain an amount of autcorrelation which
is approximately known and can be corrected for in the inference.)

S is a Toeplitz matrix

S =   / r0  r1  r2  r3  r4  ....... \
     |  r-1 r0  r1  r2  r3  ....... |
     |  r-2 r-1 r0  r1  r2  ....... |
     |  r-3 r-2 r-1 r0  r1  ....... |
        ...........................
        ...........................
        ...........................
     |  ....... r-2 r-1 r0  r1  r2  |
     |  ....... r-3 r-2 r-1 r0  r1  |
     \  ....... r-4 r-3 r-2 r-1 r0  /

this is in theory equivalent to convolving the time series in a column
vector (Y or X or E) with 1D convolution kernel r[i];

SY[i] = SUM(r[j-i] * Y[j])

the difference in practice is that it is easier to sort out
end-effects (padding of the data to allow simple processing with a
constant width convolution kernel) if this is seen as a convolution
rather than matrix multiplication. In fact, the convolution is
normally carried out in fourier space (ie a fourier space
multiplication rather than real space convolution) for computational
efficiency.


***********************************************************

generating prewhitening matrix S:

E = N(0,sigma^2 V)

where V is the autocorrelation matrix

use Cholesky to get K where V=KK'

so if we set S = K^-1

there are more details in Mark's report which we've just put on the web:
http://www.fmrib.ox.ac.uk/analysis/techrep/tr01mw1/tr01mw1/node3.html

in detail:

1) fit model to data and remove that part of signal which correlates.
2) take resulting residuals and estimate autocorrelation parameters
3) regularise these by applying a Tukey taper and the smoothing spatially with the estimates at neighbouring voxels
4) create S=K^-1 by inverting the autocorrelation estimates in the spectral domain
5) Use S to prewhiten data and model, and refit.

again - there is more detail in the paper (and the source code ;-)


*/

/* }}} */
/* {{{ defines, includes and typedefs */

#include "featlib.h"
using namespace NEWMAT;
using namespace NEWIMAGE;

/* }}} */
/* {{{ usage */

void usage(void)
{
  printf("Usage: prewhiten <feat_directory.feat> [options]\n");
  printf("[-o <output_directory>] change output directory from default of input feat directory\n");
  exit(1);
}

/* }}} */

int main(int argc, char **argv)
{
  /* {{{ variables */

int          argi=1, x, y, z;
char         fmridata[10000], featdir[10000], outputdir[10000], thestring[10000];
ColumnVector pwts;

/* }}} */

  /* {{{ process arguments */

if (argc<2) usage();

strcpy(featdir,argv[argi++]);
sprintf(fmridata,"%s/filtered_func_data",featdir);

strcpy(outputdir,featdir);

for (;argi<argc;argi++)
{
  if (!strcmp(argv[argi], "-o"))
    /* {{{ output dir */

{
  argi++;
  if (argc<argi+1)
    {
      printf("Error: no value given following -o\n");
      usage();
    }
  strcpy(outputdir,argv[argi]);
}

/* }}} */
}

/* }}} */
  /* {{{ read filtered_func_data */

volume4D<int> im;
read_volume4D(im, fmridata);

/* }}} */
  /* {{{ read auto correlation estimates for prewhitening */

volume4D<float> acs;
sprintf(thestring,"%s/stats/threshac1",featdir);

if(fsl_imageexists(thestring)) 
  read_volume4D(acs, thestring);

/* }}} */

  for(z=0;z<im.zsize();z++) for(y=0;y<im.ysize();y++) for(x=0;x<im.xsize();x++)
    {
      prewhiten_timeseries(acs.voxelts(x,y,z), im.voxelts(x,y,z), pwts, im.tsize());
      im.voxelts(x,y,z)=pwts;
    }

  sprintf(thestring,"%s/pw_res",outputdir);
  save_volume4D(im,thestring);

  return 0;
}