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/* MELODIC - Multivariate exploratory linear optimized decomposition into
independent components
meldata.cc - data handler / container class
Christian F. Beckmann, FMRIB Image Analysis Group
using namespace Utilities;
using namespace NEWIMAGE;
Matrix MelodicData::process_file(string fname, int numfiles)
volume4D<float> RawData;
//read data
message("Reading data file " << fname << " ... ");
read_volume4D(RawData,fname);
message(" done" << endl);
del_vols(RawData,opts.dummy.value());
Mean += meanvol(RawData)/numfiles;
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//reshape
Matrix tmpData;
tmpData = RawData.matrix(Mask);
//estimate smoothness
if(Resels == 0)
Resels = est_resels(RawData,Mask);
//convert to percent BOLD signal change
if(opts.pbsc.value()){
message(" Converting data to percent BOLD signal change ...");
Matrix meanimg = convert_to_pbsc(tmpData);
meanR = meanimg.Row(1);
message(" done" << endl);
}
else{
message(string(" Removing mean image ..."));
meanR = mean(tmpData);
tmpData = remmean(tmpData);
message(" done" << endl);
}
//convert to power spectra
if(opts.pspec.value()){
message(" Converting data to powerspectra ...");
tmpData = calc_FFT(tmpData);
message(" done" << endl);
}
meanC = mean(tmpData,2);
//switch dimension in case temporal ICA is required
if(opts.temporal.value()){
message(string(" Switching dimensions for temporal ICA") << endl);
tmpData = tmpData.t();
Matrix tmp;
tmp = meanC;
meanC = meanR.t();
meanR = tmp.t();
message(" Data size : " << Data.Nrows() << " x " << Data.Ncols() <<endl);
}
//variance - normalisation
if(opts.varnorm.value()){
message(" Normalising by voxel-wise variance ...");
stdDev = varnorm(tmpData,tmpData.Nrows(),3.1);
stdDevi = pow(stdDev,-1);
message(" done" << endl);
return tmpData;
}
Matrix MelodicData::expand_mix()
{
Matrix out;
out = expand_dimred(mixMatrix);
return out;
}
Matrix MelodicData::expand_dimred(const Matrix& Mat)
{
int first, last;
first = 1;
last = DWM.at(0).Ncols();
Matrix tmp = DWM.at(0) * Mat.Rows(first,last);
for(unsigned int ctr = 1; ctr < DWM.size(); ctr++){
first = last + 1;
last += DWM.at(ctr).Ncols();
tmp &= DWM.at(ctr) * Mat.Rows(first, last);
return tmp;
}
Matrix MelodicData::reduce_dimred(const Matrix& Mat)
{
int first, last;
first = 1;
last = WM.at(0).Ncols();
Matrix tmp = WM.at(0) * Mat.Rows(first,last);
for(unsigned int ctr = 1; ctr < WM.size(); ctr++){
first = last + 1;
last += WM.at(ctr).Ncols();
tmp &= WM.at(ctr) * Mat.Rows(first, last);
void MelodicData::set_TSmode()
{
Matrix tmp, tmpT, tmpS, tmpT2, tmpS2;
tmp = expand_dimred(mixMatrix);
tmpT = zeros(tmp.Nrows()/numfiles, tmp.Ncols());
tmpS = zeros(numfiles, tmp.Ncols());
krfact(tmp,tmpT,tmpS);
for(int ctr = 1; ctr <= tmp.Ncols(); ctr++){
tmpT2 << tmpT.Column(ctr);
tmpS2 << tmpS.Column(ctr);
add_Tmodes(tmpT2);
add_Smodes(tmpS2);
}
void MelodicData::setup()
{
setup_misc();
numfiles = (int)opts.inputfname.value().size();
if((numfiles > 1) && (opts.approach.value()==string("defl") || opts.approach.value()==string("symm")))
opts.approach.set_T("tica");
Matrix alldat, tmpData;
alldat = process_file(opts.inputfname.value().at(0), numfiles) / numfiles;
for(int ctr = 1; ctr < numfiles; ctr++){
tmpData = process_file(opts.inputfname.value().at(ctr), numfiles);
alldat += tmpData / numfiles;
}
//update mask
if(opts.update_mask.value()){
message(" Excluding voxels with constant value ...");
update_mask(Mask, alldat);
message(" done" << endl);
}
message(endl << "Initial data size : "<<alldat.Nrows()<<" x "<<alldat.Ncols()<<endl<<endl);
//estimate model order
ColumnVector PPCA;
RowVector AdjEV, PercEV;
Matrix Corr, tmpE;
int order;
if(opts.pca_dim.value() == 0){
order = ppca_dim(alldat, RXweight, PPCA, AdjEV, PercEV, Corr, pcaE, pcaD, Resels, opts.pca_est.value());
calc_white(pcaE, pcaD, order, whiteMatrix, dewhiteMatrix);
opts.pca_dim.set_T(order);
}
else{
order = opts.pca_dim.value();
std_pca(tmpData, RXweight, Corr, pcaE, pcaD);
calc_white(pcaE, pcaD, order, whiteMatrix, dewhiteMatrix);
}
if(numfiles < 2){
Data = alldat;
Matrix tmp = Identity(Data.Nrows());
DWM.push_back(tmp);
WM.push_back(tmp);
} else {
for(int ctr = 0; ctr < numfiles; ctr++){
tmpData = process_file(opts.inputfname.value().at(ctr), numfiles);
// whiten (separate / joint)
if(!opts.joined_whiten.value()){
std_pca(tmpData, RXweight, Corr, pcaE, pcaD);
calc_white(pcaE, pcaD, order, whiteMatrix, dewhiteMatrix);
tmpData = whiteMatrix * tmpData;
DWM.push_back(dewhiteMatrix);
WM.push_back(whiteMatrix);
//concatenate Data
if(Data.Storage() == 0)
Data = tmpData;
else
Data &= tmpData;
message(" Data size : "<<Data.Nrows()<<" x "<<Data.Ncols()<<endl);
/* {//remove row mean
message(string("Removing mean image ... "));
}else{
message(string("Removing mean time course ... "));
}*/
//save the mean & mask
save_volume(Mask,logger.appendDir("mask"));
save_volume(Mean,logger.appendDir("mean"));
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} // void setup()
// }}}
void MelodicData::setup_misc()
{
//initialize Mean
read_volume(Mean,opts.inputfname.value().at(0),tempInfo);
//save first image
tmpnam(Mean_fname); // generate a tmp name
save_volume(Mean,Mean_fname);
//create mask
create_mask(Mask);
// clean /tmp
char callRMstr[1000];
ostrstream osc(callRMstr,1000);
osc << "rm " << string(Mean_fname) <<"* " << '\0';
system(callRMstr);
if(!samesize(Mean,Mask)){
cerr << "ERROR:: mask and data have different dimensions \n\n";
exit(2);
}
//reset mean
Mean *= 0;
//set up weighting
}
//seed the random number generator
double tmptime = time(NULL);
void MelodicData::save()
{
//check for temporal ICA
if(opts.temporal.value()){
message(string("temporal ICA: transform back the data ... "));
Matrix tmpIC = mixMatrix.t();
mixMatrix=IC.t();
IC=tmpIC;
unmixMatrix=pinv(mixMatrix);
Data = Data.t();
tmpIC = meanC;
meanC = meanR.t();
meanR = tmpIC.t();
// whiteMatrix = whiteMatrix.t;
// dewhiteMatrix = dewhiteMatrix.t();
message(string("done") << endl);
opts.temporal.set_T(false); // Do not switch again!
message(endl << "Writing results to : " << endl);
if((IC.Storage()>0)&&(opts.output_origIC.value())&&(after_mm==false))
save4D(IC,opts.outputfname.value() + "_oIC");
//Output IC -- adjusted for noise
if(IC.Storage()>0){
volume4D<float> tempVol;
//Matrix ICadjust;
if(after_mm){
save4D(IC,opts.outputfname.value() + "_IC");
// ICadjust = IC;
} else{
stdNoisei = pow(stdev(Data - mixMatrix * IC)*std::sqrt((float)(Data.Nrows()-1))/
std::sqrt((float)(Data.Nrows()-IC.Nrows())),-1);
ColumnVector diagvals;
diagvals=pow(diag(unmixMatrix*unmixMatrix.t()),-0.5);
save4D(SP(IC,diagvals*stdNoisei),opts.outputfname.value() + "_IC");
}
// tempVol.setmatrix(ICadjust,Mask);
//strncpy(tempInfo.header.hist.aux_file,"render3",24);
//save_volume4D(tempVol,logger.appendDir(opts.outputfname.value()
// + "_IC"),tempInfo);
//message(" " << logger.appendDir(opts.outputfname.value() + "_IC") <<endl);
if(opts.output_origIC.value())
save4D(stdNoisei,string("Noise_stddev_inv"));
//Output T- & S-modes
save_Tmodes();
save_Smodes();
saveascii(mixMatrix, opts.outputfname.value() + "_mix");
mixFFT=calc_FFT(mixMatrix, opts.logPower.value());
saveascii(mixFFT,opts.outputfname.value() + "_FTmix");
//Output ICstats
if(ICstats.Storage()>0)
saveascii(ICstats,opts.outputfname.value() + "_ICstats");
if(opts.output_unmix.value() && unmixMatrix.Storage()>0)
saveascii(unmixMatrix,opts.outputfname.value() + "_unmix");
//Output Mask
message(" "<< logger.appendDir("mask") <<endl);
//Output mean
if(opts.output_mean.value() && meanC.Storage()>0 && meanR.Storage()>0){
saveascii(meanR,opts.outputfname.value() + "_meanR");
saveascii(meanC,opts.outputfname.value() + "_meanC");
}
//Output white
if(opts.output_white.value() && whiteMatrix.Storage()>0&&
dewhiteMatrix.Storage()>0){
saveascii(whiteMatrix,opts.outputfname.value() + "_white");
saveascii(dewhiteMatrix,opts.outputfname.value() + "_dewhite");
tmp=calc_FFT(dewhiteMatrix, opts.logPower.value());
saveascii(tmp,opts.outputfname.value() + "_FTdewhite");
}
//Output PCA
if(opts.output_pca.value() && pcaD.Storage()>0&&pcaE.Storage()>0){
saveascii(pcaE,opts.outputfname.value() + "_pcaE");
saveascii((Matrix) diag(pcaD),opts.outputfname.value() + "_pcaD");
if(whiteMatrix.Ncols()==Data.Ncols()){
PCAmaps = dewhiteMatrix.t();
}else
PCAmaps = whiteMatrix * Data;
save4D(PCAmaps,opts.outputfname.value() + "_pca");
}
// }}}
// {{{ remove_components
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int MelodicData::remove_components()
{
message("Reading " << opts.filtermix.value() << endl)
mixMatrix = read_ascii_matrix(opts.filtermix.value());
if (mixMatrix.Storage()<=0) {
cerr <<" Please specify the mixing matrix correctly" << endl;
exit(2);
}
unmixMatrix = pinv(mixMatrix);
IC = unmixMatrix * Data;
string tmpstr;
tmpstr = opts.filter.value();
Matrix noiseMix;
Matrix noiseIC;
int ctr=0;
char *p;
char t[1024];
const char *discard = ", [];{(})abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ~!@#$%^&*_-=+|\':><./?";
message("Filtering the data...");
strcpy(t, tmpstr.c_str());
p=strtok(t,discard);
ctr = atoi(p);
if(ctr>0 && ctr<=mixMatrix.Ncols()){
message(" "<< ctr );
noiseMix = mixMatrix.Column(ctr);
noiseIC = IC.Row(ctr).t();
}else{
cerr << endl<< "component number "<<ctr<<" does not exist" << endl;
}
do{
p=strtok(NULL,discard);
if(p){
ctr = atoi(p);
if(ctr>0 && ctr<=mixMatrix.Ncols()){
message(" "<<ctr);
noiseMix |= mixMatrix.Column(ctr);
noiseIC |= IC.Row(ctr).t();
}
else{
cerr << endl<< "component number "<<ctr<<" does not exist" << endl;
}
}
}while(p);
message(endl);
Matrix newData;
newData = Data - noiseMix * noiseIC.t();
//cerr << newData.Nrows() << " x " << newData.Ncols() << endl;
//cerr << meanC.Nrows() << " x " << meanC.Ncols() << endl;
//cerr << meanR.Nrows() << " x " << meanR.Ncols() << endl;
newData = newData + meanC*ones(1,newData.Ncols());
newData = newData + ones(newData.Nrows(),1)*meanR;
volume4D<float> tmp;
read_volume4D(tmp,opts.inputfname.value().at(0));
tmp.setmatrix(newData,Mask);
save_volume4D(tmp,logger.appendDir(opts.outputfname.value() + "_ICAfiltered"));
return 0;
} // int remove_components()
// }}}
// {{{ create_RXweight
void MelodicData::create_RXweight()
{
message("Reading the weights for the covariance R_X from file "<< opts.segment.value() << endl);
volume4D<float> tmpRX;
read_volume4D(tmpRX,opts.segment.value());
RXweight = tmpRX.matrix(Mask);
}
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void MelodicData::est_smoothness(){
if(Resels == 0){
string SM_path = opts.binpath + "smoothest";
string Mask_fname = logger.appendDir("mask");
if(opts.segment.value().length()>0){
Mask_fname = opts.segment.value();
}
// Setup external call to smoothest:
char callSMOOTHESTstr[1000];
ostrstream osc(callSMOOTHESTstr,1000);
osc << SM_path << " -d " << data_dim()
<< " -r " << opts.inputfname.value().at(0) << " -m "
<< Mask_fname << " > " << logger.appendDir("smoothest") << '\0';
message(" Calling Smoothest: " << callSMOOTHESTstr << endl);
system(callSMOOTHESTstr);
//read back the results
ifstream in;
string str;
Resels = 1.0;
in.open(logger.appendDir("smoothest").c_str(), ios::in);
if(in>0){
for(int ctr=1; ctr<7; ctr++){ in >> str;}
in.close();
if(str!="nan"){
Resels = atof(str.c_str());
}
}
}
}
unsigned long MelodicData::standardise(volume<float>& mask,
volume4D<float>& R)
{
unsigned long count = 0;
int M=R.tsize();
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( mask(x,y,z) > 0.5) {
count ++;
if( M > 2 ) {
// For each voxel
// calculate mean and standard deviation...
double Sx = 0.0, SSx = 0.0;
for ( int t = 0; t < M; t++ ) {
float R_it = R(x,y,z,t);
Sx += R_it;
SSx += (R_it)*(R_it);
}
float mean = Sx / M;
float sdsq = (SSx - ((Sx)*(Sx) / M)) / (M - 1) ;
if (sdsq<=0) {
// trap for differences between mask and invalid data
mask(x,y,z)=0;
count--;
} else {
// ... and use them to standardise to N(0, 1).
for ( unsigned short t = 0; t < M; t++ ) {
R(x,y,z,t) = (R(x,y,z,t) - mean) / sqrt(sdsq);
}
}
}
}
}
}
}
return count;
}
float MelodicData::est_resels(volume4D<float> R, volume<float> mask)
{
message(" Estimating data smoothness ... ");
unsigned long mask_volume = standardise(mask, R);
int dof = R.tsize();
unsigned long N = mask_volume;
// MJ additions to make it cope with 2D images
bool usez = true;
if (R.zsize() <= 1) { usez = false; }
enum {X = 0, Y, Z};
float SSminus[3] = {0, 0, 0}, S2[3] = {0, 0, 0};
int zstart=1;
if (!usez) zstart=0;
for ( unsigned short z = zstart; z < R.zsize() ; z++ )
for ( unsigned short y = 1; y < R.ysize() ; y++ )
for ( unsigned short x = 1; x < R.xsize() ; x++ )
// Sum over N
if( (mask(x, y, z)>0.5) &&
(mask(x-1, y, z)>0.5) &&
(mask(x, y-1, z)>0.5) &&
( (!usez) || (mask(x, y, z-1)>0.5) ) ) {
N++;
for ( unsigned short t = 0; t < R.tsize(); t++ ) {
// Sum over M
SSminus[X] += R(x, y, z, t) * R(x-1, y, z, t);
SSminus[Y] += R(x, y, z, t) * R(x, y-1, z, t);
if (usez) SSminus[Z] += R(x, y, z, t) * R(x, y, z-1, t);
S2[X] += 0.5 * (R(x, y, z, t)*R(x, y, z, t) + R(x-1, y, z, t)*R(x-1, y, z, t));
S2[Y] += 0.5 * (R(x, y, z, t)*R(x, y, z, t) + R(x, y-1, z, t)*R(x, y-1, z, t));
if (usez) S2[Z] += 0.5 * (R(x, y, z, t)*R(x, y, z, t) + R(x, y, z-1, t)*R(x, y, z-1, t));
}
}
float norm = 1.0/(float) N;
float v = dof; // v - degrees of freedom (nu)
if(R.tsize() > 1) {
norm = (v - 2) / ((v - 1) * N * R.tsize());
}
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// for extreme smoothness
if (SSminus[X]>=0.99999999*S2[X])
SSminus[X]=0.99999*S2[X];
if (SSminus[Y]>=0.99999999*S2[Y])
SSminus[Y]=0.99999*S2[Y];
if (usez)
if (SSminus[Z]>=0.99999999*S2[Z])
SSminus[Z]=0.99999*S2[Z];
// Convert to sigma squared
float sigmasq[3] = {0,0,0};
sigmasq[X] = -1.0 / (4 * log(fabs(SSminus[X]/S2[X])));
sigmasq[Y] = -1.0 / (4 * log(fabs(SSminus[Y]/S2[Y])));
if (usez) { sigmasq[Z] = -1.0 / (4 * log(fabs(SSminus[Z]/S2[Z]))); }
// Convert to full width half maximum
float FWHM[3] = {0,0,0};
FWHM[X] = sqrt(8 * log(2) * sigmasq[X]);
FWHM[Y] = sqrt(8 * log(2) * sigmasq[Y]);
if (usez) { FWHM[Z] = sqrt(8 * log(2) * sigmasq[Z]); }
float resels = FWHM[X] * FWHM[Y];
if (usez) resels *= FWHM[Z];
message(" done " <<endl);
return resels;
}
// }}}
// {{{ create_mask
void MelodicData::create_mask(volume<float>& theMask)
{
if(opts.use_mask.value() && opts.maskfname.value().size()>0){ // mask provided
read_volume(theMask,opts.maskfname.value());
message("Mask provided : " << opts.maskfname.value()<<endl<<endl);
}
else{
if(opts.perf_bet.value() && opts.use_mask.value()){ //use BET
message("Create mask ... ");
// set up all strings
string BET_outputfname = string(Mean_fname)+"_brain";
string BET_path = opts.binpath + "bet";
string BET_optarg = "-m -f 0.4"; // see man bet
char callBETstr[1000];
ostrstream osc(callBETstr,1000);
<< BET_outputfname << " " << BET_optarg << " > /dev/null " << '\0';
message(" Calling BET: " << callBETstr << endl);
system(callBETstr);
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// read back the Mask file
read_volume(theMask,Mask_fname);
message("done" << endl);
}
else{
if(opts.use_mask.value()){ //just threshold the Mean
message("Create mask ... ");
float Mmin, Mmax, Mtmp;
Mmin = Mean.min(); Mmax = Mean.max();
theMask = binarise(Mean,Mmin + opts.threshold.value()* (Mmax-Mmin),Mmax);
Mtmp = Mmin + opts.threshold.value()* (Mmax-Mmin);
message("done" << endl);
}
else{ //well, don't threshold then
theMask = Mean;
theMask = 1.0;
}
}
}
if(opts.remove_endslices.value()){
// just in case mc introduced something nasty
message(" Deleting end slices" << endl);
for(int ctr1=theMask.miny(); ctr1<=theMask.maxy(); ctr1++){
for(int ctr2=theMask.minx(); ctr2<=theMask.maxx(); ctr2++){
theMask(ctr2,ctr1,Mask.minz()) = 0.0;
theMask(ctr2,ctr1,Mask.maxz()) = 0.0;
}
}
}
} //void create_mask()
void MelodicData::sort()
{
int numComp = mixMatrix.Ncols(), numVox = IC.Ncols(),
numTime = mixMatrix.Nrows(), i,j;
for(int ctr_i = 1; ctr_i <= numComp; ctr_i++){
flipres(ctr_i); };}
// cerr << "HERE2" << endl << endl;
// re-order wrt standard deviation of IC maps
message("Sorting IC maps" << endl);
Matrix tmpscales, tmpICrow, tmpMIXcol;
tmpscales = stdev(IC,2);
ICstats = tmpscales;
double max_val, min_val = tmpscales.Minimum()-1;
for(int ctr_i = 1; ctr_i <= numComp; ctr_i++){
max_val = tmpscales.Maximum2(i,j);
ICstats(ctr_i,1)=max_val;
tmpICrow = IC.Row(ctr_i);
tmpMIXcol = mixMatrix.Column(ctr_i);
IC.SubMatrix(ctr_i,ctr_i,1,numVox) = IC.SubMatrix(i,i,1,numVox);
mixMatrix.SubMatrix(1,numTime,ctr_i,ctr_i) =
mixMatrix.SubMatrix(1,numTime,i,i);
IC.SubMatrix(i,i,1,numVox) = tmpICrow.SubMatrix(1,1,1,numVox);
mixMatrix.SubMatrix(1,numTime,i,i) = tmpMIXcol.SubMatrix(1,numTime,1,1);
tmpscales(i,1)=tmpscales(ctr_i,1);
tmpscales(ctr_i,1)=min_val;
ICstats /= ICstats.Column(1).Sum();
ICstats *= 100;
if(EVP.Storage()>0){
tmpscales = ICstats.Column(1).AsMatrix(ICstats.Nrows(),1) * EVP(1,numComp);
ICstats |= tmpscales;
}
if(Data.Storage()>0&&stdDev.Storage()>0){
//if(DataVN.Storage()>0&&stdDev.Storage()>0){
//cerr << " ICstats " << ICstats << endl << endl;
Matrix copeP(tmpscales), copeN(tmpscales);
Matrix max_ICs(tmpscales), min_ICs(tmpscales);
for(int ctr_i = 1; ctr_i <= numComp; ctr_i++){
int i,j;
max_ICs(ctr_i,1) = IC.Row(ctr_i).Maximum2(i,j);
//cerr << " ICstats " << ICstats << endl << endl;
//cerr << endl <<(pinv(mixMatrix)*DataVN.Column(j)) << endl;
copeP(ctr_i,1) = std::abs((pinv(mixMatrix)*Data.Column(j)).Row(ctr_i).AsScalar()*stdDev(1,j)*100*(mixMatrix.Column(ctr_i).Maximum()-mixMatrix.Column(ctr_i).Minimum())/meanR(1,j));
min_ICs(ctr_i,1) = IC.Row(ctr_i).Minimum2(i,j);
copeN(ctr_i,1) = -1.0*std::abs((pinv(mixMatrix)*Data.Column(j)).Row(ctr_i).AsScalar()*stdDev(1,j)*100*(mixMatrix.Column(ctr_i).Maximum()-mixMatrix.Column(ctr_i).Minimum())/meanR(1,j));
}
ICstats |= copeP;
ICstats |= copeN;
}
mixFFT=calc_FFT(mixMatrix, opts.logPower.value());
unmixMatrix = pinv(mixMatrix);
//if(ICstats.Storage()>0){cout << "ICstats: " << ICstats.Nrows() <<"x" << ICstats.Ncols() << endl;}else{cout << "ICstats empty " <<endl;}
}
void MelodicData::status(const string &txt)
{
cout << "MelodicData Object " << txt << endl;
if(Data.Storage()>0){cout << "Data: " << Data.Nrows() <<"x" << Data.Ncols() << endl;}else{cout << "Data empty " <<endl;}
if(pcaE.Storage()>0){cout << "pcaE: " << pcaE.Nrows() <<"x" << pcaE.Ncols() << endl;}else{cout << "pcaE empty " <<endl;}
if(pcaD.Storage()>0){cout << "pcaD: " << pcaD.Nrows() <<"x" << pcaD.Ncols() << endl;}else{cout << "pcaD empty " <<endl;}
if(whiteMatrix.Storage()>0){cout << "white: " << whiteMatrix.Nrows() <<"x" << whiteMatrix.Ncols() << endl;}else{cout << "white empty " <<endl;}
if(dewhiteMatrix.Storage()>0){cout << "dewhite: " << dewhiteMatrix.Nrows() <<"x" << dewhiteMatrix.Ncols() << endl;}else{cout << "dewhite empty " <<endl;}
if(mixMatrix.Storage()>0){cout << "mix: " << mixMatrix.Nrows() <<"x" << mixMatrix.Ncols() << endl;}else{cout << "mix empty " <<endl;}
if(unmixMatrix.Storage()>0){cout << "unmix: " << unmixMatrix.Nrows() <<"x" << unmixMatrix.Ncols() << endl;}else{cout << "unmix empty " <<endl;}
if(IC.Storage()>0){cout << "IC: " << IC.Nrows() <<"x" << IC.Ncols() << endl;}else{cout << "IC empty " <<endl;}