Skip to content
Snippets Groups Projects
Commit 28a169c5 authored by Moises Fernandez's avatar Moises Fernandez
Browse files

Changed to save only the voxels of each new subpart

parent 816b3031
No related branches found
No related tags found
No related merge requests found
Hide whitespace changes
Inline Side-by-side
Showing
with 302 additions and 322 deletions
CUDA/samples.cu
+ 302
− 322
View file @ 28a169c5
......@@ -17,342 +17,322 @@ using namespace Xfibres;
// MCMC SAMPLE STORAGE
////////////////////////////////////////////
Samples::Samples( NEWIMAGE::volume<int> vol2matrixkey,Matrix matrix2volkey,int nvoxels,int nmeasures):
opts(xfibresOptions::getInstance()),m_vol2matrixkey(vol2matrixkey),m_matrix2volkey(matrix2volkey){
/////////////// GPU version /////////////////////
m_sum_d=new float[nvoxels];
m_sum_S0=new float[nvoxels];
for(int i=0;i<nvoxels;i++){
m_sum_d[i]=0;
m_sum_S0[i]=0;
}
m_vec=new ColumnVector[nvoxels];
m_dyad=new vector<SymmetricMatrix>[nvoxels];
m_sum_f=new vector<float> [nvoxels];
m_sum_lam=new vector<float> [nvoxels];
////////////////////////////////////////////////
Samples::Samples(int nvoxels,int nmeasures):
opts(xfibresOptions::getInstance()){
/////////////// GPU version /////////////////////
m_sum_d=new float[nvoxels];
m_sum_S0=new float[nvoxels];
for(int i=0;i<nvoxels;i++){
m_sum_d[i]=0;
m_sum_S0[i]=0;
}
m_vec=new ColumnVector[nvoxels];
m_dyad=new vector<SymmetricMatrix>[nvoxels];
m_sum_f=new vector<float> [nvoxels];
m_sum_lam=new vector<float> [nvoxels];
////////////////////////////////////////////////
m_beenhere=m_vol2matrixkey*0;
int count=0;
int nsamples=0;
//m_beenhere=m_vol2matrixkey*0;
int count=0;
int nsamples=0;
for(int i=0;i<opts.njumps.value();i++){
count++;
if(count==opts.sampleevery.value()){
count=0;nsamples++;
}
}
for(int i=0;i<opts.njumps.value();i++){
count++;
if(count==opts.sampleevery.value()){
count=0;nsamples++;
}
}
m_dsamples.ReSize(nsamples,nvoxels);
m_dsamples=0;
m_S0samples.ReSize(nsamples,nvoxels);
m_S0samples=0;
m_lik_energy.ReSize(nsamples,nvoxels);
m_dsamples.ReSize(nsamples,nvoxels);
m_dsamples=0;
m_S0samples.ReSize(nsamples,nvoxels);
m_S0samples=0;
m_lik_energy.ReSize(nsamples,nvoxels);
// m_mean_sig.ReSize(nmeasures,nvoxels);
// m_mean_sig=0;
// m_std_sig.ReSize(nmeasures,nvoxels);
// m_std_sig=0;
// m_sig2.ReSize(nmeasures,nvoxels);
// m_sig2=0;
m_mean_dsamples.ReSize(nvoxels);
m_mean_dsamples=0;
m_mean_S0samples.ReSize(nvoxels);
m_mean_S0samples=0;
Matrix tmpvecs(3,nvoxels);
tmpvecs=0;
//m_sum_d=0; changed GPU version
//m_sum_S0=0; changed GPU version
if(opts.modelnum.value()==2){
m_d_stdsamples.ReSize(nsamples,nvoxels);
m_d_stdsamples=0;
m_mean_d_stdsamples.ReSize(nvoxels);
m_mean_d_stdsamples=0;
//m_sum_d_std=0; changed GPU version
/////////////// GPU version /////////////////////
m_sum_d_std=new float[nvoxels];
for(int i=0;i<nvoxels;i++){
m_sum_d_std[i]=0;
}
////////////////////////////////////////////////
}
if (opts.f0.value()){
m_f0samples.ReSize(nsamples,nvoxels);
m_f0samples=0;
m_mean_f0samples.ReSize(nvoxels);
m_mean_f0samples=0;
//m_sum_f0=0; changed GPU version
/////////////// GPU version /////////////////////
m_sum_f0=new float[nvoxels];
for(int i=0;i<nvoxels;i++)
m_sum_f0[i]=0;
////////////////////////////////////////////////
}
if (opts.rician.value()){
m_mean_tausamples.ReSize(nvoxels);
m_mean_tausamples=0;
//m_sum_tau=0; changed GPU version
/////////////// GPU version /////////////////////
m_sum_tau=new float[nvoxels];
for(int i=0;i<nvoxels;i++)
m_sum_tau[i]=0;
////////////////////////////////////////////////
}
SymmetricMatrix tmpdyad(3);
tmpdyad=0;
m_nsamps=nsamples;
//m_vec.ReSize(3); changed GPU version
/////////////// GPU version /////////////////////
for(int i=0;i<nvoxels;i++){
m_vec[i].ReSize(3);
for(int f=0;f<opts.nfibres.value();f++){
m_dyad[i].push_back(tmpdyad);
m_sum_f[i].push_back(0);
m_sum_lam[i].push_back(0);
}
}
////////////////////////////////////////////////
for(int f=0;f<opts.nfibres.value();f++){
m_thsamples.push_back(m_S0samples);
m_phsamples.push_back(m_S0samples);
m_fsamples.push_back(m_S0samples);
m_lamsamples.push_back(m_S0samples);
m_dyadic_vectors.push_back(tmpvecs);
m_mean_fsamples.push_back(m_mean_S0samples);
m_mean_lamsamples.push_back(m_mean_S0samples);
//m_sum_lam.push_back(0); changed GPU version
//m_sum_f.push_back(0); changed GPU version
//m_dyad.push_back(tmpdyad); changed GPU version
}
}
m_mean_dsamples.ReSize(nvoxels);
m_mean_dsamples=0;
m_mean_S0samples.ReSize(nvoxels);
m_mean_S0samples=0;
Matrix tmpvecs(3,nvoxels);
tmpvecs=0;
//m_sum_d=0; changed GPU version
//m_sum_S0=0; changed GPU version
if(opts.modelnum.value()==2){
m_d_stdsamples.ReSize(nsamples,nvoxels);
m_d_stdsamples=0;
m_mean_d_stdsamples.ReSize(nvoxels);
m_mean_d_stdsamples=0;
//m_sum_d_std=0; changed GPU version
/////////////// GPU version /////////////////////
m_sum_d_std=new float[nvoxels];
for(int i=0;i<nvoxels;i++){
m_sum_d_std[i]=0;
}
////////////////////////////////////////////////
}
if (opts.f0.value()){
m_f0samples.ReSize(nsamples,nvoxels);
m_f0samples=0;
m_mean_f0samples.ReSize(nvoxels);
m_mean_f0samples=0;
//m_sum_f0=0; changed GPU version
/////////////// GPU version /////////////////////
m_sum_f0=new float[nvoxels];
for(int i=0;i<nvoxels;i++)
m_sum_f0[i]=0;
////////////////////////////////////////////////
}
if (opts.rician.value()){
m_mean_tausamples.ReSize(nvoxels);
m_mean_tausamples=0;
//m_sum_tau=0; changed GPU version
/////////////// GPU version /////////////////////
m_sum_tau=new float[nvoxels];
for(int i=0;i<nvoxels;i++)
m_sum_tau[i]=0;
////////////////////////////////////////////////
}
SymmetricMatrix tmpdyad(3);
tmpdyad=0;
m_nsamps=nsamples;
//m_vec.ReSize(3); changed GPU version
/////////////// GPU version /////////////////////
for(int i=0;i<nvoxels;i++){
m_vec[i].ReSize(3);
for(int f=0;f<opts.nfibres.value();f++){
m_dyad[i].push_back(tmpdyad);
m_sum_f[i].push_back(0);
m_sum_lam[i].push_back(0);
}
}
////////////////////////////////////////////////
for(int f=0;f<opts.nfibres.value();f++){
m_thsamples.push_back(m_S0samples);
m_phsamples.push_back(m_S0samples);
m_fsamples.push_back(m_S0samples);
m_lamsamples.push_back(m_S0samples);
m_dyadic_vectors.push_back(tmpvecs);
m_mean_fsamples.push_back(m_mean_S0samples);
m_mean_lamsamples.push_back(m_mean_S0samples);
//m_sum_lam.push_back(0); changed GPU version
//m_sum_f.push_back(0); changed GPU version
//m_dyad.push_back(tmpdyad); changed GPU version
}
}
//new version for GPU
void Samples::record(float rd,float rf0,float rtau,float rdstd,float rs0,float rlikelihood_energy, float *rth,float *rph, float *rf, int vox, int samp){
m_dsamples(samp,vox)=rd;
m_sum_d[vox-1]+=rd;
if(opts.modelnum.value()==2){
m_d_stdsamples(samp,vox)=rdstd;
m_sum_d_std[vox-1]+=rdstd;
}
if (opts.f0.value()){
m_f0samples(samp,vox)=rf0;
m_sum_f0[vox-1]+=rf0;
}
if (opts.rician.value()){
m_sum_tau[vox-1]+=rtau;
}
m_S0samples(samp,vox)=rs0;
m_sum_S0[vox-1]+=rs0;
m_lik_energy(samp,vox)=rlikelihood_energy;
for(int f=0;f<opts.nfibres.value();f++){
float th=rth[f];
float ph=rph[f];
m_thsamples[f](samp,vox)=th;
m_phsamples[f](samp,vox)=ph;
m_fsamples[f](samp,vox)=rf[f];
//for means
m_vec[vox-1] << sin(th)*cos(ph) << sin(th)*sin(ph)<<cos(th) ;
m_dyad[vox-1][f] << m_dyad[vox-1][f]+m_vec[vox-1]*m_vec[vox-1].t();
m_sum_f[vox-1][f]+=rf[f];
m_sum_lam[vox-1][f]+=0;
}
}
//new version for GPU
void Samples::finish_voxel(int vox){
m_mean_dsamples(vox)=m_sum_d[vox-1]/m_nsamps;
if(opts.modelnum.value()==2)
m_mean_d_stdsamples(vox)=m_sum_d_std[vox-1]/m_nsamps;
if(opts.f0.value())
m_mean_f0samples(vox)=m_sum_f0[vox-1]/m_nsamps;
if(opts.rician.value())
m_mean_tausamples(vox)=m_sum_tau[vox-1]/m_nsamps;
m_mean_S0samples(vox)=m_sum_S0[vox-1]/m_nsamps;
m_sum_d[vox-1]=0;
m_sum_S0[vox-1]=0;
void Samples::record(float rd,float rf0,float rtau,float rdstd,float rs0,float rlikelihood_energy, float *rth,float *rph, float *rf, int vox, int samp){
m_dsamples(samp,vox)=rd;
m_sum_d[vox-1]+=rd;
if(opts.modelnum.value()==2){
m_d_stdsamples(samp,vox)=rdstd;
m_sum_d_std[vox-1]+=rdstd;
}
if (opts.f0.value()){
m_f0samples(samp,vox)=rf0;
m_sum_f0[vox-1]+=rf0;
}
if (opts.rician.value()){
m_sum_tau[vox-1]+=rtau;
}
m_S0samples(samp,vox)=rs0;
m_sum_S0[vox-1]+=rs0;
m_lik_energy(samp,vox)=rlikelihood_energy;
for(int f=0;f<opts.nfibres.value();f++){
float th=rth[f];
float ph=rph[f];
m_thsamples[f](samp,vox)=th;
m_phsamples[f](samp,vox)=ph;
m_fsamples[f](samp,vox)=rf[f];
//for means
m_vec[vox-1] << sin(th)*cos(ph) << sin(th)*sin(ph)<<cos(th) ;
m_dyad[vox-1][f] << m_dyad[vox-1][f]+m_vec[vox-1]*m_vec[vox-1].t();
m_sum_f[vox-1][f]+=rf[f];
m_sum_lam[vox-1][f]+=0;
}
}
//new version for GPU
void Samples::finish_voxel(int vox){
m_mean_dsamples(vox)=m_sum_d[vox-1]/m_nsamps;
if(opts.modelnum.value()==2)
m_mean_d_stdsamples(vox)=m_sum_d_std[vox-1]/m_nsamps;
if(opts.f0.value())
m_mean_f0samples(vox)=m_sum_f0[vox-1]/m_nsamps;
if(opts.rician.value())
m_mean_tausamples(vox)=m_sum_tau[vox-1]/m_nsamps;
m_mean_S0samples(vox)=m_sum_S0[vox-1]/m_nsamps;
m_sum_d[vox-1]=0;
m_sum_S0[vox-1]=0;
if(opts.rician.value())
m_sum_tau[vox-1]=0;
if(opts.modelnum.value()==2)
m_sum_d_std[vox-1]=0;
if (opts.f0.value())
m_sum_f0[vox-1]=0;
DiagonalMatrix dyad_D; //eigenvalues
Matrix dyad_V; //eigenvectors
int nfibs=0;
for(int f=0;f<opts.nfibres.value();f++){
EigenValues(m_dyad[vox-1][f],dyad_D,dyad_V);
int maxeig;
if(dyad_D(1)>dyad_D(2)){
if(dyad_D(1)>dyad_D(3)) maxeig=1;
else maxeig=3;
}
else{
if(dyad_D(2)>dyad_D(3)) maxeig=2;
else maxeig=3;
}
m_dyadic_vectors[f](1,vox)=dyad_V(1,maxeig);
m_dyadic_vectors[f](2,vox)=dyad_V(2,maxeig);
m_dyadic_vectors[f](3,vox)=dyad_V(3,maxeig);
if(opts.rician.value())
m_sum_tau[vox-1]=0;
if(opts.modelnum.value()==2)
m_sum_d_std[vox-1]=0;
if (opts.f0.value())
m_sum_f0[vox-1]=0;
DiagonalMatrix dyad_D; //eigenvalues
Matrix dyad_V; //eigenvectors
int nfibs=0;
for(int f=0;f<opts.nfibres.value();f++){
EigenValues(m_dyad[vox-1][f],dyad_D,dyad_V);
int maxeig;
if(dyad_D(1)>dyad_D(2)){
if(dyad_D(1)>dyad_D(3)) maxeig=1;
else maxeig=3;
}
else{
if(dyad_D(2)>dyad_D(3)) maxeig=2;
else maxeig=3;
}
m_dyadic_vectors[f](1,vox)=dyad_V(1,maxeig);
m_dyadic_vectors[f](2,vox)=dyad_V(2,maxeig);
m_dyadic_vectors[f](3,vox)=dyad_V(3,maxeig);
if((m_sum_f[vox-1][f]/m_nsamps)>0.05){
nfibs++;
}
m_mean_fsamples[f](vox)=m_sum_f[vox-1][f]/m_nsamps;
m_mean_lamsamples[f](vox)=m_sum_lam[vox-1][f]/m_nsamps;
if((m_sum_f[vox-1][f]/m_nsamps)>0.05){
nfibs++;
}
m_mean_fsamples[f](vox)=m_sum_f[vox-1][f]/m_nsamps;
m_mean_lamsamples[f](vox)=m_sum_lam[vox-1][f]/m_nsamps;
m_dyad[vox-1][f]=0;
m_sum_f[vox-1][f]=0;
m_sum_lam[vox-1][f]=0;
}
m_beenhere(int(m_matrix2volkey(vox,1)),int(m_matrix2volkey(vox,2)),int(m_matrix2volkey(vox,3)))=nfibs;
}
m_dyad[vox-1][f]=0;
m_sum_f[vox-1][f]=0;
m_sum_lam[vox-1][f]=0;
}
//m_beenhere(int(m_matrix2volkey(vox,1)),int(m_matrix2volkey(vox,2)),int(m_matrix2volkey(vox,3)))=nfibs;
}
void save_part(RowVector data, string name, int idpart){
Log& logger = LogSingleton::getInstance();
xfibresOptions& opts = xfibresOptions::getInstance();
int nvox = data.Ncols();
string file_name;
file_name = logger.appendDir(name+"_"+num2str(idpart));
ofstream out;
out.open(file_name.data(), ios::out | ios::binary);
out.write((char*)&data(1),nvox*sizeof(Real));
out.close();
}
void save_part(Matrix data, string name, int idpart){
Log& logger = LogSingleton::getInstance();
xfibresOptions& opts = xfibresOptions::getInstance();
int nvox = data.Ncols();
int nsamples = data.Nrows();
string file_name;
file_name = logger.appendDir(name+"_"+num2str(idpart));
ofstream out;
out.open(file_name.data(), ios::out | ios::binary);
out.write((char*)&data(1,1),nvox*nsamples*sizeof(Real));
out.close();
}
void Samples::save(const NEWIMAGE::volume<float>& mask){
NEWIMAGE::volume4D<float> tmp;
//So that I can sort the output fibres into
// files ordered by fibre fractional volume..
vector<Matrix> thsamples_out=m_thsamples;
vector<Matrix> phsamples_out=m_phsamples;
vector<Matrix> fsamples_out=m_fsamples;
vector<Matrix> lamsamples_out=m_lamsamples;
void Samples::save(int idpart){
vector<Matrix> thsamples_out=m_thsamples;
vector<Matrix> phsamples_out=m_phsamples;
vector<Matrix> fsamples_out=m_fsamples;
vector<Matrix> lamsamples_out=m_lamsamples;
vector<Matrix> dyadic_vectors_out=m_dyadic_vectors;
vector<Matrix> mean_fsamples_out;
for(unsigned int f=0;f<m_mean_fsamples.size();f++)
mean_fsamples_out.push_back(m_mean_fsamples[f]);
Log& logger = LogSingleton::getInstance();
if(opts.modelnum.value()==1){
tmp.setmatrix(m_mean_dsamples,mask);
tmp.setDisplayMaximumMinimum(tmp.max(),0);
save_volume4D(tmp,logger.appendDir("mean_dsamples"));
}
else if(opts.modelnum.value()==2){
tmp.setmatrix(m_mean_dsamples,mask);
tmp.setDisplayMaximumMinimum(tmp.max(),0);
save_volume4D(tmp,logger.appendDir("mean_dsamples"));
tmp.setmatrix(m_mean_d_stdsamples,mask);
tmp.setDisplayMaximumMinimum(tmp.max(),0);
save_volume4D(tmp,logger.appendDir("mean_d_stdsamples"));
tmp.setmatrix(m_dsamples,mask);
tmp.setDisplayMaximumMinimum(tmp.max(),0);
save_volume4D(tmp,logger.appendDir("dsamples"));
tmp.setmatrix(m_d_stdsamples,mask);
tmp.setDisplayMaximumMinimum(tmp.max(),0);
save_volume4D(tmp,logger.appendDir("d_stdsamples"));
}
if (opts.f0.value()){
tmp.setmatrix(m_mean_f0samples,mask);
tmp.setDisplayMaximumMinimum(1,0);
save_volume4D(tmp,logger.appendDir("mean_f0samples"));
tmp.setmatrix(m_f0samples,mask);
tmp.setDisplayMaximumMinimum(1,0);
save_volume4D(tmp,logger.appendDir("f0samples"));
}
if (opts.rician.value()){
tmp.setmatrix(m_mean_tausamples,mask);
tmp.setDisplayMaximumMinimum(tmp.max(),0);
save_volume4D(tmp,logger.appendDir("mean_tausamples"));
}
tmp.setmatrix(m_mean_S0samples,mask);
tmp.setDisplayMaximumMinimum(tmp.max(),0);
save_volume4D(tmp,logger.appendDir("mean_S0samples"));
//tmp.setmatrix(m_lik_energy,mask);
//save_volume4D(tmp,logger.appendDir("lik_energy"));
//Sort the output based on mean_fsamples
//
vector<Matrix> sumf;
for(int f=0;f<opts.nfibres.value();f++){
Matrix tmp=sum(m_fsamples[f],1);
sumf.push_back(tmp);
}
for(int vox=1;vox<=m_dsamples.Ncols();vox++){
vector<pair<float,int> > sfs;
pair<float,int> ftmp;
vector<Matrix> dyadic_vectors_out=m_dyadic_vectors;
vector<Matrix> mean_fsamples_out;
for(unsigned int f=0;f<m_mean_fsamples.size();f++)
mean_fsamples_out.push_back(m_mean_fsamples[f]);
Log& logger = LogSingleton::getInstance();
if(opts.modelnum.value()==1){
save_part(m_mean_dsamples,"mean_dsamples",idpart);
}
else if(opts.modelnum.value()==2){
save_part(m_mean_dsamples,"mean_dsamples",idpart);
save_part(m_mean_d_stdsamples,"mean_d_stdsamples",idpart);
//save_part(m_dsamples,"m_d_stdsamples",idpart);
//save_part(m_d_stdsamples,"d_stdsamples",idpart);
}
if (opts.f0.value()){
save_part(m_mean_f0samples,"mean_f0samples",idpart);
//save_part(m_f0samples,"f0samples",idpart);
}
if (opts.rician.value()){
save_part(m_mean_tausamples,"mean_tausamples",idpart);
}
save_part(m_mean_S0samples,"mean_S0samples",idpart);
//Sort the output based on mean_fsamples
//
vector<Matrix> sumf;
for(int f=0;f<opts.nfibres.value();f++){
Matrix tmp=sum(m_fsamples[f],1);
sumf.push_back(tmp);
}
for(int vox=1;vox<=m_dsamples.Ncols();vox++){
vector<pair<float,int> > sfs;
pair<float,int> ftmp;
for(int f=0;f<opts.nfibres.value();f++){
ftmp.first=sumf[f](1,vox);
ftmp.second=f;
sfs.push_back(ftmp);
}
sort(sfs.begin(),sfs.end());
for(int f=0;f<opts.nfibres.value();f++){
ftmp.first=sumf[f](1,vox);
ftmp.second=f;
sfs.push_back(ftmp);
}
sort(sfs.begin(),sfs.end());
for(int samp=1;samp<=m_dsamples.Nrows();samp++){
for(int f=0;f<opts.nfibres.value();f++){;
thsamples_out[f](samp,vox)=m_thsamples[sfs[(sfs.size()-1)-f].second](samp,vox);
phsamples_out[f](samp,vox)=m_phsamples[sfs[(sfs.size()-1)-f].second](samp,vox);
fsamples_out[f](samp,vox)=m_fsamples[sfs[(sfs.size()-1)-f].second](samp,vox);
lamsamples_out[f](samp,vox)=m_lamsamples[sfs[(sfs.size()-1)-f].second](samp,vox);
}
}
for(int samp=1;samp<=m_dsamples.Nrows();samp++){
for(int f=0;f<opts.nfibres.value();f++){;
thsamples_out[f](samp,vox)=m_thsamples[sfs[(sfs.size()-1)-f].second](samp,vox);
phsamples_out[f](samp,vox)=m_phsamples[sfs[(sfs.size()-1)-f].second](samp,vox);
fsamples_out[f](samp,vox)=m_fsamples[sfs[(sfs.size()-1)-f].second](samp,vox);
lamsamples_out[f](samp,vox)=m_lamsamples[sfs[(sfs.size()-1)-f].second](samp,vox);
}
}
for(int f=0;f<opts.nfibres.value();f++){
mean_fsamples_out[f](1,vox)=m_mean_fsamples[sfs[(sfs.size()-1)-f].second](vox);
dyadic_vectors_out[f](1,vox)=m_dyadic_vectors[sfs[(sfs.size()-1)-f].second](1,vox);
dyadic_vectors_out[f](2,vox)=m_dyadic_vectors[sfs[(sfs.size()-1)-f].second](2,vox);
dyadic_vectors_out[f](3,vox)=m_dyadic_vectors[sfs[(sfs.size()-1)-f].second](3,vox);
}
}
// save the sorted fibres
for(int f=0;f<opts.nfibres.value();f++){
for(int f=0;f<opts.nfibres.value();f++){
mean_fsamples_out[f](1,vox)=m_mean_fsamples[sfs[(sfs.size()-1)-f].second](vox);
dyadic_vectors_out[f](1,vox)=m_dyadic_vectors[sfs[(sfs.size()-1)-f].second](1,vox);
dyadic_vectors_out[f](2,vox)=m_dyadic_vectors[sfs[(sfs.size()-1)-f].second](2,vox);
dyadic_vectors_out[f](3,vox)=m_dyadic_vectors[sfs[(sfs.size()-1)-f].second](3,vox);
}
}
// save the sorted fibres
for(int f=0;f<opts.nfibres.value();f++){
// element_mod_n(thsamples_out[f],M_PI);
// element_mod_n(phsamples_out[f],2*M_PI);
tmp.setmatrix(thsamples_out[f],mask);
tmp.setDisplayMaximumMinimum(tmp.max(),tmp.min());
string oname="th"+num2str(f+1)+"samples";
save_volume4D(tmp,logger.appendDir(oname));
tmp.setmatrix(phsamples_out[f],mask);
tmp.setDisplayMaximumMinimum(tmp.max(),tmp.min());
oname="ph"+num2str(f+1)+"samples";
save_volume4D(tmp,logger.appendDir(oname));
tmp.setmatrix(fsamples_out[f],mask);
tmp.setDisplayMaximumMinimum(1,0);
oname="f"+num2str(f+1)+"samples";
save_volume4D(tmp,logger.appendDir(oname));
// tmp.setmatrix(lamsamples_out[f],mask);
// oname="lam"+num2str(f+1)+"samples";
// save_volume4D(tmp,logger.appendDir(oname));
tmp.setmatrix(mean_fsamples_out[f],mask);
tmp.setDisplayMaximumMinimum(1,0);
oname="mean_f"+num2str(f+1)+"samples";
save_volume(tmp[0],logger.appendDir(oname));
tmp.setmatrix(dyadic_vectors_out[f],mask);
tmp.setDisplayMaximumMinimum(1,-1);
oname="dyads"+num2str(f+1);
save_volume4D(tmp,logger.appendDir(oname));
}
}
save_part(thsamples_out[f],"th"+num2str(f+1)+"samples",idpart);
save_part(phsamples_out[f],"ph"+num2str(f+1)+"samples",idpart);
save_part(fsamples_out[f],"f"+num2str(f+1)+"samples",idpart);
//save_part(mean_fsamples_out[f],"mean_f"+num2str(f+1)+"samples",idpart);
//save_part(dyadic_vectors_out[f],"dyads"+num2str(f+1),idpart);
}
}
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment