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- centre;
for (int i=1; i<=3; i++) { params(i+3) = transl(i); }
ColumnVector rotparams(3);
(*rotmat2params)(rotparams,rotmat);
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for (int i=1; i<=3; i++) { params(i) = rotparams(i); }
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return 0;
}
int decompose_aff(ColumnVector& params, const Matrix& affmat,
int (*rotmat2params)(ColumnVector& , const Matrix& ))
{
Tracer tr("decompose_aff");
ColumnVector centre(3);
centre = 0.0;
return decompose_aff(params,affmat,centre,rotmat2params);
}
int compose_aff(const ColumnVector& params, int n, const ColumnVector& centre,
Matrix& aff,
int (*params2rotmat)(const ColumnVector& , int , Matrix& ,
const ColumnVector& ) )
{
Tracer tr("compose_aff");
if (n<=0) return 0;
// order of parameters is 3 rotation + 3 translation + 3 scales + 3 skews
// angles are in radians
(*params2rotmat)(params,n,aff,centre);
if (n<=6) return 0;
if (n>=7) {
scale(1,1)=params(7);
if (n>=8) scale(2,2)=params(8);
else scale(2,2)=params(7);
if (n>=9) scale(3,3)=params(9);
else scale(3,3)=params(7);
}
// fix the translation so that the centre is not moved
ColumnVector strans(3);
strans = centre - scale.SubMatrix(1,3,1,3)*centre;
scale.SubMatrix(1,3,4,4) = strans;
if (n>=10) {
if (n>=10) skew(1,2)=params(10);
if (n>=11) skew(1,3)=params(11);
if (n>=12) skew(2,3)=params(12);
}
// fix the translation so that the centre is not moved
ColumnVector ktrans(3);
ktrans = centre - skew.SubMatrix(1,3,1,3)*centre;
skew.SubMatrix(1,3,4,4) = ktrans;
aff = aff * skew * scale;
return 0;
}
float rms_deviation(const Matrix& affmat1, const Matrix& affmat2,
const ColumnVector& centre, const float rmax)
{
Tracer trcr("rms_deviation");
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Matrix isodiff(4,4), a1(4,4), a2(4,4);
if ((affmat1.Nrows()==4) && (affmat1.Ncols()==4)) { a1=affmat1; }
else if ((affmat1.Nrows()==3) && (affmat1.Ncols()==3)) { a1=IdentityMatrix(4); a1.SubMatrix(1,3,1,3)=affmat1; }
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else { cerr << "ERROR:: Can only calculate RMS deviation for 4x4 or 3x3 matrices" << endl; exit(-5); }
if ((affmat2.Nrows()==4) && (affmat2.Ncols()==4)) { a2=affmat2; }
else if ((affmat2.Nrows()==3) && (affmat2.Ncols()==3)) { a2=IdentityMatrix(4); a2.SubMatrix(1,3,1,3)=affmat2; }
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else { cerr << "ERROR:: Can only calculate RMS deviation for 4x4 or 3x3 matrices" << endl; exit(-5); }
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isodiff = a1*a2.i() - IdentityMatrix(4);
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} catch(...) {
cerr << "RMS_DEVIATION ERROR:: Could not invert matrix" << endl;
exit(-5);
}
Matrix adiff(3,3);
adiff = isodiff.SubMatrix(1,3,1,3);
ColumnVector tr(3);
tr = isodiff.SubMatrix(1,3,4,4) + adiff*centre;
float rms = std::sqrt( (tr.t() * tr).AsScalar() +
(rmax*rmax/5.0)*Trace(adiff.t()*adiff) );
return rms;
}
float rms_deviation(const Matrix& affmat1, const Matrix& affmat2,
const float rmax)
{
ColumnVector centre(3);
centre = 0;
return rms_deviation(affmat1,affmat2,centre,rmax);
}
// helper function - calls nifti, but with FSL default case
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Matrix Mat44ToNewmat(mat44 m)
{
Matrix r(4,4);
for(unsigned short i = 0; i < 4; ++i)
for(unsigned short j = 0; j < 4; ++j)
r(i+1, j+1) = m.m[i][j];
return r;
}
mat44 NewmatToMat44(const Matrix& m)
{
mat44 r;
for(unsigned short i = 0; i < 4; ++i)
for(unsigned short j = 0; j < 4; ++j)
r.m[i][j] = m(i+1, j+1);
return r;
}
void get_axis_orientations(const Matrix& sform_mat, int sform_code,
const Matrix& qform_mat, int qform_code,
int& icode, int& jcode, int& kcode)
Matrix vox2mm(4,4);
if (sform_code!=NIFTI_XFORM_UNKNOWN) {
vox2mm = sform_mat;
} else if (qform_code!=NIFTI_XFORM_UNKNOWN) {
vox2mm = qform_mat;
} else {
// ideally should be sampling_mat(), but for orientation it doesn't matter
mat44 v2mm;
for (int ii=0; ii<4; ii++) { for (int jj=0; jj<4; jj++) {
v2mm.m[ii][jj] = vox2mm(ii+1,jj+1);
} }
nifti_mat44_to_orientation(v2mm,&icode,&jcode,&kcode);
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Matrix mat44_to_newmat(mat44 inmat)
{
Matrix retmat(4,4);
for (int ii=0; ii<4; ii++) {
for (int jj=0; jj<4; jj++) {
retmat(ii+1,jj+1) = inmat.m[ii][jj];
}
}
return retmat;
}
mat44 newmat_to_mat44(const Matrix& inmat)
{
mat44 retmat;
for (int ii=0; ii<4; ii++) {
for (int jj=0; jj<4; jj++) {
retmat.m[ii][jj] = inmat(ii+1,jj+1);
}
}
return retmat;
}
// Matlab style functions for percentiles, quantiles and median
// AUG 06 CB
{
}
float interp1(const ColumnVector& x, const ColumnVector& y, float xi)
// Look-up function for data table defined by x, y
// Returns the values yi at xi using linear interpolation
// Assumes that x is sorted in ascending order
{
float ans;
if(xi >= x.Maximum())
ans = y(x.Nrows());
else
if(xi <= x.Minimum())
ans = y(1);
else{
int ind=2;
while(xi >= x(ind)) { ind++; }
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float xa = x(ind-1), xb = x(ind), ya = y(ind-1), yb = y(ind);
ans = ya + (xi - xa)/(xb - xa) * (yb - ya);
}
return ans;
}
float quantile(const ColumnVector& in, int which)
{
float p;
switch (which)
{
case 0 : p = 0.0; break;
case 1 : p = 25.0; break;
case 2 : p = 50.0; break;
case 3 : p = 75.0; break;
case 4 : p =100.0; break;
default: p = 0.0;
}
return percentile(in,p);
}
float percentile(const ColumnVector& in, float p)
{
ColumnVector y = in;
int num = y.Nrows();
ColumnVector xx,yy,sequence,a(1),b(1),c(1),d(1);
sequence = 100*(seq(num)-0.5)/num; a << y(1); b << y(num); c = 0; d = 100;
xx = (c & sequence & d);
yy = (a & y & b);
return interp1(xx,yy,p);
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ReturnMatrix quantile(const Matrix& in, int which)
{
int num = in.Ncols();
Matrix res(1,num);
for (int ctr=1; ctr<=num; ctr++){
ColumnVector tmp = in.Column(ctr);
res(1,ctr) = quantile(tmp,which);
}
res.Release();
return res;
}
ReturnMatrix percentile(const Matrix& in, float p)
{
int num = in.Ncols();
Matrix res(1,num);
for (int ctr=1; ctr<=num; ctr++){
ColumnVector tmp = in.Column(ctr);
res(1,ctr) = percentile(tmp,p);
}
res.Release();
return res;
}
void cart2sph(const ColumnVector& dir, float& th, float& ph)
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float mag=sqrt(dir(1)*dir(1)+dir(2)*dir(2)+dir(3)*dir(3));
if(mag==0){
ph=M_PI/2;
th=M_PI/2;
}
else{
if(dir(1)==0 && dir(2)>=0) ph=M_PI/2;
else if(dir(1)==0 && dir(2)<0) ph=-M_PI/2;
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else if(dir(1)>0) ph=atan(dir(2)/dir(1));
else if(dir(2)>0) ph=atan(dir(2)/dir(1))+M_PI;
else ph=atan(dir(2)/dir(1))-M_PI;
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else if(dir(3)>0) th=atan(sqrt(dir(1)*dir(1)+dir(2)*dir(2))/dir(3));
else th=atan(sqrt(dir(1)*dir(1)+dir(2)*dir(2))/dir(3))+M_PI;
}
}
void cart2sph(const Matrix& dir,ColumnVector& th,ColumnVector& ph)
{
if(th.Nrows()!=dir.Ncols()){
th.ReSize(dir.Ncols());
}
if(ph.Nrows()!=dir.Ncols()){
ph.ReSize(dir.Ncols());
}
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float mag=sqrt(dir(1,i)*dir(1,i)+dir(2,i)*dir(2,i)+dir(3,i)*dir(3,i));
if(mag==0){
ph(i)=M_PI/2;
th(i)=M_PI/2;
}
else{
if(dir(1,i)==0 && dir(2,i)>=0) ph(i)=M_PI/2;
else if(dir(1,i)==0 && dir(2,i)<0) ph(i)=-M_PI/2;
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else if(dir(1,i)>0) ph(i)=atan(dir(2,i)/dir(1,i));
else if(dir(2,i)>0) ph(i)=atan(dir(2,i)/dir(1,i))+M_PI;
else ph(i)=atan(dir(2,i)/dir(1,i))-M_PI;
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else if(dir(3,i)>0) th(i)=atan(sqrt(dir(1,i)*dir(1,i)+dir(2,i)*dir(2,i))/dir(3,i));
else th(i)=atan(sqrt(dir(1,i)*dir(1,i)+dir(2,i)*dir(2,i))/dir(3,i))+M_PI;
// added by SJ
void cart2sph(const vector<ColumnVector>& dir,ColumnVector& th,ColumnVector& ph)
{
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ph.ReSize(dir.size());
}
//double _2pi=2*M_PI;
double _pi2=M_PI/2;
int j=1;
for (unsigned int i=0;i<dir.size();i++) {
float mag=std::sqrt(dir[i](1)*dir[i](1)+dir[i](2)*dir[i](2)+dir[i](3)*dir[i](3));
if(mag==0){
ph(j)=_pi2;
th(j)=_pi2;
}
else{
if(dir[i](1)==0 && dir[i](2)>=0) ph(j)=_pi2;
else if(dir[i](1)==0 && dir[i](2)<0) ph(j)=-_pi2;
else if(dir[i](1)>0) ph(j)=std::atan(dir[i](2)/dir[i](1));
else if(dir[i](2)>0) ph(j)=std::atan(dir[i](2)/dir[i](1))+M_PI;
else ph(j)=std::atan(dir[i](2)/dir[i](1))-M_PI;
//ph(j)=fmod(ph(j),_2pi);
if(dir[i](3)==0) th(j)=_pi2;
else if(dir[i](3)>0) th(j)=std::atan(std::sqrt(dir[i](1)*dir[i](1)+dir[i](2)*dir[i](2))/dir[i](3));
else th(j)=std::atan(std::sqrt(dir[i](1)*dir[i](1)+dir[i](2)*dir[i](2))/dir[i](3))+M_PI;
//th(j)=fmod(th(j),M_PI);
}
j++;
}
}
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// Added by CFB --- Matlab style Matrix functions
ReturnMatrix ones(const int dim1, const int dim2)
{
int tdim = dim2;
if(tdim<0){tdim=dim1;}
Matrix res(dim1,tdim); res = 1.0;
res.Release();
return res;
}
ReturnMatrix zeros(const int dim1, const int dim2)
{
int tdim = dim2;
if(tdim<0){tdim=dim1;}
Matrix res(dim1,tdim); res = 0.0;
res.Release();
return res;
}
ReturnMatrix repmat(const Matrix &mat, const int rows, const int cols)
{
Matrix res = mat;
for(int ctr = 1; ctr < cols; ctr++){res |= mat;}
Matrix tmpres = res;
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for(int ctr = 1; ctr < rows; ctr++){res &= tmpres;}
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res.Release();
return res;
}
ReturnMatrix dist2(const Matrix &mat1, const Matrix &mat2)
{
Matrix res(mat1.Ncols(),mat2.Ncols());
for(int ctr1 = 1; ctr1 <= mat1.Ncols(); ctr1++)
for(int ctr2 =1; ctr2 <= mat2.Ncols(); ctr2++)
{
ColumnVector tmp;
tmp=mat1.Column(ctr1)-mat2.Column(ctr2);
res(ctr1,ctr2) = std::sqrt(tmp.SumSquare());
}
res.Release();
return res;
}
ReturnMatrix abs(const Matrix& mat)
{
Matrix res = mat;
for (int mc=1; mc<=mat.Ncols(); mc++) {
for (int mr=1; mr<=mat.Nrows(); mr++) {
res(mr,mc)=std::abs(res(mr,mc));
}
}
res.Release();
return res;
}
ReturnMatrix sqrt(const Matrix& mat)
{
Matrix res = mat;
bool neg_flag = false;
for (int mc=1; mc<=mat.Ncols(); mc++) {
for (int mr=1; mr<=mat.Nrows(); mr++) {
if(res(mr,mc)<0){ neg_flag = true; }
res(mr,mc)=std::sqrt(std::abs(res(mr,mc)));
}
}
if(neg_flag){
//cerr << " Matrix contained negative elements" << endl;
//cerr << " return sqrt(abs(X)) instead" << endl;
}
res.Release();
return res;
}
ReturnMatrix sqrtm(const Matrix& mat)
{
Matrix res, tmpU, tmpV;
DiagonalMatrix tmpD;
SVD(mat, tmpD, tmpU, tmpV);
res = tmpU*sqrt(tmpD)*tmpV.t();
res.Release();
return res;
}
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ReturnMatrix log(const Matrix& mat)
{
Matrix res = mat;
bool neg_flag = false;
for (int mc=1; mc<=mat.Ncols(); mc++) {
for (int mr=1; mr<=mat.Nrows(); mr++) {
if(res(mr,mc)<0){ neg_flag = true; }
res(mr,mc)=std::log(std::abs(res(mr,mc)));
}
}
if(neg_flag){
// cerr << " Matrix contained negative elements" << endl;
// cerr << " return log(abs(X)) instead" << endl;
}
res.Release();
return res;
}
ReturnMatrix exp(const Matrix& mat)
{
Matrix res = mat;
for (int mc=1; mc<=mat.Ncols(); mc++) {
for (int mr=1; mr<=mat.Nrows(); mr++) {
res(mr,mc)=std::exp(res(mr,mc));
}
}
res.Release();
return res;
}
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// optimised code for calculating matrix exponential
ReturnMatrix expm(const Matrix& mat){
float nmat = sum(mat).Maximum();
int nc=mat.Ncols(),nr=mat.Nrows();
Matrix res(nr,nc);
IdentityMatrix id(nr);
Matrix U(nr,nc),V(nr,nc);
if(nmat <= 1.495585217958292e-002){ // m=3
Matrix mat2(nr,nc);
mat2=mat*mat;
U = mat*(mat2+60.0*id);
V = 12.0*mat2+120.0*id;
res = (-U+V).i()*(U+V);
}
else if(nmat <= 2.539398330063230e-001){ // m=5
Matrix mat2(nr,nc),mat4(nr,nc);
mat2=mat*mat;mat4=mat2*mat2;
U = mat*(mat4+420.0*mat2+15120.0*id);
V = 30.0*mat4+3360.0*mat2+30240.0*id;
res = (-U+V).i()*(U+V);
}
else if(nmat <= 9.504178996162932e-001){ // m=7
Matrix mat2(nr,nc),mat4(nr,nc),mat6(nr,nc);
mat2=mat*mat;mat4=mat2*mat2,mat6=mat4*mat2;
U = mat*(mat6+1512.0*mat4+277200.0*mat2+8648640.0*id);
V = 56.0*mat6+25200.0*mat4+1995840.0*mat2+17297280.0*id;
res = (-U+V).i()*(U+V);
}
else if(nmat <= 2.097847961257068e+000){
Matrix mat2(nr,nc),mat4(nr,nc),mat6(nr,nc),mat8(nr,nc);
mat2=mat*mat;mat4=mat2*mat2,mat6=mat4*mat2,mat8=mat6*mat2;
U = mat*(mat8+3960.0*mat6+2162160.0*mat4+302702400.0*mat2+8821612800.0*id);
V = 90.0*mat8+110880.0*mat6+30270240.0*mat4+2075673600.0*mat2+17643225600.0*id;
res = (-U+V).i()*(U+V);
}
else if(nmat <= 5.371920351148152e+000){
Matrix mat2(nr,nc),mat4(nr,nc),mat6(nr,nc);
mat2=mat*mat;mat4=mat2*mat2,mat6=mat4*mat2;
U = mat*(mat6*(mat6+16380.0*mat4+40840800.0*mat2)+
+33522128640.0*mat6+10559470521600.0*mat4+1187353796428800.0*mat2+32382376266240000.0*id);
V = mat6*(182.0*mat6+960960.0*mat4+1323241920.0*mat2)
+ 670442572800.0*mat6+129060195264000.0*mat4+7771770303897600.0*mat2+64764752532480000.0*id;
res = (-U+V).i()*(U+V);
}
else{
double t;int s;
t = frexp(nmat/5.371920351148152,&s);
if(t==0.5) s--;
t = std::pow(2.0,s);
res = (mat/t);
Matrix mat2(nr,nc),mat4(nr,nc),mat6(nr,nc);
mat2=res*res;mat4=mat2*mat2,mat6=mat4*mat2;
U = res*(mat6*(mat6+16380*mat4+40840800*mat2)+
+33522128640.0*mat6+10559470521600.0*mat4+1187353796428800.0*mat2+32382376266240000.0*id);
V = mat6*(182.0*mat6+960960.0*mat4+1323241920.0*mat2)
+ 670442572800.0*mat6+129060195264000.0*mat4+7771770303897600.0*mat2+64764752532480000.0*id;
res = (-U+V).i()*(U+V);
for(int i=1;i<=s;i++)
res = res*res;
}
res.Release();
return res;
}
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ReturnMatrix tanh(const Matrix& mat)
{
Matrix res = mat;
for (int mc=1; mc<=mat.Ncols(); mc++) {
for (int mr=1; mr<=mat.Nrows(); mr++) {
res(mr,mc)=std::tanh(res(mr,mc));
}
}
res.Release();
return res;
}
ReturnMatrix pow(const Matrix& mat, const double exp)
{
Matrix res = mat;
for (int mc=1; mc<=mat.Ncols(); mc++) {
for (int mr=1; mr<=mat.Nrows(); mr++) {
res(mr,mc)=std::pow(res(mr,mc),exp);
}
}
res.Release();
return res;
}
ReturnMatrix max(const Matrix& mat)
{
Matrix res;
if(mat.Nrows()>1){
res=zeros(1,mat.Ncols());
res=mat.Row(1);
for(int mc=1; mc<=mat.Ncols();mc++){
for(int mr=2; mr<=mat.Nrows();mr++){
if(mat(mr,mc)>res(1,mc)){res(1,mc)=mat(mr,mc);}
}
}
}
else{
res=zeros(1);
res=mat(1,1);
for(int mc=2; mc<=mat.Ncols(); mc++){
if(mat(1,mc)>res(1,1)){res(1,1)=mat(1,mc);}
}
}
res.Release();
return res;
}
ReturnMatrix max(const Matrix& mat,ColumnVector& index)
{
index.ReSize(mat.Nrows());
index=1;
Matrix res;
if(mat.Nrows()>1){
res=zeros(1,mat.Ncols());
res=mat.Row(1);
for(int mc=1; mc<=mat.Ncols();mc++){
for(int mr=2; mr<=mat.Nrows();mr++){
if(mat(mr,mc)>res(1,mc))
{
res(1,mc)=mat(mr,mc);
index(mr)=mc;
}
}
}
}
else{
res=zeros(1);
res=mat(1,1);
for(int mc=2; mc<=mat.Ncols(); mc++){
if(mat(1,mc)>res(1,1))
{
res(1,1)=mat(1,mc);
index(1)=mc;
}
}
}
res.Release();
return res;
}
ReturnMatrix min(const Matrix& mat)
{
Matrix res;
if(mat.Nrows()>1){
res=zeros(1,mat.Ncols());
res=mat.Row(1);
for(int mc=1; mc<=mat.Ncols();mc++){
for(int mr=2; mr<=mat.Nrows();mr++){
if(mat(mr,mc)<res(1,mc)){res(1,mc)=mat(mr,mc);}
}
}
}
else{
res=zeros(1);
res=mat(1,1);
for(int mc=2; mc<=mat.Ncols(); mc++){
if(mat(1,mc)<res(1,1)){res(1,1)=mat(1,mc);}
}
}
res.Release();
return res;
}
ReturnMatrix sum(const Matrix& mat, const int dim)
{
Matrix tmp;
if (dim == 1) {tmp=mat;}
else {tmp=mat.t();}
Matrix res(1,tmp.Ncols());
res = 0.0;
for (int mc=1; mc<=tmp.Ncols(); mc++) {
for (int mr=1; mr<=tmp.Nrows(); mr++) {
res(1,mc) += tmp(mr,mc);
}
}
if (!(dim == 1)) {res=res.t();}
res.Release();
return res;
}
ReturnMatrix mean(const Matrix& mat, const int dim)
{
Matrix tmp;
if (dim == 1) {tmp=mat;}
else {tmp=mat.t();}
int N = tmp.Nrows();
Matrix res(1,tmp.Ncols());
res = 0.0;
for (int mc=1; mc<=tmp.Ncols(); mc++) {
for (int mr=1; mr<=tmp.Nrows(); mr++) {
res(1,mc) += tmp(mr,mc)/N;
}
}
if (!(dim == 1)) {res=res.t();}
res.Release();
return res;
}
ReturnMatrix var(const Matrix& mat, const int dim)
{
Matrix tmp;
if (dim == 1) {tmp=mat;}
else {tmp=mat.t();}
int N = tmp.Nrows();
Matrix res(1,tmp.Ncols());
res = 0.0;
if(N>1){
tmp -= ones(tmp.Nrows(),1)*mean(tmp,1);
for (int mc=1; mc<=tmp.Ncols(); mc++)
for (int mr=1; mr<=tmp.Nrows(); mr++)
res(1,mc) += tmp(mr,mc) / (N-1) * tmp(mr,mc);
}
if (!(dim == 1)) {res=res.t();}
res.Release();
return res;
}
ReturnMatrix stdev(const Matrix& mat, const int dim)
{
return sqrt(var(mat,dim));
}
ReturnMatrix gt(const Matrix& mat1,const Matrix& mat2)
{
Mark Jenkinson
committed
int ctrcol = std::min(mat1.Ncols(),mat2.Ncols());
int ctrrow = std::min(mat1.Nrows(),mat2.Nrows());
Matrix res(ctrrow,ctrcol);
res=0.0;
for (int ctr1 = 1; ctr1 <= ctrrow; ctr1++) {
for (int ctr2 =1; ctr2 <= ctrcol; ctr2++) {
if( mat1(ctr1,ctr2) > mat2(ctr1,ctr2)){
res(ctr1,ctr2) = 1.0;
}
}
}
res.Release();
return res;
}
ReturnMatrix lt(const Matrix& mat1,const Matrix& mat2)
{
Mark Jenkinson
committed
int ctrcol = std::min(mat1.Ncols(),mat2.Ncols());
int ctrrow = std::min(mat1.Nrows(),mat2.Nrows());
Matrix res(ctrrow,ctrcol);
res=0.0;
for (int ctr1 = 1; ctr1 <= ctrrow; ctr1++) {
for (int ctr2 =1; ctr2 <= ctrcol; ctr2++) {
if( mat1(ctr1,ctr2) < mat2(ctr1,ctr2)){
res(ctr1,ctr2) = 1.0;
}
}
}
res.Release();
return res;
}
ReturnMatrix geqt(const Matrix& mat1,const Matrix& mat2)
{
Mark Jenkinson
committed
int ctrcol = std::min(mat1.Ncols(),mat2.Ncols());
int ctrrow = std::min(mat1.Nrows(),mat2.Nrows());
Matrix res(ctrrow,ctrcol);
res=0.0;
for (int ctr1 = 1; ctr1 <= ctrrow; ctr1++) {
for (int ctr2 =1; ctr2 <= ctrcol; ctr2++) {
if( mat1(ctr1,ctr2) >= mat2(ctr1,ctr2)){
res(ctr1,ctr2) = 1.0;
}
}
}
res.Release();
return res;
}
ReturnMatrix geqt(const Matrix& mat,const float a)
{
int ncols = mat.Ncols();
int nrows = mat.Nrows();
Matrix res(nrows,ncols);
res=0.0;
for (int ctr1 = 1; ctr1 <= nrows; ctr1++) {
for (int ctr2 =1; ctr2 <= ncols; ctr2++) {
if( mat(ctr1,ctr2) >= a){
res(ctr1,ctr2) = 1.0;
}
}
}
res.Release();
return res;
}
ReturnMatrix leqt(const Matrix& mat1,const Matrix& mat2)
{
Mark Jenkinson
committed
int ctrcol = std::min(mat1.Ncols(),mat2.Ncols());
int ctrrow = std::min(mat1.Nrows(),mat2.Nrows());
Matrix res(ctrrow,ctrcol);
res=0.0;
for (int ctr1 = 1; ctr1 <= ctrrow; ctr1++) {
for (int ctr2 =1; ctr2 <= ctrcol; ctr2++) {
if( mat1(ctr1,ctr2) <= mat2(ctr1,ctr2)){
res(ctr1,ctr2) = 1.0;
}
}
}
res.Release();
return res;
}
ReturnMatrix eq(const Matrix& mat1,const Matrix& mat2)
{
Mark Jenkinson
committed
int ctrcol = std::min(mat1.Ncols(),mat2.Ncols());
int ctrrow = std::min(mat1.Nrows(),mat2.Nrows());
Matrix res(ctrrow,ctrcol);
res=0.0;
for (int ctr1 = 1; ctr1 <= ctrrow; ctr1++) {
for (int ctr2 =1; ctr2 <= ctrcol; ctr2++) {
if( mat1(ctr1,ctr2) == mat2(ctr1,ctr2)){
res(ctr1,ctr2) = 1.0;
}
}
}
res.Release();
return res;
}
ReturnMatrix neq(const Matrix& mat1,const Matrix& mat2)
{
Mark Jenkinson
committed
int ctrcol = std::min(mat1.Ncols(),mat2.Ncols());
int ctrrow = std::min(mat1.Nrows(),mat2.Nrows());
Matrix res(ctrrow,ctrcol);
res=0.0;
for (int ctr1 = 1; ctr1 <= ctrrow; ctr1++) {
for (int ctr2 =1; ctr2 <= ctrcol; ctr2++) {
if( mat1(ctr1,ctr2) != mat2(ctr1,ctr2)){
res(ctr1,ctr2) = 1.0;
}
}
}
res.Release();
return res;
}
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ReturnMatrix SD(const Matrix& mat1,const Matrix& mat2)
{
if((mat1.Nrows() != mat2.Nrows()) ||
(mat1.Ncols() != mat2.Ncols()) ){
cerr <<"MISCMATHS::SD - matrices are of different dimensions"<<endl;
exit(-1);
}
Matrix ret(mat1.Nrows(),mat1.Ncols());
for (int r = 1; r <= mat1.Nrows(); r++) {
for (int c =1; c <= mat1.Ncols(); c++) {
if( mat2(r,c)==0)
ret(r,c)=0;
else
ret(r,c) = mat1(r,c)/mat2(r,c);
}
}
ret.Release();
return ret;
}
ReturnMatrix vox_to_vox(const ColumnVector& xyz1,const ColumnVector& dims1,const ColumnVector& dims2,const Matrix& xfm){
ColumnVector xyz1_mm(4),xyz2_mm,xyz2(3);
xyz1_mm<<xyz1(1)*dims1(1)<<xyz1(2)*dims1(2)<<xyz1(3)*dims1(3)<<1;
xyz2_mm=xfm*xyz1_mm;
xyz2_mm=xyz2_mm/xyz2_mm(4);
xyz2<<xyz2_mm(1)/dims2(1)<<xyz2_mm(2)/dims2(2)<<xyz2_mm(3)/dims2(3);
xyz2.Release();
return xyz2;
}
ReturnMatrix mni_to_imgvox(const ColumnVector& mni,const ColumnVector& mni_origin,const Matrix& mni2img, const ColumnVector& img_dims){
ColumnVector mni_new_origin(4),img_mm;//homogeneous
ColumnVector img_vox(3);
mni_new_origin<<mni(1)+mni_origin(1)<<mni(2)+mni_origin(2)<<mni(3)+mni_origin(3)<<1;
img_mm=mni2img*mni_new_origin;
img_vox<<img_mm(1)/img_dims(1)<<img_mm(2)/img_dims(2)<<img_mm(3)/img_dims(3);
img_vox.Release();
return img_vox;
}
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ReturnMatrix remmean(const Matrix& mat, const int dim)
{
Matrix res;
if (dim == 1) {res=mat;}
else {res=mat.t();}
Matrix Mean;
Mean = mean(res);
for (int ctr = 1; ctr <= res.Nrows(); ctr++) {
for (int ctr2 =1; ctr2 <= res.Ncols(); ctr2++) {
res(ctr,ctr2)-=Mean(1,ctr2);
}
}
if (dim != 1) {res=res.t();}
res.Release();
return res;
}
void remmean(const Matrix& mat, Matrix& demeanedmat, Matrix& Mean, const int dim)
{
if (dim == 1) {demeanedmat=mat;}
else {demeanedmat=mat.t();}
Mean = mean(demeanedmat);
for (int ctr = 1; ctr <= demeanedmat.Nrows(); ctr++) {
for (int ctr2 =1; ctr2 <= demeanedmat.Ncols(); ctr2++) {
demeanedmat(ctr,ctr2)-=Mean(1,ctr2);
}
}
if (dim != 1){demeanedmat = demeanedmat.t();Mean = Mean.t();}
}
ReturnMatrix cov(const Matrix& mat, const int norm)
{
SymmetricMatrix res;
Matrix tmp;
int N;
tmp=remmean(mat);
if (norm == 1) {N = mat.Nrows();}
else {N = mat.Nrows()-1;}
res << tmp.t()*tmp;
res = res/N;
res.Release();
return res;
}
ReturnMatrix corrcoef(const Matrix& mat, const int norm)
{
SymmetricMatrix res;
SymmetricMatrix C;
C = cov(mat,norm);
Matrix D;
D=diag(C);
D=pow(sqrt(D*D.t()),-1);
res << SP(C,D);
res.Release();
return res;
}
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ReturnMatrix flipud(const Matrix& mat)
{
Matrix rmat(mat.Nrows(),mat.Ncols());
for(int j=1;j<=mat.Ncols();j++)
for(int i=1;i<=mat.Nrows();i++)
rmat(i,j)=mat(mat.Nrows()-i+1,j);
rmat.Release();
return rmat;
}
ReturnMatrix fliplr(const Matrix& mat)
{
Matrix rmat(mat.Nrows(),mat.Ncols());
for(int j=1;j<=mat.Ncols();j++)
for(int i=1;i<=mat.Nrows();i++)
rmat(i,j)=mat(i,mat.Ncols()-j+1);
rmat.Release();
return rmat;
}
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void symm_orth(Matrix &Mat)
{
SymmetricMatrix Metric;
Metric << Mat.t()*Mat;
Metric = Metric.i();
Matrix tmpE;
DiagonalMatrix tmpD;
EigenValues(Metric,tmpD,tmpE);
Mat = Mat * tmpE * sqrt(abs(tmpD)) * tmpE.t();
}
void powerspectrum(const Matrix &Mat1, Matrix &Result, bool useLog)
//calculates the powerspectrum for every column of Mat1
{
Matrix res;
for(int ctr=1; ctr <= Mat1.Ncols(); ctr++)
{
ColumnVector tmpCol;
tmpCol=Mat1.Column(ctr);
ColumnVector FtmpCol_real;
ColumnVector FtmpCol_imag;
ColumnVector tmpPow;
RealFFT(tmpCol,FtmpCol_real,FtmpCol_imag);
tmpPow = pow(FtmpCol_real,2)+pow(FtmpCol_imag,2);
tmpPow = tmpPow.Rows(2,tmpPow.Nrows());
if(useLog){tmpPow = log(tmpPow);}
if(res.Storage()==0){res= tmpPow;}else{res|=tmpPow;}
}
Result=res;
}