int main()
{
read_pars("analysis_pars");
read_ensemble_pars(base_path,T,TSEP,ibeta,nmass,mass,iml_un,nlight,ntheta,theta,ist_th,njack,data_list_file);
TH=L=T/2;
//we stick to the light-heavy
int im_spec=0;
//load the 2 points and fits M and Z
jack M[nmass];
jack Z[nmass];
for(int im_S0=0;im_S0<nmass;im_S0++)
{
int ith_S0=ist_th;
fit_ZM_2pts(M[im_S0],Z[im_S0],im_spec,im_S0,ith_S0);
}
ofstream FT_file("FT.dat");
ofstream FP_file("FP.dat");
ofstream FM_file("FM.dat");
ofstream F0_file("F0.dat");
FILE *Q2_file=open_file("Q2_combo","w");
//loop over all the theta combination
for(int ith_S1=0;ith_S1<ntheta;ith_S1++)
for(int ith_S0=ith_S1;ith_S0<ntheta;ith_S0++)
{
//compute energy
jack E_source=Eth(M[gen_im_S0],ith_S0);
jack E_sink=Eth(M[gen_im_S1],ith_S1);
//construct time dependance
jvec time_dep(T,njack);
for(int t=0;t<T;t++)
{
jack coef=Z[gen_im_S0]*Z[gen_im_S1]/sqrt(2*E_source*2*E_sink);
jack forw,back;
if(t<TSEP)
{
forw=exp(-E_source*t-E_sink*(TSEP-t));
back=exp(-E_source*(T-t)-E_sink*(T-(TSEP-t)));
}
else
{
forw=exp(-E_source*(T-t)-E_sink*(t-TSEP));
back=exp(-E_source*t-E_sink*(T-(t-TSEP)));
}
time_dep[t]=coef*(forw+back);
}
//loop over matrix element
int iV0=0,iVK=1,iTK=2;
char MEL_name[3][4]={"V0","VK","TK"};
jack MEL[3];
jvec MEL_corr[3];
for(int iMEL=0;iMEL<3;iMEL++)
{
//load 3pts
jvec P5MELP5;
switch(iMEL)
{
case 0:P5MELP5=load_P5V0P5(gen_im_S0,ith_S0,gen_im_S1,ith_S1);break;
case 1:P5MELP5=load_P5VKP5(gen_im_S0,ith_S0,gen_im_S1,ith_S1);break;
case 2:P5MELP5=load_P5TKP5(gen_im_S0,ith_S0,gen_im_S1,ith_S1);break;
}
P5MELP5.subset(1,TSEP).print_to_file(combine("P5%sP5/%02d_%d_%02d_%d.xmg",MEL_name[iMEL],gen_im_S0,ith_S0,gen_im_S1,ith_S1).c_str());
time_dep.subset(1,TSEP).print_to_file(combine("time_dep/%02d_%d_%02d_%d.xmg",gen_im_S0,ith_S0,gen_im_S1,ith_S1).c_str());
//compute remotion of time dependance
MEL_corr[iMEL]=P5MELP5/time_dep;
MEL[iMEL]=constant_fit(MEL_corr[iMEL].subset(1,TSEP),tmin_MEL,tmax_MEL,combine("%s/%02d_%d_%02d_%d.xmg",MEL_name[iMEL],gen_im_S0,ith_S0,gen_im_S1,ith_S1).c_str());
}
//ratios
jvec VK_fr_TK_corr=MEL_corr[iVK]/MEL_corr[iTK];
jack VK_fr_TK=constant_fit(VK_fr_TK_corr.subset(1,TSEP),tmin_MEL,tmax_MEL,combine("VK_fr_TK/%02d_%d_%02d_%d.xmg",gen_im_S0,ith_S0,gen_im_S1,ith_S1).c_str());
jvec V0_fr_TK_corr=MEL_corr[iV0]/MEL_corr[iTK];
jack V0_fr_TK=constant_fit(V0_fr_TK_corr.subset(1,TSEP),tmin_MEL,tmax_MEL,combine("V0_fr_TK/%02d_%d_%02d_%d.xmg",gen_im_S0,ith_S0,gen_im_S1,ith_S1).c_str());
//////////////////////////// compute_form_factors ///////////////////////
//2*Zv
MEL[iVK]/=-2*1.69;
MEL[iV0]/=-2*1.69;
//impulses
jack M_source=M[gen_im_S0],M_sink=M[gen_im_S1];
double p_source=M_PI*theta[ith_S0]/L,p_sink=M_PI*theta[ith_S1]/L;
jack P0=E_source+E_sink,Q0=E_source-E_sink;
double Pi=p_source+p_sink,Qi=p_source-p_sink;
jack Q2=Q0*Q0-3*Qi*Qi;
jack P2=P0*P0-3*Pi*Pi;
jack DM2=M_source*M_source-M_sink*M_sink;
//TK=(E_source*p_sink-p_source*E_sink)*2FT/(M_source+M_sink)
jack FT=MEL[iTK]*0.5*(M_source+M_sink)/(E_source*p_sink-p_source*E_sink);
//FP,F0
jack FM,FP,F0;
if(ith_S0!=iopp[ith_S1])
{
jack DET=P0*Qi-Q0*Pi;
//calculate (f+) and (f-)*Q2/(M_K^2-M_Pi^2)
FP=(MEL[iV0]*Qi-Q0*MEL[iVK])/DET;
FM=(P0*MEL[iVK]-MEL[iV0]*Pi)/DET;
F0=FP+Q2/DM2*FM;
}
else
{
//calculate (f+) and (f-)*Q2/(M_K^2-M_Pi^2)
FM=MEL[iVK]/Qi;
FP=(MEL[iV0]-Q0*FM)/P0;
F0=FP+Q2/DM2*FM;
}
FT_file<<Q2<<" "<<FT<<" "<<Pi<<endl;
FP_file<<Q2<<" "<<FP<<" "<<Pi<<endl;
FM_file<<Q2<<" "<<FM<<" "<<Pi<<endl;
F0_file<<Q2<<" "<<F0<<" "<<Pi<<endl;
fprintf(Q2_file,"th0=%f\t" "th1=%f\t" "Q2=%f\n",theta[ith_S0],theta[ith_S1],Q2.med());
}
fclose(Q2_file);
return 0;
}
void normalToStdVector(omxMatrix *cov, omxMatrix *mean, omxMatrix *slope, omxMatrix *thr,
int numOrdinal, std::vector< omxThresholdColumn > &ti,
Eigen::Ref<Eigen::VectorXd> out)
{
// order of elements: (c.f. lav_model_wls, lavaan 0.6-2)
// 1. thresholds + means (interleaved)
// 2. slopes (if any, columnwise per exo)
// 3. variances (continuous indicators only)
// 4. covariances; not correlations (lower triangle)
EigenMatrixAdaptor Ecov(cov);
if (numOrdinal == 0) {
int dx = 0;
if (mean) {
EigenVectorAdaptor Emean(mean);
for (int rx=0; rx < cov->cols; ++rx) {
out[dx++] = Emean(rx);
}
}
if (slope) {
EigenMatrixAdaptor Eslope(slope);
for (int cx=0; cx < Eslope.cols(); ++cx) {
for (int rx=0; rx < Eslope.rows(); ++rx) {
out[dx++] = Eslope(rx,cx);
}
}
}
for (int cx=0; cx < cov->cols; ++cx) {
out[dx++] = Ecov(cx,cx);
}
for (int cx=0; cx < cov->cols-1; ++cx) {
for (int rx=cx+1; rx < cov->rows; ++rx) {
out[dx++] = Ecov(rx,cx);
}
}
return;
}
if (!mean) Rf_error("ordinal indicators and no mean vector");
EigenVectorAdaptor Emean(mean);
EigenMatrixAdaptor Eth(thr);
Eigen::VectorXd sdTmp(1.0/Ecov.diagonal().array().sqrt());
Eigen::DiagonalMatrix<double, Eigen::Dynamic> sd(Emean.size());
sd.setIdentity();
int dx = 0;
for (auto &th : ti) {
for (int t1=0; t1 < th.numThresholds; ++t1) {
double sd1 = sdTmp[th.dColumn];
out[dx++] = (Eth(t1, th.column) - Emean[th.dColumn]) * sd1;
sd.diagonal()[th.dColumn] = sd1;
}
if (!th.numThresholds) {
out[dx++] = Emean[th.dColumn];
}
}
if (slope) {
EigenMatrixAdaptor Eslope(slope);
for (int cx=0; cx < Eslope.cols(); ++cx) {
for (int rx=0; rx < Eslope.rows(); ++rx) {
out[dx++] = Eslope(rx,cx);
}
}
}
Eigen::MatrixXd stdCov(sd * Ecov * sd);
for (int cx=0; cx < cov->cols; ++cx) {
if (ti[cx].numThresholds) continue;
out[dx++] = stdCov(cx,cx);
}
for (int cx=0; cx < cov->cols-1; ++cx) {
for (int rx=cx+1; rx < cov->rows; ++rx) {
out[dx++] = stdCov(rx,cx);
}
}
}
