int main(int argc, char** argv)
{
try {
/* Define the name of the model to be used. */
std::string ModelName = "NPEpsilons";
/* Create an object of the class InputParameters. */
InputParameters IP;
/* Read a map for the mandatory model parameters. (Default values in InputParameters.h) */
std::map<std::string, double> DPars_IN = IP.getInputParameters(ModelName);
/* Change the default values of the mandatory model parameters if necessary. */
/* This can also be done with DPars after creating an object of ComputeObservables. */
DPars_IN["mcharm"] = 1.3;
DPars_IN["mub"] = 4.2;
/* Create objects of the classes ModelFactory and ThObsFactory */
ModelFactory ModelF;
ThObsFactory ThObsF;
/* Create an object of the class ComputeObservables. */
ComputeObservables CO(ModelF, ThObsF, ModelName, DPars_IN);
/* Add the observables to be returned. */
CO.AddObservable("Mw");
CO.AddObservable("GammaZ");
CO.AddObservable("AFBbottom");
/* Remove a previously added observables if necessary. */
//CO.RemoveObservable("AFBbottom");
/* Set the flags for the model being used, if necessary. */
std::map<std::string, std::string> DFlags;
DFlags["epsilon2SM"] = "TRUE";
DFlags["epsilonbSM"] = "TRUE";
CO.setFlags(DFlags);
/* Get the map of observables if necessary. */
std::map<std::string, double> DObs = CO.getObservables();
/* Define a map for the parameters to be varied. */
std::map<std::string, double> DPars;
for (int i = 0; i < 2; i++) {
/* Vary the parameters that need to be varied in the analysis. */
DPars["epsilon_1"] = 0. + i * 0.01;
DPars["epsilon_3"] = 0. + i * 0.01;
/* Get the map of observables with the parameter values defined above. */
DObs = CO.compute(DPars);
std::cout << "\nParameters[" << i + 1 << "]:"<< std::endl;
for (std::map<std::string, double>::iterator it = DPars.begin(); it != DPars.end(); it++) {
std::cout << it->first << " = " << it->second << std::endl;
}
std::cout << "\nObservables[" << i + 1 << "]:" << std::endl;
for (std::map<std::string, double>::iterator it = DObs.begin(); it != DObs.end(); it++) {
std::cout << it->first << " = " << it->second << std::endl;
}
}
return EXIT_SUCCESS;
} catch (const std::runtime_error& e) {
std::cerr << e.what() << std::endl;
return EXIT_FAILURE;
}
}
double hepfit_call(double *theta, int nDims, double *phi, int nDerived, int index){
std::string selected_pars[nDims] = {"mtop","AlsMz"};
std::string selected_obsvs[nDerived] = {"Mw"};
std::string ModelName = "StandardModel";
/* Create an object of the class InputParameters. */
InputParameters IP;
/* Read a map for the mandatory model parameters. (Default values in InputParameters.h) */
std::map<std::string, double> DPars_IN = IP.getInputParameters(ModelName);
/* Create objects of the classes ModelFactory and ThObsFactory */
ModelFactory ModelF;
ThObsFactory ThObsF;
/* Create an object of the class ComputeObservables. */
ComputeObservables CO(ModelF, ThObsF, ModelName, DPars_IN);
/* Add the observables to be returned. */
CO.AddObservable( selected_obsvs[0] );
/* Set the flags for the model being used, if necessary. */
// std::map<std::string, std::string> DFlags;
// DFlags["epsilon2SM"] = "TRUE";
// CO.setFlags(DFlags);
/* Get the map of observables */
std::map<std::string, double> DObs = CO.getObservables();
/* Define a map for the parameters to be varied. */
std::map<std::string, double> DPars;
/* Vary the parameters that need to be varied in the analysis. */
// to be obtained from sampler: PolyChord or MultiNest
for(int i = 0; i < nDims; i++){
DPars[ selected_pars[i] ] = theta[i];
std::cout << "DPars[ "<< selected_pars[i] << " ] = " << DPars[ selected_pars[i] ];
}
/* Get the map of observables with the parameter values defined above. */
DObs = CO.compute(DPars);
for (int i = 0; i < nDerived; i++){
phi[i] = DObs[ selected_obsvs[i] ];
std::cout << "DObs[ "<< selected_obsvs[i] << " ] = " << DObs[ selected_obsvs[i] ] << std::endl;
}
//@@@@ HEPfit should have option for return log-likelihood ??
// compute and retrun log likelihood
// Observable: Mw = 80.3613 +- 0.03, say
double Mw = 80.3613;
double Mw_err = 0.03;
double MwPredicted = phi[0];
double chi = (MwPredicted - Mw) / ( sqrt(2.) * Mw_err );
std::cout << "log-likelihood = " << - chi * chi - log( Mw_err * sqrt(2.0 * M_PI) );
return - chi * chi - log( Mw_err * sqrt(2.0 * M_PI) );
}