EWP-BplusToKstMuMu-AngAna/Code/FCNCFitter/sources/Core/toystudy.cc

/**
 * @file toystudy.cc
 * @author Renata Kopecna (cause I find 500 lines functioned defined in a header disgusting)
 * @date 2021-02-25
 *
 */

#include <toystudy.hh>

#include <iostream>

#include <event.hh>
#include <parameters.hh>
#include <funcs.hh>
#include <pdf.hh>
#include <options.hh>
#include <fitter.hh>
#include <generator.hh>
#include <multifit.hh>


void fcnc::toystudy::toy(unsigned int nevents, unsigned int nruns, pdf* prob, parameters* params, generator* gen){
    std::vector<unsigned int> the_nevents;
    the_nevents.push_back(nevents);

    std::vector<pdf*> probs;
    probs.push_back(prob);

    std::vector<parameters*> the_params;
    the_params.push_back(params);

    std::vector<generator*> gens;
    gens.push_back(gen);

    toy(the_nevents, nruns, probs, the_params, gens);
}

void fcnc::toystudy::toy(std::vector<unsigned int> nevents, unsigned int nruns, std::vector<pdf*> pdfs, std::vector<parameters*> params, std::vector<generator*> gens){

    //Open texFile
    std::ofstream myFile;
    open_Latex_noteFile(latex_toyFile(), myFile);

    spdlog::info("Starting toy study on toy data");
    //unsigned int run_no = 0;

    spdlog::info("Will run over {0:d} runs with  {1:d}", nruns, nevents.at(0));
    if (spdlog_info()){
        for (unsigned int i=1; i<nevents.size(); i++) std::cout << "/" << nevents.at(i);
    }
    spdlog::info(" events each.");

    //need this for jitter on constraints
    TRandom3* rnd = new TRandom3();
    rnd->SetSeed(0);//non-static seed
    //initialisation for histos and arrrays
    unsigned int nparams = 0;
    for (unsigned int i=0; i<params.size(); i++){
        nparams += params.at(i)->nparameters();
    }

    std::vector<double> pull_width(nparams, 0.0);
    std::vector<double> pull_mean(nparams, 0.0);
    std::vector<double> pull_width_sigma(nparams, 0.0);
    std::vector<double> pull_mean_sigma(nparams, 0.0);
    std::vector<double> pull_chisquared(nparams, 0.0);
    std::vector<double> value_width(nparams, 0.0);
    std::vector<double> value_mean(nparams, 0.0);
    std::vector<double> value_width_sigma(nparams, 0.0);
    std::vector<double> value_mean_sigma(nparams, 0.0);
    std::vector<double> value_chisquared(nparams, 0.0);
    std::vector<double> error_width(nparams, 0.0);
    std::vector<double> error_mean(nparams, 0.0);
    std::vector<double> error_width_sigma(nparams, 0.0);
    std::vector<double> error_mean_sigma(nparams, 0.0);
    std::vector<double> error_chisquared(nparams, 0.0);
    std::vector<std::vector<double> > errors(nparams, std::vector<double>());
    std::vector<std::vector<double> > errors_up(nparams, std::vector<double>());
    std::vector<std::vector<double> > errors_down(nparams, std::vector<double>());
    std::vector<std::vector<double> > values(nparams, std::vector<double>());
    std::vector<std::vector<double> > nominal_errors(nparams, std::vector<double>());
    std::vector<std::vector<double> > nominal_values(nparams, std::vector<double>());
    std::vector<int> return_values(nruns);
    std::vector<std::vector<std::vector<double> > > corrs(nparams, std::vector<std::vector<double> >());

    fcnc::fitter fit(opts);
    for (unsigned int i=0; i<pdfs.size(); i++){
        pdfs.at(i)->init(params.at(i));
    }
    fit.set_common_parameters(common_params);

    for (unsigned int i=0; i < nruns; i++) {
        spdlog::info("Starting run no. {0:d}", i + 1);
        //run_no = i;

        std::vector< std::vector<event> > temp_events;
        for (unsigned int j=0; j<gens.size(); j++){
            std::vector<event> ev = gens.at(j)->generate(nevents.at(j), params.at(j), pdfs.at(j));
            temp_events.push_back(ev);
        }
        std::vector< std::vector<event>* > events;
        for (unsigned int j=0; j<gens.size(); j++){
            events.push_back(&temp_events.at(j));
        }

        std::string num;
        std::stringstream out;
        out << (i+1);
        num = out.str();
        int result = 0;

        //in the case of constraints, jitter the mean value
        for (unsigned int j = 0; j < params.size(); j++){
            for (unsigned int k = 0; k < params.at(j)->nparameters(); k++){
                parameter* param = params.at(j)->get_parameter(k);
                double shift = rnd->Rndm() > 0.5 ? -fabs(rnd->Gaus(0.0, param->get_previous_error_low())) : fabs(rnd->Gaus(0.0, param->get_previous_error_high()));//TODO, actually should probably use PDG method
                if (param->get_gaussian_constraint()){
                    param->set_previous_measurement(param->get_start_value() + shift);//rnd->Gaus(0.0, sigma));
                }
            }
        }
        if (opts->eos_bsz_ff){
            TMatrixD mU = fcnc::get_bsz_mu_invcov();

            std::vector<double> rndvec(21, 0.0);
            for (unsigned int i=0; i<21; i++){
                rndvec.at(i) = rnd->Gaus(0.0, 1.0);
            }
            std::vector<double> shiftvec(21, 0.0);
            for (unsigned int i=0; i<21; i++){
                for (unsigned int j=0; j<21; j++){
                    shiftvec.at(i) += mU(j,i)*rndvec.at(j);//new order, corrected and tested
                }
            }
            for (unsigned int j = 0; j < params.size(); j++){
                if (params.at(j)->get_parameter("a0_0") != 0){
                    //TODO: jitter according to covariance matrix given
                    //std::vector<double> coeffvec(21, 0.0);// = nominal;//fcnc::legendre_coefficients_nominal;
                    for (unsigned int i=0; i<21; i++){
                        parameter* param = 0;
                        switch (i) {
                        case 0 : param = params.at(j)->get_parameter("a0_0"); break;
                        case 1 : param = params.at(j)->get_parameter("a0_1"); break;
                        case 2 : param = params.at(j)->get_parameter("a0_2"); break;
                        case 3 : param = params.at(j)->get_parameter("a1_0"); break;
                        case 4 : param = params.at(j)->get_parameter("a1_1"); break;
                        case 5 : param = params.at(j)->get_parameter("a1_2"); break;
                        //case 6 : param = params.at(j)->get_parameter(""); break;
                        case 7 : param = params.at(j)->get_parameter("a12_1"); break;
                        case 8 : param = params.at(j)->get_parameter("a12_2"); break;
                        case 9 : param = params.at(j)->get_parameter("v_0"); break;
                        case 10 : param = params.at(j)->get_parameter("v_1"); break;
                        case 11 : param = params.at(j)->get_parameter("v_2"); break;
                        case 12 : param = params.at(j)->get_parameter("t1_0"); break;
                        case 13 : param = params.at(j)->get_parameter("t1_1"); break;
                        case 14 : param = params.at(j)->get_parameter("t1_2"); break;
                        //case 15 : param = params.at(j)->get_parameter(""); break;
                        case 16 : param = params.at(j)->get_parameter("t2_1"); break;
                        case 17 : param = params.at(j)->get_parameter("t2_2"); break;
                        case 18 : param = params.at(j)->get_parameter("t23_0"); break;
                        case 19 : param = params.at(j)->get_parameter("t23_1"); break;
                        case 20 : param = params.at(j)->get_parameter("t23_2"); break;
                        };
                        if (param != 0){
                            param->set_previous_measurement(param->get_start_value() + shiftvec.at(i));
                        }
                    }
                }
            }
        }
        //perform the actual fit
        result = fit.fit(pdfs, params, events, num);
        return_values.at(i) = result;

        if (result != 300 && opts->repeat_on_fail) {
            spdlog::error("Fit failed, repeating run");
            for (unsigned int j = 0; j < params.size(); j++){
                params.at(j)->reset_parameters();
            }
            i--;
            continue;
        }

        //extract the parameters from the fit  
        unsigned int start_param = 0;
        for (unsigned int j = 0; j < params.size(); j++) {
            for (unsigned int k = 0; k < params.at(j)->nparameters(); k++){
                parameter* param = params.at(j)->get_parameter(k);
                unsigned int idx = start_param+k;
                values.at(idx).push_back(param->get_value());
                errors.at(idx).push_back(param->get_error());
                errors_up.at(idx).push_back(param->get_error_up());
                errors_down.at(idx).push_back(param->get_error_down());
                corrs.at(idx).push_back(param->correlations);//for some, this is empty
            }
            start_param += params.at(j)->nparameters();
        }
        //todo whats this? //I don't know, David. Sincerely, Renata
        for (unsigned int j = 0; j < nparams; j++){
            nominal_values.at(j).push_back(0.0);
            nominal_errors.at(j).push_back(0.0);
        }

        //Update pull histos every 10 steps or when finished.
        //if ((i+1 >= 100) && (((i+1)%100 == 0) || (i+1 == nruns)))
        if (i+1 == nruns)   {
            //This should be done a little differently:
            //recreate histos every time, use min and max values as axis.
            start_param = 0;
            for (unsigned int j = 0; j < params.size(); j++){//this plots the pull histos
                for (unsigned int k = 0; k < params.at(j)->nparameters(); k++){//this plots the pull histos
                    parameter* par = params.at(j)->get_parameter(k);
                    if (par->get_step_size() != 0.0) {
                        unsigned int idx = start_param+k;
                        update_pull(par, values.at(idx),
                                    errors.at(idx),
                                    pull_mean[idx], pull_mean_sigma[idx],
                                    pull_width[idx], pull_width_sigma[idx],
                                    pull_chisquared[idx]);
                        update_value(par, values.at(idx), errors.at(idx),
                                     value_mean[idx], value_mean_sigma[idx],
                                     value_width[idx], value_width_sigma[idx],
                                     value_chisquared[idx]);
                        update_error(par, values.at(idx), errors.at(idx),
                                     error_mean[idx], error_mean_sigma[idx],
                                     error_width[idx], error_width_sigma[idx],
                                     error_chisquared[idx]);
                    }
                }
                start_param += params.at(j)->nparameters();
            }
        }

        for (unsigned int j = 0; j < params.size(); j++){
            params.at(j)->reset_parameters();
        }

        spdlog::info("Run {0:d} finished", i + 1);
    }
    //delete rnd;

    //save result trees to root file
    //TFile* output;
    output->cd();
    int param_index = 0;

    unsigned int start_param = 0;
    for (unsigned int j = 0; j < params.size(); j++){
        for (unsigned int k = 0; k < params.at(j)->nparameters(); k++){
            parameter* param = params.at(j)->get_parameter(k);
            unsigned int idx = start_param+k;
            //for (unsigned int j = 0; j < nparams; j++)//this gives nice output
            if (param->get_step_size() != 0.0) {//todo common parameters, only save once...
                std::string parname(param->get_name());
                std::string pardesc(param->get_description());
                TTree* t = new TTree(parname.c_str(), pardesc.c_str());
                double value, error, error_up, error_down, start_value, nominal_value, nominal_error;
                int run, migrad, status_cov;
                double tmp_corr[corrs.at(idx).at(0).size()];
                t->Branch("run",&run,"run/I");
                t->Branch("value",&value,"value/D");
                t->Branch("error",&error,"error/D");
                t->Branch("error_up",&error_up,"error_up/D");
                t->Branch("error_down",&error_down,"error_down/D");
                t->Branch("start_value",&start_value,"start_value/D");
                t->Branch("migrad",&migrad,"migrad/I");
                t->Branch("status_cov",&status_cov,"status_cov/I");
                t->Branch("nominal_value",&nominal_value,"nominal_value/D");
                t->Branch("nominal_error",&nominal_error,"nominal_error/D");
                t->Branch("index",&param_index, "index/I");
                std::string corr_string = "correlations";
                std::stringstream corr_stream;
                corr_stream << "correlations[" << corrs.at(idx).at(0).size() << "]/D";
                t->Branch("correlations",&tmp_corr,corr_stream.str().c_str());
                for (unsigned int i=0; i<nruns; i++){
                    run = i;
                    if (param->is_blind()){
                        value = values.at(idx).at(i) + param->get_blinding_delta();//TODO add delta
                    }
                    else{
                        value = values.at(idx).at(i);//TODO add delta
                    }
                    error = errors.at(idx).at(i);
                    if (param->is_blind()){
                        nominal_value = nominal_values.at(idx).at(i) + param->get_blinding_delta();
                    }
                    else{
                        nominal_value = nominal_values.at(idx).at(i);
                    }
                    nominal_error = nominal_errors.at(idx).at(i);
                    error_up = errors_up.at(idx).at(i);
                    error_down = errors_down.at(idx).at(i);
                    if (param->is_blind()){
                        start_value = param->get_start_value() + param->get_blinding_delta();
                    }
                    else{
                        start_value = param->get_start_value();
                    }
                    migrad = return_values.at(i) % 100;
                    status_cov = return_values.at(i) / 100;
                    //corr = corrs.at(idx).at(i);
                    for (unsigned int l = 0; l < corrs.at(idx).at(i).size(); l++){
                        tmp_corr[l] = corrs.at(idx).at(i).at(l);
                    }
                    t->Fill();
                }
                t->Write();
                delete t;
                param_index++;
            }
        }
        start_param += params.at(j)->nparameters();
    }
    //latex output
    bool blind = false;
    for (unsigned int j = 0; j < params.size(); j++){
        if (!params.at(j)->is_blind()) blind = true;
    }
    spdlog::info("Toy Study results");
    if (!blind){
        myFile <<"\\begin{tabular}{|cccccccc|}\\hline" << std::endl;
        myFile <<"\\# & parameter & Mean Value & Mean Width & Mean Error & Error Width & Pull Mean & Pull Width \\\\ \\hline \\hline" << std::endl;
        start_param = 0;
        for (unsigned int j = 0; j < params.size(); j++)
        {
            for (unsigned int k = 0; k < params.at(j)->nparameters(); k++)
            {
                parameter* param = params.at(j)->get_parameter(k);
                unsigned int idx = start_param+k;
                if (param->get_step_size() != 0.0){
                    std::string partex = param->get_description();
                    myFile <<std::setw(2) << idx << " & $" << std::setw(22) << partex << "$ & $"
                              << std::fixed << std::setprecision(4)
                              << std::setw(7) << value_mean[idx] << " \\pm " << std::setw(7) << value_mean_sigma[idx] << "$ & $"
                              << std::setw(7) << value_width[idx] << " \\pm " << std::setw(7) << value_width_sigma[idx] << "$ & $"
                              << std::setw(7) << error_mean[idx] << " \\pm " << std::setw(7) << error_mean_sigma[idx] << "$ & $"
                              << std::setw(7) << error_width[idx] << " \\pm " << std::setw(7) << error_width_sigma[idx] << "$ & $"
                              << std::setw(7) << pull_mean[idx] << " \\pm " << std::setw(7) << pull_mean_sigma[idx] << "$ & $"
                              << std::setw(7) << pull_width[idx] << " \\pm " << std::setw(7) << pull_width_sigma[idx] << "$ \\\\"
                              << std::endl;
                }
            }
            start_param += params.at(j)->nparameters();
        }
        myFile <<"\\hline\\end{tabular}" << std::endl;
    }

    delete rnd;

    //Close Latex file
    myFile.close();

}