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230 lines
9.0 KiB
230 lines
9.0 KiB
//Renata Kopecna
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#include <TCanvas.h>
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#include <TPaveText.h>
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#include <TTree.h>
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#include <TBranch.h>
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#include <TF1.h>
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#include <TH1D.h>
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#include <TStyle.h>
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#include <string>
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#include <vector>
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#include <event.hh>
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#include <parameters.hh>
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#include <funcs.hh>
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#include <pdf.hh>
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#include <options.hh>
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#include <multifit.hh>
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using namespace std;
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using namespace fcnc;
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multifit::multifit(options* o):
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opts(o)
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{
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output = new TFile(("result" + opts->name + ".root").c_str(), "RECREATE");
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};
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multifit::~multifit(){
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output->Write();
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output->Close();
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delete output;
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};
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void multifit::fit_gaussian(vector<double>& values, double& gauss_mean, double& sigma_gauss_mean, double& gauss_width, double& sigma_gauss_width, double& chi_squared) const
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{
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double max = *max_element(std::begin(values), std::end(values));
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double min = *min_element(std::begin(values), std::end(values));
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double mean=0.0;
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double rms=0.0;
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TTree* t = new TTree("tree", "pull values");
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double value;
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TBranch *branch = t->Branch("value",&value,"value/D");
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for (unsigned int i = 0; i < values.size(); i++) {
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value = values.at(i);
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branch->Fill();
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t->Fill();
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mean += value/values.size();
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}
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for (unsigned int i = 0; i < values.size(); i++){
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value = values.at(i);
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rms += (value - mean)*(value - mean)/values.size();
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}
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rms = sqrt(rms);
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TF1* f1 = new TF1("f1", "gaus(0)/sqrt(2.0*3.1415926536)/abs([2])", min-5.0, max+5.0);
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f1->SetParameters(1.0,mean,rms);
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//f1->SetParLimits(0, 1.0, 1.0);
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f1->FixParameter(0, 1.0);
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t->UnbinnedFit("f1", "value");
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gauss_mean = f1->GetParameter(1);
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sigma_gauss_mean = f1->GetParError(1);
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gauss_width = f1->GetParameter(2);
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sigma_gauss_width = f1->GetParError(2);
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delete f1;
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delete t;
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};
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void multifit::update_pull(parameter* p, vector<double>& values, vector<double>& errors, double& gauss_mean, double& sigma_gauss_mean, double& gauss_width, double& sigma_gauss_width, double& chi_squared, string appendix) const
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{
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unsigned int runs=values.size();
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string parname(p->get_name());
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string descr(p->get_description());
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gStyle->SetOptStat(0);
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gStyle->SetOptFit(0);
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//finally the pull histo
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TH1D* pull_histo = new TH1D((parname + "_pull_").c_str(),
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(descr + " pull distribution;" + "(" + descr
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+ "^{fitted}-" + descr
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+ "^{generated})/#sigma").c_str(),
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50, -5.0, 5.0);//sensible for pulls
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//fill histos
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//this is done every time because the time is negligible
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double start_value = p->get_start_value();
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vector<double> pull_values;
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for (unsigned int k=0; k<runs; k++){
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if (errors.at(k) != 0.0){
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double pull = (values.at(k)-start_value)/errors.at(k);
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pull_histo->Fill(pull);
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pull_values.push_back(pull);
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}
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}
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fit_gaussian(pull_values, gauss_mean, sigma_gauss_mean, gauss_width, sigma_gauss_width, chi_squared);
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TCanvas *pull_canvas = new TCanvas((parname + "_pull_" + appendix).c_str(), "Bs Likelihood Analysis: Pull distribution",1600,1200);
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pull_canvas->cd();
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pull_histo->Draw();
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TF1 *g = new TF1("g","[0]*exp(-(x-[1])*(x-[1])/2.0/[2]/[2])",-5.0,5.0);
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//for normalization: multiply with dx*histint = width/Nbins*histint
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g->SetParameters(1.0/sqrt(2.0*TMath::Pi())/gauss_width*10.0/50.0*pull_histo->Integral(), gauss_mean, gauss_width);
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g->Draw("same");
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TPaveText* text = new TPaveText(0.6,0.7,0.88,0.88,"NDC");
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text->SetFillColor(0);
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std::ostringstream stream_mean;
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stream_mean << "Mean: " << fixed << setprecision(3) << gauss_mean << "#pm" << sigma_gauss_mean;
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std::ostringstream stream_width;
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stream_width << "Width: " << fixed << setprecision(3) << gauss_width << "#pm" << sigma_gauss_width;
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text->AddText(stream_mean.str().c_str());
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text->AddText(stream_width.str().c_str());
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text->Draw("same");
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string afilename; //TODO: move to paths
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if (appendix != "") afilename = "plots/pull_" + parname + "_" + appendix + ".eps";
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else afilename = "plots/pull_" + parname + ".eps";
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if (opts->write_eps)pull_canvas->Print(afilename.c_str(), "eps");
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output->cd();
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pull_canvas->Write();
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delete text;
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delete g;
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delete pull_histo;
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delete pull_canvas;
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}
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void multifit::update_value(parameter * p, vector<double>& values, vector<double>& errors, double& gauss_mean, double& sigma_gauss_mean, double& gauss_width, double& sigma_gauss_width, double& chi_squared, string appendix) const
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{
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unsigned int runs=values.size();
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string parname(p->get_name());
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string descr(p->get_description());
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//first find min and max values
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double max = *max_element(std::begin(values), std::end(values));
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double min = *min_element(std::begin(values), std::end(values));
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double width = max-min;
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double hist_min = min - 0.5*width;
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double hist_max = max + 0.5*width;
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gStyle->SetOptStat(0);
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gStyle->SetOptFit(0);
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TH1D* value_histo = new TH1D((parname+"_values").c_str(),
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(descr + ";" + descr).c_str(),
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50, hist_min, hist_max);
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for (unsigned int k=0; k<runs; k++) {
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value_histo->Fill(values.at(k));
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}
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fit_gaussian(values, gauss_mean, sigma_gauss_mean, gauss_width, sigma_gauss_width, chi_squared);
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TCanvas *value_canvas = new TCanvas((parname + "_value_" + appendix).c_str(), "Bs Likelihood Analysis: Value distribution",1600,1200);
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value_canvas->cd();
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value_histo->Draw();
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TF1 *g = new TF1("g","[0]*exp(-(x-[1])*(x-[1])/2.0/[2]/[2])",-5.0,5.0);
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g->SetParameters(1.0/sqrt(2.0*TMath::Pi())/gauss_width*(hist_max-hist_min)/50.0*value_histo->Integral(), gauss_mean, gauss_width);
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g->Draw("same");
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TPaveText* text = new TPaveText(0.6,0.7,0.88,0.88,"NDC");
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text->SetFillColor(0);
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std::ostringstream stream_mean;
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stream_mean << "Mean: " << fixed << setprecision(3) << gauss_mean << "#pm" << sigma_gauss_mean;
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std::ostringstream stream_width;
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stream_width << "Width: " << fixed << setprecision(3) << gauss_width << "#pm" << sigma_gauss_width;
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text->AddText(stream_mean.str().c_str());
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text->AddText(stream_width.str().c_str());
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text->Draw("same");
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//TODO: move
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string afilename;
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if (appendix != "") afilename = "plots/value_" + parname + "_" + appendix + ".eps";
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else afilename = "plots/value_" + parname + ".eps";
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if (opts->write_eps) value_canvas->Print(afilename.c_str(), "eps");
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output->cd();
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value_canvas->Write();
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delete text;
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delete value_histo;
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delete value_canvas;
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delete g;
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}
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void multifit::update_error(parameter* p, vector<double>& values, vector<double>& errors, double& gauss_mean, double& sigma_gauss_mean, double& gauss_width, double& sigma_gauss_width, double& chi_squared, string appendix) const
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{
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unsigned int runs=values.size();
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string parname(p->get_name());
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string descr(p->get_description());
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//then find min and max errors
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double max = *max_element(std::begin(errors), std::end(errors));
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double min = *min_element(std::begin(errors), std::end(errors));
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double width = max - min;
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double hist_min = min - 0.5*width;
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double hist_max = max + 0.5*width;
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gStyle->SetOptStat(0);
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gStyle->SetOptFit(0);
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TH1D* error_histo = new TH1D((parname+"_errors").c_str(),
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("#sigma(" + descr + ");#sigma(" + descr + ")").c_str(),
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100, hist_min, hist_max);
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for (unsigned int k=0; k<runs; k++){
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error_histo->Fill(errors.at(k));
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}
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fit_gaussian(errors, gauss_mean, sigma_gauss_mean, gauss_width, sigma_gauss_width, chi_squared);
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TCanvas *error_canvas = new TCanvas((parname + "_error_" + appendix).c_str(), "Bs Likelihood Analysis: Error distribution",1600,1200);
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error_canvas->cd();
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error_histo->Draw();
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TF1 *g = new TF1("g","[0]*exp(-(x-[1])*(x-[1])/2.0/[2]/[2])",-5.0,5.0);
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g->SetParameters(1.0/sqrt(2.0*TMath::Pi())/gauss_width*(hist_max-hist_min)/100.0*error_histo->Integral(), gauss_mean, gauss_width);
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g->Draw("same");
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TPaveText* text = new TPaveText(0.6,0.7,0.88,0.88,"NDC");
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text->SetFillColor(0);
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std::ostringstream stream_mean;
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stream_mean << "Mean: " << fixed << setprecision(3) << gauss_mean << "#pm" << sigma_gauss_mean;
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std::ostringstream stream_width;
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stream_width << "Width: " << fixed << setprecision(3) << gauss_width << "#pm" << sigma_gauss_width;
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text->AddText(stream_mean.str().c_str());
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text->AddText(stream_width.str().c_str());
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text->Draw("same");
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//TODO: move
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string afilename;
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if (appendix != "") afilename = "plots/error_" + parname + "_" + appendix + ".eps";
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else afilename = "plots/error_" + parname + ".eps";
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if (opts->write_eps) error_canvas->Print(afilename.c_str(), "eps");
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output->cd();
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error_canvas->Write();
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delete text;
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delete error_histo;
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delete error_canvas;
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delete g;
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}
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