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541 lines
18 KiB
541 lines
18 KiB
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#ifndef BASIC_ANALYSIS
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#define BASIC_ANALYSIS
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#include <string>
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#include <iostream>
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#include <cmath>
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#include <algorithm>
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#include <filesystem>
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#include <string_view>
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#include "TTree.h"
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#include "RooHist.h"
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#include "constants.h"
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class FourVect
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{
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private:
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Float_t px, py, pz, energy;
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Float_t *PtrPX()
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{
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return &px;
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}
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Float_t *PtrPY()
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{
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return &py;
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}
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Float_t *PtrPZ()
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{
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return &pz;
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}
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Float_t *PtrEnergy()
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{
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return &energy;
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}
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public:
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static FourVect *Init(TTree *tree, const char *particle)
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{
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FourVect *v = new FourVect{};
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tree->SetBranchAddress(TString::Format("%s_PX", particle).Data(), v->PtrPX());
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tree->SetBranchAddress(TString::Format("%s_PY", particle).Data(), v->PtrPY());
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tree->SetBranchAddress(TString::Format("%s_PZ", particle).Data(), v->PtrPZ());
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tree->SetBranchAddress(TString::Format("%s_ENERGY", particle).Data(), v->PtrEnergy());
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return v;
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}
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TLorentzVector LorentzVector()
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{
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return TLorentzVector(px, py, pz, energy);
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}
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TLorentzVector LorentzVector(double sub_mass)
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{
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TVector3 momentum(px, py, pz);
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float energy = TMath::Sqrt(TMath::Sq(sub_mass) + momentum.Mag2());
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return TLorentzVector(momentum, energy);
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}
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std::string ToString()
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{
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return TString::Format("(%f, %f, %f, %f)", px, py, pz, energy).Data();
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}
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};
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struct FittedParam
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{
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std::string title;
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std::string name;
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double value;
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double error;
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bool at_limit;
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bool constant;
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int decimals;
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FittedParam(RooRealVar var, int decimals)
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{
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this->title = var.GetTitle();
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this->name = var.GetName();
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double val = var.getVal();
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this->value = val;
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this->constant = var.isConstant();
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if (!this->constant)
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{
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this->error = var.getError();
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this->at_limit = ((val == var.getMin()) || (val == var.getMax()));
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}
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else
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{
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this->error = 0.;
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}
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this->decimals = decimals;
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}
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FittedParam(std::string name, std::string title, double value, double err, int decimals)
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{
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this->title = title;
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this->name = name;
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this->value = value;
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this->error = err;
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this->decimals = decimals;
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this->constant = false;
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this->at_limit = false;
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}
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std::string ToString() const
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{
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if (this->constant)
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{
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return TString::Format("%s = %.*f", this->title.c_str(), this->decimals, this->value).Data();
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}
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else
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{
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return TString::Format("%s = %.*f #pm %.*f", this->title.c_str(), this->decimals, this->value, this->decimals, this->error).Data();
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}
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}
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};
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struct ShapeParamters
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{
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double alpha_left;
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double n_left;
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double alpha_right;
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double n_right;
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double sigma_lr;
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};
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struct RooFitSummary
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{
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RooPlot *fit_histogram;
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RooPlot *pull_histogram;
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std::map<std::string, std::string> pdf_names;
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std::vector<FittedParam> fitted_params;
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ShapeParamters shape_parameters;
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};
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void DrawInDefaultCanvas(TH1 *histogram, const char *folder, double margin_left = 0.1, Option_t *option = "")
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{
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std::filesystem::create_directory(TString::Format("output_files/analysis/%s", folder).Data());
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TString name = TString::Format("%s_canvas", histogram->GetName());
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TCanvas *c = new TCanvas(name, histogram->GetName(), 0, 0, 800, 600);
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c->SetLeftMargin(margin_left);
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histogram->SetStats(0);
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histogram->Draw(option);
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c->Draw();
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c->SaveAs(TString::Format("output_files/analysis/%s/%s.pdf", folder, name.Data()).Data());
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}
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void DrawInDefaultCanvasStacked(std::vector<TH1D *> histograms, std::vector<Color_t> colors, std::vector<Style_t> fill_style, const char *folder, double margin_left = 0.1, Option_t *option = "")
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{
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std::filesystem::create_directory(TString::Format("output_files/analysis/%s", folder).Data());
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TString name = TString::Format("%s_stack_canvas", histograms[0]->GetName());
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TCanvas *c = new TCanvas(name, histograms[0]->GetName(), 0, 0, 800, 600);
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c->SetLeftMargin(margin_left);
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std::string drwopt_2 = std::string(option).empty() ? "SAME HIST" : TString::Format("%s SAME HIST", option).Data();
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for (size_t i = 0; i < histograms.size(); i++)
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{
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const Double_t scaling_factor = 1.;
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auto hist_clone = (TH1 *)histograms[i]->Clone(TString::Format("%s_clone", histograms[i]->GetName()));
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hist_clone->Scale(scaling_factor / histograms[i]->GetMaximum());
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hist_clone->SetLineColor(colors[i]);
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hist_clone->SetMaximum(scaling_factor + (scaling_factor * 0.05));
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hist_clone->SetMinimum(0.);
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hist_clone->SetStats(0);
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if (fill_style[i] != 0)
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{
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hist_clone->SetFillStyle(fill_style[i]);
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hist_clone->SetFillColor(colors[i]);
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}
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if (i > 0)
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{
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hist_clone->Draw(drwopt_2.c_str());
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}
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else
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{
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hist_clone->Draw(drwopt_2.c_str());
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}
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}
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c->BuildLegend(0.55, 0.79, 0.87, 0.89);
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c->Draw();
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c->SaveAs(TString::Format("output_files/analysis/%s/%s.pdf", folder, name.Data()).Data());
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}
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void DrawInDefaultCanvas(RooPlot *rooPlot, const char *folder, double margin_left = 0, Option_t *option = "")
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{
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std::filesystem::create_directory(TString::Format("output_files/analysis/%s", folder).Data());
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TString name = TString::Format("%s_canvas", rooPlot->GetName());
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TCanvas *c = new TCanvas(name, rooPlot->GetName(), 0, 0, 800, 600);
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c->SetLeftMargin(margin_left);
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rooPlot->Draw(option);
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c->Draw();
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c->SaveAs(TString::Format("output_files/analysis/%s/%s.pdf", folder, name.Data()).Data());
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}
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void PrintStyles(RooPlot *plt)
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{
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auto Xaxis = plt->GetXaxis();
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auto Yaxis = plt->GetYaxis();
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std::cout << "## Styles for plot " << plt->GetName() << std::endl;
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std::cout << "## X axis\n"
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<< "Label Size: " << Xaxis->GetLabelSize() << ",\n"
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<< "Label Offset: " << Xaxis->GetLabelOffset() << ",\n"
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<< "Title Size: " << Xaxis->GetTitleSize() << ",\n"
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<< "Title Offset: " << Xaxis->GetTitleOffset() << "." << std::endl;
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std::cout << "## Y axis\n"
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<< "Label Size: " << Yaxis->GetLabelSize() << ",\n"
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<< "Label Offset: " << Yaxis->GetLabelOffset() << ",\n"
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<< "Title Size: " << Yaxis->GetTitleSize() << ",\n"
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<< "Title Offset: " << Yaxis->GetTitleOffset() << "." << std::endl;
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}
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void DrawInDefaultCanvas(RooFitSummary fitSummary, const char *folder)
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{
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std::filesystem::create_directory(TString::Format("output_files/analysis/%s", folder).Data());
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TString name = TString::Format("%s_canvas", fitSummary.fit_histogram->GetName());
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TCanvas *c = new TCanvas(name, fitSummary.fit_histogram->GetTitle(), 0, 0, 800, 600);
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Double_t xlow, ylow, xup, yup;
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c->GetPad(0)->GetPadPar(xlow, ylow, xup, yup);
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c->Divide(1, 2);
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Double_t upPad_ylow = ylow + 0.25 * (yup - ylow);
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Double_t dwPad_yup = ylow + 0.25 * (yup - ylow);
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TVirtualPad *upPad = c->GetPad(1);
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upPad->SetPad(xlow, upPad_ylow, xup, yup);
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TVirtualPad *dwPad = c->GetPad(2);
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dwPad->SetPad(xlow, ylow, xup, dwPad_yup);
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dwPad->SetTopMargin(0);
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Double_t upPad_area = (yup - upPad_ylow) * (xup - xlow);
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Double_t dwPad_area = (dwPad_yup - ylow) * (xup - xlow);
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// std::cout << "### UP AREA: " << upPad_area << " LOW AREA: " << dwPad_area << std::endl;
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const Double_t pull_title_size = 0.09;
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const Double_t pull_label_size = 0.09;
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Double_t fit_title_size = (pull_title_size * dwPad_area) / upPad_area;
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Double_t fit_label_size = (pull_label_size * dwPad_area) / upPad_area;
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// canvas->Update();
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c->cd(1);
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// TPad *pull_pad = new TPad(TString::Format("%s_canv_pull", fitSummary.fit_histogram->GetName()), "Pull Pad", 0., 0., 1., 0.3);
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// pull_pad->Draw();
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// pull_pad->SetTopMargin(0.001);
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// pull_pad->SetBottomMargin(0.3);
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// pull_pad->SetGrid();
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// TPad *fit_pad = new TPad(TString::Format("%s_canv_fit", fitSummary.fit_histogram->GetName()), "Fit Pad", 0., 0.3, 1., 1.);
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// fit_pad->Draw();
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// fit_pad->SetBottomMargin(0.001);
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// fit_pad->cd();
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auto fit_Xaxis = fitSummary.fit_histogram->GetXaxis();
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auto fit_Yaxis = fitSummary.fit_histogram->GetYaxis();
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fit_Xaxis->SetLabelOffset(0.02);
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fit_Xaxis->SetLabelSize(fit_label_size);
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fit_Xaxis->SetTitleOffset(1.4);
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fit_Xaxis->SetTitleSize(fit_title_size);
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fit_Yaxis->SetLabelOffset(0.01);
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fit_Yaxis->SetLabelSize(fit_label_size);
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fit_Yaxis->SetTitleSize(fit_title_size);
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fit_Yaxis->SetTitleOffset(0);
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fitSummary.fit_histogram->Draw();
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TLegend *leg1 = new TLegend(0.55, 0.45, 0.87, 0.89);
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leg1->SetFillColor(kWhite);
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leg1->SetLineColor(kWhite);
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for (const auto &[name, title] : fitSummary.pdf_names)
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{
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leg1->AddEntry(fitSummary.fit_histogram->findObject(name.c_str()), title.c_str(), "LP");
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}
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for (const auto &par : fitSummary.fitted_params)
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{
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if (!par.constant)
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{
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leg1->AddEntry((TObject *)0, par.ToString().c_str(), "");
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}
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}
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leg1->Draw();
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c->cd(2);
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Double_t pull_min = fitSummary.pull_histogram->GetMinimum();
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Double_t pull_max = fitSummary.pull_histogram->GetMaximum();
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// std::cout << "### (" << fitSummary.pull_histogram->GetName() << ") PULL MIN: " << pull_min << " PULL MAX: " << pull_max << std::endl;
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double bound = 0;
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if (TMath::Abs(pull_min) > TMath::Abs(pull_max))
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{
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bound = TMath::Abs(pull_min);
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}
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else
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{
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bound = TMath::Abs(pull_max);
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}
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auto pull_Xaxis = fitSummary.pull_histogram->GetXaxis();
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auto pull_Yaxis = fitSummary.pull_histogram->GetYaxis();
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pull_Xaxis->SetLabelOffset(0.02);
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pull_Xaxis->SetLabelSize(pull_label_size);
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pull_Xaxis->SetTitleSize(pull_title_size);
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pull_Xaxis->SetTitle("");
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pull_Yaxis->SetLabelOffset(0.01);
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pull_Yaxis->SetLabelSize(pull_label_size);
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pull_Yaxis->SetTitleSize(pull_title_size);
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pull_Yaxis->SetTitleOffset(0.45);
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pull_Yaxis->SetTitle("Pull Distribution");
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fitSummary.pull_histogram->SetTitle("");
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fitSummary.pull_histogram->SetMaximum(bound);
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fitSummary.pull_histogram->SetMinimum(-bound);
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fitSummary.pull_histogram->Draw();
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auto line_mid = new TLine(B_MASS_VAR_MIN, 0, B_MASS_VAR_MAX, 0);
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line_mid->SetLineColor(kOrange + 4);
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line_mid->Draw();
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auto line_up = new TLine(B_MASS_VAR_MIN, bound / 2, B_MASS_VAR_MAX, bound / 2);
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line_up->SetLineStyle(kDashed);
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line_up->SetLineColor(kOrange + 4);
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line_up->Draw();
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auto line_low = new TLine(B_MASS_VAR_MIN, -bound / 2, B_MASS_VAR_MAX, -bound / 2);
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line_low->SetLineStyle(kDashed);
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line_low->SetLineColor(kOrange + 4);
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line_low->Draw();
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c->Draw();
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c->SaveAs(TString::Format("output_files/analysis/%s/%s.pdf", folder, name.Data()).Data());
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}
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void PrintProgress(const char *title, unsigned int total, unsigned int every, unsigned int current)
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{
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if ((current + 1) % every == 0 || current + 1 == total)
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{
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std::cout << "["
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<< title
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<< "] Processed event: " << current + 1 << " (" << TString::Format("%.2f", ((double)(current + 1) / (double)total) * 100.) << "%)" << std::endl;
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}
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}
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RooPlot *CreateRooFitHistogram(TH1D *hist)
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{
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RooRealVar roo_var_mass("var_mass", "B Mass Variable", B_MASS_VAR_MIN, B_MASS_VAR_MAX);
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roo_var_mass.setRange("fitting_range", B_MASS_VAR_MIN, B_MASS_VAR_MAX);
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RooDataHist roohist_B_M("roohist_B_M", "B Mass Histogram", roo_var_mass, RooFit::Import(*hist));
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RooPlot *roo_frame_mass = roo_var_mass.frame(RooFit::Title(hist->GetTitle()), RooFit::Name(TString::Format("%s_rplt", hist->GetName())));
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roohist_B_M.plotOn(roo_frame_mass, RooFit::Binning(B_MASS_HIST_BINS), RooFit::Name("B Mass Distribution"));
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roo_frame_mass->GetXaxis()->SetTitle(hist->GetXaxis()->GetTitle());
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return roo_frame_mass;
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}
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RooFitSummary CreateRooDataSetAndFitCB(TTree *dataSet, TString var_name, TString xAxis, bool hasExpBkg, bool useExtShape, ShapeParamters extShape, bool const_sigma = false, Double_t fit_low = 0, Double_t fit_up = 0)
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{
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auto suffix_name = [name = dataSet->GetName()](const char *text)
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{
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return TString::Format("%s_%s", text, name);
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};
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Double_t fitRangeUp = fit_low == 0 ? B_MASS_VAR_MIN : fit_low;
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Double_t fitRangeLow = fit_up == 0 ? B_MASS_VAR_MAX : fit_up;
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RooRealVar roo_var_mass(var_name, "B Mass Variable", B_MASS_VAR_MIN, B_MASS_VAR_MAX);
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const char *fitting_range_name = "fitting_range";
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roo_var_mass.setRange(fitting_range_name, fitRangeUp, fitRangeLow);
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TString dataset_name = suffix_name("roodataset_B_M");
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// RooDataHist roodataset_B_M(hist_name, "B Mass Histogram", roo_var_mass, RooFit::Import(*hist));
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RooDataSet roodataset_B_M(dataset_name, "B Mass Data Set", roo_var_mass, RooFit::Import(*dataSet));
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RooPlot *roo_frame_mass = roo_var_mass.frame(RooFit::Title(dataSet->GetTitle()), RooFit::Name(TString::Format("%s_rplt", dataSet->GetName())));
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roodataset_B_M.plotOn(roo_frame_mass, RooFit::Name(dataset_name));
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roo_frame_mass->GetXaxis()->SetTitle(xAxis);
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// Crystal Ball for Signal
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RooRealVar var_mass_x0(suffix_name("var_mass_x0"), "#mu", 5278., 5170., 5500.);
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RooRealVar var_mass_sigmaLR(suffix_name("var_mass_sigmaLR"), "#sigma_{LR}", 16., 5., 40.);
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RooRealVar var_mass_alphaL(suffix_name("var_mass_alphaL"), "#alpha_{L}", 2., 0., 4.);
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RooRealVar var_mass_nL(suffix_name("var_mass_nL"), "n_{L}", 5., 0., 15.);
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RooRealVar var_mass_alphaR(suffix_name("var_mass_alphaR"), "#alpha_{R}", 2., 0., 4.);
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RooRealVar var_mass_nR(suffix_name("var_mass_nR"), "n_{R}", 5., 0., 15.);
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if (useExtShape)
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{
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if (extShape.alpha_left != 0.)
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{
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var_mass_alphaL.setConstant(true);
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var_mass_alphaL.setVal(extShape.alpha_left);
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}
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if (extShape.n_left != 0.)
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{
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var_mass_nL.setConstant(true);
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var_mass_nL.setVal(extShape.n_left);
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}
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if (extShape.alpha_right != 0.)
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{
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var_mass_alphaR.setConstant(true);
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var_mass_alphaR.setVal(extShape.alpha_right);
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}
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if (extShape.n_right != 0.)
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{
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var_mass_nR.setConstant(true);
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var_mass_nR.setVal(extShape.n_right);
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}
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if (extShape.sigma_lr != 0. && const_sigma)
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{
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var_mass_sigmaLR.setConstant(true);
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var_mass_sigmaLR.setVal(extShape.sigma_lr);
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}
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}
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TString signal_name = suffix_name("sig_cb");
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RooCrystalBall sig_cb(signal_name, "CB Signal", roo_var_mass, var_mass_x0, var_mass_sigmaLR, var_mass_alphaL, var_mass_nL, var_mass_alphaR, var_mass_nR);
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std::vector<FittedParam> fitted_params{};
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std::map<std::string, std::string> pdf_names{};
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pdf_names[signal_name.Data()] = sig_cb.getTitle().Data();
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TString pull_compare_name{};
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if (hasExpBkg)
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{
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// Exponential for Background
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RooRealVar var_mass_bkg_c(suffix_name("var_mass_bkg_c"), "#lambda", -0.0014, -0.004, -0.000);
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TString background_name = suffix_name("bgk_exp");
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RooExponential bkg_exp(background_name, "Exp Background", roo_var_mass, var_mass_bkg_c);
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pdf_names[background_name.Data()] = bkg_exp.getTitle().Data();
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RooRealVar var_mass_nsig(suffix_name("nsig"), "N_{Sig}", 0., dataSet->GetEntries());
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RooRealVar var_mass_nbkg(suffix_name("nbkg"), "N_{Bkg}", 0., dataSet->GetEntries());
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|
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TString fitted_pdf_name = suffix_name("sigplusbkg");
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RooAddPdf fitted_pdf(fitted_pdf_name, "Sig + Bkg", RooArgList(sig_cb, bkg_exp), RooArgList(var_mass_nsig, var_mass_nbkg));
|
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pdf_names[fitted_pdf_name.Data()] = fitted_pdf.getTitle().Data();
|
|
|
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RooFitResult *fitres = fitted_pdf.fitTo(roodataset_B_M, RooFit::Save(), RooFit::PrintLevel(1), RooFit::Range(fitting_range_name));
|
|
|
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// sigplusbkg.plotOn(roo_frame_mass, RooFit::VisualizeError(*fitres, 1), RooFit::FillColor(kOrange + 1), RooFit::FillStyle(3144));
|
|
fitted_pdf.plotOn(roo_frame_mass, RooFit::LineColor(kRed), RooFit::LineStyle(kSolid), RooFit::Range(fitting_range_name), RooFit::Name(fitted_pdf_name));
|
|
fitted_pdf.plotOn(roo_frame_mass, RooFit::Components(RooArgSet(bkg_exp)), RooFit::LineColor(kBlue - 7), RooFit::LineStyle(kDashed), RooFit::Range(fitting_range_name), RooFit::Name(background_name));
|
|
fitted_pdf.plotOn(roo_frame_mass, RooFit::Components(RooArgSet(sig_cb)), RooFit::LineColor(kRed - 7), RooFit::LineStyle(kDashed), RooFit::Range(fitting_range_name), RooFit::Name(signal_name));
|
|
|
|
fitted_params.push_back(FittedParam(var_mass_nsig, 2));
|
|
fitted_params.push_back(FittedParam(var_mass_nbkg, 2));
|
|
|
|
double sig_val = var_mass_nsig.getVal();
|
|
double sig_err = var_mass_nsig.getError();
|
|
double bkg_val = var_mass_nbkg.getVal();
|
|
double bkg_err = var_mass_nbkg.getError();
|
|
|
|
double sig_over_bkg_val = sig_val / TMath::Sqrt(sig_val + bkg_val);
|
|
double err_prop_sig = (sig_val + 2 * bkg_val) / (2 * TMath::Power((sig_val + bkg_val), (3 / 2)));
|
|
double err_prop_bkg = -sig_val / (2 * TMath::Power((sig_val + bkg_val), (3 / 2)));
|
|
double sig_over_bkg_err = TMath::Sqrt(TMath::Sq(err_prop_sig * sig_err) + TMath::Sq(err_prop_bkg * bkg_err));
|
|
|
|
fitted_params.push_back(FittedParam("sig_over_bkg", "N_{Sig}/#sqrt{N_{Sig} + N_{Bkg}}", sig_over_bkg_val, sig_over_bkg_err, 2));
|
|
|
|
fitted_params.push_back(FittedParam(var_mass_bkg_c, 5));
|
|
|
|
pull_compare_name = fitted_pdf_name;
|
|
}
|
|
else
|
|
{
|
|
RooFitResult *fitres = sig_cb.fitTo(roodataset_B_M, RooFit::Save(), RooFit::PrintLevel(1), RooFit::Range(fitting_range_name));
|
|
// sigplusbkg.plotOn(roo_frame_mass, RooFit::VisualizeError(*fitres, 1), RooFit::FillColor(kOrange + 1), RooFit::FillStyle(3144));
|
|
sig_cb.plotOn(roo_frame_mass, RooFit::LineColor(kRed), RooFit::LineStyle(kSolid), RooFit::Range(fitting_range_name), RooFit::Name(signal_name));
|
|
pull_compare_name = signal_name;
|
|
}
|
|
|
|
fitted_params.push_back(FittedParam(var_mass_x0, 2));
|
|
fitted_params.push_back(FittedParam(var_mass_sigmaLR, 2));
|
|
fitted_params.push_back(FittedParam(var_mass_alphaL, 2));
|
|
fitted_params.push_back(FittedParam(var_mass_nL, 2));
|
|
fitted_params.push_back(FittedParam(var_mass_alphaR, 2));
|
|
fitted_params.push_back(FittedParam(var_mass_nR, 2));
|
|
|
|
RooPlot *roo_frame_pull = roo_var_mass.frame(RooFit::Title("Pull Distribution"), RooFit::Name(TString::Format("%s_pull_rplt", dataSet->GetName())));
|
|
RooHist *pull_hist = roo_frame_mass->pullHist(dataset_name, pull_compare_name);
|
|
roo_frame_pull->addPlotable(pull_hist, "P");
|
|
|
|
// Double_t pull_min = pull_hist->GetMinimum();
|
|
// Double_t pull_max = pull_hist->GetMaximum();
|
|
|
|
// std::cout << "##### (" << pull_hist->GetName() << ") PULL MIN: " << pull_min << " PULL MAX: " << pull_max << std::endl;
|
|
|
|
return RooFitSummary{
|
|
roo_frame_mass,
|
|
roo_frame_pull,
|
|
pdf_names,
|
|
fitted_params,
|
|
ShapeParamters{
|
|
var_mass_alphaL.getVal(),
|
|
var_mass_nL.getVal(),
|
|
var_mass_alphaR.getVal(),
|
|
var_mass_nR.getVal(),
|
|
var_mass_sigmaLR.getVal()}};
|
|
}
|
|
|
|
bool InRange(double value, double center, double low_intvl, double up_intvl)
|
|
{
|
|
return center - low_intvl < value && value < center + up_intvl;
|
|
}
|
|
|
|
bool InRange(double value, double center, double intvl)
|
|
{
|
|
return InRange(value, center, intvl, intvl);
|
|
}
|
|
|
|
#endif
|