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bias_fitter.C
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// Standalone code to fit for biases in mass distribtions per eta, pT, eta, pT bins
#include "TFile.h"
#include "TTree.h"
#include "TChain.h"
#include "TMath.h"
#include "TCanvas.h"
#include "TVector.h"
#include "TVectorT.h"
#include "Minuit2/FunctionMinimum.h"
#include "Minuit2/MnMinimize.h"
#include "Minuit2/MnMigrad.h"
#include "Minuit2/MnPrint.h"
#include "Minuit2/MnUserParameterState.h"
#include "Minuit2/FCNGradientBase.h"
#include <string.h>
#include <eigen3/Eigen/Core>
#include <eigen3/Eigen/Dense>
#include <sys/time.h>
using namespace ROOT;
using namespace ROOT::VecOps;
using namespace ROOT::Minuit2;
using namespace std;
using ROOT::RDF::RNode;
using Eigen::MatrixXd;
using Eigen::VectorXd;
const int nbinseta = 24;
const int nbinspt = 5;
//--------------------------------------------------------
// Functions for TTree
//--------------------------------------------------------
//dxy_significance
RVecF dxy_significance(RVecF Muon_dxy, RVecF Muon_dxyErr){
return abs(Muon_dxy)/Muon_dxyErr;
}
// MuonisGood
RVecB MuonisGood(RVecF Muon_pt, RVecF Muon_eta, RVecB Muon_isGlobal, RVecB Muon_mediumId, RVecF Muon_pfRelIso04_all, RVec<UChar_t> Muon_genPartFlav, RVecF dxy_significance){
RVecB muonisgood;
for(int i=0;i<Muon_pt.size();i++){
if (Muon_pt[i] > 10.0 && abs(Muon_eta[i]) < 2.4 && Muon_isGlobal[i] && Muon_mediumId[i] && Muon_pfRelIso04_all[i] < 0.15 && Muon_genPartFlav[i]==1 && dxy_significance[i] < 4){
muonisgood.push_back(1);
} else {
muonisgood.push_back(0);
}
}
return muonisgood;
}
// MuonisGoodData
RVecB MuonisGoodData(RVecF Muon_pt, RVecF Muon_eta, RVecB Muon_isGlobal, RVecB Muon_mediumId, RVecF Muon_pfRelIso04_all, RVecF dxy_significance){
RVecB muonisgood;
for(int i=0;i<Muon_pt.size();i++){
if (Muon_pt[i] > 10.0 && abs(Muon_eta[i]) < 2.4 && Muon_isGlobal[i] && Muon_mediumId[i] && Muon_pfRelIso04_all[i] < 0.15 && dxy_significance[i] < 4){
muonisgood.push_back(1);
} else {
muonisgood.push_back(0);
}
}
return muonisgood;
}
//--------------------------------------------------------
// Functions for histograms
//--------------------------------------------------------
// Functions for label names
string stringify_name(int a, int b, int c, int d){
string hyp = "_";
return to_string(a) + hyp + to_string(b) + hyp + to_string(c) + hyp + to_string(d);
}
string stringify_title(int a, int b, int c, int d, vector<double> e, vector<double> p){
string comma = ",";
string txt1 = "#eta+ in [", txt2="] pT+ in [", txt3="] #eta- in [", txt4="] pT- in [", txt5 = "]";
return txt1+to_string(e[a]).substr(0,4)+comma+to_string(e[a+1]).substr(0,4)+txt2+to_string(p[b]).substr(0,4)+comma+to_string(p[b+1]).substr(0,4)+txt3+to_string(e[c]).substr(0,4)+comma+to_string(e[c+1]).substr(0,4)+txt4+to_string(p[d]).substr(0,4)+comma+to_string(p[d+1]).substr(0,4)+txt5;
}
// Function for Gaussian fit and integral
vector<double> fitHisto(TH1* histogram, int draw_option, int color, int nsigma){
vector<double> fitresult;
double mean = histogram->GetMean();
double sigma = histogram->GetRMS();
double mean_err, sigma_err, integral, chi2;
TF1 *gaussianFunc = new TF1("gaussianFunc", "gaus", mean - 5 * sigma, mean + 5 * sigma);
// first fit
if(0 == histogram->Fit(gaussianFunc, "QNR")){
mean = gaussianFunc->GetParameter(1);
sigma = gaussianFunc->GetParameter(2);
// second fit: few sigma of first fit around mean of first fit
gaussianFunc->SetRange(mean - nsigma * sigma, mean + nsigma * sigma);
if (0 == histogram->Fit(gaussianFunc, "Q0R")) { // don't draw yet
if (histogram->GetFunction(gaussianFunc->GetName())) {
histogram->GetFunction(gaussianFunc->GetName())->SetLineColor(color);
if (draw_option == 1){ histogram->GetFunction(gaussianFunc->GetName())->ResetBit(TF1::kNotDraw);} // draw fit
}
mean = gaussianFunc->GetParameter(1);
sigma = gaussianFunc->GetParameter(2);
mean_err = gaussianFunc->GetParError(1);
sigma_err = gaussianFunc->GetParError(2);
chi2 = gaussianFunc->GetChisquare();
TF1 *gaussianNewFunc = new TF1("gaussianNewFunc", "gaus", mean - 5 * sigma, mean + 5 * sigma);
gaussianNewFunc->SetParameter(0, 1.0/(sigma*pow(2*M_PI,0.5)));
gaussianNewFunc->SetParameter(1, mean);
gaussianNewFunc->SetParameter(2, sigma);
// integral of fitted gaussian function in range of the histogram
integral = gaussianNewFunc->Integral(75.0, 105.0);
// TODO implement line below
//integral = gaussianNewFunc->Integral(histogram->GetXaxis()->GetBinLowEdge(1), histogram->GetXaxis()->GetBinLowEdge(histogram->GetNbins()+1)); // TODO histogram->GetNbins() includes under/over flow??
} else {
mean = -90.0;
sigma = -5.0;
mean_err = 90.0;
sigma_err = 5.0;
integral = -100.0;
chi2 = -100.0;
}
} else {
mean = -90.0;
sigma = -5.0;
mean_err = 90.0;
sigma_err = 5.0;
integral = -100.0;
chi2 = -100.0;
}
fitresult.push_back(mean);
fitresult.push_back(sigma);
fitresult.push_back(mean_err);
fitresult.push_back(sigma_err);
fitresult.push_back(integral);
fitresult.push_back(chi2);
return fitresult;
}
//--------------------------------------------------------
// Class and functions for minimization
//--------------------------------------------------------
class TheoryFcn : public FCNGradientBase {
//class TheoryFcn : public FCNBase {
public:
TheoryFcn(const vector<double> &meas, const vector<double> &err, const vector<string> &bin_labels, const TMatrixD &V_phys_to_int, const TMatrixD &V_int_to_phys) : scaleSquared(meas), scaleSquaredError(err), binLabels(bin_labels), VPhysToInt(V_phys_to_int), VIntToPhys(V_int_to_phys), errorDef(1.0) {}
~TheoryFcn() {}
virtual double Up() const {return errorDef;}
virtual void SetErrorDef(double def) {errorDef = def;}
virtual double operator()(const vector<double>&) const;
virtual vector<double> Gradient(const vector<double>& ) const;
virtual bool CheckGradient() const {return true;}
// virtual std::vector< double > ROOT::Minuit2::FCNGradientBase::Hessian(const std::vector< double > & )const -> allows to do Hessian analytically?
vector<int> getIndices(string bin_label) const;
double getK(const int pT_index) const;
const vector<double> pT_binning {25.0, 33.2011, 38.3067, 42.2411, 46.055, 55.0}; // pT binning goes here, this is for 2016 //TODO add fct SetPtbinning
static constexpr double scaling_A = 0.001, scaling_e = 0.001 * 40.0, scaling_M = 0.001 / 40.0, scaling_e_prime = 0.001 / 0.01; // this scaling makes fitter parameters of order 1
vector<string> binLabels; // made public for the closure test
TMatrixD VPhysToInt, VIntToPhys;
private:
vector<double> scaleSquared;
vector<double> scaleSquaredError;
double errorDef;
};
//-----------------------------------------------
// Function to get bin index from label name
vector<int> TheoryFcn::getIndices(string bin_label) const{
vector<int> indices;
int pos = 0;
for (int i=0; i<4-1;i++){ //4 for 4D binning
pos = bin_label.find("_");
indices.push_back(stoi(bin_label.substr(0,pos)));
bin_label.erase(0,pos+1); // 1 is the length of the delimiter, "_"
}
indices.push_back(stoi(bin_label));
return indices;
}
//-----------------------------------------------
// Function to get average k from pT bin index
double TheoryFcn::getK(const int pT_index) const{
// TODO what about errors on k?
// TODO could add the pT histo per eta,pt,eta,pt to get more accurate average pT
return 2.0 / (pT_binning[pT_index] + pT_binning[pT_index+1]); // k = 1 / avg_pT
}
//-----------------------------------------------
// Function to build reduced chi2
double TheoryFcn::operator()(const vector<double>& par) const {
// par has size n_parameters = 3*number_eta_bins, idices from 0 to n-1 contain A, from n to 2n-1 epsilon, from 2n to 3n-1 M
assert(par.size() == 3*nbinseta);
double chi2(0.0);
double diff(0.0);
double k_plus, k_minus, term_pos, term_neg;
double my_func;
int eta_pos_index, eta_neg_index;
vector<int> bin_indices(4); // for 4D binning
double k_middle = (1.0/pT_binning[nbinspt] + 1.0/pT_binning[0])/2.0;
vector<double> physical_M_pars(nbinseta);
TMatrixD internal_M_matrix(nbinseta,1), physical_M_matrix(nbinseta,1);
TArrayD internal_M_elements(nbinseta);
for(int i=0; i<nbinseta; i++){
internal_M_elements[i] = par[2*nbinseta + i];
}
internal_M_matrix.SetMatrixArray(internal_M_elements.GetArray());
physical_M_matrix = TMatrixD(VIntToPhys,TMatrixD::kMult,internal_M_matrix);
//std::cout<< "\n" << "physical M array"<< "\n";
for(int i=0; i<nbinseta; i++){
physical_M_pars[i] = TMatrixDColumn(physical_M_matrix,0)(i);
//std::cout<< " "<< physical_M_pars[i];
}
//std::cout<<"\n";
for(unsigned int n(0); n < scaleSquared.size() ; n++) {
bin_indices = getIndices(binLabels[n]);
eta_pos_index = bin_indices[0];
eta_neg_index = bin_indices[2];
// get k value and TODO error from pT bin index
k_plus = getK(bin_indices[1]);
k_minus = getK(bin_indices[3]);
// (1 + A(+) - e(+)k + M(+)/k)(1 + A(-) - e(-)k - M(-)/k) and scaling of A,e,M
//term_pos = (1. + scaling_A*par[eta_pos_index] - scaling_e*par[eta_pos_index + nbinseta]*k_plus + scaling_M*par[eta_pos_index + 2*nbinseta]/k_plus);
//term_neg = (1. + scaling_A*par[eta_neg_index] - scaling_e*par[eta_neg_index + nbinseta]*k_minus - scaling_M*par[eta_neg_index + 2*nbinseta]/k_minus);
// decorrelate A, e by shifting origin of k
// (1 + A'(+) - e'(+)k' + M(+)/k)(1 + A'(-) - e'(-)k' - M(-)/k) and scaling of A,e,M
//par[eta_pos_index] now has the meaning of A' rather than A
//par[eta_pos_index + nbinseta] now has the meaning of e' rather than e
//term_pos = (1. + scaling_A*par[eta_pos_index] - scaling_e_prime*par[eta_pos_index + nbinseta]*(k_plus - k_middle) + scaling_M*par[eta_pos_index + 2*nbinseta]/k_plus);
//term_neg = (1. + scaling_A*par[eta_neg_index] - scaling_e_prime*par[eta_neg_index + nbinseta]*(k_minus - k_middle) - scaling_M*par[eta_neg_index + 2*nbinseta]/k_minus);
// decorrelate A, e by shifting origin of k and decorrelate M by fitting a linear combination of physical parameters
term_pos = (1. + scaling_A*par[eta_pos_index] - scaling_e_prime*par[eta_pos_index + nbinseta]*(k_plus - k_middle) + scaling_M*physical_M_pars[eta_pos_index]/k_plus);
term_neg = (1. + scaling_A*par[eta_neg_index] - scaling_e_prime*par[eta_neg_index + nbinseta]*(k_minus - k_middle) - scaling_M*physical_M_pars[eta_neg_index]/k_minus);
my_func = term_pos*term_neg;
diff = my_func - scaleSquared[n];
chi2 += diff*diff/(scaleSquaredError[n]*scaleSquaredError[n]);
}
int ndof = scaleSquared.size() - par.size();
return (chi2/ndof - 1.0); // minimise reduced chi2 directly
}
//-----------------------------------------------
// Function to build gradient of reduced chi2 analytically
vector<double> TheoryFcn::Gradient(const vector<double> &par ) const {
assert(par.size() == 3*nbinseta);
vector<double> grad(par.size(),0.0);
TArrayD grad_wrt_M_physical_array(nbinseta), grad_wrt_M_internal_array(nbinseta); // TODO and if not eta 24 bins?
TMatrixD grad_wrt_M_physical_matrix(nbinseta,1), grad_wrt_M_internal_matrix(nbinseta,1);
double temp(0.0), local_func(0.0), local_grad(0.0);
double k_plus, k_minus, term_pos, term_neg;
double k_middle = (1.0/pT_binning[nbinspt] + 1.0/pT_binning[0])/2.0;
int eta_pos_index, eta_neg_index;
vector<int> bin_indices(4); // for 4D binning
int ndof = scaleSquared.size() - par.size();
vector<double> physical_M_pars(nbinseta);
TMatrixD internal_M_matrix(nbinseta,1), physical_M_matrix(nbinseta,1);
TArrayD internal_M_elements(nbinseta);
for(int i=0; i<nbinseta; i++){
internal_M_elements[i] = par[2*nbinseta + i];
}
internal_M_matrix.SetMatrixArray(internal_M_elements.GetArray());
physical_M_matrix = TMatrixD(VIntToPhys,TMatrixD::kMult,internal_M_matrix);
for(int i=0; i<nbinseta; i++){
physical_M_pars[i] = TMatrixDColumn(physical_M_matrix,0)(i);
}
for(unsigned int n(0); n < scaleSquared.size() ; n++) { // loops over measurements
// TODO I need everything from the chi2 function, maybe better solution to this
bin_indices = getIndices(binLabels[n]);
eta_pos_index = bin_indices[0];
eta_neg_index = bin_indices[2];
// get k value and TODO error from pT bin index
k_plus = getK(bin_indices[1]);
k_minus = getK(bin_indices[3]);
// (1 + A(+) - e(+)k + M(+)/k)(1 + A(-) - e(-)k - M(-)/k) and scaling of e,M
//term_pos = (1. + scaling_A*par[eta_pos_index] - scaling_e*par[eta_pos_index + nbinseta]*k_plus + scaling_M*par[eta_pos_index + 2*nbinseta]/k_plus);
//term_neg = (1. + scaling_A*par[eta_neg_index] - scaling_e*par[eta_neg_index + nbinseta]*k_minus - scaling_M*par[eta_neg_index + 2*nbinseta]/k_minus);
// decorrelate A, e by shifting origin of k
// (1 + A'(+) - e'(+)k' + M(+)/k)(1 + A'(-) - e'(-)k' - M(-)/k) and scaling of A,e,M
//par[eta_pos_index] now has the meaning of A' rather than A
//par[eta_pos_index + nbinseta] now has the meaning of e' rather than e
//term_pos = (1. + scaling_A*par[eta_pos_index] - scaling_e_prime*par[eta_pos_index + nbinseta]*(k_plus - k_middle) + scaling_M*par[eta_pos_index + 2*nbinseta]/k_plus);
//term_neg = (1. + scaling_A*par[eta_neg_index] - scaling_e_prime*par[eta_neg_index + nbinseta]*(k_minus - k_middle) - scaling_M*par[eta_neg_index + 2*nbinseta]/k_minus);
// decorrelate A, e by shifting origin of k and decorrelate M by fitting a linear combination of physical parameters
term_pos = (1. + scaling_A*par[eta_pos_index] - scaling_e_prime*par[eta_pos_index + nbinseta]*(k_plus - k_middle) + scaling_M*physical_M_pars[eta_pos_index]/k_plus);
term_neg = (1. + scaling_A*par[eta_neg_index] - scaling_e_prime*par[eta_neg_index + nbinseta]*(k_minus - k_middle) - scaling_M*physical_M_pars[eta_neg_index]/k_minus);
local_func = term_pos*term_neg;
temp=2.0*(local_func - scaleSquared[n])/(scaleSquaredError[n]*scaleSquaredError[n])/ndof;
// derivative wrt A'(+), which is fpar[eta_pos_index]
local_grad = scaling_A*term_neg;
grad[eta_pos_index] += temp*local_grad;
// derivative wrt e'(+)
local_grad = -scaling_e_prime*(k_plus-k_middle)*term_neg;
grad[eta_pos_index + nbinseta] += temp*local_grad;
// derivative wrt M(+), next transform to derivative wrt M_internal(+)
local_grad = scaling_M/k_plus*term_neg;
grad[eta_pos_index + 2*nbinseta] += temp*local_grad;
// derivative wrt A'(-)
local_grad = scaling_A*term_pos;
grad[eta_neg_index] += temp*local_grad;
// derivative wrt e'(-)
local_grad = -scaling_e_prime*(k_minus-k_middle)*term_pos;
grad[eta_neg_index + nbinseta] += temp*local_grad;
// derivative wrt M(-), next transform to derivative wrt M_internal(-)
local_grad = -scaling_M/k_minus*term_pos;
grad[eta_neg_index + 2*nbinseta] += temp*local_grad;
}
// -----------------------------------------------
// Transform M terms to derivatives wrt M_internal
for(int i=0; i<nbinseta; i++){
grad_wrt_M_physical_array[i] = grad[2*nbinseta + i];
}
grad_wrt_M_physical_matrix.SetMatrixArray(grad_wrt_M_physical_array.GetArray());
grad_wrt_M_internal_matrix = TMatrixD(VPhysToInt,TMatrixD::kMult,grad_wrt_M_physical_matrix);
for(int i=0; i<nbinseta; i++){
grad[2*nbinseta + i] = TMatrixDColumn(grad_wrt_M_internal_matrix,0)(i);
}
// -----------------------------------------------
return grad;
}
//-----------------------------------------------
// Function to get parameter name (A0, e1, etc.) and appropriate scaling from index in parameters array
tuple<string,double,string> getParameterNameAndScaling(int index){
int whole = index / nbinseta;
int rest = index % nbinseta;
double scaling;
string name, physical_name;
if (whole == 0) {
name = "A_prime" + to_string(rest);
physical_name = "A" + to_string(rest);
scaling = TheoryFcn::scaling_A;
}
else if (whole == 1) {
name = "e_prime" + to_string(rest);
physical_name = "e" + to_string(rest);
scaling = TheoryFcn::scaling_e_prime;
}
else if (whole == 2) {
name = "M_internal" + to_string(rest);
physical_name = "M" + to_string(rest);
scaling = TheoryFcn::scaling_M;
}
else {
cout<<"\n"<<"ERROR counting parameters"<<"\n";
}
return make_tuple(name, scaling, physical_name);
}
//-----------------------------------------------
// Overload << operator
template <typename S>
ostream& operator<<(ostream& os,
const vector<S>& vector)
{
for (auto element : vector) {
os << element << " ";
}
return os;
}
//--------------------------------------------------------
// Main
//--------------------------------------------------------
int bias_fitter(){
double t1(0.);
// Get start time
struct timeval tv_start, tv_stop;
gettimeofday(&tv_start, 0);
ROOT::EnableImplicitMT();
// Choose validation/analysis mode
//string mode_option("simulation"); //TODO pass as command line argument
//TODO make it so that label of data sample is attached to output files
//--------------------------------------------------------
// Define dataframe
TChain chain("Events");
//chain.Add("/scratch/wmass/y2016/DYJetsToMuMu_H2ErratumFix_TuneCP5_13TeV-powhegMiNNLO-pythia8-photos/NanoV9MCPostVFP_TrackFitV722_NanoProdv6/240509_040854/0000/NanoV9MCPostVFP_1.root");
//chain.Add("/scratch/wmass/y2016/DYJetsToMuMu_H2ErratumFix_PDFExt_TuneCP5_13TeV-powhegMiNNLO-pythia8-photos/NanoV9MCPostVFP_TrackFitV722_NanoProdv6/240509_041233/0000/NanoV9MCPostVFP_1.root");
//chain.Add("/scratch/wmass/y2017/DYJetsToMuMu_H2ErratumFix_TuneCP5_13TeV-powhegMiNNLO-pythia8-photos/NanoV9MC2017_TrackFitV722_NanoProdv3/NanoV9MC2017_1.root");
string line;
ifstream file("InOutputFiles/MCFilenames_2016.txt");
if (file.is_open()) {
while (getline(file, line)) {
chain.Add(line.c_str());
}
file.close();
}
RDataFrame df(chain); // TODO write for analysis mode, will need 2 data frames, but honestly should be a different script that people use
auto d0 = std::make_unique<RNode>(df);
// Random numbers for pairs
unsigned int nslots = d0->GetNSlots();
std::cout<<"1st frame: nslots = "<<nslots<<"\n";
std::vector<TRandom3*> rans = {};
for(unsigned int i = 0; i < nslots; i++){
rans.emplace_back( new TRandom3(4357 + i*10) );
}
d0 = std::make_unique<RNode>(d0->Filter("HLT_IsoMu24 == 1"));
d0 = std::make_unique<RNode>(d0->Filter("nMuon >= 2"));
d0 = std::make_unique<RNode>(d0->Filter("PV_npvsGood >= 1"));
//if (mode_option.compare("simulation") == 0) {
d0 = std::make_unique<RNode>(d0->Define("dxy_significance","dxy_significance(Muon_dxy, Muon_dxyErr)"));
d0 = std::make_unique<RNode>(d0->Define("MuonisGood", "MuonisGood(Muon_pt, Muon_eta, Muon_isGlobal, Muon_mediumId, Muon_pfRelIso04_all, Muon_genPartFlav, dxy_significance)"));
//} else if (mode_option.compare("data") == 0){
//d0 = std::make_unique<RNode>(d0->Define("dxy_significance","dxy_significance(Muon_dxy, Muon_dxyErr)"));
//d0 = std::make_unique<RNode>(d0->Define("MuonisGood", "MuonisGoodData(Muon_pt, Muon_eta, Muon_isGlobal, Muon_mediumId, Muon_pfRelIso04_all, dxy_significance)"));
//}
// Binning (pT done later)
double pt_low = 25.0, pt_high = 55.0, eta_low = -2.4, eta_high = 2.4, mll_diff_low = -7.0, mll_diff_high = 7.0, mll_low = 75.0, mll_high = 105.0;
int nbinsmll_diff_over_gen=32, nbinsmll_diff=22, nbinsmll=32;
vector<double> etabinranges, ptbinranges, mllbinranges;
vector<double> mll_diffbinranges, mll_diff_over_genbinranges;
vector<float> A_values(nbinseta), e_values(nbinseta), M_values(nbinseta);
std::cout<<"\n etabinranges = [";
for (int i=0; i<=nbinseta; i++){
etabinranges.push_back(eta_low + i * (eta_high - eta_low)/nbinseta);
std::cout<<etabinranges[i]<<", ";
}
std::cout<<"] \n";
double myetaboundaries[etabinranges.size()];
for (int i=0; i<etabinranges.size(); i++){
myetaboundaries[i] = etabinranges[i];
}
for (int i=0; i<=nbinsmll_diff; i++) mll_diffbinranges.push_back(mll_diff_low + i*(mll_diff_high - mll_diff_low)/nbinsmll_diff);
double mymll_diffboundaries[mll_diffbinranges.size()];
for (int i=0; i<mll_diffbinranges.size(); i++){
mymll_diffboundaries[i] = mll_diffbinranges[i];
}
for (int i=0; i<=nbinsmll; i++) mllbinranges.push_back(mll_low + i*(mll_high-mll_low)/nbinsmll);
double mymllboundaries[mllbinranges.size()];
for (int i=0; i<mllbinranges.size(); i++){
mymllboundaries[i] = mllbinranges[i];
}
for (int i=0; i<=nbinsmll_diff_over_gen; i++) mll_diff_over_genbinranges.push_back(-0.1 + i*2.0*0.1/nbinsmll_diff_over_gen);
int nbins = nbinseta*nbinseta*nbinspt*nbinspt;
int nbins_plus_one = nbins+1;
double mybinsboundaries[nbins_plus_one];
for (int i=0; i<=nbins; i++){
mybinsboundaries[i] = i;
}
float kmean_val = 0.5*( 1./25. + 1./55. ); // TODO don't input pt bounds by hand
TRandom3* ran0 = new TRandom3(932);
for (int i=0; i<nbinseta; i++){
//std::cout<< "\n" << "i: " << i << "\n";
// A from -0.0002 to 0.0005 and back
A_values[i] = (-7.0*4.0/nbinseta/nbinseta*(i - nbinseta/2)*(i - nbinseta/2) + 5.0)*0.0001;
std::cout<< i << " A: "<<A_values[i] << " ";
//A_values[i] = -0.001*( 1 + ((etabinranges[i]+etabinranges[i+1])/2.0/2.4)*((etabinranges[i]+etabinranges[i+1])/2.0/2.4) );
//A_values[i] = 0.0004;
//std::cout<< "A: " << A_values[i] << ", ";
// e from 0.01 to 0.001 and back
e_values[i] = (9.0*4.0/nbinseta/nbinseta*(i - nbinseta/2)*(i - nbinseta/2) + 1.0)*0.001;
std::cout<< i << " e: "<<e_values[i] << " ";
//e_values[i] = ran0->Uniform(-0.0001/kmean_val, 0.0001/kmean_val);
//e_values[i] = 0.002;
//std::cout<< "e: " << e_values[i] << ", ";
// M from 4*10^-5 to -2*10^-5 and back
M_values[i] = (6.0*4.0/nbinseta/nbinseta*(i - nbinseta/2)*(i - nbinseta/2) - 2.0)*0.00001;
std::cout<< i << " M: "<<M_values[i] << "\n";
//M_values[i] = ran0->Uniform(-0.001*kmean_val, 0.001*kmean_val);
//M_values[i] = 0.00001;
//std::cout<< "M: " << M_values[i] << ", ";
}
auto GetEtaBin = [&](float eta)->int{
for (int i=0; i<nbinseta; i++){
if( etabinranges[i] <= eta && eta < etabinranges[i+1]) { return i; }
}
return -9;
};
//Pairs
auto pairs = [&](unsigned int nslot, RVecF Muon_pt, RVecI Muon_charge, RVecF Muon_eta, RVecF Muon_phi, RVecB MuonisGood, RVecF Muon_dxy, RVecF Muon_dz, RVecI GenPart_status, RVecI GenPart_pdgId, RVecI GenPart_genPartIdxMother, RVecF GenPart_pt, RVecF GenPart_eta, RVecF GenPart_phi)->std::tuple<int,int,float,float,float,float,float,float,float,float,float,float,float,float,int,int>{
// <0 pos_muon_index, 1 neg_muon_index, 2 mll_reco, 3 posPt_reco, 4 negPt_reco , 5 mll_smear, 6 posPt_smear, 7 negPt_smear, 8 mll_gen, 9 posPt_gen, 10 negPt_gen, 11 mll_smear_beta_val, 12 posPt_smear_beta_val, 13 negPt_smear_beta_val>
RVec<std::tuple<int,int,float,float,float,float,float,float,float,float,float,float,float,float,int,int>> pairs;
std::tuple<int,int,float,float,float,float,float,float,float,float,float,float,float,float,int,int> temp, pair_to_return;
float rest_mass = 0.105658; // muMass = 0.105658 GeV
float firstPt_reco, secondPt_reco, mll_reco, firstPt_smear, secondPt_smear, mll_smear, firstPt_gen, secondPt_gen, mll_gen, firstPt_smear_beta_val, secondPt_smear_beta_val, smear_beta_weight;
float smear_pt, mean, width, smeared_mean, smeared_curvature, smear_beta_weight_first_term, smear_beta_weight_second_term;
//float A = 0.0004, e = 0.002, M = 0.00001; //for now constant per eta bin
float A, e, M;
int eta_bin_idx, first_gen_idx, second_gen_idx;
for(int i=1;i<Muon_pt.size();i++){
if(MuonisGood[i]){
for(int j=0;j<i;j++){
if(MuonisGood[j] && Muon_charge[i]*Muon_charge[j]==-1 && abs(Muon_dxy[i]-Muon_dxy[j])<0.1 && abs(Muon_dz[i]-Muon_dz[j])<0.6){
TLorentzVector firstTrack, secondTrack, mother, firstGenTrack, secondGenTrack, motherGen, firstSmearTrack, secondSmearTrack, motherSmear, firstSmearBiasTrack, secondSmearBiasTrack, motherSmearBias;
firstTrack.SetPtEtaPhiM(Muon_pt[i], Muon_eta[i], Muon_phi[i], rest_mass);
secondTrack.SetPtEtaPhiM(Muon_pt[j], Muon_eta[j], Muon_phi[j], rest_mass);
mother = firstTrack + secondTrack;
mll_reco = mother.M();
firstPt_reco = Muon_pt[i];
secondPt_reco = Muon_pt[j];
if(mll_low<mll_reco && mll_reco<mll_high){ //Cut in mll
//Gen match
bool firstGenMatched = false, secondGenMatched = false;
for (int k=0;k<GenPart_eta.size();k++){
if (GenPart_status[k]==1 && abs(GenPart_pdgId[k])==13 && GenPart_pdgId[GenPart_genPartIdxMother[k]]==23){ // mu(-) has PDGID 13
if(pow(pow(GenPart_eta[k]-Muon_eta[i],2) + pow(TVector2::Phi_mpi_pi(GenPart_phi[k]-Muon_phi[i]),2),0.5)<0.3){
firstGenTrack.SetPtEtaPhiM(GenPart_pt[k], GenPart_eta[k], GenPart_phi[k], rest_mass);
firstPt_gen = GenPart_pt[k];
first_gen_idx = k;
//smear 1st muon
mean = GenPart_pt[k]; //beta = 1
width = (0.004*pow(GenPart_eta[k],2)+0.01)*GenPart_pt[k];
firstPt_smear = rans[nslot]->Gaus(mean, width);
firstSmearTrack.SetPtEtaPhiM(firstPt_smear, GenPart_eta[k], GenPart_phi[k], rest_mass);
//smear_beta_val, weight for beta != 1 use Eqn 22 in note AN2021_131_v5
eta_bin_idx = GetEtaBin(Muon_eta[i]);
if (eta_bin_idx < 0 || eta_bin_idx > 23){
std::cout<<"\n"<<"WARNING eta out of bounds";
pair_to_return = make_tuple(0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0);
return pair_to_return ;
}
A = A_values[eta_bin_idx];
e = e_values[eta_bin_idx];
M = M_values[eta_bin_idx];
if (GenPart_pdgId[k]>0){ //mu(-)
if ( (1.0+A)*(1.0+A) + 4.0*e*(-1.0*M - 1.0/mean) >= 0.0 ){
smeared_curvature = ( -1.0*(1.0+A) + pow( (1.0+A)*(1.0+A) + 4.0*e*(-1.0*M - 1.0/mean) ,0.5) )/(-2.0)/e;
if (smeared_curvature < 0.0 || std::isnan(smeared_curvature)){
std::cout<<"\n"<<"WARNING WRONG CURVATURE";
pair_to_return = make_tuple(0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0);
return pair_to_return ;
}
} else {
std::cout<<"\n"<<"WARNING negative delta";
pair_to_return = make_tuple(0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0);
return pair_to_return ;
}
//smeared_curvature = 1.0/mean + M; // for M only
} else { //mu(+)
if ( (1.0+A)*(1.0+A) + 4.0*e*(M - 1.0/mean) >= 0.0 ){
smeared_curvature = ( -1.0*(1.0+A) + pow( (1.0+A)*(1.0+A) + 4.0*e*(M - 1.0/mean) ,0.5) )/(-2.0)/e;
if (smeared_curvature < 0.0 || std::isnan(smeared_curvature)){
std::cout<<"\n"<<"WARNING WRONG CURVATURE";
pair_to_return = make_tuple(0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0);
return pair_to_return ;
}
} else {
std::cout<<"\n"<<"WARNING negative delta";
pair_to_return = make_tuple(0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0);
return pair_to_return ;
}
//smeared_curvature = 1.0/mean - M; // for M only
}
smeared_mean = 1.0/smeared_curvature;
firstPt_smear_beta_val = rans[nslot]->Gaus(smeared_mean, width);
firstSmearBiasTrack.SetPtEtaPhiM(firstPt_smear_beta_val, GenPart_eta[k], GenPart_phi[k], rest_mass);
//smear_beta_weight_first_term = TMath::Gaus(firstPt_smear, smeared_mean, width) / TMath::Gaus(firstPt_smear, mean, width);
firstGenMatched = true;
if(secondGenMatched == true){break;}
} else if(pow(pow(GenPart_eta[k]-Muon_eta[j],2) + pow(TVector2::Phi_mpi_pi(GenPart_phi[k]-Muon_phi[j]),2),0.5)<0.3){
secondGenTrack.SetPtEtaPhiM(GenPart_pt[k], GenPart_eta[k], GenPart_phi[k], rest_mass);
secondPt_gen = GenPart_pt[k];
second_gen_idx = k;
//smear 2nd muon
mean = GenPart_pt[k]; //beta = 1
width = (0.004*pow(GenPart_eta[k],2)+0.01)*GenPart_pt[k];
secondPt_smear = rans[nslot]->Gaus(mean, width);
secondSmearTrack.SetPtEtaPhiM(secondPt_smear, GenPart_eta[k], GenPart_phi[k], rest_mass);
//smear_beta_val, weight for beta != 1
eta_bin_idx = GetEtaBin(Muon_eta[j]);
if (eta_bin_idx < 0 || eta_bin_idx > 23){
std::cout<<"\n"<<"WARNING eta out of bounds";
pair_to_return = make_tuple(0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0);
return pair_to_return ;
}
A = A_values[eta_bin_idx];
e = e_values[eta_bin_idx];
M = M_values[eta_bin_idx];
if (GenPart_pdgId[k]>0){ //mu(-)
if ( (1.0+A)*(1.0+A) + 4.0*e*(-1.0*M - 1.0/mean) >= 0.0 ){
smeared_curvature = ( -1.0*(1.0+A) + pow( (1.0+A)*(1.0+A) + 4.0*e*(-1.0*M - 1.0/mean) ,0.5) )/(-2.0)/e;
if (smeared_curvature < 0.0 || std::isnan(smeared_curvature)){
std::cout<<"\n"<<"WARNING WRONG CURVATURE";
pair_to_return = make_tuple(0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0);
return pair_to_return ;
}
} else {
std::cout<<"\n"<<"WARNING negative delta";
pair_to_return = make_tuple(0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0);
return pair_to_return ;
}
//smeared_curvature = 1.0/mean + M; // for M only
} else { //mu(+)
if ( (1.0+A)*(1.0+A) + 4.0*e*(M - 1.0/mean) >= 0.0 ){
smeared_curvature = ( -1.0*(1.0+A) + pow( (1.0+A)*(1.0+A) + 4.0*e*(M - 1.0/mean) ,0.5) )/(-2.0)/e;
if (smeared_curvature < 0.0 || std::isnan(smeared_curvature)){
std::cout<<"\n"<<"WARNING WRONG CURVATURE";
pair_to_return = make_tuple(0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0);
return pair_to_return ;
}
} else {
std::cout<<"\n"<<"WARNING negative delta";
pair_to_return = make_tuple(0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0);
return pair_to_return ;
}
//smeared_curvature = 1.0/mean - M; // for M only
}
smeared_mean = 1.0/smeared_curvature;
secondPt_smear_beta_val = rans[nslot]->Gaus(smeared_mean, width);
secondSmearBiasTrack.SetPtEtaPhiM(secondPt_smear_beta_val, GenPart_eta[k], GenPart_phi[k], rest_mass);
//smear_beta_weight_second_term = TMath::Gaus(secondPt_smear, smeared_mean, width) / TMath::Gaus(secondPt_smear, mean, width);
secondGenMatched = true;
if(firstGenMatched == true){break;}
}
}
}
if(firstGenMatched == false || secondGenMatched == false){
continue;
}
motherSmear = firstSmearTrack + secondSmearTrack;
mll_smear = motherSmear.M();
//smear_beta_weight = smear_beta_weight_first_term * smear_beta_weight_second_term;
motherSmearBias = firstSmearBiasTrack + secondSmearBiasTrack;
float mll_smear_beta_val = motherSmearBias.M();
motherGen = firstGenTrack + secondGenTrack;
float mll_gen = motherGen.M();
//--------------------------------------------------------------------------------
// ATTENTION OVERWRITING smear
// smear mass directly
//mll_smear = rans[nslot]->Gaus(mll_gen, 0.02*mll_gen);
//smear_beta_weight = TMath::Gaus(mll_smear, mll_gen*(1-0.001), 0.02*mll_gen) / TMath::Gaus(mll_smear, mll_gen, 0.02*mll_gen);
//--------------------------------------------------------------------------------
if(Muon_charge[i]==1){
// <0 pos_muon_index, 1 neg_muon_index, 2 mll_reco, 3 posPt_reco, 4 negPt_reco , 5 mll_smear, 6 posPt_smear, 7 negPt_smear, 8 mll_gen, 9 posPt_gen, 10 negPt_gen, 11 mll_smear_beta_val, 12 posPt_smear_beta_val, 13 negPt_smear_beta_val, 14 pos_gen_idx, 15 neg_gen_idx>
temp=make_tuple(i,j,mll_reco,firstPt_reco,secondPt_reco,mll_smear,firstPt_smear,secondPt_smear,mll_gen,firstPt_gen,secondPt_gen,mll_smear_beta_val,firstPt_smear_beta_val,secondPt_smear_beta_val,first_gen_idx, second_gen_idx);
} else {
temp=make_tuple(j,i,mll_reco,secondPt_reco,firstPt_reco,mll_smear,secondPt_smear,firstPt_smear,mll_gen,secondPt_gen,firstPt_gen,mll_smear_beta_val,secondPt_smear_beta_val,firstPt_smear_beta_val,second_gen_idx, first_gen_idx);
}
pairs.push_back(temp);
}
}
}
}
}
if(pairs.size()==1){
pair_to_return=pairs.at(0);
} else if(pairs.size()>1){
float diff=100.0;
int best=0;
for(int i=0;i<pairs.size();i++){
if(abs(get<2>(pairs.at(i))-91)<diff){
diff=(abs(get<2>(pairs.at(i))-91));
best=i;
}
}
pair_to_return=pairs.at(best);
} else {
pair_to_return=make_tuple(0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0);
}
return pair_to_return;
};
/*
//PairsData
auto pairsData = [&](RVecF Muon_pt, RVecI Muon_charge, RVecF Muon_eta, RVecF Muon_phi, RVecB MuonisGood, RVecF Muon_dxy, RVecF Muon_dz)->std::tuple<int,int,float,float,float>{
// <0 pos_muon_index, 1 neg_muon_index, 2 mll_reco, 3 posPt_reco, 4 negPt_reco>
RVec<std::tuple<int,int,float,float,float>> pairs;
std::tuple<int,int,float,float,float> temp, pair_to_return;
float rest_mass = 0.105658; // muMass = 0.105658 GeV
float firstPt_reco, secondPt_reco, mll_reco;
for(int i=1;i<Muon_pt.size();i++){
if(MuonisGood[i]){
for(int j=0;j<i;j++){
if(MuonisGood[j] && Muon_charge[i]*Muon_charge[j]==-1 && abs(Muon_dxy[i]-Muon_dxy[j])<0.1 && abs(Muon_dz[i]-Muon_dz[j])<0.6){ //TODO better deltaR matching
TLorentzVector firstTrack, secondTrack, mother;
firstTrack.SetPtEtaPhiM(Muon_pt[i], Muon_eta[i], Muon_phi[i], rest_mass);
secondTrack.SetPtEtaPhiM(Muon_pt[j], Muon_eta[j], Muon_phi[j], rest_mass);
mother = firstTrack + secondTrack;
mll_reco = mother.M();
firstPt_reco = Muon_pt[i];
secondPt_reco = Muon_pt[j];
if(mll_low<mll_reco && mll_reco<mll_high){ //Cut in mll
if(Muon_charge[i]==1){
// <0 pos_muon_index, 1 neg_muon_index, 2 mll_reco, 3 posPt_reco, 4 negPt_reco >
temp=make_tuple(i,j,mll_reco,firstPt_reco,secondPt_reco);
} else {
temp=make_tuple(j,i,mll_reco,secondPt_reco,firstPt_reco);
}
pairs.push_back(temp);
}
}
}
}
}
if(pairs.size()==1){
pair_to_return=pairs.at(0);
} else if(pairs.size()>1){
float diff=100.0;
int best=0;
for(int i=0;i<pairs.size();i++){
if(abs(get<2>(pairs.at(i))-91)<diff){
diff=(abs(get<2>(pairs.at(i))-91));
best=i;
}
}
pair_to_return=pairs.at(best);
} else {
pair_to_return=make_tuple(0,0,0.0,0.0,0.0);
}
return pair_to_return;
};
*/
//if (mode_option.compare("simulation") == 0) {
d0 = std::make_unique<RNode>(d0->DefineSlot("pairs", pairs, {"Muon_pt", "Muon_charge", "Muon_eta", "Muon_phi", "MuonisGood", "Muon_dxy", "Muon_dz", "GenPart_status", "GenPart_pdgId", "GenPart_genPartIdxMother", "GenPart_pt", "GenPart_eta", "GenPart_phi"}));
//} else if (mode_option.compare("data") == 0){
//d0 = std::make_unique<RNode>(d0->Define("pairs", pairsData, {"Muon_pt", "Muon_charge", "Muon_eta", "Muon_phi", "MuonisGood", "Muon_dxy", "Muon_dz"}));
//}
d0 = std::make_unique<RNode>(d0->Define("mll_reco","return get<2>(pairs);"));
d0 = std::make_unique<RNode>(d0->Filter("mll_reco>1.0")); // this means only events with one mu pair are kept
// This below works because actually we kept only one pair per event
// Accessing properties through the idices of pairs ensures the muons passed MuonisGood
// <0 pos_muon_index, 1 neg_muon_index, 2 mll_reco, 3 posPt_reco, 4 negPt_reco , 5 mll_smear, 6 posPt_smear, 7 negPt_smear, 8 mll_gen, 9 posPt_gen, 10 negPt_gen, 11 smear_beta_weight, 12 posPt_smear_beta_val, 13 negPt_smear_beta_val>
d0 = std::make_unique<RNode>(d0->Define("posTrackPt","return get<3>(pairs);")); //TOOD make binning on smear not reco
//dlast = std::make_unique<RNode>(dlast->Define("negTrackPt","return get<4>(pairs);"));
//dlast = std::make_unique<RNode>(dlast->Define("posTrackEta","return Muon_eta[get<0>(pairs)];"));
//dlast = std::make_unique<RNode>(dlast->Define("negTrackEta","return Muon_eta[get<1>(pairs)];"));
////dlast = std::make_unique<RNode>(dlast->Define("bin_index_reco", GetEtaPtEtaPtBin ,{"posTrackEta", "posTrackPt", "negTrackEta", "negTrackPt"}));
////if (mode_option.compare("simulation") == 0) { //NOTE ATTENTION can't yet run analysis mode with optimised pT binning
d0 = std::make_unique<RNode>(d0->Define("weight", "std::copysign(1.0, genWeight)"));
////}
//pT bin optimisation starts
// Call 1st event loop
std::cout<<"Call 1st event loop: pT binning optimization"<<"\n";
auto pt_pos_uni = d0->Histo1D({"pt_pos_uni", "pt mu+", nbinspt*3, pt_low, pt_high},"posTrackPt","weight");
// Get quartiles
double xq[nbinspt+1], myptboundaries[nbinspt+1];
for (int i=0;i<=nbinspt;i++) xq[i] = float(i)/nbinspt;
pt_pos_uni->GetQuantiles(nbinspt+1,myptboundaries, xq);
ptbinranges.clear();
std::cout<<"ptbinranges = [";
for (int i=0; i<=nbinspt; i++){
ptbinranges.push_back(myptboundaries[i]);
std::cout<<ptbinranges[i]<<", ";
}
std::cout<<"] \n";
//---------------------------------------------------
// Sort events in eta pt eta pt bins
// GetEtaPtEtaPtBin
auto GetEtaPtEtaPtBin = [&](float pos_eta, float pos_pt, float neg_eta, float neg_pt )->int{
int idx = 0;
for (int i=0; i<nbinseta; i++){
for (int j=0; j<nbinspt; j++){
for (int k=0; k<nbinseta; k++){
for (int l=0; l<nbinspt; l++){
if(etabinranges[i] <= pos_eta && pos_eta < etabinranges[i+1] && ptbinranges[j] <= pos_pt && pos_pt < ptbinranges[j+1] && etabinranges[k] <= neg_eta && neg_eta < etabinranges[k+1] && ptbinranges[l] <= neg_pt && neg_pt < ptbinranges[l+1] ) { return idx; }
idx++;
}
}
}
}
return -9;
};
auto d1 = std::make_unique<RNode>(df);
d1 = std::make_unique<RNode>(d1->Filter("event%2==0")); //MC like
// Random numbers for pairs
nslots = d1->GetNSlots();
std::cout<<"2nd frame: nslots = "<<nslots<<"\n";
for(unsigned int i = 0; i < nslots; i++){
rans[i] = new TRandom3(8724 + i*10) ;
}
d1 = std::make_unique<RNode>(d1->Filter("HLT_IsoMu24 == 1"));
d1 = std::make_unique<RNode>(d1->Filter("nMuon >= 2"));
d1 = std::make_unique<RNode>(d1->Filter("PV_npvsGood >= 1"));
d1 = std::make_unique<RNode>(d1->Define("dxy_significance","dxy_significance(Muon_dxy, Muon_dxyErr)"));
d1 = std::make_unique<RNode>(d1->Define("MuonisGood", "MuonisGood(Muon_pt, Muon_eta, Muon_isGlobal, Muon_mediumId, Muon_pfRelIso04_all, Muon_genPartFlav, dxy_significance)"));
d1 = std::make_unique<RNode>(d1->DefineSlot("pairs", pairs, {"Muon_pt", "Muon_charge", "Muon_eta", "Muon_phi", "MuonisGood", "Muon_dxy", "Muon_dz", "GenPart_status", "GenPart_pdgId", "GenPart_genPartIdxMother", "GenPart_pt", "GenPart_eta", "GenPart_phi"}));
d1 = std::make_unique<RNode>(d1->Define("mll_reco","return get<2>(pairs);"));
d1 = std::make_unique<RNode>(d1->Filter("mll_reco>1.0")); // this means only events with one mu pair are kept
// This below works because actually we kept only one pair per event
// Accessing properties through the idices of pairs ensures the muons passed MuonisGood
// <0 pos_muon_index, 1 neg_muon_index, 2 mll_reco, 3 posPt_reco, 4 negPt_reco , 5 mll_smear, 6 posPt_smear, 7 negPt_smear, 8 mll_gen, 9 posPt_gen, 10 negPt_gen, 11 smear_beta_weight, 12 posPt_smear_beta_val, 13 negPt_smear_beta_val>
d1 = std::make_unique<RNode>(d1->Define("posPtSmear","return get<6>(pairs);"));
d1 = std::make_unique<RNode>(d1->Define("negPtSmear","return get<7>(pairs);"));
d1 = std::make_unique<RNode>(d1->Define("posGenEta","return GenPart_eta[get<14>(pairs)];"));
d1 = std::make_unique<RNode>(d1->Define("negGenEta","return GenPart_eta[get<15>(pairs)];"));
d1 = std::make_unique<RNode>(d1->Define("bin_index_smear", GetEtaPtEtaPtBin ,{"posGenEta", "posPtSmear", "negGenEta", "negPtSmear"}));
d1 = std::make_unique<RNode>(d1->Define("mll_smear","return get<5>(pairs);"));
d1 = std::make_unique<RNode>(d1->Define("mll_gen","return get<8>(pairs);"));
d1 = std::make_unique<RNode>(d1->Define("mll_diff_smear","return mll_smear - mll_gen;"));
d1 = std::make_unique<RNode>(d1->Define("weight", "std::copysign(1.0, genWeight)"));
// Take MC frame and fit diff smear per k bin, save results in a histo
// MC diff
// Call 2nd event loop
std::cout<<"Call 2nd event loop: get MC resolution"<<"\n";
auto mll_diff_smear_diffbin_4Dbin = d1->Histo2D({"mll_diff_smear_diffbin_4Dbin", "mll_diff_smear_diffbin_4Dbin", nbinsmll_diff, mymll_diffboundaries, nbins, mybinsboundaries},"mll_diff_smear", "bin_index_smear", "weight");
auto mDh_diff_mc_ptr = mll_diff_smear_diffbin_4Dbin;
auto mDh_proj_diff_mc = mDh_diff_mc_ptr->ProjectionX("mDh_proj_diff_mc", 0, 1, "e");
double evt_threshold = 300.0;
int pos_eta_bin, pos_pt_bin, neg_eta_bin, neg_pt_bin;
vector<double> fitresult;
TH1D *mean_diff_by_idx = new TH1D("mean_diff_by_idx", "mean_diff_by_idx", nbins, 0, nbins);
TH1D *sigma_diff_by_idx = new TH1D("sigma_smear_by_idx", "mean_diff_by_idx", nbins, 0, nbins);
cout << "\n" << "First loop over 4D bins starts" << "\n";
for (int multi_dim_bin=0; multi_dim_bin<nbins; multi_dim_bin++){
pos_eta_bin = multi_dim_bin/(nbinspt*nbinseta*nbinspt);
pos_pt_bin = (multi_dim_bin%(nbinspt*nbinseta*nbinspt))/(nbinseta*nbinspt);
neg_eta_bin = ((multi_dim_bin%(nbinspt*nbinseta*nbinspt))%(nbinseta*nbinspt))/nbinspt;
neg_pt_bin = ((multi_dim_bin%(nbinspt*nbinseta*nbinspt))%(nbinseta*nbinspt))%nbinspt;
delete gROOT->FindObject("mDh_proj_diff_mc");
mDh_proj_diff_mc = mDh_diff_mc_ptr->ProjectionX("mDh_proj_diff_mc", multi_dim_bin+1, multi_dim_bin+1, "e");
if (mDh_proj_diff_mc->Integral(1,nbinsmll_diff) >= evt_threshold){
// Fit diff_mc
fitresult = fitHisto(mDh_proj_diff_mc, 0, 8, 5);
if (fitresult[0] == -90.0 || fitresult[1] == -5.0){
std::cout<<"WARNING bad diff_mc fit "<<" \n";
}
mean_diff_by_idx->SetBinContent(multi_dim_bin+1, fitresult[0]);
mean_diff_by_idx->SetBinError(multi_dim_bin+1, fitresult[2]);
sigma_diff_by_idx->SetBinContent(multi_dim_bin+1, fitresult[1]);
sigma_diff_by_idx->SetBinError(multi_dim_bin+1, fitresult[3]);
} else {
mean_diff_by_idx->SetBinContent(multi_dim_bin+1, -10.0);
mean_diff_by_idx->SetBinError(multi_dim_bin+1, 5.0);
sigma_diff_by_idx->SetBinContent(multi_dim_bin+1, -10.0);
sigma_diff_by_idx->SetBinError(multi_dim_bin+1, 5.0);
}
}
// define jacobians with the info from that histo
auto jacobian_weight_alpha_mass = [&](int multi_dim_bin, float m_reco, float m_gen, double MC_weight)->float{
float mean_MC, sigma_MC;
mean_MC = mean_diff_by_idx->GetBinContent(multi_dim_bin+1);
sigma_MC = sigma_diff_by_idx->GetBinContent(multi_dim_bin+1);
float jac_alpha = (m_reco-(mean_MC+m_gen))*(m_reco-(mean_MC+m_gen))/sigma_MC/sigma_MC-1.0;
return jac_alpha*MC_weight;
};
auto jacobian_weight_beta_mass = [&](int multi_dim_bin, float m_reco, float m_gen, double MC_weight)->float{
float mean_MC, sigma_MC;
mean_MC = mean_diff_by_idx->GetBinContent(multi_dim_bin+1);
sigma_MC = sigma_diff_by_idx->GetBinContent(multi_dim_bin+1);
float jac_beta = (m_reco-(mean_MC+m_gen))*(mean_MC+m_gen)/sigma_MC/sigma_MC;
return jac_beta*MC_weight;
};
auto jacobian_weight_alpha_diff = [&](int multi_dim_bin, float m_diff, double MC_weight)->float{
float mean_MC, sigma_MC;
mean_MC = mean_diff_by_idx->GetBinContent(multi_dim_bin+1);
sigma_MC = sigma_diff_by_idx->GetBinContent(multi_dim_bin+1);
float jac_alpha = (m_diff-mean_MC)*(m_diff-mean_MC)/sigma_MC/sigma_MC - 1.0;
return jac_alpha*MC_weight;
};
auto jacobian_weight_epsilon_diff = [&](int multi_dim_bin, float m_diff, double MC_weight)->float{
float mean_MC, sigma_MC;