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deeplot.cc
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// Differential distributions with DeepEfficiency inversion
// ------------------------------------------------------------------------
//
//
// Compile with makefile: make && ./deeplot
//
// [email protected], 23/07/2018
#include <iostream>
#include <fstream>
#include <map>
#include <memory>
// ROOT
#include "TLorentzVector.h"
#include "TCanvas.h"
#include "TH1.h"
#include "TH2.h"
#include "TStyle.h"
#include "TROOT.h"
#include "TColor.h"
// Own classes
#include "tripletclass.h"
const double PI = 3.14159265359;
// Maximum event count cut (for quick testing)
const int MAXEVENTS = 100000000;
// Particle mass
const double mPI = 0.139570;
const double mK = 0.493677;
// ****************** FIDUCIAL DEFINITION ******************
// We cut events here for the DeepEfficiency training phase
// Note that these should be kept the same as in the training
// phase, or more tight.
const double FID_PT = 0.1; // track pt
const double FID_ETA = 0.9; // track eta
// *********************************************************
bool Processor(const std::string& PREDICTFILE);
void SetROOTStyle();
void SetPlotStyle();
void PlotFilled(TH1D* h1, std::string& name, bool logscale, bool normalize);
bool ReadKinematics(TLorentzVector& p1_gen, TLorentzVector& p2_gen,
TLorentzVector& p1_rec, TLorentzVector& p2_rec, int& reco);
// Kinematic input
FILE* fp;
// Main function
int main(void) {
SetPlotStyle();
std::vector<std::string> filenames;
filenames.push_back("tree2track_kPipmExp");
filenames.push_back("tree2track_kPipmOrexp");
filenames.push_back("tree2track_kPipmPower");
filenames.push_back("tree2track_kCohRhoToPi");
filenames.push_back("tree2track_kKpkmExp");
filenames.push_back("tree2track_kKpkmOrexp");
filenames.push_back("tree2track_kKpkmPower");
for (uint i = 0; i < filenames.size(); ++i) {
Processor(filenames.at(i));
}
return EXIT_SUCCESS;
}
// Processor
bool Processor(const std::string& PREDICTFILE) {
// Create output directory in a case
system("mkdir figs");
std::string cmd = "mkdir ./figs/" + PREDICTFILE + "/";
system(cmd.c_str());
// 1. Open kinematics
std::string filename = "./data/" + PREDICTFILE + ".csv";
if ((fp = fopen(filename.c_str(), "r")) == NULL) {
printf("Cannot open kinematics inputfile: %s \n", filename.c_str());
return false;
}
// 2. Open DeepEfficiency weights
std::string deepfilename = "./output/" + PREDICTFILE + ".out";
std::ifstream deepnetfile;
deepnetfile.open(deepfilename);
if (!deepnetfile) {
printf("Cannot open DeepEfficiency outputfile: %s \n", deepfilename.c_str());
return false;
}
// -------------------------------------------------------------------------
// Create histograms
// Number of bins
const int Nbin1D = 100;
const int Nbin2D = 80;
// (Generated, Reconstructed, Corrected) 1D-histogram triplets
std::vector<h1Triplet*> h1;
h1Triplet h1M(PREDICTFILE + "/h1M", ";System M (GeV); events", Nbin1D, 0.0, 4.0, "northeast");
h1.push_back(&h1M);
h1Triplet h1Y(PREDICTFILE + "/h1Y", ";System Y; events", Nbin1D, -1.0, 1.0, "northeast");
h1.push_back(&h1Y);
h1Triplet h1Pt(PREDICTFILE + "/h1Pt", ";System P_{T} (GeV); events", Nbin1D, 0.0, 2.0, "northeast");
h1.push_back(&h1Pt);
h1Triplet h1pt1(PREDICTFILE + "/h1pt1", ";Track p_{T} (GeV); events", Nbin1D, 0.0, 2.0, "northeast");
h1.push_back(&h1pt1);
h1Triplet h1eta1(PREDICTFILE + "/h1eta1", ";Track #eta; events", Nbin1D, -1.0, 1.0, "southeast");
h1.push_back(&h1eta1);
h1Triplet h1dY(PREDICTFILE + "/h1dY", ";#Deltay #equiv y_{1}-y_{2}; events", Nbin1D, -2.0, 2.0, "northeast");
h1.push_back(&h1dY);
// (Generated, Reconstructed, Corrected) 2D-histogram triplets
std::vector<h2Triplet*> h2;
h2Triplet h2etaphi(PREDICTFILE + "/h2etaphi", ";Track #eta; Track #phi (rad)", Nbin2D, -1.0, 1.0, Nbin2D, -PI, PI);
h2.push_back(&h2etaphi);
h2Triplet h2etaeta(PREDICTFILE + "/h2etaeta", ";Track #eta^{(1)}; Track #eta^{(2)}", Nbin2D, -1.0, 1.0, Nbin2D, -1.0, 1.0);
h2.push_back(&h2etaeta);
h2Triplet h2pt1pt2(PREDICTFILE + "/h2pt1pt2", ";Track p_{T}^{(1)}; Track p_{T}^{(2)}", Nbin2D, 0.0, 2.0, Nbin2D, 0.0, 2.0);
h2.push_back(&h2pt1pt2);
h2Triplet h2Mdeltaphi(PREDICTFILE + "/h2Mdeltaphi", ";System M (GeV); Pair #Delta#phi (rad)", Nbin2D, 0.0, 4.0, Nbin2D, 0, PI);
h2.push_back(&h2Mdeltaphi);
h2Triplet h2MPt(PREDICTFILE + "/h2MPt", ";System M (GeV); System P_{T} (GeV)", Nbin2D, 0.0, 4.0, Nbin2D, 0, 2.5);
h2.push_back(&h2MPt);
h2Triplet h2Mpt1(PREDICTFILE + "/h2Mpt1", ";System M (GeV); Track p_{T} (GeV)", Nbin2D, 0.0, 4.0, Nbin2D, 0, 2.5);
h2.push_back(&h2Mpt1);
// Aux variables
int reco = 0;
int k = 0;
double weight = 0.0;
TH1D* h1W = new TH1D("h1W", ";DeepEfficiency-6D output w; events", 200, 0, 1.0);
// Generated 4-momentum
TLorentzVector p1_gen;
TLorentzVector p2_gen;
TLorentzVector system_gen;
// Reconstructed 4-momentum
TLorentzVector p1_rec;
TLorentzVector p2_rec;
TLorentzVector system_rec;
// Event loop
while (true) {
if (k >= MAXEVENTS - 1) {
printf("Maximum event count = %d reached \n", MAXEVENTS);
break;
}
// Read kinematic input
if (!ReadKinematics(p1_gen, p2_gen, p1_rec, p2_rec, reco)) {
break;
}
// System 4-momentum, generated and reconstructed
system_gen = p1_gen + p2_gen;
system_rec = p1_rec + p2_rec;
// Read in DeepEfficiency efficiency estimate
if (!(deepnetfile >> weight)) {
printf("Weight not found (k = %d)!\n", k);
break;
}
// Inverse weight
weight = 1.0 / std::min(std::max(weight, 1e-6), 1.0); // max operator regularizator for safety
// ----------------------------------------------------------------
// ***** FIDUCIAL CUTS *****
// Note that DeepEfficiency network should not be trained with more restrictive cuts than what
// one applied here.
// Use generator level variables here, in order to be able to make "ground truth comparison".
// When working with data, this option is not possible.
if (p1_gen.Perp() > FID_PT && p2_gen.Perp() > FID_PT &&
std::abs(p1_gen.Eta()) < FID_ETA && std::abs(p2_gen.Eta()) < FID_ETA ) {
// Event within fiducial
} else {
//printf("Event outside fiducial phase space!! Check numerics:: (pt,eta) = (%0.3f,%0.3f), (%0.3f, %0.3f) \n", p1_gen.Perp(), p1_gen.Eta(), p2_gen.Perp(), p2_gen.Eta());
continue;
}
// ----------------------------------------------------------------
// Construct observables of interest
TLorentzVector p1;
TLorentzVector p2;
TLorentzVector system;
// Generator level
const double M_gen = system_gen.M();
const double Y_gen = system_gen.Y();
const double Pt_gen = system_gen.Perp();
const double dY_gen = p1_gen.Rapidity() - p2_gen.Rapidity();
const double eta1_gen = p1_gen.Eta();
const double pt1_gen = p1_gen.Perp();
const double pt2_gen = p2_gen.Perp();
const double deltaphi_gen = p1_gen.DeltaPhi(p2_gen);
// Reconstruction level
const double M_rec = system_rec.M();
const double Y_rec = system_rec.Y();
const double Pt_rec = system_rec.Perp();
const double dY_rec = p1_rec.Rapidity() - p2_rec.Rapidity();
const double eta1_rec = p1_rec.Eta();
const double pt1_rec = p1_rec.Perp();
const double pt2_rec = p2_rec.Perp();
const double deltaphi_rec = p1_rec.DeltaPhi(p2_rec);
// ----------------------------------------------------------------
// *** Efficiency correction and plotting ***
// 1D
h1M.Fill(reco, M_gen, M_rec, weight);
h1Y.Fill(reco, Y_gen, Y_rec, weight);
h1Pt.Fill(reco, Pt_gen, Pt_rec, weight);
h1pt1.Fill(reco, pt1_gen, pt1_rec, weight);
h1eta1.Fill(reco, eta1_gen, eta1_rec, weight);
h1dY.Fill(reco, dY_gen, dY_rec, weight);
// 2D
h2etaphi.Fill(reco, p1_gen.Eta(), p1_gen.Phi(), p1_rec.Eta(), p1_rec.Phi(), weight);
h2etaeta.Fill(reco, p1_gen.Eta(), p2_gen.Eta(), p1_rec.Eta(), p2_rec.Eta(), weight);
h2Mdeltaphi.Fill(reco, M_gen, deltaphi_gen, M_rec, deltaphi_rec, weight);
h2MPt.Fill(reco, M_gen, Pt_gen, M_rec, Pt_rec, weight);
h2Mpt1.Fill(reco, M_gen, pt1_gen, M_rec, pt1_rec, weight);
h2pt1pt2.Fill(reco, pt1_gen, pt2_gen, pt1_rec, pt2_rec, weight);
// DEBUG fills
h1W->Fill(1.0/weight);
++k; // event count
}
// Plot
std::string name = PREDICTFILE + "/hx_weights";
PlotFilled(h1W, name, false, false);
// Save 1D-histograms
double chi2sum = 0.0;
for (uint i = 0; i < h1.size(); ++i) {
chi2sum += h1.at(i)->SaveFig();
}
printf("=======================================================\n");
printf("AVERAGE: <Chi2 / ndf> = %0.2f \n", chi2sum / (double)h1.size());
printf("=======================================================\n");
// Save 2D-histograms
for (uint i = 0; i < h2.size(); ++i) {
h2.at(i)->SaveFig();
}
fclose(fp);
deepnetfile.close();
delete h1W;
return true;
}
// Global Style Setup
void SetROOTStyle() {
gStyle->SetOptStat(0); // Statistics BOX OFF with argument 0
gStyle->SetTitleSize(0.0475,"t"); // Title with "t" (or anything else than xyz)
gStyle->SetStatY(1.0);
gStyle->SetStatX(1.0);
gStyle->SetStatW(0.15);
gStyle->SetStatH(0.09);
}
// Set "nice" 2D-plot style
void SetPlotStyle() {
// Set Smooth color gradients
const Int_t NRGBs = 5;
const Int_t NCont = 255;
Double_t stops[NRGBs] = { 0.00, 0.34, 0.61, 0.84, 1.00 };
Double_t red[NRGBs] = { 0.00, 0.00, 0.87, 1.00, 0.51 };
Double_t green[NRGBs] = { 0.00, 0.81, 1.00, 0.20, 0.00 };
Double_t blue[NRGBs] = { 0.51, 1.00, 0.12, 0.00, 0.00 };
TColor::CreateGradientColorTable(NRGBs, stops, red, green, blue, NCont);
gStyle->SetNumberContours(NCont);
// Black-Red palette
gStyle->SetPalette(53); // 53,56 for inverted
// Number of decimals in text in TH2 plots
//gStyle->SetPaintTextFormat("4.2f");
//gStyle->SetTitleOffset(1.4,"x"); //X-axis title offset from axis
//gStyle->SetTitleOffset(1.4,"y"); //X-axis title offset from axis
//gStyle->SetTitleSize(0.04,"x"); //X-axis title size
//gStyle->SetTitleSize(0.04,"y");
//gStyle->SetTitleSize(0.04,"z");
//gStyle->SetLabelOffset(0.025);
}
//
void PlotFilled(TH1D* h1, std::string& name, bool logscale, bool normalize) {
TCanvas* c = new TCanvas("c","c", 800, 650);
if (logscale) {
c->cd()->SetLogy();
}
if (normalize) { // Make it discrete probability distribution
double norm = 1.0/h1->GetEntries();
//double norm = 1.0/h1->Integral();
h1->Scale(norm);
}
h1->GetYaxis()->SetTitleOffset(1.45);
h1->SetFillColor(19);
h1->SetLineWidth(1.5);
h1->SetMarkerStyle(20);
h1->SetMarkerSize(0.5);
h1->SetStats(0);
h1->Draw(); // "ep"
c->SaveAs( ("./figs/" + name + ".pdf").c_str() );
delete c;
}
// Read event kinematics
bool ReadKinematics(TLorentzVector& p1_gen, TLorentzVector& p2_gen,
TLorentzVector& p1_rec, TLorentzVector& p2_rec, int& reco) {
double px1_gen, py1_gen, pz1_gen, px2_gen, py2_gen, pz2_gen,
px1_rec, py1_rec, pz1_rec, px2_rec, py2_rec, pz2_rec = 0.0;
int pidCode[2] = {0};
// Read event kinematics
int ret = fscanf(fp, "%lf,%lf,%lf,%lf,%lf,%lf,%lf,%lf,%lf,%lf,%lf,%lf,%d,%d,%d\n",
&px1_gen, &py1_gen, &pz1_gen,
&px2_gen, &py2_gen, &pz2_gen,
&px1_rec, &py1_rec, &pz1_rec,
&px2_rec, &py2_rec, &pz2_rec,
&pidCode[0], &pidCode[1],
&reco);
if (ret == 15) { // check we got the right number of variables!
// ok
} else if (errno != 0) {
printf("Kinematics inputfile:: Error in parsing! \n");
return false;
} else if (ret == EOF) {
printf("Kinematics inputfile:: EOF! \n");
return false;
} else {
printf("Kinematics inputfile:: General error! \n");
return false;
}
// Assign masses
double mass[2] = {0,0};
for (int i = 0; i < 2; ++i) {
if (std::abs(pidCode[i]) == 211) { // Charged pion
mass[i] = mPI;
}
if (std::abs(pidCode[i]) == 321) { // Charged kaon
mass[i] = mK;
}
}
// Construct 4-momenta
p1_gen.SetXYZM(px1_gen, py1_gen, pz1_gen, mass[0]);
p2_gen.SetXYZM(px2_gen, py2_gen, pz2_gen, mass[1]);
p1_rec.SetXYZM(px1_rec, py1_rec, pz1_rec, mass[0]);
p2_rec.SetXYZM(px2_rec, py2_rec, pz2_rec, mass[1]);
return true;
}