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draw_results_helper.cc
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draw_results_helper.cc
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/* Helps draw_results.py to perform CPU-intensive tasks: fills and fits
* histograms.
*/
#include <vector>
#include <TF1.h>
#include <TH1D.h>
#include <TFile.h>
#include <TTree.h>
#include <TMath.h>
#include <TSystem.h>
#include <TCanvas.h>
#include <TString.h>
#include <TGraphErrors.h>
// prints a message and exits gracefully
#define FATAL(msg) do { fprintf(stderr, "FATAL: %s\n", msg); gSystem->Exit(1); } while (0)
using namespace std;
struct pair_t {
vector<float> x;
vector<float> y;
};
struct trio_t {
vector<float> x;
vector<float> y;
vector<float> z;
};
// global variables
pair_t gDataE; // array of (mcE, pfE/mcE)
pair_t gDataPt; // array of (mcPt, pfE/mcE)
trio_t gDataEta; // array of (mcEta, pfE/mcE, mcE)
trio_t gDataVtx; // array of (nVtx, pfE/mcE, mcE)
TGraphErrors* grMean = NULL;
TGraphErrors* grSigma = NULL; // NOTE: sigma = width/position
//______________________________________________________________________________
void SetBranchAddress(TTree* tree, const char* bname, void* ptr)
{
/* Associates tree branch with address of a variable.
*/
// verify branch existence
if (!tree->GetBranch(bname))
FATAL(Form("tree branch \"%s\" does not exist", bname));
// activate this branch
tree->SetBranchStatus(bname, 1);
// associate tree branch with address and check return value
Int_t ret = tree->SetBranchAddress(bname, ptr);
if (ret != 0 && ret != 4)
FATAL("TTree::SetBranchAddress() returned bad code");
}
//______________________________________________________________________________
void fill_arrays(const char* infile, const char* friendname,
const char* mva_branch, bool isEE)
{
// Fills global variables-arrays.
// open root file
TFile* fi = TFile::Open(infile);
if (!fi || fi->IsZombie())
FATAL("TFile::Open() failed");
// get TTree with PFClusters
TTree* tree = dynamic_cast<TTree*>(fi->Get("ntuplizer/PFClusterTree"));
if (!tree) FATAL("TFile::Get() failed");
// add branches with outputs from MVAs
if (!tree->AddFriend("ntuplizer/PFClusterTree", friendname))
FATAL("TTree::AddFriend() failed");
// disable all branches by default
tree->SetBranchStatus("*", 0);
// variables to be associated with the input tree branches
Int_t nVtx;
float mcE, mcPt, mcEta, pfE, pfEta;
float mva_output;
// associate tree branches with variables
SetBranchAddress(tree, "mcE", &mcE);
SetBranchAddress(tree, "mcPt", &mcPt);
SetBranchAddress(tree, "mcEta", &mcEta);
SetBranchAddress(tree, "pfE", &pfE);
SetBranchAddress(tree, "pfEta", &pfEta);
SetBranchAddress(tree, "nVtx", &nVtx);
if (mva_branch[0] != '\0')
SetBranchAddress(tree, mva_branch, &mva_output);
// cleanup from previous execution
gDataE.x.clear();
gDataE.y.clear();
gDataPt.x.clear();
gDataPt.y.clear();
gDataEta.x.clear();
gDataEta.y.clear();
gDataEta.z.clear();
gDataVtx.x.clear();
gDataVtx.y.clear();
gDataVtx.z.clear();
// loop over events and collect data
for (Long64_t ev = 1; ev < tree->GetEntriesFast(); ev += 2) {// NOTE: take only test events
if (tree->GetEntry(ev) <= 0)
FATAL("TTree::GetEntry() failed");
float resol = pfE/mcE;
// apply correction, if necessary
if (mva_branch[0] != '\0')
resol *= mva_output;
gDataEta.x.push_back(mcEta);
gDataEta.y.push_back(resol);
gDataEta.z.push_back(mcE);
// barrel vs endcaps
if (isEE) {
if (fabs(pfEta) < 1.479)
continue;
} else {
if (fabs(pfEta) > 1.479)
continue;
}
gDataE.x.push_back(mcE);
gDataE.y.push_back(resol);
gDataPt.x.push_back(mcPt);
gDataPt.y.push_back(resol);
gDataVtx.x.push_back((float)nVtx);
gDataVtx.y.push_back(resol);
gDataVtx.z.push_back(mcE);
} // event loop
}
//______________________________________________________________________________
void MeanSigma(vector<float> &numbers, double &mean, double &sigma)
{
/* Evaluates average (mean) and dispersion (sigma) of numbers "numbers".
*
* Mean and sigma are recalculated iteratively several times. During each
* calculation, a region [mean - 3*sigma, mean + 3*sigma] is used, where
* "mean" and "sigma" are taken from a previous iteration.
*/
// zero-order iteration
mean = 0;
sigma = 0;
for (size_t i = 0; i < numbers.size(); i++) {
mean += numbers[i];
sigma += numbers[i] * numbers[i];
}
mean /= numbers.size();
sigma = sqrt(sigma/numbers.size() - mean*mean);
// iterations
for (int c = 0; c < 1000; c++) {
double mean_prev = mean;
double sigma_prev = sigma;
double xmin = mean - 3*sigma;
double xmax = mean + 3*sigma;
// evaluate peak position and width
mean = 0;
sigma = 0;
int nent = 0;
for (size_t i = 0; i < numbers.size(); i++) {
if (numbers[i] < xmin || numbers[i] > xmax) continue;
mean += numbers[i];
sigma += numbers[i] * numbers[i];
nent++;
}
mean /= nent;
sigma = sqrt(sigma/nent - mean*mean);
// break when converged
if (fabs(mean - mean_prev) <= 1e-6 * fabs(mean) &&
fabs(sigma - sigma_prev) <= 1e-6 * fabs(sigma))
return;
}
const char* fmt = "nent_total=%lli, mean=%f, sigma=%f\n";
fprintf(stderr, fmt, numbers.size(), mean, sigma);
FATAL("mean and/or sigma did not converged");
}
//______________________________________________________________________________
void fit_slices_real(vector<float>& x, vector<float>& y, int blockSize,
const char* title, const char* xtitle)
{
/* Fits distributions of sorted blocks of data points, result is given in
* grMean and grSigma -- positions and widths vs X axis.
*
* NOTE: sigma = width/position.
*/
// cleanup from previous execution
if (grMean) delete grMean;
if (grSigma) delete grSigma;
grMean = new TGraphErrors();
grSigma = new TGraphErrors();
size_t siz = x.size();
if (siz < 1) FATAL("x.size() < 1");
// sort by X axis
size_t* ind = new size_t[siz]; // allocate on the heap to avoid segfaults
TMath::Sort(siz, &x.front(), ind, false);
// NOTE: last block is excluded if it has less than 0.5 * blockSize entries
int nblocks = TMath::Nint(round(((float)siz)/blockSize));
TCanvas* c = NULL;
vector<TObject*> todel(64);
// counter of accepted blocks
int b0 = 0;
// loop over blocks of ordered data
for (int b = 0; b < nblocks; b++) {
vector<float> bx;
vector<float> by;
// fill separate arrays with current block data
for (size_t i = b * blockSize; i < (size_t) (b + 1) * blockSize; i++) {
if (i >= siz) break;
bx.push_back(x[ind[i]]);
by.push_back(y[ind[i]]);
}
double meanX, sigmaX;
double meanY, sigmaY;
// mean and sigma in the block and Etrue/Erec
MeanSigma(bx, meanX, sigmaX);
MeanSigma(by, meanY, sigmaY);
// fill histogram
TH1* h = new TH1D("h", "", 100, 0.55, 1.3);
for (size_t i = 0; i < by.size(); i++)
h->Fill(by[i]);
// create new canvas, if necessary
if (b % 9 == 0) {
if (c) {
c->SaveAs(Form("output/plots_results/fits/%s.png", c->GetTitle()));
// memory cleanup
delete c;
for (size_t k = 0; k < todel.size(); k++)
delete todel[k];
todel.clear();
}
TString cname = TString::Format("fits_%s_blk%03dto%03d", title, b + 1, b + 9);
c = new TCanvas(cname, cname, 1000, 700);
c->SetLeftMargin(0);
c->SetRightMargin(0);
c->SetTopMargin(0);
c->SetBottomMargin(0);
c->Divide(3, 3);
}
c->cd(b % 9 + 1);
gPad->SetLeftMargin(0.12);
gPad->SetRightMargin(0.02);
gPad->SetTopMargin(0.08);
gPad->SetBottomMargin(0.08);
h->SetTitle(Form("%s = %.2f #pm %.2f", xtitle, meanX, sigmaX));
h->SetXTitle("E^{rec}/E^{gen}");
h->SetYTitle("Entries");
h->SetTitleOffset(1.6, "Y");
h->Sumw2(true);
h->SetLineColor(kBlack);
h->Draw();
// Gaussian + left power-law tail + right exponential tail
const char* expr = "[0] * ( (x-[1])/[2] > -[3] ? "
"( (x-[1])/[2] < [5] ? exp(-(x-[1])^2/(2*[2]*[2])) : exp(0.5*[5]*[5] - [5]*(x-[1])/[2]) ) : "
"([4]/[3])^[4] * exp(-0.5*[3]^2) * (-(x-[1])/[2]-[3]+[4]/[3])^(-[4]) )";
TF1* fit = new TF1("fit", expr, 0.55, 1.3);
fit->SetLineWidth(1);
fit->SetNpx(2000);
fit->SetParameters(h->GetMaximum(), meanY, 0.5 * sigmaY, 1.5, 5, 1.5);
fit->SetParLimits(0, 0.33 * h->GetMaximum(), 2 * h->GetMaximum());
fit->SetParLimits(1, meanY - sigmaY, meanY + sigmaY);
fit->SetParLimits(2, 0.1 * sigmaY, 1.1 * sigmaY);
// pre-fit to improve convergence (especially in the EB/EE gap region)
fit->FixParameter(3, 1.5);
fit->FixParameter(4, 5);
fit->FixParameter(5, 1.5);
h->Fit(fit, "QEM", "same", 0.55, 1.3);
fit->SetParLimits(3, 0.4, 10);
fit->SetParLimits(4, 1.01, 100);
fit->SetParLimits(5, 0.4, 10);
h->Fit(fit, "QEM", "same", 0.55, 1.3);
fit->ReleaseParameter(0);
fit->ReleaseParameter(0);
fit->SetParLimits(1, 0.65, 1.2);
fit->SetParLimits(2, 0, 1.1 * sigmaY);
fit->ReleaseParameter(3);
fit->ReleaseParameter(4);
fit->ReleaseParameter(5);
h->Fit(fit, "QEML", "same", 0.55, 1.3);
// do not accept really bad fitting results
if (fit->GetParError(1)/fit->GetParameter(1) < 0.15 &&
fit->GetParError(2)/fit->GetParameter(2) < 0.15) {
grMean->SetPoint(b0, meanX, fit->GetParameter(1));
grMean->SetPointError(b0, sigmaX, fit->GetParError(1));
grSigma->SetPoint(b0, meanX, fit->GetParameter(2)/fit->GetParameter(1));
grSigma->SetPointError(b0, sigmaX, fit->GetParError(2)/fit->GetParameter(1));
b0++;
}
todel.push_back(h);
todel.push_back(fit);
} // block loop
// save the very last canvas
if (c) {
c->SaveAs(Form("output/plots_results/fits/%s.png", c->GetTitle()));
// memory cleanup
delete c;
for (size_t k = 0; k < todel.size(); k++)
delete todel[k];
}
delete ind;
}
//______________________________________________________________________________
void fit_slices(int type, int blockSize, const char* title, const char* xtitle,
double e1 = 0, double e2 = 0)
{
// Steers work of fit_slices_real().
// mcE
if (type == 0)
fit_slices_real(gDataE.x, gDataE.y, blockSize, title, xtitle);
// mcPt
else if (type == 1)
fit_slices_real(gDataPt.x, gDataPt.y, blockSize, title, xtitle);
// mcEta in mcE region
else if (type == 2) {
pair_t data;
for (size_t i = 0; i < gDataEta.x.size(); i++) {
if (gDataEta.z[i] < e1 || gDataEta.z[i] >= e2) continue;
data.x.push_back(gDataEta.x[i]);
data.y.push_back(gDataEta.y[i]);
}
fit_slices_real(data.x, data.y, blockSize, title, xtitle);
}
// mcVtx in mcE region
else if (type == 3) {
pair_t data;
for (size_t i = 0; i < gDataVtx.x.size(); i++) {
if (gDataVtx.z[i] < e1 || gDataVtx.z[i] >= e2) continue;
data.x.push_back(gDataVtx.x[i]);
data.y.push_back(gDataVtx.y[i]);
}
fit_slices_real(data.x, data.y, blockSize, title, xtitle);
}
else
FATAL("invalid type");
}