void FillHisto(TH2D * h, int n, double * p) {
const double mx1 = p[1];
const double my1 = p[3];
const double sx1 = p[2];
const double sy1 = p[4];
const double mx2 = p[6];
const double my2 = p[8];
const double sx2 = p[7];
const double sy2 = p[9];
//const double w1 = p[0]*sx1*sy1/(p[5]*sx2*sy2);
const double w1 = 0.5;
double x, y;
for (int i = 0; i < n; ++i) {
// generate randoms with larger gaussians
rndm.Rannor(x,y);
double r = rndm.Rndm(1);
if (r < w1) {
x = x*sx1 + mx1;
y = y*sy1 + my1;
}
else {
x = x*sx2 + mx2;
y = y*sy2 + my2;
}
h->Fill(x,y);
}
}
RooDataHist * genHistFromModelPdf(const char * name, RooAbsPdf *model, RooRealVar *var, double ScaleLumi, int range, int rebin, int seed ) {
double genEvents = model->expectedEvents(*var);
TRandom3 *rndm = new TRandom3();
rndm->SetSeed(seed);
double nEvt = rndm->PoissonD( genEvents) ;
int intEvt = ( (nEvt- (int)nEvt) >= 0.5) ? (int)nEvt +1 : int(nEvt);
RooDataSet * data = model->generate(*var , intEvt );
cout<< " expected events for " << name << " = "<< genEvents << endl;
cout<< " data->numEntries() for name " << name << " == " << data->numEntries()<< endl;
// cout<< " nEvt from PoissonD for" << name << " == " << nEvt<< endl;
//cout<< " cast of nEvt for" << name << " == " << intEvt<< endl;
RooAbsData *binned_data = data->binnedClone();
TH1 * toy_hist = binned_data->createHistogram( name, *var, Binning(range/rebin ) );
for(int i = 1; i <= toy_hist->GetNbinsX(); ++i) {
toy_hist->SetBinError( i, sqrt( toy_hist->GetBinContent(i)) );
if(toy_hist->GetBinContent(i) == 0.00) {
cout<< " WARNING: histo " << name << " has 0 enter in bin number " << i << endl;
}
if(toy_hist->GetBinContent(i) < 0.1) {
toy_hist->SetBinContent(i, 0.0);
toy_hist->SetBinError(i, 0.0);
cout<< " WARNING: setting value 0.0 to histo " << name << " for bin number " << i << endl;
}
}
RooDataHist * toy_rooHist = new RooDataHist(name, name , RooArgList(*var), toy_hist );
return toy_rooHist;
}
void gtime2(Int_t nsteps = 200, Int_t np=5000) {
if (np > 5000) np = 5000;
Int_t color[5000];
Double_t cosphi[5000], sinphi[5000], speed[5000];
TRandom3 r;
Double_t xmin = 0, xmax = 10, ymin = -10, ymax = 10;
TGraphTime *g = new TGraphTime(nsteps,xmin,ymin,xmax,ymax);
g->SetTitle("TGraphTime demo 2;X;Y");
Int_t i,s;
Double_t phi,fact = xmax/Double_t(nsteps);
for (i=0;i<np;i++) { //calculate some object parameters
speed[i] = r.Uniform(0.5,1);
phi = r.Gaus(0,TMath::Pi()/6.);
cosphi[i] = fact*speed[i]*TMath::Cos(phi);
sinphi[i] = fact*speed[i]*TMath::Sin(phi);
Double_t rc = r.Rndm();
color[i] = kRed;
if (rc > 0.3) color[i] = kBlue;
if (rc > 0.7) color[i] = kYellow;
}
for (s=0;s<nsteps;s++) { //fill the TGraphTime step by step
for (i=0;i<np;i++) {
Double_t xx = s*cosphi[i];
if (xx < xmin) continue;
Double_t yy = s*sinphi[i];
TMarker *m = new TMarker(xx,yy,25);
m->SetMarkerColor(color[i]);
m->SetMarkerSize(1.5 -s/(speed[i]*nsteps));
g->Add(m,s);
}
g->Add(new TPaveLabel(.70,.92,.98,.99,Form("shower at %5.3f nsec",3.*s/nsteps),"brNDC"),s);
}
g->Draw();
}
int decroissance_pi(float _p_pi = 1.0){
// random.seed(60934386)
TRandom3 rand;
rand.SetSeed();
//float _p_pi;
//cout << "Entrez l'impulsion des pions (en GeV) : p = ";
//cin >> _p_pi;
double x[1000] = {0.0};
double y[1000] = {0.0};
for (int i=0;i<1000;i++){
//double _theta_cm_mu = rand.Uniform(TMath::Pi());
double _theta_cm_mu = TMath::ACos(gRandom->Uniform(2.0)-1.0);
double _phi_cm_mu = rand.Uniform(2*TMath::Pi());
if (i < 10){
cout << "i: " << i << " th_cm_mu = " << _theta_cm_mu << endl;
}
double _theta_lab_mu = 0.0, _p_lab_mu = 0.0;
ThetaLab_mu(_p_pi, _theta_cm_mu, _theta_lab_mu, _p_lab_mu);
cout << "th_lab_mu= " << _theta_lab_mu << " _p_lab_mu= " << _p_lab_mu << endl;
x[i] = 1000*_theta_lab_mu;
y[i] = _p_lab_mu;
}
TGraph *graph = new TGraph(1000, x, y);
graph->SetTitle("Theta vs p_{LAB}");
graph->Draw("A*");
return 0;
}
int main(int argc, char *argv[]) {
TRandom3 *ran = new TRandom3(0);
vektor b = new double[N];
vektor x = new double[N];
// vektor b = malloc_vektor(N);
// vektor x = malloc_vektor(N);
// matrix A = new double[N][N];
matrix A = malloc_matrix(N);
double dummy = 0.;
for (int i=0; i<N; ++i) {
for (int j=0; j<N; ++j) {
dummy = ran->Uniform();
A[i][j] = dummy;
A[j][i] = dummy;
}
b[i] = ran->Uniform();
x[i] = ran->Uniform();
}
// print_matrix(A);
vektor x2 = malloc_vektor(N);
x2 = vec_copy(cg(N, A, x, b, 1000, 1e-10, 0));
// print_vektor(x2);
delete[] b;
delete[] x;
// delete[] A;
return 0;
}
void plot_distortion(){
TRandom3* rand = new TRandom3();
// TF1 *func = new TF1("func","TMath::Abs([0]+[1]*x)",0,12);
//
// rand->SetSeed(0);
// Double_t slope=0.2*rand->Gaus(0,1);
// Double_t anchor_point=3.5;
// //want offset to be set by requiring func at anchor point to be 1
// Double_t offset=(1-slope*anchor_point);
// func->SetParameter(0,offset);
// func->SetParameter(1,slope);
//FN distortion
TF1 *func = new TF1("func","[0]/(0.2*pow(x,0.1))+[1]",0,12);
rand->SetSeed(0);
Double_t scaling =0.2*rand->Gaus(0,1);
func->SetParameter(0,scaling);
func->SetParameter(1,0);
//set offset so that func(FinalEnergy)=1;
func->SetParameter(1,1-1*func->Eval(12));
func->Draw();
}
//____________________________________________________________________
void fitCircle(Int_t n=10000) {
//generates n points around a circle and fit them
TCanvas *c1 = new TCanvas("c1","c1",600,600);
c1->SetGrid();
gr = new TGraph(n);
if (n> 999) gr->SetMarkerStyle(1);
else gr->SetMarkerStyle(3);
TRandom3 r;
Double_t x,y;
for (Int_t i=0;i<n;i++) {
r.Circle(x,y,r.Gaus(4,0.3));
gr->SetPoint(i,x,y);
}
c1->DrawFrame(-5,-5,5,5);
gr->Draw("p");
//Fit a circle to the graph points
TVirtualFitter::SetDefaultFitter("Minuit"); //default is Minuit
TVirtualFitter *fitter = TVirtualFitter::Fitter(0, 3);
fitter->SetFCN(myfcn);
fitter->SetParameter(0, "x0", 0, 0.1, 0,0);
fitter->SetParameter(1, "y0", 0, 0.1, 0,0);
fitter->SetParameter(2, "R", 1, 0.1, 0,0);
Double_t arglist[1] = {0};
fitter->ExecuteCommand("MIGRAD", arglist, 0);
//Draw the circle on top of the points
TArc *arc = new TArc(fitter->GetParameter(0),
fitter->GetParameter(1),fitter->GetParameter(2));
arc->SetLineColor(kRed);
arc->SetLineWidth(4);
arc->Draw();
}
//____________________________________________________________________
void fitCircle(Int_t n=10000) {
//generates n points around a circle and fit them
TCanvas *c1 = new TCanvas("c1","c1",600,600);
c1->SetGrid();
gr = new TGraph(n);
if (n> 999) gr->SetMarkerStyle(1);
else gr->SetMarkerStyle(3);
TRandom3 r;
Double_t x,y;
for (Int_t i=0;i<n;i++) {
r.Circle(x,y,r.Gaus(4,0.3));
gr->SetPoint(i,x,y);
}
c1->DrawFrame(-5,-5,5,5);
gr->Draw("p");
auto chi2Function = [&](const Double_t *par) {
//minimisation function computing the sum of squares of residuals
// looping at the graph points
Int_t np = gr->GetN();
Double_t f = 0;
Double_t *x = gr->GetX();
Double_t *y = gr->GetY();
for (Int_t i=0;i<np;i++) {
Double_t u = x[i] - par[0];
Double_t v = y[i] - par[1];
Double_t dr = par[2] - std::sqrt(u*u+v*v);
f += dr*dr;
}
return f;
};
// wrap chi2 funciton in a function object for the fit
// 3 is the number of fit parameters (size of array par)
ROOT::Math::Functor fcn(chi2Function,3);
ROOT::Fit::Fitter fitter;
double pStart[3] = {0,0,1};
fitter.SetFCN(fcn, pStart);
fitter.Config().ParSettings(0).SetName("x0");
fitter.Config().ParSettings(1).SetName("y0");
fitter.Config().ParSettings(2).SetName("R");
// do the fit
bool ok = fitter.FitFCN();
if (!ok) {
Error("line3Dfit","Line3D Fit failed");
}
const ROOT::Fit::FitResult & result = fitter.Result();
result.Print(std::cout);
//Draw the circle on top of the points
TArc *arc = new TArc(result.Parameter(0),result.Parameter(1),result.Parameter(2));
arc->SetLineColor(kRed);
arc->SetLineWidth(4);
arc->Draw();
}
// Integral Theta
float GetIntegralTheta(float thetaMin, float thetaMax, float ymin, float ymax,
int loopnumber, float sigma) {
//xmin und xmax sind die thetas
float dy, theta, dTheta, y, integral, xmin, xmax, x;
int accepted = 0;
dTheta = thetaMax - thetaMin;
//cout << "dtheta = " << dTheta << endl;
dy = ymax - ymin;
for (int i = 0; i < loopnumber; i++) {
//cout << "NUMBER = " << i << endl;
theta = tr3.Rndm() * dTheta + thetaMin;
//cout << "theta = " << theta << endl;
y = tr3.Rndm() * dy + ymin;
//cout << "y = " << y << endl;
xmin = theta - 5 * sigma;
xmax = theta + 5 * sigma;
x = GetRndGaus(theta, xmin, xmax, sigma);
//cout << "x = " << x << endl;
if (CheckCrossFctComplete(x, y)) {
accepted++;
}
}
integral = (((float) accepted) / (float) loopnumber) * dTheta * dy;
cout << "Integralwert: " << integral << endl;
return integral;
}
// Code from http://www.hongliangjie.com/2012/12/19/how-to-generate-gamma-random-variables/
// Parameter b could be theta...
double gsl_ran_gamma(const double a, const double b, TRandom3 &rand)
{
if (a < 1)
{
double u = rand.Uniform(1);
return gsl_ran_gamma(1.0 + a, b, rand) * pow (u, 1.0 / a);
}
double x, v, u;
double d = a - 1.0 / 3.0;
double c = (1.0 / 3.0) / sqrt (d);
while (1)
{
do
{
x = rand.Gaus(0, 1.0);
v = 1.0 + c * x;
}
while (v <= 0);
v = v * v * v;
u = rand.Uniform(1);
if (u < 1 - 0.0331 * x * x * x * x)
break;
if (log (u) < 0.5 * x * x + d * (1 - v + log (v)))
break;
}
return b * d * v;
}
// ------------------------------------------------------------------------------------------------------//
// ------------------------------------------------------------------------------------------------------//
void frac_error(double a, double b, double *ret){
double c = (b-a);
gStyle->SetOptFit(0011);
gStyle->SetOptStat(0);
TCanvas *can = new TCanvas("can","can",900,600);
double frac = a/b;
TH1F *h_ = new TH1F("h_","h_",100,frac*0.98,frac*1.02);
TRandom3 *r = new TRandom3();
for(int i=0;i<50000;i++){
double ap = r->Poisson(a);
double cp = r->Poisson(c);
h_->Fill((double)ap/(ap+cp));
}
h_->Fit("gaus","Q","Q");
h_->Draw();
can->SaveAs("statEP.png");
double val = h_->GetFunction("gaus")->GetParameter(2);
delete h_;
ret[0] = val;
}
void eta()
{
// declare and define two histograms (their number of bins and boundaries)
TH1F* h_eta = new TH1F("h_eta","",100,-10,10);
TH1F* h_theta = new TH1F("h_theta","",100,0,TMath::Pi());
// start producing distributions
// set the number of events to be 10000
const int neve=10000;
TRandom3* myrndm = new TRandom3();
for(unsigned int i=0; i<neve; i++){
// produce a flat theta distributions
float theta = myrndm->Rndm()*TMath::Pi();
h_theta->Fill(theta);
float eta = -TMath::Log(TMath::Tan(0.5*theta));
h_eta->Fill(eta);
}
h_theta->Draw();
h_eta->Draw();
// save the histograms in a root file
TFile* outFile = new TFile("eta_theta.root","recreate");
h_theta->Write();
h_eta->Write();
outFile->Close();
}
int main(int argc, char *argv[]) {
TRandom3 *ran = new TRandom3(0);
double eta[N*N];
double phi[N*N];
geom_pbc();
init_matrix();
double dummy;
for (int i=0; i<nvol; ++i){
for (int j=0; j<nvol; ++j){
dummy = ran->Uniform();
A[i][j] = dummy;
A[j][i] = dummy;
}
eta[i] = ran->Uniform();
}
cg(phi, eta, laplace, 1000, 1e-10, 1);
for (int i=0; i<nvol; ++i){
if (nn[0][i] == 1){
eta[i] = 0;
}
}
cg(phi, eta, dirichlet, 1000, 1e-10, 1);
return 0;
}
// ------------------------------------------------------------------------------------------------------//
// ------------------------------------------------------------------------------------------------------//
void bias_corr_stat_error(double Sa, double Sb, double Sc, double Sd,
double Qa, double Qb, double Qc, double Qd,
double Scale, double *ret){
double ea = (Sa)/(Sa+Sb);
double ed = (Sd)/(Sd+Sc);
double ew = (Sa+Sb)/(Sa+Sb+Sd+Sc);
double st = Sa+Sb+Sc+Sd;
double na = (Qa + Sa);
double nb = (Qb + Sb);
double nc = (Qc + Sc);
double nd = (Qd + Sd);
double S[4] = {0,0,0,0};
signal(na,nb,nc,nd,ea,ed,ew,S);
double s1 = S[0];
double s2 = S[2];
double cen1 = st/s1;
double cen2 = st/s2;
TH1F *tmp_u = new TH1F("tmp_u","tmp_u",100,cen1*(1-0.01),cen1*(1+0.01));
TH1F *tmp_d = new TH1F("tmp_d","tmp_d",100,cen2*(1-0.01),cen2*(1+0.01));
TRandom3 *rnd = new TRandom3();
for(int i=0;i<50000; i++){
double qa = Scale*rnd->Poisson((1./Scale)*Qa);
double qb = Scale*rnd->Poisson((1./Scale)*Qb);
double qc = Scale*rnd->Poisson((1./Scale)*Qc);
double qd = Scale*rnd->Poisson((1./Scale)*Qd);
na = (qa + Sa);
nb = (qb + Sb);
nc = (qc + Sc);
nd = (qd + Sd);
signal(na,nb,nc,nd,ea,ed,ew,S);
tmp_u->Fill(st/S[0]);
tmp_d->Fill(st/S[2]);
}
tmp_u->Fit("gaus","Q","Q");
tmp_d->Fit("gaus","Q","Q");
double val1 = tmp_u->GetFunction("gaus")->GetParameter(2);
double val2 = tmp_d->GetFunction("gaus")->GetParameter(2);
delete tmp_u;
delete tmp_d;
ret[0] = val1;
ret[1] = val2;
}
void macro6(){
TH1F* sig_h=new TH1F("sig_h","Signal Histo",50,0,10);
TH1F* gaus_h1=new TH1F("gaus_h1","Gauss Histo 1",30,0,10);
TH1F* gaus_h2=new TH1F("gaus_h2","Gauss Histo 2",30,0,10);
TH1F* bkg_h=new TH1F("exp_h","Exponential Histo",50,0,10);
// simulate the measurements
TRandom3 rndgen;
for (int imeas=0;imeas<4000;imeas++){
bkg_h->Fill(rndgen.Exp(4));
if (imeas%4==0) gaus_h1->Fill(rndgen.Gaus(5,2));
if (imeas%4==0) gaus_h2->Fill(rndgen.Gaus(5,2));
if (imeas%10==0)sig_h->Fill(rndgen.Gaus(5,.5));}
// Format Histograms
TH1F* histos[4]={sig_h,bkg_h,gaus_h1,gaus_h2};
for (int i=0;i<4;++i){
histos[i]->Sumw2(); // *Very* Important
format_h(histos[i],i+1);
}
// Sum
TH1F* sum_h= new TH1F(*bkg_h);
sum_h->Add(sig_h,1.);
sum_h->SetTitle("Exponential + Gaussian");
format_h(sum_h,kBlue);
TCanvas* c_sum= new TCanvas();
sum_h->Draw("hist");
bkg_h->Draw("SameHist");
sig_h->Draw("SameHist");
// Divide
TH1F* dividend=new TH1F(*gaus_h1);
dividend->Divide(gaus_h2);
// Graphical Maquillage
dividend->SetTitle(";X axis;Gaus Histo 1 / Gaus Histo 2");
format_h(dividend,kOrange);
gaus_h1->SetTitle(";;Gaus Histo 1 and Gaus Histo 2");
gStyle->SetOptStat(0);
TCanvas* c_divide= new TCanvas();
c_divide->Divide(1,2,0,0);
c_divide->cd(1);
c_divide->GetPad(1)->SetRightMargin(.01);
gaus_h1->DrawNormalized("Hist");
gaus_h2->DrawNormalized("HistSame");
c_divide->cd(2);
dividend->GetYaxis()->SetRangeUser(0,2.49);
c_divide->GetPad(2)->SetGridy();
c_divide->GetPad(2)->SetRightMargin(.01);
dividend->Draw();
}
void histo_graph(TH1D *histo,TGraph *graph,Int_t N_to_fill) {
Int_t N_Points=graph->GetN();
Double_t x,y;
Double_t y_min=0.0;
Double_t y_max=0.0;
Double_t x_min=0.0;
Double_t x_max=0.0;
for (Int_t i=0;i<N_Points;i++) {
graph->GetPoint(i,x,y);
if (y>y_max) y_max=y;
if (x>x_max) x_max=x;
if (y<y_min) y_min=y;
if (x<x_min) x_min=x;
}
Double_t histo_x_min=histo->GetXaxis()->GetXmin();
Double_t histo_x_max=histo->GetXaxis()->GetXmax();
if (histo_x_min>x_min) x_min=histo_x_min;
if (histo_x_max<x_max) x_max=histo_x_max;
//reverse graph order if necessary
TGraph graph2(N_Points);
Double_t x0,xNm1;
graph->GetPoint(0,x0,y);
graph->GetPoint(N_Points-1,xNm1,y);
if (xNm1<x0) {
for (Int_t iPoint=0;iPoint<N_Points;iPoint++) {
Double_t x,y;
graph->GetPoint(N_Points-iPoint,x,y);
graph2.SetPoint(iPoint,x,y);
}
graph=&graph2;
}
TRandom3 r;
r.SetSeed(0);
Int_t N_tried=0;
Int_t N_filled=0;
while (N_filled<N_to_fill) {
N_tried++;
//rejection method
x=r.Uniform(x_min,x_max);
y=r.Uniform(0.0,y_max);
if (y<graph->Eval(x)) {
histo->Fill(x);
N_filled++;
}
}
printf("x_max=%8.6g; x_min=%8.6g; y_max=%8.6g; N_filled=%d; N_tried=%d\n",x_max,x_min,y_max,N_filled,N_tried);
Double_t norm=(x_max-x_min)*y_max*N_filled/N_tried;
histo->Scale(norm/histo->Integral("width"));
}
//ks-test for Gaussian Random samples
void test(Int_t num)
{
Double_t *d=new Double_t[num];
Double_t p,t;
TRandom3 r;
for (int i=0;i<num;i++)
d[i]=r.Gaus();
ROOT::Math::GoFTest *tt=new ROOT::Math::GoFTest(num,d,ROOT::Math::GoFTest::kGaussian);
tt->KolmogorovSmirnovTest(p,t);
cout<<"p:"<<p<<",t:"<<t<<endl;
}
void generateGaussRandom(){
TRandom3 *gr = new TRandom3(12345);
cout<<"generating gaus rand for use "<< Nrand<<endl;
for(int j=0;j<Nrand;j++){
if(j%100000000==0) cout<<" j " << j <<endl;
float tmp = gr->Gaus();
vrandgaus.push_back(tmp);
}
}
float GetRndGaus(float mu, float xmin, float xmax, float sigma) {
float xRnd = 0.;
float yRnd = 0.;
const float width = xmax - xmin;
do {
xRnd = width * tr3.Rndm() + xmin;
yRnd = tr3.Rndm() / (sigma * sqrt(2 * M_PI));
} while (!CheckGaus(xRnd, yRnd, mu, sigma));
return xRnd;
}
//______________________________________________________________________________
// Fill y and error vectors with random points from TRandom3
void FillRandVectors(int nPoints, vector<double> &xVector, vector< double > &yVector, vector< double > &yErrorVector, int seed = 250, double lowerBound= 10, double upperBound= 20, double lowerErrorBound = 1, double upperErrorBound= 2)
{
//Call TRandom3
TRandom3 *jrand = new TRandom3(seed);
for (int i = 0; i < nPoints; i++)
{
xVector.push_back(i);
yVector.push_back(jrand->Uniform(lowerBound, upperBound));
yErrorVector.push_back(jrand->Uniform(lowerErrorBound, upperErrorBound));
}
delete jrand;
}
Double_t Reconstruct( Double_t xt, TRandom3& R )
{
// apply some Gaussian smearing + bias and efficiency corrections to fake reconstruction
const Double_t cutdummy = -99999.0;
Double_t xeff = 0.3 + (1.0 - 0.3)/20.0*(xt + 10.0); // efficiency
Double_t x = R.Rndm();
if (x > xeff) return cutdummy;
else {
Double_t xsmear= R.Gaus(-2.5,0.2); // bias and smear
return xt+xsmear;
}
}
// Read in an event spectrum from a text file containing
// spectral data, and then generate some random events.
// This assumes that the input data file contains two columns,
// the first with (evenly spaced!) energy bins in units of eV, and the second
// with a number of events per kg per day per keV
void FillRecoilSpectrumFromFile(TH1F* recoilHisto, Double_t time, Double_t mass, const char* filename, Int_t seed)
{
FILE* dataFile;
dataFile = fopen(filename,"r");
if (dataFile == NULL) {
cout << "The file " << filename << " is not found!!! Quitting now." << endl;
exit(1);
}
// Count the number of lines to set the array sizes
fstream lineCountStream;
lineCountStream.open(filename, fstream::in);
Int_t lineCount = 0;
while (lineCountStream.peek() != EOF) {
lineCountStream.ignore(128, '\n');
lineCount++;
}
lineCountStream.close();
cout << "The file has " << lineCount << " lines." << endl;
// Should add a section here to double check the formatting of the file
// Warning message about units
cout << "Reading data and filling histogram from " << filename << "..." << endl;
cout << "I am assuming that the first column in the file is in units of MeV "
<< "and that the second column is in units of 1/(kg day keV)!" << endl << endl;
// Read the data
const Int_t nLines = lineCount;
Float_t energies[nLines];
Float_t rates[nLines];
for (Int_t thisLine = 0; thisLine < nLines; thisLine++)
fscanf(dataFile, "%E %E", &energies[thisLine], &rates[thisLine]);
fclose(dataFile);
// Set up the source histogram
TH1F SpectralDataHistogram("SpectralDataHistogram", "Spectrum of Events from File", nLines-1, energies);
for (Int_t thisBin = 0; thisBin < (nLines - 1); thisBin++) {
SpectralDataHistogram.SetBinContent(thisBin,rates[thisBin]);
}
// Fill the recoil histogram with the correct number
// of events, randomly chosen from the source histogram.
TRandom3* randGen = new TRandom3(seed);
Float_t meanNEvents = time * mass * SpectralDataHistogram.ComputeIntegral();
Int_t nEvents = randGen->Poisson(meanNEvents);
cout << "nEvents = " << nEvents << endl;
for (Int_t thisEvent; thisEvent<nEvents; thisEvent++)
recoilHisto->Fill(SpectralDataHistogram.GetRandom());
}
void TestBkg(){
TFile * f = new TFile("./TestBkg.root","recreate");
TTree * tree = new TTree("flt","data");
LendaEvent *e = new LendaEvent();
tree->Branch("Event",&e);
TRandom3 * r = new TRandom3();
for (int i=0;i<100000;i++){
Double_t v = r->Uniform()*30.0;
e->pushTime(30);
e->pushTime(30);
e->pushTime(v);
e->pushEnergy(1);
e->pushEnergy(1);
e->pushEnergy(1);
e->pushLongGate(2);
e->pushLongGate(2);
e->pushLongGate(2);
e->pushShortGate(2);
e->pushShortGate(2);
e->pushShortGate(2);
e->pushCubicTime(e->times[0]);
e->pushCubicTime(e->times[1]);
e->pushCubicTime(e->times[2]);
e->pushSoftwareCFD(1); e->pushSoftwareCFD(1); e->pushSoftwareCFD(1);
e->Finalize();
tree->Fill();
e->Clear();
}
tree->Write();
f->Close();
}
void randomize(TH1F* hist, unsigned int seed, unsigned int debug=0.)
{
TRandom3* rnd = new TRandom3(seed); rnd->SetSeed();
for(int idx=0; idx<hist->GetNbinsX(); ++idx){
if(debug>0){
std::cerr << "[" << idx+1 << "] : " << "mean=" << hist->GetBinContent(idx+1) << " rnd=" << rnd->Poisson(hist->GetBinContent(idx+1)) << std::endl;
}
float value = rnd->Poisson(hist->GetBinContent(idx+1));
hist->SetBinContent(idx+1, value); hist->SetBinError(idx+1, TMath::Sqrt(value));
}
// Make sure there is no rounding error, and the total is truly an integer.
hist->Scale(TMath::Nint(hist->Integral())/hist->Integral());
delete rnd;
}
void write_ntuple_to_file(){
TFile ofile("conductivity_experiment.root","RECREATE");
// Initialise the TNtuple
TNtuple cond_data("cond_data",
"Example N-Tuple",
"Potential:Current:Temperature:Pressure");
// Fill it randomly to fake the acquired data
TRandom3 rndm;
float pot,cur,temp,pres;
for (int i=0;i<10000;++i){
pot=rndm.Uniform(0.,10.); // get voltage
temp=rndm.Uniform(250.,350.); // get temperature
pres=rndm.Uniform(0.5,1.5); // get pressure
cur=pot/(10.+0.05*(temp-300.)-0.2*(pres-1.)); // current
// add some random smearing (measurement errors)
pot*=rndm.Gaus(1.,0.01); // 1% error on voltage
temp+=rndm.Gaus(0.,0.3); // 0.3 abs. error on temp.
pres*=rndm.Gaus(1.,0.02);// 1% error on pressure
cur*=rndm.Gaus(1.,0.01); // 1% error on current
// write to ntuple
cond_data.Fill(pot,cur,temp,pres);
}
// Save the ntuple and close the file
cond_data.Write();
ofile.Close();
}
void NeuSmear( TH1D* NeuDet, TH1* NeuSp, TH1* Reso )
{
TRandom3 r;
for( int b=1; b<=nBin; b++ ) {
double mea = 1.5 + (12-1.5)/nBin*(b-0.5);
double wei = NeuSp->GetBinContent( b );
double res = Reso ->GetBinContent( b );
for( int s=1; s<=1000; s++ ) {
double d = r.Gaus( mea, res );
NeuDet->Fill( d, wei/nBin );
}
}
}
//------------------------------------------------------------------------------
void generate_rndm_numbers(double* rnd_numbers,const unsigned int n_rnd_numbers,double min, double max){
/**
* Generate between -MAX_RND and MAX_RND double numbers
**/
TRandom3 rndgen;
double init = omp_get_wtime();
for (unsigned int i=0;i<n_rnd_numbers;++i){
rnd_numbers[i] = rndgen.Uniform(min,max);
// std::cout << "o " << min << " " << max << " " << rnd_numbers[i] << std::endl;
}
double delta_t = omp_get_wtime() - init;
std::cout << "\n*** " << n_rnd_numbers
<< " numbers (" << min << "," << max<< ") generated in "
<< delta_t << " s \n";
}
int main(int argc, char** argv)
{
file = TFile::Open("~/lhcb/rd/Kll/tuples/fromPatrick/Kmm_Q17_reduced.root");
DecayTree = dynamic_cast<TTree*>(file->Get("finalTree_KMuMu"));
cosTheta = new RooRealVar("cosThetaL", "cosThetaL", -1., 1.);
sWeight = new RooRealVar("sWeight", "sWeight", -5., 5.);
data = new RooDataSet("data", "", DecayTree, RooArgList(*cosTheta,*sWeight), 0, "sWeight");
fout.open("results.txt",std::ofstream::out);
r.SetSeed(123456);
Double_t a(0.), b(0.);
for(Int_t j=-2; j<3; ++j) {
for(Int_t k=0; k<10; ++k) {
for(Int_t i=0; i<20; ++i) {
a = k/-10.;
b = j/10.;
fit(i,a,b);
}
}
}
fout.close();
return 0;
}
// Generate the number of events for this run.
// This is just Poisson statistics and the cross section.
Int_t GenerateNumOfNuRecoils(Double_t time, Double_t detMass, Double_t distance, Double_t activity, Int_t nNeutrons, Int_t nProtons, TF1* RecoilSpectrum, Double_t SpectrumMin, Double_t SpectrumMax, UInt_t seed)
{
// Set up the constants
Double_t AvogadroConst = 6.022e23;
Double_t NucleonMass = (nNeutrons * 939.565) + (nProtons * 938.272); // [MeV]
Double_t molarMass = nNeutrons + nProtons;
// Calculate mean number of events
Double_t SpectrumWeightedCrossSection = RecoilSpectrum->Integral(SpectrumMin, SpectrumMax);
Double_t MeanEvents = time * AvogadroConst * activity * detMass * SpectrumWeightedCrossSection / (4 * TMath::Pi() * molarMass * pow(distance,2));
// Generate a random number of events from Poisson distribution
TRandom3* randGen = new TRandom3(seed);
Int_t nEvt = randGen->Poisson(MeanEvents);
return nEvt;
}
void smearEmEnergy( const EnergyScaleCorrection_class& egcor, TLorentzVector& p, const ZGTree& myTree, float r9 ) {
float smearSigma = egcor.getSmearingSigma(myTree.run, fabs(p.Eta())<1.479, r9, p.Eta(), p.Pt(), 0., 0.);
float cor = myRandom_.Gaus( 1., smearSigma );
p.SetPtEtaPhiM( p.Pt()*cor, p.Eta(), p.Phi(), p.M() );
}
