void GenerateNonStd(unsigned int Npart)
{
frodo *fr = frodo::instance();
static TRandom randy(0);
fr->HBDParticles.clear();
double s = randy.Rndm();
//cout<<"s= "<<s<<endl;
double s1 = 0;
if(s < 0.5) s1 = 1;
else s1 = -1;
double a = randy.Rndm();
//cout<<"a= "<<a <<endl;
double Cx = 2*fr->Ring[0].C()*a - fr->Ring[0].C();
double Cy = s1*sqrt(fr->Ring[0].C()*fr->Ring[0].C()-Cx*Cx);
fr->CX.push_back(Coordinates(Cx));
fr->CY.push_back(Coordinates(Cy));
//cout<<"ring center is: ( "<< Cx <<" , " << Cy << " ) " <<endl;
for (unsigned int i=0; i<Npart; i++)
{
//cout<<"Npart= " <<i<<endl;
double S = randy.Rndm();
//cout<<"S= "<<S<<endl;
double s2 = 0;
if(S < 0.5) s2 = 1;
else s2 = -1;
double b = randy.Rndm();
//cout<<"b= "<<b<<endl;
double x = Cx + 2*fr->Ring[0].R()*b - fr->Ring[0].R();
double y = Cy + s2*sqrt(fr->Ring[0].R()*fr->Ring[0].R() - (x-Cx)*(x-Cx));
if(fabs(x) <= 1000 || fabs(y) <= 1125)
{
i--;
//cout<<"reject!"<<endl;
continue;
}
double q = -1;
while (q<=0) q=randy.Exp(80000);
//cout<<"q= " <<q<<endl;
fr->HBDParticles.push_back( AParticle(x,y,q) );
}
//cout<<"# of particles: " <<fr->HBDParticles.size()<<endl;
}
void drawing_pion_decay(){
double Pi_lifetime = 0.26033e-6; //s
double c = 3e8;//m/s
TCanvas *c1 = new TCanvas("test", "test");
TRandom *r = new TRandom();
TView *view = TView::CreateView(1, 0, 0);
view->ShowAxis();
view->SetRange(-5, -5, 0, 5, 5, 10);
for (int i = 0; i < 100; ++i)
{
double px = 0.3;
double py = 0.3;
double pz = -1; // GeV
double energy = sqrt(px*px+py*py+pz*pz+M_pion*M_pion);
//life time
double t = r->Exp(Pi_lifetime);
//decay length
double gamma = energy/M_pion;
double length = c*t*gamma/1e3;
// decay position
double vx = 0;
double vy = 0;
double vz = 10 - length;
TLorentzVector pion(px, py, pz, energy);
double decay_particle_mass[2] = {M_muon, M_neu};
TGenPhaseSpace event;
event.SetDecay(pion, 2, decay_particle_mass);
event.Generate();
TLorentzVector Muon = *(event.GetDecay(0));
TLorentzVector Neu = *(event.GetDecay(1));
plot_particle(Muon, vx, vy, vz, 2);
plot_particle(Neu, vx, vy, vz, 4);
}
}
void principal(Int_t n=10, Int_t m=10000)
{
//
// Principal Components Analysis (PCA) example
//
// Example of using TPrincipal as a stand alone class.
//
// We create n-dimensional data points, where c = trunc(n / 5) + 1
// are correlated with the rest n - c randomly distributed variables.
//
// Here's the plot of the eigenvalues Begin_Html
// <IMG SRC="gif/principal_eigen.gif">
// End_Html
//Authors: Rene Brun, Christian Holm Christensen
Int_t c = n / 5 + 1;
cout << "*************************************************" << endl;
cout << "* Principal Component Analysis *" << endl;
cout << "* *" << endl;
cout << "* Number of variables: " << setw(4) << n
<< " *" << endl;
cout << "* Number of data points: " << setw(8) << m
<< " *" << endl;
cout << "* Number of dependent variables: " << setw(4) << c
<< " *" << endl;
cout << "* *" << endl;
cout << "*************************************************" << endl;
// Initilase the TPrincipal object. Use the empty string for the
// final argument, if you don't wan't the covariance
// matrix. Normalising the covariance matrix is a good idea if your
// variables have different orders of magnitude.
TPrincipal* principal = new TPrincipal(n,"ND");
// Use a pseudo-random number generator
TRandom* random = new TRandom;
// Make the m data-points
// Make a variable to hold our data
// Allocate memory for the data point
Double_t* data = new Double_t[n];
for (Int_t i = 0; i < m; i++) {
// First we create the un-correlated, random variables, according
// to one of three distributions
for (Int_t j = 0; j < n - c; j++) {
if (j % 3 == 0)
data[j] = random->Gaus(5,1);
else if (j % 3 == 1)
data[j] = random->Poisson(8);
else
data[j] = random->Exp(2);
}
// Then we create the correlated variables
for (Int_t j = 0 ; j < c; j++) {
data[n - c + j] = 0;
for (Int_t k = 0; k < n - c - j; k++)
data[n - c + j] += data[k];
}
// Finally we're ready to add this datapoint to the PCA
principal->AddRow(data);
}
// We delete the data after use, since TPrincipal got it by now.
delete [] data;
// Do the actual analysis
principal->MakePrincipals();
// Print out the result on
principal->Print();
// Test the PCA
principal->Test();
// Make some histograms of the orginal, principal, residue, etc data
principal->MakeHistograms();
// Make two functions to map between feature and pattern space
principal->MakeCode();
// Start a browser, so that we may browse the histograms generated
// above
TBrowser* b = new TBrowser("principalBrowser", principal);
}
