void
LagrangianAttributes::SetFromNode(DataNode *parentNode)
{
if(parentNode == 0)
return;
DataNode *searchNode = parentNode->GetNode("LagrangianAttributes");
if(searchNode == 0)
return;
DataNode *node;
if((node = searchNode->GetNode("seedPoint")) != 0)
SetSeedPoint(node->AsDoubleArray());
if((node = searchNode->GetNode("numSteps")) != 0)
SetNumSteps(node->AsInt());
if((node = searchNode->GetNode("XAxisSample")) != 0)
{
// Allow enums to be int or string in the config file
if(node->GetNodeType() == INT_NODE)
{
int ival = node->AsInt();
if(ival >= 0 && ival < 6)
SetXAxisSample(sampleType(ival));
}
else if(node->GetNodeType() == STRING_NODE)
{
sampleType value;
if(sampleType_FromString(node->AsString(), value))
SetXAxisSample(value);
}
}
if((node = searchNode->GetNode("YAxisSample")) != 0)
{
// Allow enums to be int or string in the config file
if(node->GetNodeType() == INT_NODE)
{
int ival = node->AsInt();
if(ival >= 0 && ival < 6)
SetYAxisSample(sampleType(ival));
}
else if(node->GetNodeType() == STRING_NODE)
{
sampleType value;
if(sampleType_FromString(node->AsString(), value))
SetYAxisSample(value);
}
}
if((node = searchNode->GetNode("variable")) != 0)
SetVariable(node->AsString());
}
ExplainTuple*
PhysSample::addSpecificExplainInfo(ExplainTupleMaster *explainTuple,
ComTdb * tdb,
Generator *generator)
{
NAString buffer = "sample_type: ";
switch(sampleType()) {
case RANDOM:
buffer += "RANDOM ";
break;
case PERIODIC:
buffer += "PERIODIC ";
break;
case FIRSTN:
buffer += "FIRST ";
break;
case CLUSTER:
buffer += "CLUSTER ";
break;
default:
buffer += "UNKNOWN ";
break;
}
explainTuple->setDescription(buffer);
return(explainTuple);
}
int main(int argc, char *argv[])
{
if(argc != 4){
std::cout << "Usage: sortForest <inputFile> <sampleType> <#>" << std::endl;
return 1;
}
int rStatus = -1;
rStatus = makeWJetAnaSkim(argv[1], sampleType(atoi(argv[2])), atoi(argv[3]));
return rStatus;
}
void EffectFilter::Filter(sampleCount len,
sampleType *buffer)
{
float *inr = new float[len];
float *ini = new float[len];
float *outr = new float[len];
float *outi = new float[len];
unsigned int i;
for(i=0; i<len; i++)
inr[i] = buffer[i]/32767.;
// Apply window and FFT
WindowFunc(3, len, inr); // Hanning window
FFT(len, false, inr, NULL, outr, outi);
// Apply filter
unsigned int half = len/2;
for(i=0; i<=half; i++) {
int j = len - i;
outr[i] = outr[i]*filterFunc[i];
outi[i] = outi[i]*filterFunc[i];
if (i!=0 && i!=len/2) {
outr[j] = outr[j]*filterFunc[i];
outi[j] = outi[j]*filterFunc[i];
}
}
// Inverse FFT and normalization
FFT(len, true, outr, outi, inr, ini);
for(i=0; i<len; i++)
buffer[i] = sampleType(inr[i]*32767);
delete[] inr;
delete[] ini;
delete[] outr;
delete[] outi;
}
LagrangianAttributes::sampleType
LagrangianAttributes::GetYAxisSample() const
{
return sampleType(YAxisSample);
}
void EffectNoiseRemoval::RemoveNoise(sampleCount len,
sampleType *buffer)
{
float *inr = new float[len];
float *ini = new float[len];
float *outr = new float[len];
float *outi = new float[len];
float *power = new float[len];
float *plog = new float[len];
int i;
for(i=0; i<len; i++)
inr[i] = buffer[i]/32767.;
// Apply window and FFT
WindowFunc(3, len, inr); // Hanning window
FFT(len, false, inr, NULL, outr, outi);
for(i=0; i<len; i++)
inr[i] = buffer[i]/32767.;
WindowFunc(3, len, inr); // Hanning window
PowerSpectrum(len, inr, power);
for(i=0; i<=len/2; i++)
plog[i] = log(power[i]);
int half = len/2;
for(i=0; i<=half; i++) {
float smooth;
if (plog[i] < noiseGate[i] + (level/2.0))
smooth = 0.0;
else
smooth = 1.0;
smoothing[i] = smooth * 0.5 + smoothing[i] * 0.5;
}
/* try to eliminate tinkle bells */
for(i=2; i<=half-2; i++) {
if (smoothing[i]>=0.5 &&
smoothing[i]<=0.55 &&
smoothing[i-1]<0.1 &&
smoothing[i-2]<0.1 &&
smoothing[i+1]<0.1 &&
smoothing[i+2]<0.1)
smoothing[i] = 0.0;
}
outr[0] *= smoothing[0];
outi[0] *= smoothing[0];
outr[half] *= smoothing[half];
outi[half] *= smoothing[half];
for(i=1; i<half; i++) {
int j = len - i;
float smooth = smoothing[i];
outr[i] *= smooth;
outi[i] *= smooth;
outr[j] *= smooth;
outi[j] *= smooth;
}
// Inverse FFT and normalization
FFT(len, true, outr, outi, inr, ini);
for(i=0; i<len; i++)
buffer[i] = sampleType(inr[i]*32767);
delete[] inr;
delete[] ini;
delete[] outr;
delete[] outi;
delete[] power;
delete[] plog;
}
void fitSignal(){
std::string shapes_file = "mlfit.root";
std::string data_file = "param_ws.root";
std::string channel = "ch1";
TFile *dfile = TFile::Open(data_file.c_str());
TFile *sfile = TFile::Open(shapes_file.c_str());
TH1F *bkg = (TH1F*)sfile->Get(Form("shapes_fit_b/%s/total",channel.c_str()));
TH1F *data = (TH1F*)dfile->Get("SR_data"); // TH1 for data
TH1F *signal= (TH1F*)dfile->Get("SR_signal"); // TH1 for signal
TH2F *covar = (TH2F*)sfile->Get(Form("shapes_fit_b/%s/total_covar",channel.c_str()));
// bkg and covariance defined as pdf / GeV, so scale by bin widhts
//
int nbins = data->GetNbinsX();
if (!isTH1Input){
for (int b=1;b<=nbins;b++){
double bw = bkg->GetBinWidth(b);
bkg->SetBinContent(b,bkg->GetBinContent(b)*bw);
for (int j=1;j<=nbins;j++){
covar->SetBinContent(b,j,covar->GetBinContent(b,j)*bw*bw);
}
}
}
RooArgList xlist_;
RooArgList olist_;
RooArgList mu_;
bkg->Print() ;
covar->Print() ;
signal->Print() ;
data->Print() ;
// Make a dataset (simultaneous)
RooCategory sampleType("bin_number","Bin Number");
RooRealVar observation("observed","Observed Events bin",1);
// You have to define the samples types first!, because RooFit suuuuucks!
for (int b=1;b<=nbins;b++){
sampleType.defineType(Form("%d",b-1),b-1);
sampleType.setIndex(b-1);
}
RooArgSet obsargset(observation,sampleType);
RooDataSet obsdata("combinedData","Data in all Bins",obsargset);
//obsdata.add(RooArgSet(observation,sampleType));
for (int b=1;b<=nbins;b++){
observation.setVal(data->GetBinContent(b));
sampleType.setIndex(b-1);
std::cout << sampleType.getLabel() << ", " << sampleType.getIndex() << std::endl;
//RooArgSet localset(observation,sampleType);
//obsdata.add(localset);
obsdata.add(RooArgSet(observation,sampleType));
std::cout << " Observed at " << b << ", " << observation.getVal() << std::endl;
}
// make a constraint term for the background, and a RooRealVar for bkg
for (int b=1;b<=nbins;b++){
double bkgy = (double)bkg->GetBinContent(b);
RooRealVar *mean_ = new RooRealVar(Form("exp_bin_%d_In",b),Form("expected bin %d",b),bkgy);
mean_->setConstant(true);
RooRealVar *x_ = new RooRealVar(Form("exp_bin_%d",b),Form("bkg bin %d",b),bkgy,0.2*bkgy,bkgy*4);
std::cout << " Exp background At " << b << ", " << x_->getVal() << std::endl;
xlist_.add(*x_);
mu_.add(*mean_);
}
// constraint PDF for background
// Convert TH2 -> TMatrix
TMatrixDSym Tcovar(nbins);
for (int i=0;i<nbins;i++){
for (int j=0;j<nbins;j++){
//if (i==j)Tcovar[i][j] = covar->GetBinContent(i+1,j+1);
//else Tcovar[i][j] = 0;
Tcovar[i][j] = covar->GetBinContent(i+1,j+1);
}
}
std::cout<< "Made Covariance" << std::endl;
RooMultiVarGaussian constraint_pdf("constraint_pdf","Constraint for background pdf",xlist_,mu_,Tcovar);
std::cout<< "Made Covariance Gauss" << std::endl;
// Make the signal component
RooRealVar r("r","r",1,-5,5);
RooArgList signals_;
for (int b=1;b<=nbins;b++) {
RooProduct *sigF = new RooProduct(Form("signal_%d",b),"signal nominal",RooArgSet(r,RooFit::RooConst(signal->GetBinContent(b))));
std::cout << " Signal At " << b << ", " << sigF->getVal() << std::endl;
signals_.add(*sigF);
}
RooArgList plist_;
RooArgList slist_;
sampleType.setIndex(1);
RooSimultaneous combined_pdf("combined_pdf","combined_pdf",sampleType);
for (int b=1;b<=nbins;b++){
RooAddition *sum = new RooAddition(Form("splusb_bin_%d",b),Form("Signal plus background in bin %d",b),RooArgList(*((RooRealVar*)(signals_.at(b-1))),*((RooRealVar*)(xlist_.at(b-1)))));
RooPoisson *pois = new RooPoisson(Form("pdf_bin_%d",b),Form("Poisson in bin %d",b),observation,(*sum));
// Who cares about poissons?
//RooGaussian *pois = new RooGaussian(Form("pdf_bin_%d",b),Form("Poisson in bin %d",b),observation,(*sum),RooFit::RooConst(TMath::Sqrt(sum->getVal())));
combined_pdf.addPdf(*pois,Form("%d",b-1));
slist_.add(*sum);
plist_.add(*pois);
}
combined_pdf.Print("v");
obsdata.Print("v");
// Make a prodpdf instread
RooProdPdf combinedpdfprod("maybefinalpdf","finalpdf",RooArgList(combined_pdf,constraint_pdf));
RooAbsReal *nll_ = combined_pdf.createNLL(obsdata,RooFit::ExternalConstraints(RooArgList(constraint_pdf)));
//
RooMinimizer m(*nll_);
m.minimize("Minuit2","minimize");
// constrained fit!
r.setConstant(true);
double nllMin = nll_->getVal();
TFile *fout = new TFile("simple.root","RECREATE");
TTree *tree = new TTree("limit","limit");
float deltaNLL_;
float r_;
tree->Branch("r",&r_,"r/F");
tree->Branch("deltaNLL",&deltaNLL_,"deltaNLL/F");
RooMinimizer mc(*nll_);
//combinedpdfprod.fitTo(obsdata);
//
for(float rv=-2;rv<=2;rv+=0.2){
r.setVal(rv);
r_=rv;
mc.minimize("Minuit2","minimize");
deltaNLL_ = nll_->getVal() - nllMin;
tree->Fill();
}
fout->cd();
tree->Write();
fout->Close();
/*
RooAbsReal *nll_ = combinedpdfprod.createNLL(obsdata);
nll_->Print("v");
// Minimize
RooMinimizer m(*nll_);
r.setConstant(true);
std::cout << combinedpdfprod.getVal() << std::endl;
std::cout << constraint_pdf.getVal() << std::endl;
//m.Print();
m.minimize("Minuit2","minimize");
*/
}
