void generateFitAndCalculatePvalue( XMLConfigReader * xmlFile, ParameterSet* parSet, MinimiserConfiguration * theMinimiser, FitFunctionConfiguration * theFunction, ParameterSet* argumentParameterSet, int nData, int repeats, vector<double> * pvalues)
{
PDFWithData * pdfAndData = xmlFile->GetPDFsAndData()[0];
pdfAndData->SetPhysicsParameters( parSet );
IPDF * pdf = pdfAndData->GetPDF();
vector<IPDF*> vectorPDFs;
vectorPDFs.push_back(pdf);
PhaseSpaceBoundary * phase = xmlFile->GetPhaseSpaceBoundaries()[0];
EdStyle * greigFormat = new EdStyle();
greigFormat->SetStyle();
ParameterSet* parSetFromFit = NULL;
// Set up to be able to generate some MC data
DataSetConfiguration * dataConfig = pdfAndData->GetDataSetConfig();
dataConfig->SetSource( "Foam" );
bool model = false;
double pvalue = 0.;
unsigned int ulTime = static_cast<unsigned int>( time( NULL ));
for ( int i = 0; i < repeats; )
{
cout << "Ensemble " << i << endl;
// First, generate some data from this PDF
pdfAndData->SetPhysicsParameters( parSet );
pdf->SetRandomFunction( (int)ulTime );
pdf->SetMCCacheStatus( false );
MemoryDataSet * subset = (MemoryDataSet*)dataConfig->MakeDataSet( phase, pdf, nData );
vector<IDataSet*> vectorData;
vectorData.push_back(subset);
if ( model ) {
// Use the same PDF for the large MC dataset (the Model approach)
pdfAndData->SetPhysicsParameters( parSet );
}
else {
// Fit the generated data (the Fit I approach)
FitResult * gofResult = FitAssembler::DoFit( theMinimiser, theFunction, argumentParameterSet, vectorPDFs, vectorData, xmlFile->GetConstraints() );
if ( gofResult->GetFitStatus() == 3 ) {
delete parSetFromFit;
parSetFromFit = gofResult->GetResultParameterSet()->GetDummyParameterSet();
pdfAndData->SetPhysicsParameters( parSetFromFit );
i++; // let us go to the next iteration of the loop so that we always get "repeats" good results
}
}
// Generate large sample of MC
ulTime = static_cast<unsigned int>( time( NULL ));
pdf->SetRandomFunction( (int)ulTime );
pdf->SetMCCacheStatus( false );
MemoryDataSet * mcData = (MemoryDataSet*)dataConfig->MakeDataSet( phase, pdf, 10*nData );
// Finally calculate the p-value relative to the large MC sample
pvalue = GoodnessOfFit::pValueFromPoint2PointDissimilarity( subset, mcData );
//pvalue = GoodnessOfFit::pValueFromPoint2PointDissimilarity( data, mcData );
pvalues->push_back(pvalue);
//delete subset;
//delete mcData;
}
parSet = parSetFromFit; // Need this so that we have the correct parameters during the second call of this function
}
int main( int argc, char* argv[] )
{
EdStyle* thisStyle = new EdStyle();
thisStyle->SetStyle();
gStyle->SetOptStat(0);
// Mute ROOT
gErrorIgnoreLevel = kFatal;
struct PlottingConfig thisConfig = PlottingConfig();
ProcessInputOptions( thisConfig, argc, argv );
if( !thisConfig.cutString.empty() && !thisConfig.weightName.empty() )
{
cout << "WARNING!!! You have made a Cut and are making a weighted plot!!!" << endl;
cout << "Be Careful!!!" << endl;
}
TTree* inputTree = ROOT_File_Processing::GetFirstTree( thisConfig.inputFileName, "READ" );
if( thisConfig.requestedParameters.empty() )
{
thisConfig.requestedParameters = StringOperations::TString2string( TTree_Processing::get_branch_names( inputTree ) );
if( !thisConfig.weightName.empty() )
{
vector<string> allParameters;
for( unsigned int i=0; i< thisConfig.requestedParameters.size(); ++i )
{
if( thisConfig.requestedParameters[i] != thisConfig.weightName ) allParameters.push_back( thisConfig.requestedParameters[i] );
}
thisConfig.requestedParameters = allParameters;
}
}
TString plotDistsOutput = "PlotDists_Output";
cout << "Output Plots Sotred in: " << plotDistsOutput << endl;
gSystem->mkdir( plotDistsOutput );
vector<Double_t>* weightData = NULL;
if( !thisConfig.weightName.empty() )
{
weightData = TTree_Processing::Buffer_Branch( inputTree, thisConfig.weightName, thisConfig.cutString );
}
for( unsigned int i=0; i< thisConfig.requestedParameters.size(); ++i )
{
string thisParameterName = thisConfig.requestedParameters[i];
vector<Double_t>* thisData = TTree_Processing::Buffer_Branch( inputTree, thisParameterName, thisConfig.cutString );
TString CanvasName = "Canvas_";
CanvasName.Append( thisParameterName );
TCanvas* thisCanvas = EdStyle::RapidFitCanvas( CanvasName );
thisCanvas->SetLogy( thisConfig.setLogY );
thisCanvas->SetLogx( thisConfig.setLogX );
TString TH1Name = "TH1_";
TH1Name.Append( thisParameterName );
Double_t thisMin=0.;
Double_t thisMax=0.;
if( thisConfig.explicit_min <= -DBL_MAX ) thisMin = Template_Functions::get_minimum( *thisData );
else thisMin = thisConfig.explicit_min;
if( thisConfig.explicit_max >= DBL_MAX ) thisMax = Template_Functions::get_maximum( *thisData );
else thisMax = thisConfig.explicit_max;
TH1* thisTH1 = new TH1D( TH1Name, TH1Name, thisConfig.num_bins, thisMin, thisMax );
for( unsigned int j=0; j< thisData->size(); ++j )
{
double weight=0.;
if( weightData == NULL ) weight = 1.;
else weight = weightData->at(j);
SafeFill( thisTH1, thisData->at(j), weight, thisMin, thisMax );
}
thisTH1->Draw( thisConfig.drawOptions.c_str() );
thisCanvas->Update();
thisTH1->GetXaxis()->SetRangeUser( thisMin, thisMax );
thisCanvas->Update();
TString XaxisLabel = EdStyle::GetParamRootName( thisParameterName );
XaxisLabel.Append( " " );
XaxisLabel.Append( EdStyle::GetParamRootUnit( thisParameterName ) );
thisTH1->GetXaxis()->SetTitle( XaxisLabel );
TString YaxisLabel = "Candidates / ( ";
stringstream thisStream;
thisStream << setprecision(2) << fabs(thisMax-thisMin)/((double)thisConfig.num_bins);
YaxisLabel.Append( thisStream.str().c_str() );
TString YaxisUnit = EdStyle::GetParamRootUnit( thisParameterName );
if( StringProcessing::is_empty( YaxisUnit ) )
{
}
else
{
YaxisLabel.Append( " " );
YaxisLabel.Append( YaxisUnit );
}
YaxisLabel.Append( " )" );
thisTH1->GetYaxis()->SetTitle( YaxisLabel );
thisCanvas->Update();
double ymin = thisTH1->GetBinContent( thisTH1->GetMinimumBin() ) - thisTH1->GetBinError( thisTH1->GetMinimumBin() );
double ymax = thisTH1->GetBinContent( thisTH1->GetMaximumBin() ) + thisTH1->GetBinError( thisTH1->GetMaximumBin() );
if( thisConfig.y_min > -DBL_MAX ) ymin = thisConfig.y_min;
if( thisConfig.y_max < DBL_MAX ) ymax = thisConfig.y_max;
thisTH1->GetYaxis()->SetRangeUser( ymin, ymax );
thisTH1->SetMinimum( ymin );
thisTH1->SetMaximum( ymax );
thisCanvas->Update();
TString localPDFName = thisParameterName.c_str();
localPDFName.Append( "_Output.pdf" );
TString TotalPDFName = plotDistsOutput;
TotalPDFName.Append("/");
TotalPDFName.Append( localPDFName );
thisCanvas->Print( TotalPDFName );
delete thisData;
}
}
