void ALEXHistogramsWidget::drawPlots()
{
if(ad->isEmptyBurst()) return;
qDebug("ALEXHistogramsWidget::drawPlots");
// density plot
plotDensity();
// pr histogram
plotHistogramPR();
// S histogram
plotHistogramS();
switch(ui->comboBoxPlot->currentIndex()) {
case 0:
plotContour();
break;
case 1:
plotTgTr();
break;
default:
AlexEvalLog::warning("Error: Plot not implemented");
break;
}
}
void ALEXHistogramsWidget::on_pushButtonLevels_clicked()
{
qDebug()<<"pushButton clicked";
DialogTable *dialog = new DialogTable(this);
QList<QString> header;
header<<"contour levels";
dialog->setTableHeader(header);
for(int i =0; i<contourLevels.size();++i) {
dialog->setItem(i,0, QString::number(contourLevels.at(i)));
}
dialog->setWindowTitle("Set Contour Levels");
dialog->exec();
if(dialog->result()==QDialog::Accepted && (dialog->getRowCount()>0)) {
contourLevels.clear();
bool ok;
double sum=0;
for(int i =0; i<dialog->getRowCount();++i) {
double val = dialog->getItemValue(i,0).toDouble(&ok);
if(ok) contourLevels.append(val);
sum+=val;
}
if(dialog->getAuto()) {
relativeLevels=true;
} else {
relativeLevels=false;
}
}
if(ui->comboBoxPlot->currentIndex()==0) plotContour();
// fd->clear();
// testCorrelator(fd->tau,fd->autoCorrDonor,ad->arrivalTimeDonor,ad->arrivalTimeDonor,ad->FCSparams);
// plotFCS();
}
void ALEXHistogramsWidget::on_comboBoxPlot_currentIndexChanged()
{
if(ui->comboBoxPlot->currentIndex()==0) plotContour();
if(ui->comboBoxPlot->currentIndex()==1) plotTgTr();
// if(ui->comboBoxPlot->currentIndex()==2) plotFCS();
}
void ALEXHistogramsWidget::on_comboBoxPalette_currentIndexChanged()
{
if(ui->comboBoxPlot->currentIndex()==0) plotContour();
plotDensity();
}
/* Main part of macro */
void fitData( TGraphErrors *gdata , TF1* ffit , double *startval )
{
cout << "\n***** fitData.C: Starting fitting procedure... *****" << endl;
g_data_global = gdata;
f_fit_global = ffit;
if ( g_data_global == NULL )
{
cout << "fitData.C: Did no find any data to fit." << endl;
return NULL;
}
const char * minName = "Minuit2";
const char *algoName = "Migrad";
// create minimizer giving a name and a name (optionally) for the specific
// algorithm
// possible choices are:
// minName algoName
// Minuit /Minuit2 Migrad, Simplex,Combined,Scan (default is Migrad)
// Minuit2 Fumili2
// Fumili
// GSLMultiMin ConjugateFR, ConjugatePR, BFGS,
// BFGS2, SteepestDescent
// GSLMultiFit
// GSLSimAn
// Genetic
ROOT::Math::Minimizer* min =
ROOT::Math::Factory::CreateMinimizer(minName, algoName);
/* Alternatice constructor for Minuit */
//ROOT::Minuit2::Minuit2Minimizer *min = new ROOT::Minuit2::Minuit2Minimizer();
//TMinuit *min = new TMinuit();
// set tolerance , etc...
min->SetMaxFunctionCalls(1000000); // for Minuit/Minuit2
min->SetMaxIterations(10000); // for GSL
min->SetTolerance(0.001);
min->SetPrintLevel(1);
// create funciton wrapper for minmizer
// a IMultiGenFunction type
ROOT::Math::Functor f(&Chi2_log,3);
double step[3] = {0.1,0.1,10};
// starting point
double variable[3] = { startval[0],startval[1],startval[2]};
// set function to minimize (this is the Chi2 function)
min->SetFunction(f);
// Set the free variables to be minimized!
min->SetVariable(0,"x",variable[0], step[0]);
min->SetVariable(1,"y",variable[1], step[1]);
min->SetVariable(2,"z",variable[2], step[2]);
// do the minimization
min->Minimize();
/* set function parameters to best fit results */
const double *xs = min->X();
f_fit_global->SetParameter(0, xs[0]);
f_fit_global->SetParameter(1, xs[1]);
f_fit_global->SetParameter(2, xs[2]);
/* Print summaries of minimization process */
std::cout << "Minimum: f(" << xs[0] << ", " << xs[1] << ", " << xs[2] << "): "
<< min->MinValue() << std::endl;
// expected minimum is 0
if ( min->MinValue() < 1.E-4 && f(xs) < 1.E-4)
std::cout << "Minimizer " << minName << " - " << algoName
<< " converged to the right minimum" << std::endl;
else {
std::cout << "Minimizer " << minName << " - " << algoName
<< " failed to converge !!!" << std::endl;
Error("NumericalMinimization","fail to converge");
}
/* Get degrees of freedom NDF */
unsigned ndf = 0;
/* get range of function used for fit */
double fit_min, fit_max;
f_fit_global->GetRange(fit_min,fit_max);
/* count data points in range of function */
for ( int p = 0; p < g_data_global->GetN(); p++ )
{
if ( g_data_global->GetX()[p] >= fit_min && g_data_global->GetX()[p] <= fit_max )
{
ndf++;
}
}
ndf -= 3; // 3 parameters in fit
cout << "Degrees of freedom: " << ndf << " --> chi2 / NDF = " << min->MinValue() / ndf << endl;
/* Get error covariance matrix from minimizer */
TMatrixD matrix0(3,3);
for ( unsigned i = 0; i < 3; i++ )
{
for ( unsigned j = 0; j < 3; j++ )
{
matrix0[i][j] = min->CovMatrix(i,j);
}
}
matrix0.Print();
/* Get minos error */
double par0_minos_error_up = 0;
double par0_minos_error_low = 0;
double par1_minos_error_up = 0;
double par1_minos_error_low = 0;
double par2_minos_error_up = 0;
double par2_minos_error_low = 0;
min->GetMinosError( 0, par0_minos_error_low, par0_minos_error_up );
min->GetMinosError( 1, par1_minos_error_low, par1_minos_error_up );
min->GetMinosError( 2, par2_minos_error_low, par2_minos_error_up );
cout << "Minos uncertainty parameter 0: up = " << par0_minos_error_up << ", low = " << par0_minos_error_low << endl;
cout << "Minos uncertainty parameter 1: up = " << par1_minos_error_up << ", low = " << par1_minos_error_low << endl;
cout << "Minos uncertainty parameter 2: up = " << par2_minos_error_up << ", low = " << par2_minos_error_low << endl;
/* Plot chi2 */
/* sigmas symmetric from covariance matrix */
//double sigmas[6] = {sqrt(matrix0[0][0]), sqrt(matrix0[0][0]), sqrt(matrix0[1][1]), sqrt(matrix0[1][1]), sqrt(matrix0[2][2]), sqrt(matrix0[2][2])};
/* sigmas asymmetric from minos */
double sigmas[6] = {par0_minos_error_low, par0_minos_error_up, par1_minos_error_low, par1_minos_error_up, par2_minos_error_low, par2_minos_error_up};
plotChi2( g_data_global, f_fit_global, 3, sigmas );
/* Draw contour plots */
TGraph2D* gcontour3D = plotContour( min , 3 , ndf );
/* Find error boundaries */
TF1* flow = (TF1*)f_fit_global->Clone("flow");
TF1* fup = (TF1*)f_fit_global->Clone("fup");
flow->SetRange(0,100000);
flow->SetLineColor(kBlue);
flow->SetLineStyle(2);
fup->SetRange(0,100000);
fup->SetLineColor(kBlue);
fup->SetLineStyle(2);
findErrorBounds( f_fit_global, flow, fup, gcontour3D );
/* plot residuals data w.r.t. fit */
plotResiduals( g_data_global, f_fit_global, flow, fup );
/* Draw data, best fit, and 1-sigma band */
TCanvas *cfit = new TCanvas();
g_data_global->Draw("AP");
//f_fit_global->SetRange(0,4000);
f_fit_global->Draw("same");
flow->Draw("same");
fup->Draw("same");
cfit->Print("new_FitResult.png");
/* Fit Extrapolation */
double t_extrapolate_months = 6;
double t_extrapolate_s = t_extrapolate_months * 30 * 24 * 60 * 60;
cout << "Extrapolation to " << t_extrapolate_months
<< " months: " << ffit->Eval( t_extrapolate_s )
<< " , up: " << fup->Eval( t_extrapolate_s ) - ffit->Eval( t_extrapolate_s )
<< " , low: " << flow->Eval( t_extrapolate_s ) - ffit->Eval( t_extrapolate_s )
<< endl;
return f_fit_global;
}
