--- /dev/null
+/**
+ * @file HistogramType.c
+ * @brief A class to store the histograms used in #Run.c
+ *
+ */
+
+#ifndef __HISTOGRAMTYPE__C
+#define __HISTOGRAMTYPE__C
+
+#include "HistogramType.h"
+using namespace std;
+
+
+//*****************
+// CONSTRUCTOR AND DECONSTRUCTOR
+//*****************
+
+HistogramType::~HistogramType( void) {
+
+}
+
+HistogramType::HistogramType(TString suffix, systemparam* gotsystempar, sensorinfostruct* gotsensorinfo, Bool_t threshold, Int_t gotcolor, Int_t gotstyle ) {
+ histogramdescription = suffix;
+ cursystempar = gotsystempar;
+ cursensorinfo = gotsensorinfo;
+ setThresholdType(threshold);
+ initHistograms(gotcolor, gotstyle);
+};
+
+
+//*****************
+// GETTER AND SETTER
+//*****************
+
+void HistogramType::setThresholdType(Bool_t threshold) {
+ if (threshold) {
+ isthresholdclustertype = 1; }
+ else {
+ isthresholdclustertype = 0; }
+}
+
+//*****************
+// INIT FUNCTIONS
+//*****************
+
+void HistogramType::initHistograms(Int_t gotcolor, Int_t gotstyle) {
+ color = gotcolor;
+ style = gotstyle;
+ initHistogram(Seed, "Seed" + histogramdescription, color, style);
+ initHistogram(Sum, "Sum" + histogramdescription, color, style);
+ initHistogram(Veto, "Veto" + histogramdescription, color, style);
+ initHistogram(Noise, "Noise" + histogramdescription, color, style);
+
+ Noise->SetBins(cursystempar->nbinsnoise, 0, cursystempar->maxbinnoise);
+}
+
+void HistogramType::initHistogram(TH1F* &histogrampointer, TString prefix, Int_t color, Int_t style) {
+ histogrampointer=new TH1F(prefix.Data(), prefix.Data(), cursystempar->nbins, 0, cursystempar->maxbin);
+ histogrampointer->SetLineStyle(style);
+ histogrampointer->SetLineColor(color);
+ histogrampointer->SetStats(kTRUE);
+ histogrampointer->SetStats(111111111);
+ histogrampointer->SetLineWidth(3);
+ histogrampointer->GetXaxis()->SetTitle("Q_coll [ADU]");
+ histogrampointer->GetYaxis()->SetTitle(Form("Entries [1/%.1f ADU]",histogrampointer->GetBinWidth(1)));
+ histogrampointer->GetXaxis()->CenterTitle();
+ histogrampointer->GetYaxis()->CenterTitle();
+}
+
+void HistogramType::initCluster(TString suffix, Float_t xcoord_min_step, Float_t xcoord_abs_min, Float_t xcoord_abs_max, Float_t ycoord_min_step, Float_t ycoord_abs_min, Float_t ycoord_abs_max) {
+ TString prefix = "Cluster" + suffix;
+ Int_t sizearrrotx= (int)((xcoord_abs_max-xcoord_abs_min)/xcoord_min_step+0.5);
+ Int_t sizearrroty = (int)((ycoord_abs_max-ycoord_abs_min)/ycoord_min_step+0.5);
+ // cout << "xcoord_abs_min: " << xcoord_abs_min << ", xcoord_abs_max: " << xcoord_abs_max << ", xcoord_min_step: " << xcoord_min_step << endl;
+ // cout << "ycoord_abs_min: " << ycoord_abs_min << ", ycoord_abs_max: " << ycoord_abs_max << ", ycoord_min_step: " << ycoord_min_step << endl;
+ histAvgCluster = new TH2F(prefix.Data(), "Avg. cluster distribution; x; y",sizearrrotx, xcoord_abs_min,xcoord_abs_max,sizearrroty,ycoord_abs_min,ycoord_abs_max);
+ // you can increase the resolution, especially usefull if you observe an rotated (originally staggered) pixel cluster
+ // histogramstructpointer->histAvgCluster = new TH2F(prefix.Data(), Form("Avg. cluster distribution; x; y %s", humanreadablestr.Data()),2*sizearrrotx, xcoord_abs_min,xcoord_abs_max,sizearrroty*2,ycoord_abs_min,ycoord_abs_max);
+}
+
+//*****************
+// METHODS APPLYABLE TO HISTOGRAMS
+//*****************
+
+Bool_t HistogramType::calibrateHistograms( Float_t gotgain ) {
+ if ( gotgain <= 0 )
+ {
+ cout << coloryellow << "Cannot rescale run from [ADU] to [e] units, no gain provided." << endlr;
+ return 1;
+ }
+ gain = gotgain;
+
+ calibrated = new HistogramType(histogramdescription+" calibrated", cursystempar, cursensorinfo, isthresholdclustertype, color, style);
+ if (Seed != 0) calibrateHistogram(calibrated->Seed, Seed);
+ if (Sum != 0) calibrateHistogram(calibrated->Sum, Sum);
+ if (Veto != 0) calibrateHistogram(calibrated->Veto, Veto);
+ if (Noise != 0) calibrateHistogram(calibrated->Noise, Noise);
+ if (histAvgCluster != 0) calibrate2DHistogramCounts(calibrated->histAvgCluster, histAvgCluster);
+
+ calibrated->posSeed = posSeed * gain;
+ calibrated->posSum = posSum * gain;
+ calibrated->posVeto = posVeto * gain;
+ calibrated->avgNoise = avgNoise * gain;
+ calibrated->avgNoisePlus = avgNoisePlus * gain;
+ calibrated->avgNoiseMinus = avgNoiseMinus * gain;
+ calibrated->sr90IntegralVal = sr90IntegralVal * gain;
+
+ calibrated->iscalibrated = true;
+
+ return 1;
+}
+
+void HistogramType::calibrateHistogram(TH1F* &histogrampointernew, TH1F* &histogrampointerold) {
+ histogrampointernew = (TH1F*)histogrampointerold->Clone();
+ histogrampointernew->SetName(Form("%sC", histogrampointerold->GetName()));
+ histogrampointernew->SetTitle(Form("%sC", histogrampointerold->GetTitle()));
+ histogrampointernew->GetXaxis()->SetTitle("Q_coll [e]");
+ int nbins = histogrampointernew->GetXaxis()->GetNbins();
+ double new_bins[nbins+1];
+ for(int i=0; i <= nbins; i++){
+ new_bins[i] = histogrampointernew->GetBinCenter(i)*gain;
+ }
+ histogrampointernew->SetBins(nbins, new_bins);
+ histogrampointernew->GetYaxis()->SetTitle(Form("Entries [1/%.1f e]",histogrampointernew->GetBinWidth(1)));
+}
+
+void HistogramType::calibrate2DHistogramCounts(TH2F* &histogrampointernew, TH2F* &histogrampointerold ) {
+ histogrampointernew = (TH2F*)histogrampointerold->Clone();
+ histogrampointernew->SetName(Form("%sC", histogrampointerold->GetName()));
+ histogrampointernew->SetTitle(Form("%sC", histogrampointerold->GetTitle()));
+ histogrampointernew->GetZaxis()->SetTitle("Q_coll [e]");
+ int nbinsx = histogrampointernew->GetXaxis()->GetNbins();
+ int nbinsy = histogrampointernew->GetYaxis()->GetNbins();
+
+ for(int y=0; y <= nbinsy; y++){
+ for(int x=0; x <= nbinsx; x++){
+ histogrampointernew->SetBinContent(x,y,histogrampointerold->GetBinContent(x,y)*gain);
+ }
+ }
+}
+
+Float_t HistogramType::FitPerform(TH1F* histogrampointer, TString fitFuncType, Bool_t verbose, Double_t* parameters) {
+ Float_t posMax = 0;
+ Float_t posMax2 = 0;
+ Float_t posMaxValHist = histogrampointer->GetXaxis()->GetXmax();
+ // posMaxValHist/10 for USB system, the value is 90
+ Float_t noiseborder = 90;
+ if (noisethresholdborder>0)
+ noiseborder = noisethresholdborder;
+ else if (noisethresholdborder > 0)
+ noiseborder = noisethresholdborder;
+
+ if (fitFuncType.Contains("gaus"))
+ {
+ histogrampointer->GetXaxis()->SetRange(histogrampointer->GetXaxis()->FindBin(noiseborder),histogrampointer->GetXaxis()->FindBin(posMaxValHist)); // look only for maxima with x greater than noiseborder, cut away noise
+ // Int_t xValMax = histogrampointer->GetBinCenter(histogrampointer->GetMaximumBin());
+ TF1* fitFunc = new TF1("fitFunc",fitFuncType,noiseborder,posMaxValHist);
+
+ if (TString(histogrampointer->GetName()).Contains("Veto"))
+ {
+ Float_t peak1 = histogrampointer->GetMaximumBin();
+ Float_t peak2;
+ if (histogrampointer->GetBinCenter(peak1)<3.3*noiseborder) // vermutlich ist veto peak nicht am höchsten
+ {
+ Float_t avg = 0;
+ for(Int_t bin=histogrampointer->GetXaxis()->FindBin(noiseborder);bin<histogrampointer->FindLastBinAbove(0);bin++)
+ avg += histogrampointer->GetBinContent(bin);
+ avg /= histogrampointer->FindLastBinAbove(0) - histogrampointer->GetXaxis()->FindBin(noiseborder);
+ peak2 = histogrampointer->FindLastBinAbove(avg/3);
+ }
+ else
+ {
+ peak2 = peak1;
+ peak1 = histogrampointer->GetXaxis()->FindBin(noiseborder);
+ }
+ histogrampointer->GetXaxis()->SetRange(peak1,peak2-1); // look only for maxima with x greater than noiseborder, cut away noise
+ Float_t min = histogrampointer->GetMinimumBin();
+ histogrampointer->GetXaxis()->SetRange(min,histogrampointer->FindLastBinAbove(0)); // look only for maxima with x greater than noiseborder, cut away noise
+ // DEBUG
+ // if (verbose)
+ // {
+ // cout << coloryellow << "peak1 " << histogrampointer->GetXaxis()->GetBinCenter(peak1) << endlr;
+ // cout << coloryellow << "peak1val " << histogrampointer->GetBinContent(peak1) << endlr;
+ // cout << coloryellow << "peak2 " << histogrampointer->GetXaxis()->GetBinCenter(peak2) << endlr;
+ // cout << coloryellow << "peak2val " << histogrampointer->GetBinContent(peak2) << endlr;
+ // cout << coloryellow << "min " << histogrampointer->GetXaxis()->GetBinCenter( min ) << endlr;
+ // }
+ histogrampointer->Fit(fitFunc, "N,Q,W", "", histogrampointer->GetXaxis()->GetBinCenter(min), posMaxValHist);
+ posMax = histogrampointer->GetXaxis()->GetBinCenter(histogrampointer->GetMaximumBin()); // Methode 1
+ histogrampointer->GetXaxis()->SetRange(histogrampointer->GetXaxis()->FindBin(noiseborder),histogrampointer->GetXaxis()->FindBin(posMaxValHist)); // look only for maxima with x greater than noiseborder, cut away noise
+ } else {
+ histogrampointer->Fit(fitFunc, "N,Q,W", "", noiseborder, posMaxValHist);
+ posMax = fitFunc->GetMaximumX(); // Methode 2
+ }
+ Float_t sigma = fitFunc->GetParameter(2);
+ posMax2 = fitFunc->GetMaximumX(); // Methode 2
+ Float_t peakposdifperc = abs(posMax-posMax2)/min(posMax,posMax2);
+ if (sigma > 260 || peakposdifperc>0.3)
+ {
+ if (verbose)
+ {
+ if (sigma > 260)
+ {
+ cout << "Sigma suspiciously height when fitting " << histogrampointer->GetName() << " spectrum: " << sigma << endl;
+ cout << colorred << " Could not find " << histogrampointer->GetName() << " peak" << endlr;
+ }
+ else if (peakposdifperc>0.3)
+ {
+ cout << "Maximum peak position and fit gaussian peak position doesn't match in " << histogrampointer->GetName() << " spectrum. Difference: " << peakposdifperc*100 <<" % " << endl;
+ cout << colorred << " Could not find " << histogrampointer->GetName() << " peak" << endlr;
+ }
+ }
+ return 0;
+ }
+ else if (sigma > 40 || peakposdifperc>0.1)
+ {
+ if (verbose)
+ {
+ if (sigma > 260)
+ {
+ cout << "Sigma or suspiciously height when fitting " << histogrampointer->GetName() << " spectrum: " << sigma << endl;
+ cout << coloryellow << " Please check " << histogrampointer->GetName() << " peak position: " << colorreset << colorwhite << posMax << endlr;
+ }
+ else if (peakposdifperc>0.1)
+ {
+ cout << "Maximum peak position and fit gaussian peak position in " << histogrampointer->GetName() << " have difference of " << peakposdifperc*100 <<" % " << endl;
+ cout << coloryellow << " Please check " << histogrampointer->GetName() << " peak position: " << colorreset << colorwhite << posMax << endlr;
+ }
+ }
+ }
+ // fitFunc->DrawCopy("same");
+ TString legendEntry = TString(Form("%s","Gaus fit"));
+ TLegend* leg = new TLegend(0.5,0.5,0.89,0.89);//(0.6,0.7,0.89,0.89);
+ // leg->SetHeader();//"Legend Title");
+ leg->SetFillStyle(0);
+ leg->AddEntry((TObject*) 0, legendEntry, "");
+ leg->SetTextSize(0.05);
+ // leg->Draw();
+
+ }
+ else if (fitFuncType=="landau")
+ {
+ histogrampointer->GetXaxis()->SetRange(histogrampointer->GetXaxis()->FindBin(noiseborder),histogrampointer->GetXaxis()->FindBin(posMaxValHist)); // look only for maxima with x greater than noiseborder, cut away noise
+ // Int_t xValMax = histogrampointer->GetBinCenter(histogrampointer->GetMaximumBin());
+ TF1* fitFunc = new TF1("fitFunc",fitFuncType,noiseborder,posMaxValHist);
+
+ Float_t fitMax1 = 1000;
+ Float_t fitMax2 = 1000;
+ Float_t fitMax3 = 1000;
+ Float_t minFitMax = 1000;
+ Float_t maxFitMax = 1000;
+ histogrampointer->Fit(fitFunc, "N,Q,W", "", noiseborder, posMaxValHist);
+ fitMax1 = fitFunc->GetMaximumX();
+ // fitFunc->DrawCopy("same");
+ histogrampointer->Fit(fitFunc, "N,Q,W", "", noiseborder, fitMax1*1.1);
+ fitMax2 = fitFunc->GetMaximumX();
+ fitFunc->SetLineColor(kBlue);
+ fitFunc->SetLineStyle(2); // dashed
+ // fitFunc->DrawCopy("same");
+ histogrampointer->Fit(fitFunc, "N,Q,W", "", fitMax1*0.9, posMaxValHist);
+ // histogrampointer->Fit(fitFunc, "N,Q,W", "", fitMax1, histogrampointer->GetBinCenter(bini));
+ fitMax3 = fitFunc->GetMaximumX();
+ fitFunc->SetLineColor(kGreen);
+ // fitFunc->DrawCopy("same");
+ fitFunc->SetLineStyle(1); // normal for the following fits
+
+ // Sort the three fits and save error estimation
+ minFitMax = TMath::Min(TMath::Min(fitMax1,fitMax2),fitMax3);
+ maxFitMax = TMath::Max(TMath::Max(fitMax1,fitMax2),fitMax3);
+ posMax = fitMax1 + fitMax2 + fitMax3 - minFitMax - maxFitMax;
+
+ //fitLandauErrorLeft.push_back(posMax - minFitMax);
+ //fitLandauErrorRight.push_back(maxFitMax - posMax);
+ } else if (fitFuncType=="lorentz")
+ {
+ // create a TF1 with the range from 0 to 3 and 6 parameters
+ TF1 *fitFcn = new TF1("fitFcn",lorentzianPeak,0,160,4);
+ fitFcn->SetNpx(1000);
+ fitFcn->SetLineWidth(4);
+ fitFcn->SetLineColor(color);
+ // set start values for some parameters
+ fitFcn->SetParName(0,"Area");
+ fitFcn->SetParameter(0,21655); // Area
+ fitFcn->SetParName(1,"Width");
+ fitFcn->SetParameter(1,34); // width
+ fitFcn->SetParName(2,"Peak pos.");
+ fitFcn->SetParameter(2,80); // peak position x
+ fitFcn->SetParName(3,"Y shift");
+ fitFcn->SetParameter(3,-22); // y-shift
+ gStyle->SetOptFit(0111);
+
+ // fitFcn->FixParameter(0,21655.90045);
+ // fitFcn->FixParameter(1,34.31885101);
+ fitFcn->FixParameter(2,histogrampointer->GetXaxis()->GetBinCenter((int)(histogrampointer->GetXaxis()->GetNbins()/2.0+0.5)));
+ // fitFcn->FixParameter(3,-22.43016284);
+
+ //histogrampointer->Fit("fitFcn","V+","ep");
+ histogrampointer->Fit("fitFcn","Q,W","ep");
+ for (Int_t pari=0; pari<4; pari++)
+ parameters[pari]=fitFcn->GetParameter(pari);
+ if (verbose)
+ {
+ Printf("width: %.6f ",fitFcn->GetParameter(1));
+ cout << "width: " << fitFcn->GetParameter(1) << endl;
+ cout << "peak: " << fitFcn->GetParameter(2) << endl;
+ cout << "y-shift:" << fitFcn->GetParameter(3) << endl;
+ }
+ }
+
+ return posMax;
+}
+
+Bool_t HistogramType::calculteCCE() {
+ if (posSeed > 0 && posVeto > 0)
+ CCE_in_Perc_1 = posSeed / posVeto * 100.0;
+ if (posSum > 0 && posVeto > 0)
+ CCE_in_Perc_25 = posSum / posVeto * 100.0;
+ if (CCE_in_Perc_1 > 0 || CCE_in_Perc_25 > 0)
+ return 0;
+ return 1;
+}
+
+Bool_t HistogramType::integrateSr90Spectra(TH1F* histogrampointer, Int_t frames_found, Float_t thresholdborder, Bool_t verbose) {
+ Float_t posMaxValHist = histogrampointer->GetXaxis()->GetXmax();
+
+ TH1F* smoothedcurce = (TH1F*)histogrampointer->Clone();
+ smoothedcurce->Smooth(4);
+
+ // Int_t random = random1->Rndm()*100000;
+ // TString canvastitle = Form("%s %s sdfasdf", histogrampointer->GetName(), runcode.Data());
+ // TString canvasname = Form("%s %s %d dfgsdfgsdfg", histogrampointer->GetName(), runcode.Data(), random);
+ // TCanvas* canvas = new TCanvas(canvasname, canvastitle, 900, 700);
+ // smoothedcurce->Draw();
+
+ Int_t thresholdbincurcandidate = 0;
+ if (thresholdborder < 0)
+ {
+ Int_t rising = 0;
+ Int_t bini =smoothedcurce->GetMaximumBin();
+ Float_t curval = smoothedcurce->GetXaxis()->GetBinCenter(bini);
+ // cout << "smoothedcurce->GetXaxis()->GetBinCenter(" << bini << "): " << curval << endl;
+
+ do {
+ curval=smoothedcurce->GetBinContent(bini++);
+ if (curval*0.95 <= smoothedcurce->GetBinContent(bini))
+ {
+ rising++;
+ // cout << "rising at " << smoothedcurce->GetXaxis()->GetBinCenter(bini) << " as " << curval << " < " << smoothedcurce->GetBinContent(bini) << endl; // debug
+ }
+ else
+ {
+ rising = 0;
+ thresholdbincurcandidate = bini;
+ }
+ } while (rising < 3 && bini<smoothedcurce->GetNbinsX());
+ }
+ else
+ {
+ thresholdbincurcandidate = histogrampointer->GetXaxis()->FindBin(thresholdborder);
+ }
+
+
+ // histogrampointer->GetXaxis()->SetRange(histogrampointer->GetXaxis()->FindBin(0),histogrampointer->GetXaxis()->FindBin(posMaxValHist));
+ // Float_t posNoiseMax= histogrampointer->GetMaximum();
+ // histogrampointer->GetXaxis()->SetRange(posNoiseMax,histogrampointer->GetXaxis()->FindBin(posMaxValHist));
+ // Float_t posChargeMax= histogrampointer->GetMaximum();
+
+ noisethresholdborder = histogrampointer->GetXaxis()->GetBinUpEdge(thresholdbincurcandidate);
+
+ if (verbose) {
+ cout << " Integrating " << histogrampointer->GetTitle() << endlr;
+ cout << " Noise threshold at " << noisethresholdborder;
+ TString histtitle=histogrampointer->GetXaxis()->GetTitle();
+ if (histtitle.Contains("[e]"))
+ {
+ cout << " e" << endl;
+ if (noisethresholdborder > 300) {
+ cout << coloryellow << " This noise threshold seems too high, please check manually the " << histogrampointer->GetName() << " spectrum." << endlr;
+ }
+ }
+ else
+ {
+ cout << " ADU" << endl;
+ if (noisethresholdborder > 75) {
+ cout << coloryellow << " This noise threshold seems too high, please check manually the " << histogrampointer->GetName() << " spectrum." << endlr;
+ }
+ }
+ }
+ sr90IntegralVal = histogrampointer->IntegralAndError(thresholdbincurcandidate,histogrampointer->GetNbinsX(), sr90IntegralErr);
+ // cout << "Integrate from bin " << thresholdbincurcandidate << " to " << histogrampointer->GetNbinsX() << endl;
+ sr90IntegralErr /= sr90IntegralVal/100;
+// cout << " Unscaled integral is " << Form("%e",sr90IntegralVal) << ", error " << sr90IntegralErr << "%" << endl;
+// cout << " ";
+ if (frames_found>0)
+ {
+ sr90IntegralVal/=frames_found;
+ if (verbose)
+ cout << " Scaled";
+ }
+ if (verbose)
+ cout << " Integral is " << Form("%e",sr90IntegralVal) << ", error " << sr90IntegralErr << "%" << endl;
+
+ return 0;
+}
+
+
+//*****************
+// OTHER HELPFUL FUNCTIONS
+//*****************
+
+// Lorenzian Peak function
+Double_t HistogramType::lorentzianPeak(Double_t *x, Double_t *par) {
+ return par[3]+(0.5*par[0]*par[1]/TMath::Pi()) /
+ TMath::Max( 1.e-10,(x[0]-par[2])*(x[0]-par[2])
+ + .25*par[1]*par[1]);
+}
+
+#endif
+
generateReadableRunCode();
cout << colorwhite << "initRootParameters():" << endlr;
initRootParameters();
- cout << colorwhite << "initHistograms():" << endlr;
- initHistograms(&histogram);
- initHistograms(&histogramthreshold, "threshold");
- initHistograms(&histogramfixedthreshold, "fixed threshold");
+ cout << colorwhite << "init Histogram classes:" << endlr;
+ // default histogram class, no special cuts applied
+ histogram = new HistogramType("", &cursystemparam, &cursensorinfo, 0, rootcolors[plotStyle], rootlinestyle[plotStyle] );
+ HistogramClassVector.push_back(histogram);
+ // dynamical cluster threshold cut
+ histogramthreshold = new HistogramType(" Threshold", &cursystemparam, &cursensorinfo, 1, rootcolors[plotStyle], rootlinestyle[plotStyle] );
+ HistogramClassVector.push_back(histogramthreshold);
+ // fixed threshold cut
+ histogramfixedthreshold = new HistogramType(" fixed Threshold", &cursystemparam, &cursensorinfo, 1, rootcolors[plotStyle], rootlinestyle[plotStyle] );
+ HistogramClassVector.push_back(histogramfixedthreshold);
debugDBreadout();
}
else
void Run::setSystemSpecificParameters()
{
+ systemparam systemparamUSB (2800/*maxbin*/,2800/4/*nbins*/, 25/*vetothreshold*/, 10/*maxbinnoise*/, 100/*nbinsnoise*/);
+ systemparam systemparamPegasus (2800,2800/2,20,10,100);
+ systemparam systemparamPXI (800*16,800,75,150,150);
+ systemparam systemparamFSBB (2800,2800/4,25,10,100);
if (labbook.system.EqualTo("USB") && labbook.chipGen.EqualTo("Mi34") )
- systemparamcur = systemparamUSB;
+ cursystemparam = systemparamUSB;
else if (labbook.system.EqualTo("USB") && labbook.chipGen.EqualTo("FSBB") )
- systemparamcur = systemparamUSB;
+ cursystemparam = systemparamUSB;
else if (labbook.system.EqualTo("PXI")) // && labbook.chipGen.EqualTo("34") )
- systemparamcur = systemparamPXI;
+ cursystemparam = systemparamPXI;
else if (labbook.system.EqualTo("Pegasus")) // && labbook.chipGen.EqualTo("34") )
- systemparamcur = systemparamPegasus;
+ cursystemparam = systemparamPegasus;
setSensorInSystemParam();
}
sensorinfostruct sensorinfoFSBB( 4, 416 );
if (labbook.system.EqualTo("USB") && labbook.chipGen.EqualTo("Mi34") )
- sensorinfocur=sensorinfoMi34USB;
+ cursensorinfo=sensorinfoMi34USB;
else if (labbook.system.EqualTo("USB") && labbook.chipGen.EqualTo("FSBB") )
- sensorinfocur=sensorinfoFSBB;
+ cursensorinfo=sensorinfoFSBB;
else if (labbook.system.EqualTo("PXI")) // && labbook.chipGen.EqualTo("34") )
- sensorinfocur=sensorinfoMi34PXI;
+ cursensorinfo=sensorinfoMi34PXI;
else if (labbook.system.EqualTo("Pegasus")) // && labbook.chipGen.EqualTo("34") )
- sensorinfocur=sensorinfoPegasus;
+ cursensorinfo=sensorinfoPegasus;
}
void Run::setMatrixSpecificParameters()
{
submatrix_x_start = 0;
- submatrix_x_end = sensorinfocur.columns;
+ submatrix_x_end = cursensorinfo.columns;
submatrix_y_start = 0;
- submatrix_y_end = sensorinfocur.rows;
+ submatrix_y_end = cursensorinfo.rows;
}
Bool_t Run::useDynamicalNoise(Bool_t var)
if (!runAllreadyAnalyzed() || force)
{
if (processed->initNewRootFile()) return 1;
- frames_found = processed->GetNumberFrames();
/// progress meter, temporal variable
ULong_t progress_tmp=-1;
/// progress meter
cout << colorwhite << "initOldRootFile():" << endlr;
}
if (processed->initOldRootFile()) return 1;
- cout << colorwhite << "binNoise():" << endlr;
- binNoise();
+ frames_found = processed->GetNumberFrames();
+// cout << colorwhite << "plotPixSignal():"<< flush << endlr;
+// processed->plotPixSignal(0,10000000,351);
cout << colorwhite << "binSeedSumVeto():" << endlr;
- binSeedSumVeto();
+ binSeedSumVeto();
cout << colorwhite << "binCluster():" << endlr;
binCluster();
- cout << colorwhite << "rescaleHistograms():" << endlr;
- histogramCalibrated.calibrated = rescaleHistograms();
- histogramthresholdCalibrated.calibrated = histogramCalibrated.calibrated;
- histogramfixedthresholdCalibrated.calibrated = histogramCalibrated.calibrated;
- cout << colorwhite << "calculateCCE():" << endlr;
- calculteCCE();
- if (labbook.source.Contains("Sr90")) {
- cout << colorwhite << "integrateSr90Spectra():" << endlr;
- integrateSr90Spectra(&histogramthresholdCalibrated, histogramthresholdCalibrated.Seed);
+ for (vector<HistogramType*>::iterator curHistogramClass = HistogramClassVector.begin(); curHistogramClass != HistogramClassVector.end(); curHistogramClass++) {
+ binNoise((*curHistogramClass));
+ if (labbook.source.Contains("Fe")||labbook.source.Contains("Cd")) {
+ cout << colorwhite << "calculateCCE():" << endlr;
+ (*curHistogramClass)->calculteCCE();
+ }
+ if (labbook.source.Contains("Sr90")) {
+ cout << colorwhite << "integrateSr90Spectra():" << endlr;
+ (*curHistogramClass)->integrateSr90Spectra((*curHistogramClass)->Seed, frames_found, -1, false);
+ }
}
+ histogramfixedthreshold->integrateSr90Spectra(histogramfixedthreshold->Seed, frames_found, 0);
+ rescaleHistogramClasses();
cout << colorwhite << "updateDatabase():" << endlr;
updateDatabase();
cout << colorwhite << "delete MAPS class:" << endlr;
return 1;
}
-Bool_t Run::calculteCCE()
-{
- if (labbook.source.Contains("Fe")||labbook.source.Contains("Cd"))
- {
- if (histogram.posSeed > 0 && histogram.posVeto > 0)
- CCE_in_Perc_1 = histogram.posSeed / histogram.posVeto * 100.0;
- if (histogram.posSum > 0 && histogram.posVeto > 0)
- CCE_in_Perc_25 = histogram.posSum / histogram.posVeto * 100.0;
- }
- return 0;
-}
-
-Bool_t Run::rescaleHistograms()
+Bool_t Run::rescaleHistogramClasses()
{
float_t vetopeakposition = -1;
- if ( histogram.posVeto > 0 )
+ if ( histogram->posVeto > 0 )
{
- vetopeakposition = histogram.posVeto;
+ vetopeakposition = histogram->posVeto;
cout << colorwhite << "Use calibration obtained from this run, position veto: " << vetopeakposition << endlr;
}
else if ( labbook.posVetoDB > 0 )
cout << coloryellow << "Cannot rescale run from [ADU] to [e] units, no calibration peak found." << endlr;
return 0;
}
- gain = 1640.0/vetopeakposition;
-
- rescaleHistogram(histogramCalibrated.Seed, histogram.Seed);
- rescaleHistogram(histogramCalibrated.Sum, histogram.Sum);
- rescaleHistogram(histogramCalibrated.Veto, histogram.Veto);
- rescaleHistogram(histogramCalibrated.Noise, histogram.Noise);
-
- if (histogram.histAvgCluster != nullptr)
- {
- rescale2DHistogramCounts(histogramCalibrated.histAvgCluster, histogram.histAvgCluster);
+ Float_t gain = 1640.0/vetopeakposition;
+ for (vector<HistogramType*>::iterator curHistogramClass = HistogramClassVector.begin(); curHistogramClass != HistogramClassVector.end(); curHistogramClass++) {
+ (*curHistogramClass)->calibrateHistograms(gain);;
}
-
- histogramCalibrated.posSeed = histogram.posSeed * gain;
- histogramCalibrated.posSum = histogram.posSum * gain;
- histogramCalibrated.posVeto = histogram.posVeto * gain;
- histogramCalibrated.avgNoise = histogram.avgNoise * gain;
- histogramCalibrated.avgNoisePlus = histogram.avgNoisePlus * gain;
- histogramCalibrated.avgNoiseMinus = histogram.avgNoiseMinus * gain;
-
- rescaleHistogram(histogramthresholdCalibrated.Seed, histogramthreshold.Seed);
- rescaleHistogram(histogramthresholdCalibrated.Sum, histogramthreshold.Sum);
- rescaleHistogram(histogramthresholdCalibrated.Veto, histogramthreshold.Veto);
- rescaleHistogram(histogramthresholdCalibrated.Noise, histogramthreshold.Noise);
-
- histogramthresholdCalibrated.posSeed = histogramthreshold.posSeed * gain;
- histogramthresholdCalibrated.posSum = histogramthreshold.posSum * gain;
- histogramthresholdCalibrated.posVeto = histogramthreshold.posVeto * gain;
- histogramthresholdCalibrated.avgNoise = histogramthreshold.avgNoise * gain;
- histogramthresholdCalibrated.avgNoisePlus = histogramthreshold.avgNoisePlus * gain;
- histogramthresholdCalibrated.avgNoiseMinus = histogramthreshold.avgNoiseMinus * gain;
-
- rescaleHistogram(histogramfixedthresholdCalibrated.Seed, histogramfixedthreshold.Seed);
- rescaleHistogram(histogramfixedthresholdCalibrated.Sum, histogramfixedthreshold.Sum);
- rescaleHistogram(histogramfixedthresholdCalibrated.Veto, histogramfixedthreshold.Veto);
- rescaleHistogram(histogramfixedthresholdCalibrated.Noise, histogramfixedthreshold.Noise);
-
- histogramfixedthresholdCalibrated.posSeed = histogramfixedthreshold.posSeed * gain;
- histogramfixedthresholdCalibrated.posSum = histogramfixedthreshold.posSum * gain;
- histogramfixedthresholdCalibrated.posVeto = histogramfixedthreshold.posVeto * gain;
- histogramfixedthresholdCalibrated.avgNoise = histogramfixedthreshold.avgNoise * gain;
- histogramfixedthresholdCalibrated.avgNoisePlus = histogramfixedthreshold.avgNoisePlus * gain;
- histogramfixedthresholdCalibrated.avgNoiseMinus = histogramfixedthreshold.avgNoiseMinus * gain;
-
- if (histogramthreshold.histAvgCluster != nullptr)
- {
- rescale2DHistogramCounts(histogramthresholdCalibrated.histAvgCluster, histogramthreshold.histAvgCluster);
- }
-
- return 1;
-}
-
-void Run::rescaleHistogram(TH1F* &histogrampointernew, TH1F* &histogrampointerold )
-{
- histogrampointernew = (TH1F*)histogrampointerold->Clone();
- histogrampointernew->SetName(Form("%sC", histogrampointerold->GetName()));
- histogrampointernew->SetTitle(Form("%sC", histogrampointerold->GetTitle()));
- histogrampointernew->GetXaxis()->SetTitle("Q_coll [e]");
- int nbins = histogrampointernew->GetXaxis()->GetNbins();
- double new_bins[nbins+1];
- for(int i=0; i <= nbins; i++){
- new_bins[i] = histogrampointernew->GetBinCenter(i)*gain;
- }
- histogrampointernew->SetBins(nbins, new_bins);
- histogrampointernew->GetYaxis()->SetTitle(Form("Entries [1/%.1f e]",histogrampointernew->GetBinWidth(1)));
-}
-
-
-void Run::rescale2DHistogramCounts(TH2F* &histogrampointernew, TH2F* &histogrampointerold )
-{
- histogrampointernew = (TH2F*)histogrampointerold->Clone();
- histogrampointernew->SetName(Form("%sC", histogrampointerold->GetName()));
- histogrampointernew->SetTitle(Form("%sC", histogrampointerold->GetTitle()));
- histogrampointernew->GetZaxis()->SetTitle("Q_coll [e]");
- int nbinsx = histogrampointernew->GetXaxis()->GetNbins();
- int nbinsy = histogrampointernew->GetYaxis()->GetNbins();
-
- double new_bins[nbinsx][nbinsy];
- for(int y=0; y < nbinsy; y++){
- for(int x=0; x < nbinsx; x++){
- histogrampointernew->SetBinContent(x,y,histogrampointerold->GetBinContent(x,y)*gain);
- }
- }
+ return 1;
}
Bool_t Run::analyzeFrame(Int_t frame)
{
if (matrixname.EqualTo("A0")) {
- setSubMatrixBorders(0, sensorinfocur.columns/2-2, 0, sensorinfocur.rows, false);
+ setSubMatrixBorders(0, cursensorinfo.columns/2-2, 0, cursensorinfo.rows, false);
runcodesuffix += "_A0_";
humanreadablesuffix += "A0, ";
}
else if (matrixname.EqualTo("A1")) {
- setSubMatrixBorders(sensorinfocur.columns/2+2, sensorinfocur.columns, 0, sensorinfocur.rows, false);
+ setSubMatrixBorders(cursensorinfo.columns/2+2, cursensorinfo.columns, 0, cursensorinfo.rows, false);
runcodesuffix += "_A1_";
humanreadablesuffix += "A1, ";
}
void Run::constructUpdateString(string *sqlupdatequery, const string databasevaluename, const Double_t value, const int precision=3)
{
- // cout << colorred << databasevaluename << " : " << value << endlr;
+// cout << colorred << databasevaluename << " : " << value << endlr;
if (!std::isinf(value))
{
if (value>0)
string sqlupdatequery = "";
- constructUpdateString(&sqlupdatequery, "Gain", gain);
- constructUpdateString(&sqlupdatequery, "SumPeak", histogram.posSum, 4);
- constructUpdateString(&sqlupdatequery, "SeedPeak", histogram.posSeed, 4);
- constructUpdateString(&sqlupdatequery, "VetoPeak", histogram.posVeto, 4);
- constructUpdateString(&sqlupdatequery, "Avg.Noise", histogramCalibrated.avgNoise);
- constructUpdateString(&sqlupdatequery, "Avg.Noise+", histogramCalibrated.avgNoisePlus, 2);
- constructUpdateString(&sqlupdatequery, "Avg.Noise-", histogramCalibrated.avgNoiseMinus, 2);
- constructUpdateString(&sqlupdatequery, "CCE_1", CCE_in_Perc_1);
- constructUpdateString(&sqlupdatequery, "CCE_25", CCE_in_Perc_25);
- constructUpdateString(&sqlupdatequery, "Avg.NoiseADC", histogram.avgNoise);
+ constructUpdateString(&sqlupdatequery, "Gain", histogram->gain);
+ constructUpdateString(&sqlupdatequery, "SumPeak", histogram->posSum, 4);
+ constructUpdateString(&sqlupdatequery, "SeedPeak", histogram->posSeed, 4);
+ constructUpdateString(&sqlupdatequery, "VetoPeak", histogram->posVeto, 4);
+ constructUpdateString(&sqlupdatequery, "Avg.Noise", histogram->calibrated->avgNoise);
+ constructUpdateString(&sqlupdatequery, "Avg.Noise+", histogram->calibrated->avgNoisePlus, 2);
+ constructUpdateString(&sqlupdatequery, "Avg.Noise-", histogram->calibrated->avgNoiseMinus, 2);
+ constructUpdateString(&sqlupdatequery, "CCE_1", histogram->CCE_in_Perc_1);
+ constructUpdateString(&sqlupdatequery, "CCE_25", histogram->CCE_in_Perc_25);
+ constructUpdateString(&sqlupdatequery, "Avg.NoiseADC", histogram->avgNoise);
constructUpdateString(&sqlupdatequery, "Frames_found", frames_found,100000000);
- constructUpdateString(&sqlupdatequery, "Sr90IntegralVal", histogramthresholdCalibrated.sr90IntegralVal,1000000000);
+ constructUpdateString(&sqlupdatequery, "Sr90IntegralVal", histogramfixedthreshold->calibrated->sr90IntegralVal,1000000000);
if (sqlupdatequery.length()>0)
{
return out.str();
}
-Bool_t Run::binNoise()
+Bool_t Run::binNoise(HistogramType* oneHistogramClass)
{
Float_t noise;
TBranch* noiseBranch;
Double_t const probabilities[] = {0.3415/2, 0.5, 1-0.3415/2}; // sigma/2 from gaus to the left and to the right //{0.17, 0.5, 1-0.17};
- histogram.Noise->Reset();
+ oneHistogramClass->Noise->Reset();
processed->fNoiseTree->SetBranchAddress("noise", &noise, &noiseBranch);
for (Int_t cnt=0; cnt<processed->fNoiseTree->GetEntries(); cnt++) {
processed->fNoiseTree->GetEntry(cnt);
- histogram.Noise->Fill(noise);
+ oneHistogramClass->Noise->Fill(noise);
}
- histogram.Noise->GetQuantiles( 3, noisequantiles, probabilities);
- histogram.avgNoise = noisequantiles[1];
- histogram.avgNoisePlus = noisequantiles[2] - noisequantiles[1];
- histogram.avgNoiseMinus = noisequantiles[1] - noisequantiles[0];
- histogramthreshold.avgNoise = histogram.avgNoise;
- histogramthreshold.avgNoisePlus = histogram.avgNoisePlus;
- histogramthreshold.avgNoiseMinus = histogram.avgNoiseMinus;
- histogramfixedthreshold.avgNoise = histogram.avgNoise;
- histogramfixedthreshold.avgNoisePlus = histogram.avgNoisePlus;
- histogramfixedthreshold.avgNoiseMinus = histogram.avgNoiseMinus;
+ oneHistogramClass->Noise->GetQuantiles( 3, noisequantiles, probabilities);
+ oneHistogramClass->avgNoise = noisequantiles[1];
+ oneHistogramClass->avgNoisePlus = noisequantiles[2] - noisequantiles[1];
+ oneHistogramClass->avgNoiseMinus = noisequantiles[1] - noisequantiles[0];
// if (labbook.system == "PXI")
// for (int j=0; j<3; j++)
// noisequantiles[j] /= 16.0; // TODO analyze PXI scales
}
Bool_t Run::binSeedSumVeto()
-{
-// /// pixel number of seed pixel, position on sensor
-// UInt_t seedPixel[10000];
-// /// Array of processed->clustersize * processed->clustersize clusters, seed pixel in the middle
-// Float_t pixelcluster[processed->clustersize*processed->clustersize][10000];
-
+{
/// collected charge in cluster
Float_t pixelSum = 0;
Float_t notSeedSum = 0;
{
for(Int_t hiti=0; (unsigned int)hiti<processed->fFrameInfo.hits;hiti++)
{
- uint pixel_column_x = processed->fFrameInfo.pixel[hiti]%sensorinfocur.columns; // column of seed
- uint pixel_row_y = processed->fFrameInfo.pixel[hiti]/sensorinfocur.columns; // row of seed
+ uint pixel_column_x = processed->fFrameInfo.pixel[hiti]%cursensorinfo.columns; // column of seed
+ uint pixel_row_y = processed->fFrameInfo.pixel[hiti]/cursensorinfo.columns; // row of seed
if (false) {
cout << "submatrix_x_start: " << submatrix_x_start << ", submatrix_x_end: " << submatrix_x_end << endl;
cout << "submatrix_y_start: " << submatrix_y_start << ", submatrix_y_end: " << submatrix_y_end << endl;
}
if (pixel_column_x >= submatrix_x_start && pixel_column_x < submatrix_x_end && pixel_row_y >= submatrix_y_start && pixel_row_y < submatrix_y_end) // Diode sitzt oben im SeedPixel, da nach PitchY angeordnet
{
- histogram.numberofhits++;
+ histogram->numberofhits++;
// histogram with the single pixel
- histogram.Seed->Fill(processed->fFrameInfo.p[12][hiti]);
+ histogram->Seed->Fill(processed->fFrameInfo.p[12][hiti]);
// sum histogram
pixelSum = 0;
notSeedSum += processed->fFrameInfo.p[clusteri][hiti];
}
}
- histogram.Sum->Fill(pixelSum);
+ histogram->Sum->Fill(pixelSum);
// veto spectrum
- if (TMath::Abs(notSeedSum) < systemparamcur.vetothreshold && (labbook.source.Contains("Fe")||labbook.source.Contains("Cd")))
- histogram.Veto->Fill(processed->fFrameInfo.p[12][hiti]); // histogram with the single pixel
+ if (TMath::Abs(notSeedSum) < cursystemparam.vetothreshold && (labbook.source.Contains("Fe")||labbook.source.Contains("Cd")))
+ histogram->Veto->Fill(processed->fFrameInfo.p[12][hiti]); // histogram with the single pixel
if (processed->fFrameInfo.pixelthreshold[hiti]>0)
{
- histogramthreshold.numberofhits++;
+ histogramthreshold->numberofhits++;
- histogramthreshold.Seed->Fill(processed->fFrameInfo.p[12][hiti]);
- histogramthreshold.Sum->Fill(pixelSum);
- if (TMath::Abs(notSeedSum) < systemparamcur.vetothreshold && (labbook.source.Contains("Fe")||labbook.source.Contains("Cd")))
- histogramthreshold.Veto->Fill(processed->fFrameInfo.p[12][hiti]); // histogram with the single pixel
+ histogramthreshold->Seed->Fill(processed->fFrameInfo.p[12][hiti]);
+ histogramthreshold->Sum->Fill(pixelSum);
+ if (TMath::Abs(notSeedSum) < cursystemparam.vetothreshold && (labbook.source.Contains("Fe")||labbook.source.Contains("Cd")))
+ histogramthreshold->Veto->Fill(processed->fFrameInfo.p[12][hiti]); // histogram with the single pixel
}
if (processed->fFrameInfo.pixelfixedthreshold[hiti]>0)
{
- histogramfixedthreshold.numberofhits++;
+ histogramfixedthreshold->numberofhits++;
- histogramfixedthreshold.Seed->Fill(processed->fFrameInfo.p[12][hiti]);
- histogramfixedthreshold.Sum->Fill(pixelSum);
- if (TMath::Abs(notSeedSum) < systemparamcur.vetothreshold && (labbook.source.Contains("Fe")||labbook.source.Contains("Cd")))
- histogramfixedthreshold.Veto->Fill(processed->fFrameInfo.p[12][hiti]); // histogram with the single pixel
+ histogramfixedthreshold->Seed->Fill(processed->fFrameInfo.p[12][hiti]);
+ histogramfixedthreshold->Sum->Fill(pixelSum);
+ if (TMath::Abs(notSeedSum) < cursystemparam.vetothreshold && (labbook.source.Contains("Fe")||labbook.source.Contains("Cd")))
+ histogramfixedthreshold->Veto->Fill(processed->fFrameInfo.p[12][hiti]); // histogram with the single pixel
}
}
}
}
}
// gROOT->SetBatch(kTRUE);
- if (labbook.source.Contains("Fe")||labbook.source.Contains("Cd"))
- histogram.posVeto=FitPerform(&histogram, histogram.Veto, "gaus", true);
- histogram.posSeed=FitPerform(&histogram, histogram.Seed, "landau", true);
- histogram.posSum=FitPerform(&histogram, histogram.Sum, "gaus", true);
- if (labbook.source.Contains("Fe")||labbook.source.Contains("Cd"))
- histogramthreshold.posVeto=FitPerform(&histogram, histogram.Veto, "gaus", false);
- histogramthreshold.posSeed=FitPerform(&histogram, histogram.Seed, "landau", false);
- histogramthreshold.posSum=FitPerform(&histogram, histogram.Sum, "gaus", false);
- if (labbook.source.Contains("Fe")||labbook.source.Contains("Cd"))
- histogramfixedthreshold.posVeto=FitPerform(&histogramfixedthreshold, histogramfixedthreshold.Veto, "gaus", false);
- histogramfixedthreshold.posSeed=FitPerform(&histogramfixedthreshold, histogramfixedthreshold.Seed, "landau", false);
- histogramfixedthreshold.posSum=FitPerform(&histogramfixedthreshold, histogramfixedthreshold.Sum, "gaus", false);
+
+ for (vector<HistogramType*>::iterator curHistogramClass = HistogramClassVector.begin(); curHistogramClass != HistogramClassVector.end(); curHistogramClass++) {
+ if (labbook.source.Contains("Fe")||labbook.source.Contains("Cd"))
+ (*curHistogramClass)->posVeto=(*curHistogramClass)->FitPerform((*curHistogramClass)->Veto, "gaus", true);
+ (*curHistogramClass)->posSeed=(*curHistogramClass)->FitPerform((*curHistogramClass)->Seed, "landau", true);
+ (*curHistogramClass)->posSum=(*curHistogramClass)->FitPerform((*curHistogramClass)->Sum, "gaus", true);
+ }
// gROOT->SetBatch(kFALSE);
return 0;
}
findRotatedClusterDimension(rotateangle);
}
else
- {
+ {
initClusters(curpixelinfo.pitchY, 0,curpixelinfo.pitchY*5,curpixelinfo.pitchX,0,curpixelinfo.pitchX*5);
// prefix = "Cluster" + suffix;
- // histogramstructpointer->histAvgCluster = new TH2F(prefix.Data(), Form("Avg. cluster distribution; x; y %s", humanreadablestr.Data()),5, 0,curpixelinfo.pitchY*5,5,0,curpixelinfo.pitchX*5); // TODO: remove hardcoded 5
+ // HistogramTypepointer->histAvgCluster = new TH2F(prefix.Data(), Form("Avg. cluster distribution; x; y %s", humanreadablestr.Data()),5, 0,curpixelinfo.pitchY*5,5,0,curpixelinfo.pitchX*5); // TODO: remove hardcoded 5
}
{
for(Int_t hiti=0; (unsigned int)hiti<processed->fFrameInfo.hits;hiti++)
{
- uint pixel_column_x = processed->fFrameInfo.pixel[hiti]%sensorinfocur.columns; // column of seed
- uint pixel_row_y = processed->fFrameInfo.pixel[hiti]/sensorinfocur.columns; // row of seed
+ uint pixel_column_x = processed->fFrameInfo.pixel[hiti]%cursensorinfo.columns; // column of seed
+ uint pixel_row_y = processed->fFrameInfo.pixel[hiti]/cursensorinfo.columns; // row of seed
if (false) {
cout << "submatrix_x_start: " << submatrix_x_start << ", submatrix_x_end: " << submatrix_x_end << endl;
cout << "submatrix_y_start: " << submatrix_y_start << ", submatrix_y_end: " << submatrix_y_end << endl;
}
// cout << colorwhite << "oben: xcoord: " << xcoord << ", ycoord: " << ycoord << endl;
- histogram.histAvgCluster->Fill(xcoord,ycoord,processed->fFrameInfo.p[clusteri][hiti]);
+ histogram->histAvgCluster->Fill(xcoord,ycoord,processed->fFrameInfo.p[clusteri][hiti]);
if (processed->fFrameInfo.pixelthreshold[hiti]>0)
- histogramthreshold.histAvgCluster->Fill(xcoord,ycoord,processed->fFrameInfo.p[clusteri][hiti]);
+ histogramthreshold->histAvgCluster->Fill(xcoord,ycoord,processed->fFrameInfo.p[clusteri][hiti]);
if (processed->fFrameInfo.pixelfixedthreshold[hiti]>0)
- histogramfixedthreshold.histAvgCluster->Fill(xcoord,ycoord,processed->fFrameInfo.p[clusteri][hiti]);
+ histogramfixedthreshold->histAvgCluster->Fill(xcoord,ycoord,processed->fFrameInfo.p[clusteri][hiti]);
}
}
else // Diode sitzt unten im SeedPixel, da nach PitchY angeordnet
}
// cout << colorwhite << "unten: xcoord: " << xcoord << ", ycoord: " << ycoord << endl;
- histogram.histAvgCluster->Fill(xcoord,ycoord,processed->fFrameInfo.p[clusteri][hiti]);
+ histogram->histAvgCluster->Fill(xcoord,ycoord,processed->fFrameInfo.p[clusteri][hiti]);
if (processed->fFrameInfo.pixelthreshold[hiti]>0)
- histogramthreshold.histAvgCluster->Fill(xcoord,ycoord,processed->fFrameInfo.p[clusteri][hiti]);
+ histogramthreshold->histAvgCluster->Fill(xcoord,ycoord,processed->fFrameInfo.p[clusteri][hiti]);
if (processed->fFrameInfo.pixelfixedthreshold[hiti]>0)
- histogramfixedthreshold.histAvgCluster->Fill(xcoord,ycoord,processed->fFrameInfo.p[clusteri][hiti]);
+ histogramfixedthreshold->histAvgCluster->Fill(xcoord,ycoord,processed->fFrameInfo.p[clusteri][hiti]);
}
}
}
}
}
- for (Int_t clusterx = 1; clusterx <=histogram.histAvgCluster->GetXaxis()->GetNbins();clusterx++) {
- for (Int_t clustery = 1; clustery <=histogram.histAvgCluster->GetYaxis()->GetNbins();clustery++) {
- histogram.histAvgCluster->SetBinContent(clusterx,clustery,histogram.histAvgCluster->GetBinContent(clusterx,clustery)/histogram.numberofhits);
- histogramthreshold.histAvgCluster->SetBinContent(clusterx,clustery,histogramthreshold.histAvgCluster->GetBinContent(clusterx,clustery)/histogramthreshold.numberofhits);
- histogramfixedthreshold.histAvgCluster->SetBinContent(clusterx,clustery,histogramfixedthreshold.histAvgCluster->GetBinContent(clusterx,clustery)/histogramfixedthreshold.numberofhits);
+ for (Int_t clusterx = 1; clusterx <=histogram->histAvgCluster->GetXaxis()->GetNbins();clusterx++) {
+ for (Int_t clustery = 1; clustery <=histogram->histAvgCluster->GetYaxis()->GetNbins();clustery++) {
+ histogram->histAvgCluster->SetBinContent(clusterx,clustery,histogram->histAvgCluster->GetBinContent(clusterx,clustery)/histogram->numberofhits);
+ histogramthreshold->histAvgCluster->SetBinContent(clusterx,clustery,histogramthreshold->histAvgCluster->GetBinContent(clusterx,clustery)/histogramthreshold->numberofhits);
+ histogramfixedthreshold->histAvgCluster->SetBinContent(clusterx,clustery,histogramfixedthreshold->histAvgCluster->GetBinContent(clusterx,clustery)/histogramfixedthreshold->numberofhits);
}
}
return 0;
}
-Bool_t Run::plotNoise()
-{
- if (!error)
- {
- TString legendEntry = Form("Noise: %.2f + %.2f - %.2f", histogram.avgNoise, histogram.avgNoisePlus, histogram.avgNoiseMinus );
- noisecanvas = plot1DHistogram(&histogram, histogram.Noise, "landau", "", legendEntry);
- return 0;
- }
- return 1;
-}
-
-Bool_t Run::plotSeed()
-{
- if (!error)
- {
- plot1DHistogram(&histogram, histogram.Seed, "landau");
- return 0;
- }
- return 1;
-}
-
-Bool_t Run::plotSeedThreshold()
-{
- if (!error)
- {
- plot1DHistogram(&histogramthreshold, histogramthreshold.Seed, "landau");
- return 0;
- }
- return 1;
-}
-
-Bool_t Run::plotSeedThresholdCalibrated()
-{
- if (!error)
- {
- plot1DHistogram(&histogramthresholdCalibrated, histogramthresholdCalibrated.Seed, "landau");
- return 0;
- }
- return 1;
-}
-
-Bool_t Run::plotSum()
-{
- if (!error)
- {
- plot1DHistogram(&histogram, histogram.Sum, "gaus");
- return 0;
- }
- return 1;
-}
-
-Bool_t Run::plotVeto()
-{
- if (!error)
- {
- plot1DHistogram(&histogram, histogram.Veto, "gaus");
- return 0;
- }
- return 1;
-}
-
+// Bool_t Run::plotNoise()
+// {
+// if (!error)
+// {
+// TString legendEntry = Form("Noise: %.2f + %.2f - %.2f", histogram->avgNoise, histogram->avgNoisePlus, histogram->avgNoiseMinus );
+// noisecanvas = plot1DHistogram(histogram, histogram->Noise, "landau", "", legendEntry);
+// return 0;
+// }
+// return 1;
+// }
Bool_t Run::plotCompareHistograms()
{
if (!error)
{
- if ( !plothistogramstructpointerarray.size() ) // XOR operator !=
+ if ( !compareHistogramClassVector.size() ) // XOR operator !=
{
- cout << colorred << "plotCompareHistograms(): No plots to compare, please push them into the vector: Run::plothistogramstructpointerarray." << endlr;
+ cout << colorred << "plotCompareHistograms(): No plots to compare, please push them into the vector: Run::compareHistogramClassVector." << endlr;
return 1;
}
Bool_t calibrated = true;
Bool_t uncalibrated = true;
- for (UInt_t histogrami=0; histogrami < plothistogramstructpointerarray.size(); histogrami++)
+ for (UInt_t histogrami=0; histogrami < compareHistogramClassVector.size(); histogrami++)
{
- calibrated = calibrated && plothistogramstructpointerarray.at(histogrami)->calibrated;
- uncalibrated = uncalibrated && !plothistogramstructpointerarray.at(histogrami)->calibrated;
- // cout << colorcyan << plothistogramstructpointerarray.at(histogrami)->histogramdescription << endlr;
+ calibrated = calibrated && compareHistogramClassVector.at(histogrami)->calibrated;
+ uncalibrated = uncalibrated && !compareHistogramClassVector.at(histogrami)->calibrated;
+ // cout << colorcyan << compareHistogramClassVector.at(histogrami)->histogramdescription << endlr;
}
// cout << "Calibrated " << (calibrated?"Yes":"No") << endl;
// cout << "Uncalibrated " << (uncalibrated?"Yes":"No") << endl;
}
// legend entries
- Float_t height = plothistogramstructpointerarray.size() * 0.04;
+ Float_t height = compareHistogramClassVector.size() * 0.04;
TLegend* leg1 = new TLegend(0.4,0.89-height,0.89,0.89);//(0.6,0.7,0.89,0.89);
leg1->SetTextSize(0.02);
leg1->SetFillColor(0); leg1->SetBorderSize(0);
TString canvasname = Form("%s%d",runcode.Data(),random);
TCanvas* canvas = new TCanvas(canvasname, canvastitle, 1200, 800);
canvas->Divide(2,2);
- for (UInt_t histogrami=0; histogrami < plothistogramstructpointerarray.size(); histogrami++)
+ for (UInt_t histogrami=0; histogrami < compareHistogramClassVector.size(); histogrami++)
{
- histogramstruct* curhistogramstructp = plothistogramstructpointerarray.at(histogrami);
+ HistogramType* curhistogramclassp = compareHistogramClassVector.at(histogrami);
TH1F* curhistogramclone;
canvas->cd(1);
- curhistogramclone = (TH1F*) curhistogramstructp->Seed->Clone();
+ curhistogramclone = (TH1F*) curhistogramclassp->Seed->Clone();
curhistogramclone->SetLineColor(rootcolors[histogrami]);
curhistogramclone->Draw("SAME");
legendEntry = Form("%s", curhistogramclone->GetTitle());
curhistogramclone->SetAxisRange(0,lastbin1*1.1,"X");
gPad->SetLogy(1);
canvas->cd(2);
- curhistogramclone = (TH1F*) curhistogramstructp->Sum->Clone();
+ curhistogramclone = (TH1F*) curhistogramclassp->Sum->Clone();
curhistogramclone->SetLineColor(rootcolors[histogrami]);
curhistogramclone->Draw("SAME");
leg1->Draw("SAME");
curhistogramclone->SetAxisRange(0,lastbin2*1.1,"X");
gPad->SetLogy(1);
canvas->cd(3);
- curhistogramclone = (TH1F*) curhistogramstructp->Veto->Clone();
+ curhistogramclone = (TH1F*) curhistogramclassp->Veto->Clone();
curhistogramclone->SetLineColor(rootcolors[histogrami]);
curhistogramclone->Draw("SAME");
leg1->Draw("SAME");
lastbin3 = (curhistogramclone->GetBinCenter(curhistogramclone->FindLastBinAbove(2,1))>lastbin3)?curhistogramclone->GetBinCenter(curhistogramclone->FindLastBinAbove(2,1)):lastbin3;
curhistogramclone->SetAxisRange(0,lastbin3*1.1,"X");
canvas->cd(4);
- curhistogramclone = (TH1F*) curhistogramstructp->Noise->Clone();
+ curhistogramclone = (TH1F*) curhistogramclassp->Noise->Clone();
curhistogramclone->SetLineColor(rootcolors[histogrami]);
curhistogramclone->Draw();
- legendEntry = Form("Noise: %.2f + %.2f - %.2f",curhistogramstructp->avgNoise, curhistogramstructp->avgNoisePlus, curhistogramstructp->avgNoiseMinus);
+ legendEntry = Form("Noise: %.2f + %.2f - %.2f",curhistogramclassp->avgNoise, curhistogramclassp->avgNoisePlus, curhistogramclassp->avgNoiseMinus);
leg2->AddEntry(curhistogramclone, legendEntry, "l");
leg2->Draw();
}
return 1;
}
-Bool_t Run::plotAllHistograms(histogramstruct* histogramstructpointer)
+Bool_t Run::plotAllHistograms(HistogramType* HistogramTypepointer)
{
if (!error)
{
Float_t lastbin;
Int_t random = random1->Rndm()*1000000;
- TString canvastitle = Form("%s %s", runcode.Data(), histogramstructpointer->histogramdescription.Data());
- if (histogramstructpointer->calibrated)
+ TString canvastitle = Form("%s %s", runcode.Data(), HistogramTypepointer->histogramdescription.Data());
+ if (HistogramTypepointer->calibrated)
canvastitle += "_el";
TString canvasname = Form("%s%d",runcode.Data(),random);
TCanvas* canvas = new TCanvas(canvasname, canvastitle, 1200, 800);
canvas->Divide(2,2);
canvas->cd(1);
- histogramstructpointer->Seed->Draw("");
- plotVerticalLine(histogramstructpointer->Seed, histogramstructpointer->posSeed);
- lastbin = histogramstructpointer->Seed->GetBinCenter(histogramstructpointer->Seed->FindLastBinAbove(2,1));
- histogramstructpointer->Seed->SetAxisRange(0,lastbin*1.1,"X");
+ HistogramTypepointer->Seed->Draw("");
+ plotVerticalLine(HistogramTypepointer->Seed, HistogramTypepointer->posSeed);
+ lastbin = HistogramTypepointer->Seed->GetBinCenter(HistogramTypepointer->Seed->FindLastBinAbove(2,1));
+ HistogramTypepointer->Seed->SetAxisRange(0,lastbin*1.1,"X");
// gPad->SetLogy(1);
canvas->cd(2);
- histogramstructpointer->Sum->Draw();
- plotVerticalLine(histogramstructpointer->Sum, histogramstructpointer->posSum);
- lastbin = histogramstructpointer->Sum->GetBinCenter(histogramstructpointer->Sum->FindLastBinAbove(2,1));
- histogramstructpointer->Sum->SetAxisRange(0,lastbin*1.1,"X");
+ HistogramTypepointer->Sum->Draw();
+ plotVerticalLine(HistogramTypepointer->Sum, HistogramTypepointer->posSum);
+ lastbin = HistogramTypepointer->Sum->GetBinCenter(HistogramTypepointer->Sum->FindLastBinAbove(2,1));
+ HistogramTypepointer->Sum->SetAxisRange(0,lastbin*1.1,"X");
canvas->cd(3);
- histogramstructpointer->Veto->Draw();
+ HistogramTypepointer->Veto->Draw();
if (labbook.source.Contains("Fe")||labbook.source.Contains("Cd"))
- plotVerticalLine(histogramstructpointer->Veto, histogramstructpointer->posVeto);
- histogramstructpointer->Veto->SetAxisRange(histogramstructpointer->posVeto*0.7,histogramstructpointer->posVeto*1.4,"X");
+ plotVerticalLine(HistogramTypepointer->Veto, HistogramTypepointer->posVeto);
+ HistogramTypepointer->Veto->SetAxisRange(HistogramTypepointer->posVeto*0.7,HistogramTypepointer->posVeto*1.4,"X");
canvas->cd(4);
- histogramstructpointer->Noise->Draw();
- TString legendEntry = Form("Noise: %.2f + %.2f - %.2f",histogramstructpointer->avgNoise, histogramstructpointer->avgNoisePlus, histogramstructpointer->avgNoiseMinus);
+ HistogramTypepointer->Noise->Draw();
+ TString legendEntry = Form("Noise: %.2f + %.2f - %.2f",HistogramTypepointer->avgNoise, HistogramTypepointer->avgNoisePlus, HistogramTypepointer->avgNoiseMinus);
TLegend* leg = new TLegend(0.5,0.8,0.89,0.89);//(0.6,0.7,0.89,0.89);
- leg->AddEntry(histogramstructpointer->Veto, legendEntry, "l");
+ leg->AddEntry(HistogramTypepointer->Veto, legendEntry, "l");
leg->SetTextSize(0.03);
leg->Draw();
return 1;
}
-Bool_t Run::plotClusterDistribution(histogramstruct* histogramstructpointer)
+Bool_t Run::plotClusterDistribution(HistogramType* HistogramTypepointer)
{
Int_t random = random1->Rndm()*100000;
TString canvastitle = Form("%s_Cluster", runcode.Data());
- if (histogramstructpointer->calibrated)
+ if (HistogramTypepointer->calibrated)
canvastitle += "_el";
- if (histogramstructpointer->thresholdcluster)
+ if (HistogramTypepointer->isthresholdclustertype)
canvastitle += "_trsh";
TString canvasname = Form("%s%d",runcode.Data(),random);
TCanvas* Canvas_1 = new TCanvas(canvasname, canvastitle, 1200, 800);
// Canvas_1->SetPhi(32.46973);
Canvas_1->TAttPad::SetFrameBorderMode(0);
Canvas_1->TAttPad::SetFrameBorderMode(0);
- histogramstructpointer->histAvgCluster->SetContour(20);
+ HistogramTypepointer->histAvgCluster->SetContour(20);
- TPaletteAxis *palette = new TPaletteAxis(0.8596098,-0.8025656,0.8949913,0.9836416,histogramstructpointer->histAvgCluster);
+ TPaletteAxis *palette = new TPaletteAxis(0.8596098,-0.8025656,0.8949913,0.9836416,HistogramTypepointer->histAvgCluster);
palette->SetLabelColor(1);
palette->SetLabelFont(42);
palette->SetLabelOffset(0.005);
palette->SetTitleSize(0.035);
palette->SetFillColor(100);
palette->SetFillStyle(1001);
- histogramstructpointer->histAvgCluster->GetListOfFunctions()->Add(palette,"br");
+ HistogramTypepointer->histAvgCluster->GetListOfFunctions()->Add(palette,"br");
TPaveStats *ptstats = new TPaveStats(0.7013721,0.7794677,0.850686,0.9226869,"brNDC");
ptstats->SetTextFont(42);
ptstats->SetOptStat(1011);
ptstats->SetOptFit(0);
ptstats->Draw();
- histogramstructpointer->histAvgCluster->GetListOfFunctions()->Add(ptstats);
- ptstats->SetParent(histogramstructpointer->histAvgCluster);
+ HistogramTypepointer->histAvgCluster->GetListOfFunctions()->Add(ptstats);
+ ptstats->SetParent(HistogramTypepointer->histAvgCluster);
Int_t ci; // for color index setting
ci = TColor::GetColor("#000099");
- histogramstructpointer->histAvgCluster->SetLineColor(ci);
- histogramstructpointer->histAvgCluster->GetXaxis()->SetTitle(" x [#mum]");
- histogramstructpointer->histAvgCluster->GetXaxis()->SetLabelFont(42);
- histogramstructpointer->histAvgCluster->GetXaxis()->SetLabelOffset(0.004);
- histogramstructpointer->histAvgCluster->GetXaxis()->SetLabelSize(0.025);
- histogramstructpointer->histAvgCluster->GetXaxis()->SetTitleSize(0.035);
- histogramstructpointer->histAvgCluster->GetXaxis()->SetTitleFont(42);
- histogramstructpointer->histAvgCluster->GetXaxis()->SetTitleOffset(1.5);
- histogramstructpointer->histAvgCluster->GetYaxis()->SetTitle(" y [#mum]");
- histogramstructpointer->histAvgCluster->GetYaxis()->SetLabelFont(42);
- histogramstructpointer->histAvgCluster->GetYaxis()->SetLabelOffset(-0.016);
- histogramstructpointer->histAvgCluster->GetYaxis()->SetLabelSize(0.025);
- histogramstructpointer->histAvgCluster->GetYaxis()->SetTitleSize(0.035);
- histogramstructpointer->histAvgCluster->GetYaxis()->SetTitleFont(42);
- histogramstructpointer->histAvgCluster->GetYaxis()->SetTitleOffset(1.5);
- if (histogramstructpointer->calibrated)
- histogramstructpointer->histAvgCluster->GetZaxis()->SetTitle(" Charge [e]");
+ HistogramTypepointer->histAvgCluster->SetLineColor(ci);
+ HistogramTypepointer->histAvgCluster->GetXaxis()->SetTitle(" x [#mum]");
+ HistogramTypepointer->histAvgCluster->GetXaxis()->SetLabelFont(42);
+ HistogramTypepointer->histAvgCluster->GetXaxis()->SetLabelOffset(0.004);
+ HistogramTypepointer->histAvgCluster->GetXaxis()->SetLabelSize(0.025);
+ HistogramTypepointer->histAvgCluster->GetXaxis()->SetTitleSize(0.035);
+ HistogramTypepointer->histAvgCluster->GetXaxis()->SetTitleFont(42);
+ HistogramTypepointer->histAvgCluster->GetXaxis()->SetTitleOffset(1.5);
+ HistogramTypepointer->histAvgCluster->GetYaxis()->SetTitle(" y [#mum]");
+ HistogramTypepointer->histAvgCluster->GetYaxis()->SetLabelFont(42);
+ HistogramTypepointer->histAvgCluster->GetYaxis()->SetLabelOffset(-0.016);
+ HistogramTypepointer->histAvgCluster->GetYaxis()->SetLabelSize(0.025);
+ HistogramTypepointer->histAvgCluster->GetYaxis()->SetTitleSize(0.035);
+ HistogramTypepointer->histAvgCluster->GetYaxis()->SetTitleFont(42);
+ HistogramTypepointer->histAvgCluster->GetYaxis()->SetTitleOffset(1.5);
+ if (HistogramTypepointer->calibrated)
+ HistogramTypepointer->histAvgCluster->GetZaxis()->SetTitle(" Charge [e]");
else
- histogramstructpointer->histAvgCluster->GetZaxis()->SetTitle(" Charge [ADU]");
- histogramstructpointer->histAvgCluster->GetZaxis()->SetTitleOffset(1.5);
- histogramstructpointer->histAvgCluster->GetZaxis()->SetLabelFont(42);
- histogramstructpointer->histAvgCluster->GetZaxis()->SetLabelSize(0.025);
- histogramstructpointer->histAvgCluster->GetZaxis()->SetTitleSize(0.035);
- histogramstructpointer->histAvgCluster->GetZaxis()->SetTitleFont(42);
- histogramstructpointer->histAvgCluster->GetZaxis()->SetNdivisions(5,5,0);
- histogramstructpointer->histAvgCluster->Draw("LEGO2Z ");
+ HistogramTypepointer->histAvgCluster->GetZaxis()->SetTitle(" Charge [ADU]");
+ HistogramTypepointer->histAvgCluster->GetZaxis()->SetTitleOffset(1.5);
+ HistogramTypepointer->histAvgCluster->GetZaxis()->SetLabelFont(42);
+ HistogramTypepointer->histAvgCluster->GetZaxis()->SetLabelSize(0.025);
+ HistogramTypepointer->histAvgCluster->GetZaxis()->SetTitleSize(0.035);
+ HistogramTypepointer->histAvgCluster->GetZaxis()->SetTitleFont(42);
+ HistogramTypepointer->histAvgCluster->GetZaxis()->SetNdivisions(5,5,0);
+ HistogramTypepointer->histAvgCluster->Draw("LEGO2Z ");
TPaveText *pt = new TPaveText(0.3177154,0.9348119,0.6822846,0.995,"blNDC");
pt->SetName("title");
f->Append(img);
f->Write();
- write2DHistogramToFile(histogramstructpointer->histAvgCluster);
+ write2DHistogramToFile(HistogramTypepointer->histAvgCluster);
// gStyle->SetPaperSize(10.,10.);
// canvas->Print(savepathresults + "/" + canvastitle + ".tex");
// create lorentz fit of a slice
- TH1F* xslicetroughcluster = new TH1F(Form("%s_xslice", histogramstructpointer->histAvgCluster->GetTitle()),"X slice",histogramstructpointer->histAvgCluster->GetXaxis()->GetNbins(),histogramstructpointer->histAvgCluster->GetXaxis()->GetBinLowEdge(1),histogramstructpointer->histAvgCluster->GetXaxis()->GetBinUpEdge(histogramstructpointer->histAvgCluster->GetXaxis()->GetNbins()));
+ TH1F* xslicetroughcluster = new TH1F(Form("%s_xslice", HistogramTypepointer->histAvgCluster->GetTitle()),"X slice",HistogramTypepointer->histAvgCluster->GetXaxis()->GetNbins(),HistogramTypepointer->histAvgCluster->GetXaxis()->GetBinLowEdge(1),HistogramTypepointer->histAvgCluster->GetXaxis()->GetBinUpEdge(HistogramTypepointer->histAvgCluster->GetXaxis()->GetNbins()));
Double_t xslicetroughclusterpar[4];
- Int_t middlebin = (int)(histogramstructpointer->histAvgCluster->GetXaxis()->GetNbins()/2.0 + 0.5);
- for (Int_t bini=1; bini <= histogramstructpointer->histAvgCluster->GetXaxis()->GetNbins(); bini++)
- xslicetroughcluster->SetBinContent(bini,histogramstructpointer->histAvgCluster->GetBinContent(bini,middlebin));
+ Int_t middlebin = (int)(HistogramTypepointer->histAvgCluster->GetXaxis()->GetNbins()/2.0 + 0.5);
+ for (Int_t bini=1; bini <= HistogramTypepointer->histAvgCluster->GetXaxis()->GetNbins(); bini++)
+ xslicetroughcluster->SetBinContent(bini,HistogramTypepointer->histAvgCluster->GetBinContent(bini,middlebin));
- TH1F* yslicetroughcluster = new TH1F(Form("%s_yslice", histogramstructpointer->histAvgCluster->GetTitle()),"Y slice",histogramstructpointer->histAvgCluster->GetYaxis()->GetNbins(),histogramstructpointer->histAvgCluster->GetYaxis()->GetBinLowEdge(1),histogramstructpointer->histAvgCluster->GetYaxis()->GetBinUpEdge(histogramstructpointer->histAvgCluster->GetYaxis()->GetNbins()));
+ TH1F* yslicetroughcluster = new TH1F(Form("%s_yslice", HistogramTypepointer->histAvgCluster->GetTitle()),"Y slice",HistogramTypepointer->histAvgCluster->GetYaxis()->GetNbins(),HistogramTypepointer->histAvgCluster->GetYaxis()->GetBinLowEdge(1),HistogramTypepointer->histAvgCluster->GetYaxis()->GetBinUpEdge(HistogramTypepointer->histAvgCluster->GetYaxis()->GetNbins()));
Double_t yslicetroughclusterpar[4];
- middlebin = (int)(histogramstructpointer->histAvgCluster->GetYaxis()->GetNbins()/2.0 + 0.5);
- for (Int_t bini=1; bini <= histogramstructpointer->histAvgCluster->GetYaxis()->GetNbins(); bini++)
- yslicetroughcluster->SetBinContent(bini,histogramstructpointer->histAvgCluster->GetBinContent(middlebin,bini));
+ middlebin = (int)(HistogramTypepointer->histAvgCluster->GetYaxis()->GetNbins()/2.0 + 0.5);
+ for (Int_t bini=1; bini <= HistogramTypepointer->histAvgCluster->GetYaxis()->GetNbins(); bini++)
+ yslicetroughcluster->SetBinContent(bini,HistogramTypepointer->histAvgCluster->GetBinContent(middlebin,bini));
random = random1->Rndm()*100000;
canvastitle = Form("%s Cluster slices", runcode.Data());
- if (histogramstructpointer->calibrated)
+ if (HistogramTypepointer->calibrated)
canvastitle += "_el";
- if (histogramstructpointer->thresholdcluster)
+ if (HistogramTypepointer->isthresholdclustertype)
canvastitle += "_trsh";
canvasname = Form("%s%d",runcode.Data(),random);
TCanvas* canvas = new TCanvas(canvasname, canvastitle, 1200, 700);
canvas->cd(1);
TPad* firstpart = (TPad*)canvas->GetPad(1);
firstpart->SetGrid();
- FitPerform(&histogram, xslicetroughcluster,"lorentz", false, &xslicetroughclusterpar[0]);
+ HistogramTypepointer->FitPerform(xslicetroughcluster,"lorentz", false, &xslicetroughclusterpar[0]);
canvas->cd(2);
TPad* secondpart = (TPad*)canvas->GetPad(2);
secondpart->SetGrid();
- FitPerform(&histogram, yslicetroughcluster,"lorentz", false, &yslicetroughclusterpar[0]);
+ HistogramTypepointer->FitPerform(yslicetroughcluster,"lorentz", false, &yslicetroughclusterpar[0]);
canvas->Draw();
return 0;
}
-Bool_t Run::plotClusterDistribution()
-{
- if (!error)
- {
- plotClusterDistribution(&histogram);
- return 0;
- }
- return 1;
-}
-
-Bool_t Run::plotAllHistograms()
-{
- if (!error)
- {
- plotAllHistograms(&histogram);
- return 0;
- }
- return 1;
-}
-
-Bool_t Run::plotAllHistogramsCalibrated()
-{
- if (!error)
- {
- if (histogramCalibrated.calibrated) {
- plotAllHistograms(&histogramCalibrated);
- return 0;
- }
- }
- return 1;
-}
-
-Bool_t Run::plotAllHistogramsThresholdCluster()
-{
- if (!error)
- {
- plotAllHistograms(&histogramthreshold);
- return 0;
- }
- return 1;
-}
-
-Bool_t Run::plotAllHistogramsThresholdClusterCalibrated()
-{
- if (!error)
- {
- plotAllHistograms(&histogramthresholdCalibrated);
- return 0;
- }
- return 1;
-}
-
-Bool_t Run::integrateSr90Spectra(histogramstruct* histogramstructpointer, TH1F* histogrampointer, Float_t thresholdborder, Bool_t verbose)
-{
- Float_t posMaxValHist = histogrampointer->GetXaxis()->GetXmax();
-
- TH1F* smoothedcurce = (TH1F*)histogrampointer->Clone();
- smoothedcurce->Smooth(4);
-
-// Int_t random = random1->Rndm()*100000;
-// TString canvastitle = Form("%s %s sdfasdf", histogrampointer->GetName(), runcode.Data());
-// TString canvasname = Form("%s %s %d dfgsdfgsdfg", histogrampointer->GetName(), runcode.Data(), random);
-// TCanvas* canvas = new TCanvas(canvasname, canvastitle, 900, 700);
-// smoothedcurce->Draw();
-
- Int_t thresholdbincurcandidate = 0;
- if (thresholdborder < 0)
- {
- Int_t rising = 0;
- Int_t bini =smoothedcurce->GetMaximumBin();
- Float_t curval = smoothedcurce->GetXaxis()->GetBinCenter(bini);
-// cout << "smoothedcurce->GetXaxis()->GetBinCenter(" << bini << "): " << curval << endl;
-
- do {
- curval=smoothedcurce->GetBinContent(bini++);
- if (curval*0.95 <= smoothedcurce->GetBinContent(bini))
- {
- rising++;
-// cout << "rising at " << smoothedcurce->GetXaxis()->GetBinCenter(bini) << " as " << curval << " < " << smoothedcurce->GetBinContent(bini) << endl; // debug
- }
- else
- {
- rising = 0;
- thresholdbincurcandidate = bini;
- }
- } while (rising < 3 && bini<smoothedcurce->GetNbinsX());
- }
- else
- {
- thresholdbincurcandidate = histogrampointer->GetXaxis()->FindBin(thresholdborder);
- }
-
-
-// histogrampointer->GetXaxis()->SetRange(histogrampointer->GetXaxis()->FindBin(0),histogrampointer->GetXaxis()->FindBin(posMaxValHist));
-// Float_t posNoiseMax= histogrampointer->GetMaximum();
-// histogrampointer->GetXaxis()->SetRange(posNoiseMax,histogrampointer->GetXaxis()->FindBin(posMaxValHist));
-// Float_t posChargeMax= histogrampointer->GetMaximum();
-
- histogramstructpointer->noisethresholdborder = histogrampointer->GetXaxis()->GetBinUpEdge(thresholdbincurcandidate);
-
- if (verbose) {
- cout << " Integrating " << histogrampointer->GetTitle() << endlr;
- cout << " Noise threshold at " << noisethresholdborder;
- TString histtitle=histogrampointer->GetXaxis()->GetTitle();
- if (histtitle.Contains("[e]"))
- {
- cout << " e" << endl;
- if (noisethresholdborder > 300) {
- cout << coloryellow << " This noise threshold seems too high, please check manually the " << histogrampointer->GetName() << " spectrum." << endlr;
- }
- }
- else
- {
- cout << " ADU" << endl;
- if (noisethresholdborder > 75) {
- cout << coloryellow << " This noise threshold seems too high, please check manually the " << histogrampointer->GetName() << " spectrum." << endlr;
- }
- }
- }
- histogramstructpointer->sr90IntegralVal = histogrampointer->IntegralAndError(thresholdbincurcandidate,histogrampointer->GetNbinsX(), histogramstructpointer->sr90IntegralErr);
- // cout << "Integrate from bin " << thresholdbincurcandidate << " to " << histogrampointer->GetNbinsX() << endl;
- histogramstructpointer->sr90IntegralErr /= histogramstructpointer->sr90IntegralVal/100;
- cout << " Unscaled integral is " << Form("%e",histogramstructpointer->sr90IntegralVal) << ", error " << histogramstructpointer->sr90IntegralErr << "%" << endl;
- cout << " ";
- if (labbook.frames_foundDB>0 || frames_found>0)
- {
- histogramstructpointer->sr90IntegralVal/=(labbook.frames_foundDB>0)?labbook.frames_foundDB:frames_found;
- cout << "Scaled ";
- }
- cout << "Integral is " << Form("%e",histogramstructpointer->sr90IntegralVal) << ", error " << histogramstructpointer->sr90IntegralErr << "%" << endl;
-
- return 0;
-}
-
-Float_t Run::FitPerform(histogramstruct* histogramstructpointer, TH1F* histogrampointer, TString fitFuncType, Bool_t verbose, Double_t* parameters)
-{
- Float_t posMax = 0;
- Float_t posMax2 = 0;
- Float_t posMaxValHist = histogrampointer->GetXaxis()->GetXmax();
- // posMaxValHist/10 for USB system, the value is 90
- Float_t noiseborder = 90;
- if (histogramstructpointer->noisethresholdborder>0)
- noiseborder = histogramstructpointer->noisethresholdborder;
- else if (noisethresholdborder > 0)
- noiseborder = noisethresholdborder;
-
- if (doFits)
- {
- if (fitFuncType.Contains("gaus"))
- {
- histogrampointer->GetXaxis()->SetRange(histogrampointer->GetXaxis()->FindBin(noiseborder),histogrampointer->GetXaxis()->FindBin(posMaxValHist)); // look only for maxima with x greater than noiseborder, cut away noise
- // Int_t xValMax = histogrampointer->GetBinCenter(histogrampointer->GetMaximumBin());
- TF1* fitFunc = new TF1("fitFunc",fitFuncType,noiseborder,posMaxValHist);
-
- if (TString(histogrampointer->GetName()).Contains("Veto"))
- {
- Float_t peak1 = histogrampointer->GetMaximumBin();
- Float_t peak2;
- if (histogrampointer->GetBinCenter(peak1)<3.3*noiseborder) // vermutlich ist veto peak nicht am höchsten
- {
- Float_t avg = 0;
- for(Int_t bin=histogrampointer->GetXaxis()->FindBin(noiseborder);bin<histogrampointer->FindLastBinAbove(0);bin++)
- avg += histogrampointer->GetBinContent(bin);
- avg /= histogrampointer->FindLastBinAbove(0) - histogrampointer->GetXaxis()->FindBin(noiseborder);
- peak2 = histogrampointer->FindLastBinAbove(avg/3);
- }
- else
- {
- peak2 = peak1;
- peak1 = histogrampointer->GetXaxis()->FindBin(noiseborder);
- }
- histogrampointer->GetXaxis()->SetRange(peak1,peak2-1); // look only for maxima with x greater than noiseborder, cut away noise
- Float_t min = histogrampointer->GetMinimumBin();
- histogrampointer->GetXaxis()->SetRange(min,histogrampointer->FindLastBinAbove(0)); // look only for maxima with x greater than noiseborder, cut away noise
- // DEBUG
-// if (verbose)
-// {
-// cout << coloryellow << "peak1 " << histogrampointer->GetXaxis()->GetBinCenter(peak1) << endlr;
-// cout << coloryellow << "peak1val " << histogrampointer->GetBinContent(peak1) << endlr;
-// cout << coloryellow << "peak2 " << histogrampointer->GetXaxis()->GetBinCenter(peak2) << endlr;
-// cout << coloryellow << "peak2val " << histogrampointer->GetBinContent(peak2) << endlr;
-// cout << coloryellow << "min " << histogrampointer->GetXaxis()->GetBinCenter( min ) << endlr;
-// }
- histogrampointer->Fit(fitFunc, "N,Q,W", "", histogrampointer->GetXaxis()->GetBinCenter(min), posMaxValHist);
- posMax = histogrampointer->GetXaxis()->GetBinCenter(histogrampointer->GetMaximumBin()); // Methode 1
- histogrampointer->GetXaxis()->SetRange(histogrampointer->GetXaxis()->FindBin(noiseborder),histogrampointer->GetXaxis()->FindBin(posMaxValHist)); // look only for maxima with x greater than noiseborder, cut away noise
- } else {
- histogrampointer->Fit(fitFunc, "N,Q,W", "", noiseborder, posMaxValHist);
- posMax = fitFunc->GetMaximumX(); // Methode 2
- }
- Float_t sigma = fitFunc->GetParameter(2);
- posMax2 = fitFunc->GetMaximumX(); // Methode 2
- Float_t peakposdifperc = abs(posMax-posMax2)/min(posMax,posMax2);
- if (sigma > 260 || peakposdifperc>0.3)
- {
- if (verbose)
- {
- if (sigma > 260)
- {
- cout << "Sigma suspiciously height when fitting " << histogrampointer->GetName() << " spectrum: " << sigma << endl;
- cout << colorred << " Could not find " << histogrampointer->GetName() << " peak" << endlr;
- }
- else if (peakposdifperc>0.3)
- {
- cout << "Maximum peak position and fit gaussian peak position doesn't match in " << histogrampointer->GetName() << " spectrum. Difference: " << peakposdifperc*100 <<" % " << endl;
- cout << colorred << " Could not find " << histogrampointer->GetName() << " peak" << endlr;
- }
- }
- return 0;
- }
- else if (sigma > 40 || peakposdifperc>0.1)
- {
- if (verbose)
- {
- if (sigma > 260)
- {
- cout << "Sigma or suspiciously height when fitting " << histogrampointer->GetName() << " spectrum: " << sigma << endl;
- cout << coloryellow << " Please check " << histogrampointer->GetName() << " peak position: " << colorreset << colorwhite << posMax << endlr;
- }
- else if (peakposdifperc>0.1)
- {
- cout << "Maximum peak position and fit gaussian peak position in " << histogrampointer->GetName() << " have difference of " << peakposdifperc*100 <<" % " << endl;
- cout << coloryellow << " Please check " << histogrampointer->GetName() << " peak position: " << colorreset << colorwhite << posMax << endlr;
- }
- }
- }
-// fitFunc->DrawCopy("same");
- TString legendEntry = TString(Form("%s",runcode.Data()));
- TLegend* leg = new TLegend(0.5,0.5,0.89,0.89);//(0.6,0.7,0.89,0.89);
- // leg->SetHeader();//"Legend Title");
- leg->SetFillStyle(0);
- leg->AddEntry((TObject*) 0, legendEntry, "");
- leg->SetTextSize(0.05);
-// leg->Draw();
-
- }
- else if (fitFuncType=="landau")
- {
- histogrampointer->GetXaxis()->SetRange(histogrampointer->GetXaxis()->FindBin(noiseborder),histogrampointer->GetXaxis()->FindBin(posMaxValHist)); // look only for maxima with x greater than noiseborder, cut away noise
- // Int_t xValMax = histogrampointer->GetBinCenter(histogrampointer->GetMaximumBin());
- TF1* fitFunc = new TF1("fitFunc",fitFuncType,noiseborder,posMaxValHist);
-
- Float_t fitMax1 = 1000;
- Float_t fitMax2 = 1000;
- Float_t fitMax3 = 1000;
- Float_t minFitMax = 1000;
- Float_t maxFitMax = 1000;
- histogrampointer->Fit(fitFunc, "N,Q,W", "", noiseborder, posMaxValHist);
- fitMax1 = fitFunc->GetMaximumX();
-// fitFunc->DrawCopy("same");
- histogrampointer->Fit(fitFunc, "N,Q,W", "", noiseborder, fitMax1*1.1);
- fitMax2 = fitFunc->GetMaximumX();
- fitFunc->SetLineColor(kBlue);
- fitFunc->SetLineStyle(2); // dashed
-// fitFunc->DrawCopy("same");
- histogrampointer->Fit(fitFunc, "N,Q,W", "", fitMax1*0.9, posMaxValHist);
-// histogrampointer->Fit(fitFunc, "N,Q,W", "", fitMax1, histogrampointer->GetBinCenter(bini));
- fitMax3 = fitFunc->GetMaximumX();
- fitFunc->SetLineColor(kGreen);
-// fitFunc->DrawCopy("same");
- fitFunc->SetLineStyle(1); // normal for the following fits
-
- // Sort the three fits and save error estimation
- minFitMax = TMath::Min(TMath::Min(fitMax1,fitMax2),fitMax3);
- maxFitMax = TMath::Max(TMath::Max(fitMax1,fitMax2),fitMax3);
- posMax = fitMax1 + fitMax2 + fitMax3 - minFitMax - maxFitMax;
-
- //fitLandauErrorLeft.push_back(posMax - minFitMax);
- //fitLandauErrorRight.push_back(maxFitMax - posMax);
- } else if (fitFuncType=="lorentz")
- {
- // create a TF1 with the range from 0 to 3 and 6 parameters
- TF1 *fitFcn = new TF1("fitFcn",lorentzianPeak,0,160,4);
- fitFcn->SetNpx(1000);
- fitFcn->SetLineWidth(4);
- fitFcn->SetLineColor(rootcolors[plotStyle]);
- // set start values for some parameters
- fitFcn->SetParName(0,"Area");
- fitFcn->SetParameter(0,21655); // Area
- fitFcn->SetParName(1,"Width");
- fitFcn->SetParameter(1,34); // width
- fitFcn->SetParName(2,"Peak pos.");
- fitFcn->SetParameter(2,80); // peak position x
- fitFcn->SetParName(3,"Y shift");
- fitFcn->SetParameter(3,-22); // y-shift
- gStyle->SetOptFit(0111);
-
-// fitFcn->FixParameter(0,21655.90045);
-// fitFcn->FixParameter(1,34.31885101);
- fitFcn->FixParameter(2,histogrampointer->GetXaxis()->GetBinCenter((int)(histogrampointer->GetXaxis()->GetNbins()/2.0+0.5)));
-// fitFcn->FixParameter(3,-22.43016284);
-
- //histogrampointer->Fit("fitFcn","V+","ep");
- histogrampointer->Fit("fitFcn","Q,W","ep");
- for (Int_t pari=0; pari<4; pari++)
- parameters[pari]=fitFcn->GetParameter(pari);
- if (verbose)
- {
- Printf("width: %.6f ",fitFcn->GetParameter(1));
- cout << "width: " << fitFcn->GetParameter(1) << endl;
- cout << "peak: " << fitFcn->GetParameter(2) << endl;
- cout << "y-shift:" << fitFcn->GetParameter(3) << endl;
- }
- }
-
- }
-
- return posMax;
-}
-
-// Lorenzian Peak function
-Double_t Run::lorentzianPeak(Double_t *x, Double_t *par) {
- return par[3]+(0.5*par[0]*par[1]/TMath::Pi()) /
- TMath::Max( 1.e-10,(x[0]-par[2])*(x[0]-par[2])
- + .25*par[1]*par[1]);
-}
void Run::plotVerticalLine(TH1F* histogrampointer, Float_t xVal) {
if (xVal > 0)
}
}
-TCanvas* Run::plot1DHistogram(histogramstruct* histogramstructpointer, TH1F* onehistogram, TString fitFuncType, TString titlestr, TString legendstr)
+TCanvas* Run::plot1DHistogram(HistogramType* HistogramTypepointer, TH1F* onehistogram, TString fitFuncType, TString titlestr, TString legendstr)
{
if (onehistogram != nullptr)
{
TCanvas* canvas = new TCanvas(canvasname, canvastitle, 900, 700);
onehistogram->SetTitle(titlestr);
onehistogram->Draw();
- Float_t maxValue = FitPerform(histogramstructpointer, onehistogram, fitFuncType);
+ Float_t maxValue = HistogramTypepointer->FitPerform(onehistogram, fitFuncType);
plotVerticalLine(onehistogram, maxValue);
TLegend* leg = new TLegend(0.8,0.8,0.89,0.89);//(0.6,0.7,0.89,0.89);
leg->SetFillColor(0);
TString filename= savepathresults + "/" + runcode + " histograms.dat";
fstream* fout = new fstream(filename,ios::out);
- TString header = Form("#%s %lu frames\n", runcode.Data(), (labbook.frames_foundDB>0)?labbook.frames_foundDB:frames_found);
- header += Form("#bin [ADU]\tbin [e]\tSeed\tSum\tVeto\tSeed thrsh\tSum thrsh\tbin noise [ADU]\tbin noise [e]\tnoise\n");
- header += Form("#posVeto, run: %.1f, DB: %.1f, Fe55 DB (%d, %.1f): %.1f\n", histogram.posVeto, labbook.posVetoDB, Fe55run.posVetorunnumber, Fe55run.temperature, Fe55run.posVeto);
- header += Form("#posSeed, run: %.1f, DB: %.1f\n", histogram.posSeed, labbook.posSeedDB);
- header += Form("#posSum, run: %.1f, DB: %.1f\n", histogram.posSum, labbook.posSumDB);
- header += Form("#Noise [ADU]: %.2f +%.2f -%.2f\n", histogram.avgNoise, histogram.avgNoisePlus, histogram.avgNoiseMinus );
- if (histogramCalibrated.calibrated)
- header += Form("#Noise [e]: %.2f +%.2f -%.2f",histogramCalibrated.avgNoise, histogramCalibrated.avgNoisePlus, histogramCalibrated.avgNoiseMinus );
+ TString header = Form("#%s %lu frames\n", runcode.Data(), frames_found);
+ header += Form("#posVeto, run: %.1f, DB: %.1f, Fe55 DB (%d, %.1f): %.1f\n", histogram->posVeto, labbook.posVetoDB, Fe55run.posVetorunnumber, Fe55run.temperature, Fe55run.posVeto);
+ header += Form("#posSeed, run: %.1f, DB: %.1f\n", histogram->posSeed, labbook.posSeedDB);
+ header += Form("#posSum, run: %.1f, DB: %.1f\n", histogram->posSum, labbook.posSumDB);
+ if (labbook.source.Contains("Sr90")) {
+ header += Form("#Sr90 integral: ");
+ for (vector<HistogramType*>::iterator curHistogramClass = HistogramClassVector.begin(); curHistogramClass != HistogramClassVector.end(); curHistogramClass++)
+ header += Form("%s: %.6f\t", (*curHistogramClass)->histogramdescription.Data(), histogram->sr90IntegralVal);
+ header += Form("\n");
+ }
+ header += Form("#Noise [ADU]: %.2f +%.2f -%.2f\n", histogram->avgNoise, histogram->avgNoisePlus, histogram->avgNoiseMinus );
+ if (histogram->calibrated != 0)
+ header += Form("#Noise [e]: %.2f +%.2f -%.2f\n",histogram->calibrated->avgNoise, histogram->calibrated->avgNoisePlus, histogram->calibrated->avgNoiseMinus );
+ header += Form("#\t\t");
+ for (vector<HistogramType*>::iterator curHistogramClass = HistogramClassVector.begin(); curHistogramClass != HistogramClassVector.end(); curHistogramClass++) {
+ header += Form("%s\t\t\t", (*curHistogramClass)->histogramdescription.Data());
+ }
+ header += Form("\n#bin [ADU]\tbin [e]\t");
+ for (vector<HistogramType*>::iterator curHistogramClass = HistogramClassVector.begin(); curHistogramClass != HistogramClassVector.end(); curHistogramClass++) {
+ header += Form("Seed\tSum\tVeto\t");
+ }
+ header += Form("bin noise [ADU]\tbin noise [e]\tnoise");
*fout << header << endl;
TString outline;
- for(Int_t bin=0;bin<histogram.Seed->GetNbinsX();bin++)
+ for(Int_t bin=1;bin<=cursystemparam.nbins;bin++)
{
- outline=Form("%.1f\t%.1f\t%.1f\t%.1f\t%.1f\t%.1f\t%.1f", histogram.Seed->GetBinCenter(bin), histogramCalibrated.calibrated?histogramCalibrated.Seed->GetBinCenter(bin):0, histogram.Seed->GetBinContent(bin), histogram.Sum->GetBinContent(bin), histogram.Veto->GetBinContent(bin), histogramthreshold.Seed->GetBinContent(bin), histogramthreshold.Sum->GetBinContent(bin));
- if (bin < systemparamcur.nbinsnoise)
- outline+=Form("\t%.1f\t%.1f\t%.1f", histogram.Noise->GetBinCenter(bin),histogramCalibrated.calibrated?histogramCalibrated.Noise->GetBinCenter(bin):0,histogram.Noise->GetBinContent(bin));
+ outline=Form("%.1f\t%.1f\t", histogram->Seed->GetBinCenter(bin), histogram->iscalibrated?histogram->calibrated->Seed->GetBinCenter(bin):0);
+ for (vector<HistogramType*>::iterator curHistogramClass = HistogramClassVector.begin(); curHistogramClass != HistogramClassVector.end(); curHistogramClass++) {
+ outline+=Form("%.1f\t%.1f\t%.1f\t", (*curHistogramClass)->Seed->GetBinContent(bin), (*curHistogramClass)->Sum->GetBinContent(bin), (*curHistogramClass)->Veto->GetBinContent(bin));
+ }
+ if (bin <= cursystemparam.nbinsnoise)
+ outline+=Form("\t%.1f\t%.1f\t%.1f", histogram->Noise->GetBinCenter(bin),histogram->iscalibrated?histogram->calibrated->Noise->GetBinCenter(bin):0,histogram->Noise->GetBinContent(bin));
*fout<<outline<<endl;
}
fout->close();
//system("okular "+ ergebnisfile + ".png &");
}
-void Run::initHistograms(histogramstruct* histogramstructpointer, TString suffix)
-{
- histogramstructpointer->histogramdescription = suffix;
- initHistogram(histogramstructpointer->Seed, "Seed " + suffix);
- initHistogram(histogramstructpointer->Sum, "Sum " + suffix);
- initHistogram(histogramstructpointer->Veto, "Veto " + suffix);
-
- TString prefix = "Noise " + suffix;
-
- if (suffix.Contains("threshold"))
- {
- histogramstructpointer->thresholdcluster = 1;
- histogramstructpointer->Noise = histogram.Noise;
- }
- else
- {
- histogramstructpointer->Noise=new TH1F(prefix.Data(), Form("%s, %s", prefix.Data(), humanreadablestr.Data()), systemparamcur.nbinsnoise, 0, systemparamcur.maxbinnoise);
- histogramstructpointer->Noise->SetLineStyle(rootlinestyle[plotStyle]);
- histogramstructpointer->Noise->SetLineColor(rootcolors[plotStyle]);
- histogramstructpointer->Noise->SetLineWidth(3);
- histogramstructpointer->Noise->GetXaxis()->SetTitle("Q_coll [ADU]");
- histogramstructpointer->Noise->GetYaxis()->SetTitle(Form("Entries [1/%.1f ADU]",histogramstructpointer->Noise->GetBinWidth(1)));
- histogramstructpointer->Noise->GetXaxis()->CenterTitle();
- histogramstructpointer->Noise->GetYaxis()->CenterTitle();
- }
- plothistogrampointer = &histogram.Seed;
- plothistogramstructpointer = &histogram;
-}
-
-void Run::initHistogram(TH1F* &histogrampointer, TString prefix)
-{
- histogrampointer=new TH1F(prefix.Data(), Form("%s, %s", prefix.Data(), humanreadablestr.Data()), systemparamcur.nbins, 0, systemparamcur.maxbin);
- histogrampointer->SetLineStyle(rootlinestyle[plotStyle]);
- histogrampointer->SetLineColor(rootcolors[plotStyle]);
- histogrampointer->SetStats(kTRUE);
- histogrampointer->SetStats(111111111);
- histogrampointer->SetLineWidth(3);
- histogrampointer->GetXaxis()->SetTitle("Q_coll [ADU]");
- histogrampointer->GetYaxis()->SetTitle(Form("Entries [1/%.1f ADU]",histogrampointer->GetBinWidth(1)));
- histogrampointer->GetXaxis()->CenterTitle();
- histogrampointer->GetYaxis()->CenterTitle();
-}
-
void Run::initClusters(Float_t xcoord_min_step, Float_t xcoord_abs_min, Float_t xcoord_abs_max, Float_t ycoord_min_step, Float_t ycoord_abs_min, Float_t ycoord_abs_max)
{
if (false) // set to true to debug
cout << "sizearrrotx: " << sizearrrotx << " sizearrroty: " << sizearrroty << endl;
}
- initCluster(&histogram, "", xcoord_min_step, xcoord_abs_min, xcoord_abs_max, ycoord_min_step, ycoord_abs_min, ycoord_abs_max);
- initCluster(&histogramthreshold, "_threshold", xcoord_min_step, xcoord_abs_min, xcoord_abs_max, ycoord_min_step, ycoord_abs_min, ycoord_abs_max);
- initCluster(&histogramfixedthreshold, "_fixed_threshold", xcoord_min_step, xcoord_abs_min, xcoord_abs_max, ycoord_min_step, ycoord_abs_min, ycoord_abs_max);
+ for (vector<HistogramType*>::iterator curHistogramClass = HistogramClassVector.begin(); curHistogramClass != HistogramClassVector.end(); curHistogramClass++) {
+ (*curHistogramClass)->initCluster((*curHistogramClass)->histogramdescription, xcoord_min_step, xcoord_abs_min, xcoord_abs_max, ycoord_min_step, ycoord_abs_min, ycoord_abs_max);
+ }
}
-void Run::initCluster(histogramstruct* histogramstructpointer, TString suffix, Float_t xcoord_min_step, Float_t xcoord_abs_min, Float_t xcoord_abs_max, Float_t ycoord_min_step, Float_t ycoord_abs_min, Float_t ycoord_abs_max)
-{
- TString prefix = "Cluster" + suffix;
- Int_t sizearrrotx= (int)((xcoord_abs_max-xcoord_abs_min)/xcoord_min_step+0.5);
- Int_t sizearrroty = (int)((ycoord_abs_max-ycoord_abs_min)/ycoord_min_step+0.5);
-// cout << "xcoord_abs_min: " << xcoord_abs_min << ", xcoord_abs_max: " << xcoord_abs_max << ", xcoord_min_step: " << xcoord_min_step << endl;
-// cout << "ycoord_abs_min: " << ycoord_abs_min << ", ycoord_abs_max: " << ycoord_abs_max << ", ycoord_min_step: " << ycoord_min_step << endl;
- histogramstructpointer->histAvgCluster = new TH2F(prefix.Data(), Form("Avg. cluster distribution; x; y %s", Form("%s, %s", prefix.Data(), humanreadablestr.Data())),sizearrrotx, xcoord_abs_min,xcoord_abs_max,sizearrroty,ycoord_abs_min,ycoord_abs_max);
- // you can increase the resolution, especially usefull if you observe an rotated (originally staggered) pixel cluster
- // histogramstructpointer->histAvgCluster = new TH2F(prefix.Data(), Form("Avg. cluster distribution; x; y %s", humanreadablestr.Data()),2*sizearrrotx, xcoord_abs_min,xcoord_abs_max,sizearrroty*2,ycoord_abs_min,ycoord_abs_max);
-}
void Run::initRootParameters()
{
jspc29 / radhard.git / commitdiff