// set file path and other variables to be set
Init();
cout << "Found " << numberRuns << " run(s) in 'runlist.txt' or given as parameters." << endl;
+ TNtupleO *datainput2;
+ datainput2 =new TNtupleO("Position Veto", "data", "x:y:xerr:yerr");
+ TNtupleO *datainput3;
+ datainput3 =new TNtupleO("ntuple3", "data", "x:y:xerr:yerr");
+ TNtupleO *datainput4;
+ datainput4 =new TNtupleO("ntuple4", "data", "x:y:xerr:yerr");
for(Int_t runi=0; runi<numberRuns; runi++) // loop over runs read from file or given as aparameters
{
if (runList[runi]>0)
runs[runi]->setLabel(runListCustomTitle[runi]);
}
}
- compareHistogramClassVectorMABSBot.push_back(runs[runi]->histogram);
+ compareHistogramClassVectorMABSBot.push_back(runs[runi]->histogramwoRTS);
// gROOT->SetBatch(kTRUE);
if (!isBatch)
gROOT->SetBatch(kFALSE);
// Uncomment below to do classical analysis withour RTS
+ compareHistogramClassVector.push_back(runs[runi]->histogramwoRTS->normalized);
compareHistogramClassVector.push_back(runs[runi]->histogram->normalized);
- compareHistogramClassVector.push_back(runs[runi]->histogramfixedthreshold->normalized);
+ compareHistogramClassVector2.push_back(runs[runi]->histogramwoRTS->normalized);
- compareHistogramClassVector2.push_back(runs[runi]->histogram->normalized);
- compareHistogramVector.push_back(runs[runi]->histogram->normalized->Seed);
- compareHistogramVector.push_back(runs[runi]->histogram->normalized->Sum);
+// compareHistogramVector.push_back(runs[runi]->histogramfixedthreshold->SeedPerc);
+
+ compareHistogramVector2.push_back(runs[runi]->histogramwoRTS->normalized->Seed);
+ compareHistogramVector3.push_back(runs[runi]->histogramwoRTS->normalized->Sum);
+
+
+ runs[runi]->plot1DHistogram(runs[runi]->histogramwoRTS->pixeltimefiredDistrib);
+ runs[runi]->plot1DHistogram(runs[runi]->histogramwoRTS->LeakageCurrentInPixelSorted);
+
+// compareHistogramClassVector2.push_back(runs[runi]->histogram->normalized);
+//
+// compareHistogramVector3.push_back(runs[runi]->histogram->normalized->Seed);
+// compareHistogramVector4.push_back(runs[runi]->histogram->normalized->Sum);
+//
+//
+// compareHistogramVector5.push_back(runs[runi]->histogramfixedthreshold->calibrated->Seed);
// compareHistogramClassVector2.push_back(runs[runi]->histogramwoRTS);
// compareHistogramClassVector2.push_back(runs[runi]->histogram->normalized);
// compareHistogramClassVector.push_back(runs[runi]->histogramfixedthreshold->normalized);
// compareHistogramClassVector.push_back(runs[runi]->histogramfixedthreshold->normalized);
// runs[runi]->plot1DHistogram(runs[runi]->histogram->Seed);
-// runs[runi]->plot1DHistogram(runs[runi]->histogramwoRTS->Seed);
+// runs[runi]->plot1DHistogram( runs[runi]->histogram->Seed, "gaus", true, false, false, runs[runi]->histogram->fixedThresholdValue);
+
- ntuple->Fill(runs[runi]->labbook.depletionV,runs[runi]->histogramfixedthreshold->posSeed,0,0);
+// Double_t AverageCharge[9];
+// Double_t TotalHits[9];
+// for (UInt_t sumi = 0; sumi < 9; sumi++) {
+// AverageCharge[sumi] = 0;
+// TotalHits[sumi] = 0;
+// for (UInt_t bini = 1; bini <= runs[runi]->histogramfixedthreshold->a_Sum[sumi]->GetXaxis()->GetNbins();++bini) {
+// AverageCharge[sumi] += runs[runi]->histogramfixedthreshold->a_Sum[sumi]->GetBinContent(bini)*runs[runi]->histogramfixedthreshold->a_Sum[sumi]->GetBinCenter(bini);
+// TotalHits[sumi] += runs[runi]->histogramfixedthreshold->a_Sum[sumi]->GetBinContent(bini);
+// }
+// AverageCharge[sumi] /= TotalHits[sumi]/runs[runi]->histogramfixedthreshold->gain;
+// }
+
+
+
+
+// TNtupleO *datainput;
+// datainput =new TNtupleO("Position Seed", "data", "x:y:xerr:yerr");
+// datainput->itsHistogramType = runs[runi]->histogramfixedthreshold;
+// datainput->xaxisTitle = "Number of pixel in cluster";
+// datainput->yaxisTitle = "Position Seed";
+// for (UInt_t sumi = 0; sumi < 9; sumi++) {
+// // if (!(sumi%2))
+// // compareHistogramVector2.push_back(runs[runi]->histogram->normalized->a_Sum[sumi]);
+// datainput->Fill(sumi+1,runs[runi]->histogramfixedthreshold->a_posSum[sumi],0,0);
+// // runs[runi]->plot1DHistogram( runs[runi]->histogram->a_Sum[sumi], "gaus", true, false, false, runs[runi]->histogram->fixedThresholdValue);
+// }
+// ntupleVector.push_back(datainput);
+//
+//
+// datainput2->itsHistogramType = runs[runi]->histogramfixedthreshold;
+// datainput2->xaxisTitle = "Voltage";
+// datainput2->yaxisTitle = "Position Veto";
+// datainput2->Fill(runs[runi]->labbook.depletionV,runs[runi]->histogramfixedthreshold->posVeto,0,0);
+//
+//
+//
+// datainput3->itsHistogramType = runs[runi]->histogramfixedthreshold;
+// datainput3->xaxisTitle = "Voltage";
+// datainput3->yaxisTitle = "Position Sum";
+// // datainput3->Fill(runs[runi]->labbook.depletionV,runs[runi]->histogramfixedthreshold->posSum,0,0);
+//
+//
+//
+//
+// datainput4->itsHistogramType = runs[runi]->histogramfixedthreshold;
+// datainput4->xaxisTitle = "Voltage";
+// datainput4->yaxisTitle = "Cluster multiplicity";
+// datainput4->Fill(runs[runi]->labbook.depletionV,runs[runi]->histogramfixedthreshold->clustermultiplicity,0,0);
+
+
+// TNtupleO *datainput;
+// datainput =new TNtupleO("ntuple", "data", "x:y:xerr:yerr");
+// datainput->itsHistogramType = runs[runi]->histogram;
+// datainput->xaxisTitle = "Number of pixel in cluster";
+// datainput->yaxisTitle = "Average charge collected [e]";
+// for (UInt_t sumi = 0; sumi < 9; sumi++) {
+// if (!(sumi%2))
+// compareHistogramVector2.push_back(runs[runi]->histogram->normalized->a_Sum[sumi]);
+// datainput->Fill(sumi+1,AverageCharge[sumi],0,0);
+// // runs[runi]->plot1DHistogram( runs[runi]->histogram->a_Sum[sumi], "gaus", true, false, false, runs[runi]->histogram->fixedThresholdValue);
+// }
+// ntupleVector.push_back(datainput);
+
+
+
+// TNtupleO *datainput;
+// datainput =new TNtupleO("ntuple", "data", "x:y:xerr:yerr");
+// datainput->itsHistogramType = runs[runi]->histogram;
+// datainput->xaxisTitle = "Number of pixel in cluster";
+// datainput->yaxisTitle = "Average charge collected [ADU]";
+// for (UInt_t sumi = 0; sumi < 9; sumi++) {
+// if (!(sumi%2))
+// compareHistogramVector2.push_back(runs[runi]->histogram->normalized->a_Sum[sumi]);
+// datainput->Fill(sumi+1,runs[runi]->histogram->a_integralSum[sumi],0,runs[runi]->histogram->a_integralSumErr[sumi]);
+// runs[runi]->plot1DHistogram( runs[runi]->histogram->a_Sum[sumi], "gaus", true, false, false, runs[runi]->histogram->fixedThresholdValue);
+// }
+// ntupleVector.push_back(datainput);
+
+// ntuple.Clear();
+
// ntuple->Fill(2,2,1,1);
// ntuple->Fill(3,3,1,1);
// ntuple->Fill(4,4,1,1);
// compareHistogramVector.push_back(runs[runi]->histogramwoRTSAggresive->Seed);
// compareHistogramVector.push_back(runs[runi]->histogramfixedthreshold->Seed);
// compareHistogramVector.push_back(runs[runi]->histogramfixedthreshold->normalized->Seed);
-// runs[runi]->plot1DHistogram( runs[runi]->histogramfixedthreshold->normalized->Seed, "gaus", true, false, false, 500);
+// Float_t integratestart = 0;
+// runs[runi]->plot1DHistogram( runs[runi]->histogram->normalized->Seed, "landau", true, false, false);
+// integratestart = runs[runi]->histogram->FindNoisethresholdborder(runs[runi]->histogram->Sum, false, false);
+// cout << "integratestart: " << integratestart << endlr;
+// runs[runi]->plot1DHistogram( runs[runi]->histogram->normalized->Sum, "landau", true, false, false, integratestart);
+// integratestart = runs[runi]->histogram->FindNoisethresholdborder(runs[runi]->histogram->a_Sum[2], false, false);
+// cout << "integratestart: " << integratestart << endlr;
+// runs[runi]->plot1DHistogram( runs[runi]->histogram->normalized->a_Sum[2], "landau", true, false, false, integratestart);
+// integratestart = runs[runi]->histogram->FindNoisethresholdborder(runs[runi]->histogram->a_Sum[5], false, false);
+// cout << "integratestart: " << integratestart << endlr;
+// runs[runi]->plot1DHistogram( runs[runi]->histogram->normalized->a_Sum[5], "landau", true, false, false, integratestart);
//runs[runi]->plot1DHistogram( runs[runi]->histogramwoRTS->normalized->Seed, "GaussTail");
//runs[runi]->plot1DHistogram( runs[runi]->histogramwoRTS->a_Sum[0]);
// compareHistogramVector.push_back(runs[runi]->histogramwoRTS->a_Sum[0]); // Seed
// plot and clear compareHistogramClassVector vector, delete these two lines if you want to compare among different runs
plotAllRuns(&compareHistogramClassVector);
compareHistogramClassVector.clear();
+
+ CompareHistograms(&compareHistogramVector);
+ compareHistogramVector.clear();
}
//cout << runs[runi]->histogram
}
}
- }
+ } // loop over all runs end
+
+ ntupleVector2.push_back(datainput2);
+ ntupleVector2.push_back(datainput3);
+
+ ntupleVector3.push_back(datainput4);
plotAllRuns(&compareHistogramClassVector3);
plotAllRuns(&compareHistogramClassVector4);
plotAllRuns(&compareHistogramClassVector5);
-plot1DData(ntuple);
+plot1DData(&ntupleVector);
+plot1DData(&ntupleVector2);
+plot1DData(&ntupleVector3);
CompareLeageCurrent(&compareHistogramClassVector4);
CompareLeageCurrent(&compareHistogramClassVector5);
writeObservableToFile();
#include <TH2C.h>
#include <TTimeStamp.h>
#include <TGraphErrors.h>
+#include "help.h"
#include "HistogramType.h"
#include "Run.h"
-#include "help.h"
using namespace std;
Int_t* ReadRunList();
Bool_t FindGoodTitle(vector<TH1FO*>* ptCompareHistogramVector);
Bool_t FindGoodTitle();
-/** @brief A function to write data from TNtuple to a file. File is save with the prefix "Oberservable_" in the result folder
- * */
-Bool_t writeNTupleToFile(TNtuple* ntuplepointer);
/** @brief A function to plot TH1F histograms of different runs into one file
/** @brief A function to plot ntuplöe data into a canvas
*
*/
-TCanvas* plot1DData(TNtuple* ntuplepointer, Bool_t verbose=0, Bool_t logscale=0, TString titlestr="", TString legendstr="" );
+TCanvas* plot1DData(std::vector<TNtupleO*>* ntuplepvectorpointer, Bool_t verbose=0, Bool_t logscale=0, TString titlestr="", TString legendstr="" );
+
+/** @brief A function to write data from TNtuple to a file. File is save with the prefix "Oberservable_" in the result folder
+ * */
+Bool_t writeNTupleVectorToFile(std::vector<TNtupleO*>* ntuplepvectorpointer);
Bool_t writeObservableToFile();
/** @brief A vector able to hold pointer of type #Run::histogramstruct
string to_str_w_prec(const Float_t a_value, int precision = 1);
vector<HistogramType*> compareHistogramClassVector, compareHistogramClassVector2, compareHistogramClassVector3, compareHistogramClassVector4, compareHistogramClassVector5, compareHistogramClassVector6, compareHistogramClassVector7, compareHistogramClassVector8, compareHistogramClassVectorMABSBot;
vector<TH1FO*> compareHistogramVector, compareHistogramVector2, compareHistogramVector3, compareHistogramVector4, compareHistogramVector5, compareHistogramVector6, compareHistogramVector7, compareHistogramVector8;
-TNtuple *ntuple, *ntuple2, *ntuple3, *ntuple4;
+vector<TNtupleO*> ntupleVector, ntupleVector2, ntupleVector3, ntupleVector4;
TString ownpath = "";
TString savepathresults;
//FindGoodTitle();
TTimeStamp timestamp = TTimeStamp();
savepathresults = Form("./results/%d%06d/", (int)timestamp.GetDate(kFALSE), (int)timestamp.GetTime(kFALSE));
- ntuple = new TNtuple("ntuple", "data", "x:y:xerr:yerr");
- ntuple2 = new TNtuple("ntuple2","data2","x:y:xerr:yerr");
- ntuple3 = new TNtuple("ntuple3","data3","x:y:xerr:yerr");
- ntuple4 = new TNtuple("ntuple4","data4","x:y:xerr:yerr");
}
-TCanvas* plot1DData(TNtuple* ntuplepointer, Bool_t verbose, Bool_t logscale, TString titlestr, TString legendstr )
+TCanvas*
+plot1DData(std::vector<TNtupleO*>* ntuplepvectorpointer, Bool_t verbose, Bool_t logscale, TString titlestr, TString legendstr )
{
- if (ntuplepointer != nullptr)
+ if (ntuplepvectorpointer != nullptr)
{
- if (ntuplepointer->GetEntries() > 0) {
- TString canvastitle = Form("%s", ntuplepointer->GetName());
- TString canvasname = Form("%s", ntuplepointer->GetName());
- TCanvas* canvas = new TCanvas(canvasname, canvastitle, 900, 700);
- canvas->SetGrid();
-
- if (titlestr.Length()>0)
- ntuplepointer->SetTitle(titlestr);
- if (logscale) {
- gPad->SetLogy(1);
+ if (ntuplepvectorpointer->size() > 0) {
+ TNtupleO* ntuplepointer = ntuplepvectorpointer->at(0);
+ if (ntuplepointer->GetEntries() > 0) {
+ // TNtuple* ntuplepointer = &(ntuplepvectorpointer->at(0));
+ TString canvastitle = Form("%s", ntuplepointer->GetName());
+ TString canvasname = Form("%s", ntuplepointer->GetName());
+ // titlestr = ntuplepointer->GetTitle();
+
+ if (titlestr.Length()>0)
+ ntuplepointer->SetTitle(titlestr);
+ if (logscale) {
+ gPad->SetLogy(1);
+ }
+
+ TCanvas* canvas = new TCanvas(canvasname, canvastitle, 900, 700);
+ canvas->SetGrid();
+
+ for(UInt_t runi=0;runi<ntuplepvectorpointer->size();runi++) // loop over runs or other stacked elements in vector<TNtuple*>
+ {
+ // TNtuple* ntuplepointer = &(ntuplepvectorpointer->at(runi));
+ TNtupleO* ntuplepointer = ntuplepvectorpointer->at(runi);
+ ntuplepointer->Draw("x:y:xerr:yerr","","goff");
+
+ TGraphErrors *gr = new TGraphErrors(ntuplepointer->GetEntries(),ntuplepointer->GetV1(),ntuplepointer->GetV2(),ntuplepointer->GetV3(),ntuplepointer->GetV4());
+ if ( ntuplepointer->Title.Length() > 0 ) { gr->SetTitle(ntuplepointer->Title); } else { gr->SetTitle("");}
+ if ( ntuplepointer->xaxisTitle.Length() > 0 ) { gr->GetXaxis()->SetTitle(ntuplepointer->xaxisTitle); } else { gr->GetXaxis()->SetTitle("");}
+ if ( ntuplepointer->yaxisTitle.Length() > 0 ) { gr->GetYaxis()->SetTitle(ntuplepointer->yaxisTitle); } else { gr->GetYaxis()->SetTitle("");}
+ if (ntuplepointer->itsHistogramType != 0) gr->SetMarkerColor(ntuplepointer->itsHistogramType->color); else gr->SetMarkerColor(runi); gr->SetMarkerStyle(21);
+ if (!runi) gr->Draw("ALP"); else gr->Draw("LP");
+ }
+
+ TPaveText *owntitle = new TPaveText(0.15,0.9,0.930401,0.995,"nbNDC");
+ owntitle->SetFillStyle(0);
+ owntitle->SetBorderSize(0);
+ owntitle->SetTextAlign(22);
+ owntitle->SetTextSize(0.05);
+ owntitle->SetTextFont(42);
+
+ owntitle->Clear();
+ if (titlestr.Length()>0) {
+ owntitle->AddText(titlestr);
+ owntitle->Draw("SAME");
+ }
+
+ canvas->Update();
+ // canvas -> SaveAs( savepathresults + "/" + runcode + " " + onehistogram->GetName() + ".eps");
+
+ TImageDump *img = new TImageDump(savepathresults + ntuplepointer->GetName() + ".png");
+ canvas->Paint();
+ img->Close();
+
+ writeNTupleVectorToFile(ntuplepvectorpointer);
+ return canvas;
}
-
- ntuplepointer->Draw("x:y:xerr:yerr","","goff");
-
- TGraphErrors *gr = new TGraphErrors(ntuplepointer->GetEntries(),ntuplepointer->GetV1(),ntuplepointer->GetV2(),ntuplepointer->GetV3(),ntuplepointer->GetV4());
- gr->SetTitle("TGraphErrors Example");
- gr->SetMarkerColor(4);
- gr->SetMarkerStyle(21);
- gr->Draw("ALP");
-
- canvas->Update();
- // canvas -> SaveAs( savepathresults + "/" + runcode + " " + onehistogram->GetName() + ".eps");
-
- TImageDump *img = new TImageDump(savepathresults + ntuplepointer->GetName() + ".png");
- canvas->Paint();
- img->Close();
-
- writeNTupleToFile(ntuplepointer);
- return canvas;
}
}
return nullptr;
}
-Bool_t writeNTupleToFile(TNtuple* ntuplepointer)
+Bool_t writeNTupleVectorToFile(std::vector<TNtupleO*>* ntuplepvectorpointer)
{
- if (ntuplepointer != nullptr)
+ if (ntuplepvectorpointer != nullptr)
{
- system("mkdir "+ savepathresults + " -p");
-
- TString filename = savepathresults + Form("Observable_%s.dat", ntuplepointer->GetName());
-
- TString header = "x\ty\txerr\tyerr";
- fstream* fout = new fstream(filename,ios::out);
-
- *fout << header << endl;
- TString outline;
- for(int i=0;i<ntuplepointer->GetEntries();i++) {
- ntuplepointer->GetEntry(i);
- Float_t *p = ntuplepointer->GetArgs();
- outline ="";
- outline+=Form("%.2f\t%.2f\t%.2f\t%.2f", p[0], p[1], p[2], p[3]);
- *fout<<outline<<endl;
+ if (ntuplepvectorpointer->size() > 0) {
+ TNtupleO* ntuplepointer = ntuplepvectorpointer->at(0);
+ system("mkdir "+ savepathresults + " -p");
+ TString filename = savepathresults + Form("Observable_%s.dat", ntuplepointer->GetName());
+
+ TString header = "";
+ for(UInt_t runi=0;runi<ntuplepvectorpointer->size();runi++) // loop over runs or other stacked elements in vector<TNtupleO*>
+ {
+ header += "x\ty\txerr\tyerr\t";
+ }
+ fstream* fout = new fstream(filename,ios::out);
+ *fout << header << endl;
+
+ // find max entries
+ Int_t maxEntries = 0;
+ for(UInt_t runi=0;runi<ntuplepvectorpointer->size();runi++) {
+ ntuplepointer = ntuplepvectorpointer->at(runi);
+ maxEntries = (ntuplepointer->GetEntries() > maxEntries)?ntuplepointer->GetEntries():maxEntries;
+ }
+
+
+ TString outline="";
+ for(int i=0;i < maxEntries;i++) {
+ for(UInt_t runi=0;runi<ntuplepvectorpointer->size();runi++) // loop over runs or other stacked elements in vector<TNtupleO*>
+ {
+ ntuplepointer = ntuplepvectorpointer->at(runi);
+ ntuplepointer->GetEntry(i);
+ Float_t *p = ntuplepointer->GetArgs();
+ outline+=Form("%.2f\t%.2f\t%.2f\t%.2f\t", p[0], p[1], p[2], p[3]);
+ }
+ *fout<<outline<<endl;
+ outline="";
+ }
+ fout->close();
+ return 0;
}
- fout->close();
- return 0;
}
return 1;
}
// legend entries
Float_t height = ptCompareHistogramVector->size() * 0.03;
- TLegend* leg1 = new TLegend(0.3,0.89-height,0.95,0.89);//(0.6,0.7,0.89,0.89);
+// TLegend* leg1 = new TLegend(0.3,0.89-height,0.95,0.89);//(0.6,0.7,0.89,0.89);
+ TLegend* leg1 = new TLegend(0.3,0.89-height,0.6,0.89);//(0.6,0.7,0.89,0.89);
leg1->SetTextSize(0.025);
leg1->SetFillStyle(1001);
leg1->SetTextFont(132);
{
TH1F* curhistogramclone = (TH1F*) ptCompareHistogramVector->at(histogrami)->Clone();
heighestval1 = (curhistogramclone->GetMaximum()>heighestval1?curhistogramclone->GetMaximum():heighestval1);
- lastbin1 = (curhistogramclone->GetBinCenter(curhistogramclone->FindLastBinAbove(2,1))>lastbin1)?curhistogramclone->GetBinCenter(curhistogramclone->FindLastBinAbove(2,1)):lastbin1;
+ lastbin1 = (curhistogramclone->GetBinCenter(curhistogramclone->FindLastBinAbove(2,2))>lastbin1)?curhistogramclone->GetBinCenter(curhistogramclone->FindLastBinAbove(2,2)):lastbin1;
canvastitle+= Form("_%s",getRunnumberAtBegin(curhistogramclone->GetName()).Data());
}
TCanvas* canvas = new TCanvas(canvasname, canvastitle, 1200, 800);
leg1->Draw("SAME");
curhistogramclone->SetAxisRange(0,lastbin1*1.1,"X");
- // curhistogramclone->GetYaxis()->SetRangeUser(5,heighestval1*4);
+ curhistogramclone->GetYaxis()->SetRangeUser(1,heighestval1*1.4);
owntitle->Clear();
owntitle->AddText(trimRunnumberAtBegin(curhistogramclone->GetName()));
}
owntitle->Draw("SAME");
- gPad->SetLogy(1);
- curhistogramclone->GetYaxis()->UnZoom();
+// gPad->SetLogy(1);
+// curhistogramclone->GetYaxis()->UnZoom();
}
canvas->Update();
// gPad->Modified(); gPad->Update();
curhistogramclone->GetXaxis()->UnZoom();
curhistogramclone->GetXaxis()->SetRange(curhistogramclone->GetXaxis()->FindBin(curhistogramclassp->noisethresholdborder),curhistogramclone->GetXaxis()->FindBin(posMaxValHist)); // look only for maxima with x greater than noiseborder, cut away noise
heighestval1 = (curhistogramclone->GetMaximum()>heighestval1?curhistogramclone->GetMaximum():heighestval1);
- lastbin1 = (curhistogramclone->GetBinCenter(curhistogramclone->FindLastBinAbove(2,1))>lastbin1)?curhistogramclone->GetBinCenter(curhistogramclone->FindLastBinAbove(2,1)):lastbin1;
+ lastbin1 = (curhistogramclone->GetBinCenter(curhistogramclone->FindLastBinAbove(2,2))>lastbin1)?curhistogramclone->GetBinCenter(curhistogramclone->FindLastBinAbove(2,2)):lastbin1;
// cout << "Last bin: " << colorcyan << lastbin1 << endlr;
// cout << "curhistogramclone->GetMaximum(): " << colorcyan << curhistogramclone->GetMaximum() << endlr;
heighestval2 = (curhistogramclone->GetMaximum()>heighestval2?curhistogramclone->GetMaximum():heighestval2);
- lastbin2 = (curhistogramclone->GetBinCenter(curhistogramclone->FindLastBinAbove(2,1))>lastbin2)?curhistogramclone->GetBinCenter(curhistogramclone->FindLastBinAbove(2,1)):lastbin2;
+ lastbin2 = (curhistogramclone->GetBinCenter(curhistogramclone->FindLastBinAbove(2,2))>lastbin2)?curhistogramclone->GetBinCenter(curhistogramclone->FindLastBinAbove(2,2)):lastbin2;
curhistogramclone = (TH1F*) curhistogramclassp->Veto->Clone();
posMaxValHist = curhistogramclone->GetXaxis()->GetXmax();
curhistogramclone->GetXaxis()->UnZoom();
curhistogramclone->GetXaxis()->SetRange(curhistogramclone->GetXaxis()->FindBin(curhistogramclassp->noisethresholdborder),curhistogramclone->GetXaxis()->FindBin(posMaxValHist)); // look only for maxima with x greater than noiseborder, cut away noise
heighestval3 = (curhistogramclone->GetMaximum()>heighestval3?curhistogramclone->GetMaximum():heighestval3);
- lastbin3 = (curhistogramclone->GetBinCenter(curhistogramclone->FindLastBinAbove(2,1))>lastbin3)?curhistogramclone->GetBinCenter(curhistogramclone->FindLastBinAbove(2,1)):lastbin3;
+ lastbin3 = (curhistogramclone->GetBinCenter(curhistogramclone->FindLastBinAbove(2,2))>lastbin3)?curhistogramclone->GetBinCenter(curhistogramclone->FindLastBinAbove(2,2)):lastbin3;
// cout << "heighestval3: " << colorcyan << heighestval3 << endlr;
// cout << "curhistogramclone->GetMaximum(): " << colorcyan << curhistogramclone->GetMaximum() << endlr;
labbooksctruct curlabbook = curhistogramclassp->run->labbook;
pixelinfo curpixelinfo = curhistogramclassp->run->curpixelinfo;
- TString title1 = Form("%s, %s, %s, %.0fx%.0f #mum^{2} pitch, %s\nT=%.0f {}^{o}C, %.1f MRad, %.1f*10^{13} n_{eq}/cm^{2}, %.2f Mhz", curlabbook.source.Data(),
+ TString title1 = Form(" %s, %s, %s, %.0fx%.0f #mum^{2} pitch, %s\nT=%.0f {}^{o}C, %.1f MRad, %.1f*10^{13} n_{eq}/cm^{2}, %.2f Mhz", curlabbook.source.Data(),
curlabbook.chipGen.Data(), curlabbook.matrix.Data(), curpixelinfo.pitchX, curpixelinfo.pitchY, curpixelinfo.comment.Data(),
curlabbook.tempSens, curlabbook.radDoseIon, curlabbook.radDoseNonIon, curlabbook.clock);
if (curlabbook.depletionV > 0)
// cout << colorred << legendstr << endlr;
}
- // Folder name suffix
- TString folderadd = "";
+// // Folder name suffix
+// TString folderadd = "";
+// if (same_RunNumber)
+// folderadd.Append(Form(", %d", firstlabbook.runnumber));
+// if (same_HistType && mayBeSameRun)
+// folderadd.Append(Form(", %s", trimRunnumberAtBegin(firsthistogramp->GetName()).Data()));
+// if (same_Source)
+// folderadd.Append(Form(", %s", firstlabbook.source.Data()));
+// if (same_ChipGen)
+// folderadd.Append(Form(", %s", firstlabbook.chipGen.Data()));
+// if (same_Matrix)
+// folderadd.Append(Form(", %s, %.0fx%.0f #mum^{2} pitch, %s", firstlabbook.matrix.Data(), firstpixelinfo.pitchX, firstpixelinfo.pitchY, firstpixelinfo.comment.Data()));
+// title1.Append(Form("\n"));
+// if (same_Temp)
+// title1.Append(Form("T=%.0f {}^{o}C", firstlabbook.tempSens));
+// if (same_IonRad && firstlabbook.radDoseIon != 0)
+// title1.Append(Form(", %.1f MRad", firstlabbook.radDoseIon));
+// if (same_NonIonRad && firstlabbook.radDoseNonIon != 0)
+// title1.Append(Form(", %.1f*10^{13} n_{eq}/cm^{2}", firstlabbook.radDoseNonIon));
+// if (same_Clock)
+// title1.Append(Form(", %.2f Mhz", firstlabbook.clock));
+// if (same_Depletion && firstlabbook.depletionV >= 0)
+// title1.Append(Form(", U_{dep}=%.1f V", firstlabbook.depletionV));
+// title1 = title1.Remove(0,2); // remove trailing " ,"
+
+
+
+
+
HistogramType* curhistogramclassp = ptCompareHistogramClassVector->at(0);
labbooksctruct curlabbook = curhistogramclassp->run->labbook;
pixelinfo curpixelinfo = curhistogramclassp->run->curpixelinfo;
calibrated = new HistogramType(histogramdescription+" calibrated", cursystempar, cursensorinfo, humanreadablestr, labbook, run, color, style);
if (Seed != 0) calibrateHistogram(calibrated->Seed, Seed);
if (Sum != 0) calibrateHistogram(calibrated->Sum, Sum);
- for (unsigned int i=0; i < a_Sum.size(); ++i)
- normalizeHistogram(calibrated->a_Sum[i], a_Sum[i]);
+ for (unsigned int i=0; i < a_Sum.size(); ++i) {
+ calibrated->a_Sum.push_back(a_Sum[i]);
+ calibrateHistogram(calibrated->a_Sum[i], a_Sum[i]);
+ }
if (Veto != 0) calibrateHistogram(calibrated->Veto, Veto);
if (Noise != 0) calibrateHistogram(calibrated->Noise, Noise);
if (pixeltimefired != 0) calibrated->pixeltimefired = pixeltimefired;
if (LeakageCurrentInPixelSorted != 0) calibrateHistogramYAxis(calibrated->LeakageCurrentInPixelSorted, LeakageCurrentInPixelSorted);
calibrated->posSeed = posSeed * gain;
- calibrated->posSum = posSum * gain;
+ calibrated->posSum = posSum * gain;
+ for (unsigned int i=0; i < a_posSum.size(); ++i){
+ calibrated->a_posSum.push_back(a_posSum[i] * gain);
+ }
calibrated->posVeto = posVeto * gain;
calibrated->integralSeed = integralSeed;
calibrated->integralSeedErr = integralSeedErr;
calibrated->integralVeto = integralVeto;
calibrated->integralSum = integralVeto;
- calibrated->integralSumErr = integralSumErr;
+ calibrated->integralSumErr = integralSumErr;
+ for (unsigned int i=0; i < a_integralSum.size(); ++i){
+ calibrated->a_integralSum.push_back( a_integralSum[i]);
+ calibrated->a_integralSumErr.push_back( a_integralSumErr[i] );
+ }
calibrated->posSeedPerc = posSeedPerc;
calibrated->sigmaSeedPerc = sigmaSeedPerc;
calibrated->avgNoise = avgNoise * gain;
calibrated->avgNoisePlus = avgNoisePlus * gain;
calibrated->avgNoiseMinus = avgNoiseMinus * gain;
calibrated->sr90IntegralVal = sr90IntegralVal;
+ calibrated->sr90IntegralErr = sr90IntegralErr;
calibrated->StoN = StoN;
calibrated->CCE_in_Perc_1 = CCE_in_Perc_1;
calibrated->CCE_in_Perc_25 = CCE_in_Perc_25;
if (Seed != 0) normalizeHistogram(normalized->Seed, Seed);
if (Sum != 0) normalizeHistogram(normalized->Sum, Sum);
for (unsigned int i=0; i < a_Sum.size(); ++i){
+ normalized->a_Sum.push_back(a_Sum[i]);
normalizeHistogram(normalized->a_Sum[i], a_Sum[i]);
}
if (Veto != 0) normalizeHistogram(normalized->Veto, Veto);
normalized->frames_found = frames_found;
normalized->posSeed = posSeed;
normalized->posSum = posSum;
+ for (unsigned int i=0; i < a_posSum.size(); ++i){
+ normalized->a_posSum.push_back(a_posSum[i]);
+ }
normalized->posVeto = posVeto;
normalized->integralSeed = integralSeed/frames_found*pow10(6);
normalized->integralSeedErr = integralSeedErr * sqrt(pow10(6)/frames_found);
normalized->integralVeto= integralVeto/frames_found*pow10(6);
normalized->integralVeto= integralVeto/frames_found*pow10(6);
normalized->integralSum = integralSum/frames_found*pow10(6);
+ for (unsigned int i=0; i < a_integralSum.size(); ++i){
+ normalized->a_integralSum.push_back(a_integralSum[i]/frames_found*pow10(6));
+ normalized->a_integralSumErr.push_back(a_integralSumErr[i]* sqrt(pow10(6)/frames_found));
+ }
normalized->integralSumErr = integralSumErr * sqrt(pow10(6)/frames_found);
normalized->posSeedPerc = posSeedPerc;
normalized->sigmaSeedPerc = sigmaSeedPerc;
normalized->avgNoise = avgNoise;
normalized->avgNoisePlus = avgNoisePlus;
normalized->avgNoiseMinus = avgNoiseMinus;
- normalized->sr90IntegralVal = sr90IntegralVal;
+ normalized->sr90IntegralVal = sr90IntegralVal/frames_found*pow10(6);;
+ normalized->sr90IntegralErr = sr90IntegralErr * sqrt(pow10(6)/frames_found);
normalized->StoN = StoN;
normalized->CCE_in_Perc_1 = CCE_in_Perc_1;
normalized->CCE_in_Perc_25 = CCE_in_Perc_25;
else if (noisethresholdborder>=0 )
noiseborder = noisethresholdborder;
+// cout << colorred << " noiseborder " << noiseborder << " : " << endlr;
// cout << colorred << " " << histogrampointer->GetName() << " : " << endlr;
// leg->Draw();
//
}
- else if (fitFuncType=="landau")
+ else if (fitFuncType=="landauSIMPLE")
{
+ TF1* fitFunc = new TF1("fitFunc",fitFuncType,noiseborder,posMaxValHist);
+ fit_result_ptr = histogrampointer->Fit(fitFunc, "N,Q,W,S", "", noiseborder, posMaxValHist);
+ parameters = (Double_t *)calloc(3, sizeof(Double_t));
+ for (Int_t pari=0; pari<3; pari++)
+ parameters[pari]=fitFunc->GetParameter(pari);
+ fitFunc->SetLineColor(kGreen);
+ fitFunc->SetLineStyle(1); // normal for the following fits
+ if (verbose)
+ fitFunc->DrawCopy("same");
+
+ } else if (fitFuncType=="landau")
+ {
+ fitFuncType = "landau";
if (posVeto > 0)
posMaxValHist = posVeto-posVeto/8;
histogrampointer->GetXaxis()->SetRange(histogrampointer->GetXaxis()->FindBin(noiseborder),histogrampointer->GetXaxis()->FindBin(posMaxValHist)); // look only for maxima with x greater than noiseborder, cut away noise
fit_result_ptr = histogrampointer->Fit(fitFunc, "N,Q,W,S", "", noiseborder, posMaxValHist);
for (Int_t pari=0; pari<3; pari++)
parameters[pari]=fitFunc->GetParameter(pari);
+
+ fitFunc->SetLineColor(kGreen);
+ fitFunc->SetLineStyle(1); // normal for the following fits
+ if (verbose)
+ fitFunc->DrawCopy("same");
// posMax = fitFunc->GetParameter(1); // new method, just use MPV of fit, as noise border is well known. Compare with old method anyway and warn if something suspecious.
// parameters[1]=fitFunc->GetMaximumX();
posMax = fitFunc->GetMaximumX(); // new method, just use MPV of fit, as noise border is well known. Compare with old method anyway and warn if something suspecious.
fit_result_ptr = histogrampointer->Fit(fitFunc, "N,Q,W,S", "", fitMax1*0.9, posMaxValHist);
// histogrampointer->Fit(fitFunc, "N,Q,W", "", fitMax1, histogrampointer->GetBinCenter(bini));
fitMax3 = fitFunc->GetMaximumX();
- fitFunc->SetLineColor(kGreen);
+ fitFunc->SetLineColor(kAzure);
fitFunc->SetLineStyle(1); // normal for the following fits
- if (verbose)
- fitFunc->DrawCopy("same");
//Sort the three fits and save error estimation
minFitMax = TMath::Min(TMath::Min(fitMax1,fitMax2),fitMax3);
return 1;
}
-Bool_t HistogramType::FindNoisethresholdborder(TH1FO* histogrampointer, Bool_t overwrite, Bool_t verbose) {
+Float_t HistogramType::FindNoisethresholdborder(TH1FO* histogrampointer, Bool_t overwrite, Bool_t verbose) {
+ Bool_t savetoclass = kFALSE;
if (overwrite || noisethresholdborder == -1)
{
+ savetoclass = kTRUE;
+ }
// cout << colorcyan << "Find border" << endl;
- Float_t posMaxValHist = histogrampointer->GetXaxis()->GetXmax();
-
- TH1FO* smoothedcurce = (TH1FO*)histogrampointer->Clone();
- // smoothedcurce->Smooth(4); // not working method, rescaling is easier and more reliable
- Int_t rebinningfactor = 2;
- smoothedcurce->RebinX(rebinningfactor);
- smoothedcurce->SetAxisRange(0,histogrampointer->GetBinCenter(histogrampointer->GetNbinsX()));
-
- if (verbose) {
- cout << colorwhite << histogrampointer->GetName() << endlr;
- TString canvastitle = Form("%s Noisethreshold border", histogrampointer->GetName());
- TString canvasname = Form("%s Noisethreshold border", histogrampointer->GetName());
- TCanvas* canvas = new TCanvas(canvasname, canvastitle, 900, 700);
- smoothedcurce->SetLineColor(color+1);
- smoothedcurce->Draw();
- histogrampointer->Draw("SAME");
- canvas->Update();
+ Float_t posMaxValHist = histogrampointer->GetXaxis()->GetXmax();
+
+ TH1FO* smoothedcurce = (TH1FO*)histogrampointer->Clone();
+ // smoothedcurce->Smooth(4); // not working method, rescaling is easier and more reliable
+ Int_t rebinningfactor = 2;
+ smoothedcurce->RebinX(rebinningfactor);
+ smoothedcurce->SetAxisRange(0,histogrampointer->GetBinCenter(histogrampointer->GetNbinsX()));
+
+ if (verbose) {
+ cout << colorwhite << histogrampointer->GetName() << endlr;
+ TString canvastitle = Form("%s Noisethreshold border", histogrampointer->GetName());
+ TString canvasname = Form("%s Noisethreshold border", histogrampointer->GetName());
+ TCanvas* canvas = new TCanvas(canvasname, canvastitle, 900, 700);
+ smoothedcurce->SetLineColor(color+1);
+ smoothedcurce->Draw();
+ histogrampointer->Draw("SAME");
+ canvas->Update();
+ }
+
+ // Int_t bini =smoothedcurce->GetMaximumBin();
+ // thresholdbincurcandidate = bini;
+ // if (verbose)
+ // cout << "GetMaximumBin: smoothedcurce->GetBinContent(" << bini << "): " << smoothedcurce->GetBinContent(bini) << endl;
+ Int_t bini = 0;
+ Int_t falling = 0;
+ Int_t noisepeakcandidate = 0;
+
+ do {
+ Float_t curval=smoothedcurce->GetBinContent(bini++);
+ if (curval*1.05 <= smoothedcurce->GetBinContent(bini))
+ {
+ falling = 0;
+ noisepeakcandidate = bini;
}
-
- // Int_t bini =smoothedcurce->GetMaximumBin();
- // thresholdbincurcandidate = bini;
- // if (verbose)
- // cout << "GetMaximumBin: smoothedcurce->GetBinContent(" << bini << "): " << smoothedcurce->GetBinContent(bini) << endl;
- Int_t bini = 0;
- Int_t falling = 0;
- Int_t noisepeakcandidate = 0;
-
- do {
- Float_t curval=smoothedcurce->GetBinContent(bini++);
- if (curval*1.05 <= smoothedcurce->GetBinContent(bini))
- {
- falling = 0;
- noisepeakcandidate = bini;
- }
- else
- {
- falling++;
- if (verbose)
- cout << "falling at " << smoothedcurce->GetXaxis()->GetBinCenter(bini) << " as " << curval << " < " << smoothedcurce->GetBinContent(bini) << endl; // debug
- }
- } while (falling < 2 && bini<smoothedcurce->GetNbinsX());
- if (verbose) {
- cout << "Noise peak at: smoothedcurce->GetBinContent(" << noisepeakcandidate << "): " << smoothedcurce->GetBinContent(noisepeakcandidate) << endl;
+ else
+ {
+ falling++;
+ if (verbose)
+ cout << "falling at " << smoothedcurce->GetXaxis()->GetBinCenter(bini) << " as " << curval << " < " << smoothedcurce->GetBinContent(bini) << endl; // debug
}
- Int_t thresholdbincurcandidate = 0;
- bini = noisepeakcandidate;
-
- Int_t rising = 0;
- do {
- Float_t curval=smoothedcurce->GetBinContent(bini++);
- if (curval*0.95 <= smoothedcurce->GetBinContent(bini))
- {
- rising++;
- if (verbose)
- cout << "rising at bin " << bini << ":" << smoothedcurce->GetXaxis()->GetBinCenter(bini) << " as " << curval << " < " << smoothedcurce->GetBinContent(bini) << endl; // debug
- }
- else
- {
- rising = 0;
- thresholdbincurcandidate = bini;
- if (verbose)
- cout << "falling at bin " << bini << ":" << smoothedcurce->GetXaxis()->GetBinCenter(bini) << " as " << curval << " < " << smoothedcurce->GetBinContent(bini) << endl; // debug
- }
- } while (rising < 3 && bini<smoothedcurce->GetNbinsX());
- thresholdbincurcandidate *= rebinningfactor;
-
- noisethresholdborder = histogrampointer->GetXaxis()->GetBinUpEdge(thresholdbincurcandidate);
- if (verbose) {
- cout << " Noise threshold at " << noisethresholdborder << endl;
+ } while (falling < 2 && bini<smoothedcurce->GetNbinsX());
+ if (verbose) {
+ cout << "Noise peak at: smoothedcurce->GetBinContent(" << noisepeakcandidate << "): " << smoothedcurce->GetBinContent(noisepeakcandidate) << endl;
+ }
+ Int_t thresholdbincurcandidate = 0;
+ bini = noisepeakcandidate;
+
+ Int_t rising = 0;
+ do {
+ Float_t curval=smoothedcurce->GetBinContent(bini++);
+ if (curval*0.95 <= smoothedcurce->GetBinContent(bini))
+ {
+ rising++;
+ if (verbose)
+ cout << "rising at bin " << bini << ":" << smoothedcurce->GetXaxis()->GetBinCenter(bini) << " as " << curval << " < " << smoothedcurce->GetBinContent(bini) << endl; // debug
}
- return 0;
+ else
+ {
+ rising = 0;
+ thresholdbincurcandidate = bini;
+ if (verbose)
+ cout << "falling at bin " << bini << ":" << smoothedcurce->GetXaxis()->GetBinCenter(bini) << " as " << curval << " < " << smoothedcurce->GetBinContent(bini) << endl; // debug
+ }
+ } while (rising < 3 && bini<smoothedcurce->GetNbinsX());
+ thresholdbincurcandidate *= rebinningfactor;
+
+ Float_t temp_noisethresholdborder = histogrampointer->GetXaxis()->GetBinUpEdge(thresholdbincurcandidate);
+ if (savetoclass)
+ noisethresholdborder = temp_noisethresholdborder;
+
+ if (verbose) {
+ cout << " Noise threshold at " << temp_noisethresholdborder << endl;
}
- return 1;
+ return temp_noisethresholdborder;
}
Double_t HistogramType::FindBorderToPeak(TH1FO* histogrampointer, Double_t startVal, Double_t endVal, Bool_t verbose) {
Bool_t HistogramType::integrateSr90Spectra(TH1FO* histogrampointer, Int_t frames_found, Float_t thresholdborder, Bool_t verbose) {
Int_t thresholdbincurcandidate = 0;
+
if (thresholdborder < 0)
{
if (noisethresholdborder < 0) {
}
}
}
- sr90IntegralVal = histogrampointer->Integral(thresholdbincurcandidate,histogrampointer->GetNbinsX(), "width");
+
+ sr90IntegralVal = histogrampointer->IntegralAndError(thresholdbincurcandidate,histogrampointer->GetNbinsX()+1, sr90IntegralErr, "width");
+
// histogrampointer->Integral(min+fittries*min/20, histogrampointer->GetXaxis()->FindBin(posMaxValHist), "width");
// for (
// cout << "Integrate from bin " << thresholdbincurcandidate << " to " << histogrampointer->GetNbinsX() << endl;
cout << " ";
if (frames_found>0)
{
+ cout << "Integral is " << colorgreen << Form("%e",sr90IntegralVal) << "+-" << Form("%e",sr90IntegralErr) << colorreset << endl;
sr90IntegralVal/=frames_found;
- cout << "Integral is " << colorgreen << Form("%e",sr90IntegralVal) << colorreset << endl;
+ sr90IntegralErr/=frames_found;
if (verbose)
cout << "Scaled ";
}
if (verbose) {
// cout << "Integral is " << colorgreen << Form("%e",sr90IntegralVal) << colorreset << ", error " << sr90IntegralErr << "%" << endl;
- cout << "Integral is " << colorgreen << Form("%e",sr90IntegralVal) << endlr;
+ cout << "Integral is " << colorgreen << Form("%e",sr90IntegralVal)<< "+-" << Form("%e",sr90IntegralErr) << endlr;
+ }
+
+ return 0;
+}
+
+
+Bool_t HistogramType::integrateSr90Spectras(Int_t frames_found, Float_t thresholdborder, Bool_t verbose) {
+
+ Int_t thresholdbincurcandidate = 0;
+ Double_t cur_sr90IntegralVal = 0;
+ Double_t cur_sr90IntegralErr = 0;
+
+ if (thresholdborder < 0)
+ {
+ if (noisethresholdborder < 0) {
+ FindNoisethresholdborder(Seed, true, verbose);
+ }
+ thresholdbincurcandidate = Seed->GetXaxis()->FindBin(noisethresholdborder);
}
+ else
+ {
+ thresholdbincurcandidate = Seed->GetXaxis()->FindBin(thresholdborder);
+ noisethresholdborder = thresholdborder;
+ }
+
+ for(UInt_t sumi=0;sumi<a_Sum.size();sumi++) // loop over all sum spectras
+ {
+ cur_sr90IntegralVal = a_Sum[sumi]->IntegralAndError(thresholdbincurcandidate,a_Sum[sumi]->GetNbinsX()+1, cur_sr90IntegralErr, "width");
+ a_sr90IntegralVal.push_back(cur_sr90IntegralVal);
+ a_sr90IntegralErr.push_back(cur_sr90IntegralErr);
+ }
return 0;
}
// GENERAL HISTOGRAM PROPERTIES
//*****************
- Int_t color;
- Int_t style;
-
-
//*****************
// GENERAL SYSTEM PROPERTIES
//*****************
/// Pointer to the run class wich initialized this histogram type
Run* run = 0;
+ //*****************
+ // GENERAL HISTOGRAM PROPERTIES
+ //*****************
+
+ Int_t color;
+ Int_t style;
//*****************
// TH HISTOGRAMS
TH1FO* Seed = 0;
/// Sum spectrum, the charge of whole cluster is summed up in binned into this TH1F histogram
TH1FO* Sum = 0;
+ /// Sum spectrum, the charge of whole cluster is summed up in binned into this TH1F histogram
+// TH1FO* Sum9 = 0;
/// Sum spectrum array, the charge of whole cluster is summed up in binned into this TH1F histogram
vector<TH1FO*> a_Sum;
/// Veto spectrum, used to find better calibration peak, only entries where Sum over not seed pixels is below @c Run::vetothreshold are binned into this histogram
Double_t integralSeedErr=0;
/// fitted position of the most probable value in the over cluster summed spectrum
Float_t posSum=0;
+ vector<Float_t> a_posSum;
/// fintegral over the sum peak, normalized to number of events
Double_t integralSum=0;
+ vector<Double_t> a_integralSum;
/// fintegral error over the Seed peak, poisson dist. assumed -> sqrt(N) for each bin
Double_t integralSumErr=0;
+ vector<Double_t> a_integralSumErr;
/// fitted position of the calibration peak of Fe55-beta-photons in the seed spectrum, from a run best suited to the current
Float_t posVeto=0;
/// fintegral over the veto peak, normalized to number of events
Double_t integralVeto=0;
+
+ /// what is the propability that a second pixel is over the threshold
+ Double_t clustermultiplicity=0;
/// Average Noise value
Float_t avgNoise = 0;
/// Integral value, after integrating from #noisethresholdborder to maxbin.
Double_t sr90IntegralVal = -1;
Double_t sr90IntegralErr = -1;
+ vector<Double_t> a_sr90IntegralVal;
+ vector<Double_t> a_sr90IntegralErr;
/// Peak and sigma of Seed to Sum ratio
Float_t posSeedPerc = 0;
*
*/
Bool_t integrateSr90Spectra(TH1FO* histogrampointer, Int_t frames_found=-1, Float_t thresholdborder=-1, Bool_t verbose=true);
+ Bool_t integrateSr90Spectras(Int_t frames_found, Float_t thresholdborder, Bool_t verbose);
/**
* @brief Tries to find a border between the noise and the signal
* @return true if succesfull
*
*/
- Bool_t FindNoisethresholdborder(TH1FO* histogrampointer, Bool_t overwrite=false, Bool_t verbose=false);
+ Float_t FindNoisethresholdborder(TH1FO* histogrampointer, Bool_t overwrite=false, Bool_t verbose=false);
/**
//db = TSQLServer::Connect("mysql://jspc29.x-matter.uni-frankfurt.de","radhard","mimosa88");
try
{
- string selectquery = prepareSQLStatement("select `System`, `TempCooling`, COALESCE(`TempChipStart`,-10000) as `TempChipStart`, COALESCE(`TempChipEnd`,-10000) as `TempChipEnd`, `ChipNum`, `RadiationSource`, `Matrix`, `Clock`, `StorePath`, `ChipGen`,COALESCE(`VetoPeak`,-1) as `VetoPeak`,COALESCE(`SeedPeak`,-1) as `SeedPeak`,COALESCE(`SumPeak`,-1) as `SumPeak`,COALESCE(`Gain`,-1) as `Gain`,COALESCE(`Avg.Noise`,-1) as `Avg.Noise`,COALESCE(`Avg.Noise+`,-1) as `Avg.Noise+`,COALESCE(`Avg.Noise-`,-1) as `Avg.Noise-`,COALESCE(`CCE_1`,-1) as `CCE_1`,COALESCE(`CCE_25`,-1) as `CCE_25`,COALESCE(`Frames_found`,-1) as `Frames_found`,COALESCE(`Sr90IntegralVal`,-1) as `Sr90IntegralVal`,COALESCE(`StoN`,-1) as `StoN`, `Comment`, `DepletionVoltage` from `radhard`.`labbook` WHERE `runnumber`=" + numberToString<>(labbook.runnumber));
+ string selectquery = prepareSQLStatement("select `System`, `TempCooling`, COALESCE(`TempChipStart`,-10000) as `TempChipStart`, COALESCE(`TempChipEnd`,-10000) as `TempChipEnd`, `ChipNum`, `RadiationSource`, `Matrix`, `Clock`, `StorePath`, `ChipGen`,COALESCE(`VetoPeak`,-1) as `VetoPeak`,COALESCE(`SeedPeak`,-1) as `SeedPeak`,COALESCE(`SumPeak`,-1) as `SumPeak`,COALESCE(`Gain`,-1) as `Gain`,COALESCE(`Avg.Noise`,-1) as `Avg.Noise`,COALESCE(`Avg.NoiseADC`,-1) as `Avg.NoiseADC`,COALESCE(`Avg.Noise+`,-1) as `Avg.Noise+`,COALESCE(`Avg.Noise-`,-1) as `Avg.Noise-`,COALESCE(`CCE_1`,-1) as `CCE_1`,COALESCE(`CCE_25`,-1) as `CCE_25`,COALESCE(`Frames_found`,-1) as `Frames_found`,COALESCE(`Sr90IntegralVal`,-1) as `Sr90IntegralVal`,COALESCE(`StoN`,-1) as `StoN`, `Comment`, `DepletionVoltage` from `radhard`.`labbook` WHERE `runnumber`=" + numberToString<>(labbook.runnumber));
res = db->Query(selectquery.c_str());
nrows = res->GetRowCount();
if (nrows > 0)
labbook.posSumDB = (rowsql->GetField(12) != NULL)?atof(rowsql->GetField(12)):-1;
labbook.gainDB = (rowsql->GetField(13) != NULL)?atof(rowsql->GetField(13)):-1;
labbook.NoiseAvgDB = (rowsql->GetField(14) != NULL)?atof(rowsql->GetField(14)):-1;
- labbook.NoiseAvgPlusDB = (rowsql->GetField(15) != NULL)?atof(rowsql->GetField(15)):-1;
- labbook.NoiseAvgMinusDB = (rowsql->GetField(16) != NULL)?atof(rowsql->GetField(16)):-1;
- labbook.CCE_in_Perc_1DB = (rowsql->GetField(17) != NULL)?atof(rowsql->GetField(17)):-1;
- labbook.CCE_in_Perc_25DB = (rowsql->GetField(18) != NULL)?atof(rowsql->GetField(18)):-1;
- labbook.frames_foundDB = (rowsql->GetField(19) != NULL)?atoi(rowsql->GetField(19)):-1;
- labbook.Sr90IntegralVal = (rowsql->GetField(20) != NULL)?atof(rowsql->GetField(20)):-1;
- labbook.StoN = (rowsql->GetField(21) != NULL)?atof(rowsql->GetField(21)):-1;
- labbook.comment = (rowsql->GetField(22) != NULL)?std::string(rowsql->GetField(22)):"";
- labbook.depletionV = (rowsql->GetField(23) != NULL)?atof(rowsql->GetField(23)):-1;
+ labbook.NoiseAvgADC_DB = (rowsql->GetField(15) != NULL)?atof(rowsql->GetField(15)):-1;
+ labbook.NoiseAvgPlusDB = (rowsql->GetField(16) != NULL)?atof(rowsql->GetField(16)):-1;
+ labbook.NoiseAvgMinusDB = (rowsql->GetField(17) != NULL)?atof(rowsql->GetField(17)):-1;
+ labbook.CCE_in_Perc_1DB = (rowsql->GetField(18) != NULL)?atof(rowsql->GetField(18)):-1;
+ labbook.CCE_in_Perc_25DB = (rowsql->GetField(19) != NULL)?atof(rowsql->GetField(19)):-1;
+ labbook.frames_foundDB = (rowsql->GetField(20) != NULL)?atoi(rowsql->GetField(20)):-1;
+ labbook.Sr90IntegralVal = (rowsql->GetField(21) != NULL)?atof(rowsql->GetField(21)):-1;
+ labbook.StoN = (rowsql->GetField(22) != NULL)?atof(rowsql->GetField(22)):-1;
+ labbook.comment = (rowsql->GetField(23) != NULL)?std::string(rowsql->GetField(23)):"";
+ labbook.depletionV = (rowsql->GetField(24) != NULL)?atof(rowsql->GetField(24)):-1;
// labbook.frames_Analyzed = (rowsql->GetField(20) != NULL)?atoi(rowsql->GetField(20)):-1;
delete res;
if (labbook.chipGen.Length() > 0)
cout << "| posSumDB: " << std::right << colorwhite << labbook.posSumDB << endlr;
cout << "| posVetoDB: " << std::right << colorwhite << labbook.posVetoDB << endlr;
cout << "| gainDB: " << std::right << colorwhite << labbook.gainDB << endlr;
+ cout << "| NoiseAvgADC_DB " << std::right << colorwhite << labbook.NoiseAvgADC_DB << endlr;
cout << "| NoiseAvgDB: " << std::right << colorwhite << labbook.NoiseAvgDB << endlr;
cout << "| NoiseAvgPlusDB: " << std::right << colorwhite << labbook.NoiseAvgPlusDB << endlr;
cout << "| NoiseAvgMinusDB: " << std::right << colorwhite << labbook.NoiseAvgMinusDB << endlr;
systemparam systemparamFSBB (2800,2800/4,25,10,100);
systemparam systemparamUSBMi19 (400/*maxbin*/,400/1/*nbins*/, 25/*vetothreshold*/, 10/*maxbinnoise*/, 100/*nbinsnoise*/);
systemparam systemparamPipper2 (1000,500,25,10,100); // change bin size to 200 if Cd109
+ systemparam systemparamPipper2_Sr (3000,1200,25,10,100); // change bin size to 200 if Cd109
systemparam systemparamPipper2_Cd (1000,200,25,10,100); // change bin size to 200 if Cd109
if (labbook.system.EqualTo("USB") && labbook.chipGen.EqualTo("Mi34") )
cursystemparam = systemparamUSB;
else if (labbook.system.EqualTo("USB") && labbook.chipGen.EqualTo("Pipper2") ) {
if (labbook.source.Contains("Cd"))
cursystemparam = systemparamPipper2_Cd;
+ else if (labbook.source.Contains("Sr"))
+ cursystemparam = systemparamPipper2_Sr;
else
cursystemparam = systemparamPipper2;
}
if (labbook.source.Contains("Sr90")) {
//cout << colorwhite << " integrateSr90Spectra():" << endlr;
(*curHistogramClass)->integrateSr90Spectra((*curHistogramClass)->Seed, frames_found, -1, false);
+ (*curHistogramClass)->integrateSr90Spectras(frames_found, 58, false);
cout << colorwhite << " calculteStoN():" << endlr;
(*curHistogramClass)->calculteStoN(true);
}
if (labbook.source.Contains("Sr90")) {
cout << colorwhite << "integrateSr90Spectra():" << endlr;
histogramthreshold->integrateSr90Spectra(histogramthreshold->Seed, frames_found, -1, true);
- if (histogramfixedthreshold != 0)
+ histogramthreshold->integrateSr90Spectras(frames_found, -1, false);
+ if (histogramfixedthreshold != 0) {
histogramfixedthreshold->integrateSr90Spectra(histogramfixedthreshold->Seed, frames_found, 0);
+ histogramfixedthreshold->integrateSr90Spectras(frames_found, -1, false);
+ }
}
cout << colorwhite << "normalizeHistogramClasses():" << endlr;
normalizeHistogramClasses();
Double_t gausssigma = 0;
+
for (Int_t filli=0; filli<3; filli++) { // first and second fill loop
for (Int_t framei=0; framei<processed->fHitTree->GetEntries(); framei++) // loop over all frames
{
histogram->a_Sum[clusterj]->Fill(a_pixelSum[clusterj]);
}
+ if (a_pixelSum[1]-a_pixelSum[0] > labbook.NoiseAvgADC_DB*5)
+ histogram->clustermultiplicity++;
+
// seed percentage spectrum
histogram->SeedPerc->Fill(processed->fFrameInfo.p[12][hiti]/pixelSum*100);
(*curHistogramClass)->integralSum = parameters[6];
(*curHistogramClass)->integralSumErr = parameters[9];
+ (*curHistogramClass)->clustermultiplicity /=(*curHistogramClass)->numberofhits;
+
+ for (u_int sumi = 0; sumi < (*curHistogramClass)->a_Sum.size(); sumi++) {
+ // Float_t integratestart = (*curHistogramClass)->FindNoisethresholdborder((*curHistogramClass)->a_Sum[sumi], false, false);
+// parameters = (*curHistogramClass)->FitPerform((*curHistogramClass)->a_Sum[sumi], "gaus", 0, false, integratestart); //TODO change back to gauss
+ parameters = (*curHistogramClass)->FitPerform((*curHistogramClass)->a_Sum[sumi], "gaus", 0, false, (*curHistogramClass)->fixedThresholdValue); //TODO change back to gauss
+ (*curHistogramClass)->a_posSum.push_back(parameters[1]);
+ (*curHistogramClass)->a_integralSum.push_back(parameters[6]);
+ (*curHistogramClass)->a_integralSumErr.push_back(parameters[9]);
+ }
+
parameters =(*curHistogramClass)->FitPerform((*curHistogramClass)->SeedPerc, "landau");
(*curHistogramClass)->posSeedPerc = parameters[1];
(*curHistogramClass)->sigmaSeedPerc = parameters[2];
histogramtypepointer->SeedPerc->Fill(processed->fFrameInfo.p[12][hiti]/pixelSum*100);
if (TMath::Abs(notSeedSum) < cursystemparam.vetothreshold && (labbook.source.Contains("Fe")||labbook.source.Contains("Cd")))
histogramtypepointer->Veto->Fill(processed->fFrameInfo.p[12][hiti]); // histogram with the single pixel
+
+ if (pt_a_pixelSum[1]-pt_a_pixelSum[0] > labbook.NoiseAvgADC_DB*5)
+ histogramtypepointer->clustermultiplicity++;
}
#include <TCanvas.h>
#include <Rtypes.h>
+#include <TNtuple.h>
#include <TString.h>
#include <TTimeStamp.h>
#include <TLegend.h>
#define MAXHITS 1000000
#define MAXPIXELS 100000
+
+
+
//####################################################################
/**
* @file help.h
Float_t posVetoDB = -1;
/// gain found in db
Float_t gainDB = -1;
- /// average noise found in db
+ /// average noise found in db in ADU
+ Float_t NoiseAvgADC_DB = -1;
+ /// average noise found in db in e
Float_t NoiseAvgDB = -1;
/// Positive noise error found in db
Float_t NoiseAvgPlusDB = -1;
return ss >> result ? result : 0;
}
+class HistogramType;
+class TNtupleO: public TNtuple {
+public:
+ HistogramType* itsHistogramType = 0;
+ TString xaxisTitle = "", yaxisTitle = "", Title = "";
+ using TNtuple::TNtuple;
+};
/**
jspc29 / radhard.git / commitdiff