Int_t fRunNumber;
/// Number of Pixels found in Raw Data, is set im @c checkConf()
Int_t fPixelsData;
- /// Number of Pixels written to #fCdsmatrix and used for analysis
+ /// Number of Pixels written to #MAPS::fCdsmatrix and used for analysis
Int_t fPixels;
/// number of rows in the sensor, set and passed initMapsRun() to in the constructor, if Mimosa34 and system==USB then it is equal to 8, for PXI system it is equal to 16
Int_t fRows;
int fNoiseDyn;
Float_t HIT_tmp[29];
- /// total number of events, stated in the config file #fRootFile
+ /// total number of events, stated in the config file #MAPS::fRootFile
UInt_t FileTotalEvNbInConfig = 0;
- /// number of events per file, stated in the config file #fRootFile
+ /// number of events per file, stated in the config file #MAPS::fRootFile
UInt_t FileEvNbInConfig = 0;
- /// Average noise over all pixels in #fPixels in current frame to be binned into #fDynNoiseTree
+ /// Average noise over all pixels in #MAPS::fPixels in current frame to be binned into #MAPS::fDynNoiseTree
Float_t fNoiseMean;
/// Average pedestal to be binned into fDynNoiseTree
Float_t fPedestalsMean;
Int_t* fEvents;
/// Array with f0 values of pixels in current frame
Int_t* fF0matrix;
- /// Array with f1 values of pixels in current frame, used to generate #fCdsmatrix = #fF0matrix - #fF1matrix
+ /// Array with f1 values of pixels in current frame, used to generate #MAPS::fCdsmatrix = #MAPS::fF0matrix - #MAPS::fF1matrix
Int_t* fF1matrix;
/// Array with CDS values of pixels in current frame
Float_t* fCdsmatrix;
* @brief Compares provided information with that found in the RAW data
* @return true on success, false on failure
*
- * It also sets #FileTotalEvNbInConfig, #FileEvNbInConfig and @a fPixelsData accordingly
+ * It also sets #MAPS::FileTotalEvNbInConfig, #MAPS::FileEvNbInConfig and @a fPixelsData accordingly
*/
bool checkConf(Int_t&);
void debugStream(const arraytype* (a), Int_t n=512, Int_t columns=8, Int_t precision=2, float highlightabove = 99999999);
/**
- * @brief Initialize histogram labels and histograms #hint1, #hint2, #fdiscriminatedhitmatrix, #fADCHitmatrix
+ * @brief Initialize histogram labels and histograms #MAPS::hint1, #MAPS::hint2, #MAPS::fdiscriminatedhitmatrix, #MAPS::fADCHitmatrix
*/
void initHistograms();
Bool_t initOldRootFile();
- /** @brief Refill the noise branch every #numberofframesfornoise frames or just use the initial initialization
+ /** @brief Refill the noise branch every #MAPS::numberofframesfornoise frames or just use the initial initialization
*
* turning this on will slow down the analysis, but allows time dependant noise investigation
*/
/**
- * @brief Calculates a first estimate of the noise and pedestial of each pixel in #frames
+ * @brief Calculates a first estimate of the noise and pedestial of each pixel in #MAPS::frames
*
* Calculate noise and pedestial, trying to avoid pixels with hit charge
*
* Steps:
* 1. Calculate noise and pedestial
* 2. Recalculate noise and pedestial without pixels with higher CDS then 5 sigma
- * 3. Recalculate noise and pedestial again using second estimate, without pixels with higher CDS then 3 sigma. This noise and pedestial is called #fPedestals and #fNoise.
+ * 3. Recalculate noise and pedestial again using second estimate, without pixels with higher CDS then 3 sigma. This noise and pedestial is called #MAPS::fPedestals and #MAPS::fNoise.
* This new noise and pedestial will be used for hit analysis (@c hitana())
*
* @return true if no errors occured
/**
* @brief Eliminates line structures in the chip
*
- * @Todo An average over the noise of a line is calculated and subtracted from each #fCdsmatrix value of every pixel in this line.<br />
+ * @Todo An average over the noise of a line is calculated and subtracted from each #MAPS::fCdsmatrix value of every pixel in this line.<br />
* Noise structure between different lines is eliminated this way.
*
* @see fCdsmatrix
Long_t plus = 0;
Long_t minus = 0;
/**
- * @brief Analysis current frame for hits, saves cluster to #fFrameInfo.p
+ * @brief Analysis current frame for hits, saves cluster to #MAPS::fFrameInfo.p
*
* loops over all pixel, compares
*
*
* if true, saves pixel as seed pixel candidate, creates 5x5 cluster
* around it. If cluster has biggest value in the middle, then count it as a valid
- * cluster and indicate a hit in the discriminator matrix #fHittedPixel, TH2F variable #fdiscriminatedhitmatrix
- * and TH2F ADC matrix #fADCHitmatrix
+ * cluster and indicate a hit in the discriminator matrix #MAPS::fHittedPixel, TH2F variable #MAPS::fdiscriminatedhitmatrix
+ * and TH2F ADC matrix #MAPS::fADCHitmatrix
*
*/
void hitana ();
Bool_t Run::binCluster()
{
- // histogram.histAvgCluster->SetBit(TH1::kCanRebin);
- // histogramthreshold.histAvgCluster->SetBit(TH1::kCanRebin);
Float_t rotateangle = 0;
if (curpixelinfo.staggered) {
rotateangle = TMath::ATan(curpixelinfo.pitchY/(curpixelinfo.pitchX/2))*180.0/TMath::Pi();
Canvas_1->Modified();
Canvas_1->cd();
Canvas_1->SetSelected(Canvas_1);
-
-
+
TImageDump *img = new TImageDump(savepathresults + "/" + canvastitle + ".png");
Canvas_1->Paint();
img->Close();
// gStyle->SetPaperSize(10.,10.);
// canvas->Print(savepathresults + "/" + canvastitle + ".tex");
- return 0;
+ // 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()));
+ 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));
+
+ 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()));
+ 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));
+
+ random = random1->Rndm()*100000;
+ canvastitle = Form("%s Cluster slices", runcode.Data());
+ if (histogramstructpointer->calibrated)
+ canvastitle += "_el";
+ if (histogramstructpointer->thresholdcluster)
+ canvastitle += "_trsh";
+ canvasname = Form("%s%d",runcode.Data(),random);
+ TCanvas* canvas = new TCanvas(canvasname, canvastitle, 1200, 700);
+ canvas->Divide(2,1);
+ canvas->SetFillColor(0);
+ canvas->SetBorderSize(2);
+
+ canvas->cd(1);
+ TPad* firstpart = (TPad*)canvas->GetPad(1);
+ firstpart->SetGrid();
+ FitPerform(xslicetroughcluster,"lorentz", false, &xslicetroughclusterpar[0]);
+
+
+ canvas->cd(2);
+ TPad* secondpart = (TPad*)canvas->GetPad(2);
+ secondpart->SetGrid();
+ FitPerform(yslicetroughcluster,"lorentz", false, &yslicetroughclusterpar[0]);
+
+ canvas->Draw();
+
+ TImageDump *img2 = new TImageDump(savepathresults + "/" + canvastitle + ".png");
+ canvas->Paint();
+ img2->Close();
+
+ f->Append(canvas);
+ f->Append(img2);
+ f->Write();
+ // .Data(), Form("Avg. cluster distribution; x; y %s", humanreadablestr.Data()),sizearrrotx, xcoord_abs_min,xcoord_abs_max,sizearrroty,ycoord_abs_min,ycoord_abs_max);
+
+ return 0;
}
Bool_t Run::plotClusterDistribution()
return 0;
}
-Float_t Run::FitPerform(TH1F* histogrampointer, TString fitFuncType, Bool_t verbose)
+Float_t Run::FitPerform(TH1F* histogrampointer, TString fitFuncType, Bool_t verbose, Double_t* parameters)
{
Float_t posMax = 0;
Float_t posMax2 = 0;
if (doFits)
{
- 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 (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();
}
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;
//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");
+ Double_t par[4];
+
+ // writes the fit results into the par array
+ fitFcn->GetParameters(par);
+ 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)
{
for (Int_t biny_i = 1; biny_i<=onehistogram->GetXaxis()->GetNbins(); biny_i++) {
for (Int_t binx_i = 1; binx_i<=onehistogram->GetXaxis()->GetNbins(); binx_i++) {
outline=Form("%.2f\t%.2f\t%.2f", onehistogram->GetXaxis()->GetBinCenter(binx_i), onehistogram->GetYaxis()->GetBinCenter(biny_i), onehistogram->GetBinContent(binx_i, biny_i));
- cout << outline << endl; // debug line
+ //cout << outline << endl; // debug line
*fout<<outline<<endl;
}
}
rootlinestyle = new Int_t[13]{1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
}
+
#endif
#include <TF1.h>
#include <TImageDump.h>
+#include <TMath.h>
+#include <TF1.h>
+
#include <TAttPad.h>
#include <TView.h>
#include <TPaletteAxis.h>
*/
string removeNumbers(std::string x);
+ /**
+ * @brief Lorenzian Peak function */
+ static Double_t lorentzianPeak(Double_t *x, Double_t *par);
+
/**
* @brief removes all punctuation from given string
*/
char hostname[1024];
/**
- * @brief fills noise #histogram */
+ * @brief fills noise histograms in #Run::histogramstruct */
Bool_t binNoise();
/**
- * @brief fills Seedm, Sum and Veto #histogram */
+ * @brief fills Seedm, Sum and Veto #Run::histogramstruct */
Bool_t binSeedSumVeto();
/**
- * @brief fills the average cluster distribution into a 2 dimension histogram #histogram*/
+ * @brief fills the average cluster distribution into a 2 dimension histogram #Run::histogramstruct */
Bool_t binCluster();
/// noise quantiles: mean value, sigma in postive direction and sigma in negative direction
Double_t noisequantiles[3];
* @return peak position of the fit
*
*/
- Float_t FitPerform(TH1F*, TString fitFuncType="landau", Bool_t verbose=true);
+ Float_t FitPerform(TH1F*, TString fitFuncType="landau", Bool_t verbose=true, Double_t* parameters=NULL);
/**
* @brief find the border between the noise and the signal in Sr90 runs
/** @brief Writes a given histogram into a file */
Bool_t write2DHistogramToFile(TH2F* onehistogram);
- /** @brief Writes all histogram of @c Run::histogramsctruct into a file */
+ /** @brief Writes all histogram of @c Run::histogramstruct into a file */
Bool_t writeAllHistogramsToFile();
/** @brief Makes a gnuplot file to plot the histogram data created in @c Run::writeAllHistogramsToFile() */
histogramstruct* plothistogramstructpointer;
TH1F** plothistogrampointer;
- /** @brief Plots all histograms from #histogram into one canvas */
+ /** @brief Plots all histograms from #Run::histogramstruct into one canvas
+ *
+ * @param histogramstruct pointer to a histogram structure of type #Run::histogramstruct
+ */
Bool_t plotAllHistograms(histogramstruct*);
- /** @brief Plots average cluster charge distribution */
+ /** @brief Plots average cluster charge distribution
+ *
+ * Creates a graphical view of the average charge distribution in a
+ * cluster
+ *
+ * @param histogramstruct Pointer to a structure of type #Run::histogramstruct
+ *
+ */
Bool_t plotClusterDistribution(histogramstruct*);
- /// init all histograms, set binwidth, bin amount and names
+ ///
+ /** @brief initializes all histograms, set binwidth, bin amount and names
+ * *
+ * @param histogramstruct Pointer to a structure of type #Run::histogramstruct
+ * @param suffix A string which will be appended to the generated title of each histograms
+ */
void initHistograms(histogramstruct*, TString suffix = "");
- /** @brief initializes the TH2F cluster for the binning for a specific structure of type #histogramstruct one points to*/
+ /** @brief initializes the TH2F cluster for the binning for a specific structure of type #Run::histogramstruct one points to*/
void initCluster(histogramstruct*, 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);
pixelinfo pixelinfoMi34[32];
jspc29 / radhard.git / commitdiff