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/data/refman/pasoursint/CMSSW_5_3_0/src/CalibTracker/SiPixelSCurveCalibration/src/SiPixelSCurveCalibrationAnalysis.cc

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00001 #include "CalibTracker/SiPixelSCurveCalibration/interface/SiPixelSCurveCalibrationAnalysis.h"
00002 #include "TMath.h"
00003 
00004 #include <iostream>
00005 #include <fstream>
00006 
00007 #include "Geometry/Records/interface/TrackerDigiGeometryRecord.h"
00008 #include "DataFormats/SiPixelDigi/interface/SiPixelCalibDigiError.h"
00009 #include "CondFormats/SiPixelObjects/interface/SiPixelFrameConverter.h"
00010 #include "CondFormats/SiPixelObjects/interface/ElectronicIndex.h"
00011 #include "CondFormats/SiPixelObjects/interface/DetectorIndex.h"
00012 #include "CondFormats/SiPixelObjects/interface/LocalPixel.h"
00013 #include <sstream>
00014 
00015 //initialize static members
00016 std::vector<float> SiPixelSCurveCalibrationAnalysis::efficiencies_(0);
00017 std::vector<float> SiPixelSCurveCalibrationAnalysis::effErrors_(0);
00018 
00019 
00020 void SiPixelSCurveCalibrationAnalysis::calibrationEnd(){
00021   if(printoutthresholds_)
00022     makeThresholdSummary();
00023 }
00024 
00025 void SiPixelSCurveCalibrationAnalysis::makeThresholdSummary(void){
00026   ofstream myfile;
00027   myfile.open (thresholdfilename_.c_str());
00028   for(detIDHistogramMap::iterator  thisDetIdHistoGrams= histograms_.begin();  thisDetIdHistoGrams != histograms_.end(); ++thisDetIdHistoGrams){
00029    // loop over det id (det id = number (unsigned int) of pixel module 
00030     const MonitorElement *sigmahist = (*thisDetIdHistoGrams).second[kSigmas];
00031     const MonitorElement *thresholdhist = (*thisDetIdHistoGrams).second[kThresholds];  
00032     uint32_t detid = (*thisDetIdHistoGrams).first;
00033     std::string name = sigmahist->getTitle();
00034     std::string rocname = name.substr(0,name.size()-7);
00035     rocname+="_ROC";
00036     int total_rows = sigmahist ->getNbinsY();
00037     int total_columns = sigmahist->getNbinsX();
00038     //loop over all rows on columns on all ROCs 
00039     for (int irow=0; irow<total_rows; ++irow){
00040       for (int icol=0; icol<total_columns; ++icol){
00041         float threshold_error = sigmahist->getBinContent(icol+1,irow+1); // +1 because root bins start at 1
00042         if(writeZeroes_ ||(!writeZeroes_ && threshold_error>0)){             
00043           //changing from offline to online numbers
00044           int realfedID=-1;
00045           for(int fedid=0; fedid<=40; ++fedid){
00046             SiPixelFrameConverter converter(theCablingMap_.product(),fedid);
00047             if(converter.hasDetUnit(detid)){
00048               realfedID=fedid;
00049               break;   
00050             }
00051           }
00052           if (realfedID==-1){
00053             std::cout<<"error: could not obtain real fed ID"<<std::endl;
00054           }
00055           sipixelobjects::DetectorIndex detector ={detid,irow,icol};
00056           sipixelobjects::ElectronicIndex cabling; 
00057           SiPixelFrameConverter formatter(theCablingMap_.product(),realfedID);
00058           formatter.toCabling(cabling,detector);
00059           // cabling should now contain cabling.roc and cabling.dcol  and cabling.pxid
00060           // however, the coordinates now need to be converted from dcl,pxid to the row,col coordinates used in the calibration info 
00061           sipixelobjects::LocalPixel::DcolPxid loc;
00062           loc.dcol = cabling.dcol;
00063           loc.pxid = cabling.pxid;
00064           // FIX to adhere to new cabling map. To be replaced with CalibTracker/SiPixelTools detid - > hardware id classes ASAP.
00065           //        const sipixelobjects::PixelFEDCabling *theFed= theCablingMap.product()->fed(realfedID);
00066           //        const sipixelobjects::PixelFEDLink * link = theFed->link(cabling.link);
00067           //        const sipixelobjects::PixelROC *theRoc = link->roc(cabling.roc);
00068           sipixelobjects::LocalPixel locpixel(loc);
00069           sipixelobjects::CablingPathToDetUnit path = {realfedID, cabling.link, cabling.roc};  
00070           const sipixelobjects::PixelROC *theRoc = theCablingMap_->findItem(path);
00071           // END of FIX
00072           int newrow= locpixel.rocRow();
00073           int newcol = locpixel.rocCol();
00074           myfile<<rocname<<theRoc->idInDetUnit()<<" "<<newcol<<" "<<newrow<<" "<<thresholdhist->getBinContent(icol+1, irow+1)<<" "<<threshold_error;  // +1 because root bins start at 1
00075           myfile<<"\n";
00076         }
00077       }
00078     }
00079   }
00080   myfile.close();
00081 }
00082 
00083 //used for TMinuit fitting
00084 void chi2toMinimize(int &npar, double* grad, double &fcnval, double* xval, int iflag)
00085 {
00086    TF1 * theFormula = SiPixelSCurveCalibrationAnalysis::fitFunction_;
00087    //setup function parameters
00088    for (int i = 0; i < npar; i++)
00089       theFormula->SetParameter(i, xval[i]);
00090    fcnval = 0;
00091    //compute Chi2 of all points
00092    const std::vector<short>* theVCalValues = SiPixelSCurveCalibrationAnalysis::getVcalValues();
00093    for (uint32_t i = 0; i < theVCalValues->size(); i++)
00094    {
00095       float chi = (SiPixelSCurveCalibrationAnalysis::efficiencies_[i] - theFormula->Eval((*theVCalValues)[i]) );
00096       chi       /= SiPixelSCurveCalibrationAnalysis::effErrors_[i];
00097       fcnval += chi*chi;
00098    }
00099 }
00100 
00101 void
00102 SiPixelSCurveCalibrationAnalysis::doSetup(const edm::ParameterSet& iConfig)
00103 {
00104    edm::LogInfo("SiPixelSCurveCalibrationAnalysis") << "Setting up calibration paramters.";
00105    std::vector<uint32_t>        anEmptyDefaultVectorOfUInts;
00106    std::vector<uint32_t>        detIDsToSaveVector_;
00107    useDetectorHierarchyFolders_ = iConfig.getUntrackedParameter<bool>("useDetectorHierarchyFolders", true);
00108    saveCurvesThatFlaggedBad_    = iConfig.getUntrackedParameter<bool>("saveCurvesThatFlaggedBad", false);
00109    detIDsToSaveVector_          = iConfig.getUntrackedParameter<std::vector<uint32_t> >("detIDsToSave", anEmptyDefaultVectorOfUInts);
00110    maxCurvesToSave_             = iConfig.getUntrackedParameter<uint32_t>("maxCurvesToSave", 1000);
00111    write2dHistograms_           = iConfig.getUntrackedParameter<bool>("write2dHistograms", true);
00112    write2dFitResult_            = iConfig.getUntrackedParameter<bool>("write2dFitResult", true);
00113    printoutthresholds_          = iConfig.getUntrackedParameter<bool>("writeOutThresholdSummary",true);
00114    thresholdfilename_           = iConfig.getUntrackedParameter<std::string>("thresholdOutputFileName","thresholds.txt");  
00115    minimumChi2prob_             = iConfig.getUntrackedParameter<double>("minimumChi2prob", 0);
00116    minimumThreshold_            = iConfig.getUntrackedParameter<double>("minimumThreshold", -10);
00117    maximumThreshold_            = iConfig.getUntrackedParameter<double>("maximumThreshold", 300);
00118    minimumSigma_                = iConfig.getUntrackedParameter<double>("minimumSigma", 0);
00119    maximumSigma_                = iConfig.getUntrackedParameter<double>("maximumSigma", 100);
00120    minimumEffAsymptote_         = iConfig.getUntrackedParameter<double>("minimumEffAsymptote", 0);
00121    maximumEffAsymptote_         = iConfig.getUntrackedParameter<double>("maximumEffAsymptote", 1000);
00122    maximumSigmaBin_             = iConfig.getUntrackedParameter<double>("maximumSigmaBin", 10);
00123    maximumThresholdBin_         = iConfig.getUntrackedParameter<double>("maximumThresholdBin", 255);
00124 
00125    writeZeroes_= iConfig.getUntrackedParameter<bool>("alsoWriteZeroThresholds", false);
00126 
00127    // convert the vector into a map for quicker lookups.
00128    for(unsigned int i = 0; i < detIDsToSaveVector_.size(); i++)
00129       detIDsToSave_.insert( std::make_pair(detIDsToSaveVector_[i], true) );
00130 }
00131 
00132 SiPixelSCurveCalibrationAnalysis::~SiPixelSCurveCalibrationAnalysis()
00133 {
00134    //do nothing
00135 }
00136 
00137 void SiPixelSCurveCalibrationAnalysis::buildACurveHistogram(const uint32_t& detid, const uint32_t& row, const uint32_t& col, sCurveErrorFlag errorFlag, const std::vector<float>& efficiencies, const std::vector<float>& errors)
00138 {
00139    if (curvesSavedCounter_ > maxCurvesToSave_)
00140    {
00141       edm::LogWarning("SiPixelSCurveCalibrationAnalysis") << "WARNING: Request to save curve for [detid](col/row):  [" << detid << "](" << col << "/" << row << ") denied. Maximum number of saved curves (defined in .cfi) exceeded.";
00142       return;
00143    }
00144    std::ostringstream rootName;
00145    rootName << "SCurve_row_" << row << "_col_" << col;
00146    std::ostringstream humanName;
00147    humanName << translateDetIdToString(detid) << "_" << rootName.str() << "_ErrorFlag_" << (int)errorFlag;
00148 
00149    unsigned int numberOfVCalPoints = vCalPointsAsFloats_.size()-1; //minus one is necessary since the lower edge of the last bin must be added
00150    if (efficiencies.size() != numberOfVCalPoints || errors.size() != numberOfVCalPoints)
00151    {
00152       edm::LogError("SiPixelSCurveCalibrationAnalysis") << "Error saving single curve histogram!  Number of Vcal values (" << numberOfVCalPoints << ") does not match number of efficiency points or error points!";
00153       return;
00154    }
00155    setDQMDirectory(detid);
00156    float * vcalValuesToPassToCrappyRoot = &vCalPointsAsFloats_[0];
00157    MonitorElement * aBadHisto = bookDQMHistogram1D(detid, rootName.str(), humanName.str(), numberOfVCalPoints, vcalValuesToPassToCrappyRoot);  //ROOT only takes an input as array. :(  HOORAY FOR CINT!
00158    curvesSavedCounter_++;
00159    for(unsigned int iBin = 0; iBin < numberOfVCalPoints; ++iBin)
00160    {
00161       int rootBin = iBin + 1;  //root bins start at 1
00162       aBadHisto->setBinContent(rootBin, efficiencies[iBin]);
00163       aBadHisto->setBinError(rootBin, errors[iBin]);
00164    }
00165 }
00166 
00167 void SiPixelSCurveCalibrationAnalysis::calibrationSetup(const edm::EventSetup& iSetup)
00168 {
00169    edm::LogInfo("SiPixelSCurveCalibrationAnalysis") << "Calibration Settings: VCalLow: " << vCalValues_[0] << "  VCalHigh: " << vCalValues_[vCalValues_.size()-1] << " nVCal: " << vCalValues_.size() << "  nTriggers: " << nTriggers_;
00170    curvesSavedCounter_ = 0;
00171    if (saveCurvesThatFlaggedBad_)
00172    {
00173       //build the vCal values as a vector of floats if we want to save single curves
00174       const std::vector<short>* theVCalValues = this->getVcalValues();
00175       unsigned int numberOfVCalPoints = theVCalValues->size();
00176       edm::LogWarning("SiPixelSCurveCalibrationAnalysis") << "WARNING: Option set to save indiviual S-Curves - max number: " 
00177                                                           << maxCurvesToSave_ << " This can lead to large memory consumption! (Got " << numberOfVCalPoints << " VCal Points";
00178       for(unsigned int i = 0; i < numberOfVCalPoints; i++)
00179       {
00180          vCalPointsAsFloats_.push_back( static_cast<float>((*theVCalValues)[i]) );
00181          edm::LogInfo("SiPixelSCurveCalibrationAnalysis") << "Adding calibration Vcal: " << (*theVCalValues)[i];
00182       }
00183       // must add lower edge of last bin to the vector
00184       vCalPointsAsFloats_.push_back( vCalPointsAsFloats_[numberOfVCalPoints-1] + 1 );
00185    }
00186 
00187    fitFunction_ = new TF1("sCurve", "0.5*[2]*(1+TMath::Erf( (x-[0]) / ([1]*sqrt(2)) ) )", vCalValues_[0], vCalValues_[vCalValues_.size()-1]);
00188 }
00189 
00190 bool
00191 SiPixelSCurveCalibrationAnalysis::checkCorrectCalibrationType()
00192 {
00193   if(calibrationMode_=="SCurve")
00194     return true;
00195   else if(calibrationMode_=="unknown"){
00196     edm::LogInfo("SiPixelSCurveCalibrationAnalysis") <<  "calibration mode is: " << calibrationMode_ << ", continuing anyway..." ;
00197     return true;
00198   }
00199   else{
00200     //    edm::LogDebug("SiPixelSCurveCalibrationAnalysis") << "unknown calibration mode for SCurves, should be \"SCurve\" and is \"" << calibrationMode_ << "\"";
00201   }
00202   return false;
00203 }
00204 
00205 sCurveErrorFlag SiPixelSCurveCalibrationAnalysis::estimateSCurveParameters(const std::vector<float>& eff, float& threshold, float& sigma)
00206 {
00207    sCurveErrorFlag output = errAllZeros;
00208    bool allZeroSoFar    = true;
00209    int turnOnBin        = -1;
00210    int saturationBin    = -1;
00211    for (uint32_t iVcalPt = 0; iVcalPt < eff.size(); iVcalPt++)
00212    {
00213       if (allZeroSoFar && eff[iVcalPt] != 0 ) {
00214          turnOnBin = iVcalPt;
00215          allZeroSoFar = false;
00216          output = errNoTurnOn;
00217       } else if (eff[iVcalPt] > 0.90)
00218       {
00219          saturationBin  = iVcalPt;
00220          short turnOnVcal       = vCalValues_[turnOnBin];
00221          short saturationVcal   = vCalValues_[saturationBin];
00222          short delta            = saturationVcal - turnOnVcal;
00223          sigma                  = delta * 0.682;
00224          if (sigma < 1)         //check to make sure sigma guess is larger than our X resolution.  Hopefully prevents Minuit from getting stuck at boundary
00225             sigma = 1;
00226          threshold              = turnOnVcal + (0.5 * delta);
00227          return errOK;
00228       }
00229    }
00230    return output;
00231 }
00232 
00233 sCurveErrorFlag SiPixelSCurveCalibrationAnalysis::fittedSCurveSanityCheck(float threshold, float sigma, float amplitude)
00234 {
00235    //check if nonsensical
00236    if (threshold > vCalValues_[vCalValues_.size()-1] || threshold < vCalValues_[0] ||
00237          sigma > vCalValues_[vCalValues_.size()-1] - vCalValues_[0] )
00238       return errFitNonPhysical;
00239 
00240    if (threshold < minimumThreshold_ || threshold > maximumThreshold_ ||
00241          sigma < minimumSigma_ || sigma > maximumSigma_ ||
00242          amplitude < minimumEffAsymptote_ || amplitude > maximumEffAsymptote_)
00243       return errFlaggedBadByUser;
00244 
00245    return errOK;
00246 }
00247 
00248 void calculateEffAndError(int nADCResponse, int nTriggers, float& eff, float& error)
00249 {
00250    eff = (float)nADCResponse / (float)nTriggers;
00251    double effForErrorCalculation = eff;
00252    if (eff <= 0 || eff >= 1)
00253       effForErrorCalculation = 0.5 / (double)nTriggers;
00254    error = TMath::Sqrt(effForErrorCalculation*(1-effForErrorCalculation) / (double)nTriggers);
00255 }
00256 
00257 //book histograms when new DetID is encountered in Event Record
00258 void SiPixelSCurveCalibrationAnalysis::newDetID(uint32_t detid)
00259 {
00260    edm::LogInfo("SiPixelSCurveCalibrationAnalysis") << "Found a new DetID (" << detid << ")!  Checking to make sure it has not been added.";
00261    //ensure that this DetID has not been added yet
00262    sCurveHistogramHolder tempMap;
00263    std::pair<detIDHistogramMap::iterator, bool> insertResult; 
00264    insertResult = histograms_.insert(std::make_pair(detid, tempMap));
00265    if (insertResult.second)     //indicates successful insertion
00266    {
00267       edm::LogInfo("SiPixelSCurveCalibrationAnalysisHistogramReport") << "Histogram Map.insert() returned true!  Booking new histogrames for detID: " << detid;
00268       // use detector hierarchy folders if desired
00269       if (useDetectorHierarchyFolders_)
00270          setDQMDirectory(detid);
00271 
00272       std::string detIdName = translateDetIdToString(detid);
00273       if (write2dHistograms_){
00274         MonitorElement * D2sigma       = bookDQMHistoPlaquetteSummary2D(detid,"ScurveSigmas", detIdName + " Sigmas");
00275         MonitorElement * D2thresh      = bookDQMHistoPlaquetteSummary2D(detid,"ScurveThresholds", detIdName + " Thresholds");
00276         MonitorElement * D2chi2        = bookDQMHistoPlaquetteSummary2D(detid,"ScurveChi2Prob",detIdName + " Chi2Prob");
00277          insertResult.first->second.insert(std::make_pair(kSigmas, D2sigma));
00278          insertResult.first->second.insert(std::make_pair(kThresholds, D2thresh));
00279          insertResult.first->second.insert(std::make_pair(kChi2s, D2chi2));
00280       }
00281       if (write2dFitResult_){
00282         MonitorElement * D2FitResult = bookDQMHistoPlaquetteSummary2D(detid,"ScurveFitResult", detIdName + " Fit Result");
00283          insertResult.first->second.insert(std::make_pair(kFitResults, D2FitResult));
00284       }
00285       MonitorElement * D1sigma       = bookDQMHistogram1D(detid,"ScurveSigmasSummary", detIdName + " Sigmas Summary", 100, 0, maximumSigmaBin_);
00286       MonitorElement * D1thresh      = bookDQMHistogram1D(detid,"ScurveThresholdSummary", detIdName + " Thresholds Summary", 255, 0, maximumThresholdBin_);
00287       MonitorElement * D1chi2        = bookDQMHistogram1D(detid,"ScurveChi2ProbSummary", detIdName + " Chi2Prob Summary", 101, 0, 1.01);
00288       MonitorElement * D1FitResult   = bookDQMHistogram1D(detid,"ScurveFitResultSummary", detIdName + " Fit Result Summary", 10, -0.5, 9.5);
00289       insertResult.first->second.insert(std::make_pair(kSigmaSummary, D1sigma));
00290       insertResult.first->second.insert(std::make_pair(kThresholdSummary, D1thresh));
00291       insertResult.first->second.insert(std::make_pair(kChi2Summary, D1chi2));
00292       insertResult.first->second.insert(std::make_pair(kFitResultSummary, D1FitResult));
00293    }
00294 }
00295 
00296 bool SiPixelSCurveCalibrationAnalysis::doFits(uint32_t detid, std::vector<SiPixelCalibDigi>::const_iterator calibDigi)
00297 {
00298    sCurveErrorFlag errorFlag = errOK;
00299    uint32_t nVCalPts = calibDigi->getnpoints();
00300    //reset and fill static datamembers with vector of points and errors
00301    efficiencies_.resize(0);
00302    effErrors_.resize(0);
00303    for (uint32_t iVcalPt = 0; iVcalPt < nVCalPts; iVcalPt++)
00304    {
00305       float eff;
00306       float error;
00307       calculateEffAndError(calibDigi->getnentries(iVcalPt), nTriggers_, eff, error);
00308       edm::LogInfo("SiPixelSCurveCalibrationAnalysis") << "Eff: " << eff << " Error:  " << error << "  nEntries: " << calibDigi->getnentries(iVcalPt) << "  nTriggers: " << nTriggers_ << " VCalPt " << vCalValues_[iVcalPt];
00309       efficiencies_.push_back(eff);
00310       effErrors_.push_back(error);
00311    } 
00312 
00313    //estimate the S-Curve parameters
00314    float thresholdGuess;
00315    float sigmaGuess;
00316    errorFlag = estimateSCurveParameters(efficiencies_, thresholdGuess, sigmaGuess);
00317 
00318    // these -1.0 default values will only be filled if the curve is all zeroes, or doesn't turn on, WHICH INDICATES A SERIOUS PROBLEM
00319    Double_t sigma                       = -1.0;
00320    Double_t sigmaError                  = -1.0;
00321    Double_t threshold                   = -1.0;
00322    Double_t thresholdError              = -1.0;
00323    Double_t amplitude                   = -1.0;
00324    Double_t amplitudeError              = -1.0;
00325    Double_t chi2                        = -1.0;
00326    //calculate NDF
00327    Int_t nDOF                           = vCalValues_.size() - 3;
00328    Double_t chi2probability             = 0;
00329 
00330    if (errorFlag == errOK)          //only do fit if curve is fittable
00331    {
00332       //set up minuit fit
00333       TMinuit *gMinuit = new TMinuit(3);
00334       gMinuit->SetPrintLevel(-1);  //save ourselves from gigabytes of stdout
00335       gMinuit->SetFCN(chi2toMinimize);
00336 
00337       //define threshold parameters - choose step size 1, max 300, min -50
00338       gMinuit->DefineParameter(0, "Threshold", (Double_t)thresholdGuess, 1, -50, 300);
00339       //sigma
00340       gMinuit->DefineParameter(1, "Sigma", (Double_t)sigmaGuess, 0.1, 0, 255); 
00341       //amplitude
00342       gMinuit->DefineParameter(2, "Amplitude", 1, 0.1, -0.001, 200);
00343 
00344       //Do Chi2 minimazation
00345       gMinuit->Migrad();
00346       gMinuit->GetParameter(0, threshold, thresholdError);
00347       gMinuit->GetParameter(1, sigma, sigmaError);
00348       gMinuit->GetParameter(2, amplitude, amplitudeError);
00349 
00350       //get Chi2
00351       Double_t params[3]   = {threshold, sigma, amplitude};
00352       gMinuit->Eval(3, NULL, chi2, params, 0);
00353       //calculate Chi2 proability
00354       if (nDOF <= 0)
00355          chi2probability = 0;
00356       else
00357          chi2probability = TMath::Prob(chi2, nDOF);
00358       
00359       //check to make sure output makes sense (i.e. threshold > 0)
00360       if (chi2probability > minimumChi2prob_)
00361          errorFlag = fittedSCurveSanityCheck(threshold, sigma, amplitude);
00362       else
00363          errorFlag = errBadChi2Prob;
00364 
00365       edm::LogInfo("SiPixelSCurveCalibrationAnalysis") << "Fit finished with errorFlag: " << errorFlag << " - threshold: " << threshold << "  sigma: " << sigma << "  chi2: " << chi2 << "  nDOF: " << nDOF << " chi2Prob: " << chi2probability << " chi2MinUser: " << minimumChi2prob_;
00366 
00367       delete gMinuit;
00368    }
00369    //get row and column for this pixel
00370    uint32_t row = calibDigi->row();
00371    uint32_t col = calibDigi->col();
00372 
00373    //get iterator to histogram holder for this detid
00374    detIDHistogramMap::iterator thisDetIdHistoGrams;
00375    thisDetIdHistoGrams = histograms_.find(detid);
00376    if (thisDetIdHistoGrams != histograms_.end())
00377    {
00378       edm::LogInfo("SiPixelSCurveCalibrationAnalysisHistogramReport") << "Filling histograms for [detid](col/row):  [" << detid << "](" << col << "/" << row << ") ErrorFlag: " << errorFlag;
00379       //always fill fit result
00380       (*thisDetIdHistoGrams).second[kFitResultSummary]->Fill(errorFlag);
00381       if (write2dFitResult_)
00382          (*thisDetIdHistoGrams).second[kFitResults]->setBinContent(col+1, row+1, errorFlag); // +1 because root bins start at 1
00383 
00384       // fill sigma/threshold result
00385       (*thisDetIdHistoGrams).second[kSigmaSummary]->Fill(sigma);
00386       (*thisDetIdHistoGrams).second[kThresholdSummary]->Fill(threshold);
00387       if (write2dHistograms_)
00388       {
00389          (*thisDetIdHistoGrams).second[kSigmas]->setBinContent(col+1, row+1, sigma); // +1 because root bins start at 1
00390          (*thisDetIdHistoGrams).second[kThresholds]->setBinContent(col+1, row+1, threshold); // +1 because root bins start at 1
00391       }
00392       // fill chi2
00393       (*thisDetIdHistoGrams).second[kChi2Summary]->Fill(chi2probability);
00394       if (write2dHistograms_)
00395          (*thisDetIdHistoGrams).second[kChi2s]->Fill(col, row, chi2probability);
00396    }
00397    // save individual curves, if requested
00398    if (saveCurvesThatFlaggedBad_)
00399    {
00400       bool thisDetIDinList = false;
00401       if (detIDsToSave_.find(detid) != detIDsToSave_.end()) //see if we want to save this histogram
00402          thisDetIDinList = true;
00403 
00404       if (errorFlag != errOK || thisDetIDinList)
00405       {
00406          edm::LogError("SiPixelSCurveCalibrationAnalysis") << "Saving error histogram for [detid](col/row):  [" << detid << "](" << col << "/" << row << ") ErrorFlag: " << errorFlag;
00407          buildACurveHistogram(detid, row, col, errorFlag, efficiencies_, effErrors_);
00408       }
00409    }
00410 
00411    return true;
00412    
00413 }