PedsFullNoiseAlgorithm Class Reference

Histogram-based analysis for pedestal run. More...

#include <PedsFullNoiseAlgorithm.h>

Inheritance diagram for PedsFullNoiseAlgorithm:

Public Member Functions
const Histo &	hNoise () const
const Histo &	hNoise2D () const
const Histo &	hPeds () const
	PedsFullNoiseAlgorithm (const edm::ParameterSet &pset, PedsFullNoiseAnalysis *const)
	~PedsFullNoiseAlgorithm () override
Public Member Functions inherited from CommissioningAlgorithm
void	analysis (const std::vector< TH1 * > &)
	CommissioningAlgorithm (CommissioningAnalysis *const)
	CommissioningAlgorithm ()
virtual	~CommissioningAlgorithm ()

Private Member Functions
void	analyse () override
void	extract (const std::vector< TH1 * > &) override
	PedsFullNoiseAlgorithm ()
void	reset (PedsFullNoiseAnalysis *)

Private Attributes
float	adProbabCut_
float	amplitudeRatio_
float	ashmanDistance_
float	chi2ProbabCut_
bool	generateRandomHisto_
Histo	hNoise2D_
Histo	hNoise_
Histo	hPeds_
int	integralNsigma_
float	integralTailCut_
float	jbProbabCut_
float	ksProbabCut_
float	kurtosisCut_
float	maxDriftResidualCut_
float	maxStripNoiseCut_
float	maxStripNoiseSignificanceCut_
float	minStripNoiseCut_

Additional Inherited Members
Public Types inherited from CommissioningAlgorithm
typedef std::pair< TH1 *, std::string >	Histo
Protected Member Functions inherited from CommissioningAlgorithm
CommissioningAnalysis *const	anal () const
uint32_t	extractFedKey (const TH1 *const)

Detailed Description

Histogram-based analysis for pedestal run.

Author

M. Wingham, R.Bainbridge

Definition at line 17 of file PedsFullNoiseAlgorithm.h.

Constructor & Destructor Documentation

PedsFullNoiseAlgorithm::PedsFullNoiseAlgorithm	(	const edm::ParameterSet &	pset,
		PedsFullNoiseAnalysis * const	anal
	)

Definition at line 26 of file PedsFullNoiseAlgorithm.cc.

References adProbabCut_, chi2ProbabCut_, integralNsigma_, integralTailCut_, jbProbabCut_, ksProbabCut_, kurtosisCut_, LogDebug, maxDriftResidualCut_, maxStripNoiseCut_, maxStripNoiseSignificanceCut_, minStripNoiseCut_, and sistrip::mlCommissioning_.

    : CommissioningAlgorithm(anal),
      hPeds_(nullptr, ""),
      hNoise_(nullptr, ""),
      hNoise2D_(nullptr, ""),
      maxDriftResidualCut_(pset.getParameter<double>("MaxDriftResidualCut")),
      minStripNoiseCut_(pset.getParameter<double>("MinStripNoiseCut")),
      maxStripNoiseCut_(pset.getParameter<double>("MaxStripNoiseCut")),
      maxStripNoiseSignificanceCut_(pset.getParameter<double>("MaxStripNoiseSignificanceCut")),
      adProbabCut_(pset.getParameter<double>("AdProbabCut")),
      ksProbabCut_(pset.getParameter<double>("KsProbabCut")),
      generateRandomHisto_(pset.getParameter<bool>("GenerateRandomHisto")),
      jbProbabCut_(pset.getParameter<double>("JbProbabCut")),
      chi2ProbabCut_(pset.getParameter<double>("Chi2ProbabCut")),
      kurtosisCut_(pset.getParameter<double>("KurtosisCut")),
      integralTailCut_(pset.getParameter<double>("IntegralTailCut")),
      integralNsigma_(pset.getParameter<int>("IntegralNsigma")),
      ashmanDistance_(pset.getParameter<double>("AshmanDistance")),
      amplitudeRatio_(pset.getParameter<double>("AmplitudeRatio")) {
  LogDebug(mlCommissioning_) << "[PedsFullNoiseAlgorithm::" << __func__ << "]"
                             << " Set maximum drift of the mean value to: " << maxDriftResidualCut_
                             << " Set minimum noise value to: " << minStripNoiseCut_
                             << " Set maximum noise value to: " << maxStripNoiseCut_
                             << " Set maximum noise significance value to: " << maxStripNoiseSignificanceCut_
                             << " Set minimum Anderson-Darling p-value to: " << adProbabCut_
                             << " Set minimum Kolmogorov-Smirnov p-value to: " << ksProbabCut_
                             << " Set minimum Jacque-Bera p-value to: " << jbProbabCut_
                             << " Set minimum Chi2 p-value to: " << chi2ProbabCut_
                             << " Set N-sigma for the integral to : " << integralNsigma_
                             << " Set maximum integral tail at N-sigma to : " << integralTailCut_
                             << " Set maximum Kurtosis to : " << kurtosisCut_;
}

PedsFullNoiseAlgorithm::~PedsFullNoiseAlgorithm ( )

inlineoverride

Definition at line 21 of file PedsFullNoiseAlgorithm.h.

References hNoise(), hNoise2D(), and hPeds().

{ ; }

PedsFullNoiseAlgorithm::PedsFullNoiseAlgorithm ( )

inlineprivate

Definition at line 28 of file PedsFullNoiseAlgorithm.h.

References analyse(), extract(), and reset().

{ ; }

Member Function Documentation

void PedsFullNoiseAlgorithm::analyse ( )

overrideprivatevirtual

Performs histogram anaysis.

Implements CommissioningAlgorithm.

Definition at line 154 of file PedsFullNoiseAlgorithm.cc.

References funct::abs(), CommissioningAnalysis::addErrorCode(), PedsFullNoiseAnalysis::adProbab_, adProbabCut_, amplitudeRatio_, CommissioningAlgorithm::anal(), ashmanDistance_, PedsFullNoiseAnalysis::badADProbab_, PedsFullNoiseAnalysis::badChi2Probab_, PedsFullNoiseAnalysis::badDoublePeakStrip_, PedsFullNoiseAnalysis::badFitStatus_, PedsFullNoiseAnalysis::badJBProbab_, PedsFullNoiseAnalysis::badKSProbab_, PedsFullNoiseAnalysis::badStrip_, PedsFullNoiseAnalysis::badStripBit_, PedsFullNoiseAnalysis::badTailStrip_, PedsFullNoiseAnalysis::chi2Probab_, chi2ProbabCut_, PedsFullNoiseAnalysis::deadStrip_, PedsFullNoiseAnalysis::deadStripBit_, CommissioningAnalysis::fecKey(), generateRandomHisto_, hNoise2D_, hNoise_, hPeds_, integralNsigma_, integralTailCut_, PedsFullNoiseAnalysis::jbProbab_, jbProbabCut_, PedsFullNoiseAnalysis::ksProbab_, ksProbabCut_, kurtosisCut_, PedsFullNoiseAnalysis::largeNoiseSignificance_, PedsFullNoiseAnalysis::largeNoiseStrip_, LogTrace, PedsFullNoiseAnalysis::lowNoiseStrip_, SiStripPI::max, maxDriftResidualCut_, sistrip::maximum_, maxStripNoiseCut_, maxStripNoiseSignificanceCut_, min(), minStripNoiseCut_, sistrip::mlCommissioning_, sistrip::mlDqmClient_, PedsFullNoiseAnalysis::noise_, PedsFullNoiseAnalysis::noiseMax_, PedsFullNoiseAnalysis::noiseMean_, PedsFullNoiseAnalysis::noiseMin_, PedsFullNoiseAnalysis::noiseSignificance_, PedsFullNoiseAnalysis::noiseSpread_, sistrip::nullPtr_, sistrip::numberOfBins_, or, PedsFullNoiseAnalysis::peds_, PedsFullNoiseAnalysis::pedsMax_, PedsFullNoiseAnalysis::pedsMean_, PedsFullNoiseAnalysis::pedsMin_, PedsFullNoiseAnalysis::pedsSpread_, funct::pow(), PedsFullNoiseAnalysis::raw_, PedsFullNoiseAnalysis::rawMax_, PedsFullNoiseAnalysis::rawMean_, PedsFullNoiseAnalysis::rawMin_, PedsFullNoiseAnalysis::rawSpread_, reset(), PedsFullNoiseAnalysis::residualIntegral_, PedsFullNoiseAnalysis::residualIntegralNsigma_, PedsFullNoiseAnalysis::residualKurtosis_, PedsFullNoiseAnalysis::residualMean_, PedsFullNoiseAnalysis::residualRMS_, PedsFullNoiseAnalysis::residualSigmaGaus_, PedsFullNoiseAnalysis::residualSkewness_, mps_fire::result, PedsFullNoiseAnalysis::shiftedStrip_, mathSSE::sqrt(), createJobs::tmp, and relativeConstraints::value.

Referenced by PedsFullNoiseAlgorithm().

                                     {
  // check base analysis object
  if (!anal()) {
    edm::LogWarning(mlCommissioning_) << "[PedsFullNoiseAlgorithm::" << __func__ << "]"
                                      << " NULL pointer to base Analysis object!";
    return;
  }

  CommissioningAnalysis* tmp = const_cast<CommissioningAnalysis*>(anal());
  PedsFullNoiseAnalysis* ana = dynamic_cast<PedsFullNoiseAnalysis*>(tmp);

  // check PedsFullNoiseAnalysis object
  if (!ana) {
    edm::LogWarning(mlCommissioning_) << "[PedsFullNoiseAlgorithm::" << __func__ << "]"
                                      << " NULL pointer to derived Analysis object!";
    return;
  }

  // check if the histograms exists
  if (!hPeds_.first) {
    ana->addErrorCode(sistrip::nullPtr_);
    return;
  }

  if (!hNoise_.first) {
    ana->addErrorCode(sistrip::nullPtr_);
    return;
  }

  if (!hNoise2D_.first) {
    ana->addErrorCode(sistrip::nullPtr_);
    return;
  }

  // take the histograms
  TProfile* histoPeds = dynamic_cast<TProfile*>(hPeds_.first);
  TProfile* histoNoiseMean = dynamic_cast<TProfile*>(hNoise_.first);
  TH2S* histoNoise = dynamic_cast<TH2S*>(hNoise2D_.first);

  // Make sanity checks about pointers
  if (not histoPeds) {
    ana->addErrorCode(sistrip::nullPtr_);
    return;
  }

  if (not histoNoiseMean) {
    ana->addErrorCode(sistrip::nullPtr_);
    return;
  }

  if (not histoNoise) {
    ana->addErrorCode(sistrip::nullPtr_);
    return;
  }

  // check the binning  --> each x-axis bin is 1 strip -> 2APV per lldChannel -> 256 strips
  if (histoPeds->GetNbinsX() != 256) {
    ana->addErrorCode(sistrip::numberOfBins_);
    return;
  }

  //check the binning  --> each x-axis bin is 1 strip -> 2APV per lldChannel -> 256 strips
  if (histoNoiseMean->GetNbinsX() != 256) {
    ana->addErrorCode(sistrip::numberOfBins_);
    return;
  }

  //check the binning  --> each y-axis bin is 1 strip -> 2APV per lldChannel -> 256 strips
  if (histoNoise->GetNbinsY() != 256) {
    ana->addErrorCode(sistrip::numberOfBins_);
    return;
  }

  //Reset values
  reset(ana);

  // loop on each strip
  uint32_t apvID = -1;

  // Save basic information at strip / APV level
  vector<float> ped_max;
  vector<float> ped_min;
  vector<float> raw_max;
  vector<float> raw_min;
  vector<float> noise_max;
  vector<float> noise_min;

  // loop on each strip in the lldChannel
  for (int iStrip = 0; iStrip < histoPeds->GetNbinsX(); iStrip++) {
    if (iStrip < histoPeds->GetNbinsX() / 2)
      apvID = 0;
    else
      apvID = 1;

    int stripBin = 0;
    if (iStrip >= 128)
      stripBin = iStrip - 128;
    else
      stripBin = iStrip;

    ana->peds_[apvID][stripBin] = histoPeds->GetBinContent(iStrip + 1);        // pedestal value
    ana->noise_[apvID][stripBin] = histoNoiseMean->GetBinContent(iStrip + 1);  // noise value
    ana->raw_[apvID][stripBin] = histoPeds->GetBinError(iStrip + 1);           // raw noise value

    ana->pedsMean_[apvID] += ana->peds_[apvID][stripBin];    // mean pedestal
    ana->rawMean_[apvID] += ana->raw_[apvID][stripBin];      // mean raw noise
    ana->noiseMean_[apvID] += ana->noise_[apvID][stripBin];  // mean noise

    // max pedestal
    if (ped_max.size() < apvID + 1)
      ped_max.push_back(ana->peds_[apvID][stripBin]);
    else {
      if (ana->peds_[apvID][stripBin] > ped_max.at(apvID))
        ped_max.at(apvID) = ana->peds_[apvID][stripBin];
    }

    // min pedestal
    if (ped_min.size() < apvID + 1)
      ped_min.push_back(ana->peds_[apvID][stripBin]);
    else {
      if (ana->peds_[apvID][stripBin] < ped_min.at(apvID))
        ped_min.at(apvID) = ana->peds_[apvID][stripBin];  // min pedestal
    }

    // max noise
    if (noise_max.size() < apvID + 1)
      noise_max.push_back(ana->noise_[apvID][stripBin]);
    else {
      if (ana->noise_[apvID][stripBin] > noise_max.at(apvID))
        noise_max.at(apvID) = ana->noise_[apvID][stripBin];
    }

    // min noise
    if (noise_min.size() < apvID + 1)
      noise_min.push_back(ana->noise_[apvID][stripBin]);
    else {
      if (ana->noise_[apvID][stripBin] < noise_min.at(apvID))
        noise_min.at(apvID) = ana->noise_[apvID][stripBin];
    }

    // max raw
    if (raw_max.size() < apvID + 1)
      raw_max.push_back(ana->raw_[apvID][stripBin]);
    else {
      if (ana->raw_[apvID][stripBin] > raw_max.at(apvID))
        raw_max.at(apvID) = ana->raw_[apvID][stripBin];
    }

    // min raw
    if (raw_min.size() < apvID + 1)
      raw_min.push_back(ana->raw_[apvID][stripBin]);
    else {
      if (ana->raw_[apvID][stripBin] < raw_min.at(apvID))
        raw_min.at(apvID) = ana->raw_[apvID][stripBin];
    }
  }

  // Mean values
  for (unsigned int iApv = 0; iApv < ana->pedsMean_.size(); iApv++) {
    ana->pedsMean_.at(iApv) /= (ana->peds_[iApv].size());    // calculate mean pedestal per APV
    ana->rawMean_.at(iApv) /= (ana->raw_[iApv].size());      // calculate mean raw noise per APV
    ana->noiseMean_.at(iApv) /= (ana->noise_[iApv].size());  // calculate mean noise per APV
  }

  // Min and Max
  for (unsigned int iApv = 0; iApv < ped_max.size(); iApv++) {
    if (ped_max.at(iApv) > sistrip::maximum_)
      ana->pedsMax_.at(iApv) = sistrip::maximum_;
    else if (ped_max.at(iApv) < -1. * sistrip::maximum_)
      ana->pedsMax_.at(iApv) = -1. * sistrip::maximum_;
    else
      ana->pedsMax_.at(iApv) = ped_max.at(iApv);

    if (ped_min.at(iApv) > sistrip::maximum_)
      ana->pedsMin_.at(iApv) = sistrip::maximum_;
    else if (ped_min.at(iApv) < -1. * sistrip::maximum_)
      ana->pedsMin_.at(iApv) = -1. * sistrip::maximum_;
    else
      ana->pedsMin_.at(iApv) = ped_min.at(iApv);

    if (noise_max.at(iApv) > sistrip::maximum_)
      ana->noiseMax_.at(iApv) = sistrip::maximum_;
    else if (noise_max.at(iApv) < -1. * sistrip::maximum_)
      ana->noiseMax_.at(iApv) = -1. * sistrip::maximum_;
    else
      ana->noiseMax_.at(iApv) = noise_max.at(iApv);

    if (noise_min.at(iApv) > sistrip::maximum_)
      ana->noiseMin_.at(iApv) = sistrip::maximum_;
    else if (noise_min.at(iApv) < -1. * sistrip::maximum_)
      ana->noiseMin_.at(iApv) = -1. * sistrip::maximum_;
    else
      ana->noiseMin_.at(iApv) = noise_min.at(iApv);

    if (raw_max.at(iApv) > sistrip::maximum_)
      ana->rawMax_.at(iApv) = sistrip::maximum_;
    else if (raw_max.at(iApv) < -1. * sistrip::maximum_)
      ana->rawMax_.at(iApv) = -1. * sistrip::maximum_;
    else
      ana->rawMax_.at(iApv) = raw_max.at(iApv);

    if (raw_min.at(iApv) > sistrip::maximum_)
      ana->rawMin_.at(iApv) = sistrip::maximum_;
    else if (raw_min.at(iApv) < -1. * sistrip::maximum_)
      ana->rawMin_.at(iApv) = -1. * sistrip::maximum_;
    else
      ana->rawMin_.at(iApv) = raw_min.at(iApv);
  }

  // Calculate the spread for noise and pedestal
  apvID = -1;

  for (int iStrip = 0; iStrip < histoNoiseMean->GetNbinsX(); iStrip++) {
    if (iStrip < histoNoiseMean->GetNbinsX() / 2)
      apvID = 0;
    else
      apvID = 1;
    ana->pedsSpread_[apvID] += pow(histoPeds->GetBinContent(iStrip + 1) - ana->pedsMean_.at(apvID), 2);
    ana->noiseSpread_[apvID] += pow(histoNoiseMean->GetBinContent(iStrip + 1) - ana->noiseMean_.at(apvID), 2);
    ana->rawSpread_[apvID] += pow(histoPeds->GetBinError(iStrip + 1) - ana->rawMean_.at(apvID), 2);
  }

  for (unsigned int iApv = 0; iApv < ana->pedsSpread_.size(); iApv++) {
    ana->pedsSpread_[iApv] = sqrt(ana->pedsSpread_[iApv]) / sqrt(ana->peds_[iApv].size() - 1);
    ana->noiseSpread_[iApv] = sqrt(ana->noiseSpread_[iApv]) / sqrt(ana->noise_[iApv].size() - 1);
    ana->rawSpread_[iApv] = sqrt(ana->rawSpread_[iApv]) / sqrt(ana->raw_[iApv].size() - 1);
  }

  // loop on each strip in the lldChannel
  apvID = 0;
  TH1S* histoResidualStrip = new TH1S("histoResidualStrip",
                                      "",
                                      histoNoise->GetNbinsX(),
                                      histoNoise->GetXaxis()->GetXmin(),
                                      histoNoise->GetXaxis()->GetXmax());
  histoResidualStrip->Sumw2();
  histoResidualStrip->SetDirectory(nullptr);
  TF1* fitFunc = new TF1("fitFunc", "gaus(0)", histoNoise->GetXaxis()->GetXmin(), histoNoise->GetXaxis()->GetXmax());
  TF1* fit2Gaus = nullptr;
  TH1F* randomHisto = nullptr;
  TFitResultPtr result;

  for (int iStrip = 0; iStrip < histoNoise->GetNbinsY(); iStrip++) {
    // tell which APV
    if (iStrip < histoNoise->GetNbinsY() / 2)
      apvID = 0;
    else
      apvID = 1;
    histoResidualStrip->Reset();

    int stripBin = 0;
    if (iStrip >= 128)
      stripBin = iStrip - 128;
    else
      stripBin = iStrip;

    for (int iBinX = 0; iBinX < histoNoise->GetNbinsX(); iBinX++) {
      histoResidualStrip->SetBinContent(iBinX + 1, histoNoise->GetBinContent(iBinX + 1, iStrip + 1));
      histoResidualStrip->SetBinError(iBinX + 1, histoNoise->GetBinError(iBinX + 1, iStrip + 1));
    }

    if (histoResidualStrip->Integral() == 0) {  // dead strip --> i.e. no data

      // set default values
      ana->adProbab_[apvID][stripBin] = 0;
      ana->ksProbab_[apvID][stripBin] = 0;
      ana->jbProbab_[apvID][stripBin] = 0;
      ana->chi2Probab_[apvID][stripBin] = 0;
      ana->noiseSignificance_[apvID][stripBin] = 0;
      ana->residualMean_[apvID][stripBin] = 0;
      ana->residualRMS_[apvID][stripBin] = 0;
      ana->residualSigmaGaus_[apvID][stripBin] = 0;
      ana->residualSkewness_[apvID][stripBin] = 0;
      ana->residualKurtosis_[apvID][stripBin] = 0;
      ana->residualIntegralNsigma_[apvID][stripBin] = 0;
      ana->residualIntegral_[apvID][stripBin] = 0;
      ana->deadStrip_[apvID].push_back(stripBin);
      ana->deadStripBit_[apvID][stripBin] = 1;
      ana->badStripBit_[apvID][stripBin] = 0;

      SiStripFecKey fec_key(ana->fecKey());
      LogTrace(mlDqmClient_) << "DeadStrip: fecCrate "
                             << " " << fec_key.fecCrate() << " fecSlot " << fec_key.fecSlot() << " fecRing "
                             << fec_key.fecRing() << " ccuAddr " << fec_key.ccuAddr() << " ccChan  "
                             << fec_key.ccuChan() << " lldChan " << fec_key.lldChan() << " apvID " << apvID
                             << " stripID " << iStrip;

      continue;
    }

    // set / calculated basic quantities
    ana->residualMean_[apvID][stripBin] = histoResidualStrip->GetMean();
    ana->residualRMS_[apvID][stripBin] = histoResidualStrip->GetRMS();
    ana->residualSkewness_[apvID][stripBin] = histoResidualStrip->GetSkewness();
    ana->residualKurtosis_[apvID][stripBin] = histoResidualStrip->GetKurtosis();
    ana->noiseSignificance_[apvID][stripBin] =
        (ana->noise_[apvID][stripBin] - ana->noiseMean_[apvID]) / ana->noiseSpread_[apvID];
    ana->residualIntegral_[apvID][stripBin] = histoResidualStrip->Integral();
    ana->residualIntegralNsigma_[apvID][stripBin] =
        (histoResidualStrip->Integral(histoResidualStrip->FindBin(ana->residualMean_[apvID][stripBin] +
                                                                  ana->residualRMS_[apvID][stripBin] * integralNsigma_),
                                      histoResidualStrip->GetNbinsX() + 1) +
         histoResidualStrip->Integral(
             0,
             histoResidualStrip->FindBin(ana->residualMean_[apvID][stripBin] -
                                         ana->residualRMS_[apvID][stripBin] * integralNsigma_))) /
        ana->residualIntegral_[apvID][stripBin];

    // performing a Gaussian fit of the residual distribution
    fitFunc->SetRange(histoNoise->GetXaxis()->GetXmin(), histoNoise->GetXaxis()->GetXmax());
    fitFunc->SetParameters(ana->residualIntegral_[apvID][stripBin],
                           ana->residualMean_[apvID][stripBin],
                           ana->residualRMS_[apvID][stripBin]);
    result = histoResidualStrip->Fit(fitFunc, "QSRN");

    // Good gaussian fit
    if (result.Get()) {
      ana->residualSigmaGaus_[apvID][stripBin] = fitFunc->GetParameter(2);
      ana->chi2Probab_[apvID][stripBin] = result->Prob();

      // jacque bera probability
      float jbVal =
          (ana->residualIntegral_[apvID][stripBin] / 6) *
          (pow(ana->residualSkewness_[apvID][stripBin], 2) + pow(ana->residualKurtosis_[apvID][stripBin], 2) / 4);
      ana->jbProbab_[apvID][stripBin] = ROOT::Math::chisquared_cdf_c(jbVal, 2);

      //Kolmogorov Smirnov and Anderson Darlong
      if (randomHisto == nullptr)
        randomHisto = (TH1F*)histoResidualStrip->Clone("randomHisto");
      randomHisto->Reset();
      randomHisto->SetDirectory(nullptr);

      if (generateRandomHisto_) {  
        randomHisto->FillRandom("fitFunc", histoResidualStrip->Integral());
        if (randomHisto->Integral() != 0) {
          ana->ksProbab_[apvID][stripBin] = histoResidualStrip->KolmogorovTest(randomHisto, "N");
          ana->adProbab_[apvID][stripBin] = histoResidualStrip->AndersonDarlingTest(randomHisto);
        } else {
          ana->ksProbab_[apvID][stripBin] = 0;
          ana->adProbab_[apvID][stripBin] = 0;
        }

      } else {
        randomHisto->Add(fitFunc);
        if (randomHisto->Integral() != 0) {
          ana->ksProbab_[apvID][stripBin] = histoResidualStrip->KolmogorovTest(randomHisto, "N");
          ROOT::Fit::BinData data1;
          ROOT::Fit::BinData data2;
          ROOT::Fit::FillData(data1, histoResidualStrip, nullptr);
          data2.Initialize(randomHisto->GetNbinsX() + 1, 1);
          for (int ibin = 0; ibin < randomHisto->GetNbinsX(); ibin++) {
            if (histoResidualStrip->GetBinContent(ibin + 1) != 0 or randomHisto->GetBinContent(ibin + 1) >= 1)
              data2.Add(randomHisto->GetBinCenter(ibin + 1),
                        randomHisto->GetBinContent(ibin + 1),
                        randomHisto->GetBinError(ibin + 1));
          }

          double probab, value;
          ROOT::Math::GoFTest::AndersonDarling2SamplesTest(data1, data2, probab, value);
          ana->adProbab_[apvID][stripBin] = probab;
        } else {
          ana->ksProbab_[apvID][stripBin] = 0;
          ana->adProbab_[apvID][stripBin] = 0;
        }
      }
    }

    // start applying selections storing output
    bool badStripFlag = false;
    ana->deadStripBit_[apvID][stripBin] = 0;  // is not dead if the strip has data

    if (fabs(ana->residualMean_[apvID][stripBin]) > maxDriftResidualCut_ and not badStripFlag) {  //mean value
      ana->shiftedStrip_[apvID].push_back(stripBin);
      badStripFlag = true;
    }

    if (ana->residualRMS_[apvID][stripBin] < minStripNoiseCut_ and not badStripFlag) {  // low noise
      ana->lowNoiseStrip_[apvID].push_back(stripBin);
      badStripFlag = true;
    }

    if (ana->residualRMS_[apvID][stripBin] > maxStripNoiseCut_ and not badStripFlag) {  // large noise
      ana->largeNoiseStrip_[apvID].push_back(stripBin);
      badStripFlag = true;
    }

    if (fabs(ana->noiseSignificance_[apvID][stripBin]) > maxStripNoiseSignificanceCut_ and
        not badStripFlag) {  // large noise significance
      ana->largeNoiseSignificance_[apvID].push_back(stripBin);
      badStripFlag = true;
    }

    if (result.Get() and result->Status() != 0)  // bad fit status
      ana->badFitStatus_[apvID].push_back(stripBin);

    if (ana->adProbab_[apvID][stripBin] < adProbabCut_ and not badStripFlag)  // bad AD p-value --> store the strip-id
      ana->badADProbab_[apvID].push_back(stripBin);

    if (ana->ksProbab_[apvID][stripBin] < ksProbabCut_ and not badStripFlag)  // bad KS p-value --> store the strip-id
      ana->badKSProbab_[apvID].push_back(stripBin);

    if (ana->jbProbab_[apvID][stripBin] < jbProbabCut_ and not badStripFlag)  // bad JB p-value  --> store the strip-id
      ana->badJBProbab_[apvID].push_back(stripBin);

    if (ana->chi2Probab_[apvID][stripBin] < chi2ProbabCut_ and
        not badStripFlag)  // bad CHI2 p-value  --> store the strip-id
      ana->badChi2Probab_[apvID].push_back(stripBin);

    if (ana->adProbab_[apvID][stripBin] < adProbabCut_ and ana->ksProbab_[apvID][stripBin] < ksProbabCut_ and
        ana->jbProbab_[apvID][stripBin] < jbProbabCut_ and ana->chi2Probab_[apvID][stripBin] < chi2ProbabCut_)
      badStripFlag = true;  // bad strip is flagged as bad by all the methods

    if (ana->residualKurtosis_[apvID][stripBin] > kurtosisCut_ and
        ana->residualIntegralNsigma_[apvID][stripBin] > integralTailCut_ and not badStripFlag) {  // bad tails
      ana->badTailStrip_[apvID].push_back(stripBin);
      badStripFlag = true;
    }

    if (badStripFlag) {  // loop for double peaked

      fit2Gaus = new TF1("dgaus",
                         "[0]*exp(-((x-[1])*(x-[1]))/(2*[2]*[2]))+[3]*exp(-((x-[4])*(x-[4]))/(2*[5]*[5]))",
                         histoNoise->GetXaxis()->GetXmin(),
                         histoNoise->GetXaxis()->GetXmax());
      fit2Gaus->SetParameter(0, fitFunc->GetParameter(0) / 2);
      fit2Gaus->SetParameter(3, fitFunc->GetParameter(0) / 2);
      fit2Gaus->SetParameter(1, 1.);
      fit2Gaus->SetParameter(4, -1.);
      fit2Gaus->SetParameter(2, fitFunc->GetParameter(2));
      fit2Gaus->SetParameter(5, fitFunc->GetParameter(2));
      fit2Gaus->SetParLimits(1, 0., histoNoise->GetXaxis()->GetXmax());
      fit2Gaus->SetParLimits(4, histoNoise->GetXaxis()->GetXmin(), 0);
      result = histoResidualStrip->Fit(fit2Gaus, "QSR");

      // ashman distance
      float ashman = TMath::Power(2, 0.5) * abs(fit2Gaus->GetParameter(1) - fit2Gaus->GetParameter(4)) /
                     (sqrt(pow(fit2Gaus->GetParameter(2), 2) + pow(fit2Gaus->GetParameter(5), 2)));
      // amplitude
      float amplitudeRatio = std::min(fit2Gaus->GetParameter(0), fit2Gaus->GetParameter(3)) /
                             std::max(fit2Gaus->GetParameter(0), fit2Gaus->GetParameter(3));

      if (ashman > ashmanDistance_ and amplitudeRatio > amplitudeRatio_)
        ana->badDoublePeakStrip_[apvID].push_back(stripBin);
    }

    if (badStripFlag) {  // save the final bit

      ana->badStrip_[apvID].push_back(stripBin);
      ana->badStripBit_[apvID][stripBin] = 1;

      SiStripFecKey fec_key(ana->fecKey());
      LogTrace(mlDqmClient_) << "BadStrip: fecCrate "
                             << " " << fec_key.fecCrate() << " fecSlot " << fec_key.fecSlot() << " fecRing "
                             << fec_key.fecRing() << " ccuAddr " << fec_key.ccuAddr() << " ccChan  "
                             << fec_key.ccuChan() << " lldChan " << fec_key.lldChan() << " apvID " << apvID
                             << " stripID " << stripBin;

    } else
      ana->badStripBit_[apvID][stripBin] = 0;
  }

  ped_max.clear();
  ped_min.clear();
  raw_max.clear();
  raw_min.clear();
  noise_max.clear();
  noise_min.clear();
  if (histoResidualStrip)
    delete histoResidualStrip;
  if (fitFunc)
    delete fitFunc;
  if (randomHisto)
    delete randomHisto;
  if (fit2Gaus)
    delete fit2Gaus;
}

void PedsFullNoiseAlgorithm::extract ( const std::vector< TH1 * > &  histos )

overrideprivatevirtual

Extracts and organises histograms.

Implements CommissioningAlgorithm.

Definition at line 62 of file PedsFullNoiseAlgorithm.cc.

References CommissioningAnalysis::addErrorCode(), CommissioningAlgorithm::anal(), sistrip::extrainfo::commonMode_, CommissioningAlgorithm::extractFedKey(), CommissioningAnalysis::fedKey(), hNoise2D_, hNoise_, hPeds_, sistrip::mlCommissioning_, sistrip::extrainfo::noise2D_, sistrip::extrainfo::noiseProfile_, sistrip::numberOfHistos_, sistrip::extrainfo::pedestals_, sistrip::PEDS_FULL_NOISE, sistrip::extrainfo::roughPedestals_, overlapproblemtsosanalyzer_cfi::title, sistrip::unexpectedExtraInfo_, and sistrip::unexpectedTask_.

Referenced by PedsFullNoiseAlgorithm().

                                                                  {
  if (!anal()) {
    edm::LogWarning(mlCommissioning_) << "[PedsFullNoiseAlgorithm::" << __func__ << "]"
                                      << " NULL pointer to Analysis object!";
    return;
  }

  // Check number of histograms --> Pedestal, noise and noise2D
  if (histos.size() != 3) {
    anal()->addErrorCode(sistrip::numberOfHistos_);
  }

  // Extract FED key from histo title --> i.e. APV pairs or LLD channel
  if (!histos.empty()) {
    anal()->fedKey(extractFedKey(histos.front()));
  }

  // Extract 1D histograms
  std::vector<TH1*>::const_iterator ihis = histos.begin();
  for (; ihis != histos.end(); ihis++) {
    // Check for NULL pointer
    if (!(*ihis)) {
      continue;
    }

    SiStripHistoTitle title((*ihis)->GetName());
    if (title.runType() != sistrip::PEDS_FULL_NOISE) {
      anal()->addErrorCode(sistrip::unexpectedTask_);
      continue;
    }

    // Extract peds histos
    if (title.extraInfo().find(sistrip::extrainfo::roughPedestals_) != std::string::npos) {
      //@@ something here for rough peds?
    } else if (title.extraInfo().find(sistrip::extrainfo::pedestals_) != std::string::npos) {
      hPeds_.first = *ihis;
      hPeds_.second = (*ihis)->GetName();
    } else if (title.extraInfo().find(sistrip::extrainfo::commonMode_) != std::string::npos) {
      //@@ something here for CM plots?
    } else if (title.extraInfo().find(sistrip::extrainfo::noiseProfile_) != std::string::npos) {
      //@@ something here for noise profile plot?
      hNoise_.first = *ihis;
      hNoise_.second = (*ihis)->GetName();
    } else if (title.extraInfo().find(sistrip::extrainfo::noise2D_) != std::string::npos) {
      hNoise2D_.first = *ihis;
      hNoise2D_.second = (*ihis)->GetName();
    } else {
      anal()->addErrorCode(sistrip::unexpectedExtraInfo_);
    }
  }
}

const PedsFullNoiseAlgorithm::Histo & PedsFullNoiseAlgorithm::hNoise ( ) const

inline

Definition at line 65 of file PedsFullNoiseAlgorithm.h.

References hNoise_.

Referenced by ~PedsFullNoiseAlgorithm().

{ return hNoise_; }

const PedsFullNoiseAlgorithm::Histo & PedsFullNoiseAlgorithm::hNoise2D ( ) const

inline

Definition at line 64 of file PedsFullNoiseAlgorithm.h.

References hNoise2D_.

Referenced by ~PedsFullNoiseAlgorithm().

{ return hNoise2D_; }

const PedsFullNoiseAlgorithm::Histo & PedsFullNoiseAlgorithm::hPeds ( ) const

inline

Definition at line 63 of file PedsFullNoiseAlgorithm.h.

References hPeds_.

Referenced by ~PedsFullNoiseAlgorithm().

{ return hPeds_; }

void PedsFullNoiseAlgorithm::reset ( PedsFullNoiseAnalysis *  ana )

private

reset vector

Definition at line 115 of file PedsFullNoiseAlgorithm.cc.

References PedsFullNoiseAnalysis::adProbab_, PedsFullNoiseAnalysis::badStripBit_, PedsFullNoiseAnalysis::chi2Probab_, PedsFullNoiseAnalysis::deadStripBit_, PedsFullNoiseAnalysis::jbProbab_, PedsFullNoiseAnalysis::ksProbab_, PedsFullNoiseAnalysis::noise_, PedsFullNoiseAnalysis::noiseMax_, PedsFullNoiseAnalysis::noiseMean_, PedsFullNoiseAnalysis::noiseMin_, PedsFullNoiseAnalysis::noiseSignificance_, PedsFullNoiseAnalysis::noiseSpread_, PedsFullNoiseAnalysis::peds_, PedsFullNoiseAnalysis::pedsMax_, PedsFullNoiseAnalysis::pedsMean_, PedsFullNoiseAnalysis::pedsMin_, PedsFullNoiseAnalysis::pedsSpread_, PedsFullNoiseAnalysis::raw_, PedsFullNoiseAnalysis::rawMax_, PedsFullNoiseAnalysis::rawMean_, PedsFullNoiseAnalysis::rawMin_, PedsFullNoiseAnalysis::rawSpread_, PedsFullNoiseAnalysis::residualIntegral_, PedsFullNoiseAnalysis::residualIntegralNsigma_, PedsFullNoiseAnalysis::residualKurtosis_, PedsFullNoiseAnalysis::residualMean_, PedsFullNoiseAnalysis::residualRMS_, PedsFullNoiseAnalysis::residualSigmaGaus_, and PedsFullNoiseAnalysis::residualSkewness_.

Referenced by analyse(), and PedsFullNoiseAlgorithm().

                                                             {
  for (size_t iapv = 0; iapv < ana->peds_.size(); iapv++) {
    ana->pedsMean_[iapv] = 0.;
    ana->rawMean_[iapv] = 0.;
    ana->noiseMean_[iapv] = 0.;
    ana->pedsSpread_[iapv] = 0.;
    ana->noiseSpread_[iapv] = 0.;
    ana->rawSpread_[iapv] = 0.;
    ana->pedsMax_[iapv] = 0.;
    ana->pedsMin_[iapv] = 0.;
    ana->rawMax_[iapv] = 0.;
    ana->rawMin_[iapv] = 0.;
    ana->noiseMax_[iapv] = 0.;
    ana->noiseMin_[iapv] = 0.;

    for (size_t istrip = 0; istrip < ana->peds_[iapv].size(); istrip++) {
      ana->peds_[iapv][istrip] = 0.;
      ana->noise_[iapv][istrip] = 0.;
      ana->raw_[iapv][istrip] = 0.;
      ana->adProbab_[iapv][istrip] = 0.;
      ana->ksProbab_[iapv][istrip] = 0.;
      ana->jbProbab_[iapv][istrip] = 0.;
      ana->chi2Probab_[iapv][istrip] = 0.;
      ana->residualRMS_[iapv][istrip] = 0.;
      ana->residualSigmaGaus_[iapv][istrip] = 0.;
      ana->noiseSignificance_[iapv][istrip] = 0.;
      ana->residualMean_[iapv][istrip] = 0.;
      ana->residualSkewness_[iapv][istrip] = 0.;
      ana->residualKurtosis_[iapv][istrip] = 0.;
      ana->residualIntegralNsigma_[iapv][istrip] = 0.;
      ana->residualIntegral_[iapv][istrip] = 0.;
      ana->deadStripBit_[iapv][istrip] = 0;
      ana->badStripBit_[iapv][istrip] = 0;
    }
  }
}

Member Data Documentation

float PedsFullNoiseAlgorithm::adProbabCut_

private

Definition at line 51 of file PedsFullNoiseAlgorithm.h.

Referenced by analyse(), and PedsFullNoiseAlgorithm().

float PedsFullNoiseAlgorithm::amplitudeRatio_

private

Definition at line 60 of file PedsFullNoiseAlgorithm.h.

Referenced by analyse().

float PedsFullNoiseAlgorithm::ashmanDistance_

private

Definition at line 59 of file PedsFullNoiseAlgorithm.h.

Referenced by analyse().

float PedsFullNoiseAlgorithm::chi2ProbabCut_

private

Definition at line 55 of file PedsFullNoiseAlgorithm.h.

Referenced by analyse(), and PedsFullNoiseAlgorithm().

bool PedsFullNoiseAlgorithm::generateRandomHisto_

private

Definition at line 53 of file PedsFullNoiseAlgorithm.h.

Referenced by analyse().

Histo PedsFullNoiseAlgorithm::hNoise2D_

private

Definition at line 44 of file PedsFullNoiseAlgorithm.h.

Referenced by analyse(), extract(), and hNoise2D().

Histo PedsFullNoiseAlgorithm::hNoise_

private

Noise and residuals

Definition at line 43 of file PedsFullNoiseAlgorithm.h.

Referenced by analyse(), extract(), and hNoise().

Histo PedsFullNoiseAlgorithm::hPeds_

private

Pedestals and raw noise

Definition at line 41 of file PedsFullNoiseAlgorithm.h.

Referenced by analyse(), extract(), and hPeds().

int PedsFullNoiseAlgorithm::integralNsigma_

private

Definition at line 58 of file PedsFullNoiseAlgorithm.h.

Referenced by analyse(), and PedsFullNoiseAlgorithm().

float PedsFullNoiseAlgorithm::integralTailCut_

private

Definition at line 57 of file PedsFullNoiseAlgorithm.h.

Referenced by analyse(), and PedsFullNoiseAlgorithm().

float PedsFullNoiseAlgorithm::jbProbabCut_

private

Definition at line 54 of file PedsFullNoiseAlgorithm.h.

Referenced by analyse(), and PedsFullNoiseAlgorithm().

float PedsFullNoiseAlgorithm::ksProbabCut_

private

Definition at line 52 of file PedsFullNoiseAlgorithm.h.

Referenced by analyse(), and PedsFullNoiseAlgorithm().

float PedsFullNoiseAlgorithm::kurtosisCut_

private

Definition at line 56 of file PedsFullNoiseAlgorithm.h.

Referenced by analyse(), and PedsFullNoiseAlgorithm().

float PedsFullNoiseAlgorithm::maxDriftResidualCut_

private

Analysis parameters

Definition at line 47 of file PedsFullNoiseAlgorithm.h.

Referenced by analyse(), and PedsFullNoiseAlgorithm().

float PedsFullNoiseAlgorithm::maxStripNoiseCut_

private

Definition at line 49 of file PedsFullNoiseAlgorithm.h.

Referenced by analyse(), and PedsFullNoiseAlgorithm().

float PedsFullNoiseAlgorithm::maxStripNoiseSignificanceCut_

private

Definition at line 50 of file PedsFullNoiseAlgorithm.h.

Referenced by analyse(), and PedsFullNoiseAlgorithm().

float PedsFullNoiseAlgorithm::minStripNoiseCut_

private

Definition at line 48 of file PedsFullNoiseAlgorithm.h.

Referenced by analyse(), and PedsFullNoiseAlgorithm().