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#include "DQM/SiStripCommissioningAnalysis/interface/PedsFullNoiseAlgorithm.h"
#include "CondFormats/SiStripObjects/interface/PedsFullNoiseAnalysis.h"
#include "DataFormats/SiStripCommon/interface/SiStripHistoTitle.h"
#include "DataFormats/SiStripCommon/interface/SiStripEnumsAndStrings.h"
#include "FWCore/MessageLogger/interface/MessageLogger.h"
#include "TProfile.h"
#include "TH1.h"
#include "TH2.h"
#include "TF1.h"
#include "TFitResult.h"
#include "TMath.h"
#include "Math/DistFunc.h"
#include "Math/ProbFuncMathCore.h"
#include "Fit/BinData.h"
#include "HFitInterface.h"
#include "Math/GoFTest.h"
#include <iostream>
#include <iomanip>
#include <cmath>
using namespace sistrip;
using namespace std;
// ----------------------------------------------------------------------------
//
PedsFullNoiseAlgorithm::PedsFullNoiseAlgorithm(const edm::ParameterSet& pset, PedsFullNoiseAnalysis* const anal)
: 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_;
}
// ----------------------------------------------------------------------------
//
void PedsFullNoiseAlgorithm::extract(const std::vector<TH1*>& histos) {
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_);
}
}
}
// resetting vectors
void PedsFullNoiseAlgorithm::reset(PedsFullNoiseAnalysis* ana) {
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;
}
}
}
// -----------------------------------------------------------------------------
//
void PedsFullNoiseAlgorithm::analyse() {
// 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
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
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;
}
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