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Histogram-based analysis for pedestal run. More...
#include <PedsFullNoiseAlgorithm.h>
Public Member Functions | |
| const Histo & | hNoise () const |
| const Histo & | hNoise1D () const |
| const Histo & | hPeds () const |
| PedsFullNoiseAlgorithm (const edm::ParameterSet &pset, PedsFullNoiseAnalysis *const ) | |
| virtual | ~PedsFullNoiseAlgorithm () |
Private Member Functions | |
| void | analyse () |
| void | extract (const std::vector< TH1 * > &) |
| PedsFullNoiseAlgorithm () | |
Private Attributes | |
| float | deadStripMax_ |
| Histo | hNoise1D_ |
| Histo | hNoise_ |
| Histo | hPeds_ |
| float | ksProbCut_ |
| std::string | noiseDef_ |
| float | noisyStripMin_ |
Histogram-based analysis for pedestal run.
Definition at line 16 of file PedsFullNoiseAlgorithm.h.
| PedsFullNoiseAlgorithm::PedsFullNoiseAlgorithm | ( | const edm::ParameterSet & | pset, |
| PedsFullNoiseAnalysis * const | anal | ||
| ) |
Definition at line 18 of file PedsFullNoiseAlgorithm.cc.
: CommissioningAlgorithm(anal), hPeds_(0,""), hNoise_(0,""), hNoise1D_(0,""), deadStripMax_(pset.getParameter<double>("DeadStripMax")), noisyStripMin_(pset.getParameter<double>("NoisyStripMin")), noiseDef_(pset.getParameter<std::string>("NoiseDefinition")), ksProbCut_(pset.getParameter<double>("KsProbCut")) { //LogDebug(mlCommissioning_) // << "[PedsFullNoiseAlgorithm::" << __func__ << "]" // << " Set maximum noise deviation for dead strip determination to: " << deadStripMax_; // LogDebug(mlCommissioning_) // << "[PedsFullNoiseAlgorithm::" << __func__ << "]" // << " Set minimal noise deviation for noise strip determination to: " << noisyStripMin_; }
| virtual PedsFullNoiseAlgorithm::~PedsFullNoiseAlgorithm | ( | ) | [inline, virtual] |
Definition at line 22 of file PedsFullNoiseAlgorithm.h.
{;}
| PedsFullNoiseAlgorithm::PedsFullNoiseAlgorithm | ( | ) | [inline, private] |
Definition at line 32 of file PedsFullNoiseAlgorithm.h.
{;}
| void PedsFullNoiseAlgorithm::analyse | ( | ) | [private, virtual] |
Performs histogram anaysis.
Implements CommissioningAlgorithm.
Definition at line 97 of file PedsFullNoiseAlgorithm.cc.
References a, CommissioningAnalysis::addErrorCode(), CommissioningAlgorithm::anal(), b, createTree::dd, PedsFullNoiseAnalysis::dead_, deadStripMax_, f, CommissioningAnalysis::fecKey(), hNoise_, hPeds_, i, if(), sistrip::invalid_, PedsFullNoiseAnalysis::ksProb_, ksProbCut_, LogTrace, sistrip::maximum_, sistrip::mlCommissioning_, sistrip::mlDqmClient_, PedsFullNoiseAnalysis::noise_, PedsFullNoiseAnalysis::noiseBin84_, noiseDef_, PedsFullNoiseAnalysis::noiseGaus_, PedsFullNoiseAnalysis::noiseMax_, PedsFullNoiseAnalysis::noiseMean_, PedsFullNoiseAnalysis::noiseMin_, PedsFullNoiseAnalysis::noiseRMS_, PedsFullNoiseAnalysis::noiseSignif_, PedsFullNoiseAnalysis::noiseSpread_, PedsFullNoiseAnalysis::noisy_, noisyStripMin_, sistrip::nullPtr_, sistrip::numberOfBins_, PedsFullNoiseAnalysis::peds_, PedsFullNoiseAnalysis::pedsMax_, PedsFullNoiseAnalysis::pedsMean_, PedsFullNoiseAnalysis::pedsMin_, PedsFullNoiseAnalysis::pedsSpread_, PedsFullNoiseAnalysis::raw_, PedsFullNoiseAnalysis::rawMax_, PedsFullNoiseAnalysis::rawMean_, PedsFullNoiseAnalysis::rawMin_, PedsFullNoiseAnalysis::rawSpread_, mathSSE::sqrt(), tmp, and ExpressReco_HICollisions_FallBack::x.
{
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 );
if ( !ana ) {
edm::LogWarning(mlCommissioning_)
<< "[PedsFullNoiseAlgorithm::" << __func__ << "]"
<< " NULL pointer to derived Analysis object!";
return;
}
if ( !hPeds_.first ) {
ana->addErrorCode(sistrip::nullPtr_);
return;
}
if ( !hNoise_.first ) {
ana->addErrorCode(sistrip::nullPtr_);
return;
}
TProfile * peds_histo = dynamic_cast<TProfile *>(hPeds_.first);
TH2S * noise_histo = dynamic_cast<TH2S *>(hNoise_.first);
if ( !peds_histo ) {
ana->addErrorCode(sistrip::nullPtr_);
return;
}
if ( !noise_histo ) {
ana->addErrorCode(sistrip::nullPtr_);
return;
}
if ( peds_histo->GetNbinsX() != 256 ) {
ana->addErrorCode(sistrip::numberOfBins_);
return;
}
if ( noise_histo->GetNbinsY() != 256 ) { // X range is configurable
ana->addErrorCode(sistrip::numberOfBins_);
return;
}
// Iterate through APVs
for ( uint16_t iapv = 0; iapv < 2; iapv++ ) {
// Used to calc mean and rms for peds and noise
float p_sum = 0., p_sum2 = 0., p_max = -1.*sistrip::invalid_, p_min = sistrip::invalid_;
float n_sum = 0., n_sum2 = 0., n_max = -1.*sistrip::invalid_, n_min = sistrip::invalid_;
float r_sum = 0., r_sum2 = 0., r_max = -1.*sistrip::invalid_, r_min = sistrip::invalid_;
// Iterate through strips of APV
for ( uint16_t istr = 0; istr < 128; istr++ ) {
ana->ksProb_[iapv].push_back(0);
ana->noiseGaus_[iapv].push_back(0);
ana->noiseBin84_[iapv].push_back(0);
ana->noiseRMS_[iapv].push_back(0);
ana->noiseSignif_[iapv].push_back(0);
// pedestals and raw noise
if ( peds_histo ) {
if ( peds_histo->GetBinEntries(iapv*128 + istr + 1) ) {
ana->peds_[iapv][istr] = peds_histo->GetBinContent(iapv*128 + istr + 1);
p_sum += ana->peds_[iapv][istr];
p_sum2 += (ana->peds_[iapv][istr] * ana->peds_[iapv][istr]);
if ( ana->peds_[iapv][istr] > p_max ) { p_max = ana->peds_[iapv][istr];}
if ( ana->peds_[iapv][istr] < p_min ) { p_min = ana->peds_[iapv][istr];}
ana->raw_[iapv][istr] = peds_histo->GetBinError(iapv*128 + istr + 1);
r_sum += ana->raw_[iapv][istr];
r_sum2 += (ana->raw_[iapv][istr] * ana->raw_[iapv][istr]);
if ( ana->raw_[iapv][istr] > r_max ) { r_max = ana->raw_[iapv][istr]; }
if ( ana->raw_[iapv][istr] < r_min ) { r_min = ana->raw_[iapv][istr]; }
}
}
// Noise from Full Distribution
if ( noise_histo ) {
// Fit the ADC Distribution from TH2S by projecting it out and fitting.
TH1S * noisehist = new TH1S("noisehist","",noise_histo->GetNbinsX(),
-noise_histo->GetNbinsX()/2,noise_histo->GetNbinsX()/2);
for(int i=0;i<=noise_histo->GetNbinsX()+1;i++){
noisehist->SetBinContent(i,noise_histo->GetBinContent(i,iapv*128 + istr + 1));
}
// If the histogram is valid.
if(noisehist->Integral() > 0){
ana->noiseRMS_[iapv][istr] = noisehist->GetRMS();
noisehist->Fit("gaus","Q");
ana->noiseGaus_[iapv][istr] = noisehist->GetFunction("gaus")->GetParameter(2);
// new Bin84 method
std::vector<float> integralFrac;
integralFrac.push_back(1.*noisehist->GetBinContent(0)/noisehist->Integral(0,noisehist->GetNbinsX()));
// Calculate the integral of distro as a function of bins.
for(int i = 1; i < noisehist->GetNbinsX();i++){
integralFrac.push_back(float(noisehist->GetBinContent(i))/
noisehist->Integral(0,noisehist->GetNbinsX())+integralFrac[i-1]);
//Take the two bins next to 84% and solve for x in 0.84 = mx+b
if (integralFrac[i] >= 0.84135 && integralFrac[i-1] < 0.84135) {
// my quadratic noise calculation
double w = noisehist->GetBinWidth(i);
double a = noisehist->GetBinContent(i-1);
double b = noisehist->GetBinContent(i);
double f = w*(0.84135 -integralFrac[i-1])/(integralFrac[i]-integralFrac[i-1]);
double x = 0;
if (a==b) {
x = f;
} else {
double aa = (b-a)/(2*w);
double bb = (b+a)/2;
double cc = -b*f;
double dd = bb*bb-4*aa*cc; //if (dd<0) dd=0;
x = (-bb+sqrt(dd))/(2*aa);
}
ana->noiseBin84_[iapv][istr] = noisehist->GetBinLowEdge(i) + x;
}
}
// Compare shape of ADC to a histogram made of Gaus Fit for KSProb, Chi2Prob, Etc...
TH1D * FitHisto = new TH1D("FitHisto","FitHisto",noisehist->GetNbinsX(),
-noisehist->GetNbinsX()/2,noisehist->GetNbinsX()/2);
FitHisto->Add(noisehist->GetFunction("gaus"));
FitHisto->Sumw2();
noisehist->Sumw2();
if(FitHisto->Integral() > 0){
// This is stored as a float but will be plotted as an int at the summary histos.
// This forces the histo to draw 10000 bins instead of 1.
ana->ksProb_[iapv][istr] = noisehist->KolmogorovTest(FitHisto)*10000;
}
delete FitHisto;
}
delete noisehist;
}
// Assigning the actual noise values used for Upload!!!!!!!!!!!!!!!!!!!!
if (noiseDef_ == "Bin84") {
if (ana->noiseBin84_[iapv][istr] > 0) {
ana->noise_[iapv][istr] = ana->noiseBin84_[iapv][istr];
} else {
ana->noise_[iapv][istr] = ana->noiseRMS_[iapv][istr];
}
} else if (noiseDef_ == "RMS") {
ana->noise_[iapv][istr] = ana->noiseRMS_[iapv][istr];
} else edm::LogWarning(mlCommissioning_)<< "[PedsFullNoiseAlgorithm::"
<< __func__ << "]"<< " Unknown noise definition!!!";
// Setting Sum of RMS and RMS^2 for Dead/Noisy Strip calculations
n_sum += ana->noise_[iapv][istr];
n_sum2 += (ana->noise_[iapv][istr] * ana->noise_[iapv][istr]);
if ( ana->noise_[iapv][istr] > n_max ) { n_max = ana->noise_[iapv][istr]; }
if ( ana->noise_[iapv][istr] < n_min ) { n_min = ana->noise_[iapv][istr]; }
} // strip loop
// Calc mean and rms for peds
if ( !ana->peds_[iapv].empty() ) {
p_sum /= static_cast<float>( ana->peds_[iapv].size() );
p_sum2 /= static_cast<float>( ana->peds_[iapv].size() );
ana->pedsMean_[iapv] = p_sum;
ana->pedsSpread_[iapv] = sqrt( fabs(p_sum2 - p_sum*p_sum) );
}
// Calc mean and rms for noise using noiseRMS.
if ( !ana->noise_[iapv].empty() ) {
n_sum /= static_cast<float>( ana->noise_[iapv].size() );
n_sum2 /= static_cast<float>( ana->noise_[iapv].size() );
ana->noiseMean_[iapv] = n_sum;
ana->noiseSpread_[iapv] = sqrt( fabs(n_sum2 - n_sum*n_sum) );
}
// Calc mean and rms for raw noise
if ( !ana->raw_[iapv].empty() ) {
r_sum /= static_cast<float>( ana->raw_[iapv].size() );
r_sum2 /= static_cast<float>( ana->raw_[iapv].size() );
ana->rawMean_[iapv] = r_sum;
ana->rawSpread_[iapv] = sqrt( fabs(r_sum2 - r_sum*r_sum) );
}
// Set max and min values for peds, noise and raw noise
if ( p_max > -1.*sistrip::maximum_ ) { ana->pedsMax_[iapv] = p_max; }
if ( p_min < 1.*sistrip::maximum_ ) { ana->pedsMin_[iapv] = p_min; }
if ( n_max > -1.*sistrip::maximum_ ) { ana->noiseMax_[iapv] = n_max; }
if ( n_min < 1.*sistrip::maximum_ ) { ana->noiseMin_[iapv] = n_min; }
if ( r_max > -1.*sistrip::maximum_ ) { ana->rawMax_[iapv] = r_max; }
if ( r_min < 1.*sistrip::maximum_ ) { ana->rawMin_[iapv] = r_min; }
// Set dead and noisy strips
for ( uint16_t istr = 0; istr < 128; istr++ ) { // strip loop
// Set the significance of the noise of each strip also compared to apv avg.
ana->noiseSignif_[iapv][istr] = (ana->noise_[iapv][istr]-ana->noiseMean_[iapv])/ana->noiseSpread_[iapv];
if ( ana->noiseMin_[iapv] > sistrip::maximum_ || ana->noiseMax_[iapv] > sistrip::maximum_ ) {
continue;
}
// Strip Masking for Dead Strips
if(ana->noiseSignif_[iapv][istr] < -deadStripMax_){
ana->dead_[iapv].push_back(istr);
SiStripFecKey fec_key(ana->fecKey());
LogTrace(mlDqmClient_)<<"DeadSignif "<<ana->noiseSignif_[iapv][istr]
<<" "<<fec_key.fecCrate()
<<" "<<fec_key.fecSlot()
<<" "<<fec_key.fecRing()
<<" "<<fec_key.ccuAddr()
<<" "<<fec_key.ccuChan()
<<" "<<fec_key.lldChan()
<<" "<<iapv*128+istr<<std::endl;
} // Strip Masking for Dead Strips
// Laurent's Method for Flagging bad strips in TIB
else if((ana->noiseMax_[iapv]/ana->noiseMean_[iapv] > 3 || ana->noiseSpread_[iapv] > 3)
&& ana->noiseSignif_[iapv][istr] > 1){
ana->noisy_[iapv].push_back(istr);
SiStripFecKey fec_key(ana->fecKey());
LogTrace(mlDqmClient_)<<"NoisyLM "<<ana->noiseMax_[iapv]/ana->noiseMean_[iapv]
<<" "<<fec_key.fecCrate()
<<" "<<fec_key.fecSlot()
<<" "<<fec_key.fecRing()
<<" "<<fec_key.ccuAddr()
<<" "<<fec_key.ccuChan()
<<" "<<fec_key.lldChan()
<<" "<<iapv*128+istr<<std::endl;
} // if NoisyLM
//Strip Masking for Non Gassian Strips which are also noisy.
else if(ana->ksProb_[iapv][istr] < ksProbCut_){
ana->noisy_[iapv].push_back(istr);
SiStripFecKey fec_key(ana->fecKey());
LogTrace(mlDqmClient_)<<"NoisyKS "<<ana->ksProb_[iapv][istr]
<<" "<<fec_key.fecCrate()
<<" "<<fec_key.fecSlot()
<<" "<<fec_key.fecRing()
<<" "<<fec_key.ccuAddr()
<<" "<<fec_key.ccuChan()
<<" "<<fec_key.lldChan()
<<" "<<iapv*128+istr<<std::endl;
} //Strip Masking for Non Gassian Strips which are also noisy.
else if(ana->noiseSignif_[iapv][istr] > noisyStripMin_){
ana->noisy_[iapv].push_back(istr);
SiStripFecKey fec_key(ana->fecKey());
LogTrace(mlDqmClient_)<<"NoisySignif "<<ana->noiseSignif_[iapv][istr]
<<" "<<fec_key.fecCrate()
<<" "<<fec_key.fecSlot()
<<" "<<fec_key.fecRing()
<<" "<<fec_key.ccuAddr()
<<" "<<fec_key.ccuChan()
<<" "<<fec_key.lldChan()
<<" "<<iapv*128+istr<<std::endl;
} // if Signif
}// strip loop to set dead or noisy strips
} // apv loop
//std::cout << std::endl;
}
| void PedsFullNoiseAlgorithm::extract | ( | const std::vector< TH1 * > & | histos | ) | [private, virtual] |
Extracts and organises histograms.
Implements CommissioningAlgorithm.
Definition at line 39 of file PedsFullNoiseAlgorithm.cc.
References CommissioningAnalysis::addErrorCode(), CommissioningAlgorithm::anal(), sistrip::extrainfo::commonMode_, CommissioningAlgorithm::extractFedKey(), CommissioningAnalysis::fedKey(), hNoise1D_, hNoise_, hPeds_, sistrip::mlCommissioning_, sistrip::extrainfo::noise2D_, sistrip::extrainfo::noiseProfile_, sistrip::numberOfHistos_, sistrip::extrainfo::pedestals_, sistrip::extrainfo::roughPedestals_, indexGen::title, and sistrip::unexpectedExtraInfo_.
{
if ( !anal() ) {
edm::LogWarning(mlCommissioning_)
<< "[PedsFullNoiseAlgorithm::" << __func__ << "]"
<< " NULL pointer to Analysis object!";
return;
}
// Check number of histograms
if ( histos.size() != 3 ) {
anal()->addErrorCode(sistrip::numberOfHistos_);
}
// Extract FED key from histo title
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; }
// TO BE UPDATED!!!
// Check run type
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?
hNoise1D_.first = *ihis;
hNoise1D_.second = (*ihis)->GetName();
} else if ( title.extraInfo().find(sistrip::extrainfo::noise2D_) != std::string::npos ) {
hNoise_.first = *ihis;
hNoise_.second = (*ihis)->GetName();
} else {
anal()->addErrorCode(sistrip::unexpectedExtraInfo_);
}
}
}
| const PedsFullNoiseAlgorithm::Histo & PedsFullNoiseAlgorithm::hNoise | ( | ) | const [inline] |
| const Histo& PedsFullNoiseAlgorithm::hNoise1D | ( | ) | const [inline] |
| const PedsFullNoiseAlgorithm::Histo & PedsFullNoiseAlgorithm::hPeds | ( | ) | const [inline] |
float PedsFullNoiseAlgorithm::deadStripMax_ [private] |
Analysis parameters
Definition at line 50 of file PedsFullNoiseAlgorithm.h.
Referenced by analyse().
Histo PedsFullNoiseAlgorithm::hNoise1D_ [private] |
Definition at line 47 of file PedsFullNoiseAlgorithm.h.
Referenced by extract().
Histo PedsFullNoiseAlgorithm::hNoise_ [private] |
Residuals and noise
Definition at line 46 of file PedsFullNoiseAlgorithm.h.
Histo PedsFullNoiseAlgorithm::hPeds_ [private] |
float PedsFullNoiseAlgorithm::ksProbCut_ [private] |
Definition at line 53 of file PedsFullNoiseAlgorithm.h.
Referenced by analyse().
std::string PedsFullNoiseAlgorithm::noiseDef_ [private] |
Definition at line 52 of file PedsFullNoiseAlgorithm.h.
Referenced by analyse().
float PedsFullNoiseAlgorithm::noisyStripMin_ [private] |
Definition at line 51 of file PedsFullNoiseAlgorithm.h.
Referenced by analyse().
1.7.3