[PWGDQ] some clang-tidy fixes for VarManager.h

PWGDQ/Core/VarManager.cxx

@@ -69,7 +69,7 @@
 o2::vertexing::FwdDCAFitterN<3> VarManager::fgFitterThreeProngFwd;
 o2::globaltracking::MatchGlobalFwd VarManager::mMatching;
 std::map<VarManager::CalibObjects, TObject*> VarManager::fgCalibs;
-bool VarManager::fgRunTPCPostCalibration[4] = {false, false, false, false};
+std::array<bool, 4> VarManager::fgRunTPCPostCalibration = {false, false, false, false};
 int VarManager::fgCalibrationType = 0;                // 0 - no calibration, 1 - calibration vs (TPCncls,pIN,eta) typically for pp, 2 - calibration vs (eta,nPV,nLong,tLong) typically for PbPb
 bool VarManager::fgUseInterpolatedCalibration = true; // use interpolated calibration histograms (default: true)
 int VarManager::fgEfficiencyType = 0;                 // type of efficiency to be applied, default is no efficiency
@@ -191,8 +191,8 @@
   // TO Do: add more systems
 
   // set the beam 4-momentum vectors
   float beamAEnergy = energy / 2.0 * sqrt(NumberOfProtonsA * NumberOfProtonsC / NumberOfProtonsC / NumberOfProtonsA); // GeV
   float beamCEnergy = energy / 2.0 * sqrt(NumberOfProtonsC * NumberOfProtonsA / NumberOfProtonsA / NumberOfProtonsC); // GeV
   float beamAMomentum = std::sqrt(beamAEnergy * beamAEnergy - NumberOfNucleonsA * NumberOfNucleonsA * MassProton * MassProton);
   float beamCMomentum = std::sqrt(beamCEnergy * beamCEnergy - NumberOfNucleonsC * NumberOfNucleonsC * MassProton * MassProton);
   fgBeamA.SetPxPyPzE(0, 0, beamAMomentum, beamAEnergy);
@@ -291,7 +291,7 @@
     double mean = calibMeanHist->GetBinContent(binTPCncls, binPin, binEta);
     double sigma = calibSigmaHist->GetBinContent(binTPCncls, binPin, binEta);
     return (nSigmaValue - mean) / sigma; // Return the calibrated nSigma value
   } else if (fgCalibrationType == 2) {
     // get the calibration histograms
     CalibObjects calibMean, calibSigma, calibStatus;
     switch (species) {
@@ -458,14 +458,14 @@
   // Bimodality coefficient = (skewness^2 + 1) / kurtosis
   // return a tuple including the coefficient, mean, RMS, skewness, and kurtosis
   size_t n = data.size();
   if (n < 3) {
     return std::make_tuple(-1.0, -1.0, -1.0, -1.0, -1.0);
   }
   float mean = std::accumulate(data.begin(), data.end(), 0.0) / n;
 
   float m2 = 0.0, m3 = 0.0, m4 = 0.0;
   float diff, diff2;
   for (float x : data) {
     diff = x - mean;
     diff2 = diff * diff;
     m2 += diff2;
@@ -504,7 +504,7 @@
   int nBins = static_cast<int>((max - min) / binWidth);
   std::vector<int> counts(nBins, 0.0);
 
   for (float x : data) {
     if (x < min || x >= max) {
       continue; // skip out-of-range values
     }