TrackResiduals.cxx

Go to the documentation of this file.

// Copyright 2019-2020 CERN and copyright holders of ALICE O2.
// See https://alice-o2.web.cern.ch/copyright for details of the copyright holders.
// All rights not expressly granted are reserved.
//
// This software is distributed under the terms of the GNU General Public
// License v3 (GPL Version 3), copied verbatim in the file "COPYING".
//
// In applying this license CERN does not waive the privileges and immunities
// granted to it by virtue of its status as an Intergovernmental Organization
// or submit itself to any jurisdiction.
 
 
#include "SpacePoints/TrackResiduals.h"
#include "CommonConstants/MathConstants.h"
#include "ReconstructionDataFormats/Track.h"
#include "MathUtils/fit.h"
 
#include "TMatrixDSym.h"
#include "TDecompChol.h"
#include "TVectorD.h"
 
#include <cmath>
#include <cstring>
#include <algorithm>
 
// for debugging
#include "TStopwatch.h"
#include "TSystem.h"
#include <iostream>
#include <fstream>
#include <limits>
#include <iomanip>
 
#include <fairlogger/Logger.h>
 
using namespace o2::tpc;
 
 
//______________________________________________________________________________
void TrackResiduals::init(bool doBinning)
{
  mSmoothPol2[VoxX] = true;
  mSmoothPol2[VoxF] = true;
  setKernelType();
  mParams = &SpacePointsCalibConfParam::Instance();
  mMaxZ2X = mParams->maxZ2X;
  mIsInitialized = true;
 
  if (doBinning) {
    // initialize binning
    initBinning();
  }
 
  LOG(info) << "Initialization complete";
}
 
//______________________________________________________________________________
void TrackResiduals::setY2XBinning(const std::vector<float>& binning)
{
  if (mIsInitialized) {
    LOG(error) << "Binning already initialized, not changing y/x binning";
    return;
  }
 
  if (binning.size() == 0) {
    LOGP(info, "Empty binning provided, will use default uniform y/x binning with {} bins", mNY2XBins);
    return;
  } else if (binning.size() == 1) {
    const int bins = static_cast<int>(binning.at(0));
    setNY2XBins(bins);
    LOGP(info, "Setting uniform binning for y/x with {} bins", bins - 1);
    return;
  }
 
  const int nBins = binning.size() - 1;
  if (fabsf(binning[0] + 1.f) > param::sEps || fabsf(binning[nBins] - 1.f) > param::sEps) {
    LOG(error) << "Provided binning for y/x not in range -1 to 1: " << binning[0] << " - " << binning[nBins] << ". Not changing y/x binning";
    return;
  }
 
  LOGP(info, "Setting custom binning for y/x with {} bins", nBins);
  setNY2XBins(nBins);
  mUniformBins[VoxF] = false;
  mY2XBinsDH.clear();
  mY2XBinsDI.clear();
  mY2XBinsCenter.clear();
  for (int iBin = 0; iBin < nBins; ++iBin) {
    mY2XBinsDH.push_back(.5f * (binning[iBin + 1] - binning[iBin]));
    mY2XBinsDI.push_back(.5f / mY2XBinsDH[iBin]);
    mY2XBinsCenter.push_back(binning[iBin] + mY2XBinsDH[iBin]);
    LOGF(info, "Bin %i: center (%.3f), half bin width (%.3f)", iBin, mY2XBinsCenter.back(), mY2XBinsDH.back());
  }
}
 
//______________________________________________________________________________
void TrackResiduals::setZ2XBinning(const std::vector<float>& binning)
{
  if (mIsInitialized) {
    LOG(error) << "Binning already initialized, not changing z/x binning";
    return;
  }
 
  if (binning.size() == 0) {
    LOGP(info, "Empty binning provided, will use default uniform z/x binning with {} bins", mNZ2XBins);
    return;
  } else if (binning.size() == 1) {
    const int bins = static_cast<int>(binning.at(0));
    setNZ2XBins(bins);
    LOGP(info, "Setting uniform binning for z/x with {} bins", bins);
    return;
  }
 
  int nBins = binning.size() - 1;
  if (fabsf(binning[0]) > param::sEps || fabsf(binning[nBins] - 1.f) > param::sEps) {
    LOG(error) << "Provided binning for z/x not in range 0 to 1: " << binning[0] << " - " << binning[nBins] << ". Not changing z/x binning";
    return;
  }
 
  LOGP(info, "Setting custom binning for z/x with {} bins", nBins);
  setNZ2XBins(nBins);
  mUniformBins[VoxZ] = false;
  mZ2XBinsDH.clear();
  mZ2XBinsDI.clear();
  mZ2XBinsCenter.clear();
  for (int iBin = 0; iBin < nBins; ++iBin) {
    mZ2XBinsDH.push_back(.5f * (binning[iBin + 1] - binning[iBin]) * mMaxZ2X);
    mZ2XBinsDI.push_back(.5f / mZ2XBinsDH[iBin]);
    mZ2XBinsCenter.push_back(binning[iBin] * mMaxZ2X + mZ2XBinsDH[iBin]);
    LOGF(info, "Bin %i: center (%.3f), half bin width (%.3f)", iBin, mZ2XBinsCenter.back(), mZ2XBinsDH.back());
  }
}
 
//______________________________________________________________________________
void TrackResiduals::initBinning()
{
  // initialize binning structures
  //
  // X binning
  if (mNXBins > 0 && mNXBins < param::NPadRows) {
    // uniform binning in X
    LOGF(info, "X-binning is uniform with %i bins from %.2f to %.2f", mNXBins, param::MinX, param::MaxX);
    mDXI = mNXBins / (param::MaxX - param::MinX);
    mDX = 1.0f / mDXI;
    mUniformBins[VoxX] = true;
  } else {
    // binning per pad row
    LOGF(info, "X-binning is per pad-row");
    mNXBins = param::NPadRows;
    mUniformBins[VoxX] = false;
    mDX = param::RowDX[0];
    mDXI = 1.f / mDX; // should not be used
  }
  //
  // Y/X binning
  mMaxY2X.resize(mNXBins);
  mDY2XI.resize(mNXBins);
  mDY2X.resize(mNXBins);
  //
  for (int ix = 0; ix < mNXBins; ++ix) {
    float x = getX(ix);
    mMaxY2X[ix] = tan(.5f * SECPHIWIDTH) - sDeadZone / x;
    mDY2XI[ix] = mNY2XBins / (2.f * mMaxY2X[ix]);
    mDY2X[ix] = 1.f / mDY2XI[ix];
  }
  if (mUniformBins[VoxF]) {
    LOGF(info, "Y/X-binning is uniform with %i bins from -MaxY2X to +MaxY2X (values depend on X-bin)", mNY2XBins);
    for (int ip = 0; ip < mNY2XBins; ++ip) {
      mY2XBinsDH.push_back(1.f / mNY2XBins);
      mY2XBinsDI.push_back(.5f / mY2XBinsDH[ip]);
      mY2XBinsCenter.push_back(-1.f + (ip + 0.5f) * 2.f * mY2XBinsDH[ip]);
      LOGF(info, "Bin %i: center (%.3f), half bin width (%.3f)", ip, mY2XBinsCenter.back(), mY2XBinsDH.back());
    }
  }
  //
  // Z/X binning
  mDZ2XI = mNZ2XBins / mMaxZ2X;
  mDZ2X = 1.0f / mDZ2XI; // for uniform case only
  if (mUniformBins[VoxZ]) {
    LOGF(info, "Z/X-binning is uniform with %i bins from 0 to %f", mNZ2XBins, mMaxZ2X);
    for (int iz = 0; iz < mNZ2XBins; ++iz) {
      mZ2XBinsDH.push_back(.5f * mDZ2X);
      mZ2XBinsDI.push_back(mDZ2XI);
      mZ2XBinsCenter.push_back((iz + 0.5f) * mDZ2X);
      LOGF(info, "Bin %i: center (%.3f), half bin width (%.3f)", iz, mZ2XBinsCenter.back(), mZ2XBinsDH.back());
    }
  }
  //
  mNVoxPerSector = mNY2XBins * mNZ2XBins * mNXBins;
  LOGF(info, "Each TPC sector is divided into %i voxels", mNVoxPerSector);
}
 
//______________________________________________________________________________
void TrackResiduals::initResultsContainer(int iSec)
{
  if (mInitResultsContainer.test(iSec)) {
    return;
  }
  mInitResultsContainer.set(iSec);
  mVoxelResults[iSec].resize(mNVoxPerSector);
  for (int ix = 0; ix < mNXBins; ++ix) {
    for (int ip = 0; ip < mNY2XBins; ++ip) {
      for (int iz = 0; iz < mNZ2XBins; ++iz) {
        const size_t binGlb = getGlbVoxBin(ix, ip, iz);
        VoxRes& resVox = mVoxelResults[iSec][binGlb];
        resVox.bvox[VoxX] = ix;
        resVox.bvox[VoxF] = ip;
        resVox.bvox[VoxZ] = iz;
        resVox.bsec = iSec;
      }
    }
  }
  LOG(debug) << "initialized the container for the main results";
}
 
//______________________________________________________________________________
void TrackResiduals::reset()
{
  for (int iSec = 0; iSec < SECTORSPERSIDE * SIDES; ++iSec) {
    mXBinsIgnore[iSec].reset();
    std::fill(mVoxelResults[iSec].begin(), mVoxelResults[iSec].end(), VoxRes());
    std::fill(mValidFracXBins[iSec].begin(), mValidFracXBins[iSec].end(), 0);
  }
}
 
//______________________________________________________________________________
int TrackResiduals::getRowID(float x) const
{
  int ix;
  if (x < param::RowX[param::NRowsAccumulated[0] - 1] + param::RowDX[0]) {
    // we are in the IROC
    ix = (x - (param::RowX[0] - .5f * param::RowDX[0])) / param::RowDX[0];
    if (ix < 0) {
      // x is smaller than the inner radius of the first pad row
      ix = -1;
    }
  } else if (x >= param::RowX[param::NRowsAccumulated[param::NROCTypes - 2]] - .5f * param::RowDX[param::NROCTypes - 1]) {
    // we are in the OROC3
    ix = (x - (param::RowX[param::NRowsAccumulated[param::NROCTypes - 2]] - .5f * param::RowDX[param::NROCTypes - 1])) / param::RowDX[param::NROCTypes - 1] + param::NRowsAccumulated[param::NROCTypes - 2];
    if (ix >= param::NPadRows) {
      // x is larger than the outer radius of the last OROC pad row
      ix = -1;
    }
  } else if (x > param::RowX[param::NRowsAccumulated[0]] - .5f * param::RowDX[1] && x < param::RowX[param::NRowsAccumulated[1] - 1] + .5f * param::RowDX[1]) {
    // we are in the OROC1
    ix = (x - (param::RowX[param::NRowsAccumulated[0]] - .5f * param::RowDX[1])) / param::RowDX[1] + param::NRowsAccumulated[0];
  } else if (x > param::RowX[param::NRowsAccumulated[1]] - .5f * param::RowDX[2] && x < param::RowX[param::NRowsAccumulated[2] - 1] + .5f * param::RowDX[2]) {
    // we are in the OROC2
    ix = (x - (param::RowX[param::NRowsAccumulated[1]] - .5f * param::RowDX[2])) / param::RowDX[2] + param::NRowsAccumulated[1];
  } else {
    // x is in one of the gaps between the ROCs
    ix = -1;
  }
  return ix;
}
 
bool TrackResiduals::findVoxelBin(int secID, float x, float y, float z, std::array<unsigned char, VoxDim>& bvox) const
{
  // Z/X bin
  if (fabs(z / x) > mMaxZ2X) {
    return false;
  }
  int bz = getZ2XBinExact(secID < SECTORSPERSIDE ? z / x : -z / x);
  if (bz < 0) {
    return false;
  }
  // X bin
  int bx = getXBinExact(x);
  if (bx < 0 || bx >= mNXBins) {
    return false;
  }
  // Y/X bin
  int bp = getY2XBinExact(y / x, bx);
  if (bp < 0 || bp >= mNY2XBins) {
    return false;
  }
 
  bvox[VoxZ] = bz;
  bvox[VoxX] = bx;
  bvox[VoxF] = bp;
  return true;
}
 
void TrackResiduals::setKernelType(KernelType kernel, float bwX, float bwP, float bwZ, float scX, float scP, float scZ)
{
  // set kernel type and widths in terms of binning in x, y/x, z/x and define aux variables
  mKernelType = kernel;
 
  mKernelScaleEdge[VoxX] = scX;
  mKernelScaleEdge[VoxF] = scP;
  mKernelScaleEdge[VoxZ] = scZ;
 
  mKernelWInv[VoxX] = (bwX > 0) ? 1. / bwX : 1.;
  mKernelWInv[VoxF] = (bwP > 0) ? 1. / bwP : 1.;
  mKernelWInv[VoxZ] = (bwZ > 0) ? 1. / bwZ : 1.;
 
  if (mKernelType == KernelType::Epanechnikov) {
    // bandwidth 1
    mStepKern[VoxX] = static_cast<int>(nearbyint(bwX + 0.5));
    mStepKern[VoxF] = static_cast<int>(nearbyint(bwP + 0.5));
    mStepKern[VoxZ] = static_cast<int>(nearbyint(bwZ + 0.5));
  } else if (mKernelType == KernelType::Gaussian) {
    // look in ~5 sigma
    mStepKern[VoxX] = static_cast<int>(nearbyint(bwX * 5. + 0.5));
    mStepKern[VoxF] = static_cast<int>(nearbyint(bwP * 5. + 0.5));
    mStepKern[VoxZ] = static_cast<int>(nearbyint(bwZ * 5. + 0.5));
  } else {
    LOG(error) << "given kernel type is not defined";
  }
  for (int i = VoxDim; i--;) {
    if (mStepKern[i] < 1) {
      mStepKern[i] = 1;
    }
  }
}
 
 
void TrackResiduals::setStats(const std::vector<TrackResiduals::VoxStats>& statsIn, int iSec)
{
  initResultsContainer(iSec);
  std::vector<VoxRes>& secDataTmp = mVoxelResults[iSec];
  for (int iVox = 0; iVox < mNVoxPerSector; ++iVox) {
    secDataTmp[iVox].stat[VoxX] = statsIn[iVox].meanPos[VoxX];
    secDataTmp[iVox].stat[VoxF] = statsIn[iVox].meanPos[VoxF];
    secDataTmp[iVox].stat[VoxZ] = statsIn[iVox].meanPos[VoxZ];
    secDataTmp[iVox].stat[VoxDim] = statsIn[iVox].nEntries;
  }
}
 
void TrackResiduals::fillStats(int iSec)
{
  initResultsContainer(iSec);
  std::vector<VoxRes>& secDataTmp = mVoxelResults[iSec];
  for (int iVox = 0; iVox < mNVoxPerSector; ++iVox) {
    const auto& voxStat = mVoxStatsIn[iVox];
    VoxRes& resVox = secDataTmp[iVox];
    for (int iDim = VoxDim; iDim--;) {
      const auto sumStat = (resVox.stat[VoxDim] + voxStat.nEntries);
      if (sumStat == 0) {
        continue;
      }
      double norm = 1. / sumStat;
      resVox.stat[iDim] = (resVox.stat[iDim] * resVox.stat[VoxDim] + voxStat.meanPos[iDim] * voxStat.nEntries) * norm;
    }
    resVox.stat[VoxDim] += voxStat.nEntries;
  }
}
 
//______________________________________________________________________________
void TrackResiduals::processSectorResiduals(int iSec)
{
  LOGP(info, "Processing {} voxel residuals for sector {}", mLocalResidualsIn.size(), iSec);
  initResultsContainer(iSec);
  // effective t0 correction changes sign between A-/C-side
  float effT0corr = (iSec < SECTORSPERSIDE) ? mEffT0Corr : -1. * mEffT0Corr;
  std::vector<size_t> binData;
  for (const auto& res : mLocalResidualsIn) {
    binData.push_back(getGlbVoxBin(res.bvox));
  }
  // sort in voxel increasing order
  std::vector<size_t> binIndices(binData.size());
  o2::math_utils::SortData(binData, binIndices);
  // fill the voxel statistics into the results container
  std::vector<VoxRes>& secData = mVoxelResults[iSec];
 
  // vectors holding the data for one voxel at a time
  std::vector<float> dyVec;
  std::vector<float> dzVec;
  std::vector<float> tgVec;
  // assuming we will always have around 1000 entries per voxel
  dyVec.reserve(1e3);
  dzVec.reserve(1e3);
  tgVec.reserve(1e3);
  size_t currVoxBin = -1;
  unsigned int nPointsInVox = 0;
  unsigned int nProcessed = 0;
  while (nProcessed < binData.size()) {
    // read all points, voxel by voxel
    int idx = binIndices[nProcessed];
    if (currVoxBin != binData[idx]) {
      if (nPointsInVox) {
        VoxRes& resVox = secData[currVoxBin];
        processVoxelResiduals(dyVec, dzVec, tgVec, resVox);
      }
      currVoxBin = binData[idx];
      nPointsInVox = 0;
      dyVec.clear();
      dzVec.clear();
      tgVec.clear();
    }
    dyVec.push_back(mLocalResidualsIn[idx].dy * param::MaxResid / 0x7fff);
    dzVec.push_back(mLocalResidualsIn[idx].dz * param::MaxResid / 0x7fff -
                    mEffVdriftCorr * secData[currVoxBin].stat[VoxZ] * secData[currVoxBin].stat[VoxX] -
                    effT0corr);
    tgVec.push_back(mLocalResidualsIn[idx].tgSlp * param::MaxTgSlp / 0x7fff);
 
    ++nPointsInVox;
    ++nProcessed;
  }
  if (nPointsInVox) {
    // process last voxel
    VoxRes& resVox = secData[currVoxBin];
    processVoxelResiduals(dyVec, dzVec, tgVec, resVox);
  }
  LOG(info) << "extracted residuals for sector " << iSec;
 
  int nRowsOK = validateVoxels(iSec);
  LOG(info) << "number of validated X rows: " << nRowsOK;
  if (!nRowsOK) {
    LOG(warning) << "sector " << iSec << ": all X-bins disabled, abandon smoothing";
    return;
  } else {
    smooth(iSec);
  }
 
  // process dispersions
  dyVec.clear();
  tgVec.clear();
  currVoxBin = -1;
  nProcessed = 0;
  nPointsInVox = 0;
  while (nProcessed < binData.size()) {
    int idx = binIndices[nProcessed];
    if (currVoxBin != binData[idx]) {
      if (nPointsInVox) {
        VoxRes& resVox = secData[currVoxBin];
        if (!getXBinIgnored(iSec, resVox.bvox[VoxX])) {
          processVoxelDispersions(tgVec, dyVec, resVox);
        }
      }
      currVoxBin = binData[idx];
      nPointsInVox = 0;
      dyVec.clear();
      tgVec.clear();
    }
    dyVec.push_back(mLocalResidualsIn[idx].dy * param::MaxResid / 0x7fff);
    tgVec.push_back(mLocalResidualsIn[idx].tgSlp * param::MaxTgSlp / 0x7fff);
    ++nPointsInVox;
    ++nProcessed;
  }
  if (nPointsInVox) {
    // process last voxel
    VoxRes& resVox = secData[currVoxBin];
    if (!getXBinIgnored(iSec, resVox.bvox[VoxX])) {
      processVoxelDispersions(tgVec, dyVec, resVox);
    }
  }
  // smooth dispersions
  for (int ix = 0; ix < mNXBins; ++ix) {
    if (getXBinIgnored(iSec, ix)) {
      continue;
    }
    for (int iz = 0; iz < mNZ2XBins; ++iz) {
      for (int ip = 0; ip < mNY2XBins; ++ip) {
        int voxBin = getGlbVoxBin(ix, ip, iz);
        VoxRes& resVox = secData[voxBin];
        getSmoothEstimate(iSec, resVox.stat[VoxX], resVox.stat[VoxF], resVox.stat[VoxZ], resVox.DS, 0x1 << VoxV);
      }
    }
  }
  LOG(info) << "Done processing residuals for sector " << iSec;
  dumpResults(iSec);
}
 
//______________________________________________________________________________
void TrackResiduals::processVoxelResiduals(std::vector<float>& dy, std::vector<float>& dz, std::vector<float>& tg, VoxRes& resVox)
{
  int nPoints = dy.size();
  if (nPoints < mParams->minEntriesPerVoxel) {
    LOG(debug) << "voxel " << getGlbVoxBin(resVox.bvox) << " is skipped due to too few entries (" << nPoints << " < " << mParams->minEntriesPerVoxel << ")";
    return;
  } else {
    LOGF(debug, "Processing voxel %i with %i entries", getGlbVoxBin(resVox.bvox), nPoints);
  }
  std::array<float, 7> zResults;
  resVox.flags = 0;
  std::vector<size_t> indices(dz.size());
  if (!o2::math_utils::LTMUnbinned(dz, indices, zResults, mParams->LTMCut)) {
    LOG(debug) << "failed trimming input array for voxel " << getGlbVoxBin(resVox.bvox);
    return;
  }
  if (!mParams->isBfieldZero) {
    std::array<float, 2> res{0.f};
    std::array<float, 3> err{0.f};
    float sigMAD = fitPoly1Robust(tg, dy, res, err, mParams->LTMCut);
    if (sigMAD < 0) {
      LOG(debug) << "failed robust linear fit, sigMAD =  " << sigMAD;
      return;
    }
    float corrErr = err[0] * err[2];
    corrErr = corrErr > 0 ? err[1] / std::sqrt(corrErr) : -999;
    //
    resVox.D[ResX] = -res[1];
    resVox.D[ResY] = res[0];
    resVox.D[ResZ] = zResults[1];
    resVox.E[ResX] = std::sqrt(err[2]);
    resVox.E[ResY] = std::sqrt(err[0]);
    resVox.E[ResZ] = zResults[4];
    resVox.EXYCorr = corrErr;
    resVox.D[ResD] = resVox.dYSigMAD = sigMAD; // later will be overwritten by real dispersion
    resVox.dZSigLTM = zResults[2];
  } else {
    // for B=0 we cannot disentangle radial distortions from distortions in y,
    // so simply use average for dy as well and set distortion in X to zero
    std::array<float, 7> yResults;
    std::vector<size_t> indicesY(dy.size());
    if (!o2::math_utils::LTMUnbinned(dy, indicesY, yResults, mParams->LTMCut)) {
      LOG(debug) << "failed trimming input array for voxel " << getGlbVoxBin(resVox.bvox);
      return;
    }
    resVox.D[ResX] = 0; // force to zero
    resVox.D[ResY] = yResults[1];
    resVox.D[ResZ] = zResults[1];
    resVox.E[ResX] = 0;
    resVox.E[ResY] = yResults[4];
    resVox.E[ResZ] = zResults[4];
    resVox.EXYCorr = 0;
    resVox.D[ResD] = resVox.dYSigMAD = yResults[2];
    resVox.dZSigLTM = zResults[2];
  }
 
  LOGF(debug, "D[0]=%.2f, D[1]=%.2f, D[2]=%.2f, E[0]=%.2f, E[1]=%.2f, E[2]=%.2f, EXYCorr=%.4f, dYSigMAD=%.3f, dZSigLTM=%.3f",
       resVox.D[0], resVox.D[1], resVox.D[2], resVox.E[0], resVox.E[1], resVox.E[2], resVox.EXYCorr, resVox.dYSigMAD, resVox.dZSigLTM);
 
  resVox.flags |= DistDone;
 
  return;
}
 
void TrackResiduals::processVoxelDispersions(std::vector<float>& tg, std::vector<float>& dy, VoxRes& resVox)
{
  size_t nPoints = tg.size();
  LOG(debug) << "processing voxel dispersions for vox " << getGlbVoxBin(resVox.bvox) << " with " << nPoints << " points";
  if (nPoints < 2) {
    return;
  }
  for (size_t i = nPoints; i--;) {
    dy[i] -= resVox.DS[ResY] - resVox.DS[ResX] * tg[i];
  }
  resVox.D[ResD] = getMAD2Sigma(dy);
  resVox.E[ResD] = resVox.D[ResD] / sqrt(2.f * nPoints); // a la gaussioan RMS error (very crude)
  resVox.flags |= DispDone;
}
 
//______________________________________________________________________________
int TrackResiduals::validateVoxels(int iSec)
{
  // apply voxel validation cuts
  // return number of good voxels for given sector
  int cntMasked = 0;  // number of voxels masked for any reason (either low statistics or bad fit)
  int cntLowStat = 0; // number of voxels which were not processed due to too low statistics
  mXBinsIgnore[iSec].reset();
  std::vector<VoxRes>& secData = mVoxelResults[iSec];
 
  int cntMaskedFit = 0;
  int cntMaskedSigma = 0;
 
  // find bad voxels in sector
  for (int ix = 0; ix < mNXBins; ++ix) {
    int cntValid = 0;
    for (int ip = 0; ip < mNY2XBins; ++ip) {
      for (int iz = 0; iz < mNZ2XBins; ++iz) {
        int binGlb = getGlbVoxBin(ix, ip, iz);
        VoxRes& resVox = secData[binGlb];
        if ((resVox.flags & DistDone) == 0) {
          ++cntLowStat;
        }
        bool voxelOK = (resVox.flags & DistDone) && !(resVox.flags & Masked);
        if (voxelOK) {
          // check fit errors
          if (resVox.E[ResY] * resVox.E[ResY] > mParams->maxFitErrY2 ||
              resVox.E[ResX] * resVox.E[ResX] > mParams->maxFitErrX2 ||
              fabs(resVox.EXYCorr) > mParams->maxFitCorrXY) {
            voxelOK = false;
            ++cntMaskedFit;
          }
          // check raw distribution sigmas
          if (resVox.dYSigMAD > mParams->maxSigY ||
              resVox.dZSigLTM > mParams->maxSigZ) {
            voxelOK = false;
            ++cntMaskedSigma;
          }
        }
        if (voxelOK) {
          ++cntValid;
        } else {
          ++cntMasked;
          resVox.flags |= Masked;
        }
      } // loop over Z
    } // loop over Y/X
    mValidFracXBins[iSec][ix] = static_cast<float>(cntValid) / (mNY2XBins * mNZ2XBins);
    LOGP(debug, "Sector {}: xBin {} has {} % of voxels valid. Total masked due to fit: {} ,and sigma: {}",
         iSec, ix, mValidFracXBins[iSec][ix] * 100., cntMaskedFit, cntMaskedSigma);
  } // loop over X
 
  // mask X-bins which cannot be smoothed
 
  short nBadReg = 0;                           // count bad regions (one or more consecutive bad X-bins)
  std::array<short, param::NPadRows> badStart; // to store indices to the beginnings of the bad regions
  std::array<short, param::NPadRows> badEnd;   // to store indices to the end of the bad regions
  bool prevBad = false;
  float fracBadRows = 0.f;
  for (int ix = 0; ix < mNXBins; ++ix) {
    if (mValidFracXBins[iSec][ix] < mParams->minValidVoxFracDrift) {
      LOG(debug) << "row " << ix << " is bad";
      ++fracBadRows;
      if (prevBad) {
        badEnd[nBadReg] = ix;
      } else {
        badStart[nBadReg] = ix;
        badEnd[nBadReg] = ix;
        prevBad = true;
      }
    } else {
      if (prevBad) {
        ++nBadReg;
        prevBad = false;
      }
    }
  }
  if (prevBad) {
    ++nBadReg;
  }
  fracBadRows /= mNXBins;
  if (fracBadRows > mParams->maxFracBadRowsPerSector) {
    LOG(warning) << "sector " << iSec << ": Fraction of bad X-bins: " << fracBadRows << " -> masking whole sector";
    mXBinsIgnore[iSec].set();
  } else {
    for (int iBad = 0; iBad < nBadReg; ++iBad) {
      LOG(debug) << "masking bad region " << iBad;
      short badInReg = badEnd[iBad] - badStart[iBad] + 1;
      short badInNextReg = iBad < (nBadReg - 1) ? badEnd[iBad] - badStart[iBad] + 1 : 0;
      if (badInReg > mParams->maxBadXBinsToCover) {
        // disable too large bad patches
        for (int i = 0; i < badInReg; ++i) {
          LOG(debug) << "disabling too large patch in bad region " << iBad << ", badStart(" << badStart[iBad] << "), i(" << i << ")";
          mXBinsIgnore[iSec].set(badStart[iBad] + i);
        }
      }
      if (badInNextReg > mParams->maxBadXBinsToCover && (badStart[iBad + 1] - badEnd[iBad] - 1) < mParams->minGoodXBinsToCover) {
        // disable too small isolated good patches`
        for (int i = badEnd[iBad] + 1; i < badStart[iBad + 1]; ++i) {
          LOG(debug) << "disabling too small good patch before bad region " << iBad + 1 << ", badStart(" << badEnd[iBad] << "), badEnd(" << badStart[iBad + 1] << ")";
          mXBinsIgnore[iSec].set(i);
        }
      }
    }
    if (nBadReg) {
      if (mXBinsIgnore[iSec].test(badStart[0]) && badStart[0] < mParams->minGoodXBinsToCover) {
        // 1st good patch is too small
        for (int i = 0; i < badStart[0]; ++i) {
          LOG(debug) << "disabling too small first good patch badStart(0), badEnd(" << badStart[0] << ")";
          mXBinsIgnore[iSec].set(i);
        }
      }
      if (mXBinsIgnore[iSec].test(badStart[nBadReg - 1]) && (mNXBins - badEnd[nBadReg - 1] - 1) < mParams->minGoodXBinsToCover) {
        // last good patch is too small
        for (int i = badEnd[nBadReg - 1] + 1; i < mNXBins; ++i) {
          LOG(debug) << "disabling too small last good patch badStart(" << badEnd[nBadReg - 1] << "), badEnd(" << mNXBins << ")";
          mXBinsIgnore[iSec].set(i);
        }
      }
    }
  }
  //
  int nMaskedRows = mXBinsIgnore[iSec].count();
  LOGP(info, "Sector {}: out of {} voxels {} are masked. {} (low stat), {} (invalid fit) and {} (raw distrib sigma)",
       iSec, mNVoxPerSector, cntMasked, cntLowStat, cntMaskedFit, cntMaskedSigma);
  //
  return mNXBins - nMaskedRows;
}
 
void TrackResiduals::smooth(int iSec)
{
  std::vector<VoxRes>& secData = mVoxelResults[iSec];
  for (int ix = 0; ix < mNXBins; ++ix) {
    if (getXBinIgnored(iSec, ix)) {
      continue;
    }
    for (int ip = 0; ip < mNY2XBins; ++ip) {
      for (int iz = 0; iz < mNZ2XBins; ++iz) {
        int voxBin = getGlbVoxBin(ix, ip, iz);
        VoxRes& resVox = secData[voxBin];
        resVox.flags &= ~SmoothDone;
        bool res = getSmoothEstimate(resVox.bsec, resVox.stat[VoxX], resVox.stat[VoxF], resVox.stat[VoxZ], resVox.DS, (0x1 << VoxX | 0x1 << VoxF | 0x1 << VoxZ));
        if (!res) {
          mNSmoothingFailedBins[iSec]++;
        } else {
          resVox.flags |= SmoothDone;
        }
      }
    }
  }
  // substract dX contribution to dZ
  for (int ix = 0; ix < mNXBins; ++ix) {
    if (getXBinIgnored(iSec, ix)) {
      continue;
    }
    for (int ip = 0; ip < mNY2XBins; ++ip) {
      for (int iz = 0; iz < mNZ2XBins; ++iz) {
        int voxBin = getGlbVoxBin(ix, ip, iz);
        VoxRes& resVox = secData[voxBin];
        if (!(resVox.flags & SmoothDone)) {
          continue;
        }
        resVox.DS[ResZ] += resVox.stat[VoxZ] * resVox.DS[ResX]; // remove slope*dX contribution from dZ
        resVox.D[ResZ] += resVox.stat[VoxZ] * resVox.DS[ResX];  // remove slope*dX contribution from dZ
      }
    }
  }
}
 
bool TrackResiduals::getSmoothEstimate(int iSec, float x, float p, float z, std::array<float, ResDim>& res, int whichDim)
{
  // get smooth estimate for distortions for point in sector coordinates
 
  std::array<int, VoxDim> minPointsDir{0}; // min number of points per direction
  const float kTrialStep = 0.5;
  std::array<bool, ResDim> doDim{false};
  for (int i = 0; i < ResDim; ++i) {
    doDim[i] = (whichDim & (0x1 << i)) > 0;
    if (doDim[i]) {
      res[i] = 0.f;
    }
  }
 
  int matSize = sSmtLinDim;
  for (int i = 0; i < VoxDim; ++i) {
    minPointsDir[i] = 3; // for pol1 smoothing require at least 3 points
    if (mSmoothPol2[i]) {
      ++minPointsDir[i];
      ++matSize;
    }
  }
 
  int ix0, ip0, iz0;
  findVoxel(x, p, iSec < SECTORSPERSIDE ? z : -z, ix0, ip0, iz0); // find nearest voxel
  std::vector<VoxRes>& secData = mVoxelResults[iSec];
  int binCenter = getGlbVoxBin(ix0, ip0, iz0); // global bin of nearest voxel
  VoxRes& voxCenter = secData[binCenter];      // nearest voxel
  LOG(debug) << "getting smooth estimate around voxel " << binCenter;
 
  // cache
  // \todo maybe a 1-D cache would be more efficient?
  std::array<std::array<double, sMaxSmtDim*(sMaxSmtDim + 1) / 2>, ResDim> cmat;
  int maxNeighb = 10 * 10 * 10;
  std::vector<VoxRes*> currVox;
  currVox.reserve(maxNeighb);
  std::vector<float> currCache;
  currCache.reserve(maxNeighb * VoxHDim);
 
  std::array<int, VoxDim> maxTrials;
  maxTrials[VoxZ] = mNZ2XBins / 2;
  maxTrials[VoxF] = mNY2XBins / 2;
  maxTrials[VoxX] = mParams->maxBadXBinsToCover * 2;
 
  std::array<int, VoxDim> trial{0};
 
  while (true) {
    std::fill(mLastSmoothingRes.begin(), mLastSmoothingRes.end(), 0);
    memset(&cmat[0][0], 0, sizeof(cmat));
 
    int nbOK = 0; // accounted neighbours
 
    float stepX = mStepKern[VoxX] * (1. + kTrialStep * trial[VoxX]);
    float stepF = mStepKern[VoxF] * (1. + kTrialStep * trial[VoxF]);
    float stepZ = mStepKern[VoxZ] * (1. + kTrialStep * trial[VoxZ]);
 
    if (!(voxCenter.flags & DistDone) || (voxCenter.flags & Masked) || getXBinIgnored(iSec, ix0)) {
      // closest voxel has no data -> increase smoothing step
      stepX += kTrialStep * mStepKern[VoxX];
      stepF += kTrialStep * mStepKern[VoxF];
      stepZ += kTrialStep * mStepKern[VoxZ];
    }
 
    // effective kernel widths accounting for the increased bandwidth at the edges and missing data
    float kWXI = getDXI(ix0) * mKernelWInv[VoxX] * mStepKern[VoxX] / stepX;
    float kWFI = getDY2XI(ix0, ip0) * mKernelWInv[VoxF] * mStepKern[VoxF] / stepF;
    float kWZI = getDZ2XI(iz0) * mKernelWInv[VoxZ] * mStepKern[VoxZ] / stepZ;
    int iStepX = static_cast<int>(nearbyint(stepX + 0.5));
    int iStepF = static_cast<int>(nearbyint(stepF + 0.5));
    int iStepZ = static_cast<int>(nearbyint(stepZ + 0.5));
    int ixMin = ix0 - iStepX;
    int ixMax = ix0 + iStepX;
    if (ixMin < 0) {
      ixMin = 0;
      ixMax = std::min(static_cast<int>(nearbyint(ix0 + stepX * mKernelScaleEdge[VoxX])), mNXBins - 1);
      kWXI /= mKernelScaleEdge[VoxX];
    }
    if (ixMax >= mNXBins) {
      ixMax = mNXBins - 1;
      ixMin = std::max(static_cast<int>(nearbyint(ix0 - stepX * mKernelScaleEdge[VoxX])), 0);
      kWXI /= mKernelScaleEdge[VoxX];
    }
 
    int ipMin = ip0 - iStepF;
    int ipMax = ip0 + iStepF;
    if (ipMin < 0) {
      ipMin = 0;
      ipMax = std::min(static_cast<int>(nearbyint(ip0 + stepF * mKernelScaleEdge[VoxF])), mNY2XBins - 1);
      kWFI /= mKernelScaleEdge[VoxF];
    }
    if (ipMax >= mNY2XBins) {
      ipMax = mNY2XBins - 1;
      ipMin = std::max(static_cast<int>(nearbyint(ip0 - stepF * mKernelScaleEdge[VoxF])), 0);
      kWFI /= mKernelScaleEdge[VoxF];
    }
 
    int izMin = iz0 - iStepZ;
    int izMax = iz0 + iStepZ;
    if (izMin < 0) {
      izMin = 0;
      izMax = std::min(static_cast<int>(nearbyint(iz0 + stepZ * mKernelScaleEdge[VoxZ])), mNZ2XBins - 1);
      kWZI /= mKernelScaleEdge[VoxZ];
    }
    if (izMax >= mNZ2XBins) {
      izMax = mNZ2XBins - 1;
      izMin = std::max(static_cast<int>(nearbyint(iz0 - stepZ * mKernelScaleEdge[VoxZ])), 0);
      kWZI /= mKernelScaleEdge[VoxZ];
    }
 
    std::vector<unsigned short> nOccX(ixMax - ixMin + 1, 0);
    std::vector<unsigned short> nOccF(ipMax - ipMin + 1, 0);
    std::vector<unsigned short> nOccZ(izMax - izMin + 1, 0);
 
    int nbCheck = (ixMax - ixMin + 1) * (ipMax - ipMin + 1) * (izMax - izMin + 1);
    if (nbCheck >= maxNeighb) {
      maxNeighb = nbCheck + 100;
      currCache.reserve(maxNeighb * VoxHDim);
      currVox.reserve(maxNeighb);
    }
    std::array<double, 3> u2Vec;
 
    // first loop, check presence of enough points
    for (int ix = ixMin; ix <= ixMax; ++ix) {
      for (int ip = ipMin; ip <= ipMax; ++ip) {
        for (int iz = izMin; iz <= izMax; ++iz) {
          int binNb = getGlbVoxBin(ix, ip, iz);
          VoxRes& voxNb = secData[binNb];
          if (!(voxNb.flags & DistDone) ||
              (voxNb.flags & Masked) ||
              getXBinIgnored(iSec, ix)) {
            // skip voxels w/o data
            continue;
          }
          // estimate weighted distance
          float dx = voxNb.stat[VoxX] - x;
          float df = voxNb.stat[VoxF] - p;
          float dz = voxNb.stat[VoxZ] - z;
          float dxw = dx * kWXI;
          float dfw = df * kWFI;
          float dzw = dz * kWZI;
          u2Vec[0] = dxw * dxw;
          u2Vec[1] = dfw * dfw;
          u2Vec[2] = dzw * dzw;
          double kernelWeight = getKernelWeight(u2Vec);
          if (kernelWeight < 1e-6) {
            continue;
          }
          // new point is validated
          ++nOccX[ix - ixMin];
          ++nOccF[ip - ipMin];
          ++nOccZ[iz - izMin];
          currVox[nbOK] = &voxNb;
          currCache[nbOK * VoxHDim + VoxX] = dx;
          currCache[nbOK * VoxHDim + VoxF] = df;
          currCache[nbOK * VoxHDim + VoxZ] = dz;
          currCache[nbOK * VoxHDim + VoxV] = kernelWeight;
          ++nbOK;
        }
      }
    }
 
    // check if we have enough points in every dimension
    std::array<int, VoxDim> nPoints{0};
    for (int i = ixMax - ixMin + 1; i--;) {
      if (nOccX[i]) {
        ++nPoints[VoxX];
      }
    }
    for (int i = ipMax - ipMin + 1; i--;) {
      if (nOccF[i]) {
        ++nPoints[VoxF];
      }
    }
    for (int i = izMax - izMin + 1; i--;) {
      if (nOccZ[i]) {
        ++nPoints[VoxZ];
      }
    }
    bool enoughPoints = true;
    std::array<bool, VoxDim> incrDone{false};
    for (int i = 0; i < VoxDim; ++i) {
      if (nPoints[i] < minPointsDir[i]) {
        // need to extend smoothing neighbourhood
        enoughPoints = false;
        if (trial[i] < maxTrials[i] && !incrDone[i]) {
          // try to increment only missing direction
          ++trial[i];
          incrDone[i] = true;
        } else if (trial[i] == maxTrials[i]) {
          // cannot increment missing direction, try others
          for (int j = VoxDim; j--;) {
            if (i != j && trial[j] < maxTrials[j] && !incrDone[j]) {
              ++trial[j];
              incrDone[j] = true;
            }
          }
        }
      }
    }
 
    if (!enoughPoints) {
      if (!(incrDone[VoxX] || incrDone[VoxF] || incrDone[VoxZ])) {
        LOG(error) << fmt::format("trial limit reached, skipping this voxel: incrDone[VoxX] {}, incrDone[VoxF] {}, incrDone[VoxZ] {}", incrDone[VoxX], incrDone[VoxF], incrDone[VoxZ]);
        return false;
      }
      LOG(debug) << "sector " << iSec << ": increasing filter bandwidth around voxel " << binCenter;
      // printf("Sector:%2d x=%.2f y/x=%.2f z/x=%.2f (iX: %d iY2X:%d iZ2X:%d)\n", iSec, x, p, z, ix0, ip0, iz0);
      // printf("not enough neighbours (need min %d) %d %d %d (tot: %d) | Steps: %.1f %.1f %.1f\n", 2, nPoints[VoxX], nPoints[VoxF], nPoints[VoxZ], nbOK, stepX, stepF, stepZ);
      // printf("trying to increase filter bandwidth (trialXFZ: %d %d %d)\n", trial[VoxX], trial[VoxF], trial[VoxZ]);
      continue;
    }
 
    // now fill matrices and solve
    for (int iNb = 0; iNb < nbOK; ++iNb) {
      double wiCache = currCache[iNb * VoxHDim + VoxV];
      double dxi = currCache[iNb * VoxHDim + VoxX];
      double dfi = currCache[iNb * VoxHDim + VoxF];
      double dzi = currCache[iNb * VoxHDim + VoxZ];
      double dxi2 = dxi * dxi;
      double dfi2 = dfi * dfi;
      double dzi2 = dzi * dzi;
      const VoxRes* voxNb = currVox[iNb];
      for (int iDim = 0; iDim < ResDim; ++iDim) {
        if (!doDim[iDim]) {
          continue;
        }
        double vi = voxNb->D[iDim];
        double wi = wiCache;
        if (mUseErrInSmoothing && fabs(voxNb->E[iDim]) > 1e-6) {
          // account for point error apart from kernel value
          wi /= (voxNb->E[iDim] * voxNb->E[iDim]);
        }
        std::array<double, sMaxSmtDim*(sMaxSmtDim + 1) / 2>& cmatD = cmat[iDim];
        double* rhsD = &mLastSmoothingRes[iDim * sMaxSmtDim];
        unsigned short iMat = 0;
        unsigned short iRhs = 0;
        // linear part
        cmatD[iMat++] += wi;
        rhsD[iRhs++] += wi * vi;
        //
        cmatD[iMat++] += wi * dxi;
        cmatD[iMat++] += wi * dxi2;
        rhsD[iRhs++] += wi * dxi * vi;
        //
        cmatD[iMat++] += wi * dfi;
        cmatD[iMat++] += wi * dxi * dfi;
        cmatD[iMat++] += wi * dfi2;
        rhsD[iRhs++] += wi * dfi * vi;
        //
        cmatD[iMat++] += wi * dzi;
        cmatD[iMat++] += wi * dxi * dzi;
        cmatD[iMat++] += wi * dfi * dzi;
        cmatD[iMat++] += wi * dzi2;
        rhsD[iRhs++] += wi * dzi * vi;
        //
        // check if quadratic part is needed
        if (mSmoothPol2[VoxX]) {
          cmatD[iMat++] += wi * dxi2;
          cmatD[iMat++] += wi * dxi * dxi2;
          cmatD[iMat++] += wi * dfi * dxi2;
          cmatD[iMat++] += wi * dzi * dxi2;
          cmatD[iMat++] += wi * dxi2 * dxi2;
          rhsD[iRhs++] += wi * dxi2 * vi;
        }
        if (mSmoothPol2[VoxF]) {
          cmatD[iMat++] += wi * dfi2;
          cmatD[iMat++] += wi * dxi * dfi2;
          cmatD[iMat++] += wi * dfi * dfi2;
          cmatD[iMat++] += wi * dzi * dfi2;
          cmatD[iMat++] += wi * dxi2 * dfi2;
          cmatD[iMat++] += wi * dfi2 * dfi2;
          rhsD[iRhs++] += wi * dfi2 * vi;
        }
        if (mSmoothPol2[VoxZ]) {
          cmatD[iMat++] += wi * dzi2;
          cmatD[iMat++] += wi * dxi * dzi2;
          cmatD[iMat++] += wi * dfi * dzi2;
          cmatD[iMat++] += wi * dzi * dzi2;
          cmatD[iMat++] += wi * dxi2 * dzi2;
          cmatD[iMat++] += wi * dfi2 * dzi2;
          cmatD[iMat++] += wi * dzi2 * dzi2;
          rhsD[iRhs++] += wi * dzi2 * vi;
        }
      }
    }
 
    bool fitRes = true;
 
    // solve system of linear equations
 
    TMatrixDSym matrix(matSize);
    TDecompChol chol(matSize);
    TVectorD rhsVec(matSize);
    for (int iDim = 0; iDim < ResDim; ++iDim) {
      if (!doDim[iDim]) {
        continue;
      }
      matrix.Zero(); // reset matrix
      std::array<double, sMaxSmtDim*(sMaxSmtDim + 1) / 2>& cmatD = cmat[iDim];
      double* rhsD = &mLastSmoothingRes[iDim * sMaxSmtDim];
      short iMat = -1;
      short row = -1;
 
      // with the studid implementation of TMatrixDSym we need to set all elements of the matrix explicitly (or maybe only upper triangle?)
      matrix(++row, 0) = cmatD[++iMat];
      matrix(++row, 0) = cmatD[++iMat];
      matrix(row, 1) = cmatD[++iMat];
      matrix(0, row) = matrix(row, 0);
      matrix(++row, 0) = cmatD[++iMat];
      matrix(row, 1) = cmatD[++iMat];
      matrix(row, 2) = cmatD[++iMat];
      matrix(0, row) = matrix(row, 0);
      matrix(1, row) = matrix(row, 1);
      matrix(++row, 0) = cmatD[++iMat];
      matrix(row, 1) = cmatD[++iMat];
      matrix(row, 2) = cmatD[++iMat];
      matrix(row, 3) = cmatD[++iMat];
      matrix(0, row) = matrix(row, 0);
      matrix(1, row) = matrix(row, 1);
      matrix(2, row) = matrix(row, 2);
      // add pol2 elements if needed
      if (mSmoothPol2[VoxX]) {
        const int colLim = (++row) + 1;
        for (int iCol = 0; iCol < colLim; ++iCol) {
          matrix(row, iCol) = cmatD[++iMat];
          matrix(iCol, row) = matrix(row, iCol);
        }
      }
      if (mSmoothPol2[VoxF]) {
        const int colLim = (++row) + 1;
        for (int iCol = 0; iCol < colLim; ++iCol) {
          matrix(row, iCol) = cmatD[++iMat];
          matrix(iCol, row) = matrix(row, iCol);
        }
      }
      if (mSmoothPol2[VoxZ]) {
        const int colLim = (++row) + 1;
        for (int iCol = 0; iCol < colLim; ++iCol) {
          matrix(row, iCol) = cmatD[++iMat];
          matrix(iCol, row) = matrix(row, iCol);
        }
      }
      rhsVec.SetElements(rhsD);
      chol.SetMatrix(matrix);
      chol.Decompose();
      fitRes = chol.Solve(rhsVec);
      if (!fitRes) {
        for (int i = VoxDim; i--;) {
          trial[i]++;
        }
        LOG(error) << "solution for smoothing failed, trying to increase filter bandwidth";
        continue;
      }
      res[iDim] = rhsVec[0];
    }
 
    break;
  }
 
  return true;
}
 
double TrackResiduals::getKernelWeight(std::array<double, 3> u2vec) const
{
  double w = 1.;
  if (mKernelType == KernelType::Epanechnikov) {
    for (size_t i = u2vec.size(); i--;) {
      if (u2vec[i] > 1) {
        return 0.;
      }
      w *= 3. / 4. * (1. - u2vec[i]);
    }
  } else if (mKernelType == KernelType::Gaussian) {
    double u2 = 0.;
    for (size_t i = u2vec.size(); i--;) {
      u2 += u2vec[i];
    }
    w = u2 < mParams->maxGaussStdDev * mParams->maxGaussStdDev * u2vec.size() ? std::exp(-u2) / std::sqrt(2. * M_PI) : 0;
  }
  return w;
}
 
 
float TrackResiduals::fitPoly1Robust(std::vector<float>& x, std::vector<float>& y, std::array<float, 2>& res, std::array<float, 3>& err, float cutLTM) const
{
  // robust pol1 fit, modifies input arrays order
  if (x.size() != y.size()) {
    LOG(error) << "x and y must not have different sizes for fitPoly1Robust (" << x.size() << " != " << y.size() << ")";
  }
  size_t nPoints = x.size();
  res[0] = res[1] = 0.f;
  if (nPoints < 2) {
    return -1;
  }
  std::array<float, 7> yResults;
  std::vector<size_t> indY(nPoints);
  if (!o2::math_utils::LTMUnbinned(y, indY, yResults, cutLTM)) {
    return -1;
  }
  // rearrange used events in increasing order
  o2::math_utils::Reorder(y, indY);
  o2::math_utils::Reorder(x, indY);
  //
  // 1st fit to get crude slope
  int nPointsUsed = std::lrint(yResults[0]);
  int vecOffset = std::lrint(yResults[5]);
  // use only entries selected by LTM for the fit
  float a, b;
  medFit(nPointsUsed, vecOffset, x, y, a, b, err);
  //
  std::vector<float> ycm(nPoints);
  for (size_t i = nPoints; i-- > 0;) {
    ycm[i] = y[i] - (a + b * x[i]);
  }
  std::vector<size_t> indices(nPoints);
  o2::math_utils::SortData(ycm, indices);
  o2::math_utils::Reorder(ycm, indices);
  o2::math_utils::Reorder(y, indices);
  o2::math_utils::Reorder(x, indices);
  //
  // robust estimate of sigma after crude slope correction
  float sigMAD = getMAD2Sigma({ycm.begin() + vecOffset, ycm.begin() + vecOffset + nPointsUsed});
  // find LTM estimate matching to sigMAD, keaping at least given fraction
  if (!o2::math_utils::LTMUnbinnedSig(ycm, indY, yResults, mParams->minFracLTM, sigMAD, true)) {
    return -1;
  }
  // final fit
  nPointsUsed = std::lrint(yResults[0]);
  vecOffset = std::lrint(yResults[5]);
  medFit(nPointsUsed, vecOffset, x, y, a, b, err);
  res[0] = a;
  res[1] = b;
  return sigMAD;
}
 
//___________________________________________________________________
void TrackResiduals::medFit(int nPoints, int offset, const std::vector<float>& x, const std::vector<float>& y, float& a, float& b, std::array<float, 3>& err) const
{
  // fitting a straight line y(x|a, b) = a + b * x
  // to given x and y data minimizing the absolute deviation
  float aa, bb, chi2 = 0.f;
  if (nPoints < 2) {
    a = b = 0.f;
    err[0] = err[1] = err[2] = 999.f;
    return;
  }
  // do least squares minimization as first guess
  float sx = 0.f, sxx = 0.f, sy = 0.f, sxy = 0.f;
  for (int j = nPoints + offset; j-- > offset;) { // same order as in AliRoot version such that resulting sums are identical
    sx += x[j];
    sxx += x[j] * x[j];
    sy += y[j];
    sxy += x[j] * y[j];
  }
  float del = nPoints * sxx - sx * sx;
  float delI = 1. / del;
  aa = (sxx * sy - sx * sxy) * delI;
  bb = (nPoints * sxy - sx * sy) * delI;
  err[0] = sxx * delI;
  err[1] = sx * delI;
  err[2] = nPoints * delI;
 
  for (int j = nPoints + offset; j-- > offset;) {
    float tmp = y[j] - (aa + bb * x[j]);
    chi2 += tmp * tmp;
  }
  float sigb = std::sqrt(chi2 * delI); // expected sigma for b
  float b1 = bb;
  float f1 = roFunc(nPoints, offset, x, y, b1, aa);
  if (sigb > 0) {
    float b2 = bb + std::copysign(3.f * sigb, f1);
    float f2 = roFunc(nPoints, offset, x, y, b2, aa);
    if (fabs(f1 - f2) < sFloatEps) {
      a = aa;
      b = bb;
      return;
    }
    while (f1 * f2 > 0.f) {
      bb = b2 + 1.6f * (b2 - b1);
      b1 = b2;
      f1 = f2;
      b2 = bb;
      f2 = roFunc(nPoints, offset, x, y, b2, aa);
    }
    sigb = .01f * sigb;
    while (fabs(b2 - b1) > sigb) {
      bb = b1 + .5f * (b2 - b1);
      if (bb == b1 || bb == b2) {
        break;
      }
      float f = roFunc(nPoints, offset, x, y, bb, aa);
      if (f * f1 >= .0f) {
        f1 = f;
        b1 = bb;
      } else {
        f2 = f;
        b2 = bb;
      }
    }
  }
  a = aa;
  b = bb;
}
 
float TrackResiduals::roFunc(int nPoints, int offset, const std::vector<float>& x, const std::vector<float>& y, float b, float& aa) const
{
  // calculate sum(x_i * sgn(y_i - a - b * x_i)) for given b
  // see numberical recipies paragraph 15.7.3
  std::vector<float> vecTmp(nPoints);
  float sum = 0.f;
  for (int j = nPoints; j-- > 0;) {
    vecTmp[j] = y[j + offset] - b * x[j + offset];
  }
  int nPointsHalf = nPoints / 2;
  if (nPoints < 20) { // it is faster to do insertion sort
    for (int i = 1; i < nPoints; i++) {
      float v = vecTmp[i];
      int j;
      for (j = i; j--;) {
        if (vecTmp[j] > v) {
          vecTmp[j + 1] = vecTmp[j];
        } else {
          break;
        }
      }
      vecTmp[j + 1] = v;
    }
    aa = (nPoints & 0x1) ? vecTmp[nPointsHalf] : .5f * (vecTmp[nPointsHalf - 1] + vecTmp[nPointsHalf]);
  } else {
    std::vector<float>::iterator nth = vecTmp.begin() + vecTmp.size() / 2;
    if (nPoints & 0x1) {
      std::nth_element(vecTmp.begin(), nth, vecTmp.end());
      aa = *nth;
    } else {
      std::nth_element(vecTmp.begin(), nth - 1, vecTmp.end());
      std::nth_element(nth, nth, vecTmp.end());
      aa = 0.5 * (*(nth - 1) + *(nth));
    }
    // aa = (nPoints & 0x1) ? selectKthMin(nPointsHalf, vecTmp) : .5f * (selectKthMin(nPointsHalf - 1, vecTmp) + selectKthMin(nPointsHalf, vecTmp));
  }
  for (int j = nPoints; j-- > 0;) {
    float d = y[j + offset] - (b * x[j + offset] + aa);
    if (y[j + offset] != 0.f) {
      d /= fabs(y[j + offset]);
    }
    if (fabs(d) > sFloatEps) {
      sum += (d >= 0.f ? x[j + offset] : -x[j + offset]);
    }
  }
  return sum;
}
 
//______________________________________________________________________________
float TrackResiduals::selectKthMin(const int k, std::vector<float>& data) const
{
  // Returns the k th smallest value in the vector. The input vector will be rearranged
  // to have this value in location data[k] , with all smaller elements moved before it
  // (in arbitrary order) and all larger elements after (also in arbitrary order).
  // From Numerical Recipes in C++ (paragraph 8.5)
  // Probably it is not needed anymore, since std::nth_element() can also be used
 
  int i, ir, j, l, mid, n = data.size();
  float a;    // partitioning element
  l = 0;      // left hand side of active partition
  ir = n - 1; // right hand side of active partition
 
  while (true) {
    if (ir <= l + 1) {                         // active partition with 1 or 2 elements
      if (ir == l + 1 && data[ir] < data[l]) { // case of 2 elements
        std::swap(data[l], data[ir]);
      }
      return data[k];
    } else {
      mid = (l + ir) >> 1;
      std::swap(data[mid], data[l + 1]);
      if (data[l] > data[ir]) {
        std::swap(data[l], data[ir]);
      }
      if (data[l + 1] > data[ir]) {
        std::swap(data[l + 1], data[ir]);
      }
      if (data[l] > data[l + 1]) {
        std::swap(data[l], data[l + 1]);
      }
      i = l + 1; // initialize pointers for partitioning
      j = ir;
      a = data[l + 1];
      while (true) {
        // innermost loop used for partitioning
        do {
          i++;
        } while (data[i] < a);
        do {
          j--;
        } while (data[j] > a);
        if (j < i) {
          // pointers crossed -> partitioning complete
          break;
        }
        std::swap(data[i], data[j]);
      }
      data[l + 1] = data[j];
      data[j] = a;
      // keep the partition which contains kth element active
      if (j >= k) {
        ir = j - 1;
      }
      if (j <= k) {
        l = i;
      }
    }
  }
}
 
//___________________________________________________________________
float TrackResiduals::getMAD2Sigma(std::vector<float> data) const
{
  // Sigma calculated from median absolute deviations
  // see: https://en.wikipedia.org/wiki/Median_absolute_deviation
  // the data is passed by value (copied!), such that the original vector
  // is not rearranged
 
  int nPoints = data.size();
  if (nPoints < 2) {
    return 0;
  }
 
  // calculate median of the input data
  float medianOfData;
  std::vector<float>::iterator nth = data.begin() + data.size() / 2;
  if (nPoints & 0x1) {
    std::nth_element(data.begin(), nth, data.end());
    medianOfData = *nth;
  } else {
    std::nth_element(data.begin(), nth - 1, data.end());
    std::nth_element(nth, nth, data.end());
    medianOfData = .5f * (*(nth - 1) + (*nth));
  }
 
  // fill vector with absolute deviations to median
  for (auto& entry : data) {
    entry = fabs(entry - medianOfData);
  }
 
  // calculate median of abs deviations
  float medianOfAbsDeviations;
  if (nPoints & 0x1) {
    std::nth_element(data.begin(), nth, data.end());
    medianOfAbsDeviations = *nth;
  } else {
    std::nth_element(data.begin(), nth - 1, data.end());
    std::nth_element(nth, nth, data.end());
    medianOfAbsDeviations = .5f * (*(nth - 1) + (*nth));
  }
 
  float k = 1.4826f; // scale factor for normally distributed data
  return k * medianOfAbsDeviations;
}
 
void TrackResiduals::fitCircle(int nCl, std::array<float, param::NPadRows>& x, std::array<float, param::NPadRows>& y, float& xc, float& yc, float& r, std::array<float, param::NPadRows>& residHelixY)
{
  // this fast algebraic circle fit is described here:
  // https://dtcenter.org/met/users/docs/write_ups/circle_fit.pdf
  float xMean = 0.f;
  float yMean = 0.f;
 
  for (int i = nCl; i--;) {
    xMean += x[i];
    yMean += y[i];
  }
  xMean /= nCl;
  yMean /= nCl;
  // define sums needed for circular fit
  float su2 = 0.f, sv2 = 0.f, suv = 0.f, su3 = 0.f, sv3 = 0.f, su2v = 0.f, suv2 = 0.f;
  for (int i = nCl; i--;) {
    float ui = x[i] - xMean;
    float vi = y[i] - yMean;
    float ui2 = ui * ui;
    float vi2 = vi * vi;
    suv += ui * vi;
    su2 += ui2;
    sv2 += vi2;
    su3 += ui2 * ui;
    sv3 += vi2 * vi;
    su2v += ui2 * vi;
    suv2 += ui * vi2;
  }
  float rhsU = .5f * (su3 + suv2);
  float rhsV = .5f * (sv3 + su2v);
  float det = su2 * sv2 - suv * suv;
  float uc = (rhsU * sv2 - rhsV * suv) / det;
  float vc = (su2 * rhsV - suv * rhsU) / det;
  float r2 = uc * uc + vc * vc + (su2 + sv2) / nCl;
  xc = uc + xMean;
  yc = vc + yMean;
  r = sqrt(r2);
  // write residuals to residHelixY
  for (int i = nCl; i--;) {
    float dx = x[i] - xc;
    float dxr = r2 - dx * dx;
    float ys = dxr > 0 ? sqrt(dxr) : 0.f; // distance of point in y from the circle center (using fit results for r and xc)
    float dy = y[i] - yc;                 // distance of point in y from the circle center (using fit result for yc)
    float dysp = dy - ys;
    float dysm = dy + ys;
    residHelixY[i] = fabsf(dysp) < fabsf(dysm) ? dysp : dysm;
  }
  // printf("r = %.4f m, xc = %.4f, yc = %.4f\n", r/100.f, xc, yc);
}
 
bool TrackResiduals::fitPoly1(int nCl, std::array<float, param::NPadRows>& x, std::array<float, param::NPadRows>& y, std::array<float, 2>& res)
{
  // fit a straight line y = ax + b to a given set of points (x,y)
  // no measurement errors assumed, no fit errors calculated
  // res[0] = a (slope)
  // res[1] = b (offset)
  if (nCl < 2) {
    // not enough points
    return false;
  }
  float sumX = 0.f, sumY = 0.f, sumXY = 0.f, sumX2 = 0.f, nInv = 1.f / nCl;
  for (int i = nCl; i--;) {
    sumX += x[i];
    sumY += y[i];
    sumXY += x[i] * y[i];
    sumX2 += x[i] * x[i];
  }
  float det = sumX2 - nInv * sumX * sumX;
  if (fabsf(det) < 1e-12f) {
    return false;
  }
  res[0] = (sumXY - nInv * sumX * sumY) / det;
  res[1] = nInv * sumY - nInv * res[0] * sumX;
  return true;
}
 
 
void TrackResiduals::createOutputFile(const char* filename)
{
  if (getNVoxelsPerSector() == 0) {
    LOG(warn) << "For the tree aliases to work you must initialize the binning before calling createOutputFile()";
  }
  mFileOut = std::make_unique<TFile>(filename, "recreate");
  mTreeOut = std::make_unique<TTree>("voxResTree", "Voxel results and statistics");
  mTreeOut->SetAlias("z2xBin", "bvox[0]");
  mTreeOut->SetAlias("y2xBin", "bvox[1]");
  mTreeOut->SetAlias("xBin", "bvox[2]");
  mTreeOut->SetAlias("z2xAV", "stat[0]");
  mTreeOut->SetAlias("y2xAV", "stat[1]");
  mTreeOut->SetAlias("xAV", "stat[2]");
  mTreeOut->SetAlias("fsector", "bsec+0.5+9.*(y2xAV)/pi");
  mTreeOut->SetAlias("phi", "(bsec%18+0.5+9.*(stat[1])/pi)/9*pi");
  mTreeOut->SetAlias("r", "stat[2]");
  mTreeOut->SetAlias("z", "z2xAV*xAV");
  mTreeOut->SetAlias("dX", "D[0]");
  mTreeOut->SetAlias("dY", "D[1]");
  mTreeOut->SetAlias("dZ", "D[2]");
  mTreeOut->SetAlias("dXS", "DS[0]");
  mTreeOut->SetAlias("dYS", "DS[1]");
  mTreeOut->SetAlias("dZS", "DS[2]");
  mTreeOut->SetAlias("dXE", "E[0]");
  mTreeOut->SetAlias("dYE", "E[1]");
  mTreeOut->SetAlias("dZE", "E[2]");
  mTreeOut->SetAlias("voxelIndex", Form("xBin + %i * (y2xBin + %i * z2xBin) + %i * bsec", getNXBins(), getNY2XBins(), getNVoxelsPerSector()));
  mTreeOut->SetAlias("entries", "stat[3]");
  mTreeOut->SetAlias("fitOK", Form("(flags & %u) == %u", DistDone, DistDone));
  mTreeOut->SetAlias("dispOK", Form("(flags & %u) == %u", DispDone, DispDone));
  mTreeOut->SetAlias("smtOK", Form("(flags & %u) == %u", SmoothDone, SmoothDone));
  mTreeOut->SetAlias("masked", Form("(flags & %u) == %u", Masked, Masked));
  mTreeOut->Branch("voxRes", &mVoxelResultsOutPtr);
}
 
void TrackResiduals::closeOutputFile()
{
  mFileOut->cd();
  mTreeOut->Write();
  mTreeOut.reset();
  mFileOut->Close();
  mFileOut.reset();
}
 
void TrackResiduals::dumpResults(int iSec)
{
  if (mTreeOut) {
    printf("Dumping results for sector %i. Don't forget the call to closeOutputFile() in the end...\n", iSec);
    for (int i = 0; i < mNVoxPerSector; ++i) {
      mVoxelResultsOut = mVoxelResults[iSec][i];
      mTreeOut->Fill();
    }
  }
}
 
void TrackResiduals::printMem() const
{
  static float mres = 0, mvir = 0, mres0 = 0, mvir0 = 0;
  static ProcInfo_t procInfo;
  static TStopwatch sw;
  const Long_t kMB = 1024;
  gSystem->GetProcInfo(&procInfo);
  mres = float(procInfo.fMemResident) / kMB;
  mvir = float(procInfo.fMemVirtual) / kMB;
  sw.Stop();
  printf("RSS: %.3f(%.3f) VMEM: %.3f(%.3f) MB | CpuTime:%.3f RealTime:%.3f s\n",
         mres, mres - mres0, mvir, mvir - mvir0, sw.CpuTime(), sw.RealTime());
  mres0 = mres;
  mvir0 = mvir;
  sw.Start();
}
 
void TrackResiduals::clear()
{
  getLocalResVec().clear();
  mInitResultsContainer.reset();
}