ShowerBayesianTrucatingdEdx_tool.cc

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//############################################################################
//### Name:        ShowerBayesianTrucatingdEdx                             ###
//### Author:      Dominic Barker                                          ###
//### Date:        13.05.19                                                ###
//### Description: Recursively adds values from the dEdx vectors and stops ###
//###              when the probability of getting that dEdx value is too  ###
//###              low. This is done for both the a electron prior and     ###
//###              photon prior. The posterior is calculated and the prior ###
//###              with the highest posterior is taken. Currently can      ###
//###              only be used with the sliding calo dEdx                 ###
//############################################################################

//Framework Includes
#include "art/Utilities/ToolMacros.h"

//LArSoft Includes
#include "larpandora/LArPandoraEventBuilding/LArPandoraShower/Tools/IShowerTool.h"

//ROOT Includes
#include "TFile.h"
#include "TH1.h"
#include "TAxis.h"

namespace ShowerRecoTools {

  class ShowerBayesianTrucatingdEdx : public IShowerTool {

  public:
    ShowerBayesianTrucatingdEdx(const fhicl::ParameterSet& pset);

    //Generic Direction Finder
    int CalculateElement(const art::Ptr<recob::PFParticle>& pfparticle,
                         art::Event& Event,
                         reco::shower::ShowerElementHolder& ShowerEleHolder) override;

  private:
    double CalculatePosterior(std::string priorname,
                              std::vector<double>& values,
                              int& minprob,
                              float& mean,
                              float& likelihood);
    double CalculatePosterior(std::string priorname, std::vector<double>& values);

    bool
    isProbabilityGood(float& old_prob, float& new_prob)
    {
      return (old_prob - new_prob) < fProbSeedCut;
    }

    bool
    isPosteriorProbabilityGood(double& prob, double& old_posteior)
    {
      return (old_posteior - prob) < fPostiorCut;
    }

    bool CheckPoint(std::string priorname, double& value);

    std::vector<double> GetLikelihooddEdxVec(double& electronprob,
                                             double& photonprob,
                                             std::string prior,
                                             std::vector<double>& dEdxVec);

    std::vector<double> MakeSeed(std::vector<double>& dEdxVec);

    void ForceSeedToFit(std::vector<double>& SeedTrack,
                        std::string& prior,
                        float& mean,
                        double& posterior);

    void RecurivelyAddHit(std::vector<double>& SeedTrack,
                          std::vector<double>& dEdxVec,
                          std::string& prior,
                          int& SkippedHitsNum,
                          float& old_mean,
                          double& old_posteior);

    TH1F* electronpriorHist;
    TH1F* photonpriorHist;

    //fcl params
    int fVerbose;
    std::string fdEdxInputLabel;
    int fNumSeedHits;
    float fProbSeedCut;
    float fProbPointCut;
    float fPostiorCut;
    int fnSkipHits;
    std::string fShowerdEdxOutputLabel;
    bool fDefineBestPlane;
    std::string fShowerBestPlaneOutputLabel;
  };

  ShowerBayesianTrucatingdEdx::ShowerBayesianTrucatingdEdx(const fhicl::ParameterSet& pset)
    : IShowerTool(pset.get<fhicl::ParameterSet>("BaseTools"))
    , fVerbose(pset.get<int>("Verbose"))
    , fdEdxInputLabel(pset.get<std::string>("dEdxInputLabel"))
    , fNumSeedHits(pset.get<int>("NumSeedHits"))
    , fProbSeedCut(pset.get<float>("ProbSeedCut"))
    , fProbPointCut(pset.get<float>("ProbPointCut"))
    , fPostiorCut(pset.get<float>("PostiorCut"))
    , fnSkipHits(pset.get<int>("nSkipHits"))
    , fShowerdEdxOutputLabel(pset.get<std::string>("ShowerdEdxOutputLabel"))
    , fDefineBestPlane(pset.get<bool>("DefineBestPlane"))
    , fShowerBestPlaneOutputLabel(pset.get<std::string>("ShowerBestPlaneOutputLabel"))
  {

    //Get the prior file name
    std::string fname;
    cet::search_path sp("FW_SEARCH_PATH");
    auto PriorPath = pset.get<std::string>("PriorFname");
    if (!sp.find_file(PriorPath, fname)) {
      throw cet::exception("ShowerBayesianTrucatingdEdx") << "Could not find the prior file";
    }
    std::string electron_histoname = pset.get<std::string>("PriorElectronHistoName");
    std::string photon_histoname = pset.get<std::string>("PriorPhotonHistoName");

    TFile fin(fname.c_str(), "READ");
    if (!fin.IsOpen()) {
      throw cet::exception("ShowerBayesianTrucatingdEdx") << "Could read the prior file. Stopping";
    }

    //Get the histograms.
    electronpriorHist = dynamic_cast<TH1F*>(fin.Get(electron_histoname.c_str()));
    if (!electronpriorHist) {
      throw cet::exception("ShowerBayesianTrucatingdEdx") << "Could not read the electron hist";
    }
    photonpriorHist = dynamic_cast<TH1F*>(fin.Get(photon_histoname.c_str()));
    if (!photonpriorHist) {
      throw cet::exception("ShowerBayesianTrucatingdEdx") << "Could not read the photon hist ";
    }

    if (electronpriorHist->GetNbinsX() != photonpriorHist->GetNbinsX()) {
      throw cet::exception("ShowerBayesianTrucatingdEdx") << "Histrogram bins do not match";
    }

    //Normalise the histograms.
    electronpriorHist->Scale(1 / electronpriorHist->Integral());
    photonpriorHist->Scale(1 / photonpriorHist->Integral());
  }

  int
  ShowerBayesianTrucatingdEdx::CalculateElement(const art::Ptr<recob::PFParticle>& pfparticle,
                                                art::Event& Event,
                                                reco::shower::ShowerElementHolder& ShowerEleHolder)
  {

    //The idea , to some peoples distaste, is to attempt to improve the dEdx value by assuming
    //The particle is either a) electron b) a e-e+ pair.
    //We will take the start of track and work down until a few hits destory our postier probability.

    //Note: tried takeing the postior with the highest sum of the probabilitys on all three
    //      planes and on the 2 planes with the most hits. Made things worse.

    //Get the vectors of the dEdx Elements
    if (!ShowerEleHolder.CheckElement(fdEdxInputLabel)) {
      if (fVerbose)
        mf::LogError("ShowerBayesianTrucatingdEdx")
          << "Start position not set, returning " << std::endl;
      return 1;
    }

    std::map<int, std::vector<double>> dEdx_plane_final;
    std::map<int, std::vector<double>> dEdx_vec_planes;
    ShowerEleHolder.GetElement(fdEdxInputLabel, dEdx_vec_planes);

    //Do this for each plane;
    for (auto const& dEdx_vec_plane : dEdx_vec_planes) {

      //Set up out final value if we don't have any points.
      if (dEdx_vec_plane.second.size() < 1) {
        dEdx_plane_final[dEdx_vec_plane.first] = {};
        continue;
      }

      std::vector<double> dEdx_vec = dEdx_vec_plane.second;

      double electronprob_eprior = 0;
      double photonprob_eprior = 0;

      double electronprob_pprior = 0;
      double photonprob_pprior = 0;

      std::vector<double> dEdx_electronprior =
        GetLikelihooddEdxVec(electronprob_eprior, photonprob_eprior, "electron", dEdx_vec);
      std::vector<double> dEdx_photonprior =
        GetLikelihooddEdxVec(electronprob_pprior, photonprob_pprior, "photon", dEdx_vec);

      //Use the vector which maximises both priors.
      if (electronprob_eprior < photonprob_pprior) {
        dEdx_plane_final[dEdx_vec_plane.first] = dEdx_photonprior;
      }
      else {
        dEdx_plane_final[dEdx_vec_plane.first] = dEdx_electronprior;
      }

    } //Plane Loop

    //Calculate the median of the of dEdx.
    std::vector<double> dEdx_final;
    std::vector<double> dEdx_finalErr;

    int max_hits = -999;
    int best_plane = -999;

    bool check = false;
    for (auto const& dEdx_plane : dEdx_plane_final) {

      //Redefine the best plane
      if ((int)(dEdx_plane.second).size() > max_hits) {
        best_plane = dEdx_plane.first;
        max_hits = (dEdx_plane.second).size();
      }

      if ((dEdx_plane.second).empty()) {
        dEdx_final.push_back(-999);
        dEdx_finalErr.push_back(-999);
        continue;
      }

      dEdx_final.push_back(TMath::Median((dEdx_plane.second).size(), &(dEdx_plane.second)[0]));
      dEdx_finalErr.push_back(-999);
      check = true;
    }

    //Check at least one plane has the information
    if (!check) return 1;

    if (fDefineBestPlane) { ShowerEleHolder.SetElement(best_plane, fShowerBestPlaneOutputLabel); }

    ShowerEleHolder.SetElement(dEdx_final, dEdx_finalErr, fShowerdEdxOutputLabel);

    return 0;
  }

  double
  ShowerBayesianTrucatingdEdx::CalculatePosterior(std::string priorname,
                                                  std::vector<double>& values)
  {
    int minprob_iter = -999;
    float mean = -999;
    float likelihood = -999;
    return CalculatePosterior(priorname, values, minprob_iter, mean, likelihood);
  }

  double
  ShowerBayesianTrucatingdEdx::CalculatePosterior(std::string priorname,
                                                  std::vector<double>& values,
                                                  int& minprob_iter,
                                                  float& mean,
                                                  float& likelihood)
  {

    //Posterior prob;
    float posterior = 1;
    float meanprob = 0;
    float likelihood_other = 1;
    likelihood = 1;

    //Minimum probability temp
    float minprob_temp = 9999;
    minprob_iter = 0;

    TH1F* prior_hist = nullptr;
    TH1F* other_hist = nullptr;

    if (priorname == "electron") {
      prior_hist = electronpriorHist;
      other_hist = photonpriorHist;
    }
    if (priorname == "photon") {
      prior_hist = photonpriorHist;
      other_hist = electronpriorHist;
    }

    TAxis* xaxis = prior_hist->GetXaxis();

    //Loop over the hits and calculate the probability
    for (int i = 0; i < (int)values.size(); ++i) {

      float value = values[i];

      Int_t bin = xaxis->FindBin(value);

      float prob = -9999;
      float other_prob = -9999;

      if (bin != xaxis->GetNbins() || bin == 0) {
        //Calculate the likelihood
        prob = prior_hist->GetBinContent(bin);
        other_prob = other_hist->GetBinContent(bin);
      }
      else {
        prob = 0;
        other_prob = 0;
      }

      if (prob < minprob_temp) {
        minprob_temp = prob;
        minprob_iter = i;
      }

      if (prob == 0 && other_prob == 0) { continue; }

      //Calculate the posterior the mean probability and liklihood
      meanprob += prior_hist->GetBinContent(bin);
      likelihood *= prob;
      likelihood_other *= other_prob;
    }

    posterior = likelihood / (likelihood + likelihood_other);

    meanprob /= values.size();
    mean = meanprob;
    return posterior;
  }

  bool
  ShowerBayesianTrucatingdEdx::CheckPoint(std::string priorname, double& value)
  {

    TH1F* prior_hist = nullptr;

    if (priorname == "electron") { prior_hist = electronpriorHist; }
    if (priorname == "photon") { prior_hist = photonpriorHist; }

    TAxis* xaxis = prior_hist->GetXaxis();

    Int_t bin = xaxis->FindBin(value);

    float prob = -9999;

    if (bin != xaxis->GetNbins() + 1 || bin == 0) {
      //Calculate the likelihood
      prob = prior_hist->GetBinContent(bin);
    }
    else {
      prob = 0;
    }

    //Return the probability of getting that point.
    return prob > fProbPointCut;
  }

  std::vector<double>
  ShowerBayesianTrucatingdEdx::GetLikelihooddEdxVec(double& electronprob,
                                                    double& photonprob,
                                                    std::string prior,
                                                    std::vector<double>& dEdxVec)
  {

    //have a pool
    std::vector<double> dEdxVec_temp = dEdxVec;

    //Get The seed track.
    std::vector<double> SeedTrack = MakeSeed(dEdxVec_temp);
    //    if(SeedTrack.size() < 1){
    //  return SeedTrack;
    // }

    //Force the seed the be a good likelihood.
    float mean = 999;
    double posterior = 999;
    ForceSeedToFit(SeedTrack, prior, mean, posterior);

    //Recursively add dEdx
    int SkippedHitsNum = 0;
    RecurivelyAddHit(SeedTrack, dEdxVec_temp, prior, SkippedHitsNum, mean, posterior);

    //Calculate the likelihood of the vector  with the photon and electron priors.
    electronprob = CalculatePosterior("electron", SeedTrack);
    photonprob = CalculatePosterior("photon", SeedTrack);

    return SeedTrack;
  }

  std::vector<double>
  ShowerBayesianTrucatingdEdx::MakeSeed(std::vector<double>& dEdxVec)
  {

    std::vector<double> seed_vector;

    //Add the first hits to the seed
    int MaxHit = fNumSeedHits;
    if (fNumSeedHits > (int)dEdxVec.size()) { MaxHit = (int)dEdxVec.size(); }

    //    if(MaxHit == 0){
    //  mf::LogError("ShowerBayesianTrucatingdEdx") << "Size of the vector is 0 cannot perform the dEdx cutting "<< std::endl;
    //}

    for (int hit_iter = 0; hit_iter < MaxHit; ++hit_iter) {
      seed_vector.push_back(dEdxVec[0]);
      dEdxVec.erase(dEdxVec.begin());
    }

    return seed_vector;
  }

  void
  ShowerBayesianTrucatingdEdx::ForceSeedToFit(std::vector<double>& SeedTrack,
                                              std::string& prior,
                                              float& mean,
                                              double& posterior)
  {

    int minprob_iter = 999;
    float likelihood = -999;
    float prob = CalculatePosterior(prior, SeedTrack, minprob_iter, mean, likelihood);
    while ((mean < fProbSeedCut || prob <= 0) && SeedTrack.size() > 1) {

      //Remove the the worse point.
      // std::cout << "removing hit with dEdx: " << SeedTrack.at(minprob_iter) << std::endl;
      SeedTrack.erase(SeedTrack.begin() + minprob_iter);
      minprob_iter = 999;

      //Recalculate
      prob = CalculatePosterior(prior, SeedTrack, minprob_iter, mean, likelihood);
    }
    posterior = prob;
    // std::cout << "seed has been fit with size: " << SeedTrack.size() << std::endl;
    return;
  }

  void
  ShowerBayesianTrucatingdEdx::RecurivelyAddHit(std::vector<double>& SeedTrack,
                                                std::vector<double>& dEdxVec,
                                                std::string& prior,
                                                int& SkippedHitsNum,
                                                float& old_mean,
                                                double& old_posteior)
  {

    //If we have no more hits to add then lets finish.
    if (dEdxVec.size() < 1) { return; }

    bool ok = CheckPoint(prior, dEdxVec[0]);
    // int minprob_iter=999;
    // float mean = -999;
    // float likelihood =999;
    // if(ok){std::cout << "passed first cut" << std::endl;}
    // else{std::cout << "failed first cut" << std::endl;}
    // double prob = CalculatePosterior(prior,SeedTrack,minprob_iter,mean,likelihood);
    // ok *= isProbabilityGood(mean,old_mean);
    // if(ok){std::cout << "passed second cut" << std::endl;}
    // else{std::cout << "failed second cut" << std::endl;}
    // ok *= isPosteriorProbabilityGood(prob,old_posteior);
    // if(ok){std::cout << "passed this cut" << std::endl;}
    // else{std::cout << "failed third cut" << std::endl;}

    //If we failed lets try the next hits
    if (!ok) {
      // std::cout << "failed the pass point: " << dEdxVec[0] << " trying another hit" << SkippedHitsNum << std::endl;
      //if(SeedTrack.size() > 1){SeedTrack.pop_back();}
      ++SkippedHitsNum;
      if (SkippedHitsNum > fnSkipHits) { return; }
    }
    else {
      //Add the next point in question.
      // std::cout << "adding value: " << dEdxVec[0] << std::endl;
      //Reset the skip number
      SkippedHitsNum = 0;
      SeedTrack.push_back(dEdxVec[0]);
    }

    //We have analysed this hit now lets remove it.
    dEdxVec.erase(dEdxVec.begin());

    RecurivelyAddHit(SeedTrack, dEdxVec, prior, SkippedHitsNum, old_mean, old_posteior);

    return;
  }
}

DEFINE_ART_CLASS_TOOL(ShowerRecoTools::ShowerBayesianTrucatingdEdx)