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Changeset 35cdc46 in git for external/fastjet/contribs


Ignore:
Timestamp:
Sep 3, 2014, 3:18:54 PM (10 years ago)
Author:
Pavel Demin <demin@…>
Branches:
ImprovedOutputFile, Timing, dual_readout, llp, master
Children:
be2222c
Parents:
5b5a56b
Message:

upgrade FastJet to version 3.1.0-beta.1, upgrade Nsubjettiness to version 2.1.0, add SoftKiller version 1.0.0

Location:
external/fastjet/contribs
Files:
16 added
14 edited

Legend:

Unmodified
Added
Removed
  • external/fastjet/contribs/Nsubjettiness/AUTHORS

    r5b5a56b r35cdc46  
    1818   JHEP 1202:093 (2012), arXiv:1108.2701.
    1919
     20New in v2.0 is the winner-take-all axis, described in:
     21
     22   Jet Shapes with the Broadening Axis.
     23   Andrew J. Larkoski, Duff Neill, and Jesse Thaler.
     24   JHEP 1404:017 (2014), arXiv:1401.2158.
     25
     26as well as in unpublished work by Gavin Salam.
     27
    2028----------------------------------------------------------------------
  • external/fastjet/contribs/Nsubjettiness/AxesFinder.cc

    r5b5a56b r35cdc46  
    55//  Jesse Thaler, Ken Van Tilburg, Christopher K. Vermilion, and TJ Wilkason
    66//
     7//  $Id: AxesFinder.cc 670 2014-06-06 01:24:42Z jthaler $
    78//----------------------------------------------------------------------
    89// This file is part of FastJet contrib.
     
    3738template <int N>
    3839std::vector<LightLikeAxis> AxesFinderFromOnePassMinimization::UpdateAxesFast(const std::vector <LightLikeAxis> & old_axes,
    39                                   const std::vector <fastjet::PseudoJet> & inputJets) {
     40                                  const std::vector <fastjet::PseudoJet> & inputJets) const {
    4041   assert(old_axes.size() == N);
    4142   
     
    4546   for (int n = 0; n < N; ++n) {
    4647      new_axes[n].reset(0.0,0.0,0.0,0.0);
    47 #ifdef FASTJET2
    48       new_jets[n].reset(0.0,0.0,0.0,0.0);
    49 #else
    50       // use cheaper reset if available
    5148      new_jets[n].reset_momentum(0.0,0.0,0.0,0.0);
    52 #endif
    5349   }
    5450
     
    135131// (This is just a wrapper for the templated version above.)
    136132std::vector<LightLikeAxis> AxesFinderFromOnePassMinimization::UpdateAxes(const std::vector <LightLikeAxis> & old_axes,
    137                                       const std::vector <fastjet::PseudoJet> & inputJets) {
     133                                      const std::vector <fastjet::PseudoJet> & inputJets) const {
    138134   int N = old_axes.size();
    139135   switch (N) {
     
    166162// uses minimization of N-jettiness to continually update axes until convergence.
    167163// The function returns the axes found at the (local) minimum
    168 std::vector<fastjet::PseudoJet> AxesFinderFromOnePassMinimization::getBetterAxes(int n_jets, const std::vector <fastjet::PseudoJet> & inputJets, const std::vector<fastjet::PseudoJet>& seedAxes) {
     164std::vector<fastjet::PseudoJet> AxesFinderFromOnePassMinimization::getAxes(int n_jets, const std::vector <fastjet::PseudoJet> & inputJets, const std::vector<fastjet::PseudoJet>& seedAxes) const {
    169165         
    170166   // convert from PseudoJets to LightLikeAxes
     
    211207}
    212208
    213 PseudoJet AxesFinderFromKmeansMinimization::jiggle(const PseudoJet& axis) {
     209PseudoJet AxesFinderFromKmeansMinimization::jiggle(const PseudoJet& axis) const {
    214210   double phi_noise = ((double)rand()/(double)RAND_MAX) * _noise_range * 2.0 - _noise_range;
    215211   double rap_noise = ((double)rand()/(double)RAND_MAX) * _noise_range * 2.0 - _noise_range;
     
    226222   
    227223// Repeatedly calls the one pass finder to try to find global minimum
    228 std::vector<fastjet::PseudoJet> AxesFinderFromKmeansMinimization::getBetterAxes(int n_jets, const std::vector <fastjet::PseudoJet> & inputJets, const std::vector<fastjet::PseudoJet>& seedAxes) {
     224std::vector<fastjet::PseudoJet> AxesFinderFromKmeansMinimization::getAxes(int n_jets, const std::vector <fastjet::PseudoJet> & inputJets, const std::vector<fastjet::PseudoJet>& seedAxes) const {
    229225   
    230226   // first iteration
     
    256252// It continually updates until it reaches convergence or it reaches the maximum number of attempts.
    257253// This is essentially the same as a stable cone finder.
    258 std::vector<fastjet::PseudoJet> AxesFinderFromGeometricMinimization::getBetterAxes(int n_jets, const std::vector <fastjet::PseudoJet> & particles, const std::vector<fastjet::PseudoJet>& currentAxes) {
     254std::vector<fastjet::PseudoJet> AxesFinderFromGeometricMinimization::getAxes(int /*n_jets*/, const std::vector <fastjet::PseudoJet> & particles, const std::vector<fastjet::PseudoJet>& currentAxes) const {
    259255
    260256   std::vector<fastjet::PseudoJet> seedAxes = currentAxes;
    261    double seedTau = _function->tau(particles, seedAxes);
     257   double seedTau = _function.tau(particles, seedAxes);
    262258   
    263259   for (int i = 0; i < _nAttempts; i++) {
     
    270266         // start from unclustered beam measure
    271267         int minJ = -1;
    272          double minDist = _function->beam_distance_squared(particles[i]);
     268         double minDist = _function.beam_distance_squared(particles[i]);
    273269         
    274270         // which axis am I closest to?
    275271         for (unsigned int j = 0; j < seedAxes.size(); j++) {
    276             double tempDist = _function->jet_distance_squared(particles[i],seedAxes[j]);
     272            double tempDist = _function.jet_distance_squared(particles[i],seedAxes[j]);
    277273            if (tempDist < minDist) {
    278274               minDist = tempDist;
     
    287283      // calculate tau on new axes
    288284      seedAxes = newAxes;
    289       double tempTau = _function->tau(particles, newAxes);
     285      double tempTau = _function.tau(particles, newAxes);
    290286     
    291287      // close enough to stop?
  • external/fastjet/contribs/Nsubjettiness/AxesFinder.hh

    r5b5a56b r35cdc46  
    55//  Jesse Thaler, Ken Van Tilburg, Christopher K. Vermilion, and TJ Wilkason
    66//
     7//  $Id: AxesFinder.hh 678 2014-06-12 20:43:03Z jthaler $
    78//----------------------------------------------------------------------
    89// This file is part of FastJet contrib.
     
    5354class AxesFinder {
    5455   
    55 protected:
    56    AxesFinder* _startingFinder; // storing a possible starting finder if needed
    57    std::vector<fastjet::PseudoJet> _seedAxes;
    58    
    59    AxesFinder(AxesFinder* startingFinder = NULL) : _startingFinder(startingFinder) {}
    60    
    61 public:
    62    virtual ~AxesFinder(){
    63       if (_startingFinder) delete _startingFinder;  //TODO: Convert to smart pointers to avoid this.
    64    }
    65    
    66    // Allow setting of seedAxes from a starting finder
    67    std::vector<fastjet::PseudoJet> getAxes(int n_jets, const std::vector<fastjet::PseudoJet> & inputs, const std::vector<fastjet::PseudoJet>& currentAxes) {
    68       if (_startingFinder) {
    69          _seedAxes = _startingFinder->getAxes(n_jets,inputs,currentAxes);
    70          return getBetterAxes(n_jets,inputs,_seedAxes);
    71       } else {
    72          _seedAxes = getBetterAxes(n_jets,inputs,currentAxes);
    73          return _seedAxes;
    74       }
    75    }
    76    
    77    // say what the current seed axes are
    78    std::vector<fastjet::PseudoJet> seedAxes() const {
    79       return _seedAxes;
    80    }
    81    
    82    // This function should be overloaded, and updates the seedAxes
    83    virtual std::vector<fastjet::PseudoJet> getBetterAxes(int n_jets, const std::vector<fastjet::PseudoJet> & inputs, const std::vector<fastjet::PseudoJet>& seedAxes) = 0;
    84    
    85 };
    86 
     56public:
     57   
     58   // This function should be overloaded, and updates the seedAxes to return new axes
     59   virtual std::vector<fastjet::PseudoJet> getAxes(int n_jets,
     60                                                   const std::vector<fastjet::PseudoJet>& inputs,
     61                                                   const std::vector<fastjet::PseudoJet>& seedAxes) const = 0;
     62   // convenient shorthand for squaring
     63   static inline double sq(double x) {return x*x;}
     64
     65   //virtual destructor
     66   virtual ~AxesFinder(){}
     67   
     68};
     69
     70   
    8771//------------------------------------------------------------------------
    8872/// \class AxesFinderFromExclusiveJetDefinition
     
    9074// with different jet algorithms.
    9175class AxesFinderFromExclusiveJetDefinition : public AxesFinder {
    92 
    93    private:
    94       fastjet::JetDefinition _def;
    95    
    96    public:
    97       AxesFinderFromExclusiveJetDefinition(fastjet::JetDefinition def) : _def(def) {}
    98      
    99       virtual std::vector<fastjet::PseudoJet> getBetterAxes(int n_jets, const std::vector <fastjet::PseudoJet> & inputs, const std::vector<fastjet::PseudoJet>& currentAxes) {
    100          fastjet::ClusterSequence jet_clust_seq(inputs, _def);
    101          return jet_clust_seq.exclusive_jets(n_jets);
    102       }
     76   
     77public:
     78   AxesFinderFromExclusiveJetDefinition(fastjet::JetDefinition def)
     79   : _def(def) {}
     80   
     81   virtual std::vector<fastjet::PseudoJet> getAxes(int n_jets,
     82                                                   const std::vector <fastjet::PseudoJet> & inputs,
     83                                                   const std::vector<fastjet::PseudoJet>& /*seedAxes*/) const {
     84      fastjet::ClusterSequence jet_clust_seq(inputs, _def);
     85      return jet_clust_seq.exclusive_jets(n_jets);
     86   }
     87   
     88private:
     89   fastjet::JetDefinition _def;
     90
    10391};
    10492
     
    10896// winner take all recombination scheme.
    10997class AxesFinderFromWTA_KT : public AxesFinderFromExclusiveJetDefinition {
    110    private:
    111       const WinnerTakeAllRecombiner *recomb;
    112    public:
    113       AxesFinderFromWTA_KT() : AxesFinderFromExclusiveJetDefinition(
    114          fastjet::JetDefinition(fastjet::kt_algorithm,
    115          fastjet::JetDefinition::max_allowable_R, //maximum jet radius constant
    116          recomb = new WinnerTakeAllRecombiner(),
    117          fastjet::Best)) {}
    118       ~AxesFinderFromWTA_KT() {delete recomb;}
    119    };
     98
     99public:
     100   AxesFinderFromWTA_KT()
     101   : AxesFinderFromExclusiveJetDefinition(
     102      fastjet::JetDefinition(fastjet::kt_algorithm,
     103      fastjet::JetDefinition::max_allowable_R, //maximum jet radius constant
     104      &_recomb,
     105      fastjet::Best)) {}
     106   
     107private:
     108   const WinnerTakeAllRecombiner _recomb;
     109
     110};
    120111   
    121112//------------------------------------------------------------------------
     
    124115// winner take all recombination scheme.
    125116class AxesFinderFromWTA_CA : public AxesFinderFromExclusiveJetDefinition {
    126    private:
    127       const WinnerTakeAllRecombiner *recomb;
    128    public:
    129       AxesFinderFromWTA_CA() : AxesFinderFromExclusiveJetDefinition(
    130          fastjet::JetDefinition(fastjet::cambridge_algorithm,
    131          fastjet::JetDefinition::max_allowable_R, //maximum jet radius constant
    132          recomb = new WinnerTakeAllRecombiner(),
    133          fastjet::Best)) {}
    134       ~AxesFinderFromWTA_CA() {delete recomb;}
    135 };
    136 
    137 //  The following classes are for testing, and are commented out for initial release
    138 //
    139 ////------------------------------------------------------------------------
    140 ///// \class AxesFinderFromWTA2_KT
    141 //// This class finds axes by finding the exlusive jets after clustering according to a kT algorithm and a
    142 //// winner take all recombination scheme with alpha = 2.
    143 //class AxesFinderFromWTA2_KT : public AxesFinderFromExclusiveJetDefinition {
    144 //   private:
    145 //      const WinnerTakeAllRecombiner *recomb;
    146 //   public:
    147 //      AxesFinderFromWTA2_KT() : AxesFinderFromExclusiveJetDefinition(
    148 //         fastjet::JetDefinition(fastjet::kt_algorithm,
    149 //         fastjet::JetDefinition::max_allowable_R, //maximum jet radius constant
    150 //         recomb = new WinnerTakeAllRecombiner(2), // uses alpha = 2 here
    151 //         fastjet::Best)) {}
    152 //      ~AxesFinderFromWTA2_KT() {delete recomb;}
    153 //   };
    154 //   
    155 ////------------------------------------------------------------------------
    156 ///// \class AxesFinderFromWTA2_CA
    157 //// This class finds axes by finding the exlusive jets after clustering according to a CA algorithm and a
    158 //// winner take all recombination scheme with alpha = 2.
    159 //class AxesFinderFromWTA2_CA : public AxesFinderFromExclusiveJetDefinition {
    160 //   private:
    161 //      const WinnerTakeAllRecombiner *recomb;
    162 //   public:
    163 //      AxesFinderFromWTA2_CA() : AxesFinderFromExclusiveJetDefinition(
    164 //         fastjet::JetDefinition(fastjet::cambridge_algorithm,
    165 //         fastjet::JetDefinition::max_allowable_R, //maximum jet radius constant
    166 //         recomb = new WinnerTakeAllRecombiner(2), //uses alpha = 2 here
    167 //         fastjet::Best)) {}
    168 //      ~AxesFinderFromWTA2_CA() {delete recomb;}
    169 //};
     117public:
     118   AxesFinderFromWTA_CA()
     119   : AxesFinderFromExclusiveJetDefinition(
     120      fastjet::JetDefinition(fastjet::cambridge_algorithm,
     121      fastjet::JetDefinition::max_allowable_R, //maximum jet radius constant
     122      &_recomb,
     123      fastjet::Best)) {}
     124   
     125private:
     126   const WinnerTakeAllRecombiner _recomb;
     127};
     128
    170129
    171130//------------------------------------------------------------------------
     
    174133// E_scheme recombination.
    175134class AxesFinderFromKT : public AxesFinderFromExclusiveJetDefinition {
    176    public:
    177       AxesFinderFromKT() : AxesFinderFromExclusiveJetDefinition(
    178          fastjet::JetDefinition(fastjet::kt_algorithm,
    179          fastjet::JetDefinition::max_allowable_R, //maximum jet radius constant
    180          fastjet::E_scheme,
    181          fastjet::Best)) {}
     135public:
     136   AxesFinderFromKT()
     137   : AxesFinderFromExclusiveJetDefinition(
     138      fastjet::JetDefinition(fastjet::kt_algorithm,
     139      fastjet::JetDefinition::max_allowable_R, //maximum jet radius constant
     140      fastjet::E_scheme,
     141      fastjet::Best)) {}
    182142};
    183143
     
    187147// E_scheme recombination.
    188148class AxesFinderFromCA : public AxesFinderFromExclusiveJetDefinition {
    189    public:
    190       AxesFinderFromCA() : AxesFinderFromExclusiveJetDefinition(
    191          fastjet::JetDefinition(fastjet::cambridge_algorithm,
    192                                 fastjet::JetDefinition::max_allowable_R,  //maximum jet radius constant
    193          fastjet::E_scheme,
    194          fastjet::Best)) {}
     149public:
     150   AxesFinderFromCA()
     151   : AxesFinderFromExclusiveJetDefinition(
     152      fastjet::JetDefinition(fastjet::cambridge_algorithm,
     153                             fastjet::JetDefinition::max_allowable_R,  //maximum jet radius constant
     154      fastjet::E_scheme,
     155      fastjet::Best)) {}
    195156};
    196157
     
    201162// This can be implemented with different jet algorithms.
    202163class AxesFinderFromHardestJetDefinition : public AxesFinder {
    203 
    204    private:
    205       fastjet::JetDefinition _def;
    206    
    207    public:
    208       AxesFinderFromHardestJetDefinition(fastjet::JetDefinition def) : _def(def) {}
    209      
    210       virtual std::vector<fastjet::PseudoJet> getBetterAxes(int n_jets, const std::vector <fastjet::PseudoJet> & inputs, const std::vector<fastjet::PseudoJet>& currentAxes) {
    211          fastjet::ClusterSequence jet_clust_seq(inputs, _def);
    212          std::vector<fastjet::PseudoJet> myJets = sorted_by_pt(jet_clust_seq.inclusive_jets());
    213          myJets.resize(n_jets);  // only keep n hardest
    214          return myJets;
    215       }     
     164public:
     165   AxesFinderFromHardestJetDefinition(fastjet::JetDefinition def)
     166   : _def(def) {}
     167   
     168   virtual std::vector<fastjet::PseudoJet> getAxes(int n_jets,
     169                                                   const std::vector <fastjet::PseudoJet> & inputs,
     170                                                   const std::vector<fastjet::PseudoJet>& /*seedAxes*/) const {
     171      fastjet::ClusterSequence jet_clust_seq(inputs, _def);
     172      std::vector<fastjet::PseudoJet> myJets = sorted_by_pt(jet_clust_seq.inclusive_jets());
     173      myJets.resize(n_jets);  // only keep n hardest
     174      return myJets;
     175   }
     176   
     177private:
     178   fastjet::JetDefinition _def;
    216179};
    217180
     
    221184// to an anti kT algorithm and E_scheme.
    222185class AxesFinderFromAntiKT : public AxesFinderFromHardestJetDefinition {
    223    public:
    224       AxesFinderFromAntiKT(double R0) : AxesFinderFromHardestJetDefinition(fastjet::JetDefinition(fastjet::antikt_algorithm,R0,fastjet::E_scheme,fastjet::Best)) {}
     186public:
     187   AxesFinderFromAntiKT(double R0)
     188   : AxesFinderFromHardestJetDefinition(
     189      fastjet::JetDefinition(fastjet::antikt_algorithm,
     190                             R0,fastjet::E_scheme,fastjet::Best)) {}
    225191};
    226192
     
    231197class AxesFinderFromUserInput : public AxesFinder {
    232198
    233    public:
    234       AxesFinderFromUserInput() {}
    235      
    236       virtual std::vector<fastjet::PseudoJet> getBetterAxes(int n_jets, const std::vector <fastjet::PseudoJet> & inputs, const std::vector<fastjet::PseudoJet>& currentAxes) {
    237          assert(currentAxes.size() == (unsigned int) n_jets);
    238          return currentAxes;
    239       }
     199public:
     200   AxesFinderFromUserInput() {}
     201   
     202   virtual std::vector<fastjet::PseudoJet> getAxes(int n_jets, const std::vector <fastjet::PseudoJet> & /*inputs*/, const std::vector<fastjet::PseudoJet>& currentAxes) const {
     203      assert(currentAxes.size() == (unsigned int) n_jets);
     204      (void)(n_jets);  // adding this line to fix unused-parameter warning
     205      return currentAxes;
     206   }
    240207};
    241208
     
    249216class AxesFinderFromOnePassMinimization : public AxesFinder {
    250217
    251    private:
    252       double _precision;  // Desired precision in axes alignment
    253       int _halt;  // maximum number of steps per iteration
    254      
    255       double _beta;
    256       double _Rcutoff;
    257      
    258       DefaultUnnormalizedMeasure _measureFunction;
    259    
    260    public:
    261 
    262       // From a startingFinder, try to minimize the unnormalized_measure
    263       AxesFinderFromOnePassMinimization(AxesFinder* startingFinder, double beta, double Rcutoff)
    264          : AxesFinder(startingFinder),
    265            _precision(0.0001), //hard coded for now
    266            _halt(1000), //hard coded for now
    267            _beta(beta),
    268            _Rcutoff(Rcutoff),
    269            _measureFunction(beta, Rcutoff)
    270            {}
    271    
    272       virtual std::vector<fastjet::PseudoJet> getBetterAxes(int n_jets, const std::vector <fastjet::PseudoJet> & inputJets, const std::vector<fastjet::PseudoJet>& currentAxes);
    273 
    274       template <int N> std::vector<LightLikeAxis> UpdateAxesFast(const std::vector <LightLikeAxis> & old_axes,
    275                                   const std::vector <fastjet::PseudoJet> & inputJets);
    276    
    277       std::vector<LightLikeAxis> UpdateAxes(const std::vector <LightLikeAxis> & old_axes,
    278                                       const std::vector <fastjet::PseudoJet> & inputJets);
     218public:
     219
     220   // From a startingFinder, try to minimize the unnormalized_measure
     221   AxesFinderFromOnePassMinimization(double beta, double Rcutoff)
     222      : _precision(0.0001), //hard coded for now
     223        _halt(1000), //hard coded for now
     224        _beta(beta),
     225        _Rcutoff(Rcutoff),
     226        _measureFunction(beta, Rcutoff)
     227        {}
     228
     229   virtual std::vector<fastjet::PseudoJet> getAxes(int n_jets,
     230                                                   const std::vector <fastjet::PseudoJet> & inputJets,
     231                                                   const std::vector<fastjet::PseudoJet>& currentAxes) const;
     232   
     233private:
     234   double _precision;  // Desired precision in axes alignment
     235   int _halt;  // maximum number of steps per iteration
     236   
     237   double _beta;
     238   double _Rcutoff;
     239   
     240   DefaultUnnormalizedMeasureFunction _measureFunction;
     241   
     242   template <int N> std::vector<LightLikeAxis> UpdateAxesFast(const std::vector <LightLikeAxis> & old_axes,
     243                                                              const std::vector <fastjet::PseudoJet> & inputJets) const;
     244   
     245   std::vector<LightLikeAxis> UpdateAxes(const std::vector <LightLikeAxis> & old_axes,
     246                                         const std::vector <fastjet::PseudoJet> & inputJets) const;
    279247
    280248};
     
    288256class AxesFinderFromKmeansMinimization : public AxesFinder{
    289257
    290    private:
    291       int _n_iterations;   // Number of iterations to run  (0 for no minimization, 1 for one-pass, >>1 for global minimum)
    292       double _noise_range; // noise range for random initialization
    293    
    294       DefaultUnnormalizedMeasure _measureFunction; //function to test whether minimum is reached
    295    
    296       AxesFinderFromOnePassMinimization _onePassFinder;  //one pass finder for minimization
    297 
    298       PseudoJet jiggle(const PseudoJet& axis);
    299    
    300    public:
    301       AxesFinderFromKmeansMinimization(AxesFinder *startingFinder, double beta, double Rcutoff, int n_iterations) :
    302          AxesFinder(startingFinder),
    303          _n_iterations(n_iterations),
    304          _noise_range(1.0), // hard coded for the time being
    305          _measureFunction(beta, Rcutoff),
    306          _onePassFinder(NULL, beta, Rcutoff)
    307          {}
    308 
    309       virtual std::vector<fastjet::PseudoJet> getBetterAxes(int n_jets, const std::vector <fastjet::PseudoJet> & inputJets, const std::vector<fastjet::PseudoJet>& currentAxes);
    310 
     258public:
     259   AxesFinderFromKmeansMinimization(double beta, double Rcutoff, int n_iterations)
     260   :  _n_iterations(n_iterations),
     261      _noise_range(1.0), // hard coded for the time being
     262      _measureFunction(beta, Rcutoff),
     263      _onePassFinder(beta, Rcutoff)
     264      {}
     265
     266   virtual std::vector<fastjet::PseudoJet> getAxes(int n_jets, const std::vector <fastjet::PseudoJet> & inputJets, const std::vector<fastjet::PseudoJet>& currentAxes) const;
     267   
     268private:
     269   int _n_iterations;   // Number of iterations to run  (0 for no minimization, 1 for one-pass, >>1 for global minimum)
     270   double _noise_range; // noise range for random initialization
     271   
     272   DefaultUnnormalizedMeasureFunction _measureFunction; //function to test whether minimum is reached
     273   
     274   AxesFinderFromOnePassMinimization _onePassFinder;  //one pass finder that is repeatedly called
     275   
     276   PseudoJet jiggle(const PseudoJet& axis) const;
    311277};
    312278
     
    317283class AxesFinderFromGeometricMinimization : public AxesFinder {
    318284
    319    private:
    320       MeasureFunction* _function;
    321       double _Rcutoff;
    322       double _nAttempts;
    323       double _accuracy;
    324 
    325    
    326    public:
    327       AxesFinderFromGeometricMinimization(AxesFinder* startingFinder, double beta, double Rcutoff) : AxesFinder(startingFinder), _Rcutoff(Rcutoff) {
    328          if (beta != 2.0) {
    329             std::cerr << "Geometric minimization is currently only defined for beta = 2.0." << std::endl;
    330             exit(1);
    331          }
    332          
    333          _nAttempts = 100;
    334          _accuracy = 0.000000001;
    335          _function = new GeometricMeasure(beta,_Rcutoff);
     285public:
     286   AxesFinderFromGeometricMinimization(double beta, double Rcutoff)
     287   :  _nAttempts(100),
     288      _accuracy(0.000000001),
     289      _function(beta,Rcutoff)
     290   {
     291      if (beta != 2.0) {
     292         throw Error("Geometric minimization is currently only defined for beta = 2.0.");
    336293      }
    337 
    338       ~AxesFinderFromGeometricMinimization() {
    339          delete _function;
    340       }
    341    
    342       virtual std::vector<fastjet::PseudoJet> getBetterAxes(int n_jets, const std::vector <fastjet::PseudoJet> & particles, const std::vector<fastjet::PseudoJet>& currentAxes);
     294   }
     295
     296   virtual std::vector<fastjet::PseudoJet> getAxes(int n_jets, const std::vector <fastjet::PseudoJet> & particles, const std::vector<fastjet::PseudoJet>& currentAxes) const;
     297
     298private:
     299   double _nAttempts;
     300   double _accuracy;
     301   GeometricMeasureFunction _function;
     302
     303
    343304};
    344305
     
    348309// in order to better facilitate calculations.
    349310class LightLikeAxis {
    350 private:
    351    double _rap, _phi, _weight, _mom;
    352    
    353    double DistanceSq(double rap2, double phi2) const {
    354       double rap1 = _rap;
    355       double phi1 = _phi;
    356      
    357       double distRap = rap1-rap2;
    358       double distPhi = std::fabs(phi1-phi2);
    359       if (distPhi > M_PI) {distPhi = 2.0*M_PI - distPhi;}
    360       return sq(distRap) + sq(distPhi);
    361    }
    362    
    363    double Distance(double rap2, double phi2) const {
    364       return std::sqrt(DistanceSq(rap2,phi2));
    365    }
    366    
    367    
     311
    368312public:
    369313   LightLikeAxis() : _rap(0.0), _phi(0.0), _weight(0.0), _mom(0.0) {}
     
    399343   }
    400344
     345private:
     346   double _rap, _phi, _weight, _mom;
     347   
     348   double DistanceSq(double rap2, double phi2) const {
     349      double rap1 = _rap;
     350      double phi1 = _phi;
     351     
     352      double distRap = rap1-rap2;
     353      double distPhi = std::fabs(phi1-phi2);
     354      if (distPhi > M_PI) {distPhi = 2.0*M_PI - distPhi;}
     355      return distRap*distRap + distPhi*distPhi;
     356   }
     357   
     358   double Distance(double rap2, double phi2) const {
     359      return std::sqrt(DistanceSq(rap2,phi2));
     360   }
     361   
     362   
    401363};
    402364
  • external/fastjet/contribs/Nsubjettiness/MeasureFunction.cc

    r5b5a56b r35cdc46  
    55//  Jesse Thaler, Ken Van Tilburg, Christopher K. Vermilion, and TJ Wilkason
    66//
     7//  $Id: MeasureFunction.cc 670 2014-06-06 01:24:42Z jthaler $
    78//----------------------------------------------------------------------
    89// This file is part of FastJet contrib.
     
    3637
    3738// Return all of the necessary TauComponents for specific input particles and axes
    38 TauComponents MeasureFunction::result(const std::vector<fastjet::PseudoJet>& particles, const std::vector<fastjet::PseudoJet>& axes) {
     39TauComponents MeasureFunction::result(const std::vector<fastjet::PseudoJet>& particles, const std::vector<fastjet::PseudoJet>& axes) const {
     40   
     41   // first find partition
     42   // this sets jetPartitionStorage and beamPartitionStorage
     43   PseudoJet beamPartitionStorage;
     44   std::vector<fastjet::PseudoJet> jetPartitionStorage = get_partition(particles,axes,&beamPartitionStorage);
     45   
     46   // then return result calculated from partition
     47   return result_from_partition(jetPartitionStorage,axes,&beamPartitionStorage);
     48}
    3949
    40    std::vector<double> jetPieces(axes.size(), 0.0);
    41    double beamPiece = 0.0;
     50std::vector<fastjet::PseudoJet> MeasureFunction::get_partition(const std::vector<fastjet::PseudoJet>& particles,
     51                                                               const std::vector<fastjet::PseudoJet>& axes,
     52                                                               PseudoJet * beamPartitionStorage) const {
    4253   
    43    // Calculates the unnormalized sub-tau values, i.e. a std::vector of the contributions to tau_N of each Voronoi region (or region within R_0)
     54   std::vector<std::vector<PseudoJet> > jetPartition(axes.size());
     55   std::vector<PseudoJet> beamPartition;
     56   
     57   // Figures out the partiting of the input particles into the various jet pieces
     58   // Based on which axis the parition is closest to
    4459   for (unsigned i = 0; i < particles.size(); i++) {
    4560     
     
    6075     
    6176      if (j_min == -1) {
    62          if (_has_beam) beamPiece += beam_numerator(particles[i]);
     77         if (_has_beam) beamPartition.push_back(particles[i]);
    6378         else assert(_has_beam);  // this should never happen.
    6479      } else {
    65          jetPieces[j_min] += jet_numerator(particles[i],axes[j_min]);
     80         jetPartition[j_min].push_back(particles[i]);
    6681      }
    6782   }
    6883   
    69    // Calculates normalization for tau and subTau if _has_denominator is true, otherwise returns 1.0 (i.e. no normalization)
    70    double tauDen = 0.0;
    71    if (_has_denominator) {
    72       for (unsigned i = 0; i < particles.size(); i++) {
    73          tauDen += denominator(particles[i]);
    74       }
    75    } else {
    76       tauDen = 1.0; // if no denominator, then 1.0 for no normalization factor
     84   // Store beam partition
     85   if (beamPartitionStorage) {
     86      *beamPartitionStorage = join(beamPartition);
     87   }
     88
     89   // Store jet partitions
     90   std::vector<PseudoJet> jetPartitionStorage(axes.size(),PseudoJet(0,0,0,0));
     91   for (unsigned j = 0; j < axes.size(); j++) {
     92      jetPartitionStorage[j] = join(jetPartition[j]);
    7793   }
    7894   
     95   return jetPartitionStorage;
     96}
     97
     98// does partition, but only stores index of PseudoJets
     99std::vector<std::list<int> > MeasureFunction::get_partition_list(const std::vector<fastjet::PseudoJet>& particles,
     100                                                                 const std::vector<fastjet::PseudoJet>& axes) const {
     101
     102   std::vector<std::list<int> > jetPartition(axes.size());
     103   
     104   // Figures out the partiting of the input particles into the various jet pieces
     105   // Based on which axis the parition is closest to
     106   for (unsigned i = 0; i < particles.size(); i++) {
     107     
     108      // find minimum distance; start with beam (-1) for reference
     109      int j_min = -1;
     110      double minRsq;
     111      if (_has_beam) minRsq = beam_distance_squared(particles[i]);
     112      else minRsq = std::numeric_limits<double>::max(); // make it large value
     113     
     114      // check to see which axis the particle is closest to
     115      for (unsigned j = 0; j < axes.size(); j++) {
     116         double tempRsq = jet_distance_squared(particles[i],axes[j]); // delta R distance
     117         if (tempRsq < minRsq) {
     118            minRsq = tempRsq;
     119            j_min = j;
     120         }
     121      }
     122     
     123      if (j_min == -1) {
     124         assert(_has_beam); // consistency check
     125      } else {
     126         jetPartition[j_min].push_back(i);
     127      }
     128   }
     129   
     130   return jetPartition;
     131}
     132   
     133
     134// Uses existing partition and calculates result
     135// TODO:  Can we cache this for speed up when doing area subtraction?
     136TauComponents MeasureFunction::result_from_partition(const std::vector<fastjet::PseudoJet>& jet_partition,
     137                                                     const std::vector<fastjet::PseudoJet>& axes,
     138                                                     PseudoJet * beamPartitionStorage) const {
     139   
     140   std::vector<double> jetPieces(axes.size(), 0.0);
     141   double beamPiece = 0.0;
     142   
     143   double tauDen = 0.0;
     144   if (!_has_denominator) tauDen = 1.0;  // if no denominator, then 1.0 for no normalization factor
     145   
     146   // first find jet pieces
     147   for (unsigned j = 0; j < axes.size(); j++) {
     148      std::vector<PseudoJet> thisPartition = jet_partition[j].constituents();
     149      for (unsigned i = 0; i < thisPartition.size(); i++) {
     150         jetPieces[j] += jet_numerator(thisPartition[i],axes[j]); //numerator jet piece
     151         if (_has_denominator) tauDen += denominator(thisPartition[i]); // denominator
     152      }
     153   }
     154   
     155   // then find beam piece
     156   if (_has_beam) {
     157      assert(beamPartitionStorage); // make sure I have beam information
     158      std::vector<PseudoJet> beamPartition = beamPartitionStorage->constituents();
     159
     160      for (unsigned i = 0; i < beamPartition.size(); i++) {
     161         beamPiece += beam_numerator(beamPartition[i]); //numerator beam piece
     162         if (_has_denominator) tauDen += denominator(beamPartition[i]); // denominator
     163      }
     164   }
    79165   return TauComponents(jetPieces, beamPiece, tauDen, _has_denominator, _has_beam);
    80166}
     167
     168   
     169   
     170   
    81171   
    82172} //namespace contrib
  • external/fastjet/contribs/Nsubjettiness/MeasureFunction.hh

    r5b5a56b r35cdc46  
    55//  Jesse Thaler, Ken Van Tilburg, Christopher K. Vermilion, and TJ Wilkason
    66//
     7//  $Id: MeasureFunction.hh 678 2014-06-12 20:43:03Z jthaler $
    78//----------------------------------------------------------------------
    89// This file is part of FastJet contrib.
     
    3132#include <limits>
    3233
     34
    3335FASTJET_BEGIN_NAMESPACE      // defined in fastjet/internal/base.hh
    3436
    3537namespace contrib{
    3638
    37 inline double sq(double x) {return x*x;}
    38 
    39 ///////
    40 //
    41 // Measure Function
    42 //
    43 ///////
    44 
     39///////
     40//
     41// TauComponents
     42// (eventually we might want to put this in a separate header file)
     43//
     44///////
     45   
    4546/// \class TauComponents
    4647// This class creates a wrapper for the various tau/subtau values calculated in Njettiness. This class allows Njettiness access to these variables
    4748// without ever having to do the calculation itself. It takes in subtau numerators and tau denominator from MeasureFunction
    4849// and outputs tau numerator, and normalized tau and subtau.
    49 // TODO:  Consider merging with NjettinessExtras.  Add axes information?
    5050class TauComponents {
    51 private:
    52    
    53    // these values are input in the constructor
    54    std::vector<double> _jet_pieces_numerator;
    55    double _beam_piece_numerator;
    56    double _denominator;
    57    bool _has_denominator; //added so that TauComponents knows if denominator is used or not
    58    bool _has_beam; //added so that TauComponents knows if beam regions is used or not
    59    
    60    // these values are derived from above values
    61    std::vector<double> _jet_pieces;
    62    double _beam_piece;
    63    double _numerator;
    64    double _tau;
    65    
    6651   
    6752public:
     
    115100   double beam_piece() const { return _beam_piece; }
    116101   double tau() const { return _tau; }
    117    
    118 };
     102
     103private:
     104   
     105   // these values are input in the constructor
     106   std::vector<double> _jet_pieces_numerator;
     107   double _beam_piece_numerator;
     108   double _denominator;
     109   bool _has_denominator; //added so that TauComponents knows if denominator is used or not
     110   bool _has_beam; //added so that TauComponents knows if beam regions is used or not
     111   
     112   // these values are derived from above values
     113   std::vector<double> _jet_pieces;
     114   double _beam_piece;
     115   double _numerator;
     116   double _tau;
     117   
     118};
     119
     120///////
     121//
     122// Measure Function
     123//
     124///////
     125
    119126
    120127//------------------------------------------------------------------------
     
    125132class MeasureFunction {
    126133   
     134public:
     135   //These functions define the measure by which tau_N is calculated,
     136   //and are overloaded by the various measures below
     137   
     138   // Distanes to axes.  These are called many times, so need to be as fast as possible
     139   virtual double jet_distance_squared(const fastjet::PseudoJet& particle, const fastjet::PseudoJet& axis) const = 0;
     140   virtual double beam_distance_squared(const fastjet::PseudoJet& particle) const = 0;
     141   
     142   // The actual measures used in N-(sub)jettiness
     143   virtual double jet_numerator(const fastjet::PseudoJet& particle, const fastjet::PseudoJet& axis) const  = 0;
     144   virtual double beam_numerator(const fastjet::PseudoJet& particle) const = 0;
     145   
     146   // a possible normalization factor
     147   virtual double denominator(const fastjet::PseudoJet& particle) const = 0;
     148   
     149   //------
     150   // The functions below call the above functions and are not virtual
     151   //------
     152   
     153   // Return all of the necessary TauComponents for specific input particles and axes
     154   // Also have optional pointers to get out information about partitioning
     155   TauComponents result(const std::vector<fastjet::PseudoJet>& particles, const std::vector<fastjet::PseudoJet>& axes) const;
     156
     157   // Just getting tau value if that is all that is needed
     158   double tau(const std::vector<fastjet::PseudoJet>& particles, const std::vector<fastjet::PseudoJet>& axes) const {
     159      return result(particles,axes).tau();
     160   }
     161   
     162   // Create the partitioning and stores internally
     163   std::vector<fastjet::PseudoJet> get_partition(const std::vector<fastjet::PseudoJet>& particles, const std::vector<fastjet::PseudoJet>& axes, PseudoJet * beamPartitionStorage = NULL) const;
     164
     165   // Essentially same as get_partition, but in the form needed for the jet algorithm
     166   std::vector<std::list<int> > get_partition_list(const std::vector<fastjet::PseudoJet>& particles, const std::vector<fastjet::PseudoJet>& axes) const;
     167
     168   // calculates the tau result using an existing partition
     169   TauComponents result_from_partition(const std::vector<fastjet::PseudoJet>& jet_partitioning, const std::vector<fastjet::PseudoJet>& axes, PseudoJet * beamPartitionStorage = NULL) const;
     170
     171   // shorthand for squaring
     172   static inline double sq(double x) {return x*x;}
     173
     174   //virtual destructor
     175   virtual ~MeasureFunction(){}
     176   
    127177protected:
    128178   //bool set by derived classes to choose whether or not to use the denominator
     
    133183   MeasureFunction(bool has_denominator = true, bool has_beam = true) : _has_denominator(has_denominator), _has_beam(has_beam) {}
    134184   
    135 public:
    136    virtual ~MeasureFunction(){}
    137    
    138    //These functions define the measure by which tau_N is calculated,
    139    //and are overloaded by the various measures below
    140    
    141    // Distanes to axes.  These are called many times, so need to be as fast as possible
    142    virtual double jet_distance_squared(const fastjet::PseudoJet& particle, const fastjet::PseudoJet& axis) = 0;
    143    virtual double beam_distance_squared(const fastjet::PseudoJet& particle) = 0;
    144    
    145    // The actual measures used in N-(sub)jettiness
    146    virtual double jet_numerator(const fastjet::PseudoJet& particle, const fastjet::PseudoJet& axis) = 0;
    147    virtual double beam_numerator(const fastjet::PseudoJet& particle) = 0;
    148    
    149    // a possible normalization factor
    150    virtual double denominator(const fastjet::PseudoJet& particle) = 0;
    151    
    152    
    153    // These functions call the above functions and are not virtual
    154    
    155    // Do I cluster a particle into a jet?
    156    bool do_cluster(const fastjet::PseudoJet& particle, const fastjet::PseudoJet& axis) {
    157       return (jet_distance_squared(particle,axis) <= beam_distance_squared(particle));
    158    }
    159    
    160    // Return all of the necessary TauComponents for specific input particles and axes
    161    TauComponents result(const std::vector<fastjet::PseudoJet>& particles, const std::vector<fastjet::PseudoJet>& axes);
    162 
    163    double tau(const std::vector<fastjet::PseudoJet>& particles, const std::vector<fastjet::PseudoJet>& axes) {
    164       return result(particles,axes).tau();
    165    }
    166 
    167    
    168 };
    169 
    170 
    171 /// \class DefaultNormalizedMeasure
     185};
     186
     187
     188/// \class DefaultNormalizedMeasureFunction
    172189// This class is the default measure, inheriting from the class above. This class will calculate tau_N
    173190// of a jet according to this measure. This measure is defined as the pT of the particle multiplied by deltaR
    174191// to the power of beta. This class includes the normalization factor determined by R0
    175 class DefaultNormalizedMeasure : public MeasureFunction {
    176 
    177    private:
    178       double _beta;
    179       double _R0;
    180       double _Rcutoff;
    181 
    182    public:
    183 
    184       DefaultNormalizedMeasure(double beta, double R0, double Rcutoff, bool normalized = true)
    185       : MeasureFunction(normalized), _beta(beta), _R0(R0), _Rcutoff(Rcutoff) {}
    186 
    187       virtual double jet_distance_squared(const fastjet::PseudoJet& particle, const fastjet::PseudoJet& axis) {
    188          return particle.squared_distance(axis);
    189       }
    190    
    191       virtual double beam_distance_squared(const fastjet::PseudoJet& particle) {
    192          return sq(_Rcutoff);
    193       }
    194 
    195       virtual double jet_numerator(const fastjet::PseudoJet& particle, const fastjet::PseudoJet& axis) {
    196          return particle.perp() * std::pow(jet_distance_squared(particle,axis),_beta/2.0);
    197       }
    198    
    199       virtual double beam_numerator(const fastjet::PseudoJet& particle) {
    200          return particle.perp() * std::pow(_Rcutoff,_beta);
    201       }
    202 
    203       virtual double denominator(const fastjet::PseudoJet& particle) {
    204          return particle.perp() * std::pow(_R0,_beta);
    205       }
    206 
     192class DefaultNormalizedMeasureFunction : public MeasureFunction {
     193
     194public:
     195
     196   DefaultNormalizedMeasureFunction(double beta, double R0, double Rcutoff, bool normalized = true)
     197   : MeasureFunction(normalized), _beta(beta), _R0(R0), _Rcutoff(Rcutoff) {}
     198
     199   virtual double jet_distance_squared(const fastjet::PseudoJet& particle, const fastjet::PseudoJet& axis) const {
     200      return particle.squared_distance(axis);
     201   }
     202
     203   virtual double beam_distance_squared(const fastjet::PseudoJet& /*particle*/) const {
     204      return sq(_Rcutoff);
     205   }
     206
     207   virtual double jet_numerator(const fastjet::PseudoJet& particle, const fastjet::PseudoJet& axis) const{
     208      return particle.perp() * std::pow(jet_distance_squared(particle,axis),_beta/2.0);
     209   }
     210
     211   virtual double beam_numerator(const fastjet::PseudoJet& particle) const {
     212      return particle.perp() * std::pow(_Rcutoff,_beta);
     213   }
     214
     215   virtual double denominator(const fastjet::PseudoJet& particle) const {
     216      return particle.perp() * std::pow(_R0,_beta);
     217   }
     218   
     219private:
     220   double _beta;
     221   double _R0;
     222   double _Rcutoff;
     223
     224   
    207225};
    208226
    209227//------------------------------------------------------------------------
    210 /// \class DefaultUnnormalizedMeasure
     228/// \class DefaultUnnormalizedMeasureFunction
    211229// This class is the unnormalized default measure, inheriting from the class above. The only difference from above
    212230// is that the denominator is defined to be 1.0 by setting _has_denominator to false.
    213 class DefaultUnnormalizedMeasure : public DefaultNormalizedMeasure {
    214 
    215    public:
    216       // Since all methods are identical, UnnormalizedMeasure inherits directly from NormalizedMeasure. R0 is defaulted to NAN since the value of R0 is unecessary for this class.
    217       // the "false" flag sets _has_denominator in MeasureFunction to false so no denominator is used.
    218       DefaultUnnormalizedMeasure(double beta, double Rcutoff) : DefaultNormalizedMeasure(beta, NAN, Rcutoff, false) {}
    219 
    220       
     231class DefaultUnnormalizedMeasureFunction : public DefaultNormalizedMeasureFunction {
     232
     233public:
     234   // Since all methods are identical, UnnormalizedMeasure inherits directly
     235   // from NormalizedMeasure. R0 is a dummy value since the value of R0 is unecessary for this class,
     236   // and the "false" flag sets _has_denominator in MeasureFunction to false so no denominator is used.
     237   DefaultUnnormalizedMeasureFunction(double beta, double Rcutoff)
     238   : DefaultNormalizedMeasureFunction(beta, std::numeric_limits<double>::quiet_NaN(), Rcutoff, false) {}
    221239};
    222240
    223241//------------------------------------------------------------------------
    224 /// \class GeometricMeasure
     242/// \class GeometricMeasureFunction
    225243// This class is the geometic measure, inheriting from the class above. This class will calculate tau_N
    226244// of a jet according to this measure. This measure is defined by the Lorentz dot product between
    227245// the particle and the axis. This class includes normalization of tau_N.
    228 class GeometricMeasure : public MeasureFunction {
    229 
    230    private:
    231       double _jet_beta;
    232       double _beam_beta;
    233       double _Rcutoff;
    234 
    235       // create light-like axis
    236       fastjet::PseudoJet lightFrom(const fastjet::PseudoJet& input) const {
    237          double length = sqrt(pow(input.px(),2) + pow(input.py(),2) + pow(input.pz(),2));
    238          return fastjet::PseudoJet(input.px()/length,input.py()/length,input.pz()/length,1.0);
    239       }
    240 
    241    public:
    242       // Right now, we are hard coded for beam_beta = 1.0, but that will need to change
    243       GeometricMeasure(double jet_beta, double Rcutoff) : _jet_beta(jet_beta), _beam_beta(1.0), _Rcutoff(Rcutoff) {}
    244    
    245       virtual double jet_distance_squared(const fastjet::PseudoJet& particle, const fastjet::PseudoJet& axis) {
    246          fastjet::PseudoJet lightAxis = lightFrom(axis);
    247          double pseudoRsquared = 2.0*dot_product(lightFrom(axis),particle)/(lightAxis.pt()*particle.pt());
    248          return pseudoRsquared;
    249       }
    250    
    251       virtual double beam_distance_squared(const fastjet::PseudoJet& particle) {
    252          return sq(_Rcutoff);
    253       }
    254 
    255       virtual double jet_numerator(const fastjet::PseudoJet& particle, const fastjet::PseudoJet& axis) {
    256          fastjet::PseudoJet lightAxis = lightFrom(axis);
    257          double weight = (_beam_beta == 1.0) ? 1.0 : std::pow(lightAxis.pt(),_beam_beta - 1.0);
    258          return particle.pt() * weight * std::pow(jet_distance_squared(particle,axis),_jet_beta/2.0);
    259       }
    260    
    261       virtual double beam_numerator(const fastjet::PseudoJet& particle) {
    262          double weight = (_beam_beta == 1.0) ? 1.0 : std::pow(particle.pt()/particle.e(),_beam_beta - 1.0);
    263          return particle.pt() * weight * std::pow(_Rcutoff,_jet_beta);
    264       }
    265 
    266       virtual double denominator(const fastjet::PseudoJet& particle) {
    267          return 1.0;
    268       }
    269 };
    270 
    271 
     246class GeometricMeasureFunction : public MeasureFunction {
     247
     248public:
     249   // Right now, we are hard coded for beam_beta = 1.0, but that will need to change
     250   GeometricMeasureFunction(double jet_beta, double Rcutoff) :
     251     MeasureFunction(false), // doesn't have denominator
     252     _jet_beta(jet_beta), _beam_beta(1.0), _Rcutoff(Rcutoff) {}
     253
     254   virtual double jet_distance_squared(const fastjet::PseudoJet& particle, const fastjet::PseudoJet& axis) const {
     255      fastjet::PseudoJet lightAxis = lightFrom(axis);
     256      double pseudoRsquared = 2.0*dot_product(lightFrom(axis),particle)/(lightAxis.pt()*particle.pt());
     257      return pseudoRsquared;
     258   }
     259
     260   virtual double beam_distance_squared(const fastjet::PseudoJet&  /*particle*/) const {
     261      return sq(_Rcutoff);
     262   }
     263
     264   virtual double jet_numerator(const fastjet::PseudoJet& particle, const fastjet::PseudoJet& axis) const {
     265      fastjet::PseudoJet lightAxis = lightFrom(axis);
     266      double weight = (_beam_beta == 1.0) ? 1.0 : std::pow(lightAxis.pt(),_beam_beta - 1.0);
     267      return particle.pt() * weight * std::pow(jet_distance_squared(particle,axis),_jet_beta/2.0);
     268   }
     269
     270   virtual double beam_numerator(const fastjet::PseudoJet& particle) const {
     271      double weight = (_beam_beta == 1.0) ? 1.0 : std::pow(particle.pt()/particle.e(),_beam_beta - 1.0);
     272      return particle.pt() * weight * std::pow(_Rcutoff,_jet_beta);
     273   }
     274
     275   virtual double denominator(const fastjet::PseudoJet&  /*particle*/) const {
     276      return std::numeric_limits<double>::quiet_NaN();
     277   }
     278   
     279   
     280private:
     281   double _jet_beta;
     282   double _beam_beta;
     283   double _Rcutoff;
     284   
     285   // create light-like axis
     286   fastjet::PseudoJet lightFrom(const fastjet::PseudoJet& input) const {
     287      double length = sqrt(pow(input.px(),2) + pow(input.py(),2) + pow(input.pz(),2));
     288      return fastjet::PseudoJet(input.px()/length,input.py()/length,input.pz()/length,1.0);
     289   }
     290
     291};
     292   
     293   
    272294} //namespace contrib
    273295
  • external/fastjet/contribs/Nsubjettiness/Njettiness.cc

    r5b5a56b r35cdc46  
    55//  Jesse Thaler, Ken Van Tilburg, Christopher K. Vermilion, and TJ Wilkason
    66//
     7//  $Id: Njettiness.cc 677 2014-06-12 18:56:46Z jthaler $
    78//----------------------------------------------------------------------
    89// This file is part of FastJet contrib.
     
    3536///////
    3637
    37 // Helper function to correlate one pass minimization with appropriate measure
    38 void Njettiness::setOnePassAxesFinder(MeasureMode measure_mode, AxesFinder* startingFinder, double beta, double Rcutoff) {
    39    if (measure_mode == normalized_measure || measure_mode == unnormalized_measure || measure_mode == normalized_cutoff_measure || measure_mode == unnormalized_cutoff_measure) {
    40       _axesFinder = new AxesFinderFromOnePassMinimization(startingFinder, beta, Rcutoff);
    41    }
    42    else if (measure_mode == geometric_measure || measure_mode == geometric_cutoff_measure) {
    43       _axesFinder = new AxesFinderFromGeometricMinimization(startingFinder, beta, Rcutoff);
    44    }
    45    else {
    46       std::cerr << "Minimization only set up for normalized_measure, unnormalized_measure, normalized_cutoff_measure, unnormalized_cutoff_measure, geometric_measure, geometric_cutoff_measure" << std::endl;
    47       exit(1); }
    48 }
    49 
    50 // Parsing needed for constructor to set AxesFinder and MeasureFunction
    51 // All of the parameter handling is here, and checking that number of parameters is correct.
    52 void Njettiness::setMeasureFunctionandAxesFinder(AxesMode axes_mode, MeasureMode measure_mode, double para1, double para2, double para3, double para4) {
     38Njettiness::Njettiness(const AxesDefinition & axes_def, const MeasureDefinition & measure_def)
     39: _axes_def(axes_def.create()), _measure_def(measure_def.create()) {
     40   setMeasureFunctionAndAxesFinder();  // call helper function to do the hard work
     41}
     42
     43Njettiness::Njettiness(AxesMode axes_mode, const MeasureDefinition & measure_def)
     44: _axes_def(createAxesDef(axes_mode)), _measure_def(measure_def.create()) {
     45   setMeasureFunctionAndAxesFinder();  // call helper function to do the hard work
     46}
     47   
     48// Convert from MeasureMode enum to MeasureDefinition
     49// This returns a pointer that will be claimed by a SharedPtr
     50MeasureDefinition* Njettiness::createMeasureDef(MeasureMode measure_mode, int num_para, double para1, double para2, double para3) const {
    5351
    5452   // definition of maximum Rcutoff for non-cutoff measures, changed later by other measures
    5553   double Rcutoff = std::numeric_limits<double>::max();  //large number
    56    // Most (but all measures have some kind of beta value)
    57    double beta = NAN;
     54   // Most (but not all) measures have some kind of beta value
     55   double beta = std::numeric_limits<double>::quiet_NaN();
    5856   // The normalized measures have an R0 value.
    59    double R0 = NAN;
    60 
     57   double R0 = std::numeric_limits<double>::quiet_NaN();
     58   
    6159   // Find the MeasureFunction and set the parameters.
    6260   switch (measure_mode) {
     
    6462         beta = para1;
    6563         R0 = para2;
    66          if(correctParameterCount(2, para1, para2, para3, para4))
    67             _measureFunction = new DefaultNormalizedMeasure(beta, R0, Rcutoff); //normalized_measure requires 2 parameters, beta and R0
    68          else {
    69             std::cerr << "normalized_measure needs 2 parameters (beta and R0)" << std::endl;
    70             exit(1); }
     64         if(num_para == 2) {
     65            return new NormalizedMeasure(beta,R0);
     66         } else {
     67            throw Error("normalized_measure needs 2 parameters (beta and R0)");
     68         }
    7169         break;
    7270      case unnormalized_measure:
    7371         beta = para1;
    74          if(correctParameterCount(1, para1, para2, para3, para4))
    75             _measureFunction = new DefaultUnnormalizedMeasure(beta, Rcutoff); //unnormalized_measure requires 1 parameter, beta
    76          else {
    77             std::cerr << "unnormalized_measure needs 1 parameter (beta)" << std::endl;
    78             exit(1); }
     72         if(num_para == 1) {
     73            return new UnnormalizedMeasure(beta);
     74         } else {
     75            throw Error("unnormalized_measure needs 1 parameter (beta)");
     76         }
    7977         break;
    8078      case geometric_measure:
    8179         beta = para1;
    82          if(correctParameterCount(1, para1, para2, para3, para4))
    83             _measureFunction = new GeometricMeasure(beta,Rcutoff); //geometric_measure requires 1 parameter, beta
    84          else {
    85             std::cerr << "geometric_measure needs 1 parameter (beta)" << std::endl;
    86             exit(1); }
     80         if (num_para == 1) {
     81            return new GeometricMeasure(beta);
     82         } else {
     83            throw Error("geometric_measure needs 1 parameter (beta)");
     84         }
    8785         break;
    8886      case normalized_cutoff_measure:
     
    9088         R0 = para2;
    9189         Rcutoff = para3; //Rcutoff parameter is 3rd parameter in normalized_cutoff_measure
    92          if(correctParameterCount(3, para1, para2, para3, para4))
    93             _measureFunction = new DefaultNormalizedMeasure(beta, R0, Rcutoff); //normalized_cutoff_measure requires 3 parameters, beta, R0, and Rcutoff
    94          else {
    95             std::cerr << "normalized_cutoff_measure has 3 parameters (beta, R0, Rcutoff)" << std::endl;
    96             exit(1); }
     90         if (num_para == 3) {
     91            return new NormalizedCutoffMeasure(beta,R0,Rcutoff);
     92         } else {
     93            throw Error("normalized_cutoff_measure has 3 parameters (beta, R0, Rcutoff)");
     94         }
    9795         break;
    9896      case unnormalized_cutoff_measure:
    9997         beta = para1;
    10098         Rcutoff = para2; //Rcutoff parameter is 2nd parameter in normalized_cutoff_measure
    101          if (correctParameterCount(2, para1, para2, para3, para4))
    102             _measureFunction = new DefaultUnnormalizedMeasure(beta, Rcutoff); //unnormalized_cutoff_measure requires 2 parameters, beta and Rcutoff
    103          else {
    104             std::cerr << "unnormalized_cutoff_measure has 2 parameters (beta, Rcutoff)" << std::endl;
    105             exit(1); }
     99         if (num_para == 2) {
     100            return new UnnormalizedCutoffMeasure(beta,Rcutoff);
     101         } else {
     102            throw Error("unnormalized_cutoff_measure has 2 parameters (beta, Rcutoff)");
     103         }
    106104         break;
    107105      case geometric_cutoff_measure:
    108106         beta = para1;
    109107         Rcutoff = para2; //Rcutoff parameter is 2nd parameter in geometric_cutoff_measure
    110          if(correctParameterCount(2, para1, para2, para3, para4))
    111             _measureFunction = new GeometricMeasure(beta,Rcutoff); //geometric_cutoff_measure requires 2 parameters, beta and Rcutoff
    112          else {
    113             std::cerr << "geometric_cutoff_measure has 2 parameters (beta,Rcutoff)" << std::endl;
    114             exit(1); }
     108         if(num_para == 2) {
     109           return new GeometricCutoffMeasure(beta,Rcutoff);
     110         } else {
     111            throw Error("geometric_cutoff_measure has 2 parameters (beta, Rcutoff)");
     112         }
    115113         break;
    116114      default:
    117115         assert(false);
    118116         break;
    119    }   
    120 
    121    // Choose which AxesFinder from user input.
    122    // Uses setOnePassAxesFinder helpful function to use beta and Rcutoff values about (if needed)
     117   }
     118   return NULL;
     119}
     120
     121// Convert from AxesMode enum to AxesDefinition
     122// This returns a pointer that will be claimed by a SharedPtr
     123AxesDefinition* Njettiness::createAxesDef(Njettiness::AxesMode axes_mode) const {
     124   
    123125   switch (axes_mode) {
    124126      case wta_kt_axes:
    125          _axesFinder = new AxesFinderFromWTA_KT();
    126          break;
     127         return new WTA_KT_Axes();
    127128      case wta_ca_axes:
    128          _axesFinder = new AxesFinderFromWTA_CA();
    129          break;
     129         return new WTA_CA_Axes();
    130130      case kt_axes:
    131          _axesFinder = new AxesFinderFromKT();
    132          break;
     131         return new KT_Axes();
    133132      case ca_axes:
    134          _axesFinder = new AxesFinderFromCA();
    135          break;
     133         return new CA_Axes();
    136134      case antikt_0p2_axes:
    137          _axesFinder = new AxesFinderFromAntiKT(0.2);     
    138          break;
     135         return new AntiKT_Axes(0.2);
    139136      case onepass_wta_kt_axes:
    140          setOnePassAxesFinder(measure_mode, new AxesFinderFromWTA_KT(), beta, Rcutoff);
    141          break;
     137         return new OnePass_WTA_KT_Axes();
    142138      case onepass_wta_ca_axes:
    143          setOnePassAxesFinder(measure_mode, new AxesFinderFromWTA_CA(), beta, Rcutoff);
    144          break;
     139         return new OnePass_WTA_CA_Axes();
    145140      case onepass_kt_axes:
    146          setOnePassAxesFinder(measure_mode, new AxesFinderFromKT(), beta, Rcutoff);
    147          break;
     141         return new OnePass_KT_Axes();
    148142      case onepass_ca_axes:
    149          setOnePassAxesFinder(measure_mode, new AxesFinderFromCA(), beta, Rcutoff);
    150          break;
     143         return new OnePass_CA_Axes();
    151144      case onepass_antikt_0p2_axes:
    152          setOnePassAxesFinder(measure_mode, new AxesFinderFromAntiKT(0.2), beta, Rcutoff);
    153          break;
     145         return new OnePass_AntiKT_Axes(0.2);
    154146      case onepass_manual_axes:
    155          setOnePassAxesFinder(measure_mode, new AxesFinderFromUserInput(), beta, Rcutoff);
    156          break;
    157       case min_axes: //full minimization is not defined for geometric_measure.
    158          if (measure_mode == normalized_measure || measure_mode == unnormalized_measure || measure_mode == normalized_cutoff_measure || measure_mode == unnormalized_cutoff_measure)
    159             //Defaults to 100 iteration to find minimum
    160             _axesFinder = new AxesFinderFromKmeansMinimization(new AxesFinderFromKT(), beta, Rcutoff, 100);
    161          else {
    162             std::cerr << "Multi-pass minimization only set up for normalized_measure, unnormalized_measure, normalized_cutoff_measure, unnormalized_cutoff_measure." << std::endl;
    163             exit(1);
    164          }
    165          break;
     147         return new OnePass_Manual_Axes();
     148      case min_axes:
     149         return new MultiPass_Axes(100);
    166150      case manual_axes:
    167          _axesFinder = new AxesFinderFromUserInput();
    168          break;
    169 // These options have been commented out because they have not been fully tested
    170 //      case wta2_kt_axes: // option for alpha = 2 added
    171 //         _axesFinder = new AxesFinderFromWTA2_KT();
    172 //         break;
    173 //      case wta2_ca_axes: // option for alpha = 2 added
    174 //         _axesFinder = new AxesFinderFromWTA2_CA();
    175 //         break;
    176 //      case onepass_wta2_kt_axes: // option for alpha = 2 added
    177 //         setOnePassAxesFinder(measure_mode, new AxesFinderFromWTA2_KT(), beta, Rcutoff);
    178 //         break;
    179 //      case onepass_wta2_ca_axes: // option for alpha = 2 added
    180 //         setOnePassAxesFinder(measure_mode, new AxesFinderFromWTA2_CA(), beta, Rcutoff);
    181 //         break;
     151         return new Manual_Axes();
    182152      default:
    183153         assert(false);
    184          break;
    185       }   
    186 
     154         return NULL;
     155   }
     156}
     157
     158   
     159// Parsing needed for constructor to set AxesFinder and MeasureFunction
     160// All of the parameter handling is here, and checking that number of parameters is correct.
     161void Njettiness::setMeasureFunctionAndAxesFinder() {
     162   // Get the correct MeasureFunction and AxesFinders
     163   _measureFunction.reset(_measure_def->createMeasureFunction());
     164   _startingAxesFinder.reset(_axes_def->createStartingAxesFinder(*_measure_def));
     165   _finishingAxesFinder.reset(_axes_def->createFinishingAxesFinder(*_measure_def));
    187166}
    188167
    189168// setAxes for Manual mode
    190 void Njettiness::setAxes(std::vector<fastjet::PseudoJet> myAxes) {
    191    if (_current_axes_mode == manual_axes || _current_axes_mode == onepass_manual_axes) {
     169void Njettiness::setAxes(const std::vector<fastjet::PseudoJet> & myAxes) {
     170   if (_axes_def->supportsManualAxes()) {
    192171      _currentAxes = myAxes;
    193    }
    194    else {
    195       std::cerr << "You can only use setAxes if using manual_axes or onepass_manual_axes measure mode" << std::endl;
    196       exit(1);
     172   } else {
     173      throw Error("You can only use setAxes for manual AxesDefinitions");
    197174   }
    198175}
     
    200177// Calculates and returns all TauComponents that user would want.
    201178// This information is stored in _current_tau_components for later access as well.
    202 TauComponents Njettiness::getTauComponents(unsigned n_jets, const std::vector<fastjet::PseudoJet> & inputJets) {
     179TauComponents Njettiness::getTauComponents(unsigned n_jets, const std::vector<fastjet::PseudoJet> & inputJets) const {
    203180   if (inputJets.size() <= n_jets) {  //if not enough particles, return zero
    204181      _currentAxes = inputJets;
     
    206183      _current_tau_components = TauComponents();
    207184      _seedAxes = _currentAxes;
     185      _currentJets = _currentAxes;
     186      _currentBeam = PseudoJet(0.0,0.0,0.0,0.0);
    208187   } else {
    209       _currentAxes = _axesFinder->getAxes(n_jets,inputJets,_currentAxes); // sets current Axes
    210       _seedAxes = _axesFinder->seedAxes(); // sets seed Axes (if one pass minimization was used)
    211       _current_tau_components = _measureFunction->result(inputJets, _currentAxes);  // sets current Tau Values
     188
     189      _seedAxes = _startingAxesFinder->getAxes(n_jets,inputJets,_currentAxes); //sets starting point for minimization
     190      if (_finishingAxesFinder) {
     191         _currentAxes = _finishingAxesFinder->getAxes(n_jets,inputJets,_seedAxes);
     192      } else {
     193         _currentAxes = _seedAxes;
     194      }
     195     
     196      // Find partition and store information
     197      // (jet information in _currentJets, beam in _currentBeam)
     198      _currentJets = _measureFunction->get_partition(inputJets,_currentAxes,&_currentBeam);
     199     
     200      // Find tau value and store information
     201      _current_tau_components = _measureFunction->result_from_partition(_currentJets, _currentAxes,&_currentBeam);  // sets current Tau Values
    212202   }
    213203   return _current_tau_components;
     
    219209// Each vector element is a list of ints corresponding to the indices in
    220210// particles of the particles belonging to that jet.
    221 // TODO:  Consider moving to MeasureFunction
    222 std::vector<std::list<int> > Njettiness::getPartition(const std::vector<fastjet::PseudoJet> & particles) {
    223    std::vector<std::list<int> > partitions(_currentAxes.size());
    224 
    225    for (unsigned i = 0; i < particles.size(); i++) {
    226      
    227       int j_min = -1;
    228       // find minimum distance
    229       double minR = std::numeric_limits<double>::max();  //large number
    230       for (unsigned j = 0; j < _currentAxes.size(); j++) {
    231          double tempR = _measureFunction->jet_distance_squared(particles[i],_currentAxes[j]); // delta R distance
    232          if (tempR < minR) {
    233             minR = tempR;
    234             j_min = j;
    235          }
    236       }
    237       if (_measureFunction->do_cluster(particles[i],_currentAxes[j_min])) partitions[j_min].push_back(i);
    238    }
    239    return partitions;
    240 }
    241 
    242 // Having found axes, assign each particle in particles to an axis, and return a set of jets.
    243 // Each jet is the sum of particles closest to an axis (Njet = Naxes).
    244 // TODO:  Consider moving to MeasureFunction
    245 std::vector<fastjet::PseudoJet> Njettiness::getJets(const std::vector<fastjet::PseudoJet> & particles) {
    246    
    247    std::vector<fastjet::PseudoJet> jets(_currentAxes.size());
    248 
    249    std::vector<std::list<int> > partition = getPartition(particles);
    250    for (unsigned j = 0; j < partition.size(); ++j) {
    251       std::list<int>::const_iterator it, itE;
    252       for (it = partition[j].begin(), itE = partition[j].end(); it != itE; ++it) {
    253          jets[j] += particles[*it];
    254       }
    255    }
    256    return jets;
    257 }
    258 
     211std::vector<std::list<int> > Njettiness::getPartitionList(const std::vector<fastjet::PseudoJet> & particles) const {
     212   // core code is in MeasureFunction
     213   return _measureFunction->get_partition_list(particles,_currentAxes);
     214}
     215
     216   
    259217} // namespace contrib
    260218
  • external/fastjet/contribs/Nsubjettiness/Njettiness.hh

    r5b5a56b r35cdc46  
    55//  Jesse Thaler, Ken Van Tilburg, Christopher K. Vermilion, and TJ Wilkason
    66//
     7//  $Id: Njettiness.hh 670 2014-06-06 01:24:42Z jthaler $
    78//----------------------------------------------------------------------
    89// This file is part of FastJet contrib.
     
    2526#define __FASTJET_CONTRIB_NJETTINESS_HH__
    2627
     28
    2729#include "MeasureFunction.hh"
    2830#include "AxesFinder.hh"
     31#include "NjettinessDefinition.hh"
    2932
    3033#include "fastjet/PseudoJet.hh"
     34#include "fastjet/SharedPtr.hh"
     35#include <fastjet/LimitedWarning.hh>
     36
    3137#include <cmath>
    3238#include <vector>
    3339#include <list>
    3440
    35 
    3641FASTJET_BEGIN_NAMESPACE      // defined in fastjet/internal/base.hh
    3742
    3843namespace contrib {
    39 
     44   
    4045///////
    4146//
     
    5459   
    5560   // The various axes choices available to the user
     61   // It is recommended to use AxesDefinition instead of these.
    5662   enum AxesMode {
    5763      kt_axes,             // exclusive kt axes
     
    7884   // "normalized_cutoff_measure" was the default in v1.0 of Nsubjettiness
    7985   // "unnormalized_measure" is now the recommended default usage
     86   // But it is recommended to use MeasureDefinition instead of these.
    8087   enum MeasureMode {
    8188      normalized_measure,           //default normalized measure
     
    8794   };
    8895
    89 private:
    90    // The chosen axes/measure modes
    91    AxesFinder* _axesFinder;  // The chosen axes
    92    MeasureFunction* _measureFunction; // The chosen measure
     96   // Main constructor that uses AxesMode and MeasureDefinition to specify measure
     97   // Unlike Nsubjettiness or NjettinessPlugin, the value N is not chosen
     98   Njettiness(const AxesDefinition & axes_def, const MeasureDefinition & measure_def);
    9399
    94    // Enum information so functions can specify output based on specific options, primarily for setAxes
    95    AxesMode _current_axes_mode;
    96    MeasureMode _current_measure_mode;
    97    
    98    // Information about the current information
    99    TauComponents _current_tau_components; //automatically set to have components of 0; these values will be set by the getTau function call
    100    std::vector<fastjet::PseudoJet> _currentAxes;
    101    std::vector<fastjet::PseudoJet> _seedAxes; // axes used prior to minimization (if applicable)
    102    
    103    // Needed for compilation of non C++11 users
    104    bool isnan(double para) { return para != para; }
     100   // Intermediate constructor (needed to enable v1.0.3 backwards compatibility?)
     101   Njettiness(AxesMode axes_mode, const MeasureDefinition & measure_def);
    105102
    106    // Helpful function to check to make sure input has correct number of parameters
    107    bool correctParameterCount(int n, double para1, double para2, double para3, double para4){
    108       int numpara;
    109       if (!isnan(para1) && !isnan(para2) && !isnan(para3) && !isnan(para4)) numpara = 4;
    110       else if (!isnan(para1) && !isnan(para2) && !isnan(para3) && isnan(para4)) numpara = 3;
    111       else if (!isnan(para1) && !isnan(para2) && isnan(para3) && isnan(para4)) numpara = 2;
    112       else if (!isnan(para1) && isnan(para2) && isnan(para3) && isnan(para4)) numpara = 1;
    113       else numpara = 0;
    114       return n == numpara;
     103   // Alternative constructor which takes axes/measure information as enums with measure parameters
     104   // This version is not recommended
     105   Njettiness(AxesMode axes_mode,
     106              MeasureMode measure_mode,
     107              int num_para,
     108              double para1 = std::numeric_limits<double>::quiet_NaN(),
     109              double para2 = std::numeric_limits<double>::quiet_NaN(),
     110              double para3 = std::numeric_limits<double>::quiet_NaN())
     111   : _axes_def(createAxesDef(axes_mode)), _measure_def(createMeasureDef(measure_mode, num_para, para1, para2, para3)) {
     112      setMeasureFunctionAndAxesFinder();  // call helper function to do the hard work
    115113   }
    116114
    117    // Helper function to set onepass_axes depending on input measure_mode and startingFinder
    118    void setOnePassAxesFinder(MeasureMode measure_mode, AxesFinder* startingFinder, double para1, double Rcutoff);
    119  
    120    // created separate function to set MeasureFunction and AxesFinder in order to keep constructor cleaner.
    121    void setMeasureFunctionandAxesFinder(AxesMode axes_mode, MeasureMode measure_mode, double para1, double para2, double para3, double para4);
    122 
    123 public:
    124 
    125 
    126    // Main constructor which takes axes/measure information, and possible parameters.
    127    // Unlike Nsubjettiness or NjettinessPlugin, the value N is not chosen
    128    Njettiness(AxesMode axes_mode,
    129               MeasureMode measure_mode,
    130               double para1 = NAN,
    131               double para2 = NAN,
    132               double para3 = NAN,
    133               double para4 = NAN)
    134    : _current_axes_mode(axes_mode),
    135    _current_measure_mode(measure_mode) {
    136       setMeasureFunctionandAxesFinder(axes_mode, measure_mode, para1, para2, para3, para4);  // call helper function to do the hard work
    137    }
    138 
    139    ~Njettiness() {
    140       // clean house
    141       delete _measureFunction;
    142       delete _axesFinder;
    143    }
     115   // destructor
     116   ~Njettiness() {};
    144117   
    145118   // setAxes for Manual mode
    146    void setAxes(std::vector<fastjet::PseudoJet> myAxes);
     119   void setAxes(const std::vector<fastjet::PseudoJet> & myAxes);
    147120   
    148121   // Calculates and returns all TauComponents that user would want.
    149122   // This information is stored in _current_tau_components for later access as well.
    150    TauComponents getTauComponents(unsigned n_jets, const std::vector<fastjet::PseudoJet> & inputJets);
     123   TauComponents getTauComponents(unsigned n_jets, const std::vector<fastjet::PseudoJet> & inputJets) const;
    151124
    152125   // Calculates the value of N-subjettiness,
    153126   // but only returns the tau value from _current_tau_components
    154    double getTau(unsigned n_jets, const std::vector<fastjet::PseudoJet> & inputJets) {
     127   double getTau(unsigned n_jets, const std::vector<fastjet::PseudoJet> & inputJets) const {
    155128      return getTauComponents(n_jets, inputJets).tau();
    156129   }
    157    
    158    // returns enum information
    159    MeasureMode currentMeasureMode() { return _current_measure_mode;}
    160    AxesMode currentAxesMode() { return _current_axes_mode;}
    161130
    162131   // Return all relevant information about tau components
    163    TauComponents currentTauComponents() {return _current_tau_components;}
    164    
     132   TauComponents currentTauComponents() const {return _current_tau_components;}
    165133   // Return axes found by getTauComponents.
    166    std::vector<fastjet::PseudoJet> currentAxes() { return _currentAxes;}
     134   std::vector<fastjet::PseudoJet> currentAxes() const { return _currentAxes;}
    167135   // Return seedAxes used if onepass minimization (otherwise, same as currentAxes)
    168    std::vector<fastjet::PseudoJet> seedAxes() { return _seedAxes;}
     136   std::vector<fastjet::PseudoJet> seedAxes() const { return _seedAxes;}
     137   // Return jet partition found by getTauComponents.
     138   std::vector<fastjet::PseudoJet> currentJets() const {return _currentJets;}
     139   // Return beam partition found by getTauComponents.
     140   fastjet::PseudoJet currentBeam() const {return _currentBeam;}
    169141   
    170142   // partition inputs by Voronoi (each vector stores indices corresponding to inputJets)
    171    std::vector<std::list<int> > getPartition(const std::vector<fastjet::PseudoJet> & inputJets);
     143   std::vector<std::list<int> > getPartitionList(const std::vector<fastjet::PseudoJet> & inputJets) const;
    172144
    173    // partition inputs by Voronoi
    174    std::vector<fastjet::PseudoJet> getJets(const std::vector<fastjet::PseudoJet> & inputJets);
     145private:
     146   
     147   // Information about Axes and Measures to be Used
     148   // Implemented as SharedPtrs to avoid memory management headaches
     149   SharedPtr<const AxesDefinition> _axes_def;
     150   SharedPtr<const MeasureDefinition> _measure_def;
     151   
     152   // The chosen axes/measure mode workers
     153   // Implemented as SharedPtrs to avoid memory management headaches
     154   // TODO: make into a SharedPtr<const AxesFinder>?
     155   SharedPtr<MeasureFunction> _measureFunction;  // The chosen measure
     156   SharedPtr<AxesFinder> _startingAxesFinder;    // The initial axes finder
     157   SharedPtr<AxesFinder> _finishingAxesFinder;   // A possible minimization step
     158   
     159   // Information about the current information
     160   // Defined as mutables, so user should be aware that these change when getTau is called.
     161   mutable TauComponents _current_tau_components; //automatically set to have components of 0; these values will be set by the getTau function call
     162   mutable std::vector<fastjet::PseudoJet> _currentAxes; //axes found after minimization
     163   mutable std::vector<fastjet::PseudoJet> _seedAxes; // axes used prior to minimization (if applicable)
     164   mutable std::vector<fastjet::PseudoJet> _currentJets; //partitioning information
     165   mutable fastjet::PseudoJet _currentBeam; //return beam, if requested
     166   
     167   // created separate function to set MeasureFunction and AxesFinder in order to keep constructor cleaner.
     168   void setMeasureFunctionAndAxesFinder();
     169   
     170   // Convert old style enums into new style MeasureDefinition
     171   AxesDefinition* createAxesDef(AxesMode axes_mode) const;
     172   
     173   // Convert old style enums into new style MeasureDefinition
     174   MeasureDefinition* createMeasureDef(MeasureMode measure_mode, int num_para, double para1, double para2, double para3) const;
    175175
    176176};
    177 
     177   
    178178} // namespace contrib
    179179
  • external/fastjet/contribs/Nsubjettiness/NjettinessPlugin.cc

    r5b5a56b r35cdc46  
    55//  Jesse Thaler, Ken Van Tilburg, Christopher K. Vermilion, and TJ Wilkason
    66//
     7//  $Id: NjettinessPlugin.cc 663 2014-06-03 21:26:41Z jthaler $
    78//----------------------------------------------------------------------
    89// This file is part of FastJet contrib.
     
    2930
    3031
    31 // Constructor with same arguments as Nsubjettiness.
    32 NjettinessPlugin::NjettinessPlugin(int N, Njettiness::AxesMode axes_mode, Njettiness::MeasureMode measure_mode, double para1, double para2, double para3, double para4)
    33   : _N(N), _njettinessFinder(axes_mode, measure_mode, para1, para2, para3, para4) {}
    34 
    35 // Old constructor for compatibility
    36 NjettinessPlugin::NjettinessPlugin(int N, Njettiness::AxesMode mode, double beta, double R0, double Rcutoff)
    37    : _N(N), _njettinessFinder(mode, Njettiness::normalized_cutoff_measure, beta, R0, Rcutoff) {}
    3832
    3933std::string NjettinessPlugin::description() const {return "N-jettiness jet finder";}
    4034
     35
    4136// Clusters the particles according to the Njettiness jet algorithm
    42 // TODO: this code should be revisited to see if if can be made more clear.
     37// Apologies for the complication with this code, but we need to make
     38// a fake jet clustering tree.  The partitioning is done by getPartitionList
    4339void NjettinessPlugin::run_clustering(ClusterSequence& cs) const
    4440{
    4541   std::vector<fastjet::PseudoJet> particles = cs.jets();
     42
     43   // HACK: remove area information from particles (in case this is called by
     44   // a ClusterSequenceArea.  Will be fixed in a future FastJet release)
     45   for (unsigned i = 0; i < particles.size(); i++) {
     46      particles[i].set_structure_shared_ptr(SharedPtr<PseudoJetStructureBase>());
     47   }
     48   
     49   
    4650   _njettinessFinder.getTau(_N, particles);
    47    std::vector<std::list<int> > partition = _njettinessFinder.getPartition(particles);
     51
     52   std::vector<std::list<int> > partition = _njettinessFinder.getPartitionList(particles);
    4853
    4954   std::vector<fastjet::PseudoJet> jet_indices_for_extras;
    5055
    5156   // output clusterings for each jet
    52    for (size_t i = 0; i < partition.size(); ++i) {
     57   for (size_t i0 = 0; i0 < partition.size(); ++i0) {
     58      size_t i = partition.size() - 1 - i0; // reversed order of reading to match axes order
    5359      std::list<int>& indices = partition[i];
    5460      if (indices.size() == 0) continue;
     
    7076   }
    7177
     78   //HACK:  Re-reverse order of reading to match CS order
     79   reverse(jet_indices_for_extras.begin(),jet_indices_for_extras.end());
     80
    7281   NjettinessExtras * extras = new NjettinessExtras(_njettinessFinder.currentTauComponents(),jet_indices_for_extras,_njettinessFinder.currentAxes());
    7382   cs.plugin_associate_extras(std::auto_ptr<ClusterSequence::Extras>(extras));
  • external/fastjet/contribs/Nsubjettiness/NjettinessPlugin.hh

    r5b5a56b r35cdc46  
    1 // $Id$
    2 //
    31//  Nsubjettiness Package
    42//  Questions/Comments?  jthaler@jthaler.net
     
    75//  Jesse Thaler, Ken Van Tilburg, Christopher K. Vermilion, and TJ Wilkason
    86//
     7//  $Id: NjettinessPlugin.hh 671 2014-06-10 17:47:52Z jthaler $
    98//----------------------------------------------------------------------
    109// This file is part of FastJet contrib.
     
    4948// to similar information
    5049class NjettinessExtras : public ClusterSequence::Extras {
    51    private:
    52    
    53       TauComponents _tau_components;
    54       std::vector<fastjet::PseudoJet> _jets;
    55       std::vector<fastjet::PseudoJet> _axes;
    56      
    57       int labelOf(const fastjet::PseudoJet& jet) const {
    58          int thisJet = -1;
    59          for (unsigned int i = 0; i < _jets.size(); i++) {
    60             if (_jets[i].cluster_hist_index() == jet.cluster_hist_index()) {
    61                thisJet = i;
    62                break;
    63             }
    64          }
    65          return thisJet;
    66       }
    67      
     50   
    6851   public:
    6952      NjettinessExtras(TauComponents tau_components, std::vector<fastjet::PseudoJet> jets, std::vector<fastjet::PseudoJet> axes) : _tau_components(tau_components), _jets(jets), _axes(axes) {}
     
    7457      std::vector<fastjet::PseudoJet> axes() const {return _axes;}
    7558     
    76       double totalTau(const fastjet::PseudoJet& jet) const {
     59      double totalTau(const fastjet::PseudoJet& /*jet*/) const {
    7760         return _tau_components.tau();
    7861      }
     62     
    7963      double subTau(const fastjet::PseudoJet& jet) const {
    80          if (labelOf(jet) == -1) return NAN;
     64         if (labelOf(jet) == -1) return std::numeric_limits<double>::quiet_NaN(); // nonsense
    8165         return _tau_components.jet_pieces()[labelOf(jet)];
    8266      }
     
    9377         return (labelOf(jet) >= 0);
    9478      }
    95 
     79   
     80private:
     81   
     82   TauComponents _tau_components;
     83   std::vector<fastjet::PseudoJet> _jets;
     84   std::vector<fastjet::PseudoJet> _axes;
     85   
     86   int labelOf(const fastjet::PseudoJet& jet) const {
     87      int thisJet = -1;
     88      for (unsigned int i = 0; i < _jets.size(); i++) {
     89         if (_jets[i].cluster_hist_index() == jet.cluster_hist_index()) {
     90            thisJet = i;
     91            break;
     92         }
     93      }
     94      return thisJet;
     95   }
    9696};
    9797
     
    122122 * onepass_wta_kt_axes  : one-pass minimization seeded by wta_kt
    123123 *
    124  * For the unnormalized_measure, N-jettiness is defined as:
     124 * For the UnnormalizedMeasure(beta), N-jettiness is defined as:
    125125 *
    126126 * tau_N = Sum_{all particles i} p_T^i min((DR_i1)^beta, (DR_i2)^beta, ...)
     
    129129 *   and jet j.
    130130 *
    131  * The normalized_meausure include an extra parameter R0, and the various cutoff
     131 * The NormalizedMeausure include an extra parameter R0, and the various cutoff
    132132 * measures include an Rcutoff, which effectively defines an angular cutoff
    133133 * similar in effect to a cone-jet radius.
     
    138138public:
    139139
    140    NjettinessPlugin(int N,
    141                     Njettiness::AxesMode axes_mode,
    142                     Njettiness::MeasureMode measure_mode,
    143                     double para1 = NAN,
    144                     double para2 = NAN,
    145                     double para3 = NAN,
    146                     double para4 = NAN);
     140   // Constructor with same arguments as Nsubjettiness.
     141   NjettinessPlugin(int N,
     142                    const AxesDefinition & axes_def,
     143                    const MeasureDefinition & measure_def)
     144   : _njettinessFinder(axes_def, measure_def), _N(N) {}
     145   
     146   
     147   // Alternative constructors that define the measure via enums and parameters
     148   // These constructors are likely be removed
     149   NjettinessPlugin(int N,
     150                 Njettiness::AxesMode axes_mode,
     151                 Njettiness::MeasureMode measure_mode)
     152   : _njettinessFinder(axes_mode, measure_mode, 0), _N(N) {}
     153   
     154   
     155   NjettinessPlugin(int N,
     156                 Njettiness::AxesMode axes_mode,
     157                 Njettiness::MeasureMode measure_mode,
     158                 double para1)
     159   : _njettinessFinder(axes_mode, measure_mode, 1, para1), _N(N) {}
     160   
     161   
     162   NjettinessPlugin(int N,
     163                 Njettiness::AxesMode axes_mode,
     164                 Njettiness::MeasureMode measure_mode,
     165                 double para1,
     166                 double para2)
     167   : _njettinessFinder(axes_mode, measure_mode, 2, para1, para2), _N(N) {}
     168   
     169   
     170   NjettinessPlugin(int N,
     171                 Njettiness::AxesMode axes_mode,
     172                 Njettiness::MeasureMode measure_mode,
     173                 double para1,
     174                 double para2,
     175                 double para3)
     176   : _njettinessFinder(axes_mode, measure_mode, 3, para1, para2, para3), _N(N) {}
     177
    147178
    148179   // Old constructor for backwards compatibility with v1.0,
    149    // where normalized_cutoff_measure was the only option
     180   // where NormalizedCutoffMeasure was the only option
    150181   NjettinessPlugin(int N,
    151182                    Njettiness::AxesMode mode,
    152183                    double beta,
    153184                    double R0,
    154                     double Rcutoff=std::numeric_limits<double>::max());
     185                    double Rcutoff=std::numeric_limits<double>::max())
     186   : _njettinessFinder(mode, NormalizedCutoffMeasure(beta, R0, Rcutoff)), _N(N) {}
     187
    155188
    156189
     
    164197private:
    165198
     199   Njettiness _njettinessFinder;
    166200   int _N;
    167    mutable Njettiness _njettinessFinder; // TODO:  should muck with this so run_clustering can be const without this mutable
    168201
    169202};
  • external/fastjet/contribs/Nsubjettiness/Nsubjettiness.cc

    r5b5a56b r35cdc46  
    55//  Jesse Thaler, Ken Van Tilburg, Christopher K. Vermilion, and TJ Wilkason
    66//
     7//  $Id: Nsubjettiness.cc 597 2014-04-16 23:07:55Z jthaler $
    78//----------------------------------------------------------------------
    89// This file is part of FastJet contrib.
  • external/fastjet/contribs/Nsubjettiness/Nsubjettiness.hh

    r5b5a56b r35cdc46  
    55//  Jesse Thaler, Ken Van Tilburg, Christopher K. Vermilion, and TJ Wilkason
    66//
     7//  $Id: Nsubjettiness.hh 670 2014-06-06 01:24:42Z jthaler $
    78//----------------------------------------------------------------------
    89// This file is part of FastJet contrib.
     
    3233#include <string>
    3334#include <climits>
    34 
    3535
    3636FASTJET_BEGIN_NAMESPACE      // defined in fastjet/internal/base.hh
     
    4949public:
    5050
    51    // Main constructor, which takes N, axes/measure modes,
    52    // and up to four parameters for parameters (i.e. beta, Rcutoff, etc depending on measure)
     51   
     52   // Main constructor, which takes N, the AxesDefiniation, and the MeasureDefinition.
     53   // The Definitions are given in NjettinessDefinition.hh
     54   //
     55   // The recommended AxesDefinitions are (more are available as listed in the README
     56   // and defined in NjettinessDefinition.hh):
     57   //   KT_Axes             : exclusive kt axes
     58   //   WTA_KT_Axes         : exclusive kt with winner-take-all recombination
     59   //   OnePass_KT_Axes     : one-pass minimization from kt starting point
     60   //   OnePass_WTA_KT_Axes : one-pass min. from wta_kt starting point
     61   //
     62   // The recommended measure definitions are (with the corresponding parameters)
     63   //   NormalizedMeasure(beta,R0)
     64   //      :  This was the original N-subjettiness measure (dimensionless)
     65   //   UnnormalizedMeasure(beta)
     66   //      :  This is the new recommended default, same as above but without
     67   //      :  the normalization factor, and hence has units of GeV
     68   //   NormalizedCutoffMeasure(beta,R0,Rcutoff)
     69   //      :  Same as normalized_measure, but cuts off at Rcutoff
     70   //   UnnormalizedCutoffMeasure(beta,Rcutoff)
     71   //      :  Same as unnormalized_measure, but cuts off at Rcutoff
     72   Nsubjettiness(int N,
     73                 const AxesDefinition& axes_def,
     74                 const MeasureDefinition& measure_def)
     75   : _njettinessFinder(axes_def,measure_def), _N(N) {}
     76   
     77   
     78   // Alternative constructors that define the measure via enums and parameters
     79   // These constructors are likely be removed
     80   // Zero parameter arguments
     81   // (Currently, no measure uses this)
     82   Nsubjettiness(int N,
     83                 Njettiness::AxesMode axes_mode,
     84                 Njettiness::MeasureMode measure_mode)
     85   : _njettinessFinder(axes_mode, measure_mode, 0), _N(N) {}
     86
     87   // One parameter argument
     88   // (for unnormalized_measure, para1=beta)
    5389   Nsubjettiness(int N,
    5490                 Njettiness::AxesMode axes_mode,
    5591                 Njettiness::MeasureMode measure_mode,
    56                  double para1 = NAN,
    57                  double para2 = NAN,
    58                  double para3 = NAN,
    59                  double para4 = NAN)
    60    : _njettinessFinder(axes_mode, measure_mode, para1, para2, para3, para4), _N(N) {}
     92                 double para1)
     93   : _njettinessFinder(axes_mode, measure_mode, 1, para1), _N(N) {}
     94
     95   // Two parameter arguments
     96   // (for normalized_measure, para1=beta, para2=R0)
     97   // (for unnormalized_cutoff_measure, para1=beta, para2=Rcutoff)
     98   Nsubjettiness(int N,
     99                 Njettiness::AxesMode axes_mode,
     100                 Njettiness::MeasureMode measure_mode,
     101                 double para1,
     102                 double para2)
     103   : _njettinessFinder(axes_mode, measure_mode, 2, para1, para2), _N(N) {}
     104
     105   // Three parameter arguments
     106   // (for unnormalized_cutoff_measure, para1=beta, para2=R0, para3=Rcutoff)
     107   Nsubjettiness(int N,
     108                 Njettiness::AxesMode axes_mode,
     109                 Njettiness::MeasureMode measure_mode,
     110                 double para1,
     111                 double para2,
     112                 double para3)
     113   : _njettinessFinder(axes_mode, measure_mode, 3, para1, para2, para3), _N(N) {}
    61114
    62115   // Old constructor for backwards compatibility with v1.0,
     
    67120                 double R0,
    68121                 double Rcutoff=std::numeric_limits<double>::max())
    69    : _njettinessFinder(axes_mode, Njettiness::normalized_cutoff_measure, beta, R0, Rcutoff), _N(N) {}
    70 
    71 
     122   : _njettinessFinder(axes_mode, NormalizedCutoffMeasure(beta,R0,Rcutoff)), _N(N) {}
     123   
    72124   /// returns tau_N, measured on the constituents of this jet
    73125   double result(const PseudoJet& jet) const;
     
    86138   }
    87139   
     140   /// returns subjet regions found by result() calculation (these have valid constituents)
     141   /// Note that the axes and the subjets are not the same
     142   std::vector<fastjet::PseudoJet> currentSubjets() const {
     143      return _njettinessFinder.currentJets();
     144   }
     145
     146   /// returns components of tau_N without recalculating anything
     147   TauComponents currentTauComponents() const {
     148      return _njettinessFinder.currentTauComponents();
     149   }
     150   
    88151   // To set axes for manual use
    89    void setAxes(std::vector<fastjet::PseudoJet> myAxes) {
     152   void setAxes(const std::vector<fastjet::PseudoJet> & myAxes) {
    90153      // Cross check that manual axes are being used is in Njettiness
    91154        _njettinessFinder.setAxes(myAxes);
     
    95158private:
    96159   
    97    mutable Njettiness _njettinessFinder; // TODO:  should muck with this so result can be const without this mutable
     160   Njettiness _njettinessFinder; // TODO:  should muck with this so result can be const without this mutable
    98161   int _N;
    99162
     
    112175   NsubjettinessRatio(int N,
    113176                      int M,
     177                      const AxesDefinition & axes_def,
     178                      const MeasureDefinition & measure_def)
     179   : _nsub_numerator(N,axes_def,measure_def),
     180   _nsub_denominator(M,axes_def,measure_def) {}
     181   
     182   // Alternative constructor with enums and parameters
     183   // Again, likely to be removed
     184   NsubjettinessRatio(int N,
     185                      int M,
     186                      Njettiness::AxesMode axes_mode,
     187                      Njettiness::MeasureMode measure_mode)
     188   : _nsub_numerator(N, axes_mode, measure_mode),
     189   _nsub_denominator(M, axes_mode, measure_mode) {}
     190
     191   
     192   NsubjettinessRatio(int N,
     193                      int M,
    114194                      Njettiness::AxesMode axes_mode,
    115195                      Njettiness::MeasureMode measure_mode,
    116                       double para1 = NAN,
    117                       double para2 = NAN,
    118                       double para3 = NAN,
    119                       double para4 = NAN)
    120    : _nsub_numerator(N, axes_mode, measure_mode, para1, para2, para3, para4),
    121    _nsub_denominator(M, axes_mode, measure_mode, para1, para2, para3, para4) {}
    122 
    123    //returns tau_N/tau_M based off the input jet using result function from Nsubjettiness
     196                      double para1)
     197   : _nsub_numerator(N, axes_mode, measure_mode, para1),
     198   _nsub_denominator(M, axes_mode, measure_mode, para1) {}
     199
     200   NsubjettinessRatio(int N,
     201                      int M,
     202                      Njettiness::AxesMode axes_mode,
     203                      Njettiness::MeasureMode measure_mode,
     204                      double para1,
     205                      double para2)
     206   : _nsub_numerator(N, axes_mode, measure_mode, para1, para2),
     207   _nsub_denominator(M, axes_mode, measure_mode, para1, para2) {}
     208   
     209   NsubjettinessRatio(int N,
     210                      int M,
     211                      Njettiness::AxesMode axes_mode,
     212                      Njettiness::MeasureMode measure_mode,
     213                      double para1,
     214                      double para2,
     215                      double para3)
     216   : _nsub_numerator(N, axes_mode, measure_mode, para1, para2, para3),
     217   _nsub_denominator(M, axes_mode, measure_mode, para1, para2, para3) {}
     218
     219   //returns tau_N/tau_M based off the input jet using result function from Nsubjettiness
    124220   double result(const PseudoJet& jet) const;
    125221
  • external/fastjet/contribs/Nsubjettiness/VERSION

    r5b5a56b r35cdc46  
    1 1.1.0-beta4
     12.1.0
  • external/fastjet/contribs/Nsubjettiness/WinnerTakeAllRecombiner.cc

    r5b5a56b r35cdc46  
    55//  Jesse Thaler, Ken Van Tilburg, Christopher K. Vermilion, and TJ Wilkason
    66//
     7//  $Id: WinnerTakeAllRecombiner.cc 597 2014-04-16 23:07:55Z jthaler $
    78//----------------------------------------------------------------------
    89// This file is part of FastJet contrib.
     
    3334
    3435// recombine pa and pb by creating pab with energy of the sum of particle energies in the direction of the harder particle
    35 
    3636// updated recombiner to use more general form of a metric equal to E*(pT/E)^(alpha), which reduces to pT*cosh(rap)^(1-alpha)
    3737// alpha is specified by the user. The default is alpha = 1, which is the typical behavior. alpha = 2 provides a metric which more
  • external/fastjet/contribs/Nsubjettiness/WinnerTakeAllRecombiner.hh

    r5b5a56b r35cdc46  
    55//  Jesse Thaler, Ken Van Tilburg, Christopher K. Vermilion, and TJ Wilkason
    66//
     7//  $Id: WinnerTakeAllRecombiner.hh 670 2014-06-06 01:24:42Z jthaler $
    78//----------------------------------------------------------------------
    89// This file is part of FastJet contrib.
     
    5051   
    5152   /// recombine pa and pb and put result into pab
    52    virtual void recombine(const fastjet::PseudoJet & pa, const fastjet::PseudoJet & pb, fastjet::PseudoJet & pab) const;
     53   virtual void recombine(const fastjet::PseudoJet & pa,
     54                          const fastjet::PseudoJet & pb,
     55                          fastjet::PseudoJet & pab) const;
    5356
    5457private:
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