VariableRPlugin.cc
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VariableRPlugin.cc
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	// VariableR Package
	// Questions/Comments? jthaler@jthaler.net
	//
	// Copyright (c) 2009-2013
	// David Krohn, Jesse Thaler, and Lian-Tao Wang
	//
	// $Id: VariableR.cc 596 2014-04-16 22:15:15Z jthaler $
	//----------------------------------------------------------------------
	// This file is part of FastJet contrib.
	//
	// It is free software; you can redistribute it and/or modify it under
	// the terms of the GNU General Public License as published by the
	// Free Software Foundation; either version 2 of the License, or (at
	// your option) any later version.
	//
	// It is distributed in the hope that it will be useful, but WITHOUT
	// ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
	// or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public
	// License for more details.
	//
	// You should have received a copy of the GNU General Public License
	// along with this code. If not, see <http://www.gnu.org/licenses/>.
	//----------------------------------------------------------------------

	#include "VariableRPlugin.hh"

	#include <cstdio>
	#include "math.h"
	#include <iomanip>
	#include <cmath>
	#include <map>
	#include <sstream>
	#include <queue>

	#include <fastjet/NNH.hh>
	#if FASTJET_VERSION_NUMBER >= 30200
	#include <fastjet/NNFJN2Plain.hh>
	#include <fastjet/NNFJN2Tiled.hh>
	#endif

	FASTJET_BEGIN_NAMESPACE // defined in fastjet/internal/base.hh

	namespace contrib {

	//----------------------------------------------------------------------
	// classes to help run a Variable R algorithm using NN-type classes

	// class carrying particle-independent information
	class VariableRNNInfo {
	public:
	VariableRNNInfo(double rho2_in, double min_r2_in, double max_r2_in,
	VariableRPlugin::ClusterType clust_type_in)
	: _rho2(rho2_in), _min_r2(min_r2_in), _max_r2(max_r2_in),
	_clust_type(clust_type_in) {}

	double rho2() const {return _rho2; }
	double min_r2() const {return _min_r2;}
	double max_r2() const {return _max_r2;}
	double momentum_scale_of_pt2(double pt2) const {
	if (_clust_type == VariableRPlugin::AKTLIKE){ return 1.0/pt2;}
	else if (_clust_type == VariableRPlugin::KTLIKE){ return pt2;}
	else { return 1.0;} // VariableRPlugin::CALIKE
	}

	private:
	double _rho2; ///< constant that controls the overall R magnitude
	double _min_r2; ///< minimal allowed radius
	double _max_r2; ///< maximal allowed radius
	VariableRPlugin::ClusterType _clust_type;
	};

	// class carrying the minimal info required for the cluysterinb
	class VariableRBriefJet {
	public:
	void init(const PseudoJet & jet, VariableRNNInfo *info) {
	_rap = jet.rap();
	_phi = jet.phi();

	_mom_factor2 = jet.pt2(); // temporary

	// get the appropriate "radius"
	_beam_R2 = info->rho2()/_mom_factor2;
	if (_beam_R2 > info->max_r2()){ _beam_R2 = info->max_r2();}
	else if (_beam_R2 < info->min_r2()){ _beam_R2 = info->min_r2();}

	// get the appropriate momentum scale
	_mom_factor2 = info->momentum_scale_of_pt2(_mom_factor2);
	}

	double geometrical_distance(const VariableRBriefJet * jet) const {
	double dphi = std::abs(_phi - jet->_phi);
	double deta = (_rap - jet->_rap);
	if (dphi > pi) {dphi = twopi - dphi;}
	return dphidphi + detadeta;
	}

	double geometrical_beam_distance() const { return _beam_R2; }

	double momentum_factor() const{ return _mom_factor2; }

	/// make this BJ class compatible with the use of NNH
	double distance(const VariableRBriefJet * other_bj_jet){
	double mom1 = momentum_factor();
	double mom2 = other_bj_jet->momentum_factor();
	return (mom1<mom2 ? mom1 : mom2) * geometrical_distance(other_bj_jet);
	}
	double beam_distance(){
	return momentum_factor() * geometrical_beam_distance();
	}

	// the following are required by N2Tiled
	inline double rap() const{ return _rap;}
	inline double phi() const{ return _phi;}

	private:
	double _rap, _phi, _mom_factor2, _beam_R2;
	};

	//----------------------------------------------------------------------
	// now the implementation of VariableR itself
	//----------------------------------------------------------------------

	// Constructor that sets VR algorithm parameters
	// - rho mass scale for effective radius (i.e. R ~ rho/pT)
	// - min_r minimum jet radius
	// - max_r maximum jet radius
	// - clust_type whether to use CA-like, kT-like, or anti-kT-like distance measure
	// - strategy one of Best (the default), N2Tiled , N2Plain or NNH
	VariableRPlugin::VariableRPlugin(double rho, double min_r, double max_r,
	ClusterType clust_type, bool precluster,
	Strategy requested_strategy)
	:_rho2(rhorho), _min_r2(min_rmin_r), _max_r(max_r), _max_r2(max_r*max_r),
	_clust_type(clust_type), _requested_strategy(requested_strategy),
	_precluster(precluster), _pre_jet_def(kt_algorithm, min_r){
	// Added errors for user input.
	if (min_r < 0.0) throw Error("VariableRPlugin: Minimum radius must be positive.");
	if (precluster && min_r==0.0) throw Error("VariableRPlugin: To apply preclustering, minimum radius must be non-zero.");
	if (max_r < 0.0) throw Error("VariableRPlugin: Maximum radius must be positive.");
	if (min_r > max_r) throw Error("VariableRPlugin: Minimum radius must be bigger than or equal to maximum radius.");

	// decide the strategy

	#if FASTJET_VERSION_NUMBER < 30200
	// this is only supported for the Best and Native strategies
	if ((requested_strategy!=Best) && (requested_strategy!=Native) && (requested_strategy!=NNH))
	throw Error("VariableRPlugin: with FastJet<3.2.0, Native and Best are the only supported strategies");
	#endif

	_strategy = requested_strategy;
	if (requested_strategy==Best){
	_strategy = _best_strategy();
	}

	// with pre-clustering we're forced into the native clustering
	// Q: do we issue a warning? throw?
	if (precluster){
	// this is only supported for the Best and Native strategies
	if ((requested_strategy!=Best) && (requested_strategy!=Native))
	throw Error("VariableRPlugin: pre-clustering is only supported for the Native and Best strategies");
	_strategy = Native;
	}
	}

	//----------------------------------------------------------------------
	// Description of algorithm, including parameters
	string VariableRPlugin::description() const{
	stringstream myStream("");

	myStream << "Variable R (0903.0392), ";

	switch (_clust_type) {
	case AKTLIKE:
	myStream << "AKT";
	break;
	case CALIKE:
	myStream << "CA";
	break;
	case KTLIKE:
	myStream << "KT";
	break;
	}

	myStream << fixed << setprecision(1) << ", rho=" << sqrt(_rho2);
	myStream << ", min_r=" << sqrt(_min_r2);
	myStream << ", max_r=" << sqrt(_max_r2);
	myStream << (_precluster ? ", with precluster" : "");
	switch (_strategy){
	case Best: myStream << ", strategy=Best"; break;
	case N2Tiled: myStream << ", strategy=N2Tiled"; break;
	case N2Plain: myStream << ", strategy=N2Plain"; break;
	case NNH: myStream << ", strategy=NNH"; break;
	case Native: myStream << ", strategy=Native"; break;
	};
	switch (_requested_strategy){
	case Best: myStream << " (requested: Best)"; break;
	case N2Tiled: myStream << " (requested: N2Tiled)"; break;
	case N2Plain: myStream << " (requested: N2Plain)"; break;
	case NNH: myStream << " (requested: NNH)"; break;
	case Native: myStream << " (requested: Native)"; break;
	};

	return myStream.str();
	}

	//----------------------------------------------------------------------
	// do the clustering
	void VariableRPlugin::run_clustering(ClusterSequence & cs) const {
	if (_strategy==Native){
	_native_clustering(cs);
	return;
	}

	VariableRNNInfo info(_rho2, _min_r2, _max_r2, _clust_type);

	#if FASTJET_VERSION_NUMBER >= 30200
	if (_strategy==N2Tiled){
	NNFJN2Tiled<VariableRBriefJet,VariableRNNInfo> nnt(cs.jets(), _max_r, &info);
	_NN_clustering(cs, nnt);
	} else if (_strategy==N2Plain){
	NNFJN2Plain<VariableRBriefJet,VariableRNNInfo> nnp(cs.jets(), &info);
	_NN_clustering(cs, nnp);
	} else { // NNH
	#endif
	fastjet::NNH<VariableRBriefJet,VariableRNNInfo> nnh(cs.jets(), &info);
	_NN_clustering(cs, nnh);
	#if FASTJET_VERSION_NUMBER >= 30200
	}
	#endif
	}

	//---------------------------------------------------------------------
	// decide the optimal strategy
	VariableRPlugin::Strategy VariableRPlugin::_best_strategy() const{
	#if FASTJET_VERSION_NUMBER >= 30200
	return N2Tiled;
	#else
	return Native;
	#endif
	}

	//---------------------------------------------------------------------
	// the "new" NN-style clustering
	template<typename NN>
	void VariableRPlugin::_NN_clustering(ClusterSequence &cs, NN &nn) const{
	int njets = cs.jets().size();
	while (njets > 0) {
	int i, j, k;
	double dij = nn.dij_min(i, j);

	if (j >= 0) {
	cs.plugin_record_ij_recombination(i, j, dij, k);
	nn.merge_jets(i, j, cs.jets()[k], k);
	} else {
	cs.plugin_record_iB_recombination(i, dij);
	nn.remove_jet(i);
	}
	njets--;
	}
	}



	//----------------------------------------------------------------------
	// the code below is for the "native" clustering

	// precluster into kT subjets if desired.
	void VariableRPlugin::_preclustering(ClusterSequence & cs, set<int>& unmerged_jets) const {
	int cntr(0);
	for(vector<PseudoJet>::const_iterator it = cs.jets().begin(); it != cs.jets().end(); it++)
	unmerged_jets.insert(unmerged_jets.end(), cntr++);

	// Make preclusters
	ClusterSequence pre_cs(cs.jets(), _pre_jet_def);
	vector<PseudoJet> preclustered_jets = pre_cs.inclusive_jets();
	vector<int> particle_jet_indices = pre_cs.particle_jet_indices(preclustered_jets);

	// This code take preclustered objects and puts them into the ClusterSequence tree of VR
	// TODO: Figure out if there is a better way to do this step.
	for(int i = 0 ; i < (int)preclustered_jets.size(); i++){
	queue<int> constit_indices;
	for(int j = 0 ; j < (int)particle_jet_indices.size(); j++)
	if(particle_jet_indices[j] == i)
	constit_indices.push(j);

	int final_jet;
	while(constit_indices.size() > 1){
	int indx1 = constit_indices.front();
	unmerged_jets.erase(indx1);
	constit_indices.pop();
	int indx2 = constit_indices.front();
	unmerged_jets.erase(indx2);
	constit_indices.pop();
	cs.plugin_record_ij_recombination(indx1, indx2, 0., final_jet);
	constit_indices.push(final_jet);
	unmerged_jets.insert(unmerged_jets.end(), final_jet);
	}
	}
	}

	// Implements VR alorithm (virtual function from JetDefinition::Plugin)
	void VariableRPlugin::_native_clustering(ClusterSequence & cs) const {
	set<int> unmerged_jets;

	if(_precluster){ // do kT preclustering
	assert(_min_r2 > 0.); // since this is (min_r)^2, this should never happen
	_preclustering(cs, unmerged_jets);
	} else // make a list of the unmerged jets
	for(int i = 0 ; i < (int)cs.jets().size(); i++)
	unmerged_jets.insert(unmerged_jets.end(), i);

	// priority_queue is part of the C++ standard library.
	// The objects being sorted are JetDistancePairs
	// The comparison function is just asking who has the smallest distance
	// The distances are set initially by _setup_distance_measures and then updated by the while loop below.
	vector<JetDistancePair> jet_vec;
	_setup_distance_measures(cs, jet_vec, unmerged_jets);
	priority_queue< JetDistancePair, vector<JetDistancePair>, CompareJetDistancePair > jet_queue(jet_vec.begin(),jet_vec.end());

	// go through the jet_queue until empty
	while(!jet_queue.empty()){

	// find the closest pair
	JetDistancePair jdpair = jet_queue.top();
	jet_queue.pop();

	// Rebuild the jet_queue
	// DK - the 1.5 below was just found empirically
	// JDT - this code is safe, but I'm not 100% sure why it is needed.
	// It rebuilds the jet_queue instead of letting the below functions do the hard work.
	if(jet_queue.size() > 50 && jet_queue.size() > 1.5unmerged_jets.size()unmerged_jets.size()){
	jet_vec.clear();
	_setup_distance_measures(cs, jet_vec, unmerged_jets);
	priority_queue< JetDistancePair, vector<JetDistancePair>, CompareJetDistancePair > tmp_jet_queue(jet_vec.begin(),jet_vec.end());
	swap(jet_queue,tmp_jet_queue);
	}

	// make sure not merged
	if((unmerged_jets.find(jdpair.j1) == unmerged_jets.end()) \|\| (jdpair.j2 != -1 && unmerged_jets.find(jdpair.j2) == unmerged_jets.end()))
	continue;


	if(jdpair.j2 == -1) // If closest distance is to beam, then merge with beam
	_merge_jet_with_beam(cs, jdpair, unmerged_jets);
	else // Otherwise, merge jets back together
	_merge_jets(cs, jdpair, jet_queue, unmerged_jets);
	}
	}


	// Add final jet to clust_seq. No need to update jet_queue, since this jet has already been deleted.
	void VariableRPlugin::_merge_jet_with_beam(ClusterSequence & clust_seq, JetDistancePair & jdp, set<int>& unmerged_jets) const{
	clust_seq.plugin_record_iB_recombination(jdp.j1, jdp.distance);
	unmerged_jets.erase(jdp.j1);
	}


	// Add jet merging to clust_seq. Here, we need to update the priority_queue since some of the measures have changes.
	void VariableRPlugin::_merge_jets(ClusterSequence & clust_seq,
	JetDistancePair & jdp,
	priority_queue< JetDistancePair, vector<JetDistancePair>, CompareJetDistancePair > &jet_queue,
	set<int>& unmerged_jets) const{


	int new_jet_num;
	clust_seq.plugin_record_ij_recombination(jdp.j1,jdp.j2,jdp.distance,new_jet_num);

	unmerged_jets.erase(jdp.j1);
	unmerged_jets.erase(jdp.j2);

	// take the resulting jet and recompute all distances with it
	for(set<int>::iterator it = unmerged_jets.begin(); it != unmerged_jets.end(); it++){
	JetDistancePair jpair;
	jpair.j1 = new_jet_num;
	jpair.j2 = (*it);
	jpair.distance = _get_JJ_distance_measure(clust_seq.jets()[*it],clust_seq.jets()[new_jet_num]);
	jet_queue.push(jpair);
	}
	unmerged_jets.insert(unmerged_jets.end(), new_jet_num);

	// also add the new distance to beam
	JetDistancePair jpair;
	jpair.j1 = new_jet_num;
	jpair.j2 = -1; // -1 is for the beam
	jpair.distance = _get_JB_distance_measure(clust_seq.jets()[new_jet_num]);
	jet_queue.push(jpair);
	}

	// Initial distance setup
	void VariableRPlugin::_setup_distance_measures(ClusterSequence & clust_seq,
	vector<JetDistancePair> &jet_vec,
	set<int> & unmerged_jets) const{
	JetDistancePair jpair;

	// Add the jet-jet distances
	for(set<int>::iterator it1 = unmerged_jets.begin(); it1 != unmerged_jets.end(); it1++){
	for(set<int>::iterator it2 = it1; it2 != unmerged_jets.end(); it2++){
	if((it1) != (it2)){
	jpair.j1 = (*it1);
	jpair.j2 = (*it2);
	jpair.distance = _get_JJ_distance_measure(clust_seq.jets()[it1],clust_seq.jets()[it2]);
	jet_vec.push_back(jpair);
	}
	}
	// Add the jet-beam distances, and set initial merge info
	jpair.j1 = (*it1);
	jpair.j2 = -1; // -1 is for the beam
	jpair.distance = _get_JB_distance_measure(clust_seq.jets()[*it1]);
	jet_vec.push_back(jpair);
	}
	}

	// get the dij between two jets
	// Different measures for AKTLIKE, CALIKE, and KTLIKE
	// TODO: add arbitrary p weighting
	double VariableRPlugin::_get_JJ_distance_measure(const PseudoJet& j1, const PseudoJet& j2) const {
	double ret;
	switch(_clust_type){
	case AKTLIKE:
	ret = min(1./j1.perp2(), 1./j2.perp2());
	break;
	case CALIKE :
	ret = 1.;
	break;
	case KTLIKE:
	ret = min(j1.perp2(), j2.perp2());
	break;
	default:
	assert(false);
	}

	ret *= j1.squared_distance(j2);
	return ret;
	}

	// jet diB between jet and beam
	// Different measures for AKTLIKE, CALIKE, and KTLIKE
	// TODO: add arbitrary p weighting
	double VariableRPlugin::_get_JB_distance_measure(const PseudoJet& jet) const{
	switch(_clust_type){
	case AKTLIKE:
	{
	if((_rho2 / jet.perp2()) < _min_r2)
	return _min_r2/jet.perp2();
	if((_rho2 / jet.perp2()) > _max_r2)
	return _max_r2/jet.perp2();
	return _rho2/jet.perp2()/jet.perp2();
	}
	case CALIKE :
	{
	if((_rho2 / jet.perp2()) < _min_r2)
	return _min_r2;
	if((_rho2 / jet.perp2()) > _max_r2)
	return _max_r2;
	return _rho2 /jet.perp2();
	}
	break;
	case KTLIKE:
	{
	if((_rho2 / jet.perp2()) < _min_r2)
	return _min_r2*jet.perp2();
	if((_rho2 / jet.perp2()) > _max_r2)
	return _max_r2*jet.perp2();
	return _rho2;
	}
	default:
	assert(false);
	}
	}



	} // namespace contrib

	FASTJET_END_NAMESPACE

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