{"action":"created","comment":{"url":"https://api.github.com/repos/PFCal-dev/cmssw/pulls/comments/105369410","pull_request_review_id":26221562,"id":105369410,"diff_hunk":"@@ -1,52 +1,100 @@\n #include \"L1Trigger/L1THGCal/interface/be_algorithms/HGCalClusteringImpl.h\"\n-\n+#include \"DataFormats/Common/interface/PtrVector.h\"\n+#include \"DataFormats/Common/interface/OrphanHandle.h\"\n+#include <typeinfo>\n \n //class constructor\n-HGCalClusteringImpl::HGCalClusteringImpl(const edm::ParameterSet& conf){    \n-    seed_CUT_ = conf.getParameter<double>(\"seeding_threshold\");\n-    tc_CUT_ = conf.getParameter<double>(\"clustering_threshold\");\n+HGCalClusteringImpl::HGCalClusteringImpl(const edm::ParameterSet & conf):\n+    seedThreshold_(conf.getParameter<double>(\"seeding_threshold\")),\n+    triggerCellThreshold_(conf.getParameter<double>(\"clustering_threshold\")),\n+    dr_(conf.getParameter<double>(\"dR_cluster\"))\n+{    \n+\n+    edm::LogInfo(\"HGCalClusterParameters\") << \"C2d seeding Thr: \" << seedThreshold_ ; \n+    edm::LogInfo(\"HGCalClusterParameters\") << \"C2d clustering Thr: \" << triggerCellThreshold_ ; \n+ \n+}\n+\n+bool HGCalClusteringImpl::isPertinent( const l1t::HGCalTriggerCell &tc, l1t::HGCalCluster &clu, double distXY ) const \n+{\n+\n+    HGCalDetId tcDetId( tc.detId() );\n+    HGCalDetId cluDetId( clu.seedDetId() );\n+    if( tcDetId.layer() != cluDetId.layer() ||\n+        tcDetId.subdetId() != cluDetId.subdetId() ||\n+        tcDetId.zside() != cluDetId.zside() )\n+        return false;\n+   \n+    if ( clu.distance(tc) < distXY )\n+        return true;\n+\n+    return false;\n+\n }\n-       \n \n-void  HGCalClusteringImpl::clusterizeBase(const l1t::HGCalTriggerCellBxCollection& trgcell_product_, l1t::HGCalClusterBxCollection& cluster_product_){\n+void HGCalClusteringImpl::clusterize(const edm::OrphanHandle<l1t::HGCalTriggerCellBxCollection> triggerCellsHandle, \n+                                     l1t::HGCalClusterBxCollection & clusters\n+    ){\n+\n+    if( triggerCellsHandle.isValid() ){\n     \n-        double_t protoClEta = 0.;\n-        double_t protoClPhi = 0.;           \n-        double_t C2d_pt  = 0.;\n-        double_t C2d_eta = 0.;\n-        double_t C2d_phi = 0.;\n-        uint32_t C2d_hwPtEm = 0;\n-        uint32_t C2d_hwPtHad = 0;\n-                \n-        for(l1t::HGCalTriggerCellBxCollection::const_iterator tc = trgcell_product_.begin(); tc != trgcell_product_.end(); ++tc)\n-        {\n-            if(tc->hwPt()>0)\n-            {\n-                        \n-                HGCalDetId trgdetid(tc->detId());                \n-                int trgCellLayer = trgdetid.layer();               \n-                        \n-                if(trgCellLayer<28){\n-                    C2d_hwPtEm+=tc->hwPt();\n-                }else if(trgCellLayer>=28){\n-                    C2d_hwPtHad+=tc->hwPt();\n+        bool isSeed[triggerCellsHandle->size()];\n+\n+        /* search for cluster seeds */\n+        int itc=0;\n+        for( l1t::HGCalTriggerCellBxCollection::const_iterator tc = triggerCellsHandle->begin(); tc != triggerCellsHandle->end(); ++tc,++itc ){\n+            isSeed[itc] = ( tc->hwPt() > seedThreshold_) ? true : false;\n+        }\n+    \n+        /* clustering the TCs */\n+        std::vector<l1t::HGCalCluster> clustersTmp;\n+\n+        itc=0;\n+        for( l1t::HGCalTriggerCellBxCollection::const_iterator tc = triggerCellsHandle->begin(); tc != triggerCellsHandle->end(); ++tc,++itc ){\n+            \n+            if( tc->hwPt() < triggerCellThreshold_ ){\n+                continue;\n+            }\n+        \n+            /* searching for TC near the center of the cluster  */\n+            int iclu=0;\n+            vector<int> tcPertinentClusters; \n+            for( std::vector<l1t::HGCalCluster>::iterator clu = clustersTmp.begin(); clu != clustersTmp.end(); ++clu,++iclu ){\n+                if( this->isPertinent(*tc, *clu, dr_) ){\n+                    tcPertinentClusters.push_back(iclu);\n                 }\n-                               \n-                C2d_pt += tc->pt();                        \n-                protoClEta += tc->pt()*tc->eta();\n-                protoClPhi += tc->pt()*tc->phi();\n             }\n+            if( tcPertinentClusters.size() == 0 && isSeed[itc] ){\n+                l1t::HGCalCluster obj( *tc );\n+                clustersTmp.emplace_back( obj );\n+            }\n+            else if ( tcPertinentClusters.size() > 0 ){\n+         \n+                unsigned minDist = 300;\n+                unsigned targetClu = 0;\n+                        \n+                for( std::vector<int>::const_iterator iclu = tcPertinentClusters.begin(); iclu != tcPertinentClusters.end(); ++iclu ){\n+                    double d = clustersTmp.at( *iclu).distance(*tc);\n+                    if( d < minDist ){\n+                        minDist = d;\n+                        targetClu = *iclu;\n+                    }\n+                } \n+                clustersTmp.at( targetClu).addTriggerCell( *tc );\n+                edm::Ptr<l1t::HGCalTriggerCell> p( triggerCellsHandle, itc, true );\n+                clustersTmp.at( targetClu).addTriggerCellList( p );\n+            \n+            }\n+\n         }\n-        l1t::HGCalCluster cluster( reco::LeafCandidate::LorentzVector(), C2d_hwPtEm + C2d_hwPtHad, 0, 0);\n-        cluster.setHwPtEm(C2d_hwPtEm);\n-        cluster.setHwPtHad(C2d_hwPtHad);\n-\n-        if((cluster.hwPtEm()+cluster.hwPtHad())>tc_CUT_){\n-            C2d_eta = protoClEta/C2d_pt;\n-            C2d_phi = protoClPhi/C2d_pt;                \n-            math::PtEtaPhiMLorentzVector calibP4(C2d_pt, C2d_eta, C2d_phi, 0 );\n-            cluster.setP4(calibP4);\n-            cluster_product_.push_back(0,cluster);\n+\n+        /* making the collection of clusters */\n+        for( unsigned i(0); i<clustersTmp.size(); ++i ){","path":"L1Trigger/L1THGCal/src/be_algorithms/HGCalClusteringImpl.cc","position":132,"original_position":132,"commit_id":"dd198b357b68a5c8f2673da3a74e905af5aaa256","original_commit_id":"dd198b357b68a5c8f2673da3a74e905af5aaa256","user":{"login":"jbsauvan","id":5569798,"avatar_url":"https://avatars0.githubusercontent.com/u/5569798?v=3","gravatar_id":"","url":"https://api.github.com/users/jbsauvan","html_url":"https://github.com/jbsauvan","followers_url":"https://api.github.com/users/jbsauvan/followers","following_url":"https://api.github.com/users/jbsauvan/following{/other_user}","gists_url":"https://api.github.com/users/jbsauvan/gists{/gist_id}","starred_url":"https://api.github.com/users/jbsauvan/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/jbsauvan/subscriptions","organizations_url":"https://api.github.com/users/jbsauvan/orgs","repos_url":"https://api.github.com/users/jbsauvan/repos","events_url":"https://api.github.com/users/jbsauvan/events{/privacy}","received_events_url":"https://api.github.com/users/jbsauvan/received_events","type":"User","site_admin":false},"body":"In principle, it should not be needed to have a temporary collection. It should be possible to fill directly the output collection.","created_at":"2017-03-10T10:24:35Z","updated_at":"2017-03-10T10:34:59Z","html_url":"https://github.com/PFCal-dev/cmssw/pull/87#discussion_r105369410","pull_request_url":"https://api.github.com/repos/PFCal-dev/cmssw/pulls/87","_links":{"self":{"href":"https://api.github.com/repos/PFCal-dev/cmssw/pulls/comments/105369410"},"html":{"href":"https://github.com/PFCal-dev/cmssw/pull/87#discussion_r105369410"},"pull_request":{"href":"https://api.github.com/repos/PFCal-dev/cmssw/pulls/87"}}},"pull_request":{"url":"https://api.github.com/repos/PFCal-dev/cmssw/pulls/87","id":109103159,"html_url":"https://github.com/PFCal-dev/cmssw/pull/87","diff_url":"https://github.com/PFCal-dev/cmssw/pull/87.diff","patch_url":"https://github.com/PFCal-dev/cmssw/pull/87.patch","issue_url":"https://api.github.com/repos/PFCal-dev/cmssw/issues/87","number":87,"state":"open","locked":false,"title":"Hgc l1clustering rebased pre5 tmp","user":{"login":"mastrolorenzo","id":5551207,"avatar_url":"https://avatars2.githubusercontent.com/u/5551207?v=3","gravatar_id":"","url":"https://api.github.com/users/mastrolorenzo","html_url":"https://github.com/mastrolorenzo","followers_url":"https://api.github.com/users/mastrolorenzo/followers","following_url":"https://api.github.com/users/mastrolorenzo/following{/other_user}","gists_url":"https://api.github.com/users/mastrolorenzo/gists{/gist_id}","starred_url":"https://api.github.com/users/mastrolorenzo/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/mastrolorenzo/subscriptions","organizations_url":"https://api.github.com/users/mastrolorenzo/orgs","repos_url":"https://api.github.com/users/mastrolorenzo/repos","events_url":"https://api.github.com/users/mastrolorenzo/events{/privacy}","received_events_url":"https://api.github.com/users/mastrolorenzo/received_events","type":"User","site_admin":false},"body":"Update to the clusters classes in the Dataformat and in the clustering function in the implementation classes. Adding of a simple 2D-clustering algorithm based on dR(x,y) + a version of a 3D-clustering used to cross-check the 2D-clustering. Modification of the calibration class, adding a function to calibrate from hwPt in transverse 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