From 6fe60514f671feed136496d7de0b277fa074b220 Mon Sep 17 00:00:00 2001
From: Christopher Dilks <christopher.j.dilks@gmail.com>
Date: Wed, 9 Nov 2022 14:38:44 -0500
Subject: [PATCH 01/32] test: `AnalysisEpic` on 22.10.0 single particle
 simulations

---
 s3tools/make-epic-config.sh | 143 ++++++++++++++++++++++++++++++++++++
 src/AnalysisEpic.cxx        |   4 +-
 tutorial/analysis_epic.C    |   5 +-
 3 files changed, 148 insertions(+), 4 deletions(-)
 create mode 100755 s3tools/make-epic-config.sh

diff --git a/s3tools/make-epic-config.sh b/s3tools/make-epic-config.sh
new file mode 100755
index 00000000..5b677d3f
--- /dev/null
+++ b/s3tools/make-epic-config.sh
@@ -0,0 +1,143 @@
+#!/bin/bash
+
+###################
+# TOP-LEVEL SCRIPT to automate the creation of a config file for a specific release,
+# supporting streaming or downloading from S3
+# - the config file consists of file names (or URLs), with Q2 minima and cross sections
+###################
+
+# RELEASE TAG AND RECO DIR: ###########################
+release="22.10.0/epic_arches"
+# release="22.10.0/epic_brycecanyon"
+releaseDir="S3/eictest/EPIC/RECO/$release/SINGLE/pi-/5GeV/130to177deg"
+filter='eicrecon'
+# filter='juggler'
+#######################################################
+
+# usage:
+if [ $# -lt 3 ]; then
+  echo """
+  USAGE: $0 [energy] [local_dir] [mode] [limit(optional)] [config_file(optional)]
+
+   - [energy]: 5x41      | - see below for available datasets
+               5x100     | - data from different Q2minima are combined,
+               10x100    |   weighted by cross sections
+               10x275
+               18x275
+               
+   - [local_dir]: output directory name: datarec/[local_dir]
+
+   - [mode]:   s - make config file for streaming from S3
+               d - download from S3, then make the local config file
+               c - just make the local config file, for local files
+   
+   - [limit]   integer>0 : only stream/download this many files per Q2 min
+               0         : stream/download all files
+               default=5
+
+   - [config_file]  name of the config file; if not specified, the
+                    config file will be in datarec/[local_dir]
+
+   See script for local and remote file path settings; they are
+   configured for a specific set of data, but you may want to change
+   them.
+
+  CURRENT RELEASE: $release
+  S3 Directory:    $releaseDir
+
+  AVAILABLE DATA ON S3 (press ^C to abort S3 query):
+  """
+  mc tree $releaseDir
+  exit 2
+fi
+energy=$1
+locDir=$2
+mode=$3
+limit=5
+configFile=""
+if [ $# -ge 4 ]; then limit=$4; fi
+if [ $# -ge 5 ]; then configFile=$5; fi
+
+# cd to the main directory 
+pushd $(dirname $(realpath $0))/..
+
+# settings #############################################################
+# sourceDir="$releaseDir/$energy"
+# targetDir="datarec/$locDir/$release/$energy"
+# Q2minima=( 1000 100 10 1 )
+# Q2max=0 # no maximum
+# FIXME: currently testing single-particle simulations:
+sourceDir="$releaseDir"
+targetDir="datarec/$locDir/$release"
+########################################################################
+
+# download files from S3
+function status { echo ""; echo "[+] $1"; }
+if [ "$mode" == "d" ]; then
+  status "downloading files from S3..."
+  ### FIXME: no Q2 minima yet ##############
+  # for Q2min in ${Q2minima[@]}; do
+  #   if [ $limit -gt 0 ]; then
+  #     s3tools/generate-s3-list.sh "$sourceDir/minQ2=$Q2min" | head -n$limit | grep -E $filter | s3tools/download.sh "$targetDir/minQ2=$Q2min"
+  #   else
+  #     s3tools/generate-s3-list.sh "$sourceDir/minQ2=$Q2min" |                 grep -E $filter | s3tools/download.sh "$targetDir/minQ2=$Q2min"
+  #   fi
+  # done
+  ##########################################
+  if [ $limit -gt 0 ]; then
+    s3tools/generate-s3-list.sh "$sourceDir" | head -n$limit | grep -E $filter | s3tools/download.sh "$targetDir"
+  else
+    s3tools/generate-s3-list.sh "$sourceDir" |                 grep -E $filter | s3tools/download.sh "$targetDir"
+  fi
+  ##########################################
+fi
+
+# build a config file
+status "build config file..."
+mkdir -p $targetDir
+if [ -z "$configFile" ]; then configFile=$targetDir/files.config; fi
+> $configFile.list
+### FIXME: no Q2minima yet ############################3
+# for Q2min in ${Q2minima[@]}; do
+#   crossSection=$(s3tools/read-xsec-table.sh "pythia8:$energy/minQ2=$Q2min")
+#   if [ "$mode" == "d" -o "$mode" == "c" ]; then
+#     s3tools/generate-local-list.sh "$targetDir/minQ2=$Q2min" $crossSection $Q2min $Q2max | grep -v UNKNOWN | tee -a $configFile.list
+#   elif [ "$mode" == "s" ]; then
+#     if [ $limit -gt 0 ]; then
+#       s3tools/generate-s3-list.sh "$sourceDir/minQ2=$Q2min" $crossSection $Q2min $Q2max | grep -v UNKNOWN | head -n$limit | tee -a $configFile.list
+#     else
+#       s3tools/generate-s3-list.sh "$sourceDir/minQ2=$Q2min" $crossSection $Q2min $Q2max | grep -v UNKNOWN | tee -a $configFile.list
+#     fi
+#   else
+#     echo "ERROR: unknown mode"
+#     exit 1
+#   fi
+# done
+#######################################################3
+crossSection=$(s3tools/read-xsec-table.sh "pythia8:$energy/minQ2=1") # just pick any cross section
+if [ "$mode" == "d" -o "$mode" == "c" ]; then
+  s3tools/generate-local-list.sh "$targetDir" $crossSection 1 0 | grep -v UNKNOWN | tee -a $configFile.list
+elif [ "$mode" == "s" ]; then
+  if [ $limit -gt 0 ]; then
+    s3tools/generate-s3-list.sh "$sourceDir" $crossSection 1 0 | grep -v UNKNOWN | head -n$limit | tee -a $configFile.list
+  else
+    s3tools/generate-s3-list.sh "$sourceDir" $crossSection 1 0 | grep -v UNKNOWN | tee -a $configFile.list
+  fi
+else
+  echo "ERROR: unknown mode"
+  exit 1
+fi
+#######################################################3
+s3tools/generate-config-file.rb $configFile $energy $configFile.list
+
+# output some info
+#status "files in target directory:"
+#tree $targetDir
+popd
+status "done building config file at:"
+echo "     $configFile"
+echo ""
+if [ -n "$(grep UNKNOWN $configFile.list)" ]; then
+  >&2 echo "ERROR: missing some cross sections"
+  exit 1
+fi
diff --git a/src/AnalysisEpic.cxx b/src/AnalysisEpic.cxx
index ddebb6d3..0811d9f5 100644
--- a/src/AnalysisEpic.cxx
+++ b/src/AnalysisEpic.cxx
@@ -74,8 +74,8 @@ void AnalysisEpic::Execute()
     
     // read particle collections for this event
     const auto& simParts    = evStore.get<edm4hep::MCParticleCollection>("MCParticles");
-    const auto& recParts    = evStore.get<edm4eic::ReconstructedParticleCollection>("ReconstructedParticles");
-    const auto& mcRecAssocs = evStore.get<edm4eic::MCRecoParticleAssociationCollection>("ReconstructedParticlesAssoc");
+    const auto& recParts    = evStore.get<edm4eic::ReconstructedParticleCollection>("ReconstructedChargedParticles");
+    const auto& mcRecAssocs = evStore.get<edm4eic::MCRecoParticleAssociationCollection>("ReconstructedChargedParticlesAssociations");
 
     // data objects
     edm4hep::MCParticle mcPartEleBeam;
diff --git a/tutorial/analysis_epic.C b/tutorial/analysis_epic.C
index 75961a96..dc7ab3b0 100644
--- a/tutorial/analysis_epic.C
+++ b/tutorial/analysis_epic.C
@@ -26,14 +26,15 @@ R__LOAD_LIBRARY(Sidis-eic)
  *   between fast and full simulations
  */
 void analysis_epic(
-    TString  infiles="tutorial/test.epic.config", // list of input files
+    // TString  configFile="tutorial/test.epic.config", // list of input files
+    TString  configFile="datarec/epic.single/22.10.0/epic_arches/files.config", // list of input files
     TString  outfilePrefix="tutorial.epic"        // output filename prefix
     )
 {
 
   // setup analysis ========================================
   // - define `AnalysisEpic` instead of `AnalysisDelphes`
-  AnalysisEpic *A = new AnalysisEpic(infiles, outfilePrefix);
+  AnalysisEpic *A = new AnalysisEpic(configFile, outfilePrefix);
 
   // settings
   A->crossCheckKinematics = true;   // enable cross check with upstream kinematics

From 57c5724d62627734758281f1b9827ab6e52d49a1 Mon Sep 17 00:00:00 2001
From: Christopher Dilks <christopher.j.dilks@gmail.com>
Date: Wed, 9 Nov 2022 15:59:03 -0500
Subject: [PATCH 02/32] feat: initial `AnalysisEpic` support for campaign
 `22.11+`

---
 datarec/xsec/xsec.dat       |  7 +++
 s3tools/download.sh         |  7 ++-
 s3tools/make-epic-config.sh | 92 +++++++++++++------------------------
 3 files changed, 46 insertions(+), 60 deletions(-)

diff --git a/datarec/xsec/xsec.dat b/datarec/xsec/xsec.dat
index d1d64305..0e52c339 100644
--- a/datarec/xsec/xsec.dat
+++ b/datarec/xsec/xsec.dat
@@ -33,3 +33,10 @@ pythia6:ep-18x275          8.830e+05           0.0011   # general Q2
 pythia6:ep-18x275-q2-low   8.796e+05           0.0006   # 1 < Q2 < 100
 pythia6:ep-18x275-q2-high  3.092e+03           0.0475   # 100 < Q2
 pythia6:ep-18x275-Lambda   8.830e+05           0.0011   # FIXME: assuming general Q2
+#
+#
+#
+# Pythia 6, for EPIC -- TODO
+# 
+#
+#
diff --git a/s3tools/download.sh b/s3tools/download.sh
index 27578945..6d433adf 100755
--- a/s3tools/download.sh
+++ b/s3tools/download.sh
@@ -19,7 +19,12 @@ while read sourceURL; do
   sourcePath=$(echo $sourceURL | awk '{print $1}' | sed 's/https:\/\///g' | sed 's/^[^\/]*\//S3\//g')
   echo "mc cp $sourcePath $targetPath; echo \"\"" >> $downloadScript
 done
-echo "generated downloader script $downloadScript"
+echo """
+generated downloader script $downloadScript
+===============================================================
+`cat $downloadScript`
+===============================================================
+"""
 
 # download
 echo "now starting download..."
diff --git a/s3tools/make-epic-config.sh b/s3tools/make-epic-config.sh
index 5b677d3f..d7e1e88f 100755
--- a/s3tools/make-epic-config.sh
+++ b/s3tools/make-epic-config.sh
@@ -7,9 +7,10 @@
 ###################
 
 # RELEASE TAG AND RECO DIR: ###########################
-release="22.10.0/epic_arches"
-# release="22.10.0/epic_brycecanyon"
-releaseDir="S3/eictest/EPIC/RECO/$release/SINGLE/pi-/5GeV/130to177deg"
+detector_config="epic_arches"
+# detector_config="epic_brycecanyon"
+release="22.11.0"
+releaseDir="S3/eictest/EPIC/RECO/$release/$detector_config/DIS/NC"
 filter='eicrecon'
 # filter='juggler'
 #######################################################
@@ -62,34 +63,25 @@ if [ $# -ge 5 ]; then configFile=$5; fi
 pushd $(dirname $(realpath $0))/..
 
 # settings #############################################################
-# sourceDir="$releaseDir/$energy"
-# targetDir="datarec/$locDir/$release/$energy"
-# Q2minima=( 1000 100 10 1 )
-# Q2max=0 # no maximum
-# FIXME: currently testing single-particle simulations:
-sourceDir="$releaseDir"
-targetDir="datarec/$locDir/$release"
+sourceDir="$releaseDir/$energy"
+targetDir="datarec/$locDir/$release/$energy"
+Q2minima=( 1000 100  10  1  )
+Q2maxima=( 0    1000 100 10 )
 ########################################################################
 
 # download files from S3
+function q2subdir { echo $1/minQ2=$2; }
 function status { echo ""; echo "[+] $1"; }
 if [ "$mode" == "d" ]; then
   status "downloading files from S3..."
-  ### FIXME: no Q2 minima yet ##############
-  # for Q2min in ${Q2minima[@]}; do
-  #   if [ $limit -gt 0 ]; then
-  #     s3tools/generate-s3-list.sh "$sourceDir/minQ2=$Q2min" | head -n$limit | grep -E $filter | s3tools/download.sh "$targetDir/minQ2=$Q2min"
-  #   else
-  #     s3tools/generate-s3-list.sh "$sourceDir/minQ2=$Q2min" |                 grep -E $filter | s3tools/download.sh "$targetDir/minQ2=$Q2min"
-  #   fi
-  # done
-  ##########################################
-  if [ $limit -gt 0 ]; then
-    s3tools/generate-s3-list.sh "$sourceDir" | head -n$limit | grep -E $filter | s3tools/download.sh "$targetDir"
-  else
-    s3tools/generate-s3-list.sh "$sourceDir" |                 grep -E $filter | s3tools/download.sh "$targetDir"
-  fi
-  ##########################################
+  for Q2min in ${Q2minima[@]}; do
+    echo " sourceDir = `q2subdir $sourceDir $Q2min`"
+    echo " targetDir = `q2subdir $targetDir $Q2min`"
+    s3tools/generate-s3-list.sh  `q2subdir $sourceDir $Q2min`   | \
+      { if [ $limit -gt 0 ]; then head -n$limit; else cat; fi } | \
+      grep -E $filter                                           | \
+      s3tools/download.sh `q2subdir $targetDir $Q2min`
+  done
 fi
 
 # build a config file
@@ -97,42 +89,24 @@ status "build config file..."
 mkdir -p $targetDir
 if [ -z "$configFile" ]; then configFile=$targetDir/files.config; fi
 > $configFile.list
-### FIXME: no Q2minima yet ############################3
-# for Q2min in ${Q2minima[@]}; do
-#   crossSection=$(s3tools/read-xsec-table.sh "pythia8:$energy/minQ2=$Q2min")
-#   if [ "$mode" == "d" -o "$mode" == "c" ]; then
-#     s3tools/generate-local-list.sh "$targetDir/minQ2=$Q2min" $crossSection $Q2min $Q2max | grep -v UNKNOWN | tee -a $configFile.list
-#   elif [ "$mode" == "s" ]; then
-#     if [ $limit -gt 0 ]; then
-#       s3tools/generate-s3-list.sh "$sourceDir/minQ2=$Q2min" $crossSection $Q2min $Q2max | grep -v UNKNOWN | head -n$limit | tee -a $configFile.list
-#     else
-#       s3tools/generate-s3-list.sh "$sourceDir/minQ2=$Q2min" $crossSection $Q2min $Q2max | grep -v UNKNOWN | tee -a $configFile.list
-#     fi
-#   else
-#     echo "ERROR: unknown mode"
-#     exit 1
-#   fi
-# done
-#######################################################3
-crossSection=$(s3tools/read-xsec-table.sh "pythia8:$energy/minQ2=1") # just pick any cross section
-if [ "$mode" == "d" -o "$mode" == "c" ]; then
-  s3tools/generate-local-list.sh "$targetDir" $crossSection 1 0 | grep -v UNKNOWN | tee -a $configFile.list
-elif [ "$mode" == "s" ]; then
-  if [ $limit -gt 0 ]; then
-    s3tools/generate-s3-list.sh "$sourceDir" $crossSection 1 0 | grep -v UNKNOWN | head -n$limit | tee -a $configFile.list
-  else
-    s3tools/generate-s3-list.sh "$sourceDir" $crossSection 1 0 | grep -v UNKNOWN | tee -a $configFile.list
-  fi
-else
-  echo "ERROR: unknown mode"
-  exit 1
-fi
-#######################################################3
+for (( i=0; i<${#Q2minima[@]}; i++)); do
+  Q2min=${Q2minima[$i]}
+  Q2max=${Q2maxima[$i]}
+  crossSection=$(s3tools/read-xsec-table.sh "pythia8:$energy/minQ2=$Q2min")
+  case $mode in
+    d) listScript=s3tools/generate-local-list.sh; listDir=`q2subdir $targetDir $Q2min`; ;;
+    c) listScript=s3tools/generate-local-list.sh; listDir=`q2subdir $targetDir $Q2min`; ;;
+    s) listScript=s3tools/generate-s3-list.sh;    listDir=`q2subdir $sourceDir $Q2min`; ;;
+    *) echo "ERROR: unknown mode" >&2; exit 1;    ;;
+  esac
+  $listScript $listDir $crossSection $Q2min $Q2max            | \
+    grep -v UNKNOWN                                           | \
+    { if [ $limit -gt 0 ]; then head -n$limit; else cat; fi } | \
+    tee -a $configFile.list
+done
 s3tools/generate-config-file.rb $configFile $energy $configFile.list
 
-# output some info
-#status "files in target directory:"
-#tree $targetDir
+# finalize
 popd
 status "done building config file at:"
 echo "     $configFile"

From 4157ee78d8ad68b18932cde875748fb6a64f3cfb Mon Sep 17 00:00:00 2001
From: Christopher Dilks <christopher.j.dilks@gmail.com>
Date: Wed, 9 Nov 2022 20:09:18 -0500
Subject: [PATCH 03/32] modified:   tutorial/analysis_epic.C

---
 tutorial/analysis_epic.C | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorial/analysis_epic.C b/tutorial/analysis_epic.C
index dc7ab3b0..0a497081 100644
--- a/tutorial/analysis_epic.C
+++ b/tutorial/analysis_epic.C
@@ -27,7 +27,7 @@ R__LOAD_LIBRARY(Sidis-eic)
  */
 void analysis_epic(
     // TString  configFile="tutorial/test.epic.config", // list of input files
-    TString  configFile="datarec/epic.single/22.10.0/epic_arches/files.config", // list of input files
+    TString  configFile="datarec/epic.22.11.0/22.11.0/18x275/files.config", // list of input files
     TString  outfilePrefix="tutorial.epic"        // output filename prefix
     )
 {

From cf7adbce7d581428bd22a2453128e9370497676f Mon Sep 17 00:00:00 2001
From: Christopher Dilks <christopher.j.dilks@gmail.com>
Date: Sat, 12 Nov 2022 00:08:03 -0500
Subject: [PATCH 04/32] introduce caching of PDG values, in preparation for PID
 smearing

---
 src/AnalysisEpic.cxx | 123 ++++++++++++++++++++++++++-----------------
 src/AnalysisEpic.h   |  13 +++--
 2 files changed, 86 insertions(+), 50 deletions(-)

diff --git a/src/AnalysisEpic.cxx b/src/AnalysisEpic.cxx
index 0811d9f5..1f78ab9a 100644
--- a/src/AnalysisEpic.cxx
+++ b/src/AnalysisEpic.cxx
@@ -55,8 +55,8 @@ void AnalysisEpic::Execute()
     if(e%10000==0) fmt::print("{} events...\n",e);
     if(verbose) fmt::print("\n\n{:=<70}\n",fmt::format("EVENT {} ",e));
 
-    // next event
-    // FIXME: check that we analyze ALL of the events: do we miss the first or last one?
+    // read next event
+    // FIXME: is this correct? are we actually reading all of the events?
     if(e>0) {
       evStore.clear();
       podioReader.endOfEvent();
@@ -71,11 +71,15 @@ void AnalysisEpic::Execute()
     int num_ion_beams        = 0;
     int num_sim_electrons    = 0;
     int num_rec_electrons    = 0;
+
+    // clear caches
+    useCachedPDG = false;
+    pdgCache.clear();
     
     // read particle collections for this event
     const auto& simParts    = evStore.get<edm4hep::MCParticleCollection>("MCParticles");
     const auto& recParts    = evStore.get<edm4eic::ReconstructedParticleCollection>("ReconstructedChargedParticles");
-    const auto& mcRecAssocs = evStore.get<edm4eic::MCRecoParticleAssociationCollection>("ReconstructedChargedParticlesAssociations");
+    // const auto& mcRecAssocs = evStore.get<edm4eic::MCRecoParticleAssociationCollection>("ReconstructedChargedParticlesAssociations"); // FIXME: broken
 
     // data objects
     edm4hep::MCParticle mcPartEleBeam;
@@ -158,7 +162,7 @@ void AnalysisEpic::Execute()
       kin->AddToHFS(GetP4(recPart));
     };
     if(verbose) fmt::print("\n{:-<60}\n","MC<->Reco ASSOCIATIONS ");
-    LoopMCRecoAssocs(mcRecAssocs, AllRecPartsToHFS, verbose);
+    // LoopMCRecoAssocs(mcRecAssocs, AllRecPartsToHFS, verbose); // FIXME: relations unavailable
 
     // find reconstructed electron
     // ============================================================================
@@ -176,15 +180,17 @@ void AnalysisEpic::Execute()
         kin->vecElectron = GetP4(recPart);
       };
     };
-    LoopMCRecoAssocs(mcRecAssocs, FindRecoEleByTruth);
+    LoopMCRecoAssocs(mcRecAssocs, FindRecoEleByTruth); // FIXME: relations unavailable
     */
 
     // use electron finder from upstream algorithm `InclusiveKinematics*`
+    // FIXME: `InclusiveKinematics` collections are not yet available
     // FIXME: is the correct upstream electron finder used here? The
     // `InclusiveKinematics*` recon algorithms seem to rely on
     // `Jug::Base::Beam::find_first_scattered_electron(mcParts)` and matching
     // to truth; this guarantees we get the correct reconstructed scattered
     // electron
+    /*
     const auto& disCalcs = evStore.get<edm4eic::InclusiveKinematicsCollection>("InclusiveKinematicsElectron");
     if(disCalcs.size() != 1) ErrorPrint(fmt::format("WARNING: disCalcs.size = {} != 1 for this event",disCalcs.size()));
     for(const auto& calc : disCalcs) {
@@ -196,6 +202,7 @@ void AnalysisEpic::Execute()
       num_rec_electrons++;
       kin->vecElectron = GetP4(ele);
     }
+    */
 
     // check for found reconstructed scattered electron
     if(num_rec_electrons == 0) { ErrorPrint("WARNING: reconstructed scattered electron not found"); continue; };
@@ -250,11 +257,13 @@ void AnalysisEpic::Execute()
       // - `IsActiveEvent()` is only true if at least one bin gets filled for this track
       if( writeSimpleTree && HD->IsActiveEvent() ) ST->FillTree(wTrack);
     };
-    LoopMCRecoAssocs(mcRecAssocs, SidisOutput);
+    // LoopMCRecoAssocs(mcRecAssocs, SidisOutput); // FIXME: relations unavailable
 
 
     // read kinematics calculations from upstream /////////////////////////
+    // FIXME: `InclusiveKinematics` collections are not yet available
     // TODO: cross check these with our calculations from `Kinematics`
+    /*
     if(crossCheckKinematics) {
       auto PrintRow = [] <typename T> (std::string name, std::vector<T> vals, bool header=false) {
         fmt::print("  {:>16}",name);
@@ -307,6 +316,7 @@ void AnalysisEpic::Execute()
       }
       else fmt::print("{:-<75}\n  method \"{}\" is not available upstream\n","DIFFERENCE: ",reconMethod);
     } // if crossCheckKinematics
+    */
 
   } // event loop
   fmt::print("end event loop\n");
@@ -340,12 +350,15 @@ void AnalysisEpic::PrintParticle(const edm4hep::MCParticle& P) {
       P.getVertex().y,
       P.getVertex().z
       );
-  fmt::print("  {:>20}:\n", "Parents");
-  for(const auto& parent : P.getParents())
-    fmt::print("    {:>20}: {}\n", "PDG", parent.getPDG());
-  fmt::print("  {:>20}:\n", "Daughters");
-  for(const auto& daughter : P.getDaughters())
-    fmt::print("    {:>20}: {}\n", "PDG", daughter.getPDG());
+  // FIXME: relations unavailable
+  // fmt::print("  {:>20}:\n", "Parents");
+  // for(const auto& parent : P.getParents()) {
+  //   // fmt::print("    {:>20}: {}\n", "PDG", parent.getPDG());
+  //   fmt::print("    {:>20}: {}\n", "id", parent.id());
+  // }
+  // fmt::print("  {:>20}:\n", "Daughters");
+  // for(const auto& daughter : P.getDaughters())
+  //   fmt::print("    {:>20}: {}\n", "PDG", daughter.getPDG());
 }
 
 void AnalysisEpic::PrintParticle(const edm4eic::ReconstructedParticle& P) {
@@ -368,6 +381,7 @@ void AnalysisEpic::PrintParticle(const edm4eic::ReconstructedParticle& P) {
   fmt::print("  {:>20}: {}\n", "# of tracks",   P.tracks_size()      );
   fmt::print("  {:>20}: {}\n", "# of PIDs",     P.particleIDs_size() );
   fmt::print("  {:>20}: {}\n", "# of recParts", P.particles_size()   );
+  // FIXME: relations unavailable
   // for(const auto& track : P.getTracks()) {
   //   // ...
   // }
@@ -376,6 +390,22 @@ void AnalysisEpic::PrintParticle(const edm4eic::ReconstructedParticle& P) {
   // }
 }
 
+// print out a reconstructed particle, and its matching truth 
+void AnalysisEpic::PrintAssociatedParticles(
+    const edm4hep::MCParticle& simPart,
+    const edm4eic::ReconstructedParticle& recPart
+    )
+{
+  fmt::print("\n   {:->35}\n"," reconstructed particle:");
+  PrintParticle(recPart);
+  fmt::print("\n   {:.>35}\n"," truth match:");
+  if(simPart.isAvailable())
+    PrintParticle(simPart);
+  else
+    fmt::print("     {:>35}\n","NO MATCH");
+  fmt::print("\n");
+}
+
 
 // helper methods /////////////////////////////////////////////////////////////
 
@@ -393,63 +423,62 @@ void AnalysisEpic::LoopMCRecoAssocs(
 {
   for(const auto& assoc : mcRecAssocs ) {
 
+    // FIXME: relations unavailable
     // get reconstructed and simulated particles, and check for matching
     auto recPart = assoc.getRec(); // reconstructed particle
     auto simPart = assoc.getSim(); // simulated (truth) particle
     // if(!simPart.isAvailable()) continue; // FIXME: consider using this once we have matching
 
-    // print out this reconstructed particle, and its matching truth 
-    if(printParticles) {
-      fmt::print("\n   {:->35}\n"," reconstructed particle:");
-      PrintParticle(recPart);
-      fmt::print("\n   {:.>35}\n"," truth match:");
-      if(simPart.isAvailable())
-        PrintParticle(simPart);
-      else
-        fmt::print("     {:>35}\n","NO MATCH");
-      fmt::print("\n");
-    }
+    // print associations
+    if(printParticles) PrintAssociatedParticles(simPart,recPart);
 
     // get reconstructed PDG from PID
-    bool usedTruthPID = false;
-    auto recPDG = GetReconstructedPDG(simPart, recPart, usedTruthPID);
-    if(verbose) fmt::print("   GetReconstructedPDG = {}\n",recPDG);
-    // if(usedTruthPID) continue; // FIXME: consider using this once we have decent PID
-
+    auto recPDG = GetReconstructedPDG(simPart, recPart);
     // run payload
     payload(simPart, recPart, recPDG);
 
-  } // end loop over Reco<->MC associations
-} // end LoopMCRecoAssocs
+  }
+  useCachedPDG = true; // looped once, enable PDG caching
+}
 
 
-// get PDG from reconstructed particle; resort to true PDG, if
-// PID is unavailable (sets `usedTruth` to true)
+// get PDG of reconstructed particle
 int AnalysisEpic::GetReconstructedPDG(
     const edm4hep::MCParticle& simPart,
-    const edm4eic::ReconstructedParticle& recPart,
-    bool& usedTruth
+    const edm4eic::ReconstructedParticle& recPart
     )
 {
   int pdg = 0;
-  usedTruth = false;
 
-  // if using edm4hep::ReconstructedParticle:
-  /*
-  if(recPart.getParticleIDUsed().isAvailable()) // FIXME: not available
-    pdg = recPart.getParticleIDUsed().getPDG();
+  // get it from the cache, if we already have it
+  // FIXME: check this, this has not yet been tested
+  if(useCachedPDG) {
+    try {
+      pdg = pdgCache.at({simPart.id(),recPart.id()});
+      return pdg;
+    } catch(const std::out_of_range &e) {
+      ErrorPrint("WARNING: a PDG value was not cached");
+    }
+  }
+
+  /* // FIXME: relations unavailable
+  pdg = recPart.getPDG(); // if using edm4eic::ReconstructedParticle
+  if(recPart.getParticleIDUsed().isAvailable())
+    pdg = recPart.getParticleIDUsed().getPDG(); // if using edm4hep::ReconstructedParticle
   */
-  
-  // if using edm4eic::ReconstructedParticle:
-  // pdg = recPart.getPDG(); // FIXME: not available either
+
+  // instead, use PID smearing
+  //   TODO  TODO  TODO
+  //   TODO  TODO  TODO
+  //   TODO  TODO  TODO
 
   // if reconstructed PID is unavailable, use MC PDG
-  if(pdg==0) {
-    usedTruth = true;
-    if(simPart.isAvailable())
-      pdg = simPart.getPDG();
-  }
+  if(pdg==0 && simPart.isAvailable())
+    pdg = simPart.getPDG();
 
+  // cache this PDG value and return it
+  if(verbose) fmt::print("   caching PDG = id({},{}) -> {}\n",simPart.id(),recPart.id(),pdg);
+  pdgCache.insert({{simPart.id(),recPart.id()},pdg});
   return pdg;
 }
 
diff --git a/src/AnalysisEpic.h b/src/AnalysisEpic.h
index f968b7a6..d2d00b26 100644
--- a/src/AnalysisEpic.h
+++ b/src/AnalysisEpic.h
@@ -42,6 +42,10 @@ class AnalysisEpic : public Analysis
     // printers
     void PrintParticle(const edm4hep::MCParticle& P);
     void PrintParticle(const edm4eic::ReconstructedParticle& P);
+    void PrintAssociatedParticles(
+        const edm4hep::MCParticle& simPart,
+        const edm4eic::ReconstructedParticle& recPart
+        );
 
 
   protected:
@@ -49,10 +53,9 @@ class AnalysisEpic : public Analysis
     // get PDG from reconstructed particle
     int GetReconstructedPDG(
         const edm4hep::MCParticle& simPart,
-        const edm4eic::ReconstructedParticle& recPart,
-        bool& usedTruth
+        const edm4eic::ReconstructedParticle& recPart
         );
-    // common loop over Reconstructed Particle <-> MC Particle associations
+    // run `payload` for all [Reconstructed Particle] <-> [MC Particle] associations
     // payload signature: (simPart, recPart, reconstructed PDG)
     void LoopMCRecoAssocs(
         const edm4eic::MCRecoParticleAssociationCollection& mcRecAssocs,
@@ -64,6 +67,10 @@ class AnalysisEpic : public Analysis
     podio::ROOTReader podioReader;
     podio::EventStore evStore;
 
+    // reconstructed PDG cache table
+    bool useCachedPDG;
+    std::map<std::pair<unsigned int,unsigned int>, int> pdgCache; // map : {simPart.id(),recPart.id()} -> recPDG
+
     ClassDefOverride(AnalysisEpic,1);
 };
 

From 699759d63cb55325688024979fd307f575cd3391 Mon Sep 17 00:00:00 2001
From: Gregtom3 <gamatousek@gmail.com>
Date: Wed, 16 Nov 2022 10:27:36 -0500
Subject: [PATCH 05/32] Integrated a Reco<-->MC matching algorithm to the Epic
 analysis. Temporary until the campaign fixes podio issues

---
 src/AnalysisEpic.cxx | 595 ++++++++++++++++++++++++-------------------
 src/AnalysisEpic.h   |   7 +
 2 files changed, 334 insertions(+), 268 deletions(-)

diff --git a/src/AnalysisEpic.cxx b/src/AnalysisEpic.cxx
index 1f78ab9a..f6053215 100644
--- a/src/AnalysisEpic.cxx
+++ b/src/AnalysisEpic.cxx
@@ -1,4 +1,5 @@
 #include "AnalysisEpic.h"
+#include "AnalysisEcce.h"
 
 AnalysisEpic::AnalysisEpic(TString infileName_, TString outfilePrefix_)
   : Analysis(infileName_, outfilePrefix_)
@@ -12,320 +13,378 @@ void AnalysisEpic::Execute()
   // setup
   Prepare();
 
-  // produce flat list of files from `infiles`
-  std::vector<std::string> infilesFlat;
-  for(const auto fileList : infiles)
-    for(const auto fileName : fileList)
-      infilesFlat.push_back(fileName);
-
-  // create PODIO event store
-  podioReader.openFiles(infilesFlat);
-  evStore.setReader(&podioReader);
-  ENT = podioReader.getEntries();
-  if(maxEvents>0) ENT = std::min(maxEvents,ENT);
+  // read EventEvaluator tree
+  TChain *chain = new TChain("events");
+  for(Int_t idx=0; idx<infiles.size(); ++idx) {
+    for(std::size_t idxF=0; idxF<infiles[idx].size(); ++idxF) {
+      // std::cout << "Adding " << infiles[idx][idxF] << " with " << inEntries[idx][idxF] << std::endl;
+      chain->Add(infiles[idx][idxF].c_str(), inEntries[idx][idxF]);
+    }
+  }
+
+  TTreeReader tr(chain);
+
+  TTreeReaderArray<Int_t> hepmcp_status(tr, "GeneratedParticles.type");
+  TTreeReaderArray<Int_t> hepmcp_PDG(tr,    "GeneratedParticles.PDG");
+  TTreeReaderArray<Float_t> hepmcp_E(tr,      "GeneratedParticles.energy");
+  TTreeReaderArray<Float_t> hepmcp_psx(tr,    "GeneratedParticles.momentum.x");
+  TTreeReaderArray<Float_t> hepmcp_psy(tr,    "GeneratedParticles.momentum.y");
+  TTreeReaderArray<Float_t> hepmcp_psz(tr,    "GeneratedParticles.momentum.z");
+
   
+
+  // All true particles (including secondaries, etc)
+  TTreeReaderArray<Int_t> mcpart_PDG(tr,      "MCParticles.PDG");
+  TTreeReaderArray<Int_t> mcpart_genStat(tr,         "MCParticles.generatorStatus");
+  TTreeReaderArray<Int_t> mcpart_simStat(tr,         "MCParticles.simulatorStatus");
+  TTreeReaderArray<Double_t> mcpart_m(tr,         "MCParticles.mass");
+  TTreeReaderArray<Float_t> mcpart_psx(tr,       "MCParticles.momentum.x");
+  TTreeReaderArray<Float_t> mcpart_psy(tr,       "MCParticles.momentum.y");
+  TTreeReaderArray<Float_t> mcpart_psz(tr,       "MCParticles.momentum.z");
+
+
+  // Reco tracks
+  TTreeReaderArray<Int_t> tracks_type(tr,  "ReconstructedChargedParticles.type"); // needs to be made an int eventually in actual EE code
+  TTreeReaderArray<Float_t> tracks_e(tr, "ReconstructedChargedParticles.energy");
+  TTreeReaderArray<Float_t> tracks_p_x(tr, "ReconstructedChargedParticles.momentum.x");
+  TTreeReaderArray<Float_t> tracks_p_y(tr, "ReconstructedChargedParticles.momentum.y");
+  TTreeReaderArray<Float_t> tracks_p_z(tr, "ReconstructedChargedParticles.momentum.z");
+  TTreeReaderArray<Int_t> tracks_PDG(tr,  "ReconstructedChargedParticles.PDG");
+  TTreeReaderArray<Float_t> tracks_CHI2PID(tr,  "ReconstructedChargedParticles.goodnessOfPID");
+  
+  // RecoAssociations
+  TTreeReaderArray<UInt_t> assoc_simID(tr, "ReconstructedChargedParticlesAssociations.simID");
+  TTreeReaderArray<UInt_t> assoc_recID(tr, "ReconstructedChargedParticlesAssociations.recID");
+  TTreeReaderArray<Float_t> assoc_weight(tr, "ReconstructedChargedParticlesAssociations.weight");
+  // TTreeReaderArray<Short_t> tracks_charge(tr,  "tracks_charge");
+  int trackSource = 0; // default track source is "all tracks"
+
   // calculate Q2 weights
   CalculateEventQ2Weights();
 
-  // upstream reconstruction methods
-  // - list of upstream methods
-  const std::vector<std::string> upstreamReconMethodList = {
-    "Truth",
-    "Electron",
-    "DA",
-    "JB",
-    "Sigma"
-  };
-  // - association of `Kinematics::CalculateDIS` reconstruction method with upstream;
-  //   for those unavailable upstream, use `"NONE"`
-  const std::map<TString,std::string> associatedUpstreamMethodMap = {
-    { "Ele",    "Electron" },
-    { "DA",     "DA"       },
-    { "JB",     "JB"       },
-    { "Sigma",  "Sigma"    },
-    { "Mixed",  "NONE"     },
-    { "eSigma", "NONE"     }
-  };
-  // - get upstream method associated with `reconMethod`
-  const auto& associatedUpstreamMethod = associatedUpstreamMethodMap.at(reconMethod);
-
-  // event loop =========================================================
-  fmt::print("begin event loop...\n");
-  for(unsigned e=0; e<ENT; e++) {
-    if(e%10000==0) fmt::print("{} events...\n",e);
-    if(verbose) fmt::print("\n\n{:=<70}\n",fmt::format("EVENT {} ",e));
-
-    // read next event
-    // FIXME: is this correct? are we actually reading all of the events?
-    if(e>0) {
-      evStore.clear();
-      podioReader.endOfEvent();
-    }
+  // counters
+  Long64_t numNoBeam, numEle, numNoEle, numNoHadrons, numProxMatched;
+  numNoBeam = numEle = numNoEle = numNoHadrons = numProxMatched = 0;
+
+  
+
+  tr.SetEntriesRange(1,maxEvents);
+  do{
 
     // resets
     kin->ResetHFS();
     kinTrue->ResetHFS();
-    double mcPartElectronP   = 0.0;
-    bool double_counted_beam = false;
-    int num_ele_beams        = 0;
-    int num_ion_beams        = 0;
-    int num_sim_electrons    = 0;
-    int num_rec_electrons    = 0;
-
-    // clear caches
-    useCachedPDG = false;
-    pdgCache.clear();
+
     
-    // read particle collections for this event
-    const auto& simParts    = evStore.get<edm4hep::MCParticleCollection>("MCParticles");
-    const auto& recParts    = evStore.get<edm4eic::ReconstructedParticleCollection>("ReconstructedChargedParticles");
-    // const auto& mcRecAssocs = evStore.get<edm4eic::MCRecoParticleAssociationCollection>("ReconstructedChargedParticlesAssociations"); // FIXME: broken
-
-    // data objects
-    edm4hep::MCParticle mcPartEleBeam;
-    edm4hep::MCParticle mcPartIonBeam;
-    edm4hep::MCParticle mcPartElectron;
-
-    // loop over generated particles
-    if(verbose) fmt::print("\n{:-<60}\n","MCParticles ");
-    for(auto simPart : simParts) {
-
-      // print out this MCParticle
-      // if(verbose) PrintParticle(simPart);
-
-      // generated particle properties
-      auto simPDG = simPart.getPDG();
-
-      // add to Hadronic Final State (HFS) sums
-      kinTrue->AddToHFS(GetP4(simPart));
-
-      // filter for beam particles
-      if(simPart.getGeneratorStatus() == constants::statusBeam) {
-        switch(simPDG) {
-          case constants::pdgElectron:
-            if(num_ele_beams>0) double_counted_beam = true;
-            mcPartEleBeam = simPart;
-            num_ele_beams++;
-            break;
-          case constants::pdgProton:
-            if(num_ion_beams>0) double_counted_beam = true;
-            mcPartIonBeam = simPart;
-            num_ion_beams++;
-            break;
-          default:
-            ErrorPrint(fmt::format("WARNING: Unknown beam particle with PDG={}",simPDG));
-        }
-      }
+    double maxP = 0;
+    int genEleID = -1;
+    bool foundBeamElectron = false;
+    bool foundBeamIon = false;
+
+    // Index maps for particle sets
+    std::map <double,int> genidmap; // <pz, index_gen>
+    std::map <int,int> mcidmap; // <index_mc, index_gen>
+    std::map <int,int>    trackidmap; // <index, index_mc>  
+
+    // ParticleEE vectors
+    // The index of the vectors correspond to their for loop idx
+    std::vector<ParticlesEE> genpart;    // mcID --> igen
+    std::vector<ParticlesEE> mcpart;     // mcID --> imc
+    std::vector<ParticlesEE> trackpart;  // mcID --> (imc of matching mcpart) or (-1 if no match is found)
+
+    /*
+      GenParticles loop
+    */
+
+    for(int igen=0; igen<hepmcp_PDG.GetSize(); igen++) {
+
+      int pid_ = hepmcp_PDG[igen];
 
-      // filter for scattered electron: select the one with the highest |p|
-      if(simPart.getGeneratorStatus() == constants::statusFinal) {
-        if(simPDG == constants::pdgElectron) {
-          auto eleP = edm4hep::utils::p(simPart);
-          if(eleP>mcPartElectronP) {
-            mcPartElectron  = simPart;
-            mcPartElectronP = eleP;
-            num_sim_electrons++;
-          }
-        }
+      double px_ = hepmcp_psx[igen];
+      double py_ = hepmcp_psy[igen];
+      double pz_ = hepmcp_psz[igen];
+      double e_  = hepmcp_E[igen];
+     
+      double p_ = sqrt(pow(hepmcp_psx[igen],2) + pow(hepmcp_psy[igen],2) + pow(hepmcp_psz[igen],2));
+      double mass_ = (fabs(pid_)==211)?pimass:(fabs(pid_)==321)?kmass:(fabs(pid_)==11)?emass:(fabs(pid_)==13)?mumass:(fabs(pid_)==2212)?pmass:0.;
+
+      // Add to genpart
+      ParticlesEE part;
+
+      part.pid=pid_;
+      part.charge = (pid_ == 211 || pid_ == 321 || pid_ == 2212 || pid_ == -11 || pid_ == -13)?1:(pid_ == -211 || pid_ == -321 || pid_ == -2212 || pid_ == 11 || pid_ == 13)?-1:0;
+      part.mcID=igen;
+      part.vecPart.SetPxPyPzE(px_,py_,pz_,e_);
+      genpart.push_back(part);
+      
+      genidmap.insert({pz_,igen});
+
+    }
+
+    /*
+      MCParticles loop
+    */
+
+    for(int imc=0; imc < mcpart_PDG.GetSize(); imc++){
+
+      int pid_ = mcpart_PDG[imc];
+      double px_ = mcpart_psx[imc];
+      double py_ = mcpart_psy[imc];
+      double pz_ = mcpart_psz[imc];
+      double m_ = mcpart_m[imc];
+      double e_ = sqrt(px_*px_+py_*py_+pz_*pz_+m_*m_);
+
+      // Add to mcpart
+      ParticlesEE part;
+
+      part.pid=pid_;
+      part.charge = (pid_ == 211 || pid_ == 321 || pid_ == 2212 || pid_ == -11 || pid_ == -13)?1:(pid_ == -211 || pid_ == -321 || pid_ == -2212 || pid_ == 11 || pid_ == 13)?-1:0;
+      part.mcID=imc;
+      part.vecPart.SetPxPyPzE(px_,py_,pz_,e_);
+      mcpart.push_back(part);
+      
+      int igen=-1;
+      if(auto search = genidmap.find(pz_); search != genidmap.end())
+	igen=search->second; //index of the GeneratedParticle
+      
+      mcidmap.insert({imc,igen});
+
+    }
+
+    /*
+      ReconstructedParticles loop
+      - Add all particles to the std::vector<> of particles
+      - Identify the 
+      - Identify closest matching MCParticle in theta,phi,E space
+
+    */
+
+   
+      
+    for(int itrack=0; itrack < tracks_PDG.GetSize(); itrack++){
+
+      int pid_ = tracks_PDG[itrack];
+      double px_ = tracks_p_x[itrack];
+      double py_ = tracks_p_y[itrack];
+      double pz_ = tracks_p_z[itrack];
+      double e_ = tracks_e[itrack];
+      double m_ = sqrt(e_*e_-px_*px_+py_*py_+pz_*pz_);
+      
+      // Add to trackpart
+      ParticlesEE part;
+
+      part.pid=pid_;
+      part.charge = (pid_ == 211 || pid_ == 321 || pid_ == 2212 || pid_ == -11 || pid_ == -13)?1:(pid_ == -211 || pid_ == -321 || pid_ == -2212 || pid_ == 11 || pid_ == 13)?-1:0;
+      part.vecPart.SetPxPyPzE(px_,py_,pz_,e_);
+
+      /*
+	Read through Associations to match particles
+	By default, we assume no association, so mcID --> -1
+	assoc_recID --> itrack (index of the RecoParticle)
+	assoc_simID --> imc (index of the MCParticle)
+      */
+
+
+      part.mcID=-1;
+      for(int iassoc = 0 ; iassoc < assoc_simID.GetSize() ; iassoc++){
+	int idx_recID = assoc_recID[iassoc]; 
+	int idx_simID = assoc_simID[iassoc];
+	if(itrack==idx_recID){ // This track has an association
+	  part.mcID=idx_simID;
+	  break; // Only one association per particle
+	}
       }
 
-    } // end loop over generated particles
-
-    // check for found generated particles
-    if(num_ele_beams==0)     { ErrorPrint("WARNING: missing MC electron beam");      continue; };
-    if(num_ion_beams==0)     { ErrorPrint("WARNING: missing MC ion beam");           continue; };
-    if(num_sim_electrons==0) { ErrorPrint("WARNING: missing scattered electron");    continue; };
-    if(double_counted_beam)  { ErrorPrint("WARNING: found multiple beam particles"); continue; };
-
-    // set Kinematics 4-momenta
-    kinTrue->vecEleBeam  = GetP4(mcPartEleBeam);
-    kinTrue->vecIonBeam  = GetP4(mcPartIonBeam);
-    kinTrue->vecElectron = GetP4(mcPartElectron);
-
-    // print beam particles
-    if(verbose) {
-      if(verbose) fmt::print("\n{:-<60}\n","GENERATED BEAMS ");
-      PrintParticle(mcPartEleBeam);
-      PrintParticle(mcPartIonBeam);
-      if(verbose) fmt::print("\n{:-<60}\n","GENERATED SCATTERED ELECTRON ");
-      PrintParticle(mcPartElectron);
+      trackpart.push_back(part);
+      trackidmap.insert({itrack,part.mcID}); 
+      
     }
 
-    // add reconstructed particles to Hadronic Final State (HFS)
-    /* the following will run loops over Reconstructed Particle <-> MC Particle associations
-     * - uses high-order function `LoopMCRecoAssocs` for common tasks, such as quality cuts
-     *   and getting the reconstructed PID (PDG)
-     * - first define a first-order function (`payload`), then call `LoopMCRecoAssocs`
-     * - see `LoopMCRecoAssocs` for `payload` signature
-     */
-    auto AllRecPartsToHFS = [&] (auto& simPart, auto& recPart, auto recPDG) {
-      kin->AddToHFS(GetP4(recPart));
-    };
-    if(verbose) fmt::print("\n{:-<60}\n","MC<->Reco ASSOCIATIONS ");
-    // LoopMCRecoAssocs(mcRecAssocs, AllRecPartsToHFS, verbose); // FIXME: relations unavailable
-
-    // find reconstructed electron
-    // ============================================================================
-    /* FIXME: need realistic electron finder; all of the following options rely
-     * on MC-truth matching; is there any common upstream realistic electron finder
-     */
-
-    // find scattered electron by simply matching to truth
-    // FIXME: not working, until we have truth matching and/or reconstructed PID
-    // FIXME: does `simPart==mcPartElectron` work as expected?
+
+
     /*
-    auto FindRecoEleByTruth = [&] (auto& simPart, auto& recPart, auto recPDG) {
-      if(recPDG==constants::pdgElectron && simPart==mcPartElectron) {
-        num_rec_electrons++;
-        kin->vecElectron = GetP4(recPart);
-      };
-    };
-    LoopMCRecoAssocs(mcRecAssocs, FindRecoEleByTruth); // FIXME: relations unavailable
+      With the GeneratedParticles, MCParticles, and ReconstructedParticles filled,
+      we can begin to search for the beam particles and hadronic final state (HFS)
+      This is done for both the Truth and Reconstructed Particles
     */
 
-    // use electron finder from upstream algorithm `InclusiveKinematics*`
-    // FIXME: `InclusiveKinematics` collections are not yet available
-    // FIXME: is the correct upstream electron finder used here? The
-    // `InclusiveKinematics*` recon algorithms seem to rely on
-    // `Jug::Base::Beam::find_first_scattered_electron(mcParts)` and matching
-    // to truth; this guarantees we get the correct reconstructed scattered
-    // electron
     /*
-    const auto& disCalcs = evStore.get<edm4eic::InclusiveKinematicsCollection>("InclusiveKinematicsElectron");
-    if(disCalcs.size() != 1) ErrorPrint(fmt::format("WARNING: disCalcs.size = {} != 1 for this event",disCalcs.size()));
-    for(const auto& calc : disCalcs) {
-      auto ele = calc.getScat();
-      if( ! ele.isAvailable()) {
-        ErrorPrint("WARNING: `disCalcs` scattered electron unavailable");
-        continue;
+      Loop over MCParticles
+    */
+
+    for(ParticlesEE mcpart_: mcpart){
+
+      int imc = mcpart_.mcID;
+      /* Beam particles have a MCParticles.generatorStatus of 4 */
+      int genStat_ = mcpart_genStat[imc];
+      if(mcpart_.pid==11 && genStat_ == 4){
+	foundBeamElectron=true;
+	kinTrue->vecEleBeam = mcpart_.vecPart;
+      }
+      else if(mcpart_.pid==2212 && genStat_ == 4){
+	foundBeamIon=true;
+	kinTrue->vecIonBeam = mcpart_.vecPart;
+      }
+      else if(genStat_==4){
+	cout << "Warning...unknown beam particle with generatorStatus == 4 found...Continuing..." << endl;
       }
-      num_rec_electrons++;
-      kin->vecElectron = GetP4(ele);
+
+      /* Assume the scattered electron is the pid==11 final state particle with the most energy */
+      if(mcpart_.pid==11 && genStat_ == 1 && mcpart_.vecPart.P() > maxP)
+	{
+	  maxP=mcpart_.vecPart.P();
+	  kinTrue->vecElectron = mcpart_.vecPart;
+	  genEleID = mcpart_.mcID; 
+	}
+
+      /*
+	Only append MCParticles to the HFS if they are matched with a GeneratedParticle
+       */
+      
+      else if(genStat_ == 1 && mcidmap[mcpart_.mcID]>-1){ 
+	kinTrue->AddToHFS(mcpart_.vecPart);
+      }
+    
     }
+
+    //check beam finding
+    if(!foundBeamElectron || !foundBeamIon) { numNoBeam++; continue;};
+
+    /*
+      Loop over RecoParticles
     */
 
-    // check for found reconstructed scattered electron
-    if(num_rec_electrons == 0) { ErrorPrint("WARNING: reconstructed scattered electron not found"); continue; };
-    if(num_rec_electrons >  1) { ErrorPrint("WARNING: found more than 1 reconstructed scattered electron"); };
+    int itrack = 0;
+    bool recEleFound=false;
+    for(ParticlesEE trackpart_ : trackpart){
+      // Skip if there is no matching MCParticle
+      if(trackidmap[itrack]==-1) continue;
+      // If the trackidmap is linked to the genEleID (generated scattered electron), identify this reco particle as the electron
+      if(trackidmap[itrack]==genEleID){	
+	recEleFound=true;
+	kin->vecElectron= trackpart_.vecPart;
+      }
+      // Add the final state particle to the HFS
+      kin->AddToHFS(trackpart_.vecPart);
+      itrack++;
+    }
+
+    // Skip event if the reco scattered electron was missing
+    if(recEleFound==false){
+      numNoEle++;
+      continue;
+    }
 
-    // subtract electron from hadronic final state variables
+    // subtrct electron from hadronic final state variables
     kin->SubtractElectronFromHFS();
     kinTrue->SubtractElectronFromHFS();
 
     // skip the event if there are no reconstructed particles (other than the
     // electron), otherwise hadronic recon methods will fail
-    if(kin->countHadrons == 0) { ErrorPrint("WARNING: no hadrons"); };
-  
-    // calculate DIS kinematics; skip the event if the calculation did not go well
-    if( ! kin->CalculateDIS(reconMethod)     ) continue; // reconstructed
-    if( ! kinTrue->CalculateDIS(reconMethod) ) continue; // generated (truth)
+    if(kin->countHadrons == 0) {
+      numNoHadrons++;
+      continue;
+    };
+   
+    // calculate DIS kinematics
+    if(!(kin->CalculateDIS(reconMethod))) continue; // reconstructed
+    if(!(kinTrue->CalculateDIS(reconMethod))) continue; // generated (truth)
 
 
+    /*
+      Loop again over the reconstructed particles
+      Calculate Hasdron Kinematics
+      Fill output data structures (Histos, SimpleTree, etc.)
+    */
 
-    // loop over Reco<->MC associations again
-    /* - calculate SIDIS kinematics
-     * - fill output data structures
-     */
-    auto SidisOutput = [&] (auto& simPart, auto& recPart, auto recPDG) {
+    for(ParticlesEE part : trackpart){
+
+      int pid_ = part.pid;
+      int mcid_ = part.mcID;
 
       // final state cut
-      // - check PID, to see if it's a final state we're interested in
-      auto kv = PIDtoFinalState.find(recPDG);
-      if(kv!=PIDtoFinalState.end()) finalStateID = kv->second; else return;
-      if(activeFinalStates.find(finalStateID)==activeFinalStates.end()) return;
+      // - check PID, to see if it's a final state we're interested in for
+      //   histograms; if not, proceed to next track
+      auto kv = PIDtoFinalState.find(pid_);
+      if(kv!=PIDtoFinalState.end()) finalStateID = kv->second; else continue;
+      if(activeFinalStates.find(finalStateID)==activeFinalStates.end()) continue;
+
+      // calculate reconstructed hadron kinematics
+      kin->vecHadron = part.vecPart;
+      kin->CalculateHadronKinematics();
+
+      // find the matching truth hadron using mcID, and calculate its kinematics
+      if(mcid_ > 0) {
+	for(auto imc : mcpart) {
+	  if(mcid_ == imc.mcID) {
+	    kinTrue->vecHadron = imc.vecPart;
+	    break;
+	  }
+	}
+      }
 
-      // set SIDIS particle 4-momenta, and calculate their kinematics
-      kinTrue->vecHadron = GetP4(simPart);
       kinTrue->CalculateHadronKinematics();
-      kin->vecHadron = GetP4(recPart);
-      kin->CalculateHadronKinematics();
 
       // weighting
-      //   FIXME: we are in a podio::EventStore event loop, thus we need an
-      //          alternative to `chain->GetTreeNumber()`; currently disabling weighting
-      //          for now, by setting `wTrack=1.0`
-      // Double_t Q2weightFactor = GetEventQ2Weight(kinTrue->Q2, inLookup[chain->GetTreeNumber()]);
-      // wTrack = Q2weightFactor * weight->GetWeight(*kinTrue);
-      wTrack = 1.0; // FIXME
+      Double_t Q2weightFactor = GetEventQ2Weight(kinTrue->Q2, inLookup[chain->GetTreeNumber()]);
+      wTrack = Q2weightFactor * weight->GetWeight(*kinTrue);
       wTrackTotal += wTrack;
 
-      // fill track histograms in activated bins
-      FillHistosTracks();
-
-      // fill simple tree
-      // - not binned
-      // - `IsActiveEvent()` is only true if at least one bin gets filled for this track
-      if( writeSimpleTree && HD->IsActiveEvent() ) ST->FillTree(wTrack);
-    };
-    // LoopMCRecoAssocs(mcRecAssocs, SidisOutput); // FIXME: relations unavailable
+      if(includeOutputSet["1h"]) {
+	// fill track histograms in activated bins
+	FillHistosTracks();
 
+	// fill simple tree
+	// - not binned
+	// - `IsActiveEvent()` is only true if at least one bin gets filled for this track
+	if( writeSimpleTree && HD->IsActiveEvent() ) ST->FillTree(wTrack);
+      }
 
-    // read kinematics calculations from upstream /////////////////////////
-    // FIXME: `InclusiveKinematics` collections are not yet available
-    // TODO: cross check these with our calculations from `Kinematics`
-    /*
-    if(crossCheckKinematics) {
-      auto PrintRow = [] <typename T> (std::string name, std::vector<T> vals, bool header=false) {
-        fmt::print("  {:>16}",name);
-        if(header) { for(auto val : vals) fmt::print(" {:>8}",   val); }
-        else       { for(auto val : vals) fmt::print(" {:8.4f}", val); }
-        fmt::print("\n");
-      };
-      // upstream calculations
-      fmt::print("\n{:-<75}\n","KINEMATICS, calculated from upstream: ");
-      PrintRow("", std::vector<std::string>({ "x", "Q2", "W", "y", "nu" }), true);
-      for(const auto upstreamReconMethod : upstreamReconMethodList)
-        for(const auto& calc : evStore.get<edm4eic::InclusiveKinematicsCollection>("InclusiveKinematics"+upstreamReconMethod) )
-          PrintRow( upstreamReconMethod, std::vector<float>({
-              calc.getX(),
-              calc.getQ2(),
-              calc.getW(),
-              calc.getY(),
-              calc.getNu()
-              }));
-      // local calculations
-      fmt::print("{:-<75}\n",fmt::format("KINEMATICS, calculated locally in SIDIS-EIC, with method \"{}\": ",reconMethod));
-      auto PrintKinematics = [&PrintRow] (std::string name, Kinematics *K) {
-        PrintRow( name, std::vector<Double_t>({
-            K->x,
-            K->Q2,
-            K->W,
-            K->y,
-            K->Nu
-            }));
-      };
-      PrintKinematics("Truth",kinTrue);
-      PrintKinematics("Reconstructed",kin);
-      // compare upstream and local
-      if(associatedUpstreamMethod != "NONE") {
-        fmt::print("{:-<75}\n",fmt::format("DIFFERENCE: upstream({}) - local({}): ",associatedUpstreamMethod,reconMethod));
-        for(const auto upstreamMethod : std::vector<std::string>({"Truth",associatedUpstreamMethod})) {
-          const auto& upstreamCalcs = evStore.get<edm4eic::InclusiveKinematicsCollection>("InclusiveKinematics"+upstreamMethod);
-          for(const auto& upstreamCalc : upstreamCalcs) {
-            auto K    = upstreamMethod=="Truth" ? kinTrue : kin;
-            auto name = upstreamMethod=="Truth" ? "Truth" : "Reconstructed";
-            PrintRow( name, std::vector<Double_t>({
-                upstreamCalc.getX()  - K->x,
-                upstreamCalc.getQ2() - K->Q2,
-                upstreamCalc.getW()  - K->W,
-                upstreamCalc.getY()  - K->y,
-                upstreamCalc.getNu() - K->Nu
-                }));
-          }
-        }
+    }//hadron loop
+    
+    
+    
+    // =======================================
+    // DEBUG PRINT STATEMENTS
+    // =======================================
+    
+    int ipart = 0;
+    for(ParticlesEE trackpart_: trackpart) {
+      cout << trackpart_.pid << "|" << trackpart_.vecPart.E() << "\t";
+      ParticlesEE genpart_;
+      ParticlesEE mcpart_;
+      int mcpart_idx=trackidmap[ipart];
+      if(mcpart_idx>-1){ // Found MCParticle
+	mcpart_ = mcpart.at(mcpart_idx);
+	cout << mcpart_.pid << "|" << mcpart_.vecPart.E() << "\t";
+	int genpart_idx=mcidmap[mcpart_.mcID];
+	if(genpart_idx>-1){ // Found GeneratedParticle
+	  genpart_ = genpart.at(genpart_idx);
+	  cout << genpart_.pid << "|" << genpart_.vecPart.E() << "\t";	  
+	}
       }
-      else fmt::print("{:-<75}\n  method \"{}\" is not available upstream\n","DIFFERENCE: ",reconMethod);
-    } // if crossCheckKinematics
-    */
+      ipart++;
+      cout<<"\n";
+    }
+    cout << "\n ============================================================ \n" <<endl;
+    
 
-  } // event loop
-  fmt::print("end event loop\n");
+  } while(tr.Next());
 
+  
   // finish execution
-  evStore.clear();
-  podioReader.endOfEvent();
-  podioReader.closeFile();
   Finish();
+
+  // final printout
+  cout << "Total number of scattered electrons found: " << numEle << endl;
+  if(numNoEle>0)
+    cerr << "WARNING: skipped " << numNoEle << " events which had no reconstructed scattered electron" << endl;
+  if(numNoHadrons>0)
+    cerr << "WARNING: skipped " << numNoHadrons << " events which had no reconstructed hadrons" << endl;
+  if(numNoBeam>0)
+    cerr << "WARNING: skipped " << numNoBeam << " events which had no beam particles" << endl;
+  if(numProxMatched>0)
+    cerr << "WARNING: " << numProxMatched << " recon. particles were proximity matched to truth (when mcID match failed)" << endl;
 }
 
 
diff --git a/src/AnalysisEpic.h b/src/AnalysisEpic.h
index d2d00b26..24d09e23 100644
--- a/src/AnalysisEpic.h
+++ b/src/AnalysisEpic.h
@@ -1,6 +1,13 @@
 #ifndef AnalysisEpic_
 #define AnalysisEpic_
 
+// TEMP FIX:
+// New includes
+
+#include "TTreeReader.h"
+#include "TTreeReaderValue.h"
+#include "TTreeReaderArray.h"
+
 // data model
 #include "podio/EventStore.h"
 #include "podio/ROOTReader.h"

From 37b5febc6cfe89ec51dafcf9e6ffb7e7ae2742ff Mon Sep 17 00:00:00 2001
From: Christopher Dilks <christopher.j.dilks@gmail.com>
Date: Wed, 23 Nov 2022 15:25:14 -0500
Subject: [PATCH 06/32] fix: `weight` -> `weightTrack`

---
 src/AnalysisEpic.cxx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/AnalysisEpic.cxx b/src/AnalysisEpic.cxx
index 2706edfb..2ff7f7a5 100644
--- a/src/AnalysisEpic.cxx
+++ b/src/AnalysisEpic.cxx
@@ -337,7 +337,7 @@ void AnalysisEpic::Execute()
 
       if(includeOutputSet["1h"]) {
         // fill single-hadron histograms in activated bins
-        auto wTrack = Q2weightFactor * weight->GetWeight(*kinTrue);
+        auto wTrack = Q2weightFactor * weightTrack->GetWeight(*kinTrue);
         wTrackTotal += wTrack;
         FillHistos1h(wTrack);
         FillHistosInclusive(wTrack);

From c450ac41822fd4e43cc6bfb1a584fc1a0ffc5edb Mon Sep 17 00:00:00 2001
From: Gregtom3 <gamatousek@gmail.com>
Date: Mon, 28 Nov 2022 12:02:38 -0500
Subject: [PATCH 07/32] Prototyping

---
 src/Analysis.cxx     |  3 +++
 src/Analysis.h       |  2 ++
 src/AnalysisEpic.cxx | 30 ++++++++++++++++++++++++---
 src/ParticleTree.cxx | 21 +++++++++++++++++++
 src/ParticleTree.h   | 48 ++++++++++++++++++++++++++++++++++++++++++++
 5 files changed, 101 insertions(+), 3 deletions(-)
 create mode 100644 src/ParticleTree.cxx
 create mode 100644 src/ParticleTree.h

diff --git a/src/Analysis.cxx b/src/Analysis.cxx
index 20d91b59..c1d8299a 100644
--- a/src/Analysis.cxx
+++ b/src/Analysis.cxx
@@ -91,6 +91,7 @@ Analysis::Analysis(
   // - these settings can be set at the macro level
   verbose         = false;
   writeSimpleTree = false;
+  writeParticleTree = false;
   maxEvents       = 0;
   useBreitJets    = false;
   errorCntMax     = 1000;
@@ -307,6 +308,7 @@ void Analysis::Prepare() {
   kin = new Kinematics(eleBeamEn,ionBeamEn,crossingAngle);
   kinTrue = new Kinematics(eleBeamEn, ionBeamEn, crossingAngle);
   ST = new SimpleTree("tree",kin,kinTrue);
+  PT = new ParticleTree("ptree",kin,kinTrue);
 
   // if including jets, define a `jet` final state
 #ifndef EXCLUDE_DELPHES
@@ -656,6 +658,7 @@ void Analysis::Finish() {
   cout << "writing ROOT file..." << endl;
   outFile->cd();
   if(writeSimpleTree) ST->WriteTree();
+  if(writeParticleTree) PT->WriteTree();
   HD->Payload([this](Histos *H){ H->WriteHists(outFile); }); HD->ExecuteAndClearOps();
   HD->Payload([this](Histos *H){ H->Write(); }); HD->ExecuteAndClearOps();
   std::vector<Double_t> vec_wInclusiveTotal { wInclusiveTotal };
diff --git a/src/Analysis.h b/src/Analysis.h
index da0fa3f6..34489a5c 100644
--- a/src/Analysis.h
+++ b/src/Analysis.h
@@ -56,6 +56,7 @@ class Analysis : public TNamed
     // common settings
     Bool_t verbose; // if true, print a lot more information
     Bool_t writeSimpleTree; // if true, write SimpleTree (not binned)
+    Bool_t writeParticleTree; // if true, write ParticleTree (not binned)
     Long64_t maxEvents; /* default=0, which runs all events;
                          * if > 0, run a maximum number of `maxEvents` events (useful for quick tests)
                          */
@@ -118,6 +119,7 @@ class Analysis : public TNamed
 
     // shared objects
     SimpleTree *ST;
+    ParticleTree *PT;
     Kinematics *kin, *kinTrue;
     HistosDAG *HD;
     Weights const* weightInclusive;
diff --git a/src/AnalysisEpic.cxx b/src/AnalysisEpic.cxx
index 2ff7f7a5..c16092cd 100644
--- a/src/AnalysisEpic.cxx
+++ b/src/AnalysisEpic.cxx
@@ -274,6 +274,8 @@ void AnalysisEpic::Execute()
       numNoEle++;
       continue;
     }
+    else
+      numEle++;
 
     // subtrct electron from hadronic final state variables
     kin->SubtractElectronFromHFS();
@@ -346,8 +348,30 @@ void AnalysisEpic::Execute()
 	// - not binned
 	// - `IsActiveEvent()` is only true if at least one bin gets filled for this track
 	if( writeSimpleTree && HD->IsActiveEvent() ) ST->FillTree(wTrack);
-      }
 
+	// fill particle tree
+	if( writeParticleTree && HD->IsActiveEvent() )
+	  {
+	    int ipart = 0;
+	    for(ParticlesEE trackpart_: trackpart){
+	      ParticlesEE mcpart_;                  
+	      int mcpart_idx=trackidmap[ipart];     // Map idx to the matched MCParticle
+	      int genStat_ = -1;                    // Default Generator Status of MCParticle is -1 (no match)
+	      if(mcpart_idx>-1){                    // RecoParticle has an MCParticle match
+		int imc = mcpart_.mcID;          
+		genStat_ = mcpart_genStat[imc];     // Get Generator status of MCParticle
+		mcpart_ = mcpart.at(mcpart_idx);    // Get MCParticle
+	      }
+	      PT->FillTree(trackpart_.vecPart,      // Fill Tree
+			   mcpart_.vecPart,
+			   trackpart_.pid,
+			   genStat_,
+			   wTrack);
+	      ipart++;
+	    }
+	  }
+      }
+      
     }//hadron loop
     
     
@@ -355,7 +379,7 @@ void AnalysisEpic::Execute()
     // =======================================
     // DEBUG PRINT STATEMENTS
     // =======================================
-    
+    /*    
     int ipart = 0;
     for(ParticlesEE trackpart_: trackpart) {
       cout << trackpart_.pid << "|" << trackpart_.vecPart.E() << "\t";
@@ -375,7 +399,7 @@ void AnalysisEpic::Execute()
       cout<<"\n";
     }
     cout << "\n ============================================================ \n" <<endl;
-    
+    */
 
   } while(tr.Next());
 
diff --git a/src/ParticleTree.cxx b/src/ParticleTree.cxx
new file mode 100644
index 00000000..66fd976d
--- /dev/null
+++ b/src/ParticleTree.cxx
@@ -0,0 +1,21 @@
+#include "ParticleTree.h"
+
+ClassImp(ParticleTree)
+
+// constructor
+ParticleTree::ParticleTree(TString treeName_) 
+  : treeName(treeName_)
+{
+  T = new TTree(treeName,treeName);
+  T->Branch("recPart",  "TLorentzVector" , &(recopart_));    
+  T->Branch("mcPart",   "TLorentzVector" , &(mcpart_));    
+  T->Branch("pid",      &(pid_)           , "pid/I");
+  T->Branch("status",      &(status_)           , "status/I");
+  T->Branch("Weight",   &(weight)        , "Weight/D");
+};
+
+
+// destructor
+ParticleTree::~ParticleTree() {
+};
+
diff --git a/src/ParticleTree.h b/src/ParticleTree.h
new file mode 100644
index 00000000..5ae4006f
--- /dev/null
+++ b/src/ParticleTree.h
@@ -0,0 +1,48 @@
+/* ParticleTree
+ * - provides a particle tree for storing reconstructed particle kinematics + pid
+ * - helpful for debugging matching algorithm
+ */
+#ifndef ParticleTree_
+#define ParticleTree_
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <iostream>
+#include <sstream>
+
+// sidis-eic
+
+// ROOT
+#include "TTree.h"
+#include "TLorentzVector.h"
+
+class ParticleTree : public TObject
+{
+  public:
+    ParticleTree(TString treeName_);
+    ~ParticleTree();
+
+    TTree *GetTree() { return T; };
+    void FillTree(TLorentzVector recopart, TLorentzVector mcpart, int pid, int status, Double_t w) {
+      recopart_ = recopart;
+      mcpart_   = mcpart;
+      pid_      = pid;
+      status_   = status;
+      weight    = w;
+      T->Fill(); };
+    void WriteTree() { T->Write(); };
+
+  private:
+  
+    Double_t weight;
+    TTree *T;
+    TString treeName;
+    TLorentzVector recopart_;
+    TLorentzVector mcpart_;
+    int status_;
+    int pid_;
+  
+  ClassDef(ParticleTree,1);
+};
+
+#endif

From 427760a44dce4adf762d602aa2ef49396ff4e75c Mon Sep 17 00:00:00 2001
From: Gregtom3 <gamatousek@gmail.com>
Date: Mon, 28 Nov 2022 12:51:40 -0500
Subject: [PATCH 08/32] Working particle matching with ParticleTree

---
 s3tools/make-epic-config.sh |  2 +-
 src/Analysis.cxx            |  2 +-
 src/Analysis.h              |  1 +
 src/AnalysisEpic.cxx        | 11 +++++++----
 src/LinkDef.h               |  1 +
 tutorial/analysis_epic.C    |  9 ++++-----
 6 files changed, 15 insertions(+), 11 deletions(-)

diff --git a/s3tools/make-epic-config.sh b/s3tools/make-epic-config.sh
index d7e1e88f..d14e22d0 100755
--- a/s3tools/make-epic-config.sh
+++ b/s3tools/make-epic-config.sh
@@ -9,7 +9,7 @@
 # RELEASE TAG AND RECO DIR: ###########################
 detector_config="epic_arches"
 # detector_config="epic_brycecanyon"
-release="22.11.0"
+release="22.11.2"
 releaseDir="S3/eictest/EPIC/RECO/$release/$detector_config/DIS/NC"
 filter='eicrecon'
 # filter='juggler'
diff --git a/src/Analysis.cxx b/src/Analysis.cxx
index c1d8299a..e7ff3385 100644
--- a/src/Analysis.cxx
+++ b/src/Analysis.cxx
@@ -308,7 +308,7 @@ void Analysis::Prepare() {
   kin = new Kinematics(eleBeamEn,ionBeamEn,crossingAngle);
   kinTrue = new Kinematics(eleBeamEn, ionBeamEn, crossingAngle);
   ST = new SimpleTree("tree",kin,kinTrue);
-  PT = new ParticleTree("ptree",kin,kinTrue);
+  PT = new ParticleTree("ptree");
 
   // if including jets, define a `jet` final state
 #ifndef EXCLUDE_DELPHES
diff --git a/src/Analysis.h b/src/Analysis.h
index 34489a5c..b29d5e39 100644
--- a/src/Analysis.h
+++ b/src/Analysis.h
@@ -29,6 +29,7 @@
 #include "HistosDAG.h"
 #include "Kinematics.h"
 #include "SimpleTree.h"
+#include "ParticleTree.h"
 #include "Weights.h"
 #include "CommonConstants.h"
 
diff --git a/src/AnalysisEpic.cxx b/src/AnalysisEpic.cxx
index c16092cd..c3916296 100644
--- a/src/AnalysisEpic.cxx
+++ b/src/AnalysisEpic.cxx
@@ -84,7 +84,8 @@ void AnalysisEpic::Execute()
     // Index maps for particle sets
     std::map <double,int> genidmap; // <pz, index_gen>
     std::map <int,int> mcidmap; // <index_mc, index_gen>
-    std::map <int,int>    trackidmap; // <index, index_mc>  
+    std::map <int,int> trackidmap; // <index, index_mc>  
+    std::map <int,int> trackstatmap; // <index, genstatus>
 
     // ParticleEE vectors
     // The index of the vectors correspond to their for loop idx
@@ -305,7 +306,7 @@ void AnalysisEpic::Execute()
 
     /*
       Loop again over the reconstructed particles
-      Calculate Hasdron Kinematics
+      Calculate Hadron Kinematics
       Fill output data structures (Histos, SimpleTree, etc.)
     */
 
@@ -358,9 +359,11 @@ void AnalysisEpic::Execute()
 	      int mcpart_idx=trackidmap[ipart];     // Map idx to the matched MCParticle
 	      int genStat_ = -1;                    // Default Generator Status of MCParticle is -1 (no match)
 	      if(mcpart_idx>-1){                    // RecoParticle has an MCParticle match
+ 		mcpart_ = mcpart.at(mcpart_idx);        // Get MCParticle
 		int imc = mcpart_.mcID;          
-		genStat_ = mcpart_genStat[imc];     // Get Generator status of MCParticle
-		mcpart_ = mcpart.at(mcpart_idx);    // Get MCParticle
+		genStat_ = mcpart_genStat[imc];         // Get Generator status of MCParticle
+		if(imc==genEleID)                       // If MCParticle was scattered electron, set status to 2
+		  genStat_=2;
 	      }
 	      PT->FillTree(trackpart_.vecPart,      // Fill Tree
 			   mcpart_.vecPart,
diff --git a/src/LinkDef.h b/src/LinkDef.h
index 6b9dcea2..0a105ce2 100644
--- a/src/LinkDef.h
+++ b/src/LinkDef.h
@@ -14,6 +14,7 @@
 // analysis objects
 #pragma link C++ class Kinematics+;
 #pragma link C++ class SimpleTree+;
+#pragma link C++ class ParticleTree+;
 #pragma link C++ class Weights+;
 #pragma link C++ class WeightsUniform+;
 #pragma link C++ class WeightsSivers+;
diff --git a/tutorial/analysis_epic.C b/tutorial/analysis_epic.C
index 0a497081..3fe221f4 100644
--- a/tutorial/analysis_epic.C
+++ b/tutorial/analysis_epic.C
@@ -26,21 +26,20 @@ R__LOAD_LIBRARY(Sidis-eic)
  *   between fast and full simulations
  */
 void analysis_epic(
-    // TString  configFile="tutorial/test.epic.config", // list of input files
-    TString  configFile="datarec/epic.22.11.0/22.11.0/18x275/files.config", // list of input files
+    TString  configFile="datarec/epic.22.11.2/22.11.2/10x100/files.config", // list of input files
     TString  outfilePrefix="tutorial.epic"        // output filename prefix
     )
 {
 
   // setup analysis ========================================
-  // - define `AnalysisEpic` instead of `AnalysisDelphes`
   AnalysisEpic *A = new AnalysisEpic(configFile, outfilePrefix);
 
   // settings
   A->crossCheckKinematics = true;   // enable cross check with upstream kinematics
   A->verbose              = true;   // print event-by-event information
-  A->maxEvents            = 300000; // use this to limit the number of events
-  A->writeSimpleTree      = true;
+  A->maxEvents            = 1000; // use this to limit the number of events
+  A->writeSimpleTree      = true; // write event-by-event info into TTree
+  A->writeParticleTree    = true; // write particle level info into TTree
 
   // set reconstruction method and final states =============================
   // - see `Analysis` constructor for methods (or other tutorials)

From e1efecbf139539392b40b357101a2c7db5e42048 Mon Sep 17 00:00:00 2001
From: Gregtom3 <gamatousek@gmail.com>
Date: Mon, 28 Nov 2022 16:11:06 -0500
Subject: [PATCH 09/32] Edit

---
 tutorial/analysis_epic.C | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorial/analysis_epic.C b/tutorial/analysis_epic.C
index 3fe221f4..11e91ff3 100644
--- a/tutorial/analysis_epic.C
+++ b/tutorial/analysis_epic.C
@@ -37,7 +37,7 @@ void analysis_epic(
   // settings
   A->crossCheckKinematics = true;   // enable cross check with upstream kinematics
   A->verbose              = true;   // print event-by-event information
-  A->maxEvents            = 1000; // use this to limit the number of events
+  //A->maxEvents            = 1000; // use this to limit the number of events
   A->writeSimpleTree      = true; // write event-by-event info into TTree
   A->writeParticleTree    = true; // write particle level info into TTree
 

From 3fcceec122ef160746553e6267bb11c9d6f1cc0d Mon Sep 17 00:00:00 2001
From: Gregtom3 <gamatousek@gmail.com>
Date: Mon, 28 Nov 2022 16:13:27 -0500
Subject: [PATCH 10/32] ParticleTree anaylsis script

---
 macro/postprocess_ParticleTree.ipynb | 495 +++++++++++++++++++++++++++
 1 file changed, 495 insertions(+)
 create mode 100644 macro/postprocess_ParticleTree.ipynb

diff --git a/macro/postprocess_ParticleTree.ipynb b/macro/postprocess_ParticleTree.ipynb
new file mode 100644
index 00000000..ab4c1010
--- /dev/null
+++ b/macro/postprocess_ParticleTree.ipynb
@@ -0,0 +1,495 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "faced-providence",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ROOT\n",
+    "ROOT.gStyle.SetPalette(ROOT.kDarkRainBow)\n",
+    "ROOT.gStyle.SetHistLineWidth(2)\n",
+    "ROOT.gStyle.SetTitleSize(0.04,\"XY\")\n",
+    "ROOT.gStyle.SetLegendBorderSize(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "mounted-conviction",
+   "metadata": {},
+   "source": [
+    "# postprocess_ParticleTree.ipynb\n",
+    "---\n",
+    "The purpose of this analysis script is to create plots of particle kinematics from the ROOT TTree named ParticleTree. This tree is only generated when the user sets the appropriate flag in the analysis script. The tree has currently has 5 columns. These are...\n",
+    "- recPart (TLorentzVector)\n",
+    "- mcPart  (TLorentzVector)\n",
+    "- pid     (int)\n",
+    "- status  (int)\n",
+    "- weight  (double)\n",
+    "\n",
+    "For each event, the TLorentzVectors of the reconstructed particles are saved into the recPart branch. Using the associations branch included in the Epic sims, a Monte Carlo particle can be matched to its reconstructed partner using event generator level information. If an mcPart entry is empty, this means that the reconstructed particle did not originate from the hepmc file (ex: secondaries, background, etc). The pid of the reconstructed particle is also stored. The status variable is -1 if no MCParticle match was found, 2 if the MCParticle match was the scattered electron (highest momentum particle with pid==11), and 1 otherwise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "cardiac-moment",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Output ROOT File and name of ParticleTree\n",
+    "rootfile=\"../out/tutorial.epic.root\"\n",
+    "treeName=\"ptree\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "working-portugal",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " File loaded\n",
+      " --------------------------------------------------\n",
+      " Num Scattered Electrons =  11826\n",
+      " Num Particles =  76822\n",
+      " Num Matches =  69266\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create RDataFrame\n",
+    "df=ROOT.RDataFrame(treeName,rootfile)\n",
+    "Nele = df.Filter(\"status==2\").Count()\n",
+    "Npar = df.Count()\n",
+    "Nmat = df.Filter(\"status!=-1\").Count()\n",
+    "print(\" File loaded\\n\",\"-\"*50)\n",
+    "print(\" Num Scattered Electrons = \",Nele.GetValue())\n",
+    "print(\" Num Particles = \",Npar.GetValue())\n",
+    "print(\" Num Matches = \",Nmat.GetValue())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "authorized-broadway",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TLatex to overlay on TCanvas\n",
+    "def drawLatex(xNDC,yNDC):\n",
+    "    latex=ROOT.TLatex()\n",
+    "    latex.SetTextFont(42)\n",
+    "    latex.SetTextSize(0.04)\n",
+    "    latex.DrawLatexNDC(xNDC,yNDC,\"#bf{epic.22.11.2} DIS NC\")\n",
+    "    latex.DrawLatexNDC(xNDC,yNDC-0.05,\"10x100 e+p collisions\")\n",
+    "    latex.DrawLatexNDC(xNDC,yNDC-0.1,\"Q^{2} > 1 GeV^{2}\")\n",
+    "    return"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "alpha-assignment",
+   "metadata": {},
+   "source": [
+    "# Comparing Particle Distributions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "stretch-twist",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Particle histogram for both MC and Reco\n",
+    "def get_both_histo1d(bins,xmin,xmax,drawString,filterString):\n",
+    "    hmc=df.Define(\"mcVar\",\"mc{}\".format(drawString)).Filter(filterString).Histo1D((\"hmc\",\"\",bins,xmin,xmax),\"mcVar\")\n",
+    "    hreco=df.Define(\"recVar\",\"rec{}\".format(drawString)).Filter(filterString).Histo1D((\"hrec\",\"\",bins,xmin,xmax),\"recVar\")\n",
+    "    return hmc,hreco"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "adjacent-timing",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Queue RDataFrame histograms\n",
+    "hmc_e_E,hreco_e_E = get_both_histo1d(100,5,12,\"Part.E()\",\"pid==11 && status==2\")\n",
+    "hmc_pip_E,hreco_pip_E = get_both_histo1d(100,0,15,\"Part.E()\",\"pid==211 && status==1\")\n",
+    "hmc_pim_E,hreco_pim_E = get_both_histo1d(100,0,15,\"Part.E()\",\"pid==-211 && status==1\")\n",
+    "\n",
+    "hmc_e_eta,hreco_e_eta = get_both_histo1d(100,-3.5,1,\"Part.Eta()\",\"pid==11 && status==2\")\n",
+    "hmc_pip_eta,hreco_pip_eta = get_both_histo1d(100,-5,5,\"Part.Eta()\",\"pid==211 && status==1\")\n",
+    "hmc_pim_eta,hreco_pim_eta = get_both_histo1d(100,-5,5,\"Part.Eta()\",\"pid==-211 && status==1\")\n",
+    "\n",
+    "hmc_e_phi,hreco_e_phi = get_both_histo1d(100,-180,180,\"Part.Phi()*180/3.14159265\",\"pid==11 && status==2\")\n",
+    "hmc_pip_phi,hreco_pip_phi = get_both_histo1d(100,-180,180,\"Part.Phi()*180/3.14159265\",\"pid==211 && status==1\")\n",
+    "hmc_pim_phi,hreco_pim_phi = get_both_histo1d(100,-180,180,\"Part.Phi()*180/3.14159265\",\"pid==-211 && status==1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "popular-discipline",
+   "metadata": {},
+   "source": [
+    "## Particle Energy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "legendary-niagara",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c=ROOT.TCanvas(\"c\",\"c\",1800,600)\n",
+    "c.Divide(3,1)\n",
+    "c.cd(1)\n",
+    "hmc_e_E.GetXaxis().SetTitle(\"E_{e} [GeV]\")\n",
+    "hmc_e_E.Draw(\"PLC\")\n",
+    "hreco_e_E.Draw(\"PLC same\")\n",
+    "\n",
+    "c.cd(2)\n",
+    "hmc_pip_E.GetXaxis().SetTitle(\"E_{#pi+} [GeV]\")\n",
+    "hmc_pip_E.Draw(\"PLC\")\n",
+    "hreco_pip_E.Draw(\"PLC same\")\n",
+    "\n",
+    "c.cd(3)\n",
+    "hmc_pim_E.GetXaxis().SetTitle(\"E_{#pi-} [GeV]\")\n",
+    "hmc_pim_E.Draw(\"PLC\")\n",
+    "hreco_pim_E.Draw(\"PLC same\")\n",
+    "legend=ROOT.TLegend(0.4,0.4,0.8,0.5)\n",
+    "legend.AddEntry(hreco_pim_E.GetValue(),\"Reconstructed\",\"l\")\n",
+    "legend.AddEntry(hmc_pim_E.GetValue(),\"Monte Carlo\",\"l\")\n",
+    "legend.Draw(\"same\")\n",
+    "drawLatex(0.2,0.8)\n",
+    "c.Draw()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "timely-compression",
+   "metadata": {},
+   "source": [
+    "## Particle Pseudorapidity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "horizontal-handle",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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fnvnlta3renKcGYBNmn+IeMlmmtQttYAzTwSTtm3TiDFN0wyav3ix+BNBwVCAp5rsGVGM+jtEiDCeDQ6Wf2DlRsYJm3mqy6S6rlNYMxr+eJ26yccWuq4bDJ1jEglgMyJvZTN3cQ/0Wo3grcZDxETmy+BpX0odfV7NAFZgMMTnlVIq6Ku3qttuAXN5w5fu7/K2bw1PBAVDAZ4n9ZJLz8rCeIixaBsiPrhgO/Go6ORgPqXxM8Cqqh6yI4Cniduzb58My3Otl2sE3yPuZgejyqSej33fp4WXZiME4Ou/31M7+/V/l71eN/liTy1gnhYaIpO0qqo0iW6xgtiuYCj7lT+P0pue50iZknmSSF3Xx+MxDSJzOBzO53Pf9+nWaH6qovXofk6IlB4GpueZ9c8Bv4ufkykVv97+9X0vLRRg2zbcCKa5Lwb94vPeElE0uP0zSgwPlH/vWrIeqzdO0Csun7RLU9akpx3FFR2n5jf1Zukd+9qGLc0CsuEWcGDQHyLuDfNnfit5Ivjbk/cHsHPjFMi6rpumKcsyRoo5nU5FUTRNczgc6rouy/LS1LSrksaOiVjn4HDSADdlWfZ9n56LpmMcjCYDsEnfPn2On6UrsphNNoJpmJe45YvGrjA34NLKKUtX6mMdfnrndmIw99z4jzkiO0n+2Pv67c+sk4+x+Ka4VuSVmd/7YcqlD06e0rheHX+Kj79Z2+gddenAo3RwGuNc3bGvLfnyz4/4Wboi77XJFjCkv9vxdWAQCY11zj8VRXE+n5e5DTxv2uYPkBn/89//+Z///s+jVoPr3X3lOZ1Ol5aMi9Zvss4zxzjDxfwOThp8nPzLw+CLxH+q//ef6v+NX6c18583N/6KNIKn0+mxFXY9v8M+T9pD7vEjHDPWNE1a59LoRlf+5b9Zz1SH6Fd75dauDHSkjTeZ/Igmtz9eUlVVHO/pdMrnipmpatrL5GpRmp/D/Dw0TRPXliv3tTaDpu2+W/WZxnc9Ted9/wBfqwWMv8z0Nv4Fpdf5X+b4ejL4u33zdL3zgjZj4/0ydDzZs0i7ePMJ0pWrwfVceR7OKb2DkwYfJ//yMPgi8bn+c9CtbzBfRLyNWSDSpBDjjQ+80BcVF5+Hc0rvsM+T9pBu8ilvMXrtxCRgsSTSu/KJpGOdtm1TZ96Z0x59vdPWxitH/fOtpek3L4l07FS9oijyuk2m1KXhC8dbruu67/tB0aBrc+xx8uPjTtCT20+1HU+bk/ePThscH0j6FbzQH/mgrbzvVn2m8V3PTf0+Lz4f5+PO5/bHDB1nofvTBGAPJvthaQThQ21pgDPgmdK4kONBIQdRzjT9yPVBz7TxK7vcps65sYsIOMbYuKkyKUgXXbnnN5jHAS+5o4N/ioSmrzen0ym2czweb+1fHAcyX8+Z01hVVQwGNf97aZomervPfyWLHU32kq7rOvaVxugAbrX9YKi7PgD2SQsIS8nzVm4dIfTv3/8osqTRPQ8wCjuR51oWRTFOPEylKdI3v1r+BWAQhbyU1Zhqkj6VbyEFQy+tE7G54mfAdLzlfOGlaGPqRf6eSWPeOfhgRCrfDDJOHmZEnN+sQNu2MYHM/F7SSMSXKvCiU43DSmw/GAoAAO/0qKBkdJC/frWvD9krsFaDSGiIqZbHDzUHa/Z9Pz9z0WQ+5kwYrq7r6Gw+mO0klRZZeO7NoGcu392lNNJ8DpYrs0QvjXB6t4j8zpzSmOYoosPj5Nwro5ORgjqfuxq/uJkNbiAnNG8QJ8eNmeQZIQ9hNnkAAJhz363X37//Mf55eN3gFU1O9V6WZR6EisS3GJJyvIX50heSZ33GtB4pQXLy0NKkPfF2Joky0g/jdT4bSZH1Lh8bB/jSyrG8/mlyR082rkx6nSYaunWDxexZjS7qRVGkid3n49GXRPWkdsJSZIYCAMDbBqHML9nryWjp9XkuM5/69skgpOxRntIY4zDm8ab50ldUVVWKi6UhOMeZg2l2nTRqZFEUl9I888E908LoBl5c7s8+MJhPaXIveTz3zQ1+qHzypY+Lknc/xZns+z7OwJvTPeXmO8u/+t/zlX4dZfvtFnP8QPHL5HpwBZmhAAAw4dunz/ETb390f+U/y9YNXtp5pKqqFBCMkFbTNFHUNE1EPOOz86WvJVU7OryP8xwH8qIUQbtpANC2beO8vRkJzadFujQze/QZj9fj6dGfLI1tWrxj2PQ0bdGb+0pn8nQ6xfoxuMGt+5rM0n3djNHUbg4aUFgbmaEAADB05S3cYL6jGAFNrgrcJOKbg07cKfrWtm3kSObBo0ulryWv88N7mqcN3hFZi7Mary9FQvP83GUjoXl26k3pmQ+RuslHJPSmXvORqHtfR/sVDhNxfegzphm89VMeQ/JAgqEAADAt75QnxAkf5Hg8DkZ4HKTmpS7h15S+kJTMWFXVw6Nab87wPlOrN6OcKQXy+cHHgTwSeilue734+MzpKsvy0iGfTqfD4XDTn2IEnSdTm+PXd+l3t+A4rW9Kgc71pIUOapKHYtktwVAAAJh2ZR7KlXPEA2ODOcrzhfMfyd+uNjA0b3Lmn2K253tabX5W95lPRfj1UpQzH3bzUmfzdUZCH5KdmoZhfXOdS97sYj8QIdTD4TD4YAxBcDgcJn8LbwZtSdYTk2VVjBkKANs0OVHv0pUCgP8TCW75xDuTwbV8cqErt3xpwvp1toyDQVEjCjYOyR0Oh1jtmpmC0vLj8RifSnP+FNnU8HES8pXTFupfpY3kexmsk3Ydm31stC6GMc1/a/mxt207WZmbNl68NQ19hCwvxZEvFc2of05PP4ixpu2MN5jSQlfVR/6BPmK80S///JAQSk5mKABs092zBwDXM4QZvMc4fnRTTuiMV2kEIzGwKIqU4ZhMRn4Hq81E7upsxvkrPzXY4yA8FwOz5gG4cY7krV3y3ymvwE3ZwTFj1fizb3a0j5hpzJXUNE1sJI8y3xGgzOeqyqV+9/m+Uhx8Pmj7uiRy8hzbD4aOLyuv0i4CG5P6cL3ZpSg92c6X5B+JFT6mmkNd18X32snn0oNaTX5k/KUwn/RgcgUANuZFG0E+WswL/+Zq838zrzh1UlLXdYqH5iZvWgdrXhO5G48hMPOpa87ki45IMDY4kBi29Zpry/l8jrN6PB7zRNr3DBow+TdQ1/WlfS07Y9UTRBbnA+ckjE19fcSm7qAFXKHtB0OFPoE1iCe6KfZXlmVcndKL3KWH6vHxGB3p/SPEXyPta3KPKeiZL0y9rg6Hw/iLWvTriUY9thlbeOnbGADmvWgjOKNt2/m+yePSdFyDR4mTK+/EzDPRDUc/xyLmlf4wZrp4x5qXztvknW/q2z75bHv8p/jmX+M199fz23lzC3GY1yy/42b/0sYvefOsFrO/svGnbq3VfftaUBpEe6nI49psrwW81aWDSn/VixzO9oOhACuRJ05G0nr6cpPfHV1qDNJXn3iRjyQ1+IKbtzeX2p4rpYBmtNzjYOj44WTf96ldH3enKssyxgVr2zY15Cv/SvfR0vPeOM8PLAVYj1dsBGeO5Xg8ToY703O+aO/S3uufxlub3NROHI/HydlmxrPD5wmkk6W3zlqzQtcHue74g1l/BO0VPfOsrvk3+BEd2x8+LWGMafPt0/fHbvZKW2oBb3Wp+YuWNLWYz89iNIESwJPUWf5jXO7zR2Ft20bvmBiH6M1NpY+nKFhqF9Pg+oPXd1c7vR5sKgb7zx/+t22b7kbabGj/tCQNeJTyQ9MjwX0qyzJ1ejoej4OhXeq6Ph6Pfd9H96jB14j5UoBVedFGcCy/bg+k53xd16UHft3PEQbz+rc/Fdsd+O9Kk43X4Flp/euoO/OlwDONI6E/ur/i5/mV+Vz/mX6ev/cZm2kBbxLJNJeO6Hg8nk6nlNjx/KoKhgI8QzR7h8Mhn1gz3SYVRXE8HqPf002PxbquSxHJ0+kUt2dpMJrUTD7kECbHuEk7umYy1kHyS1mWMVnqbqN4ceCn0ykiwilnNkojmtw0zfl8Pp/PTdMMppqdKQVYlQ00gsn5fM6nPs/lTweTuq7jKj35kT2nhc70Dqnr+nQ6xbwxcS+dn/PJ0t1+l4A1iLnaHzhje4qobmCWwi21gDeJI7qUtp9SZJaycDf5SzfPeXumhyCwDallarPBYvKi5PquXpPtXDxdjFuyx+abDHaUH0JZlvORuHFlUtZMfHaHtzHxpHSQ25JOY3ynyWOjcc+cvjbNlAKszQYawTflnRMj5+XN9d9cZ8MmY8dJ/XMYzWLqfn6+dHviH8UejhTe4+u/eS/4FUVR99AC3ipd/9MIAE+uwNrHDK2zye8i7J3/rcyXAqxEXJ1SBkTkUc7cA1w5TWdsKua+HCwvsqeF76n59bWK55D15amQ4plnviRVO332IfV8IeNRz4pfvwoMvg8N1p8vBViJbTSC14s7lDdzFaOGO2z7bjJ/fvZz9tzkwovaWwt4k67rDodDVVWLTHu+cDf580hVVVVVpZFf9BAENiDGdhxcoPJv8HlK4JvXsWhEU45A3ErFFmK4maIoYv73908mMGhiB6mLeTZoqkZqwtts/NDxQeVb3u3dYEqMja9E0VtifMIvGZTu8xwC6/e6jeAdohfk+Xx+85rcdd3K03YAXsVD+ul/+/R58PP+iu2qBbxJJLHGcGHL1GAcjlxQfCFIb8c1LIoiwsZvlqYlH1RV1u9//vs///Pf/3nUanC9yStPPgVqURTpQU66cMUKUVpV1el0mvx4SB8ffDD/1ODtfaL73mDjqQL5rvNHU2lhXsnB9Tnf8qBobMMX88E3lXQa4/zkv+hz1krOl4bX/TIAK7GSbwgrqcb1ttQIXhIX4cGS8/lcVdXgypzXc1B0/bXXVfoOTho8x0wjlRddudr8mvdV6aZqDH7u2Pv44rOxFvBWg5Yxv6O8ppIfdzFfVyMx+NWO75Dzu+v50rTOx9SUFyAYylJmrjyn02l80c+X3N1ufWiDN7/xydIr63Plalu9mE/GkfNvSDPhzmuCoR9UbdiJlXxDWEk1rre9RnByd5MP/AbPmfJblUEwdBxOneF6fgcP/+A5HhUMfX8U8lKVnhyTvXSd2UwLeKtBMDRioOPxsi89Svy46/aKxgxNXePHC+c/kr81XBqwZpPXtHFHiUdt+VHuGLHryvrsuWd3jOaTZpEqfk4JODOz8HwvEgPFACv3oo3g5O7OWbgtXbTPl2Nw43ucmZV5CGcYWI/NtIC3GtyhpCvz4pfotQRD454wDw9P3tSlcOf1t3yXJqwfWPw3AcAOTT7VS9NJiX4CALBhf//+x3jhl+fXg51ZSzA0za6Vljxq6kBRTgBWKIU78xYt4puxZDw7fGSSxuvJ0vWPlQ4AAMmP7q+lq8AeLTybfJLf4M2QIwPAZlRVlXeKb9s2D2gOHhMOBpOZLwU25nP9Z/pZui4AO5IHGbqum3n75nbEK2A9VhEMjZu3yVs40U8AtqrruoiHlmVZluXxeKyqKjVtdV2fTqe+76N0MJjMZOnKxwwCAOaVZXnpJrdt27quy7Ks6/o9jz+7rpsZSi7fy2RNolNLfP3Y/BePrusOh0P+KPpwOKTSw+GQv51308rAR1tFN/m4/Rsv10MQeFFXjlYMcZuR947PxQwb95UCW/L13+/Zu7X3KNQIwn1mQpz5XMF938fr+0KiM5/K//H2fX84HAaPWiM4mK9TluWeB6a7Pv4wDl+wVRrBl7CKzNDiwl2cHoLAKzpvyHoOZ+nf6seq63ommvmeUoAnW7q5eKT1HM7Sv1WeoW3b4/F4qSjiaPmfxPF4vLWjZCR1XgrJxa10VVWxi+iPMkhmjLdN08Q6kai029DP+ecz6Wt0Xeff8h48uXX4OOs5lg/6TS0fDB1PnZToIQgAwJ59+edH+lm6LsCHSKPlXFohivKgQNwX35QJVJbl4XCYSU6MveTD9USsM+0liqqqyvOTbuqa+ahh7u4Yf/PNjzy2bg8fS3R+NQOSwq1WEQw9n8+XgpjRB/B0Op1Op/Fq85HECB8AACAASURBVKUAsGfllKUrBQD84nQ6NU3TNM31gcW4802RzRjEcxAOG7T7zU+TG0yBznxhBD1TlHZyqo9rOmjGKKURjY0Xd0TuyrJs2zY2FeNvxpJrPph/ZLDr99RtcIbzuo2rN/4a1rZtXrHBrqO067rBaoPqxa8+lQqJwJWWD4ZeQw9BPlo+Sat5WoFteGZPEwDgPjEhUsxcNC6dDFMOlsQ6eZf22FTesbL9aTLkev0Q5OP8pGI2rTINM9o0TYR9o6qXZmeaCUcej8fD4VBVVYrqHo/H+XhoxB+rqprc9U11m5c2le9rZiiDNCpC7Dp+KeMZlmK1QfVSaQx6ECckNtL3vdgIXGMVEygBAADAo0x2hnjdh4KXIlwxDGhRFKfT6XA4xBTwXdfFzMOPjYtNdrF/cxcRrEwj2kXkN5IZ7/h1NE2Td9KP4QUuxUNjj+kjkUQVs8Pn4eO8brHCHXWLDeabKn4GQydP0WDog8gAPR6P8RtMq/V9n9aJor7v0zbjN5LHdmP8wJtqDvv0Gpmh8NF+dH/Fz9IVAQAA3msz3SOuzFKs6zoSA9u2jRjfHXMLj8N2V/bcnwnAReriYMux2TsSMAcHNRjVdLJWeWmcpXidUm7zuuUr3GHQz/18Pk/WLRYOhiyIhYMzOajMm3m4r/t3Dk+2/WCo4dK4xrdPn+Pn67/fv/77fenqAAAAXNV1PUSMLFIO8w7y7/HONMM8aTF339bGYco3T874I6kCl0YGeDPgOCmlgkbq6zUfvyb0PDkowWD9NJrqTRV+j5vGlzMMHeu0/WDoNp4HAgAAsE+XQl2D6FhKNryvg/x8QG1msNFL2ZRR2vf94Vd5/+7o9B3SHE1pyXyVZgKXV46COjP+wPwHx9uJAHTf9zG26cz8TvO/zet33XVdGpw0TaD00VFRkU22YfvBUJj35Z8f+c/S1QEAAHjDZM7mYOb3692af3rTZpummRy1ICoZ42AmRVHkb+d392Zl7l7hjmhyXddxXE3TRHQ4EkWv3/h94wacz+c0vVLEnW/dyB2uH2XOYHSsk2AoAAAArFE+Vc41a0Z+YoqK3rSXwacGWZ+TeYvXJGCOY3x5Z/kI54Wo/Ol0GgRMw/gkzOz90nmLhNNLhzO55BqDI+q6LvqkzvzixjuKle8ITEdS7fl8jl/WHcPFMikNpvft0+el68KDCYYCAMBLyu/T3KrBVo2nGxrPwNO2bZpBPkKKt+Y2XtpLPnt7MQqYxtv5+dzHAcHoLH9T9fI6XLn3yWmaUmUm6xZpqsXtZy/mrboykDp5JucHHBjruu5wOMwPKvpBvv77XbvDqxMMBYBtGk8haBZB2BJ3obATETtLsba2bQdBwK7r8iX5zPLv2cs4LBihuhiYMsb6LEazog9EZDZN8tN1XXwbmf/UJcfjcbCdmehhOqLBR9JpiTqUZRmlEdAs7pp+Kk5FnL385Fyq3qVdX/8rS8HcdHTjP4yPcOtswy8aM/379z/+/v0PI+lt2H8tXQEA4EOYMxBulc8L8XXBetwi7tNe8VYTuFIkew6yKfNWfhzCi8BfhA4ftZfi52RH+cCUTdPM7yJt9ng8plzINz81KQ3EmbZTVdVMMualXafwbsrQzA85X+F6EZEcDNk5U73JXZ9Op5t2PT664q5I7h12EiKMbwWv8n2Am5TbvlMqy40fIDPiruDWy/R9n4KtchV9XX53cKvBDLmR/LKqrwSDbyn5W19gPoIL6eva6u8upRx+aG/oa/aSJnx/7GaLn/HW8fKyLCO2mEbnvL4Cb+76USf2I+r2ETt9j/U3N/e1lYOiXx6Oru/7wBNs9SqayAwFAID/lSa9/fbptp6AAB/tOdGua/ZyR17nlZV/c507TsKbH3nUif2Iuj1hC4ylLwOF7wMbJRgKAAAvQ494AID3EAwFAICi+N8ZciWAwBZMzhm47V6fAFxp+8HQcSuoCeRNec7F3gYHAYB9unWG3OfznQSu56ZvY6qq+tBJ0oFd2X4wVCsIAMCVBBwBVmhm1niAW20/GAo3+fv3P4qf4yUbkwsAAICdc2vMxgiGwoTP9Z9FUXxduhoAAACwIJFQtue3pSsAAHyIcsrSlQIA3qWu62jTjaEZnJDn+PLPjy///Pj79z+iM+Xr+lz/mf8sXR2WITMUfhEd5IP5ZIGXZtRsANiYuq6LojidTkVRHA6Hrut2PpimE/I0m4kbjuZL/Gt6PTZNMBQAAABeQN/3p9MpRQAPh8PSNVqYE8IkXfuZJxgKAAAAL6Bpmgj8EZyQD5LngcZcGnkfyhf15Z8f+dsImJovZJ8EQwEAAGB5+aiXdV2Pw3z5CofDoaqqZ1TrFnlH9clDuHIjxc8u8PMrrP+EvKKX7hE/OaTpl+fXg3UTDAUAYL/0pAPWoG3b4/GYL4m3qQ/4eOWqqj56fMyyLCcrMKnrukEv9TiEpmlundqobdu8//u4dHD4Tzsh+zEaWHOzIuPVfCF7IxgKAMBOiYTCVpVlOV642qkFU21Tp+/Ir+z7/nA4DGKCsfL1Mcq73RTBTMHcqqpSQmjXdcfj8Xg83jqvUV3Xfd+3bTv5qdhRqt7TTsh+vHQkdAPd+XmC7QdDx63gaptAAACeL+9SpycdbMML3fRFCG+Q1RgLI8J4OBzS4ZRl+YT8x4hdDjJV59efTAKNbuxlWUZk8/roahx43/cz68Qpes4J2afBCJuwJdsPhr5QKwgAAMB+RPpn8XMQzIGIHh6Px7quU3LlIF/y4emQk0m186Kel7rDxyTvx+NxsrTruslDqKpqMoQab2Ns0OecELZNH5F92n4wFAAAZuhSBywlQnun02lmhZQjGSG/wbicD8/+aZomXlyfGRrVu5T4Wdf15AEOoq6Dfu5t2x4Oh3GMOO8j/5wTAmxPue0rRVlu/ACZEU943pPb//4twKtzFX1dl9I6/EJhnAOygbbel5aPoBF8XS/0u4v2er62MYDmrWNiRhf196RJXrnfmDfp1r7qceBVVUVe52Qv+8mTc80Z4520Kbl9no0Xuore57elKwAAfIjzlKUrBQvTGw7WI/pHR8xuXBqBvPtKNyZNRnTTp2LypbIsP/oURcVuDdQWRXE6neJvoG3b+IoyyEWNvvB5/VN//HfWGdg5wVAAAPbl79//yH+Wrg7sUXSCju7Vx+Nx0KGhruvoG973fYyYeX3pC/nQaX/O53MEDeP0ftxZunQU5UhaM37vgypFbfOtpSFTB/t63d84sBKCoQCwjPFNwuBWoZAXAw/yuf4zfuLtj+6v/GfZusEORbfopmnO53PXdTGgZIpwtW3b932URkSv7/vUOM6XvpaPDupFxuXpdIrJiCIk+vBzdekoql+l5VGB6FY/UPwaDB1veTKKCnArEygBwErFWF3xOu70BncIM6VAkmKgmzc4UnFeViue4aUneXVd5x2f8xlyip8zCKUZw+dLN+md6ZApABrnKqYbevjIOePzP3i4m760hOjF/+Zmm6aJ32+aVl4feeD9ZIYCwDLGA3pG6kTc7ewnLwaeQxIorEff93mqYFEUKdQVBqWR2Hhl6WsZD4s59qh0yLquB6fuIaLy87+C8VeUqqomBzcfnIq8p3wK6T6q5sBuCYYCwCpEfDPdLYwzX/K386XAPun4zwtJc51P9t2eD/yNhxB9ePWeZjwsZogzE/MLFaP4761iOzFIazxJfc/Wxt4M6eah0ku/r/k+Ll3XvW7Im4359ulz/rN0dbiHYCgArEKMnpYv2U9eDPAQ6cbs67/fv/77fenqwLTU2z0igNG54XA4jIeMTFL47Po+EJcG5h579wG9S8rWHIQII7CY5pi6u/NH27ZlWeZh0I94dJp+p5MbnzzJ4x4th8NhsuN8fDuKIn3kWZzo5zZsPxi6ttYOAMZS1/jxwvmPXLky7NzXf79L34C1idmTYpKf4me066a2b37jV3rvYbxb13X5BEcx/mkeKLwvLTRyS4/HY1VVp9Ppg8KgSZq5Pj+ECMUWoyBmTJl1OBzSmvGbnYx15tXWCYaV+PLPjy///Fi6Ftxv+xMoraF5A4AZ0fMrbgzSkvFqacakK9NDrn/+p61k2/aQIzm4JRP2Zc0m+33HPDmXPjLf8G1gyOyIGx6Px77v+75PpyLOUt/3d8wCH6mgT4sexqAHkYI6OIR85qtQ1/XpdDocDilBONa8VNsIFn/EgKfAPm0/GAoAKxdf/fNsl4fkxQhxQk4GB6zKfFu2+ejnWEwhlXJC67pOpyjipLdu8P1fA249z3VdR7ZvPt/jpfrkK+cH+5CaAMwTDAWAhUXuxpur7fDOEIBNqqpqML7koFf4YBTsvKGcLN1MzuBkWDDipAvU5i5vRjbvXhnW43P9Z1EUX5euBnfb/pihALBmMxPBi34CsEmDLhFt2+bhzkHpYFjt+VIAeNMqMkNjhJSiKGKs5UFpdBa4rxQAVi5mNhgv33NeDADbVtd1DBKahrfOx4tMA0qm0nxY7clS2YXAM/3o/iqK4tun7Q9KvlXLB0PTACjRV+J4POaDiaTJIoqi6Pt+MK3efCkAvITJW7i2bQ+HQ5owYZwXM1MKACuXhsgsptrBNKDkHaUAMG/hbvJd1x2Px6ZpojGLJ37j7hLn8/l8PjdNExHPa0oBYP3GUyclkfnS931ZlmVZDqabnyx1QwjAa5kfMvI9pQBwSbnsVLOR2pnXIR86LTo+5KVlWVZVFRHP+dK0xFy6u/Xt0+fifVPHvn8L8OpcRddgPvPlUqnfHSQ7bNB3eMgfwYX0dfndwXtoRHKDs5G/3fCJ2vxVdOFu8uMBzgZd/AalgyHS5ksBYAPm014kxQAAAFxv+TFDY+KjrusiMNq2bX5fd9MdYD6EKHCT8ZC78e9LnOUahqwCAABeWuQ5wh4sGQyN8EGaPSnmEzwcDjHq2eTonyncef3YoGmSwXnbTgCGN8UAvvmS9HaF/zo+Ivj4nm0eDofBGB2wBpMt4Ar/RQPAw2kE4SYioezK8pmhRdYmtW1bluXhcDifz4/qFajBg+sN/r20bXs8Hi89nFjQRwQfIxzsisGW+HsGYLc0gnCHv3//I73+smA94IMtOZt8BDQH4342TTPzkfnYx9riNfDqYgxfo08AAAAA27BkMDTM53iKfsKyBo8ruq4rM+NhRlNRPhnazKfibf7BvDRfnq4V0emp7/t4EQMNxy7SOKeDqdjyzU5Wpq7rGBYgX3Ny7yHS2MdHCgAA8KJ+dH+ln6XrAh9o4W7yVVUdj8dB0CQvHaSk9X2fUkcnSweBG1inpQZk+fLPj5vWj5nN8nzt6J8e/2bbtk2D/BY/Y5Sn06nI+tdHmHL8qbzj0uFwaJomZnA6HA6pNMYIbpomNhLXirZtT6dTvsGoZGwkajKTyjqoTNpdbLnv+3Q4472n4QLi6PKim84qACxr8D3k1q8HALA3n+s/48XXZevBgywcDI2wSB5iyCMvg9KIUKTI6XwpcIc8/zFCiilumEoHKZZt23ZdF+ukEGcURcpnClnmRfk4pGkXdV3nDzni8UYqKkYT3Oe1TUHMeYPKRFw16hnRz3SMg8Bokc0odTwe84rVdX04HN7cNQCsgSkyAICdWzgYWtd1TCKfJvsbRF4iVJFKI+lspvSxs0vDB3mJFIyIS+ZZluPk6xS7HI9ZkQKjg9zSYpTWnf+zjYhkvsfI2SzeetRx5b/9QWXqup4cXH88s3z0o0+HmVfGZQeAlxNfRQRGAeAaadyAb5++L1sTHmL5MUPbtj2fz6fT6XQ6nc/n8RCEeekg6DBfCtyq+1Xq855WiJE6k0EW55V7Gf9DnlztfD7HSBrPH5ozLkT5kUbuZz6c6NMqAwAP97n+M/X4AwDYleWDoSG6mn5EKXC3Qb/4oiiapjmPFFNj+M64fuqzruvigUdERe/7l37HTGuxo/GRpoCs2dsAAADgFa0lGAqsX0Qk8yVpSNBxmDKfgX3wqSvTSPMRgbuuuynemhsELgdvJ3NOx1HgGFf0UtEdtQLgQ3379Dn9LF2XNTJTMACwW4KhwBtSCHIwdmfMeJYXpehnvuagr/31c53FHPHxOmY0urLCETZNMco8FHs6nWIc0vnKxPJ874fDIVUggsJp+2ZPYrXKKUtXCp5BABQAgEsWnkDpCcY3fpPzpQCTUmAxxqOIGc9SeLFpmhRJHExo1jRNSqUcf+qaDu+DDRbZP96oVVmWk/+c27Y9HA4pRhnbidfjyqRp2WKKpLIsYyq2mb3H2UjbvH6wVHgy7R079/fvf+RvvyxVD2AJk8//tIzAfTxn3ZjpUMJmXIqVsAdxtXrPvO3v38JWjSdbf2fRm/saf/DNrc2vMFk6XjgzY9J9h/NyXEVfl98dexYt+CAYuqte4TPfYfIiX3XmuZC+Lr87uJUW4ZLJMOjmm9HNX0W3nxkKPNz8hGZ3FN2xrze3Nr/CZOl44cMPB4Cn2VX0c1J+/7bJWzUA+Gga0E0SDAUAYAs+13/Gi6/L1gMAgBUTDAUA4OV9rv/8+u/3pWuxFvkQAU4LAEBOMBQAgJcn5JfLhwj49smZAQD4P4KhAABshIG9AACY99vSFQAAAAAAeAaZoQCwTWVZjheez+fn1wR4FfkE9IVMWwBgiwRDAWCbxD2BmwwioQAAm7T9YOg4L8bNIQAATIpsUIFRAGCrth8MFfrknT7Xf+Zv8+lZAQCAFTJWDACXbD8YCu/09d/vvy4QDAUAgFUT9wTgEsFQAADYr9QJ5uuy9QAAeArBULhoMIOqwbMAgM2L2KjAKACwVYKhAACwX2k89G+fvs+vCcCWSPd5iMFpHORUsU6CoUBRFEXXdV3X1XVd1/W4tG3bS0XPFJWM1++pT2xk8cMBgKe5/o43YqMCowDbJhLKngmGAkVRFG3b9n2fRxuTuq77vj8ej+8fhz7irfd9djAl6PF4rKpqXNsrq/GQwwGAbUvDiYaUQwrANkhjfI+/f/+j+KWDhfjyyxAMBf5P3/dXLrxD27Z3hyAjEno6nVIsNbbWtm3btg+pHmzP4BFC8BgA9iZu1Qa+XPfZr/8O8kMFQwHgF+YhfEWCocAvBuHFSL2squpRIdH7qlT8GgktfgZDIx66UL1g7cQ9geKKdE6ZLADArvy2dAWAtaiqatzxfDLU2HVdmUkfieXRFz6Vpu0cj8eiKMqyTNts2zatNtN9PnrEj1c4nU5N06S3+U4na5XX55rDAYDd+vLPj/xn6eoAwOr86P7Kf5auDjfYfmboOPYhU4bFDUbgepo3L9Bt2x4Oh3xJ3/dN0+Txwa7rDodDVVUR0zwcDofDIf9ndTgc4iMRAK3rOl4XRXE8HlOCZwxF2jRNrJDWnKzYZKg0n0MpxjyNraUDGdRqMqL65uHAR0v/QOq6Hj9+aNs2Tfl1aynAvEdFOU2kCwC8kO0HQwU14HopkhhRlQiyxFTyaZ2Ilqao5fl8jmTPFIhJgcUI0wz616dNRewyxYDquh7EYcP1eZppv8VUVPfSbEtvHg58qJQ0XVVVjPyQN1vxzCBej6c4my8FeA697AGA17L9YCis0JpT6CMik6KZk+vkndOLUZwx/1QerMmlXLbx8vummx+MczqZQHfps/OHAx8ncqLTU4HIU04p0infOf17PB6P6d/IfCnAk0U2qMAo62EWQQAuMWYo8Is8aBihlms+dev0ShHrORwOaaTOQYZmMh/ZSX2Ei2zM0MPhMK7PzHbGRQvOFsWuDJ461HWdhnooiiIyRvN85/ztfCkA7Nx5ytKVAmAVBEOBX6Sks5Sb9nH7Gn9DvbS7iPsMpP7FRVGUZRmh29jO6XS6vhrjCGxVVdd/HO7W9/3gj20wRMOgtKqqPFI/XwoAAMCYYCgwlPeUnzSIHo4DOm+KkOtgXqZLyZuRnTquT0RC06dSf+FbjQ/njo3AfWLio8hrHs8hNp8ZPSjVQR4AAOBNgqHAUIQUL/WRb5qm7/u8Y29xewJpfOpwOEToJ4ZKvBSFbNs2xWfT+rGFPAM0RZFia8V1ky9NHo4xQ3mC+DOLSZOKn3+K+T+K8UdSuPPKP9Hyao84IAAAVufbp8+Dn6VrBMsTDAWGUsBlMsTZtm3TNMfjMWIoETO9JiUt1om52oufA9jHsKERu5zp2951XcRD0/qD/UYgKY0ZGptKcaUZKdKaDuemLvbwfufzOWb9Sv8oihtzQme2fKUHHAYswa0dvK7xk7nBN8/oORFdKMYfny8FgvYRJplNHiiKUaLZIDgyKI1hDcczwtd1PfhgPgBilObd4eNtKr2mhpfWH1cp39dMrQZb1tGYp4k/tsEQE/Gk4dJH5oP7MprZGzd48LrebLPquk59hvq+77ou/8h8KTDw9+9/xIuv/35ftiawEoKhcJv81uvLPz8WrMni7osbvnOUw48bQlEYlEXM/+GJfsKbdt4Ww0u71DWhbdvoAxRPr2POzPSQe74UAN6kmzwALCOGaMiX5PHN8ezw+Ui+k6W3TmUGAIuYf54XjWPqx5OCnteUAmM/ur/iZ+mKwFpsPxhqmgge5cs/P9LP0nUBtiDu3PJRevNw56B0MFnZfCkArFk+7+VkYHTweG/wCHC+FADmbb+bvHkhAFinuq7TdGSxpKqqfJjd0+kUk4bFknx2r8lSPQQBeCF5nkpVVYNRQWc+OB4ZSTAUgOttPzMUAFYrJpE/nU6n0ymfUizEtGOpdHzvN1MKAKsVscumac7n8/l8jtTOeBw4mSia2rjrx8sedxC85AHHA8BL2X5mKACs3E35LzeVAsAKDXozxPRHx+OxbdtHtXo6CAJwicxQAAAAnmcc04wllxI/5xNCr08XBYBiDZmh444JTdPkU0C0bRvNW13X46kh5ksBYLcmu/7JlAHu9rn+M702KzEf4cru8KKfALzHwsHQN5uxfDDsvu8Hsw3Ol7JD+Xf0rwvWA2AFxD0BWKGu6w6HwyADJr+PG88O3/d90zQzpYP55YEZcdfsfpk9W0U3+fOvUqPYtm00e7G8aZqIeF5Tyg7lkVAAAB7rR/eXhFDeL9I/j8fj+M4uvS2yLNF4kd8kzpQCwJvKZdNG2rY9Ho+X6hD9+/LSsiyrqopWc740LZEXsx+f6z+//vt9sPDLPz8+Yl/fPn3+uI3DeriKvi6/OzZPW/xMcbZz6cznv4iN/VJcSD/UYCyXcaLo4XBIb8cTLs2UFn538NPgsrzhK/YabOmUbv4qupZu8mncz8EKg/4Ogz4R86XszTgSCgAArND5fI5Rzuq6npxPKVYoLsy2NFMKzBs/34K9WX4CpeLXp4KD1M75tm1Qmg8hym5t4zkMAMB65N+v4i7avTTvNxkGHaxwdykAXLJwMDRil6lPREQz27ZNc8QPpHDn9WODTs6lO7btBGAAgG0QgwOA+8gcgrBwMHQ8+Etd18fjsW3bRz0GFOUEANgGkdA1+Pv3P8YLvzy/HgAAd1l4NvnJ8V+Ky4mf8wmhppIHANi8L//8+PLPj79//2MyKgdQFEU5ZelKAbAKqxgzdCwFSUU/AQBgPX50fy1dBXibDoIAXLJkZmjXdWVZxmih+cL0ejw7fN/3TdPMlA7mlweA3ZIUAwAAMLBkMDTSP4/HYwqAtm2bhzvTrEr5+il4Ol8KT/Dt0+f8Z+nqAPziPGXpSgEAACxp4W7y5/O5LMvD4ZCWpJnli6Ko6/p0Oh0Oh5TJcjqd0pqTpddPrAQAwCv6XP+5dBUAAHhVy48Zej6fu66LeeQn51OKFYoLsy3NlMLH+fLPj/yttFAA+CB56PPrgvUAAGATlg+GFkUxGQYdrHB3KQAAL2oyCdQEPgAA3G0VwVAAALgkRT+/ffq+bE0AAHh1S06gBAAAAADwNNvPDE3TKyXm0gUAeBVf//0uIRQAgEfZfjBU6BMA4EV9/VcYFACAR9p+MBQA9mncN6LwjJDX9OWfH0tXAQCAjRAMBYBtEvcEYLc8EWTPPtd/xouvy9Zjl9LJL7IZIFkbwVAAAAA2RdyT3cqDcTzfryP8CIaulGAoAAAAwEYYcRvmCYYCAAAAbIFI6FLyIc6/ffq8YE14k2AoAAAAwHaYexBm/LZ0BQAAAAAAnkEwFAAAAADYBd3kAQAAAF6VESrhJtsPhpZlOVhyPp8XqQkAAADAA4mEwq22HwwV+gRgn8aPAwvNIgDAFqUZkz7XfxZF8WXRysDKbT8YCgD7JO4JALATEQMFriEYCgAAwKboHgHAJYKhAAAAbIq4J3vzo/tr6SrAy/ht6QoAwH6VI23b5iu0bVvXdV3Xg+XXlAIAADAgMxQAltF13fwKdV33fR+v+77vui7/yHwpvLQ08NnXZesBAMDmyAwFgCWdf5VyPNu27fu+aZpY3jRNRDyvKYWXZgoIAAA+jmAoACxjPnZ5PB6Loshjo/nb+VLYgB/dX4Y/AwDg4XSTB4BlpGBovKjrerBCVVWDt6lf/Jul8NK+/vv926fvS9cCAIANEgwFgCWVZZleV1U1GBV05oOD0nwIUXhpX/8VBgUA4KNsPxia32SG8/m8SE0AIBexy6Zpont7RDPbtm3bdrIHfQp3Xjk26LgFvETLyAp9+efH0lXgTt8+fc7f+lUCAKuy/TFDzyNL1wgAiqIoTqdTPmNS13VVVcVgoDflhF4ybgEveeeBAAAAvIrtZ4YCwDqNY5qR+9l13WS4cz4h1FTywOL+/v2P/K0RDwCAFRIMBYB1SZFQ0U8AuM/kWDE6QwBQCIYCwCK6rjscDmnA0LQwvR7PDt/3fdM0M6WD+eUBnuxH91f+9tsnmaEsRtwTgEu2P2YoPM23T5/Tz9J1AdYu0j+Px2MKgLZtm4c706xK+fopcjpfCgAAwCSZoQCwjPP5XJbl4XBIS/JE0bquT6fT4XBIHf1Op1Nac7L0yomVAAAAdkswFB4gny7AXAHA9c7nc9d1MWPS5HxKsUJxYbalmVIAAADGBEMBYEmTYdDBCneXAsD61XXdtu2gRWvb1SjpwgAAIABJREFUNj3wG48DM18KADMEQ+EB8ukCzBUArISJdAFYv7qu+76PThKDhfE6SvM5BudLAWCeCZQAYJvOU5auFAD8n67rUlgzSTMKRsvVNE1EPK8pBYA3bT8YWo4sXSMAAACKw+FQVdVg4fF4LIoidX6PF+ntfCkAvGn73eRlwQAAAKxNWZZVVXVdN05YGURIq6rKE0jnSwFg3vYzQwEAAFiVyOW81L39pskDzSUIwE22nxkKAMDKffv0eekqAM/Tdd3xeDydTpNF44VpxqTrxwa9fng0XQl5UZpOuJtgKAAAS3I7B3sTQ4VOZnTelBM6Q4iTbdN0wnusKxha13XbtoMWrm3beAAYpYOPzJcCAPAS/v79j/ztl6XqAXywuGsb3L51XRd3gpPhzvmEUFPJs1tf/vmxdBXgJa0oGBp9H7quy9u/1CGiKIoozZu6+VIAAABWKCaFT/q+7/s+3QmKfgLwcdYygVLXdeMZANu27fu+aZrz+Xw+n5umiYjnNaUAALyQH91f+c/S1QE+Stu2518VRRG3dREMHc8OH/d98XqydDC/PADMWEswNEaNGSyMp4Wp90S8SG/nSwFg58opS1cKAN6Q+tHH23gxuO+7VAoAb1pFN/myLKuq6rpufJM2iJAOHgPOlwLAnpk7AoBXVNf16XQ6HA7p9jCfd36y9PqJlQBg+WBoPMS71L39pskE8yFEAQAAWL/x07u6rs/nc5op96ZSAJi3cDC067rj8Zg/6MuLxgtTuPP6sUGv7BIofYbH+lz/mb819hkAANzkpswYALjSwsHQGCp0shl7VMsnyskivv77/dcFgqEAAAAAC1syGJqGvs6Hu+66rm3buq4nw53zCaGmkgcAAAAALll+zNCYFD7p+77v+xQJFf3k5Xz550f+9tunz0vVBAAAAIDcbwvuu23b86+Komia5nw+RzB0PDt83/dN08TrydLB/PIAAADsTTll6UoBsApLBkPflPrRx9t4kfrUz5cCAACwT+cpS1cKgFVYvpv8jLquT6fT4XBID/HyeecnS00pCAAAAABMWlcwdPywrq7r8/kcY4OOA53zpQCwZ5P9AeXFAAAAe7auYOgl84FOYVAAGBP3ZOXMMQgAwPO9RjAUAIBXJ/oJAMDiBEPhGfLbvy///FiwJgDwNJ/rP9Prr/9+H6+gTQQA4Mm2Hwwdj5im2yAAwEfLI6GJ6CcAAMvafjBU6JNl/f37H+n1ZFIMAGyVhg8AbjIeUsZzRHi47QdDYVk/ur/S62+f3BMCsBcioQBwE4Nrw3MIhgIA8FHkswDATVLTGbHRPEKa+h1+fX61YEMEQwEAANiU8dQRhSHUeH16XcBDCIYCwDa5DwRgt7R3bEDeu0IPenggwVAA2Cb3gQAA22DYGXig35auAAAAAADAMwiGAgAAAAC7IBgKAKtQ13XXdYOFbdvWdV3Xddu244/MlwIAADBgzFAAWF5d133fd11X1/VgYbyO0jxaOl8KAADA2PYzQ8uRpWsEAL/oui6FNZO2bfu+b5rmfD6fz+emaSLieU0pAAAAk7YfDD2PLF0jAPjF4XCoqmqw8Hg8FkWR+r/Hi/R2vhQAAIBJ2w+GAsCalWVZVdVkUucgQlpVVZ5AOl8KAADAmGAoACwmcjkvdW/Pxw99s3R+ZQAAAArBUABYStd1x+PxdDpNFo0XpnDnlWODjkfNvuTuQwCAddLeAXCJ2eQBYBkxVOhkRudNOaGXGCYbgN3SCAJwiWAoACwgOsjXdZ3PetR1Xdu2dV1PhjvnE0JNJQ8AAPAmwVAAWExMCp/0fd/3/ZXd4UU/AQAAbmXMUABYQNu2518VRdE0zfl8jmDoeHb4vu+bponXk6WD+eUBAAAYEAwFgDVK/ejjbbxIfernSwEAAJikmzwArFFd16fT6XA4pNlv83nnJ0uvnFgJAABgt7YfDE13iYmJBVnW5/rP/O2P7q+lagKsyrh5quv6fD7H2KDjQOd8KQAAAGPbD4YKfQLw0uYDncKgrM23T5+XrgIAAFy0/WAorM3Xf7//ukBmKAAbIRIKAMDKCYYCwDaNB4opdJjgKb7886P4OSzMl6UrAwAr51EiPJlgKDxP3Bwm2jzgQ4l7spTB6NgAwCXuCuH5BEMBAADYFN0jeC2DvBngQwmGAgDwSD86w2EDCxP3BOCS35auAAAAAADAMwiGAgAAAAC7IBgKAAAAAOyCYCgAAADP1rZtXdd1Xbdt+9hSAJix/QmUxtMIGksbAABgKV3XHQ6HoiiqqiqK4ng8Ho/H/Datruu+7+N13/dd13Vdd2UpAMzbfmboeWTpGgHAM5RTlq4UABQRCT2fzxHHPJ1ORVGkHM+2bfu+b5ombt+apomI5zWlAPCm7QdDAWCfxo8DPREEYCWapkmv67ouiiIFNI/HY/FrbDR/O18KAG/afjd5AAAA1uN0OkUANJcvie7z+dvUL/7NUgCYJxgKAADA86S4Z2SDRq/5PLtzHCqd/Hh6KxjKa/n26fPSVYBdW0UwtG3baAUnZwN8TykAAADrFGHQIus1Pzn6Zwp3Xj826PXDZBtDhicTCYXFLRwMNY0g5G3hl39+LFgTAAB4pjSHUhoJ9Kac0Pktv7t28IHSrd/n+s+iKL4sWhnYm4UnUDKNIAAAwG5FD7+qqiIeOmn+Ls89IK/oc/1n/CxdEdij5WeTN40gu/X373/8/fsfX/75ISEUAICd6LpufvakQvQTgI+0cDd50whCPAz8unQ1AADgOfq+b9s2T2TJ45vj27roEThTOrgxhPX70f21dBVgvxbODM2nEey6Lka5fs80go+tHgAAAA8Ud23H43E8AFp6W2Q3d/Fi0CPwUikAvGkVs8kXK5hG0ADbPF/+MPDbp+8L1gTYpMkWUHsHwOLO53NZlukesCiKqqpSQLOu69PpdDgcUkMWc0vMlEqLAeB6awmGmkYQAB5LCwjAasUNYLyeHDktrXBrKQDMW0swtCiKuq7ruo546KVuDgbSBgAA2IA345iPyo8BgNySY4aaRhAAAAAAeJqFJ1CKaQTzJaYRBAAAAAA+wpLBUNMIAgC8us/1n+ln6boA/K9yytKVAmAVFh4z1DSCAACvSwAUWCezCAJwyfITKJlGEADgpf3o/ooX3z59X7YmAAAwb/lgaGEaQQCAl/X13+9ioAAAvIpVBEMBgIebHBxNt0Ee6+u/wqAAALwSwVAA2CZxT57myz8/lq4CAABcZfvB0HFejJtDAAAA4GnSlINfl60HsIdgqNAnAAAA8EzfPn3O334tir9//2OpygC57QdDAWDN2rbtuq4oirqu27Z9YCkAAIsYRELDj+6voijMOgiLEwwFgGV0XXc4HIqiqKqqKIrj8Xg8HvMODXVd930fr/u+77ouQp/XlAIAsKyUChrzDU5GSNmwNDZCiGg4a/Db0hUAgJ2KSOj5fI445ul0KooiJXi2bdv3fdM05/P5fD43TRMRz2tKAQAAmFRue0jNstz4AZKL52wvOqHtS1eeDXMV/VBlWTZNk3dvL8uyqqqIacYEgPn5v7608LvjWbRfzHj1Pw8X0tfld8fiXv0CyDsN/gAiRfSFMkM3fxXVTR4AlnE6neq6HizMl0T3+fxt6hf/ZikA7Fk8NRzY9r09sDZpYISv/7vgZYKhm6ebPAAsI8U9o5t83LbliaLjUOnkx69ZGQB25Txl6UoBsAoyQwFgYTF4aFEUTdPEi8nRP9OMSVeODTqZFDPJ/SEAADzKYIQEc2etjWAoACwszaF0PB6Lomjb9qac0JnNPqJ2AAAA26GbPAAsr67rtm2rqop46KT5hFBTyQMAALxp+8HQcmTpGgFA0XXd/OxJhegnAADAo22/m7xOgryQwUgig3FGgI3p+75t23zGpDy+OZ4dvu/7NKjoZOlgfnkAAAAGtp8ZCgArFEmgx+MxBUDbts3DnREkTbmi8SJFTudLAQAAmLT9zFB4CSabgx06n89lWaap5IuiqKoqBTTruj6dTofDIQ3wcjqd0pqTpVdOrATvp50CAOBFCYYCwGJiHvl4PTmEaFrh1lL4OCKhAAC8LsFQWK/8btP4obBVb8Yx51cQBmUpf//+R3r9ZcF6AADALQRDAQC42Y/ur6WrAHBRGkYmZ3JdAArBUFinPA9Ub0TgPu4DAdgt7R0AlwiGAsA2uQ8EAAAY+G3pCgAAAAAAPMP2M0PHnQRlygAAAADADm0/GCr0CQAAAAAUewiGAgDwTp/rP9PrrwvWAwAA3kcwFACAOZ/rP7/++33pWgAAwAMIhgIAMEcklPfI04p/dH8tWBMAgEIwFACAa3z558fSVQAAgPcSDAWAbSrLcrzQvILAk0U2aJ4fCgCwIMFQANgmcU9gDb59+lz837xbusnzJJ4IAnCJYCgAAACbIu4JwCWCoQAAwOPl48xGfijAtrnWwUv4bekKAAAAALw2kVB4FdvPDB0PFqPHBAAAAPBweVI8sE7bD4YKfbINg8eMmlgAAACAW+kmDwAAwLO1bVvXdV3Xbds+thQAZmw/MxRe3d+//5G//frv96VqAgAADxGjmVVVVRTF8Xg8Ho//v727SXbbOBcACqRSWYN38FT1tAQrBCepysSbUDagoe+E4EQZegNPm/DAHmQA0NYSlCpnB94F36ATBAJIXPAH6AZwTrkSXjav+F2w0U186J+qqoqiCKVFUZxOp/D4dDrVdV3XdfO7w6UAMMzIUEjdl/pj+7/Y4QAAwENC0rOqqpDHDCub7ff7UFqW5el0OhwO5/P5fD4fDoeQ8RxTCgCvSiIZan4EADxdfknsoAAgO51Ou92uGQeaZdnhcGgeH4/HLMua67vwoPlxuBQAXhV/mrz5EQAwBVsIApCm3W7XSV92ruPC5WH7x+a679VSABgWeWSo+REAAACbUtd1e1holmWdbGantKNTOvxiAOiInAw1PwIAAGCz6roOkwWrqsp6Q0SD5oJx/NiXi2vFWEAGgCz6NHnzIwAAALapWfesWSrtpjGhA6wVw3TeFi/tH21yC4sTeWSo+REAAABbEwaENuueDV/KDQ8ItVQac+pkQoElir+BUqOu67Ba6PD8iJAtvWl+xJiXuXMIAAAwg3Dpt9vtrl3WyX6Ssg+///j1E0aGwsKkkgw1PwIAAGALwiCYaznN/upnYQDpQGln/TSYTi8TCixP/GRoMyD0cDi8uv2RO4QAAADL1Vy19a/+mk1x9/t9URThlWEcTHvj3IFSmMf7375kWfbpzdvYgQD3iJwMNT8C7tNeqsaK3QAALM7xeOw8E3KaRVFUVbXf75sVz8JCasHFUrtHADBe5GSo+RFwn69nZ0iGAhdcXDXb6jEAxFUUxaudUXhNe+zn+FIAGBYzGWp+BABMR94TgEV71jYSANAWf83QzPwIuEVYniawSA0AAADAeDGToeZHAEBZlk1HdnGqxN2lAAAAdCQxMvRV5kcAsEphckNY8Pp4PB6Px/Ysh6IomtWxT6dTXdftVbaHSwFgyyycDcA1f4gdAABsVEh6VlUV8pjhCi1sLZhlWVmWYdvA8/l8Pp8Ph0PIeI4pBYCNO18SOygAkpCvu0vI85X/gbSFBTTbS2qu3gb/ZGamFZ1Unue73a6dwSzL8ng8hmMehrS0j3/79cOlmc+OZ+isTK274RFL/NKiIV0unx3TabdmF7dwWFZDxzwW1wmuvhVdxjT5R/TnR6z7EwVgKXa7XWehz87QzjB9vv1jMy/+1VJ4kD36AABYpfUnQ6U+AUhTf1Z7J5t505LZ7SVE4VkWNIQBAGb2wzff9Z98P38cwI2sGQoA8dV1HaYyVFWVXcqTZq3s58i1QfPRnvRHAAAApG79I0MBIHHNoM5mK/mbxoReY24EtzI1HgDG+1J/jB0CcA8jQwEgmjAgtNkXfjjLOTwg1FbyPEgmFACALTAyFADiqOt6v993NpTvvGD41ycIiq3rLH9m4TMAAFZGMhRWpTOux8YXkLL9fp9dz2n2d4cPA0gHSjv7ywMA8Li3xUvz+EPEOIAnkQyF9TDDERakyYGWZdkpCs+UZbnf74uiCK8MM+ibFw+Xwt0sfwYAbe1MKLAOkqGwNmE0qMQoLMXxeOw8E3KaRVFUVbXf75vd3sNG88HF0pEbKwHA6jX9Y5utBbnPh99/jB0C8EySoQAQQVEUr16Shde0x36OLwWALZP35FlkQmF9JEMBIGnDiU5pUACAqdmMAdZk/cnQ/vwINwlZGTPiAQAAAMZYfzJU6pMt66z2bVsM2BTLpQEAAHSsPxkKK2ayBjBA3hMAAKBDMhTWrLfat5GhAAAAwHZJhgIAbJeFpwEA2BTJUFinzgx617oA9OkdAADYGslQAIBN++Gb75rH7yPGAQAA05MMBQDYtC+1FaUB4Ctvi5fw4EPcOIAJ/CF2AAAAAACpaDKhwCoZGQoAAMCq5Hnef/J8Ps8fCQv14fcfY4cATEUyFAAAgFWR9+QRMqGwbutPhvZvCeoXAdgCg2IAAO72/rcvsUMAJrH+ZKirPgC2SQ8IAADQYQMlAAAAAGATJEMBAAAAgE1Y/zR5oPHpzdvmsRVwAAAAgK0xMhQAAAAA2AQjQ2ET2uNA2+NDAQAAALbDyFAAAAAAYBMkQwEAAACATTBNHgDWKc/z/pPn83n+SABgZjpBAK6RDAWAdXLJB8Bm6QQBuGb9ydD+LUH9IgAAAABs0PqToVKfAAAAwIBPb97GDgGYiQ2UAAAAgO2SCYVNWf/IUAAAGq73gKQURVGWZVEUnefLsqzrunnBTaVwnx+++a794/tYcQATkwwFANgKmVAgKXVdn06n/vNFUTTPn06nuq5D6nNMKdztS/0xdgjAHBKaJl8UxcU+LNwnvHbHb7gUAIC3xUv4L/z4/rcv7f/ixgZsU13XZVnu9/t+UVmWp9PpcDicz+fz+Xw4HELGc0wpALwqT2R/obqu9/t9VVWd+RHtm35Zlu12u2u3BPulWZbleSp/IDMIo11c1L3q1WFBjiENrehy+exoNDnQLMs+/P5jpp0nhiV+T9OQTirP8+Zx5zIwFLUPfp7nzbXecGnzjM+OmyyxjWJBFlfBVt+Kxh8Z6pYgLMWnN287/8WOCICxvtQfzf4D0hEu4qqquli62+06P3YGwQyUAsCw+GuGXkyDBsfjMcuyZv57WZbH47FZKnu4FLhm4H7UQH5T6hMWpz3oprHue7xc8+H3Hz+9+TF2FABj9fdTGijtzBeE8VzjwDbFHxnqliAsiAXmYEHOl8QOigjC1HiARbg4tKXJfo4f+JKP9py4WSCZUNis+CNDh7klCADwOPexgEW46QJwgPt/jPTDN981j99HjAOYUbrJ0Gu3BEO686ZbgmNeprMEAABI0PDVn3XSeIQFtWGD0k2GuiUIwHYURVGWZb93a9bCDi+4qRQAFkr2k4mYGg9kKSdDL9IpzqDfPTQT6waKALhbXdcXl3lpL/9yOp3qum73dMOlALBQ/a0gTqfT4XAYKO1sJgEXyYQCQfwNlIbJfs7MZuIAc6rruizL/X7fLyrLMlz7hY2PDodDyHiOKQWA5QpzHZrZEuFBMwFiuBT63hYv4b/wY7MlrI1hYbOSHhnqluCDmuY+6GwmO9DuN2tI9/efHRglCsCtLqZBg+PxmH197Xc8Hpt58cOlALBcRVFUVbXf75vtH6qqGi4dv4oaW/O2eOlf1QIbl3QyNAyWKYqiWRAt+/rCb6CU4Uzo1G+XWYgaYISwsHVd1xezop07fJ27gMOlAJC+oigu7vEQnm9f6I0vhTaZUKAv6WSoW4IP6rf7YchneL4ztLM9ULTJY356M6rnuHK3TTJ0DQwBhohu2kuwvYQoAKzAs/bUBdPhgbZUkqFuCT7dE++AvZoOG34v2y4tl0woxHJxtnuT7hw5F765Wfiqi10wAADA+qSSDB3mlmBHZ076wIT0Ttrx/b///6vXh4RX+Dc/PBDVxeVEZdOWaGDUMDCPp3R8UpwAAAAdy0iG0nZpTvpUE9KbnZQa7y++7pJ2dnVgRyYARhoeEGrrJABoXJwe4TYhAJlk6BJNlE+UpiQzCBQSI/sJAPeR9wTgmvUnQ/u3BNfRL4akVZjOPN2k5kd2hG9nV6/tyGQPeoBr+rvDn06nw+EwUNrZXx4AAICO9SdD15H6nNT8gwGb7O2lJUolQwGyLMvKstzv90VRtPcJLMtyTCkAwGbZuAIYtv5k6Lp11vScf6r7xW5m6uyq7emBLSiKoqqq/X7fTHGoqmq4dIM7CgIAtMmEAq+SDE3XKnf07u/I1JpBP6rT0rcB61MUxcV5DOH59tjP8aUAAJvVufAcvw8wsAWSoYnqp/ya5TXbU8s7K2yGFTnnSRfel5x91pKgtqcHtmM40SkNCgAAMJ5kaNJCyi/k+2T92q7tyARAo7+FYGYp7c0wkQKALbMxLzBAMjRpoQUfn+9bx1T6O7jkA+iT99ws3SIAAFwjGboAm0px/nc1gN5I2E0dBwB4kOXSAACgTzKUtNy6GkB/RyYXewAAAABcJBlKusbskmQtmET0p2QayQsQly4SAAD6JEMTsvEVvtq5s2ayfNvGj0/KfDQAUWh+Aa6xi+DW/He9tbhxAEuw/mRovxdMswt0PdPWGcxiy/hFaNLZKjPADDS2AAPSvOhjIm+Ll1vXWwO2bP3J0GX1gmYWX+SwAMBFukgAkAkFbrL+ZCgAAACwbm4QAiNJhgL3M0kTAAAAWBDJUABYJ3tHrJK7UAAA8AjJUOAeP3zzXeeZ91HiAK6T91wfmVAAAHiQZGhkb4uX8OBD3DjgRl/qj7FDANio9u0oN6IAAOAmkqExvS1ebHsHAAAAAPOQDI1JJhQAuJWx+QAAcDfJ0Pje//YldggAQKKaFXUyi+oAwH9YRxu4m2QoMIn2txMZf4D7WFEH4D55nveftLXgasiEAo9YfzK03wvqAgGARZAJBbiPi74taO8omNlUEBht/clQvSDMrD0O1D1bgGs6LeTAIHrj6wHYpv7VRLtPtIg2cJ/1J0MBYJvMEEyZe0UAMOxiX6kDBR4nGQoA6yTvmb4wv89ceAC4phkKKg0KPItkKDCr8dNCAVYvzO/79ObHrLNrvPQoAHzNhQPwLJKhwHzczgW4RgIUAABmIBkKzC3c1JUYBRhg/AsAAExBMhSYnLwnwAB5TwC2zMUCMDPJ0Lkl29DneZ7gVhuiGi/NqNKU5rFKMyp4UCIVWxgJhpElE4kwhAETSaQmJx7GzBfIiR8NYQgjkTBWTzJ0VslmQmEif/vXPzXlAABAm11VgYgkQyOQHgIAAJhOnuf9J12FJcIgISCu9SdD+72gLhAAAGDFXPSlz2hQIJb1J0P1gpAyE2RgOgbFAAARtb/q/9///G/ESADa1p8MBRZk5JQZOVMYQ94TAIil/8Xe7HggEZKhQBydhOZN342MJwWS9bZ4af/44fcfm8cGxQCwNeGLujQokJRNJ0PzPB8YNTNdqahEJar+7/YTmhf/5ZFfpF6NaiCdmv6xSicqFmrgQ3960UbCaDcpHwbf7lojtqaj8XgY6UQijKe/1+O3MxM5GixXIlVoa2G8/+3LnH9y4kdDGMJYdBjrsPhk6MvLy6+//ppl2bt37z5+/Bg7HGBazVVTaLjDNVU/ufB///O/4cmLV1kGZ7EaZVnWdZ1lWVEUZVlGjmarXr1J88M332VfDxEF4EF6QADutuxk6Lt37z5//hwef/78+ddffw2JUYBXXZuz0yRS2y8LPr152y41PZ+4iqI4nU7h8el0qus6XBYykc4uENcGuPXvsb//9/9/3PLtdwh3BRpuD/AIPWBc/U5wTBFAOhacDH15efn8+fP3338fBoS+vLz8/e9///nnn//617/GDg2YybV0ZJ7n9w3/HPj25osdSSnL8nQ6HQ6HMBymLMvj8VjXdVEUkSNbEVd08ERf6q+mcH16IxnKnfSA0+nc9uusgh3OYj0j3K05fQZuqzOPBY9QyPM8+3qr3DzPv/322/bg0Fir7F0rDdX9b//6Z1JRKVW6ytJXk6HtkaFN39P+8eJXvXD+9ufgj+nMkj1WVhRdooud4G63a4bGJLIy1+KKhq/xBoZ/zhbhaorSiURRlKL+uTZy/e5Eiojo1R4wS6aezFk0cE6N/AdvynIOjEhI4WgoUpRa0avn14K+ZK7DgkeGZln27bffdn5sZs0DjHffLe6RvzVwA7DtptJO9tbk/W3a7XadH5s5gyvTGahy7WUD99jvntDnhILZjDzTIVtFDzh+XNjAqTHyu+hNnaDN32EKnbVi/nn6+7U8qUGj81hwrjfP82aOfBCWEB0/viMWI0OVKk2n9GIrMdDfhN+99lv5vdPzU6b3TVOe580MwSAsoHbf+A7aFnRnftFF6USiKHpRyu2VTjBBr/aAWdqVajqdm+UP/iOpNRSKFK21KNn2at094FJHhv7888/9J9v7KSUuz3OlSpUmW/q3u3534Lf+9q9/DvyDN+knW5t/fH15WK65uE1EezeJ9Qn1/NVKPnA6jD8N2+fyQFuh6PGidCJRlFSR7owBa+oBx3+FG+gE271b03/pBBUpWlCR67solpoMHblLUpqZ7PevvwRYlUnPek3KBo3ZIyLNHvBuI+v5wMucKQArMHKXpEV0guM7psd7N50gLIgTdgZ/iB3AM7W3TgKATbk4WAYAVk8PCMBNlp0Mlf0EYLNc+wGwTXpAAB6x4GRof+/4z58/f//997HiAYDZ9HfOPZ1Oh8MhVjwAMA89IAAPWnAy9OXlJcuyd+/ehR/Dgz/96U9FURRF0d5e8KK859Vfea6yLEeGGj0AxyooiuLVu9DRj1XbmICfLvF6FbcuJX5wrolSkXhVqBLN0mnhQaeeJFWRgrjVKZHeJJEw2ub/XFI7CGpmOmE0In4oCTaetI3pAbMkP0dNTTphtM38uaR2BFTLdMJo6AHncF6yzjjQn376KctG1zrsAAAKLklEQVSy3W632+2G/7qqqvqH4nA4zBN28+5jQp1IP4Cqqi6+Mu6xuinUSYXjMPzW0Y9VP5iZj1XzGTWf2rVXRjlW48OL++4qEiN1RsF0PqakKlIQzoIoMaTQ8waJdGpt85/mcVvjvrgNXQpVIp0TpJFCc5FO40nfcA94TvJzTKFWRz/HU2jxOmbuAvSAbSnUh3TOjkYKbUU6Led0lp0MDX766aeQBm3X2vApXvvYUjjnO8HMWcPC2dX+8wfO+bjH6qZQJ1JVVfN9a0wyNIUefWTAzxXetKnJ4cfhJHuy4cV9941XJG5VVdXFDyhUpNnDuar5dhXlG1X0njdIoVNri3Kax22NO6I3dIlUiUROkE4AcS8F539f7nCtBzyn9znqBM/JtHiN+bsAPWBbIvUhkbOjE4AecAbr+Tv71SW7fqclnPYzRHXRTaFOFEDn7QYOSPRjNT7U6WJoDPcTcY9VY3zAU7xv55l0zsGbwov77huvSDxLIhWpEep8xOvAuD3vtTeN3snOf5rHbY0vBhOxoUukSiRygnTePVZzkVrjyX1S+xx1ghffdGudoB6wE0AK9SGRs6Pz7nrAGSx4zdCOqqr6axk0S8l0NOsv1HU9/1oMN4U6hd1u1wlg4CDEPVY3hTqRcKpcHDHeEfdYNcYH/HT9eR+d5e0bUY7V+PDivruKxFMkUpGCPM93u13EMKL3vEEKnVpbrNM8bmvcFr2hS6RKJHKCBNGbi6QaT+6W1OcYvVYnco4n0uI1onQBesBGIvUhkbMjiN5WJNVyTu2PsQN4mqa+hs9sv99nlxbSbsvzvHk8Z527I9Tn6v+lrzbBsY7VHaFGF+tYpeDWbmPmYxWrV7vv3bdckXiiFCpS6ODi1uHoPW/73dvS79SmELc1TkoiVSKREyRLo7kIUmg8eVwKn2MKtTqRczyRFi8uPWAjkfqQyNmRpdFWBCm0nDNYz8jQxn6/DzW4s652WzjNmoHH4YbM/DV+TKhTq+s61PVrN4USOVbZiFCjS+dYze9iEznQ2c98rG4NL+67b7ki8USJVKS6ro/HYzrtdgo9b5B+pzaFuK1x4lKoEnFPkESai0QaTx6UyOeYSK1u6AQj0gMOSKE+6AGzZFrOeSxmZOhwNrrdiJzP5zCm93g8ZleS+lVVtX+lruuiKI7H47M+5pHRjgl1uncPj0N17xyQtkSO1ZhQp3v3kaY+VtmzA4741jMcq7ZljQmd+eCwXMNn5WwV6R//+MdA6V/+8pfdbjfDORi35701jIk6tTsimZmrvmtmqBJjTH2CDNvv9/M0F8P0wkuhE2zTCd4XyZyiN27J0gNmesAYlpQMvdaiFUXRqTThmVCPL35s/UoWzsDwYc8Z7auhTvTudV039z2G3zf6sRof6hTvftO/NvWxyp4d8NRvPfAtZIZj9arhL0kR3z2Fg8MiDJ+VF9c/mqIi/fLLL7/88svFoj//+c/9YOq6Lsvy6U1W3J53fBiTdmo3RTLdW98qbmsc3WxVYqRJT5AB4b3maS6G6YWXQifYphO8NZLp3vomekA9YKYHjOWOTZcSVFVVf8Ovm3bCmm3brMdDfUoM2QNbpM0Z7YOhPj2SWzfai7gd230BP6L/SYUFwkf++tTH6sHw4r77pioS05m/Ig3MM5qzUqXQ8zaRJNKptc18msdtjS+K2NClUCUSOUESaS4GYosbA4/bbK1O5Bw/p9Hi9c3ZBegB+2+tBzwn01YMxBY3homsZ83Q/loG126z1HWd53nEncvGhzqRcPtlzJtGP1bjQ40u+rGKrr8Z4ul0utiyRzlW48OL++4qEk+RSEUqy7LztSP7zzpEM99ejt7zBgvq1KYTtzVOTSJVIoUTJJHmIpHGkwcl8jkmUquzNM7xLJkWLyI9YFsi9SGFsyORtiKRlnM+z8mpJiD8OU3iPLQpzcqv4bZDeyHYgRfHDXVqzaK8h572C1I4Vq+GOqeLN83SOVZ989/l69zc69znjH6shsObWuIHZ4CRocsVt6+5JlYMKRyNpDq1fmCzneZxW+OLYjV06VSJFE6Qvi03Fzwuzc9xy7U6nRbvYmDzdAF6wM77plAfUjg7+rbcVsxmPcnQ838+uUZ7wHN/DHbnxTN/wAOhTq1pd/raL0jhWL0a6pwGkqEpHKu+KB3b4esbm+13T+FYDYQ3g8QPzjWSoYuWTkVqhxQrjM7RmH9aVlKdWj+wOU/zuK1xX/RLwRSqRPQT5GJIiTQXKTSe3CHBzzGdWq0T7AQ2WxegB2y/byL1IfrZcTGkRNqKFFrOieTn3l+7aM0g3jHDicOyyrFWUL4p1OjiHqtlcaxC3U72HBwfXtx3V5F4ChWpbVk97+rFbY3pc4K0aTzXwefY5hxPhx4wNc6Oto20nGtLhgIAAAAAXLSeDZQAAAAAAAZIhgIAAAAAmyAZCgAAAABsgmQoAAAAALAJkqEAAAAAwCb8MXYAAAAAAMBDyrLMsqwoiqIowo91XWdZFv6XRn4+n2PHAMC0dIoArJ7ODoAtCx3f6XTKsqyqqv1+v9vtwo9Zlsn+tUmGAqycThGA1dPZAbBxRVHUdV0URdMbhruD4ZnD4RDuGpJZMxRg9eq6rut6t9tlWbbf76uqquv6fD6HZ/SIAKyAzg6AjWtmSGRZdjgcwoPmGfMk2iRDAVZOpwjA6unsANi4pu8beEwgGQqwcjpFAFZPZwcAjCQZCgAAAABsgmQoAAAAALAJkqEAAAAAwCZIhgIAAAAAmyAZCgAAAABsgmQoAAAAACxeWZbn87koitiBJE0yFGATdIoArJ7ODgB41R9jBwBAKuq6rus6y7KyLCOHAgAAwLPled55pqqqrd1HNDIUgCzLsrIs9/t9yIfmeR6yogCwJnlPu7+r69rtQABWpp3oLMuyqqrdbldV1eFwCA+2lgnNjAwF2KxOn3c8HpuOMCRGz+dzlMAA4Fn6V4BlWZZlGW7+lWXZfkEzQwIA1mS324UHzT2/oijqui6KoukHm6It3BeUDAXYrqZTbK79woOiKI7HY6SgAOCZxlwBAsCKXbzVF7rC5vGmbgdKhgJsVycH2r4H2Fw6AsCijbkCDD3g6XTK/jOYtDNoFABWI/R3beE2YYxY4pAMBeDfQ0E766ZFiwYAJnPxCjAkQ5u581lvMRkAWIeiKMLAl5AAbaYGxo1qZjZQAuC/o2CaH/f7fcR4AGAK/SvA5iKwuQ40fR6AFTudTs1tv9PptN/vN9jrGRkKQJZlWVVV+/2+WSq0qqq48QDA051Op9DBhSkRdgsEYGuajq8ois12gvlm/3IA+rY5SwIAAICNkAwFAAAAADbBmqEAAAAAwCZIhgIAAAAAmyAZCgAAAABsgmQoAAAAALAJkqEAAAAAwCZIhgIAAAAAmyAZCgAAAABsgmQoAAAAALAJkqEAAAAAwCZIhgIAAAAAmyAZCgAAAABsgmQoAAAAALAJkqEAAAAAwCZIhgIAAAAAm/D/nG7RwLZnw5UAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c=ROOT.TCanvas(\"c\",\"c\",1800,600)\n",
+    "c.Divide(3,1)\n",
+    "c.cd(1)\n",
+    "hmc_e_eta.GetXaxis().SetTitle(\"#eta_{e}\")\n",
+    "hmc_e_eta.Draw(\"PLC\")\n",
+    "hreco_e_eta.Draw(\"PLC same\")\n",
+    "legend=ROOT.TLegend(0.48,0.6,0.88,0.7)\n",
+    "legend.AddEntry(hreco_e_eta.GetValue(),\"Reconstructed\",\"l\")\n",
+    "legend.AddEntry(hmc_e_eta.GetValue(),\"Monte Carlo\",\"l\")\n",
+    "legend.Draw(\"same\")\n",
+    "\n",
+    "c.cd(2)\n",
+    "hmc_pip_eta.GetXaxis().SetTitle(\"#eta_{#pi+}\")\n",
+    "hmc_pip_eta.Draw(\"PLC\")\n",
+    "hreco_pip_eta.Draw(\"PLC same\")\n",
+    "\n",
+    "c.cd(3)\n",
+    "hmc_pim_eta.GetXaxis().SetTitle(\"#eta_{#pi-}\")\n",
+    "hmc_pim_eta.Draw(\"PLC\")\n",
+    "hreco_pim_eta.Draw(\"PLC same\")\n",
+    "drawLatex(0.2,0.8)\n",
+    "c.Draw()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "graduate-boards",
+   "metadata": {},
+   "source": [
+    "## Particle Phi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "smaller-detector",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c=ROOT.TCanvas(\"c\",\"c\",1800,600)\n",
+    "c.Divide(3,1)\n",
+    "c.cd(1)\n",
+    "hmc_e_phi.GetXaxis().SetTitle(\"#phi_{e}\")\n",
+    "hmc_e_phi.Draw(\"PLC\")\n",
+    "hreco_e_phi.Draw(\"PLC same\")\n",
+    "\n",
+    "\n",
+    "c.cd(2)\n",
+    "hmc_pip_phi.GetXaxis().SetTitle(\"#phi_{#pi+}\")\n",
+    "hmc_pip_phi.Draw(\"PLC\")\n",
+    "hreco_pip_phi.Draw(\"PLC same\")\n",
+    "legend=ROOT.TLegend(0.3,0.65,0.65,0.75)\n",
+    "legend.AddEntry(hreco_pip_phi.GetValue(),\"Reconstructed\",\"l\")\n",
+    "legend.AddEntry(hmc_pip_phi.GetValue(),\"Monte Carlo\",\"l\")\n",
+    "legend.Draw(\"same\")\n",
+    "\n",
+    "c.cd(3)\n",
+    "hmc_pim_phi.GetXaxis().SetTitle(\"#phi_{#pi-}\")\n",
+    "hmc_pim_phi.Draw(\"PLC\")\n",
+    "hreco_pim_phi.Draw(\"PLC same\")\n",
+    "drawLatex(0.2,0.8)\n",
+    "c.Draw()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "velvet-upset",
+   "metadata": {},
+   "source": [
+    "# Particle Resolutions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "attractive-julian",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Delta histogram\n",
+    "def get_compare_histo1d(bins,xmin,xmax,drawString,filterString):\n",
+    "    h=df.Define(\"Var\",drawString).Filter(filterString).Histo1D((\"h\",\"\",bins,xmin,xmax),\"Var\")\n",
+    "    return h"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "right-console",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Queue RDataFrame histograms\n",
+    "hres_e_E = get_compare_histo1d(100,-1,1,\"recPart.E()-mcPart.E()\",\"pid==11 && status==2\")\n",
+    "hres_e_eta = get_compare_histo1d(100,-0.01,0.01,\"recPart.Eta()-mcPart.Eta()\",\"pid==11 && status==2\")\n",
+    "hres_e_phi = get_compare_histo1d(100,-1,1,\"(recPart.Phi()-mcPart.Phi())*180/3.14159265\",\"pid==11 && status==2\")\n",
+    "\n",
+    "hres_pip_E = get_compare_histo1d(100,-0.4,0.4,\"recPart.E()-mcPart.E()\",\"pid==211 && status==1\")\n",
+    "hres_pip_eta = get_compare_histo1d(100,-0.01,0.01,\"recPart.Eta()-mcPart.Eta()\",\"pid==211 && status==1\")\n",
+    "hres_pip_phi = get_compare_histo1d(100,-1,1,\"(recPart.Phi()-mcPart.Phi())*180/3.14159265\",\"pid==211 && status==1\")\n",
+    "\n",
+    "hres_pim_E = get_compare_histo1d(100,-0.4,0.4,\"recPart.E()-mcPart.E()\",\"pid==-211 && status==1\")\n",
+    "hres_pim_eta = get_compare_histo1d(100,-0.01,0.01,\"recPart.Eta()-mcPart.Eta()\",\"pid==-211 && status==1\")\n",
+    "hres_pim_phi = get_compare_histo1d(100,-1,1,\"(recPart.Phi()-mcPart.Phi())*180/3.14159265\",\"pid==-211 && status==1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "human-holocaust",
+   "metadata": {},
+   "source": [
+    "## Particle Energy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "arbitrary-commerce",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c=ROOT.TCanvas(\"c\",\"c\",1800,600)\n",
+    "c.Divide(3,1)\n",
+    "c.cd(1)\n",
+    "hres_e_E.GetXaxis().SetTitle(\"#DeltaE_{e} [GeV]\")\n",
+    "hres_e_E.Draw(\"PLC\")\n",
+    "c.cd(2)\n",
+    "hres_pip_E.GetXaxis().SetTitle(\"#DeltaE_{#pi+} [GeV]\")\n",
+    "hres_pip_E.Draw(\"PLC\")\n",
+    "\n",
+    "c.cd(3)\n",
+    "hres_pim_E.GetXaxis().SetTitle(\"#DeltaE_{#pi-} [GeV]\")\n",
+    "hres_pim_E.Draw(\"PLC\")\n",
+    "drawLatex(0.13,0.8)\n",
+    "c.Draw()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "injured-croatia",
+   "metadata": {},
+   "source": [
+    "## Particle Pseudorapidity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "clean-sculpture",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c=ROOT.TCanvas(\"c\",\"c\",1800,600)\n",
+    "c.Divide(3,1)\n",
+    "c.cd(1)\n",
+    "hres_e_eta.GetXaxis().SetTitle(\"#Delta#eta_{e}\")\n",
+    "hres_e_eta.GetXaxis().SetNdivisions(505)\n",
+    "hres_e_eta.Draw(\"PLC\")\n",
+    "drawLatex(0.13,0.8)\n",
+    "c.cd(2)\n",
+    "hres_pip_eta.GetXaxis().SetTitle(\"#Delta#eta_{#pi+}\")\n",
+    "hres_pip_eta.GetXaxis().SetNdivisions(505)\n",
+    "hres_pip_eta.Draw(\"PLC\")\n",
+    "\n",
+    "c.cd(3)\n",
+    "hres_pim_eta.GetXaxis().SetTitle(\"#Delta#eta_{#pi-}\")\n",
+    "hres_pim_eta.GetXaxis().SetNdivisions(505)\n",
+    "hres_pim_eta.Draw(\"PLC\")\n",
+    "\n",
+    "c.Draw()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "warming-radical",
+   "metadata": {},
+   "source": [
+    "## Particle Phi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "immediate-methodology",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c=ROOT.TCanvas(\"c\",\"c\",1800,600)\n",
+    "c.Divide(3,1)\n",
+    "c.cd(1)\n",
+    "hres_e_phi.GetXaxis().SetTitle(\"#Delta#phi_{e}\")\n",
+    "hres_e_phi.GetXaxis().SetNdivisions(505)\n",
+    "hres_e_phi.Draw(\"PLC\")\n",
+    "drawLatex(0.13,0.8)\n",
+    "c.cd(2)\n",
+    "hres_pip_phi.GetXaxis().SetTitle(\"#Delta#phi_{#pi+}\")\n",
+    "hres_pip_phi.GetXaxis().SetNdivisions(505)\n",
+    "hres_pip_phi.Draw(\"PLC\")\n",
+    "\n",
+    "c.cd(3)\n",
+    "hres_pim_phi.GetXaxis().SetTitle(\"#Delta#phi_{#pi-}\")\n",
+    "hres_pim_phi.GetXaxis().SetNdivisions(505)\n",
+    "hres_pim_phi.Draw(\"PLC\")\n",
+    "\n",
+    "c.Draw()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "forced-penny",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc-autonumbering": false
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 6b9f6a25edbf9be5b6d70389ec96e07bb312d6a3 Mon Sep 17 00:00:00 2001
From: Christopher Dilks <christopher.j.dilks@gmail.com>
Date: Mon, 28 Nov 2022 14:00:30 -0500
Subject: [PATCH 11/32] ci: add `AnalysisEpic` comparison plots

---
 .github/workflows/ci.yml   | 25 ++++++++++++++++++++++---
 macro/ci/analysis_p_eta.C  |  6 ++++--
 macro/ci/analysis_x_q2.C   |  3 ++-
 macro/ci/analysis_yRatio.C |  6 ++++--
 macro/ci/comparator.C      | 10 ++++++----
 src/PostProcessor.cxx      | 10 +++++-----
 6 files changed, 43 insertions(+), 17 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 26654b95..d651e13a 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -139,6 +139,7 @@ jobs:
       fail-fast: true
       matrix:
         include:
+          - { detector: epic,   num_files: 1  }
           - { detector: athena, num_files: 20 }
           - { detector: ecce,   num_files: 40 }
     steps:
@@ -238,11 +239,16 @@ jobs:
     strategy:
       fail-fast: true
       matrix:
-        detector: [athena, ecce]
+        detector: [epic, athena, ecce]
         aname: [x_q2, p_eta, yRatio]
         recon: [Ele]
         include: # enable other recon methods for `aname==x_q2` only
-        - { aname: x_q2, recon: Mixed,  detector: athena } # FIXME: not sure how to avoid being repetitive... 
+        - { aname: x_q2, recon: Mixed,  detector: epic } # FIXME: not sure how to avoid being repetitive... 
+        - { aname: x_q2, recon: JB,     detector: epic }
+        - { aname: x_q2, recon: DA,     detector: epic }
+        - { aname: x_q2, recon: Sigma,  detector: epic }
+        - { aname: x_q2, recon: eSigma, detector: epic }
+        - { aname: x_q2, recon: Mixed,  detector: athena }
         - { aname: x_q2, recon: JB,     detector: athena }
         - { aname: x_q2, recon: DA,     detector: athena }
         - { aname: x_q2, recon: Sigma,  detector: athena }
@@ -292,6 +298,7 @@ jobs:
       matrix:
         mode:
           - fastsim
+          - epic
           - athena
           - ecce
         pname: # list only jobs which will only use one (primary) recon method
@@ -311,6 +318,12 @@ jobs:
           - { mode: fastsim, pname: coverage2D_x_q2, recon: JB,     aname: x_q2, pmacro: postprocess_x_q2.C }
           - { mode: fastsim, pname: coverage2D_x_q2, recon: Mixed,  aname: x_q2, pmacro: postprocess_x_q2.C }
           - { mode: fastsim, pname: coverage2D_x_q2, recon: Sigma,  aname: x_q2, pmacro: postprocess_x_q2.C }
+          - { mode: epic,    pname: coverage2D_x_q2, recon: Ele,    aname: x_q2, pmacro: postprocess_x_q2.C }
+          - { mode: epic,    pname: coverage2D_x_q2, recon: DA,     aname: x_q2, pmacro: postprocess_x_q2.C }
+          - { mode: epic,    pname: coverage2D_x_q2, recon: eSigma, aname: x_q2, pmacro: postprocess_x_q2.C }
+          - { mode: epic,    pname: coverage2D_x_q2, recon: JB,     aname: x_q2, pmacro: postprocess_x_q2.C }
+          - { mode: epic,    pname: coverage2D_x_q2, recon: Mixed,  aname: x_q2, pmacro: postprocess_x_q2.C }
+          - { mode: epic,    pname: coverage2D_x_q2, recon: Sigma,  aname: x_q2, pmacro: postprocess_x_q2.C }
           - { mode: athena,  pname: coverage2D_x_q2, recon: Ele,    aname: x_q2, pmacro: postprocess_x_q2.C }
           - { mode: athena,  pname: coverage2D_x_q2, recon: DA,     aname: x_q2, pmacro: postprocess_x_q2.C }
           - { mode: athena,  pname: coverage2D_x_q2, recon: eSigma, aname: x_q2, pmacro: postprocess_x_q2.C }
@@ -400,6 +413,10 @@ jobs:
         with:
           name: root_analysis_fastsim_${{matrix.aname}}_${{matrix.recon}}
           path: out
+      - uses: actions/download-artifact@v3
+        with:
+          name: root_analysis_epic_${{matrix.aname}}_${{matrix.recon}}
+          path: out
       - uses: actions/download-artifact@v3
         with:
           name: root_analysis_athena_${{matrix.aname}}_${{matrix.recon}}
@@ -423,10 +440,12 @@ jobs:
             echo "[CI] COMPARATOR MACRO"
             ls out
             mv -v out/fastsim.{${{matrix.aname}},${{matrix.pname}}}.${{matrix.recon}}.root # rename aname -> pname
+            mv -v out/epic.{${{matrix.aname}},${{matrix.pname}}}.${{matrix.recon}}.root # rename aname -> pname
             mv -v out/athena.{${{matrix.aname}},${{matrix.pname}}}.${{matrix.recon}}.root # rename aname -> pname
             mv -v out/ecce.{${{matrix.aname}},${{matrix.pname}}}.${{matrix.recon}}.root # rename aname -> pname
-            ${{env.root_no_delphes}} 'macro/ci/comparator.C("out/fastsim.${{matrix.pname}}.${{matrix.recon}}.root","out/athena.${{matrix.pname}}.${{matrix.recon}}.root","out/ecce.${{matrix.pname}}.${{matrix.recon}}.root","out/comparison.${{matrix.pname}}.${{matrix.recon}}","${{matrix.xvar}}","${{matrix.yvar}}")'
+            ${{env.root_no_delphes}} 'macro/ci/comparator.C("out/fastsim.${{matrix.pname}}.${{matrix.recon}}.root","out/epic.${{matrix.pname}}.${{matrix.recon}}.root","out/athena.${{matrix.pname}}.${{matrix.recon}}.root","out/ecce.${{matrix.pname}}.${{matrix.recon}}.root","out/comparison.${{matrix.pname}}.${{matrix.recon}}","${{matrix.xvar}}","${{matrix.yvar}}")'
             rm -v out/fastsim.${{matrix.pname}}.${{matrix.recon}}.root # rm analysis_root artifact
+            rm -v out/epic.${{matrix.pname}}.${{matrix.recon}}.root # rm analysis_root artifact
             rm -v out/athena.${{matrix.pname}}.${{matrix.recon}}.root # rm analysis_root artifact
             rm -v out/ecce.${{matrix.pname}}.${{matrix.recon}}.root # rm analysis_root artifact
       - uses: actions/upload-artifact@v3
diff --git a/macro/ci/analysis_p_eta.C b/macro/ci/analysis_p_eta.C
index 4cf5adb0..8acd17bd 100644
--- a/macro/ci/analysis_p_eta.C
+++ b/macro/ci/analysis_p_eta.C
@@ -5,11 +5,13 @@ void analysis_p_eta(
     TString configFile,
     TString outfilePrefix,
     TString reconMethod="Ele"
-) {
+    )
+{
 
   // setup analysis ========================================
   Analysis *A;
-  if     (outfilePrefix.Contains("athena")) A = new AnalysisAthena( configFile, outfilePrefix );
+  if   (outfilePrefix.Contains("epic"))     A = new AnalysisEpic(   configFile, outfilePrefix );
+  else if(outfilePrefix.Contains("athena")) A = new AnalysisAthena( configFile, outfilePrefix );
   else if(outfilePrefix.Contains("ecce"))   A = new AnalysisEcce(   configFile, outfilePrefix );
 #ifndef EXCLUDE_DELPHES
   else A = new AnalysisDelphes( configFile, outfilePrefix );
diff --git a/macro/ci/analysis_x_q2.C b/macro/ci/analysis_x_q2.C
index f52b1f49..5357eda4 100644
--- a/macro/ci/analysis_x_q2.C
+++ b/macro/ci/analysis_x_q2.C
@@ -10,7 +10,8 @@ void analysis_x_q2(
 
   // setup analysis ========================================
   Analysis *A;
-  if     (outfilePrefix.Contains("athena")) A = new AnalysisAthena( configFile, outfilePrefix );
+  if   (outfilePrefix.Contains("epic"))     A = new AnalysisEpic(   configFile, outfilePrefix );
+  else if(outfilePrefix.Contains("athena")) A = new AnalysisAthena( configFile, outfilePrefix );
   else if(outfilePrefix.Contains("ecce"))   A = new AnalysisEcce(   configFile, outfilePrefix );
 #ifndef EXCLUDE_DELPHES
   else A = new AnalysisDelphes( configFile, outfilePrefix );
diff --git a/macro/ci/analysis_yRatio.C b/macro/ci/analysis_yRatio.C
index 974b0f53..e6f34d96 100644
--- a/macro/ci/analysis_yRatio.C
+++ b/macro/ci/analysis_yRatio.C
@@ -5,11 +5,13 @@ void analysis_yRatio(
     TString configFile,
     TString outfilePrefix,
     TString reconMethod="Ele"
-) {
+    )
+{
 
   // setup analysis ========================================
   Analysis *A;
-  if     (outfilePrefix.Contains("athena")) A = new AnalysisAthena( configFile, outfilePrefix );
+  if   (outfilePrefix.Contains("epic"))     A = new AnalysisEpic(   configFile, outfilePrefix );
+  else if(outfilePrefix.Contains("athena")) A = new AnalysisAthena( configFile, outfilePrefix );
   else if(outfilePrefix.Contains("ecce"))   A = new AnalysisEcce(   configFile, outfilePrefix );
 #ifndef EXCLUDE_DELPHES
   else A = new AnalysisDelphes( configFile, outfilePrefix );
diff --git a/macro/ci/comparator.C b/macro/ci/comparator.C
index 9e05bba2..90fbea27 100644
--- a/macro/ci/comparator.C
+++ b/macro/ci/comparator.C
@@ -4,8 +4,9 @@ R__LOAD_LIBRARY(Sidis-eic)
 // - depending on infile, different histograms will be drawn
 void comparator(
     TString infile0="out/resolution.fastsim.root",
-    TString infile1="out/resolution.athena.root",
-    TString infile2="out/resolution.ecce.root",
+    TString infile1="out/resolution.epic.root",
+    TString infile2="out/resolution.athena.root",
+    TString infile3="out/resolution.ecce.root",
     TString outfile="out/resolution.comparison.root",
     TString gx="x", TString gy="q2" // plotgrid vars
     ) {
@@ -73,7 +74,8 @@ void comparator(
   std::vector<TFile*> infiles = {
     new TFile(infile0),
     new TFile(infile1),
-    new TFile(infile2)
+    new TFile(infile2),
+    new TFile(infile3)
   };
   bool first=true;
   Int_t numXbins, numYbins;
@@ -114,9 +116,9 @@ void comparator(
   // set legend labels
   auto makeLegendName = [] (TString infileName) -> TString {
     if     (infileName.Contains("fastsim")) return "Delphes";
+    else if(infileName.Contains("epic"))    return "EPIC";
     else if(infileName.Contains("athena"))  return "ATHENA";
     else if(infileName.Contains("ecce"))    return "ECCE";
-    else if(infileName.Contains("epic"))    return "EPIC";
     return "UNKNOWN";
   };
   for(auto infile : infiles) {
diff --git a/src/PostProcessor.cxx b/src/PostProcessor.cxx
index a20db7ec..90febbf0 100644
--- a/src/PostProcessor.cxx
+++ b/src/PostProcessor.cxx
@@ -495,17 +495,17 @@ void PostProcessor::DrawInBins(
             hist->SetFillColor(kGray);
             break;
           case 1:
+            hist->SetLineColor(kAzure);
+            hist->SetLineStyle(1);
+            break;
+          case 2:
             hist->SetLineColor(kRed);
             hist->SetLineStyle(7);
             break;
-          case 2:
+          case 3:
             hist->SetLineColor(kGreen+2);
             hist->SetLineStyle(9);
             break;
-          case 3:
-            hist->SetLineColor(kAzure);
-            hist->SetLineStyle(1);
-            break;
         }
 
         if(legendLabels.size()>0 && i==0 && j==0) {

From 1db144c701e688957d5846dfe665fd1793db2cd8 Mon Sep 17 00:00:00 2001
From: Christopher Dilks <christopher.j.dilks@gmail.com>
Date: Mon, 28 Nov 2022 16:14:21 -0500
Subject: [PATCH 12/32] modified:   .github/workflows/ci.yml

---
 .github/workflows/ci.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index d651e13a..0807593d 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -139,7 +139,7 @@ jobs:
       fail-fast: true
       matrix:
         include:
-          - { detector: epic,   num_files: 1  }
+          - { detector: epic,   num_files: 8  }
           - { detector: athena, num_files: 20 }
           - { detector: ecce,   num_files: 40 }
     steps:

From 721ccb1f78a94f92361d02db74c9958e90d950fc Mon Sep 17 00:00:00 2001
From: Christopher Dilks <christopher.j.dilks@gmail.com>
Date: Mon, 28 Nov 2022 19:26:29 -0500
Subject: [PATCH 13/32] add copyright notices

---
 s3tools/make-epic-config.sh | 4 ++++
 src/AnalysisEpic.cxx        | 3 +++
 src/AnalysisEpic.h          | 3 +++
 src/ParticleTree.cxx        | 3 +++
 src/ParticleTree.h          | 3 +++
 5 files changed, 16 insertions(+)

diff --git a/s3tools/make-epic-config.sh b/s3tools/make-epic-config.sh
index d14e22d0..6ee28a3d 100755
--- a/s3tools/make-epic-config.sh
+++ b/s3tools/make-epic-config.sh
@@ -1,5 +1,9 @@
 #!/bin/bash
 
+# SPDX-License-Identifier: LGPL-3.0-or-later
+# Copyright (C) 2022 Christopher Dilks
+
+
 ###################
 # TOP-LEVEL SCRIPT to automate the creation of a config file for a specific release,
 # supporting streaming or downloading from S3
diff --git a/src/AnalysisEpic.cxx b/src/AnalysisEpic.cxx
index c3916296..66b26c03 100644
--- a/src/AnalysisEpic.cxx
+++ b/src/AnalysisEpic.cxx
@@ -1,3 +1,6 @@
+// SPDX-License-Identifier: LGPL-3.0-or-later
+// Copyright (C) 2022 Gregory Matousek, Christopher Dilks
+
 #include "AnalysisEpic.h"
 #include "AnalysisEcce.h"
 
diff --git a/src/AnalysisEpic.h b/src/AnalysisEpic.h
index 24d09e23..1a26d64a 100644
--- a/src/AnalysisEpic.h
+++ b/src/AnalysisEpic.h
@@ -1,3 +1,6 @@
+// SPDX-License-Identifier: LGPL-3.0-or-later
+// Copyright (C) 2022 Gregory Matousek, Christopher Dilks
+
 #ifndef AnalysisEpic_
 #define AnalysisEpic_
 
diff --git a/src/ParticleTree.cxx b/src/ParticleTree.cxx
index 66fd976d..8164d063 100644
--- a/src/ParticleTree.cxx
+++ b/src/ParticleTree.cxx
@@ -1,3 +1,6 @@
+// SPDX-License-Identifier: LGPL-3.0-or-later
+// Copyright (C) 2022 Gregory Matousek
+
 #include "ParticleTree.h"
 
 ClassImp(ParticleTree)
diff --git a/src/ParticleTree.h b/src/ParticleTree.h
index 5ae4006f..37effa70 100644
--- a/src/ParticleTree.h
+++ b/src/ParticleTree.h
@@ -1,3 +1,6 @@
+// SPDX-License-Identifier: LGPL-3.0-or-later
+// Copyright (C) 2022 Gregory Matousek
+
 /* ParticleTree
  * - provides a particle tree for storing reconstructed particle kinematics + pid
  * - helpful for debugging matching algorithm

From c463d8ab12f7b5bc813710d78c638c4550ab3aad Mon Sep 17 00:00:00 2001
From: Christopher Dilks <christopher.j.dilks@gmail.com>
Date: Wed, 30 Nov 2022 00:35:50 -0500
Subject: [PATCH 14/32] remove all `podio` dependence from `AnalysisEpic`

---
 src/AnalysisEpic.cxx     | 156 ---------------------------------------
 src/AnalysisEpic.h       |  68 +----------------
 tutorial/analysis_epic.C |   1 -
 3 files changed, 2 insertions(+), 223 deletions(-)

diff --git a/src/AnalysisEpic.cxx b/src/AnalysisEpic.cxx
index 66b26c03..049cee77 100644
--- a/src/AnalysisEpic.cxx
+++ b/src/AnalysisEpic.cxx
@@ -6,7 +6,6 @@
 
 AnalysisEpic::AnalysisEpic(TString infileName_, TString outfilePrefix_)
   : Analysis(infileName_, outfilePrefix_)
-  , crossCheckKinematics(false)
 {};
 
 AnalysisEpic::~AnalysisEpic() {};
@@ -424,158 +423,3 @@ void AnalysisEpic::Execute()
   if(numProxMatched>0)
     cerr << "WARNING: " << numProxMatched << " recon. particles were proximity matched to truth (when mcID match failed)" << endl;
 }
-
-
-// particle printers //////////////////////////////////////////////
-
-void AnalysisEpic::PrintParticle(const edm4hep::MCParticle& P) { 
-  fmt::print("\n");
-  fmt::print("  {:>20}: {}\n", "PDG",          P.getPDG()             );
-  fmt::print("  {:>20}: {}\n", "Status",       P.getGeneratorStatus() );
-  fmt::print("  {:>20}: {}\n", "Energy",       P.getEnergy()          );
-  fmt::print("  {:>20}: {}\n", "p=|Momentum|", edm4hep::utils::p(P)   );
-  fmt::print("  {:>20}: {}\n", "pT_lab",       edm4hep::utils::pT(P)  );
-  fmt::print("  {:>20}: ({}, {}, {})\n",
-      "3-Momentum",
-      P.getMomentum().x,
-      P.getMomentum().y,
-      P.getMomentum().z
-      );
-  fmt::print("  {:>20}: ({}, {}, {})\n",
-      "Vertex",
-      P.getVertex().x,
-      P.getVertex().y,
-      P.getVertex().z
-      );
-  // FIXME: relations unavailable
-  // fmt::print("  {:>20}:\n", "Parents");
-  // for(const auto& parent : P.getParents()) {
-  //   // fmt::print("    {:>20}: {}\n", "PDG", parent.getPDG());
-  //   fmt::print("    {:>20}: {}\n", "id", parent.id());
-  // }
-  // fmt::print("  {:>20}:\n", "Daughters");
-  // for(const auto& daughter : P.getDaughters())
-  //   fmt::print("    {:>20}: {}\n", "PDG", daughter.getPDG());
-}
-
-void AnalysisEpic::PrintParticle(const edm4eic::ReconstructedParticle& P) {
-  fmt::print("\n");
-  fmt::print("  {:>20}: ", "PDG");
-  if(P.getParticleIDUsed().isAvailable()) fmt::print("{}\n", P.getParticleIDUsed().getPDG());
-  else fmt::print("???\n");
-  fmt::print("  {:>20}: {}\n", "Mass",         P.getMass()           );
-  fmt::print("  {:>20}: {}\n", "Charge",       P.getCharge()         );
-  fmt::print("  {:>20}: {}\n", "Energy",       P.getEnergy()         );
-  fmt::print("  {:>20}: {}\n", "p=|Momentum|", edm4hep::utils::p(P)  );
-  fmt::print("  {:>20}: {}\n", "pT_lab",       edm4hep::utils::pT(P) );
-  fmt::print("  {:>20}: ({}, {}, {})\n",
-      "3-Momentum",
-      P.getMomentum().x,
-      P.getMomentum().y,
-      P.getMomentum().z
-      );
-  fmt::print("  {:>20}: {}\n", "# of clusters", P.clusters_size()    );
-  fmt::print("  {:>20}: {}\n", "# of tracks",   P.tracks_size()      );
-  fmt::print("  {:>20}: {}\n", "# of PIDs",     P.particleIDs_size() );
-  fmt::print("  {:>20}: {}\n", "# of recParts", P.particles_size()   );
-  // FIXME: relations unavailable
-  // for(const auto& track : P.getTracks()) {
-  //   // ...
-  // }
-  // for(const auto& cluster : P.getClusters()) {
-  //   // ...
-  // }
-}
-
-// print out a reconstructed particle, and its matching truth 
-void AnalysisEpic::PrintAssociatedParticles(
-    const edm4hep::MCParticle& simPart,
-    const edm4eic::ReconstructedParticle& recPart
-    )
-{
-  fmt::print("\n   {:->35}\n"," reconstructed particle:");
-  PrintParticle(recPart);
-  fmt::print("\n   {:.>35}\n"," truth match:");
-  if(simPart.isAvailable())
-    PrintParticle(simPart);
-  else
-    fmt::print("     {:>35}\n","NO MATCH");
-  fmt::print("\n");
-}
-
-
-// helper methods /////////////////////////////////////////////////////////////
-
-// common loop over Reconstructed Particle <-> MC Particle associations
-/* - get PID
- * - basic quality cuts
- * - execute `payload`
-//     payload signature: (simPart, recPart, reconstructed PDG)
- */
-void AnalysisEpic::LoopMCRecoAssocs(
-    const edm4eic::MCRecoParticleAssociationCollection& mcRecAssocs,
-    std::function<void(const edm4hep::MCParticle&, const edm4eic::ReconstructedParticle&, int)> payload,
-    bool printParticles
-    )
-{
-  for(const auto& assoc : mcRecAssocs ) {
-
-    // FIXME: relations unavailable
-    // get reconstructed and simulated particles, and check for matching
-    auto recPart = assoc.getRec(); // reconstructed particle
-    auto simPart = assoc.getSim(); // simulated (truth) particle
-    // if(!simPart.isAvailable()) continue; // FIXME: consider using this once we have matching
-
-    // print associations
-    if(printParticles) PrintAssociatedParticles(simPart,recPart);
-
-    // get reconstructed PDG from PID
-    auto recPDG = GetReconstructedPDG(simPart, recPart);
-    // run payload
-    payload(simPart, recPart, recPDG);
-
-  }
-  useCachedPDG = true; // looped once, enable PDG caching
-}
-
-
-// get PDG of reconstructed particle
-int AnalysisEpic::GetReconstructedPDG(
-    const edm4hep::MCParticle& simPart,
-    const edm4eic::ReconstructedParticle& recPart
-    )
-{
-  int pdg = 0;
-
-  // get it from the cache, if we already have it
-  // FIXME: check this, this has not yet been tested
-  if(useCachedPDG) {
-    try {
-      pdg = pdgCache.at({simPart.id(),recPart.id()});
-      return pdg;
-    } catch(const std::out_of_range &e) {
-      ErrorPrint("WARNING: a PDG value was not cached");
-    }
-  }
-
-  /* // FIXME: relations unavailable
-  pdg = recPart.getPDG(); // if using edm4eic::ReconstructedParticle
-  if(recPart.getParticleIDUsed().isAvailable())
-    pdg = recPart.getParticleIDUsed().getPDG(); // if using edm4hep::ReconstructedParticle
-  */
-
-  // instead, use PID smearing
-  //   TODO  TODO  TODO
-  //   TODO  TODO  TODO
-  //   TODO  TODO  TODO
-
-  // if reconstructed PID is unavailable, use MC PDG
-  if(pdg==0 && simPart.isAvailable())
-    pdg = simPart.getPDG();
-
-  // cache this PDG value and return it
-  if(verbose) fmt::print("   caching PDG = id({},{}) -> {}\n",simPart.id(),recPart.id(),pdg);
-  pdgCache.insert({{simPart.id(),recPart.id()},pdg});
-  return pdg;
-}
-
diff --git a/src/AnalysisEpic.h b/src/AnalysisEpic.h
index 1a26d64a..f8df2ca7 100644
--- a/src/AnalysisEpic.h
+++ b/src/AnalysisEpic.h
@@ -1,32 +1,16 @@
 // SPDX-License-Identifier: LGPL-3.0-or-later
 // Copyright (C) 2022 Gregory Matousek, Christopher Dilks
 
-#ifndef AnalysisEpic_
-#define AnalysisEpic_
-
-// TEMP FIX:
-// New includes
+#pragma once
 
+// ROOT
 #include "TTreeReader.h"
 #include "TTreeReaderValue.h"
 #include "TTreeReaderArray.h"
 
-// data model
-#include "podio/EventStore.h"
-#include "podio/ROOTReader.h"
-#include "podio/CollectionBase.h"
-#include "edm4hep/utils/kinematics.h"
-
-// data model collections
-#include "edm4hep/MCParticleCollection.h"
-#include "edm4eic/ReconstructedParticleCollection.h"
-#include "edm4eic/MCRecoParticleAssociationCollection.h"
-#include "edm4eic/InclusiveKinematicsCollection.h"
-
 // sidis-eic
 #include "Analysis.h"
 
-
 class AnalysisEpic : public Analysis
 {
   public:
@@ -35,53 +19,5 @@ class AnalysisEpic : public Analysis
 
     void Execute() override;
 
-    // settings
-    bool crossCheckKinematics;
-
-    // return Lorentz vector for a given particle
-    template <class ParticleType>
-    TLorentzVector GetP4(ParticleType& P) {
-      return TLorentzVector(
-          P.getMomentum().x,
-          P.getMomentum().y,
-          P.getMomentum().z,
-          P.getEnergy()
-          );
-    }
-
-    // printers
-    void PrintParticle(const edm4hep::MCParticle& P);
-    void PrintParticle(const edm4eic::ReconstructedParticle& P);
-    void PrintAssociatedParticles(
-        const edm4hep::MCParticle& simPart,
-        const edm4eic::ReconstructedParticle& recPart
-        );
-
-
-  protected:
-
-    // get PDG from reconstructed particle
-    int GetReconstructedPDG(
-        const edm4hep::MCParticle& simPart,
-        const edm4eic::ReconstructedParticle& recPart
-        );
-    // run `payload` for all [Reconstructed Particle] <-> [MC Particle] associations
-    // payload signature: (simPart, recPart, reconstructed PDG)
-    void LoopMCRecoAssocs(
-        const edm4eic::MCRecoParticleAssociationCollection& mcRecAssocs,
-        std::function<void(const edm4hep::MCParticle&, const edm4eic::ReconstructedParticle&, int)> payload,
-        bool printParticles=false
-        );
-
-  private:
-    podio::ROOTReader podioReader;
-    podio::EventStore evStore;
-
-    // reconstructed PDG cache table
-    bool useCachedPDG;
-    std::map<std::pair<unsigned int,unsigned int>, int> pdgCache; // map : {simPart.id(),recPart.id()} -> recPDG
-
     ClassDefOverride(AnalysisEpic,1);
 };
-
-#endif
diff --git a/tutorial/analysis_epic.C b/tutorial/analysis_epic.C
index 2841a611..0bb524c7 100644
--- a/tutorial/analysis_epic.C
+++ b/tutorial/analysis_epic.C
@@ -38,7 +38,6 @@ void analysis_epic(
   AnalysisEpic *A = new AnalysisEpic(configFile, outfilePrefix);
 
   // settings
-  A->crossCheckKinematics = true;   // enable cross check with upstream kinematics
   A->verbose              = true;   // print event-by-event information
   //A->maxEvents            = 1000; // use this to limit the number of events
   A->writeSimpleTree      = true; // write event-by-event info into TTree

From f4150a7b141f093fd2202e607e16969a4196adde Mon Sep 17 00:00:00 2001
From: Christopher Dilks <christopher.j.dilks@gmail.com>
Date: Wed, 30 Nov 2022 00:53:23 -0500
Subject: [PATCH 15/32] new file DataModel.h to hold common `Particles` class;
 add particle masses to `CommonConstants.h`

---
 src/Analysis.h        |  1 +
 src/AnalysisAthena.h  | 25 -------------------------
 src/AnalysisEcce.cxx  | 12 ++++++------
 src/AnalysisEcce.h    | 33 ---------------------------------
 src/AnalysisEpic.cxx  | 31 +++++++++++++++----------------
 src/CommonConstants.h | 12 ++++++++----
 src/DataModel.h       | 31 +++++++++++++++++++++++++++++++
 src/LinkDef.h         |  1 +
 8 files changed, 62 insertions(+), 84 deletions(-)
 create mode 100644 src/DataModel.h

diff --git a/src/Analysis.h b/src/Analysis.h
index ef5881fc..85ca6a2a 100644
--- a/src/Analysis.h
+++ b/src/Analysis.h
@@ -28,6 +28,7 @@
 #include "adage/BinSet.h"
 
 // sidis-eic
+#include "DataModel.h"
 #include "Histos.h"
 #include "HistosDAG.h"
 #include "Kinematics.h"
diff --git a/src/AnalysisAthena.h b/src/AnalysisAthena.h
index ec1b4772..851bc4c1 100644
--- a/src/AnalysisAthena.h
+++ b/src/AnalysisAthena.h
@@ -10,31 +10,6 @@
 
 #include "Analysis.h"
 
-class Clusters
-{
-  public:
-    Clusters() {}
-    Clusters(double E_, double x_, double y_, double z_, double theta_, double phi_) {}
-    virtual ~Clusters() {}
-
-    double E;
-    double x;
-    double y;
-    double z;
-    double theta;
-    double phi;
-
-};
-
-class Particles
-{
-  public:
-    int pid;
-    int charge;
-    int mcID;
-    TLorentzVector vecPart;
-};
-
 class AnalysisAthena : public Analysis
 {
   public:
diff --git a/src/AnalysisEcce.cxx b/src/AnalysisEcce.cxx
index 43e959bd..f35fcad7 100644
--- a/src/AnalysisEcce.cxx
+++ b/src/AnalysisEcce.cxx
@@ -123,7 +123,7 @@ void AnalysisEcce::Execute()
      * - find scattered electron
      * - find beam particles
      */
-    std::vector<ParticlesEE> mcpart;
+    std::vector<Particles> mcpart;
     double maxP = 0;
     int genEleID = -1;
     bool foundBeamElectron = false;
@@ -142,10 +142,10 @@ void AnalysisEcce::Execute()
       double e_  = hepmcp_E[imc];
       
       double p_ = sqrt(pow(hepmcp_psx[imc],2) + pow(hepmcp_psy[imc],2) + pow(hepmcp_psz[imc],2));
-      double mass_ = (fabs(pid_)==211)?pimass:(fabs(pid_)==321)?kmass:(fabs(pid_)==11)?emass:(fabs(pid_)==13)?mumass:(fabs(pid_)==2212)?pmass:0.;
+      double mass_ = (fabs(pid_)==211)?constants::pimass:(fabs(pid_)==321)?constants::kmass:(fabs(pid_)==11)?constants::emass:(fabs(pid_)==13)?constants::mumass:(fabs(pid_)==2212)?constants::pmass:0.;
       
       // add to `mcpart`
-      ParticlesEE part;
+      Particles part;
       
       if(genStatus_ == 1) { // final state
 	
@@ -215,7 +215,7 @@ void AnalysisEcce::Execute()
      * - find the scattered electron
      *
      */
-    std::vector<ParticlesEE> recopart;
+    std::vector<Particles> recopart;
     int recEleFound = 0;
     for(int ireco=0; ireco<tracks_id.GetSize(); ireco++) {
 
@@ -235,7 +235,7 @@ void AnalysisEcce::Execute()
       if(pid_ == 0) continue; // pid==0: reconstructed tracks with no matching truth pid
 
       // add reconstructed particle `part` to `recopart`
-      ParticlesEE part;
+      Particles part;
       part.pid = pid_;
       part.mcID = tracks_trueID[ireco];
       //      part.charge = tracks_charge[ireco];
@@ -244,7 +244,7 @@ void AnalysisEcce::Execute()
       double reco_px = tracks_p_x[ireco];
       double reco_py = tracks_p_y[ireco];
       double reco_pz = tracks_p_z[ireco];
-      reco_mass = (fabs(pid_)==211)?pimass:(fabs(pid_)==321)?kmass:(fabs(pid_)==11)?emass:(fabs(pid_)==13)?mumass:(fabs(pid_)==2212)?pmass:0.;
+      reco_mass = (fabs(pid_)==211)?constants::pimass:(fabs(pid_)==321)?constants::kmass:(fabs(pid_)==11)?constants::emass:(fabs(pid_)==13)?constants::mumass:(fabs(pid_)==2212)?constants::pmass:0.;
 
       double reco_p = sqrt(reco_px*reco_px + reco_py*reco_py + reco_pz*reco_pz);
       double reco_E = sqrt(reco_p*reco_p + reco_mass * reco_mass);
diff --git a/src/AnalysisEcce.h b/src/AnalysisEcce.h
index 0f34749b..d4460992 100644
--- a/src/AnalysisEcce.h
+++ b/src/AnalysisEcce.h
@@ -10,31 +10,6 @@
 
 #include "Analysis.h"
 
-class ClustersEE
-{
-  public:
-    ClustersEE() {}
-    ClustersEE(double E_, double x_, double y_, double z_, double theta_, double phi_) {}
-    virtual ~ClustersEE() {}
-
-    double E;
-    double x;
-    double y;
-    double z;
-    double theta;
-    double phi;
-
-};
-
-class ParticlesEE
-{
-  public:
-    int pid;
-    int charge;
-    int mcID;
-    TLorentzVector vecPart;
-};
-
 class AnalysisEcce : public Analysis
 {
   public:
@@ -53,12 +28,4 @@ class AnalysisEcce : public Analysis
     ClassDefOverride(AnalysisEcce,1);
 };
 
-
-static const double pimass = 0.13957061;
-static const double kmass  = 0.493677;
-static const double pmass = 0.938272081;
-static const double emass = 0.000511;
-static const double mumass = 0.105658376;
-
-
 #endif
diff --git a/src/AnalysisEpic.cxx b/src/AnalysisEpic.cxx
index 049cee77..9f2175c4 100644
--- a/src/AnalysisEpic.cxx
+++ b/src/AnalysisEpic.cxx
@@ -2,7 +2,6 @@
 // Copyright (C) 2022 Gregory Matousek, Christopher Dilks
 
 #include "AnalysisEpic.h"
-#include "AnalysisEcce.h"
 
 AnalysisEpic::AnalysisEpic(TString infileName_, TString outfilePrefix_)
   : Analysis(infileName_, outfilePrefix_)
@@ -91,9 +90,9 @@ void AnalysisEpic::Execute()
 
     // ParticleEE vectors
     // The index of the vectors correspond to their for loop idx
-    std::vector<ParticlesEE> genpart;    // mcID --> igen
-    std::vector<ParticlesEE> mcpart;     // mcID --> imc
-    std::vector<ParticlesEE> trackpart;  // mcID --> (imc of matching mcpart) or (-1 if no match is found)
+    std::vector<Particles> genpart;    // mcID --> igen
+    std::vector<Particles> mcpart;     // mcID --> imc
+    std::vector<Particles> trackpart;  // mcID --> (imc of matching mcpart) or (-1 if no match is found)
 
     /*
       GenParticles loop
@@ -109,10 +108,10 @@ void AnalysisEpic::Execute()
       double e_  = hepmcp_E[igen];
      
       double p_ = sqrt(pow(hepmcp_psx[igen],2) + pow(hepmcp_psy[igen],2) + pow(hepmcp_psz[igen],2));
-      double mass_ = (fabs(pid_)==211)?pimass:(fabs(pid_)==321)?kmass:(fabs(pid_)==11)?emass:(fabs(pid_)==13)?mumass:(fabs(pid_)==2212)?pmass:0.;
+      double mass_ = (fabs(pid_)==211)?constants::pimass:(fabs(pid_)==321)?constants::kmass:(fabs(pid_)==11)?constants::emass:(fabs(pid_)==13)?constants::mumass:(fabs(pid_)==2212)?constants::pmass:0.;
 
       // Add to genpart
-      ParticlesEE part;
+      Particles part;
 
       part.pid=pid_;
       part.charge = (pid_ == 211 || pid_ == 321 || pid_ == 2212 || pid_ == -11 || pid_ == -13)?1:(pid_ == -211 || pid_ == -321 || pid_ == -2212 || pid_ == 11 || pid_ == 13)?-1:0;
@@ -138,7 +137,7 @@ void AnalysisEpic::Execute()
       double e_ = sqrt(px_*px_+py_*py_+pz_*pz_+m_*m_);
 
       // Add to mcpart
-      ParticlesEE part;
+      Particles part;
 
       part.pid=pid_;
       part.charge = (pid_ == 211 || pid_ == 321 || pid_ == 2212 || pid_ == -11 || pid_ == -13)?1:(pid_ == -211 || pid_ == -321 || pid_ == -2212 || pid_ == 11 || pid_ == 13)?-1:0;
@@ -174,7 +173,7 @@ void AnalysisEpic::Execute()
       double m_ = sqrt(e_*e_-px_*px_+py_*py_+pz_*pz_);
       
       // Add to trackpart
-      ParticlesEE part;
+      Particles part;
 
       part.pid=pid_;
       part.charge = (pid_ == 211 || pid_ == 321 || pid_ == 2212 || pid_ == -11 || pid_ == -13)?1:(pid_ == -211 || pid_ == -321 || pid_ == -2212 || pid_ == 11 || pid_ == 13)?-1:0;
@@ -215,7 +214,7 @@ void AnalysisEpic::Execute()
       Loop over MCParticles
     */
 
-    for(ParticlesEE mcpart_: mcpart){
+    for(auto mcpart_: mcpart){
 
       int imc = mcpart_.mcID;
       /* Beam particles have a MCParticles.generatorStatus of 4 */
@@ -259,7 +258,7 @@ void AnalysisEpic::Execute()
 
     int itrack = 0;
     bool recEleFound=false;
-    for(ParticlesEE trackpart_ : trackpart){
+    for(auto trackpart_ : trackpart){
       // Skip if there is no matching MCParticle
       if(trackidmap[itrack]==-1) continue;
       // If the trackidmap is linked to the genEleID (generated scattered electron), identify this reco particle as the electron
@@ -312,7 +311,7 @@ void AnalysisEpic::Execute()
       Fill output data structures (Histos, SimpleTree, etc.)
     */
 
-    for(ParticlesEE part : trackpart){
+    for(auto part : trackpart){
 
       int pid_ = part.pid;
       int mcid_ = part.mcID;
@@ -356,8 +355,8 @@ void AnalysisEpic::Execute()
 	if( writeParticleTree && HD->IsActiveEvent() )
 	  {
 	    int ipart = 0;
-	    for(ParticlesEE trackpart_: trackpart){
-	      ParticlesEE mcpart_;                  
+	    for(auto trackpart_: trackpart){
+	      Particles mcpart_;                  
 	      int mcpart_idx=trackidmap[ipart];     // Map idx to the matched MCParticle
 	      int genStat_ = -1;                    // Default Generator Status of MCParticle is -1 (no match)
 	      if(mcpart_idx>-1){                    // RecoParticle has an MCParticle match
@@ -386,10 +385,10 @@ void AnalysisEpic::Execute()
     // =======================================
     /*    
     int ipart = 0;
-    for(ParticlesEE trackpart_: trackpart) {
+    for(Particles trackpart_: trackpart) {
       cout << trackpart_.pid << "|" << trackpart_.vecPart.E() << "\t";
-      ParticlesEE genpart_;
-      ParticlesEE mcpart_;
+      Particles genpart_;
+      Particles mcpart_;
       int mcpart_idx=trackidmap[ipart];
       if(mcpart_idx>-1){ // Found MCParticle
 	mcpart_ = mcpart.at(mcpart_idx);
diff --git a/src/CommonConstants.h b/src/CommonConstants.h
index ab826c4f..60e1a321 100644
--- a/src/CommonConstants.h
+++ b/src/CommonConstants.h
@@ -2,8 +2,8 @@
 // Copyright (C) 2022 Christopher Dilks
 
 // common constants for analysis
-#ifndef CommonConstants_
-#define CommonConstants_
+
+#pragma once
 
 namespace constants {
 
@@ -19,6 +19,10 @@ namespace constants {
     statusBeam  = 4
   };
 
-};
+  static const double pimass = 0.13957061;
+  static const double kmass  = 0.493677;
+  static const double pmass  = 0.938272081;
+  static const double emass  = 0.000511;
+  static const double mumass = 0.105658376;
 
-#endif
+};
diff --git a/src/DataModel.h b/src/DataModel.h
new file mode 100644
index 00000000..8dab31f9
--- /dev/null
+++ b/src/DataModel.h
@@ -0,0 +1,31 @@
+// SPDX-License-Identifier: LGPL-3.0-or-later
+// Copyright (C) 2022 Sanghwa Park, Christopher Dilks
+
+#pragma once
+
+#include "TLorentzVector.h"
+
+class Particles {
+  public:
+    int pid;
+    int charge;
+    int mcID;
+    TLorentzVector vecPart;
+};
+
+/*
+class Clusters {
+  public:
+    Clusters() {}
+    Clusters(double E_, double x_, double y_, double z_, double theta_, double phi_) {}
+    virtual ~Clusters() {}
+
+    double E;
+    double x;
+    double y;
+    double z;
+    double theta;
+    double phi;
+};
+*/
+
diff --git a/src/LinkDef.h b/src/LinkDef.h
index bdb67e51..c57b48e3 100644
--- a/src/LinkDef.h
+++ b/src/LinkDef.h
@@ -15,6 +15,7 @@
 #pragma link C++ class HistosDAG+;
 
 // analysis objects
+#pragma link C++ class DataModel+;
 #pragma link C++ class Kinematics+;
 #pragma link C++ class SimpleTree+;
 #pragma link C++ class ParticleTree+;

From 000c45137654f94b9a8830d33e99d876da76f21a Mon Sep 17 00:00:00 2001
From: Christopher Dilks <christopher.j.dilks@gmail.com>
Date: Wed, 30 Nov 2022 01:05:29 -0500
Subject: [PATCH 16/32] minor changes

---
 src/AnalysisEpic.cxx | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/AnalysisEpic.cxx b/src/AnalysisEpic.cxx
index 9f2175c4..3a85b712 100644
--- a/src/AnalysisEpic.cxx
+++ b/src/AnalysisEpic.cxx
@@ -57,8 +57,6 @@ void AnalysisEpic::Execute()
   TTreeReaderArray<UInt_t> assoc_simID(tr, "ReconstructedChargedParticlesAssociations.simID");
   TTreeReaderArray<UInt_t> assoc_recID(tr, "ReconstructedChargedParticlesAssociations.recID");
   TTreeReaderArray<Float_t> assoc_weight(tr, "ReconstructedChargedParticlesAssociations.weight");
-  // TTreeReaderArray<Short_t> tracks_charge(tr,  "tracks_charge");
-  int trackSource = 0; // default track source is "all tracks"
 
   // calculate Q2 weights
   CalculateEventQ2Weights();
@@ -69,8 +67,10 @@ void AnalysisEpic::Execute()
 
   
 
+  cout << "begin event loop..." << endl;
   tr.SetEntriesRange(1,maxEvents);
   do{
+    if(tr.GetCurrentEntry()%10000==0) cout << tr.GetCurrentEntry() << " events..." << endl;
 
     // resets
     kin->ResetHFS();
@@ -88,7 +88,7 @@ void AnalysisEpic::Execute()
     std::map <int,int> trackidmap; // <index, index_mc>  
     std::map <int,int> trackstatmap; // <index, genstatus>
 
-    // ParticleEE vectors
+    // Particles vectors
     // The index of the vectors correspond to their for loop idx
     std::vector<Particles> genpart;    // mcID --> igen
     std::vector<Particles> mcpart;     // mcID --> imc

From ed307833f4566fb9aa0bee9e9a5e41308e62604d Mon Sep 17 00:00:00 2001
From: Gregtom3 <gamatousek@gmail.com>
Date: Mon, 5 Dec 2022 13:02:34 -0500
Subject: [PATCH 17/32] Refactoring track variables to recpart

---
 src/AnalysisEpic.cxx | 72 ++++++++++++++++++++++----------------------
 1 file changed, 36 insertions(+), 36 deletions(-)

diff --git a/src/AnalysisEpic.cxx b/src/AnalysisEpic.cxx
index 3a85b712..02ce9df6 100644
--- a/src/AnalysisEpic.cxx
+++ b/src/AnalysisEpic.cxx
@@ -45,13 +45,13 @@ void AnalysisEpic::Execute()
 
 
   // Reco tracks
-  TTreeReaderArray<Int_t> tracks_type(tr,  "ReconstructedChargedParticles.type"); // needs to be made an int eventually in actual EE code
-  TTreeReaderArray<Float_t> tracks_e(tr, "ReconstructedChargedParticles.energy");
-  TTreeReaderArray<Float_t> tracks_p_x(tr, "ReconstructedChargedParticles.momentum.x");
-  TTreeReaderArray<Float_t> tracks_p_y(tr, "ReconstructedChargedParticles.momentum.y");
-  TTreeReaderArray<Float_t> tracks_p_z(tr, "ReconstructedChargedParticles.momentum.z");
-  TTreeReaderArray<Int_t> tracks_PDG(tr,  "ReconstructedChargedParticles.PDG");
-  TTreeReaderArray<Float_t> tracks_CHI2PID(tr,  "ReconstructedChargedParticles.goodnessOfPID");
+  TTreeReaderArray<Int_t> recparts_type(tr,  "ReconstructedChargedParticles.type"); // needs to be made an int eventually in actual EE code
+  TTreeReaderArray<Float_t> recparts_e(tr, "ReconstructedChargedParticles.energy");
+  TTreeReaderArray<Float_t> recparts_p_x(tr, "ReconstructedChargedParticles.momentum.x");
+  TTreeReaderArray<Float_t> recparts_p_y(tr, "ReconstructedChargedParticles.momentum.y");
+  TTreeReaderArray<Float_t> recparts_p_z(tr, "ReconstructedChargedParticles.momentum.z");
+  TTreeReaderArray<Int_t> recparts_PDG(tr,  "ReconstructedChargedParticles.PDG");
+  TTreeReaderArray<Float_t> recparts_CHI2PID(tr,  "ReconstructedChargedParticles.goodnessOfPID");
   
   // RecoAssociations
   TTreeReaderArray<UInt_t> assoc_simID(tr, "ReconstructedChargedParticlesAssociations.simID");
@@ -85,14 +85,14 @@ void AnalysisEpic::Execute()
     // Index maps for particle sets
     std::map <double,int> genidmap; // <pz, index_gen>
     std::map <int,int> mcidmap; // <index_mc, index_gen>
-    std::map <int,int> trackidmap; // <index, index_mc>  
+    std::map <int,int> recidmap; // <index, index_mc>  
     std::map <int,int> trackstatmap; // <index, genstatus>
 
     // Particles vectors
     // The index of the vectors correspond to their for loop idx
     std::vector<Particles> genpart;    // mcID --> igen
     std::vector<Particles> mcpart;     // mcID --> imc
-    std::vector<Particles> trackpart;  // mcID --> (imc of matching mcpart) or (-1 if no match is found)
+    std::vector<Particles> recpart;  // mcID --> (imc of matching mcpart) or (-1 if no match is found)
 
     /*
       GenParticles loop
@@ -163,16 +163,16 @@ void AnalysisEpic::Execute()
 
    
       
-    for(int itrack=0; itrack < tracks_PDG.GetSize(); itrack++){
+    for(int irec=0; irec < recparts_PDG.GetSize(); irec++){
 
-      int pid_ = tracks_PDG[itrack];
-      double px_ = tracks_p_x[itrack];
-      double py_ = tracks_p_y[itrack];
-      double pz_ = tracks_p_z[itrack];
-      double e_ = tracks_e[itrack];
+      int pid_ = recparts_PDG[irec];
+      double px_ = recparts_p_x[irec];
+      double py_ = recparts_p_y[irec];
+      double pz_ = recparts_p_z[irec];
+      double e_ = recparts_e[irec];
       double m_ = sqrt(e_*e_-px_*px_+py_*py_+pz_*pz_);
       
-      // Add to trackpart
+      // Add to recpart
       Particles part;
 
       part.pid=pid_;
@@ -182,7 +182,7 @@ void AnalysisEpic::Execute()
       /*
 	Read through Associations to match particles
 	By default, we assume no association, so mcID --> -1
-	assoc_recID --> itrack (index of the RecoParticle)
+	assoc_recID --> irec (index of the RecoParticle)
 	assoc_simID --> imc (index of the MCParticle)
       */
 
@@ -191,14 +191,14 @@ void AnalysisEpic::Execute()
       for(int iassoc = 0 ; iassoc < assoc_simID.GetSize() ; iassoc++){
 	int idx_recID = assoc_recID[iassoc]; 
 	int idx_simID = assoc_simID[iassoc];
-	if(itrack==idx_recID){ // This track has an association
+	if(irec==idx_recID){ // This track has an association
 	  part.mcID=idx_simID;
 	  break; // Only one association per particle
 	}
       }
 
-      trackpart.push_back(part);
-      trackidmap.insert({itrack,part.mcID}); 
+      recpart.push_back(part);
+      recidmap.insert({irec,part.mcID}); 
       
     }
 
@@ -256,19 +256,19 @@ void AnalysisEpic::Execute()
       Loop over RecoParticles
     */
 
-    int itrack = 0;
+    int irec = 0;
     bool recEleFound=false;
-    for(auto trackpart_ : trackpart){
+    for(auto recpart_ : recpart){
       // Skip if there is no matching MCParticle
-      if(trackidmap[itrack]==-1) continue;
-      // If the trackidmap is linked to the genEleID (generated scattered electron), identify this reco particle as the electron
-      if(trackidmap[itrack]==genEleID){	
+      if(recidmap[irec]==-1) continue;
+      // If the recidmap is linked to the genEleID (generated scattered electron), identify this reco particle as the electron
+      if(recidmap[irec]==genEleID){	
 	recEleFound=true;
-	kin->vecElectron= trackpart_.vecPart;
+	kin->vecElectron= recpart_.vecPart;
       }
       // Add the final state particle to the HFS
-      kin->AddToHFS(trackpart_.vecPart);
-      itrack++;
+      kin->AddToHFS(recpart_.vecPart);
+      irec++;
     }
 
     // Skip event if the reco scattered electron was missing
@@ -311,7 +311,7 @@ void AnalysisEpic::Execute()
       Fill output data structures (Histos, SimpleTree, etc.)
     */
 
-    for(auto part : trackpart){
+    for(auto part : recpart){
 
       int pid_ = part.pid;
       int mcid_ = part.mcID;
@@ -355,9 +355,9 @@ void AnalysisEpic::Execute()
 	if( writeParticleTree && HD->IsActiveEvent() )
 	  {
 	    int ipart = 0;
-	    for(auto trackpart_: trackpart){
+	    for(auto recpart_: recpart){
 	      Particles mcpart_;                  
-	      int mcpart_idx=trackidmap[ipart];     // Map idx to the matched MCParticle
+	      int mcpart_idx=recidmap[ipart];     // Map idx to the matched MCParticle
 	      int genStat_ = -1;                    // Default Generator Status of MCParticle is -1 (no match)
 	      if(mcpart_idx>-1){                    // RecoParticle has an MCParticle match
  		mcpart_ = mcpart.at(mcpart_idx);        // Get MCParticle
@@ -366,9 +366,9 @@ void AnalysisEpic::Execute()
 		if(imc==genEleID)                       // If MCParticle was scattered electron, set status to 2
 		  genStat_=2;
 	      }
-	      PT->FillTree(trackpart_.vecPart,      // Fill Tree
+	      PT->FillTree(recpart_.vecPart,      // Fill Tree
 			   mcpart_.vecPart,
-			   trackpart_.pid,
+			   recpart_.pid,
 			   genStat_,
 			   wTrack);
 	      ipart++;
@@ -385,11 +385,11 @@ void AnalysisEpic::Execute()
     // =======================================
     /*    
     int ipart = 0;
-    for(Particles trackpart_: trackpart) {
-      cout << trackpart_.pid << "|" << trackpart_.vecPart.E() << "\t";
+    for(Particles recpart_: recpart) {
+      cout << recpart_.pid << "|" << recpart_.vecPart.E() << "\t";
       Particles genpart_;
       Particles mcpart_;
-      int mcpart_idx=trackidmap[ipart];
+      int mcpart_idx=recidmap[ipart];
       if(mcpart_idx>-1){ // Found MCParticle
 	mcpart_ = mcpart.at(mcpart_idx);
 	cout << mcpart_.pid << "|" << mcpart_.vecPart.E() << "\t";

From ee675bf1db9381871fb28d85c592d4366880db8b Mon Sep 17 00:00:00 2001
From: Gregtom3 <gamatousek@gmail.com>
Date: Mon, 5 Dec 2022 13:07:06 -0500
Subject: [PATCH 18/32] Mass calculation typo

---
 src/AnalysisEpic.cxx | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/AnalysisEpic.cxx b/src/AnalysisEpic.cxx
index 02ce9df6..43a4e206 100644
--- a/src/AnalysisEpic.cxx
+++ b/src/AnalysisEpic.cxx
@@ -170,7 +170,6 @@ void AnalysisEpic::Execute()
       double py_ = recparts_p_y[irec];
       double pz_ = recparts_p_z[irec];
       double e_ = recparts_e[irec];
-      double m_ = sqrt(e_*e_-px_*px_+py_*py_+pz_*pz_);
       
       // Add to recpart
       Particles part;
@@ -179,6 +178,8 @@ void AnalysisEpic::Execute()
       part.charge = (pid_ == 211 || pid_ == 321 || pid_ == 2212 || pid_ == -11 || pid_ == -13)?1:(pid_ == -211 || pid_ == -321 || pid_ == -2212 || pid_ == 11 || pid_ == 13)?-1:0;
       part.vecPart.SetPxPyPzE(px_,py_,pz_,e_);
 
+      double m_ = part.vecPart.M();
+
       /*
 	Read through Associations to match particles
 	By default, we assume no association, so mcID --> -1

From d7cf364955e7f58ce5d4f68e13270a7a079b8b42 Mon Sep 17 00:00:00 2001
From: Gregtom3 <gamatousek@gmail.com>
Date: Mon, 5 Dec 2022 13:40:53 -0500
Subject: [PATCH 19/32] Incrementor

---
 src/AnalysisEpic.cxx | 19 ++++++++++---------
 1 file changed, 10 insertions(+), 9 deletions(-)

diff --git a/src/AnalysisEpic.cxx b/src/AnalysisEpic.cxx
index 43a4e206..7a17f7bc 100644
--- a/src/AnalysisEpic.cxx
+++ b/src/AnalysisEpic.cxx
@@ -260,16 +260,17 @@ void AnalysisEpic::Execute()
     int irec = 0;
     bool recEleFound=false;
     for(auto recpart_ : recpart){
-      // Skip if there is no matching MCParticle
-      if(recidmap[irec]==-1) continue;
-      // If the recidmap is linked to the genEleID (generated scattered electron), identify this reco particle as the electron
-      if(recidmap[irec]==genEleID){	
-	recEleFound=true;
-	kin->vecElectron= recpart_.vecPart;
+      // If there is a matching MCParticle
+      if(recidmap[irec]!=-1){
+	// If the recidmap is linked to the genEleID (generated scattered electron), identify this reco particle as the electron
+	if(recidmap[irec]==genEleID){	
+	  recEleFound=true;
+	  kin->vecElectron= recpart_.vecPart;
+	}
+	// Add the final state particle to the HFS
+	kin->AddToHFS(recpart_.vecPart);
       }
-      // Add the final state particle to the HFS
-      kin->AddToHFS(recpart_.vecPart);
-      irec++;
+      irec++; // Increment to next particle
     }
 
     // Skip event if the reco scattered electron was missing

From 85d8470044b1550cad91dc39b821347703115f29 Mon Sep 17 00:00:00 2001
From: Gregtom3 <gamatousek@gmail.com>
Date: Mon, 5 Dec 2022 13:49:34 -0500
Subject: [PATCH 20/32] index mc bound change

---
 src/AnalysisEpic.cxx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/AnalysisEpic.cxx b/src/AnalysisEpic.cxx
index 7a17f7bc..0bcb65ad 100644
--- a/src/AnalysisEpic.cxx
+++ b/src/AnalysisEpic.cxx
@@ -330,7 +330,7 @@ void AnalysisEpic::Execute()
       kin->CalculateHadronKinematics();
 
       // find the matching truth hadron using mcID, and calculate its kinematics
-      if(mcid_ > 0) {
+      if(mcid_ >= 0) {
 	for(auto imc : mcpart) {
 	  if(mcid_ == imc.mcID) {
 	    kinTrue->vecHadron = imc.vecPart;

From b2e90fd2a1c5697a33f24de7625a7f9bd7242576 Mon Sep 17 00:00:00 2001
From: Gregtom3 <gamatousek@gmail.com>
Date: Mon, 5 Dec 2022 13:59:02 -0500
Subject: [PATCH 21/32] ParticleTree removed from nested loop and placed in
 separate loop over all reconstructed particles

---
 src/AnalysisEpic.cxx | 83 ++++++++++++++++----------------------------
 1 file changed, 30 insertions(+), 53 deletions(-)

diff --git a/src/AnalysisEpic.cxx b/src/AnalysisEpic.cxx
index 0bcb65ad..c2a95687 100644
--- a/src/AnalysisEpic.cxx
+++ b/src/AnalysisEpic.cxx
@@ -310,9 +310,10 @@ void AnalysisEpic::Execute()
     /*
       Loop again over the reconstructed particles
       Calculate Hadron Kinematics
-      Fill output data structures (Histos, SimpleTree, etc.)
+      Fill output data structures (Histos)
     */
-
+    
+    int ipart = 0;
     for(auto part : recpart){
 
       int pid_ = part.pid;
@@ -352,61 +353,37 @@ void AnalysisEpic::Execute()
 	// - not binned
 	// - `IsActiveEvent()` is only true if at least one bin gets filled for this track
 	if( writeSimpleTree && HD->IsActiveEvent() ) ST->FillTree(wTrack);
-
-	// fill particle tree
-	if( writeParticleTree && HD->IsActiveEvent() )
-	  {
-	    int ipart = 0;
-	    for(auto recpart_: recpart){
-	      Particles mcpart_;                  
-	      int mcpart_idx=recidmap[ipart];     // Map idx to the matched MCParticle
-	      int genStat_ = -1;                    // Default Generator Status of MCParticle is -1 (no match)
-	      if(mcpart_idx>-1){                    // RecoParticle has an MCParticle match
- 		mcpart_ = mcpart.at(mcpart_idx);        // Get MCParticle
-		int imc = mcpart_.mcID;          
-		genStat_ = mcpart_genStat[imc];         // Get Generator status of MCParticle
-		if(imc==genEleID)                       // If MCParticle was scattered electron, set status to 2
-		  genStat_=2;
-	      }
-	      PT->FillTree(recpart_.vecPart,      // Fill Tree
-			   mcpart_.vecPart,
-			   recpart_.pid,
-			   genStat_,
-			   wTrack);
-	      ipart++;
-	    }
-	  }
       }
-      
-    }//hadron loop
-    
-    
+    } //hadron loop
     
-    // =======================================
-    // DEBUG PRINT STATEMENTS
-    // =======================================
-    /*    
-    int ipart = 0;
-    for(Particles recpart_: recpart) {
-      cout << recpart_.pid << "|" << recpart_.vecPart.E() << "\t";
-      Particles genpart_;
-      Particles mcpart_;
-      int mcpart_idx=recidmap[ipart];
-      if(mcpart_idx>-1){ // Found MCParticle
-	mcpart_ = mcpart.at(mcpart_idx);
-	cout << mcpart_.pid << "|" << mcpart_.vecPart.E() << "\t";
-	int genpart_idx=mcidmap[mcpart_.mcID];
-	if(genpart_idx>-1){ // Found GeneratedParticle
-	  genpart_ = genpart.at(genpart_idx);
-	  cout << genpart_.pid << "|" << genpart_.vecPart.E() << "\t";	  
-	}
-      }
-      ipart++;
-      cout<<"\n";
-    }
-    cout << "\n ============================================================ \n" <<endl;
+    /*
+      Loop again over the reconstructed particles
+      Fill output data structures (ParticleTree)
     */
 
+    // fill particle tree
+    if ( writeParticleTree ) {
+      int ipart = 0;
+      for( auto part: recpart ){
+	  Particles mcpart_;                  
+	  int mcpart_idx=recidmap[ipart];     // Map idx to the matched MCParticle
+	  int genStat_ = -1;                    // Default Generator Status of MCParticle is -1 (no match)
+	  if(mcpart_idx>-1){                    // RecoParticle has an MCParticle match
+	    mcpart_ = mcpart.at(mcpart_idx);        // Get MCParticle
+	    int imc = mcpart_.mcID;          
+	    genStat_ = mcpart_genStat[imc];         // Get Generator status of MCParticle
+	    if(imc==genEleID)                       // If MCParticle was scattered electron, set status to 2
+	      genStat_=2;
+	  }
+	  PT->FillTree(part_.vecPart,      // Fill Tree
+		       mcpart_.vecPart,
+		       recpart_.pid,
+		       genStat_,
+		       wTrack);
+      } // particle loop
+      ipart++;
+    }
+    
   } while(tr.Next());
 
   

From 3fc21c5d16862f61fd55570a285fbec354e2a1de Mon Sep 17 00:00:00 2001
From: Gregtom3 <gamatousek@gmail.com>
Date: Mon, 5 Dec 2022 14:14:13 -0500
Subject: [PATCH 22/32] ParticleTree tweaks, remove weights

---
 macro/postprocess_ParticleTree.ipynb | 51 ++++++++++++++--------------
 src/AnalysisEpic.cxx                 |  7 ++--
 src/ParticleTree.cxx                 |  1 -
 src/ParticleTree.h                   |  4 +--
 4 files changed, 29 insertions(+), 34 deletions(-)

diff --git a/macro/postprocess_ParticleTree.ipynb b/macro/postprocess_ParticleTree.ipynb
index ab4c1010..52c6c130 100644
--- a/macro/postprocess_ParticleTree.ipynb
+++ b/macro/postprocess_ParticleTree.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 79,
-   "id": "faced-providence",
+   "id": "available-instruction",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -16,17 +16,16 @@
   },
   {
    "cell_type": "markdown",
-   "id": "mounted-conviction",
+   "id": "radical-baking",
    "metadata": {},
    "source": [
     "# postprocess_ParticleTree.ipynb\n",
     "---\n",
-    "The purpose of this analysis script is to create plots of particle kinematics from the ROOT TTree named ParticleTree. This tree is only generated when the user sets the appropriate flag in the analysis script. The tree has currently has 5 columns. These are...\n",
+    "The purpose of this analysis script is to create plots of particle kinematics from the ROOT TTree named ParticleTree. This tree is only generated when the user sets the appropriate flag in the analysis script. The tree has currently has 4 columns. These are...\n",
     "- recPart (TLorentzVector)\n",
     "- mcPart  (TLorentzVector)\n",
     "- pid     (int)\n",
     "- status  (int)\n",
-    "- weight  (double)\n",
     "\n",
     "For each event, the TLorentzVectors of the reconstructed particles are saved into the recPart branch. Using the associations branch included in the Epic sims, a Monte Carlo particle can be matched to its reconstructed partner using event generator level information. If an mcPart entry is empty, this means that the reconstructed particle did not originate from the hepmc file (ex: secondaries, background, etc). The pid of the reconstructed particle is also stored. The status variable is -1 if no MCParticle match was found, 2 if the MCParticle match was the scattered electron (highest momentum particle with pid==11), and 1 otherwise."
    ]
@@ -34,7 +33,7 @@
   {
    "cell_type": "code",
    "execution_count": 80,
-   "id": "cardiac-moment",
+   "id": "thrown-baghdad",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +45,7 @@
   {
    "cell_type": "code",
    "execution_count": 81,
-   "id": "working-portugal",
+   "id": "frequent-speaking",
    "metadata": {},
    "outputs": [
     {
@@ -76,7 +75,7 @@
   {
    "cell_type": "code",
    "execution_count": 82,
-   "id": "authorized-broadway",
+   "id": "according-document",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,7 +92,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "alpha-assignment",
+   "id": "competitive-struggle",
    "metadata": {},
    "source": [
     "# Comparing Particle Distributions"
@@ -102,7 +101,7 @@
   {
    "cell_type": "code",
    "execution_count": 83,
-   "id": "stretch-twist",
+   "id": "civil-lender",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -116,7 +115,7 @@
   {
    "cell_type": "code",
    "execution_count": 84,
-   "id": "adjacent-timing",
+   "id": "peripheral-controversy",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -136,7 +135,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "popular-discipline",
+   "id": "seventh-music",
    "metadata": {},
    "source": [
     "## Particle Energy"
@@ -145,7 +144,7 @@
   {
    "cell_type": "code",
    "execution_count": 85,
-   "id": "legendary-niagara",
+   "id": "surrounded-welding",
    "metadata": {},
    "outputs": [
     {
@@ -186,7 +185,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "timely-compression",
+   "id": "threaded-massage",
    "metadata": {},
    "source": [
     "## Particle Pseudorapidity"
@@ -195,7 +194,7 @@
   {
    "cell_type": "code",
    "execution_count": 86,
-   "id": "horizontal-handle",
+   "id": "future-prague",
    "metadata": {},
    "outputs": [
     {
@@ -236,7 +235,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "graduate-boards",
+   "id": "constitutional-research",
    "metadata": {},
    "source": [
     "## Particle Phi"
@@ -245,7 +244,7 @@
   {
    "cell_type": "code",
    "execution_count": 87,
-   "id": "smaller-detector",
+   "id": "heard-civilization",
    "metadata": {},
    "outputs": [
     {
@@ -287,7 +286,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "velvet-upset",
+   "id": "decreased-academy",
    "metadata": {},
    "source": [
     "# Particle Resolutions"
@@ -296,7 +295,7 @@
   {
    "cell_type": "code",
    "execution_count": 88,
-   "id": "attractive-julian",
+   "id": "chicken-notification",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -309,7 +308,7 @@
   {
    "cell_type": "code",
    "execution_count": 111,
-   "id": "right-console",
+   "id": "cubic-military",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -329,7 +328,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "human-holocaust",
+   "id": "frozen-fashion",
    "metadata": {},
    "source": [
     "## Particle Energy"
@@ -338,7 +337,7 @@
   {
    "cell_type": "code",
    "execution_count": 112,
-   "id": "arbitrary-commerce",
+   "id": "brief-vegetation",
    "metadata": {},
    "outputs": [
     {
@@ -371,7 +370,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "injured-croatia",
+   "id": "animal-robin",
    "metadata": {},
    "source": [
     "## Particle Pseudorapidity"
@@ -380,7 +379,7 @@
   {
    "cell_type": "code",
    "execution_count": 116,
-   "id": "clean-sculpture",
+   "id": "extended-buyer",
    "metadata": {},
    "outputs": [
     {
@@ -417,7 +416,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "warming-radical",
+   "id": "binary-event",
    "metadata": {},
    "source": [
     "## Particle Phi"
@@ -426,7 +425,7 @@
   {
    "cell_type": "code",
    "execution_count": 117,
-   "id": "immediate-methodology",
+   "id": "governing-behavior",
    "metadata": {},
    "outputs": [
     {
@@ -464,7 +463,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "forced-penny",
+   "id": "unnecessary-processor",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/AnalysisEpic.cxx b/src/AnalysisEpic.cxx
index c2a95687..9bdada7e 100644
--- a/src/AnalysisEpic.cxx
+++ b/src/AnalysisEpic.cxx
@@ -375,11 +375,10 @@ void AnalysisEpic::Execute()
 	    if(imc==genEleID)                       // If MCParticle was scattered electron, set status to 2
 	      genStat_=2;
 	  }
-	  PT->FillTree(part_.vecPart,      // Fill Tree
+	  PT->FillTree(part.vecPart,      // Fill Tree
 		       mcpart_.vecPart,
-		       recpart_.pid,
-		       genStat_,
-		       wTrack);
+		       part.pid,
+		       genStat_);
       } // particle loop
       ipart++;
     }
diff --git a/src/ParticleTree.cxx b/src/ParticleTree.cxx
index 8164d063..79d7bc0c 100644
--- a/src/ParticleTree.cxx
+++ b/src/ParticleTree.cxx
@@ -14,7 +14,6 @@ ParticleTree::ParticleTree(TString treeName_)
   T->Branch("mcPart",   "TLorentzVector" , &(mcpart_));    
   T->Branch("pid",      &(pid_)           , "pid/I");
   T->Branch("status",      &(status_)           , "status/I");
-  T->Branch("Weight",   &(weight)        , "Weight/D");
 };
 
 
diff --git a/src/ParticleTree.h b/src/ParticleTree.h
index 37effa70..fc5a172e 100644
--- a/src/ParticleTree.h
+++ b/src/ParticleTree.h
@@ -26,18 +26,16 @@ class ParticleTree : public TObject
     ~ParticleTree();
 
     TTree *GetTree() { return T; };
-    void FillTree(TLorentzVector recopart, TLorentzVector mcpart, int pid, int status, Double_t w) {
+    void FillTree(TLorentzVector recopart, TLorentzVector mcpart, int pid, int status) {
       recopart_ = recopart;
       mcpart_   = mcpart;
       pid_      = pid;
       status_   = status;
-      weight    = w;
       T->Fill(); };
     void WriteTree() { T->Write(); };
 
   private:
   
-    Double_t weight;
     TTree *T;
     TString treeName;
     TLorentzVector recopart_;

From 56cb967c94303a7f2eed99f746079f16cf9ff90f Mon Sep 17 00:00:00 2001
From: Gregtom3 <gamatousek@gmail.com>
Date: Mon, 5 Dec 2022 14:20:43 -0500
Subject: [PATCH 23/32] Postprocess notebook rerun with recent commit changes

---
 macro/postprocess_ParticleTree.ipynb | 102 ++++++++++++---------------
 1 file changed, 47 insertions(+), 55 deletions(-)

diff --git a/macro/postprocess_ParticleTree.ipynb b/macro/postprocess_ParticleTree.ipynb
index 52c6c130..01ce8572 100644
--- a/macro/postprocess_ParticleTree.ipynb
+++ b/macro/postprocess_ParticleTree.ipynb
@@ -2,8 +2,8 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 79,
-   "id": "available-instruction",
+   "execution_count": 15,
+   "id": "atomic-american",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "radical-baking",
+   "id": "taken-knowing",
    "metadata": {},
    "source": [
     "# postprocess_ParticleTree.ipynb\n",
@@ -32,8 +32,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
-   "id": "thrown-baghdad",
+   "execution_count": 16,
+   "id": "herbal-costume",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,8 +44,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
-   "id": "frequent-speaking",
+   "execution_count": 17,
+   "id": "vertical-information",
    "metadata": {},
    "outputs": [
     {
@@ -54,9 +54,9 @@
      "text": [
       " File loaded\n",
       " --------------------------------------------------\n",
-      " Num Scattered Electrons =  11826\n",
-      " Num Particles =  76822\n",
-      " Num Matches =  69266\n"
+      " Num Scattered Electrons =  31108\n",
+      " Num Particles =  309850\n",
+      " Num Matches =  294452\n"
      ]
     }
    ],
@@ -74,8 +74,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
-   "id": "according-document",
+   "execution_count": 18,
+   "id": "behind-saskatchewan",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "competitive-struggle",
+   "id": "other-slide",
    "metadata": {},
    "source": [
     "# Comparing Particle Distributions"
@@ -100,8 +100,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
-   "id": "civil-lender",
+   "execution_count": 19,
+   "id": "falling-somerset",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -114,8 +114,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
-   "id": "peripheral-controversy",
+   "execution_count": 29,
+   "id": "bored-provider",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -124,7 +124,7 @@
     "hmc_pip_E,hreco_pip_E = get_both_histo1d(100,0,15,\"Part.E()\",\"pid==211 && status==1\")\n",
     "hmc_pim_E,hreco_pim_E = get_both_histo1d(100,0,15,\"Part.E()\",\"pid==-211 && status==1\")\n",
     "\n",
-    "hmc_e_eta,hreco_e_eta = get_both_histo1d(100,-3.5,1,\"Part.Eta()\",\"pid==11 && status==2\")\n",
+    "hmc_e_eta,hreco_e_eta = get_both_histo1d(100,-3.5,5,\"Part.Eta()\",\"pid==11 && status==2\")\n",
     "hmc_pip_eta,hreco_pip_eta = get_both_histo1d(100,-5,5,\"Part.Eta()\",\"pid==211 && status==1\")\n",
     "hmc_pim_eta,hreco_pim_eta = get_both_histo1d(100,-5,5,\"Part.Eta()\",\"pid==-211 && status==1\")\n",
     "\n",
@@ -135,7 +135,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "seventh-music",
+   "id": "virtual-infrared",
    "metadata": {},
    "source": [
     "## Particle Energy"
@@ -143,13 +143,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
-   "id": "surrounded-welding",
+   "execution_count": 30,
+   "id": "registered-malta",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -185,7 +185,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "threaded-massage",
+   "id": "original-effort",
    "metadata": {},
    "source": [
     "## Particle Pseudorapidity"
@@ -193,13 +193,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
-   "id": "future-prague",
+   "execution_count": 36,
+   "id": "geological-palestinian",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -235,7 +235,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "constitutional-research",
+   "id": "deluxe-michigan",
    "metadata": {},
    "source": [
     "## Particle Phi"
@@ -243,13 +243,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
-   "id": "heard-civilization",
+   "execution_count": 37,
+   "id": "anonymous-discovery",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -286,7 +286,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "decreased-academy",
+   "id": "alternative-hazard",
    "metadata": {},
    "source": [
     "# Particle Resolutions"
@@ -294,8 +294,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
-   "id": "chicken-notification",
+   "execution_count": 38,
+   "id": "fitting-catalyst",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -307,8 +307,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
-   "id": "cubic-military",
+   "execution_count": 39,
+   "id": "approximate-operation",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -328,7 +328,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "frozen-fashion",
+   "id": "flying-hepatitis",
    "metadata": {},
    "source": [
     "## Particle Energy"
@@ -336,13 +336,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
-   "id": "brief-vegetation",
+   "execution_count": 40,
+   "id": "exposed-grenada",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -370,7 +370,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "animal-robin",
+   "id": "swedish-maker",
    "metadata": {},
    "source": [
     "## Particle Pseudorapidity"
@@ -378,13 +378,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
-   "id": "extended-buyer",
+   "execution_count": 41,
+   "id": "equal-gates",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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Euq4vTSgTXS2Kouu6eYQetlw6fpTeTNMUTwgf8A1eLM+zs/ntHtcK8D5eGASvj0eb793cHmPU5lvGcVzcqf6ff/pf/+t/rY9z+AgIwFvdBrZtGwHr0vCIXLw5j2jRgVxPGrnRnPe8+U5wGIZxHNu2naZpUXzzaE8aJg9APFJrZ4pZjIkHZcOfMQLz4BdRJ/4bB3lmt6Mni4eTwzDUdR23cOudY4DhIuzNH3guPmL+1PF4t4Lx+4p/93Rd13XdYsqX+eQD65N2cyvAW3ltENyMR192OP67vrTmm7d8wPkXKYoiJhgtiuL//t//m9/1v//3/z5bBASgeHUEXItPWWdXI7pFKnZe9ZIDYnQgvkIcYd6lG+4EY8/8BSVDAY4snsXNt+SoEAGgLMt4Ijd/cviqEviUUh4Un9e6jceY8y5FV9vZFDObZfsx5iJeR1owngEOf+adOVilf3yd+bmKmbLzPyzGcczPitu27bpu/g+Cm1sB3tbzg+C3hpHF0779mcs2I2P0eX7j+j//8z//8z//Mz9yfoJ1hggIwMKb3Abm3GsEo0u7zYfzzx/7pT9ju4c/ha433wnOazueXNUhGfpY878ur+zH29v8e3/ppC3q1Obb5yf8mnN+6VBftt7wWVD8eVC2jm358WC+odr/y/kIZVl+axm6+XQweYq0uNPLFTFr8fXTn2mz5ynCfKhbev+uYlag+Vltmmb+j4Din2VBMTtP/tfJza0Ab+idg2Axi4NVVcXtaC6QuUbEwXjXZhw8WwQEIHu3CBihJy8YuBkB88L3ObrFlnm8yxUeN98JxqD778bcO/j+AvSf5OVf8F7neZ2tb5pmsc/i71xVVTH9xLeOv7NPHH/9uZvm/5NHZ6759LXNN252dfGJl87SpXfttC5O46XeXnNm/lP99z/Vf7/bxMdZ/42Kvza5Nf5S5f9J43+u+PuW96n+KP7cHe0c/31cf9n51nvf+SvvW1+K17/oR7ROn3zSOCpB8CSOEQTzAq03vPHu73I9v4GTxruZRzpR78AWF5+3jYD7cWfdetuWn/fncRfzgweJl0fBnCn7yUGuyb5tpgLXibyb+5n7sJ/WXBxt4cuDNzPzb7R5/PWWfE3p+z6nhvdzlPlTNndbJ0Pn56FpmviH8pWfNbkPPI3b/pdfp903t5/Tyy/mdzS/gq2vG4t/DC1a42pzTet0rJPGMQiCJyEI3p3r+Q2cNN7NOhm6+PPa7nEvN1x8RMAdj7uYv8Vq8gfW//GTg+SxqJF9y6m3mB6u+OcMRLFPzvHNx7GuxURFO7PwRrV2mi0T9qX5lLeL774/G26soZkNfxbWLL4qEY/DRj15np+ibdvpzwrX13Q7n8x9cR5i0Zh2tohzfM0rPws2Lf6e5x9NwnAYwzDE9TauGPvTg9xltHt5tZ9/FsBPCIIAnJMI+BLmDN2TJ25YTwq5mAw0zxR7/d/XfPArF8yKXGH+xMi7LSayzStp5FvuHfOpai+5Pgea5YTg9KcAKuaYiKbvrg4WX2S/nzunMSae+HJC+pjC78vJ8vMsHuvPiskvYl1RK4ECa/mSmydm3b8u3eVfP/vXNAAAnu/38Gv+41/p71f1BE5LZei2yMHVdd11XaTJFvel9R+x55e7zTemlOYHL8ty56Z3cwmmRenQ5j7zlb82jxz5uzwbxaYvd7jGD2/poxL2ywzj5tcchuGaBWHatp0vf7az284+wzDk6YcBsvxQJ0a1718l9gtCf9IKAABAcYbK0HWB5JeVMsMwrCsix3Esy3L93sWe4zjOSzXXNusxdyoKU0oxoHJ+/7xZl1pcSDteuvGef9ylMtL8QZvnZNMP5wRYSyntj2GPlcuiInVdnHtldjIntXfyofGL2zmgmlBgIS6eeV3IzR32335zKwAAAGvHrwzdnIF1X8765Qlrc2nkZrYrL9oTP+6M6Z5P7rnoz850k+sEX945j7Vc7DP/oCdbdya/Xqx3f/0Bi92zGiWuRVFEcW5U2t6QI4juKe0E7igCyqUrUkyvMd8SBaQ7rTke7bcCAACw6fjJ0JtVVbVeyGKdspzP/rafM50fZ54WzK+vzN/lKs5Lg7LnVZx3r9P8rrZt8+364wonYzh8PpPjOOas6PUHicHyUaW7+RF36ChwJvm60a7k7cVqlvS7tAIAALDp+MPkvyvfu8aA9y/3n+8T47WLy4mzzck9r19yZ57lzGslLbRtmzO210yX+VDzOQFuXscjL1v05WellPL6UZGEvTS5wc5nXRosr2IUuNn6QVpOZcYSc/kp1/wJ1mbrPPu50woAAMAmydCleR7z7iPN8wFvuF+dZzkvZULnycfX3hXP87Y7k+U9SB4mHzmCb42aj5XlbxtoP0/IAqSUvnwYE/vEBWd90f5JKwAAn2K+pvxiuXng7iRDl2K5nqIoqqq6e1YrT/E2DMO3blyvyXLm4qDnJx8Xrqlgvd5iKOhaWZaXvnKUTX0rqR1J53Ec1weMX9+l390L52kFPt1+RPhJKwCc0+YKsTcPVgPgSMwZurRY+We9HNDCPGW2v6r7zrvKsizL8lIGcz7t5jRNH5QJ7fv+5wnl+O77x9nPQn53RZEYppq/RRZ9WG+ft8pKAADAy63X0ZUJ5T39Hn7lP6/uC5yFZOhF89rAmAy0rut1Sq6u69jtmpWC8vau6+JdwzCsl4aP3Oh853yE9E/5IPNPWeyTPzoOe99sXUyyGRbfMV5vduZbBy++WoY+cp2bB78tQZn+rIW1yLHOp+pbd/WapC3AM5VbXt0pAACAVzJMfkOMrS62agA3iy4Xu+1k7iLLFlmzK9+1+MRFeq5t21gs6NIOxfeH5P/QvAPfGja+WLEqv/fLgfaRM421kpqmiYPMs8w3JChzHnYhj7uff1bOg+8nbQGeTAkMAMCrzKcBBd6KytANKaX5er7Z5l3lYs9rMnfrUds777pmzPthpqocZ4qiqKrqyoH20zTFWe26Lmp488SvN+cCNv8OxHIli8+K3t5lTgB4iXml+ZdNUfG9rgGfzxMyr0PMtfOLQ6235Lff7YsBwHf8MCBGBJwHshzs8pYoU9jc8tqproD7kgnl49w3CG7eFb5RENycS+UwfvgF+75vmqZpmr7v10een8DY7YaDr4/MT+z8yt7hs/5T/fc/1X+/28TH2bzyzAuHI00/TdPmX57F2xcVx9+91FzT277vI/u/aKqqKj4u9plvyS+apomvk7dk+ZiXXiw+t+/7eNKw2cm7feHTcNJ4N4LgSXxWEFx89E8CYlVV8dWiz4st8SIffLElf+4154RrOGm83G2hTUD8dOuLz0dEwOkBQTBb3wxeHwQfdzE/eJB44Ik7RzaZ+3IfeBJfpvPmMePLK34OFbn1jrn+yD/mD1qE2EWa8tKW3J91z6Np/swgvvI8c5pPQnRAMvRenDTejSB4Eh8UBOfuEhAX+y96Pv3zwWG+IZwHxM17XdfzGzhpvJxk6DmtLz7vHwGnxwTBLN8M3hAEH3cxN0we4NliLECsuxXTB+e1yL6cbGE+IcN6JELe7crB5ovBCPMf98cppJTyBB2XPitPWBxjKIo/S40t9s+r1bVta64JgMN7nyC46NWlH68MiNOfaom8/3yimFzgk/tWVVVM/Z+/0TpEAnAk7xkBi8cEwfkR1l19eRCUDAV4hqqqImLF5T4mRolHgkVR1HUdj8u+nColx5s4wjRNbdvGel/zrOL1gWRnz/kEx9+dm3gxg0z0Kr5m8WeJs4ji3zosAJ/obYPg4uA7/c+v9wNiSqmu63xPmO1/tfgWfd9LhgIczEdEwOJhQXB9V7j2kiAoGXqjmBChsXo4cJ1hGKZpiieBeQLp3FT8CT/Xl0ZGHJofp/mzFFvXdVceZzFT9eKh4jzUrVd++7J786NFnI4zEFsifseACPd+AMf2nkFw7ucBMb5a3KDmLUVRtG0bRUD7Hz0PkQAcxvtHwOIxQTD3dj+6vSoI/vuZH3YkVnsErpfjXASnGA5waZ2+Lw81j0BxkHgwk58NXm8e22KcwqUPzR2L123bRjdigEPEsNyxRSfbts0ROu+QP86oQIBje9sguPjoHwbEqO6Zf4X1ltg/uh3hLz7InQXAIX1EBCweEwTXfX6rICgZCvAMm4PmwjxmfPkoL480L/4MkZgfIaosv1W03jRNWZYRpeI4eZBC3/fRNI7j9GfS7rqu51ti8HtsyR1bzP7ZdV3+bxwkBlBEpYwSe4DDe9sgOPfDgBjdy0ebpmm+z/x+Ne45Y0uEwnwTu3h2CMCn+4gIWDwgCBaru8L3CoIPWpjpTZzwK/POLKR7EpvXmbjc5ydjsTEm3Ji3xg7rN2Z5ib2IhbF/Xljw0mrsP7FetfCaLbcd+RKX7hs4abwbQfAkDhYE524Lfz8Pmq7nN3DSeDmryZ/T+uJzjAg4veIecHrkxbycLmQMj6EsD/4F+Sx/pb+Lovg9/PpWEx9n58ozH1mw3rJu/dINb/lELuY3uDRYxpnkVQTBkxAE704QvIGTxsvdFtoExE936eIjAt7mcRdzw+QBnmcdsRZTif38gJC5DwTeiiAIwDmJgO/GavIAAAAAwClIhgIAAAAApyAZyucZhmH+eufHL49z/c4AAAAAfDrJ0K+VZXkpZda2bUqpLMuUUtu2N3/EMAyXVrpYfMpmT2Lq3LIsY5+bu/ERhmGo6zqf7bZt67rOrXVdz3/c962dAQCAT1FueXWnAHgLFlD6wk6KM6U0jmO8HscxXt+WEt151zxmj+NY13Xf9/OMZyQH5/ucfPHEqqqu3zP/BuG+/GsbgNMSBHkHZ74hAl5IEPwIKkP3tG3bdd2lpsijTX8URdF13XeHXUdR56WUXCRJq6qKj+j7viiKRTFj/Ng0TezTNE1x4v/9pmn61jB5/0jiEaYDeZ+v8+rfKgBXeXW4uKf3+Tqv/q0CcJVXh4u7eZ/v8qDflGTothhGcSkTWhRFNM1/MZGp/FZlaFmWdV3vFCfGp+TsXkopcp35U6Kpqqr5sPHrSyOLf86/+RM3zL/55Vvu27e7zyW6v5sJSQEAAADejWTotr7vm6Zpmub6xGIMXc+ZzZjEc5EOW0xV0/yxecCc6JxvjKRnztLGj4sMbPRkPy0bs5RGNjZe3JC5K8uybds4VMy/GVuueeP8LYuP/knfFmd43rd199YzB7VtO+/Y4qOjdRiGxW6L7sWvPrcefhZXAAAAgE8hGbotFkSKlYvWrZtpysWW2Gc+pD0OFQWkof1jM+UaR7gmlbbYJ37cL1rMg+sj7RtdvbQ60046suu6uq6rqspZ3a7r9vOhkX+sqmrzo7/Vt335UPPP2pnKIM+KEB8dv5T1Ckux26J7uTUmPYgTEgcZx1E+FHgJa0cAAAAsWEDpdpcyXDENaFEUfd/XdR1LwA/DMI5j0zT3zYttDrH/8iMiWZkXYorMbxQz3jAjQ9M080H6Mb3ApXxofGJ+S0oppRSrw8/Tx/O+xQ439C0OOD9U8ScZunmKFlMfRAVo13XxG8y7jeOY94mmcRzzMeM3Ms/tlmVpmSbgJR43yQ4AAMCHUhl6iyurFFNKURjYtm3k+G5Ya36dtrty5P5OAi5KFxdHjsPeUIC5+FKLWU03ezVvjbMUr3PJ7bxv8x1usBjnPk3TZt9i42LKgti4OJOLznxZh/vQSX8BAAAAuJ5k6C2ur+6MHFmUHM4HyP/ED8sM50WLc7cdbZ2m/PLkrN+SO3BpZoAvE46bcilolL5e8/ZrUs+bkxIs9s+zqX6rwwAAAAA8lGTo7S6luhbZsVxseNsA+f2E2s5ko5eqKaN1HMf6n+bju2PQd8hrNOUt+13aSVxeOQvqzvwD+29cHycS0OM4xtymO+s77f82r//oYRjy5KR5ASVZUaD4M7HGenu+wF6qW7+5FQAAgAXJ0HvarNlcrPx+ve/Wn37rsE3TTFuikzEPZlYUxfzH/Y/7sjM373BDNjmlFN+raZrIDkeh6PUHv23egGma8vJKkXf+7kGAg4nr6np7zJyUQ0UAACAASURBVLMcr7uuWyxwlFLqui6uvevL134rAAAAa8dPhj5iId35UjnX7Bn1ifl291ufsnjXoupzs27xmgLMdY5vPlg+0nkhOt/3/SJhGtYnYefTL523KGu69HU2t1xj8Y2GYYi5O3d+cesPip1vSExHodY0TfHLUrQFpxWrsW0+FMkxIq5Xcb2dL0kXK+/lhzrzx1H7rQAAAGw6fjJ0Xfx4l8Oulxtar8CT71TzeO3vlu1c+pT5rXKxSpjGj/vrua8TgjFY/lvdm/fhyk/fXKYpd2azb7mc6rtnL7IPV6YGNs/k/oQDa8Mw1HW9P6kocDZ1XV96GLa4uC0eCC0up4vr/34rALwPE8UA8FaOnwx9kAi6OdfWtu3ivnQYhvmWNFtZ/iefsk4LRqou/oURc30Wq1XRFyIzmxf5GYYhCmb333VJ13WL4+xkD/M3Wrwln5boQ1mW0ZrLqW5YfipORZy9+cm51L1LH339rywnc/O3W//FAM5mXmW/8OWzlsUOEUeubAWAd3BpopifTAVjohgAfmRz4sjD+PkXjATZfJB4tr6zXXz0+o2Xznnc0G52YP9T5m/PLs0Hun/YS+9aD5Off52qqtZ34z/86HVOdr5DvD1vWZy6xSlaJxrm3Vufz/VHz794tC56u+jPZr4jH+Q/1X//U/1387TsNMELbV5zuMHmtTRfMZqmydef3Hrp8nhN6+R3x/sRBPk4LqQ/lOfQX0fAxb+rF/dcP2kNfne83G2hTUDkTRz+KlpOdxo2/p7K8uFfMJccPvSB5DWfkoeK3PewxZ/F5dfby7KsqirXXRaz2T9//tH3OrGP6NvNH/pX+rsoit/Dr/VbdprghZ5wFT2JmEaj7/t1bcu8XqZpmryQXV3X+ccQxebxGynLcqc1dri+e37LPIEgyMcRBH9oHokWETCa5qc331n8sDVv8bvjtW4LbQIib+LwV9F/v7oDH+/RadDrP+WGgdhXdv7LfW44CV++5V4n9hF9e8IRgDOITGjOaUY2cz6txw8d+18wHE/cAWZuBeHTRRiKx3vr1p9MBWOiGAB+wpyhAPAa80xoURRt2+7fzu2vCHflenEA8A6+LIy4+UcA2KcyFABeIE+mMd8YtaK5MlT2k5NYFIEuSkSBg9mMX3nemHtFt+unizGQAuBsVIZyu6qqLJIOcJvNdOc8Q7quEh3HMQ8M3G8FgLf1rZrQm12/jMZdPg6ADyIZyu3uNasdwDlVVdV13XyY/DyhGdvzZTZezHfeaQWAz/KTwRCGSgDwLYbJA8BrxCOlruu6rost88VwU0p939d1nQf6zZfi3W8FgDcn+wnAq0iGAsDDpZQ2B+LF7dzm/KH5Xbe1AsDb2pzspWmanVYTxQBwL4bJ30dKqSzLsiwNUQxOCMD1Uko72cyftMJr/ZX+zn9e3RfgjfxkKhgTxQDwQypD7yACcN/3RVHUdT0Mw8kHbjghAIAEKHDJ5mQv+60miuE8FgH09/DrVT2Bo5IMvYNxHHMAjsD86h69mBMC8A7yXeKcZXN5MrdwcHKXJor5yVQwJooB4CckQ++gaRoxeM4JAXgH8p4AvLn9u4aftMInWjxBNMYCHuT4ydB1Xcx3bw7nE9BsTs0236Gu6zecvXs+UP3m2eW+fPSad3j/EwIAAADACR0/GfqTupi2bbuum2+JHzdnpYmdq6p69PyYZVlePy3OMAyLUerxFZqm+e4s423bzse/r1sXX/9pJwQAAAAArnH8ZOjNcklpHvQd9ZXjONZ1vcgJxs5PmLr7WxnMnMytqioXhA7D0HVd13XfXdcopTSOY9u2m++KD8rde9oJAQAAAIArSYZuixTeoqoxNkaGsa7rXHNaluUT6h8jd7moVN3ff7MINIaxl2UZmc3rs6vxxcdx3NknTtFzTggAAAAAfMu/Xt2BdxTln8WfSTAX2rZtmqb4k/iLfaJeMrt7l8qyrOv6+kxo8adI89Jw+L7viz/lnGuXvkLM/rk+YGyJ1uecEAAAAAD4LpWhGyK1F+nCSzvkGsnI9C3m5bz7Ar6Rfi0upy/XonuXCj9TSptfcLHe1GKce9u2dV2vk5vzMfLPOSEAAAAA8F0qQzdEGnF/sstcBdm27bRy6V0ppdvKJNs/rlyZPT5lf+f1svKRCa2qqu/7SL/Wdb0YYl/8OT+bB4yuXn9CAAAAAOBpJENvNB8jf71YfKksy++u5P5d0bFvLV4UO/d9PwxDzCsaScxFLep6pHwej//DPgMAAMDn+iv9HX9e3RFgj2To0kMnuJymKZKGXdeVZfm4ldYvfYtyJe+5WQ8bvZ0fLVKf8wzpDYlXAJ5gfc1fzIUCAMC9yIHCpzBn6NKjk3ox2j3G14/jGAuvt217389NKW0OZl8MnM/75GH1iyxq/Bi1ovnIi2NeM6sAAM9nlhIATmvz+Z/IyBP8Hn69ugvAFyRDb/TDcsg8eWisxRTLDd09Nq/rQxc1nouEaYzi//KwTdN0XZenMS2MkQcAAN6JvCcAlzxpmHxUPsZMlM9svc16Wsy1e5VDppSuXBPpW6Lzl1Y6CutUaVVV67WPpmlanIr5SPmc0r1XzwEAAADgQZ6RDC3LMk8xGXNlztNwKaWu68ZxHMex67pFevEnrTdbT4uZPy46Hx/0wyRmHKeu63Ecm6a5+6PLL1O681TppVM3DMPOJKrDMOznWwEAAADgfTw8GTpfo3wYhkj55bHYMVI7UoGxuNA4jjn79pPWH/Y5MomLFGEkFiN9WfxgqaW2bcuynKdBH1FZGd2L8ezr1s05dNYnsK7rzYHzMS4+moyRBwAAAOAjPDwZOo5jVVXzrOI8dxbVlzlbFy/yjz9p/aFhGKqqigWOYgz+okbytrLQqC3tuq6qqr7vH5QGzfLK9fOvEKnYYpXE7Pu+KIq6rvOe8VvbzHXOu22MPAAAAAAf4eELKMVS6fMti9rDRVYxUpB3af2hyBvOh+HPP3Qcx7wI0vWiFPRp2cOYUDVKUBdfIdeN5p1TSn3f13Xddd18z0u9jbP9iAlPAQAAAOARHp4MXacLF/nK/Yk+15OE7sx0uWj9uVgtPdeExkpNuWk9qeiXfj4x6HfTrymlaZrmZa3z5OaiP/Od51/2Lj0BAAAAgNd6eDJ0bhiGmGUyRmRvZtNyQvNeubbNyTHXdtKUm2nByJP+oF9P9WVm8+adAQAAAOBTPC8ZmrOcfd9Hru1bNaE3u/sq7QDwETYfBwqLAADAmT18AaWiKIZhKMsyr5z+k8HXP2kFgFOZtry6UwAAAK/0jDlD67rOK/Zs7rD/9ptbAQAAAACyh1eGxiShl7KW6/Xfo4B0pzUvX77fCgAAAAAw99hk6HwF84W8vZhNDxov7tIKAG8lpbT5aHAYhli5bjOEtW17cysAAAALT1pAqeu6xZacyuz7vq7rvMhDLDQfNlvn2c+dVgB4H8MwLEYzhLZtI0RWVdV1Xdd182k989qDRVGM4zgMwzydut8KAADA2mMrQ1NKm6s3LO70pmnq+77v+/XySj9pBYCXG4ahbduYNGbd1HVdrC44DEM8DsyxrG3bvPbgNE1N00TG85pWAAAANpXHXli2LA/+Bfksf6W/i6L4Pfz6VhO8kKvoz+XhC8VqBEOUds7PcAybiP/GG+etZVnmBQn3Wwu/O97AzaFNTORNuJB+Lr87XuLu8UtA5FUOfxV90jB5ADin+GfEMAzr4tD1un+LqT8XrYuVA/db4dPFHWBwHwgAwL08fDV5AOCSWPsopVSW5XqFpfXkMNf/CABnVm55dacAeAsqQwHgBSLvmVdPapqm67q6rmMo/V1m/7z+ru/Yo2D4OPM60Hl9KMD1hDYALpEMBYBXyndrbduWZVnX9b2WBHQfCAAAsGCYPAC8QKQ7F/N+Nk2z85b9clFLyQMAAHxJMhQA3ktOa8p+AgAA3JdkKAC8xnr998hv5qLRRet89fn9VgAAADZJhgLAa7RtW8xWgW/bdhzHPFJ+0RovYuOXrQAAAGw6/gJK67V0LSgBwDtIKcUi8jlUVVWVE5oppb7v67rOrbHQ/DWtAAAAbDp+MlTqE4CXSyltxqO2bdu2nY+OX7/rtlYAAADWjp8MBYA3t5/K/EkrAAAAc+YMBQAAAABOQTIUAAAAADgFw+QB4JjWSwgWptIGAADOTTIUAI5J3hMAAGBBMhQAgLv5K/396i4AAMBF5gwFAOA+ZEIBAHhzKkMBALin38OvV3cBAAC2qQwFAAAAAE5BZSgAAACHUpbleqOlBQEoJEMBAAA4GHlPAC4xTB4AAAAAOAXJUAAAAADgFAyTB4BjMl0aAMBD/ZX+fnUXgG+TDAWAY5L3BAB4HJlQ+FDHT4au62LcHAIAAAA/93v49eouAN9z/GSo1CcAAAAAUFhACQAAAAA4CclQAAAAAOAUjj9MHgAAAOATzZdpMj8p3IXKUAAAAADgFFSGAgAA8Gxt2w7DUBRFSqlt2zu2wjHM60Dn9aHAD0mGAgAA8FRlWRZFUVVVURRd13VdN01Tbk0pjeMYr8dxHIYhUp/XtALAPsPkAeCYyi2v7hQAFCmloij6vo88Zt/3RVHkAs+2bcdxbJpmmqZpmpqmiYznNa0A8CXJUAA4pmnLqzsFAEXUdUZKNL/ICc2u64p/5kbnP+63AsCXJEMBAAB4nhgdv5Bzo+sdqqrK4+K/bAWAfZKhAAAAPE8UcpZl2bZt27Yxi8u8unOeGF1btO7vDAALkqEA8Awppf0ZzS6tlptSurRU7n4rALynlNJ86aSiKJqmiabNWJnTndfPDbo5cbbZtAEorCYPAE8wDMP+CL5YGHdd6mItXQCOJ0JY0zR5xs88E+i3akJ3mCYbgEtUhgLAAw3D0LZtXdf7+6xTpdbSBeCQIurlTGhRFG3bVlUV+dBLb9k/4D37B8DRSYYCwAPVdb1zd5f3WS8lYS1dAA5sc97PnNaU/QTgcY6fDDUpDAAvFJWbfd9f2mFx+zdnLV0Ajmcz8MWP0bSOaFFJGq83WzeXpweATcdPhk4rr+4RAPw/Mdr9UmzaXy3XWroAfKgYFD8f3zBPaMb2HNfixWIwxKVWAPiSBZQA4DWGYei6brNo9C4DAK8fDOFJIQDPNAxDSikvJV8URVVVOfallPq+r+s6B7J5rNxs9UQQgOtJhgLAa8RUoZv3b3e5qZPiBOBtRepzPjp+LqU0TdNtrQCwTzIUAF4gj/Kbj+yLpedTSpu3dlaTAOBg9lOZP2kFgEskQwHgZRYLzY/jOI5jvruT/QQAALiv4y+gBABvqG3b9fp+TdNM07Szlm5eXMJaugAAADeQDAWAd2QtXQAAgLszTB4A3tH+arnW0gUAALiBZCgAPFyse7u/z3oHa+kCAADcl2QoALw1a+kCAADcizlDAQAAAIBTUBkKAMeUpxOd+3K0PgAAwIFJhgLAMcl7AgAALEiGAgAAcCiGR3BIf6W/5z/+Hn69qifw0SRDAQAAOBR5TwAukQwFAAAAeF+LItBFiSjwLcdPhq7HR3hICAAAAAAndPxkqNQnAAAAAFAUxb9e3QEAAAAAgGeQDAUAAAAATkEyFAAAAAA4hePPGQoA57ReQrAwlTYAAHBukqEAcEzyngAAAAuGyQMAAAAApyAZCgAAAACcgmHyAAAAAFf5K/396i4AP6IyFAAAAOBrMqFwAE9NhqaUhmFYbCxX2rad79C2bUoppbTYfk0rAAAAwB39Hn7lP6/uC/BtzxsmPwzDOI7rjfvvSinld43jOAzD/C37rfBxFo8ZRVYAAACAO3pGZegwDG3b1nV9aYe+76eZXOPZtu04jk3TxPamaSLjeU0rAAAAAMBcOU3Twz+jLPPrvu9TSvnHtm27rrvUh3jjvLUsy6qqIuO535q3POELwpWi8POaes/r94SHchX9XH53vMTd45eAyAu5kH4uvzse500C05t0g6M6/FX0GZWhUbnZ9/26KScuL41wr6pq8eN8rP1+KwCc2XpW7vnjSQA4MEEQgEveYjX5sizruq7ruizLed1oURQ/+REAzmza8upOAcAzCIIAXPLiZGgUcuZ5P6O0M+YMvdfsn5uPBD0kBAAAAICzeXEyNJZOyismDcNQVVXXdcX9yjw3Hwl6SAgAAAAAZ/PiZOg64xlbLpWF7peLWkoegLeVUtqMU23bppRSSvnR4L1aAQAAWPj3qzuwLSdJZT8BOIBhGDaX+It5WmI9wK7ruq7r+z4HwZRSftc4jovFBvdbAQA4tlhTPlhZHq73ysrQYRjKslwUs8xv5Narw4/jmFeQ328FgHcwDEPbtnVdr5si6dn3feQxY86WvGfbtuM45mm1m6aJjOc1rXAwf6W/539e3R0AAD7YK5OhKaWYIXR9a5d/LGZVovEiJ0/3WwHgHdR1HXNhr8UzvPmMMTkCFkUR71pEvfzjfisAAAf2e/iV/7y6L/B5XjxMPopD5/UyTdPke7mUUt/3dV3n1d4Xgwd3WgHgHUS95zAM6+LQRSZ0bTHcYTEkYr8VjmFxj6csFACAH3peMjSltLlo+zRNMTwwloDYfFeUjn63FQDe2XpU+6KGdBHa5pOEftkKAADA2lssoLSZBl3scHMrALy/XDra931xpxUC88iJL20+rQQAADiet0iGAsCZ5aLOPN/LXZ7zSXECAAAsvHIBJQA4uZg7O68Lv58D3S8XtZQ8AADAl1SGAsBrxND4qqou5TFlPwEAAO5LZSgAvEZMEnopp7leHX4cx7yC/H4rAAAAm1SGwsP9lf5+dReAt5NzoG3bLppiS9u2dV2nlGLPGEGfd95vBQAAYJNkKDyWTCiwr+u6xZbIaaaU+r6v6zovCp+XV/qyFQCAe3FPBwcjGQrP8Hv49eouAK+UUlqs7b7eculd89rP61sBAPg5mVA4HslQAHhr+4lOaVAAWMsjJ+a+fAwJl6hugSM5fjJ0HQWFQACAe1EyA7whN30AXHL8ZKgoCADwIDKhAAB8luMnQwHgnIwQ5GkMHgQA4FNIhgLAMcl7AgAALPzr1R0AAAAAAHgGyVAAAAAA4BQkQwEAAACAU5AMBQAAAABOQTIUAAAAADgFyVAAAAAA4BQkQwEAAACAU/j3qzsAAAAAwO3+Sn/Pf/w9/HpVT+D9qQwFgGMqt7y6UwDw/wzDkFJKKbVtu25t2/bmVgDYoTIUAI5pmqZXdwEAtrVt23VdURRVVXVd13XdPGyllMZxjNfjOA7DMAzDla1wNosi0EWJKLCmMhQAAIDnGYah67qmaaZpGoah7/uiKFJK0dq27TiO0TpNU9M0kfG8phUAviQZCgAAwPPE2PY8wj2l1DRNToZGxWhuXey83woAXzJMHgAAgOcZx7GqqvmWRTZz0VpVVR4X/2UrAOw7fmWotSMAAADeSqx9lFIqyzKltBjnnqtEL733+p0BYOH4laGWjwAAAHgTkffMqyc1TdN1XV3Xfd+vs6Ihr5h0/dyg1xfBuGFkk2WI4MCOnwwFAADg3eQsZNu2ZVnWdT1N07dqQq85ONxAJhSOTTIUAACAJ4mE5mLez6gPvfSW/YJQS8nzIL+HX6/uAvAQx58zFAAAgLeyX+Mp+wnA40iGAsAxrZcQtIogAO+gqqpFHeg8v7leHX4cx6ZpdloXdaYAsEMyFACOadry6k4BQNG2bTErDm3bdp7uXLTGi9j4ZSsAfMmcoQAAADxPSikmCc1DFqqqygnNlFLf93Vd59a+7+fvXbdev7ASAEiGAgAA8FRt27ZtG6Pj16nMlNI0Tbe1AsA+yVAAeIaUUtu263u2+a3gepTfT1oB4M3tpzJ/0goAl0iGAsDDDcOwWO0hpJTy9nEch2GYryDxk1YAAADWLKAEAA80DEPbtnVdr5vyehGxtFHTNJHT/HkrAAAAm8pjLyxblgf/gry/v9LfRVH8Hn494V1wd66iP5eXdyhWKzxE0/wMl2VZVVXkNH/SWvjd8SxPDljiI8/kQvq5/O74oY8ONx/ded7E4a+iKkMB4IGicnO+DO5cVVWLH+ej6X/SCgAAwJpkKAC8zLeWhvjWjwAAAKxJhgLAC2zO75kTmneZ/bO82s8/CwAA4CMcfzX59T3esSc+AOAjfKsm9DbiHQAAwMLxk6FuBQH4FPsFoT9pBQAAoDBMHgBeSPYTAADgmY5fGQoA72m9/vs4jk3T7LTmFeT3WwHg5DZnxDZqEIBCZSgAvErbtsVsetB4ERt/2AoAJzdteXWnAHgLKkMB4DVSSn3f13Wdq1f6vt9vnWc/d1oBAADYJBkKAA+XUtosSIntMfvnOpX5k1YAAADWJEMB4MX2U5k/aQUA4IT+Sn/n17+HXy/sCbwhyVAAAL5nfosFAAAfRDIUAI7JQro8iEwoALyteR2okA2bJEMB4JjkPXkoY+4AAPhE/3p1BwAAAAAAnkEyFAAAAAA4BclQAAAAAOAUJEMBAAAAgFOQDAUAAAAATkEyFAAAAAA4hX+/ugMPV5blYss0TS/pCQAAAADwQsdPhkp9AgAcyV/p7/z69/DrhT0BAODjHD8ZCgDntB4bUXhGCACwZf6kDTg2yVAAOCZ5T45nXgfqrhWAexFT4FQkQwEAADgUwyO4galX4CQkQwEAADgUeU8ALvnXqzsAAAAAAPAMkqEAAAAAwClIhgIAAAAApyAZCgAAAACcgmQoAAAAAHAKT02GppSGYVhvb9s2pZRSatv2vq0AAAAAAOHfT/ukYRjGcVxvTynl7eM4DsMwT5j+pBUAzqwsy/XGaZqe3xMAAIA38YzK0GEY2rat63rd1LbtOI5N00zTNE1T0zSR0/x5KwCc3LTl1Z0CAAB4pfIJ90XzypS+71NKi6Z5H8qyrKoqcpo/ac1b3PjxWn+lv4ui+D38esK74O5cRT+X3x2P8yZB6k26wYG5kH4uvzu+66gx5ajfi0c7/FX0GZWhUYrS9/1ma1VVix/no+l/0goAAAAAkD1vztBL5oWiX7bOJwn9shUAAADgzKI+NFMoCk9dTX5hc37PnN+81+yf5XXu8lkA8F1t26aUUkpt2963FQAAgIVXVoZ+qyb0Zsee5gCAjxZP42LWl67ruq6bh635iIdYJHD+pHC/FQCAk1sUgS5KROG0XlkZumn/Ru4nrQDwVuKxX9/3kceMybVzjWfbtuM4Nk0TU283TRMZz2ta4e7+Sn/nP6/uCwAA3O71yVDZTwDOKeo680iIeJFDW9d1xT9zo/Mf91vhviRAAQA4jBcvoLRe/z3qXHZa8wry+60A8ObWgaz45ywxi6C22H+/Fe7OegsAABzAiytDo4ZlURSzqHO5rRUA3lzErLIs27Zt2zbmD50Hsm9Nrn2vubYB4ACsmgvAJS+uDE0p9X1f13WOTDFj2k7rPPu50woAby6lFOWcMea9KIo8NmJzHpi8YtKVs8Rcf9dnsUEADkZoA+CS5yVDU0qbASm2x33dOpX5k1YAeGeR3GyaJs/4mWcC/VZN6CXuAwEAABZeXBma/eSuTxoUgI8zDMM8E1oURdu2wzB0XXdpyheLCsLaYnEnE5sCALDv9avJA8Bpbc77mdOasp8AAAD39S6VoQBwKjnvOc+Hzid+Wa8OH5Wk8XqzdbG+PBzbogh0USIKAACbVIYCwGtUVTUfFN+27TyhGdvnywYWs7Xm91sBAADYpDIUAF4jykK7rsuryVdVlQe/p5T6vq/rOi8K3/d9fu9mq0m0AQCuZ1QBnJNkKAC8TKQ+56Pj51JK0zTd1goAwA6ZUDgtyVAAeLH9VOZPWgEA2LGYgRo4A3OGAgAAAACnoDIUAI4pTyc6N03T83sCAADwJo5fGVquvLpHAPAM05ZXdwoAllJKef3ArG3blFJKqW3b9Vv2WwFgx/ErQ934AQAAvKeU0jiOwzDMZ8GOjfE6WufZ0v1WANh3/MpQAAAA3tAwDDmtmbVtO45j0zQxpqFpmsh4XtMKAF+SDAUAAOAF6rquqmqxseu6oijy+Pd4kX/cbwWAL0mGAgAA8GxlWVZVtVnUuciQVlU1LyDdbwWAfZKhAAAAPFXUcl4a3j6fP/TL1v2dAWBBMhQAAIDnGYah67q+7zeb1htzuvP6uUHLq932FQD4XMdfTR4AAID3EVOFblZ0fqsmdMc0Td/uFgDnIBkKAADAk8QA+ZTSfNWjYRjatk0pbaY79wtCLSUPwLdIhgLAMW0O/VMpA8A7iEXhs3Ecx3G8cji87CcAP2HOUAA4pmnLqzsFwNm1bbuOTU3TTNMUydD16vDjODZNE683WxfrywM7/kp/5z+v7gu8hspQeF/z4PR7+PXCngBwQu6RgJdo27au65RSVIBGhjSPqd9vhX1CG1BIhgIAsOZ2EXiVlFLf93Vd5/le5uvOb7Zev7ASZya0zStsnA3OTDIU3pEoBcA7MC4BeIL1LC4ppWma5rWf17fCPqENkAwFAADg7ewnOqVBuZLiEmBBMhTuT7gFAAB4ObdmwNrxk6F5KpnMWro8lHALAADwPgyNB+aOnwyV+uQlhFsAAACAd3P8ZCgAACcxH5/hwSQAAGuSoQBwTOuJYgoDJgAA+GMxyZvniJyEZCgAHJO8J6cyv38zfzcAAJdIhgIAAAAH4ZHYNRZFoE4ap/KvV3cAAAAA4A4k9YAvqQwFgFcahqFt26IoUkrxYq5t22EYbmsFADgnc18COyRDAeBl2rbtuq4oiqqquq7rum4+0WdKaRzHeD2O4zAMkfq8phUAzswqggBcYpg8ALzGMAxd1zVNM03TMAx93xdFkVKK1rZtx3GM1mmamqaJjOc1rQBwctOWV3cKgLcgGQoArxED2/Pw9pRS0zQ5GRoVo7l1sfN+KwAAAJsMkweA1xjHsaqq+ZZFNnPRWlVVHhf/ZSsAAABrKkMB4GVi4aOUUlmWKaXFOPdcJXrpvdfvDAAAQCEZCgAvEXnPWDSpKIqY9LOu69i+OftnTndeOTdoebV7fCEAAIAPYJg8ALxSi7nyfgAAGN9JREFUXs+hbduyLOu6nqbpWzWhXx4ZAACAoDIUAF4gEpqLeT+bptl5y35BqKXkAQAAviQZCgAvs1/jKfsJAABwX8dPhpoZDYD3VFVVTBiazfOb69Xhx3HMpaObrYs6UwAAABaOnwydVl7dIwAoiqJo27aYFYe2bTtPdy5a40Vs/LIVAAC+66/0d/7z6r7AA1lACQBeI6XUNE3XdXnUQlVVOaGZUur7vq7r3Nr3/fy969YrF1YCAAA4LclQAHiZtm3bto3R8etUZkppmqbbWgEA4Eq/h1/5tbJQDk8yFABebD+V+ZNW+BY3PwAAHN7x5wwFAOBLMqEAAJyBylAAOKY8neichQTZNx8lBwAAxyMZCgDHJO/JyS1qXeV5AQAoDJMHAAAAAE5CZSgAAIeyKAI1HSqckLliALhEMhQAAIBDkfcE4BLJUAAAAAD+f6be5sDMGQoAAAAAnILKUAAAAACKwtTbnIDKUAAAAADgFCRDAQAAAIBTMEweAI6pLMv1RqvrAgDHYyg3cL3jJ0PXt4LuAwE4A/EOADgDmdBnWp9tC83zcY6fDHUrCAAAAMcmJfcE8s4cw/GToQAAML9/c8MMADfLYVRulA9lASUAAAAA4BRUhgIAcGTzOlA1LAAAJ6cyFAAAAAA4BZWhAADnpVISAIBTURkKAHBSMqEAAJyNylAAgFOztDoAAOehMhQAAAAAOAWVoQBwTGVZrjdO0/T8ngDAkwmCAFzy+mToOko1TdO2bf6xbdthGIqiSCnNt1/TCgCn5ZaPTeYJBc5AEATgkhcnQyOPuSOlNI5jvB7HcRiG+Vv2WwEAmJMJBQDg5N5iztDpn3KBZ9u24zg2TRPbm6aJjOc1rQAAbPo9/Jr/eXV3AADgeV6cDN3PXXZdVxTFPDc6/3G/FQA+S0ppHRbbtk0pXZoKZr8VAACAhXcZJp/n/VzsUFXV4sc8Lv7LVgD4FDHxyzAM81BorhgAAID7eoth8mVZ1nVd13VZlot86Do9utO6vzMAvKdhGNYP88wVAwAAcHcvTobGvV++l4vSzhjrt3lHl9Od19/vlde5w5cBgJvUdb0Y61CYKwYAAOABXjxMvu/7eTlnDA/sui4mQdt54/VFoNM03d4/AHiwsiyrqhqGYf1kzlwx8CB/pb/nP1pFCgD2LUInfLQXV4auc5qx5VLh535BqOGBAHyWncEQhbliAAAA7u3FlaGXXDkcXvYTgM81DEPXdX3fbzatN+YVk64Mf9dPAmMUBeexKAJV5wIA+74cPzEPpgZb8BFemQwdhqGu66Zp5nOczW/w1iP+YrGIndb1nGsA8J5iqtDNis67zBUjxQkAHJWnWcDNXpkMjXu5rutSSvE6r40bO7RtW9d1SikypHmfa1oB4J1FwEopLZ4IxqzZm+lOoyUAOJK2bfOt3Po+7ietHIaM55ub14H6ZfFBXjxMfpqmsizrus5b5oWiKaW+7+u6zgP95mMJN1vNmAbAB4lF4bNxHMf/r727SXfUxhoADHl6N98OqgbGS8heKoNvZDzpDJK1VJYADLpW0L0e90AJTcDG2GB+pPcd3XshLgLyOeggiaaxVgwA0QuduDCx73q9hnVj2gzYrgyTZVnTNHVdd7Pe+Fai8bS4ZkY28J7t1wy93W4he90dCFMURdghe/C2pZGtALBbZVn2RrLked59ImitGABiFfpu3epnGCITFnhp5wuGnFiW5fV6DR3Gp1uJj4onsLiN3yYfhKkNI9nr0YTBKVsB4IjaefTh1+FaMSNbAWDPwgO8bieufdqX/TVtopfy2l/HtwLAU9uPDAUAhqwVA0CsTqdTr3zZm+fem+vQmw8xvhUAximGAsAuDF/+bq0YAKI0XOKzV80cz2u9rd0lRAHgKcVQANi1lzqEcJcXvAK7Vdd1eKFumABx91VIbblz+ouS2pkTTw0fRgJv691yWPKVfVIMBQCImUroU91TpNsGa2qrnO1iL0s9AlTiBOARxVAAiNPdQTE6h8lS4wN2pR0Q2r4Xfnznt7dyOJ7hHVTvTiNcR48b2SfFUACIk7onPNXtmOl+w2pCJfR0Oj2qY6p+JksoBlagGArHYO0VAADiEMaEPqppDt8O3zTN5XIZ2dp7vzzHMiyA6uxEwONG9kwxFAAgNnodwG61NdDh7Pjwl7Isz+dzURRhz7BOaLvz+FYOR8IC1hd/MXS4YpppgxzL3bVXAOARmQI4hOv12vtLqGkWRVFV1fl8brty4UXzwd2t01+sxD4ZCgqsKf5iqNInAJAgHcv3WJcGPq0oiqd9tLBPd+zn9K0AMC7+YigAAACHM17oVAYF4D2KoQAAYF0aAIAkKIbCMnSZAAAA4C4L0bAfP219ABADlVAAAACA/TMyFBbj0RawK+1rdru8VzBinswBADtkIRr2RjEUAOKk7pkU/QoAAJhCMRQAIBLmKACwT8OHdnIWsBVrhgIAAACfYvoCsCtGhgIAAACf1Q4FVRslG20GRg3zaUaGAgAAAABJMDIUAADu645bMVAFDiTP8+EfvVoQ9mAknxo1zDoUQwEAAIiKuuceKGwB+6QYCgAAfd1xK/rzAK8SOYHdUgwFAAAAlmeBEWCH4i+GDheLMWMCgBRYLg0AAKAn/mKoXh8AaZIBYVm9KZ+GOwEAHFH8xVAAgChZjg0AAF6lGAoAcDwqoWvqDQJ18gEAjksxFAC2VJZlXddZlhVFUZblgltJgZnaAByUB0s80m0bbnX4BMVQANhMeMfR6XTKsux6vV6v16qqiqIIW4uiaJom/Nw0TV3XofQ5ZSsAAABDiqEAsI1Q9OxWP/M8P5/P4cVHZVk2TXO5XMKQz7Isr9drXddh5/GtAADrmzjY01g/Hum2jdCcvL2QT/hp6wMAgEQ1TXM6nbrly8vl0v58vV6zLGsnv7dFzylbAQBWZto7cBRGhgLANk6nU6982ZvnHqbPd39t58U/3Qp8mhXNAIbEQ5bi7YV8jmIoAGxjuMRnr5o5Pue9t7W7hCgxGd7662cCAMDbFEMBYHt1XZ/P5yzLqqrK7tVJs065c+KLksLbmaYIq5SyQwZB7NNwRbMuq5sB8RHZgJgohgLAxtoqZ/sypZfGhD6ixHlcj/qcd98kwK64OkB8xiObuAccjhcoAcBm6rrO8zy8F/52u41XOccHhE4cLsr+6VVG4N/1Pw2bAiJzN7LJWcARGRkKh+SlDRCBMDX+dDo9qmOqfqbsbmwX8AHYIekJOBbFUHiTp6DATGGR0Ec1zeHb4cMA0pGtvffLAwAA0KMYCu/YsBI6/tIG4CjaGmhZlr1N4S9lWZ7P56Iowp5hBn278/hWAEjc3bcIWk17Oh0NIGKKofA+80GA+a7Xa+8voaZZFEVVVefzue3OhRfNB3e3TnyxErA4VQPYG3XPKYaxK3RwxDT271HrhSniL4YOHwnKiwBsriiKp/ko7NMd+zl9KwDAiKcVT6UldkilnkXEXwxV+gTg0MYLncqgsC3FAuDQ2iD2Uo1JQYo9eK/1QpZCMRQAAACA4/L0kQUphgJAnLw7AgBYloIUu9UdH6qhMk4xFADipO4Je9Cbu6d7BgCwLcVQAICNWesqHcatAMCyuvl0eE81fpclF6dJMRQAYAMKoNHr9a9ccQBYmeTLXYqhAABrMDAhcePjVgCApUxZoybsY8ZGmhRD4fAsRgawf3eLXyI2U0j0AAALUgwFAFiJMhavMoYUAKabfq9lxkbKFEPhwCxGBrBzIjOLCBlfcwLmG48k4gyQAsVQeIGbAwCGHs1iljUYp4UAKxN2YIR1adKhGApTuXUAYOhpdnAnDcC4xUsw4x949/NlKyAdiqHwGncJwFHkeT784+12W/9IUmAWMy9xOwG0FskdEhDMYQG61CiGAkCc1D0B4CjeeKg2vrOndACPKIYCADw3vT+p58lHWdEMyO7lmpFoIDEBdCmGAgD8yauQAOKQyFoxHocAvCH+YugwC8aXAgGA+ea/CkmnlI+6u6JZt91qgdDS6QuEBXibDBux+IuhsiCpEbIB5ugusmZAKMcy0mLdEkAEZCWARcRfDIU53HAAxE2c57i69U0tGY7FOtSwZ8MM63FjZBRDeUGe50mNtF3hzmPZU6pTlFoTBWZ66b42z/Msc7O7JEF7Qf+u//nofKZ5SwB7NqcSulXZRcRelvO5uB2e0vFvuhLqthRDoc9LWgGSIs6Tsv2UWuCg3v4STX/5u28lbGj6V/XuX9gnxVD4G8ELID5iO/gWwCeMf7M8bIAUvDqDXkbeA8VQiPk2xVNlAHecMK69PfB6enjPyJeo52lK8qWDo3vjW2xC/foUQ0mdTjIQqzzPh3/c22pKd01/RjU9hruPJFlLNX5PWEnZ/LnwOh0QnzmpUEzY1u6WmF3WDtfQPbQoz2eIQdGvRB7x6NeuKJvotpzS4zrutfvQreH8oHfcU7pbTumylj2fS41fO3QJVRM9rn1+HQ7N12FZzufiDnRK74aUiRPql402I59/oPP5HiNDgWN3VIADmR5tpg+lEbJgNeHr5n0RpMkYT2ARC44nfS8uPcrmSVEMfWKRcvj8D9nJYSwimrNxrPPZDbjDxYz+0/z6f6f/X+EwVviQ+XbSNnZyGCRuYiua+FT51bdMtB8VzdchpuDglO7wMBbRHsb0rlrvyzu8qZhYQu3+i3medz/kjX7jTs4nG5r/sL/XDt/4qJ0Eh518HWI6G9Gc0p2cjWjO5yIf0v2EYS/+ruGm8Q7+3cXBU6uNHr4Y+ssvv/z48SPLsi9fvvz2229bHw4cladDcEQrJMGnr1J5NW7cjTaCDxzOo0pTnv86Uiqa8mV/OyCIJElZKgMuMswK4NN6uXV8Bv2jXCyytY5dDP369WtIgVmW/fjx48ePH//617+2PSQOJNlAMAyLef5r91ez5uEQPp0EX5oJO/Ii3enPsUUbSE336cjdgDCyafj3Hi9Yi9iUDPionbzUbLQNYM/eG5Sw4Ace14GLoeFh4Ldv38KTwF9++eX333//448/fv75560Pjb1I55v8UU/HhQHrezUJjsfDkcqCYeNAz3s3AwveQjx9MCNkxe29bqBmA0BrF0szvCfP8yzLusef5/mXL1+6TwV3snqFw1j8MO6uRdUzfkPzn+bXaM7G5w5j+ntLep/w3kP1nZ8Nh8GuPE2C0xfrHCFUOoydf4jDSO0wpr9Xd/6Sx9M/hJW92g18+3XMO/86OAyH4TAcxs4PY88OPDI0y7IvX770fm2nSxzF9JrRxD3Hd3s0xO+9fvLbk48Wf+46ZeZmT29iOHc9bSQzZ8K+tAj005Fr4//WiPcO46XPNKKWT3iaBKe0w6frDc09SoDlrJBPDQ88hJe6gW7DAOiJqhh61/wbmkVuiSZ+yPR/a/4HfroAOv+/GmFp8/W9WtEemXD0XrNcfNOIaL44viBxm5IEn9JFBFI2f3AAm1gkAwKQrtsxff/+Pcuyb9++df/47du33v/R1mcX4PDWje5MMiUJbt1wAGKweoDnCd1AgHWsG93XdtSRoRPfknSTCAGIzpQkKAMCEB/dQADm+2nrA1jS4RYMBYClSIIApEkGBOAlxy6GSnsAJEsSBCBNMiAAcxy4GDp8aeCPHz/CejEAEDdJEIA0yYAAzHTgYmhIeF+/fi3LsiiKr1+/Zln222+/jf9XYeeiKMqyfLRPURR1XS95rDs25YRM3Hlkaz4w5Z+L20tnPina5DoWPM9BUpFzD9okGC7NlCToOg4JOFuRBB/RJtchCR6abuBSBJxNyIAjtMkVyIB/2vgFTvP0HgB+//79dDqN7H86nbr73925qqosy6qq+swh78uUEzJx5/Gtw4Z3uVwW/985kJfOfFK0yXUseJ6DpCLnfgyT4MjOruOQgLMVSfARbXIdkmAEdAPnE3A2IQOO0CZXIAO2jl0MvVwuWZZ9//499ADDr48uQ9jatvLhzlVVhT8e9Fq+6ukJmb7z+Nbjfj0+5KUznxRtch0LnudbepFzP9pLE5Kg6/gqAWcrkuAj2uQ6JME46AbOJOBsQgYcoU2uQAbsOnYxNJz33l8e1baf7tyteR/xWr5qwbM3vjV8Q5Y56Ci8dOaTok2uQ+SMg+s4k4CzFUnwEW1yHYJnHFzHmQScTciAI7TJFYicXQdeMzQYDtxtmua9ncMZCQ8KErHg2ZvyUXVdH3ItiQ946cwnRZtch8gZB9dxJgFnK5LgI9rkOgTPOLiOMwk4m5ABR2iTKxA5W//Y+gDmKoriQzunYMGz19taFEX7PQkhJs/zduvpdEow7nRpio9ok+sQOePgOs4k4GxFa3xEm1yH4BkH13EmAWcTmuIIbXIFImfrwCND77bXR1frpZ1TsODZexo4QtzpLjbRNE2yJ19TfESbXIfIGQfXcSYBZyta4yPa5DoEzzi4jjMJOJvQFEdokysQOXv2PjJ0qQp9ZJdtivFT97mnLkNVVXX3KcuyrutkpwMk2BQn0ibX4WHggciAc0iC+5Rma5xCm1yHJHggkuAckuAOpdkUJ9ImVyAD9hygGPoolN+9PC9lzbjHQo+fuk+fve7W4b9VFEXTNHVdp/AdmyLupjiHNrkOkXOfZMA5JMEDib41vk2bXIfguU+S4ByS4FFE3xTn0CZXkHLk3HsxtCzLka3X6zXlizdu/NQFH4ovPOV0PaJNrkPkPAQZcA5JcM+crke0yXUInocgCc4hCe6WczVCm1yByPk/S76afnXh5Vbdv2Sd1R/e2zm8DKuqqgWPc58WPHt3t55Op0cfO9w/KS+d+aRok+sQOePgOs4k4GxFEnxEm1yH4BkH13EmAWcTMuAIbXIFImfXsS95OO9ts+5drbC1vVrjO/f+qyNey1ctePbubm3PYfi1u0Rx4kF/YlNMkDa5DpEzDq7jTALOViTBR7TJdQiecXAdZxJwNiEDjtAmVyBydh3+uxfabqt7DXoXr/3L3Z17+xzxWr5hwbM3vjV8c1qJxJoRI2c+cdrkOkTOOLiOMwk4W5EEH9Em1yF4xsF1nEnA2YQMOEKbXIHI2cpvt1t2fGEtg4nr3b60cwoWPHsjW8My3o9W7E6TpviINrkOkTMOruNMAs5WtMZHtMl1CJ5xcB1nEnA2oSmO0CZXIHJmWRZJMRQAAAAAYNxPWx8AAAAAAMAaFEMBAAAAgCQohgIAAAAASVAMBQAAAACSoBgKAAAAAMdQFEV4zzvvUQwFiIekCED0JDsAEtc0TVmWWx/FgeW3223rYwBgGXmen04nXUQAIibZAZCysiyv12uWZQp6bzMyFCAS4dlg0zRbHwgAfIpkB0DiQiU0+ysndpVlWZZl+7ywLMuiKIqiWO/gDsLIUIBI5HkefrhcLr28GH5tE2GbIA2rAeBYJDsAUtbmvuHg0JD4wvPCqqrO5/PpdGofH6r+dSmGAsRAUgQgepIdAInL8z/reOHpYFVV7cDPsKZ2URRtNgybwl+GDxFTZpo8QAyu12uYExF+7Y6Cqeu6ruvT6ZRl2fl8rqqqruvb7Rb+IiMCcBSSHQApK8sy5LUsy4YJrp0hkWXZ5XLpFkkz8yT+TjEU4PAkRQCiJ9kBkLjr9dpdDzT7+yLa3bVBH/1MoBgKcHiSIgDRk+wASFk7ASJoE5zZD29QDAU4NkkRgOhJdgAk7nw+97JeVVVZ5+XyTKcYCnBskiIA0ZPsAEhZeCjYm+7Q/mo1mFcphgIcmKQIQPQkOwAS131/YNflcsnMk3idYijAgUmKAERPsgMgZXVdN01zdxXs8MfuItpMoRgKcFSSIgDRk+wASFx47Jffcz6fu/swkWIowFFJigBET7IDIHF1Xd+eaVNhWZa32+3uQ0Ra/9j6AAB400urpD2aYwgAeybZAQDLUgwF4E91XYc+p54kAABAfPI87/2lqqrURpKaJg9AlmVZWZbn8znUQ/M893JeAOIznGvfzXd1XXscCEBkuoXOsiyrqjqdTlVVXS6X8ENqldDMyFCAZPVy3vV6bRNhKIzebrdNDgwAljLsAYap9OHhX1mW3R3aGRIAEJPT6RR+aJ/5FUVR13VRFG0e7K46uu7RbUAxFCBdbVJs+37hh6IortfrRgcFAEua0gMEgIjdfdQXUmH7c1KPAxVDAdLVq4F2nwG2XUcAOLQpPcCQAZumyf4aTNobNAoA0Qj5ris8JtziWLahGArAn0NBe+umbXY0APAxd3uAoRjazp3PBovJAEAciqIIA19CAbSdGrjtUa3MC5QA+N8omPbX8/m84fEAwCcMe4BtJ7DtB5o+D0DEmqZpH/s1TXM+nxPMekaGApBlWVZV1fl8bpcKrapq2+MBgMU1TRMSXJgS4W2BAKSmTXxFUSSbBPNk/88BGEpzlgQAAACJUAwFAAAAAJJgzVAAAAAAIAmKoQAAAABAEhRDAQAAAIAkKIYCAAAAAElQDAUAAAAAkqAYCgAAAAAkQTEUAAAAAEiCYigAAAAAkATFUAAAAAAgCYqhAAAAAEASFEMBAAAAgCQohgIAAAAASfgvYiUNXH8Mp8sAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -416,7 +416,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "binary-event",
+   "id": "diagnostic-miracle",
    "metadata": {},
    "source": [
     "## Particle Phi"
@@ -424,13 +424,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
-   "id": "governing-behavior",
+   "execution_count": 42,
+   "id": "diagnostic-campaign",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -459,14 +459,6 @@
     "\n",
     "c.Draw()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "unnecessary-processor",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From 3572a818bebf82609abe1d3b476e79bc35a3f463 Mon Sep 17 00:00:00 2001
From: Christopher Dilks <christopher.j.dilks@gmail.com>
Date: Mon, 5 Dec 2022 15:08:16 -0500
Subject: [PATCH 24/32] update documentation and tutorial

---
 tutorial/README.md                            | 21 +++++++++-------
 .../{analysis_dd4hep.C => analysis_athena.C}  |  4 ++--
 tutorial/analysis_epic.C                      | 24 ++-----------------
 ..._dd4hep_draw.C => postprocess_epic_draw.C} |  4 ++--
 4 files changed, 18 insertions(+), 35 deletions(-)
 rename tutorial/{analysis_dd4hep.C => analysis_athena.C} (97%)
 rename tutorial/{postprocess_dd4hep_draw.C => postprocess_epic_draw.C} (97%)

diff --git a/tutorial/README.md b/tutorial/README.md
index ab9772fa..28893950 100644
--- a/tutorial/README.md
+++ b/tutorial/README.md
@@ -40,15 +40,15 @@ To run tutorials, you need to generate or obtain ROOT files, from fast or full s
 
 
 ### Full Simulation
-- full simulation files can be streamed from S3 using `tutorial/s3files.*.config` config files
-- use `s3tools/` scripts to make new config files, download files from S3, and more; for example:
+- use `s3tools/` scripts to make `config` files, download files from S3, and more; for example:
 ```bash
-s3tools/make-athena-config.sh 10x100 tutorial.athena s 8  # stream ATHENA files
-s3tools/make-ecce-config.sh 10x100 tutorial.ecce d 12     # download ECCE files
+s3tools/make-epic-config.sh 10x100 tutorial.epic s 4  # stream EPIC files
+s3tools/make-ecce-config.sh 10x100 tutorial.ecce d 12 # download ECCE files (legacy production from Fun4all+EventEvaluator)
 ```
 - run `s3tools/` scripts with no arguments to print usage guide
 - downloading from S3 is preferred, if disk space is available
-
+- similar to the fast simulations, note where the `config` file is produced; the tutorial
+  macros require this file as an argument
 
 ## Introductory Notes
 
@@ -56,6 +56,7 @@ s3tools/make-ecce-config.sh 10x100 tutorial.ecce d 12     # download ECCE files
 - many of these examples focus on fast simulations; to switch between fast and
   full simulations, change the `Analysis`-derived class in the macro:
   - `AnalysisDelphes` for Delphes trees (fast simulations)
+  - `AnalysisEpic` for trees from the DD4hep+EICrecon stack (EPIC full simulations)
   - `AnalysisAthena` for trees from the DD4hep+Juggler stack (ATHENA full simulations)
   - `AnalysisEcce` for trees from the Fun4all+EventEvaluator stack (ECCE full simulations)
 - note: some extra settings and features differ between these
@@ -98,13 +99,15 @@ Each of these examples has two macros:
     the given binning scheme
 
 3. Full Simulations (all other tutorials are for fast simulations)
-  - `analysis_dd4hep.C`: basically a copy of `analysis_xqbins.C`,
+  - `analysis_epic.C`: basically a copy of `analysis_xqbins.C`,
     but shows how to analyze full simulation data; the main difference
-    is using `AnalysisAthena` instead of `AnalysisDelphes`
-  - `postprocess_dd4hep_draw.C`: clone of `postprocess_xqbins_draw.C`,
+    is using `AnalysisEpic` instead of `AnalysisDelphes`
+  - `postprocess_epic_draw.C`: clone of `postprocess_xqbins_draw.C`,
     specific for this example
   - see also `analysis_eventEvaluator.C` and `postprocess_eventEvaluator_draw.C`
-    for similar full simulation scripts using the `EventEvaluator` output
+    for similar full simulation scripts using the `EventEvaluator` output from
+    ECCE simulations
+  - see also `analysis_athena.C` for the ATHENA version
 
 4. Average kinematics table
   - `analysis_qbins.C`: bin the analysis in several Q2 bins, for a couple
diff --git a/tutorial/analysis_dd4hep.C b/tutorial/analysis_athena.C
similarity index 97%
rename from tutorial/analysis_dd4hep.C
rename to tutorial/analysis_athena.C
index c1e25293..7f31c55a 100644
--- a/tutorial/analysis_dd4hep.C
+++ b/tutorial/analysis_athena.C
@@ -16,9 +16,9 @@ R__LOAD_LIBRARY(Sidis-eic)
  *     - `export S3_SECRET_KEY=<password>`
  *   - a sample config file is `s3files.athena.config`, with a list of S3 URLs
  */
-void analysis_dd4hep(
+void analysis_athena(
     TString configFile="tutorial/s3files.athena.config", // input config file
-    TString outfilePrefix="tutorial.dd4hep"              // output filename prefix
+    TString outfilePrefix="tutorial.athena"              // output filename prefix
 ) {
 
   // setup analysis ========================================
diff --git a/tutorial/analysis_epic.C b/tutorial/analysis_epic.C
index 0bb524c7..46c36e4f 100644
--- a/tutorial/analysis_epic.C
+++ b/tutorial/analysis_epic.C
@@ -3,34 +3,14 @@
 
 R__LOAD_LIBRARY(Sidis-eic)
 
-/////////////////////////////////////////////////////////////////////////
-// this is currently a script to support development of AnalysisEpic;  //
-// when AnalysisEpic is ready, this will become the tutorial script    //
-/////////////////////////////////////////////////////////////////////////
-
-//
-// currently testing with files produced from benchmarks:
-//   repo:        physics_benchmarks, from https://eicweb.phy.anl.gov/EIC/benchmarks/physics_benchmarks
-//   CI stage:    finish
-//   CI job:      summary
-//   CI artifact: results/dis/10on100/minQ2=1/rec-dis_10x100_minQ2=1.root
-// 
-// test procedure:
-// 1. download this artifact from a recent pipeline, and store in `datarec/epic_test/`
-// 2. run this macro
-//
-// if you use a different artifact, edit `tutorial/test.epic.config`
-//
-//
-
 /* EPIC simulation example
  * - note the similarity of the macro to the fast simulation
  * - you only need to swap `AnalysisDelphes` with `AnalysisEpic` to switch
  *   between fast and full simulations
  */
 void analysis_epic(
-    TString  configFile="datarec/epic.22.11.2/22.11.2/10x100/files.config", // list of input files
-    TString  outfilePrefix="tutorial.epic"        // output filename prefix
+    TString configFile="tutorial/s3files.epic.config", // input config file
+    TString outfilePrefix="tutorial.epic"              // output filename prefix
     )
 {
 
diff --git a/tutorial/postprocess_dd4hep_draw.C b/tutorial/postprocess_epic_draw.C
similarity index 97%
rename from tutorial/postprocess_dd4hep_draw.C
rename to tutorial/postprocess_epic_draw.C
index 93cd071f..03621721 100644
--- a/tutorial/postprocess_dd4hep_draw.C
+++ b/tutorial/postprocess_epic_draw.C
@@ -4,8 +4,8 @@
 R__LOAD_LIBRARY(Sidis-eic)
 
 // make kinematics coverage plots, such as eta vs. p in bins of (x,Q2)
-void postprocess_dd4hep_draw(
-    TString infile="out/tutorial.dd4hep.root"
+void postprocess_epic_draw(
+    TString infile="out/tutorial.epic.root"
 ) {
 
   // setup postprocessor ========================================

From 55ac090c78b54ec39da4dd270336c2ce0fc60046 Mon Sep 17 00:00:00 2001
From: Christopher Dilks <christopher.j.dilks@gmail.com>
Date: Mon, 5 Dec 2022 15:08:40 -0500
Subject: [PATCH 25/32] fix: unused class rule warning

---
 src/LinkDef.h | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/LinkDef.h b/src/LinkDef.h
index c57b48e3..bdb67e51 100644
--- a/src/LinkDef.h
+++ b/src/LinkDef.h
@@ -15,7 +15,6 @@
 #pragma link C++ class HistosDAG+;
 
 // analysis objects
-#pragma link C++ class DataModel+;
 #pragma link C++ class Kinematics+;
 #pragma link C++ class SimpleTree+;
 #pragma link C++ class ParticleTree+;

From bc4fab77eab097b6c8c2ed5eb94a6d59fb73c8ac Mon Sep 17 00:00:00 2001
From: Christopher Dilks <christopher.j.dilks@gmail.com>
Date: Mon, 5 Dec 2022 17:17:00 -0500
Subject: [PATCH 26/32] ci: increase stats for `AnalysisEpic`

---
 .github/workflows/ci.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 2fe4dbb8..f6901812 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -142,7 +142,7 @@ jobs:
       fail-fast: true
       matrix:
         include:
-          - { detector: epic,   num_files: 8  }
+          - { detector: epic,   num_files: 20 }
           - { detector: athena, num_files: 20 }
           - { detector: ecce,   num_files: 40 }
     steps:

From 18f62dcfc030f1d520d676e64f8f0f8ae96e7547 Mon Sep 17 00:00:00 2001
From: Ralf Seidl <http.proxy>
Date: Wed, 7 Dec 2022 00:04:05 -0500
Subject: [PATCH 27/32] Added pythia 6 cross sections for epic simulations with
 and without radiative corrections to the cross section table

---
 datarec/xsec/xsec.dat | 28 +++++++++++++++++++++++++---
 1 file changed, 25 insertions(+), 3 deletions(-)

diff --git a/datarec/xsec/xsec.dat b/datarec/xsec/xsec.dat
index 0e52c339..a29017d5 100644
--- a/datarec/xsec/xsec.dat
+++ b/datarec/xsec/xsec.dat
@@ -36,7 +36,29 @@ pythia6:ep-18x275-Lambda   8.830e+05           0.0011   # FIXME: assuming genera
 #
 #
 #
-# Pythia 6, for EPIC -- TODO
-# 
-#
+# Pythia 6, for EPIC
+# qbins from 1-10-100-1000-100000 
+# noradcor is without radgen, radcor is including radgen 
 #
+pythia6:ep_noradcor.18x275_q2_1_10		8.089e+05 0.0010 #  20	40.0 M	4.95e+01  nb-1
+pythia6:ep_noradcor.18x275_q2_10_100 		7.087e+04 0.0007 #  20 	20.0 M	2.82e+02  nb-1
+pythia6:ep_noradcor.18x275_q2_100_1000		3.034e+03 0.0347 #  40 	4.0 M 	1.32e+03  nb-1
+pythia6:ep_noradcor.18x275_q2_1000_100000	5.697e+01 0.0018 #  20 	1.0 M 	1.76e+04  nb-1
+pythia6:ep_noradcor.10x100_q2_1_10 		5.387e+05 0.0015 #  20 	40.0 M	7.42e+01  nb-1
+pythia6:ep_noradcor.10x100_q2_10_100 		3.964e+04 0.0005 #  20 	20.0 M	5.05e+02  nb-1
+pythia6:ep_noradcor.10x100_q2_100_1000		1.196e+03 0.0819 #  20 	2.0 M 	1.67e+03  nb-1
+pythia6:ep_noradcor.10x100_q2_1000_100000	4.286e+00 0.0104 #  20 	1.0 M 	2.33e+05  nb-1
+pythia6:ep_noradcor.5x100_q2_1_10 		4.462e+05 0.0010 #  20 	40.0 M	8.96e+01  nb-1
+pythia6:ep_noradcor.5x100_q2_10_100 		2.902e+04 0.0291 #  20 	20.0 M	6.89e+02  nb-1
+pythia6:ep_noradcor.5x100_q2_100_1000		6.471e+02 0.0146 #  20 	2.0 M 	3.09e+03  nb-1
+pythia6:ep_noradcor.5x100_q2_1000_100000	2.092e-01 0.0245 #  20 	0.2 M 	9.56e+05  nb-1
+pythia6:ep_noradcor.5x41_q2_1_10 		3.433e+05 0.0015 #  20 	40.0 M	1.17e+02  nb-1
+pythia6:ep_noradcor.5x41_q2_10_100 		1.935e+04 0.0006 #  20 	20.0 M	1.03e+03  nb-1
+pythia6:ep_noradcor.5x41_q2_100_1000		2.219e+02 0.0074 #  20 	2.0 M 	9.01e+03  nb-1
+pythia6:ep_radcor.18x275_q2_1_10   	  8.540e+05 0.0004 #	20 	40.0 M 4.68e+01  nb-1   4.68e+01
+pythia6:ep_radcor.18x275_q2_10_100	  1.455e+05 0.1198 # 	20 	20.0 M 1.37e+02  nb-1   1.38e+02
+pythia6:ep_radcor.18x275_q2_100_1000	  6.919e+03 0.0483 #	40 	4.0 M  5.78e+02  nb-1   5.78e+02
+pythia6:ep_radcor.18x275_q2_1000_100000	  1.212e+02 0.0460 #	 20 	1.0 M  8.25e+03  nb-1   8.25e+03
+pythia6:ep_radcor.5x41_q2_1_10      	  3.730e+05 0.0740 #	200 	20.0 M 5.36e+01  nb-1   5.36e+01
+pythia6:ep_radcor.5x41_q2_10_100 	  2.286e+04 0.2940 #	1000 	10.0 M 4.37e+02  nb-1   4.37e+02
+pythia6:ep_radcor.5x41_q2_100_1000  	  2.576e+02 0.0074 #	 20 	2.0 M  7.76e+03  nb-1   7.76e+03

From e722c025aff98b5c31d3b62b89b1abf8b8ea8772 Mon Sep 17 00:00:00 2001
From: Christopher Dilks <christopher.j.dilks@gmail.com>
Date: Wed, 7 Dec 2022 12:43:26 -0500
Subject: [PATCH 28/32] move EPIC cross sections to the top of `xsec.dat`

---
 datarec/xsec/xsec.dat | 57 +++++++++++++++++++++----------------------
 1 file changed, 28 insertions(+), 29 deletions(-)

diff --git a/datarec/xsec/xsec.dat b/datarec/xsec/xsec.dat
index a29017d5..f3de8d23 100644
--- a/datarec/xsec/xsec.dat
+++ b/datarec/xsec/xsec.dat
@@ -1,3 +1,30 @@
+# Pythia 6, for EPIC
+# qbins from 1-10-100-1000-100000 
+# noradcor is without radgen, radcor is including radgen 
+#label                                    cross_section_[pb] relative_uncertainty
+pythia6:ep_noradcor.18x275_q2_1_10        8.089e+05          0.0010      # 20   40.0 M 4.95e+01 nb-1
+pythia6:ep_noradcor.18x275_q2_10_100      7.087e+04          0.0007      # 20   20.0 M 2.82e+02 nb-1
+pythia6:ep_noradcor.18x275_q2_100_1000    3.034e+03          0.0347      # 40   4.0  M 1.32e+03 nb-1
+pythia6:ep_noradcor.18x275_q2_1000_100000 5.697e+01          0.0018      # 20   1.0  M 1.76e+04 nb-1
+pythia6:ep_noradcor.10x100_q2_1_10        5.387e+05          0.0015      # 20   40.0 M 7.42e+01 nb-1
+pythia6:ep_noradcor.10x100_q2_10_100      3.964e+04          0.0005      # 20   20.0 M 5.05e+02 nb-1
+pythia6:ep_noradcor.10x100_q2_100_1000    1.196e+03          0.0819      # 20   2.0  M 1.67e+03 nb-1
+pythia6:ep_noradcor.10x100_q2_1000_100000 4.286e+00          0.0104      # 20   1.0  M 2.33e+05 nb-1
+pythia6:ep_noradcor.5x100_q2_1_10         4.462e+05          0.0010      # 20   40.0 M 8.96e+01 nb-1
+pythia6:ep_noradcor.5x100_q2_10_100       2.902e+04          0.0291      # 20   20.0 M 6.89e+02 nb-1
+pythia6:ep_noradcor.5x100_q2_100_1000     6.471e+02          0.0146      # 20   2.0  M 3.09e+03 nb-1
+pythia6:ep_noradcor.5x100_q2_1000_100000  2.092e-01          0.0245      # 20   0.2  M 9.56e+05 nb-1
+pythia6:ep_noradcor.5x41_q2_1_10          3.433e+05          0.0015      # 20   40.0 M 1.17e+02 nb-1
+pythia6:ep_noradcor.5x41_q2_10_100        1.935e+04          0.0006      # 20   20.0 M 1.03e+03 nb-1
+pythia6:ep_noradcor.5x41_q2_100_1000      2.219e+02          0.0074      # 20   2.0  M 9.01e+03 nb-1
+pythia6:ep_radcor.18x275_q2_1_10          8.540e+05          0.0004      # 20   40.0 M 4.68e+01 nb-1 4.68e+01
+pythia6:ep_radcor.18x275_q2_10_100        1.455e+05          0.1198      # 20   20.0 M 1.37e+02 nb-1 1.38e+02
+pythia6:ep_radcor.18x275_q2_100_1000      6.919e+03          0.0483      # 40   4.0  M 5.78e+02 nb-1 5.78e+02
+pythia6:ep_radcor.18x275_q2_1000_100000   1.212e+02          0.0460      # 20   1.0  M 8.25e+03 nb-1 8.25e+03
+pythia6:ep_radcor.5x41_q2_1_10            3.730e+05          0.0740      # 200  20.0 M 5.36e+01 nb-1 5.36e+01
+pythia6:ep_radcor.5x41_q2_10_100          2.286e+04          0.2940      # 1000 10.0 M 4.37e+02 nb-1 4.37e+02
+pythia6:ep_radcor.5x41_q2_100_1000        2.576e+02          0.0074      # 20   2.0  M 7.76e+03 nb-1 7.76e+03
+
 # Pythia 8, from ATHENA production HEPMC files: S3/eictest/ATHENA/EVGEN/DIS/NC
 #label                     cross_section_[pb]  relative_uncertainty
 pythia8:5x100/minQ2=1000   0.43023             0.00258
@@ -19,6 +46,7 @@ pythia8:18x275/minQ2=1000  79.451              0.00223
 pythia8:18x275/minQ2=100   3370.2              0.00245
 pythia8:18x275/minQ2=10    69275               0.00266
 pythia8:18x275/minQ2=1     7.4167e+05          0.00277
+
 # Pythia 6, from ECCE production files:
 # S3/eictest/ECCE/ProductionInputFiles/SIDIS/pythia6
 # Note: the following are values for noradcor files, radcor 
@@ -33,32 +61,3 @@ pythia6:ep-18x275          8.830e+05           0.0011   # general Q2
 pythia6:ep-18x275-q2-low   8.796e+05           0.0006   # 1 < Q2 < 100
 pythia6:ep-18x275-q2-high  3.092e+03           0.0475   # 100 < Q2
 pythia6:ep-18x275-Lambda   8.830e+05           0.0011   # FIXME: assuming general Q2
-#
-#
-#
-# Pythia 6, for EPIC
-# qbins from 1-10-100-1000-100000 
-# noradcor is without radgen, radcor is including radgen 
-#
-pythia6:ep_noradcor.18x275_q2_1_10		8.089e+05 0.0010 #  20	40.0 M	4.95e+01  nb-1
-pythia6:ep_noradcor.18x275_q2_10_100 		7.087e+04 0.0007 #  20 	20.0 M	2.82e+02  nb-1
-pythia6:ep_noradcor.18x275_q2_100_1000		3.034e+03 0.0347 #  40 	4.0 M 	1.32e+03  nb-1
-pythia6:ep_noradcor.18x275_q2_1000_100000	5.697e+01 0.0018 #  20 	1.0 M 	1.76e+04  nb-1
-pythia6:ep_noradcor.10x100_q2_1_10 		5.387e+05 0.0015 #  20 	40.0 M	7.42e+01  nb-1
-pythia6:ep_noradcor.10x100_q2_10_100 		3.964e+04 0.0005 #  20 	20.0 M	5.05e+02  nb-1
-pythia6:ep_noradcor.10x100_q2_100_1000		1.196e+03 0.0819 #  20 	2.0 M 	1.67e+03  nb-1
-pythia6:ep_noradcor.10x100_q2_1000_100000	4.286e+00 0.0104 #  20 	1.0 M 	2.33e+05  nb-1
-pythia6:ep_noradcor.5x100_q2_1_10 		4.462e+05 0.0010 #  20 	40.0 M	8.96e+01  nb-1
-pythia6:ep_noradcor.5x100_q2_10_100 		2.902e+04 0.0291 #  20 	20.0 M	6.89e+02  nb-1
-pythia6:ep_noradcor.5x100_q2_100_1000		6.471e+02 0.0146 #  20 	2.0 M 	3.09e+03  nb-1
-pythia6:ep_noradcor.5x100_q2_1000_100000	2.092e-01 0.0245 #  20 	0.2 M 	9.56e+05  nb-1
-pythia6:ep_noradcor.5x41_q2_1_10 		3.433e+05 0.0015 #  20 	40.0 M	1.17e+02  nb-1
-pythia6:ep_noradcor.5x41_q2_10_100 		1.935e+04 0.0006 #  20 	20.0 M	1.03e+03  nb-1
-pythia6:ep_noradcor.5x41_q2_100_1000		2.219e+02 0.0074 #  20 	2.0 M 	9.01e+03  nb-1
-pythia6:ep_radcor.18x275_q2_1_10   	  8.540e+05 0.0004 #	20 	40.0 M 4.68e+01  nb-1   4.68e+01
-pythia6:ep_radcor.18x275_q2_10_100	  1.455e+05 0.1198 # 	20 	20.0 M 1.37e+02  nb-1   1.38e+02
-pythia6:ep_radcor.18x275_q2_100_1000	  6.919e+03 0.0483 #	40 	4.0 M  5.78e+02  nb-1   5.78e+02
-pythia6:ep_radcor.18x275_q2_1000_100000	  1.212e+02 0.0460 #	 20 	1.0 M  8.25e+03  nb-1   8.25e+03
-pythia6:ep_radcor.5x41_q2_1_10      	  3.730e+05 0.0740 #	200 	20.0 M 5.36e+01  nb-1   5.36e+01
-pythia6:ep_radcor.5x41_q2_10_100 	  2.286e+04 0.2940 #	1000 	10.0 M 4.37e+02  nb-1   4.37e+02
-pythia6:ep_radcor.5x41_q2_100_1000  	  2.576e+02 0.0074 #	 20 	2.0 M  7.76e+03  nb-1   7.76e+03

From 05adfbf50b320db0999cc0faac20fcc929be4534 Mon Sep 17 00:00:00 2001
From: Christopher Dilks <christopher.j.dilks@gmail.com>
Date: Wed, 7 Dec 2022 13:18:53 -0500
Subject: [PATCH 29/32] update comments to clarify HEPMC file paths

---
 datarec/xsec/xsec.dat | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/datarec/xsec/xsec.dat b/datarec/xsec/xsec.dat
index f3de8d23..d2bb252a 100644
--- a/datarec/xsec/xsec.dat
+++ b/datarec/xsec/xsec.dat
@@ -1,4 +1,4 @@
-# Pythia 6, for EPIC
+# Pythia 6, for EPIC: S3/eictest/EPIC/EVGEN/DIS/NC
 # qbins from 1-10-100-1000-100000 
 # noradcor is without radgen, radcor is including radgen 
 #label                                    cross_section_[pb] relative_uncertainty
@@ -26,6 +26,7 @@ pythia6:ep_radcor.5x41_q2_10_100          2.286e+04          0.2940      # 1000
 pythia6:ep_radcor.5x41_q2_100_1000        2.576e+02          0.0074      # 20   2.0  M 7.76e+03 nb-1 7.76e+03
 
 # Pythia 8, from ATHENA production HEPMC files: S3/eictest/ATHENA/EVGEN/DIS/NC
+# Q2 binning by minimum only (no maxima)
 #label                     cross_section_[pb]  relative_uncertainty
 pythia8:5x100/minQ2=1000   0.43023             0.00258
 pythia8:5x100/minQ2=100    778.99              0.00223
@@ -47,8 +48,7 @@ pythia8:18x275/minQ2=100   3370.2              0.00245
 pythia8:18x275/minQ2=10    69275               0.00266
 pythia8:18x275/minQ2=1     7.4167e+05          0.00277
 
-# Pythia 6, from ECCE production files:
-# S3/eictest/ECCE/ProductionInputFiles/SIDIS/pythia6
+# Pythia 6, from ECCE production files: S3/eictest/ECCE/ProductionInputFiles/SIDIS/pythia6
 # Note: the following are values for noradcor files, radcor 
 #label                     cross_section_[pb]  relative_uncertainty
 pythia6:ep-5x41            3.189e+05           0.0011   # general Q2

From 2b152fd2cb9e4a3ce67ac82df2855548230557c7 Mon Sep 17 00:00:00 2001
From: Christopher Dilks <christopher.j.dilks@gmail.com>
Date: Thu, 15 Dec 2022 10:52:50 -0500
Subject: [PATCH 30/32] fix: disable unused `Particles::charge` in
 `AnalysisEpic`

---
 src/AnalysisEpic.cxx | 6 +++---
 src/DataModel.h      | 6 +++---
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/AnalysisEpic.cxx b/src/AnalysisEpic.cxx
index 9bdada7e..caaa8ef2 100644
--- a/src/AnalysisEpic.cxx
+++ b/src/AnalysisEpic.cxx
@@ -114,7 +114,7 @@ void AnalysisEpic::Execute()
       Particles part;
 
       part.pid=pid_;
-      part.charge = (pid_ == 211 || pid_ == 321 || pid_ == 2212 || pid_ == -11 || pid_ == -13)?1:(pid_ == -211 || pid_ == -321 || pid_ == -2212 || pid_ == 11 || pid_ == 13)?-1:0;
+      // part.charge = // TODO; not used yet
       part.mcID=igen;
       part.vecPart.SetPxPyPzE(px_,py_,pz_,e_);
       genpart.push_back(part);
@@ -140,7 +140,7 @@ void AnalysisEpic::Execute()
       Particles part;
 
       part.pid=pid_;
-      part.charge = (pid_ == 211 || pid_ == 321 || pid_ == 2212 || pid_ == -11 || pid_ == -13)?1:(pid_ == -211 || pid_ == -321 || pid_ == -2212 || pid_ == 11 || pid_ == 13)?-1:0;
+      // part.charge = // TODO; not used yet
       part.mcID=imc;
       part.vecPart.SetPxPyPzE(px_,py_,pz_,e_);
       mcpart.push_back(part);
@@ -175,7 +175,7 @@ void AnalysisEpic::Execute()
       Particles part;
 
       part.pid=pid_;
-      part.charge = (pid_ == 211 || pid_ == 321 || pid_ == 2212 || pid_ == -11 || pid_ == -13)?1:(pid_ == -211 || pid_ == -321 || pid_ == -2212 || pid_ == 11 || pid_ == 13)?-1:0;
+      // part.charge = // TODO; not used yet
       part.vecPart.SetPxPyPzE(px_,py_,pz_,e_);
 
       double m_ = part.vecPart.M();
diff --git a/src/DataModel.h b/src/DataModel.h
index 8dab31f9..594e06a8 100644
--- a/src/DataModel.h
+++ b/src/DataModel.h
@@ -7,9 +7,9 @@
 
 class Particles {
   public:
-    int pid;
-    int charge;
-    int mcID;
+    int pid    = 0;
+    int charge = 0;
+    int mcID   = -1;
     TLorentzVector vecPart;
 };
 

From 92e75bec53d343a67e3b0e01bbb7c4cddd9a3877 Mon Sep 17 00:00:00 2001
From: Christopher Dilks <christopher.j.dilks@gmail.com>
Date: Thu, 15 Dec 2022 10:57:43 -0500
Subject: [PATCH 31/32] doc: clarify comment

---
 src/AnalysisEpic.cxx | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/src/AnalysisEpic.cxx b/src/AnalysisEpic.cxx
index caaa8ef2..47d8af5c 100644
--- a/src/AnalysisEpic.cxx
+++ b/src/AnalysisEpic.cxx
@@ -156,9 +156,7 @@ void AnalysisEpic::Execute()
     /*
       ReconstructedParticles loop
       - Add all particles to the std::vector<> of particles
-      - Identify the 
-      - Identify closest matching MCParticle in theta,phi,E space
-
+      - Look up associated MC particle
     */
 
    

From f7ca1f68352ed35ae830cdf85e2f42935f3a6840 Mon Sep 17 00:00:00 2001
From: Christopher Dilks <christopher.j.dilks@gmail.com>
Date: Thu, 15 Dec 2022 12:12:10 -0500
Subject: [PATCH 32/32] deleted:    tutorial/s3files.*.config

---
 .gitignore                     |  1 +
 tutorial/s3files.athena.config | 61 ---------------------------------
 tutorial/s3files.ecce.config   | 62 ----------------------------------
 tutorial/test.epic.config      |  8 -----
 4 files changed, 1 insertion(+), 131 deletions(-)
 delete mode 100644 tutorial/s3files.athena.config
 delete mode 100644 tutorial/s3files.ecce.config
 delete mode 100644 tutorial/test.epic.config

diff --git a/.gitignore b/.gitignore
index 50106dc8..385be451 100644
--- a/.gitignore
+++ b/.gitignore
@@ -25,6 +25,7 @@ results*
 # tmp files
 tutorial/delphes.config
 tutorial/delphes.config.bak
+tutorial/s3files.*.config
 get-files.sh
 *.xsec
 
diff --git a/tutorial/s3files.athena.config b/tutorial/s3files.athena.config
deleted file mode 100644
index 417cfa7b..00000000
--- a/tutorial/s3files.athena.config
+++ /dev/null
@@ -1,61 +0,0 @@
-############################################################
-# EXAMPLE CONFIGURATION FILE, for AnalysisAthena
-############################################################
-
-# Global Settings
-# ===============
-:eleBeamEn 10
-:ionBeamEn 100
-:crossingAngle -25
-:totalCrossSection 555660.0
-
-# Group Settings   | NOTE: they must be sorted by increasing strictness
-# ==============   | of Q2 cuts, or at least by decreasing cross section
-
-# Q2 range 1
-:q2min 1.0
-:crossSection 555660.0
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=1/pythia8NCDIS_10x100_minQ2=1_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0001.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=1/pythia8NCDIS_10x100_minQ2=1_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0002.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=1/pythia8NCDIS_10x100_minQ2=1_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0003.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=1/pythia8NCDIS_10x100_minQ2=1_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0004.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=1/pythia8NCDIS_10x100_minQ2=1_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_001.0001.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=1/pythia8NCDIS_10x100_minQ2=1_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_001.0002.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=1/pythia8NCDIS_10x100_minQ2=1_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_001.0003.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=1/pythia8NCDIS_10x100_minQ2=1_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_001.0004.root
-
-# Q2 range 2
-:q2min 10.0
-:crossSection 40026.0
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=10/pythia8NCDIS_10x100_minQ2=10_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0001.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=10/pythia8NCDIS_10x100_minQ2=10_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0002.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=10/pythia8NCDIS_10x100_minQ2=10_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0003.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=10/pythia8NCDIS_10x100_minQ2=10_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0004.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=10/pythia8NCDIS_10x100_minQ2=10_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0005.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=10/pythia8NCDIS_10x100_minQ2=10_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_001.0001.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=10/pythia8NCDIS_10x100_minQ2=10_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_001.0002.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=10/pythia8NCDIS_10x100_minQ2=10_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_001.0003.root
-
-# Q2 range 3
-:q2min 100.0
-:crossSection 1343.0
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=100/pythia8NCDIS_10x100_minQ2=100_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0001.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=100/pythia8NCDIS_10x100_minQ2=100_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0002.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=100/pythia8NCDIS_10x100_minQ2=100_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0003.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=100/pythia8NCDIS_10x100_minQ2=100_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0004.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=100/pythia8NCDIS_10x100_minQ2=100_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0005.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=100/pythia8NCDIS_10x100_minQ2=100_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0006.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=100/pythia8NCDIS_10x100_minQ2=100_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0007.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=100/pythia8NCDIS_10x100_minQ2=100_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_001.0001.root
-
-# Q2 range 4
-:q2min 1000.0
-:crossSection 6.8238
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=1000/pythia8NCDIS_10x100_minQ2=1000_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0001.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=1000/pythia8NCDIS_10x100_minQ2=1000_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0002.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=1000/pythia8NCDIS_10x100_minQ2=1000_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0003.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=1000/pythia8NCDIS_10x100_minQ2=1000_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0004.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=1000/pythia8NCDIS_10x100_minQ2=1000_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0005.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=1000/pythia8NCDIS_10x100_minQ2=1000_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0006.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=1000/pythia8NCDIS_10x100_minQ2=1000_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0007.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/ATHENA/RECO/deathvalley-v1.0/DIS/NC/10x100/minQ2=1000/pythia8NCDIS_10x100_minQ2=1000_beamEffects_xAngle=-0.025_hiDiv_vtxfix_1_000.0008.root
diff --git a/tutorial/s3files.ecce.config b/tutorial/s3files.ecce.config
deleted file mode 100644
index c3190b8a..00000000
--- a/tutorial/s3files.ecce.config
+++ /dev/null
@@ -1,62 +0,0 @@
-############################################################
-# EXAMPLE CONFIGURATION FILE, for AnalysisEcce
-############################################################
-
-# Global Settings
-# ===============
-:eleBeamEn 10
-:ionBeamEn 100
-:crossingAngle -25
-:totalCrossSection 579700.0
-
-# Group Settings   | NOTE: they must be sorted by increasing strictness
-# ==============   | of Q2 cuts, or at least by decreasing cross section
-
-# Q2 range 1
-:q2min 1.0
-:crossSection 579700.0
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100/DST_SIDIS_pythia6_ep-10x100_000_0000000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100/DST_SIDIS_pythia6_ep-10x100_000_0002000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100/DST_SIDIS_pythia6_ep-10x100_000_0004000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100/DST_SIDIS_pythia6_ep-10x100_000_0006000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100/DST_SIDIS_pythia6_ep-10x100_000_0008000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100/DST_SIDIS_pythia6_ep-10x100_000_0010000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100/DST_SIDIS_pythia6_ep-10x100_000_0012000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100/DST_SIDIS_pythia6_ep-10x100_000_0014000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100/DST_SIDIS_pythia6_ep-10x100_000_0016000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100/DST_SIDIS_pythia6_ep-10x100_000_0018000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100/DST_SIDIS_pythia6_ep-10x100_000_0020000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100/DST_SIDIS_pythia6_ep-10x100_000_0022000_02000_g4event_eval.root
-
-# Q2 range 2
-:q2min 1.0
-:q2max 100.0
-:crossSection 578400.0
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-low/DST_SIDIS_pythia6_ep-10x100-q2-low_000_0000000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-low/DST_SIDIS_pythia6_ep-10x100-q2-low_000_0002000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-low/DST_SIDIS_pythia6_ep-10x100-q2-low_000_0004000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-low/DST_SIDIS_pythia6_ep-10x100-q2-low_000_0006000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-low/DST_SIDIS_pythia6_ep-10x100-q2-low_000_0008000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-low/DST_SIDIS_pythia6_ep-10x100-q2-low_000_0010000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-low/DST_SIDIS_pythia6_ep-10x100-q2-low_000_0012000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-low/DST_SIDIS_pythia6_ep-10x100-q2-low_000_0014000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-low/DST_SIDIS_pythia6_ep-10x100-q2-low_000_0016000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-low/DST_SIDIS_pythia6_ep-10x100-q2-low_000_0018000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-low/DST_SIDIS_pythia6_ep-10x100-q2-low_000_0020000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-low/DST_SIDIS_pythia6_ep-10x100-q2-low_000_0022000_02000_g4event_eval.root
-
-# Q2 range 3
-:q2min 100.0
-:crossSection 1159.0
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-high/DST_SIDIS_pythia6_ep-10x100-q2-high_000_0000000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-high/DST_SIDIS_pythia6_ep-10x100-q2-high_000_0002000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-high/DST_SIDIS_pythia6_ep-10x100-q2-high_000_0004000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-high/DST_SIDIS_pythia6_ep-10x100-q2-high_000_0006000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-high/DST_SIDIS_pythia6_ep-10x100-q2-high_000_0008000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-high/DST_SIDIS_pythia6_ep-10x100-q2-high_000_0010000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-high/DST_SIDIS_pythia6_ep-10x100-q2-high_000_0012000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-high/DST_SIDIS_pythia6_ep-10x100-q2-high_000_0014000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-high/DST_SIDIS_pythia6_ep-10x100-q2-high_000_0016000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-high/DST_SIDIS_pythia6_ep-10x100-q2-high_000_0018000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-high/DST_SIDIS_pythia6_ep-10x100-q2-high_000_0020000_02000_g4event_eval.root
-s3https://dtn01.sdcc.bnl.gov:9000/eictest/EPIC/Campaigns/22.1/SIDIS/pythia6/ep-10x100-q2-high/DST_SIDIS_pythia6_ep-10x100-q2-high_000_0022000_02000_g4event_eval.root
diff --git a/tutorial/test.epic.config b/tutorial/test.epic.config
deleted file mode 100644
index b46a69a0..00000000
--- a/tutorial/test.epic.config
+++ /dev/null
@@ -1,8 +0,0 @@
-:eleBeamEn 10
-:ionBeamEn 100
-:crossingAngle -25
-:totalCrossSection 555660.0
-
-:q2min 1.0
-:crossSection 555660.0
-datarec/epic_test/rec-dis_10x100_minQ2=1.root # FIXME: currently a local test file, from `physics_benchmarks` artifacts