diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 7b9e3b24..f6901812 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -142,6 +142,7 @@ jobs:
       fail-fast: true
       matrix:
         include:
+          - { detector: epic,   num_files: 20 }
           - { detector: athena, num_files: 20 }
           - { detector: ecce,   num_files: 40 }
     steps:
@@ -241,11 +242,16 @@ 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 }
@@ -295,6 +301,7 @@ jobs:
       matrix:
         mode:
           - fastsim
+          - epic
           - athena
           - ecce
         pname: # list only jobs which will only use one (primary) recon method
@@ -314,6 +321,12 @@ 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 }
@@ -403,6 +416,10 @@ 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}}
@@ -426,10 +443,12 @@ 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/.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/datarec/xsec/xsec.dat b/datarec/xsec/xsec.dat
index d1d64305..d2bb252a 100644
--- a/datarec/xsec/xsec.dat
+++ b/datarec/xsec/xsec.dat
@@ -1,4 +1,32 @@
+# 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
+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
+# 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
@@ -19,8 +47,8 @@ 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
+
+# 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
diff --git a/macro/ci/analysis_p_eta.C b/macro/ci/analysis_p_eta.C
index 679afc10..625ad79a 100644
--- a/macro/ci/analysis_p_eta.C
+++ b/macro/ci/analysis_p_eta.C
@@ -8,11 +8,13 @@ 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 6951320b..fd701314 100644
--- a/macro/ci/analysis_x_q2.C
+++ b/macro/ci/analysis_x_q2.C
@@ -13,7 +13,8 @@ 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 4d0ea6ea..19ef5ace 100644
--- a/macro/ci/analysis_yRatio.C
+++ b/macro/ci/analysis_yRatio.C
@@ -8,11 +8,13 @@ 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 1f89214f..fe8de205 100644
--- a/macro/ci/comparator.C
+++ b/macro/ci/comparator.C
@@ -7,8 +7,9 @@ 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
     ) {
@@ -76,7 +77,8 @@ 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;
@@ -117,9 +119,9 @@ 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/macro/postprocess_ParticleTree.ipynb b/macro/postprocess_ParticleTree.ipynb
new file mode 100644
index 00000000..01ce8572
--- /dev/null
+++ b/macro/postprocess_ParticleTree.ipynb
@@ -0,0 +1,486 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "atomic-american",
+   "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": "taken-knowing",
+   "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 4 columns. These are...\n",
+    "- recPart (TLorentzVector)\n",
+    "- mcPart  (TLorentzVector)\n",
+    "- pid     (int)\n",
+    "- status  (int)\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": 16,
+   "id": "herbal-costume",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Output ROOT File and name of ParticleTree\n",
+    "rootfile=\"../out/tutorial.epic.root\"\n",
+    "treeName=\"ptree\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "vertical-information",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " File loaded\n",
+      " --------------------------------------------------\n",
+      " Num Scattered Electrons =  31108\n",
+      " Num Particles =  309850\n",
+      " Num Matches =  294452\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": 18,
+   "id": "behind-saskatchewan",
+   "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": "other-slide",
+   "metadata": {},
+   "source": [
+    "# Comparing Particle Distributions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "falling-somerset",
+   "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": 29,
+   "id": "bored-provider",
+   "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,-3.5,5,\"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",
+    "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": "virtual-infrared",
+   "metadata": {},
+   "source": [
+    "## Particle Energy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "registered-malta",
+   "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": "original-effort",
+   "metadata": {},
+   "source": [
+    "## Particle Pseudorapidity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "geological-palestinian",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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KSZf3v/rFJXe73U6WRpM2COym/40r9rgVj+fGG1YagEXVdZ0/CuavA9OdfMo9Sdkoec5IJFHGI+Ld3gUO+r9Pfk5L4kmv+NjepXWiHc9X1uQBvI4nbQGLExvB+BBPtfke0iNw3/exw7qu05632+0yPST2i0rnaLvdpufqfIXBw/Z2uz2+dL/fL36Aj+NPf+27f/pr3z21CDjD5JUnFm632xTxjM+xPH5NK6fV4n8Huy2KIvU3H2x4T9vtNlUjt9vt8voX2UNgLI/mPz+0tNohLuZncNJYh+9u/+i72z86pij/dWYrbm2mEcwbsvS/eROQtxqpmUhNRt78DdqRG8mPZdzqpfpEtfPBXuIhJV9/sqWLld88ENfzMzhprMORjeBMg8idTV7q90/YAu5PbwT32WPsYNtDIbtoN4+sw3UtnBkaAe/9fh+vUuMkpiynpmn6vs+f+dMwc2+WAjyg9Opv0CU8OhekbMr8alaW5WazGbwuu8Pbs3LKkRtGRkzxsZ7pSGN5ZJXmhz94fwjAWo27+MWvg55eqfm4f/M3Y/Dt0WkjH8cmxKNN3qXjkP23u4Os0kw3vs3IYB0dBIE1eeoWcFyBcSMYH8bjhxYfg7z5sGndx7FE9wtNn7v8bPKDDOEiS68dzLMcg7CmYQXmSwEeUPRrSM1eHvEsRt0NoovEbreL0jsPD3r2tTRFQsNkb/pxT/nFW3cAbi31ExxMlzd4Q1Zkj4Ljp8GljEf2LMsyPcKl15aDlv2QNBhc83FO4Qc5zOuKW5e4E2jbNgaJm7znGRv0rBwEEeZL4XW8Kz9bugoc5albwOK4RjASFtND62aziYBpZL2UZRnjhBYfw3epRVjEwsHQyYOfeYrebrf5v8F8KcAD2m63VVUNLl/ltydnj5YjxUyLC0KTd5NHeGNI01geLwDTUcSHfPKomeFHAViTNNJZngYSLWBqJqIF6fs+PSYseHsfz6iDbJ3UlkVjnV7vDWo733DnHUTW+vwSJyR/3NtsNvkdQnHgYbDIugDmj83pzM+XvrIjY/FM/ud56KTl/4EPlucn/Jhz/pd/+ZdFUfzwhz88tfQ//qf/+su/+av4/L3v/+B3f+e33vwuHs3TtYDF6Y1g/l/WZrOJI43Zk9LVO0oXj4QWxcOMpRLDIgyqVHx7qJ39x7EGDpWOhxt4nANcnDFD4W4mrzxpYT5iZj5MTP6SMB8RpshGVY5hVopsmJjHGTM0vwLnwc20Tt4RfjBA6uTYozkX8zM4aazDm8OlDX7e3Ipbm28EBzfz8SFakChKI2unJdEI7r/d/BV3GTEtDXg9+Op8lLRB6fhY8l2lXwcHOF+NJ72ejw8tv1WYHydu/KCa722+NC05u+bP61rP+OPBi8a3auPstuP/kxw/+I/F/t+8RQxvTiUy+e1jkxtOVnXyRf6btZ2cJWVQ+hd/8Rf5wl/82Z9PVvVHP/7JzBdpBBc0/vctnrMF3J/VCCbFtyN7yf64K8zkfq7rURqJ8VnIB2FNUqs5Xzre7ZtueXAPQTAU7uaSS8q4IRk8Sp295/ubrO15h/AKV+mrc9JYB8HQp3P2xWemBVy2+Zv/9ksauyNXe9Lr+Tg6lgcC0ud8qpCkODBx7jGlaZ1LD+AJXeXZ9lCsMH/0PtSn5/g/6fl67rJplo/f25EP+GnndSY/osn9j5fk84Wm0PB8PDR9y09/+tNDpXkwNEVCv/f9H/zoxz/5xZ/9+S/+7M9/9OOfxMKZeKhGcEHn/Qf4sC3gmxU4vr17tMfAB2ok0kUkT4maCXfOl6Z1blbfJyMYCnfjynN1TukZnDTW4bwnOs+BC3LxubrVnNI8qDSOpuVhL4+B59l9dMlO0r9IXdd5mK/4dsemfJ2ZSOKgeoN80sn653t7Mxg6qN6gboeqcWjPk8nag6qmrL3x5m+egfzQBhmg+6lg6KGgZwqSHvoijeCCXvPiczu3O58LzyafKz9OMTwz8eL84DuPP6YeANzNeKLeO0/DBQDFx1mDi6JIsaoYCC+Fq2LuhxhO7lrPdJON4CO3jF3XxfB84zPQfRS/HlrtmJ0fuX6MFRhP6CngGN+YvjeGbR3PijmpLMuqqmae9IuiqKqqqqqTBklMO9zv9zFwZ/obm/+uSXEg8xWIdSbPZEQz3zwbP/3pT4ui+I3f+I351f7jf/qvRVF87/s/+G//73Cil9/9nd/63vd/UBTFj9//b/M7AQ5ZMhg6Ocr1YMkjRz/flZ8NfhasDAAMHHq/CgB3E1Gw4tvTJUWOYYoodV2XcmKuNaXG8flBV/m6S0SwOGKFbdtWVTU4CdVHseabq+ULy7LMdx6TPs/UJG2V7yEvnVwn5TweekIvy3L70aFvf3OFY1z49xOR3zejxpOH2XXd7og5Ydq2jWMc5MkO/Mkf/6uiKFKn+IG/+ev//qMf/yRCosAZFs4MTS8Ak/yyMp4dPuYNnCm925TEQp8AAACHROQunuAicS8VHcqJORRKe+QUmQvFFNKDhX3fT6asDtbs+34+9FaW5TjPcfwMnq8fnc3zFQbRz6ZpYp3Jrz5Un0hlzfNbx9IKM3McDaSRAY5c/03zf4fFxzhp27aTyblHhmJjw5/97Gcz68T08f/H//O3h1b445//n6aVh7MtGQyNK0XbtnnCfx7ujAtNuqCki+8xpffxofs8fu75pQAAAI8sYnwxtOLxz2iDJMSZnV9St4eS4ptprM+U3zN53gYDd8706e66LpUO0mBnupBHZ/N8SVo5lpcfTX7RnY0rkz7P513O7LCYPauR4loURSTnRqbtGX+Q0Vn+WnnQwKkWzgyNy3FcRCLhP0YnidJ4MRWvxeKlYv7OZ7LU1QQAAGBZEeM71Jt4s9mc2kEwhQiX7SB4I9vtdhwIHocs0wNvCskVh/OB8sE908LBAKBvSgmqdV1PPmvnma1XzNA8TyRXpc83+pbIXU1nsu/7FBU9ficR+shTwXIxYChwO8OxeO9vv99PDjuSlqQVTi29v7zvvHRRAADgBeU9/wZFMeVOjBCacvrGHQRjQMz8QS/vIDhT+lzSiXqzw3vI14nzUBwxtsBgqyPPVR7ljLmSxus0TZMitotnJuVjApw9FGyctDdj6/Gnm6b8ij/gyNM6/qvjrcDPfvazcdT7d3/nt37vn5xad+AEywdDiyPimG+OhHK9ugAAAHAF4yhPih/FrD5peR5uiy6AkW0XS/JA23zpc8njmFfvaZ52eMbJyaOchyKhefBx2X+CPG673W7vPIpCisvHH+RJveZ/+tOf/uxnPzuvo/33/8E//P73/+Ef//z/PHVDoHiQYOgK5Hmg5lYCAABeVnTgm18nevh1XTcepLJ4tg6CZyvLMmKO+WBx15LGE4iTfFKt3oxypkj0/YOPA8dksB5vMDHJ2GazOXTIEaM/Kajdtu3Pfvazvu//8i//clD0ve//4Jd/81f/+//665NzKP3H//RfY4Yl4DwLjxkKAADAC4qOxjOBp8k46ZGlT2Ew8894OqCBPAaXJ9LOf8tgq5hy41AEMx92c7/fP1EkdLfbXR5QjmOf3898uPPU4Wv/4i/+oiiK3/iN3xgs/9GPf1IUxe/9k380udWf/PG/Kori+9//hyd9F5AIhgIAAMBi+r7PB1qtqqqqqnFIrqqqWO2YmYLS8rZtY6uu68ZTw0dsNF857aH8trST/FsG66Svjt1eN1odoyuEwTEWH4ejPSamPLPz4q1p6CPWObnzN7NKJ/3whz+MfaZ/0HflZ+/Kz1JC6P/y/w1jryktVB95OJtu8gB30n1Ufhxw/c31B7dTsXn69enmCsgr/+ypHAAAl4u+1UVR5COohsmky8FqM5G7sixTT/kjtxp84yAFsmmamCzo0ArF6V3yL5RX4KT+6YMZq9K2b3a0j5hpzJVU13XsJI8yn3F/nuKwA7/4sz//vX/yj375N3+12Wx+9OOffO/7PyiK4k/++F9FJDRSR4HzyAwFuId4yR83iIM7nslbxsG9ZtpJvNsP+Wv8+3vzZnfQ8S2lOQySHVJKwuRdIADrkK72bwZKIuAyWDJOT3sEh1rhaMQHpZMLi8NTgfMiyrLc7Xbj5ZODrg7WPCZyN+61PbPVMX+NV5/oaSl9piiK7XZ7ZEf7/X4fZ7Vt27itTQO/nj2LfXSWTz50n3/oPv/d3/mt/X6fYqC/90/+UcRGi6L4xZ/9ubTQJ7K+FvDC5q/5ttvVc8b6g6GbkRt90RefvIuf919/+f7rL2/0LcCTatt2t9tFSxA3pqktPOmesq7rFAzd7Xap39M9xVGM8xfyFcqPEwLk4h4xRLMXnbxiSX5OgBcXnQRNSrka8QS42+0ilJNuyCfvzGeGMoxnwsnuw/eXT7edS4MY5u8+U7s5TgFLHZ95WWVZ7vf73W5X13Vd17vd7lBMLdaM1dLdVJLusvKFXdelncee862iNL8x2x8Wf6gzK6QxRtN+Jv+2J+s5Pszx5ml52na+MvM7Hx/d5I3o5K7ys5pO7Jv/IcdWk0U//OEP44v+8Gf/bVD0N3/93/f7/S/+7M9/9OOf/OjHP/nFn/35fr//3d/5rfnv4nGsrwW8vPlb5Bl2YP3d5Gcushd6//WXX3wi6AmcI3V4jyZhs9nExar8OH3n/HBFIXo/pfeHqVlKg9mX2bwEKQ57rUOYHx6+zOYhDZO3mHnCwmT8FHhBYqCrlB7e0uNQ93H8wdROTfaKCGkEwPiQPw3GhmmFvLm5XY/dmQSLpmlS5l00xNFAp1m5o2NHNNCrSbLjcscPc3lGKOT4nXO8e57V3/2d3xIAfV5ragGv0vwVD9ArYv2ZoTcymfv56VcfPv3qw/0rAzy+uq6rqiqz0UKjSYg3hBEJjaYl3jMfGRbMW7toaVJQNS2PD1d8hVgeMfdr0zSDaGnf923bDnqIRGZo3lJeq5IUU30jjEXAE4lOgvGzdF24grxzXzRV6U1ekWWOxGB8b+4qbZ4a1vRwmCda3i7pMhrryaK8018alzCNLRjSIdwubwOAB7GmFvAqzV9aecGQqGDoRSL6mX6Wrg7wuJqmiXdiERBMzcAgapkatmMyQ0M0mfEiLrWOaWEKqkbM8ToHc5btdhtduvb7fWod438foaPEKh3fZQzgpuLiU1XV4H1Y8bHha9t2v99H19rjdxvtXWTTpPeIqXtEalKvdxxHSQ3cZrNJI8CkJjilzNy5VgAs4nVawCObv/y5L7KF7lnJRDAU4E7i3V16mXatHL1IwMzzLtOe824XxdKPXnnXj91uF+HaCPvGKEtpNlUA1qf7OMReMWoBB+/D5odhGW8Y3QPzx614JozXhBdW+2zRruVvIqN3pGzQu1lT94jtdhsvlZeuCHCOl2oB32z+yrKM4G88Gi81XIxgKMDNdaPp46+VpNm2bQpxRh/5QQJgvB5c9oEwTOZ+phTRYulYLQA3kr8MSykhM+3gkc9F0WrE2NkhnjPzThKLdInIDzamPSw+9oKMqRTvX6XXtKbuEfPjCQIP66VawOdq/gRDAW5u0EegmIoMDtZ5c8zQNCR2bJIGq44l+fx9bdvepzmcb+HyYWuajyOKRhOeFt6ydgAsI4aIGbQRkwNoFm81JcXH94vRiJRlGS/VYg+pe0GM0318fs21pEPI02HSgDBpBgmAd+Vng5+la8RNvEgLeFLzlz+rLhkhnXxjthq3O8A//bXv/umvfffUogU9XYXheU1eecbjTOfrb7fbwTrRH2qwk0GrNlghL8qzRMdrXkVd1/lu00xQeW3HK0we/mS1c6tvrW7BSeN5fXf7R9/d/tGye+Bskxef1DshGrI0hHRqoWKF9J5s0BwMOjekzQcbDtq+Q23KtQwavtQsjtu78T1AfghvVtX1/AxOGo8sGqmZn6vs/ypV5VTji8/6WsDLm790sAu2gJv903YWOMZmc6sD/OKTd0VRTE6aNFO0oKerMDyvmSvPobE706uzwedTPcLYoPMma/hmtW93MV8xJ43nMk6KuWQS+dibaegXcWojeJUW8JKm82GRvmgAACAASURBVEau2CK7np/BSeORDRqp67aA4/1zT4cuPq/TAh7f/B1T89tdzFfeSAiGJk9XYXhe7r+vzik9g5PGE5nsHigY+qRcfK7OKT2Dk8Yju3UjpRFckIvPdd3ufP7KLXYKAAAn8dgGAMAdmEAJAAAAAHgJgqEAAAAAQ/ls113Xzfz65n6WnDgb+Lb1B0M3I0vXCAAAAIY2m82hkFnTNGVZbjabsiybpjn7K7qum3kozr9lsiYx50k8WT/UtC230HVdVVXpbDdNU1VVKq2qKv913kkrA7e2/jFDDV4L3J/3LgC8LI0gnGcmxFmWZd/38bnv+/h8Xkh0Zqv8P96+76uq2u12ecQzgoP5Oi8+Xcx2uz1+zfQveB/55IRG5b4njeBTWH8wFODO1nRH+OI3uMBqDCas91h4O2tqNTSC3FPTNG3bHiqKOFr6g9xsNm3blmV5Um5m13VpV5PfUhTFdruNhNCIe1ZVlf9XEJHQuq5j5ajzy/6XctJR6yP/Ilbz38Lq/7tefzd5AABe2SASCvBQosv5oUhoURRRlAcmdrtdcWJm6GazqapqJjkxviXF7MqyrOs6/5Yo2m63ebfx41Mji+sFBM8Yf/PNTa5bt+P39u9/8V/+/S/+y5G7PbL0Q/d5+jmyGvBqBEMBYJ3Go2brtsNLeVd+Fj/xq8dC4DHtdru6ruu6Pj6wGAmhKbIZg3gOgmWDdr/+aHKHKdCZL4ygZ4rSpmzQcU3mw7IxSmlEY+PDGZHHzWbTNE3sKlJWY8kxG+abDL76kroNznBet3H1xrdh//xH/3Kz2fzB7//2H/z+b0dpHhVtmiYqEx8O1T/+6VPp6kdxhWsRDAWAddpPWbpSAMC3xIRIMXPRuHQyTDlYEuvko3nGriKBNDQfTYZcYw/HhNIG68Sv80mLqXN9hH2jqodmZ5oJR7ZtW1XVdrtNUd22befjoRF/3G63k199Ut3mpV3l39W27aFdVX//f/7bf/Ovi6L4Z3/4L/7df/jPv/69f1AUxR/8/m8PVouBCAbVS6UxkmyckN1uF8OSiofCMYwZCgDAAm7Xe10GKLAyhyJcMbd7URS73a6qqpgCvuu6vu/rur5uXGyyi/2bXxHByjQRU0R+I5nxjHe0abjS4mPu5Ew8NL4xbRJDrMbs8Hn4OK9brHBG3WKH+a6Kj8HQyVMUkdD0Lf/09/7xP//Rv/y3/+Zff+/7P/jl3/xVWq3v+7RO/OP2fZ/2Gf8ieWx3s9nceZomeFIyQwEAuDfjeAI3tZqxYo7MUizLMhIDm6aJGN8Zc82Pw3ZH9tyfCcBF6uJgz7HbMxIwBwc1GNV0slZ5aZyl+JxSbvO65SucIT+ipmn2+/1k3aq//z+Lovhnf/gv8oX/95/8X0VR/O0v/zpfOKjMm3m4h7oBpXFj8tFj4JUJhgIAsAyTPAA3spqxYo7P7owYWQzxmXeQv8SFaYZ50mLuvL2Nw5RvnpzxJqkCh0YGeDPgOCmlgkbq6zGbR9f4+SWTgxIkcXRpNNWTKgwvTjAUAAAAHtehUNcgOpbmRzqvg/x8QG1msNFD2ZRR2vd99W15/+7o9B3SHE1pyXyVZgKXR46COjP+wPyG4/1EALrv+xjbdGZ+p7/95V9NLv/17/2gKIpjJpdPlUyDk6YJlAY1z186eu8IiWAoAAAAPJPJnM3BzO/HOzX/9KTd1nU9magblYxxMJOiKPJf57/uzcqcvcIZ0eSyLOO46rqO6HAkio7XjKDn2KEg6YzojJ+mV4q486k7gRckGAoAAACPKJ8q55g1Iz8xRUVP+pbBVoOsz8k0zGMSMMcBx7yzfITzQlR+t9sNAqZhfBJmvv3QeYuE00OHM7nkGIMj6rouhmWY+YcbDA+alvzT3/vHx39pfIik2v1+H/9YZwwXC69GMBQAbiv19hrfm47ndhisM7Ptm6UAwAqMpxuKdj91io8laQb5CCmemtt46Fvy2duLUcA0fp2fz30cEIzO8idVL6/Dkd8+OU1Tqsxk3SJNtTj97MW8VUcGUnf/4+8VHyeUT6J3/HjY0EO6rquqan5Q0UNMpgS/snQFVmtwWTE8B8BrirlrU2+ptm13u92RY/OXZZnu0aOnWL7JfCkww30a8EQi0FZVVdxCNE0zCAJ2XZcvyWeWP/516fhbxmHB2G0+uGfx7Zjs2G63i+EsI1BbFEWEQee3OiQOM9/PzOTv6YgGX53OSV3XbdtuNpsIH6fTeMb0U3FXFmcvfftM9f7ZH/6Lf/tv/vVms/l3/+E/F0Xxt7/864iNDqaYn//GoijiXzndWM5Hh4Fk/cHQeArNPek0ggA8ndRhLT1IbDabqqryligvzaUUj/S80bZtTDLwZim8pi8+eZf/+ulXH5aqCcAVRbLnIJsyv5eI5XkIr+u6zWbTtu3xcbE3v6X4ONlRPjBluhV5c7fxSvjIrSblr5bTkpk3wYe+Ot0spVzX/JDzFY4XXeMHQ3bOVG/3P/5exEP/4Pd/Oy38d//hPx/fR774GGjOj654K5Kbv/yTFsor26w7MrjZXPkAj7nJjnVSUVxiFs84GNTqyCLgxV39KvpqNpvN4D44opZxVvPPk9sW334Iyfc2X1r4t+PhXf0GaXCTVhx3b/Mg92k8JhfS57XWf7voBZJGvVzwW9JAPdfdbfEx3jpenu5zUleY4yvw5ldf68QeWbe86fn3v/gvf/vLv/717/2Dk8KgZ3zpfDVgYK1X0WT9maFXNL7JBoAZ2+12kPiQB0bT50PD/w+6VkXftCNL4TVFANQ9G7BKtw6DHv8tZ+R1Hln5N9c54yS8ucm1TuwZ+/mnv/ePzw6Dnv2lgGDoyWRQAnCkcd+ocbwyH85lkEY6uLUts0FC3ywFAABgzGzyAHAPMXpXkY3lFLHLuq73+/1+v09zHRRHzK10jPFU9Ydc/l0AAABPQTAUAG6uLMs0uUHK6Nztdvv9Pp8Kdrvd5tOkXmh/tMu/CwDgdsZDDwGcTTAUAG4oEkJj5vf9fp9HOccRz1hyKC10Pl30KsmkAAAP6NDESgBnMGboraSR+99/s8AcbQAvp+u6qqoGI4G+Kd3ri34CAABcl8xQALiV6Bo/GbWMjNGZuebHs8P3fZ9mkJ8vBQAAYJLM0OsbTDefUkQBeCkpsjke4qppmrIsY4TQsiwjFbRpmuhNn9apqqosy9hPWueYUngR7rKAQyanB1zHMNllWaY5GDX9hRMCnE4wFABuK+ZEyqUp4zebTWSPhvwmvizL3W5XVVV6nMsnX5ovhVcgEgrMWEfccyza+t1uVxRFVVVd1734sDlOCHAGwVAAuImyLN98Etvv93HXnvJDx3vIcz+PL4UXMeiRA7Bufd+n15/xWnTpGi3MCQHOIBgKAEuaDIMOVji7FABYk7quNf05J+QS77/+8otPvsyXeMXIi1h/MHQ8WMxae0wAAKye3vHAiuVDXk6+Ls1XqKrqAedOzDuqv/nGd2Ynxewb37TC45+Qh/X+6y/fXglWav3BUKFPAIB1EAkF1qppmsEg4/Hr5JjgsfJ2u731+Jibzeb4Qcm7rhv0Uo9DOGNeo5hV8tBXjw//bifkGY2bzjz9M33WwvJS1h8MBQDgec0/xR2/oa5/wMNK3RlTp+/Ir+z7vqqqQUwwVr7DxIknRTBTMHe73aaE0K7r2rZt2/bUeY1igvimaSa3ii9K1bvbCXlGQpwwSTAUAIAH5SkOWL0I4Q2yGmNhRBirqkr9HTebzR3yHyN2OchUnV9/Mgk0urFvNpuIbB4fXY0D7/t+Zp04Rfc5Ic9O+icMCIYCAPDQTk3qHKzv2Q94WJH+WXwcBHMgoodt25ZlmZIrB/mSV0+HHM+68aao56Hu8DHJe9u2k6Vd100ewna7nQyhxq8xNuh9TgiwPoKhALBOkw8zhtIGgMcRob3dbjezQsqRjJDfYFzOq7fsdV3Hh+MzQ6N6hxI/y7KcPMDBjcqgn3vTNFVVjWPEeR/5+5wQYH2+s3QFAICb2E9ZulIAwN+JMOJ8MmPKgmya5viWPZJJz6hS89GRM7PHt8yvPJ5WPiKh2+12t9tF+LWqqkEX++Lj+ZncYVT1lW91vvjkXf6zdHXgmQiGAgDAhHflZ/nP0tUBXlSajOikrWLypc1mc+pM7qeKip3UOT1W3u120Uc+YprFKBc1Aqx5/VN//AvrvAKin3CJhwiGNk0Tb4rGl+nNyHjEkEPbAgDAeUQ/gVu76bQ/+/0+goZt2242m9uNpHnoKMbP8mnNyXzYqG2+tzRk6uC7jAqafPrVh1OH1QaKRxgzNKXHF0XRtm3btmmskDfbhrIsU9p83/dpSGkAALjch+7zQmAUuI1bB/Wit3v0r+/7PiZej3SiK35L/lSeG3ScT+ukbvWDh/f4NZ9PaVzPY0YVeFiXNyXHZ4PKG4V5C2eG5unxXddFbvxg/OPdbpePAJIyQOOCXtd1LK/rOuKhdz4EAAAAuJEL0yFj8ND0yBx9569YvTB+Eu8yk2HN6tsmI6qRLhpBgKfuIy8SCg9l4WBo3/fb7Ta/MuaXtvmLfj6LXPHt6yMAAAA8uPGwmGPXSocsy/LIOZFOEpU/NNNRGIdKt9vt5EyP42Hxio/P/rGTp37k/9B9nn7O20P0i08/R6553nfBii0cDI0s/XxJfpVMnw/1fx9cyrfb7fwlGAAAgNUbD1h5i3TIy42HxQxlWcYgmxEDvTCIGfuJ7MvoW3nJ3sbeDOnmz+mHorrzo951Xedhf55p5eF4CwdDxwnz4wvcZrOJtPnxqM/zvwIAAPCCJrMOl67UhJStOXiYjcBi6jx+9nBwTdPEA3UKg94iszKq17bt5M4nw9DjMe7iqX+8ZnQejaIn7SMPPJrlJ1BKuq6LC9xut4slcd2v6zouqTEwcxoE+sjdHvkC8DGbRgAArivPmnn/zf+f2V0R4HKRIZQmOCrLMkb5TCuclxaa5jW6xaRJY3Vdx3zIcThpSuRIeo3StPJut4vQZ13XsebMeKBN06Rtn7qP/O2c2hFe9igsnBmaRNJ+URRpKvni49RJ6XrXdd12u43r4PGX8slXgk/xkhAAgOvyBAg8oK7rIg7Y933btlVVxWPvdruNseDOCGWmVNDJ+YuurmmayGpKhxBHEWODDoKYZVnGymnNmE3kUKwzwsG3GPAUeE3LZ4amhNCUAZqML9nxduvQ1dxU8gAATMrDoCmJRmwUeBCpB2SaRjg98+apkce7POPn1Ofrsiwj9po2zB/wB/XJV84P9io14ZA8hzQmuP90ucrAghYOhkYkdLvdnnR1SxdK10QAAADWYTIsGHHSBWpzljcjm2evDHAtCwdDIyd0MqYZcdJBuuhg5JTBbEuRWn+bmgIA8JROHUwNAF5c5I3mPnTG12Y9lgyGTibPpyUxrV7btullUdM0Me5JWqeqqjS2dD7uMgAAAACnGkdCYWWWHzO0KIrxACgR0+y6brPZRPZoyBNFY9DlqqrSfPH55EsA8OJS+5gzZyAL8nAFAM8ipYJqvlmfJYOhMWTy/DrzYyqnQZeLU+aXB4BXIO7JQ/EoBQDAI3iIzNB5b46pLAwKAPAUjDgGAA/ri0/eFUXx/pvfNNms1neWrgAAAAArFFNBlGU5ObXD7UoBYMYTZIYCdxDjUeRLIuda5vUxDNYBADAQQ1dvt9uiKNq2bds2n+OhLMu+7+Nz3/eDe9FLSuFBPNf4MJ9+9SF9jvzQ+N9CoihrJDMUKIqi6Lqu/baqqvIJyh7KLW55L9lnVVWyEgCu6ItP3sXP0hUBzhRBz91uF7dYMYx1mhq3aZq+7+u63u/3+/2+ruuIaV5eCg/iuSKh8GoEQ4G/s/+2uq6Lh0x4vEXwseu6dIMOwILEQGEF+r7fbrf5bWTcWIa2bYuiSLdz8SH9ekkpPJQP3ef5zxl7WOTt4M9/9Td//qu/+elXH+Lnnl8N97H+bvLjvDaz68KRmqZp2zb1QgKAu3mEp6/3X3/5xSdfFnoIwum22+0gQDlI3ozu8/mv+T3nJaWwGt4Owo2sPzN0P7J0jeCZDO41u67bZMbDjKai/PZ3Zqv4Nd9wMCBUWp4yC+INR9/38SGGz4+vSOOcDm6+891OVqYsy8gyyNec/PbQNM3kkQKwGu+//nLpKsATixu8fMkgXjnf92hQetKvsDIpQ/MRXhPCOqw/MxQe0FKv+E5tPruuiyGZ0pKqqtJ7/qZpqqpKA+FHaHK32xUfU0pjis/ofj7YKn8tUVVVXdcxnlQMVBqlMTR+Xdexk7Ztm6Zpmma32+U7jErGTqImM6kBg8qkr4s9932fDmf87fEhHV1edNJZBeCJRNMpNwcukQYjihvFyfE905xI1xr98/iB76XL8FC0OHAHgqHA38nfq8f9aN7FKUoHKZZN03RdF+ukW8koioyAFLLMi1JgMf+KsizzXk4x1FQqKkYT3Oe1zScnnTGoTMRVo55xC56OcRAYLT4OUBUf8oqVZWmwUQCASSnKObizmln/Kt8rxMkzEgmF+xAMhQU8RQeHiEvmWZYRnRyvU0y9w083oIPc0mI0rlN+y5tul9O2kbNZvDUu/pH3zYPKlGU5eaOc+s7na7Ztm09jeupXA/Cw8ofPp2ij4SmkhNC6rt8cVmg+IfSSUng6WiK4NcFQ4O8MbiXjFjaFI4tspM6BcZx0xiDieSiYuN/vIwQZKZnH3EZfS5yH8ZGm0a8EQAGO8a78bOkqAMtIYxMdilSKfgKwlPVPoAScbdAvviiKuq4nJyU7aRLP4+9fu67b7/e73W673caonSfU/vSvS+KLxkeaorFuwQHe9BSR0J//6m/+/Fd/06wUcHWRE3rolml865h335ksTe/d50sB4E2CocCxIiKZL0lDgo7DlPkM7IOtjrxhTeOKxoeT4q25cbrroJ7jhNNxFHiQEzooOqNWcAebKUtXipfzofs8/zm02hefvEs/96zeTE2WrQY8tXxkoYG0vBgNBH+VUgB4k2Ao8IYUghyM3RnTr+dF6a40XzPNLx9Fx9+wxhzx8TlmNDqywhE2TXfheSh2t9vFOKTzlYnl+bdXVZUqEEHhtH+zJ/GwxtnNZpPgMYk8wlq1I7G8LMu4K4sXdTFxZdpqsjSPfs6UAsCbjBkKzEmBxZg2va7r/EY2H8czZmZPeWd1XadUyvFWx9ywDnZYZJMyRa02m81kZKdpmqqqUowy9hOfx5VJd94xPulms4n76Zlvj7OR9qlbFsBVLN5RfVCB6Ob/6UKVgad2aI7K8TrjWSsvLwWAedOhhNU4FCs5T6QtnHqnft5WVzdTjSj6+a/+Zloy04sNiqnJ1i8sevO7xhu+ubf5FSZLxwsPffsxFViH615FuSf/diwugonH3FQseLM0U8m8aFzDQTbr4nd63IIL6fPyb8fijm8Ec/dsEI9sAe9cKx7E6q+iMkOBk81EAM8rOuO73tzb/AqTpZN5B+ftH4AV068fAOB5CYbyjfdff5n9JjMUAGBO5MgIjAJwIU0J3JlgKBMiKz7Rax4AWLH8zsdtDwD3JBIK97f+YGg+/0lY98AHZ5gZAAsAAAC4KSNywj2tPxgq9HmGlBMxSBEFAFiTPA/UbQ8Ar0avUF7T+oOhAABwOR1oAHhZeSMoj5VnJxjKhHSZe//NAm+HAAAAYCUGSaC6R/BSBEMBAGDOz3/1NwudB+GpjKeOKAyhBmfJG0GdJFgHwVC+ZZDu7koHAAA8HXFPOMMZ+aGDoIEe9DyF7yxdAQAAAACejPQpnpTMUAAAAIDXddJQMJPZoAKjPBHBUABYJ8OlAQAADAiGAsA6iXsCAHBFhgRlHQRDAQAAAC51xgREwP0JhgJFURRd13VdV5ZlWZbj0qZpDhXdU1QyPl9Sn9jJ4ocDAACshkgoPAvBUKAoiqJpmr7v82hjUpZl3/dt217e5TbiredtOxj9sG3b7XY7ru2R1bjK4QAw5lEQgFd20kxEwCLWHwwdTx8hAgKH9H1/5MIzNE1zdggy/kPe7XYplhp7a5qmaZqrVA+Ay60gEnreIUzOqwsAr0aDyFP4ztIVuLn9yNI1goc2iC1G6uV2u12kMiGqlEdC08K2bZepEwCHfeg+Tz9L1wUAAL5l/cFQ4Ejb7Xbc8Xwy77Lruk0mbRLLoy98Kk37icDlZrNJ+2yaJq02030+esSPV9jtdnVdp1/zL52sVV6fYw4HgJeSx3CPD+Z++tWH/OcO9QSAB6RB5Imsv5s8PKClehG++VDXNE1VVfmSvu/rus7jg13XVVW13W4jpllVVVVVec51VVWxSQRAy7KMz0VRtG2bEjxjKNK6rmOFtOZkxSZDpfkcSjHmaewtHcigVpMR1TcPBy7XNE2atmv8guF2pcBS8k6CHggBAB6KYCjwd1IkMaIqEWSJqeTTOhEtTVHL/X4fyZ4pEJMCixGmGQw5mnYVsctYM8KagzhsOD5PM31vMRXVPTTb0puHAxeKfOQYa6Jt2/yVQPHxrUB8Hk9idkkpAAAAY4KhsIBHHkNtu93GxETFgT7yRVHkndOLUZwx3yoP1uRSLtt4+XnTzedfmhJRD60wMH84cIn4e86jn5vNJmUfp4zm9F9c27bpv4JLSuEpDOZYWIc8D3SVBwgA8OyMGQp8Sx40jFDLMVudOuN8RBurqkojdQ4yNJP5yE7qI1xkY4ZWVTWuz8x+xkWnHg4c0vf9YHyG/L+pGEg3z2jOf72kFB6fQCEAAIuQGQp8S0o6Sx9u913HD82ZklVzaUzS4mNP5JQlFyOBHrnzcTJd9GiGy+WjN4RBxH/wx7bdbvNY/CWl8BSedzzNwfDfj9znA17T5LSZxoXnQTz1G8HxBBjHNILjQ37eewBWQGYoMJT3lJ80iOZE7ttJXxHBx8G8TIeSNyOTblyfyIxLW6VI6KnGh3PGTmDS+A/70Ci6kwalJ/0K3M5SEyECx9tPWbpSUBSri4Qe46kPmVUSDAWGIqR4qI98Xdd936ew43kJpLFVVVURiIxEzkNRyKZpUnw2rZ+GYkyrpZhmSgs9ZujPycMxZii30HVdZKnE3+3kn1kKaF7lj3BztMu/C17Qh+5zCaEAnOfTrz7kP0tX5zTRAp7aCD7pwbJKgqHAUArHTIY4m6ap67pt24ihRMz0mJS01J89dhsv52PY0Ihd5pHNga7rIh6a1h98b8Q005ihsasUbJ2RIq3pcGaqAWcryzL9ncff7Uk5oeeZTIqRKQMAALwyY4YCRTFKQxsERwalTdOkJM08ZFOW5WDDWDMvzXsNx6+p9JgaHlp/XKX8u2ZqNdizjsZcXUpVPmYkh/nw/SWlAAAAFIKhwNnOixteOMrhJQMsXrJnOE9EQrfb7aFIpegnL8WQYQDwUjT9PCbd5AHgVuaHrx3P/56P1TtZmiYrmy+FB+RxCACAR7D+zNDxvBAGRwPgDlIMdNw7PpY0TVNVVVmW+SgNaeVLSuFhmTYBAF6BFp9Htv5gqNAnAMtq23awJKKWZVnudruYFiyW5/N3TZam8RzmS4FbeFd+tnQVAAC41PqDoQCwiPGUYofWOTR/1yWlAADw+MYD6cgq5dYEQwFgYSfNDHZSKXAVH7rPl64CAKyQIcVZhGAoAAAAAAfddKyYlAoqNsp9mE0eAAAAAHgJMkMBAAAAmGCsGNZHZigAAAAA8BIeIhjaNE1ZlmVZNk1z3VIAAAAAgLB8N/nNZlMUxXa7LYqibdu2bXe7XZobtyzLvu/jc9/3Xdd1XZe2nS8FAADgBcVj5sB+v79/TYBJ5kpiQQtnhkbQc7fbRRwzGqeqqqK0aZq+7+u63u/3+/2+ruuIeB5TCgAvbjNl6UoBwD3spyxdKQAewsLB0L7vt9ttygMtiqKu6/S5bduiKFL/9/iQfp0vBYAX5zkQAIBH8+lXHwY/S9eIl7NwMHS73Q7Cl4PUzug+n/+a+sW/WQoAAAAAkCw8Zui4V/sgmpknjY4NSvMhRAEAAAAAcstPoJR0XRejhe52u2IqTlpk4c7jxwY9cnw0PQcBALi6wQQROgMCACzrUYKhKcqZppI/KSd0hignAAAAAFA8QjA0JYTWdf3m9EfzCaGmkgcA4EEMkkAHKaIAACxi+TFDq6rabreH4piinwAAAMBT80rsePm5MrwMt7DwbPKRE3oopjmeHb7v+7quZ0oH88sDAAAALEgkFB7KkpmhKQY67h0fS5qmqaqqLMtYM8YJTSvPlwIAAAA8CEmOb8pPkQgyt7P8mKFFUbRtO1gSMc2yLHe7XVVVaUb4mGg+TJYeP7ESAAAAAPBSlgyGlmX55lTvsU6e+3l8KQAAAABPapAfKruWq3iIzNA3zQc6hUEBAHgu495/HvAAAO7gOYKhAACwGsZBA1iHd+VnS1dhtQbvCAdNp3eKXEIwFADWKY2pnXtzgBrgbtJjWzzR5c91nugAHp9I6FK8U+RCgqEAsE7invCAPL/BfXgjyN186D5fugovavBOEY4nGAoAAEvK80DHWaKFRFE4nbgnAIesPxg6fiWoXQQA4M4ENAEAHsH6g6FCnwAAPIv5+SIAALjQ+oOhAAAsQiAPAIBHIxh6b+P55h5/uOW8zo9fWwDgEYiEXpGJ5gGgOO7uYryOppMBwdC7GkdCAQCe2vztjccPAOBuzn4XK4T6UgRDF5CSK58lNhoVfpbaAgB34/bg1sYTzQPACzopNJlWPrLp1MK+GsFQAAAuYhQdAGBZlwc0Tw2h8rwEQwEAAACuSUANHpZgKAAAXGQwXIBUWYAXJxJ6Twb35FSCobwtruPvv/nNzT0AwBMbPKJ7huTWyrJsmqYsy3zhZrMZrFbXddM06demabquS5sPVp4vhQfhAguPSTAUAADONEgCffwZpSQrcWdd1/V9P144v1VZlmmrvu+7rss3mS8FGMjbPhFqCsHQ+3v/9ZdffPLlN5+/Wfa4uZYmMAUAWJ+4x3N3x01FjLJt20Mr7Ha7QbpoaJqmk/7k+QAAIABJREFU7/uUKNo0Tdu2XdfFyvOlwP3lLwINFMNT+M7SFXgt77/+cukqAPAqNlOWrhQAr6KqqkOR0NTDfbI0tkqd31PQ85hSgNynX31IP0vXhQciM3QB6T9Cb+MBuJ39fr90FQB4XdEMdV1XVdWgKPVqPxQV3W63g1/zvvbzpcDd5Hmgjz9QDCTrD4aOs2AWeTiM68L7N9cDAAB4AfmT2na7HYwKmq+ZDxL6ZinADLMIUrxCN/n9yNI1AgAAeF0Ru6zrOh7QIrUzurpfayqkybFiDCADQPEKmaEPIrLH09RJAABwIektPKnB1Ekx/VHbtk3TXGseJEkwwMCglTRu4Stbf2YoAACsj6c4ntc44hlLDqWFzqeLXiuZFIAXITP0TtytAgBwdZHnErea5yWKSi/lcaQgqegncB9iNa9JMBQAAIA7ifnl67qOQULTwvR5PDt83/dpBvn5UuBxDOaXz6eeh2UJht6ct+sAANzUeeOgGT2NRZRlud1u27YtyzJSQZum6fu+rutYoWmaqqrKsowIaVrnmFLgQQwioQ9IrOaVCYYCAMA15U+Az5sIM46Nem7kWrqu22w2VVWlJXmiaFmWu92uqqo01Xs+4dJ8KbCsyWzQxw+M8moEQwEA4FaerpOg/FCuqyzLyYnd9/t913Uxj/zkfEqxQnFgtqWZUgCYJxgKAADXkcc6HycR5uz4ZkoFFSHlFibDoIMVzi4F7uzNt31P93aQFRMMBQCA6xs85i0VGz0+jqkXPMCk8QVcIA+emmAoAACsnEAnwHkeJ83/eT3I28FT5W8TNaMrIxgKAOuUZpbITQ7cBtzNOuZWAng16Yr9LIE8YMb6g6HjR0HPgQC8Au0dAACcKs8DNWr2Kq0/GOpREADgbjwzHHKtuZUWPMM6DALM0wjCU/jO0hUAAGAlPAQC8LI0gvAs1p8ZCgDAPckZvJEFT6wOgwBH0gjC4xMMBQCAFRK15JWZRRCAQwRDAQAAWBVxTwAOEQwFAIBV0UkTAOAQwVAAAN4wnv08nxsdAACehdnkAeAeyrLsum6wcDPSNE2+QtM0ZVmWZTlYfkwpXMs4EgoAr+xd+ZnGEZ6XzFAAuLmu6/q+Hy+c36osy7RV3/dd1+WbzJfC1aVUUI9/AAA8L5mhAHBDXdc1TVNV1aEVdrvdPpNyPJum6fu+rutYXtd1RDyPKQUA4BY+dJ8PfpauEXAywVAAuKGqqtq2nSyK2GVZlpOlsVUeG81/nS8FAACu5YtP3uU/S1eHS+kmDwA3tN/vi6Loum6cHJoSOQ9FRbfb7eDXvK/9fCkAAHBT48Dop199WKQmnEQwFACWtNls0uftdjsYFTRfMx8k9M1SuDUjhwIAqzcIbubRTymiz2v93eTHE/UuXSMAKIqiiNhlGvczUjujq/tVRv8ct4CHXP5dAADwgj796kP8LF0RTrD+zNDonwgAj2a32+XZnV3XlWXZtm3TNIcGEj2JFpAbMVkEAADPa/2ZoQDwmMYRz1hyKC10Pl3UVPIAAABvEgwFgMeSgqSinwAAANclGAoAC+i6brPZxAih+cL0eTw7fN/3aQb5+VIAAAAmCYYCwALKstxut23bpgBo0zR939d1nX4tsizR+JCCp/OlAAAATFr/BEoA8JgiObSqqrSkrusU0CzLcrfbVVWVZnvPJ1yaLwWezrvys/xXs1TBhVL7mDO1IHAVX3zybukqcJEHCoaWZTmeP3fchuUPikVRNE0TOTWx+Y3rCADnKMty8gFsv993XRfzyE/OpxQrFAdmW5opBZ7IIBIKXE7cE4BDHiUY2nXdYOyz4oipIcqyTFv1fR/Pkzeo3bPKb6zfL1gPAA6bDIMOVji7FHgikQ36LIHRQVLMp199WKomAHA32rt1WD4YGhHMtm0PrXCo318aWy0SQpumiZHXPBaGZ7mTBgCemp5iAMDZxrELY8Vwa8sHQ/Ox0gbmu/5F/DSfSqJt29RrnpAuIl988uWyNQEA1kck9AUNkmL8DQBwNllcLGL5YGgM5tJ13TgqmsKah6Ki2+128Ou4rz0AADelyxgAcLaUxSU2yn0sHwx9Uz6H0na7zRM/B+HRfAjRJzLzX7vkcAAAAAC4locOhkZkM40KGrHOpmlO6gs/no9+ktkGAQDgPHlneZnCwOswVAg8o4cOhg6mTorJkWJg0ONnSXr8KOf7r/9uNM9073iV5PD3X39pqFCAlzX5OvDxm0UAgKcgEnoJPeJZ0EMHQ8cRz0gOPTRl/AqmTkoX0/ffLDi/m3weYwXgBYl7woObeQ4cFD3y0El5Hqi4APCCpMPD03noYOghKRL67NHPO9w7ui4DAAAAD+KR3/DxIh43GBrzy6cBQ9PC9Hk8d3zf94P55Z/IIGrpvToAwFrNPAcOivQiBAC4ru8sXYGDyrLcbrdt26YAaNM0fd/XdZ1+LbIs0fiQR04BAAAAeCLvys/Sz9J1YZ0eNzO0KIqu6zabTVVVaUmeKFqW5W63q6oqTRAxmHAJAAAAAOaNA6+686/YowRDy7KcnOdhv993XRczJk3OpxQrFFOzLQEAAADwFPL44z3TQqWgvppHCYbOmAyDDla4U1UAAGC9nmgWewC4rtTqiY2u3hMEQwEAgFvz7MeapLHUcpOdEQF4NYKhAADANyIvRmCUZyfuCcAhjzubPAAAAADAFQmGAgAAAAAvQTd5AAAAgKN88cm7pasAXGT9wdDxyNmGjwEAAABOJRLKvPwv5NOvPixYE2asPxgq9AkAcF0eBQF4ZYJc8NTWHwwFgNc07htReEfINYiEMm/wFyJkAMAryNs7N0sPTjCU07wrP8t//dB9vlRNAJgn7slNiXABsGKDJ19gTQRDAQCAKxiEyOXFAE9KJBTWTTCU06RUUM0DAAAAa6UfJKyVYCgAAAAAT2OcniV4zfEEQzlN6u70/psFLjcAALeVP/J52APgxemoyoUEQwEAAAB4Jgbx42yCoRzLiPgAAHeW54He/2Fv8I2SUgF4HbplrJhgKAAAE+RZPD7BSgBex3l3JjdtKw1d+qQEQwEAGBIJfXw3/TcaPMv5e+DpbDab8cL9fn//mgBXcXxLdHmbdWS3DI3j8xIM5TlIfACA+9PgPqDJm6L55zF5K7wgcU9Yh1NDAZMN4jFt5dkMXfqM1h8MHb8S1C4+HdcUAFiWscKf16lxUgB4dt75MW/9wVChz9W46cscAOAQkdAHccmj3WTeyrVuq/TgAeDqtCbczvqDoaySe24AuLNPv/qwdBW4iePvoybn1fWiGgB4LoKhALBO5o4A7kYPHgDgWQiG8hzef/3lF598WRTF++lymaEAQ+Ke8MquO1TokfPqAsAT0aK9LMFQnsD7r79cugoAAE/D0x3AMQy/9spOais1rCsjGMrTmByqzJQOAACTJp/qPeoDBOGt1yQCTiEYCgDwyjwScB5BBGAd8iGPXdkYc2u0SoKhAAAvylMfAPAihDVJBEMBAF6aecA5iYdJTlWWZdM0ZVkOljdN03VdWuGKpXAqVzZ4KYKhAAAA3ETXdX3fj5eXZZmW933fdV0ENy8vBYB531m6AgAAAKxN13VN01RVNS5qmqbv+7qu9/v9fr+v6zpimpeXAsCbBEMBAPjGu/Kz+Fm6IsDTq6qqbdvJolieurfHh/TrJaUA8CbBUAAAAK4sMjd3u91k6Xa7Hfya96a/pBQA5q1/zNDNZjNYst/vF6kJAMDDMncEcE/j+ZRmSvNBQt8shav74pN3S1cBuKb1Z4buR5auEQAAwIuaHN8zxTevNfrn5mhX+TpWTCQU1mf9maEAAAA8iJNyQs8mCYbr+vSrD0tXAbgawVAAAFgDM1/xvOYTQi8pBYABwVAAWKfJrn8yZQB4BKKfACxFMBQA1kncE16H+a94LuP53/u+r+t6pjTNID9fCgBvWv8ESgAAADyOpmmKbHjQ+BALLywFgDfJDGUN8hGyZEYAAMAjK8tyt9tVVZVGdNntdvOlefRzphR4WQbO5niCoQAAANxEWZaTw7bE8hj9cxzKvKQUAOYJhj40CY9HipPjRRAAwOLSLdn7ZevBM5gPZV5SCrwO0RJOJRjKGnzxybvi7264XQcBAAAAmCAY+tAkPAIA8CwGuTlffPLlUjUBADjEbPI8t0+/+pB+lq4LwJyyLGN0s4GmacqyLMtycibcS0oBAAAYkBkKADfXdV3f9+PlZVmm5X3fd12XB0wvKQUAAGBs/Zmhm5Gla3SCLz5598Un795//eX7r3UyAnhKXdc1TVNV1bioaZq+7+u63u/3+/2+ruuIaV5eCgDAJeJhPGanAFZm/cHQ/cjSNQLghVRV1bbtZFEsTz3c40P69ZJSAADOJgYK66ab/IPKR8B0IQZ4XvESruu6yeTQ7XY7+DXvTX9JKQAAlzAvBWeIGbDfL10N5gmGAsBiyrI8vjQfJPTNUric17HA85ocHk03QQAKwVAAWMTk+J4poHmV0T+PHybbwyFjIqHAU9O0AXf2ofs8ff7iE/O+PDTBUABYwEk5oefxHMjl9BAEAGBl1j+BEgA8i/mE0EtKAQAAKB4qGFqW5eSDXNM0ZVmWZTk5Se58KQA8MtFPAACAe3qUbvJd101O+5BPB9H3fdd1+bPffCkAPLLx/O9939d1PVOaZpCfLwUAAGDS8pmhXdc1TVNV1bioaZp4LNzv9/v9vq7riHgeUwoADy76NKThQeND6uhwSSkAAACTlg+GVlXVtu1kUSwfPPilX+dLAeDBlWW52+36vt9sNpvNpu/73W43X5pHP2dKAQAAmLR8N/mY67brusnk0EGPv0GvwPlSAHgQZVlOzu0ey6NbwziUeUkpAAAAY8sHQ+fNP90NSvMhRLmPd+Vn6fOH7vMFawLwvE5q7E4qBQAAILd8N/lDJkf/TI98x48NujnOdSoNAAAAADyqx80MvVYizGS3RK4lskHz/FAAAAAAeEyPGwydNJ8Qaip5iqL44pN3+a+ffvVhqZoAAAAA8FAePRgq+gkAAABcTqdGoHjwYOh4dvi+7+u6nikdzC/PSxkkgQ5SRAGAwnMgAK9KCwiEhw6GNk1TVVVZlpEBGuOENk1zTCn3EQHH99/8ZjZ5AHhongMBeHEx7wXwyh46GFqW5W73/7d3L0eOW1cDgAGVg1AGVpU6BhPceOkkxgn00rMhuJGWSuBXErP1gqBiGFXJGUwW/BdXA2EAEo3mA/cQ+L5SqboJdvcZPO4hDu7jsN1u29XeD4fD+NbpCysBwLK1+bHLuoIU7gOBFZAEAbgkSjG0qqqzmSm93u37OX0rD9Udk576h/Y6m7jRAsjLLR8AqyUJ8l6mWYP1iFIMHTde6FQGBQAAAK6jEgqr8hzFUJ5C2xXUfGQAAAA8l96SvMBSKYY+jd6jqoDNdBuh9ZQA4BnpFwMAwOIphgIAoBIKwDIZuQj0KIY+gV4n0ID3KvEjBACmCDj0BACuphIKDCmGAgAAAIvVrm8BUKyhGFqWZe+V0+mUJRIAAAAAIKPlF0OVPgGAlTNIEAAAku9yBwAAwAOphAIAQGv5PUOXqntjYwIUAGDcpU8Llj0EAGBVFEOf1euXT53vFEMBgHdTCQUAYG0UQwEAVu3DH59zhwAAADNRDH0+3TsWHToAuKQsy+GL1hUEAJbNZNnAOMVQAFgmdU8AYG1UQoE3KYYCAACwKIZHrJxFhoERiqEAAEujXwywcuqeTGHeOVin73IHAADAPamEAsCbVEJhtfQMBQBYICMEAeBN3QWKgZXQMxQAAAAAWIXl9wwdzpxt+hgAAAAAWKHlF0OVPgEAAACAwjB5AAAAAGAlFEMBAAAAgFVQDAUAAAAAVkExFAAAAABYBcVQAAAAAGAVlr+aPACsU1mWwxdPp9P8kRDKrz+85A4BAGD5XqqP3W8/Nz/lioQexVAAWCZ1T4ZUQgEAWDnF0CXwtAEAmO7DH59zhwAAsHBtcaZXtCE7c4YCAAAAAKugZ+gSvH759O0LeoYCAADrZeJsAC5RDAUAAB6lO1OtKRqYjbonAJcohj633gdKqyIAAAAAwCXLL4YOx0d4SAgAAI/2y/f/ar8eTOsEAJDH8ouhSp8AADC/dhXdoih+/UExFAAIYfnFUFbONFUAAAAUZpYDiqIoiu9yBwAAAADwWCqhQKJnKIvV7Qcq7QEAAGC8ILNpCxGvf77w08W3Mi89QwEAAACAVdAzFAAAAADuo9cB2VjVaPQMBQAAAABWQc9QgnqpPrZfv468D4ALyrIcvng6neaPBAB6hklqt9vVdd1+W9d10zRFUVRV1X19ylYAGKEYSkTdSigA11H3BCCmVMccUVXV8XhMXx+Px6Zpuj8yvhUAximG8ijdSTGuWLDv9cunu4YDAAAEcumhXV3Xx+Ox7Sha1/V+v2+apqqqN7eyWvrTANOZM5SIVEIB4I5+/eEl/Zc7EICieKtn6H6/L4qiHfzeFj2nbGWdVEKBd9EzlPvr9gO95b7riv6kAECPGigQTVsMbef97L1hs9n0vm3Hxb+5ldX63PyUOwTgOSy/GDqcmdscagDA8oz3i/GIEYime6e22Wx6s4KO/GBva3cKURjyUBDoWf4w+dNA7ogAAO7MCEHgiaTa5W63SzdoqWtnGup+dgR9W/2cvlBSOdkd/j0EphIKDC2/ZygAwEoYIQg8hcPh0O3dmZY/2u/3dV2/q0/oCJ1g6DI8Auhafs9QaL1UH7v/5Q4H4Ey/ld4SEOm2sKqqs0tDjG8FgJiGNc30yqWOn+MdQqd3FwWAQs9QAMjlzZu37iRox+OxaZrefGojWwHg6UwcDi/fAXALPUNZkc/NT+m/3IEA/KU3sXXbx7Ou6+Px2M6nttvtUsVzylYACKtpmuFIiG4KG64On1LeyNbe+vIAMEIxFADyGK9d7vf7oii6tdHut+NbASCs1P1zv98Pn/C13xadXqLpi17Ku7QVAN5kmDwA5NHeBKYvhhOo9fq59PrCjG8FgLBOp1NZltvttn1lt9u1Bc2qqg6Hw3a7bZd6PxwO7TvPbp2+sBJPrbfwgzF/wHWiF0PbDNfqpsmiKOq6bu8hPQ9k3K8/vKQvXv98Qe4E8utmus1m05sVdOQHe1u7U4hCm/IAYjqdTmm267QSYG9rVVXpDcWF1ZZGtrJU05fAlQSBcaGLoTeuLAHjutnUQ0VgfimFtQ/5UlKr67p9ztfTZr2JyW74QPGS0+k08Z1kN6VTjJtA4CmcLYP23nD1VpYqJb6RwqgkCLwpdDE0uXSH1s4s006UluadkRQZ+vDH5+63KUG+fvnUeU0xFJhbb1hfSmH7/b6u67vc/ilxLs/0TjHFIPcBwHpIgsCI0Aso3bKyBAAEd3bcX3E5/Y2nRWMj1uNz85MBDQAAcJ3QPUNvXFkCLuk+JzSMAoimzXeqnwAAAPcVuhiaXL2yxGqlMXSvb75vRr2CozELAE3TbLfb3qqA3Rw3fMKXJocZ2dp7RggAAEBP6GHy7coSp9PpdDqlG79003hpZYnhi+U0D/2HzOz1y6dvZ8MM59cfXtr/cscCkEfKWWm26/RKOxd2+23RSW3pi97kMJe2AgAAcFbonqFXryzRZfmIvM6uXARAURSn06ksy+12277S7ShaVdXhcNhut+0Tu8Ph0L7z7FYDJlbu9cunX38I/TQUAACyC10MPbuyxPF4vLRkvNnT4g8//+X7f7VfB+++CjCD0+nUNE3Ka2ezXnpDcSEnjmxlbWRVAACYInQx9JKJK0sAQHxny6C9N1y9lbWJ/1gUYB5nZ0IzanDBDI8Apos7Z2jTNGVZ9qY/m76yBDF9bn5q/8sdCwAAsEync3IHxaMYHgG8S9xi6I0rSwAAAAAr8eGPz+1/uWMBQgs9TP6WlSUAAAAAALpCF0OL21aWAAAAABbppfqYvnjNGwfwbKIXQ4ubV5YAAAAAACieohgKAAAAkPTW47WOPPAuiqEAsEztnNpd1tIFAADWTDEUAJZJ3XMNTJcGwIK1aQ7gjhRDoe/XH166337443OuSAAAANZJJRR4kOUXQ4eDBPWUYUSvEgoAMZkuDYA16OU7gNstvxiq9MkUZ3uDKowCAAAALMnyi6FwhTQiI+/8a0brAwAAANyXYihr1ysyBpmYRqdUAJJeYnr98smIeAAAuJpiKHyjOyVN9rtNo/UBVm5YCc0VCQAALINiKABAaO2DuvSUzsQpAKyTmcSAu1AMhbgiTF0KAABPpyzL4YsW131qwwFzhtAB11EMBQAAYFHUPZfKTGLA7RRDIa40LjL71KUAAABxGCAP3OK73AEAAAAAAMxBz1CIwlgPAAAAgIdSDIUQVEKBu7N2xDK8fvlkvhQAALgXxVAIxNw3wB2pey7A6xdlUAAAuCfFUACA0DwqAwCAe1l+MXQ4SFBPmVB6w8Pd7wEAAADwIMsvhip9AgAAwNMxcTbwCMsvhhJWrxOoFYQAIHmpPqYvXvPGAQD5mDgbeBDFUACAQNpKKACsUO+JoInUgLtTDAUACOdz81NRFMYGArAqnggCM1AMBQDIzL0fACRGxwOPphgKb7DePQAPNayEWi8CgPXo5kGVUGAGiqHEYhklANYpjYsvpEIA1uTs2AgdUICHUgyFi375/l+Fu1MAcnAfCHCLsiyHL55Op/kjYYrOPZeeocDDKYYSRao8dn3IEsdAbzXDaMZLtO6lAQBYIXXP4EyWDWSkGApXGlYh5688vtlZ1YSnAGG5DwRgnWRAIC/FUKJoR0bE0QupO2TjbBWy++JDy46XSpxvTj0+W4QAjHMfCMDKBbwBBFZCMRSu19YT55xOdOLfMuEpYLq0+LqttFnSAABgBoqhcAcf/vj819Si57pkXnL1hKSpDpt+vJ1ctftwNW36q+dRZ0rWd0UIPC91z2fheRUAayYPAjNbfjF02C/GzSH3MnGQY+9tbcnypfqoLglAYvYSABbs0q2TSigwv+UXQ5U+mU2qcvbGOZ6reP5ZDH1oJXRkCp4UobWVAGZmnlAA1unNDOhmBJjT8ouh8DhT5vx+s+Ip8QOswfA+8PXLJ/OEArAeVkwCglAMhTmkiucdx4Dc0r3ol878oYUpRAFmZF07AADISzEUKIpvb8t1VgWYh/YWgHXyUBDISDEUntItY0x6P2uQJsA8jIsHgEIlFMhNMRTWrtsvyecSgAcxJwkAdBkeAeSiGArvc3u58LrfEKFMOYzBJxiAd9FsArBgtyxsADAbxVBgkgjVWID4Lt0Hvs4cBwDMa5gBB6MirCYPhKAYClPd3p2nt4z7n7/28X/3Fr0aaBuM2ijAkB4xAKxcuz6B+wUgLMVQmM8tqx5l4RMMwBW6rb2GFIA1S30pUjaUE4EgFEPhIZaU6dMnmNTd6c1+rMml/qQAi2fJeIAIyrIcvng6neaPZFUkQeApKIYCd7Ck4i8shvvA+Z1dMt4DIYD5yXezaaeIOZsEE6kQCGX5xdDhreBS82JZlpH/aesJL00M2o6RfFeHykuC772zldDuiJi8gu+94OHx1CKcWkHO8JnDuHTLt869MSJIJMIQBjxIkDP5oWEMJ8uWBIUhjAWEsXjLL4Y6jcjiqdfQuK6C2f3c81J9/KX6WHxdPdmoeWDZIjz4AYBcUkcQA+SBZ7H8YuiNuiWt14xxQD7vLew+dSEY4L1UQgEA4Ikoho55qT6OzHsCZz3dkvFdvT6bt5Q10354qb55sXdBDSsIUzqNXvdTd6GLK5BkbIgAYAbDG4HePGAAz0sxdIxKKCv30MLudX2pMvbA0vkL1ubSfaDWAIBlGyl3nu0wZIA88FwUQ9/W9vUwkS30dCsCZz8z9cqpw89J7fXVKy6M97q69FMziLMwFPBQwzbt9cunbiOmKygAy3a2K6gOQ8ACKIYCc3uzknj2DfetPxrwDpzlfg8Aim8fAX5dPOOvXg4+PANPbV3F0De7dt7Y93P8x2//68Jbani3dzqeP7xfvv9X8fVxcVmWP27+MzKm/rrwruj+OXEWv3fVVVd4cFm8keN+903xw+g1CK9f27ezldDUpLxUH38//vzhwi986r0RJIw4kQhDGCxPkFMoThg/bv4zfP1sEpzyEfrZ94YwhCGMlXj6Ymhd103TFEVRVVVd15mjAUZ1S6jT/ft/v09po9/bddSAdxZAEpzu7FQeZ2/2ui+2D1TSj/9iyQiAGGTAd5m45FEvLWacmQrgoZ67GFpV1fF4TF8fj8emaVJGBOZx3VKSw596qT6m0TdTPml9M4h18FOp3lpMWLn+dul3/t/ff0xfGC7EzCTBid5sqdzswWy6V9n//f3HjJHw1GTAZMpH8bNTYP/1zd9//PC1m8JIEux9yi3L8tLwCGBEe5XJgNk9cTG0ruvj8bjb7dKTwLqu9/t90zRVVWWODLiH7ke3l+pj6k/63vLrrz+8tMXK8bddESFkJAkmI21Ce12/FkXx9x+LCROAjtzs9bq0l+XP7wsUgDtZWwa8lOl6rw8/8XaT2ufLFc9LP6XiCSzYE08QUJZlURTd+Muy3Gw23aeCvRkQ3ju5Xq+316Pn5sv7+4X3vD++jPBGKhq/H3/uTWbUXdqyt3Xko17babT7zusG1/fiT0/2zvYMXfzBJZc3k+CckwpN39S90n8//nzFTz1iUaNL3bpj7sPFbIoTiU3zbOol+uG13L0SowVPKFfcBvZ+PGASvLTppfo4nvium/dp5Kem3PzaZJNN7900/DR79uY0ZvCL9MQ9Q4ui2Gw2vW/b4RLAcxldf+maHljdT3JnJ4Zv3zbMUtfVSWFmT5cE+3d0l3tt97q3vJ590wTtp8zxwqueLxCBEfRM93QZsHgrCX6T6Tqb3syA3V/y3llfeh+DgTkNnnO8b10NbvTEbV9Zlu3giCTNHdP9F92llqFnaITfv+zwlv2vCxher3PKpZ6n6W3DrT2/H39e9j2buVBjejMJLriaP6Uj5/Ayj/Ag3aaYkdiv6bygAAAKgUlEQVSUfVPk9koSDGi228A40nnYGxE10r1aErTJpsibnqWBWnYGfNaeoWdnyO5OpH1HZVme/frNN793a/bfL7zn/fFH//7Fh/f78ZvOp72+qL2tkN2cSfDu/v2/32/9DZev6JGL3aaAm+JEYlOoTct+xMiNnjoDFtcmwW7iaz+X/nv0bT3Zr2ubbLLpElkvi2cthk6cHvsulWyj5wAIZUoSDPssV1YF4Gpz3gY+giQIEMF3uQO4p7PPCQFgDSRBANZJBgTgXZ67GCrtAbBakiAA6yQDAnCLJy6GDhcNPB6Pu90uVzwAMBtJEIB1kgEBuNETF0PTAoLtrDHpi+6qgo/7u1VVVVU1w9+6QvDwuqqqivBQ91n2WJDd1RN87wUPrxXw4JYDwffhCk1JggGPY96zPUibECSMrvmPS7Sd4MyME0Yr40EJ2HjSNfE2MOBx1NTECaNr5uMSbQ84LeOE0ZIB53B6Zr0HgIfD4aF/7nA4pD+02Ww2m03AHTgM79H75GppZ2YPr91R7a7LG88lQXZXT+TzLf7V2krh7Xa73IH8pd17XaEiJBlPggGPY8azPU6bELDlnD/FREu+eZNshFMizgXSitBcxGk8GXrzNjDgcYxwVme/xiO0eD0zpwAZsCvC+RDn6mhFaCvitJyPE7Q68C6Hw2Gea6Z3YaQTJc6Zka6Z7q6IcCUPHQ6H9uNL3uSXwmiPYPo2ez7uibO7eoKfb8Gv1labb0LFlqLKHQVTXUqC0Y5j3rM9SJsQreXMkmJCJd/sSTbIKRHkAukFkPdWcP6/yxVGbgOjHUdJ8BSmxWvNnwJkwK4g50OQq6MXgAw4g7X8O+9ieEYWAR7mtIbBpNYtUzgXdZ4xZK7uDVvbUAc0ibO7eoKfb8Gv1laKKloxNNSh5GrRjmPesz1ImxCt5cySYkIl3+xJNsgpEeQC6f31XM1FtMaT60Q7jpLg2T+6tiQoA/YCiHA+BLk6en9dBpzB3womOxwO7dw0reEruWw2m95sDtFmIUxOp1NRFE3TbLfb3LEUw3EKvenYswu1u7qCn2/Br9akLMvNZtM0TVmWuWP5Rnso0xfR9hsThTqO2c/2IG1CtJYzV4qJk3yzJ9kgp0SQCyTJ3lyEajy5WqjjmP2sDnKNB2nxWllSgAzYCnI+BLk6kuxtRaiW89EUQ9+hPRXSmZFajTizyQ7bjmh1vYCWfXk/VPDzLfjVWnwNJlQFuaebg1NWzhcL14twHCOc7UHahOAt52wk31aQUyLIBVLEaC6SCI0nt4twHCOc1UGu8SAtXl4yYCvI+RDk6ihitBVJhJZzBk+8mnxG2+02XSS9qbvjaB8mnJ0Bl+JCKyM5XSfy+Rbzam2aZr/fB9xdSfog0g7NSI+sQ5WSmSLIcYx2tsdpEyK3nI8j+Y6IcErkvUCCNBdBGk9uFOQ4BjmrW5JgRjLgiAjngwxYhGk556Fn6DfGa95tU3U6nZqmSedrMeNzg4nhVVWVTuKzXb4famKEEYQK5qllPN+myHW1jttut5vNJuDuSnqHsmmaqqr2+32QvUdrvMmd7Tj+97//Hdn6z3/+c56zPUgGj5OpY2bksO1edkGSad6kGSQ5SoLPQhLskgSvi2RO2Ru3sGTAQgbMQTH0G+nsP7upqqruaZG+TZfKnMXQ8fCar1N+7Ha7LOfr9B0Y01J7gD9I9vNtoixX64gUQ1VV3WCapqnrOshlcnbenOPxmNJhhoC4YLzJHZ7tDzqOv/3222+//XZ20z/+8Y9hMA8624Nk8DiZ+oky8sqTb7RkmitpxkmOkuCzkAS7JMH3RvK4P/0uMqAMWMiAuTx0eaYlORwOwzXFQi22lfpUB1wv+6wUbfbV5Hu7K01onSmcMRF2V0/k8y341Toy8iLUIe6Ks/e4xfzHMcjZHqdNiNlyzpxiAibfjEk2wikR5AIJ0lyMxJY3Bm632rM6yDV+itHiDc2ZAmTA4Z+WAU9h2oqR2PLG8CDmDH2H4XQJoZ7kpIcqoUIKbrh43/F4zD6BzrMIfr5Fvlrruu41xMXXmVkiPHBrmqYsy7B7j4mCHMc4Z3uQNiF4yzkPybcryCkR4QIJ0lwEaTy5UZDjGOSsLmJc40WYFi8jGbAryPkQ4eoI0lYEaTlnY5j8VOks3O/3bUfluq7jNF7tOTrszh2hw3lMdV1vt9vUE774eojtrimCn2/Br9bg7L1lcBy7guyN4C3nbCTfVpBTIsgFEoS9sQyOY1eQvRGkxctLBmwFOR+CXB1BrG5vXNmjdK16ey9OJ/+Rdcdyh3ZekHHfvQs7ezyXBNldrac438JerUNFZ82+IHp7L1p4TBTwOGYMI3ubELblnD/FREu+uZJsqFMi+wVyNqQgzUWExpMrBDyOcc5qSbAX2GwpQAbs/t0g50P2q+NsSEHaiggt54OUp8G/lnHtQ4wIA1q5i+6jOZbE1XqLNPF8qDnmuYLj2KVNCEXyjcYF0qXxXAbHscs1HocMGI2ro2slLadiKAAAAACwChZQAgAAAABWQTEUAAAAAFgFxVAAAAAAYBUUQwEAAACAVVAMBQAAAABW4W+5AwAAAAAAblLXdVEUVVVVVZW+bZqmKIr0f1rl6XTKHQMAjyUpArB4kh0Aa5YS3/F4LIricDhst9vNZpO+LYpC9a9LMRRg4SRFABZPsgNg5aqqapqmqqo2G6ang+mV3W6XnhpSmDMUYPGapmmaZrPZFEWx3W4Ph0PTNKfTKb0iIwKwAJIdACvXjpAoimK326Uv2leMk+hSDAVYOEkRgMWT7ABYuTb3jXxNohgKsHCSIgCLJ9kBABMphgIAAAAAq6AYCgAAAACsgmIoAAAAALAKiqEAAAAAwCoohgIAAAAAq6AYCgAAAABPr67r0+lUVVXuQEJTDAVYBUkRgMWT7ACAN/0tdwAARNE0TdM0RVHUdZ05FAAAAO6tLMveK4fDYW3PEfUMBaAoiqKu6+12m+qhZVmmqigALEk50M13TdN4HAjAwnQLnXVdHw6HzWZzOBx2u136Ym2V0ELPUIDV6uW8/X7fJsJUGD2dTlkCA4B7Gd4B1nVd13V6+FfXdfcN7QgJAFiSzWaTvmif+VVV1TRNVVVtHmw3reG5oGIowHq1SbG990tfVFW13+8zBQUA9zTlDhAAFuzso76UCtuvV/U4UDEUYL16NdDuM8D21hEAntqUO8CUAY/HY/G1M2mv0ygALEbKd13pMWGOWPJQDAXgz66gvXnTskUDAA9z9g4wFUPbsfPFYDIZAFiGqqpSx5dUAG2HBuaNamYWUALgr14w7bfb7TZjPADwCMM7wPYmsL0PNHwegAU7Ho/tY7/j8bjdbleY9fQMBaAoiuJwOGy323aq0MPhkDceALi74/GYElwaEmG1QADWpk18VVWtNgmWq/2XAzC0zlESAAAArIRiKAAAAACwCuYMBQAAAABWQTEUAAAAAFgFxVAAAAAAYBUUQwEAAACAVVAMBQAAAABWQTEUAAAAAFgFxVAAAAAAYBUUQwEAAACAVVAMBQAAAABWQTEUAAAAAFgFxVAAAAAAYBUUQwEAAACAVVAMBQAAAABWQTEUAAAAAFiF/wcdkH03Cz/ZXAAAAABJRU5ErkJggg==\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": "deluxe-michigan",
+   "metadata": {},
+   "source": [
+    "## Particle Phi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "anonymous-discovery",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7x2GqNCAeBEMBAEhTNuToRgEAgFFZlo2FzKqqktmorbXPTBXdtu3E9wH3WQZbIrOgyJeKsxQrXKxt26Io9N2uqqooCl1bFIX747SHNgawNSZQAoD1EXJCDCZmkweA7cTWCf78zVtza5X7GNfx8vn145tXY8zL7wsinRd+IsRpre26Th53XSePl4VEJ/Zy/zS6riuKomkaN+IpwUF3m+3mej6FPM/nb6m/QaTtpF3M1fpKMkMBYGV9QuJ5OUf/VgEAs2zaF7zNP7zNP4SPxc/fvHX/eT1I+PiurV/OfIf9OpPw8vn16CbMUlVVXddjqySO5n4k6rp+dNi1JHWOheQkSJrnuTxF0zTGGC+ZUX4sy1K2KcvSpB4xmdD3/UPD5PlbvoLtOgKv1ws7wSe57d/6tTxko99U+sFQRggCAAAASXpnv5d/H9+8O7otiIt39feX//7HX/77H8c2aYy0cywSaoyRVW5QQCKVD2WGZllWFMVEcqI8i0b3rLUS69RnkVV5nrvDxuenRpov628+Y0H9zbu7rNu21WuJTm9GQVLgUekHQ3eLKwMAAAA40PvfPr3/7dPRrUAUvKu/mK8Em6Ypy7Isy/mBRRm6rpFNKeLphcO8TKDyZvCAGuh0F0rQU6O08qMXgZWWTIdlpUqpRGPlwYLIXZZlVVXJoaT+piyZs6O7i/fUz7TNe4fdtoXNCxOzqqpyG+Y9taxt29bbzGue/Op1bfJVXOHRO4IUwnpU+sFQAAAAAIn51P7g/ju6OcByMiGSzFwUrh0MU3pLZBt3SLscShJIRXUzGHKVI8wJpXnbyI/TSYs6uF7CvtLUsdmZJsKRdV0XRZHnuUZ167qejodK/DHP88Gnfqht0/RQ7nNNlDLQqgjy1PJLCWdYks285ulaKXogb4gcpOs64qHAHEygBABAmrxbxAQLAAA4qbEIl5QBNcY0TVMUhUwB37Zt13VlWa4bFxscYn/3KSRYqRMxSeRXkhkX5OqWZekO0sSpPC4AACAASURBVJfyAmPxUHlG3cVaa62V2eHd8LHbNtlgQdvkgO6hzC0YOvgWeaUPJAO0rmv5DepmXdfpNrKq6zo9pvxG3NhulmVM03QR7hd70kIXIDMUAAAAAIAYzcxStNZKYmBVVRLjWzDXfBi2mzlyfyIAJ6mL3pHlsAsSML0X5VU1HWyVu1beJXmsKbdu29wNFvDGufd9P9g2WeiVLJCF3jvpNeZuHm7M5SCAqBAMBQAgTYweBQDg7OZnd0qMTFIO3QHyz3gyzdBNWnQtO1oYprz75oS7aAPGKgPcDTgO0lRQSX2ds/uc0PNgUQJve62m+lCDgYsjGAoAAAAAQLzGQl1edEyTDZcNkJ8OqE0UGx3LppS1XdcVX3LHd8ugb6FzNOmS6SZNBC5nVkGdqD8wvWN4HAlAd10ntU0n5nea/m3Of+q2bbU4qU6gRFQUmINgKAAAacpu/tH9+I/ux3AaUwAAcFKDOZvezO/zPZp/+tBhy7Lsh0gjpQ6mMsa4P04/3d3GLN5gQTTZWiuvqyxLiQ5Louj8gy+rG9D3vU6vJHHnRw8CXBDBUAAA0qQXG2/zD2/zD5SRAgDgdNypcuZsKfmJGhV96Fm8vbysz8G8xTkJmGGMzx0sL+E8IY1vmsYLmIrwTZh49rH3TRJOx17O4JI5vFfUtq186Zr4xYVPJBsvCExLUm3f9/LLWlAuFrgagqEAAAAAAEQqnG4onIGnqiqdQV5Cio/mNo49izt7uwkCpvLj9HzuYUBQBss/1Dy3DTOffXCaJm3MYNskTdU8/u7JvFUzA6mD7+R0wYFQ27ZFUUwXFQUw5g9HNwA4k3f2e/dHpiXB4bzPpOFjGTG5aR9+SdWa91oqS4Wj2suydLeZ2BcAAKRBAm1FUTRNIz2+FwRs29Zd4s4sP//rQfgsYVhQDusW9zTBrOiepmmknKUEao0xEgad3muMvEz3OBPRQ31F3lPre1KWZV3XWZZJ+FjfxgXTT1lrZYi67ivPMta8saee//vSYK5+vVxwEFzBxzfvvCXvf/t0SEuikn4wNLySZJAglgmjTsCx+EyeiCYaeOSrszyWwljzB2dN7/vzN2/1q8/L78sIlAMArkIvA3/+5q3+KI9PR5I9vWxK96pWlrshvLZtsyyr6/qh4Nr0s5jbZEduYUrvNu3EYeu61lzIu3sN0kKcepw8zye+OI09tYZ3NUPTfcnuBvPJ/WmvZOdE8wafWsLQ8580fHVmUSQXCQsjoRDpB0MJfWJdknZHEArx0FRQPpZxkhjlYOkuHdGm6RV1XctlhrmNlhr7Wjy9LwAAF6eXgRILkB8jjwtMJHLK5DzypUKrXqrBa96xC+Hp6OHEs7i764TvY4d69LC65XRAsL0xTvXPZ55a3vOxDWR399ndtWGkeKJt4a9j+qkHPwxee9xXN/ikgNBU0MjPgXtKPxgKAMCBJkpieUOZJKCpI98n5gS4u68x5i///Q/vOhAAAJzaPtGuOc+yIK9zZuPnxDcffRPu7rLWG7tF23Y4AnBBBEMBANiQRCSlyH241qskJaW45LGGNceiohP7PspNK6bsLAAAAICERT2bfDbCm+FO7oRQJBgAcDp37+RnWSbVu7IsC4duPXQoAAAAAMC5M0PtE/NOAABwoMEOy+vXjDPDgAyEt9bO7+kG5464rfrR3VLSVyk7C+AivOIhzKt7FK3uEqa2hLPgelPuTOyLJOV5zi8amOB9k2ew17Sog6FhjWFJe5H/MncEAOC87vZW3gQCOkvp/KcYnDtCvifx9QgAcCCtHiM1Xry8lru3/VbMiXn5/Prxzav+SGQ8WqQ9AVhR1MPkPRL91JNgOHeEWVTIGQCASLhf9MNoqXVmmZ/eFwAw6P1vn9x/RzfnuiQSKhNh933fNE3Xdd6lXNM0vcO97pOcGFlelqV7kfiQl8+v9zcCgLh9an9w/x3dnHOIOjPUU9d1WZbukhXnjgAAYH8LLt40SJpY9JOhPQBwKe6VnXe3b2zmQBHmxNR1raPmF5CwuFc8AQCQsNMEQ3VofLhwprB7424wHqXjaF5+X8C1OoDlwnt4kupibuMHvfpo7mXe4L7ePcIToVzpYuHgULemEKZNB1wAbGTwT8/t1+5GRS+eE6NVArzvCZfFGwLgUecIhkqVtKZp3CXhZm7tGCW1t2XuiMFVIqxPCngYRwNgXVVVFUWhcyK5t/2stXmey4xJXqXsu/uel2SDEhh9SNu2kiSl9McIv9tsEXx85phFUeR5nliSNRA/zQN1/3LHruOE96fq/ckPXgamSl67XBoXRcEcwrwhABY4R81QvTLUJfO/8kopGXnslQdya9Cs11gkjgpTANZirZUqaVmWZVk2eNuvKApZK7ViNNw5uO/qCW4vn18/vnn38c27l8+v3BCKWf8lCZpHmPBYFMXqIXudhgUwxryz33ND5Sxk8lt57J0ZNMtPzmmS+CnbzI9zZTfuj4OrzkXeCrlXKt8Ejm7RwXhDACxwjmComwszgVtAAIA4WWv7vh+cE0kmjpBpIrwNZFVZlrI2rBUzse/ziH6el3xUuCAEEC3N49Mbfu7Id6/Xa9tWRkuYx3NiNOslfHzShJiyLCO813Ug3hAAC5wgGDoxTTzRTwBAAnQs/OAqyXdYsO8q5qTDS/ao+2+79mAmr6Be27aZIywzqqu8MrVje8mP7o7eCFZdrp9PScKSdGZjjHyw5Sm0zqn3fc897GBjrLUSH3G3HHx2UVXV4CtFMphI9yzkfl5548UlB+8dmvGrv5SuCj98/2/6769/+3u4gXvuklof+zVunrZtq5vFv5q7o911g/jfEAAROkHNUO8+oZqYdwIAAOzj4qHPPV/+/CItUmzd/VIk14d6g7koCq2rIKFJydKSSZklwi7Dz7293ICFzPEll6NFUehaKd4nqTpS0lQuiZumcQ8ojZSDSEsmUlm9xujTyZHdMhHhs2tpXXl17qqZ7yeAjbhhrDnJ7BokTSn6qT58/2//+eN/hMv/91//z5//5//wFsoJbYeqx1mWza/DE9YtkTPtgnmNvHN7uNZ7+bu9IQDScIJgqBkZDZHk3BEAAKxFq6HJLILy49v8wxbPpXG6i8dGjxJOQqJxQxNkVEmKpSTsyDYa4pRVkvLplefTFE5dok8h831pIKPrOneV91+vtTOvsb3GSFxV2inRT32NXmDUODNKyXWyNsxaS7FR4ChZloVxK7mFIwE1L3zmbjmYE/NMPqDUmX1ZvP8atMv+lw//+sc/fmuM+fXXX3795b9+/fWXf/7zP3nxUL2JtfXw8IcuriUcaYzJ81yHrcidJ6kP+1CYUs7tY7ml8kTavN3eEADJiD0YGk6dpKRAshSakSXuvBMAAFycRrgkQCk/MrXIuiKcUk/CBG6kIAwTaCghvMjUj0044MYLQLhfz+yXUznLFexEpaPBg0zwGiMDbMPNwpnlZRz94CQtXDMngLsv56VzIkm0S+5MuPdXNEvd3JIE9SSQXk7Mt9/+yRjzxz9++8sv/6ULJfop6aL//Od/0pPeYBx5dRK7nJ9Brxt7UWy5tSYzPWq/MIeEVqfzhXV8AwmhAB4Ve83QqqomJoWwG88dAQAAEL/2SzrmXTeQSp3Ky+Kc+SzeF62x710y9XNd1/uX5pSLYfeVSoTFLSe6W2OwNSKhp9be5kTSv1M3r0X+Zt3pldwQm71NGq4ntCdTAo8tNfvXv/39119/Mca4kVD14w///i8f/tXcAqZa9MM956/eJPmlPFRLRH47Y8Ph5Zc7dsCxlyDdU3hAWSJr93lDAKQn9szQOfheCwAAoMKZRsYuUMPRphPmX2HKljIMf8HoyEefTkmC6kTSqIysf/SwiFmE2dmYSUNXmgHq6vt+Yq3kxITJ4GckdUL/91//z9gGP/7w7//5439IwFTDxO4Ggye9Z2gS7vx4qHQlY3e/JH4dLtchnsILaksKcNgXuGPk93lDAKQn9sxQPIOJfZEk5q0GgIdI+pW7REuChkEEdwZ2b6+ZaaTu2FVJ/pofb3V5F8Dej4M5p2EUWKOfg6sWtArAiuRcNDEKcGKtuRX/XfC8UX2HlChnOEWSS6qI/vVvf5dxk56xvaxT5fkht6ngq5lDB+RZpjcOf1kSCc3zvGkaCb8WRREWMxnrQbQ8wvw3BABU+sFQGT0RPk5eJL07sC4+2ECqXj6/yqXpy+fXl8+vRzcnBXoB6dXulOp77iq9QHW39Mbaz6/KJ3PEy+O2bedHQiVsqpfubihWhsTebYwsd5+9KAptgASF9fjMngTgLP747Z/MLWw6n5yNdyhXsiBFVzZumqa9TdknQUzvJlw4Ul7H4z/ZZgAXl34w1L07dME7Re9/+yT/fvr6u5++/u7o5gDr0A/20Q0BsA6in6tzi6lZa8uy1CKeXvU9t/SerNJUSncvmb1kzrWuRFHdkp367UvCnWN3pqVJWiXQHVMZNkbX6gQa8mInnl3SVPX4eZ4/M/00gMhp7WD3R7km0m+Sh3+f/Ovf/r7dwfu+l6ChnDy3qycwln+aBXRLuU3lNUla6x5N+gU3QppGbQQAh0uhZigAAEiAXJGSAP6Qscl5vUtTnbLZDM2DNFZ6b2Iv7+6y2wz3gN6ObvHQ8OI5bIn7LGON0VJ0GsMde3a3AVxIA8nTE4h0K/LjO/v9kW0KTI+Of56eOWVMgNwHmi4+sIDUaw6XezecdBsdVj9YDsUt7hy2czCKCgCPIhgKAABwCdOl9xasWvBcd482vcHg2nDh6i8HAA706y//ZW6VQxfQ4qEyqZ2XNb+W8BaXl+PpBUzdmioTZFiAljE1jJEHsIb0h8kDAAAAABAhCXF++P7fJraZM8nSHNbaLcqDSIxyukJ0GCrN8zyc+6jve2+4gztSXkO6a7UcwGURDAUAIE2D5dKObRIAAHD9y4d/Ncb854//4S3/9ts/ZVn217/9/dtv/2SeSAsVMvZc5pQry3L1tNBwpiOPGyody9B3S6kMrp0/Ix8ATCMYCgBAmjTJwv3x2CYBAADXn//n/5BApwQ9lQRJ//nP/yRpob/88l/Ljl9VlUwop2HQLTIrJYgp49nDtYP3Yruu80KfRVEMDpyXcfGyijHyAFZBMBQAAAAAgGP88st//fGP3/766y9Zln377Z8+fP9vf/3b3yUGKpalhVprsyyr6zrP86ZpNgqDKp253lorszbJxE0SCfWCmDLxXVEUuqWkiw7GOt1mM0YewCqYQAkAAAAAgGO8s9+b/++P//LhT//543/8+usvYRj0119/+fbbPz2aHCqpoLtFD2WSeklB7bpOqnwaZ9Z4XWKMsdY2TVMURV3X7pZjrc3zvOu6LQqeApH7+OadPHj5fcEPKx7TGPP+t0/PH/CMCIYm7p39/ugmANjQy+fXj29ef3/8+7IVOkgAAADs6ccf/v3HH/5dc0L/+MdvdcakD9//W1hU9K7na+NMVPAcZK3t+94t/ekGN732uBtba8cKiS5rCZAGN2qJdaUfDJW0/J+/eauPqZgGIA0vn1+PbgIAAEB0fv7m7UmDCP/r/++MMZ/aL+5tS5z0oBY97G5kc/HGwDVJ8uYq5zQ3D/SkJ8m1pB8MldCn/JovGAb1+lEA6dEu7eL9GQAAT/J6UkmnAAAAiUk/GAoAAAAA07inmJK//Pc//CVZZox5m384ojkAgLgQDAUAAACQmvnBzcGpJIiNnp0OCpRJFGTA3A4TKnhPwUA9AIjQV0c3AAAAbCK7cX88tkkAACSM2WsB4BTIDAUAIE2aFCP5Tb8Xv/t93i0SVQAk66evvzPzMvLcqSSAteyWhQoAWIZgKAAAAAAAABA1r4QLt/QWIxgKAEDi3CQpquAlpm3btm2ttdbacG1VVWOr9iSNlMfPtEcOcvjLAQAAwKkRDAUAADirqqq6rnOjjcpa23VdXddaMGExibcu29erVFvXdZ7nYWtnNmOVlwMAAHAuXgUY8huedK1gqNZteTm2HQAAAOvpum7mwgWqqlocgpRIaNM0GkuVo1VVVVXVKs0DAAC4ODc2eot3MUPAlPRnk3cnz/1H9+M/uh+PbQ8AAAn4+Oad/Du6IfidF1uU1Ms8zw9pjJAmuZFQXVjX9TFtAgAASAtfyBdIPxja973mMsjjOTNLAgCQMA1lLgto3t3lnf1e/y1tI+bK8zwceD6Yd9m2bebQXWS5jIXXtXocCVxmWabHrKpKN5sYPi8j4sMNmqYpy1J/dJ90sFVue+a8HAAAgKt5/9sn+Xd0Q87hWsPkAQDAWviyZZwKPDuYuJtbVVVRFO6SruvKsnTjg23bFkWR57nENIuiKIrCHfxeFIXsIgFQa608NsbUda0JnlKKtCxL2UC3HGzYYKjUnUNJap7K0fSFeK0ajKjefTkAAADAIIKhAABcyD7F1+X4pIXuRiOJEhmU0KRMJa/bSLRUo5Z930uyp+Z7amCxqqq2bb2So3ooiV3KlhLW9OKwYn6epj6vGYrqjs22dPflAAu4Zy3mGDg7L6M8y340xrzNPxzUHABARAiGAgCQJq4DdxBP7Z08z2ViIjMyRt4Y4w5ON0Gc0d1L0j/DI8j2g3may6abd59UE1HHNvBMvxzgUdy/SYymistvlrt0AACVfs1QAACuqb95m394m39wi2gjPW7QUJI35+z16IzzEm0sikIrdXoZmmo6NirJp7qlHipsz8RxwlWPvhwg9Kn9Qf4d3RAAALAVMkMBAMAsTFUZMx0prw+2e675UXVNVnVpTVJzy1/WcfdSCXTmwcN01DzPZ+4LAA95+fz68c2ru4TC2QBwXmSGAgAApMAdKT/Iy9/suu7R6KEEH715mcaSNyU7NWyPTE+ve2kk9FHhy1lwEAC46+Xz6/2NAADnQWYoAAB7sNZ6E9oIHS8sG6y4dkXetEuIk8w+NDZGvixLCZXq3Efm8QRS2asoCplcfjqRUz6fGvp0p6dvmkY305imHm1OBdLBl0PNULi86pCcwfAkTQWdP06CfFIAkbvswC+CoQAwF5dVWCycm1u409R0Xde2rRvNeWYtLkgDiIMhTllY17VEJ40xZVnOmfXIWlvXdZZlksIp87a7MVA3sumRsKb7pN7zSkxTJ/tqmqYoCgm2TrdKI6165Lu7AMCeyCcFgGilHwyVr9c/f/NWHzN9BIAFmH4Uy0iM0o0EqaqqJIlPZwCv61pz4p5Zi+vwIuDelxxvreRRhjPCW2u9HTXjUte6ny75UdfOaeHY9mGT3OeaaJV3ZD75cHl3K+nBcaAF+aQAsIOLJ6qnHwyVr9HS9xAGBfAkub7isgrzTQwilgipBnckoKmBoWfWPkp6yZfffyLlOXHL4obeXo8eZHr7Zw5OGBQAAAAPYQIlAAA21Pd93/djA3i96WvyPHdH0z+zFgAAAAAQSj8zFACAaD2aLueGO6fXzuQOkGEEHwAgGVqM+Pbjj8aYt/mHg5oDAIgIwVAAAA4wOJ5dA5qrTIU0eB0YPjaUkQEAJEe7NqluRKUjAIA6QTC0bVupiWat9WZH9S7zjDE6lQQAADF7KCd0mYnrQG92kUOQiAoAAABgf7EHQ2VGCGNMnud1Xdd1rZd2q2TNAAAQj+mu7Zm1ZnY6zD4xSiKhAAAAAA4RdTC0bdu6rjXZs23boiiste71XtM0zCIKADipTaOfB5o5Pb1WLJVA7fvN2wUAwBS9V3e3CwMAnFfUwVCJgeqwd2ttWZa6Vi4CiYQCAE4qnP+96zrt6QbX6gzy02s9MwfF//T1d+7GBCgBAJuqqkqv6cJaZ8+sBQBgQtTB0PC6zu3nNCOGqCgA4IyqqnJHPEhHpj3dM2sP5EZUGQsPABgkY/7M7d5e13Vt27ojHnRGQWPMo2uX8e4I3u3C3A10oAMA4BS+OroBd8iNPmttlmXeAHmRZVlRFEVRyAYHNBEAgEWstU3TdF2XZVmWZV3XNU0zvVZ7uum1AADETCKhfd+3bdv3vfRo7g0/GSrR933f92VZSsRzzloAAO6KNxgq/ZlMmmSMkU6uKArt5+RmoPaCclMxTIqRq0T3cfhjOCs9AAArstb2fR8GK2V50zRN04QbPLMWAICYuQXQpAvTCz25AHRjo+6P02t38P63T/pvtycFAKwo6mHyQqePr6pK8kBliZcC07attbaua68jlI1lFIMeyvsRAIADTccxn1kLAIl5+fz68c3r0a3AUwarnHm1sL1qaQ+tBQBgWryZodI7ev1ceP8w3IUhEgAAmC8HQDASAsCJfHzzzv2ny18+EwZNweBVmxfNfOhGIPcFAQAPiT0zdEHHRl8IAICJcgAEUyoBuOvuiYKxyWmo69paG04AOJjaojMmzU988e7/ZdmPg49NlN0lAGBT8WaGGmPyPJeKMMqdQT7LMm9EPDmhAABEi0gogPlWL8j4zn6v/1Y8LJaRCQNlFtwsy+q61hGBaxWH6W/e5h/e5h/Cx+qpVwIAOKGoM0OrqiqKQieR13kDjTHWWgmVurcTdS0AAIgTKV0AAJkD0J0EaTrKOZ31Qk4MAOAhUQdDrbVlWdZ1rWMc8jzXLlOSQ4ui0O3LstxzGkEAAAAAkfvU/qCPSQuNinvt5tUMJfoJAM+Q/u7l6GZEK+pgqDGmqqqqqgYnHDTG9H3ftq3MI0+pUAAAAACIX5ZleZ57MU0d5BfODu8OARxc6827CwDAhNiDoWIi0EkYFAAAAABORAKamvgio/3cUfNutTRvkqXptQAQv3CYgjuIYRVywI9vXtc9bDLOEQyNhzf5A4XPnhROphHDW8pvGWNePr9Kd3IbbrByjwUAAHbgXYWufgmKu2RsX13XOl+uTKkkrLVN08j0SjPXkh8D4Cwo2BKD9IOh0kf+/M1bfbx4xkCmwV1XnO9nnK1CDF4+c1cNAIDVcPv54tqbwaF+MsPSWLW06bUAED+9D/dMbJTwxWLpB0Ml9CkfkcVhUJd8UeMztxb94hvVW8pvGWP4bAAA8LyjelIvCZT0nGPdrXj2zFoAAMakHwwFAOCadPyga5X7ggCwCm4xAgCwAMMpnkQwFACANBH3BHDXDnM4KIKeAIDLYixCVAiGAgAAAFe054XZo5FQadvL3e2AEd7wiCz70RjzNv9wUHMAXBqR0NgQDAUAAACua5U5HGZiWB92o8MjiEEAiMR2Yy/wKIKhAABgQwyMBbCAXDF+fPN6dENwekQfAGAOvXV0hWEZBEMBAAAAAACABHkJ8oO3iK6WRE8wFAAAbOKnr78zpOQAAAAAZ3CdYRkEQx+2oJr7nDA84PEGllJjK0n8lgEAl3Wp4XgAABxlz+LgZ5F+MFSmEfz5m7f6WGtp74NPGxagxN4V8FsGAFwNX4yRJD7YAE7EPWVdNlfvq6MbsLm+7zX66T5e7FP7w4KPy7K9cHHvf/tEqmDy+C0DAC6Lb8gAAGB/6WeGAgBwTTIewrPz8AgAcMUZ+iStD0/a9INNVSUAa3FPVhfv+wiGAgCQJuKeAAA84/CiRoc3AACSRDAUAAAkiFQaAHfFmakKeKQLIzAKAGshGAoAAFLDFSMAXNyTtWJ++vo7cwuXy2DS9+u1DQBwLIKhAADgKdGWHCKVBgAui1oxAIAx6c8mDwAAthNtJBQAAAAAQmSGAgCAZ1F3DwAAAMApEAxFXNwMIy6txzArCAAAAAAAz/BGOKUUgqBO1LT0h8lnWabFs93HwElxUgMAAAAA4BnUerqy9DNDpXK2xI+ooh0/d8ZGTGBWEAB3PTmRLgAAAJC22EIQz1/jM3J0jvSDoQAAXBNxTwAAAOAsyHbaDcFQAAAAAAAA4Hikdu4g/ZqhAAAAAAAAAGAIhgIAcKyqqqy11tqqqrxVWcDbZmJfAACuLOxDmUoXACAYJg8AwDHati2KwhiT53nXdV3XtW3btq2und7dWtt1nTz29gUA4OIonA0AGENmKAAAx5BIaN/3bdv2fd80Tdd1Xo5n0zS9Q9dWVdV1XVmWsrwsS4mH7v0aAADAuHf2e/m3w14AgJnIDAUA4DBlWepja61xEkLlgSwM1XVtjHFjo3VdV1VFPBQAAAA4nDs1/Mvv//9hzo7ejZBP7ay98BCCoanRP5uX6e1iJeeLh84U8XNPguaRueHkt3nSXyVW5H2EDDMMJmEw1inj5d0NxraUjcf2BQAAMdAoxsc3rw9t/9BeAGITXsEhKukHQ6VO9s/fvNXHCZePYSRFhDgJ4kl8hFKleaBulDOMZrqzPeR57iZ+euFRt4QoAAAAgGNpCstD13R6U4QIz3bSD4ZK6FM+eQmHQV3yl3O6u4huplt60R95dY++rpP+KrGFZf0o4lfXtUwHb5wx70Iim2VZynKJdVZVNX8s/PxpcyPsHxfn1AMAAADAhPSDoQAAxKlpmqIoZBol4Q51b5rGzf2UHFIpDDpWSNQTYYjTxb1uAAAAAPsjGAoAwDGstd4E8W6UM4x4SnKoN7JenWvqpIlI6E9ff+f++PKZ7HgAAAAAqyEYCgDAkdzR8XOKfmok9FzRz0GDk2MydwQAAACA7ZwgGNq2rZZL8+qpGWO0dNrgWgAAopVlmTcnkjGmLEtjTNu2RVFowVDhbhnOHd91nTe/fCS8JNDBACgAAFf28vnVvflHpWwA2FTswdCqquq6NsbkeV7XdV3XbgU0d+ZcGTmYQJoMAOAiJKCpcyJJ8VC9/ycdnzu9Utd1EiqVH4uisNbqHUETTMEEAPtgxjNEaHAWwTiraVMQBjivcHpbOsFTiDoY2rZtXdeaFyMXinrhp5eFslbCpmOV1AAAiI3OiSS3/YwxTdO4a7Msc6dXchNFrbUy/5Je7HkTLsXASwJlxiQgSeF1IBCDOOOeEySAwh8UcCJr/cF6ueF7uuz386iDoRrllB+ttZoRY4yRS0d33gmZY5fkUADAWbQ3mgHq6vt+Yq3Mv+Rmhu7pst+cAAwijgMAuCZNBV3WCZIbfoiog6Fh+TNvAKC3NiygBgBAizqWaAAAIABJREFU5AYDnTPXmiPCoIZIKAAAALAeiajKd+z3uzzjxev4Rx0MNbdpkdq2lcBoVVXuVd+TV4Bu2H7Tsg6a8/zy+4INP3MH5lcDK7pO7RXp8F7ubgfE5+JfoQAAAACc0VdHN2CUjPvTSmplWXZdVxSFLB8cCz8YG82yTOupZTcbtXnQnjnP5FcjDQyyA1aRDTm6UQB28s5+7/47ujnAAMl0kfQXb1XYf3nbTOwLAMC02DNDjVP6uqoqmUqi7/v5OaGyu8RWBqto7xN22bOOUqoJdLiaJ2uvnIXk1pHQjS2cbu4IAGsh+on4yf05qXsmGTDabd2dBMJaq+XRuq6T+trbNfUhaX9xBYA0xJsZKuFOryqoO4FSKJ4uEAAAADjWp/YHylkgTnKt1zSNxDGbpjHB/BBN0/QOd+LcruvKspTlMoIwkitBIqEAJnx8807/Hd2Wq4s9M3Q6AzSSPg8AAAAAMJPkdeq1njzQizt5MHYlKFXU3NhoXdcyz8RWzX3QxEC93WatAABMiDoYmue5dGy6xO3hwrnj5Q7hXq0DAAAArsJLYyGOg2eEl3LGiX7ejYp6wwcHj3Zx/MECUfnp6+/M/6uQRlro8aIOhlZVVRSFtVZ6QR0QMbhW+kjqZwMA8AxKDQIIHXvlxnVjeuRSLssyubjzkj2FO+lfnuduWowXHnVLiEZrz1AIfzK4uPDbLEVj4Ik6GGqtLcuyrmvtCPM81z7SWts0jXSiskRqzQAAgC0QJwUubsGMoARlMMhaK+mcEgY1X04OIZHNsizl0k9inVVVPTQW3o2lTkt1vsE9p/AF4sH3VcwRdTDUGOP2eeH4CGtt3/fTNWUAAMAc3DMHsK7nozBuMp25XeK+f/KgiIDENzXcKXU/zS05tGka9+KubVtrrdRPm3/Rl2qIE8AcXscBeGIPhorpPo8wKAAAmyJOCmAxSU8jjgnVtq0bCTXGSPqLThcxmAQjU8YPXvrFM3USAOAUzhEMxZ7ir68RZhksqAjuvczYXiOM8zt6ObYdceDdwAKDIwTJlAEAxGCw7udYuNPbhegnAOAZXx3dgM1lWaZXg+5jDIo/h3yVqjfxv0wAeF4/5OhGAQCuTmKaXkBTS5+1bZtlmTeZkrtxOHd813Xe/PIAAExIPzNULvwkgsZF4Ezx19fQVNBnYqPyMqN9jdDP4cc3r8e2JAa8G8D+4h8qAQAnlee5WyS0qioNaMrcSnVdW2slbCprdYYlmYlewqbmFlr1gqdbY8gOcCmLww4T0QYmNztW+sFQAACAR3GrDAC2o3Mi6WzyeZ5r+qckhxZFodu7BUattU3TFEWhY/68CZeeMefkv1YHQUQVOAWilkkiGAoAADAs/qESAHBSEvp0sztdfd+3bSsx08H5lGSDwX33IR0EQ3aAi1gwT4kZGVTkHYoJBg9BMBQAAAAAcIDp6ZKmA53rhkH3L4RCESQAOArBUAAAAAAAFmIULQCcC8FQAAAAXJdXA4FpsmaaiP4QGAIAADEjGAoAAICLohoskCqdXsnV9/26z7KsjCAA4FgEQwEAAHBpkg1KYHSmiegPgSHEY/W4JwAgGekHQ+V77cvRzdjO6gORXj6/ag3v2/vGeDE8xvtYzrw00qvQnf9gp/+IFlzXhQfk4hCH2CcpBgAAAABOJP1g6D+6H40x5pu35nZZmNJ14BaR0HUPiAta9rE8Kh9n9T8iCqUhHin1dwAAAACwivSDoXIpKOGJVC8LV0860wMS1sFi8il69CMkAxU1N3k3g39Ez3z++SMCAABICZU0ACAZ6QdDAQBAMihDAQBIHnfTAWBTBEMBAMA5DF4cugsJjAIAVidDlwAAySAYCgAAzoQyFACAVHl39WRs/vuDGgMAqSIYCgAATsm9YiQwCgAAAGAOgqEAACB2xDoBAAAArIJgKAAAAAAgKVmWhQv7vt+/JQCA2BAMBQAA8frp6+8Mk1cAAB5E3HOMN9iCuQcBXFDiwdCfv3nLwLo5pDK3MeZlpQOu0sVu2k+/fH79+ObV3F6yPMZl6Z+A6/bZGD2HTOwFxICkGAAA4OLqGABM8sFQj1wWch3oGQzoPCP+LvblM6FPAOmjvwMAACHJMon/qg0ANpJ4MPQv//0P91Lw/YFNiZ6MQFwxQfKZLtZLAt2on5ZnkViwDsCUH/moXM3gCNx39otV4WdjcC+yjAEAALAzN8Fl8UAlRtADuIivjm4AAACXVlWVtdZaW1XVumsBAMAVrDLUj0RRANeReGYoAADRatu2KApjTJ7nXdd1Xde2bdu2uoG1tus6efzoWjxPq0ub/5dlwzxOwFYIxABP0kFLzwxUYgQ9YubF/dedYNP94idIjk4YmaEAABxDIqF937dt2/d90zRd12mOZ1VVXdeVZdn3fd/3ZVlKxHPOWjyP6tIAAADxWH2yExdf/K6GzFAAAA5TlqU+ttYaYzSgWde1McaNjdZ1XVWVbDC99rL0W/LicmkezQjYIkdm0+wG4ER++vo7QwF3AMAM0llsFBhd5YsfidWnQGYoAAAHkKilBECVjJd3f1y8FpHbNLsBAAAAwBgyQwEAOIDmgbrxUC+a6YVKB4/g/kgwdJVyaXtysxtIFAUAXFN4g5BOEM84quy7W2OUUQ6RIzMUAIDD1HXtlgHV5YOj3TX6OXMsfDbbMy8BAIAI0d+dBUMlsC6qf2IOMkOf4hWDmDnX2IK9wqoT8c9rJr3aWlXbBg/+0PHdXjbcK5k+eCKxiJyj1S0oBzP9OZx/cqASTRqapimKQqZREjrU/aGc0DF93z/VPuzFOyEn0yUBhs8zjpNqJzj2dTGcCPt5m14+jB2ckwZWsWnZdyQg/WBoeAPwdP0if73Pu0ifOvEyL/IORI7fAjzW2r7v3UmQpqOc0wmhF586CcAO3I5sTliEjg/Yx+lS4Tg5ADhW+sHQjUKfXsbWzHjlsr28fc8SG5WvyBtVbVtWFW5wr/QSJCfm19t06r1LeSY1e/Bz6E2kO/Fn7m5JJZo0uKPjvaKfRD8BJCC971rAIe5eS+oGa31F3Dpbk5MDgKNQMxQAgGNkWRamgpZlKQ/C2eG7rpte680vDwDr+tT+QPACAACcHcFQAACOIQFNyQxt21bqurij5o1THlQezFwLAAAAABgU+zD5sOJnWZZ6sTe9FgCAmLVta62t67qua1nSNI2utdbKDEva2d1dO3NiJQDnFQ5WXZCqqaNrb1P5kewJAAAuJOpgKLXSAABpa2+stWEoU2ZYkv7u0bUA0rNK2b6zVJ8HAGB1zJ8BEXUwVEzPgEQiDADg1AbDoN4Gi9ciPd6XeAo4XtAqU5rIRCsERgEA10EkFCrqYOiczFAuAgEAwEXwJR4AAOAZ3EiGOUswdDDuOb0WAICLCytrm3vjLXAK8iWewCgAAACwQNTBUOFey+V57qWLTq+dzxslJEOHoqLXPC/T22HEy+fXj29e1z1mJGMV3WboywynRJhYNfPg5pHXKDve/bgONh7AKoh7uuLv6AEAK+KOIABgzFdHN2BK13XGmLIs+77v+z7P867rdLL46bUqu2fXl4SDvHy+RIht4mUuewf2TDu6yO8IAAAAO+iHHN0oAEAUos4M9SZHksl267qWiOf0WnW3z5Nwj+a7RVtI3mkhMaOFVs8DWmUGgxWbIZ+NiSkRls2WsGw8pux19+M62HgA8LinoEfT8L0TS7QdPQAAAIAdRJ0ZGpYBlSVjRULdtQAAAADu+vjmnfx7+fzKQA0AAJC8qDNDx0zPlcRMSgAAJMPNA2XKIABAMhipAABHiTcztG3bLMu8Me/uDPITawEAAN7Z7/Xf0W0BYvTT19/99PV373/7JP+Obg4up6oqa621Npz44cm1AABMiDczVBI867qWTs4YU1VV13VlWcraPM/H1gIAALg0HvpybDsAPI77GUmSmWzzPDfG1HVd17U72YO1VubLNcZ0Xde2rZv4Mr02fj99/Z15vAT2o+QPh14PAELxZoaa29xHRVHItO91XZdlqbf+pMMbWwsAAC7uU/vD1peaAIAFJJ2laRqJYzZNY4zRSzlNc5Ep4MuylIjnnLVAYtxhLtwZAtYSb2ao6PteOkjNAJ2/FgAAwIuHfnzD/DAnE177EeO+Dn7XqZK8Tr2C8ybCrevafBkbreu6qirZYHotzJd/OPR6p0b0E9hI7MFQY8x0oJMwKAAAg2QEoscdhAjEj+vA+dzJWG4DY4kkIlJ5nus4d+Ve1snw+bHtp9cCiZHoNh0isKITBEMBAMACxD1X5wabmG1mT5rlxKXgGKalxrlUVSXlzmTKBy/Z03wZGA15a90SogAA3EUw9HcRzquwZ5NePr8ygGIBvfBYJf/CvcB76Jf+/PWPdwT3Cl8/G+SYrOKhv7WJsvcT4QCCNQBwWXraJzaKyMl0uF3XSRjUGKMT4Q6Odtdw5/yx8IPDIwZx7xAAribqCZRWkQWObtEseyY+vHwmDHq8A1NdJq6X+Gysi/cTOK+fvv7up6+/e//bJ+4xAMDzJLjpToIkdT/NgzmhE/rZnn0xAICzST8z9G73FvO8CtK2fZrE1d1DvLdrrfwL/TTO/KWv+FuTQw2+kIlVWGD+b23wDDAxlYR7ZH5fAAAgTm3bSiTUnQSpbVuNhw7uMn3AdVsIAEhb+pmhAAAAAICohHU/jRPWJPoJANgOwVAAAAAAwE68uKeQH2VVODu8ZJLK48G13vzyAABMIBgKAAAAANhPnufuoPiqqtyAplc8VB64G0+sBQDgrvRrhgIAAHjcaesmSvECALbQtq21tq5rnU0+z3PNFbXWNk1TFIVOfts0je47uHb+xEoAABAMBQAgTXqV6GLaXABADCT06Y6Od1lr+75fthYhvQX4cmw7ACAOBEMBAEgTcc8Jkg3q5ocCAPY3Hcp8Zi0APIrbBtdBMBQAAADAOrjHAERIC8J8fPN6bEsQlfCMTe0gXATB0GF7lhL7+OadPn7/26dNn2s77quIR5w3drz36qevv5MHL59f+Xaym/mfWC7qAACYiU4T8bhIrZg4r8JwCpyxQ9w2uI70g6FhL5heF3i4OPvgc53cXz5ztt1PnJ9Y4CLOdXIGsACJRYgBF33AHHrG5hsaLiX9YOiyXnCfUmKSEijPlUB0RtNa5X17f2hj1O3tjSLU6P7Gze2X7rbwvKnBZyTv9sTHlWs5AAAAxMm9cIjq+gsA4pd+MBQAAMSAGwwAAAAADkcwFAAAAEiQjEG5VU7/4m7ExPinNMrZAwAQCifqoKe7pq+ObgAAAAAAAACwISbqgCIzFAAAAEjK3cL0g2Urwr0SKGoPICVeVjsVeK5G8zoHBz3MpKmg9HFXRjAUAIA0ZVkWLmR2XQAAcEZMd35x5HViRQRDAQBIE3FPAIt5NdSYqxpAJCQblMDoZUn3RFInnkQwFAAAAJsIL1YZ0ggAAIBjEQwdNjH5Jp606T0c9+DR/vr0ylBaeK6bWl5rn59671wvf2un/mwAJ8Wkotshbeek3F8cwWsAAJCe9IOhYcU0hg0eaLdIKACcRVVVbdsaY6y1VVW5q8IurCxLd5uJfTHHzsWnvOCgF4dNNQir0TRiowD2FGfh7EjOhNKMl7vbpYJplwB40g+GPtrhuZciBNc2Im/yRsWnYp4bLuh3z9QNuzPMmjXe3lQv+5c59WcDz5BLtTzPjTF1Xdd1rd2WRDknWGu7rpPHXde1bXt3Fwzap+MII6HbPRewjNsZRRKyARY7PO6JSHA2wyoIqScm/WAoAABxstYaY5qmkQdt2xZFUVWVm+Opaz1VVXVdp4miVVXVdd227eDG1xHhbTCPc0vp1TAJAOAgYIG0RRU6kcZ4VWLSxrRLAFwEQwEAOIbkdWr4UkOi8qOOfx/ct65rY4yGTSUYqqPmgenrvaiuyQEAiAGT/mECZX8SQzAUAIBj5Hmu49yVRj/vRkVlcP300a7DK+VxcXxNx7nwlwsk47zxRLpOhBi7kzCCoQAAHKOqqqIosiwry9IEyZ7Cnf8hz3M38dMLj7olRAEzcv3Jxd5lcUUHYGsJdDFk/wEXQTAUAIBjWGslnVPCoMYYiYoKiWxqVVCJdUpF0Zlj4Qcn0h3ELBMAAFzT6ndKiCciAe6Qo41mfsaxCIYCAHAMiW96kyCZW3KoN3WSTI4khUFnzpJEiBOPOu/wRkzwikhwUQcAAC6OYCgAAAdo29aNhBpjJOVTwp1mqEioBE/Hpoxn6iQ8iRQeALgUym1vjVuMe6IaDB5CMDReL59fP755PboV69Bu4GXL4w8e/OLnxPnv/MR7uOyAMfC+f3hfPrjsRwwG636OhTu9XYh+YgsMb1xX+D3k/W+fDmkJAGBPdKN7uvhVPxZIPxgaVkw7xbDBl8+JhEGBo/D9A5GTmKYX99SJ49u2LYrCzRs1X0Y/w7nju67z5pcHcKy0r82m7zgCAAy3GPcltxupBoM50g+GniL0OSaZ3AHtAzbKdZXjewdn3Id55J0ffA8HDzXzgJFwi14PrgIOlOe5WyS0qioNaMrcSnVdW2slWiprdYYlmYlewqbmFlr1ZqIHEAP9Opd2bBSIzeAsgqe+NgQArCX9YCgAAHHSOZF0Nvk8zzX9s23bLMuKotDt3URRa23TNEVR6MWeN+ESAGyEsjM4BeKeseFcgdXNv8u42/1IPudnQTAUAIDDSOjTze509X3ftq3ETAfnU5INBvfFYut+Xdaj3Uotk5MOALgcIkS4gmc+5/yN7Cz2YGg4usEroCZz7xpjrLUMDwQAnNH0dEnTgc4LhkEjr1TIUGgAwLrcjm/PXm/1+3mxddk4r/k18dwt9yknyuf8FKIOht6dJ1dm3ZXHMv0uU+sCACAol/ao578uD0ZCpWQkQVIAAAB4CJ4eIupgqBi7bNOpJHTeibquvWl5AQC4rPTinmepVJjM/IfYwlEZXgDOyD1LbNTrDY638NLuuJ8HIDFfHd2AKdNpnu4MvMaZinfjRgEAAABRePn8+vHNO+IUAAAA80WdGerOqGuGyqLlee79qKPmAQAAgNi4dRie9PL59fmDALiss4y3QAwir9gOPCrqYKhwS57lee6mi+4zIn7rm+1yWnm5u92XIkwBmOg+vdZu2tHSi8808RHS9/DRj+Xh3Bf18vuSV8MkzoHwt8+oXgDHCrtvLrRm4gQOANgUl9hIT9TBUEnz1KqgMl1SVVU6g7zHnU9JDU4fEUqvsBpwNRHeIYgTbxSA2HCVBQDnxTn8IlYc2RAehzug2FnUwdCmadzcT5kcqa7rqqrm54Q+E+Xc+k67+wcv+WtzPD/X7UYGz1+D7+GmDeY0epdXEP3/tnc/Z64i6Z6AUT/XiPJg+nlu+0DgQDlRFpzlzEawmW05MOVEOQD40ItrQnmhWUSfaBokUkr9IQLed1GVKSSdSJIkxI+I+JZH0WSt9PLm31095MR/S2lH2Tnwcj7ff8+sYwJ4xtUxMUbAvJwzNt/gsGFzWYehy8Qzjv28VTJ+veASAAD5cC0EvI/c85Pc9uMbXjvUFB6SdRh6S0pCpZ8AQG6McLyHnQMAwCbyDUOHYWiaJi0Ymh5MXy9rx4/jeD6fP9VAAACeYjARQCmmaysp3cajlqVulbdlQ/mGoXH4Z9d1IYT4ddu207izbdumaUIIMSFNz9mktQCQG8ulfUwa5Phj/XmwX4b6Anzb7lcbV6WA3OQbhlZVdblcTqdT0zTpkelA0RBC3/dN06SLvb7vP99IAMiT3PMzZEAAj7p6u66aVNBdPmE2ZbBt2zQmxoCYd5vWX5VqvdxxPkio4Eo+sg5Dq6q6XC7DMMSKSVfrKcUnVNeqLQEAfMbPS8Q/t24IO5QuGvOcV7i/EUxs7svKELGsbvw61tc9ZjGJ44RoR6CaUKH8ygqVexhaVdXVGHT2hA81BQAAPsjwGXZpOXchXtNNr+zSKNGZtHhaHBDatm3XdXH0zJtamycRDGzOn2G5CghDueqAf3XxR7Yc20NcQWXCLwJ4kwxXGTvgR5QPiFML9SbsVcw3U0K6PvOv67pqUisihqFp1vzR5HDah4PzZ1ii/Yehy+VmrKEGAGxi92GWJBT4hq7rUpncajJN/lYqWtf17Ns0ax4AvrT/MHR/0edhbztYju0haXVqtjVdbz5mBL9t3CJgJ2afB7JKIQ/7WQX4hph1LosgTQe11HU9Hfg5i0enS4gCfEBWn7v4hv2HoQAAm5veGgEgGoZhHMe+76cPxmQzrQoas862bR+aC3+rYP3S/kbPALBOGAoAHM7up6tTNMcnx5HizumDs9JJsThSXBj0/ipJIk7gHdzY3gdhKAAAX1ifDubC4IUkoRxKrAs/e3CZeMbBobdKxh+zdBIA3yYMBYB9ujpD0EiZIpZU/vHXn9M1sjdvs4WxPm/lly4tZTfisNDlaqG3pCRU+gnAM4ShALBPB8k995fT/fjr+6UCn9wb0xD2x78e+/eQz6vDP/e3/zMx27HG3rJLXdfN6sJXVTUMQ9M0acHQ9GD6elk7fhzH5fsAwC3CUACA7MSBgZ8cA/hMCHsEy9j38wHlrAxXbNJvH24EvNTVGfF1XXddF0JIheans+nbtm2aJoQQE9JbxeiBUswmxFQZzIlh94ShAECRjJWbetXeSJcf5mJPfX4A7CzxhP25WjopGobhdDo1TZMemQ4UDSH0fd80TVoNZlZwad+cEzLnF/Soq/dipx9CBKMOqncQhgIAwNcElPBCbduuDOe8XC7DMMSKSVdHj8YnVDfiVL7N+e0Z9t66tH9+LDZteC/W3d9jEoa+RRF/Tt9oZBE/Vw6mveDyRJ+/4n7RcYeXuKsBODILg8KKqzHo7AkfakoenCJK4Tf1jOk40OIuS1/OsfQ++w9Dl7V0311QYq9/sXv9uV7O/UAAAKAsYhfeLR1jsxVCt2IC/pHtPwzdqpZuzn9Xz6y+n36u+Cpd5i25nejvlPNxuyLu7bJ2NQBHNvsE5U4qQEGM64fS7T8MBQDge+K8kJ/rkLjYA4qxnCBYbTdQZt9mpcBvja7YTcVwt69gB4ShALBPrgMBOCz93WdcLQX+7acVJI4GFYw+w0J8bEgYCgD75DqQZ0wX1XG5AsCKOMbzy85iw4rhAFPCUAAAAIAPOfiQ0ukKCQ+VMIFXEYYCAPzbwa9PAIC38kkDNicMBYAttW07DENVVSGEtm1fuBUAgDypQQ8bEoYCwGZijaO6rquq6rqu67rpQp8hhHEc49fjOA7DEKPPe7byDS5L+IDpSnmFVlIGinO0NTqnP++Pf/1fFw/8mzCUPcitd//x159//D33gomvnZ2x7a8gtwOgCOkA+LH+PN4phFBVVd/38YthGJqmads2jvFs23Ycx/P5nL7tum4Yhvjk9a18QDrzuMoqwqzXE3wD7JVLA+BL+w9D46CbKdV1easff+Ueg76cDxzwPXFcZ4ovUyQav+26rqqqNPk9xp1pXvz6Vo5pdjY26jDJZHW233/5tfqZw+o6gc84bF9wq3L9ctjKYXcRHNn+w1DR575l23Vl27DqbcNh0o/8yYKAOe/nzKXDIP9RzDtW13Wa555Mh3bG6fO3nr++lfeZnXkySbUyaUbO4nkvk2D0HdZ/tB3/4AB3+sCwlVnY6moF8rT/MBQA8tS2bdM0p9PpfD5Xi8Ge1X8Go0uzrdMlRDmseNElGD0gWSdQnLeuobyyJNStQaP3vNu6A84RhEIJQwFgGyGEOJwzxqBVVcVUtJpMlp89P8add86FXy4Uc4tZFHeaXgvNRtnLH8nB1ckfFkjlmK52gvq74uRwp+fRNrgxCfkThgLANmK4OSuCFL94aEzoLS75ADgsnWDOpuNAV0LDZ5LQly8J5cbSW6lzyIcJQwFgA8MwTJPQqqpi+aNYB+nWS9bf8LUtZGr6oXz2eX1aFQcAeK07e9iVefHcMl3h9Od+83mGQxCGAsBmrq77OQzDrLL8VdLP40ijZmYXKqbgAUCVx2z6F/rMj5PJCqezsHtnv0qyJQwFgA2kuHOah8Z8Mz6yrA4fR5LGr69undWXZ9++TEJnT1DQFoDizIqzV6vdWYzVXjUv/iC+UU4KdkAYCgDbqOt6WkG+bdtpoBlrzYcQpgnpdE79ylZ2Y3bJt7xQEXECsFeZDF38JKvuLElpeQdh6HF9b/y5Uevc4tjI3J3rKM1+j/9d/++3tYh/DQvtui5Vk6/rOk1+DyH0fd80TaqH2/d9eu3VrXcWVmL3voxQAeBR8VPi51fkNHQReLn9h6HpKjFRWBA4mjujaon258Xoczq6cyqEcLlcvrcVKM5WQQMAZGh2czf2kr9t1Bh2Zv9hqOhz6Zmx98btc4tjI393rqOUfpWy0Y9ZjzKf2QoA8KTp53wrcpInVy48ZP9hKAAc03JuROUeIQWaVs/4OWpyb7ffngkaXP4Bn2fGOlnRFfIoYSgA7JPck6seqsy7uQNWzwBewh1BOBpTFblfSWFoCKFt2+l8wGUPdz6f1dIFAHZjOtjh+dUkC80WM6+esWzVZ/JlV32wQu75Jr//8mu1WFXJMo5AWYoJQ0MI4zjGwrvxkVRvFwBgl9407SvzbLEsV/fhbkohmXgIAOxPGWHoMAzjOF7d1Pe92hEAwI6lATjKVmRLvgwAUIoywtCmaeq6nuWhcWSoJBQA4Bm7GcaYj5hfFx1em4MPAOxVAWHo6XSq63oYhtkKoWmavFQUAAAAsrIs2Zch64HAAeUehsZqSCvLg04T0piZvr1Nx5PJhK+tmpHJj0/RtvqMlY7enwO+DPMBrvjYMMbpVbHzEsC+FVqyDziCrMPQYRi6ruv7/urWOGs+lY+PFZbatp1Vk19WnL9KtUEAgPcp5ap4dvvqrbPFZzdcP1ODHuCT4pkt56LzVgWBA8o6DI1Lhd6a/z4rnRQLzXddNwtDpZzPyORD+VbN+P2XX6tJ75hzF062tvp0NTt6DXAGMqHQEABFSLMUvwggAAAZeklEQVQZTGX4vOmNSWk175BvGJrGe07DzWEY2rYNP81eEgeHxlT0Y+0EgDxdnRvhHiHcMrvceusKJ7MbvaJhgG9434m6lNkMwPfkG4ZGXddNvx3HcRzH9axTEgoAldyzNOIwAMhKvHH1gQ7aqinJ9Mak2la8T75h6NXVP9MKocMwNE2Tvo1UTwIAAMD0iH17dOp0znccc24b7FW+Yei6EEJd113XpfnybduO43g+n7duGgDAA2YDQCxRDfA8uSdl+dgoVO5naOqOlRqGVlU1DMPpdGqaJj0yGygKAAAAHNb0jqPbjUBUUhi6vLl3uVyGYYgVkywVCgBAVOJoDgOCANiWMu7RkX/2gygpDL1KDAoAsD/xeuzHE68FAICl4sNQeLdMLqhSM753WUh1yBE3s8PmgHsAOLKChnUcuXAwx3S1ulFVVX3fp5EubdvGArkhhOViaOtbge+JXWcml8DwPvsPQ5cdrbW0AQCyNQ0x//j7nxu2BNhKCGEcx/j1OI5xbbQ7t1KidM/+58iPh+9muet/yzLZLOhmIbzJ/sNQ0SffllUnkRrjsvAbDjjiZnH0ZnQwAwAHt7xGiwNC43/bth3HMVXHbdu267pYKOLLrRyTJPQWYzzhqv2HoQBwTFcnIbpHCK/yzKqmwFTMN1MP1XVdfDBt7bouzYtf30pxZqMWnok1nxwAseNENY2TkI1CJAwFgH2Se7JXat3CznRddz6fp4/UdT37Ns2L/3IrPOStGejz0/+/R+gJ64ShAADwgM+vauqylh1Lk9+XD66/ZPqtMJSXSGNL41n3t00b820bdhnWJ6UUwlAAAEqSQ63bHc+mhE8ahmEcx77vp48sn5bizvvnwt8qWL9kIsVrFXR6/P2XX6u3pXUvnP7/Pa/9uX789We8+bcyxNV9OwoiDAUAgAd88prWmBr2LQ4InY70fGhM6AoRJ7zKj78emANhfVKKIAwFAKBIaaBK8mT1jIfEf6voqZSwuVgX/sunrQ8IVTopE7Mz8JtOj9PbUZ88589ug33yn/6GWf/4ktbGN1neDhR6UiJhKAAA23jmmvbqQJX4hh+uUwF8TxwWOlstNJJ+HlxBc+0z9NBAzieVkoSW0k4+Rhj6SvEP7MeXz4PHFf2BoOjGH8Q0Pki3kaUJsCffOxXn/9kmRaj6GihO13WzuvDRsjr8dADp1a1X34edmd4z++Q5f7a0aCndza2BnC+3yF5dO1CA/Yehy5WzLR8DAJQr88uwO6fmTa8tX/ITbXWRDDzj6hqgbds2TRNCiCNAZ+Xm17dSumnvYB2S/H1yFOr3WHqbq/Yfhn4y+vz5gT730wFlyXw9mnVFN/4glp84i7vvzS1XC+m6I3hA01Px/ReW04uH+z/b5H9RBGRiWTopCSH0fd80TerIpuXmr269v7ASvIRVWaY+NgoVXmX/YSgAHJPcc39KucO0y4uiZbEm4Blt264M5wwhXC6X6djP+7fCAe2sz4V3E4YCAMAaI15hE+tBpxiUTWS4KksmzYCCCEMBAOBrpYzMBeCAUidlrVX4kjAUALZxdU3ParL22fIJ5/N5Oqmwbds0SVDtCD4pDUKxXBoAAGURhgJAjmLKuSKEMI5j/Hocx2EYvnwJvITpeED+VBGEzVlum2wJQwFgG8tLsjggdLoI2q0KuW3bjuOYBoq2bdt13TAMFlDLUJytlrPvhZu7rJL0DvkfALBLck/YluW2yZkwFACyEPPNdPG2XiS367r4kvTaruvSrHlYl+K5H+vPAwB4Qrx1ahlTciMMBYAsdF13Pp/TtynWvJWK1nU9+zbNmicT/xxyX0nz919+rUpo51vNJvF9r0rSSr588N0LQBKnU1huGzYnDIWXMVXwrezetzKLc3Mx61wWQZoueVbX9XTg5ywenS4hCrfsL5ibnb4e/QFN4gOA5Pm41lUbRdh/GLpcOdvyMQBkZRiGcRz7vp8+GJPNtCpozDrbtr1/LvytavVLekbusePLm2fWP53FrypFAGRuk0EA06kYO+5PoRT7D0Nd4PEB35tSx52mHx0sN/Ny+xsmVqIUd04fnJVOisWR4sKgd1ZJ0gOSg3df8qWTmBHuAPBt00vaZyorJi7cyNn+w1AAyFysCz97cJl4xsGht0rGK53E+3z+nt/LI9T0ho9O/YvXcipNAeyAQQCvksngVvdB+TZhKABsKQ4LXa4WektKQqWfZO7bI0Tel4QCQNGeXCn78+SV5EkYCgBb6rpuVhe+qqphGJqmSQuGpgfT18va8eM4Lt8HyvXy4aiPLgw6vcK0EigAm5jNbIgLiG0okwXi8k+ByZwwFAA2dnVGfF3XXdeFEFKh+els+rZtm6YJIcSE9FYxegA4pqtVBK2mvTO7H/i//AELWilbXknOhKEAsJmrpZOiYRhOp1PTNOmR6UDREELf903TpIu9WcElADgyuSfPyyRwfHRmA/AlYSgAbKZt25XhnJfLZRiGWDHp6ujR+ITqRpwKALBLmUzWBgolDAWAfF2NQWdP+FBT4LZ8hqso/g7A8x6a4p1PJwjcSRj6Ss+fBJ1G4ZZMZqkAAAAA5dp/GLpcOdvyMQAcgdoRfEAsazur5/DbFi1R/B2AD7ta232TThB4yP7D0M9c9T2/ZIlFT+AWhQjhe+SeAAAAM/sPQwEAAABey6ANKNTftm4AAAAAAMAnlDQyNITQtu2sbG7btsMwpK1btAsAOBxV3QAAoETFhKEhhHEch2GYhqHxwfh13BqDUQCAoglbAQDgHcoIQ4dhSKFn0rbtOI7n8zkOCG3btuu6WVoKAPBaFggDyN/pdFo+qLQg3O+Pv/9j6ybAu5QRhjZNU9f1LA/tuq6qqjQ1PoahadY8AECJhK3bcu0H+yD3fCvTF4CiFRCGnk6nuq6HYVje3KvrevbtcgApAAAAAPf47X/+uXUT4L1yD0PjwM9bgz3NiAeAW8wQhIe49gP4kukLH2COArxb1mHoMAxd1/V9f3XT8sFpPaXk6qXgkotDAHZG18ZePX+V6DoTAOCwsg5D41KhV4d/3j8m1KUgAED+BJQAHNxDcxR+/PXnH3//832NgR3LNwyNE+RDCKlEUlVVwzC0bRtCuBqGKp0EALBjz89k//2XX6vJNM9YA+S3J98UAD7rx19iUPi+fMPQKJaMT8ZxHMcxJaHSTwCA0lmsEwC+IXagbuzBo/62dQNuatv28p+qqjqfz5fLJYahy9rx4ziez+dNWgsAAAAAZC7fMPRLaR59/DZ+MZ1TDwAAAACQ5D5NfkUIoe/7pmlSvfirdecBAMhEnMoHAABbKSkMXdaFDyFcLpe4cuj99eUBAADYsTRiZmp5RQm74XYj3K+kMPQWMSgAQOZSAXeAD5B7ZkJCB2RoD2EoALBkUAwAwO653QiP2n8YurwUdB0IwBHo7wCArUjogGztPwx1KQgAAAAAVFX1t60bAAAAwOEMwxBCCCG0bTvbdFqYPadt21uvZQf+Ef6PxUaB99n/yFAAAACy0rZt13VVVdV13XVd13VpSt8wDOuvDSGM4xi/HsdxGIYvXwIAiTAUAIBjMeAItjUMQ9d15/M5juschqFpmhDCNNPs+z6EsHxt27bjOKbXxlA1DjL9RNN5MyuNAh8gDAUAAOBzUo4Zvw0hnM/ntDVGorfCzTieNL02hqFt2xocSln++Ps/tm5CGdy/5B2EoQAAHIUxR5CDcRzrup4+Ml36M8Wat1LR2Wvruk6z5gHgS8JQAAAAPirWPhqGIQajsSDS9Amn0yl9Xdf1dODn7JnTJUQhf7//8mv12ZtzaRTqj389UMZ9QfcveR/V5AEA4JX++Ps/zH+EW2KsGYsmVVV1Pp/HcWyaJsWdMdk8n8+Xy+VyucSBn2l10Tv/lWU9+lte/gMCkDkjQwFgn65e4KVavQCwrdQltW17Op2apomPzEonxeJIcWHQ+6sk6e/I0OeXv/ztf/45/daNOoj2H4YuLwX1iwAcgf4OPm922QksxUBztu7n+XyOA0Wra4uExonwt0rGK50EwEP2H4a6FAQAOCDjXyBn94/xXL5E+klxLH8JWbFmKABs49biZdNrvDglMFaZWL7D+lYAyFNd12kcaDStIH86nWb92rRnXNaOX9amB4AV+x8ZCgCFmpbHjdMDZ7V0V7bCkZmrDplr27ZpmhBC7Lnath3H8Xw+V1UVQohRabzbN9u6fG16zhY/BwBFMjIUALZxWajruq7r2bVf3BSL7aa4c30rAOQshBB7rjglouu6uq5ToBm7s6Zp0tbz+Zy2hhD6vk+vHcdxVnAJANYJQwEgCzHfTIFmnD+Yrv3iF+nb9a0AkLm2bS+XS9/3fd9fLpfZ/by46Xw+x62zDi6EMH2tJBSAh5gmDwBZiCNfpo/MVkCbrZK2vhUA8reSY6Zp8t94LQCsMDIUALZ3dcmzhy4CXRMCAAB8SRgKABsbhiEueTZ9ZPm0FHfeuTborWr1S8//CACQFf0dALeYJg983x9//8fWTYA9iANCp0M7XzIx8HK5PNEoeMA/wv/ZugkA/0EnCLfEXvvH1s2ADe0/DF3eANQvApCVWBf+y6etDwhVSh4AgC/9+OvPrZsAG9t/GCr6hHf47X/+uXUTYCdWCsFLP8nfP4f/u3UTAADgAfsPQwEgZ13XzerCR8vq8NMBpFe3Xn0fAACojGiBnxRQAoCNXV0DdLaQ6Kzc/PpWAAAArjIyFAA2syydlIQQ+r5vmiYtfj0tN391652FlQAAKJECtvASwlAA2EzbtivDOUMIl8slrg26DDrXtwIAALAkDAWArK0HnWJQAIDds9wnvJA1Q18pzVUsUbmNL7flVcmNL7flVcmNL7flHEG5x2e5La80fiPltrwqufHltpwjKPf4LLflVcmNL7flVcmNL7flVeGNz5CRoQCwT1c/M10ul8+3BAAAIBPCUADYJ7knAIfljiAAtwhDAQAA2BW5JwC37D8MXd4S1C8CAAAAwAHtPwwVfQIAAAAAlWryAAAAAMBBCEOvL6397ae96q1e2KoPt/zOp9nt97PbX/5Wr/rn7nxa0bud3fOXdT8ntNe+1Z1233jHzENPe9Vb6QSJ/GXdzwnttW91J7v9tW91J7v9CAoIQ9u2DSGEENq2nW06LSyfAwAAAABQZR6GDsNwOp26rovfdl03DbCHYdimWQAAAABAgbIuoNQ0TTWpgDQMQ9M0bdtOh3/2fR9C2KJ1AAAAAEBJsg5Dq6o6n8/p6xh6pgGh8QtJKABcdXU9oHSLEQAA4ICyDkOvjvpMj0hFAWCF3BMAAGAm6zB0lnvGWfOzEknTYS91XVtIFAAA4OBMjwDglqzD0CTGoNV/zpofxzE+EuPREMI4jrMVRasbveDMPc/xVt7KW3mro73V//tf/33Pv0W2Cj3wvNVx3urz/6K38lb3P0cnuEvL33tuB5638lbeyltt/lZH6AFPpdwcG4ZhGIau61L6OQzDbGp8zENL+YkAAAAAgE8qJgyN1uPOtm27rlNfHgAAAABY+tvWDbhpOfCzuq9KkiQUAAAAAFjKNwytqiquATp9ZFpB/nQ63doKAAAAADCTbxgaB3h2XZcizrZtx3GMNZRCCHVd39r6sRYu49fTwiyxbds2hBBCmD3+YVcbX33VvEwaX5Wzn1fk38Ko0F1d7hFe9ImFQyn3WC33/JAUsZ/X5d/CqNBdXe5BXu6JhaMp91gt9/yQFLGfV2TevKkSd3XRR3i5J5YiXfI2a21d1ytbz+fzxxrW931VVX3fLx9caVVd1ys/zsdcbfzlq+Zl0vhLOft5Rf4tjArd1eUe4UWfWDiUco/Vcs8PSRH7eV3+LYwK3dXlHuTlnlg4mnKP1XLPD0kR+3lF5s2bKnFXF32El3tiKVTuYejlcul/urX1fD7f2vqm9qTxp1eP1FuNia9KB2789pMtv6w2fr15OTQ+ifv51tasmnpV/i1MitvV5R7hRZ9YOJRyj9Vyzw8zxZ2ZZ/JvYVLcri73IC/3xMLRlHuslnt+mCnuzDyVefNmytrVRR/h5Z5YilZAGJqbae5+9c9s/YWzRz6c3K80fr15OTQ+yX8/r8u/hUlxu7rcI7zoEwuHUu6xWu75YSbz/fyl/FuYFLeryz3Iyz2xcDTlHqvlnh9mMt/P6zJv3kxZu7roI7zcE0vR8l0zNFtxx10drjyt73R1oYrlGOZxHF/fxNtWGl991bzNG5/kv5+/lH8Lo+J2dblHeNEnFg6l3GO13PPDTOb7+R75tzAqbleXe5CXe2LhaMo9Vss9P8xkvp+/lHnzpsra1UUf4eWeWIr2X1s3YIdOp1P6uq7r6SEbQpg+M4SQ1ZE6a9761s0bf/9+zlD+LZwqeldPlXWEz5R7YuFoCj1Wizs/FH1mzr+FU0Xv6qniDvKpQk8sHFChx2px54dyz8yZN2+p3F09VdwRPlPoiSVzRoa+Ujzs0pINMZWPJb2upvj5uNq89HeVW+Mf3c9Znabzb+FU0bt6qqwjfKbcEwtHU+ixWtz5oegzc/4tnCp6V08Vd5BPFXpi4YAKPVaLOz+Ue2bOvHlL5e7qqeKO8JlCTyxFMDJ0bv2QWv8L7/t++oRhGEIIXde1bfuZU8O3G//QrZIPWP9BNt/Pz8i/hVNF7+qp3I7wh+zmt0ARyu0Ed9MDVjrBbBS9q6cyPMjvt5vfAkXQCT609U322glm3rylcnf1VIZH+EP28VvIkzB07tZaDFVVhRAe/VsKIYzjGA/Zq//Wd5p42zONv/pu3976pPUfJN4JmT34yf38ctm2cPND+q02PMIfsu/fArkptxPcTQ9Y6QSzse/Tr04QlnSC6d2+vfV5h+oEc27ejk+/pfSA1a5/C5sThs4tT6/PS4fpuw/NJxufz0nhez/Ix/bz8/Jv4bqCdvVUPkf4SxT6WyBz5XaCu+kBK51g9gra1VNZHeTPK/S3QOZ0gt/Y+nL77gQzb949StnVU1kd4S9R4m8hO9eLzPOVWOqr7/vZI2k1hyjW9lp+HVVVVdf1u5u6tGz85UbzpotTZNX4R/fz7Pnbyr+FUbm7utwjvOgTC4dS7rFa7vkhKvfMnOTfwqjcXV3uQV7uiYWjKfdYLff8EJV7Zo4yb95Uobu66CO83BNLoYSh37TyZ5YePJ/P02M3viQdmrMnf9LVxl9t3vrWTRp/uVxijv/Qft6glbfl38Kk0F1d7hFe9ImFQyn3WC33/JAUemZO8m9hUuiuLvcgL/fEwtGUe6yWe35ICj0zR5k3b6bEXV30EV7uiaVQ2x+vhbp6pF5+njKSWYofX5VsdZjeavx68zJpfFTEfl4Rz2I5tzApcVeXe4QXfWLhUMo9Vss9P0zlv5/X6QTfqtyDvNwTC0dT7rFa7vlhKv/9vKKgHvBS4K4u+ggv98RSqNNlsXN5UlzyeWWl6risw6PrWH/MevPyaXzp+7kqoYXRDnb1VClH+MzOfgvsWNHHakHnh6L3c5R/C6Md7Oqpgg7yqZ39Ftixoo/Vgs4PRe/nKvvmTZW+q6cKOsJn9vRbyIcwFAAAAAA4hL9t3QAAAAAAgE8QhgIAAAAAhyAMBQAAAAAOQRgKAAAAALsSayuxJAwFOBydIgC7p7MD4OCaptm6CZlSTR7gcE4nJ38Adk5nB8DB6QpvMTIUAAAAADgEYSgAAAAA7MQwDG3bVlXVtq11Y5aEoQAHolMEYPd0dgAcWdu2TdOEEKqqGoYhfU0iDAU4Cp0iALunswPgyNq27brucrmkrrCu63Ec421CImupAhxC6hSrnwtphxDGcTyfz/pFAPZBZwfAwZ1Op6qqpl1hvDWYHqQShgIchE4RgN3T2QFwZLHXq+s6rhKTqsnH/rHve7MlItPkAfYv9oV1XU8fTB2h9dQA2AGdHQAHp7O7kzAUYP90igDsns4OALiHMBQAAAAAyhbnQ4zjuLKVShgKcAQ6RQB2T2cHwMGtLA5zPp8/3JicCUMB9k+nCMDu6ewAoO/7qqpi8cCobdv0XyJhKMAh6BQB2D2dHQAHF0KIvWGsIB9CGIYh1pQnOdkjAAcxDEO6PozFdtWaAGBndHYAEJ1OQr/r7BeAw9EpArB7OjsADk5XeMt/bd0AAHIxDEMcPmM6IQAAwP7E6fNTfd8frcygNUMBqKqqatu2aZqYh55OJ5MKAdif08K0vxuGwe1AAHasbdu+7+u67vv+fD7HL46WhFZGhgIQdV2XOsIYjJpSAcCexCvAtm3bto03/9q2nV4BphkSALAD5/N59ki65xcLK4UQUj+YNh3hvqAwFOBwlp1iuvaLX4QQuq77bKMA4JUeugIEgP1ZjzVjV5i+PtTtQGEowOEsO8XlUqGxAi8AFOqhK8D45HEcq6pKkyREpQDsUuzvpuJtwi3asg1hKAD/Ggo6Wzdts9YAwNtcvQKMYWiaO1/9jEQBYGdCCHHgSwxA09TAbVv1YQooAfDvUTDp26ZpNmwPALzD8gowXQSm60DT5wHYsXEc022/cRybpjlgr2dkKABVVVV93zdNk5YK7ft+2/YAwMuN4xg7uDglQrVAAI4mdXwhhMN2gqfD/uQALB1zlgQAAAAHIQwFAAAAAA7BmqEAAAAAwCEIQwEAAACAQxCGAgAAAACHIAwFAAAAAA5BGAoAAAAAHIIwFAAAAAA4BGEoAAAAAHAIwlAAAAAA4BCEoQAAAADAIQhDAQAAAIBDEIYCAAAAAIcgDAUAAAAADuH/A9dCatxbThyKAAAAAElFTkSuQmCC\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": "alternative-hazard",
+   "metadata": {},
+   "source": [
+    "# Particle Resolutions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "fitting-catalyst",
+   "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": 39,
+   "id": "approximate-operation",
+   "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": "flying-hepatitis",
+   "metadata": {},
+   "source": [
+    "## Particle Energy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "exposed-grenada",
+   "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": "swedish-maker",
+   "metadata": {},
+   "source": [
+    "## Particle Pseudorapidity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "equal-gates",
+   "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": "diagnostic-miracle",
+   "metadata": {},
+   "source": [
+    "## Particle Phi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "diagnostic-campaign",
+   "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_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()"
+   ]
+  }
+ ],
+ "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
+}
diff --git a/s3tools/download.sh b/s3tools/download.sh
index 5d33c3d1..411f6e2d 100755
--- a/s3tools/download.sh
+++ b/s3tools/download.sh
@@ -23,7 +23,12 @@ 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
new file mode 100755
index 00000000..6ee28a3d
--- /dev/null
+++ b/s3tools/make-epic-config.sh
@@ -0,0 +1,121 @@
+#!/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
+# - the config file consists of file names (or URLs), with Q2 minima and cross sections
+###################
+
+# RELEASE TAG AND RECO DIR: ###########################
+detector_config="epic_arches"
+# detector_config="epic_brycecanyon"
+release="22.11.2"
+releaseDir="S3/eictest/EPIC/RECO/$release/$detector_config/DIS/NC"
+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  )
+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..."
+  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
+status "build config file..."
+mkdir -p $targetDir
+if [ -z "$configFile" ]; then configFile=$targetDir/files.config; fi
+> $configFile.list
+for (( i=0; i<${#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
+
+# finalize
+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/Analysis.cxx b/src/Analysis.cxx
index 46361ad2..67acdd0a 100644
--- a/src/Analysis.cxx
+++ b/src/Analysis.cxx
@@ -94,6 +94,7 @@ Analysis::Analysis(
   // - these settings can be set at the macro level
   verbose         = false;
   writeSimpleTree = false;
+  writeParticleTree = false;
   maxEvents       = 0;
   useBreitJets    = false;
   errorCntMax     = 1000;
@@ -310,6 +311,7 @@ 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");
 
   // if including jets, define a `jet` final state
 #ifndef EXCLUDE_DELPHES
@@ -659,6 +661,7 @@ 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 9f749db3..85ca6a2a 100644
--- a/src/Analysis.h
+++ b/src/Analysis.h
@@ -28,10 +28,12 @@
 #include "adage/BinSet.h"
 
 // sidis-eic
+#include "DataModel.h"
 #include "Histos.h"
 #include "HistosDAG.h"
 #include "Kinematics.h"
 #include "SimpleTree.h"
+#include "ParticleTree.h"
 #include "Weights.h"
 #include "CommonConstants.h"
 
@@ -59,6 +61,7 @@ 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)
                          */
@@ -121,6 +124,7 @@ class Analysis : public TNamed
 
     // shared objects
     SimpleTree *ST;
+    ParticleTree *PT;
     Kinematics *kin, *kinTrue;
     HistosDAG *HD;
     Weights const* weightInclusive;
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
new file mode 100644
index 00000000..47d8af5c
--- /dev/null
+++ b/src/AnalysisEpic.cxx
@@ -0,0 +1,400 @@
+// SPDX-License-Identifier: LGPL-3.0-or-later
+// Copyright (C) 2022 Gregory Matousek, Christopher Dilks
+
+#include "AnalysisEpic.h"
+
+AnalysisEpic::AnalysisEpic(TString infileName_, TString outfilePrefix_)
+  : Analysis(infileName_, outfilePrefix_)
+{};
+
+AnalysisEpic::~AnalysisEpic() {};
+
+void AnalysisEpic::Execute()
+{
+  // setup
+  Prepare();
+
+  // 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> 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");
+  TTreeReaderArray<UInt_t> assoc_recID(tr, "ReconstructedChargedParticlesAssociations.recID");
+  TTreeReaderArray<Float_t> assoc_weight(tr, "ReconstructedChargedParticlesAssociations.weight");
+
+  // calculate Q2 weights
+  CalculateEventQ2Weights();
+
+  // counters
+  Long64_t numNoBeam, numEle, numNoEle, numNoHadrons, numProxMatched;
+  numNoBeam = numEle = numNoEle = numNoHadrons = numProxMatched = 0;
+
+  
+
+  cout << "begin event loop..." << endl;
+  tr.SetEntriesRange(1,maxEvents);
+  do{
+    if(tr.GetCurrentEntry()%10000==0) cout << tr.GetCurrentEntry() << " events..." << endl;
+
+    // resets
+    kin->ResetHFS();
+    kinTrue->ResetHFS();
+
+    
+    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> 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> recpart;  // 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];
+
+      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)?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
+      Particles part;
+
+      part.pid=pid_;
+      // part.charge = // TODO; not used yet
+      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
+      Particles part;
+
+      part.pid=pid_;
+      // part.charge = // TODO; not used yet
+      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
+      - Look up associated MC particle
+    */
+
+   
+      
+    for(int irec=0; irec < recparts_PDG.GetSize(); irec++){
+
+      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];
+      
+      // Add to recpart
+      Particles part;
+
+      part.pid=pid_;
+      // part.charge = // TODO; not used yet
+      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
+	assoc_recID --> irec (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(irec==idx_recID){ // This track has an association
+	  part.mcID=idx_simID;
+	  break; // Only one association per particle
+	}
+      }
+
+      recpart.push_back(part);
+      recidmap.insert({irec,part.mcID}); 
+      
+    }
+
+
+
+    /*
+      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
+    */
+
+    /*
+      Loop over MCParticles
+    */
+
+    for(auto 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;
+      }
+
+      /* 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
+    */
+
+    int irec = 0;
+    bool recEleFound=false;
+    for(auto recpart_ : recpart){
+      // 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);
+      }
+      irec++; // Increment to next particle
+    }
+
+    // Skip event if the reco scattered electron was missing
+    if(recEleFound==false){
+      numNoEle++;
+      continue;
+    }
+    else
+      numEle++;
+
+    // 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) {
+      numNoHadrons++;
+      continue;
+    };
+   
+    // calculate DIS kinematics
+    if(!(kin->CalculateDIS(reconMethod))) continue; // reconstructed
+    if(!(kinTrue->CalculateDIS(reconMethod))) continue; // generated (truth)
+
+    // Get the weight for this event's Q2
+    auto Q2weightFactor = GetEventQ2Weight(kinTrue->Q2, inLookup[chain->GetTreeNumber()]);
+
+    // fill inclusive histograms, if only `inclusive` is included in output
+    // (otherwise they will be filled in track and jet loops)
+    if(includeOutputSet["inclusive_only"]) {
+      auto wInclusive = Q2weightFactor * weightInclusive->GetWeight(*kinTrue);
+      wInclusiveTotal += wInclusive;
+      FillHistosInclusive(wInclusive);
+    }
+
+    /*
+      Loop again over the reconstructed particles
+      Calculate Hadron Kinematics
+      Fill output data structures (Histos)
+    */
+    
+    int ipart = 0;
+    for(auto part : recpart){
+
+      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 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;
+	  }
+	}
+      }
+
+      kinTrue->CalculateHadronKinematics();
+
+      if(includeOutputSet["1h"]) {
+        // fill single-hadron histograms in activated bins
+        auto wTrack = Q2weightFactor * weightTrack->GetWeight(*kinTrue);
+        wTrackTotal += wTrack;
+        FillHistos1h(wTrack);
+        FillHistosInclusive(wTrack);
+
+	// 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);
+      }
+    } //hadron loop
+    
+    /*
+      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,
+		       part.pid,
+		       genStat_);
+      } // particle loop
+      ipart++;
+    }
+    
+  } while(tr.Next());
+
+  
+  // finish execution
+  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
new file mode 100644
index 00000000..f8df2ca7
--- /dev/null
+++ b/src/AnalysisEpic.h
@@ -0,0 +1,23 @@
+// SPDX-License-Identifier: LGPL-3.0-or-later
+// Copyright (C) 2022 Gregory Matousek, Christopher Dilks
+
+#pragma once
+
+// ROOT
+#include "TTreeReader.h"
+#include "TTreeReaderValue.h"
+#include "TTreeReaderArray.h"
+
+// sidis-eic
+#include "Analysis.h"
+
+class AnalysisEpic : public Analysis
+{
+  public:
+    AnalysisEpic(TString infileName_="", TString outfilePrefix_="");
+    ~AnalysisEpic();
+
+    void Execute() override;
+
+    ClassDefOverride(AnalysisEpic,1);
+};
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..594e06a8
--- /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    = 0;
+    int charge = 0;
+    int mcID   = -1;
+    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 0e7507fb..bdb67e51 100644
--- a/src/LinkDef.h
+++ b/src/LinkDef.h
@@ -17,6 +17,7 @@
 // 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/src/ParticleTree.cxx b/src/ParticleTree.cxx
new file mode 100644
index 00000000..79d7bc0c
--- /dev/null
+++ b/src/ParticleTree.cxx
@@ -0,0 +1,23 @@
+// SPDX-License-Identifier: LGPL-3.0-or-later
+// Copyright (C) 2022 Gregory Matousek
+
+#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");
+};
+
+
+// destructor
+ParticleTree::~ParticleTree() {
+};
+
diff --git a/src/ParticleTree.h b/src/ParticleTree.h
new file mode 100644
index 00000000..fc5a172e
--- /dev/null
+++ b/src/ParticleTree.h
@@ -0,0 +1,49 @@
+// 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
+ */
+#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) {
+      recopart_ = recopart;
+      mcpart_   = mcpart;
+      pid_      = pid;
+      status_   = status;
+      T->Fill(); };
+    void WriteTree() { T->Write(); };
+
+  private:
+  
+    TTree *T;
+    TString treeName;
+    TLorentzVector recopart_;
+    TLorentzVector mcpart_;
+    int status_;
+    int pid_;
+  
+  ClassDef(ParticleTree,1);
+};
+
+#endif
diff --git a/src/PostProcessor.cxx b/src/PostProcessor.cxx
index f3e3836d..99693b10 100644
--- a/src/PostProcessor.cxx
+++ b/src/PostProcessor.cxx
@@ -498,17 +498,17 @@ 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) {
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 b93ec7dd..46c36e4f 100644
--- a/tutorial/analysis_epic.C
+++ b/tutorial/analysis_epic.C
@@ -3,46 +3,25 @@
 
 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  infiles="tutorial/test.epic.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
     )
 {
 
   // 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
   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)
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 ========================================
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
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-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
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-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
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-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
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-
-# Q2 range 3
-:q2min 100.0
-:crossSection 1159.0
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-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
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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