UE_in_pp/plotting/dijet_ntopoclusters_varyingthresholds.ipynb

0001 {
0002  "cells": [
0003   {
0004    "cell_type": "code",
0005    "execution_count": 174,
0006    "id": "0727af2b-0e8b-44b8-af33-eeeb6ffc8f85",
0007    "metadata": {},
0008    "outputs": [],
0009    "source": [
0010     "import ROOT\n",
0011     "from ROOT import TCanvas, TFile, TProfile, TNtuple, TH1I, TH1F, TH2F, TH3F, TColor, TEfficiency\n",
0012     "from ROOT import gROOT, gBenchmark, gRandom, gSystem\n",
0013     "import numpy as np\n",
0014     "import pdb"
0015    ]
0016   },
0017   {
0018    "cell_type": "code",
0019    "execution_count": 175,
0020    "id": "09dbc289-1f1f-4b62-9c83-efd18859c012",
0021    "metadata": {},
0022    "outputs": [
0023     {
0024      "name": "stdout",
0025      "output_type": "stream",
0026      "text": [
0027       "/direct/sphenix+u/egm2153/spring_2023\n"
0028      ]
0029     },
0030     {
0031      "data": {
0032       "text/plain": [
0033        "0"
0034       ]
0035      },
0036      "execution_count": 175,
0037      "metadata": {},
0038      "output_type": "execute_result"
0039     },
0040     {
0041      "name": "stdout",
0042      "output_type": "stream",
0043      "text": [
0044       "sPhenixStyle: Applying nominal settings.\n",
0045       "sPhenixStyle: ROOT6 mode\n"
0046      ]
0047     }
0048    ],
0049    "source": [
0050     "%cd /sphenix/u/egm2153/spring_2023\n",
0051     "gROOT.LoadMacro(\"sPhenixStyle.C\");\n",
0052     "gROOT.ProcessLine(\"SetsPhenixStyle()\")"
0053    ]
0054   },
0055   {
0056    "cell_type": "code",
0057    "execution_count": 176,
0058    "id": "34ddc2fe-b709-44e8-ad2c-2800084add08",
0059    "metadata": {},
0060    "outputs": [
0061     {
0062      "name": "stdout",
0063      "output_type": "stream",
0064      "text": [
0065       "/gpfs/mnt/gpfs02/sphenix/user/egm2153/calib_study/JetValidation/analysis\n"
0066      ]
0067     }
0068    ],
0069    "source": [
0070     "%cd /sphenix/user/egm2153/calib_study/JetValidation/analysis"
0071    ]
0072   },
0073   {
0074    "cell_type": "code",
0075    "execution_count": 177,
0076    "id": "eb8b160b-8dc7-4fdd-89b7-8468492f8d66",
0077    "metadata": {},
0078    "outputs": [],
0079    "source": [
0080     "direct = 'results_11_11'\n",
0081     "topo_thres = ['-9999','0','100','200','300','500']\n",
0082     "thres_string = ['All E_{topo}','E_{topo} > 0 MeV','E_{topo} > 100 MeV','E_{topo} > 200 MeV','E_{topo} > 300 MeV','E_{topo} > 500 MeV']"
0083    ]
0084   },
0085   {
0086    "cell_type": "code",
0087    "execution_count": 183,
0088    "id": "05ff6fe0-3897-4591-95a3-9e8e6761c72a",
0089    "metadata": {},
0090    "outputs": [],
0091    "source": [
0092     "#f2 = ROOT.TFile.Open(\"pt10cut/dijet_calo_analysis_fullrunlist.root\")\n",
0093     "f2 = ROOT.TFile.Open(\"ue_unfolding/dijet_calo_analysis_data_topo_pt7cut_wAj_wntopo.root\")\n",
0094     "h_ntopo_towards = []\n",
0095     "h_ntopo_transverse = []\n",
0096     "h_ntopo_away = []\n",
0097     "h_topo_towards = []\n",
0098     "h_topo_transverse = []\n",
0099     "h_topo_away = []\n",
0100     "h_2D_topo_towards = []\n",
0101     "h_2D_topo_transverse = []\n",
0102     "h_2D_topo_away = []\n",
0103     "for i, t in enumerate(topo_thres):\n",
0104     "    h_ntopo_towards.append(f2.Get('h_ntopo'+t+'_towards'))\n",
0105     "    h_ntopo_transverse.append(f2.Get('h_ntopo'+t+'_transverse'))\n",
0106     "    h_ntopo_away.append(f2.Get('h_ntopo'+t+'_away'))\n",
0107     "    h_ntopo_towards[i].Scale(1.0/h_ntopo_towards[i].Integral())\n",
0108     "    h_ntopo_transverse[i].Scale(1.0/h_ntopo_transverse[i].Integral())\n",
0109     "    h_ntopo_away[i].Scale(1.0/h_ntopo_away[i].Integral())\n",
0110     "    h_ntopo_towards[i].SetDirectory(0)\n",
0111     "    h_ntopo_transverse[i].SetDirectory(0)\n",
0112     "    h_ntopo_away[i].SetDirectory(0)\n",
0113     "    h_topo_towards.append(f2.Get('h_topo'+t+'_towards'))\n",
0114     "    h_topo_transverse.append(f2.Get('h_topo'+t+'_transverse'))\n",
0115     "    h_topo_away.append(f2.Get('h_topo'+t+'_away'))\n",
0116     "    h_topo_towards[i].Rebin(10)\n",
0117     "    h_topo_transverse[i].Rebin(2)\n",
0118     "    h_topo_away[i].Rebin(10)\n",
0119     "    h_topo_towards[i].Scale(1.0/h_topo_towards[i].Integral())\n",
0120     "    h_topo_transverse[i].Scale(1.0/h_topo_transverse[i].Integral())\n",
0121     "    h_topo_away[i].Scale(1.0/h_topo_away[i].Integral())\n",
0122     "    h_topo_towards[i].SetDirectory(0)\n",
0123     "    h_topo_transverse[i].SetDirectory(0)\n",
0124     "    h_topo_away[i].SetDirectory(0)\n",
0125     "    h_2D_topo_towards.append(f2.Get('h_2D_topo'+t+'_towards'))\n",
0126     "    h_2D_topo_transverse.append(f2.Get('h_2D_topo'+t+'_transverse'))\n",
0127     "    h_2D_topo_away.append(f2.Get('h_2D_topo'+t+'_away'))\n",
0128     "    h_2D_topo_towards[i].SetDirectory(0)\n",
0129     "    h_2D_topo_transverse[i].SetDirectory(0)\n",
0130     "    h_2D_topo_away[i].SetDirectory(0)\n",
0131     "f2.Close()"
0132    ]
0133   },
0134   {
0135    "cell_type": "code",
0136    "execution_count": 179,
0137    "id": "0a2d0ac2-c67a-4e70-9fde-08add90aa40b",
0138    "metadata": {},
0139    "outputs": [],
0140    "source": [
0141     "f2 = ROOT.TFile.Open(\"ue_unfolding/dijet_calo_analysis_waveform_topocluster_simulation_pt7cut_wAj_wntopo.root\")\n",
0142     "h_mc_ntopo_towards = []\n",
0143     "h_mc_ntopo_transverse = []\n",
0144     "h_mc_ntopo_away = []\n",
0145     "h_mc_topo_towards = []\n",
0146     "h_mc_topo_transverse = []\n",
0147     "h_mc_topo_away = []\n",
0148     "h_mc_2D_topo_towards = []\n",
0149     "h_mc_2D_topo_transverse = []\n",
0150     "h_mc_2D_topo_away = []\n",
0151     "for i, t in enumerate(topo_thres):\n",
0152     "    h_mc_ntopo_towards.append(f2.Get('h_ntopo'+t+'_towards'))\n",
0153     "    h_mc_ntopo_transverse.append(f2.Get('h_ntopo'+t+'_transverse'))\n",
0154     "    h_mc_ntopo_away.append(f2.Get('h_ntopo'+t+'_away'))\n",
0155     "    h_mc_ntopo_towards[i].Scale(1.0/h_mc_ntopo_towards[i].Integral())\n",
0156     "    h_mc_ntopo_transverse[i].Scale(1.0/h_mc_ntopo_transverse[i].Integral())\n",
0157     "    h_mc_ntopo_away[i].Scale(1.0/h_mc_ntopo_away[i].Integral())\n",
0158     "    h_mc_ntopo_towards[i].SetDirectory(0)\n",
0159     "    h_mc_ntopo_transverse[i].SetDirectory(0)\n",
0160     "    h_mc_ntopo_away[i].SetDirectory(0)\n",
0161     "    h_mc_topo_towards.append(f2.Get('h_topo'+t+'_towards'))\n",
0162     "    h_mc_topo_transverse.append(f2.Get('h_topo'+t+'_transverse'))\n",
0163     "    h_mc_topo_away.append(f2.Get('h_topo'+t+'_away'))\n",
0164     "    h_mc_topo_towards[i].Rebin(10)\n",
0165     "    h_mc_topo_transverse[i].Rebin(2)\n",
0166     "    h_mc_topo_away[i].Rebin(10)\n",
0167     "    h_mc_topo_towards[i].Scale(1.0/h_mc_topo_towards[i].Integral())\n",
0168     "    h_mc_topo_transverse[i].Scale(1.0/h_mc_topo_transverse[i].Integral())\n",
0169     "    h_mc_topo_away[i].Scale(1.0/h_mc_topo_away[i].Integral())\n",
0170     "    h_mc_topo_towards[i].SetDirectory(0)\n",
0171     "    h_mc_topo_transverse[i].SetDirectory(0)\n",
0172     "    h_mc_topo_away[i].SetDirectory(0)\n",
0173     "    h_mc_2D_topo_towards.append(f2.Get('h_2D_topo'+t+'_towards'))\n",
0174     "    h_mc_2D_topo_transverse.append(f2.Get('h_2D_topo'+t+'_transverse'))\n",
0175     "    h_mc_2D_topo_away.append(f2.Get('h_2D_topo'+t+'_away'))\n",
0176     "    h_mc_2D_topo_towards[i].SetDirectory(0)\n",
0177     "    h_mc_2D_topo_transverse[i].SetDirectory(0)\n",
0178     "    h_mc_2D_topo_away[i].SetDirectory(0)\n",
0179     "f2.Close()  \n",
0180     "f2 = ROOT.TFile.Open(\"ue_unfolding/dijet_calo_analysis_cluster_topocluster_simulation_pt7cut_wAj_wntopo.root\")\n",
0181     "h_clus_ntopo_towards = []\n",
0182     "h_clus_ntopo_transverse = []\n",
0183     "h_clus_ntopo_away = []\n",
0184     "h_clus_topo_towards = []\n",
0185     "h_clus_topo_transverse = []\n",
0186     "h_clus_topo_away = []\n",
0187     "h_clus_2D_topo_towards = []\n",
0188     "h_clus_2D_topo_transverse = []\n",
0189     "h_clus_2D_topo_away = []\n",
0190     "for i, t in enumerate(topo_thres):\n",
0191     "    h_clus_ntopo_towards.append(f2.Get('h_ntopo'+t+'_towards'))\n",
0192     "    h_clus_ntopo_transverse.append(f2.Get('h_ntopo'+t+'_transverse'))\n",
0193     "    h_clus_ntopo_away.append(f2.Get('h_ntopo'+t+'_away'))\n",
0194     "    h_clus_ntopo_towards[i].Scale(1.0/h_clus_ntopo_towards[i].Integral())\n",
0195     "    h_clus_ntopo_transverse[i].Scale(1.0/h_clus_ntopo_transverse[i].Integral())\n",
0196     "    h_clus_ntopo_away[i].Scale(1.0/h_clus_ntopo_away[i].Integral())\n",
0197     "    h_clus_ntopo_towards[i].SetDirectory(0)\n",
0198     "    h_clus_ntopo_transverse[i].SetDirectory(0)\n",
0199     "    h_clus_ntopo_away[i].SetDirectory(0)\n",
0200     "    h_clus_topo_towards.append(f2.Get('h_topo'+t+'_towards'))\n",
0201     "    h_clus_topo_transverse.append(f2.Get('h_topo'+t+'_transverse'))\n",
0202     "    h_clus_topo_away.append(f2.Get('h_topo'+t+'_away'))\n",
0203     "    h_clus_topo_towards[i].Rebin(10)\n",
0204     "    h_clus_topo_transverse[i].Rebin(2)\n",
0205     "    h_clus_topo_away[i].Rebin(10)\n",
0206     "    h_clus_topo_towards[i].Scale(1.0/h_clus_topo_towards[i].Integral())\n",
0207     "    h_clus_topo_transverse[i].Scale(1.0/h_clus_topo_transverse[i].Integral())\n",
0208     "    h_clus_topo_away[i].Scale(1.0/h_clus_topo_away[i].Integral())\n",
0209     "    h_clus_topo_towards[i].SetDirectory(0)\n",
0210     "    h_clus_topo_transverse[i].SetDirectory(0)\n",
0211     "    h_clus_topo_away[i].SetDirectory(0)\n",
0212     "    h_clus_2D_topo_towards.append(f2.Get('h_2D_topo'+t+'_towards'))\n",
0213     "    h_clus_2D_topo_transverse.append(f2.Get('h_2D_topo'+t+'_transverse'))\n",
0214     "    h_clus_2D_topo_away.append(f2.Get('h_2D_topo'+t+'_away'))\n",
0215     "    h_clus_2D_topo_towards[i].SetDirectory(0)\n",
0216     "    h_clus_2D_topo_transverse[i].SetDirectory(0)\n",
0217     "    h_clus_2D_topo_away[i].SetDirectory(0)\n",
0218     "f2.Close()\n",
0219     "f2 = ROOT.TFile.Open(\"ue_unfolding/dijet_calo_analysis_nozero_topocluster_simulation_pt7cut_wAj_wntopo.root\")\n",
0220     "h_nz_ntopo_towards = []\n",
0221     "h_nz_ntopo_transverse = []\n",
0222     "h_nz_ntopo_away = []\n",
0223     "h_nz_topo_towards = []\n",
0224     "h_nz_topo_transverse = []\n",
0225     "h_nz_topo_away = []\n",
0226     "h_nz_2D_topo_towards = []\n",
0227     "h_nz_2D_topo_transverse = []\n",
0228     "h_nz_2D_topo_away = []\n",
0229     "for i, t in enumerate(topo_thres):\n",
0230     "    h_nz_ntopo_towards.append(f2.Get('h_ntopo'+t+'_towards'))\n",
0231     "    h_nz_ntopo_transverse.append(f2.Get('h_ntopo'+t+'_transverse'))\n",
0232     "    h_nz_ntopo_away.append(f2.Get('h_ntopo'+t+'_away'))\n",
0233     "    h_nz_ntopo_towards[i].Scale(1.0/h_nz_ntopo_towards[i].Integral())\n",
0234     "    h_nz_ntopo_transverse[i].Scale(1.0/h_nz_ntopo_transverse[i].Integral())\n",
0235     "    h_nz_ntopo_away[i].Scale(1.0/h_nz_ntopo_away[i].Integral())\n",
0236     "    h_nz_ntopo_towards[i].SetDirectory(0)\n",
0237     "    h_nz_ntopo_transverse[i].SetDirectory(0)\n",
0238     "    h_nz_ntopo_away[i].SetDirectory(0)\n",
0239     "    h_nz_topo_towards.append(f2.Get('h_topo'+t+'_towards'))\n",
0240     "    h_nz_topo_transverse.append(f2.Get('h_topo'+t+'_transverse'))\n",
0241     "    h_nz_topo_away.append(f2.Get('h_topo'+t+'_away'))\n",
0242     "    h_nz_topo_towards[i].Rebin(10)\n",
0243     "    h_nz_topo_transverse[i].Rebin(2)\n",
0244     "    h_nz_topo_away[i].Rebin(10)\n",
0245     "    h_nz_topo_towards[i].Scale(1.0/h_nz_topo_towards[i].Integral())\n",
0246     "    h_nz_topo_transverse[i].Scale(1.0/h_nz_topo_transverse[i].Integral())\n",
0247     "    h_nz_topo_away[i].Scale(1.0/h_nz_topo_away[i].Integral())\n",
0248     "    h_nz_topo_towards[i].SetDirectory(0)\n",
0249     "    h_nz_topo_transverse[i].SetDirectory(0)\n",
0250     "    h_nz_topo_away[i].SetDirectory(0)\n",
0251     "    h_nz_2D_topo_towards.append(f2.Get('h_2D_topo'+t+'_towards'))\n",
0252     "    h_nz_2D_topo_transverse.append(f2.Get('h_2D_topo'+t+'_transverse'))\n",
0253     "    h_nz_2D_topo_away.append(f2.Get('h_2D_topo'+t+'_away'))\n",
0254     "    h_nz_2D_topo_towards[i].SetDirectory(0)\n",
0255     "    h_nz_2D_topo_transverse[i].SetDirectory(0)\n",
0256     "    h_nz_2D_topo_away[i].SetDirectory(0)\n",
0257     "f2.Close()\n",
0258     "f2 = ROOT.TFile.Open(\"ue_unfolding/dijet_calo_analysis_detroit_jet10_topocluster_simulation_pt7cut_wAj_wntopo.root\")\n",
0259     "h_dt_ntopo_towards = []\n",
0260     "h_dt_ntopo_transverse = []\n",
0261     "h_dt_ntopo_away = []\n",
0262     "h_dt_topo_towards = []\n",
0263     "h_dt_topo_transverse = []\n",
0264     "h_dt_topo_away = []\n",
0265     "h_dt_2D_topo_towards = []\n",
0266     "h_dt_2D_topo_transverse = []\n",
0267     "h_dt_2D_topo_away = []\n",
0268     "for i, t in enumerate(topo_thres):\n",
0269     "    h_dt_ntopo_towards.append(f2.Get('h_ntopo'+t+'_towards'))\n",
0270     "    h_dt_ntopo_transverse.append(f2.Get('h_ntopo'+t+'_transverse'))\n",
0271     "    h_dt_ntopo_away.append(f2.Get('h_ntopo'+t+'_away'))\n",
0272     "    h_dt_ntopo_towards[i].Scale(1.0/h_dt_ntopo_towards[i].Integral())\n",
0273     "    h_dt_ntopo_transverse[i].Scale(1.0/h_dt_ntopo_transverse[i].Integral())\n",
0274     "    h_dt_ntopo_away[i].Scale(1.0/h_dt_ntopo_away[i].Integral())\n",
0275     "    h_dt_ntopo_towards[i].SetDirectory(0)\n",
0276     "    h_dt_ntopo_transverse[i].SetDirectory(0)\n",
0277     "    h_dt_ntopo_away[i].SetDirectory(0)\n",
0278     "    h_dt_topo_towards.append(f2.Get('h_topo'+t+'_towards'))\n",
0279     "    h_dt_topo_transverse.append(f2.Get('h_topo'+t+'_transverse'))\n",
0280     "    h_dt_topo_away.append(f2.Get('h_topo'+t+'_away'))\n",
0281     "    h_dt_topo_towards[i].Rebin(10)\n",
0282     "    h_dt_topo_transverse[i].Rebin(2)\n",
0283     "    h_dt_topo_away[i].Rebin(10)\n",
0284     "    h_dt_topo_towards[i].Scale(1.0/h_dt_topo_towards[i].Integral())\n",
0285     "    h_dt_topo_transverse[i].Scale(1.0/h_dt_topo_transverse[i].Integral())\n",
0286     "    h_dt_topo_away[i].Scale(1.0/h_dt_topo_away[i].Integral())\n",
0287     "    h_dt_topo_towards[i].SetDirectory(0)\n",
0288     "    h_dt_topo_transverse[i].SetDirectory(0)\n",
0289     "    h_dt_topo_away[i].SetDirectory(0)\n",
0290     "    h_dt_2D_topo_towards.append(f2.Get('h_2D_topo'+t+'_towards'))\n",
0291     "    h_dt_2D_topo_transverse.append(f2.Get('h_2D_topo'+t+'_transverse'))\n",
0292     "    h_dt_2D_topo_away.append(f2.Get('h_2D_topo'+t+'_away'))\n",
0293     "    h_dt_2D_topo_towards[i].SetDirectory(0)\n",
0294     "    h_dt_2D_topo_transverse[i].SetDirectory(0)\n",
0295     "    h_dt_2D_topo_away[i].SetDirectory(0)\n",
0296     "f2.Close()"
0297    ]
0298   },
0299   {
0300    "cell_type": "code",
0301    "execution_count": 180,
0302    "id": "f09a395a-caa3-4b88-a03c-4bdfcb86d212",
0303    "metadata": {},
0304    "outputs": [],
0305    "source": [
0306     "mean_ntopo_towards = []\n",
0307     "mean_ntopo_transverse = []\n",
0308     "mean_ntopo_away = []\n",
0309     "mean_mc_ntopo_towards = []\n",
0310     "mean_mc_ntopo_transverse = []\n",
0311     "mean_mc_ntopo_away = []\n",
0312     "mean_clus_ntopo_towards = []\n",
0313     "mean_clus_ntopo_transverse = []\n",
0314     "mean_clus_ntopo_away = []\n",
0315     "mean_nz_ntopo_towards = []\n",
0316     "mean_nz_ntopo_transverse = []\n",
0317     "mean_nz_ntopo_away = []\n",
0318     "mean_dt_ntopo_towards = []\n",
0319     "mean_dt_ntopo_transverse = []\n",
0320     "mean_dt_ntopo_away = []\n",
0321     "mean_topo_towards = []\n",
0322     "mean_topo_transverse = []\n",
0323     "mean_topo_away = []\n",
0324     "mean_mc_topo_towards = []\n",
0325     "mean_mc_topo_transverse = []\n",
0326     "mean_mc_topo_away = []\n",
0327     "mean_clus_topo_towards = []\n",
0328     "mean_clus_topo_transverse = []\n",
0329     "mean_clus_topo_away = []\n",
0330     "mean_nz_topo_towards = []\n",
0331     "mean_nz_topo_transverse = []\n",
0332     "mean_nz_topo_away = []\n",
0333     "mean_dt_topo_towards = []\n",
0334     "mean_dt_topo_transverse = []\n",
0335     "mean_dt_topo_away = []\n",
0336     "\n",
0337     "std_ntopo_towards = []\n",
0338     "std_ntopo_transverse = []\n",
0339     "std_ntopo_away = []\n",
0340     "std_mc_ntopo_towards = []\n",
0341     "std_mc_ntopo_transverse = []\n",
0342     "std_mc_ntopo_away = []\n",
0343     "std_clus_ntopo_towards = []\n",
0344     "std_clus_ntopo_transverse = []\n",
0345     "std_clus_ntopo_away = []\n",
0346     "std_nz_ntopo_towards = []\n",
0347     "std_nz_ntopo_transverse = []\n",
0348     "std_nz_ntopo_away = []\n",
0349     "std_dt_ntopo_towards = []\n",
0350     "std_dt_ntopo_transverse = []\n",
0351     "std_dt_ntopo_away = []\n",
0352     "std_topo_towards = []\n",
0353     "std_topo_transverse = []\n",
0354     "std_topo_away = []\n",
0355     "std_mc_topo_towards = []\n",
0356     "std_mc_topo_transverse = []\n",
0357     "std_mc_topo_away = []\n",
0358     "std_clus_topo_towards = []\n",
0359     "std_clus_topo_transverse = []\n",
0360     "std_clus_topo_away = []\n",
0361     "std_nz_topo_towards = []\n",
0362     "std_nz_topo_transverse = []\n",
0363     "std_nz_topo_away = []\n",
0364     "std_dt_topo_towards = []\n",
0365     "std_dt_topo_transverse = []\n",
0366     "std_dt_topo_away = []\n",
0367     "\n",
0368     "for i in range(len(h_ntopo_towards)):\n",
0369     "    mean_ntopo_towards.append(h_ntopo_towards[i].GetMean())\n",
0370     "    mean_ntopo_transverse.append(h_ntopo_transverse[i].GetMean())\n",
0371     "    mean_ntopo_away.append(h_ntopo_away[i].GetMean())\n",
0372     "    mean_mc_ntopo_towards.append(h_mc_ntopo_towards[i].GetMean())\n",
0373     "    mean_mc_ntopo_transverse.append(h_mc_ntopo_transverse[i].GetMean())\n",
0374     "    mean_mc_ntopo_away.append(h_mc_ntopo_away[i].GetMean())\n",
0375     "    mean_clus_ntopo_towards.append(h_clus_ntopo_towards[i].GetMean())\n",
0376     "    mean_clus_ntopo_transverse.append(h_clus_ntopo_transverse[i].GetMean())\n",
0377     "    mean_clus_ntopo_away.append(h_clus_ntopo_away[i].GetMean())\n",
0378     "    mean_nz_ntopo_towards.append(h_nz_ntopo_towards[i].GetMean())\n",
0379     "    mean_nz_ntopo_transverse.append(h_nz_ntopo_transverse[i].GetMean())\n",
0380     "    mean_nz_ntopo_away.append(h_nz_ntopo_away[i].GetMean())\n",
0381     "    mean_dt_ntopo_towards.append(h_dt_ntopo_towards[i].GetMean())\n",
0382     "    mean_dt_ntopo_transverse.append(h_dt_ntopo_transverse[i].GetMean())\n",
0383     "    mean_dt_ntopo_away.append(h_dt_ntopo_away[i].GetMean())\n",
0384     "    mean_topo_towards.append(h_topo_towards[i].GetMean())\n",
0385     "    mean_topo_transverse.append(h_topo_transverse[i].GetMean())\n",
0386     "    mean_topo_away.append(h_topo_away[i].GetMean())\n",
0387     "    mean_mc_topo_towards.append(h_mc_topo_towards[i].GetMean())\n",
0388     "    mean_mc_topo_transverse.append(h_mc_topo_transverse[i].GetMean())\n",
0389     "    mean_mc_topo_away.append(h_mc_topo_away[i].GetMean())\n",
0390     "    mean_clus_topo_towards.append(h_clus_topo_towards[i].GetMean())\n",
0391     "    mean_clus_topo_transverse.append(h_clus_topo_transverse[i].GetMean())\n",
0392     "    mean_clus_topo_away.append(h_clus_topo_away[i].GetMean())\n",
0393     "    mean_nz_topo_towards.append(h_nz_topo_towards[i].GetMean())\n",
0394     "    mean_nz_topo_transverse.append(h_nz_topo_transverse[i].GetMean())\n",
0395     "    mean_nz_topo_away.append(h_nz_topo_away[i].GetMean())\n",
0396     "    mean_dt_topo_towards.append(h_dt_topo_towards[i].GetMean())\n",
0397     "    mean_dt_topo_transverse.append(h_dt_topo_transverse[i].GetMean())\n",
0398     "    mean_dt_topo_away.append(h_dt_topo_away[i].GetMean())\n",
0399     "    \n",
0400     "    std_ntopo_towards.append(h_ntopo_towards[i].GetRMS())\n",
0401     "    std_ntopo_transverse.append(h_ntopo_transverse[i].GetRMS())\n",
0402     "    std_ntopo_away.append(h_ntopo_away[i].GetRMS())\n",
0403     "    std_mc_ntopo_towards.append(h_mc_ntopo_towards[i].GetRMS())\n",
0404     "    std_mc_ntopo_transverse.append(h_mc_ntopo_transverse[i].GetRMS())\n",
0405     "    std_mc_ntopo_away.append(h_mc_ntopo_away[i].GetRMS())\n",
0406     "    std_clus_ntopo_towards.append(h_clus_ntopo_towards[i].GetRMS())\n",
0407     "    std_clus_ntopo_transverse.append(h_clus_ntopo_transverse[i].GetRMS())\n",
0408     "    std_clus_ntopo_away.append(h_clus_ntopo_away[i].GetRMS())\n",
0409     "    std_nz_ntopo_towards.append(h_nz_ntopo_towards[i].GetRMS())\n",
0410     "    std_nz_ntopo_transverse.append(h_nz_ntopo_transverse[i].GetRMS())\n",
0411     "    std_nz_ntopo_away.append(h_nz_ntopo_away[i].GetRMS())\n",
0412     "    std_dt_ntopo_towards.append(h_dt_ntopo_towards[i].GetRMS())\n",
0413     "    std_dt_ntopo_transverse.append(h_dt_ntopo_transverse[i].GetRMS())\n",
0414     "    std_dt_ntopo_away.append(h_dt_ntopo_away[i].GetRMS())\n",
0415     "    std_topo_towards.append(h_topo_towards[i].GetRMS())\n",
0416     "    std_topo_transverse.append(h_topo_transverse[i].GetRMS())\n",
0417     "    std_topo_away.append(h_topo_away[i].GetRMS())\n",
0418     "    std_mc_topo_towards.append(h_mc_topo_towards[i].GetRMS())\n",
0419     "    std_mc_topo_transverse.append(h_mc_topo_transverse[i].GetRMS())\n",
0420     "    std_mc_topo_away.append(h_mc_topo_away[i].GetRMS())\n",
0421     "    std_clus_topo_towards.append(h_clus_topo_towards[i].GetRMS())\n",
0422     "    std_clus_topo_transverse.append(h_clus_topo_transverse[i].GetRMS())\n",
0423     "    std_clus_topo_away.append(h_clus_topo_away[i].GetRMS())\n",
0424     "    std_nz_topo_towards.append(h_nz_topo_towards[i].GetRMS())\n",
0425     "    std_nz_topo_transverse.append(h_nz_topo_transverse[i].GetRMS())\n",
0426     "    std_nz_topo_away.append(h_nz_topo_away[i].GetRMS())\n",
0427     "    std_dt_topo_towards.append(h_dt_topo_towards[i].GetRMS())\n",
0428     "    std_dt_topo_transverse.append(h_dt_topo_transverse[i].GetRMS())\n",
0429     "    std_dt_topo_away.append(h_dt_topo_away[i].GetRMS())"
0430    ]
0431   },
0432   {
0433    "cell_type": "code",
0434    "execution_count": 25,
0435    "id": "a331cbee-6a06-4f57-ad18-bf95d362f162",
0436    "metadata": {},
0437    "outputs": [],
0438    "source": []
0439   },
0440   {
0441    "cell_type": "code",
0442    "execution_count": 72,
0443    "id": "fccb848a-2c25-44d3-a20b-f215861a4d3f",
0444    "metadata": {},
0445    "outputs": [
0446     {
0447      "name": "stderr",
0448      "output_type": "stream",
0449      "text": [
0450       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0451       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_ntopo_towards-9999_Topoclusters.png has been created\n",
0452       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0453       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_ntopo_towards0_Topoclusters.png has been created\n",
0454       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0455       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_ntopo_towards100_Topoclusters.png has been created\n",
0456       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0457       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_ntopo_towards200_Topoclusters.png has been created\n",
0458       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0459       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_ntopo_towards300_Topoclusters.png has been created\n",
0460       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0461       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_ntopo_towards500_Topoclusters.png has been created\n"
0462      ]
0463     },
0464     {
0465      "data": {
0466       "image/png": 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\n",
0467       "text/plain": [
0468        "<IPython.core.display.Image object>"
0469       ]
0470      },
0471      "metadata": {},
0472      "output_type": "display_data"
0473     }
0474    ],
0475    "source": [
0476     "for i in range(len(h_ntopo_towards)):\n",
0477     "    canvas = ROOT.TCanvas(\"canvas\",\"\",600,500)\n",
0478     "    leg = ROOT.TLegend(.55,.65,.92,.92)\n",
0479     "    leg.AddEntry(\"\",\"#bf{Towards Region}\",\"\")\n",
0480     "    leg.AddEntry(\"\",thres_string[i],\"\")\n",
0481     "    leg.AddEntry(h_ntopo_towards[i],\"Jet Trig. Data Topoclusters\",\"pl\")\n",
0482     "    leg.AddEntry(h_mc_ntopo_towards[i],\"Waveform Topoclusters\",\"pl\")\n",
0483     "    leg.AddEntry(h_clus_ntopo_towards[i],\"Cluster Topoclusters\",\"pl\")\n",
0484     "    leg.AddEntry(h_nz_ntopo_towards[i],\"No ZS Topoclusters\",\"pl\")\n",
0485     "    leg.AddEntry(h_dt_ntopo_towards[i],\"Detroit Waveform Topoclusters\",\"pl\")\n",
0486     "    h_ntopo_towards[i].SetStats(0)\n",
0487     "    h_mc_ntopo_towards[i].SetStats(0)\n",
0488     "    h_clus_ntopo_towards[i].SetStats(0)\n",
0489     "    h_nz_ntopo_towards[i].SetStats(0)\n",
0490     "    h_dt_ntopo_towards[i].SetStats(0)\n",
0491     "    h_mc_ntopo_towards[i].SetLineColor(2)\n",
0492     "    h_mc_ntopo_towards[i].SetMarkerColor(2)\n",
0493     "    h_clus_ntopo_towards[i].SetLineColor(4)\n",
0494     "    h_clus_ntopo_towards[i].SetMarkerColor(4)\n",
0495     "    h_nz_ntopo_towards[i].SetLineColor(6)\n",
0496     "    h_nz_ntopo_towards[i].SetMarkerColor(6)\n",
0497     "    h_dt_ntopo_towards[i].SetLineColor(7)\n",
0498     "    h_dt_ntopo_towards[i].SetMarkerColor(7)\n",
0499     "    h_mc_ntopo_towards[i].SetMarkerStyle(20)\n",
0500     "    h_clus_ntopo_towards[i].SetMarkerStyle(20)\n",
0501     "    h_nz_ntopo_towards[i].SetMarkerStyle(20)\n",
0502     "    h_mc_ntopo_towards[i].GetXaxis().SetRangeUser(0,80)\n",
0503     "    h_mc_ntopo_towards[i].Draw()\n",
0504     "    h_dt_ntopo_towards[i].Draw('same')\n",
0505     "    h_clus_ntopo_towards[i].Draw('same')\n",
0506     "    h_nz_ntopo_towards[i].Draw('same')\n",
0507     "    h_ntopo_towards[i].Draw('same')\n",
0508     "    h_mc_ntopo_towards[i].SetXTitle(\"N_{topo}\") \n",
0509     "    canvas.SetLogy(1)\n",
0510     "    leg.SetTextSize(0.035)\n",
0511     "    leg.Draw()\n",
0512     "    canvas.Draw()\n",
0513     "    canvas.SaveAs(\"/sphenix/u/egm2153/fall_2024/\"+direct+\"/h_ntopo_towards\"+topo_thres[i]+\"_Topoclusters.png\")"
0514    ]
0515   },
0516   {
0517    "cell_type": "code",
0518    "execution_count": 26,
0519    "id": "68c380de-7d13-4208-89b8-4a90fafad2a9",
0520    "metadata": {},
0521    "outputs": [
0522     {
0523      "name": "stderr",
0524      "output_type": "stream",
0525      "text": [
0526       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0527       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_ntopo_transverse-9999_Topoclusters.png has been created\n",
0528       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0529       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_ntopo_transverse0_Topoclusters.png has been created\n",
0530       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0531       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_ntopo_transverse100_Topoclusters.png has been created\n",
0532       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0533       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_ntopo_transverse200_Topoclusters.png has been created\n",
0534       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0535       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_ntopo_transverse300_Topoclusters.png has been created\n",
0536       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0537       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_ntopo_transverse500_Topoclusters.png has been created\n"
0538      ]
0539     },
0540     {
0541      "data": {
0542       "image/png": 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\n",
0543       "text/plain": [
0544        "<IPython.core.display.Image object>"
0545       ]
0546      },
0547      "metadata": {},
0548      "output_type": "display_data"
0549     }
0550    ],
0551    "source": [
0552     "for i in range(len(h_ntopo_transverse)):\n",
0553     "    canvas = ROOT.TCanvas(\"canvas\",\"\",600,500)\n",
0554     "    leg = ROOT.TLegend(.55,.65,.92,.92)\n",
0555     "    leg.AddEntry(\"\",\"#bf{Transverse Region}\",\"\")\n",
0556     "    leg.AddEntry(\"\",thres_string[i],\"\")\n",
0557     "    leg.AddEntry(h_ntopo_transverse[i],\"Jet Trig. Data Topoclusters\",\"pl\")\n",
0558     "    leg.AddEntry(h_mc_ntopo_transverse[i],\"Waveform Topoclusters\",\"pl\")\n",
0559     "    leg.AddEntry(h_clus_ntopo_transverse[i],\"Cluster Topoclusters\",\"pl\")\n",
0560     "    leg.AddEntry(h_nz_ntopo_transverse[i],\"No ZS Topoclusters\",\"pl\")\n",
0561     "    leg.AddEntry(h_dt_ntopo_transverse[i],\"Detroit Waveform Topoclusters\",\"pl\")\n",
0562     "    h_ntopo_transverse[i].SetStats(0)\n",
0563     "    h_mc_ntopo_transverse[i].SetStats(0)\n",
0564     "    h_clus_ntopo_transverse[i].SetStats(0)\n",
0565     "    h_nz_ntopo_transverse[i].SetStats(0)\n",
0566     "    h_dt_ntopo_transverse[i].SetStats(0)\n",
0567     "    h_mc_ntopo_transverse[i].SetLineColor(2)\n",
0568     "    h_mc_ntopo_transverse[i].SetMarkerColor(2)\n",
0569     "    h_clus_ntopo_transverse[i].SetLineColor(4)\n",
0570     "    h_clus_ntopo_transverse[i].SetMarkerColor(4)\n",
0571     "    h_nz_ntopo_transverse[i].SetLineColor(6)\n",
0572     "    h_nz_ntopo_transverse[i].SetMarkerColor(6)\n",
0573     "    h_dt_ntopo_transverse[i].SetLineColor(7)\n",
0574     "    h_dt_ntopo_transverse[i].SetMarkerColor(7)\n",
0575     "    h_mc_ntopo_transverse[i].SetMarkerStyle(20)\n",
0576     "    h_clus_ntopo_transverse[i].SetMarkerStyle(20)\n",
0577     "    h_nz_ntopo_transverse[i].SetMarkerStyle(20)\n",
0578     "    h_mc_ntopo_transverse[i].GetXaxis().SetRangeUser(0,80)\n",
0579     "    h_mc_ntopo_transverse[i].Draw()\n",
0580     "    h_dt_ntopo_transverse[i].Draw('same')\n",
0581     "    h_clus_ntopo_transverse[i].Draw('same')\n",
0582     "    h_nz_ntopo_transverse[i].Draw('same')\n",
0583     "    h_ntopo_transverse[i].Draw('same')\n",
0584     "    h_mc_ntopo_transverse[i].SetXTitle(\"N_{topo}\") \n",
0585     "    canvas.SetLogy(1)\n",
0586     "    leg.SetTextSize(0.035)\n",
0587     "    leg.Draw()\n",
0588     "    canvas.Draw()\n",
0589     "    canvas.SaveAs(\"/sphenix/u/egm2153/fall_2024/\"+direct+\"/h_ntopo_transverse\"+topo_thres[i]+\"_Topoclusters.png\")"
0590    ]
0591   },
0592   {
0593    "cell_type": "code",
0594    "execution_count": 27,
0595    "id": "19ff26eb-e474-4eec-9441-ba385b1ef79d",
0596    "metadata": {},
0597    "outputs": [
0598     {
0599      "name": "stderr",
0600      "output_type": "stream",
0601      "text": [
0602       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0603       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_ntopo_away-9999_Topoclusters.png has been created\n",
0604       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0605       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_ntopo_away0_Topoclusters.png has been created\n",
0606       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0607       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_ntopo_away100_Topoclusters.png has been created\n",
0608       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0609       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_ntopo_away200_Topoclusters.png has been created\n",
0610       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0611       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_ntopo_away300_Topoclusters.png has been created\n",
0612       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0613       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_ntopo_away500_Topoclusters.png has been created\n"
0614      ]
0615     },
0616     {
0617      "data": {
0618       "image/png": 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\n",
0619       "text/plain": [
0620        "<IPython.core.display.Image object>"
0621       ]
0622      },
0623      "metadata": {},
0624      "output_type": "display_data"
0625     }
0626    ],
0627    "source": [
0628     "for i in range(len(h_ntopo_away)):\n",
0629     "    canvas = ROOT.TCanvas(\"canvas\",\"\",600,500)\n",
0630     "    leg = ROOT.TLegend(.55,.65,.92,.92)\n",
0631     "    leg.AddEntry(\"\",\"#bf{Away Region}\",\"\")\n",
0632     "    leg.AddEntry(\"\",thres_string[i],\"\")\n",
0633     "    leg.AddEntry(h_ntopo_away[i],\"Jet Trig. Data Topoclusters\",\"pl\")\n",
0634     "    leg.AddEntry(h_mc_ntopo_away[i],\"Waveform Topoclusters\",\"pl\")\n",
0635     "    leg.AddEntry(h_clus_ntopo_away[i],\"Cluster Topoclusters\",\"pl\")\n",
0636     "    leg.AddEntry(h_nz_ntopo_away[i],\"No ZS Topoclusters\",\"pl\")\n",
0637     "    leg.AddEntry(h_dt_ntopo_away[i],\"Detroit Waveform Topoclusters\",\"pl\")\n",
0638     "    h_ntopo_away[i].SetStats(0)\n",
0639     "    h_mc_ntopo_away[i].SetStats(0)\n",
0640     "    h_clus_ntopo_away[i].SetStats(0)\n",
0641     "    h_nz_ntopo_away[i].SetStats(0)\n",
0642     "    h_dt_ntopo_away[i].SetStats(0)\n",
0643     "    h_mc_ntopo_away[i].SetLineColor(2)\n",
0644     "    h_mc_ntopo_away[i].SetMarkerColor(2)\n",
0645     "    h_clus_ntopo_away[i].SetLineColor(4)\n",
0646     "    h_clus_ntopo_away[i].SetMarkerColor(4)\n",
0647     "    h_nz_ntopo_away[i].SetLineColor(6)\n",
0648     "    h_nz_ntopo_away[i].SetMarkerColor(6)\n",
0649     "    h_dt_ntopo_away[i].SetLineColor(7)\n",
0650     "    h_dt_ntopo_away[i].SetMarkerColor(7)\n",
0651     "    h_mc_ntopo_away[i].SetMarkerStyle(20)\n",
0652     "    h_clus_ntopo_away[i].SetMarkerStyle(20)\n",
0653     "    h_nz_ntopo_away[i].SetMarkerStyle(20)\n",
0654     "    h_mc_ntopo_away[i].GetXaxis().SetRangeUser(0,80)\n",
0655     "    h_mc_ntopo_away[i].Draw()\n",
0656     "    h_dt_ntopo_away[i].Draw('same')\n",
0657     "    h_clus_ntopo_away[i].Draw('same')\n",
0658     "    h_nz_ntopo_away[i].Draw('same')\n",
0659     "    h_ntopo_away[i].Draw('same')\n",
0660     "    h_mc_ntopo_away[i].SetXTitle(\"N_{topo}\") \n",
0661     "    canvas.SetLogy(1)\n",
0662     "    leg.SetTextSize(0.035)\n",
0663     "    leg.Draw()\n",
0664     "    canvas.Draw()\n",
0665     "    canvas.SaveAs(\"/sphenix/u/egm2153/fall_2024/\"+direct+\"/h_ntopo_away\"+topo_thres[i]+\"_Topoclusters.png\")"
0666    ]
0667   },
0668   {
0669    "cell_type": "code",
0670    "execution_count": 181,
0671    "id": "5f3fe02f-2513-42ab-b08a-45d1e9262922",
0672    "metadata": {},
0673    "outputs": [],
0674    "source": [
0675     "xlist0 = [-125,-25,75,175,275,475]\n",
0676     "xlist1 = [-115,-15,85,185,285,485]\n",
0677     "xlist2 = [-105,-5,95,195,295,495]\n",
0678     "xlist3 = [-95,5,105,205,305,505]\n",
0679     "xlist4 = [-85,15,115,215,315,515]\n",
0680     "xlist5 = [-75,25,125,225,325,525]\n",
0681     "xerr = [0,0,0,0,0,0]\n",
0682     "x0 = np.array(xlist0, dtype='float64')\n",
0683     "x1 = np.array(xlist1, dtype='float64')\n",
0684     "x2 = np.array(xlist2, dtype='float64')\n",
0685     "x3 = np.array(xlist3, dtype='float64')\n",
0686     "x4 = np.array(xlist4, dtype='float64')\n",
0687     "x5 = np.array(xlist5, dtype='float64')"
0688    ]
0689   },
0690   {
0691    "cell_type": "code",
0692    "execution_count": 170,
0693    "id": "58f462b2-9ceb-4322-a30b-7ec8870ab7be",
0694    "metadata": {},
0695    "outputs": [
0696     {
0697      "name": "stderr",
0698      "output_type": "stream",
0699      "text": [
0700       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0701       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_mean_ntopo_towards.png has been created\n"
0702      ]
0703     },
0704     {
0705      "data": {
0706       "image/png": 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\n",
0707       "text/plain": [
0708        "<IPython.core.display.Image object>"
0709       ]
0710      },
0711      "metadata": {},
0712      "output_type": "display_data"
0713     }
0714    ],
0715    "source": [
0716     "# Create a canvas\n",
0717     "canvas = ROOT.TCanvas(\"canvas\", \"Graph\", 600, 600)\n",
0718     "\n",
0719     "# Define marker styles and colors\n",
0720     "marker_styles = {\n",
0721     "    \"towards\": ROOT.kFullCircle,\n",
0722     "    \"transverse\": ROOT.kFullCircle,\n",
0723     "    \"away\": ROOT.kFullCircle\n",
0724     "}\n",
0725     "\n",
0726     "colors = {\n",
0727     "    \"ntopo\": ROOT.kBlack,\n",
0728     "    \"mc_ntopo\": ROOT.kRed,\n",
0729     "    \"clus_ntopo\": ROOT.kBlue,\n",
0730     "    \"nz_ntopo\": ROOT.kMagenta,\n",
0731     "    \"dt_ntopo\": ROOT.kCyan\n",
0732     "}\n",
0733     "\n",
0734     "# Prepare a list of all data groups\n",
0735     "data_groups = [\n",
0736     "    (\"mc_ntopo\", x0, mean_mc_ntopo_towards, mean_mc_ntopo_transverse, mean_mc_ntopo_away, std_mc_ntopo_towards, std_mc_ntopo_transverse, std_mc_ntopo_away),\n",
0737     "    (\"clus_ntopo\", x1, mean_clus_ntopo_towards, mean_clus_ntopo_transverse, mean_clus_ntopo_away, std_clus_ntopo_towards, std_clus_ntopo_transverse, std_clus_ntopo_away),\n",
0738     "    (\"nz_ntopo\", x2, mean_nz_ntopo_towards, mean_nz_ntopo_transverse, mean_nz_ntopo_away, std_nz_ntopo_towards, std_nz_ntopo_transverse, std_nz_ntopo_away),\n",
0739     "    (\"dt_ntopo\", x3, mean_dt_ntopo_towards, mean_dt_ntopo_transverse, mean_dt_ntopo_away, std_dt_ntopo_towards, std_dt_ntopo_transverse, std_dt_ntopo_away),\n",
0740     "    (\"ntopo\", x4, mean_ntopo_towards, mean_ntopo_transverse, mean_ntopo_away, std_ntopo_towards, std_ntopo_transverse, std_ntopo_away)\n",
0741     "]\n",
0742     "\n",
0743     "graphs = []\n",
0744     "    \n",
0745     "# Create TGraphs for each data group\n",
0746     "for group, x, towards, transverse, away, towards_width, transverse_width, away_width in data_groups:\n",
0747     "    n_points = len(towards)\n",
0748     "\n",
0749     "    # Create graphs for towards, transverse, and away\n",
0750     "    graph_towards = ROOT.TGraphErrors(n_points, x, np.array(towards, dtype='float64'), np.array(xerr, dtype='float64'), np.array(towards_width, dtype='float64'))\n",
0751     "    graph_transverse = ROOT.TGraphErrors(n_points, x, np.array(transverse, dtype='float64'), np.array(xerr, dtype='float64'), np.array(transverse_width, dtype='float64'))\n",
0752     "    graph_away = ROOT.TGraphErrors(n_points, x, np.array(away, dtype='float64'), np.array(xerr, dtype='float64'), np.array(away_width, dtype='float64'))\n",
0753     "\n",
0754     "    # Set marker styles and colors\n",
0755     "    graph_towards.SetMarkerStyle(marker_styles[\"towards\"])\n",
0756     "    graph_transverse.SetMarkerStyle(marker_styles[\"transverse\"])\n",
0757     "    graph_away.SetMarkerStyle(marker_styles[\"away\"])\n",
0758     "\n",
0759     "    graph_towards.SetMarkerColor(colors[group])\n",
0760     "    graph_transverse.SetMarkerColor(colors[group])\n",
0761     "    graph_away.SetMarkerColor(colors[group])\n",
0762     "\n",
0763     "    graph_towards.SetLineColor(colors[group])\n",
0764     "    graph_transverse.SetLineColor(colors[group])\n",
0765     "    graph_away.SetLineColor(colors[group])\n",
0766     "\n",
0767     "    graphs.extend([graph_towards, graph_transverse, graph_away])\n",
0768     "\n",
0769     "# Draw all graphs on the same canvas\n",
0770     "for i, graph in enumerate(graphs):\n",
0771     "    if i % 3 == 0:\n",
0772     "        if i == 0:\n",
0773     "            graph.Draw(\"AP\")\n",
0774     "            graph.GetYaxis().SetRangeUser(0,35)\n",
0775     "            graph.GetXaxis().SetTitle(\"E_{topo} thres [MeV]\")\n",
0776     "            graph.GetYaxis().SetTitle(\"<N_{topo}>\")\n",
0777     "        else:\n",
0778     "             graph.Draw(\"P SAME\")\n",
0779     "\n",
0780     "# Adding a legend\n",
0781     "legend = ROOT.TLegend(0.53, 0.65, 0.92, 0.92)\n",
0782     "legend.SetNColumns(1)\n",
0783     "legend.AddEntry(\"\",\"#bf{Towards Region}\",\"\")\n",
0784     "legend.AddEntry(graphs[1], \"Waveform\", \"pe\")\n",
0785     "legend.AddEntry(graphs[4], \"Clusters\", \"pe\")\n",
0786     "legend.AddEntry(graphs[7], \"No ZS\", \"pe\")\n",
0787     "legend.AddEntry(graphs[10], \"Detroit Waveform\", \"ep\")\n",
0788     "legend.AddEntry(graphs[13], \"Jet Trig. Data\", \"pe\")\n",
0789     "legend.Draw()\n",
0790     "\n",
0791     "# Update and display the canvas\n",
0792     "canvas.Update()\n",
0793     "canvas.Draw()\n",
0794     "canvas.SaveAs(\"/sphenix/u/egm2153/fall_2024/\"+direct+\"/h_mean_ntopo_towards.png\")"
0795    ]
0796   },
0797   {
0798    "cell_type": "code",
0799    "execution_count": 169,
0800    "id": "7eaddce7-57e6-4b57-944d-66734e1eaefd",
0801    "metadata": {},
0802    "outputs": [
0803     {
0804      "name": "stderr",
0805      "output_type": "stream",
0806      "text": [
0807       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0808       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_mean_ntopo_transverse.png has been created\n"
0809      ]
0810     },
0811     {
0812      "data": {
0813       "image/png": 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\n",
0814       "text/plain": [
0815        "<IPython.core.display.Image object>"
0816       ]
0817      },
0818      "metadata": {},
0819      "output_type": "display_data"
0820     }
0821    ],
0822    "source": [
0823     "# Create a canvas\n",
0824     "canvas = ROOT.TCanvas(\"canvas\", \"Graph\", 600, 600)\n",
0825     "\n",
0826     "# Define marker styles and colors\n",
0827     "marker_styles = {\n",
0828     "    \"towards\": ROOT.kFullCircle,\n",
0829     "    \"transverse\": ROOT.kFullCircle,\n",
0830     "    \"away\": ROOT.kFullCircle\n",
0831     "}\n",
0832     "\n",
0833     "colors = {\n",
0834     "    \"ntopo\": ROOT.kBlack,\n",
0835     "    \"mc_ntopo\": ROOT.kRed,\n",
0836     "    \"clus_ntopo\": ROOT.kBlue,\n",
0837     "    \"nz_ntopo\": ROOT.kMagenta,\n",
0838     "    \"dt_ntopo\": ROOT.kCyan\n",
0839     "}\n",
0840     "\n",
0841     "# Prepare a list of all data groups\n",
0842     "data_groups = [\n",
0843     "    (\"mc_ntopo\", x0, mean_mc_ntopo_towards, mean_mc_ntopo_transverse, mean_mc_ntopo_away, std_mc_ntopo_towards, std_mc_ntopo_transverse, std_mc_ntopo_away),\n",
0844     "    (\"clus_ntopo\", x1, mean_clus_ntopo_towards, mean_clus_ntopo_transverse, mean_clus_ntopo_away, std_clus_ntopo_towards, std_clus_ntopo_transverse, std_clus_ntopo_away),\n",
0845     "    (\"nz_ntopo\", x2, mean_nz_ntopo_towards, mean_nz_ntopo_transverse, mean_nz_ntopo_away, std_nz_ntopo_towards, std_nz_ntopo_transverse, std_nz_ntopo_away),\n",
0846     "    (\"dt_ntopo\", x3, mean_dt_ntopo_towards, mean_dt_ntopo_transverse, mean_dt_ntopo_away, std_dt_ntopo_towards, std_dt_ntopo_transverse, std_dt_ntopo_away),\n",
0847     "    (\"ntopo\", x4, mean_ntopo_towards, mean_ntopo_transverse, mean_ntopo_away, std_ntopo_towards, std_ntopo_transverse, std_ntopo_away)\n",
0848     "]\n",
0849     "\n",
0850     "graphs = []\n",
0851     "    \n",
0852     "# Create TGraphs for each data group\n",
0853     "for group, x, towards, transverse, away, towards_width, transverse_width, away_width in data_groups:\n",
0854     "    n_points = len(towards)\n",
0855     "\n",
0856     "    # Create graphs for towards, transverse, and away\n",
0857     "    graph_towards = ROOT.TGraphErrors(n_points, x, np.array(towards, dtype='float64'), np.array(xerr, dtype='float64'), np.array(towards_width, dtype='float64'))\n",
0858     "    graph_transverse = ROOT.TGraphErrors(n_points, x, np.array(transverse, dtype='float64'), np.array(xerr, dtype='float64'), np.array(transverse_width, dtype='float64'))\n",
0859     "    graph_away = ROOT.TGraphErrors(n_points, x, np.array(away, dtype='float64'), np.array(xerr, dtype='float64'), np.array(away_width, dtype='float64'))\n",
0860     "\n",
0861     "    # Set marker styles and colors\n",
0862     "    graph_towards.SetMarkerStyle(marker_styles[\"towards\"])\n",
0863     "    graph_transverse.SetMarkerStyle(marker_styles[\"transverse\"])\n",
0864     "    graph_away.SetMarkerStyle(marker_styles[\"away\"])\n",
0865     "\n",
0866     "    graph_towards.SetMarkerColor(colors[group])\n",
0867     "    graph_transverse.SetMarkerColor(colors[group])\n",
0868     "    graph_away.SetMarkerColor(colors[group])\n",
0869     "\n",
0870     "    graph_towards.SetLineColor(colors[group])\n",
0871     "    graph_transverse.SetLineColor(colors[group])\n",
0872     "    graph_away.SetLineColor(colors[group])\n",
0873     "\n",
0874     "    graphs.extend([graph_towards, graph_transverse, graph_away])\n",
0875     "\n",
0876     "# Draw all graphs on the same canvas\n",
0877     "for i, graph in enumerate(graphs):\n",
0878     "    if (i-1) % 3 == 0:\n",
0879     "        if i == 1:\n",
0880     "            graph.Draw(\"AP\")\n",
0881     "            graph.GetYaxis().SetRangeUser(0,35)\n",
0882     "            graph.GetXaxis().SetTitle(\"E_{topo} thres [MeV]\")\n",
0883     "            graph.GetYaxis().SetTitle(\"<N_{topo}>\")\n",
0884     "        else:\n",
0885     "             graph.Draw(\"P SAME\")\n",
0886     "\n",
0887     "# Adding a legend\n",
0888     "legend = ROOT.TLegend(0.53, 0.65, 0.92, 0.92)\n",
0889     "legend.SetNColumns(1)\n",
0890     "legend.AddEntry(\"\",\"#bf{Transverse Region}\",\"\")\n",
0891     "legend.AddEntry(graphs[1], \"Waveform\", \"pe\")\n",
0892     "legend.AddEntry(graphs[4], \"Clusters\", \"pe\")\n",
0893     "legend.AddEntry(graphs[7], \"No ZS\", \"pe\")\n",
0894     "legend.AddEntry(graphs[10], \"Detroit Waveform\", \"ep\")\n",
0895     "legend.AddEntry(graphs[13], \"Jet Trig. Data\", \"pe\")\n",
0896     "legend.Draw()\n",
0897     "\n",
0898     "# Update and display the canvas\n",
0899     "canvas.Update()\n",
0900     "canvas.Draw()\n",
0901     "canvas.SaveAs(\"/sphenix/u/egm2153/fall_2024/\"+direct+\"/h_mean_ntopo_transverse.png\")"
0902    ]
0903   },
0904   {
0905    "cell_type": "code",
0906    "execution_count": 168,
0907    "id": "bfdff84e-e079-4bcd-b1cb-260983962484",
0908    "metadata": {},
0909    "outputs": [
0910     {
0911      "name": "stderr",
0912      "output_type": "stream",
0913      "text": [
0914       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
0915       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_mean_ntopo_away.png has been created\n"
0916      ]
0917     },
0918     {
0919      "data": {
0920       "image/png": 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\n",
0921       "text/plain": [
0922        "<IPython.core.display.Image object>"
0923       ]
0924      },
0925      "metadata": {},
0926      "output_type": "display_data"
0927     }
0928    ],
0929    "source": [
0930     "# Create a canvas\n",
0931     "canvas = ROOT.TCanvas(\"canvas\", \"Graph\", 600, 600)\n",
0932     "\n",
0933     "# Define marker styles and colors\n",
0934     "marker_styles = {\n",
0935     "    \"towards\": ROOT.kFullCircle,\n",
0936     "    \"transverse\": ROOT.kFullCircle,\n",
0937     "    \"away\": ROOT.kFullCircle\n",
0938     "}\n",
0939     "\n",
0940     "colors = {\n",
0941     "    \"ntopo\": ROOT.kBlack,\n",
0942     "    \"mc_ntopo\": ROOT.kRed,\n",
0943     "    \"clus_ntopo\": ROOT.kBlue,\n",
0944     "    \"nz_ntopo\": ROOT.kMagenta,\n",
0945     "    \"dt_ntopo\": ROOT.kCyan\n",
0946     "}\n",
0947     "\n",
0948     "# Prepare a list of all data groups\n",
0949     "data_groups = [\n",
0950     "    (\"mc_ntopo\", x0, mean_mc_ntopo_towards, mean_mc_ntopo_transverse, mean_mc_ntopo_away, std_mc_ntopo_towards, std_mc_ntopo_transverse, std_mc_ntopo_away),\n",
0951     "    (\"clus_ntopo\", x1, mean_clus_ntopo_towards, mean_clus_ntopo_transverse, mean_clus_ntopo_away, std_clus_ntopo_towards, std_clus_ntopo_transverse, std_clus_ntopo_away),\n",
0952     "    (\"nz_ntopo\", x2, mean_nz_ntopo_towards, mean_nz_ntopo_transverse, mean_nz_ntopo_away, std_nz_ntopo_towards, std_nz_ntopo_transverse, std_nz_ntopo_away),\n",
0953     "    (\"dt_ntopo\", x3, mean_dt_ntopo_towards, mean_dt_ntopo_transverse, mean_dt_ntopo_away, std_dt_ntopo_towards, std_dt_ntopo_transverse, std_dt_ntopo_away),\n",
0954     "    (\"ntopo\", x4, mean_ntopo_towards, mean_ntopo_transverse, mean_ntopo_away, std_ntopo_towards, std_ntopo_transverse, std_ntopo_away)\n",
0955     "]\n",
0956     "\n",
0957     "graphs = []\n",
0958     "    \n",
0959     "# Create TGraphs for each data group\n",
0960     "for group, x, towards, transverse, away, towards_width, transverse_width, away_width in data_groups:\n",
0961     "    n_points = len(towards)\n",
0962     "\n",
0963     "    # Create graphs for towards, transverse, and away\n",
0964     "    graph_towards = ROOT.TGraphErrors(n_points, x, np.array(towards, dtype='float64'), np.array(xerr, dtype='float64'), np.array(towards_width, dtype='float64'))\n",
0965     "    graph_transverse = ROOT.TGraphErrors(n_points, x, np.array(transverse, dtype='float64'), np.array(xerr, dtype='float64'), np.array(transverse_width, dtype='float64'))\n",
0966     "    graph_away = ROOT.TGraphErrors(n_points, x, np.array(away, dtype='float64'), np.array(xerr, dtype='float64'), np.array(away_width, dtype='float64'))\n",
0967     "\n",
0968     "    # Set marker styles and colors\n",
0969     "    graph_towards.SetMarkerStyle(marker_styles[\"towards\"])\n",
0970     "    graph_transverse.SetMarkerStyle(marker_styles[\"transverse\"])\n",
0971     "    graph_away.SetMarkerStyle(marker_styles[\"away\"])\n",
0972     "\n",
0973     "    graph_towards.SetMarkerColor(colors[group])\n",
0974     "    graph_transverse.SetMarkerColor(colors[group])\n",
0975     "    graph_away.SetMarkerColor(colors[group])\n",
0976     "\n",
0977     "    graph_towards.SetLineColor(colors[group])\n",
0978     "    graph_transverse.SetLineColor(colors[group])\n",
0979     "    graph_away.SetLineColor(colors[group])\n",
0980     "\n",
0981     "    graphs.extend([graph_towards, graph_transverse, graph_away])\n",
0982     "\n",
0983     "# Draw all graphs on the same canvas\n",
0984     "for i, graph in enumerate(graphs):\n",
0985     "    if (i-2) % 3 == 0:\n",
0986     "        if i == 2:\n",
0987     "            graph.Draw(\"AP\")\n",
0988     "            graph.GetYaxis().SetRangeUser(0,35)\n",
0989     "            graph.GetXaxis().SetTitle(\"E_{topo} thres [MeV]\")\n",
0990     "            graph.GetYaxis().SetTitle(\"<N_{topo}>\")\n",
0991     "        else:\n",
0992     "             graph.Draw(\"P SAME\")\n",
0993     "\n",
0994     "# Adding a legend\n",
0995     "legend = ROOT.TLegend(0.53, 0.65, 0.92, 0.92)\n",
0996     "legend.SetNColumns(1)\n",
0997     "legend.AddEntry(\"\",\"#bf{Away Region}\",\"\")\n",
0998     "legend.AddEntry(graphs[1], \"Waveform\", \"pe\")\n",
0999     "legend.AddEntry(graphs[4], \"Clusters\", \"pe\")\n",
1000     "legend.AddEntry(graphs[7], \"No ZS\", \"pe\")\n",
1001     "legend.AddEntry(graphs[10], \"Detroit Waveform\", \"ep\")\n",
1002     "legend.AddEntry(graphs[13], \"Jet Trig. Data\", \"pe\")\n",
1003     "legend.Draw()\n",
1004     "\n",
1005     "# Update and display the canvas\n",
1006     "canvas.Update()\n",
1007     "canvas.Draw()\n",
1008     "canvas.SaveAs(\"/sphenix/u/egm2153/fall_2024/\"+direct+\"/h_mean_ntopo_away.png\")"
1009    ]
1010   },
1011   {
1012    "cell_type": "code",
1013    "execution_count": 151,
1014    "id": "6ff060cd-2eda-4b07-a0cd-66681ebbd5d9",
1015    "metadata": {},
1016    "outputs": [
1017     {
1018      "name": "stderr",
1019      "output_type": "stream",
1020      "text": [
1021       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
1022       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_mean_ntopo.png has been created\n"
1023      ]
1024     },
1025     {
1026      "data": {
1027       "image/png": 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\n",
1028       "text/plain": [
1029        "<IPython.core.display.Image object>"
1030       ]
1031      },
1032      "metadata": {},
1033      "output_type": "display_data"
1034     }
1035    ],
1036    "source": [
1037     "# Create a canvas\n",
1038     "canvas = ROOT.TCanvas(\"canvas\", \"Graph\", 800, 600)\n",
1039     "\n",
1040     "# Define marker styles and colors\n",
1041     "marker_styles = {\n",
1042     "    \"towards\": ROOT.kOpenTriangleUp,\n",
1043     "    \"transverse\": ROOT.kFullCircle,\n",
1044     "    \"away\": ROOT.kOpenTriangleDown\n",
1045     "}\n",
1046     "\n",
1047     "colors = {\n",
1048     "    \"ntopo\": ROOT.kBlack,\n",
1049     "    \"mc_ntopo\": ROOT.kRed,\n",
1050     "    \"clus_ntopo\": ROOT.kBlue,\n",
1051     "    \"nz_ntopo\": ROOT.kMagenta,\n",
1052     "    \"dt_ntopo\": ROOT.kCyan\n",
1053     "}\n",
1054     "\n",
1055     "# Prepare a list of all data groups\n",
1056     "data_groups = [\n",
1057     "    (\"mc_ntopo\", x0, mean_mc_ntopo_towards, mean_mc_ntopo_transverse, mean_mc_ntopo_away, std_mc_ntopo_towards, std_mc_ntopo_transverse, std_mc_ntopo_away),\n",
1058     "    (\"clus_ntopo\", x1, mean_clus_ntopo_towards, mean_clus_ntopo_transverse, mean_clus_ntopo_away, std_clus_ntopo_towards, std_clus_ntopo_transverse, std_clus_ntopo_away),\n",
1059     "    (\"nz_ntopo\", x2, mean_nz_ntopo_towards, mean_nz_ntopo_transverse, mean_nz_ntopo_away, std_nz_ntopo_towards, std_nz_ntopo_transverse, std_nz_ntopo_away),\n",
1060     "    (\"dt_ntopo\", x3, mean_dt_ntopo_towards, mean_dt_ntopo_transverse, mean_dt_ntopo_away, std_dt_ntopo_towards, std_dt_ntopo_transverse, std_dt_ntopo_away),\n",
1061     "    (\"ntopo\", x4, mean_ntopo_towards, mean_ntopo_transverse, mean_ntopo_away, std_ntopo_towards, std_ntopo_transverse, std_ntopo_away)\n",
1062     "]\n",
1063     "\n",
1064     "graphs = []\n",
1065     "    \n",
1066     "# Create TGraphs for each data group\n",
1067     "for group, x, towards, transverse, away, width_towards, width_transverse, width_away in data_groups:\n",
1068     "    n_points = len(towards)\n",
1069     "    xlist = [-100,0,100,200,300,500]\n",
1070     "    x = np.array(xlist, dtype='float64')\n",
1071     "\n",
1072     "    # Create graphs for towards, transverse, and away\n",
1073     "    graph_towards = ROOT.TGraph(n_points, x, np.array(towards, dtype='float64'))\n",
1074     "    graph_transverse = ROOT.TGraph(n_points, x, np.array(transverse, dtype='float64'))\n",
1075     "    graph_away = ROOT.TGraph(n_points, x, np.array(away, dtype='float64'))\n",
1076     "\n",
1077     "    # Set marker styles and colors\n",
1078     "    graph_towards.SetMarkerStyle(marker_styles[\"towards\"])\n",
1079     "    graph_transverse.SetMarkerStyle(marker_styles[\"transverse\"])\n",
1080     "    graph_away.SetMarkerStyle(marker_styles[\"away\"])\n",
1081     "\n",
1082     "    graph_towards.SetMarkerColor(colors[group])\n",
1083     "    graph_transverse.SetMarkerColor(colors[group])\n",
1084     "    graph_away.SetMarkerColor(colors[group])\n",
1085     "\n",
1086     "    graph_towards.SetLineColor(colors[group])\n",
1087     "    graph_transverse.SetLineColor(colors[group])\n",
1088     "    graph_away.SetLineColor(colors[group])\n",
1089     "\n",
1090     "    graphs.extend([graph_towards, graph_transverse, graph_away])\n",
1091     "\n",
1092     "# Draw all graphs on the same canvas\n",
1093     "for i, graph in enumerate(graphs):\n",
1094     "    if i == 0:\n",
1095     "        graph.Draw(\"AP\")\n",
1096     "        graph.GetYaxis().SetRangeUser(0,30)\n",
1097     "        graph.GetXaxis().SetTitle(\"E_{topo} thres [MeV]\")\n",
1098     "        graph.GetYaxis().SetTitle(\"<N_{topo}>\")\n",
1099     "    else:\n",
1100     "         graph.Draw(\"P SAME\")\n",
1101     "\n",
1102     "# Adding a legend\n",
1103     "legend = ROOT.TLegend(0.33, 0.76, 0.92, 0.92)\n",
1104     "legend.SetNColumns(2)\n",
1105     "legend.AddEntry(graphs[1], \"Waveform\", \"p\")\n",
1106     "legend.AddEntry(graphs[4], \"Clusters\", \"p\")\n",
1107     "legend.AddEntry(graphs[7], \"No ZS\", \"p\")\n",
1108     "legend.AddEntry(graphs[12], \"Towards\", \"p\")\n",
1109     "legend.AddEntry(graphs[10], \"Detroit Waveform\", \"p\")\n",
1110     "legend.AddEntry(graphs[13], \"Transverse\", \"p\")\n",
1111     "legend.AddEntry(graphs[13], \"Jet Trig. Data\", \"p\")\n",
1112     "legend.AddEntry(graphs[14], \"Away\", \"p\")\n",
1113     "legend.Draw()\n",
1114     "\n",
1115     "# Update and display the canvas\n",
1116     "canvas.Update()\n",
1117     "canvas.Draw()\n",
1118     "canvas.SaveAs(\"/sphenix/u/egm2153/fall_2024/\"+direct+\"/h_mean_ntopo.png\")"
1119    ]
1120   },
1121   {
1122    "cell_type": "code",
1123    "execution_count": 108,
1124    "id": "c53bb34e-dfa7-4fe6-b10e-2cb16967604d",
1125    "metadata": {},
1126    "outputs": [
1127     {
1128      "name": "stderr",
1129      "output_type": "stream",
1130      "text": [
1131       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
1132       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_topo_spectra_towards-9999_Topoclusters.png has been created\n",
1133       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
1134       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_topo_spectra_towards0_Topoclusters.png has been created\n",
1135       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
1136       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_topo_spectra_towards100_Topoclusters.png has been created\n",
1137       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
1138       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_topo_spectra_towards200_Topoclusters.png has been created\n",
1139       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
1140       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_topo_spectra_towards300_Topoclusters.png has been created\n",
1141       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
1142       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_topo_spectra_towards500_Topoclusters.png has been created\n"
1143      ]
1144     },
1145     {
1146      "data": {
1147       "image/png": 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\n",
1148       "text/plain": [
1149        "<IPython.core.display.Image object>"
1150       ]
1151      },
1152      "metadata": {},
1153      "output_type": "display_data"
1154     }
1155    ],
1156    "source": [
1157     "for i in range(len(h_topo_towards)):\n",
1158     "    canvas = ROOT.TCanvas(\"canvas\", \"\", 600, 800)\n",
1159     "    pad1 = ROOT.TPad(\"pad1\", \"\", 0, 0.3, 1, 1.0)\n",
1160     "    pad1.SetBottomMargin(0.02)  # Adjust the margin for better separation\n",
1161     "    pad1.Draw()\n",
1162     "    pad1.cd()\n",
1163     "    pad1.SetLogy(1)  # Set logarithmic scale for the spectra plot\n",
1164     "\n",
1165     "    # Customize the histograms (color, marker, etc.)\n",
1166     "    h_topo_towards[i].SetStats(0)\n",
1167     "    h_mc_topo_towards[i].SetStats(0)\n",
1168     "    h_clus_topo_towards[i].SetStats(0)\n",
1169     "    h_nz_topo_towards[i].SetStats(0)\n",
1170     "    h_mc_topo_towards[i].SetLineColor(2)\n",
1171     "    h_mc_topo_towards[i].SetMarkerColor(2)\n",
1172     "    h_clus_topo_towards[i].SetLineColor(4)\n",
1173     "    h_clus_topo_towards[i].SetMarkerColor(4)\n",
1174     "    h_nz_topo_towards[i].SetLineColor(6)\n",
1175     "    h_nz_topo_towards[i].SetMarkerColor(6)\n",
1176     "    h_dt_topo_towards[i].SetLineColor(7)\n",
1177     "    h_dt_topo_towards[i].SetMarkerColor(7)\n",
1178     "\n",
1179     "    h_mc_topo_towards[i].GetXaxis().SetLabelSize(0)\n",
1180     "    h_topo_towards[i].GetXaxis().SetLabelSize(0)\n",
1181     "    h_clus_topo_towards[i].GetXaxis().SetLabelSize(0)\n",
1182     "    h_nz_topo_towards[i].GetXaxis().SetLabelSize(0)\n",
1183     "    h_dt_topo_towards[i].GetXaxis().SetLabelSize(0)\n",
1184     "    \n",
1185     "    h_mc_topo_towards[i].GetXaxis().SetRangeUser(-5,45)\n",
1186     "\n",
1187     "    h_mc_topo_towards[i].Draw()\n",
1188     "    h_dt_topo_towards[i].Draw(\"same\")\n",
1189     "    h_clus_topo_towards[i].Draw(\"same\")\n",
1190     "    h_nz_topo_towards[i].Draw(\"same\")  # Uncomment if needed\n",
1191     "    h_topo_towards[i].Draw(\"same\")\n",
1192     "\n",
1193     "    # Add legend\n",
1194     "    leg = ROOT.TLegend(.57, .6, .92, .92)\n",
1195     "    leg.AddEntry(\"\",\"#bf{Towards Region}\",\"\")\n",
1196     "    leg.AddEntry(\"\",thres_string[i],\"\")\n",
1197     "    leg.AddEntry(h_topo_towards[i],\"Jet Trig. Data\",\"l\")\n",
1198     "    leg.AddEntry(h_mc_topo_towards[i],\"Calo Waveform\",\"l\")\n",
1199     "    leg.AddEntry(h_clus_topo_towards[i],\"Calo Cluster\",\"l\")\n",
1200     "    leg.AddEntry(h_nz_topo_towards[i],\"Calo No ZS\",\"l\")\n",
1201     "    leg.AddEntry(h_dt_topo_towards[i],\"Detriot Waveform\",\"l\")\n",
1202     "    leg.Draw()\n",
1203     "    leg.SetTextSize(0.04)\n",
1204     "\n",
1205     "    canvas.cd()\n",
1206     "    pad2 = ROOT.TPad(\"pad2\", \"\", 0, 0.05, 1, 0.3)\n",
1207     "    pad2.SetTopMargin(0.02)\n",
1208     "    pad2.SetBottomMargin(0.4)\n",
1209     "    pad2.Draw()\n",
1210     "    pad2.cd()\n",
1211     "    ratio1 = h_mc_topo_towards[i].Clone(\"ratio1\")\n",
1212     "    ratio1.Divide(h_topo_towards[i])\n",
1213     "    ratio2 = h_clus_topo_towards[i].Clone(\"ratio2\")\n",
1214     "    ratio2.Divide(h_topo_towards[i])\n",
1215     "    ratio3 = h_nz_topo_towards[i].Clone(\"ratio3\")\n",
1216     "    ratio3.Divide(h_topo_towards[i])\n",
1217     "    ratio4 = h_dt_topo_towards[i].Clone(\"ratio4\")\n",
1218     "    ratio4.Divide(h_topo_towards[i])\n",
1219     "\n",
1220     "    ratio1.GetYaxis().SetTitle(\"MC/Data Ratio\")\n",
1221     "    ratio1.GetYaxis().SetNdivisions(208)\n",
1222     "    ratio1.GetYaxis().SetRangeUser(0.5,1.5)\n",
1223     "    ratio1.GetYaxis().SetTitleSize(25)\n",
1224     "    ratio1.GetYaxis().SetTitleFont(43)\n",
1225     "    ratio1.GetYaxis().SetTitleOffset(1.5)\n",
1226     "    ratio1.GetYaxis().SetLabelFont(43)\n",
1227     "    ratio1.GetYaxis().SetLabelSize(25)\n",
1228     "    ratio1.GetXaxis().SetTitle(\"E_{T,topo} [GeV]\")\n",
1229     "    ratio1.GetXaxis().SetTitleSize(25)\n",
1230     "    ratio1.GetXaxis().SetTitleFont(43)\n",
1231     "    ratio1.GetXaxis().SetTitleOffset(0)\n",
1232     "    ratio1.GetXaxis().SetLabelFont(43)\n",
1233     "    ratio1.GetXaxis().SetLabelSize(25)\n",
1234     "    \n",
1235     "    ratio1.GetXaxis().SetRangeUser(-5,45)\n",
1236     "\n",
1237     "    # Draw ratio plots\n",
1238     "    ratio1.Draw(\"ep\")\n",
1239     "    ratio2.Draw(\"same ep\")\n",
1240     "    ratio3.Draw(\"same ep\")  # Uncomment if needed\n",
1241     "    ratio4.Draw(\"same ep\")\n",
1242     "\n",
1243     "    # Update canvas\n",
1244     "    canvas.Update()\n",
1245     "    canvas.Draw()\n",
1246     "    canvas.SaveAs(\"/sphenix/u/egm2153/fall_2024/\"+direct+\"/h_topo_spectra_towards\"+topo_thres[i]+\"_Topoclusters.png\")"
1247    ]
1248   },
1249   {
1250    "cell_type": "code",
1251    "execution_count": 109,
1252    "id": "779d8904-fa06-446f-acce-4f18560e614c",
1253    "metadata": {},
1254    "outputs": [
1255     {
1256      "name": "stderr",
1257      "output_type": "stream",
1258      "text": [
1259       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
1260       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_topo_spectra_transverse-9999_Topoclusters.png has been created\n",
1261       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
1262       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_topo_spectra_transverse0_Topoclusters.png has been created\n",
1263       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
1264       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_topo_spectra_transverse100_Topoclusters.png has been created\n",
1265       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
1266       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_topo_spectra_transverse200_Topoclusters.png has been created\n",
1267       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
1268       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_topo_spectra_transverse300_Topoclusters.png has been created\n",
1269       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
1270       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_topo_spectra_transverse500_Topoclusters.png has been created\n"
1271      ]
1272     },
1273     {
1274      "data": {
1275       "image/png": 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6VdpIJ7LKFYK+wJk+O9JLKAFWZdkjvVzfQvEDHDkgSL9hSWnWajh4EPU6TBMATBOWhbExq1YzG623yJqUk1ImpcxAJqWckLJGFWwTarfMVc8Bjl00b7brjaJOV+Hw+rW3Tz0MJVSt2hRUeWpcl9PX8sqw/tE1gJBT+poIyiR1Da66Wd4Gff58+oTBiQmMj7s/GLnc4KFD4Sfai/YL71x+cN+yhFT/Hti3/EJ22V602zPy/sFbfNx37dH3rYY1+XyXPaWj2p/qR11dde5q40T11nz1u/BC7a6pyVWEz/muayS6Q2/7kNJ9HUh0k3C/T+5TU1MhVYwpfv3AYCLhnSAADBpG+InmR5fvGV1TPvXvq6PLZmG5PSPvH1zi59VCr+Q0P8uHi5+v6Dqv4jxXTyZRiF/QNwDXXXvF0lvCvsmxdQLRjbbflz2LxWKxWPT9PkX6AcuyltRqp4clyzID3lJc8EW8b2HlE1TFSumc9y8MXPDF1o6RuFH7FM6VPfW6oQ/n5ghaw1RLiFNTU85soq29tJqa9ABc/p96JTbo9Ewm4xweVh1BuY+Izt/za7hIHbTuT0gzGIaR2L7d/63BwUSAUQhAWnL7WdRQm8RkEskMMkkkJzBRQ237EqTZrTt/ou1sbpzeL6wRfYUN8e3UO3xNtt80uuqsS+8bznjqy73zFMqepqOD3CuVirMWpRdXvE61Wp2eni6Xy70U5kKiZlxKC3D5duaBodCzTgOLqN2M3ChGTZgATJgWrDGM/WfMpjASKJukFVSrVV/hjL1Wg7Oy4NZxTYBK8MJnRRfK+WVDp/QJHW35hTthauVLp9M6MsZ5Vsu/bJLeQ1pWfmlpHsg5DuaBE0B+aUkGL3sahvg4Pn8ao7OY1QdLKD2HvR/D5xOJbv0bi2h/JQTvGNRaTriNoj71XlqufJ3zTbpYLGaz2Wq1mk6np6am1E5e+ClKiavVqm4fhWHapXSi5afs9IbfnlSDqakpXbi5WCwqYVPGfsO/DEKEYaQSiTnTnAEMwAIMYEildzEMEbbsaf0NZs7ijOv4t1Dajh3SvCXkXBJOM5aKbwB4m9f0VLYXb0HBFvYPxzqqmvE2NK0ppeRSpy8dZ/kpe66h8jkjWpzH1R9KRPvepDcZH5c3HBL4PwSeeSPuE3hG4FM44Bf/4OA04FU+xVmcsaIZaZ/gdcpwZe1yuYEohBBtDtj1TcLZqslHL2+GbOw19IeAx3KlEK7R/lWOcNQCpkL9ifv6K4e8pe6rYXCPtzffrprvhHQpC9859R92f/frL/v+M7CXYT8D+96Xzf9a8rsL366Fn7h91y7fz9TghRe2Z+Q9jHqSyonfG4ykgxDUx1z/GEWog+u4b6iDWlQsl8va6UaPRLUPGYkzoEKj/5acV3cd0Y/F2bnrQbmeknd4nU90k3BHT+4hfzchv8LWit9Gaf6ipEO4tbD8yJ5zM7B1rN7tsL938dKn8g3C9QZvvhm5nPsvIJ8f/N3fbc/Iexhv8LjrE+1t4JoNgk70shXxk+tnCbUTCY9ENSN+Lryn+D4T18zjkjfXDp8envdxtQffO21IVIOJqN+WsBXxa8mvNrrnTjqHTw8uO5VP69+nB8PEz7RtLCzguuvW6V8+j2uvxcmTdZtJXlqAWgcKUa/wBmoBKZqhBQ5DzVob+vK96WspXD/6NnAeafhUOwqKnxv9xcf71kbTC4VA8et5TNN+Bu4sLerfs1iu18M0LGHbWFzEoUMwDAAwDNx8MxYWDCpff+BrGnZdCpUOJ7qH2YnenoS0jWFAwj8swUCDmJhxwNqzp3T4MA4fRqWCTAZAXsrwAEHSMyhfkmw2q0IsVAi5crCMe2ikMRS/BgQFBUrGUfQEwhBnBu2dSz6/5bODcmcizB26AIwBOSlLQmjlOwHMRTRW0nmUy2VXCT2GFmyCWGKvOy7UYUP4/pG1NpdBiDFOeoMffxj3jNqug18dtX98Y4MTU0LMAQZgSAkpDSlVgOAIsyj0DZlMRkqpQ+yllFS+TRCyOBndRbvV8kun0+Ei1zl5GUiHM3xIvOsx+eBF9v7jK98FH9hn/5yBHRONNSwlxGHgMFCRMkPN61c423QjPWj5KfjnSJpEpMSOO8VV1whpSBtSGnL/e8SOO4UY2YCYUfkI6S66Vfx0pWbf44RsCJES4rAQphBlCFOIIxtTPkJI19Gt4qcNO5eFpxN+tupCTI3dV4gMf7OEtJtYihB0q/hhNbuBym+rKn3oJ9VC+48OL4QQEimxOLx0sfipIsUAqtVqNpvV+WSpTIT0EqrCkRBi0wWrGxZIanj1rR+PCF3Nu21X7B0aBMF3AzoRdssT9vTG8yGkSwnav9ho/qZNnKLxzRgVkiGzzfNqL83kvkR3a11s+WmKq/DrD+kcjn/WHTtINoSuVu1KXAlgenq6beF03vpKzh9dx5Vh2rYML+pySoDbc8VeohfEj5DO4amH7PLb7fqA/dMfF/UBu7zffuohquBm0LVbnV9qM5mM0r+2+XX7OpZrzfM93uZv4fzSvzkofg2gtydpnqceshevx4CFpBwYgEjKAfEcateD+rdRtE3jfUvN9V7VyaxSLBYb2oXO9pswIlUCTzU85+ne8rPOC7mupdarvANzjV+taXnvS//o7cd5iqt/dUSPSl/ReVCf5Rx8E09l88Ti7dmzK8Utgc+HbIj79y9XXu+uEVG+Yvn+qxqUBiQuNjQ7+S4zusrAOjfnvO3Dy966Cv4p01M5GcBTQdc5bF/x1v341n9w9eAdqr6c6y3n2IKuqMtQOO9aDVKX0HFeyNVPyCOKjuiuy8k9jLh+36RLeVasKV8FFf26DorfxtjQbOtqrCZ3V31zrRnedxsWQdPy4PzRe2nf63oHpo+4utWn6B68A/MV15Bq764rumTP2Ymv4rp0OpYSgBS/eKD4keapn1xexvIiFicwkUACQAKJQzi0iMVlLNfnqX8boHnxK5fLqkB5yOlOCfHtueHlnA2cr53Wm1fMmhlYiJjBzyR1HnS191VTp265tDDoLO8Rr43bNqKbhLnnR0hrSIwOPIbaQRyso27CBGDCtGCNYewfUUtc0iWfNSHa/W9rqI0056ZUQ18Y32XPDV1Ut1fX1Xtm8NvwCxmY67rKx8fpX+q72RaU0987ANfYFCE7qc7XPe9H0yUfSEK6gcPi98/HhbOY1UdKKG3D0KfEJ2McVU/inM11gieFznfRPL7xDE6USmnfEC0wTmnx1aTwgTl7U51oZVI/Tk9Pb9EBpOGt9S3dWtKobbCYLWkSaVl/Ie+2sOw6/j/wJ8PyPGl+ViQSsQxsY3TGH7aqWeaynDSZTKZarer66aqWbDqd1q6JLfcSLBaL2WxW1WqHxypSQ8V6S66Zgal+pqenna6bzgZTU1M9b4EhpmK2FL8GUORIk5wGvMq3+tayBXSD9HUKSmyCghaUjeVcclRC2GTnXhOtYXyetp+8505NTSn1cvWg+mw4MF2a1GUXZjIZ5yqos9vwDl0NYgk93Cgh02x0ushlT0Jag2EYicFB/7cGBxNdYfZ1DDqYLyhSTdtYTiFUhC/x6QVM58HwytiuZq49M2fcoWvDD02ojlM1nT1ro9DZWFmTQZuariVZb//ECcWPkJbxoY98ZPQDH3AdHP3lX/7QTTfFMp6uRmcy0/HdyqZReqDVS4mZMy5bLTYGoVqqNUzVXtkWvhFyTlxOLpogXXG5jegL+TbzaidWtVCHxutbCxI/5zcG/bhUPD7Fz4eIvEh7Az4fsiEKCwuj7343crm1D1g+P/JzP5c/eTLuoXUlvoLk6/3vZGpqSguVbuB13Hed0vxgvG+pyzUzMG9QXcjp0i9M3hk74Y3zk57gd2e3vhEL3k50LHz4ie0huklYSO5pBSMEnw/ZAEkpzVOnMDODUgmWBcPA+DgKBSOVMpkSbwtoOybIgvE2UEdCwh4a9tkSNjGwZjppbftOJrpJmJN7GBQ/0jyWlAlgLXCtUoGeeqSsAwnqHyEbhOIXDxQ/siGSUvpaeEbAcUJIONFNwgx1aADj/EjzjAOWlKX1fzN5KYfiGhAh3UAscX709mxAyDYsIS4KwDyQc/x55KU8ARTiGxIhnU+IW0p0F6X4EdIyUkLMAQZgSAkpDSmHgDlghGuehHQY3NMKg3t+ZNNUpMxQ8wjZGtFNwrT8CIkEX+WrVvldipCOgOJHSOTUanJyUiaT8pMZmUzKiQlZq1EFCYkTih8h0VKryYkD8h3fkE+aqEA8aSL9l/LQAepfsxSLRVUPQeU520QPOkPYFtGR40GDaZh1mnQQrUkU06Pw+ZCtc2th+ZHR5RnYElL9ux3290aXP5VnbfcG+JZdxcbzbG3iFC9BBW+dacCchd23TrlcdqUu60Oim4Rp+RESLdu+iG8tiDzWtgALEMcWxOAfxTioLqBYLKoc1t4kk9PT022uzqozRHtTa4bn0d4KDZN0k61A8WuACCDucZHuwLLkby5BK18VK6VzChA3LsE0ufIZiFI+KaVzITGTySj929z65+YoFotK+VyrmsVi0VVTgmyOoGk20pmW4teAEGOckIYMAwmghtokJpNIZpBJIjmBiRpqBmDEPbyORcmJ77KnswaQRu/G6fpH4f072zdsrGTYV+F0saGgu3C95U1p7Rq599xisegt0edt7+xc9dlFkhyyOBnPVQmfD9k6T+Cf34v3jmNcf+hyyF2H657AP8c9tM5lQ7OT726cc30S6/f8vO19ywltYjCuPT9vz65qQb7q7ryoa3i+NZ5cvek24TfVLUQ3CdPyIyRCpGXNIi+RnMWsPlhCCbjoKPLSNGMcW8+grEA9qYWvi1YqFbWGqdun02nfkvG6PRyVbFuLXtrVI9FXlKtiJqXU5qPaAnSpnWvk2WxWKX2bt0W7DoofIREiDOOPcd+9+Irr+L24+0u4TyQSsYyql6hUKsoPRR/xXRfVKP1wCoN6rXQoiCgCGNR1ncZfpVIJcnDFqsiVy2U9GLXp6LrTdDrdRQueMULxIyRCLMsyYfu/Bduk5bdl1Kadyw8l/BTfZc9Wj6sxaszT09Ourb6Qgr3wyLDe5HN1SxpC8SMkQgzDSBj+fi3G8HCi8yw/Idr9b3O4TLdisaj9A8NtOF+UYISsE4a/tekFRh25oUYe7n2jLDyXM6TXkKX4NQnr+RESLb+Wy93zzDMLX/iC8+Do2Nj7ksm4htT5qNW8oIQpKupOLQDqYLh0Oq3dJlvoIh++iIrVdVTZnF+iS9symYzanKtUKtPT09VqNZvNOhc2vfj6vJDNEJEjTW/A50O2TmFhYfS665DLrX3q8vmRa6/NnzwZ99A6FzXFB/krOucutWLpyoTimtzg8Pb0nffCM7Ood31zxLjG2dDbUztk+l5IC5uzccNBer09g26kG4luEuayJyHR8pVUamFuDoYBtf5pGBgaWpyb+8rISNxD61y0veXdwFNH9C6dssmcplL4IqTTo1ITYtjpK/qmlVFmX/Mrjc4lWbVU67zB8H7Uu64HkslkmAVmk0Qkqr0Bnw/ZIqZtw7ZXk3pKlMtrr227bttxD7BzccarTU1NqUSXWvN0M5dZ5lwV1G18GygLyfVjENoJU3mWlstl5xHXYJzXdV5aD9730vrudIdeS87ZYTPte4DoJmFO7mFQ/MjWSTjFz/HPoPI1wnd/y7sW6mowNTXl0kisX7T0dttM2mvfIATXiS7x816oYZC78+6ChDyoPcVvQ7BSeRgh2+Z8bqRJJqS0gNL6v6W8lEPAESaJbQ7lEqKcWZpsoI6EhD007DPkLADNn7iJwYe/pQ+iV3w7wx2UIppsKX5hCMHnQ7ZKTcoxYK9D//JSngDmgBEhqlWZTlMCCfEnukmYDi+EREtKiDnAAAy12inlEDBdk6VbwNruhMQFLZswaPmR1lKRMiOEqu3+IRNvOy4SgAl8Z5+8O4Ejd4pUilYgIWvQ8iOkF8gIAeCuI/J3nlj+/nGxG2IAYjfEk8fFzf+0fOdhftMipE3QsgmDlmf1lIoAACAASURBVB+Jgs9sX96xNOCs7Q5gBvLsNvs/LZ0X16gI6UCim4Q5uYdB8SMtx7LkckLu9lt0qcMWdZFIcOWTkBW47ElIj6Bqu2uqWMstwtruhLQNih8hbeb0WZytoTaJySSSGWSSSE5goobaWSwBVtzDI6QvoPgR0laEYcxf8LlfwoE66iZMACZMC9Yv4obvX/A5lrclpD1Q/AhpN3e98Rtv3vmKWczqIyWUrtz5qrve8PUYR9WxqPquqtzd5mqUh9fJa3IAQT1ssfNMKK6enY9ii9clzF0ZBp8PiYKQ8rZxD62z8E2nieZScTrZxCmu00NmSwSXXtpQ5+F3GlTGbyuX7gqim4Rp+RHSVizLMi3/jT3r9GnTNNs8no6lWCyqAkDOTM269HksRs/m7M5wfOdlLXU6daeqW+QtW6hK/rZ8VP0AxY+QtmIYRiJgYy/krT5EKZ+U0pm7OZPJKGGIQoeaGVJ7lEZLnbOG39TUlCuNtdK/WB5FD3B+3AMgpO/4tfHxeyxroVRyHhzN5983NBTXkDoNPd1739J1bp0HnQUc9IZZSP/O9mojreGQyuVyNpvNZrOy0VplZRU1mI2KkxpMOp12jcp3kLp4E9kwES2n9gZ8PiQKCgsLo9ddh1xu7XOYz49ce23+5Mm4h9YpbGh28hUAVw1Y556ft334zpkejBJj1/Yh/IrqbXqa1af7HuyxWn3NEN0kzMk9DIofiYKEbWNxEYcOQXm+GAZuvhkLCyxvq9mQZrgaq3VRpyDB4zniLb8e4hHj7N87MGdveq9Ov6t0q0m3FO/prjFgta59M731BhS/ePB+g9vEVzlCnJi2DWdt93J57bVt16l/UsqNiF+5XE6n015PEKyvfq4Fw7fn8Ms53/VVVv2j0tHwwYQQbt75ln3vDVswZKal+MUDRY5EQcIpfo5/yvKrVKh/G7P8XHhXDl3i57XDlGg1ORiXpejs0HfYajwNVUqd29CqK5fLU1NTzpXb3jYEo5uE6e1JSLsZB3Keb7sHFu3Lb5HJpPxkxo6zvK1o+79N4fS6VN4rYhXlJrohlC9Jk56cqlnQVYI8UMI7104uDb1jlAdNpVKRq7K6ifslYKgDIe2nAMyv178Di/bxA8uHvvyvT5qoYOBJE+kvnTr0K2f6try7kpCQpCrZbFa9W6lUstns9PS02gxTBlbUw1Oq4+t+6XJD1YQ4lBaLRXVWyP36imKxWFQPitEOmyEii7I34PMhEbFo24ds27Bt2LZh2+/7yAt/f+GPZmDrNdDbYX9v+w8+9etm3CONB+/WmhPn3NXMNhsa7fltaNnTeYrLn8W3Zfiyp3ZyCVkXDZmrfR1Qe4noJmFO7mFQ/EjUlG1bSvnp85a08lVQ0fr36fOW4h5gbKgZ3zutN9Qbr8+ks59N+KQEvatNiK04vATdppMQf9Set2EofvHQ239VpEMwTfsZLC9icQITCSQAJJA4hEOLWHwWy/V6n/q/aA3T65nKsdM13buEwZkDU7fxbaAkaiuGl/ascTl/6svpzoO0Td9OOgA9Ku+j0FfvYbNPUvziguJH2sDyyfpJnHwv3juOcT1x55C7DtedxMnl+XrcA4wN32zO3rVQVwOnM6Ru4FQIb7fh+hFiXXmHtKHOG+ZnCY/N73nlk1FOwlFViO8NhODzIe3gd8TvPo/nnEWOAOSQ24kLPy3/z7hG1TmobGEhScu8DdSREE+Qhn1GOuAt9ozVLG6t7bwDiW4S5uQeBsWPtIfE+cPW8nPe48Pn7bJe8jlOSJ8Q3STMUAdCYsayLF/lA3B6+XkWOSIkCih+hMQMixwR0n5Y0oiQ+GGRI0LaDC0/QjqAfB7z864iR/LECeTz8Y2JkF6G4kdI/HwllVqYm4NhrBU5GhpanJv7yshI3EMjpDehN2MY9PYkbcCSMgFArOZ4rlSgXdilrAPGsWOCBbtJX8JQh3ig+JH2kJTSFD4FDowXX6zv2QPTRCKB8XEUCiKVav/wCIkLhjoQ0sv4FjnK33PP+LFjwjQfRkGYJiwLY2OyVotlhIT0GH0qfs7qX/2QJYF0ON4iR/kHHvjh85dlfn53HS/9ND5Xx0uV0q89tePfYWYmvmG2lYwHVccuossVi8UWFgYKSr8ScrydZYlU9pl+L4QUUdq0TkZnhXcmh/Vt2Z/Ph8SCq8jRoY/d+cDAjyr4ni5yVMYj38a/1i68Ou6RtomgKSuozlEIKhN0M5drVZ+qN1cDPdu4jofXb2o5zozh7bniVohuEu7Hyd37W0dwuZD2DIkQTdm2bdO8H3/tVD6tf/fjW3a9L1Jdez+nvhUbmiG8XJ9iampqQ0miw4fhW4RIZ6b2Pd62FNW+dZc6lugm4T5d9vSuPES3nELIhsgIIQzjzdifxhvVkSpWioNn8KZ92C/6NedLJpORq3N3y5fsWr7sCc+soou8T09Pe4+3ef+F2z20bFa+TtLyI51D/eTyckCFv2Us1+eX4x5gO0Bzldw1qpKRrnjnPK7a6+Nqv0Ofog56LT/VTPfpuparz4aD1POM1wz13o6qXBh0ae8VvYNv5mm4yjwF3ax+XGpU+rXzGTrPCupnc0Q3CXfo5N7wwYX8qjZKyJcAih+Ji38IqPD3P3Ey7qG1iRDxc+mHb9k/VxlYhepQLzNqGZCeeUC/6zUVXJcLmn98O9R+Bnp43g0/37p9rtsJudZGn0b4zTpH7rxfNUjn/1gtyes64vtwmsd7s62iEyf3htu/vr+qTSxhB+0/a6J77oSE86GLD/288RHXH/kvJMb+/atujntobSJkEnDtWrk+xXqadrV3/Yj1uuU6xVcA0p6K7SHj9x2k99ZUz65C897quLqBd8fO1WCjT8PbwHWzviXjXeWCpUNWm39EzdBf4hf+lUH/Jlzums4/rCB0J65vMSEjadldEbIRjCH/jb3hXUbcQ2sTDScBvf7m/SC7rCtf8XOd4pwifF1CXJ00nNm9mqRvJ+RaamnRdWnnuUGWou9FnU/DJZ+u071X1G28wu97lveI67ewOfpC/NQatP6Qhy/3+/7hNrn+2bxjMcWPxEJ4Db96lN6eaPu/wJE0Ej+9V+c7wzac7r3tsf4LdMhFg9oE9RlivTXsp6HRCY+V5vs0dJtmVLzhE/Y+Q++RDhe/TvH2LBaL09PTLico32auFwr1lBuershms+l0mu6dpGNhhb9w1IfX6VGZzWbFetBqF25fB84mUfOVa/qqVCqqN9cmn4pA1zfindbUdKfOdfUc8jQ2xFZutlvolHp+Tr/bSqWifYJd+P6tYFU7sfp3E3Ih/et0yWdQ5gVCYqFhhT9ZrUaR7Tps26Bj8M4PU1NTHfj5TafT1WrVO6G5pMU1+2WzWXWuTm3jUq9MJjM9Pa3y3Ti/B2g682l0Gh0kfvq3VSwWg8SvVQEx1WrVdQn9Z+Rio1+aJBNhk5aQz+PgQeRy0PqnKvx9+tNychKlEkxT9mW2a/W1VX8DzmQy1WrV++W14ffgEJRouQ76ykzDoWazWdeAvZdw9qkah08jqr06vVqtOneLWvU0NnGzzbAJGzQ6OmXZs1U0tNNVnKyXoBM3uo7c8jsi/Ulghb/RUdTrwjQF0IfZriuVilrj0Ss3aoJ2LeQo1dn0qp1vn+q6G9IDp0p5l5rg920+SHRdKMFTJzpP38TTUF1t/WabYRM7c60dwFZHEzW+zkUKNWbfHVRfD64t0pnPh/Q8pm3Dtte8QsrltdfLy/XhYQnof3YuZx86FPeQW4/6sKcd6FnL9THXsWVOBw0EOzQ2dHjRPzqDuxEaGhF+F96WQQk2XRfyerM37LnJp+Htyuk96x1D7zm8dOLkTvEjJOEUP8c/49lnleZV8I41/TN6MP7B98u6NwxA4fUDcL7rjI+SzYmfN1TcV3Ebzjle7XFd0Xu667paekMUa0NPw3v74Tfbq+LXicValfeKr0OmWjIul8tee1wtdk9NTbUwQV/ICnUHPjfSS0xIaQGl9X+B+dtvlzWZuO10Cbk3wvgHWOOYLWBmD55Cvd63OT8VarpQm1u+yXuD3grvM+SszfW5uetuYhNuo8OL7nYaEr4XGNFk25Xi56twIW9tGlZyJ3FRk3IM2OvQv7yUj/0/1ZffOPCb53a+DT+TAEzgO/ju3TAPD/3WyOl/jHW8hEQCK7mv4DXnXdDBl/QGKSHmAAMw1GqnlEPA22/f+XvbXv99/MxuiAGI3RBP4mdu3vXmu17zhbjHS0iX0WXipwhx4qL4kZ4hJcRhIUwhyoApxBEhdh//mW89f1Eea2tEBYhjz180+Oj+GMdJSDfSZeKnljS93sAtL+5FSOeQEQKAZcnfXBJa+XSRvwLEjeeEaXJ9npAN0GXipw07l4WnolJ8qz1sERFAyy9ESDjDQAKihtokJpNIZpBJIjmBiRpqBoQR9/AI2TRB02ykM22XiR9Wy0qpLAaVSqVYLOoHFIX9F+KAS0g7EYZ4ctviQRyso27CBGDCtGCNYezJbYsiwS9kpFsJCUiI7qLdJ36ZTEZZeNVqNZvN6qyvFCTS83zuDaUXd43MYlYfKaF0Ztcln73iaIyjIqQb6WJXfmd+o4j8XBjqQDqKHYnkWcun2tH2YeOM33FCup3oJmFO7mFQ/EjnYFlWSDGjer1uPPJIFHUeCIkRxvnFBh1eSKcwPIwg8TMM+aY3IZORyaScmOifPNekN6DDSydChxfSIRhCDI6PI5dzv5HPD950U3JxsT/rPJAegA4vhJAwbsrnR+fn1+lfPj/6d3934/lrhTlFqYS9ezEzE8P4COkeuKcVBvf8SEdRk/JArWYePXq8VIJlwTD27d8//MEP3nXLLSOnTjlbSsMQJl1gSNfDPT9CCFJC3JlKXXPrrUa9jvvvN5599j1XX62Vr4p3rDW1LEnxIyQYWjZh0PIjHUtFyowQMpk8Ze48inwJOROPJPDTK0WOjBdo+ZEegJYfIWQdKuFn7UO/c+DiL38Df23i1cA7Tbz6L1E5cPGXax/6nbgHSEhHQ/FrAEMdSCdz24u/ePKZWy7ApYCy88xteM3JZyaPvPBLMY+MkKaJJdSBy3phcNmTdDgvG5x46znzXkfCMwDvRe5vtw0/s3QkrlER0iqY4SUeKH6kk7Es+ROJ3Wfhl/AMxg/qdeORY8z5Qroa7vkRQryc9lU+AGdhyb1J5nwhJAiKHyHdyjCGE/BPeGbASFiSOV8ICYLiR0i3Igxx0+DBHNwJz/LI33TehwVOrzRjzhdCPFD8COlidt6Ur14879S/PPLli09c+OsHnc1EqYRSqe2jI6Rzofg1gKEOpGOxpPyjQ6nffe0d/7LPMGAAMGA8sW/o5p+6449//yfN4eH1rZnzhXQoDHXoOOjtSTqcpJQ7T6Ewg1wJx63KPiMzO46ZAp7fZZq7dztbMtsn6Uaim4TPb9yEENKpjAPWHtxyWNxyGFdXMscyAkC+Uhl68EEAVUAFOsh8HkNDsY6UkM6Cy56EdDEFYB7ISQlgRfmee+6x559//p57kkAGSAITV15Ze+wxFArxDpWQjoLiR0gXkxJiDjAAQ0pIaUgpTVP8l/+yfPbsSrozwJLyoBCnuFFNiAOKHyHdTUqIw0KYQpQBU4hEqZS6+Kdmjx/XDUrHj78s9boZhjoQ4oAOHWHQ4YV0HYlE0rJ8HFuGhw11XFarzHlGugWmNyOENMayLF/lA3D6tFX/xCdkkjnPCAEofoT0EoZh7AhIeLYdRuLMGWGazHlGCLjsGQ6XPUl3YVmymJhYwukS1uVzySN/AXYVMZtYzXkGQOZyMAxx+HDbh0lIs7CkUTyE5BfgcyMdiLTkYmJxHON7sVfrXx75Ezgxi9lRvEk4xA+MfCedQXgmF+75xYMMIO5xEeLD6WHsGkzNYc7AWsKzIQzNYW54cMQa9pzAnGekAwiaZiOdaWn5hcFlT9J1fKZgb/8aCgsDACqoZJABcPuovfRzS//pj3e6GtPyIx0OvT0JIU2xdAg/+2rM7LMBrCjfPvvfXWQtZf5WNaiutpT5PMbHYxkkIbFD8SOkpziQEp/+Ml59DeqGtCHrhvyp9+DT/237u/7s85NXXumb80xWq416JaTXoPgR0lOkhDiSEtVbxaV1ZO+Xl9ZRPiz+4/an/8Cy6lKuy3l27lztttsY+Uf6E+5phcE9P9LVVKTMCAFgcnKyXq/Pzs46382NjhoXXXR4NRGazOUwP4+5OZFKxTBWQvxgqEM8UPxIb5BMJk0/xxYDcB5l5B/pNCh+8UDxIz2AZVmJhH/aFwB1rEsJQ/9P0lHQ25MQskkMw9i+3V/8BuF5g5F/pD+g+BHS++y0D74XOdfB65G/EJ5QB8MQwWYiIT0DxY+QHsey5E3n8icxn3PoXx757+PETfi4ibW8L4z8I/0Dxa8BIoC4x0VIsxiGOCRS3/DkPPsG5iYwolNdy3weJ06oyD9C2knQNBvpTEuHjjDo8EJ6ANO0h5MYWP2mq3OeAbBhW3vemDj1GAwD4+MoFMTISGwDJcQDvT3jgeJHeoN6wk5aPss89WE7aQ3ISkVkMs7jrPZOOgR6exJCNs/DOfzVDbbr4F8dsB/OAYBWPlmryclJ5nwh/QDFj5De57V5GE+jklvTv0reNn6Ey/JrbWSthoMHUa+z2jvpByh+hPQ+F48MpO6ANGAatg1pGrYcwp47sGfUMQMcPYrRUeFIgSZKJezdi5mZGEZMSMRwTysM7vmR3uN42d6XXfet97OfrXzsYxmZTPrmdmHOFxIj3PMjhLQGrXwPPbT49rdPDgwkP/7x7IBIvt1800N4pc8JzPlCehGKHyH9yEMPLV53/fg/W8+qMkcS5r/8xCuvHbjER/+Y84X0IufHPQBCSAzkPnZ0IDHyb4/doY88/a9/+vJLfnP8qeHv/viH+qDM5zE0FMcACYkW7mmFwT0/0qucJ5I2fBYzB2Asw1KvV3K+zM0x8p3EBff8CCEtY2HB9FU+ADas+aE9EpCGgaEhrXyyWm3vGAmJFoofIX2HMWIY8N/GG4aRsBZRLgvTFEeOQAiGvZOehOJHSN8xfBrjOJjzFDnKIz+Og4a1kvOFYe+kh6H4EdJ3CEPcOJSf9xQ5OoETN+7Ki8RqKn2GvZPeheJHSD/yykLqAx+YdRU5+qX/dfYnCiMAPlupAECp5FQ+hSiVUCq1e7iEtBp6M4YRUk2Kz410NbImz4zJR/Zif2lAFTl6IG8PP1b7rStK1a/MStMUicTb3vSmz/3TP73lhz90nwugXmfwH2kV4XX76O0ZDzKAuMdFyJYQKbFjTlxlCGnIq5GWhkzI2rUi96NlU+Vzkab53MDA9SMjD72SYe8kWoKm2UhnWoofIX2KSAlxWAhTiDKEKT6cmDVeO/qYY53z0UrlFXv2/MfXvtZ5lsznMT6+8prxD6Rr4bJnGAxyJ/3DQDLpn8PTMKS1LuxdTk+LP/9zlEowTSQSKyXgU6m2Dpf0BwxyJ4REyIJpBmavtqz5PWth73J6WkxPM/6BdDu0bMKg5Uf6B7EjibN++jdoyLOmrFRWgv8mJ1Gvu7xAZS4HwxCHD7dlpKSPiG4S5uQeBsWP9A+JwUM/e+7017EujOF65L+1bZe5dJs+wrJ/pJ1w2ZMQEiGWJcfO5U96wt6/jxNj5/KmKatSApCWheDVUZb9I10ExY8QAsMQEyL1Dcy5wt6/gbkJjFxqIAMkpZwcHq5dfnlgF4x/IN0Dl/XC4LIn6ROkJWUCA1iJNVZh7+q1DTtZF6cfOYZ0Oifl/OOP33HnnSNHjqw7PZ/H0JBYf5CQrcNlT0JIhAhDSGNtitHKV0Ptf9t2y+lLdyOTQTJZmpx8mRBHf/qnZW5tdXSl7F+hAEb+ke6B4kcIAYCBnHjuBtt5pIbatXsO/s3rzZV9PtOEZd31iU/M7N8Pw5CG4Yx/QKnEykeki6D4EUIAAAXselrI3Jr9d9OVM5cMjH7nYUdUQ6mEvXufm521br1VmKYq+4ePfpSRf6Tr4J5WGNzzI32FrEnMACXAAgxceHb3maWAnC+mCaBarabTaUb+kehgnF88UPxIfyIr8vS+04lg783f/u3f/tKXvmSaZiKRGD9zprC05E1uxsg/snXo8EIIaR8iIwzDMLb7i9/5A+efOXPGNE0ApmlaS0tjgM8SJyP/SAdDyyYMWn6knzm0/dDppdOl9TlfrsSV9oD9sP2w82AOMADXEictP7J1aPm1mGKxKFbJZDJxD4eQjkNaMr9UmPfkfPkevudSPgAlwFXc3Vn5iJAOpB/Fr1gsTk9PT01NlcvlqamparVK/SPEhTDEkNgztz7nyzZsewkv+ba3AG3lOSP/COlM+nFZTwiRTqcrlYr6UWmh73PgsifpWyxLziSkAVGAgCPny04kz8BnMXPwwgvPnH8+LAuGsVLhb2QEgKxWRTrd3rGT3oHLni2mWCzq18rs01pICAFgGOLuYewFZiCxmvPldsj3YfzCCzzrmfk8PvpRHfknjhyBEHJykmHvpGPpR/GTUjrXOZUQcuWTEBe/kBffukFaQB3ShqxDPgf89QfGXxg5CUd6M+TzOHFiKZ83pVyp+Ver4eBBhr2TTqYfxU9RqVSUw0u1Wi2Xy3EPh5COo1DAI0+LWg67IbLAbohaHk+/OILPzuJhA8IAAGHg74cwfYcxMpIQK3mxcfQoRkedYe+iVMLevZiZieM+CPGhQ/e0MplMJpNxLk66qKyiGoe0DKFYLFYqFZWlwnfZk3t+pM+p1eTMDEqltb28H/yS/Y8T+AVT5I6Lh1F5MzKz++SfJ+T/8mVxdGTlyzQL3pJWEeEkLDsPZYel0+mgBlNTU94bKZfL6t2N3qy63NTUlPetznw+hLSfctlWL24pvPRXo8szsCWk+nc77G+OLk/kX5JSVmzbNk0bkH7/bMCu12O9D9JlRDcJd+KyZzabDXlXOWcCSKfTKlbBdVb4c6xUKi4zkbt9hDQkk1lZ0kx+UTyxIPKrlf8AFCAeXxDGH4mklBlg9/Dw5Gc+U7v4Yp9eWPCWdAwdJH7FYlHFnoc3U8o3NTVVqVTUgqdctfaaXPycnp6mbychm8Cy5G8uwal8igLE2BJMCxDCFMLav3/sG99w6R/D3klnEZFFuVG8K5m+y566WZPHfXG1TKfTQSd2zvMhpBOwTXvZseDp/LcMe7hu6wO5r3710L33ri145vP2tdfaCwsr/VQq8d4I6Raim4TPb4/ENsS59qicUHybKYst7YmZ1WuhyhwMv1a5XM5ms04Tk96ehDSDMMSZQXvnks/yjDUoTycGUK0inQZQev/7jRdfvNUwVlxlhoYwN6eC/1AqwTRlIrESC5/yFoQgJHI6SPy0aBWLxSDxU8e3uEuXyWSklHrlM7y3hsuwLiS9Q0lP8+MP45577PcvrNsx+d8vXrjvJ0pIzsI0sapq1p49Vr1uVKvrgv9GR8VqXXipgv/m5qh/fcJGp9NI6aA9v5bQ/GZeZpXwZhs1pbc4fkI6nOFD4l2vxoP7bH3k1isW7jmb+7vXm1hVNRXSPlSrJYQ4xuA/ssomFiejG0xXip+vYnnXQgkhLUekxI47xVXXCGlIG1Ia8tGh0kvvHXnhbkcl91Lpspe9bOhXfzWZTGYymWQyOTExUZuZcVV7h9K/kqsgBCHtoFOWPQkh3YJICRwGDgMVKTLiL5Kz1t+6Q9efuOuugW3b7HPnAJim+ZfPPPPtpaU7AZ/1TcuSpskQCNJmutLyI4R0AiIjLMuyApK2KOVTHP/CF55897uP7Nrl084wRCIhA7b5CYmIrhQ/3429IB+ZLSICiOJahHQdhmEYRlNG28LXvvZFzxaOfeCAvPxyFn/oc4Km2Uhn2i4Tv4Ybey1P10LHFkLCef3rx6+4Ite4HbD0wgv1G2/UP9oHDuDee/Ga17D4Q59Dh5dmCXHpZK4yQtrMo4/mH310HnDq36B/U8PAyIg0DAlIw8ATT+Caawbuvlu/T/9P0ja6TPxUAjPvCufmqjoQQraIZcnTp1OvxB1XwxiGAWAYxgj27cAN7qb5/OD4eLJYXKt5+/jjTuVT0P+TtIcuEz9t2LksPJ3ws+0jIqSvMQxx+bD8IlK/ilstmGWULZhj+JMr8PROZ8H3fH70xIkb83n1k8hkpGUhqLyRZUnTpAsMiZQuEz+spiKrVquZTEaVaNCbolHYf3R4ISSc26/A0BVSZbvOIAPg9zDyoctm0y83YBgAYBj7hoYuveOOiZERfZYwDPiFN0hADg7i0kvpAtM/0OGlKTKZjLLwqtVqNptVNh8iyytGhxdCwkk/hvSj7pnkt58Y/dPnbzXqddx/v1Gvv+fw4btGRkZcc9n4uMyt85SRgLzkElx+OV1g+opYHF66uFK5tvOayVK2OVjJnZBwpCWRgPAUOQIgIVHHZx+WH8+4pbEqZVoIWathbAx794rVTT77yivxb/82sLi4rp9cDoYhDh+O6BZIJxPdJMzJPQyKHyENkUkpTB/xe3HQ3rNDODJdA3twFCgBJpAAxoH8D36Q+vznUSqp4g/y7NmBpSWfSxiGCNogJD1NdJNw9y17EkI6i3HInHt6umfUPnY5TFO8AzBNYVk4MCYP1GQdMIVYqXkLHHzVq07deqvy/8TJk8JP+YAVF5jIb4T0E7RswgjZbuVzI0QhaxJjwF6I0srn5YEr7cQP5Dd/IP4DRAIwgVng2A32j/aI44cHoD5WUgLISWkAh1c/aDKZ9LXwaPn1NuGOLRFNtkxs3QCKHCHhiJSQcxIzkIaEBRj4h/8pX31OnA+xGwLAbsCA/K27Bm66cBFitbBDMonx8VKhYOzZs7abNz4uLUusj/OT+TyGhtp4Q6TdhEyz0Tl8ctmTELJVREqIw0KYAmWcPomnz4knIPIOL5gCxHHUjBdy+GF95dCqJ6dVq5l67isUMD/vdAGV+TxOnECh0L6bIf0BxY8Q0jJERhiGyAvkPf6fL6L0HjnXugAAIABJREFUsxjBH68r+4e9ewePHk2sfrsXqRTm5mAYaynQhoYwNydGRgAw7J20EO75hUFvT0I2irSkTGDAI35JJE347NsNGsZZ332+SkVkMgBkrYajR1EqweE5KlI+lQFJ70FvT0JIdyAMIQ33bGXB8lU+AEuWZfqJ35ryHTyIep1h76S1UPwIIS1mICeeu8F2HjFgJLb5l/0zDCMRUsb96FGMjorZtcVSVn4gLYHLemEw1IGQTeANfpB5OfmNSetnrdJd6zw58/n80NDQbbfdhoDPFIMf+oFYQh1o+TWAuT0J2SgiJTAHGJCGlJDSkBhC/r/m55+ezzk8OfP5/PHHHvur559XPw4kk/snJh5y5DZrWPkhypsg7YPFbAkhPYIz+EGYQhwRI28dmZubMwzDMAwAhmE8L+UTQpxdXlanSNN8zrKuP3hQ619Q5QcAMAwRslhKSCMofoSQCBGZtRWtPdhzq7i1Lur34f66qL/0sDGy57LHHPt5j5ZKr9i79z8ePbp2vqfyA1Tw3/g4CNkC3PMLg6EOhLQKWZNnfl1+r46rjq98577wvOSLy35Ll4ahlzS9lR9Wwt4dwX8inW7HDZA4YKgDIaS7OX2bvP9f1pTPguWvfAAs62R9JRFMUNg7hJCTkzKZZM1bsjlo2YRBy4+QVvHiDnvn2XXftoPC3mEYsCx43PzWhb0fPOgMgZC5HObnMTcnUinagr0ELb/YEAHEPS5Cuglpye1n3Z+acYzn4N7Pe+2BAxdffrl6nUwmJyYmaqsmndA1q/2C/+TLXiZ/5VdoC3YjQdNspDMtxa8BDHUgZOucBky4PzUFFOYxfyPWXFdee+DAiXvv3fXqV6sfTdP8S8s6MDZWc8lYqeRUPgASEHfdhe9+l4lguhGGOhBCehPDEP/3IGbW618Kqf2448R5hm5kPvHE7ve854mvfEW3OV4qPbl37xFHPpeQ4L+Bc+f0ayaCIeFwTysM7vkR0ipuLdjv/xqOLYjCas7r2yEzo/Kr1+OW0nkApJQ7kknfJNeu5NfetC8SnkTa6jgTwXQ53PMjhHQ3BybEba/Gq/fJOqQNWYf8qX1y9mJZGICdsG3YZsL0VT54k1+vD/4LmxqZCIYEQPEjhLSDVEocuVNUrxGXGshCXmrg767Cf94phpeFMIWASFiJHQHJr2EYyWRyzf1hfc1bAcjBwaATmQiG+ELxI4S0iVRKHD4sTFNMl4VpismE2HGZELNrC5YfOzf+jivc/p84cOC89f6fpwB38N++ffYNN7jOYyIYEgLFjxDSbjIq51kJTuUDUEBh16PzN25z6N+BA4P33pv0+H+eApSQolwWpin+9E/F00+vWwtViWAKBbAEPPGD4kcIiQFpSenZjEshNYe5l58bXvnZMF72xBN4z3ueDvb/VMF/volg5PQ0SiVG/hFf6M0YBuv5ERId3pwvitODtrF0HgDDtp/fvXs52P9TfUJ9E8GEZ4Fp/c2QLcB6fp0Ig9wJiYhZ6Y78A3A75BdXX588fdpX+QAsWdZbP/EJ9dpVCHAlEQxLwHcPDHInhPQLliU/s4S9WKd/t0P+JPB/LaFet6WUGB4OrOd3/vnPvfiieuktBAj4ZIGB0r9SCYRQ/AghsWAY4sWEGAMsQEf+PQeMAc8bIpEQ1ao0hBgcH4ennh+uvBJveMPjd9yhD7gKATYsAU8XGMI9vzCY4YWQ6JiYkJaFUkkAuBryGASAAwfsJ57A448L00QigUs/tPjDfxx/6nV71yy2fB5zc3jpJZ8enYUAPVlgAEhADg6KHTugeh8fR6HALcBOhhleCCG9RqGA+XnkchKAVr5778VrXoPV9NTiDTJ1FrNXiDX/zysuuMBf+bCuEKC3BLwE5CWX4PLLmfyagOJHCImLVErMzcEwsBqeIJ94Atdcg7vvHgDwDkgAd5UGrn9damj4VnWKUa+/7w//MHAj0DAuSSZXXq/PAgNAXnklbHvg4Yf1EbrA9DNc1guDy56EtIdKRWYyIpmUO03kgRyQAExgFpgBXjBgWQNY9XrfPzHxnGU9ut515Q35/IVDQ3975EhVyrQKgajVMDODUgmWBcOQZ88OLC15L83k150Mlz0JIb1MJiMsS+405R1AEtgNMQCxG8IA5oALLbni/wkA+Fw+/6P5+SscVt0b8vmnTpx4bS6XlDIDJKWckPLUnj06CwxOnhR+ygcw+XWfQvEjhHQEhiE+MYgFIO8oT1SAmAc+MYhEYu3gW0ZG/uKOO4aMtUKAF+za9brZ2Z2jo6YQEMIUwgLGgJqUAEQmIwwjZLGUya/7EC7rhcFlT0LayYvb7Z1LPt/IXxz0zwWjM7xMSlkHZtcnCslJaQCHVw/KiQlYlli/WCrzeQwNiSNHWnYPpKVENwlzcg+D4kdI25CWlAkM+FWltSFFHSLhfkuLX1JK0y9FluE4Lms1jI1h716tfyvJr+fmxMgIAFmtinS6hXdEtg73/GJDBBD3uAjpNYQhRMDqozAgEqJadU+CKgOWX4rsFSzAXJ06fZNfY24OQsjJSea/jpGgaTbSmZbi1wDm9iSkfYxD5twfrucO2NXLZTIpP7mmTevaBIsmDCDhmEDFakXBlUJIR45ACBw8iHqdwX8xwtyehJD+pgDMr9O/5w7Y//I1aT+HJ01UIJ40kf5LeeiAW//GgZxnosxLGVTKdiX5NZj/un/hnlYY3PMjpM3ImsQMUAIswMCDP2knfyjKTwntAjoDefWovOd9mJxZ++5ek3IM2AuUBgYAQMq8lCeAOWAkvFyOXxY0rAb/cRcwdrjnRwjpC0RKiMNCmAJlCFM8+Ng65QNQgDi2IAb/aN1ZKSHmgNXQBxhSDjWjfAH5ryUgLctOJLgL2MPQsgmDlh8hMWJZcjkhd/t9R6/DFvWV4g/ptGNXz6+8bQhey0+dKbdtGzh3buUIq+DGBy0/QkjfMQwk/CIfABjAZ/9AJpMyk4GyzR56aHFyclK9m0wmJyYmas2Ya6v5rwXWriSvuEIrH7gL2KNQ/AghHYowxNlB/2/9zw3gX88I0xTvAExTnDpVu/768VptpaSDaZqWZY2Njfnq3zofem/+68HBgUcfdZ/CKrg9B8WPENK5/PjDuGfUdh28c9j+29fI1CyegawAz0Au3FVKJUfuumvNabNUKu3du3dmZgZANXjdTAf/qR/t4WGmAO0TuKcVBvf8CIkXWZNnDshHTOw/vvJN/YF99rnvyZeWxRNYc4TZieQZ+CjToGHsqNdNIAGMAwUgtWr26Y92rVY7evTo4cOHASQSifEzZwpLS97NPRZ/iAXu+RFC+hGREjvuFFddI6QhbUhpyH1pfG+98lmwfJUPwJJlmQMDGBhQqa4P1Gq/tX5f8KGHHjp48GC97lgvff3rx0ZHXaulMp/HeFDQIOlKKH6EkI5GBz+IMoQpdvzhwA1iXeUHA0YCgRUb9MvSqVNPjo//TX3dvuAHP/jBiy66aNYR5F46fnwvMLNvnz6ykgK0UGjpbZGY4bJeGFz2JKTT8M1/PYEJC1YJ631S8nkMDeG22wBASkxOol6HQ+cAbNu27ZzDsVNjDA7Wt29XVXAxPo5CQSW/Jm2GVR3igeJHSAeynLDPs9atWtVQG8PYJRdcMvvjVW3L5/HYY7jiipUQhUQCZ84gyJnFj3q9bjz88FoiNACs/NB2uOdHCCErDOTEczescwFNIfWZ60rGy1cWOQ1hvP1Bue2cwPLySgvT3JDyDRpGIpHQyidrNVZ+6DEofoSQbqOAXU8LZ/5r+4D92r8bufWdt6ofTWm+Rhi/uHiZa5HTn23b3EfWu7fIWo2VH3oPLuuFwWVPQjoTV/5rebnEazBw94CAACAhk0iaAS6g6zhwAN/6Fq65Zi2GXbm33HFHfWRElUOSk5Oo18V6HZW5HAxDHD7c8lsjTrjnFw8hpRT53AjpBGRFioyQSSlMAUCJnwkz0P/TidK56Wn82Z/hyBEA2r3FSKXWSsCHVn5o3a30L+FFayl+MUDLj5DOR1oSiRXZa2z5DQ6ubP4pncvnkUphYABq/pUSQF7KIeCIMvssC4mE79wsAdTrItGEypLNEt0kfH4UnRJCSNsQhpAJqZROrlRlwDjGGwQ/KKNNyvOl/IiUup0uBLjauSETCd/KRzAMKl/3QocXQkj3M76u/juAAgrzo/O5fWsZq6/M50c8sep54CPBhQBXkoKuVn5wwpwv3Q7FjxDS/RRw5nH54JVr8Q8LV178uT2zxv7V4AfDuGpo6NV33JFz1ORTRt6EEIdX95xMIY4IIYBJKZNSZoCklJOHDtVOn15X+YE5X7ofih8hpOs5BRwETIk6pA1ZhzwtMT2YKkysBj+Y5syRI3eNjAQZeVJKtbdUk/IgUAdMISCEKYQ1PHxwbq6WSknDkIA0DAwNYW4uJOeLrFajvmWyRejQEQYdXgjpCiYnZb2O2VkB4GrIYxAAcjlpGDhyZAAej8GKlJkAD8NJKevA7Pp3c1IawGEhZKXiyvkCR9oXWavh6FGUSjBNJBIredFY/30LMNQhHih+hHQFyaQ0TR8xMwxpWT7iF9aVlKafLhqe4y6pkx/6EB5/XFx2mY4IlLkc5ucxN0f92zQUv3ig+BHS+ViWTCQABMUjbED8LCkTAHyNQinrQEIH/6m0L6OjWursK6/Ev/3bwOLiupMYC781KH7xQPEjpCsIsPzk4KDcsUO80ZT/kBDj4ygUkEqFxVOjacvPm/ZFBskvY+G3AMUvHih+hHQFExPSslAqOdVHvnWP/ZEfiw/8SCQAE/jOPnl3AkfuFOH6NyGlBZTW658z7H2l9/VpX9Q0wVj4lsOqDoQQEkihgPl55Byhfte8wf7UM+Lcj8RuiAGI3RBPHhc3P4k7DzeYSQvAPJBzTLgqIqIACEBICZX2Zb0xFyanjIXvSCh+hJCuJ5USc3MwDKwGI8jMP4onzgpnwfcCxLEFMfhHAFCtBkpgSog5wAAMKSGlioiYVilgpISK/Bserl1+ufdcb6eMhe9YuKwXBpc9Cek6KhW5bx+WE3K335f7OuxLDViWWI1ECNsFVBERKvJv1BH/kJNy/vHH77jzzpEjR4SUkFIODEhAjo7ioosGjh9XzVZi4UMjAkk4XPYkhJCmyGTEMJAIWIk0AGmpqnzCsjA2hlotcG5VsYBH1ysfgJIQe1OpI2972+TXv75iDj7zzOS999be8hbs3+8TCy+EDC1cQNoPLZswaPkR0qW8uN3eueRr+cndDl1UgfCHD4cpk7//p5TnS/nhb397dv/+la4eeGD+zW+e27FjRAhIqdRuJRZQhTow7H3j0NszHih+hHQp1kftY/fg/Qvr9O92yOeAyfVGoWH4B8iv9BMU+SclpMTAuv5vsO1/AR4UAkACGD99Ov/Rj6Z27WLY+6ah+MUDxY+QLkXW5JkD8hET+4+v6NPtsH8SYgw45V4RlfV6UM0+INjycyuilAC2SXluVRFzDz44v2vX3LXXpp56aq0Vw943Avf8oqJYLIYXESaEdCMiJXbcKa66RkhD2pDSkOcG4ad8MIww5QMwvj7yAVjRudUrCacKnnPYgqX9+/ceOzazvviDKJVQWl9lkMRBX1s2lUolm80iOPURLT9CegBZkSIjdCC8BCTkAASAfF4ODeHIkTDxq0k5Bux1RL7npZyT8iWlc7oEvNcWBAAY9bq5e/e68YBh781Cyy8SlPIRQnobkREACgWcflx+/UpbVXt/BvLeK23rMdmwKp+O/IMj8m/MZQ4GT9CWYZjDw+sOMey9A+hf8ctkMgCmpqbiHgghpB3sAeYEEhICQkDshhiWmBNolOwTAFKq4K0QWC14OyHEvFP/hBgM0D/DshKnT+sfGfbeIfSp+FUqlWq1Wi6X4x4IIaRdHMWOy4T2fwHw9uMDO14nMNNsBxLQ4Xpr5iAAwJByH3CDbbtOyX/1q+Pf/vZaD7oEPCP/4qZDxS+TyRSLxZAGlUqlWCxmMpmGLX3JZrNTU1MZT1FKQkjPUoKYdeuNKAk07X0ihFjnH3fqlLjllpW3du/+mVtuqZ065U4K+u53548fl0o4DQNDQ3J6GqWSaiSTSTkxIWu1Td8T2TSdKH7KLKtUKkENisViNpudnp6uVqvVanV6eloIEdLehRAinU5vQjIJIV2KtCSCygpZkKbfiqWAFIE7ebVa7eDBg/V6Xf1omqZtWTh4UNRqzq3Bue3bRycmlGAK08RHPyqmp1GvC5Um2zRhWRgbo/61n04Uv3A/lGKxOD09DSCdTpfLZb1pp88SwajT1f+VVQDoF4SQnkQYAkEuJgbE+lAHWZNyUiq/GJmUckJKTwq0o0ePjo6Ozjrq+ZVKpdft3Tt89Khza3Bk1RdUlYPA0aPO+rdQkQ9792JmBgDXQtuK7Bimpqac7ifpdNq3mXp3amqq4cGgqzT/KDrq+RBCtoJ9yLZztlwJSlj5Z+dt+2Z7XbNF236vbY+vtbRztn2dbS/azokiEeCuaRiGOs9/DInEyhLo+n/LQ0P2xIQN2ICdSNiHDtmLixE/j+4gukm4UyZ3ryb5ip9u1uTxJq9bLpd936X4EdIz2Iu2fd06/bPztn2tbS+sF7+Jdcq3pn+H1sTPDK3MXq/X/Qdgmraf8tnAMrB8441rR3I5+7rrqH8yykm4U5Y9M5nM1CrpdDqomVqc9DbQG3ibW72k5wshPY9ICcwBxv/P3rtHR3bVd77fXbYlg106VcJ5uLukaAg4thsSumduAtwLqg6ZGz/yuANJ7r0w3cxKQF0PWJOEuWNJnllST2hLJmHILCyVqBAW001u1s0fScAYyLoBlQgYbAd3Qm47xoRGaZ22YzPddY7adrs7Xft3/9h1Tp13nSrVU/p9lheodu2zz67T0v7Wb+/fA2QdyiEJlCEm3DuNMfxiNE2LsPzC3hKahrDwvpGRxKc+5bidYy+U6Q7X93sCdZTfpvp5cXFxY2MjsJtq76VWtZr8jDgjDMMMKiIjsAQsg0DCk8xagEAwYPvFCAgA6uRPh75qrqr2dDqdy+Xe8573SClL7kRlhUIhmUxGzSCXI9MUpVK9Iryq/DA6mrhyxTvVUknVm2j7ww4gA5VLclAsv07RquW3uLgYLVetmtI7mj3DMD2AIKixCjvdWzAFGvX+FevQj+HYxRsuqpeGYZim+dRTTz399NP5fN7uVigUzp49W4xOGFMsYnOT1FVEAORv/IbwKV8d06TI/dWho43Nye5NZijFL9Dyi9gsZRiGCYR0wjGgWk/7IgzxbAr/42ZXrPoqVq9PT3zinz9ht5RKpdtvv/2OO+7QtHqYu6ZpyWSyXC5PRBZtF5mM/slP3v8rv1IvgXvx4v3vfvfW7bcH9+YsaN1kULY9GYZh+sAqMOk65Nv/vPhH0CYa+vf7KF2uei2wUqmkaZphGMvLywCiXWBsdKLcrbdO3nprPRwinTYPH8796Z9+4uTJieVlZ08qFBC9g8rsjKG0/BiGYTqDz71FQPwYxJ3Wy3+EcTkkPN40zZiaZ7MKTAJrjqOvkhBTmczqT/0UOXZQG1nQmK4xlOIXeLAX5iOzQyLi5RmGGWrIJAoSLwFxo1X2bwophITHRzh2hlFyK1+9MZks/eqvQtOcWdBQLovIHdTdRHRmki4xZNue09PT0SLXcUdQ9mFhmN2K0MTlG+WrXwlYYU3Xq9yBA+aZMy06dvrHDJZaADATCfOBB1LLy6Rynu0xIpbZ7unf7rH8FByxxzBMfNYIK/CuvL+Dc+/CvPUq/YY3bD/33NNHjrTo2OlDEyI8wxpSQgC+MvNM1xgy8VPB7H7jj7NUMwzTKqZJH72CKbj073ew9TDyj6NqNRg33EA/8RMYG6uvljEdO50IS9VynhK4AIACkarvJ+wUoEz3GTLxsw07j4WnUl13ozItn/kxzG5F08TLKTEDmKinfamCvozSU5h8CY3AhtOnS2960+32Jqfy8IyvfE6KwKZb/wpEZ4E97tnSlzO/IRM/AKoC7cbGRjabVVX97AfUDfuPg9kZZheTy+GX87gfQuVzGYfYQOklrHm6lUolTzKXliGCswSus+wRMGGv8ntybeEg91ioLKAANjY2VFU/1c6CxDBMqxSL2NxEPk+o5zMzER7Y0Mb4VvYYAqCKIwFYEsJT9sjfTecFrcuI4dUM285z5gXtLEIM8fNhGCYOuk4rK1Ah5pqGV14Zv3IlQP80TVP613RNUHtRRKQTHXMH9uWJNpWpl0jYQ0V0y/j3/awCge192KGje4swL+5RsPgxzB7BEiwxNzdnmmZgxuoHH3wQrYjfPFHVF9iXJ9KAZYf4RXRbChI/AvaOX0z3FuHh2/ZkGIbpOEQgEgCKxeLm5qYnY/VTT5198cWCeplO09wc6b7a7n6CQ9qFKMXvZm2QAiBdp/l5dVeyJhH787mGYsDi1xT29mSYPUUmkymXy86M1UBSiE/UanX3TsMQpomZGUTrX1RIe+xuxthYXe10HceOoVpVURPCMGCamJlpSf8G1lrsi7cnb+tFwdueDLNnaexezlO1irU1O1qPAOTzqtxewOpsX5gmMtTy7T6o04hMx7ZnaLeXX67edBMApFJ0xx143esSp045b0T5PDRNNKv5R7qO1dV6acBUCrkcikWRybT6QPoCn/n1BxY/htmzNDQsTUa98m1D/ABomt0efOEckQmU7P1GIgAFoiTwoEP8grs9/HDyhhuW775bjSlHRhJXr/rvRZoWnQ6tbjJOToq1evwG5fPY3ES5PBT6x2d+DMMwfUAVlN0H+rCleRdAJ0D7QKYJw3Cty45TOaTTdGmentapaUh7QOT7o4+enZoqvu996iUBIkj5GvOLYHXVqXwARKmEqSmsrERdtQdg8WMYhglF08QdY/QJIG1lgRmH0IAycHuSUqmG5afrdOwYqlZaNMMQ0hQ4BqE3dju9Ie0AnJHvdrdvfrN8990T58+rlqiDL1XwNsKZpVRyKl99wFIJO4zZH35Y/BiGYaJ46ACSB6jg0KAixM0HaOWAq9vqKsbH9bW1+62GdKk0n7nl/Nhq/UIV0i7On5+fr2fNTqfTc3Nzuq5nhLCjGqrb20sf+pBSPme2T/V/rpZCAblcxMyVcRr8XlOTcbczZCWNek+YuxGfBTLMHmH6KVxneu2E6TOJ2pgEGueAKyv69nYOmLS6GIB56tSxZLKRJlTX9WPHjk1O1vsYhmGa5szMTLlczlgncELTKJUKFC05MmL/XC94Wy7XXwYZiBFD1U3GwaAv/vNs+TWBc3syzF6GTEqYwUuz2BbHf1sCIFAqJbe3SypPi6NLCZi6dGnVfr26ujo5Obnm2IcslUpTU1MrnhO4XK5e2N3azxQAHT2KQ4cAgMgueAshmgT/2UM5P1Qzk7HH9CW3J3szRsHengyzZ7GdNilNIsir89J1ta/9pLjrtABQBTIYvxycF1RToX1ElE6njSA7TNM0wzAad9R1fXa29J73LN1zD4DUhQu5xx//N5///J+fOLE0NgYgBeSAwnPPZd7//mhPTtJ1zMxgakpYh3y2ySiUATrYSxx7ezIMw/SJHCjvXX+feEPt5ZvE908r00xch+0Q5YMd1G6aZqDy+d86v39/rlyuWtuSxvj4+de85hd+//f1ZFLZgoYQJnBse1t/4xujPTlFJoNyGZpGAAG2ySjaKsm0m2DLJgq2/Bhmz+KwwwgzwBREyco0VqCzJfoa4b0O+yGNtBGkf2Nj2vZ2a5ZfI9unHflHdIDoTMJlruQ/+UnthReW7r/fM1pw8J8/KegwpAlly49hGKY/iIxAGdDqoQ6k0SsjNO5WPgA55PLwnq69611H77zzDvVzOp2+/fbbjxw54ulTKBTe/e53O11A/+v8/JoV52DjUT4Apfe9r+Q7zwNCPTntrdudpgndFbD4MQzDBOB0uBD1WAQBQBjixkWh+ZwriyhuYjPn0L+jR49+9atffN3rXqdeGobx+te//ktf+pJT/wqFwlNPPfWd73ynakUIGoZxxUoeak8lbJKmphljY97WSE/OjqQJ3QWw+DEMw8Slrn+aeGXUK0gZZMoo33RdXYo0TXvmmWd+/ud//jOf+Yzd59SpU3fdddd3v/tdu08ymTxw4MBtt93mdAGF5+guPBJAu3w5tb3tbAn15LSK5bac82WXloPgM60oIqJP+LkxzJ7DkdrT/ID86sP4xXMu++Fzk3L6XqRK16HZCZ+zLm5YN2gaVPn4kDO/wqVLyS98YWljI8CTM9yfhdLpwHSgoWlCu18+NzrOj8/8+gPH+TEMU4caZYHGZsU7fhzfOCjtNx89KH/utRibq6/j0b6dzp/DusHR7SjR88AR2bhdgejszTcX3va2ljw528v50u31ri9xfix+DMMwLSMy4lUnxZt/XtheMG/5X8WNDwJWysyxqbHUaPDBm10sUP2cCjmfG7VrChLdKsQjwH4hrLvV04RO7tsnlpYSRAkiYRhieTk6hkFoGsKOA30nhbvbL4bFj2EYph08XjD4AMRxASuxtTBE7s5cftLrjVkoFHLuM7lcLpf3OW0ePXr04B11N1ExPi7m5/c9++ySdfym0oROtHcUFy/ny673i2HxYxiG2RFK/7AKTEKsOfJfny5uYjN/sKE0hULh7NmzxaKrqFGxWNzc3HTq39GjR7/4RZebqEoBqivh2aH7SbGIzU2n/tVPCt2z2vW1kFj8GIZhOkHJpXxQ/p/nytpT1u6lpiWTyXK5POHemcxkMuVy2d4LDXQTDU4B6iT28VjcnC+7vRYSe3tGwRleGIaJA5mElBUIAQGAQLBKAF6H6wDIlEQOKEJkhJ3MxTmIo3Z8uJuoYcDhgaITrQJLAKyEn0UgE9M0DM/wQqYJZ61C51sAqtWeVYTgDC8MwzCDi9AEfHKg9A9WGSJhCJjADEiPWs2buIk63tKJjgFVwJnwcwbQ41uBYe2t+MUMKSx+DMMwnSAo/zULRlqjAAAgAElEQVQdoMTVxjIrSgJTQOSpWYT/p6ZplErZ91hVJZQcdl5JiGbDx2YYaiHtBN7Wi4KD3BmGiYmd/zpRSgAgkByViSsJuDdCAZBGCTOB8G3Pubk50zRL7tO1QqGQTCaXl5ftljSREbRGaSHtXiKj16NqIXW6IgQHuQ8iHOTOMEwc7PzXCjkmxZWQNd0Mbrbx+3/63URNoogSSiJ8S7OBnfAsiF7WQuJitgMHO7wwDNMq/iq4rVp+AHRdX1lZUXaepmm5XK5YLHrcRCMsP1MN1aHP08fKR91bhHlxj4LFj2GYVmmI3xzBhCgJl/9ngZBE4sEm4hfW4mSOyARKbv0rECWBB9sWP/9eaPdze0ZOh709GYZhhosisOnygqEC4SxQjLim1eGRd2hDgahzw+9yWPwYhmE6iYrtg+8UkDRCEihDTIiOHGhlhKgP7074OdFXW21Y4G29KHjbk2GYnRO9exnRLe6FqpMQCA97d5RjajaUp1tfN0J525NhGGb30PkCsZby7TDsPYBIp9DhhcWPYRhmaBBCBETFWVq6k7B3VcCo/nNHChgNdgl4Fj+GYZheQ52KQ3BTcitfvVGIprmo7QJG6mXTAka7wBJk8WMYhtkNRIe9A5GndM4CRkQIKWC0m8rbsvgxDMP0Dod8wJKPzthRmhApKQPeIBqVUklammiOKOAIsFQ6/7nPzX/4w/VuFy7MnThx/rOfdRYw2mXlbVn8miBC6Pe8GIYZAjxSVyzK976XrM1FGIYwTczMoFP6l/vqV/OViquJ6McuXLjjueciXGDINPVXv/rYJz5RTafr3cbHTU2bKZf1m24iu45E6+VtY36qsGW2qystu/JHwaEODMO0ja7TsWOYnMSaVeT20CH5gx9gaytBAIESEADyedI0LC0FLPQxgx/sxq077zz2u787tblZsrKAHnriiR/80A9tTU05++eJNGDJIS3zH/1o9VWvWisUXN1WVrRXXln60IfUS0qnRVCtJdI0TzvpOlZXsbQEAKkUcjkUiyKTCX1S4XB6s/7A4scwTNvMz1O12lA+AADtAwqA8qqsAmvACvCSBsPYqfhJw0AqdX7fvpVicXluDoBmGK+Mjl559av9I3sqP6RfftkI7Ga1xy9vW98dddiIlM9jcxPlchv6x+LXH1j8GIZpm3Sa3JJG+0CfgDgHFKyiCyugKWAGdKYq/OLSquVnG2eCCESGpqUMA4mg4y2iKpASAoBJlCIK7iZlVQjVLablR/PzqFadu6NQ+qdpQtmCrcDi1x9Y/BiGaQ/TpFQKnspCH4ZMQxTcjSugV0bpQ68EaE/L4jc3B9MUpZIqwkBCpC9cMMbH/SN7Lb8YpQHtwZ0dqFBAMikcVQbj747GgTO8MAzDDBOaJvz12PMOm8+mCKEK93UgKLxYxOamswJ77vHH8w8/7OlVIPKUY8+5E2QHd/MNXi9v66gySKaJMIUzTWpd/LoHix/DMExXyOWQd5R0SIJSISVmb7wiyOiAfWNXoFUvSdMK3/rW5jve0bTyQ5wCEXHK2wpNg1/zFYFfB/oHb+tFwdueDMO0ja7TzAymplAq1TXvxVF505UAk4M0EoZw5ovWdVpdxdJSAkAqJXM5vPOd9Gd/JpwtxSIyGYHA3VE1rJoG0Qqg9iU1K9X1hM/GjNkNaFLeNubuaEx425NhGGbIyGSEZYYpS4keO4jtI944dCoQGtuLqow7OXKNwTDE+fN0772NcMCWAgQzQixZO6qGEMtCBEpazG6KqN3ZGLujgwBbNlGw5ccwzM6xDDNBOmEGmIKwbEFV3va54/TQnws7Lu6OO+h1r8OpU07jhA4coDNnEk6jzg4QjLb84rfE7daspBHpOlZWoOw8TavH+Tl2R+PD3p79gcWPYZid4xQL0gnu7cXn3knvPy6csfAjI/LqVc+2HFky5BIjTSPDCBK/tuQwbjef+EX7oGIH8LYnwzDMbkBY24sAhCHEsnjoz13KB9DVq55txfrhnRUcDyANzAG6aeq//dv1xnQ6PTc3pw9nps3ec32/J8AwDLMXEdbBWankSe8iguyxLSAHTFotBmACR6+7Tly+fFu9yTBM05yZmSmXy12e+26At/Wi4G1PhmF2TsAZmbWZGBgLb4mfs3E+nb5YrX7C2emWWw4lEvTCC6edjfl8XtO05eVlDMO2Z+AcPIPwmV8fYPFjGKYrOFZ9OwuaQ/EIwMgIXbmaUC0jI+mrV+NGiGuaZpomWPwi4TM/hmGYfuKJhQcAiKNH6dCh+ouxMSO+8gFQyheLmLri6UYU90IPHchh0zFY/BiGYfpJsYjNTZf+FQr0wgviT/6krhOmmUq1khtFszK8hKETzVsCFlreNna3IYXFrwlczJZhmM5Djc0+Ryw8AGgaJZMolzEx0Vhncrlc3hE2rjh06NDBgwc9jYVCIZfzZO503VAnOgZUgcDytiRAonm3zsLFbAcOPvNjGKZ3hB+46bo+MzMzNTVVstKGFQqFp556Sghx++23OxvPnj1bLpcnJycRct42T1QF1ty60ihva92xSTfPxGOe+fkPC32f2D8yn/kxDMPsUTKZTLlctvczNU1LJpMnT548efKkp7FcLk9E5lIp+SQNQEmIUlvdhhe2bKJgy49hmN4hQCBBIqIlpo0V5mlpEqWAYK8TVd42IQCYslk391s7svyIKCKJKFt+DMMwuxWyfEsAUJpojuQT0tNCMXJYN0UTIsxzRnNIWsxuO6Hv3jQsfgzDMP2EdMIxoAph+ZbQecK9IJ3sFpjADDqif7Hq1kZ2E9FVHSKJ43TTG1j8GIZh+soqMAmx5tjbPCXww0g4qjqIksAUsNKBuzWtW6vMzTjlbQGQrtN8PbkopdM0N0dByUVVN7K6ra6vT774ovNMsSTElP35ehIOyOLHMAzTV0ou5VMkzngXZ1ES6IS3SUaIMqChHquuESWBMpA5D+dG6/55fFInfzdnkT/SdTgKDwrDgGliZsajf3Y3ZTIKwyj9zM+sJZPex9Bbbxp26IiCHV4YhukqZBJSjSTXsAwvEbSzSKAEEmjP4cUXVeB0NqlvvToMUMoTNoEyEhnh8UmxR6L5eVSrwipIUTfs8nlomlhasudgd1OXm8lkyjCQCDK96k43CfUzOLdnv2DxYxim21CahKuqAwgULH4aJczOiZ+zyuA8oeo1QClP0JBYEu7rHOKXTgvD8I9NmiYMoyF+Vjeb9IULxvi4/wNqRIa958ni10dY/BiG6TY0RzAbtd0BEIgOkGfnkwqEJBIPdkf8fAJcv6lGCSNY/KRpIpUKKLCrfqhWE+k0AGkYdjebuRMnTE0rFV0HiIVLl5I337zcK/HjMz+GYZi+UgQ2QY7cnnSU8DzkEdloKRD83iZB6Lo+P99aeVsyCWF5s02MGcHaIzQNqVRAgV1ATybv/8hHVOP41NT86KhnBoWVlc2pqVypccZXqFTOPv104bnnoqfaQVj8GIZh+onICJQBDWT5lohbBR6B2C/sFiSBMsREgHHmRNf1Y8eOVS0PFLu8bbT+CU0gPKxv22u2OW737ncfm5ysWi/rBXb37ft36bRrDnfeOTM56ZxB5tln1z760VS1Wv981WryG9/4xJ/+6cTHPx79ATsIb+tFwdueDMP0jh1neJmfn69Wq2uWa4lClbddWl4Cwrc9fVuvsDdal4O2PYlIiPkPfrD62c+ubW05rzqUTNLk5OkzZ1xzmJzUXvOapdP1urtydDRx5QoAQQQispxf1GEhn/n1HxY/hmF6R4xKstHil06nDSNgB1PTNMM0fEOpiyxvzxlgqqF/9Y3WMhITDfHTiVaBJQBACrg8Pn4l6HaBaKOj1StXANDYmNjerh8W1ktJWDcFUK2KdJqst/jMj2EYhvHiiB1HKmUEKh8A0zRVshj3VQRABaafBzxbr42NVssDpZGWJZFAImFsb8dXPgDmlSuqxm7CNGGXJ3THs1MyiY98RAAJK2S+lYfRGix+DMMwQ4mukyPEHKaZGhkJPrvTRuuVHyhN5gfk3FGqVmFFnAvTxMwMzgPCqmkkDCGWheeIcRWYdJZ60DS0WGI3ZedFy+XIV55QHj2KH/ohb8i8ipHvAntR/CqVChenZRhm2FldxeQk1hzBeVev5g4c8IpKYbKQu6OeuVMY4qlv4D9+D59zHAuWSmJqCitW7rTAEEMEFjnK5eDTMBw6lGhaYrdYxOamU/+oUMDXvoa3vlU4DiyFcgdd6URWNx978UyrUqkcPnx4YWHB2bi4uOjvyWd+DMP0jhbP/NJpMgxPoJ0OzIyMTF29apW3PVQ4+4Oz5a3yJCZhpY9ZAZnA/W6R0zQyDBF2R0PKRpEj9b9SQuUtm5qCHbRQKOCppyDEr99++6d8JXYnJieBuicL6TpWVhLLywCkpiGXo7W1hGl6PrQApPKC6TTXd3zEwadSqSBE7RiGYYYC06QgRcgA5atX66aSpmnJM8nylfIEXOVtixBV0P3eAWEYlAox+zQhUkSuGwqBTAblcsMy0zQkkzh5Mgn88OpqYw5BJXZFJoOlJSwvAxCGQaYpHnww4qOKVrZY47AXLZtsNruxsRHng7PlxzBM7+iA5QcAmkammQAgDWknDlX/S1YfCUoD2w6pi7b8iGiOyARKzgwsRAeIziQSzuCEAlESWLaOk5oUs3X0CUyW1j3Lby+e+Smy2aw67WMTkGGYYSSXQz7v/XY+d1T+6R1W4xRoNPjru+lWvkKBcrnAjg38RY6OEj0PHJGNTDSBZY/iEuQFo9rbGy+avSh+Gxsb6oeFhYXp6enjx4+zwwvDMENHsYjNTZf+zR2Vv/xF/E+vq78UhsCdkJPSc+HnJuVjB4ks66pQoLNnUWwmWY1aSAAAjehWIR4B9tv134PKHrX6ebxeMKq9Cwyo+GWz2WiDrFKpLC4uZrPZpj39LCwsrK+vqxEqlcr6+jr4CJBhmL5D7j3PwBYHmYwol6FZcqRp9HPP4M6fR/Izjiq4pwUAedCVJvRfvxan39K4KplEuYyJZrnTAGSEWLKETUVD/HQi4WlpU/nUKaDj85A6QQSE+7CwUwzimZbyxpyenlaeKX4WFxePHz/uaVxfX89ms7A2kQMJ+7BCiMDb8ZkfwzADRXSGF7s4g/OEj0A0StdduQ6A1CRyQFFFrwPwiWuMnDKxikgEV5aIOvMLbNxbGV4OHz4c8a6tfNPT0+vr63bEgn0VhdPtmTMMw/SLsOIMAkJcqZsEgdHrPUMQiYFZhwdI/BYXFxcXF5sevynlW1hYqFQqas/TVrU4W5cqwt1v5CmrkWEYZjAhncjKZEZpojkinZyNEe4tjZM6xibCTuolnpBzANPT0xHdYrYHonqur6+rl9PT02EXDs7zYRhmLyO3pLxHylz96I5AMi9rh2u1n605G2sHa7XJmh3PoBJ1yoKU98mAFVIFK7hRx4zuFu+FcVqCu/lu2PTC7i3Cg3KmValUbGusUqlsbGwEHsKpEL2w8zk4Tv6i7+XZWQ27is/8GIYZBGieUIVYE87DPHlIgpA4nbAbCUSThNfgutPX1Vvs4gyTvhLwQWd+vlO5Tp75BcQx9u/Mb1AyvCi/TfXz4uKiHY3gQbXvcIsym80SkS2f0aO1GgXBYskwTOcpQRjetUg8KTx5OAUEzoGet4yn2FVwe4l/UQ1cZrsdgTYo4tcp1EFgnJ4xu7GYMQzTXwI9WSgkBkJA4Ir1s08vB4GWTMbuSeAAObzEJ1C31NEdwzDMLkNoAr7ElmG1F4Cdu7fsiW/8Qyl+DMMwe4scyJfJjA6RM3q93lggxE4H5jQf/RVudX03qyCLXxP8lf+4/h/DML2mCGy69I8KhDEg5WuMkVtTBUjUQ+DTRHP07BPSqovrqnDbG/0LXF27vdIOpfgFZn4J85HZIWFust24F8MwTCAiI+BIrKk8WcRJIU4KT2NT9xbSCceAKgSEgBCGgInt/wNvHCdnXVxPhduuEri6dnulHTKHl+np6WiR41h1hmF2JSIjsARaJgK5PFkCGyNYBSYhHDonSuJrIzT2Ge/lpZLQtF37RX/IxE8RlvMTLH4Mw+x2Al1dovxfPARFTbzvasJf3haAabY6u2YQYTCOjYZs21MlMPMbf1yTgWEYpilh+T8BaMCYz89T61BeNOuQkQDYqdlCuwF2N+zb15kZ+Bgy8bMNO4+FZyf87Pgd2eGFYZghwnLaBIKcNgOjJhSXR2jbbT7GqXAba0pEx4AqYB0yChOYAWz9q6+q+/dPPPLIUqmkGg0hlldXUS4HyuTOGTLxA6DK721sbGSzWVWTz5aibth/7PDCMMywoOtkOW0CQLDTZlDUxKWj8vMZV13cmBVu47AKTAJrDpuhJMQUYDvTqEV1Ttdz994LVcBWUSxic7NLPjfDJ37ZbFZZeBsbG4cPH7YL+7EgMQyzx1ldxeQkPE6bt9yi/9qv1Y/z0un0/KV5/WndEyBx8wvibX+sNjkJIFXh9vhxvVRqXDg3N/fEE0/Mz887W3Rd90/DU4Dilnn63Hlvn9L58x+73zX4iq6vnT8Pa3yk05ibwy//cmknTyScQRQ/VaUowqtFdViwUPUZejhBhmGYQaRUcikfAEA/dSr3rW/VjUHDMExpHsMxXej1RNhWgMS+n04sLQmVfdowxAc+cP748Vy12rjw/Pnz9957r612hmGYpjkzM+PRPzuUQiEMMWqiPIN9TutT13Hs2BXH4D8wze33vhfvfa/TboVpYmbG1HWjCys8Vy2Igqs6MAwzWISXXzcNSqXsHna/eaAKrDm75/N5TdOWlpcIJMidGtuq6jA/P1+tVtfWXBceOHDgzJkz/qGWl5dhbb8FFqBYyZOp4f4l617z86hW4R48ceiQJMLp03DfYFTTXllaav5kWoQX9yhY/BiGGSzCxQ+EdJoMwyN+aQT5d2qaZpiGfyhb/NLptGGEOIb6hjJNE7b4pesRh07xA1DVaNwOsUinEW9wAKOa9krszvEZxG3PgYK9PRmGGRZyOZfTCmAGKh8A0zSNsKAH9W5svTEdwYBRoRQmxgxSF8RXPgBXWplMfFj8msDengzDDAvFIjY3nfqnjYwERzaMjWmpsKAHQNM0TQt919/Z/lloQoYkhbk8RtsaQKSNjY2m4g6uxk+10j8mLH4MwzC7hExGlMuNyHRNo1tuOXbbbd5gvQMHCnfe2SSC7847cwcO5D2N6fQBT0uhUMi5gwG/eicqB7y1JioH5ON3ghKCEsIQ4rdyuXzeO/ihQ4cOHjzon0muI8GGPlj8GIZhdg+ZjFiy/EoMQ7z4YvGZZ/4RcCpN4cyZs2fOFAIvtzlzpnDmzKb7wqPV6vM33HCkMVChcPbs2aI7GLB4Bi+dESuOs8SHQC+fEUWHo0yxWNzc3HTqX6FQGBsbS6VSnkbVudmHbgcWP4ZhmN2JadL2dgbOehDQgCRQvnRpovULbwUe+ed/3l9/rWnJZLJcLk9MTDgv/PttMQPYx4BV0CVgBvj7Sw1XiUwmUy6X7f1SNdTJkydPnjzpaQTgHL+DsDdjFOztyTDMYBHp7dloEAIAEQX5fwKAppFhCv9QDm/P0AtNMwF3XhH7dkDUhc47kk5YRWIpAUCmJHJAESIjPKN1bxFmy68J7O3JMMzw4vP/BOIl7Wz7wvfkaDLvPfObLMj35Bw5ZXyB8KvLq1+Y+MJ+sZ+L2Q4K7O3JMMwAQT6zL6wRQID/Z9yknW1fiAKwCTiFs0B0FnAeMvpqChZRvCd/jz6r96yYLYsfwzDMrsXv/5lMolzGRGS1951c+EcZca4sHGeFhCS2yuKPnBeWXMqnECWBLuXxDILPtKLo6pnfUB8o8uT7xfBOfnhnjiGcvP/YzNlidQIQeuYXOFTTFpMoZY/iGYuIEgIAGYRUPf+LJwsMgVBFIt04U+QzP4ZhGGbQ0YQIC0e3TcGImoLQIFI98qi4vje3YRiGYQafnVtZOcAkKrl9VQpESXcnMkmUXH2oQHB16i5s+TEMwzAdo1j3d3F4yhCdBYq+Tp6agvB26i4sfgzDMEzHyAjhio0nSgJlYMJhC4qMcAbQ2zUFRTNvmg4yZKe4PSYiymTnz23ojtCd8OT7xfBOfnhnjiGcfNsOL9FDxW8JbgwJxo+mS0+ez/yaMFy/8QzDMHEZmLUtQjW7F+fO256dpKv5CFodvKX+3c5Z09XJ8OQ7NfjemfxA/c602n+gJt/qZGhgJJfFj2EYhokF6UTzVkxemmiODuGQp4X0AHlTFyrlU92efULOWxem0zQ3R3rQhd2DxY9hGIZpjj8hJ52nL+ALttoJQ8AEZuDRP/tCASEghCFePE/6vZBb9W6GIUwTMzPopf6x+DEMwzAx8CXkFKfEC3ghcaqhI6IkMAWsNLkweSrx8g9j7DONllJJTE1hxXNhNxkyF6Ye06qLV0v9uzo4T6Zf/XkyPJlu94/TOZa3Z7zx7QspTcJw5SQjUHCWMo2EIfwXeqiCxusGmKPskePCmB+2PdjyYxiGYZpAJsFwt0S4rpggwxJC34U2mq/FNGEYXZE6P2z5RcF1+xiGYRQXcGEc44hh+VVRVT09F3rwW35AFb6eHOfXB/ibAcMwjILmSCXkdNp88oBMnEk4W6hAqWSKlsl/oXO0ygH5jTPCGWxYKFAymVpe7tGqy9ueDMMwTAz8CTmPEp6HPNKo2x6cotN34aWj8qbnsX2k9Uq5nYPFj2EYhmmOnZCTNCIQaSRuFXgEYr+wWwJTdPovvPlWsf8RYL/QNAIofqXcTn4c3tljGIZhWoIqJLIiuiXmhZUKZWNc2HFY/BiGYZg9B297MgzDMHsOFj+GYRhmz8HixzAMw+w5WPwYhmGYPQeLH8MwDLPnYPFjGIZh9hwsfgzDDAe0sdHvKQQzsBNjImDxYxhmoCFdp/l5SqeRzZJV87vfkwJ6NTFW1i7B4scwzOBCuo5jx1CtCsMQgDAMmCZmZvquf92e2MBK/u6BesXCwsL09LS66fT09MLCQs9u3Tb9/adhGKZliNChv1xVsqczd4w5K3+3gAsJcWYVNHoXL2z3sQc/ZPdoXVree5HebHFx8fjx44FvTU9PVyqVbk+gbfpejpknM3T9h28yqmhldNVsR5/uTsbXn9JpYQTUQiVNE4bhn7zKEdn0NvGeTGMsf397YoIIAFm1P9XEvMXQm83KM3man0e1KtbWnINTPg9NSywvIeCfIvJz1Cu5I+aFvkruURe29Nj9gwc+GmfbEFdyt5Vvenp6fX19fX2diNbX15UVuLGxkc1muz0HhmFaQAgMRhlnMk0EKR8AmCaFvdV92plYzBVcdSuVxNqa5x1RKqFUamGWTCRdt/xUMfT19XW/yFUqlcOHD6NDNWMrlUrbOqqsT//l3fvS0e3Buw1Pvl90fvL+L/ZxWtq5Tzszj2/51e+i3m13ki52YPnZ3XSiVWAJAJACckARyPi+W7i6EeWWl4srK5lnn/UODiRA6LLl57u8+YXtP/bdavkpUZmeng6UpWw2u7CwAGBxcXGHN8pms0pH20Bp8M7nwDBM58nlKJ/3tFGhgFyuwzdq1d6NMTGd6BhQtQY3hDCBGUB3r+bebomE+cM/PFMu6/v2eW+qaS3MkImkF+IXYZB1ZM+zUqls7MAbuG3V3CHDa3yAJ98/ujH5OCPu/K5tzrxYxOamU2aoUECPa34HTj58YraMrgKTwJpDU0tCTAEr7pECur3vfVM33bTi/oxtS/5Q/8J3j+6Kn9K2CJeWHXq7VCqVxcXFnaiXGIyzDYbpPXVnevVziDN9nD5dRWQyKJehCn4DpGlIJlEui4mJXk4jYmLqZeDESm5JqzcK4Tm4C+6WzZb+/b+3X/ZF8nc5nXUe9aPuovxcWn03Gjtwou3PojZd1TjT09OB02tjYgwz+MitLXnPPTKXI8uTXebz8u675dYWARJo0qf3WLPyNJK7UXnJtze+Z6iYY/l7qZEMKSElrA6u/6SsSqk6R3Wr1dQPUtPkfffJc+cCp9n25+vghTt47AFXOtu6twh3/eQ/m82qPcmFhYVsNmvbgpVKxfYCbc/+U4Oon9VQLX0WddQ3PT2tbMfAaQy1ZwTDRGA70zsb5ZEj+Id/EN/4BgCkUnTHHXjd6xKnTrkuzOehaWJpKdZtOuEpEzVUBx1eAtx8Yo3l72WPlCYygvaWNHd7RDdTBTlETjMm7PDivXMPFndb//x0Ks5P7V629FnsS2wVZPFj9g5+L8r63ubISOLqVdUiHT+7erodGqPY2+I3R2QCJbewFYiSwLKjMaLbg22Ln29aLH4eepHerFKp2IF9Nirsr18R7soAXV9f78vdGaa/RISp2WpHgAhSPsAKZRuYcMCBpQhsAnnH2l0gOgsU2+rGdJbre3ObbDY7OJlcFhcXNzY2wgIwPER7xLBdyAwjQtMolfLrn3D/HPrLrWkilerO1HYVGSHKRCuo200akATKwIR7VYnZbRfjXWatlD1dvWmPxG9wsM8aY4oxyxuzO8nlyDSFlTEk7Lec3IoI5XaYTHZ1aruJjBBLwDKAkIO9lrrtVjzLrHA0dk8COy9+yg/F9m2JEzzeywBzFRfBG57MXqdYxMwM5fNK/wQgR0fFlSv+js6Tv7rDfbnc06lG4pfnASXmIr73lK9fdP4sUQm17T8SR7d3PoeYDi92QjXnAaTtjKManRYhO7wwuxjSdaysYHkZADSN7rgDr3+9x7dTHj2KZ54R3/ym6oNcDsViPZQtliNEtxxeSNexugrldJpK1SeWyQyUw0uLIwUNFWPwmGOxw4uHzlt+KnjOPk5TLweNQO/TnaSJYZihQ2QyWFrC8jIBwjCg605bEAAVCuLsWfzJn4jJyXqfwaBeS29ysv7N2jBI1dIrl5HJ9HduzLCwSyybNkIdbCLya7Plx+x+HF/sPbZgw84LCTMgK61znME7Oc+gIMV6xZ+lJXTO8iOQoCZ7V2z5Bd0t/vUx13IAACAASURBVOgBV+6SUIfFxcVo15KmHXY+gThniv58MQyz1xCZjFhaUkuPMAyxvByYRaylnGddWbe6XPGHdKJ5UkVWKU00R6Tzl+DdRtfF7/jx49G5PaM7dGQC3b4Fw+wyIoyd+pZjtSoAofZC1ZajW/+6lxQ0upZeB8bXCceAKoRViwEmMAPWv11GV0IdnInHYOUeCyTiLYZhBpHVVUxOOg0vUSpRPo+VFVg5zyLO5MSOz+TCghSBDlX8WQUmIdYaXwBESVCesGIV3GN2BV3ZTrVP0WISWOp2EOAzP2YvElnMtkl1WfVz+Jlc3KSgkbOiuTk4ghTr4xcKSCYTy8todmwWPT6lSRgBpi9pwe185hd0t/ijB1w53Lk97WO248ePR+dSsSMCBxAWP2YvEi5+ZJpIpQI3RQlAtaoyv8QRyJ3MinQdMzOYmnI6pqoAxMTEBIjs0udoUfzIkEhBBO37EghVCN+nZ/ELulv80QOuHG7xa9xAiIWFhSGtk+4PUmQtZHY/O7P8YgrkDmfld0zVP/jB0r59yq5MATmgCGSsv+HuWn4euWXxa2F075VCiPrNrEc6NHF+HoZdLYZ9/gzTAZx/Be68aPX3HTnPos/kOpUU1BOkqBPlgEnUV0wDMIlmgDJRyzlTciCTRMl1FRUIvpxuOtGq9XOayJbb9j4RoyCi3qQ360VVh2iEEENqFzLM7kRVEw2jWMTmJuXzje7+IuO5nLNDo1su1+GpWpbDKjDprodeEmIKWGljxCKwCco3ngAVCL4iCzrRMaAKWD6hwgRmAL3jX5cH8Pu3iHQIHhJ6lNg6TN46G4Ggcoq2eon6YWDPHRkmgDgB5l26cyZD5TJWVur31jQkkyiXXRGB7sShQNeTgpYAfz7okhBa649IZASVCStQcX52kQUx4Ro/UG7zqjhDJ2Czsut0qUK8TZwU0uvr6zu/kYpSj9/fn3dtenra06cHz4dhWkJubcm5OQlIQKZScnZWbm31ZyqADP8DkVtbcna2Pk9Nk/fdJ8+d2+HtyH87gABDSkhZN1g9/1ntccb3fhyQhAzrngq5oyalf6Yx52B325LyHilzjlvkpbxbyq2gwZuM1fh88S70j+S/0D94zIccMHrAlc627i3CvXB4UbqSzWZVVJ+tOuplR+IcIlKUBbK4uKjuriZWqVTsxJ7OEdjbkxkoGvFzVhQB5fPY3EQn4udaJl5i6+5aqNYc0iGVgDQi01cM3UNYjuwILw6TKAUEnyYSIQVsuz50qw4v80RVt1kJIE+kAcsJ5WETeyy7oYMOL/7BvQ3xRw+4sjfent21bJTZZ1tU6qXTzgOwsLCww1s4bbiYV6nOHotTNTrn0+3nwzAtIefmZC5X/ypu/SfzeTk7G9C77a/6MYkzfq/mMCtl3meKFaS8r5nlJ7e25D33OJ+qzOfl3XfLra1oc6arll9LgzcZy25gy893ly6i1M4jJ86XSrfaHt+fkDPOVeqm/k1Oj1QTix8zYMhUyqN89fVa0wJ6B25LdlCNBkn8tqS8261/BSnvkvJcU/GL+D4ReWWU3O5M/Jrs4o5FbDZH3ZLFz0N3vT0D/VmcjWrDs223l8XFxQWLVmcVttfKhY2YwSQ6pyU53upeXs2BJSNEGdBQd1XViJJAGZho6iHSbo7sIrAJ5B07cgUin09oO2hChIWDaAC22eelQ3RJVBV+W8rjluI3Ddsm/sdZX18PdLGxfXOcY+58YgzTKeJYflH7eBRiDraHzyII+BLf861XZR41mZWFNAwZ9DzrT6mZObMl5ayy0qTUpLxPynNSBk2q5W3PlszKJmPZDWz5uemu5aesq42NDTvUQbXYL/tSbCEsoZpymRnM6rsMA0TGzwlRTzISlHgaU1O0tLRXzMHY8QBC0xAUdK9j3/2jv6t+th5VgM9FRoglIeph9UIsC9Hc0IxH98xKpkGXRNXG1hLbvFMvp6en7RO7jtxoJ0M54zH8Y/bx6TGME7m1Je++W+bzDQOlUKhls7Vi0Q5+qI2OBtoxteuvDzUH22NgLT9Pl+gzv9lZ5/MkYAv77vmxb+fe9Ki6EiDrUQWHPQTccceWH7ViVjYfK2RWMemB5edfWOFYftsbuCm9KGa7vr7u9ExRcrixsaFO1+IEAnaVbDarbD61Pet5t8k/HMN0H7LOoUUmg3IZmlZfkDSNiJBIiFqtnnPDMMSVK8GjXLvmNwexsoOYbPcKFZ/BSg/iS1izeuiTk3Jz7W/eYreUSmKHj6oNumdWDiCB62rX19jO62k8FhYWOhLbbtPGx3HucAaeO/bx+TBMPZ49lQqIZ7eO7vzOioGHWKEnW4Fuou0S0/Jr30oIuOVOLT/yxeOnRl+sj2pZftY3jZ5afvEHjznWIFt+EW3dW4R3TxC3iqaP/3Gy2awyPSOKTnCQO9NtNjZoejqohkC8eHZ/jQX1+yoiW1xvtV1mwUdAvHJQZHX7AdEBt/SOH6cwQthQBGwbMpWqX+R7blStBtSr2IsljXZFkHv/E1v3Jau1rXxExGm1md6jghHSafrPWQp2qQjxW3HuvkUEP0jni0KBrg/J4tu5MgvBtLs12hdE5PPQtLBKTXsaFVdT/3moHKm6WMzWzhk2PT1te3VWLNRLW4F2fsf4ll/8XGhs+TGdRdl5uk5zR+ndBn7mtEgBBvD4QfpMCssnRSZj1TBrVjZPDRXYjQAaHa0f/mkacjm6dEkQBdYhEqokXifwV7YL7TaQlp8aam6OTBOlknBafoUCJZNYXg6p57dXLT97fyJRKgEgIVrOt7fL0pv5E6+oG4VFEXTkphFDqSh4z/TiBBd26fkwew378O5tqKVS8hcPXXtyf20F0o7hegjy25O1E4UaEVUq0hN/JvE2++dz2Df3Wy83zgHf/JVzR2a9J3mFgrzvPnKE9AW6icq77tppsmn7A0o5Z7kmpqSclXJLhqaE7uCZX6xDspi3s462rEcl7TM/61ENzplf6LONuLIbZ372kXO9AY78OHFH79uZX+fHtb03p6enlVeLEhtbEVW7ooM+LxHip96y7xX/CwGLH7Nztrbkvz1c+8LB2gXIGuQFyK+j9mnUPKmrHkJt+bpaKiXfhmuplJwd/eh5HJL4sMSFGmoSFyROnMehe67/Yi4nAXobJED5I9t3/9BjTv1rqJp70ep8mQX7A4aXIAjs3wfxC1diR7/G41KPSo1lPaoWtLxL4hdUzyP8c/VG/KysC07xo5YcqXaT+AVmzrSVr7Menk5Y/Jg+IiveZejJ36+pHx4o1v520mXnvexTPgJJyJfQEMi/vPE7T9xgbuBv7A6P4om/u/kHH3796Q9D2t1OQM6+c3v2zV+Jq2odzPBiMedWPlv/ZjsqfoFXNVGLVuzRtiWrN+Jn5e1p7AhERx/2QPyc+xNe8QNktRpv9L6JX+e3U5UviadQUaslhwYEPvNjoiGdsAqUQAZECsjhn/5n+fQD4k3fhEbCFPQ3P4PvPEnyaqJguQ0SCIBwe1+qRgIlLB+0v4b8EVybwIiz23/HP78V1/2/EPZoK6Ap4D8k6e8vXUdxige1dfITfWQVUU4osD3OAVjMo7uIbqrSurPebJ5oEyiHlYRt96SuN2d+8/NUrWJtzV3kKE+ahqWlwI/jHasrZ37WkbP6xbOPe+2T6Rije+eJXp35dX7cMMeTVkMRBgEWv73GBtF0jFBi2iAxLUiny++lb1fx5tN1xXr0ttrt3xffmcJbvltv+fLt8m1PY8TtVk2gQPFzNvr7qMYroBvdo62ATFChKmL5InZa/KIr21WBlO8tv19MLEePoDlEqUV4SbylmJI8SOKXTpNhBH3D0ILbeyR+c3MwTVEqOcWvNUcqFr/BhMVvj6ATrQIlwABSQA4oBtkHHjvvldfS1/8JP/dsQ4oI9J3r6fZrrhanPWc3IlLqAq1DfzcbAzLVzWxNnbL81HNeAuB+zt0Qvw7YowMjfoZJdvShj+Dow955e87MYGoqsboKgISgQgFnz6JcFhMT8Ub3zhPur0e7Oc5vwBFu+j0dpvOo/bEqYAgBIQwhTGAG0N1/csrOe+xLJAyRgBCGSJx2KZ/CqXwABESgXAG4gmv1kUHkXXUDLqHwxXMMgoy+fUvLuVMwKwpEOXeL/ZwR+Zw7gkkUtulmAkYvvtF28hYDG31o59tTL0nTkEy2oHw+dCJx4oRSWnHxonjgAezb17HpumHLLwq2/PYCTffH1F6o+UH5V5/DL5yra1ug9YYQc81v/P0Fzr0dz9+I1xE0AfOfcfMIbvBc5R/tMuSrgr6w1sbkdWaXLT8KDeDTiWaAKaBkdVAlCDzl9CKe87JaHDx37JrlZ/puFzx+65afrtPqKpaWACCVQi6HYhGZTIBp23QoT4sdfejsHxF9OIwZXgKPaUtf+MLWPfcEH9PuDLb8mL1OybciAygJsUI0T5QmygJpot+ZwJuuOY+p/EdZoX+f14Bv3LJpv/w6vvU2fPdG/BuBcYHDAuOXUXoY3/dcpYvaXztStTwEujSGb9xW83TbPioTeQFgY6MzX9SEY39NJ5pX3ndAmmiOyG+oRRSSdQ4V9pybFI1tl5j2aAfRdTp2DNUq1CM0DGGamJlBYDmkVikWsbmJfL4xVKFAZ8+iuIuqHK26lQ/q69TmZpcyinfL8vPXalDenoE1HMKKqveFSqViz4ctv11PqL8GEYh+HfhUov4FMbci/3FKlGeQedZyaQsy/v4O9JPu75SVW569ga689cIfA3ll5xHW6La/vO6Zr9h9JPa9Mvr//E1i/K2X76xfld5KXhrbfu3om54Z1QATePwgPTxKH3mVeNXtQlgWABXolafovx3Ag38kDMNlbcR8AhEHVK06TEZ4skT7xaALll+EPTrZHcvPdsgkR7tyyFxeCrhjxFCBLbpOKytQfiSaVv+HnpgI+YceQsuv1WPandLx4IlBmEPbeArND9TcmC6R8sWoqbDokVrNG7v2kJw90QjXUxFkzqC9P8S1FxK1z6av2S3rb6g9Jl7Q8S8JKv7p7aSioIDayIhqJEAWCtfe8q7aoS/WUK2hVkO19jNf0T/37Oys1DT5dtTsUGup6rxp9TpvRqF25HCthfAvHwgPStt5AJ+zJfg5E2lWe/yhYnYLLYkXb6hW4/xSKavMev0Qt/6fpgV8wOiholpAg5PhJWKesUevX2mof6mg3xBIWY2TpqD1O3eYwNxmQyF+tlVqtwzO3JjuMStl3vdXN+pTvvpKfVESqPI2aavdo7h2EbIGeRHySz9y7v0/cfGLP3KuiloNtSpqX/7R721Zyuf8TwI1oDY25o9Mr33sSc8M19cD/vLluiSiuTlX4LOtf7OzscUvcMGVkiLlqoWhIp9zQcr7uiZ+no/T6lAtiZ9hSLtylEf8gI6LXztjDbj4Ueu/bzuEF3ciovX1dWfeUbudxW8vsCXl3e51+TdqteAvoVKiVtMu1lCTqQvyX5+ofWnqhV98zVkNF9+Oaxou/scDn9sc/8nakSMEPIkPEiDz+dr11/vFj6wUUHJnOY9sa8PzX1jxOdcHV+myiECk0mU9fr5mp0TRwh5C+Dfx6LXb/5wLUt4l5bkdil8zYWtbR1sSP+qp5dfOWIMvfoFfj/Dxj9/XHfFjh5d6Jffjx4/3eyJMf7D9NTT1NZPoFiE08m3gEwG4/iqZ6QQSwhgXI6+p/vpq4j+N/oqB8eP4WQPjy2d+aeLWWuLUKQAH8XEAolTCG98oDx70DlYoIJcDIHZw4G2a5Eyj8TbHmYtpwogMfnB4ZwBEhiHOg+7dgv4iqVAEM5EY8T8EAIAWFLrelAi/mFaHQjxPnB6Ty7kcUhSFAuW652OzuygCm243pQIRXvvaLvn0sEMHnCWWlATaz4QdXvYaFaKsEADmiEyHrwQAEN229U/PTN7q7H9k5dP7zWeX7r+/3iUoDpkAuv568f732xWFWg4EDiedplcbKAB5QFVHWgNWgJc0BCf+sJifpy1Jn0kKqFpsVeApuu06PPMWxxdiogNEZxKur8gFoqQVnOAh2F+DvDESwX4xrbREeOJMdM53plWHF12nmRlMTWG1JFS7csgslzE50QGHl3pLTL+VIXR4AaATrQAqN4wG5IAHJya6VSCwG+bk8OJ5Jvx89iz+PbrRy5eDDyQuXrSP8YK3INXx3m/+ptS0jldUOFGofXsyoDrSh/O16AvH7qjhYYkVxy7Ty75jTik9jj/2RiXF2OQMSyod3e3xWq1pywdqtfieOD3b9qTwchC87Rk+esCVzt3s7i3CvO3JMAFkhPgvf/m88ZAxVpWQMlmtXRkZCexpapoxNoaQ3FMKoWmJj31MGAbW14VhiOXlndt8G0QAign8I6HguHkRYpPwgcitRNOk7ffg1ecIBasfEW70XSMEgKtoeaMyZjIXf7fzRPeqbuEtJrDW25DB+GQywk4zbf07d8FHfy/Q/XRavK3nwpOGpmk+M356u5Xzn39Of9foKzddnq7ur7ydsl8VyQvXXhy/3t9Tq1aN8XH1MwF04EDizBlnh84WTPemIf0YFT8q7NDDxk01EoZQ1d4Dx7muKmXa/d2XKHDFqadEoaDdy5AdueikOY3AOH83/0ZrUEvjZt6PHZBKu2fbnhFNMXcF9+y2pzdBhPufuEvLLFt+TYg2nPs9O6ZbPJO7opQPQParAsAH1q775bWqp1v+058+9md/Zr+ko0fx/PPyyJFGizre61AejoA0pKOYKUPf5/1VJBOTKZnNIp2muTnyJBkxiaQWdAPfr3QjJUor38RjJnMJ7OY5YgxoCZ+JBqQSEZl2di1C9MBS6iKB62q311gWP4YJ4E3nJ5Ty2RRXcHUi9esrjexihUrl+7ffXviHfyBNUy7t4tZb8cgjYv9+u2WHeX49BOR/KiamNrHi09bLI7RlJsKSbGlCpMzgW4xcbiRUO/SofOoVitLtoLUpZlLpgG4hHrbBSOlp6GrqMmb3wdueLvzbnvx89iDV/6+qvVFL+L4a6vvooSKV8pe2taS2vZ07fbpw222T+/cDqDz5ZPbQIWdnqlR2EsYQSGj+pyoZ44329QPym2fEHASBEhBwVD1Vv+DSwPwKmZooFV2jHf2U/M5P4LG3CgAwcPAx+rFP0ad/RKQ+ngAg06SqEJ3fj+DKRNb+WHSeKnuLLKCbf+s1cDOW6Hqi9wsRkEo7IeqjW7Sw7enZ2h2ebc9YO5Pd3vb0bYzHG907lKdt15Y0qlQqQojFxcX+ToNhFLSxASD9hrQZtFJlnhWz98NMa1/52781Uqnlw4cT+/ap5NeHDx70RJt1XPmiLCoNxlj9vn8wIuUZ3AQShITEBdAJ0GdLWFmh+XnluYfxKfz6J2lzCvkVR0zVQ/TCj4pP/u/Wajcunr9b/JfHxFNfr3cQhoAJfZaOXaZoZ5aYSaUDux3wmXT+lgLw/l0dMjhEDO/T64X4qVTRgahs1wzTX0jXaX6e0mlks5RO09zc6R/ZrKS90UWVcf30recAHD54ELELAXYKTYiQmm7QXiFNgEBSk69cpRf20Rc+XP+u/uMXUDlBD+yTyUv0/PNkFxz4Xkr86nGpmZZ+VCl5CW+doYfPKw8EAaAA/NU58ZbTCRIg1VwSpfdg8m+9qfenACv1PiEkWvks4NlB9Xc7SvQ8cMShdv4WNdScEEvWSZchxLIQrSmfddNeVhncfQz30+tc1EQwzrRhYazvLMNTB/E8kx48H6bvyK0tec89Mpezw/JkPr916P2P3XBhPb1lx5Gtj289dsPFrc8+a1/Yat7nmFTCL49Ij0lE69+qEdF/+hfXDjxcyzkC+PIPyTs/X5vLXFNCJyEB2gf57cnaowdr9Zgq0MdRewRyv5WIEqALjvBB+7/UheCcZ6Mv1VSso50pLTCpNPni/DzdHqt5L/S3BA5FIU3RYYVRIYOtx/nFmUNLQ8WM84sVihczI7Z/Vv4LrcE78CcQFufXuHu3FuGuL+5KThYWFtbX11XO63WL3qhvS7D47UHk3JxT+Rr6964HvrLv+3aK6q/c+n2n8lGzPLwRGubE7qbW5VRQVLizjz89ZrZWK9Zq9oU//fXakU8H1KP4zRO1D6O+8F+APAF5CLXfG633lJr86GhtP6QtkEnImk/8jKRELSThZ01irB7lb1eWqCtrM/zd4rRQi8KzJeU97sU6L+X1YRnMg8QvWHgGXvzqSVwdX01iFv0IvdAavAOpqHer+CmFm56edr5cWFho3N7x7iDA4rcHkamUR/nq+qdpqsO3it/xXxVagUUlv67V/BpG4VJXqNV+1m2C5KU8XKt9wKFqaih3RSOZ91044k/Uotaji7U/QMPOewjy85D7ULMXmtlZOXvEJZAvOao12f+FWX646MpvoypLdHLRDxqqJeEJMFOiy+iMyV0gfta+RstFr6IuBBE6VISof+LX3TM/lTPTrg2rfrATaSoh3NjYcLb0F/txM3sEMk0YIX4kVt7oQw/d5n8z+ASOCMAIYCYSziOQJ6R0FoUvSvleIudh4TeB73piGIDvAV/3HSgCWBLCEGIdMIRICXGbEI0Lia76E7WoTzNGT/1ufYbpC6SfwOl99NujsM/zPvhO+t++hP/lAAkIATEO8U9pnEt4nU3e8X/Tb2x4G/EQYa2+RtbnX7JTmQ4KAWGF0SGD28McOmexuorJSaytOX61SmJqCivN6qM3vTDqELqt1Oc9prvi17REu18OGaaXCE1DKuRPWNNE2FsAwpwVia46grJLQtxC9H+6/WL8UvekEFu+4O5zQpx2DzXV8CuBSsDtXdDDVhyi62vi8qtF3TFhXJgaHi2LX3JEx9/6Z+LOuzB9pnHHf1EVr7oRf42G1D0Eyv13+qtx9wd/SOK1jplZmCGhhH0hwld2d4cMlkouAbMam381iXNhTLfewaRH3p72z9PT0xsbGz24aacQbvo9HaYL5HKUz3va7JJDEfidFUd9NRAAnBJiK1rqwjYbmiVJCV3QfQMe+mt6w99hreD4Fl8UU5v4w/cJqlLdXisheco1eQFxy8viTkDtQ1VBl4B/99eJ537NFWYwehWYAc57Z6sFJpHpExFmyvUxHFOHFE/RK/dbUUWvoi+0ienW2xL2MtvtJbcXlt/GxkY2m3VugdqBfeqHpgZiH/FuRjO7j2IRm5tO/YuZk8xTCHBMyishf6tXo6UuKIg77KbOJCkRC/rIVUdswKPy2z+Fv/mX3j/2UlGsFSBSAgCZhKDFTkDcCKEyho1DzEOch7j0tLjPhB1m8Fs/EPlf8l44gHXsgs2UHYcMOjd7B42IzQtNQyoVvusbeaFNZ2s0KuxltutLbpfOEm1sr07bz6X3c2ibQZ4b00GkKkWzg5JD61JSoPNboFNAnG7x/OgCgx+O1mpvrtU0KVGTmpS/WashxKcRtYZjgkwFBDYQ6KLl82k7Pqgy8bZXwtaWvPtumc9LsuIoCgV5113y3LkQh5cY/iDdcHiJKCVP1jN3XejzIgl0NglgkBxeZmdlPu+ttVUoyPvua+KQEnVh4AdsL8Knfw4vPVrcVaiD+tmWQ0VHgvxU7ETb14a9xeK315A7+20MlKJRv/AESd1krXbQ0fNQrTbhu9AO6bOJXtDXrc6payEuoNcaZf/krJR5r/6tH6g94BY/e910rlDnH699+S015SZ6EbUvv7mmPxZeULDL4ifhi5FwPK74IYO7Q/zsryZOAVNfTaInE3Vhux8wgF0vfn46FdjuD6Jv+0JnDIaCxY9piUApem2tdsQjYz6pU+F6BWWuWTEMh2u1MFXz3NQZ/HBfUJ9ZKfPbPik1a04pldbs7R7bR2uP31KbPVKzxc+5btorlLQC6OwLZV7Ku6UMc6bvjvjJoPq5obeLETIYJ3I8mEESP7JK7KpwPWeJ3aaEXsjiFwenzddeh+hrlW5NT0+rCPqY+ue8cGFhwb7Wo38sfkyr+KXosVotMDLdKXVOxVp3xAI2VTUn6+HvRhuINtJnGZ1/rOZf/uqxz0QgSqXkl99S2z7ijQiUeSlneyd+UQIcb6XexeLn6NPhDC8R84w9+u4Vv0CLysYf9t7q4J7LlYw1DZz377gGZpxh8dtrVCo7ykzmxClFETIWoViBQ7VNxI6fF//OoWP5s2OfyTrhe3EkIBaeQFKLK37tyaGzUc65lM8lwCx+4RfGIdbgQyh+XakWUalU7PCG48ePK30K7Gm/20Zhh8XFRXW55yN4yhIFTk8l1A68cH193fY+5ZJGewRdp9VVlEp4o0F/lxK5HIpFZDKdd7OuEGX7GjATq/RMZDWc+XmSW5T8jJgHAFRBKYhEUAFZAqWAbQjv35AAgQRFFQ/yl9GJLr9OaRJG0Bw0EmbzIkeBjXFK+QQTVJrI+5HjsZOSRp47Dmwld/+T6U1Jo+u7MSgsVQt76aG9UAelr2GyqkpJtDTg0MUgMh1B12nuKL3bwH8wRArCMPD4X9DsY1g+2Xn966/yQS0oO5vDn6/QR7bFOcCOf7gM+arA6ukatk1XO+mEVetnqzqgiPeQA9c/1RgWpwEA/Q6038lHHpY7tkff59kV8ctms7YmHT9+fHp6OkKH2i7mp4TKP7LSsAjxs9udfSqVStiAzO7m1DL9X9/D186Ju+urOb53GvdN0sklml8ZuCWjF/h0xv7mbZr0nm2cAwoOtbsR4tuQP+kOGqYCIQk86GjRCceAybpqwgCZhBlQmUSgdsZGaIJSIfqn9VP/oj5yd1b5iDtiZw+5s/T+yQRNostgB0d6TUdGUKSE0t3om7LDC2Pze6O1FV/5gocgf2803Fl/D3NR+HxbICXkZUcWbGm50zhPb+Sc9PvFbB+pqWO5oFPGFmYVGKchC1KGBaX5RujsmZ/6OFEnkfFo9cwv4o4DdeYX/WR2icNL9wgTbyVsTX1enN6hthAG3qKPXx2YbmMY8kJQ7QICXUStWu2Y/8vuQBoBdY7UWl9DzeMmOjdX76mqod3lMAAAGe1JREFU4VxNBjxnCXltpOaPT2hZ/HxxGoECrOie+HnCLWqjLboCtTcrp/iFZCqQ2oCJX2hGhYtqYXWeMbYz6Rj0IrdnNEKItnc+2yabzapNThVoofRSpWHz9Ix+fD2eNtNxxoBUyHaQBjFIySkHAqEJ0gJ+7QUEjdW3sIQhzn8A7z8uqtX6u4YhXnmBrrvkvYrUEeRVWPm2BUxgBqS3/JclMgJlQKunISWNkATKEBM7ybPVmntIfSuvWv84MCCuhNzdBIXn1Wyb6LPPsYHJwhYxzxRSslrPzNftNbZbDi8ewuSte/Ucok8ZlfLZTzabzS4uLgohNjY2FhcXey/GTL8Qmrg8Kl8dtEi9MkqvTvX/2+GgkciLS+elJwX29lGZvFXgwbr+2dVwSnWFE7//qcR/hhwPHNBxWChKgvJkFYhwHVM1dVYUGYElYBmEYM/PrrMKTEJYlRAEBIXpjVZPqdpZos8+Pc5HfSR6nt14MsF0yaK08SQzC6S9IPewa9V+ZsSZX9iF/lC/Hjwfpu8YxdrnJr3bU5+drBl5PvMLIM4GYyol90G6C8fX/htqD7m3TGXQDiqBXrJ2C53Vw+Pu2gVsFLa57RlrcOeT8W3lqY/vbVQnkfFoedsz/OxzsLY9I85oe3Xm1/UvtiqiTvmVqJYFC1uE2nOwVCIXZjvGLCXY0iXMrmRsVrzjx/GNg40yCI8elD/3WozNDco35YGi6QajadKrDfoEkAbsurgaxJ2gnwCtWMZQxJ7ijZYhbhjCNDEzA12vr8HeyYiowA1dp/n5+kXpNM3Nkd76hmpMIrbypKMgIhUIXS2YVAQ2QXnHgVm379gegzDPLomqQtlStiOJeuk0ubADX9Awx5amn0upJlt+jI20UrDUHOlP+j2pgcfnoml/Yw9xoK09JK6dsMzBi5AvhboauVJp5/NyNihRC0UaJf5MNPm8vPtuubUVXGsi2nZRGd389qiTQCcOCVkbrXky68R6vCGzaprhxZ+jTt1xoCy/iHl6RuveItwL8XPKm+elErC2x1da5ZSxQEVUhqanj/++qtEz27bnxgwjcp01LzbhC95LIV6ORqLmrHz02TfU/vBGb8+PQz4QWESpRfGbm5P3/duae+tVzh6pzc62LH5OHSVLj5WOOrtFh1t4vyjEow3xs6/cYfRI9OA7Fz/7Sv+T2Q3i5w+58yiT3xZsY3y4nTb9A/obbcPXc6HnQbP4MUwoIQteWDgEgWqoLfxmXe00Tf7CwWtP7nMdBH4ctUcg9/vED2hZ/O4Yqz0M6TRAH4L8POTtyVqr4jc3J3M5abl/uu1R5wdvJdwiJjsRP68+Dar4Bfyz7gLx82x7krXl6Omwkyh4f7ieX0oD22PG+bU9MYbZs0REmxE11jblF+PcCH0Atf3wykyE5edsdO5MalrtwwjOXXACweIXQSpleYv4Z+X54GFbeSx+oaP3Tfy6G+qgXEic8QMqwM5+ufNQh4qFehkYpUAOUy/swmw2yw4vDNMZciCTRMmd2FMlPLMwTTIMGBD3A/MAgcbdAZdkhToUCpR0Z0rzo+t07BgmJ+3BE3nIcV8EZxHCAN3fykdR8wx5C4ZBKYdrfv/DLZjYdL1qgV17wS7doIonKMPLE283aHBVB4ZpA9IJM8AUbP2r+/Ipp1AraX86TYYhYLlxqkC+faAPJnCfFACqwOOH6JOj9KOHxEMrCQKNp+CquWENNT9P1SrW1oQaagxkAIG1JiQoHVhrIpygeQKAptXbvQRVdfC0xKyxsJOqDu3dMWAOXa7q4L+yN1Uduh7qsLi4uL6+7txjtNOpKOWLEwjYR4Sbfk+HYYaAmPlWcjnk8651bR/w+f14/S2NGIkbr2Du7/HOSj0dzPcMTP8FzR71Bi2USlhbawx+CcIIWYppjLZbTPHsnCdZwxYKlMu1NAwTC3uZ7faS2zfLRu03DvhOI1t+DLMjAuvYWV/sdZ1mZjA1hdWSUM1fOCSv/QC/uNX4Uk6gCzfT5ov4V9Y39RXQ2yfpj7OE/YkHlgSAlCZNU9SLFVp3OAHSQEUEZKJJPKjKGsb9ELpOs0fpPSbuerJhj35mDEsnxURg+jS2/FoYPeDKXWL5hcFnbAyzF/CXKxLW6pbJiHIZmpVBVdPo7Wdcyqd4zYviXzlWqiLEV89h7FQjd6hpJkZGvOvjCjAF8QcjrgDz5AuijTDq/UBZIEUNe3SMUBYYvBp5TAt03bKxnUrs4nlDJHts+THMjohpESjbwiCkvGJJCK72V4UcR4IAAiUgADpwgM6cSTiP5eaOyp97Boe/KQBAQ71c6oRo1QaieUK1kbSz3pgnaBBLbPkFzzP26AFX9sby6+Libru6BGL7v3SEnWyiRlzL4scwO6IV8QOB0i4nSXXAFih+NciPAR9CfR9yDbQC/I8RXLmaUOMVCnT2LMplTEwKz9Zry+L3/7d3/z5yo/cdx79Uc3ZxGG0AN3fKQa2BFLlODhDMTBPf2ch/YKgUslJ6na4hp7BPBq47SQMIrtylTe7gdMMJEDcJEDh/gaFbBGns1Ra2ERjLJ8V399GzfMhnyBlyyGf5fkHFisvhPORw+ZmHfH6cVDfdNLOaJp0dhp+3GuHXlb62a+cMUrbBi7tQumjn6Uds822WCunnMeEHHKTZRdFeXs0zIxfi9pGorPkZMX8W8wtJ7ITyL8XcF/NIzP/IHRGZza4ahf6l07j05tsZY6pyy2MuKuqjthhyLslJUt5Hwq/F1iteeZzw66X/YN3E6FbdiCptuROyuw1Km7zWruy+ttTPvafjA8Blu1H7g6T86a8u/9cbAu0/5PI/vXFBX0jx5XuXosPB3Nh6uSO8SCFS1A3R6WvYZ7/yHeuWNOxy7q9W+cLwXrd6xyZluB2d3Lvfru26sHPQsnBANuFvQWOs4TTuftSJNwra3mUD0JB7eS0NknJ5evnHv7n89cfvou7fP778c1I9dugfrsKvtPWr65wdolOcIcr8ITp94fl3hg2/2jjvOfzKA4e2z5LAWOHHCb/uW3vqI7T5fL7zCZzGZOC5YJi9Reneq9R3L91c9ekKpfFltB7Z3/y6AHZK7iXJF4mdFP7Oqzvf+afkwQ8T22XwwVzu1NwG+87/JYH5yu0Uu+b6ltp6ndy/Ly9f7irTGObfqaLj2pyfizahdWeA6ukdzZkxnxt9FmtOjHlmzF7v5ZRc5ObcVR2WdofO47RuwqBKh5ShbtJa3WagAHVzIVUWb7+yAThIVeXJ1jYC9yEDNb/mQ3T6AvPvDFjzsyNuu//qZoA6vOZXfFsUPyqKf3BG7j4tik+LomaWqIDwWOGx3vbcmT3+yt2+0c6Z3Heu4L7FfmUDcJBgfrSbr1yMEfP2rd4brAg/keL8vNl8Q5UzEw0XfjbOS/8qxwHvIPye3Ui+d/lXM0tUQPiLSKy3PdWAPfmav3WWZe7QZR12vQDQo/b3IWez5O7dul+JOzh1WGWzz0GER9zuxbrc2VFEknUi63ab2TlW+F6Fa22wEV66Uhd1gUd3+sBvtVrp40bb1HO1WvlbS4I62AEALTUcO7TEH0pU+h+i8+zMfP751ZuenJhnz8qjkjZQvX44zjtnLozUJJa0zNpAyUXenpxcpVLf19jow+8Qeuczz3Nz3Uh1u92WUjNccR6m3MDkldrFJM+TcPKJyJMn8tvf3sg/7Qj/pLd2K4c067hOTSP1qXnMOE9midQlVvusrSv506d37XW172tsX/P5DX4LMXDzcz6fb7fb+XzuFnKxWKRpulqtsiyjwScQi+b3Ie/dS16/NrZt52xm3n9fXr+W6sGpu2Dbl9r7gut1cnpqdrYvdaYnTETk7Vu5uDCPHsnr18aZUkmePJFHj+T01KyvhwWw49qEpz/cU4NpGhuyJRe/5EfT+VPEo5VBX9thgxfbQ9F9i/3KBqBjlW0qmjTrCDYc7aMMTdqXhpufBJpx+v38Pvvsqp/fbFY8fVq8qZk7vpvWnjeHICgeF8UnRfGmdYMXW3Ldzo2SH6vBS/c1vzRNj1Nz0gqcHS+7ZI8WN7EMtw1gP+bMyKvrn0/M1VDX/czOcEiDlPVa/Gly1+tkNivXLu7dS774Qp4/F5GrmXX1funPJBGRkxNzY+7fwyT3EvPayMvrSQ1n0uRRax0tuTwXkYqdPYaeQvUI6rrr7dwvreH5L/SXR318gNuvTY0j0E1tv7ezY5QUNaOr7Kr5Vb+v2yvD+1ddZbQL7Sg29h3tKDZjG+El8Mq4uzocgT6xKzVR0YXuOJ+6sPR4z3+hiCyXSzli/S/qxqIUfijxFn74kr8S+ehGY/1knch9kZ0jvFQV/uzMPHto/vZfNWOqp5ivbNbx8GHx/e+HWrIc0ozTPmW0S9br5Fe/erV7FJuWxtPlY3+dx6ludj6fN+znfgg7sHWappvNxv639Nb+wroXlg5IH8fnOBvvG4UfSryF76XkrWp+4fGpw++jhXfe7mdPLn/z0eVLebfNF1L890eXP318aVf79tvi00+L09N39bCHDy+/973Ln/zk0n2S548v+tlnxelpufL3+HHxtLIXv1M52/spY3Dfdz9GjbHm18t20zR150noNQVLlTw/+UxN0xg38GxR/Re2Kkyr9XvdOIUZan0Kc9SNO5fJ8PrF26KQmvCTotg1wosffl++dyP5bP59+d6lu1qpWceDBzeSz+afyE/dt7Op6SbfJ58Ub96Ewi88ik3z8PPqAKHw05Wbh1/5Yxou/Pqdry7P8yzLtFO5DnXdRxcIO1m8tO9iYdevnF++7VRSrdbvdeMUZqj1KcxoC9N6Tlp/49cTzV1cmMu75i+q+kmfS3Giy92yXL/w5MTUNO44N+bE/f/ZmXn5Uluy3JieMDyfn92+cd5WRGYzc3GR+C8M7ay3cX937MrN5/Mrf0y3bzLbkuOkYOdu97WAwvSxPoUZbWH8mXJFrrqpJc/bhZ+5MOau3Kl67lWIuVrupcXFW3P37vV/vNednyf+KGv+vLvh8Hv2zFxcyHqd3Ay/F0+fPvn5zwm/mzqsRTZResB2yGR+R9DLEQcwhA/kg6/l6xfywt6l/Eq++ka++VA+bLiF63uJIiK/kz9U3kT9vfzRXc174e9qmnH+vvY9y5niL3EXfiDytVzvo4gR+UrkG5EPa17YbL9rd8cuMo1ytWLT3qa8rfV0eT9Szc+n9yp1dM00TaOoCAKImjkz8lJkLXIhMpOrfn57dVO7+Mfi3/5F/v7NjTuf//xRMf+xzF7VtqK3NTN34ePH5v335fmu2mdDer90vZaLixv3SzvZ+G0yWPgBwFBMbpLFQXlgzsyfHprfvJUf/NdV1P364+KvZ/LdX4ZGGT07M48eyf374g9I1nk+5blZHLaPt9sA/fwYPBPAsA5MPhFJ7iXf/WXy4IeJmZlCjJmZH/xdEk4+uRpfVGYzmc2MiOl1fFGSL6yvmp9t4eLf0rTdRefzOSno02MS11hrEZU5oqL6oi68inQXwsXerx55nJpZXAe8brhKdwXpanf6eJAYbtJS6pnXRwHipYdu5O2AXH53ydEW3i/q0CVqIaLjHBDd6W0Hu4/xsPvnzBEGHjmERkPdb0u743fLbquXv39buMCxtnty+D7cGvYvLZY/MHcog9LIBkMXrcw939yvX0OXq5GIjnNAdKe3m3zuYY+i/HXnzGjzz59Ux+XuTld/v93/8WvJmvxZhvd2guL6dmk/PvfPqXLhGPgHtvmJOqy4jnNAXKe3cb7E2yWxXLIqy2m//w1VqjqBASbtCv7Zfvi51P2BaHV89QIU0R9wf+y1OJarQ11R62bbGFDdn/04rwUlER3ngOhO77qci+KStfeMN8fnj09Zt05pd+om52mur5ncG1osFoE5+abDNg4SER0HZ/y0nFF8cPqQ3H8EYn875r2I6DjXifH0rjtnbkEbvVGd8G7jf+327dNzptRw0k7Os/dbRzyl0a1hO/vH1dN/s9lsNpvx/BUF1OWHfqMc+eUsouNcKdLTW8sc6WGvDAZ7no9qp3Soy9Ksc3VrVi7f+++X8BueziPotysbucqhwOX6qhHFlW5UV4E6sR/nSE9vK8uyxDHyr0pqsVhonTVJEs2VxWIR+wfh82+ZttJ9+LX6Nh3116tO6L6naXoLDkKe59qJU8cuH7o4ZZ1/cxzKyI+zK/bTe7lc6jXKXmeXy+X4v3CISJZlWubVarVarbQWGOMHsfPPc0Q1Pz0z9FtGmP0Yovs8upLn+Xa7nc/nUfw5hdmvloxd0KuIjvPtOL21eU6e57aRxWq1GvmRF5EkSWzgKRFZrVZRfxYd27upTMDODjGbzWb8/U72VtewwqV77X8Eg/cCbl74ypeM89OsO9UHP9qtjP84l4zw9G5OC+83JoyiwWpdN56Rnzx1f6d1yw9sedtLa097W0Zr3O7NGf3GZJ/ExlgNb2JnhCwWi8rnz3pk7FfLQb5gNim8/dkOVjf+qTn8Rm7j//5uRXSc1WhP71b8Q20bqA9Qmsb0OPuFTNNUK38jL39Dh7Yc3i8zm9h5DR3tF5DjaPLkeegy7qCFHH9Xs7pv61H02TLxHGdX7Kd33blxePeyI6g7vCMvfF2x6/LiwLOox9aeWZaZ6977c0eaprobt7LO19xisfA/j1Lv1KHLGKJfisf/5EmuKx9+OaPoQhfRcXbFfnrXnTNxjRNdEtcpZFU2ouxgX/aOTfQhuoci4682Kb+0sYyQEtdxDovo9DY1FYsoPo5b9syvcrSdwx++0s8PB1kul0mVsX011suutlPX+bZi6SenYjnOt4leXrWrnJ4zsfQwsU0rtAt5XIX3LRYL+1nkea4P7yuHfWml+wYvWrhWL4nlAoR46SVgu91qIyxdGPXIKeibvci650wsDY42m81yudxut1p+XRjdzXPLfhZuJ7oDO+x3P5mtbZbWXOdlACq538yiuIRhcPacqRtqZ8yiLryv293pN/waVrG5DAEAjqn7257alUR/trdlb8GXDgDArdF9zU/pI1a3E6KOckQKAgAG11f4WaQgAGBseg8/ixQEAIzE8cLP8lOQ1p4AgGMaIPxUnue2xwbhBwA4pgFGeNGxBmzyHTgbLwAAbfUypVElO6CU0i6A9PADABxf7+FH5gEAxqav8CPzAACj1X342fG2hcwDAIxS72N7NnlJpAONAwAixawOAIDJ6f6254FzLAEA0LfBOrkDADCUATq5AwAwLMIPADA5hB8AYHIIPwDA5BB+AIDJIfwAoIXFYpEERTFBt1tg/7d5npd2c7FY7D0aiW4qPNSXLYlO+6N6HR2M8AOAFtyJuPdbYeR0yrnSXmy32+VyuV+u66vc0Z5L3JBzI7bXwb8IPwBoLU1TU2/o0jXll9ZWBDebjbtHaZqKyHa73SP/dlbgNBf1LfI8N8YcYbAUwg8AIHJdRRMRY0wp5LIs00Dabrd7VMh0nOdwCh55CgTCDwAgeZ7rrc66WtdisQhnWJ7nWZZVRmPgzqdureEsCB0i/AAA70IocGMzz/PNZuOHnzZpWS6Xq9VquVwmSVKKQPsSPxo1EY/fSojwAwBctdPZee9xsViUgipJEn1tmqZpmmodbrlclnJOl9fdMj3+tK99zeQOALeY3uKr/JUfDxFpW3JbX3RTLcuy1WpVugWaZZlWDd3jNtQ9T2FWBwBoZeeUpWmaHr8e05buhXv995eoxWLhd96wq9W9qnK5LtxsNjZi/SUqz/PlclnK1G5R8wOA1ubz+fgTrhP7dVucz+faLtRNNV3oN4oZpKJM+AFAa1Hf2wwoxZXU1N7syv7CwNb0zqdN0wHveQrhBwCw/PBroi7ASpuy/9V30ewcqgJN+AEAJE3T1WpVapBSUvcErvmTOX0XvfOpVcChKtB0dQCA7gXGvx66aNVs5gXCb7lcuv8N5FaWZZW/1YX65E+Gu+cphB8A9EHHA9tsNjpkZZqmm2ulNfM8H0kialHrKn+VYabp5bfVXK1WlS1l7Jrat33ARkN0dQCAFpoEVantfpIkfmt+S5v1H/lS3KRjg2ahOCOfadRtt1u/j4QODaNP8jTY6nZZewHqz3V7fYSuDhIYmBwAUNLkulqaEsFfYrkVQV3HXWLnjthsNvP53F9Z2ZQKvFHlXuwskr9T+l47128y68V8Pq9bQbcZWOFwhB8A9CucSW6c6EVfk8P/WbejQWhfoi93k6lJ/u2s/OgNW9Vkg3b9hum7c2uEHwDELRxIeqHXn+fzuXvFt7mo67gbsblYqmaVthAoUjj8hnWE8KPBCwCMRWm22Lp+cnJzOM3SS2KfSv44CD8AGClNtZ2NPvbuKjfCrhdZlunsSH2/EeEHAGNRat+oP7sDo9hfudW70ksG7Dx3OHdfeu3/TvgBwLHVjSKWZZmOB63/1eb+7m/dH2w/cVtP0j4JTTrPuU+/9t2JXuR5bgvWay9AhjcDgGOzPefkun5j+wK6YSbePU97lzJNU3tT1L17GZ6KHRad3AFgAEly4/Jbqgv6NzxtR/jKWmNpfexEzQ8Ajs1/Mhdo2FkSGDMTzfHMDwCOzU7og6Fw2xMAIrDfTHuoQ/gBACaH254AgMkh/AAAk0P4AQAmh/ADAEwO4QcAmBzCDwAwOYQfAGByCD8AwOQQfgCAySH8AACTQ/gBACaH8AMATA7hBwCYHMIPADA5hB8AYHIIPwDA5BB+AIDJIfwAAJND+AEAJofwAwBMDuEHAJgcwg8AMDmEHwBgcgg/AMDkEH4AgMkh/AAAk0P4AQAmh/ADAEwO4QcAmBzCDwAwOYQfAGByCD8AwOQQfgCAySH8AACTQ/gBACaH8AMATA7hBwCYHMIPADA5hB8AYHIIPwDA5BB+AIDJIfwAAJND+AEAJofwAwBMzv8DFW/eccSp27kAAAAASUVORK5CYII=\n",
1276       "text/plain": [
1277        "<IPython.core.display.Image object>"
1278       ]
1279      },
1280      "metadata": {},
1281      "output_type": "display_data"
1282     }
1283    ],
1284    "source": [
1285     "for i in range(len(h_topo_transverse)):\n",
1286     "    canvas = ROOT.TCanvas(\"canvas\", \"\", 600, 800)\n",
1287     "    pad1 = ROOT.TPad(\"pad1\", \"\", 0, 0.3, 1, 1.0)\n",
1288     "    pad1.SetBottomMargin(0.02)  # Adjust the margin for better separation\n",
1289     "    pad1.Draw()\n",
1290     "    pad1.cd()\n",
1291     "    pad1.SetLogy(1)  # Set logarithmic scale for the spectra plot\n",
1292     "\n",
1293     "    # Customize the histograms (color, marker, etc.)\n",
1294     "    h_topo_transverse[i].SetStats(0)\n",
1295     "    h_mc_topo_transverse[i].SetStats(0)\n",
1296     "    h_clus_topo_transverse[i].SetStats(0)\n",
1297     "    h_nz_topo_transverse[i].SetStats(0)\n",
1298     "    h_mc_topo_transverse[i].SetLineColor(2)\n",
1299     "    h_mc_topo_transverse[i].SetMarkerColor(2)\n",
1300     "    h_clus_topo_transverse[i].SetLineColor(4)\n",
1301     "    h_clus_topo_transverse[i].SetMarkerColor(4)\n",
1302     "    h_nz_topo_transverse[i].SetLineColor(6)\n",
1303     "    h_nz_topo_transverse[i].SetMarkerColor(6)\n",
1304     "    h_dt_topo_transverse[i].SetLineColor(7)\n",
1305     "    h_dt_topo_transverse[i].SetMarkerColor(7)\n",
1306     "\n",
1307     "    h_mc_topo_transverse[i].GetXaxis().SetLabelSize(0)\n",
1308     "    h_topo_transverse[i].GetXaxis().SetLabelSize(0)\n",
1309     "    h_clus_topo_transverse[i].GetXaxis().SetLabelSize(0)\n",
1310     "    h_nz_topo_transverse[i].GetXaxis().SetLabelSize(0)\n",
1311     "    h_dt_topo_transverse[i].GetXaxis().SetLabelSize(0)\n",
1312     "    \n",
1313     "    h_mc_topo_transverse[i].GetXaxis().SetRangeUser(-5,10)\n",
1314     "\n",
1315     "    h_mc_topo_transverse[i].Draw()\n",
1316     "    h_dt_topo_transverse[i].Draw(\"same\")\n",
1317     "    h_clus_topo_transverse[i].Draw(\"same\")\n",
1318     "    h_nz_topo_transverse[i].Draw(\"same\")  # Uncomment if needed\n",
1319     "    h_topo_transverse[i].Draw(\"same\")\n",
1320     "\n",
1321     "    # Add legend\n",
1322     "    leg = ROOT.TLegend(.57, .6, .92, .92)\n",
1323     "    leg.AddEntry(\"\",\"#bf{Transverse Region}\",\"\")\n",
1324     "    leg.AddEntry(\"\",thres_string[i],\"\")\n",
1325     "    leg.AddEntry(h_topo_transverse[i],\"Jet Trig. Data\",\"l\")\n",
1326     "    leg.AddEntry(h_mc_topo_transverse[i],\"Calo Waveform\",\"l\")\n",
1327     "    leg.AddEntry(h_clus_topo_transverse[i],\"Calo Cluster\",\"l\")\n",
1328     "    leg.AddEntry(h_nz_topo_transverse[i],\"Calo No ZS\",\"l\")\n",
1329     "    leg.AddEntry(h_dt_topo_transverse[i],\"Detriot Waveform\",\"l\")\n",
1330     "    leg.Draw()\n",
1331     "    leg.SetTextSize(0.04)\n",
1332     "\n",
1333     "    canvas.cd()\n",
1334     "    pad2 = ROOT.TPad(\"pad2\", \"\", 0, 0.05, 1, 0.3)\n",
1335     "    pad2.SetTopMargin(0.02)\n",
1336     "    pad2.SetBottomMargin(0.4)\n",
1337     "    pad2.Draw()\n",
1338     "    pad2.cd()\n",
1339     "    ratio1 = h_mc_topo_transverse[i].Clone(\"ratio1\")\n",
1340     "    ratio1.Divide(h_topo_transverse[i])\n",
1341     "    ratio2 = h_clus_topo_transverse[i].Clone(\"ratio2\")\n",
1342     "    ratio2.Divide(h_topo_transverse[i])\n",
1343     "    ratio3 = h_nz_topo_transverse[i].Clone(\"ratio3\")\n",
1344     "    ratio3.Divide(h_topo_transverse[i])\n",
1345     "    ratio4 = h_dt_topo_transverse[i].Clone(\"ratio4\")\n",
1346     "    ratio4.Divide(h_topo_transverse[i])\n",
1347     "\n",
1348     "    ratio1.GetYaxis().SetTitle(\"MC/Data Ratio\")\n",
1349     "    ratio1.GetYaxis().SetNdivisions(208)\n",
1350     "    ratio1.GetYaxis().SetRangeUser(0.5,1.5)\n",
1351     "    ratio1.GetYaxis().SetTitleSize(25)\n",
1352     "    ratio1.GetYaxis().SetTitleFont(43)\n",
1353     "    ratio1.GetYaxis().SetTitleOffset(1.5)\n",
1354     "    ratio1.GetYaxis().SetLabelFont(43)\n",
1355     "    ratio1.GetYaxis().SetLabelSize(25)\n",
1356     "    ratio1.GetXaxis().SetTitle(\"E_{T,topo} [GeV]\")\n",
1357     "    ratio1.GetXaxis().SetTitleSize(25)\n",
1358     "    ratio1.GetXaxis().SetTitleFont(43)\n",
1359     "    ratio1.GetXaxis().SetTitleOffset(0)\n",
1360     "    ratio1.GetXaxis().SetLabelFont(43)\n",
1361     "    ratio1.GetXaxis().SetLabelSize(25)\n",
1362     "    \n",
1363     "    ratio1.GetXaxis().SetRangeUser(-5,10)\n",
1364     "\n",
1365     "    # Draw ratio plots\n",
1366     "    ratio1.Draw(\"ep\")\n",
1367     "    ratio2.Draw(\"same ep\")\n",
1368     "    ratio3.Draw(\"same ep\")  # Uncomment if needed\n",
1369     "    ratio4.Draw(\"same ep\")\n",
1370     "\n",
1371     "    # Update canvas\n",
1372     "    canvas.Update()\n",
1373     "    canvas.Draw()\n",
1374     "    canvas.SaveAs(\"/sphenix/u/egm2153/fall_2024/\"+direct+\"/h_topo_spectra_transverse\"+topo_thres[i]+\"_Topoclusters.png\")"
1375    ]
1376   },
1377   {
1378    "cell_type": "code",
1379    "execution_count": 110,
1380    "id": "f653ff62-e736-44c3-97aa-056ccbe331de",
1381    "metadata": {},
1382    "outputs": [
1383     {
1384      "name": "stderr",
1385      "output_type": "stream",
1386      "text": [
1387       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
1388       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_topo_spectra_away-9999_Topoclusters.png has been created\n",
1389       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
1390       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_topo_spectra_away0_Topoclusters.png has been created\n",
1391       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
1392       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_topo_spectra_away100_Topoclusters.png has been created\n",
1393       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
1394       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_topo_spectra_away200_Topoclusters.png has been created\n",
1395       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
1396       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_topo_spectra_away300_Topoclusters.png has been created\n",
1397       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
1398       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_topo_spectra_away500_Topoclusters.png has been created\n"
1399      ]
1400     },
1401     {
1402      "data": {
1403       "image/png": 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\n",
1404       "text/plain": [
1405        "<IPython.core.display.Image object>"
1406       ]
1407      },
1408      "metadata": {},
1409      "output_type": "display_data"
1410     }
1411    ],
1412    "source": [
1413     "for i in range(len(h_topo_away)):\n",
1414     "    canvas = ROOT.TCanvas(\"canvas\", \"\", 600, 800)\n",
1415     "    pad1 = ROOT.TPad(\"pad1\", \"\", 0, 0.3, 1, 1.0)\n",
1416     "    pad1.SetBottomMargin(0.02)  # Adjust the margin for better separation\n",
1417     "    pad1.Draw()\n",
1418     "    pad1.cd()\n",
1419     "    pad1.SetLogy(1)  # Set logarithmic scale for the spectra plot\n",
1420     "\n",
1421     "    # Customize the histograms (color, marker, etc.)\n",
1422     "    h_topo_away[i].SetStats(0)\n",
1423     "    h_mc_topo_away[i].SetStats(0)\n",
1424     "    h_clus_topo_away[i].SetStats(0)\n",
1425     "    h_nz_topo_away[i].SetStats(0)\n",
1426     "    h_mc_topo_away[i].SetLineColor(2)\n",
1427     "    h_mc_topo_away[i].SetMarkerColor(2)\n",
1428     "    h_clus_topo_away[i].SetLineColor(4)\n",
1429     "    h_clus_topo_away[i].SetMarkerColor(4)\n",
1430     "    h_nz_topo_away[i].SetLineColor(6)\n",
1431     "    h_nz_topo_away[i].SetMarkerColor(6)\n",
1432     "    h_dt_topo_away[i].SetLineColor(7)\n",
1433     "    h_dt_topo_away[i].SetMarkerColor(7)\n",
1434     "\n",
1435     "    h_mc_topo_away[i].GetXaxis().SetLabelSize(0)\n",
1436     "    h_topo_away[i].GetXaxis().SetLabelSize(0)\n",
1437     "    h_clus_topo_away[i].GetXaxis().SetLabelSize(0)\n",
1438     "    h_nz_topo_away[i].GetXaxis().SetLabelSize(0)\n",
1439     "    h_dt_topo_away[i].GetXaxis().SetLabelSize(0)\n",
1440     "    \n",
1441     "    h_mc_topo_away[i].GetXaxis().SetRangeUser(-5,30)\n",
1442     "\n",
1443     "    h_mc_topo_away[i].Draw()\n",
1444     "    h_dt_topo_away[i].Draw(\"same\")\n",
1445     "    h_clus_topo_away[i].Draw(\"same\")\n",
1446     "    h_nz_topo_away[i].Draw(\"same\")  # Uncomment if needed\n",
1447     "    h_topo_away[i].Draw(\"same\")\n",
1448     "\n",
1449     "    # Add legend\n",
1450     "    leg = ROOT.TLegend(.57, .6, .92, .92)\n",
1451     "    leg.AddEntry(\"\",\"#bf{Away Region}\",\"\")\n",
1452     "    leg.AddEntry(\"\",thres_string[i],\"\")\n",
1453     "    leg.AddEntry(h_topo_away[i],\"Jet Trig. Data\",\"l\")\n",
1454     "    leg.AddEntry(h_mc_topo_away[i],\"Calo Waveform\",\"l\")\n",
1455     "    leg.AddEntry(h_clus_topo_away[i],\"Calo Cluster\",\"l\")\n",
1456     "    leg.AddEntry(h_nz_topo_away[i],\"Calo No ZS\",\"l\")\n",
1457     "    leg.AddEntry(h_dt_topo_away[i],\"Detriot Waveform\",\"l\")\n",
1458     "    leg.Draw()\n",
1459     "    leg.SetTextSize(0.04)\n",
1460     "\n",
1461     "    canvas.cd()\n",
1462     "    pad2 = ROOT.TPad(\"pad2\", \"\", 0, 0.05, 1, 0.3)\n",
1463     "    pad2.SetTopMargin(0.02)\n",
1464     "    pad2.SetBottomMargin(0.4)\n",
1465     "    pad2.Draw()\n",
1466     "    pad2.cd()\n",
1467     "    ratio1 = h_mc_topo_away[i].Clone(\"ratio1\")\n",
1468     "    ratio1.Divide(h_topo_away[i])\n",
1469     "    ratio2 = h_clus_topo_away[i].Clone(\"ratio2\")\n",
1470     "    ratio2.Divide(h_topo_away[i])\n",
1471     "    ratio3 = h_nz_topo_away[i].Clone(\"ratio3\")\n",
1472     "    ratio3.Divide(h_topo_away[i])\n",
1473     "    ratio4 = h_dt_topo_away[i].Clone(\"ratio4\")\n",
1474     "    ratio4.Divide(h_topo_away[i])\n",
1475     "\n",
1476     "    ratio1.GetYaxis().SetTitle(\"MC/Data Ratio\")\n",
1477     "    ratio1.GetYaxis().SetNdivisions(208)\n",
1478     "    ratio1.GetYaxis().SetRangeUser(0.5,1.5)\n",
1479     "    ratio1.GetYaxis().SetTitleSize(25)\n",
1480     "    ratio1.GetYaxis().SetTitleFont(43)\n",
1481     "    ratio1.GetYaxis().SetTitleOffset(1.5)\n",
1482     "    ratio1.GetYaxis().SetLabelFont(43)\n",
1483     "    ratio1.GetYaxis().SetLabelSize(25)\n",
1484     "    ratio1.GetXaxis().SetTitle(\"E_{T,topo} [GeV]\")\n",
1485     "    ratio1.GetXaxis().SetTitleSize(25)\n",
1486     "    ratio1.GetXaxis().SetTitleFont(43)\n",
1487     "    ratio1.GetXaxis().SetTitleOffset(0)\n",
1488     "    ratio1.GetXaxis().SetLabelFont(43)\n",
1489     "    ratio1.GetXaxis().SetLabelSize(25)\n",
1490     "    \n",
1491     "    ratio1.GetXaxis().SetRangeUser(-5,30)\n",
1492     "\n",
1493     "    # Draw ratio plots\n",
1494     "    ratio1.Draw(\"ep\")\n",
1495     "    ratio2.Draw(\"same ep\")\n",
1496     "    ratio3.Draw(\"same ep\")  # Uncomment if needed\n",
1497     "    ratio4.Draw(\"same ep\")\n",
1498     "\n",
1499     "    # Update canvas\n",
1500     "    canvas.Update()\n",
1501     "    canvas.Draw()\n",
1502     "    canvas.SaveAs(\"/sphenix/u/egm2153/fall_2024/\"+direct+\"/h_topo_spectra_away\"+topo_thres[i]+\"_Topoclusters.png\")"
1503    ]
1504   },
1505   {
1506    "cell_type": "code",
1507    "execution_count": 163,
1508    "id": "5b42faf2-80fe-41e0-b429-4be2e2848720",
1509    "metadata": {},
1510    "outputs": [
1511     {
1512      "name": "stderr",
1513      "output_type": "stream",
1514      "text": [
1515       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
1516       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_mean_etopo.png has been created\n"
1517      ]
1518     },
1519     {
1520      "data": {
1521       "image/png": 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\n",
1522       "text/plain": [
1523        "<IPython.core.display.Image object>"
1524       ]
1525      },
1526      "metadata": {},
1527      "output_type": "display_data"
1528     }
1529    ],
1530    "source": [
1531     "# Create a canvas\n",
1532     "canvas = ROOT.TCanvas(\"canvas\", \"Graph\", 800, 600)\n",
1533     "\n",
1534     "# Define marker styles and colors\n",
1535     "marker_styles = {\n",
1536     "    \"towards\": ROOT.kOpenTriangleUp,\n",
1537     "    \"transverse\": ROOT.kFullCircle,\n",
1538     "    \"away\": ROOT.kOpenTriangleDown\n",
1539     "}\n",
1540     "\n",
1541     "colors = {\n",
1542     "    \"ntopo\": ROOT.kBlack,\n",
1543     "    \"mc_ntopo\": ROOT.kRed,\n",
1544     "    \"clus_ntopo\": ROOT.kBlue,\n",
1545     "    \"nz_ntopo\": ROOT.kMagenta,\n",
1546     "    \"dt_ntopo\": ROOT.kCyan\n",
1547     "}\n",
1548     "\n",
1549     "# Prepare a list of all data groups\n",
1550     "data_groups = [\n",
1551     "    (\"mc_ntopo\", mean_mc_topo_towards, mean_mc_topo_transverse, mean_mc_topo_away),\n",
1552     "    (\"clus_ntopo\", mean_clus_topo_towards, mean_clus_topo_transverse, mean_clus_topo_away),\n",
1553     "    (\"nz_ntopo\", mean_nz_topo_towards, mean_nz_topo_transverse, mean_nz_topo_away),\n",
1554     "    (\"dt_ntopo\", mean_dt_topo_towards, mean_dt_topo_transverse, mean_dt_topo_away),\n",
1555     "    (\"ntopo\", mean_topo_towards, mean_topo_transverse, mean_topo_away)\n",
1556     "]\n",
1557     "\n",
1558     "graphs = []\n",
1559     "    \n",
1560     "# Create TGraphs for each data group\n",
1561     "for group, towards, transverse, away in data_groups:\n",
1562     "    n_points = len(towards)\n",
1563     "    xlist = [-500,0,100,200,300,500]\n",
1564     "    x = np.array(xlist, dtype='float64')\n",
1565     "\n",
1566     "    # Create graphs for towards, transverse, and away\n",
1567     "    graph_towards = ROOT.TGraph(n_points, x, np.array(towards, dtype='float64'))\n",
1568     "    graph_transverse = ROOT.TGraph(n_points, x, np.array(transverse, dtype='float64'))\n",
1569     "    graph_away = ROOT.TGraph(n_points, x, np.array(away, dtype='float64'))\n",
1570     "\n",
1571     "    # Set marker styles and colors\n",
1572     "    graph_towards.SetMarkerStyle(marker_styles[\"towards\"])\n",
1573     "    graph_transverse.SetMarkerStyle(marker_styles[\"transverse\"])\n",
1574     "    graph_away.SetMarkerStyle(marker_styles[\"away\"])\n",
1575     "\n",
1576     "    graph_towards.SetMarkerColor(colors[group])\n",
1577     "    graph_transverse.SetMarkerColor(colors[group])\n",
1578     "    graph_away.SetMarkerColor(colors[group])\n",
1579     "\n",
1580     "    graph_towards.SetLineColor(colors[group])\n",
1581     "    graph_transverse.SetLineColor(colors[group])\n",
1582     "    graph_away.SetLineColor(colors[group])\n",
1583     "\n",
1584     "    graphs.extend([graph_towards, graph_transverse, graph_away])\n",
1585     "\n",
1586     "# Draw all graphs on the same canvas\n",
1587     "for i, graph in enumerate(graphs):\n",
1588     "    if i == 0:\n",
1589     "        graph.Draw(\"AP\")\n",
1590     "        graph.GetYaxis().SetRangeUser(-0.5,3)\n",
1591     "        graph.GetXaxis().SetTitle(\"E_{topo} thres [MeV]\")\n",
1592     "        graph.GetYaxis().SetTitle(\"<E_{T,topo}> [GeV]\")\n",
1593     "    else:\n",
1594     "         graph.Draw(\"P SAME\")\n",
1595     "\n",
1596     "# Adding a legend\n",
1597     "legend = ROOT.TLegend(0.17, 0.76, 0.7, 0.92)\n",
1598     "legend.SetNColumns(2)\n",
1599     "legend.AddEntry(graphs[1], \"Waveform\", \"p\")\n",
1600     "legend.AddEntry(graphs[4], \"Clusters\", \"p\")\n",
1601     "legend.AddEntry(graphs[7], \"No ZS\", \"p\")\n",
1602     "legend.AddEntry(graphs[12], \"Towards\", \"p\")\n",
1603     "legend.AddEntry(graphs[10], \"Detroit Waveform\", \"p\")\n",
1604     "legend.AddEntry(graphs[13], \"Transverse\", \"p\")\n",
1605     "legend.AddEntry(graphs[13], \"Jet Trig. Data\", \"p\")\n",
1606     "legend.AddEntry(graphs[14], \"Away\", \"p\")\n",
1607     "legend.Draw()\n",
1608     "\n",
1609     "# Update and display the canvas\n",
1610     "canvas.Update()\n",
1611     "canvas.Draw()\n",
1612     "canvas.SaveAs(\"/sphenix/u/egm2153/fall_2024/\"+direct+\"/h_mean_etopo.png\")"
1613    ]
1614   },
1615   {
1616    "cell_type": "code",
1617    "execution_count": 171,
1618    "id": "f8dbaf5e-1bdd-4388-9934-145ed9c21eff",
1619    "metadata": {},
1620    "outputs": [
1621     {
1622      "name": "stderr",
1623      "output_type": "stream",
1624      "text": [
1625       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
1626       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_mean_etopo_towards.png has been created\n"
1627      ]
1628     },
1629     {
1630      "data": {
1631       "image/png": 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\n",
1632       "text/plain": [
1633        "<IPython.core.display.Image object>"
1634       ]
1635      },
1636      "metadata": {},
1637      "output_type": "display_data"
1638     }
1639    ],
1640    "source": [
1641     "# Create a canvas\n",
1642     "canvas = ROOT.TCanvas(\"canvas\", \"Graph\", 600, 600)\n",
1643     "\n",
1644     "# Define marker styles and colors\n",
1645     "marker_styles = {\n",
1646     "    \"towards\": ROOT.kFullCircle,\n",
1647     "    \"transverse\": ROOT.kFullCircle,\n",
1648     "    \"away\": ROOT.kFullCircle\n",
1649     "}\n",
1650     "\n",
1651     "colors = {\n",
1652     "    \"ntopo\": ROOT.kBlack,\n",
1653     "    \"mc_ntopo\": ROOT.kRed,\n",
1654     "    \"clus_ntopo\": ROOT.kBlue,\n",
1655     "    \"nz_ntopo\": ROOT.kMagenta,\n",
1656     "    \"dt_ntopo\": ROOT.kCyan\n",
1657     "}\n",
1658     "\n",
1659     "# Prepare a list of all data groups\n",
1660     "data_groups = [\n",
1661     "    (\"mc_ntopo\", x0, mean_mc_topo_towards, mean_mc_topo_transverse, mean_mc_topo_away, std_mc_topo_towards, std_mc_topo_transverse, std_mc_topo_away),\n",
1662     "    (\"clus_ntopo\", x1, mean_clus_topo_towards, mean_clus_topo_transverse, mean_clus_topo_away, std_clus_topo_towards, std_clus_topo_transverse, std_clus_topo_away),\n",
1663     "    (\"nz_ntopo\", x2, mean_nz_topo_towards, mean_nz_topo_transverse, mean_nz_topo_away, std_nz_topo_towards, std_nz_topo_transverse, std_nz_topo_away),\n",
1664     "    (\"dt_ntopo\", x3, mean_dt_topo_towards, mean_dt_topo_transverse, mean_dt_topo_away, std_dt_topo_towards, std_dt_topo_transverse, std_dt_topo_away),\n",
1665     "    (\"ntopo\", x4, mean_topo_towards, mean_topo_transverse, mean_topo_away, std_topo_towards, std_topo_transverse, std_topo_away)\n",
1666     "]\n",
1667     "\n",
1668     "graphs = []\n",
1669     "    \n",
1670     "# Create TGraphs for each data group\n",
1671     "for group, x, towards, transverse, away, towards_width, transverse_width, away_width in data_groups:\n",
1672     "    n_points = len(towards)\n",
1673     "\n",
1674     "    # Create graphs for towards, transverse, and away\n",
1675     "    graph_towards = ROOT.TGraphErrors(n_points, x, np.array(towards, dtype='float64'), np.array(xerr, dtype='float64'), np.array(towards_width, dtype='float64'))\n",
1676     "    graph_transverse = ROOT.TGraphErrors(n_points, x, np.array(transverse, dtype='float64'), np.array(xerr, dtype='float64'), np.array(transverse_width, dtype='float64'))\n",
1677     "    graph_away = ROOT.TGraphErrors(n_points, x, np.array(away, dtype='float64'), np.array(xerr, dtype='float64'), np.array(away_width, dtype='float64'))\n",
1678     "\n",
1679     "    # Set marker styles and colors\n",
1680     "    graph_towards.SetMarkerStyle(marker_styles[\"towards\"])\n",
1681     "    graph_transverse.SetMarkerStyle(marker_styles[\"transverse\"])\n",
1682     "    graph_away.SetMarkerStyle(marker_styles[\"away\"])\n",
1683     "\n",
1684     "    graph_towards.SetMarkerColor(colors[group])\n",
1685     "    graph_transverse.SetMarkerColor(colors[group])\n",
1686     "    graph_away.SetMarkerColor(colors[group])\n",
1687     "\n",
1688     "    graph_towards.SetLineColor(colors[group])\n",
1689     "    graph_transverse.SetLineColor(colors[group])\n",
1690     "    graph_away.SetLineColor(colors[group])\n",
1691     "\n",
1692     "    graphs.extend([graph_towards, graph_transverse, graph_away])\n",
1693     "\n",
1694     "# Draw all graphs on the same canvas\n",
1695     "for i, graph in enumerate(graphs):\n",
1696     "    if i % 3 == 0:\n",
1697     "        if i == 0:\n",
1698     "            graph.Draw(\"AP\")\n",
1699     "            graph.GetYaxis().SetRangeUser(-2,8)\n",
1700     "            graph.GetXaxis().SetTitle(\"E_{topo} thres [MeV]\")\n",
1701     "            graph.GetYaxis().SetTitle(\"<E_{T,topo}> [GeV]\")\n",
1702     "        else:\n",
1703     "             graph.Draw(\"P SAME\")\n",
1704     "\n",
1705     "# Adding a legend\n",
1706     "legend = ROOT.TLegend(0.17, 0.65, 0.5, 0.92)\n",
1707     "legend.SetNColumns(1)\n",
1708     "legend.AddEntry(\"\",\"#bf{Towards Region}\",\"\")\n",
1709     "legend.AddEntry(graphs[1], \"Waveform\", \"pe\")\n",
1710     "legend.AddEntry(graphs[4], \"Clusters\", \"pe\")\n",
1711     "legend.AddEntry(graphs[7], \"No ZS\", \"pe\")\n",
1712     "legend.AddEntry(graphs[10], \"Detroit Waveform\", \"ep\")\n",
1713     "legend.AddEntry(graphs[13], \"Jet Trig. Data\", \"pe\")\n",
1714     "legend.Draw()\n",
1715     "\n",
1716     "# Update and display the canvas\n",
1717     "canvas.Update()\n",
1718     "canvas.Draw()\n",
1719     "canvas.SaveAs(\"/sphenix/u/egm2153/fall_2024/\"+direct+\"/h_mean_etopo_towards.png\")"
1720    ]
1721   },
1722   {
1723    "cell_type": "code",
1724    "execution_count": 172,
1725    "id": "df9c4284-854a-4353-882c-58a7e021e6c2",
1726    "metadata": {},
1727    "outputs": [
1728     {
1729      "name": "stderr",
1730      "output_type": "stream",
1731      "text": [
1732       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
1733       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_mean_etopo_transverse.png has been created\n"
1734      ]
1735     },
1736     {
1737      "data": {
1738       "image/png": 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\n",
1739       "text/plain": [
1740        "<IPython.core.display.Image object>"
1741       ]
1742      },
1743      "metadata": {},
1744      "output_type": "display_data"
1745     }
1746    ],
1747    "source": [
1748     "# Create a canvas\n",
1749     "canvas = ROOT.TCanvas(\"canvas\", \"Graph\", 600, 600)\n",
1750     "\n",
1751     "# Define marker styles and colors\n",
1752     "marker_styles = {\n",
1753     "    \"towards\": ROOT.kFullCircle,\n",
1754     "    \"transverse\": ROOT.kFullCircle,\n",
1755     "    \"away\": ROOT.kFullCircle\n",
1756     "}\n",
1757     "\n",
1758     "colors = {\n",
1759     "    \"ntopo\": ROOT.kBlack,\n",
1760     "    \"mc_ntopo\": ROOT.kRed,\n",
1761     "    \"clus_ntopo\": ROOT.kBlue,\n",
1762     "    \"nz_ntopo\": ROOT.kMagenta,\n",
1763     "    \"dt_ntopo\": ROOT.kCyan\n",
1764     "}\n",
1765     "\n",
1766     "# Prepare a list of all data groups\n",
1767     "data_groups = [\n",
1768     "    (\"mc_ntopo\", x0, mean_mc_topo_towards, mean_mc_topo_transverse, mean_mc_topo_away, std_mc_topo_towards, std_mc_topo_transverse, std_mc_topo_away),\n",
1769     "    (\"clus_ntopo\", x1, mean_clus_topo_towards, mean_clus_topo_transverse, mean_clus_topo_away, std_clus_topo_towards, std_clus_topo_transverse, std_clus_topo_away),\n",
1770     "    (\"nz_ntopo\", x2, mean_nz_topo_towards, mean_nz_topo_transverse, mean_nz_topo_away, std_nz_topo_towards, std_nz_topo_transverse, std_nz_topo_away),\n",
1771     "    (\"dt_ntopo\", x3, mean_dt_topo_towards, mean_dt_topo_transverse, mean_dt_topo_away, std_dt_topo_towards, std_dt_topo_transverse, std_dt_topo_away),\n",
1772     "    (\"ntopo\", x4, mean_topo_towards, mean_topo_transverse, mean_topo_away, std_topo_towards, std_topo_transverse, std_topo_away)\n",
1773     "]\n",
1774     "\n",
1775     "graphs = []\n",
1776     "    \n",
1777     "# Create TGraphs for each data group\n",
1778     "for group, x, towards, transverse, away, towards_width, transverse_width, away_width in data_groups:\n",
1779     "    n_points = len(towards)\n",
1780     "\n",
1781     "    # Create graphs for towards, transverse, and away\n",
1782     "    graph_towards = ROOT.TGraphErrors(n_points, x, np.array(towards, dtype='float64'), np.array(xerr, dtype='float64'), np.array(towards_width, dtype='float64'))\n",
1783     "    graph_transverse = ROOT.TGraphErrors(n_points, x, np.array(transverse, dtype='float64'), np.array(xerr, dtype='float64'), np.array(transverse_width, dtype='float64'))\n",
1784     "    graph_away = ROOT.TGraphErrors(n_points, x, np.array(away, dtype='float64'), np.array(xerr, dtype='float64'), np.array(away_width, dtype='float64'))\n",
1785     "\n",
1786     "    # Set marker styles and colors\n",
1787     "    graph_towards.SetMarkerStyle(marker_styles[\"towards\"])\n",
1788     "    graph_transverse.SetMarkerStyle(marker_styles[\"transverse\"])\n",
1789     "    graph_away.SetMarkerStyle(marker_styles[\"away\"])\n",
1790     "\n",
1791     "    graph_towards.SetMarkerColor(colors[group])\n",
1792     "    graph_transverse.SetMarkerColor(colors[group])\n",
1793     "    graph_away.SetMarkerColor(colors[group])\n",
1794     "\n",
1795     "    graph_towards.SetLineColor(colors[group])\n",
1796     "    graph_transverse.SetLineColor(colors[group])\n",
1797     "    graph_away.SetLineColor(colors[group])\n",
1798     "\n",
1799     "    graphs.extend([graph_towards, graph_transverse, graph_away])\n",
1800     "\n",
1801     "# Draw all graphs on the same canvas\n",
1802     "for i, graph in enumerate(graphs):\n",
1803     "    if (i-1) % 3 == 0:\n",
1804     "        if i == 1:\n",
1805     "            graph.Draw(\"AP\")\n",
1806     "            graph.GetYaxis().SetRangeUser(-0.5,3)\n",
1807     "            graph.GetXaxis().SetTitle(\"E_{topo} thres [MeV]\")\n",
1808     "            graph.GetYaxis().SetTitle(\"<E_{T,topo}> [GeV]\")\n",
1809     "        else:\n",
1810     "             graph.Draw(\"P SAME\")\n",
1811     "\n",
1812     "# Adding a legend\n",
1813     "legend = ROOT.TLegend(0.17, 0.65, 0.5, 0.92)\n",
1814     "legend.SetNColumns(1)\n",
1815     "legend.AddEntry(\"\",\"#bf{Transverse Region}\",\"\")\n",
1816     "legend.AddEntry(graphs[1], \"Waveform\", \"pe\")\n",
1817     "legend.AddEntry(graphs[4], \"Clusters\", \"pe\")\n",
1818     "legend.AddEntry(graphs[7], \"No ZS\", \"pe\")\n",
1819     "legend.AddEntry(graphs[10], \"Detroit Waveform\", \"ep\")\n",
1820     "legend.AddEntry(graphs[13], \"Jet Trig. Data\", \"pe\")\n",
1821     "legend.Draw()\n",
1822     "\n",
1823     "# Update and display the canvas\n",
1824     "canvas.Update()\n",
1825     "canvas.Draw()\n",
1826     "canvas.SaveAs(\"/sphenix/u/egm2153/fall_2024/\"+direct+\"/h_mean_etopo_transverse.png\")"
1827    ]
1828   },
1829   {
1830    "cell_type": "code",
1831    "execution_count": 173,
1832    "id": "472eddb0-1ded-47ae-9710-279b69e0477a",
1833    "metadata": {},
1834    "outputs": [
1835     {
1836      "name": "stderr",
1837      "output_type": "stream",
1838      "text": [
1839       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
1840       "Info in <TCanvas::Print>: png file /sphenix/u/egm2153/fall_2024/results_11_11/h_mean_etopo_away.png has been created\n"
1841      ]
1842     },
1843     {
1844      "data": {
1845       "image/png": 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\n",
1846       "text/plain": [
1847        "<IPython.core.display.Image object>"
1848       ]
1849      },
1850      "metadata": {},
1851      "output_type": "display_data"
1852     }
1853    ],
1854    "source": [
1855     "# Create a canvas\n",
1856     "canvas = ROOT.TCanvas(\"canvas\", \"Graph\", 600, 600)\n",
1857     "\n",
1858     "# Define marker styles and colors\n",
1859     "marker_styles = {\n",
1860     "    \"towards\": ROOT.kFullCircle,\n",
1861     "    \"transverse\": ROOT.kFullCircle,\n",
1862     "    \"away\": ROOT.kFullCircle\n",
1863     "}\n",
1864     "\n",
1865     "colors = {\n",
1866     "    \"ntopo\": ROOT.kBlack,\n",
1867     "    \"mc_ntopo\": ROOT.kRed,\n",
1868     "    \"clus_ntopo\": ROOT.kBlue,\n",
1869     "    \"nz_ntopo\": ROOT.kMagenta,\n",
1870     "    \"dt_ntopo\": ROOT.kCyan\n",
1871     "}\n",
1872     "\n",
1873     "# Prepare a list of all data groups\n",
1874     "data_groups = [\n",
1875     "    (\"mc_ntopo\", x0, mean_mc_topo_towards, mean_mc_topo_transverse, mean_mc_topo_away, std_mc_topo_towards, std_mc_topo_transverse, std_mc_topo_away),\n",
1876     "    (\"clus_ntopo\", x1, mean_clus_topo_towards, mean_clus_topo_transverse, mean_clus_topo_away, std_clus_topo_towards, std_clus_topo_transverse, std_clus_topo_away),\n",
1877     "    (\"nz_ntopo\", x2, mean_nz_topo_towards, mean_nz_topo_transverse, mean_nz_topo_away, std_nz_topo_towards, std_nz_topo_transverse, std_nz_topo_away),\n",
1878     "    (\"dt_ntopo\", x3, mean_dt_topo_towards, mean_dt_topo_transverse, mean_dt_topo_away, std_dt_topo_towards, std_dt_topo_transverse, std_dt_topo_away),\n",
1879     "    (\"ntopo\", x4, mean_topo_towards, mean_topo_transverse, mean_topo_away, std_topo_towards, std_topo_transverse, std_topo_away)\n",
1880     "]\n",
1881     "\n",
1882     "graphs = []\n",
1883     "    \n",
1884     "# Create TGraphs for each data group\n",
1885     "for group, x, towards, transverse, away, towards_width, transverse_width, away_width in data_groups:\n",
1886     "    n_points = len(towards)\n",
1887     "\n",
1888     "    # Create graphs for towards, transverse, and away\n",
1889     "    graph_towards = ROOT.TGraphErrors(n_points, x, np.array(towards, dtype='float64'), np.array(xerr, dtype='float64'), np.array(towards_width, dtype='float64'))\n",
1890     "    graph_transverse = ROOT.TGraphErrors(n_points, x, np.array(transverse, dtype='float64'), np.array(xerr, dtype='float64'), np.array(transverse_width, dtype='float64'))\n",
1891     "    graph_away = ROOT.TGraphErrors(n_points, x, np.array(away, dtype='float64'), np.array(xerr, dtype='float64'), np.array(away_width, dtype='float64'))\n",
1892     "\n",
1893     "    # Set marker styles and colors\n",
1894     "    graph_towards.SetMarkerStyle(marker_styles[\"towards\"])\n",
1895     "    graph_transverse.SetMarkerStyle(marker_styles[\"transverse\"])\n",
1896     "    graph_away.SetMarkerStyle(marker_styles[\"away\"])\n",
1897     "\n",
1898     "    graph_towards.SetMarkerColor(colors[group])\n",
1899     "    graph_transverse.SetMarkerColor(colors[group])\n",
1900     "    graph_away.SetMarkerColor(colors[group])\n",
1901     "\n",
1902     "    graph_towards.SetLineColor(colors[group])\n",
1903     "    graph_transverse.SetLineColor(colors[group])\n",
1904     "    graph_away.SetLineColor(colors[group])\n",
1905     "\n",
1906     "    graphs.extend([graph_towards, graph_transverse, graph_away])\n",
1907     "\n",
1908     "# Draw all graphs on the same canvas\n",
1909     "for i, graph in enumerate(graphs):\n",
1910     "    if (i-2) % 3 == 0:\n",
1911     "        if i == 2:\n",
1912     "            graph.Draw(\"AP\")\n",
1913     "            graph.GetYaxis().SetRangeUser(-2,8)\n",
1914     "            graph.GetXaxis().SetTitle(\"E_{topo} thres [MeV]\")\n",
1915     "            graph.GetYaxis().SetTitle(\"<E_{T,topo}> [GeV]\")\n",
1916     "        else:\n",
1917     "             graph.Draw(\"P SAME\")\n",
1918     "\n",
1919     "# Adding a legend\n",
1920     "legend = ROOT.TLegend(0.17, 0.65, 0.5, 0.92)\n",
1921     "legend.SetNColumns(1)\n",
1922     "legend.AddEntry(\"\",\"#bf{Away Region}\",\"\")\n",
1923     "legend.AddEntry(graphs[1], \"Waveform\", \"pe\")\n",
1924     "legend.AddEntry(graphs[4], \"Clusters\", \"pe\")\n",
1925     "legend.AddEntry(graphs[7], \"No ZS\", \"pe\")\n",
1926     "legend.AddEntry(graphs[10], \"Detroit Waveform\", \"ep\")\n",
1927     "legend.AddEntry(graphs[13], \"Jet Trig. Data\", \"pe\")\n",
1928     "legend.Draw()\n",
1929     "\n",
1930     "# Update and display the canvas\n",
1931     "canvas.Update()\n",
1932     "canvas.Draw()\n",
1933     "canvas.SaveAs(\"/sphenix/u/egm2153/fall_2024/\"+direct+\"/h_mean_etopo_away.png\")"
1934    ]
1935   },
1936   {
1937    "cell_type": "code",
1938    "execution_count": 219,
1939    "id": "41544a67-86d3-48c3-a334-f6915bc194dd",
1940    "metadata": {},
1941    "outputs": [],
1942    "source": [
1943     "#f2 = ROOT.TFile.Open(\"pt10cut/dijet_calo_analysis_fullrunlist.root\")\n",
1944     "f2 = ROOT.TFile.Open(\"ue_unfolding/dijet_calo_analysis_data_topo_pt7cut_wAj_wntopo.root\")\n",
1945     "h_towards = TH1F(f2.Get('h_topo-9999_towards'))\n",
1946     "h_transverse = TH1F(f2.Get('h_topo-9999_transverse'))\n",
1947     "h_away = TH1F(f2.Get('h_topo-9999_away'))\n",
1948     "h_towards.Rebin(2)\n",
1949     "h_transverse.Rebin(2)\n",
1950     "h_away.Rebin(2)\n",
1951     "h_towards.Scale(1.0/h_towards.GetBinContent(h_towards.FindBin(-0.2)))\n",
1952     "h_transverse.Scale(1.0/h_transverse.GetBinContent(h_transverse.FindBin(-0.2)))\n",
1953     "h_away.Scale(1.0/h_away.GetBinContent(h_away.FindBin(-0.2)))\n",
1954     "h_towards.SetDirectory(0)\n",
1955     "h_transverse.SetDirectory(0)\n",
1956     "h_away.SetDirectory(0)\n",
1957     "f2.Close()"
1958    ]
1959   },
1960   {
1961    "cell_type": "code",
1962    "execution_count": 220,
1963    "id": "82acf2a6-a79d-4548-9ca2-f2c299e42b19",
1964    "metadata": {},
1965    "outputs": [
1966     {
1967      "name": "stderr",
1968      "output_type": "stream",
1969      "text": [
1970       "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n"
1971      ]
1972     },
1973     {
1974      "data": {
1975       "image/png": 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\n",
1976       "text/plain": [
1977        "<IPython.core.display.Image object>"
1978       ]
1979      },
1980      "metadata": {},
1981      "output_type": "display_data"
1982     }
1983    ],
1984    "source": [
1985     "canvas = ROOT.TCanvas(\"canvas\", \"\", 600, 800)\n",
1986     "pad1 = ROOT.TPad(\"pad1\", \"\", 0, 0.3, 1, 1.0)\n",
1987     "pad1.SetBottomMargin(0.02)  # Adjust the margin for better separation\n",
1988     "pad1.Draw()\n",
1989     "pad1.cd()\n",
1990     "pad1.SetLogy(1)  # Set logarithmic scale for the spectra plot\n",
1991     "\n",
1992     " # Customize the histograms (color, marker, etc.)\n",
1993     "h_towards.SetStats(0)\n",
1994     "h_towards.GetXaxis().SetLabelSize(0)\n",
1995     "h_transverse.SetStats(0)\n",
1996     "h_transverse.GetXaxis().SetLabelSize(0)\n",
1997     "h_away.SetStats(0)\n",
1998     "h_away.GetXaxis().SetLabelSize(0)\n",
1999     "h_transverse.SetLineColor(2)\n",
2000     "h_transverse.SetMarkerColor(2)\n",
2001     "h_away.SetLineColor(4)\n",
2002     "h_away.SetMarkerColor(4)\n",
2003     "\n",
2004     "ratio1 = h_towards.Clone(\"ratio1\")\n",
2005     "ratio1.Divide(h_transverse)\n",
2006     "ratio2 = h_away.Clone(\"ratio2\")\n",
2007     "ratio2.Divide(h_transverse)\n",
2008     "ratio3 = h_away.Clone(\"ratio3\")\n",
2009     "ratio3.Divide(h_towards)\n",
2010     "\n",
2011     "ratio1.SetLineColor(1)\n",
2012     "ratio1.SetMarkerColor(1)\n",
2013     "ratio1.SetMarkerStyle(ROOT.kOpenTriangleUp)\n",
2014     "ratio2.SetLineColor(1)\n",
2015     "ratio2.SetMarkerColor(1)\n",
2016     "ratio2.SetMarkerStyle(ROOT.kOpenTriangleDown)\n",
2017     "ratio3.SetLineColor(1)\n",
2018     "ratio3.SetMarkerColor(1)\n",
2019     "ratio3.SetMarkerStyle(ROOT.kOpenCircle)\n",
2020     "\n",
2021     "h_towards.GetXaxis().SetRangeUser(-3,0.5)\n",
2022     "h_towards.Draw(\"same\")\n",
2023     "h_transverse.Draw(\"same\")\n",
2024     "h_away.Draw(\"same\")\n",
2025     "\n",
2026     "# Add legend\n",
2027     "leg = ROOT.TLegend(.17, .6, .6, .92)\n",
2028     "leg.AddEntry(\"\",\"Jet Trig. Data\",\"\")\n",
2029     "leg.AddEntry(\"\",thres_string[0],\"\")\n",
2030     "leg.AddEntry(h_towards,\"Towards Region\",\"lp\")\n",
2031     "leg.AddEntry(h_transverse,\"Transverse Region\",\"lp\")\n",
2032     "leg.AddEntry(h_away,\"Away Region\",\"lp\")\n",
2033     "leg.AddEntry(ratio1,\"Towards/Transverse\",\"ep\")\n",
2034     "leg.AddEntry(ratio2,\"Away/Transverse\",\"ep\")\n",
2035     "leg.AddEntry(ratio3,\"Away/Towards\",\"ep\")\n",
2036     "leg.Draw()\n",
2037     "leg.SetTextSize(0.04)\n",
2038     "\n",
2039     "canvas.cd()\n",
2040     "pad2 = ROOT.TPad(\"pad2\", \"\", 0, 0.05, 1, 0.3)\n",
2041     "pad2.SetTopMargin(0.02)\n",
2042     "pad2.SetBottomMargin(0.4)\n",
2043     "pad2.Draw()\n",
2044     "pad2.cd()\n",
2045     "    \n",
2046     "ratio1.GetYaxis().SetTitle(\"MC/Data Ratio\")\n",
2047     "ratio1.GetYaxis().SetNdivisions(208)\n",
2048     "ratio1.GetYaxis().SetRangeUser(0,2)\n",
2049     "ratio1.GetYaxis().SetTitleSize(25)\n",
2050     "ratio1.GetYaxis().SetTitleFont(43)\n",
2051     "ratio1.GetYaxis().SetTitleOffset(1.5)\n",
2052     "ratio1.GetYaxis().SetLabelFont(43)\n",
2053     "ratio1.GetYaxis().SetLabelSize(25)\n",
2054     "ratio1.GetXaxis().SetTitle(\"E_{T,topo} [GeV]\")\n",
2055     "ratio1.GetXaxis().SetTitleSize(25)\n",
2056     "ratio1.GetXaxis().SetTitleFont(43)\n",
2057     "ratio1.GetXaxis().SetTitleOffset(0)\n",
2058     "ratio1.GetXaxis().SetLabelFont(43)\n",
2059     "ratio1.GetXaxis().SetLabelSize(25)\n",
2060     "ratio1.GetXaxis().SetRangeUser(-3,0.5)\n",
2061     "ratio1.Draw(\"ep\")\n",
2062     "ratio2.Draw(\"same ep\")\n",
2063     "ratio3.Draw(\"same ep\")  # Uncomment if needed\n",
2064     "\n",
2065     "# Update canvas\n",
2066     "canvas.Update()\n",
2067     "canvas.Draw()\n",
2068     "#canvas.SaveAs(\"/sphenix/u/egm2153/fall_2024/\"+direct+\"/h_topo_spectra_towards\"+topo_thres[i]+\"_Topoclusters.png\")"
2069    ]
2070   },
2071   {
2072    "cell_type": "code",
2073    "execution_count": null,
2074    "id": "d62f8dce-6c59-4c42-8e25-c493f35e6263",
2075    "metadata": {},
2076    "outputs": [],
2077    "source": []
2078   },
2079   {
2080    "cell_type": "code",
2081    "execution_count": null,
2082    "id": "a49d3c71-9e4a-42d6-bc58-32ebaa6beac0",
2083    "metadata": {},
2084    "outputs": [],
2085    "source": []
2086   },
2087   {
2088    "cell_type": "code",
2089    "execution_count": null,
2090    "id": "18d540fc-e8dc-4c36-9498-f0ae9595df9b",
2091    "metadata": {},
2092    "outputs": [],
2093    "source": []
2094   },
2095   {
2096    "cell_type": "code",
2097    "execution_count": null,
2098    "id": "81a27968-596f-47ce-9d47-af2b3c22e024",
2099    "metadata": {},
2100    "outputs": [],
2101    "source": []
2102   },
2103   {
2104    "cell_type": "code",
2105    "execution_count": null,
2106    "id": "791b5f47-a6a5-4b08-842f-c7fd5e93bdb4",
2107    "metadata": {},
2108    "outputs": [],
2109    "source": []
2110   },
2111   {
2112    "cell_type": "code",
2113    "execution_count": null,
2114    "id": "59ae0a66-34d4-4a5c-9bce-4748fe721315",
2115    "metadata": {},
2116    "outputs": [],
2117    "source": []
2118   },
2119   {
2120    "cell_type": "code",
2121    "execution_count": null,
2122    "id": "8c520f5b-0c28-4f01-8e34-13a6d9a1ad40",
2123    "metadata": {},
2124    "outputs": [],
2125    "source": []
2126   }
2127  ],
2128  "metadata": {
2129   "kernelspec": {
2130    "display_name": "Python (sPHENIX)",
2131    "language": "python",
2132    "name": "sphenix-env"
2133   },
2134   "language_info": {
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2149 }