LYSO_OPT_array.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8a171321-cb1b-4dad-b619-965f7f877dd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ROOT as R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f6888bb6-a5a3-49b5-ba9a-157647433b5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "files={}\n",
    "# files['PMT']=R.TFile(\"LYSO_Optimisation.root\")\n",
    "files['MS3_DRY']=R.TFile('data/MS3.root')\n",
    "files['OPT_DRY']=R.TFile('LYSO_ARRAY_DRY_Optimisation.root')\n",
    "files['OPT2_DRY']=R.TFile('data/OPT2/prodAnalysis_c40_OptStep1_Jan2022.root')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "d2190f00-9430-45a6-92b3-44cf5d73a60b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=0.1, y[0]=2586.22, ex[0]=0, ey[0]=77.5866\n",
      "x[1]=1.1, y[1]=2757.33, ex[1]=0, ey[1]=82.7198\n",
      "x[2]=2.1, y[2]=2488.67, ex[2]=0, ey[2]=74.6601\n",
      "x[3]=3.1, y[3]=2695.03, ex[3]=0, ey[3]=80.8508\n",
      "x[4]=4.1, y[4]=2697.97, ex[4]=0, ey[4]=80.939\n",
      "x[5]=5.1, y[5]=2867.02, ex[5]=0, ey[5]=86.0105\n",
      "x[0]=0.05, y[0]=75.1409, ex[0]=0, ey[0]=9.23377\n",
      "x[1]=1.05, y[1]=0, ex[1]=0, ey[1]=0\n",
      "x[2]=2.05, y[2]=0, ex[2]=0, ey[2]=0\n",
      "x[3]=3.05, y[3]=0, ex[3]=0, ey[3]=0\n",
      "x[4]=4.05, y[4]=72.7343, ex[4]=0, ey[4]=6.86714\n",
      "x[5]=5.05, y[5]=0, ex[5]=0, ey[5]=0\n",
      "x[6]=6.05, y[6]=0, ex[6]=0, ey[6]=0\n",
      "x[7]=7.05, y[7]=0, ex[7]=0, ey[7]=0\n",
      "x[8]=8.05, y[8]=83.7046, ex[8]=0, ey[8]=7.55401\n"
     ]
    }
   ],
   "source": [
    "g={}\n",
    "# for tag in ['MS3','OPT']:\n",
    "#     for t in ['lyAbs','lyNorm','tau','lyDT']:\n",
    "#         g['%s_%s_PMT'%(t,tag)]=files['PMT'].Get('%s_%s'%(t,tag))\n",
    "g['LY_MS3_DRY']=files['MS3_DRY'].Get('ly_array')\n",
    "g['LY_OPT_DRY']=files['OPT_DRY'].Get('LY_OPT_DRY')\n",
    "g['LY_OPT2_DRY']=files['OPT2_DRY'].Get('ly_byProd_PREIRR')\n",
    "\n",
    "g['LY_OPT_DRY'].Print()\n",
    "g['LY_OPT2_DRY'].Print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "52ea1c18-6827-45b9-a6e7-a63e1678aac2",
   "metadata": {},
   "outputs": [],
   "source": [
    "arrays={}\n",
    "arrays['MS3']={\n",
    "    416:{'file':'data/tree_FirstRun006084_LastRun006087_ARRAY000416_OVSCAN_SUMMARY.root','prod':1},\n",
    "    586:{'file':'data/tree_FirstRun006096_LastRun006099_ARRAY000586_OVSCAN_SUMMARY.root','prod':3},\n",
    "    708:{'file':'data/tree_FirstRun006108_LastRun006111_ARRAY000708_OVSCAN_SUMMARY.root','prod':4},\n",
    "    531:{'file':'data/tree_FirstRun006120_LastRun006123_ARRAY000531_OVSCAN_SUMMARY.root','prod':5},\n",
    "    606:{'file':'data/tree_FirstRun006134_LastRun006137_ARRAY000606_OVSCAN_SUMMARY.root','prod':9},\n",
    "    481:{'file':'data/tree_FirstRun006152_LastRun006155_ARRAY000481_OVSCAN_SUMMARY.root','prod':10},\n",
    "}\n",
    "\n",
    "arrays['OPT']={\n",
    "    809:{'file':'data/tree_FirstRun006090_LastRun006093_ARRAY000809_OVSCAN_SUMMARY.root','prod':1},\n",
    "    805:{'file':'data/tree_FirstRun006102_LastRun006105_ARRAY000805_OVSCAN_SUMMARY.root','prod':3},\n",
    "    799:{'file':'data/tree_FirstRun006114_LastRun006117_ARRAY000799_OVSCAN_SUMMARY.root','prod':4},\n",
    "    800:{'file':'data/tree_FirstRun006126_LastRun006129_ARRAY000800_OVSCAN_SUMMARY.root','prod':5},\n",
    "    803:{'file':'data/tree_FirstRun006146_LastRun006149_ARRAY000803_OVSCAN_SUMMARY.root','prod':9},\n",
    "    796:{'file':'data/tree_FirstRun006158_LastRun006161_ARRAY000796_OVSCAN_SUMMARY.root','prod':10}\n",
    "}\n",
    "\n",
    "arrays['OPT2']={\n",
    "    810:{'file':'data/tree_FirstRun007056_LastRun007059_ARRAY000810_OVSCAN_SUMMARY.root','prod':1},\n",
    "    821:{'file':'data/tree_FirstRun007062_LastRun007065_ARRAY000821_OVSCAN_SUMMARY.root','prod':5},\n",
    "    830:{'file':'data/tree_FirstRun007068_LastRun007071_ARRAY000830_OVSCAN_SUMMARY.root','prod':10}\n",
    "}\n",
    "\n",
    "map_prod={\n",
    "    1:0,\n",
    "    3:1,\n",
    "    4:2,\n",
    "    5:3,\n",
    "    9:4,\n",
    "    10:5\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "62211d81-f364-4d00-97df-0ba7b55e1b68",
   "metadata": {},
   "outputs": [],
   "source": [
    "for tag in ['MS3','OPT','OPT2']:\n",
    "    for t in ['LY','LYDT','sigmaT','sigmaT_4V','sigmaTvsLYDT','sigmaT4VvsLYDT','XT']:\n",
    "        g['%s_%s'%(t,tag)]=R.TGraphErrors(len(map_prod))\n",
    "        \n",
    "for tag in ['MS3','OPT','OPT2']:\n",
    "    for ia,ar in enumerate(arrays[tag]):\n",
    "        files[ar]=R.TFile(arrays[tag][ar]['file'])\n",
    "        g['sigmaT_%d'%ar]=files[ar].Get('sigmaT_vs_bar')\n",
    "        g['LY_%d'%ar]=files[ar].Get('LY_vs_bar')\n",
    "        g['XT_%d'%ar]=files[ar].Get('XT_vs_bar')\n",
    "        iv=map_prod[arrays[tag][ar]['prod']]\n",
    "#         if (arrays[tag][ar]['prod']==9 and tag=='OPT'):\n",
    "#             tau=g['tau_MS3_PMT'].GetY()[iv]\n",
    "#         else:\n",
    "#             tau=g['tau_%s_PMT'%tag].GetY()[iv]\n",
    "#         print(ar,tau,arrays[tag][ar]['prod'])\n",
    "        pos=iv\n",
    "        if (tag=='OPT'):\n",
    "            pos+=0.1\n",
    "        elif(tag=='OPT2'):\n",
    "            pos+=0.2\n",
    "        g['LY_%s'%tag].SetPoint(iv,pos,g['LY_%d'%ar].GetY()[1]*3950/48.8) #use 4V measure\n",
    "        g['LY_%s'%tag].SetPointError(iv,0,g['LY_%d'%ar].GetEY()[1]*3950/48.8)\n",
    "#         g['LYDT_%s'%tag].SetPoint(iv,pos,g['LY_%d'%ar].GetY()[1]*3950/48.8/tau) #use 4V measure\n",
    "#         g['LYDT_%s'%tag].SetPointError(iv,0,g['LY_%d'%ar].GetEY()[1]*3950/48.8/tau)\n",
    "        g['sigmaT_%s'%tag].SetPoint(iv,pos,g['sigmaT_%d'%ar].GetY()[0]) #use 8V measure\n",
    "        g['sigmaT_%s'%tag].SetPointError(iv,0,g['sigmaT_%d'%ar].GetEY()[0]) #use 8V measure\n",
    "        g['sigmaT_4V_%s'%tag].SetPoint(iv,pos,g['sigmaT_%d'%ar].GetY()[1]) #use 4V measure\n",
    "        g['sigmaT_4V_%s'%tag].SetPointError(iv,0,g['sigmaT_%d'%ar].GetEY()[1]) #use 4V measure\n",
    "#         g['sigmaTvsLYDT_%s'%tag].SetPoint(iv,g['LY_%d'%ar].GetY()[1]*3950/48.8/tau,g['sigmaT_%d'%ar].GetY()[0]) #use 8V measure\n",
    "#         g['sigmaTvsLYDT_%s'%tag].SetPointError(iv,g['LY_%d'%ar].GetEY()[1]*3950/48.8/tau,g['sigmaT_%d'%ar].GetEY()[0])\n",
    "#         g['sigmaT4VvsLYDT_%s'%tag].SetPoint(iv,g['LY_%d'%ar].GetY()[1]*3950/48.8/tau,g['sigmaT_%d'%ar].GetY()[1]) #use 4V measure\n",
    "#         g['sigmaT4VvsLYDT_%s'%tag].SetPointError(iv,g['LY_%d'%ar].GetEY()[1]*3950/48.8/tau,g['sigmaT_%d'%ar].GetEY()[1])\n",
    "        g['XT_%s'%tag].SetPoint(iv,pos,g['XT_%d'%ar].GetY()[0]) #use 8V measure\n",
    "        g['XT_%s'%tag].SetPointError(iv,0,g['XT_%d'%ar].GetEY()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "60c00a41",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for tag in ['OPT2']:\n",
    "#     for ia,ar in enumerate(arrays[tag]):\n",
    "#         files[ar]=R.TFile(arrays[tag][ar]['file'])\n",
    "#         g['sigmaT_%d'%ar]=files[ar].Get('sigmaT_vs_bar')\n",
    "#         g['LY_%d'%ar]=files[ar].Get('LY_vs_bar')\n",
    "#         g['XT_%d'%ar]=files[ar].Get('XT_vs_bar')\n",
    "#         iv=map_prod[arrays[tag][ar]['prod']]\n",
    "#         pos=iv\n",
    "#         if (tag=='OPT2'):\n",
    "#             pos+=0.2\n",
    "#         g['LY_%s'%tag].SetPoint(iv,pos,g['LY_%d'%ar].GetY()[1]*3950/48.8) #use 4V measure\n",
    "#         g['LY_%s'%tag].SetPointError(iv,0,g['LY_%d'%ar].GetEY()[1]*3950/48.8)\n",
    "# #         g['LYDT_%s'%tag].SetPoint(iv,pos,g['LY_%d'%ar].GetY()[1]*3950/48.8/tau) #use 4V measure\n",
    "# #         g['LYDT_%s'%tag].SetPointError(iv,0,g['LY_%d'%ar].GetEY()[1]*3950/48.8/tau)\n",
    "#         g['sigmaT_%s'%tag].SetPoint(iv,pos,g['sigmaT_%d'%ar].GetY()[0]) #use 8V measure\n",
    "#         g['sigmaT_%s'%tag].SetPointError(iv,0,g['sigmaT_%d'%ar].GetEY()[0]) #use 8V measure\n",
    "#         g['sigmaT_4V_%s'%tag].SetPoint(iv,pos,g['sigmaT_%d'%ar].GetY()[1]) #use 4V measure\n",
    "#         g['sigmaT_4V_%s'%tag].SetPointError(iv,0,g['sigmaT_%d'%ar].GetEY()[1]) #use 4V measure\n",
    "# #         g['sigmaTvsLYDT_%s'%tag].SetPoint(iv,g['LY_%d'%ar].GetY()[1]*3950/48.8/tau,g['sigmaT_%d'%ar].GetY()[0]) #use 8V measure\n",
    "# #         g['sigmaTvsLYDT_%s'%tag].SetPointError(iv,g['LY_%d'%ar].GetEY()[1]*3950/48.8/tau,g['sigmaT_%d'%ar].GetEY()[0])\n",
    "# #         g['sigmaT4VvsLYDT_%s'%tag].SetPoint(iv,g['LY_%d'%ar].GetY()[1]*3950/48.8/tau,g['sigmaT_%d'%ar].GetY()[1]) #use 4V measure\n",
    "# #         g['sigmaT4VvsLYDT_%s'%tag].SetPointError(iv,g['LY_%d'%ar].GetEY()[1]*3950/48.8/tau,g['sigmaT_%d'%ar].GetEY()[1])\n",
    "#         g['XT_%s'%tag].SetPoint(iv,pos,g['XT_%d'%ar].GetY()[0]) #use 8V measure\n",
    "#         g['XT_%s'%tag].SetPointError(iv,0,g['XT_%d'%ar].GetEY()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f47c5a7f-0beb-4c93-b8d1-f630f1eba4aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "g['relGain_LY_OPT']=R.TGraphErrors(6)\n",
    "for v,iv in map_prod.items():\n",
    "    ly_arr_opt=g['LY_OPT'].GetY()[iv]\n",
    "    ly_arr_ms3=g['LY_MS3'].GetY()[iv]\n",
    "    ly_arr_opt2=g['LY_OPT2'].GetY()[iv]\n",
    "#     if (v!=9):\n",
    "#         ly_pmt_opt=g['lyNorm_OPT_PMT'].GetY()[iv]\n",
    "#     else:\n",
    "#         ly_pmt_opt=g['lyNorm_MS3_PMT'].GetY()[iv]\n",
    "#     ly_pmt_ms3=g['lyNorm_MS3_PMT'].GetY()[iv]\n",
    "#     if (ly_arr_ms3>0 and ly_arr_opt>0):\n",
    "#         print(v,ly_arr_opt,ly_pmt_opt,ly_arr_ms3,ly_pmt_ms3)\n",
    "#         g['relGain_LY_OPT'].SetPoint(iv,iv,(ly_arr_opt/ly_pmt_opt)/(ly_arr_ms3/ly_pmt_ms3))\n",
    "#         g['relGain_LY_OPT'].SetPointError(iv,0,0.07)\n",
    "# g['relGain_LY_OPT'].Print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "765bd4f8-5f4e-4829-9766-50e945fcac4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=0, y[0]=3949.34, ex[0]=0, ey[0]=104.712\n",
      "x[1]=1, y[1]=3825.94, ex[1]=0, ey[1]=137.756\n",
      "x[2]=2, y[2]=3744.46, ex[2]=0, ey[2]=142.393\n",
      "x[3]=3, y[3]=4615.7, ex[3]=0, ey[3]=77.2372\n",
      "x[4]=4, y[4]=3924.52, ex[4]=0, ey[4]=157.001\n",
      "x[5]=5, y[5]=4269.84, ex[5]=0, ey[5]=163.209\n",
      "x[0]=0.26, y[0]=0.997918, ex[0]=0, ey[0]=0.0292477\n",
      "x[1]=1.26, y[1]=1.1318, ex[1]=0, ey[1]=0.0375413\n",
      "x[2]=2.26, y[2]=0.961765, ex[2]=0, ey[2]=0.0326065\n",
      "x[3]=3.26, y[3]=0.979477, ex[3]=0, ey[3]=0.0338506\n",
      "x[4]=4.26, y[4]=1.07134, ex[4]=0, ey[4]=0.0401968\n",
      "x[5]=5.26, y[5]=0.903504, ex[5]=0, ey[5]=0.0794499\n",
      "x[6]=6.26, y[6]=0.870008, ex[6]=0, ey[6]=0.0354183\n",
      "x[7]=7.26, y[7]=1.02556, ex[7]=0, ey[7]=0.0325095\n",
      "x[8]=8.26, y[8]=1.05497, ex[8]=0, ey[8]=0.0400633\n",
      "x[0]=1, y[0]=1, ex[0]=0.03, ey[0]=0.03\n",
      "x[1]=0.968754, y[1]=0.963771, ex[1]=0.03, ey[1]=0.03\n",
      "x[2]=0.948124, y[2]=0.98152, ex[2]=0.03, ey[2]=0.03\n",
      "x[3]=1.16873, y[3]=1.07357, ex[3]=0.03, ey[3]=0.03\n",
      "x[4]=0.993715, y[4]=1.0277, ex[4]=0.03, ey[4]=0.03\n",
      "x[5]=1.08115, y[5]=1.05717, ex[5]=0.03, ey[5]=0.03\n",
      "x[0]=0, y[0]=1.52215, ex[0]=0.03, ey[0]=0.03\n",
      "x[1]=1, y[1]=1.53002, ex[1]=0.03, ey[1]=0.03\n",
      "x[2]=2, y[2]=1.47036, ex[2]=0.03, ey[2]=0.03\n",
      "x[3]=3, y[3]=1.65706, ex[3]=0.03, ey[3]=0.03\n",
      "x[4]=4, y[4]=1.47181, ex[4]=0.03, ey[4]=0.03\n",
      "x[5]=5, y[5]=1.55668, ex[5]=0.03, ey[5]=0.03\n"
     ]
    }
   ],
   "source": [
    "map_prod_ms3_dry={\n",
    "    1:0,\n",
    "    2:1,\n",
    "    3:2,\n",
    "    4:3,\n",
    "    5:4,\n",
    "    6:5,\n",
    "    8:6,\n",
    "    9:7,\n",
    "    10:8\n",
    "}\n",
    "g['LY_MS3'].Print()\n",
    "g['LY_MS3_DRY'].Print()\n",
    "g['rel_MS3_DRY_GREASE']=R.TGraphErrors(6)\n",
    "for v,iv in map_prod.items():\n",
    "    ly_arr_grease_ms3=g['LY_MS3'].GetY()[iv]/g['LY_MS3'].GetY()[0]\n",
    "    ly_arr_dry_ms3=g['LY_MS3_DRY'].GetY()[map_prod_ms3_dry[v]]/g['LY_MS3_DRY'].GetY()[0]\n",
    "    if (ly_arr_grease_ms3>0 and ly_arr_dry_ms3>0):\n",
    "        g['rel_MS3_DRY_GREASE'].SetPoint(iv,ly_arr_grease_ms3,ly_arr_dry_ms3)\n",
    "        g['rel_MS3_DRY_GREASE'].SetPointError(iv,0.03,0.03)\n",
    "g['rel_MS3_DRY_GREASE'].Print()\n",
    "\n",
    "g['LY_DRY_GREASE_MS3']=R.TGraphErrors(6)\n",
    "for v,iv in map_prod.items():\n",
    "    ly_arr_grease_ms3=g['LY_MS3'].GetY()[iv]/g['LY_MS3_DRY'].GetY()[map_prod_ms3_dry[v]]/2600.\n",
    "    if (ly_arr_grease_ms3>0):\n",
    "        g['LY_DRY_GREASE_MS3'].SetPoint(iv,iv,ly_arr_grease_ms3)\n",
    "        g['LY_DRY_GREASE_MS3'].SetPointError(iv,0.03,0.03)\n",
    "g['LY_DRY_GREASE_MS3'].Print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "bdd74c8b-2969-43dc-a154-0e39414dbe5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=1, y[0]=1, ex[0]=0.03, ey[0]=0.03\n",
      "x[1]=1.02024, y[1]=1.06616, ex[1]=0.03, ey[1]=0.03\n",
      "x[2]=0.917177, y[2]=0.96228, ex[2]=0.03, ey[2]=0.03\n",
      "x[3]=1.05731, y[3]=1.04207, ex[3]=0.03, ey[3]=0.03\n",
      "x[4]=0.980939, y[4]=1.04321, ex[4]=0.03, ey[4]=0.03\n",
      "x[5]=1.08976, y[5]=1.10857, ex[5]=0.03, ey[5]=0.03\n",
      "x[0]=0, y[0]=1.67511, ex[0]=0.03, ey[0]=0.03\n",
      "x[1]=1, y[1]=1.60296, ex[1]=0.03, ey[1]=0.03\n",
      "x[2]=2, y[2]=1.59659, ex[2]=0.03, ey[2]=0.03\n",
      "x[3]=3, y[3]=1.69961, ex[3]=0.03, ey[3]=0.03\n",
      "x[4]=4, y[4]=1.57512, ex[4]=0.03, ey[4]=0.03\n",
      "x[5]=5, y[5]=1.64667, ex[5]=0.03, ey[5]=0.03\n"
     ]
    }
   ],
   "source": [
    "g['rel_OPT_DRY_GREASE']=R.TGraphErrors(6)\n",
    "\n",
    "for v,iv in map_prod.items():\n",
    "    ly_arr_grease=g['LY_OPT'].GetY()[iv]/g['LY_OPT'].GetY()[0]\n",
    "    ly_arr_dry=g['LY_OPT_DRY'].GetY()[iv]/g['LY_OPT_DRY'].GetY()[0]\n",
    "    if (ly_arr_grease>0 and ly_arr_dry>0):\n",
    "        g['rel_OPT_DRY_GREASE'].SetPoint(iv,ly_arr_grease,ly_arr_dry)\n",
    "        g['rel_OPT_DRY_GREASE'].SetPointError(iv,0.03,0.03)\n",
    "g['rel_OPT_DRY_GREASE'].Print()\n",
    "\n",
    "\n",
    "g['LY_DRY_GREASE_OPT']=R.TGraphErrors(6)\n",
    "for v,iv in map_prod.items():\n",
    "    ly_arr_grease=g['LY_OPT'].GetY()[iv]/g['LY_OPT_DRY'].GetY()[iv]\n",
    "    if (ly_arr_grease>0):\n",
    "        g['LY_DRY_GREASE_OPT'].SetPoint(iv,iv,ly_arr_grease)\n",
    "        g['LY_DRY_GREASE_OPT'].SetPointError(iv,0.03,0.03)\n",
    "g['LY_DRY_GREASE_OPT'].Print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "7e7bba8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=0.2, y[0]=4354.82, ex[0]=0, ey[0]=253.865\n",
      "x[1]=0, y[1]=0, ex[1]=0, ey[1]=0\n",
      "x[2]=0, y[2]=0, ex[2]=0, ey[2]=0\n",
      "x[3]=3.2, y[3]=4265.54, ex[3]=0, ey[3]=160.528\n",
      "x[4]=0, y[4]=0, ex[4]=0, ey[4]=0\n",
      "x[5]=5.2, y[5]=4740.44, ex[5]=0, ey[5]=161.569\n",
      "x[0]=0.05, y[0]=75.1409, ex[0]=0, ey[0]=9.23377\n",
      "x[1]=1.05, y[1]=0, ex[1]=0, ey[1]=0\n",
      "x[2]=2.05, y[2]=0, ex[2]=0, ey[2]=0\n",
      "x[3]=3.05, y[3]=0, ex[3]=0, ey[3]=0\n",
      "x[4]=4.05, y[4]=72.7343, ex[4]=0, ey[4]=6.86714\n",
      "x[5]=5.05, y[5]=0, ex[5]=0, ey[5]=0\n",
      "x[6]=6.05, y[6]=0, ex[6]=0, ey[6]=0\n",
      "x[7]=7.05, y[7]=0, ex[7]=0, ey[7]=0\n",
      "x[8]=8.05, y[8]=83.7046, ex[8]=0, ey[8]=7.55401\n",
      "x[0]=1, y[0]=1, ex[0]=0.03, ey[0]=0.03\n",
      "x[1]=0, y[1]=0, ex[1]=0, ey[1]=0\n",
      "x[2]=0, y[2]=0, ex[2]=0, ey[2]=0\n",
      "x[3]=0.979498, y[3]=0.967973, ex[3]=0.03, ey[3]=0.03\n",
      "x[4]=0, y[4]=0, ex[4]=0, ey[4]=0\n",
      "x[5]=1.08855, y[5]=1.11397, ex[5]=0.03, ey[5]=0.03\n",
      "x[0]=0, y[0]=0.0222906, ex[0]=0.03, ey[0]=0.03\n",
      "x[1]=0, y[1]=0, ex[1]=0, ey[1]=0\n",
      "x[2]=0, y[2]=0, ex[2]=0, ey[2]=0\n",
      "x[3]=3, y[3]=0.022556, ex[3]=0.03, ey[3]=0.03\n",
      "x[4]=0, y[4]=0, ex[4]=0, ey[4]=0\n",
      "x[5]=5, y[5]=0.0217819, ex[5]=0.03, ey[5]=0.03\n"
     ]
    }
   ],
   "source": [
    "map_prod_opt2_dry={\n",
    "    1:0,\n",
    "    2:1,\n",
    "    3:2,\n",
    "    4:3,\n",
    "    5:4,\n",
    "    6:5,\n",
    "    8:6,\n",
    "    9:7,\n",
    "    10:8\n",
    "}\n",
    "\n",
    "g['LY_OPT2'].Print()\n",
    "g['LY_OPT2_DRY'].Print()\n",
    "\n",
    "g['rel_OPT2_DRY_GREASE']=R.TGraphErrors(6)\n",
    "for v,iv in map_prod.items():\n",
    "    ly_arr_grease_opt2=g['LY_OPT2'].GetY()[iv]/g['LY_OPT2'].GetY()[0]\n",
    "    ly_arr_dry_opt2=g['LY_OPT2_DRY'].GetY()[map_prod_opt2_dry[v]]/g['LY_OPT2_DRY'].GetY()[0]\n",
    "    if (ly_arr_grease_opt2>0 and ly_arr_dry_opt2>0):\n",
    "        g['rel_OPT2_DRY_GREASE'].SetPoint(iv,ly_arr_grease_opt2,ly_arr_dry_opt2)\n",
    "        g['rel_OPT2_DRY_GREASE'].SetPointError(iv,0.03,0.03)\n",
    "g['rel_OPT2_DRY_GREASE'].Print()\n",
    "\n",
    "g['LY_DRY_GREASE_OPT2']=R.TGraphErrors(6)\n",
    "for v,iv in map_prod.items():\n",
    "    if g['LY_OPT2_DRY'].GetY()[map_prod_opt2_dry[v]]>0:\n",
    "        ly_arr_grease_opt2=g['LY_OPT2'].GetY()[iv]/g['LY_OPT2_DRY'].GetY()[map_prod_opt2_dry[v]]/2600.\n",
    "    else:\n",
    "        ly_arr_grease_opt2=0\n",
    "    if (ly_arr_grease_opt2>0):\n",
    "        g['LY_DRY_GREASE_OPT2'].SetPoint(iv,iv,ly_arr_grease_opt2)\n",
    "        g['LY_DRY_GREASE_OPT2'].SetPointError(iv,0.03,0.03)\n",
    "g['LY_DRY_GREASE_OPT2'].Print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d095a870",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=0.2, y[0]=4354.82, ex[0]=0, ey[0]=253.865\n",
      "x[1]=0, y[1]=0, ex[1]=0, ey[1]=0\n",
      "x[2]=0, y[2]=0, ex[2]=0, ey[2]=0\n",
      "x[3]=3.2, y[3]=4265.54, ex[3]=0, ey[3]=160.528\n",
      "x[4]=0, y[4]=0, ex[4]=0, ey[4]=0\n",
      "x[5]=5.2, y[5]=4740.44, ex[5]=0, ey[5]=161.569\n",
      "x[0]=0.05, y[0]=75.1409, ex[0]=0, ey[0]=9.23377\n",
      "x[1]=1.05, y[1]=0, ex[1]=0, ey[1]=0\n",
      "x[2]=2.05, y[2]=0, ex[2]=0, ey[2]=0\n",
      "x[3]=3.05, y[3]=0, ex[3]=0, ey[3]=0\n",
      "x[4]=4.05, y[4]=72.7343, ex[4]=0, ey[4]=6.86714\n",
      "x[5]=5.05, y[5]=0, ex[5]=0, ey[5]=0\n",
      "x[6]=6.05, y[6]=0, ex[6]=0, ey[6]=0\n",
      "x[7]=7.05, y[7]=0, ex[7]=0, ey[7]=0\n",
      "x[8]=8.05, y[8]=83.7046, ex[8]=0, ey[8]=7.55401\n"
     ]
    }
   ],
   "source": [
    "_GREASE'].SetPoint(iv,ly_arr_grease_ms3,ly_arr_dry_ms3)\n",
    "        g['rel_MS3_DRY_GREASE'].SetPointError(iv,0.03,0.03)\n",
    "g['rel_MS3_DRY_GREASE'].Print()\n",
    "\n",
    "g['LY_DRY_GREASE_MS3']=R.TGraphErrors(6)\n",
    "for v,iv in map_prod.items():\n",
    "    ly_arr_grease_ms3=g['LY_MS3'].GetY()[iv]/g['LY_MS3_DRY'].GetY()[map_prod_ms3_dry[v]]/2600.\n",
    "    if (ly_arr_grease_ms3>0):\n",
    "        g['LY_DRY_GREASE_MS3'].SetPoint(iv,iv,ly_arr_grease_ms3)\n",
    "        g['LY_DRY_GREASE_MS3'].SetPointError(iv,0.03,0.03)\n",
    "g['LY_DRY_GREASE_MS3'].Print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b4a1ac93-f0ff-4022-8515-f5637c0b88ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c1\n"
     ]
    }
   ],
   "source": [
    "c1=R.TCanvas('c1','c1',800,600)\n",
    "l=R.TLegend(0.8,0.8,0.9,0.88)\n",
    "l.SetFillColorAlpha(0,0)\n",
    "l.SetBorderSize(0)\n",
    "l.SetTextSize(0.04)\n",
    "\n",
    "tL=R.TLatex()#pm                                                                                                                               \n",
    "tL.SetTextSize(0.04)\n",
    "R.gStyle.SetOptTitle(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f9eeb305-e93f-4107-a86a-30d020a1d15c",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'LY_DRY_GREASE_OPT2'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Input \u001b[0;32mIn [41]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMS3\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOPT\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOPT2\u001b[39m\u001b[38;5;124m'\u001b[39m]:\n\u001b[1;32m      2\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m h \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLY\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLYDT\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msigmaT\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msigmaT_4V\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mXT\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLY_DRY_GREASE\u001b[39m\u001b[38;5;124m'\u001b[39m]:\n\u001b[0;32m----> 3\u001b[0m         \u001b[43mg\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;132;43;01m%s\u001b[39;49;00m\u001b[38;5;124;43m_\u001b[39;49m\u001b[38;5;132;43;01m%s\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;241;43m%\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mh\u001b[49m\u001b[43m,\u001b[49m\u001b[43mk\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mGetHistogram()\u001b[38;5;241m.\u001b[39mSetBins(\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m0.5\u001b[39m,\u001b[38;5;241m6\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m0.5\u001b[39m)\n\u001b[1;32m      4\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m kk,vendor \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m([\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m9\u001b[39m,\u001b[38;5;241m10\u001b[39m]):\n\u001b[1;32m      5\u001b[0m             g[\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m_\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m%\u001b[39m(h,k)]\u001b[38;5;241m.\u001b[39mGetHistogram()\u001b[38;5;241m.\u001b[39mGetXaxis()\u001b[38;5;241m.\u001b[39mSetBinLabel(kk\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprod\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m%\u001b[39mvendor)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'LY_DRY_GREASE_OPT2'"
     ]
    }
   ],
   "source": [
    "for k in ['MS3','OPT','OPT2']:\n",
    "    for h in ['LY','LYDT','sigmaT','sigmaT_4V','XT','LY_DRY_GREASE']:\n",
    "        g['%s_%s'%(h,k)].GetHistogram().SetBins(6,-0.5,6-0.5)\n",
    "        for kk,vendor in enumerate([1,3,4,5,9,10]):\n",
    "            g['%s_%s'%(h,k)].GetHistogram().GetXaxis().SetBinLabel(kk+1,'prod%d'%vendor)\n",
    "\n",
    "for gr in ['relGain_LY_OPT']:\n",
    "    g[gr].GetHistogram().SetBins(6,-0.5,6-0.5)\n",
    "    for kk,vendor in enumerate([1,3,4,5,9,10]):\n",
    "        g[gr].GetHistogram().GetXaxis().SetBinLabel(kk+1,'prod%d'%vendor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f3068f38-b5e3-439b-b65a-e66ec309ca8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=0, y[0]=3949.34, ex[0]=0, ey[0]=104.712\n",
      "x[1]=1, y[1]=3825.94, ex[1]=0, ey[1]=137.756\n",
      "x[2]=2, y[2]=3744.46, ex[2]=0, ey[2]=142.393\n",
      "x[3]=3, y[3]=4615.7, ex[3]=0, ey[3]=77.2372\n",
      "x[4]=4, y[4]=3924.52, ex[4]=0, ey[4]=157.001\n",
      "x[5]=5, y[5]=4269.84, ex[5]=0, ey[5]=163.209\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TCanvas::Print>: png file plots/LYSO_OPT_LY_ARRAY_byProd.png has been created\n",
      "Info in <TCanvas::Print>: pdf file plots/LYSO_OPT_LY_ARRAY_byProd.pdf has been created\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l.Clear()\n",
    "g['LY_MS3'].Print()\n",
    "g['LY_MS3'].Draw(\"AP*\")\n",
    "l.AddEntry(g['LY_MS3'],\"MS3\",\"PL\")\n",
    "g['LY_MS3'].GetHistogram().GetYaxis().SetLimits(3000,6000)\n",
    "g['LY_MS3'].GetHistogram().GetYaxis().SetRangeUser(3000,6000)\n",
    "g['LY_MS3'].GetHistogram().GetYaxis().SetTitle(\"LO (ph/MeV)\")\n",
    "g['LY_OPT'].SetMarkerStyle(24)\n",
    "g['LY_OPT'].SetMarkerSize(1.2)\n",
    "l.AddEntry(g['LY_OPT'],\"OPT\",\"PL\")\n",
    "g['LY_OPT'].Draw(\"PSAME\")\n",
    "l.Draw()\n",
    "tL.DrawLatexNDC(0.11,0.92,'CMS MTD - POST MS3 Optimisation - Array Grease coupling')\n",
    "tL.DrawLatexNDC(0.15,0.83,'<MS3> = %.0f ph/MeV'%R.TMath.Mean(6,g['LY_MS3'].GetY()))\n",
    "tL.DrawLatexNDC(0.15,0.79,'<OPT> = %.0f ph/MeV'%R.TMath.Mean(6,g['LY_OPT'].GetY()))\n",
    "c1.Draw()\n",
    "for ext in ['png','pdf']:\n",
    "    c1.SaveAs('plots/LYSO_OPT_LY_ARRAY_byProd.%s'%ext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b14f5389-a5eb-458e-971f-9024a136e926",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=0, y[0]=1.52215, ex[0]=0.03, ey[0]=0.03\n",
      "x[1]=1, y[1]=1.53002, ex[1]=0.03, ey[1]=0.03\n",
      "x[2]=2, y[2]=1.47036, ex[2]=0.03, ey[2]=0.03\n",
      "x[3]=3, y[3]=1.65706, ex[3]=0.03, ey[3]=0.03\n",
      "x[4]=4, y[4]=1.47181, ex[4]=0.03, ey[4]=0.03\n",
      "x[5]=5, y[5]=1.55668, ex[5]=0.03, ey[5]=0.03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TCanvas::Print>: png file plots/LYSO_OPT_LY_DRY_GREASE_ARRAY_byProd.png has been created\n",
      "Info in <TCanvas::Print>: pdf file plots/LYSO_OPT_LY_DRY_GREASE_ARRAY_byProd.pdf has been created\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['LY_DRY_GREASE_MS3'].Print()\n",
    "g['LY_DRY_GREASE_MS3'].Draw(\"AP*\")\n",
    "g['LY_DRY_GREASE_MS3'].GetHistogram().GetYaxis().SetLimits(1.2,1.9)\n",
    "g['LY_DRY_GREASE_MS3'].GetHistogram().GetYaxis().SetRangeUser(1.2,1.9)\n",
    "g['LY_DRY_GREASE_MS3'].GetHistogram().GetYaxis().SetTitle(\"rel LO GREASE/DRY\")\n",
    "g['LY_DRY_GREASE_OPT'].SetMarkerStyle(24)\n",
    "g['LY_DRY_GREASE_OPT'].SetMarkerSize(1.2)\n",
    "g['LY_DRY_GREASE_OPT'].Draw(\"PSAME\")\n",
    "l.Draw()\n",
    "tL.DrawLatexNDC(0.11,0.92,'CMS MTD - POST MS3 Optimisation - Array Dry/Grease coupling')\n",
    "tL.DrawLatexNDC(0.15,0.83,'<MS3> = %.2f'%R.TMath.Mean(6,g['LY_DRY_GREASE_MS3'].GetY()))\n",
    "tL.DrawLatexNDC(0.15,0.79,'<OPT> = %.2f'%R.TMath.Mean(6,g['LY_DRY_GREASE_OPT'].GetY()))\n",
    "c1.Draw()\n",
    "for ext in ['png','pdf']:\n",
    "    c1.SaveAs('plots/LYSO_OPT_LY_DRY_GREASE_ARRAY_byProd.%s'%ext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0a5bfd58-5f11-42ba-8aff-c460e9dfd7a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=0, y[0]=96.8065, ex[0]=0, ey[0]=3.88181\n",
      "x[1]=1, y[1]=99.3941, ex[1]=0, ey[1]=5.24674\n",
      "x[2]=2, y[2]=99.799, ex[2]=0, ey[2]=4.1517\n",
      "x[3]=3, y[3]=91.1651, ex[3]=0, ey[3]=3.15793\n",
      "x[4]=4, y[4]=101.733, ex[4]=0, ey[4]=9.05018\n",
      "x[5]=5, y[5]=92.061, ex[5]=0, ey[5]=6.67867\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TCanvas::Print>: png file plots/LYSO_OPT_sigmaT_ARRAY_byProd.png has been created\n",
      "Info in <TCanvas::Print>: pdf file plots/LYSO_OPT_sigmaT_ARRAY_byProd.pdf has been created\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['sigmaT_MS3'].Print()\n",
    "g['sigmaT_MS3'].Draw(\"AP*\")\n",
    "g['sigmaT_MS3'].GetHistogram().GetYaxis().SetLimits(70,120)\n",
    "g['sigmaT_MS3'].GetHistogram().GetYaxis().SetRangeUser(70,120)\n",
    "g['sigmaT_MS3'].GetHistogram().GetYaxis().SetTitle(\"#sigma_{t} (ps)\")\n",
    "g['sigmaT_OPT'].SetMarkerStyle(24)\n",
    "g['sigmaT_OPT'].SetMarkerSize(1.2)\n",
    "g['sigmaT_OPT'].Draw(\"PSAME\")\n",
    "l.Draw()\n",
    "tL.DrawLatexNDC(0.11,0.92,'CMS MTD - POST MS3 Optimisation - Array Grease coupling')\n",
    "tL.DrawLatexNDC(0.15,0.83,'<MS3> = %.0f ps'%R.TMath.Mean(6,g['sigmaT_MS3'].GetY()))\n",
    "tL.DrawLatexNDC(0.15,0.79,'<OPT> = %.0f ps'%R.TMath.Mean(6,g['sigmaT_OPT'].GetY()))\n",
    "c1.Draw()\n",
    "for ext in ['png','pdf']:\n",
    "    c1.SaveAs('plots/LYSO_OPT_sigmaT_ARRAY_byProd.%s'%ext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2d4370df-e2a7-481e-a337-643a39eb5afa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=0, y[0]=118.983, ex[0]=0, ey[0]=4.49715\n",
      "x[1]=1, y[1]=124.461, ex[1]=0, ey[1]=7.28023\n",
      "x[2]=2, y[2]=130.78, ex[2]=0, ey[2]=5.41889\n",
      "x[3]=3, y[3]=104.711, ex[3]=0, ey[3]=3.81231\n",
      "x[4]=4, y[4]=128.895, ex[4]=0, ey[4]=10.4804\n",
      "x[5]=5, y[5]=109.975, ex[5]=0, ey[5]=6.55757\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TCanvas::Print>: png file plots/LYSO_OPT_sigmaT4V_ARRAY_byProd.png has been created\n",
      "Info in <TCanvas::Print>: pdf file plots/LYSO_OPT_sigmaT4V_ARRAY_byProd.pdf has been created\n"
     ]
    },
    {
     "data": {
      "image/png": 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yJ6ekriDRyvMuctfkphWiulHt2tr2LgAAI1rbaXf/1kqWapmTpH9wssPG17Z3AQAY0dpOuyvb2pXtXQAARrS20+5RV/9JC9Zk26UAAAAG0y1Cyv1hZNSUeX896058k7W2yAwAmJS1nYOePHmyqk3ucJsamchARqxHD3NBySAqfWEgAADoz95LsnQbh7tOURRma0hPV5WtWtWazKQgf+d5HkVRnufyX/PvKeu0vQAAhPXncfQEjWmaNq2jz+/mwpubm9oMcHNzYz395uZGT5yUJIm7QidrO+0eOKbKmm8z4kYxAAAMpamDqPZcXBTF27dvoyhK01RC0s3NjeSzt2/fmk+RNXVrVlmWb9++NaeBhF+HUGVGKPM+MHubIicl9hq7dAAAtFKbn2rDliyUICUna6VUmqaSq8ynSPYyW6eiKCrLknaTljqEKmmd0uFDN1ZJhp1LqPI33I1dOgAA9pAwZN30VtTe+lbWdE/T1utIcrLGWkm/Ye17wdWt+y/P8yRJkiTRaTeKoiRJZIgVAAAYhtsoJUv0uKs2zOFWkpxqn05LVUvdQpXMSiVX/MkSfeuY4CUDAAC1JPpYWadpNI60XT158qS2c1CvL4PfrafLUzjLt3TIQHWZ81M03Q0QAAB08uRJ25OyOxwqauj7i6JI9yZtt9snT55IuvI3Psn9fDebjbxmp9avNeswT1UURe6UVGVZbrfb/m6iDADAsj158sScCqE9c6iTv+/vzz//lJwkT9Gncj1c3SIj1vU6B5RtnTq0VMn+kBFUemS35F+utwQA4DB//vmnbqPq2lilG5z2XomvlMrz3JxPIYoiaY6qLZI57QJTfLfULVTJv+Y+k50UMYoNAIBD6VzVvsnK6gFs6vtzyXwKeoh604wJerWoeVosWLqFqtodRscfAACdPHE0Lfe3XZndebX9dDKOyhOb9IvIlWdHbtfKdRhTpZSqzarzmvxz4swDuva2Tf5HzVs++ffIIq/ZXORGAVgkq0VKhlUd0FglQ849J2JpDdlut3u/HmXE1WGju/BPnW5qI/vGHVOVpmmn1xlL1+0dmPUjw6pVz6NNowjd/eKuOZGbNkrBag8kq300SZK968zlgASwNk03yLP+aKK/Ks3nyhdgkiTmQvOl5L+1d/Ez13Rf3JzFyl+wJhM/7QbXYWtl5s89GS2KplyDUy5b5SQeKz005Qb/dRnmizStOXqu0pf7umGozTFWuwK5CsAE7Y0g/hVqQ5Ww7i1jvpQORjI5ws3NjTlW3X01d52Db6scTfu0G1znUNVGb6U91sT3rht6zEebQkNtjDBfSmcmc4kVkYfZQEue52mamsXwNM5ZZdaHmV5Hrks1nzL8FgGA394I4l/BDVX6S896HWuJzlXuqWTvagcnqj/XF6riak03vIvjSW+vOw2YWVrrfs9pmuq7MborR1GklNL3HMiyrCgKuW5WP9Hz3GG4d7A2yxYZm2BOhGaV2d2E2mdZzMFnejhC00y25ngFxmwBOMa456CiKPTUVtY9/mpX86zT0pMnT6Z82g1vb+zq2jc0el+SR5vtHZHZ6CJ/6MrUHWT6IWnUMe+66L5g/sBc09xB7Q+DPuimTV0Mq6WqtnjWQl0tegWzTbv2fc3K7DSOzf+yALDXwU0+MzXx025w+6dU2Gw28mt+75pFUcRxPPEb18Rew5en6YI1iRq6MuUPd0yb+VypfOsKQd24opSSXa6fotfULzvwfYeKB57BXsLzCu5CdwObyP0AzCXb7da8r2Xt5a7mOgAAaPtDlZyGN5tNHMdmX4mQmysrpeI43mw2eZ5PfJYLf8YcrBiSpaTSzPsMaJIM9EPyR238MhOJzI0rAdEKWKYsy+StoyhKksRcbbvdyo5unxtkW5ockz/MUKhLLn9Y+dKakLb9W7hjs3SQ0tWim6Z0vJv4QQ4AGEf7Ri3PVWbWvWsmq9P29sStRvc6PqlPcwfJ3+ZI7TZTKkTtJiDQy902oTZ7tv3lh3tfwd+z5g5Cb3qiu2mmpj5TvVA22RwUb/bD6h5VAOhq7O64oTV9Dy9VhxnVsyyT58hJJU1TfXZh0sW9pHEojmPdEKJP1bVtOWYfljmquunF9a6xIk5Zlu6zsiwzV9P9nrqLUD9alqU0fY3e4WXOPVsZY9IDvoXeamvcuq4EaalluDoAoFaHUKXpbh3OLu25WaplEu103wDZLxKMdEOL272oV9PrWJlJHjUjmmcgkX7HWkF6yuI41peiVHW9tFaXtPyxd1o1/wrq4QYOmozBiuOY7j8AgKvDbWoQRPskmiSJeZ/L2gQgY9qiKJKMaz1kDr2vnThAzz7gGfFtXoI7PD0TRORMuHC8vduVZZkMTcuyzFx5s9nUZjsAGJJ5MU3T9Af+C3r8q+19FiyEqoFIQoqiaLvdbrfbJEncGGTJsswcxt60sqyw9zRvXsvmRhM3kLnXvjUVQAe7WkeOVfcnKl0kM/FYl0y2p7dXp0n9X/nbTHh0eQMYkZxK3OU3NzfWV9Pbt2+bXiRNU2mPL4rCs5o4fs6qNSBUDUS3EknDjwzTiaLIk66shXvX0X2y0ePTvzRx6TFJcltNCQpWjIjqJiBN09TfutZfU5aZitwQI1Wq06p6uDR1bww16flgrQa8yMhzehLRg68xBICA5L7LURTpm1JIt0ZZlm/fvnVzVfT4OvHo4XtbfuHLN7y1gpwIzIUt71O3dgEHvU/fdLbXOnythfqKOXcd9+o/zzRO1i5u+kjotzNXaH9FZ39X/7XcrtpHPRcS+mtMb3XXayoBYK/jr6dzb5+stbxljWdlITerqX2LrqLJnHaHcchAdRxPjwT3Z3/zqv6mdZRSTSnBGtZdFIX7OtY8VTLJeNXlis4BBqr71c4E0abvr3ZGdbPRrjZXWTUGAIPRjfG1X/u6O083tHv4fw/jMPvvhWd2Ks3dxO/9dyQ97ifaNxxeuskWdvGmHom/d7t036jEo721YV6AOfrUEgBmzX8OKsvS/0tbBtrWdvBp0jmo26is/3rW1GSIlTlj4sHWdu+//SFDX0HWZmz1xC07VKElK1SNXRwAK1J7Dtrtdh8/fvz48eO3b99OT08vLy//8pe/vHjxwl3Tk5A0K3j5k5PnIULVAfZ3/+nJisx5IDkVAQBwvN1ud3l5eXt7+7e//e0f//jHzc3N3d3du3fvdrvdYS+oHt/lTBSPbbdbSVR0Aoa1P1SZ80lK7esbzNEVAgDAMT5+/Pj999//8ssvb968OT09ff369S+//PLq1avffvvNWvOY5oy3j8nFfe64Uhyp25QKMhdi9HDhvZ4nI/isjEB/lFJ6momxywJg7T5+/Pi3v/3t2bNnesnTp0//4z/+Y7PZ/M///I+55jFfWW54Yt6pPhw7xkhPyT2LTlNzhvFas9gKAMBM6bOMnmuqjTajzrWWY6o8GFN1sAOnVJBJtM07sgUtVY/8M0yMXToAwCromZxOT09///13a3qn33///eTkxJztScjZtnYudU3Ph9zzFqBG5xnVrRm36fjrj3klf9MF/01d7LWTj/vxCQSA4V1eXn769OmXX355+vSpLLm/v//f//3fy8tLd+U0TWVEVNNYKH1pc38Fhk/LSULdORJbPnFS2m/vuJo+LW61e/asOet3wCOhJ7LJtceVVRu1s5lb3yAt54IHgIG5c47//e9///d///f//M///P33329vb3///fe//OUv//Zv//Z///d/tXOUh5pR3YMZ1Q+2f2ut89msT1ez2Lv+XxidbuSit3fvauPWjO62d0NVbW2Yuaqpy3/WByqApapNHn//+9//+7//++TkJIqik5OT//qv/2pKVMK82Uaapjc3N+aPz5ubGyvWRISqobQKVXPPUtr0965198r8gZktzH2hF6Zpqlc2X0RiSm6wXkobfmOlqGZ5rFBlBiYppFsJVg2Y2z78FgGA39400zKsNN1EqzbWEKoGs7KtnfzebYoX1eM2G3d9KxW592Zu89DA3G8Ea6t1Ud2bHEvhdcwyt0VXlCcpmlFSopj/BsyywjJ+WgAY0fExxSRtVNJYFfaVQ5n+aTesw7dW2jzClWQIE9+7e1tZPO001vnebNRpepfRd1/yoClU1W6CuVBvi/lET2eitULtDZXNNWt/C850QCGAKRg75Axt4qfd4A6cUiEy7pWNY8itfPXf8kfTQHWdP/ZecalfqtM1IDJ7fvv1j6RvmNDTlL57r3ksy9K6Mnm73eoakDs5uM/abrfcpgkA4NofquQmyi453+j/9l7SZZEsFcexzNImC/UfTfXpqWfzNG+mga67ZrvdxnHcKV3JtjTpNaW5eTRqkThNurlOBztddWbGlXV0ExfTiAAAXK3mqfK0SNFY1ZU10Vd00P0s3WrXtwxy3679y+r58cuylFacJEkkVXueNWKbpS5YWZZSTrMwbUqlk5O5XyQm6oekOU0y4vHzCwMAlqpVS5WcSKwhOBIF9H97L+nMyQT0cRzrM7e+pvKAZo+WnXpdE4B15+woisqynPjNs80Wps1mYybLrtPf6ZeSOGUmNqkEWULTLACgVqsxVUqpqqrKsozjmNEkh3GzlDmaSj8kfzRVsqfyZcy1Jm9x8Llf0pU5Q4E52Kh25SZ9HzA69GvtW/78qSvLMuuldLriUwDgMPHKjF3fQ+swUF0aMDabDT/Tj+Fp59DLm+7rpPuz3HyTPRZkH0lRp3+7Awn9mt72vZWwt39Qh0urEuRGEAAAmLpd/SetAjRZHUCflfVIcLcCzajkBgJzSd+5Vt8w2xxH3/Sm/Q1Ud5vu3KsaJUTWDlQ/YJC++UR9caLsrOrxRBUc/wAA2yHzMBjzKx729LGMXmBrAvHIma3ePG1Lj541S3jUMKN6+3kp/fNUuX1ne6dl8ne3tZkNq3a6qcqZUb1qMVNXmxnVrUp2t6L2la2FezcKALC2b8ujJv+c3QTTnnP/wGdKK4iYDx12778gocp86/b3JuovVFXNtbF3Bf906p4Cu7O3H7ZRAIBoZaHq8Mk/Z3oNlL86BitG02CdKIo8k2Hmed73VXjSPFbVjaNv0utA9dqnm3uqKAq3DtM0bVP42hnV9RPdger6WfT9AQBc8ZBJYnRxPKftlQE98vdMI2wouio89SBxs01FFUUhI80lHpnTUPnfPWLaTwDoYl6n3eOtbGtXtndRywpVYxcHABZrbafdw7v/AAAAoBGqAAAAAmh17z9gSfSMpmsepgYACK5DZ6ceiSLa3Gp3atbWuQsAwIjWdtpt2/2XZZke2yszWMp90LgYCgAAIGrZ/aeUKsvSvVQqy7Ltdhv8an+5t4nnNWvvf6LvVXLkrVEAAAAOsD9UFUWhE5U1IaTkmM1mE7BxT97Os4IkPCtyyUL5uyxLc2IhAACAAezv/pNWH8kom83GuhmwDjftJ+BuIjfxNYdt1a7jRq4sy8qy1Hc4SdNUctUxhQEAAOhk/wiyOI7TNNXRyhqrLhFH7kay2WzyPD84WsVxrP9uep04juVNdZH0E80NkdXcXLW2EXMAAIxobafdPS1VepSS/Hez2UiDUJ7n0iAURZHcnlbWOaZ9SNqZPDe79byFdfc3nfYAAACG0erqPzPN6DusyW10+yuZRfr4mt7RHWI1QJEAAAC0Y2dUN7vh+lMUxXa7rW3E6to2Fh8qzJYAAICF2hOqzDYq+dttBBqgo22z2SRJUtv+1LVRqjpUkA0BAABL1WqeKj0FVJ7ncgFgmqbRw7V4+u+on343s8NRL5RLBZumyOLSPwAAMLD9oUqClMyYoJSqqkoptd1u5VG5WU1kRJ+eCqrfUZRlac5WRYoCAADj2j+mSu4+a86kUBSFXKYnMylED01Wngv3jiEj4q2eOLkIUUKVe62fzFbaR2EAAABqtRqoLskpjmOzQUi3EsmMnU1jngZgNZLJH9ypBgAmjuuBsDCtxlRFUVRVVRzHEp7MvCIzHUSjdsAppfRgL1lyzBykAIBhSM/D2uaHxIJ1O5TlDsrWwmFmVWhj72B5PiVxk8oAABxjSURBVLoAMDV8My/Y2nbugVvr3vtvFta2dwFg+vhmXrC17dyVbe3K9i4ATB/fzAu2tp177IzqAAAAiAhVAAAAQRCqAAAAAiBUAQAABECoAgAACCBYqJKbA5pLlFITvCVf7DV26QAAwFyFudaxKY5M7ULKtV3bCQDTxzfzgq1t54ZpqZL7K8tNjq07HwMAAKzBuiLk2iIzAEwf38wLtrad262laprDpAAAAEbXLULGcZwkieQquYmyUmpGt/9bW2QGgOnjm3nB1rZzvzvsaXpk+na7jaY3IB0AAGBghwxUl/aqNE1lcHoURTNqrAIAAOhD5+6/KIrSNJW+P3P5LBqr5lJOAFgPvpkXbG07t3NLVZIkVqKShQxgBwAAaxZmnqqyLOkBBAAAa9YtVKVpWpal1ShFnAIAAOjc2SnDqpIkkTmryrKMoijP81lEq7V17gLA9PHNvGBr27mdt7Yois1mYy6ZS6KK1rd3AWD6+GZesLXt3AO3tiiKoijmNfNntL69C+xl3Q2dDwiGxzfzgq1t565sax+fP1yrqg1AW9sXHyaFw2/B1rZzD5xRfb5WtXcBAMBgwkypAAAAsHKEKgAAgAAIVQAAAAEQqgAAAAIgVAEAAARAqAIAAAiAUAUAABAAoQoAACAAQhUAAEAAhCoAAIAACFUAAAABEKoAAAACIFQBAAAEQKgCAAAI4LuxCzC0OI49j1ZVNVhJAADAkqwuVBGbAABAH+j+AwAACIBQBQAAEAChCgAAIABCFQAAQACEKgAAgAAIVQAAAAEQqgAAAAIgVAEAAARAqAIAAAiAUAUAABAAoQoAACAAQhUAAEAAhCoAAIAAvhu7AEOL49jzaFVVg5UEAAAsyepCFbEJAAD0YXWhCstjtT6SmwEAoyBUYfYkRcVxTJwCAIyIgeoAAAABEKoAAAACIFQB6+W/GBYA0AmhClivqqp0riJgAcCRCFXAqulcxTB/ADjSFEOVUqooCnd5lmVKKaVUlmVdHwVgiR9Y/6XJCgAOM7mr0Iui2Gw2eZ4rpayFURQlSRJFUVmW0eMf1kopWSiSJKmNZVx1v2Ds3MNIvdFYhRHx4V2wte3cCbVUFUWRZZmEJ4ssrKqqKIqiKPI8j6JIt0hlWVaWZZqmVVVVVZWmaVmWtaEKgMn8vlvVFx8A9GFCEdLsdLBaquI4TtPU7NeL41g3R7k/ss1HrbeYzvYiLHbuMag9jIjDb8HWtnMnNKO61Lvu6TNJ05TFTF3SLWj+1+wNBAAA6NuEQpWHzk/S+CRNVmaoMv+OnCFWAAAAfZtHqNJ0I1aaphKkuo6dOvjKplU1YAIAgK5mFqr0WPXtdhs9TKPQ9RV6KRkAAFi3CV3915LMRJUkieSqWlz6BwAABjaDUFUUhTsdqNVARYoCAADjmkGoklHnVmwy/+te61eWpXU9IAAAQK9mEKrEdrvVQUrP9qn/GxltV/IHN6sBAABDmsdAdbmNhjl/VZIkOjYppfI832w2+so+a+5QAMDUrG1aSKzBnI5p3VLVFJhkBU+c4jO8YOzcY1B7GAW3nly8tX23rGxrV7Z3V4WdewxqD2MhUS3b2r5b5tH9BwBYEmseZvO/qzoHY2EIVQCAoUlyovsPCzObq/8AAEtidgyRqLAMhCoAwAgIUlgeQhUAAEAAhCoAAIAAVjdQ3brkxEJzNAAAOMzqQhWxCQAA9IHuPwAAgAAIVQAAAAEQqgAAAAIgVAEAAARAqAIAAAiAUAUAABAAoQoAACAAQhUAAEAAhCoAAIAACFUAAAABEKoAAAACIFQBAAAEQKgCAAAIgFAFAAAQwHdjF2BocRx7Hq2qarCSAACAJVldqCI2AQCAPtD9BwAAEAChCgAAIABCFQAAQACrG1MFwKQv3ZA/GHQIAAcjVAGrRooCgFDo/gMAAAiAlirMWxzHtLX0yprajdoGgCa0VGHeqqqyRgUhrKqqJEjpPwAAtQhVmD2dqzjlAwBGRPcfZsxqmjL/S8ACAAyMUIUZk+Qkw6porAIAjIvuP8ybOVCdRAUAGBGhCvNGkAIATMTquv/8F4hxhgYAAIdZXagiNgEAgD7Q/QcAABAAoQoAACCA1XX/TRN3AgEAYO4IVZNgzrc0dlkAAMAh6P4DAAAIgFAFAAAQAKEKAAAgAEIVAABAAIQqAACAAAhVAAAAARCqAAAAAiBUAQAABECoAgAACGB1M6pbN4SxMKE5AAA4zOpCFbEJAAD0ge4/AACAAAhVAAAAARCqAAAAAiBUAQAABLC6gepYIeuSTy5WAAD0gVCF5ZMUFccxcQoA0B+6/wAAAAIgVAEAAAQwxVCllCqKwl2eZZlSSimVZVnXRwEAAHo1uVEmRVFsNps8z5VS5nIZa5wkSRRFZVlGUWSuo5SShSJJktpYNvFRNRMv3sTtrT2q9xjUHvrD0bVga9u5E2qpKooiy7LNZuM+JOEpz/OiKIqikD2k18yyrCzLNE2rqqqqKk3TsixrQxUAAEBPJhQhzeverZaqOI6txqcsy7bbrb6qK3p8nby7vl4+ne11Tbx4E0dLVa+oPfSHo2vB1rZzJ9RSJe1MeZ67DyVJYvUGuitY/zV7AwEAAPo2j3mq3Dan7XZr/teKXNYQKwAAgL5NqKWqpaIopL9P2rS6jp2KD9XHtgAAgMWYWahSSsn4dD3oyt8t6KoO1cPWAFO32+0+fPhwdnYWRdHZ2dn79+93u93YhQKAiZpNqJIGKn2Vnz9LcekfcLzdbvfu3bvb29ubm5vb29s8z+/u7i4uLshVAFBrHqFKJq9KkqSqqtq5PUlRQHC//fbb999/f3V19ebNm9PT09evX19dXb169erXX38du2gAMEWTu9axdvJPd9IEkwxLn++UCrpU0yzeXDClQnBnZ2c3Nzdv3rwxF37+/Fkp9e3bt7FKheXhs7lga9u5M7j6T2cjt41KlsiUofrmNpLGZnSzmqqqyFWYmru7u2/fvp2fn1vLz8/P5aHT09NRCgYAkzWDUKVZ0yhED8lJKZXn+Waz0dfouXe5mTjJVVFzaxwwsJOTk9PT069fv75+/dpc/uXLF3lorIIBwGQtql3EbKmqNcF2IM9kDVMr6sTR/Rfc+/fv7+7urq6unj59Kkvu7+9/+umn58+f//Wvfx23bFgSPpsLtradu7KtnerelYLRWHUMQlVwu93u4uLi1atXFxcX5+fnX758+fTp0x9//HF9ff3y5cuxS4fl4LO5YGvbufO4+m/ZzGNuVQcfJu7FixfX19cnJydKqdPTU6XU8+fPSVQA0GRdEXLikXnixZs4Wqp6Re2hPxxdC7a2nUtLFQAAQABzuvoPALAk+kodRpRiGQhVAIBxkKKwMHT/AQAABLC6lirPvFARP5sAAMChVheqiE0AAKAPdP8BAAAEQKgCAAAIgFAFAAAQAKEKAAAggNUNVMfyMH8gAGAKCFWYPVIUAGAK6P4DAAAIgJYqAMAUWXM10yaN6SNUAQCmSFJUHMfEKcwFoQoAgPmhJW+CCFUAAMwPLXkTxEB1AACAAAhVAAAAAayu+8/qhLbQiAoAAA6zulBFbAIAAH2g+w8AACAAQhUAAEAAhCoAAIAACFUAAAABEKoAAAACIFQBAAAEQKgCAAAIgFCFhdvtdh8+fDg7O4ui6Ozs7P3797vdbuxCAQAWiFCFJdvtdu/evbu9vb25ubm9vc3z/O7u7uLiglwFAAhuXXe3nvjdvCdevDn68OHD7e3t1dXVs2fPZMn9/f2PP/54cnLy888/j1u2eeHgxFg49vwmXj8TL15wK9vaqe5d646E0yzkHJ2dnd3c3Lx588Zc+PnzZ6XUt2/fxirVHE32s4PF49jzm3j9TLx4wdH9NwnVY2MXZyHu7u6+fft2fn5uLT8/P5eHRikVAGCpCFVYrJOTk9PT069fv1rLv3z5Ig+NUSgAwGKtLlTFXmOXDoFdXl5eX1/f39/rJff3958+fbq8vByxVACARVpXZ+faOnex2+0uLi5evXp1cXFxfn7+5cuXT58+/fHHH9fX1y9fvhy7dHPCZwdj4djzm3j9TLx4wa2upQqr8uLFi+vr65OTE6XU6empUur58+ckqk50Iy6tuQDgt64IubbIDBN7H5gjPrl+E6+fiRcvOFqqAAAAAiBUAQAABECoAgAACIBQBQAAEAChCgAAIABCFQAAQACEKgAAgAAIVQAAAAEQqgAAAAIgVAEAAARAqAIAAAiAUAUAABDAd2MXYGhxHHseXdV9HwEAQECrC1XEJgAA0Ae6/wAAAAIgVAEAAARAqAIAAAiAUAUAABAAoQoAACAAQhUAAEAAhCoAAIAAphiqlFJFUXR9NMsypZRSKsuy3ooGAABQb3KTfxZFUZZl10eVUnp5WZZFUXhiGQAAQHATaqkqiiLLss1m0/XRLMvKskzTtKqqqqrSNJVc1W9xAQAADPF0btti3pUvz3OlVMtH5SFzQ+I4TpLEzVVxPKHtxcDY+8Ac8cmtpatl4vUz8eIFN6GWKmlnyvP8gEeTJLH+6+lDBABg1qqq0m0NZqMDxjWhUHUMq1nL+i8AYF52u92HDx/Ozs6iKDo7O3v//v1utxu7UNOic9WqmoImbvahquvYqfhQ/RQfAGDb7Xbv3r27vb29ubm5vb3N8/zu7u7i4oJcJawTE6eq6Zh9qOraKFUdqp/iAwBsv/322/fff391dfXmzZvT09PXr19fXV29evXq119/Hbtok6DPSvrcxKlqImYfqmpx6R8AzNfHjx8vLi6ePXumlzx9+vTi4uLjx48jlmpSzAHgZKnpWEioIkUBwDLc3d19+/bt/PzcWn5+fi4PjVKqqSFITdMSQpV7rV9Zltb1gACAWTg5OTk9Pf369au1/MuXL/LQGIUCWllCqJL70ujBVfIHN6sBgJm6vLy8vr6+v7/XS+7v7z99+nR5eTliqYC9lhCqlFJ5npdlKdc+lGXpzh0KAJiLH3744evXrz/++OPnz5/v7u4+f/78008//fHHHz/88MPYRQN8FjXVqYys8sSptU3tChN7H5iR3W7366+/fvz48e7u7uTk5PLy8ocffnj58uXY5ZqciX+zTbx4wa1sa1e2d2Fi7wNzxCfXb+L1M/HiBbeE7j8AAIDREaoAAAAC+G7sAgDAjFk3BllVTwcAC6EKAA4nKWptA0cA1KL7DwAAIABCFQAAQACr6/6zBkBYaMAHAACHWV2oIjYBGAzD2IFVWV2oAoDBMIwdWBXGVAEAAARAS9U80IkAAMDEEarmgU6EY+hIKn9QhwCAPhCqsHykKADAABhTBQAAEAChCgAAIABCFQAAQACEKgAAgAAIVQAAAAEQqgAAAAIgVAEAAARAqAIAAAhgdZN/Wvd7sTBLJAAAOMzqQhWxCQAA9IHuPwAAgABW11IFAMDiWWNd6KUZBi1VM7Db7T58+HB2dhZF0dnZ2fv373e73diFAgBMV1VVEqT0HxgAoWrqdrvdu3fvbm9vb25ubm9v8zy/u7u7uLggVwEAMCnxqgJsHM9vez98+HB7e3t1dfXs2TNZcn9//+OPP56cnPz888/jlg2A8H+3zPGbZzqoPb+JH3ujF2BgK9vaGe7ds7Ozm5ubN2/emAs/f/6slPr27dtYpQIQGV8pEz+xzRq15zfxY2/0AgyM7r9Ju7u7+/bt2/n5ubX8/PxcHhqlVABEVVV6OLB/DjwAa0ComrSTk5PT09OvX79ay798+SIPjVEoAP+ic9Wqfo4DqEWomrrLy8vr6+v7+3u95P7+/tOnT5eXlyOWCkD8wPovTVbAaq2rs3OOnbu73e7i4uLVq1cXFxfn5+dfvnz59OnTH3/8cX19/fLly7FLB6ydfKv4G6vm+M0zHdSeH2OqJoWWqql78eLF9fX1ycmJUur09FQp9fz5cxIVMAXmCWNVZw4AtdYVIecemedefmCpJt5aMGvUnt/Ej73RCzAwWqoAAAACIFQBAAAEsLobKvsvzFlVKyUAAAhodaGK2AQAAPpA9x8AAEAAhCoAAIAAVtf9BwDAAlj3nWRwyxQQqgAAmB9S1ATR/QcAABAAoQoAerHb7T58+HB2dhZF0dnZ2fv373e73diFAtAjQhUAhLfb7d69e3d7e3tzc3N7e5vn+d3d3cXFBbkKWLB13ZRn7jchmnv5gaVyP5sfPny4vb29urp69uyZLLm/v//xxx9PTk5+/vnnMco4V3zvHWP02hu9AAOjpQoAwvv48ePFxYVOVFEUPX369OLi4uPHjyOWCitB1/NYCFXzEMexXDSr/wAwWXd3d9++fTs/P7eWn5+fy0OjlGp2+N47DF3PIyJUzUP12NjFAeBzcnJyenr69etXa/mXL1/koTEKNT987x3mt99++/7776+urt68eXN6evr69eurq6tXr179+uuvYxdt+dbV2bm2zl0Aw3C/W96/f393d3d1dfX06VNZcn9//9NPPz1//vyvf/3rGGXEWpydnd3c3Lx588Zc+PnzZ6XU8K2kazvtrmxrV7Z3AQzD/W7Z7XYXFxevXr26uLg4Pz//8uXLp0+f/vjjj+vr65cvX45VTize3d3d6enp7e2t1SDatLxvazvtrq77L/Yau3QAFuLFixfX19cnJydKqdPTU6XU8+fPSVToG13P41pXhFxbZAYwDP93C988GNKkup7XdvCvbGtXtncBDINQhemYVNfz2g7+1XX/AQCwYHQ9j2hdEXJtkRnAMGipwjSNfuyNXoCB0VIFAAAQAKEKAAAgAEIVAABAAN+NXQAAmDE9v538sarhIwAsU2ypUkoVReEuz7JMKaWUyrKs66MA0AfuTwdAm1xLVVEUZVm6y5VSenlZlkVRmMHL/ygAAEDfJtRSVRRFlmWbzcZ9KMuysizTNJXfgmmaSnJq8ygAAMAAJjSBhHnrvTzPlVLWQ2ZR4zhOkkSSk/9R6y2ms70AVoJvHoxl9GNv9AIMbEItVdLOlOd57aNJklj/NXsJ/Y8CAAD0bUKhys9suNr7qH9lAACA4GYQqmpHR+nY1HXsVHyoYzcDAAAs2uSu/nN1aqPaa1WduwAAYDAzaKmq5W+g4tI/AAAwsNmEKlIUAACYsnmEKvdqPpmYyvOodT0gAABAr+YRquTOM3r4lPyhb0fjfxQAAGAAMxioHkWRUirP881mo6/CM6ezqn2UWRUAAMCQZjbVqYydagpM/kej9U3tCmAK+ObBWEY/9kYvwMBWtrUr27sApoBvHoxl9GNv9AIMbB5jqgAAACaOUAUAABDAPAaqAwCA9vSVW/LHqvrgRkSoAgBgaUhRo6D7DwAAIABCFQAAQACr6/7T3cy1aC8FAACHWV2oIjYBAIA+0P0HAAAQAKEKAAAgAEIVAABAAIQqAACAAAhVAAAAARCqAAAAAljdlAoAMBjuvwasCqEKAPpCigJWhe4/AACAAAhVAAAAARCqAAAAAiBUAQAABECoAgAACIBQBQAAEMDqplTQ08bU4vpnAABwmNWFKmITAADoA91/AAAAARCqAAAAAiBUAQAABECoAgAACIBQBQAAEAChCgAAIABCFQAAQACEKgAAgAAIVQAAAAEQqgAAAAIgVAEAAARAqAIAAAiAUAUAABDAd2MXYGhxHHserapqsJIAAIAlWV2oIjYBAIA+0P0HAAAQAKEKAAAgAEIVAABAAIQqAACAAAhVAAAAARCqAAAAAiBUAQAABECoAgAACIBQBQAAEAChCgAAIABCFQAAQACEKgAAgAAIVQAAAAF8N3YBhhbHsefRqqoGKwkAAFiS1YUqYhMAAOgD3X9oy9/IBz9q70hU4DGovWNQe2iPUAUAABAAoQoAACAAQhUAAEAAhCoAAIAACFUAAAABEKoAAAACmFOoyrJMKaWUyrKs66MAAAC9imcxGWZRFJvNJoqiJEnKspQ/iqLQKyilZLmwHtXieB7bO03U3jGovSNRgceg9o5B7R1jbbU3j5YqSVRVVRVFUVVVnudlWeoWqSzLyrJM07Sqqqqq0jQty7I2VAEAAPRkHhEyjuM0Tc1+vTiOdXOUTHdrboj5qPU6s9jeaaL2jkHtHYkKPAa1dwxq7xhrq70ZtFRJNlJKmQt1P6D+r+dRAACAvs0gVEmcspqdrMxkRS4AAICBfTd2AdrabrdycV8URWY/YO3YKWvcuolbYx6D2jsGtXckKvAY1N4xqD20NI9Qlef5ZrOR4epCd/B1aqNaVc8uAAAY0jxClVKqqirzcj9/luLSPwAAMLB5hCph9vpZvXukKAAAMK4ZDFSPoiiOY7dpKk1T+cO91k+mrRqmbAAAANFcQpXEJmmpKopCxgyavYGRMbjKHcwOAADQt9nMymVd0Jfnudl2pe9jU/soAABA32YTqqIoKoqiKAo9sULtCtHi5qzSN4qW/0oNjFmg+TCrTjdnUnstWQeeLKEBuL2mW7xTh23w4T3A3pOF1CRHYK/m0f0n5OvJ87la5KfOGoO/2WwYld+Srqg4juXvzWbDF0pL1mGWZdl2ux2pLLOkK3C73fKZ7ar2w0s1+vlPFlKTevwMejKnUDVTclhnWaaPb/lDjm9Z4rYBFEVhPiWKIqXU2j4MQapOPyXLsjRN1/O9HOrAk4UrTFRhKzB70GuZpyNI7elxtFmWyWyF/Rd8TP2dLLIsk/vhFkWRJMl6jsPhEar6VRTFZrOR41s3k0h7m/6CcH9AZFkmj242Gz2SrCiKGfXVHi9U1ckkZ6NswogCHnjy3zzPB96EcYWtQHniqgJ9qNrT93VdXi+EpdeTRWF0Ba7qUBxBhT7JqUj/V/5OkiRJElmSJEmapvJ3mqay3HpKnudN/12wsFWnM0H/BZ+EgLWXJIn8sZ7aq8JVoLxOmqYyyYt++rKFrT292rKPwF5PFtaX4UqOw1HQUjW04vFo+rIszR8QZVlavyH0DzUcU3VKKfkqWfzv3SaH1V72eL6SNTusAvXdILIsq6qq6Z6ki3dw7aVpGsfx2kY+iIAnC84jgyFUjWzvsb7ab+G9WladHo4gjd7Up2hfe2VZ6rOaHjUMPrnHaF97EkYr4zZlq3XkIWeN00JPCFVD0IMNI+dHv3nFtYyklhU47sXxVVcYc5itrVaD1J5u1o6iqKqqVbVaHV+BWZbpVhYOP6399545eGgNzS09nSzMkezb7ZaE2qMx+hxXRA/lka8D6RE3u8arx8MJZYkefiH/rnlMVZCqM1ej9vQ6nQ68apVjqoJUoP4vh9+RtTf8hgyp75OFDM+KVjOwbywLP0xHp4cE+r9MZUimu7CnUs1C2KqrXW3BOPCOFLwCV1WrfHgPMMBndg3VOLrvIgzC32lS++iq+lk8glTdaiuTA+9IoSpwnbXKh/cA/X1m11aTo2BMVe84jg9G1R2D2jsSFXgMau8AVNoCzOnefwAAAJNFSxUAAEAAhCoAAIAACFUAAAABEKoAAAACIFQBAAAEQKgCAAAIgFAFAAAQAKEKAAAgAEIVAABAAIQqAACAAAhVAAAAARCqAAAAAiBUAQAABECoAgAACIBQBQAAEAChCgAAIABCFQAAQACEKgAAgAAIVQAAAAEQqgAAAAIgVAEAAARAqAIAAAiAUAUAABAAoQoAACAAQhUAAEAAhCoAAIAACFUAAAABEKoAAAACIFQBAAAEQKgCAAAIgFAFAAAQAKEKAAAgAEIVAABAAIQqAACAAAhVAAAAARCqAAAAAiBUAQAABECoAgAACOD/AT1dYbdXFkFSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['sigmaT_4V_MS3'].Print()\n",
    "g['sigmaT_4V_MS3'].Draw(\"AP*\")\n",
    "g['sigmaT_4V_MS3'].GetHistogram().GetYaxis().SetLimits(90,150)\n",
    "g['sigmaT_4V_MS3'].GetHistogram().GetYaxis().SetRangeUser(90,150)\n",
    "g['sigmaT_4V_MS3'].GetHistogram().GetYaxis().SetTitle(\"#sigma_{t} @ 4V (ps)\")\n",
    "g['sigmaT_4V_OPT'].SetMarkerStyle(24)\n",
    "g['sigmaT_4V_OPT'].SetMarkerSize(1.2)\n",
    "g['sigmaT_4V_OPT'].Draw(\"PSAME\")\n",
    "l.Draw()\n",
    "tL.DrawLatexNDC(0.11,0.92,'CMS MTD - POST MS3 Optimisation - Array Grease coupling')\n",
    "tL.DrawLatexNDC(0.15,0.83,'<MS3> = %.0f ps'%R.TMath.Mean(6,g['sigmaT_4V_MS3'].GetY()))\n",
    "tL.DrawLatexNDC(0.15,0.79,'<OPT> = %.0f ps'%R.TMath.Mean(6,g['sigmaT_4V_OPT'].GetY()))\n",
    "c1.Draw()\n",
    "for ext in ['png','pdf']:\n",
    "    c1.SaveAs('plots/LYSO_OPT_sigmaT4V_ARRAY_byProd.%s'%ext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "412cc606-aa23-4fec-9311-40ae1e2f0c21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=0, y[0]=0, ex[0]=0, ey[0]=0\n",
      "x[1]=0, y[1]=0, ex[1]=0, ey[1]=0\n",
      "x[2]=0, y[2]=0, ex[2]=0, ey[2]=0\n",
      "x[3]=0, y[3]=0, ex[3]=0, ey[3]=0\n",
      "x[4]=0, y[4]=0, ex[4]=0, ey[4]=0\n",
      "x[5]=0, y[5]=0, ex[5]=0, ey[5]=0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TCanvas::Print>: png file plots/LYSO_OPT_LYDT_ARRAY_byProd.png has been created\n",
      "Info in <TCanvas::Print>: pdf file plots/LYSO_OPT_LYDT_ARRAY_byProd.pdf has been created\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['LYDT_MS3'].Print()\n",
    "g['LYDT_MS3'].Draw(\"AP*\")\n",
    "g['LYDT_MS3'].GetHistogram().GetYaxis().SetLimits(70,140)\n",
    "g['LYDT_MS3'].GetHistogram().GetYaxis().SetRangeUser(70,140)\n",
    "g['LYDT_MS3'].GetHistogram().GetYaxis().SetTitle(\"LO/#tau (ph/MeV/ns)\")\n",
    "g['LYDT_OPT'].SetMarkerStyle(24)\n",
    "g['LYDT_OPT'].SetMarkerSize(1.2)\n",
    "g['LYDT_OPT'].Draw(\"PSAME\")\n",
    "l.Draw()\n",
    "tL.DrawLatexNDC(0.11,0.92,'CMS MTD - POST MS3 Optimisation - Array Grease Coupling')\n",
    "tL.DrawLatexNDC(0.15,0.83,'<MS3> = %.0f ph/MeV/ns'%R.TMath.Mean(6,g['LYDT_MS3'].GetY()))\n",
    "tL.DrawLatexNDC(0.15,0.79,'<OPT> = %.0f ph/MeV/ms'%R.TMath.Mean(6,g['LYDT_OPT'].GetY()))\n",
    "c1.Draw()\n",
    "for ext in ['png','pdf']:\n",
    "    c1.SaveAs('plots/LYSO_OPT_LYDT_ARRAY_byProd.%s'%ext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "63a0a59f-aa30-4ed3-b4e3-75031d6f0a88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=0, y[0]=0.161331, ex[0]=0, ey[0]=0.0111908\n",
      "x[1]=1, y[1]=0.186814, ex[1]=0, ey[1]=0.0165171\n",
      "x[2]=2, y[2]=0.183841, ex[2]=0, ey[2]=0.0154871\n",
      "x[3]=3, y[3]=0.211497, ex[3]=0, ey[3]=0.0151006\n",
      "x[4]=4, y[4]=0.162088, ex[4]=0, ey[4]=0.0172198\n",
      "x[5]=5, y[5]=0.150696, ex[5]=0, ey[5]=0.0156174\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TCanvas::Print>: png file plots/LYSO_OPT_XT_ARRAY_byProd.png has been created\n",
      "Info in <TCanvas::Print>: pdf file plots/LYSO_OPT_XT_ARRAY_byProd.pdf has been created\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['XT_MS3'].Print()\n",
    "g['XT_MS3'].Draw(\"AP*\")\n",
    "g['XT_MS3'].GetHistogram().GetYaxis().SetLimits(0.000001,0.5)\n",
    "g['XT_MS3'].GetHistogram().GetYaxis().SetRangeUser(0.000001,0.5)\n",
    "g['XT_MS3'].GetHistogram().GetYaxis().SetTitle(\"XT\")\n",
    "g['XT_OPT'].SetMarkerStyle(24)\n",
    "g['XT_OPT'].SetMarkerSize(1.2)\n",
    "g['XT_OPT'].Draw(\"PSAME\")\n",
    "l.Draw()\n",
    "tL.DrawLatexNDC(0.11,0.92,'CMS MTD - POST MS3 Optimisation - Array Grease coupling')\n",
    "\n",
    "c1.Draw()\n",
    "for ext in ['png','pdf']:\n",
    "    c1.SaveAs('plots/LYSO_OPT_XT_ARRAY_byProd.%s'%ext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b57c06a4-c90f-46fa-8955-5a353dee9cc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=0, y[0]=0, ex[0]=0, ey[0]=0\n",
      "x[1]=0, y[1]=0, ex[1]=0, ey[1]=0\n",
      "x[2]=0, y[2]=0, ex[2]=0, ey[2]=0\n",
      "x[3]=0, y[3]=0, ex[3]=0, ey[3]=0\n",
      "x[4]=0, y[4]=0, ex[4]=0, ey[4]=0\n",
      "x[5]=0, y[5]=0, ex[5]=0, ey[5]=0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TCanvas::Print>: png file plots/LYSO_OPT_sigmaTVsLYDT_ARRAY.png has been created\n",
      "Info in <TCanvas::Print>: pdf file plots/LYSO_OPT_sigmaTVsLYDT_ARRAY.pdf has been created\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['sigmaTvsLYDT_MS3'].Print()\n",
    "g['sigmaTvsLYDT_MS3'].Draw(\"AP*\")\n",
    "g['sigmaTvsLYDT_MS3'].GetHistogram().GetYaxis().SetLimits(75,120)\n",
    "g['sigmaTvsLYDT_MS3'].GetHistogram().GetYaxis().SetRangeUser(75,120)\n",
    "g['sigmaTvsLYDT_MS3'].GetHistogram().GetXaxis().SetLimits(80,130)\n",
    "g['sigmaTvsLYDT_MS3'].GetHistogram().GetXaxis().SetRangeUser(80,130)\n",
    "g['sigmaTvsLYDT_MS3'].GetHistogram().GetYaxis().SetTitle(\"#sigma_{t} (ps)\")\n",
    "g['sigmaTvsLYDT_MS3'].GetHistogram().GetXaxis().SetTitle(\"LO/#tau (ph/MeV/ns)\")\n",
    "g['sigmaTvsLYDT_OPT'].SetMarkerStyle(24)\n",
    "g['sigmaTvsLYDT_OPT'].SetMarkerSize(1.2)\n",
    "g['sigmaTvsLYDT_OPT'].Draw(\"PSAME\")\n",
    "f1=R.TF1('ff','[0]+[1]*x',0,200)\n",
    "f1.SetParameter(0,145.5)\n",
    "f1.SetParameter(1,-0.5)\n",
    "f1.Draw('SAME')\n",
    "l.Draw()\n",
    "tL.DrawLatexNDC(0.11,0.92,'CMS MTD - POST MS3 Optimisation - Array Grease coupling')\n",
    "c1.Draw()\n",
    "for ext in ['png','pdf']:\n",
    "    c1.SaveAs('plots/LYSO_OPT_sigmaTVsLYDT_ARRAY.%s'%ext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a61046ac-7782-4c31-aa20-cf35e686309a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=0, y[0]=0, ex[0]=0, ey[0]=0\n",
      "x[1]=0, y[1]=0, ex[1]=0, ey[1]=0\n",
      "x[2]=0, y[2]=0, ex[2]=0, ey[2]=0\n",
      "x[3]=0, y[3]=0, ex[3]=0, ey[3]=0\n",
      "x[4]=0, y[4]=0, ex[4]=0, ey[4]=0\n",
      "x[5]=0, y[5]=0, ex[5]=0, ey[5]=0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TCanvas::Print>: png file plots/LYSO_OPT_sigmaT4VVsLYDT_ARRAY.png has been created\n",
      "Info in <TCanvas::Print>: pdf file plots/LYSO_OPT_sigmaT4VVsLYDT_ARRAY.pdf has been created\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['sigmaT4VvsLYDT_MS3'].Print()\n",
    "g['sigmaT4VvsLYDT_MS3'].Draw(\"AP*\")\n",
    "g['sigmaT4VvsLYDT_MS3'].GetHistogram().GetYaxis().SetLimits(90,160)\n",
    "g['sigmaT4VvsLYDT_MS3'].GetHistogram().GetYaxis().SetRangeUser(90,160)\n",
    "g['sigmaT4VvsLYDT_MS3'].GetHistogram().GetXaxis().SetLimits(80,130)\n",
    "g['sigmaT4VvsLYDT_MS3'].GetHistogram().GetXaxis().SetRangeUser(80,130)\n",
    "g['sigmaT4VvsLYDT_MS3'].GetHistogram().GetYaxis().SetTitle(\"#sigma_{t} @ 4V (ps)\")\n",
    "g['sigmaT4VvsLYDT_MS3'].GetHistogram().GetXaxis().SetTitle(\"LO/#tau (ph/MeV/ns)\")\n",
    "g['sigmaT4VvsLYDT_OPT'].SetMarkerStyle(24)\n",
    "g['sigmaT4VvsLYDT_OPT'].SetMarkerSize(1.2)\n",
    "g['sigmaT4VvsLYDT_OPT'].Draw(\"PSAME\")\n",
    "f2=R.TF1('ff2','[0]+[1]*x',0,200)\n",
    "f2.SetParameter(0,205)\n",
    "f2.SetParameter(1,-0.84)\n",
    "f2.Draw('SAME')\n",
    "l.Draw()\n",
    "tL.DrawLatexNDC(0.11,0.92,'CMS MTD - POST MS3 Optimisation - Array Grease coupling')\n",
    "c1.Draw()\n",
    "for ext in ['png','pdf']:\n",
    "    c1.SaveAs('plots/LYSO_OPT_sigmaT4VVsLYDT_ARRAY.%s'%ext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9462bdb1-59f2-435c-9c72-3fbcd565af3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['relGain_LY_OPT'].Draw(\"AP\")\n",
    "g['relGain_LY_OPT'].GetYaxis().SetTitle('Rel. LY gain OPT/MS3 (package only)')\n",
    "g['relGain_LY_OPT'].GetYaxis().SetLimits(0.85,1.35)\n",
    "g['relGain_LY_OPT'].GetYaxis().SetRangeUser(0.85,1.35)\n",
    "g['relGain_LY_OPT'].SetMarkerStyle(24)\n",
    "g['relGain_LY_OPT'].SetMarkerSize(1.2)\n",
    "tL.DrawLatexNDC(0.11,0.92,'CMS MTD - POST MS3 Optimisation')\n",
    "tL.DrawLatexNDC(0.15,0.83,'<rel LY> = %.2f'%R.TMath.Mean(6,g['relGain_LY_OPT'].GetY()))\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d1588b54-f8c7-4a42-9b79-3ab890455abd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['rel_MS3_DRY_GREASE'].Draw('AP*')\n",
    "g['rel_MS3_DRY_GREASE'].GetYaxis().SetTitle('Rel. LY DRY')\n",
    "g['rel_MS3_DRY_GREASE'].GetYaxis().SetLimits(0.8,1.3)\n",
    "g['rel_MS3_DRY_GREASE'].GetYaxis().SetRangeUser(0.8,1.3)\n",
    "g['rel_MS3_DRY_GREASE'].GetXaxis().SetTitle('Rel. LY GREASE')\n",
    "g['rel_MS3_DRY_GREASE'].GetXaxis().SetLimits(0.8,1.3)\n",
    "g['rel_MS3_DRY_GREASE'].GetXaxis().SetRangeUser(0.8,1.3)\n",
    "g['rel_OPT_DRY_GREASE'].SetMarkerStyle(24)\n",
    "g['rel_OPT_DRY_GREASE'].SetMarkerSize(1.2)\n",
    "g['rel_OPT_DRY_GREASE'].Draw('PSAME')\n",
    "f3=R.TF1('f3','x',0.85,1.2)\n",
    "f3.Draw('SAME')\n",
    "l.Draw()\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "22410f6b-5de4-4ebe-935c-57df83fe5624",
   "metadata": {},
   "outputs": [],
   "source": [
    "fOut=R.TFile('LYSO_ARRAY_Optimisation.root','RECREATE')\n",
    "for hn,h in g.items():\n",
    "    h.Write(hn)\n",
    "fOut.Close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}