THSCAN_analysis-LYSO_OPT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8a171321-cb1b-4dad-b619-965f7f877dd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.24/02\n"
     ]
    }
   ],
   "source": [
    "import ROOT as R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f6888bb6-a5a3-49b5-ba9a-157647433b5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "files={}\n",
    "files['MS3']=R.TFile(\"data/MS3.root\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d2190f00-9430-45a6-92b3-44cf5d73a60b",
   "metadata": {},
   "outputs": [],
   "source": [
    "g={}\n",
    "g['lyDT']=files['MS3'].Get(\"lyDTArray\")\n",
    "g['tau']=files['MS3'].Get(\"tau_pmt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "52ea1c18-6827-45b9-a6e7-a63e1678aac2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#missing Prod5\n",
    "arrays={}\n",
    "arrays['MS3_DRY']={\n",
    "    416:{'file':'data/tree_FirstRun005166_LastRun005193_ARRAY000416_THSCAN_SUMMARY.root','prod':1},\n",
    "    586:{'file':'data/tree_FirstRun005226_LastRun005253_ARRAY000586_THSCAN_SUMMARY.root','prod':3},\n",
    "    708:{'file':'data/tree_FirstRun005256_LastRun005283_ARRAY000708_THSCAN_SUMMARY.root','prod':4},\n",
    "    531:{'file':'data/tree_FirstRun005196_LastRun005223_ARRAY000531_THSCAN_SUMMARY.root','prod':5},\n",
    "    606:{'file':'data/tree_FirstRun005292_LastRun005319_ARRAY000606_THSCAN_SUMMARY.root','prod':9},\n",
    "    481:{'file':'data/tree_FirstRun005070_LastRun005097_ARRAY000481_THSCAN_SUMMARY.root','prod':10}\n",
    "}\n",
    "\n",
    "arrays['OPT_DRY']={\n",
    "    809:{'file':'data/tree_FirstRun005907_LastRun005934_ARRAY000809_THSCAN_SUMMARY.root','prod':1},\n",
    "    805:{'file':'data/tree_FirstRun005817_LastRun005844_ARRAY000805_THSCAN_SUMMARY.root','prod':3},\n",
    "    799:{'file':'data/tree_FirstRun005637_LastRun005664_ARRAY000799_THSCAN_SUMMARY.root','prod':4},\n",
    "    801:{'file':'data/tree_FirstRun005697_LastRun005724_ARRAY000801_THSCAN_SUMMARY.root','prod':5},\n",
    "#    800:{'file':'data/tree_FirstRun005667_LastRun005694_ARRAY000800_THSCAN_SUMMARY.root','prod':5},\n",
    "    803:{'file':'data/tree_FirstRun005757_LastRun005784_ARRAY000803_THSCAN_SUMMARY.root','prod':9},\n",
    "    796:{'file':'data/tree_FirstRun005040_LastRun005067_ARRAY000796_THSCAN_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": 5,
   "id": "17641c81-b160-4621-86af-0489998c413d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sigmaTvsTH=R.TF1(\"sigmaTvsTH\",\"TMath::Sqrt([0]*[0]/TMath::Power(x,[1])+[2]*[2]*TMath::Power(x,[3]))\",0,1000)\n",
    "sigmaTvsTH.SetParameter(0,5.67032e+04)\n",
    "sigmaTvsTH.SetParameter(2,6.21645e+01)\n",
    "sigmaTvsTH.SetParLimits(1,0.,5)\n",
    "sigmaTvsTH.SetParLimits(3,0,5)\n",
    "sigmaTvsTH.SetParameter(1,3.1)\n",
    "sigmaTvsTH.SetParameter(3,0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "62211d81-f364-4d00-97df-0ba7b55e1b68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "416 130.77797346170925 2620.858943799735\n",
      "586 132.34572119194485 2532.1213829811204\n",
      "708 138.02099843621147 2473.123223083949\n",
      "531 125.61110587867933 2862.7417013010313\n",
      "606 130.2758141396899 2667.23556295393\n",
      "481 124.0820169000153 2736.916621199681\n",
      "809 128.6561909778912 2586.2210430065907\n",
      "805 131.24874140253408 2757.3256601905086\n",
      "799 134.05100514352972 2488.6689030857056\n",
      "801 121.66715924027287 2695.0267306062005\n",
      "803 127.16445128059303 2697.9662164230003\n",
      "796 119.29640277520008 2867.018319879154\n",
      " FCN=0.547109 FROM HESSE     STATUS=OK             23 CALLS        1168 TOTAL\n",
      "                     EDM=1.16347e-06    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
      "  EXT PARAMETER                                   STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  p0           4.72167e+03   2.43242e+04   2.88657e-02  -2.56844e-06\n",
      "   2  p1           1.73129e+00   3.36047e+00   1.09104e-06   6.36125e-02\n",
      "   3  p2           2.51102e+01   1.19340e+02   5.52937e-05  -6.52530e-04\n",
      "   4  p3           6.11231e-01   2.74437e+00   5.30210e-07  -6.53541e-02\n",
      " FCN=0.19269 FROM MIGRAD    STATUS=CONVERGED     521 CALLS         522 TOTAL\n",
      "                     EDM=4.6155e-07    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
      "  EXT PARAMETER                                   STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  p0          -4.54745e+03   1.72140e+04   1.31259e-01  -8.88917e-07\n",
      "   2  p1           1.75410e+00   1.82382e+00   5.20838e-06  -2.71638e-02\n",
      "   3  p2           2.60573e+01   8.14308e+01   2.13354e-04   1.70128e-03\n",
      "   4  p3           6.14101e-01   1.03478e+00   1.97031e-06   1.91372e-01\n",
      " FCN=0.163122 FROM MIGRAD    STATUS=CONVERGED     554 CALLS         555 TOTAL\n",
      "                     EDM=1.70286e-07    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
      "  EXT PARAMETER                                   STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  p0           9.99299e+03   4.49440e+04   3.07617e-01   9.60275e-07\n",
      "   2  p1           2.18870e+00   2.05706e+00   5.67088e-06  -5.23237e-02\n",
      "   3  p2           4.65276e+01   8.65219e+01   2.98232e-04   6.19174e-04\n",
      "   4  p3           4.22781e-01   6.51011e-01   1.84391e-06   9.32103e-02\n",
      " FCN=0.221549 FROM HESSE     STATUS=OK             23 CALLS         914 TOTAL\n",
      "                     EDM=1.99557e-06    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
      "  EXT PARAMETER                                   STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  p0           7.24340e+03   1.70076e+04   2.78469e-02  -8.48862e-07\n",
      "   2  p1           1.90262e+00   1.18609e+00   6.77459e-07   3.16439e-02\n",
      "   3  p2           1.62944e+01   4.01054e+01   2.28942e-05  -7.56056e-04\n",
      "   4  p3           7.64130e-01   8.49135e-01   3.05965e-07  -6.19903e-02\n",
      " FCN=0.139791 FROM MIGRAD    STATUS=CONVERGED     706 CALLS         707 TOTAL\n",
      "                     EDM=4.40334e-07    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
      "  EXT PARAMETER                                   STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  p0          -3.10274e+03   1.04716e+04   8.83959e-02   3.05308e-06\n",
      "   2  p1           1.60877e+00   1.69630e+00   5.19370e-06   5.67179e-02\n",
      "   3  p2           3.41076e+01   8.75911e+01   2.34803e-04  -1.69049e-03\n",
      "   4  p3           5.03241e-01   8.50333e-01   1.78585e-06  -2.23993e-01\n",
      " FCN=0.500252 FROM MIGRAD    STATUS=CALL LIMIT   1486 CALLS        1487 TOTAL\n",
      "                     EDM=0.00128373    STRATEGY= 1  ERROR MATRIX UNCERTAINTY   8.6 per cent\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  p0          -1.39032e+03   1.18953e+03   1.80730e+01   6.35981e-04\n",
      "   2  p1           1.16152e+00   4.09495e-01  -3.76759e-03   5.00064e+00\n",
      "   3  p2           8.12493e+00   7.01646e+00  -2.34784e-01  -3.21470e-01\n",
      "   4  p3           1.00126e+00   2.89965e-01   4.84839e-03  -1.41614e+01\n",
      " FCN=0.25774 FROM MIGRAD    STATUS=CONVERGED    1483 CALLS        1484 TOTAL\n",
      "                     EDM=5.76787e-07    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
      "  EXT PARAMETER                                   STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  p0           3.52507e+04   1.83882e+05   1.91105e+00  -1.37458e-07\n",
      "   2  p1           2.91367e+00   3.72046e+00   1.31067e-05   2.71814e-02\n",
      "   3  p2           6.84502e+01   6.03332e+01   4.16330e-04  -1.26137e-03\n",
      "   4  p3           2.51351e-01   3.21674e-01   2.19876e-06  -2.36973e-01\n",
      " FCN=0.216553 FROM MIGRAD    STATUS=CALL LIMIT   1481 CALLS        1482 TOTAL\n",
      "                     EDM=2.9784e-05    STRATEGY= 1  ERROR MATRIX UNCERTAINTY  18.6 per cent\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  p0           2.59413e+03   3.01877e+03  -7.62696e-01   4.48381e-06\n",
      "   2  p1           1.40320e+00   6.09605e-01  -1.70292e-04   1.74600e-02\n",
      "   3  p2           8.77302e+00   1.53798e+01  -2.47758e-02  -2.05389e-02\n",
      "   4  p3           1.00015e+00   6.02687e-01   5.06460e-04  -9.95480e-01\n",
      " FCN=0.2472 FROM MIGRAD    STATUS=CALL LIMIT   1483 CALLS        1484 TOTAL\n",
      "                     EDM=0.00427989    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  p0           8.48810e+03   1.23512e+04   5.36618e+02  -9.08018e-05\n",
      "   2  p1           2.20021e+00   8.07285e-01   1.47290e-02   4.18684e+00\n",
      "   3  p2           6.35106e+01   2.85285e+01   9.84408e-01  -1.20940e-01\n",
      "   4  p3           2.89599e-01   1.66964e-01  -4.74486e-03  -2.23193e+01\n",
      " FCN=1.26207 FROM MIGRAD    STATUS=CONVERGED     562 CALLS         563 TOTAL\n",
      "                     EDM=4.38388e-06    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
      "  EXT PARAMETER                                   STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  p0           1.03551e+04   4.84357e+04   3.95686e-01  -1.10099e-06\n",
      "   2  p1           2.16383e+00   2.08301e+00   6.64499e-06   6.03250e-02\n",
      "   3  p2           3.05929e+01   8.17803e+01   2.58095e-04  -1.46594e-05\n",
      "   4  p3           5.27289e-01   9.01702e-01   2.17025e-06   4.93562e-03\n",
      " FCN=0.302182 FROM HESSE     STATUS=OK             23 CALLS         668 TOTAL\n",
      "                     EDM=3.13609e-07    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
      "  EXT PARAMETER                                   STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  p0           6.65730e+03   2.53360e+04   4.09087e-02  -1.31108e-06\n",
      "   2  p1           1.94558e+00   1.80226e+00   1.09602e-06   5.31678e-02\n",
      "   3  p2           2.62983e+01   6.47854e+01   4.10676e-05  -2.05306e-03\n",
      "   4  p3           6.01497e-01   8.41695e-01   3.77464e-07  -2.24805e-01\n",
      " FCN=0.602765 FROM MIGRAD    STATUS=CONVERGED     661 CALLS         662 TOTAL\n",
      "                     EDM=2.01326e-06    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
      "  EXT PARAMETER                                   STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  p0          -3.62954e+03   1.13202e+04   1.24026e-01   4.43276e-06\n",
      "   2  p1           1.72470e+00   1.57401e+00   6.11108e-06   8.47234e-02\n",
      "   3  p2           2.54440e+01   5.74572e+01   1.97427e-04   9.38252e-05\n",
      "   4  p3           5.97193e-01   7.77662e-01   1.88817e-06   3.39647e-02\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <Fit>: Abnormal termination of minimization.\n",
      "Warning in <Fit>: Abnormal termination of minimization.\n",
      "Warning in <Fit>: Abnormal termination of minimization.\n"
     ]
    }
   ],
   "source": [
    "for tag in ['MS3_DRY','OPT_DRY']:\n",
    "    for t in ['LY','sigmaT','XT','SR']:\n",
    "        g['%s_%s'%(t,tag)]=R.TGraphErrors(len(map_prod))\n",
    "\n",
    "for tag in ['MS3_DRY','OPT_DRY']:\n",
    "    for ia,ar in enumerate(arrays[tag]):\n",
    "        files[ar]=R.TFile(arrays[tag][ar]['file'])\n",
    "        g['SR_%d'%ar]=files[ar].Get('DT_vs_bar')\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 ('OPT' in tag):\n",
    "            pos+=0.1\n",
    "        sigmaTvsTH.SetParameter(0,5.67032e+04)\n",
    "        sigmaTvsTH.SetParameter(2,6.21645e+01)\n",
    "        g['sigmaT_%d'%ar].Fit(sigmaTvsTH,\"BR\")\n",
    "        sr=1./g['SR_%d'%ar].GetFunction('pol2').Derivative(150)*1000\n",
    "        ly=R.TMath.Mean(g['LY_%d'%ar].GetN(),g['LY_%d'%ar].GetY())*2600/75.\n",
    "        xt=R.TMath.Mean(g['XT_%d'%ar].GetN(),g['XT_%d'%ar].GetY())\n",
    "        #sigmaT=sigmaTvsTH.GetMinimum()\n",
    "        sigmaT=R.TMath.MinElement(g['sigmaT_%d'%ar].GetN(),g['sigmaT_%d'%ar].GetY())\n",
    "        print(ar,sigmaT,ly)\n",
    "        g['SR_%s'%tag].SetPoint(iv,pos,sr)\n",
    "        g['SR_%s'%tag].SetPointError(iv,0,sr*0.02)\n",
    "        g['LY_%s'%tag].SetPoint(iv,pos,ly)\n",
    "        g['LY_%s'%tag].SetPointError(iv,0,ly*0.03)\n",
    "        g['sigmaT_%s'%tag].SetPoint(iv,pos,sigmaT)\n",
    "        g['sigmaT_%s'%tag].SetPointError(iv,0,4.)\n",
    "        g['XT_%s'%tag].SetPoint(iv,pos,xt)\n",
    "        g['XT_%s'%tag].SetPointError(iv,0,0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b4a1ac93-f0ff-4022-8515-f5637c0b88ad",
   "metadata": {},
   "outputs": [],
   "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": 8,
   "id": "f3068f38-b5e3-439b-b65a-e66ec309ca8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in ['MS3_DRY','OPT_DRY']:\n",
    "    for h in ['LY','SR','sigmaT','XT']:\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0a5bfd58-5f11-42ba-8aff-c460e9dfd7a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=0, y[0]=2620.86, ex[0]=0, ey[0]=78.6258\n",
      "x[1]=1, y[1]=2532.12, ex[1]=0, ey[1]=75.9636\n",
      "x[2]=2, y[2]=2473.12, ex[2]=0, ey[2]=74.1937\n",
      "x[3]=3, y[3]=2862.74, ex[3]=0, ey[3]=85.8823\n",
      "x[4]=4, y[4]=2667.24, ex[4]=0, ey[4]=80.0171\n",
      "x[5]=5, y[5]=2736.92, ex[5]=0, ey[5]=82.1075\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TCanvas::Print>: png file plots/LYSO_OPT_LY_ARRAY_DRY_byProd.png has been created\n",
      "Info in <TCanvas::Print>: pdf file plots/LYSO_OPT_LY_ARRAY_DRY_byProd.pdf has been created\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxwAAAI8CAIAAAD0vjrdAAAABmJLR0QAAAAAAAD5Q7t/AAAgAElEQVR4nO3dMZLrOnaAYbDrJuNkprwAr2GqvACCN/J4H65649hVHkckE0cv9gT2VtwEF+BX3oLvAjyhs0sH5zYeGgAhSgRBSvi/oKslUCQISuLRAQg2y7IoAAAA7PN2dgUAAABeAUEVAABABgRVAAAAGRBUAQAAZEBQBQAAkAFBFQAAQAYEVQAAABkQVAEAAGRAUAUAAJABQRUAAEAGBFUAAAAZEFQBAABkQFAFAACQAUEVAABABgRVAAAAGRBUAQAAZEBQBQAAkAFBFQAAQAYEVQAAABkQVAEAAGRAUAUAAJABQRUAAEAGBFUAAAAZEFQBAABkQFAFAACQAUEVAABABgRVAAAAGRBUAQAAZPDl7ApckTHGGCP/a6211uEC7sP0AtHS9PqjVUpsMSz1nlkj64kuvKVW263VJ72VuxrKXVgpNQzDlgpsqc+9jb+9VuGGHmj2B95O967frvw6q7rUth6T/pa4vuu3cHbhLlfYCLhhwWdt24at1Pe9u0zf927pNE2JUve1XtHa+j3TNLkLt217szS6F9GNrlUpumsPS9ck3Iq3U2v7vn3h7Q1ys/IbS9dqFa5hmiaveun3w2Ot9DBbt51vBlvbvNVbs9ba1+Ee9FwftJKu38LZhZ+Fpz6COALdf78yxjRNM89zWDSOY9M0ay/00g/jOK4ttlY0jmM6h+HyargxB/OYrusOXb/divvQGOM9Y83z7B2IuxbOLtE4a7VSSnm16rrOO6bjON784XvEjg/D0DRN0zTP9bNbay3VLvBezcg96Ns//gCujO6/X7mnqL7vbdeYjYSGYYh+90XjsJBdT9/3dj1aa3n5XXGVK3oicVfl7kIifdK2rX2V+5Ku65Zbqabt1rbidlnePBDuwnZtbdu6T3pH7YEGWeN20m3p1pymye6C3TW7C3ZJ2VNbt3me072Bd7XSHu57dc96tNYPtPbDSm7rAd6HfeN3CK7Gvs2e63cIjkNQ9YN7+rFnQfUxSEVOYFvinrXTmH3ejSrUxxlavlK3D6lxl4x+Hbvr0Vq7J9rEat29lqSFPFyLJh/gDvqRf2wMEdbQDebcA+EGHPaF4dAlWbMXu6h7GiRqS1Bla+W9l6ZpsrvgLSl1lkjo5vvh3lbyRn7IMKyHB2C5a7Mjuty13bv+6EqiCySW2S6xuWhDZdmoJ0xah4fbq8wwDFKNaCW9b5Votc3KAKC158MaypLRb4O1WuXaaOIQpJeJbiisbVj5xw76vauSxpS/G9sEl3Zy9+Nl2M7y6IAPr7nsOKSwiz0coiTjY9zfzd7Kpw9rdXNfK1t0K+nVZEv9Xe6+bC+6l9ca0V3zlow2SLibiV1LNOxd7//tjb9lL7xahS1smz3xlni4lcL8TbhOb+WJcSThULDE+qNjqqJD+rydio6HCyvjbTGswJbN2QXWPshZuK2U+JS5NXTr4B4R77XRHbQ1X/uWiLaVJzzW3qvcprMLr63cLpBo1WiuMT2ANVxmbSSfVyu3Pom1bRlTFW2iaPOGo2/XlsQT4eD9sPahjQqDKu9ry/0CskXe52f7wMboV7BXlA6AEp/Vh194l7A1vK3LJhKhp0gEYXe16l37tb3xVfAdfe+x3lK3na0UCussbgZVW9j3VXh6S/TQ2W0lrjCIViYRbd9c1VLqug03/ohWda2dvaDK2+ub7Rl953gRW1T6Oo9E0y0rn/21XQ5rtba5tS26+7vcH1Slt3hXUBW6+XMi3CKeDgPVfffmXeVjZtO20nETXYn7uR3Hses6GV3rZsjvrd72F16QcQYAec249h0XNqz75eu2qs40oii6dVlzIlfv9o94tVrrSJU+AtvlmjipPNBKrjB1arsevAj75mchei6Xs4tdVWK0kG2KMNHrfaBULKMmyyyfT3VrB918dKqqWMQW3dPwNJnrHeUO+FPOcUysv21bO3IufN7tQlLr7Rnt/7o5Zi5sui3j1fq+l62Hx9St/NrLba3CNJstckepesskLhPZUnPvuD/WGRemD93Pglf5iw8BxB3yxGbP764Gcc89XvZC/vdyS+ELQ4kuNu/3pd2E/d9+f62tJ7F3OzNV02dri218H27pcLQvcX+MbklC3LVfawvbBl+crORa6ihRq2i3YHoB64FWsg/dl4TVjq458es8rFIiGxHmDNxauUfTfS/Je9ttjbCJopM+eFtf629aa4Hoxzb9Id3yQVg2N/uykuNZ1nvxpmmS5krUPNzB8Gh6ok2X6P4LV7W25JZvjPBJ2ZebjebFfxszVRsX25Kp2tIC6c8jnhGZqr3ca9luDjMchkHa3R12IOZ5vmswuPuz76xRjVrr7rPH1vPYrzS715KUWj5OKl7D5p1Vwc2oJbKSIlGrsK201u5iuSaz8KrnvsdOHwzr/oKXZJ5UyR3VKxdJ2DTe/qPp7fWWLNHNhrrrg+DmYLxn1rJ6a98M3vOSB7V/m6YJh8PbfXEv41Cfu7A93rUU3npC4aq8Rr75PbmWi5UvT28lXiOkO/JuKvOhiFb+9M8jsiCo8kW/W82H9AtlgS2fajlPyHeEXX5tFiuPPa8ffanIEX2Lktuz5OedGx7JP2tnl3SV5HQiDZsesvAw/fmSQ7XtjGtr5YaP4b7IYrbma+fmna10HcMwhD8tJLpyzzoSHOycdCB6eekpbE1kNjLvurA9l9kaY5qm6bou0Vxr4wfu3e5dzWhXLv9I3XZGP2u8PvqHXy62xNyAi6Dqh7DD3pX+AepFOdGvG3tVbfjhvPfjar+Yjv6cu1NARRfQWref3VynRBiW11bRMR/pKkmrhkfNu8L8ZsU2cjMo3jPeYtFaSYu5tbKZGLvMzdPbA610WTbWXMvkJcbN3OXQs+P2D4K39fnD2gJ3sY1mu1OjOWA3Q7bxh1zornq6PwMeDuM22vlTMzxAe9aGGpXqZ7w699vH6+lPjB7wevfdl9uHfXA3mMSmb9ZNnnE3lx5h4C0fFq29MNEgD/BaI2FjQ6VHMOwpurmw+mxJHqCw6bxhGdGa3Kyee9r2itwGDFspOoYjHHBTZkxVOALJa8lofcK9O3pM1dq4nAdsiQjX6hauxHt3JY7L2lChaJusbW7jmKro2txBS9Gd8kT33d2XPWOqwg8sY6qQC5mqH9wUQtd1kjwwxrg/5tZ6lBLpFsvrO7e/h4wzy/ZjPVa5fkLN82w7I6QTQZ5v27bkrzS3oeylkZLnS1fJ7TMyD92ZeCP3vLh2yLz3klurtd++9hI8O2xoY8LDayX3gsqwEezQPfN5eve1DR1q+1C86LCeu7iXjIXJjGIpvfBiRiudLL+LHXW0MRGV3mLYdNvXHK7k3r6/cGyA/pgK1a4wmjb23tU2SWbW7+/krc1+ErOPJXBTd+HnEc+tfBx3ZYmGcn9ShL8zwia1D+3vtpufzLVaJX5XqeAK9oczVYe+Q8LWSEiPWw93MF3/aJrtrr3zFg5TQdGfmBtrldjZjK2UrszaOqdb81TZFz6WqUqcWfsN15mHmSoV+0ja+iQ2l96RXJmqRBY2Wrq28M1MVcirufepv1nzdAzkrXPtM+6+5Gbye8vnIvHdFc1ZJta2ZQeXfJmq7VvE0yFT9cmy8l7v+z49hiBMboeGYVj7ppCvvO31TPwgy6j/PDNhMVrrcJCNrVJ4INYWlpEl2dtnY+OvvZdUcO+a6Lnh5hWR97bS2mrdQ1w4ZWWMWav/8HHTHq9x5PoG+d/+sncTLYnx7O5FANa9H7097EGJHvH9Carw4NpnvGZxt7UlDRMeqQeu2HU3dPOdplfuFOkerGEY1j477voTzZKuZLjFjMImPejaGhTWnHLWvD531Hn2M43NQh+0/ldy14E49Kg9zLuCYa1ieyp/87XunKL644Z9awunS4+Qrr8t3dJLtf19kj4cz2vj4bNvie2ngJ1vDNvD1Tr3Pt+4UbXj3n/uMmsL6I9++S0fkIy8DT1wUHA1BFXA6/OCqlPrgkuQt8Rd8c1OdnzqBd+EXlB19ObMxw2wtXON8GNBJ67my9kVAAAUIqfzwjMFyIUUTE/gktaQ4fPSIN7Ni/CkCKoAoCI2uGnbtsz5271U8LHbJ7wYGTEmeSlvorI+dodHPBGCKuD12aHrfF9D5tooOWZO3n56/W7ip5M5eEtuUWu9LIsdDaYuNhIUD2NMFQAAQAbnTKmgY3drkZuWrP2a2VMKAABwtBMyVXKdhZ2Kxn3SPvQuf9hTCgAAUEDpTJV7DYglV6O4M4979918uBQAAKCM0pmqpmnatvUyVTJjilsTWUxioz2lAAAAZRTNVCXCHW/Cfgm8spQCAAAUUC6osvfijpamLyX1Su96CAAAUECheaqMMeM4Rqd9i4ZZdux53l48e7MOAABQQFUzNxUKqrqukxnnwqK7clT7VXV0AQA4UW25jBJBVXjnSPVxC6q1OWTTCao9pQAAAEcod5sa9/ZP6uOGRzaiIooCAABPrcRA9WEYls+UUjKzlARV4fV6MueC/B8ttVf8pUsBAADKOOc2NR7bPygP5R/bV7inFAAAoIxI95/5oD5ilKPvnq21nqap6zo7os29TjBa6kZRiVIAAIAyfp1RXUaOJ6bN9O7WdwQ3kstbKprmhHsdAgBQp9pOuz/2VuaFattWrsjzFrK5q/BGyM+ltqMLAMCJajvtNsuyNE2zPVSSkOtJL7ir7egCAHCi2k67le1tZUcXAIAT1XbafVOVXSvXJJ1dOwDAK1sqc3Z7l/aj+08ptTag6pXUFjIDAC5l/zno5lVZdnxOdIS0vRxt7d5xeb29vdV12l2Wpe97d7bMtm2naTozsj2Mqu9XAgDgOr7vY6fF7vt+bRl7NneffH9/j8YA7+/v3svf39/d6bXDBe5S22n3R/efMWb5iK7meZZpn7TWTzogHQCAF+bd+c2KnrWNMV+/flVK9X0vQdL7+7vEZ1+/fnVfIkvabNY8z1+/fu26Lnf1X1c01Jqm6SVzV2v7CwBAAXuyPt+dTJWKJZm+f//unru9J8PlZW1t27qJJW/JxLa2qO20G79NjeSolo/oSnJXeYM5AADwAAmGopN1R+9+K0uGI6i89UjKyhtrJf2GiYnB4Urd++/mHOsAAOAUYQ+gPONms2767gy3ktN99OWMBdooElQZY7TWTdN0XScxr/T9la8cAAAISejjxTprFwZK7urt7S06EssuL4PfvZfLS157ZoCMfg2qwliq7/tlWeT582oIAEAV3t5S3UcuCaq8ICna96eUmqZJ/hnH8e3tTaKrdPLJGDOOoxsPbKxY5b4opYZhcA/MU9/dDwCA5/L29vbdmQphO3d8Trrv7/v37xInyUvsSb/v++hL5GpBu8wDdavTm3KOhHTzEVEBAFDM9+/fbY7q3mSVTTjdnBRUaz1N03dnPgWllKSjolVyp11Ym8EBnqaqDj5mVAcAnChxDpJwKp2yGsdxHEebXnp7e5Nxz+H/N1elnPmr3t/fE2HAxrWtvbaq0+6bWglsjTEvOdqfe/8BAE73Flh7Pp27crvzov10Mo4qekLXWruzKrzqeb+kL+FTWmvbTbssS9M0bdu+TENfPGR22zl626Z0qXvLp3T28RnTkxesc+IeWxtfGH15uvSxyjxcVQBH8BI/Mqzq3pxQ3/cy5DzR9ydD18dxvPnZlxFXj2Wk8IM3Gag8ae8GuCyL/NO2bZnZSA8V7u+leD8y5OrLLaVrowi9NUSXPHeu/C31sdet3Kyzu6S3jLeSXO9ne6HNxma0B2XLx3CtdI1tzOje3bs2AEdYm3bc+2eNfMzdG/+pj7ApnBjdexidFd1dMly5O4tVumJravvO+bS30qByepD/3efLVy67i++FF2F4p0bvQlkbMKWvy3BXsrbkWXHVlvrcVedo+yRWsn8X7gqqJLALq+rVJAwit9dn7SV2na/x6wh4XjdDkPQC0aBKhPeWsQ9tYCSTI7y/v7tj1cO1hctwm5qNPu2tzU4tQSC18bRxcRc/uuG53y31imzQcDOMsAfOfca7vWOZHfS4NZ+mya3z2jLuvdO9ta1l8rz0lbuSMJN3r7uCKvcz5R3NdBy5vT7RFbr13L/LAPa4GYKkFwiDKvuN4a3He8bGVZ61vJTr4YjqO0GVIqg6z86gylubdx61sYV7Wn3gtJ1LtD5ejBJdpv/grVCWDKMH26ruG/jmjk8f7EM754grrHC0bnajNhZcO5phUVhJu5WwPu5Pz7BxLv7+B2rwcHSShSSfRCJUsovtCadEbV87kX6HsPvPDbae2sX3wh0T48UBbvdNNFSK7pobGdgl74otjhONdbyYwO7szbXJkvIt4EUV0ZXczDC57eyFOO5LNi4mVCxrGI5Z9A60W/OwZzDcUOJV9P0Bp9sZozydU84vJ/p0oabWum3bruu01nIpwTAMTdPM85weuIOHrV3RJudUOxGr/BPef8B9bdM0wzB4VwjaywC11nLI7Uvskna1WutiU78OwyABn7sL3hVq7mTBwzDIvoQ1NMbIkg9U/uZlreGUd9GJ8m4uJnULj7W3p+lr9KKbjj7psutkXl8AOFYYZ235zf2kovt7iuiQJjdTZf+XIvm//7gqU20YqB7tHgpf4mYv3BFL2wffyL6suWsQj9cgG9+37rs0zFSlu//WqucenbCn1Taau5iXEVRBTitMRynnnlxe/cMcZJhdc98wds1hC0QbDcApzs4clVbbN09qb18mlrKucHTDGCg8/dtZcdXn8GJtkHUijxjt8fEyXvb5sHdJanLvHt2sQCgai3jriQY6snW7lTCkcNfsjXZX24Iq93nvyejhiPYtqvXRbO4abG9gGFRtqY/7pDd+i74/4ArODnJKUxc47ZaUmqeVSQIzko7UpmlsP5GNV6KdMm4n3c1pG4dhkMPpXUOnlJrnOXzVMAzuYnYqedtFaEvnee66TjoWN+7pA4ZhsH1Yfd+HFe77Xtph+QgRbDPKP4lePOnUtgvLTde3121jx/fNqVYTy0j1ZBmpW3pt2pFYzOsSpe8PAA63fHRM3HRK0JfXltY4etOJ3I/XmyP/u4Ovl3uujV/LtUSXia7NC9Hu6sjbLuxBs6L1b52B526LeakstdIppj4nvbYMVI8+Ly+Mrmetn25t19zja1/oZaqiQ9Rd0fH+blru5oEAUMDZmaPSavvy+aI+Dwd+ecvZt6m5mWCw2rad5zkcTu4yxkgGQsZxe0Xu3QxtxsvNWNhbEq3dFko5w8CPYGvY7rsVUrSG3jh3b8e9f+7d0F0vHMcxevjsqiTfZmtorxQJtW2bTm3KeuwKpcKJrQN4Xu4lMm3bpm9R5UrfBm0N/Ve3LduSKK9BnRcyh0mUxCRDkmLxkhPeLANbplRwi6IzGK1NaxR2I6rkzWESCc5Efqt1xsWnF0jslLc5r5GXlVmdEo3mbTqx9WVzpipsPW890WqvLeNV1Z1PK1r/xLEDUFiuDNDayIRwZqnoYqL/mPlzbWpQ76vpgXqGX1mv7de99fpHXvJb+PSj616+FzZ1eDFXeF4Mu6Wiq3JPyeFJOnGdWnQcffrN8NhAda+GnrUa3ozD0gPV19ow5B6msKnDSbASQVXY97ckg6q1cHmtV9FdJmyHaCmAs+yMpbw4qXfuJ2O/Iry4yi7pChf2Fghf9dhEoLV9/9yYUuHFoqvrHN3oOS8dVMkzYUBwc7SN3ehaH1AbmyBg+6F/LKhKvyqMWtZ2am21fexSu40rSWzXe+GWoErCRG/93qrc1ggDSlkmcaCjR8qWrh0CAOXtjae+f2+D2ydb9pvEC2tU7O430YWFJK4eS0150t+0rydy9Z+9lKzve3vxV5ae1MT8jYeWXpA0snd5f8hNzKwto7VeO922zoB3pZQxJlxP+3kwk+2wW5uVNGTfMFFr/fQbR1BF63wzjvQssbhq40qi3aB3bT16AabHXWBt4bUDPX2eQNWy7fYsHwoAN9lBrtFvA5tkujknsNp8aTPu0kTPNy476PXmkgnGGDnG8kUv7wl3hTJgea3UjqcWXhyQLnU1ze39fV6y1/bq/cSJXAatbx8yfzo7yn5Pne9aiX1TSciyp8W2h6fbV7i/NQCcIn0Omuc5/UtbJoV5f39PfPbf3t6UUjZH5T1MLGkZY75+/dp+njDvMW9vby982o1YSzBsnCNxI29bcpy8Lh5vPFD/eXZH76E3rGStNKzGnr1APaL9egCwU7SP7Nu3b//yL//yu9/9Tin1u9/97k9/+tO3b9/WetNUrMPOJV9fdgjU2kvs4PS1Irr/HhC5kihjLPXrZoJVqWDk0BGlYTUeqz9qQ1AF4Ahh2PHt27e///u//+mnn/7rv/7rf//3f3/55Zc//vGPf/jDH6Jx1ZagyuYa3Je8f+YOPw3XQFD1sC/SrDLdke1E6/s+b89CdNiHfaZt2/RMSOEVc15/X6IUAIDL+vOf//w3f/M3P//881/91V8ppX7/+9///PPP//RP//Rv//Zv//qv/+ouuWcyv69fv4ZPts59ZpHFF+VMwJg9lrLsOuU9IeOr3BtoyHB4ObrhjUe8KnmDqNKlwAO8yUIB4CB//vOf//M//1MiKvGb3/zmH/7hH7qu84KqPWfnMHhqVyYLxR5f3AfjOLrTs3qWHGPN3Fu82Se11pJeslu3pXsC8yh3kvG7ZNl9PIvEnOYAsJ8MEhd/+7d/m17me3IOT498d3l9OGSkyviiyt7CYlkWuXbJvS+H5Jb6vrd3XBnH0V5vlb0CeVcIAMC9bJz013/91+/v77///e/d0v/+7//uuu4vf/mL9yqbgEgESVvuy46DfFEHZIPSpIdR4iqJotyISik1DEP6fnPpCr9MgsFeOa/WL55f21lv4de4qdMRQfZO7gF67IXRl6dLH6vMw1UFcKiffvrp3//933/++eff/OY38sz//d///cd//MdPP/0ULtz3/devXxNBlTt7EU6wfFwud9xVTnJFofekvXeHTKDgbd2dGUEFV/PJ20X+T5d61p6/mrVPS3gxZuLIus2y8Z1QXnRqTW+ZcKKUcIEw0f3Ahh5w70WCtm7pPXrs0IQz8kfXuXFtAI4QXh/37du3P/zhD3/84x9/+eWXv/zlL7/88ss//uM//t3f/d3//M//RK+nyzWjegJX/z3s1ztg2OOUaxoFy5t3SniBUaI0DJLc00a61PMURzf9CyOcmSJt42KntMxa7OgGKGvL2AXW5qbrY/eNTmzoMXcFVVJV+T/R+DeDyIS1l6TjLQDFRCOPb9++/elPf/rtb3+rlPrtb3/7z//8z2sRlXBvtiF35XO/5aL3/rsrEiKoetjqj/680ZV34vFm7JQtehN42m9/Ocd4UZQ3U+haaViNXHt0kPDGi8KNtNy9c2MIu7C7kv7j7rxrq7LK76xXebfaa8u4XyVrLXBzJdENPeauoMpmZ5cgqErHkdvrE13hvfUEcJyb0czGYCX6WzEaBknRXZEQQdXDUjOqR+9N+/iWgmPvloZzTXmVWTsD3Sz16rB/Rw7lnv69IreJwuWj/adhS6aLSoq+u7xzvz2yXtpJyMNwX7xXRVdyM8jwYk35OIQLhxV26+Zx6+B9HPrk7Z/DuslWwvqsfWbXVgWgsP1hiktyVJKsyrvmXGr72rm9tza62v8b92ZS5LhScfGjezODEoZQa0FVNGHjbeUiQVU0SSMxgQ1ZEuuJ7ou7ki0bCtlNpwdjbVzM1io8cDYxHO5ReASnlRsqh/sefRV9f8Dpzg5ySkt/gb+eyvb2ekd3ckbxu2fo6MKJzq8smaojRtStiUbYXuLHjQ/6vm/bNqxhmHMKQ7GbGwqlR7bdu9jyue9v+ZySdBd282rherZsKHwyGlYCOMXZQU5p6nqn3UPdvpAq+sX9pK6zF147y5P24drJL4yH3HOzu/Lo82srsbzBj3fty5q7orS1Bkm/G6Pv2HQAcfNd7a4z7LML42AVdFmqIKcVpqNkGbcmbgDkVTIMBKOHMkzC3dxZAMWcHeSUVts3T/yyo+jZ8aQa5nSFoxv2E4Unv5tBldoQdqydR7cksax2w1wb0fGS7hq2NEs0FvHWEw10oj1iaj3btxb0eMKkoFclb7GbA7bUysCp5XNnpe0NDIMq7+HNJ73I+zU+wsCzOzvIKU1d4LRbUuQX/1lVKeDEvdsSr9w82ScyVVHR9WwZU5WI/LYs7O1mumW8NXiBSLh1L4zwlknHTO6G0im0ta5Yb+vR+Cl8cnImUwjX0zrDquxGvaDKfej+1PHqE66cvj/gUs4OckpTLx1UhPyg6rV/zq6c939VYNOJ3M/aiTxcIAyqpKPNSpxBtw9Un2KzM2QX9qBZ0ePSOkOmovuyNkg/saFErdKDsTYGVd6AqmUl7rHPyyAwd5m1hFx0j9xAqg1GmAFAMbV9+Xy6obLWOnFD5dewnH3vv7UbzqiPeyAqpewNfDz21j1h6TAM2W8/IitM3zJoJ3t/67Zt99xfyN33aDs8vCHv3ji2Ke5q7XEcowO/7KrcW2Gq5O2c27aNvjfc+sjdM+UfqXBi6wCAXN7cB/Z+xqdU5bXZs9o4jk3TRM+absuH5+ybcUNGxphhGJqm6bruZhghYceaxNvJrnAt0ImGAjcjvHBVNzd019oes6UdJBKK7rVdZp5nr4UTG01E4QCA/LZnbs5OqmVw+l7IvABuq3q9gd7QmT6YAVytjDraPmIm3f1312iqtZd4Oxh91dogIbF2JZ3XARpe5+itduOGQtFB8eEzW7r/wr6/ZX1kWHRH1rYVnXYhelCi+wgAR6vt++fHONktzq5qBtc5uolzXrqnZm3cdJagyt10IuBI74tn7Z2TflX0AsBoiyWaa8uGbg5f27j1RFAV/fgkKhkGlLJMYlhV9EjZ0tf48AJ4Ruoyp90yKtvbix1dO47Ye34tDgjPnenTalQ6qGpLzf+Zjlqi2bi1nY1eWblxQzeDqnDl0cUSQVV0K97a0iGUfVU0rlo79DcXACxhl1kAAB38SURBVICjqYuddo/WLLGTljFGhpLIiI2jR/AU0zTx/b0mexRUcnh7Ddw3ZGJolx1Uvr+t7EUD0zTJADg7dOzeVXlD3ffb0hoAcAXPddrdz99bY0zXdd5C7b4rs66jtqOLh3lB1dnVAYCnVNtp9817LBGVnehI+qfkgqMTagcAAPAkIlMqTNNkJz2Sjo++7+d5fo1kFQAAwBE+BVXuOCoXk9ygNsMwyLD9sysCAHga/ozq0ZkVXylHZWfWjqqq6xcJiTnNAQCI8keQNU3T972Xmmqa5jXGqtc2Yg4AgBPVdtr9tLdycxLvZmHhvcPS9x65stqOLgAAJ6rttPtpb+19WG960jaq7egCAHCi2k67le1tZUcXAIAT1Xba9eepAgAAwAPelFJN02wfI2WnsAIAAID1ppRalmUcx6ZpEpeRSyzVNI17QzoAAACIH/NULcsil/6FN/6z2ratqmcUAABgu8gIMvNBHuoPhWt2hNpGzAEAcKLaTruV7W1lRxcAgBPVdtrl6j8AAIAMvtxe5LVw7z8AAHCE6oIqwiYAAHAEuv8AAAAyIKgCAADIINL9506mULIqAAAAz+vXTJUxRuZM7z40TSNTqOfamEzLrrWO3hVHKrBWmn5tuhQAAOBoPyaQGIZhHEelVNu2NkFljJnnWSnV9/3OYMUYI3O1t22rlJLVumPG3QqEpVpreVK0beuGeunST3tb2YQZAACcqLrT7rIs0zSpj7vQhCQMmqYpWrqR3ZaQLfZ9n3ho69P3vVsqD2190qVhNfbsBQAA2K62026zLItM3bSsx5I3F7ipaRov3dU0jU0pSapp+Zy4sn/DrbuvTZeG1dizFwAAYLvaTrs/BqpLgmdN3/fjOMqYp8c2M01T+Fr7zDzPkg+zvN5Gr9R2EW4pBQAAKODN5ooSC+0f/e2O0zLGSHrJXa2MMZeR8lprL8/kVe+uhwAAAAVsmlE94wWAMlxdObkxWbkdpS5Zsa7rJLmVcdMifZuahKoSmAAA4F5fJK+T7trbks3aaFkWSVZJFOUmq2zUMgyDzOywLEv2tBOxEQAAOMKPeaokxFmTLr2X9PS1bSurlbDJGxeVHuOVTl9lT24BAADc9KaUkikM1ub5lKBHlnlMNA22JQVl60MUBQAALu6LUkprbUcyrU3+ubMbbp7nYRjczj43Egqv13M7HMNS92rBdCkAAEAh7qRVYSzStu3OaT+FrG1txs70bJ9eqTcZabo0rMb+fQEAAFvUdtqNz8q1Z0qqNd5ld978nPY2NdFSe5cb4c16lS716hDdXwAAkF1tp91NeyvX6+2frcrGSWtBT/oywz2lorajCwDAiWo77W7aW0kjvUC71HZ0AQA4UW2n3bezKwAAAPAKCKoAAAAyIKgCAADIYNO9/15J+t5/VXX9AgCAjKoLqgibAADAEb6oW8kbAAAA3PRFBTczBgAAwL3qmkCitgkzAAA4UW2nXa7+AwAAyICgCgAAIAOCKgAAgAwIqgAAADIgqAIAAMiAoAoAACCD6mZU5zY1AADgCNUFVYRNAADgCHT/AQAAZEBQBQAAkAFBFQAAQAYEVQAAABkQVAEAAGRAUAUAAJABQRUAAEAGBFUAAAAZEFQBAABkQFAFAACQQXW3qeHefwAA4AjVBVWETQAA4Ah0/wEAAGRAUAUAAJABQRUAAEAGRYOqYRi01lrrYRgSi0UXSL9245oBAAAO0pQZuG2M6bpOKdW2rVJqnme1MmZcaz3Pc9/3bngkT9qHbdsaYzaWupqm0P4CAIDaTruFMlUSUS3LYowxxkzTpJQKs0rGGDc8EsMwSJi1LMuyLH3fz/Nsw6Z0KQAAQBmFQsimabzkU9M0YUpJnvQyVTKzlFtP97Xp0rAaVYXMAACcqLbTbqF5qqZp0lp7T3rPyENjTDg/p3Qaug+9/r5EKQAAQAGFuv9s/CTdfxI2uYkr6cVbi2ej4dfGhwAAAAWUnlFdBlcppfq+t08aY8ZxlIFWnuyjo9K3qUmoKoEJAADuVTqosmPVx3FUH8mqruvato1mmLKnnYiNAADAEU64959MKCVx1TAMEld5U0wZY+zUU+Ea0ukrLv0DAADllQiqJELyYh1vcilJXFnzPM/z7I7ESq8/T0UBAAAeVWig+jzP3qxU7kRTy2dKKZl3SoKq8Gq+eZ7tFX/pUgAAgDJKBFUSG43jGM7YueXltn/QXZsN0dKlAAAAZZSblcu77C59MxlvplB7lxvhzXqVLvXWzEB1AADKqO20W3RvbRT12DV98vK116ZLRW1HFwCAE9V22q1sbys7ugAAnKi2026hgeoAAACvjaAKAAAggxMm/zxX+jY1VWUpAQBARtUFVYRNAADgCHT/AQAAZEBQBQAAkAFBFQAAQAYEVQAAABkQVAEAAGRAUAUAAJABQRUAAEAGBFUAAAAZEFQBAABkQFAFAACQQXW3qeHefwAA4AjVBVWETQAA4Ah0/wEAAGRAUAUAAJABQRUAAEAGBFUAAAAZEFQBAABkQFAFAACQAUEVAABABgRVAAAAGRBUAQAAZFDdjOrcpgYAAByhuqCKsAkAAByB7j8AAIAMCKoAAAAyIKgCAADIgKAKAAAgg6JB1TAMWmut9TAMJUsBAACO1pS5Gs4Y03WdUqptW6XUPM/q84V4MtOBWzpNk9ZaSrXW8qRo29YYYx+mS11NU2h/AQBAbafdQpkqiaiWZTHGGGOmaVJK2aySBE/TNEmpHAB5iSw2z3Pf98uyLMvS9/08zzZsSpcCAACUUSiEbJqm73u3b65pGptScv8XwzCM4yh1kySWl9ZyX5soDatRVcgMAMCJajvtFpr80+3Ls+wzbduGpS7pFnQfev19iVIAAIACCgVVNmaSBJJ07dnEVZhVGscx+nL70A2b0qUAAAAFlL5NjR0p1fd9dAE7pF3GXWUfHZW+919CVQlMAABwr9JBlR2rLrkobwYEm2Sy3YXpbsHHKpB3hQAAAOqUyT9lNqm2bd0+PmNM0zT2Or50LJVOX3HpHwAAKK9EUGWMSYxSVx9dfm3bLssSnb2TKAoAAFxcoUzVPM9etORGQjKIai02Cq/mm+fZXvGXLgUAACijxJgqSUqN4yh3klHOjJ3KiaXCHJU8MwxD13Vaa1nSrsEukygFAAAoo9ysXN5ld3Z+Tnu5X8jWzVvGm/UqXerVgYHqAACUUdtpt+je2qTUY9f0ubmoe0tFbUcXAIAT1XbarWxvKzu6AACcqLbT7glTKgAAALwegioAAIAMSs+ofrr0bWqqylICAICMqguqCJsAAMAR6P4DAADIgKAKAAAgA4IqAACADAiqAAAAMiCoAgAAyICgCgAAIAOCKgAAgAwIqgAAADIgqAIAAMiAoAoAACCD6m5Tw73/AADAEaoLqgibAADAEej+AwAAyICgCgAAIAOCKgAAgAwIqgAAADIgqAIAAMiAoAoAACADgioAAIAMCKoAAAAyIKgCAADIoLoZ1blNDQAAOEJ1QRVhEwAAOALdfwAAABkQVAEAAGRAUAUAAJABQRUAAEAGRYOqYRi01lrrYRhKlgIAABytKXM1nDGm6zqlVNu2Sql5ntXnC/G01vKkaNvWGJOl1NU0hfYXAADUdtotlKmSiGpZFmOMMWaaJqWUzSoNwzDPc9/3y7Isy9L3/TzPNjDaUwoAAFBGoRCyaZq+792+uaZpbEpJJuR0a5KrNKxGVSEzAAAnqu20W2jyz2matNbek+4z0i3oPvR69B4uBQAAKKBQ95+Nn6T7T9JLbuIqDLmiL3/gIQAAQAGlb1Mjg6uUUn3fyz/Rfjo79jz76Kj0vf8SqkpgAgCAe5UOquxY9XEc1cdUCInls6ediI0AAMARTpj8U2aTattW4qqodIJqTykAAMARSgRVxpj0KHVFFAUAAJ5coUzVPM/eXOduJBRerydTTyVK7RV/6VIAAIAySgRVkpQaxzGcsdM+VE7uSv5xpwZ9uBQAAKCMcrNyeZfdefNz2vvYCG9eqz2lXh0YqA4AQBm1nXaL7q2NotaCHlngiFJR29EFAOBEtZ12K9vbyo4uAAAnqu20e8KUCgAAAK+HoAoAACCD0jOqny59m5qqspQA9vO+UvgOAWpWXVDFVx6AjOQrpbaBIwCi6P4DAADIgKAKAAAgA4IqAACADAiqAAAAMiCoAgAAyICgCgAAIAOCKgAAgAwIqgAAADIgqAIAAMiguhnVuU0NAAA4QnVBFWETAAA4At1/AAAAGRBUAQAAZEBQBQAAkAFBFQAAQAYEVQDwoPTVxABqQ1AFAA9alsXGVQRYAAiqAOBxNq5iuhYA1c1TBQBZeKkp9yEBFlAngioAeIRETk3TkKwCIOj+A4AHSUQl/xNRAaguU8W9/wDkwjcGAFd1QRVfggAA4Ah0/wEAAGRAUAUAAJABQRUAAEAGRYOqYRi01lrrYRhKlgIAABytKTZwWy67a9tWKTXPs1Jqmiat9VqpWzGttTwp2rY1xmws9erAQHUA2fHdAkTV9tEolKmS4GmaJmOMMUaauOu6aOk0TUopm3MahmGe577vl2VZlqXv+3mebdiULgUA4CU1n51dHShVLFPVNI2XQBqGYRxHOyWx+pyacpffUxpWo6qQGUAZfLfgLBd/7128etkVylS1beuNdnKDHun1S7/ce+j19yVKAQAACigUVBlj7PAp4cY9Em81TTMMwzAMknzyRk25r73rIQAAQAEnzKhujJHRVDJ2SimltZb00jiO8kzf93bhvFt/uOO5qgQmAAC4V+l5qrTWNqKyKSW5fM8dbD6Oo5RmTzstj8pbDQAA8GLKBVXGmKZpbPDkRkvypHu5X3pcVDp9xaV/AACgvHJjqrqua9t2WZboiPXouCgbHhFFAQCAiysUVEmXXzT68eIn4UZaYdZqnmd7xV+6FAAAoIwSA9XdiTq9InmmbVsZoi4PZT5PGxgNw9B1ndbajbTcvsJEKQAAQBklZuWyl/uF7NbTt5rx1uAOcr9Z6qptFjIAR/MuKOYbBoVd/Lx28epld629jY6vylIqaju6AIDXdvHz2sWrl11le1vZ0QUAvLaLn9cuXr3sSs9TBQAA8JIIqgAAADIgqAIAAMjghHv/nSt977+qun4B4FxcO/mw2sYqPYvqgirehQBwEfKFTHzwgGVZbLvRgNdB9x8AAM9H4ipFsuBKqstUAQDw1LxuU/chAda5CKoAAHgmbq8fyapLofsPAIAn446jIqK6DoIqAACeDIHUNRFUAQAAZEBQBQAAkAFBFQAAQAYEVQAAABlUN6XCNW9Tw70aAAB4dtUFVdeMV7jVAAAAz47uPwAAgAwIqgAAADIgqAIAAMiAoAoAcIL0ZUPAMyKoAgCcwN4MWBFg4VUQVAEAzmHjKi58xmuobkoFAMDpvNSU+5AAC8+LoAoAUJo7OR/JKrwMgioAwAnc6Y6JqB7gjUijDa+AoAoAcAKCgJ1owAuqLqi65r3/AADAs6suqCJsAgAAR2BKBQAAgAwIqs7HrHcAALwAgqrzMa0wAAAvoGhQNQyD1lprPQxDWGqMSZSmX5suvT5magEA4Nk1xc7iEjS0bauUmudZKTVNk9ZaSodhGMdRFpBSt2Jaa3lStG1rjNlY6tXhalFLIjV1taoCwBEu+M2MXGo7uIUyVRI8TdNkjDHGSBN3XSelxphxHPu+X5bFGDNNk32JUmoYhnmepXRZlr7v53m2YVO69Pqk2soJoZYPp9YLAADcp1AI2TSNl0CS1JRsXVJNbk2kF0/+hv1i7trSpWE1Lhis2Fpds3oAcCi++l5YbQe30DxVbdt6o53coGeeZ+kWtLyFvVLbRbil9PqqesMdwetCpT0BAKco1P0ng9DdZ7y4R8aYa62bptFae3km77V3PcTLc7tQiagAAGc5YUoFY4ykFmTslMRP4zjKQHUZFNV1nTyffXRU86i81QAAAC+m9G1q7JV67qV/wuYYhmFomqbrumVZsqedyGQAAIAjlMtUSYLKXqlnoyX5xxsX1fd9elUPlwIAAByhUKbKGNN1XWIGqbVXSchFFAUAr8e7mQQ9CXh25aZUUOsfmHBKBfeZsNSdNCFdGlbjyh/ai1fv4mg9ALia2r6ZS2Sq3Ik6vSI7H1XXdfaiPzufp13GLZXclV1VuhQAAKCMEiGk9P1Fi9zB6XL1n/BSTd4avEHu6VLXxUPmi1fv4mg9ALia2r6Zr7W3brYpb6m4+NG9ePUujtYDgKup7Zu5sr299tG9ePUujtYDgKup7Zv5hMk/AQAAXg9BFQAAQAalZ1Q/XfqGM1VlKQEAQEbVBVWETYDL+5nBBwQAHlZdUHVNTCv8sNpGQWYnrUczAsB+BFWXwPnsYcuy2ICAyAAAcCIGquPpSVyliE0BAKciU4Un5o0Hch8SYAEACiOowhNze/1IVgEAzkX3H56bO46KiAoAcCKCKjw3AikAwEUQVAEAAGRAUAUAAJABQRUAAEAG1V39x73/AADAEaoLqgibAADAEej+AwAAyICgCqhXujccAHAXgiqgXnYmekWABQC7EVQBVeMOPwCQS3UD1QEIbkcNAHkRVAGV4nbUAJAX3X9AvbgdNQBkRFAF1ItACgAyovsPT8+7fo1AAQBwiuqCKm5T83o4agCAK6guqOIEDAAAjsCYKgAAgAwIqgAAADIgqAIAAMiAoAoAACCDokHVMAxaa631MAyJxaILpF+7cc0AAAAHaYpdDSdzGbRtq5Sa51kpNU2T1tpbTGs9z3Pf9254JE/ah23bGmM2lnp14Oo/IMRHA8ARavtuKZSpkuBpmiZjjDFGmrjrOm8xY4wbHolhGCTMWpZlWZa+7+d5tmFTuhQAAKCMQiFk0zReAmkYhnEcva3LYl6mKpwm211bujSsRlUhM7ARHw0AR6jtu6VQpqptW2+0Uxj0SDYrGgxJp6H70OvvS5QCAAAUUCioMsZ4w6e8uEd68dbiWe+1dz0EAAAo4IQpFYwx0mc3TZN9ZhxH+9BbOO/Wm0flrQZwEfbtzfscAHYqfe8/e6Wee+lf13Vt20YzTNnTTlV17gI38YkAgFzKBVXGGLncz5suQf73ppgyxtipp6KrSm8oQ3UBAADuUSiokogqMYPUOI7uw3me53m2ERVRFAAAuLhyUyqozR0NTdO42SzpMVybNCFdGq6Zzg4AAMqo7bRbIlPlTtTpFW25q8wwDF3Xaa1tFOW+MF0KAABQRtGB6l4fn9oW/Witp2nqus5emuQOck+XAgAAlPFMeTk3F3VvqagtDwkAwIlqO+1WtreVHV0AAE5U22n3hMk/AQAAXg9BFQAAQAalZ1Q/XfpGHFVlKQEAQEbVBVWETQAA4Ah0/wEAAGRAUAUAAJABQRUAAEAGBFUAAAAZEFQBAABkQFAFAACQAUEVAABABgRVAAAAGRBUAQAAZEBQBQAAkEF1t6nh3n8AAOAI1QVVhE0AAOAIdP8BAABkQFAFAACQAUEVAABABgRVAAAAGRBUAQAAZEBQBQAAkAFBFQAAQAYEVQAAABkQVAEAAGRQ3Yzq3KYGAAAcobqgirAJAAAcge4/AACADAiqAAAAMiCoAgAAyICgCgAAIIOiQdUwDFprrfUwDCVLAQAAjtYUuxpO5jJo21YpNc+zUmqaJq31llKttTwp2rY1xtiH6VKvDlz9BwBAGbWddgtlqiQ8mqbJGGOMkSbuum5L6TAM8zz3fb8sy7Isfd/P82zDpnQpAABAGYVCyKZpvATSMAzjOMrWb5aqz/NLucunS8NqVBUyAwBwotpOu4UyVW3beqOd3KCnbVvb07f2cu+h19+XKAUAACig0IzqYd7IjXvC0nEc3YdeyOUNokqXAgAAFHDClArGGOmzm6bpZmn20VHNo/JWAwAAvJjS9/6zaST34r5Eabpb8AFVde4CAIBiymWqJAVlr9TzoqV0abiqh0sBAACOUG5MVdd1axflpUsVURQAALi8clMqqPWut3Sp9AmuTZqQLg03RPcfAABl1HbaLZGpcifq9IqGYUiXyt+u67TWNopyF06XAgAAlFEihJTevWjRsizp0ugavEHu6VJXbSEzAAAnqu20+0x76+ai7i0VtR1dAABOVNtpt7K9rezoAgBwotpOuydM/gkAAPB6CKoAAAAyKD2j+unSN5ypKksJAAAyqi6oImwCAABHoPsPAAAgA4IqAACADAiqAAAAMiCoAgAAyICgCgAAIAOCKgAAgAwIqgAAADIgqAIAAMiAoAoAACCD6mZU5zY1AADgCNUFVYRNAADgCHT/AQAAZEBQBQAAkAFBFQAAQAYEVQAAABkQVAEAAGRAUAUAAJABQRUAAEAGBFUAAAAZEFRhq/Rk9Eij9XaiAfeg9fag9bAdQRUAAEAG1d2mhnv/AQCAI1QXVBE2AQCAI9D9BwAAkAFBFQAAQAYEVQAAABkUDaqGYdBaa62HYShZCgAAcLSm2MBtueyubVul1DzPSqlpmrTWUqq1lidF27bGGPtwT6lXBwaqP4zW24PW24kG3IPW24PW26O21iuUqZLgaZomY4wxRpq46zopHYZhnue+75dlWZal7/t5nm1gtKcUAACgjEIhZNM0XgJpGIZxHGXrksRya+Iuv6c0rEZVIXNetN4etN5ONOAetN4etN4etbVeoUxV27beaCcv6JFuQfeh16P3cCkAAEABhYIqY4wdPiW8uMcr9Xildz0EAAAo4IQZ1Y0xMppqmiYVpKyEHXuefXQUt8bcg9bbg9bbiQbcg9bbg9bDRqWDKhst2Uv/7spR7VRVzy4AACip3DxVxpimaeyVeuloKZ2g2lMKAABwhEKZKunyS8wgRRQFAACeWrkpFdR675v0CXrTIvR9LxcMRkttfJYuBQAAKKNEUGVHpvd97xVJ2OTlsbw4KVpqh2SlSwEAAMooGlSFvMjJPu9FRXtKAQAACrjWVKc225S99HnZe0XLw3DGL6xxm852JdN6G3lvPHmGG5Zvt3aXd9pwCz68D7h5spCW5B14qHJX/22R/tjsKX1e3uCwrusYLraRbaimaeT/ruv4QtnIe5vJfaVOqstTsg04jiOf2XtFP7w0Y1r6ZCEtKZfhF65YVa4VVL0keVsPw2Df3/KPvL/lmTAHYIxxX6KU0lrX9mHI0nT2JcMw9H1fz/dyrjeePFlhRJW3AYcPh9b5OrK0nh10OwzDNE1rw0hexnEni2EYZOSxMSa8axwyIqg6ljGm6zp5f9s0iSRp7RdE+ANiGAYp7brO3s/HGHOpvtqj5Wo6rXVV7SYyvvHkodz/oB55G1BeWFVAn6v17K1dX7IjwnXoycI4XYFVvRVPsOBIciqyD+X/tm3btpVn2raV2VCXZen7Xp73XjJN09rDF5a36WxMcHzFLyFj67VtK//U03pLvgaU9fR9L9c+25e/trytZxd77XfgoScL78uwkvfhKchUlWY+D6if59n9ATHPs/cbwv5Qw56m01rLV8nL/95d81jr2THChWp5YY81oCRKpctmWRbvRvL1eLj1+r5vmqa2kQ8i48mC80gxBFUnu/ler/Zb+KaNTWeHI0jSm/YU21tvnmd7VrOjhsEnd4/trSfBqASmh1fr2na+5bxxWjgIQVUJdrChCn70u1dcy0hq/TGpacEKXtf+pjPONGa1tWqW1rNpbaXUcuuunS9mfwMOw2CzLLz9rO3fe+7goRrSLQedLNyR7OM4EqEe6Iw+x4rYoTzydSA94m7X+PJ5OKE8Y4dfyN+ax1RlaTp3MVrPLnPXG2+pckxVlga0D3n77Wy98jtS0tEnCxmepaoZ2HeWF3+bns4OCUx/mcqQzPDJg2r1FPI2XXSxF8Ybb6fsDVhVq/LhfUCBz2wNzXi6LwpFpDtNoqVV9bMkZGm6ahuTN95OuRqwzlblw/uA4z6ztbXkKRhTdTjexw+j6fag9XaiAfeg9R5Ao72Aa937DwAA4EmRqQIAAMiAoAoAACADgioAAIAMCKoAAAAyIKgCAADIgKAKAAAgA4IqAACADAiqAAAAMiCoAgAAyICgCgAAIAOCKgAAgAwIqgAAADIgqAIAAMiAoAoAACADgioAAIAMCKoAAAAyIKgCAADIgKAKAAAgA4IqAACADAiqAAAAMiCoAgAAyICgCgAAIAOCKgAAgAwIqgAAADIgqAIAAMiAoAoAACADgioAAIAMCKoAAAAyIKgCAADIgKAKAAAgA4IqAACADAiqAAAAMiCoAgAAyICgCgAAIAOCKgAAgAwIqgAAADIgqAIAAMiAoAoAACCD/wcorVl4eu+H6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l.Clear()\n",
    "g['LY_MS3_DRY'].Print()\n",
    "g['LY_MS3_DRY'].Draw(\"AP*\")\n",
    "l.AddEntry(g['LY_MS3_DRY'],\"MS3\",\"PL\")\n",
    "g['LY_MS3_DRY'].GetHistogram().GetYaxis().SetLimits(2000,4000)\n",
    "g['LY_MS3_DRY'].GetHistogram().GetYaxis().SetRangeUser(2000,4000)\n",
    "g['LY_MS3_DRY'].GetHistogram().GetYaxis().SetTitle(\"LO (ph/MeV)\")\n",
    "g['LY_OPT_DRY'].SetMarkerStyle(24)\n",
    "g['LY_OPT_DRY'].SetMarkerSize(1.2)\n",
    "l.AddEntry(g['LY_OPT_DRY'],\"OPT\",\"PL\")\n",
    "g['LY_OPT_DRY'].Draw(\"PSAME\")\n",
    "l.Draw()\n",
    "tL.DrawLatexNDC(0.11,0.92,'CMS MTD - POST MS3 Optimisation - Array dry coupling')\n",
    "tL.DrawLatexNDC(0.15,0.83,'<MS3> = %.0f ph/MeV'%R.TMath.Mean(6,g['LY_MS3_DRY'].GetY()))\n",
    "tL.DrawLatexNDC(0.15,0.79,'<OPT> = %.0f ph/MeV'%R.TMath.Mean(6,g['LY_OPT_DRY'].GetY()))\n",
    "c1.Draw()\n",
    "for ext in ['png','pdf']:\n",
    "    c1.SaveAs('plots/LYSO_OPT_LY_ARRAY_DRY_byProd.%s'%ext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "22570826-83dc-493b-a245-f5687b607a69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=0, y[0]=130.778, ex[0]=0, ey[0]=4\n",
      "x[1]=1, y[1]=132.346, ex[1]=0, ey[1]=4\n",
      "x[2]=2, y[2]=138.021, ex[2]=0, ey[2]=4\n",
      "x[3]=3, y[3]=125.611, ex[3]=0, ey[3]=4\n",
      "x[4]=4, y[4]=130.276, ex[4]=0, ey[4]=4\n",
      "x[5]=5, y[5]=124.082, ex[5]=0, ey[5]=4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TCanvas::Print>: png file plots/LYSO_OPT_sigmaT_ARRAY_DRY_byProd.png has been created\n",
      "Info in <TCanvas::Print>: pdf file plots/LYSO_OPT_sigmaT_ARRAY_DRY_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_DRY'].Print()\n",
    "g['sigmaT_MS3_DRY'].Draw(\"AP*\")\n",
    "g['sigmaT_MS3_DRY'].GetHistogram().GetYaxis().SetLimits(100,150)\n",
    "g['sigmaT_MS3_DRY'].GetHistogram().GetYaxis().SetRangeUser(100,150)\n",
    "g['sigmaT_MS3_DRY'].GetHistogram().GetYaxis().SetTitle(\"#sigma_{t} (ps)\")\n",
    "g['sigmaT_OPT_DRY'].SetMarkerStyle(24)\n",
    "g['sigmaT_OPT_DRY'].SetMarkerSize(1.2)\n",
    "g['sigmaT_OPT_DRY'].Draw(\"PSAME\")\n",
    "l.Draw()\n",
    "tL.DrawLatexNDC(0.11,0.92,'CMS MTD - POST MS3 Optimisation - Array Dry coupling')\n",
    "tL.DrawLatexNDC(0.15,0.83,'<MS3> = %.0f ps'%R.TMath.Mean(6,g['sigmaT_MS3_DRY'].GetY()))\n",
    "tL.DrawLatexNDC(0.15,0.79,'<OPT> = %.0f ps'%R.TMath.Mean(6,g['sigmaT_OPT_DRY'].GetY()))\n",
    "c1.Draw()\n",
    "for ext in ['png','pdf']:\n",
    "    c1.SaveAs('plots/LYSO_OPT_sigmaT_ARRAY_DRY_byProd.%s'%ext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d31de83c-8d8d-4cd0-8303-fb9afdd98cf6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=0, y[0]=0.0138488, ex[0]=0, ey[0]=0.01\n",
      "x[1]=1, y[1]=0.0455465, ex[1]=0, ey[1]=0.01\n",
      "x[2]=2, y[2]=0.0709276, ex[2]=0, ey[2]=0.01\n",
      "x[3]=3, y[3]=0.0865404, ex[3]=0, ey[3]=0.01\n",
      "x[4]=4, y[4]=0.0218292, ex[4]=0, ey[4]=0.01\n",
      "x[5]=5, y[5]=0.0186736, ex[5]=0, ey[5]=0.01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TCanvas::Print>: png file plots/LYSO_OPT_XT_ARRAY_DRY_byProd.png has been created\n",
      "Info in <TCanvas::Print>: pdf file plots/LYSO_OPT_XT_ARRAY_DRY_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_DRY'].Print()\n",
    "g['XT_MS3_DRY'].Draw(\"AP*\")\n",
    "g['XT_MS3_DRY'].GetHistogram().GetYaxis().SetLimits(0.000001,0.5)\n",
    "g['XT_MS3_DRY'].GetHistogram().GetYaxis().SetRangeUser(0.000001,0.5)\n",
    "g['XT_MS3_DRY'].GetHistogram().GetYaxis().SetTitle(\"XT\")\n",
    "g['XT_OPT_DRY'].SetMarkerStyle(24)\n",
    "g['XT_OPT_DRY'].SetMarkerSize(1.2)\n",
    "g['XT_OPT_DRY'].Draw(\"PSAME\")\n",
    "l.Draw()\n",
    "tL.DrawLatexNDC(0.11,0.92,'CMS MTD - POST MS3 Optimisation - Array Dry coupling')\n",
    "\n",
    "c1.Draw()\n",
    "for ext in ['png','pdf']:\n",
    "    c1.SaveAs('plots/LYSO_OPT_XT_ARRAY_DRY_byProd.%s'%ext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a8100d21-d680-4df6-b6f8-038778a36647",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=0, y[0]=276.448, ex[0]=0, ey[0]=5.52896\n",
      "x[1]=1, y[1]=277.934, ex[1]=0, ey[1]=5.55869\n",
      "x[2]=2, y[2]=270.587, ex[2]=0, ey[2]=5.41174\n",
      "x[3]=3, y[3]=279.149, ex[3]=0, ey[3]=5.58298\n",
      "x[4]=4, y[4]=275.991, ex[4]=0, ey[4]=5.51983\n",
      "x[5]=5, y[5]=283.418, ex[5]=0, ey[5]=5.66836\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TCanvas::Print>: png file plots/LYSO_OPT_SR_ARRAY_DRY_byProd.png has been created\n",
      "Info in <TCanvas::Print>: pdf file plots/LYSO_OPT_SR_ARRAY_DRY_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['SR_MS3_DRY'].Print()\n",
    "g['SR_MS3_DRY'].Draw(\"AP*\")\n",
    "g['SR_MS3_DRY'].GetHistogram().GetYaxis().SetLimits(200,400)\n",
    "g['SR_MS3_DRY'].GetHistogram().GetYaxis().SetRangeUser(200,400)\n",
    "g['SR_MS3_DRY'].GetHistogram().GetYaxis().SetTitle(\"SR (mV/ns)\")\n",
    "g['SR_OPT_DRY'].SetMarkerStyle(24)\n",
    "g['SR_OPT_DRY'].SetMarkerSize(1.2)\n",
    "g['SR_OPT_DRY'].Draw(\"PSAME\")\n",
    "l.Draw()\n",
    "tL.DrawLatexNDC(0.11,0.92,'CMS MTD - POST MS3 Optimisation - Array Dry coupling')\n",
    "\n",
    "c1.Draw()\n",
    "for ext in ['png','pdf']:\n",
    "    c1.SaveAs('plots/LYSO_OPT_SR_ARRAY_DRY_byProd.%s'%ext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "412cc606-aa23-4fec-9311-40ae1e2f0c21",
   "metadata": {},
   "outputs": [],
   "source": [
    "fOut=R.TFile('LYSO_ARRAY_DRY_Optimisation.root','RECREATE')\n",
    "for hn,h in g.items():\n",
    "    h.Write(hn)\n",
    "fOut.Close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8cc8348",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}