LO_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "659bf4de",
   "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": "957ff835",
   "metadata": {},
   "outputs": [],
   "source": [
    "files={}\n",
    "files['array']=R.TFile(\"MS3/prodAnalysis.root\")\n",
    "files['pmt']=R.TFile(\"MS3/prodAnalysis_PMT.root\")\n",
    "files['tofpet']=R.TFile(\"MS3/prodAnalysis_TOFPET.root\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "41593f61",
   "metadata": {},
   "outputs": [],
   "source": [
    "#files['tofpet'].ls()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "85667fca",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "g={}\n",
    "g['ly_array']=files['array'].Get(\"lyNorm_byProd_geo2_PREIRR\")\n",
    "#g['ly_array'].Print()\n",
    "g['sigmaT_array']=files['array'].Get(\"sigmaT_byProd_geo2_PREIRR\")\n",
    "g['ly_pmt']=files['pmt'].Get(\"lyAbs_byProd_geo2_PREIRR\")\n",
    "g['ly_esr_pmt']=files['pmt'].Get(\"lyAbs_byProd_geo2_esr_PREIRR\")\n",
    "g['tau_pmt']=files['pmt'].Get(\"dt_byProd_geo2_PREIRR\")\n",
    "g['ly_tofpet']=files['tofpet'].Get(\"ly_byProd_geo2\")\n",
    "g['ly_esr_tofpet']=files['tofpet'].Get(\"ly_byProd_geo2_esr\")\n",
    "g['sigmaT_esr_tofpet']=files['tofpet'].Get(\"sigmaT_byProd_geo2_esr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "76487de6-8cc2-45c0-8e70-0cfb9e6381bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "c1=R.TCanvas(\"c1\",\"c1\",800,600)\n",
    "#g['array'].Draw(\"AP\")\n",
    "#g['pmt'].Draw(\"PSAME\")\n",
    "g['relLossArray']=R.TGraphErrors()\n",
    "g['relLossArrayESR']=R.TGraphErrors()\n",
    "g['maxlyDTArray']=R.TGraphErrors()\n",
    "g['lyArrayVsPMT']=R.TGraphErrors()\n",
    "g['lyArrayVsPMTESR']=R.TGraphErrors()\n",
    "g['lyArrayVsTOFPETESR']=R.TGraphErrors()\n",
    "g['sigmaTArrayVsTOFPETESR']=R.TGraphErrors()\n",
    "\n",
    "g['relLossESRTOFPETVsPMT']=R.TGraphErrors()\n",
    "g['relLossArrayVsTOFPETESR']=R.TGraphErrors()\n",
    "\n",
    "g['lyDTArray']=R.TGraphErrors()\n",
    "g['sigmaTVsLYDT']=R.TGraphErrors()\n",
    "\n",
    "g['lyTOFPETVsPMT']=R.TGraphErrors()\n",
    "g['lyTOFPETVsPMTESR']=R.TGraphErrors()\n",
    "g['lyLossTOFPETVsPMTESR']=R.TGraphErrors()\n",
    "\n",
    "data={}\n",
    "data['ly_array']=[]\n",
    "data['sigmaT_array']=[]\n",
    "data['ly_pmt']=[]\n",
    "data['ly_esr_pmt']=[]\n",
    "data['tau_pmt']=[]\n",
    "data['ly_tofpet']=[]\n",
    "data['ly_esr_tofpet']=[]\n",
    "data['sigmaT_esr_tofpet']=[]\n",
    "\n",
    "producers=['prod1','prod2','prod3','prod4','prod5','prod6','prod8','prod9','prod10']\n",
    "\n",
    "import ctypes\n",
    "for i in range(9):\n",
    "    x,y,x1,y1,x2,y2,x3,y3,x4,y4=ctypes.c_double(0),ctypes.c_double(0),ctypes.c_double(0),ctypes.c_double(0),ctypes.c_double(0),ctypes.c_double(0),ctypes.c_double(0),ctypes.c_double(0),ctypes.c_double(0),ctypes.c_double(0)\n",
    "    x5,y5,x6,y6,x7,y7=ctypes.c_double(0),ctypes.c_double(0),ctypes.c_double(0),ctypes.c_double(0),ctypes.c_double(0),ctypes.c_double(0)\n",
    "    g['ly_array'].GetPoint(i,x,y)\n",
    "    g['ly_pmt'].GetPoint(i,x1,y1)\n",
    "    g['tau_pmt'].GetPoint(i,x2,y2)\n",
    "    g['sigmaT_array'].GetPoint(i,x3,y3)\n",
    "    g['ly_esr_pmt'].GetPoint(i,x4,y4)\n",
    "    g['ly_tofpet'].GetPoint(i,x5,y5)\n",
    "    g['ly_esr_tofpet'].GetPoint(i,x6,y6)\n",
    "    g['sigmaT_esr_tofpet'].GetPoint(i,x7,y7)\n",
    "    \n",
    "    data['ly_array'].append(y.value)\n",
    "    data['sigmaT_array'].append(y3.value)\n",
    "    data['ly_pmt'].append(y1.value)\n",
    "    data['ly_esr_pmt'].append(y4.value)\n",
    "    data['tau_pmt'].append(y2.value)\n",
    "    data['ly_tofpet'].append(y5.value)\n",
    "    data['ly_esr_tofpet'].append(y6.value)\n",
    "    data['sigmaT_esr_tofpet'].append(y7.value)\n",
    "    \n",
    "    g['lyArrayVsPMT'].SetPoint(i,(data['ly_array'][i]/data['ly_array'][0]),(data['ly_pmt'][i]/data['ly_pmt'][0]))\n",
    "    g['lyArrayVsPMT'].SetPointError(i,0.03,0.04)\n",
    "    g['lyArrayVsPMTESR'].SetPoint(i,(data['ly_array'][i]/data['ly_array'][0]),(data['ly_esr_pmt'][i]/data['ly_esr_pmt'][0]))\n",
    "    g['lyArrayVsPMTESR'].SetPointError(i,0.03,0.04)\n",
    "\n",
    "    g['lyArrayVsTOFPETESR'].SetPoint(i,(data['ly_array'][i]/data['ly_array'][0]),(data['ly_esr_tofpet'][i]/data['ly_esr_tofpet'][0]))\n",
    "    g['lyArrayVsTOFPETESR'].SetPointError(i,0.03,0.04)\n",
    "\n",
    "    g['sigmaTArrayVsTOFPETESR'].SetPoint(i,(data['sigmaT_array'][i]/data['sigmaT_array'][0]),(data['sigmaT_esr_tofpet'][i]/data['sigmaT_esr_tofpet'][0]))\n",
    "    g['sigmaTArrayVsTOFPETESR'].SetPointError(i,0.03,0.04)\n",
    "    \n",
    "    g['lyTOFPETVsPMT'].SetPoint(i,(data['ly_tofpet'][i]/data['ly_tofpet'][0]),(data['ly_pmt'][i]/data['ly_pmt'][0]))\n",
    "    g['lyTOFPETVsPMT'].SetPointError(i,0.03,0.04)\n",
    "    g['lyTOFPETVsPMTESR'].SetPoint(i,(data['ly_esr_tofpet'][i]/data['ly_esr_tofpet'][0]),(data['ly_esr_pmt'][i]/data['ly_esr_pmt'][0]))\n",
    "    g['lyTOFPETVsPMTESR'].SetPointError(i,0.03,0.04)\n",
    "\n",
    "    g['lyLossTOFPETVsPMTESR'].SetPoint(i,(data['ly_esr_tofpet'][i]/data['ly_tofpet'][i]),(data['ly_esr_pmt'][i]/data['ly_pmt'][i]))\n",
    "    g['lyLossTOFPETVsPMTESR'].SetPointError(i,0.03,0.04)\n",
    "    \n",
    "    g['relLossArray'].SetPoint(i,i,(data['ly_array'][i]/data['ly_array'][0])/(data['ly_pmt'][i]/data['ly_pmt'][0]))\n",
    "    g['relLossArray'].SetPointError(i,0,0.03)\n",
    "    g['relLossArrayESR'].SetPoint(i,i,(data['ly_array'][i]/data['ly_array'][0])/(data['ly_esr_pmt'][i]/data['ly_esr_pmt'][0]))\n",
    "    g['relLossArrayESR'].SetPointError(i,0,0.03)\n",
    "    g['lyDTArray'].SetPoint(i,i,(data['ly_array'][i]/data['tau_pmt'][i])*4700)\n",
    "    g['lyDTArray'].SetPointError(i,0,(data['ly_array'][i]/data['tau_pmt'][i])*4700*0.03)\n",
    "    g['sigmaTVsLYDT'].SetPoint(i,(data['ly_array'][0]/data['tau_pmt'][0])/(data['ly_array'][i]/data['tau_pmt'][i]),data['sigmaT_array'][i]/data['sigmaT_array'][0])\n",
    "    g['sigmaTVsLYDT'].SetPointError(i,0.01,0.02)\n",
    "    \n",
    "    g['relLossESRTOFPETVsPMT'].SetPoint(i,i,(data['ly_esr_tofpet'][i]/data['ly_tofpet'][i])/(data['ly_esr_pmt'][i]/data['ly_pmt'][i]))\n",
    "    g['relLossESRTOFPETVsPMT'].SetPointError(i,0,0.04)\n",
    "    \n",
    "    g['relLossArrayVsTOFPETESR'].SetPoint(i,i,(data['ly_array'][i]/data['ly_array'][0])/(data['ly_esr_tofpet'][i]/data['ly_esr_tofpet'][0]))\n",
    "    g['relLossArrayVsTOFPETESR'].SetPointError(i,0,0.03)\n",
    "    \n",
    "for k in ['ly_array','ly_pmt','tau_pmt','relLossArray','lyDTArray','relLossArrayVsTOFPETESR','relLossESRTOFPETVsPMT']:\n",
    "    g[k].GetHistogram().SetBins(len(data['ly_array']),-0.5,len(data['ly_array'])-0.5)\n",
    "    for i in range(len(producers)):\n",
    "        g[k].GetHistogram().GetXaxis().SetBinLabel(i+1,producers[i])\n",
    "    g[k].SetMarkerStyle(24)\n",
    "    g[k].SetMarkerColor(1)\n",
    "    g[k].SetLineColor(1)\n",
    "    g[k].SetMarkerSize(0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c2a60316-38c0-4054-96a1-0c5978117797",
   "metadata": {},
   "outputs": [],
   "source": [
    "t=R.TLatex()\n",
    "t.SetTextSize(0.04)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f250a8bf-b47d-44ef-8105-53adf1ae2df7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['relLossArray'].GetHistogram().GetYaxis().SetTitle('Rel. ratio ARRAY / BAR_{naked}')\n",
    "g['relLossArray'].Draw(\"AP\")\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "996a00be-4983-4f4d-b930-00f71616fee8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['lyDTArray'].Draw(\"AP\")\n",
    "g['lyDTArray'].GetHistogram().GetYaxis().SetTitle(\"LO/#tau(array) ph/MeV/ns\")\n",
    "t.DrawLatexNDC(0.1,0.92,\"BTL - LYSO MS3\")\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "962f7a85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['ly_array'].Draw(\"AP\")\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "73ddda59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['tau_pmt'].Draw(\"AP\")\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e92c3eee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " FCN=12.9302 FROM MIGRAD    STATUS=CONVERGED      56 CALLS          57 TOTAL\n",
      "                     EDM=2.5344e-08    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
      "  EXT PARAMETER                                   STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  p0           5.40677e-01   1.25191e-01   1.24973e-05  -2.25878e-03\n",
      "   2  p1           4.82331e-01   1.29465e-01   1.29239e-05  -3.91583e-03\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['sigmaTVsLYDT'].Draw(\"AP*\")\n",
    "g['sigmaTVsLYDT'].Fit(\"pol1\")\n",
    "g['sigmaTVsLYDT'].GetHistogram().GetXaxis().SetTitle(\"Rel. #tau/LO_{array}\")\n",
    "g['sigmaTVsLYDT'].GetHistogram().GetYaxis().SetTitle(\"Rel. #sigma_{t}\")\n",
    "t.DrawLatexNDC(0.1,0.92,\"BTL - LYSO MS3\")\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "adc02d4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['sigmaT_array'].Draw(\"AP\")\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bf9d9674",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['lyArrayVsPMT'].Draw(\"AP*\")\n",
    "f=R.TF1('f','x',0,2)\n",
    "f.Draw('SAME')\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "82f6301e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7IBovh/+GjupDhBqG/5qmufrkKoDLM9gHkXlZqRrmUU1uD2HrujT/BO5AdQris9b8c/H2606oCjT/BN6K+r9YmnlCrN40/6yqalKaCp81F80fqc2Y24Wdxs9E9WILKxPXJ94x431R7XZuJrVX13RrJwWqcKWaoQVo+HWetK4itaO7CzuNn4ntxRZRovo3TuW7rlVsu507Se3VNd3ajUXvHfdRWZZ1Xb+d9l7X9TDNa7F4NvYq9qV2dHdhp/EzUQ+6nWf89ht2kFDFJaT26pqe/ffjje+6bnLZ5kXjqzv3fT/0IwVuJp7P3yiG/2ZX7nv+52bpE2K01qfqUF3X1XU9by46Fy7hPLTLCr2yhlAVfmjbdjzf/KKjk0Akxv+9jidRAZE7rS43/p/WuBvWqyXH65nn+dDYva7rpmk2bkVqdchd2Gmk7JzX/2/jlPc4x0nt1XVapWro0r5l4WGm/PDrMBo4LlkZE9zX0LtLEy/4HQUquKz3F1SOwdtp7OOv/EtfnTAqSf33As4nTsHFnVap2mgxHo0z1tDlYTzjan0w8Tu7bxrA/0hUcH1vKlVDpjnrSn9vn3cyHyt0Xlg5o1D1BYiLOAV3sVypCtdLzvP88V+hWhPJsNp4NeapK9wSyaoCvDS+4EwmUcHlLYSq0Omg7/uiKKqqatu2qqowVfzxeJzSreCLhHRWaQ3gvXmckqjg+qbDf13XNU0zmes9BJSyLJumKcvyl5FlfK5fENpWhbV9PB6T/ulqVEC8jrl+HxCDaaUqdON8lUvC7Vs6dv5F13V5ng85KfwwDnbjG4uiaJpmWOGhU+ihawjwMdUpuLuFierriaSqqhBiji5WjefIt20b5nWFW8bdrUICG+e8617vGbgn1SlIwz+tTsNo2np/8yzL8jx/u8wRhgsqL/4p5Ly3a+7sP2C7HT40op+K7oOR46T26vq4+eeJM5ZWAtOPp3kBvBd9nAL29c+cqi3NCFbKRQD8h0QF6ZnW5eZXL57e4d0CMXvbGP2i2wV3Mnmfnvuu/Gbw4mpxKrUBGn4ptVfX9Oy/MAf8VZ/PUKDaeBXkOD1Xnb12QDa8GS/5rrxaogJ2tBAh67pumibLsqIohmG+4dovlz63LrXIDNcVybv1g9W4bJyKZFdzS6m9ul5ubVmWk5abRVHUdX3p2VSpHV24rkjerVtX47KJKotmV3NLqb263m/tD1pS/UxqRxeuK5J36/vVuHKcCiLZ1dxSaq+u5Qsqj00SVdd11x3+A9iNyyED/3ofqibCxQGPWBWAa3DBGWDJx80/AdLlgjPAa0IVwAa3i1OpTXaBH0guVK33//QRA0ztF6eiyjHP53NYn6hWLItvfWCj5EKVNyqw3T+fF7f79Ai5KvPBCDtJLlQBbHLMmX1vL5Z1ijjXCi5nGqq8tQCO65UQVU0ojLJFWKzyTcRFTUNVURSnrAdAFP6NU3meR5Q1djWetxRVooLrSmsyoMmPcBXnvFtnBaoUPjRS2EbOktqry5wqgDtcbQY4nVAFpO12DaiAswhVQKrEKWBXQhWQHnGKT0zORkxqkhAfSS5U6agOSROn+FycfeeJUHKhylsCEiVOAQebhqqyLLfcreu63VcF4ChO7gOONy1mbuxje9F6j+ItXMVu79a/xakUPjRS2MZd2FFfSG2nTStVSW08cGd/G+8b/ocZ4VVcgDj9v43LGe8DLiPPpwWqzyPR8197rh5wU29CVVmWeZ7nef54PLIsy/O8rutfrBfAd/4cpwC+sxaq8jzv+76qquEqy0VRNE2zcTI7wE+NC1TiFPBzL0NVqEi1bVvX9ZCiuq6rqqrv+5+sG8Bmzu8DzvYyVIVJVPOiVAhbplgBsVCgAuKQXPNPHdXhVhSogGi8Gf6bV6TC7dedVvVcdfbaAZspUAGReVmpKsuyKIrH4zHMUq/ruuu6MHX9V6sHsESBCojPm1andV03TTO+paqq63ZVSK21K1zXy3erOLU3H4wb2VFfSG2nbd3arusuN+RXluX41MUsvaML17XwbnVF5GP4YNzIjvpCajttbU7VOI5cLlGFkcqz1wLYwx4d0gGOtnb230VDSdd1XddNRi2BS5j+v1Z1CriOtbpcnudFUVyuJdW4aULbtob/YF1s74v/rE98cSq2HbWXu27X7uyoL6S2015WqkLz9KZpQrSa//XY9fqDcPy6rgvXKwSu5ZmZjQ5c0lqoGkbQLjoOuGi9+eeKpLI2qfn6fbG78dvsP+sUzboBrFubqH7LJpnrzT9vucnw1tfviz2NE9XzmcezYv911tEBruJlqAL4naXxPjkGuJbkrv0HTJycXa4zfUrIA9YJVXBnk8lS0cWC6yQqgLeEKrizkKJiPKtZnAJux5wq4OckKuCOVKqAHxKngPuKb1DgSDEOgsDxonjlf9ghPYp1ToNdvZEd9YXUdppKFXCw+C44A3CE5ELVeufopAI1HE6cAlKSXKgSm+AXxCkgPcmFKuBY4tR1DJX78IP/c8IfCVXAfpzcdylSFOxLqAL2IE4ByROqgL8x3geQZZlQBXxPnCIBqXVa4i9cpgb4ymS8z7cOu1pvf/NLz+dzMqM/KhGuUspUqoAPmT5FYoZcpWTFOqEK+IRExa9EWIOJcJWISnKhSkd1+NLovZP/e4s3DkeI53UVplXFWayS86KSXKiK7f0A1/Bvger5n9tM4OX+xq9zL3jWJReqgM8Y7+MM8cSXeNZkUeSrlxpn/wGvSVQAm6lUAUvEKYAPCVXAv7T0BPiKUAX8lzgF8AdCFSBOAexAqIK0iVMAO0kuVGn+Cf8hTgHsKrlQJTZBljm5D2B/yYUqSJ04BXAMzT8hJRIVwGFUqiAN4hTAwVSq4P7+yVASFcAxVKrg1hSoAH5FpQruS6IC+CGhCu4oz/+XqJ7PteZsAOzE8B/ci5aeACdJLlTpqM5tzeJUnudXfEEPb9Lwg3clcBXJhSof0Bwtz/Nfv8xeVKeeIVc9n+es1aqV9YlqPQG2Sy5Uwa28G+wLuSqTVACOJ1TB/tZHmXcxiUjDgNmr5Q9apR9sKcBVnHn2X13XZVmWZVnX9V8Wzme2PCAc53m0f59s5fmyUY3qiLX4y30Bbua0SlVZln3fh5/7vu+6ruu6xSW7rns8HlmWFUXR9/36wnBzn7SeGs9bkmMAjnZOpaqu677vq6oK/2etqipEpcWFQ6J6Pp9d1z2fz7Zt+74falHhXm3bjv8TrFLFiQ6MLx8284w5SMW8bgDfOeeEoPnM2TzPi6JYzFV5nldVNc5JocoV7l7XddM0G7cithOgYKu/9UY/6JXvDUVSvOC/kNpOO21OVVEUk1+H0cCxELPKsnz1OEMOMybIbbnaDMAVnDmnavtiXdeNl5/Hr/EpSK8qXnA94hTAdZxQqVpMPOsZq2ma4V6T+VIhYE2mZ6082vxUwY0+3Er4m/HF+zKJCuACTqhUbaxRDdq2fTweYbp6MB4rbNt2/IB1XXddtziSGCQ1uMsluXgfwDWd2adqbGXArizLUIIKJqloHtGGEcO91xEONq9OSVQA13HanKpPQ8+nXRI+rYfBmVSnAK7vnErV/Fy/0LZqceE8zycJaVi467p5/3Q1Kq5EdQrgLk5r/pmNiknhh3E/z3FUCnPPw6/hT+NHKIpiMo19JZ9BXMQpgBs5rStXaNo5/Dqebx6uSzPujDC+pk02m5w+OTVv0il0LLUuZMTrt2f2af4Jf+cF/4XUdtrJW/u2t+d4ydCtanHh9b8OUju6xOiM6VP7vvKHR/OGIile8F9IbacltrWJHV3i8rc49ZdX7+6v/PCA8+tNxcDbnIN4aX0htZ122tl/Z1lv45nUseen7tXJM9pEBUcYvji87FmXXKjyZuDX9otTf+nsf9BVAVxsgBT44mCj5EIV/NSuBSrDf1vIecBZhCo4xr3G+8bGES22RAVwolguUwO3ckCiiie+xLMmiyJfPeDGVKpgV/ctUAGwTqiC/UhUHGkyXUxNDmIjVMEexCmOp+cqRE6ogr85o0M6ABESquBb4hQAI8mFKh3V2YE4BcBMcqFKbOJPxCkAXkguVMGXxCkAVglVsIGT+wB4R6iCVeIUANu4TA28JlEBsJlKFSwRpwD4kEoVzEhUAHxOpQpGxCkAvqVSBf8lUQHwB8lVqnRUZ4E4BcCfJReqxCb+oaUnADtJLlTBf4hTAOxKqCI9KcWpYbw7/KBSC3AcoYqUpBSnAikK4GeEKtKQXpwC4MeEKhLg5D4AjidUcWviFAC/IlRxU8b7APit5EKV5p/3J04BcIbkQpXYdHPG+wA4SXKhitsSpwA4lQsqcwsSFQBni7pSVdd113VZlpVlWdf1jgtzH+IUAHHIo51jVJZl3/fDr0VRhMw013Xd4/EIy4S7vFo4z+PdXr4hUZ3KG+oUdjsXktrLNdLhv7qu+76vqur5fD6fz6qq+r5/FapCono+n13XPZ/Ptm37vlesurk8/1+iej4lKgBOF2mEnF/8Nc/zlfpTVVXjFBWqXPNNSy0y35YCVRy8oU5ht3Mhqb1c451TVRTF5NfxaOBgmEf1k5XibOIUALGKdPgv25yTwmKTCtZi/OLaxuN9mUQFQHRiDFWLY3zrGatpmuFe67Op8m99uzX82TxOSVQAxCfG4b9Px/Latn08HmG6evBqrDDTUf1aXHAmVsN/M+bTHwGSFWOoWvTq1L8sy8qyfD6fQ4GqrmtTrC5PnIqbFPV7qU34hSuKN1StpKhFeijchDhFNKLKMc/nc1ifqFYsi2994CwxzqnKlsbvQtuqxYXzPJ+UplYWJmrmTsFrIVdlKoUQq0hDVSg7DVEp/DDUorquy/N8+DW0Bg2/hj9lCleXo5knUfr61JaDxLxWQLw127qum6YZfm3bdshY4bo0416gk2vajBcey9Wo46RXAlGK7RMjrE8eX7Eqth1FPFJ7bcS+tdt7e3Zd13VdWZYrC6d2dC9AnCJiUX1i5LFOqMqiXCUikdprI7GtTezoxk6igs/5HONCUnu5xnv2H3cmTgFwO8mFqvU5lUkF6tNIVADcUXKhSmw6kzgFwH0lF6o4h5aeANydUMXBxCkA0iBUcRhxCoCUCFUcQJwCID1CFbsSpwBIlVDFfpzcB0DChCr2IE4BkLzkQpXmn/uTqAAgwVAlNu1JnAKA//p/Z68AlyVRAcBIcpUqdiBOAcCMUMUndEwAgBeEKrYRpwBglVDFO+IUAGwgVPGaOAUAmwlVLBGnAOBDQhUzTu4DgM8lF6p0VF8jTgHAt5ILVanHpleM9wHA3yQXqpgSpwBgD0JV2oz3AcBOhKpUiVMAsCsXVE6SRAUAe1OpSow4BQDHUKlKiUQFAIdRqUqDOAUAB1OpSoBEBQDHS65SlVZHdXEKAH4luVB1t9j0ipaeAPBbyYWq+xOnAOAMZ86pquu6LMuyLOu6/svC+cyWB7yhPJ+O90lUAPArp1WqyrLs+z783Pd913Vd171aOEyEKooiy7KmaZqmSWUUbyPVKQA42zmVqrqu+76vqur5fD6fz6qqQq5aXLgsyyzL2rYNwatt2/AI4a/hXm3bPkcSqlSpTkEyQiV+/AMQlfyUkk/4OBg/dZ7nRVEs5qr1heu63l64yvNztvcoTu4DIGJ3+9p957Q5VWEsb/zrMBq4vuTEkMPWBxDvZlygUp0CgAicOadq45J1XT8ejzzPq6rKsqxpmmyUpYJxJfxVxes+FKgAID4nVKoWE89KxirLcjxFPcuykK6CUN+aTM9aebT5qYIbfb29e1KgAoBYnRCqtteohuUns9qbphkeJExRH2am13W9MpKYZdnzW99t7J4UqAAgYrFc+29lwC4kqlexaR7Rwi23GgFUoAKA6J0WqjaGnrDYJDltiU2f1sPipUAFAFdwTqiaj9CFctR8ycX8NCStruvm/dPvU6NSoAKA6zit+Wc2KiaFH8b9PMdRqSiKpmnGw39934ep62EOe9M0Q5Aa2or+YjOOM2/pCQDE7bSuXKFp5/Br27ZDxnYZpcAAAA7uSURBVOq67vF4jDsjjK9pk82aJkxOzRtPwJq4QBcyF5wB4C4u8LW7q5O3dnHK1BcLh86f4YrLK48Q9dEVpwC4l6i/dg+Q2NbGeXTFKQDuKNKv3cOc1lGdLBOnAOA+kgtV673RfxqoTUUHgBtJLlRFUYcUpwDgdpILVScz3gcANyVU/Yo4BQC3JlT9hPE+ALg7oepg4hQApOG0CyonQaICgGSoVB1DnAKAxKhUHUCiAoD0qFTtSpwCgFQlF6oO7KguUQFAwpILVYd0VBenACB5yYWqnWnpCQBkWSZUfU+cAgBGhKrPiVMAwIxQ9QlxCgB4QajaRpwCAFYJVRs4uQ8AeEeoWiVOAQDbJBeqPmj+KVEBAJslF6o2Nf8UpwCAD7mg8oxEBQB8LrlK1Rpx6oIm47mHXIYIADYQqv5LorqmkKLyPBenADiXUCVOAQA7SDtUaekJAOwk1VAlTgEAu0oyVBnvAwD2lmSoCsQpAGA/yYWq/52Bv9Ra3RlkAMB3kgtVYhMAcAQd1QEAdiBUAQDsIOpQVdd1WZZlWdZ1ve/CAAD7ivfiHmVZ9n0//FoURdd1rxYO08+LosiyLNxrcbtczOSuHFmACKX24Rxppaqu677vq6p6Pp/P57Oqqr7vX4WqsiyzLGvbtuu6ruvatg2P8LvVBQCSF2mEDJWn8brlef6qWLV94dQiczocWYAIpfbhHGmlKvvvWN741/Fo4MqSAAC/F2+oCoN6W4SRvjzP67qu6zoUrlYmYAEA7C7G5p+LeWgyb33yp1DHapom3FJV1asHz5caqW+RVAETAPhUjJWq7TWqYfnJrPamaV49yPNbf98uAODGYgxVi1aG80KiGk73q+t6ZQIWAMAR4g1VGydFhcUmdanwq2lVAMDPRBqq5qWmUI6aL7mYnxaTFgDAcSINVWEsb0hF4YdhgK/runCuX/i1KIqmacbDf33f67MAAPxSvF256roezubLsqxt2yFjdV33eDzG7T03XtMmtS5k6XBkASKU2odz7Fv70UDe24VTO7rpcGQBIpTah3NiW5vY0U2HIwsQodQ+nGNs/nmo9eafSR17AGBHyYUqsQkAOEKkZ/8BAFyLUAUAsAOhCgBgB0IVAMAOhCoAgB0IVQAAOxCqAAB2IFQBAOwgueafOqrfSWoXQAAgZsmFKt/BfxRVjnk+n8P6RLViWXzrA8DRDP9xbSFXZeIyAGdLrlLF360PoZ4lzrUCIB1CFR+LqiYURtkiLFYJeQCpMfzHhY3nLUWVqABIkFDFZ6LKLlGtzETM6wbAEYQqAIAdCFUAADsQqgAAdpDc2X86qgMAR0guVIlNAMARDP8BAOxAqAIA2IFQBQCwA6EKAGAHQhUAwA6EKgCAHQhVAAA7SK5PleafAMARkgtVYhMAcATDfwAAOxCqAAB2cGaoquu6LMuyLOu6Xlksf6HrulcLrD8gAMDuTptTVZZl3/fh577vu64bQhIAwOWcU6mq67rv+6qqns/n8/msqirkqsWFnzNFURRFUZZllmXhXm3bjhdQqQIAfiw/5Wy40Ndg/NR5nhdFsaVYVdd10zTDfSe/vn1eZ//dkiMLEKHUPpxPm1NVFMXk12E0cF3TNFVVDb8OOcwAIgBwojPnVH19r/no3ril58aKFwDAjk6oVC0mno0Zq+/7tm0nt2RZNpmetfJor84lfOuTTQQAknNCpeq7GtVwx8nd27Yd31LXddd1KyOJSQ3uAgA/E0vzzy0DduGEwcmN84g2PisQAOA3TgtVn4aeMI9qe6+Er+thAABfOCdUzc/1W6xCjTVNM7+x67p5/3Q1KgDg905r/pmNikmTc/oWo1KWZYtjf0VRNE0zBKmhregRqw0A8Mo5LRXKsqyqqmma4ay6yTl92b8Fp/Dz4oheSGCPx2O4paoqHdUBgB87udXpSlr69HG6rguXZ15ZLLXWrulwZAEilNqHc2Jbm9jRTYcjCxCh1D6cY2mpAABwaaddpuYs673RkwrUAMCOkgtVYhMAcITkQhU3M5Qeww9CMwBnEaq4NikKgEiYqA4AsAOhCgBgB0IVAMAOhCoAgB0IVQAAO0ju7D/NPwGAIyQXqsQmAOAIhv8AAHYgVAEA7ECoAgDYgVAFALADoQoAYAdCFQDADoQqAIAdCFUAADtIrvmnjuoAwBGSC1ViEwBwBMN/AAA7EKoAAHYgVAEA7ECoAgDYgVAFALADoQoAYAdC1SWtd9u6K1udlDQ33FYnJdkNvzGhCgBgB8k1/9RRHQA4QnKhSmwCAI5g+A8AYAdRh6q6rsuyLMuyruuVxfIXuq770YoCAMmLd/ivLMu+78PPfd93XbdLSMrz/KMRwE+X/81TfCrCrUhzq7+7y6ci3IoIt/qLu9jq4+7yqQi3wlaTRVupquu67/uqqp7P5/P5rKoq5KrFhZ8zRVEURVGW5U9XGgBIWKQZM5yjN163PM+LothSrKrrummaxe2aP+yWNYkw6d/gKSJcJU/hKW6/Sp4iqaeIYZVSK2VFWqnKsqwoismvw2jguqZpqqo6ZqUAAJbFG6q+G7wL91qf2A4AsLsYQ9XiGN/GjNX3fdu268u8Oltw0bD8x5sBAKQkxrP/vp5gHu749u5ObQAAdhdjqFq0ZYp6OGHw7WKflp2+KFN5iiOW9xSe4nJPEeEqeYqkniLCVbq3eEPVp12pwjyq9dlUak4AwEEiHdsKnT8nLRWqqlrJTF+0SwAA2EuME9Wz/xachtlRk3P6uq7L83wesHRSAADOEmmoKssydFEPZ94tntM3Hh8MP2uhDgCcJdLhv4G0BABcQqSVqkFZlikkqrquw5ZuaVu6svC8z9a1+qDutR9i5lhvV5blLpdRP93GDUnhcH90TC/6Avj7Nt7mcKdofjVifmx+QZ6VhYdlhnvN/zo2XJQ6ftv3wzAWPNxlfafFY8dtvPSx3iLsgbZtz16Rv9q4IeuvjXsc7o+O6UVfALts4z0Od5qEqpOFyfXDGyb8+uoNGT52h7+GN+Rw34t+BgUf7YdJmpzsh2jtuI2XPtZvtW07nHRy6W3cviHrr40bHO6PjulFXwB7beMNDnfKhKqTTb47wy2vChjrC4e36CFrebxP98MkQoW4edja7WPHbbz0sX5r/B/0S3+1bN+QG7+1g4+O6UVfAHtt4w0Od8pin1OVgnnZv+/7LUtODAPzXdddcSLCxv1w6XMX9trGqx/rdeGz6e1FPOP30YasvDZucLg/2hUXfQHstY03ONwpi7ejejq254O6rh+PR+iDmmVZ0zTZrPX8+IoBRVFc6D25cT+ExbquGy//KobGZt9tvO6xZu7ta8PhTorDfVEqVWdafJ+sfLaWZRn+O9s0TUhU436n4Ut3PC2j7/tLVHQ+3Q9ZljVNM9zrEufF7LuN1z3WzL19bTjcSXG4L02l6kyfvk/C1XuGy/XUdR2+d8OHctu24wes67rrukuUcD7dD23bPh6Px+Mx3LIyZhqJfbfxuseaubevDYc7KQ73palURWelzDtOVFmW1XU9/qKdfzQPo0i7r+QPrKx2WZbhP3DBM+4Gtiu+3sabHWvmxofS4U6Kw31pKlXn2/hWWZy8HGpXk8k3E1epG3/6kXGJUb+Jo7fxKseauS++Mh3upDjcl6BSdbL5uFUoR82XXPzPypC0Fi8yfaH/2WzfD1mW5Xk++XxZWTgee23j1Y81cyuvDYc7KQ735e3do4HPhFNqh4Y0k35Lk5aP4a+TDoGT+w4tTybtBCP30X4Yb9pwTvKPV/gLO27jpY/1Rrdpgbi4IYvdXF+9Nm5zuLfsivWF4/f3bbzN4U7TBb6Kbm9Sqxi/wSYftc8Pr2VxrffhX/bDVT55d9zGSx/rLS76nTq38i07PtyTlkW3PNwbd8XKwvHbZRvvcbjTlD8vO8n3Zj7qabmycDgZMFyWdbeV+6Ht++G6W7rXNl53D/DKjd/afMThviihCgBgByaqAwDsQKgCANiBUAUAsAOhCgDON1xzjOsSqgDge/NWvYOyLPM8X797aPiZ53m43Gf4eWO6evvgPxDDOsRDqAKAc9R1Ha6bXlVV27Zt24Zudo/H4+1VqsqyjOFKElVV6fsw0FIBAL6X53lRFIu1pXB51pXv2VDmmS8Q7ti27au80nXd4/GI5Bs8z/OVVU2KShUA/E9d1yEh1XU9LhcN3Tj3upp7SCGTZvrDc2Wrl1Sv63py0YVw4+Iazrfo1TYuPkLXdfM1GR4hy7KiKK54hftDnNnOHQAik2XZMKw2uSDj4rdntnQJmvG9Vp7o1R233Hd+navwgK/WcLxF81tWHmF+OZ3JLeHXV6uaFHsBAP5nEqee/71q5yRDDAv8JVStXNdv8qRj8xCzeIH24b7zLZrf8vYRVhZ+zkJesgz/AcDUeI5U0zRFUQxzhsIM8b7v93r8RStTlOb3ret6PIwY7jtZbH6v8S3rj1AUxXh7+76fl+70g8iy7P/OXgEAiMs8MWT/znD6e4B4O6175SnmfxpnoK7rmqaZLDDfoskt648QzlIMs8pezfcSqjKhCgBWhKzQ9/28NBVCxt8ffOVPGx8/nAwYfi6Koqqqea76yyOE1QiT00Occq7fIsN/APBSSA+Lk5/+GCwmY2oTK3+aP+/j8SiKIsxqWjxZ7623jzCsbd/3i/2xxKxMqAKAtyYlpS2t0t9aKfmsdFt4tW6hG8L4lu22PMJ4bTVQeEWoAoA1YVr6kCTqun5VrVlUzwxDe+GR8zwfbgxXrQmPv3L1m2yUeyZBZzyQt9GWRwjLLNbPPhqpvLmfnmsIAHHLllokTCLUpD3BekuFufFg4mI56m17gsmDTFZveMywzHwN57dMHmH4df4s85HQcPv6CifCZWoAYJNDSzKTytO6UNkaD9LN7/7p2r59hLqum6aZx4Z9G81fmlAFANeT57/+Bn91lcPfr0m0zKkCgOtZmXS1u7quw8T8xYsAbp9ednvSJQBc0tCK8wdP1Pd927bzGPezdbgEoQoAYAeG/wAAdiBUAQDsQKgCANiBUAUAsAOhCgBgB0IVAMAOhCoAgB0IVQAAOxCqAAB2IFQBAOxAqAIA2IFQBQCwA6EKAGAHQhUAwA6EKgCAHfx/m4wpt4LzeUsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['lyArrayVsPMTESR'].Draw(\"AP*\")\n",
    "g['lyArrayVsPMTESR'].GetHistogram().GetXaxis().SetTitle(\"rel LO (array)\")\n",
    "g['lyArrayVsPMTESR'].GetHistogram().GetYaxis().SetTitle(\"rel LO bar_{ESR} (PMT)\")\n",
    "f.Draw(\"SAME\")\n",
    "t.DrawLatexNDC(0.1,0.92,\"BTL - LYSO MS3\")\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "de446562",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['lyTOFPETVsPMTESR'].Draw(\"AP*\")\n",
    "f.Draw('SAME')\n",
    "#g['lyTOFPETVsPMT'].Fit('pol1')\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "99800733-7f2c-4dbb-a8ce-e78cf4644413",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nt1dXopWqoiiqqlr8OO8GTqZxVD/ZKQ4lUQGQsHRD1cacFDZbVLCexi9OrCwlKgASl2KoetrjW89YbdtO91ofTVV+6tOj4WsGUQFwBilOqfBuL6/rutvtFoarB696hYW1/05HgQqAk0gxVD316tK/oijquh7HcSpQNU1jiNUVKFABcCrphqqVFPWUORQuRaIC4GxSHFNVPOvfhWmrnm5cluWiNLWyMalbjEmXqAA4iURDVSg7TVEp3JhqUX3fl2U5/RimBg0/hn8qFK5OSoEKfshsohBXoqGqrusQlcJbfRiGrusW28wv96uqqm3bsizDcPXHjUmdAhX83Pivo3cHTi/1qU63z+3Z933f93Vdr2yc29Sup6FAtTMzqgOHyO1TIrOjzezsnoNJE/YnVAGHyO1TIt2r/7g+BSoALiTRMVVcn0QFwLUIVRzBmPSfcD0XwC9l1/5b/5rJqvV7mKsPokpnDME4jtPOpLNXQWr7A/C97EKVz/Ejafn9XMhVhVc+wP6yC1UcJqdElWDfbaddSvBIAY4iVPETV2/5LaRTFgpdtp2KVd+08KQx4HqEKnaWU4EqNfPQk07OA7gqV/+xpywTVTrxJZ09eZTyvgF8RqWKfWQZpwDImUoVO5CoAMiPShVRiVMA5EqlingkKgAyplJFJJlNmgAAC0IVX1OgAoAMQ5W1/yKTqPJj2T6Ap7ILVb4MYtLy+6F0okzK6zQXSe4SkAkD1fmURJUx6zQDPMquUkUEWn4H+Wa9PAsqA+xNqOJNEtVxvlm9OG5Jadd1mr8k5wFHEap4h5Yf1mkGeEGoYhsFqqOlE1/S2ZOnEt894MIMVGcDiQoA/qJSxSpxCgC2UaniNYkKADZTqeIZcQoA3qRSxQOJCgDel12lytp/a8QpAPhUdqEq99i0QqICgC9kF6p4zqyeAPAdoSp7ClQAEIOB6nmTqAAgEpWqjGn5AUA8QlWWFKgAIDbtv/xIVACwA5WqzGj5AcA+VKpyIlEBwG5UqvKg5QcAO1OpyoBEBQD7y65Sldfaf+IUAPxKdqHqarFphUQFAD+UXajKgjgFAD9nTNXlSFQAcASVqmsxaQIAHESougoFKgA41JHtv6Zp6rqu67ppmm82Lh9secBLkagA4GiHVarquh6GIdwehqHv+77vX20c5kGoqqooirZt27bN6CK+P2n5AUACjqlUNU0zDMP9fh/HcRzH+/0ectXTjeu6Loqi67oQvLquC48Q/jXcq+u6cSaXSlVZSlQAkIjykJJPqDzN/3RZllVVPc1V6xs3TbO9cFWWxxzvLrT82GynV/6l3lDADnL7lDhsTFXo5c1/nLqB61suTDlsvYF4NfMCVU6vVwBI1pFjqjZu2TTN7XYry/J+vxdF0bZtMctSwXzxmVcVr+vQ8gOA9BxQqXqaeFYyVl3X8yHqRVGEdBWE+tZieNbKoz1eKrjRx8cbmUQFAEk6IFRtr1FN2y9GtbdtOz1IGKI+jUxvmmalk1gUxfipzw42JsPSASBhqSxTs9KwC4nqVWx6jGjhN1frABqWDgBpOyxUbQw9YbNFctoSm96thyXNsHQ+NTWv0+piA1zRMaHqsUMXylGPWz7NT1PS6vv+cf70y9aoxCnel1wXG+C6Dpv8s5gVk8KN+Xye86hUVVXbtvP23zAMYeh6GMPetu0UpKZpRX9xGHsziAoAzuOwWbnCpJ3Tj13XTRmr7/vb7TafGWG+pk3xMGnCoqkxH4C1cJpZyBZtmlPsM/k5zRsKOEhunxIHH+3TIVMfbBxm/gwrLq88wgnOrjjFeZzgDQUcKrdPicyONvGz6xI/TiX1NxRwtNw+JQ6bUZ0lw6cA4MyEqgQoUAHA+aUy+We+JCoAuITsKlXr8x/+uvWr5QcAV5FdqEpoxJxEBQAXkl2oSoKWHwBcjlD1cxIV5ze10cONhArAAMcRqn5Ly49LkKIAHglVv6JABQCXZkqFn5CoAODqVKp2Jk4BQB5UqvYkUQFANlSq9iFOAUBmVKp2IFEBQH5UqmIzaQIAZCm7ULXj2n8KVACQsexC1V6TFkpUAJC37ELVLrT8ACB7Bqp/TaICAFSqvqLlBwD8R6XqUxIVADCjUvURLT8A4F9C1ZsUqACAZ7T/3iFRAQAvqFRtI04BAKtUqjaQqACAv6hUrRKnAIBtsgtVb6z9J1EBAJtlF6q2rv1n0gQA4B3Zhaq/KVABAO8zUP1fEhUA8BGVqhktPwDgUypV/5GoAIAvqFRp+QEAEWRfqZKoAIAY8q5UafkBAJHkGqoUqACAqLJs/0lUAEBsuVaqCnEKAIgpu1D1v7X/ni0CuHURGwCAf2UXqsSmi1mskO38AnCU7EIVFxNSVFmW4hQAx8pyoDoAQGxCFQBABEIVAEAESYeqpmnquq7rummauBsDAMSV7vDeuq6HYZh+rKqq7/tXG4dLwKqqKooi3OvpcRnOfFXOLECCcvtwTrRS1TTNMAz3+30cx3Ec7/f7MAyvQlVd10VRdF3X933f913XhUf43e4CANlLNEKGytN838qyfFWs2r5xbpE5H84sQIJy+3BOtFJV/NfLm/847waubAkA8HvphqrQ1NsidPrKsmyapmmaULhaGYAFABBdijOqP81Di3Hri38Kday2bcNv7vf7qwcvny35t0VWBUwA4F0pVqq216im7Rej2tu2ffUg46e+Py4A4MJSDFVPrbTzQqKaLvdrmmZlABYAwB7SDVUbB0WFzRZ1qfCjYVUAwM8kGqoeS02hHPW45dP89DRpAQDsJ9FQFXp5UyoKN6YGX9/34Vq/8GNVVW3bztt/wzCYZwEA+KV0Z+Vqmma6mq8oiq7rpozV9/3tdptP77lxTZvcZiHLhzMLkKDcPpxTP9q3Gnl/bpzb2c2HMwuQoNw+nDM72szObj6cWYAE5fbhnOiYKgCAcxGqAAAiEKoAACIQqgAAIkhxQeVdrS+onNV4OgAgouxCldgEAOxB+w8AIAKhCgAgAqEKACACoQoAIAKhCgAgAqEKACACoQoAIAKhCgAgAqEKACACoQoAIILslqmx9h8AsIfsQpXYBADsQfsPACACoQoAIAKhCgAgAqEKACACoQoAIAKhCgAgAqEKACACoYoTW5/KFQB+SajiPUnlmHEcp/1JaseK9PYHgL0JVZzblKvMlQ/AsbJbpsbaf99LswaT5l4BkI/sQpXY9L2knsOyLNMsVgl5ALnR/uPEQqIKt5NKVABkSKjiPUlll6R2ZiHlfQNgD0IVAEAEQhUAQARCFQBABEIVAEAEQhUAQARCFQBABEIVAEAEQhUAQATZLVNj7T8AYA/ZhSqxCQDYg/YfAEAEQhUAQARCFQBABEIVAEAER4aqpmnquq7rummalc3KF/q+f7XB+gMCAER32NV/dV0PwxBuD8PQ9/0UkgAATueYSlXTNMMw3O/3cRzHcbzf7yFXPd14fFBVVVVVdV0XRRHu1XXdfAOVKgDgx8pD5m0KM3DO/3RZllVVbSlWNU3Ttu1038WPf/5d81RdkjMLkKDcPpwPG1NVVdXix6kbuK5t2/v9Pv045TANRADgQEeOqfr4Xo/dvfniMxsrXgAAER1QqXqaeDZmrGEYuq5b/KYoisXwrJVHe3Ut4Z/eOUQAIDsHVKo+q1FNd1zcveu6+W+apun7fqWTmFVzFwD4mVQm/9zSsAsXDC5++RjR5lcFAgD8xmGh6t3QE8ZRbZ8r4eN6GADAB44JVY/X+j2tQs21bfv4y77vH+dPV6MCAH7vsMk/i1kxaXFN39OoVBTF095fVVVt205BappWdI/dBgB45ZgpFeq6vt/vbdtOV9Utrukr/i04hdtPO3ohgd1ut+k39/vdjOoAwI8dPNXpSlp693H6vg/LM69sltvUrvlwZgESlNuHc2ZHm9nZzYczC5Cg3D6cU5lSAQDg1IQqAIAIhCoAgAgOW1D5KOur+GXV+gUAIsouVIlNAMAetP8AACIQqgAAIhCqAAAiEKoAACIQqgAAIhCqAAAiEKoAACIQqgAAIhCqAAAiEKoAACLIbpkaa/9dzHRCww1nEICjZBeqfOlejBMKQCK0/wAAIhCqAAAiEKoAACIQqgAAIhCqAAAiEKoAACIQqgAAIhCqAAAiEKoAACIQqgAAIshumRpr/wEAe8guVIlNAMAetP8AACIQqgAAIhCqAAAiEKoAACIQqgAAIhCqAAAiEKoAACIQqgAAIhCqAAAiEKoAACLIbpkaa/8BAHvILlRdIzaVZXmNA3mLo85KngfuqLOS7YFfmPYfAEAEQhUAQARCFQBABEIVAEAESYeqpmnquq7rummalc3KF/q+/9GOAgDZS/fqv7quh2EIt4dh6Ps+Skh692qLD67O+MGfeFeCR5HnUX92l3cleBQJHvUHd3HU+93lXQkehaOmSLZS1TTNMAz3+30cx3Ec7/d7yFVPNx4fVFVVVVVd1z/daQAgY4lmzDBF53zfyrKsqmpLsappmrZtnx7X48Nu2ZMEk/4F/kSCu+RP+BOX3yV/Iqs/kcIu5VbKSrRSVRRFVVWLH6du4Lq2be/3+z47BQDwXLqh6rPmXbjX+sB2AIDoUgxVT3t8GzPWMAxd161v8+pqwaem7d8+DAAgJyle/ffxAPNwxz/v7tIGACC6FEPVU1uGqIcLBv/c7N2y0wdlKn9ij+39CX/idH8iwV3yJ7L6Ewnu0rWlG6renZUqjKNaH02l5gQA7CTR3laY+XMxpcL9fl/JTB9MlwAAEEuKA9WL/wpO0+ioxTV9fd+XZfkYsMykAAAcJdFQVdd1mEU9XHn39Jq+eX8w3DaFOgBwlETbfxNpCQA4hUQrVZO6rnNIVE3ThCPdMm3pysaP82ydax7UWM9Dypzr7eq6jrKM+uE2HkgOp/utc3rSF8D3x3iZ052jx9WI+bHHBXlWNp62me71+K9z06LU6dv+PEy94Oku609aOiIe46nP9RbhGei67ugd+dbGA1l/bVzjdL91Tk/6AohyjNc43XkSqg4WBtdPb5jw46s3ZPjYnf41vCGn+570Myh463lYpMnF85CsiMd46nP9p67rpotOTn2M2w9k/bVxgdP91jk96Qsg1jFe4HTnTKg62OK7M/zmVQFjfePwFt1lL/f37vOwiFAhbu62d3FEPMZTn+s/zf+Dfuqvlu0HcuG3dvDWOT3pCyDWMV7gdOcs9TFVOXgs+w/DsGXLhakx3/f9GQcibHweTn3tQqxjPPu5Xhc+m/5cxDN9bx3IymvjAqf7rafipC+AWMd4gdOds3RnVM/H9nzQNM3tdgvzoBZF0bZt8TD1/HzFgKqqTvSe3Pg8hM36vp9v/yqGpibuMZ73XPPoz9eG050Vp/ukVKqO9PR9svLZWtd1+O9s27YhUc3nOw1fuvNhGcMwnKKi8+7zUBRF27bTvU5xXUzcYzzvuebRn68NpzsrTvepqVQd6d33SVi9Z1qup2ma8L0bPpS7rps/YNM0fd+fooTz7vPQdd3tdrvdbtNvVnqmiYh7jOc91zz687XhdGfF6T41larkrJR554mqKIqmaeZftI8fzVMXKfpO/sDKbtd1Hf4DF4xpT2C74uNjvNi55tH8VDrdWXG6T02l6ngb3ypPBy+H2tVi8M3CWerG735knKLrt7D3MZ7lXPPog69MpzsrTvcpqFQd7LFvFcpRj1s+/c/KlLSeLjJ9ov/ZbH8eiqIoy3Lx+bKycTpiHePZzzWPVl4bTndWnO7Tiz1HA+8Jl9ROE9Is5ltaTPkY/nUxQ+DivtOUJ4vpBBP31vMwP7TpmuQf7/AHIh7jqc/1RpeZAvHpgTydzfXVa+Myp3vLU7G+cfq+P8bLnO48neCr6PIWtYr5G2zxUTu+uZbFud6H3zwPZ/nkjXiMpz7XW5z0O/XRyrfs/HQvpiy65One+FSsbJy+KMd4jdOdp3I87SDfi3lrTsuVjcPFgGFZ1mg790Pbn4fzHmmsYzzvM8ArF35r8xan+6SEKgCACAxUBwCIQKgCAIhAqAIAiL9wdGkAAACxSURBVECoAgCIQKgCAIhAqAIAiECoAgCIQKgCAIhAqAIAiECoAgCIQKgCAIhAqAIAiECoAgCIQKgCAIhAqAIAiECoAgCIQKgCAIhAqAIAiECoAgCIQKgCAIhAqAIAiECoAgCIQKgCAIhAqAIAiECoAgCIQKgCAIhAqAIAiECoAgCIQKgCAIhAqAIAiECoAgCIQKgCAIhAqAIAiECoAgCIQKgCAIhAqAIAiECoAgCI4P8BRd+sU/qmFw4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['lyArrayVsTOFPETESR'].Draw(\"AP*\")\n",
    "f.Draw('SAME')\n",
    "#g['lyTOFPETVsPMT'].Fit('pol1')\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6caee40b-e42b-4290-aacf-0230c1bd268f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['sigmaTArrayVsTOFPETESR'].Draw(\"AP*\")\n",
    "f.Draw('SAME')\n",
    "#g['lyTOFPETVsPMT'].Fit('pol1')\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "491885c9-8e45-4e2d-bd42-1f29430de8fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['relLossESRTOFPETVsPMT'].Draw(\"AP\")\n",
    "#f.Draw('SAME')\n",
    "#g['lyTOFPETVsPMT'].Fit('pol1')\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c47b8121-58ac-4751-b205-01fe32bde4f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l=R.TLegend(0.7,0.73,0.85,0.88)\n",
    "l.SetBorderSize(0)\n",
    "l.SetTextSize(0.035)\n",
    "l.SetFillColorAlpha(0,0)\n",
    "g['relLossArrayVsTOFPETESR'].Draw(\"AP\")\n",
    "g['relLossArray'].Draw(\"PSAME\")\n",
    "g['relLossArrayVsTOFPETESR'].GetHistogram().GetYaxis().SetTitle(\"Rel. ratio ARRAY/BAR\")\n",
    "g['relLossArrayVsTOFPETESR'].SetMarkerStyle(20)\n",
    "g['relLossArrayVsTOFPETESR'].GetHistogram().GetYaxis().SetRangeUser(0.7,1.3)\n",
    "l.AddEntry(g['relLossArray'],\"BAR Naked\",\"P\")\n",
    "l.AddEntry(g['relLossArrayESR'],\"BAR ESR\",\"P\")\n",
    "l.Draw(\"SAME\")\n",
    "t.DrawLatexNDC(0.1,0.92,\"BTL - LYSO MS3\")\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "88e022f7-4fad-4d17-afe1-84df3c8ba52d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['lyLossTOFPETVsPMTESR'].Draw(\"AP*\")\n",
    "g['lyLossTOFPETVsPMTESR'].GetHistogram().GetXaxis().SetTitle(\"LO_{ESR}/LO_{naked}(TOFPET)\")\n",
    "g['lyLossTOFPETVsPMTESR'].GetHistogram().GetYaxis().SetTitle(\"LO_{ESR}/LO_{naked}(PMT)\")\n",
    "f.Draw(\"SAME\")\n",
    "t.DrawLatexNDC(0.1,0.92,\"BTL - LYSO MS3\")\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e607f641-9da4-49b2-ac45-9964944f08a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "g['ly_rms']=R.TGraphErrors()\n",
    "g['ly_q5']=R.TGraphErrors()\n",
    "g['ly_q5overRMS']=R.TGraphErrors()\n",
    "prob=np.array([0.05,0.5-0.683/2,0.5,0.5+0.683/2.,0.95])\n",
    "q=np.array([0.,0.,0.,0.,0.])\n",
    "for ip,p in enumerate(producers):\n",
    "    g['ly_array_allxtals_%s'%p]=files['array'].Get(\"h1_ly_byProd_%s_geo2_PREIRR\"%p)\n",
    "    g['ly_array_allxtals_%s'%p].GetQuantiles(5,q,prob)\n",
    "    g['ly_rms'].SetPoint(ip,ip,g['ly_array_allxtals_%s'%p].GetRMS()/g['ly_array_allxtals_%s'%p].GetMean())\n",
    "    g['ly_rms'].SetPointError(ip,0.,0.005)\n",
    "    g['ly_q5'].SetPoint(ip,ip,(g['ly_array_allxtals_%s'%p].GetMean()-q[0])/g['ly_array_allxtals_%s'%p].GetMean())\n",
    "    g['ly_q5'].SetPointError(ip,0.,0.005)\n",
    "    g['ly_q5overRMS'].SetPoint(ip,ip,(g['ly_array_allxtals_%s'%p].GetMean()-q[0])/g['ly_array_allxtals_%s'%p].GetMean())\n",
    "    g['ly_q5overRMS'].SetPointError(ip,0.,0.01)\n",
    "\n",
    "for k in ['ly_rms','ly_q5','ly_q5overRMS']:\n",
    "    g[k].GetHistogram().SetBins(len(data['ly_array']),-0.5,len(data['ly_array'])-0.5)\n",
    "    for i in range(len(producers)):\n",
    "        g[k].GetHistogram().GetXaxis().SetBinLabel(i+1,producers[i])\n",
    "    g[k].SetMarkerStyle(24)\n",
    "    g[k].SetMarkerColor(1)\n",
    "    g[k].SetLineColor(1)\n",
    "    g[k].SetMarkerSize(0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "082b2afe-7022-4634-856a-bd559ea9a4f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g['ly_rms'].Draw(\"AP\")\n",
    "g['ly_rms'].GetHistogram().GetYaxis().SetTitle(\"LO_{array} (RMS)\")\n",
    "#f.Draw(\"SAME\")\n",
    "#t.DrawLatexNDC(0.1,0.92,\"BTL - LYSO MS3\")\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b9a8f5d9-9a32-4608-9704-f5e48d9a89ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l2=R.TLegend(0.69,0.83,0.9,0.88)\n",
    "l2.SetFillColorAlpha(0,0)\n",
    "l2.SetBorderSize(0)\n",
    "l2.SetTextSize(0.035)\n",
    "g['ly_q5overRMS'].Draw(\"AP\")\n",
    "g['ly_q5overRMS'].GetHistogram().GetYaxis().SetTitle(\"LO_{array} 1-Q(0.05)/#mu\")\n",
    "f1=R.TF1('f1','1.62',0,10)\n",
    "f1.Draw(\"SAME\")\n",
    "l2.AddEntry(f1,\"gaussian PDF\",\"L\")\n",
    "#l2.Draw()\n",
    "#t.DrawLatexNDC(0.1,0.92,\"BTL - LYSO MS3\")\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "abdae553-04eb-4fa7-8af6-28b49fe1560c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving ly_array\n",
      "Saving sigmaT_array\n",
      "Saving ly_pmt\n",
      "Saving ly_esr_pmt\n",
      "Saving tau_pmt\n",
      "Saving ly_tofpet\n",
      "Saving ly_esr_tofpet\n",
      "Saving sigmaT_esr_tofpet\n",
      "Saving relLossArray\n",
      "Saving relLossArrayESR\n",
      "Saving maxlyDTArray\n",
      "Saving lyArrayVsPMT\n",
      "Saving lyArrayVsPMTESR\n",
      "Saving lyArrayVsTOFPETESR\n",
      "Saving sigmaTArrayVsTOFPETESR\n",
      "Saving relLossESRTOFPETVsPMT\n",
      "Saving relLossArrayVsTOFPETESR\n",
      "Saving lyDTArray\n",
      "Saving sigmaTVsLYDT\n",
      "Saving lyTOFPETVsPMT\n",
      "Saving lyTOFPETVsPMTESR\n",
      "Saving lyLossTOFPETVsPMTESR\n",
      "Saving ly_rms\n",
      "Saving ly_q5\n",
      "Saving ly_q5overRMS\n",
      "Saving ly_array_allxtals_prod1\n",
      "Saving ly_array_allxtals_prod2\n",
      "Saving ly_array_allxtals_prod3\n",
      "Saving ly_array_allxtals_prod4\n",
      "Saving ly_array_allxtals_prod5\n",
      "Saving ly_array_allxtals_prod6\n",
      "Saving ly_array_allxtals_prod8\n",
      "Saving ly_array_allxtals_prod9\n",
      "Saving ly_array_allxtals_prod10\n"
     ]
    }
   ],
   "source": [
    "out=R.TFile('data/MS3.root','RECREATE')\n",
    "for h,hh in g.items():\n",
    "    print('Saving %s'%h)\n",
    "    hh.SetName(h)\n",
    "    hh.Write()\n",
    "out.Close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc17143d-2101-4bae-b1eb-a4e1d763e927",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7642948",
   "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
}