LYSO_OPT2_array_with_grease.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['PMT']=R.TFile(\"LYSO_OPT_OPT2_PMT.root\")\n",
    "files['MS3_DRY']=R.TFile('data/MS3.root')\n",
    "files['OPT_OPT2_DRY']=R.TFile('LYSO_ARRAY_DRY_OPT_OPT2.root')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d2190f00-9430-45a6-92b3-44cf5d73a60b",
   "metadata": {},
   "outputs": [],
   "source": [
    "g={}\n",
    "for tag in ['MS3','OPT', 'OPT2','OPT2bis']:\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_OPT2_DRY'].Get('LY_OPT_DRY')\n",
    "g['LY_OPT2_DRY']=files['OPT_OPT2_DRY'].Get('LY_OPT2_DRY')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
    "arrays['OPT2bis']={\n",
    "    811:{'file':'data/tree_FirstRun007074_LastRun007077_ARRAY000811_OVSCAN_SUMMARY.root','prod':1},\n",
    "    822:{'file':'data/tree_FirstRun007080_LastRun007083_ARRAY000822_OVSCAN_SUMMARY.root','prod':5},\n",
    "    831:{'file':'data/tree_FirstRun007086_LastRun007089_ARRAY000831_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": 5,
   "id": "62211d81-f364-4d00-97df-0ba7b55e1b68",
   "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]=0.1, y[0]=4332.2, ex[0]=0, ey[0]=103.095\n",
      "x[1]=1.1, y[1]=4419.88, ex[1]=0, ey[1]=92.771\n",
      "x[2]=2.1, y[2]=3973.39, ex[2]=0, ey[2]=92.4805\n",
      "x[3]=3.1, y[3]=4580.49, ex[3]=0, ey[3]=48.1255\n",
      "x[4]=4.1, y[4]=4249.62, ex[4]=0, ey[4]=197.21\n",
      "x[5]=5.1, y[5]=4721.04, ex[5]=0, ey[5]=91.7828\n",
      "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"
     ]
    }
   ],
   "source": [
    "for tag in ['MS3','OPT','OPT2','OPT2bis']:\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','OPT2bis']:\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",
    "        elif (tag=='OPT2bis'):\n",
    "            tau=g['tau_OPT2_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",
    "        elif(tag=='OPT2bis'):\n",
    "            pos+=0.3\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])\n",
    "        \n",
    "g['LY_MS3'].Print()\n",
    "g['LY_OPT'].Print()\n",
    "g['LY_OPT2'].Print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f47c5a7f-0beb-4c93-b8d1-f630f1eba4aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 4332.1987164360635 0.7830257093592288 3949.3398168315557 0.8497261839751226\n",
      "3 4419.884756041635 1.0610635620696511 3825.939483034411 1.0049200652610786\n",
      "4 3973.3939881484903 0.9003008916263927 3744.4647840520242 1.0139715795188575\n",
      "5 4580.492510340601 0.9569671263485415 4615.698030449256 0.9546059554381784\n",
      "9 4249.621201724178 1.015656232995119 3924.518019756242 1.015656232995119\n",
      "10 4721.035328173878 1.078853965767325 4269.835933536657 0.9590313731024819\n",
      "x[0]=0, y[0]=1.19038, ex[0]=0, ey[0]=0.07\n",
      "x[1]=1, y[1]=1.09411, ex[1]=0, ey[1]=0.07\n",
      "x[2]=2, y[2]=1.19512, ex[2]=0, ey[2]=0.07\n",
      "x[3]=3, y[3]=0.989924, ex[3]=0, ey[3]=0.07\n",
      "x[4]=4, y[4]=1.08284, ex[4]=0, ey[4]=0.07\n",
      "x[5]=5, y[5]=0.98287, ex[5]=0, ey[5]=0.07\n"
     ]
    }
   ],
   "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",
    "    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": 7,
   "id": "60d60cc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 4354.8247809046725 0.8970757748102424 3949.3398168315557 0.8497261839751226\n",
      "5 4265.540987665711 0.9043813664721796 4615.698030449256 0.9546059554381784\n",
      "10 4740.439022148354 1.1553751052428083 4269.835933536657 0.9590313731024819\n",
      "x[0]=0, y[0]=1.04447, ex[0]=0, ey[0]=0.07\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]=0.97546, ex[3]=0, ey[3]=0.07\n",
      "x[4]=0, y[4]=0, ex[4]=0, ey[4]=0\n",
      "x[5]=5, y[5]=0.921546, ex[5]=0, ey[5]=0.07\n"
     ]
    }
   ],
   "source": [
    "g['relGain_LY_OPT2']=R.TGraphErrors(6)\n",
    "for v,iv in map_prod.items():\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_opt2=g['lyNorm_OPT2_PMT'].GetY()[iv]\n",
    "    else:\n",
    "        ly_pmt_opt2=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_opt2>0):\n",
    "        print(v,ly_arr_opt2,ly_pmt_opt2,ly_arr_ms3,ly_pmt_ms3)\n",
    "        g['relGain_LY_OPT2'].SetPoint(iv,iv,(ly_arr_opt2/ly_pmt_opt2)/(ly_arr_ms3/ly_pmt_ms3))\n",
    "        g['relGain_LY_OPT2'].SetPointError(iv,0,0.07)\n",
    "g['relGain_LY_OPT2'].Print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e78af4fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 4337.246367567669 0.8970757748102424 3949.3398168315557 0.8497261839751226\n",
      "5 4187.295673092939 0.9043813664721796 4615.698030449256 0.9546059554381784\n",
      "10 4501.983587177374 1.1553751052428083 4269.835933536657 0.9590313731024819\n",
      "x[0]=0, y[0]=1.04025, ex[0]=0, ey[0]=0.07\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]=0.957566, ex[3]=0, ey[3]=0.07\n",
      "x[4]=0, y[4]=0, ex[4]=0, ey[4]=0\n",
      "x[5]=5, y[5]=0.87519, ex[5]=0, ey[5]=0.07\n"
     ]
    }
   ],
   "source": [
    "g['relGain_LY_OPT2bis']=R.TGraphErrors(6)\n",
    "for v,iv in map_prod.items():\n",
    "    ly_arr_ms3=g['LY_MS3'].GetY()[iv]\n",
    "    ly_arr_OPT2bis=g['LY_OPT2bis'].GetY()[iv]\n",
    "    if (v!=9):\n",
    "        ly_pmt_OPT2bis=g['lyNorm_OPT2_PMT'].GetY()[iv]\n",
    "    else:\n",
    "        ly_pmt_OPT2bis=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_OPT2bis>0):\n",
    "        print(v,ly_arr_OPT2bis,ly_pmt_OPT2bis,ly_arr_ms3,ly_pmt_ms3)\n",
    "        g['relGain_LY_OPT2bis'].SetPoint(iv,iv,(ly_arr_OPT2bis/ly_pmt_OPT2bis)/(ly_arr_ms3/ly_pmt_ms3))\n",
    "        g['relGain_LY_OPT2bis'].SetPointError(iv,0,0.07)\n",
    "g['relGain_LY_OPT2bis'].Print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "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]=0.1, y[0]=1.67511, ex[0]=0.03, ey[0]=0.03\n",
      "x[1]=1.1, y[1]=1.60296, ex[1]=0.03, ey[1]=0.03\n",
      "x[2]=2.1, y[2]=1.59659, ex[2]=0.03, ey[2]=0.03\n",
      "x[3]=3.1, y[3]=1.69961, ex[3]=0.03, ey[3]=0.03\n",
      "x[4]=4.1, y[4]=1.57512, ex[4]=0.03, ey[4]=0.03\n",
      "x[5]=5.1, 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",
    "    if g['LY_OPT_DRY'].GetY()[iv]>0:\n",
    "        ly_arr_grease=g['LY_OPT'].GetY()[iv]/g['LY_OPT_DRY'].GetY()[iv]\n",
    "    else:\n",
    "        ly_arr_grease=0\n",
    "    if (ly_arr_grease>0):\n",
    "        g['LY_DRY_GREASE_OPT'].SetPoint(iv,iv+0.1,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": 11,
   "id": "7e7bba8f",
   "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]=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]=0.951704, 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]=1.09689, ex[5]=0.03, ey[5]=0.03\n",
      "x[0]=0.2, y[0]=1.60216, 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]=3.2, y[3]=1.64895, ex[3]=0.03, ey[3]=0.03\n",
      "x[4]=0, y[4]=0, ex[4]=0, ey[4]=0\n",
      "x[5]=5.2, y[5]=1.58997, 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['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[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[v]]>0:\n",
    "        ly_arr_grease_opt2=g['LY_OPT2'].GetY()[iv]/g['LY_OPT2_DRY'].GetY()[map_prod[v]]\n",
    "    else:\n",
    "        ly_arr_grease_opt2=0\n",
    "    if (ly_arr_grease_opt2>0):\n",
    "        g['LY_DRY_GREASE_OPT2'].SetPoint(iv,iv+0.2,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": 12,
   "id": "488c9765",
   "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]=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]=0.965427, y[3]=0.951704, ex[3]=0.03, ey[3]=0.03\n",
      "x[4]=0, y[4]=0, ex[4]=0, ey[4]=0\n",
      "x[5]=1.03798, y[5]=1.09689, ex[5]=0.03, ey[5]=0.03\n",
      "x[0]=0.3, y[0]=1.59569, 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]=3.3, y[3]=1.6187, ex[3]=0.03, ey[3]=0.03\n",
      "x[4]=0, y[4]=0, ex[4]=0, ey[4]=0\n",
      "x[5]=5.3, y[5]=1.50999, ex[5]=0.03, ey[5]=0.03\n"
     ]
    }
   ],
   "source": [
    "map_prod_OPT2bis_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['rel_OPT2bis_DRY_GREASE']=R.TGraphErrors(6)\n",
    "for v,iv in map_prod.items():\n",
    "    ly_arr_grease_OPT2bis=g['LY_OPT2bis'].GetY()[iv]/g['LY_OPT2bis'].GetY()[0]\n",
    "    ly_arr_dry_OPT2bis=g['LY_OPT2_DRY'].GetY()[map_prod[v]]/g['LY_OPT2_DRY'].GetY()[0]\n",
    "    if (ly_arr_grease_OPT2bis>0 and ly_arr_dry_OPT2bis>0):\n",
    "        g['rel_OPT2bis_DRY_GREASE'].SetPoint(iv,ly_arr_grease_OPT2bis,ly_arr_dry_OPT2bis)\n",
    "        g['rel_OPT2bis_DRY_GREASE'].SetPointError(iv,0.03,0.03)\n",
    "g['rel_OPT2bis_DRY_GREASE'].Print()\n",
    "\n",
    "g['LY_DRY_GREASE_OPT2bis']=R.TGraphErrors(6)\n",
    "for v,iv in map_prod.items():\n",
    "    if g['LY_OPT2_DRY'].GetY()[map_prod[v]]>0:\n",
    "        ly_arr_grease_OPT2bis=g['LY_OPT2bis'].GetY()[iv]/g['LY_OPT2_DRY'].GetY()[map_prod[v]]\n",
    "    else:\n",
    "        ly_arr_grease_OPT2bis=0\n",
    "    if (ly_arr_grease_OPT2bis>0):\n",
    "        g['LY_DRY_GREASE_OPT2bis'].SetPoint(iv,iv+0.3,ly_arr_grease_OPT2bis)\n",
    "        g['LY_DRY_GREASE_OPT2bis'].SetPointError(iv,0.03,0.03)\n",
    "g['LY_DRY_GREASE_OPT2bis'].Print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b4a1ac93-f0ff-4022-8515-f5637c0b88ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "c1=R.TCanvas('c1','c1',800,600)\n",
    "l=R.TLegend(0.725,0.7,0.9,0.88)\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": 14,
   "id": "f9eeb305-e93f-4107-a86a-30d020a1d15c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in ['MS3','OPT','OPT2','OPT2bis']:\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','relGain_LY_OPT2']:\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": 15,
   "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",
    "g['LY_OPT2'].SetMarkerStyle(22)\n",
    "g['LY_OPT2'].SetMarkerSize(1.2)\n",
    "l.AddEntry(g['LY_OPT2'],\"OPT2\",\"PL\")\n",
    "g['LY_OPT2'].Draw(\"PSAME\")\n",
    "g['LY_OPT2bis'].SetMarkerStyle(26)\n",
    "g['LY_OPT2bis'].SetMarkerSize(1.2)\n",
    "l.AddEntry(g['LY_OPT2bis'],\"OPT2bis\",\"PL\")\n",
    "g['LY_OPT2bis'].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(0.15,0.78,'<OPT> = %.0f ph/MeV'%(R.TMath.Mean(6,g['LY_OPT'].GetY())))\n",
    "tL.DrawLatexNDC(0.15,0.73,'<OPT2> = %.0f ph/MeV'%(R.TMath.Mean(6,g['LY_OPT2'].GetY())*2))#multiply by 2 because half of elements are empty\n",
    "tL.DrawLatexNDC(0.15,0.68,'<OPT2bis> = %.0f ph/MeV'%(R.TMath.Mean(6,g['LY_OPT2bis'].GetY())*2))#multiply by 2 because half of elements are empty\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": 16,
   "id": "b14f5389-a5eb-458e-971f-9024a136e926",
   "metadata": {
    "scrolled": false
   },
   "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",
    "g['LY_DRY_GREASE_OPT2'].SetMarkerStyle(22)\n",
    "g['LY_DRY_GREASE_OPT2'].SetMarkerSize(1.2)\n",
    "g['LY_DRY_GREASE_OPT2'].Draw(\"PSAME\")\n",
    "g['LY_DRY_GREASE_OPT2bis'].SetMarkerStyle(26)\n",
    "g['LY_DRY_GREASE_OPT2bis'].SetMarkerSize(1.2)\n",
    "g['LY_DRY_GREASE_OPT2bis'].Draw(\"PSAME\")\n",
    "l.Draw()\n",
    "tL.DrawLatexNDC(0.11,0.92,'CMS MTD - POST MS3 Optimisation - Array Dry/Grease coupling')\n",
    "tL.DrawLatexNDC(0.15,0.84,'<MS3> = %.2f'%(R.TMath.Mean(6,g['LY_DRY_GREASE_MS3'].GetY())))\n",
    "tL.DrawLatexNDC(0.15,0.80,'<OPT> = %.2f'%(R.TMath.Mean(6,g['LY_DRY_GREASE_OPT'].GetY())))\n",
    "tL.DrawLatexNDC(0.15,0.76,'<OPT2> = %.2f'%(R.TMath.Mean(6,g['LY_DRY_GREASE_OPT2'].GetY())*2))#multiply by 2 because half of elements are empty\n",
    "tL.DrawLatexNDC(0.15,0.72,'<OPT2bis> = %.2f'%(R.TMath.Mean(6,g['LY_DRY_GREASE_OPT2bis'].GetY())*2))#multiply by 2 because half of elements are empty\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": 17,
   "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",
    "g['sigmaT_OPT2'].SetMarkerStyle(22)\n",
    "g['sigmaT_OPT2'].SetMarkerSize(1.2)\n",
    "g['sigmaT_OPT2'].Draw(\"PSAME\")\n",
    "g['sigmaT_OPT2bis'].SetMarkerStyle(26)\n",
    "g['sigmaT_OPT2bis'].SetMarkerSize(1.2)\n",
    "g['sigmaT_OPT2bis'].Draw(\"PSAME\")\n",
    "l.Draw()\n",
    "tL.DrawLatexNDC(0.11,0.92,'CMS MTD - POST MS3 Optimisation - Array Grease coupling')\n",
    "tL.DrawLatexNDC(0.15,0.84,'<MS3> = %.0f ps'%(R.TMath.Mean(6,g['sigmaT_MS3'].GetY())))\n",
    "tL.DrawLatexNDC(0.15,0.80,'<OPT> = %.0f ps'%(R.TMath.Mean(6,g['sigmaT_OPT'].GetY())))\n",
    "tL.DrawLatexNDC(0.15,0.76,'<OPT2> = %.0f ps'%(R.TMath.Mean(6,g['sigmaT_OPT2'].GetY())*2))#multiply by 2 because half of elements are empty\n",
    "tL.DrawLatexNDC(0.15,0.72,'<OPT2bis> = %.0f ps'%(R.TMath.Mean(6,g['sigmaT_OPT2bis'].GetY())*2))#multiply by 2 because half of elements are empty\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": 18,
   "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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\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",
    "g['sigmaT_4V_OPT2'].SetMarkerStyle(22)\n",
    "g['sigmaT_4V_OPT2'].SetMarkerSize(1.2)\n",
    "g['sigmaT_4V_OPT2'].Draw(\"PSAME\")\n",
    "g['sigmaT_4V_OPT2bis'].SetMarkerStyle(26)\n",
    "g['sigmaT_4V_OPT2bis'].SetMarkerSize(1.2)\n",
    "g['sigmaT_4V_OPT2bis'].Draw(\"PSAME\")\n",
    "l.Draw()\n",
    "tL.DrawLatexNDC(0.11,0.92,'CMS MTD - POST MS3 Optimisation - Array Grease coupling')\n",
    "tL.DrawLatexNDC(0.15,0.84,'<MS3> = %.0f ps'%(R.TMath.Mean(6,g['sigmaT_4V_MS3'].GetY())))\n",
    "tL.DrawLatexNDC(0.15,0.80,'<OPT> = %.0f ps'%(R.TMath.Mean(6,g['sigmaT_4V_OPT'].GetY())))\n",
    "tL.DrawLatexNDC(0.15,0.76,'<OPT2> = %.0f ps'%(R.TMath.Mean(6,g['sigmaT_4V_OPT2'].GetY())*2))#multiply by 2 because half of elements are empty\n",
    "tL.DrawLatexNDC(0.15,0.72,'<OPT2bis> = %.0f ps'%(R.TMath.Mean(6,g['sigmaT_4V_OPT2bis'].GetY())*2))#multiply by 2 because half of elements are empty\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": 19,
   "id": "412cc606-aa23-4fec-9311-40ae1e2f0c21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=0, y[0]=95.1625, ex[0]=0, ey[0]=2.52311\n",
      "x[1]=1, y[1]=98.9939, ex[1]=0, ey[1]=3.56436\n",
      "x[2]=2, y[2]=90.2181, ex[2]=0, ey[2]=3.43079\n",
      "x[3]=3, y[3]=118.542, ex[3]=0, ey[3]=1.98364\n",
      "x[4]=4, y[4]=94.7121, ex[4]=0, ey[4]=3.78897\n",
      "x[5]=5, y[5]=105.182, ex[5]=0, ey[5]=4.02043\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": {
      "image/png": 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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",
    "g['LYDT_OPT2'].SetMarkerStyle(22)\n",
    "g['LYDT_OPT2'].SetMarkerSize(1.2)\n",
    "g['LYDT_OPT2'].Draw(\"PSAME\")\n",
    "g['LYDT_OPT2bis'].SetMarkerStyle(26)\n",
    "g['LYDT_OPT2bis'].SetMarkerSize(1.2)\n",
    "g['LYDT_OPT2bis'].Draw(\"PSAME\")\n",
    "l.Draw()\n",
    "tL.DrawLatexNDC(0.11,0.92,'CMS MTD - POST MS3 Optimisation - Array Grease Coupling')\n",
    "tL.DrawLatexNDC(0.15,0.84,'<MS3> = %.0f ph/MeV/ns'%(R.TMath.Mean(6,g['LYDT_MS3'].GetY())))\n",
    "tL.DrawLatexNDC(0.15,0.80,'<OPT> = %.0f ph/MeV/ms'%(R.TMath.Mean(6,g['LYDT_OPT'].GetY())))\n",
    "tL.DrawLatexNDC(0.15,0.76,'<OPT2> = %.0f ph/Mev/ms'%(R.TMath.Mean(6,g['LYDT_OPT2'].GetY())*2))#multiply by 2 because half of elements are empty\n",
    "tL.DrawLatexNDC(0.15,0.72,'<OPT2bis> = %.0f ph/Mev/ms'%(R.TMath.Mean(6,g['LYDT_OPT2bis'].GetY())*2))#multiply by 2 because half of elements are empty\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": 20,
   "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",
    "g['XT_OPT2'].SetMarkerStyle(22)\n",
    "g['XT_OPT2'].SetMarkerSize(1.2)\n",
    "g['XT_OPT2'].Draw(\"PSAME\")\n",
    "g['XT_OPT2bis'].SetMarkerStyle(26)\n",
    "g['XT_OPT2bis'].SetMarkerSize(1.2)\n",
    "g['XT_OPT2bis'].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": 21,
   "id": "b57c06a4-c90f-46fa-8955-5a353dee9cc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=95.1625, y[0]=96.8065, ex[0]=2.52311, ey[0]=3.88181\n",
      "x[1]=98.9939, y[1]=99.3941, ex[1]=3.56436, ey[1]=5.24674\n",
      "x[2]=90.2181, y[2]=99.799, ex[2]=3.43079, ey[2]=4.1517\n",
      "x[3]=118.542, y[3]=91.1651, ex[3]=1.98364, ey[3]=3.15793\n",
      "x[4]=94.7121, y[4]=101.733, ex[4]=3.78897, ey[4]=9.05018\n",
      "x[5]=105.182, y[5]=92.061, ex[5]=4.02043, ey[5]=6.67867\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",
    "g['sigmaTvsLYDT_OPT2'].SetMarkerStyle(22)\n",
    "g['sigmaTvsLYDT_OPT2'].SetMarkerSize(1.2)\n",
    "g['sigmaTvsLYDT_OPT2'].Draw(\"PSAME\")\n",
    "g['sigmaTvsLYDT_OPT2bis'].SetMarkerStyle(26)\n",
    "g['sigmaTvsLYDT_OPT2bis'].SetMarkerSize(1.2)\n",
    "g['sigmaTvsLYDT_OPT2bis'].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": 22,
   "id": "a61046ac-7782-4c31-aa20-cf35e686309a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x[0]=95.1625, y[0]=118.983, ex[0]=2.52311, ey[0]=4.49715\n",
      "x[1]=98.9939, y[1]=124.461, ex[1]=3.56436, ey[1]=7.28023\n",
      "x[2]=90.2181, y[2]=130.78, ex[2]=3.43079, ey[2]=5.41889\n",
      "x[3]=118.542, y[3]=104.711, ex[3]=1.98364, ey[3]=3.81231\n",
      "x[4]=94.7121, y[4]=128.895, ex[4]=3.78897, ey[4]=10.4804\n",
      "x[5]=105.182, y[5]=109.975, ex[5]=4.02043, ey[5]=6.55757\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",
    "g['sigmaT4VvsLYDT_OPT2'].SetMarkerStyle(22)\n",
    "g['sigmaT4VvsLYDT_OPT2'].SetMarkerSize(1.2)\n",
    "g['sigmaT4VvsLYDT_OPT2'].Draw(\"PSAME\")\n",
    "g['sigmaT4VvsLYDT_OPT2bis'].SetMarkerStyle(26)\n",
    "g['sigmaT4VvsLYDT_OPT2bis'].SetMarkerSize(1.2)\n",
    "g['sigmaT4VvsLYDT_OPT2bis'].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": 23,
   "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": 24,
   "id": "7b1f2602",
   "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_OPT2'].Draw(\"AP\")\n",
    "g['relGain_LY_OPT2'].GetYaxis().SetTitle('Rel. LY gain OPT2/MS3')\n",
    "g['relGain_LY_OPT2'].GetYaxis().SetLimits(0.85,1.35)\n",
    "g['relGain_LY_OPT2'].GetYaxis().SetRangeUser(0.85,1.35)\n",
    "g['relGain_LY_OPT2'].SetMarkerStyle(22)\n",
    "g['relGain_LY_OPT2'].SetMarkerSize(1.2)\n",
    "tL.DrawLatexNDC(0.11,0.92,'CMS MTD - OPT2')\n",
    "tL.DrawLatexNDC(0.15,0.83,'<rel LY> = %.2f'%R.TMath.Mean(6,g['relGain_LY_OPT2'].GetY()))\n",
    "c1.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
    "g['rel_OPT2_DRY_GREASE'].SetMarkerStyle(22)\n",
    "g['rel_OPT2_DRY_GREASE'].SetMarkerSize(1.2)\n",
    "g['rel_OPT2_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": 26,
   "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",
    "    if \"OPT2bis_PMT\" in hn:\n",
    "        continue\n",
    "    h.Write(hn)\n",
    "fOut.Close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "775bd338",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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