LYSO_OPT2_array_with_grease_average_over_producer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "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": 4,
   "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": 5,
   "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",
    "arrays['OPT2avg']={\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",
    "    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",
    "\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": 19,
   "id": "62211d81-f364-4d00-97df-0ba7b55e1b68",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104.71169703846552\n",
      "137.7563237509357\n",
      "142.39339134314162\n",
      "77.23715822825368\n",
      "157.00087435378626\n",
      "163.20890586940192\n",
      "MS3 LY [1, 1, 1, 1, 1, 1]\n",
      "MS3 LYDT [1, 1, 1, 1, 1, 1]\n",
      "MS3 sigmaT [1, 1, 1, 1, 1, 1]\n",
      "MS3 XT [1, 1, 1, 1, 1, 1]\n",
      "MS3 sigmaT_4V [1, 1, 1, 1, 1, 1]\n",
      "103.09481016561267\n",
      "92.7710154493997\n",
      "92.48051042709419\n",
      "48.12550346083652\n",
      "197.20976531073538\n",
      "91.7828208942994\n",
      "OPT LY [1, 1, 1, 1, 1, 1]\n",
      "OPT LYDT [1, 1, 1, 1, 1, 1]\n",
      "OPT sigmaT [1, 1, 1, 1, 1, 1]\n",
      "OPT XT [1, 1, 1, 1, 1, 1]\n",
      "OPT sigmaT_4V [1, 1, 1, 1, 1, 1]\n",
      "253.86512281360362\n",
      "160.52763430324399\n",
      "161.56942800682046\n",
      "OPT2 LY [1, 0, 0, 1, 0, 1]\n",
      "OPT2 LYDT [1, 0, 0, 1, 0, 1]\n",
      "OPT2 sigmaT [1, 0, 0, 1, 0, 1]\n",
      "OPT2 XT [1, 0, 0, 1, 0, 1]\n",
      "OPT2 sigmaT_4V [1, 0, 0, 1, 0, 1]\n",
      "218.4548320998602\n",
      "52.64498392746241\n",
      "115.69343987703192\n",
      "OPT2bis LY [1, 0, 0, 1, 0, 1]\n",
      "OPT2bis LYDT [1, 0, 0, 1, 0, 1]\n",
      "OPT2bis sigmaT [1, 0, 0, 1, 0, 1]\n",
      "OPT2bis XT [1, 0, 0, 1, 0, 1]\n",
      "OPT2bis sigmaT_4V [1, 0, 0, 1, 0, 1]\n",
      "253.86512281360362\n",
      "160.52763430324399\n",
      "161.56942800682046\n",
      "218.4548320998602\n",
      "52.64498392746241\n",
      "115.69343987703192\n",
      "OPT2avg LY [2, 0, 0, 2, 0, 2]\n",
      "OPT2avg LYDT [2, 0, 0, 2, 0, 2]\n",
      "OPT2avg sigmaT [2, 0, 0, 2, 0, 2]\n",
      "OPT2avg XT [2, 0, 0, 2, 0, 2]\n",
      "OPT2avg sigmaT_4V [2, 0, 0, 2, 0, 2]\n",
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      "x[0]=0, y[0]=0, ex[0]=0, ey[0]=0\n",
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      "x[5]=0, y[5]=0, ex[5]=0, ey[5]=0\n",
      "x[0]=0.4, y[0]=4354.82, ex[0]=0, ey[0]=64447.5\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",
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      "x[5]=0, y[5]=0, ex[5]=0, ey[5]=0\n",
      "x[0]=0.4, y[0]=4354.82, ex[0]=0, ey[0]=64447.5\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]=3.4, y[3]=4265.54, ex[3]=0, ey[3]=25769.1\n",
      "x[4]=0, y[4]=0, ex[4]=0, ey[4]=0\n",
      "x[5]=0, y[5]=0, ex[5]=0, ey[5]=0\n",
      "x[0]=0.4, y[0]=4354.82, ex[0]=0, ey[0]=64447.5\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]=3.4, y[3]=4265.54, ex[3]=0, ey[3]=25769.1\n",
      "x[4]=0, y[4]=0, ex[4]=0, ey[4]=0\n",
      "x[5]=5.4, y[5]=4740.44, ex[5]=0, ey[5]=26104.7\n",
      "x[0]=0.4, y[0]=8692.07, ex[0]=0, ey[0]=112170\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]=3.4, y[3]=4265.54, ex[3]=0, ey[3]=25769.1\n",
      "x[4]=0, y[4]=0, ex[4]=0, ey[4]=0\n",
      "x[5]=5.4, y[5]=4740.44, ex[5]=0, ey[5]=26104.7\n",
      "x[0]=0.4, y[0]=8692.07, ex[0]=0, ey[0]=112170\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]=3.4, y[3]=8452.84, ex[3]=0, ey[3]=28540.6\n",
      "x[4]=0, y[4]=0, ex[4]=0, ey[4]=0\n",
      "x[5]=5.4, y[5]=4740.44, ex[5]=0, ey[5]=26104.7\n",
      "x[0]=0.4, y[0]=8692.07, ex[0]=0, ey[0]=112170\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]=3.4, y[3]=8452.84, ex[3]=0, ey[3]=28540.6\n",
      "x[4]=0, y[4]=0, ex[4]=0, ey[4]=0\n",
      "x[5]=5.4, y[5]=9242.42, ex[5]=0, ey[5]=39489.7\n",
      "x[0]=0.4, y[0]=4346.04, ex[0]=0, ey[0]=167.459\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]=3.4, y[3]=4226.42, ex[3]=0, ey[3]=84.4698\n",
      "x[4]=0, y[4]=0, ex[4]=0, ey[4]=0\n",
      "x[5]=5.4, y[5]=4621.21, ex[5]=0, ey[5]=99.36\n"
     ]
    }
   ],
   "source": [
    "for tag in ['MS3','OPT','OPT2','OPT2bis','OPT2avg']:\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",
    "#If more than one array from a producer are under the same tag, evaluate LY,XT,sigmaT average\n",
    "for tag in ['MS3','OPT','OPT2','OPT2bis','OPT2avg']:\n",
    "#for tag in ['MS3']:\n",
    "\n",
    "    array_per_prod = [0] * len(map_prod)\n",
    "\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",
    "        array_per_prod[iv]+=1\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",
    "        elif (tag=='OPT2avg'):\n",
    "            tau=g['tau_OPT2_PMT'].GetY()[iv]\n",
    "        else:\n",
    "            tau=g['tau_%s_PMT'%tag].GetY()[iv]\n",
    "        \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",
    "        elif(tag=='OPT2avg'):\n",
    "            pos+=0.4\n",
    "        \n",
    "        g['LY_%s'%tag].SetPoint(iv,pos,g['LY_%s'%tag].GetY()[iv]+g['LY_%d'%ar].GetY()[1]*3950/48.8) #use 4V measure\n",
    "        g['LY_%s'%tag].SetPointError(iv,0,g['LY_%s'%tag].GetEY()[iv]+(g['LY_%d'%ar].GetEY()[1]*3950/48.8)**2)\n",
    "        g['LYDT_%s'%tag].SetPoint(iv,pos,g['LYDT_%s'%tag].GetY()[iv]+(g['LY_%d'%ar].GetY()[1]*3950/48.8/tau)) #use 4V measure\n",
    "        g['LYDT_%s'%tag].SetPointError(iv,0,g['LYDT_%s'%tag].GetEY()[iv]+(g['LY_%d'%ar].GetEY()[1]*3950/48.8/tau)**2)\n",
    "        g['sigmaT_%s'%tag].SetPoint(iv,pos,g['sigmaT_%s'%tag].GetY()[iv]+g['sigmaT_%d'%ar].GetY()[0]) #use 8V measure\n",
    "        g['sigmaT_%s'%tag].SetPointError(iv,0,g['sigmaT_%s'%tag].GetEY()[iv]+(g['sigmaT_%d'%ar].GetEY()[0])**2) #use 8V measure\n",
    "        g['sigmaT_4V_%s'%tag].SetPoint(iv,pos,g['sigmaT_4V_%s'%tag].GetY()[iv]+g['sigmaT_%d'%ar].GetY()[1]) #use 4V measure\n",
    "        g['sigmaT_4V_%s'%tag].SetPointError(iv,0,g['sigmaT_4V_%s'%tag].GetEY()[iv]+(g['sigmaT_%d'%ar].GetEY()[1])**2) #use 4V measure\n",
    "        g['XT_%s'%tag].SetPoint(iv,pos,g['XT_%s'%tag].GetY()[iv]+g['XT_%d'%ar].GetY()[0]) #use 8V measure\n",
    "        g['XT_%s'%tag].SetPointError(iv,0,g['XT_%s'%tag].GetEY()[iv]+(g['XT_%d'%ar].GetEY()[0])**2)\n",
    "\n",
    "        print(g['LY_%d'%ar].GetEY()[1]*3950/48.8)\n",
    "        g['LY_OPT2avg'].Print()\n",
    "\n",
    "    for var in ['LY','LYDT','sigmaT','XT','sigmaT_4V']:\n",
    "        print(tag,var,array_per_prod)\n",
    "        for iv in range(len(map_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",
    "            elif(tag=='OPT2avg'):\n",
    "                pos+=0.4\n",
    "            #print(iv, g['LY_%s'%tag].GetY()[iv], array_per_prod[iv])\n",
    "            if array_per_prod[iv]!=0:\n",
    "                print \n",
    "                g['%s_%s'%(var,tag)].SetPoint(iv, pos, g['%s_%s'%(var,tag)].GetY()[iv]/array_per_prod[iv])\n",
    "                g['%s_%s'%(var,tag)].SetPointError(iv, 0, (g['%s_%s'%(var,tag)].GetEY()[iv]**0.5)/array_per_prod[iv])\n",
    "\n",
    "    for iv in range(len(map_prod)):\n",
    "        \n",
    "        g['sigmaTvsLYDT_%s'%tag].SetPoint(iv,g['LYDT_%s'%tag].GetY()[iv],g['sigmaT_%s'%tag].GetY()[iv]) #use 8V measure\n",
    "        g['sigmaTvsLYDT_%s'%tag].SetPointError(iv,g['LYDT_%s'%tag].GetEY()[iv],g['sigmaT_%s'%tag].GetEY()[iv])\n",
    "        g['sigmaT4VvsLYDT_%s'%tag].SetPoint(iv,g['LYDT_%s'%tag].GetY()[iv],g['sigmaT_4V_%s'%tag].GetY()[iv]) #use 4V measure\n",
    "        g['sigmaT4VvsLYDT_%s'%tag].SetPointError(iv,g['LYDT_%s'%tag].GetEY()[iv],g['sigmaT_4V_%s'%tag].GetEY()[iv])\n",
    "\n",
    "# print(array_per_prod)        \n",
    "# g['LY_OPT2'].Print()\n",
    "# g['LYDT_OPT2'].Print()\n",
    "# g['LYDT_OPT2bis'].Print()\n",
    "g['LY_OPT2avg'].Print()\n",
    "# g['LY_OPT2bis'].Print()\n",
    "# g['LY_OPT2avg'].Print()\n",
    "# g['sigmaT_MS3'].Print()\n",
    "# g['sigmaT_OPT2'].Print()\n",
    "# g['sigmaT_OPT2bis'].Print()\n",
    "# g['sigmaT_OPT2avg'].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": "91ae5d93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 4346.035574236171 0.8970757748102424 3949.3398168315557 0.8497261839751226\n",
      "5 4226.418330379325 0.9043813664721796 4615.698030449256 0.9546059554381784\n",
      "10 4621.211304662864 1.1553751052428083 4269.835933536657 0.9590313731024819\n",
      "x[0]=0, y[0]=1.04236, 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]=0.966513, 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]=0.898368, ex[5]=0, ey[5]=0.07\n"
     ]
    }
   ],
   "source": [
    "g['relGain_LY_OPT2avg']=R.TGraphErrors(6)\n",
    "for v,iv in map_prod.items():\n",
    "    ly_arr_ms3=g['LY_MS3'].GetY()[iv]\n",
    "    ly_arr_OPT2avg=g['LY_OPT2avg'].GetY()[iv]\n",
    "    if (v!=9):\n",
    "        ly_pmt_OPT2avg=g['lyNorm_OPT2_PMT'].GetY()[iv]\n",
    "    else:\n",
    "        ly_pmt_OPT2avg=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_OPT2avg>0):\n",
    "        print(v,ly_arr_OPT2avg,ly_pmt_OPT2avg,ly_arr_ms3,ly_pmt_ms3)\n",
    "        g['relGain_LY_OPT2avg'].SetPoint(iv,iv,(ly_arr_OPT2avg/ly_pmt_OPT2avg)/(ly_arr_ms3/ly_pmt_ms3))\n",
    "        g['relGain_LY_OPT2avg'].SetPointError(iv,0,0.07)\n",
    "g['relGain_LY_OPT2avg'].Print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "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": 12,
   "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": 13,
   "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": 14,
   "id": "643569d1",
   "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]=0.972477, 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]=1.06332, y[5]=1.09689, ex[5]=0.03, ey[5]=0.03\n",
      "x[0]=0.4, y[0]=1.59892, 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]=3.4, y[3]=1.63382, ex[3]=0.03, ey[3]=0.03\n",
      "x[4]=0, y[4]=0, ex[4]=0, ey[4]=0\n",
      "x[5]=5.4, y[5]=1.54998, ex[5]=0.03, ey[5]=0.03\n"
     ]
    }
   ],
   "source": [
    "map_prod_OPT2avg_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_OPT2avg_DRY_GREASE']=R.TGraphErrors(6)\n",
    "for v,iv in map_prod.items():\n",
    "    ly_arr_grease_OPT2avg=g['LY_OPT2avg'].GetY()[iv]/g['LY_OPT2avg'].GetY()[0]\n",
    "    ly_arr_dry_OPT2avg=g['LY_OPT2_DRY'].GetY()[map_prod[v]]/g['LY_OPT2_DRY'].GetY()[0]\n",
    "    if (ly_arr_grease_OPT2avg>0 and ly_arr_dry_OPT2avg>0):\n",
    "        g['rel_OPT2avg_DRY_GREASE'].SetPoint(iv,ly_arr_grease_OPT2avg,ly_arr_dry_OPT2avg)\n",
    "        g['rel_OPT2avg_DRY_GREASE'].SetPointError(iv,0.03,0.03)\n",
    "g['rel_OPT2avg_DRY_GREASE'].Print()\n",
    "\n",
    "g['LY_DRY_GREASE_OPT2avg']=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_OPT2avg=g['LY_OPT2avg'].GetY()[iv]/g['LY_OPT2_DRY'].GetY()[map_prod[v]]\n",
    "    else:\n",
    "        ly_arr_grease_OPT2avg=0\n",
    "    if (ly_arr_grease_OPT2avg>0):\n",
    "        g['LY_DRY_GREASE_OPT2avg'].SetPoint(iv,iv+0.4,ly_arr_grease_OPT2avg)\n",
    "        g['LY_DRY_GREASE_OPT2avg'].SetPointError(iv,0.03,0.03)\n",
    "g['LY_DRY_GREASE_OPT2avg'].Print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 16,
   "id": "f9eeb305-e93f-4107-a86a-30d020a1d15c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in ['MS3','OPT','OPT2','OPT2bis','OPT2avg']:\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": 17,
   "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",
    "g['LY_OPT2avg'].SetMarkerStyle(22)\n",
    "g['LY_OPT2avg'].SetMarkerColor(R.kRed)\n",
    "g['LY_OPT2avg'].SetMarkerSize(1.2)\n",
    "l.AddEntry(g['LY_OPT2avg'],\"OPT2avg\",\"PL\")\n",
    "g['LY_OPT2avg'].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": 18,
   "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",
    "g['LY_DRY_GREASE_OPT2avg'].SetMarkerStyle(22)\n",
    "g['LY_DRY_GREASE_OPT2avg'].SetMarkerColor(R.kRed)\n",
    "g['LY_DRY_GREASE_OPT2avg'].SetMarkerSize(1.2)\n",
    "g['LY_DRY_GREASE_OPT2avg'].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": 19,
   "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",
    "g['sigmaT_OPT2avg'].SetMarkerStyle(22)\n",
    "g['sigmaT_OPT2avg'].SetMarkerColor(R.kRed)\n",
    "g['sigmaT_OPT2avg'].SetMarkerSize(1.2)\n",
    "g['sigmaT_OPT2avg'].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": 20,
   "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",
    "g['sigmaT_4V_OPT2avg'].SetMarkerStyle(22)\n",
    "g['sigmaT_4V_OPT2avg'].SetMarkerColor(R.kRed)\n",
    "g['sigmaT_4V_OPT2avg'].SetMarkerSize(1.2)\n",
    "g['sigmaT_4V_OPT2avg'].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": 21,
   "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",
    "g['LYDT_OPT2avg'].SetMarkerStyle(22)\n",
    "g['LYDT_OPT2avg'].SetMarkerColor(R.kRed)\n",
    "g['LYDT_OPT2avg'].SetMarkerSize(1.2)\n",
    "g['LYDT_OPT2avg'].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": 22,
   "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",
    "g['XT_OPT2avg'].SetMarkerStyle(22)\n",
    "g['XT_OPT2avg'].SetMarkerColor(R.kRed)\n",
    "g['XT_OPT2avg'].SetMarkerSize(1.2)\n",
    "g['XT_OPT2avg'].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": 23,
   "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",
    "g['sigmaTvsLYDT_OPT2avg'].SetMarkerStyle(22)\n",
    "g['sigmaTvsLYDT_OPT2avg'].SetMarkerColor(R.kRed)\n",
    "g['sigmaTvsLYDT_OPT2avg'].SetMarkerSize(1.2)\n",
    "g['sigmaTvsLYDT_OPT2avg'].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": 24,
   "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",
    "g['sigmaT4VvsLYDT_OPT2avg'].SetMarkerStyle(22)\n",
    "g['sigmaT4VvsLYDT_OPT2avg'].SetMarkerColor(R.kRed)\n",
    "g['sigmaT4VvsLYDT_OPT2avg'].SetMarkerSize(1.2)\n",
    "g['sigmaT4VvsLYDT_OPT2avg'].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": 25,
   "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": 26,
   "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": 30,
   "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",
    "g['rel_OPT2bis_DRY_GREASE'].SetMarkerStyle(26)\n",
    "g['rel_OPT2bis_DRY_GREASE'].SetMarkerSize(1.2)\n",
    "g['rel_OPT2bis_DRY_GREASE'].Draw(\"PSAME\")\n",
    "g['rel_OPT2avg_DRY_GREASE'].SetMarkerStyle(22)\n",
    "g['rel_OPT2avg_DRY_GREASE'].SetMarkerColor(R.kRed)\n",
    "g['rel_OPT2avg_DRY_GREASE'].SetMarkerSize(1.2)\n",
    "g['rel_OPT2avg_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": 28,
   "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": {
   "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
}