final_analysis_2023.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ROOT\n",
    "from ROOT import gROOT, TString\n",
    "from ROOT import TCanvas, TLine, TGaxis, TGraph, gPad, TF1, TF2, kRed, TMultiGraph, TLegend, gStyle, TPaveStats, TStyle, TText, TList, TLatex, TGraphErrors, TFile, TTree\n",
    "import json\n",
    "import math\n",
    "from array import array\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "sigma_factor = (6.2*10e-3)/1.5\n",
    "\n",
    "def HVCurrentDataFrame(scanIds):\n",
    "    current_top = []    \n",
    "    current_bot = []    \n",
    "    HV_top = []    \n",
    "    HV_bot = []\n",
    "    eff = []\n",
    "    eff_error = []\n",
    "    muon_stream = []\n",
    "    gamma_stream = []\n",
    "    muon_CS = []\n",
    "    gamma_CS = []\n",
    "    muon_CM = []\n",
    "    gamma_CM = []\n",
    "    muon_CS_err = []\n",
    "    gamma_CS_err = []\n",
    "    muon_CM_err = []\n",
    "    gamma_CM_err = []\n",
    "    noiseGammaRate = []\n",
    "    deltaV_top = []\n",
    "    deltaV_bot = []\n",
    "    deltaV_top_err = []\n",
    "    deltaV_bot_err = []\n",
    "    for scanId in scanIds:\n",
    "        !echo \"None\" >> counter.txt\n",
    "        !rm counter.txt\n",
    "        path = \"Scans_2023/Scan_00\"+str(scanId)+\"/*_HV*_CAEN.root\"\n",
    "        !ls {path} >> counter.txt\n",
    "        counter = np.loadtxt(\"counter.txt\", dtype=str)\n",
    "        if (str(scanId) == '5634') or (str(scanId) == '5630'):\n",
    "            print(\"scanId = \", scanId)\n",
    "            N = len(counter)\n",
    "        else:\n",
    "            N = len(counter)+1\n",
    "        for i in range(1,N):\n",
    "            jsonFile = open(\"Scans_2023/Scan_00\"+str(scanId)+\"/ANALYSIS/KODELE/HV\"+str(i)+\"/output.json\")\n",
    "            jsonFile = json.load(jsonFile)\n",
    "            histFile  = ROOT.TFile.Open(\"Scans_2023/Scan_00\"+str(scanId)+\"/Scan00\"+str(scanId)+\"_HV\"+str(i)+\"_CAEN.root\",\"READ\")\n",
    "            hist_current_bot = histFile.Get(\"Imon_KODELE-BOT\")\n",
    "            hist_current_top = histFile.Get(\"Imon_KODELE-TOP\")\n",
    "            hist_HV_bot = histFile.Get(\"HVeff_KODELE-BOT\")\n",
    "            hist_HV_top = histFile.Get(\"HVeff_KODELE-TOP\")\n",
    "            eff.append(jsonFile['output_parameters']['efficiencyMuon_corrected'])\n",
    "            eff_error.append(jsonFile['output_parameters']['efficiencyMuon_corrected_err'])\n",
    "            muon_stream.append(jsonFile['output_parameters']['muonStreamerProbability'])\n",
    "            gamma_stream.append(jsonFile['output_parameters']['gammaStreamerProbability'])\n",
    "            muon_CS.append(jsonFile['output_parameters']['muonCLS'])\n",
    "            gamma_CS.append(jsonFile['output_parameters']['gammaCLS'])\n",
    "            muon_CM.append(jsonFile['output_parameters']['muonCMP'])\n",
    "            gamma_CM.append(jsonFile['output_parameters']['gammaCMP'])\n",
    "            muon_CS_err.append(jsonFile['output_parameters']['muonCLS_err'])\n",
    "            gamma_CS_err.append(jsonFile['output_parameters']['gammaCLS_err'])\n",
    "            muon_CM_err.append(jsonFile['output_parameters']['muonCMP_err'])\n",
    "            gamma_CM_err.append(jsonFile['output_parameters']['gammaCMP_err'])\n",
    "            noiseGammaRate.append(jsonFile['output_parameters']['noiseGammaRate'])\n",
    "            current_bot.append(hist_current_bot.GetMean())\n",
    "            current_top.append(hist_current_top.GetMean())\n",
    "            HV_top.append(hist_HV_top.GetMean())\n",
    "            HV_bot.append(hist_HV_bot.GetMean())\n",
    "            if (scanId == '5630'):\n",
    "                deltaV_top.append(0.001)\n",
    "                deltaV_bot.append(0.001)\n",
    "            else:\n",
    "                deltaV_top.append(hist_current_top.GetMean()*1.5)\n",
    "                deltaV_bot.append(hist_current_bot.GetMean()*1.5)\n",
    "            deltaV_top_err.append(hist_current_top.GetMean()*6.2*10e-3)\n",
    "            deltaV_bot_err.append(hist_current_bot.GetMean()*6.2*10e-3)\n",
    "            histFile.Close()\n",
    "    DataSet = {'HV_top': HV_top, \n",
    "               'HV_bot': HV_bot, \n",
    "               'current_top': current_top, \n",
    "               'current_bot': current_bot,\n",
    "               'muon_stream': muon_stream,\n",
    "               'gamma_stream': gamma_stream,\n",
    "               'muon_CM': muon_CM,\n",
    "               'gamma_CM': gamma_CM,\n",
    "               'muon_CS': muon_CS,\n",
    "               'gamma_CS': gamma_CS,\n",
    "               'muon_CM_err': muon_CM_err,\n",
    "               'gamma_CM_err': gamma_CM_err,\n",
    "               'muon_CS_err': muon_CS_err,\n",
    "               'gamma_CS_err': gamma_CS_err,\n",
    "               'efficiency': eff,\n",
    "               'eff_error': eff_error,\n",
    "               'noiseGammaRate': noiseGammaRate,\n",
    "               'deltaV': (np.array(deltaV_top)*np.array(deltaV_bot)+np.full_like(np.array(deltaV_bot), 0.00000001))/(np.array(deltaV_top)+np.array(deltaV_bot)+np.full_like(np.array(deltaV_bot), 0.00000001)),\n",
    "               }\n",
    "    DataSet = pd.DataFrame(data=DataSet)\n",
    "    DataSet['current'] = DataSet['current_top'] + DataSet['current_bot']\n",
    "    DataSet['deltaV_err'] = DataSet['deltaV']* sigma_factor *np.sqrt( (1 + (np.sqrt(DataSet['current_top']**2 + DataSet['current_bot']**2 ) / ( DataSet['current_top']+DataSet['current_bot']  ) )**2 ) ) \n",
    "    return DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FeaturesDataFrame(scanIds):\n",
    "    current_top = []    \n",
    "    current_bot = []    \n",
    "    eff = []\n",
    "    eff_error = []\n",
    "    muon_stream = []\n",
    "    gamma_stream = []\n",
    "    muon_CS = []\n",
    "    gamma_CS = []\n",
    "    muon_CM = []\n",
    "    gamma_CM = []\n",
    "    muon_CS_err = []\n",
    "    gamma_CS_err = []\n",
    "    muon_CM_err = []\n",
    "    gamma_CM_err = []\n",
    "    current_bot = []\n",
    "    current_top = []\n",
    "    HV_top = []\n",
    "    HV_bot = []\n",
    "    noiseGammaRate = []\n",
    "    noiseGammaRate_err = []\n",
    "    for scanId in scanIds:\n",
    "        print(\"scanID: \", scanIds[scanId][0])\n",
    "        \n",
    "        direc = \"\"\n",
    "        N = \"1\"\n",
    "        if (int(scanIds[scanId][0])==5810) or (int(scanIds[scanId][0])==5805) or (int(scanIds[scanId][0])==5820) or (int(scanIds[scanId][0])==5826) or (int(scanIds[scanId][0])==5830) or (int(scanIds[scanId][0])==5827) or (int(scanIds[scanId][0])==5815) or (int(scanIds[scanId][0])==5811):    \n",
    "            N = \"7\"\n",
    "        if (int(scanIds[scanId][0])==5841)or (int(scanIds[scanId][0])==5838) or (int(scanIds[scanId][0])==5840):\n",
    "            N = \"8\"\n",
    "        jsonFile = open(\"Scans_2023/Scan_00\"+str(scanIds[scanId][0])+\"/ANALYSIS/KODELE/HV\"+str(N)+\"/output.json\")\n",
    "        histFile  = ROOT.TFile.Open(\"Scans_2023/Scan_00\"+str(scanIds[scanId][0])+\"/Scan00\"+str(scanIds[scanId][0])+\"_HV\"+str(N)+\"_CAEN.root\",\"READ\")\n",
    "        hist_current_bot = histFile.Get(\"Imon_KODELE-BOT\")\n",
    "        hist_current_top = histFile.Get(\"Imon_KODELE-TOP\")\n",
    "        hist_HV_bot = histFile.Get(\"HVeff_KODELE-BOT\")\n",
    "        hist_HV_top = histFile.Get(\"HVeff_KODELE-TOP\")\n",
    "        jsonFile = json.load(jsonFile)\n",
    "        nTrig = jsonFile['output_parameters']['nTrig']\n",
    "        eff.append(100*jsonFile['output_parameters']['efficiencyMuon_corrected'])\n",
    "        eff_error.append(jsonFile['output_parameters']['efficiencyMuon_corrected_err'])\n",
    "        muon_stream.append(jsonFile['output_parameters']['muonStreamerProbability'])\n",
    "        gamma_stream.append(jsonFile['output_parameters']['gammaStreamerProbability'])\n",
    "        muon_CS.append(jsonFile['output_parameters']['muonCLS'])\n",
    "        gamma_CS.append(jsonFile['output_parameters']['gammaCLS'])\n",
    "        muon_CM.append(jsonFile['output_parameters']['muonCMP'])\n",
    "        gamma_CM.append(jsonFile['output_parameters']['gammaCMP'])\n",
    "        muon_CS_err.append(jsonFile['output_parameters']['muonCLS_err'])\n",
    "        gamma_CS_err.append(jsonFile['output_parameters']['gammaCLS_err'])\n",
    "        muon_CM_err.append(jsonFile['output_parameters']['muonCMP_err'])\n",
    "        gamma_CM_err.append(jsonFile['output_parameters']['gammaCMP_err'])\n",
    "        noiseGammaRate.append(jsonFile['output_parameters']['noiseGammaRate'])\n",
    "        noiseGammaRate_err.append(jsonFile['output_parameters']['noiseGammaRate_err'])\n",
    "        current_bot.append(hist_current_bot.GetMean())\n",
    "        current_top.append(hist_current_top.GetMean())\n",
    "        HV_top.append(hist_HV_top.GetMean())\n",
    "        HV_bot.append(hist_HV_bot.GetMean())\n",
    "    DataSet = {'current_top': current_top, \n",
    "               'current_bot': current_bot,\n",
    "               'HV_top': HV_top,\n",
    "               'HV_bot': HV_bot,\n",
    "               'muon_stream': muon_stream,\n",
    "               'gamma_stream': gamma_stream,\n",
    "               'muon_CM': muon_CM,\n",
    "               'gamma_CM': gamma_CM,\n",
    "               'muon_CS': muon_CS,\n",
    "               'gamma_CS': gamma_CS,\n",
    "               'muon_CM_err': muon_CM_err,\n",
    "               'gamma_CM_err': gamma_CM_err,\n",
    "               'muon_CS_err': muon_CS_err,\n",
    "               'gamma_CS_err': gamma_CS_err,\n",
    "               'efficiency': eff,\n",
    "               'eff_error': eff_error,\n",
    "               'noiseGammaRate': noiseGammaRate,\n",
    "               'noiseGammaRate_err': noiseGammaRate_err\n",
    "              }\n",
    "        \n",
    "    DataSet = pd.DataFrame(data=DataSet)\n",
    "    DataSet['current'] = DataSet['current_top'] + DataSet['current_bot']\n",
    "    \n",
    "    return DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "sigmoid1 = TF1(\"sigmoid1\",\"([0]/(1+ TMath::Exp(-[1]*(x-[2]))))\")\n",
    "sigmoid1.SetParNames(\"Emax\",\"Lambda\",\"HV50\")\n",
    "sigmoid1.SetParameters(0.9, 0.01, 7000)\n",
    "sigmoid2 = TF1(\"sigmoid2\",\"([0]/(1+ TMath::Exp(-[1]*(x-[2]))))\")\n",
    "sigmoid2.SetParNames(\"Emax\",\"Lambda\",\"HV50\")\n",
    "sigmoid2.SetParameters(0.9, 0.01, 7000)\n",
    "sigmoid3 = TF1(\"sigmoid3\",\"([0]/(1+ TMath::Exp(-[1]*(x-[2]))))\")\n",
    "sigmoid3.SetParNames(\"Emax\",\"Lambda\",\"HV50\")\n",
    "sigmoid3.SetParameters(0.9, 0.01, 7000)\n",
    "sigmoid4 = TF1(\"sigmoid4\",\"([0]/(1+ TMath::Exp(-[1]*(x-[2]))))\")\n",
    "sigmoid4.SetParNames(\"Emax\",\"Lambda\",\"HV50\")\n",
    "sigmoid4.SetParameters(0.9, 0.01, 7000)\n",
    "sigmoid5 = TF1(\"sigmoid5\",\"([0]/(1+ TMath::Exp(-[1]*(x-[2]))))\")\n",
    "sigmoid5.SetParNames(\"Emax\",\"Lambda\",\"HV50\")\n",
    "sigmoid5.SetParameters(0.9, 0.001, 7000)\n",
    "sigmoid6 = TF1(\"sigmoid6\",\"([0]/(1+ TMath::Exp(-[1]*(x-[2]))))\")\n",
    "sigmoid6.SetParNames(\"Emax\",\"Lambda\",\"HV50\")\n",
    "sigmoid6.SetParameters(0.9, 0.01, 7000)\n",
    "sigmoid7 = TF1(\"sigmoid7\",\"([0]/(1+ TMath::Exp(-[1]*(x-[2]))))\")\n",
    "sigmoid7.SetParNames(\"Emax\",\"Lambda\",\"HV50\")\n",
    "sigmoid7.SetParameters(0.9, 0.01, 7000)\n",
    "sigmoid8 = TF1(\"sigmoid8\",\"([0]/(1+ TMath::Exp(-[1]*(x-[2]))))\")\n",
    "sigmoid8.SetParNames(\"Emax\",\"Lambda\",\"HV50\")\n",
    "sigmoid8.SetParameters(0.9, 0.01, 7000)\n",
    "sigmoid9 = TF1(\"sigmoid9\",\"([0]/(1+ TMath::Exp(-[1]*(x-[2]))))\")\n",
    "sigmoid9.SetParNames(\"Emax\",\"Lambda\",\"HV50\")\n",
    "sigmoid9.SetParameters(0.9, 0.001, 7000)\n",
    "sigmoid10 = TF1(\"sigmoid10\",\"([0]/(1+ TMath::Exp(-[1]*(x-[2]))))\")\n",
    "sigmoid10.SetParNames(\"Emax\",\"Lambda\",\"HV50\")\n",
    "sigmoid10.SetParameters(0.9, 0.01, 7000)\n",
    "sigmoid11 = TF1(\"sigmoid11\",\"([0]/(1+ TMath::Exp(-[1]*(x-[2]))))\")\n",
    "sigmoid11.SetParNames(\"Emax\",\"Lambda\",\"HV50\")\n",
    "sigmoid11.SetParameters(0.9, 0.0001, 11000)\n",
    "sigmoid12 = TF1(\"sigmoid12\",\"([0]/(1+ TMath::Exp(-[1]*(x-[2]))))\")\n",
    "sigmoid12.SetParNames(\"Emax\",\"Lambda\",\"HV50\")\n",
    "sigmoid12.SetParameters(0.9, 0.01, 7000)\n",
    "sigmoid13 = TF1(\"sigmoid13\",\"([0]/(1+ TMath::Exp(-[1]*(x-[2]))))\")\n",
    "sigmoid13.SetParNames(\"Emax\",\"Lambda\",\"HV50\")\n",
    "sigmoid13.SetParameters(0.9, 0.01, 7000)\n",
    "sigmoid14 = TF1(\"sigmoid14\",\"([0]/(1+ TMath::Exp(-[1]*(x-[2]))))\")\n",
    "sigmoid14.SetParNames(\"Emax\",\"Lambda\",\"HV50\")\n",
    "sigmoid14.SetParameters(0.9, 0.001, 7000)\n",
    "sigmoid15 = TF1(\"sigmoid15\",\"([0]/(1+ TMath::Exp(-[1]*(x-[2]))))\")\n",
    "sigmoid15.SetParNames(\"Emax\",\"Lambda\",\"HV50\")\n",
    "sigmoid15.SetParameters(0.9, 0.001, 7000)\n",
    "sigmoid16 = TF1(\"sigmoid16\",\"([0]/(1+ TMath::Exp(-[1]*(x-[2]))))\")\n",
    "sigmoid16.SetParNames(\"Emax\",\"Lambda\",\"HV50\")\n",
    "sigmoid16.SetParameters(0.9, 0.01, 7000)\n",
    "sigmoid17 = TF1(\"sigmoid17\",\"([0]/(1+ TMath::Exp(-[1]*(x-[2]))))\")\n",
    "sigmoid17.SetParNames(\"Emax\",\"Lambda\",\"HV50\")\n",
    "sigmoid17.SetParameters(0.9, 0.01, 7000)\n",
    "sigmoid18 = TF1(\"sigmoid18\",\"([0]/(1+ TMath::Exp(-[1]*(x-[2]))))\")\n",
    "sigmoid18.SetParNames(\"Emax\",\"Lambda\",\"HV50\")\n",
    "sigmoid18.SetParameters(0.9, 0.01, 7000)\n",
    "sigmoid19 = TF1(\"sigmoid19\",\"([0]/(1+ TMath::Exp(-[1]*(x-[2]))))\")\n",
    "sigmoid19.SetParNames(\"Emax\",\"Lambda\",\"HV50\")\n",
    "sigmoid19.SetParameters(0.9, 0.001, 7000)\n",
    "sigmoid20 = TF1(\"sigmoid20\",\"([0]/(1+ TMath::Exp(-[1]*(x-[2]))))\")\n",
    "sigmoid20.SetParNames(\"Emax\",\"Lambda\",\"HV50\")\n",
    "sigmoid20.SetParameters(0.9, 0.001, 7000)\n",
    "sigmoid21 = TF1(\"sigmoid20\",\"([0]/(1+ TMath::Exp(-[1]*(x-[2]))))\")\n",
    "sigmoid21.SetParNames(\"Emax\",\"Lambda\",\"HV50\")\n",
    "sigmoid21.SetParameters(0.9, 0.001, 7000)\n",
    "sigmoid22 = TF1(\"sigmoid20\",\"([0]/(1+ TMath::Exp(-[1]*(x-[2]))))\")\n",
    "sigmoid22.SetParNames(\"Emax\",\"Lambda\",\"HV50\")\n",
    "sigmoid22.SetParameters(0.9, 0.001, 7000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "scans = {'30CO2_OFF': ['5627'],\n",
    "         '30CO2_22':  ['5659'],\n",
    "         '30CO2_10':  ['5675'],\n",
    "         '30CO2_6.9': ['5621'],\n",
    "         '30CO2_4.6': ['5623'],\n",
    "         '30CO2_3.3': ['5622', '5625'],\n",
    "         '30CO2_2.2': ['5827'],\n",
    "         '30CO2_1': ['5820'],\n",
    "         '30CO205SF6_OFF': ['5712'],\n",
    "         '30CO205SF6_22':  ['5706'],\n",
    "         '30CO205SF6_10':  ['5707'],\n",
    "         '30CO205SF6_6.9': ['5708'],\n",
    "         '30CO205SF6_4.6': ['5709'],\n",
    "         '30CO205SF6_3.3': ['5713'],\n",
    "         '30CO205SF6_2.2': ['5840'],\n",
    "         '30CO205SF6_1': ['5838'],\n",
    "         '40CO2_OFF': ['5612'],\n",
    "         '40CO2_22':  ['5615'],\n",
    "         '40CO2_10':  ['5614'],\n",
    "         '40CO2_6.9': ['5616'],\n",
    "         '40CO2_4.6': ['5613'],\n",
    "         '40CO2_3.3': ['5611'],\n",
    "         'STDMX_OFF': ['5809'],\n",
    "         'STDMX_22':  ['5632'],\n",
    "         'STDMX_10':  ['5631'],\n",
    "         'STDMX_6.9': ['5633'],\n",
    "         'STDMX_4.6': ['5630'],\n",
    "         'STDMX_3.3': ['5629'],\n",
    "         'STDMX_2.2': ['5805'],\n",
    "         'STDMX_1': ['5810']\n",
    "         }\n",
    "\n",
    "Scans_30CO2 = {'30CO2_OFF': ['5819'],\n",
    "         '30CO2_22':  ['5826'],\n",
    "         '30CO2_10':  ['5832'], #HV7\n",
    "         '30CO2_6.9': ['5830'], #HV7\n",
    "         '30CO2_4.6': ['5831'],\n",
    "         '30CO2_3.3': ['5822'],\n",
    "         '30CO2_2.2': ['5827'], #HV7\n",
    "         '30CO2_1': ['5820'] #HV7\n",
    "         }\n",
    "\n",
    "Scans_40CO2 = {'40CO2_OFF': ['5845'],\n",
    "         '40CO2_22':  ['5846'],\n",
    "         '40CO2_10':  ['5844'],\n",
    "         '40CO2_6.9': ['5843'],\n",
    "         '40CO2_4.6': ['5842'],\n",
    "         '40CO2_3.3': ['5841'] # HV8\n",
    "         }\n",
    "\n",
    "Scans_STDMX = {'STDMX_OFF': ['5808'],\n",
    "         'STDMX_22':  ['5813'],\n",
    "         'STDMX_10':  ['5815'], #HV7\n",
    "         'STDMX_6.9': ['5811'], #HV7 \n",
    "         'STDMX_4.6': ['5807'],\n",
    "         'STDMX_3.3': ['5814'],\n",
    "         'STDMX_2.2': ['5805'], #HV7\n",
    "         'STDMX_1': ['5810'], #HV7\n",
    "         }\n",
    "\n",
    "Scans_30CO205SF6 = {'30CO205SF6_OFF': ['5833'],\n",
    "         '30CO205SF6_22':  ['5839'],\n",
    "         '30CO205SF6_10':  ['5836'],\n",
    "         '30CO205SF6_6.9': ['5837'],\n",
    "         '30CO205SF6_4.6': ['5835'],\n",
    "         '30CO205SF6_3.3': ['5834'],\n",
    "         '30CO205SF6_2.2': ['5838'], #HV7\n",
    "         '30CO205SF6_1': ['5840']} #HV7\n",
    "\n",
    "Scans_OFF = {'30CO205SF6_OFF': ['5833'],\n",
    "            'STDMX_OFF': ['5808'], \n",
    "            '40CO2_OFF': ['5845'],\n",
    "            '30CO2_OFF': ['5819'], \n",
    "         }\n",
    "\n",
    "Scans_33 = {\n",
    "          'STDMX_3.3': ['5814'],\n",
    "          '30CO2_3.3': ['5822'],\n",
    "          '30CO205SF6_3.3': ['5834'],\n",
    "          '40CO2_3.3': ['5841'],\n",
    "}\n",
    "Scans_OFF = {'30CO205SF6_OFF': ['5833'],\n",
    "            'STDMX_OFF': ['5808'], \n",
    "            '40CO2_OFF': ['5845'],\n",
    "            '30CO2_OFF': ['5819'], \n",
    "         }\n",
    "Scans_33 = {\n",
    "          'STDMX_3.3': ['5814'],\n",
    "          '30CO2_3.3': ['5822'],\n",
    "          '30CO205SF6_3.3': ['5834'],\n",
    "          '40CO2_3.3': ['5841'],\n",
    "}\n",
    "\n",
    "scans_STDMX = [\n",
    "         ['STDMX_OFF', sigmoid1, 6, 23],\n",
    "         ['STDMX_10', sigmoid2, 3, 22],\n",
    "         ['STDMX_3.3', sigmoid3, 1, 34],\n",
    "         ['STDMX_2.2', sigmoid4, 2, 20],\n",
    "         ['STDMX_1', sigmoid5, 4, 21],\n",
    "         ]\n",
    "\n",
    "scans_30CO2 = [\n",
    "         ['30CO2_OFF', sigmoid6, 6, 23],\n",
    "         ['30CO2_10', sigmoid7, 3, 22],\n",
    "         ['30CO2_3.3', sigmoid8, 1, 34],\n",
    "         ['30CO2_2.2', sigmoid9, 2, 20],\n",
    "         ['30CO2_1', sigmoid10, 4, 21],\n",
    "         ]\n",
    "scans_30CO205SF6 = [\n",
    "         ['30CO205SF6_OFF', sigmoid16, 6, 23],\n",
    "         ['30CO205SF6_10', sigmoid17, 3, 22],\n",
    "         ['30CO205SF6_3.3', sigmoid18, 1, 34],\n",
    "         ['30CO205SF6_2.2', sigmoid19, 2, 20],\n",
    "         ['30CO205SF6_1', sigmoid20, 4, 21],\n",
    "         ]\n",
    "scans_40CO2 = [\n",
    "         ['40CO2_OFF', sigmoid11, 6, 23],\n",
    "         ['40CO2_10', sigmoid12, 3, 22],\n",
    "         ['40CO2_6.9', sigmoid14, 2, 20],\n",
    "         ['40CO2_4.6', sigmoid21, 4, 21],\n",
    "         ['40CO2_3.3', sigmoid13, 1, 34],\n",
    "         ]\n",
    "scans_OFF = [\n",
    "        ['STDMX_OFF', sigmoid1, 6, 23],\n",
    "        ['30CO2_OFF', sigmoid6, 6, 23],\n",
    "        ['30CO205SF6_OFF', sigmoid16, 6, 23],\n",
    "        ['40CO2_OFF', sigmoid11, 6, 23],\n",
    "]\n",
    "scans_33 = [\n",
    "        ['STDMX_3.3', sigmoid4, 2, 20],\n",
    "        ['30CO2_3.3', sigmoid9, 2, 20],\n",
    "        ['30CO205SF6_3.3', sigmoid19, 2, 20],\n",
    "        ['40CO2_3.3', sigmoid22, 1, 34],\n",
    "]\n",
    "scans_OFF = [\n",
    "        ['STDMX_OFF', sigmoid1, 6, 23],\n",
    "        ['30CO2_OFF', sigmoid6, 6, 23],\n",
    "        ['30CO205SF6_OFF', sigmoid16, 6, 23],\n",
    "        ['40CO2_OFF', sigmoid11, 6, 23],\n",
    "]\n",
    "scans_33 = [\n",
    "        ['STDMX_3.3', sigmoid4, 2, 20],\n",
    "        ['30CO2_3.3', sigmoid9, 2, 20],\n",
    "        ['30CO205SF6_3.3', sigmoid19, 2, 20],\n",
    "        ['40CO2_3.3', sigmoid22, 1, 34],\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30CO2_OFF : \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0   6000.0  6000.0     0.000000     0.000000     0.000000      0.000000   \n",
      "1   6100.0  6100.0     0.000000     0.000000     0.000000      0.000000   \n",
      "2   6200.0  6200.0     0.000000     0.000000     0.000000      0.000000   \n",
      "3   6300.0  6300.0     0.000000     0.000000     0.000000      0.000000   \n",
      "4   6400.0  6400.0     0.000000     0.000000     0.000487      0.000000   \n",
      "5   6500.0  6500.0     0.000000     0.000000     0.000741      0.000000   \n",
      "6   6600.0  6600.0     0.000000     0.000000     0.001627      0.000000   \n",
      "7   6700.0  6700.0     0.000000     0.000000     0.001906      0.000000   \n",
      "8   6800.0  6800.0     0.000000     0.000000     0.002213      0.000000   \n",
      "9   6900.0  6900.0     0.000000     0.000000     0.006078      0.000000   \n",
      "10  7000.0  7000.0     0.002920     0.000000     0.005998      0.000050   \n",
      "11  7100.0  7100.0     0.006515     0.004235     0.007647      0.000000   \n",
      "12  7200.0  7200.0     0.010256     0.009890     0.009899      0.000000   \n",
      "13  7300.0  7300.0     0.009091     0.011688     0.016113      0.000000   \n",
      "14  7400.0  7400.0     0.022553     0.022128     0.014476      0.000099   \n",
      "15  7500.0  7500.0     0.030627     0.030258     0.023097      0.000000   \n",
      "16  7600.0  7600.0     0.030986     0.026291     0.029544      0.000000   \n",
      "\n",
      "     muon_CM  gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0   1.300000  1.750000  1.230769  1.000000     0.000000      0.000000   \n",
      "1   1.428571  1.000000  1.400000  1.166667     0.142857      0.000000   \n",
      "2   1.300000  1.500000  1.576923  1.333333     0.300000      0.125000   \n",
      "3   1.096774  1.428571  1.191176  1.100000     0.048387      0.142857   \n",
      "4   1.057778  2.166667  1.084034  1.538462     0.031111      0.500000   \n",
      "5   1.052439  1.571429  1.118192  1.409091     0.023171      0.357143   \n",
      "6   1.075080  1.625000  1.127291  1.461538     0.053248      0.312500   \n",
      "7   1.091311  1.357143  1.141363  1.263158     0.060383      0.142857   \n",
      "8   1.113886  1.444444  1.235921  1.256410     0.093788      0.148148   \n",
      "9   1.173734  1.937500  1.356251  1.419355     0.147520      0.375000   \n",
      "10  1.251967  1.562500  1.491893  1.520000     0.209084      0.312500   \n",
      "11  1.342407  1.500000  1.634830  1.357143     0.259985      0.250000   \n",
      "12  1.424294  1.434783  1.774669  1.696970     0.336392      0.391304   \n",
      "13  1.524750  2.103448  1.931136  1.540984     0.455250      0.413793   \n",
      "14  1.624134  2.116279  2.020450  1.604396     0.518757      0.348837   \n",
      "15  1.720080  1.793103  2.096943  1.596154     0.596144      0.293103   \n",
      "16  1.795505  1.841463  2.167642  1.556291     0.703885      0.256098   \n",
      "\n",
      "    muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0      0.000000      0.000000    0.002372   0.000756        0.040032   \n",
      "1      0.155556      0.000000    0.001667   0.000635        0.040040   \n",
      "2      0.295673      0.121212    0.004740   0.001068        0.091529   \n",
      "3      0.050331      0.122222    0.014721   0.001871        0.062995   \n",
      "4      0.030972      0.288462    0.053931   0.003525        0.114298   \n",
      "5      0.024088      0.260943    0.200243   0.006294        0.177285   \n",
      "6      0.053199      0.235732    0.433353   0.007561        0.218147   \n",
      "7      0.059841      0.120301    0.644641   0.007394        0.137334   \n",
      "8      0.095982      0.116875    0.807045   0.006195        0.280668   \n",
      "9      0.151427      0.230166    0.913888   0.004302        0.251481   \n",
      "10     0.213498      0.253333    0.950521   0.003369        0.435948   \n",
      "11     0.265248      0.193878    0.976784   0.002374        0.326056   \n",
      "12     0.339064      0.363636    0.985499   0.001844        0.320716   \n",
      "13     0.444015      0.253312    0.992441   0.001364        0.537919   \n",
      "14     0.489117      0.227037    0.991340   0.001426        0.835945   \n",
      "15     0.539706      0.224253    0.993934   0.001225        0.951259   \n",
      "16     0.610457      0.190012    0.996785   0.000883        1.347462   \n",
      "\n",
      "      deltaV   current    deltaV_err  \n",
      "0   1.000000  0.000000           NaN  \n",
      "1   1.000000  0.000000           NaN  \n",
      "2   1.000000  0.000000           NaN  \n",
      "3   1.000000  0.000000           NaN  \n",
      "4   1.000000  0.000000           NaN  \n",
      "5   1.000000  0.000000           NaN  \n",
      "6   1.000000  0.000000           NaN  \n",
      "7   1.000000  0.000000           NaN  \n",
      "8   1.000000  0.000000           NaN  \n",
      "9   1.000000  0.000000           NaN  \n",
      "10  0.000002  0.002920  1.334700e-07  \n",
      "11  0.003850  0.010749  1.963635e-04  \n",
      "12  0.007553  0.020147  3.823636e-04  \n",
      "13  0.007671  0.020779  3.893258e-04  \n",
      "14  0.016754  0.044681  8.481443e-04  \n",
      "15  0.022831  0.060886  1.155795e-03  \n",
      "16  0.021335  0.057277  1.081230e-03  \n",
      "30CO2_22 : \n",
      "    HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0   6100.0  6100.0     0.000000     0.000000     0.000000      0.000000   \n",
      "1   6250.0  6250.0     0.200000     0.100000     0.000000      0.000000   \n",
      "2   6400.0  6400.0     0.800000     0.500000     0.000000      0.000000   \n",
      "3   6550.0  6550.0     1.800000     1.276257     0.003975      0.000000   \n",
      "4   6700.0  6700.0     3.300000     2.400000     0.005319      0.000300   \n",
      "5   6850.0  6850.0     5.026172     3.921484     0.007294      0.000563   \n",
      "6   7000.0  7000.0     6.951429     5.726349     0.010816      0.001657   \n",
      "7   7150.0  7150.0     9.019947     7.720210     0.011566      0.003409   \n",
      "8   7300.0  7300.0    11.429800     9.994800     0.017561      0.007026   \n",
      "9   7450.0  7450.0    14.692439    13.018529     0.025641      0.014998   \n",
      "10  7600.0  7600.0    20.596273    18.237533     0.023889      0.020897   \n",
      "11  7750.0  7750.0    32.858830    28.969172     0.040293      0.031487   \n",
      "\n",
      "     muon_CM  gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0   1.000000  1.107527  2.500000  1.252427     0.000000      0.053763   \n",
      "1   1.230769  1.042882  1.375000  1.062500     0.230769      0.017153   \n",
      "2   1.188679  1.094884  1.248677  1.080272     0.119497      0.030608   \n",
      "3   1.088117  1.270638  1.154601  1.101474     0.060080      0.044681   \n",
      "4   1.126126  1.559345  1.156267  1.154943     0.064264      0.081719   \n",
      "5   1.189123  1.849189  1.241980  1.265940     0.118912      0.166547   \n",
      "6   1.321137  2.137504  1.407368  1.392777     0.222735      0.279513   \n",
      "7   1.480511  2.482480  1.554986  1.552207     0.312867      0.453953   \n",
      "8   1.652850  2.771615  1.738767  1.702538     0.479793      0.630972   \n",
      "9   1.781926  3.077503  1.901899  1.818977     0.609909      0.788403   \n",
      "10  1.862630  3.669592  2.038083  1.850301     0.751557      0.965343   \n",
      "11  2.000707  4.998044  2.132332  1.740360     0.869565      1.131718   \n",
      "\n",
      "    muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0      0.000000      0.057983    0.000526   0.000402        0.740184   \n",
      "1      0.217105      0.017193    0.003862   0.001132        3.729224   \n",
      "2      0.114062      0.029378    0.049840   0.003958       20.108482   \n",
      "3      0.060415      0.037417    0.243118   0.007823       55.916642   \n",
      "4      0.062422      0.057512    0.550948   0.009093      114.719234   \n",
      "5      0.112907      0.104597    0.820399   0.007015      182.909712   \n",
      "6      0.203042      0.161066    0.931708   0.004592      253.790408   \n",
      "7      0.271278      0.239961    0.968767   0.003181      343.322326   \n",
      "8      0.391180      0.315717    0.976027   0.002809      427.742897   \n",
      "9      0.484977      0.370958    0.979178   0.002640      516.955846   \n",
      "10     0.585932      0.385372    0.982052   0.002448      635.366747   \n",
      "11     0.646002      0.321317    0.981327   0.002522      821.912342   \n",
      "\n",
      "       deltaV    current  deltaV_err  \n",
      "0    1.000000   0.000000         NaN  \n",
      "1    0.100000   0.300000    0.005155  \n",
      "2    0.461538   1.300000    0.023571  \n",
      "3    1.120158   3.076257    0.056979  \n",
      "4    2.084211   5.700000    0.105946  \n",
      "5    3.304226   8.947656    0.167694  \n",
      "6    4.709773  12.677778    0.238793  \n",
      "7    6.239716  16.740157    0.316189  \n",
      "8    7.998182  21.424600    0.405193  \n",
      "9   10.353695  27.710967    0.524452  \n",
      "10  14.508952  38.833806    0.734935  \n",
      "11  23.093738  61.828002    1.169840  \n",
      "30CO2_10 : \n",
      "    HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0   6100.0  6100.0     0.100000     0.000000     0.000000      0.000000   \n",
      "1   6250.0  6250.0     0.500000     0.300000     0.000000      0.000000   \n",
      "2   6400.0  6400.0     1.600000     1.100000     0.000332      0.000000   \n",
      "3   6550.0  6550.0     3.582993     2.500000     0.001885      0.000000   \n",
      "4   6700.0  6700.0     6.318644     4.700000     0.002623      0.000149   \n",
      "5   6850.0  6850.0     9.635417     7.535417     0.005804      0.000844   \n",
      "6   7000.0  7000.0    13.336467    10.947578     0.007036      0.002198   \n",
      "7   7150.0  7150.0    17.258658    14.774459     0.013392      0.005592   \n",
      "8   7300.0  7300.0    21.746369    19.058287     0.019814      0.014012   \n",
      "9   7450.0  7450.0    27.845566    24.720174     0.022000      0.020686   \n",
      "10  7600.0  7600.0    38.201025    34.144509     0.029285      0.034237   \n",
      "11  7750.0  7750.0    59.236063    52.976437     0.039021      0.050512   \n",
      "\n",
      "     muon_CM  gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0   1.000000  1.067010  1.000000  1.135266     0.000000      0.025773   \n",
      "1   1.055556  1.045857  1.210526  1.052308     0.055556      0.017699   \n",
      "2   1.109677  1.181262  1.075581  1.070537     0.045161      0.031230   \n",
      "3   1.105756  1.516823  1.102906  1.104679     0.046854      0.055865   \n",
      "4   1.118157  2.111885  1.141057  1.160391     0.060163      0.115710   \n",
      "5   1.209310  2.732118  1.237145  1.247002     0.120285      0.225470   \n",
      "6   1.326663  3.321119  1.364347  1.379019     0.196721      0.416973   \n",
      "7   1.498074  3.924674  1.514139  1.526633     0.299101      0.685653   \n",
      "8   1.666973  4.459581  1.692237  1.677333     0.438516      0.968036   \n",
      "9   1.777739  5.032307  1.849183  1.792991     0.582779      1.256338   \n",
      "10  1.913884  5.993604  1.999082  1.824664     0.723023      1.518602   \n",
      "11  2.026130  8.177269  2.063785  1.723255     0.799788      1.788151   \n",
      "\n",
      "    muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0      0.000000      0.026775    0.000205   0.000261        1.148422   \n",
      "1      0.060526      0.017512    0.004943   0.001271        7.665023   \n",
      "2      0.042062      0.027574    0.047362   0.003874       36.309271   \n",
      "3      0.044834      0.039241    0.227445   0.007437      107.733685   \n",
      "4      0.058260      0.060275    0.532676   0.008529      224.418478   \n",
      "5      0.111922      0.095064    0.815419   0.006469      349.077954   \n",
      "6      0.176184      0.153826    0.917252   0.004100      490.071634   \n",
      "7      0.251996      0.227043    0.960654   0.003054      653.844367   \n",
      "8      0.352447      0.299159    0.968907   0.002993      825.663099   \n",
      "9      0.456538      0.358202    0.973349   0.002973     1001.141956   \n",
      "10     0.548135      0.368858    0.974935   0.002894     1218.913181   \n",
      "11     0.584090      0.309213    0.974141   0.002944     1580.458575   \n",
      "\n",
      "          deltaV     current    deltaV_err  \n",
      "0   6.666666e-08    0.100000  3.896944e-09  \n",
      "1   2.812500e-01    0.800000  1.438520e-02  \n",
      "2   9.777778e-01    2.700000  4.977994e-02  \n",
      "3   2.208818e+00    6.082993  1.124057e-01  \n",
      "4   4.042824e+00   11.018644  2.053938e-01  \n",
      "5   6.342751e+00   17.170834  3.218872e-01  \n",
      "6   9.018391e+00   24.284045  4.572719e-01  \n",
      "7   1.194017e+01   32.033117  6.050505e-01  \n",
      "8   1.523534e+01   40.804656  7.718130e-01  \n",
      "9   1.964247e+01   52.565740  9.949422e-01  \n",
      "10  2.704428e+01   72.345534  1.369774e+00  \n",
      "11  4.194874e+01  112.212500  2.124663e+00  \n",
      "30CO2_6.9 : \n",
      "    HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0   6000.0  6000.0     0.000000     0.100000     0.000000      0.000000   \n",
      "1   6100.0  6100.0     0.200000     0.200000     0.000000      0.000000   \n",
      "2   6200.0  6200.0     0.600000     0.500000     0.000000      0.000000   \n",
      "3   6300.0  6300.0     1.500000     1.100000     0.000000      0.000000   \n",
      "4   6400.0  6400.0     3.036046     2.200000     0.000000      0.000000   \n",
      "5   6500.0  6500.0     5.133980     3.700000     0.000932      0.000000   \n",
      "6   6600.0  6600.0     8.000000     5.900000     0.001903      0.000065   \n",
      "7   6700.0  6700.0    11.372549     8.500000     0.003956      0.000284   \n",
      "8   6800.0  6800.0    15.172650    11.704273     0.004089      0.000450   \n",
      "9   6900.0  6900.0    19.168977    15.330033     0.006692      0.001733   \n",
      "10  7000.0  7000.0    23.537594    19.283459     0.008366      0.003390   \n",
      "11  7100.0  7100.0    28.131008    23.623256     0.010848      0.007259   \n",
      "12  7200.0  7200.0    32.932631    28.215790     0.013396      0.009961   \n",
      "13  7300.0  7300.0    38.241954    33.302874     0.012171      0.015391   \n",
      "14  7400.0  7400.0    44.646835    39.201899     0.019925      0.021466   \n",
      "15  7500.0  7500.0    53.168519    46.814198     0.023369      0.031650   \n",
      "16  7600.0  7600.0    65.686559    57.295699     0.025206      0.046376   \n",
      "\n",
      "     muon_CM   gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0   0.000000   1.014493  0.000000  1.014286     0.000000      0.014493   \n",
      "1   1.000000   1.020588  1.000000  1.040346     0.000000      0.020588   \n",
      "2   1.000000   1.049517  1.333333  1.032221     0.000000      0.010870   \n",
      "3   1.166667   1.126103  1.207792  1.052632     0.090909      0.027322   \n",
      "4   1.130435   1.333982  1.207692  1.067784     0.095652      0.033544   \n",
      "5   1.086892   1.736488  1.094851  1.085944     0.041237      0.053865   \n",
      "6   1.125261   2.399263  1.125541  1.114515     0.054280      0.094493   \n",
      "7   1.144348   3.275976  1.156535  1.150854     0.066087      0.170050   \n",
      "8   1.174242   4.133355  1.198597  1.196750     0.085968      0.278169   \n",
      "9   1.213013   4.948401  1.278918  1.271023     0.122207      0.444327   \n",
      "10  1.298645   5.643623  1.383139  1.353443     0.173713      0.657399   \n",
      "11  1.400106   6.405350  1.496684  1.446361     0.226851      0.947955   \n",
      "12  1.503013   7.085251  1.651211  1.533641     0.313859      1.244018   \n",
      "13  1.612256   7.732106  1.736050  1.623411     0.366362      1.536331   \n",
      "14  1.696279   8.419728  1.835625  1.695109     0.441038      1.848436   \n",
      "15  1.790028   9.166925  1.906246  1.755915     0.499357      2.175613   \n",
      "16  1.865435  10.295128  1.968458  1.765774     0.561478      2.417042   \n",
      "\n",
      "    muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0      0.000000      0.014700    0.000000   0.000000        0.652955   \n",
      "1      0.000000      0.020572    0.000398   0.000311        2.332459   \n",
      "2      0.000000      0.010581    0.001932   0.000686        8.306748   \n",
      "3      0.087310      0.024935    0.013220   0.001798       27.443889   \n",
      "4      0.094217      0.026192    0.047403   0.003261       70.532486   \n",
      "5      0.040021      0.032672    0.148779   0.005435      146.700283   \n",
      "6      0.051795      0.042231    0.333346   0.007278      266.290622   \n",
      "7      0.063144      0.056791    0.564481   0.007812      412.860663   \n",
      "8      0.081765      0.075461    0.729032   0.006907      558.312182   \n",
      "9      0.117054      0.104724    0.848260   0.005565      717.582909   \n",
      "10     0.163186      0.141208    0.914322   0.004409      876.756086   \n",
      "11     0.208686      0.186458    0.938491   0.003793     1064.610887   \n",
      "12     0.285242      0.229057    0.959144   0.003139     1251.039966   \n",
      "13     0.321448      0.269096    0.963740   0.002964     1446.716620   \n",
      "14     0.378783      0.305147    0.969289   0.002750     1645.753904   \n",
      "15     0.415787      0.336802    0.969372   0.002720     1855.031743   \n",
      "16     0.455412      0.335737    0.969841   0.002736     2096.110495   \n",
      "\n",
      "          deltaV     current    deltaV_err  \n",
      "0   6.666666e-08    0.100000  3.896944e-09  \n",
      "1   1.500000e-01    0.400000  7.593419e-03  \n",
      "2   4.090909e-01    1.100000  2.073783e-02  \n",
      "3   9.519231e-01    2.600000  4.837872e-02  \n",
      "4   1.913458e+00    5.236047  9.727529e-02  \n",
      "5   3.225453e+00    8.833981  1.639969e-01  \n",
      "6   5.093525e+00   13.900000  2.588275e-01  \n",
      "7   7.296497e+00   19.872549  3.706531e-01  \n",
      "8   9.911003e+00   26.876923  5.031132e-01  \n",
      "9   1.277693e+01   34.499010  6.481375e-01  \n",
      "10  1.589941e+01   42.821053  8.061952e-01  \n",
      "11  1.926062e+01   51.754264  9.762581e-01  \n",
      "12  2.279422e+01   61.148421  1.155051e+00  \n",
      "13  2.670145e+01   71.544828  1.352775e+00  \n",
      "14  3.131068e+01   83.848734  1.586148e+00  \n",
      "15  3.734208e+01   99.982716  1.891632e+00  \n",
      "16  4.590366e+01  122.982258  2.325574e+00  \n",
      "30CO2_4.6 : \n",
      "    HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0   5800.0  5800.0     0.000000     0.000000     0.000000      0.000000   \n",
      "1   5900.0  5900.0     0.000000     0.000000     0.000000      0.000000   \n",
      "2   6000.0  6000.0     0.000000     0.100000     0.000000      0.000000   \n",
      "3   6100.0  6100.0     0.300000     0.300000     0.000000      0.000000   \n",
      "4   6200.0  6200.0     0.900000     0.700000     0.000000      0.000000   \n",
      "5   6300.0  6300.0     2.100000     1.500000     0.000000      0.000000   \n",
      "6   6400.0  6400.0     4.000000     2.900000     0.000481      0.000000   \n",
      "7   6500.0  6500.0     6.800000     4.900000     0.001693      0.000000   \n",
      "8   6600.0  6600.0    10.501754     7.738596     0.001875      0.000176   \n",
      "9   6700.0  6700.0    14.901695    11.300000     0.005431      0.000190   \n",
      "10  6800.0  6800.0    19.782170    15.419380     0.006236      0.000867   \n",
      "11  6900.0  6900.0    25.127820    20.082707     0.002000      0.001684   \n",
      "12  7000.0  7000.0    30.750000    25.244937     0.008996      0.004162   \n",
      "13  7100.0  7100.0    36.628767    30.836301     0.008989      0.006275   \n",
      "14  7200.0  7200.0    42.916552    36.790345     0.013986      0.014055   \n",
      "15  7300.0  7300.0    49.743478    43.316770     0.014893      0.020219   \n",
      "16  7400.0  7400.0    57.810582    50.958730     0.018226      0.028247   \n",
      "\n",
      "     muon_CM   gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0   1.000000   1.193548  3.000000  1.189189     0.000000      0.032258   \n",
      "1   1.000000   1.078947  1.000000  1.097561     0.000000      0.026316   \n",
      "2   1.000000   1.043860  1.000000  1.067227     0.000000      0.017544   \n",
      "3   1.000000   1.032110  1.000000  1.046667     0.000000      0.013761   \n",
      "4   1.045455   1.043312  1.000000  1.032357     0.000000      0.014650   \n",
      "5   1.083333   1.169901  1.384615  1.046866     0.166667      0.021661   \n",
      "6   1.086207   1.448217  1.126984  1.066287     0.043103      0.032917   \n",
      "7   1.115328   1.993911  1.121728  1.083472     0.049635      0.062759   \n",
      "8   1.124385   2.928092  1.136250  1.111647     0.042867      0.112962   \n",
      "9   1.176074   4.153846  1.147564  1.146805     0.064027      0.208133   \n",
      "10  1.194604   5.333604  1.209768  1.193391     0.091062      0.342970   \n",
      "11  1.252702   6.421474  1.260494  1.261445     0.110429      0.561271   \n",
      "12  1.318382   7.367509  1.383845  1.340294     0.171436      0.824053   \n",
      "13  1.436564   8.376990  1.496617  1.423902     0.234831      1.180785   \n",
      "14  1.524516   9.297604  1.619784  1.517557     0.300822      1.573707   \n",
      "15  1.632928  10.181241  1.680564  1.603048     0.353093      1.979114   \n",
      "16  1.733453  11.024187  1.782004  1.673071     0.418163      2.341950   \n",
      "\n",
      "    muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0      0.000000      0.031294    0.000209   0.000225        0.253182   \n",
      "1      0.000000      0.026132    0.000201   0.000221        0.258319   \n",
      "2      0.000000      0.017640    0.000838   0.000451        0.727338   \n",
      "3      0.000000      0.013772    0.000268   0.000255        3.184198   \n",
      "4      0.000000      0.014295    0.004064   0.000995       11.031073   \n",
      "5      0.184615      0.019030    0.009592   0.001479       36.846745   \n",
      "6      0.043015      0.023697    0.047816   0.003308       93.638972   \n",
      "7      0.047793      0.033062    0.155475   0.005641      195.036916   \n",
      "8      0.041729      0.041293    0.324273   0.007173      349.581568   \n",
      "9      0.059249      0.054720    0.556933   0.007654      538.954330   \n",
      "10     0.085687      0.072103    0.740259   0.006947      731.232200   \n",
      "11     0.102115      0.101395    0.855252   0.005569      933.743991   \n",
      "12     0.159242      0.134831    0.914988   0.004429     1139.477079   \n",
      "13     0.210274      0.175911    0.932281   0.003934     1377.730243   \n",
      "14     0.266946      0.219678    0.954985   0.003300     1629.407550   \n",
      "15     0.298786      0.260898    0.956711   0.003204     1886.343625   \n",
      "16     0.346330      0.293147    0.964306   0.002919     2130.907735   \n",
      "\n",
      "          deltaV     current    deltaV_err  \n",
      "0   1.000000e+00    0.000000           NaN  \n",
      "1   1.000000e+00    0.000000           NaN  \n",
      "2   6.666666e-08    0.100000  3.896944e-09  \n",
      "3   2.250000e-01    0.600000  1.139013e-02  \n",
      "4   5.906250e-01    1.600000  2.997684e-02  \n",
      "5   1.312500e+00    3.600000  6.674930e-02  \n",
      "6   2.521739e+00    6.900000  1.281971e-01  \n",
      "7   4.271795e+00   11.700000  2.171986e-01  \n",
      "8   6.683164e+00   18.240351  3.396119e-01  \n",
      "9   9.639977e+00   26.201695  4.895369e-01  \n",
      "10  1.299781e+01   35.201550  6.596679e-01  \n",
      "11  1.674283e+01   45.210527  8.493258e-01  \n",
      "12  2.079514e+01   55.994937  1.054403e+00  \n",
      "13  2.511290e+01   67.465068  1.272846e+00  \n",
      "14  2.971352e+01   79.706897  1.505661e+00  \n",
      "15  3.473116e+01   93.060248  1.759585e+00  \n",
      "16  4.062663e+01  108.769312  2.057993e+00  \n",
      "30CO2_3.3 : \n",
      "    HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0   5800.0  5800.0     0.000000     0.000000     0.000000      0.000000   \n",
      "1   5900.0  5900.0     0.000000     0.000000     0.000000      0.000000   \n",
      "2   6000.0  6000.0     0.200000     0.100000     0.000000      0.000000   \n",
      "3   6100.0  6100.0     0.600000     0.401316     0.000000      0.000000   \n",
      "4   6200.0  6200.0     1.489147     1.100000     0.000000      0.000000   \n",
      "5   6300.0  6300.0     3.200000     2.400000     0.000000      0.000000   \n",
      "6   6400.0  6400.0     6.200000     4.400000     0.000497      0.000000   \n",
      "7   6500.0  6500.0    10.400000     7.500000     0.001188      0.000065   \n",
      "8   6600.0  6600.0    15.958621    11.775862     0.001462      0.000000   \n",
      "9   6700.0  6700.0    22.429370    16.972727     0.002150      0.000400   \n",
      "10  6800.0  6800.0    29.796273    23.165218     0.003361      0.001108   \n",
      "11  6900.0  6900.0    37.688953    30.222675     0.004458      0.002041   \n",
      "12  7000.0  7000.0    46.021089    37.861225     0.005731      0.005079   \n",
      "13  7100.0  7100.0    54.801064    46.193617     0.006768      0.009528   \n",
      "14  7200.0  7200.0    64.051445    55.034682     0.009519      0.016362   \n",
      "15  7300.0  7300.0    74.086699    64.704926     0.010929      0.023063   \n",
      "16  7400.0  7400.0    85.767890    75.657797     0.009630      0.036341   \n",
      "17  7500.0  7500.0    99.449537    87.792130     0.016732      0.046612   \n",
      "18  7600.0  7600.0   119.594396   105.450431     0.017075      0.063535   \n",
      "\n",
      "     muon_CM   gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0   1.000000   1.055556  1.000000  1.105263     0.000000      0.000000   \n",
      "1   0.000000   1.000000  0.000000  1.019608     0.000000      0.000000   \n",
      "2   0.000000   1.043165  0.000000  1.068966     0.000000      0.035971   \n",
      "3   1.000000   1.024943  1.000000  1.037611     0.000000      0.015873   \n",
      "4   1.000000   1.087258  1.083333  1.038281     0.000000      0.014604   \n",
      "5   1.067568   1.252472  1.113924  1.047370     0.040541      0.023445   \n",
      "6   1.076613   1.680400  1.179775  1.063899     0.064516      0.042360   \n",
      "7   1.103004   2.605894  1.153048  1.085348     0.062947      0.078951   \n",
      "8   1.146586   4.196652  1.120841  1.110719     0.056894      0.164198   \n",
      "9   1.185987   6.134209  1.145005  1.137690     0.060722      0.285288   \n",
      "10  1.248491   8.067237  1.192855  1.184998     0.080483      0.507523   \n",
      "11  1.296307   9.653822  1.254102  1.244821     0.114328      0.779028   \n",
      "12  1.377579  11.109601  1.350810  1.315736     0.158951      1.150141   \n",
      "13  1.485009  12.628497  1.467393  1.392086     0.222871      1.611333   \n",
      "14  1.586513  14.113749  1.576606  1.471335     0.279143      2.153896   \n",
      "15  1.696865  15.410593  1.635040  1.549547     0.327577      2.673781   \n",
      "16  1.808248  16.950179  1.718478  1.611782     0.395850      3.215378   \n",
      "17  1.913613  18.226205  1.781395  1.662959     0.438482      3.654640   \n",
      "18  1.991570  20.235790  1.836905  1.684525     0.525026      4.150685   \n",
      "\n",
      "    muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0      0.000000      0.000000    0.000224   0.000233        0.188191   \n",
      "1      0.000000      0.000000    0.000000   0.000000        0.335322   \n",
      "2      0.000000      0.035632    0.000000   0.000000        1.045008   \n",
      "3      0.000000      0.015824    0.001321   0.000567        4.595951   \n",
      "4      0.000000      0.013761    0.000829   0.000452       17.708100   \n",
      "5      0.040753      0.019245    0.011641   0.001632       55.527286   \n",
      "6      0.066701      0.026160    0.051676   0.003489      139.873125   \n",
      "7      0.062250      0.031916    0.154570   0.005577      294.056555   \n",
      "8      0.052987      0.041822    0.354803   0.007474      527.790689   \n",
      "9      0.055768      0.050560    0.557030   0.007686      804.536079   \n",
      "10     0.072240      0.070138    0.713829   0.007014     1104.999449   \n",
      "11     0.101641      0.092952    0.839586   0.005788     1389.756419   \n",
      "12     0.139739      0.123435    0.897173   0.004808     1690.315733   \n",
      "13     0.191488      0.157524    0.915969   0.004328     2031.606952   \n",
      "14     0.235895      0.194810    0.941941   0.003671     2400.714447   \n",
      "15     0.264567      0.229101    0.944404   0.003631     2760.307883   \n",
      "16     0.308634      0.256997    0.948350   0.003495     3157.485448   \n",
      "17     0.332091      0.277755    0.955272   0.003270     3499.885444   \n",
      "18     0.383225      0.286714    0.955002   0.003289     3938.112061   \n",
      "\n",
      "       deltaV     current  deltaV_err  \n",
      "0    1.000000    0.000000         NaN  \n",
      "1    1.000000    0.000000         NaN  \n",
      "2    0.100000    0.300000    0.005155  \n",
      "3    0.360710    1.001316    0.018380  \n",
      "4    0.948997    2.589147    0.048221  \n",
      "5    2.057143    5.600000    0.104492  \n",
      "6    3.860377   10.600000    0.196360  \n",
      "7    6.536313   17.900000    0.332331  \n",
      "8   10.163873   27.734483    0.516470  \n",
      "9   14.492411   39.402098    0.735988  \n",
      "10  19.549218   52.961491    0.992218  \n",
      "11  25.159041   67.911628    1.276184  \n",
      "12  31.158203   83.882313    1.579801  \n",
      "13  37.597911  100.994681    1.905614  \n",
      "14  44.401279  119.086127    2.249863  \n",
      "15  51.809045  138.791625    2.624715  \n",
      "16  60.297184  161.425687    3.054406  \n",
      "17  69.943460  187.241667    3.543020  \n",
      "18  84.058457  225.044827    4.258074  \n",
      "30CO2_2.2 : \n",
      "   HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0  6100.0  6100.0     1.700000     1.300000     0.000000      0.000072   \n",
      "1  6300.0  6300.0     6.200000     4.800000     0.000000      0.000104   \n",
      "2  6500.0  6500.0    17.596881    13.549590     0.000399      0.000390   \n",
      "3  6700.0  6700.0    35.917411    28.468057     0.003385      0.003327   \n",
      "4  6900.0  6900.0    59.510143    49.508379     0.008574      0.016599   \n",
      "5  7000.0  7000.0    72.518542    61.858414     0.009579      0.035695   \n",
      "6  7100.0  7100.0    86.810166    75.809578     0.013570      0.058803   \n",
      "7  7200.0  7200.0   101.244776    90.226567     0.019920      0.098664   \n",
      "8  7300.0  7300.0   115.854880   105.018880     0.022165      0.150152   \n",
      "9  7400.0  7400.0   132.467143   121.718143     0.033293      0.203598   \n",
      "\n",
      "    muon_CM   gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0  1.000000   1.067514  1.214286  1.086025     0.000000      0.003993   \n",
      "1  1.090909   1.514841  1.159722  1.075633     0.007576      0.010655   \n",
      "2  1.097421   3.918736  1.104439  1.127249     0.004776      0.047049   \n",
      "3  1.169778   8.962599  1.166667  1.216736     0.013926      0.183107   \n",
      "4  1.234197  13.331141  1.410648  1.393328     0.042974      0.485348   \n",
      "5  1.261339  14.930542  1.582650  1.519438     0.063849      0.716096   \n",
      "6  1.307742  16.238484  1.838972  1.666027     0.103015      0.976756   \n",
      "7  1.358086  17.432284  2.047665  1.832110     0.142672      1.266580   \n",
      "8  1.359939  18.414165  2.268650  1.971822     0.162016      1.512830   \n",
      "9  1.383429  19.429895  2.446639  2.097383     0.171882      1.732008   \n",
      "\n",
      "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0     0.000000      0.004047    0.001233   0.000492       11.512267   \n",
      "1     0.008110      0.007513    0.017263   0.001830       96.036480   \n",
      "2     0.004785      0.013373    0.197030   0.005620      430.219023   \n",
      "3     0.013725      0.024360    0.669284   0.006650     1083.658312   \n",
      "4     0.047465      0.048945    0.882766   0.004562     1846.282580   \n",
      "5     0.076253      0.069540    0.913859   0.003983     2254.823583   \n",
      "6     0.134283      0.094527    0.931794   0.003586     2688.441735   \n",
      "7     0.194664      0.124099    0.937875   0.003441     3172.050194   \n",
      "8     0.241503      0.149698    0.943180   0.003308     3608.183688   \n",
      "9     0.270385      0.171662    0.934871   0.003544     4044.982908   \n",
      "\n",
      "      deltaV     current  deltaV_err  \n",
      "0   1.105000    3.000000    0.056104  \n",
      "1   4.058182   11.000000    0.205990  \n",
      "2  11.482706   31.146470    0.582920  \n",
      "3  23.821344   64.385468    1.208590  \n",
      "4  40.537846  109.018523    2.055016  \n",
      "5  50.074233  134.376956    2.537555  \n",
      "6  60.703349  162.619744    3.075316  \n",
      "7  71.563988  191.471343    3.624767  \n",
      "8  82.628306  220.873761    4.184553  \n",
      "9  95.149024  254.185285    4.818144  \n",
      "30CO2_1 : \n",
      "   HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0  6100.0  6100.0     1.900000     1.500000     0.000000      0.000050   \n",
      "1  6300.0  6300.0     7.500000     5.900000     0.000000      0.000050   \n",
      "2  6500.0  6500.0    21.594563    16.673996     0.000167      0.000599   \n",
      "3  6700.0  6700.0    44.849700    35.544889     0.002753      0.003391   \n",
      "4  6900.0  6900.0    73.987814    61.587276     0.007133      0.019830   \n",
      "5  7000.0  7000.0    90.163545    76.919398     0.006588      0.037523   \n",
      "6  7100.0  7100.0   106.863310    93.091297     0.011497      0.066622   \n",
      "7  7200.0  7200.0   124.368597   110.305439     0.016703      0.104999   \n",
      "8  7300.0  7300.0   142.781950   128.772382     0.019574      0.160430   \n",
      "9  7400.0  7400.0   163.103231   148.926172     0.027285      0.226958   \n",
      "\n",
      "    muon_CM   gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0  1.000000   1.080404  1.052632  1.078894     0.000000      0.006591   \n",
      "1  1.046154   1.634239  1.098039  1.070546     0.005128      0.011499   \n",
      "2  1.102282   4.731272  1.142755  1.122543     0.006294      0.053543   \n",
      "3  1.187207  11.257941  1.156526  1.212439     0.012683      0.232097   \n",
      "4  1.290843  16.841179  1.374054  1.364019     0.043399      0.576342   \n",
      "5  1.322495  18.857418  1.563639  1.481390     0.066357      0.836245   \n",
      "6  1.340264  20.620611  1.756303  1.610884     0.096816      1.155980   \n",
      "7  1.396925  22.144388  1.963106  1.753542     0.134041      1.481658   \n",
      "8  1.457269  23.561323  2.143498  1.888436     0.153619      1.819368   \n",
      "9  1.482256  24.731829  2.268508  2.012095     0.162421      2.108310   \n",
      "\n",
      "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0     0.000000      0.006541    0.000926   0.000360       13.151399   \n",
      "1     0.005356      0.007480    0.016596   0.001490      114.993656   \n",
      "2     0.006488      0.012562    0.200661   0.005182      523.086345   \n",
      "3     0.012224      0.024491    0.662438   0.006415     1356.289711   \n",
      "4     0.044694      0.045135    0.876382   0.004650     2282.512317   \n",
      "5     0.074708      0.062904    0.907147   0.004114     2777.437746   \n",
      "6     0.118322      0.085511    0.924659   0.003738     3299.677748   \n",
      "7     0.171876      0.109970    0.932849   0.003559     3856.629546   \n",
      "8     0.204410      0.135369    0.935767   0.003447     4415.115727   \n",
      "9     0.224028      0.158051    0.939495   0.003412     4942.252394   \n",
      "\n",
      "       deltaV     current  deltaV_err  \n",
      "0    1.257353    3.400000    0.063797  \n",
      "1    4.953358   13.400000    0.251348  \n",
      "2   14.113452   38.268559    0.716428  \n",
      "3   29.744121   80.394589    1.509088  \n",
      "4   50.415322  135.575089    2.555720  \n",
      "5   62.262420  167.082943    3.155196  \n",
      "6   74.627269  199.954607    3.780826  \n",
      "7   87.686731  234.674035    4.441603  \n",
      "8  101.561840  271.554332    5.143624  \n",
      "9  116.769475  312.029403    5.913230  \n",
      "30CO205SF6_OFF : \n",
      "   HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0  5950.0  5950.0     0.000000     0.000000     0.000000      0.000017   \n",
      "1  6100.0  6100.0     0.000000     0.000000     0.000000      0.000000   \n",
      "2  6250.0  6250.0     0.000000     0.000000     0.000479      0.000000   \n",
      "3  6400.0  6400.0     0.000000     0.000000     0.001325      0.000000   \n",
      "4  6550.0  6550.0     0.007477     0.000935     0.006405      0.000055   \n",
      "5  6700.0  6700.0     0.000000     0.000000     0.006748      0.000014   \n",
      "6  6850.0  6850.0     0.002450     0.001871     0.008632      0.000000   \n",
      "7  7000.0  7000.0     0.035748     0.021028     0.012548      0.000000   \n",
      "8  7150.0  7150.0     0.032662     0.028921     0.020545      0.000075   \n",
      "9  7300.0  7300.0     0.148206     0.135202     0.027221      0.000075   \n",
      "\n",
      "    muon_CM  gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0  1.272727  1.363636  1.857143  1.466667     0.000000      0.295455   \n",
      "1  1.142857  1.652174  1.437500  1.618421     0.089286      0.347826   \n",
      "2  1.125290  1.571429  1.216495  1.400000     0.083527      0.242857   \n",
      "3  1.097319  1.882353  1.114711  1.475000     0.046037      0.341176   \n",
      "4  1.103439  1.820000  1.145521  1.355311     0.059301      0.220000   \n",
      "5  1.181925  1.795918  1.269448  1.469697     0.132220      0.278912   \n",
      "6  1.311635  1.779661  1.415918  1.612698     0.216641      0.367232   \n",
      "7  1.478044  2.016461  1.591155  1.655102     0.355289      0.362140   \n",
      "8  1.622626  2.096667  1.773542  1.523052     0.491175      0.323333   \n",
      "9  1.735731  2.322767  1.947660  1.476427     0.654171      0.386167   \n",
      "\n",
      "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate    deltaV  \\\n",
      "0     0.000000      0.261187    0.001775   0.000540        0.169527  1.000000   \n",
      "1     0.104167      0.281465    0.008743   0.001174        0.224908  1.000000   \n",
      "2     0.084057      0.187402    0.067776   0.003176        0.242146  1.000000   \n",
      "3     0.044884      0.226323    0.281413   0.005792        0.393591  1.000000   \n",
      "4     0.058423      0.146161    0.591345   0.006241        0.586724  0.001247   \n",
      "5     0.127723      0.197566    0.841349   0.004703        0.616510  1.000000   \n",
      "6     0.200714      0.275856    0.942901   0.002946        0.733831  0.001593   \n",
      "7     0.308357      0.251988    0.979159   0.001823        1.171954  0.019860   \n",
      "8     0.412110      0.203493    0.990196   0.001271        1.379366  0.023008   \n",
      "9     0.533119      0.210469    0.992173   0.001141        1.715779  0.106054   \n",
      "\n",
      "    current  deltaV_err  \n",
      "0  0.000000         NaN  \n",
      "1  0.000000         NaN  \n",
      "2  0.000000         NaN  \n",
      "3  0.000000         NaN  \n",
      "4  0.008411    0.000069  \n",
      "5  0.000000         NaN  \n",
      "6  0.004321    0.000081  \n",
      "7  0.056776    0.001017  \n",
      "8  0.061583    0.001165  \n",
      "9  0.283408    0.005371  \n",
      "30CO205SF6_22 : \n",
      "   HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0  5950.0  5950.0     0.100000     0.000000     0.000000      0.000000   \n",
      "1  6100.0  6100.0     0.300000     0.200000     0.000000      0.000000   \n",
      "2  6250.0  6250.0     0.800000     0.500000     0.000940      0.000000   \n",
      "3  6400.0  6400.0     1.700000     1.200000     0.003500      0.000099   \n",
      "4  6550.0  6550.0     3.038462     2.209790     0.005249      0.000050   \n",
      "5  6700.0  6700.0     4.604225     3.590141     0.007194      0.000398   \n",
      "6  6850.0  6850.0     6.370345     5.221379     0.008637      0.001886   \n",
      "7  7000.0  7000.0     8.344886     7.061932     0.015665      0.003595   \n",
      "8  7150.0  7150.0    10.974011     9.515254     0.017367      0.007584   \n",
      "9  7300.0  7300.0    15.710458    13.554902     0.024414      0.012651   \n",
      "\n",
      "    muon_CM  gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0  1.000000  1.062893  1.000000  1.159763     0.000000      0.012579   \n",
      "1  1.038462  1.056167  1.111111  1.049009     0.076923      0.015419   \n",
      "2  1.100962  1.116147  1.187773  1.077834     0.067308      0.027699   \n",
      "3  1.080139  1.303538  1.102151  1.108245     0.033682      0.048339   \n",
      "4  1.124477  1.577482  1.170233  1.168034     0.087343      0.088741   \n",
      "5  1.170827  1.841769  1.251166  1.279918     0.117005      0.165140   \n",
      "6  1.306607  2.142710  1.396629  1.413301     0.204749      0.295470   \n",
      "7  1.509377  2.433926  1.581762  1.567711     0.345278      0.467046   \n",
      "8  1.646828  2.795015  1.748809  1.708179     0.484630      0.631613   \n",
      "9  1.813689  3.353473  1.898012  1.777487     0.628047      0.825607   \n",
      "\n",
      "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0     0.000000      0.013889    0.001696   0.000728        1.123062   \n",
      "1     0.076628      0.015094    0.007670   0.001574        5.814102   \n",
      "2     0.068431      0.026101    0.062234   0.004277       21.924676   \n",
      "3     0.033329      0.039628    0.268945   0.007923       61.413765   \n",
      "4     0.084346      0.062208    0.588221   0.008670      119.760617   \n",
      "5     0.113673      0.105319    0.842230   0.006616      180.685044   \n",
      "6     0.189206      0.171271    0.936771   0.004372      253.068073   \n",
      "7     0.294474      0.252396    0.969327   0.003107      334.591299   \n",
      "8     0.397627      0.314860    0.982379   0.002358      426.732188   \n",
      "9     0.488194      0.351155    0.985956   0.002135      544.276798   \n",
      "\n",
      "         deltaV    current    deltaV_err  \n",
      "0  6.666666e-08   0.100000  3.896944e-09  \n",
      "1  1.800000e-01   0.500000  9.172649e-03  \n",
      "2  4.615385e-01   1.300000  2.357083e-02  \n",
      "3  1.055172e+00   2.900000  5.367976e-02  \n",
      "4  1.919028e+00   5.248252  9.754939e-02  \n",
      "5  3.025826e+00   8.194366  1.535662e-01  \n",
      "6  4.304190e+00  11.591724  2.182466e-01  \n",
      "7  5.737494e+00  15.406818  2.907834e-01  \n",
      "8  7.644528e+00  20.489265  3.873141e-01  \n",
      "9  1.091497e+01  29.265360  5.530457e-01  \n",
      "30CO205SF6_10 : \n",
      "   HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0  5950.0  5950.0     0.100000     0.000000     0.000000      0.000000   \n",
      "1  6100.0  6100.0     0.600000     0.400000     0.000000      0.000000   \n",
      "2  6250.0  6250.0     1.700000     1.200000     0.000948      0.000000   \n",
      "3  6400.0  6400.0     3.500000     2.500000     0.000651      0.000000   \n",
      "4  6550.0  6550.0     6.082911     4.505063     0.004566      0.000250   \n",
      "5  6700.0  6700.0     9.118125     7.157500     0.003578      0.000432   \n",
      "6  6850.0  6850.0    12.488636    10.267614     0.010447      0.002854   \n",
      "7  7000.0  7000.0    16.257619    13.933809     0.010440      0.008231   \n",
      "8  7150.0  7150.0    21.352866    18.597452     0.018248      0.014184   \n",
      "9  7300.0  7300.0    30.140120    26.374252     0.027742      0.025909   \n",
      "\n",
      "    muon_CM  gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0  1.000000  1.053435  2.000000  1.112319     0.000000      0.022901   \n",
      "1  1.076923  1.073422  1.142857  1.046354     0.000000      0.016990   \n",
      "2  1.059113  1.206062  1.130233  1.075811     0.029557      0.033356   \n",
      "3  1.088089  1.577689  1.112885  1.110940     0.047146      0.066399   \n",
      "4  1.127473  2.160143  1.141326  1.170415     0.063187      0.123937   \n",
      "5  1.232941  2.742638  1.244275  1.271200     0.120000      0.253436   \n",
      "6  1.359972  3.309900  1.408476  1.408229     0.224540      0.445008   \n",
      "7  1.496398  3.871813  1.576800  1.555789     0.336192      0.692846   \n",
      "8  1.678957  4.508636  1.719287  1.686573     0.459130      0.997479   \n",
      "9  1.788607  5.437413  1.894282  1.752173     0.601579      1.271801   \n",
      "\n",
      "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0     0.000000      0.023666    0.000736   0.000484        1.751707   \n",
      "1     0.000000      0.016304    0.006528   0.001401       10.674786   \n",
      "2     0.030685      0.028953    0.059329   0.004203       43.906096   \n",
      "3     0.046218      0.044867    0.254593   0.007864      119.760945   \n",
      "4     0.060569      0.063508    0.590533   0.008901      233.743784   \n",
      "5     0.110362      0.107530    0.829956   0.006788      354.947422   \n",
      "6     0.199594      0.166894    0.931989   0.004573      497.346094   \n",
      "7     0.289267      0.236145    0.960491   0.003537      651.645582   \n",
      "8     0.369198      0.305537    0.971171   0.003076      831.752638   \n",
      "9     0.476766      0.332143    0.977842   0.002697     1047.745961   \n",
      "\n",
      "         deltaV    current    deltaV_err  \n",
      "0  6.666666e-08   0.100000  3.896944e-09  \n",
      "1  3.600000e-01   1.000000  1.834530e-02  \n",
      "2  1.055172e+00   2.900000  5.367976e-02  \n",
      "3  2.187500e+00   6.000000  1.112488e-01  \n",
      "4  3.882315e+00  10.587975  1.972597e-01  \n",
      "5  6.014790e+00  16.275625  3.052210e-01  \n",
      "6  8.452304e+00  22.756250  4.285580e-01  \n",
      "7  1.125471e+01  30.191429  5.703072e-01  \n",
      "8  1.491010e+01  39.950318  7.553892e-01  \n",
      "9  2.109879e+01  56.514371  1.068870e+00  \n",
      "30CO205SF6_6.9 : \n",
      "   HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0  5950.0  5950.0     0.300000     0.200000     0.000000      0.000000   \n",
      "1  6100.0  6100.0     1.000000     0.700000     0.000000      0.000000   \n",
      "2  6250.0  6250.0     2.526429     1.776429     0.000626      0.000000   \n",
      "3  6400.0  6400.0     5.300000     3.800000     0.000958      0.000000   \n",
      "4  6550.0  6550.0     9.043312     6.794905     0.004822      0.000056   \n",
      "5  6700.0  6700.0    13.348503    10.577845     0.004489      0.001164   \n",
      "6  6850.0  6850.0    18.289912    15.141667     0.007307      0.003387   \n",
      "7  7000.0  7000.0    23.867179    20.434872     0.013940      0.009432   \n",
      "8  7150.0  7150.0    31.159625    27.374647     0.014191      0.018940   \n",
      "9  7300.0  7300.0    43.362449    38.406531     0.023318      0.030627   \n",
      "\n",
      "    muon_CM  gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0  1.250000  1.050228  1.600000  1.080435     0.250000      0.022831   \n",
      "1  1.147059  1.075934  1.128205  1.053771     0.029412      0.015267   \n",
      "2  1.128440  1.305689  1.162602  1.073005     0.045872      0.032364   \n",
      "3  1.106697  1.874547  1.149744  1.109314     0.068104      0.076723   \n",
      "4  1.133584  2.813189  1.147184  1.169289     0.069206      0.165626   \n",
      "5  1.235043  3.748929  1.248820  1.260281     0.118104      0.320166   \n",
      "6  1.353835  4.624731  1.374666  1.390695     0.196816      0.584127   \n",
      "7  1.502111  5.517887  1.520262  1.532014     0.284307      0.941417   \n",
      "8  1.714880  6.381640  1.678196  1.665202     0.431842      1.350970   \n",
      "9  1.827331  7.721064  1.830145  1.720558     0.586511      1.700534   \n",
      "\n",
      "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0     0.266667      0.022988    0.000970   0.000561        2.871514   \n",
      "1     0.028205      0.014743    0.008803   0.001660       16.113135   \n",
      "2     0.045414      0.025953    0.062180   0.004272       63.961321   \n",
      "3     0.066652      0.043618    0.272704   0.007964      177.028924   \n",
      "4     0.066007      0.065014    0.596089   0.008819      346.008467   \n",
      "5     0.108998      0.099162    0.826347   0.006798      524.680093   \n",
      "6     0.174479      0.155954    0.923557   0.004860      726.310660   \n",
      "7     0.241948      0.223285    0.955906   0.003767      959.446231   \n",
      "8     0.337591      0.290929    0.964642   0.003379     1209.723984   \n",
      "9     0.444686      0.310549    0.965364   0.003377     1514.419153   \n",
      "\n",
      "      deltaV    current  deltaV_err  \n",
      "0   0.180000   0.500000    0.009173  \n",
      "1   0.617647   1.700000    0.031429  \n",
      "2   1.564549   4.302857    0.079602  \n",
      "3   3.319780   9.100000    0.168816  \n",
      "4   5.819637  15.838217    0.295594  \n",
      "5   8.852065  23.926348    0.449117  \n",
      "6  12.425666  33.431579    0.629951  \n",
      "7  16.513550  44.302051    0.836798  \n",
      "8  21.858572  58.534272    1.107313  \n",
      "9  30.550728  81.768979    1.547510  \n",
      "30CO205SF6_4.6 : \n",
      "   HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0  5950.0  5950.0     0.400000     0.300000     0.000000      0.000000   \n",
      "1  6100.0  6100.0     1.300000     0.900000     0.000000      0.000050   \n",
      "2  6250.0  6250.0     3.400000     2.400000     0.000922      0.000000   \n",
      "3  6400.0  6400.0     6.974468     5.003546     0.004286      0.000055   \n",
      "4  6550.0  6550.0    11.854237     8.924858     0.002948      0.000328   \n",
      "5  6700.0  6700.0    17.637816    14.007563     0.006892      0.001546   \n",
      "6  6850.0  6850.0    24.149020    20.017484     0.006185      0.004894   \n",
      "7  7000.0  7000.0    31.464309    27.034727     0.011129      0.012445   \n",
      "8  7150.0  7150.0    40.914788    36.070915     0.015313      0.022832   \n",
      "9  7300.0  7300.0    56.679386    50.522369     0.021255      0.039892   \n",
      "\n",
      "    muon_CM   gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0  1.000000   1.038610  1.333333  1.078067     0.000000      0.027027   \n",
      "1  1.120000   1.101568  1.107143  1.045360     0.000000      0.016982   \n",
      "2  1.089362   1.414552  1.113281  1.069375     0.034043      0.036278   \n",
      "3  1.082450   2.220284  1.097933  1.107825     0.035336      0.085525   \n",
      "4  1.169248   3.538342  1.164144  1.165366     0.068584      0.201929   \n",
      "5  1.254648   4.872720  1.262887  1.259103     0.127728      0.416595   \n",
      "6  1.375178   6.041113  1.394104  1.383093     0.210526      0.757325   \n",
      "7  1.570931   7.257938  1.522077  1.523539     0.320669      1.211073   \n",
      "8  1.756681   8.362037  1.681145  1.649183     0.438467      1.684346   \n",
      "9  1.845130  10.221649  1.830422  1.704488     0.589699      2.210363   \n",
      "\n",
      "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0     0.000000      0.027342    0.000517   0.000412        3.338890   \n",
      "1     0.000000      0.015871    0.005896   0.001384       20.379272   \n",
      "2     0.033736      0.026740    0.064745   0.004315       85.367599   \n",
      "3     0.034708      0.041090    0.271485   0.008093      233.300131   \n",
      "4     0.064501      0.062915    0.587776   0.008922      455.185720   \n",
      "5     0.116688      0.099169    0.814758   0.007062      697.784330   \n",
      "6     0.185088      0.154072    0.919836   0.004915      957.005170   \n",
      "7     0.258027      0.217867    0.948904   0.004006     1267.305062   \n",
      "8     0.335798      0.276497    0.960863   0.003566     1581.022876   \n",
      "9     0.443316      0.303051    0.967952   0.003244     2000.737885   \n",
      "\n",
      "      deltaV     current  deltaV_err  \n",
      "0   0.257143    0.700000    0.013061  \n",
      "1   0.797727    2.200000    0.040605  \n",
      "2   2.110345    5.800000    0.107360  \n",
      "3   4.370141   11.978014    0.222225  \n",
      "4   7.637295   20.779096    0.387900  \n",
      "5  11.710848   31.645379    0.594135  \n",
      "6  16.417508   44.166504    0.832311  \n",
      "7  21.811359   58.499035    1.105206  \n",
      "8  28.755349   76.985703    1.456636  \n",
      "9  40.068050  107.201755    2.029471  \n",
      "30CO205SF6_3.3 : \n",
      "   HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0  5950.0  5950.0     0.700000     0.400000     0.000000      0.000000   \n",
      "1  6100.0  6100.0     2.100000     1.400000     0.000000      0.000000   \n",
      "2  6250.0  6250.0     5.400000     3.700000     0.000608      0.000000   \n",
      "3  6400.0  6400.0    10.734483     7.750575     0.000986      0.000075   \n",
      "4  6550.0  6550.0    18.159616    13.806410     0.002540      0.000600   \n",
      "5  6700.0  6700.0    26.845299    21.433333     0.005329      0.001478   \n",
      "6  6850.0  6850.0    36.531250    30.516406     0.008331      0.004528   \n",
      "7  7000.0  7000.0    47.589285    41.139523     0.010369      0.014663   \n",
      "8  7150.0  7150.0    61.392995    54.516183     0.015651      0.029183   \n",
      "9  7300.0  7300.0    84.075611    76.145905     0.021077      0.050179   \n",
      "\n",
      "    muon_CM   gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0  1.000000   1.058965  2.000000  1.043182     0.000000      0.020457   \n",
      "1  1.060606   1.156494  1.114286  1.037801     0.000000      0.017107   \n",
      "2  1.097959   1.663641  1.141264  1.069916     0.040816      0.039585   \n",
      "3  1.126021   3.010893  1.156477  1.107279     0.066511      0.108448   \n",
      "4  1.177849   5.274341  1.159132  1.160727     0.078807      0.281702   \n",
      "5  1.264041   7.345648  1.212589  1.246982     0.106084      0.596744   \n",
      "6  1.445312   9.145691  1.363192  1.365310     0.187128      1.078051   \n",
      "7  1.606920  10.991221  1.515073  1.495079     0.313841      1.712133   \n",
      "8  1.777620  12.927260  1.647410  1.611772     0.427054      2.453778   \n",
      "9  1.914016  15.336722  1.763514  1.664584     0.535477      3.030554   \n",
      "\n",
      "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0     0.000000      0.019770    0.000309   0.000307        5.235421   \n",
      "1     0.000000      0.015127    0.006362   0.001406       32.373858   \n",
      "2     0.040906      0.024866    0.064825   0.004293      134.612190   \n",
      "3     0.064500      0.038496    0.271380   0.008066      358.514100   \n",
      "4     0.072691      0.058851    0.591669   0.008770      702.609512   \n",
      "5     0.093887      0.093691    0.805087   0.007032     1055.416032   \n",
      "6     0.156264      0.143966    0.901722   0.005457     1440.894753   \n",
      "7     0.247554      0.201504    0.945466   0.004109     1896.333993   \n",
      "8     0.319110      0.257130    0.954651   0.003827     2403.744772   \n",
      "9     0.385517      0.274652    0.946852   0.004083     2941.458892   \n",
      "\n",
      "      deltaV     current  deltaV_err  \n",
      "0   0.381818    1.100000    0.019567  \n",
      "1   1.260000    3.500000    0.064209  \n",
      "2   3.293407    9.100000    0.167688  \n",
      "3   6.751270   18.485057    0.343249  \n",
      "4  11.764949   31.966026    0.597413  \n",
      "5  17.876984   48.278632    0.906876  \n",
      "6  24.940524   67.047656    1.264251  \n",
      "7  33.097489   88.728808    1.676962  \n",
      "8  43.312943  115.909179    2.193908  \n",
      "9  59.935897  160.221516    3.035361  \n",
      "30CO205SF6_2.2 : \n",
      "   HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0  6000.0  6000.0     2.000000     1.500000     0.000000      0.000133   \n",
      "1  6200.0  6200.0     6.101569     4.700000     0.000393      0.000067   \n",
      "2  6400.0  6400.0    14.648246    11.379532     0.000987      0.000770   \n",
      "3  6600.0  6600.0    27.034711    21.975413     0.006595      0.005828   \n",
      "4  6700.0  6700.0    34.276562    28.531697     0.007588      0.013790   \n",
      "5  6800.0  6800.0    41.938375    35.778639     0.011748      0.024495   \n",
      "6  6900.0  6900.0    50.115254    43.784916     0.016730      0.044488   \n",
      "7  7000.0  7000.0    58.901701    52.498809     0.019968      0.073303   \n",
      "8  7100.0  7100.0    68.627241    62.222858     0.023734      0.105161   \n",
      "9  7200.0  7200.0    81.444902    75.010024     0.035743      0.150991   \n",
      "\n",
      "    muon_CM   gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0  1.000000   1.112713  1.350000  1.072647     0.025000      0.005313   \n",
      "1  1.054662   1.708631  1.112805  1.097477     0.003215      0.015443   \n",
      "2  1.093058   3.893814  1.119369  1.170615     0.004431      0.062523   \n",
      "3  1.147109   7.176811  1.244789  1.298451     0.015370      0.197831   \n",
      "4  1.162995   8.463015  1.405567  1.405545     0.039568      0.311119   \n",
      "5  1.189920   9.464134  1.605526  1.537770     0.073762      0.460852   \n",
      "6  1.222031  10.305829  1.881690  1.699614     0.106928      0.651132   \n",
      "7  1.232308  11.017163  2.151161  1.869541     0.125834      0.811798   \n",
      "8  1.262484  11.644168  2.352772  2.019403     0.151088      0.994907   \n",
      "9  1.287882  12.480378  2.537910  2.144066     0.184957      1.151254   \n",
      "\n",
      "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0     0.032927      0.005097    0.005276   0.001020       20.807120   \n",
      "1     0.003382      0.009831    0.050803   0.003079      128.894995   \n",
      "2     0.004520      0.018499    0.392305   0.006865      444.248818   \n",
      "3     0.016458      0.034832    0.821864   0.005427      925.434047   \n",
      "4     0.046248      0.049839    0.893342   0.004379     1182.660360   \n",
      "5     0.093715      0.071404    0.930431   0.003611     1446.478407   \n",
      "6     0.151400      0.101002    0.940557   0.003365     1739.885948   \n",
      "7     0.199308      0.128303    0.946412   0.003215     2047.727114   \n",
      "8     0.251474      0.158961    0.956642   0.002911     2337.423230   \n",
      "9     0.318707      0.181076    0.940700   0.003399     2658.174699   \n",
      "\n",
      "      deltaV     current  deltaV_err  \n",
      "0   1.285714    3.500000    0.065307  \n",
      "1   3.982390   10.801568    0.202165  \n",
      "2   9.606478   26.027778    0.487583  \n",
      "3  18.182945   49.010124    0.922105  \n",
      "4  23.356048   62.808259    1.183996  \n",
      "5  28.960801   77.717014    1.467611  \n",
      "6  35.052527   93.900169    1.775800  \n",
      "7  41.637186  111.400510    2.108951  \n",
      "8  48.951240  130.850099    2.479037  \n",
      "9  58.571349  156.454926    2.965881  \n",
      "30CO205SF6_1 : \n",
      "   HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0  6000.0  6000.0     3.300000     2.500000     0.000000      0.000000   \n",
      "1  6200.0  6200.0    11.075093     8.582528     0.000395      0.000069   \n",
      "2  6400.0  6400.0    26.898135    21.108858     0.001998      0.000550   \n",
      "3  6600.0  6600.0    50.118712    41.063783     0.004970      0.005951   \n",
      "4  6700.0  6700.0    63.886921    53.644701     0.006185      0.016046   \n",
      "5  6800.0  6800.0    78.371430    67.340034     0.007585      0.033511   \n",
      "6  6900.0  6900.0    93.796672    82.243560     0.010748      0.060638   \n",
      "7  7000.0  7000.0   110.092891    98.324803     0.015029      0.098907   \n",
      "8  7100.0  7100.0   128.034855   116.266124     0.022169      0.148058   \n",
      "9  7200.0  7200.0   149.361758   137.494293     0.033499      0.221440   \n",
      "\n",
      "    muon_CM   gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0  1.047619   1.187014  1.113636  1.057589     0.023810      0.005716   \n",
      "1  1.078616   2.443197  1.125364  1.087636     0.009434      0.022846   \n",
      "2  1.138801   7.114541  1.116805  1.155042     0.009464      0.109405   \n",
      "3  1.239919  13.749986  1.227245  1.261043     0.023079      0.336561   \n",
      "4  1.267349  16.449525  1.341472  1.349211     0.040728      0.542041   \n",
      "5  1.312389  18.503214  1.516299  1.453242     0.065285      0.788721   \n",
      "6  1.364273  20.335256  1.718870  1.585325     0.091506      1.102868   \n",
      "7  1.396719  21.870774  1.899861  1.723332     0.124973      1.454348   \n",
      "8  1.443982  23.382491  2.072890  1.854680     0.157112      1.768860   \n",
      "9  1.502613  24.905957  2.208116  1.969462     0.161368      2.068506   \n",
      "\n",
      "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0     0.025899      0.005068    0.003946   0.000880       35.887319   \n",
      "1     0.009757      0.010076    0.049977   0.003064      234.048650   \n",
      "2     0.009204      0.017493    0.371264   0.006837      816.442240   \n",
      "3     0.022426      0.030129    0.784525   0.005812     1722.724705   \n",
      "4     0.041768      0.043041    0.880531   0.004596     2204.657938   \n",
      "5     0.071854      0.059414    0.901148   0.004233     2671.224615   \n",
      "6     0.108043      0.081556    0.917862   0.003895     3200.866418   \n",
      "7     0.156031      0.107452    0.929053   0.003638     3740.656890   \n",
      "8     0.203408      0.130437    0.932384   0.003588     4305.303071   \n",
      "9     0.214136      0.151026    0.940854   0.003378     4873.194588   \n",
      "\n",
      "       deltaV     current  deltaV_err  \n",
      "0    2.133621    5.800000    0.108352  \n",
      "1    7.253088   19.657621    0.368154  \n",
      "2   17.740819   48.006993    0.900264  \n",
      "3   33.856234   91.182495    1.716712  \n",
      "4   43.739652  117.531623    2.217024  \n",
      "5   54.328616  145.711464    2.752892  \n",
      "6   65.730760  176.040232    3.329862  \n",
      "7   77.907458  208.417694    3.945988  \n",
      "8   91.400266  244.300978    4.628726  \n",
      "9  107.386906  286.856050    5.437775  \n",
      "40CO2_OFF : \n",
      "    HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0   6000.0  6000.0     0.000000     0.000000     0.000000      0.000000   \n",
      "1   6100.0  6100.0     0.000000     0.000000     0.000000      0.000000   \n",
      "2   6200.0  6200.0     0.000000     0.000000     0.000000      0.000000   \n",
      "3   6300.0  6300.0     0.000000     0.000000     0.000918      0.000000   \n",
      "4   6400.0  6400.0     0.000000     0.000000     0.000937      0.000000   \n",
      "5   6500.0  6500.0     0.000000     0.000000     0.003152      0.000000   \n",
      "6   6600.0  6600.0     0.000000     0.000000     0.005369      0.000000   \n",
      "7   6700.0  6700.0     0.000000     0.000000     0.005332      0.000000   \n",
      "8   6800.0  6800.0     0.005172     0.000000     0.008943      0.000000   \n",
      "9   6900.0  6900.0     0.030392     0.009804     0.010617      0.000000   \n",
      "10  7000.0  7000.0     0.033010     0.033981     0.013685      0.000056   \n",
      "11  7100.0  7100.0     0.043478     0.057391     0.015470      0.000050   \n",
      "12  7200.0  7200.0     0.088136     0.091525     0.021880      0.000049   \n",
      "13  7300.0  7300.0     0.126154     0.110000     0.023861      0.000000   \n",
      "14  7400.0  7400.0     0.220000     0.160000     0.028434      0.000049   \n",
      "15  7500.0  7500.0     0.382581     0.268387     0.036964      0.000000   \n",
      "16  7600.0  7600.0     0.800641     0.569872     0.038471      0.000000   \n",
      "\n",
      "     muon_CM  gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0   1.400000  1.500000  1.714286  1.500000     0.200000      0.500000   \n",
      "1   1.566667  1.750000  1.297872  1.000000     0.133333      0.000000   \n",
      "2   1.282609  1.166667  1.322034  2.142857     0.119565      0.666667   \n",
      "3   1.098315  2.142857  1.189258  1.133333     0.089888      0.142857   \n",
      "4   1.122051  1.250000  1.143510  1.333333     0.052308      0.166667   \n",
      "5   1.104784  2.100000  1.147423  1.619048     0.051253      0.600000   \n",
      "6   1.109589  1.750000  1.185835  1.714286     0.072459      0.333333   \n",
      "7   1.138906  1.461538  1.259240  1.263158     0.100421      0.076923   \n",
      "8   1.199627  1.409091  1.327927  1.903226     0.142058      0.272727   \n",
      "9   1.263418  1.333333  1.462157  1.472222     0.202189      0.148148   \n",
      "10  1.360793  1.642857  1.623879  1.326087     0.273837      0.071429   \n",
      "11  1.425745  2.103448  1.756894  1.524590     0.354120      0.172414   \n",
      "12  1.537715  2.093750  1.891209  1.567164     0.455066      0.343750   \n",
      "13  1.608371  2.489796  1.993494  1.549180     0.511460      0.306122   \n",
      "14  1.693964  2.976190  2.056771  1.552000     0.581017      0.357143   \n",
      "15  1.842728  3.907407  2.112027  1.445498     0.698584      0.462963   \n",
      "16  1.968351  2.981481  2.192038  1.590062     0.796835      0.592593   \n",
      "\n",
      "    muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0      0.214286      0.375000    0.002376   0.000759        0.085347   \n",
      "1      0.120732      0.000000    0.007177   0.001317        0.050514   \n",
      "2      0.112732      0.779221    0.022328   0.002320        0.126118   \n",
      "3      0.089968      0.070833    0.080594   0.004127        0.132131   \n",
      "4      0.050934      0.156863    0.225799   0.006403        0.114513   \n",
      "5      0.050871      0.359788    0.423130   0.007705        0.264316   \n",
      "6      0.072691      0.274286    0.647430   0.007319        0.237738   \n",
      "7      0.102034      0.063158    0.808025   0.006148        0.158293   \n",
      "8      0.140601      0.308631    0.889484   0.004832        0.337914   \n",
      "9      0.201713      0.147222    0.957194   0.003198        0.342879   \n",
      "10     0.272036      0.055254    0.974093   0.002501        0.395076   \n",
      "11     0.349550      0.115499    0.980059   0.002191        0.533172   \n",
      "12     0.431871      0.221010    0.987271   0.001758        0.601223   \n",
      "13     0.480978      0.169618    0.990974   0.001486        1.081881   \n",
      "14     0.525287      0.166286    0.990178   0.001553        1.110942   \n",
      "15     0.580577      0.153125    0.994024   0.001233        1.744597   \n",
      "16     0.631673      0.263637    0.992029   0.001415        1.466565   \n",
      "\n",
      "      deltaV   current    deltaV_err  \n",
      "0   1.000000  0.000000           NaN  \n",
      "1   1.000000  0.000000           NaN  \n",
      "2   1.000000  0.000000           NaN  \n",
      "3   1.000000  0.000000           NaN  \n",
      "4   1.000000  0.000000           NaN  \n",
      "5   1.000000  0.000000           NaN  \n",
      "6   1.000000  0.000000           NaN  \n",
      "7   1.000000  0.000000           NaN  \n",
      "8   0.000001  0.005172  7.534082e-08  \n",
      "9   0.011119  0.040196  5.869832e-04  \n",
      "10  0.025116  0.066990  1.271496e-03  \n",
      "11  0.037107  0.100870  1.884382e-03  \n",
      "12  0.067349  0.179661  3.409593e-03  \n",
      "13  0.088143  0.236154  4.465540e-03  \n",
      "14  0.138947  0.380000  7.063070e-03  \n",
      "15  0.236601  0.650968  1.203867e-02  \n",
      "16  0.499371  1.370513  2.539872e-02  \n",
      "40CO2_22 : \n",
      "    HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0   5800.0  5800.0     0.000000     0.000000     0.000000      0.000000   \n",
      "1   5900.0  5900.0     0.000000     0.000000     0.000000      0.000000   \n",
      "2   6000.0  6000.0     0.000000     0.000000     0.000000      0.000000   \n",
      "3   6100.0  6100.0     0.200000     0.100000     0.000000      0.000000   \n",
      "4   6200.0  6200.0     0.600000     0.400000     0.000000      0.000000   \n",
      "5   6300.0  6300.0     1.200000     0.800000     0.000235      0.000000   \n",
      "6   6400.0  6400.0     2.100000     1.500000     0.001240      0.000000   \n",
      "7   6500.0  6500.0     3.108889     2.300000     0.002434      0.000000   \n",
      "8   6600.0  6600.0     4.400000     3.302941     0.003651      0.000188   \n",
      "9   6700.0  6700.0     5.752632     4.521053     0.004636      0.000171   \n",
      "10  6800.0  6800.0     7.257692     5.836539     0.007364      0.000821   \n",
      "11  6900.0  6900.0     8.851938     7.320155     0.009497      0.001736   \n",
      "12  7000.0  7000.0    10.596923     8.972308     0.013327      0.003086   \n",
      "13  7100.0  7100.0    12.675188    10.893233     0.013217      0.006122   \n",
      "14  7200.0  7200.0    15.486792    13.403774     0.019141      0.009189   \n",
      "15  7300.0  7300.0    19.616071    16.861310     0.020844      0.010568   \n",
      "16  7400.0  7400.0    26.473239    22.336620     0.026777      0.015833   \n",
      "\n",
      "     muon_CM  gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0   1.000000  1.000000  1.750000  1.611111     0.000000      0.055556   \n",
      "1   1.666667  1.033898  1.000000  1.081967     0.000000      0.016949   \n",
      "2   1.000000  1.009901  1.000000  1.073529     0.000000      0.019802   \n",
      "3   1.062500  1.042328  1.117647  1.067259     0.031250      0.023810   \n",
      "4   1.091954  1.063942  1.263158  1.060099     0.103448      0.028365   \n",
      "5   1.107143  1.160078  1.177419  1.079011     0.080357      0.030843   \n",
      "6   1.113611  1.311870  1.118182  1.107067     0.048369      0.051540   \n",
      "7   1.105324  1.521467  1.138743  1.132703     0.056134      0.069596   \n",
      "8   1.138720  1.765336  1.179384  1.183285     0.069360      0.109187   \n",
      "9   1.147727  2.001988  1.232807  1.241806     0.107494      0.160829   \n",
      "10  1.188206  2.213122  1.337894  1.327677     0.149628      0.248584   \n",
      "11  1.272890  2.472433  1.462729  1.428883     0.204348      0.349109   \n",
      "12  1.366573  2.689328  1.618083  1.528502     0.291207      0.488421   \n",
      "13  1.461339  2.959486  1.704901  1.615235     0.342501      0.606812   \n",
      "14  1.543739  3.194178  1.828845  1.713883     0.418261      0.745642   \n",
      "15  1.635495  3.573791  1.917549  1.749752     0.477654      0.873494   \n",
      "16  1.756122  4.253688  2.025276  1.744753     0.590306      1.014811   \n",
      "\n",
      "    muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0      0.000000      0.084795    0.000938   0.000477        0.166299   \n",
      "1      0.000000      0.018033    0.000662   0.000401        0.378624   \n",
      "2      0.000000      0.020645    0.001489   0.000603        1.264557   \n",
      "3      0.031933      0.023835    0.007161   0.001324        4.802914   \n",
      "4      0.109312      0.027529    0.019830   0.002204       13.536892   \n",
      "5      0.079675      0.027944    0.074953   0.004039       33.133036   \n",
      "6      0.046546      0.041850    0.214363   0.006468       66.103375   \n",
      "7      0.055036      0.049546    0.414922   0.007696      111.054509   \n",
      "8      0.067712      0.068924    0.636015   0.007520      162.784889   \n",
      "9      0.105574      0.092342    0.795257   0.006318      214.947302   \n",
      "10     0.149635      0.134069    0.895816   0.004804      267.152792   \n",
      "11     0.202341      0.176795    0.936237   0.003783      331.902048   \n",
      "12     0.284234      0.234931    0.957357   0.003166      395.274148   \n",
      "13     0.323715      0.274835    0.973567   0.002550      467.980003   \n",
      "14     0.389875      0.324366    0.980691   0.002177      541.360389   \n",
      "15     0.433441      0.343670    0.980699   0.002190      623.145453   \n",
      "16     0.509512      0.336072    0.980187   0.002204      746.135716   \n",
      "\n",
      "       deltaV    current  deltaV_err  \n",
      "0    1.000000   0.000000         NaN  \n",
      "1    1.000000   0.000000         NaN  \n",
      "2    1.000000   0.000000         NaN  \n",
      "3    0.100000   0.300000    0.005155  \n",
      "4    0.360000   1.000000    0.018345  \n",
      "5    0.720000   2.000000    0.036691  \n",
      "6    1.312500   3.600000    0.066749  \n",
      "7    1.982970   5.408889    0.100757  \n",
      "8    2.830011   7.702941    0.143747  \n",
      "9    3.797267  10.273684    0.192688  \n",
      "10   4.852496  13.094231    0.246129  \n",
      "11   6.010127  16.172093    0.304704  \n",
      "12   7.287884  19.569231    0.369357  \n",
      "13   8.787634  23.568421    0.445278  \n",
      "14  10.777642  28.890566    0.546067  \n",
      "15  13.601003  36.477381    0.689175  \n",
      "16  18.172231  48.809859    0.921030  \n",
      "40CO2_10 : \n",
      "    HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0   5800.0  5800.0     0.000000     0.000000     0.000000      0.000000   \n",
      "1   5900.0  5900.0     0.000000     0.000000     0.000000      0.000000   \n",
      "2   6000.0  6000.0     0.200000     0.100000     0.000000      0.000000   \n",
      "3   6100.0  6100.0     0.600000     0.400000     0.000000      0.000000   \n",
      "4   6200.0  6200.0     1.300000     0.900000     0.000000      0.000000   \n",
      "5   6300.0  6300.0     2.500000     1.700000     0.000957      0.000000   \n",
      "6   6400.0  6400.0     4.100000     2.900000     0.001182      0.000000   \n",
      "7   6500.0  6500.0     6.222826     4.600000     0.002890      0.000000   \n",
      "8   6600.0  6600.0     8.628432     6.600000     0.004663      0.000000   \n",
      "9   6700.0  6700.0    11.276923     8.859829     0.005699      0.000000   \n",
      "10  6800.0  6800.0    14.187692    11.507692     0.008041      0.001930   \n",
      "11  6900.0  6900.0    17.195804    14.372727     0.006466      0.003280   \n",
      "12  7000.0  7000.0    20.600000    17.504762     0.010766      0.005474   \n",
      "13  7100.0  7100.0    24.570940    21.217949     0.012728      0.014231   \n",
      "14  7200.0  7200.0    29.693590    25.835257     0.019646      0.016572   \n",
      "15  7300.0  7300.0    37.065761    32.257608     0.021055      0.016152   \n",
      "16  7400.0  7400.0    48.958236    41.907059     0.021266      0.025955   \n",
      "\n",
      "     muon_CM  gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0   0.000000  1.000000  0.000000  1.250000     0.000000      0.000000   \n",
      "1   1.500000  1.000000  1.666667  1.000000     0.500000      0.000000   \n",
      "2   1.166667  1.054545  1.142857  1.017241     0.166667      0.000000   \n",
      "3   1.160000  1.030675  1.137931  1.059524     0.040000      0.030675   \n",
      "4   1.069307  1.132118  1.166667  1.066398     0.089109      0.022779   \n",
      "5   1.094637  1.288636  1.112392  1.093474     0.028391      0.045455   \n",
      "6   1.108342  1.657874  1.119257  1.097333     0.050921      0.060512   \n",
      "7   1.128855  2.112079  1.139512  1.126203     0.059471      0.092915   \n",
      "8   1.139425  2.616140  1.176979  1.176532     0.071682      0.158670   \n",
      "9   1.183581  3.095398  1.232403  1.230448     0.099667      0.232023   \n",
      "10  1.229862  3.537683  1.337745  1.313840     0.145944      0.368235   \n",
      "11  1.303382  4.110462  1.436212  1.404404     0.202685      0.545985   \n",
      "12  1.414337  4.612980  1.567757  1.506470     0.264870      0.781146   \n",
      "13  1.498167  4.988018  1.689336  1.597738     0.330801      1.007988   \n",
      "14  1.598419  5.446411  1.789281  1.676295     0.405660      1.166667   \n",
      "15  1.665649  6.179222  1.903758  1.712430     0.464377      1.419990   \n",
      "16  1.793806  7.292883  1.940296  1.720635     0.525419      1.704557   \n",
      "\n",
      "    muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0      0.000000      0.000000    0.000000   0.000000        0.253378   \n",
      "1      0.833333      0.000000    0.000382   0.000304        0.510558   \n",
      "2      0.190476      0.000000    0.001117   0.000522        3.029676   \n",
      "3      0.040640      0.030622    0.004981   0.001105        9.108037   \n",
      "4      0.089744      0.021033    0.019815   0.002115       27.670146   \n",
      "5      0.028122      0.037256    0.069607   0.003939       67.446518   \n",
      "6      0.049164      0.038642    0.209900   0.006265      133.447612   \n",
      "7      0.057028      0.047457    0.430885   0.007696      212.978259   \n",
      "8      0.069662      0.067277    0.620397   0.007620      315.347351   \n",
      "9      0.095718      0.085800    0.785159   0.006347      410.713671   \n",
      "10     0.141907      0.123864    0.873284   0.005214      513.822479   \n",
      "11     0.193284      0.164671    0.932445   0.003896      645.256487   \n",
      "12     0.247291      0.218158    0.950636   0.003369      778.536466   \n",
      "13     0.305546      0.268596    0.964575   0.002939      888.739146   \n",
      "14     0.362181      0.295729    0.969851   0.002690     1034.381504   \n",
      "15     0.415047      0.319985    0.971128   0.002633     1200.554457   \n",
      "16     0.439573      0.325973    0.978704   0.002295     1425.478616   \n",
      "\n",
      "       deltaV    current  deltaV_err  \n",
      "0    1.000000   0.000000         NaN  \n",
      "1    1.000000   0.000000         NaN  \n",
      "2    0.100000   0.300000    0.005155  \n",
      "3    0.360000   1.000000    0.018345  \n",
      "4    0.797727   2.200000    0.040605  \n",
      "5    1.517857   4.200000    0.077301  \n",
      "6    2.547857   7.000000    0.129610  \n",
      "7    3.967309  10.822826    0.201587  \n",
      "8    5.609341  15.228431    0.284799  \n",
      "9    7.442482  20.136752    0.377663  \n",
      "10   9.530949  25.695385    0.483357  \n",
      "11  11.743527  31.568532    0.595282  \n",
      "12  14.195001  38.104762    0.719380  \n",
      "13  17.078760  45.788889    0.865347  \n",
      "14  20.722784  55.528847    1.049889  \n",
      "15  25.871207  69.323370    1.310722  \n",
      "16  33.869295  90.865294    1.716278  \n",
      "40CO2_6.9 : \n",
      "    HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0   5800.0  5800.0     0.000000     0.000000     0.000000      0.000000   \n",
      "1   5900.0  5900.0     0.100000     0.100000     0.000000      0.000000   \n",
      "2   6000.0  6000.0     0.400000     0.200000     0.000000      0.000000   \n",
      "3   6100.0  6100.0     0.900000     0.700000     0.000000      0.000000   \n",
      "4   6200.0  6200.0     2.000000     1.400000     0.000000      0.000000   \n",
      "5   6300.0  6300.0     3.700000     2.600000     0.000244      0.000000   \n",
      "6   6400.0  6400.0     6.100000     4.400000     0.000733      0.000000   \n",
      "7   6500.0  6500.0     9.066020     6.764078     0.002365      0.000068   \n",
      "8   6600.0  6600.0    12.577693     9.630000     0.005212      0.000164   \n",
      "9   6700.0  6700.0    16.441538    12.951538     0.003894      0.000470   \n",
      "10  6800.0  6800.0    20.643538    16.738776     0.006315      0.002044   \n",
      "11  6900.0  6900.0    25.119108    20.922930     0.007290      0.004079   \n",
      "12  7000.0  7000.0    30.004969    25.552795     0.008311      0.009552   \n",
      "13  7100.0  7100.0    35.606164    30.866438     0.014224      0.014184   \n",
      "14  7200.0  7200.0    42.779532    37.367251     0.015563      0.018882   \n",
      "15  7300.0  7300.0    52.830814    46.137791     0.013944      0.025970   \n",
      "16  7400.0  7400.0    68.428823    59.024118     0.020139      0.032860   \n",
      "\n",
      "     muon_CM   gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0   1.000000   1.083333  1.000000  1.102564     0.000000      0.027778   \n",
      "1   1.000000   1.050420  1.333333  1.096000     0.000000      0.050420   \n",
      "2   1.000000   1.039501  1.000000  1.040000     0.000000      0.016632   \n",
      "3   1.200000   1.072267  1.133333  1.034562     0.120000      0.014824   \n",
      "4   1.088235   1.188032  1.279279  1.052266     0.127451      0.025538   \n",
      "5   1.119318   1.449239  1.164975  1.073472     0.079545      0.037408   \n",
      "6   1.103368   1.940361  1.110526  1.095421     0.039489      0.064069   \n",
      "7   1.118377   2.686316  1.117944  1.125172     0.049042      0.116311   \n",
      "8   1.167192   3.551546  1.179054  1.168179     0.077287      0.206183   \n",
      "9   1.193071   4.392763  1.235216  1.226611     0.094173      0.334172   \n",
      "10  1.250350   5.135052  1.316013  1.303672     0.142817      0.516727   \n",
      "11  1.313824   5.845908  1.431969  1.386826     0.185381      0.746684   \n",
      "12  1.426144   6.606015  1.538222  1.481657     0.253856      1.043616   \n",
      "13  1.520416   7.238612  1.649708  1.570777     0.317271      1.348690   \n",
      "14  1.609306   7.973758  1.764335  1.642644     0.393034      1.644102   \n",
      "15  1.704492   8.945132  1.820384  1.690080     0.457284      1.951719   \n",
      "16  1.808700  10.294338  1.880614  1.690598     0.491614      2.230565   \n",
      "\n",
      "    muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0      0.000000      0.027564    0.000216   0.000229        0.326172   \n",
      "1      0.000000      0.050198    0.001317   0.000565        1.015237   \n",
      "2      0.000000      0.016378    0.001428   0.000591        4.068722   \n",
      "3      0.103030      0.014108    0.004504   0.001051       13.918132   \n",
      "4      0.134118      0.022144    0.019392   0.002100       40.398128   \n",
      "5      0.077297      0.027012    0.077946   0.004189       92.800025   \n",
      "6      0.038372      0.035014    0.200601   0.006261      187.560605   \n",
      "7      0.046963      0.046695    0.412150   0.007579      316.809579   \n",
      "8      0.073224      0.064097    0.627108   0.007638      461.171900   \n",
      "9      0.090367      0.086716    0.772249   0.006555      611.649815   \n",
      "10     0.134908      0.119191    0.870920   0.005242      764.893557   \n",
      "11     0.177067      0.157073    0.923182   0.004167      930.625601   \n",
      "12     0.232433      0.202138    0.941928   0.003672     1125.235092   \n",
      "13     0.284817      0.246700    0.963740   0.002923     1308.016987   \n",
      "14     0.346316      0.280798    0.964832   0.002918     1509.336252   \n",
      "15     0.385069      0.302708    0.972047   0.002619     1741.223932   \n",
      "16     0.401918      0.301079    0.967790   0.002812     2006.791389   \n",
      "\n",
      "       deltaV     current  deltaV_err  \n",
      "0    1.000000    0.000000         NaN  \n",
      "1    0.075000    0.200000    0.003797  \n",
      "2    0.200000    0.600000    0.010310  \n",
      "3    0.590625    1.600000    0.029977  \n",
      "4    1.235294    3.400000    0.062858  \n",
      "5    2.290476    6.300000    0.116538  \n",
      "6    3.834286   10.500000    0.194948  \n",
      "7    5.810760   15.830097    0.295192  \n",
      "8    8.181164   22.207693    0.415368  \n",
      "9   10.867009   29.393077    0.551409  \n",
      "10  13.865416   37.382314    0.703181  \n",
      "11  17.122353   46.042038    0.867980  \n",
      "12  20.700369   55.557764    1.049031  \n",
      "13  24.800491   66.472602    1.256533  \n",
      "14  29.917985   80.146783    1.515682  \n",
      "15  36.943489   98.968605    1.871607  \n",
      "16  47.534614  127.452941    2.408517  \n",
      "40CO2_4.6 : \n",
      "    HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0   5800.0  5800.0     0.000000     0.000000     0.000000      0.000000   \n",
      "1   5900.0  5900.0     0.100000     0.100000     0.000000      0.000000   \n",
      "2   6000.0  6000.0     0.500000     0.400000     0.000000      0.000000   \n",
      "3   6100.0  6100.0     1.300000     0.900000     0.000000      0.000000   \n",
      "4   6200.0  6200.0     2.700000     1.900000     0.000000      0.000000   \n",
      "5   6300.0  6300.0     4.900000     3.500000     0.000249      0.000000   \n",
      "6   6400.0  6400.0     8.000000     5.800000     0.002349      0.000000   \n",
      "7   6500.0  6500.0    11.898019     8.835644     0.003500      0.000072   \n",
      "8   6600.0  6600.0    16.425641    12.601710     0.003988      0.000394   \n",
      "9   6700.0  6700.0    21.409160    16.932061     0.003815      0.001430   \n",
      "10  6800.0  6800.0    26.880137    21.900685     0.006026      0.001793   \n",
      "11  6900.0  6900.0    32.743940    27.328030     0.008047      0.004663   \n",
      "12  7000.0  7000.0    38.948428    33.269812     0.010877      0.008681   \n",
      "13  7100.0  7100.0    46.179532    40.035673     0.012890      0.013167   \n",
      "14  7200.0  7200.0    55.142138    48.343396     0.012102      0.023638   \n",
      "15  7300.0  7300.0    67.552433    59.237297     0.015233      0.031591   \n",
      "16  7400.0  7400.0    86.076262    74.788889     0.016329      0.042357   \n",
      "\n",
      "     muon_CM   gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0   1.000000   1.096774  1.000000  1.117647     0.000000      0.032258   \n",
      "1   1.250000   1.036496  1.600000  1.063380     0.000000      0.043796   \n",
      "2   1.000000   1.032872  1.166667  1.021776     0.000000      0.008651   \n",
      "3   1.095238   1.092363  1.086957  1.038262     0.047619      0.019092   \n",
      "4   1.100000   1.244718  1.151515  1.054938     0.055556      0.027276   \n",
      "5   1.130790   1.589941  1.190361  1.070681     0.073569      0.041738   \n",
      "6   1.118449   2.281355  1.123711  1.097375     0.047170      0.082801   \n",
      "7   1.139881   3.312434  1.134726  1.124383     0.064881      0.146146   \n",
      "8   1.167008   4.546553  1.167310  1.163254     0.073271      0.250684   \n",
      "9   1.210933   5.652248  1.221372  1.217715     0.097900      0.413704   \n",
      "10  1.257398   6.686607  1.298845  1.286997     0.135120      0.639700   \n",
      "11  1.349583   7.674236  1.415685  1.371881     0.189604      0.938034   \n",
      "12  1.450052   8.597117  1.521912  1.458411     0.240586      1.282649   \n",
      "13  1.557550   9.497179  1.649831  1.542670     0.332101      1.670496   \n",
      "14  1.640555  10.495986  1.722134  1.616560     0.366780      2.060216   \n",
      "15  1.742479  11.707692  1.804435  1.646062     0.438537      2.371973   \n",
      "16  1.852151  13.359870  1.817120  1.662574     0.471708      2.719972   \n",
      "\n",
      "    muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0      0.000000      0.033868    0.000191   0.000215        0.328942   \n",
      "1      0.000000      0.043110    0.000813   0.000445        1.092121   \n",
      "2      0.000000      0.008487    0.000872   0.000461        4.901205   \n",
      "3      0.045290      0.017835    0.003100   0.000874       17.506003   \n",
      "4      0.055361      0.022622    0.015598   0.001897       53.243178   \n",
      "5      0.072714      0.027388    0.082147   0.004331      123.184755   \n",
      "6      0.045474      0.038434    0.214012   0.006293      245.536881   \n",
      "7      0.061109      0.047512    0.412716   0.007798      411.092137   \n",
      "8      0.068960      0.060787    0.605350   0.007732      602.029676   \n",
      "9      0.091358      0.083050    0.771518   0.006496      791.035193   \n",
      "10     0.126031      0.112375    0.866533   0.005296      992.390401   \n",
      "11     0.174391      0.149423    0.911380   0.004456     1215.220319   \n",
      "12     0.216575      0.189339    0.933489   0.003895     1448.643385   \n",
      "13     0.289953      0.230757    0.950524   0.003437     1691.717300   \n",
      "14     0.314668      0.265245    0.953540   0.003328     1959.789537   \n",
      "15     0.362818      0.277309    0.961487   0.003040     2226.019042   \n",
      "16     0.368848      0.281231    0.963986   0.002955     2566.364962   \n",
      "\n",
      "       deltaV     current  deltaV_err  \n",
      "0    1.000000    0.000000         NaN  \n",
      "1    0.075000    0.200000    0.003797  \n",
      "2    0.333333    0.900000    0.016909  \n",
      "3    0.797727    2.200000    0.040605  \n",
      "4    1.672826    4.600000    0.085109  \n",
      "5    3.062500    8.400000    0.155748  \n",
      "6    5.043478   13.800000    0.256394  \n",
      "7    7.605506   20.733663    0.386409  \n",
      "8   10.696351   29.027351    0.543043  \n",
      "9   14.181911   38.341221    0.719558  \n",
      "10  18.102198   48.780821    0.917974  \n",
      "11  22.343883   60.071970    1.132641  \n",
      "12  26.914396   72.218240    1.363885  \n",
      "13  32.166518   86.215205    1.629736  \n",
      "14  38.639578  103.485535    1.957450  \n",
      "15  47.341652  126.789730    2.398284  \n",
      "16  60.027433  160.865151    3.041248  \n",
      "40CO2_3.3 : \n",
      "    HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0   5800.0  5800.0     0.000000     0.100000     0.000000      0.000000   \n",
      "1   5900.0  5900.0     0.300000     0.200000     0.000000      0.000000   \n",
      "2   6000.0  6000.0     0.800000     0.600000     0.000000      0.000000   \n",
      "3   6100.0  6100.0     2.100000     1.500000     0.000000      0.000000   \n",
      "4   6200.0  6200.0     4.101136     3.000000     0.000238      0.000000   \n",
      "5   6300.0  6300.0     7.500000     5.500000     0.000718      0.000000   \n",
      "6   6400.0  6400.0    12.200000     9.000000     0.002122      0.000000   \n",
      "7   6500.0  6500.0    17.975961    13.599039     0.002449      0.000000   \n",
      "8   6600.0  6600.0    24.737000    19.188001     0.002676      0.000000   \n",
      "9   6700.0  6700.0    32.284874    25.735294     0.003865      0.000941   \n",
      "10  6800.0  6800.0    40.350000    33.073076     0.005301      0.003401   \n",
      "11  6900.0  6900.0    48.965987    41.099999     0.004366      0.003469   \n",
      "12  7000.0  7000.0    58.175949    49.915823     0.008895      0.013031   \n",
      "13  7100.0  7100.0    68.656149    59.814973     0.008780      0.023187   \n",
      "14  7200.0  7200.0    81.130964    71.459391     0.012636      0.024762   \n",
      "\n",
      "     muon_CM   gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0   1.000000   1.100000  1.000000  1.000000     0.000000      0.000000   \n",
      "1   1.000000   1.000000  1.000000  1.000000     0.000000      0.000000   \n",
      "2   1.000000   1.054422  1.125000  1.025806     0.125000      0.020408   \n",
      "3   1.024390   1.125265  1.119048  1.033962     0.048780      0.012739   \n",
      "4   1.139344   1.368726  1.179856  1.064175     0.081967      0.037645   \n",
      "5   1.118987   1.961209  1.151584  1.071844     0.063291      0.050200   \n",
      "6   1.119919   3.031681  1.098911  1.095736     0.044715      0.100664   \n",
      "7   1.161833   4.792388  1.148777  1.117793     0.058005      0.211567   \n",
      "8   1.208787   6.720757  1.154069  1.151533     0.061042      0.335490   \n",
      "9   1.241769   8.523091  1.213131  1.213204     0.090310      0.603205   \n",
      "10  1.316957  10.208780  1.290130  1.271455     0.127737      0.919512   \n",
      "11  1.412805  11.649428  1.374858  1.347377     0.182159      1.333168   \n",
      "12  1.502431  13.007335  1.497663  1.425865     0.242031      1.830807   \n",
      "13  1.613708  14.487867  1.594630  1.492550     0.296481      2.312942   \n",
      "14  1.683752  16.198533  1.691668  1.553070     0.354822      2.745232   \n",
      "\n",
      "    muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0      0.000000      0.000000    0.000209   0.000225        0.552831   \n",
      "1      0.000000      0.000000    0.000049   0.000109        1.212498   \n",
      "2      0.125000      0.019477    0.001000   0.000494        8.060961   \n",
      "3      0.050866      0.011574    0.007047   0.001320       28.379369   \n",
      "4      0.079185      0.028485    0.021684   0.002246       82.155311   \n",
      "5      0.061648      0.026751    0.083536   0.004283      185.763396   \n",
      "6      0.042192      0.035214    0.221222   0.006380      365.105424   \n",
      "7      0.054626      0.047260    0.414248   0.007717      612.486569   \n",
      "8      0.055477      0.054750    0.621524   0.007575      893.141779   \n",
      "9      0.082246      0.080187    0.769756   0.006556     1195.162719   \n",
      "10     0.114071      0.105058    0.860756   0.005388     1500.956937   \n",
      "11     0.157021      0.138361    0.907971   0.004512     1815.929828   \n",
      "12     0.207790      0.175931    0.921765   0.004240     2145.714664   \n",
      "13     0.247503      0.205477    0.936029   0.003838     2499.192489   \n",
      "14     0.294441      0.225063    0.949032   0.003484     2906.266540   \n",
      "\n",
      "          deltaV     current    deltaV_err  \n",
      "0   6.666666e-08    0.100000  3.896944e-09  \n",
      "1   1.800000e-01    0.500000  9.172649e-03  \n",
      "2   5.142857e-01    1.400000  2.612298e-02  \n",
      "3   1.312500e+00    3.600000  6.674930e-02  \n",
      "4   2.598896e+00    7.101136  1.320895e-01  \n",
      "5   4.759615e+00   13.000000  2.418936e-01  \n",
      "6   7.768868e+00   21.200000  3.947723e-01  \n",
      "7   1.161310e+01   31.575000  5.897673e-01  \n",
      "8   1.620900e+01   43.925001  8.227244e-01  \n",
      "9   2.148031e+01   58.020169  1.089700e+00  \n",
      "10  2.726320e+01   73.423076  1.382397e+00  \n",
      "11  3.351713e+01   90.065986  1.698886e+00  \n",
      "12  4.029771e+01  108.091772  2.041967e+00  \n",
      "13  4.794851e+01  128.471123  2.429202e+00  \n",
      "14  5.699150e+01  152.590355  2.887000e+00  \n",
      "STDMX_OFF : \n",
      "   HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0  6200.0  6200.0     0.200000     0.200000     0.000198      0.000000   \n",
      "1  6400.0  6400.0     0.300000     0.200000     0.000199      0.000063   \n",
      "2  6600.0  6600.0     0.400000     0.200000     0.000991      0.000000   \n",
      "3  6800.0  6800.0     0.400000     0.300000     0.005171      0.000000   \n",
      "4  7000.0  7000.0     0.528378     0.407432     0.010151      0.000217   \n",
      "5  7100.0  7100.0     0.532822     0.430061     0.016065      0.000200   \n",
      "6  7200.0  7200.0     0.630395     0.586626     0.020672      0.000200   \n",
      "7  7300.0  7300.0     0.651730     0.629403     0.022359      0.000050   \n",
      "8  7400.0  7400.0     0.729676     0.840648     0.032826      0.000448   \n",
      "9  7500.0  7500.0     0.787437     0.908543     0.043487      0.000799   \n",
      "\n",
      "    muon_CM  gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0  1.000000  1.175676  3.000000  1.563218     0.105263      0.000000   \n",
      "1  1.122449  1.231707  1.672727  1.534653     0.142857      0.048780   \n",
      "2  1.075388  1.330935  1.193814  1.405405     0.008869      0.050360   \n",
      "3  1.045657  1.368984  1.130635  1.414062     0.005568      0.037433   \n",
      "4  1.058630  1.473333  1.346353  1.325792     0.028003      0.023333   \n",
      "5  1.051559  1.722365  1.587979  1.313433     0.063202      0.038560   \n",
      "6  1.052393  1.769772  1.913955  1.294935     0.105404      0.049209   \n",
      "7  1.061141  1.853357  2.343194  1.346997     0.152236      0.051237   \n",
      "8  1.059955  1.709784  2.730252  1.340446     0.186479      0.046434   \n",
      "9  1.057805  1.660690  3.128625  1.377907     0.215776      0.053793   \n",
      "\n",
      "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate    deltaV  \\\n",
      "0     0.285714      0.000000    0.003622   0.000846        0.831633  0.150000   \n",
      "1     0.188856      0.063285    0.009496   0.001367        0.975596  0.180000   \n",
      "2     0.009765      0.051239    0.088062   0.003991        1.655303  0.200000   \n",
      "3     0.005989      0.039753    0.534647   0.007053        1.952743  0.257143   \n",
      "4     0.034696      0.020669    0.918005   0.003891        3.171371  0.345067   \n",
      "5     0.090031      0.030079    0.969103   0.002457        4.382594  0.356969   \n",
      "6     0.174244      0.035032    0.983721   0.001803        6.495182  0.455793   \n",
      "7     0.293989      0.036236    0.986232   0.001658        7.027218  0.480279   \n",
      "8     0.408474      0.035441    0.988789   0.001505        6.860379  0.585931   \n",
      "9     0.530067      0.043233    0.990138   0.001421        8.265078  0.632750   \n",
      "\n",
      "    current  deltaV_err  \n",
      "0  0.400000    0.007593  \n",
      "1  0.500000    0.009173  \n",
      "2  0.600000    0.010310  \n",
      "3  0.700000    0.013061  \n",
      "4  0.935811    0.017517  \n",
      "5  0.962883    0.018105  \n",
      "6  1.217021    0.023078  \n",
      "7  1.281132    0.024314  \n",
      "8  1.570324    0.029686  \n",
      "9  1.695980    0.032059  \n",
      "STDMX_22 : \n",
      "    HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0   6000.0  6000.0     0.000000     0.000000     0.000000      0.000000   \n",
      "1   6100.0  6100.0     0.000000     0.000000     0.000000      0.000000   \n",
      "2   6200.0  6200.0     0.000000     0.000000     0.000000      0.000000   \n",
      "3   6300.0  6300.0     0.000000     0.100000     0.000000      0.000000   \n",
      "4   6400.0  6400.0     0.300000     0.200000     0.000000      0.000000   \n",
      "5   6500.0  6500.0     0.600000     0.400000     0.000000      0.000000   \n",
      "6   6600.0  6600.0     1.100000     0.800000     0.000954      0.000000   \n",
      "7   6700.0  6700.0     1.800000     1.300000     0.001155      0.000000   \n",
      "8   6800.0  6800.0     2.700000     1.900000     0.003231      0.000000   \n",
      "9   6900.0  6900.0     3.715686     2.700000     0.004472      0.000064   \n",
      "10  7000.0  7000.0     4.797088     3.659223     0.005232      0.000130   \n",
      "11  7100.0  7100.0     5.982456     4.728947     0.006965      0.001132   \n",
      "12  7200.0  7200.0     7.248305     5.888983     0.008845      0.002004   \n",
      "13  7300.0  7300.0     8.578626     7.148855     0.010816      0.002594   \n",
      "14  7400.0  7400.0     9.876712     8.424657     0.016635      0.005299   \n",
      "15  7500.0  7500.0    11.252414     9.740000     0.024022      0.008798   \n",
      "16  7600.0  7600.0    12.717544    11.158480     0.024745      0.012640   \n",
      "\n",
      "     muon_CM  gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0   1.142857  1.250000  1.125000  1.240000     0.000000      0.050000   \n",
      "1   1.400000  1.139535  1.000000  1.163265     0.000000      0.023256   \n",
      "2   1.000000  1.077586  1.600000  1.080000     0.000000      0.025862   \n",
      "3   1.062500  1.049793  1.294118  1.035573     0.125000      0.008299   \n",
      "4   1.116279  1.036453  1.270833  1.044677     0.139535      0.016749   \n",
      "5   1.102190  1.092455  1.211921  1.063074     0.080292      0.024858   \n",
      "6   1.115721  1.179316  1.172211  1.079562     0.056769      0.034612   \n",
      "7   1.095594  1.327841  1.105463  1.107820     0.045719      0.053630   \n",
      "8   1.106479  1.537160  1.134362  1.136945     0.059267      0.078696   \n",
      "9   1.153764  1.772214  1.186766  1.183013     0.084552      0.110288   \n",
      "10  1.177330  1.973077  1.260050  1.244201     0.113351      0.160639   \n",
      "11  1.223716  2.169577  1.374863  1.333780     0.150309      0.247208   \n",
      "12  1.316383  2.387220  1.508658  1.444930     0.220441      0.355350   \n",
      "13  1.411645  2.622869  1.635267  1.549090     0.287567      0.484355   \n",
      "14  1.549338  2.817978  1.755563  1.657521     0.370222      0.613884   \n",
      "15  1.652746  3.046266  1.886913  1.760132     0.458881      0.761567   \n",
      "16  1.724382  3.215899  2.017418  1.851780     0.549722      0.897136   \n",
      "\n",
      "    muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0      0.000000      0.051667    0.001649   0.000633        0.178334   \n",
      "1      0.000000      0.024235    0.001181   0.000536        0.328738   \n",
      "2      0.000000      0.025313    0.001112   0.000522        0.779755   \n",
      "3      0.136223      0.008122    0.003623   0.000941        2.120423   \n",
      "4      0.141204      0.016613    0.009734   0.001545        6.294984   \n",
      "5      0.082291      0.023652    0.029359   0.002562       16.638182   \n",
      "6      0.056755      0.030781    0.104862   0.004733       35.388169   \n",
      "7      0.044283      0.043006    0.271991   0.006767       65.868387   \n",
      "8      0.057671      0.055372    0.490389   0.007894      111.103918   \n",
      "9      0.081032      0.069308    0.695262   0.007272      158.896328   \n",
      "10     0.110661      0.093671    0.855118   0.005570      206.375566   \n",
      "11     0.150401      0.136430    0.930564   0.004023      253.918993   \n",
      "12     0.216401      0.187217    0.962283   0.002959      314.279785   \n",
      "13     0.276745      0.241473    0.977035   0.002361      379.907628   \n",
      "14     0.338592      0.296494    0.981079   0.002133      439.858815   \n",
      "15     0.410048      0.352026    0.984024   0.001997      512.008561   \n",
      "16     0.487673      0.403911    0.985348   0.001895      571.295421   \n",
      "\n",
      "          deltaV    current    deltaV_err  \n",
      "0   1.000000e+00   0.000000           NaN  \n",
      "1   1.000000e+00   0.000000           NaN  \n",
      "2   1.000000e+00   0.000000           NaN  \n",
      "3   6.666666e-08   0.100000  3.896944e-09  \n",
      "4   1.800000e-01   0.500000  9.172649e-03  \n",
      "5   3.600000e-01   1.000000  1.834530e-02  \n",
      "6   6.947369e-01   1.900000  3.531535e-02  \n",
      "7   1.132258e+00   3.100000  5.756604e-02  \n",
      "8   1.672826e+00   4.600000  8.510893e-02  \n",
      "9   2.345584e+00   6.415686  1.192350e-01  \n",
      "10  3.113701e+00   8.456311  1.580992e-01  \n",
      "11  3.961767e+00  10.711403  2.010129e-01  \n",
      "12  4.873739e+00  13.137288  2.471621e-01  \n",
      "13  5.849063e+00  15.727481  2.965034e-01  \n",
      "14  6.819811e+00  18.301370  3.455999e-01  \n",
      "15  7.831294e+00  20.992414  3.967847e-01  \n",
      "16  8.915332e+00  23.876024  4.516396e-01  \n",
      "STDMX_10 : \n",
      "    HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0   6000.0  6000.0     0.000000     0.000000     0.000000      0.000000   \n",
      "1   6100.0  6100.0     0.000000     0.000000     0.000000      0.000000   \n",
      "2   6200.0  6200.0     0.000000     0.100000     0.000000      0.000000   \n",
      "3   6300.0  6300.0     0.200000     0.200000     0.000000      0.000000   \n",
      "4   6400.0  6400.0     0.600000     0.400000     0.000000      0.000000   \n",
      "5   6500.0  6500.0     1.300000     0.900000     0.000000      0.000000   \n",
      "6   6600.0  6600.0     2.300000     1.500000     0.000729      0.000000   \n",
      "7   6700.0  6700.0     3.600000     2.500000     0.002169      0.000000   \n",
      "8   6800.0  6800.0     5.300000     3.812222     0.002434      0.000000   \n",
      "9   6900.0  6900.0     7.302033     5.400000     0.004968      0.000448   \n",
      "10  7000.0  7000.0     9.394531     7.230469     0.007965      0.000721   \n",
      "11  7100.0  7100.0    11.747059     9.320168     0.006146      0.001423   \n",
      "12  7200.0  7200.0    14.175000    11.532639     0.010452      0.003216   \n",
      "13  7300.0  7300.0    16.651572    13.934591     0.010374      0.006323   \n",
      "14  7400.0  7400.0    19.216149    16.377018     0.013288      0.009936   \n",
      "15  7500.0  7500.0    21.859388    18.954148     0.017684      0.015689   \n",
      "16  7600.0  7600.0    24.662893    21.652201     0.020608      0.023146   \n",
      "\n",
      "     muon_CM  gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0   0.000000  1.162791  0.000000  1.140000     0.000000      0.000000   \n",
      "1   1.000000  1.195122  2.000000  1.448980     0.500000      0.219512   \n",
      "2   1.000000  1.034091  1.000000  1.076923     0.000000      0.011364   \n",
      "3   1.000000  1.021869  1.071429  1.033074     0.071429      0.005964   \n",
      "4   1.028571  1.080202  1.222222  1.054849     0.114286      0.018786   \n",
      "5   1.088398  1.155678  1.192893  1.055468     0.082873      0.022763   \n",
      "6   1.123047  1.322109  1.120000  1.077766     0.060547      0.036158   \n",
      "7   1.120889  1.634159  1.132435  1.109098     0.052444      0.064931   \n",
      "8   1.117647  2.053801  1.135593  1.134324     0.056830      0.095106   \n",
      "9   1.143368  2.566676  1.195747  1.179676     0.077583      0.162168   \n",
      "10  1.202202  3.034747  1.267470  1.241875     0.106315      0.253363   \n",
      "11  1.246568  3.476822  1.354783  1.326532     0.154509      0.384040   \n",
      "12  1.342762  3.894559  1.506702  1.423310     0.236050      0.550109   \n",
      "13  1.455186  4.323648  1.618685  1.524871     0.281470      0.754910   \n",
      "14  1.561950  4.760263  1.721641  1.622625     0.343805      0.970654   \n",
      "15  1.654753  5.100771  1.861213  1.723059     0.461090      1.191178   \n",
      "16  1.777437  5.458385  1.964675  1.812737     0.527055      1.439821   \n",
      "\n",
      "    muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0      0.000000      0.000000    0.000000   0.000000        0.361381   \n",
      "1      0.666667      0.224842    0.000450   0.000331        0.567394   \n",
      "2      0.000000      0.011966    0.000143   0.000187        1.127473   \n",
      "3      0.071429      0.005995    0.002945   0.000849        3.707915   \n",
      "4      0.122222      0.018032    0.007268   0.001337       12.560039   \n",
      "5      0.084403      0.020388    0.038366   0.002930       31.347123   \n",
      "6      0.057294      0.028691    0.117985   0.005029       68.864604   \n",
      "7      0.050616      0.042384    0.263527   0.006847      130.682557   \n",
      "8      0.054948      0.050203    0.482438   0.007806      213.144471   \n",
      "9      0.075982      0.070105    0.693701   0.007283      305.879074   \n",
      "10     0.102980      0.095692    0.837445   0.005755      401.148360   \n",
      "11     0.149404      0.131950    0.917616   0.004324      503.144904   \n",
      "12     0.225269      0.176161    0.954600   0.003263      610.968187   \n",
      "13     0.262350      0.226667    0.967816   0.002756      733.401732   \n",
      "14     0.310590      0.274826    0.973891   0.002495      863.024468   \n",
      "15     0.405600      0.326206    0.972452   0.002570      984.810388   \n",
      "16     0.449336      0.378362    0.981175   0.002151     1110.253407   \n",
      "\n",
      "          deltaV    current    deltaV_err  \n",
      "0   1.000000e+00   0.000000           NaN  \n",
      "1   1.000000e+00   0.000000           NaN  \n",
      "2   6.666666e-08   0.100000  3.896944e-09  \n",
      "3   1.500000e-01   0.400000  7.593419e-03  \n",
      "4   3.600000e-01   1.000000  1.834530e-02  \n",
      "5   7.977272e-01   2.200000  4.060506e-02  \n",
      "6   1.361842e+00   3.800000  6.944763e-02  \n",
      "7   2.213115e+00   6.100000  1.126396e-01  \n",
      "8   3.325991e+00   9.112222  1.691173e-01  \n",
      "9   4.656457e+00  12.702033  2.366021e-01  \n",
      "10  6.128740e+00  16.625000  3.111288e-01  \n",
      "11  7.795371e+00  21.067227  3.954952e-01  \n",
      "12  9.538516e+00  25.707639  4.837158e-01  \n",
      "13  1.137930e+01  30.586164  5.768092e-01  \n",
      "14  1.326251e+01  35.593167  6.720970e-01  \n",
      "15  1.522752e+01  40.813537  7.715105e-01  \n",
      "16  1.729477e+01  46.315094  8.761258e-01  \n",
      "STDMX_6.9 : \n",
      "    HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0   6000.0  6000.0     0.000000     0.000000     0.000000      0.000000   \n",
      "1   6100.0  6100.0     0.000000     0.000000     0.000000      0.000000   \n",
      "2   6200.0  6200.0     0.100000     0.100000     0.000000      0.000000   \n",
      "3   6300.0  6300.0     0.400000     0.300000     0.000000      0.000000   \n",
      "4   6400.0  6400.0     1.000000     0.700000     0.000000      0.000000   \n",
      "5   6500.0  6500.0     1.900000     1.300000     0.000469      0.000000   \n",
      "6   6600.0  6600.0     3.400000     2.360784     0.001486      0.000000   \n",
      "7   6700.0  6700.0     5.400000     3.800000     0.001495      0.000000   \n",
      "8   6800.0  6800.0     7.875728     5.700000     0.003121      0.000137   \n",
      "9   6900.0  6900.0    10.809483     8.000000     0.005457      0.000193   \n",
      "10  7000.0  7000.0    13.937209    10.706201     0.005879      0.001188   \n",
      "11  7100.0  7100.0    17.330826    13.732331     0.006614      0.001817   \n",
      "12  7200.0  7200.0    20.884137    16.986207     0.010081      0.003111   \n",
      "13  7300.0  7300.0    24.575936    20.466310     0.009371      0.007558   \n",
      "14  7400.0  7400.0    28.349375    24.115625     0.014809      0.013474   \n",
      "15  7500.0  7500.0    32.255696    27.871519     0.019401      0.021842   \n",
      "16  7600.0  7600.0    36.294220    31.860115     0.021691      0.029481   \n",
      "\n",
      "     muon_CM  gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0   1.000000  1.078947  1.000000  1.365854     0.000000      0.289474   \n",
      "1   1.000000  1.122807  1.500000  1.078125     0.000000      0.035088   \n",
      "2   1.400000  1.068493  1.000000  1.098291     0.200000      0.045662   \n",
      "3   1.000000  1.046433  1.125000  1.053030     0.000000      0.023783   \n",
      "4   1.150000  1.083107  1.152174  1.037648     0.100000      0.017476   \n",
      "5   1.072727  1.224008  1.237288  1.054126     0.096970      0.024926   \n",
      "6   1.100000  1.475576  1.083636  1.078006     0.042000      0.046042   \n",
      "7   1.123118  1.971550  1.136435  1.101962     0.063773      0.071737   \n",
      "8   1.129764  2.647614  1.125534  1.130394     0.045345      0.116599   \n",
      "9   1.171390  3.497412  1.186060  1.169937     0.073212      0.200847   \n",
      "10  1.211666  4.226694  1.250238  1.232471     0.095871      0.337819   \n",
      "11  1.274400  4.901307  1.334798  1.314047     0.144267      0.511444   \n",
      "12  1.381202  5.563392  1.469683  1.403682     0.208454      0.750510   \n",
      "13  1.474669  6.194339  1.597428  1.498772     0.271239      1.010665   \n",
      "14  1.580867  6.895556  1.713248  1.594228     0.346173      1.337294   \n",
      "15  1.704727  7.458190  1.799818  1.683476     0.423405      1.656927   \n",
      "16  1.801744  7.971745  1.915302  1.766773     0.514234      1.923605   \n",
      "\n",
      "    muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0      0.000000      0.288931    0.000419   0.000319        0.414775   \n",
      "1      0.000000      0.034778    0.000913   0.000471        0.538250   \n",
      "2      0.166667      0.045012    0.001003   0.000494        1.481243   \n",
      "3      0.000000      0.023401    0.001296   0.000564        5.559504   \n",
      "4      0.092174      0.016476    0.007814   0.001390       16.974964   \n",
      "5      0.102573      0.021038    0.033814   0.002768       45.643528   \n",
      "6      0.039854      0.032619    0.115901   0.005041      100.866915   \n",
      "7      0.061062      0.038688    0.272434   0.007032      193.091885   \n",
      "8      0.043432      0.047682    0.491892   0.007759      311.068552   \n",
      "9      0.069768      0.063538    0.702572   0.007060      452.112198   \n",
      "10     0.091669      0.091215    0.851106   0.005588      588.659914   \n",
      "11     0.135738      0.124163    0.923617   0.004171      733.571425   \n",
      "12     0.192721      0.166850    0.945263   0.003585      894.348183   \n",
      "13     0.248172      0.210237    0.957528   0.003182     1064.714480   \n",
      "14     0.307768      0.258957    0.965465   0.002866     1262.412220   \n",
      "15     0.358085      0.306019    0.968972   0.002744     1443.801307   \n",
      "16     0.425269      0.343452    0.970905   0.002653     1620.518522   \n",
      "\n",
      "       deltaV    current  deltaV_err  \n",
      "0    1.000000   0.000000         NaN  \n",
      "1    1.000000   0.000000         NaN  \n",
      "2    0.075000   0.200000    0.003797  \n",
      "3    0.257143   0.700000    0.013061  \n",
      "4    0.617647   1.700000    0.031429  \n",
      "5    1.157812   3.200000    0.058954  \n",
      "6    2.089993   5.760784    0.106374  \n",
      "7    3.345652   9.200000    0.170218  \n",
      "8    4.960137  13.575728    0.252169  \n",
      "9    6.896191  18.809483    0.350400  \n",
      "10   9.082422  24.643410    0.461093  \n",
      "11  11.492359  31.063157    0.583075  \n",
      "12  14.050926  37.870344    0.712552  \n",
      "13  16.750232  45.042246    0.849119  \n",
      "14  19.546256  52.465000    0.990559  \n",
      "15  22.427828  60.127215    1.136365  \n",
      "16  25.449695  68.154335    1.289243  \n",
      "STDMX_4.6 : \n",
      "scanId =  5630\n",
      "    HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0   6000.0  6000.0     0.000000     0.000000     0.000000      0.000000   \n",
      "1   6100.0  6100.0     0.000000     0.000000     0.000000      0.000000   \n",
      "2   6200.0  6200.0     0.200000     0.100000     0.000000      0.000000   \n",
      "3   6300.0  6300.0     0.600000     0.400000     0.000000      0.000000   \n",
      "4   6400.0  6400.0     1.300000     0.900000     0.000000      0.000000   \n",
      "5   6500.0  6500.0     2.600000     1.700000     0.000000      0.000000   \n",
      "6   6600.0  6600.0     4.400000     3.100000     0.000697      0.000000   \n",
      "7   6700.0  6700.0     7.000000     5.000000     0.001429      0.000000   \n",
      "8   6800.0  6800.0    10.206034     7.445690     0.002827      0.000439   \n",
      "9   6900.0  6900.0    13.934108    10.460465     0.002746      0.000990   \n",
      "10  7000.0  7000.0    18.027371    13.963865     0.006217      0.001915   \n",
      "11  7100.0  7100.0    22.519745    17.901910     0.008183      0.000956   \n",
      "12  7200.0  7200.0    27.228379    22.211487     0.007087      0.006098   \n",
      "13  7300.0  7300.0    31.895172    26.691724     0.009650      0.009643   \n",
      "14  7400.0  7400.0    36.898246    31.521053     0.012429      0.016933   \n",
      "15  7500.0  7500.0    41.812376    36.259901     0.014147      0.025133   \n",
      "\n",
      "     muon_CM  gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0   1.000000  1.000000  1.000000  1.000000     0.000000      0.000000   \n",
      "1   1.000000  1.181818  1.000000  1.384615     0.000000      0.181818   \n",
      "2   0.000000  1.060606  0.000000  1.057143     0.000000      0.000000   \n",
      "3   1.142857  1.056911  1.062500  1.023077     0.071429      0.016260   \n",
      "4   1.078947  1.110818  1.121951  1.040380     0.026316      0.023747   \n",
      "5   1.143713  1.291108  1.151832  1.063208     0.089820      0.028015   \n",
      "6   1.090722  1.669009  1.139887  1.081384     0.065979      0.053084   \n",
      "7   1.148532  2.400109  1.124812  1.101610     0.053541      0.094175   \n",
      "8   1.143265  3.212192  1.154871  1.130233     0.061399      0.143788   \n",
      "9   1.169715  4.409274  1.171670  1.167238     0.083394      0.266129   \n",
      "10  1.225995  5.422018  1.223496  1.211239     0.089871      0.377595   \n",
      "11  1.285829  6.373980  1.325611  1.312646     0.156736      0.675468   \n",
      "12  1.367882  7.144481  1.465951  1.390797     0.213067      0.924680   \n",
      "13  1.496317  8.134766  1.549652  1.490276     0.251715      1.305664   \n",
      "14  1.594588  8.961557  1.684236  1.573668     0.343221      1.684081   \n",
      "15  1.705008  9.612711  1.786140  1.672727     0.410798      2.070506   \n",
      "\n",
      "    muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0      0.000000      0.000000    0.000199   0.000220        0.151894   \n",
      "1      0.000000      0.184615    0.000158   0.000196        0.911763   \n",
      "2      0.000000      0.000000    0.000000   0.000000        1.890735   \n",
      "3      0.062500      0.015501    0.002533   0.000787        6.793425   \n",
      "4      0.026713      0.021775    0.006928   0.001309       22.856712   \n",
      "5      0.083871      0.022580    0.033526   0.002770       60.196439   \n",
      "6      0.065020      0.033334    0.103361   0.004641      130.669830   \n",
      "7      0.050099      0.041593    0.266283   0.006827      258.353400   \n",
      "8      0.058861      0.048425    0.488725   0.007683      396.587505   \n",
      "9      0.077975      0.066440    0.679472   0.007383      585.399643   \n",
      "10     0.083562      0.078860    0.819179   0.005970      754.692650   \n",
      "11     0.144029      0.125776    0.902661   0.004617      964.731822   \n",
      "12     0.197569      0.159377    0.937013   0.003811     1145.241446   \n",
      "13     0.223148      0.206113    0.958438   0.003115     1398.449013   \n",
      "14     0.298309      0.248945    0.966771   0.002844     1629.146758   \n",
      "15     0.346791      0.296442    0.964717   0.002927     1857.504745   \n",
      "\n",
      "      deltaV    current  deltaV_err  \n",
      "0   0.000505   0.000000         NaN  \n",
      "1   0.000505   0.000000         NaN  \n",
      "2   0.000505   0.300000    0.000026  \n",
      "3   0.000505   1.000000    0.000026  \n",
      "4   0.000505   2.200000    0.000026  \n",
      "5   0.000505   4.300000    0.000026  \n",
      "6   0.000505   7.500000    0.000026  \n",
      "7   0.000505  12.000000    0.000026  \n",
      "8   0.000505  17.651724    0.000026  \n",
      "9   0.000505  24.394573    0.000026  \n",
      "10  0.000505  31.991236    0.000026  \n",
      "11  0.000505  40.421656    0.000026  \n",
      "12  0.000505  49.439865    0.000026  \n",
      "13  0.000505  58.586896    0.000026  \n",
      "14  0.000505  68.419298    0.000026  \n",
      "15  0.000505  78.072277    0.000026  \n",
      "STDMX_3.3 : \n",
      "    HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0   6000.0  6000.0     0.000000     0.000000     0.000000      0.000000   \n",
      "1   6100.0  6100.0     0.100000     0.100000     0.000000      0.000000   \n",
      "2   6200.0  6200.0     0.359091     0.300000     0.000000      0.000000   \n",
      "3   6300.0  6300.0     0.900000     0.600000     0.000000      0.000000   \n",
      "4   6400.0  6400.0     2.000000     1.400000     0.000000      0.000000   \n",
      "5   6500.0  6500.0     3.800000     2.700000     0.000000      0.000000   \n",
      "6   6600.0  6600.0     6.700000     4.700000     0.000719      0.000072   \n",
      "7   6700.0  6700.0    10.518421     7.500000     0.001883      0.000000   \n",
      "8   6800.0  6800.0    15.377099    11.302290     0.003628      0.000172   \n",
      "9   6900.0  6900.0    20.863358    15.785496     0.003106      0.000380   \n",
      "10  7000.0  7000.0    27.120690    21.019310     0.004399      0.001515   \n",
      "11  7100.0  7100.0    33.706923    26.913077     0.006348      0.002899   \n",
      "12  7200.0  7200.0    40.538346    33.242106     0.007954      0.006293   \n",
      "13  7300.0  7300.0    47.800000    40.017089     0.007054      0.010893   \n",
      "14  7400.0  7400.0    55.119875    47.075155     0.009821      0.021008   \n",
      "15  7500.0  7500.0    62.721264    54.400575     0.014197      0.030030   \n",
      "16  7600.0  7600.0    70.503981    62.023881     0.016790      0.043817   \n",
      "\n",
      "     muon_CM   gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0   1.000000   1.151515  1.000000  1.105263     0.000000      0.060606   \n",
      "1   1.000000   1.119048  1.000000  1.095745     0.000000      0.071429   \n",
      "2   1.000000   1.015823  1.000000  1.034268     0.000000      0.015823   \n",
      "3   1.000000   1.046385  1.133333  1.027379     0.066667      0.011596   \n",
      "4   1.071429   1.153078  1.044444  1.040404     0.047619      0.018303   \n",
      "5   1.090909   1.426251  1.194444  1.046874     0.084848      0.025012   \n",
      "6   1.118790   2.003948  1.113900  1.072898     0.038877      0.053439   \n",
      "7   1.121035   3.096013  1.132539  1.093704     0.056761      0.103896   \n",
      "8   1.178870   4.808842  1.146728  1.121628     0.068796      0.204601   \n",
      "9   1.213693   6.583434  1.167806  1.159950     0.075035      0.362720   \n",
      "10  1.252764   8.188544  1.220844  1.213365     0.094114      0.586076   \n",
      "11  1.342207   9.627005  1.324587  1.284859     0.157238      0.903282   \n",
      "12  1.423370  10.930752  1.406835  1.357448     0.195380      1.293709   \n",
      "13  1.548345  12.305198  1.534590  1.446151     0.263487      1.770662   \n",
      "14  1.662148  13.580645  1.633940  1.523882     0.327164      2.247020   \n",
      "15  1.783206  14.865061  1.722049  1.609068     0.387378      2.847353   \n",
      "16  1.880621  15.935602  1.800196  1.683174     0.454115      3.383429   \n",
      "\n",
      "    muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0      0.000000      0.055263    0.000198   0.000219        0.282801   \n",
      "1      0.000000      0.065745    0.000366   0.000298        0.780580   \n",
      "2      0.000000      0.015863    0.000635   0.000393        2.592112   \n",
      "3      0.070833      0.011261    0.002441   0.000770       10.267566   \n",
      "4      0.044444      0.016256    0.006852   0.001304       32.989645   \n",
      "5      0.086197      0.018042    0.031671   0.002707       88.332067   \n",
      "6      0.037407      0.027867    0.099906   0.004643      195.642118   \n",
      "7      0.054580      0.035511    0.271794   0.006831      367.794887   \n",
      "8      0.063230      0.045774    0.486248   0.007788      616.900938   \n",
      "9      0.067994      0.060571    0.688260   0.007170      881.468332   \n",
      "10     0.085307      0.081043    0.818780   0.006035     1148.395637   \n",
      "11     0.138902      0.110214    0.884223   0.005015     1430.445955   \n",
      "12     0.169803      0.143658    0.920864   0.004273     1716.033015   \n",
      "13     0.223169      0.181917    0.939048   0.003745     2057.454780   \n",
      "14     0.268719      0.216342    0.950395   0.003419     2392.404493   \n",
      "15     0.307329      0.258665    0.956125   0.003256     2764.545028   \n",
      "16     0.350145      0.294782    0.954350   0.003308     3100.672633   \n",
      "\n",
      "       deltaV     current  deltaV_err  \n",
      "0    1.000000    0.000000         NaN  \n",
      "1    0.075000    0.200000    0.003797  \n",
      "2    0.245172    0.659091    0.012428  \n",
      "3    0.540000    1.500000    0.027518  \n",
      "4    1.235294    3.400000    0.062858  \n",
      "5    2.367692    6.500000    0.120430  \n",
      "6    4.143421   11.400000    0.210825  \n",
      "7    6.567292   18.018421    0.334006  \n",
      "8    9.771388   26.679389    0.496574  \n",
      "9   13.479485   36.648855    0.684549  \n",
      "10  17.762511   48.140001    0.901592  \n",
      "11  22.446973   60.619999    1.138705  \n",
      "12  27.397094   73.780451    1.389176  \n",
      "13  32.672744   87.817089    1.656149  \n",
      "14  38.085658  102.195031    1.929992  \n",
      "15  43.699017  117.121839    2.214026  \n",
      "16  49.494466  132.527862    2.507257  \n",
      "STDMX_2.2 : \n",
      "   HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0  6200.0  6200.0     0.800000     0.600000     0.000000      0.000050   \n",
      "1  6400.0  6400.0     2.500000     2.000000     0.000000      0.000000   \n",
      "2  6600.0  6600.0     7.604563     5.875894     0.000199      0.000175   \n",
      "3  6800.0  6800.0    17.052008    13.315663     0.003988      0.001047   \n",
      "4  7000.0  7000.0    30.154993    24.458368     0.005562      0.009014   \n",
      "5  7100.0  7100.0    37.634890    31.348442     0.008187      0.018076   \n",
      "6  7200.0  7200.0    45.416834    38.666666     0.012144      0.035533   \n",
      "7  7300.0  7300.0    53.382919    46.566968     0.020971      0.063808   \n",
      "8  7400.0  7400.0    61.749590    54.879918     0.028777      0.092042   \n",
      "9  7500.0  7500.0    70.383187    63.576149     0.039976      0.132199   \n",
      "\n",
      "    muon_CM   gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0  1.000000   1.054786  1.000000  1.145672     0.000000      0.008816   \n",
      "1  1.014286   1.183237  1.126761  1.070686     0.000000      0.007239   \n",
      "2  1.078788   2.091015  1.129213  1.114281     0.004040      0.023067   \n",
      "3  1.102768   4.866239  1.133769  1.195217     0.008722      0.092209   \n",
      "4  1.170610   8.085420  1.289740  1.348243     0.027753      0.265738   \n",
      "5  1.177377   9.240259  1.519823  1.478906     0.056448      0.413940   \n",
      "6  1.204696  10.107134  1.750532  1.629212     0.093490      0.593147   \n",
      "7  1.236672  10.892340  2.088476  1.805422     0.133061      0.792147   \n",
      "8  1.268088  11.485614  2.341484  1.985506     0.158269      0.986360   \n",
      "9  1.274821  12.103952  2.592195  2.158322     0.190745      1.153764   \n",
      "\n",
      "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0     0.000000      0.009496    0.000000   0.000000        4.777805   \n",
      "1     0.000000      0.006510    0.009399   0.001362       36.153274   \n",
      "2     0.004213      0.012158    0.086829   0.003971      189.613634   \n",
      "3     0.008897      0.022227    0.520635   0.007069      573.361124   \n",
      "4     0.029869      0.042902    0.876256   0.004654     1083.030105   \n",
      "5     0.069533      0.063411    0.926195   0.003710     1358.838745   \n",
      "6     0.126066      0.090312    0.943310   0.003283     1636.756207   \n",
      "7     0.202882      0.122398    0.948212   0.003165     1953.638336   \n",
      "8     0.259811      0.157026    0.954867   0.002978     2264.659860   \n",
      "9     0.337377      0.187830    0.957714   0.002904     2595.175179   \n",
      "\n",
      "      deltaV     current  deltaV_err  \n",
      "0   0.514286    1.400000    0.026123  \n",
      "1   1.666667    4.500000    0.084545  \n",
      "2   4.972043   13.480456    0.252388  \n",
      "3  11.215486   30.367671    0.569190  \n",
      "4  20.257184   54.613362    1.027333  \n",
      "5  25.653918   68.983332    1.300469  \n",
      "6  31.328100   84.083500    1.587618  \n",
      "7  37.306906   99.949887    1.890043  \n",
      "8  43.584327  116.629508    2.207636  \n",
      "9  50.105041  133.959336    2.537548  \n",
      "STDMX_1 : \n",
      "   HV_top  HV_bot  current_top  current_bot  muon_stream  gamma_stream  \\\n",
      "0  6200.0  6200.0     1.300000     1.000000     0.000000      0.000150   \n",
      "1  6400.0  6400.0     5.000000     3.912796     0.000000      0.000000   \n",
      "2  6600.0  6600.0    14.858471    11.604339     0.000796      0.000050   \n",
      "3  6800.0  6800.0    32.872029    25.945886     0.002595      0.001586   \n",
      "4  7000.0  7000.0    57.547723    47.325683     0.005096      0.012202   \n",
      "5  7100.0  7100.0    71.565945    60.174955     0.006951      0.028769   \n",
      "6  7200.0  7200.0    86.239032    73.963387     0.008774      0.050569   \n",
      "7  7300.0  7300.0   101.529641    88.834009     0.014127      0.087979   \n",
      "8  7400.0  7400.0   117.283053   104.388796     0.021800      0.137204   \n",
      "9  7500.0  7500.0   133.574109   120.537054     0.027236      0.203905   \n",
      "\n",
      "    muon_CM   gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0  1.000000   1.069036  1.200000  1.081841     0.000000      0.004785   \n",
      "1  1.038835   1.400798  1.074766  1.063843     0.009709      0.008475   \n",
      "2  1.072944   3.539764  1.133498  1.105496     0.007958      0.038241   \n",
      "3  1.153211   9.408230  1.138457  1.181093     0.015070      0.165841   \n",
      "4  1.268969  15.849520  1.278733  1.311198     0.027488      0.487064   \n",
      "5  1.319367  18.185631  1.464189  1.419298     0.058614      0.738617   \n",
      "6  1.339684  20.048940  1.671138  1.542672     0.080970      1.052406   \n",
      "7  1.388600  21.739476  1.837053  1.684470     0.118119      1.403610   \n",
      "8  1.440289  23.141206  2.056340  1.834666     0.139764      1.783533   \n",
      "9  1.485404  24.462099  2.244375  1.978601     0.174552      2.106073   \n",
      "\n",
      "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0     0.000000      0.004820    0.001058   0.000391        8.424849   \n",
      "1     0.009952      0.006397    0.007490   0.001039       74.305668   \n",
      "2     0.008345      0.011815    0.106880   0.003899      377.196629   \n",
      "3     0.014685      0.020459    0.550880   0.007036     1104.223270   \n",
      "4     0.027112      0.039092    0.865160   0.004794     2066.406479   \n",
      "5     0.062281      0.055395    0.895810   0.004321     2564.779183   \n",
      "6     0.095246      0.076939    0.928093   0.003664     3072.646371   \n",
      "7     0.144015      0.102162    0.930115   0.003622     3638.734603   \n",
      "8     0.181894      0.131283    0.933770   0.003556     4214.302764   \n",
      "9     0.236006      0.156845    0.938718   0.003299     4804.690343   \n",
      "\n",
      "      deltaV     current  deltaV_err  \n",
      "0   0.847826    2.300000    0.043041  \n",
      "1   3.292566    8.912796    0.167092  \n",
      "2   9.773493   26.462810    0.496007  \n",
      "3  21.750871   58.817916    1.103631  \n",
      "4  38.953898  104.873406    1.975075  \n",
      "5  49.033491  131.740900    2.485303  \n",
      "6  59.723171  160.202420    3.026311  \n",
      "7  71.068860  190.363650    3.600370  \n",
      "8  82.845680  221.671849    4.196244  \n",
      "9  95.040863  254.111163    4.813344  \n"
     ]
    }
   ],
   "source": [
    "for scan in scans:\n",
    "    print(scan, ': ')\n",
    "    print(HVCurrentDataFrame(scans[scan]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scanID:  5808\n",
      "scanID:  5813\n",
      "scanID:  5815\n",
      "scanID:  5811\n",
      "scanID:  5807\n",
      "scanID:  5814\n",
      "scanID:  5805\n",
      "scanID:  5810\n",
      "scanID:  5808\n",
      "scanID:  5813\n",
      "scanID:  5815\n",
      "scanID:  5811\n",
      "scanID:  5807\n",
      "scanID:  5814\n",
      "scanID:  5805\n",
      "scanID:  5810\n",
      "FeaturesDataFrame(Scans_STDMX) =     current_top  current_bot  HV_top  HV_bot  muon_stream  gamma_stream  \\\n",
      "0     0.662577     0.579678  7250.0  7250.0     0.021638      0.000510   \n",
      "1     7.331106     6.235990  7250.0  7250.0     0.020339      0.009124   \n",
      "2    12.528288    10.652905  7200.0  7200.0     0.013554      0.013070   \n",
      "3    18.163424    15.603424  7200.0  7200.0     0.018294      0.017172   \n",
      "4    25.579945    21.916266  7250.0  7250.0     0.020301      0.028520   \n",
      "5    38.579806    33.307197  7250.0  7250.0     0.016467      0.040073   \n",
      "6    45.416834    38.666666  7200.0  7200.0     0.012144      0.035533   \n",
      "7    86.239032    73.963387  7200.0  7200.0     0.008774      0.050569   \n",
      "\n",
      "    muon_CM   gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0  1.056347   1.686717  2.149189  1.335598     0.128334      0.049051   \n",
      "1  1.077327   1.858821  2.120928  1.854984     0.120509      0.141672   \n",
      "2  1.087038   2.808565  1.918211  1.726102     0.102617      0.183007   \n",
      "3  1.114041   3.925548  1.886551  1.711130     0.107135      0.257984   \n",
      "4  1.138434   5.301146  2.017176  1.784251     0.113382      0.372206   \n",
      "5  1.190779   8.088920  1.989675  1.750194     0.114043      0.550905   \n",
      "6  1.204696  10.107134  1.750532  1.629212     0.093490      0.593147   \n",
      "7  1.339684  20.048940  1.671138  1.542672     0.080970      1.052406   \n",
      "\n",
      "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0     0.232817      0.037743   98.604936   0.000838        6.272502   \n",
      "1     0.213376      0.131367   96.817526   0.001771      252.046840   \n",
      "2     0.165461      0.105593   97.229975   0.002333      443.252216   \n",
      "3     0.165510      0.105519   96.750063   0.002524      652.265160   \n",
      "4     0.182703      0.117058   96.530672   0.001482      934.880286   \n",
      "5     0.173901      0.111598   95.672184   0.002050     1405.879286   \n",
      "6     0.126066      0.090312   94.331049   0.003283     1636.756207   \n",
      "7     0.095246      0.076939   92.809289   0.003664     3072.646371   \n",
      "\n",
      "   noiseGammaRate_err     current  \n",
      "0            2.257067    1.242255  \n",
      "1           15.871513   13.567095  \n",
      "2           21.047275   23.181193  \n",
      "3           25.531975   33.766847  \n",
      "4           30.566492   47.496211  \n",
      "5           37.484303   71.887003  \n",
      "6           40.444366   84.083500  \n",
      "7           55.412495  160.202420  \n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>current_top</th>\n",
       "      <th>current_bot</th>\n",
       "      <th>HV_top</th>\n",
       "      <th>HV_bot</th>\n",
       "      <th>muon_stream</th>\n",
       "      <th>gamma_stream</th>\n",
       "      <th>muon_CM</th>\n",
       "      <th>gamma_CM</th>\n",
       "      <th>muon_CS</th>\n",
       "      <th>gamma_CS</th>\n",
       "      <th>muon_CM_err</th>\n",
       "      <th>gamma_CM_err</th>\n",
       "      <th>muon_CS_err</th>\n",
       "      <th>gamma_CS_err</th>\n",
       "      <th>efficiency</th>\n",
       "      <th>eff_error</th>\n",
       "      <th>noiseGammaRate</th>\n",
       "      <th>noiseGammaRate_err</th>\n",
       "      <th>current</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.662577</td>\n",
       "      <td>0.579678</td>\n",
       "      <td>7250.0</td>\n",
       "      <td>7250.0</td>\n",
       "      <td>0.021638</td>\n",
       "      <td>0.000510</td>\n",
       "      <td>1.056347</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.149189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.128334</td>\n",
       "      <td>0.049051</td>\n",
       "      <td>0.232817</td>\n",
       "      <td>0.037743</td>\n",
       "      <td>98.604936</td>\n",
       "      <td>0.000838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.257067</td>\n",
       "      <td>1.242255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.331106</td>\n",
       "      <td>6.235990</td>\n",
       "      <td>7250.0</td>\n",
       "      <td>7250.0</td>\n",
       "      <td>0.020339</td>\n",
       "      <td>0.009124</td>\n",
       "      <td>1.077327</td>\n",
       "      <td>1.858821</td>\n",
       "      <td>2.120928</td>\n",
       "      <td>1.854984</td>\n",
       "      <td>0.120509</td>\n",
       "      <td>0.141672</td>\n",
       "      <td>0.213376</td>\n",
       "      <td>0.131367</td>\n",
       "      <td>96.817526</td>\n",
       "      <td>0.001771</td>\n",
       "      <td>252.046840</td>\n",
       "      <td>15.871513</td>\n",
       "      <td>13.567095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.528288</td>\n",
       "      <td>10.652905</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>0.013554</td>\n",
       "      <td>0.013070</td>\n",
       "      <td>1.087038</td>\n",
       "      <td>2.808565</td>\n",
       "      <td>1.918211</td>\n",
       "      <td>1.726102</td>\n",
       "      <td>0.102617</td>\n",
       "      <td>0.183007</td>\n",
       "      <td>0.165461</td>\n",
       "      <td>0.105593</td>\n",
       "      <td>97.229975</td>\n",
       "      <td>0.002333</td>\n",
       "      <td>443.252216</td>\n",
       "      <td>21.047275</td>\n",
       "      <td>23.181193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.163424</td>\n",
       "      <td>15.603424</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>0.018294</td>\n",
       "      <td>0.017172</td>\n",
       "      <td>1.114041</td>\n",
       "      <td>3.925548</td>\n",
       "      <td>1.886551</td>\n",
       "      <td>1.711130</td>\n",
       "      <td>0.107135</td>\n",
       "      <td>0.257984</td>\n",
       "      <td>0.165510</td>\n",
       "      <td>0.105519</td>\n",
       "      <td>96.750063</td>\n",
       "      <td>0.002524</td>\n",
       "      <td>652.265160</td>\n",
       "      <td>25.531975</td>\n",
       "      <td>33.766847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25.579945</td>\n",
       "      <td>21.916266</td>\n",
       "      <td>7250.0</td>\n",
       "      <td>7250.0</td>\n",
       "      <td>0.020301</td>\n",
       "      <td>0.028520</td>\n",
       "      <td>1.138434</td>\n",
       "      <td>5.301146</td>\n",
       "      <td>2.017176</td>\n",
       "      <td>1.784251</td>\n",
       "      <td>0.113382</td>\n",
       "      <td>0.372206</td>\n",
       "      <td>0.182703</td>\n",
       "      <td>0.117058</td>\n",
       "      <td>96.530672</td>\n",
       "      <td>0.001482</td>\n",
       "      <td>934.880286</td>\n",
       "      <td>30.566492</td>\n",
       "      <td>47.496211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>38.579806</td>\n",
       "      <td>33.307197</td>\n",
       "      <td>7250.0</td>\n",
       "      <td>7250.0</td>\n",
       "      <td>0.016467</td>\n",
       "      <td>0.040073</td>\n",
       "      <td>1.190779</td>\n",
       "      <td>8.088920</td>\n",
       "      <td>1.989675</td>\n",
       "      <td>1.750194</td>\n",
       "      <td>0.114043</td>\n",
       "      <td>0.550905</td>\n",
       "      <td>0.173901</td>\n",
       "      <td>0.111598</td>\n",
       "      <td>95.672184</td>\n",
       "      <td>0.002050</td>\n",
       "      <td>1405.879286</td>\n",
       "      <td>37.484303</td>\n",
       "      <td>71.887003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>45.416834</td>\n",
       "      <td>38.666666</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>0.012144</td>\n",
       "      <td>0.035533</td>\n",
       "      <td>1.204696</td>\n",
       "      <td>10.107134</td>\n",
       "      <td>1.750532</td>\n",
       "      <td>1.629212</td>\n",
       "      <td>0.093490</td>\n",
       "      <td>0.593147</td>\n",
       "      <td>0.126066</td>\n",
       "      <td>0.090312</td>\n",
       "      <td>94.331049</td>\n",
       "      <td>0.003283</td>\n",
       "      <td>1636.756207</td>\n",
       "      <td>40.444366</td>\n",
       "      <td>84.083500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>86.239032</td>\n",
       "      <td>73.963387</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>0.008774</td>\n",
       "      <td>0.050569</td>\n",
       "      <td>1.339684</td>\n",
       "      <td>20.048940</td>\n",
       "      <td>1.671138</td>\n",
       "      <td>1.542672</td>\n",
       "      <td>0.080970</td>\n",
       "      <td>1.052406</td>\n",
       "      <td>0.095246</td>\n",
       "      <td>0.076939</td>\n",
       "      <td>92.809289</td>\n",
       "      <td>0.003664</td>\n",
       "      <td>3072.646371</td>\n",
       "      <td>55.412495</td>\n",
       "      <td>160.202420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   current_top  current_bot  HV_top  HV_bot  muon_stream  gamma_stream  \\\n",
       "0     0.662577     0.579678  7250.0  7250.0     0.021638      0.000510   \n",
       "1     7.331106     6.235990  7250.0  7250.0     0.020339      0.009124   \n",
       "2    12.528288    10.652905  7200.0  7200.0     0.013554      0.013070   \n",
       "3    18.163424    15.603424  7200.0  7200.0     0.018294      0.017172   \n",
       "4    25.579945    21.916266  7250.0  7250.0     0.020301      0.028520   \n",
       "5    38.579806    33.307197  7250.0  7250.0     0.016467      0.040073   \n",
       "6    45.416834    38.666666  7200.0  7200.0     0.012144      0.035533   \n",
       "7    86.239032    73.963387  7200.0  7200.0     0.008774      0.050569   \n",
       "\n",
       "    muon_CM   gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
       "0  1.056347   1.000000  2.149189  1.000000     0.128334      0.049051   \n",
       "1  1.077327   1.858821  2.120928  1.854984     0.120509      0.141672   \n",
       "2  1.087038   2.808565  1.918211  1.726102     0.102617      0.183007   \n",
       "3  1.114041   3.925548  1.886551  1.711130     0.107135      0.257984   \n",
       "4  1.138434   5.301146  2.017176  1.784251     0.113382      0.372206   \n",
       "5  1.190779   8.088920  1.989675  1.750194     0.114043      0.550905   \n",
       "6  1.204696  10.107134  1.750532  1.629212     0.093490      0.593147   \n",
       "7  1.339684  20.048940  1.671138  1.542672     0.080970      1.052406   \n",
       "\n",
       "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
       "0     0.232817      0.037743   98.604936   0.000838        1.000000   \n",
       "1     0.213376      0.131367   96.817526   0.001771      252.046840   \n",
       "2     0.165461      0.105593   97.229975   0.002333      443.252216   \n",
       "3     0.165510      0.105519   96.750063   0.002524      652.265160   \n",
       "4     0.182703      0.117058   96.530672   0.001482      934.880286   \n",
       "5     0.173901      0.111598   95.672184   0.002050     1405.879286   \n",
       "6     0.126066      0.090312   94.331049   0.003283     1636.756207   \n",
       "7     0.095246      0.076939   92.809289   0.003664     3072.646371   \n",
       "\n",
       "   noiseGammaRate_err     current  \n",
       "0            2.257067    1.242255  \n",
       "1           15.871513   13.567095  \n",
       "2           21.047275   23.181193  \n",
       "3           25.531975   33.766847  \n",
       "4           30.566492   47.496211  \n",
       "5           37.484303   71.887003  \n",
       "6           40.444366   84.083500  \n",
       "7           55.412495  160.202420  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Features_STDMX = FeaturesDataFrame(Scans_STDMX)\n",
    "Features_STDMX['gamma_CM'][0] = 1\n",
    "Features_STDMX['gamma_CS'][0] = 1\n",
    "Features_STDMX['noiseGammaRate'][0] = 1\n",
    "print(\"FeaturesDataFrame(Scans_STDMX) = \", FeaturesDataFrame(Scans_STDMX))\n",
    "Features_STDMX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de scans no dicionário: 5\n",
      "Processando scan: STDMX_OFF\n",
      "Funções no gráfico após ajuste: ['sigmoid1']\n",
      "Lambda = 0.01216180708246331\n",
      "HV95_err = 3.7603528229451637\n",
      "Fit concluído para o scan: STDMX_OFF\n",
      "Processando scan: STDMX_10\n",
      "Funções no gráfico após ajuste: ['sigmoid2']\n",
      "Lambda = 0.010579609411315853\n",
      "HV95_err = 3.062315569430728\n",
      "Fit concluído para o scan: STDMX_10\n",
      "Processando scan: STDMX_3.3\n",
      "Funções no gráfico após ajuste: ['sigmoid3']\n",
      "Lambda = 0.01056025004644915\n",
      "HV95_err = 3.4716011042430694\n",
      "Fit concluído para o scan: STDMX_3.3\n",
      "Processando scan: STDMX_2.2\n",
      "Funções no gráfico após ajuste: ['sigmoid4']\n",
      "Lambda = 0.011864636183976537\n",
      "HV95_err = 4.383025771544109\n",
      "Fit concluído para o scan: STDMX_2.2\n",
      "Processando scan: STDMX_1\n",
      "Funções no gráfico após ajuste: ['sigmoid5']\n",
      "Lambda = 0.011974763187287744\n",
      "HV95_err = 4.129418620077054\n",
      "Fit concluído para o scan: STDMX_1\n",
      "Tabela salva em 'tabela_dados_STDMX.csv'.\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      14.1711\n",
      "NDf                       =            7\n",
      "Edm                       =  1.14577e-05\n",
      "NCalls                    =           90\n",
      "Emax                      =     0.989618   +/-   0.000818564 \n",
      "Lambda                    =    0.0121618   +/-   0.000163772 \n",
      "HV50                      =      6787.79   +/-   1.87384     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      162.076\n",
      "NDf                       =           13\n",
      "Edm                       =  6.07303e-07\n",
      "NCalls                    =           80\n",
      "Emax                      =      0.97403   +/-   0.00118495  \n",
      "Lambda                    =    0.0105796   +/-   9.36429e-05 \n",
      "HV50                      =      6808.49   +/-   1.81916     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      135.693\n",
      "NDf                       =           14\n",
      "Edm                       =  2.13604e-10\n",
      "NCalls                    =           78\n",
      "Emax                      =     0.946878   +/-   0.00161884  \n",
      "Lambda                    =    0.0105603   +/-   0.000109083 \n",
      "HV50                      =      6803.85   +/-   1.93829     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      11.4509\n",
      "NDf                       =            6\n",
      "Edm                       =  8.68755e-07\n",
      "NCalls                    =           79\n",
      "Emax                      =     0.952942   +/-   0.00155244  \n",
      "Lambda                    =    0.0118646   +/-   0.00018404  \n",
      "HV50                      =      6788.26   +/-   2.09574     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      37.1688\n",
      "NDf                       =            7\n",
      "Edm                       =  1.63675e-08\n",
      "NCalls                    =          216\n",
      "Emax                      =     0.931301   +/-   0.00170656  \n",
      "Lambda                    =    0.0119748   +/-   0.000170121 \n",
      "HV50                      =      6772.99   +/-   2.20214     \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6070 xmax = 7630 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6070.000000,7630.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6070 xmax = 7630 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6070.000000,7630.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6070 xmax = 7630 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6070.000000,7630.000000] is not a valid interval\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c_all\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6070 xmax = 7630 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6070.000000,7630.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6070 xmax = 7630 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6070.000000,7630.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6070 xmax = 7630 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6070.000000,7630.000000] is not a valid interval\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ROOT import TCanvas, TGraphErrors, TLatex, TLine, TLegend\n",
    "import math\n",
    "import pandas as pd\n",
    "\n",
    "# Inicializando listas para gráficos e funções\n",
    "TGraphs_list_STDMX = []\n",
    "func_list_STDMX = []\n",
    "\n",
    "canvas = TCanvas(\"c\", \"c\", 564, 232, 600, 600)\n",
    "HV_ref = 95\n",
    "\n",
    "markers = [23, 22, 20, 29]\n",
    "colors = [6, 3, 2, 1]  # Rosa, verde, vermelho, preto\n",
    "\n",
    "k = 1\n",
    "\n",
    "# Contagem dos scans no dicionário\n",
    "num_scans_total = len(scans_STDMX)\n",
    "print(f\"Total de scans no dicionário: {num_scans_total}\")\n",
    "\n",
    "# Listas para armazenar os dados\n",
    "tabela_dados = []\n",
    "\n",
    "for scan, sigmoid, color, marker in scans_STDMX:\n",
    "    if scan not in scans:\n",
    "        print(f\"Scan '{scan}' não encontrado no dicionário de scans. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    print(f\"Processando scan: {scan}\")\n",
    "    \n",
    "    df = HVCurrentDataFrame(scans[scan])\n",
    "    n = len(df['HV_top'])\n",
    "\n",
    "    gr_E = TGraphErrors(n, \n",
    "                        df['HV_top'].values, \n",
    "                        df['efficiency'].values,\n",
    "                        0,\n",
    "                        df['eff_error'].values) # valores do df[].values para converter objeto dataframe em array adequado para o TGraph\n",
    "    \n",
    "    # Ajuste e verificação do resultado\n",
    "    fit_result = gr_E.Fit(sigmoid, \"S\")\n",
    "    if not fit_result.IsValid():\n",
    "        print(f\"Fit falhou para o scan: {scan}\")\n",
    "        continue\n",
    "    \n",
    "    # Listando funções após o ajuste para depuração\n",
    "    functions = gr_E.GetListOfFunctions()\n",
    "    function_names = [f.GetName() for f in functions]\n",
    "    print(f\"Funções no gráfico após ajuste: {function_names}\")\n",
    "\n",
    "    # Tentando encontrar a função ajustada correta\n",
    "    sig = None\n",
    "    for name in function_names:\n",
    "        if name.startswith(\"sigmoid\"):\n",
    "            sig = gr_E.GetFunction(name)\n",
    "            break\n",
    "    \n",
    "    if sig is None:\n",
    "        print(f\"Função 'sigmoid' não encontrada para o scan: {scan}. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    sig.SetLineColor(color)\n",
    "    gr_E.SetMarkerColor(color)\n",
    "    gr_E.SetMarkerStyle(marker)\n",
    "\n",
    "    Emax = sig.GetParameter(0)\n",
    "    Emax_err = sig.GetParError(0)     \n",
    "\n",
    "    Lambda = sig.GetParameter(1)    \n",
    "    Lambda_err = sig.GetParError(1)      \n",
    "\n",
    "    HV50 = sig.GetParameter(2)\n",
    "    HV50_err = sig.GetParError(2)      \n",
    "    \n",
    "    print(f\"Lambda = {Lambda}\")\n",
    "    HV95 = sig.GetX(HV_ref)\n",
    "    HV95_err = math.sqrt(((math.log(19) / Lambda**2) * Lambda_err)**2 + HV50_err**2) # erro HV95\n",
    "    \n",
    "    print(f\"HV95_err = {HV95_err}\")\n",
    "\n",
    "    # Adicionando gráficos e funções às listas\n",
    "    TGraphs_list_STDMX.append(gr_E)\n",
    "    func_list_STDMX.append(sig)\n",
    "    \n",
    "    # Adicionando dados à tabela\n",
    "    for i in range(n):\n",
    "        HV_top = df['HV_top'].values[i]\n",
    "        efficiency = df['efficiency'].values[i]\n",
    "        eff_error = df['eff_error'].values[i]\n",
    "        tabela_dados.append([scan, HV_top, efficiency, eff_error])\n",
    "    \n",
    "    gr_E.Draw(\"AP\")\n",
    "    k += 1\n",
    "\n",
    "    print(f\"Fit concluído para o scan: {scan}\")\n",
    "\n",
    "# Criando o canvas final\n",
    "canvas_all = TCanvas(\"c_all\", \"c_all\", 0, 0, 600, 600)\n",
    "\n",
    "if TGraphs_list_STDMX:\n",
    "    TGraphs_list_STDMX[0].GetHistogram().SetMaximum(1.4)\n",
    "    TGraphs_list_STDMX[0].SetTitle(\"\")\n",
    "    TGraphs_list_STDMX[0].GetXaxis().SetTitle(\"HV_{eff} [V]\")\n",
    "    TGraphs_list_STDMX[0].GetYaxis().SetTitle(\"Efficiency\")\n",
    "    TGraphs_list_STDMX[0].Draw(\"AP\")\n",
    "    \n",
    "    for graph in TGraphs_list_STDMX[1:]:\n",
    "        graph.Draw(\"PSAME\")\n",
    "\n",
    "    Emax_STDMX = []\n",
    "    Emax_err_STDMX = []\n",
    "    Lambda_STDMX = []\n",
    "    Lambda_err_STDMX = []\n",
    "    HV50_STDMX = []\n",
    "    HV50_err_STDMX = []\n",
    "    HV95_STDMX = []\n",
    "    HV95_err_STDMX = []\n",
    "\n",
    "    for sig in func_list_STDMX:\n",
    "        Emax_ = 1 - math.sqrt((1 - sig.GetParameter(0)) * (1 - sig.GetParameter(0)))\n",
    "        Emax_STDMX.append(Emax_)\n",
    "        Emax_err_STDMX.append(sig.GetParError(0))     \n",
    "        Lambda_STDMX.append(sig.GetParameter(1))  \n",
    "        Lambda_err_STDMX.append(sig.GetParError(1))\n",
    "        HV50_STDMX.append(sig.GetParameter(2))  \n",
    "        HV50_err_STDMX.append(sig.GetParError(2)) \n",
    "        HV95_STDMX.append(sig.GetX(HV_ref))\n",
    "        HV95_err_STDMX.append((math.log(19) / sig.GetParameter(1)**2) * sig.GetParError(1) + sig.GetParError(2)) # erro HV95\n",
    "\n",
    "    WP_STDMX = []    \n",
    "\n",
    "    for Emax_, Lambda_, HV50_ in zip(Emax_STDMX, Lambda_STDMX, HV50_STDMX):\n",
    "        WP_STDMX.append((HV50_ - math.log(1 / 0.95 - 1) / Lambda_ + 150.))\n",
    "\n",
    "    txt_STDMX_10 = Features_STDMX['noiseGammaRate'][2] / (Features_STDMX['gamma_CS'][2] * 1000)\n",
    "    txt_STDMX_3_3 = Features_STDMX['noiseGammaRate'][5] / (Features_STDMX['gamma_CS'][5] * 1000)\n",
    "    txt_STDMX_2_2 = Features_STDMX['noiseGammaRate'][6] / (Features_STDMX['gamma_CS'][6] * 1000)\n",
    "    txt_STDMX_1 = Features_STDMX['noiseGammaRate'][7] / (Features_STDMX['gamma_CS'][7] * 1000)\n",
    "\n",
    "    eff_1_STDMX = 'plateau = {a:.0%}, WP = {b:.2f} kV, no hit bkg gamma rate, Eff(WP) = {c:.0%}'.format(a=Emax_STDMX[0], b=WP_STDMX[0] / 1000., c=(func_list_STDMX[0].Eval(WP_STDMX[0])))\n",
    "    eff_2_STDMX = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm2, Eff(WP) = {c:.0%}'.format(a=Emax_STDMX[1], b=WP_STDMX[1] / 1000., c=(func_list_STDMX[1].Eval(WP_STDMX[1])), d=txt_STDMX_10)\n",
    "    eff_3_STDMX = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm2, Eff(WP) = {c:.0%}'.format(a=Emax_STDMX[2], b=WP_STDMX[2] / 1000., c=(func_list_STDMX[2].Eval(WP_STDMX[2])), d=txt_STDMX_3_3)\n",
    "    eff_4_STDMX = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm2, Eff(WP) = {c:.0%}'.format(a=Emax_STDMX[3], b=WP_STDMX[3] / 1000., c=(func_list_STDMX[3].Eval(WP_STDMX[3])), d=txt_STDMX_2_2)\n",
    "    eff_5_STDMX = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm2, Eff(WP) = {c:.0%}'.format(a=Emax_STDMX[4], b=WP_STDMX[4] / 1000., c=(func_list_STDMX[4].Eval(WP_STDMX[4])), d=txt_STDMX_1)\n",
    "\n",
    "    cms_tex = TLatex()\n",
    "    cms_tex.SetNDC()\n",
    "    cms_tex.SetTextFont(61)\n",
    "    cms_tex.SetTextSize(0.04)\n",
    "    cms_tex.SetLineWidth(2)\n",
    "    cms_tex.DrawLatex(0.10, 0.905, \"CMS MUON\")\n",
    "\n",
    "    cms_tex_1 = TLatex()\n",
    "    cms_tex_1.SetNDC()\n",
    "    cms_tex_1.SetTextFont(61)\n",
    "    cms_tex_1.SetTextSize(0.04)\n",
    "    cms_tex_1.SetLineWidth(2)\n",
    "    cms_tex_1.DrawLatex(0.79, 0.905, \"GIF++\")\n",
    "\n",
    "    cms_tex_2 = TLatex()\n",
    "    cms_tex_2.SetNDC()\n",
    "    cms_tex_2.SetTextFont(52)\n",
    "    cms_tex_2.SetTextSize(0.04)\n",
    "    cms_tex_2.SetLineWidth(2)\n",
    "    cms_tex_2.DrawLatex(0.32, 0.905, \"Preliminary\")\n",
    "\n",
    "    legend = TLegend(0.1, 0.68, 0.3, 0.89)\n",
    "    legend.SetTextFont(42)\n",
    "    legend.SetBorderSize(0) \n",
    "    legend.SetFillStyle(4000)\n",
    "    legend.SetFillColor(0)  \n",
    "    legend.SetTextSize(0.02376) \n",
    "    legend.AddEntry(TGraphs_list_STDMX[0], eff_1_STDMX, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_STDMX[1], eff_2_STDMX, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_STDMX[2], eff_3_STDMX, \"P\")\n",
    "    if len(TGraphs_list_STDMX) > 3:\n",
    "        legend.AddEntry(TGraphs_list_STDMX[3], eff_4_STDMX, \"P\")\n",
    "    if len(TGraphs_list_STDMX) > 4:\n",
    "        legend.AddEntry(TGraphs_list_STDMX[4], eff_5_STDMX, \"P\")\n",
    "    legend.Draw()\n",
    "    canvas_all.Draw()\n",
    "\n",
    "    ltx_data = TLatex()\n",
    "    ltx_data.SetTextFont(42)\n",
    "    ltx_data.SetTextSize(0.025)\n",
    "    ltx_data.SetTextColor(1)\n",
    "    ltx_data.DrawLatex(5900, 0.88, \"Standard gas mixture\")\n",
    "    ltx_data.DrawLatex(5900, 0.81, \"Test Beam in April+July+August 2023\")\n",
    "    ltx_data.DrawLatex(5900, 0.74, \"Before irradiation\")\n",
    "    ltx_data.DrawLatex(5900, 0.67, \"1.4 mm double gap RPC\")\n",
    "    ltx_data.DrawLatex(5900, 0.60, \"Threshold = 60 [fC]\")\n",
    "\n",
    "    line = TLine(5850, 1., 7750, 1.)\n",
    "    line.SetLineColor(1)\n",
    "    line.SetLineStyle(9)\n",
    "    line.SetLineWidth(2)\n",
    "    line.Draw()\n",
    "\n",
    "    canvas_all.cd()\n",
    "    canvas_all.Draw()\n",
    "else:\n",
    "    print(\"Nenhum gráfico válido para desenhar.\")\n",
    "\n",
    "# Salvando a tabela em um arquivo CSV\n",
    "tabela_df = pd.DataFrame(tabela_dados, columns=[\"Scan\", \"HV_top\", \"Efficiency\", \"Eff_Error\"])\n",
    "tabela_df.to_csv(\"tabela_dados_STDMX.csv\", index=False)\n",
    "print(\"Tabela salva em 'tabela_dados_STDMX.csv'.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de scans no dicionário: 5\n",
      "Processando scan: STDMX_OFF\n",
      "Funções no gráfico após ajuste: ['sigmoid1']\n",
      "Lambda = 0.012161863504274928\n",
      "HV95_err = 3.7542363428695236\n",
      "Fit concluído para o scan: STDMX_OFF\n",
      "Processando scan: STDMX_10\n",
      "Funções no gráfico após ajuste: ['sigmoid2']\n",
      "Lambda = 0.010579614651716553\n",
      "HV95_err = 3.0623164087980044\n",
      "Fit concluído para o scan: STDMX_10\n",
      "Processando scan: STDMX_3.3\n",
      "Funções no gráfico após ajuste: ['sigmoid3']\n",
      "Lambda = 0.010560250817668895\n",
      "HV95_err = 3.464012429265514\n",
      "Fit concluído para o scan: STDMX_3.3\n",
      "Processando scan: STDMX_2.2\n",
      "Funções no gráfico após ajuste: ['sigmoid4']\n",
      "Lambda = 0.011864559316741997\n",
      "HV95_err = 4.394308826482921\n",
      "Fit concluído para o scan: STDMX_2.2\n",
      "Processando scan: STDMX_1\n",
      "Funções no gráfico após ajuste: ['sigmoid5']\n",
      "Lambda = 0.011974884081125844\n",
      "HV95_err = 4.129320358734606\n",
      "Fit concluído para o scan: STDMX_1\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      14.1711\n",
      "NDf                       =            7\n",
      "Edm                       =  8.58791e-07\n",
      "NCalls                    =           37\n",
      "Emax                      =     0.989617   +/-   0.000817386 \n",
      "Lambda                    =    0.0121619   +/-   0.000163803 \n",
      "HV50                      =      6787.78   +/-   1.86051     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      162.076\n",
      "NDf                       =           13\n",
      "Edm                       =  4.07144e-09\n",
      "NCalls                    =           39\n",
      "Emax                      =      0.97403   +/-   0.00118495  \n",
      "Lambda                    =    0.0105796   +/-   9.3643e-05  \n",
      "HV50                      =      6808.49   +/-   1.81916     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      135.693\n",
      "NDf                       =           14\n",
      "Edm                       =  3.82506e-11\n",
      "NCalls                    =           38\n",
      "Emax                      =     0.946878   +/-   0.00161813  \n",
      "Lambda                    =    0.0105603   +/-   0.000109037 \n",
      "HV50                      =      6803.85   +/-   1.92647     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      11.4509\n",
      "NDf                       =            6\n",
      "Edm                       =  6.80646e-07\n",
      "NCalls                    =           67\n",
      "Emax                      =     0.952943   +/-   0.00154112  \n",
      "Lambda                    =    0.0118646   +/-   0.000184316 \n",
      "HV50                      =      6838.26   +/-   2.10865     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      37.1688\n",
      "NDf                       =            7\n",
      "Edm                       =  6.99686e-06\n",
      "NCalls                    =           83\n",
      "Emax                      =     0.931298   +/-   0.00170654  \n",
      "Lambda                    =    0.0119749   +/-   0.00017012  \n",
      "HV50                      =      6852.98   +/-   2.20211     \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6070 xmax = 7630 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6070.000000,7630.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6120 xmax = 7680 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6120.000000,7680.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6150 xmax = 7710 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6150.000000,7710.000000] is not a valid interval\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c_all\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6070 xmax = 7630 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6070.000000,7630.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6120 xmax = 7680 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6120.000000,7680.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6150 xmax = 7710 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6150.000000,7710.000000] is not a valid interval\n",
      "Info in <TCanvas::Print>: pdf file STDMX_eff_2023.pdf has been created\n",
      "Info in <TCanvas::Print>: png file STDMX_eff_2023.png has been created\n",
      "Info in <TCanvas::SaveAs>: ROOT file STDMX_eff_2023.root has been created\n"
     ]
    }
   ],
   "source": [
    "from ROOT import TCanvas, TGraphErrors, TLatex, TLine, TLegend\n",
    "import math\n",
    "import pandas as pd\n",
    "\n",
    "# Nome do arquivo CSV original\n",
    "arquivo_csv = 'tabela_dados_STDMX.csv'\n",
    "\n",
    "# Ler o arquivo CSV\n",
    "df_csv = pd.read_csv(arquivo_csv)\n",
    "\n",
    "# Ajustar os valores de HV_top para os scans especificados\n",
    "df_csv.loc[df_csv['Scan'] == 'STDMX_1', 'HV_top'] += 80\n",
    "df_csv.loc[df_csv['Scan'] == 'STDMX_2.2', 'HV_top'] += 50\n",
    "\n",
    "# Inicializando listas para gráficos e funções\n",
    "TGraphs_list_STDMX = []\n",
    "func_list_STDMX = []\n",
    "\n",
    "# Configurações do canvas\n",
    "canvas = TCanvas(\"c\", \"c\", 564, 232, 600, 600)\n",
    "canvas.SetTopMargin(0.1)\n",
    "canvas.SetBottomMargin(0.12)\n",
    "canvas.SetLeftMargin(0.14)\n",
    "canvas.SetRightMargin(0.05)\n",
    "\n",
    "HV_ref = 95\n",
    "\n",
    "\n",
    "k = 1\n",
    "\n",
    "# Contagem dos scans no dicionário\n",
    "num_scans_total = len(scans_STDMX)\n",
    "print(f\"Total de scans no dicionário: {num_scans_total}\")\n",
    "\n",
    "for scan, sigmoid, color, marker in scans_STDMX:\n",
    "    if scan not in scans:\n",
    "        print(f\"Scan '{scan}' não encontrado no dicionário de scans. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    print(f\"Processando scan: {scan}\")\n",
    "\n",
    "    # Filtrar dados do DataFrame ajustado\n",
    "    df = df_csv[df_csv['Scan'] == scan]\n",
    "    n = len(df['HV_top'])\n",
    "\n",
    "    gr_E = TGraphErrors(n, \n",
    "                        df['HV_top'].values, \n",
    "                        df['Efficiency'].values,\n",
    "                        0,\n",
    "                        df['Eff_Error'].values) # valores do df[].values para converter objeto dataframe em array adequado para o TGraph\n",
    "    \n",
    "    # Ajuste e verificação do resultado\n",
    "    fit_result = gr_E.Fit(sigmoid, \"S\")\n",
    "    if not fit_result.IsValid():\n",
    "        print(f\"Fit falhou para o scan: {scan}\")\n",
    "        continue\n",
    "    \n",
    "    # Listando funções após o ajuste para depuração\n",
    "    functions = gr_E.GetListOfFunctions()\n",
    "    function_names = [f.GetName() for f in functions]\n",
    "    print(f\"Funções no gráfico após ajuste: {function_names}\")\n",
    "\n",
    "    # Tentando encontrar a função ajustada correta\n",
    "    sig = None\n",
    "    for name in function_names:\n",
    "        if name.startswith(\"sigmoid\"):\n",
    "            sig = gr_E.GetFunction(name)\n",
    "            break\n",
    "    \n",
    "    if sig is None:\n",
    "        print(f\"Função 'sigmoid' não encontrada para o scan: {scan}. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    sig.SetLineColor(color)\n",
    "    gr_E.SetMarkerColor(color)\n",
    "    gr_E.SetMarkerStyle(marker)\n",
    "    gr_E.SetLineWidth(2)\n",
    "    gr_E.SetMarkerSize(1.2)\n",
    "\n",
    "    Emax = sig.GetParameter(0)\n",
    "    Emax_err = sig.GetParError(0)     \n",
    "\n",
    "    Lambda = sig.GetParameter(1)    \n",
    "    Lambda_err = sig.GetParError(1)      \n",
    "\n",
    "    HV50 = sig.GetParameter(2)\n",
    "    HV50_err = sig.GetParError(2)      \n",
    "    \n",
    "    print(f\"Lambda = {Lambda}\")\n",
    "    HV95 = sig.GetX(HV_ref)\n",
    "    HV95_err = math.sqrt(((math.log(19) / Lambda**2) * Lambda_err)**2 + HV50_err**2) # erro HV95\n",
    "    \n",
    "    print(f\"HV95_err = {HV95_err}\")\n",
    "\n",
    "    # Adicionando gráficos e funções às listas\n",
    "    TGraphs_list_STDMX.append(gr_E)\n",
    "    func_list_STDMX.append(sig)\n",
    "    gr_E.Draw(\"AP\")\n",
    "    k += 1\n",
    "\n",
    "    print(f\"Fit concluído para o scan: {scan}\")\n",
    "\n",
    "# Criando o canvas final\n",
    "canvas_all = TCanvas(\"c_all\", \"c_all\", 0, 0, 600, 600)\n",
    "canvas_all.SetTopMargin(0.1)\n",
    "canvas_all.SetBottomMargin(0.12)\n",
    "canvas_all.SetLeftMargin(0.14)\n",
    "canvas_all.SetRightMargin(0.05)\n",
    "\n",
    "if TGraphs_list_STDMX:\n",
    "    TGraphs_list_STDMX[0].GetHistogram().SetMaximum(1.4)\n",
    "    TGraphs_list_STDMX[0].GetHistogram().SetMinimum(0)\n",
    "    TGraphs_list_STDMX[0].SetTitle(\"\")\n",
    "    TGraphs_list_STDMX[0].GetXaxis().SetTitle(\"HV_{eff} [V]\")\n",
    "    TGraphs_list_STDMX[0].GetYaxis().SetTitle(\"Efficiency\")\n",
    "    TGraphs_list_STDMX[0].GetXaxis().SetTitleSize(0.05)\n",
    "    TGraphs_list_STDMX[0].GetYaxis().SetTitleSize(0.05)\n",
    "    TGraphs_list_STDMX[0].GetXaxis().SetLabelSize(0.04)\n",
    "    TGraphs_list_STDMX[0].GetYaxis().SetLabelSize(0.04)\n",
    "    TGraphs_list_STDMX[0].Draw(\"AP\")\n",
    "    \n",
    "    for graph in TGraphs_list_STDMX[1:]:\n",
    "        graph.Draw(\"PSAME\")\n",
    "\n",
    "    Emax_STDMX = []\n",
    "    Emax_err_STDMX = []\n",
    "    Lambda_STDMX = []\n",
    "    Lambda_err_STDMX = []\n",
    "    HV50_STDMX = []\n",
    "    HV50_err_STDMX = []\n",
    "    HV95_STDMX = []\n",
    "    HV95_err_STDMX = []\n",
    "\n",
    "    for sig in func_list_STDMX:\n",
    "        Emax_ = 1 - math.sqrt((1 - sig.GetParameter(0)) * (1 - sig.GetParameter(0)))\n",
    "        Emax_STDMX.append(Emax_)\n",
    "        Emax_err_STDMX.append(sig.GetParError(0))     \n",
    "        Lambda_STDMX.append(sig.GetParameter(1))  \n",
    "        Lambda_err_STDMX.append(sig.GetParError(1))\n",
    "        HV50_STDMX.append(sig.GetParameter(2))  \n",
    "        HV50_err_STDMX.append(sig.GetParError(2)) \n",
    "        HV95_STDMX.append(sig.GetX(HV_ref))\n",
    "        HV95_err_STDMX.append((math.log(19) / sig.GetParameter(1)**2) * sig.GetParError(1) + sig.GetParError(2)) # erro HV95\n",
    "\n",
    "    WP_STDMX = []    \n",
    "\n",
    "    for Emax_, Lambda_, HV50_ in zip(Emax_STDMX, Lambda_STDMX, HV50_STDMX):\n",
    "        WP_STDMX.append((HV50_ - math.log(1 / 0.95 - 1) / Lambda_ + 150.))\n",
    "\n",
    "    txt_STDMX_10 = Features_STDMX['noiseGammaRate'][2] / (Features_STDMX['gamma_CS'][2] * 1000)\n",
    "    txt_STDMX_3_3 = Features_STDMX['noiseGammaRate'][5] / (Features_STDMX['gamma_CS'][5] * 1000)\n",
    "    txt_STDMX_2_2 = Features_STDMX['noiseGammaRate'][6] / (Features_STDMX['gamma_CS'][6] * 1000)\n",
    "    txt_STDMX_1 = Features_STDMX['noiseGammaRate'][7] / (Features_STDMX['gamma_CS'][7] * 1000)\n",
    "\n",
    "    eff_1_STDMX = 'plateau = {a:.0%}, WP = {b:.2f} kV, no hit bkg gamma rate, Eff(WP) = {c:.0%}'.format(a=Emax_STDMX[0], b=WP_STDMX[0] / 1000., c=(func_list_STDMX[0].Eval(WP_STDMX[0])))\n",
    "    eff_2_STDMX = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_STDMX[1], b=WP_STDMX[1] / 1000., c=(func_list_STDMX[1].Eval(WP_STDMX[1])), d=txt_STDMX_10)\n",
    "    eff_3_STDMX = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_STDMX[2], b=WP_STDMX[2] / 1000., c=(func_list_STDMX[2].Eval(WP_STDMX[2])), d=txt_STDMX_3_3)\n",
    "    eff_4_STDMX = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_STDMX[3], b=WP_STDMX[3] / 1000., c=(func_list_STDMX[3].Eval(WP_STDMX[3])), d=txt_STDMX_2_2)\n",
    "    eff_5_STDMX = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_STDMX[4], b=WP_STDMX[4] / 1000., c=(func_list_STDMX[4].Eval(WP_STDMX[4])), d=txt_STDMX_1)\n",
    "\n",
    "    cms_tex = TLatex()\n",
    "    cms_tex.SetNDC()\n",
    "    cms_tex.SetTextFont(61)\n",
    "    cms_tex.SetTextSize(0.04)\n",
    "    cms_tex.SetLineWidth(2)\n",
    "    cms_tex.DrawLatex(0.14, 0.905, \"CMS\")\n",
    "\n",
    "    cms_tex_1 = TLatex()\n",
    "    cms_tex_1.SetNDC()\n",
    "    cms_tex_1.SetTextFont(61)\n",
    "    cms_tex_1.SetTextSize(0.04)\n",
    "    cms_tex_1.SetLineWidth(2)\n",
    "    cms_tex_1.DrawLatex(0.85, 0.905, \"GIF++\")\n",
    "\n",
    "    cms_tex_2 = TLatex()\n",
    "    cms_tex_2.SetNDC()\n",
    "    cms_tex_2.SetTextFont(52)\n",
    "    cms_tex_2.SetTextSize(0.04)\n",
    "    cms_tex_2.SetLineWidth(2)\n",
    "    cms_tex_2.DrawLatex(0.23, 0.905, \"Preliminary\")\n",
    "\n",
    "    legend = TLegend(0.15, 0.68, 0.3, 0.89)\n",
    "    legend.SetTextFont(42)\n",
    "    legend.SetBorderSize(0) \n",
    "    legend.SetFillStyle(4000)\n",
    "    legend.SetFillColor(0)  \n",
    "    legend.SetTextSize(0.02376) \n",
    "    legend.AddEntry(TGraphs_list_STDMX[0], eff_1_STDMX, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_STDMX[1], eff_2_STDMX, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_STDMX[2], eff_3_STDMX, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_STDMX[3], eff_4_STDMX, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_STDMX[4], eff_5_STDMX, \"P\")\n",
    "    legend.Draw()\n",
    "    canvas_all.Draw()\n",
    "\n",
    "    ltx_data = TLatex()\n",
    "    ltx_data.SetTextFont(42)\n",
    "    ltx_data.SetTextSize(0.025)\n",
    "    ltx_data.SetTextColor(1)\n",
    "    ltx_data.DrawLatex(6150, 0.88, \"Standard gas mixture\")\n",
    "    ltx_data.DrawLatex(6150, 0.81, \"Test Beam in April+July+August 2023\")\n",
    "    ltx_data.DrawLatex(6150, 0.74, \"Before irradiation\")\n",
    "    ltx_data.DrawLatex(6150, 0.67, \"1.4 mm double gap RPC\")\n",
    "    ltx_data.DrawLatex(6150, 0.60, \"Threshold = 60 [fC]\")\n",
    "\n",
    "    line = TLine(6100, 1., 7630, 1.)\n",
    "    line.SetLineColor(1)\n",
    "    line.SetLineStyle(9)\n",
    "    line.SetLineWidth(2)\n",
    "    line.Draw()\n",
    "\n",
    "    canvas_all.Update()\n",
    "    canvas_all.SaveAs(\"STDMX_eff_2023.pdf\")\n",
    "    canvas_all.SaveAs(\"STDMX_eff_2023.png\")\n",
    "    canvas_all.SaveAs(\"STDMX_eff_2023.root\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scanID:  5819\n",
      "scanID:  5826\n",
      "scanID:  5832\n",
      "scanID:  5830\n",
      "scanID:  5831\n",
      "scanID:  5822\n",
      "scanID:  5827\n",
      "scanID:  5820\n",
      "scanID:  5819\n",
      "scanID:  5826\n",
      "scanID:  5832\n",
      "scanID:  5830\n",
      "scanID:  5831\n",
      "scanID:  5822\n",
      "scanID:  5827\n",
      "scanID:  5820\n",
      "FeaturesDataFrame(Scans_30CO2) =     current_top  current_bot  HV_top  HV_bot  muon_stream  gamma_stream  \\\n",
      "0     0.516166     0.416491  7060.0  7060.0     0.018003      0.000250   \n",
      "1     8.236441     7.194407  7100.0  7100.0     0.019441      0.008320   \n",
      "2    14.676590    12.431754  7060.0  7060.0     0.018289      0.012954   \n",
      "3    22.606310    19.578012  7100.0  7100.0     0.017575      0.023247   \n",
      "4    27.569545    23.575042  7060.0  7060.0     0.015797      0.023081   \n",
      "5    42.202164    36.301476  7060.0  7060.0     0.016382      0.034044   \n",
      "6    86.810166    75.809578  7100.0  7100.0     0.013570      0.058803   \n",
      "7   106.863310    93.091297  7100.0  7100.0     0.011497      0.066622   \n",
      "\n",
      "    muon_CM   gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0  1.046605   1.380734  2.027942  1.553710     0.114498      0.041284   \n",
      "1  1.069188   1.834037  2.156432  1.882689     0.123208      0.140533   \n",
      "2  1.089290   2.788576  1.974940  1.778704     0.108266      0.187523   \n",
      "3  1.114720   4.001052  2.056186  1.836094     0.123787      0.286302   \n",
      "4  1.133903   5.077845  1.914788  1.732481     0.105372      0.325085   \n",
      "5  1.170880   7.823211  1.878253  1.715422     0.100166      0.493850   \n",
      "6  1.307742  16.238484  1.838972  1.666027     0.103015      0.976756   \n",
      "7  1.340264  20.620611  1.756303  1.610884     0.096816      1.155980   \n",
      "\n",
      "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0     0.199979      0.045108   97.984488   0.001414        3.491967   \n",
      "1     0.222820      0.133994   97.329474   0.002293      250.622640   \n",
      "2     0.178546      0.112076   96.694180   0.001115      453.798524   \n",
      "3     0.205514      0.122611   96.345960   0.002675      714.527983   \n",
      "4     0.162809      0.104240   95.713718   0.001444      868.616270   \n",
      "5     0.148017      0.101858   94.920933   0.001565     1332.901642   \n",
      "6     0.134283      0.094527   93.179415   0.003586     2688.441735   \n",
      "7     0.118322      0.085511   92.465901   0.003738     3299.677748   \n",
      "\n",
      "   noiseGammaRate_err     current  \n",
      "0            1.797203    0.932656  \n",
      "1           15.825827   15.430847  \n",
      "2           21.296105   27.108345  \n",
      "3           26.722011   42.184322  \n",
      "4           29.463607   51.144587  \n",
      "5           36.497840   78.503639  \n",
      "6           51.835132  162.619744  \n",
      "7           57.423139  199.954607  \n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>current_top</th>\n",
       "      <th>current_bot</th>\n",
       "      <th>HV_top</th>\n",
       "      <th>HV_bot</th>\n",
       "      <th>muon_stream</th>\n",
       "      <th>gamma_stream</th>\n",
       "      <th>muon_CM</th>\n",
       "      <th>gamma_CM</th>\n",
       "      <th>muon_CS</th>\n",
       "      <th>gamma_CS</th>\n",
       "      <th>muon_CM_err</th>\n",
       "      <th>gamma_CM_err</th>\n",
       "      <th>muon_CS_err</th>\n",
       "      <th>gamma_CS_err</th>\n",
       "      <th>efficiency</th>\n",
       "      <th>eff_error</th>\n",
       "      <th>noiseGammaRate</th>\n",
       "      <th>noiseGammaRate_err</th>\n",
       "      <th>current</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.516166</td>\n",
       "      <td>0.416491</td>\n",
       "      <td>7060.0</td>\n",
       "      <td>7060.0</td>\n",
       "      <td>0.018003</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>1.046605</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.027942</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.114498</td>\n",
       "      <td>0.041284</td>\n",
       "      <td>0.199979</td>\n",
       "      <td>0.045108</td>\n",
       "      <td>97.984488</td>\n",
       "      <td>0.001414</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.797203</td>\n",
       "      <td>0.932656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.236441</td>\n",
       "      <td>7.194407</td>\n",
       "      <td>7100.0</td>\n",
       "      <td>7100.0</td>\n",
       "      <td>0.019441</td>\n",
       "      <td>0.008320</td>\n",
       "      <td>1.069188</td>\n",
       "      <td>1.834037</td>\n",
       "      <td>2.156432</td>\n",
       "      <td>1.882689</td>\n",
       "      <td>0.123208</td>\n",
       "      <td>0.140533</td>\n",
       "      <td>0.222820</td>\n",
       "      <td>0.133994</td>\n",
       "      <td>97.329474</td>\n",
       "      <td>0.002293</td>\n",
       "      <td>250.622640</td>\n",
       "      <td>15.825827</td>\n",
       "      <td>15.430847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.676590</td>\n",
       "      <td>12.431754</td>\n",
       "      <td>7060.0</td>\n",
       "      <td>7060.0</td>\n",
       "      <td>0.018289</td>\n",
       "      <td>0.012954</td>\n",
       "      <td>1.089290</td>\n",
       "      <td>2.788576</td>\n",
       "      <td>1.974940</td>\n",
       "      <td>1.778704</td>\n",
       "      <td>0.108266</td>\n",
       "      <td>0.187523</td>\n",
       "      <td>0.178546</td>\n",
       "      <td>0.112076</td>\n",
       "      <td>96.694180</td>\n",
       "      <td>0.001115</td>\n",
       "      <td>453.798524</td>\n",
       "      <td>21.296105</td>\n",
       "      <td>27.108345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22.606310</td>\n",
       "      <td>19.578012</td>\n",
       "      <td>7100.0</td>\n",
       "      <td>7100.0</td>\n",
       "      <td>0.017575</td>\n",
       "      <td>0.023247</td>\n",
       "      <td>1.114720</td>\n",
       "      <td>4.001052</td>\n",
       "      <td>2.056186</td>\n",
       "      <td>1.836094</td>\n",
       "      <td>0.123787</td>\n",
       "      <td>0.286302</td>\n",
       "      <td>0.205514</td>\n",
       "      <td>0.122611</td>\n",
       "      <td>96.345960</td>\n",
       "      <td>0.002675</td>\n",
       "      <td>714.527983</td>\n",
       "      <td>26.722011</td>\n",
       "      <td>42.184322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27.569545</td>\n",
       "      <td>23.575042</td>\n",
       "      <td>7060.0</td>\n",
       "      <td>7060.0</td>\n",
       "      <td>0.015797</td>\n",
       "      <td>0.023081</td>\n",
       "      <td>1.133903</td>\n",
       "      <td>5.077845</td>\n",
       "      <td>1.914788</td>\n",
       "      <td>1.732481</td>\n",
       "      <td>0.105372</td>\n",
       "      <td>0.325085</td>\n",
       "      <td>0.162809</td>\n",
       "      <td>0.104240</td>\n",
       "      <td>95.713718</td>\n",
       "      <td>0.001444</td>\n",
       "      <td>868.616270</td>\n",
       "      <td>29.463607</td>\n",
       "      <td>51.144587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>42.202164</td>\n",
       "      <td>36.301476</td>\n",
       "      <td>7060.0</td>\n",
       "      <td>7060.0</td>\n",
       "      <td>0.016382</td>\n",
       "      <td>0.034044</td>\n",
       "      <td>1.170880</td>\n",
       "      <td>7.823211</td>\n",
       "      <td>1.878253</td>\n",
       "      <td>1.715422</td>\n",
       "      <td>0.100166</td>\n",
       "      <td>0.493850</td>\n",
       "      <td>0.148017</td>\n",
       "      <td>0.101858</td>\n",
       "      <td>94.920933</td>\n",
       "      <td>0.001565</td>\n",
       "      <td>1332.901642</td>\n",
       "      <td>36.497840</td>\n",
       "      <td>78.503639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>86.810166</td>\n",
       "      <td>75.809578</td>\n",
       "      <td>7100.0</td>\n",
       "      <td>7100.0</td>\n",
       "      <td>0.013570</td>\n",
       "      <td>0.058803</td>\n",
       "      <td>1.307742</td>\n",
       "      <td>16.238484</td>\n",
       "      <td>1.838972</td>\n",
       "      <td>1.666027</td>\n",
       "      <td>0.103015</td>\n",
       "      <td>0.976756</td>\n",
       "      <td>0.134283</td>\n",
       "      <td>0.094527</td>\n",
       "      <td>93.179415</td>\n",
       "      <td>0.003586</td>\n",
       "      <td>2688.441735</td>\n",
       "      <td>51.835132</td>\n",
       "      <td>162.619744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>106.863310</td>\n",
       "      <td>93.091297</td>\n",
       "      <td>7100.0</td>\n",
       "      <td>7100.0</td>\n",
       "      <td>0.011497</td>\n",
       "      <td>0.066622</td>\n",
       "      <td>1.340264</td>\n",
       "      <td>20.620611</td>\n",
       "      <td>1.756303</td>\n",
       "      <td>1.610884</td>\n",
       "      <td>0.096816</td>\n",
       "      <td>1.155980</td>\n",
       "      <td>0.118322</td>\n",
       "      <td>0.085511</td>\n",
       "      <td>92.465901</td>\n",
       "      <td>0.003738</td>\n",
       "      <td>3299.677748</td>\n",
       "      <td>57.423139</td>\n",
       "      <td>199.954607</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   current_top  current_bot  HV_top  HV_bot  muon_stream  gamma_stream  \\\n",
       "0     0.516166     0.416491  7060.0  7060.0     0.018003      0.000250   \n",
       "1     8.236441     7.194407  7100.0  7100.0     0.019441      0.008320   \n",
       "2    14.676590    12.431754  7060.0  7060.0     0.018289      0.012954   \n",
       "3    22.606310    19.578012  7100.0  7100.0     0.017575      0.023247   \n",
       "4    27.569545    23.575042  7060.0  7060.0     0.015797      0.023081   \n",
       "5    42.202164    36.301476  7060.0  7060.0     0.016382      0.034044   \n",
       "6    86.810166    75.809578  7100.0  7100.0     0.013570      0.058803   \n",
       "7   106.863310    93.091297  7100.0  7100.0     0.011497      0.066622   \n",
       "\n",
       "    muon_CM   gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
       "0  1.046605   1.000000  2.027942  1.000000     0.114498      0.041284   \n",
       "1  1.069188   1.834037  2.156432  1.882689     0.123208      0.140533   \n",
       "2  1.089290   2.788576  1.974940  1.778704     0.108266      0.187523   \n",
       "3  1.114720   4.001052  2.056186  1.836094     0.123787      0.286302   \n",
       "4  1.133903   5.077845  1.914788  1.732481     0.105372      0.325085   \n",
       "5  1.170880   7.823211  1.878253  1.715422     0.100166      0.493850   \n",
       "6  1.307742  16.238484  1.838972  1.666027     0.103015      0.976756   \n",
       "7  1.340264  20.620611  1.756303  1.610884     0.096816      1.155980   \n",
       "\n",
       "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
       "0     0.199979      0.045108   97.984488   0.001414        1.000000   \n",
       "1     0.222820      0.133994   97.329474   0.002293      250.622640   \n",
       "2     0.178546      0.112076   96.694180   0.001115      453.798524   \n",
       "3     0.205514      0.122611   96.345960   0.002675      714.527983   \n",
       "4     0.162809      0.104240   95.713718   0.001444      868.616270   \n",
       "5     0.148017      0.101858   94.920933   0.001565     1332.901642   \n",
       "6     0.134283      0.094527   93.179415   0.003586     2688.441735   \n",
       "7     0.118322      0.085511   92.465901   0.003738     3299.677748   \n",
       "\n",
       "   noiseGammaRate_err     current  \n",
       "0            1.797203    0.932656  \n",
       "1           15.825827   15.430847  \n",
       "2           21.296105   27.108345  \n",
       "3           26.722011   42.184322  \n",
       "4           29.463607   51.144587  \n",
       "5           36.497840   78.503639  \n",
       "6           51.835132  162.619744  \n",
       "7           57.423139  199.954607  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Features_30CO2 = FeaturesDataFrame(Scans_30CO2)\n",
    "Features_30CO2['gamma_CM'][0] = 1\n",
    "Features_30CO2['gamma_CS'][0] = 1\n",
    "Features_30CO2['noiseGammaRate'][0] = 1\n",
    "print(\"FeaturesDataFrame(Scans_30CO2) = \", FeaturesDataFrame(Scans_30CO2))\n",
    "Features_30CO2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de scans no dicionário: 5\n",
      "Processando scan: 30CO2_OFF\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Funções no gráfico após ajuste: ['sigmoid6']\n",
      "Lambda = 0.010507181067274114\n",
      "HV95_err = 3.070743795517659\n",
      "Fit concluído para o scan: 30CO2_OFF\n",
      "Processando scan: 30CO2_10\n",
      "Funções no gráfico após ajuste: ['sigmoid7']\n",
      "Lambda = 0.010707059404383412\n",
      "HV95_err = 4.01032316508076\n",
      "Fit concluído para o scan: 30CO2_10\n",
      "Processando scan: 30CO2_3.3\n",
      "Funções no gráfico após ajuste: ['sigmoid8']\n",
      "Lambda = 0.010827702470037677\n",
      "HV95_err = 3.25248257526441\n",
      "Fit concluído para o scan: 30CO2_3.3\n",
      "Processando scan: 30CO2_2.2\n",
      "Funções no gráfico após ajuste: ['sigmoid9']\n",
      "Lambda = 0.0115481112169792\n",
      "HV95_err = 4.28354503233753\n",
      "Fit concluído para o scan: 30CO2_2.2\n",
      "Processando scan: 30CO2_1\n",
      "Funções no gráfico após ajuste: ['sigmoid10']\n",
      "Lambda = 0.011663337846026777\n",
      "HV95_err = 3.8839891856288804\n",
      "Fit concluído para o scan: 30CO2_1\n",
      "Tabela salva em 'tabela_dados.csv'.\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      240.082\n",
      "NDf                       =           14\n",
      "Edm                       =  4.59774e-11\n",
      "NCalls                    =           93\n",
      "Emax                      =     0.993042   +/-   0.000545708 \n",
      "Lambda                    =    0.0105072   +/-   9.68828e-05 \n",
      "HV50                      =      6646.61   +/-   1.65919     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      121.192\n",
      "NDf                       =            9\n",
      "Edm                       =  4.82823e-06\n",
      "NCalls                    =           87\n",
      "Emax                      =      0.96981   +/-   0.00132631  \n",
      "Lambda                    =    0.0107071   +/-   0.000124968 \n",
      "HV50                      =      6684.45   +/-   2.40429     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      340.053\n",
      "NDf                       =           14\n",
      "Edm                       =   7.8647e-06\n",
      "NCalls                    =           96\n",
      "Emax                      =     0.943462   +/-   0.00144715  \n",
      "Lambda                    =    0.0108277   +/-   0.000102732 \n",
      "HV50                      =      6672.01   +/-   1.98035     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      43.3897\n",
      "NDf                       =            7\n",
      "Edm                       =  7.04166e-08\n",
      "NCalls                    =          198\n",
      "Emax                      =     0.934804   +/-   0.00161829  \n",
      "Lambda                    =    0.0115481   +/-   0.000164082 \n",
      "HV50                      =      6621.54   +/-   2.28568     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      72.1213\n",
      "NDf                       =            7\n",
      "Edm                       =  6.00067e-07\n",
      "NCalls                    =          145\n",
      "Emax                      =     0.930121   +/-   0.00163731  \n",
      "Lambda                    =    0.0116633   +/-   0.000147258 \n",
      "HV50                      =      6621.74   +/-   2.21944     \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5935 xmax = 7915 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5935.000000,7915.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5620 xmax = 7780 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5620.000000,7780.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5970 xmax = 7530 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5970.000000,7530.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5970 xmax = 7530 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5970.000000,7530.000000] is not a valid interval\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c_all\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5935 xmax = 7915 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5935.000000,7915.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5620 xmax = 7780 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5620.000000,7780.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5970 xmax = 7530 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5970.000000,7530.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5970 xmax = 7530 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5970.000000,7530.000000] is not a valid interval\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ROOT import TCanvas, TGraphErrors, TLatex, TLine, TLegend\n",
    "import math\n",
    "import pandas as pd\n",
    "\n",
    "# Inicializando listas para gráficos e funções\n",
    "TGraphs_list_30CO2 = []\n",
    "func_list_30CO2 = []\n",
    "\n",
    "canvas = TCanvas(\"c\", \"c\", 564, 232, 600, 600)\n",
    "HV_ref = 95\n",
    "\n",
    "\n",
    "k = 1\n",
    "\n",
    "# Contagem dos scans no dicionário\n",
    "num_scans_total = len(scans_30CO2)\n",
    "print(f\"Total de scans no dicionário: {num_scans_total}\")\n",
    "\n",
    "# Listas para armazenar os dados\n",
    "tabela_dados = []\n",
    "\n",
    "for scan, sigmoid, color, marker in scans_30CO2:\n",
    "    if scan not in scans:\n",
    "        print(f\"Scan '{scan}' não encontrado no dicionário de scans. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    print(f\"Processando scan: {scan}\")\n",
    "    \n",
    "    df = HVCurrentDataFrame(scans[scan])\n",
    "    n = len(df['HV_top'])\n",
    "\n",
    "    gr_E = TGraphErrors(n, \n",
    "                        df['HV_top'].values, \n",
    "                        df['efficiency'].values,\n",
    "                        0,\n",
    "                        df['eff_error'].values) # valores do df[].values para converter objeto dataframe em array adequado para o TGraph\n",
    "    \n",
    "    # Ajuste e verificação do resultado\n",
    "    fit_result = gr_E.Fit(sigmoid, \"S\")\n",
    "    if not fit_result.IsValid():\n",
    "        print(f\"Fit falhou para o scan: {scan}\")\n",
    "        continue\n",
    "    \n",
    "    # Listando funções após o ajuste para depuração\n",
    "    functions = gr_E.GetListOfFunctions()\n",
    "    function_names = [f.GetName() for f in functions]\n",
    "    print(f\"Funções no gráfico após ajuste: {function_names}\")\n",
    "\n",
    "    # Tentando encontrar a função ajustada correta\n",
    "    sig = None\n",
    "    for name in function_names:\n",
    "        if name.startswith(\"sigmoid\"):\n",
    "            sig = gr_E.GetFunction(name)\n",
    "            break\n",
    "    \n",
    "    if sig is None:\n",
    "        print(f\"Função 'sigmoid' não encontrada para o scan: {scan}. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    sig.SetLineColor(color)\n",
    "    gr_E.SetMarkerColor(color)\n",
    "    gr_E.SetMarkerStyle(marker)\n",
    "\n",
    "    Emax = sig.GetParameter(0)\n",
    "    Emax_err = sig.GetParError(0)     \n",
    "\n",
    "    Lambda = sig.GetParameter(1)    \n",
    "    Lambda_err = sig.GetParError(1)      \n",
    "\n",
    "    HV50 = sig.GetParameter(2)\n",
    "    HV50_err = sig.GetParError(2)      \n",
    "    \n",
    "    print(f\"Lambda = {Lambda}\")\n",
    "    HV95 = sig.GetX(HV_ref)\n",
    "    HV95_err = math.sqrt(((math.log(19) / Lambda**2) * Lambda_err)**2 + HV50_err**2) # erro HV95\n",
    "    \n",
    "    print(f\"HV95_err = {HV95_err}\")\n",
    "\n",
    "    # Adicionando gráficos e funções às listas\n",
    "    TGraphs_list_30CO2.append(gr_E)\n",
    "    func_list_30CO2.append(sig)\n",
    "    \n",
    "    # Adicionando dados à tabela\n",
    "    for i in range(n):\n",
    "        HV_top = df['HV_top'].values[i]\n",
    "        efficiency = df['efficiency'].values[i]\n",
    "        eff_error = df['eff_error'].values[i]\n",
    "        tabela_dados.append([scan, HV_top, efficiency, eff_error])\n",
    "    \n",
    "    gr_E.Draw(\"AP\")\n",
    "    k += 1\n",
    "\n",
    "    print(f\"Fit concluído para o scan: {scan}\")\n",
    "\n",
    "# Criando o canvas final\n",
    "canvas_all = TCanvas(\"c_all\", \"c_all\", 0, 0, 600, 600)\n",
    "\n",
    "if TGraphs_list_30CO2:\n",
    "    TGraphs_list_30CO2[0].GetHistogram().SetMaximum(1.4)\n",
    "    TGraphs_list_30CO2[0].SetTitle(\"\")\n",
    "    TGraphs_list_30CO2[0].GetXaxis().SetTitle(\"HV_{eff} [V]\")\n",
    "    TGraphs_list_30CO2[0].GetYaxis().SetTitle(\"Muon Efficiency\")\n",
    "    TGraphs_list_30CO2[0].Draw(\"AP\")\n",
    "    \n",
    "    for graph in TGraphs_list_30CO2[1:]:\n",
    "        graph.Draw(\"PSAME\")\n",
    "\n",
    "    Emax_30CO2 = []\n",
    "    Emax_err_30CO2 = []\n",
    "    Lambda_30CO2 = []\n",
    "    Lambda_err_30CO2 = []\n",
    "    HV50_30CO2 = []\n",
    "    HV50_err_30CO2 = []\n",
    "    HV95_30CO2 = []\n",
    "    HV95_err_30CO2 = []\n",
    "\n",
    "    for sig in func_list_30CO2:\n",
    "        Emax_ = 1 - math.sqrt((1 - sig.GetParameter(0)) * (1 - sig.GetParameter(0)))\n",
    "        Emax_30CO2.append(Emax_)\n",
    "        Emax_err_30CO2.append(sig.GetParError(0))     \n",
    "        Lambda_30CO2.append(sig.GetParameter(1))  \n",
    "        Lambda_err_30CO2.append(sig.GetParError(1))\n",
    "        HV50_30CO2.append(sig.GetParameter(2))  \n",
    "        HV50_err_30CO2.append(sig.GetParError(2)) \n",
    "        HV95_30CO2.append(sig.GetX(HV_ref))\n",
    "        HV95_err_30CO2.append((math.log(19) / sig.GetParameter(1)**2) * sig.GetParError(1) + sig.GetParError(2)) # erro HV95\n",
    "\n",
    "    WP_30CO2 = []    \n",
    "\n",
    "    for Emax_, Lambda_, HV50_ in zip(Emax_30CO2, Lambda_30CO2, HV50_30CO2):\n",
    "        WP_30CO2.append((HV50_ - math.log(1 / 0.95 - 1) / Lambda_ + 150.))\n",
    "\n",
    "    txt_30CO2_10 = Features_30CO2['noiseGammaRate'][2] / (Features_30CO2['gamma_CS'][2] * 1000)\n",
    "    txt_30CO2_3_3 = Features_30CO2['noiseGammaRate'][5] / (Features_30CO2['gamma_CS'][5] * 1000)\n",
    "    txt_30CO2_2_2 = Features_30CO2['noiseGammaRate'][6] / (Features_30CO2['gamma_CS'][6] * 1000)\n",
    "    txt_30CO2_1 = Features_30CO2['noiseGammaRate'][7] / (Features_30CO2['gamma_CS'][7] * 1000)\n",
    "\n",
    "    eff_1_30CO2_OFF = 'plateau = {a:.0%}, WP = {b:.2f} kV, no hit bkg gamma rate, Eff(WP) = {c:.0%}'.format(a=Emax_30CO2[0], b=WP_30CO2[0] / 1000., c=(func_list_30CO2[0].Eval(WP_30CO2[0])))\n",
    "    eff_2_30CO2_10 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_30CO2[1], b=WP_30CO2[1] / 1000., c=(func_list_30CO2[1].Eval(WP_30CO2[1])), d=txt_30CO2_10)\n",
    "    eff_3_30CO2_3_3 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_30CO2[2], b=WP_30CO2[2] / 1000., c=(func_list_30CO2[2].Eval(WP_30CO2[2])), d=txt_30CO2_3_3)\n",
    "    eff_4_30CO2_2_2 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_30CO2[3], b=WP_30CO2[3] / 1000., c=(func_list_30CO2[3].Eval(WP_30CO2[3])), d=txt_30CO2_2_2)\n",
    "    eff_5_30CO2_1 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_30CO2[4], b=WP_30CO2[4] / 1000., c=(func_list_30CO2[4].Eval(WP_30CO2[4])), d=txt_30CO2_1)\n",
    "\n",
    "    cms_tex = TLatex()\n",
    "    cms_tex.SetNDC()\n",
    "    cms_tex.SetTextFont(61)\n",
    "    cms_tex.SetTextSize(0.04)\n",
    "    cms_tex.SetLineWidth(2)\n",
    "    cms_tex.DrawLatex(0.10, 0.905, \"CMS\")\n",
    "\n",
    "    cms_tex_1 = TLatex()\n",
    "    cms_tex_1.SetNDC()\n",
    "    cms_tex_1.SetTextFont(61)\n",
    "    cms_tex_1.SetTextSize(0.04)\n",
    "    cms_tex_1.SetLineWidth(2)\n",
    "    cms_tex_1.DrawLatex(0.79, 0.905, \"GIF++\")\n",
    "\n",
    "    cms_tex_2 = TLatex()\n",
    "    cms_tex_2.SetNDC()\n",
    "    cms_tex_2.SetTextFont(52)\n",
    "    cms_tex_2.SetTextSize(0.04)\n",
    "    cms_tex_2.SetLineWidth(2)\n",
    "    cms_tex_2.DrawLatex(0.32, 0.905, \"Preliminary\")\n",
    "\n",
    "    legend = TLegend(0.1, 0.68, 0.3, 0.89)\n",
    "    legend.SetTextFont(42)\n",
    "    legend.SetBorderSize(0) \n",
    "    legend.SetFillStyle(4000)\n",
    "    legend.SetFillColor(0)  \n",
    "    legend.SetTextSize(0.02376) \n",
    "    legend.AddEntry(TGraphs_list_30CO2[0], eff_1_30CO2_OFF, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_30CO2[1], eff_2_30CO2_10, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_30CO2[2], eff_3_30CO2_3_3, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_30CO2[3], eff_4_30CO2_2_2, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_30CO2[4], eff_5_30CO2_1, \"P\")\n",
    "    legend.Draw()\n",
    "    canvas_all.Draw()\n",
    "\n",
    "    ltx_data = TLatex()\n",
    "    ltx_data.SetTextFont(42)\n",
    "    ltx_data.SetTextSize(0.025)\n",
    "    ltx_data.SetTextColor(1)\n",
    "    ltx_data.DrawLatex(5900, 0.88, \"30% CO2 + 1% SF6\")\n",
    "    ltx_data.DrawLatex(5900, 0.81, \"Test Beam in April+July+August 2023\")\n",
    "    ltx_data.DrawLatex(5900, 0.74, \"1.4 mm double gap RPC\")\n",
    "    ltx_data.DrawLatex(5900, 0.67, \"Threshold = 60 [fC]\")\n",
    "\n",
    "    line = TLine(5850, 1., 7750, 1.)\n",
    "    line.SetLineColor(1)\n",
    "    line.SetLineStyle(9)\n",
    "    line.SetLineWidth(2)\n",
    "    line.Draw()\n",
    "\n",
    "    canvas_all.cd()\n",
    "    canvas_all.Draw()\n",
    "else:\n",
    "    print(\"Nenhum gráfico válido para desenhar.\")\n",
    "\n",
    "# Salvando a tabela em um arquivo CSV\n",
    "tabela_df = pd.DataFrame(tabela_dados, columns=[\"Scan\", \"HV_top\", \"Efficiency\", \"Eff_Error\"])\n",
    "tabela_df.to_csv(\"tabela_dados_30CO2.csv\", index=False)\n",
    "print(\"Tabela salva em 'tabela_dados.csv'.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de scans no dicionário: 5\n",
      "Processando scan: 30CO2_OFF\n",
      "Funções no gráfico após ajuste: ['sigmoid6']\n",
      "Lambda = 0.010507181048171464\n",
      "HV95_err = 3.070670008817123\n",
      "Fit concluído para o scan: 30CO2_OFF\n",
      "Processando scan: 30CO2_10\n",
      "Funções no gráfico após ajuste: ['sigmoid7']\n",
      "Lambda = 0.010707074819000326\n",
      "HV95_err = 4.010310013336792\n",
      "Fit concluído para o scan: 30CO2_10\n",
      "Processando scan: 30CO2_3.3\n",
      "Funções no gráfico após ajuste: ['sigmoid8']\n",
      "Lambda = 0.010827850913363206\n",
      "HV95_err = 3.2525853255005543\n",
      "Fit concluído para o scan: 30CO2_3.3\n",
      "Processando scan: 30CO2_2.2\n",
      "Funções no gráfico após ajuste: ['sigmoid9']\n",
      "Lambda = 0.011548113007111937\n",
      "HV95_err = 4.283532907398575\n",
      "Fit concluído para o scan: 30CO2_2.2\n",
      "Processando scan: 30CO2_1\n",
      "Funções no gráfico após ajuste: ['sigmoid10']\n",
      "Lambda = 0.011663255667846619\n",
      "HV95_err = 3.8839630920326873\n",
      "Fit concluído para o scan: 30CO2_1\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      240.082\n",
      "NDf                       =           14\n",
      "Edm                       =  8.28055e-12\n",
      "NCalls                    =           40\n",
      "Emax                      =     0.993042   +/-   0.000545727 \n",
      "Lambda                    =    0.0105072   +/-   9.6879e-05  \n",
      "HV50                      =      6646.61   +/-   1.6592      \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      121.192\n",
      "NDf                       =            9\n",
      "Edm                       =  2.19793e-08\n",
      "NCalls                    =           37\n",
      "Emax                      =     0.969812   +/-   0.0013263   \n",
      "Lambda                    =    0.0107071   +/-   0.000124968 \n",
      "HV50                      =      6684.44   +/-   2.4043      \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      340.053\n",
      "NDf                       =           14\n",
      "Edm                       =  2.48089e-07\n",
      "NCalls                    =           31\n",
      "Emax                      =     0.943465   +/-   0.00144715  \n",
      "Lambda                    =    0.0108279   +/-   0.000102739 \n",
      "HV50                      =      6672.01   +/-   1.98037     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      43.3897\n",
      "NDf                       =            7\n",
      "Edm                       =  2.46354e-07\n",
      "NCalls                    =           77\n",
      "Emax                      =     0.934804   +/-   0.00161829  \n",
      "Lambda                    =    0.0115481   +/-   0.000164081 \n",
      "HV50                      =      6691.54   +/-   2.28568     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      72.1213\n",
      "NDf                       =            7\n",
      "Edm                       =  2.38144e-09\n",
      "NCalls                    =           92\n",
      "Emax                      =     0.930122   +/-   0.00163731  \n",
      "Lambda                    =    0.0116633   +/-   0.000147254 \n",
      "HV50                      =      6731.74   +/-   2.21944     \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5935 xmax = 7915 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5935.000000,7915.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5620 xmax = 7780 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5620.000000,7780.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6040 xmax = 7600 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6040.000000,7600.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6080 xmax = 7640 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6080.000000,7640.000000] is not a valid interval\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c_all\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5935 xmax = 7915 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5935.000000,7915.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5620 xmax = 7780 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5620.000000,7780.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6040 xmax = 7600 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6040.000000,7600.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6080 xmax = 7640 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6080.000000,7640.000000] is not a valid interval\n",
      "Info in <TCanvas::SaveAs>: ROOT file 30CO2_eff.root has been created\n",
      "Info in <TCanvas::Print>: png file 30CO2_eff.png has been created\n",
      "Info in <TCanvas::Print>: pdf file 30CO2_eff.pdf has been created\n"
     ]
    }
   ],
   "source": [
    "from ROOT import TCanvas, TGraphErrors, TLatex, TLine, TLegend, TImage\n",
    "import math\n",
    "import pandas as pd\n",
    "\n",
    "# Nome do arquivo CSV original\n",
    "arquivo_csv = 'tabela_dados_30CO2.csv'\n",
    "\n",
    "# Ler o arquivo CSV\n",
    "df_csv = pd.read_csv(arquivo_csv)\n",
    "\n",
    "# Ajustar os valores de HV_top para os scans especificados\n",
    "df_csv.loc[df_csv['Scan'] == '30CO2_1', 'HV_top'] += 110\n",
    "df_csv.loc[df_csv['Scan'] == '30CO2_2.2', 'HV_top'] += 70\n",
    "\n",
    "# Inicializando listas para gráficos e funções\n",
    "TGraphs_list_30CO2 = []\n",
    "func_list_30CO2 = []\n",
    "\n",
    "# Configurações do canvas\n",
    "canvas = TCanvas(\"c\", \"c\", 564, 232, 600, 600)\n",
    "canvas.SetTopMargin(0.1)\n",
    "canvas.SetBottomMargin(0.12)\n",
    "canvas.SetLeftMargin(0.14)\n",
    "canvas.SetRightMargin(0.05)\n",
    "\n",
    "HV_ref = 95\n",
    "\n",
    "\n",
    "\n",
    "k = 1\n",
    "\n",
    "# Contagem dos scans no dicionário\n",
    "num_scans_total = len(scans_30CO2)\n",
    "print(f\"Total de scans no dicionário: {num_scans_total}\")\n",
    "\n",
    "for scan, sigmoid, color, marker in scans_30CO2:\n",
    "    if scan not in scans:\n",
    "        print(f\"Scan '{scan}' não encontrado no dicionário de scans. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    print(f\"Processando scan: {scan}\")\n",
    "\n",
    "    # Filtrar dados do DataFrame ajustado\n",
    "    df = df_csv[df_csv['Scan'] == scan]\n",
    "    n = len(df['HV_top'])\n",
    "\n",
    "    gr_E = TGraphErrors(n, \n",
    "                        df['HV_top'].values, \n",
    "                        df['Efficiency'].values,\n",
    "                        0,\n",
    "                        df['Eff_Error'].values) # valores do df[].values para converter objeto dataframe em array adequado para o TGraph\n",
    "    \n",
    "    # Ajuste e verificação do resultado\n",
    "    fit_result = gr_E.Fit(sigmoid, \"S\")\n",
    "    if not fit_result.IsValid():\n",
    "        print(f\"Fit falhou para o scan: {scan}\")\n",
    "        continue\n",
    "    \n",
    "    # Listando funções após o ajuste para depuração\n",
    "    functions = gr_E.GetListOfFunctions()\n",
    "    function_names = [f.GetName() for f in functions]\n",
    "    print(f\"Funções no gráfico após ajuste: {function_names}\")\n",
    "\n",
    "    # Tentando encontrar a função ajustada correta\n",
    "    sig = None\n",
    "    for name in function_names:\n",
    "        if name.startswith(\"sigmoid\"):\n",
    "            sig = gr_E.GetFunction(name)\n",
    "            break\n",
    "    \n",
    "    if sig is None:\n",
    "        print(f\"Função 'sigmoid' não encontrada para o scan: {scan}. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    sig.SetLineColor(color)\n",
    "    gr_E.SetMarkerColor(color)\n",
    "    gr_E.SetMarkerStyle(marker)\n",
    "    gr_E.SetLineWidth(2)\n",
    "    gr_E.SetMarkerSize(1.2)\n",
    "\n",
    "    Emax = sig.GetParameter(0)\n",
    "    Emax_err = sig.GetParError(0)     \n",
    "\n",
    "    Lambda = sig.GetParameter(1)    \n",
    "    Lambda_err = sig.GetParError(1)      \n",
    "\n",
    "    HV50 = sig.GetParameter(2)\n",
    "    HV50_err = sig.GetParError(2)      \n",
    "    \n",
    "    print(f\"Lambda = {Lambda}\")\n",
    "    HV95 = sig.GetX(HV_ref)\n",
    "    HV95_err = math.sqrt(((math.log(19) / Lambda**2) * Lambda_err)**2 + HV50_err**2) # erro HV95\n",
    "    \n",
    "    print(f\"HV95_err = {HV95_err}\")\n",
    "\n",
    "    # Adicionando gráficos e funções às listas\n",
    "    TGraphs_list_30CO2.append(gr_E)\n",
    "    func_list_30CO2.append(sig)\n",
    "    gr_E.Draw(\"AP\")\n",
    "    k += 1\n",
    "\n",
    "    print(f\"Fit concluído para o scan: {scan}\")\n",
    "\n",
    "# Criando o canvas final\n",
    "canvas_all = TCanvas(\"c_all\", \"c_all\", 0, 0, 600, 600)\n",
    "canvas_all.SetTopMargin(0.1)\n",
    "canvas_all.SetBottomMargin(0.12)\n",
    "canvas_all.SetLeftMargin(0.14)\n",
    "canvas_all.SetRightMargin(0.05)\n",
    "\n",
    "if TGraphs_list_30CO2:\n",
    "    TGraphs_list_30CO2[0].GetHistogram().SetMaximum(1.4)\n",
    "    TGraphs_list_30CO2[0].GetHistogram().SetMinimum(0)\n",
    "    TGraphs_list_30CO2[0].SetTitle(\"\")\n",
    "    TGraphs_list_30CO2[0].GetXaxis().SetTitle(\"HV_{eff} [V]\")\n",
    "    TGraphs_list_30CO2[0].GetYaxis().SetTitle(\"Efficiency\")\n",
    "    TGraphs_list_30CO2[0].GetXaxis().SetTitleSize(0.05)\n",
    "    TGraphs_list_30CO2[0].GetYaxis().SetTitleSize(0.05)\n",
    "    TGraphs_list_30CO2[0].GetXaxis().SetLabelSize(0.04)\n",
    "    TGraphs_list_30CO2[0].GetYaxis().SetLabelSize(0.04)\n",
    "    TGraphs_list_30CO2[0].GetXaxis().SetLimits(6000, 7400)\n",
    "    TGraphs_list_30CO2[0].Draw(\"AP\")\n",
    "    \n",
    "    for graph in TGraphs_list_30CO2[1:]:\n",
    "        graph.Draw(\"PSAME\")\n",
    "\n",
    "    Emax_30CO2 = []\n",
    "    Emax_err_30CO2 = []\n",
    "    Lambda_30CO2 = []\n",
    "    Lambda_err_30CO2 = []\n",
    "    HV50_30CO2 = []\n",
    "    HV50_err_30CO2 = []\n",
    "    HV95_30CO2 = []\n",
    "    HV95_err_30CO2 = []\n",
    "\n",
    "    for sig in func_list_30CO2:\n",
    "        Emax_ = 1 - math.sqrt((1 - sig.GetParameter(0)) * (1 - sig.GetParameter(0)))\n",
    "        Emax_30CO2.append(Emax_)\n",
    "        Emax_err_30CO2.append(sig.GetParError(0))     \n",
    "        Lambda_30CO2.append(sig.GetParameter(1))  \n",
    "        Lambda_err_30CO2.append(sig.GetParError(1))\n",
    "        HV50_30CO2.append(sig.GetParameter(2))  \n",
    "        HV50_err_30CO2.append(sig.GetParError(2)) \n",
    "        HV95_30CO2.append(sig.GetX(HV_ref))\n",
    "        HV95_err_30CO2.append((math.log(19) / sig.GetParameter(1)**2) * sig.GetParError(1) + sig.GetParError(2)) # erro HV95\n",
    "\n",
    "    WP_30CO2 = []    \n",
    "\n",
    "for Emax_, Lambda_, HV50_ in zip(Emax_30CO2, Lambda_30CO2, HV50_30CO2):\n",
    "        WP_30CO2.append((HV50_ - math.log(1 / 0.95 - 1) / Lambda_ + 150.))\n",
    "\n",
    "txt_30CO2_10 = Features_30CO2['noiseGammaRate'][2] / (Features_30CO2['gamma_CS'][2] * 1000)\n",
    "txt_30CO2_3_3 = Features_30CO2['noiseGammaRate'][5] / (Features_30CO2['gamma_CS'][5] * 1000)\n",
    "txt_30CO2_2_2 = Features_30CO2['noiseGammaRate'][6] / (Features_30CO2['gamma_CS'][6] * 1000)\n",
    "txt_30CO2_1 = Features_30CO2['noiseGammaRate'][7] / (Features_30CO2['gamma_CS'][7] * 1000)\n",
    "\n",
    "eff_1_30CO2_OFF = 'plateau = {a:.0%}, WP = {b:.2f} kV, no hit bkg gamma rate, Eff(WP) = {c:.0%}'.format(a=Emax_30CO2[0], b=WP_30CO2[0] / 1000., c=(func_list_30CO2[0].Eval(WP_30CO2[0])))\n",
    "eff_2_30CO2_10 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_30CO2[1], b=WP_30CO2[1] / 1000., c=(func_list_30CO2[1].Eval(WP_30CO2[1])), d=txt_30CO2_10)\n",
    "eff_3_30CO2_3_3 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_30CO2[2], b=WP_30CO2[2] / 1000., c=(func_list_30CO2[2].Eval(WP_30CO2[2])), d=txt_30CO2_3_3)\n",
    "eff_4_30CO2_2_2 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_30CO2[3], b=WP_30CO2[3] / 1000., c=(func_list_30CO2[3].Eval(WP_30CO2[3])), d=txt_30CO2_2_2)\n",
    "eff_5_30CO2_1 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_30CO2[4], b=WP_30CO2[4] / 1000., c=(func_list_30CO2[4].Eval(WP_30CO2[4])), d=txt_30CO2_1)\n",
    "\n",
    "cms_tex = TLatex()\n",
    "cms_tex.SetNDC()\n",
    "cms_tex.SetTextFont(61)\n",
    "cms_tex.SetTextSize(0.04)\n",
    "cms_tex.SetLineWidth(2)\n",
    "cms_tex.DrawLatex(0.14, 0.905, \"CMS\")\n",
    "\n",
    "cms_tex_1 = TLatex()\n",
    "cms_tex_1.SetNDC()\n",
    "cms_tex_1.SetTextFont(61)\n",
    "cms_tex_1.SetTextSize(0.04)\n",
    "cms_tex_1.SetLineWidth(2)\n",
    "cms_tex_1.DrawLatex(0.85, 0.905, \"GIF++\")\n",
    "\n",
    "cms_tex_2 = TLatex()\n",
    "cms_tex_2.SetNDC()\n",
    "cms_tex_2.SetTextFont(52)\n",
    "cms_tex_2.SetTextSize(0.04)\n",
    "cms_tex_2.SetLineWidth(2)\n",
    "cms_tex_2.DrawLatex(0.23, 0.905, \"Preliminary\")\n",
    "\n",
    "legend = TLegend(0.15, 0.68, 0.3, 0.89)\n",
    "legend.SetTextFont(42)\n",
    "legend.SetBorderSize(0) \n",
    "legend.SetFillStyle(4000)\n",
    "legend.SetFillColor(0)  \n",
    "legend.SetTextSize(0.02376) \n",
    "legend.AddEntry(TGraphs_list_30CO2[0], eff_1_30CO2_OFF, \"P\")\n",
    "legend.AddEntry(TGraphs_list_30CO2[1], eff_2_30CO2_10, \"P\")\n",
    "legend.AddEntry(TGraphs_list_30CO2[2], eff_3_30CO2_3_3, \"P\")\n",
    "legend.AddEntry(TGraphs_list_30CO2[3], eff_4_30CO2_2_2, \"P\")\n",
    "legend.AddEntry(TGraphs_list_30CO2[4], eff_5_30CO2_1, \"P\")\n",
    "legend.Draw()\n",
    "canvas_all.Draw()\n",
    "\n",
    "ltx_data = TLatex()\n",
    "ltx_data.SetTextFont(42)\n",
    "ltx_data.SetTextSize(0.025)\n",
    "ltx_data.SetTextColor(1)\n",
    "ltx_data.DrawLatex(6100, 0.88, \"30% CO_{2} + 1.0% SF_{6}\")\n",
    "ltx_data.DrawLatex(6100, 0.81, \"Test Beam in April+July+August\")\n",
    "ltx_data.DrawLatex(6100, 0.74, \"2023, before irradiation\")\n",
    "ltx_data.DrawLatex(6100, 0.67, \"1.4 mm double gap RPC\")\n",
    "ltx_data.DrawLatex(6100, 0.60, \"Threshold = 60 [fC]\")\n",
    "\n",
    "line = TLine(6000, 1., 7400, 1.)\n",
    "line.SetLineColor(1)\n",
    "line.SetLineStyle(9)\n",
    "line.SetLineWidth(2)\n",
    "line.Draw()\n",
    "\n",
    "\n",
    "canvas_all.Update()\n",
    "canvas_all.SaveAs(\"30CO2_eff.root\")\n",
    "canvas_all.SaveAs(\"30CO2_eff.png\")\n",
    "canvas_all.SaveAs(\"30CO2_eff.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scanID:  5833\n",
      "scanID:  5839\n",
      "scanID:  5836\n",
      "scanID:  5837\n",
      "scanID:  5835\n",
      "scanID:  5834\n",
      "scanID:  5838\n",
      "scanID:  5840\n",
      "scanID:  5833\n",
      "scanID:  5839\n",
      "scanID:  5836\n",
      "scanID:  5837\n",
      "scanID:  5835\n",
      "scanID:  5834\n",
      "scanID:  5838\n",
      "scanID:  5840\n",
      "FeaturesDataFrame(Scans_30CO205SF6) =     current_top  current_bot  HV_top  HV_bot  muon_stream  gamma_stream  \\\n",
      "0     0.729052     0.573356  6950.0  6950.0     0.023953      0.000650   \n",
      "1     9.885142     8.778891  7060.0  7060.0     0.035293      0.017356   \n",
      "2    17.803002    15.848965  7060.0  7060.0     0.028751      0.031319   \n",
      "3    25.854911    22.977375  7060.0  7060.0     0.030122      0.042524   \n",
      "4    33.528084    30.002467  7060.0  7060.0     0.029922      0.054032   \n",
      "5    50.053757    44.876121  7060.0  7060.0     0.023695      0.072797   \n",
      "6   110.092891    98.324803  7000.0  7000.0     0.015029      0.098907   \n",
      "7    58.901701    52.498809  7000.0  7000.0     0.019968      0.073303   \n",
      "\n",
      "    muon_CM   gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0  1.049668   1.337696  2.242913  1.526419     0.137472      0.052356   \n",
      "1  1.076826   1.936739  2.647883  2.178276     0.176868      0.180727   \n",
      "2  1.105946   3.066759  2.534077  2.120079     0.164453      0.273759   \n",
      "3  1.128806   4.346750  2.479932  2.098029     0.155544      0.389662   \n",
      "4  1.149686   5.661482  2.418002  2.055092     0.159480      0.490002   \n",
      "5  1.204097   8.739607  2.339360  1.995886     0.154333      0.728377   \n",
      "6  1.396719  21.870774  1.899861  1.723332     0.124973      1.454348   \n",
      "7  1.232308  11.017163  2.151161  1.869541     0.125834      0.811798   \n",
      "\n",
      "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0     0.259732      0.057492   98.277432   0.000658        3.886514   \n",
      "1     0.373557      0.185917   98.179328   0.001361      314.975965   \n",
      "2     0.328036      0.173742   97.463618   0.001594      604.938009   \n",
      "3     0.300337      0.172604   96.877498   0.001764      890.845187   \n",
      "4     0.294557      0.163700   96.523118   0.001858     1150.351354   \n",
      "5     0.265778      0.153545   95.413920   0.002117     1732.501391   \n",
      "6     0.156031      0.107452   92.905250   0.003638     3740.656890   \n",
      "7     0.199308      0.128303   94.641179   0.003215     2047.727114   \n",
      "\n",
      "   noiseGammaRate_err     current  \n",
      "0            1.844694    1.302408  \n",
      "1           17.739920   18.664033  \n",
      "2           24.588330   33.651967  \n",
      "3           29.836912   48.832286  \n",
      "4           33.905786   63.530550  \n",
      "5           41.610476   94.929878  \n",
      "6           61.139394  208.417694  \n",
      "7           45.236057  111.400510  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>current_top</th>\n",
       "      <th>current_bot</th>\n",
       "      <th>HV_top</th>\n",
       "      <th>HV_bot</th>\n",
       "      <th>muon_stream</th>\n",
       "      <th>gamma_stream</th>\n",
       "      <th>muon_CM</th>\n",
       "      <th>gamma_CM</th>\n",
       "      <th>muon_CS</th>\n",
       "      <th>gamma_CS</th>\n",
       "      <th>muon_CM_err</th>\n",
       "      <th>gamma_CM_err</th>\n",
       "      <th>muon_CS_err</th>\n",
       "      <th>gamma_CS_err</th>\n",
       "      <th>efficiency</th>\n",
       "      <th>eff_error</th>\n",
       "      <th>noiseGammaRate</th>\n",
       "      <th>noiseGammaRate_err</th>\n",
       "      <th>current</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.729052</td>\n",
       "      <td>0.573356</td>\n",
       "      <td>6950.0</td>\n",
       "      <td>6950.0</td>\n",
       "      <td>0.023953</td>\n",
       "      <td>0.000650</td>\n",
       "      <td>1.049668</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.242913</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.137472</td>\n",
       "      <td>0.052356</td>\n",
       "      <td>0.259732</td>\n",
       "      <td>0.057492</td>\n",
       "      <td>98.277432</td>\n",
       "      <td>0.000658</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.844694</td>\n",
       "      <td>1.302408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.885142</td>\n",
       "      <td>8.778891</td>\n",
       "      <td>7060.0</td>\n",
       "      <td>7060.0</td>\n",
       "      <td>0.035293</td>\n",
       "      <td>0.017356</td>\n",
       "      <td>1.076826</td>\n",
       "      <td>1.936739</td>\n",
       "      <td>2.647883</td>\n",
       "      <td>2.178276</td>\n",
       "      <td>0.176868</td>\n",
       "      <td>0.180727</td>\n",
       "      <td>0.373557</td>\n",
       "      <td>0.185917</td>\n",
       "      <td>98.179328</td>\n",
       "      <td>0.001361</td>\n",
       "      <td>314.975965</td>\n",
       "      <td>17.739920</td>\n",
       "      <td>18.664033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.803002</td>\n",
       "      <td>15.848965</td>\n",
       "      <td>7060.0</td>\n",
       "      <td>7060.0</td>\n",
       "      <td>0.028751</td>\n",
       "      <td>0.031319</td>\n",
       "      <td>1.105946</td>\n",
       "      <td>3.066759</td>\n",
       "      <td>2.534077</td>\n",
       "      <td>2.120079</td>\n",
       "      <td>0.164453</td>\n",
       "      <td>0.273759</td>\n",
       "      <td>0.328036</td>\n",
       "      <td>0.173742</td>\n",
       "      <td>97.463618</td>\n",
       "      <td>0.001594</td>\n",
       "      <td>604.938009</td>\n",
       "      <td>24.588330</td>\n",
       "      <td>33.651967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25.854911</td>\n",
       "      <td>22.977375</td>\n",
       "      <td>7060.0</td>\n",
       "      <td>7060.0</td>\n",
       "      <td>0.030122</td>\n",
       "      <td>0.042524</td>\n",
       "      <td>1.128806</td>\n",
       "      <td>4.346750</td>\n",
       "      <td>2.479932</td>\n",
       "      <td>2.098029</td>\n",
       "      <td>0.155544</td>\n",
       "      <td>0.389662</td>\n",
       "      <td>0.300337</td>\n",
       "      <td>0.172604</td>\n",
       "      <td>96.877498</td>\n",
       "      <td>0.001764</td>\n",
       "      <td>890.845187</td>\n",
       "      <td>29.836912</td>\n",
       "      <td>48.832286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.528084</td>\n",
       "      <td>30.002467</td>\n",
       "      <td>7060.0</td>\n",
       "      <td>7060.0</td>\n",
       "      <td>0.029922</td>\n",
       "      <td>0.054032</td>\n",
       "      <td>1.149686</td>\n",
       "      <td>5.661482</td>\n",
       "      <td>2.418002</td>\n",
       "      <td>2.055092</td>\n",
       "      <td>0.159480</td>\n",
       "      <td>0.490002</td>\n",
       "      <td>0.294557</td>\n",
       "      <td>0.163700</td>\n",
       "      <td>96.523118</td>\n",
       "      <td>0.001858</td>\n",
       "      <td>1150.351354</td>\n",
       "      <td>33.905786</td>\n",
       "      <td>63.530550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50.053757</td>\n",
       "      <td>44.876121</td>\n",
       "      <td>7060.0</td>\n",
       "      <td>7060.0</td>\n",
       "      <td>0.023695</td>\n",
       "      <td>0.072797</td>\n",
       "      <td>1.204097</td>\n",
       "      <td>8.739607</td>\n",
       "      <td>2.339360</td>\n",
       "      <td>1.995886</td>\n",
       "      <td>0.154333</td>\n",
       "      <td>0.728377</td>\n",
       "      <td>0.265778</td>\n",
       "      <td>0.153545</td>\n",
       "      <td>95.413920</td>\n",
       "      <td>0.002117</td>\n",
       "      <td>1732.501391</td>\n",
       "      <td>41.610476</td>\n",
       "      <td>94.929878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110.092891</td>\n",
       "      <td>98.324803</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>0.015029</td>\n",
       "      <td>0.098907</td>\n",
       "      <td>1.396719</td>\n",
       "      <td>21.870774</td>\n",
       "      <td>1.899861</td>\n",
       "      <td>1.723332</td>\n",
       "      <td>0.124973</td>\n",
       "      <td>1.454348</td>\n",
       "      <td>0.156031</td>\n",
       "      <td>0.107452</td>\n",
       "      <td>92.905250</td>\n",
       "      <td>0.003638</td>\n",
       "      <td>3740.656890</td>\n",
       "      <td>61.139394</td>\n",
       "      <td>208.417694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>58.901701</td>\n",
       "      <td>52.498809</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>0.019968</td>\n",
       "      <td>0.073303</td>\n",
       "      <td>1.232308</td>\n",
       "      <td>11.017163</td>\n",
       "      <td>2.151161</td>\n",
       "      <td>1.869541</td>\n",
       "      <td>0.125834</td>\n",
       "      <td>0.811798</td>\n",
       "      <td>0.199308</td>\n",
       "      <td>0.128303</td>\n",
       "      <td>94.641179</td>\n",
       "      <td>0.003215</td>\n",
       "      <td>2047.727114</td>\n",
       "      <td>45.236057</td>\n",
       "      <td>111.400510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   current_top  current_bot  HV_top  HV_bot  muon_stream  gamma_stream  \\\n",
       "0     0.729052     0.573356  6950.0  6950.0     0.023953      0.000650   \n",
       "1     9.885142     8.778891  7060.0  7060.0     0.035293      0.017356   \n",
       "2    17.803002    15.848965  7060.0  7060.0     0.028751      0.031319   \n",
       "3    25.854911    22.977375  7060.0  7060.0     0.030122      0.042524   \n",
       "4    33.528084    30.002467  7060.0  7060.0     0.029922      0.054032   \n",
       "5    50.053757    44.876121  7060.0  7060.0     0.023695      0.072797   \n",
       "6   110.092891    98.324803  7000.0  7000.0     0.015029      0.098907   \n",
       "7    58.901701    52.498809  7000.0  7000.0     0.019968      0.073303   \n",
       "\n",
       "    muon_CM   gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
       "0  1.049668   1.000000  2.242913  1.000000     0.137472      0.052356   \n",
       "1  1.076826   1.936739  2.647883  2.178276     0.176868      0.180727   \n",
       "2  1.105946   3.066759  2.534077  2.120079     0.164453      0.273759   \n",
       "3  1.128806   4.346750  2.479932  2.098029     0.155544      0.389662   \n",
       "4  1.149686   5.661482  2.418002  2.055092     0.159480      0.490002   \n",
       "5  1.204097   8.739607  2.339360  1.995886     0.154333      0.728377   \n",
       "6  1.396719  21.870774  1.899861  1.723332     0.124973      1.454348   \n",
       "7  1.232308  11.017163  2.151161  1.869541     0.125834      0.811798   \n",
       "\n",
       "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
       "0     0.259732      0.057492   98.277432   0.000658        1.000000   \n",
       "1     0.373557      0.185917   98.179328   0.001361      314.975965   \n",
       "2     0.328036      0.173742   97.463618   0.001594      604.938009   \n",
       "3     0.300337      0.172604   96.877498   0.001764      890.845187   \n",
       "4     0.294557      0.163700   96.523118   0.001858     1150.351354   \n",
       "5     0.265778      0.153545   95.413920   0.002117     1732.501391   \n",
       "6     0.156031      0.107452   92.905250   0.003638     3740.656890   \n",
       "7     0.199308      0.128303   94.641179   0.003215     2047.727114   \n",
       "\n",
       "   noiseGammaRate_err     current  \n",
       "0            1.844694    1.302408  \n",
       "1           17.739920   18.664033  \n",
       "2           24.588330   33.651967  \n",
       "3           29.836912   48.832286  \n",
       "4           33.905786   63.530550  \n",
       "5           41.610476   94.929878  \n",
       "6           61.139394  208.417694  \n",
       "7           45.236057  111.400510  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Features_30CO205SF6 = FeaturesDataFrame(Scans_30CO205SF6)\n",
    "Features_30CO205SF6['gamma_CM'][0] = 1\n",
    "Features_30CO205SF6['gamma_CS'][0] = 1\n",
    "Features_30CO205SF6['noiseGammaRate'][0] = 1\n",
    "print(\"FeaturesDataFrame(Scans_30CO205SF6) = \", FeaturesDataFrame(Scans_30CO205SF6))\n",
    "Features_30CO205SF6\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de scans no dicionário: 5\n",
      "Processando scan: 30CO205SF6_OFF\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Funções no gráfico após ajuste: ['sigmoid16']\n",
      "Lambda = 0.009868736723696913\n",
      "HV95_err = 3.2132227438513024\n",
      "Fit concluído para o scan: 30CO205SF6_OFF\n",
      "Processando scan: 30CO205SF6_10\n",
      "Funções no gráfico após ajuste: ['sigmoid17']\n",
      "Lambda = 0.01057168237103946\n",
      "HV95_err = 4.548988916384935\n",
      "Fit concluído para o scan: 30CO205SF6_10\n",
      "Processando scan: 30CO205SF6_3.3\n",
      "Funções no gráfico após ajuste: ['sigmoid18']\n",
      "Lambda = 0.010911543003148966\n",
      "HV95_err = 4.443635047976753\n",
      "Fit concluído para o scan: 30CO205SF6_3.3\n",
      "Processando scan: 30CO205SF6_2.2\n",
      "Funções no gráfico após ajuste: ['sigmoid19']\n",
      "Lambda = 0.011603430790325048\n",
      "HV95_err = 4.20298684002922\n",
      "Fit concluído para o scan: 30CO205SF6_2.2\n",
      "Processando scan: 30CO205SF6_1\n",
      "Funções no gráfico após ajuste: ['sigmoid20']\n",
      "Lambda = 0.011322393307012558\n",
      "HV95_err = 4.297504860225976\n",
      "Fit concluído para o scan: 30CO205SF6_1\n",
      "Tabela salva em 'tabela_dados_30CO205SF6.csv'.\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      137.155\n",
      "NDf                       =            7\n",
      "Edm                       =  3.54505e-07\n",
      "NCalls                    =          112\n",
      "Emax                      =     0.990414   +/-   0.000794203 \n",
      "Lambda                    =   0.00986874   +/-   8.89584e-05 \n",
      "HV50                      =      6513.27   +/-   1.75829     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      72.8542\n",
      "NDf                       =            7\n",
      "Edm                       =  7.51815e-07\n",
      "NCalls                    =          134\n",
      "Emax                      =     0.970629   +/-   0.00173901  \n",
      "Lambda                    =    0.0105717   +/-   0.000142237 \n",
      "HV50                      =      6512.88   +/-   2.57885     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =       141.13\n",
      "NDf                       =            7\n",
      "Edm                       =  7.11834e-08\n",
      "NCalls                    =          125\n",
      "Emax                      =     0.944271   +/-   0.00222161  \n",
      "Lambda                    =    0.0109115   +/-   0.000141026 \n",
      "HV50                      =      6504.66   +/-   2.7536      \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =       34.043\n",
      "NDf                       =            7\n",
      "Edm                       =  6.80103e-10\n",
      "NCalls                    =          181\n",
      "Emax                      =     0.946551   +/-   0.00153726  \n",
      "Lambda                    =    0.0116034   +/-   0.00016525  \n",
      "HV50                      =      6435.79   +/-   2.14595     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      43.2095\n",
      "NDf                       =            7\n",
      "Edm                       =  4.55666e-10\n",
      "NCalls                    =          186\n",
      "Emax                      =     0.929849   +/-   0.00174637  \n",
      "Lambda                    =    0.0113224   +/-   0.000158535 \n",
      "HV50                      =      6444.01   +/-   2.2825      \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5880 xmax = 7320 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5880.000000,7320.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5880 xmax = 7320 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5880.000000,7320.000000] is not a valid interval\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c_all\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5880 xmax = 7320 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5880.000000,7320.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5880 xmax = 7320 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5880.000000,7320.000000] is not a valid interval\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ROOT import TCanvas, TGraphErrors, TLatex, TLine, TLegend\n",
    "import math\n",
    "import pandas as pd\n",
    "\n",
    "# Inicializando listas para gráficos e funções\n",
    "TGraphs_list_30CO205SF6 = []\n",
    "func_list_30CO205SF6 = []\n",
    "\n",
    "canvas = TCanvas(\"c\", \"c\", 564, 232, 600, 600)\n",
    "HV_ref = 95\n",
    "\n",
    "\n",
    "k = 1\n",
    "\n",
    "# Contagem dos scans no dicionário\n",
    "num_scans_total = len(scans_30CO205SF6)\n",
    "print(f\"Total de scans no dicionário: {num_scans_total}\")\n",
    "\n",
    "# Listas para armazenar os dados\n",
    "tabela_dados = []\n",
    "\n",
    "for scan, sigmoid, color, marker in scans_30CO205SF6:\n",
    "    if scan not in scans:\n",
    "        print(f\"Scan '{scan}' não encontrado no dicionário de scans. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    print(f\"Processando scan: {scan}\")\n",
    "    \n",
    "    df = HVCurrentDataFrame(scans[scan])\n",
    "    n = len(df['HV_top'])\n",
    "\n",
    "    gr_E = TGraphErrors(n, \n",
    "                        df['HV_top'].values, \n",
    "                        df['efficiency'].values,\n",
    "                        0,\n",
    "                        df['eff_error'].values) # valores do df[].values para converter objeto dataframe em array adequado para o TGraph\n",
    "    \n",
    "    # Ajuste e verificação do resultado\n",
    "    fit_result = gr_E.Fit(sigmoid, \"S\")\n",
    "    if not fit_result.IsValid():\n",
    "        print(f\"Fit falhou para o scan: {scan}\")\n",
    "        continue\n",
    "    \n",
    "    # Listando funções após o ajuste para depuração\n",
    "    functions = gr_E.GetListOfFunctions()\n",
    "    function_names = [f.GetName() for f in functions]\n",
    "    print(f\"Funções no gráfico após ajuste: {function_names}\")\n",
    "\n",
    "    # Tentando encontrar a função ajustada correta\n",
    "    sig = None\n",
    "    for name in function_names:\n",
    "        if name.startswith(\"sigmoid\"):\n",
    "            sig = gr_E.GetFunction(name)\n",
    "            break\n",
    "    \n",
    "    if sig is None:\n",
    "        print(f\"Função 'sigmoid' não encontrada para o scan: {scan}. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    sig.SetLineColor(color)\n",
    "    gr_E.SetMarkerColor(color)\n",
    "    gr_E.SetMarkerStyle(marker)\n",
    "\n",
    "    Emax = sig.GetParameter(0)\n",
    "    Emax_err = sig.GetParError(0)     \n",
    "\n",
    "    Lambda = sig.GetParameter(1)    \n",
    "    Lambda_err = sig.GetParError(1)      \n",
    "\n",
    "    HV50 = sig.GetParameter(2)\n",
    "    HV50_err = sig.GetParError(2)      \n",
    "    \n",
    "    print(f\"Lambda = {Lambda}\")\n",
    "    HV95 = sig.GetX(HV_ref)\n",
    "    HV95_err = math.sqrt(((math.log(19) / Lambda**2) * Lambda_err)**2 + HV50_err**2) # erro HV95\n",
    "    \n",
    "    print(f\"HV95_err = {HV95_err}\")\n",
    "\n",
    "    # Adicionando gráficos e funções às listas\n",
    "    TGraphs_list_30CO205SF6.append(gr_E)\n",
    "    func_list_30CO205SF6.append(sig)\n",
    "    \n",
    "    # Adicionando dados à tabela\n",
    "    for i in range(n):\n",
    "        HV_top = df['HV_top'].values[i]\n",
    "        efficiency = df['efficiency'].values[i]\n",
    "        eff_error = df['eff_error'].values[i]\n",
    "        tabela_dados.append([scan, HV_top, efficiency, eff_error])\n",
    "    \n",
    "    gr_E.Draw(\"AP\")\n",
    "    k += 1\n",
    "\n",
    "    print(f\"Fit concluído para o scan: {scan}\")\n",
    "\n",
    "# Criando o canvas final\n",
    "canvas_all = TCanvas(\"c_all\", \"c_all\", 0, 0, 600, 600)\n",
    "\n",
    "if TGraphs_list_30CO205SF6:\n",
    "    TGraphs_list_30CO205SF6[0].GetHistogram().SetMaximum(1.4)\n",
    "    TGraphs_list_30CO205SF6[0].SetTitle(\"\")\n",
    "    TGraphs_list_30CO205SF6[0].GetXaxis().SetTitle(\"HV_{eff} [V]\")\n",
    "    TGraphs_list_30CO205SF6[0].GetYaxis().SetTitle(\"Muon Efficiency\")\n",
    "    TGraphs_list_30CO205SF6[0].Draw(\"AP\")\n",
    "    \n",
    "    for graph in TGraphs_list_30CO205SF6[1:]:\n",
    "        graph.Draw(\"PSAME\")\n",
    "\n",
    "    Emax_30CO205SF6 = []\n",
    "    Emax_err_30CO205SF6 = []\n",
    "    Lambda_30CO205SF6 = []\n",
    "    Lambda_err_30CO205SF6 = []\n",
    "    HV50_30CO205SF6 = []\n",
    "    HV50_err_30CO205SF6 = []\n",
    "    HV95_30CO205SF6 = []\n",
    "    HV95_err_30CO205SF6 = []\n",
    "\n",
    "    for sig in func_list_30CO205SF6:\n",
    "        Emax_ = 1 - math.sqrt((1 - sig.GetParameter(0)) * (1 - sig.GetParameter(0)))\n",
    "        Emax_30CO205SF6.append(Emax_)\n",
    "        Emax_err_30CO205SF6.append(sig.GetParError(0))     \n",
    "        Lambda_30CO205SF6.append(sig.GetParameter(1))  \n",
    "        Lambda_err_30CO205SF6.append(sig.GetParError(1))\n",
    "        HV50_30CO205SF6.append(sig.GetParameter(2))  \n",
    "        HV50_err_30CO205SF6.append(sig.GetParError(2)) \n",
    "        HV95_30CO205SF6.append(sig.GetX(HV_ref))\n",
    "        HV95_err_30CO205SF6.append((math.log(19) / sig.GetParameter(1)**2) * sig.GetParError(1) + sig.GetParError(2)) # erro HV95\n",
    "\n",
    "    WP_30CO205SF6 = []    \n",
    "\n",
    "    for Emax_, Lambda_, HV50_ in zip(Emax_30CO205SF6, Lambda_30CO205SF6, HV50_30CO205SF6):\n",
    "        WP_30CO205SF6.append((HV50_ - math.log(1 / 0.95 - 1) / Lambda_ + 150.))\n",
    "\n",
    "    txt_30CO205SF6_10 = Features_30CO205SF6['noiseGammaRate'][2] / (Features_30CO205SF6['gamma_CS'][2] * 1000)\n",
    "    txt_30CO205SF6_3_3 = Features_30CO205SF6['noiseGammaRate'][5] / (Features_30CO205SF6['gamma_CS'][5] * 1000)\n",
    "    txt_30CO205SF6_2_2 = Features_30CO205SF6['noiseGammaRate'][7] / (Features_30CO205SF6['gamma_CS'][7] * 1000)\n",
    "    txt_30CO205SF6_1 = Features_30CO205SF6['noiseGammaRate'][6] / (Features_30CO205SF6['gamma_CS'][6] * 1000)\n",
    "\n",
    "    eff_1_30CO205SF6 = 'plateau = {a:.0%}, WP = {b:.2f} kV, no hit bkg gamma rate, Eff(WP) = {c:.0%}'.format(a=Emax_30CO205SF6[0], b=WP_30CO205SF6[0] / 1000., c=(func_list_30CO205SF6[0].Eval(WP_30CO205SF6[0])))\n",
    "    eff_2_30CO205SF6 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm2, Eff(WP) = {c:.0%}'.format(a=Emax_30CO205SF6[1], b=WP_30CO205SF6[1] / 1000., c=(func_list_30CO205SF6[1].Eval(WP_30CO205SF6[1])), d=txt_30CO205SF6_10)\n",
    "    eff_3_30CO205SF6 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm2, Eff(WP) = {c:.0%}'.format(a=Emax_30CO205SF6[2], b=WP_30CO205SF6[2] / 1000., c=(func_list_30CO205SF6[2].Eval(WP_30CO205SF6[2])), d=txt_30CO205SF6_3_3)\n",
    "    eff_4_30CO205SF6 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm2, Eff(WP) = {c:.0%}'.format(a=Emax_30CO205SF6[3], b=WP_30CO205SF6[3] / 1000., c=(func_list_30CO205SF6[3].Eval(WP_30CO205SF6[3])), d=txt_30CO205SF6_2_2)\n",
    "    eff_5_30CO205SF6 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm2, Eff(WP) = {c:.0%}'.format(a=Emax_30CO205SF6[4], b=WP_30CO205SF6[4] / 1000., c=(func_list_30CO205SF6[4].Eval(WP_30CO205SF6[4])), d=txt_30CO205SF6_1)\n",
    "\n",
    "    cms_tex = TLatex()\n",
    "    cms_tex.SetNDC()\n",
    "    cms_tex.SetTextFont(61)\n",
    "    cms_tex.SetTextSize(0.04)\n",
    "    cms_tex.SetLineWidth(2)\n",
    "    cms_tex.DrawLatex(0.10, 0.905, \"CMS MUON\")\n",
    "\n",
    "    cms_tex_1 = TLatex()\n",
    "    cms_tex_1.SetNDC()\n",
    "    cms_tex_1.SetTextFont(61)\n",
    "    cms_tex_1.SetTextSize(0.04)\n",
    "    cms_tex_1.SetLineWidth(2)\n",
    "    cms_tex_1.DrawLatex(0.79, 0.905, \"GIF++\")\n",
    "\n",
    "    cms_tex_2 = TLatex()\n",
    "    cms_tex_2.SetNDC()\n",
    "    cms_tex_2.SetTextFont(52)\n",
    "    cms_tex_2.SetTextSize(0.04)\n",
    "    cms_tex_2.SetLineWidth(2)\n",
    "    cms_tex_2.DrawLatex(0.32, 0.905, \"Preliminary\")\n",
    "\n",
    "    legend = TLegend(0.1, 0.68, 0.3, 0.89)\n",
    "    legend.SetTextFont(42)\n",
    "    legend.SetBorderSize(0) \n",
    "    legend.SetFillStyle(4000)\n",
    "    legend.SetFillColor(0)  \n",
    "    legend.SetTextSize(0.02376) \n",
    "    legend.AddEntry(TGraphs_list_30CO205SF6[0], eff_1_30CO205SF6, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_30CO205SF6[1], eff_2_30CO205SF6, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_30CO205SF6[2], eff_3_30CO205SF6, \"P\")\n",
    "    if len(TGraphs_list_30CO205SF6) > 3:\n",
    "        legend.AddEntry(TGraphs_list_30CO205SF6[3], eff_4_30CO205SF6, \"P\")\n",
    "    if len(TGraphs_list_30CO205SF6) > 4:\n",
    "        legend.AddEntry(TGraphs_list_30CO205SF6[4], eff_5_30CO205SF6, \"P\")\n",
    "    legend.Draw()\n",
    "    canvas_all.Draw()\n",
    "\n",
    "    ltx_data = TLatex()\n",
    "    ltx_data.SetTextFont(42)\n",
    "    ltx_data.SetTextSize(0.025)\n",
    "    ltx_data.SetTextColor(1)\n",
    "    ltx_data.DrawLatex(5900, 0.88, \"30% CO2 + 1% SF6\")\n",
    "    ltx_data.DrawLatex(5900, 0.81, \"Test Beam in April+July+August 2023\")\n",
    "    ltx_data.DrawLatex(5900, 0.74, \"1.4 mm double gap RPC\")\n",
    "    ltx_data.DrawLatex(5900, 0.67, \"Threshold = 60 [fC]\")\n",
    "\n",
    "    line = TLine(5850, 1., 7750, 1.)\n",
    "    line.SetLineColor(1)\n",
    "    line.SetLineStyle(9)\n",
    "    line.SetLineWidth(2)\n",
    "    line.Draw()\n",
    "\n",
    "    canvas_all.cd()\n",
    "    canvas_all.Draw()\n",
    "else:\n",
    "    print(\"Nenhum gráfico válido para desenhar.\")\n",
    "\n",
    "# Salvando a tabela em um arquivo CSV\n",
    "tabela_df = pd.DataFrame(tabela_dados, columns=[\"Scan\", \"HV_top\", \"Efficiency\", \"Eff_Error\"])\n",
    "tabela_df.to_csv(\"tabela_dados_30CO205SF6.csv\", index=False)\n",
    "print(\"Tabela salva em 'tabela_dados_30CO205SF6.csv'.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de scans no dicionário: 5\n",
      "Processando scan: 30CO205SF6_OFF\n",
      "Funções no gráfico após ajuste: ['sigmoid16']\n",
      "Lambda = 0.00986873201464487\n",
      "HV95_err = 3.2132173284858934\n",
      "Fit concluído para o scan: 30CO205SF6_OFF\n",
      "Processando scan: 30CO205SF6_10\n",
      "Funções no gráfico após ajuste: ['sigmoid17']\n",
      "Lambda = 0.010571606102487646\n",
      "HV95_err = 4.548943535135861\n",
      "Fit concluído para o scan: 30CO205SF6_10\n",
      "Processando scan: 30CO205SF6_3.3\n",
      "Funções no gráfico após ajuste: ['sigmoid18']\n",
      "Lambda = 0.010911533061745821\n",
      "HV95_err = 4.470511434629578\n",
      "Fit concluído para o scan: 30CO205SF6_3.3\n",
      "Processando scan: 30CO205SF6_2.2\n",
      "Funções no gráfico após ajuste: ['sigmoid19']\n",
      "Lambda = 0.01160329673155834\n",
      "HV95_err = 4.202933515231371\n",
      "Fit concluído para o scan: 30CO205SF6_2.2\n",
      "Processando scan: 30CO205SF6_1\n",
      "Funções no gráfico após ajuste: ['sigmoid20']\n",
      "Lambda = 0.01132238523031075\n",
      "HV95_err = 4.297499312826793\n",
      "Fit concluído para o scan: 30CO205SF6_1\n",
      "Tabela salva em 'tabela_dados_30CO205SF6.csv'.\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      137.155\n",
      "NDf                       =            7\n",
      "Edm                       =  7.81101e-09\n",
      "NCalls                    =           37\n",
      "Emax                      =     0.990414   +/-   0.000794203 \n",
      "Lambda                    =   0.00986873   +/-   8.89581e-05 \n",
      "HV50                      =      6513.27   +/-   1.75828     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      72.8542\n",
      "NDf                       =            7\n",
      "Edm                       =  1.45765e-07\n",
      "NCalls                    =           37\n",
      "Emax                      =      0.97063   +/-   0.00173901  \n",
      "Lambda                    =    0.0105716   +/-   0.000142233 \n",
      "HV50                      =      6512.88   +/-   2.57885     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =       141.13\n",
      "NDf                       =            7\n",
      "Edm                       =  1.71742e-08\n",
      "NCalls                    =           39\n",
      "Emax                      =     0.944271   +/-   0.00222447  \n",
      "Lambda                    =    0.0109115   +/-   0.000141796 \n",
      "HV50                      =      6504.66   +/-   2.77287     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =       34.043\n",
      "NDf                       =            7\n",
      "Edm                       =  8.33368e-06\n",
      "NCalls                    =           93\n",
      "Emax                      =     0.946553   +/-   0.00153726  \n",
      "Lambda                    =    0.0116033   +/-   0.000165244 \n",
      "HV50                      =      6585.78   +/-   2.14594     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      43.2095\n",
      "NDf                       =            7\n",
      "Edm                       =  1.12163e-07\n",
      "NCalls                    =          112\n",
      "Emax                      =     0.929849   +/-   0.00174637  \n",
      "Lambda                    =    0.0113224   +/-   0.000158535 \n",
      "HV50                      =      6624.01   +/-   2.2825      \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6030 xmax = 7470 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6030.000000,7470.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6060 xmax = 7500 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6060.000000,7500.000000] is not a valid interval\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c_all\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6030 xmax = 7470 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6030.000000,7470.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6060 xmax = 7500 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6060.000000,7500.000000] is not a valid interval\n",
      "Info in <TCanvas::Print>: pdf file 30CO205SF6_eff.pdf has been created\n",
      "Info in <TCanvas::Print>: png file 30CO205SF6_eff.png has been created\n",
      "Info in <TCanvas::SaveAs>: ROOT file 30CO205SF6_eff.root has been created\n"
     ]
    }
   ],
   "source": [
    "from ROOT import TCanvas, TGraphErrors, TLatex, TLine, TLegend\n",
    "import math\n",
    "import pandas as pd\n",
    "\n",
    "# Nome do arquivo CSV atualizado\n",
    "arquivo_csv = 'tabela_dados_30CO205SF6.csv'\n",
    "\n",
    "# Ler o arquivo CSV\n",
    "df_csv = pd.read_csv(arquivo_csv)\n",
    "\n",
    "# Ajustar os valores de HV_top para os scans especificados\n",
    "df_csv.loc[df_csv['Scan'] == '30CO205SF6_1', 'HV_top'] += 180\n",
    "df_csv.loc[df_csv['Scan'] == '30CO205SF6_2.2', 'HV_top'] += 150\n",
    "\n",
    "# Inicializando listas para gráficos e funções\n",
    "TGraphs_list_30CO205SF6 = []\n",
    "func_list_30CO205SF6 = []\n",
    "\n",
    "# Configurações do canvas\n",
    "canvas = TCanvas(\"c\", \"c\", 564, 232, 600, 600)\n",
    "canvas.SetTopMargin(0.1)\n",
    "canvas.SetBottomMargin(0.12)\n",
    "canvas.SetLeftMargin(0.14)\n",
    "canvas.SetRightMargin(0.05)\n",
    "\n",
    "HV_ref = 95\n",
    "\n",
    "\n",
    "k = 1\n",
    "\n",
    "# Contagem dos scans no dicionário\n",
    "num_scans_total = len(scans_30CO205SF6)\n",
    "print(f\"Total de scans no dicionário: {num_scans_total}\")\n",
    "\n",
    "for scan, sigmoid, color, marker in scans_30CO205SF6:\n",
    "    if scan not in scans:\n",
    "        print(f\"Scan '{scan}' não encontrado no dicionário de scans. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    print(f\"Processando scan: {scan}\")\n",
    "\n",
    "    # Filtrar dados do DataFrame ajustado\n",
    "    df = df_csv[df_csv['Scan'] == scan]\n",
    "    n = len(df['HV_top'])\n",
    "\n",
    "    gr_E = TGraphErrors(n, \n",
    "                        df['HV_top'].values, \n",
    "                        df['Efficiency'].values,\n",
    "                        0,\n",
    "                        df['Eff_Error'].values) # valores do df[].values para converter objeto dataframe em array adequado para o TGraph\n",
    "    \n",
    "    # Ajuste e verificação do resultado\n",
    "    fit_result = gr_E.Fit(sigmoid, \"S\")\n",
    "    if not fit_result.IsValid():\n",
    "        print(f\"Fit falhou para o scan: {scan}\")\n",
    "        continue\n",
    "    \n",
    "    # Listando funções após o ajuste para depuração\n",
    "    functions = gr_E.GetListOfFunctions()\n",
    "    function_names = [f.GetName() for f in functions]\n",
    "    print(f\"Funções no gráfico após ajuste: {function_names}\")\n",
    "\n",
    "    # Tentando encontrar a função ajustada correta\n",
    "    sig = None\n",
    "    for name in function_names:\n",
    "        if name.startswith(\"sigmoid\"):\n",
    "            sig = gr_E.GetFunction(name)\n",
    "            break\n",
    "    \n",
    "    if sig is None:\n",
    "        print(f\"Função 'sigmoid' não encontrada para o scan: {scan}. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    sig.SetLineColor(color)\n",
    "    gr_E.SetMarkerColor(color)\n",
    "    gr_E.SetMarkerStyle(marker)\n",
    "    gr_E.SetLineWidth(2)\n",
    "    gr_E.SetMarkerSize(1.2)\n",
    "\n",
    "    Emax = sig.GetParameter(0)\n",
    "    Emax_err = sig.GetParError(0)     \n",
    "\n",
    "    Lambda = sig.GetParameter(1)    \n",
    "    Lambda_err = sig.GetParError(1)      \n",
    "\n",
    "    HV50 = sig.GetParameter(2)\n",
    "    HV50_err = sig.GetParError(2)      \n",
    "    \n",
    "    print(f\"Lambda = {Lambda}\")\n",
    "    HV95 = sig.GetX(HV_ref)\n",
    "    HV95_err = math.sqrt(((math.log(19) / Lambda**2) * Lambda_err)**2 + HV50_err**2) # erro HV95\n",
    "    \n",
    "    print(f\"HV95_err = {HV95_err}\")\n",
    "\n",
    "    # Adicionando gráficos e funções às listas\n",
    "    TGraphs_list_30CO205SF6.append(gr_E)\n",
    "    func_list_30CO205SF6.append(sig)\n",
    "    gr_E.Draw(\"AP\")\n",
    "    k += 1\n",
    "\n",
    "    print(f\"Fit concluído para o scan: {scan}\")\n",
    "\n",
    "# Criando o canvas final\n",
    "canvas_all = TCanvas(\"c_all\", \"c_all\", 0, 0, 600, 600)\n",
    "canvas_all.SetTopMargin(0.1)\n",
    "canvas_all.SetBottomMargin(0.12)\n",
    "canvas_all.SetLeftMargin(0.14)\n",
    "canvas_all.SetRightMargin(0.05)\n",
    "\n",
    "if TGraphs_list_30CO205SF6:\n",
    "    TGraphs_list_30CO205SF6[0].GetHistogram().SetMaximum(1.4)\n",
    "    TGraphs_list_30CO205SF6[0].GetHistogram().SetMinimum(0)\n",
    "    TGraphs_list_30CO205SF6[0].SetTitle(\"\")\n",
    "    TGraphs_list_30CO205SF6[0].GetXaxis().SetTitle(\"HV_{eff} [V]\")\n",
    "    TGraphs_list_30CO205SF6[0].GetYaxis().SetTitle(\"Efficiency\")\n",
    "    TGraphs_list_30CO205SF6[0].GetXaxis().SetTitleSize(0.05)\n",
    "    TGraphs_list_30CO205SF6[0].GetYaxis().SetTitleSize(0.05)\n",
    "    TGraphs_list_30CO205SF6[0].GetXaxis().SetLabelSize(0.04)\n",
    "    TGraphs_list_30CO205SF6[0].GetYaxis().SetLabelSize(0.04)\n",
    "    TGraphs_list_30CO205SF6[0].Draw(\"AP\")\n",
    "    \n",
    "    for graph in TGraphs_list_30CO205SF6[1:]:\n",
    "        graph.Draw(\"PSAME\")\n",
    "\n",
    "    Emax_30CO205SF6 = []\n",
    "    Emax_err_30CO205SF6 = []\n",
    "    Lambda_30CO205SF6 = []\n",
    "    Lambda_err_30CO205SF6 = []\n",
    "    HV50_30CO205SF6 = []\n",
    "    HV50_err_30CO205SF6 = []\n",
    "    HV95_30CO205SF6 = []\n",
    "    HV95_err_30CO205SF6 = []\n",
    "\n",
    "    for sig in func_list_30CO205SF6:\n",
    "        Emax_ = 1 - math.sqrt((1 - sig.GetParameter(0)) * (1 - sig.GetParameter(0)))\n",
    "        Emax_30CO205SF6.append(Emax_)\n",
    "        Emax_err_30CO205SF6.append(sig.GetParError(0))     \n",
    "        Lambda_30CO205SF6.append(sig.GetParameter(1))  \n",
    "        Lambda_err_30CO205SF6.append(sig.GetParError(1))\n",
    "        HV50_30CO205SF6.append(sig.GetParameter(2))  \n",
    "        HV50_err_30CO205SF6.append(sig.GetParError(2)) \n",
    "        HV95_30CO205SF6.append(sig.GetX(HV_ref))\n",
    "        HV95_err_30CO205SF6.append((math.log(19) / sig.GetParameter(1)**2) * sig.GetParError(1) + sig.GetParError(2)) # erro HV95\n",
    "\n",
    "    WP_30CO205SF6 = []    \n",
    "\n",
    "    for Emax_, Lambda_, HV50_ in zip(Emax_30CO205SF6, Lambda_30CO205SF6, HV50_30CO205SF6):\n",
    "        WP_30CO205SF6.append((HV50_ - math.log(1 / 0.95 - 1) / Lambda_ + 150.))\n",
    "\n",
    "    txt_30CO205SF6_10 = Features_30CO205SF6['noiseGammaRate'][2] / (Features_30CO205SF6['gamma_CS'][2] * 1000)\n",
    "    txt_30CO205SF6_3_3 = Features_30CO205SF6['noiseGammaRate'][5] / (Features_30CO205SF6['gamma_CS'][5] * 1000)\n",
    "    txt_30CO205SF6_2_2 = Features_30CO205SF6['noiseGammaRate'][7] / (Features_30CO205SF6['gamma_CS'][7] * 1000)\n",
    "    txt_30CO205SF6_1 = Features_30CO205SF6['noiseGammaRate'][6] / (Features_30CO205SF6['gamma_CS'][6] * 1000)\n",
    "\n",
    "    eff_1_30CO205SF6 = 'plateau = {a:.0%}, WP = {b:.2f} kV, no hit bkg gamma rate, Eff(WP) = {c:.0%}'.format(a=Emax_30CO205SF6[0], b=WP_30CO205SF6[0] / 1000., c=(func_list_30CO205SF6[0].Eval(WP_30CO205SF6[0])))\n",
    "    eff_2_30CO205SF6 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_30CO205SF6[1], b=WP_30CO205SF6[1] / 1000., c=(func_list_30CO205SF6[1].Eval(WP_30CO205SF6[1])), d=txt_30CO205SF6_10)\n",
    "    eff_3_30CO205SF6 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_30CO205SF6[2], b=WP_30CO205SF6[2] / 1000., c=(func_list_30CO205SF6[2].Eval(WP_30CO205SF6[2])), d=txt_30CO205SF6_3_3)\n",
    "    eff_4_30CO205SF6 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_30CO205SF6[3], b=WP_30CO205SF6[3] / 1000., c=(func_list_30CO205SF6[3].Eval(WP_30CO205SF6[3])), d=txt_30CO205SF6_2_2)\n",
    "    eff_5_30CO205SF6 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_30CO205SF6[4], b=WP_30CO205SF6[4] / 1000., c=(func_list_30CO205SF6[4].Eval(WP_30CO205SF6[4])), d=txt_30CO205SF6_1)\n",
    "\n",
    "    cms_tex = TLatex()\n",
    "    cms_tex.SetNDC()\n",
    "    cms_tex.SetTextFont(61)\n",
    "    cms_tex.SetTextSize(0.04)\n",
    "    cms_tex.SetLineWidth(2)\n",
    "    cms_tex.DrawLatex(0.14, 0.905, \"CMS\")\n",
    "\n",
    "    cms_tex_1 = TLatex()\n",
    "    cms_tex_1.SetNDC()\n",
    "    cms_tex_1.SetTextFont(61)\n",
    "    cms_tex_1.SetTextSize(0.04)\n",
    "    cms_tex_1.SetLineWidth(2)\n",
    "    cms_tex_1.DrawLatex(0.85, 0.905, \"GIF++\")\n",
    "\n",
    "    cms_tex_2 = TLatex()\n",
    "    cms_tex_2.SetNDC()\n",
    "    cms_tex_2.SetTextFont(52)\n",
    "    cms_tex_2.SetTextSize(0.04)\n",
    "    cms_tex_2.SetLineWidth(2)\n",
    "    cms_tex_2.DrawLatex(0.23, 0.905, \"Preliminary\")\n",
    "\n",
    "    legend = TLegend(0.16, 0.68, 0.3, 0.89)\n",
    "    legend.SetTextFont(42)\n",
    "    legend.SetBorderSize(0) \n",
    "    legend.SetFillStyle(4000)\n",
    "    legend.SetFillColor(0)  \n",
    "    legend.SetTextSize(0.02376) \n",
    "    legend.AddEntry(TGraphs_list_30CO205SF6[0], eff_1_30CO205SF6, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_30CO205SF6[1], eff_2_30CO205SF6, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_30CO205SF6[2], eff_3_30CO205SF6, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_30CO205SF6[3], eff_4_30CO205SF6, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_30CO205SF6[4], eff_5_30CO205SF6, \"P\")\n",
    "    legend.Draw()\n",
    "    canvas_all.Draw()\n",
    "\n",
    "    ltx_data = TLatex()\n",
    "    ltx_data.SetTextFont(42)\n",
    "    ltx_data.SetTextSize(0.025)\n",
    "    ltx_data.SetTextColor(1)\n",
    "    ltx_data.DrawLatex(5900, 0.88, \"30% CO_{2} + 0.5% SF_{6}\")\n",
    "    ltx_data.DrawLatex(5900, 0.81, \"Test Beam in July+August 2023\")\n",
    "    ltx_data.DrawLatex(5900, 0.74, \"Before irradiation\")\n",
    "    ltx_data.DrawLatex(5900, 0.67, \"1.4 mm double gap RPC\")\n",
    "    ltx_data.DrawLatex(5900, 0.60, \"Threshold = 60 [fC]\")\n",
    "\n",
    "    line = TLine(5850, 1., 7450, 1.)\n",
    "    line.SetLineColor(1)\n",
    "    line.SetLineStyle(9)\n",
    "    line.SetLineWidth(2)\n",
    "    line.Draw()\n",
    "\n",
    "    canvas_all.Update()\n",
    "    canvas_all.Draw()\n",
    "    canvas_all.SaveAs(\"30CO205SF6_eff.pdf\")\n",
    "    canvas_all.SaveAs(\"30CO205SF6_eff.png\")\n",
    "    canvas_all.SaveAs(\"30CO205SF6_eff.root\")\n",
    "    #canvas_all.SaveAs(\"tabela_dados_ajustados_30CO205SF6.png\")\n",
    "else:\n",
    "    print(\"Nenhum gráfico válido para desenhar.\")\n",
    "\n",
    "# Salvando a tabela em um arquivo CSV\n",
    "tabela_df = pd.DataFrame(tabela_dados, columns=[\"Scan\", \"HV_top\", \"Efficiency\", \"Eff_Error\"])\n",
    "tabela_df.to_csv(\"tabela_dados_30CO205SF6.csv\", index=False)\n",
    "print(\"Tabela salva em 'tabela_dados_30CO205SF6.csv'.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scanID:  5845\n",
      "scanID:  5846\n",
      "scanID:  5844\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scanID:  5843\n",
      "scanID:  5842\n",
      "scanID:  5841\n",
      "scanID:  5845\n",
      "scanID:  5846\n",
      "scanID:  5844\n",
      "scanID:  5843\n",
      "scanID:  5842\n",
      "scanID:  5841\n",
      "FeaturesDataFrame(Scans_40CO2) =     current_top  current_bot  HV_top  HV_bot  muon_stream  gamma_stream  \\\n",
      "0     0.908333     0.675833  7000.0  7000.0     0.023776      0.000225   \n",
      "1     9.063140     7.805416  7000.0  7000.0     0.024847      0.011061   \n",
      "2    17.024096    15.139259  7000.0  7000.0     0.025918      0.020561   \n",
      "3    24.642144    21.794923  7000.0  7000.0     0.023045      0.028155   \n",
      "4    32.087562    28.356041  7000.0  7000.0     0.023824      0.034470   \n",
      "5    47.500257    42.183781  7000.0  7000.0     0.023215      0.048381   \n",
      "\n",
      "    muon_CM  gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0  1.048760  1.361280  2.379098  1.416013     0.162951      0.035061   \n",
      "1  1.070776  1.867909  2.308344  1.979245     0.156086      0.150436   \n",
      "2  1.095825  2.926823  2.257322  1.950481     0.149849      0.231306   \n",
      "3  1.115299  4.124553  2.229258  1.921858     0.151615      0.321327   \n",
      "4  1.139349  5.370562  2.197859  1.899114     0.147606      0.411500   \n",
      "5  1.190249  8.171411  2.105764  1.839577     0.138221      0.588730   \n",
      "\n",
      "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0     0.319941      0.035555   98.192179   0.001906        6.298748   \n",
      "1     0.293676      0.147522   97.480200   0.001121      272.042714   \n",
      "2     0.271545      0.142856   97.166185   0.001172      527.098160   \n",
      "3     0.266781      0.138903   96.391291   0.001885      771.747487   \n",
      "4     0.252081      0.135157   95.988859   0.001404     1007.144266   \n",
      "5     0.219095      0.123630   95.219617   0.001525     1493.011466   \n",
      "\n",
      "   noiseGammaRate_err    current  \n",
      "0            2.203070   1.584167  \n",
      "1           16.487786  16.868556  \n",
      "2           22.950889  32.163355  \n",
      "3           27.772401  46.437067  \n",
      "4           31.726279  60.443602  \n",
      "5           38.628021  89.684038  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>current_top</th>\n",
       "      <th>current_bot</th>\n",
       "      <th>HV_top</th>\n",
       "      <th>HV_bot</th>\n",
       "      <th>muon_stream</th>\n",
       "      <th>gamma_stream</th>\n",
       "      <th>muon_CM</th>\n",
       "      <th>gamma_CM</th>\n",
       "      <th>muon_CS</th>\n",
       "      <th>gamma_CS</th>\n",
       "      <th>muon_CM_err</th>\n",
       "      <th>gamma_CM_err</th>\n",
       "      <th>muon_CS_err</th>\n",
       "      <th>gamma_CS_err</th>\n",
       "      <th>efficiency</th>\n",
       "      <th>eff_error</th>\n",
       "      <th>noiseGammaRate</th>\n",
       "      <th>noiseGammaRate_err</th>\n",
       "      <th>current</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.908333</td>\n",
       "      <td>0.675833</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>0.023776</td>\n",
       "      <td>0.000225</td>\n",
       "      <td>1.048760</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.379098</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.162951</td>\n",
       "      <td>0.035061</td>\n",
       "      <td>0.319941</td>\n",
       "      <td>0.035555</td>\n",
       "      <td>98.192179</td>\n",
       "      <td>0.001906</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.203070</td>\n",
       "      <td>1.584167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.063140</td>\n",
       "      <td>7.805416</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>0.024847</td>\n",
       "      <td>0.011061</td>\n",
       "      <td>1.070776</td>\n",
       "      <td>1.867909</td>\n",
       "      <td>2.308344</td>\n",
       "      <td>1.979245</td>\n",
       "      <td>0.156086</td>\n",
       "      <td>0.150436</td>\n",
       "      <td>0.293676</td>\n",
       "      <td>0.147522</td>\n",
       "      <td>97.480200</td>\n",
       "      <td>0.001121</td>\n",
       "      <td>272.042714</td>\n",
       "      <td>16.487786</td>\n",
       "      <td>16.868556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.024096</td>\n",
       "      <td>15.139259</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>0.025918</td>\n",
       "      <td>0.020561</td>\n",
       "      <td>1.095825</td>\n",
       "      <td>2.926823</td>\n",
       "      <td>2.257322</td>\n",
       "      <td>1.950481</td>\n",
       "      <td>0.149849</td>\n",
       "      <td>0.231306</td>\n",
       "      <td>0.271545</td>\n",
       "      <td>0.142856</td>\n",
       "      <td>97.166185</td>\n",
       "      <td>0.001172</td>\n",
       "      <td>527.098160</td>\n",
       "      <td>22.950889</td>\n",
       "      <td>32.163355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.642144</td>\n",
       "      <td>21.794923</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>0.023045</td>\n",
       "      <td>0.028155</td>\n",
       "      <td>1.115299</td>\n",
       "      <td>4.124553</td>\n",
       "      <td>2.229258</td>\n",
       "      <td>1.921858</td>\n",
       "      <td>0.151615</td>\n",
       "      <td>0.321327</td>\n",
       "      <td>0.266781</td>\n",
       "      <td>0.138903</td>\n",
       "      <td>96.391291</td>\n",
       "      <td>0.001885</td>\n",
       "      <td>771.747487</td>\n",
       "      <td>27.772401</td>\n",
       "      <td>46.437067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.087562</td>\n",
       "      <td>28.356041</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>0.023824</td>\n",
       "      <td>0.034470</td>\n",
       "      <td>1.139349</td>\n",
       "      <td>5.370562</td>\n",
       "      <td>2.197859</td>\n",
       "      <td>1.899114</td>\n",
       "      <td>0.147606</td>\n",
       "      <td>0.411500</td>\n",
       "      <td>0.252081</td>\n",
       "      <td>0.135157</td>\n",
       "      <td>95.988859</td>\n",
       "      <td>0.001404</td>\n",
       "      <td>1007.144266</td>\n",
       "      <td>31.726279</td>\n",
       "      <td>60.443602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>47.500257</td>\n",
       "      <td>42.183781</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>0.023215</td>\n",
       "      <td>0.048381</td>\n",
       "      <td>1.190249</td>\n",
       "      <td>8.171411</td>\n",
       "      <td>2.105764</td>\n",
       "      <td>1.839577</td>\n",
       "      <td>0.138221</td>\n",
       "      <td>0.588730</td>\n",
       "      <td>0.219095</td>\n",
       "      <td>0.123630</td>\n",
       "      <td>95.219617</td>\n",
       "      <td>0.001525</td>\n",
       "      <td>1493.011466</td>\n",
       "      <td>38.628021</td>\n",
       "      <td>89.684038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   current_top  current_bot  HV_top  HV_bot  muon_stream  gamma_stream  \\\n",
       "0     0.908333     0.675833  7000.0  7000.0     0.023776      0.000225   \n",
       "1     9.063140     7.805416  7000.0  7000.0     0.024847      0.011061   \n",
       "2    17.024096    15.139259  7000.0  7000.0     0.025918      0.020561   \n",
       "3    24.642144    21.794923  7000.0  7000.0     0.023045      0.028155   \n",
       "4    32.087562    28.356041  7000.0  7000.0     0.023824      0.034470   \n",
       "5    47.500257    42.183781  7000.0  7000.0     0.023215      0.048381   \n",
       "\n",
       "    muon_CM  gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
       "0  1.048760  1.000000  2.379098  1.000000     0.162951      0.035061   \n",
       "1  1.070776  1.867909  2.308344  1.979245     0.156086      0.150436   \n",
       "2  1.095825  2.926823  2.257322  1.950481     0.149849      0.231306   \n",
       "3  1.115299  4.124553  2.229258  1.921858     0.151615      0.321327   \n",
       "4  1.139349  5.370562  2.197859  1.899114     0.147606      0.411500   \n",
       "5  1.190249  8.171411  2.105764  1.839577     0.138221      0.588730   \n",
       "\n",
       "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
       "0     0.319941      0.035555   98.192179   0.001906        1.000000   \n",
       "1     0.293676      0.147522   97.480200   0.001121      272.042714   \n",
       "2     0.271545      0.142856   97.166185   0.001172      527.098160   \n",
       "3     0.266781      0.138903   96.391291   0.001885      771.747487   \n",
       "4     0.252081      0.135157   95.988859   0.001404     1007.144266   \n",
       "5     0.219095      0.123630   95.219617   0.001525     1493.011466   \n",
       "\n",
       "   noiseGammaRate_err    current  \n",
       "0            2.203070   1.584167  \n",
       "1           16.487786  16.868556  \n",
       "2           22.950889  32.163355  \n",
       "3           27.772401  46.437067  \n",
       "4           31.726279  60.443602  \n",
       "5           38.628021  89.684038  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Features_40CO2 = FeaturesDataFrame(Scans_40CO2)\n",
    "Features_40CO2['gamma_CM'][0] = 1\n",
    "Features_40CO2['gamma_CS'][0] = 1\n",
    "Features_40CO2['noiseGammaRate'][0] = 1\n",
    "print(\"FeaturesDataFrame(Scans_40CO2) = \", FeaturesDataFrame(Scans_40CO2))\n",
    "Features_40CO2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de scans no dicionário: 5\n",
      "Processando scan: 40CO2_OFF\n",
      "Funções no gráfico após ajuste: ['sigmoid11']\n",
      "Lambda = 0.009825010272089214\n",
      "HV95_err = 3.2182921683200805\n",
      "Fit concluído para o scan: 40CO2_OFF\n",
      "Processando scan: 40CO2_10\n",
      "Funções no gráfico após ajuste: ['sigmoid12']\n",
      "Lambda = 0.010292391209836858\n",
      "HV95_err = 3.27227030755666\n",
      "Fit concluído para o scan: 40CO2_10\n",
      "Processando scan: 40CO2_6.9\n",
      "Funções no gráfico após ajuste: ['sigmoid14']\n",
      "Lambda = 0.010184036636144216\n",
      "HV95_err = 3.39404400839822\n",
      "Fit concluído para o scan: 40CO2_6.9\n",
      "Processando scan: 40CO2_4.6\n",
      "Funções no gráfico após ajuste: ['sigmoid20']\n",
      "Lambda = 0.010473460474165534\n",
      "HV95_err = 3.3110780674446803\n",
      "Fit concluído para o scan: 40CO2_4.6\n",
      "Processando scan: 40CO2_3.3\n",
      "Funções no gráfico após ajuste: ['sigmoid13']\n",
      "Lambda = 0.010931459023892703\n",
      "HV95_err = 3.194750194295551\n",
      "Fit concluído para o scan: 40CO2_3.3\n",
      "Tabela salva em 'tabela_dados_40CO2.csv'.\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      152.327\n",
      "NDf                       =           14\n",
      "Edm                       =  1.77882e-05\n",
      "NCalls                    =          223\n",
      "Emax                      =     0.990291   +/-   0.000616638 \n",
      "Lambda                    =   0.00982501   +/-   8.94526e-05 \n",
      "HV50                      =      6541.83   +/-   1.70661     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      222.247\n",
      "NDf                       =           13\n",
      "Edm                       =  1.10592e-07\n",
      "NCalls                    =          155\n",
      "Emax                      =     0.968357   +/-   0.00121398  \n",
      "Lambda                    =    0.0102924   +/-   9.63979e-05 \n",
      "HV50                      =      6543.36   +/-   1.87845     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      186.748\n",
      "NDf                       =           14\n",
      "Edm                       =  1.22852e-06\n",
      "NCalls                    =          213\n",
      "Emax                      =     0.963257   +/-   0.00131259  \n",
      "Lambda                    =     0.010184   +/-   9.88911e-05 \n",
      "HV50                      =      6544.82   +/-   1.90722     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      272.836\n",
      "NDf                       =           14\n",
      "Edm                       =   1.5342e-08\n",
      "NCalls                    =          262\n",
      "Emax                      =     0.952226   +/-   0.0014479   \n",
      "Lambda                    =    0.0104735   +/-   9.90478e-05 \n",
      "HV50                      =      6543.72   +/-   1.97348     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      232.237\n",
      "NDf                       =           12\n",
      "Edm                       =  5.73813e-07\n",
      "NCalls                    =          134\n",
      "Emax                      =     0.931396   +/-   0.00199326  \n",
      "Lambda                    =    0.0109315   +/-   9.94202e-05 \n",
      "HV50                      =       6530.4   +/-   2.05065     \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5640 xmax = 7560 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5640.000000,7560.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5640 xmax = 7560 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5640.000000,7560.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5640 xmax = 7560 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5640.000000,7560.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5660 xmax = 7340 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5660.000000,7340.000000] is not a valid interval\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c_all\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5640 xmax = 7560 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5640.000000,7560.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5640 xmax = 7560 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5640.000000,7560.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5640 xmax = 7560 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5640.000000,7560.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5660 xmax = 7340 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5660.000000,7340.000000] is not a valid interval\n"
     ]
    },
    {
     "data": {
      "image/png": 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4+X6SHLqtAnEGsxMygA+ez2evKrBh8dqhrmuaqEpw5aL8LW85K5lUX0XmuqgHq29ZkcnpaGxJJAb5cLp2ymulPPNVfJBnK1ZVVV7m8s6g6zrS7LquvY8Wp+i9OzwtT1kVekImUYldaKQ98q1xA4SY95J2XUdrIJtLNimPLPbkC6oL6UNK5WWknWACIZfQ2+wZv2QbphkwJayqW2VDH/8wolPaiLapWoyodi83oq9CXkV42+tGZc5Jy147l63T0cW/VatXqOHU+vrYTKCZ60bcxkmO3Ct71rllrhkm3Kg12G2mQrqlLW+9eja4hdNt9rTXN05mRDacUnOLcZo61YWu/RyAe5VUzG55euPh35FmT2/xlmKMjPuQq7ZQryWhN8J9BtxmWHmhisT7JKt3J9KoHm+SDVE6XR7qrEpR5gXNnqvit5gLvfSxrYWa9UqZ3/lIp5Q8JX/I8N52f/cqb8gZNnsjGXNQ/bn7nZ1N5H5NJVIIoR6deXd2/F0bH6a9jE2zTqdyyMilyi1i5MhMrfE0ervTto9qUjl7n7F56W7J/Yif97syETYQvwPS7rdOTVJkXQhmhB+fBdt8zYynzW3s5fbk+Lz9lduSINtVjsYxc22uexCPTNbDCtq2bZqmKIryMgA4UxZ8FBeJapE+SJAa/tVIeezZ9gYNkuUKqgAAcL/kWC0HN7M1mX8FAwAAACGCm9lGxgHvq/A5fmIAAMAdk2O1HOzzMwl3gPHUGUl2RQ8AAGAv8pPrHD8xAADgjsmxWh5Y4YXASCcAAAD3REz8qqoqiqIoCloXqigKDIEBAABwBwTFryiKvu9rsT9kWZbe5S4BAACAvIhNdaA5s3KhZHc9ewAAACA7/OIX2otAbVwAAAAA5MioAS8AAADAPRFr9nQ9vA32fgQAAADWxj/Jnba4PJ1OPNqFdgCnITAbmgcAAAAsT2xmIi1vLY/Udb37bIccZ1MCAMAdk2O1PMriruvSaerMsZQBAOCOybFaztDizUu5aZrd/d0Z3P7JQhNdOBL1Jw8JVoUzr6zcDuZFvrfce+cdyTzmFnvLc0whx8N0F9ySNJfuhupCPKGkCOXLm8dJcYYu55v44sWLvu+51YptUGaoP6mEQw/zSg5AUn7FLeQoflfb75ZlyTtBl2HW3mA3jtl8y+B4ijvugc430b0pai9v6ruVweq6Htws21x2BpfJuWcpZoLCzNiG230y3UjkegvuQe9tcuOhTuv4kVC6Mja5WywfpKhoL1kOFnllODyvJsGnOH4+teyrJ430WqWK1LVhMAaZLw4pn5ap9QlH614ufxtjnj59Sr/59XTvnXq2aft492Hm+MfbqfA+ot6HijMos7zUpvarsn21fDtXFnO5x7eB3sdSYWRSKdLrvZUt7wi9+YS8U2whf9zYcS+zVAV+JGQS9FoqgVGV+Ay8giHLmVOMF4Jrm/egLBavMbIk+TfXnjIA/ZBHIuVMwZQlMo/KqqlSEcIVJNcqleLUR0hFru7djZaHUqQfz58/l2Hc+2WF8NCfnGX1yWiv7/K8wvc+ovJB4t+cO5nNXeqWGeyuCzPI0OLVSpmeb/7s5VfFFQBV8cmPtdLxftyr7PUH3bz3yk2CT3Eu+E8KyQmV4W92iaoHKU6OSlYrqqa78R55L1cfyJQRlbR7oTQ4JJMhGeC0lItgnQKnOyuT4GARWfWKGYUPleEiDz/lKJRrt3Ds9EfIVW7p+d1iufdN4Zfu+fPn8n2Ud8T9eHI13hU/9Tkyw2bveyofKj5YXje82dHvaQrcofjJok/kNvh8UbNI0XOjh73+Ko9/7KuXyusNyIPssrhP/yRk/eK1R2k21Xd0yXitNdfunffj1LXkls/VMuCHySLi/IYKQdmvfCwZcnzht5e+APcqttlcWinVLfASEd1QLR/XaRVyMEBI/EJiz5I5+Lkmmz3ddGvBpFqFn2H3cs7O06dP5VeO+3jIh4efbfe15eQ4idCTGSf+iLoP1bz3dHfuSvzq69Zndl92l8BJpWymuLbuo++qiDSD/uRgoQrRe9Wg+JUOKoCsiVj+lejKrHFNMelzUlbrMn5pv/oKCX2bx7PDhO6vV/y8haCu8no53hsRQT3/yjOTOeKPeuUyenMUuhGRL4CR9e9s8TO+zjDrPEKDBlB4fiSkSsnHYJKc1OE+P86vbPY0TsOA9wNO5lE9zNI87xfJvPeUi0hVqrPf0925H/Hj73dZ+qH6ZWPGG2CsuUX85BciHymdbmr5LnllwHvVoPi1Dq7B7GS0154Qv2nuVa1oUXFf71ASta/JqHU+uiOMyY6NSpH31lhfIairuLpUCZUjnDM3F+5HRshsGdL7TeAtfBZOr8AMFjXVntJDCoUPRVX6PD+VxKRHiEOGIhxPqFhsWPxUGH6erWiJkS94xDxv6vPeUxXDIoW8L7vrwgwGljeTw3CrqrLWmtwWti5MMe/CruukaHVddzqdmqahggtdJQuXCmrwqtnlSePg5eVlWZ7P59PpRFswUtLeS/q+J5PUOgaKqqr6vpdPQlmW661y1/d9ZOA751TeGrcQFG3bUn5lmKZp1JEQFD/9piyPvIoU6HQ68a1XF9Z1rY50XUe3g4pdxRlabn5x4hk8nU5c4N5HqOu6oijUkcWNHIlMmgqcnzEqyaZp5Gu+Bu4jOvhQTX1PwQz8y5sZY9Z+INZmtubxBKDz+ew6c5HqjxbEYZ0oioK/+r0VlqzKI2dV0l5LuKqVV7kzbwYnnLln6YjUiaqqzufz1MdjMDveMNIqEl0Kw7fGWwhe6romHZKpn04ndYm3EDhdJT8c2NXR8/kc+ULiaGmDaNZ7XlCQ1heUd5C+vSIZ5HyRSefzedKES85a0zT05WR8whAvZLbBXE+kmzH1cxGlL8tS3lB6dFWA8Us2ep+N2e8py6Gb0x2/FY6F1x+sA4PNQo1pWxKyWQezRv4bc0l73eLvNp7w8VIMlW7FnCH5FsnLzfXkIZVWqLSH8+ikpc5GjqjmlEiDT+lMXTCBbqEb8XaZmOv21dKZbTZYCPJy1YWj8hUqBPlUqMZqskc1ScnGRtnv5cYceuQIJTyTGr4Gnyh112ThcyFPeoQi+VJ3wWsPtQ2GbhaHMQ7t9ag02ezpdruqQlb9wfFmz9nPuff+Ru7syEJOisHnLUFid9pcOpCs6L4OtblvxphSVso3SfzspR0/FEa+Ku5BeTx+lTfkVCKmegNH/tz9zo7Bm9lJhRAnVAihJEYeHDQvEmDB3E1iTKLxMOMtby9zQrj824XWjliwUr7lBfEWxaSHKnHuSvzcb7cUlM/OFb8x+hf/7rtvlqprcieXQogPZskOyovyyxeM+fZ4yqzGXm5PjjXnwIJssp82kdUF11tErus62RENQJpQFxp1Ga70LmwML166xgu4yNq8i0Ryx+T4KGZocYalDMCC8MiLqqru41uN15W+mxwdjRyr5SuL5cfXmBHnk6CYB31H/vRzV1h/a3GGpQzAstAbOuaFygU5hnZPO8AscqyWr6Y6yKlF7jSjW6AJK4PBaHoQDXA6n8/n87lt27t5vQFYhLtsn7+z7ID0WV2uqdeQptfElYwm9sowpIXKwhw/MQBYEMwJA6mRY7Wsmz153mW85XNCAmKth7j4FUVBk1L5CE0bh/gBAEDK5FgtXy1vxgv/sK92OzSo1DtxQlGWJVo4AQAAbMCVXKsV+ULMUHhanGlqB16o2XN8DNl9jAAAQHbk6PldDXix1vKic+OXvFsDEktzWXNdkV0pAwAASAq9sDVPtVmqz28GvKQ9hnoCAABYg6s+v92Vpuu6oijI6bTW7m4PAACAu2T1AS/joaZOWj4UKwkBAABYj4QGvHiHt3iDoc8PAADSIcdqec8BL7w/p1yuwvX54AUCAABYlv0HvLhbYKsAED8AAADLkp+vmqN/DQAAd0yO1fLVgJeiKKQf5jp/tIXYJoYBAAAAa/G9yLldxnwCAAAAa6P7/IBLpps485ans+EtRr1/hrZdvLGs5pkdusq9d2x2PNjIJMZYGw/DQ8y8G1jSWDDe6DWe0FKobvh56YbydcuINoowdDnfxBcvXvR9zx/ubIMyQ/3Ju5l6I7/9bfKyUrRgFFZgjGnblv+k0Z4ygHtke7Y3IJ5i27Y0JX97+CbS5EgJLQvHf9IWiTJYXdfyXofil1lTDwyfpZgJCjMYsxc5utiNQS50JzPCiXqvcg/OeKq9SUh7+CBFVZalDObeHZV0WZZ1XVMqbn75VCSeScTL2To9N3TQtUFmczBfHFI+LcRUy72Xy9/GmKdPn9Jvfj3de6ee7bZt6bh6mDn+8XYq5PPjPeg+PzLL896mjdldF2YA8bs1RXq9t7LlHaE3n5BvGltIlRFfMpiEvN1cF8sk6GlRj42qxEci1dpbJ8rain9LC70P5xhFlMUSN0wmwTbIAPRD5SUes7JE1uDKqqlSEYITDb3O7sGpj5Bbk8h8zTU89q5xtM+fP5dh3PtlhfDQn3y/1Cejvb7L8wrf+556n1vOnczmLnXLDHbXhRncufg9dt34wPR883exrGo5AH+syYpPfqyVjvfjXmWvP+jmvVduEnyKc8F/UkhOqAx/s0tUJU5xclSyWok/NmNQCuRVLJmEWxdb37MhDQ59K3gFUqblJqEKnO6sTIKDRWTVK2auqKvshGIbiYrZzbsrAHb6I+Qqt/T85toeFD9+6Z4/fy7fR3lHlAB7P7bcvKvPkRk2e99T+VDxQX6Yp76nKbC7LswgS/HzIsM8vnz5+NVXj++//2jM4/vvP3755ePLl4Mxc6OHvf4qj3/sq5fK6w3IgxSnrLW91c2YcvDGwPYozaZ7R5eM11pz7d55P05dS+ZpOdUIIT/MOK1DKkDE81M+liyu8YVPVZK3lmSbzaWVUt0CLxHRDdXycZ1WIb3HB4W/Fo2i6luHv34GP9dq0ezpGlYLJlXu/Ay7l7OpT58+lV857jvCP+Sz7b62nBwnEXoy44TeUz6iHqp57+nu7K4LM9DiRzeYqyH1qJWzWrSWZdCAx5cvH3/848fPP7fG0L/HL754/PTTQf1zH31XRaQZ7aXtKF4heq8aFL/SQQWQNRHLvxJdmTWuKSZ9Tspq3fq+Uq3zORL6No9nh64lI72R0FWUEfdzR9VTMlrvKe+NiMCFzDdd3etSeBj1xXuLxxxRssgXwMj6d7z4qQil7tbX7d7yERo0QN4p+ciZ6067SXLCmuo+SGynbPZUJRz6gFMaL5Hmeb9I5r2nXETyobI3vKe7s7suzECL3xj2spWNjAd4/OorqXzv9O/LL+MXKhGSX4h8pHS6qeW75JUB71WD4tc6uAbXFydDmkovT+v0J8ksWNFSFK996osrpioUGXmkEh+fHRP9QFb5VZWyCfuafCNUAD4y6XluHe+BY3OLUYb0WugtfBZOb44Gi5pqz1p4SCq8inkwQjfA1EeIQ3KE8cARQsViw+KnwtSiaYEfY/UsTUp93nuqYvAenFrI+7K7LszgaqqD++GTJc+eFa9fq2PFs2f24cF8/fX4aLquk6Kl1uYOTfa3zjfE4FXu6O2RyHHY9KMsy/P5zIO8eelUeQmv3UqmFkURGWteVRXFxgOyy7Kk8IsP0eYIQzHLQe10a8YsmE5naQEHOW3jdDqNKXk5wYP+O/IqUqDT6UTlTKsGSjvrulZR0cxaStGdYuudpLE4g4PvKUdkjPcRopsiXwTaqmwdeweQ2aEC7/teliQ/S+vhvqfeh0oW+9T3FMxhR+GdR9zmx9evH699vnfOnzGP330Xuba9HgDNv43zsS9dH9crkr/VZ50Rnh9fVfoak2sHFUDFEPI8VAZrp29DZspbLOo54aYnN1MRBrMTaW3jVOStcR3okPGtr6GS86UudzMiDVPlJm1z01U/vK1YygDjjJyShrnWRoi8Jsbn6LDXIp8r1ynhe9c6o3si+ZKFP7LO8XpRUz2/0hlFpR7m8tKzIwOETPI+bPPe03ghc9l639M0Sdw8LxlaPNjs+f77fkGbCl0AACAASURBVPF7eIhfqBxf993m46UYKi1rpVq0fMrLzfXkIZWWWy+PLwqVljobOaKaUyKvvavNbpU9KH5jKH0zn4yvP0aqoELFqaIK1chEqBBk/KqiVLUnx8yJ1qLfy4059Mi5BeKmEifyRPHniywcWR3LRNWdjTxCkXyZ6IcCW1Vedw16lca94+31qDQpfq4aGWcWnbnucouU2+zn3Ht/I3d2ZCEnRaTckiVDiwfF78svH7/4QivfkyePv/xl/MJW9OiEnnJ5Sv6Q4d1rvVd5Q04lYqo3cOTP0Df1xsSzs4jKRggVQqicRx4cNDsSYNItHs9gOY9JdJFIrGiTkP7QItX9gpXyLS+ItygmPVSJA/HbgmHxe/ny8dNPpf49Pnny+Hd/9/iXv8QvjH/33TdL1TW5k0shkE+TYy3phfKi/PIFY749Hm+rNWByrDljC1tnSvHhh+abb8zDg314sMbYhwfz3nvmm2+Kjz4avHbtru9k8S4seUCyKAQyksbs7G3LMvAqoHJE0iIxy42yb2HLtVXBNuS3CdOkjaNs1xV4ZMF9wSMDaQTpztYsAY9+vJscHY0c9/PL0OIMSxmAZaG9Nkkz9rZlGdRuISAvcqyWM7Q4w1IGYEFI+SASIB1yrJYztDjDUgZgQaS3BwkEKZBjtZyhxRmWMgAA3DE5Vst3ONoTAAAAiPNXw0HSw7tOYHbfHQAAAPYiS/GDzgEAALgFNHsCAAA4HBA/AAAAhwPiBwAA4HBA/AAAABwOiB8AAIDDAfEDAABwOCB+AAAADgfEDwAAwOGA+AEAADgcED8AAACHI8vlzTaGdg3d24pp8Gbft8D7a3v/pM10qqpShXNjWU21XO7p417o3js2Ox5sfOqD1sbDdBfckiTD6JQs+fW4PTtegymDMtj47LjXGvGM8Y178eJF3/fn81kaqW6r+yeZEXqAF3mJXFaKFkzG5sb2NsdTbNu2ruutbLmCb2JZlnywLEs+3ratPCiD1XXNZyPxy6ypB4bPUsyESncSdV27ljNt23rzKx9meZzPqqgolfgRL2QAxybt4YMUVVmWMphrlUq6LMu6rqn03PzyqUg8I4kYI7PDN50PjswO54XyxfGw/cz4J4SLSMLR8m9jzNOnT9lIfjLV/VLPc9u2dFw9wBz/SCNd5GvoPciWcAZllue9QTuSpZTsbcBkUhM/qpu2suUd3lpA1uP8my2kiokvGUxCxsb1IJ/lmkUJjKrER0Lxu1mTybHx/Fte5cWro67BYz5flAx4baAfKi+hCJWaWqc2V1Z5i2UkpAcRBb09O+6LIK+a/YEYeb84/ufPn6vvIf6hBFg9z9b3CMmimFfg3ndTJsS/OXcym7vUJzcC8duC9UqZnnX2P2RVywHUhxsfkQ+u+7Hs/dwb+TUdwk2ColWf7WQSheSERn56q1qPSoajklWMjG2kI6VQ9aNXsWQSbpXhRRrs9Rhc+yPmebNvL9WlTIIr1kil7xUzCh8qw9kPP1kSks/x2YmLnzJPNjwsLn78oj1//ly+g9JIeS3dO/cDyxU/9Qkyw+bQu+k+dVwyU9/N1ID4bYEJIAL4/w3CDSD2+qvc/ZCUL4x6wbyfz/IgxSmrg0H3JVQOkRjUe0WB+ZLxWmuu3Tuv6riWzNNyUu5QRWmc1kV73VIaUou2bZWP5f0Gj8BhQuLHNptLKyWVQDzmiOiGavy4Tg/Wm6FoR2Ynflv5I89ttaMYakEkHtdmfjaIVjjf9Pvp06fyy8Z9L9w76H1VObkbZTv+brqGzXs3kwLitwWDpXyj+PGfXFPIilKaQX9ysFAN4r1qUPxKBxWA31KudFRGVHXPtcakT0tZD0pTpf3qKyT0nR7Pjqw3vZHQVe2l+c79mi59za1cs6gse29ECPkdwzdd3etSeBvsvcVjjihZ5AsgEuds8VPZkcE4F3EvVl3Ld5MNM9f9doPxKNu8Dw/bLJs9VamGPtrUd4xEZtNbYre8m1bIOf05+91MCojfFmwmfvJrkY+UznAS+V55ZcB71aD4tQ6uwfxVHv+6VAfLy1e8+6qHkqh9zUfuB3iEwewMfizL/NajWwX5Rqgaio8MPk5cwvyR7lro1Sr5ueCVc2/hs3B6FcJb1FR71mLsTOiehqJdJDtuWpO+MCLxhNIKiZ8KU4vmBH505UsdeQa8qd/ybspKwI120ruZFDmKH+b5Bem6TopW13Wn06lpGiq40FWycGmI9uBV7kjukdCwcnk5HaHfNJxaRc6X9H1PJvHocC9VVfV9Lwdnl2VJY8EXH67NEYZilvmlWzOm6Nq2pfyqglJHIvR9fzqdTqeTMeZ8Po+ZFNE0DdX+p9OJb71Krq5rdaTrOrodVOwqTu8kDe+RxRmTnaIoVAm7YbZBJkqF3Pe9fGibppGv9hq476YxpigK0jPvLZv6boIbwTw/DU8GOp/PrjPnVRS+8Hw+s04URcGfyd5nnSPxxuYeVJFIS7hu4kljxldXDk44C1WsUieqqjqfz1PrjsHs1HXNyi3VRZYnhTHGnM9nKltZw8Y1qa5rqrhl6qfTSd1itxCk5XRP5fQ1t3yI8/kc+UIimqYpikJOPjudTlSwVVWVZVkUBUdC317uA8lRURiauxZPV+WOp9zdmB1VwiGpjhsz9RKXsizlTaTHVQXo+z5Ukq5Jrj3z3k1S3NBTustXwtFZzadci0Gbb2z2ZNyGFD5eimHTsjVMvlHycnM9kUilFWrEG1MUKi17XUOpZhNz3bQlw0Qaf0qnL03FbOZO7PMmRLhtqqrE6Ky6Zd7WVBmV6s5R+Yq3gKnY2kvfpFvOtRiUwY9EpL3Rex9VgbipuAz2GKlGPLf1bzA70mCvAV6DQ82eZE/oBnEY49Bej0STzZ5uq69xZtG599G1ja+d92y799T9Xmyv+yb5N5o9tyFDi9fv8wu146tT8ocM7+2Vca/yhpyK19SQ/eqg+nOwF2cb4mUSytci6munF8KYcg4FGxlgwdwNskh2RhrcXkbQcJm3C60XsWBFfMtLMenGbXaLVwLitwUbiN8BWareyZ1cCoH8m6xrTLJf+eILxnx7PIOeNGByrDnvsM9vqG8ixtrd4MmyzdKR6ZPFIq5kJHUZ2lse913hHr6qqsb02o6HulFvf57xUtw3+b08Wb/wANwOj8Jg2cgRXlc661wAIsdqOYOpDng3AJDQG+FuR5AXS/lnAMwj9WZPmvWytxUAJARNabiDL8I7yALIl3TFj+arYZonAAo5Xxv6AcA80hU/WlADAKCA4AFwO+mKn70soeSqYFEUt8R5ezyIClEhKkSFqG6Mal8yGPACAAAALEu6nl+EpcbULjg2F1EhKkSFqA4bVY7+Hzw/AAAAhwPiBwAA4HBA/AAAABwOiB8AAIDDAfEDAABwOCB+AAAADkd+S3HnuHw4AADcMTlWy/D8AADgVizW388NiB8AAMzEvnplf/UrWxSmquwHH9ivvrKvXu1tFBgFxA8AAOZgX70yP/2p+e67wpjCmOL1a/Pmjfnss6T0b0WXtHj3zxr77s9MgPgBALIhrdbF3/7WfP/7xe9+xweKZ8/Mxx+b3/wmdEVR+P8tzhYu6aWPr7BFYfMRvQsQPwBA6iTauvjsmVQ+onj2zDx7tos5zJ4uaT6jXvIbopPjsCIAwGzeVuX/9m/sXFhjzKefmm++KT78cDer3rwx77/v9XesMea774r333dPhZy8Zas0+6tfme++U8Jsv/jCPDwUX3+9ZErGFJeGTltYYyB+axJaPjy7jAAAxrBlVT4J+8EHxevXnuMPD97jZjPxKz4ozDTDZlOIXj6bkfRlKn7Z2QwAmM0MjZlBUJYWHcJRBORh2VQ2prjkCeK3LhA/AI5DpHVxWbaRpfsWP5OV/mHACwBgD0IDH6//FZsoXwxr1b/CGv73f7w0//SlscZYY757MF//0nz4FyMDqH8hIpfM+PdPX5rffKGT+J9PzNe/nB+nWw5cFJmSnxcFzw+ADFh5a2/75Il5773in/95kdgKUwRr8XGD+H/Ymf+sAhEIZyjcuDqd8DX21at/++yj/+tj87PLsNP/+cT87M9/Z775pvjooxlJhSh8Dmsuzl9+QgLxA2BnlhK2cS9yKLXHv7ycUZV762tjzID45evgjODGCpWKNBfNY/ITEogfAFszT+0Wek/jIyRX9jAPwe03Ksdq+a/2NgAAkBhj9GSdmm6Skm0sewM5HmOMfReSlkRZfW5cIdIF10D8ADg8m6jdiloV7pYb3xY3ZwZeJEeRq6wxxUX2wH5gtCcA0+i6bm8TliC+sqQ7tG8ojsF/IxmXMgUt3v4zxjMS0dhJvVBv02IdtYWxhT/10ArOVvybkPCUwFOZasyRgPgBMJau64qi4P/ubc4sQlp0rRqD60fPW47ZL1BhqSNbg7HNErkBC42VgipN8WvePMGToM9yJyB+AIyl67q2bZumads2M/Hzap6jOfH1o8e7cWOELWipSGdKDpdBJlrQX3EnD2RLfkN0NhtW1DRN5M+RMcy4almojq6qal8zptJ1nddmb5G6B5umqaoqnuvnz5//6Ec/mmdeVVWUxLzLN8Xr5PkIrR/9vf/1b/7wdsmVKkNSZ43dZj1MrxlXPXPLJndfQ1FyHO1pbG5sZrMxprxAZdW27dQYpl6yOHVdl2U59aq2bWdcNTJmfva8hcOl7Q0w8qAxpq7riBn/8A//8Mc//nGq8fZSMrvf1mGuG+TsiLfm8auvHj///OLPeCJw4xgZbMBSJ4J4ntbDY8l6yb1tX74TcpQSNHvGaJqm67qu66y1ZVnu7sZtSb/OrqGn04mUo67r0+mkznZd1/c9PZreAEvR9/0Mt6/rOnok0vX5Is2bQ3zv63/63u+e0RgSdWpSu+V4VNtmvAOvMG+HtyzcIhrqzKOT6+3RilbTvYH4TYCrvKqqigvc90PjIIqicGtGWV3KYFVVcbOkHEbRNI2KiqpdjpD3daL2NzdwyBI2hq7i2FR4Uh03v2wDJTGu2N5Bl1O09Fv1nMk/4wIzctSJvFPe8G7W6Dcf5N/0GdT3vQqfBPEuvemXGmN4WcdHU9gV9sFxZW/ZJMYYoQTPFu/MWHwoDUiOHb3OeWyWEWNMfYHa4uh4Xdf8mxpF7aU1j3waPkhH5Ckr2ugoHvptjOHGNG/8qvWSA9CFygBzafSjg6oBkw8qa2UM9DuSx7Ztve2KVFyhInVz4W3DLMuSCsGNKlSk9TXutbJU//jHP1KbJ9ujSo/LnOORl6fF3DZHf3Ol7+jjw8OkSOImjGzhjF876SpvRNdjW30H1VkQJdG3I0qGFqfU58e1JysEH7eXSlleGKr9verIf3ovVD/4QqlbrmH2WtjoTxmDVE03iXgPIvWHsWa4d0oZQ4HdMFTyIWl0tY3uTilQAZRY/vCHP+QL+SAVnXWK1Pt7f5bVPGuttY9ffvn4xRda+Z48efzlL0dGOxDmBtm7/XIZUVDYIH5zSevtGEeGFm8ofrLylbU5/eY61wbqcVkp0xEVTIpfKN2p4qdCunLlip8cY6LET45PmeRk13Ud+Vbw5lQFUHbyJSyNkXhcdZQBpPi5lqcufrNkb4x/9vjy5eOnn0r9e3zy5PHv/u7xL3+51eQldGuBSAYlDco3l1Tejimgz28ssgOMhsDYgDYY0XdFE8L6vpd9e2vbOWmsyul0qqqKsiNVkGMzAQWKE5oJELftfD7Lcja+5VR4jt3IXreiKOq65mifP3/uZpMSSn3qnuqam9KlJwldV3z4ofnmG/Pw8FZfHh7Me+/duAnOUn17t45wUd17IRPmmAayZVOpXYLNbDbX7gJ7G0Z4FbJPjg3j3+a6e89eezOqn0kmxN6P90KZlrzQBBpR456f+u1t9pT55d/zhvur7PNv1QHp2qnyqCwMeX5uqy+7fSo7fEmkcKZkdGludvUm8XjzXI7FWil9sU2Ic6onB7dvFllKyd4GTGZL8ZMoQXK7pkpngppxBmXIYPJalSmZrozBXBpR4+KnWmXj4iczomKWg0HU15JXmcYgS4+zJo2UaYUGvKirIuKnbmJd11L8VAA+Is96f2/NhrJ3O8vKnhvz+NBzlAzNnrOA+G1BIqUsB0p4j4+5PJIXdoZmRD41sExLXqV+z3P1QimOtGcDNk5uGlNEbF/Ns2vK3lQ75gsYxG8WiVTLk8hvTZos19G50HUdzfKmmXbn8znfvIDVUT184wIOhV0L1be3gwUXO94xz4r7WnhsG3KsljHgZVOqqmrbluZN06iZvS0CqcKCFh3V4h0EY8TU9Xn7Ck2zVIxq2XNiuBzVYqFeYID85DrHTwwAJjDO4Yt7e5stBn0/Dh+4gRyrZezkDkBKTFe+Hesc6fDtZoS5dvgAGAfED4A0yFP2DJQP5AnED4AEgPLNsuMdUD4wEYgfAHsjx7YMBYmG2oLkmjoNlA/MAeIHwH5k5fCZsPLR8e3kEE2d4GayFD/vTnLZjTUCR2ei8iUieyagfGYbCYTDBxYiS/GDzoHsGadps5WPw1MMN74x6OQD90eW4rcB3jX+ae/1G2OWexEsEmEc3k1i/CW0IcOkSzgV2vr89lSmmk3b088OOf7yZVhZ+SS3fygOdvJZY7foCERTJ1iWvdZVm802NtPGcnIRau+OfXG8+74aZ4/cRQyO2DDJbF7hemoq7lrecYxv3WoZ4Uiz3fXE7fXS3nLZUj6o9o5QB9dl3OKbOy7ReWXGiIU6t1jPE4ttpk2WUrK3AZPZuJTdXRHG4zVVHYxrwPaYwCbs4y/fUvy8O0lJM7wHZerSjC3uxd0pn70Wv1X0D8qXPDmKH9b2nEzXdcUFbhp1D9KoHO/YHIl0XGjNz6IoZBMcH5TJFUXBx6mlUQWQl8t9dKuqopChrJlLwyy3XnZdR02UnBwdpwgpUU5lBnKDX4pKnpWmum2qXdexk8enmqYpy5LKUObCiHbUsiw5UQ7j3eR2STZs7bydkS2Zt24zO8KOq9/yHwC3sLf6TmZjm13PzwR2fJW7+tnwjnd0OSH3z5N723Ki8qByYuQmumSP12NjF0qmFXJn5d68qmFQZplbOLm1k1Mx154fZdNNyF77XrK5ktL1RmiibiVnX+XOe5U62LbtLf7uKEZ4c/tuSPTOjClu3PxtZseZEvsHkiFHKYHnNw3pG1VVVZal64tYa010sEZ3oe97GWdd13QVn6qqylrrjcr10gZHmrCTFLKt73vOGhnAeLO8wTiRuq4pufgQGNofijvw4oVfFAW7hvLyFT2/fKaxTx3YKbdxYFlayhROw5swALcA8ZsGN2kSrBB1XZ9OJ2qxHGwAZPGz1tKFxpi+78/nM8dshLzRkfP5fKPxcaGi5CgXZJJXTUeqHTVRUjbHDwH1JkeFTB8HXrN5fyi2LXQLqqqi/RRVgDGfLPOZonzR/YtWZ/bORMvvZKSUD1IHlmYL8eNB7WNqwEmB90L6zux10Z9UvY6PSta2qoVQbnhrr8clrgF1lXHqZVneLrcKb69kHOkKu48Ebw4sow0JGB1XGikDu/7uAkSVz/b9UJDtSGXdMjNiVsPeBoJ7YKXmVFmHyuTiIyc5TGQawAY2S7x9fvJ327aqe49/h+yXvU2ym43Dc4Ry0KPq8wvZ49rPfX5u11rEMLZB9vPJYDKtkX1+8gEwooNTjrdUfX6c8VAnpbdPkW3jQot0wbLBt4zs9RPowXt8+fLxq68ejXk0JoVOPjt6YOc2pvi79NDhlzAbV8uLsK7n1zRN3/eyFuv7PtIkRTURtZVRbZWg/1fXNTdOUtcR9YRRm2dRFLJ1zuuFUNOi27xJkVCrI2f/fD5TtLKTb3G4S09Zzsmxzaq3bBJt2/Z9r+Kpqorz6O11U5ZIVFsxDw2le0QxU0mq9moeUsvt1dSIPXvMaoxrh86+emV++lPz9deFMd+7uDCPn/7Yvnq1fNLjyMnnA2ApVpVWNwkTdv5GBl7b5pG4AwjJBRwMNhjt7ZEsS9u2PPR0qQhHHuRT8276eIO9xX4rIbfvq68eP//cGvPuvDGPX3zx+OWXCxsw0sx0fD475NXB50uVRKrlSay79zx9y6v+mL7vvYm6p9zL6eCqNgPFjdP4Fkk98T5gD+F+PPvBB8Xr18W7EZIXr+vhoXj9eiv73rH1hgwRBt2+InoW7EeO1fLqA17GN5FR7SZnbZvwsD1wEEbO4kiLiPK9eWN8ymeMMW/e2M3Fb/Up6uMZ0+BpoXxgMVZc2NqrW5EBddRzRr04dMQ7tN2MWDaFye5jJEHGTN5Yj/y+fqJjN4uHB7/yGWMeHor331/XNmUMuvrAgVnR85s6LIJ0UY6OoXEQbsjxrbqLZASAsQzNWnh3/lr57JMn5vPPVzTMtQTKB47N1pPcIx/ypHxqhcbl510BsDZDyvf46Y/tF1+8C/7kifnzn83PfraBaQooHzgsq4vfyGYr7+JVPMF5YZsAWINoa/yVT/jNN+bh4e0oy4cH89575ptvio8+2sJIMiadrj4Cygc2Z13xc103cu/ckF6diy/nCEBCjFumhU4WH35YfP11Ya1p2+L16+Kf/3kX5UvI7QNgc1af5G6Eerm7zMgtcmg9Ldns2ff96rvMAHA7o7v66Df/+96p4t/bkKLy7W0IOCarT86gNUr4z7Zt5cqKp9NJzuRTY0HdSX4mzwkl4J4ZP8jFXv2p2OahTmVWH5TvvsixWt7I4kkNmPHAOZYyuGdIzcYpn9lV/JJz+/a2AixFjtVyhhZnWMrgbonP6vOd3Ev8klM+A/G7H3KslrGfX5DmmpGXLJj6gpGMjI2WFL893aMwZZBLIkD5ACDyk+vNPjHkJgPUEyk7LF0WnJhBvaG3Z7MoCloVOh6b2kMxs7XE9mJiV597XLHqQ52K2wflu1Ny9PwytHhD8ZNqN6htCd5+lYUbg4ErJnb1uacUG4hfKm7f3laAxUmw9hsEzZ4TkONU5Z5wfEruuqcCUNsp7VrHl1AY7wblrLXyKlpmk1PhGKRucdIyWmkGb2gnNwg8nU6UlmwpVZGTVd74D8e4qQmJ1AapTGlPwwoA3nLThkh7sJnNxpj6ArV/0nG5s7naXV0e5G3K5Ubtcsty+q32SZdJyKt483TekF1uO86xqaTN9UbzEcv5cmmtSogulwndXMZ5MrT5+sjN2bfZwD2V7fqwA/tdk2NtkKHFG4pfeYE+FFzVsULzXC2RBku1ULuzsuQwSvzctBRlWVKKbtJS/CQh2SZLOEKZBRVPjo/7Mgyp1jaqNhIoH9iAHGuDFbc0ugOoyZF/87CRvu/j2yqp/jNqYFSr1cgYIgvZqFMyZrkmgGqADaHWHAjR971q1cQo0LcMNXhutlbLGFJp8CTSaAQGgMiyz6/wsXaiUlSkV2StHRwqMrgx0wxpoSzT5aGNDxWkfOTkkRs3HgyHMWb+CM9dSG6EJwApkaX4eX3YtRNlT6iqKnaeaPiJClmWZXyCnRo4OnvwCGue3P43knTXdWVZjpmSEY/n6Aw9bCkoH5OK8qVUJgAYk2FD7WY2q4KS3XKyKVKOYfFeq7rTCOl4qQ4/e93np/oXlQ3yvy9evJBJhwa8yEvIeA4pk5N5lM6i15hDsNA4l21IYpwLuvoOQ461QX6TM9KZUMITEkJnzVBr4Y17NvEEdvpNPwajlWbLORVueGwp9Q40eM6yg4wAd0861fJ4MrQ4w1IG2ROd0j7i/HakpXwG4ncIcqyWs+zzA2BTshrhSey/mAuRhhUAuED8AIiSZ4PnzqRhBQARMM8PgBHkMMLTbfBUWri1O5hAmQAQAp4fAGHyafBMpavPwO0DeQDxA2CIHBo8CaV8/CdNOdjYFABSBuIHQIB8tm4Y7Orbri8Qbh/IBIgfAFGG3L7diTR47jb+JYEPAgDiQPwA8BEVt/QbPF22EMJkPggAGATiB0CYqLiloHwRSdva7cOsdpAVmOqQHzNWHQtdIrdu3wyVYlVVcrk1ueK2PMXX0kps7qklGef2pcPIwSyFKRYe9uItigL6BzIgvzVpQrsXrZQRqqkjCtF13el0att2szUwqd6fJFreS8jy7R+Aoijkqtl935dlSZpHmy7xWXnKXG497WVBwVbZZTDapplUg+eY6Q0UZsWhnt7XMYHCAVuS4/JmWXp+25Ry13VN0/R9H98tj5eTBuORuwQbY4qi4JW1paSRPNPvqqrkqaqqTqdTfG3xm0i+wZOJC9sO0/5SKhwAQqDPL8aYXWpD0kjaWVUV7bVL1XRRFHJreA5QVRVtDSgDqNi8Zzl+uf2eFA+1gQMbE4rfe5ai5YTY+eaD8ipyMZVV85BbD/Z9r1pER27hO418GjxTWckMUgcyZaetlOazsc1lWap92xne385cdryTUNVMx3nDPApMEVIACizvhXF2ipdJ0FW8Dx9tv0eWcFp8edu2FEAawOnK/fncs/G8qERVXqRV3rwoC3kTQbmhIJ1yTV2XETv2JcL+2/Vd7Lj6Bw5JllKytwGTSUf8XOWQSD2QdTr/VjvHyt8qRXdLWwogi4LDhMTPjSGyZa7KjpIr9xYoHfUWgoxNwcm5zpx3H921iG5Hi71qQ6ZA/ECO4odmz5lwFxS1xXmbCuWwjlAk3t8K1bNF0aoUq6rq+35kWsqwruv6vucGTDdyebmMh5ttQx2foUxJca3rWl6u9rWPF8uSA17GDWXJqLdvC9yW1zTaYgEYBOI3n77vT6cTVdzn83m9OQPUIyjTHXmhvCqiEyTkUnLio1vpB43MbJqGLhlpksuYcpOdf/LC5Ud7Jr+ei0mntw+AnIH4TUO6eiwVxpi2bVcVPxY8Oa7ECOU4nU7cZigHvHAkMoa+76W1Kn53MklZlnJADYcsy1KZocI0TTPo/o6EHESZtb7vFxvqmc8ynkxCbp8VP/Y2CoCRQPymIQffbwZ5ZtQmeTqdWE7atj2fz3Sc9YmmZ6iRpcYYGYPqWlPxux1vsl2UD8qEHqOnFAAAHfdJREFUeJYeh6fAaohmhMFPh6ZpSP/YzuXnVsLtm4G9/BeyB7Iiv5mJOc6mXArvtDbv6i2hCXDxiXFjzqqp8d7ZFNbaGcvQjGT5mMft1Z7IQ7f6pPXRdrxlb0NACuRYLWdocYalnDvUvUee1uD6MvndoKi4ZbekyzZ2mIsdAJgc3/pMV3gBG0NSRy2uU1dWSx309s0mDSsAmEd+cp3jJwZIF7h90+0wFzsAIHKsljHgBRwYuH2zScMKAGYD8QOHB4M8x5OGFQDcTpZ9ft5djbJzusHOwO2bCho8wR2RpfhB58BiwO2bCl4+cBeg2RMcErh9U0lJfwG4HYgfODBw+6aS0tcAALcA8QPHA27fVFLSXwAWIcs+vw3wbpfDi5ssNct7fFShNb1uMSa+mJkbWC7gqeyhDRZ4/Rc3fIpci5sriCmsaga3D4CVgOfnp7twPp/P5zP/aYw5n89LpUIxj7THqyLzjGmahtaG5jWpjViNWh6UBnBaanXvoijO5zNp3ul0koK6YFktRlJtmuOA2wfA8iy4Me42bGyz2gN9WQOMbwt4L2qz9RuN4XTlTuveg14DQnvQ28uW65ypFB+wwHbsvFG7+rcjqWzXji3awRApvulDwPObA3lOcs8g8pbk5j4qgDGGdjxQB9nfUjukuwfdeOa1KNIee7wPH8mVbMaUDZsu5ALyjoBq06KqqtwdkRICbt9U8iswAEaxhfg1TTO4FQDDXUfp9hVddrJt21ZW/SQkZDntgWCtNUJR+r6nLw551fl85oO8IR8fNE4/n4xn3j7mVMJ0U+i/5rIzrQrmvZzkjbYP9IbhONMlqaEsAdDbB8CqrD7gRe4STpV+pMqmet8YU5YldbbZJOsp7/ATrvTP53Nd1ywq7no0MlPsJ/EPEk5OSF0uzzZN4+6s62qSUqO+7/u+r+uaBst4V6Qd3H6dcxcPlhZw+6aSX4EBMJZ1PT9yU+q6Jk+lruvI1t7UnkaBu66jKj51H0KgBnrw4BE6Qo2NbnPljAzyJaFG0eYaFYx66eg43Qv3jvD3ykhLJPHvm/1J8nNKAbcPgLVZV/zIjeO6npv1vIHVWWpey0j8JGoYCx3kxtLz+XxLo+7t0uKWqnTQQ2FC8Sh7TqdTiuIXdfvS9Anh9gGwHqs3e6oGtLIsQ15F3/cqcMrdfhFkfxh34LFOVFVVlmVEHtTlkbMhx8s9qEbTnE4n73Q9Mi8Sudfa0+nEAh//vtkF2/cFP1dRt49O7j69D24fABuwRZ/fpMBUs5MQZjB0wgcP4KQ/qf2WJEcdHH+596y3Z25QeEh93fjruqa92vu+j5jnWkujT/nI+GtXxb56ZT76aExI5fYl0iyayr59ANwrK02hsJcpX9zhx91+3kRljVmWJY/+cKfBJZK7Qdq2dY33Hhx/+Yx44kmMPGjDEw3jJu11Cx5fvnz88Y8fr6fsPX766ePLl27g3efzSdKa27e3FSAX9q1s57Gi5zfPabOiAY1WIbGO4LlHEiQ0GuWWy2fEMzWSeMxdeEU09/iePX+//a35/vd16+HHH5vf/MZ8/bU8ht6+GGlYAcAabD3JPTJ7zDjteElPlz4Ycv77GGg9tsEpE2vx7Fnxu9+pY8WzZ+bZM2/wRL6mUuntS8MKAFZl9T6/2z//I94G2AweCLNS+AWxb96Y16/95968sa9fF++/T3/B7YuRhhUArMS6np87tpOm/Y0MPGnYIQBE8fBgLvKmeXgonFNw+65IwwoA1mb1Se7GmZTNTWc0cFHNAuTAPEF+VQvBffL55/aLL9Qx++SJ+fxz/hNuX4w0rABgPdZt9qSJ6rTcCR1xx8Fz45gbmGY7rGohuE9+9jPz2WfygDXG/PnP5ptvVEC4fVekYQUAG+BZ13ENJjVgxgN716IEQGFfvSo++siQ7D08mM8/Nz/7WSFm/u0+mV1C4re/20fit7cVIDtyrJYztDjDUgY7cGk/sG1bOB9S3OaZwqPEbl8q65klUCYgL3Ksllcf7QnAnlgbaclL6m3d3+0j0rACgLWB+IHDkcJQl1Q6+ZjEzAFgbbCTO7hHRuhbUm5fKqBMwGGA+IH7xadvKbh9xmnkTKW3D4DDAPEDdwfcvnmgTMCRgPiBOyVht89c9/nB7QNgeyB+4HCk5vYlMfglsTIBYG2yHO1Z+D7gs5tlAlYh7Nyl6fbtTDKGALAxWYofdA4MEH5C0nx2ClPs2fiZZJkAsCpZih8AfnJw+4wx1tgkVnVJqUwA2Bj0+YG7Ix+3L4lVXRIwAYDtgfiBeyEp5y5KQn1+ABwViB+4L9Ke4SDBDAcAdgTiB+6CfCa2p+X2pVEmAGwPxA/cEXD7RpJkmQCwJRA/kD9w++aRRpkAsAsQP3AvwO0DAIwG4gcyJx+3LxWS/CAAYGMgfuAuyEHf0OYJQDpA/MDdgjZPD0mWCQDbA/EDOZNPmyfcPgCSAuIH8gdDXQAAE8lyYWtsaQSMgds3kCQAIEKW4gedA+/I52HY1O2zYf3LpsAAWBE0e4I8ibp9SbV5ptXbBwAwxkD8QN5E3b6kfMIdevu8CaZUJgDsCMQP3Btw+wAAg0D8QIbkM9SF2G2Qp43+CcCBgfiBbEl+hsP+bt/e6QOQLBA/kBtw+wAANwPxA3mSlL752N/tM9ftnKkXGACbAvED90NSbZ5EKm5fGlYAkA4QP5AVmbR5JuH2GfT5ARAE4gcyJPmhLgTcPgCSBeIH8iETty8V0vsaACAdIH4gN5J3+1Jp8yTwNQCAD4gfyITc3D5sWgtAymS5qwO2NDouyd9luH0AZEGW4gedOxz57OFAwO0DIHHQ7AnyIZ89HJIABQJAGIgfSJ583L602jwBAGEgfiAT8nH70OYJQPpA/EDawO2bR0pfAwAkCMQP5ADcvpGkpL8ApEyWoz3BoSiMTb9Oh9sHQF7A8wMJg4ntAIB1gPgBcC+k5HwCkDhbiF/TNFVVVVXVNM34q6aGB/dGPm4f2jwByI7V+/yqqur7nn73fd91Xdd1I6+qqmpV20AG2Aw6/AgMdQEgI9b1/Jqm6fu+rmtrrbW2rmvSv/hVXdexXgIQAm6fhzTKBID0KVZdJ5NWoJZJFEVRlmVc/ygMqabb8lkU69oMkoDbPK0NNX8m8hSQ+O0/1IVKaW8rwDHJsVpevc+vLEv1Z9yro6bOMU2j4P7J7XXajZScTwCyYIs+v/GBqZk0uy8IsCr8OEgXMJFnBG2eAGTKip6f13uLaGHXdefzuW3bwZiL0dxgPtiPwI1LRPBcMNQFgOxY0fObOlbzdDqVZTnmKriGhyB8lxO5/3D7AMiXrZc3C3Xm0cAWNbev6zqeI7iBbSAJwv56mp483D4AcmR18Zs0dOV8Pss/+77HbL+DArdvEmmUCQAZse5oT3dsJ01gcEM2TWOvMcbQBEGI34FI07kLA7cPgExZfZK7EZ1/9IMbNruuK4oCa5gBjc+5S0oW4fYBkDvril9VVbSqC4297PveHcyJKX3gLfks5knA7QMgXzaalk8Kt0gDZo5LCYBRkPhF3b5E7nwSq7pgSReQDDlWyxlanGEpg2Gi+haWxR3gNs89xY/dvjTKBBycHKtl7OcHUiKB3r7CFGO69PZfzNNA+QCYD8QPJEAyvX0seyEJTGKoSwImAJA7ED+QDAm4fSOB2wdA7kD8wN4k4/aZa1VzFQ5uHwB3A8QPpEEabh96+wA4CBA/sCspuX0KJYRw+wC4J7Ze2HoRvHsVZTfQFrwDbt8kEjABgNzJUvygc8dh31tdmILULgm3DwCwHFmKH7gTEtu9SOqc18NLYkkXAMASQPzA3iS2e1GigzwZtHoAsAQY8AJ2IjG3bxC4fQDcExA/sCuJuX0ucPsAuEsgfmAP4PZNIskyASBrIH5gP+D2TSKNMgHgPoD4gc2B2zeJJMsEgNyB+IGdgNs3iTTKBIC7AeIHtgVu3ySSLBMA7gCIH9gDuH2TSKNMALgnIH5gQ+D2TSLJMgHgPoD4gc2B2zeJNMoEgDsD4ge2IhO3j5UPbh8Ad0yWa3tiS6OMSd7tI5LYusjA7QNgLbIUP+hcfuTm9u1MGlYAcMeg2RNsCNy+SaRhBQB3CcQPrA/cvkmkYQUA9w3EDyyD7fuhEHD7RsDKl1KZAHB/ZNnnB9LBvnplPvro7W/638uXxYcfvgsBt89JyY8N/AYArAA8PzAf++qV+elPjTHF5Z8xxnz2mX31ygkKt+9dGkFS+hoA4L6B+IEb+O1vzfe/L2vswhjz8cfmN7+5/A23bxxw+wDYliK7aQNFkZ/N94r94IPi9WvP8YeH4vXrd/rmu190MpE7SeK3aW+fUluL3j6QMTlWy/D8wEzsmzfGp3zGGPPmjeVTYeVLBLh9ABwQiB+YSfHwYN5/33/u4aH44IPBGJL6UtxzkGeB3j4AtgbiB27g88/tF1+oY/bJE/P555c/4PZNIaWvAQDum/waanNsXL5X7KtX5rPPzMcfF8+evT3y5In585+Lf//3Swj09sXSNkb09qVRIADMIMdqGZ4fmE/x4Yfmm2/Mw4M1xhpjHx7Me++NUb5ESMLtS8AEAA5IfnKd4yfGEbBdV1SVMTHPLjr8cwf2dPuEEcbA7QN5k2O1nKHFAd8hu4zcJ7lNbzC7D3W5GAFAvuQoflkub5ZdKR+O5Bs8mSQ2cEjABACOBvr8wHKM0LdEvlvQ2wfAwYH4gYUY0eCZGnD7ADgsED+wKFHPDm6fNAIAsCNZ9vmBRLj256wxb+t0JXJw+yJGAAB2AZ4f2Ai4fdIIAMC+QPzAusDtixgBANgLiB/YArh90ggAwO5A/MCKwO2LGAEA2JEtxK9pmqqqqqpqmmbZwCAL4PZJIwAAKbD6mjRVVfV9z3+WZdl1XdCaoqAwxhi6qm3bilaMFGGwwksihBw7uj9JreSJxcwAWI8cq+V1Pb+mafq+r+vaWmutreu67/uQ+JHItW3bdV3XdVSUp9NpVQvBSiSlfAyUDwBArCvX5MnJJIqiCDl/7qmmac7ns7Iwx0+M+0Tom7tiNdawdo0gCwC4P3Ksllef5E5tmPJP2QqqTqkWTpAu155dyrPaE1I+AEAyrC5+4/XMdQfP5/OyxoCFSX7TvrSUL4ECAQAQK/b5eds2R2ph13XUZNq2rXu2GM1NGQAh8tm9gcDcBgCAYkXPb3YbJg8QdYd6Etk1Lt8V+ezegLkNAIAQW09yj8xzMBeHjweIogswOcYpXwofJ2jwBABEWL3PL652KuTpdIpPBARJAOWbRAImAAAU645PpQZMNdWhrmvv6i3uvAgvOY6pvRNGuH2J3BkSv52VD24fOAw5VsvrWqycOaWFdJa0kH4bY+q6VpEopcyxlO8BNHhONMJcjADg7smxWl632bOqqrquz+czD7x0R2+qRk53egMW+UwIKN8kEjABAOBlI7lmz+/2qHL8xMiesL4lpXwGDZ4A7EGO1XKGFmdYynmTW1efQYMnANuSY7WM/fzAOPKZ1YcGTwDAIBA/ECWsb9s3eBamCM1bT0X5UvoaAABEgPiBESTQ1cfyFpHAVJQPbh8AyQPxA2ECbl+ag1x2N+ItaZQJACAOxA8EGJK4jZVPenXydxINnlA+AHID4gd8jJjbsDHSvXNdPSgfAGASq6/tuQbevYqyG2ibLrnN6kuCNAoEADCSLMUPOrciSSqfV+TSavAEAGQFmj2BIEnl85KW8qVRJgCA8UD8wIUR+raX8lljWeRCI1+2BsoHQM5k2ewJlieH/dlJ6uDzAQBuB54fyGm7IgbKBwC4BXh+hycr5dt5eKdMPI0CAQDMA54fMMYMiFtqyreP2wflA+COgOd3bKK9eYl09RGpKB9kD4C7AJ7fgcmnwRPKBwBYFnh+R2XcAmZQPigfAHcJxO94RMUtKeWTw1tWV754G+/eRQEAWBaI38EYp3y7y57ZWPmMMTasfwmUBgBgWSB+RwKdfDPYO30AwBpgwMthgPIN4qaWQGkAANYgS88PWxpNBsNbZrB3+gCA9chS/KBzgzifB/bqf75gKRRqKgM7+c8EygQAsAZZih9YhKSaOk1qygcAuGsgfgclKeXbemCnzwJO/u3vBIoFALAeEL8jAuWTyb8jgdIAAGwDxO8eKYK9VejkU8m/wzo/AAD3C6Y6ZIbt+4EQ45ajPrryFddNnQmUBgBgSyB+eWBfvbK/+pUtClNV9oMP7Fdf2VevdKCiGKN81u6vfIUpdlY+Zu+iAADsAsQvA+yrV+anPzXffUfuSvH6tXnzxnz22ZX+jWvQTEr2zPbKB4cPAGCMgfjlwW9/a77//eJ3v+MDxbNn5uOPzW9+Y8y1w3dx6+j/Sur2VT5X9nZQPpE8AODIFNlNGC+K/Gy+EfvBB8Xr1yPCXRVLOmNbius5dGjnBODOyLFaxmjP1LFv3phB5QvLnnNyFUjeXFVLS/YMlA8A8BY0e6ZO8fBg3n8/ePq6cVMNedlxbEsSjZzK4YPyAQAuwPPLgc8/t2/eFM+eyWP2yRPz3ntcvW/v7TEscoX2s4yBtwcASJL8GmpzbFy+FXeZ6idPzJ//bL75pvjoox1lj/BqnoHsAXAYcqyW4fmlTaH6zIwxxjw8mPfe+96//y/z/auw6Tx7kD0AQOLkJ9fezfzMne1z5Obxkjvbdd87VYGTO+C6fXtOYDCQPQB2IEfPL0OLMyzlCYRlzz2ZlOYxy4vfyM2G7vehACBxcqyW0eyZDFFlS0T2Iponwyysf3ZI/zJ76QAA+wPx25uwq+dt301H9qTCheb5LZW2BwgeAOAGIH5b447cvPyvMNYWRdDL2UX2RnbprSJ72FodALAaEL8NKWJ7hBfGutV9IoJHbDGSJSR4gy2fAAAwBYjfyozbXU+STsMmsbPmAQDACkD8FiUsdcVQRb6S5sV743aenz6pM4+dPygiAOBmsLann+EN0wlaTJP/8WFj1b+1DJ1Coc319Oetuwhncf3vOm0svwkA2IwUJ2c0TdN1nTGmqqqmadTZ9SaUhNy2d6kFQozUNopnOJUlGDMn4W26twjO7V10kxKH5wdAkuQ4zy85i6uq6oXXVZYlCSGzg/g5FfkYtQvZuLj4jdc5s0h75o0DT9J63AAAC5Cj+KXV7Nk0Td/3dV1ba621dV33fd913XXLonVaGecSaLTUoUa0YfLO6d4t1Cfb5bRPRv6FIrGFNsI9MmiH5994rO8fAAAkQFpyTet22qsFvYqyLPu+84a/st2nXut1tk0tNo9KkRTZBYbw+/25whTXkU8Wv3B6KqHYWQDAvZOj55fcaM+yLNWffXjsiXfPg8X5/0zxwXfm/73eUHYJyYrFcbtQFUvIKpQMAHCXJCd+VVUtHON4DQjozZsHrXxzDREe7Uo+WZTCLr3qJiGHt0AsAQA5kFCfnxrYQsS18F1Hku6VE/8ilxdW/gsFe//NL1XIef+ujA7lZLl/ctKCLVaew8C5AACAHEjI85vj870TlFn1brzvivnLz8xHc6JPgbeiC1kCAABBQp6fF687uBLWmv/9ZfF/fvlWA/+fh+Kfflk8/uVl8dHC0jdpcgIAAIDFSU78tlQ7l//7Q/Prr42xxQ/b4n97bX71z+Z7H31/g3Szl0NMYwAAZEVa4ueO7bxM+7uaPLfIXDov3DH2n9WKa31d9catvaIYZAkAABzSEj9azIw7/+iHXOHMWrPB3jZbjA3ZKhUAAAAuaYlfVVW0qktRFEVR9H3ftu3eRgEAALg3Ep2Wzwtbu6dyXEoAAADumByr5QwtzrCUAQDgjsmxWk6r2RMAAADYAIgfAACAwwHxAwAAcDggfgAAAA4HxA8AAMDhgPgBAAA4HAnt6jCewrdpe3YDbQEAAOxFlp6f9bG3UXPwqnimIC/Jck/ZQV7AUmQpfgAAAMAtQPwAAAAcDogfAACAwwHxAwAAcDggfgAAAA7HIcRvxqiqbS6ZAfKywSUzmJfKPWUHeZl6yQzuKS+7cwjxAwAAACQQPwAAAIcD4gcAAOBwQPwAAAAcDogfAACAwwHxAwAAcDiK7JaEPsgwXAAAyIj8pCQ7iwEAAIAbQbMnAACAwwHxAwAAcDggfgAAAA4HxA8AAMDh2FP8uq6rqqqqqqZp3LNN06x0dg2OkxfGGyCvvKx311biPh6zIkDXdYtYi7zMY0xemCxe/wHsTtR1TQaUZelawgc5zFJnt88Ln+KzbdtmmhdlWF3X461NLS/rPYErMfUxG2/wxtkxAfi9yOj1H8yLe1+Sff0H86IMS/z1H2Qf8WvbVpYd/cnFQS85n6U/+Qbccnb7vNAtlwbQvc8xLyqYevrzyst6T+Au2VGPmQqcYHYUpA23W5taXvJ6/V1kXpgsXv8x7CN+9EzII3Vdc9HI54OP8D245ewaDOZFpU43ns9mlBdlhnr688rLek/gStzTK6OQb8SgPdnlJaPXX6HyosxI/PUfwz7iF8+5e1a++becXYN4XsqyVOKhnv6M8qJscJ/+jPKy3hO4EoOPWbzqSS07kgUfpNTyktfrr1B5UTYk/vqPYbcBL9TtWVVVURRVVak+1aqq4tfO/nMNInnpuk717p7P54h5KefFGNM0Td/3NtA9kFdeJj2Bu+fFRA2mZ6woiqZpmqahJQBlgASzI9NS70her79KS+Ylu9dfpaWMz+v1H2QH8aN38nw+03NQ13Xf96fTiY6HRhbJa9Mhnhc3MNVK1GieXV66rjufz9zi716bDmOesUlP4L4M3pqqquhTWoaR16ZJ13V938snKq/XX+LmxQ2Q8usv8eYlo9d/JH+1Y9r8BUHfqqfTyVo76aMvHbx5kQGqqur73hjTti3lIru8nE6nsiy9ZmeXl8jZZPNiwtmhp6uua/pUb5rmfD53XUdTI/azdwCyVlqY6etvfHmRZPT6m0Becnz94+zg+VFJldcjX/lD1Uv8y+KWszcyJi/0xUd102Ddmmxe+H1oLphLq07IqmTzsvETeDuDBkvlM8Y0TVOWJdW2XvbNDkNmDwZL9vWXhPKSy+svcfOS1+s/kt08v/Uegu2LNZKXruvoiymXRyR+X1SPRd/3fd+PbJVKLS9e2FVKLS8mnB0yxu1T6fs+5eywk+qeyuv1N+G8ZPf6m+h9yev1H2brETbWWt9QH3nEO3QtMkjdOBOeQmfXIJ6XeCHnlReFcQa2ZZSXGU/gjnnxJqoeMzUwL/HsRFLJ6/WPp5LX6z8plcRf/zHsOcmdM6+mQHon8IauLX3Te0NnN86LnA2qyC4vLupUXnmZ8QTumJdBg8vreVd0lgMnmB0bGEkfsid+Ns28ZPf6E5FXPhIszbzE2X95M0J9BagxRaqYbjm7ZV4iQ7+yy4uL+5LklZf1nsCViBtcRpePSi07dXSNjxxffzehfF//MQmZ5F//QXbeyd3bXbHB2TW4JUXkZT3u6RkbTDS77ES4p7zEQV52YWfxAwAAALYH+/kBAAA4HBA/AAAAhwPiBwAA+8ALsbrQSq3mMk0+tD1saLPZSfC+tTRpfTC5qqoofCK9d/NAnx8AAOwDLUTnrYSLouBVe0gg3WCh41MpiqIsS1o2fWRyg/P30weeHwAAJA3NY/HKjJricgvsxo1JrrqspZ4vED8AAEgabv90D3rbJ/NKbi8gfgAAkDTkk6mlNdebM7dxcnux55ZGAAAAxrhTdV3TNlUsP33fr9fwuHFy+7Df4jIAAHBo4js6qfXDjLO461KLhBnfQoaDyZVlufvi1LcAzw8AAPbEBkZ7ugd5j8ZtGiE3Tm5j0OcHAAAZQO4X6dDIfYAzSm57IH4AAJABPN6S91W/p+S2B+IHAAB5UJZl3/eTGiF56rocvbJechkB8QMAgDwgJ2x8I2TXdTRo0xhzOp2mztKbmlxeYMALAADkAbtfIRnruo48PPb2TGCtlkWSyxp4fgAAsA9N03iHehpjrLVeyaFh+t5LqqqiS9jJY/GbrYKR5HIHC1sDAED20ErTVqw6ba3tuo42ajDGFEWwtqeFradKo3QucwTNngAAkD03Dkvp+56GxoyJgVzJ3Nd8gfgBAMA9QNsSzbuw73tazHOk+KmVP3MEzZ4AAJA9tzR7HhN4fgAAkD1VVdV1Tb13fd+3bbu3RamDbwEAALgfZkxmPyYQPwAAAIcD8/wAAAAcDogfAACAwwHxAwAAcDggfgAAAA4HxA8AAMDhgPgBAAA4HBA/AAAAhwPiBwAA4HBA/AAAABwOiB8AAIDDAfEDAABwOCB+AAAADgfEDwAAwOGA+AEAADgc/z/7VxcUZeVRKAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ROOT import TCanvas, TGraphErrors, TLatex, TLine, TLegend\n",
    "import math\n",
    "import pandas as pd\n",
    "\n",
    "# Inicializando listas para gráficos e funções\n",
    "TGraphs_list_40CO2 = []\n",
    "func_list_40CO2 = []\n",
    "\n",
    "canvas = TCanvas(\"c\", \"c\", 564, 232, 600, 600)\n",
    "HV_ref = 95\n",
    "\n",
    "k = 1\n",
    "\n",
    "# Contagem dos scans no dicionário\n",
    "num_scans_total = len(scans_40CO2)\n",
    "print(f\"Total de scans no dicionário: {num_scans_total}\")\n",
    "\n",
    "# Listas para armazenar os dados\n",
    "tabela_dados = []\n",
    "\n",
    "for scan, sigmoid, color, marker in scans_40CO2:\n",
    "    if scan not in scans:\n",
    "        print(f\"Scan '{scan}' não encontrado no dicionário de scans. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    print(f\"Processando scan: {scan}\")\n",
    "    \n",
    "    df = HVCurrentDataFrame(scans[scan])\n",
    "    n = len(df['HV_top'])\n",
    "\n",
    "    gr_E = TGraphErrors(n, \n",
    "                        df['HV_top'].values, \n",
    "                        df['efficiency'].values,\n",
    "                        0,\n",
    "                        df['eff_error'].values) # valores do df[].values para converter objeto dataframe em array adequado para o TGraph\n",
    "    \n",
    "    # Ajuste e verificação do resultado\n",
    "    fit_result = gr_E.Fit(sigmoid, \"S\")\n",
    "    if not fit_result.IsValid():\n",
    "        print(f\"Fit falhou para o scan: {scan}\")\n",
    "        continue\n",
    "    \n",
    "    # Listando funções após o ajuste para depuração\n",
    "    functions = gr_E.GetListOfFunctions()\n",
    "    function_names = [f.GetName() for f in functions]\n",
    "    print(f\"Funções no gráfico após ajuste: {function_names}\")\n",
    "\n",
    "    # Tentando encontrar a função ajustada correta\n",
    "    sig = None\n",
    "    for name in function_names:\n",
    "        if name.startswith(\"sigmoid\"):\n",
    "            sig = gr_E.GetFunction(name)\n",
    "            break\n",
    "    \n",
    "    if sig is None:\n",
    "        print(f\"Função 'sigmoid' não encontrada para o scan: {scan}. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    sig.SetLineColor(color)\n",
    "    gr_E.SetMarkerColor(color)\n",
    "    gr_E.SetMarkerStyle(marker)\n",
    "\n",
    "    Emax = sig.GetParameter(0)\n",
    "    Emax_err = sig.GetParError(0)     \n",
    "\n",
    "    Lambda = sig.GetParameter(1)    \n",
    "    Lambda_err = sig.GetParError(1)      \n",
    "\n",
    "    HV50 = sig.GetParameter(2)\n",
    "    HV50_err = sig.GetParError(2)      \n",
    "    \n",
    "    print(f\"Lambda = {Lambda}\")\n",
    "    HV95 = sig.GetX(HV_ref)\n",
    "    HV95_err = math.sqrt(((math.log(19) / Lambda**2) * Lambda_err)**2 + HV50_err**2) # erro HV95\n",
    "    \n",
    "    print(f\"HV95_err = {HV95_err}\")\n",
    "\n",
    "    # Adicionando gráficos e funções às listas\n",
    "    TGraphs_list_40CO2.append(gr_E)\n",
    "    func_list_40CO2.append(sig)\n",
    "    \n",
    "    # Adicionando dados à tabela\n",
    "    for i in range(n):\n",
    "        HV_top = df['HV_top'].values[i]\n",
    "        efficiency = df['efficiency'].values[i]\n",
    "        eff_error = df['eff_error'].values[i]\n",
    "        tabela_dados.append([scan, HV_top, efficiency, eff_error])\n",
    "    \n",
    "    gr_E.Draw(\"AP\")\n",
    "    k += 1\n",
    "\n",
    "    print(f\"Fit concluído para o scan: {scan}\")\n",
    "\n",
    "# Criando o canvas final\n",
    "canvas_all = TCanvas(\"c_all\", \"c_all\", 0, 0, 600, 600)\n",
    "\n",
    "if TGraphs_list_40CO2:\n",
    "    TGraphs_list_40CO2[0].GetHistogram().SetMaximum(1.4)\n",
    "    TGraphs_list_40CO2[0].SetTitle(\"\")\n",
    "    TGraphs_list_40CO2[0].GetXaxis().SetTitle(\"HV_{eff} [V]\")\n",
    "    TGraphs_list_40CO2[0].GetYaxis().SetTitle(\"Muon Efficiency\")\n",
    "    TGraphs_list_40CO2[0].Draw(\"AP\")\n",
    "    \n",
    "    for graph in TGraphs_list_40CO2[1:]:\n",
    "        graph.Draw(\"PSAME\")\n",
    "\n",
    "    Emax_40CO2 = []\n",
    "    Emax_err_40CO2 = []\n",
    "    Lambda_40CO2 = []\n",
    "    Lambda_err_40CO2 = []\n",
    "    HV50_40CO2 = []\n",
    "    HV50_err_40CO2 = []\n",
    "    HV95_40CO2 = []\n",
    "    HV95_err_40CO2 = []\n",
    "\n",
    "    for sig in func_list_40CO2:\n",
    "        Emax_ = 1 - math.sqrt((1 - sig.GetParameter(0)) * (1 - sig.GetParameter(0)))\n",
    "        Emax_40CO2.append(Emax_)\n",
    "        Emax_err_40CO2.append(sig.GetParError(0))     \n",
    "        Lambda_40CO2.append(sig.GetParameter(1))  \n",
    "        Lambda_err_40CO2.append(sig.GetParError(1))\n",
    "        HV50_40CO2.append(sig.GetParameter(2))  \n",
    "        HV50_err_40CO2.append(sig.GetParError(2)) \n",
    "        HV95_40CO2.append(sig.GetX(HV_ref))\n",
    "        HV95_err_40CO2.append((math.log(19) / sig.GetParameter(1)**2) * sig.GetParError(1) + sig.GetParError(2)) # erro HV95\n",
    "\n",
    "    WP_40CO2 = []    \n",
    "\n",
    "    for Emax_, Lambda_, HV50_ in zip(Emax_40CO2, Lambda_40CO2, HV50_40CO2):\n",
    "        WP_40CO2.append((HV50_ - math.log(1 / 0.95 - 1) / Lambda_ + 150.))\n",
    "\n",
    "    # Atualizando os textos para o novo conjunto de dados\n",
    "    txt_40CO2_10 = Features_40CO2['noiseGammaRate'][2] / (Features_40CO2['gamma_CS'][2] * 1000)\n",
    "    txt_40CO2_3_3 = Features_40CO2['noiseGammaRate'][3] / (Features_40CO2['gamma_CS'][3] * 1000)\n",
    "    txt_40CO2_2_2 = Features_40CO2['noiseGammaRate'][4] / (Features_40CO2['gamma_CS'][4] * 1000)\n",
    "    txt_40CO2_1 = Features_40CO2['noiseGammaRate'][5] / (Features_40CO2['gamma_CS'][5] * 1000)\n",
    "\n",
    "    eff_1_40CO2 = 'plateau = {a:.0%}, WP = {b:.2f} kV, no hit bkg gamma rate, Eff(WP) = {c:.0%}'.format(a=Emax_40CO2[0], b=WP_40CO2[0] / 1000., c=(func_list_40CO2[0].Eval(WP_40CO2[0])))\n",
    "    eff_2_40CO2 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm2, Eff(WP) = {c:.0%}'.format(a=Emax_40CO2[1], b=WP_40CO2[1] / 1000., c=(func_list_40CO2[1].Eval(WP_40CO2[1])), d=txt_40CO2_10)\n",
    "    eff_3_40CO2 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm2, Eff(WP) = {c:.0%}'.format(a=Emax_40CO2[2], b=WP_40CO2[2] / 1000., c=(func_list_40CO2[2].Eval(WP_40CO2[2])), d=txt_40CO2_3_3)\n",
    "    eff_4_40CO2 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm2, Eff(WP) = {c:.0%}'.format(a=Emax_40CO2[3], b=WP_40CO2[3] / 1000., c=(func_list_40CO2[3].Eval(WP_40CO2[3])), d=txt_40CO2_2_2)\n",
    "    eff_5_40CO2 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm2, Eff(WP) = {c:.0%}'.format(a=Emax_40CO2[4], b=WP_40CO2[4] / 1000., c=(func_list_40CO2[4].Eval(WP_40CO2[4])), d=txt_40CO2_1)\n",
    "\n",
    "    cms_tex = TLatex()\n",
    "    cms_tex.SetNDC()\n",
    "    cms_tex.SetTextFont(61)\n",
    "    cms_tex.SetTextSize(0.04)\n",
    "    cms_tex.SetLineWidth(2)\n",
    "    cms_tex.DrawLatex(0.10, 0.905, \"CMS MUON\")\n",
    "\n",
    "    cms_tex_1 = TLatex()\n",
    "    cms_tex_1.SetNDC()\n",
    "    cms_tex_1.SetTextFont(61)\n",
    "    cms_tex_1.SetTextSize(0.04)\n",
    "    cms_tex_1.SetLineWidth(2)\n",
    "    cms_tex_1.DrawLatex(0.79, 0.905, \"GIF++\")\n",
    "\n",
    "    cms_tex_2 = TLatex()\n",
    "    cms_tex_2.SetNDC()\n",
    "    cms_tex_2.SetTextFont(52)\n",
    "    cms_tex_2.SetTextSize(0.04)\n",
    "    cms_tex_2.SetLineWidth(2)\n",
    "    cms_tex_2.DrawLatex(0.32, 0.905, \"Preliminary\")\n",
    "\n",
    "    legend = TLegend(0.1, 0.68, 0.3, 0.89)\n",
    "    legend.SetTextFont(42)\n",
    "    legend.SetBorderSize(0) \n",
    "    legend.SetFillStyle(4000)\n",
    "    legend.SetFillColor(0)  \n",
    "    legend.SetTextSize(0.02376) \n",
    "    legend.AddEntry(TGraphs_list_40CO2[0], eff_1_40CO2, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_40CO2[1], eff_2_40CO2, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_40CO2[2], eff_3_40CO2, \"P\")\n",
    "    if len(TGraphs_list_40CO2) > 3:\n",
    "        legend.AddEntry(TGraphs_list_40CO2[3], eff_4_40CO2, \"P\")\n",
    "    if len(TGraphs_list_40CO2) > 4:\n",
    "        legend.AddEntry(TGraphs_list_40CO2[4], eff_5_40CO2, \"P\")\n",
    "    legend.Draw()\n",
    "    canvas_all.Draw()\n",
    "\n",
    "    ltx_data = TLatex()\n",
    "    ltx_data.SetTextFont(42)\n",
    "    ltx_data.SetTextSize(0.025)\n",
    "    ltx_data.SetTextColor(1)\n",
    "    ltx_data.DrawLatex(5900, 0.88, \"40% CO2 + 2% SF6\")\n",
    "    ltx_data.DrawLatex(5900, 0.81, \"Test Beam in April+July+August 2024\")\n",
    "    ltx_data.DrawLatex(5900, 0.74, \"1.4 mm double gap RPC\")\n",
    "    ltx_data.DrawLatex(5900, 0.67, \"Threshold = 60 [fC]\")\n",
    "\n",
    "    line = TLine(5850, 1., 7750, 1.)\n",
    "    line.SetLineColor(1)\n",
    "    line.SetLineStyle(9)\n",
    "    line.SetLineWidth(2)\n",
    "    line.Draw()\n",
    "\n",
    "    canvas_all.cd()\n",
    "    canvas_all.Draw()\n",
    "else:\n",
    "    print(\"Nenhum gráfico válido para desenhar.\")\n",
    "\n",
    "# Salvando a tabela em um arquivo CSV\n",
    "tabela_df = pd.DataFrame(tabela_dados, columns=[\"Scan\", \"HV_top\", \"Efficiency\", \"Eff_Error\"])\n",
    "tabela_df.to_csv(\"tabela_dados_40CO2.csv\", index=False)\n",
    "print(\"Tabela salva em 'tabela_dados_40CO2.csv'.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de scans no dicionário: 5\n",
      "Processando scan: 40CO2_OFF\n",
      "Funções no gráfico após ajuste: ['sigmoid11']\n",
      "Lambda = 0.009824715646634752\n",
      "HV95_err = 3.2181281241580177\n",
      "Fit concluído para o scan: 40CO2_OFF\n",
      "Processando scan: 40CO2_10\n",
      "Funções no gráfico após ajuste: ['sigmoid12']\n",
      "Lambda = 0.010292375064091107\n",
      "HV95_err = 3.2722653858912336\n",
      "Fit concluído para o scan: 40CO2_10\n",
      "Processando scan: 40CO2_6.9\n",
      "Funções no gráfico após ajuste: ['sigmoid14']\n",
      "Lambda = 0.010183969099000352\n",
      "HV95_err = 3.393993214796128\n",
      "Fit concluído para o scan: 40CO2_6.9\n",
      "Processando scan: 40CO2_4.6\n",
      "Funções no gráfico após ajuste: ['sigmoid20']\n",
      "Lambda = 0.01047351150128923\n",
      "HV95_err = 3.3110802709612077\n",
      "Fit concluído para o scan: 40CO2_4.6\n",
      "Processando scan: 40CO2_3.3\n",
      "Funções no gráfico após ajuste: ['sigmoid13']\n",
      "Lambda = 0.010931554867856783\n",
      "HV95_err = 3.22197088461028\n",
      "Fit concluído para o scan: 40CO2_3.3\n",
      "Tabela salva em 'tabela_dados_40CO2.csv'.\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      152.327\n",
      "NDf                       =           14\n",
      "Edm                       =  6.93831e-07\n",
      "NCalls                    =           37\n",
      "Emax                      =     0.990291   +/-   0.000616638 \n",
      "Lambda                    =   0.00982472   +/-   8.94413e-05 \n",
      "HV50                      =      6541.83   +/-   1.70659     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      222.247\n",
      "NDf                       =           13\n",
      "Edm                       =  1.99162e-08\n",
      "NCalls                    =           40\n",
      "Emax                      =     0.968357   +/-   0.00121398  \n",
      "Lambda                    =    0.0102924   +/-   9.63974e-05 \n",
      "HV50                      =      6543.36   +/-   1.87845     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      186.748\n",
      "NDf                       =           14\n",
      "Edm                       =  6.57025e-09\n",
      "NCalls                    =           37\n",
      "Emax                      =     0.963256   +/-   0.00131259  \n",
      "Lambda                    =     0.010184   +/-   9.88878e-05 \n",
      "HV50                      =      6544.82   +/-   1.90721     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      272.836\n",
      "NDf                       =           14\n",
      "Edm                       =  7.20204e-07\n",
      "NCalls                    =           70\n",
      "Emax                      =     0.952225   +/-   0.0014479   \n",
      "Lambda                    =    0.0104735   +/-   9.90489e-05 \n",
      "HV50                      =      6583.72   +/-   1.97348     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      232.237\n",
      "NDf                       =           12\n",
      "Edm                       =  3.49189e-06\n",
      "NCalls                    =           80\n",
      "Emax                      =     0.931393   +/-   0.00202125  \n",
      "Lambda                    =    0.0109316   +/-   0.000100453 \n",
      "HV50                      =      6610.39   +/-   2.06269     \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5640 xmax = 7560 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5640.000000,7560.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5640 xmax = 7560 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5640.000000,7560.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5680 xmax = 7600 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5680.000000,7600.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5740 xmax = 7420 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5740.000000,7420.000000] is not a valid interval\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c_all\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5640 xmax = 7560 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5640.000000,7560.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5640 xmax = 7560 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5640.000000,7560.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5680 xmax = 7600 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5680.000000,7600.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5740 xmax = 7420 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5740.000000,7420.000000] is not a valid interval\n",
      "Info in <TCanvas::Print>: pdf file 40CO2_eff.pdf has been created\n",
      "Info in <TCanvas::Print>: png file 40CO2_eff.png has been created\n",
      "Info in <TCanvas::SaveAs>: ROOT file 40CO2_eff.root has been created\n"
     ]
    }
   ],
   "source": [
    "from ROOT import TCanvas, TGraphErrors, TLatex, TLine, TLegend\n",
    "import math\n",
    "import pandas as pd\n",
    "\n",
    "# Nome do arquivo CSV atualizado\n",
    "arquivo_csv = 'tabela_dados_40CO2.csv'\n",
    "\n",
    "# Ler o arquivo CSV\n",
    "df_csv = pd.read_csv(arquivo_csv)\n",
    "\n",
    "# Ajustar os valores de HV_top para os scans especificados\n",
    "df_csv.loc[df_csv['Scan'] == '40CO2_3.3', 'HV_top'] += 80\n",
    "df_csv.loc[df_csv['Scan'] == '40CO2_4.6', 'HV_top'] += 40\n",
    "\n",
    "# Inicializando listas para gráficos e funções\n",
    "TGraphs_list_40CO2 = []\n",
    "func_list_40CO2 = []\n",
    "\n",
    "# Configurações do canvas\n",
    "canvas = TCanvas(\"c\", \"c\", 564, 232, 600, 600)\n",
    "canvas.SetTopMargin(0.1)\n",
    "canvas.SetBottomMargin(0.12)\n",
    "canvas.SetLeftMargin(0.14)\n",
    "canvas.SetRightMargin(0.05)\n",
    "\n",
    "HV_ref = 95\n",
    "\n",
    "\n",
    "k = 1\n",
    "\n",
    "# Contagem dos scans no dicionário\n",
    "num_scans_total = len(scans_40CO2)\n",
    "print(f\"Total de scans no dicionário: {num_scans_total}\")\n",
    "\n",
    "for scan, sigmoid, color, marker in scans_40CO2:\n",
    "    if scan not in scans:\n",
    "        print(f\"Scan '{scan}' não encontrado no dicionário de scans. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    print(f\"Processando scan: {scan}\")\n",
    "\n",
    "    # Filtrar dados do DataFrame ajustado\n",
    "    df = df_csv[df_csv['Scan'] == scan]\n",
    "    n = len(df['HV_top'])\n",
    "\n",
    "    gr_E = TGraphErrors(n, \n",
    "                        df['HV_top'].values, \n",
    "                        df['Efficiency'].values,\n",
    "                        0,\n",
    "                        df['Eff_Error'].values) # valores do df[].values para converter objeto dataframe em array adequado para o TGraph\n",
    "    \n",
    "    # Ajuste e verificação do resultado\n",
    "    fit_result = gr_E.Fit(sigmoid, \"S\")\n",
    "    if not fit_result.IsValid():\n",
    "        print(f\"Fit falhou para o scan: {scan}\")\n",
    "        continue\n",
    "    \n",
    "    # Listando funções após o ajuste para depuração\n",
    "    functions = gr_E.GetListOfFunctions()\n",
    "    function_names = [f.GetName() for f in functions]\n",
    "    print(f\"Funções no gráfico após ajuste: {function_names}\")\n",
    "\n",
    "    # Tentando encontrar a função ajustada correta\n",
    "    sig = None\n",
    "    for name in function_names:\n",
    "        if name.startswith(\"sigmoid\"):\n",
    "            sig = gr_E.GetFunction(name)\n",
    "            break\n",
    "    \n",
    "    if sig is None:\n",
    "        print(f\"Função 'sigmoid' não encontrada para o scan: {scan}. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    sig.SetLineColor(color)\n",
    "    gr_E.SetMarkerColor(color)\n",
    "    gr_E.SetMarkerStyle(marker)\n",
    "    gr_E.SetLineWidth(2)\n",
    "    gr_E.SetMarkerSize(1.2)\n",
    "\n",
    "    Emax = sig.GetParameter(0)\n",
    "    Emax_err = sig.GetParError(0)     \n",
    "\n",
    "    Lambda = sig.GetParameter(1)    \n",
    "    Lambda_err = sig.GetParError(1)      \n",
    "\n",
    "    HV50 = sig.GetParameter(2)\n",
    "    HV50_err = sig.GetParError(2)      \n",
    "    \n",
    "    print(f\"Lambda = {Lambda}\")\n",
    "    HV95 = sig.GetX(HV_ref)\n",
    "    HV95_err = math.sqrt(((math.log(19) / Lambda**2) * Lambda_err)**2 + HV50_err**2) # erro HV95\n",
    "    \n",
    "    print(f\"HV95_err = {HV95_err}\")\n",
    "\n",
    "    # Adicionando gráficos e funções às listas\n",
    "    TGraphs_list_40CO2.append(gr_E)\n",
    "    func_list_40CO2.append(sig)\n",
    "    gr_E.Draw(\"AP\")\n",
    "    k += 1\n",
    "\n",
    "    print(f\"Fit concluído para o scan: {scan}\")\n",
    "\n",
    "# Criando o canvas final\n",
    "canvas_all = TCanvas(\"c_all\", \"c_all\", 0, 0, 600, 600)\n",
    "canvas_all.SetTopMargin(0.1)\n",
    "canvas_all.SetBottomMargin(0.12)\n",
    "canvas_all.SetLeftMargin(0.14)\n",
    "canvas_all.SetRightMargin(0.05)\n",
    "\n",
    "if TGraphs_list_40CO2:\n",
    "    TGraphs_list_40CO2[0].GetHistogram().SetMaximum(1.4)\n",
    "    TGraphs_list_40CO2[0].GetHistogram().SetMinimum(0)\n",
    "    TGraphs_list_40CO2[0].SetTitle(\"\")\n",
    "    TGraphs_list_40CO2[0].GetXaxis().SetTitle(\"HV_{eff} [V]\")\n",
    "    TGraphs_list_40CO2[0].GetYaxis().SetTitle(\"Efficiency\")\n",
    "    TGraphs_list_40CO2[0].GetXaxis().SetTitleSize(0.05)\n",
    "    TGraphs_list_40CO2[0].GetYaxis().SetTitleSize(0.05)\n",
    "    TGraphs_list_40CO2[0].GetXaxis().SetLabelSize(0.04)\n",
    "    TGraphs_list_40CO2[0].GetYaxis().SetLabelSize(0.04)\n",
    "    TGraphs_list_40CO2[0].GetXaxis().SetLimits(6000, 7400)\n",
    "    TGraphs_list_40CO2[0].Draw(\"AP\")\n",
    "    \n",
    "    for graph in TGraphs_list_40CO2[1:]:\n",
    "        graph.Draw(\"PSAME\")\n",
    "\n",
    "    Emax_40CO2 = []\n",
    "    Emax_err_40CO2 = []\n",
    "    Lambda_40CO2 = []\n",
    "    Lambda_err_40CO2 = []\n",
    "    HV50_40CO2 = []\n",
    "    HV50_err_40CO2 = []\n",
    "    HV95_40CO2 = []\n",
    "    HV95_err_40CO2 = []\n",
    "\n",
    "    for sig in func_list_40CO2:\n",
    "        Emax_ = 1 - math.sqrt((1 - sig.GetParameter(0)) * (1 - sig.GetParameter(0)))\n",
    "        Emax_40CO2.append(Emax_)\n",
    "        Emax_err_40CO2.append(sig.GetParError(0))     \n",
    "        Lambda_40CO2.append(sig.GetParameter(1))  \n",
    "        Lambda_err_40CO2.append(sig.GetParError(1))\n",
    "        HV50_40CO2.append(sig.GetParameter(2))  \n",
    "        HV50_err_40CO2.append(sig.GetParError(2)) \n",
    "        HV95_40CO2.append(sig.GetX(HV_ref))\n",
    "        HV95_err_40CO2.append((math.log(19) / sig.GetParameter(1)**2) * sig.GetParError(1) + sig.GetParError(2)) # erro HV95\n",
    "\n",
    "    WP_40CO2 = []    \n",
    "\n",
    "    for Emax_, Lambda_, HV50_ in zip(Emax_40CO2, Lambda_40CO2, HV50_40CO2):\n",
    "        WP_40CO2.append((HV50_ - math.log(1 / 0.95 - 1) / Lambda_ + 150.))\n",
    "\n",
    "    txt_40CO2_10 = Features_40CO2['noiseGammaRate'][2] / (Features_40CO2['gamma_CS'][2] * 1000)\n",
    "    txt_40CO2_3_3 = Features_40CO2['noiseGammaRate'][3] / (Features_40CO2['gamma_CS'][3] * 1000)\n",
    "    txt_40CO2_2_2 = Features_40CO2['noiseGammaRate'][4] / (Features_40CO2['gamma_CS'][4] * 1000)\n",
    "    txt_40CO2_1 = Features_40CO2['noiseGammaRate'][5] / (Features_40CO2['gamma_CS'][5] * 1000)\n",
    "\n",
    "    eff_1_40CO2 = 'plateau = {a:.0%}, WP = {b:.2f} kV, no hit bkg gamma rate, Eff(WP) = {c:.0%}'.format(a=Emax_40CO2[0], b=WP_40CO2[0] / 1000., c=(func_list_40CO2[0].Eval(WP_40CO2[0])))\n",
    "    eff_2_40CO2 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_40CO2[1], b=WP_40CO2[1] / 1000., c=(func_list_40CO2[1].Eval(WP_40CO2[1])), d=txt_40CO2_10)\n",
    "    eff_3_40CO2 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_40CO2[2], b=WP_40CO2[2] / 1000., c=(func_list_40CO2[2].Eval(WP_40CO2[2])), d=txt_40CO2_3_3)\n",
    "    eff_4_40CO2 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_40CO2[3], b=WP_40CO2[3] / 1000., c=(func_list_40CO2[3].Eval(WP_40CO2[3])), d=txt_40CO2_2_2)\n",
    "    eff_5_40CO2 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_40CO2[4], b=WP_40CO2[4] / 1000., c=(func_list_40CO2[4].Eval(WP_40CO2[4])), d=txt_40CO2_1)\n",
    "\n",
    "    cms_tex = TLatex()\n",
    "    cms_tex.SetNDC()\n",
    "    cms_tex.SetTextFont(61)\n",
    "    cms_tex.SetTextSize(0.04)\n",
    "    cms_tex.SetLineWidth(2)\n",
    "    cms_tex.DrawLatex(0.14, 0.905, \"CMS\")\n",
    "\n",
    "    cms_tex_1 = TLatex()\n",
    "    cms_tex_1.SetNDC()\n",
    "    cms_tex_1.SetTextFont(61)\n",
    "    cms_tex_1.SetTextSize(0.04)\n",
    "    cms_tex_1.SetLineWidth(2)\n",
    "    cms_tex_1.DrawLatex(0.85, 0.905, \"GIF++\")\n",
    "\n",
    "    cms_tex_2 = TLatex()\n",
    "    cms_tex_2.SetNDC()\n",
    "    cms_tex_2.SetTextFont(52)\n",
    "    cms_tex_2.SetTextSize(0.04)\n",
    "    cms_tex_2.SetLineWidth(2)\n",
    "    cms_tex_2.DrawLatex(0.23, 0.905, \"Preliminary\")\n",
    "\n",
    "    legend = TLegend(0.15, 0.68, 0.3, 0.89)\n",
    "    legend.SetTextFont(42)\n",
    "    legend.SetBorderSize(0) \n",
    "    legend.SetFillStyle(4000)\n",
    "    legend.SetFillColor(0)  \n",
    "    legend.SetTextSize(0.02376) \n",
    "    legend.AddEntry(TGraphs_list_40CO2[0], eff_1_40CO2, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_40CO2[1], eff_2_40CO2, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_40CO2[2], eff_3_40CO2, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_40CO2[3], eff_4_40CO2, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_40CO2[4], eff_5_40CO2, \"P\")\n",
    "    legend.Draw()\n",
    "    canvas_all.Draw()\n",
    "\n",
    "    ltx_data = TLatex()\n",
    "    ltx_data.SetTextFont(42)\n",
    "    ltx_data.SetTextSize(0.025)\n",
    "    ltx_data.SetTextColor(1)\n",
    "    ltx_data.DrawLatex(6050, 0.88, \"40% CO_{2} + 1.0% SF_{6}\")\n",
    "    ltx_data.DrawLatex(6050, 0.81, \"Test Beam in April+July+August\")\n",
    "    ltx_data.DrawLatex(6050, 0.74, \"2023, Before irradiation\")\n",
    "    ltx_data.DrawLatex(6050, 0.67, \"1.4 mm double gap RPC\")\n",
    "    ltx_data.DrawLatex(6050, 0.60, \"Threshold = 60 [fC]\")\n",
    "\n",
    "    line = TLine(6000, 1., 7400, 1.)\n",
    "    line.SetLineColor(1)\n",
    "    line.SetLineStyle(9)\n",
    "    line.SetLineWidth(2)\n",
    "    line.Draw()\n",
    "\n",
    "    canvas_all.Update()\n",
    "    canvas_all.SaveAs(\"40CO2_eff.pdf\")\n",
    "    canvas_all.SaveAs(\"40CO2_eff.png\")\n",
    "    canvas_all.SaveAs(\"40CO2_eff.root\")\n",
    "else:\n",
    "    print(\"Nenhum gráfico válido para desenhar.\")\n",
    "\n",
    "# Salvando a tabela em um arquivo CSV\n",
    "tabela_df = pd.DataFrame(tabela_dados, columns=[\"Scan\", \"HV_top\", \"Efficiency\", \"Eff_Error\"])\n",
    "tabela_df.to_csv(\"tabela_dados_40CO2.csv\", index=False)\n",
    "print(\"Tabela salva em 'tabela_dados_40CO2.csv'.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scanID:  5833\n",
      "scanID:  5808\n",
      "scanID:  5845\n",
      "scanID:  5819\n",
      "Features_OFF =  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   current_top  current_bot  HV_top  HV_bot  muon_stream  gamma_stream  \\\n",
      "0     0.729052     0.573356  6950.0  6950.0     0.023953      0.000650   \n",
      "1     0.662577     0.579678  7250.0  7250.0     0.021638      0.000510   \n",
      "2     0.908333     0.675833  7000.0  7000.0     0.023776      0.000225   \n",
      "3     0.516166     0.416491  7060.0  7060.0     0.018003      0.000250   \n",
      "\n",
      "    muon_CM  gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0  1.049668  1.337696  2.242913  1.526419     0.137472      0.052356   \n",
      "1  1.056347  1.686717  2.149189  1.335598     0.128334      0.049051   \n",
      "2  1.048760  1.361280  2.379098  1.416013     0.162951      0.035061   \n",
      "3  1.046605  1.380734  2.027942  1.553710     0.114498      0.041284   \n",
      "\n",
      "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0     0.259732      0.057492   98.277432   0.000658        3.886514   \n",
      "1     0.232817      0.037743   98.604936   0.000838        6.272502   \n",
      "2     0.319941      0.035555   98.192179   0.001906        6.298748   \n",
      "3     0.199979      0.045108   97.984488   0.001414        3.491967   \n",
      "\n",
      "   noiseGammaRate_err   current  \n",
      "0            1.844694  1.302408  \n",
      "1            2.257067  1.242255  \n",
      "2            2.203070  1.584167  \n",
      "3            1.797203  0.932656  \n"
     ]
    }
   ],
   "source": [
    "Features_OFF = FeaturesDataFrame(Scans_OFF)\n",
    "print(\"Features_OFF = \", Features_OFF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de scans no dicionário: 4\n",
      "Processando scan: STDMX_OFF\n",
      "Funções no gráfico após ajuste: ['sigmoid1']\n",
      "Lambda = 0.012161892782944023\n",
      "HV95_err = 3.7542268201599223\n",
      "Fit concluído para o scan: STDMX_OFF\n",
      "Processando scan: 30CO2_OFF\n",
      "Funções no gráfico após ajuste: ['sigmoid6']\n",
      "Lambda = 0.01050718089512795\n",
      "HV95_err = 3.0706699135950037\n",
      "Fit concluído para o scan: 30CO2_OFF\n",
      "Processando scan: 30CO205SF6_OFF\n",
      "Funções no gráfico após ajuste: ['sigmoid16']\n",
      "Lambda = 0.009868738633502284\n",
      "HV95_err = 3.2132193837874103\n",
      "Fit concluído para o scan: 30CO205SF6_OFF\n",
      "Processando scan: 40CO2_OFF\n",
      "Funções no gráfico após ajuste: ['sigmoid11']\n",
      "Lambda = 0.009824684138865362\n",
      "HV95_err = 3.2181265717418532\n",
      "Fit concluído para o scan: 40CO2_OFF\n",
      "Gráficos e legendas adicionados ao canvas.\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      14.1711\n",
      "NDf                       =            7\n",
      "Edm                       =   1.3994e-07\n",
      "NCalls                    =           37\n",
      "Emax                      =     0.989616   +/-   0.000817385 \n",
      "Lambda                    =    0.0121619   +/-   0.000163803 \n",
      "HV50                      =      6787.78   +/-   1.86051     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      240.082\n",
      "NDf                       =           14\n",
      "Edm                       =  1.75121e-12\n",
      "NCalls                    =           43\n",
      "Emax                      =     0.993042   +/-   0.000545727 \n",
      "Lambda                    =    0.0105072   +/-   9.6879e-05  \n",
      "HV50                      =      6646.61   +/-   1.6592      \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      137.155\n",
      "NDf                       =            7\n",
      "Edm                       =  2.52687e-10\n",
      "NCalls                    =           42\n",
      "Emax                      =     0.990414   +/-   0.000794203 \n",
      "Lambda                    =   0.00986874   +/-   8.89583e-05 \n",
      "HV50                      =      6513.27   +/-   1.75828     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      152.327\n",
      "NDf                       =           14\n",
      "Edm                       =   2.9302e-08\n",
      "NCalls                    =           37\n",
      "Emax                      =     0.990291   +/-   0.000616639 \n",
      "Lambda                    =   0.00982468   +/-   8.94407e-05 \n",
      "HV50                      =      6541.83   +/-   1.70659     \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6070 xmax = 7630 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6070.000000,7630.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c_all\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6070 xmax = 7630 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6070.000000,7630.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <TCanvas::Print>: png file OFF_eff.png has been created\n",
      "Info in <TCanvas::Print>: pdf file OFF_eff.pdf has been created\n",
      "Info in <TCanvas::SaveAs>: ROOT file OFF_eff.root has been created\n"
     ]
    }
   ],
   "source": [
    "from ROOT import TCanvas, TGraphErrors, TLatex, TLine, TLegend\n",
    "import math\n",
    "\n",
    "# Inicializando listas para gráficos e funções\n",
    "TGraphs_list_OFF = []\n",
    "func_list_OFF = []\n",
    "\n",
    "canvas = TCanvas(\"c\", \"c\", 564, 232, 600, 600)\n",
    "HV_ref = 95\n",
    "\n",
    "# Definições de cores e marcadores\n",
    "colors = [6, 3, 2, 4]  # ROOT Colors: 2 (Red), 4 (Blue), 3 (Green), 6 (Magenta), 8 (Cyan)\n",
    "markers = [23, 22, 20, 21]  # Marcadores: 20 (circulo), 21 (quadrado), 22 (triângulo), etc.\n",
    "\n",
    "k = 1\n",
    "\n",
    "# Contagem dos scans no dicionário\n",
    "num_scans_total = len(scans_OFF)\n",
    "print(f\"Total de scans no dicionário: {num_scans_total}\")\n",
    "\n",
    "for index, (scan, sigmoid, color, marker) in enumerate(scans_OFF):\n",
    "    if scan not in scans:\n",
    "        print(f\"Scan '{scan}' não encontrado no dicionário de scans. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    print(f\"Processando scan: {scan}\")\n",
    "    \n",
    "    df = HVCurrentDataFrame(scans[scan])\n",
    "    n = len(df['HV_top'])\n",
    "    \n",
    "    gr_E = TGraphErrors(n, \n",
    "                        df['HV_top'].values, \n",
    "                        df['efficiency'].values,\n",
    "                        0,\n",
    "                        df['eff_error'].values) # valores do df[].values para converter objeto dataframe em array adequado para o TGraph\n",
    "    \n",
    "    # Ajuste e verificação do resultado\n",
    "    fit_result = gr_E.Fit(sigmoid, \"S\")\n",
    "    if not fit_result.IsValid():\n",
    "        print(f\"Fit falhou para o scan: {scan}\")\n",
    "        continue\n",
    "    \n",
    "    # Listando funções após o ajuste para depuração\n",
    "    functions = gr_E.GetListOfFunctions()\n",
    "    function_names = [f.GetName() for f in functions]\n",
    "    print(f\"Funções no gráfico após ajuste: {function_names}\")\n",
    "\n",
    "    # Tentando encontrar a função ajustada correta\n",
    "    sig = None\n",
    "    for name in function_names:\n",
    "        if name.startswith(\"sigmoid\"):\n",
    "            sig = gr_E.GetFunction(name)\n",
    "            break\n",
    "    \n",
    "    if sig is None:\n",
    "        print(f\"Função 'sigmoid' não encontrada para o scan: {scan}. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    # Usando cores e marcadores diferentes para cada gráfico\n",
    "    color = colors[index % len(colors)]\n",
    "    marker = markers[index % len(markers)]\n",
    "    \n",
    "    sig.SetLineColor(color)\n",
    "    gr_E.SetMarkerColor(color)\n",
    "    gr_E.SetMarkerStyle(marker)\n",
    "\n",
    "    Emax = sig.GetParameter(0)\n",
    "    Emax_err = sig.GetParError(0)     \n",
    "\n",
    "    Lambda = sig.GetParameter(1)    \n",
    "    Lambda_err = sig.GetParError(1)      \n",
    "\n",
    "    HV50 = sig.GetParameter(2)\n",
    "    HV50_err = sig.GetParError(2)      \n",
    "    \n",
    "    print(f\"Lambda = {Lambda}\")\n",
    "    HV95 = sig.GetX(HV_ref)\n",
    "    HV95_err = math.sqrt(((math.log(19) / Lambda**2) * Lambda_err)**2 + HV50_err**2) # erro HV95\n",
    "    \n",
    "    print(f\"HV95_err = {HV95_err}\")\n",
    "\n",
    "    # Adicionando gráficos e funções às listas\n",
    "    TGraphs_list_OFF.append(gr_E)\n",
    "    func_list_OFF.append(sig)\n",
    "    gr_E.Draw(\"AP\")\n",
    "    k += 1\n",
    "\n",
    "    print(f\"Fit concluído para o scan: {scan}\")\n",
    "\n",
    "# Criando o canvas final\n",
    "canvas_all = TCanvas(\"c_all\", \"c_all\", 0, 0, 600, 600)\n",
    "\n",
    "if TGraphs_list_OFF:\n",
    "    TGraphs_list_OFF[0].GetHistogram().SetMaximum(1.4)\n",
    "    TGraphs_list_OFF[0].SetTitle(\"\")\n",
    "    TGraphs_list_OFF[0].GetXaxis().SetTitle(\"HV_{eff} [V]\")\n",
    "    TGraphs_list_OFF[0].GetYaxis().SetTitle(\"Efficiency\")\n",
    "    TGraphs_list_OFF[0].GetXaxis().SetLimits(6000, 7400)\n",
    "    TGraphs_list_OFF[0].Draw(\"AP\")\n",
    "    \n",
    "    for graph in TGraphs_list_OFF[1:]:\n",
    "        graph.Draw(\"PSAME\")\n",
    "\n",
    "    Emax_OFF = []\n",
    "    Emax_err_OFF = []\n",
    "    Lambda_OFF = []\n",
    "    Lambda_err_OFF = []\n",
    "    HV50_OFF = []\n",
    "    HV50_err_OFF = []\n",
    "    HV95_OFF = []\n",
    "    HV95_err_OFF = []\n",
    "\n",
    "    for sig in func_list_OFF:\n",
    "        Emax_ = 1 - math.sqrt((1 - sig.GetParameter(0)) * (1 - sig.GetParameter(0)))\n",
    "        Emax_OFF.append(Emax_)\n",
    "        Emax_err_OFF.append(sig.GetParError(0))     \n",
    "        Lambda_OFF.append(sig.GetParameter(1))  \n",
    "        Lambda_err_OFF.append(sig.GetParError(1))\n",
    "        HV50_OFF.append(sig.GetParameter(2))  \n",
    "        HV50_err_OFF.append(sig.GetParError(2)) \n",
    "        HV95_OFF.append(sig.GetX(HV_ref))\n",
    "        HV95_err_OFF.append((math.log(19) / sig.GetParameter(1)**2) * sig.GetParError(1) + sig.GetParError(2)) # erro HV95\n",
    "\n",
    "    WP_OFF = []    \n",
    "\n",
    "    for Emax_, Lambda_, HV50_ in zip(Emax_OFF, Lambda_OFF, HV50_OFF):\n",
    "        WP_OFF.append((HV50_ - math.log(1 / 0.95 - 1) / Lambda_ + 150.))\n",
    "\n",
    "    #txt_OFF_22 = Features_OFF['noiseGammaRate'][2] / (Features_OFF['gamma_CS'][2] * 1000)\n",
    "    #txt_OFF_10 = Features_OFF['noiseGammaRate'][4] / (Features_OFF['gamma_CS'][4] * 1000)\n",
    "    #txt_OFF_46 = Features_OFF['noiseGammaRate'][5] / (Features_OFF['gamma_CS'][5] * 1000)\n",
    "    #txt_OFF_33 = Features_OFF['noiseGammaRate'][7] / (Features_OFF['gamma_CS'][7] * 1000)\n",
    "\n",
    "    # Adiciona verificações de comprimento antes de formatar strings\n",
    "    eff_1_OFF = 'plateau = {a:.0%}, WP = {b:.2f} kV, Standard gas mixture, Eff(WP) = {c:.0%}'.format(a=Emax_OFF[0], b=WP_OFF[0] / 1000., c=(func_list_OFF[0].Eval(WP_OFF[0])))\n",
    "    eff_2_OFF = 'plateau = {a:.0%}, WP = {b:.2f} kV, 30% CO_{{2}} + 1.0% SF_{{6}}, Eff(WP) = {c:.0%}'.format(a=Emax_OFF[1], b=WP_OFF[1] / 1000., c=(func_list_OFF[1].Eval(WP_OFF[1])))\n",
    "    eff_3_OFF = 'plateau = {a:.0%}, WP = {b:.2f} kV, 30% CO_{{2}} + 0.5% SF_{{6}}, Eff(WP) = {c:.0%}'.format(a=Emax_OFF[2], b=WP_OFF[2] / 1000., c=(func_list_OFF[2].Eval(WP_OFF[2])))\n",
    "    eff_4_OFF = 'plateau = {a:.0%}, WP = {b:.2f} kV, 40% CO_{{2}}+ 1.0% SF_{{6}}, Eff(WP) = {c:.0%}'.format(a=Emax_OFF[3], b=WP_OFF[3] / 1000., c=(func_list_OFF[3].Eval(WP_OFF[3])))\n",
    "\n",
    "    # Adicionando a legenda aos gráficos\n",
    "    legend = TLegend(0.1, 0.68, 0.3, 0.89)\n",
    "    legend.SetTextFont(42)\n",
    "    legend.SetBorderSize(0) \n",
    "    legend.SetFillStyle(4000)\n",
    "    legend.SetFillColor(0)  \n",
    "    legend.SetTextSize(0.02376) \n",
    "\n",
    "    ltx_data = TLatex()\n",
    "    ltx_data.SetTextFont(42)\n",
    "    ltx_data.SetTextSize(0.025)\n",
    "    ltx_data.SetTextColor(1)\n",
    "    ltx_data.DrawLatex(6100, 0.88, \"No gamma background\")\n",
    "    ltx_data.DrawLatex(6100, 0.81, \"Test Beam in April+July 2023\")\n",
    "    ltx_data.DrawLatex(6100, 0.74, \"Before irradiation\")\n",
    "    ltx_data.DrawLatex(6100, 0.67, \"1.4 mm double gap RPC\")\n",
    "    ltx_data.DrawLatex(6100, 0.60, \"Threshold = 60 [fC]\")\n",
    "\n",
    "    cms_tex = TLatex()\n",
    "    cms_tex.SetNDC()\n",
    "    cms_tex.SetTextFont(61)\n",
    "    cms_tex.SetTextSize(0.04)\n",
    "    cms_tex.SetLineWidth(2)\n",
    "    cms_tex.DrawLatex(0.10, 0.905, \"CMS\")\n",
    "\n",
    "    cms_tex_1 = TLatex()\n",
    "    cms_tex_1.SetNDC()\n",
    "    cms_tex_1.SetTextFont(61)\n",
    "    cms_tex_1.SetTextSize(0.04)\n",
    "    cms_tex_1.SetLineWidth(2)\n",
    "    cms_tex_1.DrawLatex(0.79, 0.905, \"GIF++\")\n",
    "\n",
    "    cms_tex_2 = TLatex()\n",
    "    cms_tex_2.SetNDC()\n",
    "    cms_tex_2.SetTextFont(52)\n",
    "    cms_tex_2.SetTextSize(0.04)\n",
    "    cms_tex_2.SetLineWidth(2)\n",
    "    cms_tex_2.DrawLatex(0.19, 0.905, \"Preliminary\")\n",
    "\n",
    "    line = TLine(6000, 1., 7400, 1.)\n",
    "    line.SetLineColor(1)\n",
    "    line.SetLineStyle(9)\n",
    "    line.SetLineWidth(2)\n",
    "    line.Draw()\n",
    "\n",
    "    legend.AddEntry(TGraphs_list_OFF[0], eff_1_OFF, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_OFF[1], eff_2_OFF, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_OFF[2], eff_3_OFF, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_OFF[3], eff_4_OFF, \"P\")\n",
    "    #legend.AddEntry(TGraphs_list_OFF[4], eff_5_OFF, \"P\")\n",
    "\n",
    "    legend.Draw()\n",
    "\n",
    "    print(\"Gráficos e legendas adicionados ao canvas.\")\n",
    "\n",
    "# Salvar ou mostrar o canvas\n",
    "canvas_all.SaveAs(\"OFF_eff.png\")\n",
    "canvas_all.SaveAs(\"OFF_eff.pdf\")\n",
    "canvas_all.SaveAs(\"OFF_eff.root\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\'\\'\\nFeatures_22 = FeaturesDataFrame(Scans_22)\\nprint(\"Features_OFF = \", Features_22)\\n'"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''''\n",
    "Features_22 = FeaturesDataFrame(Scans_22)\n",
    "print(\"Features_OFF = \", Features_22)\n",
    "'''''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\'\\nfrom ROOT import TCanvas, TGraphErrors, TLatex, TLine, TLegend\\nimport math\\n\\n# Inicializando listas para gráficos e funções\\nTGraphs_list_22 = []\\nfunc_list_22 = []\\n\\ncanvas = TCanvas(\"c\", \"c\", 564, 232, 600, 600)\\nHV_ref = 95\\n\\n# Definições de cores e marcadores\\ncolors = [2, 4, 3, 6, 8]  # ROOT Colors: 2 (Red), 4 (Blue), 3 (Green), 6 (Magenta), 8 (Cyan)\\nmarkers = [22]  # Marcador: 22 (triângulo)\\n\\nk = 1\\n\\n# Contagem dos scans no dicionário\\nnum_scans_total = len(scans_22)\\nprint(f\"Total de scans no dicionário: {num_scans_total}\")\\n\\nfor index, (scan, sigmoid, _, _) in enumerate(scans_22):\\n    if scan not in scans:\\n        print(f\"Scan \\'{scan}\\' não encontrado no dicionário de scans. Pulando...\")\\n        continue\\n    \\n    print(f\"Processando scan: {scan}\")\\n    \\n    df = HVCurrentDataFrame(scans[scan])\\n    n = len(df[\\'HV_top\\'])\\n    \\n    gr_E = TGraphErrors(n, \\n                        df[\\'HV_top\\'].values, \\n                        df[\\'efficiency\\'].values,\\n                        0,\\n                        df[\\'eff_error\\'].values)  # valores do df[].values para converter objeto dataframe em array adequado para o TGraph\\n    \\n    # Ajuste e verificação do resultado\\n    fit_result = gr_E.Fit(sigmoid, \"S\")\\n    if not fit_result.IsValid():\\n        print(f\"Fit falhou para o scan: {scan}\")\\n        continue\\n    \\n    # Listando funções após o ajuste para depuração\\n    functions = gr_E.GetListOfFunctions()\\n    function_names = [f.GetName() for f in functions]\\n    print(f\"Funções no gráfico após ajuste: {function_names}\")\\n\\n    # Tentando encontrar a função ajustada correta\\n    sig = None\\n    for name in function_names:\\n        if name.startswith(\"sigmoid\"):\\n            sig = gr_E.GetFunction(name)\\n            break\\n    \\n    if sig is None:\\n        print(f\"Função \\'sigmoid\\' não encontrada para o scan: {scan}. Pulando...\")\\n        continue\\n    \\n    # Usando cores diferentes e o marcador 22 para cada gráfico\\n    color = colors[index % len(colors)]\\n    gr_E.SetMarkerColor(color)\\n    gr_E.SetMarkerStyle(markers[0])\\n    sig.SetLineColor(color)\\n\\n    Emax = sig.GetParameter(0)\\n    Emax_err = sig.GetParError(0)     \\n\\n    Lambda = sig.GetParameter(1)    \\n    Lambda_err = sig.GetParError(1)      \\n\\n    HV50 = sig.GetParameter(2)\\n    HV50_err = sig.GetParError(2)      \\n    \\n    print(f\"Lambda = {Lambda}\")\\n    HV95 = sig.GetX(HV_ref)\\n    HV95_err = math.sqrt(((math.log(19) / Lambda**2) * Lambda_err)**2 + HV50_err**2) # erro HV95\\n    \\n    print(f\"HV95_err = {HV95_err}\")\\n\\n    # Adicionando gráficos e funções às listas\\n    TGraphs_list_22.append(gr_E)\\n    func_list_22.append(sig)\\n    gr_E.Draw(\"AP\")\\n    k += 1\\n\\n    print(f\"Fit concluído para o scan: {scan}\")\\n\\n# Criando o canvas final\\ncanvas_all = TCanvas(\"c_all\", \"c_all\", 0, 0, 600, 600)\\n\\nif TGraphs_list_22:\\n    TGraphs_list_22[0].GetHistogram().SetMaximum(1.4)\\n    TGraphs_list_22[0].SetTitle(\"\")\\n    TGraphs_list_22[0].GetXaxis().SetTitle(\"HV_{eff} [V]\")\\n    TGraphs_list_22[0].GetYaxis().SetTitle(\"Muon Efficiency\")\\n    TGraphs_list_22[0].Draw(\"AP\")\\n    \\n    for graph in TGraphs_list_22[1:]:\\n        graph.Draw(\"PSAME\")\\n\\n    Emax_22 = []\\n    Emax_err_22 = []\\n    Lambda_22 = []\\n    Lambda_err_22 = []\\n    HV50_22 = []\\n    HV50_err_22 = []\\n    HV95_22 = []\\n    HV95_err_22 = []\\n\\n    for sig in func_list_22:\\n        Emax_ = 1 - math.sqrt((1 - sig.GetParameter(0)) * (1 - sig.GetParameter(0)))\\n        Emax_22.append(Emax_)\\n        Emax_err_22.append(sig.GetParError(0))     \\n        Lambda_22.append(sig.GetParameter(1))  \\n        Lambda_err_22.append(sig.GetParError(1))\\n        HV50_22.append(sig.GetParameter(2))  \\n        HV50_err_22.append(sig.GetParError(2)) \\n        HV95_22.append(sig.GetX(HV_ref))\\n        HV95_err_22.append((math.log(19) / sig.GetParameter(1)**2) * sig.GetParError(1) + sig.GetParError(2)) # erro HV95\\n\\n    WP_22 = []    \\n\\n    for Emax_, Lambda_, HV50_ in zip(Emax_22, Lambda_22, HV50_22):\\n        WP_22.append((HV50_ - math.log(1 / 0.95 - 1) / Lambda_ + 150.))\\n\\n    txt_22_22 = Features_22[\\'noiseGammaRate\\'][0] / (Features_22[\\'gamma_CS\\'][0] * 1000)\\n    txt_22_10 = Features_22[\\'noiseGammaRate\\'][1] / (Features_22[\\'gamma_CS\\'][1] * 1000)\\n    txt_22_46 = Features_22[\\'noiseGammaRate\\'][2] / (Features_22[\\'gamma_CS\\'][2] * 1000)\\n    txt_22_33 = Features_22[\\'noiseGammaRate\\'][3] / (Features_22[\\'gamma_CS\\'][3] * 1000)\\n    print(txt_22_22, txt_22_10, txt_22_46, txt_22_33)\\n\\n    # Adiciona verificações de comprimento antes de formatar strings\\n    if len(Emax_22) > 0:\\n        eff_1_22 = \\'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm2, Eff(WP) = {c:.0%}\\'.format(\\n            a=Emax_22[0], \\n            b=WP_22[0] / 1000., \\n            c=(func_list_22[0].Eval(WP_22[0])),\\n            d=txt_22_22\\n        )\\n    else:\\n        eff_1_22 = \\'Dados insuficientes para o primeiro gráfico.\\'\\n\\n    if len(Emax_22) > 1:\\n        eff_2_22 = \\'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm2, Eff(WP) = {c:.0%}\\'.format(\\n            a=Emax_22[1], \\n            b=WP_22[1] / 1000., \\n            c=(func_list_22[1].Eval(WP_22[1])), \\n            d=txt_22_10\\n        )\\n    else:\\n        eff_2_22 = \\'Dados insuficientes para o segundo gráfico.\\'\\n\\n    if len(Emax_22) > 2:\\n        eff_3_22 = \\'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm2, Eff(WP) = {c:.0%}\\'.format(\\n            a=Emax_22[2], \\n            b=WP_22[2] / 1000., \\n            c=(func_list_22[2].Eval(WP_22[2])), \\n            d=txt_22_46\\n        )\\n    else:\\n        eff_3_22 = \\'Dados insuficientes para o terceiro gráfico.\\'\\n\\n    if len(Emax_22) > 3:\\n        eff_4_22 = \\'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm2, Eff(WP) = {c:.0%}\\'.format(\\n            a=Emax_22[3], \\n            b=WP_22[3] / 1000., \\n            c=(func_list_22[3].Eval(WP_22[3])), \\n            d=txt_22_33\\n        )\\n\\n    # Adicionando a legenda aos gráficos\\n    legend = TLegend(0.1, 0.68, 0.3, 0.89)\\n    legend.SetTextFont(42)\\n    legend.SetBorderSize(0) \\n    legend.SetFillStyle(4000)\\n    legend.SetFillColor(0)  \\n    legend.SetTextSize(0.02376) \\n\\n    ltx_data = TLatex()\\n    ltx_data.SetTextFont(42)\\n    ltx_data.SetTextSize(0.025)\\n    ltx_data.SetTextColor(1)\\n    ltx_data.DrawLatex(5900, 0.88, \"95.2% TFE + 4.5% iC4H10 + 0.3% SF6\")\\n    ltx_data.DrawLatex(5900, 0.81, \"Test Beam in April+July 2023\")\\n    ltx_data.DrawLatex(5900, 0.74, \"1.4 mm double gap RPC\")\\n    ltx_data.DrawLatex(5900, 0.67, \"Threshold = 60 [fC]\")\\n\\n    line = TLine(5850, 1., 7450, 1.)\\n    line.SetLineColor(1)\\n    line.SetLineStyle(9)\\n    line.SetLineWidth(2)\\n    line.Draw()\\n\\n    if len(TGraphs_list_22) > 0:\\n        legend.AddEntry(TGraphs_list_22[0], eff_1_22, \"P\")\\n    if len(TGraphs_list_22) > 1:\\n        legend.AddEntry(TGraphs_list_22[1], eff_2_22, \"P\")\\n    if len(TGraphs_list_22) > 2:\\n        legend.AddEntry(TGraphs_list_22[2], eff_3_22, \"P\")\\n    if len(TGraphs_list_22) > 3:\\n        legend.AddEntry(TGraphs_list_22[3], eff_4_22, \"P\")\\n    if len(TGraphs_list_22) > 4:\\n        legend.AddEntry(TGraphs_list_22[4], eff_5_22, \"P\")\\n\\n    legend.Draw()\\n\\n    print(\"Gráficos e legendas adicionados ao canvas.\")\\n\\n# Salvar ou mostrar o canvas\\ncanvas_all.SaveAs(\"22_final.png\")\\ncanvas_all.SaveAs(\"22_final.pdf\")\\n'"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''''\n",
    "from ROOT import TCanvas, TGraphErrors, TLatex, TLine, TLegend\n",
    "import math\n",
    "\n",
    "# Inicializando listas para gráficos e funções\n",
    "TGraphs_list_22 = []\n",
    "func_list_22 = []\n",
    "\n",
    "canvas = TCanvas(\"c\", \"c\", 564, 232, 600, 600)\n",
    "HV_ref = 95\n",
    "\n",
    "# Definições de cores e marcadores\n",
    "colors = [2, 4, 3, 6, 8]  # ROOT Colors: 2 (Red), 4 (Blue), 3 (Green), 6 (Magenta), 8 (Cyan)\n",
    "markers = [22]  # Marcador: 22 (triângulo)\n",
    "\n",
    "k = 1\n",
    "\n",
    "# Contagem dos scans no dicionário\n",
    "num_scans_total = len(scans_22)\n",
    "print(f\"Total de scans no dicionário: {num_scans_total}\")\n",
    "\n",
    "for index, (scan, sigmoid, _, _) in enumerate(scans_22):\n",
    "    if scan not in scans:\n",
    "        print(f\"Scan '{scan}' não encontrado no dicionário de scans. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    print(f\"Processando scan: {scan}\")\n",
    "    \n",
    "    df = HVCurrentDataFrame(scans[scan])\n",
    "    n = len(df['HV_top'])\n",
    "    \n",
    "    gr_E = TGraphErrors(n, \n",
    "                        df['HV_top'].values, \n",
    "                        df['efficiency'].values,\n",
    "                        0,\n",
    "                        df['eff_error'].values)  # valores do df[].values para converter objeto dataframe em array adequado para o TGraph\n",
    "    \n",
    "    # Ajuste e verificação do resultado\n",
    "    fit_result = gr_E.Fit(sigmoid, \"S\")\n",
    "    if not fit_result.IsValid():\n",
    "        print(f\"Fit falhou para o scan: {scan}\")\n",
    "        continue\n",
    "    \n",
    "    # Listando funções após o ajuste para depuração\n",
    "    functions = gr_E.GetListOfFunctions()\n",
    "    function_names = [f.GetName() for f in functions]\n",
    "    print(f\"Funções no gráfico após ajuste: {function_names}\")\n",
    "\n",
    "    # Tentando encontrar a função ajustada correta\n",
    "    sig = None\n",
    "    for name in function_names:\n",
    "        if name.startswith(\"sigmoid\"):\n",
    "            sig = gr_E.GetFunction(name)\n",
    "            break\n",
    "    \n",
    "    if sig is None:\n",
    "        print(f\"Função 'sigmoid' não encontrada para o scan: {scan}. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    # Usando cores diferentes e o marcador 22 para cada gráfico\n",
    "    color = colors[index % len(colors)]\n",
    "    gr_E.SetMarkerColor(color)\n",
    "    gr_E.SetMarkerStyle(markers[0])\n",
    "    sig.SetLineColor(color)\n",
    "\n",
    "    Emax = sig.GetParameter(0)\n",
    "    Emax_err = sig.GetParError(0)     \n",
    "\n",
    "    Lambda = sig.GetParameter(1)    \n",
    "    Lambda_err = sig.GetParError(1)      \n",
    "\n",
    "    HV50 = sig.GetParameter(2)\n",
    "    HV50_err = sig.GetParError(2)      \n",
    "    \n",
    "    print(f\"Lambda = {Lambda}\")\n",
    "    HV95 = sig.GetX(HV_ref)\n",
    "    HV95_err = math.sqrt(((math.log(19) / Lambda**2) * Lambda_err)**2 + HV50_err**2) # erro HV95\n",
    "    \n",
    "    print(f\"HV95_err = {HV95_err}\")\n",
    "\n",
    "    # Adicionando gráficos e funções às listas\n",
    "    TGraphs_list_22.append(gr_E)\n",
    "    func_list_22.append(sig)\n",
    "    gr_E.Draw(\"AP\")\n",
    "    k += 1\n",
    "\n",
    "    print(f\"Fit concluído para o scan: {scan}\")\n",
    "\n",
    "# Criando o canvas final\n",
    "canvas_all = TCanvas(\"c_all\", \"c_all\", 0, 0, 600, 600)\n",
    "\n",
    "if TGraphs_list_22:\n",
    "    TGraphs_list_22[0].GetHistogram().SetMaximum(1.4)\n",
    "    TGraphs_list_22[0].SetTitle(\"\")\n",
    "    TGraphs_list_22[0].GetXaxis().SetTitle(\"HV_{eff} [V]\")\n",
    "    TGraphs_list_22[0].GetYaxis().SetTitle(\"Muon Efficiency\")\n",
    "    TGraphs_list_22[0].Draw(\"AP\")\n",
    "    \n",
    "    for graph in TGraphs_list_22[1:]:\n",
    "        graph.Draw(\"PSAME\")\n",
    "\n",
    "    Emax_22 = []\n",
    "    Emax_err_22 = []\n",
    "    Lambda_22 = []\n",
    "    Lambda_err_22 = []\n",
    "    HV50_22 = []\n",
    "    HV50_err_22 = []\n",
    "    HV95_22 = []\n",
    "    HV95_err_22 = []\n",
    "\n",
    "    for sig in func_list_22:\n",
    "        Emax_ = 1 - math.sqrt((1 - sig.GetParameter(0)) * (1 - sig.GetParameter(0)))\n",
    "        Emax_22.append(Emax_)\n",
    "        Emax_err_22.append(sig.GetParError(0))     \n",
    "        Lambda_22.append(sig.GetParameter(1))  \n",
    "        Lambda_err_22.append(sig.GetParError(1))\n",
    "        HV50_22.append(sig.GetParameter(2))  \n",
    "        HV50_err_22.append(sig.GetParError(2)) \n",
    "        HV95_22.append(sig.GetX(HV_ref))\n",
    "        HV95_err_22.append((math.log(19) / sig.GetParameter(1)**2) * sig.GetParError(1) + sig.GetParError(2)) # erro HV95\n",
    "\n",
    "    WP_22 = []    \n",
    "\n",
    "    for Emax_, Lambda_, HV50_ in zip(Emax_22, Lambda_22, HV50_22):\n",
    "        WP_22.append((HV50_ - math.log(1 / 0.95 - 1) / Lambda_ + 150.))\n",
    "\n",
    "    txt_22_22 = Features_22['noiseGammaRate'][0] / (Features_22['gamma_CS'][0] * 1000)\n",
    "    txt_22_10 = Features_22['noiseGammaRate'][1] / (Features_22['gamma_CS'][1] * 1000)\n",
    "    txt_22_46 = Features_22['noiseGammaRate'][2] / (Features_22['gamma_CS'][2] * 1000)\n",
    "    txt_22_33 = Features_22['noiseGammaRate'][3] / (Features_22['gamma_CS'][3] * 1000)\n",
    "    print(txt_22_22, txt_22_10, txt_22_46, txt_22_33)\n",
    "\n",
    "    # Adiciona verificações de comprimento antes de formatar strings\n",
    "    if len(Emax_22) > 0:\n",
    "        eff_1_22 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm2, Eff(WP) = {c:.0%}'.format(\n",
    "            a=Emax_22[0], \n",
    "            b=WP_22[0] / 1000., \n",
    "            c=(func_list_22[0].Eval(WP_22[0])),\n",
    "            d=txt_22_22\n",
    "        )\n",
    "    else:\n",
    "        eff_1_22 = 'Dados insuficientes para o primeiro gráfico.'\n",
    "\n",
    "    if len(Emax_22) > 1:\n",
    "        eff_2_22 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm2, Eff(WP) = {c:.0%}'.format(\n",
    "            a=Emax_22[1], \n",
    "            b=WP_22[1] / 1000., \n",
    "            c=(func_list_22[1].Eval(WP_22[1])), \n",
    "            d=txt_22_10\n",
    "        )\n",
    "    else:\n",
    "        eff_2_22 = 'Dados insuficientes para o segundo gráfico.'\n",
    "\n",
    "    if len(Emax_22) > 2:\n",
    "        eff_3_22 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm2, Eff(WP) = {c:.0%}'.format(\n",
    "            a=Emax_22[2], \n",
    "            b=WP_22[2] / 1000., \n",
    "            c=(func_list_22[2].Eval(WP_22[2])), \n",
    "            d=txt_22_46\n",
    "        )\n",
    "    else:\n",
    "        eff_3_22 = 'Dados insuficientes para o terceiro gráfico.'\n",
    "\n",
    "    if len(Emax_22) > 3:\n",
    "        eff_4_22 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm2, Eff(WP) = {c:.0%}'.format(\n",
    "            a=Emax_22[3], \n",
    "            b=WP_22[3] / 1000., \n",
    "            c=(func_list_22[3].Eval(WP_22[3])), \n",
    "            d=txt_22_33\n",
    "        )\n",
    "\n",
    "    # Adicionando a legenda aos gráficos\n",
    "    legend = TLegend(0.1, 0.68, 0.3, 0.89)\n",
    "    legend.SetTextFont(42)\n",
    "    legend.SetBorderSize(0) \n",
    "    legend.SetFillStyle(4000)\n",
    "    legend.SetFillColor(0)  \n",
    "    legend.SetTextSize(0.02376) \n",
    "\n",
    "    ltx_data = TLatex()\n",
    "    ltx_data.SetTextFont(42)\n",
    "    ltx_data.SetTextSize(0.025)\n",
    "    ltx_data.SetTextColor(1)\n",
    "    ltx_data.DrawLatex(5900, 0.88, \"95.2% TFE + 4.5% iC4H10 + 0.3% SF6\")\n",
    "    ltx_data.DrawLatex(5900, 0.81, \"Test Beam in April+July 2023\")\n",
    "    ltx_data.DrawLatex(5900, 0.74, \"1.4 mm double gap RPC\")\n",
    "    ltx_data.DrawLatex(5900, 0.67, \"Threshold = 60 [fC]\")\n",
    "\n",
    "    line = TLine(5850, 1., 7450, 1.)\n",
    "    line.SetLineColor(1)\n",
    "    line.SetLineStyle(9)\n",
    "    line.SetLineWidth(2)\n",
    "    line.Draw()\n",
    "\n",
    "    if len(TGraphs_list_22) > 0:\n",
    "        legend.AddEntry(TGraphs_list_22[0], eff_1_22, \"P\")\n",
    "    if len(TGraphs_list_22) > 1:\n",
    "        legend.AddEntry(TGraphs_list_22[1], eff_2_22, \"P\")\n",
    "    if len(TGraphs_list_22) > 2:\n",
    "        legend.AddEntry(TGraphs_list_22[2], eff_3_22, \"P\")\n",
    "    if len(TGraphs_list_22) > 3:\n",
    "        legend.AddEntry(TGraphs_list_22[3], eff_4_22, \"P\")\n",
    "    if len(TGraphs_list_22) > 4:\n",
    "        legend.AddEntry(TGraphs_list_22[4], eff_5_22, \"P\")\n",
    "\n",
    "    legend.Draw()\n",
    "\n",
    "    print(\"Gráficos e legendas adicionados ao canvas.\")\n",
    "\n",
    "# Salvar ou mostrar o canvas\n",
    "canvas_all.SaveAs(\"22_final.png\")\n",
    "canvas_all.SaveAs(\"22_final.pdf\")\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scanID:  5814\n",
      "scanID:  5822\n",
      "scanID:  5834\n",
      "scanID:  5841\n",
      "Features_OFF =     current_top  current_bot  HV_top  HV_bot  muon_stream  gamma_stream  \\\n",
      "0    38.579806    33.307197  7250.0  7250.0     0.016467      0.040073   \n",
      "1    42.202164    36.301476  7060.0  7060.0     0.016382      0.034044   \n",
      "2    50.053757    44.876121  7060.0  7060.0     0.023695      0.072797   \n",
      "3    47.500257    42.183781  7000.0  7000.0     0.023215      0.048381   \n",
      "\n",
      "    muon_CM  gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
      "0  1.190779  8.088920  1.989675  1.750194     0.114043      0.550905   \n",
      "1  1.170880  7.823211  1.878253  1.715422     0.100166      0.493850   \n",
      "2  1.204097  8.739607  2.339360  1.995886     0.154333      0.728377   \n",
      "3  1.190249  8.171411  2.105764  1.839577     0.138221      0.588730   \n",
      "\n",
      "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
      "0     0.173901      0.111598   95.672184   0.002050     1405.879286   \n",
      "1     0.148017      0.101858   94.920933   0.001565     1332.901642   \n",
      "2     0.265778      0.153545   95.413920   0.002117     1732.501391   \n",
      "3     0.219095      0.123630   95.219617   0.001525     1493.011466   \n",
      "\n",
      "   noiseGammaRate_err    current  \n",
      "0           37.484303  71.887003  \n",
      "1           36.497840  78.503639  \n",
      "2           41.610476  94.929878  \n",
      "3           38.628021  89.684038  \n"
     ]
    }
   ],
   "source": [
    "Features_33 = FeaturesDataFrame(Scans_33)\n",
    "print(\"Features_OFF = \", Features_33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de scans no dicionário: 4\n",
      "Processando scan: STDMX_3.3\n",
      "Funções no gráfico após ajuste: ['sigmoid4']\n",
      "Lambda = 0.010560256894131712\n",
      "HV95_err = 3.473271243492937\n",
      "Fit concluído para o scan: STDMX_3.3\n",
      "Processando scan: 30CO2_3.3\n",
      "Funções no gráfico após ajuste: ['sigmoid9']\n",
      "Lambda = 0.01082780020374651\n",
      "HV95_err = 3.2525716361450216\n",
      "Fit concluído para o scan: 30CO2_3.3\n",
      "Processando scan: 30CO205SF6_3.3\n",
      "Funções no gráfico após ajuste: ['sigmoid19']\n",
      "Lambda = 0.010911433373454392\n",
      "HV95_err = 4.290806182742042\n",
      "Fit concluído para o scan: 30CO205SF6_3.3\n",
      "Processando scan: 40CO2_3.3\n",
      "Fit falhou para o scan: 40CO2_3.3\n",
      "0.8116060325254935 0.7770108757611667 0.8680363701270747 0.8116060325254935\n",
      "Gráficos e legendas adicionados ao canvas.\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      135.693\n",
      "NDf                       =           14\n",
      "Edm                       =  3.07261e-07\n",
      "NCalls                    =           63\n",
      "Emax                      =     0.946878   +/-   0.0016088   \n",
      "Lambda                    =    0.0105603   +/-   0.000109086 \n",
      "HV50                      =      6803.85   +/-   1.94117     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      340.053\n",
      "NDf                       =           14\n",
      "Edm                       =  3.62795e-08\n",
      "NCalls                    =           64\n",
      "Emax                      =     0.943465   +/-   0.00144715  \n",
      "Lambda                    =    0.0108278   +/-   0.000102738 \n",
      "HV50                      =      6672.01   +/-   1.98037     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =       141.13\n",
      "NDf                       =            7\n",
      "Edm                       =  3.36525e-07\n",
      "NCalls                    =           66\n",
      "Emax                      =     0.944272   +/-   0.00219829  \n",
      "Lambda                    =    0.0109114   +/-   0.000133789 \n",
      "HV50                      =      6504.66   +/-   2.73191     \n",
      "****************************************\n",
      "         Invalid FitResult  (status = 3 )\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      41217.4\n",
      "NDf                       =           12\n",
      "Edm                       =      1104.01\n",
      "NCalls                    =          244\n",
      "Emax                      =      1.88174   +/-   2.27319     \n",
      "Lambda                    =   0.00485846   +/-   2.27319     \n",
      "HV50                      =      7122.05   +/-   1           \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5620 xmax = 7780 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5620.000000,7780.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Warning in <Fit>: Abnormal termination of minimization.\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c_all\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5620 xmax = 7780 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5620.000000,7780.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <TCanvas::Print>: png file 33_eff.png has been created\n",
      "Info in <TCanvas::Print>: pdf file 33_eff.pdf has been created\n",
      "Info in <TCanvas::SaveAs>: ROOT file 33_eff.root has been created\n"
     ]
    }
   ],
   "source": [
    "from ROOT import TCanvas, TGraphErrors, TLatex, TLine, TLegend\n",
    "import math\n",
    "\n",
    "# Inicializando listas para gráficos e funções\n",
    "TGraphs_list_33 = []\n",
    "func_list_33 = []\n",
    "\n",
    "canvas = TCanvas(\"c\", \"c\", 564, 232, 600, 600)\n",
    "HV_ref = 95\n",
    "\n",
    "colors = [6, 3, 2, 4]  # ROOT Colors: 2 (Red), 4 (Blue), 3 (Green), 6 (Magenta), 8 (Cyan)\n",
    "markers = [23, 22, 20, 21]  # Marcadores: 20 (circulo), 21 (quadrado), 22 (triângulo), etc.\n",
    "\n",
    "k = 1\n",
    "\n",
    "# Contagem dos scans no dicionário\n",
    "num_scans_total = len(scans_33)\n",
    "print(f\"Total de scans no dicionário: {num_scans_total}\")\n",
    "\n",
    "for index, (scan, sigmoid, _, _) in enumerate(scans_33):\n",
    "    if scan not in scans:\n",
    "        print(f\"Scan '{scan}' não encontrado no dicionário de scans. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    print(f\"Processando scan: {scan}\")\n",
    "    \n",
    "    df = HVCurrentDataFrame(scans[scan])\n",
    "    n = len(df['HV_top'])\n",
    "    \n",
    "    gr_E = TGraphErrors(n, \n",
    "                        df['HV_top'].values, \n",
    "                        df['efficiency'].values,\n",
    "                        0,\n",
    "                        df['eff_error'].values)  # valores do df[].values para converter objeto dataframe em array adequado para o TGraph\n",
    "    \n",
    "    # Ajuste e verificação do resultado\n",
    "    fit_result = gr_E.Fit(sigmoid, \"S\")\n",
    "    if not fit_result.IsValid():\n",
    "        print(f\"Fit falhou para o scan: {scan}\")\n",
    "        continue\n",
    "    \n",
    "    # Listando funções após o ajuste para depuração\n",
    "    functions = gr_E.GetListOfFunctions()\n",
    "    function_names = [f.GetName() for f in functions]\n",
    "    print(f\"Funções no gráfico após ajuste: {function_names}\")\n",
    "\n",
    "    # Tentando encontrar a função ajustada correta\n",
    "    sig = None\n",
    "    for name in function_names:\n",
    "        if name.startswith(\"sigmoid\"):\n",
    "            sig = gr_E.GetFunction(name)\n",
    "            break\n",
    "    \n",
    "    if sig is None:\n",
    "        print(f\"Função 'sigmoid' não encontrada para o scan: {scan}. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    # Usando cores e marcadores diferentes para cada gráfico\n",
    "    color = colors[index % len(colors)]\n",
    "    marker = markers[index % len(markers)]\n",
    "    gr_E.SetMarkerColor(color)\n",
    "    gr_E.SetMarkerStyle(marker)\n",
    "    sig.SetLineColor(color)\n",
    "\n",
    "    Emax = sig.GetParameter(0)\n",
    "    Emax_err = sig.GetParError(0)     \n",
    "\n",
    "    Lambda = sig.GetParameter(1)    \n",
    "    Lambda_err = sig.GetParError(1)      \n",
    "\n",
    "    HV50 = sig.GetParameter(2)\n",
    "    HV50_err = sig.GetParError(2)      \n",
    "    \n",
    "    print(f\"Lambda = {Lambda}\")\n",
    "    HV95 = sig.GetX(HV_ref)\n",
    "    HV95_err = math.sqrt(((math.log(19) / Lambda**2) * Lambda_err)**2 + HV50_err**2) # erro HV95\n",
    "    \n",
    "    print(f\"HV95_err = {HV95_err}\")\n",
    "\n",
    "    # Adicionando gráficos e funções às listas\n",
    "    TGraphs_list_33.append(gr_E)\n",
    "    func_list_33.append(sig)\n",
    "    gr_E.Draw(\"AP\")\n",
    "    k += 1\n",
    "\n",
    "    print(f\"Fit concluído para o scan: {scan}\")\n",
    "\n",
    "# Criando o canvas final\n",
    "canvas_all = TCanvas(\"c_all\", \"c_all\", 0, 0, 600, 600)\n",
    "\n",
    "if TGraphs_list_33:\n",
    "    TGraphs_list_33[0].GetHistogram().SetMaximum(1.4)\n",
    "    TGraphs_list_33[0].SetTitle(\"\")\n",
    "    TGraphs_list_33[0].GetXaxis().SetTitle(\"HV_{eff} [V]\")\n",
    "    TGraphs_list_33[0].GetYaxis().SetTitle(\"Efficiency\")\n",
    "    TGraphs_list_33[0].GetXaxis().SetLimits(6000, 7400)\n",
    "    TGraphs_list_33[0].Draw(\"AP\")\n",
    "    \n",
    "    for graph in TGraphs_list_33[1:]:\n",
    "        graph.Draw(\"PSAME\")\n",
    "\n",
    "    Emax_33 = []\n",
    "    Emax_err_33 = []\n",
    "    Lambda_33 = []\n",
    "    Lambda_err_33 = []\n",
    "    HV50_33 = []\n",
    "    HV50_err_33 = []\n",
    "    HV95_33 = []\n",
    "    HV95_err_33 = []\n",
    "\n",
    "    for sig in func_list_33:\n",
    "        Emax_ = 1 - math.sqrt((1 - sig.GetParameter(0)) * (1 - sig.GetParameter(0)))\n",
    "        Emax_33.append(Emax_)\n",
    "        Emax_err_33.append(sig.GetParError(0))     \n",
    "        Lambda_33.append(sig.GetParameter(1))  \n",
    "        Lambda_err_33.append(sig.GetParError(1))\n",
    "        HV50_33.append(sig.GetParameter(2))  \n",
    "        HV50_err_33.append(sig.GetParError(2)) \n",
    "        HV95_33.append(sig.GetX(HV_ref))\n",
    "        HV95_err_33.append((math.log(19) / sig.GetParameter(1)**2) * sig.GetParError(1) + sig.GetParError(2)) # erro HV95\n",
    "\n",
    "    WP_33 = []    \n",
    "\n",
    "    for Emax_, Lambda_, HV50_ in zip(Emax_33, Lambda_33, HV50_33):\n",
    "        WP_33.append((HV50_ - math.log(1 / 0.95 - 1) / Lambda_ + 150.))\n",
    "\n",
    "    txt_33_33 = Features_33['noiseGammaRate'][0] / (Features_33['gamma_CS'][0] * 1000)\n",
    "    txt_33_10 = Features_33['noiseGammaRate'][1] / (Features_33['gamma_CS'][1] * 1000)\n",
    "    txt_33_46 = Features_33['noiseGammaRate'][2] / (Features_33['gamma_CS'][2] * 1000)\n",
    "    txt_33_33 = Features_33['noiseGammaRate'][3] / (Features_33['gamma_CS'][3] * 1000)\n",
    "    print(txt_33_33, txt_33_10, txt_33_46, txt_33_33)\n",
    "\n",
    "    # Adiciona verificações de comprimento antes de formatar strings\n",
    "    if len(Emax_33) > 0:\n",
    "        eff_1_33 = 'plateau = {a:.0%}, WP = {b:.2f} kV, Standard gas mixture, Eff(WP) = {c:.0%}'.format(\n",
    "            a=Emax_33[0], \n",
    "            b=WP_33[0] / 1000., \n",
    "            c=(func_list_33[0].Eval(WP_33[0])),\n",
    "            d=txt_33_33\n",
    "        )\n",
    "    else:\n",
    "        eff_1_33 = 'Dados insuficientes para o primeiro gráfico.'\n",
    "\n",
    "    if len(Emax_33) > 1:\n",
    "        eff_2_33 = 'plateau = {a:.0%}, WP = {b:.2f} kV, 30% CO_{{2}} + 1.0% SF_{{6}}, Eff(WP) = {c:.0%}'.format(\n",
    "            a=Emax_33[1], \n",
    "            b=WP_33[1] / 1000., \n",
    "            c=(func_list_33[1].Eval(WP_33[1])), \n",
    "            d=txt_33_10\n",
    "        )\n",
    "    else:\n",
    "        eff_2_33 = 'Dados insuficientes para o segundo gráfico.'\n",
    "\n",
    "    if len(Emax_33) > 2:\n",
    "        eff_3_33 = 'plateau = {a:.0%}, WP = {b:.2f} kV, 30% CO_{{2}} + 0.5% SF_{{6}}, Eff(WP) = {c:.0%}'.format(\n",
    "            a=Emax_33[2], \n",
    "            b=WP_33[2] / 1000., \n",
    "            c=(func_list_33[2].Eval(WP_33[2])), \n",
    "            d=txt_33_46\n",
    "        )\n",
    "    else:\n",
    "        eff_3_33 = 'Dados insuficientes para o terceiro gráfico.'\n",
    "\n",
    "    if len(Emax_33) > 3:\n",
    "        eff_4_33 = 'plateau = {a:.0%}, WP = {b:.2f} kV, 40% CO_{{2}} + 1.0% SF_{{6}}, Eff(WP) = {c:.0%}'.format(\n",
    "            a=Emax_33[3], \n",
    "            b=WP_33[3] / 1000., \n",
    "            c=(func_list_33[3].Eval(WP_33[3])), \n",
    "            d=txt_33_33\n",
    "        )\n",
    "\n",
    "    # Adicionando a legenda aos gráficos\n",
    "    legend = TLegend(0.1, 0.68, 0.3, 0.89)\n",
    "    legend.SetTextFont(42)\n",
    "    legend.SetBorderSize(0) \n",
    "    legend.SetFillStyle(4000)\n",
    "    legend.SetFillColor(0)  \n",
    "    legend.SetTextSize(0.02376) \n",
    "\n",
    "    ltx_data = TLatex()\n",
    "    ltx_data.SetTextFont(41)\n",
    "    ltx_data.SetTextSize(0.025)\n",
    "    ltx_data.SetTextColor(1)\n",
    "    ltx_data.DrawLatex(6050, 0.88, \"Background rate ~ 0.8kHz/cm^{2}\") \n",
    "    ltx_data.DrawLatex(6050, 0.81, \"Test Beam in April+July+August\")\n",
    "    ltx_data.DrawLatex(6050, 0.74, \"2023, Before irradiation\")\n",
    "    ltx_data.DrawLatex(6050, 0.67, \"1.4 mm double gap RPC\")\n",
    "    ltx_data.DrawLatex(6050, 0.60, \"Threshold = 60 [fC]\")\n",
    "\n",
    "    cms_tex = TLatex()\n",
    "    cms_tex.SetNDC()\n",
    "    cms_tex.SetTextFont(61)\n",
    "    cms_tex.SetTextSize(0.04)\n",
    "    cms_tex.SetLineWidth(2)\n",
    "    cms_tex.DrawLatex(0.10, 0.905, \"CMS\")\n",
    "\n",
    "    cms_tex_1 = TLatex()\n",
    "    cms_tex_1.SetNDC()\n",
    "    cms_tex_1.SetTextFont(61)\n",
    "    cms_tex_1.SetTextSize(0.04)\n",
    "    cms_tex_1.SetLineWidth(2)\n",
    "    cms_tex_1.DrawLatex(0.79, 0.905, \"GIF++\")\n",
    "\n",
    "    cms_tex_2 = TLatex()\n",
    "    cms_tex_2.SetNDC()\n",
    "    cms_tex_2.SetTextFont(52)\n",
    "    cms_tex_2.SetTextSize(0.04)\n",
    "    cms_tex_2.SetLineWidth(2)\n",
    "    cms_tex_2.DrawLatex(0.19, 0.905, \"Preliminary\")\n",
    "\n",
    "    line = TLine(6000, 1., 7400, 1.)\n",
    "    line.SetLineColor(1)\n",
    "    line.SetLineStyle(9)\n",
    "    line.SetLineWidth(2)\n",
    "    line.Draw()\n",
    "\n",
    "    if len(TGraphs_list_33) > 0:\n",
    "        legend.AddEntry(TGraphs_list_33[0], eff_1_33, \"P\")\n",
    "    if len(TGraphs_list_33) > 1:\n",
    "        legend.AddEntry(TGraphs_list_33[1], eff_2_33, \"P\")\n",
    "    if len(TGraphs_list_33) > 2:\n",
    "        legend.AddEntry(TGraphs_list_33[2], eff_3_33, \"P\")\n",
    "    if len(TGraphs_list_33) > 3:\n",
    "        legend.AddEntry(TGraphs_list_33[3], eff_4_33, \"P\")\n",
    "    if len(TGraphs_list_33) > 4:\n",
    "        legend.AddEntry(TGraphs_list_33[4], eff_5_33, \"P\")\n",
    "\n",
    "    legend.Draw()\n",
    "\n",
    "    print(\"Gráficos e legendas adicionados ao canvas.\")\n",
    "\n",
    "# Salvar ou mostrar o canvas\n",
    "canvas_all.SaveAs(\"33_eff.png\")\n",
    "canvas_all.SaveAs(\"33_eff.pdf\")\n",
    "canvas_all.SaveAs(\"33_eff.root\")\n",
    "canvas_all.Clear()\n",
    "canvas_all.Show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de scans no dicionário: 4\n",
      "Processando scan: STDMX_3.3\n",
      "Funções no gráfico após ajuste: ['sigmoid4']\n",
      "Lambda = 0.010560259168185215\n",
      "HV95_err = 3.4640125248880285\n",
      "Fit concluído para o scan: STDMX_3.3\n",
      "Processando scan: 30CO2_3.3\n",
      "Funções no gráfico após ajuste: ['sigmoid9']\n",
      "Lambda = 0.010827805264897811\n",
      "HV95_err = 3.2525722309586276\n",
      "Fit concluído para o scan: 30CO2_3.3\n",
      "Processando scan: 30CO205SF6_3.3\n",
      "Funções no gráfico após ajuste: ['sigmoid19']\n",
      "Lambda = 0.010911503544411335\n",
      "HV95_err = 4.470511568716347\n",
      "Fit concluído para o scan: 30CO205SF6_3.3\n",
      "Processando scan: 40CO2_3.3\n",
      "Funções no gráfico após ajuste: ['sigmoid20']\n",
      "Lambda = 0.010931518041517459\n",
      "HV95_err = 3.213488391021091\n",
      "Fit concluído para o scan: 40CO2_3.3\n",
      "Gráficos e legendas adicionados ao canvas.\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      135.693\n",
      "NDf                       =           14\n",
      "Edm                       =  1.80793e-08\n",
      "NCalls                    =           37\n",
      "Emax                      =     0.946878   +/-   0.00161813  \n",
      "Lambda                    =    0.0105603   +/-   0.000109037 \n",
      "HV50                      =      6803.85   +/-   1.92647     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      340.053\n",
      "NDf                       =           14\n",
      "Edm                       =  2.50202e-09\n",
      "NCalls                    =           35\n",
      "Emax                      =     0.943465   +/-   0.00144715  \n",
      "Lambda                    =    0.0108278   +/-   0.000102738 \n",
      "HV50                      =      6672.01   +/-   1.98037     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =       141.13\n",
      "NDf                       =            7\n",
      "Edm                       =  9.22262e-08\n",
      "NCalls                    =           37\n",
      "Emax                      =     0.944271   +/-   0.00222448  \n",
      "Lambda                    =    0.0109115   +/-   0.000141795 \n",
      "HV50                      =      6504.66   +/-   2.77287     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      232.237\n",
      "NDf                       =           12\n",
      "Edm                       =  1.88146e-06\n",
      "NCalls                    =          159\n",
      "Emax                      =     0.931399   +/-   0.00201081  \n",
      "Lambda                    =    0.0109315   +/-   0.00010033  \n",
      "HV50                      =       6530.4   +/-   2.05305     \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5620 xmax = 7780 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5620.000000,7780.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5660 xmax = 7340 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5660.000000,7340.000000] is not a valid interval\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c_all\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5620 xmax = 7780 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5620.000000,7780.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5660 xmax = 7340 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5660.000000,7340.000000] is not a valid interval\n",
      "Info in <TCanvas::Print>: png file 33_eff.png has been created\n",
      "Info in <TCanvas::Print>: pdf file 33_eff.pdf has been created\n",
      "Info in <TCanvas::SaveAs>: ROOT file 33_eff.root has been created\n"
     ]
    }
   ],
   "source": [
    "from ROOT import TCanvas, TGraphErrors, TLatex, TLine, TLegend\n",
    "import math\n",
    "\n",
    "# Inicializando listas para gráficos e funções\n",
    "TGraphs_list_33 = []\n",
    "func_list_33 = []\n",
    "\n",
    "canvas = TCanvas(\"c\", \"c\", 564, 232, 600, 600)\n",
    "HV_ref = 95\n",
    "\n",
    "# Definições de cores e marcadores\n",
    "colors = [6, 3, 2, 4]  # ROOT Colors: 2 (Red), 4 (Blue), 3 (Green), 6 (Magenta), 8 (Cyan)\n",
    "markers = [23, 22, 20, 21]  # Marcadores: 20 (circulo), 21 (quadrado), 22 (triângulo), etc.\n",
    "\n",
    "k = 1\n",
    "\n",
    "# Contagem dos scans no dicionário\n",
    "num_scans_total = len(scans_33)\n",
    "print(f\"Total de scans no dicionário: {num_scans_total}\")\n",
    "\n",
    "for index, (scan, sigmoid, color, marker) in enumerate(scans_33):\n",
    "    if scan not in scans:\n",
    "        print(f\"Scan '{scan}' não encontrado no dicionário de scans. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    print(f\"Processando scan: {scan}\")\n",
    "    \n",
    "    df = HVCurrentDataFrame(scans[scan])\n",
    "    n = len(df['HV_top'])\n",
    "    \n",
    "    gr_E = TGraphErrors(n, \n",
    "                        df['HV_top'].values, \n",
    "                        df['efficiency'].values,\n",
    "                        0,\n",
    "                        df['eff_error'].values) # valores do df[].values para converter objeto dataframe em array adequado para o TGraph\n",
    "    \n",
    "    # Ajuste e verificação do resultado\n",
    "    fit_result = gr_E.Fit(sigmoid, \"S\")\n",
    "    if not fit_result.IsValid():\n",
    "        print(f\"Fit falhou para o scan: {scan}\")\n",
    "        continue\n",
    "    \n",
    "    # Listando funções após o ajuste para depuração\n",
    "    functions = gr_E.GetListOfFunctions()\n",
    "    function_names = [f.GetName() for f in functions]\n",
    "    print(f\"Funções no gráfico após ajuste: {function_names}\")\n",
    "\n",
    "    # Tentando encontrar a função ajustada correta\n",
    "    sig = None\n",
    "    for name in function_names:\n",
    "        if name.startswith(\"sigmoid\"):\n",
    "            sig = gr_E.GetFunction(name)\n",
    "            break\n",
    "    \n",
    "    if sig is None:\n",
    "        print(f\"Função 'sigmoid' não encontrada para o scan: {scan}. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    # Usando cores e marcadores diferentes para cada gráfico\n",
    "    color = colors[index % len(colors)]\n",
    "    marker = markers[index % len(markers)]\n",
    "    \n",
    "    sig.SetLineColor(color)\n",
    "    gr_E.SetMarkerColor(color)\n",
    "    gr_E.SetMarkerStyle(marker)\n",
    "\n",
    "    Emax = sig.GetParameter(0)\n",
    "    Emax_err = sig.GetParError(0)     \n",
    "\n",
    "    Lambda = sig.GetParameter(1)    \n",
    "    Lambda_err = sig.GetParError(1)      \n",
    "\n",
    "    HV50 = sig.GetParameter(2)\n",
    "    HV50_err = sig.GetParError(2)      \n",
    "    \n",
    "    print(f\"Lambda = {Lambda}\")\n",
    "    HV95 = sig.GetX(HV_ref)\n",
    "    HV95_err = math.sqrt(((math.log(19) / Lambda**2) * Lambda_err)**2 + HV50_err**2) # erro HV95\n",
    "    \n",
    "    print(f\"HV95_err = {HV95_err}\")\n",
    "\n",
    "    # Adicionando gráficos e funções às listas\n",
    "    TGraphs_list_33.append(gr_E)\n",
    "    func_list_33.append(sig)\n",
    "    gr_E.Draw(\"AP\")\n",
    "    k += 1\n",
    "\n",
    "    print(f\"Fit concluído para o scan: {scan}\")\n",
    "\n",
    "# Criando o canvas final\n",
    "canvas_all = TCanvas(\"c_all\", \"c_all\", 0, 0, 600, 600)\n",
    "\n",
    "if TGraphs_list_33:\n",
    "    TGraphs_list_33[0].GetHistogram().SetMaximum(1.4)\n",
    "    TGraphs_list_33[0].SetTitle(\"\")\n",
    "    TGraphs_list_33[0].GetXaxis().SetTitle(\"HV_{eff} [V]\")\n",
    "    TGraphs_list_33[0].GetYaxis().SetTitle(\"Efficiency\")\n",
    "    TGraphs_list_33[0].GetXaxis().SetLimits(6000, 7400)\n",
    "    TGraphs_list_33[0].Draw(\"AP\")\n",
    "    \n",
    "    for graph in TGraphs_list_33[1:]:\n",
    "        graph.Draw(\"PSAME\")\n",
    "\n",
    "    Emax_33 = []\n",
    "    Emax_err_33 = []\n",
    "    Lambda_33 = []\n",
    "    Lambda_err_33 = []\n",
    "    HV50_33 = []\n",
    "    HV50_err_33 = []\n",
    "    HV95_33 = []\n",
    "    HV95_err_33 = []\n",
    "\n",
    "    for sig in func_list_33:\n",
    "        Emax_ = 1 - math.sqrt((1 - sig.GetParameter(0)) * (1 - sig.GetParameter(0)))\n",
    "        Emax_33.append(Emax_)\n",
    "        Emax_err_33.append(sig.GetParError(0))     \n",
    "        Lambda_33.append(sig.GetParameter(1))  \n",
    "        Lambda_err_33.append(sig.GetParError(1))\n",
    "        HV50_33.append(sig.GetParameter(2))  \n",
    "        HV50_err_33.append(sig.GetParError(2)) \n",
    "        HV95_33.append(sig.GetX(HV_ref))\n",
    "        HV95_err_33.append((math.log(19) / sig.GetParameter(1)**2) * sig.GetParError(1) + sig.GetParError(2)) # erro HV95\n",
    "\n",
    "    WP_33 = []    \n",
    "\n",
    "    for Emax_, Lambda_, HV50_ in zip(Emax_33, Lambda_33, HV50_33):\n",
    "        WP_33.append((HV50_ - math.log(1 / 0.95 - 1) / Lambda_ + 150.))\n",
    "\n",
    "    #txt_33_22 = Features_33['noiseGammaRate'][2] / (Features_33['gamma_CS'][2] * 1000)\n",
    "    #txt_33_10 = Features_33['noiseGammaRate'][4] / (Features_33['gamma_CS'][4] * 1000)\n",
    "    #txt_33_46 = Features_33['noiseGammaRate'][5] / (Features_33['gamma_CS'][5] * 1000)\n",
    "    #txt_33_33 = Features_33['noiseGammaRate'][7] / (Features_33['gamma_CS'][7] * 1000)\n",
    "\n",
    "    # Adiciona verificações de comprimento antes de formatar strings\n",
    "    eff_1_33 = 'plateau = {a:.0%}, WP = {b:.2f} kV, Standard gas mixture, Eff(WP) = {c:.0%}'.format(a=Emax_33[0], b=WP_33[0] / 1000., c=(func_list_33[0].Eval(WP_33[0])))\n",
    "    eff_2_33 = 'plateau = {a:.0%}, WP = {b:.2f} kV, 30% CO_{{2}} + 1.0% SF_{{6}}, Eff(WP) = {c:.0%}'.format(a=Emax_33[1], b=WP_33[1] / 1000., c=(func_list_33[1].Eval(WP_33[1])))\n",
    "    eff_3_33 = 'plateau = {a:.0%}, WP = {b:.2f} kV, 30% CO_{{2}} + 0.5% SF_{{6}}, Eff(WP) = {c:.0%}'.format(a=Emax_33[2], b=WP_33[2] / 1000., c=(func_list_33[2].Eval(WP_33[2])))\n",
    "    eff_4_33 = 'plateau = {a:.0%}, WP = {b:.2f} kV, 40% CO_{{2}}+ 1.0% SF_{{6}}, Eff(WP) = {c:.0%}'.format(a=Emax_33[3], b=WP_33[3] / 1000., c=(func_list_33[3].Eval(WP_33[3])))\n",
    "\n",
    "    # Adicionando a legenda aos gráficos\n",
    "    legend = TLegend(0.1, 0.68, 0.3, 0.89)\n",
    "    legend.SetTextFont(42)\n",
    "    legend.SetBorderSize(0) \n",
    "    legend.SetFillStyle(4000)\n",
    "    legend.SetFillColor(0)  \n",
    "    legend.SetTextSize(0.02376) \n",
    "\n",
    "    ltx_data = TLatex()\n",
    "    ltx_data.SetTextFont(42)\n",
    "    ltx_data.SetTextSize(0.025)\n",
    "    ltx_data.SetTextColor(1)\n",
    "    ltx_data.DrawLatex(6100, 0.88, \"Background rate: ~ 0.8 kHz/cm^{2}\")\n",
    "    ltx_data.DrawLatex(6100, 0.81, \"Test Beam in April+July+August\")\n",
    "    ltx_data.DrawLatex(6100, 0.74, \"2023, Before irradiation\")\n",
    "    ltx_data.DrawLatex(6100, 0.67, \"1.4 mm double gap RPC\")\n",
    "    ltx_data.DrawLatex(6100, 0.60, \"Threshold = 60 [fC]\")\n",
    "\n",
    "    cms_tex = TLatex()\n",
    "    cms_tex.SetNDC()\n",
    "    cms_tex.SetTextFont(61)\n",
    "    cms_tex.SetTextSize(0.04)\n",
    "    cms_tex.SetLineWidth(2)\n",
    "    cms_tex.DrawLatex(0.10, 0.905, \"CMS\")\n",
    "\n",
    "    cms_tex_1 = TLatex()\n",
    "    cms_tex_1.SetNDC()\n",
    "    cms_tex_1.SetTextFont(61)\n",
    "    cms_tex_1.SetTextSize(0.04)\n",
    "    cms_tex_1.SetLineWidth(2)\n",
    "    cms_tex_1.DrawLatex(0.79, 0.905, \"GIF++\")\n",
    "\n",
    "    cms_tex_2 = TLatex()\n",
    "    cms_tex_2.SetNDC()\n",
    "    cms_tex_2.SetTextFont(52)\n",
    "    cms_tex_2.SetTextSize(0.04)\n",
    "    cms_tex_2.SetLineWidth(2)\n",
    "    cms_tex_2.DrawLatex(0.19, 0.905, \"Preliminary\")\n",
    "\n",
    "    line = TLine(6000, 1., 7400, 1.)\n",
    "    line.SetLineColor(1)\n",
    "    line.SetLineStyle(9)\n",
    "    line.SetLineWidth(2)\n",
    "    line.Draw()\n",
    "\n",
    "    legend.AddEntry(TGraphs_list_33[0], eff_1_33, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_33[1], eff_2_33, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_33[2], eff_3_33, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_33[3], eff_4_33, \"P\")\n",
    "    #legend.AddEntry(TGraphs_list_33[4], eff_5_33, \"P\")\n",
    "\n",
    "    legend.Draw()\n",
    "\n",
    "    print(\"Gráficos e legendas adicionados ao canvas.\")\n",
    "\n",
    "# Salvar ou mostrar o canvas\n",
    "canvas_all.SaveAs(\"33_eff.png\")\n",
    "canvas_all.SaveAs(\"33_eff.pdf\")\n",
    "canvas_all.SaveAs(\"33_eff.root\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de scans no dicionário: 5\n",
      "Processando scan: STDMX_OFF\n",
      "Funções no gráfico após ajuste: ['sigmoid1']\n",
      "Lambda = 0.012161934819029394\n",
      "HV95_err = 3.754251662685071\n",
      "Fit concluído para o scan: STDMX_OFF\n",
      "Processando scan: STDMX_10\n",
      "Funções no gráfico após ajuste: ['sigmoid2']\n",
      "Lambda = 0.010579614489368764\n",
      "HV95_err = 3.0623172505238796\n",
      "Fit concluído para o scan: STDMX_10\n",
      "Processando scan: STDMX_3.3\n",
      "Funções no gráfico após ajuste: ['sigmoid3']\n",
      "Lambda = 0.01056025036443764\n",
      "HV95_err = 3.4640120796863125\n",
      "Fit concluído para o scan: STDMX_3.3\n",
      "Processando scan: STDMX_2.2\n",
      "Funções no gráfico após ajuste: ['sigmoid4']\n",
      "Lambda = 0.011864675106728742\n",
      "HV95_err = 4.394343090822958\n",
      "Fit concluído para o scan: STDMX_2.2\n",
      "Processando scan: STDMX_1\n",
      "Funções no gráfico após ajuste: ['sigmoid5']\n",
      "Lambda = 0.011974955576955364\n",
      "HV95_err = 4.129483840207931\n",
      "Fit concluído para o scan: STDMX_1\n",
      "Gráficos e legendas adicionados ao canvas.\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      14.1711\n",
      "NDf                       =            7\n",
      "Edm                       =  2.47816e-08\n",
      "NCalls                    =           37\n",
      "Emax                      =     0.989616   +/-   0.000817385 \n",
      "Lambda                    =    0.0121619   +/-   0.000163806 \n",
      "HV50                      =      6787.78   +/-   1.86051     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      162.076\n",
      "NDf                       =           13\n",
      "Edm                       =  8.97533e-10\n",
      "NCalls                    =           36\n",
      "Emax                      =      0.97403   +/-   0.00118495  \n",
      "Lambda                    =    0.0105796   +/-   9.36431e-05 \n",
      "HV50                      =      6808.49   +/-   1.81916     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      135.693\n",
      "NDf                       =           14\n",
      "Edm                       =   7.4964e-12\n",
      "NCalls                    =           38\n",
      "Emax                      =     0.946878   +/-   0.00161813  \n",
      "Lambda                    =    0.0105603   +/-   0.000109037 \n",
      "HV50                      =      6803.85   +/-   1.92647     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      11.4509\n",
      "NDf                       =            6\n",
      "Edm                       =  1.34264e-08\n",
      "NCalls                    =           62\n",
      "Emax                      =     0.952942   +/-   0.00154112  \n",
      "Lambda                    =    0.0118647   +/-   0.000184321 \n",
      "HV50                      =      6838.26   +/-   2.10865     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      37.1688\n",
      "NDf                       =            7\n",
      "Edm                       =  1.62334e-06\n",
      "NCalls                    =           37\n",
      "Emax                      =       0.9313   +/-   0.00170655  \n",
      "Lambda                    =     0.011975   +/-   0.000170131 \n",
      "HV50                      =      6852.98   +/-   2.20213     \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6070 xmax = 7630 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6070.000000,7630.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6120 xmax = 7680 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6120.000000,7680.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6150 xmax = 7710 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6150.000000,7710.000000] is not a valid interval\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c_all\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6070 xmax = 7630 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6070.000000,7630.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6120 xmax = 7680 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6120.000000,7680.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6150 xmax = 7710 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6150.000000,7710.000000] is not a valid interval\n",
      "Info in <TCanvas::Print>: png file STDMX_eff_10.png has been created\n",
      "Info in <TCanvas::Print>: pdf file STDMX_eff_10.pdf has been created\n",
      "Info in <TCanvas::SaveAs>: ROOT file STDMX_eff_10.root has been created\n"
     ]
    }
   ],
   "source": [
    "from ROOT import TCanvas, TGraphErrors, TLatex, TLine, TLegend, TF1\n",
    "import math\n",
    "import pandas as pd\n",
    "\n",
    "# Nome do arquivo CSV original\n",
    "arquivo_csv = 'tabela_dados_STDMX.csv'\n",
    "\n",
    "# Ler o arquivo CSV\n",
    "df_csv = pd.read_csv(arquivo_csv)\n",
    "\n",
    "# Ajustar os valores de HV_top para os scans especificados\n",
    "df_csv.loc[df_csv['Scan'] == 'STDMX_1', 'HV_top'] += 80\n",
    "df_csv.loc[df_csv['Scan'] == 'STDMX_2.2', 'HV_top'] += 50\n",
    "\n",
    "# Inicializando listas para gráficos e funções\n",
    "TGraphs_list_STDMX = []\n",
    "func_list_STDMX = []\n",
    "\n",
    "# Configurações do canvas\n",
    "canvas = TCanvas(\"c\", \"c\", 564, 232, 600, 600)\n",
    "canvas.SetTopMargin(0.1)\n",
    "canvas.SetBottomMargin(0.12)\n",
    "canvas.SetLeftMargin(0.14)\n",
    "canvas.SetRightMargin(0.05)\n",
    "\n",
    "HV_ref = 95\n",
    "k = 1\n",
    "\n",
    "# Contagem dos scans no dicionário\n",
    "num_scans_total = len(scans_STDMX)\n",
    "print(f\"Total de scans no dicionário: {num_scans_total}\")\n",
    "\n",
    "for scan, sigmoid, color, marker in scans_STDMX:\n",
    "    if scan not in scans:\n",
    "        print(f\"Scan '{scan}' não encontrado no dicionário de scans. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    print(f\"Processando scan: {scan}\")\n",
    "\n",
    "    # Filtrar dados do DataFrame ajustado\n",
    "    df = df_csv[df_csv['Scan'] == scan]\n",
    "    n = len(df['HV_top'])\n",
    "\n",
    "    gr_E = TGraphErrors(n, \n",
    "                        df['HV_top'].values, \n",
    "                        df['Efficiency'].values,\n",
    "                        0,\n",
    "                        df['Eff_Error'].values)\n",
    "    \n",
    "    # Ajuste e verificação do resultado\n",
    "    fit_result = gr_E.Fit(sigmoid, \"S\")\n",
    "    if not fit_result.IsValid():\n",
    "        print(f\"Fit falhou para o scan: {scan}\")\n",
    "        continue\n",
    "\n",
    "    # Listando funções após o ajuste para depuração\n",
    "    functions = gr_E.GetListOfFunctions()\n",
    "    function_names = [f.GetName() for f in functions]\n",
    "    print(f\"Funções no gráfico após ajuste: {function_names}\")\n",
    "\n",
    "    # Tentando encontrar a função ajustada correta\n",
    "    sig = None\n",
    "    for name in function_names:\n",
    "        if name.startswith(\"sigmoid\"):\n",
    "            sig = gr_E.GetFunction(name)\n",
    "            break\n",
    "    \n",
    "    if sig is None:\n",
    "        print(f\"Função 'sigmoid' não encontrada para o scan: {scan}. Pulando...\")\n",
    "        continue\n",
    "\n",
    "    # Configurando cores e estilos\n",
    "    sig.SetLineColor(color)\n",
    "    gr_E.SetMarkerColor(color)\n",
    "    gr_E.SetLineColor(color)\n",
    "    gr_E.SetMarkerStyle(marker)\n",
    "    gr_E.SetLineWidth(2)\n",
    "    gr_E.SetMarkerSize(1.2)\n",
    "\n",
    "    Emax = sig.GetParameter(0)\n",
    "    Emax_err = sig.GetParError(0)     \n",
    "\n",
    "    Lambda = sig.GetParameter(1)    \n",
    "    Lambda_err = sig.GetParError(1)      \n",
    "\n",
    "    HV50 = sig.GetParameter(2)\n",
    "    HV50_err = sig.GetParError(2)      \n",
    "    \n",
    "    print(f\"Lambda = {Lambda}\")\n",
    "    HV95 = sig.GetX(HV_ref)\n",
    "    HV95_err = math.sqrt(((math.log(19) / Lambda**2) * Lambda_err)**2 + HV50_err**2)\n",
    "    \n",
    "    print(f\"HV95_err = {HV95_err}\")\n",
    "\n",
    "    TGraphs_list_STDMX.append(gr_E)\n",
    "    func_list_STDMX.append(sig)\n",
    "\n",
    "    # Desenhar o gráfico\n",
    "    gr_E.Draw(\"AP\")\n",
    "\n",
    "    # Desenhar a função ajustada\n",
    "    sig.Draw(\"same\")\n",
    "\n",
    "    k += 1\n",
    "\n",
    "    print(f\"Fit concluído para o scan: {scan}\")\n",
    "\n",
    "# Criando o canvas final\n",
    "canvas_all = TCanvas(\"c_all\", \"c_all\", 0, 0, 600, 600)\n",
    "canvas_all.SetTopMargin(0.1)\n",
    "canvas_all.SetBottomMargin(0.12)\n",
    "canvas_all.SetLeftMargin(0.14)\n",
    "canvas_all.SetRightMargin(0.05)\n",
    "\n",
    "if TGraphs_list_STDMX:\n",
    "    TGraphs_list_STDMX[0].GetHistogram().SetMaximum(1.4)\n",
    "    TGraphs_list_STDMX[0].GetHistogram().SetMinimum(0)\n",
    "    TGraphs_list_STDMX[0].SetTitle(\"\")\n",
    "    TGraphs_list_STDMX[0].GetXaxis().SetTitle(\"HV_{eff} [V]\")\n",
    "    TGraphs_list_STDMX[0].GetYaxis().SetTitle(\"Efficiency\")\n",
    "    TGraphs_list_STDMX[0].GetXaxis().SetTitleSize(0.05)\n",
    "    TGraphs_list_STDMX[0].GetYaxis().SetTitleSize(0.05)\n",
    "    TGraphs_list_STDMX[0].GetXaxis().SetLabelSize(0.04)\n",
    "    TGraphs_list_STDMX[0].GetYaxis().SetLabelSize(0.04)\n",
    "    TGraphs_list_STDMX[0].Draw(\"AP\")\n",
    "    \n",
    "    for graph in TGraphs_list_STDMX[1:]:\n",
    "        graph.Draw(\"PSAME\")\n",
    "\n",
    "    for func in func_list_STDMX:\n",
    "        func.Draw(\"same\")\n",
    "\n",
    "    Emax_STDMX = []\n",
    "    Emax_err_STDMX = []\n",
    "    Lambda_STDMX = []\n",
    "    Lambda_err_STDMX = []\n",
    "    HV50_STDMX = []\n",
    "    HV50_err_STDMX = []\n",
    "    HV95_STDMX = []\n",
    "    HV95_err_STDMX = []\n",
    "\n",
    "    for sig in func_list_STDMX:\n",
    "        Emax_ = 1 - math.sqrt((1 - sig.GetParameter(0)) * (1 - sig.GetParameter(0)))\n",
    "        Emax_STDMX.append(Emax_)\n",
    "        Emax_err_STDMX.append(sig.GetParError(0))     \n",
    "        Lambda_STDMX.append(sig.GetParameter(1))  \n",
    "        Lambda_err_STDMX.append(sig.GetParError(1))\n",
    "        HV50_STDMX.append(sig.GetParameter(2))  \n",
    "        HV50_err_STDMX.append(sig.GetParError(2)) \n",
    "        HV95_STDMX.append(sig.GetX(HV_ref))\n",
    "        HV95_err_STDMX.append((math.log(19) / sig.GetParameter(1)**2) * sig.GetParError(1) + sig.GetParError(2))\n",
    "\n",
    "    WP_STDMX = []    \n",
    "\n",
    "    for Emax_, Lambda_, HV50_ in zip(Emax_STDMX, Lambda_STDMX, HV50_STDMX):\n",
    "        WP_STDMX.append((HV50_ - math.log(1 / 0.95 - 1) / Lambda_ + 150.))\n",
    "\n",
    "    txt_STDMX_10 = Features_STDMX['noiseGammaRate'][2] / (Features_STDMX['gamma_CS'][2] * 1000)\n",
    "    txt_STDMX_3_3 = Features_STDMX['noiseGammaRate'][5] / (Features_STDMX['gamma_CS'][5] * 1000)\n",
    "    txt_STDMX_2_2 = Features_STDMX['noiseGammaRate'][6] / (Features_STDMX['gamma_CS'][6] * 1000)\n",
    "    txt_STDMX_1 = Features_STDMX['noiseGammaRate'][7] / (Features_STDMX['gamma_CS'][7] * 1000)\n",
    "\n",
    "    eff_1_STDMX = 'plateau = {a:.0%}, WP = {b:.2f} kV, no hit bkg gamma rate, Eff(WP) = {c:.0%}'.format(a=Emax_STDMX[0], b=WP_STDMX[0] / 1000., c=(func_list_STDMX[0].Eval(WP_STDMX[0])))\n",
    "    eff_2_STDMX = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_STDMX[1], b=WP_STDMX[1] / 1000., c=(func_list_STDMX[1].Eval(WP_STDMX[1])), d=txt_STDMX_10)\n",
    "    eff_3_STDMX = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_STDMX[2], b=WP_STDMX[2] / 1000., c=(func_list_STDMX[2].Eval(WP_STDMX[2])), d=txt_STDMX_3_3)\n",
    "    eff_4_STDMX = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_STDMX[3], b=WP_STDMX[3] / 1000., c=(func_list_STDMX[3].Eval(WP_STDMX[3])), d=txt_STDMX_2_2)\n",
    "    eff_5_STDMX = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_STDMX[4], b=WP_STDMX[4] / 1000., c=(func_list_STDMX[4].Eval(WP_STDMX[4])), d=txt_STDMX_1)\n",
    "\n",
    "    legend = TLegend(0.15, 0.68, 0.3, 0.89)\n",
    "    legend.SetTextFont(42)\n",
    "    legend.SetBorderSize(0) \n",
    "    legend.SetFillStyle(4000)\n",
    "    legend.SetFillColor(0)  \n",
    "    legend.SetTextSize(0.02376) \n",
    "\n",
    "    ltx_data = TLatex()\n",
    "    ltx_data.SetTextFont(42)\n",
    "    ltx_data.SetTextSize(0.025)\n",
    "    ltx_data.SetTextColor(1)\n",
    "    ltx_data.DrawLatex(6150, 0.88, \"Standard gas mixture\")\n",
    "    ltx_data.DrawLatex(6150, 0.81, \"Test Beam in April+July+August\")\n",
    "    ltx_data.DrawLatex(6150, 0.74, \"2023, Before irradiation\")\n",
    "    ltx_data.DrawLatex(6150, 0.67, \"1.4 mm double gap RPC\")\n",
    "    ltx_data.DrawLatex(6150, 0.60, \"Threshold = 60 [fC]\")\n",
    "\n",
    "    cms_tex = TLatex()\n",
    "    cms_tex.SetNDC()\n",
    "    cms_tex.SetTextFont(61)\n",
    "    cms_tex.SetTextSize(0.04)\n",
    "    cms_tex.SetLineWidth(2)\n",
    "    cms_tex.DrawLatex(0.15, 0.905, \"CMS\")\n",
    "\n",
    "    cms_tex_1 = TLatex()\n",
    "    cms_tex_1.SetNDC()\n",
    "    cms_tex_1.SetTextFont(61)\n",
    "    cms_tex_1.SetTextSize(0.04)\n",
    "    cms_tex_1.SetLineWidth(2)\n",
    "    cms_tex_1.DrawLatex(0.85, 0.905, \"GIF++\")\n",
    "\n",
    "    cms_tex_2 = TLatex()\n",
    "    cms_tex_2.SetNDC()\n",
    "    cms_tex_2.SetTextFont(52)\n",
    "    cms_tex_2.SetTextSize(0.04)\n",
    "    cms_tex_2.SetLineWidth(2)\n",
    "    cms_tex_2.DrawLatex(0.23, 0.905, \"Preliminary\")\n",
    "\n",
    "    line = TLine(6100, 1., 7630, 1.)\n",
    "    line.SetLineColor(1)\n",
    "    line.SetLineStyle(9)\n",
    "    line.SetLineWidth(2)\n",
    "    line.Draw()\n",
    "\n",
    "    legend.AddEntry(TGraphs_list_STDMX[0], eff_1_STDMX, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_STDMX[1], eff_2_STDMX, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_STDMX[2], eff_3_STDMX, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_STDMX[3], eff_4_STDMX, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_STDMX[4], eff_5_STDMX, \"P\")\n",
    "\n",
    "    legend.Draw()\n",
    "\n",
    "    print(\"Gráficos e legendas adicionados ao canvas.\")\n",
    "\n",
    "# Salvar ou mostrar o canvas\n",
    "canvas_all.SaveAs(\"STDMX_eff_10.png\")\n",
    "canvas_all.SaveAs(\"STDMX_eff_10.pdf\")\n",
    "canvas_all.SaveAs(\"STDMX_eff_10.root\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de scans no dicionário: 5\n",
      "Processando scan: 30CO2_OFF\n",
      "Funções no gráfico após ajuste: ['sigmoid6']\n",
      "Lambda = 0.010507180882886295\n",
      "HV95_err = 3.070669922626277\n",
      "Fit concluído para o scan: 30CO2_OFF\n",
      "Processando scan: 30CO2_10\n",
      "Funções no gráfico após ajuste: ['sigmoid7']\n",
      "Lambda = 0.010707091729899662\n",
      "HV95_err = 4.010321439195105\n",
      "Fit concluído para o scan: 30CO2_10\n",
      "Processando scan: 30CO2_3.3\n",
      "Funções no gráfico após ajuste: ['sigmoid8']\n",
      "Lambda = 0.010827822954784912\n",
      "HV95_err = 3.2525722515983113\n",
      "Fit concluído para o scan: 30CO2_3.3\n",
      "Processando scan: 30CO2_2.2\n",
      "Funções no gráfico após ajuste: ['sigmoid9']\n",
      "Lambda = 0.011548191313254496\n",
      "HV95_err = 4.283613013503648\n",
      "Fit concluído para o scan: 30CO2_2.2\n",
      "Processando scan: 30CO2_1\n",
      "Funções no gráfico após ajuste: ['sigmoid10']\n",
      "Lambda = 0.01166325051991364\n",
      "HV95_err = 3.883960013062234\n",
      "Fit concluído para o scan: 30CO2_1\n",
      "Gráficos e legendas adicionados ao canvas.\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      240.082\n",
      "NDf                       =           14\n",
      "Edm                       =  1.03314e-12\n",
      "NCalls                    =           40\n",
      "Emax                      =     0.993042   +/-   0.000545727 \n",
      "Lambda                    =    0.0105072   +/-   9.6879e-05  \n",
      "HV50                      =      6646.61   +/-   1.6592      \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      121.192\n",
      "NDf                       =            9\n",
      "Edm                       =  3.02299e-09\n",
      "NCalls                    =           41\n",
      "Emax                      =     0.969812   +/-   0.0013263   \n",
      "Lambda                    =    0.0107071   +/-   0.000124969 \n",
      "HV50                      =      6684.44   +/-   2.4043      \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      340.053\n",
      "NDf                       =           14\n",
      "Edm                       =  6.41234e-08\n",
      "NCalls                    =           33\n",
      "Emax                      =     0.943465   +/-   0.00144715  \n",
      "Lambda                    =    0.0108278   +/-   0.000102738 \n",
      "HV50                      =      6672.01   +/-   1.98036     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      43.3897\n",
      "NDf                       =            7\n",
      "Edm                       =   1.0273e-06\n",
      "NCalls                    =           57\n",
      "Emax                      =     0.934805   +/-   0.00161829  \n",
      "Lambda                    =    0.0115482   +/-   0.000164087 \n",
      "HV50                      =      6691.54   +/-   2.28568     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      72.1213\n",
      "NDf                       =            7\n",
      "Edm                       =   1.1826e-10\n",
      "NCalls                    =           44\n",
      "Emax                      =     0.930122   +/-   0.00163731  \n",
      "Lambda                    =    0.0116633   +/-   0.000147254 \n",
      "HV50                      =      6731.74   +/-   2.21944     \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5935 xmax = 7915 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5935.000000,7915.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5620 xmax = 7780 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5620.000000,7780.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6040 xmax = 7600 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6040.000000,7600.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6080 xmax = 7640 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6080.000000,7640.000000] is not a valid interval\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c_all\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5935 xmax = 7915 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5935.000000,7915.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5620 xmax = 7780 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5620.000000,7780.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6040 xmax = 7600 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6040.000000,7600.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6080 xmax = 7640 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6080.000000,7640.000000] is not a valid interval\n",
      "Info in <TCanvas::Print>: png file 30CO2_eff_10.png has been created\n",
      "Info in <TCanvas::Print>: pdf file 30CO2_eff_10.pdf has been created\n",
      "Info in <TCanvas::SaveAs>: ROOT file 30CO2_eff_10.root has been created\n"
     ]
    }
   ],
   "source": [
    "from ROOT import TCanvas, TGraphErrors, TLatex, TLine, TLegend, TF1\n",
    "import math\n",
    "import pandas as pd\n",
    "\n",
    "# Nome do arquivo CSV original\n",
    "arquivo_csv = 'tabela_dados_30CO2.csv'\n",
    "\n",
    "# Ler o arquivo CSV\n",
    "df_csv = pd.read_csv(arquivo_csv)\n",
    "\n",
    "# Ajustar os valores de HV_top para os scans especificados\n",
    "df_csv.loc[df_csv['Scan'] == '30CO2_1', 'HV_top'] += 110\n",
    "df_csv.loc[df_csv['Scan'] == '30CO2_2.2', 'HV_top'] += 70\n",
    "\n",
    "# Inicializando listas para gráficos e funções\n",
    "TGraphs_list_30CO2 = []\n",
    "func_list_30CO2 = []\n",
    "\n",
    "# Configurações do canvas\n",
    "canvas = TCanvas(\"c\", \"c\", 564, 232, 600, 600)\n",
    "canvas.SetTopMargin(0.1)\n",
    "canvas.SetBottomMargin(0.12)\n",
    "canvas.SetLeftMargin(0.14)\n",
    "canvas.SetRightMargin(0.05)\n",
    "\n",
    "HV_ref = 95\n",
    "k = 1\n",
    "\n",
    "# Contagem dos scans no dicionário\n",
    "num_scans_total = len(scans_30CO2)\n",
    "print(f\"Total de scans no dicionário: {num_scans_total}\")\n",
    "\n",
    "for scan, sigmoid, color, marker in scans_30CO2:\n",
    "    if scan not in scans:\n",
    "        print(f\"Scan '{scan}' não encontrado no dicionário de scans. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    print(f\"Processando scan: {scan}\")\n",
    "\n",
    "    # Filtrar dados do DataFrame ajustado\n",
    "    df = df_csv[df_csv['Scan'] == scan]\n",
    "    n = len(df['HV_top'])\n",
    "\n",
    "    gr_E = TGraphErrors(n, \n",
    "                        df['HV_top'].values, \n",
    "                        df['Efficiency'].values,\n",
    "                        0,\n",
    "                        df['Eff_Error'].values)\n",
    "    \n",
    "    # Ajuste e verificação do resultado\n",
    "    fit_result = gr_E.Fit(sigmoid, \"S\")\n",
    "    if not fit_result.IsValid():\n",
    "        print(f\"Fit falhou para o scan: {scan}\")\n",
    "        continue\n",
    "\n",
    "    # Listando funções após o ajuste para depuração\n",
    "    functions = gr_E.GetListOfFunctions()\n",
    "    function_names = [f.GetName() for f in functions]\n",
    "    print(f\"Funções no gráfico após ajuste: {function_names}\")\n",
    "\n",
    "    # Tentando encontrar a função ajustada correta\n",
    "    sig = None\n",
    "    for name in function_names:\n",
    "        if name.startswith(\"sigmoid\"):\n",
    "            sig = gr_E.GetFunction(name)\n",
    "            break\n",
    "    \n",
    "    if sig is None:\n",
    "        print(f\"Função 'sigmoid' não encontrada para o scan: {scan}. Pulando...\")\n",
    "        continue\n",
    "\n",
    "    # Configurando cores e estilos\n",
    "    sig.SetLineColor(color)\n",
    "    gr_E.SetMarkerColor(color)\n",
    "    gr_E.SetLineColor(color)\n",
    "    gr_E.SetMarkerStyle(marker)\n",
    "    gr_E.SetLineWidth(2)\n",
    "    gr_E.SetMarkerSize(1.2)\n",
    "\n",
    "    Emax = sig.GetParameter(0)\n",
    "    Emax_err = sig.GetParError(0)     \n",
    "\n",
    "    Lambda = sig.GetParameter(1)    \n",
    "    Lambda_err = sig.GetParError(1)      \n",
    "\n",
    "    HV50 = sig.GetParameter(2)\n",
    "    HV50_err = sig.GetParError(2)      \n",
    "    \n",
    "    print(f\"Lambda = {Lambda}\")\n",
    "    HV95 = sig.GetX(HV_ref)\n",
    "    HV95_err = math.sqrt(((math.log(19) / Lambda**2) * Lambda_err)**2 + HV50_err**2)\n",
    "    \n",
    "    print(f\"HV95_err = {HV95_err}\")\n",
    "\n",
    "    TGraphs_list_30CO2.append(gr_E)\n",
    "    func_list_30CO2.append(sig)\n",
    "\n",
    "    # Desenhar o gráfico\n",
    "    gr_E.Draw(\"AP\")\n",
    "\n",
    "    # Desenhar a função ajustada\n",
    "    sig.Draw(\"same\")\n",
    "\n",
    "    k += 1\n",
    "\n",
    "    print(f\"Fit concluído para o scan: {scan}\")\n",
    "\n",
    "# Criando o canvas final\n",
    "canvas_all = TCanvas(\"c_all\", \"c_all\", 0, 0, 600, 600)\n",
    "canvas_all.SetTopMargin(0.1)\n",
    "canvas_all.SetBottomMargin(0.12)\n",
    "canvas_all.SetLeftMargin(0.14)\n",
    "canvas_all.SetRightMargin(0.05)\n",
    "\n",
    "if TGraphs_list_30CO2:\n",
    "    TGraphs_list_30CO2[0].GetHistogram().SetMaximum(1.4)\n",
    "    TGraphs_list_30CO2[0].GetHistogram().SetMinimum(0)\n",
    "    TGraphs_list_30CO2[0].SetTitle(\"\")\n",
    "    TGraphs_list_30CO2[0].GetXaxis().SetTitle(\"HV_{eff} [V]\")\n",
    "    TGraphs_list_30CO2[0].GetYaxis().SetTitle(\"Efficiency\")\n",
    "    TGraphs_list_30CO2[0].GetXaxis().SetTitleSize(0.05)\n",
    "    TGraphs_list_30CO2[0].GetYaxis().SetTitleSize(0.05)\n",
    "    TGraphs_list_30CO2[0].GetXaxis().SetLabelSize(0.04)\n",
    "    TGraphs_list_30CO2[0].GetYaxis().SetLabelSize(0.04)\n",
    "    TGraphs_list_30CO2[0].Draw(\"AP\")\n",
    "    \n",
    "    for graph in TGraphs_list_30CO2[1:]:\n",
    "        graph.Draw(\"PSAME\")\n",
    "\n",
    "    for func in func_list_30CO2:\n",
    "        func.Draw(\"same\")\n",
    "\n",
    "    Emax_30CO2 = []\n",
    "    Emax_err_30CO2 = []\n",
    "    Lambda_30CO2 = []\n",
    "    Lambda_err_30CO2 = []\n",
    "    HV50_30CO2 = []\n",
    "    HV50_err_30CO2 = []\n",
    "    HV95_30CO2 = []\n",
    "    HV95_err_30CO2 = []\n",
    "\n",
    "    for sig in func_list_30CO2:\n",
    "        Emax_ = 1 - math.sqrt((1 - sig.GetParameter(0)) * (1 - sig.GetParameter(0)))\n",
    "        Emax_30CO2.append(Emax_)\n",
    "        Emax_err_30CO2.append(sig.GetParError(0))     \n",
    "        Lambda_30CO2.append(sig.GetParameter(1))  \n",
    "        Lambda_err_30CO2.append(sig.GetParError(1))\n",
    "        HV50_30CO2.append(sig.GetParameter(2))  \n",
    "        HV50_err_30CO2.append(sig.GetParError(2)) \n",
    "        HV95_30CO2.append(sig.GetX(HV_ref))\n",
    "        HV95_err_30CO2.append((math.log(19) / sig.GetParameter(1)**2) * sig.GetParError(1) + sig.GetParError(2))\n",
    "\n",
    "    WP_30CO2 = []    \n",
    "\n",
    "    for Emax_, Lambda_, HV50_ in zip(Emax_30CO2, Lambda_30CO2, HV50_30CO2):\n",
    "        WP_30CO2.append((HV50_ - math.log(1 / 0.95 - 1) / Lambda_ + 150.))\n",
    "\n",
    "    txt_30CO2_10 = Features_30CO2['noiseGammaRate'][2] / (Features_30CO2['gamma_CS'][2] * 1000)\n",
    "    txt_30CO2_3_3 = Features_30CO2['noiseGammaRate'][5] / (Features_30CO2['gamma_CS'][5] * 1000)\n",
    "    txt_30CO2_2_2 = Features_30CO2['noiseGammaRate'][6] / (Features_30CO2['gamma_CS'][6] * 1000)\n",
    "    txt_30CO2_1 = Features_30CO2['noiseGammaRate'][7] / (Features_30CO2['gamma_CS'][7] * 1000)\n",
    "\n",
    "    eff_1_30CO2 = 'plateau = {a:.0%}, WP = {b:.2f} kV, no hit bkg gamma rate, Eff(WP) = {c:.0%}'.format(a=Emax_30CO2[0], b=WP_30CO2[0] / 1000., c=(func_list_30CO2[0].Eval(WP_30CO2[0])))\n",
    "    eff_2_30CO2 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_30CO2[1], b=WP_30CO2[1] / 1000., c=(func_list_30CO2[1].Eval(WP_30CO2[1])), d=txt_30CO2_10)\n",
    "    eff_3_30CO2 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_30CO2[2], b=WP_30CO2[2] / 1000., c=(func_list_30CO2[2].Eval(WP_30CO2[2])), d=txt_30CO2_3_3)\n",
    "    eff_4_30CO2 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_30CO2[3], b=WP_30CO2[3] / 1000., c=(func_list_30CO2[3].Eval(WP_30CO2[3])), d=txt_30CO2_2_2)\n",
    "    eff_5_30CO2 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_30CO2[4], b=WP_30CO2[4] / 1000., c=(func_list_30CO2[4].Eval(WP_30CO2[4])), d=txt_30CO2_1)\n",
    "\n",
    "    legend = TLegend(0.15, 0.68, 0.3, 0.89)\n",
    "    legend.SetTextFont(42)\n",
    "    legend.SetBorderSize(0) \n",
    "    legend.SetFillStyle(4000)\n",
    "    legend.SetFillColor(0)  \n",
    "    legend.SetTextSize(0.02376) \n",
    "\n",
    "    ltx_data = TLatex()\n",
    "    ltx_data.SetTextFont(42)\n",
    "    ltx_data.SetTextSize(0.025)\n",
    "    ltx_data.SetTextColor(1)\n",
    "    ltx_data.DrawLatex(6000, 0.88, \"30% CO_{2} + 1.0% SF_{6}\")\n",
    "    ltx_data.DrawLatex(6000, 0.81, \"Test Beam in April+July+August\")\n",
    "    ltx_data.DrawLatex(6000, 0.74, \"2023, Before irradiation\")\n",
    "    ltx_data.DrawLatex(6000, 0.67, \"1.4 mm double gap RPC\")\n",
    "    ltx_data.DrawLatex(6000, 0.60, \"Threshold = 60 [fC]\")\n",
    "\n",
    "    cms_tex = TLatex()\n",
    "    cms_tex.SetNDC()\n",
    "    cms_tex.SetTextFont(61)\n",
    "    cms_tex.SetTextSize(0.04)\n",
    "    cms_tex.SetLineWidth(2)\n",
    "    cms_tex.DrawLatex(0.15, 0.905, \"CMS\")\n",
    "\n",
    "    cms_tex_1 = TLatex()\n",
    "    cms_tex_1.SetNDC()\n",
    "    cms_tex_1.SetTextFont(61)\n",
    "    cms_tex_1.SetTextSize(0.04)\n",
    "    cms_tex_1.SetLineWidth(2)\n",
    "    cms_tex_1.DrawLatex(0.85, 0.905, \"GIF++\")\n",
    "\n",
    "    cms_tex_2 = TLatex()\n",
    "    cms_tex_2.SetNDC()\n",
    "    cms_tex_2.SetTextFont(52)\n",
    "    cms_tex_2.SetTextSize(0.04)\n",
    "    cms_tex_2.SetLineWidth(2)\n",
    "    cms_tex_2.DrawLatex(0.23, 0.905, \"Preliminary\")\n",
    "\n",
    "    line = TLine(5900, 1., 7780, 1.)\n",
    "    line.SetLineColor(1)\n",
    "    line.SetLineStyle(9)\n",
    "    line.SetLineWidth(2)\n",
    "    line.Draw()\n",
    "\n",
    "    legend.AddEntry(TGraphs_list_30CO2[0], eff_1_30CO2, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_30CO2[1], eff_2_30CO2, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_30CO2[2], eff_3_30CO2, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_30CO2[3], eff_4_30CO2, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_30CO2[4], eff_5_30CO2, \"P\")\n",
    "\n",
    "    legend.Draw()\n",
    "\n",
    "    print(\"Gráficos e legendas adicionados ao canvas.\")\n",
    "\n",
    "# Salvar ou mostrar o canvas\n",
    "canvas_all.SaveAs(\"30CO2_eff_10.png\")\n",
    "canvas_all.SaveAs(\"30CO2_eff_10.pdf\")\n",
    "canvas_all.SaveAs(\"30CO2_eff_10.root\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de scans no dicionário: 5\n",
      "Processando scan: 30CO205SF6_OFF\n",
      "Funções no gráfico após ajuste: ['sigmoid16']\n",
      "Lambda = 0.009868738030935287\n",
      "HV95_err = 3.2132189038265526\n",
      "Fit concluído para o scan: 30CO205SF6_OFF\n",
      "Processando scan: 30CO205SF6_10\n",
      "Funções no gráfico após ajuste: ['sigmoid17']\n",
      "Lambda = 0.010571604155513925\n",
      "HV95_err = 4.548955342875589\n",
      "Fit concluído para o scan: 30CO205SF6_10\n",
      "Processando scan: 30CO205SF6_3.3\n",
      "Funções no gráfico após ajuste: ['sigmoid18']\n",
      "Lambda = 0.010911523338421202\n",
      "HV95_err = 4.47050386071553\n",
      "Fit concluído para o scan: 30CO205SF6_3.3\n",
      "Processando scan: 30CO205SF6_2.2\n",
      "Funções no gráfico após ajuste: ['sigmoid19']\n",
      "Lambda = 0.011603439977896072\n",
      "HV95_err = 4.202996377106831\n",
      "Fit concluído para o scan: 30CO205SF6_2.2\n",
      "Processando scan: 30CO205SF6_1\n",
      "Funções no gráfico após ajuste: ['sigmoid20']\n",
      "Lambda = 0.011322391717376165\n",
      "HV95_err = 4.297505968323349\n",
      "Fit concluído para o scan: 30CO205SF6_1\n",
      "Gráficos e legendas adicionados ao canvas.\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      137.155\n",
      "NDf                       =            7\n",
      "Edm                       =  1.70496e-11\n",
      "NCalls                    =           38\n",
      "Emax                      =     0.990414   +/-   0.000794203 \n",
      "Lambda                    =   0.00986874   +/-   8.89583e-05 \n",
      "HV50                      =      6513.27   +/-   1.75828     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      72.8542\n",
      "NDf                       =            7\n",
      "Edm                       =   2.8528e-08\n",
      "NCalls                    =           36\n",
      "Emax                      =      0.97063   +/-   0.00173901  \n",
      "Lambda                    =    0.0105716   +/-   0.000142233 \n",
      "HV50                      =      6512.88   +/-   2.57885     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =       141.13\n",
      "NDf                       =            7\n",
      "Edm                       =  4.16212e-09\n",
      "NCalls                    =           38\n",
      "Emax                      =     0.944271   +/-   0.00222447  \n",
      "Lambda                    =    0.0109115   +/-   0.000141795 \n",
      "HV50                      =      6504.66   +/-   2.77287     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =       34.043\n",
      "NDf                       =            7\n",
      "Edm                       =  1.38791e-07\n",
      "NCalls                    =           80\n",
      "Emax                      =     0.946551   +/-   0.00153726  \n",
      "Lambda                    =    0.0116034   +/-   0.000165251 \n",
      "HV50                      =      6585.79   +/-   2.14595     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      43.2095\n",
      "NDf                       =            7\n",
      "Edm                       =  1.99202e-09\n",
      "NCalls                    =           37\n",
      "Emax                      =     0.929849   +/-   0.00174637  \n",
      "Lambda                    =    0.0113224   +/-   0.000158535 \n",
      "HV50                      =      6624.01   +/-   2.2825      \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6030 xmax = 7470 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6030.000000,7470.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6060 xmax = 7500 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6060.000000,7500.000000] is not a valid interval\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c_all\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6030 xmax = 7470 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6030.000000,7470.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6060 xmax = 7500 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6060.000000,7500.000000] is not a valid interval\n",
      "Info in <TCanvas::Print>: png file 30CO205SF6_eff_10.png has been created\n",
      "Info in <TCanvas::Print>: pdf file 30CO205SF6_eff_10.pdf has been created\n",
      "Info in <TCanvas::SaveAs>: ROOT file 30CO205SF6_eff_10.root has been created\n"
     ]
    }
   ],
   "source": [
    "from ROOT import TCanvas, TGraphErrors, TLatex, TLine, TLegend, TF1\n",
    "import math\n",
    "import pandas as pd\n",
    "\n",
    "# Nome do arquivo CSV original\n",
    "arquivo_csv = 'tabela_dados_30CO205SF6.csv'\n",
    "\n",
    "# Ler o arquivo CSV\n",
    "df_csv = pd.read_csv(arquivo_csv)\n",
    "\n",
    "# Ajustar os valores de HV_top para os scans especificados\n",
    "df_csv.loc[df_csv['Scan'] == '30CO205SF6_1', 'HV_top'] += 180\n",
    "df_csv.loc[df_csv['Scan'] == '30CO205SF6_2.2', 'HV_top'] += 150\n",
    "\n",
    "# Inicializando listas para gráficos e funções\n",
    "TGraphs_list_30CO205SF6 = []\n",
    "func_list_30CO205SF6 = []\n",
    "\n",
    "# Configurações do canvas\n",
    "canvas = TCanvas(\"c\", \"c\", 564, 232, 600, 600)\n",
    "canvas.SetTopMargin(0.1)\n",
    "canvas.SetBottomMargin(0.12)\n",
    "canvas.SetLeftMargin(0.14)\n",
    "canvas.SetRightMargin(0.05)\n",
    "\n",
    "HV_ref = 95\n",
    "k = 1\n",
    "\n",
    "# Contagem dos scans no dicionário\n",
    "num_scans_total = len(scans_30CO205SF6)\n",
    "print(f\"Total de scans no dicionário: {num_scans_total}\")\n",
    "\n",
    "for scan, sigmoid, color, marker in scans_30CO205SF6:\n",
    "    if scan not in scans:\n",
    "        print(f\"Scan '{scan}' não encontrado no dicionário de scans. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    print(f\"Processando scan: {scan}\")\n",
    "\n",
    "    # Filtrar dados do DataFrame ajustado\n",
    "    df = df_csv[df_csv['Scan'] == scan]\n",
    "    n = len(df['HV_top'])\n",
    "\n",
    "    gr_E = TGraphErrors(n, \n",
    "                        df['HV_top'].values, \n",
    "                        df['Efficiency'].values,\n",
    "                        0,\n",
    "                        df['Eff_Error'].values)\n",
    "    \n",
    "    # Ajuste e verificação do resultado\n",
    "    fit_result = gr_E.Fit(sigmoid, \"S\")\n",
    "    if not fit_result.IsValid():\n",
    "        print(f\"Fit falhou para o scan: {scan}\")\n",
    "        continue\n",
    "\n",
    "    # Listando funções após o ajuste para depuração\n",
    "    functions = gr_E.GetListOfFunctions()\n",
    "    function_names = [f.GetName() for f in functions]\n",
    "    print(f\"Funções no gráfico após ajuste: {function_names}\")\n",
    "\n",
    "    # Tentando encontrar a função ajustada correta\n",
    "    sig = None\n",
    "    for name in function_names:\n",
    "        if name.startswith(\"sigmoid\"):\n",
    "            sig = gr_E.GetFunction(name)\n",
    "            break\n",
    "    \n",
    "    if sig is None:\n",
    "        print(f\"Função 'sigmoid' não encontrada para o scan: {scan}. Pulando...\")\n",
    "        continue\n",
    "\n",
    "    # Configurando cores e estilos\n",
    "    sig.SetLineColor(color)\n",
    "    gr_E.SetMarkerColor(color)\n",
    "    gr_E.SetLineColor(color)\n",
    "    gr_E.SetMarkerStyle(marker)\n",
    "    gr_E.SetLineWidth(2)\n",
    "    gr_E.SetMarkerSize(1.2)\n",
    "\n",
    "    Emax = sig.GetParameter(0)\n",
    "    Emax_err = sig.GetParError(0)     \n",
    "\n",
    "    Lambda = sig.GetParameter(1)    \n",
    "    Lambda_err = sig.GetParError(1)      \n",
    "\n",
    "    HV50 = sig.GetParameter(2)\n",
    "    HV50_err = sig.GetParError(2)      \n",
    "    \n",
    "    print(f\"Lambda = {Lambda}\")\n",
    "    HV95 = sig.GetX(HV_ref)\n",
    "    HV95_err = math.sqrt(((math.log(19) / Lambda**2) * Lambda_err)**2 + HV50_err**2)\n",
    "    \n",
    "    print(f\"HV95_err = {HV95_err}\")\n",
    "\n",
    "    TGraphs_list_30CO205SF6.append(gr_E)\n",
    "    func_list_30CO205SF6.append(sig)\n",
    "\n",
    "    # Desenhar o gráfico\n",
    "    gr_E.Draw(\"AP\")\n",
    "\n",
    "    # Desenhar a função ajustada\n",
    "    sig.Draw(\"same\")\n",
    "\n",
    "    k += 1\n",
    "\n",
    "    print(f\"Fit concluído para o scan: {scan}\")\n",
    "\n",
    "# Criando o canvas final\n",
    "canvas_all = TCanvas(\"c_all\", \"c_all\", 0, 0, 600, 600)\n",
    "canvas_all.SetTopMargin(0.1)\n",
    "canvas_all.SetBottomMargin(0.12)\n",
    "canvas_all.SetLeftMargin(0.14)\n",
    "canvas_all.SetRightMargin(0.05)\n",
    "\n",
    "if TGraphs_list_30CO205SF6:\n",
    "    TGraphs_list_30CO205SF6[0].GetHistogram().SetMaximum(1.4)\n",
    "    TGraphs_list_30CO205SF6[0].GetHistogram().SetMinimum(0)\n",
    "    TGraphs_list_30CO205SF6[0].SetTitle(\"\")\n",
    "    TGraphs_list_30CO205SF6[0].GetXaxis().SetTitle(\"HV_{eff} [V]\")\n",
    "    TGraphs_list_30CO205SF6[0].GetYaxis().SetTitle(\"Efficiency\")\n",
    "    TGraphs_list_30CO205SF6[0].GetXaxis().SetTitleSize(0.05)\n",
    "    TGraphs_list_30CO205SF6[0].GetYaxis().SetTitleSize(0.05)\n",
    "    TGraphs_list_30CO205SF6[0].GetXaxis().SetLabelSize(0.04)\n",
    "    TGraphs_list_30CO205SF6[0].GetYaxis().SetLabelSize(0.04)\n",
    "    TGraphs_list_30CO205SF6[0].Draw(\"AP\")\n",
    "    \n",
    "    for graph in TGraphs_list_30CO205SF6[1:]:\n",
    "        graph.Draw(\"PSAME\")\n",
    "\n",
    "    for func in func_list_30CO205SF6:\n",
    "        func.Draw(\"same\")\n",
    "\n",
    "    Emax_30CO205SF6 = []\n",
    "    Emax_err_30CO205SF6 = []\n",
    "    Lambda_30CO205SF6 = []\n",
    "    Lambda_err_30CO205SF6 = []\n",
    "    HV50_30CO205SF6 = []\n",
    "    HV50_err_30CO205SF6 = []\n",
    "    HV95_30CO205SF6 = []\n",
    "    HV95_err_30CO205SF6 = []\n",
    "\n",
    "    for sig in func_list_30CO205SF6:\n",
    "        Emax_ = 1 - math.sqrt((1 - sig.GetParameter(0)) * (1 - sig.GetParameter(0)))\n",
    "        Emax_30CO205SF6.append(Emax_)\n",
    "        Emax_err_30CO205SF6.append(sig.GetParError(0))     \n",
    "        Lambda_30CO205SF6.append(sig.GetParameter(1))  \n",
    "        Lambda_err_30CO205SF6.append(sig.GetParError(1))\n",
    "        HV50_30CO205SF6.append(sig.GetParameter(2))  \n",
    "        HV50_err_30CO205SF6.append(sig.GetParError(2)) \n",
    "        HV95_30CO205SF6.append(sig.GetX(HV_ref))\n",
    "        HV95_err_30CO205SF6.append((math.log(19) / sig.GetParameter(1)**2) * sig.GetParError(1) + sig.GetParError(2))\n",
    "\n",
    "    WP_30CO205SF6 = []    \n",
    "\n",
    "    for Emax_, Lambda_, HV50_ in zip(Emax_30CO205SF6, Lambda_30CO205SF6, HV50_30CO205SF6):\n",
    "        WP_30CO205SF6.append((HV50_ - math.log(1 / 0.95 - 1) / Lambda_ + 150.))\n",
    "\n",
    "    txt_30CO205SF6_10 = Features_30CO205SF6['noiseGammaRate'][2] / (Features_30CO205SF6['gamma_CS'][2] * 1000)\n",
    "    txt_30CO205SF6_3_3 = Features_30CO205SF6['noiseGammaRate'][5] / (Features_30CO205SF6['gamma_CS'][5] * 1000)\n",
    "    txt_30CO205SF6_2_2 = Features_30CO205SF6['noiseGammaRate'][7] / (Features_30CO205SF6['gamma_CS'][7] * 1000)\n",
    "    txt_30CO205SF6_1 = Features_30CO205SF6['noiseGammaRate'][6] / (Features_30CO205SF6['gamma_CS'][6] * 1000)\n",
    "\n",
    "    eff_1_30CO205SF6 = 'plateau = {a:.0%}, WP = {b:.2f} kV, no hit bkg gamma rate, Eff(WP) = {c:.0%}'.format(a=Emax_30CO205SF6[0], b=WP_30CO205SF6[0] / 1000., c=(func_list_30CO205SF6[0].Eval(WP_30CO205SF6[0])))\n",
    "    eff_2_30CO205SF6 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_30CO205SF6[1], b=WP_30CO205SF6[1] / 1000., c=(func_list_30CO205SF6[1].Eval(WP_30CO205SF6[1])), d=txt_30CO205SF6_10)\n",
    "    eff_3_30CO205SF6 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_30CO205SF6[2], b=WP_30CO205SF6[2] / 1000., c=(func_list_30CO205SF6[2].Eval(WP_30CO205SF6[2])), d=txt_30CO205SF6_3_3)\n",
    "    eff_4_30CO205SF6 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_30CO205SF6[3], b=WP_30CO205SF6[3] / 1000., c=(func_list_30CO205SF6[3].Eval(WP_30CO205SF6[3])), d=txt_30CO205SF6_2_2)\n",
    "    eff_5_30CO205SF6 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_30CO205SF6[4], b=WP_30CO205SF6[4] / 1000., c=(func_list_30CO205SF6[4].Eval(WP_30CO205SF6[4])), d=txt_30CO205SF6_1)\n",
    "\n",
    "    legend = TLegend(0.15, 0.68, 0.3, 0.89)\n",
    "    legend.SetTextFont(42)\n",
    "    legend.SetBorderSize(0) \n",
    "    legend.SetFillStyle(4000)\n",
    "    legend.SetFillColor(0)  \n",
    "    legend.SetTextSize(0.02376) \n",
    "\n",
    "    ltx_data = TLatex()\n",
    "    ltx_data.SetTextFont(42)\n",
    "    ltx_data.SetTextSize(0.025)\n",
    "    ltx_data.SetTextColor(1)\n",
    "    ltx_data.DrawLatex(5900, 0.88, \"30% CO_{2} + 0.5% SF_{6}\")\n",
    "    ltx_data.DrawLatex(5900, 0.81, \"Test Beam in April+July+August\")\n",
    "    ltx_data.DrawLatex(5900, 0.74, \"2023, Before irradiation\")\n",
    "    ltx_data.DrawLatex(5900, 0.67, \"1.4 mm double gap RPC\")\n",
    "    ltx_data.DrawLatex(5900, 0.60, \"Threshold = 60 [fC]\")\n",
    "\n",
    "    cms_tex = TLatex()\n",
    "    cms_tex.SetNDC()\n",
    "    cms_tex.SetTextFont(61)\n",
    "    cms_tex.SetTextSize(0.04)\n",
    "    cms_tex.SetLineWidth(2)\n",
    "    cms_tex.DrawLatex(0.15, 0.905, \"CMS\")\n",
    "\n",
    "    cms_tex_1 = TLatex()\n",
    "    cms_tex_1.SetNDC()\n",
    "    cms_tex_1.SetTextFont(61)\n",
    "    cms_tex_1.SetTextSize(0.04)\n",
    "    cms_tex_1.SetLineWidth(2)\n",
    "    cms_tex_1.DrawLatex(0.85, 0.905, \"GIF++\")\n",
    "\n",
    "    cms_tex_2 = TLatex()\n",
    "    cms_tex_2.SetNDC()\n",
    "    cms_tex_2.SetTextFont(52)\n",
    "    cms_tex_2.SetTextSize(0.04)\n",
    "    cms_tex_2.SetLineWidth(2)\n",
    "    cms_tex_2.DrawLatex(0.23, 0.905, \"Preliminary\")\n",
    "\n",
    "    line = TLine(5830, 1., 7430, 1.)\n",
    "    line.SetLineColor(1)\n",
    "    line.SetLineStyle(9)\n",
    "    line.SetLineWidth(2)\n",
    "    line.Draw()\n",
    "\n",
    "    legend.AddEntry(TGraphs_list_30CO205SF6[0], eff_1_30CO205SF6, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_30CO205SF6[1], eff_2_30CO205SF6, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_30CO205SF6[2], eff_3_30CO205SF6, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_30CO205SF6[3], eff_4_30CO205SF6, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_30CO205SF6[4], eff_5_30CO205SF6, \"P\")\n",
    "\n",
    "    legend.Draw()\n",
    "\n",
    "    print(\"Gráficos e legendas adicionados ao canvas.\")\n",
    "\n",
    "# Salvar ou mostrar o canvas\n",
    "canvas_all.SaveAs(\"30CO205SF6_eff_10.png\")\n",
    "canvas_all.SaveAs(\"30CO205SF6_eff_10.pdf\")\n",
    "canvas_all.SaveAs(\"30CO205SF6_eff_10.root\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>current_top</th>\n",
       "      <th>current_bot</th>\n",
       "      <th>HV_top</th>\n",
       "      <th>HV_bot</th>\n",
       "      <th>muon_stream</th>\n",
       "      <th>gamma_stream</th>\n",
       "      <th>muon_CM</th>\n",
       "      <th>gamma_CM</th>\n",
       "      <th>muon_CS</th>\n",
       "      <th>gamma_CS</th>\n",
       "      <th>muon_CM_err</th>\n",
       "      <th>gamma_CM_err</th>\n",
       "      <th>muon_CS_err</th>\n",
       "      <th>gamma_CS_err</th>\n",
       "      <th>efficiency</th>\n",
       "      <th>eff_error</th>\n",
       "      <th>noiseGammaRate</th>\n",
       "      <th>noiseGammaRate_err</th>\n",
       "      <th>current</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.908333</td>\n",
       "      <td>0.675833</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>0.023776</td>\n",
       "      <td>0.000225</td>\n",
       "      <td>1.048760</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.379098</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.162951</td>\n",
       "      <td>0.035061</td>\n",
       "      <td>0.319941</td>\n",
       "      <td>0.035555</td>\n",
       "      <td>98.192179</td>\n",
       "      <td>0.001906</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.203070</td>\n",
       "      <td>1.584167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.063140</td>\n",
       "      <td>7.805416</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>0.024847</td>\n",
       "      <td>0.011061</td>\n",
       "      <td>1.070776</td>\n",
       "      <td>1.867909</td>\n",
       "      <td>2.308344</td>\n",
       "      <td>1.979245</td>\n",
       "      <td>0.156086</td>\n",
       "      <td>0.150436</td>\n",
       "      <td>0.293676</td>\n",
       "      <td>0.147522</td>\n",
       "      <td>97.480200</td>\n",
       "      <td>0.001121</td>\n",
       "      <td>272.042714</td>\n",
       "      <td>16.487786</td>\n",
       "      <td>16.868556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.024096</td>\n",
       "      <td>15.139259</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>0.025918</td>\n",
       "      <td>0.020561</td>\n",
       "      <td>1.095825</td>\n",
       "      <td>2.926823</td>\n",
       "      <td>2.257322</td>\n",
       "      <td>1.950481</td>\n",
       "      <td>0.149849</td>\n",
       "      <td>0.231306</td>\n",
       "      <td>0.271545</td>\n",
       "      <td>0.142856</td>\n",
       "      <td>97.166185</td>\n",
       "      <td>0.001172</td>\n",
       "      <td>527.098160</td>\n",
       "      <td>22.950889</td>\n",
       "      <td>32.163355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.642144</td>\n",
       "      <td>21.794923</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>0.023045</td>\n",
       "      <td>0.028155</td>\n",
       "      <td>1.115299</td>\n",
       "      <td>4.124553</td>\n",
       "      <td>2.229258</td>\n",
       "      <td>1.921858</td>\n",
       "      <td>0.151615</td>\n",
       "      <td>0.321327</td>\n",
       "      <td>0.266781</td>\n",
       "      <td>0.138903</td>\n",
       "      <td>96.391291</td>\n",
       "      <td>0.001885</td>\n",
       "      <td>771.747487</td>\n",
       "      <td>27.772401</td>\n",
       "      <td>46.437067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.087562</td>\n",
       "      <td>28.356041</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>0.023824</td>\n",
       "      <td>0.034470</td>\n",
       "      <td>1.139349</td>\n",
       "      <td>5.370562</td>\n",
       "      <td>2.197859</td>\n",
       "      <td>1.899114</td>\n",
       "      <td>0.147606</td>\n",
       "      <td>0.411500</td>\n",
       "      <td>0.252081</td>\n",
       "      <td>0.135157</td>\n",
       "      <td>95.988859</td>\n",
       "      <td>0.001404</td>\n",
       "      <td>1007.144266</td>\n",
       "      <td>31.726279</td>\n",
       "      <td>60.443602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>47.500257</td>\n",
       "      <td>42.183781</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>0.023215</td>\n",
       "      <td>0.048381</td>\n",
       "      <td>1.190249</td>\n",
       "      <td>8.171411</td>\n",
       "      <td>2.105764</td>\n",
       "      <td>1.839577</td>\n",
       "      <td>0.138221</td>\n",
       "      <td>0.588730</td>\n",
       "      <td>0.219095</td>\n",
       "      <td>0.123630</td>\n",
       "      <td>95.219617</td>\n",
       "      <td>0.001525</td>\n",
       "      <td>1493.011466</td>\n",
       "      <td>38.628021</td>\n",
       "      <td>89.684038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   current_top  current_bot  HV_top  HV_bot  muon_stream  gamma_stream  \\\n",
       "0     0.908333     0.675833  7000.0  7000.0     0.023776      0.000225   \n",
       "1     9.063140     7.805416  7000.0  7000.0     0.024847      0.011061   \n",
       "2    17.024096    15.139259  7000.0  7000.0     0.025918      0.020561   \n",
       "3    24.642144    21.794923  7000.0  7000.0     0.023045      0.028155   \n",
       "4    32.087562    28.356041  7000.0  7000.0     0.023824      0.034470   \n",
       "5    47.500257    42.183781  7000.0  7000.0     0.023215      0.048381   \n",
       "\n",
       "    muon_CM  gamma_CM   muon_CS  gamma_CS  muon_CM_err  gamma_CM_err  \\\n",
       "0  1.048760  1.000000  2.379098  1.000000     0.162951      0.035061   \n",
       "1  1.070776  1.867909  2.308344  1.979245     0.156086      0.150436   \n",
       "2  1.095825  2.926823  2.257322  1.950481     0.149849      0.231306   \n",
       "3  1.115299  4.124553  2.229258  1.921858     0.151615      0.321327   \n",
       "4  1.139349  5.370562  2.197859  1.899114     0.147606      0.411500   \n",
       "5  1.190249  8.171411  2.105764  1.839577     0.138221      0.588730   \n",
       "\n",
       "   muon_CS_err  gamma_CS_err  efficiency  eff_error  noiseGammaRate  \\\n",
       "0     0.319941      0.035555   98.192179   0.001906        1.000000   \n",
       "1     0.293676      0.147522   97.480200   0.001121      272.042714   \n",
       "2     0.271545      0.142856   97.166185   0.001172      527.098160   \n",
       "3     0.266781      0.138903   96.391291   0.001885      771.747487   \n",
       "4     0.252081      0.135157   95.988859   0.001404     1007.144266   \n",
       "5     0.219095      0.123630   95.219617   0.001525     1493.011466   \n",
       "\n",
       "   noiseGammaRate_err    current  \n",
       "0            2.203070   1.584167  \n",
       "1           16.487786  16.868556  \n",
       "2           22.950889  32.163355  \n",
       "3           27.772401  46.437067  \n",
       "4           31.726279  60.443602  \n",
       "5           38.628021  89.684038  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Features_40CO2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de scans no dicionário: 5\n",
      "Processando scan: 40CO2_OFF\n",
      "Funções no gráfico após ajuste: ['sigmoid11']\n",
      "Lambda = 0.009824672903229118\n",
      "HV95_err = 3.2181203972904053\n",
      "Fit concluído para o scan: 40CO2_OFF\n",
      "Processando scan: 40CO2_10\n",
      "Funções no gráfico após ajuste: ['sigmoid12']\n",
      "Lambda = 0.010292373181645938\n",
      "HV95_err = 3.2722664640470986\n",
      "Fit concluído para o scan: 40CO2_10\n",
      "Processando scan: 40CO2_6.9\n",
      "Funções no gráfico após ajuste: ['sigmoid14']\n",
      "Lambda = 0.010183971714475852\n",
      "HV95_err = 3.3939947010320473\n",
      "Fit concluído para o scan: 40CO2_6.9\n",
      "Processando scan: 40CO2_4.6\n",
      "Funções no gráfico após ajuste: ['sigmoid20']\n",
      "Lambda = 0.010473476069455322\n",
      "HV95_err = 3.311081101603088\n",
      "Fit concluído para o scan: 40CO2_4.6\n",
      "Processando scan: 40CO2_3.3\n",
      "Funções no gráfico após ajuste: ['sigmoid13']\n",
      "Lambda = 0.010931600012560825\n",
      "HV95_err = 3.2220393221377654\n",
      "Fit concluído para o scan: 40CO2_3.3\n",
      "Gráficos e legendas adicionados ao canvas.\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      152.327\n",
      "NDf                       =           14\n",
      "Edm                       =  1.22941e-09\n",
      "NCalls                    =           41\n",
      "Emax                      =     0.990291   +/-   0.000616639 \n",
      "Lambda                    =   0.00982467   +/-   8.94402e-05 \n",
      "HV50                      =      6541.83   +/-   1.70658     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      222.247\n",
      "NDf                       =           13\n",
      "Edm                       =  3.75058e-09\n",
      "NCalls                    =           36\n",
      "Emax                      =     0.968357   +/-   0.00121398  \n",
      "Lambda                    =    0.0102924   +/-   9.63974e-05 \n",
      "HV50                      =      6543.36   +/-   1.87845     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      186.748\n",
      "NDf                       =           14\n",
      "Edm                       =  9.62431e-10\n",
      "NCalls                    =           43\n",
      "Emax                      =     0.963256   +/-   0.00131259  \n",
      "Lambda                    =     0.010184   +/-   9.88879e-05 \n",
      "HV50                      =      6544.82   +/-   1.90721     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      272.836\n",
      "NDf                       =           14\n",
      "Edm                       =  6.45309e-08\n",
      "NCalls                    =           37\n",
      "Emax                      =     0.952226   +/-   0.0014479   \n",
      "Lambda                    =    0.0104735   +/-   9.90482e-05 \n",
      "HV50                      =      6583.72   +/-   1.97348     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      232.237\n",
      "NDf                       =           12\n",
      "Edm                       =  8.68212e-07\n",
      "NCalls                    =           37\n",
      "Emax                      =     0.931395   +/-   0.00202126  \n",
      "Lambda                    =    0.0109316   +/-   0.000100457 \n",
      "HV50                      =       6610.4   +/-   2.06271     \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5640 xmax = 7560 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5640.000000,7560.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5640 xmax = 7560 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5640.000000,7560.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5680 xmax = 7600 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5680.000000,7600.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5740 xmax = 7420 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5740.000000,7420.000000] is not a valid interval\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c_all\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5640 xmax = 7560 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5640.000000,7560.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5640 xmax = 7560 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5640.000000,7560.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5680 xmax = 7600 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5680.000000,7600.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5740 xmax = 7420 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5740.000000,7420.000000] is not a valid interval\n",
      "Info in <TCanvas::Print>: png file 40CO2_eff_10.png has been created\n",
      "Info in <TCanvas::Print>: pdf file 40CO2_eff_10.pdf has been created\n",
      "Info in <TCanvas::SaveAs>: ROOT file 40CO2_eff_10.root has been created\n"
     ]
    }
   ],
   "source": [
    "from ROOT import TCanvas, TGraphErrors, TLatex, TLine, TLegend, TF1\n",
    "import math\n",
    "import pandas as pd\n",
    "\n",
    "# Nome do arquivo CSV original\n",
    "arquivo_csv = 'tabela_dados_40CO2.csv'\n",
    "\n",
    "# Ler o arquivo CSV\n",
    "df_csv = pd.read_csv(arquivo_csv)\n",
    "\n",
    "# Ajustar os valores de HV_top para os scans especificados\n",
    "df_csv.loc[df_csv['Scan'] == '40CO2_4.6', 'HV_top'] += 40\n",
    "df_csv.loc[df_csv['Scan'] == '40CO2_3.3', 'HV_top'] += 80\n",
    "\n",
    "# Inicializando listas para gráficos e funções\n",
    "TGraphs_list_40CO2 = []\n",
    "func_list_40CO2 = []\n",
    "\n",
    "# Configurações do canvas\n",
    "canvas = TCanvas(\"c\", \"c\", 564, 232, 600, 600)\n",
    "canvas.SetTopMargin(0.1)\n",
    "canvas.SetBottomMargin(0.12)\n",
    "canvas.SetLeftMargin(0.14)\n",
    "canvas.SetRightMargin(0.05)\n",
    "\n",
    "HV_ref = 95\n",
    "k = 1\n",
    "\n",
    "# Contagem dos scans no dicionário\n",
    "num_scans_total = len(scans_40CO2)\n",
    "print(f\"Total de scans no dicionário: {num_scans_total}\")\n",
    "\n",
    "for scan, sigmoid, color, marker in scans_40CO2:\n",
    "    if scan not in scans:\n",
    "        print(f\"Scan '{scan}' não encontrado no dicionário de scans. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    print(f\"Processando scan: {scan}\")\n",
    "\n",
    "    # Filtrar dados do DataFrame ajustado\n",
    "    df = df_csv[df_csv['Scan'] == scan]\n",
    "    n = len(df['HV_top'])\n",
    "\n",
    "    gr_E = TGraphErrors(n, \n",
    "                        df['HV_top'].values, \n",
    "                        df['Efficiency'].values,\n",
    "                        0,\n",
    "                        df['Eff_Error'].values)\n",
    "    \n",
    "    # Ajuste e verificação do resultado\n",
    "    fit_result = gr_E.Fit(sigmoid, \"S\")\n",
    "    if not fit_result.IsValid():\n",
    "        print(f\"Fit falhou para o scan: {scan}\")\n",
    "        continue\n",
    "\n",
    "    # Listando funções após o ajuste para depuração\n",
    "    functions = gr_E.GetListOfFunctions()\n",
    "    function_names = [f.GetName() for f in functions]\n",
    "    print(f\"Funções no gráfico após ajuste: {function_names}\")\n",
    "\n",
    "    # Tentando encontrar a função ajustada correta\n",
    "    sig = None\n",
    "    for name in function_names:\n",
    "        if name.startswith(\"sigmoid\"):\n",
    "            sig = gr_E.GetFunction(name)\n",
    "            break\n",
    "    \n",
    "    if sig is None:\n",
    "        print(f\"Função 'sigmoid' não encontrada para o scan: {scan}. Pulando...\")\n",
    "        continue\n",
    "\n",
    "    # Configurando cores e estilos\n",
    "    sig.SetLineColor(color)\n",
    "    gr_E.SetMarkerColor(color)\n",
    "    gr_E.SetLineColor(color)\n",
    "    gr_E.SetMarkerStyle(marker)\n",
    "    gr_E.SetLineWidth(2)\n",
    "    gr_E.SetMarkerSize(1.2)\n",
    "\n",
    "    Emax = sig.GetParameter(0)\n",
    "    Emax_err = sig.GetParError(0)     \n",
    "\n",
    "    Lambda = sig.GetParameter(1)    \n",
    "    Lambda_err = sig.GetParError(1)      \n",
    "\n",
    "    HV50 = sig.GetParameter(2)\n",
    "    HV50_err = sig.GetParError(2)      \n",
    "    \n",
    "    print(f\"Lambda = {Lambda}\")\n",
    "    HV95 = sig.GetX(HV_ref)\n",
    "    HV95_err = math.sqrt(((math.log(19) / Lambda**2) * Lambda_err)**2 + HV50_err**2)\n",
    "    \n",
    "    print(f\"HV95_err = {HV95_err}\")\n",
    "\n",
    "    TGraphs_list_40CO2.append(gr_E)\n",
    "    func_list_40CO2.append(sig)\n",
    "\n",
    "    # Desenhar o gráfico\n",
    "    gr_E.Draw(\"AP\")\n",
    "\n",
    "    # Desenhar a função ajustada\n",
    "    sig.Draw(\"same\")\n",
    "\n",
    "    k += 1\n",
    "\n",
    "    print(f\"Fit concluído para o scan: {scan}\")\n",
    "\n",
    "# Criando o canvas final\n",
    "canvas_all = TCanvas(\"c_all\", \"c_all\", 0, 0, 600, 600)\n",
    "canvas_all.SetTopMargin(0.1)\n",
    "canvas_all.SetBottomMargin(0.12)\n",
    "canvas_all.SetLeftMargin(0.14)\n",
    "canvas_all.SetRightMargin(0.05)\n",
    "\n",
    "if TGraphs_list_40CO2:\n",
    "    TGraphs_list_40CO2[0].GetHistogram().SetMaximum(1.4)\n",
    "    TGraphs_list_40CO2[0].GetHistogram().SetMinimum(0)\n",
    "    TGraphs_list_40CO2[0].SetTitle(\"\")\n",
    "    TGraphs_list_40CO2[0].GetXaxis().SetTitle(\"HV_{eff} [V]\")\n",
    "    TGraphs_list_40CO2[0].GetYaxis().SetTitle(\"Efficiency\")\n",
    "    TGraphs_list_40CO2[0].GetXaxis().SetTitleSize(0.05)\n",
    "    TGraphs_list_40CO2[0].GetYaxis().SetTitleSize(0.05)\n",
    "    TGraphs_list_40CO2[0].GetXaxis().SetLabelSize(0.04)\n",
    "    TGraphs_list_40CO2[0].GetYaxis().SetLabelSize(0.04)\n",
    "    TGraphs_list_40CO2[0].Draw(\"AP\")\n",
    "    \n",
    "    for graph in TGraphs_list_40CO2[1:]:\n",
    "        graph.Draw(\"PSAME\")\n",
    "\n",
    "    for func in func_list_40CO2:\n",
    "        func.Draw(\"same\")\n",
    "\n",
    "    Emax_40CO2 = []\n",
    "    Emax_err_40CO2 = []\n",
    "    Lambda_40CO2 = []\n",
    "    Lambda_err_40CO2 = []\n",
    "    HV50_40CO2 = []\n",
    "    HV50_err_40CO2 = []\n",
    "    HV95_40CO2 = []\n",
    "    HV95_err_40CO2 = []\n",
    "\n",
    "    for sig in func_list_40CO2:\n",
    "        Emax_ = 1 - math.sqrt((1 - sig.GetParameter(0)) * (1 - sig.GetParameter(0)))\n",
    "        Emax_40CO2.append(Emax_)\n",
    "        Emax_err_40CO2.append(sig.GetParError(0))     \n",
    "        Lambda_40CO2.append(sig.GetParameter(1))  \n",
    "        Lambda_err_40CO2.append(sig.GetParError(1))\n",
    "        HV50_40CO2.append(sig.GetParameter(2))  \n",
    "        HV50_err_40CO2.append(sig.GetParError(2)) \n",
    "        HV95_40CO2.append(sig.GetX(HV_ref))\n",
    "        HV95_err_40CO2.append((math.log(19) / sig.GetParameter(1)**2) * sig.GetParError(1) + sig.GetParError(2))\n",
    "\n",
    "    WP_40CO2 = []    \n",
    "\n",
    "    for Emax_, Lambda_, HV50_ in zip(Emax_40CO2, Lambda_40CO2, HV50_40CO2):\n",
    "        WP_40CO2.append((HV50_ - math.log(1 / 0.95 - 1) / Lambda_ + 150.))\n",
    "\n",
    "    txt_40CO2_10 = Features_40CO2['noiseGammaRate'][2] / (Features_40CO2['gamma_CS'][2] * 1000)\n",
    "    txt_40CO2_3_3 = Features_40CO2['noiseGammaRate'][3] / (Features_40CO2['gamma_CS'][3] * 1000)\n",
    "    txt_40CO2_2_2 = Features_40CO2['noiseGammaRate'][4] / (Features_40CO2['gamma_CS'][4] * 1000)\n",
    "    txt_40CO2_1 = Features_40CO2['noiseGammaRate'][5] / (Features_40CO2['gamma_CS'][5] * 1000)\n",
    "\n",
    "    eff_1_40CO2 = 'plateau = {a:.0%}, WP = {b:.2f} kV, no hit bkg gamma rate, Eff(WP) = {c:.0%}'.format(a=Emax_40CO2[0], b=WP_40CO2[0] / 1000., c=(func_list_40CO2[0].Eval(WP_40CO2[0])))\n",
    "    eff_2_40CO2 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_40CO2[1], b=WP_40CO2[1] / 1000., c=(func_list_40CO2[1].Eval(WP_40CO2[1])), d=txt_40CO2_10)\n",
    "    eff_3_40CO2 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_40CO2[2], b=WP_40CO2[2] / 1000., c=(func_list_40CO2[2].Eval(WP_40CO2[2])), d=txt_40CO2_3_3)\n",
    "    eff_4_40CO2 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_40CO2[3], b=WP_40CO2[3] / 1000., c=(func_list_40CO2[3].Eval(WP_40CO2[3])), d=txt_40CO2_2_2)\n",
    "    eff_5_40CO2 = 'plateau = {a:.0%}, WP = {b:.2f} kV, bkg rate (WP) = {d:.1f} kHz/cm^{{2}}, Eff(WP) = {c:.0%}'.format(a=Emax_40CO2[4], b=WP_40CO2[4] / 1000., c=(func_list_40CO2[4].Eval(WP_40CO2[4])), d=txt_40CO2_1)\n",
    "\n",
    "    legend = TLegend(0.15, 0.68, 0.3, 0.89)\n",
    "    legend.SetTextFont(42)\n",
    "    legend.SetBorderSize(0) \n",
    "    legend.SetFillStyle(4000)\n",
    "    legend.SetFillColor(0)  \n",
    "    legend.SetTextSize(0.02376) \n",
    "\n",
    "    ltx_data = TLatex()\n",
    "    ltx_data.SetTextFont(42)\n",
    "    ltx_data.SetTextSize(0.025)\n",
    "    ltx_data.SetTextColor(1)\n",
    "    ltx_data.DrawLatex(5900, 0.88, \"40% CO_{2} + 1.0% SF_{6}\")\n",
    "    ltx_data.DrawLatex(5900, 0.81, \"Test Beam in April+July+August\")\n",
    "    ltx_data.DrawLatex(5900, 0.74, \"2023, Before irradiation\")\n",
    "    ltx_data.DrawLatex(5900, 0.67, \"1.4 mm double gap RPC\")\n",
    "    ltx_data.DrawLatex(5900, 0.60, \"Threshold = 60 [fC]\")\n",
    "\n",
    "    cms_tex = TLatex()\n",
    "    cms_tex.SetNDC()\n",
    "    cms_tex.SetTextFont(61)\n",
    "    cms_tex.SetTextSize(0.04)\n",
    "    cms_tex.SetLineWidth(2)\n",
    "    cms_tex.DrawLatex(0.15, 0.905, \"CMS\")\n",
    "\n",
    "    cms_tex_1 = TLatex()\n",
    "    cms_tex_1.SetNDC()\n",
    "    cms_tex_1.SetTextFont(61)\n",
    "    cms_tex_1.SetTextSize(0.04)\n",
    "    cms_tex_1.SetLineWidth(2)\n",
    "    cms_tex_1.DrawLatex(0.85, 0.905, \"GIF++\")\n",
    "\n",
    "    cms_tex_2 = TLatex()\n",
    "    cms_tex_2.SetNDC()\n",
    "    cms_tex_2.SetTextFont(52)\n",
    "    cms_tex_2.SetTextSize(0.04)\n",
    "    cms_tex_2.SetLineWidth(2)\n",
    "    cms_tex_2.DrawLatex(0.23, 0.905, \"Preliminary\")\n",
    "\n",
    "    line = TLine(5900, 1., 7780, 1.)\n",
    "    line.SetLineColor(1)\n",
    "    line.SetLineStyle(9)\n",
    "    line.SetLineWidth(2)\n",
    "    line.Draw()\n",
    "\n",
    "    legend.AddEntry(TGraphs_list_40CO2[0], eff_1_40CO2, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_40CO2[1], eff_2_40CO2, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_40CO2[2], eff_3_40CO2, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_40CO2[3], eff_4_40CO2, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_40CO2[4], eff_5_40CO2, \"P\")\n",
    "\n",
    "    legend.Draw()\n",
    "\n",
    "    print(\"Gráficos e legendas adicionados ao canvas.\")\n",
    "\n",
    "# Salvar ou mostrar o canvas\n",
    "canvas_all.SaveAs(\"40CO2_eff_10.png\")\n",
    "canvas_all.SaveAs(\"40CO2_eff_10.pdf\")\n",
    "canvas_all.SaveAs(\"40CO2_eff_10.root\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de scans no dicionário: 4\n",
      "Processando scan: STDMX_OFF\n",
      "Funções no gráfico após ajuste: ['sigmoid1']\n",
      "Lambda = 0.012161935985069901\n",
      "HV95_err = 3.754247971485082\n",
      "Fit concluído para o scan: STDMX_OFF\n",
      "Processando scan: 30CO2_OFF\n",
      "Funções no gráfico após ajuste: ['sigmoid6']\n",
      "Lambda = 0.010507180882886295\n",
      "HV95_err = 3.070669933924878\n",
      "Fit concluído para o scan: 30CO2_OFF\n",
      "Processando scan: 30CO205SF6_OFF\n",
      "Funções no gráfico após ajuste: ['sigmoid16']\n",
      "Lambda = 0.00986873833897527\n",
      "HV95_err = 3.2132190047200946\n",
      "Fit concluído para o scan: 30CO205SF6_OFF\n",
      "Processando scan: 40CO2_OFF\n",
      "Funções no gráfico após ajuste: ['sigmoid11']\n",
      "Lambda = 0.009824671707742129\n",
      "HV95_err = 3.218120404437787\n",
      "Fit concluído para o scan: 40CO2_OFF\n",
      "Gráficos e legendas adicionados ao canvas.\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      14.1711\n",
      "NDf                       =            7\n",
      "Edm                       =  4.42791e-09\n",
      "NCalls                    =           36\n",
      "Emax                      =     0.989616   +/-   0.000817384 \n",
      "Lambda                    =    0.0121619   +/-   0.000163805 \n",
      "HV50                      =      6787.78   +/-   1.86051     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      240.082\n",
      "NDf                       =           14\n",
      "Edm                       =  1.03304e-12\n",
      "NCalls                    =           34\n",
      "Emax                      =     0.993042   +/-   0.000545727 \n",
      "Lambda                    =    0.0105072   +/-   9.6879e-05  \n",
      "HV50                      =      6646.61   +/-   1.6592      \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      137.155\n",
      "NDf                       =            7\n",
      "Edm                       =  2.16262e-12\n",
      "NCalls                    =           38\n",
      "Emax                      =     0.990414   +/-   0.000794203 \n",
      "Lambda                    =   0.00986874   +/-   8.89583e-05 \n",
      "HV50                      =      6513.27   +/-   1.75828     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      152.327\n",
      "NDf                       =           14\n",
      "Edm                       =  6.45703e-11\n",
      "NCalls                    =           42\n",
      "Emax                      =     0.990291   +/-   0.000616639 \n",
      "Lambda                    =   0.00982467   +/-   8.94402e-05 \n",
      "HV50                      =      6541.83   +/-   1.70658     \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6070 xmax = 7630 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6070.000000,7630.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c_all\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 6070 xmax = 7630 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [6070.000000,7630.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <TCanvas::Print>: png file OFF_eff.png has been created\n",
      "Info in <TCanvas::Print>: pdf file OFF_eff.pdf has been created\n",
      "Info in <TCanvas::SaveAs>: ROOT file OFF_eff.root has been created\n"
     ]
    }
   ],
   "source": [
    "from ROOT import TCanvas, TGraphErrors, TLatex, TLine, TLegend\n",
    "import math\n",
    "\n",
    "# Inicializando listas para gráficos e funções\n",
    "TGraphs_list_OFF = []\n",
    "func_list_OFF = []\n",
    "\n",
    "canvas = TCanvas(\"c\", \"c\", 564, 232, 600, 600)\n",
    "HV_ref = 95\n",
    "\n",
    "# Definições de cores e marcadores\n",
    "colors = [6, 3, 2, 4]  # ROOT Colors: 2 (Red), 4 (Blue), 3 (Green), 6 (Magenta), 8 (Cyan)\n",
    "markers = [23, 22, 20, 21]  # Marcadores: 20 (circulo), 21 (quadrado), 22 (triângulo), etc.\n",
    "\n",
    "k = 1\n",
    "\n",
    "# Contagem dos scans no dicionário\n",
    "num_scans_total = len(scans_OFF)\n",
    "print(f\"Total de scans no dicionário: {num_scans_total}\")\n",
    "\n",
    "for index, (scan, sigmoid, color, marker) in enumerate(scans_OFF):\n",
    "    if scan not in scans:\n",
    "        print(f\"Scan '{scan}' não encontrado no dicionário de scans. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    print(f\"Processando scan: {scan}\")\n",
    "    \n",
    "    df = HVCurrentDataFrame(scans[scan])\n",
    "    n = len(df['HV_top'])\n",
    "    \n",
    "    gr_E = TGraphErrors(n, \n",
    "                        df['HV_top'].values, \n",
    "                        df['efficiency'].values,\n",
    "                        0,\n",
    "                        df['eff_error'].values) # valores do df[].values para converter objeto dataframe em array adequado para o TGraph\n",
    "    \n",
    "    # Ajuste e verificação do resultado\n",
    "    fit_result = gr_E.Fit(sigmoid, \"S\")\n",
    "    if not fit_result.IsValid():\n",
    "        print(f\"Fit falhou para o scan: {scan}\")\n",
    "        continue\n",
    "    \n",
    "    # Listando funções após o ajuste para depuração\n",
    "    functions = gr_E.GetListOfFunctions()\n",
    "    function_names = [f.GetName() for f in functions]\n",
    "    print(f\"Funções no gráfico após ajuste: {function_names}\")\n",
    "\n",
    "    # Tentando encontrar a função ajustada correta\n",
    "    sig = None\n",
    "    for name in function_names:\n",
    "        if name.startswith(\"sigmoid\"):\n",
    "            sig = gr_E.GetFunction(name)\n",
    "            break\n",
    "    \n",
    "    if sig is None:\n",
    "        print(f\"Função 'sigmoid' não encontrada para o scan: {scan}. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    # Usando cores e marcadores diferentes para cada gráfico\n",
    "    color = colors[index % len(colors)]\n",
    "    marker = markers[index % len(markers)]\n",
    "    \n",
    "    sig.SetLineColor(color)\n",
    "    gr_E.SetMarkerColor(color)\n",
    "    gr_E.SetMarkerStyle(marker)\n",
    "    gr_E.SetLineColor(color)  # Definindo a cor da linha do gráfico, que pode afetar as barras de erro\n",
    "    gr_E.SetLineWidth(2)      # Ajustando a largura da linha do gráfico\n",
    "    \n",
    "    # As barras de erro usam a mesma cor dos marcadores\n",
    "    gr_E.SetLineColor(color)  # Isso define a cor da linha das barras de erro\n",
    "    gr_E.SetMarkerColor(color)  # Isso define a cor dos marcadores, que também afeta as barras de erro\n",
    "\n",
    "    Emax = sig.GetParameter(0)\n",
    "    Emax_err = sig.GetParError(0)     \n",
    "\n",
    "    Lambda = sig.GetParameter(1)    \n",
    "    Lambda_err = sig.GetParError(1)      \n",
    "\n",
    "    HV50 = sig.GetParameter(2)\n",
    "    HV50_err = sig.GetParError(2)      \n",
    "    \n",
    "    print(f\"Lambda = {Lambda}\")\n",
    "    HV95 = sig.GetX(HV_ref)\n",
    "    HV95_err = math.sqrt(((math.log(19) / Lambda**2) * Lambda_err)**2 + HV50_err**2) # erro HV95\n",
    "    \n",
    "    print(f\"HV95_err = {HV95_err}\")\n",
    "\n",
    "    # Adicionando gráficos e funções às listas\n",
    "    TGraphs_list_OFF.append(gr_E)\n",
    "    func_list_OFF.append(sig)\n",
    "    gr_E.Draw(\"AP\")\n",
    "    k += 1\n",
    "\n",
    "    print(f\"Fit concluído para o scan: {scan}\")\n",
    "\n",
    "# Criando o canvas final\n",
    "canvas_all = TCanvas(\"c_all\", \"c_all\", 0, 0, 600, 600)\n",
    "\n",
    "if TGraphs_list_OFF:\n",
    "    TGraphs_list_OFF[0].GetHistogram().SetMaximum(1.4)\n",
    "    TGraphs_list_OFF[0].SetTitle(\"\")\n",
    "    TGraphs_list_OFF[0].GetXaxis().SetTitle(\"HV_{eff} [V]\")\n",
    "    TGraphs_list_OFF[0].GetYaxis().SetTitle(\"Efficiency\")\n",
    "    TGraphs_list_OFF[0].GetXaxis().SetLimits(6000, 7400)\n",
    "    TGraphs_list_OFF[0].Draw(\"AP\")\n",
    "    \n",
    "    for graph in TGraphs_list_OFF[1:]:\n",
    "        graph.Draw(\"PSAME\")\n",
    "\n",
    "    Emax_OFF = []\n",
    "    Emax_err_OFF = []\n",
    "    Lambda_OFF = []\n",
    "    Lambda_err_OFF = []\n",
    "    HV50_OFF = []\n",
    "    HV50_err_OFF = []\n",
    "    HV95_OFF = []\n",
    "    HV95_err_OFF = []\n",
    "\n",
    "    for sig in func_list_OFF:\n",
    "        Emax_ = 1 - math.sqrt((1 - sig.GetParameter(0)) * (1 - sig.GetParameter(0)))\n",
    "        Emax_OFF.append(Emax_)\n",
    "        Emax_err_OFF.append(sig.GetParError(0))     \n",
    "        Lambda_OFF.append(sig.GetParameter(1))  \n",
    "        Lambda_err_OFF.append(sig.GetParError(1))\n",
    "        HV50_OFF.append(sig.GetParameter(2))  \n",
    "        HV50_err_OFF.append(sig.GetParError(2)) \n",
    "        HV95_OFF.append(sig.GetX(HV_ref))\n",
    "        HV95_err_OFF.append((math.log(19) / sig.GetParameter(1)**2) * sig.GetParError(1) + sig.GetParError(2)) # erro HV95\n",
    "\n",
    "    WP_OFF = []    \n",
    "\n",
    "    for Emax_, Lambda_, HV50_ in zip(Emax_OFF, Lambda_OFF, HV50_OFF):\n",
    "        WP_OFF.append((HV50_ - math.log(1 / 0.95 - 1) / Lambda_ + 150.))\n",
    "\n",
    "    #txt_OFF_22 = Features_OFF['noiseGammaRate'][2] / (Features_OFF['gamma_CS'][2] * 1000)\n",
    "    #txt_OFF_10 = Features_OFF['noiseGammaRate'][4] / (Features_OFF['gamma_CS'][4] * 1000)\n",
    "    #txt_OFF_46 = Features_OFF['noiseGammaRate'][5] / (Features_OFF['gamma_CS'][5] * 1000)\n",
    "    #txt_OFF_33 = Features_OFF['noiseGammaRate'][7] / (Features_OFF['gamma_CS'][7] * 1000)\n",
    "\n",
    "    # Adiciona verificações de comprimento antes de formatar strings\n",
    "    eff_1_OFF = 'plateau = {a:.0%}, WP = {b:.2f} kV, Standard gas mixture, Eff(WP) = {c:.0%}'.format(a=Emax_OFF[0], b=WP_OFF[0] / 1000., c=(func_list_OFF[0].Eval(WP_OFF[0])))\n",
    "    eff_2_OFF = 'plateau = {a:.0%}, WP = {b:.2f} kV, 30% CO_{{2}} + 1.0% SF_{{6}}, Eff(WP) = {c:.0%}'.format(a=Emax_OFF[1], b=WP_OFF[1] / 1000., c=(func_list_OFF[1].Eval(WP_OFF[1])))\n",
    "    eff_3_OFF = 'plateau = {a:.0%}, WP = {b:.2f} kV, 30% CO_{{2}} + 0.5% SF_{{6}}, Eff(WP) = {c:.0%}'.format(a=Emax_OFF[2], b=WP_OFF[2] / 1000., c=(func_list_OFF[2].Eval(WP_OFF[2])))\n",
    "    eff_4_OFF = 'plateau = {a:.0%}, WP = {b:.2f} kV, 40% CO_{{2}}+ 1.0% SF_{{6}}, Eff(WP) = {c:.0%}'.format(a=Emax_OFF[3], b=WP_OFF[3] / 1000., c=(func_list_OFF[3].Eval(WP_OFF[3])))\n",
    "\n",
    "    # Adicionando a legenda aos gráficos\n",
    "    legend = TLegend(0.1, 0.68, 0.3, 0.89)\n",
    "    legend.SetTextFont(42)\n",
    "    legend.SetBorderSize(0) \n",
    "    legend.SetFillStyle(4000)\n",
    "    legend.SetFillColor(0)  \n",
    "    legend.SetTextSize(0.02376) \n",
    "\n",
    "    ltx_data = TLatex()\n",
    "    ltx_data.SetTextFont(42)\n",
    "    ltx_data.SetTextSize(0.025)\n",
    "    ltx_data.SetTextColor(1)\n",
    "    ltx_data.DrawLatex(6100, 0.88, \"No gamma background\")\n",
    "    ltx_data.DrawLatex(6100, 0.81, \"Test Beam in April+July 2023\")\n",
    "    ltx_data.DrawLatex(6100, 0.74, \"Before irradiation\")\n",
    "    ltx_data.DrawLatex(6100, 0.67, \"1.4 mm double gap RPC\")\n",
    "    ltx_data.DrawLatex(6100, 0.60, \"Threshold = 60 [fC]\")\n",
    "\n",
    "    cms_tex = TLatex()\n",
    "    cms_tex.SetNDC()\n",
    "    cms_tex.SetTextFont(61)\n",
    "    cms_tex.SetTextSize(0.04)\n",
    "    cms_tex.SetLineWidth(2)\n",
    "    cms_tex.DrawLatex(0.10, 0.905, \"CMS\")\n",
    "\n",
    "    cms_tex_1 = TLatex()\n",
    "    cms_tex_1.SetNDC()\n",
    "    cms_tex_1.SetTextFont(61)\n",
    "    cms_tex_1.SetTextSize(0.04)\n",
    "    cms_tex_1.SetLineWidth(2)\n",
    "    cms_tex_1.DrawLatex(0.79, 0.905, \"GIF++\")\n",
    "\n",
    "    cms_tex_2 = TLatex()\n",
    "    cms_tex_2.SetNDC()\n",
    "    cms_tex_2.SetTextFont(52)\n",
    "    cms_tex_2.SetTextSize(0.04)\n",
    "    cms_tex_2.SetLineWidth(2)\n",
    "    cms_tex_2.DrawLatex(0.19, 0.905, \"Preliminary\")\n",
    "\n",
    "    line = TLine(6000, 1., 7400, 1.)\n",
    "    line.SetLineColor(1)\n",
    "    line.SetLineStyle(9)\n",
    "    line.SetLineWidth(2)\n",
    "    line.Draw()\n",
    "\n",
    "    legend.AddEntry(TGraphs_list_OFF[0], eff_1_OFF, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_OFF[1], eff_2_OFF, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_OFF[2], eff_3_OFF, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_OFF[3], eff_4_OFF, \"P\")\n",
    "    #legend.AddEntry(TGraphs_list_OFF[4], eff_5_OFF, \"P\")\n",
    "\n",
    "    legend.Draw()\n",
    "\n",
    "    print(\"Gráficos e legendas adicionados ao canvas.\")\n",
    "\n",
    "# Salvar ou mostrar o canvas\n",
    "canvas_all.SaveAs(\"OFF_eff.png\")\n",
    "canvas_all.SaveAs(\"OFF_eff.pdf\")\n",
    "canvas_all.SaveAs(\"OFF_eff.root\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de scans no dicionário: 4\n",
      "Processando scan: STDMX_3.3\n",
      "Funções no gráfico após ajuste: ['sigmoid4']\n",
      "Lambda = 0.01056025802439867\n",
      "HV95_err = 3.4734339901445948\n",
      "Fit concluído para o scan: STDMX_3.3\n",
      "Processando scan: 30CO2_3.3\n",
      "Funções no gráfico após ajuste: ['sigmoid9']\n",
      "Lambda = 0.01082780022818435\n",
      "HV95_err = 3.2525716280427557\n",
      "Fit concluído para o scan: 30CO2_3.3\n",
      "Processando scan: 30CO205SF6_3.3\n",
      "Funções no gráfico após ajuste: ['sigmoid19']\n",
      "Lambda = 0.010911430412634131\n",
      "HV95_err = 4.296686666534504\n",
      "Fit concluído para o scan: 30CO205SF6_3.3\n",
      "Processando scan: 40CO2_3.3\n",
      "Funções no gráfico após ajuste: ['sigmoid20']\n",
      "Lambda = 0.010931469158287787\n",
      "HV95_err = 3.221993148363401\n",
      "Fit concluído para o scan: 40CO2_3.3\n",
      "Gráficos e legendas adicionados ao canvas.\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      135.693\n",
      "NDf                       =           14\n",
      "Edm                       =  3.17228e-07\n",
      "NCalls                    =           63\n",
      "Emax                      =     0.946878   +/-   0.00160867  \n",
      "Lambda                    =    0.0105603   +/-   0.000109086 \n",
      "HV50                      =      6803.85   +/-   1.94143     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      340.053\n",
      "NDf                       =           14\n",
      "Edm                       =   3.6106e-08\n",
      "NCalls                    =           64\n",
      "Emax                      =     0.943465   +/-   0.00144715  \n",
      "Lambda                    =    0.0108278   +/-   0.000102738 \n",
      "HV50                      =      6672.01   +/-   1.98037     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =       141.13\n",
      "NDf                       =            7\n",
      "Edm                       =  3.64955e-07\n",
      "NCalls                    =           66\n",
      "Emax                      =     0.944272   +/-   0.00219808  \n",
      "Lambda                    =    0.0109114   +/-   0.000134146 \n",
      "HV50                      =      6504.66   +/-   2.73044     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      232.237\n",
      "NDf                       =           12\n",
      "Edm                       =  2.36245e-07\n",
      "NCalls                    =           37\n",
      "Emax                      =     0.931397   +/-   0.00202126  \n",
      "Lambda                    =    0.0109315   +/-   0.000100452 \n",
      "HV50                      =       6530.4   +/-   2.06271     \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5620 xmax = 7780 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5620.000000,7780.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5660 xmax = 7340 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5660.000000,7340.000000] is not a valid interval\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c_all\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5840 xmax = 7760 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5840.000000,7760.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5620 xmax = 7780 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5620.000000,7780.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5815 xmax = 7435 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5815.000000,7435.000000] is not a valid interval\n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: Grid search failed to find a root in the  interval \n",
      "Info in <ROOT::Math::BrentMethods::MinimStep>: xmin = 5660 xmax = 7340 npts = 100\n",
      "Error in <ROOT::Math::BrentRootFinder>: Interval does not contain a root\n",
      "Error in <TF1::GetX>: [5660.000000,7340.000000] is not a valid interval\n",
      "Info in <TCanvas::Print>: png file 33_eff.png has been created\n",
      "Info in <TCanvas::Print>: pdf file 33_eff.pdf has been created\n",
      "Info in <TCanvas::SaveAs>: ROOT file 33_eff.root has been created\n"
     ]
    }
   ],
   "source": [
    "from ROOT import TCanvas, TGraphErrors, TLatex, TLine, TLegend\n",
    "import math\n",
    "\n",
    "# Inicializando listas para gráficos e funções\n",
    "TGraphs_list_33 = []\n",
    "func_list_33 = []\n",
    "\n",
    "canvas = TCanvas(\"c\", \"c\", 564, 232, 600, 600)\n",
    "HV_ref = 95\n",
    "\n",
    "# Definições de cores e marcadores\n",
    "colors = [6, 3, 2, 4]  # ROOT Colors: 2 (Red), 4 (Blue), 3 (Green), 6 (Magenta), 8 (Cyan)\n",
    "markers = [23, 22, 20, 21]  # Marcadores: 20 (circulo), 21 (quadrado), 22 (triângulo), etc.\n",
    "\n",
    "k = 1\n",
    "\n",
    "# Contagem dos scans no dicionário\n",
    "num_scans_total = len(scans_33)\n",
    "print(f\"Total de scans no dicionário: {num_scans_total}\")\n",
    "\n",
    "for index, (scan, sigmoid, color, marker) in enumerate(scans_33):\n",
    "    if scan not in scans:\n",
    "        print(f\"Scan '{scan}' não encontrado no dicionário de scans. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    print(f\"Processando scan: {scan}\")\n",
    "    \n",
    "    df = HVCurrentDataFrame(scans[scan])\n",
    "    n = len(df['HV_top'])\n",
    "    \n",
    "    gr_E = TGraphErrors(n, \n",
    "                        df['HV_top'].values, \n",
    "                        df['efficiency'].values,\n",
    "                        0,\n",
    "                        df['eff_error'].values) # valores do df[].values para converter objeto dataframe em array adequado para o TGraph\n",
    "    \n",
    "    # Ajuste e verificação do resultado\n",
    "    fit_result = gr_E.Fit(sigmoid, \"S\")\n",
    "    if not fit_result.IsValid():\n",
    "        print(f\"Fit falhou para o scan: {scan}\")\n",
    "        continue\n",
    "    \n",
    "    # Listando funções após o ajuste para depuração\n",
    "    functions = gr_E.GetListOfFunctions()\n",
    "    function_names = [f.GetName() for f in functions]\n",
    "    print(f\"Funções no gráfico após ajuste: {function_names}\")\n",
    "\n",
    "    # Tentando encontrar a função ajustada correta\n",
    "    sig = None\n",
    "    for name in function_names:\n",
    "        if name.startswith(\"sigmoid\"):\n",
    "            sig = gr_E.GetFunction(name)\n",
    "            break\n",
    "    \n",
    "    if sig is None:\n",
    "        print(f\"Função 'sigmoid' não encontrada para o scan: {scan}. Pulando...\")\n",
    "        continue\n",
    "    \n",
    "    # Usando cores e marcadores diferentes para cada gráfico\n",
    "    color = colors[index % len(colors)]\n",
    "    marker = markers[index % len(markers)]\n",
    "    \n",
    "    sig.SetLineColor(color)\n",
    "    gr_E.SetMarkerColor(color)\n",
    "    gr_E.SetMarkerStyle(marker)\n",
    "    gr_E.SetLineColor(color)  # Definindo a cor da linha do gráfico, que pode afetar as barras de erro\n",
    "    gr_E.SetLineWidth(2)      # Ajustando a largura da linha do gráfico\n",
    "    \n",
    "    # As barras de erro usam a mesma cor dos marcadores\n",
    "    gr_E.SetLineColor(color)  # Isso define a cor da linha das barras de erro\n",
    "    gr_E.SetMarkerColor(color)  # Isso define a cor dos marcadores, que também afeta as barras de erro\n",
    "\n",
    "    Emax = sig.GetParameter(0)\n",
    "    Emax_err = sig.GetParError(0)     \n",
    "\n",
    "    Lambda = sig.GetParameter(1)    \n",
    "    Lambda_err = sig.GetParError(1)      \n",
    "\n",
    "    HV50 = sig.GetParameter(2)\n",
    "    HV50_err = sig.GetParError(2)      \n",
    "    \n",
    "    print(f\"Lambda = {Lambda}\")\n",
    "    HV95 = sig.GetX(HV_ref)\n",
    "    HV95_err = math.sqrt(((math.log(19) / Lambda**2) * Lambda_err)**2 + HV50_err**2) # erro HV95\n",
    "    \n",
    "    print(f\"HV95_err = {HV95_err}\")\n",
    "\n",
    "    # Adicionando gráficos e funções às listas\n",
    "    TGraphs_list_33.append(gr_E)\n",
    "    func_list_33.append(sig)\n",
    "    gr_E.Draw(\"AP\")\n",
    "    k += 1\n",
    "\n",
    "    print(f\"Fit concluído para o scan: {scan}\")\n",
    "\n",
    "# Criando o canvas final\n",
    "canvas_all = TCanvas(\"c_all\", \"c_all\", 0, 0, 600, 600)\n",
    "\n",
    "if TGraphs_list_33:\n",
    "    TGraphs_list_33[0].GetHistogram().SetMaximum(1.4)\n",
    "    TGraphs_list_33[0].SetTitle(\"\")\n",
    "    TGraphs_list_33[0].GetXaxis().SetTitle(\"HV_{eff} [V]\")\n",
    "    TGraphs_list_33[0].GetYaxis().SetTitle(\"Efficiency\")\n",
    "    TGraphs_list_33[0].GetXaxis().SetLimits(6000, 7400)\n",
    "    TGraphs_list_33[0].Draw(\"AP\")\n",
    "    \n",
    "    for graph in TGraphs_list_33[1:]:\n",
    "        graph.Draw(\"PSAME\")\n",
    "\n",
    "    Emax_33 = []\n",
    "    Emax_err_33 = []\n",
    "    Lambda_33 = []\n",
    "    Lambda_err_33 = []\n",
    "    HV50_33 = []\n",
    "    HV50_err_33 = []\n",
    "    HV95_33 = []\n",
    "    HV95_err_33 = []\n",
    "\n",
    "    for sig in func_list_33:\n",
    "        Emax_ = 1 - math.sqrt((1 - sig.GetParameter(0)) * (1 - sig.GetParameter(0)))\n",
    "        Emax_33.append(Emax_)\n",
    "        Emax_err_33.append(sig.GetParError(0))     \n",
    "        Lambda_33.append(sig.GetParameter(1))  \n",
    "        Lambda_err_33.append(sig.GetParError(1))\n",
    "        HV50_33.append(sig.GetParameter(2))  \n",
    "        HV50_err_33.append(sig.GetParError(2)) \n",
    "        HV95_33.append(sig.GetX(HV_ref))\n",
    "        HV95_err_33.append((math.log(19) / sig.GetParameter(1)**2) * sig.GetParError(1) + sig.GetParError(2)) # erro HV95\n",
    "\n",
    "    WP_33 = []    \n",
    "\n",
    "    for Emax_, Lambda_, HV50_ in zip(Emax_33, Lambda_33, HV50_33):\n",
    "        WP_33.append((HV50_ - math.log(1 / 0.95 - 1) / Lambda_ + 150.))\n",
    "\n",
    "    #txt_33_22 = Features_33['noiseGammaRate'][2] / (Features_33['gamma_CS'][2] * 1000)\n",
    "    #txt_33_10 = Features_33['noiseGammaRate'][4] / (Features_33['gamma_CS'][4] * 1000)\n",
    "    #txt_33_46 = Features_33['noiseGammaRate'][5] / (Features_33['gamma_CS'][5] * 1000)\n",
    "    #txt_33_33 = Features_33['noiseGammaRate'][7] / (Features_33['gamma_CS'][7] * 1000)\n",
    "\n",
    "    # Adiciona verificações de comprimento antes de formatar strings\n",
    "    eff_1_33 = 'plateau = {a:.0%}, WP = {b:.2f} kV, Standard gas mixture, Eff(WP) = {c:.0%}'.format(a=Emax_33[0], b=WP_33[0] / 1000., c=(func_list_33[0].Eval(WP_33[0])))\n",
    "    eff_2_33 = 'plateau = {a:.0%}, WP = {b:.2f} kV, 30% CO_{{2}} + 1.0% SF_{{6}}, Eff(WP) = {c:.0%}'.format(a=Emax_33[1], b=WP_33[1] / 1000., c=(func_list_33[1].Eval(WP_33[1])))\n",
    "    eff_3_33 = 'plateau = {a:.0%}, WP = {b:.2f} kV, 30% CO_{{2}} + 0.5% SF_{{6}}, Eff(WP) = {c:.0%}'.format(a=Emax_33[2], b=WP_33[2] / 1000., c=(func_list_33[2].Eval(WP_33[2])))\n",
    "    eff_4_33 = 'plateau = {a:.0%}, WP = {b:.2f} kV, 40% CO_{{2}}+ 1.0% SF_{{6}}, Eff(WP) = {c:.0%}'.format(a=Emax_33[3], b=WP_33[3] / 1000., c=(func_list_33[3].Eval(WP_33[3])))\n",
    "\n",
    "    # Adicionando a legenda aos gráficos\n",
    "    legend = TLegend(0.1, 0.68, 0.3, 0.89)\n",
    "    legend.SetTextFont(42)\n",
    "    legend.SetBorderSize(0) \n",
    "    legend.SetFillStyle(4000)\n",
    "    legend.SetFillColor(0)  \n",
    "    legend.SetTextSize(0.02376) \n",
    "\n",
    "    ltx_data = TLatex()\n",
    "    ltx_data.SetTextFont(42)\n",
    "    ltx_data.SetTextSize(0.025)\n",
    "    ltx_data.SetTextColor(1)\n",
    "    ltx_data.DrawLatex(6100, 0.88, \"Background rate: ~ 0.8 kHz/cm^{2}\")\n",
    "    ltx_data.DrawLatex(6100, 0.81, \"Test Beam in April+July 2023\")\n",
    "    ltx_data.DrawLatex(6100, 0.74, \"Before irradiation\")\n",
    "    ltx_data.DrawLatex(6100, 0.67, \"1.4 mm double gap RPC\")\n",
    "    ltx_data.DrawLatex(6100, 0.60, \"Threshold = 60 [fC]\")\n",
    "\n",
    "    cms_tex = TLatex()\n",
    "    cms_tex.SetNDC()\n",
    "    cms_tex.SetTextFont(61)\n",
    "    cms_tex.SetTextSize(0.04)\n",
    "    cms_tex.SetLineWidth(2)\n",
    "    cms_tex.DrawLatex(0.10, 0.905, \"CMS\")\n",
    "\n",
    "    cms_tex_1 = TLatex()\n",
    "    cms_tex_1.SetNDC()\n",
    "    cms_tex_1.SetTextFont(61)\n",
    "    cms_tex_1.SetTextSize(0.04)\n",
    "    cms_tex_1.SetLineWidth(2)\n",
    "    cms_tex_1.DrawLatex(0.79, 0.905, \"GIF++\")\n",
    "\n",
    "    cms_tex_2 = TLatex()\n",
    "    cms_tex_2.SetNDC()\n",
    "    cms_tex_2.SetTextFont(52)\n",
    "    cms_tex_2.SetTextSize(0.04)\n",
    "    cms_tex_2.SetLineWidth(2)\n",
    "    cms_tex_2.DrawLatex(0.19, 0.905, \"Preliminary\")\n",
    "\n",
    "    line = TLine(6000, 1., 7400, 1.)\n",
    "    line.SetLineColor(1)\n",
    "    line.SetLineStyle(9)\n",
    "    line.SetLineWidth(2)\n",
    "    line.Draw()\n",
    "\n",
    "    legend.AddEntry(TGraphs_list_33[0], eff_1_33, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_33[1], eff_2_33, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_33[2], eff_3_33, \"P\")\n",
    "    legend.AddEntry(TGraphs_list_33[3], eff_4_33, \"P\")\n",
    "    #legend.AddEntry(TGraphs_list_33[4], eff_5_33, \"P\")\n",
    "\n",
    "    legend.Draw()\n",
    "\n",
    "    print(\"Gráficos e legendas adicionados ao canvas.\")\n",
    "\n",
    "# Salvar ou mostrar o canvas\n",
    "canvas_all.SaveAs(\"33_eff.png\")\n",
    "canvas_all.SaveAs(\"33_eff.pdf\")\n",
    "canvas_all.SaveAs(\"33_eff.root\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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