SemaringProject_MuonsAndGammaSim_Din_vz5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d95042e9",
   "metadata": {},
   "source": [
    "# Read Gamma + Muon Simulations and Fit to Lab Data + Smear (vz5)\n",
    "#### Din-Ammar Tolj - Y25\n",
    "Maruyama Group, Summer 2023\n",
    "\n",
    "This notebook imports the Geant4 simulated gamma + muon backgrounds and then applies a normal distribution transformation to the data in order to \"smear\" it so that it matches lab data. $\\chi^2/\\text{ndf}$ is calculated and is improved by normalizing gamma lines/muon flux.\n",
    "\n",
    "The smeared simulated data energy deposited $R$ can mathematically be described as a normal distirbution:\n",
    "\n",
    "$$R\\sim\\mathcal{N}(E_{PE},\\sigma^2)$$\n",
    "\n",
    "where $E_{PE}$ is the energy deposited in units of PE and $\\sigma$ is the energy resolution function (also in units of PE) which this notebook is attempting to model. Therefore, $R$ has values in PE and since both the lab and simulated data to be imported have units of MeV, a conversion is necessary using the pe_mev constant calculated in $\\texttt{readDataAndFit-AndrewUpdate-Copy1.ipynb}$\n",
    "\n",
    "The energy resolution function $\\sigma$ has been modeled in literature previously as having the form:\n",
    "$$ \\sigma^2 = c_0 + c_1 E_{PE} + c_2 E_{PE}^2 $$\n",
    "\n",
    "where $c_1$, $c_2$, $c_3$ are scalars that need to be determined by minimizing the $\\chi^2/\\text{ndf}$ between the smeared simulated data and the lab data. Before doing this however, the notebook attempts to model $\\sigma$ as a constant and as linearly scaled, which are found to be good approximations (more explanation on the individual models later)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "236b8cec",
   "metadata": {},
   "source": [
    "### Import needed libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "93e1eb0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.24/02\n",
      "/vast/palmer/home.grace/dt592/SmearingProjectCUORE/libs\n",
      "/vast/palmer/home.grace/dt592/SmearingProjectCUORE\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "import ROOT\n",
    "from ROOT import *\n",
    "import ctypes\n",
    "from ctypes import *\n",
    "from root_numpy import array2hist, hist2array, fill_hist\n",
    "\n",
    "import sys\n",
    "import math\n",
    "\n",
    "import os\n",
    "from scipy.optimize import *\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from datetime import datetime\n",
    "from glob import glob\n",
    "from array import array\n",
    "\n",
    "%cd libs/\n",
    "from dt5202 import dt5202_crunch # Import parser that Tom wrote. This is containted in the file \n",
    "                                 # named dt5202.py in the lib folder.\n",
    "from dt5202oldDAQ import dt5202_crunch as dt5202_crunch_oldDAQ # Old ver\n",
    "\n",
    "from langaus import LanGausFit \n",
    "%cd -\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "363840f4",
   "metadata": {},
   "source": [
    "### Declare constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a7879d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "ped = 50 # Pedestal correction for dt5202\n",
    "\n",
    "# Declare constants for conversion from ADC to PE/MeV\n",
    "adc_mev = 538.3933387436877 # from \"MuonPeakCalibration_Din_ThanksEmily_v1_cut.ipynb\"\n",
    "pe_mev = 53.95 # gotten from averaging two values from \"OLD_MuonsAndGammas_SimulationFitting_Din_v2_w_Caesium.ipynb\"\n",
    "\n",
    "sig662 = 0.1601 # Get the sigma in [MeV] from \"Cs137_GaussFitting_ForResolution_Din_vz1.ipynb\"\n",
    "\n",
    "hmin = 0.4 # Lower bound of full spectrum hist. (not simulating anything below 500 KeV) [MeV]\n",
    "hmax = 14 # Upper bound of full spectrum hist. [MeV]\n",
    "hspl = 2.8 # Approximate division point between gamma/muon spectra\n",
    "R = 0.07\n",
    "bins = int((hmax-hmin)/R) # Want the number of bins to be ~250"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba6d73d2",
   "metadata": {},
   "source": [
    "### Import simulated .root files and lab data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6fd46eef",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "%run libs/ImportData_LabAndSimulation_Din_vzd1.ipynb # This notebook imports both lab and simulated data and \n",
    "                                                     # appends it to arrays. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d3413ec4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate the energies into np arrays\n",
    "g_mev = np.concatenate((energyDeposited1460,energyDeposited2614,\n",
    "                        energyDeposited1120,energyDeposited1238,\n",
    "                        energyDeposited1764,energyDeposited2204,\n",
    "                        energyDeposited968,energyDeposited934,\n",
    "                        energyDeposited911,energyDeposited609,\n",
    "                        energyDeposited583)) # individually simulated gamma lines, arrays declared in ext. notebook.\n",
    "mu_mev = np.array(muonsEnergy) # muons [MeV]\n",
    "sim_mev = np.concatenate((g_mev, mu_mev)) # full spectrum [PE]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "77024421",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot imported lab data vs simulation\n",
    "\n",
    "c1_1 = TCanvas('c1_1','',1200,400)\n",
    "c1_1.cd()\n",
    "\n",
    "lab_root = TH1D('lab_root','',bins,hmin,hmax) # Lab data [MeV]\n",
    "fill_hist(lab_root, lab_mev)\n",
    "sim_root = TH1D('sim_root','',bins,hmin,hmax) # Sim. data [MeV]\n",
    "# sim_root.Reset() \n",
    "fill_hist(sim_root, sim_mev)\n",
    "\n",
    "gStyle.SetPalette(kBird) # Colorblind-friendly palette\n",
    "\n",
    "lab_g = TH1D('lab_g','',int(bins/2),hmin,hspl) # Lab data, gammas [MeV]\n",
    "fill_hist(lab_g, lab_mev)\n",
    "lab_mu = TH1D('lab_mu','',int(bins/2),hspl,hmax) # Lab data, muons [MeV]\n",
    "fill_hist(lab_mu, lab_mev)\n",
    "sim_g = TH1D('sim_g','',int(bins/2),hmin,hspl) # Simulated data, gammas [MeV]\n",
    "fill_hist(sim_g, g_mev)\n",
    "sim_mu = TH1D('sim_mu','',int(bins/2),hspl,hmax) # Simulated data, muons [MeV]\n",
    "fill_hist(sim_mu, mu_mev)\n",
    "\n",
    "c1_1.Divide(2,0)\n",
    "\n",
    "c1_1.cd(1) # Plot gamma part of spectrum only\n",
    "\n",
    "lab_g.Draw()\n",
    "lab_g.SetTitle(\"Lab Data, LG vs. Simulation (Gammas)\")\n",
    "lab_g.GetXaxis().SetTitle(\"Energy deposited (MeV)\")\n",
    "lab_g.GetXaxis().SetLabelSize(.05)\n",
    "lab_g.GetXaxis().SetTitleSize(.05)\n",
    "lab_g.GetYaxis().SetTitle(\"Counts/bin\")\n",
    "lab_g.GetYaxis().SetLabelSize(.05)\n",
    "lab_g.GetYaxis().SetTitleSize(.05)\n",
    "lab_g.GetYaxis().SetTitleOffset(.9)\n",
    "lab_g.SetLineColor(kBlue+2)\n",
    "lab_g.SetLineWidth(2)\n",
    "\n",
    "sim_g.Draw(\"same\")\n",
    "sim_g.SetLineWidth(2)\n",
    "sim_g.SetLineColor(kTeal-1)\n",
    "\n",
    "gPad.SetLogy()\n",
    "gPad.SetGridx()\n",
    "gStyle.SetOptStat(0)\n",
    "\n",
    "legend = TLegend(0.48,0.69,0.88,0.86)\n",
    "legend.AddEntry(lab_g,\"Lab data (single module)\",\"l\")\n",
    "legend.AddEntry(sim_g,\"Simulation\",\"l\")\n",
    "legend.Draw()\n",
    "\n",
    "c1_1.cd(2) # Plot muon part of spectrum only\n",
    "\n",
    "lab_mu.Draw()\n",
    "lab_mu.SetTitle(\"Lab Data, LG vs. Simulation (Muons)\")\n",
    "lab_mu.GetXaxis().SetTitle(\"Energy deposited (MeV)\")\n",
    "lab_mu.GetXaxis().SetLabelSize(.05)\n",
    "lab_mu.GetXaxis().SetTitleSize(.05)\n",
    "lab_mu.GetYaxis().SetTitle(\"Counts/bin\")\n",
    "lab_mu.GetYaxis().SetLabelSize(.05)\n",
    "lab_mu.GetYaxis().SetTitleSize(.05)\n",
    "lab_mu.GetYaxis().SetTitleOffset(.9)\n",
    "lab_mu.SetLineWidth(2)\n",
    "lab_mu.SetLineColor(kBlue+2)\n",
    "\n",
    "sim_mu.Draw(\"same\")\n",
    "sim_mu.SetLineWidth(2)\n",
    "sim_mu.SetLineColor(kTeal-1)\n",
    "\n",
    "gPad.SetLogy()\n",
    "gPad.SetGridx()\n",
    "gStyle.SetOptStat(0)\n",
    "\n",
    "legend2 = TLegend(0.48,0.69,0.88,0.86)\n",
    "legend2.AddEntry(lab_g,\"Lab data (single module)\",\"l\")\n",
    "legend2.AddEntry(sim_g,\"Simulation\",\"l\")\n",
    "legend2.Draw()\n",
    "\n",
    "# Plot full spectrum\n",
    "c1_2 = TCanvas('c2_2','',1200,400)\n",
    "c1_2.cd()\n",
    "\n",
    "lab_root.Draw()\n",
    "lab_root.SetTitle(\"Lab Data, LG vs. Simulation\")\n",
    "lab_root.GetXaxis().SetTitle(\"Energy deposited (MeV)\")\n",
    "lab_root.GetXaxis().SetLabelSize(.05)\n",
    "lab_root.GetXaxis().SetTitleSize(.05)\n",
    "lab_root.GetYaxis().SetTitle(\"Counts/bin\")\n",
    "lab_root.GetYaxis().SetLabelSize(.05)\n",
    "lab_root.GetYaxis().SetTitleSize(.05)\n",
    "lab_root.GetYaxis().SetTitleOffset(.65)\n",
    "lab_root.SetLineWidth(2)\n",
    "lab_root.SetLineColor(kBlue+2)\n",
    "\n",
    "sim_root.Draw(\"same\") \n",
    "sim_root.SetLineWidth(2)\n",
    "sim_root.SetLineColor(kTeal-1)\n",
    "\n",
    "gPad.SetLogy()\n",
    "gPad.SetGrid()\n",
    "gStyle.SetOptStat(0)\n",
    "\n",
    "legend3 = TLegend(0.56,0.67,0.88,0.86)\n",
    "legend3.AddEntry(lab_g,\"Lab data (single module)\",\"l\")\n",
    "legend3.AddEntry(sim_g,\"Simulation\",\"l\")\n",
    "legend3.Draw()\n",
    "\n",
    "c1_1.Draw()\n",
    "c1_2.Draw()\n",
    "\n",
    "# c1_1.SaveAs(\"NBv5_LabVsSimulation_GammasAndMuons.png\")\n",
    "# c1_2.SaveAs(\"NBv5_LabVsSimulation_FullSpectrum.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f375185",
   "metadata": {},
   "source": [
    "### ATTEMPT 1: Constant sigma model\n",
    "\n",
    "In $\\texttt{Cs137_GaussFitting_ForResolution_Din_vz1.ipynb}$ a value for $\\sigma$ is gotten from fitting a gaussian to the Cs-137 compton maximum (approximated value). This $\\sigma$ is denoted by sig662 in this notebook, because the Cs-137 peak is at 662 KeV and it is an estimation of the value of the resolution function at this energy.\n",
    "\n",
    "sig662 is converted from MeV to PE units and is used as the 'first pass' test for sigma, the energy resolution function. Physically we do not expect the energy resolution of the single module to be the same for all energies and so the fit is not expected to be excellent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5b7acd1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the const. smearing function\n",
    "# Takes in data in units of [MeV], output in [MeV]\n",
    "def smear_const(dat_mev):\n",
    "    dat_pe = dat_mev * pe_mev # Convert from [MeV] to [PE]\n",
    "    dat_smeared = np.random.normal(loc=dat_pe, scale = sig662*pe_mev) # Has units [PE]\n",
    "    dat_smeared = dat_smeared/pe_mev # Convert from [PE] to [MeV]\n",
    "    return dat_smeared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2a2621b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TH1D::Chi2TestX>: There is a bin in h1 with less than 1 event.\n",
      "\n",
      "Info in <TH1D::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1D::Chi2TestX>: There is a bin in h1 with less than 1 event.\n",
      "\n",
      "Info in <TH1D::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Declare a TH1 and fill it with the smeared data\n",
    "const_smear = TH1D('const_smear','',bins,hmin,hmax)\n",
    "fill_hist(const_smear,smear_const(sim_mev))\n",
    "\n",
    "# Create two plots, one with unsmeared vs. lab data, and the other with const. smear\n",
    "# vs. lab data. Calculate chi2/ndf for both.\n",
    "\n",
    "c2 = TCanvas('c2','',800,650)\n",
    "\n",
    "gStyle.SetPalette(kBird) # Colorblind friendly palette\n",
    "\n",
    "const_smear.Draw()\n",
    "\n",
    "lab_root.SetLineColor(kBlue+2)\n",
    "lab_root.SetLineWidth(1)\n",
    "const_smear.SetLineColor(kOrange+8)\n",
    "const_smear.SetLineWidth(2)\n",
    "\n",
    "const_smear.GetXaxis().SetTitle(\"Energy deposited (MeV)\")\n",
    "const_smear.GetXaxis().SetLabelSize(.05)\n",
    "const_smear.GetXaxis().SetTitleSize(.05)\n",
    "const_smear.GetYaxis().SetTitle(\"Counts/bin\")\n",
    "const_smear.GetYaxis().SetLabelSize(.05)\n",
    "const_smear.GetYaxis().SetTitleSize(.05)\n",
    "const_smear.GetYaxis().SetTitleOffset(.9)\n",
    "\n",
    "c2.Clear() # Erase what was drawn because a ratio plot will be made instead\n",
    "\n",
    "gStyle.SetStatFontSize(.08)\n",
    "gStyle.SetStatW(0.4)\n",
    "gStyle.SetPalette(kBird)\n",
    "c2.Divide(0,2)\n",
    "\n",
    "c2.cd(1) # Unsmeared sim. vs. lab data\n",
    "\n",
    "gPad.SetLogy()\n",
    "gStyle.SetOptStat(0)\n",
    "gStyle.SetTitleFontSize(0.085)\n",
    "\n",
    "rp1 = ROOT.TRatioPlot(sim_root,lab_root)\n",
    "rp1.Draw()\n",
    "\n",
    "sim_root.SetTitle(\"No Smearing, Full Spectrum\")\n",
    "sim_root.SetLineColor(kTeal-1)\n",
    "sim_root.SetLineWidth(2)\n",
    "\n",
    "rp1.GetLowerRefYaxis().SetTitle(\"Ratio\")\n",
    "rp1.GetUpperRefYaxis().SetTitle(\"Counts/bin\")\n",
    "rp1.GetUpperRefYaxis().SetTitleOffset(0.7)\n",
    "rp1.GetLowerRefYaxis().SetTitleOffset(0.7)\n",
    "rp1.GetLowerRefYaxis().SetRangeUser(0,3)\n",
    "\n",
    "rp1.GetLowerRefYaxis().SetLabelSize(.04)\n",
    "rp1.GetLowerRefYaxis().SetTitleSize(.05)\n",
    "rp1.GetLowerRefXaxis().SetLabelSize(.05)\n",
    "rp1.GetLowerRefXaxis().SetTitleSize(.045)\n",
    "\n",
    "chi2_1 = \" #chi^{2}/ndf = \" + str(round(sim_root.Chi2Test(lab_root, \"UW CHI2/NDF\"), 8)) + \" \"\n",
    "\n",
    "c2.cd(2) # Const. smeared sim. vs. lab data\n",
    "\n",
    "gPad.SetLogy()\n",
    "gStyle.SetOptStat(0)\n",
    "gStyle.SetTitleFontSize(0.085)\n",
    "\n",
    "rp2 = TRatioPlot(const_smear,lab_root)\n",
    "rp2.Draw()\n",
    "\n",
    "const_smear.SetTitle(\"Constant Sigma Smearing, Full Spectrum\")\n",
    "\n",
    "rp2.GetLowerRefYaxis().SetTitle(\"Ratio\")\n",
    "rp2.GetLowerRefYaxis().SetTitleOffset(1.1)\n",
    "rp2.GetUpperRefYaxis().SetTitle(\"Counts/bin\")\n",
    "rp2.GetUpperRefYaxis().SetTitleOffset(0.7)\n",
    "rp2.GetLowerRefYaxis().SetTitleOffset(0.7)\n",
    "rp2.GetLowerRefYaxis().SetRangeUser(0,3)\n",
    "\n",
    "rp2.GetLowerRefYaxis().SetLabelSize(.04)\n",
    "rp2.GetLowerRefYaxis().SetTitleSize(.05)\n",
    "rp2.GetLowerRefXaxis().SetLabelSize(.05)\n",
    "rp2.GetLowerRefXaxis().SetTitleSize(.045)\n",
    "\n",
    "chi2_2 = \" #chi^{2}/ndf = \" + str(round(const_smear.Chi2Test(lab_root, \"UW CHI2/NDF\"), 8)) + \" \"\n",
    "\n",
    "c2.cd()\n",
    "\n",
    "# Create the legends. Probably not the best way to do this for symmetry, better if done\n",
    "# within the individual cd()s.\n",
    "legend1 = TLegend(0.6,0.85,0.88,0.93)\n",
    "legend1.AddEntry(lab_root,\"Lab data (single module)\",\"le\")\n",
    "legend1.AddEntry(sim_root,\"Simulation\",\"l\")\n",
    "legend1.Draw()\n",
    "\n",
    "legend2 = TLegend(0.6,0.35,0.88,0.43)\n",
    "legend2.AddEntry(lab_root,\"Lab data (single module)\",\"le\")\n",
    "legend2.AddEntry(const_smear,\"Smeared sim. (const)\",\"l\")\n",
    "legend2.Draw()\n",
    "\n",
    "# Show the calculated chi2/ndf values on both plots.\n",
    "title1 = TPaveText(.31,.865,.56,.915)\n",
    "title1.AddText(chi2_1)\n",
    "title1.SetMargin(0.05)\n",
    "title1.SetBorderSize(1)\n",
    "title1.SetFillColor(0)\n",
    "title1.Draw()\n",
    "\n",
    "title2 = TPaveText(.31,.365,.56,.415)\n",
    "title2.AddText(chi2_2)\n",
    "title2.SetMargin(0.05)\n",
    "title2.SetBorderSize(1)\n",
    "title2.SetFillColor(0)\n",
    "title2.Draw()\n",
    "\n",
    "c2.Draw()\n",
    "# c2.SaveAs(\"NBv5_ConstVsNoSmearing_Full.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "588a484e",
   "metadata": {},
   "source": [
    "\n",
    "### ATTEMPT 2: Linearly scaled sigma model, comparison with const. sigma model\n",
    "\n",
    "$\\sigma^2$ is attempted to be modeled using the assumption that is scales linearly with energy (in PE). By this assumption, the following mathematical relationship is used to extrapolate the value of $\\sigma$ at 1 PE:\n",
    "\n",
    "$$\\sigma_{1\\text{PE}} = \\frac{\\sigma_{662\\text{KeV}}}{0.662} \\text{PE}$$\n",
    "\n",
    "where $\\sigma_{662\\text{KeV}}$ is denoted by the previously-declared sig662 constant. Different smearing is applied to muons and gammas, the muons having an additional $x$-axis displacement of +16 PE. Moreover, because $\\sigma^2$ is linear, the scale of the Normal distribution will take the form:\n",
    "\n",
    "$$\\text{scale} = a \\sqrt{\\sigma_{1\\text{PE}} E_{PE}}$$\n",
    "\n",
    "where $a$ is a scalar value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d480eeee",
   "metadata": {},
   "outputs": [],
   "source": [
    "sig1pe = (sig662*pe_mev) / (.662*pe_mev) # Assuming that sigma scales linearly, calculate its\n",
    "                                         # value at 1 PE based on the value of sig662.\n",
    "\n",
    "# Calculate the coefficient for the square root fn that will ensure sigma has value of sig662 at .662 KeV\n",
    "# Denote this scalar by 'a'\n",
    "\n",
    "a = (sig662*pe_mev) / np.sqrt(sig1pe*.662*pe_mev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "123f9417",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linearly scaled sigma model at 662 KeV:  0.1601\n",
      "Reference value:  0.1601\n"
     ]
    }
   ],
   "source": [
    "# Define the linear smearing function\n",
    "# Takes in data in units of [MeV], x-axis shift in units of [PE], output in [MeV]\n",
    "def smear_lin(dat_mev, dis_pe):\n",
    "    dat_pe = dat_mev * pe_mev # Convert from [MeV] to [PE]\n",
    "    dat_smeared = np.random.normal(loc=dat_pe, scale = a*np.power(sig1pe * dat_pe, 0.5)) + dis_pe # [PE]\n",
    "    dat_smeared = dat_smeared/pe_mev # Convert from [PE] to [MeV]\n",
    "    return dat_smeared\n",
    "\n",
    "# Check if smearing gives correct value of sigma at 662 KeV.\n",
    "print(\"Linearly scaled sigma model at 662 KeV: \", a*np.power(sig1pe * .662 * pe_mev, 0.5) / pe_mev)\n",
    "print(\"Reference value: \", sig662)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e589b900",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TH1D::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c3 = TCanvas('c3','',1200,420)\n",
    "\n",
    "c3.cd()\n",
    "\n",
    "# Part 1: Apply linear smearing to gammas\n",
    "smeared_lin_g_th = TH1D('smeared1', 'Linearly Modeled Smearing, Gammas', int(bins/2), hmin, hspl) # TH1 object for \n",
    "                                                                                             # smeared gammas\n",
    "fill_hist(smeared_lin_g_th, smear_lin(g_mev,0)) # Convert bg_smeared_lin_g_pe from [PE] to [MeV]\n",
    "\n",
    "smeared_lin_g_th.SetLineColor(ROOT.kPink + 2)\n",
    "smeared_lin_g_th.SetLineWidth(2)\n",
    "\n",
    "smeared_lin_g_th.GetXaxis().SetTitle(\"Energy deposited (MeV)\")\n",
    "smeared_lin_g_th.GetYaxis().SetTitle(\"Counts/bin\")\n",
    "\n",
    "lab_g.SetLineColor(kBlue+2)\n",
    "lab_g.SetLineWidth(1)\n",
    "\n",
    "# Part 2: Apply linear smearing to muons\n",
    "smeared_lin_mu_th = ROOT.TH1D('smeared2', 'Linearly Modeled Smearing, Muons', int(bins/2), hspl, hmax) # TH1 object for\n",
    "                                                                                                  # smeared muons\n",
    "fill_hist(smeared_lin_mu_th, smear_lin(mu_mev, 16))\n",
    "\n",
    "smeared_lin_mu_th.SetLineColor(ROOT.kPink + 2)\n",
    "smeared_lin_mu_th.SetLineWidth(2)\n",
    "\n",
    "smeared_lin_mu_th.GetXaxis().SetTitle(\"Energy deposited (MeV)\")\n",
    "smeared_lin_mu_th.GetYaxis().SetTitle(\"Counts/bin\")\n",
    "\n",
    "lab_mu.SetLineColor(kBlue+2)\n",
    "lab_mu.SetLineWidth(1)\n",
    "\n",
    "c3.Clear() # Clear everything so that TRatioPlots can be drawn\n",
    "\n",
    "c3.Divide(2,0)\n",
    "\n",
    "c3.cd(1) # Subplot 1, draw gammas\n",
    "\n",
    "gPad.SetLogy()\n",
    "gStyle.SetStatFontSize(.08)\n",
    "gStyle.SetTitleFontSize(0.085)\n",
    "gStyle.SetStatW(0.4)\n",
    "\n",
    "rp01 = TRatioPlot(smeared_lin_g_th,lab_g)\n",
    "rp01.Draw()\n",
    "\n",
    "rp01.GetLowerRefYaxis().SetTitle(\"Ratio\")\n",
    "rp01.GetUpperRefYaxis().SetTitle(\"Counts/bin\")\n",
    "rp01.GetUpperRefYaxis().SetTitleOffset(0.7)\n",
    "rp01.GetLowerRefYaxis().SetTitleOffset(0.7)\n",
    "rp01.GetLowerRefYaxis().SetRangeUser(-1,3)\n",
    "\n",
    "rp01.GetLowerRefYaxis().SetLabelSize(.04)\n",
    "rp01.GetLowerRefYaxis().SetTitleSize(.05)\n",
    "\n",
    "rp01.GetUpperRefYaxis().SetLabelSize(.04)\n",
    "rp01.GetUpperRefYaxis().SetTitleSize(.05)\n",
    "\n",
    "rp01.GetLowerRefXaxis().SetLabelSize(.05)\n",
    "rp01.GetLowerRefXaxis().SetTitleSize(.045)\n",
    "\n",
    "chi2_1 = \" #chi^{2}/ndf = \" + str(round(lab_g.Chi2Test(smeared_lin_g_th, \"UW CHI2/NDF\"), 8)) + \" \" \n",
    "\n",
    "# The SMART/BETTER way to do legends and titles from how it was done previously\n",
    "# (The reason it's smarter is that now that it's within the cd'd TCanvas, I can\n",
    "#  just copy paste the coordinates between cd'd TCanvases to ensure symmetry.)\n",
    "legend1 = TLegend(0.14,0.395,0.51,0.54)\n",
    "legend1.AddEntry(lab_g, \"Lab data (single module)\",\"le\")\n",
    "legend1.AddEntry(smeared_lin_g_th, \"Smeared sim. (lin.)\",\"l\")\n",
    "legend1.Draw()\n",
    "\n",
    "title1 = TPaveText(0.51,0.78,0.87,0.89, \"NDC\")\n",
    "title1.AddText(chi2_1)\n",
    "title1.SetMargin(0.05)\n",
    "title1.SetBorderSize(1)\n",
    "title1.SetFillColor(0)\n",
    "title1.Draw()\n",
    "\n",
    "c3.cd(2) # Subplot 2, draw muons\n",
    "\n",
    "gPad.SetLogy()\n",
    "gStyle.SetStatFontSize(.08)\n",
    "gStyle.SetTitleFontSize(0.085)\n",
    "gStyle.SetStatW(0.4)\n",
    "\n",
    "rp02 = TRatioPlot(smeared_lin_mu_th,lab_mu)\n",
    "rp02.Draw()\n",
    "\n",
    "rp02.GetLowerRefYaxis().SetTitle(\"Ratio\")\n",
    "rp02.GetUpperRefYaxis().SetTitle(\"Counts/bin\")\n",
    "rp02.GetUpperRefYaxis().SetTitleOffset(0.7)\n",
    "rp02.GetLowerRefYaxis().SetTitleOffset(0.7)\n",
    "rp02.GetLowerRefYaxis().SetRangeUser(-1,3)\n",
    "\n",
    "rp02.GetLowerRefYaxis().SetLabelSize(.04)\n",
    "rp02.GetLowerRefYaxis().SetTitleSize(.05)\n",
    "\n",
    "rp02.GetUpperRefYaxis().SetLabelSize(.04)\n",
    "rp02.GetUpperRefYaxis().SetTitleSize(.05)\n",
    "\n",
    "rp02.GetLowerRefXaxis().SetLabelSize(.05)\n",
    "rp02.GetLowerRefXaxis().SetTitleSize(.045)\n",
    "\n",
    "chi2_2 = \" #chi^{2}/ndf = \" + str(round(lab_mu.Chi2Test(smeared_lin_mu_th, \"UW CHI2/NDF\"), 8)) + \" \"\n",
    "\n",
    "legend2 = TLegend(0.14,0.395,0.51,0.54)\n",
    "legend2.AddEntry(lab_mu, \"Lab data (single module)\",\"le\")\n",
    "legend2.AddEntry(smeared_lin_mu_th, \"Smeared sim. (lin.)\",\"l\")\n",
    "legend2.Draw()\n",
    "\n",
    "title2 = TPaveText(0.51,0.78,0.87,0.89, \"NDC\")\n",
    "title2.AddText(chi2_2)\n",
    "title2.SetMargin(0.05)\n",
    "title2.SetBorderSize(1)\n",
    "title2.SetFillColor(0)\n",
    "title2.Draw()\n",
    "\n",
    "c3.Draw()\n",
    "\n",
    "# c3.SaveAs(\"NBv5_LinSmear_GammasAndMuons_Separated.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f19d1fe",
   "metadata": {},
   "source": [
    "### Compare linear smearing chi2/ndf with unsmeared and const. smeared data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b5e8edcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TH1D::Chi2TestX>: There is a bin in h1 with less than 1 event.\n",
      "\n",
      "Info in <TH1D::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1D::Chi2TestX>: There is a bin in h1 with less than 1 event.\n",
      "\n",
      "Info in <TH1D::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lin_smear = TH1D('lin_smear','',bins,hmin,hmax) # Declare TH1 for full spectrum\n",
    "fill_hist(lin_smear,np.concatenate((smear_lin(g_mev,0), smear_lin(mu_mev, 16)))) # Concatenate arrays for full\n",
    "                                                                                 # spectrum.\n",
    "\n",
    "# Create two TRatioPlots, unsmeared sim. vs. lab data and lin. smear vs. lab data\n",
    "\n",
    "c4 = TCanvas('c4','',800,650)\n",
    "lin_smear.Draw()\n",
    "\n",
    "lin_smear.GetXaxis().SetTitle(\"Energy deposited (MeV)\")\n",
    "lin_smear.GetXaxis().SetLabelSize(.05)\n",
    "lin_smear.GetXaxis().SetTitleSize(.05)\n",
    "\n",
    "lin_smear.GetYaxis().SetTitle(\"Counts/bin\")\n",
    "lin_smear.GetYaxis().SetLabelSize(.05)\n",
    "lin_smear.GetYaxis().SetTitleSize(.05)\n",
    "lin_smear.GetYaxis().SetTitleOffset(.9)\n",
    "\n",
    "lin_smear.SetLineColor(kPink+2)\n",
    "lin_smear.SetLineWidth(2)\n",
    "\n",
    "c4.Clear()\n",
    "\n",
    "gStyle.SetStatFontSize(.08)\n",
    "gStyle.SetStatW(0.4)\n",
    "\n",
    "gStyle.SetPalette(kBird)\n",
    "\n",
    "c4.Divide(0,2)\n",
    "\n",
    "c4.cd(1) # Unsmeared sim. vs. lab data\n",
    "\n",
    "gPad.SetLogy()\n",
    "gStyle.SetOptStat(0)\n",
    "gStyle.SetTitleFontSize(0.085)\n",
    "\n",
    "rp1 = TRatioPlot(sim_root,lab_root)\n",
    "rp1.Draw()\n",
    "\n",
    "sim_root.SetTitle(\"No Smearing, Full Spectrum\")\n",
    "\n",
    "rp1.GetLowerRefYaxis().SetTitle(\"Ratio\")\n",
    "rp1.GetUpperRefYaxis().SetTitle(\"Counts/bin\")\n",
    "rp1.GetUpperRefYaxis().SetTitleOffset(0.7)\n",
    "rp1.GetLowerRefYaxis().SetTitleOffset(0.7)\n",
    "\n",
    "rp1.GetLowerRefYaxis().SetRangeUser(-1,2)\n",
    "\n",
    "rp1.GetLowerRefYaxis().SetLabelSize(.04)\n",
    "rp1.GetLowerRefYaxis().SetTitleSize(.05)\n",
    "rp1.GetLowerRefXaxis().SetLabelSize(.05)\n",
    "rp1.GetLowerRefXaxis().SetTitleSize(.045)\n",
    "\n",
    "chi2_1 = \" #chi^{2}/ndf = \" + str(round(sim_root.Chi2Test(lab_root, \"UW CHI2/NDF\"), 8)) + \" \"\n",
    "\n",
    "c4.cd(2) # Lin. smeared sim. vs lab data\n",
    "\n",
    "gPad.SetLogy()\n",
    "gStyle.SetOptStat(0)\n",
    "\n",
    "rp2 = TRatioPlot(lin_smear,lab_root)\n",
    "rp2.Draw()\n",
    "\n",
    "lin_smear.SetTitle(\"Linearly Modeled Smearing, Full Spectrum\")\n",
    "\n",
    "rp2.GetLowerRefYaxis().SetTitle(\"Ratio\")\n",
    "rp2.GetUpperRefYaxis().SetTitle(\"Counts/bin\")\n",
    "rp2.GetUpperRefYaxis().SetTitleOffset(0.7)\n",
    "rp2.GetLowerRefYaxis().SetTitleOffset(0.7)\n",
    "rp2.GetLowerRefYaxis().SetRangeUser(-1,2)\n",
    "\n",
    "rp2.GetLowerRefYaxis().SetLabelSize(.04)\n",
    "rp2.GetLowerRefYaxis().SetTitleSize(.05)\n",
    "rp2.GetLowerRefXaxis().SetLabelSize(.05)\n",
    "rp2.GetLowerRefXaxis().SetTitleSize(.045)\n",
    "\n",
    "chi2_2 = \" #chi^{2}/ndf = \" + str(round(lin_smear.Chi2Test(lab_root, \"UW CHI2/NDF\"), 8)) + \" \"\n",
    "\n",
    "c4.cd()\n",
    "\n",
    "# The bad way of doing TLegends and TPaveTexts, this code was copied from an old ver. of the nb\n",
    "legend1 = TLegend(0.6,0.85,0.88,0.93)\n",
    "legend1.AddEntry(lab_root,\"Lab data (single module)\",\"le\")\n",
    "legend1.AddEntry(sim_root,\"Simulation\",\"l\")\n",
    "legend1.Draw()\n",
    "\n",
    "title1 = TPaveText(.31,.865,.56,.915)\n",
    "title1.AddText(chi2_1)\n",
    "title1.SetMargin(0.05)\n",
    "title1.SetBorderSize(1)\n",
    "title1.SetFillColor(0)\n",
    "title1.Draw()\n",
    "\n",
    "legend2 = TLegend(0.6,0.35,0.88,0.43)\n",
    "legend2.AddEntry(lab_root,\"Lab data (single module)\",\"le\")\n",
    "legend2.AddEntry(lin_smear,\"Smeared sim. (linear)\",\"l\")\n",
    "legend2.Draw()\n",
    "\n",
    "title2 = TPaveText(.31,.365,.56,.415)\n",
    "title2.AddText(chi2_2)\n",
    "title2.SetMargin(0.05)\n",
    "title2.SetBorderSize(1)\n",
    "title2.SetFillColor(0)\n",
    "title2.Draw()\n",
    "\n",
    "c4.Draw()\n",
    "# c4.SaveAs(\"NBv5_LinVsNoSmearing_Full.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9ec8b0c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TH1D::Chi2TestX>: There is a bin in h1 with less than 1 event.\n",
      "\n",
      "Info in <TH1D::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1D::Chi2TestX>: There is a bin in h1 with less than 1 event.\n",
      "\n",
      "Info in <TH1D::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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X5qxUlbVCZVn6zp5ySlYSFUUhnyUOoc5cTjlOrNpz9na7zV+6oWvo3I4+GHMH9lOalxFIB7zx3Yr1tbG78OvmTDIGS0zkKaecKETm5eSbrswVYG9TS5fctWyQpmnsMPHp6+kOMgSfiq3Um0twamCX+sws1P7+C/8QxGq1NxOer56cspmYdwOM8wvVeFYfB5tVLMqOuPfqeasFfwTbWmkLk24Zx3TkX+ik9FvVKaL39gAxEatV2k7WR2xjut1iFcu8cAJrv6l2eRMC5JDonPTbfsfpv155CGq2r8Yi/cD6sIu0+iuwmBPcmwnP11kHpKexWuWorhfoGW/WKTbYxzRWzvRfTLGl79Gm8QvVtxwUVX1o9s0vMdOHya+nXzcHp6qONUvXzaa0HsQ65+CFk1846Qafz9r5A0wm0Tnpt/2+ouogTBilkrmpRYIOiKo9mPM914vD7RHdSUq1Hbgk8O0ujizwh3OtDIIJLNNElSwKCeoPmyYhqoJyxClRF6E9pbTzinPWb0/fdGTz1I1ZhlaJ6yIcyteKd12ZmL0tk8kXHiR/gMkkOif9tt83pEIZYvuarNQPhtUVbMKc5zs4MyXoYd6/RMsyfzatX81R3UELqVGiqn/XVQk54mfrZ5LjYy7YEhNN4benX8/gNF+6qoMgquCyIKrS7OaoXnkE98j8XPr+q++/RFdpx/a/KYjP/iGUyf3LjDH5XTfhEr44smmBLUj71Pu1lRDMiR3T7CtDjohkSRQxqm5Os1iHfVFgo14Ltp5+5pozvWoA4EBsr+MS7FKfbQp9s1cxD7ghc55vqQI3x9Bzf/2Qparf0KcqVpA+Gyy0fW11EKyP/tOxWul7901Buj45dQs2glPtYCyGRLMnvNpHsfZL42hvZgBLonPSb/uj+VQNDmArFZo5Si1R1ps/O6JqAyY/yuBUnY8TxnZQVGkvouBitJie6EeKqmBB2uHJr4Nzoa5PMKWuj382cSpRt/TyyYSjerBEZ/2mUxkc1QHGkuic9Nv+UKKqfY9Uuxkb9wNMVhsz+fnK6DsYpda8e+0Miqo+4qK0hk9V0K0+WA1dvZiRyU+ZLsKXR/qmnNJ9q5WDLweddvNLdO7XyZCQCgATSHRO+m3f90UfN9WsisRIdA6WZbl9LLKi2K0RXIcqwoeuwLTnK/2zruuqqqz3T9AbaXIgwc3CUaYLyqyGhOoV98dYJsF80r5N0xohJ88Jlcln7ZfGni8lgCSJzkm/NcbQBPv0g6J4GPNbZgP7/utHXSGqVmDa8xVHabmwKAoxgWjfargyiCq4LIiqNP/sXYHFfjt+Fn3/ZQy7RhwU6ZM6eqRYVRMuTQBX5siv8Zmb0gCMYk9d2TTN/X7XR9q23b73H0JcMw+4GhOerz8nJVsB8moGAUuVxm5YKezyGo8h8/g5jSnj0SLNvsHjs0qxqqqNzedYqtLsFqeq67r7/a59Ra09YHuKEJtW4IHV6kD4+snZXxkAhKqq9IKAuq73eo3PpGmaRUzRTdPEFlsshfZIFqfPVYuDUeymK8U9xZHY4ryy8ei1r7h+hQP9bcw3zlVrwI8nWBwsVZaiKOq61uO6DPNiQZH/9RIHSanT24HAEQf2TyduraR3zjo/hGyhxpiYpcq/0F4iH3RVg6XEGkQXF7y72EFbqFMrazu3bXi/3+1YuXFvwVKVBlF1sH6AqFqaYz1fOAW7iaq9NmCIv5FkM4zge1sfeT6fZVna/81rFYjoAwmTofOxIfV1Yjklmcjwcbvd7LVW20mV7LX2ckvwQjv9Z89WVSUOKjaKR+xOnZxtcU5N7F3IQZ2hLrTrOnvcbx9pNNsI5iW2NrNXIarS7LlNzfP5dH7fmEP6Oe6A3cGGTWwA4MDIO/x2uxVF4fv3PJ9PUT91XT+fT5EsepZNLC42jcxqSSaSWAZpm63NRAqt61qubdtWBJBVJHJtMFps0zRWlsWm6kRmWRlkTQBpB6au62yJUhPnLnT1JLEeBPX2TfK/OMnYqmprVqcWB6RrBVuy2+q/qqratr3dbtpXfY3VVd2kQDgAAIfjeDbsqqqszal7+fpYc0VQsuiJLf3BWoZsnv5shiOSHD1hlYotomka38dLCpI5ypiBRw8Z9nNQomk6tdJQpJs9ZVtJZ+hUzy+oLEtpE9Fbsao6S75gR/YMqSDfnFVFT9d18s3JXACyIy/nqv9+nKvMfqZ+AIA8rIywQ35RFAkF4Fxr3v2KrJiwc2SxoSF2bY7Nxlp6RIs4PmELEqy8VlqDA58MYff7Xap6qJWVEGT/OFVrmy7tuq3u2NFKfoSUH7zKSqvj/Uj9FDZeywlwHfzfq/kbY4iJxTE1mZeistkGzTD2ra6vFe8inT5WE+3nLlarnAqPRZduRyLr6ZWonk5gFWrMOsDc36HYx6eqqio9zjVNI/PxaxQkHFxRwXqM3roJIIO9+/WB0K9W8RnK1CiOiUtEg3zWnkmxy4PTfI6/UUyQ2QsnjwvBe9Q2AnEnl8/WKuZUz5ki9Lndbrp9YskG5yVhO/Z6GTn7xYo3lbMBak5WzhHpW2VZOvn7RxKZHA62Wwa4Eh/wUlI4Y4qNPliWpf0szlX6Evng+NHKW1ofLMtS3uqSYWKTbPuG145cTrnBQqWSNqWc1UXYnO0dSRp/THGu1TWJbRkevNAW5CS2JZr3Lcbn7A4+lkTn/Kx+uxJbL4DskvFtRy3IdEzEcrksppCfBX3fN69A2DrWyJxCN0BHrjLsDAhwPY72UhpEr0SbfLlzrT6YmGqIeeUOzk6s5M7rBwYK1mRU6bH2qYhTdTwOJ6pyHPEatb+NzcrJWULSiT1ZwoR8cD9gExuAK/EBLyWIIF5Qm/k5JewFK4GoSrNbnKo5SPgQxy7qdGIb0qNXYUJiBLep2X3vGgAA+Cw2dt7tVHB5OAJbr/5L2HKdAB5j6VTUNUEvskhzdHFtTVMEWQAAODZbLsdj6d/R2MFSJTE//T2PJLTu9vUxB9hQOUZRPIqiKIrHy9HKGGMItg4AAHBAdohTVYViqZvZYc38eFcnWGXa919FYUNYAQAAwHHZJ/hnpXYhMAtNQmfGfAty5Om/N0XlzAMSFxQAAOAw7BxRfUGfPsdby27onUNwsu9oSqsoHlGTlZ4KRGABfD4H8UAAgFHsv03NgsisonyWeAqZFx5NP/kUxcOY3/Ka7fsvXNcBTszx30gAEOSEUSXG7kjzKaE1UpYqgXBWAACwE58ymK4KTRA1s39ey8SsVqgrAABYGUSVQVSZT+sHehObLBerIMgsAABYlM8aTFfiVD5Vk/kIR3VBhJSNs2DnBN8mB/G4AgAA2Bx05aeKa2ff5bcNmBP4MgurFQAAzOZDB9NloQlO0g98k5W2Xf39nLBdoa4AAGAq5xhMZ8L030mwQspGXlAffj73/Vfx+PprymJyEAAAYDkQVcZ8lE9Vmr7/MsZap4yyVMn/b5Lr73Qh6goAAGA2GOuuZbEMzxJ+Pd4SMQ8IAAAjudRgGoMmuHQ/ENvVy9Xd/KgrRBUAAIzkyoOphSa4ej8Qk5VWVwPrBwEAADwuPpgKNMGJIqrPQ88DFo+v/uuByQoAADJBVBlElaEfaH7/Kh5ipkpGbAcAAHiHwdQgqgz9IMSP1QpLFQAA5MFgahBVhn4QhMDrAAAwBgZTY8z/9q4AHJrXbCAAAAAMgK5EXIfx9hZkVWCcYPRUbHsAcCUYTA0R1YXTRFRfkFdoUNN/PYrHlw3FjrRyWTMevd7AEQAADg6iypjL66cEff9lfj/6r0fxYDFgEm2XesksXxKNEkl6WyEaHwDg+CCqIAsG9R8y7FI/jmiPH0mko3/9FUl2ayClxhzJ1fdfegNHAAA4ODiqA8T5/evvPxNQVI4jf1E8XrHpRTZ9i3iSZPbIa0b17aqXXeptH0a1JfZDp1/uDgEAYDFwK8O3bghREhd0u44Zpb7/+Hv7mHcBZDertnnpLYCcC39CrX49fgLZ24j25qfZw+n7L397bACAvWAwNReZ/uu6rqqqvWtxEi43fouE0lN4j7+uTlbcBJ2flMxyj79PC/4YtJQF63fx+DbG9N8qvcrNvHtcBUtHcgEAbMzJRVXXdbfbra7r2+3Wti3Sajq/fxnHK+gC4/RfCfVwLEPa5uSKHr9lIkde6b/N+2ejJNd7nr9/GWN+Mvv+o6rhyiljzI9H1wUeEwDAQTi5T1XXdW3bNk3Ttm3XdXtX5+P58Qrqv846VLsuTV+ior7NX8tQ2Odp2Qb5m1vSo8vHirx0tTO9snDeAgAYxWeLKj++VFVVRVFUVSUSqmkasU7ZDzCa7z8//4wx1zBQGV9dvWSK3P6ejWCd237/6r8eIrnkg9VPtvJOteW4n0xAQgEAzOSDp/98kVQURVmWYpS63W7iMdd1XdM0iKo1OJmzzruX0l/2uUclnv7+n8QJJxazounJSuVxb+x0ofavt61xpgcNALASHymqmqa53+/OQTFNyf9VVd3vdxFSTdMw8QdB3kTh71/914/P+I/rmHIVF44iLIIrMcXdKlJDLZveD/71kNP+7y8HfKuxcIQHABjmU0WVoKWVo5zqupYjz+dTZgnrum6aJphhcJuaIKwXVR7r5nyu0OI/3n+7rt97MiqYhW/Q+v5jzLs/+4/p6/EjIj2Dlr13+39ma8yMIA8A8Ol8pKgK0nVdWZb6yPP5lLm/wWuRSmPpvx7vq88+bOx8m/D6CvgSfdbtZPJXUekjmRcaY0LThcEPwWw/rpMAAIzlPKJqDmyonIXj5fOJysPW/MsYFSDqb7DNz8U3aDnOWDrB95+Ek9bgY/XiSiQiO1woAAcAwGev/tP4fuiO4QrWILa/yhF5m7hUiNpQKxwhSGzZYCSl8RYe/lbLD1MXAgB8KOexVIlzuv1zlHM6RqnJBE0UB6QoxIVIeaD3P75TOkL6we9iHPnrB/WpuLKMxTgdjHpqO4lR5isdTBUA4BycSlQZtSPN8/ls23bfKl2Qgw+TTtAB8zmicC1iSityvO+HjXk5m/Y4yYzJWmYIAHBwziOqjDFt295uN/lc13V+YCp8qmZy2DFP73wsBqogh63/MuRMa2ovq4yYWAlyVgvm7Pbjh8vKfEyIMADYiw8WVRJVQR+pqqrv+wnbJ6Of5uOsCNtsVMsZQd82MMZxSoi1Q9Cx3bwrLX+60NVhw4rKEou5akLaK/GsE2czZRZqDABm8sGiKsaEyOlYquZztHm0YLhLWADfjuUdGRWpwbdsTd4QOkfZo5wAYD1OKKomgH5aj1XHsJQXzk+shO+Pj5WwGaNseH5QhrDJagqOQH9fP/iWRrBaSgeCN87k75CrFgEgAGA+iCqYSmieyIkSuV7hkn/AfeenVl8/FXugqI5EZJlhTldx1JI9rnbLCeej99vRaw8T+QMATANRZQzTf6ux3a//12j9d/+cx3f//bPtzIrlXo2Mub8R1yZJ7mOY6lRBy5Y6/pbJepEdmGcEuCCIKmPQT2PxQqs7vI9n3wsOLeEJHVUNZ/8c2JOgXcrOG+YFx/KJWZ6cJx6zbJm4/99mdlYAOCuIKliexHi2cP6/f5nfr/xZ1rcqfvOmG3wJ5yqHoAbKEdBJo5fJNKbmK63JjvZjCwKAo4GoMobpv5nEFuEvR3iYmbF7HayL7hL+Y9L9ZJL2GrW6MHFw2paFg6LnaCthAWAzEFXGoJ/WJB0aW3kZDwxU0RAJGKhOw6QJwcnCJXbhhBAPC9qWWIQI8NEgqmAGceeqyeNBbHxyQyR8//k78Td0LWzHZI3rdKH1zZ+WzNnDbSxPfkiItUsEgAVBVMGh+WskIOLUR5M5zRecLvRzGAwHn042Bq1sfHNpIg7WWLd3JBTACUBUGYNP1fo4Eyj+tm7B0OdvIYi+Hub7T/9tfAMVfDxBwRTbGCcnE+fISHWVmBk0GVsWjo0CH5tSZB4Q4ONAVBmDfloffwLFG58iP/p1ME/N+wg6c70VrMhMc5Gjt5xwDH4Y92Bg98H0IZxONb9fZeqt2O49o/LP90YM1DkAACAASURBVFYEgAVBVMGmpNVPIARR3qwf660+lfkzdEEjViKU2qDR65Wg/zKJmGeJfaDf05hYMu/sOLvUfLUUFGGL5AZwWRBVsCnv/ubRV3Bg1s8mZrnfRQg+6JhaivUKJ3061oOfbYiYJcn/wZC2wgaNtYOlx/BE3hZrFQHAAVEFm/D7l5Gf/u84s4FOenaYgdVJh30fQ/oHw4RlfcFwJLG4JJ5r18+plWKQTlZm218IsCWIKlgf36gQ3+fY2XZmFLxzIcpUA+dwp1ohdnya9KyixXerj8U4tblNlju6PunQdP4p1BKcCUQVLEfa/9c/Mpj+8WCyD9YiJ7B7cv1g4AdA6AfDnPjviZTTdoKK7cs5Z/IRj0YAC6LKGEIqrEF6NMqLOMU7GgZY1ko0OTfVyX/6dqL///5lzEuNff8Z7ORBf/Y5AmjVfTlnslmACcxjsBKIKmPQT/MZu5vb7N3fANYlJ1xWjhlVXdL3f8zvX8XjS5a1GmP67+EM3ly1vNWIg6FHTfZ04Xvit/SjNu2JJYutdsxRSxMCTADsAqIKlia23gpgQZadFx4pj7KyiqTvvx4qUMgkhy277OP3o/8yxeP7ZyLSxsXVljNvgs/KplExTmNnY2SGpsvJCuCDQFTBMUB7wUcQDC6aSCnkd+/YasT42o5x+UeYL2vmh7kyxuRMbs4MrzUzUDDzhpAGUQWrgY85wCjytVFQ2+VFgkgsx5NKOLvuxOYBRzGoRWJWq8VFDOYxWJVTiaqiKBzvqKqqns9nWZZN01RVtVO9IAnaC47PqpbUDG1k13ast614MM7WcRzb19Nzo5IBpDmPqPI1U1EUZVm2bdt13e12wxsdAI5A33/99X+aI9e+/5gfb/fZasCXcRmGJTNei8zchGfB8FpjSYTjOgKIwoNwBlHVNM39fncOdl1n/6+q6n6/N03TNM3WlQOAj2ZBS2pCP61vr80acQedt5YrNLqbwlCGTuD4zZQE84aQw//2rsACNE3T931d1/qgyClLXdfOEQCAQzPZiPX718+/5XiZiB5jJwH/bo4+48IJM4/BS/yD6Zwn1HlaNeA0nMFSFaTrurIs9ZHn87lXZQDghIz1K9+yxCULCm8gnckoA48faivzwrFRQ313+H3n9VaavGNOcHtOK6pGEYyoHgTHLABYnckibChK1iC+I/yo1YLzWTbbTC346cFFdxeFYDmtqKqqypnvcwxXGqQSAIxg+yWri5cYU12x8Frvu+uYfEvSTyZf5vevnLuY6bo03zYzLfbVxrV1LvRczYb3sV6kGuBzZlGlvdfTDlXs/QcAF2KsHWuOd9fSJGbxLN4iwZxtc/wcjG8wmxzlyy8UHXNKziyqjDFd18mH5/PZtm0sMfoJAE5Icl/zlextAbOHXVQYjBcfQ5u4ctIr/PBaGdvmjNuNx7cV+en9WcV8O9yyJiX/QsJ3rcRpRZUxpm3b2+0mn+u6TgT/xFIFAJDmLbzW2GunxiwdvPDTbT8rhb/69Gb5XM4jqvwwVFVV9X1vjVUJ0E8AsAPrreaLeawvUqKzNtDNM2Rb8l21/Jr4tqiMnRYHbT+ZkmJt5REz+fgGs0VqQlStvTiPqIqRszsNlioAOD+n3rY87SM1JpMRRqPJBqFFLvQrmc42x9UMZnJ+UZUD+gkANmXHLS/nFO1dG9gkZzAmu1+B+F6Hw7xcr/qvh/n+8yMOfv8qHmOcsV4V6L9M8fgelhevtZD9l/lb6BgmW5JiF8qf6WxzXM1gJogqY7BUAQAswY8t5PFtfvTWygRtb2uvbRTFlrzNmbN4+csGBwsKGvBiBjmbm3xwkk32dr8UiCpj0E8AAEvwYwv5sTNtNdyGtiwMutX7CuBvsu8/+btK91+P4vHt32Y4f//y8cFIl7JsWQPVFWKi7gKiCgDg7OziTbVGoZ/uFpYOKqEjrK4hSf38R4ZmhUEQVcYw/QcAcHw+VlGpebSvqLvY+6zi8vit97HteWQQVcagnwDgrOxifli10FDmk+NgbcPbrGhsnvH3Q88qFo8RjlOJcocThSZP0+BKlQBRBQAAcTazZ6Tjv+dUQ6a3RmU7WJmxF8Yy2czDbD5rz0KeGkSVMUz/AQCcgvkmK4nIMPqSF++r7eKTfd6FE0qMLeJLZBv23/96FI8v8/g7R+nMQg7unIjhyoKoMgb9BADgs9nUYSz+e3410psbTjO2hebpBvm72i4+2bcUC67O+xvl62fy8W0WcnDnRLD8b+8KAAAAAJwBRBUAACzGKU0Xr9muv6x9m9MCXOXntuyWiH77XBam/wAA4EisNF+Ws4XzqMtnkumYP20SdkJtg27+b7kRzmoYLFXGGFOE2LtSAABwaYqH3StmjCloWf2ncptYnyuBpcoYHNUBAI7ASlaQjC2cJ+aTf9Xj8ffyTMf837/edomeVu6olLaSfuSq7z/m8ZhVn2uApQoAAGBdlvVhgsOCqAIAAABYAEQVAAAAwALgUwUAAPBirLvVzGV6Y3Gr9xU6OCGfUReyDDAKlipjWP0HAAAh/q53exzA2ymohDbbnDGCWg/4PZz67GCpMobVfwAAEDK9vO/f8hVO78ma4BZ7iyHl/v71VtCEtX4eb7vQ+FsE9l8/LfBaGyjp7W45RVGwATOWKgAAAIAFQFQBAAAALACiCgAAAGABEFUAAAAAC3AhUcWCPgAAAFiPq4iqqqr2rgIAAACcmfOLqqZpiqJ4Pp97VwQAAGA0R9sQsP9aLVrE53MJUdX3fV3Xe1cEAAAAzgzBP40Z425FmFAAgJOQGYtcNmZZKrexpLfBWTucek7+r41r+q9/163MJ4CoMgapBAAAMIG9N8k5GogqYyKWKpQWAMA5ydzUxSZ7PFKXrLS1cHwbnBULnZz/95+iKPrL7/6HqDIG/QQAAACzOb+jeg5FiL0rtQDnuItloU18aBMHGsSHNnGgQSAIlipjsFQBAADAbK5iqZLACrGzZ7VUAQAAwGZcRVSl6RX2zzkZLqLJDiLsjnMvB2kQc6TbOUibHOdeDtIg5ki3c6Y2OVuDPP5bIJNztcmnw/SfMV5/kj+ZEwQAAIB8EFXGvOunoiiQUwAAADAWRBUAAECKdXffGxHY/Uio+hxtd8IdwSrDXDIAAMACoCgQVQAAAAALwOo/AAAAgAVAVAEAAAAsAKIKAAAAYAEQVQAAAAALgKgCAAAAWABEFQAAAMACIKoAAAAAFgBRBQAAALAAiCoAAACABbiKqGqapqqqqqq6rtu7LgAAAHBCLrFNTdd1t9ut73v7Ye8aAQAAwNm4iqWqrmtjTFVVZVlirAIAAIDFuYSlSui6rmka+bBzVQAAAOB0/LN3Bbbm+Xw6R4qi2KUmAAAAZ+I6ZpoYO1uqxHpkhc4Gc3NirJL/haK4kLkOAADycX51M1g40D4OO/tU3W43Y0zbtn3ft21r1rEbydI/+RwUbUWIxasBAACfRd/3IhTsB9Do9tm7LodgTyNN0zRd1zkqpyiKtm2tBloKLZKcW8ZSBQAACRgm0kj70Epmd0vVZoglLPZTA0sVAAAAzGRPUVVV1fP51JYq+by4mcoWFzvVh1ijDgAAAHBWdhZVdV3fbreiKKqqKoridruJZ9U0uq6TsOnaD11omsaaoHy3KixVAAAAMJP9Z0C1W5UvhkYh/ljGGBFn2jSVcNViGhgAABIwTKTBp8pyniaQ6Ayiz0bFTYgZpU7TMgAAMIfryIVpIRIQVZYdmkA24LMmJT/B/Cr5dqmiKMqylIBYsdV/dAgAAPC52ugw9n4RVZYdIqpXVWUVzxwPqiCi2Oq6dmb66rq25quqqvw4Ds6HGPQYAAAACLKnrgyu9WuaZrJnlWSVjskuqkvfNdN/AACQ4Go2GCxVk9lt77/uhSOh7ve7rOCbkKGJKCrtbtV1XVmWTgKm/wAAZsKOJaeHRzzIPqJKzEXy2XGrKstyWpyqruuez6d95DLfp52rrFuV3w+C0390FwCAfPh1enp4xIPsPP3nW6rWLtFXbPQPAIClOOUbdYObOpQRKH2//lmm/yyHa4Kg7lkVfKoAAJbilCPrZjd1kNZDVE1mN58qwbdUPZ/PNTZUtgUF463TDwAAAGAmO0//3W438XMSV6r7/V6WZXr53jTSwdaDl6C0AADGckpzBZaq9FksVZY99/6ThXhd14ncaZqm73uJz7lGQbKosK5rX7SxoTIAAORwyl1i0zd1ylteiT1FlaWqKqul1rBU6WifErLBSRDcUDnIshUDAIDPwv7qPtPP7/RNnfKWV2JPUSVaysod+bCGpcrmXxSFH2zdRCxVmK8+CLQvAADszp6O6lVVlWUp8c3rurYBq9ZY/Sd5xlRRcBhGQn0QRE8BAIDdOdAgFNy1ZqmcbUR1H0bi08CjBNidU34NY67ZGxS0Jbb0YDUSZ3FUtxzCp0oQR/I1lv7ZYOuCH1IB9ykAAACYyT66Um9T0/e9WJKsN9XGVUJcnwYeJcDunPJriKUqfRZLlWUfnyoJT2W35DPGlGVZ17VZYvovaO5qmuZ+v8tnP7goPlUAAAAwk310pdazsgZwkWpYi1dQYscCtQfV92EVd/qXxGWxv5MMahhgV075asJSlT6Lpcqyg6XKMSMt6Jku+wbGgjIkCrKWqqO5UsUEn3MWdqQ40jaoAHAmeL18HAdyVJ9P0zS+E7qlqqqYB/oxY1BJVW2d/cofsM47spcgJiweACSYswSK18vHcSpRlUB2p5GAWLGI6sGDay8J9I1kibLst8uvoV/hxat6cPa1mTsfAACEgwij4ABx8VFjDXaYP5Klf+KWbowR/3H7pzEmYW3KIT0pJqXrBLEptlXny/WH+dk6M4MXnNsudvWpumyzw1iuYGze+FuwTZP6NzX2NjPTB5Mt2KTp/IOjYSKZc4oXoNnRUT1xdmaV/Oeqg3/KB+3XFavMUtUIarV8UWW1glOxfDV2nV6+11d6pS4EZ+XcY88ud7d2oYiq9FlElWWfkAqbtXuhFv0VRVGWZXBt4IL1SfQqO0mn/8xBd9acq7TNJlYcXV9YsFnsY9rLYAYwB14RAPO5lq6U5YHOwYRMcYbJ2BEnk8nzes6FsbLSaSYUFCvdL+vgR3LuwienndPXxo4crX04wpE5R/JJv5HWrvMab7Zg5pu9Mxcsa8L4ld8aWKrM1URVkAl2Tv2nbzVNF+enn/kIEm+QTLHlWMISL6b5rPprOKcxnTSFsloHn3Xvmf2CrZqprfm6gRn5rd+4CyXqln5V2j9NaHxde7gNvugWLDH2cpiTQyyZc2SmWIm9rIJ1Sw9M6WERUSVcZfWfJrb6T2MP6jTB3GLpBelh/YvlbuKtCI2uqn3BxS7Uyfz628ovXlv9YRGcp6Cfo5PM+TAZW3nnLvybcpoxXUP4XFZ6sit9ZVZll479iQ0VZNkbOU2zfArXElWx6KC9hwl1Qf2uTPROnYOT0h+J1+jlMY3lFJdzL3sR07ixEct/fE4+6bLMu95yytXJgun93PzjuqrOh/SNw6fA6GVZ5Pb5LsAnckVR5R8vPOxxEzFBCQkRdpC3akxtaBx15eiJfX90OjX0Tw3mEBTHTspYQSaug4PayGnndDP6YjGYP5yMQdk9KqsTy44ji9TEQ0w/lMGzsWyDdVjp6S/YRS/IFWdAi5ATQCb9508b6/oXISf3/t15aI1bzsywyJvp9y+J1V8fdy7s83ynfILtk18NncwkBR98BDld1Ix/kzhfW+fsgv0kUavgqVjnz892cn2CyfLfFfpa/WfMhOyPGomHmG6W2BP0X0Gx3BIFpZOln07wxZWTyUojxSeyT0iFY+L3eGfY0+g0H3fEvxGdxvmZYv+fU3qiATPzcRRJ+iq/iIQ8cp5v8Fdaog1j7RNrxliVgtLWfzqxmh+tj53jiM+oq9L9eWx9bB9znrvTtSbfuy/6J9zX4kcS9Ym1T+JIIh//VZO+d13KhPpYcvqGX/qc9vFfI4myipCajP0JxmCpineLC7aM8X6pLJ65Cf0OG6yG/pD+PZT/JQ9qZT/BnJ/XwZedP3ol6hP7OeifgpXI7wOZT8fvBtOyjY2R6dLn1D9YYf9gZrJRtcpP5n+YVlC6VQcfYvDdol8FwUrG3pDpN9789nEOxu4u0baDb+brgKXKGMand4K/2FbK3x7MfC/ELtc5BEWY8dRM4pdr8M+xxJrRr3bs9+5gtolkmaKTnu8wtn3ST2Gz1p4mI3wStzP5tTBZ5eybvy9Bts9hDmOLS7fe2g/xTFzLUT1GEWLvSu1D/85S2Qb1U/+ay8j8qWfTB/MZi/9Lyzk7pwUSzZguNyfn4IfM1pjZaKfHPq/Mp6/Tm0gvcnKb8AgScj9TZGeif2AMvgmXLfqzWOl7tGC2wb7nP7Jpv80gwRUtVX5HoevksLGRwzfh6Grkv+tjrw/nl/fat5O2zznDZMKyFXstxv40kYnFZQfCzL4RK3Txxp/TV32TTP6v82BTz8f/LqytY/rIVM60cidfpb8gaTtK8Eii3JwfcjkPfXvT7yjj6IScYzn4LyLGzSCnslRJxISqqvR+yULTNPaHl382aKlyxjmO+I02Np9gDjZZoTAZOIqkeM3x6a96TC05aUbdxbQjuj79ywldH4/VJ1jnwT81iUyKELHjCZycE60RrPZgGw6W7iRznnswTSIf43XX/Ar4V/mXB3NOHwm2nm4fp8T8nO2RdIvpCuj7GpXzqPoEbzPR+AmC74REDo51J1HD2DfXeG1oQk/Huc3gET+f4I3k18f54BQaex/GfgHmP4WLcCqxWRRF27bGmNvt5r997M7K/lVnaoQ1sG9G5ys9Kof8H/HOtzd2YR93qEzU/1OedaytgtNMCQabvX/3PMu8vH/3pU3X1lKEXNz8hxJ8yzudIefCRLLgfSXuxREcvv4I3o4+ksg2Nqr5dQ4WZ7xmGczNyTlWq8RtGu9pJton87uf2ZecFjahXpEQCgkblX87zs06lySyjbVS8M/EhZn5+7fjnE30lsHWs22Y6Fqx7/IFOY+lquu6sizFUlWWZdM0ToKgohKKEKvW9hPxf7KY+BswgWNF0MeD2TpKLviDKYFN9lnPNP1u6pOY91FBZ2j/dxIEH64JNZoz8jlfFnt5rP7O47D/O1+9wXycPGPViOUfbBb/NhOt4bSMfzv+ceM9OH3QxL8awRITaQZzC7a2TR+8Tefr6dxXgsRtBm/c+WwyvgsTTuWz4AskP4ddCrUkno4JtepnvVrX5lSiysqmoH6qqiqmlmIjE5jQF8YZOfzhytemwTEy/cb0tVROSp9Pf6w5w61P4qnF8ncudI47CWKixxcTadXl5+OP64n8Y9n65AzP+fjiyX9Mwbv2a27eb8r/7vgSTePc0WBB/uPQFc4Zy4OSyMkh9qZdm3ypl7jc/zyzPpZ0396S2PduFH53AnMmUZWmruuu6/q+r+s6Z0PlGHvUfWfSv1pMxjBpRr5t7bCdVkK6Pp+oltIEh7fM24y1iT7uq59gBfxqBC9PF5q4r1hncMZpp3RdjWA9/fTB4S1xm/7xYIXTnTORIH2VU+jkHh57fcW0l1OQ/s4mvrb+M9JPJ9h0wQZ30uua+M8x8QUJXphDLHGwkvnZpktJN2xOhrGn7OQzp84W3bF9ST0//xNwKlHle6Bb7Gxg5obKMVar+6EJjhCD3yX9RjYhkRQbz9LtHHyZno9FOl4ik5zuHTzlDPOxuvlP2Rl0g53BeI+1T45AwToH0/cRI4F/a8EaOl+BwWrHGPyyOLXyn5qTPvgliuWT35Fi6fVz9FWUfqDBHpVuJT+T+aRFWOa1sceRqa5GaaNguelMgu2f1pprvEUXfGofzXlElVZL9/tdm6P0zKC4XjnXFiG2qPQn4zdRrOmCXzZ/WM38TqZ1AMwh0baxU7HhPKZ+BkWYo378sSGdv5M+LUrsh7Gd0Mkwv1mcNM73JXgXsXJjjLqLTBtG+hunLw9+/dONE8tBf3DaKlhtXUS6BTLf9om+GrypWIaxivnJcl5r6daeP3Jpbcc4OIFT+eo7QsoYU7wW/YnkKsvy+Xw6t1ycd8HCBrcWfIfagUp/SNQk+NVdqeYnftyTWalNCrXyyB70v33Ba+2FfWhVXc616TRG9c85mfiJdbWNp5P6+Oo5/89YZQYHXf/W+viyuGBtY9VLt0bi7oz3NE3oFRG8fSf/xO1k4tczdr859cy/nWBnMF7/8ds/cRc5zTKqoRLjY6zRzPvDzSzorFyrCbTJyjL2Z8oHsWUXHzvwxC7cQAWe4Mkuy9ptMvkRpy+MqYFpJMa5TJGUqHbwRnJkQULNZH6hnHwGh/9gDoNqKX13Y0WVf4851bYpfUGcLjfYjP5dJ/LR6ROtOk1UjdWaifrEmiXWen61EVWD0ARnHmU3FlX6FZB4P+7LiR/3ZDYQVf64ski2+s85OQdlRKKgRYp2xr9EfSbnP8pEMViTTLXkK62EVgvWM9+04yuVYKGDYs653/TtOPXMET3B2wzi3NpYDTrtNnNaL3FTTm3TN3h6zuNTlSYRbN2EptjHvon8DOdcvmAm88mvRvGaiZc/e+VusnFN1uY4z/cgbZKuhu0A6Z4wYfjXTMskkVXibDp9fk1i1zpljbuZ7Dr0IXLqabN1PqS1oH879k2bMNjEytXX2sv9Gwn2Pb9Zgg3l5B+8hSB+czm36fcl/cFJH8wzSOxxx27EyTbdDQY7CQhX0ZVFMti6832b3yYHyeQg1SCT02dykGp8ViZpCbJsTQYLWvbC2LWJ2/FNLL4M8o+PqoNP71mkYpUMmoj0JfZsLNvBuiXuyzEImffmCt7OYOY5A5+v6mI5D97vdbiEpWow2LpjoJpvqQIASBO0rGxQ1tpX+ddmXu7bvRyDzUxbpl8TP//0EOAbkHTKUTpJV8YRZ0GClUynNKoFnASjRjp9uc4wp9oX5J+9K7AFg8HWBwU7AACsR1p/2M/zx2/HjBS0PGli5jrfWGXz0f/PHE0cAafr4DdFZonTNO7MZNfhEqJqEKc3LNI5DpLJQapBJqfP5CDVIJPTZ7JgNfz/R5WeM3DkZDu2GpkqZyxjs0VFBbmKqEoEW8cuBQAAAPO5xFRX13XWP714hQPdu1IAAABwKi7hqC7+6dZR/QqKqqoqcUL0vfIvTtM0V+gAmTRNQz/R8MXR+PM70j6x2DSnx2kQ8da9eIeJTQJWVXXNNmH674SIaLAhJLquu9Ttp7nf73tX4Sg0TXO/320/MWrf8Wsiw0Pbtl3XST+5coMEN58oy1Lax49Nc3r8BrndbtIg5qrfoNgP1K7r7Fa8lyO46BQ+GmNM27byWb7wu1bnQBhjZDvtvStyCJx+UpblnrU5ALpB6rq+bD+Re3cGCOdNYoyp63qHyu1BToNcrcME28Qix6/5SrnE9N/VqOuaGS6f60z+5iDGSx1qBHOmUSZtCW63a112o2ma/n3UNJ6xv67r63SYYIMYY/wj1yHWJsaYoijqur7s1wdRdUK0CVoM1PvV5SiIOfo6w0Am1iOE1dHGmLZt7/e7tAa9xcFXmded3zHGvPsMyXzxlTWWpWmaYITt64CoOi3ig1yWJWODMeZ2u4m5HgTbK8RkXZYlukpGgrquZXS88sAA+TRNc7vd6rqmw4i4vPiIcxVH9athXW6Z7TKvSa7uhTGmaZqLvwGrqtKvPxkYdq3Rzogts3+5g1RVdbvdLt5JNP4EMSZwcdgXX3XetOY1MSLfGjFkXvBNi6XqhIiBqu97vueCeFOJopKvOisiHegqTn+gQRyqqtLzfXx9jDFioNLboF0cMfHqt+sF+wmWqrNhLTG6N1/8O69/KkkcgQt+1R2kS9jfkRfvIUaZ7mzL7Fyhg2HNvfLh+XxefD7dfnF401r0t8Z2mJ3qshuIqrMhndiZyukvFlEGcmjb9na72cBdFx8jq6qq61p/cS7eID7SYeQzS4x500KQS2xTAwAxnNgKQIOkYbYLIAGiCgAAAGABcFQHAAAAWABEFQAAnIHKY5dqOOuElmLs7XRdF1xvIQdlGtdf9JooxTnFqo4gOKoDAMAZeD6fR/CgX8ntzK6ny9/NOqjt5KDdZEJXNR0l30l/5bgJCbBUAQDASQhaqoKBZoxny7HJnLNWhTjXOkVL+mARjv7wbUjBa53ImTpIgc4weGFM6OjddcqytIt/zWuHGb9W9k8navz9fpc/m6bZXcgeB0QVAACcmdvtJqO+/WCMKYpCxIfdoEnOWmkl4RKKorjdbhLQMrEBuU1/u92svccqGBudX3YPk89FUThlaXUiZ231xEBl3kWVn7+kFzEU3CPh+XxqAaT3Mbvf7/pUsJ721vQiWScw7NXpAQAAPp/YAGc/SOyxvu/LspRtJ/q+r+taPhtj2rZ1LtHH/Qx1Gie9LStRrnmFttLlOtfWda1z8D84+Ut6pyybzLlre++2aMlHH/fTOAelLHsLFwefKgAAOAnpbfjsqefzWZal/lMnSDgJySl/mkz/KWdjAc/sEdndwTko+95YE1HCRSyYv7g92TwHpyzFciYfROQFM7ftIzOAXdfd73cnOi4BzAREFQAAXAtRVHo6L+cq61SUTm8liN1dWJN267bSpO97mf673W6J4P7B/EeJG5kBvN/vUqI9HmwfEWExvQgGnyoAALgaYsUR3eC7jQddws3Lqej5fCbS64PiwGTdueS4lVzaMdxeK5eIc5V80G5PfqFO/o6PlH+J7/ykXdf1wVj7lGUpO0nr9I6r1qXZe/4RAABgAfwBThyMzLvDk5/YOdW/vJTELcm8+zw5jkqCiAxJbB2M9EE5YjPUB21ZOnOjJhn7dw8qe19+/vq+fJ8qfY+Op5TjuRVsH90yjgcVWsJCQwAAwBURl/DYKfu/CTmtJ65KHLSO5H7K4JF09RIXxmpY13W+R3miAk6yoNC8Juz9BwAA8IZ1Eu9emJdr+ZxB007qLVXPaXVI+3XtnuFHg6gCCFsB4wAAIABJREFUAAAAWAAc1QEAAAAWAFEFAAAAsACIKgAAAIAFQFQBAAAALACiCgAAAGABEFUAAAAAC4CoAgAAAFgARBUAAADAAiCqAAAAABYAUQUAAACwAIgqAAAAgAVAVAEAAAAsAKIKAAAAYAEQVQAAAAALgKgCAAAAWABEFQAAAMAC/LN3BQCWoes6Y0xVVcFTXdc1TXOEyiQu6bquqqrgVVL5abfQNE3OhZnJghfGqm0TyId0sm2Y8GjGEmvGwebVdRusp3SYzJwBYCN6gFNQlmWsP9d1vXFXT1QmhlQyeFXbtnKqbdsJlcm8cE7+dV0HT9mb0kwrZSkmPJpR2Ic14WVblmVZlvI50arBImKJl2LfpwbwKTD9B+enqqrg6H5MrPnB8qF2iK7r7ve7VQmCMeZ2u+1Yq6ZpErpnKYISZ6nMpQF1zmVZ3u93v+csRdd1t9ttvfwBTgOiCs5PVVWfokvKsvSr+nw+xb7yWcgY7IzEGwiaNEeYgpyDtKfTjMGmBoDtQVTB+WmapigK+7mqqq7rihfOEFtVVc6poijsGCa52TxjRceOOGefz6c+EvSt0fXXNfHr6ZxKXxjLxNF5ifz9mvvZWkHgtJstaNVHo0WVqG15Iv6dOp0k/eDycRpNShmbid+2bdvayksRuk2cazM7j/XkE9vY7XaTptP5i2uXk49uK7+RbYLBLgTweaw5twiwHZk+Vdbk07Zt27byp/UXkT/rum7bVq6ys1fyp5yyskCfksSSla6MeZ8M0nkGK2nevY7Ez0ZKtMcTtyCn9C04ZyW34IXB/O2Nx5rIRGa7bCtJ4kQCXR+bfptHoxvEeSX6p3JemJI44eHkPF+nc+b4VDl3FKxA5n0FO49/yukDtjXkiNM5/ZuyD2uwbgCfDr0ZTsIoUaUHgMyxoa5rPcg5p5wh0Bm57UjpF+FnqEdWWz19oe93b0cmP399v34T6brZZLFM+pBi8O9d48xSOTrAKcjPfINHoxtElxJsq3xR5RMUr/0kUdWr3wbSqjqlVEB3Id2wiT4Q7FfB9k+0W/CmdGLnz+0XkQCsCtN/cEVikRecU3pxu444oFezW2JuW3Vd2xk9SZP26dEzgLZop56Oi5W1x/i3oK/1fbPKsnRmG4OVtJ/9+qSd1aqqkhdNXddS1u12c2aj9GSc2fbRGGN0g+j8g20VyySYbfvOso5cXdf1fS/2pOfzeb/fnXm0WFsl+oDfbn3fJ3p1fm0/0SMQYBqIKoAfZFDR7ibiSmIHG+tucrvdfC0SQ4YlO6QNjkYyBFp3Fn9A8ot2RE/wlL1W32DiLnSy+/0ezDyfpmlEB8jtj103EHs0lmmPJs0cGVR5LFIlvxSrrsz7skqnRN2LYn3gQ9dDABwKRBXADzIO+eZc6z39fD6duap8RFWYvKG6LEsrwnL0h5Z9sVM2Z/8Gg3n6ycYqg8LzcDfvEjOf2KOxftNzHk2MYzpQB/3lq6pK6yEtNGPhHoJmy2kcs+kANgBRBfCDniURZImTPVjXdcImlEBmAHPm/gSZAYyl9wc/R1Q5t5C4MBhF3S9Rlkyadyuan79fTzFxzWfw0ej5tUVGdL+tlhIcJv6ApuUQJPZY/YdSvQKO+M+98FZZZrJgWwF8GGmXK4BPwa4Oc+iTbrP9u0ew/lI4S5OM529u3r2h/co4pZik67GTiaQPerg7nsjBlVk6pfOnTelf2L4vEtRFawdqP//E6j/tnO60m/zptFJwDUG/zqMxnuku1sjWDhTL1rnr9Oq/2I2MWv2nW9V2/j70UPwSE33A6VfBtQt+3fwLgzfV46gOZ4feDCchNv3RjxFV/syRTeb4QunhMF9UJervZOKsUgwulLP4S70semj078JZZpiTf/8+XThq9Z9tOn3WyTkmqtZ4NMFbC7aVKAPz3ot0VZ1bHlTPTgPabDNX//mt6jSU3yaJCjiVD56y7RCsW3DlhH9TPaIKzk7RL7d5AsA5iLkoBWfZMudHiqKwnlJLkahAum6ZNR901cq899iqtAms8WhySrTO5rqgOU9zkRrKUkfHEb7rutvtJvOh03pI7FROp0okALgCiCqA1dHj3N51gSyaprnf7/r1WBRFXdfW0f52ux3z5UlnA9gRHNUB1qWqKlnrziD3Qdgln+KkbzdXsQmOqagAYF8QVQCrIw4oe9cCxtH3fV3XduGnfoJH1seD4RUAYD32nP6rqspfebt9fRbZJBUAAODi8Ovxn70Klkg8OvDPjtAPAAAA5oCFwuwoqowxZVmO3a0CAAAA4Jjs5lN1BAMVAAAAwFLs7FNlvO0UthdbRUFcCQAAgFkwmJodRZUEU/GPr1Qfu1OYD/0AAABgJgym5iLBP5umkdDDwbP0AwAAgJkwmJorxKlKyCkAAACApdhaVHVdVxSFCJ0ixKjc/PQS+1hvyyVmqiXqDgAAABBl65AKVVVZ8+BMO6HvIyV71rZta3fmkq1Gu67zo4y+X/gw5rcx333/NadKAAAAcFl2ngHtXlRVlR+zSvY6lc+2/s4Wp7L7qTVZ3e/32A6jRVH8+fqWz/9+I6oAAABGg0+V2T2kwvP5lG2qxJJkN4HPwdlG3v9Te1PpCUGHUXOO9BgAAAAfRJXZMaK6TMnpByCmpskx1ruuc7YR1VN+abeqvu//+/1wDmK1AgAAgHz2FFWOBpK5uURAqfUIGqt6RBUAAABks+c2NUHn8cmKyr/QEW0J+nf+fH1bLysAAACAHHawVOmZOOuf3nXd/X6v63pytlVVWe91MzTf5xB2q3r8NubbGMOSQAAAABhka7ey2O40lvz6OJ7pxpiiKOwSP/05je9bJ/5Vvx7mz5cxOFcBwLaMjdgHsCWxYRpHdbNvnKrFadvWKjaJp5B5Ydin6uW9XhQP1BUAbAmDExwTFH+aHXSlaJ1RgalGMdbVPSGu//v9+PUwEhfUMA8IAJvAL344LInOSb81O8ap6rpORFX1YpdqmLju1i3jBFzAZAUA68HgBIcFUZVm/yYQdSVRQJum2SWewmAjvExWRqxWf0KaCqUFAIvA4ASHBVGV5kBNYPer2VhXjeoH1r/KB1EFAIuwxuBUFA8cGGA+iKo0ezaBRD2QDWRkKvCwlqo0TA4CwIIgquCwIKrS7Bb80xhjN6WxS/bS0RbWowgRT/woikdRFEXh7mwj/Pf7If9Wqy8AwCEYFRHwgPkfocQ5fFZtr8BuoqppmrIsbYewmx/v0kX6EPHEX33/JQlEXf16mF+PgIEKdQUA52bUhq0TFn3fbrf0oDBhyEjPh0z7bd80zfaxBgbjPg42+I5LxM7Kbnv/WVYKrLABL1v6l3mpKxt5ASF1HIiqAmvANMdBuN1umXGehUER1rbtzCp9EM2LvStyHnYTVbKrjERFl91pbISF7SsTC/6Zn0Pffyl19ZBFglZdaY2Fx9X2MP7BsqDUc7BDtTNm23mJ2FgeS2CP2/VMdn7DDhyxQi0iwgYrabMNVkYvqwpGRkzfo56isWn0/7H7dUr3a+t/9mulc6uqapStEYYJznxtg2ipsiz7vpdeXtf19tVYtRFkb2b9b72yIMi+nRxOydqdao38jVnl5RN7b8v4Yne1l4P2T+e4xo4LNk3btvZae9w5KH8OZt6/t20wvf5QvrBDlS5UD6NSbSeBfBi8R/nfbn0raWRMtGlsIwdLl8Q2/7IsJb394F/oJ84k0Tl52fZ9v+f0n7Y6rrp9zS683Nh/TFbmZa+yVitMVgAwitjimLUvH7tsUOwr9pVeFIW2nejjYunR18rchTXb3O93Of58Pu00n7UPdV1XFIUsG++6TvvpOoUKjh1IVyY2SWKNSVKTruuez6e+BT+9TmCtWemc5dbEbiRnb7eb0w5N0wyWHkOCQeoL7fgbqx5MYzdRJWEUnGcpXnXncJ2zHld6QtAYEwtzBQCQZk5MhC1DKtgfyXYCy6JNLHVdW80kOFNmWlRZQSDCwh8mrDjwCw1WQG+YFhMW+me/FVU5t2BzsxcmqnG/321ldCZOO8igmS49hqM49edYDWEaO4iqTi1Y8IX2ORSVRvuzC5isAODcVFUl+2QkXumjhnPJ0KhpMh8ZUBKFOrartm2bppHxSOuMBINpfFcqLYOWJb8Bpel0rc431B6EHURVVVVt28qPCcc/bq/HPN9RPbsgTFYAcHJEANlXaGzgz591cqa9ghdWVaWFUbBQxyKljVvirz3osj04WaY96BO1HSwlp/T8nEVo4pC+AfvEqZKnK0ZXzS6VMSPjVM0r6EtcrPr+CwPVRyO9l5cUQBBrnnEG/ufzqXWPY3ZyZsGc6TCdSbr0mNqw5i5JY39O548+kkNCujkJxHspM3M/K/lgnZ8GSzcvAepnpRPrWjkTmjCX9X3ho7RtW3tsX40dG4GFgRuwxvPV3yC7Jgiuw9ovjXU67Vqr/xzqutYxC/QiOL2mL/bdca41r4V+zpfOOd62rX9hsBmNt/rPqYxRq/+kiP59eZ0WgnJH/fvqP0cp+nWo69oW5yzcM++r/yy2JjpzXahzL7HVf37Ls/pvWXZrAttjyne2r8ku/UBmAI35NgZRtS6LP195kekV3fps27aTu3FwtbN9e06ucLCI/Dz1/caycs4GvV50GmdgSOTsN6YzpAXrLK8Xv87pcvNZ+6WxRv4riaoEInTs59ipwWsHM8z5bLHxF3Se6crEati/y6NEbScTyyR2cLBEP8HYzoaoSrNbEyT64sbs1Q/0Ow5RtR6LP1+xsMpnX1TN+W3giCr9U3X+27mPzGinL7F1cCrg56M1SnA2webgn9Utlq5k5i34VQpeO/lJDbbbTNbIf3tRdVjmNK+j1/2vxmcxYYIo0Xprfy8+gj03VL746gN/eTN7BX4E1pVKfBccifB8PpdytLKeE33fz/+y2FrZX9hOKX56HTVnMCvtsWFXaWlscfasvda6idic5ayVdLbBE2dtnYPrToJ11u4pcB0yF/oFqaqqruvb7VYURVEUdV1/9EDmLxeDuewi5QTHDLsX+zaCgHPVeqz0fPV+ABbtHiEmK/EvsbGMY5NockpbqsSUK39KJjMrbAtyjgR/p7be9me6AnLENy/ZrBLvFidstHOtPwGq31R+mzs35dTZN575dZ5mrFr7pXGElxJAkETnpN/2R/Cp2l3k7Ss3xa3KGCPGeUTV4kx7lM6o3L62jJA/Y3JET2rH+pWvToJ9L6EPphFTMzG5Zn1NYtXWVZIjzr23r5Uo+tq0uJEGDM4k9uqlEauJU+dEDfuQgMtn7ffDZu8fgLEkOif9tt9RVPXqDajZvhpH6AfW3QFRtTgTnq+VAvZaPfrqMdvxSDCetca8rFBW0zjKzLwMUXoa0VYjrXvqCLH0toh8//eEqAo6QvWR30tOs+SLG+dy3Yx+VrGrgqU4rT0KRBVcFkRVGprgWP2ADZgXZ5qosh+s5cMf1/1ROT1mO0f8gT8mqhK3FiQ2peV7iE8TVbZiQTmodY9uq/Z9y1u/Vokb9ItO36x/d+k6j2XaVcfJH2Ay6TfSljU5JntO/5Uhtq/JofoBompx5jxfbWHKSWw8CRVTD0Gx4siLwaKD36AyEnXGZu44qqd1Vf7qP31rjtVZN0WmqNLmLnvQqXOrwrI41Q7eWqLOY0FUwWVBVKXZswmcOYvB9/tKHLYfIKoWYebzzVEegvOrYANRNeFGxjoVxURV/z596XtKBVMmqqGP+FOlY28h9tS0LMOnCmACiKo0O+z9Z3FWcjZNMyeifwxZOnv8Va9FIZEUfss+NvY4+y4fgZxu+Xw+Y8svMhncfGM+C34R9KJ0iWLg7HrmlGXF0/P51Hfq3LXeNzdnI7ZRjaY3d7Ob7+ZfDgCQZs84VUGWDRsjQTj0Hk+HRTYE7Pvej18FeyEDsO/H4zNWu9uUNhDUhJ5fREhUw48mJaqi6zrZWSynGhILyn6nrEyRcm8v7H1JQXJW7zhm1F3r0F82t07hVNu/nTTym83W2SkXAGAR9rRUOawUhU/iAZrXsLFGESthTVOEA92L/DF7wqakdV1L/tso/lhxVlWIWMn5mjRNI5pJZ1WWpVyYLshXk4IjufwEfd+3beuXa4zJMRDaDWWdaz/rnQAAB2c3S5VYjzS3282+l5eievFxigp2x0qEHLk/oYM1TePYwMbKstikfrDCfnHGmHpSPGgJKq2PlCpEdbAgx/HcvxH5kJ7L88s12bcQvNavCQDAHIqPfq0UhVt/8bEoy9IaqOxx50gik33RzlXGmL7/cixVOFeNYvLzLYpCtILOISae5qj2jd3+ZDZNfmysmlXMuWp+Nfa61rL2S+NoLyUAS6Jz0m/NEURV+s2bQPSTrr+MguJEdb/f+74XIWU3awv6TxyzHxTFwzpXIarmMO35Su9q29YaO8XHeREtAp8Oosph8mt8A3acprBFn2mqBFE1wAorCnMJzhGMvdAe9KME2aDYMqsSy23fRhgFQRYmMOH5OpvSBPsbXJm1e8Jn9bRpr/FtcMaFBPI1X6RQo8Km6CMrsXF8x3MMpuuxp0/V/X539pFwXFNjNE3Te5rM8SMRV5iqqvq+77quT8rn2Coqn/E3Ch+JdqUSJ6GyLGdGTAA4H1VVOTHGMl/jR6NpmkW+4IM+hWuw0jIvmMBuq/9ktZSejxPnlclmUn/51cyoP2kdthl6HlAgctXa+HN8LLwHCPJ8PrWGEO8LeY3b/7UTm++JYUNm+JELnQ86Q+es8521hSZqHrxQF6SrmkjsIJ4nsbP6FsxC7SCrcQ8yYMGBQirsyGG7Y1E8jPktBjLiVwFcnL2iqyR+v5Vleb/ftdroQjHA7ve7xH2V/63yaJpGThljiqIQL0ajQrM6iY0xkokUJGvGJX8bLdYuV4qFRAleKDWRmQ27FP1+v9vqSWJbw1jOweNW9Nhy5Ua0kJrcDudz2/podpv+k36vv3XSISZ3C//C/AXqh53p6/svCbAuiurf7y/5J2f/+/2Qf7vWEQCui7zDb7ebRJ11VIXVDXVdy7qiruv0XJjIFJtGpg4lE+25YbO1mUihMk0veYqE6rpOlpjItcFRoGkaWdgrhcbuS/xMjIpDa4Ym2vwJk1gy2ybBdrCxcGVqNd0O5j2mCezLbpaqqqoklJ/+MTFn4rl6BfcTRvWww1qqTJ6BSusq5gQBzsoBv93it2peE3+iiuwbNShZfJtW94oqIu9wm6eVMhZHsjgyTtLreIc2UK1z1f1+l8W8sZl9/Svdfh4UTDnmIr0Dgdyvs82Aefmu2Lvz28FvWETVQdhz+s96kZslFuI6JtD5G7EdDe1cRbB1ADgC9pVrlUFRFGLmybnWvM8SWtVip/BiQ0Ps2hxtYe1AomlydpncgGA72Om/9BDp2whhL/b3qVqwN9hdLMzIUNHByb5Dma9izlX6lysCK8ZBJnMBzofvIp0/FSWmGsfUZLwYhEHXKPsrWl8rrl05Uxba+1usVjkVXpVgO+jGTOyahaI6Dvv4VFVVpce5pmnSu8AGsRPeOtu+7yVKyqgvSTDaxKjKrI3jXJUARyuHWSFHACLs3a8PhLPsznGWHbxQT4fZX8XWVJOQC8FpPmuFkuMxQWYvnDxJErzHaTYCp852UvL5fOobTOSgU8K+7GCpEjml5+Zkwvh2uy1itZrQt45vqTLjV/8ReQEA1qbve2dlz6hZAsezVsYFUUh25ktCGPov5O61gay+Viqgpyx8XaXzNxHHrzQydehHWHDsZPnoaRbzGn1044jKTIROOIKxDcz229SIeIoVukuQ+0+MrO8Hr9LEzFSoK4CP4ONeStrKMvly51p9MOEAHvPKHfQZX8qd10EHRBhLrB20a3/wTmWVwIQSJ8A2NWkOJ6omd8fJfFY/0Nst9/3XKHWFqAL4CD7rpQSajSWOeU0XbjZuIqrS7O+ofgQ+YvpPEAkl9dXe6yKzTMSNXdQVE4IAAKuyi28TDlXHYWtRlbDl2mC4G1fJHFU/JbDKqSj+yixCrgMA7M7Ga/FY+ncodlj9J853zp5H4t93sshSG2CFlJJZD+eDMebXw+hQ7AAAALA4+8yA2sC7mu29qYQzTQPLhKDMA6oPRvtg/XlXVigtgKNxppcSnAx8qtLs3AQrrb8YRSwy5Kd3Du3DrqxWv435/vNlfj2MqCtEFcDRYHCCw4KoSkMTXKsfOOpKPqGuAA4FewDAkUFUJaAJrtsP/vv9+PUwwWWDAAAAo7jsYKqhCa7eD6yX1a8HugoAACZy8cFU2GfvPzgOIqTEZFUUD5kf1CsHAQAAIAdElTHGFCH2rtSm/Pky4sBu/sYURVcBAACMgIjqxnz+Qr9FEEX1mgr8/sNMIAAAwBgQVRBY9/fHYKYCAAAYB9N/AAAAAAuAqIIUeFYBAABkwvQfhPn1MOYhHus/Rwi4AAAAkABLFYT58/W2JFAHXIDNoMEBAD4IQnWddu+/pfjv98MQGnQSevvF2JH05cS7B4BPgeCfhuk/gX4wyJ+vH3XFFoGDxJTTKwCYMRGR5FzY919FgZwCAPgYEFUA00nqJyMS6s+X+fX4mU61AcD0hXqXa62i/vv9sFpWWwpHmbsAAGAzEFUwgDVNyegOQUToWC1lzLfYmcxrC6Bfj59ZPLuPtZp2/pteVJSIMItOr+UU6goA4FDgqA7jwHXavBrB7udj26Tvv/r+SxSSeTn72yNWANkVAHo1QFCziqINbiIk6eX/4EPhSQEAbMwlLFVd11VVtXctzkDMZHJuQlN11s70/TJQ/W0WMUel87SGqH+/v2z8epkolPx/LFvfL0vh42HUJkJGG8Ae41y1AABgJU7uq9913e12q+v6fr+3bRuUVixYyMQ698jQfgWPdW8670edaJniNIvPhIYKyiBHqP37/VUUj5fMsnrLOLOQuv7p/AEA5sBgak5vqeq6TrRUVVXYq2AQf/2dMUaMQNYcpSfydEpf9EyuRlDxBLZo/HI/mJd7u1G7Ymdujz0zAAQAAHy2rvR1cVVVz+ezLMumabSEqqrKOZLIBNIsqB6OhmOL0lYobRmy/Pv9dZBIE9b/3Y9rJdWW+tvJypcRK7WoMGiZAwAIwmBqPtpS5SukoijKsmzbVmb95Ol2Xdc0TUxRwRwSc16fiHaNMko7fso96pANznHzuh2xY/16fFu1ZN4dxdTsoXlfoggAAAN8pKhqmuZ+vzsHu66z/1dVdb/fRUg1TSMHYSn+WmUen72+LBZsU9SVDoIgCbQz2TEDTMT0n+P/bgM3aA93PctpQzz4bvg2T+xYAAAOnyqqBC2tHOVU17UceT6fshFNXddN0wQzjO1U44NtU7C2jV+P7/5758osiIgDGzJKc0wVZUnMP/77/SXPyLkpx7Kl5gH/toMZE9gdrywAuDgfKaqCdF1XlqU+8nw+Ze5v8Fqk0lisB/enTI05OBNefoKcsAjm8ErLIXCnGbZGHRDLWV34+jHifiCyAwBck/OIqjkELVUorUGsovq48dKKwpwJrN2d0NfD0Y7y2Y9E6khnr/XcD8LgXocAACfjPKJKgiboI47hKgH6aTK/HsYGn/yIgTNH/+XYn86ntBJ3LYscx4Z+1V5ZOiDFx0lwAIBMTiWqEi5WabBUTWZUJKSj4c9kZc76nQ971wmxqJcNGqWrbKMFt382nmXLgg8WAJyMU4kqo3akeT6fbdtmXot+moa/DPCwg6IO0aRtLbHx/nyGqDUICtCEQSvtyja4zBAA4OCcR1QZY9q2vd1u8rmu6/zAVFiqZvIRZioboukjarsZo+SjtUXZqKdOPk4cLI1eXWi8qGASp16LLbQUAHwcHyyqJKqCPlJVVd/3E7ajQT/NRztXmcM4JmsDlYRosmhNoLXC1lX8THKmC3ULO8n8hQLBHPzJ2Zy6ocYAYC8+WFTFIHL6Lrw2QtlaTuWMoDrulG9i0VzToSoHq4pymshfP6gJzQm+ySZvH+usRZqJs5kyCzUGADM5oaiaANN/y5IwUWxDeqOVYJWQU5mMeqCOhHU6Riy4aMzRbZAcyxbKCQDWA1FlDPppOfzVc6uOYYlISM6yxLSByjDxtxCZs4GjUOarNx8smyBt2cr0fyeqFgDMB1EFi6FHzbX3WpYRMX8HFQxR+6J9sJxnIb7tQbd3jaOW7HG1BWE0OH4wXFYifwCAaSCqjGH6bzXGhoucz9umhP1X/42cOijp5xJUWsKgSSlo2TLxWcWVjKnMMwJcEESVMein2QQmfR4/0SCDq+tnkhhW/U0Jmdfbl5z212kGRXDM8uT3hNjqwli4LKI5AMBMEFWwPMEQBkvlPGrWDw5IWmYllhHEBHTO8s/ghWaMK1W+0hrcrnvwcvo2wIeCqILl0SEMfu0x+UZ4z1OyyEzuIssMc4pA9wNcEESVMfhUrcki40o6RAJcDelUVmP99/sRtG+l+14iqNXYoAzYlgBAQFQZg35ageDOxInRaHBYCv763z0gFmyDH3rUfhi0SmZ2Ep2h4wg4czpvFInIEQBwfBBVsC45+5loQtGDjDOexaaBiuLBxN+lsAEa/G4W7CTB3hj8AWDRIRvMGB+saXoo4V8PAMfnf3tXAC7Br4cdjR7GGFm3ZY+8f/iLdUuXoI5yMBbQiNAJ12HwWQ9GvQoymEz3xldYURPrvZa0NrJnkVAAJwBLlTH4VK2Dljv+zoDelroDv85lmMwJlMA84KVIqGrdE0aJ71FGJmct6lL+VUR4B/hEEFXGoJ82wU7TJETPtIHk3++vt5ify0dygKMwqJgzDZY5s9LO1PN8ZZOpt2JbIo7KP99bEQAWBFEFm/LrIXFBB7YKCe4okhj/WMQOi+NsH+ngR2yPpDGxZN7ZcT8n5quloAhbJDeAy4KogtX5K4Yej/RAZXFEEu7nMMhYx6nBfXJiB2X/HFkkaLfrNqFFFemp7dgM+NqgfgDWA1EF2+FrI8fFCuBQ/Pv9JRsumbz9c0xcG02Y2naWwaan8zzL2ZsB7ARxInflAAAfp0lEQVTfL7QgfASIKtgObbIKJtDb6EriUWv6eOfC2rzZwyLdOIgTK2FOX/VnFf29mwYNYBNinOafTVee7ymcGEQV7A/REGAv0uGsTHLqORbgyjlofyekyZcazhx65k5QjppJ+y+OwhF5QQNboj7ILDgTiCpjCKmwOXqg8kMKOcGyCZEAZ0JHK/33e3hRoe/PPuiSOJTbm+P8fI+uBX3CciY3l4pYgZKDNUBUGYN+OgC+csJ8BasSFOvzFXzwl4B0ZrFs/XoYEUbGmJzwH74/u0R9k8lHyc1XGzHFMBixPbjP5iKb9qTXQqaZEGACYBcQVQAAsxi1F5NeNjgff6dCBxv5PV/NjFq0GCO9FvLdYEaMUzgPiCo4HMz3wYcy1rwaTB+0chkvcG56y0JNTCQ5IiZW0DTS2uvdYLZAjNPNYN4Q0iCqAAAmMmr3m1GSa+3pb53/v99f8y1n82OHGmM2CACxWUFwTRBVAABzGWvaCabXtiI38ZjwDWliJq7XKsK3vZ4cx/bJKmTQwBOzWi1uGWL3BVgVRBUAwLq4EipPIemrrCVpwd0F9EpbyVl2PddFDDq2b8Z6em5UMoA0/9u7AkviR0aoqqooiqqquq7bo0aQ4r/fD5b4wUewbEddw/SyODmGJTNeacmFNq5ErGH3FXCfCC12EM5jqaqqyjlSFEVZlm3bdl13u92ImwAAlyUhktb7bRMs1G6hmEgTI7gbz2Z7JtoSDWsVIcIZRFXTNPf73Tkopin5v6qq+/3eNE3TNFtXDkKwvg8+he376uISZ06G1qSkA2JZPZTTODHHcGfyMX2heYmY2G48wcv9U/7B/N14cpyxMvNnqvHEnERUCVpaOfN9dV0zAwgAVyMtXNKqyPG4mhZea5RjuB+/dKYhKhaK3Zc+mKBgEc4gqoJ0XVeWpT7yfD73qgwAwCCDhp/JNqd8e5vIJl3QX5HxeIwKIeGwqgNlzPaTGYp994jtK9muMIltz2lF1SiCe/8FwTELAMBICIbHrJ2YgxBHagJY2o7DaUWVv+LPMVxpkEoAcGRGGbEk8TTL0CibloRgyB/FM7fxMWbKVKPc76/H3KgTE6w7kw1CS13ouZpFs8XHa23OLKoSLlYOQUsVSgsAPgJfP31osJKcaufsG63d6gXfAJbeEjFmMEuIFT+9b0DCDnd6ThWnSiMRFqyWej6fiaV/fYgtagkAsBC+Hejf7y/5t2U15gdMcjy38gPa2Qv/fBljvvv+S6/d6/svOegf8fWNn97B32faT+/nb5P9+fp7dzn5Jw7m4F+YmRXhr8ZyWkuVMaZt29vtJp/ruvYDWVmwVAEApJljWZk8JTd4obX96N11PgjtmmaW84jCJLYX5xFVfhiqqqr6vu+6LiGnBPQTAHwoO870WVvLfGOYfxfBlYa+65iNvBCUX//9flibkD0SrO3aysP3W5ItHR3XtKXcm9jicC/OI6piDCoqg6UKAGA/MnVhOpk1+fx6fI/KVjN2Gd1kg5Cu7SgDWzqwe7o+Xjx6DFrLc35RlQP6CQA+jh13JvjrvWQWs5Pp20kM8LFQ7M6G0L8ef9P/TTS0lfXYgFWTDULB7avnlGgjzifq42+PjUFrcRBVxmCpAoATsf2EoFOi/Jmp+SabqewRtr2C44CoMgb9BACwBBLLQCbgPtFtPJPBWcKZrlH5sRgGCwpGjojV3+YmH5xkY3dOvCaIKgCAk7C9zcYpsf+O+owPXpufLGe3nL4PzBL6FUtrgtjZ2CxhzpFgNQbznzxP51xoZ/0+YveeTwRRZQzTfwAAkI0fYio2O7mNzPUXRcJeIKqMQT8BAHwyM7emGcUaiwqVGSkc4X1afTAybQ+iCgAAYDR/bUIPL/bV+zLDCdNtOpR5jutSbFHkGuBKlQBRBQAAa7FXbNJEucFTsVmzXWbW9PSisz22Dl6a07b6wjWqCg6IKmPwqQIAWIHtB/Kgf/rYHOznzKycBXTGmMwlkLEo8Dkl/vf7EVtraevjZxWPX+UsEhy3HTWGKwuiyhj0EwDAamzsOp0oLnhqUMSMmllLb5szB9nWRh/582V+PaJbPicu9PEWCQ7ECCVqaIz/7V0BAAAAgDOAqAIAgMU4penil2foWfs2pwW4ys8ts/6ZybRb/cVh+g8AAJbnszyj/dpu7JZ+nDx9N3nIB0uVMcYUIfauFAAAXJpfj5/gVb6pLMF8RRWjKB6vYFqfpJi3BEuVMTiqAwAsx2eZN/zabmZjCzTUe8irP1/m3++vaW7vazwF/NMHwVIFAAAAsACIKgAAgHVZ1jEcDguiCgAAAGABEFUAAAAAC4CjOgAAwDCTHdjXW443Nn3ibI5je3ALQtBgqTKGkAoAABBiWlADzWfF65qACrKQ3OnwGmCpMoaQCgAAEMIGNRjlQu7v6zzBrvP3kkfULKRrlS4icdZW1VY7uC+1hHXQOwlK6X3/0z5FURhzdfMVlioAAACABUBUAQAAACwAogoAAABgARBVAAAAAAuAqAIAAABYgAuJKqIkAAAAwHpcRVRVVbV3FQAAAODMnF9UNU1TFMXz+dy7IgAAAKM52i7Lf45VnWNxCVHV931d13tXBAAAAM4MEdWNGeNuRex1AIBDsdI+MItku0bdtr/f4KnTb78zDUSVMUglAACAEIinUSCqjIlYqlBaAABHZsKGeptlu0bddrxfnUZ/RnI5IKqMQT8BAADAbBBVxmCpAgAAgNmcf/VfDn2IvSu1AMQ79aFNfGgTBxrEhzZxoEEgyFVElQRWiJ0tQmxZPQAAAPh0mP4zhpk+AAAAmM1VLFX5LGKjOk4m8znOvRykQcyRbucgbXKcezlIg5gj3c6Z2oQGOXImYLBUCU5/kj8xXwEAAEA+iCpj3vVTURTIKQAAABgL038AAAApjral8WZkxvb880UU0B+wyjCXDAAAsAAoCkQVAAAAwAIw/QcAAACwAIgqAAAAgAVAVAEAAAAsAKIKAAAAYAEQVQAAAAALgKgCAAAAWABEFQAAAMACIKoAAAAAFgBRBQAAALAAVxFVTdNUVVVVVdd1e9cFAAAATsgltqnpuu52u/V9bz/sXSMAAAA4G1exVNV1bYypqqosS4xVAAAAsDiXsFQJXdc1TSMfdq4KAAAAnI5/9q7A1jyfT+dIURS71AQAAOBMXMdME2NnS5VYj6zQ2WBuToxV8r9QFBcy1wEAwFjGDhPXHFauedcOO/tU3W43Y0zbtn3ft21r1rEbydI/+RwUbUWIxasBAAAAJ2ZPXdk0Tdd1jsopiqJtW6uBlkKLJOeWEdcAAJAAS1UO17xrh6us/hNLWN/3wUeOpQoAAABmsqeoqqrq+XxqS5V8XtxMZYuLnepDrFEHAAAAOCs7i6q6rm+3W1EUVVUVRXG73cSzahpd10nYdO2HLjRNY01QvlsVlioAAACYyf4zoNqtyhdDoxB/LGOMiDNtmkq4ajENDAAACfCpyuGad+1wniaQ6Ayiz0bFTYgZpU7TMgAAMIcDiipn5DrCgIWoMruIKtmAz5qU/ATzq+TbpYqiKMtSAmKx+g8AAPI5oKjauKAcDlWZvdghonpVVVbxzPGgCiKKra5rZ6avrmtrvqqqyo/jkJk/PQYAAACC7Kkrg2v9mqaZ7FklWaVjsovq0nfN9B8AwKez6nQYlqocDlWZvdht77/uhSOh7ve7rOCbkKGJKCrtbtV1XVmWTgL6AQDARyOv8ZON60dznDpafQ7IPqJKzEXy2XGrKstyWpyqruuez6d95DLfp52rrFuV3w+Cxiq6CwAA7MjRlOLR6nNAdp7+8y1Va5foKzb6BwD8v71zS3IVBMIwVp19qStTV5ZkZZ6HrqE6fZN4iUb+72HKQWia5tZBRHAPDhrPz338F7+9/v35y8sUk2m64JEKpt9zKNhTBQAA9wBO1ReAUxVw2p4qQq9UvV6vIz6onDMyz1tHOwAAAPA1sDnprpz8+K/ve9rnRFuppmlq2zZ+fW8d8WHrZhK0cgAA+C12XywhgTRNlEsuVGN7NKxUXY0zv/1HL+I9n09yd8ZxnOeZzuc8IiN6qXAYBu204YPKAACwEXw+9QagEjdyplOV6bou+1JHrFTx0z7pyAYRwfygssm+igEAwG3IP0dv9ru0qpH/rpX4Nc50qsiXyu4OXRyxUpXlN02jD1tPzkoVlq8AAABg5AflnLlRveu6tm3pfPNhGPKBVUe8/Ucyg8fSOhAdCQAAAADlXGhbmfnVmr0k5xPVNdhbBwAAe3HuiPpbRypcdqN6LBYb1QMusaeKoI3kR7z6lw9bJ/SRCtg+BQAAoATMFCDgHL+Sf6ZmnmdaScq7qb6s0pWd66zblZUEAIBMJStV69Zydo+Glaqrcc6eKjqeKn+SL6XUtu0wDGmPx3/mctc4jtM00bU+XPSae6pIq6zbNZUEAAAAAHGOX8n9WXoHcBc18oqXltawLysHylyNRp2626w6iQ4AAL4DVqrK1cBK1f04YaVKLCPtuDOdvhvoHcoQZHSpRSDdLrMXFaxdoSkDALagf8KdpQkAv8uFNqpvZxxHvQk903Wdt6/wakdSiS2QJXshr7NrErs4AfhFLnLqY7UDiPm+FJ8F1pmlWnuexa2cqgD6Og0diHXNE9U94Xqk00Me//f0znORoRkAsBffnJh/awDZ0TLxUL/aLEfbEx6b4IQnoPTqH21LTynR/vH8b0opWG0qoQkf61Luwgu5QtcNnCovfn4sqJOcXqjTFQDggjSXf8QW9NzCTr1L318txEu40fJaLA+J764Ta8rfMaMt8T01MOynEzeqB3c3qqTrlR/+SRd8X9en3sy+8N6is44HOBHi+VgiznpdP+HLvev6cxUAmSvPPb/rVPF5PXCt9vLV4FTpkCs37K9xzpEK35za80t/TdO0bWu+G/j9diA6jHc3UEwn99ys+etvC3KVvpOpN+IAAMA3iZ0tcHvqqnJ6PVAEBqs72nEpD/FWngL1RMK9cj86rzj3fSXHIUeXCyEIqTkkB8ZjJv8ht2Neham0PseV3dTQXDXfPoZ/eWRbnL/MaHAiU21OlcnqddF4edbrEnP4kG6X6tBZ55WqvZaszNKZd4Nbu2gihKM9g70I2urGZryxoZ7VicxbOnCXbrhCSMOeQHnJV+sWF7PxnzzExtQDcuH88mmJVk9zhXdjs1dFLW//cQrf/gsk5AhmZDM5T0Ih8zxT++MXW8qVEWJzjnGh1uWSfLXNcF1qwClvhOBogrb6zWasm8RBueuBAq1xHWI8hxmroi6nyjsddLYQcRY7g9lhyoe8fYdm7vEETtsu/lbJeHH0sHKQ47idFQUv8bALxTYOn5YCnMiJv0PO/Qn0aaPlGubfk1do9p4Z0RNvSY1OlQ4PJh7e7nl4MNZoJ0Z4Nrrzf4dG/QBNji+YCsYjYYrFiy+Pzlr/s0bYEicpwFNbG1knCdS4yHxTIebYchG2q1RSusK2t33QOGVBMVljo6fe9kzFxRZRQaVcZzi9PjU+AW3en/uuaxDz+/Pjxn/mfTW88mb9dXhjbQWbw61aph1iK5lOwIq7gRpc4b2q6SO1Y0wjJ0tts0ZMUeJCCEl7W+N+eMZZbb11CXXk3WutCXf5eGqv7mJxhNUjamyoxUz5v/GAFo//i/UVW3tRPld4u7Xj3IO7ekTCSHLOkQq/i/AwchsyZzj9e+U6IYuzsiiFjp+v+QWPI8YLYTHtBvG0XilEXiKO1s2L4AXGuZshPFUuo1eu+F/t/WiDa7ObYs3a0UJ2t8aKdrg9Ly1tX511W82YNi/RUF8s6qP7Du9ii7nHLHoA4m6cF29mq+tUlHevOg1KkSkfW5KqncKQoFxBXjpVerd2UApz7E1WIxe5By3BU7JmavQr4/FCs/gT5Lco7wOFXpeXVmQqfuzqODqmluYZX8jnIfz3k54dzWhcbFAoM1osYQvCFRYXOpynKomWHDN6ypQUs7CnxF0yaA867Y6stoYph/+bPlmpKmzbhXLikCCa18hXl66wL2vXxFRDKCAwB5CPBpm4mHp22GhtraoptmREXcxoMffgrrj49clxF7BSJdFtxYzwu5SPRN7cpgcRc9r2EHeDQTDf4nHMoTAXyvsBZ/oZJerF0yrPblZrS2ZxFr0cb9AXBREX4t8szYtWaA3PhzM19KKJIphmSe/juLCGN6MEE8AueAqXWCNwE/dSbHexiwSNfEdi53Wxus0xXLeWoxtPIQcZc4tYs0uCQuBUSbyR8cZtK5g5eARv8lgMF/8urpEka+BLvhcSZJqWpnldOhE/mDJjb8bzxmJnyMOcznmIp3ZSTXff2tQGMXPR3pIQ4nkJZshiZyyZUUp6tC5UkCpYYFhsDLFinrUXf8AsyjEDF2XqxuZ5wyW565gl5Qpc2K9R3jYCFn/Gx2m9wW2LWLCFGp0qsw+nir1ysfKUA4NoPJAu9DxRvoIVdPhF3y5Ia07zXkJPPW/YMg0VO2GenrmYsQK6FOKHOI+w2JJFpS9O28KJ9MIXnS1PvggXrcjLxat9PaNoR4ezZYZePWgESuqYH7ngJTkKt1ur4bXJXcobe3IlnvSKQXt3x+JTp7Ycs5uXFDkwXaFLHd8qVKNmbnWkAp2Y0HUd/14yMY5j84e+mwfx5h0R4cYhnmeQnCUWL05ig13z3ifFv3nubMLhlf9b4ijwaI3jPHl+oVBbJNdyeJJAyYahA3Ux43yDUvBAL69YrFC7PFpSBde18KkCMXr5TdhZ5Cuy1vG5WB2oxQaidLWK+KY+uixCSLJauNcOhW5mRkI9rr8ZTf+r7RCXIshdN2kvdy3Ks5jOMRAShJSUwitXEFKSeyDfTMhtaI4GSbWfxd975bmDlO61Ub1pmsfjkVLq+16PufnLyjrVnYxwFk3BxnDd98yfUIVrG3olTOeYdQuiefron+/B2BEU5PqtSxttsbw8fhzNq7VYTuHCodkYeKE+jV9ekBV3C4vjrd59tGLB0ZI9a4sfGEFf88yoFTN7ve5iIq03Vgj54noxo0VtzZAg2uKIVCi2pI9oU2gJOtBL7sWPa0fI0QmvP9wdzX1Wqp7PZ9u2tFLVtu04jiKC6VERjcWh2t6SxvldFbs7Odxb6ljspbqrm3e9aPy3GtdHxBf6m+rN75QofxHM4Z7f9crlRTPlBDmKZhDXaVKNzctFRNDRvP6ei6lLJO4GpTA15wkD4+gseFlycYIhi4fHfVMnNLVah9dZCtOKVEGN61aqU5k6lJgxvQ8Oq9F5abGiFZVYL+4In45Fi623UE6d3Mqpym6T6T91Xef1itniSGVvyKL19Kz8aVf/aGJYVEwMQ2IcEfELx+vfRVsjrk3Tl+IRRHI96HOz63qJKyu9V5NQzPTURUYr5i1x12vkQXIdQejDrapdKKG22aRF2b3iCxbbcOyblndhU6xQ+9MOZcYv0dBsJDpm3BfMVhqQhQRiF+0ZuIDgdO7jVMUMw/B8Pud5Hoah8IPKJmfo/qvEo6QONONrtyaQUJKvkGbOyl6qFdF+gnja2B4/+fOW6SXoSl/Mbna8otgbCBD+jRgE9Gjg/VuYb+z0LNpZTNVxXlofnUSoLexfWOlevtrtK6m1LY6XEM7r0dRwXRa5FIHCn4rVDTuoa1M4j7O64gJ9gOBWTpXegZ7JTwPLP6hcPqADk0LT6fF9ZmsbehrTAoUTtrG+ClN9NIcBMamsriZvcvI8cq8Xz2r+E8mFVvP7lKbVLo+vL0QpAu/BSxJfBHhxZjVbB9Jip9P8jWSKDXw4UWt8iBAKeKUTNVJihNV81LYXPaGP8tViTfm7uKfA5D5OFfeWpmniy1H8ySBtvRJpG4tvKF035vwn/g1m39UTc6xMedUHsyPg7FhTpigxAaeCGjGdle3qBT6f6W0sOk/r9OHZcfnCZdRmFHLiaNpNKaxoz9pBktjzKBFoVoHXWnhMkdCbILRZdOR9pxVdyyVJVtwCH3GrvfrCkUopNX8v/ZHL1bbt6/USRW7u+8LClYvWWG8Lfi3f7+T1K9zGJp5rUh7flGAueAQ5fqpGIFBLaJberUtlBVnUVmdkxtf5BtF0Zxe2bf4WMk0Jc/gSYolZEht5PDkiPK5xLxcePxbr+WeN/3ZesvqsGFG13XQx+V0dTWsVWClZtVAndZmAL1llvE5yA8tcuYnDqboOsInA9Cf4fH9o1vm63IEr8RLW6ZCsOV7H9/qyOYt7+gdDwaKL5jkZgbZprVMVGCTwdczstPW8YprhXkZxQi9CoXpwqmJggjvPKFcu2pZf8xvzvaxNzgI2EcTLHofa6tP5yZznzJgb1Y6dKp1RoW/nqW06lCUrYYsKB26ElqOXizwPe1GClzwWu6iGty6VLLPHXpFHoa8Gp4q4z56qmOCw9XTAnqqNyXcUsp2DyjK/8zVNduE69XsRm1ynLEcYZEVbXadJTqIvPoqflcwKHz3VleSiJ2OtdrmReUztgTVq+5RJjsZLYepWrg/X3xQirs3InzaewG7lVc+NqQVqOd9pWj9HLX5lEx62XvL75tPsriDkImpAyO2FXEQNCEnWksnRmngrVWbkFZqULGKVCzfjB0svyS/RYnbxypC55BOUzkxi1m8gVqycaT2FwGBJTKi0V0v7dapYqVo8bF0sUG1fqQIAgFNYt652RI75eqMyi8VZLVmsVPHsdHhauzYTL3rF9WXeDdQQkxdPqCc4Putx9QJf8/ut6+f4d7YC32DxsHXeOOBrAwDAdvhsfZ1BVS+oBL6a+bxyxbM5vj706YNFTaBGYOe4CvSj2CBTbhksQAiqcKoWMXvOvjLPEnIRNSDk9kIuogaEXFPIjnOwlrBCeEnMRbGF2cWu2EaDrPPzCsXqay/kCB1+lFqcquCw9ev8hAIAAADA73KhVdnjeD6feX9683cc6NlKAQAAAOBWVLFRnfan543qNXhUXdfRw3u9K79yxnGsoQEUMo4j2gkHHYejH+iQfbyzaW6PMAjt1q28wXhP/bquq9MmePx3Q8hpyEdIPJ/PqoofM03T2SpchXEcp2nK7SSx747XCU0Pj8fj+XxSO6nZIObHJ9q2Jfvos2lujzZI3/dkkFRrD/J+oD6fz/wp3uqYwe1IKT0eD7qmDn+qOhcipUSf0z5bkUsg2knbtmdqcwG4QYZhqLadUNnFBCFGkpTSMAwnKHcGJQaprcGYNslQeJ1DShWP/2pjGAY84dLU8/C3BFq85EeNYDkzsSVtOtzuVF1OYxzH+X3WTGqxfxiGehqMaZCUkg6pB88mKaWmaYZhqLb7wKm6IXwJmhaoz9PlKtBydD3TQCF5Rwheh04pPR6PaZrIGmgtAu1l1vt8J6X0vmeInhfX7GNlxnE0T9iuBzhVt4X2ILdti7khpdT3PS3XAyK3ClqybtsWfhXNBMMw0OxY88QAyhnHse/7YRjQYMi5rHzGqWWjem3kLbd42pX+HnI9/0gpjeNY+QjYdR0f/mhiOFWjk6G1zPlvO0jXdX3fV95IOPoBMZbAacM+7VXHSJv+HoxQr6GFzApHWqxU3RBaoJrnGf2coN1U5FFRV8cbkQI0FdEeYBBB13X8eR+6T0qJFqj4Z9Aqh5Z4+ehaYTvBStXdyCsxvDVX3uf5TyU6R6DCri6gJpF/R1beQhJbusuWOVmhi5GXe+ni9XpV/jw9dxyMtBnea3KDOUmX04BTdTeoEYtHOXNlJ8qAEh6PR9/3+eCuyufIruuGYeAdp3KDaKjB0DVeMcZIC0yq+EwNAMBDnK0AYJAYPO0CIABOFQAAAADADmCjOgAAAADADsCpAgAAcAc6xSlqiPeE9uLT4jyfT/N9Cwqkx7j6pdcgF3ELb3WYYKM6AACAO/B6va6wg/6gbWf5fbryr1mbvh0F5o9McFXjU/JF/JrPTQjAShUAAICbYK5UmQfNJLWWk6OJu9kLEWlF1hTfzEL4H3oNyUwrTs7khxRwgWZCz9HhX9dp2za//Jv+vjCjtcr/ilPjp2mif8dxPN2RvQ5wqgAAANyZvu9p1s8XKaWmacj5yB9oorvZtaLjEpqm6fueDrQMPkCe4/d9n9d7sgeTT+enr4fRddM0Ii/undDdrB4tUKV3p0rLp/jkDJnfSHi9XtwB4t8xm6aJ3zL1zEXjL8mKg2FrZwYAAAB+H2+Cyxd09tg8z23b0mcn5nkehoGuU0qPx0Mk4eFaII8j4ue8gnzT39FWPF+RdhgGLkFfCPkUX+SVo4lS57LnrEkOD9dxRCDllYtQOdhTBQAA4CbEn+HLt16vV9u2/F8eIdgkRLf0YzL+L931DjzLIfR1BxFI373JS0TBFjFTPm17yjIXH1nSyhldkJNnCs/2oSeAz+dzmiZxOi4OMCPgVAEAAKgL8qj447ySVHlTURw/uyD568KceFt3dk3meabHf33fB4f7m/I/cm7oCeA0TZRjDjftQ06Y5y+ChD1VAAAAaoNWcchv0NvGzS3h6W9T0ev1CuLzQNrAlLdzUXh2ufjG8JyWktDmKrrg2550pkK+2COlk+jNT3zrOg/07NO2LX1JmscXW7Wq5uznjwAAAMAO6AmONhil9w1POrK4Nf/tUqJtSel9z5PYqESQk0GR8wYjHkghWSAPzHlx4Yk9ZJzfd1Dlcmn5vFx6TxUvo9gpJXZumfbhlhE7qOBLZGAIAAAANUJbwr1b+W+yNq0HqYLAvJFcxzRDYvWChJ6GwzCU7ygPFBDRTEezTvDtPwAAAOCNvEn8+Uf621q+ZdLMD/X20nOdDvG+rtMF/jRwqgAAAAAAdgAb1QEAAAAAdgBOFQAAAADADsCpAgAAAADYAThVAAAAAAA7AKcKAAAAAGAH4FQBAAAAAOzAf1E9rOU5FlW0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Redo but with the two TRatioPlots being const. smearing vs. lab data and lin. smearing vs. lab data\n",
    "\n",
    "c5 = TCanvas('c5','',800,650)\n",
    "\n",
    "gStyle.SetStatFontSize(.08)\n",
    "gStyle.SetStatW(0.4)\n",
    "gStyle.SetPalette(kBird)\n",
    "\n",
    "c5.Divide(0,2)\n",
    "\n",
    "c5.cd(1) # Const. smeared sim. vs. lab data\n",
    "\n",
    "gPad.SetLogy()\n",
    "gStyle.SetOptStat(0)\n",
    "gStyle.SetTitleFontSize(0.085)\n",
    "\n",
    "rp1 = TRatioPlot(const_smear,lab_root)\n",
    "rp1.Draw()\n",
    "\n",
    "rp1.GetLowerRefYaxis().SetTitle(\"Ratio\")\n",
    "rp1.GetUpperRefYaxis().SetTitle(\"Counts/bin\")\n",
    "rp1.GetUpperRefYaxis().SetTitleOffset(0.7)\n",
    "rp1.GetLowerRefYaxis().SetTitleOffset(0.7)\n",
    "rp1.GetLowerRefYaxis().SetRangeUser(0,3)\n",
    "\n",
    "rp1.GetLowerRefYaxis().SetLabelSize(.04)\n",
    "rp1.GetLowerRefYaxis().SetTitleSize(.05)\n",
    "rp1.GetLowerRefXaxis().SetLabelSize(.05)\n",
    "rp1.GetLowerRefXaxis().SetTitleSize(.045)\n",
    "\n",
    "chi2_1 = \" #chi^{2}/ndf = \" + str(round(const_smear.Chi2Test(lab_root, \"UW CHI2/NDF\"), 8)) + \" \"\n",
    "\n",
    "c5.cd(2) # Lin. smeared sim. vs lab data\n",
    "\n",
    "gPad.SetLogy()\n",
    "gStyle.SetOptStat(0)\n",
    "gStyle.SetTitleFontSize(0.085)\n",
    "\n",
    "rp2 = TRatioPlot(lin_smear,lab_root)\n",
    "rp2.Draw()\n",
    "\n",
    "lin_smear.SetTitle(\"Linearly Modeled Smearing, Full Spectrum\")\n",
    "\n",
    "rp2.GetLowerRefYaxis().SetTitle(\"Ratio\")\n",
    "rp2.GetUpperRefYaxis().SetTitle(\"Counts/bin\")\n",
    "rp2.GetUpperRefYaxis().SetTitleOffset(0.7)\n",
    "rp2.GetLowerRefYaxis().SetTitleOffset(0.7)\n",
    "rp2.GetLowerRefYaxis().SetRangeUser(0,3)\n",
    "\n",
    "rp2.GetLowerRefYaxis().SetLabelSize(.04)\n",
    "rp2.GetLowerRefYaxis().SetTitleSize(.05)\n",
    "rp2.GetLowerRefXaxis().SetLabelSize(.05)\n",
    "rp2.GetLowerRefXaxis().SetTitleSize(.045)\n",
    "\n",
    "chi2_2 = \" #chi^{2}/ndf = \" + str(round(lin_smear.Chi2Test(lab_root, \"UW CHI2/NDF\"), 8)) + \" \"\n",
    "\n",
    "c5.cd()\n",
    "\n",
    "legend1 = TLegend(0.6,0.85,0.88,0.93)\n",
    "legend1.AddEntry(lab_root,\"Lab data (single module)\",\"le\")\n",
    "legend1.AddEntry(const_smear,\"Smeared sim. (const)\",\"l\")\n",
    "legend1.Draw()\n",
    "\n",
    "title1 = TPaveText(.31,.865,.56,.915)\n",
    "title1.AddText(chi2_1)\n",
    "title1.SetMargin(0.05)\n",
    "title1.SetBorderSize(1)\n",
    "title1.SetFillColor(0)\n",
    "title1.Draw()\n",
    "\n",
    "legend2 = TLegend(0.6,0.35,0.88,0.43)\n",
    "legend2.AddEntry(lab_root,\"Lab data (single module)\",\"le\")\n",
    "legend2.AddEntry(lin_smear,\"Smeared sim. (linear)\",\"l\")\n",
    "legend2.Draw()\n",
    "\n",
    "title2 = TPaveText(.31,.365,.56,.415)\n",
    "title2.AddText(chi2_2)\n",
    "title2.SetMargin(0.05)\n",
    "title2.SetBorderSize(1)\n",
    "title2.SetFillColor(0)\n",
    "title2.Draw()\n",
    "\n",
    "c5.Draw()\n",
    "# c5.SaveAs(\"NBv5_LinVsConstSmearing_Full.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "761c6dcf",
   "metadata": {},
   "source": [
    "### Normalize the linearly smeared gamma + muon rates to lab data to improve chi2/ndf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "20c9bc07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " **********\n",
      " **    1 **SET PRINT           0\n",
      " **********\n",
      " **********\n",
      " **    2 **SET NOGRAD\n",
      " **********\n",
      " PARAMETER DEFINITIONS:\n",
      "    NO.   NAME         VALUE      STEP SIZE      LIMITS\n",
      "     1 frac0        1.25000e-01  1.00000e-02    1.00000e-02  1.00000e+01\n",
      "     2 frac1        1.25000e-01  1.00000e-02    1.00000e-02  1.00000e+01\n",
      "     3 frac2        1.25000e-01  1.00000e-02    1.00000e-02  1.00000e+01\n",
      "     4 frac3        1.25000e-01  1.00000e-02    1.00000e-02  1.00000e+01\n",
      "     5 frac4        1.25000e-01  1.00000e-02    1.00000e-02  1.00000e+01\n",
      "     6 frac5        1.25000e-01  1.00000e-02    1.00000e-02  1.00000e+01\n",
      "     7 frac6        1.25000e-01  1.00000e-02    1.00000e-02  1.00000e+01\n",
      "     8 frac7        1.25000e-01  1.00000e-02    1.00000e-02  1.00000e+01\n",
      " **********\n",
      " **    3 **SET ERR         0.5\n",
      " **********\n",
      " **********\n",
      " **    4 **SET PRINT           0\n",
      " **********\n",
      " **********\n",
      " **    5 **SET STR           1\n",
      " **********\n",
      " **********\n",
      " **    6 **MIGRAD        2120        0.01\n",
      " **********\n",
      " MIGRAD MINIMIZATION HAS CONVERGED.\n",
      " FCN=-5.72204e+06 FROM MIGRAD    STATUS=CONVERGED     897 CALLS         898 TOTAL\n",
      "                     EDM=1.38998e-07    STRATEGY= 1  ERROR MATRIX UNCERTAINTY   5.8 per cent\n",
      "  EXT PARAMETER                                   STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  frac0        5.19210e-01   5.55616e-03  -6.21876e-07  -3.75501e-02\n",
      "   2  frac1        1.63201e-01   2.39292e-03  -4.03001e-08  -6.57528e-02\n",
      "   3  frac2        1.00000e-02   6.29735e-04   1.75176e-05** at limit **\n",
      "   4  frac3        1.00000e-02   5.50623e-04   8.54578e-06** at limit **\n",
      "   5  frac4        1.00000e-02   9.11371e-04  -4.64311e-05** at limit **\n",
      "   6  frac5        1.00000e-02   8.27427e-04  -1.75863e-05** at limit **\n",
      "   7  frac6        1.00000e-02   8.57144e-04   1.64906e-06** at limit **\n",
      "   8  frac7        2.67833e-01   4.41523e-03  -2.86844e-08  -4.31029e-02\n",
      "                               ERR DEF= 0.5\n"
     ]
    }
   ],
   "source": [
    "# Gammas, create TH1 objects for each of the simulated gamma lines, they will be smeared individually\n",
    "h_sme1 = TH1D('smear1', '', bins, hmin, hmax) # The range is now the full spectrum, because muons will be added later.\n",
    "h_sme2 = TH1D('smear2', '', bins, hmin, hmax)\n",
    "h_sme3 = TH1D('smear3', '', bins, hmin, hmax)\n",
    "h_sme4 = TH1D('smear4', '', bins, hmin, hmax)\n",
    "h_sme5 = TH1D('smear5', '', bins, hmin, hmax)\n",
    "h_sme6 = TH1D('smear6', '', bins, hmin, hmax)\n",
    "h_sme7 = TH1D('smear7', '', bins, hmin, hmax)\n",
    "h_sme8 = TH1D('smear8', '', bins, hmin, hmax)\n",
    "# h_sme9 = TH1D('smear9', '', bins, hmin, hmax)\n",
    "# h_sme10 = TH1D('smear10', '', bins, hmin, hmax) # TFractionFitter is not liking adding these lower energy lines.\n",
    "                                                  # Ignore for now, maybe try fixing later.\n",
    "\n",
    "# Apply linear smearing to each of them\n",
    "fill_hist(h_sme1, smear_lin(np.array(energyDeposited1460),0))\n",
    "fill_hist(h_sme2, smear_lin(np.array(energyDeposited2614),0))\n",
    "fill_hist(h_sme3, smear_lin(np.array(energyDeposited1120),0))\n",
    "fill_hist(h_sme4, smear_lin(np.array(energyDeposited1238),0))\n",
    "fill_hist(h_sme5, smear_lin(np.array(energyDeposited1764),0))\n",
    "fill_hist(h_sme6, smear_lin(np.array(energyDeposited968),0))\n",
    "fill_hist(h_sme7, smear_lin(np.array(energyDeposited934),0))\n",
    "fill_hist(h_sme8, smear_lin(np.array(energyDeposited911),0))\n",
    "\n",
    "lab_root.Reset() # Reset lab histogram\n",
    "fill_hist(lab_root,lab_mev)\n",
    "lab_root.Sumw2()\n",
    "\n",
    "# Pre-scale the MC histos so the templates have similar error. This helps the fitter give same weight to all MC\n",
    "# so it's independant of MC statistics, but if statistics are too low, it won't work.\n",
    "\n",
    "lx = int(0.1/R) # [MeV]\n",
    "ux = int(2.6/R) # [MeV]\n",
    "\n",
    "dat_int = lab_root.Integral(lx,ux) # Ref. integral\n",
    "\n",
    "int1 = h_sme1.Integral(lx,ux)\n",
    "h_sme1.Scale(dat_int/int1)\n",
    "# h_sme1.Sumw2()\n",
    "int2 = h_sme2.Integral(lx,ux)\n",
    "h_sme2.Scale(dat_int/int2)\n",
    "# h_sme2.Sumw2()\n",
    "int3 = h_sme3.Integral(lx,ux)\n",
    "h_sme3.Scale(dat_int/int3)\n",
    "# h_sme3.Sumw2()\n",
    "int4 = h_sme4.Integral(lx,ux)\n",
    "h_sme4.Scale(dat_int/int4)\n",
    "# h_sme4.Sumw2()\n",
    "int5 = h_sme5.Integral(lx,ux)\n",
    "h_sme5.Scale(dat_int/int5)\n",
    "# h_sme5.Sumw2()\n",
    "int6 = h_sme6.Integral(lx,ux)\n",
    "h_sme6.Scale(dat_int/int6)\n",
    "# h_sme6.Sumw2()\n",
    "int7 = h_sme7.Integral(lx,ux)\n",
    "h_sme7.Scale(dat_int/int7)\n",
    "# h_sme7.Sumw2()\n",
    "int8 = h_sme8.Integral(lx,ux)\n",
    "h_sme8.Scale(dat_int/int8)\n",
    "# h_sme8.Sumw2()\n",
    "\n",
    "mcs = TObjArray(8) # Create a TObjArray for the gamma lines which will be normalized, add the 8 sim. gamma lines\n",
    "mcs.Add(h_sme1)\n",
    "mcs.Add(h_sme2)\n",
    "mcs.Add(h_sme3)\n",
    "mcs.Add(h_sme4)\n",
    "mcs.Add(h_sme5)\n",
    "mcs.Add(h_sme6)\n",
    "mcs.Add(h_sme7)\n",
    "mcs.Add(h_sme8)\n",
    "\n",
    "fit = TFractionFitter(lab_root, mcs) \n",
    "fit.Constrain(0, 0.01, 10.0) # Have it between 0 and 10 for now, but this can be fixed later if necessary.\n",
    "fit.Constrain(1, 0.01, 10.0)\n",
    "fit.Constrain(2, 0.01, 10.0)\n",
    "fit.Constrain(3, 0.01, 10.0)\n",
    "fit.Constrain(4, 0.01, 10.0)\n",
    "fit.Constrain(5, 0.01, 10.0)\n",
    "fit.Constrain(6, 0.01, 10.0)\n",
    "fit.Constrain(7, 0.01, 10.0)\n",
    "\n",
    "fit.SetRangeX(lx,ux)\n",
    "status = fit.Fit() # Do the fit!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7e3bfc22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value0 =  0.5192097324705788  +/-  0.005556161798803416\n",
      "value1 =  0.1632014607441057  +/-  0.002392923866760377\n",
      "value2 =  0.01000000000029447  +/-  0.0006297347169429389\n",
      "value3 =  0.010000000016398787  +/-  0.0005506234957679771\n",
      "value4 =  0.010000000153757417  +/-  0.000911371160495349\n",
      "value5 =  0.010000000017723068  +/-  0.0008274266926381743\n",
      "value6 =  0.010000000001497302  +/-  0.0008571439680859763\n",
      "value7 =  0.26783334583887886  +/-  0.0044152337041885314\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tot = TH1D('tot','',bins,hmin,hmax) # Declare a TH1 that will concatenate all of the simulated gamma lines. This\n",
    "                                    # will have to be done through the .Add() function now because the data is no\n",
    "                                    # longer stored in the form of np arrays.\n",
    "tot.Sumw2()\n",
    "\n",
    "value = ctypes.c_double(0.0)\n",
    "error = ctypes.c_double(0.0)\n",
    "\n",
    "# Scale each of the simulated gamma lines by the fit values. Keep in mind that the results are counted 0-7 even\n",
    "# though the simulated gamma lines were labeled 1-8. Then, add the scaled hists to tot.\n",
    "fit.GetResult(0, value, error)\n",
    "print(\"value0 = \",value.value,\" +/- \",error.value)\n",
    "h_sme1.Scale(value.value)\n",
    "\n",
    "fit.GetResult(1, value, error)\n",
    "print(\"value1 = \",value.value,\" +/- \",error.value)\n",
    "h_sme2.Scale(value.value)\n",
    "\n",
    "fit.GetResult(2, value, error)\n",
    "print(\"value2 = \",value.value,\" +/- \",error.value)\n",
    "h_sme3.Scale(value.value)\n",
    "\n",
    "fit.GetResult(3, value, error)\n",
    "print(\"value3 = \",value.value,\" +/- \",error.value)\n",
    "h_sme4.Scale(value.value)\n",
    "\n",
    "fit.GetResult(4, value, error)\n",
    "print(\"value4 = \",value.value,\" +/- \",error.value)\n",
    "h_sme5.Scale(value.value)\n",
    "\n",
    "fit.GetResult(5, value, error)\n",
    "print(\"value5 = \",value.value,\" +/- \",error.value)\n",
    "h_sme6.Scale(value.value)\n",
    "\n",
    "fit.GetResult(6, value, error)\n",
    "print(\"value6 = \",value.value,\" +/- \",error.value)\n",
    "h_sme7.Scale(value.value)\n",
    "\n",
    "fit.GetResult(7, value, error)\n",
    "print(\"value7 = \",value.value,\" +/- \",error.value)\n",
    "h_sme8.Scale(value.value)\n",
    "\n",
    "tot.Add(h_sme1)\n",
    "tot.Add(h_sme2)\n",
    "tot.Add(h_sme3)\n",
    "tot.Add(h_sme4)\n",
    "tot.Add(h_sme5)\n",
    "tot.Add(h_sme6)\n",
    "tot.Add(h_sme7)\n",
    "tot.Add(h_sme8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fa602109",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "# Normalize the muon flux so that it matches the lab data \n",
    "h_mu = TH1D('h_mu','',bins,hmin,hmax) # Declare a TH1 for muons with full hist range\n",
    "fill_hist(h_mu, smear_lin(mu_mev,16))\n",
    "\n",
    "lx = int(3/R)\n",
    "ux = int(14/R)\n",
    "\n",
    "dat_int_mu = lab_root.Integral(lx,ux) # Ref. integral\n",
    "\n",
    "int_mu = h_mu.Integral(lx,ux)\n",
    "h_mu.Scale(dat_int_mu/int_mu)\n",
    "\n",
    "tot.Add(h_mu) # Add it to the TH1 with normalized gammas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "00fcb1fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TH1D::Chi2TestX>: First histogram is not unweighted and option UW has been requested\n",
      "Info in <TH1D::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot TRatioPlot between lab data and normalized lin. smeared sim, get new chi2/ndf\n",
    "\n",
    "c6 = TCanvas('c6','',900,450)\n",
    "\n",
    "gPad.SetLogy()\n",
    "\n",
    "gStyle.SetStatFontSize(.08)\n",
    "gStyle.SetTitleFontSize(0.085)\n",
    "gStyle.SetStatW(0.4)\n",
    "\n",
    "gStyle.SetPalette(kBird)\n",
    "\n",
    "tot.SetTitle(\"Linearly Modeled Smearing Normalized with TFractionFitter, Full Spectrum\")\n",
    "tot.GetXaxis().SetTitle(\"Energy deposited (MeV)\")\n",
    "\n",
    "lab_root.SetLineWidth(1)\n",
    "tot.SetLineWidth(2)\n",
    "\n",
    "lab_root.SetAxisRange(0.8, 14, \"X\")\n",
    "tot.SetAxisRange(0.8, 14, \"X\")\n",
    "\n",
    "tot.Draw(\"hist same\")\n",
    "tot.SetLineColor(kPink+2)\n",
    "\n",
    "c6.Clear()\n",
    "\n",
    "rp = TRatioPlot(tot,lab_root)\n",
    "rp.Draw()\n",
    "\n",
    "rp.GetLowerRefYaxis().SetTitle(\"Ratio\")\n",
    "rp.GetUpperRefYaxis().SetTitle(\"Counts/bin\")\n",
    "rp.GetUpperRefYaxis().SetTitleOffset(0.7)\n",
    "rp.GetLowerRefYaxis().SetTitleOffset(0.7)\n",
    "rp.GetLowerRefYaxis().SetRangeUser(-1,3)\n",
    "\n",
    "rp.GetLowerRefYaxis().SetLabelSize(.04)\n",
    "rp.GetLowerRefYaxis().SetTitleSize(.05)\n",
    "rp.GetUpperRefYaxis().SetLabelSize(.04)\n",
    "rp.GetUpperRefYaxis().SetTitleSize(.05)\n",
    "rp.GetLowerRefXaxis().SetLabelSize(.05)\n",
    "rp.GetLowerRefXaxis().SetTitleSize(.035)\n",
    "\n",
    "chi2 = \" #chi^{2}/ndf = \" + str(round(tot.Chi2Test(lab_root, \"UW CHI2/NDF\"), 8)) + \" \"\n",
    "\n",
    "legend = TLegend(0.57,0.73,0.88,0.89)\n",
    "legend.AddEntry(lab_root,\"Lab data (single module)\",\"le\")\n",
    "legend.AddEntry(tot,\"Smeared sim. (lin.)\",\"l\")\n",
    "legend.Draw()\n",
    "\n",
    "title = TPaveText(0.26, 0.76, 0.54, 0.86, \"NDC\")\n",
    "title.AddText(chi2)\n",
    "title.SetMargin(0.05)\n",
    "title.SetBorderSize(1)\n",
    "title.SetFillColor(0)\n",
    "title.Draw()\n",
    "\n",
    "c6.Draw()\n",
    "\n",
    "# c6.SaveAs(\"NBv5_NORMALIZED_GammasAndMuons_LinSmear_Full.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d62f600b",
   "metadata": {},
   "source": [
    "### ATTEMPT 3: Second degree polynomial model, fit using TMinuit (WIP)\n",
    "\n",
    "Use TMinuit to model the energy resolution as $ \\sigma^2 = c_0 + c_1 E_{PE} + c_2 E_{PE}^2 $. \n",
    "\n",
    "*NOTE:* Because the version of ROOT used on Grace is having issues defining a ROOT.Math.Functor, Minuit2Minimizer can't be used. Instead, the minimization is attempted to be done by Minuit Migrad, which for now seems to be unsuccessful (try varying the parameters). Also, the CHI2 may have to be calculated explicitly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9a7124a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TFile::Append>: Replacing existing TH1: smearedg (Potential memory leak).\n",
      "Warning in <TFile::Append>: Replacing existing TH1: labg (Potential memory leak).\n"
     ]
    }
   ],
   "source": [
    "smeared_g = TH1F('smearedg', '', 150, 0.6, 2.6) \n",
    "lab_g = TH1D('labg', '', 150, 0.6, 2.6)\n",
    "\n",
    "# Define the energy resolution function\n",
    "def scale_poly(a, dat_pe, par): # Takes in data in [PE]\n",
    "    return a * np.power((par[0]+par[1]*dat_pe+par[2]*np.power(dat_pe,2)), 0.5)\n",
    "\n",
    "# Define the polynomial smearing function\n",
    "# Takes in data in units of [MeV], x-axis shift in units of [PE], output in [MeV]\n",
    "def smear_poly(dat_mev, par, dis_pe, a): # Takes in data to be smeared, [c0, c1, c2] coefficient array, \n",
    "                                         # x-shift in [PE], and scale factor 'a'.\n",
    "    dat_pe = dat_mev * pe_mev # Convert from [MeV] to [PE]\n",
    "    smeared_data = np.random.normal(loc=dat_pe, scale = scale_poly(a, dat_pe, par)) + dis_pe # all [PE]\n",
    "    return smeared_data/pe_mev # Return it in [MeV]\n",
    "\n",
    "# Define a function that returns chi2 of gamma hists\n",
    "def chi2_g( npar, gin, f, par, iflag ):\n",
    "    a = (sig662*pe_mev) / scale_poly(1, .662*pe_mev, par)\n",
    "    lab_g.Reset()\n",
    "    smeared_g.Reset()\n",
    "    fill_hist(lab_g, lab_mev)\n",
    "    fill_hist(smeared_g, smear_poly(sim_mev, par, 0, a)) \n",
    "    if (( par[0]+ par[1]*sim_mev*pe_mev + par[2]*np.power(sim_mev*pe_mev,2) ).any() <= 0):\n",
    "        f.value = 10**6\n",
    "    else:\n",
    "#       print(par[0], par[1], par[2], lab_g.Chi2Test(smeared_g, \"UW CHI2/NDF\"))\n",
    "#       f = lab_g.Chi2Test(smeared_g, \"UW CHI2/NDF\")\n",
    "        f.value = lab_g.Chi2Test(smeared_g, \"CHI2 UW\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3363c81a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " **********\n",
      " **    1 **SET ERR           1\n",
      " **********\n",
      " PARAMETER DEFINITIONS:\n",
      "    NO.   NAME         VALUE      STEP SIZE      LIMITS\n",
      "     1 c0           1.00000e+00  1.00000e-01    1.00000e-04  1.00000e+01\n",
      "     2 c1           1.00000e+00  1.00000e-01    1.00000e-04  1.00000e+01\n",
      "     3 c2           1.00000e+00  1.00000e-01    1.00000e-04  1.00000e+01\n",
      " **********\n",
      " **    2 **MINIMIZE         500           1\n",
      " **********\n",
      " FIRST CALL TO USER FUNCTION AT NEW START POINT, WITH IFLAG=4.\n",
      " START MIGRAD MINIMIZATION.  STRATEGY  1.  CONVERGENCE WHEN EDM .LT. 1.00e-03\n",
      " FCN=756.409 FROM MIGRAD    STATUS=INITIATE       28 CALLS          29 TOTAL\n",
      "                     EDM= unknown      STRATEGY= 1      NO ERROR MATRIX       \n",
      "  EXT PARAMETER               CURRENT GUESS       STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.99991e-01   1.00000e-01  -3.15188e-06  -5.77910e+07\n",
      "   2  c1           1.00000e+00   1.00000e-01   0.00000e+00   1.99252e+07\n",
      "   3  c2           1.00000e+00   1.00000e-01   0.00000e+00   5.92408e+07\n",
      " MIGRAD FAILS TO FIND IMPROVEMENT\n",
      " MINUIT WARNING IN HESSE   \n",
      " ============== Negative diagonal element 1 in Error Matrix\n",
      " MINUIT WARNING IN HESSE   \n",
      " ============== Negative diagonal element 3 in Error Matrix\n",
      " MINUIT WARNING IN HESSE   \n",
      " ============== 3.17029e+14 added to diagonal of error matrix\n",
      " EIGENVALUES OF SECOND-DERIVATIVE MATRIX:\n",
      "        -1.5127e+07  1.7779e+00  1.5127e+07\n",
      " MINUIT WARNING IN HESSE   \n",
      " ============== MATRIX FORCED POS-DEF BY ADDING 15142584.625019 TO DIAGONAL.\n",
      " FCN=800.974 FROM HESSE     STATUS=NOT POSDEF     22 CALLS          52 TOTAL\n",
      "                     EDM=5.89043e+12    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.99991e-01   1.22224e-09   4.59520e-07  -6.61247e+08\n",
      "   2  c1           1.00000e+00   5.14479e-10   4.59520e-07   5.39221e+07\n",
      "   3  c2           1.00000e+00   2.43961e-02   4.59520e-07  -5.96868e+08\n",
      " MINUIT WARNING IN MIGRAD  \n",
      " ============== Negative diagonal element 1 in Error Matrix\n",
      " MINUIT WARNING IN MIGRAD  \n",
      " ============== Negative diagonal element 2 in Error Matrix\n",
      " MINUIT WARNING IN MIGRAD  \n",
      " ============== Negative diagonal element 3 in Error Matrix\n",
      " MINUIT WARNING IN MIGRAD  \n",
      " ============== 1.00016 added to diagonal of error matrix\n",
      " MIGRAD FAILS TO FIND IMPROVEMENT\n",
      " MIGRAD TERMINATED WITHOUT CONVERGENCE.\n",
      " FCN=628.343 FROM MIGRAD    STATUS=FAILED        121 CALLS         122 TOTAL\n",
      "                     EDM=95265.2    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.25672e-01   1.99388e+00   0.00000e+00   1.87380e+01\n",
      "   2  c1           9.90441e+00   5.71797e+00   0.00000e+00  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   0.00000e+00  -4.98638e+02\n",
      " EXTERNAL ERROR MATRIX.    NDIM=  25    NPAR=  3    ERR DEF=1\n",
      "  5.487e+00  1.324e-03  6.776e-02 \n",
      "  1.324e-03  9.469e-01 -2.385e-04 \n",
      "  6.776e-02 -2.385e-04  8.318e-04 \n",
      "ERR MATRIX NOT POS-DEF\n",
      " PARAMETER  CORRELATION COEFFICIENTS  \n",
      "       NO.  GLOBAL      1      2      3\n",
      "        1  1.00300   1.000  0.001  1.003\n",
      "        2  0.00000   0.001  1.000 -0.008\n",
      "        3  1.00300   1.003 -0.008  1.000\n",
      " ERR MATRIX NOT POS-DEF\n",
      " START SIMPLEX MINIMIZATION.    CONVERGENCE WHEN EDM .LT. 1\n",
      " FCN=628.343 FROM SIMPLEX   STATUS=PROGRESS        9 CALLS         131 TOTAL\n",
      "                     EDM=72.7632    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.25672e-01   1.99388e+00   1.31738e-01   1.87380e+01\n",
      "   2  c1           9.90441e+00   5.71797e+00   9.40248e-01  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   2.23064e-01  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS       21 CALLS         143 TOTAL\n",
      "                     EDM=136.355    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   2.10780e-02   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   5.01466e-01  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   3.56902e-02  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS       36 CALLS         158 TOTAL\n",
      "                     EDM=69.5197    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   3.37248e-03   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   8.02345e-02  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   5.71044e-03  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS       48 CALLS         170 TOTAL\n",
      "                     EDM=99.3665    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   5.39597e-04   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   1.71167e-02  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   9.13670e-04  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS       62 CALLS         184 TOTAL\n",
      "                     EDM=120.296    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   8.63355e-05   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   3.04297e-03  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   1.54309e-04  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS       80 CALLS         202 TOTAL\n",
      "                     EDM=122.289    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   1.57321e-05   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   4.86873e-04  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   2.74327e-05  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS       91 CALLS         213 TOTAL\n",
      "                     EDM=82.9651    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   2.51713e-06   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   1.03861e-04  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   4.38923e-06  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS      102 CALLS         224 TOTAL\n",
      "                     EDM=85.6459    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   4.02797e-07   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   1.66148e-05  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   7.02275e-07  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS      115 CALLS         237 TOTAL\n",
      "                     EDM=101.541    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   6.44941e-08   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   2.65986e-06  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   1.12363e-07  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS      131 CALLS         253 TOTAL\n",
      "                     EDM=80.3696    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   1.02445e-08   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   4.24683e-07  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   1.19835e-08  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS      142 CALLS         264 TOTAL\n",
      "                     EDM=80.6164    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   1.62981e-09   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   6.70552e-08  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   1.91721e-09  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS      156 CALLS         278 TOTAL\n",
      "                     EDM=115.47    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   2.32831e-10   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   1.49012e-08  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   3.05590e-10  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS      173 CALLS         295 TOTAL\n",
      "                     EDM=72.9519    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   0.00000e+00   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   7.45058e-09  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   4.00178e-11  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS      184 CALLS         306 TOTAL\n",
      "                     EDM=83.6235    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   0.00000e+00   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   0.00000e+00  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   7.27596e-12  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS      195 CALLS         317 TOTAL\n",
      "                     EDM=115.949    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   0.00000e+00   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   0.00000e+00  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   0.00000e+00  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS      206 CALLS         328 TOTAL\n",
      "                     EDM=105.441    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   0.00000e+00   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   0.00000e+00  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   0.00000e+00  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS      217 CALLS         339 TOTAL\n",
      "                     EDM=76.1637    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   0.00000e+00   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   0.00000e+00  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   0.00000e+00  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS      228 CALLS         350 TOTAL\n",
      "                     EDM=116.799    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   0.00000e+00   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   0.00000e+00  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   0.00000e+00  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS      241 CALLS         363 TOTAL\n",
      "                     EDM=131.211    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   0.00000e+00   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   0.00000e+00  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   0.00000e+00  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS      252 CALLS         374 TOTAL\n",
      "                     EDM=111.207    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   0.00000e+00   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   0.00000e+00  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   0.00000e+00  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS      265 CALLS         387 TOTAL\n",
      "                     EDM=66.6606    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   0.00000e+00   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   0.00000e+00  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   0.00000e+00  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS      279 CALLS         401 TOTAL\n",
      "                     EDM=101.178    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   0.00000e+00   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   0.00000e+00  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   0.00000e+00  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS      290 CALLS         412 TOTAL\n",
      "                     EDM=74.1124    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   0.00000e+00   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   0.00000e+00  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   0.00000e+00  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS      301 CALLS         423 TOTAL\n",
      "                     EDM=106.349    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   0.00000e+00   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   0.00000e+00  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   0.00000e+00  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS      314 CALLS         436 TOTAL\n",
      "                     EDM=120.711    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   0.00000e+00   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   0.00000e+00  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   0.00000e+00  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS      329 CALLS         451 TOTAL\n",
      "                     EDM=83.6976    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   0.00000e+00   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   0.00000e+00  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   0.00000e+00  -4.98638e+02\n",
      " FCN=593.641 FROM SIMPLEX   STATUS=PROGRESS      340 CALLS         462 TOTAL\n",
      "                     EDM=107.712    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   0.00000e+00   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   0.00000e+00  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   0.00000e+00  -4.98638e+02\n",
      " FCN=591.86 FROM SIMPLEX   STATUS=PROGRESS      353 CALLS         475 TOTAL\n",
      "                     EDM=87.778    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   0.00000e+00   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   0.00000e+00  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   0.00000e+00  -4.98638e+02\n",
      " FCN=591.86 FROM SIMPLEX   STATUS=PROGRESS      366 CALLS         488 TOTAL\n",
      "                     EDM=73.7099    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   0.00000e+00   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   0.00000e+00  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   0.00000e+00  -4.98638e+02\n",
      " FCN=591.86 FROM SIMPLEX   STATUS=PROGRESS      379 CALLS         501 TOTAL\n",
      "                     EDM=120.841    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   0.00000e+00   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   0.00000e+00  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   0.00000e+00  -4.98638e+02\n",
      " SIMPLEX TERMINATES WITHOUT CONVERGENCE.\n",
      " FCN=591.86 FROM SIMPLEX   STATUS=CALL LIMIT    380 CALLS         502 TOTAL\n",
      "                     EDM= unknown      STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   0.00000e+00   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   0.00000e+00  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   0.00000e+00  -4.98638e+02\n",
      " FCN=591.86 FROM SIMPLEX   STATUS=CALL LIMIT    380 CALLS         502 TOTAL\n",
      "                     EDM= unknown      STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           6.77683e-01   1.99388e+00   0.00000e+00   1.87380e+01\n",
      "   2  c1           9.76832e+00   5.71797e+00   0.00000e+00  -1.09494e+00\n",
      "   3  c2           1.40082e-03   1.58996e-01   0.00000e+00  -4.98638e+02\n"
     ]
    }
   ],
   "source": [
    "gMinuit = TMinuit(3)\n",
    "gMinuit.SetFCN( chi2_g )\n",
    "\n",
    "arglist = array( 'd', 10*[0.] )\n",
    "ierflg = ctypes.c_int(1982)\n",
    "\n",
    "arglist[0] = 1\n",
    "#mnexcm(command, plist, ilist, ierflg)\n",
    "gMinuit.mnexcm( \"SET ERR\", arglist, 1, ierflg )\n",
    "\n",
    "# Set starting values and step sizes for parameters\n",
    "vstart = array( 'd', ( 1, 1,  1 ) )\n",
    "step   = array( 'd', ( 0.1, 0.1, 0.1 ) )\n",
    "gMinuit.mnparm( 0, \"c0\", vstart[0], step[0], .0001, 10, ierflg )\n",
    "gMinuit.mnparm( 1, \"c1\", vstart[1], step[1], .0001, 10, ierflg )\n",
    "gMinuit.mnparm( 2, \"c2\", vstart[2], step[2], .0001, 10, ierflg )\n",
    "\n",
    "# Now ready for minimization step\n",
    "arglist[0] = 500\n",
    "arglist[1] = 1.\n",
    "gMinuit.mnexcm( \"MINImize\", arglist, 2, ierflg )\n",
    "\n",
    "# Print results\n",
    "amin, edm, errdef = ctypes.c_double(0.18), ctypes.c_double(0.19), ctypes.c_double(0.20)\n",
    "nvpar, nparx, icstat = ctypes.c_int(1983), ctypes.c_int(1984), ctypes.c_int(1985)\n",
    "gMinuit.mnstat( amin, edm, errdef, nvpar, nparx, icstat )\n",
    "gMinuit.mnprin( 3, amin.value ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "98b39d94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Polynomial scaled sigma model at 662 KeV:  0.1601\n",
      "Reference value:  0.1601\n",
      "Scale factor a:  0.4608075150609532\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c8\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check if smearing gives correct value of sigma at 662 KeV.\n",
    "par = [6.77683e-01, 9.76832e+00, 1.40082e-03]\n",
    "a = (sig662*pe_mev) / scale_poly(1, .662*pe_mev, par)\n",
    "\n",
    "print(\"Polynomial scaled sigma model at 662 KeV: \", scale_poly(a, .662*pe_mev,par)/pe_mev)\n",
    "print(\"Reference value: \", sig662)\n",
    "print(\"Scale factor a: \", a)\n",
    "\n",
    "smeared_g.Reset()\n",
    "lab_g.Reset()\n",
    "# par = [8.77816e+00,4.59079e-01,7.95828e-03]\n",
    "# c0 = minuit.GetParameter(0,.0,.0)\n",
    "# c1 = minuit.GetParameter(1,.0,.0)\n",
    "# c2 = minuit.GetParameter(2,.0,.0)\n",
    "fill_hist(smeared_g, smear_poly(sim_mev, par, 0, a))\n",
    "fill_hist(lab_g, lab_mev)\n",
    "\n",
    "c8 = TCanvas('c8','',900,450)\n",
    "\n",
    "gPad.SetLogy()\n",
    "\n",
    "gStyle.SetStatFontSize(.08)\n",
    "gStyle.SetTitleFontSize(0.085)\n",
    "gStyle.SetStatW(0.4)\n",
    "\n",
    "gStyle.SetPalette(kBird)\n",
    "\n",
    "smeared_g.SetTitle(\"Polynomial Modeled Smearing, Gammas\")\n",
    "smeared_g.GetXaxis().SetTitle(\"Energy deposited (MeV)\")\n",
    "\n",
    "lab_g.SetLineWidth(1)\n",
    "smeared_g.SetLineWidth(2)\n",
    "\n",
    "# lab_g.SetAxisRange(0.8, 14, \"X\")\n",
    "# smeared_g.SetAxisRange(0.8, 14, \"X\")\n",
    "\n",
    "lab_g.Draw(\"hist\")\n",
    "smeared_g.Draw(\"hist same\")\n",
    "smeared_g.SetLineColor(kYellow-6)\n",
    "\n",
    "c8.Clear()\n",
    "\n",
    "rp = TRatioPlot(smeared_g,lab_g)\n",
    "rp.Draw()\n",
    "\n",
    "rp.GetLowerRefYaxis().SetTitle(\"Ratio\")\n",
    "rp.GetUpperRefYaxis().SetTitle(\"Counts/bin\")\n",
    "rp.GetUpperRefYaxis().SetTitleOffset(0.7)\n",
    "rp.GetLowerRefYaxis().SetTitleOffset(0.7)\n",
    "rp.GetLowerRefYaxis().SetRangeUser(-1,3)\n",
    "\n",
    "rp.GetLowerRefYaxis().SetLabelSize(.04)\n",
    "rp.GetLowerRefYaxis().SetTitleSize(.05)\n",
    "rp.GetUpperRefYaxis().SetLabelSize(.04)\n",
    "rp.GetUpperRefYaxis().SetTitleSize(.05)\n",
    "rp.GetLowerRefXaxis().SetLabelSize(.05)\n",
    "rp.GetLowerRefXaxis().SetTitleSize(.035)\n",
    "\n",
    "chi2 = \" #chi^{2}/ndf = \" + str(round(lab_g.Chi2Test(smeared_g, \"UW CHI2/NDF\"), 8)) + \" \"\n",
    "\n",
    "legend = TLegend(0.57,0.73,0.88,0.89)\n",
    "legend.AddEntry(lab_g,\"Lab data (single module)\",\"le\")\n",
    "legend.AddEntry(smeared_g,\"Smeared sim. (poly.)\",\"l\")\n",
    "legend.Draw()\n",
    "\n",
    "title = TPaveText(0.14, 0.41, 0.43, 0.51, \"NDC\")\n",
    "title.AddText(chi2)\n",
    "title.SetMargin(0.05)\n",
    "title.SetBorderSize(1)\n",
    "title.SetFillColor(0)\n",
    "title.Draw()\n",
    "\n",
    "c8.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "bd4e76dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " **********\n",
      " **    1 **SET ERR           1\n",
      " **********\n",
      " PARAMETER DEFINITIONS:\n",
      "    NO.   NAME         VALUE      STEP SIZE      LIMITS\n",
      "     1 c0           1.00000e+00  1.00000e-01    1.00000e+00  1.00000e+01\n",
      " MINUIT WARNING IN PARAM DEF\n",
      " ============== STARTING VALUE IS AT LIMIT.\n",
      " MINUIT WARNING IN PARAMETR\n",
      " ============== VARIABLE1 IS AT ITS LOWER ALLOWED LIMIT.\n",
      " MINUIT WARNING IN PARAMETR\n",
      " ============== VARIABLE1 BROUGHT BACK INSIDE LIMITS.\n",
      "     2 c1           1.00000e-01  1.00000e-02    1.00000e-01  1.00000e+00\n",
      " MINUIT WARNING IN PARAM DEF\n",
      " ============== STARTING VALUE IS AT LIMIT.\n",
      " MINUIT WARNING IN PARAMETR\n",
      " ============== VARIABLE2 IS AT ITS LOWER ALLOWED LIMIT.\n",
      " MINUIT WARNING IN PARAMETR\n",
      " ============== VARIABLE2 BROUGHT BACK INSIDE LIMITS.\n",
      "     3 c2           1.00000e-02  1.00000e-03    1.00000e-02  1.00000e-01\n",
      " MINUIT WARNING IN PARAM DEF\n",
      " ============== STARTING VALUE IS AT LIMIT.\n",
      " MINUIT WARNING IN PARAMETR\n",
      " ============== VARIABLE3 IS AT ITS LOWER ALLOWED LIMIT.\n",
      " MINUIT WARNING IN PARAMETR\n",
      " ============== VARIABLE3 BROUGHT BACK INSIDE LIMITS.\n",
      " **********\n",
      " **    2 **MINIMIZE         500           1\n",
      " **********\n",
      " FIRST CALL TO USER FUNCTION AT NEW START POINT, WITH IFLAG=4.\n",
      " MINUIT WARNING IN MINImize  \n",
      " ============== VARIABLE1 IS AT ITS LOWER ALLOWED LIMIT.\n",
      " MINUIT WARNING IN MINImize  \n",
      " ============== VARIABLE2 IS AT ITS LOWER ALLOWED LIMIT.\n",
      " MINUIT WARNING IN MINImize  \n",
      " ============== VARIABLE3 IS AT ITS LOWER ALLOWED LIMIT.\n",
      " START MIGRAD MINIMIZATION.  STRATEGY  1.  CONVERGENCE WHEN EDM .LT. 1.00e-03\n",
      " FCN=332.536 FROM MIGRAD    STATUS=INITIATE       39 CALLS          40 TOTAL\n",
      "                     EDM= unknown      STRATEGY= 1      NO ERROR MATRIX       \n",
      "  EXT PARAMETER               CURRENT GUESS       STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           1.00001e+00   1.00000e-01   1.05167e-05  -2.47384e+07\n",
      "   2  c1           1.00001e-01   1.00000e-02   0.00000e+00  -2.59000e+07\n",
      "   3  c2           1.00001e-02   1.00000e-03   0.00000e+00  -2.85962e+07\n",
      " MIGRAD FAILS TO FIND IMPROVEMENT\n",
      " EIGENVALUES OF SECOND-DERIVATIVE MATRIX:\n",
      "        -8.7790e+00  8.2253e-01  1.0956e+01\n",
      " MINUIT WARNING IN HESSE   \n",
      " ============== MATRIX FORCED POS-DEF BY ADDING 8.789964 TO DIAGONAL.\n",
      " FCN=349.354 FROM HESSE     STATUS=NOT POSDEF     22 CALLS          80 TOTAL\n",
      "                     EDM=560525    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           1.00001e+00   2.41374e-08   7.48078e-07  -8.89329e+06\n",
      "   2  c1           1.00001e-01   1.36821e-10   7.48083e-07   6.81234e+06\n",
      "   3  c2           1.00001e-02   3.88904e-10   7.48081e-07   3.44585e+08\n",
      " MINUIT WARNING IN MIGRAD  \n",
      " ============== Negative diagonal element 1 in Error Matrix\n",
      " MINUIT WARNING IN MIGRAD  \n",
      " ============== Negative diagonal element 3 in Error Matrix\n",
      " MINUIT WARNING IN MIGRAD  \n",
      " ============== 1 added to diagonal of error matrix\n",
      " MIGRAD FAILS TO FIND IMPROVEMENT\n",
      " MIGRAD TERMINATED WITHOUT CONVERGENCE.\n",
      " FCN=326.383 FROM MIGRAD    STATUS=FAILED        103 CALLS         104 TOTAL\n",
      "                     EDM=610.16    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           1.00001e+00   5.53051e+00  -0.00000e+00   2.67826e+07\n",
      "   2  c1           1.00001e-01   5.53063e-01   0.00000e+00  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   0.00000e+00  -6.63432e+06\n",
      " EXTERNAL ERROR MATRIX.    NDIM=  25    NPAR=  3    ERR DEF=1\n",
      "  8.209e-05  6.568e-20 -2.493e-19 \n",
      "  6.568e-20  7.706e-07  1.252e-21 \n",
      " -2.493e-19  1.252e-21  8.395e-09 \n",
      "ERR MATRIX NOT POS-DEF\n",
      " PARAMETER  CORRELATION COEFFICIENTS  \n",
      "       NO.  GLOBAL      1      2      3\n",
      "        1  0.00000   1.000  0.000 -0.000\n",
      "        2  0.00000   0.000  1.000  0.000\n",
      "        3  0.00000  -0.000  0.000  1.000\n",
      " ERR MATRIX NOT POS-DEF\n",
      " START SIMPLEX MINIMIZATION.    CONVERGENCE WHEN EDM .LT. 1\n",
      " FCN=326.383 FROM SIMPLEX   STATUS=PROGRESS        9 CALLS         113 TOTAL\n",
      "                     EDM=1205.34    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           1.00001e+00   5.53051e+00   9.76638e+01   2.67826e+07\n",
      "   2  c1           1.00001e-01   5.53063e-01   1.00805e+02  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   9.65738e+01  -6.63432e+06\n",
      " FCN=307.667 FROM SIMPLEX   STATUS=PROGRESS       25 CALLS         129 TOTAL\n",
      "                     EDM=1005.85    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98570e+00   5.53051e+00   1.56262e+01   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   1.00805e+02  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   1.93148e+00  -6.63432e+06\n",
      " FCN=307.667 FROM SIMPLEX   STATUS=PROGRESS       42 CALLS         146 TOTAL\n",
      "                     EDM=164.204    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98570e+00   5.53051e+00   2.50019e+00   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   1.61288e+01  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   3.86295e-02  -6.63432e+06\n",
      " FCN=307.667 FROM SIMPLEX   STATUS=PROGRESS       55 CALLS         159 TOTAL\n",
      "                     EDM=170.922    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98570e+00   5.53051e+00   4.44479e-01   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   3.15407e+00  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   6.40964e-03  -6.63432e+06\n",
      " FCN=307.667 FROM SIMPLEX   STATUS=PROGRESS       71 CALLS         175 TOTAL\n",
      "                     EDM=84.5436    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98570e+00   5.53051e+00   7.11166e-02   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   2.52326e-01  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   6.83695e-04  -6.63432e+06\n",
      " FCN=307.667 FROM SIMPLEX   STATUS=PROGRESS       93 CALLS         197 TOTAL\n",
      "                     EDM=72.4946    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98570e+00   5.53051e+00   4.26700e-03   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   1.68217e-02  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   3.63371e-05  -6.63432e+06\n",
      " FCN=307.667 FROM SIMPLEX   STATUS=PROGRESS      108 CALLS         212 TOTAL\n",
      "                     EDM=101.056    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98570e+00   5.53051e+00   6.82720e-04   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   2.69147e-03  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   2.90697e-06  -6.63432e+06\n",
      " FCN=306.126 FROM SIMPLEX   STATUS=PROGRESS      122 CALLS         226 TOTAL\n",
      "                     EDM=100.409    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98574e+00   5.53051e+00   1.09235e-04   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   4.30636e-04  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   4.65115e-07  -6.63432e+06\n",
      " FCN=306.126 FROM SIMPLEX   STATUS=PROGRESS      135 CALLS         239 TOTAL\n",
      "                     EDM=37.6196    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98574e+00   5.53051e+00   1.74776e-05   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   6.89017e-05  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   3.72092e-08  -6.63432e+06\n",
      " FCN=306.126 FROM SIMPLEX   STATUS=PROGRESS      150 CALLS         254 TOTAL\n",
      "                     EDM=65.9146    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98574e+00   5.53051e+00   1.81249e-06   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   1.46990e-05  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   6.61497e-09  -6.63432e+06\n",
      " FCN=306.126 FROM SIMPLEX   STATUS=PROGRESS      167 CALLS         271 TOTAL\n",
      "                     EDM=89.5935    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98574e+00   5.53051e+00   1.42314e-07   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   2.87448e-06  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   1.05840e-09  -6.63432e+06\n",
      " FCN=303.766 FROM SIMPLEX   STATUS=PROGRESS      184 CALLS         288 TOTAL\n",
      "                     EDM=56.5721    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98574e+00   5.53051e+00   2.27703e-08   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   5.74896e-07  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   1.69343e-10  -6.63432e+06\n",
      " FCN=303.766 FROM SIMPLEX   STATUS=PROGRESS      196 CALLS         300 TOTAL\n",
      "                     EDM=75.1266    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98574e+00   5.53051e+00   3.64325e-09   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   1.22644e-07  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   2.70950e-11  -6.63432e+06\n",
      " FCN=303.766 FROM SIMPLEX   STATUS=PROGRESS      209 CALLS         313 TOTAL\n",
      "                     EDM=91.2981    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98574e+00   5.53051e+00   6.47690e-10   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   2.61641e-08  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   4.33498e-12  -6.63432e+06\n",
      " FCN=303.766 FROM SIMPLEX   STATUS=PROGRESS      220 CALLS         324 TOTAL\n",
      "                     EDM=137.892    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98574e+00   5.53051e+00   1.03631e-10   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   5.58168e-09  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   6.93667e-13  -6.63432e+06\n",
      " FCN=303.766 FROM SIMPLEX   STATUS=PROGRESS      237 CALLS         341 TOTAL\n",
      "                     EDM=108.851    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98574e+00   5.53051e+00   7.36833e-12   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   1.19076e-09  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   1.35447e-13  -6.63432e+06\n",
      " FCN=303.766 FROM SIMPLEX   STATUS=PROGRESS      251 CALLS         355 TOTAL\n",
      "                     EDM=68.9639    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98574e+00   5.53051e+00   1.17950e-12   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   2.63427e-10  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   2.24265e-14  -6.63432e+06\n",
      " FCN=303.766 FROM SIMPLEX   STATUS=PROGRESS      268 CALLS         372 TOTAL\n",
      "                     EDM=104.404    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98574e+00   5.53051e+00   1.58096e-13   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   2.80949e-11  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   1.99840e-15  -6.63432e+06\n",
      " FCN=303.766 FROM SIMPLEX   STATUS=PROGRESS      280 CALLS         384 TOTAL\n",
      "                     EDM=73.4373    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98574e+00   5.53051e+00   2.48690e-14   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   5.99698e-12  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   4.44089e-16  -6.63432e+06\n",
      " FCN=303.766 FROM SIMPLEX   STATUS=PROGRESS      294 CALLS         398 TOTAL\n",
      "                     EDM=63.8199    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98574e+00   5.53051e+00   3.55271e-15   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   9.66338e-13  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   0.00000e+00  -6.63432e+06\n",
      " FCN=303.766 FROM SIMPLEX   STATUS=PROGRESS      307 CALLS         411 TOTAL\n",
      "                     EDM=81.1411    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98574e+00   5.53051e+00   0.00000e+00   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   1.84741e-13  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   0.00000e+00  -6.63432e+06\n",
      " FCN=303.766 FROM SIMPLEX   STATUS=PROGRESS      320 CALLS         424 TOTAL\n",
      "                     EDM=79.6318    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98574e+00   5.53051e+00   0.00000e+00   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   2.84217e-14  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   0.00000e+00  -6.63432e+06\n",
      " FCN=303.766 FROM SIMPLEX   STATUS=PROGRESS      332 CALLS         436 TOTAL\n",
      "                     EDM=85.4418    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98574e+00   5.53051e+00   0.00000e+00   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   1.42109e-14  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   0.00000e+00  -6.63432e+06\n",
      " FCN=303.766 FROM SIMPLEX   STATUS=PROGRESS      348 CALLS         452 TOTAL\n",
      "                     EDM=75.125    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98574e+00   5.53051e+00   0.00000e+00   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   0.00000e+00  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   0.00000e+00  -6.63432e+06\n",
      " FCN=303.766 FROM SIMPLEX   STATUS=PROGRESS      366 CALLS         470 TOTAL\n",
      "                     EDM=75.0815    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98574e+00   5.53051e+00   0.00000e+00   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   0.00000e+00  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   0.00000e+00  -6.63432e+06\n",
      " FCN=303.766 FROM SIMPLEX   STATUS=PROGRESS      380 CALLS         484 TOTAL\n",
      "                     EDM=63.4598    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98574e+00   5.53051e+00   0.00000e+00   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   0.00000e+00  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   0.00000e+00  -6.63432e+06\n",
      " FCN=303.766 FROM SIMPLEX   STATUS=PROGRESS      393 CALLS         497 TOTAL\n",
      "                     EDM=75.5244    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98574e+00   5.53051e+00   0.00000e+00   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   0.00000e+00  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   0.00000e+00  -6.63432e+06\n",
      " FCN=303.766 FROM SIMPLEX   STATUS=PROGRESS      405 CALLS         509 TOTAL\n",
      "                     EDM=116.652    STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98574e+00   5.53051e+00   0.00000e+00   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   0.00000e+00  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   0.00000e+00  -6.63432e+06\n",
      " SIMPLEX TERMINATES WITHOUT CONVERGENCE.\n",
      " FCN=303.766 FROM SIMPLEX   STATUS=CALL LIMIT    406 CALLS         510 TOTAL\n",
      "                     EDM= unknown      STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98574e+00   5.53051e+00   0.00000e+00   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   0.00000e+00  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   0.00000e+00  -6.63432e+06\n",
      " FCN=303.766 FROM SIMPLEX   STATUS=CALL LIMIT    406 CALLS         510 TOTAL\n",
      "                     EDM= unknown      STRATEGY= 1      ERR MATRIX NOT POS-DEF\n",
      "  EXT PARAMETER                APPROXIMATE        STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  c0           9.98574e+00   5.53051e+00   0.00000e+00   2.67826e+07\n",
      "   2  c1           1.17010e-01   5.53063e-01   0.00000e+00  -3.75079e+07\n",
      "   3  c2           1.00001e-02   5.53046e-02   0.00000e+00  -6.63432e+06\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TFile::Append>: Replacing existing TH1: smearedmu (Potential memory leak).\n",
      "Warning in <TFile::Append>: Replacing existing TH1: labmu (Potential memory leak).\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "smeared_mu = TH1F('smearedmu', '', 150, 2.8, 8) \n",
    "lab_mu = TH1F('labmu', '', 150, 2.8, 8)\n",
    "\n",
    "# Define a function that returns chi2 of muon hists\n",
    "def chi2_mu( npar, gin, f, par, iflag ):\n",
    "    lab_mu.Reset()\n",
    "    smeared_mu.Reset()\n",
    "    fill_hist(lab_mu, lab_mev)\n",
    "    fill_hist(smeared_mu, smear_poly(sim_mev, par, 16, 1)) \n",
    "    if (( par[0]+ par[1]*sim_mev*pe_mev + par[2]*np.power(sim_mev*pe_mev,2) ).any() <= 0):\n",
    "        f.value = 10**6\n",
    "    else:\n",
    "        f.value = lab_mu.Chi2Test(smeared_mu, \"UW CHI2\")\n",
    "\n",
    "muMinuit = TMinuit(3)\n",
    "muMinuit.SetFCN( chi2_mu )\n",
    "\n",
    "arglist = array( 'd', 10*[0.] )\n",
    "ierflg = ctypes.c_int(1982)\n",
    "\n",
    "arglist[0] = 1\n",
    "#mnexcm(command, plist, ilist, ierflg)\n",
    "muMinuit.mnexcm( \"SET ERR\", arglist, 1, ierflg )\n",
    "\n",
    "# Set starting values and step sizes for parameters\n",
    "vstart = array( 'd', ( 1, 0.1,  0.01 ) )\n",
    "step   = array( 'd', ( 0.1, 0.01, 0.001 ) )\n",
    "muMinuit.mnparm( 0, \"c0\", vstart[0], step[0], 1, 10, ierflg )\n",
    "muMinuit.mnparm( 1, \"c1\", vstart[1], step[1], .1, 1, ierflg )\n",
    "muMinuit.mnparm( 2, \"c2\", vstart[2], step[2], .01, .1, ierflg )\n",
    "\n",
    "# Now ready for minimization step\n",
    "arglist[0] = 500\n",
    "arglist[1] = 1.\n",
    "muMinuit.mnexcm( \"MINImize\", arglist, 2, ierflg )\n",
    "\n",
    "# Print results\n",
    "amin, edm, errdef = ctypes.c_double(0.18), ctypes.c_double(0.19), ctypes.c_double(0.20)\n",
    "nvpar, nparx, icstat = ctypes.c_int(1983), ctypes.c_int(1984), ctypes.c_int(1985)\n",
    "muMinuit.mnstat( amin, edm, errdef, nvpar, nparx, icstat )\n",
    "muMinuit.mnprin( 3, amin.value ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9d720570",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: c9\n",
      "Info in <TH1F::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "smeared_mu.Reset()\n",
    "lab_mu.Reset()\n",
    "par = [9.98574e+00,1.17010e-01,1.00001e-02]\n",
    "\n",
    "fill_hist(smeared_mu, smear_poly(sim_mev, par, 16, 1))\n",
    "fill_hist(lab_mu, lab_mev)\n",
    "\n",
    "c9 = TCanvas('c9','',900,450)\n",
    "\n",
    "gPad.SetLogy()\n",
    "\n",
    "gStyle.SetStatFontSize(.08)\n",
    "gStyle.SetTitleFontSize(0.085)\n",
    "gStyle.SetStatW(0.4)\n",
    "\n",
    "gStyle.SetPalette(kBird)\n",
    "\n",
    "smeared_g.SetTitle(\"Polynomial Modeled Smearing, Muons\")\n",
    "smeared_g.GetXaxis().SetTitle(\"Energy deposited (MeV)\")\n",
    "\n",
    "lab_g.SetLineWidth(1)\n",
    "smeared_g.SetLineWidth(2)\n",
    "\n",
    "# lab_g.SetAxisRange(0.8, 14, \"X\")\n",
    "# smeared_g.SetAxisRange(0.8, 14, \"X\")\n",
    "\n",
    "lab_mu.Draw(\"hist\")\n",
    "smeared_mu.Draw(\"hist same\")\n",
    "smeared_mu.SetLineColor(kYellow-6)\n",
    "smeared_mu.SetLineWidth(2)\n",
    "\n",
    "c9.Clear()\n",
    "\n",
    "rp = TRatioPlot(smeared_mu,lab_mu)\n",
    "rp.Draw()\n",
    "\n",
    "rp.GetLowerRefYaxis().SetTitle(\"Ratio\")\n",
    "rp.GetUpperRefYaxis().SetTitle(\"Counts/bin\")\n",
    "rp.GetUpperRefYaxis().SetTitleOffset(0.7)\n",
    "rp.GetLowerRefYaxis().SetTitleOffset(0.7)\n",
    "# rp.GetLowerRefYaxis().SetRangeUser(-1,3)\n",
    "\n",
    "rp.GetLowerRefYaxis().SetLabelSize(.04)\n",
    "rp.GetLowerRefYaxis().SetTitleSize(.05)\n",
    "rp.GetUpperRefYaxis().SetLabelSize(.04)\n",
    "rp.GetUpperRefYaxis().SetTitleSize(.05)\n",
    "rp.GetLowerRefXaxis().SetLabelSize(.05)\n",
    "rp.GetLowerRefXaxis().SetTitleSize(.035)\n",
    "\n",
    "chi2 = \" #chi^{2}/ndf = \" + str(round(lab_mu.Chi2Test(smeared_mu, \"UW CHI2/NDF\"), 8)) + \" \"\n",
    "\n",
    "legend = TLegend(0.57,0.73,0.88,0.89)\n",
    "legend.AddEntry(lab_mu,\"Lab data (single module)\",\"le\")\n",
    "legend.AddEntry(smeared_mu,\"Smeared sim. (poly.)\",\"l\")\n",
    "legend.Draw()\n",
    "\n",
    "title = TPaveText(0.14, 0.41, 0.43, 0.51, \"NDC\")\n",
    "title.AddText(chi2)\n",
    "title.SetMargin(0.05)\n",
    "title.SetBorderSize(1)\n",
    "title.SetFillColor(0)\n",
    "title.Draw()\n",
    "\n",
    "c9.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "995b4153",
   "metadata": {},
   "source": [
    "### Normalize the gamma lines and muon flux using TFractionFitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "cbc3e595",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " **********\n",
      " **    7 **SET PRINT           0\n",
      " **********\n",
      " **********\n",
      " **    8 **SET NOGRAD\n",
      " **********\n",
      " PARAMETER DEFINITIONS:\n",
      "    NO.   NAME         VALUE      STEP SIZE      LIMITS\n",
      "     1 frac0        1.25000e-01  1.00000e-02    1.00000e-02  1.00000e+01\n",
      "     2 frac1        1.25000e-01  1.00000e-02    1.00000e-02  1.00000e+01\n",
      "     3 frac2        1.25000e-01  1.00000e-02    1.00000e-02  1.00000e+01\n",
      "     4 frac3        1.25000e-01  1.00000e-02    1.00000e-02  1.00000e+01\n",
      "     5 frac4        1.25000e-01  1.00000e-02    1.00000e-02  1.00000e+01\n",
      "     6 frac5        1.25000e-01  1.00000e-02    1.00000e-02  1.00000e+01\n",
      "     7 frac6        1.25000e-01  1.00000e-02    1.00000e-02  1.00000e+01\n",
      "     8 frac7        1.25000e-01  1.00000e-02    1.00000e-02  1.00000e+01\n",
      " **********\n",
      " **    9 **SET ERR         0.5\n",
      " **********\n",
      " **********\n",
      " **   10 **SET PRINT           0\n",
      " **********\n",
      " **********\n",
      " **   11 **SET STR           1\n",
      " **********\n",
      " **********\n",
      " **   12 **MIGRAD        2120        0.01\n",
      " **********\n",
      " MIGRAD MINIMIZATION HAS CONVERGED.\n",
      " MIGRAD WILL VERIFY CONVERGENCE AND ERROR MATRIX.\n",
      " MIGRAD MINIMIZATION HAS CONVERGED.\n",
      " MIGRAD WILL VERIFY CONVERGENCE AND ERROR MATRIX.\n",
      " FCN=-5.72259e+06 FROM MIGRAD    STATUS=CONVERGED    1065 CALLS        1066 TOTAL\n",
      "                     EDM=2.25759e-06    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
      "  EXT PARAMETER                                   STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  frac0        5.19564e-01   6.26164e-03   2.90001e-03  -8.22290e-02\n",
      "   2  frac1        1.63269e-01   2.47051e-03   3.00576e-03   1.20057e-01\n",
      "   3  frac2        1.00000e-02   7.33092e-04   2.69771e-02** at limit **\n",
      "   4  frac3        1.00000e-02   1.36743e-03   3.36048e-02** at limit **\n",
      "   5  frac4        1.00000e-02   2.26408e-03   3.45922e-02** at limit **\n",
      "   6  frac5        1.00000e-02   2.08559e-03   3.43060e-02** at limit **\n",
      "   7  frac6        1.00000e-02   1.99550e-03   3.39160e-02** at limit **\n",
      "   8  frac7        2.67377e-01   5.56384e-03   3.41121e-03  -4.29270e-01\n",
      "                               ERR DEF= 0.5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TH1D::Sumw2>: Sum of squares of weights structure already created\n"
     ]
    }
   ],
   "source": [
    "# Gammas, create TH1 objects for each of the simulated gamma lines, they will be smeared individually\n",
    "h_sme1.Reset()\n",
    "h_sme2.Reset()\n",
    "h_sme3.Reset()\n",
    "h_sme4.Reset()\n",
    "h_sme5.Reset()\n",
    "h_sme6.Reset()\n",
    "h_sme7.Reset()\n",
    "h_sme8.Reset()\n",
    "# h_sme9 = TH1D('smear9', '', bins, hmin, hmax)\n",
    "# h_sme10 = TH1D('smear10', '', bins, hmin, hmax) # TFractionFitter is not liking adding these lower energy lines.\n",
    "                                                  # Ignore for now, maybe try fixing later.\n",
    "\n",
    "# Apply poly smearing to each of them\n",
    "par = [6.77683e-01, 9.76832e+00, 1.40082e-03]\n",
    "a = (sig662*pe_mev) / scale_poly(1, .662*pe_mev, par)\n",
    "\n",
    "fill_hist(h_sme1, smear_poly(np.array(energyDeposited1460),par,0,a))\n",
    "fill_hist(h_sme2, smear_poly(np.array(energyDeposited2614),par,0,a))\n",
    "fill_hist(h_sme3, smear_poly(np.array(energyDeposited1120),par,0,a))\n",
    "fill_hist(h_sme4, smear_poly(np.array(energyDeposited1238),par,0,a))\n",
    "fill_hist(h_sme5, smear_poly(np.array(energyDeposited1764),par,0,a))\n",
    "fill_hist(h_sme6, smear_poly(np.array(energyDeposited968),par,0,a))\n",
    "fill_hist(h_sme7, smear_poly(np.array(energyDeposited934),par,0,a))\n",
    "fill_hist(h_sme8, smear_poly(np.array(energyDeposited911),par,0,a))\n",
    "\n",
    "lab_root.Reset() # Reset lab histogram\n",
    "fill_hist(lab_root,lab_mev)\n",
    "lab_root.Sumw2()\n",
    "\n",
    "# Pre-scale the MC histos so the templates have similar error. This helps the fitter give same weight to all MC\n",
    "# so it's independant of MC statistics, but if statistics are too low, it won't work.\n",
    "\n",
    "lx = int(0.1/R) # [MeV]\n",
    "ux = int(2.6/R) # [MeV]\n",
    "\n",
    "dat_int = lab_root.Integral(lx,ux) # Ref. integral\n",
    "\n",
    "int1 = h_sme1.Integral(lx,ux)\n",
    "h_sme1.Scale(dat_int/int1)\n",
    "# h_sme1.Sumw2()\n",
    "int2 = h_sme2.Integral(lx,ux)\n",
    "h_sme2.Scale(dat_int/int2)\n",
    "# h_sme2.Sumw2()\n",
    "int3 = h_sme3.Integral(lx,ux)\n",
    "h_sme3.Scale(dat_int/int3)\n",
    "# h_sme3.Sumw2()\n",
    "int4 = h_sme4.Integral(lx,ux)\n",
    "h_sme4.Scale(dat_int/int4)\n",
    "# h_sme4.Sumw2()\n",
    "int5 = h_sme5.Integral(lx,ux)\n",
    "h_sme5.Scale(dat_int/int5)\n",
    "# h_sme5.Sumw2()\n",
    "int6 = h_sme6.Integral(lx,ux)\n",
    "h_sme6.Scale(dat_int/int6)\n",
    "# h_sme6.Sumw2()\n",
    "int7 = h_sme7.Integral(lx,ux)\n",
    "h_sme7.Scale(dat_int/int7)\n",
    "# h_sme7.Sumw2()\n",
    "int8 = h_sme8.Integral(lx,ux)\n",
    "h_sme8.Scale(dat_int/int8)\n",
    "# h_sme8.Sumw2()\n",
    "\n",
    "mcs = TObjArray(8) # Create a TObjArray for the gamma lines which will be normalized, add the 8 sim. gamma lines\n",
    "mcs.Add(h_sme1)\n",
    "mcs.Add(h_sme2)\n",
    "mcs.Add(h_sme3)\n",
    "mcs.Add(h_sme4)\n",
    "mcs.Add(h_sme5)\n",
    "mcs.Add(h_sme6)\n",
    "mcs.Add(h_sme7)\n",
    "mcs.Add(h_sme8)\n",
    "\n",
    "fit = TFractionFitter(lab_root, mcs) \n",
    "fit.Constrain(0, 0.01, 10.0) # Have it between 0 and 10 for now, but this can be fixed later if necessary.\n",
    "fit.Constrain(1, 0.01, 10.0)\n",
    "fit.Constrain(2, 0.01, 10.0)\n",
    "fit.Constrain(3, 0.01, 10.0)\n",
    "fit.Constrain(4, 0.01, 10.0)\n",
    "fit.Constrain(5, 0.01, 10.0)\n",
    "fit.Constrain(6, 0.01, 10.0)\n",
    "fit.Constrain(7, 0.01, 10.0)\n",
    "\n",
    "fit.SetRangeX(lx,ux)\n",
    "status = fit.Fit() # Do the fit!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5d398b9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value0 =  0.5195639078419821  +/-  0.00626164221757558\n",
      "value1 =  0.16326923891391254  +/-  0.002470505682718921\n",
      "value2 =  0.010000000000148621  +/-  0.0007330924525454103\n",
      "value3 =  0.01000000004184128  +/-  0.001367425379103511\n",
      "value4 =  0.010000001814361587  +/-  0.0022640770131161607\n",
      "value5 =  0.010000000006958575  +/-  0.0020855888862496884\n",
      "value6 =  0.01000000106070391  +/-  0.001995496329392962\n",
      "value7 =  0.26737732040915846  +/-  0.005563836731165894\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totp = TH1D('totp','',bins,hmin,hmax) # Declare a TH1 that will concatenate all of the simulated gamma lines. This\n",
    "                                    # will have to be done through the .Add() function now because the data is no\n",
    "                                    # longer stored in the form of np arrays.\n",
    "totp.Sumw2()\n",
    "\n",
    "value = ctypes.c_double(0.0)\n",
    "error = ctypes.c_double(0.0)\n",
    "\n",
    "# Scale each of the simulated gamma lines by the fit values. Keep in mind that the results are counted 0-7 even\n",
    "# though the simulated gamma lines were labeled 1-8. Then, add the scaled hists to tot.\n",
    "fit.GetResult(0, value, error)\n",
    "print(\"value0 = \",value.value,\" +/- \",error.value)\n",
    "h_sme1.Scale(value.value)\n",
    "\n",
    "fit.GetResult(1, value, error)\n",
    "print(\"value1 = \",value.value,\" +/- \",error.value)\n",
    "h_sme2.Scale(value.value)\n",
    "\n",
    "fit.GetResult(2, value, error)\n",
    "print(\"value2 = \",value.value,\" +/- \",error.value)\n",
    "h_sme3.Scale(value.value)\n",
    "\n",
    "fit.GetResult(3, value, error)\n",
    "print(\"value3 = \",value.value,\" +/- \",error.value)\n",
    "h_sme4.Scale(value.value)\n",
    "\n",
    "fit.GetResult(4, value, error)\n",
    "print(\"value4 = \",value.value,\" +/- \",error.value)\n",
    "h_sme5.Scale(value.value)\n",
    "\n",
    "fit.GetResult(5, value, error)\n",
    "print(\"value5 = \",value.value,\" +/- \",error.value)\n",
    "h_sme6.Scale(value.value)\n",
    "\n",
    "fit.GetResult(6, value, error)\n",
    "print(\"value6 = \",value.value,\" +/- \",error.value)\n",
    "h_sme7.Scale(value.value)\n",
    "\n",
    "fit.GetResult(7, value, error)\n",
    "print(\"value7 = \",value.value,\" +/- \",error.value)\n",
    "h_sme8.Scale(value.value)\n",
    "\n",
    "totp.Add(h_sme1)\n",
    "totp.Add(h_sme2)\n",
    "totp.Add(h_sme3)\n",
    "totp.Add(h_sme4)\n",
    "totp.Add(h_sme5)\n",
    "totp.Add(h_sme6)\n",
    "totp.Add(h_sme7)\n",
    "totp.Add(h_sme8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "4de3e25c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TFile::Append>: Replacing existing TH1: h_mu (Potential memory leak).\n"
     ]
    }
   ],
   "source": [
    "%%capture\n",
    "\n",
    "par = [9.98574e+00,1.17010e-01,1.00001e-02]\n",
    "# Normalize the muon flux so that it matches the lab data \n",
    "h_mu = TH1D('h_mu','',bins,hmin,hmax) # Declare a TH1 for muons with full hist range\n",
    "fill_hist(h_mu, smear_poly(mu_mev,par,15,1))\n",
    "\n",
    "lx = int(3/R)\n",
    "ux = int(14/R)\n",
    "\n",
    "dat_int_mu = lab_root.Integral(lx,ux) # Ref. integral\n",
    "\n",
    "int_mu = h_mu.Integral(lx,ux)\n",
    "h_mu.Scale(dat_int_mu/int_mu)\n",
    "\n",
    "totp.Add(h_mu) # Add it to the TH1 with normalized gammas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5ad3067f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TH1D::Chi2TestX>: There is a bin in h2 with less than 10 effective events.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot TRatioPlot between lab data and normalized lin. smeared sim, get new chi2/ndf\n",
    "\n",
    "c10 = TCanvas('c10','',900,450)\n",
    "\n",
    "gPad.SetLogy()\n",
    "\n",
    "gStyle.SetStatFontSize(.08)\n",
    "gStyle.SetTitleFontSize(0.085)\n",
    "gStyle.SetStatW(0.4)\n",
    "\n",
    "gStyle.SetPalette(kBird)\n",
    "\n",
    "lab_root.Reset()\n",
    "fill_hist(lab_root,lab_mev)\n",
    "\n",
    "totp.SetTitle(\"Polynomial Modeled Smearing Normalized with TFractionFitter, Full Spectrum\")\n",
    "totp.GetXaxis().SetTitle(\"Energy deposited (MeV)\")\n",
    "\n",
    "lab_root.SetLineWidth(1)\n",
    "totp.SetLineWidth(2)\n",
    "\n",
    "lab_root.SetAxisRange(0.8, 14, \"X\")\n",
    "totp.SetAxisRange(0.8, 14, \"X\")\n",
    "\n",
    "totp.Draw(\"hist same\")\n",
    "totp.SetLineColor(kYellow-6)\n",
    "\n",
    "c10.Clear()\n",
    "\n",
    "rp = TRatioPlot(totp,lab_root)\n",
    "rp.Draw()\n",
    "\n",
    "rp.GetLowerRefYaxis().SetTitle(\"Ratio\")\n",
    "rp.GetUpperRefYaxis().SetTitle(\"Counts/bin\")\n",
    "rp.GetUpperRefYaxis().SetTitleOffset(0.7)\n",
    "rp.GetLowerRefYaxis().SetTitleOffset(0.7)\n",
    "rp.GetLowerRefYaxis().SetRangeUser(-1,3)\n",
    "\n",
    "rp.GetLowerRefYaxis().SetLabelSize(.04)\n",
    "rp.GetLowerRefYaxis().SetTitleSize(.05)\n",
    "rp.GetUpperRefYaxis().SetLabelSize(.04)\n",
    "rp.GetUpperRefYaxis().SetTitleSize(.05)\n",
    "rp.GetLowerRefXaxis().SetLabelSize(.05)\n",
    "rp.GetLowerRefXaxis().SetTitleSize(.035)\n",
    "\n",
    "chi2 = \" #chi^{2}/ndf = \" + str(round(lab_root.Chi2Test(totp, \"UW CHI2/NDF\"), 8)) + \" \"\n",
    "\n",
    "legend = TLegend(0.57,0.73,0.88,0.89)\n",
    "legend.AddEntry(lab_root,\"Lab data (single module)\",\"le\")\n",
    "legend.AddEntry(totp,\"Smeared sim. (poly.)\",\"l\")\n",
    "legend.Draw()\n",
    "\n",
    "title = TPaveText(0.26, 0.76, 0.54, 0.86, \"NDC\")\n",
    "title.AddText(chi2)\n",
    "title.SetMargin(0.05)\n",
    "title.SetBorderSize(1)\n",
    "title.SetFillColor(0)\n",
    "title.Draw()\n",
    "\n",
    "c10.Draw()\n",
    "\n",
    "# c10.SaveAs(\"NBv5_NORMALIZED_GammasAndMuons_LinSmear_Full.png\")"
   ]
  }
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