VHHAnalysis/Tools/Normalization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "correct-single",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ROOT as R\n",
    "from analysis_branch import *\n",
    "from util import *\n",
    "import ctypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "posted-colors",
   "metadata": {},
   "outputs": [],
   "source": [
    "R.EnableImplicitMT()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "reasonable-moral",
   "metadata": {},
   "outputs": [],
   "source": [
    "root_file = 'Histos_used_for_BKGNorm_2016.root'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cubic-fortune",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fin = R.TFile(root_file,\"READ\")\n",
    "h_Data_3b = fin.Get('Data_SB_3B_Z_High_V_mass_Z')\n",
    "h_DY_3b = fin.Get('DY_SB_3B_Z_High_V_mass_Z')\n",
    "h_Other_3b = fin.Get('Other_SB_3B_Z_High_V_mass_Z')\n",
    "h_TT_3b = fin.Get('TT_SB_3B_Z_High_V_mass_Z')\n",
    "h_TTBB_3b = fin.Get('TTBB_SB_3B_Z_High_V_mass_Z')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "charged-ballet",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TH1F::Sumw2>: Sum of squares of weights structure already created\n"
     ]
    }
   ],
   "source": [
    "hs = R.THStack(\"stack\",\"V_mass_Z\")\n",
    "\n",
    "hs.Add(h_Other_3b)\n",
    "h_Other_3b.SetFillColor(800)\n",
    "\n",
    "hs.Add(h_TTBB_3b)\n",
    "h_TTBB_3b.SetFillColor(432)\n",
    "\n",
    "hs.Add(h_TT_3b)\n",
    "h_TT_3b.SetFillColor(600)\n",
    "\n",
    "hs.Add(h_DY_3b)\n",
    "h_DY_3b.SetFillColor(400)\n",
    "\n",
    "h_Data_3b.SetMarkerSize(1)\n",
    "h_Data_3b.SetMarkerStyle(20)\n",
    "h_Data_3b.Sumw2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "olympic-privacy",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = R.TCanvas()\n",
    "R.gStyle.SetOptStat(0000)\n",
    "hs.Draw('hist')\n",
    "h_Data_3b.Draw('SAME')\n",
    "hs.Draw('histSAME')\n",
    "h_Data_3b.Draw('SAME')\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "alpine-trustee",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "h_Data_3b_for_fit = h_Data_3b - h_Other_3b\n",
    "# h_Data_3b_for_fit = h_Data_3b\n",
    "h_TT_3b_for_fit = h_TTBB_3b + h_TT_3b\n",
    "\n",
    "hs = R.THStack(\"stack\",\"V_mass_Z\")\n",
    "\n",
    "hs.Add(h_TT_3b_for_fit)\n",
    "h_TT_3b_for_fit.SetFillColor(600)\n",
    "hs.Add(h_DY_3b)\n",
    "h_DY_3b.SetFillColor(400)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "later-screening",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = R.TCanvas()\n",
    "R.gStyle.SetOptStat(0000)\n",
    "hs.Draw('hist')\n",
    "h_Data_3b_for_fit.Draw('SAME')\n",
    "hs.Draw('histSAME')\n",
    "h_Data_3b_for_fit.Draw('SAME')\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "accurate-routine",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fit status: \n",
      "****************************************\n",
      "Minimizer is Minuit / Migrad\n",
      "MinFCN                    =     -2320.06\n",
      "NDf                       =            0\n",
      "Edm                       =  4.74636e-08\n",
      "NCalls                    =           90\n",
      "TT+TTBB                   =     0.231784   +/-   0.0632742    \t (limited)\n",
      "DY                        =     0.768206   +/-   0.0815065    \t (limited)\n",
      "\n",
      " **********\n",
      " **   31 **SET PRINT           0\n",
      " **********\n",
      " **********\n",
      " **   32 **SET NOGRAD\n",
      " **********\n",
      " PARAMETER DEFINITIONS:\n",
      "    NO.   NAME         VALUE      STEP SIZE      LIMITS\n",
      "     1 TT+TTBB      1.00000e+00  1.00000e-04    0.00000e+00  3.00000e+00\n",
      "     2 DY           1.00000e+00  1.00000e-04    0.00000e+00  3.00000e+00\n",
      " **********\n",
      " **   33 **SET ERR         0.5\n",
      " **********\n",
      " **********\n",
      " **   34 **SET PRINT           0\n",
      " **********\n",
      " **********\n",
      " **   35 **SET STR           1\n",
      " **********\n",
      " **********\n",
      " **   36 **MIGRAD        1220        0.01\n",
      " **********\n",
      " MIGRAD MINIMIZATION HAS CONVERGED.\n",
      " MIGRAD WILL VERIFY CONVERGENCE AND ERROR MATRIX.\n",
      " FCN=-2320.06 FROM MIGRAD    STATUS=CONVERGED      89 CALLS          90 TOTAL\n",
      "                     EDM=4.74636e-08    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
      "  EXT PARAMETER                                   STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  TT+TTBB      2.31784e-01   6.32742e-02   2.18709e-03   3.29315e-03\n",
      "   2  DY           7.68206e-01   8.15065e-02   1.71915e-03  -1.99216e-03\n",
      "                               ERR DEF= 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mc = R.TObjArray(2)\n",
    "mc.Add(h_TT_3b_for_fit)\n",
    "mc.Add(h_DY_3b)\n",
    "data = h_Data_3b_for_fit\n",
    "fit = R.TFractionFitter(data, mc)\n",
    "fitter = fit.GetFitter()\n",
    "fitter.Config().ParSettings(0).Set('TT+TTBB', 1.0, 0.0001, 0.0, 3.0)\n",
    "fitter.Config().ParSettings(1).Set('DY', 1.0, 0.0001, 0.0, 3.0)\n",
    "\n",
    "# fit.SetRangeX(1,8)\n",
    "status = fit.Fit()\n",
    "print(\"fit status: \"+str(status))\n",
    "if status == 0:\n",
    "    result = fit.GetPlot()\n",
    "    c = R.TCanvas()\n",
    "    R.gStyle.SetOptStat(0000)\n",
    "    data.Draw(\"Ep\")\n",
    "    result.Draw(\"same\")\n",
    "    c.Draw()\n",
    "\n",
    "SF_0 = ctypes.c_double(0.)\n",
    "ERR_0 = ctypes.c_double(0.)\n",
    "\n",
    "SF_1 = ctypes.c_double(0.)\n",
    "ERR_1 = ctypes.c_double(0.)\n",
    "\n",
    "fit.GetResult(0, SF_0, ERR_0)\n",
    "fit.GetResult(1, SF_1, ERR_1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "sexual-swing",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.003785600614182\n",
      "0.9893223150722679\n",
      "0.27402159603463394\n",
      "0.10496697717471898\n"
     ]
    }
   ],
   "source": [
    "test_TT_hist_new = fit.GetMCPrediction(0)\n",
    "test_DY_hist_new = fit.GetMCPrediction(1)\n",
    "\n",
    "print(SF_0.value * data.Integral()/test_TT_hist_new.Integral())\n",
    "print(SF_1.value * data.Integral()/test_DY_hist_new.Integral())\n",
    "print(ERR_0.value * data.Integral()/test_TT_hist_new.Integral())\n",
    "print(ERR_1.value * data.Integral()/test_DY_hist_new.Integral())\n",
    "\n",
    "sf_0 = SF_0.value * data.Integral()/test_TT_hist_new.Integral()\n",
    "sf_1 = SF_1.value * data.Integral()/test_DY_hist_new.Integral()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "arranged-northwest",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TH1F::Sumw2>: Sum of squares of weights structure already created\n"
     ]
    }
   ],
   "source": [
    "hs = R.THStack(\"stack\",\"V_mass_Z\")\n",
    "\n",
    "hs.Add(h_Other_3b)\n",
    "h_Other_3b.SetFillColor(800)\n",
    "\n",
    "hs.Add(h_TTBB_3b*sf_0)\n",
    "h_TTBB_3b.SetFillColor(432)\n",
    "\n",
    "hs.Add(h_TT_3b*sf_0)\n",
    "h_TT_3b.SetFillColor(600)\n",
    "\n",
    "hs.Add(h_DY_3b*sf_1)\n",
    "h_DY_3b.SetFillColor(400)\n",
    "\n",
    "h_Data_3b.SetMarkerSize(1)\n",
    "h_Data_3b.SetMarkerStyle(20)\n",
    "h_Data_3b.Sumw2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "valid-julian",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = R.TCanvas()\n",
    "R.gStyle.SetOptStat(0000)\n",
    "h_Data_3b.Draw()\n",
    "hs.Draw('histSAME')\n",
    "h_Data_3b.Draw('SAME')\n",
    "hs.Draw('histSAME')\n",
    "h_Data_3b.Draw('SAME')\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "natural-canadian",
   "metadata": {},
   "outputs": [],
   "source": [
    "fin = R.TFile(root_file,\"READ\")\n",
    "h_Data_3b = fin.Get('Data_SB_3B_Z_SM_V_mass_Z')\n",
    "h_DY_3b = fin.Get('DY_SB_3B_Z_SM_V_mass_Z')\n",
    "h_Other_3b = fin.Get('Other_SB_3B_Z_SM_V_mass_Z')\n",
    "h_TT_3b = fin.Get('TT_SB_3B_Z_SM_V_mass_Z')\n",
    "h_TTBB_3b = fin.Get('TTBB_SB_3B_Z_SM_V_mass_Z')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "genetic-message",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TH1F::Sumw2>: Sum of squares of weights structure already created\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hs = R.THStack(\"stack\",\"V_mass_Z\")\n",
    "\n",
    "hs.Add(h_Other_3b)\n",
    "h_Other_3b.SetFillColor(800)\n",
    "\n",
    "hs.Add(h_TTBB_3b)\n",
    "h_TTBB_3b.SetFillColor(432)\n",
    "\n",
    "hs.Add(h_TT_3b)\n",
    "h_TT_3b.SetFillColor(600)\n",
    "\n",
    "hs.Add(h_DY_3b)\n",
    "h_DY_3b.SetFillColor(400)\n",
    "\n",
    "h_Data_3b.SetMarkerSize(1)\n",
    "h_Data_3b.SetMarkerStyle(20)\n",
    "h_Data_3b.Sumw2()\n",
    "\n",
    "c = R.TCanvas()\n",
    "R.gStyle.SetOptStat(0000)\n",
    "hs.Draw('hist')\n",
    "h_Data_3b.Draw('SAME')\n",
    "hs.Draw('histSAME')\n",
    "h_Data_3b.Draw('SAME')\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "lightweight-circus",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h_Data_3b_for_fit = h_Data_3b - h_Other_3b\n",
    "# h_Data_3b_for_fit = h_Data_3b\n",
    "\n",
    "h_TT_3b_for_fit = h_TTBB_3b + h_TT_3b\n",
    "\n",
    "hs = R.THStack(\"stack\",\"V_mass_Z\")\n",
    "\n",
    "hs.Add(h_TT_3b_for_fit)\n",
    "h_TT_3b_for_fit.SetFillColor(600)\n",
    "hs.Add(h_DY_3b)\n",
    "h_DY_3b.SetFillColor(400)\n",
    "\n",
    "c = R.TCanvas()\n",
    "R.gStyle.SetOptStat(0000)\n",
    "hs.Draw('hist')\n",
    "h_Data_3b_for_fit.Draw('SAME')\n",
    "hs.Draw('histSAME')\n",
    "h_Data_3b_for_fit.Draw('SAME')\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "japanese-tomato",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fit status: \n",
      "****************************************\n",
      "Minimizer is Minuit / Migrad\n",
      "MinFCN                    =     -3851.28\n",
      "NDf                       =            0\n",
      "Edm                       =  2.25552e-09\n",
      "NCalls                    =           68\n",
      "TT+TTBB                   =     0.396277   +/-   0.0581108    \t (limited)\n",
      "DY                        =      0.60372   +/-   0.063309     \t (limited)\n",
      "\n",
      " **********\n",
      " **   37 **SET PRINT           0\n",
      " **********\n",
      " **********\n",
      " **   38 **SET NOGRAD\n",
      " **********\n",
      " PARAMETER DEFINITIONS:\n",
      "    NO.   NAME         VALUE      STEP SIZE      LIMITS\n",
      "     1 TT+TTBB      1.00000e+00  1.00000e-04    0.00000e+00  3.00000e+00\n",
      "     2 DY           1.00000e+00  1.00000e-04    0.00000e+00  3.00000e+00\n",
      " **********\n",
      " **   39 **SET ERR         0.5\n",
      " **********\n",
      " **********\n",
      " **   40 **SET PRINT           0\n",
      " **********\n",
      " **********\n",
      " **   41 **SET STR           1\n",
      " **********\n",
      " **********\n",
      " **   42 **MIGRAD        1220        0.01\n",
      " **********\n",
      " MIGRAD MINIMIZATION HAS CONVERGED.\n",
      " MIGRAD WILL VERIFY CONVERGENCE AND ERROR MATRIX.\n",
      " FCN=-3851.28 FROM MIGRAD    STATUS=CONVERGED      67 CALLS          68 TOTAL\n",
      "                     EDM=2.25552e-09    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
      "  EXT PARAMETER                                   STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  TT+TTBB      3.96277e-01   5.81108e-02   2.02343e-03   9.91953e-04\n",
      "   2  DY           6.03720e-01   6.33090e-02   1.86125e-03  -7.56948e-04\n",
      "                               ERR DEF= 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mc = R.TObjArray(2)\n",
    "mc.Add(h_TT_3b_for_fit)\n",
    "mc.Add(h_DY_3b)\n",
    "data = h_Data_3b_for_fit\n",
    "fit = R.TFractionFitter(data, mc)\n",
    "fitter = fit.GetFitter()\n",
    "fitter.Config().ParSettings(0).Set('TT+TTBB', 1.0, 0.0001, 0.0, 3.0)\n",
    "fitter.Config().ParSettings(1).Set('DY', 1.0, 0.0001, 0.0, 3.0)\n",
    "\n",
    "# fit.SetRangeX(1,8)\n",
    "status = fit.Fit()\n",
    "print(\"fit status: \"+str(status))\n",
    "if status == 0:\n",
    "    result = fit.GetPlot()\n",
    "    c = R.TCanvas()\n",
    "    R.gStyle.SetOptStat(0000)\n",
    "    data.Draw(\"Ep\")\n",
    "    result.Draw(\"same\")\n",
    "    c.Draw()\n",
    "\n",
    "SF_0 = ctypes.c_double(0.)\n",
    "ERR_0 = ctypes.c_double(0.)\n",
    "\n",
    "SF_1 = ctypes.c_double(0.)\n",
    "ERR_1 = ctypes.c_double(0.)\n",
    "\n",
    "fit.GetResult(0, SF_0, ERR_0)\n",
    "fit.GetResult(1, SF_1, ERR_1)\n",
    "\n",
    "\n",
    "test_TT_hist_new = fit.GetMCPrediction(0)\n",
    "test_DY_hist_new = fit.GetMCPrediction(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "obvious-preview",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.006976981564217\n",
      "0.9572997906779195\n",
      "0.14766476484490082\n",
      "0.10038704429530976\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TH1F::Sumw2>: Sum of squares of weights structure already created\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(SF_0.value * data.Integral()/test_TT_hist_new.Integral())\n",
    "print(SF_1.value * data.Integral()/test_DY_hist_new.Integral())\n",
    "print(ERR_0.value * data.Integral()/test_TT_hist_new.Integral())\n",
    "print(ERR_1.value * data.Integral()/test_DY_hist_new.Integral())\n",
    "\n",
    "sf_0 = SF_0.value * data.Integral()/test_TT_hist_new.Integral()\n",
    "sf_1 = SF_1.value * data.Integral()/test_DY_hist_new.Integral()\n",
    "\n",
    "hs = R.THStack(\"stack\",\"V_mass_Z\")\n",
    "\n",
    "hs.Add(h_Other_3b)\n",
    "h_Other_3b.SetFillColor(800)\n",
    "\n",
    "hs.Add(h_TTBB_3b*sf_0)\n",
    "h_TTBB_3b.SetFillColor(432)\n",
    "\n",
    "hs.Add(h_TT_3b*sf_0)\n",
    "h_TT_3b.SetFillColor(600)\n",
    "\n",
    "hs.Add(h_DY_3b*sf_1)\n",
    "h_DY_3b.SetFillColor(400)\n",
    "\n",
    "h_Data_3b.SetMarkerSize(1)\n",
    "h_Data_3b.SetMarkerStyle(20)\n",
    "h_Data_3b.Sumw2()\n",
    "\n",
    "c = R.TCanvas()\n",
    "R.gStyle.SetOptStat(0000)\n",
    "hs.Draw('hist')\n",
    "h_Data_3b.Draw('SAME')\n",
    "hs.Draw('histSAME')\n",
    "h_Data_3b.Draw('SAME')\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "adolescent-enforcement",
   "metadata": {},
   "outputs": [],
   "source": [
    "fin = R.TFile(root_file,\"READ\")\n",
    "h_Data_4b = fin.Get('Data_SB_4B_Z_SM_V_mass_Z')\n",
    "h_DY_4b = fin.Get('DY_SB_4B_Z_SM_V_mass_Z')\n",
    "h_Other_4b = fin.Get('Other_SB_4B_Z_SM_V_mass_Z')\n",
    "h_TT_4b = fin.Get('TT_SB_4B_Z_SM_V_mass_Z')\n",
    "h_TTBB_4b = fin.Get('TTBB_SB_4B_Z_SM_V_mass_Z')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "designed-terrorist",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cppyy.gbl.TH1F object at 0x560bff9fb190>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h_Data_4b.Rebin(2)\n",
    "h_DY_4b.Rebin(2)\n",
    "h_Other_4b.Rebin(2)\n",
    "h_TT_4b.Rebin(2)\n",
    "h_TTBB_4b.Rebin(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "remarkable-tissue",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TH1F::Sumw2>: Sum of squares of weights structure already created\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hs = R.THStack(\"stack\",\"V_mass_Z\")\n",
    "\n",
    "hs.Add(h_Other_4b)\n",
    "h_Other_4b.SetFillColor(800)\n",
    "\n",
    "hs.Add(h_TTBB_4b)\n",
    "h_TTBB_4b.SetFillColor(432)\n",
    "\n",
    "hs.Add(h_TT_4b)\n",
    "h_TT_4b.SetFillColor(600)\n",
    "\n",
    "hs.Add(h_DY_4b)\n",
    "h_DY_4b.SetFillColor(400)\n",
    "\n",
    "h_Data_4b.SetMarkerSize(1)\n",
    "h_Data_4b.SetMarkerStyle(20)\n",
    "h_Data_4b.Sumw2()\n",
    "\n",
    "c = R.TCanvas()\n",
    "R.gStyle.SetOptStat(0000)\n",
    "hs.Draw('hist')\n",
    "h_Data_4b.Draw('SAME')\n",
    "hs.Draw('histSAME')\n",
    "h_Data_4b.Draw('SAME')\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "careful-distinction",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h_Data_4b_for_fit = h_Data_4b - h_Other_4b\n",
    "# h_Data_4b_for_fit = h_Data_4b\n",
    "\n",
    "h_TT_4b_for_fit = h_TTBB_4b + h_TT_4b\n",
    "\n",
    "hs = R.THStack(\"stack\",\"V_mass_Z\")\n",
    "\n",
    "hs.Add(h_TT_4b_for_fit)\n",
    "h_TT_4b_for_fit.SetFillColor(600)\n",
    "hs.Add(h_DY_4b)\n",
    "h_DY_4b.SetFillColor(400)\n",
    "\n",
    "c = R.TCanvas()\n",
    "R.gStyle.SetOptStat(0000)\n",
    "hs.Draw('hist')\n",
    "h_Data_4b_for_fit.Draw('SAME')\n",
    "hs.Draw('histSAME')\n",
    "h_Data_4b_for_fit.Draw('SAME')\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "experienced-pacific",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fit status: \n",
      "****************************************\n",
      "Minimizer is Minuit / Migrad\n",
      "MinFCN                    =     -60.7325\n",
      "NDf                       =            0\n",
      "Edm                       =  1.49571e-07\n",
      "NCalls                    =           29\n",
      "TT+TTBB                   =      0.50002   +/-   0.313574     \t (limited)\n",
      "DY                        =     0.500001   +/-   0.344773     \t (limited)\n",
      "\n",
      " **********\n",
      " **   61 **SET PRINT           0\n",
      " **********\n",
      " **********\n",
      " **   62 **SET NOGRAD\n",
      " **********\n",
      " PARAMETER DEFINITIONS:\n",
      "    NO.   NAME         VALUE      STEP SIZE      LIMITS\n",
      "     1 TT+TTBB      1.00000e+00  1.00000e-04    5.00000e-01  2.00000e+00\n",
      "     2 DY           1.00000e+00  1.00000e-04    5.00000e-01  2.00000e+00\n",
      " **********\n",
      " **   63 **SET ERR         0.5\n",
      " **********\n",
      " **********\n",
      " **   64 **SET PRINT           0\n",
      " **********\n",
      " **********\n",
      " **   65 **SET STR           1\n",
      " **********\n",
      " **********\n",
      " **   66 **MIGRAD        1220        0.01\n",
      " **********\n",
      " MIGRAD MINIMIZATION HAS CONVERGED.\n",
      " MIGRAD WILL VERIFY CONVERGENCE AND ERROR MATRIX.\n",
      " MINUIT WARNING IN HESSE   \n",
      " ============== Negative diagonal element 2 in Error Matrix\n",
      " MINUIT WARNING IN HESSE   \n",
      " ============== 1.05328 added to diagonal of error matrix\n",
      " FCN=-60.7325 FROM MIGRAD    STATUS=CONVERGED      28 CALLS          29 TOTAL\n",
      "                     EDM=1.49571e-07    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
      "  EXT PARAMETER                                   STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  TT+TTBB      5.00020e-01   3.13574e-01   1.41421e-03   3.99736e-04\n",
      "   2  DY           5.00001e-01   3.44773e-01   1.41421e-03   7.37342e-05\n",
      "                               ERR DEF= 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mc = R.TObjArray(2)\n",
    "mc.Add(h_TT_4b_for_fit)\n",
    "mc.Add(h_DY_4b)\n",
    "data = h_Data_4b_for_fit\n",
    "fit = R.TFractionFitter(data, mc)\n",
    "fitter = fit.GetFitter()\n",
    "fitter.Config().ParSettings(0).Set('TT+TTBB', 1.0, 0.0001, 0.5, 2.0)\n",
    "fitter.Config().ParSettings(1).Set('DY', 1.0, 0.0001, 0.5, 2.0)\n",
    "\n",
    "# fit.SetRangeX(1,8)\n",
    "status = fit.Fit()\n",
    "print(\"fit status: \"+str(status))\n",
    "if status == 0:\n",
    "    result = fit.GetPlot()\n",
    "    c = R.TCanvas()\n",
    "    R.gStyle.SetOptStat(0000)\n",
    "    data.Draw(\"Ep\")\n",
    "    result.Draw(\"same\")\n",
    "    c.Draw()\n",
    "\n",
    "SF_0 = ctypes.c_double(0.)\n",
    "ERR_0 = ctypes.c_double(0.)\n",
    "\n",
    "SF_1 = ctypes.c_double(0.)\n",
    "ERR_1 = ctypes.c_double(0.)\n",
    "\n",
    "fit.GetResult(0, SF_0, ERR_0)\n",
    "fit.GetResult(1, SF_1, ERR_1)\n",
    "\n",
    "\n",
    "test_TT_hist_new = fit.GetMCPrediction(0)\n",
    "test_DY_hist_new = fit.GetMCPrediction(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "departmental-municipality",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6086549697942826\n",
      "0.632654390739837\n",
      "0.38170104072905103\n",
      "0.43624320485294255\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TH1F::Sumw2>: Sum of squares of weights structure already created\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArgAAAHYCAIAAAApvgy/AAAABmJLR0QAAAAAAAD5Q7t/AAAZqUlEQVR4nO3dP28j6X0H8OcR3AnC5Qq7SoD0UumFsGpmWKvyvQjHdnFufEXckKxS+JpdIEjgF3HX7NUcFr41oKTcLVNdsbjGFvQCmGKSAY/STytpKc48M58PhIM05PKemXnI+fL5N3mz2SQAgLsc9V0AAGC4BAUAICQoAAAhQQEACAkKAEBIUAAAQoICHFrTNDnnnPOTnzBK+WMWi0XfZYQpEhTg0Oq6bn9pmubOJ7RXxKqqDlQggFi24BIc3mKxWC6XVVXdmRXatoTVatVFiimr63q9XqeUfFhBLwQF6EebBm6/AZummc1mdz40QW2iSmIT9EfXA/TpdotCu2U+nz/2ddoOi6Zp6rrOOdd13XXqLxaLbuOd/8f20Z1/dc9zPvoiUa/KY3eqTQnz+VxKgN5sgD60UaCqqp3t7RtztVo94dVux4s7A8f2i0eJ5PaL337lhz/hadrXuX2IgEMSFKA3ty/Jq9XqaQm+u1R3IWD74t09rR0guX0Jvx0dPrrldiE/+oQn6MZyfsqLAJ9O1wP0pr0Wbjf1P63fYfsFuyb67mW7y3a3sesX6P532w377f+9e077T7afU9d1VVXdhfyjT3iCxWJhACMMRd9JBaarvYRvN60/+V3ZdT1sb7zz1dLHGvN3XqrLGVFvyEef8Fh7f0HgU2hRgN60X8Hbr84pXlbhsS/4BO1YyHY0Yjt+cPs127aB2Wx250DFjz7hsdpJHwYwwkAICtCn7d6HT+x3eJo2HMxms+VyuV6v7+wyaJpmPp+329frdRsIdnpM7n/Cw7WzRquqsg4jDEXfTRowadvj/j7lLdnGi522+jtfMG11PXSZYPsftkWK5iysVqs7/9XDn3APAxhhgLQoQJ92lnM+8LLN3YDB7Ub+nY6DboWGVtuz0OaSriHk/ic8UDeAcXv0JdA7QQF61l5T2475IfTK74xRaFc9uvOS35b2o094CGsrwWAJCtCz7evigTvm2waMbvhhe9fK9qGuXaEtXhsFmqZp2w/ai3r70Eef8BDdji+XS3ePhGHpu+8D2MMShE8bo7C5tUpB+9DO58PTlnd81ACFh/S5fPpSj8ATuCkUTF3bDJB+2p7RzpbcGbvQPm1n+8OfAJRIUAAAQj/ruwBA6IErFw356/sIdgEmTosCDFdd1926jfdYrVaDvdB2oyPv54MIBktQAABCpkcCACFBAQAICQoAQEhQAABCggIAEBIUAICQoAAAhAQFACAkKAAAIUEBAAgJCgBASFAAAEKCAgAQEhQAgJCgAACEBAUAICQoAAAhQQEACAkKAEDoZ30XYM9yzn0XAYBJ22w2fRdhn8YWFNLozhAcWM7ZmwiebHzfV3U9AAAhQQEACAkKAEBIUAAAQoICABASFICUUrq6urq4uDg6OkopHR0dvXz58urqqu9CAf0TFIB0dXV1eXl5fX3dTozcbDY3NzeXl5eyAjC2CdOmgMMTXFxcXF9fv3//fnvj6enpycnJ27dv+yoVlGh8l6HR7c/ozhAcwNHRUfTG8YaCRxnfZWiEKzNGq2KN7MxRnELXaxtssb2j4TBGGBR8fDBMQ66ZWhSAyAiDAvBY5+fnNzc379692954dnZ2fHzcV5GAgTDrAUivX7/+8ccfT09Puy1nZ2cfPnx49epVj6UChkBQANKLFy++++67k5OTbsvx8fGbN2/Oz897LBUwBGMbnDm+4aZwYN5E8CnG9w7SogAAhAxmhAMZ7DzD24oo6si+tMFgCQpwOEVc2nIuoJwlJBkYCV0PAEBIUAAAQoICABASFACAkKAAAIRGOOvB3SMBYF9GGBQEAgDYF10PAEBIUAAAQoICABASFACAkKAAAIQEBQAgJCgAAKGhB4XFYnHnxrqu73wIANijPOTliZqmmc1mq9WqrutuY7vwYlVV6/U6pXT70SHvEVOWcy6ibuachl/OnC2txkCN7zI00JUZm6ZZLBZtFNjWZoLuHNR1PZvNRnZKAGA4htv1UNf1fD7f2bher7c3brclAAB7N/QWkpzzTufCtrqu1+v19i6Mr82H0dD1sEe6Hhis8V2GBtr18BBtSlitVjvbo7tH3mNkJxUA9mW4XQ/3WCwWOec2JdxubNg8Xh87AQAFKK9FoW1ImM/npkcCwHMrLCi0UyG0AQDAYRQWFJbLZVVVTdNsbzT3AQCeSWFBIaW0Xq9ns9n2Fg0MAPBMhh4UdkKATAAAh1TkrAcA4DAEBQAgJCgAACFBAQAICQoAQEhQAABCggIAEBr6OgpPEN090hoMAPBYIwwKAgEA7IuuBwAgJCgAACFBAQAICQoAQEhQAABCggIAEBIUAICQoAAAhAQFACAkKAAAIUEBAAgJCgBAaIQ3hXL3SADYlxEGBYEAAPZF1wMAEBIUAICQoAAAhAQFACAkKAAAIUEBAAgJCgBASFAAAEKCAgAQEhQAgJCgAACEBAUAICQoAAChEd490m2mAWBfRhgUBAIA2BddDwBASFAAAEKCAgAQEhQAgJCgAACEBAUAICQoAAChAoLCYrG4c2Nd103THLo0ADAlQw8KTdMsl8udQJBzXi6XKaXZbFbXdS8FA4ApGO7KjE3TLBaL9Xq9s71tYOiWX8w5N00jLgDAcxh0i0Jd1/P5fGfjcrmsqqr7s6qqO/smAIBPN9wWhbqu23aCtpdh56Ht328/AQDYi+EGhSeL7h55D/eRAoA7jTAouOoDwL4MeowCANCv8oJCVVXbsyWbptke2wgA7FF5QaGdM9lmhaZp1uu1WQ8A8EzKG6PQzpmczWbtn/P53CIKAPBMcrlD/+5cZynngveIccs5F1E3c07DL2fOhi0zUOO7DI1uf0Z3hhgNQWGPBAUGa3yXofLGKAAAByMoAAAhQQHguTxhoVgYGkEBAAgJCgBASFAAAELlLbj0UVGn4MjmqwDAAYwwKAgEALAvuh4AgJCgAACEBAXgJ/TdAdsEBQAgJCgAACFBAQAICQoAQEhQAABCggIAEBIUAICQoAAAhAQFACA0wptCuXskAOzLCIOCQAAA+6LrAQAICQoAQEhQAABCggIAEBIUAICQoAAAhAQFACAkKAAAIUEBAAgJCgBASFAAAEKCAgAQGuFNodw9EgD2ZYRBQSAAgH3R9QAAhAQFACAkKAAAIUEBAAgJCgBASFAAAEKCAgAQKjIoNE2zWCzqum6apu+yAMCYlRcUFovFbDZrI8JsNqvruucCAcB45eLWMcw5z+fzxWKRUmqaZjabrVarLi7kXN4eMRE5Z3VzX3IuYw1Wn0gTNL6TXl6LQkqpiwWaEwDgWZUXFKqqarsemqZpg4K4AADPpMgWku37Q273O6T41pH3K/EgUBxdD3uk64HBGt9JL69FIedcVdVms9lsNvP5vBvY2Nk8Xk+7AgBDV1hQaDNBlwy6IY19lQcAxq2woHBbVVWCAgA8k8KCQjscoW1ISCk1TbNer7s/AYD9Km/MRbt2Qvdnt6ZCa3yjSBgNgxn3yGBGBmt8J73U/Wm7G25PjBzfGWI0BIU9EhQYrPGd9NHtz+jOEKMhKOyRoMBgje+kFzZGAQA4JEEBAAgJCgBASFAAAEKCAgAQEhQAgNDP+i7A/kU3kBzZfBUAOIARBgWBAAD2RdcDABASFACAkKAAAIQEBQAgJCgAACFBAQAICQr9iBZ7AIBBERQAgJCgALBnV1dXFxcXR0dHKaWjo6OXL19eXV31XSh4IkEBYJ+urq4uLy+vr6/bVWI3m83Nzc3l5aWsQKHyyBY8zrmMPSqlnOxRztk535ech7tY+8XFxfX19fv377c3np6enpycvH37tq9ScTDj+3gf3f4UcoZKKSd7JCjs0ZCDwtHRUVS2wZaZPRrfx/sIbwrl7pEwBSVOHRpymX1CEhlhUFDdYQoG+07XosDIjDAoAPTo/Pz85ubm3bt32xvPzs6Oj4/7KhJ8CrMeAPbp9evXP/744+npabfl7Ozsw4cPr1696rFU8GSCAsA+vXjx4rvvvjs5Oem2HB8fv3nz5vz8vMdSwZONbXBmKcNNSykne2TWwx4NedbDNu/0CRrfSdeiAACEBAUAICQoAAAhQQEACAkKAEBIUAAAQoICABASFACA0Ajv9eDukQCwLyMMCgIBAOyLrgcAICQoAAAhQQEACAkKAEBIUAAAQoICABASFACAUKlBYbFY1HW9WCz6LggAjFkucXmidu3FqqrW63VVVU3TbD9UxB6VUk72KOfsnO9LzmUsreadPkHjO+nltSjUdV1V1WazaZpmtVqt1+vtoAAA7FF5wSfnvFqt6rqOHi1ij0opJ3ukRWGPtCgwWOM76YXd66FtPNgenWCYAgA8n8KCQivnXFVVSmm9Xi+Xy53sFt098h4jS38A4zO+b+qlKG+MQkppPp83TdM0TVtpdhoVNo/Xz24AwOAVGRS2k8HOrAcAYI8KCwrtGMbtZLBer/sqDACMXmFBIaVUVVXXotAmBuMZAeCZlDeYsWmanHM3YnE+n0dTJQGAT1ReUEgptastpf/viQAAnsnYZpuUMn+mlHKyRxZc2iMLLk1QKQezlHI+XHljFACAgxEUAICQoAAAhAQFACAkKAAAIUEBAAgVuY7C/aK7R45svgoAHMAIg4JAAAD7MsKgAExB1HY4NKWU01csIoICUJ5SLmo5l1HUQsIM/TCYEQAICQoAQEhQAABCggIAEBIUAICQoAAAhASFg7q6urq4uDg6OkopHR0dvXz58urqqu9CAUBIUDicq6ury8vL6+vrdmGTzWZzc3NzeXkpKwAwWHlkq3HlPNw9uri4uL6+fv/+/fbG09PTk5OTt2/f9lUqDibnPNS6yXMpaMGlwX5ydob88b6tlHI+3Oj2Z8Bn6OjoKCrbYMvMHgkKEyQo7NGQP963lVLOhxvhEs6lrKy+bbBlHll1B+CxRhgUBntt06IAQHFGGBQG6/z8/Obm5t27d9sbz87Ojo+P+yoSANzPrIfDef369Y8//nh6etptOTs7+/Dhw6tXr3osFQDcQ1A4nBcvXnz33XcnJyfdluPj4zdv3pyfn/dYKgC4x9gGZ5Yy3LSIcg52iGW5Bn/O2TOzHvaoiI/NVE45H84YBe4zrtoOwKPpegAAQoICABASFACAkKAAAIRGOJixlLH6pZQTgCkbW1AoZZR+KZOmAJg4XQ8AQEhQAABCggIAEBIUAICQoAAAhAQFACAkKAAAIUEB4LlYLoUREBQAgJCgAACEBAUAIFR2UKjruu8iAMCYFRwU6rper9dN02xvzLmMn1KKCtCvq6uri4uLo6OjlNLR0dHLly+vrq76LtS0lBoUmqZZr9d3PbIp5KeIogL06erq6vLy8vr6erPZpJQ2m83Nzc3l5aWscEilBoXZbDafz/suBQDP6Pe///3Pf/7z9+/fd1vevXv3i1/84ssvv+yxVFOTNwXO863ruq7rxWKRc16tVtsjFcppLc8lfGXPBdYO4NFyTsO8FhwdHUUFG2aBU0o5F3lhvcfP+i7Aoy0Wi9tDEwD4FLmcr1mt4gpcrsKCQtM0y+VyZGENoHfD/FwttEWh7yLsWWFBYbFYpJ/OipzNZlVVaWAAGJ/z8/Obm5t3795tbzw7Ozs+Pu6rSBNUXlDYzgTr9bqqqjY9ADAyr1+/vry8PD097bLC2dnZhw8f3rx502/BJqXsMRcGMz4zgxlhEgY7mDGldHV19eWXX/71r39t/zw/P3/16tX5+Xm/pbqHwYwAcDgvXrx4+/ZtGuMFuBRlBwWVBgCeVakLLgEAByAoAAAhQQEACAkKAEBIUAAAQmXPeoCClLPIRxnMeYLDEBTgkFzc9kXsggPR9QAAhAQFACAkKFA8ff8Az0dQAABCBjMCkHIJTXNFFHJ8BAWAqStirmnOxZRzZAQF7lNKjS+lnADFERS4RwnpPaWUcjlFBSiMwYwAQEhQAABCggIAEBrfGIWCRrUVUVR9/wCTNrqgUMTsmVTIRB9zCRgw1XO/hv+BRF9GFxSASXBZ2y+xi5CgAEAZLTRFFHJ8BAUAimihKWXFlLHFGbMeAICQoAAAhAQFACAkKAAAIUEBAAgJCj2xuAkAJRAUAICQoAAAhAQFACAkKAAAIUs4w8GMbWHXvhkRDIcgKMABme2yL+4OBIei6wEACAkKAEBIUAAAQoICABASFACAkKAAAIQEBQAgZB0F7lfKbPVSyglQGEGBWCmrA+VcTFEBSlNk10PTNIvFoq7rxWLRd1kAYMzKCwqLxWI2mzVNk1JaLpfZSq6T9cMP6Y9/TJ9/nlJKn3+e/vVf0w8/9F0mgLEpLygsl8v5fN40TdM0m80mpaRdYYp++CH9y7+kv/0t/f3vKaX097+n6+v061/LClOS/Uzsp5STPjZ5U1rnbs55tVrVdd3+2f7SNjCkUZ4i7vTHP6a//S3953/+ZONvf5s++yz927/1VCbgOZUyGimXd2G9X/H7k3Oez+ddo4KgMBWff/5/bQk7Pvvs7u1A6QSFnhS8P03TzGazlNL2LggK+2T8x94V+3aD/gkKPSlvjEKrruvZbFZV1cjOx7BsNsP9+Yd/uLvMn33Wf9nu+QGezDuoJ0UGhXamw2q16oYmMDm/+U367W93N/7ud+k3v+mjNACjVV4LSc65qqooImgrn4offki//nX6539O//Ef/7fld79L//M/6c9/Tv/0T72WDJi20XU9FLYyY5sP6rreCQrdJAim4h//Mf35z+nf/z199lm6vk6ffZZOTqQEgL0rLPgsFovlcrmzcbuBQYvCFJUyxAmYgtG1KIxuf/ouAD0QFIDhGF1QKHIwIwBwGIICABASFACAkKAAAIQEBQAgJCgAACFBAQAICQoAQEhQAABCggIAEBIUAICQoAAAhAQFACAkKAAAIUEBAAj9rO8C7FvOfZdgXMZ1V3UAHmtsQWHzX32XYETyL/suAQB90/UAAIQEBQAgJCgAACFBAQAICQoAQEhQAABCggIAEBIUAICQoAAAhAQFACAkKAAAIUEBAAgJCgBASFAAAEJju800DFfOfZdgXDabvksAkyAowOFs/qvvEoxF/mXfJYDJ0PUAAIS0KFA+TdDwifSLERMUANAvtjfj6xfT9QAAhAQFACAkKAAAIUEBAAgJCgBAyKwHoExm9MFBCArcy2cxg2QuHxyMoEDIZzEApQaFr776KqV0cXHxq1/9qu+yAMBolTeY8dtvv805f//9999///0XX3zRJgYA4DnkTWnr5OecLy4u/vKXv6SUvvrqq6+//vonu/Df+tQB6E3+ZSruwnq/8loUUkp/+MMf2l/+9Kc/pZS+/fbbXosDAKNVWFBoM8HOuITvv/++p+IAwMiVOphx23ZQGN9tuwCgR2MIChcXF93vI+sZAoB+Fdb1AAAcUmFBoR2dsDN6cbtFAQDYo8KCQkrp4uLiiy++aH9vF1Gw5hIAPJPygkK7gkLOOef89ddff/PNN3Vd13W9WCzaJ9Q/1TRNj6XtRdM0i8Xizn2Pto9edEzUlvbILBYLtaUVHZDJVpXuo3Vn450HoWma7U/jEXv4YWk3dko8OOUFhZTSarX65ptvvvnmm6qqXr16lVJar9ftuWmaZr1e91y+Xi0Wi9ls1h6N2WxW13X3UM55uVze3j560TFRW7oj0zTNbDbb/gibZm2JDshkq0rTNMvlcufKF9WN9ui1/yrnPOIs9ajDslwui688m1HodmS1Wo1mp54mpTSfz9vf26OxWq02m818Pt8+Mt32KYiOidqyXQ22j8Zka0t0QCZYVVarVVVV7WVi++zfUze2f6+qqqqqQxX2cD7xsBRqDFW/qqruMrBztiZop1J2f6aUtt+3Y30b3yk6JhOvLbcvfl2immZtueeATLCqrFar+Xze7vjO2+fOurFziMYarR57WDZb32PLVWTXw7bFYrFer7dbCNP/j2DIOZfYG/SJqqrq2k7b5q+uEWy7Nayu6+Jbwx4sOiZqyz0mW1vuNMGq0vam37mnd9aN20M6bm8cgacdlrqu25pTaC9e8QsuLZfLNrpum8/n7YiStsdoCu/qTts72PYUppRuH5wJuv+YTLa2tJ9Z3ade+9/xfbI/3EcPyGSrygN1bfJ0uqDQ1pZ2+EJ577K+mzQ+yUfbA9uKe7DyDEHaagHbbh9LW/30m4k1pUbHZMcEa8t2Zmp3vz1Qk60t0QHZMamqkm61sd9ZN273T0VvtHF44GHZsT1GqiBldz0sl8v2fESmFvm7qR/tn74jpscck6nVlpRSXdebzWa1Wq1Wq+6rT89l6tUDD8gEq8pH7fRPqU53KvSAFBwU2oq4847dadWZ+DUypVRVVXsQul9aTdNMtp2wOxRqS/v2aad3t1vaXyZbW6IDoqp0orqxcwmc2iGKDks0oKEwfTdpPN2dbTs7O5V+2hw0BeneqYC3f5+C6JioLWmraX27OX2ytSU6IFOuKjtn/566sX1YUtBrMxoPPCw7fZ2FdlqVV+JOVBG3Y9C4a+qdbo/U6x7a7qaZzsfc5t5jorZEXxumWVvuOSCTrSq3Y2JUN+45euPz8MOy0xpXYubOmzHel1n32D1HoJsiODXRMVFb1JYdqspDRHVj4kdplIdlnEEBANiLggczAgDPTVAAAEKCAgAQEhQAgJCgAACEBAUAICQoAAAhQQEACAkKAEBIUAAAQoICABASFACAkKAAAIQEBQAgJCgAACFBAQAICQoAQEhQAABCggIAEBIUAICQoAAAhAQFACAkKAAAIUEBAAgJCgBASFAAAEKCAgAQEhQAgJCgAACEBAUAIPS/j0FMNIdK36EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(SF_0.value * data.Integral()/test_TT_hist_new.Integral())\n",
    "print(SF_1.value * data.Integral()/test_DY_hist_new.Integral())\n",
    "print(ERR_0.value * data.Integral()/test_TT_hist_new.Integral())\n",
    "print(ERR_1.value * data.Integral()/test_DY_hist_new.Integral())\n",
    "\n",
    "sf_0 = SF_0.value * data.Integral()/test_TT_hist_new.Integral()\n",
    "sf_1 = SF_1.value * data.Integral()/test_DY_hist_new.Integral()\n",
    "\n",
    "hs = R.THStack(\"stack\",\"V_mass_Z\")\n",
    "\n",
    "hs.Add(h_Other_4b)\n",
    "h_Other_4b.SetFillColor(800)\n",
    "\n",
    "hs.Add(h_TTBB_4b*sf_0)\n",
    "h_TTBB_4b.SetFillColor(432)\n",
    "\n",
    "hs.Add(h_TT_4b*sf_0)\n",
    "h_TT_4b.SetFillColor(600)\n",
    "\n",
    "hs.Add(h_DY_4b*sf_1)\n",
    "h_DY_4b.SetFillColor(400)\n",
    "\n",
    "h_Data_4b.SetMarkerSize(1)\n",
    "h_Data_4b.SetMarkerStyle(20)\n",
    "h_Data_4b.Sumw2()\n",
    "\n",
    "c = R.TCanvas()\n",
    "R.gStyle.SetOptStat(0000)\n",
    "hs.Draw('hist')\n",
    "h_Data_4b.Draw('SAME')\n",
    "hs.Draw('histSAME')\n",
    "h_Data_4b.Draw('SAME')\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "municipal-aquarium",
   "metadata": {},
   "outputs": [],
   "source": [
    "fin = R.TFile(root_file,\"READ\")\n",
    "h_Data_4b = fin.Get('Data_SB_4B_Z_High_V_mass_Z')\n",
    "h_DY_4b = fin.Get('DY_SB_4B_Z_High_V_mass_Z')\n",
    "h_Other_4b = fin.Get('Other_SB_4B_Z_High_V_mass_Z')\n",
    "h_TT_4b = fin.Get('TT_SB_4B_Z_High_V_mass_Z')\n",
    "h_TTBB_4b = fin.Get('TTBB_SB_4B_Z_High_V_mass_Z')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "stone-statement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cppyy.gbl.TH1F object at 0x560bff9646c0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h_Data_4b.Rebin(3)\n",
    "h_DY_4b.Rebin(3)\n",
    "h_Other_4b.Rebin(3)\n",
    "h_TT_4b.Rebin(3)\n",
    "h_TTBB_4b.Rebin(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "vocational-nursing",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TH1F::Sumw2>: Sum of squares of weights structure already created\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArgAAAHYCAIAAAApvgy/AAAABmJLR0QAAAAAAAD5Q7t/AAAdh0lEQVR4nO3dPY8b16EG4HOIdIuFrgq7Si5uv1tGWGgbDuut4h+R+AOwm7iIGw6rW8SNBNybwD9CbqSawyK2Ad5WKoMUKgQ19kI/gLeYhKG4e1b7MdyZc/g8MIzdITU6M3M48/J8Ka5WqwAAcJlR3wUAAIZLUAAAkgQFACBJUAAAkgQFACBJUAAAkgQFuG9N08QYY4y3fkOR4ofUdd13GWEfCQpw36qqan9omubSN7RPxPF4fE8FAkiLFlyC+1fX9Ww2G4/Hl2aFti1hPp+vI8U+q6pqsViEENysoBeCAvSjTQMXP4BN00wmk0tf2kNtogpiE/RH1wP06WKLQrtlOp3edD9th0XTNFVVxRirqlp36td1vd546d/Yvrr1p654zwd3kupVuelBtSlhOp1KCdCbFdCHNgqMx+Ot7e0Hcz6f32JvF+PFpYFjc+epRHJx5xf3fP033E67n4unCLhPggL05uIjeT6f3y7Brx/V6xCw+fBev60dILn5CL8YHT645WIhP/iGW1iP5bzLToC70/UAvWmfhZtN/bfrd9jc4bqJfr3b9WN7vXHdL7D+6zYb9tu/ff2e9o9svqeqqvF4vH6Qf/ANt1DXtQGMMBR9JxXYX+0jfLNp/dafynXXw+bGS/cWPtSYv7Wrdc5I9YZ88A031fkOgbvQogC9ab+Ct1+dQ3pZhZvu8BbasZDtaMR2/ODmPtu2gclkculAxQ++4abaSR8GMMJACArQp83ehzv2O9xOGw4mk8lsNlssFpd2GTRNM51O2+2LxaINBFs9Jle/4fraWaPj8dg6jDAUfTdpwF7bHPd3l49kGy+22uov3WHY6HpYZ4LNP9gWKTVnYT6fX/qnrv+GKxjACAOkRQH6tLWc8z0v27weMLjZyL/VcbBeoaHV9iy0uWTdEHL1G65pPYBxc/Ql0DtBAXrWPlPbjvkh9MpvjVFoVz269JHflvaDb7gOayvBYAkK0LPN5+I9d8y3DRjr4Yftv1rZvrRuV2iL10aBpmna9oP2od6+9ME3XMf6wGezmX89Eoal774PoIMlCG83RmF1YZWC9qWt+8Ptlne80QCF6/S53H2pR+AW/KNQsO/aZoDwfntGO1tya+xC+7at7dd/A5AjQQEASPpV3wUAkq65ctGQv74XcAiw57QowHBVVbVet/EK8/l8sA/a9ejIq7kRwWAJCgBAkumRAECSoAAAJAkKAECSoAAAJAkKAECSoAAAJAkKAECSoAAAJAkKAECSoAAAJAkKAECSoAAAJAkKAECSoAAAJAkKAECSoAAAJAkKAECSoAAAJAkKAEDSr/ouQMdijH0XAYC9tlqt+i5Cl0oLCqG4KwRXiDEOv8JnUUjoSnnfV3U9AABJBbYopNKc7zQAcFMFBgWBAAC6ousBAEgSFACAJEEBAEgSFACAJEEBAEgSFACApAKnR1pHAQC6UmBQEAgAoCu6HgCAJEEBAEgSFACAJEEBAEgSFACApAJnPQB3kZpgPLR9mt8E90NQALZ1+wiOseMdtvsE7keBQcGCSwDQlQKDgkAAAF0xmBEASBIUAIAkQQEASBIUAIAkQQEASBIUAICkAqdHWkcBALpSYFAQCACgK7oeAIAkQQEASMoyKDRNU9d1VVVN0/RdFgAoWX5Boa7ryWTSRoTJZFJVVc8Fgnu3XC5PT09Ho1EIYTQaPX78eLlc9l2oba9fh2++CQ8fhhDCw4fhT38Kr1/3XSbg5vILCrPZbDqdNk3TNM18Pl8sFtoV2CvL5fLs7Oz8/Lwdt7tard69e3d2djaorPD6dfjDH8LPP4dffgkhhF9+Cefn4fe/lxUgP/kFhRDCuhVBcwJ76Kuvvvroo49evXq13vLy5cuPP/74yy+/7LFUW/73f8N//mf461//veUvfwn/9V/hf/6nvzIBtxKzm0xYVdVisZjP5yGEuq4Xi8XmIcSY3xHBjYxGo1Ql76TyxxjvvpuHD//ZlrDlwYPLt99UjCZCM1DlPYayPJ7NJZXm8/lmu0JqtaWr5XgSyM7tKicpPrYMU3lBIb+uhxjjeDxerVar1Wo6na4HNq6tbq6nQ2G/3KJmXuqKwNHJ/kMIq9Vd//uP/7i8hA8edLBzH1m4T5kFhTYTrJNBXdebv8I+ODk5OTo62tp4fHx8cnLSS3ku9emn4bPPtjd+/nn49NM+SgPcQWZB4aLxeCwosFeePn369u3bzaxwfHz85s2bJ0+e9FiqLV98Ef7xj/eywuefh7//PXzxRX9lAm4ls6DQDkdoGxJCCE3TLBaL9a+wDx49evTixYvDw8P1loODg+fPnw+qReHXvw7ffRcePAgPHoQQwoMH4fAwfPdd+M1v+i4ZcEP5jblommYymax/nU6nm0GhvFEkcIVdVPhOZj28v8PuRxWY9cBglfcYyvV42u6Gi+solHeF4AqCAgxNeY+h4o6nuCsEVxAUYGjKewz9qu8CdC81eaywKwcA96DAoCAQAEBXMpv1AADcJ0EBAEgSFACAJEEBAEgSFACAJEEBAEgSFACApALXUbDgEgB0pcCgIBAAQFd0PQAASYICAJAkKAAASYICAJAkKAAASYICAJBU4PRI6ygAQFcKDAoCAQB0RdcDAJAkKAAASYICAJAkKAAASYICAJAkKACEkJ5ZDXtOUAAAkgpcR8GCSwDQlQKDgkAAAF3R9QAAJGXWotA0TV3XWxurqrq4EQC4u8yCwkWLxaLvIgBAsWLuPfoxvncIW79C2XZR4WOM3e4yxtD5hzLG7kcjuXvQifIqUt5jFKqqmk6nfZcCAIqVcddDXdeLxaJpmr4LAgDFyriFJMY4n8+rqtraeItd5XsS2HO6HjrdZ8b3Q4ajvIqUa4tCO81hKyW0CrtCANCjXMcozGYzoxMAYNeybCFpmmYymVxa8vLafOAKuh463ae7Bx0oryJl2aJgACMA3I8sg0+McTweXxoXyotycAUtCp3u092DDpRXkbIczFjYNQCAwcqy6wEAuB+CAgCQlGXXw9VSay7psACAmyowKAgEANAVXQ8AQJKgAAAkCQoAQJKgAAAkCQoAQJKgAAAkFTg90joKANCVAoOCQAAAXdH1AAAkCQoAQJKgAAAkCQoAQJKgAAAkCQoAQJKgAAAkFbiOggWXAKArBQYFgQAAuqLrAQBIEhQAgCRBAQBIEhQAgCRBAQBIEhQAgKQCp0daRwEAulJgUBAIAKAruh4AgCRBAQBIyjUo1HVdVVVd130XBABKFnPs0W+HK47H48ViMR6Pm6bZfCnHI4Lb2UWFjzF2u8sYQ+cfyhi7H43k7kEnyqtI+bUoVFU1Ho9Xq1XTNPP5fLFYbAYFAKBD+QWfGON8Pq+qKvVqdkcEt6ZFodN9unvQgfIqUmbTI9vGg83RCYYpAMDuZBYUWjHG8XgcQlgsFrPZbCu7pRZcukJh6Q8AupLfGIUQwnQ6bZqmaZr2Ab/VqLC6uX4OAwAGL8ugsJkMtmY9AAAdyiwotGMYN5PBYrHoqzAAULzMgkIIYTwer1sU2sRgPCMA7Eh+gxmbpokxrkcsTqfT1FRJAOCO8gsKIYR2taXwr54IAGBHSlsXoryVLuAKFlzqdJ/uHnSgvIqUZYvC1VLrKBR25QDgHhQYFAQCAOhKfrMeAIB7IygAAEmCAgCQJCgAAEmCAgCQJCgAAEmCAgCQVOA6ChZcAoCuFBgUBAIA6IquBwAgSVAAAJIEBQAgSVAAAJIKHMwIw5SajzPM3QK0BAW4P53PyImx+30CbCowKFhHAQC6UmBQEAgAoCsGMwIASYICAJAkKAAASYICAJAkKAAASYICAJAkKAAASQWuo2DBJQDoSoFBQSAAgK7oeoCMZZGKsygkkJJfi0LTNHVdb23ppygAULr8WhSaplksFn2XAijEcrk8PT0djUYhhNFo9Pjx4+Vy2XehYEBidj36VVWFdCtCjPkdEXsixqhudiXGbkYjLZfLs7Ozjz766NWrV+2Wo6Ojt2/fvnjx4tGjR3ffP3uovMdQfi0Ki8WizQoAd/TVV19tpoQQwsuXLz/++OMvv/yyx1LBoOQXFEIITdPEGGOMVVUZoADc2k8//bSZElovX7786aefeikPDFCWQSGEMJ/P5/N5CGEymWy9FG+ujyMA7uQWn/SLrmgi7mT/0e2F/GXflRJjnE6n63kQV3/yoUfRGIXudDVGYTQapfbjTsLtlPcYym965JbxeKz3Abidk5OTd+/evXz5cnPj8fHxwcFBX0WCocms66Fpmq2RjMY2Arf29OnTt2/fHh0drbccHx+/efPmyZMnPZYKBiWzoFBV1WKxWHc0tD8ICsDtPHr06MWLF4eHh+stBwcHz58/Pzk56bFUMCj5daU0TbM5gHFzgEIosXOIYhij0KGuxii8v093DzpQXkXK9XjacQkX2xLKu0IUQ1DokKDAYJVXkYo7nuKuEMUQFDokKDBY5VWkzMYoAAD3KfvpkRelVjgpLOIBwD0oMCgIBADQFV0PAECSoFAOq8oD0DlBAQBIEhQAgCRBAQBIEhQAgKQCp0daRwEAulJgUBAIAKAruh4AgCRBAQBIEhQAgCRBAQBIEhQAgCRBAQBIEhQAgKQC11Gw4BIAdKXAoCAQAEBXdD0AAEmCAgCQJCgAAEmCAgCQJCgAAEmCAgCQVOD0SOsoAEBXCgwKAgEAdEXXAwCQJCgAAEl5B4WqqvouAgCULOOgUFXVYrFomqbvggBAsXINCk3TLBaLvksBAIXLNShMJpPpdNp3KQCgcFkGhaqqptNpXdd9FwQACpffOgp1XV89NCG14NIVLL0AAJfKLCg0TTObza5+rnvqA0BXMgsKbXfD5qzIyWQyHo/NfQCAXcgvKGxmgsViMR6PDVYAgB2JWTfUxxjn8/lmA0OMeR/RXezzsWchxuj6dCXG7jsZfYLoRHkVKctZDwDA/cis62FLYakNAIZGiwIAkJR3i8KlUusoaH4AgJsqMCgIBADQFV0PAECSoAAAJAkKAECSoAAAJAkKAECSoAAAJAkKAEBSgesoWHAJALpSYFAQCACgK7oeAIAkQQEASBIUAIAkQQEASCpwMCOwD1Lzm4a2T8OryZ2gAORnFw/fGLvf7Q6CB9w3XQ/cq118YwNgdwpsUbDgEgB0pcCgIBAAQFd0PQAASYICAJAkKAAASYICAJAkKAAASYICAJBU4PRI6ygAQFcKDAoCAQB0RdcDAJAkKAAASVkGhaZp6rquqqppmr7LAgAlyy8o1HU9mUzaiDCZTKqq6rlAfVsul6enp6PRKIQwGo0eP368XC77LhQAhcgvKMxms+l02jRN0zTT6XSxWPRdoj4tl8uzs7Pz8/N2COdqtXr37t3Z2ZmsAEAnYl5zBJqmmUwmm2WOMc7n83W7QoyZHdEdnZ6enp+fv3r1anPj0dHR4eHhjz/+2FeprrBvF2hTjHFfDz0PMYbOL1CM5mHtnfLucnkfT1VVi8ViKzdkfUQ3NRqNUsc7zPOwbxdok6AwcIICnSjvLpfrOgptRAghzOfzrZdSCy5d4f4v6i0K2ctfUVh1B+Cmcg0Kzb9s9USETJ5tXRUyuxYFAPKSfQtJjHE6ndZ1vf419yO6kcePH7979+7ly5ebG4+Pjw8ODn766ae+SnWFfbtAm3Q9DJyuBzpR3l0us1kPdV3fQ6N9Rp4+ffr27dujo6P1luPj4zdv3jx58qTHUgFQjPyCwvr/IYR2NYV9Xkrh0aNHL168ODw8XG85ODh4/vz5yclJj6UCoBj5tZDUdT2bzda/bvY7hBLbfK4vi2PPopA7outh4HQ90Iny7nK5Hk+qLaG8K3R9WRx7FoXcEUFh4AQFOlHeXa644ynuCl1fFseeRSF3RFAYOEGBTpR3l8t1euQVUqMdC7tyAHAPCgwKAgEAdCWzWQ8AwH0SFABCCN0PUIAyCAoAQJKgAAAkCQoAQJKgAAAkCQoAQFKB6yhYcAkAulJgUBAIAKAruh4AgCRBAQBIEhQAgCRBAQBIEhQAgCRBAQBIKnB6pHUUAKArBQYFgQAAuqLrAQBIEhQAgCRBAQBIEhQAgCRBAQBIKnDWA/smNSEWgLsTFCiBKbEAO1JgULDgEgB0pcCgIBAAQFcMZgQAkrIMCk3T1HVdVVVd132XBQBKll9QqOt6Mpk0TRNCmM1mRrxnYblcnp6ejkajEMJoNHr8+PFyuey7UAB8WH5BYTabTafTpmmapmmHI2hXGLjlcnl2dnZ+ft5er9Vq9e7du7OzM1kBYPhidkP/Yozz+byqqvbX9oe2gaF9Nbsj6spgj/309PT8/PzVq1ebG4+Ojg4PD3/88ce77z/GOMjjhhCj4dV7Z7C34lvL/nhijNPpdN2oUN4Vur7BHvtoNEoVrJMCCwoMlqCwhwZ7K761jI+naZrJZBLef9jcbshCvidhU+e1M6PxH51fwBgt4kQH9jYo5PKw3EU5czn268tvjEKrqqrJZDIejy9ej9XN9XIIw3eLM3mpKwJHJ/u/z3MCsG+yDArtg2c+n6+HJjBkJycnR0dHWxuPj49PTk56KQ8A15dfC0mMcTwepyJCeW0+1zfYY29nPXz88ccvX75stxwfH7958+b58+edZIVdjFHQ9UAndD0MnK6H68hsCec2H1RVtRUU1pMgGKBHjx69ePHiyy+/XG85ODjoKiUAsFOZBZ+6rmez2dbGzQaG8qLc9WVx7DvL793uUosC3dCiMHBaFK6juOMp7gpdXxbHLiiwVwSFgRMUriPLwYwAwP0QFACApMwGM15HatZ+YW1BAHAPCgwKAgEAdEXXAwCQJCgAAEmCAgCQJCgAAEmCAgCQJCgAAEkFTo+0jgIAdKXAoCAQAEBXdD0AAEkFtijQlVQnzjB3C8AuCApcxT/fDLDndD0AAEmCAgCQJCgAAEnGKPTDOEEAslBgUMhlwSXjBAEYvgKDwtACAQDkyxgFACBJUAAAkgQFACBJUAAAkgSFchjECUDnCpz1ADAcuaxuYr4YKQUGhVzWUQCKl8tdJ5MwQz8KDAoCAQB0xRgFACCpwBaFXHoEAWD4sm9RqOt689fVKpv/gFuLMZv/IHd5B4WmaWazWdM0fReEQrx+Hb75Jjx8GEIIDx+GP/0pvH7dd5lIWuXw335ZLpenp6ej0SiEMBqNHj9+vFwu+y7UJXIp50DkGhSapqmqajKZ9F0QyvH6dfjDH8LPP4dffgkhhF9+Cefn4fe/7ywr9P7V1pdgdmq5XJ6dnZ2fn7cjyler1bt3787Ozob2DM6lnMMRM50j0DRN25Awm83m83lVVf96JY+bXC734r36t7C/+Sb8/HP461/f2/jZZ+HBg/Df/93B/mMMmXzFjIO9RmtOZrdi7GC+2Onp6fn5+atXrzY3Hh0dHR4e/vjjj3fceYd2Xc4Yc32wpmR/PDHGbIPC8M989/e4IQeFhw//2Zaw5cGDy7ffVCYXPeTyIcrlZA62wm/qJCiMRqPUTgb1oNl1OcsLCgXOeqBDu2j5yKU1Ze38PL8y301R9ziuaafzxXKZjJZLOe+ZoMAVdvHAiDvYbTdf2u6lRQEGSotC6K5F4e47GRRBAf7p00/D+Xn4y1/e2/j55+HwsKcCQVZOTk7evXv38uXLzY3Hx8cHBwd9FelSuZRzOHKd9QCd++KL8I9/hM8++/eWzz8Pf/97+OKL/soE+Xj69Onbt2+Pjo7WW46Pj9+8efPkyZMeS3VRLuUcjtKCQu/zykw/y9evfx2++y48eBAePAghhAcPwuFh+O678Jvf9F0yyMGjR49evHhxuNEEd3Bw8Pz585OTkx5LdVEu5RyO7Adnbs168AwevOGOUXhvjzuYnZHPrAc6tEezHt7fYR4Pl12UM5djv77sxygUdj0AetH5ELxcxvTlUs4eZR8UgL3k5t6lLNrkdmFnbYdFERSAPOXyIILMlTaYEQDokBYFuE+5fL/M4cs6cC8EBUqQzVLT2bSW5xJogJ0TFPqSy404hwdbPktNd73D3cgizQD3RVDoz/Bvx8ZhAYOxoxuS+9wHCQpcbRefoZ206e9gn8DQ7KKdb1/bDq9NUCBtF20epi0DZMX0SAAgSVAAAJJ0PXC/hj+EEzq1tz1je3vg5REUAHZqP8PxLgYJ0o/ygoIQCwCdKS4oaNkGgO4YzAgAJBXXogBAmXbUs6zD+gMEBcqQywqSwB3sYrk2S8B9iKBA/nJZQRIgQ8YoAABJggIAkCQoAABJxigA7FRpQ9uubW8PvDSCAsDO7O2QWMOBC6LrAQBIEhQAgCRdD8D7clkuRss23AtBAe5LLg/gEFb/13cJPiT+tu8SwN4QFOD+DP8BDLDFGAUAICnXFoWvv/46hHB6evq73/2u77KUK5+mct3VADsSV7ndYb///vtPPvnk9PQ0hPDDDz/88Y9//POf/7x+NZ8nWw5izKKpPP42m39TLovzmYWdXHQ6lMs6Cjv6pGdx7NeW3/HEGE9PT//2t7+FEL7++utvv/128xAEhS5l8mDLaFxbFuczC4LC0AkKBcnveGKMz549W/c4bP/aX8FuJpNW/b19sMXf7u+xZyGjdJjH87JzgkJBMhuj8P3334cQtsYl/PDDD//ekskDOOzxMxjuLpePT/xtTjelju3iwMt6AOcis6BwqR9++KHvItxGTl+J9pILBIOzi/Cxt0nu2koICu3AxlZhDT4A0C/rKAAASZkFhXYsQjtSYW2zRQEA6FBmQSGEcHp6+sknn7Q/t8suWXMJAHYkv6DQrqAQY4wxfvvtt8+ePauqqqqquq7bN1Tva5qmx9L2ommauq4vPfbU9uKlzona0p6Zuq7VllbqhOxtVVnfWrc2XnoSmqbZvBsX7Pqnpd24luPJyS8ohBDm8/mzZ8+ePXs2Ho+fPHkSQlgsFu21aZpmsVj0XL5e1XU9mUzaszGZTKqqWr8UY5zNZhe3Fy91TtSW9ZlpmmYymWzewvaztqROyN5WlaZpZrPZ1pMvVTfas9f+qRhjwVnqRqdlNptlX3lWRVgfyHw+L+agbieEMJ1O25/bszGfz1er1XQ63Twz6+37IHVO1JbNarB5Nva2tqROyB5Wlfl8Ph6P28fE5tW/om5s/jwej8fj8X0V9v7c8bRkqoSqPx6P14+Brau1h7Yq5frXEMLm57bUj/GlUudkz2vLxYffOlHtZ2254oTsYVWZz+fT6bQ98K2Pz6V1Y+sUlRqtbnpaVhvfY/OVZdfDprquF4vFZgth+NcIhhhjjr1BdzQej9dtp23z17oRbLM1rKqq7FvDri11TtSWK+xtbbnUHlaVtjf90iO9tG5cHNJxcWMBbndaqqpqa06mvXjZL7g0m83a6LppOp22I0raHqN9+FSvtb2DbU9hCOHiydlDV5+Tva0t7T1rfddr/1/enf36PnhC9raqXNO6TZ61dVBoa0s7fCG/T1nfTRp38sH2wLbi3lt5hiBstIBtto+FjX761Z41pabOyZY9rC2bmak9/PZE7W1tSZ2QLXtVVcKFNvZL68bF/qnUB60M1zwtWzbHSGUk766H2WzWXo+UfYv866kf7a++I4abnJN9qy0hhKqqVqvVfD6fz+frrz49l6lX1zwhe1hVPmirf0p1ulSmJyTjoNBWxK1P7Farzp4/I0MI4/G4PQnrH1pN0+xtO+H6VKgt7cennd7dbml/2Nvakjohqspaqm5sPQL37RSlTktqQENm+m7SuL1L23a2Diq83xy0D8KVUwEv/rwPUudEbQkbTeubzel7W1tSJ2Sfq8rW1b+ibmyelpDotSnGNU/LVl9npp1W+ZV4LVURN2NQ2TX1UhdH6q1f2uym2Z/b3OrKc6K2pL427GdtueKE7G1VuRgTU3XjirNXnuuflq3WuBwzd1yV+O8y6x674gyspwjum9Q5UVvUli2qynWk6saen6UiT0uZQQEA6ETGgxkBgF0TFACAJEEBAEgSFACAJEEBAEgSFACAJEEBAEgSFACAJEEBAEgSFACAJEEBAEgSFACAJEEBAEgSFACAJEEBAEgSFACAJEEBAEgSFACAJEEBAEgSFACAJEEBAEgSFACAJEEBAEgSFACAJEEBAEgSFACAJEEBAEgSFACAJEEBAEgSFACApP8HRH6g7ydPLxYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hs = R.THStack(\"stack\",\"V_mass_Z\")\n",
    "\n",
    "hs.Add(h_Other_4b)\n",
    "h_Other_4b.SetFillColor(800)\n",
    "\n",
    "hs.Add(h_TTBB_4b)\n",
    "h_TTBB_4b.SetFillColor(432)\n",
    "\n",
    "hs.Add(h_TT_4b)\n",
    "h_TT_4b.SetFillColor(600)\n",
    "\n",
    "hs.Add(h_DY_4b)\n",
    "h_DY_4b.SetFillColor(400)\n",
    "\n",
    "h_Data_4b.SetMarkerSize(1)\n",
    "h_Data_4b.SetMarkerStyle(20)\n",
    "h_Data_4b.Sumw2()\n",
    "\n",
    "c = R.TCanvas()\n",
    "R.gStyle.SetOptStat(0000)\n",
    "hs.Draw('hist')\n",
    "h_Data_4b.Draw('SAME')\n",
    "hs.Draw('histSAME')\n",
    "h_Data_4b.Draw('SAME')\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "supposed-footwear",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h_Data_4b_for_fit = h_Data_4b - h_Other_4b\n",
    "# h_Data_4b_for_fit = h_Data_4b\n",
    "\n",
    "h_TT_4b_for_fit = h_TTBB_4b + h_TT_4b\n",
    "\n",
    "hs = R.THStack(\"stack\",\"V_mass_Z\")\n",
    "\n",
    "hs.Add(h_TT_4b_for_fit)\n",
    "h_TT_4b_for_fit.SetFillColor(600)\n",
    "hs.Add(h_DY_4b)\n",
    "h_DY_4b.SetFillColor(400)\n",
    "\n",
    "c = R.TCanvas()\n",
    "R.gStyle.SetOptStat(0000)\n",
    "hs.Draw('hist')\n",
    "h_Data_4b_for_fit.Draw('SAME')\n",
    "hs.Draw('histSAME')\n",
    "h_Data_4b_for_fit.Draw('SAME')\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "unauthorized-chicken",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fit status: \n",
      "****************************************\n",
      "Minimizer is Minuit / Migrad\n",
      "MinFCN                    =     -7.80132\n",
      "NDf                       =            0\n",
      "Edm                       =  2.69073e-08\n",
      "NCalls                    =           64\n",
      "TT+TTBB                   =          0.5   +/-   0.932134     \t (limited)\n",
      "DY                        =     0.564591   +/-   0.240271     \t (limited)\n",
      "\n",
      " **********\n",
      " **   67 **SET PRINT           0\n",
      " **********\n",
      " **********\n",
      " **   68 **SET NOGRAD\n",
      " **********\n",
      " PARAMETER DEFINITIONS:\n",
      "    NO.   NAME         VALUE      STEP SIZE      LIMITS\n",
      "     1 TT+TTBB      1.00000e+00  1.00000e-04    5.00000e-01  2.00000e+00\n",
      "     2 DY           1.00000e+00  1.00000e-04    5.00000e-01  2.00000e+00\n",
      " **********\n",
      " **   69 **SET ERR         0.5\n",
      " **********\n",
      " **********\n",
      " **   70 **SET PRINT           0\n",
      " **********\n",
      " **********\n",
      " **   71 **SET STR           1\n",
      " **********\n",
      " **********\n",
      " **   72 **MIGRAD        1220        0.01\n",
      " **********\n",
      " MIGRAD MINIMIZATION HAS CONVERGED.\n",
      " MIGRAD WILL VERIFY CONVERGENCE AND ERROR MATRIX.\n",
      " FCN=-7.80132 FROM MIGRAD    STATUS=CONVERGED      63 CALLS          64 TOTAL\n",
      "                     EDM=2.69073e-08    STRATEGY= 1      ERROR MATRIX ACCURATE \n",
      "  EXT PARAMETER                                   STEP         FIRST   \n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \n",
      "   1  TT+TTBB      5.00000e-01   9.32134e-01   2.04745e-03** at limit **\n",
      "   2  DY           5.64591e-01   2.40271e-01   1.71290e-03  -2.83035e-05\n",
      "                               ERR DEF= 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mc = R.TObjArray(2)\n",
    "mc.Add(h_TT_4b_for_fit)\n",
    "mc.Add(h_DY_4b)\n",
    "data = h_Data_4b_for_fit\n",
    "fit = R.TFractionFitter(data, mc)\n",
    "fitter = fit.GetFitter()\n",
    "fitter.Config().ParSettings(0).Set('TT+TTBB', 1.0, 0.0001, 0.5, 2.0)\n",
    "fitter.Config().ParSettings(1).Set('DY', 1.0, 0.0001, 0.5, 2.0)\n",
    "\n",
    "# fit.SetRangeX(1,8)\n",
    "status = fit.Fit()\n",
    "print(\"fit status: \"+str(status))\n",
    "if status == 0:\n",
    "    result = fit.GetPlot()\n",
    "    c = R.TCanvas()\n",
    "    R.gStyle.SetOptStat(0000)\n",
    "    data.Draw(\"Ep\")\n",
    "    result.Draw(\"same\")\n",
    "    c.Draw()\n",
    "\n",
    "SF_0 = ctypes.c_double(0.)\n",
    "ERR_0 = ctypes.c_double(0.)\n",
    "\n",
    "SF_1 = ctypes.c_double(0.)\n",
    "ERR_1 = ctypes.c_double(0.)\n",
    "\n",
    "fit.GetResult(0, SF_0, ERR_0)\n",
    "fit.GetResult(1, SF_1, ERR_1)\n",
    "\n",
    "\n",
    "test_TT_hist_new = fit.GetMCPrediction(0)\n",
    "test_DY_hist_new = fit.GetMCPrediction(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "demanding-layer",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.941013025161123\n",
      "0.8889711359001609\n",
      "3.6185689243548502\n",
      "0.3783166789816394\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TH1F::Sumw2>: Sum of squares of weights structure already created\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(SF_0.value * data.Integral()/test_TT_hist_new.Integral())\n",
    "print(SF_1.value * data.Integral()/test_DY_hist_new.Integral())\n",
    "print(ERR_0.value * data.Integral()/test_TT_hist_new.Integral())\n",
    "print(ERR_1.value * data.Integral()/test_DY_hist_new.Integral())\n",
    "\n",
    "sf_0 = SF_0.value * data.Integral()/test_TT_hist_new.Integral()\n",
    "sf_1 = SF_1.value * data.Integral()/test_DY_hist_new.Integral()\n",
    "\n",
    "hs = R.THStack(\"stack\",\"V_mass_Z\")\n",
    "\n",
    "# hs.Add(h_Other_4b)\n",
    "# h_Other_4b.SetFillColor(800)\n",
    "\n",
    "hs.Add(h_TTBB_4b*sf_0)\n",
    "h_TTBB_4b.SetFillColor(432)\n",
    "\n",
    "hs.Add(h_TT_4b*sf_0)\n",
    "h_TT_4b.SetFillColor(600)\n",
    "\n",
    "hs.Add(h_DY_4b*sf_1)\n",
    "h_DY_4b.SetFillColor(400)\n",
    "\n",
    "h_Data_4b.SetMarkerSize(1)\n",
    "h_Data_4b.SetMarkerStyle(20)\n",
    "h_Data_4b.Sumw2()\n",
    "\n",
    "c = R.TCanvas()\n",
    "R.gStyle.SetOptStat(0000)\n",
    "h_Data_4b.Draw()\n",
    "hs.Draw('histSAME')\n",
    "h_Data_4b.Draw('SAME')\n",
    "hs.Draw('histSAME')\n",
    "h_Data_4b.Draw('SAME')\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "negative-isaac",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "my_ml_env",
   "language": "python",
   "name": "my_ml_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}