03 Hto4L.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ROOT as r\n",
    "import numpy as np\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mcfiles = [\n",
    "    {'name':'ZZto4mu'   ,'color':r.kRed,'f':'./samples/ZZTo4mu_skim.root','Nevents':1499064,'xsec':7.6865000E-02 },    \n",
    "    {'name':'ZZto4e'    ,'color':r.kBlue,'f':'./samples/ZZTo4e_skim.root','Nevents':1499093,'xsec':7.7241300E-02 },\n",
    "    {'name':'ZZto2e2mu' ,'color':r.kGreen,'f':'./samples/ZZTo2e2mu_skim.root','Nevents':1497445,'xsec': 1.7656500E-01 },\n",
    "    {'name':'DYJetsToLL','color':r.kOrange,'f':'./samples/DYJetsToLL_skim.root','Nevents':30458871,'xsec': 2.7903452E+03 },\n",
    "    {'name':'SMHiggs'   ,'color':r.kMagenta,'f':'./samples/SMHiggsToZZTo4L_skim.root','Nevents':299973,'xsec': 1.2932700E+01 },    \n",
    "]\n",
    "datafiles=[\n",
    "    {'name':'DoubleMuBC'      ,'color':r.kBlack,'f':'./samples/Run2012BC_DoubleMuParked_skim.root','lumi':11470},\n",
    "    {'name':'DoubleElectronB'  ,'color':r.kBlack,'f':'./samples/Run2012B_DoubleElectron_skim.root','lumi':4410},    \n",
    "    {'name':'DoubleElectronC' ,'color':r.kBlack,'f':'./samples/Run2012C_DoubleElectron_skim.root','lumi':7060},        \n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "h_2e2mu_Zmass_ee = r.TH1F('h_2e2mu_Zmass_ee','h_2e2mu_Zmass_ee',130,70,200)\n",
    "\n",
    "hlist = [\n",
    "    {'h':h_2e2mu_Zmass_ee, 'fill_var':\"Zmass_ee\"},\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = r.TCanvas('c1','c1',800,600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "for sample in mcfiles+datafiles:\n",
    "    histograms = []\n",
    "    for h_dict in hlist:\n",
    "        htmp = h_dict['h'].Clone(h_dict['h'].GetName()+\"_\"+sample['name'])\n",
    "        histograms.append({'h':htmp,'fill_var':h_dict['fill_var']})\n",
    "    sample['hists']= histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'color': 1L,\n",
       "  'f': './samples/Run2012BC_DoubleMuParked_skim.root',\n",
       "  'hists': [{'fill_var': 'Zmass_ee',\n",
       "    'h': <cppyy.gbl.TH1F object at 0x7ffc3143eb10>}],\n",
       "  'lumi': 11470,\n",
       "  'name': 'DoubleMuBC'},\n",
       " {'color': 1L,\n",
       "  'f': './samples/Run2012B_DoubleElectron_skim.root',\n",
       "  'hists': [{'fill_var': 'Zmass_ee',\n",
       "    'h': <cppyy.gbl.TH1F object at 0x7ffc3143f110>}],\n",
       "  'lumi': 4410,\n",
       "  'name': 'DoubleElectronB'},\n",
       " {'color': 1L,\n",
       "  'f': './samples/Run2012C_DoubleElectron_skim.root',\n",
       "  'hists': [{'fill_var': 'Zmass_ee',\n",
       "    'h': <cppyy.gbl.TH1F object at 0x7ffc3143f710>}],\n",
       "  'lumi': 7060,\n",
       "  'name': 'DoubleElectronC'}]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datafiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'h_2e2mu_Zmass_ee_ZZto4mu'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mcfiles[0]['hists'][0]['h'].GetName()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_goodMuons(Muons):\n",
    "    ## fill your code here\n",
    "    #  \n",
    "    #  Good Muons should contain the list of good muons pt/eta/phi/mass\n",
    "    #     Good_Muons=[\n",
    "    #         {'pt':,'eta':,'phi':,'mass':}\n",
    "    #         {'pt':,'eta':,'phi':,'mass':}        \n",
    "    #     ]\n",
    "    GoodMuons =Muons\n",
    "    return GoodMuons\n",
    "def select_goodElectrons(electrons):\n",
    "    Good_electrons = []\n",
    "    for j in range(0,electrons['n']):\n",
    "        if electrons['pt'][j]>7 and abs(electrons['eta'][j])<2.5:\n",
    "            Good_electrons.append({\n",
    "                'pt':electrons['pt'][j],\n",
    "                'eta':electrons['eta'][j],\n",
    "                'phi':electrons['phi'][j],\n",
    "                'mass':electrons['mass'][j]\n",
    "            })\n",
    "    return Good_electrons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_2e2mu(electrons,muons):\n",
    "    ## fill your code here\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def invmass(Leptons):\n",
    "    m = 0\n",
    "    px = 0\n",
    "    py = 0\n",
    "    pz = 0\n",
    "    e = 0\n",
    "    for lep in Leptons:\n",
    "        px += lep['pt']*math.cos(lep['phi'])\n",
    "        py += lep['pt']*math.sin(lep['phi'])\n",
    "        theta = 2.*math.atan(np.exp(-lep['eta'])) \n",
    "        pz += lep['pt']/math.tan(theta)\n",
    "        e += np.sqrt(lep['pt']**2 + (lep['pt']/np.tan(theta))**2 + lep['mass']**2)\n",
    "    \n",
    "    m = np.sqrt(e**2 - px**2 - py**2 - pz**2)    \n",
    "    ## OR in the massless muon limit, the formula can be more compact:\n",
    "    #m  = np.sqrt(2*pt[0]*pt[1]*(np.cosh(eta[0]-eta[1])-np.cos(phi[0]-phi[1])))\n",
    "    return m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FillHistogram(t,histograms,weight,N=-1):\n",
    "    if N==-1:\n",
    "        #Process all the events by default\n",
    "        nEntries = t.GetEntries()\n",
    "    else:\n",
    "        nEntries = N\n",
    "    print('Processing %s events in the tree'%nEntries)\n",
    "    for i in range(0,nEntries):\n",
    "        t.GetEntry(i)\n",
    "        nMuon   = t.nMuon\n",
    "        mu_pt      = np.array(t.Muon_pt)          # note: this is an array\n",
    "        mu_phi     = np.array(t.Muon_phi)         # note: this is an array\n",
    "        mu_eta     = np.array(t.Muon_eta)         # note: this is an array \n",
    "        mu_mass    = np.array(t.Muon_mass)        # note: this is an array\n",
    "        mu_isTight = np.array(t.Muon_tightId)     # note: this is an array\n",
    "        mu_charge  = np.array(t.Muon_charge)      # note: this is an array\n",
    "        mu_iso     = np.array(t.Muon_pfRelIso03_all)\n",
    "        mu_dxy     = np.array(t.Muon_dxy)\n",
    "        mu_dxyErr  = np.array(t.Muon_dxyErr)        \n",
    "        mu_dz      = np.array(t.Muon_dz)        \n",
    "        mu_dzErr   = np.array(t.Muon_dzErr)                \n",
    "        all_muons   = {\n",
    "            'n'      :nMuon,\n",
    "            'pt'     :mu_pt,\n",
    "            'phi'    :mu_phi,\n",
    "            'eta'    :mu_eta,\n",
    "            'mass'   :mu_mass,\n",
    "            'isTight':mu_isTight,\n",
    "            'charge' :mu_charge,\n",
    "            'iso'    :mu_iso,\n",
    "            'dxy'    :mu_dxy,\n",
    "            'dxyErr' :mu_dxyErr,\n",
    "            'dz'     :mu_dz,\n",
    "            'dzErr'  :mu_dzErr,\n",
    "        }\n",
    "        nElectron   = t.nElectron\n",
    "        el_pt       = np.array(t.Electron_pt)         \n",
    "        el_phi     = np.array(t.Electron_phi)         \n",
    "        el_eta     = np.array(t.Electron_eta)         \n",
    "        el_mass    = np.array(t.Electron_mass)         \n",
    "        el_charge  = np.array(t.Electron_charge)      \n",
    "        el_iso     = np.array(t.Electron_pfRelIso03_all)\n",
    "        el_dxy     = np.array(t.Electron_dxy)\n",
    "        el_dxyErr  = np.array(t.Electron_dxyErr)        \n",
    "        el_dz      = np.array(t.Electron_dz)        \n",
    "        el_dzErr   = np.array(t.Electron_dzErr)   \n",
    "        all_electrons  = {\n",
    "            'n'      :nElectron,\n",
    "            'pt'     :el_pt,\n",
    "            'phi'    :el_phi,\n",
    "            'eta'    :el_eta,\n",
    "            'mass'   :el_mass,\n",
    "            'charge' :el_charge,\n",
    "            'iso'    :el_iso,\n",
    "            'dxy'    :el_dxy,\n",
    "            'dxyErr' :el_dxyErr,\n",
    "            'dz'     :el_dz,\n",
    "            'dzErr'  :el_dzErr,\n",
    "        }\n",
    "        ## collect all the quantities you need to fill here\n",
    "        quantity={\n",
    "            'Zmass_ee':0\n",
    "        }\n",
    "        goodElectrons  = select_goodElectrons(all_electrons)\n",
    "        goodMuons      = select_goodMuons(all_muons)   \n",
    "        \n",
    "        nGoodMuons = len(goodMuons)\n",
    "        nGoodElectrons = len(goodElectrons)\n",
    "        \n",
    "        if nGoodElectrons==2:\n",
    "            quantity['Zmass_ee'] = invmass(goodElectrons)\n",
    "\n",
    "        # Fill all your histograms at the end\n",
    "        for histogram in histograms:\n",
    "            histogram['h'].Fill(quantity[histogram['fill_var']],weight)\n",
    "    return "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "for sample in mcfiles+ datafiles:\n",
    "    for h_dict in sample['hists']:\n",
    "        h_dict['h'].Reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Processing sample = ', 'DoubleMuBC')\n",
      "Processing 28859 events in the tree\n",
      "('Processing sample = ', 'DoubleElectronB')\n",
      "Processing 54023 events in the tree\n",
      "('Processing sample = ', 'DoubleElectronC')\n",
      "Processing 82561 events in the tree\n"
     ]
    }
   ],
   "source": [
    "for sample in datafiles:\n",
    "    print('Processing sample = ',sample['name'])\n",
    "    tf_sample = r.TFile(sample['f'])\n",
    "    sample_tree = tf_sample.Get(\"Events\")\n",
    "    \n",
    "    ## Calculate weights for MC here\n",
    "    FillHistogram(sample_tree,sample['hists'],weight=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datafiles[2]['hists'][0]['h'].Draw()\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'f': './samples/Run2012B_DoubleElectron_skim.root',\n",
       "  'hists': [{'fill_var': 'Zmass_ee',\n",
       "    'h': <cppyy.gbl.TH1F object at 0x7fe4d37d1730>}],\n",
       "  'lumi': 4410,\n",
       "  'name': 'DoubleElectronB'}]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datafiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Nevents': 1499064,\n",
       "  'color': 632L,\n",
       "  'f': './samples/ZZTo4mu_skim.root',\n",
       "  'hists': [{'fill_var': 'Zmass_ee',\n",
       "    'h': <cppyy.gbl.TH1F object at 0x7f8183cbdc00>}],\n",
       "  'name': 'ZZto4mu',\n",
       "  'xsec': 0.076865},\n",
       " {'Nevents': 1499093,\n",
       "  'color': 600L,\n",
       "  'f': './samples/ZZTo4e_skim.root',\n",
       "  'hists': [{'fill_var': 'Zmass_ee',\n",
       "    'h': <cppyy.gbl.TH1F object at 0x7f81863e7a80>}],\n",
       "  'name': 'ZZto4e',\n",
       "  'xsec': 0.0772413},\n",
       " {'Nevents': 1497445,\n",
       "  'color': 416L,\n",
       "  'f': './samples/ZZTo2e2mu_skim.root',\n",
       "  'hists': [{'fill_var': 'Zmass_ee',\n",
       "    'h': <cppyy.gbl.TH1F object at 0x7f81863e8380>}],\n",
       "  'name': 'ZZto2e2mu',\n",
       "  'xsec': 0.176565},\n",
       " {'Nevents': 30458871,\n",
       "  'color': 800L,\n",
       "  'f': './samples/DYJetsToLL_skim.root',\n",
       "  'hists': [{'fill_var': 'Zmass_ee',\n",
       "    'h': <cppyy.gbl.TH1F object at 0x7f81863e8e00>}],\n",
       "  'name': 'DYJetsToLL',\n",
       "  'xsec': 2790.3452},\n",
       " {'Nevents': 299973,\n",
       "  'color': 616L,\n",
       "  'f': './samples/SMHiggsToZZTo4L_skim.root',\n",
       "  'hists': [{'fill_var': 'Zmass_ee',\n",
       "    'h': <cppyy.gbl.TH1F object at 0x7f81863e9950>}],\n",
       "  'name': 'SMHiggs',\n",
       "  'xsec': 12.9327}]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mcfiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "h_2e2mu_Zmass_ee_DoubleMuBC\n",
      "h_2e2mu_Zmass_ee_DoubleElectronB\n",
      "h_2e2mu_Zmass_ee_DoubleElectronC\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "leg = r.TLegend(0.7,0.7,0.9,0.9)\n",
    "r.gStyle.SetOptStat(0)\n",
    "for i,h in enumerate(hlist):\n",
    "    hStack = r.THStack(h['h'].GetName()+\"_st\",\"\")\n",
    "    for sample in datafiles:        \n",
    "        h_sample= sample['hists'][i]['h']\n",
    "        print(h_sample.GetName())\n",
    "        leg.AddEntry(h_sample,sample['name'])\n",
    "        h_sample.SetLineColor(sample['color'])\n",
    "        h_sample.SetFillColor(0)        \n",
    "#        h_sample.Draw(\"same\")\n",
    "        hStack.Add(h_sample)\n",
    "#    leg.AddEntry(h['h'],\"All mc\")\n",
    "    hStack.Draw('same hist')\n",
    "leg.Draw(\"same\")\n",
    "c.Draw() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def output(samples):\n",
    "    outf = r.TFile.Open('Hto4l_histograms.root','RECREATE')\n",
    "    for sample in samples:\n",
    "        for h_dict in sample['hists']:\n",
    "            h_dict['h'].Write()\n",
    "    outf.Close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "output(datafiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TFile**\t\tHto4l_histograms.root\t\n",
      " TFile*\t\tHto4l_histograms.root\t\n",
      "  KEY: TH1F\th_2e2mu_Zmass_ee_DoubleMuBC;1\th_2e2mu_Zmass_ee\n",
      "  KEY: TH1F\th_2e2mu_Zmass_ee_DoubleElectronB;1\th_2e2mu_Zmass_ee\n",
      "  KEY: TH1F\th_2e2mu_Zmass_ee_DoubleElectronC;1\th_2e2mu_Zmass_ee\n"
     ]
    }
   ],
   "source": [
    "f = r.TFile.Open('Hto4l_histograms.root')\n",
    "f.ls()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "st = r.THStack()\n",
    "st.Add(f.Get('h_2e2mu_Zmass_ee_DoubleMuBC'))\n",
    "st.Add(f.Get('h_2e2mu_Zmass_ee_DoubleElectronB'))\n",
    "st.Add(f.Get('h_2e2mu_Zmass_ee_DoubleElectronC'))\n",
    "st.Draw()\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}