MuInJet_class.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead.\n",
      "  \"You should import from ipykernel or jupyter_client instead.\", ShimWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import pandautils as pup\n",
    "import numpy as np\n",
    "from rootpy.vector import LorentzVector\n",
    "from rootpy.plotting.style import get_style, set_style\n",
    "import matplotlib.pyplot as plt\n",
    "import fit\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:ROOT.TClass.TClass:no dictionary for class xAOD::EventFormat_v1 is available\n",
      "WARNING:ROOT.TClass.TClass:no dictionary for class xAOD::AuxContainerBase is available\n",
      "WARNING:ROOT.TClass.TClass:no dictionary for class xAOD::AuxInfoBase is available\n",
      "WARNING:ROOT.TClass.TClass:no dictionary for class SG::AuxElement is available\n",
      "WARNING:ROOT.TClass.TClass:no dictionary for class SG::IAuxElement is available\n",
      "WARNING:ROOT.TClass.TClass:no dictionary for class xAOD::EventInfo_v1 is available\n",
      "WARNING:ROOT.TClass.TClass:no dictionary for class xAOD::Photon_v1 is available\n",
      "WARNING:ROOT.TClass.TClass:no dictionary for class xAOD::Electron_v1 is available\n",
      "WARNING:ROOT.TClass.TClass:no dictionary for class xAOD::Jet_v1 is available\n",
      "WARNING:ROOT.TClass.TClass:no dictionary for class xAOD::Muon_v1 is available\n",
      "WARNING:ROOT.TClass.TClass:no dictionary for class SG::IAuxStore is available\n",
      "WARNING:ROOT.TClass.TClass:no dictionary for class SG::IConstAuxStore is available\n",
      "WARNING:ROOT.TClass.TClass:no dictionary for class SG::IAuxStoreIO is available\n",
      "WARNING:ROOT.TClass.TClass:no dictionary for class SG::IAuxStoreHolder is available\n",
      "WARNING:ROOT.TClass.TClass:no dictionary for class xAOD::Egamma_v1 is available\n",
      "WARNING:ROOT.TClass.TClass:no dictionary for class xAOD::IParticle is available\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -- open a file to svae the names of the branches we are interested in (the ones containing Muons or Jets)\n",
    "from rootpy.io import root_open\n",
    "f = root_open(\"../MxAOD/Truth_11-30/user.mpaganin.mc15_13TeV.341175.A14NNPDF23LO_X350tohh_yybb.merge.DAOD_HIGG1D1.e3873_a766_a768_r6282_p2421.Truth_11-30_MxAOD.root/user.mpaganin.7141844._000001.MxAOD.root\", \"read\")\n",
    "t = f[\"CollectionTree\"]\n",
    "\n",
    "features = [key.GetName() for key in t.GetListOfBranches() if (\n",
    "        ('HGamMuonsAuxDyn' in key.GetName()) \n",
    "        or ('HGamAntiKt4EMTopoJetsAuxDyn' in key.GetName()) \n",
    "        or ('HGamPhotonsAuxDyn' in key.GetName()) \n",
    "        or ('m_yy' in key.GetName())\n",
    "    )]\n",
    "f.Close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X350_df = pup.root2panda(\"../MxAOD/muons/muons2/user.mpaganin.mc15_13TeV.341175.A14NNPDF23LO_X350tohh_yybb.merge.DAOD_HIGG1D1.e3873_a766_a768_r6282_p2421.muons2_MxAOD.root/*\", \"CollectionTree\",\n",
    "                        branches = features) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "signal_df = X350_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Jet(object):\n",
    "    \"\"\"\n",
    "    Better Jet Class!\n",
    "\n",
    "    Example:\n",
    "\n",
    "    >>> j = Jet(100.1, 1.2, 1.0, 12) # instantiation with assignment\n",
    "    >>> j.lv.Pt() # print pt\n",
    "    100.1\n",
    "    >>> j.pt = 205 # assign pt\n",
    "    >>> j.lv.Pt()\n",
    "    205\n",
    "\n",
    "    \"\"\"\n",
    "    def __init__(self, pt = 0 , eta = 0, phi = 0, m = 0, btag = 0):\n",
    "        super(Jet, self).__init__()\n",
    "\n",
    "        self.btag = btag\n",
    "        \n",
    "        # -- hidden to most usage\n",
    "        self._pt = pt\n",
    "        self._eta = eta\n",
    "        self._phi = phi\n",
    "        self._m = m\n",
    "\n",
    "        # -- Lorentz 4-vector\n",
    "        self.lv = LorentzVector()\n",
    "        self.lv.SetPtEtaPhiM(self._pt, self._eta, self._phi, self._m)\n",
    "\n",
    "    def _internal_update(self):\n",
    "        self.lv.SetPtEtaPhiM(self._pt, self._eta, self._phi, self._m)\n",
    "\n",
    "    @property\n",
    "    def pt(self):\n",
    "        return self._pt\n",
    "\n",
    "    @pt.setter\n",
    "    def pt(self, value):\n",
    "        self._pt = value\n",
    "        self._internal_update()\n",
    "        \n",
    "    @property\n",
    "    def eta(self):\n",
    "        return self._eta\n",
    "\n",
    "    @eta.setter\n",
    "    def eta(self, value):\n",
    "        self._eta = value\n",
    "        self._internal_update()        \n",
    "\n",
    "    @property\n",
    "    def phi(self):\n",
    "        return self._phi\n",
    "\n",
    "    @phi.setter\n",
    "    def phi(self, value):\n",
    "        self._phi = value\n",
    "        self._internal_update()\n",
    "        \n",
    "    @property\n",
    "    def m(self):\n",
    "        return self._m\n",
    "\n",
    "    @m.setter\n",
    "    def m(self, value):\n",
    "        self._m = value\n",
    "        self._internal_update()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Photon(object):\n",
    "    \"\"\"\n",
    "    Photon Class\n",
    "    \"\"\"\n",
    "    def __init__(self, pt = 0 , eta = 0, phi = 0, m = 0):\n",
    "        super(Photon, self).__init__()\n",
    "        \n",
    "        # -- hidden to most usage\n",
    "        self._pt = pt\n",
    "        self._eta = eta\n",
    "        self._phi = phi\n",
    "        self._m = m\n",
    "\n",
    "        # -- Lorentz 4-vector\n",
    "        self.lv = LorentzVector()\n",
    "        self.lv.SetPtEtaPhiM(self._pt, self._eta, self._phi, self._m)\n",
    "\n",
    "    def _internal_update(self):\n",
    "        self.lv.SetPtEtaPhiM(self._pt, self._eta, self._phi, self._m)\n",
    "\n",
    "    @property\n",
    "    def pt(self):\n",
    "        return self._pt\n",
    "\n",
    "    @pt.setter\n",
    "    def pt(self, value):\n",
    "        self._pt = value\n",
    "        self._internal_update()\n",
    "        \n",
    "    @property\n",
    "    def eta(self):\n",
    "        return self._eta\n",
    "\n",
    "    @eta.setter\n",
    "    def eta(self, value):\n",
    "        self._eta = value\n",
    "        self._internal_update()        \n",
    "\n",
    "    @property\n",
    "    def phi(self):\n",
    "        return self._phi\n",
    "\n",
    "    @phi.setter\n",
    "    def phi(self, value):\n",
    "        self._phi = value\n",
    "        self._internal_update()\n",
    "        \n",
    "    @property\n",
    "    def m(self):\n",
    "        return self._m\n",
    "\n",
    "    @m.setter\n",
    "    def m(self, value):\n",
    "        self._m = value\n",
    "        self._internal_update()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Muon(object):\n",
    "    \"\"\"\n",
    "    Muon Class\n",
    "    \"\"\"\n",
    "    def __init__(self, pt = 0 , eta = 0, phi = 0, m = 0):\n",
    "        super(Muon, self).__init__()\n",
    "        \n",
    "        # -- hidden to most usage\n",
    "        self._pt = pt\n",
    "        self._eta = eta\n",
    "        self._phi = phi\n",
    "        self._m = m\n",
    "\n",
    "        # -- Lorentz 4-vector\n",
    "        self.lv = LorentzVector()\n",
    "        self.lv.SetPtEtaPhiM(self._pt, self._eta, self._phi, self._m)\n",
    "\n",
    "    def _internal_update(self):\n",
    "        self.lv.SetPtEtaPhiM(self._pt, self._eta, self._phi, self._m)\n",
    "\n",
    "    @property\n",
    "    def pt(self):\n",
    "        return self._pt\n",
    "\n",
    "    @pt.setter\n",
    "    def pt(self, value):\n",
    "        self._pt = value\n",
    "        self._internal_update()\n",
    "        \n",
    "    @property\n",
    "    def eta(self):\n",
    "        return self._eta\n",
    "\n",
    "    @eta.setter\n",
    "    def eta(self, value):\n",
    "        self._eta = value\n",
    "        self._internal_update()        \n",
    "\n",
    "    @property\n",
    "    def phi(self):\n",
    "        return self._phi\n",
    "\n",
    "    @phi.setter\n",
    "    def phi(self, value):\n",
    "        self._phi = value\n",
    "        self._internal_update()\n",
    "        \n",
    "    @property\n",
    "    def m(self):\n",
    "        return self._m\n",
    "\n",
    "    @m.setter\n",
    "    def m(self, value):\n",
    "        self._m = value\n",
    "        self._internal_update()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "jets = [Jet(pt, eta, phi, m, btag) for (pt, eta, phi, m, btag) in zip(\n",
    "    pup.flatten(signal_df['HGamAntiKt4EMTopoJetsAuxDyn.pt']),\n",
    "    pup.flatten(signal_df['HGamAntiKt4EMTopoJetsAuxDyn.eta']),\n",
    "    pup.flatten(signal_df['HGamAntiKt4EMTopoJetsAuxDyn.phi']),\n",
    "    pup.flatten(signal_df['HGamAntiKt4EMTopoJetsAuxDyn.m']),\n",
    "    pup.flatten(signal_df['HGamAntiKt4EMTopoJetsAuxDyn.MV2c20_85'])\n",
    "    )]\n",
    "\n",
    "jets = pup.match_shape(np.array(jets), signal_df['HGamAntiKt4EMTopoJetsAuxDyn.pt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "photons = [Photon(pt, eta, phi, m) for (pt, eta, phi, m) in zip(\n",
    "    pup.flatten(signal_df['HGamPhotonsAuxDyn.pt']),\n",
    "    pup.flatten(signal_df['HGamPhotonsAuxDyn.eta']),\n",
    "    pup.flatten(signal_df['HGamPhotonsAuxDyn.phi']),\n",
    "    pup.flatten(signal_df['HGamPhotonsAuxDyn.m'])\n",
    "    )]\n",
    "\n",
    "photons = pup.match_shape(np.array(photons), signal_df['HGamPhotonsAuxDyn.pt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "muons = [Muon(pt, eta, phi, 105.65837) for (pt, eta, phi) in zip(\n",
    "    pup.flatten(signal_df['HGamMuonsAuxDyn.pt']),\n",
    "    pup.flatten(signal_df['HGamMuonsAuxDyn.eta']),\n",
    "    pup.flatten(signal_df['HGamMuonsAuxDyn.phi'])\n",
    "    )]\n",
    "\n",
    "muons = pup.match_shape(np.array(muons), signal_df['HGamMuonsAuxDyn.pt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# -- cut out only b-jets\n",
    "bjets = [((np.array(jets[ev]))[signal_df['HGamAntiKt4EMTopoJetsAuxDyn.MV2c20_85'][ev] == 1]).tolist() for ev in xrange(len(jets)\n",
    "                                                                                                                )]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# -- select events with == 2 b-tagged jets (2-Tag Category)\n",
    "a = [(len(bjets[ev]) == 2) for ev in xrange(len(bjets))] \n",
    "bjets_2 = np.array(bjets)[np.array(a)]\n",
    "photons_2 = np.array(photons)[np.array(a)]\n",
    "muons_2 = np.array(muons)[np.array(a)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def deltaR(eta1, eta2, phi1, phi2):\n",
    "    import math\n",
    "    '''\n",
    "    Definition:\n",
    "    -----------\n",
    "        Function that calculates DR between two objects given their etas and phis\n",
    "    Args:\n",
    "    -----\n",
    "        eta1 = float, eta of first object\n",
    "        eta2 = float, eta of second object\n",
    "        phi1 = float, phi of first object\n",
    "        phi2 = float, phi of second object\n",
    "    Output:\n",
    "    -------\n",
    "        deltaR = float, distance between the two objects \n",
    "    '''\n",
    "    DEta = abs(eta1-eta2)\n",
    "    DPhi = math.acos(math.cos( abs( phi1-phi2 ) ) ) # hack to avoid |phi1-phi2| larger than 180 degrees\n",
    "    return math.sqrt( pow(DEta,2) + pow(DPhi,2) ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# -- now we need to corrcet the jets by the muons, if the muons are inside of them\n",
    "# -- start by calculating the distances between each muon and each jet. If the distance is less than a threshold, correct the jet by the muon\n",
    "def apply_correction(DRMAX, bjets_2, muons_2):\n",
    "\n",
    "    corr_bjets_2 = []\n",
    "    for ev in xrange(len(bjets_2)): # loop thru events\n",
    "        corr_bjets_2_perevent = []\n",
    "        for jet in bjets_2[ev]: # loop thru jets\n",
    "            muon_add = Muon() # save all muons before adding them to the jet\n",
    "            if (len(muons_2[ev]) != 0): # if there is at least one muon, do correction\n",
    "                for muon in muons_2[ev]: # loop thru muons\n",
    "                    dr = deltaR(jet.eta, muon.eta, jet.phi, muon.phi) # calculate dr between muon and original jet\n",
    "                if (dr < DRMAX): # if it's closer that the fixed DR, add in the muon\n",
    "                    muon_add.lv += muon.lv\n",
    "            corr_bjets_2_perevent.append(Jet(\n",
    "                    (jet.lv + muon_add.lv).pt(),\n",
    "                    (jet.lv + muon_add.lv).eta(),\n",
    "                    (jet.lv + muon_add.lv).phi(),\n",
    "                    (jet.lv + muon_add.lv).m(),\n",
    "                    jet.btag\n",
    "                ))\n",
    "        corr_bjets_2.append(corr_bjets_2_perevent)\n",
    "    \n",
    "    return corr_bjets_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bjets_2_corr04 = apply_correction(0.4, bjets_2, muons_2)\n",
    "bjets_2_corr02 = apply_correction(0.2, bjets_2, muons_2)\n",
    "bjets_2_corr01 = apply_correction(0.1, bjets_2, muons_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# -- uncorrected four vectors for dijet, diphoton and dihiggs systems\n",
    "bb = []\n",
    "yy = []\n",
    "yybb = []\n",
    "bb04 = []\n",
    "yybb04 = []\n",
    "bb02 = []\n",
    "yybb02 = []\n",
    "bb01 = []\n",
    "yybb01 = []\n",
    "\n",
    "for ev in xrange(len(bjets_2)):\n",
    "    bb.append(bjets_2[ev][0].lv + bjets_2[ev][1].lv)\n",
    "    yy.append(photons_2[ev][0].lv + photons_2[ev][1].lv)\n",
    "    yybb.append(bjets_2[ev][0].lv + bjets_2[ev][1].lv + photons_2[ev][0].lv + photons_2[ev][1].lv)\n",
    "    \n",
    "    bb04.append(bjets_2_corr04[ev][0].lv + bjets_2_corr04[ev][1].lv)\n",
    "    yybb04.append(bjets_2_corr04[ev][0].lv + bjets_2_corr04[ev][1].lv + photons_2[ev][0].lv + photons_2[ev][1].lv)\n",
    "    bb02.append(bjets_2_corr02[ev][0].lv + bjets_2_corr02[ev][1].lv)\n",
    "    yybb02.append(bjets_2_corr02[ev][0].lv + bjets_2_corr02[ev][1].lv + photons_2[ev][0].lv + photons_2[ev][1].lv)\n",
    "    bb01.append(bjets_2_corr01[ev][0].lv + bjets_2_corr01[ev][1].lv)\n",
    "    yybb01.append(bjets_2_corr01[ev][0].lv + bjets_2_corr01[ev][1].lv + photons_2[ev][0].lv + photons_2[ev][1].lv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# set_style('ATLAS', mpl=True)\n",
    "\n",
    "# bins = np.linspace(80,160,30)\n",
    "\n",
    "# _ = plt.hist([bb[ev].M()/1000 for ev in xrange(len(bb))],\n",
    "#             bins = bins,\n",
    "#             histtype = 'step',\n",
    "#             normed = True,\n",
    "#             label = 'No Correction')\n",
    "# _ = plt.hist([bb04[ev].M()/1000 for ev in xrange(len(bb04))],\n",
    "#             bins = bins,\n",
    "#             histtype = 'step',\n",
    "#             normed = True,\n",
    "#             label = 'Corrected, DR = 0.4')\n",
    "# _ = plt.hist([bb02[ev].M()/1000 for ev in xrange(len(bb02))],\n",
    "#             bins = bins,\n",
    "#             histtype = 'step',\n",
    "#             normed = True,\n",
    "#             label = 'Corrected, DR = 0.2')\n",
    "# _ = plt.hist([bb01[ev].M()/1000 for ev in xrange(len(bb01))],\n",
    "#             bins = bins,\n",
    "#             histtype = 'step',\n",
    "#             normed = True,\n",
    "#             label = 'Corrected, DR = 0.1')\n",
    "\n",
    "# # -- fancy stuff\n",
    "# plt.xlabel(r'$m_{b b} \\ (GeV)$')\n",
    "# plt.ylabel('Normalized')\n",
    "# plt.legend(fontsize = 12)\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def dr_plots(corrected_recomass_04, corrected_recomass_02, corrected_recomass_01, recomass, xmin = 80, xmax = 160, ):\n",
    "    set_style('ATLAS', mpl=True)\n",
    "    \n",
    "#     hista = np.asarray(corrected_recomass_04)/1000\n",
    "#     histb = np.asarray(corrected_recomass_02)/1000\n",
    "#     histd = np.asarray(corrected_recomass_01)/1000                 \n",
    "#     histc = np.asarray(recomass)/1000\n",
    "\n",
    "    hista = [corrected_recomass_04[ev].M()/1000 for ev in xrange(len(corrected_recomass_04))]\n",
    "    histb = [corrected_recomass_02[ev].M()/1000 for ev in xrange(len(corrected_recomass_02))]\n",
    "    histd = [corrected_recomass_01[ev].M()/1000 for ev in xrange(len(corrected_recomass_01))] \n",
    "    histc = [recomass[ev].M()/1000 for ev in xrange(len(recomass))]   \n",
    "\n",
    "    a, bina = np.histogram(hista, bins = 1000, range = (xmin, xmax), normed=True)\n",
    "    b, binb = np.histogram(histb, bins = 1000, range = (xmin, xmax), normed=True)\n",
    "    d, bind = np.histogram(histd, bins = 1000, range = (xmin, xmax), normed=True)\n",
    "    c, binc = np.histogram(histc, bins = 1000, range = (xmin, xmax), normed=True)\n",
    "    \n",
    "    plt.hist(hista, label = 'Corrected All Muons, DR = 0.4', linewidth = 1,\n",
    "               bins = np.linspace(xmin, xmax, 30), histtype = 'step', alpha = 0.5, color = 'blue', normed = True)\n",
    "    \n",
    "    plt.hist(histb, label = 'Corrected All Muons, DR = 0.2', linewidth = 1,\n",
    "               bins = np.linspace(xmin, xmax, 30), histtype = 'step', alpha = 0.5, color = 'green', normed = True)\n",
    "    \n",
    "    plt.hist(histd, label = 'Corrected All Muons, DR = 0.1', linewidth = 1,\n",
    "               bins = np.linspace(xmin, xmax, 30), histtype = 'step', alpha = 0.5, color = 'red', normed = True)\n",
    "    \n",
    "    plt.hist(histc, label = 'No Correction', linewidth = 1,\n",
    "               bins = np.linspace(xmin, xmax, 30), histtype = 'step', alpha = 0.5, color = 'black', normed = True)\n",
    "\n",
    "    \n",
    "    bincentersa = 0.5*(bina[1:]+bina[:-1])\n",
    "    bincentersb = 0.5*(binb[1:]+binb[:-1])\n",
    "    bincentersd = 0.5*(bind[1:]+bind[:-1])\n",
    "    bincentersc = 0.5*(binc[1:]+binc[:-1])\n",
    "    (xfa,yfa),coeffa,erra,_ = fit.fit(fit.crystal_ball, bincentersa, a)\n",
    "    (xfb,yfb),coeffb,errb,_ = fit.fit(fit.crystal_ball, bincentersb, b)\n",
    "    (xfd,yfd),coeffd,errd,_ = fit.fit(fit.crystal_ball, bincentersd, d)\n",
    "    (xfc,yfc),coeffc,errc,_ = fit.fit(fit.crystal_ball, bincentersc, c)\n",
    "    \n",
    "    plt.plot(xfa,yfa, color = 'blue')\n",
    "    plt.plot(xfb,yfb, color = 'green')\n",
    "    plt.plot(xfd,yfd, color = 'red')\n",
    "    plt.plot(xfc,yfc, color = 'black')\n",
    "    \n",
    "    #plt.xlim(xmin=80, xmax=160)\n",
    "    #plt.ylim( ymax = 0.015)\n",
    "    plt.legend(loc = 'best', fontsize = 10)\n",
    "    plt.ylabel('Normalized')\n",
    "    plt.xlabel(r'$m_{b\\bar{b}}\\ (\\mathsf{GeV})$')\n",
    "    plt.title('Invariant Mass of bb system, 2-Tag Category')\n",
    "    plt.show()\n",
    "    \n",
    "    print 'Peaks:'\n",
    "    print r'DR = 0.4, Peak = {0:.2f} +- {1:.2f} GeV, Sigma = {2:.2f} +- {3:.2f} GeV'.format(coeffa[3], erra[3], coeffa[4], erra[4] )\n",
    "    print r'DR = 0.2, Peak = {0:.2f} +- {1:.2f} GeV, Sigma = {2:.2f} +- {3:.2f} GeV'.format(coeffb[3], errb[3], coeffb[4], errb[4])\n",
    "    print r'DR = 0.1, Peak = {0:.2f} +- {1:.2f} GeV, Sigma = {2:.2f} +- {3:.2f} GeV'.format(coeffd[3], errd[3], coeffd[4], errd[4])\n",
    "    print r'No Correction, Peak = {0:.2f} +- {1:.2f} GeV, Sigma = {2:.2f} +- {3:.2f} GeV'.format(coeffc[3], errc[3], coeffc[4], errc[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:rootpy.plotting.style:using matplotlib style 'ATLAS'\n"
     ]
    },
    {
     "data": {
      "image/png": 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lMmY3Nzf+97//oa2tTe3atYHkJT3WrVtHREQEmpqa6OvrK9uYm5vz4sULIiMjlX24u7sz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\nKA7Yvx+ePoXu3QHwOexDzXhXfmxSjFq11Jio8FkaNGgQYX/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cuUO3bt0y/cF//vw5vXv3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\n/D+iJ4CS2gGv4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f159c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Peaks:\n",
      "DR = 0.4, Peak = 344.15 +- 0.41 GeV, Sigma = 22.74 +- 0.45 GeV\n",
      "DR = 0.2, Peak = 343.87 +- 0.41 GeV, Sigma = 22.70 +- 0.45 GeV\n",
      "DR = 0.1, Peak = 343.03 +- 0.40 GeV, Sigma = 22.37 +- 0.44 GeV\n",
      "No Correction, Peak = 341.23 +- 0.42 GeV, Sigma = 20.39 +- 0.43 GeV\n"
     ]
    }
   ],
   "source": [
    "dr_plots(yybb04, yybb02, yybb01, yybb, xmin = 280, xmax = 400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:rootpy.plotting.style:using matplotlib style 'ATLAS'\n"
     ]
    },
    {
     "data": {
      "image/png": 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+fPlU/9bU1CQ+Pp5vvvmGixcvsnfvXsaOHUuDBg0YN25clvp58eJFatSoAaA2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iRVTf3777+xsbFhxYoVnDlzhjdv3qCnp0fZsmXp2rUrFhYWaTVO8Tu4fl11cO1/9hJevHeP+pMn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\nfzA8kY5ipTV2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f4724d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Peaks:\n",
      "DR = 0.4, Peak = 120.26 +- 0.33 GeV, Sigma = 17.19 +- 0.36 GeV\n",
      "DR = 0.2, Peak = 120.05 +- 0.33 GeV, Sigma = 17.16 +- 0.36 GeV\n",
      "DR = 0.1, Peak = 119.41 +- 0.33 GeV, Sigma = 16.91 +- 0.36 GeV\n",
      "No Correction, Peak = 117.81 +- 0.32 GeV, Sigma = 15.91 +- 0.34 GeV\n"
     ]
    }
   ],
   "source": [
    "dr_plots(bb04, bb02, bb01, bb, xmin = 80, xmax = 160)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}