plots_for_JAA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:rootpy.plotting.style:using matplotlib style 'ATLAS'\n"
     ]
    }
   ],
   "source": [
    "set_style('ATLAS', mpl=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "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, 1) # 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": 288,
   "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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -- open a file to save 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/framework-00-03-00_rel2.3.38/correct_weights/yybb/sample.root', \"read\")\n",
    "t = f[\"CollectionTree\"]\n",
    "#print [key.GetName() for key in t.GetListOfBranches()]\n",
    "features = [key.GetName() for key in t.GetListOfBranches() if (\n",
    "        ('HH2yybbMuonsAuxDyn' in key.GetName()) or ('HH2yybbAntiKt4EMTopoJetsAuxDyn' in key.GetName()) \n",
    "        or ('m_yy' in key.GetName()) or ('weight' in key.GetName()) \n",
    "    )]\n",
    "f.Close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -- open a file to save 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/framework-00-03-00_rel2.3.38/correct_weights/periodD/sample.root', \"read\")\n",
    "t = f[\"CollectionTree\"]\n",
    "#print [key.GetName() for key in t.GetListOfBranches()]\n",
    "datafeatures = [key.GetName() for key in t.GetListOfBranches() if (\n",
    "        ('HH2yybbMuonsAuxDyn' in key.GetName()) or ('HH2yybbAntiKt4EMTopoJetsAuxDyn' in key.GetName()) \n",
    "        or ('m_yy' in key.GetName()) or ('weight' in key.GetName()) \n",
    "    )]\n",
    "f.Close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "yybb = pup.root2panda('../MxAOD/framework-00-03-00_rel2.3.38/correct_weights/yybb/sample.root', 'CollectionTree',\n",
    "                     branches = features)\n",
    "ybbj = pup.root2panda('../MxAOD/framework-00-03-00_rel2.3.38/correct_weights/ybbj/sample.root', 'CollectionTree',\n",
    "                     branches = features)\n",
    "yybj = pup.root2panda('../MxAOD/framework-00-03-00_rel2.3.38/correct_weights/yybj/sample.root', 'CollectionTree',\n",
    "                     branches = features)\n",
    "ybjj = pup.root2panda('../MxAOD/framework-00-03-00_rel2.3.38/correct_weights/ybjj/sample.root', 'CollectionTree',\n",
    "                     branches = features)\n",
    "yjjj = pup.root2panda('../MxAOD/framework-00-03-00_rel2.3.38/correct_weights/yjjj/sample.root', 'CollectionTree',\n",
    "                     branches = features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bkg_df = pd.concat([yybb, ybbj, yybj, ybjj, yjjj], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "periodD = pup.root2panda('../MxAOD/framework-00-03-00_rel2.3.38/correct_weights/periodD/sample.root', 'CollectionTree',\n",
    "                     branches = datafeatures)\n",
    "periodE = pup.root2panda('../MxAOD/framework-00-03-00_rel2.3.38/correct_weights/periodE/sample.root', 'CollectionTree',\n",
    "                     branches = datafeatures)\n",
    "periodF = pup.root2panda('../MxAOD/framework-00-03-00_rel2.3.38/correct_weights/periodF/sample.root', 'CollectionTree',\n",
    "                     branches = datafeatures)\n",
    "# periodG = pup.root2panda('../MxAOD/framework-00-03-00_rel2.3.38/correct_weights/periodG/sample.root', 'CollectionTree',\n",
    "#                      branches = datafeatures)\n",
    "periodH = pup.root2panda('../MxAOD/framework-00-03-00_rel2.3.38/correct_weights/periodH/sample.root', 'CollectionTree',\n",
    "                     branches = datafeatures)\n",
    "# periodJ = pup.root2panda('../MxAOD/framework-00-03-00_rel2.3.38/correct_weights/periodJ/sample.root', 'CollectionTree',\n",
    "#                      branches = datafeatures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_df = pd.concat([periodD, periodE, periodF, periodH], ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_df = data_df[data_df['HH2yybbEventInfoAuxDyn.m_yyjj'] != -99]\n",
    "bkg_df = bkg_df[bkg_df['HH2yybbEventInfoAuxDyn.m_yyjj'] != -99]\n",
    "\n",
    "yybb = yybb[yybb['HH2yybbEventInfoAuxDyn.m_yyjj'] != -99]\n",
    "ybbj = ybbj[ybbj['HH2yybbEventInfoAuxDyn.m_yyjj'] != -99]\n",
    "yybj = yybj[yybj['HH2yybbEventInfoAuxDyn.m_yyjj'] != -99]\n",
    "ybjj = ybjj[ybjj['HH2yybbEventInfoAuxDyn.m_yyjj'] != -99]\n",
    "yjjj = yjjj[yjjj['HH2yybbEventInfoAuxDyn.m_yyjj'] != -99]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# -- number of muon in jets (data):\n",
    "nMiJ_data = [len(data_df['HH2yybbMuonsAuxDyn.pt'].values[ev]) for ev in xrange(data_df.shape[0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# -- number of muon in jets (bkg):\n",
    "#bkg_muons_df = bkg_df.ix[ix_bkg]\n",
    "nMiJ_bkg = [len(bkg_df['HH2yybbMuonsAuxDyn.pt'].values[ev]) for ev in xrange(bkg_df.shape[0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# -- number of muon in jets for individual backgrounds:\n",
    "nMiJ_yybb = [len(yybb['HH2yybbMuonsAuxDyn.pt'].values[ev]) for ev in xrange(yybb.shape[0])]\n",
    "nMiJ_ybbj = [len(ybbj['HH2yybbMuonsAuxDyn.pt'].values[ev]) for ev in xrange(ybbj.shape[0])]\n",
    "nMiJ_yybj = [len(yybj['HH2yybbMuonsAuxDyn.pt'].values[ev]) for ev in xrange(yybj.shape[0])]\n",
    "nMiJ_ybjj = [len(ybjj['HH2yybbMuonsAuxDyn.pt'].values[ev]) for ev in xrange(ybjj.shape[0])]\n",
    "nMiJ_yjjj = [len(yjjj['HH2yybbMuonsAuxDyn.pt'].values[ev]) for ev in xrange(yjjj.shape[0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# -- muon in jets pt (data):\n",
    "MiJpT_data = pup.flatten(data_df['HH2yybbMuonsAuxDyn.pt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "MiJpT_bkg = pup.flatten(bkg_df['HH2yybbMuonsAuxDyn.pt'])\n",
    "MiJpT_yybb = pup.flatten(yybb['HH2yybbMuonsAuxDyn.pt'])\n",
    "MiJpT_ybbj = pup.flatten(ybbj['HH2yybbMuonsAuxDyn.pt'])\n",
    "MiJpT_yybj = pup.flatten(yybj['HH2yybbMuonsAuxDyn.pt'])\n",
    "MiJpT_ybjj = pup.flatten(ybjj['HH2yybbMuonsAuxDyn.pt'])\n",
    "MiJpT_yjjj = pup.flatten(yjjj['HH2yybbMuonsAuxDyn.pt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "event_weights_bkg = np.array( [data_df.shape[0] /  sum(bkg_df['HH2yybbEventInfoAuxDyn.weightFinal'].values)] \n",
    "         * len(nMiJ_bkg) ) * bkg_df['HH2yybbEventInfoAuxDyn.weightFinal'].values / bkg_df['HH2yybbEventInfoAuxDyn.weightXsecLumi'].values\n",
    "\n",
    "event_weights_yybb = np.array([data_df.shape[0] / sum(bkg_df['HH2yybbEventInfoAuxDyn.weightFinal'].values)] * len(nMiJ_yybb)) * yybb['HH2yybbEventInfoAuxDyn.weightFinal'].values/yybb['HH2yybbEventInfoAuxDyn.weightXsecLumi'].values\n",
    "\n",
    "event_weights_ybbj = np.array([data_df.shape[0] / sum(bkg_df['HH2yybbEventInfoAuxDyn.weightFinal'].values)] * len(nMiJ_ybbj)) *ybbj['HH2yybbEventInfoAuxDyn.weightFinal'].values/ybbj['HH2yybbEventInfoAuxDyn.weightXsecLumi'].values\n",
    "\n",
    "event_weights_yybj = np.array([data_df.shape[0] / sum(bkg_df['HH2yybbEventInfoAuxDyn.weightFinal'].values)] * len(nMiJ_yybj)) *yybj['HH2yybbEventInfoAuxDyn.weightFinal'].values/yybj['HH2yybbEventInfoAuxDyn.weightXsecLumi'].values\n",
    "\n",
    "event_weights_ybjj = np.array([data_df.shape[0] / sum(bkg_df['HH2yybbEventInfoAuxDyn.weightFinal'].values)] * len(nMiJ_ybjj)) *ybjj['HH2yybbEventInfoAuxDyn.weightFinal'].values/ybjj['HH2yybbEventInfoAuxDyn.weightXsecLumi'].values\n",
    "\n",
    "event_weights_yjjj = np.array([data_df.shape[0] / sum(bkg_df['HH2yybbEventInfoAuxDyn.weightFinal'].values)] * len(nMiJ_yjjj)) *yjjj['HH2yybbEventInfoAuxDyn.weightFinal'].values/yjjj['HH2yybbEventInfoAuxDyn.weightXsecLumi'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "weights_bkg = pup.flatten([[event_weights_bkg[ev]]*nMiJ_bkg[ev] for ev in xrange(len(nMiJ_bkg))])\n",
    "weights_yybb = pup.flatten([[event_weights_yybb[ev]]*nMiJ_yybb[ev] for ev in xrange(len(nMiJ_yybb))])\n",
    "weights_ybbj = pup.flatten([[event_weights_ybbj[ev]]*nMiJ_ybbj[ev] for ev in xrange(len(nMiJ_ybbj))])\n",
    "weights_yybj = pup.flatten([[event_weights_yybj[ev]]*nMiJ_yybj[ev] for ev in xrange(len(nMiJ_yybj))])\n",
    "weights_ybjj = pup.flatten([[event_weights_ybjj[ev]]*nMiJ_ybjj[ev] for ev in xrange(len(nMiJ_ybjj))])\n",
    "weights_yjjj = pup.flatten([[event_weights_yjjj[ev]]*nMiJ_yjjj[ev] for ev in xrange(len(nMiJ_yjjj))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "NBINS = 9\n",
    "bins_def = np.linspace(0,8,NBINS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GjRs3AgDWr18PAAgNDUV6ejoGDx4sZniiYtKkAdxygIiIjEXXrl0RExOD4cOHY/PmzQgK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\nP+Div3P4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1231fd7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ____DATA____\n",
    "# -- use hist to get bin_heights, bins:\n",
    "bin_heights, bins = np.histogram(nMiJ_data, bins = bins_def); \n",
    "# -- find center of each bin:\n",
    "bins_mean = [(bins[i]+bins[i+1])/2 for i in range (0,(len(bins)-1))]\n",
    "# -- horizontal error bars of lenght = bin length: \n",
    "xerr = [bins_mean[i]-bins[i+1] for i in range (0, len(bins_mean))] \n",
    "\n",
    "_ = plt.errorbar(bins_mean, bin_heights, xerr=xerr, yerr=np.sqrt(bin_heights), \n",
    "                 fmt='o', capsize = 0, color = 'black', label='data')\n",
    "\n",
    "\n",
    "\n",
    "# ___BKG___\n",
    "# _ = plt.hist(nMiJ_bkg, bins = bins_def, \n",
    "#              alpha = 0.5, histtype = 'stepfilled', color = 'red', label = 'MC Background', \n",
    "#              weights = \n",
    "#              np.array([ data_df.shape[0] /  sum(bkg_df['HH2yybbEventInfoAuxDyn.weightFinal'].values)] * len(nMiJ_bkg))*\n",
    "#             bkg_df['HH2yybbEventInfoAuxDyn.weightFinal'].values )\n",
    "\n",
    "plt.hist([nMiJ_yybb, nMiJ_ybbj, nMiJ_yybj, nMiJ_ybjj, nMiJ_yjjj], \n",
    "                 bins=bins_def, \n",
    "                 color = (\"#e75555\", \"#a9e185\", '#6cdcdf', '#f4df62', '#f47cbf'), \n",
    "                 stacked=True, \n",
    "                 histtype='stepfilled', \n",
    "                 label=[r'$\\gamma$$\\gamma$bb', r'$\\gamma$bbj',r'$\\gamma$$\\gamma$bj',r'$\\gamma$bjj',r'$\\gamma$jjj'], \n",
    "                 edgecolor = \"black\",\n",
    "                 weights = [\n",
    "        \n",
    "        np.array([data_df.shape[0] / sum(bkg_df['HH2yybbEventInfoAuxDyn.weightFinal'].values)] * len(nMiJ_yybb)) *\n",
    "        yybb['HH2yybbEventInfoAuxDyn.weightFinal'].values/yybb['HH2yybbEventInfoAuxDyn.weightXsecLumi'].values,\n",
    "        \n",
    "        np.array([data_df.shape[0] / sum(bkg_df['HH2yybbEventInfoAuxDyn.weightFinal'].values)] * len(nMiJ_ybbj)) *\n",
    "        ybbj['HH2yybbEventInfoAuxDyn.weightFinal'].values/ybbj['HH2yybbEventInfoAuxDyn.weightXsecLumi'].values,\n",
    "        \n",
    "        np.array([data_df.shape[0] / sum(bkg_df['HH2yybbEventInfoAuxDyn.weightFinal'].values)] * len(nMiJ_yybj)) *\n",
    "        yybj['HH2yybbEventInfoAuxDyn.weightFinal'].values/yybj['HH2yybbEventInfoAuxDyn.weightXsecLumi'].values,\n",
    "        \n",
    "        np.array([data_df.shape[0] / sum(bkg_df['HH2yybbEventInfoAuxDyn.weightFinal'].values)] * len(nMiJ_ybjj)) *\n",
    "        ybjj['HH2yybbEventInfoAuxDyn.weightFinal'].values/ybjj['HH2yybbEventInfoAuxDyn.weightXsecLumi'].values,\n",
    "        \n",
    "        np.array([data_df.shape[0] / sum(bkg_df['HH2yybbEventInfoAuxDyn.weightFinal'].values)] * len(nMiJ_yjjj)) *\n",
    "        yjjj['HH2yybbEventInfoAuxDyn.weightFinal'].values/yjjj['HH2yybbEventInfoAuxDyn.weightXsecLumi'].values]\n",
    "                    \n",
    "                ) \n",
    "\n",
    "plt.title('Muons in Jets in All Events')\n",
    "plt.xlabel('# of Muons in Jets')\n",
    "plt.ylabel('Number of Events')\n",
    "plt.yscale('log')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HlZUVCgsLcfDgQezcuRNvvfUWVq9erVI8w4YNQ69evQAAjx49wrlz55CUlIRdu3Zh+/bt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\nJk1EREREJmDSRERERGQCJk1EREREJvg/tbAXdlyeld0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1204f0890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ____DATA____\n",
    "# -- use hist to get bin_heights, bins:\n",
    "bin_heights, bins = np.histogram(nMiJ_data, bins = bins_def); \n",
    "# -- find center of each bin:\n",
    "bins_mean = [(bins[i]+bins[i+1])/2 for i in range (0,(len(bins)-1))]\n",
    "# -- horizontal error bars of lenght = bin length: \n",
    "xerr = [bins_mean[i]-bins[i+1] for i in range (0, len(bins_mean))] \n",
    "\n",
    "_ = plt.errorbar(bins_mean, bin_heights, xerr=xerr, yerr=np.sqrt(bin_heights), \n",
    "                 fmt='o', capsize = 0, color = 'black', label='data')\n",
    "\n",
    "\n",
    "\n",
    "#___BKG___\n",
    "_ = plt.hist(nMiJ_bkg, bins = bins_def, \n",
    "             alpha = 0.5, histtype = 'stepfilled', color = 'red', label = 'MC Background', \n",
    "             weights = \n",
    "             np.array([ data_df.shape[0] /  sum(bkg_df['HH2yybbEventInfoAuxDyn.weightFinal'].values)] * len(nMiJ_bkg))*\n",
    "            bkg_df['HH2yybbEventInfoAuxDyn.weightFinal'].values/bkg_df['HH2yybbEventInfoAuxDyn.weightXsecLumi'].values )\n",
    "\n",
    "plt.title('Muons in Jets in All Events')\n",
    "plt.xlabel('# of Muons in Jets')\n",
    "plt.ylabel('Number of Events')\n",
    "plt.yscale('log')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Muons' pT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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376K8vBxmZmZwc3NDSEgI3pfDH98dAQ0XtrHvvvsOCQkJKCgogKGhIcaNG4fI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\nhVKzw6YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1204f04d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins_def = np.linspace(0,100, 30)\n",
    "# ____DATA____\n",
    "# -- use hist to get bin_heights, bins:\n",
    "bin_heights, bins = np.histogram(MiJpT_data/1000, bins = bins_def); \n",
    "# -- find center of each bin:\n",
    "bins_mean = [(bins[i]+bins[i+1])/2 for i in range (0,(len(bins)-1))]\n",
    "# -- horizontal error bars of lenght = bin length: \n",
    "xerr = [bins_mean[i]-bins[i+1] for i in range (0, len(bins_mean))] \n",
    "\n",
    "_ = plt.errorbar(bins_mean, bin_heights, xerr=xerr, yerr=np.sqrt(bin_heights), \n",
    "                 fmt='o', capsize = 0, color = 'black', label='data')\n",
    "\n",
    "\n",
    "\n",
    "#___BKG___\n",
    "_ = plt.hist(MiJpT_bkg/1000, bins = bins_def, \n",
    "             alpha = 0.5, histtype = 'stepfilled', color = 'red', label = 'MC Background', \n",
    "             weights = weights_bkg)\n",
    "\n",
    "plt.title('pT of Muons in Jets in All Events')\n",
    "plt.xlabel(r'p$_{T muon}$ [GeV]')\n",
    "plt.ylabel('Number of Events')\n",
    "plt.yscale('log')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cP34cN2/exM2bN7Flyxb4+vpiwoQJWL9+PWbOnAkACs8Z4h6GTLJI5+pbNd6y\nRQtYOTggav9+te9ZUVoKcWQkkywiIgP35ptvon///vD19cWyZcvw6aefom3bttLNocPCwrBmzRpp\nkqXsnKFhkkU6V9+q8d0nTkb3iZM1uueN6B9wauECjfogIiLdc3BwwLx587Bw4UKIRCK4urrKjEpJ\nJBKZ1zXPGXJleMONjBoNVo0nIiJlevXqhejoaEybNg0SiQRpaWk4c+YMAGDXrl3o3bu3tG3Nc/7+\n/nqJWRUcySKty715o1YVeGXkVY13fL4TRh46Uqtt9bVdD//8Ey2efbbOtV1ERGTYnJyc0LlzZ/j6\n+iI1NRUdO3bEunXrMH78eHh5eWHy5P/Nbig6xzVZjdSFjd+qfW32xQsw3J0L6+8NNze0q7GtTmx2\nNrKfPFF4jZOlJQKdnKSvLz98iBu3b9VqJ29tV1FmhsK1XUREVH/qPhGoCbFYDEdHRwBAu3btcP36\ndbntFJ3Lzc2Fg4ODTmNUB5MsLbhdj73+5HG3s9NSJPo3tn17oH17mWNLrl7Fups3FV7zlru7TImI\nxVeu4Iacx3Dru7aLiIjqR1877ZWXl0tLNNRXZmYmAgMDMXv2bC1HpTkmWVpwbdAgfYdg0GZ6eeFs\nTo7CqvEzvbxU6odru4iIGidLS0scOnRIrWudnZ1xU8kf8vrEDaI1JBAIkDV0qL7DMHgllZXSqvF/\nFBSgvZ2dwqrxi69cwTe3bwMafmsKhEKZbY3UIpGgsqJCsz6IiMhgcYNoMnr1rhovkcC+XTuZQ0XZ\n2ahUsrbLxNISNv+s7aooKYGppRW8Ro5UJ9yn98vKwo39P6h9PRERNW1MsuTYvXs31q1bhytXrqCo\nqAhisVhp+8+T5U9TvdyqFV5u3VoXITZqnZo3h1+rVsDjxzLHUwUCZCi5TiQQ4Jl/rvn7yRPcy8lB\nt/feVzuO+9euMskiImoE9LVBNKcL5YiJicHDhw9RXFyMCRMmKE2yOF3YcEoqKzHs1CmFa7v2BgRI\npx73pqZi7qVLGH/jttr3u3/tKg6MCUNpQYHafRARkWHjdGED69+/PwDoJeslxaxMTLA3IEDltV1E\nRET6xCSLjEq913bJwYKmRETGJTU1FaGhobh27Vq9zukbkyxqUljQlIhIMV1UTW/Kq5KMNsnasWMH\n4uLikJSUhOTkZFRUVCA2NhYBAQEKr4mPj8eiRYtw7tw5SCQS+Pj4YP78+QgMDGzAyEmfWNCUiEg5\nba4zbrPWTBgEAAAgAElEQVRvn9b6qqioQFhYGC5evAgvLy9ERkZKC5gqO6dPRptkzZ8/H2lpaRCJ\nRBCJRMjIyFCagR89ehQDBw6EnZ0dwsLCYGFhgd27dyM4OBjR0dEIDQ1twOhJX+pb0FRcUYEOHToo\nvSY3N1e6HURNZWVlSEtLQ7NmzeoXqBwnTpxAjx49NO6HiMiQbN68GWKxGOHh4Th+/DhSUlJgZWUl\nfV1WVgZPT0/cvHkTW7Zsga+vLyZMmID169dj5syZAKD0nD4ZbZK1detWdOjQAS4uLpg1axZWr16t\nsG1ZWRkmTpwIS0tLJCQkoFOnTgCAOXPmwNvbG5MmTUJwcDAs/5kmEovFKCsrQ1lZGQCgtLQUEokE\nFhYWBrkBJclXVllZr42qgdqbVTdzaoN8OX8NScRiPL5/H+UlJSgvKkJhWRnMrKzQTCSSKYAqsbCA\ndWsRWnburP4bAZAWdwo///wzkywianTefPNN9O/fH76+vli2bBm2bduGIUOGSF/v3LkTZWVlaNu2\nLXx9fQEAYWFhWLNmjTSRUnZOn1ROsv766y9cv34dffr0gc0/GwBXVFRg8eLFOHDgAKytrTF79mwM\nGTJEZ8FWV58pvuPHjyMtLQ3h4eHSBAt4uuv31KlTMX/+fBw5ckQae2RkJMaPHw/g6fx01ZDjyZMn\n0adPHy2+C9IlcxMTTKpRwuHAuDFIjz+t8Jq2/v54Y9sOpf1WresqfvDgf8eKilBeVAQ717Y6Wde1\nyaerVvsjIjIUDg4OmDdvHhYuXAiRSAQ3NzeZ187OzkhNTZUZ5JBIJDKvlZ3TJ5X3HFm8eDFGjx4t\nHe0BgCVLlmDJkiW4du0azp49i+HDh+PMmTM6CVQTcXFxAIDg4OBa56qOVbUBgHHjxkEsFkMsFqOy\nslL6byZYxq+Vp/J9ElvWcR5QbV0XERGprlevXoiOjsa0adPkvgaAtLQ0aY6xa9cu9O7dW6Vz+qRy\nkpWYmIigoCCYmj4d/BKLxVi/fj06duyItLQ0nDt3DtbW1kqn7fTlzp07AAAPD49a59q3by/Thhq3\nHtOmw6lrN7nnnLp2Q89pM+rsgxtVExFpl5OTEzp37iyd8qv5WiAQoGPHjli3bh08PT2Rn5+PyZMn\nS69Xdk6fVJ4uvH//vszi8MuXLyMnJwcLFiyAq6srXF1d8cYbbyA+Pl4ngWqi4J+K3XZ2drXOVR3L\nz89v0JhIP0wtLTF4RxTOrvkSOSnJSI+PR1t/f7T09ELPaTNqTfOd/epLnF/7Vb3uUXNd10tTp6Pn\n9LqTNyIiQ6DNJwJVJRaLZR4gqvna3d0d169fl3ttu3btFJ7TN5WTrPLycpk5zqpkKigoSHrM1dUV\nmZmZWgzPOCjau1AZ7muoP6aWltIyDV97PKN0DVbP6TNqJUjaWNdFRGSI9FXTqry8XKbkQs3XqtDX\n/oTKqJxkubi44OrVq9LXP//8M1q2bAlPT0/psb///lvuaJG+VcVUIGcPuqpj9vb2avc/y6vudTxk\nmF6aOr3e17Ty9FKaZKmyrouIiP7H0tIShw4dUvhaFX379kXfvn3rfe9FixbV+xpVqZxkhYaGYvXq\n1Zg5cyYsLS0RExODd999V6bN7du34e7urvUgNVW1Fuv27dvw9vaWOadsvRYZt0qxGJs71v11/X39\n1wrPVYjFENZ4SkUikUAAQN7fewIAyf/diJRN/5XpY/Kdv1SMmoioaRIKhUpfGyOVk6zZs2fjxx9/\nxBdffAHg6chW9ezv/v37+O2332SeBDAUAQEBWLlyJY4dO4ahNSrZHjt2DAD45GAj093REUu6alb2\nILe0FAIAzUxr/29SLhbj16wsZD15gjuFhfCwtUUbS0sEtWkDs2o/GMrEYixroP20IiIiEBER0SD3\nIiKiuqmcZIlEIly9ehUnTpwA8HRYztbWVno+NzcX//nPf/Dqq69qP0oVKZpL7tevH9zc3BAVFYUZ\nM2ZIpzizsrKwdu1aODs7Y+DAgWrfV9GaLK670p/2trZoX+37UxcmdewI4Oki0dMKvu+LKyq0lmRt\n375dZsq+pv3799e5QerUqVPVGk4nIjJm+lqvJZAY6c6NmzZtki6+T0pKQkpKCgYMGACRSAQACA8P\nh5+fn7T90aNHERISAhsbG4wcORLm5ubYs2cPcnJysH//frW31REIBFrd54mMz+fJyQrX5RVXVKDj\njz9i0u0/NbrH/neGozAjQ2mbwowM2Lq4KDz/+O8HaNfWFX/88YdGsRARNaaRc4FAoLMF/yqPZAmF\nQkRERGDBggUK2yxduhQLFixAZWWlVoJTJiEhAZGRkdInHgUCAWJiYqSVXoOCgmSSrAEDBuDkyZOI\niIjAzp07AQA+Pj5YsGAB/7InjdT14EOlRILITsr3P6xLmViMvmu+hscA2RGz8pISnF+7Bg9SklGY\nkYHmzzyLVp5e6DFteq1yFPvefhOQ8/BHdYb0g9OQYiEioLi4GIsXL8alS5cQExODxMREdO3aFQsX\nLtTaZswRERGwtbVVuCXOgQMH0KFDB5ndWwyZVvculEgkDfb459atW7F169Z6XePn5yddg0XUEMyF\nQuzUQuXhiYmJKH7wt8yxqu19qlefT48/jfT408g8f06t7X0WLVpkMImNIcVC1NQVFxejX79+SExM\nlB6LiYlBTEwM4uLicOLECa0kWnVthxMdHY3Q0NCmmWQ9fPhQZtsdoqbOVChEkJOTxv2YyXnKRpXt\nfarqgRERaWLx4sUyCVZ1iYmJiIiIwIoVK9Tqe+nSpYiMjETr1q3Rtm1bdO/eHZs2bcLGjRtRVlYG\nDw8P7NixA5cuXcLBgwcRFxeHpUuX4vvvv8eJEyfw3//+V6adtkbVtEFpklW1n1/V6FRqaqrMHn9V\nKisrcffuXezatQsd/1kMTES6Vf/tfQT4KzUVwhpPS0okEqDqA//8JfnPh7y/Krt5eyMpKUmj2BWp\nPh0BPJ3m1/Z0BBHVX9X/k4pcvnxZrX4vXLiAPXv24MqVKygvL0e3bt3g4+ODIUOG4L333gMAzJ8/\nH5s3b8aUKVMwaNAghIaGYsiQIQCA5s2bIzw8vFY7Q6E0yaq5Vmnbtm3Ytm2bwvZCoRCff/65NuIy\nKny6kDSlbPF8ldTYWMRFLFS5z5rb+wCA68t+cPP3l76uLC9H8p7dKKq5U4NEAps2beA1fARMzMyk\nh/88FoOLF+WPnlWnznqqhpqOIKL6iYiIQExMjNI2MTExav1/f/r0aQwZMgSWlpawtLTEoEGDIJFI\ncO3aNXzyySfIz89HUVGRTOWC6suSarYbMGCA3Pvo6+lCpUlW9UXuixcvRkBAAAICAmq1MzExgaOj\nI4KCgvD8889rP0oDx4rvpKlVKSl1fh+1CwzEoM3/W4eoje19flu5onaC9Y+izEyUPX4sM+VYmJmJ\n+yr8xarOeipdTkcQkfoiIiKQmJioNNHq37+/WmsoFT3Z9+677+LAgQN44YUXsH37dpkEqfoI+7hx\n4/DTTz/JbVedsmrweqv4Xv0Ttm3bNgwePBjTp9d/GxIi0j5tbO9T/ynHp39Ftm/fvs6+lbXp1asX\noqKiZI7pajqCiDTXtWtXpUlWzd1UVNWnTx+MGzcOc+fORXl5OQ4ePIiJEyeisLAQTk5OKC8vx86d\nO9G2bVsAgK2trcwWeUVFRTLtXF1d1YpDV4y2TpahYJ0sUldxRQVWp6Tg90ePcOr+fQSIROjcvDlm\nennBysREpq3ngQN44eN56BI2Rnqs4skT/Dh6lNzF705du8l9uvDsV1/i/NqvNIq7mUgEmzZtah2X\niMUoys5GRUkJygoLYW5rC1MrK9g4OUFQbeF+SU4uCjLuSdeAqatly5Z48OCBRn0QkWpKSkrwyiuv\nyB1t9vX11Wg6f9myZdi+fTtat24Nd3d3dOvWDdbW1li5ciVatWqFnj17oqioCFu2bMFvv/2G8PBw\nWFpaYt++fYiJiZHbrj50WSeLSZaGmGSROoorKjA8Lg5Jubm1zvk4OmJvQIBMoiUvyQKeJlpn13yJ\nnJRkpMfHo62/P1p6eqHntBkqlW/QxpQjIL+cRJWaCV96Qjwubd5UK8n6+9o1PHn0UOE9LJs3R+sX\nugAASgvykXfnDsqKiuqMjYi0o6SkBBEREbh8+TJiYmLQv39/eHt7IyIiwqjXSxpEMVIAuHXrFr76\n6iucP38eDx8+VFh09M8/NatuTdTYrU5JkZtgAUBSbi5WJSfjky5d6uzH1NJSumbqa49nVEqIqtPG\nlCNQv3ISbf380dbPv1a731auwMWN3yq8R6dhw6V9ZF1IwsH3xqsUGxFph5WVlXRdJIsFq0blJCsx\nMRGvvPIKnjx5AhMTE4hEIpjK2Ti3rkJiRAT8/uiRRufVsbGzJ0zKy2SOSSQSCADI+xtOAOD6pv/i\nxuZNMsebd+2Kt3bvkzmmztqumnpMm47M8+cUjob1nDajzj6IqGEwwVKNyknW3LlzUVZWhm+//Rbj\nx4+Xm2A1VSzhQMp8npyMVSkp9brm1P37aLNPNpFJjY2tNV1YH5aVFRj6zDN43t5e5ni5WIyYzExk\nlpTgVkEBOtjZwdnKCv2dnWsVQY1OS0PipUu1SkPUpWY5iZemTkfP6bJJk6mlJQbviNJo+pOISB6D\n3yC6WbNmCAkJwZ49e3Qdk1HhmixSx4i4OJy6f1/h+QCRCLv79JG+VrQmq7qvPZ7BlDt/KTwf2akD\nvunVS2kF+jb79in9fp578SJ23LuHyck3ZI5ra21XdcreT9V0YWkdezESEdVFl2uyau/VoYCZmRnc\n3d11EgRRU9O5eXONzsvz0lT9lVdpVcfaLVXXdhERNSYqJ1l+fn511rEhItXM9PKCj6Oj3HM+jo6Y\nqUaB25rTbw2px7TpcOraTe45fa6n4roRqouhfI8YShykXSpPF16+fBl+fn745ptvMGaM+utCGhtO\nF5K6SiorsSo5WeU6WQ/LymBmba32/YRPnmCLn59OpgsBzctJ1KSN6UJdTgNQ42Ao3yOGEkdTZBAl\nHA4cOICgoCCMGzcOmzZtgo+PD5ormNKovh0PEclnZWIiLdPQZt8+mTVYNY1+9lnklJZqdD8hABcN\nkrS6aFpOghqHv/76q8597lRhbm6Od999VwsREemPyklW9b194uPjER8fr7Atkywi7Zr7wgv6DkGq\nsrQU0WEj62ynrI2Td1f4zpqjzbDIQISGhiLl+nWYapLQi8WoLC9nkkVGT+Uk69dff9VlHERkAGZ6\neio938HODi80bw7cqD1dWF0GAAcFbfJKS/H7uXN1Jll1LeSvLC2DqYWF0jYAFLaRAJBUVECsoKgy\nqc/x+U4Y8dMhta9//Pff2BEUoMWIiPRD5SRL0e7VRNR4zKpjwf27Hh5418Ojzn7a7NuHmOBgued2\n/vknFqiw2bOyhfy2rm3x8ocf1tnH6U8X4+UPP5J77smjfJxf+xWEpmZ19qOURIxvv/kG77//vmb9\nEFGjw4qiRGR0bEQivDhW/lRSeUkJzq9dI61Cnxobi1aeXugxbbrMAnyJRIJu70/UOJYtvV7igmUj\nU1xcjMWLF0ufmB8wYAC6du2KhQsX1tqDz9TCAiYqjJgqI5FI0NreHvfu3VM7DjJO9U6yrly5gl27\nduH69et4/PgxTpw4AQBITU3FuXPn0K9fPzg4OGg9UCJqPMQSCbY9/5xGfTyprMSk27L7pMrbqDo9\n/jTS408j8/w5mY2qBQIBzLTxi8xAthJ77bXXcObMGY37efnll3H48GG1r6+oqEDZ42I8uHFd7T6e\n5OWioqys7oZqKC4uRr9+/ZCYmCg9FhMTg5iYGMTFxeHEiRMyCY6JhQV8Z86GY8eOat/z3Nqv8HdS\nkkZxkHGqV5I1f/58LFu2TPpXW/V9CisrKzFixAh8+eWXmDZtmnajJKJGw791a3z50ksa9fFHYSE2\n3r5d63h9NqpubGJ+/RX2bd1g6+Kidh+FGRn45dgxjeJIS0vDk5IS7B8UonYfEolEZ9Msixcvlkls\nqktMTERERIR0E+QqLT090aZbd7XvaeVQuyaeOnGQ8VH5+3j37t1YunQpBgwYgOXLl2Pv3r347LPP\npOfbt28PHx8fHDx4kEkWESnUzsYG7WxsNOrjXE6O3CRLGxtVGyuBQIAXwkajy2j16xhejdyOhBWf\n1d2wDl7Nm+OYgjV5qrhfUgLfn3/WOA556iqqfVmF9YKNKQ7SLZWTrDVr1qB9+/b48ccfYWFhgejo\n6FptOnXqhFOnTmk1wIYmFovxySefYMuWLXj8+DH8/f2xYcMGuLm5KbyGG0STpup6qs/YNMT7eVJZ\nqZONqg1VREREo6oK/nlycp0PWmhDRESETAmiusTExMjM0gBAyt49Go1kAU+nUWv2W984Fi5c2CDf\nA43tew3Q3wbRKidZ165dw7hx42ChZAGgs7MzsrOztRKYvqxcuRK7d+/G6dOn4ezsjA8++AChoaG4\nfPmywv9BGuIHBTVuje17qCHej6WJCd6/9YfMMV1sVG0oFi1a1Kh+8a1KSWmwJKv6523AgAFKi6X2\n798fR48elb62sLOD57DhGsdhamqK0moFhesbR0NqbN9rwNMKCYqqJNQnCa8vlfculEgkEAqVN79/\n/z4s1dg+w5B8++23+PDDD/Hcc8+hWbNmWLlyJW7duqW0+CoRGQZuVK0d5eXlan8Yuq5duyo97+3t\n3aTiIN1SOcny8PDAb7/9pvC8WCxGQkICvIz4L/L8/HykpaXBx8dHesze3h7t27fHlStX9BgZEalC\nHxtVC4qLMXvKFNiZman90czUFI4KNgxvaJWlpTA3N1f7o6SkRN9vQamFCxfC19dX7jlfX98GG8Ex\nlDhIt1SeLhw+fDjmzZuHzz//HLNmzap1ftmyZbh9+7ZRL3ov+Gez2Zp7MjZv3lx6jogMw5PKSmyR\nUwZCIpHAVCCAWCKBGE//khQKBMi/egWR3rLbE5VXVmJijTIQ9SUUCPBp166wN1O/qOmCy5eR++SJ\nRnFoQ5cxY9FlzFiN+vhx9CjgeoqWItI+KysrnDhxAhEREbh8+TJiYmLQv39/eHt7IyIiosHKJhhK\nHKRbKidZ06dPx759+zBnzhzs27dPenzWrFmIi4tDUlISevXq1SBVj3fs2CG9Z3JyMioqKhAbG4uA\nAMXbMMTHx2PRokU4d+4cJBIJfHx8MH/+fAQGBkrb2NnZAXg6olXdo0ePpOeISP+etbHBf7rXvRB5\n+vnz+EJBuYjiigos1NIIdaCTE1prsFRiVUoKcnVUF4pqs7KykpZHEAgEelv7ZChxkO6onGRZW1vj\n119/xYwZM7Bz506IxWIAwOrVqyEUCjF69Gh8/fXXMNPgrzlVzZ8/H2lpaRCJRBCJRMjIyFD61MbR\no0cxcOBA2NnZISwsDBYWFti9ezeCg4MRHR2N0NBQAE+nBt3d3XH+/Hl06/Z0yuHRo0e4c+cO58eJ\nDEhLS0sMa9euznbTz59X2O5hWZnWkqzGYu+QwShOSYYm5VUrJBI48o9SgzRkyBAc/PkXCEzqXilk\nrqTMSmVZGSr5R4FK6lXvrXnz5ti2bRtWrVqF8+fPIzc3F/b29ujZsydatWqlqxhr2bp1Kzp06AAX\nFxfMmjULq1evVti2rKwMEydOhKWlJRISEtCpUycAwJw5c+Dt7Y1JkyYhODhYumB/0qRJ+M9//oOg\noCC0adMGc+bMwfPPPw9/f/8GeW9ERPry8M5t9HRwwLj27TXqx1HDbWhINy5duoRmIhF61FG25NgH\nM9D306Vyzz3Jy0Pi6s91EV6jpFZRXUdHR7z66qvajkVl1af46nL8+HGkpaUhPDxcmmABgJOTE6ZO\nnYr58+fjyJEjGDJkCICnyVd+fj78/f3x+PFj9O7dGz/99JPW3wMRUV30sbddW2trDHR11UnfxRUV\nWJ2Sgt8fPQIAjIiLQ+fmzTHTywtWJiY6uSfJsmzRAh0HvVHreM09P2/s/0Hunp8F9+4xyaoHlZ8u\nHDZsGI4cOSKdJjQWcXFxAIBgOdWHq45VtQGezosvW7YM9+/fR1FREX7++WelhUiJiHSham+7FStW\nSOspxcTEYMWKFXjllVcM/im+moorKjA8Lg7rbt7Eqfv3AQCn7t/Hups3MezUKZRUVuo5wqaras/P\nixu/ldaZS48/jYsbv8WPo0ehwgAeyjBWKidZ33//PUJCQqRTdL///rsu49KaO3fuAHhagqKm9v8M\niVe1ISIyFKrsbWdMVqekICk3V+65pNxcrFKwcwbpnip7fpJ6VE6yEhMTMWnSJJSWlmL16tXo0qUL\nunfvjjVr1iAnJ0eXMWqkqvSCvKcDFT1NSESkb41tb7uqKUJ1z5PuNOU9P3VN5TVZPXv2RM+ePfHF\nF1/g4MGD2L59O3755RfMmDEDs2fPxuuvv46xY8ciJCQEpqa62j/dMCnau1AZ7mtIRNUVFxdrvLfd\nb4sjkLRksdoxCCSSesWgyOfJyViVUr9aWafu30ebauWBTAUCpU+4qaL88WMITc1Q1+OSJubmCs9p\n4/NhSIqysjTe81NgoL/j9bU/oTL1/kxZWFjg7bffxttvv42///4bUVFR2L59Ow4cOIADBw7A0dER\nDx480EWsaqkarZJXTLTqmL29vUb3aGz7zhE1FZUSSb1/4dRkqqVfwkJzc7j17iN9nX3pIp7k5Sls\nb+ngIFPdPiv2V4S4uqKPSKRRHB62thpdDzz9mVjz5+KIuDjpWix5AkQi7O7z9P3fLymB788/Y0j0\nAY3iuHPkMMysmyltE79sCV6e/aHSNrYuunkQQB9s2rTBuwmy09D12fOz4N497Hp9gE5jVJey/QmV\n0eXehRqlo61bt8b//d//YcaMGVi9ejU+/vhj5Cn5oaAPVWuxbt++XavWlbL1WkTUuFmamOADT0+t\n9NVMC3/Zu7zUAyEb/it9/dvKFbi48VuF7Tu9PRR+cz6Svv5vh/bo7uioUv0wfejcvLnSJKtzjZ02\nAKDFs5qVknhpSt07kMQvWwLv8RM0uo+xa+XppTTJ4p6f6tPoJ8ONGzewfft2REVF4d69ewAML2EJ\nCAjAypUrcezYMQwdOlTm3LFjxwAAffr0kXcpETViVlpMsnShx7TpyDx/Tu6CZF3tw6hLM728cDYn\nR+7idx9HR8zkjIDeNLbvNUNS7yTr4cOH+O6777B9+3acP38eAGBra4v33nsPY8eOhZ+fn9aDVIVE\nIpF7vF+/fnBzc0NUVBRmzJgBz39+qGZlZWHt2rVwdnbGwIEDNbq3ojVZXHdFROoytbTE4B1ROLvm\nS+SkJCM9Ph5t/f3R0tMLPafNkKldZAysTEywNyAAq5KT8fujRzh1/z4CRCLWyTIAje17TR59rddS\nOcn66aefEBkZiUOHDqGsrAxCoRDBwcEYO3YshgwZIq2Y3hA2bdqE+Ph4AEBSUhIAYPny5di6dSsA\nIDw8XJrsmZmZYcOGDQgJCYGfnx9GjhwJc3Nz7NmzB3l5edi/fz8sNKxOzDVZRKQLppaW0inBrz2e\nka6LMVZWJib4pEsXAECbffuka7BI/xrb91pNytZrGcSarMGDBwMAOnTogLFjx2LMmDFwcXHRWWDK\nJCQkIDIyUvrUh0AgQExMDCT/PBkTFBQkM6I2YMAAnDx5EhEREdi5cycAwMfHBwsWLFBrkRwRERFR\nXVROst5//32MGzcOvXr10mU8Ktm6dat01EpVfn5+0jVYRERETU1mZiYEaWnY3LHutdPK2pgoWJ5D\ntamcZH37reKnXIiIiLSpXCzGOhWSgbqEHTsBezd3LURk/CQSCTra2WG7v7/Sdt0OHULCa6/JPZdZ\nXIxhp07pIrxGSWmSFRcXB3d3d7i7q/YNeuXKFVy5cgVjxozRSnBERNT0WJuaYmKHDhBrOGKy8fZt\nFGZlM8mqxlwoRBsVNhdX1KbCyPYv1jelSVbfvn0RERGBBQsWSI+tWLECK1askFsPKzo6Gp9++mmT\nS7L4dCGRYZppwCUaSDFbMzPpAnlNbP3jjzrbvDR1usb30YaFCxfqO4RGzeCfLqxSUlKCR0r2mFJU\nSqEx49OFRIaJ/29SXXpON4waUMa24bex0dfThSpvEE1EREREqmOSRURERKQDTLKIiIiIdIBJFhER\nEZEO1DvJqqqyXt9zRERERE1JnU8XLlq0SGblfdXTgyZyNvOs2tamqWEJByIiIsNlsCUcFJVkqO/x\nxoyPiRMRERkug9wgWszKrkRERERq4cJ3IiIiIh1gkkVERESkA0yyiIiIiHSASRYRERGRDjDJIiIi\nItIBJllEREREOsAki4iIiEgHFNbJatGiBebOnYs5c+YAeFqsKzAwEH369Gmw4IiImgITgQC5ZxIR\n2amD0nZ1nScyFhs3btRK8fJBgwahTZs2WohINxQmWfn5+Xjy5In09aJFiyAQCJhkERFp2RcvvYTC\n8nKlbd6IjcUOf3+lbdybNdNmWEQ6M2nyZJhaWQEabMVX/vgxfvzxR/z8889ajEy7FCZZrVu3xr17\n9xoyFiKiJqmTvb1K7Xq0bKnjSIgaiECI8WfOw8zKSu0uIoMCtBiQbihMsnx9fREZGQmhUCgdilN1\nc8UFCxZoJTh92b17N9atW4crV66gqKiozu2FuEE0ERGR4TK4DaJXrlyJW7duYePGjdJjqgZp7EmW\ng4MDpkyZguLiYkyYMKHO9twgmoiIyHAZ3AbRzz33HK5evYq//voLmZmZ6Nu3L8aOHYuxY8fqLBhD\n0b9/fwCqj9wRERER1aQwyQIAExMTeHh4wMPDAwDQrl07hZkgEREREf2PynWyxGIxFi5cqMtYdGrc\nuHEQCoUKP4YNG6bvEImIiKgRUasYaXp6Og4ePIgdO3bgp59+0tpTiDt27EB4eDi6du0Kc3NzCIVC\nnDp1Suk18fHxCA4Ohr29Pezs7BAUFITY2Nha7datW4ecnByFH1u3btXKeyAiIiIC6pgurCk1NRUT\nJ7EqWD8AACAASURBVE7EsWPHZI4LBAL069cPGzZsQLt27dQOZv78+UhLS4NIJIJIJEJGRgYESmpo\nHD16FAMHDoSdnR3CwsJgYWGB3bt3Izg4GNHR0QgNDZW2bdasGZqxhgwRERE1EJWTrOzsbPj7+yMz\nMxPu7u7o06cP2rRpg6ysLJw+fRrHjh2Dn58fLly4ACcnJ7WC2bp1Kzp06AAXFxfMmjULq1evVti2\nrKwMEydOhKWlJRISEtCpUycAwJw5c+Dt7Y1JkyYhODgYlpaW9Y5DLBajrKwMZWVlAIDS0lJIJBJY\nWFgoTfqIiIiIqqg8Xfjpp58iMzMTy5cvx507d7B9+3YsX74c27dvx61bt7By5UpkZWXh008/VTuY\nwMBAuLi4qNT2+PHjSEtLw6hRo6QJFgA4OTlh6tSpyMrKwpEjR9SKIzIyEtbW1nj11VchEAhgZWUF\na2trnD59Wq3+iIiIqOlROck6fPgwgoODMWfOHJiYmMicMzU1xaxZsxAcHIzDhw9rPUh54uLiAADB\nwcG1zlUdq2pTX+PGjYNYLIZYLEZlZaX039xSiIiIiFSlcpKVnZ0NHx8fpW26d++OrKwsjYNSxZ07\ndwBAWl6iuvbt28u0ISIiImpoKidZdnZ2uHv3rtI26enpsFdxDy5NFRQUAHgaV01Vx/Lz8xskFiIi\nIqKaVF743rt3b3z//feYPHky/Pz8ap0/e/Ys9u3bh9dff12rARoDRXsXKsN9DYmIiLRHX/sTKqNy\nkvXxxx/j0KFD6Nu3L4YPH46goCC0adMG2dnZiI2NxXfffQehUIiPP/5Yl/FKVY1WVY1oVVd1rKFG\n1bh3IRERkX4p259QGb3sXVhT9+7d8cMPP2Ds2LHYtWsXdu3aJXPewcEBW7ZsqXPdlrZUrcW6ffs2\nvL29Zc4pW69FRERE1BDqVYw0JCQEd+/exYEDB3Dx4kXk5+fD3t4e3bp1w+DBgxu02GdAQABWrlyJ\nY8eOYejQoTLnqoql8mlAIiIi0pd6JVkAYGNjg1GjRmHUqFG6iKcWiUQi93i/fv3g5uaGqKgozJgx\nA56engCArKwsrF27Fs7Ozhg4cGCDxEhERERUU72TLF3atGkT4uPjAQBJSUkAgOXLl0v3FQwPD5cu\nujczM8OGDRsQEhICPz8/jBw5Eubm5tizZw/y8vKwf/9+WFhYNEjciha+c3E7ERGR/ulrUbxBJVkJ\nCQmIjIyUbl0jEAgQExMDiUQCgUCAoKAgmScbBwwYgJMnTyIiIgI7d+4EAPj4+GDBggVqLX5TFxe+\nExERGS5li+INYuF7Q9i6dat01EpVfn5+tTasJiIiItI3lYuREhEREZHqmGQRERER6YBBTRcaKy58\nJyIiMlwGv/A9MDAQ/v7++PTTT3UZj1HiwnciIiLDpa+F7ypPF549exaVlZU6C4SIiIioMVE5yfLw\n8EB6erouYyEiIiJqNFSeLgwPD8eCBQtw9+5duLu76zImIiIirchKOofyoiKN+hB5e8Pa0VFLEVFT\nonKSFRISgmPHjsHf3x9z5sxBjx494OTkJC0cWp2bm5tWgyQiIqovB3NzXFu7RqM+Sioq8MyAAXh9\n3bdaioqaEpWTrPbt20v/PX36dIXtBAIB124REZHeXQgJ0biPPr/8Av5GI3WpnGSNGTNGpXbyRrYa\nO5ZwICIiMlwGX8Jh27ZtOgzDuLGEAxERkeEy+BIORERERKQ6tSq+X79+HdevX8fjx48xevRobcdE\nREREZPTqNZJ16dIldO/eHV5eXnj77bcxbtw46bmTJ0/C2toaP/30k7ZjJCIiIjI6KidZt27dQmBg\nIG7duoXp06fjtddeg0QikZ7v06cPWrRogR9++EEngRIREREZE5WTrEWLFqG0tBRnzpzBF198gZde\nekm2I6EQvr6+OH/+vNaDJCIiIjI2KidZJ06cwJAhQ+Cl5Em6tm3bIjMzUyuBERERERkzlZOshw8f\nom3btkrbSCQSlJaWahwUERERkbFTOclq3bo17ty5o7RNSkpKnYkYERERUVOgcpL1yiuv4ODBg7hx\n44bc8+fPn8eJEycwYMAArQVHREREZKxUTrI++ugjmJiYoE+fPvjmm2+QlZUFAPj999+xfv16hISE\nwMbGBrNmzdJZsERERETGQuVipM8//zz279+PkSNH4t///rf0eJcuXQAAzZs3R3R0NNzd3bUfJRER\nEZGRqVfF91dffRV//vknIiMjkZiYiNzcXNjb28PX1xfvvvsuHBwcdBWnQeMG0URERIbL4DeIrtKi\nRQtMnz4d06dP10U8BuHDDz/E4cOHkZ6eDhsbG7z22mtYuXKlwiSSG0QTEREZLm4QbUBMTU0RFRWF\nvLw8XL58Genp6TJbCBERERHVpd5J1s6dOxEUFAQHBweYmprCwcEBr7zyCnbu3KmL+PRi6dKlePHF\nF2FiYoJWrVph6tSpOHXqlL7DIiIiIiOi8nRheXk53nrrLRw6dAjA0210WrZsiZycHMTGxiI2NhZ7\n9+7FDz/8ADMzM50FrA8nTpyAt7e3vsMgIiIiI6LySNZnn32GQ4cOoVevXoiNjcWTJ0+QnZ2NJ0+e\n4Ndff0XPnj1x6NAhLF++XJfxqm3cuHEQCoUKP4YNGyb3ur1792Lz5s346quvGjhiIiIiMmYqJ1mR\nkZFo3749YmNjERAQAFPTp4Ngpqam6Pv/7d19XFR1vgfwzxke5VFMBUFBwYfQva0iWygmD0HWIptd\nH7lokVeS9OLqzWrdkiBtNe/VfIhteYVSAoWmktdXFoqKCLnelw/ZTTKxpBGaBFRACUWG3/2jnVlx\nmHFmmMMM8Xm/Xue1+ju/c+Z75rfE13O+5/eLjMSRI0cQGBiIDz74wOxgcnNzkZycjHHjxsHR0REK\nheK+j+nKysoQGxsLT09PeHh4IDo6GkeOHNHpl5mZifr6er1bTk6OzjEFBQVISUnBvn37eCeLiIiI\nTGL048Lq6mqkpqbCycmp0/3Ozs546qmnkJmZaXYwK1euhFKphLe3N7y9vVFTUwNJkvT2LyoqQlxc\nHDw8PDB37lw4OTmhoKAAsbGxKCwsRHx8vLavq6srXF1djY5l69atePnll/Hpp59iwoQJZl8TERER\n9U5G38kaNGgQ7ty5Y7BPW1sbfH19zQ4mJycHly9fhkqlwuzZsw32bW1txcKFC+Hs7Izy8nJkZmZi\nw4YNOH36NPr374+UlBTcunXLrDg2b96MP/3pTzh48CATLCIiIjKL0UlWYmIiPv74YzQ2Nna6v6Gh\nAbt27UJiYqLZwURFRcHPz8+ovsXFxVAqlUhMTERwcLC23cfHB6mpqVCpVNi/f79ZcSxduhRNTU2I\niIiAu7s73N3d4eHhgerqarPOR0RERL2P0UlWWloaQkND8cgjjyA/Px/V1dW4c+cOqqurkZeXh0ce\neQQPP/ww0tLS5IxXq7S0FAAQGxurs0/Tpuljqvb2dty+fRs3btzQbk1NTRg8eLD5ARMREVGvorcm\nS6FQ6NRDCSEAAPPmzQMASJKkbQOAyspKODs7Q61WyxFrBxcvXgQADB8+XGdfUFBQhz5ERERE3U1v\nkjV58mSzTmioUN2SmpqaAAAeHh46+zRt+h5tEhEREclNb5JljYUUeyp9C0QbwsWjiYiILMdai0Ab\nYvIC0bZCc7dKc0frbpo2T0/PbomFC0QTERFZl6FFoA3hAtGd0NRiVVZW6uwzVK9FRERE1B1MupMl\nhMC+fftw9uxZ7duFndm2bZtFgjMkIiIC69atw8GDBzFz5swO+w4ePAjA/LoyIiIioq4yOsn64Ycf\nMHXqVJwzov7IkknW3W8v3i0mJgb+/v7Iz8/H0qVLMXr0aACASqXCli1b4Ovri7i4OIvFYYi+mizW\nXREREVmfteq1jE6ylixZgnPnzmH+/Pl45pln4Ovrq12/0FKys7NRVlYGADh58iQAYO3atdp1BZOT\nkxEeHg4AcHBwQFZWFqZOnYrw8HAkJCTA0dERO3bswLVr17Bnzx69SwBZGmuyiIiIbJehei05a7KM\nzpIOHz6Mxx9/HNnZ2bIFU15eju3bt2ungZAkCQcOHIAQApIkITo6WptkAcCUKVNQUlKC9PR05OXl\nAQBCQ0ORlpZmVvEbERERkaUYnWTZ29vjoYcekjMW5OTkaO9aGSs8PFxbg0VERERkK4xOsiZOnIiv\nv/5azliIiIjIxrW2t6NPF8uF7Nrb8XN9PTyHDLFQVLbJ6G9p1apVmDRpEj766CMkJCTIGVOPw8J3\nIiLqDbwcHfHmuHFdPs+K06fR1vKzBSIyjs0XvoeEhKC4uBi///3vkZWVhfHjx+ud7LO7Fom2FSx8\nJyKi3sDNwQHP/GN94K549cwZSPYOFojIODZf+N7Y2IgVK1agqakJpaWlKC0t1du3tyVZRERERPcy\nOslatmwZjh07hpiYGMybNw+DBg2y+BQORERERL8WRmdJ+/btw4QJE1BUVKSdYoGIiIiIOmf02oW3\nbt1CeHg4EywiIiIiIxh9J2vs2LH4/vvv5YyFiIiIyCgKhR2Kj5bCyd3d7HOI9nYLRqTL6CQrLS0N\nU6dOxbFjx/Doo4/KGVOPwykciIiIulfEG6twu6nJqL5XL1zAtcoLOu0/nT6FOz/LN5WE0UnWjz/+\niKlTp+Kxxx5DQkICQkND9U7h8Mwzz1gswJ6AUzgQERF1ryETw+/f6R+GP/Fkp+2lL72Irwr3WCok\nHUYnWc8995z2z7m5ucjNze20nyRJvS7JIiIiIrqX0UnWtm3bjOrHwngiIiIiE5KspKQkGcMgIiIi\n+nUxegoHIiIiIjIekywiIiIiGRj9uHDYsGH3rbcSQkCSJM6nRUREvxrfFxXhneHDunQO1iv3TkYn\nWUIICCF02hsaGtD0j3kqfH194eDQfatqExERyen5kSNR08V5lPbX1OCCkfM50a+L0UlWVVWV3n0X\nL17EkiVL0NzcjM8//9wScREREVnd3MDALp/j0s2bTLJ6KYvUZA0fPhy7d+9GTU0NMjIyLHFKIiIi\noh7NYoXvffr0QUxMDAoKCix1SiIiIqIey6JvF9rb20OlUlnylEREREQ9ktE1WfdTV1eHTz75BEOG\nDLHUKXsMLhBNRERku6r//nfUnPi7TvtP31TI+rlGJ1kZGRmdvoLa1tYGpVKJvXv3orGxEWvWrLFo\ngN1t9erVeP/991FfXw+FQoExY8bg9ddfR0xMjN5juEA0ERGR7RocFobBYWE67berL6P2/HnZPtek\nJMsQDw8PrFy5Eq+88kqXg7Km2bNnIzU1FZ6enmhra8PmzZsRHx+P69evw9nZ2drhERERUQ9hdJJ1\n+PDhTtsVCgW8vLwQHBwMe3uLPX20mhEjRmj/rFaroVAo4OPjw/m/iIiIyCRGZ0WRkZEyhmFbPv30\nU8ydOxeNjY3w9/fH/v37YWdnZ+2wiIiIqAfpFWsXJiUlQaFQ6N1mzZrVoX9cXByuX7+Oq1evIj4+\nHr///e/R3NxspeiJiIioJzKYZLW3t5u1mSM3NxfJyckYN24cHB0doVAocPToUYPHlJWVITY2Fp6e\nnvDw8EB0dDSOHDmi0y8zMxP19fV6t5ycnE7P7+Xlhc2bN+PatWs4dOiQWddFREREvZPBx4X29vYm\nLWqpWSBarVabHMjKlSuhVCrh7e0Nb29v1NTUGPzsoqIixMXFwcPDA3PnzoWTkxMKCgoQGxuLwsJC\nxMfHa/u6urrC1dXV5JiAX+qy1Go13N3dzTqeiIiIeieDSZa/v7/RJ2pubsbVq1fNDiQnJwcjR46E\nn58fli9fjg0bNujt29raioULF8LZ2Rnl5eUIDg4GALz88ssYO3YsUlJSEBsba9bbgJs3b8bs2bPh\n7e2Nuro6vPrqq/Dz80NYJ69+EhEREelj8HFhVVXVfbfKykosWbJE+5gwICDArECioqLg5+dnVN/i\n4mIolUokJiZqEywA8PHxQWpqKlQqFfbv329WHEeOHMHYsWPh5uaGkJAQtLS04MCBA+jTp49Z5yMi\nIqLeqUuF7zt37sSDDz6I5cuXQwiBdevW4byMk3pplJaWAgBiY2N19mnaNH1MVVhYCJVKhZs3b+Ly\n5cvIzc3F0KFDzY6ViIiIeiezJrYqLy/H8uXLceLECTg4OOCPf/wj0tLS4OXlZen4OnXx4kUAwPDh\nw3X2BQUFdehDREREZA0mJVkXL17EK6+8gsLCQgDAjBkzsGbNGm1i012ampoA/DLL/L00bY2Njd0a\nExEREdHdjEqyrl69ioyMDGRlZeHOnTuYMGEC1q9fz2Lwf9C3QLQhXDyaiIjIcvQtAm2IVReIvn37\nNjZu3Ii1a9eisbERQUFBWLt2LaZPny5rUPejuVuluaN1N02bp6dnt8XDBaKJiIisS98i0IZYdYHo\nUaNGQalUol+/fnj77bexePFim1ifUFOLVVlZibFjx3bYZ6hei4iIiKi7GHy7UKlUAvhlktH169cj\nMDAQ/v7+993kFhERAQA4ePCgzj5N2+TJk2WPg4iIiEgfo25LXb9+HdevX5c7Fh1CiE7bY2Ji4O/v\nj/z8fCxduhSjR48GAKhUKmzZsgW+vr6Ii4vrtjj11WSx7oqIiMj69NVrWbUmy9x1CM2RnZ2NsrIy\nAMDJkycBAGvXrtWuK5icnIzw8HAAgIODA7KysjB16lSEh4cjISEBjo6O2LFjB65du4Y9e/bAycmp\n22JnTRYREZHt0levZdWarO5UXl6O7du3a9crlCQJBw4c0K6HGB0drU2yAGDKlCkoKSlBeno68vLy\nAAChoaFIS0tDZGSkNS6BiIiISMtmkqycnBztXStjhYeHd1qXRURERGRtXVpWh4iIiIg6ZzN3snoy\nFr4TERHZLpssfCfjsPCdiIjIdlmr8J2PC4mIiIhkwCSLiIiISAZMsoiIiIhkwCSLiIiISAZMsoiI\niIhkwLcLLYBTOBAREdkuTuHQg3EKByIiItvFKRyIiIiIfkWYZBERERHJgEkWERERkQyYZBERERHJ\ngIXvRERENq6mpgY//fRTl87R3t5uoWjIWEyyiIiIbNzgwYMBAApJ6tJ5XO35a7878dsmIiLqAeYH\nBeHNkBBrh0EmYE0WERERkQyYZBERERHJgEkWERERkQyYZBERERHJgIXvFsAFoomIiGwXF4i2QU8/\n/TT27t2LkpISTJ48WW8/LhBNRERku7hAtI3Zvn07WlparB0GERER9VC8k9WJ6upqrFy5EmVlZQgI\nCLB2OERERNQD8U7WPYQQmD9/PlauXIkhQ4ZYOxwiIiLqoXpFkpWUlASFQqF3mzVrlrbvu+++C0mS\nsGDBAitGTERERD2dzSRZubm5SE5Oxrhx4+Do6AiFQoGjR48aPKasrAyxsbHw9PSEh4cHoqOjceTI\nEZ1+mZmZqK+v17vl5OQAAL777jusXr0a7733HoBf7mrd/b9ERERExrKZmqyVK1dCqVTC29sb3t7e\nqKmpgWRgIcyioiLExcXBw8MDc+fOhZOTEwoKChAbG4vCwkLEx8dr+7q6usLV1fW+MRw7dgxXr17F\n+PHjO7Q/9dRTSExMRGZmpvkXSERERL2KzdzJysnJweXLl6FSqTB79myDfVtbW7Fw4UI4OzujvLwc\nmZmZ2LBhA06fPo3+/fsjJSUFt27dMjmG2bNn49KlSzh79izOnj2LL7/8EgCwdetW/OUvfzHruoiI\niKh3spkkKyoqCn5+fkb1LS4uhlKpRGJiIoKDg7XtPj4+SE1NhUqlwv79+02OoU+fPvD19dVumngG\nDBgAT09Pk89HREREvZfNJFmmKC0tBQDExsbq7NO0afp0VXt7u8GJSImIiIg60yOTrIsXLwIAhg8f\nrrMvKCioQx8iIiIia+iRSVZTUxMAwMPDQ2efpq2xsbFbYyIiIiK6m828XdiT6Vsg2hAuHk1ERGQ5\n+haBNoQLRHdCc7dKc0frbpq27ixU5wLRRERE1qVvEWhDuEB0JzS1WJWVlTr7DNVrEREREXWXHplk\nRUREAAAOHjyos0/TxjcCiYiIyJps+nGhvuVsYmJi4O/vj/z8fCxduhSjR48GAKhUKmzZsgW+vr6I\ni4vrtjj11WSx7oqIiMj69NVr9ZqarOzsbJSVlQEATp48CQBYu3atdl3B5ORkhIeHAwAcHByQlZWF\nqVOnIjw8HAkJCXB0dMSOHTtw7do17NmzB05OTt0WO2uyiIiIbJe+ei25a7JsJskqLy/H9u3btesV\nSpKEAwcOQAgBSZIQHR2tTbIAYMqUKSgpKUF6ejry8vIAAKGhoUhLS0NkZKQ1LoGIiIhIy2aSrJyc\nHO1dK2OFh4d3WpdFREREZG09svCdiIiIyNbZzJ2snoyF70RERLar1xe+92QsfCciIrJd1ip85+NC\nIiIiIhkwySIiIiKSAZMsIiIiIhkwySIiIiKSAZMsIiIiIhnw7UIL4BQORERE3eu9kUFwsrMzqm9b\nezvaOlkPWd8ayZbCJMsCOIUDERFR93JUKJDx299iuIeH2ed4q6ICZT/9ZMGoOmKSRURERD1ScN++\n+K2Xl9nH+3h6AjImWazJIiIiIpIBkywiIiIiGTDJIiIiIpIBkywiIiIiGTDJIiIiIpIBkywiIiIi\nGTDJIiIiIpIBkywiIiIiGTDJIiIiIpIBkywiIiIiGXBZHQvgAtFERES264vaWnxRV6fTfq6+XtbP\nZZJ1j/T0dKxatQouLi7atj/84Q/Iz8/XewwXiLYdX9TWMrG1ERwL28LxsB0lJSWIjIy0dhi9ysSB\nAzv9//8PajW+uXpVts/l48JORERE4MaNG9rNUIJFtqWzf6mQdXAsbAvHw3aUlJRYOwTqJkyyOiGE\nsHYIRERE1MMxybqHJEk4efIkBg4ciKFDhyIxMRFVVVXWDouIiIh6mF6RZCUlJUGhUOjdZs2ape07\nY8YMVFRUoLa2FsePH4e9vT1iYmLQ3NxsxSsgIiKinsZmkqzc3FwkJydj3LhxcHR0hEKhwNGjRw0e\nU1ZWhtjYWHh6esLDwwPR0dE4cuSITr/MzEzU19fr3XJycrR9x4wZgyFDhgAABg0ahK1bt0KlUuH4\n8eOWvWAiIiL6VbOZtwtXrlwJpVIJb29veHt7o6amBpIk6e1fVFSEuLg4eHh4YO7cuXByckJBQQFi\nY2NRWFiI+Ph4bV9XV1e4urqaFZcQApIksU6LiIiITGIzd7JycnJw+fJlqFQqzJ4922Df1tZWLFy4\nEM7OzigvL0dmZiY2bNiA06dPo3///khJScGtW7fMimPHjh2o/8e8GbW1tXj++ecxcOBATJw40azz\nERERUe9kM0lWVFQU/Pz8jOpbXFwMpVKJxMREBAcHa9t9fHyQmpoKlUqF/fv3mxXHhx9+iNGjR8PV\n1RUhISFobW1FcXGx2XfCiIiIqHeymSTLFKWlpQCA2NhYnX2aNk0fU+3duxe1tbVobm5GdXU18vPz\nERgYaH6wRERE1Cv1yCTr4sWLAIDhw4fr7AsKCurQh4iIiMgaemSS1dTUBADw8PDQ2adpa2xs7NaY\niIiIiDoQNujFF18UkiSJo0ePdro/NjZWSJIkvvvuO519ra2tQpIkMWnSJLnDFEIIAYAbN27cuHHj\n1oM3udjMFA6m0Nyt0tzRupumzdPTs1tiEZzagYiIiDrRIx8XamqxKisrdfYZqtciIiIi6i49MsmK\niIgAABw8eFBnn6Zt8uTJ3RoTERER0d1sOsnS9yguJiYG/v7+yM/PR0VFhbZdpVJhy5Yt8PX1RVxc\nXHeFSURERKTDZpKs7OxsJCUlISkpCZ9//jkAYO3atdq28vJybV8HBwdkZWWhtbUV4eHhWLRoEZYu\nXYqQkBBcu3YN7777LpycnGSL1dg1E8kyqqur8fbbbyMmJgZDhgyBk5MTBg8ejMTERJw7d67TY86d\nO4dp06ahX79+cHNzQ1hYGHbt2tXNkfce06ZNg0KhwIABAzrdz/GQlxAC27dvx6OPPgpPT0+4u7vj\nN7/5DRYvXqzTl2Mhn5aWFmzYsAHjxo2Dl5cXvLy8EBISgg0bNnS6CgnHoutMXffY1O/88uXLmDdv\nHgYOHAgXFxf89re/RVZWlvEBylZSb6KkpCQhSZJQKBQdNk3bBx98oHNMWVmZiImJEe7u7sLd3V1E\nRUWJI0eOyBrn559/Luzs7ISXl5dYtGiRWLZsmRg0aJCws7MT//M//yPrZ/dWr7zyipAkSYwaNUos\nXLhQrFixQkydOlUoFArh7OysM+ZnzpwRbm5uwsXFRcyfP1+8/PLLIigoSEiSJN555x3rXMSv2Icf\nfijs7OxEnz59xIABA3T2czzk1dbWJhISEoQkSWL8+PHixRdfFC+//LKYPn26znhwLOSjVqvF5MmT\nhSRJ4l/+5V/EsmXLxLJly8SYMWOEJEkiIiJCtLe3a/tzLCwjICBASJIkfHx8xODBgw3OTGDqd65U\nKrW/3+fMmSP+9Kc/ibFjxwpJksTy5cuNis9mkqye4Pbt2yIgIEC4urqKiooKbbtKpRLe3t7C19dX\ntLS0WDHCX6c9e/aIsrIynfaPP/5YSJIkgoODO7SHhYUJOzs7cejQIW3bjRs3xOjRo4WLi4tQqVSy\nx9xbXLlyRfTv318sW7ZMDB06tNMki+MhrzVr1ghJksSGDRt09qnV6g5/51jI5/Dhw0KSJPHYY491\naFer1SIyMlJIkiRKSkq07RwLyzh8+LCorq4WQtx/+idTv/M5c+YISZJETk6Otu3OnTsiOjpaKBQK\ncebMmfvGxyTLBJ9++qmQJEk8//zzOvtWr14tJEkSu3fvtkJkvdfIkSOFQqEQV69eFUIIce7cOSFJ\nknj88cd1+ubl5QlJksT69eu7O8xfrRkzZohhw4aJ5uZmERAQoJNkcTzkdfPmTeHh4SGio6Pv25dj\nIa+dO3cKSZLEW2+9pbPvL3/5S4ffDxwLeRhKskz9zhsaGoSDg4MYNWqUTv+ysjIhSZJITU29Ya9j\nMgAADz1JREFUb0w2U5PVE8i5ZiKZx9HREQBgb//LlG+GxigmJqZDH+qa3bt3Y/fu3cjKyoKLi0un\nfTge8jpw4ABu3LiB6dOno6mpCbm5uVizZg22b9+Ourq6Dn05FvKaOHEinJycUFRU1OGlLbVajaKi\nIjg7OyMsLAwAx8IaTP3Ojx8/jra2Nu2+u4WFhcHFxcWoMeqRk5FaC9dMtC2nTp3CuXPn8Lvf/U47\nQa2hMfL29oarqyvHyAKuXr2KxYsXY968eZ3+R0uD4yGvU6dOAQCuXbuGUaNG4cqVK9p9rq6uyMrK\nwr/9278B4FjIzc/PD3l5eVi4cCEeeugh7S/nAwcOoLa2Fnl5efD19QXAsbAGU79zQ/3t7OwwbNgw\no8aId7JMwDUTbcfNmzfx7LPPQqFQ4K233tK2GxojTTvHqOuWLFkCANi4caPBfhwPedXX1wMAMjIy\n8PDDD+Pbb79FY2MjCgoK4ODggKSkJJw9exYAx6I7hIeHY8aMGaioqMCmTZuwadMmfPvtt5g5cyYm\nTZqk7cex6H6mfufG9G9paYFarTb4uUyyqMdpbW3FzJkzUVFRgfT0dERGRlo7pF5l3759+Oijj7Bx\n40Z4eXlZO5xerb29HQDg4+ODnTt3YsSIEXB3d8esWbOwdu1atLW1YcuWLVaOsneoq6vDI488goKC\nAmzbtg11dXW4du0a8vLysGvXLoSFheH69evWDpO6GZMsE9jSmom9VVtbG2bPno2ioiIsX74cr732\nWof9hsZI084xMl9zczNSUlIQFxeHOXPm3Lc/x0Nemu8uJiZGZ27A+Ph4AMDp06cBcCzktmnTJly+\nfBlr1qzBs88+iwceeAB9+/bFnDlzsHnzZlRVVeHtt98GwLGwBlO/c2P69+nTB3Z2dgY/l0mWCbhm\nonW1tbUhISEBe/fuxZIlS7Bu3TqdPiNGjADQ+RhduXIFzc3NHKMuqKurg0qlwqeffgqFQtFhUyqV\nqK+vh0Kh0N7hMvQzw/HoulGjRgHo/B93ml8SLS0tADgWcjtz5gyAfy77djdN25dffgmAY2ENpv5u\nMNRfrVbj0qVLRo0RkywTcM1E61Gr1Zg3bx52796NF154QW8tkOb75xjJw8PDA//+7/+OBQsW6Gxu\nbm5wcnLCggUL8OyzzwLgz4zcNI/K715eTOObb74BAPj7+wPgz4bcNG863/tWJ/DP2jnN3UaORfcz\n9TufMGECHBwccOjQIZ3+x48fx88//2zcGJk2C0Xv1traKgICAoSLi4s4d+6ctv3HH38UAwcOFH5+\nfuLWrVtWjPDXSa1Wi7lz5wpJkkRycvJ9+0+YMEEoFApRXFysbWtqahLBwcHC1dWVk/zJpLN5soTg\neMgtKipKKBSKDisftLa2iri4OCFJksjKytK2cyzks3HjRiFJknjyySdFa2urtr2trU3MnDlTSJIk\ntmzZom3nWFieZp6suyd9vZup37lmJYVt27Zp21pbW0VUVJSws7MTX3755X1jkoTQswozdaqoqAhT\np06Fm5sbEhIS4OjoiB07dqC+vh579uzR1kGQ5bz++utYtWoV+vbti9TUVEiSpNNn2bJl2kcmZ8+e\nxaRJk6BWqzFnzhw88MADKCwsxKVLl7BlyxYsWrSouy+hVxg6dCh+/vln1NbWdmjneMjr22+/xcSJ\nE3Hz5k1Mnz4dPj4+OHToEP7v//4P0dHROHDgABSKXx5acCzk8/PPPyMsLAxff/01RowYgdjYWNjZ\n2aG4uBjffPMNxo4diy+++ALOzs4AOBaWkp2djbKyMgDAyZMnUVFRgSlTpsDb2xsAkJycjPDwcACm\nf+fV1dV4+OGHUVtbixkzZmDo0KH4/PPP8dVXX2H58uWdlqzoMCtd7OWssWZib5aUlNRhLct7N4VC\nIX744YcOx3z99ddi2rRpwsvLS7i4uIhHHnlE7Nq1y0pX0DvoW1ZHCI6H3L777juRkJAgBgwYIJyc\nnMSoUaPEG2+80eGOigbHQj5NTU1ixYoVIjg4WDg7O4s+ffqI0aNHi1dffVXcvHlTpz/HoutMXffY\n1O9cqVSKuXPnigEDBghnZ2fx0EMPib/97W9Gx8c7WUREREQyYOE7ERERkQyYZBERERHJgEkWERER\nkQyYZBERERHJgEkWERERkQyYZBERERHJgEkWERERkQyYZBERERHJgEkWERERkQyYZBERERHJgEkW\nEZERSkpKoFAotFtwcLC1QzJLfX19h+vQLB5NRJbHny4iIhNERkYiPT0dqampevtcuHAB//mf/4mQ\nkBD069cPjo6OeOCBBxAWFoaXXnoJp0+f7lIMiYmJUCgUePfdd+/b9/HHH4dCocDevXsBAK6urkhP\nT0d6ejoCAgIgSVKXYiEi/bhANBGREUpKShAdHY309HSkpaXp7ZeRkYE33ngDQgiMHz8eDz/8MPr1\n64cbN27g7NmzOH78OFpbW/HOO+9g0aJFZsVy9OhRREVFYdy4cTh16pTeflVVVQgMDISvry+USqXO\nXavIyEgcO3YMarXarDiIyDB7awdARPRrkZGRgYyMDPj7++Ojjz7ChAkTdPrU1dVh48aNaGpqMvtz\nIiIiMHLkSJw5cwZnzpzBuHHjOu23detWAMBzzz3Hx4JEVsCfOiLqkqqqKigUCjz33HM4f/48pk2b\nhn79+sHNzQ2PPvooDh48aO0Qu8X333+P1atXw8nJCZ999lmnCRYADBgwAG+++SZeeumlTvefOHEC\nM2bMgI+PD5ycnODv74+UlBSoVKoO/ZKTkwEA7733XqfnUavVyMnJgUKhwIIFC7pwZURkLiZZRGQR\nly5dwsSJE9HQ0IAXXngBM2fOxKlTp/Dkk09i586d1g5Pdjk5OVCr1ZgxY4ZRRfF2dnY6bdu2bUN4\neDiKiorw2GOPYdmyZQgNDUV2djZCQ0Nx+fJlbd9nn30WDg4OKCgoQEtLi865PvvsM/z444+IiYlB\nQEBA1y6OiMzCJIuILKK0tBTJyckoKSnBm2++iZycHBw7dgwKhQIpKSm4ceOGtUOUVXl5OQAgOjra\nrOMvXLiAlJQUBAYG4sKFC8jPz8fatWuxZ88eHDhwAFeuXMEf//hHbf/+/fvj6aefRkNDQ6dJrOYO\n1/PPP29WPETUdUyyiMgi+vbtq1MQPn78eCQmJqKhoQGFhYXaN930bV988YWVou+6n376CQDg5+en\ns6+qqkr7Rp9m27RpU4c+7777Ltra2rBp0yYMGjSow77o6GjEx8dj3759aG5u1rZrEqjs7OwO/VUq\nFfbv3w9vb2889dRTFrk+IjIdC9+JyCJCQkLg6uqq0x4REYEPPvgAX375JXJzc3H79m0AwMcff4z1\n69fj73//u7bvwIEDuy3e7lRVVYU33nijQ1tAQECHO1PHjx8H8MtbjCdOnNA5R21tLdRqNb799luE\nhIQA+CX5CgoKQnl5Oc6fP48HH3wQwD8fXSYlJXX6WJKIugeTLCKyCG9v707bfXx8AACNjY0d+jzw\nwAOws7ODv79/t8QnNx8fH5w/fx41NTU6+yIjI9He3g7gl4J0BwcHnfmprl69CgD4r//6L72fIUlS\nhztZALBgwQKsWLEC2dnZ+O///m8IIbB161YoFAptcTwRWQcfFxKRRVy5cqXTds1jNE9PT6POU1dX\nh9GjRyMtLQ0+Pj4ICgpCYWEhUlNT0b9/f4SGhqKqqkr7VqNGSUkJhg0bpv37oUOHMGbMGLi5uWHC\nhAnaR5F1dXUYM2YMXn31VQwaNAjDhg3DoUOHzL1srUmTJmk/1xB9UxN6enpCkiQ0NTWhvb29002t\nVuPRRx/tcNxzzz0He3t75Obm4s6dOzh8+DAuXbqEqKgoBAYGdvm6iMh8TLKIyCJOnz6Nmzdv6rSX\nlJQAgN65nO7V3NyM8+fPo6KiAmVlZZgyZQqmT5+OO3fu4OTJkxg8eDBWr15tcKbyH3/8Ef/6r/+K\nxYsX49y5c5gzZw6eeOIJVFdXo7m5Gd988w0qKytRXl6OadOm4bXXXjPrmu+WlJQEe3t77Nq1C+fP\nnzf5+AkTJkAIgdLSUpOOGzhwIJ566inU1dXhk08+0dZnseCdyPqYZBGRRTQ0NOjUHZ08eRL5+fno\n27cvnn76aZPOt337dgwfPhxPP/00BgwYgL/97W8YOnQo4uPjcenSpfseGxUVhUWLFmlrn6KiopCX\nl6ft8/777yMwMBDx8fF678KZIjAwEK+99hpaW1vx5JNPamus7tXQ0NBp+3/8x3/AwcEBy5YtQ2Vl\npc7+1tZWHDt2rNNjNY8F169fj08++QQDBgww+fsmIstjTRYRWcTkyZORnZ2NEydOYOLEiVCpVNix\nYwcAICsrC25ubiadz8XFBQDg4OCg/TPwy/xSmvomfZRKpbYIXOPBBx9ETU2N9g6Y5pwKhULvIzxT\npaWlQQiBVatWITw8HOPHj8fvfvc79OvXDw0NDaiqqkJxcTEkScLkyZM7HDtq1Chs27YN8+fPx5gx\nY/DEE09gxIgRuHPnDpRKJY4dOwZvb29UVFTofO7jjz+OoUOH4n//938B/DKHlr09//NOZG28k0VE\nFhEYGIjjx4/Dy8sLWVlZ2LVrF0JDQ7F//37MnDnT4p937+PC2traDrF8/fXXHfafOnUKwcHBFkuo\n9Hn99ddRUVGBpUuXoq2tDR999BHWrVuHgoIC1NXVYfHixTh16hTef/99nWMTExNx6tQpJCYm4quv\nvkJmZiY+/PBDfP/995g1axb++te/6v1czazukiSx4J3IRvCfOkRkMaNGjcInn3zSLZ+leTtx586d\nGDx4MJYvX65NvObNm4fVq1dj06ZNiI+Px65du3D27FkUFhZq3+IzlzFJ2siRI7Fhwwazzv+b3/wG\nOTk5Jh/35z//GX/+85/N+kwikgfvZBGRVUiSpLd4/d72u/+uOc7V1RVvvvkmFi5ciBdeeAGvvfaa\ndjoIb29v7N27F9u2bcPo0aOxe/dufPbZZ3B3d7/v+e8nIyMDCoXCqKVzbFF9fb128ldTi+yJyDSS\nkPveORH9qlVVVSEwMBBJSUnYtm2btcORzQ8//ID3339fm5D1798fixYtsnJUpmtpacG6des6JJb3\nztRPRJbBJIuIuqS3JFlERKZikkVEREQkA9ZkEREREcmASRYRERGRDJhkEREREcmASRYRERGRDJhk\nEREREcmASRYRERGRDJhkEREREcmASRYRERGRDP4f4X/gPFFM2loAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f297390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ____DATA____\n",
    "# -- use hist to get bin_heights, bins:\n",
    "bin_heights, bins = np.histogram(MiJpT_data/1000, bins = bins_def); \n",
    "# -- find center of each bin:\n",
    "bins_mean = [(bins[i]+bins[i+1])/2 for i in range (0,(len(bins)-1))]\n",
    "# -- horizontal error bars of lenght = bin length: \n",
    "xerr = [bins_mean[i]-bins[i+1] for i in range (0, len(bins_mean))] \n",
    "\n",
    "_ = plt.errorbar(bins_mean, bin_heights, xerr=xerr, yerr=np.sqrt(bin_heights), \n",
    "                 fmt='o', capsize = 0, color = 'black', label='data')\n",
    "\n",
    "\n",
    "\n",
    "# ___BKG___\n",
    "# _ = plt.hist(nMiJ_bkg, bins = bins_def, \n",
    "#              alpha = 0.5, histtype = 'stepfilled', color = 'red', label = 'MC Background', \n",
    "#              weights = \n",
    "#              np.array([ data_df.shape[0] /  sum(bkg_df['HH2yybbEventInfoAuxDyn.weightFinal'].values)] * len(nMiJ_bkg))*\n",
    "#             bkg_df['HH2yybbEventInfoAuxDyn.weightFinal'].values )\n",
    "\n",
    "plt.hist([MiJpT_yybb/1000, MiJpT_ybbj/1000, MiJpT_yybj/1000, MiJpT_ybjj/1000, MiJpT_yjjj/1000], \n",
    "                 bins=bins_def, \n",
    "                 color = (\"#e75555\", \"#a9e185\", '#6cdcdf', '#f4df62', '#f47cbf'), \n",
    "                 stacked=True, \n",
    "                 histtype='stepfilled', \n",
    "                 label=[r'$\\gamma$$\\gamma$bb', r'$\\gamma$bbj',r'$\\gamma$$\\gamma$bj',r'$\\gamma$bjj',r'$\\gamma$jjj'], \n",
    "                 edgecolor = \"black\",\n",
    "                 weights = [weights_yybb, weights_ybbj, weights_yybj, \n",
    "                            weights_ybjj, weights_yjjj]) \n",
    "\n",
    "plt.title('pT of Muons in Jets in All Events')\n",
    "plt.xlabel(r'p$_{T muon}$ [GeV]')\n",
    "plt.ylabel('Number of Events')\n",
    "plt.yscale('log')\n",
    "plt.legend(fontsize = 'medium')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apply cuts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# -- create lists of jets, using the Jet class\n",
    "jets_bkg = [Jet(pt, eta, phi, m, btag) for (pt, eta, phi, m, btag) in zip(\n",
    "    pup.flatten(bkg_df['HH2yybbAntiKt4EMTopoJetsAuxDyn.pt']),\n",
    "    pup.flatten(bkg_df['HH2yybbAntiKt4EMTopoJetsAuxDyn.eta']),\n",
    "    pup.flatten(bkg_df['HH2yybbAntiKt4EMTopoJetsAuxDyn.phi']),\n",
    "    pup.flatten(bkg_df['HH2yybbAntiKt4EMTopoJetsAuxDyn.m']),\n",
    "    pup.flatten(bkg_df['HH2yybbAntiKt4EMTopoJetsAuxDyn.MV2c20_85'])\n",
    "    )]\n",
    "\n",
    "jets_bkg = pup.match_shape(np.array(jets_bkg), bkg_df['HH2yybbAntiKt4EMTopoJetsAuxDyn.pt'])\n",
    "\n",
    "jets_data = [Jet(pt, eta, phi, m, btag) for (pt, eta, phi, m, btag) in zip(\n",
    "    pup.flatten(data_df['HH2yybbAntiKt4EMTopoJetsAuxDyn.pt']),\n",
    "    pup.flatten(data_df['HH2yybbAntiKt4EMTopoJetsAuxDyn.eta']),\n",
    "    pup.flatten(data_df['HH2yybbAntiKt4EMTopoJetsAuxDyn.phi']),\n",
    "    pup.flatten(data_df['HH2yybbAntiKt4EMTopoJetsAuxDyn.m']),\n",
    "    pup.flatten(data_df['HH2yybbAntiKt4EMTopoJetsAuxDyn.MV2c20_85'])\n",
    "    )]\n",
    "\n",
    "jets_data = pup.match_shape(np.array(jets_data), data_df['HH2yybbAntiKt4EMTopoJetsAuxDyn.pt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# -- cut out only b-jets\n",
    "bjets_bkg = [((np.array(jets_bkg[evn]))[bkg_df['HH2yybbAntiKt4EMTopoJetsAuxDyn.MV2c20_85'][ev] == 1]).tolist() for (evn,ev) \n",
    "         in enumerate(bkg_df.index.values) ] \n",
    "\n",
    "bjets_data = [((np.array(jets_data[evn]))[data_df['HH2yybbAntiKt4EMTopoJetsAuxDyn.MV2c20_85'][ev] == 1]).tolist() for (evn,ev) \n",
    "         in enumerate(data_df.index.values) ] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# -- select 1 and 2 Tag Cateroies\n",
    "a_bkg = [(len(bjets_bkg[ev]) > 0) for ev in xrange(len(bjets_bkg))] \n",
    "a_data = [(len(bjets_data[ev]) > 0) for ev in xrange(len(bjets_data))] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# -- all the bjets in the events with 1 or 2 bjets total\n",
    "# -- not actually used afterwards unless we want to calculate m_bb\n",
    "bjets_2_bkg = np.array(bjets_bkg)[np.array(a_bkg)]\n",
    "bjets_2_data = np.array(bjets_data)[np.array(a_data)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# -- slice to only keep events with 1 or 2 b-tagged jets\n",
    "events_to_keep_bkg = np.where(a_bkg)[0]\n",
    "events_to_keep_data = np.where(a_data)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# -- slice the dataframe using the slice above, aka keeping only events with 1 or 2 b-tagged jets\n",
    "bkg_df_12TagCat = bkg_df.iloc[events_to_keep_bkg] # bkg_df listing only events with == 1 or 2 b-jets\n",
    "data_df_12TagCat = data_df.iloc[events_to_keep_data] # data_df listing only events with == 1 or 2 b-jets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# -- boolean array that tells whether an event in the 1-2TagCat belongs to the sidebands\n",
    "sidebands_bkg = np.logical_or(bkg_df_12TagCat['HH2yybbEventInfoAuxDyn.m_yy'].values/1000 < 120, \n",
    "                           bkg_df_12TagCat['HH2yybbEventInfoAuxDyn.m_yy'].values/1000 > 130)\n",
    "\n",
    "sidebands_data = np.logical_or(data_df_12TagCat['HH2yybbEventInfoAuxDyn.m_yy'].values/1000 < 120, \n",
    "                           data_df_12TagCat['HH2yybbEventInfoAuxDyn.m_yy'].values/1000 > 130)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# -- ONLY RELEVANT FOR THE 2TagCategory!!\n",
    "\n",
    "# m_bb_bkg = np.array([ (bjets_2_bkg[ev][0].lv + bjets_2_bkg[ev][1].lv).M() \n",
    "#                       for ev in xrange(len(bjets_2_bkg))])\n",
    "# m_bb_data = np.array([ (bjets_2_data[ev][0].lv + bjets_2_data[ev][1].lv).M() \n",
    "#                       for ev in xrange(len(bjets_2_data))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# -- ONLY RELEVANT FOR THE 2TagCategory!!\n",
    "\n",
    "# bb_window_bkg = np.array(np.logical_and(m_bb_bkg/1000 > 95, m_bb_bkg/1000 < 135))\n",
    "# bb_window_data = np.array(np.logical_and(m_bb_data/1000 > 95, m_bb_data/1000 < 135))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# -- ADD SECOND PART IN THE 2 TAG CATEGORY IF YOU WANT TO APPLY m_bb CUT!!\n",
    "\n",
    "all_cuts_bkg = np.where(sidebands_bkg)[0]# & bb_window_bkg)[0]\n",
    "all_cuts_data = np.where(sidebands_data)[0]# & bb_window_data)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bkg_df_allcuts = bkg_df_12TagCat.iloc[all_cuts_bkg] # bkg_df listing only events with == 2 b-jets\n",
    "data_df_allcuts = data_df_12TagCat.iloc[all_cuts_data] # data_df listing only events with == 2 b-jets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Re-calculate interesting variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# -- number of muon in jets (data):\n",
    "nMiJ_12Tag_data = [len(data_df_allcuts['HH2yybbMuonsAuxDyn.pt'].values[ev]) for ev in xrange(data_df_allcuts.shape[0])]\n",
    "# -- number of muon in jets (bkg):\n",
    "nMiJ_12Tag_bkg = [len(bkg_df_allcuts['HH2yybbMuonsAuxDyn.pt'].values[ev]) for ev in xrange(bkg_df_allcuts.shape[0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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2btw4tn379mqXU4qPj2ccx0nMYq5Jfzk5OWzAgAHMyMiICQQCtnnzZpadnc2G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\nIYSQNkGJJyGEEEIIaRP/B3tMSd1MfZPbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12bd4b0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "NBINS = 9\n",
    "bins_def = np.linspace(0,8,NBINS)\n",
    "\n",
    "#fig = plt.figure()\n",
    "#fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, sharex=True)\n",
    "\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "gs = gridspec.GridSpec(2, 1, height_ratios=[3,1])\n",
    "ax1 = plt.subplot(gs[0])\n",
    "ax2 = plt.subplot(gs[1])\n",
    "\n",
    "\n",
    "# TOP PLOT\n",
    "ax1.set_yscale('log') #label_coords(labelx, 0.5)\n",
    "ax1.set_ylabel('Number of Events')\n",
    "\n",
    "# ____DATA____\n",
    "# -- use hist to get bin_heights, bins:\n",
    "bin_heights, bins = np.histogram(nMiJ_12Tag_data, bins = bins_def); \n",
    "# -- find center of each bin:\n",
    "bins_mean = [(bins[i]+bins[i+1])/2 for i in range (0,(len(bins)-1))]\n",
    "# -- horizontal error bars of lenght = bin length: \n",
    "xerr = [bins_mean[i]-bins[i+1] for i in range (0, len(bins_mean))] \n",
    "\n",
    "_ = ax1.errorbar(bins_mean, bin_heights, xerr=xerr, yerr=np.sqrt(bin_heights), \n",
    "                 fmt='o', capsize = 0, color = 'black', label='data')\n",
    "\n",
    "#___BKG___\n",
    "bin_h, bin_n, _ = ax1.hist(nMiJ_12Tag_bkg, bins = bins_def, \n",
    "             alpha = 0.5, histtype = 'stepfilled', color = 'red', label = 'MC Background', \n",
    "             weights = \n",
    "             np.array([ data_df_allcuts.shape[0] / sum(bkg_df_allcuts['HH2yybbEventInfoAuxDyn.weightFinal'].values)] * len(nMiJ_12Tag_bkg))*\n",
    "            bkg_df_allcuts['HH2yybbEventInfoAuxDyn.weightFinal'].values/bkg_df_allcuts['HH2yybbEventInfoAuxDyn.weightXsecLumi'].values )\n",
    "ax1.legend()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# RATIO PLOT\n",
    "ax2.set_ylabel('Data/MC')\n",
    "_ = ax2.errorbar(bins_mean, bin_heights/bin_h, xerr=xerr, \n",
    "                 yerr=((bin_heights/bin_h) * ( (np.sqrt(bin_heights)/bin_heights) + (np.sqrt(bin_h)/bin_h)) ), \n",
    "                 fmt='o', capsize = 0, color = 'black', label='data/MC')\n",
    "\n",
    "plt.suptitle('Muons in Jets in Events with 1 or 2 b-Tagged Jets in the Sidebands', fontsize = 15)\n",
    "\n",
    "plt.xlabel('# of Muons in Jets')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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