jpsi.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Measuring the J/psi mass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:rootpy.plotting.style:using ROOT style 'ATLAS'\n"
     ]
    }
   ],
   "source": [
    "# Required imports and setup\n",
    "import os\n",
    "import numpy as np\n",
    "from rootpy.plotting import Hist, Canvas, set_style, get_style\n",
    "from rootpy import asrootpy, log\n",
    "from root_numpy import root2array, fill_hist\n",
    "from ROOT import (RooFit, RooRealVar, RooDataHist, RooArgList,\n",
    "                  RooVoigtian, RooAddPdf, RooPolynomial, TLatex)\n",
    "style = get_style('ATLAS')\n",
    "style.SetCanvasDefW(900)\n",
    "style.SetCanvasDefH(600)\n",
    "set_style('ATLAS')\n",
    "log['/ROOT.TClassTable.Add'].setLevel(log.ERROR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "def get_masses(filename, treename='events'):\n",
    "    # Get array of squared mass values selecting opposite charge events\n",
    "    expr = '(e1 + e2)**2 - ((px1 + px2)^2 + (py1 + py2)^2 + (pz1 + pz2)^2)'\n",
    "    m2 = root2array(filename, treename=treename,\n",
    "                    branches=expr, selection='q1 * q2 == -1')\n",
    "    # Remove bad events\n",
    "    m2 = m2[m2 > 0]\n",
    "    # Return the masses\n",
    "    return np.sqrt(m2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "def plot_hist(hist, logy=True, logx=True):\n",
    "    # A function to plot the mass values\n",
    "    canvas = Canvas()\n",
    "    if logy:\n",
    "        canvas.SetLogy()\n",
    "    if logx:\n",
    "        canvas.SetLogx()\n",
    "    hist.xaxis.title = 'm_{#mu#mu} [GeV]'\n",
    "    hist.yaxis.title = 'Events'\n",
    "    hist.Draw('hist')\n",
    "    return canvas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Fetch the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "if not os.path.exists('events.root'):\n",
    "    !curl https://cernbox.cern.ch/index.php/s/ZE45HERahm7DeZ6/download -o events.root"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Plot the dimuon mass spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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ExT+Xa0bfxyKiRtPPaUqs7Lrucrmkf95ut9vtdrlcdvBhAgBKk9YIHdxeTvCo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\n",
      "text/plain": [
       "Canvas('Canvas_DQRULywTh2owcC5kVgjVK4')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masses = get_masses('events.root')\n",
    "mass_hist = Hist(1500, 0.5, 120)\n",
    "fill_hist(mass_hist, masses)\n",
    "plot_hist(mass_hist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Zoom in on the J/psi resonance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3+1nKVeXL5TLx540XAgAAFKjKvFHvZJMWIqV4ZFTXx3NeYr7URPb462oao0Lt1lcMVx4J\nqeqheCEAAECBqswb9V7ApCayExcJqcL4ZfHbN1VJfHJcRcm48L2nmsYkVq6m4+PVysL4vaQAAAAN\nU+8wmpcm/8WTq/pD4sKjeELVrIJXNWhy6tZCAACA5ql3GHVdVw0mJz4q/xDJdhmjnj6MxmmSbt7K\nNcO0AAAATVLvMKoRXpae94lZLsubFyPN0D+LkVEAANASzQyj4S2WMk67J1ayw0vrK4/fBqq/npFR\nAADQbE0Lo/I0I5VEwxs8NYa1H9PNBwAA5dozKlScFh5V+WJlC59mpDnEqO4HMafdIwsAACCKiApV\n5tGGjIx6nmdZVvjUpXgSzRtAdwuseSfW084U3eGlAQAAaqcJYdR13fAhnA8PD/sMfFaz1j7js2o9\nggsAALBV7cOo53ly53l5XlHi7vfxp2SpWb9ePl6YZad6VWfGygmjAACg2WofRuWYqOYO0TBNXlQp\nNr47aWLN8X2jNMFREy6ZpgcAAG1W7zAaPzZp5+sTa5BZM3zmZ+TicLiMx9n4Q+Hr5RlOicl4tx1S\nAQAAaseq9dJstXxec3y8+Ml0qFaHhX9w13Vl4pxOp+GL1X6lkfLESsLtCZenVaLqiQzrJlYSfkqt\n3zIAAHD4qswb9U42GfcdWC6X4Tn38Gon8ZNnNSWuwVePykrUQGm42niTIpWLpHCpQrC6PvzXxPtf\nCaMAAKBshNGsdgij4ifzqKK56zS8fWlanfrKRfqOX+E8qqQlUUEYBQAA5SOMVkGmPbkBvrT1eplW\nHcfZumbfe6nwygmjAACgbIRRpCKMAgCAslWZN+q9mh4AAAC1RhgFAACAMYRRAAAAGEMYBQAAgDGE\nUQAwKfEYNgBoD8IoABjg+/7l5WW323Ucp9vtXlxc+L5vulEAYABhtH4sLdOtA7Cd7/vn5+fr9Xqz\n2QghNptNEARnZ2fkUQDV0GeJiuMEm1bWDPuMAg1weXm5Xq9vb2/DhaPRyLbtq6srU60CAIVN75GK\nMAo0QLfblWOiEbZtJ5YDQMXY9B4AGisIgrTEqXkIAJqKMAoAlbJt++joKO9DANBUj0w3AABaZzgc\nBkEwn8/DhePxuNPpmGoSAJjCyCgAVG0ymdzf349GI1UyHo/v7u4mk4nBVgGAEYRRAKhar9dbLBa2\nbdu2LYSwbbvT6SwWi+PjY9NNA4CqsTS7ZlhNDzSM53mO45huBQD8BFbTA0BbHGwS5ZxSANUgjAIA\n/g/nlAKoGGEUAPBjnFMKoHqEUQDAj93c3JycnITPKZ3P56enp9fX1wZbBaDZWA1TMyxgAlAezikF\nILGACQBQNc4pBWAEYRQAIATnlAIwhONAAQA/xjmlAKrHyCgA4Mc4pxRA9Qij9WNpmW4dgBrjnFKg\nJfRZouI4wdLsmmE1PYBqcE4p0GZV5g2STc0QRgEAQNnY2gkAAACtQBgFAACAMYRRAAAAGEMYBQAA\ngDGEUQAAABhDGAUAAIAxhFEAAAAYQxgFAACAMYRRAAAAGEMYBQAAgDGEUQAAABhDGAUAAIAxhFEA\nAAAYQxgFAACAMYRRAAAAGEMYBQAAgDGPTDcAuVmWpXn04eGhspYAAIA60meJihFG64e4CQAA9rE1\nS1SZVpmmBwAAgDGEUQAAABhDGAUAAIAxhFEAAAAYQxgFAACAMYRRAAAAGEMYBQAAgDGEUQAAABhD\nGAUAAIAxhFEAqMhqtTLdBAA4OIRRACiX7/uXl5fdbtdxnG63e3Fx4fu+6UYBwKEgjAJAiXzfPz8/\nX6/Xm81GCLHZbIIgODs7I48CgEQYBYAS3dzcnJyc3N7eqpL5fH56enp9fW2wVQBwOKyHhwfTbUAO\nlsVbBtRJt9uVY6IRtm0nlgPAIagybzAyCgBlCYIgLXFqHgKAViGMAkBZbNs+OjrK+xAAtMoj0w1A\nbpZlaR5lEh84KMPhMAiC+XweLhyPx51Ox1STAECfJSrGyGj9PGiZbh2AnzCZTO7v70ejkSoZj8d3\nd3eTycRgqwC0nD5LVBwnCKMAUKJer7dYLGzbtm1bCGHbdqfTWSwWx8fHppsGAAeBpdk1w2p6oL48\nz3Mcx3QrAGC7KvMGyaZmCKMAAKBsbO0EAACAViCMAgAAwBjCKAAAAIxpYBi1LEuzRMDzPEsr8Vmu\n6zqOIy9wHMfzPH0b1PWO47iuq79YrmnIXjkAmLJarUw3AUDTNG01jOu6s9ms3++nRTp5gaaGeIck\nJtS0l/A8bzAYxMuXy2ViRHYcJ/7lrmk/C5gAVM/3/Zubm/l8vtlsjo6OhsPhZDLp9Xqm2wWgLFXm\njaadwKQPmkIIFfL6/X780XheVCX9fl/+Wb7EarVyXTcy6hlOotPpNNykwWAQf1NVElWVe563Wq1W\nqxVDpAAOhO/75+fnJycnm81GCLHZbIIgODs7WywW5FEABdi6BX8tLJdLFf6EEP1+P+1KecF0Os1S\nraozcr0KsomVR8qXy2Viq9LKVeXL5TKx/VlaDgBFubi4GA6Hkd8do9Ho6dOnppsGoCxV5o0mzPnG\np9H109xCiOl0uvVWznDN8V6K16OGReOVq3sDwvWoYdG0yhN/CqbpAVSs2+3KMdEI27YTywE0APuM\nli7XISjhMdeI8F0BKjjGY656uXC4VBP08WplIQsFABgXBEFa4tQ8BADZNSGMhkd6E7OdorJgljCq\nvzieUDW3eKoaNDl1ayEAVM+27aOjo7wPAUB2TQijOwjvpiTFY2Ku5Cq0I52JDdBUnjiSCgBGDIfD\n0WgUKRyPx/EbSQFgB+0KoyrbDQaDyCT4bDZL22Q0Ud68GAmp+mcxMgrgcEwmk/v7+3AeHY/Hd3d3\nk8nEYKsANEZLw6gQYjqdLpfLyDL8cArcbVRSnyPjt4Hqr2dkFIBxvV5vsVjYtm3bthDCtu1Op7NY\nLI6Pj003DUATNG2f0Ywiq9rlgUlyg085g2+uadvlGsGNYzE+gLx6vd7V1dXV1dXhf0MCEHtHhYq1\nbmRU7Z4Vf0j+IcuWT2btuZuX6eYDqDGSKFALe0aFitNCu8JoFmomfbfv3LwT62lniu7w0gAAALVD\nGP0/GRfCS3uutc/4LAYhAABAsxFGU+nXy8cLs+xUr+rMWDlhFAAANFuLwqjneXJL0bRJ8LTBy8Tr\n47uKaoKjJlwyTQ8AANqsRWFUcwySSMmLMmuGz/zUXKyqjdevSsLXyy2lEkdSc+2fDwAAUF8tCqMi\nNJMeyYue5w0GA/nn8ENp+TLxYpESXj3Pk+EycoJoYkIV+bfTBwAAqC+rYXv9yO1C+/1+WpIL77wV\nv8tzuVzGo6Eap5QPqawZvzhcv4yeKomKpA0+XddVtcnrw39NHMG1rKa9ZQAA4NBUmTealmy2hlGR\nvhNsYrgUoTya5eLwIGtYWj+H86iSlkQFYRQAAJSPMFo67yUhhPOS/imu66rrt26Mr+ovvHLCKAAA\nKBthFKkIowAAoGxV5o12LWACAADAQSGMAgAAwBjCKAAAAIwhjAIAAMAYwigAAACMIYwCAADAGMIo\nABQvflIGACARYbR+LC3TrQNazff9y8vLbrfrOE632724uPB933SjACBKnyUqjhOE0fp50DLdOqC9\nfN8/Pz9fr9ebzUYIsdlsgiA4OzsjjwI4NPosUXGcIIwCQDFubm5OTk5ub29VyXw+Pz09vb6+Ntgq\nADhwnC1ZMxwHChysbrcrx0QjbNtOLAeAg8VxoABQM0EQpCVOzUMAAMIoABTAtu2jo6O8DwEAHplu\nAAA0xHA4DIJgPp+HC8fjcafTMdUkADh8jIwCQDEmk8n9/f1oNFIl4/H47u5uMpkYbBUAHDjCKAAU\no9frLRYL27Zt2xZC2Lbd6XQWi8Xx8bHppgHA4WJpds2wmh6oBc/zHMcx3QoA2FGVeYNkUzOEUQAA\nUDa2dgIAAEArEEYBAABgDGEUAAAAxhBGAQAAYAxhFAAAAMYQRgEAe1mtVqabAKDGCKMAgF34vn95\nedntdh3H6Xa7FxcXvu+bbhSA+iGM1o+lZbp1AFrB9/3z8/P1er3ZbIQQm80mCIKzszPyKFAL+ixR\ncZxgB/WaYdN7AIfg8vJyvV7f3t6GC0ejkW3bV1dXploFoCicwIRUhFEAh6Db7cox0QjbthPLAdQL\nJzABAA5XEARpiVPzEAAkIowCAPKxbfvo6CjvQwCQ6JHpBgAA6mc4HAZBMJ/Pw4Xj8bjT6ZhqEoCa\nYmQUAJDbZDK5v78fjUaqZDwe393dTSYTg60CUEeEUQBAbr1eb7FY2LZt27YQwrbtTqezWCyOj49N\nNw1AzbA0u2ZYTQ/g0Hie5ziO6VYAKBJbOyEVYRQAAJSNrZ0AAADQCoRRAAAAGEMYBQAAgDGEUQAA\nABhDGAUAAIAxhFEAAAAYQxgFAACAMYRRAAAAGPPIdAOQm2VZmkfZEh8AAOjps0TFCKP1Q9wEAAD7\n2JolqkyrTNMDAADAGMIoAOxltVqZbgIA1BhhFAB24fv+5eVlt9t1HKfb7V5cXPi+b7pRAFA/hFEA\nyM33/fPz8/V6vdlshBCbzSYIgrOzM/IoAORFGAWA3G5ubk5OTm5vb1XJfD4/PT29vr422CoAqCOL\npdn1Ylm8ZYB53W5XjolG2LadWA4A9VJl3mBkFADyCYIgLXFqHgIAJCKMAkA+tm0fHR3lfQgAkIhN\n7wEgt+FwGATBfD4PF47H406nY6pJAFBTjIwCQG6TyeT+/n40GqmS8Xh8d3c3mUwMtgoA6ogwCgC5\n9Xq9xWJh27Zt20II27Y7nc5isTg+PjbdNACoGZZm1wyr6YFD43me4zimWwEARaoyb5BsaoYwCgAA\nysbWTgAAAGgFwigAAACMIYwCAADAGMIoAAAAjGHT+/qxLEvzKMubAACAnj5LVIyR0fp50DLdOgAQ\nQojVamW6CQBS6bNExXGCMAoAKIzv+5eXl91u13Gcbrd7cXHh+77pRgE4aIRRAEAxfN8/Pz9fr9eb\nzUYIsdlsgiA4OzsjjwLQIIwCAIpxc3NzcnJye3urSubz+enp6fX1tcFWAThwHOdTM5zABOBgdbtd\nOSYaYdt2YjmAg8UJTACAmgmCIC1xah4CAMIoAKAAtm0fHR3lfQgAGhhGLctyHEd/jed5rus6jiMv\ndl1Xf726WF7veV7G67NU7nlersoB4DANh8PRaBQpHI/Hw+HQSHsA1ELTbkB0XXc2m/X7fU2kk9fE\ny9O6InFj2LSX8DxvMBjEy5fLZWJEdhwnvhufpv3cMwrgYPm+f3Z2dnp6Op/PZcl4PL67u1ssFsfH\nx2bbBiCXKvNG05KNDI6aMBcOi9PpVJaoOBjvDRUW+/2+TJMqyE6n08ioZ7zy8PVZKleNSfsRCKMA\nDpnv+9fX1/P5PAgC27aHw+FkMiGJArVTad7YugV/LSyXSxX+hBD9fj/tysQffLlcysLpdBouV3VG\nyvv9fmLv6SuPtCqtXFW+XC4T25/2owHA4Uj8BgNQF1XmjSYMs8Wn0bfOoccnzdUgZbhDVM3xXpIP\nhQdHVeXxEVN1Y0C4nsRXDFee+FMwMgoAAMrG1k5lURkxfvumKonnv/CYa0T43lP1xPiKpcTK1XR8\nvFpZyMnOAACg8ZoQRsMjvYnZTtHkv/AAZ+QPiQuP4glVs2RK1aDJqVsLAQAAmqcJYTSvjFFPH0bj\nNEk3b+WaYVoAAIAmaWMY1cuY//LmxUhI1T+LkVEAANASLQqjeUc6dxuV1Fcevw1Ufz0jo8Dh4DZu\nAChDi8JoY1j7Md18oGZ837+8vOx2u47jdLvdi4sL3/dNNwoAdPaMChWnhTaG0boPN+65m5fp5gN1\n4vv++fn5er3ebDZCiM1mEwTB2dkZeRTAIdszKlScFloURvPeiLnbjZt5k27afqg7vDSAwt3c3Jyc\nnNze3qqS+Xx+enp6fX1tsFUA0CQtCqMZVbPWPuOzWMkEmDWfz8NJVBWqs9cBAHtqYxgtZL18vDDL\nTvWqzoyVE0YBg4IgkLPzuR4CAOTSrjCqyYtqO/r4KU2JeTG+q6gmOGrCJdP0wMGybfvo6CjvQwCA\nXNoVRuPHLCmJ+U9mzfCZn5GLw+EyHmfjD4Wvl2c4JSbjXPvnAyjPcDgcjXKLiwsAACAASURBVEaR\nwvF4PBwOjbQHAJqnXWFUZcHBYBAud11X5r/IIZ9p+VI9PVKeGF49z9NXHhku5fgl4HBMJpP7+/tw\nHh2Px3d3d5PJxGCrAKBJHpluQNWWy6WMkpZlyXSowmK/34+ES8dx+v3+arWazWae58mYqLLmcrmM\nVO55ntyaK165SBoxnU6ns9lstVqp61XlkeQKwIher7dYLK6vr23bDoLAtu1Op7NYLI6Pj003DQAa\nwmrYxpOO46xWq36/rxlW9DwvMjIqhNA8RdYZKVwul4k3iSZWLoRI62fXdeO3AUyn03hylSyraW8Z\nUBfqf6QA0HhV5o32JhuZ9uRvF2nr9TKtOo6TlhQV76XCKyeMAgCAshFGkYowCgAAylZl3mjXAiYA\nAAAcFMIoAAAAjCGMAgAAwBjCKAAAAIwhjAIAAMAYwigAAACMad0JTA0gD3lKw8ZPAABAT58lKkYY\nrR/iJgAA2MfWLFFlWmWaHgBQkfjRygBAGAUAlMv3/cvLy2636zhOt9u9uLjwfd90owAcCsIoAKBE\nvu+fn5+v1+vNZiOE2Gw2QRCcnZ2RRwFIhFEAQIlubm5OTk5ub29VyXw+Pz09vb6+NtgqAIfDYjVM\nvVgWbxmAOul2u3JMNMK27cRyAIegyrzByCgAoCxBEKQlTs1DAFqFMAoAKItt20dHR3kfAtAq7DMK\nAFGr1arf75tuRUMMh8MgCObzebhwPB53Oh1TTQJwUBgZBYAfYweiMkwmk/v7+9FopErG4/Hd3d1k\nMjHYKgCHgzAKAEKwA1Fper3eYrGwbdu2bSGEbdudTmexWBwfH5tuGoCDwNLsmmE1PVCSy8vL9Xod\n3oFICDEajWzbvrq6MtWqhvE8z3Ec060AsF2VeYNkUzOEUaAk7EAEAApbOwFApdiBCABMIYwCADsQ\nAYAxbO0EAEKwAxEAGMLIKAAIwQ5EAGAIYRQAhGAHIgAwhKXZNWNZlv4C3lBgf+xABKDZtsYJUWGi\nIIzWDFs7AQCAsrG1EwAAAFqBMAoAAABjCKMAAAAwhjAKAAAAYwijAAAAMIYwCgAAAGMIowAAw1ar\nlekmADCGMAoAMMP3/cvLy2636zhOt9u9uLjwfd90owBUjTAKADDA9/3z8/P1er3ZbIQQm80mCIKz\nszPyKNA2hFEAgAE3NzcnJye3t7eqZD6fn56eXl9fG2wVgOpxtmTNcBwogGbodrtyTDTCtu3EcgBV\n4jhQAECTBUGQljg1DwFoJMIogFZjHbcRtm0fHR3lfQhAIxFGAbQR67iNGw6Ho9EoUjgej4fDoZH2\nADCFMAqgdVjHfQgmk8n9/X04j47H47u7u8lkYrBVAKpHGAXQOqzjPgS9Xm+xWNi2bdu2EMK27U6n\ns1gsjo+PTTcNQKVYml0zlmXpL+ANBbZiHfeh8TzPcRzTrQBaZGucEBUmCsJozbC1E7CnIAg062PW\n6zWrZwCArZ0AoCys4waAg/LIdAMAoGrD4TAIgvl8Hi4cj8edTsdUkwCgtRgZBdA6rOMGgMNBGAXQ\nOqzjBoDDwWqYmmEBE1As1nEDQFyVeYNkUzOEUQAAUDZW0wMAAKAVCKMAAAAwhjAKAAAAYwijAAAA\nMIYwCgAAAGMIowAAADCGMAoAAABjCKMA2mK1WpluAnLg/QJagjAKoOF837+8vOx2u47jdLvdi4sL\n3/dNNwqpeL+AtiGMAmgy3/fPz8/X6/VmsxFCbDabIAjOzs7IN4eJ9wtoIc6WrBnLsvQX8IYCYZeX\nl+v1+vb2Nlw4Go1s2766ujLVKqTh/QKqsTVOiAoTBWG0ZjibHsil2+3KMbYI27YTy2EW7xdwIDib\nHgAKEARBWoLRPARTeL+AdiKMAmgs27aPjo7yPgRTeL+AdnpkugEAUKLhcBgEwXw+DxeOx+NOp2Oq\nSdDg/QJaiJFRAE02mUzu7+9Ho5EqGY/Hd3d3k8nEYKuQhvcLaCHCKIAm6/V6i8XCtm3btoUQtm13\nOp3FYnF8fGy6aUjA+wW0EEuza4bV9MDOPM9zHMd0K5AV7xdgUJV5g2RTM4RRAABQNrZ2Kpel5Xle\n/Cme57mu6ziOZVmO47iuq38JdbG8PrHOxOuzVA4AANAYbRxm0586sFwuIxNDruvOZrP4lWldl1h/\nv99Pi7mDwSBLM1TlLXzLAABAlarMG63b2kklwn6/n/F6lUSn06ksWa1WIuV9Ugmy3+/LP8unr1Yr\n13Ujo57hJCorV9cPBgNCJwAAaLzWDbOpYc6MP7ga5gxfr0LkdDoN50tVeaTccRyZXyMvqq88cTCV\nkVEAAFA27hk9FCoLLpfLcLnjOHJgNTJ9r/4aHwGNl6tCNSaqKpclMr8C2AH/fJqKdxZontaFURkB\nM87Rq+wYv31TlcQHLyPhMiwcXhMT6tbKAWj4vn95edntdh3H6Xa7FxcXvu+bbhQKwDsLNFjrwqj8\nX3XGvevkxYnJVSVIlRfVHxIrjydUTdBUNbCyHsjO9/3z8/P1er3ZbIQQm80mCIKzszNSS93xzgLN\n1rowqriuG97RSRNPMyZXfRiN0yRdADu4ubk5OTm5vb1VJfP5/PT09Pr62mCrsD/eWaDZWhpGZ7NZ\n5HbP1WqVtsmoXsan5J15J6QCec3n83BeUYXz+dxIe1AU3lmg2doVRsNBsN/vL5fLh4eH5XKpkt9g\nMMg47a6vPDt95dyqD2QUBIGcw831EA4f7yzQeO0Ko8p0OlWnHssTktR6+cO/TVN/gtRWppsPlMK2\n7aOjo7wP4fDxzgI72DMqVJwW2rXpveM4aZtmyd2aVqtVfDBSxdYDwT6jQKLhcBgEQWTqdjwedzod\nU01CIXhngbz2jwpV5tGWjowmitzWmTeA7hZY2bwJKMpkMrm/vx+NRqpkPB7f3d1NJhODrcL+eGeB\nZiOM/p/d0iRr7YED0ev1FouFbdu2bQshbNvudDqLxeL4+Nh007AX3lmg2Qij2xWyXj5eKIOmfonS\nQd0eABy+Xq93dXW12WyWy+Vms3n27Bl5pRl4Z4EGa1cYlffkpi1Rig9eavKiqiR+SlNiGI2PdGqC\nZt5hVAAR/NtpKt5ZoHnaFUYTD5RX4uXxY5aUxMSZVn9iuIzH2fhDfO0Cemx/BgB1164wmuWsebXH\nU7hwMBhE6pG/AiOHfKblS/X0SHliePU8L7FyAAonlQNAY1ht2yfIcRw1lKImzcMlkSFPz/NUlJTp\nUIXF+MXh+vv9vsyyKmsul8t4CFZbJ0QqFyn7MlhW694yIEKeVB4+H3I0Gt3f3y8Wi16vZ7ZtMGK1\nWrHcEyhWlXmjjcnGdd3EmfrpdJp4O2k4jyqJSVQK510lMYmmVS7SdwgjjAKXl5fr9TpyPuRoNLJt\n++rqylSrUD3f929ububz+WazOTo6Gg6Hk8mE/5AAhSCMVkHmTrWh/daDl8LXS1uvV/uVbq3ce2lr\n5YRRoNvtJh4Cads2h0O2BwPkQKkIo0hFGEXLBUGgOQFyvV5zPmRLMEAOlIowilSEUYCRUQg+BkDJ\nqswb7TqbHkADcFI5giBIS5zyIQbIgRpp19ZOABqAk8ph23Za3NQ8BOAwEUYB1AwnlUMIMRwOw/8h\nkcbj8XA4NNIeADvjBsSa4Z5RtFbiXpJqQwy0je/7Z2dnp6en6oYNOUDOf0uAQlSZNxgZBXDQ9Ict\nkURbiwFyoDEYZqsZdWJTGt5QNAl7SSILBsiBvLbGCVFhoiCM1gzT9GgV9pIEACPYZxSpCKNoFfaS\nBAAjuGcUALbvJVlxe1Avq9XKdBMAZEIYBXCg2EsSO9CveANwgAijAA4Xe0kiF7nibb1ey4HzzWYT\nBMHZ2Rl5FDhkhFEAB0dNsHLYEnK5ubkJ770ghJjP56enp9fX1wZbBUCP1TA1wwImNJjv+zc3N/P5\nXJ4tPhwOZei8vr6ez+dBENi2LQvZSxKJWPEGFIUFTABaJ22CVQhxdXW12WyWy+Vms3n27BlJFIlY\n8QbUFMNsNcPIKJqKLUWxP0ZGgaKwzyhSEUbRVMQI7O/i4iIIAnVavTQejzudzrNnz0y1CqgjpukB\ntAsTrCgEK96AOiKMAjCPLUVRiF6vt1gsbNu2bVsIYdt2p9NZLBbcZwwcMuZ8a4ZpejTMarXq9/uC\nCVYUzfM8x3Hi5eojB0CDaXoADRc/JufNN99kghUFiiRRTmYCDhZhFEDVEndxmk6ns9mMCVaUgZOZ\ngEPGnG/NME2PBti6i1PaBCuwGzYOA/JiayekIoyiAdjFCRXjIwfkRRhFKsuy9BfwhuLABUGgWR2/\nXq9ZO49i8ZED4rbGCVFhouCe0fp50DLdOiDVarUS7OKEyvGRA+L0WaLiOPGoyhcD0EK+79/c3Mzn\n881mc3R0NBwOf+u3futHP/pR4i5OphqJZhsOh2kbh8k/s98TYBAjowBKlLiK+Z/+6Z8+/fRTdnFC\nZdJOZnrzzTfZ7wkwjjAKYHdy5l1TeHNzc3JyEl7FPJ/Pf/Znf/bnfu7n2MUJlUk8mWk2m81mM/Z7\nAoxjAVPNsJoehyA+8y4HNeOFP//zP69fxcwuTqiY+six3xOgwWp6pCKMwjg58x4e7xyNRp9++qll\nWV/60pfChf/yL//yN3/zN2n1sIoZZrHfE6BBGEUqwiiMSxxPev311x8eHv7xH/8xXDgajT744IMf\n/vCH8Ur4fQ+zsuz3xKomtBlhFKkIozAubTwp0ePHj995553EVczPnj0roXVAVmmf5E6n841vfCNy\nw0mv16u+hYBBVeYNFjAByEQuSwqCINeI5g9/+MN//dd/ZeE8DtBwOAx/MqW33377p3/6p1nVBFSJ\nMApAx/f98N43z549k+uRM7Jt+4MPPmDhPA5Q4n5Pf/u3f/vGG29E9n84PT29vr6Wf03cQQLAPgij\nQKvpf7Mm7hL6hS984a233opc+frrr//iL/5ipHA8Hg+Hw16vd3V1tdlslsvlZrN59uwZSRSHIHG/\np//5n//58z//88iV8/n8+vqaHUmBkhBGgVaIhM7IeGfkN6u6OHGX0K985St/93d/FxlP+qmf+qmj\noyP9dDxbOOHQRP6ndHFxEQRB4pX/+7//+x//8R/M3QNlIIwCTZYYOhPHO8/Ozr773e9GLr65uYms\nmhdCfPjhh//1X/8VGU/61re+9a1vfYvpeNSU/J+S5qj6V1555YMPPlB/jczdA9gHS7NrhtX0CEvc\nekYVJm4Ien9//9prr33++eeRlPnWW299/PHHX/3qV1X5O++8E/7tGyH3vkncsp597FFfcnA0sv/D\n48eP2aEMbcPWTkhFGG0DfcQUmQ9A+sEPfhAPnZq9P1955ZXPPvtMXyLxOxhN5fv+2dnZ6empyqPv\nvvvun/7pn6Zdz46kaCq2dsJhYfVoLjt3V9qUeqTwu9/9bnyS/e233/6d3/mdSOHt7W18kn0+nycm\nUSFEPHd+9tlnr7zySqRQLkva7WcEDlx8VdMXv/jFtB0kOp3OH/7hH7KqCdgTYbSuEhNPrhi0tYaM\na1z2acMOrSq7hmKjZPZWJd7HmRgxf+M3fuMLX/hCZFFREASRU5Hm8/nnn3++288S9tlnn73zzjvq\nr+wSisaL7/8wGo3YkRQo0QNqRQhxcXEhb7E/Ojp6+vTpixcvXrx4ES9MqyHx4njhd77znV/91V8N\nD4CNRqOvf/3r3/nOdxJfK1cbdm7V/j9XuAbP8/RPD9cfvjhe+OLFi+zdlfhaFxcX8eHGxP2S4kOV\neT1+/Dj+m/Xx48eJF3c6nadPn6pRom9+85vf//73s7+zQAO8ePHi61//emSziFdfffW3f/u3I/9e\nRqPR06dP1RMTvzeAWhAVRkRGRutE/odb/pddCKG2bDw+Pg4Xxv93rkbg9hx+++IXv/ibv/mbiauw\nE1dnqzboxwWzt2r/nytxzbim/Rmnzv/gD/4gvgVSYneltSpx3fo//MM/RE57F0mT6bmMx+N33nkn\nvtf3z/zMz8R3Dx2Px+PxmF1C0XK5diSdz+f6eRLgEFjbVNqaymIv9ndxcRF/BxMHz+T/zuMjcN/4\nxjf2HH6Ll49GoydPnsSrHY1G4/E4y7hg9lbt/3O99dZbr732WmQI89VXX43nMNn++HjnYDD45V/+\n5Ujho0ePRJJ4dyW2KjwPXqxHjx5FQufXvva173//+y9evIiMd/793/99fOxHXmz6gw8ckOVy+fDw\noF/A9yu/8ivxeRI12cJwKWpBVBgRCaN1krYBXqJOpxMPUmmZqSSPHj3aJ8kl2v/nigfEtOT9+PHj\njBm3Yhkz7ng8lvFdM8kuf7NK8YRKEgXSpH0hJ35vpP3nXNVGQsWhEYRRxO2wk0554207KyTJHeDP\nVYj4z5XYXW+//farr74aGcJ0HGcwGGjGNcOhc6tcFwPt9PTp0/i91+PxOO326/h/zjV34UskVBgk\nuGcUcZqjQdJodizPIj78lvYlm13iTZB57flzVSZXd3U6nX//93/Pcsbmf/7nf37729/OewBSrl3o\n2bIe2GoymcTvvf7nf/7ntK3TIpv+6u/C32EnE6DGKou92N/Tp0/j72Di4Nm7776b/TOQffgt8d7K\n8Xj8S7/0S/ERgj2V9HOlBcTE3TRzpcn43ZmJ3ZU2n/7Nb34zcZZcP3WeOITJuCZQjcR/nrlGDeLf\nPIn3tW8dQ03D2Cp2JhgZRSK5uWOWwbN/+7d/S9ulOZ6Zsg+/ffvb3/7v//7vyJV3d3d/8id/Eh8h\nyHUfZ8ZW7f9zJa4Zf/vtt3u9Xvznevfdd+MhOy1N/t7v/V6W7kpsldy5M7674fHxcWKhem7iECbj\nmkA1Ev95DofD7P85j2+O8eGHH7548SLjGGraiOluu0QDphBG66TX6wkhMs7PJu7SnJiZ0mr48pe/\nHPme/fKXvxzf30ReGS8/OzvbJ8mV9HMlBsTE5L1YLC4uLuIhOy1NXlxcZOmurfPpgogJ1E34n2fi\n9H2u/5xnTKinp6fX19cZD2kzNftP8EUmlY3BohDqLds6P5u4S/PWFS3ZZ3jTrlTliQ3YbZ1NsT9X\nronvHabO9d2ytRBA3cW/IuTWFpHfv/vfhZ+4u0jiDUK7zf6nzfLrDwF52PsYFBwCwWp6pMn14TC+\nU09RSS5LtTs0b/8F5qRJABr6/5wnpsa8CTW+C0f2XaLzJtQ9D/DT77Sa6/bW7DXs/1rZG1BlDRXc\nDSwIo0iz24fDeGYqKckZ/7kAIKOMJ00UklCzy5VQMx4CojlGZIdjn7cOuO5ZmPZa2RuQ/elba8jy\nKapsyFkQRpGmyg8HAKBw+ruGsifUXLuL5JL9pL1cB/hlPAZFjqFmHHBNrCF7Ydpr5RrxTRxITqtz\nnzHj3Z6+tdo0osK8YcnXQ11YFm8ZADSN53lqFZTv+9fX1/P5PAgC27aHw+Gbb77puu7p6el8PpfX\nyHWTn3zySRAE8dpeeeWVyCqox48fp22AatDrr7/+8PAQ2Xx6NBpZlnV/f39ycqLWbI1Go7/+67/+\nyle+8uGHH26tIXth2mt9+umnlmV96Utf2tqAt9566+OPP/7qV7+69en39/evvfZaZLtZ1Qbbtufz\n+WazOTo6Gg6HcvOcm5ubcOEPfvCD7E/v9Xq+70dqSKxWXixrW61W/X5f/rnSvFFZ7EUheMsAoCW2\nrrxMPAUq+y7R5c3+7ynxSNW0AdcyXivXiG/G85lHo1H207lyHZ2ddrhXxoHktMFdwcgo0jAyCgBt\nFhlDPTs7i4+Yzmazv/iLv9g6tvpXf/VX8aG+QsZQ40OzyCVxHDe70Wj0ve997xd+4Rciw6iJ1SYO\n7t7f3//lX/5ldXmjstiLQvCWAQCU7HvV7XN/atpJe9kP8Mu10yoqlniXsOAEJgAAsFX2Q9riVyYe\nzJHrpL3sB/jlOgYl+7nNiTVkL8x77HO8Abme/vjx48KPzi5EfBhbjaBXgzAKAEDtZT+kbYeEmnbS\nXvYD/HIdaJd4FHPiuc2JNWQvzHXsc2IDEo+YTgu+77zzTuFHZ5c65CwP8aoAYbR+LC3TrQMA1I8+\nocrR1sTyjEOzvV4ve5ZNTK6JA66JNWQvzHXsc2IDEgeSNadGF350duLTc91WoSGXNFWhshsCUAje\nMgBArWU5BiXXuc17Fu5wWODWjQ62nhRY1NHZaUdkJ94QnFht2l3CVeYNkk3NEEYBAO1R5Ul7ex4W\nuPPT9zw6O+3KjNUmxtavfe1rVeYN9gmqGbZ2AgCgkcL7dukLsz89S7XxcxYmk8nJyUlleYNkUzOE\n0WrQz9Wgn6tBP1eDfq4G/VyecEKtsp95R2uGf4TVoJ+rQT9Xg36uBv1cDfq5GlX2M6vpAQAAYAxh\nFAAAAMYQRgEAAGAMYdS8999//4033rAs64033njjjTc++ugj0y0CAACoCHcBG5Z4ZtJ77733R3/0\nR2nX85ZVgH6uBv1cDfq5GvRzNejnarCavi1UEn3y5Mkbb7whhPjjP/5jWfL8+fNf+7VfS3xKIW8Z\n9VAP9VAP9VAP9VBP2fVkei3CqCnvv/++jJ6R3KkSauJbc2gfMuqhHuqhHuqhHuqhnn1wz6gxMok+\nefIkMgL6/Plz+QduHgUAAI1HGDVDBU05Ox+msqmasgcAAGgqwqgZn3zyifxD4kKlJ0+ehK8BAABo\nKsKoGR9//LHm0fhwKQAAQCMRRgEAAGAMYRQAAADGPDLdgJaS94PKe0PjVPlHH30U3200cZ/8HVAP\n9VAP9VAP9VAP9RhHGDXjyZMnn3zySdoSJVUeT6LsCwsAAJqEaXoAAAAYQxgFAACAMYRRM/SbN+k3\nfgIAAGgMwqgZaonS+++/H39Uv7wJAACgMQijZqiVSfFBUHVS6HvvvVdpmwAAACpHGDVGZs1PPvnk\no48+cl3XcRzLsizL+vVf/3V5QXwpvZ7neaoSx3Fc1y28zXUX7ueduyhSied5Bbey6TzPU33oOA59\nuI/CO1NWVVDrmqCoHi7ky6fB6OdqlPT1Kzt8ryoeYI6aiH/y5Ml7770XHgp9/vz5crnMXlXa+5ur\nkgabTqf7d9FyueTf0Z76/X5iB/b7fdNNq58yOpPPc1ghPaz53phOp2U1vVYK6edCvuSbraSvX1XP\nXpXs82TsablcJt4Y+vz581z/hMIfqel0Op1Ow5+5kn+IGgj/MphOp8vlMtJFWfo5XAn9vJtwd6k3\noqgvxLYpozNVnUU3tpaK6mH99wZ5tJB+5vt5q5K+fsOV7NM83iGT5Pv3/Pnz9957Tw6OPn/+/OEn\n/11trUR9FCKJSlXC7/i0zszVz+rKSD+r/ueXylZpH0j1LckARnbFduZyueQ3d0QhPaz5fqC3pUL6\nOe1i9SXP93MZX7+RUf+9mrfPk7EP/T+S7EMUmisZ53goqJ+LerPaTP1Wjj/EL4y8CuzMxJm7gptb\nQ0X1sKZL+dg/FN3PiVemhbBWKenrt8DvDRYwGaPuGk68w1oVbr25eLVaCSESf6M4jrNr65qjkH7O\nWAl2wwe1QHRm2Xbo4cTbGVU9LOBLlL2fVQcmPkX+cpS/KBG38zeGfKK8KWL/ZnA2fUMk/kvjO64a\n4V8qRAGNfr9P/xSlqM4Mf0u4rjubzfavsxn272F9SIJUyCdZJk76WaPYr1/XdWXqkMvzC6hxn2FV\nlCf7zG/aPUnMAWWRsZ81cxwPdPXe0u57xg726Uz95xxSUR9Xvjf0CulnOnmrHfpZ9ap8SiHfG3zp\nHKK8a48iq+TCqwhbfqOMXvZ+jvzbi2AN0w6Wy2VkWwN6b2dFdSZhNE0ZH1eGhOKK6mdZD2vy0uzZ\nz5GnEEabKdcSbyXxnlGSqEbeftZcTIfvIPJZJYnuo6jOJIymKfzjyic/USH9HLmFka/luH36OT6d\nWMj3BguYDoi843AwGMi/arZKjnAcR90z2u/31WdltVpxD03cbv2s/r1ZliVvBZP3ynBczW4i/32a\nzWasA9sZnVm2Ans4/KUxnU55p8LK+CTzezBu5372PE+GjXii3dc+SRYFCn84cv1PLm2YXYUn/l8Y\ntnM/P6QfX8E0/T7Cs2ncM7qnPTuTkdGt9unhyP97+bRrFPW1wO9Bvbz9rA8b+7SELx3z9vmG0t+d\nzbqQsEJ+E8T3sJD1EEb3pPrTdEOaYOfOJIxmtEMPc+rSDgr5WuD34FYZ+1l+huOxnjDaBOFws8M3\n1NYPAf8plPbsZz398iZkQQwq0M6dybuQUd6OUl8+fBXnUtQHkv8D6GXsZ5HZDm3gnlGTPM+Tu/rJ\nb6gdbo5hJ9Es9u/nrfXLP3BnkobjOOp2W+yJzixbgT2s7hBdLpe8ZRGF9LO8DZevX43D/8Zg03uT\n5Bqafr+/80ckvHQJafbvZ/HymCXHceJfeYf8L/xwyA+q67p01/7ozLIV1cPq6yLXwFJ7FPhJ5leh\nRiH9nLbYV22An33VdYIdRlNRiEImIPT3jLLf70PREz2Js2x0chb68wX4RsqlpM5kml4pqof5ctAr\npJ8z7gPd5tuoSv36LeR7g5FRY/THnSvhR9U4qPr0qP92z2azyKCd53lq96I27x5SSD8LIabT6Ww2\nW61WruuGL1ZzcG3u5Cxc15UfSMdxIv87V5/bQs44boMdOjPxU400hfRw5KjVtNdKnG9piUL6WV05\nGAyWy2Xk96A64ba1nSxq8Y2xT5LFPjK+QeH/zCW+a5GB8fA+o/EaWqiofo5UFennlndyRpF9tSJ9\nyNqOXPJ2ZtqnOoyR0bD9ezjjf65aPm5ayCc5/HswXongK7qcbwyJ1fT1luVLSmQLSZra+BdYbD8n\nbjVKJ2eXtlcrSXQHuTpT86lWCKMRe/Zw2tMjWh5GHwr6JGuifxU/Qx0U/o0hFfK9YT0wZdMU3kvi\n5bxPm2clyqP6mU7emZyyVJ9V7nDYB51ZNnq4GoX0M78HtzrMzzNhFAAAAMawzygAAACMIYwCAADA\nGMIoAAAAjCGMAgAAwBjCKAAAAIwhjAIAAMAYwigAAACMIYwCAADAC4GPTAAABQtJREFUGMIoAAAA\njCGMAgAAwBjCKAAAAIwhjAIAAMAYwigAAACMIYwCAADAGMIoAAAAjCGMAgAAwBjCKAAAAIwhjAIA\nAMAYwigAAACMIYwCAADAGMIoAAAAjCGMAgAAwBjCKAAAAIx5ZLoBAAAAted5nud56q+O4ziOo7ky\ncrHruoU0Q9ajefXEK7M3vgzWw8NDZS8GAADQSK7rzmazcEk8YsWvCev3++FEuBvLstJePczzvMFg\nIISYTqeu66pnSbJwz5Zkx8goAABAYfr9vhAiPrLoOM5qtYpcJoRQhavVyrL2HSXs9/vhV0mjUq9s\n53Q6lSVZnls4wigAAEBhEkc3w0l0uVxGoqrnea7rygv2zKOu68ohT9d1NaObaoBWtkRdGRkirQYL\nmAAAAEqkgma/3394eEgcNPU8T42V7jNFrirX3A+g4vJ0Ot35hQpEGAUAACiRyoX6W0LVo5ocmYUK\ntWkvF5mjN44wCgAAaiO80NtxHOslObgoy+WKHKXKtThxuYYh9ddEfq7wjxy5LPLSEZE5evMeAAAA\nakKml+VymZhqlsulGhcMk/PjpVI5MlKu2rNP5Wk/rxBiOp3Gr9fEPFWV5omJD5WHkVEAAFAzco3O\ncrmUaUYFwcFgsFqtVPRUwcvIIvHwSydG5OzkzyuEmE6n8qdWP9psNosP/aoOiQ+OhseP92lSgQij\nAACgfh5CK4HCuSq8W6fjOCq07b+F5z72mRBXz10ul67ryr86jqMGMuP3mGpm6ve8IbUMhFEAAFAz\n8YHGtFU7h3JbZAorRTheq7HV+M+yNWqnRc8DWUcvEUYBAEDNJO6OZKAd5dPPqqufOh5GE2fqVSWH\nM0cv2PQeAADUTu2iZ9rIZXyEMjKWqZ4YOT5+K3X0qOu6kU2j9ryBtXCEUQAAgHKlraCKj1CmTazr\n7/VMzKnyaND4Sx9alCeMAgAAlCXjYfFb6e/yTMyX6mhQz/McxznMOXpBGAUAACiPOpVef1i8pJmI\n3yFBhncb8DzvMOfoBQuYAAAAyqNCZJY9ldR+oopmiZKqP3xXaISMnuGh2UMbFhWEUQAA0B7x4clI\nktt6wQ7UDLtlWZrLEqfaVWE8p4qXq5Rms1nabaDqZwmfobqtvVUjjAIAgLaQ0U39VU5eh7Pm1gt2\n4LqumhyP7CGqXkXN5sepLBvJkVlm3tVTCjkIqiSEUQAAgHJ5nqeC4Gw2kzvbO47jOI5lWfIUUyFE\n+GhTRWXZ1WqlnuU4jhor1WflcIUHOEcvCKMAAAAV8DxPHZgkhfddmk6n8njPxGn0cJZdvSSE6Pf7\nkTrjwhUe4By9YDU9AACoEXUge4RmrXraU6onD5T3XpIlUuSa+HPl9eoG1vgT9S9aRPPLQhgFAACo\nTsYQmegw59n3xDQ9AAAAjCGMAgCAltq6TH7PdfTIgjAKAADaxXEcz/PkJp0idDtm9gs0rJcKb3Z5\nzLaZe0YBAEC7rFarwWDQ7/cfHh4sy5JbJoVX+Wy9YKvD3NEzTb/fT9vltAL5ehYAAKC+ZKaUm8yr\nwvBft16AwhFGAQBAW1iWtVwuNcly6wUoHPeMAgCAVpC3fmrWJG29AGUgjAIAgBbZOurJsGjFmKYH\nAACAMYyMAgAAwBjCKAAAAIwhjAIAAMAYwigAAACMIYwCAADAGMIoAAAAjCGMAgAAwBjCKAAAAIwh\njAIAAMAYwigAAACMIYwCAADAGMIoAAAAjPn/cWDxWeS2QbQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "Canvas('Canvas_F3putpHSnbKuPNhuAgPQJR')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mass_hist_zoomed = Hist(100, 2.8, 3.4, drawstyle='EP')\n",
    "fill_hist(mass_hist_zoomed, masses)\n",
    "plot_hist(mass_hist_zoomed, logx=False, logy=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Measure the mass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def fit(hist):\n",
    "    hmin = hist.GetXaxis().GetXmin()\n",
    "    hmax = hist.GetXaxis().GetXmax()\n",
    "\n",
    "    # Declare observable x\n",
    "    x = RooRealVar(\"x\",\"x\",hmin,hmax)\n",
    "    dh = RooDataHist(\"dh\",\"dh\",RooArgList(x),RooFit.Import(hist))\n",
    "\n",
    "    frame = x.frame(RooFit.Title(\"Z mass\"))\n",
    "    # this will show histogram data points on canvas \n",
    "    dh.plotOn(frame,RooFit.MarkerColor(2),RooFit.MarkerSize(0.9),RooFit.MarkerStyle(21))\n",
    "\n",
    "    # Signal PDF\n",
    "    mean = RooRealVar(\"mean\",\"mean\",3.1, 0, 5)\n",
    "    width = RooRealVar(\"width\",\"width\",1, 0, 100)\n",
    "    sigma = RooRealVar(\"sigma\",\"sigma\",5, 0, 100)\n",
    "    voigt = RooVoigtian(\"voigt\",\"voigt\",x,mean,width,sigma)\n",
    "    \n",
    "    # Background PDF\n",
    "    pol0 = RooPolynomial(\"pol0\",\"pol0\",x,RooArgList())\n",
    "    \n",
    "    # Combined model\n",
    "    jpsi_yield = RooRealVar(\"jpsi_yield\",\"yield of j/psi\",0.9,0,1)\n",
    "    model = RooAddPdf(\"model\",\"pol0+gauss\",RooArgList(voigt,pol0),RooArgList(jpsi_yield))\n",
    "\n",
    "    result = asrootpy(model.fitTo(dh, RooFit.Save(True)))\n",
    "    mass = result.final_params['mean'].value\n",
    "    bin1 = hist.FindFirstBinAbove(hist.GetMaximum()/2)\n",
    "    bin2 = hist.FindLastBinAbove(hist.GetMaximum()/2)\n",
    "    hwhm = (hist.GetBinCenter(bin2) - hist.GetBinCenter(bin1)) / 2\n",
    "    # this will show fit overlay on canvas\n",
    "    model.plotOn(frame,RooFit.LineColor(4))\n",
    "\n",
    "    # Draw all frames on a canvas\n",
    "    canvas = Canvas()\n",
    "    frame.GetXaxis().SetTitle(\"m_{#mu#mu} [GeV]\")\n",
    "    frame.GetXaxis().SetTitleOffset(1.2)\n",
    "    frame.Draw()\n",
    "    \n",
    "    # Draw the mass and error label\n",
    "    label = TLatex(0.6, 0.8, \"m_{{J/#psi}} = {0:.2f} #pm {1:.2f} GeV\".format(mass, hwhm))\n",
    "    label.SetNDC()\n",
    "    label.Draw()\n",
    "    \n",
    "    return canvas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KV+MJo3AUJwtwp2nlBcY4z6L3nYqiqKoqbF4PcHrSG8CAtdcZLcuyKIrhPUIB\nALiIlVpGy7IMK4/ebreYRLMsC5uCzvtYYdr+wJl8sbBWGNsaDhizJkA8Ps/zt1O1YuVYcAAAuJy3\nC01NMbwx/RJC73+WZcMHfFQhnYf1PcTz+ew8/vl8dh7fWT8D57/0SwbMJUn+/u/wkqRKkl9P5yTP\nChiyZt5Yqpu+PUspWWVFp/aDNsR2x84U2G5SjbdkWRZ+Dg/xer2Komg8nbIsb7db+Dmm3nD87Xar\nWrk2z/PQThwLD8tgvV4vTaTAPqVJZUMpYE4Lhdx61Lvf733tgnN5Pp/1Js/hlsVkdNNsLLNxfHx2\nnYU3bo9tpY2z6rs9Ft5Zacu9ZMCMYjviGT6yv7eM/t04CpzamnljwTGjWZaFOLX0YqJhKOrbNtEv\nxDLbLaDt2+ONjZEAeZ6HWxrTtuJ9Gy2gnYUDbEtrKLCQpcJoWZZhXs5C5U/00YkNDDOtJ+CBEBkf\nrp47Ywd9u9hwozUH4ATOuq5T33RPgE9tsM5oXcisEwupt/R2Zrv6w4UfxoTR4YPbCXXgicQSBnLq\n2xsB9qM6a8oGVjdPGI3rJXX+KmxP3/mr2+0WZ/ysqb6aUtCOiR8l12SwpbPzBAYK72xJBQA4n9nC\naN+vwrT6nQx/jOdZX+40eDweH/U6fZoXGyF1+F5aRoEdqaq///uRJppFgdls3E2/snoEDHP8G9Pw\n6ynwu1bJ4RzZHgY6fLyWUTgiwykBxlt7O9CdqI92CpskhSU/X6/XnuddBRPnDRjpBXynquRsOIZj\nTTG8XMtoXD2r/avww05GFAyYuJrX1qcPF+IDB2xiYlRYOS1cK4yOEXvSv2sf/bRjvW9q1xcPDQBw\nOMLoLyMnwgcT59qPvNfOBwwAAEwkjPYani/fvnHMSvWxzJGFC6MAwLldKIzGxVD7OsH7Gi87j2+v\nKjoQHAfCpW564IgONTsC2LULhdGBbZCSnrwYsmZ71/vOg2Ox7fLjLfXjOzesDz5aPx/YFSkN4CMX\nCqNJrSe9kRfDXlDh5/qv+vJl58FJT3gtyzKEy8YOop0JNbH9ErBj1gcAZjfzOqMDPeDtX60ftkJP\nfZIkj8fj8Xi0R3k+n8/68XmeZ1n2er0ej0dcfzRmzcbB9fLTNA3RMybRpKvF9H6/Px6P1+sVj4+F\nN5IrcDhyG8AY6SxLSYU9P7+++4zLWYW167MsG0i6fSvBPp/Pvp3i253pfT2za+AAAB+BSURBVAfX\nG1nr+p5jZ9Xd7/e+5U7TdJ6XDFhIvMCc9ZN6+icIJOvmjbOF0ZHKH0mS5D+G71IURTz+7cL4sfzZ\nCxdGYedOn9VO/wSB5IhhNJnW524Bo/GEUdi502e1et/SWZ8jcMgwyjqEUdg5YRQ4gTXzxrVm0wMs\n6grrOgmgwLyEUQAANiOMAsxP8yHASMIoAACbEUYB+NIVxsgCSxNGAQDYzMzbgbKCvh2kAgs/wVZ+\n+2ieeoWnqtImCsc2nCVWJowej7gJAEzxNkusmVZ10wPMrEp21OQAsHPCKAAAmxFGARa0q4FZSzj7\n8wMWJ4wCLMggb4BhwigAH5OxgbmYTQ8wg996qyU1gNG0jAIAsBlhFGBOWkUBPiKMAjCJCfXAFMIo\nAACbEUYB+IYBCcAshFEAADYjjAJMZdAkwNeEUQAANiOMAszmssMotQ0DXxNGAfhcmjYTqEAKfMV2\noMeTDl7xq8u2zACrq5I0TVxz4HiGs8TKhNHjETcBgCneZok106puegAANqNlFGCSPXV2bSkNnfZ6\nb4APCaMAzEMMBb6gmx5gHtdMYlWiZRiYRMsoAJ+rR29xFJhAyygAAJsRRgEA2IwwCsA8LCwAfEEY\nBQBgM8IowPe0BSZXXUYAmIswCgDAZoRRgBloHQT4jjAKwGyMWwA+JYwCALAZYRSAqYxSAL4mjAJ8\nSZc0wHT2pj+edPALsNJAAQAMGs4SKxNGj0fchL3xoQSO5W2WWDOt6qYHYE57anABDkAYBQBgM8Io\nwOfS37uw0lR7oLEKwHeEUQAANiOMAkxSJVdvEwWYQhgFAGAzwijAPHa1bt+21AQwnjAKMA9rAAN8\nQRgF+FiayJ0dpHHgC3ZgAphGBAOYQMsowPcEUYCJhFEAADYjjAIAsBlhFOAz1i0aQy0BIwmjAABs\nRhgF+JLZS23qBPiUMAoAwGasM3o8w1sO2gMGABi2q+2LhdHjETdhQ3u6gAN86W2WWDOt6qYHYCZp\nWk/raSq8A+8JowDf0EUBMAthFIA5VYnWUOADwigAAJsRRgHGMgDyC2ma7mreLrA3ZtMDsCALgADD\nTtgymqZpnufDx5RlWRRFnufh4KIoho+PB4fjy7IcefyYwsuy/KhwYHPy1UhpoqaAN9KT/c1aFMXj\n8ciybCDShWPat/dVRWcHU99DlGV5u93atz+fz86InOf56/UaWXg4mZO9ZHAg8WLgUzisftVUV3BE\na+aNsyWbEBwHwlw9LN7v93BLjIPt2ohhMcuykCZjkL3f741Wz3bh9ePHFB5Ppu8pCKOwFQHrI4I7\nHNqqeaM6hefzGcNfkiRZlvUd2fnEn89nuPF+v9dvj2U2bs+yrLP2hgtvnFXf7bHw5/PZef59Tw1Y\nVJL8+o+31BUc2pp54wzNbO1u9Ld96O1O89hIWa+QWHK7lsKv6o2jsfB2i2kcGFAvp/MR64V3Pgst\no7AVTX0fUV1waGvmjRNOYBoQM2J7+Ga8pZ3/6m2uDfWxp/GO7RlLnYXH7vh2seHG9lhSgMOxrBMw\n7AxhtN7S25ntooH8V2/gbPzQOfGonVAHpkzFEgZy6tsbAQDO5wxh9FMjo95wGG0bSLqfFj7QTAts\nQvPep/TOAyNdMYwOG5n/Ps2LjZA6fC8towDARVwojH7a0vldq+Rw4e1hoMPHaxmFvdHg9wXtysAA\n24Eez8Rdnk3Ghw/8+rhVv27xIQL2bWJUWNmFWkajozc3TlzNa+vTB67C9Qa2MjEqrJwWLhRGPx2I\n+d3AzU+Tbt96qF88NLAQG6wDLOdCYXSkdebaj7yXmUwAwLldMYzOMl++feOYlepjmSMLF0ZhV6ok\nTZIkTdNjjcfaAxUG9LlWGB3Ii3E5+vYuTZ15sb2q6EBwHAiXuunhcAy/BpjRtcJoe5ulqDP/haxZ\n3/OzcXA9XLbjbPtX9ePDHk6dyfij9fMBdktoB966VhiNWfB2u9VvL4oi5L/GJp99+TLevXF7Z3gt\ny3K48EZzqe2XYEeq6rc81fgnAJOlJ+tsyvP89XplWdaX5MqyjFEypMMYFjvvFQoMvw0xMWbN5/PZ\n7naPI8kahSddC3wWRRFLC8fX/9luYQ3ln+wlg52rD3b04ftCrEC1BweyZt44W7J5G0aT3/NoNHCX\nmEfrOpNoX+FJ/1Lz9Twa9SXRRBiF1QmjE6lAOCJhdA0h7ZVlmf94e3xIq3me9yXFqPwxe+HCKKxM\nw95EwigckTBKL2EUViaMTqcO4XDWzBvXmsAEAMCuCKMAAGxGGAXoZd8ggKUJowCsRLgH2oRRgPfM\nvAFYiDAKwLJEeWDAP7Y+AT6WDnZ0WfgJABg2nCVWJowej7gJ69jTtRpgTm+zxJppVTc9AACbEUYB\n3tAbMUma1huZ01SbM/AbYRQAgM0IowAdNN7Nq0pUKNBNGAUAYDNm0wMMMWB0dmlShZZna4MAiTAK\nwPrEUCDSTQ/QZMDoEgwbBTppGQVIkqSRQLXbzafeCCqOAi1aRgF66UxeiLZnIBJGAQDYjDAK8JtU\nH/1itDQDbcIoQK80TVM9ygBLMoEJoFuVpJrylpOqXSBJEi2jAABsSMsoQJIktfGMaesW5lNVptID\nv9EyCvCLnASwMi2jxzM8ncIme8BRGDYKW9nV1Exh9HjETViBzxlwYm+zxJppVTc9AKsS9IE6YRTg\nb3vqtroKdQ4IowAAbEYYBWjSj7w0NQxEwigAAJsRRgHYkmGjcHHCKECSiEQAGxFGAdhAldTif5r+\n/R9wPcIowG/MrVlfmqh0uC5hFACAzQijAPqHt/FbTz1wVcIoAHux5nbYwE4IowC/GDC6lTBstPIC\nwPX8Y+sTAFjd781vZs8AbEgYBWALVZUkSVX/00CzKFySbnqAv8lCAOvTMno8wwP8jbgCDipN/T0A\nK9nVZEFh9HjETZhRfcBomqY+X8AVvL3WrZlWddMD/E0S3YRah4sTRgGSRCQC2IhueuB6foLnngZN\nkSSGjcIlaRkFAGAzwiiA1riNqX+4MmEUuCh99AB7IIwCsCP+SICrEUaBq9NHDLAhYRS4Is1ve+NP\nArgsYRQAgM0Io8ClaZDbIe3WcCnCKHA5sg7AfgijAOxCvZXaHwxwHcIocF366AE2Z2964NRaLWxp\nIoHuV5Wkv16g+Nr5owFOTcsoAHvkzwa4CC2jx5MOjqWqNCHAOD4rwGUNZ4mVCaPHI27C1zS27V+9\npz5NqirZ0VcmnMbbLLFmWtVND8Cu7aoJB5idMApckQ6GPWu0huoOgnMTRoGr0Ed/RF41OD1jRoFT\n+20h9e1Og5Hi6+XFgsvQMgpcjl7f/fMawXUIo8AlmAMDsE/CKAC75g8JOLcrhtF0UFmW7buUZVkU\nRZ7naZrmeV4UxfBDxIPD8Z1ldh4/pnBgCv2/ALuSXnDJjOEl657PZ57n9VuKong8Hu0j+6qus/ws\ny/pi7u12G3MasfALvmQwUf1D6QN0IHanh62smTcuN5s+JsIsy0YeH5Po/X4Pt7xer6TndYoJMsuy\n8HO4++v1Koqi0epZT6Kh8Hj87XYTOgGCNJVH4bQu18wWmzlHPvHYzFk/PobI+/1ez5ex8MbteZ6H\n/Np40OHCOxtTtYzCpzSLHpfXDrayZt644pjR8WIWfD6f9dvzPA8Nq43u+/jPdgto+/Z4Y2wTjYWH\nW0J+BbgsARSu4HJhNETAkX30MTu2h2/GW9qNl41wWVcPr50J9W3hQK80bf+nae000vT3lxg4i8uF\n0dDc2Dk3qO/gzuQaE2TMi/GHzsLbCXUgaMYSzKwHAM7tcmE0KoqivqLTQDwdmVyHw2jbQNIFZlHf\n1lyz6EH9vp+rVxFO6KJh9PF4NIZ7vl6vvkVGh428y6c970IqAHAF1wqj9SCYZdnz+ayq6vl8xuR3\nu91GdrsPFz7ecOHmMMEsNIseWpX8GiEaG0dDp9ZGZwTM6VphNLrf72VZhiAYdkiK8+X3P0xzeAep\nt7Y+fViJLt1zq6rKOnfQZ2JUWDktXGvR+zzP+y5eYbWm1+vVboyMsXUnXH+Bq6mS9FebaFLV20qB\ntulRYc08etGW0U6NYZ2fBtDvAqvFm2A2VRX/M3XpVMLLWqPZG85EGP3luzRprj3ACvxRAWcljL43\ny3z59o0haA5PUdrV8AA4IgnmTH5b5klHPZzFtcJoGJPbN0Wp3Xg5kBdjIe1dmjrDaLulcyBoftqM\nCtSJKQAHcq0w2rmhfNS+vb3NUtSZOPvK7wyX7Tjb/pUwClCncRTO51phdMxe83GNp/qNt9utUU5o\n6Wxs8tmXL+PdG7d3hteyLDsLB/7WtQd9/ZeRPnqA/Uuvtk5Qnuex2z12mtdvaTR5lmUZo2RIhzEs\ntg+ul59lWciyMWs+n892CI5LJzQKT3rWZUjTy71k0NTZIPbzuRBGr8CrDEtbM29cMdkURdHZU3+/\n3zuHk9bzaNSZRIN63o06k2hf4Un/CmHCKAyEURnlIn57oROvOsxPGF1DyJ1xQfu3Gy/Vjw/eHh/X\nK31bePnjbeHCKPSF0cbNPijn1p1HveowE2GUXsIodIbRNEkSC91fiTAKi1ozb1xrAhNwXpLotdR7\n523IBIcmjAKHJ4sQhMWktz4L4DP/2PoEAD7Ubvk0g+WSqiSNf4ekSVUZeAbHpGUUODYz6Ak0kMNB\naRkFDkyX7HVVVZIklfcAHJ8wCuzY4Pr2427m5Krq19skTb0N4HiE0eMZHp5vyBTXoYOeNnkUxtjV\nVD9h9HjETYC6euMoMMbbLLFmWjWBCTgkzaLU1d8DgikcizAKHIylJHnLOwQORBgFDqbRu6RZlKDx\nTkjTJElTsRT2TxgFDkYHPX3qe4QmVh6FgzCBCdixVtjUzsWw+rZMwCEIo8A+jFhStHGIZlE6tbYJ\nBXZNNz1wDJIo30nTfS2pCDQIo8ABSKJ8pDF4NNFxDzsmjAJ7J4nyheZkJm2jsFfGjAL7laZpo01L\nEmVIY9mv3zOonUJhn7SMAnsmiTJJx+KjwM4Io8AepUmVSqLMQR6FndNND6zu3SpO7d9LokzRWHw0\nTZPKok+wG1pGgX2RRFmCzZlgt4RRYC/aG4lXlSTKbMyvh30SRoFd0CDKCuRR2CFjRo9neCuRyhc4\nB9TuM/VGZiHt8aOJ9xvXs6ttyYTR4xE3ORMxlPU18mhiCVKu522WWDOtCqPAkgYvZ5IoW5FHYT+E\nUWB1VZUYJMqaut5bVZomv/9FpMseNiGMAmsTQ9mPzibSxHsSViSMAh8aP5Co6/tc1zx7086jiUgK\nKxJGgZV0LjPuy549CEs+iaSwCWEUWJwYyiFUP+GzcbtICosSRoEF9W266HudfaqSJNFKCusSRoFF\npM3Nbv7mu5xD0HEPqxFGgX5fLXqsNZRj6HxH/v6eF0lhBcIoMBsxlFMajqSJdzhMI4zC9XS2d47/\nOm0dOdB+6kua0+iLpMnvHwHvefiUMArn9VHo/LBH/u3hvpI5pYFIGmguhU8Jo8BY6a+pxkN8B3N6\nVZL27WobaS6FkYRROJqvJhV9/2j9LUANvm45gxGzmn5uS5MkqWrHjwmmPibQJozCPqwbMd8amUF9\ns3JZf7/3a5/cKkk0l8IXhFF6TJzjcjXHqa40TftOSwCFWdQ/I4IpvCWMHkq4dI1pQptpksqou1fV\n1Cg2/qymP6/RfXAzfDlMf14LqGqvly54GGX8daP/rsOHC6ZcmTB6PMPXPxexNz6K4zvrOp/iV+5M\nk/FvE1+K8LGeP25HNpe2f+tjyBLSPX3BCaPHM/a6tPn7bKET2Px57d749s5OvvngawPDYOrGB9O+\nA3xOmah69x5aM60Ko3BUE0Nn5FsN5lL1DVvqT5QfBdMR5cHxCKPnNPKvc491lMeSO+GEWomyEU4/\nbZmSUDkoYfScev8691i7fawfcz2mbyBY1kKfscZaUa3HmiWh9nHdYBPCKD0mzB69onfV9aZpc0K9\n+vKA85s7oY54nF9cZFiaMHoooa1uyoVh4kWl7+4Tk+v0S93EZaS+OoHf8mX3c535Eu4rAej028Xh\n87Xbhu1ykTpORRjlYjrbEnovtZtdWV3T4ajW7FYaLLZ62+cyd9vq9EJc+q5JGD2vzT/TC53AT7G/\nXfUOMnxg89cE4Je1ev/HE2evSRhlThccU+rCB7y3QHPpUit+9CXUwbOda0jAdGt+Dbn+z0UYPZ4L\nBr5FuZoA25iWUNdc8eOt90MC3tlPnB1vN9X/3s6/6YTRIznQ+341O/+AAXzmqiuZ/Iqz34bsI8bZ\n1ez8HSSMHsme/gweIiACzMlVdYRvWmcF30ET1+8ZTxg9HhclAHodaHzqYa0ZfL9Re6wpuXm1vCGM\nAsAxjQ8LxxmfuqutmE/gy+G8VbVmT6wwCgCXtND41OME37OG7MMRRgGAHx/ttMdogu8AYRQAWNia\n2z4vZHpD8hJbZ3fdfXrwTdedLi2MHs/wW6Ta/OMKAFOs+UU28bEO+527q7V5hNHjETcB4CQWSsPv\nin2z62tVrdk4KowCAFzVDlq4/t/WJwAAwHUJo9Bh5bHbl6We16Ge16Ge16Gez0cYBQBgM8IoAACb\nEUYBANiMMAoAwGaE0e3961//+uc//5mm6T//+c9//vOf//73v7c+IwCAlaRWUN9W56zAP//883/+\n53/6jveSrUA9r0M9r0M9r0M9r0M9r2PNevaKbikm0T/++OOf//xnkiT/+7//G27566+//uu//qvz\nLrO8ZMpRjnKUoxzlKEc5S5cz6rGE0a3861//CtGzkTtjQu18afb2JlOOcpSjHOUoRznKmcKY0c2E\nJPrHH380WkD/+uuv8IPBowDA6Qmj24hBM/TO18VsGrvsAQDOShjdxn/+85/wQ+dEpT/++KN+DADA\nWQmj2/i///u/gd+2m0sBAE5JGAUAYDPCKAAAm/nH1idwUWE8aBgb2hZv//e//91ebbRznfwvKEc5\nylGOcpSjHOVsThjdxh9//PGf//ynb4pSvL2dRK0LCwCciW56AAA2I4wCALAZYXQbw4s3DS/8BABw\nGsLoNuIUpX/961/t3w5PbwIAOA1hdBtxZlK7ETTuFPrnn3+uek4AAKsTRjcTsuZ//vOff//730VR\n5Hmepmmapv/93/8dDmhPpR9WlmUsJM/zoihmP+ejq9fz11XUKKQsy5nP8uzKsox1mOe5Opxi9soM\nRc10dmcwVw3PcvE5MfW8joUuv6HCJxVRsZ3YEf/HH3/8+eef9abQv/766/l8ji+q7/X9qJATu9/v\n06vo+Xz6HE2UZVlnBWZZtvWpHc8Slen9XDdLDQ9cN+73+1Knfiiz1PMsF/lzW+jyG8uZVMiUOzPR\n8/nsHBj6119/ffQRqr+l7vf7/X6vv+cWfhIHUP8yuN/vz+ezUUVj6rleiHr+Tr264gsx1wXxapao\nzFjm3Cd7SHPV8PB1Qx6dpZ5dn99a6PJbL2TK6XmFthRev7/++uvPP/8MjaN//fVX9fvn6m0h8a3Q\nSFSxEN/xfZX5UT3HIxv1HOvfl8pbfW/IeJXUgDHevJX5fD59czfMUsMD1we1HcxSz30Hx4u86/MS\nl99Gq/+k05tyZ6YY/pCMb6IYOFI7RzVTPc/1Yl1Z/FZu/8oXxqdmrMzOnruZT/eA5qrhgSr1tq/m\nrufOI/tC2KUsdPmd8bphAtNm4qjhzhHW8ca3g4tfr1eSJJ3fKHmef3t25zFLPY8shO94o85IZS7t\nixruHM4YyzGBr9P4eo4V2HmX8OUYvihp+/qKEe4YBkVMPw17059E5yfNNW4d9S8VUWBAlmXqZy5z\nVWb9KlEUxePxmF7mOUyv4eGQRDDLOzkkTvU8YN7Lb1EUIXWE6fkzlDilWZXljO/57RuTpA9ojJH1\nPNDHUanqyfrGPfOFKZU5/D4nmOvt6roxbJZ6VslvfVHPsVbDXWa5brjo7NGnc48as+TqswgvPlBm\n2Ph6bnz2Gsxh+sLz+Wwsa6D2vjZXZQqjfZZ4u2oSapurnkM55uT1mVjPjbsIo+f00RTvqHPMqCQ6\n4NN6HjhYhX+h8V6VRKeYqzKF0T6zv1298zvNUs+NIYwuy21T6rndnTjLdcMEph0JIw5vt1v458BS\nyQ15nscxo1mWxffK6/Uyhqbtu3qOn7c0TcNQsDBWxnY132n8+fR4PMwD+5rKXNqMNVy/aNzvd69U\n3RLvZN+DbV/Xc1mWIWy0E+1UU5IsM6q/OT76S66vmT2GJ38X1n1dz1X/9hW66aeo96YZMzrRxMrU\nMvrWlBpu/N3r3T5grsuC78Fhn9bzcNiYciYuOtubcoUaHp1tXkjdLN8E7TUsQjnC6ESxPrc+kTP4\nujKF0ZG+qGG7Ln1hlsuC78G3RtZzeA+3Y70wegb1cPPFFertm8AfhcHEeh42PL2JMcSgGX1dmV6F\nkT6tqHjxcSn+yFxvSH8DDBtZz8loX5yDMaNbKssyrOoXrlBfDI6xkugY0+v5bfnhByOTBuR5Hofb\nMpHKXNqMNRxHiD6fTy9Zwyz1HIbhuvw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      "text/plain": [
       "Canvas('Canvas_BGgZij9UFihuBX3tjHrZfZ')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit(mass_hist_zoomed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "* These events were extracted from the CMS Mu Primary Dataset on the CERN Open Data Portal: http://opendata.cern.ch/record/14. \n",
    "* Thanks to http://openstack.cern.ch for providing a CernVM running SL5 where I could set up CMSSW 4.2.8 and to https://github.com/tpmccauley/dimuon-filter for generating the CSV from CMS AODs."
   ]
  }
 ],
 "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": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}