{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  <div id=\"logo ATLAS\" style=\"float:left; clear:both; padding: 0.0cm 0.0cm 0.0cm 0.0cm; text-align: center; width:92%; margin:0px auto; background-color:white; border:none!important;\">\n",
    "    <img class=\"img-responsive\" src=\"http://atlas-opendata.web.cern.ch/atlas-opendata/DataAndTools/pictures/opendata-top-transblack.png\" alt=\"Visualisations\" style=\"width:60%\">\n",
    "\n",
    "    <h1><b>Python example to read a ROOT file</b></h1>\n",
    "  </div>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "require(['codemirror/mode/clike/clike'], function(Clike) { console.log('ROOTaaS - C++ CodeMirror module loaded'); });"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "IPython.CodeCell.config_defaults.highlight_modes['magic_text/x-c++src'] = {'reg':[/^%%cpp/]};"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to ROOTaaS 6.06/00\n"
     ]
    }
   ],
   "source": [
    "import ROOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = ROOT.TFile.Open(\"/home/adminuser/Dropbox/Public/CEVALE_Tarea_3/input_MC_samples_Tarea4_cevale2ven.root\")\n",
    "\n",
    "t = f.Get(\"tree\")\n",
    "\n",
    "h = ROOT.TH1F(\"variable\",\"Example variable from the mini tree\",40,0,200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "797827L"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = ROOT.TCanvas(\"testCanvas\",\"a first way to plot a variable\",800,600)\n",
    "t.Draw(\"lepZ_m:realZ_LJ_m\",\"lepZ_m>0.\",\"colz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CIM/zOI49j95VAACmztXlnqt+E8/zfN83TXxGEARZlpmnURTJd104K4QqAACmz9Xlnpqq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\nQhUAAIAD/x8eOX90EEROlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    }
   ],
   "source": [
    "for event in t:\n",
    "    if t.RunNumber==167809 or t.RunNumber==167812:\n",
    "        h.Fill(t.lepZ_m/1000)\n",
    "\n",
    "print \"Done!\"\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XofaS9XrdvjBlWbZcLgefG7bc9oqdZ/WvgHMXek7332k7CYRD1E8LcVV8bjst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\nAAASEKoAABL4f6C/H06TpqVJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h.Draw()\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YrcIgnn6aCef7xWIR+rPquh6/zr8zEUPnWUL3VuwaC+PW42PjJAIX1/fXPoxx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\nJi4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scale = h.Integral()\n",
    "h.Scale(1/scale)\n",
    "h.Draw()\n",
    "c.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ROOT.TFitResultPtr object at 0x7eeb0d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " FCN=0.130725 FROM MIGRAD    STATUS=CONVERGED     119 CALLS         120 TOTAL\r\n",
      "                     EDM=1.49134e-08    STRATEGY= 1      ERROR MATRIX ACCURATE \r\n",
      "  EXT PARAMETER                                   STEP         FIRST   \r\n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \r\n",
      "   1  Constant     4.02352e-01   6.25378e-01   2.23778e-04  -3.82700e-04\r\n",
      "   2  Mean         9.04647e+01   4.67614e+00   2.40089e-03  -1.01752e-05\r\n",
      "   3  Sigma        4.30916e+00   4.80556e+00   7.48278e-05  -9.47525e-04\r\n"
     ]
    }
   ],
   "source": [
    "h.Fit(\"gaus\",\"S\")\n",
    "\n",
    "## h.Fit(\"gaus\",\"S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " FCN=0.130725 FROM MIGRAD    STATUS=CONVERGED     119 CALLS         120 TOTAL\r\n",
      "                     EDM=1.49134e-08    STRATEGY= 1      ERROR MATRIX ACCURATE \r\n",
      "  EXT PARAMETER                                   STEP         FIRST   \r\n",
      "  NO.   NAME      VALUE            ERROR          SIZE      DERIVATIVE \r\n",
      "   1  Constant     4.02352e-01   6.25378e-01   2.23778e-04  -3.82700e-04\r\n",
      "   2  Mean         9.04647e+01   4.67614e+00   2.40089e-03  -1.01752e-05\r\n",
      "   3  Sigma        4.30916e+00   4.80556e+00   7.48278e-05  -9.47525e-04\r\n"
     ]
    },
    {
     "data": {
      "image/png": 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F2ATAcUPVoCFvIVTVdZ3n+SCEGaQKPiHP/sw/aHwE4NKO2PyXZVlRFOtDVVmW\n9/u9bdv00Xc6ZgE/Yk76d7QEgL8ctKYqNNvFTuV1XXddlw6yUJZlrJ0KySnkqrh+GJBdqAIAfsdB\na6rKsqyq6n6/x1y1PPdf0zS32+12u6UL9UkHAH7N0cePD1VWZVmuqXNq2zbWXU2uf7Xx8uE142ln\nRvPMPO3vLZimBi7hapfdi+3txd5deI1QBWziapfdg/apAgA4F6EKAGADB+2o/jnL09RcqpYSANjQ\n5UKV2AQAfILmPwCADQhVAAAbEKoAADYgVAG/Kg5YBfBlhCoAgA0IVcADm9QtvTwUO8BZCFUAABsQ\nqoC11DYBLLjc4J9GVAcAPuFyoUpsAgA+QfMfAMAGhCoAgA0IVQAAGxCqAAA2IFQBAGxAqAIA2IBQ\nBQCwAaEKAGADQhUAwAYuF6ryRXuXDi6hz/7duwgA2zNNDQDABi5XUwXsJc/8SQN8M6EKAGADQhUA\nwAaEKgCADQhVwJI+c1cswCpCFQDABoQqYBX37gEsE6oAADYgVAEAbOByI6ovz0VjvHUA4DWXC1Vi\nEwDwCZr/AAA2IFQBAGxg5+a/uq6zLCvLsizLhyu3bdu27eT68aHUys0CALxvt1BV1/X9fg8/3+/3\noijGqShVlmXXdXH9LMuapomZqa7r+OjgWZuVGABgXr5Lx+22bW+3W/YTjELAWshVIVHFFeLTY+HD\nPX1N0wyeNdhOnu+zv3Auef7fz4//xGlqNhn8M530Js/+9P0/728TOKyrXXb32dsQktKqprBkrjAh\nM6WPhhxWVVVoQMzz/GFdV3a9dxdeI1QBm7jaZXefjuqhqS6tSQo/L6SiqqrSX8e1UFr64PjMdQN8\nsd3u/iuKIv11OVQ1TRNqpKJ0zdgmWJZlnud5ng9WBgD4tH3q5SZb61Y24WWjPlXx1yzLiqKIPdbH\nu3a1ekh4zeea/7KkBVDzH3y9q112d6ipisMivPb0uq5jJ/d0g1VV9X3ftm3f96GtcPIl8le9VloA\n4CJ2q6mKfczThcs1VbFGamWf9GxUWXW1yAyvUVMFbOJql93d+lQNUtHD6quyLG+3W1EUTdOsaSIc\n9NkCAPio3ULVYKzO5VAVxvasqir0Rh88OjmcOgDAb9pznKr0pR+OU/VwaNBxS1+m+Q9eEpv/+uzf\n/1uo+Q940tUuu/vUVMURO+OvYcD0uEIYHCGOlZBlWdd15Uh4KG4tHVshG8dI30UAABZnSURBVA2w\nDgDwOfvM/VeWZVVV9/s95qqFiqi4fDy7X8hPcWtxYIUsy6qqMhwobMWgnQAP7VwvFyqZQjB6f2uh\nc9XC1q5WDwmvGTf/bRiqNP/BdVztsnuxvb3YuwuvEaqATVztsrvb3X8AAN9knz5VO1oeG/1SgRoA\n2NDlaqr6RXuXDi4kHa8B4AtcLlQBAHyCUAX8KqMzAN9KqAIA2IBQBQCwAaEKAGADQhUAwAaEKgCA\nDQhVAAAbEKoAADZgmpq/GFQdAHjN5UKV2AQAfILmPwCADQhVwDQTHgM8RagCANiAUAU8YApkgDWE\nKgCADQhVAAAbEKqA/SyOGwdwLkIVAMAGhCrgt+XZn72LALC9y42obpoaAOATLheqxCYA4BM0/wEA\nbECoAgDYgFAFALABoQoAYANCFQDABoQqAIANCFUAABsQqgAANiBUAQBs4HIjqpumBgD4hMuFKrEJ\n1uizf/cuAsDJaP4DANiAUAUsyTOVuwCrHL35r67rLMvKsizL8uHKbdu2bbt+fQCAreSH7WNU1/X9\nfo+/FkURAtOcsiy7rkuXNE0ziFZ5ftz9hePI8/9in6rP1FT991efLZ9K+FJXu+wetPmvbduQqJqm\n6fu+qqqu6xYqn0KiKoqi7/u+75umybLsdrv9WoEBgIs7aKgKrX6xqqmu66IoBhVRqfBQrMoqy7Kq\nqrgd4Gjy7M/eRQDY2EFDVQhJadVU+HmhBTCkqMH6AAC/46ChKsuyoijSX5dDVdM0g0qp5Q5YAADb\nOm6oGlgOVYN6qdglS/MfAPA7jhiq4rAIrz29ruvQRT10Vx/IX/X6/gAAF3DEcapejlNt24Y4tTD+\nwqXu7QQAfs0Ra6qCQSp6WH1VluXtdiuKomkaHaoAgF923FA1GEBhOVTVdd11XVVVbdu67w8A+H0H\nDVWDW/+yR6Hqfr8XRaFbOgCwl4OGqhCPYvfwUBGVJq2yLPM8D0kr/DcMuT6gHRAA+B1H7Kie/QyJ\nfr/fY65a6Hsel4+HXNcaCAD8jqPPdBiqrEK10/tbu9rMjvCaX5hQOcuy/5tT2acSvtTVLrsHramK\ndJMCAE7hoH2qAADO5eg1VZtbHhv9UrWUMOf/GuYAWO1yoUpsAgA+QfMfMOszvdTHL2NuTeAbCFUA\nABsQqoB95NmfvYsAsCWhCgBgA0IVAMAGhCoAgA0IVQAAGxCqAAA2IFQBAGzgciOqm6YGAPiEy4Uq\nsQkA+ATNfwAAGxCqAAA2IFQBAGxAqAIA2IBQBfxt8Q5ZAOYIVQAAGxCqgGl59mfvIgCciVAFALAB\noQo4AB25gPO73IjqpqkBAD7hcqFKbIID6Xt1VMDX0PwHALABoQoAYANCFQDABoQqAIANCFUAABsQ\nqgAANiBUAQBsQKgCEkaNAniVUAUAsIHLjahumhpYI8/+7F0EgJO5XKgSmwCAT9D8BwCwAaEKAGAD\nOzf/1XWdZVlZlmVZrnxK27bhKYOFYXnqqc0CALxjt1BV1/X9fg8/3+/3oijGqWjS7XarqmqQluq6\n7rpuvLJQBQD8jn1CVdu2IVE1TVOWZQhYZVk+zFVzISkkqqZp1qwMALC5fUJVaPULiSr82rbtZFVT\n0Lbt7XZb3mZRFFIUnFieZ27OBc5sn47qIT+lGSj8PFdTVZZl9WNumxIVALCj3e7+K4oi/XU5VGVZ\nVtd1XdeTySk8q23bsizzPM/zPNSEASegdgr4FkcZUuFhqHqo67qu60JWu9/vyyOnAwBsa4dQNTkm\nwvsbrKqq7/u2bfu+D62Eky+Rv2qr0gIAX2mHULV556e6rvu+T5v8ws+TPd/7V21bZjgifzwAvGG3\n5r9BS9/m1VeDPlsAAB+1W6gaVCO9E6omh1MHXpZnf/YuAsD57BOqxtVI74Squq7Ho1gtjHoFALC5\nfUJV6PMUe3+HSWbSpBUGR1hZ/xS3lo6tkI0GWAcA+Jx9RlQPg3mmAx+sn/tvYWtpfdV4fkAAgM/J\n972vLVQylWW5SQAKnasWtpbnO+8vHNrPHzlJn6p/PvNK/4X/9f0/6esaCBS+zNUuuxfb24u9u/Ac\noQrY1NUuu0cZUR0A4NT26VO1o+Wx0S8VqAGADV0uVIlNAMAnaP4DANiAUAUAsAGhCjgMMzoDZyZU\nAVmWCTQA7xKqgL25fQT4CkIV8DcRB+AlQhUAwAaEKgCADQhVAAAbuNyI6qapAQA+4XKhSmwCAD5B\n8x8AwAaEKgCADQhVAAAbEKoAADYgVAEAbECoAgDYgFAFZNni+G0ArCFUAUci3gGnJVQBCaPjArzq\nciOqm6YGjqjv1VEBZ3e5UCU2AQCfoPkPAGADQhUAwAaEKgCADQhVAAAbEKoAADYgVAEAbECoAgDY\ngFAFALABoQoAYAOXG1HdNDUwdLT5YfLcFITAGV0uVIlNAMAnaP4Dfuz7J4c/eICTO01NVV3XWZaV\nZVmW5cqntG0bnvKhIgEARCeoqarrOs/z+/1+v99vt9v6kHS73UKuAgD4tKOHqrZt7/d7lmVN0/R9\nX1VV13VrcpUKKgDgNx09VIVWv6ZpQkiq67ooiq7r5tZv2zbP8zzPF9YBANjc0UNVyEZptVP4ea5d\nryzL6scvFA8AIDh6qMqyrCiK9NflUJVlWV3XdV1r/gMAftMJQtXAw1AFAPD7Dj2kwifGRFgeUX2B\nUUMBgAWHDlWfaMKTjQCATzhB89+gpc+QnvD9jjYdIcAKJwhVg8ERhCoA4ICOHqoGt/5lQhVs61B1\nQlrngTM7eqgKg3/G3uV1XXddlyatsizzPHczIACwr0N3VM9+BvO83+8xVxVFIULB9tQSAbwnP8vd\ncKHKqizLdxr+8vw0+wu/JDb/JR+NPP/v58d/PvOq//285mj7U+UBTupql92j11RFIVQBABzT0ftU\nAQCcglAFALCB0zT/bWV5mppLNf0CABu6XKgSmwCAT9D8BwCwAaEKAGADl2v+A44jGRDrf9LBsvLs\nz18PjQe1AjgSNVVwYYea+O+RcQIDOBShCvh9s3VOg9opgBPR/AfsMifMQlvev6N11FEBJ6CmCgBg\nA0IVAMAGhCoAgA1crk+VaWoAgE+4XKgSmwCAT9D8BwCwAaEKAGADQhVc1amGUwc4PqEKAGADQhVc\n3lHv3ugzdWnAmQhVAAAbEKqAY8mzg9acASy73DhVcE15PpyTuJ9/CIAXqKmC7yc2AfyCy9VUmaYG\nVvtn7wIAnMnlQpXYxLWlOenfqYUAvEjzH1yR0QoANidUAQBsQKiCSzN+AcBWhCoAgA0IVQAAGxCq\ngOPSoR44EaEKAGADQhVwOLrPA2ckVAEAbOByI6qbpgYA+ITLhSqxCfT+BvgEzX8AABvYuaaqruss\ny8qyLMvy4cpt27ZtO7l+fCi1crNwWfqDA2xot1BV1/X9fg8/3+/3oijGqWj9+nVdd103fpZQBQD8\njn2a/9q2DQmpaZq+76uq6rpuIQCF9Yui6Pu+7/uiKLquC7VcQUhUzd/SFYCT0gMMOIt8l47bZVl2\nXdc0TQxSYclcYcIte+mjgyV5nj+s6wqr6ajOBeX5fz8//pMlMeXIzX9plsqzP/9b2P+zU3GAV1zt\nsrtPTVWoWEqrpsLPC6moKIr016qqButr6YNvcuTABzBpt7v/BiFpIVTFzunj9dMV2rYtyzLP8zzP\nNfwBAL/sKEMqPKypmpSu33Vd13Uhq93v9+VBPgEAtrVDqJqseXp/g1VV9X3ftm3o+T73Evmrtiot\n7Eu/b4AP2SFUbd75qa7rvu/TJr/w8+QgC/2rti0zsF6f/bt3EQAe2635b9DS91r11cL6gz5bwICe\n4ADb2i1UDaqRFkLVZHer9NfJ4dSBsxP7gHPZc5yq9KXfGadq8rnjp2TXGzADgnScqlMMUhUlpf2T\nGacKzuZql919aqpCn6fY+ztMMpM22IXBEWL9U9M0WVKPFZ4eeqOnW0vHVojPAgD4BfvM/VeWZVVV\n6cAHy+OhT64fe6bHR2+3W3xKVVWGA4Wv0Wf/xnHVAY5p53q5EIzKslwZgJbXD52rFrZ2tXpICL6g\n+S/Lsjz7o/kPzuVql92L7e3F3l0Izhuqsr+7VQlVcC5Xu+weZUR1AIBTE6rgQgynDvA5+3RU39Hy\nhDOXqqUEADZ0uZoqc9FAdp4OVSmT1QAHd7lQBQDwCUIVcGhnrFQDrkmoAgDYgFAFALABoQquQkdv\ngI8SqoDzWBwSBWBfQhVcjq7fAJ8gVAFHl2d/9i4CwGNCFVyCDlUAn2aamr8YVB0AeM3lQpXYxMWd\nvkNVnmc+xcAhaf4DANjA5Wqq4Dvk+X97F+FX5dkf3cKAg1NTBefzbKISRwB+gVAFALABzX9wav+s\nW+1/NVWn76UOcGBCFXA2ef5wONC+Xxk3ATaj+Q++XJ9dcb68q3XkB45AqAKO75/MZDXA4Wn+g6s4\neYeq0Jz379+/TlJHBezjcqHKNDXwBfosP3lGBL7Q5UKV2AQAfII+VfDNvqyXutop4MiEKuCUviwv\nAl9AqIJLUMcD8GlCFXAmaTpUWQUcyuU6qsMxfWKwSpkD4DepqYL9Gf77KZoygWMSqs5keZCtgzt1\n4bODln/t9HZ51menr7WaLv8pauMOefI8Qfl3dOrCX5DmPziUzaYBPkXa+Kin6v9MwAy8T00VfKGv\nT1SbtwBqgQXed7maKtPU8DuOc5H++h5Ioylr/jH9H7CLy4UqsYlfsG+iSqupvj5RzXiqLU8CA7Zx\nuVAFB7ZBt56vb/iL8qyPO7vJ/Mr6YAFv+rZQVdd1lmVlWZZl+f7W8jx/qmbr0+s/69Tl/4XCZ9mf\nJ4v0VJXGv1n255mclD8ZqvJsFCMGieq9nDGx/V3Xf8qzG//3yZPh3zz/YK469Sf3F8pz9m8eB/+L\nfU+oquv6fr+Hn+/3e1EUbdvuWiKGnm0U+/T63+0KDX8bVVb9k2X/blgq4LK+JFS1bRsSVdM0ZVmG\ngFWWpVx1EFeNO7/aQqQrVTgCb0Srlf53Ml/1rAZmfUmoCq1+IVGFX9u27bpu31JxeE+Fnn+fX/+X\nXKcf1VhaWRX02RFHS1TtClfwJaEq5Ke0H1VZll3XtW27SecqtrM+l7wQYg4aej5nMk5drZpqnKs+\n7IjNhdrW4Qi+JFRlWVYURfprWZb3+12o+jRftTuSqKKw1+kB+Xxj6OUS/FOOFvKeWv9QheFcvqQb\nf57n457p44Xxhq9+3Xfc0W5/OvX6hyrMV67/KDocfw82WP+nW9UBimL9c65/qMKcev38f3fU/vsd\nMWOlb6ipCrFpdY3Uv9kzU8s+26hg/b02bv0VGzj8Hry9fj76YbeiWP+06x+qMGde/3IVtNl3hKpn\nGvj+NxTNypoqOLor/Qn4iiP2WYdLyJ8cCPA7fEOoCgZtf5PVV8lgfUZDhgsQOmEn1/zs/X97F2Az\ngwEUnmwTBAB4y5eEqsGtf5lQBQD8ri9p/qvr+na7xTmJ6rruum6QtLadFvCj2raNoXCytHGFsFPH\nFAo5LuHxC79cwuOXf/lUP2b5F/4Keljg3T/aawo/V7zdC5+t+xO0ruvJQh6//Msl3L385z3z379O\n7X7wP6L/FlVVpftVFMWahw5oXOvWNM3CClVV7VPQRyZPsIMXvmmaUx/88548cx/M5QKP36/fKe3A\nZOHHZRsU7yCF7+cPfhS+Qo958Pv58i9/8x+k/CtPnqMV/s2vmt3L/znfsydBVVVVVaXvbnzzwsLw\nMTtsrhoUb1D4/udMjSfo5Nl8BPETNV545MKnRYoHPz568PLPnTxxhcOWPxRs/Kl8WOAjfLTnCj+4\nloyLd4TC9/Plj+KJNLguHrz8gyKNc+ERyr988sx97fd7F/7969QRDv6HfFuoGgvvbvp2hiX7lWjJ\nOIiE8zWecOOT74CnY/oHYrr84IUPxU5PlcGSg5d/fGIfv/xZYvLSslDg8fs1/rB/1ELhw8d2kELS\nN2j3wvePDv54tXR3jl/+ye+f4xz/hyfP+Mw/WuHTJU9dp3Yv/0cdNFtsaPz2j9/R41i+tEx+U493\ncF+xkIOCHb/wy4U5Y/nTU/2Y5a9+rMwlaYHnvtl/rU1zofCTXzLpwt0L3y+WP4pXu2yqpiRd81Dl\nf1iY3ct/6jP/zevU7uX/qKNcDz7nhc/bjsZRL/0LYPKb+mgVb7G0g0/O8QuffikE6aPHL/9cA0f6\n6GHLvzKXpAWeTIQL+eBzJl90/Fk+ZuEXXje+BWtC1cJ2Pm355Jn8OB+n/HMBZfzlOWhNW7OdD3nz\nOrV7+T/qS4ZUeEq40WAwWOhBjG+CuN1u2U9pJ+8TOdR9EwvH9viFD/I8v/3I8zzuy/HLX9d1URT3\n+z3/kf3UMWRnKP/AmgKPe8sex6Co41uSj1z44H6/V1U1d5IcufzxCzP9OJ/o5Akf2/hBvt/vRVGk\n99DtW/j3r1NHPvhv+vJQderRquq6HlwXFxwhI4bLxprSDhyh8KEMYQjZtL0sfFmsee4RhPIXRRG/\ns9aU7TjlX+l0BS7L8n6/Z6cqefjyOdS4G88KoTBUUxVF0XXdWa4F8TyJn+XB6NbHcbrr1Kd9eag6\ny0doIPyBFf46aZpmzV4cYU+X/65dcITCR33fh/KUZRm+Jh5eVw5S/vjVFoaH6fs+VFw9/CI7SPnX\nO1GBwyWn67rQdrN3cdYK5/yJCjypqqo4vFbbtiFX7V2ox9q2DV/+fd+Hz3KsuNq7aH856XXq43Zr\nePwt2an6VPXJrXPjdusjd4uJxS5+pL/2xy58MD5V0oUHL//cWX2W8o8P/ol6Zsy9aPwIzD20cjuf\nNn7dyc9yWDLX0X5yO79j5cmzfKPA5HZ+wfhFJz+VRyv8O9epI5T/c768pio40bSAdV3Hv1HGJZzs\nsXSQv71iabsf8dd0hWMWfg3l/2UvFPhQH+204nDN+ocqfGxySj/L2eLxP1T5X3Dq8v9y4Te/Tp36\n4A/tm+l+wTj1H+ev87GHb0p2vKGG5oz35eCFH58Yg+qfg5d/XJinBo/Z12RJlgt8nNFu5o78QnX4\ncQrfrzgNxrtz/PJPnjzxA36c8o/L+bCmZ/fCv3md2r38H3XQbLGh2Hsu/Lo8KMu+YlGLkfh1Vhx1\nUOyx8Qfv4IWPxz/tqJ7uwsHLX/w9OvO5yj/5qXxY4HQHBwnyN41fN74XY+mzjlD4/qVQ1R++/IOT\nZ/Dp6A9T/rlEmJ7qhyr8Jtepgxz8T/j+UNWfZ+6/hbsn0jIXf9+MepCL4tjgih4cvPCDU2VcwoOX\nf1C8E5V/7oO5XODxR+Z3SjswLvz4jRjvwkEK378aqo5f/sG7UEzVJu5e/snCj8t2nMJvcp06yMH/\nhLw/+f0d633ThNgPZ/8+suMXfvlUOXj53586/mgeFjiscNKP9qkLnx2+/Kc+eVZ+lo9Z+OzkB/9l\nFwpVAACfc4m7/wAAPk2oAgDYgFAFALABoQoAYANCFQDABoQqAIANCFUAABsQqgAANiBUAQBsQKgC\nANiAUAUAsAGhCgBgA0IVAMAGhCoAgA0IVQAAGxCqAAA2IFQBAGxAqAIA2IBQBQCwAaEKAGADQhUA\nwAaEKgCADQhVAAAbEKoAADYgVAEAbECoAgDYgFAFALABoQoAYANCFQDABoQqAIANCFUAABsQqgAA\nNiBUAQBsQKgCANiAUAUAsAGhCgBgA0IVAMAG/n+ZO+himpKeSAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%cpp\n",
    "variable->SetFillColor(kBlue);\n",
    "variable->Fit(\"gaus\",\"S\");\n",
    "testCanvas->Draw();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}