AsimovSpurious.ipynb

{
 "cells": [
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Create an input example\n",
    "Create a weighted histogram, as it should be from a MC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "entries 10.0\n",
      "sum of weights 1360.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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5jv9Ylv8pfZI766MQ9X0/anRIwzWGEOJwjcs8xyXL/LhHsc4mHcztdhvrb+Yu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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = ROOT.TH1F(\"h\", \"H\", 10, 0, 10)\n",
    "h.Sumw2()\n",
    "for i in xrange(10):\n",
    "    h.Fill(i, 1 + i * 30)  # note that the histogram is weighted, as it should be from a MC\n",
    "\n",
    "print 'entries', h.GetEntries()\n",
    "print 'sum of weights', h.GetSumOfWeights()\n",
    "    \n",
    "canvas = ROOT.TCanvas()\n",
    "h.Draw()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create extended pdf from the TH1F\n",
    "\n",
    "TH1F -> RooDataHist -> RooDataHistPdf -> RooExtendedPdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[1mRooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby\u001b[0m \r\n",
      "                Copyright (C) 2000-2013 NIKHEF, University of California & Stanford University\r\n",
      "                All rights reserved, please read http://roofit.sourceforge.net/license.txt\r\n",
      "\r\n",
      "[#1] INFO:DataHandling -- RooDataHist::adjustBinning(data_hist): fit range of variable x expanded to nearest bin boundaries: [0,10] --> [0,10]\r\n"
     ]
    }
   ],
   "source": [
    "x = ROOT.RooRealVar('x', 'x', 0, 10)\n",
    "data_hist = ROOT.RooDataHist(\"data_hist\", \"hist\", ROOT.RooArgList(x), h)\n",
    "pdf_hist = ROOT.RooHistPdf(\"pdf_hist\", \"pdf_hist\", ROOT.RooArgSet(x), data_hist)\n",
    "norm = ROOT.RooRealVar(\"norm\", \"norm\", h.GetSumOfWeights())\n",
    "pdf_hist_extended = ROOT.RooExtendPdf(\"pdf_hist_extended\", \"pdf_hist_extended\", pdf_hist, norm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate the Asimov dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pdf_hist_extended.generateBinned(ROOT.RooArgSet(x), ROOT.RooFit.ExpectedData())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1360.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sumEntries()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zqhEAgFFz36LQ5/RKAADgoBwnCvVmf9MTQeoA4Dg1P7pWa+08oNsqDfE4cg5uj8yyTClV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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#1] INFO:InputArguments -- RooAbsData::plotOn(genData) INFO: dataset has non-integer weights, auto-selecting SumW2 errors instead of Poisson errors\r\n"
     ]
    }
   ],
   "source": [
    "canvas = ROOT.TCanvas()\n",
    "frame = x.frame()\n",
    "data.plotOn(frame)\n",
    "frame.Draw()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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
}