CreateData.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you forget RooPlot and the namespace, the Jupyter kernel will die miserably with no explanation. Thanks, ROOT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1mRooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby\u001b[0m \n",
      "                Copyright (C) 2000-2013 NIKHEF, University of California & Stanford University\n",
      "                All rights reserved, please read http://roofit.sourceforge.net/license.txt\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#include <RooArgusBG.h>\n",
    "#include <RooPlot.h>\n",
    "\n",
    "using namespace RooFit;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "TFile f(\"workspace.root\", \"READ\");\n",
    "RooWorkspace* w = (RooWorkspace*) f.Get(\"work\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "RooRealVar* M = work->var(\"M\");\n",
    "RooAbsPdf* sig1 = work->pdf(\"sig1\");\n",
    "RooAbsPdf* Argus = work->pdf(\"Argus\");\n",
    "RooAbsPdf* model = work->pdf(\"model\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#1] INFO:NumericIntegration -- RooRealIntegral::init(Argus_Int[M]) using numeric integrator RooIntegrator1D to calculate Int(M)\n",
      "[#1] INFO:NumericIntegration -- RooRealIntegral::init(Argus_Int[M]) using numeric integrator RooIntegrator1D to calculate Int(M)\n"
     ]
    }
   ],
   "source": [
    "RooDataSet* dataset = model->generate(*M,10000);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Error in <TFile::TFile>: file hsimple.root already exists\n",
      "Warning in <TFile::Write>: file hsimple.root not opened in write mode\n"
     ]
    }
   ],
   "source": [
    "TFile fout(\"hsimple.root\", \"CREATE\");\n",
    "// Opened after file, so tree is magically part of file.\n",
    "TTree *tree = dataset->GetClonedTree();\n",
    "fout.Write();\n",
    "fout.Close();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "RooPlot* frame = M->frame(Name(\"mbc frame\"),Title(\"mbc Neutral Btag\"), Bins(20));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#1] INFO:NumericIntegration -- RooRealIntegral::init(Argus_Int[M]) using numeric integrator RooIntegrator1D to calculate Int(M)\n",
      "[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) directly selected PDF components: (Argus)\n",
      "[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) indirectly selected PDF components: ()\n",
      "[#1] INFO:NumericIntegration -- RooRealIntegral::init(Argus_Int[M]) using numeric integrator RooIntegrator1D to calculate Int(M)\n",
      "[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) directly selected PDF components: (sig1)\n",
      "[#1] INFO:Plotting -- RooAbsPdf::plotOn(model) indirectly selected PDF components: ()\n",
      "[#1] INFO:NumericIntegration -- RooRealIntegral::init(Argus_Int[M]) using numeric integrator RooIntegrator1D to calculate Int(M)\n"
     ]
    }
   ],
   "source": [
    "dataset->plotOn(frame);\n",
    "model->plotOn(frame);\n",
    "model->plotOn(frame, Components(*Argus), LineColor(kRed));\n",
    "model->plotOn(frame, Components(*sig1), LineColor(kGreen+2));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "TCanvas c;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "frame->Draw();\n",
    "c.Draw();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.Close();"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ROOT C++",
   "language": "c++",
   "name": "root"
  },
  "language_info": {
   "codemirror_mode": "text/x-c++src",
   "file_extension": ".C",
   "mimetype": " text/x-c++src",
   "name": "c++"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}

MakeWorkspace.ipynb

ReadData.ipynb