{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Framework\n",
    "The ROOT Framework\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "**Author:** Wim Lavrijsen  \n",
    "<i><small>This notebook tutorial was automatically generated with <a href= \"https://github.com/root-project/root/blob/master/documentation/doxygen/converttonotebook.py\">ROOTBOOK-izer</a> from the macro found in the ROOT repository  on Tuesday, May 24, 2022 at 04:09 PM.</small></i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.27/01\n"
     ]
    }
   ],
   "source": [
    "from ROOT import TCanvas, TPavesText\n",
    "from ROOT import gROOT\n",
    "\n",
    "c1 = TCanvas(\"c1\",\"The ROOT Framework\",200,10,700,500)\n",
    "c1.Range(0,0,19,12)\n",
    "\n",
    "rootf = TPavesText(0.4,0.6,18,2.3,20,\"tr\")\n",
    "rootf.AddText(\"ROOT Framework\")\n",
    "rootf.SetFillColor(42)\n",
    "rootf.Draw()\n",
    "\n",
    "eventg = TPavesText(0.99,2.66,3.29,5.67,4,\"tr\")\n",
    "eventg.SetFillColor(38)\n",
    "eventg.AddText(\"Event\")\n",
    "eventg.AddText(\"Generators\")\n",
    "eventg.Draw()\n",
    "\n",
    "simul = TPavesText(3.62,2.71,6.15,7.96,7,\"tr\")\n",
    "simul.SetFillColor(41)\n",
    "simul.AddText(\"Detector\")\n",
    "simul.AddText(\"Simulation\")\n",
    "simul.Draw()\n",
    "\n",
    "recon = TPavesText(6.56,2.69,10.07,10.15,11,\"tr\")\n",
    "recon.SetFillColor(48)\n",
    "recon.AddText(\"Event\")\n",
    "recon.AddText(\"Reconstruction\")\n",
    "recon.Draw()\n",
    "\n",
    "daq = TPavesText(10.43,2.74,14.0,10.81,11,\"tr\")\n",
    "daq.AddText(\"Data\")\n",
    "daq.AddText(\"Acquisition\")\n",
    "daq.Draw()\n",
    "\n",
    "anal = TPavesText(14.55,2.72,17.9,10.31,11,\"tr\")\n",
    "anal.SetFillColor(42)\n",
    "anal.AddText(\"Data\")\n",
    "anal.AddText(\"Analysis\")\n",
    "anal.Draw()\n",
    "\n",
    "c1.Update()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw all canvases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ROOT import gROOT \n",
    "gROOT.GetListOfCanvases().Draw()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}