{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# fillrandom\n",
    "Fill a 1-D histogram from a parametric function. Original tutorial by Rene Brun.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "**Author:** Alberto Ferro  \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, August 16, 2022 at 09:36 AM.</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": [
    "import ROOT\n",
    "\n",
    "c1 = ROOT.TCanvas(\"c1\",\"The FillRandom example\",200,10,700,900)\n",
    "pad1 = ROOT.TPad(\"pad1\",\"The pad with the function\",0.05,0.50,0.95,0.95)\n",
    "pad2 = ROOT.TPad(\"pad2\",\"The pad with the histogram\",0.05,0.05,0.95,0.45)\n",
    "pad1.Draw()\n",
    "pad2.Draw()\n",
    "pad1.cd()\n",
    "ROOT.gBenchmark.Start(\"fillrandom\")\n",
    "\n",
    "form1 = ROOT.TFormula(\"form1\",\"abs(sin(x)/x)\")\n",
    "sqroot = ROOT.TF1(\"sqroot\",\"x*gaus(0) + [3]*form1\",0,10)\n",
    "sqroot.SetParameters(10,4,1,20)\n",
    "pad1.SetGridx()\n",
    "pad1.SetGridy()\n",
    "pad1.GetFrame().SetBorderMode(-1)\n",
    "pad1.GetFrame().SetBorderSize(5)\n",
    "sqroot.SetLineColor(4)\n",
    "sqroot.SetLineWidth(6)\n",
    "sqroot.Draw()\n",
    "lfunction = ROOT.TPaveLabel(5,39,9.8,46,\"The sqroot function\")\n",
    "lfunction.Draw()\n",
    "c1.Update()\n",
    "\n",
    "pad2.cd()\n",
    "pad2.GetFrame().SetBorderMode(-1)\n",
    "pad2.GetFrame().SetBorderSize(5)\n",
    "h1f = ROOT.TH1F(\"h1f\",\"Test random numbers\",200,0,10)\n",
    "h1f.SetFillColor(45)\n",
    "h1f.FillRandom(\"sqroot\",10000)\n",
    "h1f.Draw()\n",
    "c1.Update()\n",
    "\n",
    "f = ROOT.TFile(\"fillrandom-py.root\",\"RECREATE\")\n",
    "form1.Write()\n",
    "sqroot.Write()\n",
    "h1f.Write()\n",
    "ROOT.gBenchmark.Show(\"fillrandom\")"
   ]
  },
  {
   "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
}