{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "582d3064",
   "metadata": {},
   "source": [
    "# hist003_TH1_draw\n",
    "Draw a 1D histogram to a canvas.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "**Author:** Rene Brun, Giacomo Parolini  \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 19, 2026 at 08:11 PM.</small></i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c13b985f",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:49.083572Z",
     "iopub.status.busy": "2026-05-19T20:11:49.083424Z",
     "iopub.status.idle": "2026-05-19T20:11:50.046358Z",
     "shell.execute_reply": "2026-05-19T20:11:50.043385Z"
    }
   },
   "outputs": [],
   "source": [
    "import ROOT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99fb9311",
   "metadata": {},
   "source": [
    "Create and fill the histogram.\n",
    "See hist002_TH1_fillrandom_userfunc.C for more information about this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "afd59fca",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:50.048090Z",
     "iopub.status.busy": "2026-05-19T20:11:50.047929Z",
     "iopub.status.idle": "2026-05-19T20:11:50.242149Z",
     "shell.execute_reply": "2026-05-19T20:11:50.240978Z"
    }
   },
   "outputs": [],
   "source": [
    "form1 = ROOT.TFormula(\"form1\", \"abs(sin(x)/x)\")\n",
    "rangeMin = 0.0\n",
    "rangeMax = 10.0\n",
    "sqroot = ROOT.TF1(\"sqroot\", \"x*gaus(0) + [3]*form1\", rangeMin, rangeMax)\n",
    "sqroot.SetLineColor(4)\n",
    "sqroot.SetLineWidth(6)\n",
    "sqroot.SetParameters(10.0, 4.0, 1.0, 20.0)\n",
    "\n",
    "nBins = 200\n",
    "h1d = ROOT.TH1D(\"h1d\", \"Test random numbers\", nBins, rangeMin, rangeMax)\n",
    "\n",
    "h1d.FillRandom(\"sqroot\", 10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "563fca77",
   "metadata": {},
   "source": [
    "Create a canvas and draw the histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "beb9ca02",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:50.243866Z",
     "iopub.status.busy": "2026-05-19T20:11:50.243738Z",
     "iopub.status.idle": "2026-05-19T20:11:50.383000Z",
     "shell.execute_reply": "2026-05-19T20:11:50.381944Z"
    }
   },
   "outputs": [],
   "source": [
    "topX = 200\n",
    "topY = 10\n",
    "width = 700\n",
    "height = 900\n",
    "c1 = ROOT.TCanvas(\"c1\", \"The FillRandom example\", topX, topY, width, height)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3827a61b",
   "metadata": {},
   "source": [
    "Split the canvas into two sections to plot both the function and the histogram\n",
    "The TPad's constructor accepts the relative coordinates (0 to 1) of the pad's boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cc16dc4f",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:50.384695Z",
     "iopub.status.busy": "2026-05-19T20:11:50.384548Z",
     "iopub.status.idle": "2026-05-19T20:11:50.496541Z",
     "shell.execute_reply": "2026-05-19T20:11:50.495463Z"
    }
   },
   "outputs": [],
   "source": [
    "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61570a7e",
   "metadata": {},
   "source": [
    "Draw the two pads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "73a42ba2",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:50.498254Z",
     "iopub.status.busy": "2026-05-19T20:11:50.498126Z",
     "iopub.status.idle": "2026-05-19T20:11:50.615491Z",
     "shell.execute_reply": "2026-05-19T20:11:50.614398Z"
    }
   },
   "outputs": [],
   "source": [
    "pad1.Draw()\n",
    "pad2.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4586e7c2",
   "metadata": {},
   "source": [
    "Select pad1 to draw the next objects into"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f34262ba",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:50.617173Z",
     "iopub.status.busy": "2026-05-19T20:11:50.617043Z",
     "iopub.status.idle": "2026-05-19T20:11:50.737070Z",
     "shell.execute_reply": "2026-05-19T20:11:50.735996Z"
    }
   },
   "outputs": [],
   "source": [
    "pad1.cd()\n",
    "pad1.SetGridx()\n",
    "pad1.SetGridy()\n",
    "pad1.GetFrame().SetBorderMode(-1)\n",
    "pad1.GetFrame().SetBorderSize(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b00287cf",
   "metadata": {},
   "source": [
    "Draw the function in pad1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c3843085",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:50.738822Z",
     "iopub.status.busy": "2026-05-19T20:11:50.738698Z",
     "iopub.status.idle": "2026-05-19T20:11:50.847977Z",
     "shell.execute_reply": "2026-05-19T20:11:50.846829Z"
    }
   },
   "outputs": [],
   "source": [
    "sqroot.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a581ec87",
   "metadata": {},
   "source": [
    "Add a label to the function.\n",
    "TPaveLabel's constructor accepts the pixel coordinates and the label string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dec0351b",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:50.849727Z",
     "iopub.status.busy": "2026-05-19T20:11:50.849567Z",
     "iopub.status.idle": "2026-05-19T20:11:51.110013Z",
     "shell.execute_reply": "2026-05-19T20:11:51.103473Z"
    }
   },
   "outputs": [
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').then(json => {\n",
       "   const obj = Core.parse(json);\n",
       "   Core.draw('root_plot_1779221511235', obj, '');\n",
       "});\n",
       "\n",
       "      }\n",
       "      const servers = ['/static/', 'https://root.cern/js/7.11.0/', 'https://jsroot.gsi.de/7.11.0/'],\n",
       "            path = 'build/jsroot';\n",
       "      if (typeof JSROOT !== 'undefined')\n",
       "         execCode(JSROOT);\n",
       "      else if (typeof requirejs !== 'undefined') {\n",
       "         servers.forEach((s,i) => { servers[i] = s + path; });\n",
       "         requirejs.config({ paths: { 'jsroot' : servers } })(['jsroot'],  execCode);\n",
       "      } else {\n",
       "         const config = document.getElementById('jupyter-config-data');\n",
       "         if (config)\n",
       "            servers[0] = (JSON.parse(config.innerHTML || '{}')?.baseUrl || '/') + 'static/';\n",
       "         else\n",
       "            servers.shift();\n",
       "         function loadJsroot() {\n",
       "            return !servers.length ? 0 : import(servers.shift() + path + '.js').catch(loadJsroot).then(() => execCode(JSROOT));\n",
       "         }\n",
       "         loadJsroot();\n",
       "      }\n",
       "   }\n",
       "   process_root_plot_1779221511235();\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lfunction = ROOT.TPaveLabel(5, 39, 9.8, 46, \"The sqroot function\")\n",
    "lfunction.Draw()\n",
    "c1.Update()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c11ea118",
   "metadata": {},
   "source": [
    "Select pad2 to draw the next objects into"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e427d711",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:51.114183Z",
     "iopub.status.busy": "2026-05-19T20:11:51.114048Z",
     "iopub.status.idle": "2026-05-19T20:11:51.236947Z",
     "shell.execute_reply": "2026-05-19T20:11:51.236551Z"
    }
   },
   "outputs": [],
   "source": [
    "pad2.cd()\n",
    "pad2.GetFrame().SetBorderMode(-1)\n",
    "pad2.GetFrame().SetBorderSize(5)\n",
    "\n",
    "h1d.SetFillColor(45)\n",
    "h1d.Draw()\n",
    "c1.Update()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8946c752",
   "metadata": {},
   "source": [
    "Draw all canvases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c6810b9e",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:11:51.244048Z",
     "iopub.status.busy": "2026-05-19T20:11:51.243919Z",
     "iopub.status.idle": "2026-05-19T20:11:51.357043Z",
     "shell.execute_reply": "2026-05-19T20:11:51.356627Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<div id=\"root_plot_1779221511355\" style=\"width: 700px; height: 900px; position: relative\">\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "<script>\n",
       "   function process_root_plot_1779221511355() {\n",
       "      function execCode(Core) {\n",
       "         Core.settings.HandleKeys = false;\n",
       "         \n",
       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').then(json => {\n",
       "   const obj = Core.parse(json);\n",
       "   Core.draw('root_plot_1779221511355', obj, '');\n",
       "});\n",
       "\n",
       "      }\n",
       "      const servers = ['/static/', 'https://root.cern/js/7.11.0/', 'https://jsroot.gsi.de/7.11.0/'],\n",
       "            path = 'build/jsroot';\n",
       "      if (typeof JSROOT !== 'undefined')\n",
       "         execCode(JSROOT);\n",
       "      else if (typeof requirejs !== 'undefined') {\n",
       "         servers.forEach((s,i) => { servers[i] = s + path; });\n",
       "         requirejs.config({ paths: { 'jsroot' : servers } })(['jsroot'],  execCode);\n",
       "      } else {\n",
       "         const config = document.getElementById('jupyter-config-data');\n",
       "         if (config)\n",
       "            servers[0] = (JSON.parse(config.innerHTML || '{}')?.baseUrl || '/') + 'static/';\n",
       "         else\n",
       "            servers.shift();\n",
       "         function loadJsroot() {\n",
       "            return !servers.length ? 0 : import(servers.shift() + path + '.js').catch(loadJsroot).then(() => execCode(JSROOT));\n",
       "         }\n",
       "         loadJsroot();\n",
       "      }\n",
       "   }\n",
       "   process_root_plot_1779221511355();\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML 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": "3.12.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
