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    "# gr103_zones\n",
    "Example of script showing how to divide a canvas\n",
    "into adjacent subpads + axis labels on the top and right side\n",
    "of the pads. Original tutorial by Rene Brun.\n",
    "\n",
    "See the [Divide documentation](https://root.cern/doc/master/classTPad.html#a2714ddd7ba72d5def84edc1fbaea8658)\n",
    "\n",
    "Note that the last 2 arguments in\n",
    "      c1->Divide(2,2,0,0)\n",
    "define 0 space between the pads. With this, the axis labels where the pads\n",
    "touch may be cut, as in this tutorial. To avoid this, either add some spacing\n",
    "between pads (instead of 0) or change the limits of the plot in the pad (histos\n",
    "in this tutorial). E.g. h3 could be defined as\n",
    "TH2F *h3 = new TH2F(\"h3\",\"test3\",10,0,1,22,-1.1,1.1);\n",
    "but note that this can change the displayed axis labels (requiring SetNdivisions\n",
    "to readjust).\n",
    "\n",
    "SetLabelOffset changes the (perpendicular) distance to the axis. The label\n",
    "position along the axis cannot be changed\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, May 19, 2026 at 08:38 PM.</small></i>"
   ]
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     "iopub.status.busy": "2026-05-19T20:38:12.629912Z",
     "iopub.status.idle": "2026-05-19T20:38:13.774129Z",
     "shell.execute_reply": "2026-05-19T20:38:13.773633Z"
    }
   },
   "outputs": [
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').then(json => {\n",
       "   const obj = Core.parse(json);\n",
       "   Core.draw('root_plot_1779223093763', 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_1779223093763();\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ROOT\n",
    "\n",
    "c1 = ROOT.TCanvas(\"c1\",\"multipads\",900,700)\n",
    "ROOT.gStyle.SetOptStat(0)\n",
    "\n",
    "c1.Divide(2,2,0,0)\n",
    "h1 = ROOT.TH2F(\"h1\",\"test1\",10,0,1,20,0,20)\n",
    "h2 = ROOT.TH2F(\"h2\",\"test2\",10,0,1,20,0,100)\n",
    "h3 = ROOT.TH2F(\"h3\",\"test3\",10,0,1,20,-1,1)\n",
    "h4 = ROOT.TH2F(\"h4\",\"test4\",10,0,1,20,0,1000)\n",
    "\n",
    "c1.cd(1)\n",
    "ROOT.gPad.SetTickx(2)\n",
    "h1.Draw()\n",
    "c1.cd(2)\n",
    "ROOT.gPad.SetTickx(2)\n",
    "ROOT.gPad.SetTicky(2)\n",
    "h2.GetYaxis().SetLabelOffset(0.01)\n",
    "h2.Draw()\n",
    "c1.cd(3)\n",
    "h3.Draw()\n",
    "c1.cd(4)\n",
    "ROOT.gPad.SetTicky(2)\n",
    "h4.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40c1b739",
   "metadata": {},
   "source": [
    "Draw all canvases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "43f56cfe",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:38:13.775406Z",
     "iopub.status.busy": "2026-05-19T20:38:13.775282Z",
     "iopub.status.idle": "2026-05-19T20:38:13.897842Z",
     "shell.execute_reply": "2026-05-19T20:38:13.897436Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<div id=\"root_plot_1779223093896\" style=\"width: 900px; height: 700px; position: relative\">\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "<script>\n",
       "   function process_root_plot_1779223093896() {\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_1779223093896', 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_1779223093896();\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
}
