{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "57ded466",
   "metadata": {},
   "source": [
    "# hist029_TRatioPlot_simple\n",
    "Example creating a simple ratio plot of two histograms using the `pois` division option.\n",
    "Two histograms are set up and filled with random numbers. The constructor of `TRatioPlot`\n",
    "takes the to histograms, name and title for the object, drawing options for the histograms (`hist` and `E` in this case)\n",
    "and a drawing option for the output graph.\n",
    "Inspired by the tutorial of Paul Gessinger.\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, May 19, 2026 at 08:12 PM.</small></i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "919fe692",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:12:36.329486Z",
     "iopub.status.busy": "2026-05-19T20:12:36.329370Z",
     "iopub.status.idle": "2026-05-19T20:12:37.491274Z",
     "shell.execute_reply": "2026-05-19T20:12:37.490889Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<div id=\"root_plot_1779221557481\" style=\"width: 700px; height: 500px; position: relative\">\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "<script>\n",
       "   function process_root_plot_1779221557481() {\n",
       "      function execCode(Core) {\n",
       "         Core.settings.HandleKeys = false;\n",
       "         \n",
       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rrMNG4ZI6HiESd/VjrCjtJoFWDHOBt5r+5UifADLXasXRUrKUrqNgZv3YgzvflKxIpXEl70bVASVjxv44hLB06UTJAJRvA44sDDnziTRUnY0Ld1xJtHr+BidFbLqncQUqgIOKv0qXMwyfZHpcvU1FV3q1S6DFvXdZo2Ye3igwSdYFudeJ2GUTV32zZHhRdcRnAOfikzTfwVI6meBVhl28ZIoX6PxdJp6mcRxtaS7kpA42FsPsUCPx5fbKfhx7M4bH5R/JPW646A+kmbJzcDvIJ7ttAbvgDLbNyA4GKBd+qHhh8Anvob3osF4qkfmvoBeepHA1B/8CiL5sekP0JAlTQafkJE0Qh4nqk/JNSQFPqc+kNGSTVa6y+osLZbRX+EioJrNP0RLepPt98S/ERBOapbNFF/dCjSRlN/t4l1j4vxiGGMlEehUz92cTZSWDhQf0wjOlJprS+PCX54r/WVEdlRmvbHOrLFhd9XXeXJ4kFsNPUlGfFZ8z5QX9INw0ZTX/IjfcMOXaC+FEa2FpWmvhRH6lKa+lJSR7fStC/lUb322BFaXxnZ/INWd3yqY2X3D5r6smVXqdH0L2rBwjB4R4UoBhY4mkCNajPTRYqIJIQRNdISqBP1wKQpVbcJxaIg8LKS4LRWgIVqSdBq2RagWhK0WvbtmKNWN9vEoigcW4skMLKoCkRXE6gWZaE7juRAtlAXCCM7n/j5LQoDaSSHUC0qA/E7JOCTZ9eSHPQ/agMB0wQYQ3GEzge7BRbVgYhpDhhDeWCdaQIdNPvgNAFO1QnX+0PgVP0hvT8U6VWJ0KfsWMC6qpHep4rlKBKVPHLAKaoEWNIEOEWZNJxhq5EEDII+tiToJpAiQTECzOo2EFNbE9h1mXeCNEF3aNh3mL/BVgo6RTciigFP2Q1SaYZmb0W3HmhaMQCqbgjRESWzUSq6IaQb6NkAsOwISUXesgFg2ROSSrOyAWDZFhL2caB1lwg3DgzNdPNUKQ3Dug1BkzDxoFm5wE/SHRu2hti1JD8AzN4QW8JKwx+bQyrMhU1GYXeITRbaA+CyQeRUtosp8IcvBcRnO5f68e51UQdwdYsIhK/WALjsEumeMzT1490D8auYTH/hT9GJIAbAZZfIgfDQWj87jMi0MwCu4N8D4aHpH5wqOk28AXAFBcPWVvWmbaS50emsCQbAFVUwCHwwAK6gYHQSYfRDx9F55J8lAXQa2cNUmvpRMDqlkgFwBQUTqC+xMSvi29kFxhTAlWBHpxMsGwBX2JIHwaGpnz15nW/FAMDCOkKnWzEAsLCSCLo6MJH6WUvo7KsGABZWE2qg6BJThPWE+hmsifQHKwqlnQGQhTWF0t4E6mdVoXQ0ALJE1qiYFskAyBLxkqoBZgBkiXhvsUSqCfAT2bPFcBTdWpUIf9DeANDClr3SwQDQEuGP98kA0BLhDzqzmS0S4Q+6Gg8/Cf5YrVgDXkuCP2hngGtJ8AcdjIefBH/Q0QDWkuAPOhtPf6TWf8gQm5SS4M8ah2kMPwn+oMWA3JLgD9obp/zAHzTb5my1wh90MsC24JJRmtUQNPzxHkscmvGtBpkBxCW38eWsi8BPjv09cxA6dRqLBJr1IuWxuqCRP2g2dzlI9OHrU86S/R89nbe4f7ZZ7rxbmMWZnh6L0Sy+Xox/ry6Y6lj6JVMd2FJMddVUb01li9c7U7031QdTfTTVJ1M9GFVM9dXUYE0NYmpwpgZvagimBhaPnBEB24qpoZoaralRTI3O1OgNR08re+AxmRrBxGJqrKYma2oSU5MzNXlTEwdNoqkpmZrA0mJqqqZma2oWU7MzNXtTczA1sxRNpuZsagaHq6nFmlrE1OJMLd7UEkwtLFKTqWCp4nc1tVpT2YOuztTqTeWAS42mVpaw2dQK7uu+On4YkNiqJ8SyA6GeUcteDBBsMRss4Gs5sGOBXQvWWgDWYldboNWCp1ZXxSCpBT4tVrIFOC12ggUiLZawBRwtiGgxDyxYaAFAC+pZjFkL3llAzmK7WuDN6sElgMxiEVggzIJbFvPTglgWkbNYmxZhs6CSRcxsO7xDCaTK4pixYI6NlFCvHEalRcdb3C8Wm9Ki4S3WpEW1W7wtFp1u8bRYViQWjW5xrlhUucWUtOhwywLBorwtLhSLDWlR3RZL3aKzLTa45QCBxYK0mNQWVW0xIC062mI6WmxPi8FosQKtqnY1GlDLFmPJopAtdqNFFdtKCcxGiya2GIyWMcdrgdHALw45MObtVBNjjiMCfc8vVvyMuRqKeBlELcR2HoIxV/tQGPN2MIAx1/NSuAbQqvzSgxTUwZiz7kc98os6GHM1CNuJLMZczUFhzFmso7v4RQnGnEU4+ghHBnUw5mr8saBGsfCLEoy5HthSO4/lMWqBX5RgzNUZKIy5no/TEy/CmOsBN5axADW/KMGY6wk7XYW2k2GMuZpvekJO7TZdFKrB1o6QMOZqrunqTO00XXdJLv/47rvvzPs6zRm/7zRni034nnOc/cQmR7a352Scjzm3lA/2H7gWFAF5FPhwPebhEA0ixGDcDAf5eLn9arU9Ci9pCUef7An7kInPV292d85fcAabo8mQ7aUdLH2g78/WL84XeEUaffR9Xt/fcLI96Qnj5Zv1zYPid3a7O6RzfPtk/fX6cr05v1yMUaiRN0cffLh8tppDXqhP6VZDoAalPz09vVxpLAo42xP3bHvle/38q4er8xdE0tjBcshZx2Auqm3h7Pn1Yruz+bj5PstcPQeTn/5hWqjD+RNa+B9/mBbuB+hHjuGHy+1RJNGHy+0sFHSZyiLT9OzR4zYnPtouv2khGI3+9GJ3CPdoRI/4aEQP+vj0YvdRO2/fosk4BL//QMcDWvDpxe6+hl71fPfXPduN4/Nk0MST9Y74sZn+fLM509PzJLRYlbub893m9fayBy7c2XVersHlnd2OGawI9T1A4H4kEjBRaHsLMqKRUIRDDErdOz+5t91ueuQW01pJzU5V91+fP++YwEvIIwiD7OPHW4JfembaD9kzM+khjwb74erF6vzkOMAG7lrqEbzyoUPiXPcccscnZkzYZ6T/uhyaxekDAiFWl9cAvKc+vlg+JxBA697HwR21YR8E19PgcZ/vKjf7rHPynPVa1ZrveruPEg/RRQ/WlwjkMT8k8b3OTrI0e843V9x6Z846p/aM17gh18fr8/Wr16/+Y7XdHOI8eHElNFHxvYW8PNquTlfbvz485G7pRx3XEo6bCafHqYd2ttSPVqcPFmO0DNo+5cvFmK+mPFmwCXCU5WlPeLQ8Fr5HyyuyReX7pEPNmnQzJPPR8uRK0+m7R8uTmyGej5Ynt0R5PlqeIOxPDt3TU55eSUEx9kAlKlw//6qHKT1aXrRYyicdNPYJTxfsTS5OHz/frlbn95fPFX1gD0w76n5I5sGR2JJ0PB5zqaP5QxbIw+whZRagJlYtz/YVELpIQ9AoGhIVM2rpvCjEsDJtrH05BzuR8wGEaDt22/XFR6vn61fLs8t9RJFCcjd13N5eOGqdZrjWPE07bh9CoolHDdzTez3VLBSsjxb6uy+1h0At05pT97lpz/x9mtPAZ/O38/PV9jOaR06mmn72cjH+nSiiDz7gl7gPhBX/B+GDMtPpg/CBJChnyRB6lgOlScfpzl7Nvn9XbH9X7AfBfhA+CJpT635Pv/4BuKyWJ6stqloDprTb9tT99e7+LDSxC42GYTGK+zcqS9prz5dnWpjR/3+b9TmJsxVwd3lxTH6+frW3JXMpVUpQxPjbq+WLFR/aA/zd5fnJ2erLl+vLr1bbz5bnL3qwckv/cPOmp7XRa6nKyVHA5r+vN2fr8zm1B222rHfX2+dn19G+vyLQFKaPFOAT7O17by6eHNs8c+LT48Snt+WcE6/kJOPHyzcfrV9oMD1C+Ol293Jzd/lqtV129HmPazXFlDnC/7qJo2G8j842b439BzseLber892j5Yk6ybar04XGQp5+cXGx2vbkKzcLkPaW9R8rMzr9CAQhZzzrb4+G5Jqyvbmqu6kBboH/60H+DWP+jPK/Ncpfhuwr/yV8NTUTHHsIrQdXZ5K4XDdEZ/kv51wI87x6A4D3dtCvBZ+9dyj9o+sAvKuDk1pr8LUmrWp+uxj/csvbqxcF3MLp99wbcIPTq5cI3MLp0Y0Ct/Ayv72d08NdAdw10K4DiOJicjnm5LKq5R+4fiAEayVJFsHJe+P+gcFaH0twrvqMq+lw/8Dt1xF463MoLpYkIUYm2+EWAW4rCDrqNQRxuB0Xp4dLBQavY2xFqrN4vRf7Cwu48yLH9jrGkvUCjH6BwW3v2s0EvOmF8O9FSrVrCuyQaoaVUsQH1e9X7ju4ldMrNyDcwuz+RoTb6t3fkHBL1b/6hQlqt/x5YcIPX5hwbbX+7hcudP/ja9TXf140XaWmUTeT3vVqBRbyDzff/FF1oFrxxypQleKfN93oTTe3aJZZ6V27B+cddGDIdihNSbrksyLrQaFKsEPgbUzRs0t00JCL8S+3vH2POvAWTmctx4U3Nzmd397O6fvXgX7w0dXA9lzKUdSnwGVFu7ddyZPFSajOc69PcHT2u+vA21TkfG3PbZruoAVvas8f1oKuaVWRmlOxGOI/Vg3exu+7KsKbtf+pCfEg/R6vDvq5mvAMJfZzNeHnm4sfuRbMaKBfaDEYrDoYftJiEEP4iiJUy/hwzdv/5hvffkU9GFIdEuNobXRXlCT3t914+ZtpwZusHCnBmy/fvw5kHXi0tGRWfb8OdOKqqzY6K5FTSL+ODry5SvxhHXhzjfhr6sCbtf+pA/9VdeBuc/FzNeADueFffSAfvc0TGvXSvL4WfXnlur9GHWnGtqf4Mx2lP/74yyfPV2fsAUXM6recXulHV267s83ZgNXam/hGT07MGzSLcfEG4g995AX+n631tI56Rff3aT5pd2hqYsvAnY+n99fby12/EnPJkxbjkl037129Wn20vrw4Wx6ZmWym7Ldj6DM95XO4MfLjzcnD5bNOf88pnO8ZKRjtw/Tt9WH69nc1THCqvpr9bmE/0PQ9J5MOw6SG3fVRQrZvDhIVtUHi6Rcfo7ecI3q3Mfrn9TH6XQ2RmtM/8mjV73GIrh19OjoX1dYa98532zX7yFJ17fH55etX31yl2F45evlmMYaaZYh49XORHNUHR7k3bkGQhrghpFKTs0m3Sj5evuEYCN4VjsguTvu5kEPCJ5vtq/nQAZLazza1W2ZPH79+BRNXz7YnDr3r2XaAnQtQ2wF3PUPrOdirR4SJcc6JeGl+Z0KzUyEEPAbCyWMixDwGE7zxwXhn9Ly+3s5GkLPRkBO97Ytz91zGpTdyEQ5qON/LgWIo7msz1GqSqSaZZBz/6t24V+4Z52gT147fPEP57r7fdt/t4TDFfP+t4vOH63POQtzbbj/lel66E/rTr1fb07PNN5wXwwuz3QLPf8w+RT88UIm4smP7znYKhyT2zvJG/eHtFMb5bUbKvxCy/igbhT55j+rvLQeF30393TBRfleDRM/9K6i/Py0UHcWjk2rd1v/9GJHvbKGwu4RY3m6hlHYaRs0QAg9CHIK1OcUciUjq5dRCCa7KEGMtoYRAYNyvbaHIUNhAE1sGYpHcUIhQ84PG8g4umpKHGIjSa1QYisdWGUrM0WTOokQTwuCIdkxliNXEOBBeF2UIFAx2SAQS+TgIYXJehliO/53Ts7rdTJAhci+yHTJVzK9dHmrm8ms/hBRvkC1V6lCICPND1FYV9mD3v2sknskNCZssDRoDiz+W1JazU3kgDqpljEPA0iIlDZ64oDBw7MTUoeJV1Tf6qyf4gUszhmxz/FcxuQqCoDFkKia1DERMmuqGdjtPqYM3kb43OQxiQhpiNNkNzsQyVBMDGYMbGM/MmHjPJ/vvllIGJMkP3OLCl1qyqwN3rQSSjx6VtgMBp8ob0cmMPmF0HknOgzCCjDT3HfCQh9RexiErkYbEaKr4UwsZNJNSfijGDSX+o9l468sdoRmPtps3zVjVc3vOqwF4/PLxbvn8q8Mh6HYQGztXow4aCQjsTfGLTYtWeiBUoJby4mX7axenD9wh7R5q+a/b5cXLQ9qdRyTeXytvrSgJjy/O1rv726Wa9xzVZV2kZxO1/A23mqbeufz21Stl8q1/WEhDPPfGHTUppvdzHkeGKw38Ge41wPUnuNcuNutz/sBSs8y+Z0lh2wKNv2FE33AY1g7ZqEAhOEjQEI0M2chQjRuY0d64ITKzjRuq8YMYJM0P0SC0fqhGZX/wIARyZQKyP4iJTI8hquDFoZo0CGBi0hBNGrJJQzUIbB68yQOCiIBWUwYx4HIZoilDNsylOohhutUBCMqgjsLM03dpbk2pJEHdBBuJuRykBEdQbfISc6UHvIs1pBCiTRpmPNjqCYq0pdpUClnS9ULFhRycS8UGcZ6OLDVkf8hHSiQKt7iaS+aeaRlC9MkHW6ITb8lSa0nRhuy8D4lo2iEkmyu60UUfYK9kF3OSnF2JnnvZhyrs5kuowcZM5O5gJaaaxedYNE538LbWINHZkDhSKUPwNlRa6nxiNT0kF2yyEq3PkXjcwVXvvLfFZf7YkFaTUy5FXJCYuD5zKE5i4kinr5mYcxrtQihohxKLOO0qYnl9LZKq95rHp2yzRBdqSo6U6w3gO/S0i5UI4fadaDN7eSlXDVW1Q07Vebv/TSlbs6RYq1Trina5eF+zuColJSKHB3Gu2Gwlx+ALleerA8V0OIxaF4kcpeSaanWhtSrllKrkuY+oO2eprhYXYsIXMYj3yXrnS0kpGDuofB3quoXd22Qx5xB8stU6F7nGatiLKp/S78ajr6LshzaGvpZQE0HMQ5PtuW5qTuJCsFLFlxxv74V9/jY7jB169liL5QMAwnFPoSyucqfT8pfwsaBeNi+2y1c3cPuB3AeEb9ssaGd3+jK8of7b4fo33g8htP09bIjQ4H+R/RCuAVic3nC1a+JNX7tqX90QaeV+cW/7n+6GH17LDjZGl0uu4mppcWVtGysNEksW71ET3N/x537Jbx5s/4feLzlyRewf5w3Q7oTYp+N1gNhvhNwijfs9kdtk+J32R9T7v1/ggMO/hB58X3sNh7uJxDpd7e17YL9NdH3f6Erj7nHk9V3sb+ye235sCxTZ/2fsbUnXS14rpUbKtQq+L0uLMOn/UfCWzD856ebXb6b8QT/+D7P4mr/uepP/xbksxmQWXzPJbjR48bV/Sym/GBN+hHtPXq5f6Or8+Oqso+3FK+vWHxhrhGjOcq2n3yJf75brFkn5bb7HGmxu4PzvtSaQ5ZakOfctTfllSuxFRK7JxFVmCrKiQkG+vRQQJfZOaCI1SZQiIRfuhLKDhJxt8Kn4GBKX4w/iUmJ5XXN2mb+LMUioEqOPNscadSErvpRSa3Ap5pRZtoqLPtWScnCZm6/sIJ6LvlxyseakPe+tiza7KDlTkfO5Ouuc524nrSfZmFNJtlYbdQ3oMvF10Vpdt7KolpBdkGxDqNaLfjaVlH2QSDAbf4RtkELcZLTFlxItpVwsPooLMVaf+Vtrg6spixeWtjHoct2HWINzUkqWXBBQV0oJudYQXS2RLzvuoorcWVUc7gbWeNZnKwEvBCECuupzgZvD1P/RviPRx+RCjg5nOXlyklRL9sWmpBz6WCV6W2qq0Wtd1CMhZO+EY1x2cPyNbrwNLsXg6DHnYibgT6RYXzTFB5dc8fQyrmfaHmA/WieZy+rs4EvyNdtqJZfSak/W2eh8tVxHpmMRg8/W1Vhd9qIchhiTc/hsii/IjsshaDBjiT6qFLga+UB0IrVUvhMkxpBszT7lqP3jnLPO11wEW5cve+dqxo+SfQmwHL3HIxOLc5V77+zgY9W/BV1cLq7SPTXWUmxIriabtDN88L6kIlJj5no1O6QixduYxFmkt6Vk/EzifEjaqT4FSdU6Df6hkK8O10tCoIP2xc0UcbWmlIqNpSQuThtcxNXiJJSsN5MOdKlEV10KEkRX9veevjNY+4SsRk/0qTq6hF73JXr8N05lKKTgcvIxBinq1xImbAxIUKwq5ZKcZTra5BJHXXT+JD3lgkTz53EGid4non+deBfpJKS1eP265a40ZkIUX2P1PuTAXz3Et+Gyr1FSiYrmRBZn/sg2sUANaXMqOedUMxG0Koo6K1wshUvmGgYQb2V9YoyEFI9MO59UHNV5iGA4z+zKoY1ioIbgSvXE08KOz64WLz7BgyXF5SCx1JASDdWUkEqqxRckMagAB1dKdQxZ4JI+Bj+7JHwqRJ3fLuZcS84W9NKR8JKKKzh5suPGPjv4UHzN+H8cQqmNYHAkemeTV6n3kq0vJbsszqkoepfp5+xdVqH3iE/Cz2YdlzjaIXJAvBbJgKr6MpkfkoDj7J2OA/PcKv4l6/mLkoNPNpfknK8uJZ2nITIdXc3O16DcaAfamqsEvbHQDinipPJJo8AVnr2PldkTXc1VJSV6gA4MqmA0kzBnJm+VbC3XGtohJnydPhVwlav4hhKDy7Em65PXuYzEpAyAZ98ccC4rGhMSpndSsl2GT1cYUb6aHGKenAuxOpXIHGuRElPIJTQ0upmCMzZxxB4k0y22QsdI8sWHwt2tQ6gVRcG3o2/7Nnc356frk9X589Xfzner7dfLs5uHitU79kMbHxra1t1p3+NJ+1EbH+0Sj6tXMLTF2Y+9WG+/9UHxJ/2w2tP+7y+x9jryQfY/rP6j10idm3vKFubuzbG5eZDqtxsbvbznf+/YrKVfKdKE9O7azXS71ejl5pub43e43+gu8wyd7bju4i4ji5YPHHG4K+3M9t1+W8b99e6z1eXrs91ilq3HL5fb1YleFHPD5/2Oh5r+PB4/H48HEt7j0TO9teavt0cz/HU+WH3bDgWxDD9qy/iJitTsR5BQLbfZLk6farr3ev2FSEFbs+H9RIWudK9KibaWdqGKpt/Mzz1Nb7udFC+tvn+X20nVnfnl/lz8l4foBaJ99heF6n7M868OJ+91oJ6tvvfG0XkTZ748CYVzy/E49j3mS09/6JbTT/YRD3z97ssNh3UX//cL3T9Zvpq394+3+m/EUMw65ij//qsoDPBe73b4HUiKrc3pxS5s5IzRD4nKLQX+ELIyHAVzvH95efwWedG4pJ8kMA1abt9s+j1Ayy1Q1PRcv56gYRSbC+9ZXPrVoz8PWW6Tlj3cHECK6fLz0eVtwqLRUT9JWBq6/PbCcgtYNEX0Nmlx7AJyExT38+s1Ub+xtOC6KgHN+u73X+/O9gjz/TKjZ2N/mkb6y9uEpq3HrkX1vYtGOrJdPl5zR/DeAj3SVdfeHIHS/ObafRHvA5pusWJ+ktXzx4Sm2caZldg+4ZeGpbcqsZ8sYkew9DsQl7eC0y3yparsjwxOP05qfgYw/fJS88XF7YrsHRe/NyJ6/gw6fh9Bxw833/yyw6QW9PEfrVChZNCP73f9lf5oxX7+XAVZXQYesfNHPwnFkvxsfa4XWF9T5HgnGJIf773A5qf00cW76r3Yeyv0Elj8X5qkUJtv/H2Z91Y/C4YrtcuvVzcNVoOYvqDh33GVwzwKjzaX6x6T/HeO5OthfDLgcJwzHdyMD9YnK50PhzCLLy5u3D31xcWt9xU/3HxzJWvTAJtvrmdWv8zqdPfxcvtireEU9N8ttyL/7fxytetDrteqxpv3UD9anny5era/QPot0kXnfPrsv1bPd3/7qEdXXDlx9G/r85OF3hy+/xIB1O0u6YCz5c7z3frr1SEO5bPV8uTT87Nv9zfQf7nevdy83j3arl+tyXp5yPtgeXnvzer5oZuPM12fJD+zNXfmUJfWJHr2ZpOcv9mRP7fee5e4tszi+ysu3333jxtz41cYw/xHGsP1cnnxQz3pf/kh/KEq429QZfjFBfUHWxl/GxF1el/vHwVmfqgXwy1Q/TMR5gerzL+6rIT061dZfv0q669eZbRMQrVTnm/XFwQh6vg/WL94ebZ+8XJ3d3N+vnq+O+jY++s3q5NmWZ8uzy5X3/1/QdpDjQ==').then(json => {\n",
       "   const obj = Core.parse(json);\n",
       "   Core.draw('root_plot_1779221557481', 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_1779221557481();\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ROOT\n",
    "\n",
    "ROOT.gStyle.SetOptStat(0)\n",
    "\n",
    "c1 = ROOT.TCanvas(\"c1\", \"A ratio example\")\n",
    "h1 = ROOT.TH1D(\"h1\", \"h1\", 50, 0, 10)\n",
    "h2 = ROOT.TH1D(\"h2\", \"h2\", 50, 0, 10)\n",
    "f1 = ROOT.TF1(\"f1\", \"exp(- x/[0] )\")\n",
    "f1.SetParameter(0,3)\n",
    "\n",
    "h1.FillRandom(f1, 1900)\n",
    "h2.FillRandom(f1, 2000)\n",
    "h1.Sumw2()\n",
    "h2.Scale(1.9/2.)\n",
    "\n",
    "h1.GetXaxis().SetTitle(\"x\")\n",
    "h1.GetYaxis().SetTitle(\"y\")\n",
    "\n",
    "rp = ROOT.TRatioPlot(h1,h2)\n",
    "\n",
    "c1.SetTicks(0,1)\n",
    "rp.GetLowYaxis().SetNdivisions(505)\n",
    "c1.Update()\n",
    "c1.Draw()\n",
    "rp.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67ee40d9",
   "metadata": {},
   "source": [
    "Draw all canvases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "85067a71",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:12:37.501466Z",
     "iopub.status.busy": "2026-05-19T20:12:37.501328Z",
     "iopub.status.idle": "2026-05-19T20:12:37.636631Z",
     "shell.execute_reply": "2026-05-19T20:12:37.636091Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<div id=\"root_plot_1779221557634\" style=\"width: 700px; height: 500px; position: relative\">\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "<script>\n",
       "   function process_root_plot_1779221557634() {\n",
       "      function execCode(Core) {\n",
       "         Core.settings.HandleKeys = false;\n",
       "         \n",
       "Core.unzipJSON(43462,'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').then(json => {\n",
       "   const obj = Core.parse(json);\n",
       "   Core.draw('root_plot_1779221557634', 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_1779221557634();\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
}
