{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "02013ec7",
   "metadata": {},
   "source": [
    "# hist032_TRatioPlot_fit_lines\n",
    "Example that shows custom dashed lines on the lower plot, specified by a vector of floats.\n",
    "\n",
    "By default, dashed lines are drawn at certain points. You can either disable them, or specify\n",
    "where you want them to appear.\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": "fef71259",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:12:43.145883Z",
     "iopub.status.busy": "2026-05-19T20:12:43.145768Z",
     "iopub.status.idle": "2026-05-19T20:12:44.574068Z",
     "shell.execute_reply": "2026-05-19T20:12:44.573489Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      22.1778\n",
      "NDf                       =           33\n",
      "Edm                       =  6.53577e-08\n",
      "NCalls                    =           55\n",
      "Constant                  =      158.988   +/-   4.47298     \n",
      "Mean                      =   0.00662197   +/-   0.0225355   \n",
      "Sigma                     =     0.993481   +/-   0.0169678    \t (limited)\n"
     ]
    },
    {
     "data": {
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ABWBT1o+KHWhhW2J3FDRYkqWB9HsM0V9iaRocIlsU6DBmBbQQ8agG0FPWhgg1OCEg8agEpRal1VUElKAvRdh4pKH4xgxbLY0jDaAUPwBRhjFAxjgrGqGwmYI7Te94IFOhlnM+2xYGKRnuDY4DLBmGWMhIpFnPZZ4Qg6CiNVYDvG2Ui9mlMlgg+w2L6OolhBUVDdGNS6HmN6s5WIFa8guGhtUBBUPLWxx6QDJgomwAQiWBwx4GFPnMCiIGhobe2x5jErmBid1WfVOggodAQ7q8ypcyCJ+6MyZXrUVXOrVKaMs64bYcbEaRcbJNwJtNWIN8Igqsfd5uao4ILJCMzhX4pMI3/lkXSPAlZx2xgp9O85sYww/aOE4VpSrwQwFsZmUyzg47HFjjD4eJTDZhfFPmm9egTUTtosuRnGK5hnC7PhC2wZxw0cXCzsnf6BwQcGT/+N34uFxdM/MP3D5OkfCUD/wSMsmh2T+QgBUdJg8AkRQSPw80z/ISGGpDDn9B8yQqrB2n9BhDVvFfMRKgKuwcxHtIg/db8l8ImCcFSzaKL/6BCkDab/8UysPi7WI4Y+8jwCnf45F2cjBcWB/mPqkZEKa3+5T+BDvfZXemhHYcYfa4+LC7uvmsqTxYLYYPpL0mOzpj7QX1KHYYPpL/meucFDF+gvhR7XosL0l2JPXwrTX0pq6FaY8aXcq9Wec4T2V3qcf8Bqjk+1r3j/gOkvW7xKDWZ+EQsWhOF3dIhgQMHRAnrUMzNTpByRgtAjRloBfSIe2DSlqptQLAICKysFTnuFsdAtBdotbgG6pUC7xW/HHrXqbBOLoHC4FilgZREVkK4W0C3CQj2OtIC2EBcQI55P7PwWgQE10kLoFpEB+e0KsMnjtaQF84/YgMC0AMQQHGHEA2+BRXRAYtoCxBAenM60gAmabHBaAKZqhBvnQ8BU7SHjfCinVyHCnOKxAHUVI+OcKi9HkCjl0QJMESWwJS0AU4RJ4zO4GingQDCuLQXqBFJOUIzAZtUNxNbWArwukydIC9RDg99hegeuFGSKOiKKgZ/iDVJqBsa3oq4HhlYMDFUdQkxEyThKRR1C6kDPBgaLR0gq9JYNDBafkFSGlQ0MFreQ4McBVi8RZhwQmuBmqVIYhNUNwZA44gGjuYBPUo8NriG8lrSHAeMbwiWsMPjhHFJiLjgZBe8QThbGA8PFQeSUtosp4IctBY6PO5f+se6NpA7DVRcRHL5aA8PFS6Q+Z2D6x7oHx69iMvOFPUU3ghgYLl4iB4cH1v7xMELTzsBwBfseHB6Y+cGootvEGxiuIGBwbVVvmiPN9U53TTAwXFEBA8EHA8MVBIxuIg79wLF3HvpHJQBOPT5MhekfAaNbKhkYriBgAv0lHLMivsUusKYwXAm2d7rBsoHhCi55ODgw/eOT1/1WDAxY0CN0uxUDAxY0iaDagYn0jy6hu68aGLCgTegBRVVMEfQJtTNYE5kPNAqFnYEhCzqFwt4E+kerUDgaGLJEdFSOFsnAkCViJdUDmIEhS8R6y0mkmgA+EZ8tB0dR16pE8AP2BgYtuOwVDgYGLRH8qE8GBi0R/IAzzmyRCH7A1XjwSeCHtmIN/FoS+AE7A7uWBH7AwXjwSeAHHA3MWhL4AWfjmY/U5g8awkkpCfyscRyNwSeBH7AYOLck8AP2xik+4AeM2xxXK/gBJwPbFkwyCqMNAYMf9ZzEgVnfaqAZmLjktr7Eugj45DjWsweB0whzIgFGX+R5Tl3A0B8wzl0CiT59dUQs2T9phN7s7vFqvvFuZmbHGj0Wo5n9MOu/qy6Y6lD9kqkO3lJMddVUb03Fxeudqd6b6oOpPprqk6keHlVM9dXUYE0NYmpwpgZvagimBpRHYkTgbcXUUE2N1tQopkZnavSmxmAqPvCYTI3wxGJqrKYma2oSU5MzNXlTE4Em0dSUTE3w0mJqqqZma2oWU7MzNXtTczA1o4omU3M2NcOHq6nFmlrE1OJMLd7UEkwtKKnJVHip8u9qarWm4oOuztTqTSXApUZTKypsNrXC99Wvjh0GTmzVEmLxQKhl1OKLgQVbjg0W5msJ2LGwXQuvtTBYy7nawlot/NSqVgwntbBPyynZwjgt5wQLi7SchC3M0cIRLccDCy+0MEAL17McZi38zsLkCJHmzMwfnoCRWU4EFhZm4VuW46eFY1lIznLatBCbhStZyMy24B2egKoshhkLz7GRJ9Qqx6HSIuMt5hfLmdIi4S2nSYtot1hbLDLdYmmxaCQWiW4xrlhEueUoaZHhFgXBIrwtJhTLGdIiui0ndYvMtpzBLQEElhOk5UhtEdWWA6RFRluOjpazp+XAaDkFWhXtemhALFsOSxaBbDk3WkSxrTzBsdEiiS0HRsuaY7Xg0MAfghxY8xbVxJpjiEDe8weNnzXXgyJWBtETYouHYM31fCiseQsMYM01XgrTAFKVPxpIQR+sOXo/4pE/9MGa64GwRWSx5nocFNYcZR3ZxR+eYM1RwpFHGDLogzXXwx8KNYKFPzzBmmvAlp7zUI8RC/zhCdZcjYHCmmt8nEa8CGuuAW6osTBq/vAEa64RdqqFtsgw1lyPbxohp+c2VQr1wNZCSFhzPa6pdqbnNNW7JJfvf/75Z/NbRXMSIn5jNGe7V/GGOM4xYpO47fUpDadQ51byyfYFly50AO5d2rh8X2N3k0UIur96leXL+frlYr13NaYV7L1yLNhe9/h68Xpz+/Q5cdiEJgO2SttZDZOn/nj5/HSGVaQ12Hs/1XdXhLcnjTCev15eDRa/vdncppzw7cPlD8vz5er0fNZHoUdq9l54f/50MV3XoT+FWw+BHhR+eHR0vtB7NPDZsXCLtle8l89e3l+cPucWkO0sQc66BtOjOhbizy8/tjmews23TabuCUx+8ocZoS7n3zDCf//DjHC7QL9wDT+dr/duQX06X09EwZQpLbJNjw8etT3x2Xr+Y7uH0eCHZ5vdnY8GjNc+GjDe/Hh4tvmsxdu3m3AEwW9fMPIDRvDwbHNXr42N7e4ux2ZXwudpoIWHyw133yb469XqWKPnKWgXVu6sTjerV+vz8eLC7c2IyyV2eXuzYQcrh3oDI3C/kBOwURj7eBVphLgO0emNu89PDz9fr1fjrTO2tYLanK7uvjp9NvIEKgH3WBjguH7UcgFmbMz4AcfGbHrAvcW+v3i+OD3cv2UDdq10j73yol3h1Pd0XZBXTDxh25D5G+nQzI7ucRFicX6JgY+lj87mz7gIoH1v7/DtjWF7gW8sA8dtu4vYbJtOxVPTS11ru8vj3ivcXTG6tzyHIPfxoYj3jegky7CndlPHbXamplPp2PASNrT6cnm6PHl18u+L9Wp3z4OKC9cqlb+3Ky8H68XRYv3n+7vWrXxv4lrB/jDBdL90N85W+tni6N6sj5ZF25Z8O+vzxZLHM5wAe02ejAUH833iO5hfoC063xbtetaiq9dJD+aHF4bO3B3MD69eTz2YH15zQ/VgfgixP95Nz1jy5EIJgnG8qESHy2cvx2tKB/Ozdg/08cg0tgVPZvgmZ0ePnq0Xi9O782fKfUAPnrY3/YDsgz2ypWh/Paan9vYPTQB3u4eSiYAaWbU26xNY6Cx1QW/RUKg8o3KLFEhZDJppA7+dLjtRdw9AdByb9fLss8Wz5cn8+Hx7o0hZ8njUcdvzwt7otMGl4WnZ/vggEi3cG+AW3sqpdkLh9NGuLW+f2rJAfaYNp25bM57p/QynMZ/VF6eni/VXDI+WbDV97fms/45bRJ98wh9xnwga/yfhkzLB6ZPwiSQgZ2kQxiY7SIv2y5292HxbV+xYV+wnwX4SPgnaUvv+jf58D3NZzA8Xa0S1XpjSadtCd5ebuxPRxJFo9BoWq7itUVrSWXs2P9aHWf1/XS1PKZxOAXfmZ/vg18uT7Vkyl1KlBOUYX5zMny940ZbB35mfHh4vvn2xPH+5WH81P30+XrRu5Z+uXo9lbfVaqWKyd2nzL8vV8fJ0Kh3vLramd5brZ8eXuf1YxW1TkN4TgI85b3/++uzx/plnKnyyX/jkupZT4YWWNPxy/vqz5XNNBAARPlxvXqzuzE8W6/nIfX5DXU15ypSd4PIRR2XcTboafIMJ2mNYgBPvGWv3pu+SYLyqgelNeWaAq/FsUK7CK+z2Lh/vcwx6afAoIf5eM3VPPrtpnqJefx212hcXLu82aG8S2+ngV07jL1dkHzxbHMPNI0L3Bj10VEKvu33prKYdGIf4WnWgidXO+tlrgD+08gr+T5eqdytZ7u7LP97ekX/cGnCD++jucn2+GRNqzPmlj5Hqw01S6GTx2fL87Hi+d98ctrhlrMyZ6uu7+99frg7vz5+O8Bv06TesFNiPy/TT5WX66Xe1TGCqAmkr90fTxBtsDLtlgi+0VWrrpdkLKLu6SDRoi8Sv975GN1gE3m2N/np5jX5XS8SE/k9YoktGjD0Lh9Vjyeenm/WSE6Fr8Nfnr05+vAhpbhZtrJWkzegk5eKqjzXZEPRMzHOvtWksnXPW5RpdxoF09OX8NQrdrL+lxu7Z0ajh7QoerNYnk/oApY5WipYz4ujRqxOQUO5zIXMQCj+JhK5aFt+UhuKSqeOuSq3rWL96SbY85fn8lVoqlSpm/Rbek3Bs0pvlG7V7x4TLllrG/cul27Xs+sEZKRNYlgeHy5PGqx+ckeSkLeNPZ6NV98HZkZ7MPNrPg8/uzghnmh3debE8/69X8zUpi1wnOecafI4B59A1q3dlfQ/ma7XboGiELmRXcymhJZ4xtrPORR+j8zXWErmB21lJNeUSfAre5eARNAfz9ZfkbvlOkxhMJXA7LRHb4euwPoUQXEo0eDQnN8RFd2bCz6nuTDwy6s4MXaHnEm0Iqaa6uBVM6ay3uIM85U7LbGetePEhpeyKL644LXPiUo62iJBqqJX5YEupxUrOxXMLGRN0sKW6yktS1ZHbEmosBVeZt0QlMHTvcAbWFIL3OVHkrPfeBis1ejxAGKptLeJizb44nwpFIWVXa7XVW+u4Et3ZVHP2OVtXmHSwwN/nbQkWRcRpSbSllhCDCyyCsZ2TElOUYGORWMXYzov3JZAkKIRcg7FdCKHYIt6FHJ0+lVyoxXvvimN6jO1Kqq6UlLMkh/tWOqmp2hBC8lW9PV3y4nPJOYeUcNx3zuI5KsmVjDfXddXXWkL2wVtcY74rpF0q1vLSYmJnSy4xCfjm6E3qYnVSUi7WBtx5pWNc1rtabGhOwy77GGyyPuaIb853gVxVUkWK+o9Sl0vxNtdcJGcCGLqUc/I12hS8XvfvHORWrPdSCBewXXDVplRSrSWK8Yn0VVEyTDETEOE7a212yUskB5aJtvMhhZLpOhRnYul8dKXmWrPH3ZdSV6PNKcUasyvB5NTZQL85xuRtMCV2oUbxOUj2RV3mnUT+K8n5oF7K0BVfqy8+BI9zVkLnQ4zB1lIczkznmaLifMgWN7d46WoortbEZvJEx3XJep9CLsnjMgypc+JZcucins0oXawkwvIhF7zJMXaxJG9TkUI8h8TS2Rwgz5grTs5YulqCj86WUHA3IilKliQp1owXM6bO2sBOyiXhSY0QSEjRlhKhTAmZSY24Tm3Apx1sFwN7tDqiTMT7zkqo1rEn9EJ86JLjeVy/6kiNXcw1iUu4dtXj3OEDFedcwZmfuqBzJdBIElNSl1MKkkMMNRGpnzuXY4jiySRWq0mlE1f4T4ovVkysXYg1xFjhTY4LCp3PFu892cNSgDqq99X65GMukXiWju1eXAw1RwJUpJPsaoKyq8/c9+xKcq4WiS7hHnXSuWBtVi6TNRK2c0m5S07B4V71XanZV3aKJ/pBpLNZsEtIFsdF/S57qS45cTVlW0zqimSbc7UuEMsTeWWojukMBEgEmFEKzpcskUgm39noYojWpSCRiwSdKyVUX22ojtgQ6VItvtSYRKoGqHUu5BykuJw8V4O6anNwOZVivc3KBVP0LoN8zkLQme1CijHDgmxgzPApV0J0VWqKIZIEpHOu+sIkleBDinDAWGKuXnIpLofGE0skRVkifsDD72z2cLWEk9xnfZGy6JSydxHmoq28y96H6KOUke87iSIlwi8lkFgCfl5ryNZLrDWRvaSztlrhNeJTsVm5sLUxQx/Rh5IDwS2088nF6qtkuGoepY1LTHOwNkVlxdZKQJIUJ9knV6spXcyxeCvRJ5gYAi12UjOhJiVnqUnLbkUTNfkWB6758fKv+7Ykyr443Syer8lrxXEEhe3V8fyq6WQsv8HRrVEobz033cESdjAn1eBmoUeFxiBgxJm/beZScqyuR8jlxCzV6kOxCcFtk6/I/dvHx7s3PSIz2C6Tl1ZwEvnv2T+daYa1Gf/v/3yNtff0uVmebszX44i07UPyHv5fBtc03dmd1en5Zn6KN+x88WxFtkpLgMHf+LovF3Nc/9Or5Fe86tHy+cl8713uZ2ZjWp5+9t2E+vf/snh99qdbtov/8qc/vb71HTh8/8//739/p2/4/p+vL/3nUSWbjpBjyrt2iuSgO18fzNfk+NSz+V8WOAD2iAo9fpr+Mc/bndXJ2ep82dJ/UgbGLSsckBJnS8y2s/pPidq0k0+XpxjtP1+vH5JLDjIFfvjDYn10vPoRTQaCWK+xPtx0EsQWdDYjMxfRbeQL0qv2XHIzziT9nyaqITVLDFzNasFCer9myqikMSiErxDqQmCTIzqM+1ixGK6vWaOxpkaCySYZbzQzUevrNw1SQXu90fCpSQwPjlc3JmzF+HkwXy9ONwfzQ53E9eJopkngjr45O1usx+IL6WApu4EfEJKypyWB3M1aErV7WpKyofeTmbU5Vz6mZiU1q9gulehqyC4FjZ95Y2ZWj0oSSw0RwY6t6UIm02sys6bOJc0J60up7MjHY55TTdMaLMJRik05qA384uuEs1HhCKvn/ItpWr24LketLS62bLPbd9+SLgYbXQmhhkL83+U0rdJlT47YmojGqyi0b8ja6rqIYcTanLMO/GIKV+9tp28LPnvvGOdePlfvauek1hp8rSiAu9pZf+ua2l0q1inTa0HJEVdQKJiHX5TptdhoLfZrV5Mwzbv3t1SvvsslRdQigl63b9dMr1cp5EKi1xSquFyt5CjR6wJvk7t21rqgc1xDEEcY52wv92vndUatHgWJIp5tc8Ei3HPLJWxjLJm6KTfsdXUt6Ss140PES0aeahlgbZdqBpVS0BDGOXgLpheSy16D7DbZ7HX9bpPPXtP1x1y0e6lp93wDv5tctJcsg28yIm5PLe043bLXvkI4/sdZk4STnVDPUO+YsZYD0/3Vj39UCauG4H0BqyL3HzP5ee5SdWpRCiFyh+CNyc9DlyT6ElLI2BZ/exFbUNrRO6M4X+lvEqIvcZV2CUsZ8eA2O3CfatVm3wVbhbzumP9Y9IuZ0N+riA3ZdqXJYJd8hoXviVjMP4HamKLnmsGuFkyv1u5E4IcQsd53MQesWyHkrLn9Jyn6ctZfRyNTtWZTtyUjZ32Vir3vl0nZ64TwlHP9Olm6k7NX5fPb5axrcluk5lQsx64LOduvxeaioL0O33cVtVd7v0bW3oI6riZ+p/RjzvcPmvP918rZY0Tkr5WzX6/OfqEem2F170mRDc2jejWG85oAzo+fGMEKsB83pSohcVNXVNFrZM+keb4c4662ymK6qt5dVESvkT2THHw560OqHVZZpI9TwTRV8umOK5V/Nxl5FZVJgF6L5yUJqaw7iovJ5ZiTyxpTu6eHsjYTqIH8VkIIuOski+DTu6x4Crx+98pLmufe+0YpiL8VN2p0mA30Qz9vUeF2Hwe5Vqy8mxi8qoq+XQxeVUQ/pBi82vsoBpnTq18/Yfd889M2fuijEPxDCcHN6uzXisB7smfdjSif99z2iyzcuOCG08F69bqZ0psVuLXbr3y0mT97ubtL0KJVOVZpEEgDobULsTv3hJerDX/2on005uie25WhNv95PT97sSu6/5jCu0tFqz1JwaOz4+Xm7nquoUBwIBiKGrn1+SveKy29ff7TyckYpMI2uOa7ZRfDf2i1r9K/p3PAGIb1ywN/Hpytlqe4XjR25/HNvg7q8XLcCp03t3wX+SPmlusqfzJ/In88f8TcEiqECqFCqBAqLBWWCkLSbzHRiIdm6Zv+w0OnESSGRrTmMZ7nRbyRV9MHndEr3YMHCIEZKIZO1LDy5O3jsl3FP1ursz7GlHCappKSxJokEwmSQPNCJIrVMUhwzscSkqs2FBAuSSOTYgr4ZnWMLmBRdMTGJNIFMD2YeiVIyYQ2GSHCyRZLKJHnDjyTEpyLMdmUfApVi2IJ2NslEpeiLuBA9IDlfn6K3CW3XZEaYiLEx9rkg+JNxIwrGn+grmrignK2OcQq5NGwnfjCnWsCc2IUXcFghbE5FwMeKkKlPCE1BXez9yyC+JC9T4QtRHVcO4KZiriaiZfQdQwhZe9j4BnufXfO5+hr8sFl0hnwxS+HE19ScT5ydx0CkVAJYqipplSKFkViOJghAiYaVTnMso5Il+AcjbwnJouonRj5tghBRz6EUmMMIsUxjlhciinZmEKJFRexr9k7T/SYVPI62C4XF7ONkdQJSdSNnKr4YmNw3Eb30GWxvpZUcskxEwdhu0C+AIYRiQvSNdsPUiD4SDopHGkIbCSGBdpO2aUUiesRV4tS7d8Wl3jRpAiHXT1fz0+u8K97cvcmlhWF09ZojWzc72a29XeOxydJwt8SkM9Z5qZofEb7PyQYvznZp/DOTv1cY0B+ZwnX/PDh3jdccX+3cO8rIfm/q7WCqN5HuPct15Xsc5RkM3GkavbVE7brgkacJQROUZvqh1/BjwH7ush7VzDHqy9qSNh+HbKtmJZ98DsV7xywryTbovW3P6cbOBqMP97GmQLzabSN1L6GGndB+dfQ8AcN0L8oCH+raKBdmiux7meUn90MTPcULl9cuKDBfI4R4q3nU/1w8CnHdLr4/PGL5XNVSfYzbu1C1MfD+qWHMGe8Q0fR7HX05B072j4EendWp0fLw8XpswWBgOsf5seqIV4I9NFjxd9Dc2r3aC8GAzWi/qW5bT7qTkqM7/mYOkb4/eJd9FbK/mOqrp+/k+5qbRCnWlZyhYB7woBTSJng6hyzz3ofQ4iH8q7EUH2KqEZEu6eCyus1EB4NN5YoVQNnYkRZcoSLl+RDdJJUx/O12BKkWutyTiiPLnFrICfC54nMJqY6Zk9wFrHpGmSN2pSj9YH4L70dYnMRr37Com/x1oaaiU/OLip+XmKowZcUMqE++hQ51kLlRYRM0pN3VaKtqHN8lqbzsVRRTFJIGqtNrG/0ObfgeX2PryGV6FFN27UaH0qQEENw3reuApdjuBIggaxvXGhBpy1VsnPJ6iCcF+fxHBNcj0roHd8fdzi9a+Tjg5331lXnCH/35C4EwUj8PZdccovG5uZBcrZWxxShSHqfpdqCPhichqB7bwuB4lmYwXGSfWg3RWzTY11JhP9XJjnwHcTOVUeGPOdBTO0DjrA5ottDkDbNUquFACRI4G4Hl3k8NGIzd4GUDGy0oQpx1JY/dG9tzVI9BopUqw3++xv4v7uifv79+L/m6PjI/9+L7Qxp/575/yWv7j+4meIteQN+V9ovms6v0H71FDZZKrBMvFvegPbce7+U/tFK8VYdF0t+5KpZwG4u3E+bVu3amg+uBH+0U7x1DdF8/ufbKfAi7hQKoH8wS4ROwGSGALhkgvgHV56KC1Uy10ZL5c6s7YS0FCEXW5Kt7aDPdfOSXChciKBNCFJd5rZkse0prqfXmEvQS+S4jGJ2IcAkeZJvMHbcGcabiAvKq/4SXao5cZ8kp6ZxcYs+RUvC6hQ8faUSUNW44E48LSUO1ag6n2ws2sTaWry1XNomx7Ltkrc1x8LN84C3joecJOe4Cx75woLtUkiR7qvFI8ahPgs3hRMToRebuYKQbPWuWsFrqJ3nUp2XJEFSdDyVaioSiq25iudznF0qFs3DSyDXABpgcrgRa/XRcvOUkpCcd6iNNnEdnpKM2Szl5EtLGYCbLpNlvMRU9CIsAUGhxBJYMc1YkGqJ3EvnNv/4nhx8IuFEKVxe4c2JK+Ipp5LxVlISbcqFRApSs44rZl9qsppsoa0Nam4tOSYbyFRtu+Cllpy4EBuSKtpow9GlkkQdvLRxuaSAXCzWqTJHdvZCpmnchJ4ptAJy3nkrKfo0qU5LGZOFNTvVnaWb4JZb8MXqx6vmtV2WwTuY2ph7dQ3cQfFCFuv1qDvSQt3ujCFvd5ebrxbnr44322CNRy/m68Whpmu7orC9o4PmY2qrKbUVovU3zJqkVyj/fH0msj9PSZGuC0j5xUnpyDlHBpDxvlWoliQTYyhl571emhLh2oEmtuQOoO3KGMrRNiYyh1DL69qTLfGmHOE8p/XvkiNcbyB+u8s89u1WgyBz6TZdt6opz17usmbpQj1dvDHv96TbTCkMb1B2cBtPqcfflmv8wTZbGW+/80LvL8/+1zeq1LWrQPzUM9w2/+Sl/GeT8r3XfvtWvRc9XQX6HVCKrY2CCF6JUUNd30wq1zzwh6CVbi8R229PL49uoBfNKXglYd67EExjLddrwL8H1nINK2pybrxyoplbNIbltyAXNmvjLFKk01upv569XEcyW56z41Ro2L+exdxEMZre8G+iGL1t+DugmGs4RmMxN5GMK6P04lM5upa/Bcmo/tVoBoH2RnKp7Qbfu8ikD0o0t26imiaj/ga5tHeC+XJJvv7tObSRk55tLtXssaZLNXskeKnmm7PrWdk7nmivhBx9zAL6W2QBvb/68f0uk4rF38v3YG7g5Xq228sO/VZ73XVRNb+jyDA42/HyVHPDX3LkEOnOkvxylYQAT57ey5N9IQ32LQ7wsHmkIyeBW/7Kh5t+w941/nS/d/che9fwsv3eNRHUhVCb327sUPV+55rP6gP1fXngH3Lcl5f8Q674ZWL3mqxr2ngHY1Ystd9cHxyWx4scXNQwt6ZsVV5DzjEvTa/aGZXuLQ8Xqsy2SziM/puzCxkaWIxvzi7fMNW8DfdXP15oOhVebqxa+OJo8+V8/Zx8rRq8dt091i9OzxebkRe0a4VXM9ofzA+/XTzd5q66ge1Avg+f/ufi2eaLz0YF+4Jd/N+WJGkDse2byAWmF5hcQLW+/WyzJFfslJLkq8X88OHp8U/br358u9y8WL3aHKyXJ0uanu/a3puff/568Ww3zfuNLnPP9zYaVmp/NBd268ePGcxuT7fI2uJfnq7t7bUrPP5XrtD5/GQxnOkniP7jfP7DQtOgvQkH9/4/5PD0+MFnd9gs1/d7gVQO5j/ox2pu2lvvMVubShnSjdnO5RJcS46bi2DJ5D63VMLWivPZhYRjoUmla5trbnHpKi4YPjDKJ1FnR4+Fkfe2CzlnF/Gz6HUl7WCsqu2StvUVF1DrZKyKzrkcLPl0YxRNdDJVTZdp8VV4zb2x/3EOhvbFKV9kU0J7MT/EvH+MKkTBndWaT+/MD5evzlsc9pY/bddqtPxtyPrPYtysv06VzZSqacZovNcf4F5Uu/L8kTHeX50+X/CdBrj/jkc3bt5OfFcOfOdQx2mzZE5pkPQLwZe52/35ZqHmqusOh+C9Z96czKHjZ0JutB1fHO0EjoOdwDZYoPGbVePHqhjR3rwAXv/RFmbjPry95Z1vlhrI5uF6+Xx5urXcBDJ9c5sdR1oMl9NV/vzz91cYygeQYfmjDHvD2rID35pD8qImgnVn/0AMvL/feeVv+zGe5dzfzMGb9ArcU7i0WX+l9Hp7n+9fWh0rx7heVLWBev/eB/q2uY3pw3eZP3yX5cN3WT94l+n90+zbyCfJhx+l+/sIH69pCv/4ClRjNfHD00p8/2z8beQZPzx5Rn8def5KcbU9PF8vPX4Hio4PNgrqTIg+SNNDmqLDlwSsIwVDmk4bfBDkSnNVdLxe8AnWxRz1A3R7io7LfOzDplKTfl/pyTspOtkF8XwHwfqWc/mjovOmBK+XjlkfFR21Vj56tl6ekXVGOc695fMXx8vnLzZ3Vqeni2ebnYns7vL14rDpUGph+/n/Axy8g24=').then(json => {\n",
       "   const obj = Core.parse(json);\n",
       "   Core.draw('root_plot_1779221564563', 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_1779221564563();\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\", \"fit residual simple\")\n",
    "h1 = ROOT.TH1D(\"h1\", \"h1\", 50, -5, 5)\n",
    "h1.FillRandom(\"gaus\", 2000)\n",
    "h1.Fit(\"gaus\")\n",
    "h1.GetXaxis().SetTitle(\"x\")\n",
    "h1.GetYaxis().SetTitle(\"y\")\n",
    "\n",
    "rp1 = ROOT.TRatioPlot(h1)\n",
    "\n",
    "lines = ROOT.std.vector('double')()\n",
    "for i in range(-3,4):lines.push_back(i)\n",
    "rp1.SetGridlines(lines)\n",
    "\n",
    "rp1.Draw()\n",
    "rp1.GetLowerRefGraph().SetMinimum(-4)\n",
    "rp1.GetLowerRefGraph().SetMaximum(4)\n",
    "\n",
    "c1.Update()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8360e0be",
   "metadata": {},
   "source": [
    "Draw all canvases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b77dff38",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:12:44.576105Z",
     "iopub.status.busy": "2026-05-19T20:12:44.575973Z",
     "iopub.status.idle": "2026-05-19T20:12:44.691142Z",
     "shell.execute_reply": "2026-05-19T20:12:44.690778Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<div id=\"root_plot_1779221564689\" style=\"width: 700px; height: 500px; position: relative\">\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "<script>\n",
       "   function process_root_plot_1779221564689() {\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_1779221564689', 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_1779221564689();\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
}
