{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c8348801",
   "metadata": {},
   "source": [
    "# hist034_TRatioPlot_fit_margin\n",
    "Example showing a fit residual plot, where the separation margin has been set to 0.\n",
    "The last label of the lower plot's y axis is hidden automatically.\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": "88c64c53",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:12:47.654856Z",
     "iopub.status.busy": "2026-05-19T20:12:47.654737Z",
     "iopub.status.idle": "2026-05-19T20:12:48.989600Z",
     "shell.execute_reply": "2026-05-19T20:12:48.988798Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      29.7219\n",
      "NDf                       =           34\n",
      "Edm                       =  7.65715e-08\n",
      "NCalls                    =           55\n",
      "Constant                  =      395.847   +/-   6.87936     \n",
      "Mean                      =   0.00695631   +/-   0.0142942   \n",
      "Sigma                     =      1.00274   +/-   0.0101683    \t (limited)\n"
     ]
    },
    {
     "data": {
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ukDpgYyDMDcQIbhhwqBpDHDAKwKeobhh1rbqLA9iRsqSlTB+jiCba2wN4kMFSqJdZphALYd9AwDsO2gZxjY4BShxDMMQMUota4qqCglAfyuQ0WlD0awYllsaRjtgEH4AowxCoIxwVjVjQTMEVrve8ECnYyzmfZYMLFIT3BscJlgzDJGQsUiTvuscGQ4CsNVIDvG2Ui9mlMlMh5gsX0dWbGCoqC6Mah1Pcb0ZisRK15BxqK1QUGG4qmNPSYdRqJgAkwMBIsjBjzsiRNYFGQYWlt7rHmsCiZGZ/VZtQ4CCh1Bzipr6hyDxP1RWTIVddXcKpUlQ9Z1I8yckHaxQUKdGLYa8UaYgaq429wclbFgMmLk0C8dTEN/pZF0jwJWcdsYKfTvkVhGmP5RwnAtqVcCGAtjsykWxuOxxY4w4/Eoh80uin3SevUIqJ20WXIzhFcwzxZWwxfIMo4bKLhYyDv9AzMeCDz9N3ovFhJP/8D0D5GnfzgA/QcPs2h2TNYjBFhJgxlPiDAagZ5n+g8JNiSFNaf/kGFSDdb+CyyseatYj1BhcA1mPaKF/an7LTGeKDBHNYsm+o8ORtpg+h9lYvVxsR8x9JHnYej0j1ycjRQUB/qPqYdHKqz95T4xHuq1v9KDOwoz/1h7XFzYfdVUniwWxAbTX5IemzX1gf6SOgwbTH/J96wNHrpAfyn0uBYVpr8Ue/pSmP5SUkO3wswv5V6t9sgR2l/pcf4Bqzk+1b7i/QOmv2zxKjWY9YUtWAYMvaNDGAMKjhbQo8rMLJFSRApCDxtpBfQJe+DQlKpuQrEwCKysFDjtFcJCtxRot7gF6JYC7Ra/HWfUqrNNLIzC4VqkgJ2FVYC6WkC3MAv1ONIC3IJdgIx4PrHzWxgG2EgLoVtYBui3K8Amj9eSFqw/bAME0wIGBuMI4zjwFlhYByimLRgYzAPpTAtYoMkGpwWMVI1w43oII1V7yLgeSumVibCmeCwYurKRcU2VlsNIFPNowUhhJZAlLWCkMJNGZ3A1UoBAMO4tBeoEUkpQjEBm1Q3E0dYCvC6TJ0gL1EOD32F6B64UeIo6IoqBnuINUmwGxreirgemVgwEVR1CLETJOEpFHULqQM8GAotHSCr4lg0EFp+QVKaVDQQWt5DgxwFWLxFmHAY0wc1SpTADVjcEU0LEA0ZzYTxJPTa4hvBa0h4CjG8Il7DCjA/nkCJzwckoeIdwsjAfCC4OIqe4XUxhfNhSoPi4c+kf696I6hBcdRFB4as1EFy8ROpzBqZ/rHtQ/Coms17YU/QgiIHg4iVyUHhg7R8PIzjtDARXsO9B4YFZH4wqeky8geAKDAbXVvWmOdJc7/TUBAPBFWUwIHwwEFyBweghQugHjr3z4D8qAXDq8WEqTP8wGD1SyUBwBQYT6C/hmBXxLXaBPYXgSrC90wOWDQRXcMlDwYHpH5+8nrdiIMCCHqHHrRgIsKBJBNUOTKR/dAk9fdVAgAVtQgUUVTFF0CfUzmBNZD3QKBR2BoIs6BQKexPoH61C4WggyBLRUREtkoEgS8RKqgKYgSBLxHqLJFJNYDwRny2Co6hrVSLjA/YGAi247BUOBgItkfFRnwwEWiLjA844s0Ui4wOuxjOexPjQVqyBXktifMDOQK4lMT7gYDzjSYwPOBqItSTGB5yNZz1SWz9wCCelJMZnjUM0ZjyJ8QGLgXJLYnzA3jgdD+MDxm2Oq5XxAScD2RZMMgqjDQEzPuqRxIHZ32rAGYi45La/xLoI48lxrOcMAqcRRiIBRl/keaQuYPAPGOcugUSfvzwkluwfNEJvdvtoNd94NzOzI40ei9HMfpr1P1QXTHWofslUB20pprpqqrem4uL1zlTvTfXBVB9N9clUD40qpvpqarCmBjE1OFODNzUEUwPKIzEi0LZiaqimRmtqFFOjMzV6U2MwFR94TKZGaGIxNVZTkzU1ianJmZq8qYlAk2hqSqYmaGkxNVVTszU1i6nZmZq9qTmYmlFFk6k5m5qhw9XUYk0tYmpxphZvagmmFpTUZCq0VOl3NbVaU/FBV2dq9aYS4FKjqRUVNptaofvqV8cOAyW2agmxeCDUMmrxxUCCLWKDhfhaAnYsZNdCay0E1iJXW0irhZ5a1YqhpBbyaZGSLYTTIidYSKRFErYQRwtFtIgHFlpoIYAWqmcRZi30zkLkiJZGZuYfnoCQWSQCCwmz0C2L+GmhWBaUs0ibFmSzUCULmtkWvMMTYJXFMGOhOTbyhFrlECotPN5ifrHIlBYOb5EmLazdYm2x8HSLpcWikVg4usW4YmHlFlHSwsMtCoKFeVtMKBYZ0sK6LZK6hWdbZHBLAIFFgrSI1BZWbREgLTzaIjpaZE+LwGiRAq2ydhUaYMsWYcnCkC1yo4UV28oTiI0WTmwRGC17jtUCoYF/CHJgz1tUE3uOIQJ+zz9o/Oy5CopYGUQlxBYPwZ6rfCjseQsMYM81XgrTAFyVfzSQgj7Yc/R+2CP/0Ad7rgJhi8hiz1UcFPYcZR3exT88wZ6jhMOPMGTQB3uuwh8KNYyFf3iCPdeALZXzUI9hC/zDE+y5GgOFPdf4OI14EfZcA9xQYyHU/MMT7LlG2KkW2iLD2HMV3zRCTuU2VQpVYGshJOy5imuqnamcpnqX5PLjr7/+at5XNCfB49dGc7YrFq+J4xwjNonbXp/QcAp1biWfbV9w4WIH4N79jYtXN3Y3WoR7JJevtHw9X79YrPeuyLSCvVeOBdubH98uXm1unjwjDpvQZMBWaTvLGmj90fLZyQyrSIP33k/17RXh7UkjjOevlpeDxW9uNjcpJ3z7YPnT8my5Ojmb9VHokZq9F96dP1lM13boT+HWQ6AHhe8fHp4t9D4NdHYs3A7b67iXT1/cXZw84zaQ7axG/BNuPj2qc6Hg4mOboyncfNtk6p7A5Md/mhnqdv4dM/y3P80Mtxv0G/fw8/l670LU5/P1hBQsmeIix/TowcN2Jr5Yz39u9zAafP90s7vz0YDx2kcDxpsf9083X7R4+3YjjiD47QtGesAM7p9ubuv1sbHd7eXY7FL4PA208GC54Q7cBH+7Wh1p9DwF7cLKrdXJZvVyfTZeXLi5GcdygVze3Gw4wUqhXkMI9LLPb6AEHBTmPl5KGiGuQ3R68+7Lk4Mv1+vVeAGNY62gNufw3n558nSkCVQC7pEwwHH/qOUCzNiY+QOOjTn0gHubfXfxbHFysH/LhtG10j3yyot2hVPf07VBXjHRhG1DlmfEQzM7vMNFiMXZBQI+lj48nT/lIoD2vb3LtzeH7V2+sYwxbtudH8226VQ8Nb3Qtba7OO+9wt0VozvLMxByfzwU8b5xOMky7and1HFbnanpVDo2vDAaWn29PFkevzz+t8V6tbvnQcW565VK39uVlwfrxeFi/Ze7u9atfG/hWsH+NBnpfulunq30i8XhnVkfLZu2Lfl+1ufzJY9mOAH2mjweCx7M95HvwfwcbtH5tmjXsxZdvlb6YH5wbuqs3YP5weVrqg/mB1fcVH0wPwDZH+2WZyx5fK4ExjheVKLD5dMX4zWlB/PTdiX00Ug0tgWPZ/gmZ4cPn64Xi5Pb86dKfRgeNG1v+QE5B3toS9H+fkxP7Z0fmgDuTg8lEwI1tGpt1seQ0Fnqgt6ioVBpRuXyKJCSGDTTBn4/XXai7g6A6Dw26+XpF4uny+P50dn2RpGS5FHUaTcE92QB9l7hC9PTsv35bRvuTVAb7c1wlKyUN7fry9untiRQa9t0apNnNkcL5jMNhOk04rP66uRksf6G6dGSo6avPZv1P3CL6LPP+EfcZ4LG/1n4rExw+ix8JgnIWRqEsckO0qL9cmfPN9/WFTvWFftZsJ+Fz4K21L7f0z8/QlwW84PFGlatF6Z02bbQ7eXm9oQ0cUQavYbFLm5rFJd01Z7Oj/RhGMe/rJYnFE5SwK356T747fJ4K0tmbg2XoBTjq+P5swUv2hL4W/OTg6PF98+XZy8W62/mJ8/GO9et/PPVq7Gs7V4r1ZHsXdr863J1tDyZSse7i63preX66dFFaj9WcduUQe8xwEfI21++On20L/NMhY/3Cx9f1XIqPNeShl/PX32xfKYJAUDC++vN89Wt+fFiPR+pz3vU1ZSmTFkKLoo4yuOu09WgGyzQHsECnGjPWLu3fBcY454GxmHUG/NMnyvyWuDaNWS9A3/5DvI+4aCzBo+M4o9asDvyxXXLFfUW7KjcPj93h7dBe2vZhIS/dzVHsv/b9dl7TxdHEPUI771GHR110asuYTqriQjGKb5SVWiiuLN+9grgT63DMv4nS1W/FUF3F+gfbS/NP2oNuMh9eHu5PtuM+TXm/NLHyPzhJmZ0vPhieXZ6NN+7dg513NJX1kzV9t018K9XB3fnT0b4NWr1a3aK0Y/b9MvFbfrlo9omRtqYvVpdsEZsOfoe4dEme1LDuE3QhbZLbb9IZ6BllzeJBm2T+PXO9+gaw8Db7dHfLu7RR7VFLOh/hy26YMvYM3RYlU6+PNmslwiG5HRhzmcvj38+D8Gx9ipJnmG7UEuOEoMP2J7H517RVKp0VVK0wYWi2W2+nr9CryNjCDbv2eGo6O0K7q3Wx5MWwShGY0VLHXH48OUxgzjvrEp4sdRZBWE/nXHbHo8Vd0v0+oyxJhmiCwzhe8GEanAa6Z1tjRRqvjsxrnIRE79jNr6W9pfxE3rjiSLU63pEO4mGqNVCTGjwBt8eFy9NNfTm9V/RHBfn8h1hniD90WU76OuSZlwwzNxW5noVh1Kfzpb0PZu/VLuqIu+s38J7jBiSfz0bpnZPqLloV2Z7fjsTvpKr3DslwQOi9b2D5XEjYvdOoWYNFX85HW3Q904PVY70mbZf3CY7EFjyfHn2ny/naxIt1S47qc6nnH3MUNNLSHYJDR/M12plQi1KXcnVJ1s9uYKqwS4VXA3OWZttCNVpkZVUXM1WUi7ewQ4fzNdfk3LmB0W7qQSarCWChokvK9uci9NHHs5JZHEdOuM+Uky2nbUSXczBeqk14nahzIWaq68121Txh1MWbKgSXY6pWleiliXOqK0+5uDxcTIl6x0P2WQtLkeKkgu5eFtSlczN+s666HHpVRdiTVE78NX6XD1/Dj8tGmtNEnKoyQUXvS5PteJTitYXn/Hs2E58qD6lgG8Ob5PtHKmjgnMuW58Sj7nqowSXXeL/grFdcCkU8bkkvFOUJPExOvL1sPzJ2K5kX0O0OfmSKw7WznkXXY21Siw4p8AIGySWnG3mbnXH8KurVrLFMew7F7N1tUq11uVqQhckuOQkV3V3xa46H2JO2VkJEk3uSqo1e+tz8F49q52PLiVbrQsaEeS7UHzOsUiWjCMvd74UWwrz1VhN1zmL+zZFNqcaVzqbYvLMzQYpxsdOXPE+SMkFF2/wXfTehZCyxz8XfRfAjBhs8pCqFDoUzsKELW7LXDobg5RaayQjl6muq5FNkRBoieuySzipS8qh4BJ0uaspS/QlZHX2hdxVZx1+txJa+GbnWJFgoyT8u1W64qN1Kbqc9R557JwrLoZM0qlAPENnS6C5+EywRGIgqVhhYbniXlLno00+Z29zjcbb0mVfSxRXWCLjXe1iiYGNphMfSucjJyqVnIoYn0IXo/fsa8AX6HPussvJ1lBDIKSk5C5yRHxOJYk3vvouWXHkBUsELfgaO7A2VJci0QW+hk5CctEHMC0bX0oXc8mhSKq5JuMzy1GKTTlHght8Sl3MKbIaHje8j7bLOdpkU/VVYDOu895bL06it854kS4H1ts7D/NxpXYRPC0p+UiIcyKDmpRYbCg5EwHC5ifnnbVB48dLF12VRrhwldfY2ZCKy9VlgiMku86naKPPECJusNmuiAd9JBT88d52KdoSbQlCXI2I62zyqUabfSJ4I3aSavKVpGhEPBQIhUs2x+pTqNWk1CWUJQt5FGdi6KoEm4q6qEPoiqTsaw22RFImpM5JsLVWi29fj0HNXpKPWaoHd8AUm0IqOcSMW7p0mnfOkTZOvb++qzlT773nRIvtMoekEhqA17p0UmyJJUiNQoBe6iTFmsH87HLkzCcrPnqfanY5e+M7aGsOCezwYGgXSsbFHqLPGRd8V6yEkH10zmZ8452rUmrJybuMA9J2VbInqsOGbImWsF0KoZQSo3OR/Cm2U9gXcaCUpYlXglBqiCETuWY7R7yAj9aLhXRDUYNLEgNUTBJZTjpbo6+5uuoS2AvZtclLkeJidS4QN2Y7GyR4liWm6n3RIpdKrd7anILNRWm/ZIfwFksp2TU2KNbGWGyRYgkngt2k6qPPxcZQQwnaow3eZUfoAYjdWIR1EKwsqQYp0E9laYn5uOis59iYG9FElZkQAudHy7/tm7ko++pks3i2JuUWsgdK5Muj+WWrzlh+jQ9eA2TeKCTdwkj3YE5CxM1C5QKlCyGlHHx2Pijns6nG5ImJhEwTEAJXpkX2IabkkQJuHh3tXvSQnGW7HGNagdTxX7N/ONXcbzP+7/98ix365JlZnmzMt+OEtO19kjP+X+bWlO/ZrdXJ2WZ+gp/ubPF0RT5NS+jD3/m6rxdzghKmV8nveNXD5bPj+d673K9qNJh2bfbDNPQf/3nx6vQfb9gu/vM//uOrGz8whh//6f/97x/0DT/+09Wl/zRqiZO4OCbjaxIjQu18/WC+JgupGiv+usA1sYdTmBam5R8z0N1aHZ+uzpYtQSlljLjlqwNS3Gwp43b+iCmFnHby+fIEd8KX6/V9styBpcD3f1qsD49WP+NyBSHWawwi14l9H50W80dZGzXV4oOj1bXpZTHRPpivFyebB/MDXdD14nCmqeoOvzs9XazH4nPJaym7hjQQOLOnHaGgXq8dUbunHSlFeid5ZNGhP7YssqPN9d17lF+XSJaVeF3i2JK7ZItLzkVEU1qfy7N6ReLY1DkUHmt9KZWzOKWV1SyywQpKA3JcUFP9+dd5i0BQa/DZe8fTe1lkkU2daG2tqTKaqVazyKYcEQRcDLb4CPKczyIriLr8lwgR1Odfk1TWdVHVJ5tzLmjA5zPMXjHUvXSzVwx1qp31N66o3WWKnRLRFgk2iys2E7U5m3LBTtUX0wJPeWpblEex0VoERFeTsM6792sm2lI7HxG9iegs2JSmVLMvGk2dQM08GwoRl8nGoHrxfubZpiLrfyGII5h0tpeLtvOKClakOkss82ybm5ZszLnNApEna2rmMVftVXUtCS0140NEbUaeahlpbZdqDrXWUlCEx0nvUtteOdJzyW6vGOw2+e1V/W6T4V7R9afcuH+a3LgXbH+vMxNuZZUmQ7dsui9hg/9+2njeZAlUyektM+giJt1d/fwn5aWawXafm3LGd2nY/5CM7H8IL73huyzBlViIvuYSxmt568gaJ8J8dRb238VMc+1iSLXGiKXlAjMNtRPUZWLQEzcnzjFTF7qYfbQ2ZltaDNB75KUh2640ZuuSV4PyxC1fzHrBA0JtTNFzyWGX2B3udLl2WtJd1vb3yUsldkGNFcnWiuXkHC+9Cin2eCumAptqcOCNrzGzD2/gWbvs7FeytCn3+1U8dMdfL/PlN/NX1/i1kKK/WDSDc7njrxzNeQar9vQLwsLbstjLvV/BY2+A5ZcT0FP6KeP8B804/3u56hEM8fdy1W9Xp79RP9UD+I4U1NAcuZcjSK8IH/1TfOjkvbJV5Ib9wC0IKYFb/P95pfMKPW7SMUftZQJJ639Jjzuvcl7Beyalkk92pNol9tFaHH7nmeTlyj+MR14eysRA+dTJpUlc4JBKuqO4mFyOOTlck1suhsLJ3pxTMDHJB2slSRbBg3NRw1Qr7e6VsLWJ7Z1/Hxqm9i8Ob2F0GAj0c0Mfgg1eVkHfzAYvK6Afkg1e7n1kg+zR5a+wcHq++2UbtvSJCf6pmOBmdfp7WeAd2bPaRlTNO277PRjue3C/6sF69aqZy5t1t7Xbr3y4mT99sbvJ0IJkEcU1qKOBHPJzsTh3hJernX72JRrxHbcrAP7Len76fFd0l7LbSx1Se4qCh6dHy83t9VzDejBv0Y8arvXxS84pLb159svx8RhwwhG44stp50N5aLWvvL8jGWCM/PrtQTz3TlfLE1wrLVD0Nb4M2yJYboSumhuhy/wT+cfzj5gbngpPhafCU+GpcFQ4KhwVjgpHhVAhVAgVQoVQYamwVBAvf4O9gHo3mX76D7clvjtclbghgQkRwZnrDW/k1fRBZ/RK94yDATEyhshYGTSjZxrMh4kxw9BVtcA8fotlQcWqGsQTQxKiI3TIBXd8zK4k60IMlOEcToQbREfcgaldJaoOV2wORCUwX2udyykFF0O0+qoQspPiJRHjELWVKqqR8AgpfO9KY3PwXpYavBddXC9S+CgWHlq86R2e6uLFp1oI86OzXIPLNcdSiFHQoJqUY0h8Ki1U3ShXnfjmQefiN5uD/9QT1eEt8dTo1iVGm2OyRA/om2OplUiHWksWnb2tlSgOtFjiG3TQuRKyk22ugfvgBBQVkiaIq7ZEolQ6m4l1iIUr+knjiWpCVacJwSj0VqNjWatLNYSoaJRTdQQtJm8LwQRoq3zgTGLytnIh3XbOEdbjLAtD7I7tpAac3s7WaoPVF7lMWAATkUokEt2FqjFgMbmYWGzbeRe9hnjYFEjwQCCUraVGomFCqAy85lLx/bsQS7W6j4X4kmpTccEGT6PsbQgu2RJ8IUJEXyQOcw/BLG0pQ/DEOrmkK6UDcJlwmBxycsSJcRqkpuyTFOc0mU0nuYSSokSnES+288W5WmpOLrTb+Z0E8VZxNqTcDqaVaL2GBCXR88pH1oq1OQTvSSLZSQ7RxwgSkEkDdHAxJ29Dkhq9TjQgc1UBYRKhD66rruRShUixEvWUO+tjSC4EYn+copp4CYRvENrktcQW73guO1dT8Y7Ft1GydZngCO+jawEIf1+c5nkDLBxq9Ww9P77EA+7I7evIfhSk1dF22zjI9aT/D75GQYqLv+ceBbLgdZcomO1/kzsULRBhDHfFidUuUeivDx6ff01qgreLz790h+Kj2iXQ6V3E59/wneUjn5mwz9g+Z9q2TKSD8tUCYU8Z9fPD7+CnGxa6yR/zJZi3vmGhKNuuV2x/TlemKJ4+JKq/x4ptzPoV2LgLcL8Ch9/qRoVGL51Tj94FC3xfsVK79GRi3a+ojbsVmC6WXLxpcm5yX2K+eaN4rh9+PkHJoYsvHz1fPlOFbj9T2t7lk6bqXHgIf8hbdBTNXkeP37Kj7UMM79bq5HB5sDh5uiBKcv3T/Eh163OhTypQ/BF6Z7v/fD48Csz/pHl+eM0TZHkXh3tPvh3DJ3/zIXzjwfgfaTf48i0MB1zXcZKL89xhaXHXxLd7JwEhhexshOUn562G+wu2A4p8lpgztgAfsmr/NoovOQVuImgmOaLGq8eEgF4UCRbAjCLeVg1dz5bklLbjOohwmyOnoNq1hJSCRY1CpecpySUVb2OMhHxhb3GCnhy5P4Hrn5IQYgoSuKET9cIVEdS1+ORi1QSD6LHVRVTp5CuBe7bzkl20MeVcC5ngUKOzK8U5DbPikzmddzVwrcMzztxKYtALEdYWcuPxnhJKICgixcBwPGFtSTCuEMbeSsg9x4WJwL0d7ZxbJQXLi2AN8Nx6sSE74hE0ft4Tz8+dGe8rae1s50PILHmlRDTKn2tLJVgueNSio/EFbV1iZHI6T+ZkfXK5uGJ1nlJidiGpEh3aHJyP3hZfYoxqnfDiPBeXolPzUBtyQBUPBKyX9ubgiOO3tnL5QGdRfBXvnONyV2uTeEe0mEb4wirrLlUcuzfaVFytueitnVjIS2g7l2OIXCAqsXLDgz1OVrgHFImdZxZOPHuZrPcl6C0IKTbkEgsmhqRX0iSwOMEJYZ56LwKzVAR/sTOpzcNiGHLelyIxCm+22RKxF12sCZsVRdF5Yg0lp+JErTXcYotEbtSITYCpcQvCc9HEOVJp6nGREr0GKsZcyOSoBy9aLDMlchrSj9ewX72n+pGwX01t84n9fgyG3/fAfi9EM/wPNy+9IU3HR2W7wAH6O2wXKkNfeaH6CvsEfbUMEO25d54D4pON6Y0Wihvq7pAsjqhyKRqQ3oxMV9Z8cDvhJyvTG/cQvXW0C/4hhsAPY2XCtL/T59Qp8TaZOf772JF0ASYjEsAFA9In3fUqn/db6q4ucMEHsbtY0oKjlyZRz3F2MeJZUqE9xErWCpx8LUdFDl58tc7HUMjObzsS3HO1OIVUnWaIQJu1aH3cYLa8XAo3pWMgI4hT560P+MizFN8SVqDw4F+3ldwcqDlEmJdcCjfMNRGFLzkEV5siSkmpwsXqwn17Homh5EK+g+JyVjUx1oIWKTW4UgQtLHnueosPNldyzHPZHo0MtdzZpDkvUmwZHNDoyODQLuQX25z4ZLDnPYnI+YwC1aaUXMSD6m3g2j06F+k4nPigmiAjxscc0BSr5KLKZYoRX62QX8KrwpNQptB6cKWrEk1YQEHhcTV5VeFTJSNKyoIiqQofWjiO3MLNZ9WlkobquVBLHbOoJK8P5Co1RrUfJBe4gZ54X9Hb6pSoC58hqHqXfCjcAODmlIvoX4lYBZJOZOwObXlSThVuSiCktomerZdgi2uOcJJqpBpzsh4Vku2qNSayI1TxFb0xRnJ3kOulStNIA0kvonep4GNnt0hOkm1NkbcUFpVMMepmdiG3e/L4lm3KmBBi1nQozudmPpDkiu66cP3flpxsLM06IIFPfVhxPpGjQDE75GpJ8BBCaYckq78/JJICJNs0WxLdVHHRVoe/XE08OLJqzc6XqqhguZGXIrEAsWZXR511KWOOsWafvbV0E9xyoT5f/XzZrLzLinoLE7PtyC+BARqNl3AKvTp5S1pw7K0xSPb2cvPN4uzl0WYb3vXw+Xy9OND0ktDSc5ryWzomP+Xgm3LwIZS8x/Ruepn6L1enTPzLlL3tqjA2fKW/KViN+8AQ9fFmZtDkFlPw9RUXQ7gfbDuy2/BfibYWvQ9LcPZV7cnuet03DYgY1Pq3+aaB3qD6fpci8fttikQyLW8/L6AK3tMXu/R+ulFPFq/9TsGkFU4pV69REwmUmD6V8KZvI9zbplXk7beea1aD2f/6TtXhdlWQnyr9bvPlXkjUOHkN9tpv36rZEpgbNyM+AkyxtWFQ1uxIGhz/elS54oE/Ba7sZ4x8//jy8Bp80eSnlzJ7vg3CNNJyte3gYyAtV5CixufGS2pWaRQKwvtAFw5royxXpAj+/YTmKuTZUp8dzeJqye8nNtfhjmZk/btwpxGbPx53rqAdjdhchzyXLzi+D+RRHbZhD/j5WnTR1Cdv4kvvHV08nG5iTjeuQ5jGqH4Dc7ogXb7uEv2FpgiijcfR6VUSjh6XnVBxQ3n1ue99aG0TKba1o5y9hVtYk4J350/utRSpTVT46/zo5Zj98u78CS+DP1/MTATv3ZPSvl7yDZVLcvWHJKiXCeTrGfDl9nvLOq3ylib9z5PV3t1xuEJW+wjw5TJNfD2+XG7/CV84Ju+efE74sieqfUB8uUK3Ux3x8v6/Hl8+DH1hB96jwDapaJOkvy3YEsZ3JKxdK+n/ZuY7Yc+esPYRYM+1ItsV6PYhTQkIau9SZPsTI8x3p1dL9m9pHLx0a+HTlx/ex5cf7q5+frfbpBaGj+VToNvzc56+qpnst3yf46rA/I/ocgmq4dHyRD8LdkEDwnp7ne7zeusupIyn9z6RdO4jSOg3sGxNXIGb4NIXe99f3+zrft+aMvecC+L99a1a3n7nN0Qz406b8GBMQUvmWr4mcUPIm4szZmqxc8HcWR4sVG1vl9xZ8u9O9/Od6ZcCvzu9kMGlGWr327WSC830MvtbZYj56uRssRk3uiXsuJw09sH84PvFk+3HuF6jT99/8h+Lp5uvvhgN0ec87/+6JMUxZvTtm8ikq6kBXEANvvl0s+SzCofzo7MFt/EX84P7J0e/bL/m9/1y83z1cvNgvTxe0vRs1/bO/OzLV4unuwXeb3TxaLyz2XAS9mdzDhE/1EfKxvw5547oxWNyhRHyw36n7KZmamg4cHHVtukhLtGR37lRZ/PjxXCqXxj997P5TwvNH/y6Mbh3/4G2J0f3vrjFmbm633MY82D+k36L8rojpra+d/xlO4K+S3CRrKcpF4E1oKeFyCcaiLsOXKifaP6Vzd2sDyV23Md2no8vaDq9R8LMe9uFnLMj6JwY/gIFeDxV1ZYFyfoa+MKGEvfxqcgHRoIVnwMBEzw1VU3ZakJOxWdo5z4yg/tfnfDBZUW05/MDvOFHWNUouLVa82XN+cHy5VnLQbQlU9u9Gh1lG77PyWZcb6CYKpuZUNMh0nivP8C9y49K9Ef6eHd18mzB99eYwtfz9TO+QkP8zMiAL/vXf4MF9O58s1DvznXGzz1v4OQ9HD//t7OKTvMbpajzs53AcbIT2CYLNH6SdvwW7VtjbpM/jpeb9j2p1jdoc3+9fLY82Up0RMDwERw++eNiuGRZ/fXHSwTlA7Cy/ImV6Vb/Pd/bvIqVqWa9L3VdPO8c6vf7kc3l3F9PwRv3ClxnvSCB/07u9eY+3z23OnrTPL1/5/N8U5fp3S/tG7v8A2YZ/hh65fViy59f9G7HML77I/EmXIkfHj2jfPBDGD/8iYjpw88yf/guy4fvsl5Fan4nt9rKzh+tnuP55h33b0P0QZoaQi5W2+VSiwi6hH6UEmGDW7+XmqPnVNvVZGMm2lc/cbGn5rgcS3I2lZqiGsfeSs3hcjKfixPr28dLPqk5n9Sc36TmYIV6ul6ekthR+dWd5bPnR8tnzze3Vicni6ebnZ3s9vLV4qBpUGpm+/X/A5NgiKU=').then(json => {\n",
       "   const obj = Core.parse(json);\n",
       "   Core.draw('root_plot_1779221568978', 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_1779221568978();\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",
    "ROOT.gPad.SetFrameFillStyle(0)\n",
    "\n",
    "h1 = ROOT.TH1D(\"h1\", \"h1\", 50, -5, 5)\n",
    "h1.FillRandom(\"gaus\", 5000)\n",
    "h1.Fit(\"gaus\", \"S\")\n",
    "\n",
    "h1.Sumw2()\n",
    "h1.GetXaxis().SetTitle(\"x\")\n",
    "h1.GetYaxis().SetTitle(\"y\")\n",
    "\n",
    "rp1 = ROOT.TRatioPlot(h1, \"errfunc\")\n",
    "rp1.SetGraphDrawOpt(\"L\")\n",
    "rp1.SetSeparationMargin(0.0)\n",
    "rp1.Draw()\n",
    "rp1.GetLowerRefGraph().SetMinimum(-2)\n",
    "rp1.GetLowerRefGraph().SetMaximum(2)\n",
    "\n",
    "c1.Update()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09291761",
   "metadata": {},
   "source": [
    "Draw all canvases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "32b12611",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:12:48.991260Z",
     "iopub.status.busy": "2026-05-19T20:12:48.991131Z",
     "iopub.status.idle": "2026-05-19T20:12:49.106967Z",
     "shell.execute_reply": "2026-05-19T20:12:49.106054Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<div id=\"root_plot_1779221569104\" style=\"width: 700px; height: 500px; position: relative\">\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "<script>\n",
       "   function process_root_plot_1779221569104() {\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_1779221569104', 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_1779221569104();\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
}
