{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1130d028",
   "metadata": {},
   "source": [
    "# pdf009_Bessel\n",
    "Show the different kinds of Bessel functions available in ROOT\n",
    "To execute the macro type in:\n",
    "\n",
    "```cpp\n",
    "root[0] .x Bessel.C\n",
    "```\n",
    "\n",
    "It will create one canvas with the representation\n",
    "of the  cylindrical and spherical Bessel functions\n",
    "regular and modified\n",
    "\n",
    "Based on Bessel.C by Magdalena Slawinska\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "**Author:** Juan Fernando Jaramillo Botero  \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 08:26 PM.</small></i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "262de66f",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:26:49.427600Z",
     "iopub.status.busy": "2026-05-19T20:26:49.427487Z",
     "iopub.status.idle": "2026-05-19T20:26:50.492861Z",
     "shell.execute_reply": "2026-05-19T20:26:50.491674Z"
    }
   },
   "outputs": [],
   "source": [
    "import ROOT\n",
    "from ROOT import TCanvas, TF1, gSystem, gPad, TLegend, TPaveLabel, kBlack\n",
    "\n",
    "\n",
    "gSystem.Load(\"libMathMore\")\n",
    "\n",
    "DistCanvas = TCanvas(\"DistCanvas\", \"Bessel functions example\", 10, 10, 800, 600)\n",
    "DistCanvas.SetFillColor(17)\n",
    "DistCanvas.Divide(2, 2)\n",
    "DistCanvas.cd(1)\n",
    "gPad.SetGrid()\n",
    "gPad.SetFrameFillColor(19)\n",
    "leg = TLegend(0.75, 0.7, 0.89, 0.89)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "378a7987",
   "metadata": {},
   "source": [
    "Drawing the set of Bessel J functions\n",
    "\n",
    "n is the number of functions in each pad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5e5f428c",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:26:50.494409Z",
     "iopub.status.busy": "2026-05-19T20:26:50.494274Z",
     "iopub.status.idle": "2026-05-19T20:26:50.637709Z",
     "shell.execute_reply": "2026-05-19T20:26:50.637043Z"
    }
   },
   "outputs": [],
   "source": [
    "n = 5\n",
    "JBessel = []\n",
    "for nu in range(n):\n",
    "    jbessel = TF1(\"J_0\", \"ROOT::Math::cyl_bessel_j([0],x)\", 0, 10)\n",
    "    jbessel.SetParameters(nu, 0.0)\n",
    "    jbessel.SetTitle(\"\")\n",
    "    jbessel.SetLineStyle(ROOT.kSolid)\n",
    "    jbessel.SetLineWidth(3)\n",
    "    jbessel.SetLineColor(nu + 1)\n",
    "    JBessel.append(jbessel)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61696dc9",
   "metadata": {},
   "source": [
    "Setting x axis for JBessel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5f677755",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:26:50.639518Z",
     "iopub.status.busy": "2026-05-19T20:26:50.639392Z",
     "iopub.status.idle": "2026-05-19T20:26:50.758278Z",
     "shell.execute_reply": "2026-05-19T20:26:50.757625Z"
    }
   },
   "outputs": [],
   "source": [
    "xaxis = JBessel[0].GetXaxis()\n",
    "xaxis.SetTitle(\"x\")\n",
    "xaxis.SetTitleSize(0.06)\n",
    "xaxis.SetTitleOffset(.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e995173",
   "metadata": {},
   "source": [
    "setting the title in a label style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d57d844b",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:26:50.760117Z",
     "iopub.status.busy": "2026-05-19T20:26:50.759995Z",
     "iopub.status.idle": "2026-05-19T20:26:50.873700Z",
     "shell.execute_reply": "2026-05-19T20:26:50.873092Z"
    }
   },
   "outputs": [],
   "source": [
    "p1 = TPaveLabel(.0, .90, .0 + .50, .90 + .10, \"Bessel J functions\", \"NDC\")\n",
    "p1.SetFillColor(0)\n",
    "p1.SetTextFont(22)\n",
    "p1.SetTextColor(kBlack)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21a29980",
   "metadata": {},
   "source": [
    "setting the legend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a1b1890b",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:26:50.875620Z",
     "iopub.status.busy": "2026-05-19T20:26:50.875478Z",
     "iopub.status.idle": "2026-05-19T20:26:50.998793Z",
     "shell.execute_reply": "2026-05-19T20:26:50.998137Z"
    }
   },
   "outputs": [],
   "source": [
    "leg.AddEntry(JBessel[0].DrawCopy(), \" J_0(x)\", \"l\")\n",
    "leg.AddEntry(JBessel[1].DrawCopy(\"same\"), \" J_1(x)\", \"l\")\n",
    "leg.AddEntry(JBessel[2].DrawCopy(\"same\"), \" J_2(x)\", \"l\")\n",
    "leg.AddEntry(JBessel[3].DrawCopy(\"same\"), \" J_3(x)\", \"l\")\n",
    "leg.AddEntry(JBessel[4].DrawCopy(\"same\"), \" J_4(x)\", \"l\")\n",
    "\n",
    "leg.Draw()\n",
    "p1.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3064591",
   "metadata": {},
   "source": [
    "Set canvas 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "73eaa870",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:26:51.000816Z",
     "iopub.status.busy": "2026-05-19T20:26:51.000664Z",
     "iopub.status.idle": "2026-05-19T20:26:51.104384Z",
     "shell.execute_reply": "2026-05-19T20:26:51.103752Z"
    }
   },
   "outputs": [],
   "source": [
    "DistCanvas.cd(2)\n",
    "gPad.SetGrid()\n",
    "gPad.SetFrameFillColor(19)\n",
    "leg2 = TLegend(0.75, 0.7, 0.89, 0.89)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9100f6a",
   "metadata": {},
   "source": [
    "Drawing Bessel k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "09836fed",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:26:51.106233Z",
     "iopub.status.busy": "2026-05-19T20:26:51.106108Z",
     "iopub.status.idle": "2026-05-19T20:26:51.216590Z",
     "shell.execute_reply": "2026-05-19T20:26:51.216002Z"
    }
   },
   "outputs": [],
   "source": [
    "KBessel = []\n",
    "for nu in range(n):\n",
    "    kbessel = TF1(\"J_0\", \"ROOT::Math::cyl_bessel_k([0],x)\", 0, 10)\n",
    "    kbessel.SetParameters(nu, 0.0)\n",
    "    kbessel.SetTitle(\"Bessel K functions\")\n",
    "    kbessel.SetLineStyle(ROOT.kSolid)\n",
    "    kbessel.SetLineWidth(3)\n",
    "    kbessel.SetLineColor(nu+1)\n",
    "    KBessel.append(kbessel)\n",
    "kxaxis = KBessel[0].GetXaxis()\n",
    "kxaxis.SetTitle(\"x\")\n",
    "kxaxis.SetTitleSize(0.06)\n",
    "kxaxis.SetTitleOffset(.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "682f7c51",
   "metadata": {},
   "source": [
    "setting title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "833ee1a4",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:26:51.218552Z",
     "iopub.status.busy": "2026-05-19T20:26:51.218430Z",
     "iopub.status.idle": "2026-05-19T20:26:51.322093Z",
     "shell.execute_reply": "2026-05-19T20:26:51.321438Z"
    }
   },
   "outputs": [],
   "source": [
    "p2 = TPaveLabel(.0, .90, .0 + .50, .90 + .10, \"Bessel K functions\", \"NDC\")\n",
    "p2.SetFillColor(0)\n",
    "p2.SetTextFont(22)\n",
    "p2.SetTextColor(kBlack)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcb1c25f",
   "metadata": {},
   "source": [
    "setting legend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b2672746",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:26:51.323908Z",
     "iopub.status.busy": "2026-05-19T20:26:51.323788Z",
     "iopub.status.idle": "2026-05-19T20:26:51.427862Z",
     "shell.execute_reply": "2026-05-19T20:26:51.427199Z"
    }
   },
   "outputs": [],
   "source": [
    "leg2.AddEntry(KBessel[0].DrawCopy(), \" K_0(x)\", \"l\")\n",
    "leg2.AddEntry(KBessel[1].DrawCopy(\"same\"), \" K_1(x)\", \"l\")\n",
    "leg2.AddEntry(KBessel[2].DrawCopy(\"same\"), \" K_2(x)\", \"l\")\n",
    "leg2.AddEntry(KBessel[3].DrawCopy(\"same\"), \" K_3(x)\", \"l\")\n",
    "leg2.AddEntry(KBessel[4].DrawCopy(\"same\"), \" K_4(x)\", \"l\")\n",
    "leg2.Draw()\n",
    "p2.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c925c7b",
   "metadata": {},
   "source": [
    "Set canvas 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1c34ba0e",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:26:51.429696Z",
     "iopub.status.busy": "2026-05-19T20:26:51.429550Z",
     "iopub.status.idle": "2026-05-19T20:26:51.533151Z",
     "shell.execute_reply": "2026-05-19T20:26:51.532523Z"
    }
   },
   "outputs": [],
   "source": [
    "DistCanvas.cd(3)\n",
    "gPad.SetGrid()\n",
    "gPad.SetFrameFillColor(19)\n",
    "leg3 = TLegend(0.75, 0.7, 0.89, 0.89)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28ce217b",
   "metadata": {},
   "source": [
    "Drawing Bessel i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "02d9a31c",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:26:51.534911Z",
     "iopub.status.busy": "2026-05-19T20:26:51.534775Z",
     "iopub.status.idle": "2026-05-19T20:26:51.645291Z",
     "shell.execute_reply": "2026-05-19T20:26:51.644693Z"
    }
   },
   "outputs": [],
   "source": [
    "iBessel = []\n",
    "for nu in range(5):\n",
    "    ibessel = TF1(\"J_0\", \"ROOT::Math::cyl_bessel_i([0],x)\", 0, 10)\n",
    "    ibessel.SetParameters(nu, 0.0)\n",
    "    ibessel.SetTitle(\"Bessel I functions\")\n",
    "    ibessel.SetLineStyle(ROOT.kSolid)\n",
    "    ibessel.SetLineWidth(3)\n",
    "    ibessel.SetLineColor(nu + 1)\n",
    "    iBessel.append(ibessel)\n",
    "\n",
    "iaxis = iBessel[0].GetXaxis()\n",
    "iaxis.SetTitle(\"x\")\n",
    "iaxis.SetTitleSize(0.06)\n",
    "iaxis.SetTitleOffset(.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e711d99e",
   "metadata": {},
   "source": [
    "setting title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "11de8550",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:26:51.647308Z",
     "iopub.status.busy": "2026-05-19T20:26:51.647186Z",
     "iopub.status.idle": "2026-05-19T20:26:51.750872Z",
     "shell.execute_reply": "2026-05-19T20:26:51.750201Z"
    }
   },
   "outputs": [],
   "source": [
    "p3 = TPaveLabel(.0, .90, .0 + .50, .90 + .10, \"Bessel I functions\", \"NDC\")\n",
    "p3.SetFillColor(0)\n",
    "p3.SetTextFont(22)\n",
    "p3.SetTextColor(kBlack)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9014dcc3",
   "metadata": {},
   "source": [
    "setting legend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "69f9b52d",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:26:51.752576Z",
     "iopub.status.busy": "2026-05-19T20:26:51.752458Z",
     "iopub.status.idle": "2026-05-19T20:26:51.856490Z",
     "shell.execute_reply": "2026-05-19T20:26:51.855868Z"
    }
   },
   "outputs": [],
   "source": [
    "leg3.AddEntry(iBessel[0].DrawCopy(), \" I_0\", \"l\")\n",
    "leg3.AddEntry(iBessel[1].DrawCopy(\"same\"), \" I_1(x)\", \"l\")\n",
    "leg3.AddEntry(iBessel[2].DrawCopy(\"same\"), \" I_2(x)\", \"l\")\n",
    "leg3.AddEntry(iBessel[3].DrawCopy(\"same\"), \" I_3(x)\", \"l\")\n",
    "leg3.AddEntry(iBessel[4].DrawCopy(\"same\"), \" I_4(x)\", \"l\")\n",
    "leg3.Draw()\n",
    "p3.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fde3279",
   "metadata": {},
   "source": [
    "Set canvas 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "af5c2cc4",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:26:51.858293Z",
     "iopub.status.busy": "2026-05-19T20:26:51.858172Z",
     "iopub.status.idle": "2026-05-19T20:26:51.961775Z",
     "shell.execute_reply": "2026-05-19T20:26:51.961169Z"
    }
   },
   "outputs": [],
   "source": [
    "DistCanvas.cd(4)\n",
    "gPad.SetGrid()\n",
    "gPad.SetFrameFillColor(19)\n",
    "leg4 = TLegend(0.75, 0.7, 0.89, 0.89)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4215775",
   "metadata": {},
   "source": [
    "Drawing sph_bessel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ecf986ce",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:26:51.963708Z",
     "iopub.status.busy": "2026-05-19T20:26:51.963570Z",
     "iopub.status.idle": "2026-05-19T20:26:52.073982Z",
     "shell.execute_reply": "2026-05-19T20:26:52.073366Z"
    }
   },
   "outputs": [],
   "source": [
    "jBessel = []\n",
    "for nu in range(5):\n",
    "    jbessel = TF1(\"J_0\", \"ROOT::Math::sph_bessel([0],x)\", 0, 10)\n",
    "    jbessel.SetParameters(nu, 0.0)\n",
    "    jbessel.SetTitle(\"Bessel j functions\")\n",
    "    jbessel.SetLineStyle(ROOT.kSolid)\n",
    "    jbessel.SetLineWidth(3)\n",
    "    jbessel.SetLineColor(nu+1)\n",
    "    jBessel.append(jbessel)\n",
    "jaxis = jBessel[0].GetXaxis()\n",
    "jaxis.SetTitle(\"x\")\n",
    "jaxis.SetTitleSize(0.06)\n",
    "jaxis.SetTitleOffset(.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a428c339",
   "metadata": {},
   "source": [
    "setting title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ef420da2",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:26:52.075950Z",
     "iopub.status.busy": "2026-05-19T20:26:52.075826Z",
     "iopub.status.idle": "2026-05-19T20:26:52.179456Z",
     "shell.execute_reply": "2026-05-19T20:26:52.178854Z"
    }
   },
   "outputs": [],
   "source": [
    "p4 = TPaveLabel(.0, .90, .0 + .50, .90 + .10, \"Bessel j functions\", \"NDC\")\n",
    "p4.SetFillColor(0)\n",
    "p4.SetTextFont(22)\n",
    "p4.SetTextColor(kBlack)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1851ec00",
   "metadata": {},
   "source": [
    "setting legend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ac4d5565",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:26:52.181404Z",
     "iopub.status.busy": "2026-05-19T20:26:52.181283Z",
     "iopub.status.idle": "2026-05-19T20:26:52.290230Z",
     "shell.execute_reply": "2026-05-19T20:26:52.289625Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cppyy.gbl.TCanvas object at 0x55f23043cbd0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leg4.AddEntry(jBessel[0].DrawCopy(), \" j_0(x)\", \"l\")\n",
    "leg4.AddEntry(jBessel[1].DrawCopy(\"same\"), \" j_1(x)\", \"l\")\n",
    "leg4.AddEntry(jBessel[2].DrawCopy(\"same\"), \" j_2(x)\", \"l\")\n",
    "leg4.AddEntry(jBessel[3].DrawCopy(\"same\"), \" j_3(x)\", \"l\")\n",
    "leg4.AddEntry(jBessel[4].DrawCopy(\"same\"), \" j_4(x)\", \"l\")\n",
    "\n",
    "leg4.Draw()\n",
    "p4.Draw()\n",
    "\n",
    "DistCanvas.cd()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d880f083",
   "metadata": {},
   "source": [
    "Draw all canvases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7d07f189",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:26:52.292194Z",
     "iopub.status.busy": "2026-05-19T20:26:52.292073Z",
     "iopub.status.idle": "2026-05-19T20:26:52.524951Z",
     "shell.execute_reply": "2026-05-19T20:26:52.524385Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Error in <GSLError>: Error 1 in bessel_Jnu.c at 182 : domain error\n",
      "Error in <GSLError>: Error 1 in bessel_Jnu.c at 211 : gsl_sf_bessel_Jnu_e(nu, x, &result)\n",
      "Error in <GSLError>: Error 1 in bessel_Jnu.c at 182 : domain error\n",
      "Error in <GSLError>: Error 1 in bessel_Jnu.c at 211 : gsl_sf_bessel_Jnu_e(nu, x, &result)\n",
      "Error in <GSLError>: Error 1 in bessel_Jnu.c at 182 : domain error\n",
      "Error in <GSLError>: Error 1 in bessel_Jnu.c at 211 : gsl_sf_bessel_Jnu_e(nu, x, &result)\n",
      "Error in <GSLError>: Error 1 in bessel_Jnu.c at 182 : domain error\n",
      "Error in <GSLError>: Error 1 in bessel_Jnu.c at 211 : gsl_sf_bessel_Jnu_e(nu, x, &result)\n",
      "Error in <GSLError>: Error 1 in bessel_Jnu.c at 182 : domain error\n",
      "Error in <GSLError>: Error 1 in bessel_Jnu.c at 211 : gsl_sf_bessel_Jnu_e(nu, x, &result)\n",
      "Error in <GSLError>: Error 1 in bessel_Knu.c at 42 : domain error\n",
      "Error in <GSLError>: Error 1 in bessel_Knu.c at 183 : gsl_sf_bessel_Knu_e(nu, x, &result)\n",
      "Error in <GSLError>: Error 1 in bessel_Knu.c at 42 : domain error\n",
      "Error in <GSLError>: Error 1 in bessel_Knu.c at 183 : gsl_sf_bessel_Knu_e(nu, x, &result)\n",
      "Error in <GSLError>: Error 1 in bessel_Knu.c at 42 : domain error\n",
      "Error in <GSLError>: Error 1 in bessel_Knu.c at 183 : gsl_sf_bessel_Knu_e(nu, x, &result)\n",
      "Error in <GSLError>: Error 1 in bessel_Knu.c at 42 : domain error\n",
      "Error in <GSLError>: Error 1 in bessel_Knu.c at 183 : gsl_sf_bessel_Knu_e(nu, x, &result)\n",
      "Error in <GSLError>: Error 1 in bessel_Knu.c at 42 : domain error\n",
      "Error in <GSLError>: Error 1 in bessel_Knu.c at 183 : gsl_sf_bessel_Knu_e(nu, x, &result)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<div id=\"root_plot_1779222412514\" style=\"width: 800px; height: 600px; position: relative\">\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "<script>\n",
       "   function process_root_plot_1779222412514() {\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_1779222412514', 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_1779222412514();\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
}