{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9f604e4b-f7a1-4a62-877b-be0a9517c23f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ROOT import TH1D, TCanvas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cca002f6-fc30-45de-84df-b12cbbcbd466",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: mycanvas\n"
     ]
    }
   ],
   "source": [
    "my_canvas = TCanvas(\"mycanvas\",\"canvas title\",800,600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "739e0569-ac18-418f-8aae-88c141745935",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TROOT::Append>: Replacing existing TH1: example (Potential memory leak).\n"
     ]
    }
   ],
   "source": [
    "example = TH1D(\"example\",\"example histogram\",100,-3,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cc3a64e7-abb4-483a-ae6b-5697805264e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "example.FillRandom(\"gaus\",10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ab2f55bf-2150-4d38-bc00-f65ae0327881",
   "metadata": {},
   "outputs": [],
   "source": [
    "example.Draw(\"E\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3b801794-e27f-4cd2-9bc1-e29efc9cf0b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"root_plot_1776420785527\" style=\"width: 800px; height: 600px; position: relative\">\n",
       "</div>\n",
       "<script>\n",
       "   function process_root_plot_1776420785527() {\n",
       "      function drawPlot(Core) {\n",
       "         Core.settings.HandleKeys = false;\n",
       "         Core.unzipJSON(21938,'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').then(json => {\n",
       "            const obj = Core.parse(json);\n",
       "            Core.draw('root_plot_1776420785527', obj, '');\n",
       "         });\n",
       "      }\n",
       "      const servers = ['/static/', 'https://root.cern/js/7.10.3/', 'https://jsroot.gsi.de/7.10.3/'],\n",
       "            path = 'build/jsroot';\n",
       "      if (typeof JSROOT !== 'undefined')\n",
       "         drawPlot(JSROOT);\n",
       "      else if (typeof requirejs !== 'undefined') {\n",
       "         servers.forEach((s,i) => { servers[i] = s + path; });\n",
       "         requirejs.config({ paths: { 'jsroot' : servers } })(['jsroot'],  drawPlot);\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(() => drawPlot(JSROOT));\n",
       "         }\n",
       "         loadJsroot();\n",
       "      }\n",
       "   }\n",
       "   process_root_plot_1776420785527();\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e575f2d4-9d30-41f1-b174-7e454067143c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.14.4"
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