{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "38870db7",
   "metadata": {},
   "source": [
    "# rf211_paramconv\n",
    "Addition and convolution: working with a pdf with a convolution operator in terms of a parameter\n",
    "\n",
    "This tutorial requires FFT3 to be enabled.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "**Author:** Wouter Verkerke  \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 08:30 PM.</small></i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f3aa4793",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:30:18.090314Z",
     "iopub.status.busy": "2026-05-19T20:30:18.090192Z",
     "iopub.status.idle": "2026-05-19T20:30:18.103457Z",
     "shell.execute_reply": "2026-05-19T20:30:18.102922Z"
    }
   },
   "outputs": [],
   "source": [
    "%%cpp -d\n",
    "#include \"RooRealVar.h\"\n",
    "#include \"RooDataHist.h\"\n",
    "#include \"RooGaussian.h\"\n",
    "#include \"RooGenericPdf.h\"\n",
    "#include \"RooFormulaVar.h\"\n",
    "#include \"RooFFTConvPdf.h\"\n",
    "#include \"RooPlot.h\"\n",
    "#include \"TCanvas.h\"\n",
    "#include \"TAxis.h\"\n",
    "#include \"TH2.h\"\n",
    "using namespace RooFit;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e4f3c73",
   "metadata": {},
   "source": [
    "Setup component pdfs\n",
    "---------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4966c049",
   "metadata": {},
   "source": [
    "Gaussian g(x ; mean,sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1d98c8b4",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:30:18.105177Z",
     "iopub.status.busy": "2026-05-19T20:30:18.105064Z",
     "iopub.status.idle": "2026-05-19T20:30:18.464942Z",
     "shell.execute_reply": "2026-05-19T20:30:18.464251Z"
    }
   },
   "outputs": [],
   "source": [
    "RooRealVar x(\"x\", \"x\", -10, 10);\n",
    "RooRealVar mean(\"mean\", \"mean\", -3, 3);\n",
    "RooRealVar sigma(\"sigma\", \"sigma\", 0.5, 0.1, 10);\n",
    "RooGaussian modelx(\"gx\", \"gx\", x, mean, sigma);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c289c2bc",
   "metadata": {},
   "source": [
    "Block function in mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "765eb025",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:30:18.467618Z",
     "iopub.status.busy": "2026-05-19T20:30:18.467484Z",
     "iopub.status.idle": "2026-05-19T20:30:18.675419Z",
     "shell.execute_reply": "2026-05-19T20:30:18.674701Z"
    }
   },
   "outputs": [],
   "source": [
    "RooRealVar a(\"a\", \"a\", 2, 1, 10);\n",
    "RooGenericPdf model_mean(\"model_mean\", \"abs(mean)<a\", RooArgList(mean, a));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77a87b42",
   "metadata": {},
   "source": [
    "Convolution in mean parameter model = g(x,mean,sigma) (x) block(mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "26b38dbb",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:30:18.677097Z",
     "iopub.status.busy": "2026-05-19T20:30:18.676977Z",
     "iopub.status.idle": "2026-05-19T20:30:18.884800Z",
     "shell.execute_reply": "2026-05-19T20:30:18.884140Z"
    }
   },
   "outputs": [],
   "source": [
    "x.setBins(1000, \"cache\");\n",
    "mean.setBins(50, \"cache\");\n",
    "RooFFTConvPdf model(\"model\", \"model\", mean, modelx, model_mean);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62065e08",
   "metadata": {},
   "source": [
    "Configure convolution to construct a 2-D cache in (x,mean)\n",
    "rather than a 1-d cache in mean that needs to be recalculated\n",
    "for each value of x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fdd8a5b8",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:30:18.886976Z",
     "iopub.status.busy": "2026-05-19T20:30:18.886852Z",
     "iopub.status.idle": "2026-05-19T20:30:19.094704Z",
     "shell.execute_reply": "2026-05-19T20:30:19.093997Z"
    }
   },
   "outputs": [],
   "source": [
    "model.setCacheObservables(x);\n",
    "model.setBufferFraction(1.0);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5642c91",
   "metadata": {},
   "source": [
    "Integrate model over mean projModel = Int model dmean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5b8571eb",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:30:19.096661Z",
     "iopub.status.busy": "2026-05-19T20:30:19.096509Z",
     "iopub.status.idle": "2026-05-19T20:30:19.305040Z",
     "shell.execute_reply": "2026-05-19T20:30:19.304416Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#1] INFO:Eval -- RooRealVar::setRange(mean) new range named 'refrange_fft_model' created with bounds [-3,3]\n",
      "[#1] INFO:NumericIntegration -- RooRealIntegral::init(gx_Int[mean,x]) using numeric integrator RooIntegrator1D to calculate Int(mean)\n",
      "[#1] INFO:NumericIntegration -- RooRealIntegral::init(model_mean_Int[mean]) using numeric integrator RooIntegrator1D to calculate Int(mean)\n",
      "[#0] WARNING:Eval -- The FFT convolution 'model' will run with 50 bins. A decent accuracy for difficult convolutions is typically only reached with n >= 1000. Suggest to increase the number of bins of the observable 'mean'.\n",
      "[#1] INFO:Caching -- RooAbsCachedPdf::getCache(model) creating new cache 0x565197864250 with pdf gx_CONV_model_mean_CACHE_Obs[mean,x]_NORM_mean for nset (mean) with code 0\n"
     ]
    }
   ],
   "source": [
    "RooAbsPdf *projModel = model.createProjection(mean);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e108b44",
   "metadata": {},
   "source": [
    "Generate 1000 toy events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "16b91400",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:30:19.306639Z",
     "iopub.status.busy": "2026-05-19T20:30:19.306501Z",
     "iopub.status.idle": "2026-05-19T20:30:19.514853Z",
     "shell.execute_reply": "2026-05-19T20:30:19.514255Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#1] INFO:NumericIntegration -- RooRealIntegral::init(gx_Int[mean,x]) using numeric integrator RooIntegrator1D to calculate Int(mean)\n",
      "[#1] INFO:NumericIntegration -- RooRealIntegral::init(model_mean_Int[mean]) using numeric integrator RooIntegrator1D to calculate Int(mean)\n",
      "[#0] WARNING:Eval -- The FFT convolution 'model' will run with 50 bins. A decent accuracy for difficult convolutions is typically only reached with n >= 1000. Suggest to increase the number of bins of the observable 'mean'.\n",
      "[#1] INFO:Caching -- RooAbsCachedPdf::getCache(model) creating new cache 0x7f6de98eec30 with pdf gx_CONV_model_mean_CACHE_Obs[x,mean]_NORM_x_mean for nset (x,mean) with code 1\n"
     ]
    }
   ],
   "source": [
    "std::unique_ptr<RooDataHist> d{projModel->generateBinned(x, 1000)};"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73b810c3",
   "metadata": {},
   "source": [
    "Fit pdf to toy data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "545335db",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:30:19.516381Z",
     "iopub.status.busy": "2026-05-19T20:30:19.516270Z",
     "iopub.status.idle": "2026-05-19T20:30:20.812543Z",
     "shell.execute_reply": "2026-05-19T20:30:20.812067Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#1] INFO:NumericIntegration -- RooRealIntegral::init(gx_Int[mean,x]) using numeric integrator RooIntegrator1D to calculate Int(mean)\n",
      "[#1] INFO:NumericIntegration -- RooRealIntegral::init(model_mean_Int[mean]) using numeric integrator RooIntegrator1D to calculate Int(mean)\n",
      "[#0] WARNING:Eval -- The FFT convolution 'model' will run with 50 bins. A decent accuracy for difficult convolutions is typically only reached with n >= 1000. Suggest to increase the number of bins of the observable 'mean'.\n",
      "[#1] INFO:Caching -- RooAbsCachedPdf::getCache(model) creating new cache 0x7f6de9d596d0 with pdf gx_CONV_model_mean_CACHE_Obs[x,mean]_NORM_x_mean for nset (x,mean) with code 1\n",
      "[#1] INFO:Fitting -- RooAbsPdf::fitTo(model_Int[mean]_Norm[mean,x]_wrapped_pdf) fixing normalization set for coefficient determination to observables in data\n",
      "[#1] INFO:Fitting -- using generic CPU library compiled with no vectorizations\n",
      "[#1] INFO:Fitting -- Creation of NLL object took 32.6568 ms\n",
      "[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_model_Int[mean]_Norm[mean,x]_wrapped_pdf_genData) Summation contains a RooNLLVar, using its error level\n",
      "[#0] WARNING:Minimization -- RooAbsMinimizerFcn::synchronize: WARNING: no initial error estimate available for a: using 0.5\n",
      "[#0] WARNING:Minimization -- RooAbsMinimizerFcn::synchronize: WARNING: no initial error estimate available for sigma: using 0.2\n",
      "[#1] INFO:Minimization -- [fitFCN] No discrete parameters, performing continuous minimization only\n",
      "sigma=0.5, [#1] INFO:NumericIntegration -- RooRealIntegral::init(model_mean_Int[mean]) using numeric integrator RooIntegrator1D to calculate Int(mean)\n",
      "[#0] WARNING:Eval -- The FFT convolution 'model' will run with 50 bins. A decent accuracy for difficult convolutions is typically only reached with n >= 1000. Suggest to increase the number of bins of the observable 'mean'.\n",
      "[#1] INFO:Caching -- RooAbsCachedPdf::getCache(model) creating new cache 0x7f6dea18a2a0 with pdf gx_CONV_model_mean_CACHE_Obs[mean] for nset () with code 2\n",
      "\n",
      "prevFCN = 2171.275755  a=2.017, \n",
      "prevFCN = 2171.275755  a=1.983, \n",
      "prevFCN = 2171.275755  a=2.172, \n",
      "prevFCN = 2171.861215  a=1.84, \n",
      "prevFCN = 2174.775121  a=2.017, \n",
      "prevFCN = 2171.275755  a=1.983, \n",
      "prevFCN = 2171.275755  a=2, sigma=0.5067, \n",
      "prevFCN = 2171.291807  sigma=0.4934, \n",
      "prevFCN = 2171.265264  sigma=0.5029, \n",
      "prevFCN = 2171.281998  sigma=0.4971, \n",
      "prevFCN = 2171.270547  sigma=0.4843, \n",
      "prevFCN = 2171.259881  a=2.172, \n",
      "prevFCN = 2171.692149  a=1.84, \n",
      "prevFCN = 2175.249474  a=2.017, \n",
      "prevFCN = 2171.259881  a=1.983, \n",
      "prevFCN = 2171.259881  a=2.172, \n",
      "prevFCN = 2171.692149  a=1.84, \n",
      "prevFCN = 2175.249474  a=2, sigma=0.4871, \n",
      "prevFCN = 2171.26042  sigma=0.4815, \n",
      "prevFCN = 2171.260367  a=2.003, sigma=0.4688, \n",
      "prevFCN = 2171.275519  a=2.001, sigma=0.479, \n",
      "prevFCN = 2171.261603  a=2, sigma=0.482, \n",
      "prevFCN = 2171.260187  a=2, sigma=0.4832, \n",
      "prevFCN = 2171.259943  a=2, sigma=0.4838, \n",
      "prevFCN = 2171.259893  a=2, sigma=0.484, \n",
      "prevFCN = 2171.259883  a=2, sigma=0.4841, \n",
      "prevFCN = 2171.259881  a=2, sigma=0.4842, \n",
      "prevFCN = 2171.25988  a=2.017, \n",
      "prevFCN = 2171.25988  a=1.983, \n",
      "prevFCN = 2171.25988  a=2.172, \n",
      "prevFCN = 2171.691427  a=1.84, \n",
      "prevFCN = 2175.251788  a=2.017, \n",
      "prevFCN = 2171.25988  a=1.983, \n",
      "prevFCN = 2171.25988  a=2, sigma=0.487, \n",
      "prevFCN = 2171.260398  sigma=0.4814, \n",
      "prevFCN = 2171.260398  sigma=0.4842, \n",
      "prevFCN = 2171.25988  a=2.017, \n",
      "prevFCN = 2171.25988  a=1.983, \n",
      "prevFCN = 2171.25988  a=2.172, \n",
      "prevFCN = 2171.691427  a=1.84, \n",
      "prevFCN = 2175.251788  a=2.083, \n",
      "prevFCN = 2171.25988  a=1.92, \n",
      "prevFCN = 2172.379556  a=2, sigma=0.487, \n",
      "prevFCN = 2171.260398  sigma=0.4814, \n",
      "prevFCN = 2171.260398  a=2.002, sigma=0.4842, \n",
      "prevFCN = 2171.25988  a=1.998, \n",
      "prevFCN = 2171.25988  a=2, sigma=0.4848, \n",
      "prevFCN = 2171.259901  sigma=0.4836, \n",
      "prevFCN = 2171.259901  sigma=0.4843, \n",
      "prevFCN = 2171.259881  sigma=0.4841, \n",
      "prevFCN = 2171.259881  a=2.083, sigma=0.487, \n",
      "prevFCN = 2171.260398  a=2.041, sigma=0.4842, \n",
      "prevFCN = 2171.25988  a=2.02, sigma=0.4842, \n",
      "prevFCN = 2171.25988  a=2.01, sigma=0.4842, \n",
      "prevFCN = 2171.25988  a=2.005, sigma=0.4842, \n",
      "prevFCN = 2171.25988  a=2.003, sigma=0.4842, \n",
      "prevFCN = 2171.25988  a=2.001, sigma=0.4842, \n",
      "prevFCN = 2171.25988  a=2.001, sigma=0.4842, \n",
      "prevFCN = 2171.25988  a=2, sigma=0.4842, \n",
      "prevFCN = 2171.25988  a=2, sigma=0.4842, \n",
      "prevFCN = 2171.25988  a=2, sigma=0.4842, \n",
      "prevFCN = 2171.25988  a=2, sigma=0.4842, \n",
      "prevFCN = 2171.25988  a=2, sigma=0.4842, \n",
      "prevFCN = 2171.25988  a=2.083, \n",
      "prevFCN = 2171.25988  a=1.92, \n",
      "prevFCN = 2172.379556  a=2, sigma=0.4843, \n",
      "prevFCN = 2171.259881  sigma=0.4841, \n",
      "prevFCN = 2171.259881  a=2.002, sigma=0.4842, \n",
      "prevFCN = 2171.25988  a=1.998, \n",
      "prevFCN = 2171.25988  a=2, sigma=0.4842, \n",
      "prevFCN = 2171.25988  sigma=0.4842, \n",
      "prevFCN = 2171.25988  a=2.083, sigma=0.4843, \n",
      "prevFCN = 2171.259881  a=2, sigma=0.4842, "
     ]
    }
   ],
   "source": [
    "projModel->fitTo(*d, Verbose(), PrintLevel(-1));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f72cd6e1",
   "metadata": {},
   "source": [
    "Plot data and fitted pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "82f20e8b",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:30:20.813901Z",
     "iopub.status.busy": "2026-05-19T20:30:20.813789Z",
     "iopub.status.idle": "2026-05-19T20:30:21.022100Z",
     "shell.execute_reply": "2026-05-19T20:30:21.021664Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#1] INFO:NumericIntegration -- RooRealIntegral::init(gx_Int[mean,x]) using numeric integrator RooIntegrator1D to calculate Int(mean)\n",
      "[#1] INFO:NumericIntegration -- RooRealIntegral::init(model_mean_Int[mean]) using numeric integrator RooIntegrator1D to calculate Int(mean)\n",
      "[#0] WARNING:Eval -- The FFT convolution 'model' will run with 50 bins. A decent accuracy for difficult convolutions is typically only reached with n >= 1000. Suggest to increase the number of bins of the observable 'mean'.\n",
      "[#1] INFO:Caching -- RooAbsCachedPdf::getCache(model) creating new cache 0x7f6dea19d020 with pdf gx_CONV_model_mean_CACHE_Obs[x,mean]_NORM_x_mean for nset (x,mean) with code 1\n"
     ]
    }
   ],
   "source": [
    "RooPlot *frame = x.frame(Bins(25));\n",
    "d->plotOn(frame);\n",
    "projModel->plotOn(frame);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0b89323",
   "metadata": {},
   "source": [
    "Make 2d histogram of model(x;mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "42245cf5",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:30:21.023661Z",
     "iopub.status.busy": "2026-05-19T20:30:21.023535Z",
     "iopub.status.idle": "2026-05-19T20:30:21.231488Z",
     "shell.execute_reply": "2026-05-19T20:30:21.231048Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#1] INFO:NumericIntegration -- RooRealIntegral::init(gx_Int[mean,x]) using numeric integrator RooIntegrator1D to calculate Int(mean)\n",
      "[#1] INFO:NumericIntegration -- RooRealIntegral::init(model_mean_Int[mean]) using numeric integrator RooIntegrator1D to calculate Int(mean)\n",
      "[#0] WARNING:Eval -- The FFT convolution 'model' will run with 50 bins. A decent accuracy for difficult convolutions is typically only reached with n >= 1000. Suggest to increase the number of bins of the observable 'mean'.\n",
      "[#1] INFO:Caching -- RooAbsCachedPdf::getCache(model) creating new cache 0x7f6dea621bb0 with pdf gx_CONV_model_mean_CACHE_Obs[x,mean]_NORM_x for nset (x) with code 3\n"
     ]
    }
   ],
   "source": [
    "TH1 *hh = model.createHistogram(\"hh\", x, Binning(50), YVar(mean, Binning(50)), ConditionalObservables(mean));\n",
    "hh->SetTitle(\"histogram of model(x|mean)\");\n",
    "hh->SetLineColor(kBlue);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79008943",
   "metadata": {},
   "source": [
    "Draw frame on canvas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f18a14f6",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:30:21.232836Z",
     "iopub.status.busy": "2026-05-19T20:30:21.232730Z",
     "iopub.status.idle": "2026-05-19T20:30:21.437733Z",
     "shell.execute_reply": "2026-05-19T20:30:21.437177Z"
    }
   },
   "outputs": [],
   "source": [
    "TCanvas *c = new TCanvas(\"rf211_paramconv\", \"rf211_paramconv\", 800, 400);\n",
    "c->Divide(2);\n",
    "c->cd(1);\n",
    "gPad->SetLeftMargin(0.15);\n",
    "frame->GetYaxis()->SetTitleOffset(1.4);\n",
    "frame->Draw();\n",
    "c->cd(2);\n",
    "gPad->SetLeftMargin(0.20);\n",
    "hh->GetZaxis()->SetTitleOffset(2.5);\n",
    "hh->Draw(\"surf\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a861f20",
   "metadata": {},
   "source": [
    "Draw all canvases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d17d572c",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:30:21.439538Z",
     "iopub.status.busy": "2026-05-19T20:30:21.439427Z",
     "iopub.status.idle": "2026-05-19T20:30:21.646746Z",
     "shell.execute_reply": "2026-05-19T20:30:21.646260Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<div id=\"root_plot_1779222621618\" style=\"width: 800px; height: 400px; position: relative\">\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "<script>\n",
       "   function process_root_plot_1779222621618() {\n",
       "      function execCode(Core) {\n",
       "         Core.settings.HandleKeys = false;\n",
       "         \n",
       "Core.unzipJSON(58888,'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').then(json => {\n",
       "   const obj = Core.parse(json);\n",
       "   Core.draw('root_plot_1779222621618', 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_1779222621618();\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gROOT->GetListOfCanvases()->Draw()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ROOT C++",
   "language": "c++",
   "name": "root"
  },
  "language_info": {
   "codemirror_mode": "text/x-c++src",
   "file_extension": ".C",
   "mimetype": " text/x-c++src",
   "name": "c++"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}