{
"cells": [
{
"cell_type": "markdown",
"id": "625899d6",
"metadata": {},
"source": [
"# rs_bernsteinCorrection\n",
"Example of the BernsteinCorrection utility in RooStats.\n",
"\n",
"The idea is that one has a distribution coming either from data or Monte Carlo\n",
"(called \"reality\" in the macro) and a nominal model that is not sufficiently\n",
"flexible to take into account the real distribution. One wants to take into\n",
"account the systematic associated with this imperfect modeling by augmenting\n",
"the nominal model with some correction term (in this case a polynomial).\n",
"The BernsteinCorrection utility will import into your workspace a corrected model\n",
"given by nominal(x) * poly_N(x), where poly_N is an n-th order polynomial in\n",
"the Bernstein basis. The degree N of the polynomial is chosen by specifying the tolerance\n",
"one has in adding an extra term to the polynomial.\n",
"The Bernstein basis is nice because it only has positive-definite terms\n",
"and works well with PDFs.\n",
"Finally, the macro makes a plot of:\n",
" - the data (drawn from 'reality'),\n",
" - the best fit of the nominal model (blue)\n",
" - and the best fit corrected model.\n",
"\n",
"\n",
"\n",
"\n",
"**Author:** Artem Busorgin, Kyle Cranmer (C++ version) \n",
"This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Tuesday, May 19, 2026 at 08:36 PM."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "3ef95cb2",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:36:09.064003Z",
"iopub.status.busy": "2026-05-19T20:36:09.063888Z",
"iopub.status.idle": "2026-05-19T20:36:10.046105Z",
"shell.execute_reply": "2026-05-19T20:36:10.045543Z"
}
},
"outputs": [],
"source": [
"import sys\n",
"import ROOT"
]
},
{
"cell_type": "markdown",
"id": "05bbd7ff",
"metadata": {},
"source": [
"set range of observable"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "db44d86c",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:36:10.048583Z",
"iopub.status.busy": "2026-05-19T20:36:10.048439Z",
"iopub.status.idle": "2026-05-19T20:36:10.158898Z",
"shell.execute_reply": "2026-05-19T20:36:10.158457Z"
}
},
"outputs": [],
"source": [
"lowRange = -1\n",
"highRange = 5"
]
},
{
"cell_type": "markdown",
"id": "4245fff6",
"metadata": {},
"source": [
"make a RooRealVar for the observable"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9a762a91",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:36:10.167481Z",
"iopub.status.busy": "2026-05-19T20:36:10.167348Z",
"iopub.status.idle": "2026-05-19T20:36:10.319686Z",
"shell.execute_reply": "2026-05-19T20:36:10.318583Z"
}
},
"outputs": [],
"source": [
"x = ROOT.RooRealVar(\"x\", \"x\", lowRange, highRange)"
]
},
{
"cell_type": "markdown",
"id": "32ad5d88",
"metadata": {},
"source": [
"true model"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "109890ad",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:36:10.321178Z",
"iopub.status.busy": "2026-05-19T20:36:10.321052Z",
"iopub.status.idle": "2026-05-19T20:36:10.552023Z",
"shell.execute_reply": "2026-05-19T20:36:10.551368Z"
}
},
"outputs": [],
"source": [
"narrow = ROOT.RooGaussian(\"narrow\", \"\", x, ROOT.RooFit.RooConst(0.0), ROOT.RooFit.RooConst(0.8))\n",
"wide = ROOT.RooGaussian(\"wide\", \"\", x, ROOT.RooFit.RooConst(0.0), ROOT.RooFit.RooConst(2.0))\n",
"reality = ROOT.RooAddPdf(\"reality\", \"\", [narrow, wide], ROOT.RooFit.RooConst(0.8))\n",
"\n",
"data = reality.generate(x, 1000)"
]
},
{
"cell_type": "markdown",
"id": "a704b06f",
"metadata": {},
"source": [
"nominal model"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "6a6d1d75",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:36:10.553764Z",
"iopub.status.busy": "2026-05-19T20:36:10.553630Z",
"iopub.status.idle": "2026-05-19T20:36:10.760000Z",
"shell.execute_reply": "2026-05-19T20:36:10.754845Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[#0] WARNING:InputArguments -- The parameter 'sigma' with range [0, 10] of the RooGaussian 'nominal' exceeds the safe range of (0, inf). Advise to limit its range.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[#1] INFO:ObjectHandling -- RooWorkspace::import(myWorksspace) importing dataset realityData\n",
"[#1] INFO:ObjectHandling -- RooWorkSpace::import(myWorksspace) changing name of dataset from realityData to data\n",
"[#1] INFO:ObjectHandling -- RooWorkspace::import(myWorksspace) importing RooRealVar::x\n",
"[#1] INFO:ObjectHandling -- RooWorkspace::import(myWorksspace) importing RooGaussian::nominal\n",
"[#1] INFO:ObjectHandling -- RooWorkspace::import(myWorksspace) importing RooConstVar::0\n",
"[#1] INFO:ObjectHandling -- RooWorkspace::import(myWorksspace) importing RooRealVar::sigma\n"
]
}
],
"source": [
"sigma = ROOT.RooRealVar(\"sigma\", \"\", 1.0, 0, 10)\n",
"nominal = ROOT.RooGaussian(\"nominal\", \"\", x, ROOT.RooFit.RooConst(0.0), sigma)\n",
"\n",
"wks = ROOT.RooWorkspace(\"myWorksspace\")\n",
"\n",
"wks.Import(data, Rename=\"data\")\n",
"wks.Import(nominal)\n",
"\n",
"if ROOT.TClass.GetClass(\"ROOT::Minuit2::Minuit2Minimizer\"):\n",
" # use Minuit2 if ROOT was built with support for it:\n",
" ROOT.Math.MinimizerOptions.SetDefaultMinimizer(\"Minuit2\")"
]
},
{
"cell_type": "markdown",
"id": "e8b48c87",
"metadata": {},
"source": [
"The tolerance sets the probability to add an unnecessary term.\n",
"lower tolerance will add fewer terms, while higher tolerance\n",
"will add more terms and provide a more flexible function."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "ce3b96d8",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:36:10.766412Z",
"iopub.status.busy": "2026-05-19T20:36:10.766282Z",
"iopub.status.idle": "2026-05-19T20:36:10.994614Z",
"shell.execute_reply": "2026-05-19T20:36:10.993905Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[#0] PROGRESS:Eval -- BernsteinCorrection::ImportCorrectedPdf - Doing initial Fit with nominal model \n",
"[#1] INFO:Fitting -- RooAbsPdf::fitTo(nominal_over_nominal_Int[x]) 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 6.28407 ms\n",
"[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_nominal_over_nominal_Int[x]_data) Summation contains a RooNLLVar, using its error level\n",
"[#1] INFO:Minimization -- [fitFCN] No discrete parameters, performing continuous minimization only\n",
"[#1] INFO:Fitting -- RooAbsPdf::fitTo(corrected_over_corrected_Int[x]) fixing normalization set for coefficient determination to observables in data\n",
"[#1] INFO:Fitting -- Creation of NLL object took 159.78 μs\n",
"[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_corrected_over_corrected_Int[x]_data) Summation contains a RooNLLVar, using its error level\n",
"[#1] INFO:Minimization -- [fitFCN] No discrete parameters, performing continuous minimization only\n",
"[#1] INFO:NumericIntegration -- RooRealIntegral::init(corrected_Int[x]) using numeric integrator RooIntegrator1D to calculate Int(x)\n",
"[#1] INFO:Fitting -- RooAbsPdf::fitTo(corrected_over_corrected_Int[x]) fixing normalization set for coefficient determination to observables in data\n",
"[#1] INFO:Fitting -- Creation of NLL object took 120.911 μs\n",
"[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_corrected_over_corrected_Int[x]_data) Summation contains a RooNLLVar, using its error level\n",
"[#1] INFO:Minimization -- [fitFCN] No discrete parameters, performing continuous minimization only\n",
"[#1] INFO:NumericIntegration -- RooRealIntegral::init(corrected_Int[x]) using numeric integrator RooIntegrator1D to calculate Int(x)\n",
"[#1] INFO:Fitting -- RooAbsPdf::fitTo(corrected_over_corrected_Int[x]) fixing normalization set for coefficient determination to observables in data\n",
"[#1] INFO:Fitting -- Creation of NLL object took 114.48 μs\n",
"[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_corrected_over_corrected_Int[x]_data) Summation contains a RooNLLVar, using its error level\n",
"[#1] INFO:Minimization -- [fitFCN] No discrete parameters, performing continuous minimization only\n",
"[#1] INFO:NumericIntegration -- RooRealIntegral::init(corrected_Int[x]) using numeric integrator RooIntegrator1D to calculate Int(x)\n",
"[#1] INFO:Fitting -- RooAbsPdf::fitTo(corrected_over_corrected_Int[x]) fixing normalization set for coefficient determination to observables in data\n",
"[#1] INFO:Fitting -- Creation of NLL object took 122.171 μs\n",
"[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_corrected_over_corrected_Int[x]_data) Summation contains a RooNLLVar, using its error level\n",
"[#1] INFO:Minimization -- [fitFCN] No discrete parameters, performing continuous minimization only\n",
"[#1] INFO:NumericIntegration -- RooRealIntegral::init(corrected_Int[x]) using numeric integrator RooIntegrator1D to calculate Int(x)\n",
"[#1] INFO:Fitting -- RooAbsPdf::fitTo(corrected_over_corrected_Int[x]) fixing normalization set for coefficient determination to observables in data\n",
"[#1] INFO:Fitting -- Creation of NLL object took 124.45 μs\n",
"[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_corrected_over_corrected_Int[x]_data) Summation contains a RooNLLVar, using its error level\n",
"[#1] INFO:Minimization -- [fitFCN] No discrete parameters, performing continuous minimization only\n",
"[#1] INFO:NumericIntegration -- RooRealIntegral::init(corrected_Int[x]) using numeric integrator RooIntegrator1D to calculate Int(x)\n",
"[#1] INFO:Fitting -- RooAbsPdf::fitTo(corrected_over_corrected_Int[x]) fixing normalization set for coefficient determination to observables in data\n",
"[#1] INFO:Fitting -- Creation of NLL object took 124.37 μs\n",
"[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_corrected_over_corrected_Int[x]_data) Summation contains a RooNLLVar, using its error level\n",
"[#1] INFO:Minimization -- [fitFCN] No discrete parameters, performing continuous minimization only\n",
"[#1] INFO:NumericIntegration -- RooRealIntegral::init(corrected_Int[x]) using numeric integrator RooIntegrator1D to calculate Int(x)\n",
"[#1] INFO:ObjectHandling -- RooWorkspace::import(myWorksspace) importing RooEffProd::corrected\n",
"[#1] INFO:ObjectHandling -- RooWorkspace::import(myWorksspace) importing RooBernstein::poly\n",
"[#1] INFO:ObjectHandling -- RooWorkspace::import(myWorksspace) importing RooRealVar::c_0\n",
"[#1] INFO:ObjectHandling -- RooWorkspace::import(myWorksspace) importing RooRealVar::c_1\n",
"[#1] INFO:ObjectHandling -- RooWorkspace::import(myWorksspace) importing RooRealVar::c_2\n",
"[#1] INFO:ObjectHandling -- RooWorkspace::import(myWorksspace) importing RooRealVar::c_3\n",
"[#1] INFO:ObjectHandling -- RooWorkspace::import(myWorksspace) importing RooRealVar::c_4\n",
"[#1] INFO:ObjectHandling -- RooWorkspace::import(myWorksspace) importing RooRealVar::c_5\n",
"[#1] INFO:ObjectHandling -- RooWorkspace::import(myWorksspace) importing RooRealVar::c_6\n",
"[#1] INFO:Eval -- ------ Begin Bernstein Correction Log --------\n",
"degree = 1 -log L(0) = 1216.78 -log L(1) = 1208.89 q = 15.7692 P(chi^2_1 > q) = 7.1557e-05\n",
"degree = 2 -log L(1) = 1208.89 -log L(2) = 1203.21 q = 11.3692 P(chi^2_1 > q) = 0.000746732\n",
"degree = 3 -log L(2) = 1203.21 -log L(3) = 1198.85 q = 8.72171 P(chi^2_1 > q) = 0.00314444\n",
"degree = 4 -log L(3) = 1198.85 -log L(4) = 1190.16 q = 17.3777 P(chi^2_1 > q) = 3.06393e-05\n",
"degree = 5 -log L(4) = 1190.16 -log L(5) = 1183.56 q = 13.1965 P(chi^2_1 > q) = 0.00028048\n",
"degree = 6 -log L(5) = 1183.56 -log L(6) = 1182.57 q = 1.98352 P(chi^2_1 > q) = 0.15902\n",
"------ End Bernstein Correction Log --------\n",
"Correction based on Bernstein Poly of degree 6\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tolerance = 0.05\n",
"bernsteinCorrection = ROOT.RooStats.BernsteinCorrection(tolerance)\n",
"degree = bernsteinCorrection.ImportCorrectedPdf(wks, \"nominal\", \"x\", \"data\")\n",
"\n",
"if degree < 0:\n",
" ROOT.Error(\"rs_bernsteinCorrection\", \"Bernstein correction failed !\")\n",
" sys.exit()\n",
"\n",
"print(\"Correction based on Bernstein Poly of degree \", degree)\n",
"\n",
"frame = x.frame()\n",
"data.plotOn(frame)"
]
},
{
"cell_type": "markdown",
"id": "0cb4cb5e",
"metadata": {},
"source": [
"plot the best fit nominal model in blue"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4b9fc419",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:36:10.998291Z",
"iopub.status.busy": "2026-05-19T20:36:10.998159Z",
"iopub.status.idle": "2026-05-19T20:36:11.173650Z",
"shell.execute_reply": "2026-05-19T20:36:11.172979Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[#1] INFO:Fitting -- RooAbsPdf::fitTo(nominal_over_nominal_Int[x]) fixing normalization set for coefficient determination to observables in data\n",
"[#1] INFO:Fitting -- Creation of NLL object took 161.541 μs\n",
"[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_nominal_over_nominal_Int[x]_realityData) Summation contains a RooNLLVar, using its error level\n",
"[#1] INFO:Minimization -- [fitFCN] No discrete parameters, performing continuous minimization only\n",
"Minuit2Minimizer: Minimize with max-calls 500 convergence for edm < 1 strategy 1\n",
"Minuit2Minimizer : Valid minimum - status = 0\n",
"FVAL = 1216.7779341626624\n",
"Edm = 4.16187387343553302e-07\n",
"Nfcn = 19\n",
"sigma\t = 1.18138\t +/- 0.0315451\t(limited)\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nominal.fitTo(data, PrintLevel=0)\n",
"nominal.plotOn(frame)"
]
},
{
"cell_type": "markdown",
"id": "8b00d744",
"metadata": {},
"source": [
"plot the best fit corrected model in red"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "384118fa",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:36:11.175115Z",
"iopub.status.busy": "2026-05-19T20:36:11.174991Z",
"iopub.status.idle": "2026-05-19T20:36:11.322792Z",
"shell.execute_reply": "2026-05-19T20:36:11.322396Z"
}
},
"outputs": [],
"source": [
"corrected = wks[\"corrected\"]\n",
"if not corrected:\n",
" sys.exit()"
]
},
{
"cell_type": "markdown",
"id": "727cc7f8",
"metadata": {},
"source": [
"fit corrected model"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "c2e8ea79",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:36:11.336409Z",
"iopub.status.busy": "2026-05-19T20:36:11.336266Z",
"iopub.status.idle": "2026-05-19T20:36:11.487956Z",
"shell.execute_reply": "2026-05-19T20:36:11.468725Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[#1] INFO:Fitting -- RooAbsPdf::fitTo(corrected_over_corrected_Int[x]) fixing normalization set for coefficient determination to observables in data\n",
"[#1] INFO:Fitting -- Creation of NLL object took 272.222 μs\n",
"[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_corrected_over_corrected_Int[x]_realityData) Summation contains a RooNLLVar, using its error level\n",
"[#1] INFO:Minimization -- [fitFCN] No discrete parameters, performing continuous minimization only\n",
"Minuit2Minimizer: Minimize with max-calls 3500 convergence for edm < 1 strategy 1\n",
"[#1] INFO:NumericIntegration -- RooRealIntegral::init(corrected_Int[x]) using numeric integrator RooIntegrator1D to calculate Int(x)\n",
"Minuit2Minimizer : Valid minimum - status = 0\n",
"FVAL = 1182.56856160934331\n",
"Edm = 0.000128899179831313442\n",
"Nfcn = 185\n",
"c_1\t = 3.18336\t +/- 0.783721\t(limited)\n",
"c_2\t = 4.8037e-06\t +/- 3.11006\t(limited)\n",
"c_3\t = 1.18953e-06\t +/- 1.51773\t(limited)\n",
"c_4\t = 0.966776\t +/- 2.14197\t(limited)\n",
"c_5\t = 0.215629\t +/- 67.5047\t(limited)\n",
"c_6\t = 10.4446\t +/- 20.5657\t(limited)\n",
"sigma\t = 1.26666\t +/- 0.209614\t(limited)\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[#1] INFO:NumericIntegration -- RooRealIntegral::init(corrected_Int[x]) using numeric integrator RooIntegrator1D to calculate Int(x)\n"
]
}
],
"source": [
"corrected.fitTo(data, PrintLevel=0)\n",
"corrected.plotOn(frame, LineColor=\"r\")"
]
},
{
"cell_type": "markdown",
"id": "a82681c0",
"metadata": {},
"source": [
"plot the correction term (* norm constant) in dashed green\n",
"should make norm constant just be 1, not depend on binning of data"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "bccae9bc",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:36:11.498832Z",
"iopub.status.busy": "2026-05-19T20:36:11.498675Z",
"iopub.status.idle": "2026-05-19T20:36:11.609068Z",
"shell.execute_reply": "2026-05-19T20:36:11.608622Z"
}
},
"outputs": [],
"source": [
"poly = wks[\"poly\"]\n",
"if poly:\n",
" poly.plotOn(frame, LineColor=\"g\", LineStyle=\"--\")"
]
},
{
"cell_type": "markdown",
"id": "0d0d8a05",
"metadata": {},
"source": [
"this is a switch to check the sampling distribution\n",
"of -2 log LR for two comparisons:\n",
"the first is for n-1 vs. n degree polynomial corrections\n",
"the second is for n vs. n+1 degree polynomial corrections\n",
"Here we choose n to be the one chosen by the tolerance\n",
"criterion above, eg. n = \"degree\" in the code.\n",
"Setting this to true is takes about 10 min."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "bb823f44",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:36:11.611286Z",
"iopub.status.busy": "2026-05-19T20:36:11.611144Z",
"iopub.status.idle": "2026-05-19T20:36:12.317493Z",
"shell.execute_reply": "2026-05-19T20:36:12.317140Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Info in : png file rs_bernsteinCorrection.png has been created\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"
\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"checkSamplingDist = True\n",
"numToyMC = 20 # increase this value for sensible results\n",
"\n",
"c1 = ROOT.TCanvas()\n",
"if checkSamplingDist:\n",
" c1.Divide(1, 2)\n",
" c1.cd(1)\n",
"\n",
"frame.Draw()\n",
"ROOT.gPad.Update()\n",
"\n",
"if checkSamplingDist:\n",
" # check sampling dist\n",
" ROOT.Math.MinimizerOptions.SetDefaultPrintLevel(-1)\n",
" samplingDist = ROOT.TH1F(\"samplingDist\", \"\", 20, 0, 10)\n",
" samplingDistExtra = ROOT.TH1F(\"samplingDistExtra\", \"\", 20, 0, 10)\n",
" bernsteinCorrection.CreateQSamplingDist(\n",
" wks, \"nominal\", \"x\", \"data\", samplingDist, samplingDistExtra, degree, numToyMC\n",
" )\n",
"\n",
" c1.cd(2)\n",
" samplingDistExtra.SetLineColor(\"kRed\")\n",
" samplingDistExtra.Draw()\n",
" samplingDist.Draw(\"same\")\n",
"\n",
"c1.SaveAs(\"rs_bernsteinCorrection.png\")"
]
},
{
"cell_type": "markdown",
"id": "ac9a574b",
"metadata": {},
"source": [
"Draw all canvases "
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "2965f732",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:36:12.326263Z",
"iopub.status.busy": "2026-05-19T20:36:12.326124Z",
"iopub.status.idle": "2026-05-19T20:36:12.442857Z",
"shell.execute_reply": "2026-05-19T20:36:12.442529Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"
\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%jsroot on\n",
"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
}