{
"cells": [
{
"cell_type": "markdown",
"id": "ea053c62",
"metadata": {},
"source": [
"# rf107_plotstyles\n",
"Basic functionality: demonstration of various plotting styles of data, functions in a RooPlot\n",
"\n",
"\n",
"\n",
"\n",
"**Author:** Clemens Lange, Wouter Verkerke (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 08:29 PM."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "0101b482",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:29:09.960995Z",
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"shell.execute_reply": "2026-05-19T20:29:10.960650Z"
}
},
"outputs": [],
"source": [
"import ROOT"
]
},
{
"cell_type": "markdown",
"id": "e083a88c",
"metadata": {},
"source": [
"Set up model\n",
"---------------------"
]
},
{
"cell_type": "markdown",
"id": "bf041821",
"metadata": {},
"source": [
"Create observables"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b02ae5d7",
"metadata": {
"collapsed": false,
"execution": {
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"shell.execute_reply": "2026-05-19T20:29:11.128226Z"
}
},
"outputs": [],
"source": [
"x = ROOT.RooRealVar(\"x\", \"x\", -10, 10)"
]
},
{
"cell_type": "markdown",
"id": "057e3830",
"metadata": {},
"source": [
"Create Gaussian"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "dd279aba",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:29:11.130923Z",
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"shell.execute_reply": "2026-05-19T20:29:11.258065Z"
}
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"outputs": [],
"source": [
"sigma = ROOT.RooRealVar(\"sigma\", \"sigma\", 3, 0.1, 10)\n",
"mean = ROOT.RooRealVar(\"mean\", \"mean\", -3, -10, 10)\n",
"gauss = ROOT.RooGaussian(\"gauss\", \"gauss\", x, mean, sigma)"
]
},
{
"cell_type": "markdown",
"id": "62d3e868",
"metadata": {},
"source": [
"Generate a sample of 100 events with sigma=3"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a2235a66",
"metadata": {
"collapsed": false,
"execution": {
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"shell.execute_reply": "2026-05-19T20:29:11.398172Z"
}
},
"outputs": [],
"source": [
"data = gauss.generate({x}, 100)"
]
},
{
"cell_type": "markdown",
"id": "b928be9f",
"metadata": {},
"source": [
"Fit pdf to data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ebc39970",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:29:11.400659Z",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[#1] INFO:Fitting -- RooAbsPdf::fitTo(gauss_over_gauss_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 6.77445 ms\n",
"[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_gauss_over_gauss_Int[x]_gaussData) Summation contains a RooNLLVar, using its error level\n",
"[#1] INFO:Minimization -- [fitFCN] No discrete parameters, performing continuous minimization only\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gauss.fitTo(data, PrintLevel=-1)"
]
},
{
"cell_type": "markdown",
"id": "7702fd1d",
"metadata": {},
"source": [
"Make plot frames\n",
"-------------------------------"
]
},
{
"cell_type": "markdown",
"id": "ef20fe54",
"metadata": {},
"source": [
"Make four plot frames to demonstrate various plotting features"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "ffc78a8f",
"metadata": {
"collapsed": false,
"execution": {
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"shell.execute_reply": "2026-05-19T20:29:11.742126Z"
}
},
"outputs": [],
"source": [
"frame1 = x.frame(Name=\"xframe\", Title=\"Red Curve / SumW2 Histo errors\", Bins=20)\n",
"frame2 = x.frame(Name=\"xframe\", Title=\"Dashed Curve / No XError bars\", Bins=20)\n",
"frame3 = x.frame(Name=\"xframe\", Title=\"Filled Curve / Blue Histo\", Bins=20)\n",
"frame4 = x.frame(Name=\"xframe\", Title=\"Partial Range / Filled Bar chart\", Bins=20)"
]
},
{
"cell_type": "markdown",
"id": "68caeef7",
"metadata": {},
"source": [
"Data plotting styles\n",
"---------------------------------------"
]
},
{
"cell_type": "markdown",
"id": "c1b756fd",
"metadata": {},
"source": [
"Use sqrt(sum(weights^2)) error instead of Poisson errors"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "6486bff6",
"metadata": {
"collapsed": false,
"execution": {
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"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.plotOn(frame1, DataError=\"SumW2\")"
]
},
{
"cell_type": "markdown",
"id": "66236c26",
"metadata": {},
"source": [
"Remove horizontal error bars"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "4694f71d",
"metadata": {
"collapsed": false,
"execution": {
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"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.plotOn(frame2, XErrorSize=0)"
]
},
{
"cell_type": "markdown",
"id": "a6a832d0",
"metadata": {},
"source": [
"Blue markers and error bors"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "8a467e06",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:29:11.986951Z",
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{
"data": {
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.plotOn(frame3, MarkerColor=\"b\", LineColor=\"b\")"
]
},
{
"cell_type": "markdown",
"id": "15c7a964",
"metadata": {},
"source": [
"Filled bar chart"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "271d688d",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:29:12.104777Z",
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"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.plotOn(frame4, DrawOption=\"B\", DataError=None, XErrorSize=0, FillColor=\"kGray\")"
]
},
{
"cell_type": "markdown",
"id": "3f02db96",
"metadata": {},
"source": [
"Function plotting styles\n",
"-----------------------------------------------"
]
},
{
"cell_type": "markdown",
"id": "162aa085",
"metadata": {},
"source": [
"Change line color to red"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "5c7d59f8",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:29:12.219213Z",
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"shell.execute_reply": "2026-05-19T20:29:12.353393Z"
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"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gauss.plotOn(frame1, LineColor=\"r\")"
]
},
{
"cell_type": "markdown",
"id": "1dd46738",
"metadata": {},
"source": [
"Change line style to dashed"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "566e9bbd",
"metadata": {
"collapsed": false,
"execution": {
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"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gauss.plotOn(frame2, LineStyle=\"--\")"
]
},
{
"cell_type": "markdown",
"id": "b308290d",
"metadata": {},
"source": [
"Filled shapes in green color"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "82fc1ddf",
"metadata": {
"collapsed": false,
"execution": {
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"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gauss.plotOn(frame3, MoveToBack=True, DrawOption=\"F\", FillColor=\"kOrange\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "b9d1fee5",
"metadata": {
"collapsed": false,
"execution": {
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[#1] INFO:Plotting -- RooAbsPdf::plotOn(gauss) only plotting range [-8,3], curve is normalized to data in given range\n",
"[#1] INFO:Eval -- RooRealVar::setRange(x) new range named 'plotRange' created with bounds [-8,3]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Info in : png file rf107_plotstyles.png has been created\n"
]
}
],
"source": [
"gauss.plotOn(frame4, Range=(-8, 3), LineColor=\"m\")\n",
"\n",
"c = ROOT.TCanvas(\"rf107_plotstyles\", \"rf107_plotstyles\", 800, 800)\n",
"c.Divide(2, 2)\n",
"c.cd(1)\n",
"ROOT.gPad.SetLeftMargin(0.15)\n",
"frame1.GetYaxis().SetTitleOffset(1.6)\n",
"frame1.Draw()\n",
"c.cd(2)\n",
"ROOT.gPad.SetLeftMargin(0.15)\n",
"frame2.GetYaxis().SetTitleOffset(1.6)\n",
"frame2.Draw()\n",
"c.cd(3)\n",
"ROOT.gPad.SetLeftMargin(0.15)\n",
"frame3.GetYaxis().SetTitleOffset(1.6)\n",
"frame3.Draw()\n",
"c.cd(4)\n",
"ROOT.gPad.SetLeftMargin(0.15)\n",
"frame4.GetYaxis().SetTitleOffset(1.6)\n",
"frame4.Draw()\n",
"\n",
"c.SaveAs(\"rf107_plotstyles.png\")"
]
},
{
"cell_type": "markdown",
"id": "76a34727",
"metadata": {},
"source": [
"Draw all canvases "
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "cf42f563",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:29:12.820556Z",
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"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"
\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"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
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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