{
"cells": [
{
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"metadata": {},
"source": [
"# rf101_basics\n",
"This tutorial illustrates the basic features of RooFit.\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 08:28 PM."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "524fd963",
"metadata": {
"collapsed": false,
"execution": {
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"shell.execute_reply": "2026-05-19T20:28:22.755837Z"
}
},
"outputs": [],
"source": [
"import ROOT"
]
},
{
"cell_type": "markdown",
"id": "16f63d54",
"metadata": {},
"source": [
"Set up model\n",
"---------------------\n",
"Declare variables x,mean,sigma with associated name, title, initial\n",
"value and allowed range"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "81069759",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:28:22.763745Z",
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"shell.execute_reply": "2026-05-19T20:28:22.927075Z"
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"outputs": [],
"source": [
"x = ROOT.RooRealVar(\"x\", \"x\", -10, 10)\n",
"mean = ROOT.RooRealVar(\"mean\", \"mean of gaussian\", 1, -10, 10)\n",
"sigma = ROOT.RooRealVar(\"sigma\", \"width of gaussian\", 1, 0.1, 10)"
]
},
{
"cell_type": "markdown",
"id": "3358efa2",
"metadata": {},
"source": [
"Build gaussian pdf in terms of x,mean and sigma"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d03ab3fa",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:28:22.929890Z",
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"shell.execute_reply": "2026-05-19T20:28:23.051422Z"
}
},
"outputs": [],
"source": [
"gauss = ROOT.RooGaussian(\"gauss\", \"gaussian PDF\", x, mean, sigma)"
]
},
{
"cell_type": "markdown",
"id": "45ac98ff",
"metadata": {},
"source": [
"Construct plot frame in 'x'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "2ad67a58",
"metadata": {
"collapsed": false,
"execution": {
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"shell.execute_reply": "2026-05-19T20:28:23.188002Z"
}
},
"outputs": [],
"source": [
"xframe = x.frame(Title=\"Gaussian pdf\") # RooPlot"
]
},
{
"cell_type": "markdown",
"id": "71fe7b8f",
"metadata": {},
"source": [
"Plot model and change parameter values\n",
"---------------------------------------------------------------------------\n",
"Plot gauss in frame (i.e. in x)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "3e77739e",
"metadata": {
"collapsed": false,
"execution": {
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"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gauss.plotOn(xframe)"
]
},
{
"cell_type": "markdown",
"id": "77bc83e0",
"metadata": {},
"source": [
"Change the value of sigma to 3"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "99646912",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:28:23.339476Z",
"iopub.status.busy": "2026-05-19T20:28:23.339337Z",
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"shell.execute_reply": "2026-05-19T20:28:23.447142Z"
}
},
"outputs": [],
"source": [
"sigma.setVal(3)"
]
},
{
"cell_type": "markdown",
"id": "c01ee55a",
"metadata": {},
"source": [
"Plot gauss in frame (i.e. in x) and draw frame on canvas"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ca9c6d5a",
"metadata": {
"collapsed": false,
"execution": {
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"shell.execute_reply": "2026-05-19T20:28:23.562478Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gauss.plotOn(xframe, LineColor=\"r\")"
]
},
{
"cell_type": "markdown",
"id": "a8d24f90",
"metadata": {},
"source": [
"Generate events\n",
"-----------------------------\n",
"Generate a dataset of 1000 events in x from gauss"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "eba1ad9b",
"metadata": {
"collapsed": false,
"execution": {
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"iopub.status.idle": "2026-05-19T20:28:23.701324Z",
"shell.execute_reply": "2026-05-19T20:28:23.700702Z"
}
},
"outputs": [],
"source": [
"data = gauss.generate({x}, 10000) # ROOT.RooDataSet"
]
},
{
"cell_type": "markdown",
"id": "a7e8eecb",
"metadata": {},
"source": [
"Make a second plot frame in x and draw both the\n",
"data and the pdf in the frame"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "d1a5e5e5",
"metadata": {
"collapsed": false,
"execution": {
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}
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"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xframe2 = x.frame(Title=\"Gaussian pdf with data\") # RooPlot\n",
"data.plotOn(xframe2)\n",
"gauss.plotOn(xframe2)"
]
},
{
"cell_type": "markdown",
"id": "d1b8ea73",
"metadata": {},
"source": [
"Fit model to data\n",
"-----------------------------\n",
"Fit pdf to data"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "cfecd5e0",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:28:23.833310Z",
"iopub.status.busy": "2026-05-19T20:28:23.833181Z",
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"shell.execute_reply": "2026-05-19T20:28:24.017799Z"
}
},
"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 792.183 μs\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": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gauss.fitTo(data, PrintLevel=-1)"
]
},
{
"cell_type": "markdown",
"id": "91b8a905",
"metadata": {},
"source": [
"Print values of mean and sigma (that now reflect fitted values and\n",
"errors)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "d777adb2",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:28:24.020528Z",
"iopub.status.busy": "2026-05-19T20:28:24.020403Z",
"iopub.status.idle": "2026-05-19T20:28:24.129317Z",
"shell.execute_reply": "2026-05-19T20:28:24.128729Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RooRealVar::mean = 1.01746 +/- 0.0300144 L(-10 - 10) \n",
"RooRealVar::sigma = 2.9787 +/- 0.0219217 L(0.1 - 10) \n"
]
}
],
"source": [
"mean.Print()\n",
"sigma.Print()"
]
},
{
"cell_type": "markdown",
"id": "87497d6a",
"metadata": {},
"source": [
"Draw all frames on a canvas"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "c0ffbe1a",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:28:24.131068Z",
"iopub.status.busy": "2026-05-19T20:28:24.130944Z",
"iopub.status.idle": "2026-05-19T20:28:24.349174Z",
"shell.execute_reply": "2026-05-19T20:28:24.348520Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Info in : png file rf101_basics.png has been created\n"
]
}
],
"source": [
"c = ROOT.TCanvas(\"rf101_basics\", \"rf101_basics\", 800, 400)\n",
"c.Divide(2)\n",
"c.cd(1)\n",
"ROOT.gPad.SetLeftMargin(0.15)\n",
"xframe.GetYaxis().SetTitleOffset(1.6)\n",
"xframe.Draw()\n",
"c.cd(2)\n",
"ROOT.gPad.SetLeftMargin(0.15)\n",
"xframe2.GetYaxis().SetTitleOffset(1.6)\n",
"xframe2.Draw()\n",
"\n",
"c.SaveAs(\"rf101_basics.png\")"
]
},
{
"cell_type": "markdown",
"id": "ecd0c9d1",
"metadata": {},
"source": [
"Draw all canvases "
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "ace03985",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:28:24.351267Z",
"iopub.status.busy": "2026-05-19T20:28:24.351139Z",
"iopub.status.idle": "2026-05-19T20:28:24.532482Z",
"shell.execute_reply": "2026-05-19T20:28:24.531875Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
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"\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",
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