{
"cells": [
{
"cell_type": "markdown",
"id": "64af9980",
"metadata": {},
"source": [
"# df002_dataModel\n",
"Show how to work with non-flat data models, e.g. vectors of tracks.\n",
"\n",
"This tutorial shows the possibility to use data models which are more\n",
"complex than flat ntuples with RDataFrame.\n",
"\n",
"\n",
"\n",
"\n",
"**Author:** Danilo Piparo (CERN) \n",
"This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Tuesday, May 19, 2026 at 08:09 PM."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d7ca8546",
"metadata": {
"collapsed": false,
"execution": {
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"shell.execute_reply": "2026-05-19T20:09:18.864439Z"
}
},
"outputs": [],
"source": [
"import ROOT"
]
},
{
"cell_type": "markdown",
"id": "3009ec0b",
"metadata": {},
"source": [
"A simple helper function to fill a test tree: this makes the example stand-alone."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4caddf00",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:18.886236Z",
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"shell.execute_reply": "2026-05-19T20:09:19.028951Z"
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"outputs": [],
"source": [
"fill_tree_code = \"\"\"\n",
"using FourVector = ROOT::Math::XYZTVector;\n",
"using FourVectorVec = std::vector;\n",
"using CylFourVector = ROOT::Math::RhoEtaPhiVector;\n",
"\n",
"// A simple helper function to fill a test tree: this makes the example\n",
"// stand-alone.\n",
"void fill_tree(const char *filename, const char *treeName)\n",
"{\n",
" const double M = 0.13957; // set pi+ mass\n",
" TRandom3 R(1);\n",
"\n",
" auto genTracks = [&](){\n",
" FourVectorVec tracks;\n",
" const auto nPart = R.Poisson(15);\n",
" tracks.reserve(nPart);\n",
" for (int j = 0; j < nPart; ++j) {\n",
" const auto px = R.Gaus(0, 10);\n",
" const auto py = R.Gaus(0, 10);\n",
" const auto pt = sqrt(px * px + py * py);\n",
" const auto eta = R.Uniform(-3, 3);\n",
" const auto phi = R.Uniform(0.0, 2 * TMath::Pi());\n",
" CylFourVector vcyl(pt, eta, phi);\n",
" // set energy\n",
" auto E = sqrt(vcyl.R() * vcyl.R() + M * M);\n",
" // fill track vector\n",
" tracks.emplace_back(vcyl.X(), vcyl.Y(), vcyl.Z(), E);\n",
" }\n",
" return tracks;\n",
" };\n",
"\n",
" ROOT::RDataFrame d(64);\n",
" d.Define(\"tracks\", genTracks).Snapshot(treeName, filename, {\"tracks\"});\n",
"}\n",
"\"\"\""
]
},
{
"cell_type": "markdown",
"id": "c0539e27",
"metadata": {},
"source": [
"We prepare an input tree to run on"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "61be9fa5",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:19.071267Z",
"iopub.status.busy": "2026-05-19T20:09:19.071109Z",
"iopub.status.idle": "2026-05-19T20:09:20.350218Z",
"shell.execute_reply": "2026-05-19T20:09:20.349598Z"
}
},
"outputs": [],
"source": [
"fileName = \"df002_dataModel_py.root\"\n",
"treeName = \"myTree\"\n",
"ROOT.gInterpreter.Declare(fill_tree_code)\n",
"ROOT.fill_tree(fileName, treeName)"
]
},
{
"cell_type": "markdown",
"id": "e149243d",
"metadata": {},
"source": [
"We read the tree from the file and create a RDataFrame, a class that\n",
"allows us to interact with the data contained in the tree."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "22cdd6f7",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:20.367206Z",
"iopub.status.busy": "2026-05-19T20:09:20.367068Z",
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"shell.execute_reply": "2026-05-19T20:09:20.757431Z"
}
},
"outputs": [],
"source": [
"d = ROOT.RDataFrame(treeName, fileName)"
]
},
{
"cell_type": "markdown",
"id": "fafbe9b5",
"metadata": {},
"source": [
"Operating on branches which are collections of objects\n",
"Here we deal with the simplest of the cuts: we decide to accept the event\n",
"only if the number of tracks is greater than 8."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "6948cacc",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:20.762398Z",
"iopub.status.busy": "2026-05-19T20:09:20.762274Z",
"iopub.status.idle": "2026-05-19T20:09:21.766223Z",
"shell.execute_reply": "2026-05-19T20:09:21.765573Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"62 events passed all filters\n"
]
}
],
"source": [
"n_cut = \"tracks.size() > 8\"\n",
"nentries = d.Filter(n_cut).Count()\n",
"\n",
"print(\"%s events passed all filters\" % nentries.GetValue())"
]
},
{
"cell_type": "markdown",
"id": "47035d2f",
"metadata": {},
"source": [
"Another possibility consists in creating a new column containing the\n",
"quantity we are interested in.\n",
"In this example, we will cut on the number of tracks and plot their\n",
"transverse momentum."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8c14ceab",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:21.785326Z",
"iopub.status.busy": "2026-05-19T20:09:21.785189Z",
"iopub.status.idle": "2026-05-19T20:09:21.980278Z",
"shell.execute_reply": "2026-05-19T20:09:21.979615Z"
}
},
"outputs": [],
"source": [
"getPt_code = \"\"\"\n",
"using namespace ROOT::VecOps;\n",
"ROOT::RVecD getPt(const RVec &tracks)\n",
"{\n",
" auto pt = [](const FourVector &v) { return v.pt(); };\n",
" return Map(tracks, pt);\n",
"}\n",
"\"\"\"\n",
"ROOT.gInterpreter.Declare(getPt_code)\n",
"\n",
"getPtWeights_code = \"\"\"\n",
"using namespace ROOT::VecOps;\n",
"ROOT::RVecD getPtWeights(const RVec &tracks)\n",
"{\n",
" auto ptWeight = [](const FourVector &v) { return 1. / v.Pt(); };\n",
" return Map(tracks, ptWeight);\n",
"};\n",
"\"\"\"\n",
"ROOT.gInterpreter.Declare(getPtWeights_code)\n",
"\n",
"augmented_d = (\n",
" d.Define(\"tracks_n\", \"(int)tracks.size()\")\n",
" .Filter(\"tracks_n > 2\")\n",
" .Define(\"tracks_pts\", \"getPt( tracks )\")\n",
" .Define(\"tracks_pts_weights\", \"getPtWeights( tracks )\")\n",
")"
]
},
{
"cell_type": "markdown",
"id": "43bf714f",
"metadata": {},
"source": [
"The histogram is initialised with a tuple containing the parameters of the\n",
"histogram"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4840bfa5",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:21.989240Z",
"iopub.status.busy": "2026-05-19T20:09:21.989113Z",
"iopub.status.idle": "2026-05-19T20:09:23.992161Z",
"shell.execute_reply": "2026-05-19T20:09:23.991526Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Info in : png file df002_trN.png has been created\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Info in : png file df002_trPts.png has been created\n",
"Info in : png file df002_trWPts.png has been created\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved figures to df002_*.png\n"
]
}
],
"source": [
"trN = augmented_d.Histo1D((\"\", \"\", 40, -0.5, 39.5), \"tracks_n\")\n",
"trPts = augmented_d.Histo1D(\"tracks_pts\")\n",
"trWPts = augmented_d.Histo1D(\"tracks_pts\", \"tracks_pts_weights\")\n",
"\n",
"c1 = ROOT.TCanvas()\n",
"trN.Draw()\n",
"c1.SaveAs(\"df002_trN.png\")\n",
"\n",
"c2 = ROOT.TCanvas()\n",
"trPts.Draw()\n",
"c2.SaveAs(\"df002_trPts.png\")\n",
"\n",
"c3 = ROOT.TCanvas()\n",
"trWPts.Draw()\n",
"c2.SaveAs(\"df002_trWPts.png\")\n",
"\n",
"print(\"Saved figures to df002_*.png\")"
]
},
{
"cell_type": "markdown",
"id": "535a6493",
"metadata": {},
"source": [
"Draw all canvases "
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "f58d0171",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:23.999201Z",
"iopub.status.busy": "2026-05-19T20:09:23.999073Z",
"iopub.status.idle": "2026-05-19T20:09:24.198173Z",
"shell.execute_reply": "2026-05-19T20:09:24.197787Z"
}
},
"outputs": [
{
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],
"source": [
"from ROOT import gROOT \n",
"gROOT.GetListOfCanvases().Draw()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
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