{
"cells": [
{
"cell_type": "markdown",
"id": "c3da9b87",
"metadata": {},
"source": [
"# df014_CSVDataSource\n",
"Process a CSV file with RDataFrame and the CSV data source.\n",
"\n",
"This tutorial illustrates how use the RDataFrame in combination with a\n",
"RDataSource. In this case we use a RCsvDS. This data source allows to read\n",
"a CSV file from a RDataFrame.\n",
"As a result of running this tutorial, we will produce plots of the dimuon\n",
"spectrum starting from a subset of the CMS collision events of Run2010B.\n",
"Dataset Reference:\n",
"McCauley, T. (2014). Dimuon event information derived from the Run2010B\n",
"public Mu dataset. CERN Open Data Portal.\n",
"DOI: [10.7483/OPENDATA.CMS.CB8H.MFFA](http://opendata.cern.ch/record/700).\n",
"\n",
"\n",
"\n",
"\n",
"**Author:** Enric Tejedor (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": "markdown",
"id": "c85640e3",
"metadata": {},
"source": [
"Let's first create a RDF that will read from the CSV file.\n",
"The types of the columns will be automatically inferred."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b5034614",
"metadata": {
"collapsed": false,
"execution": {
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"shell.execute_reply": "2026-05-19T20:09:42.341046Z"
}
},
"outputs": [],
"source": [
"auto fileUrl = \"http://root.cern/files/tutorials/df014_CsvDataSource_MuRun2010B.csv\";\n",
"auto df = ROOT::RDF::FromCSV(fileUrl);"
]
},
{
"cell_type": "markdown",
"id": "2febcd69",
"metadata": {},
"source": [
"Now we will apply a first filter based on two columns of the CSV,\n",
"and we will define a new column that will contain the invariant mass.\n",
"Note how the new invariant mass column is defined from several other\n",
"columns that already existed in the CSV file."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f8197b40",
"metadata": {
"collapsed": false,
"execution": {
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"shell.execute_reply": "2026-05-19T20:09:42.562079Z"
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"source": [
"auto filteredEvents =\n",
" df.Filter(\"Q1 * Q2 == -1\")\n",
" .Define(\"m\", \"sqrt(pow(E1 + E2, 2) - (pow(px1 + px2, 2) + pow(py1 + py2, 2) + pow(pz1 + pz2, 2)))\");"
]
},
{
"cell_type": "markdown",
"id": "68704ac5",
"metadata": {},
"source": [
"Next we create a histogram to hold the invariant mass values and we draw it."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "1ed64cb9",
"metadata": {
"collapsed": false,
"execution": {
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"shell.execute_reply": "2026-05-19T20:09:45.479534Z"
}
},
"outputs": [],
"source": [
"auto invMass =\n",
" filteredEvents.Histo1D({\"invMass\", \"CMS Opendata: #mu#mu mass;#mu#mu mass [GeV];Events\", 512, 2, 110}, \"m\");\n",
"\n",
"auto c = new TCanvas();\n",
"c->SetLogx();\n",
"c->SetLogy();\n",
"invMass->DrawClone();"
]
},
{
"cell_type": "markdown",
"id": "8fae4ffb",
"metadata": {},
"source": [
"We will now produce a plot also for the J/Psi particle. We will plot\n",
"on the same canvas the full spectrum and the zoom in on the J/psi particle.\n",
"First we will create the full spectrum histogram from the invariant mass\n",
"column, using a different histogram model than before."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "bef18571",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:45.482106Z",
"iopub.status.busy": "2026-05-19T20:09:45.481984Z",
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"shell.execute_reply": "2026-05-19T20:09:45.684262Z"
}
},
"outputs": [],
"source": [
"auto fullSpectrum =\n",
" filteredEvents.Histo1D({\"Spectrum\", \"Subset of CMS Run 2010B;#mu#mu mass [GeV];Events\", 1024, 2, 110}, \"m\");"
]
},
{
"cell_type": "markdown",
"id": "ea4033bd",
"metadata": {},
"source": [
"Next we will create the histogram for the J/psi particle, applying first\n",
"the corresponding cut."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c5a41535",
"metadata": {
"collapsed": false,
"execution": {
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"shell.execute_reply": "2026-05-19T20:09:46.083383Z"
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},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"input_line_69:4:1: warning: captures will be by reference, not copy\n",
"auto jpsiCut = [jpsiLow, jpsiHigh](double m) { return m < jpsiHigh && m > jpsiLow; };\n",
"^\n"
]
}
],
"source": [
"double jpsiLow = 2.95;\n",
"double jpsiHigh = 3.25;\n",
"auto jpsiCut = [jpsiLow, jpsiHigh](double m) { return m < jpsiHigh && m > jpsiLow; };\n",
"auto jpsi =\n",
" filteredEvents.Filter(jpsiCut, {\"m\"})\n",
" .Histo1D({\"jpsi\", \"Subset of CMS Run 2010B: J/#psi window;#mu#mu mass [GeV];Events\", 128, jpsiLow, jpsiHigh},\n",
" \"m\");"
]
},
{
"cell_type": "markdown",
"id": "6f372418",
"metadata": {},
"source": [
"Finally we draw the two histograms side by side."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2c983ecd",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2026-05-19T20:09:46.086366Z",
"iopub.status.busy": "2026-05-19T20:09:46.086238Z",
"iopub.status.idle": "2026-05-19T20:09:47.598796Z",
"shell.execute_reply": "2026-05-19T20:09:47.593389Z"
}
},
"outputs": [],
"source": [
"auto dualCanvas = new TCanvas(\"DualCanvas\", \"DualCanvas\", 800, 512);\n",
"dualCanvas->Divide(2, 1);\n",
"auto leftPad = dualCanvas->cd(1);\n",
"leftPad->SetLogx();\n",
"leftPad->SetLogy();\n",
"fullSpectrum->DrawClone(\"Hist\");\n",
"dualCanvas->cd(2);\n",
"jpsi->SetMarkerStyle(20);\n",
"jpsi->DrawClone(\"HistP\");\n",
"\n",
"return 0;"
]
},
{
"cell_type": "markdown",
"id": "b5a4831a",
"metadata": {},
"source": [
"Draw all canvases "
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "05801eeb",
"metadata": {
"collapsed": false,
"execution": {
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"shell.execute_reply": "2026-05-19T20:09:47.816385Z"
}
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