{ "cells": [ { "cell_type": "markdown", "id": "dfad2597", "metadata": {}, "source": [ "# df104_HiggsToTwoPhotons\n", "The Higgs to two photons analysis from the ATLAS Open Data 2020 release, with RDataFrame.\n", "\n", "This tutorial is the Higgs to two photons analysis from the ATLAS Open Data release in 2020\n", "(http://opendata.atlas.cern/release/2020/documentation/). The data was taken with the ATLAS detector\n", "during 2016 at a center-of-mass energy of 13 TeV. Although the Higgs to two photons decay is very rare,\n", "the contribution of the Higgs can be seen as a narrow peak around 125 GeV because of the excellent\n", "reconstruction and identification efficiency of photons at the ATLAS experiment.\n", "\n", "The analysis is translated to a RDataFrame workflow processing 1.7 GB of simulated events and data.\n", "\n", "\n", "\n", "\n", "**Author:** Stefan Wunsch (KIT, 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 08:10 PM." ] }, { "cell_type": "code", "execution_count": 1, "id": "52a0e1e2", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:10:33.012580Z", "iopub.status.busy": "2026-05-19T20:10:33.012466Z", "iopub.status.idle": "2026-05-19T20:10:34.001253Z", "shell.execute_reply": "2026-05-19T20:10:33.998978Z" } }, "outputs": [], "source": [ "import ROOT\n", "import os" ] }, { "cell_type": "markdown", "id": "2662ed1d", "metadata": {}, "source": [ "Enable multi-threading" ] }, { "cell_type": "code", "execution_count": 2, "id": "4478ec1b", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:10:34.013448Z", "iopub.status.busy": "2026-05-19T20:10:34.013300Z", "iopub.status.idle": "2026-05-19T20:10:34.139803Z", "shell.execute_reply": "2026-05-19T20:10:34.139406Z" } }, "outputs": [], "source": [ "ROOT.ROOT.EnableImplicitMT()" ] }, { "cell_type": "markdown", "id": "257363a9", "metadata": {}, "source": [ "Create a ROOT dataframe for each dataset" ] }, { "cell_type": "code", "execution_count": 3, "id": "d7a5b3b2", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:10:34.154622Z", "iopub.status.busy": "2026-05-19T20:10:34.154461Z", "iopub.status.idle": "2026-05-19T20:10:36.714702Z", "shell.execute_reply": "2026-05-19T20:10:36.708679Z" } }, "outputs": [], "source": [ "path = \"root://eospublic.cern.ch//eos/opendata/atlas/OutreachDatasets/2020-01-22\"\n", "df = {}\n", "df[\"data\"] = ROOT.RDataFrame(\"mini\", (os.path.join(path, \"GamGam/Data/data_{}.GamGam.root\".format(x)) for x in (\"A\", \"B\", \"C\", \"D\")))\n", "df[\"ggH\"] = ROOT.RDataFrame(\"mini\", os.path.join(path, \"GamGam/MC/mc_343981.ggH125_gamgam.GamGam.root\"))\n", "df[\"VBF\"] = ROOT.RDataFrame(\"mini\", os.path.join(path, \"GamGam/MC/mc_345041.VBFH125_gamgam.GamGam.root\"))\n", "processes = list(df.keys())" ] }, { "cell_type": "markdown", "id": "3e44bab0", "metadata": {}, "source": [ "Apply scale factors and MC weight for simulated events and a weight of 1 for the data" ] }, { "cell_type": "code", "execution_count": 4, "id": "5d292aa0", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:10:36.726223Z", "iopub.status.busy": "2026-05-19T20:10:36.726038Z", "iopub.status.idle": "2026-05-19T20:10:36.958279Z", "shell.execute_reply": "2026-05-19T20:10:36.947569Z" } }, "outputs": [], "source": [ "for p in [\"ggH\", \"VBF\"]:\n", " df[p] = df[p].Define(\"weight\",\n", " \"scaleFactor_PHOTON * scaleFactor_PhotonTRIGGER * scaleFactor_PILEUP * mcWeight\");\n", "df[\"data\"] = df[\"data\"].Define(\"weight\", \"1.0\")" ] }, { "cell_type": "markdown", "id": "1c634fd1", "metadata": {}, "source": [ "Select the events for the analysis" ] }, { "cell_type": "code", "execution_count": 5, "id": "8a2dce74", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:10:36.979354Z", "iopub.status.busy": "2026-05-19T20:10:36.979177Z", "iopub.status.idle": "2026-05-19T20:10:37.351800Z", "shell.execute_reply": "2026-05-19T20:10:37.336535Z" } }, "outputs": [], "source": [ "for p in processes:\n", " # Apply preselection cut on photon trigger\n", " df[p] = df[p].Filter(\"trigP\")\n", "\n", " # Find two good muons with tight ID, pt > 25 GeV and not in the transition region between barrel and encap\n", " df[p] = df[p].Define(\"goodphotons\", \"photon_isTightID && (photon_pt > 25000) && (abs(photon_eta) < 2.37) && ((abs(photon_eta) < 1.37) || (abs(photon_eta) > 1.52))\")\\\n", " .Filter(\"Sum(goodphotons) == 2\")\n", "\n", " # Take only isolated photons\n", " df[p] = df[p].Filter(\"Sum(photon_ptcone30[goodphotons] / photon_pt[goodphotons] < 0.065) == 2\")\\\n", " .Filter(\"Sum(photon_etcone20[goodphotons] / photon_pt[goodphotons] < 0.065) == 2\")" ] }, { "cell_type": "markdown", "id": "7d18b67a", "metadata": {}, "source": [ "Compile a function to compute the invariant mass of the diphoton system" ] }, { "cell_type": "code", "execution_count": 6, "id": "34f8eb84", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:10:37.378328Z", "iopub.status.busy": "2026-05-19T20:10:37.378098Z", "iopub.status.idle": "2026-05-19T20:10:37.562754Z", "shell.execute_reply": "2026-05-19T20:10:37.551718Z" } }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ROOT.gInterpreter.Declare(\n", "\"\"\"\n", "using namespace ROOT;\n", "float ComputeInvariantMass(RVecF pt, RVecF eta, RVecF phi, RVecF e) {\n", " ROOT::Math::PtEtaPhiEVector p1(pt[0], eta[0], phi[0], e[0]);\n", " ROOT::Math::PtEtaPhiEVector p2(pt[1], eta[1], phi[1], e[1]);\n", " return (p1 + p2).mass() / 1000.0;\n", "}\n", "\"\"\")" ] }, { "cell_type": "markdown", "id": "0aee2a6f", "metadata": {}, "source": [ "Define a new column with the invariant mass and perform final event selection" ] }, { "cell_type": "code", "execution_count": 7, "id": "7abbe325", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:10:37.565165Z", "iopub.status.busy": "2026-05-19T20:10:37.565033Z", "iopub.status.idle": "2026-05-19T20:10:37.929862Z", "shell.execute_reply": "2026-05-19T20:10:37.909676Z" } }, "outputs": [], "source": [ "hists = {}\n", "for p in processes:\n", " # Make four vectors and compute invariant mass\n", " df[p] = df[p].Define(\"m_yy\", \"ComputeInvariantMass(photon_pt[goodphotons], photon_eta[goodphotons], photon_phi[goodphotons], photon_E[goodphotons])\")\n", "\n", " # Make additional kinematic cuts and select mass window\n", " df[p] = df[p].Filter(\"photon_pt[goodphotons][0] / 1000.0 / m_yy > 0.35\")\\\n", " .Filter(\"photon_pt[goodphotons][1] / 1000.0 / m_yy > 0.25\")\\\n", " .Filter(\"m_yy > 105 && m_yy < 160\")\n", "\n", " # Book histogram of the invariant mass with this selection\n", " hists[p] = df[p].Histo1D(\n", " ROOT.RDF.TH1DModel(p, \"Diphoton invariant mass; m_{#gamma#gamma} [GeV];Events\", 30, 105, 160),\n", " \"m_yy\", \"weight\")" ] }, { "cell_type": "markdown", "id": "c0837308", "metadata": {}, "source": [ "Run the event loop" ] }, { "cell_type": "markdown", "id": "594e0d39", "metadata": {}, "source": [ "RunGraphs allows to run the event loops of the separate RDataFrame graphs\n", "concurrently. This results in an improved usage of the available resources\n", "if each separate RDataFrame can not utilize all available resources, e.g.,\n", "because not enough data is available." ] }, { "cell_type": "code", "execution_count": 8, "id": "304d2be9", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:10:37.940430Z", "iopub.status.busy": "2026-05-19T20:10:37.940272Z", "iopub.status.idle": "2026-05-19T20:10:50.042499Z", "shell.execute_reply": "2026-05-19T20:10:50.042005Z" } }, "outputs": [], "source": [ "ROOT.RDF.RunGraphs([hists[s] for s in [\"ggH\", \"VBF\", \"data\"]])\n", "\n", "ggh = hists[\"ggH\"].GetValue()\n", "vbf = hists[\"VBF\"].GetValue()\n", "data = hists[\"data\"].GetValue()" ] }, { "cell_type": "markdown", "id": "142c1328", "metadata": {}, "source": [ "Create the plot" ] }, { "cell_type": "markdown", "id": "491b6b96", "metadata": {}, "source": [ "Set styles" ] }, { "cell_type": "code", "execution_count": 9, "id": "3a03b1a5", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:10:50.054838Z", "iopub.status.busy": "2026-05-19T20:10:50.054691Z", "iopub.status.idle": "2026-05-19T20:10:50.160694Z", "shell.execute_reply": "2026-05-19T20:10:50.160238Z" } }, "outputs": [], "source": [ "ROOT.gROOT.SetStyle(\"ATLAS\")" ] }, { "cell_type": "markdown", "id": "5e509683", "metadata": {}, "source": [ "Create canvas with pads for main plot and data/MC ratio" ] }, { "cell_type": "code", "execution_count": 10, "id": "059d62b9", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:10:50.170370Z", "iopub.status.busy": "2026-05-19T20:10:50.170215Z", "iopub.status.idle": "2026-05-19T20:10:50.334128Z", "shell.execute_reply": "2026-05-19T20:10:50.333398Z" } }, "outputs": [], "source": [ "c = ROOT.TCanvas(\"c\", \"\", 700, 750)\n", "\n", "upper_pad = ROOT.TPad(\"upper_pad\", \"\", 0, 0.35, 1, 1)\n", "lower_pad = ROOT.TPad(\"lower_pad\", \"\", 0, 0, 1, 0.35)\n", "for p in [upper_pad, lower_pad]:\n", " p.SetLeftMargin(0.14)\n", " p.SetRightMargin(0.05)\n", " p.SetTickx(False)\n", " p.SetTicky(False)\n", "upper_pad.SetBottomMargin(0)\n", "lower_pad.SetTopMargin(0)\n", "lower_pad.SetBottomMargin(0.3)\n", "\n", "upper_pad.Draw()\n", "lower_pad.Draw()" ] }, { "cell_type": "markdown", "id": "f6eb6600", "metadata": {}, "source": [ "Fit signal + background model to data" ] }, { "cell_type": "code", "execution_count": 11, "id": "b90d80a0", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:10:50.343243Z", "iopub.status.busy": "2026-05-19T20:10:50.343087Z", "iopub.status.idle": "2026-05-19T20:10:50.525867Z", "shell.execute_reply": "2026-05-19T20:10:50.525371Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "****************************************\n", "Minimizer is Minuit2 / Migrad\n", "Chi2 = 19.9699\n", "NDf = 26\n", "Edm = 3.10817e-10\n", "NCalls = 161\n", "p0 = 94325 +/- 72.0526 \n", "p1 = -1777.22 +/- 0.778157 \n", "p2 = 11.5606 +/- 0.00536058 \n", "p3 = -0.0256281 +/- 2.66824e-05 \n", "p4 = 119.1 \t (fixed)\n", "p5 = 125 \t (fixed)\n", "p6 = 2.39 \t (fixed)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "Error in : TFitResult is empty - use the fit option S\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Error in : TFitResult is empty - use the fit option S\n", "Error in : TFitResult is empty - use the fit option S\n", "Error in : TFitResult is empty - use the fit option S\n", "Error in : TFitResult is empty - use the fit option S\n", "Error in : TFitResult is empty - use the fit option S\n", "Error in : TFitResult is empty - use the fit option S\n", "Error in : TFitResult is empty - use the fit option S\n", "Error in : TFitResult is empty - use the fit option S\n", "Error in : TFitResult is empty - use the fit option S\n", "Error in : TFitResult is empty - use the fit option S\n", "Error in : TFitResult is empty - use the fit option S\n", "Error in : TFitResult is empty - use the fit option S\n", "Error in : TFitResult is empty - use the fit option S\n", "Error in : TFitResult is empty - use the fit option S\n", "Error in : TFitResult is empty - use the fit option S\n", "Error in : TFitResult is empty - use the fit option S\n", "Error in : TFitResult is empty - use the fit option S\n", "Error in : TFitResult is empty - use the fit option S\n", "Error in : TFitResult is empty - use the fit option S\n", "Error in : TFitResult is empty - use the fit option S\n", "Error in : TFitResult is empty - use the fit option S\n" ] } ], "source": [ "fit = ROOT.TF1(\"fit\", \"([0]+[1]*x+[2]*x^2+[3]*x^3)+[4]*exp(-0.5*((x-[5])/[6])^2)\", 105, 160)\n", "fit.FixParameter(5, 125.0)\n", "fit.FixParameter(4, 119.1)\n", "fit.FixParameter(6, 2.39)\n", "fit.SetLineColor(2)\n", "fit.SetLineStyle(ROOT.kSolid)\n", "fit.SetLineWidth(2)\n", "data.Fit(\"fit\", \"0\", \"\", 105, 160)" ] }, { "cell_type": "markdown", "id": "8aa18962", "metadata": {}, "source": [ "Draw data" ] }, { "cell_type": "code", "execution_count": 12, "id": "590f375e", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:10:50.542576Z", "iopub.status.busy": "2026-05-19T20:10:50.542424Z", "iopub.status.idle": "2026-05-19T20:10:50.677950Z", "shell.execute_reply": "2026-05-19T20:10:50.677549Z" } }, "outputs": [], "source": [ "upper_pad.cd()\n", "data.SetMarkerStyle(20)\n", "data.SetMarkerSize(1.2)\n", "data.SetLineWidth(2)\n", "data.SetLineColor(\"black\")\n", "data.SetMinimum(1e-3)\n", "data.SetMaximum(8e3)\n", "data.GetYaxis().SetLabelSize(0.045)\n", "data.GetYaxis().SetTitleSize(0.05)\n", "data.SetStats(0)\n", "data.SetTitle(\"\")\n", "data.Draw(\"E\")" ] }, { "cell_type": "markdown", "id": "18e11ef9", "metadata": {}, "source": [ "Draw fit" ] }, { "cell_type": "code", "execution_count": 13, "id": "99e722d1", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:10:50.682553Z", "iopub.status.busy": "2026-05-19T20:10:50.682421Z", "iopub.status.idle": "2026-05-19T20:10:50.797207Z", "shell.execute_reply": "2026-05-19T20:10:50.796716Z" } }, "outputs": [], "source": [ "fit.Draw(\"SAME\")" ] }, { "cell_type": "markdown", "id": "4030c166", "metadata": {}, "source": [ "Draw background" ] }, { "cell_type": "code", "execution_count": 14, "id": "6cc9b596", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:10:50.799581Z", "iopub.status.busy": "2026-05-19T20:10:50.799387Z", "iopub.status.idle": "2026-05-19T20:10:50.918647Z", "shell.execute_reply": "2026-05-19T20:10:50.918173Z" } }, "outputs": [], "source": [ "bkg = ROOT.TF1(\"bkg\", \"([0]+[1]*x+[2]*x^2+[3]*x^3)\", 105, 160)\n", "for i in range(4):\n", " bkg.SetParameter(i, fit.GetParameter(i))\n", "bkg.SetLineColor(4)\n", "bkg.SetLineStyle(ROOT.kDashed)\n", "bkg.SetLineWidth(2)\n", "bkg.Draw(\"SAME\")" ] }, { "cell_type": "markdown", "id": "d25ddd1d", "metadata": {}, "source": [ "Scale simulated events with luminosity * cross-section / sum of weights\n", "and merge to single Higgs signal" ] }, { "cell_type": "code", "execution_count": 15, "id": "fca64e84", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:10:50.943523Z", "iopub.status.busy": "2026-05-19T20:10:50.943370Z", "iopub.status.idle": "2026-05-19T20:10:51.108813Z", "shell.execute_reply": "2026-05-19T20:10:51.099650Z" } }, "outputs": [], "source": [ "lumi = 10064.0\n", "ggh.Scale(lumi * 0.102 / 55922617.6297)\n", "vbf.Scale(lumi * 0.008518764 / 3441426.13711)\n", "higgs = ggh.Clone()\n", "higgs.Add(vbf)\n", "higgs.Draw(\"HIST SAME\")" ] }, { "cell_type": "markdown", "id": "2a276931", "metadata": {}, "source": [ "Draw ratio" ] }, { "cell_type": "code", "execution_count": 16, "id": "ea94a438", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:10:51.114245Z", "iopub.status.busy": "2026-05-19T20:10:51.114096Z", "iopub.status.idle": "2026-05-19T20:10:51.291440Z", "shell.execute_reply": "2026-05-19T20:10:51.290853Z" } }, "outputs": [], "source": [ "lower_pad.cd()\n", "\n", "ratiobkg = ROOT.TH1I(\"zero\", \"\", 100, 105, 160)\n", "ratiobkg.SetLineColor(4)\n", "ratiobkg.SetLineStyle(ROOT.kDashed)\n", "ratiobkg.SetLineWidth(2)\n", "ratiobkg.SetMinimum(-125)\n", "ratiobkg.SetMaximum(250)\n", "ratiobkg.GetXaxis().SetLabelSize(0.08)\n", "ratiobkg.GetXaxis().SetTitleSize(0.12)\n", "ratiobkg.GetXaxis().SetTitleOffset(1.0)\n", "ratiobkg.GetYaxis().SetLabelSize(0.08)\n", "ratiobkg.GetYaxis().SetTitleSize(0.09)\n", "ratiobkg.GetYaxis().SetTitle(\"Data - Bkg.\")\n", "ratiobkg.GetYaxis().CenterTitle()\n", "ratiobkg.GetYaxis().SetTitleOffset(0.7)\n", "ratiobkg.GetYaxis().SetNdivisions(503, False)\n", "ratiobkg.GetYaxis().ChangeLabel(-1, -1, 0)\n", "ratiobkg.GetXaxis().SetTitle(\"m_{#gamma#gamma} [GeV]\")\n", "ratiobkg.Draw(\"AXIS\")\n", "\n", "ratiosig = ROOT.TH1F(\"ratiosig\", \"ratiosig\", 5500, 105, 160)\n", "ratiosig.Eval(fit)\n", "ratiosig.SetLineColor(2)\n", "ratiosig.SetLineStyle(ROOT.kSolid)\n", "ratiosig.SetLineWidth(2)\n", "ratiosig.Add(bkg, -1)\n", "ratiosig.Draw(\"SAME\")\n", "\n", "ratiodata = data.Clone()\n", "ratiodata.Add(bkg, -1)\n", "for i in range(1, data.GetNbinsX()):\n", " ratiodata.SetBinError(i, data.GetBinError(i))\n", "ratiodata.Draw(\"E SAME\")" ] }, { "cell_type": "markdown", "id": "33d5883f", "metadata": {}, "source": [ "Add legend" ] }, { "cell_type": "code", "execution_count": 17, "id": "809f1b11", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:10:51.293137Z", "iopub.status.busy": "2026-05-19T20:10:51.293013Z", "iopub.status.idle": "2026-05-19T20:10:51.421351Z", "shell.execute_reply": "2026-05-19T20:10:51.420878Z" } }, "outputs": [], "source": [ "upper_pad.cd()\n", "legend = ROOT.TLegend(0.55, 0.55, 0.89, 0.85)\n", "legend.SetTextFont(42)\n", "legend.SetFillStyle(0)\n", "legend.SetBorderSize(0)\n", "legend.SetTextSize(0.05)\n", "legend.SetTextAlign(32)\n", "legend.AddEntry(data, \"Data\" ,\"lep\")\n", "legend.AddEntry(bkg, \"Background\", \"l\")\n", "legend.AddEntry(fit, \"Signal + Bkg.\", \"l\")\n", "legend.AddEntry(higgs, \"Signal\", \"l\")\n", "legend.Draw()" ] }, { "cell_type": "markdown", "id": "1482f03e", "metadata": {}, "source": [ "Add ATLAS label" ] }, { "cell_type": "code", "execution_count": 18, "id": "01c84bd8", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:10:51.423225Z", "iopub.status.busy": "2026-05-19T20:10:51.423107Z", "iopub.status.idle": "2026-05-19T20:10:51.538917Z", "shell.execute_reply": "2026-05-19T20:10:51.537353Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "text = ROOT.TLatex()\n", "text.SetNDC()\n", "text.SetTextFont(72)\n", "text.SetTextSize(0.05)\n", "text.DrawLatex(0.18, 0.84, \"ATLAS\")\n", "text.SetTextFont(42)\n", "text.DrawLatex(0.18 + 0.13, 0.84, \"Open Data\")\n", "text.SetTextSize(0.04)\n", "text.DrawLatex(0.18, 0.78, \"#sqrt{s} = 13 TeV, 10 fb^{-1}\")" ] }, { "cell_type": "markdown", "id": "0c90531a", "metadata": {}, "source": [ "Save the plot" ] }, { "cell_type": "code", "execution_count": 19, "id": "a5ad27d6", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:10:51.540534Z", "iopub.status.busy": "2026-05-19T20:10:51.540399Z", "iopub.status.idle": "2026-05-19T20:10:51.713129Z", "shell.execute_reply": "2026-05-19T20:10:51.712324Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Info in : png file df104_HiggsToTwoPhotons.png has been created\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Saved figure to df104_HiggsToTwoPhotons.png\n" ] } ], "source": [ "c.SaveAs(\"df104_HiggsToTwoPhotons.png\")\n", "print(\"Saved figure to df104_HiggsToTwoPhotons.png\")" ] }, { "cell_type": "markdown", "id": "0b45b013", "metadata": {}, "source": [ "Draw all canvases " ] }, { "cell_type": "code", "execution_count": 20, "id": "c5daf506", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:10:51.714571Z", "iopub.status.busy": "2026-05-19T20:10:51.714438Z", "iopub.status.idle": "2026-05-19T20:10:51.889270Z", "shell.execute_reply": "2026-05-19T20:10:51.888421Z" } }, "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 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.12" } }, "nbformat": 4, "nbformat_minor": 5 }