{ "cells": [ { "cell_type": "markdown", "id": "735e9721", "metadata": {}, "source": [ "# rf402_datahandling\n", "Data and categories: tools for manipulation of (un)binned datasets\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 08:31 PM." ] }, { "cell_type": "code", "execution_count": 1, "id": "132f7ba9", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:31:33.035909Z", "iopub.status.busy": "2026-05-19T20:31:33.035790Z", "iopub.status.idle": "2026-05-19T20:31:33.992251Z", "shell.execute_reply": "2026-05-19T20:31:33.991568Z" } }, "outputs": [], "source": [ "import ROOT\n", "import math" ] }, { "cell_type": "markdown", "id": "15188d7b", "metadata": {}, "source": [ "WVE Add reduction by range" ] }, { "cell_type": "markdown", "id": "60aa1d79", "metadata": {}, "source": [ "Binned (RooDataHist) and unbinned datasets (RooDataSet) share\n", "many properties and inherit from a common abstract base class\n", "(RooAbsData), provides an interface for all operations\n", "that can be performed regardless of the data format" ] }, { "cell_type": "code", "execution_count": 2, "id": "30443c2c", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:31:33.994372Z", "iopub.status.busy": "2026-05-19T20:31:33.994238Z", "iopub.status.idle": "2026-05-19T20:31:34.164334Z", "shell.execute_reply": "2026-05-19T20:31:34.163765Z" } }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = ROOT.RooRealVar(\"x\", \"x\", -10, 10)\n", "y = ROOT.RooRealVar(\"y\", \"y\", 0, 40)\n", "c = ROOT.RooCategory(\"c\", \"c\")\n", "c.defineType(\"Plus\", +1)\n", "c.defineType(\"Minus\", -1)" ] }, { "cell_type": "markdown", "id": "02906e5c", "metadata": {}, "source": [ "Basic operations on unbinned datasetss\n", "--------------------------------------------------------------" ] }, { "cell_type": "markdown", "id": "7ac644d2", "metadata": {}, "source": [ "ROOT.RooDataSet is an unbinned dataset (a collection of points in\n", "N-dimensional space)" ] }, { "cell_type": "code", "execution_count": 3, "id": "ebaf92fa", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:31:34.166654Z", "iopub.status.busy": "2026-05-19T20:31:34.166510Z", "iopub.status.idle": "2026-05-19T20:31:34.360377Z", "shell.execute_reply": "2026-05-19T20:31:34.359716Z" } }, "outputs": [], "source": [ "d = ROOT.RooDataSet(\"d\", \"d\", {x, y, c})" ] }, { "cell_type": "markdown", "id": "0dc54a6c", "metadata": {}, "source": [ "Unlike ROOT.RooAbsArgs (ROOT.RooAbsPdf, ROOT.RooFormulaVar,....) datasets are not attached to\n", "the variables they are constructed from. Instead they are attached to an internal\n", "clone of the supplied set of arguments" ] }, { "cell_type": "markdown", "id": "c6b8209e", "metadata": {}, "source": [ "Fill d with dummy values" ] }, { "cell_type": "code", "execution_count": 4, "id": "1104a9fe", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:31:34.362552Z", "iopub.status.busy": "2026-05-19T20:31:34.362420Z", "iopub.status.idle": "2026-05-19T20:31:34.532873Z", "shell.execute_reply": "2026-05-19T20:31:34.532267Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RooRealVar::x = -10 L(-10 - 10) \n", " RooRealVar::y = 0 L(0 - 40) \n", " { {\"Minus\" , -1}, {\"Plus\" , 1} }\n", "\n", "RooRealVar::x = -9.98 L(-10 - 10) \n", " RooRealVar::y = 1 L(0 - 40) \n", " { {\"Minus\" , -1}, {\"Plus\" , 1} }\n", "\n", "RooRealVar::x = -9.96 L(-10 - 10) \n", " RooRealVar::y = 1.41421 L(0 - 40) \n", " { {\"Minus\" , -1}, {\"Plus\" , 1} }\n", "\n", "\n", "DataStore d (d)\n", " Contains 1000 entries\n", " Observables: \n", " 1) y = 31.607 L(0 - 40) \"y\"\n", " 2) x = 9.98 L(-10 - 10) \"x\"\n", " 3) c = Plus(idx = 1)\n", " \"c\"\n" ] } ], "source": [ "for i in range(1000):\n", " x.setVal(i / 50 - 10)\n", " y.setVal(math.sqrt(1.0 * i))\n", " if i % 2:\n", " c.setLabel(\"Plus\")\n", " else:\n", " c.setLabel(\"Minus\")\n", "\n", " # We must explicitly refer to x,y, here to pass the values because\n", " # d is not linked to them (as explained above)\n", " if i < 3:\n", " print(x, y, c)\n", " print(type(x))\n", " d.add({x, y, c})\n", "\n", "d.Print(\"v\")\n", "print(\"\")" ] }, { "cell_type": "markdown", "id": "6ab6a504", "metadata": {}, "source": [ "The get() function returns a pointer to the internal copy of the RooArgSet(x,y,c)\n", "supplied in the constructor" ] }, { "cell_type": "code", "execution_count": 5, "id": "41bcdd07", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:31:34.534683Z", "iopub.status.busy": "2026-05-19T20:31:34.534539Z", "iopub.status.idle": "2026-05-19T20:31:34.644542Z", "shell.execute_reply": "2026-05-19T20:31:34.644117Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 1) 0x556825620140 RooRealVar:: y = 31.607 L(0 - 40) \"y\"\n", " 2) 0x55682c48e260 RooRealVar:: x = 9.98 L(-10 - 10) \"x\"\n", " 3) 0x55682cbcecf0 RooCategory:: c = Plus(idx = 1)\n", " \"c\"\n", "\n" ] } ], "source": [ "row = d.get()\n", "row.Print(\"v\")\n", "print(\"\")" ] }, { "cell_type": "markdown", "id": "ba935212", "metadata": {}, "source": [ "Get with an argument loads a specific data point in row and returns\n", "a pointer to row argset. get() always returns the same pointer, unless\n", "an invalid row number is specified. In that case a null ptr is returned" ] }, { "cell_type": "code", "execution_count": 6, "id": "e538035b", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:31:34.663840Z", "iopub.status.busy": "2026-05-19T20:31:34.663687Z", "iopub.status.idle": "2026-05-19T20:31:34.772382Z", "shell.execute_reply": "2026-05-19T20:31:34.771817Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 1) 0x556825620140 RooRealVar:: y = 30 L(0 - 40) \"y\"\n", " 2) 0x55682c48e260 RooRealVar:: x = 8 L(-10 - 10) \"x\"\n", " 3) 0x55682cbcecf0 RooCategory:: c = Minus(idx = -1)\n", " \"c\"\n" ] } ], "source": [ "d.get(900).Print(\"v\")\n", "print(\"\")" ] }, { "cell_type": "markdown", "id": "09a29f1f", "metadata": {}, "source": [ "Reducing, appending and merging\n", "-------------------------------------------------------------" ] }, { "cell_type": "markdown", "id": "5eba4b29", "metadata": {}, "source": [ "The reduce() function returns a dataset which is a subset of the\n", "original" ] }, { "cell_type": "code", "execution_count": 7, "id": "6f66a2db", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:31:34.774135Z", "iopub.status.busy": "2026-05-19T20:31:34.774008Z", "iopub.status.idle": "2026-05-19T20:31:34.905780Z", "shell.execute_reply": "2026-05-19T20:31:34.905062Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " >> d1 has only columns x,c\n", "\n", " >> d2 has only column y\n", "\n", " >> d3 has only the points with y>5.17\n", "DataStore d (d)\n", " Contains 1000 entries\n", " Observables: \n", " 1) c = Plus(idx = 1)\n", " \"c\"\n", " 2) x = 9.98 L(-10 - 10) \"x\"\n", "DataStore d (d)\n", " Contains 1000 entries\n", " Observables: \n", " 1) y = 31.607 L(0 - 40) \"y\"\n", "\n", " >> d4 has only columns x, for data points with y>5.17\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "DataStore d (d)\n", " Contains 973 entries\n", " Observables: \n", " 1) y = 31.607 L(0 - 40) \"y\"\n", " 2) x = 9.98 L(-10 - 10) \"x\"\n", " 3) c = Plus(idx = 1)\n", " \"c\"\n", "DataStore d (d)\n", " Contains 973 entries\n", " Observables: \n", " 1) c = Plus(idx = 1)\n", " \"c\"\n", " 2) x = 9.98 L(-10 - 10) \"x\"\n" ] } ], "source": [ "print(\"\\n >> d1 has only columns x,c\")\n", "d1 = d.reduce({x, c})\n", "d1.Print(\"v\")\n", "\n", "print(\"\\n >> d2 has only column y\")\n", "d2 = d.reduce({y})\n", "d2.Print(\"v\")\n", "\n", "print(\"\\n >> d3 has only the points with y>5.17\")\n", "d3 = d.reduce(\"y>5.17\")\n", "d3.Print(\"v\")\n", "\n", "print(\"\\n >> d4 has only columns x, for data points with y>5.17\")\n", "d4 = d.reduce({x, c}, \"y>5.17\")\n", "d4.Print(\"v\")" ] }, { "cell_type": "markdown", "id": "cd90fc88", "metadata": {}, "source": [ "The merge() function adds two data set column-wise" ] }, { "cell_type": "code", "execution_count": 8, "id": "e5c545c5", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:31:34.907365Z", "iopub.status.busy": "2026-05-19T20:31:34.907203Z", "iopub.status.idle": "2026-05-19T20:31:35.027118Z", "shell.execute_reply": "2026-05-19T20:31:35.026560Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " >> merge d2(y) with d1(x,c) to form d1(x,c,y)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "DataStore d (d)\n", " Contains 1000 entries\n", " Observables: \n", " 1) c = Plus(idx = 1)\n", " \"c\"\n", " 2) x = 9.98 L(-10 - 10) \"x\"\n", " 3) y = 31.607 L(0 - 40) \"y\"\n" ] } ], "source": [ "print(\"\\n >> merge d2(y) with d1(x,c) to form d1(x,c,y)\")\n", "d1.merge(d2)\n", "d1.Print(\"v\")" ] }, { "cell_type": "markdown", "id": "3c68e9d0", "metadata": {}, "source": [ "The append() function adds two datasets row-wise" ] }, { "cell_type": "code", "execution_count": 9, "id": "ed299180", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:31:35.028877Z", "iopub.status.busy": "2026-05-19T20:31:35.028754Z", "iopub.status.idle": "2026-05-19T20:31:35.137531Z", "shell.execute_reply": "2026-05-19T20:31:35.136815Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " >> append data points of d3 to d1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "DataStore d (d)\n", " Contains 1973 entries\n", " Observables: \n", " 1) c = Plus(idx = 1)\n", " \"c\"\n", " 2) x = 9.98 L(-10 - 10) \"x\"\n", " 3) y = 31.607 L(0 - 40) \"y\"\n" ] } ], "source": [ "print(\"\\n >> append data points of d3 to d1\")\n", "d1.append(d3)\n", "d1.Print(\"v\")" ] }, { "cell_type": "markdown", "id": "d56211f3", "metadata": {}, "source": [ "Operations on binned datasets\n", "---------------------------------------------------------" ] }, { "cell_type": "markdown", "id": "92c20f9e", "metadata": {}, "source": [ "A binned dataset can be constructed empty, an unbinned dataset, or\n", "from a ROOT native histogram (TH1,2,3)" ] }, { "cell_type": "code", "execution_count": 10, "id": "d28e4c43", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:31:35.139294Z", "iopub.status.busy": "2026-05-19T20:31:35.139165Z", "iopub.status.idle": "2026-05-19T20:31:35.242589Z", "shell.execute_reply": "2026-05-19T20:31:35.241855Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">> construct dh (binned) from d(unbinned) but only take the x and y dimensions, \n", ">> the category 'c' will be projected in the filling process\n" ] } ], "source": [ "print(\">> construct dh (binned) from d(unbinned) but only take the x and y dimensions, \")\n", "print(\">> the category 'c' will be projected in the filling process\")" ] }, { "cell_type": "markdown", "id": "fb9ddf92", "metadata": {}, "source": [ "The binning of real variables (like x,y) is done using their fit range\n", "'get/setRange()' and number of specified fit bins 'get/setBins()'.\n", "Category dimensions of binned datasets get one bin per defined category\n", "state" ] }, { "cell_type": "code", "execution_count": 11, "id": "5aef72b4", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:31:35.244586Z", "iopub.status.busy": "2026-05-19T20:31:35.244416Z", "iopub.status.idle": "2026-05-19T20:31:35.427177Z", "shell.execute_reply": "2026-05-19T20:31:35.426458Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DataStore dh (binned version of d)\n", " Contains 100 entries\n", " Observables: \n", " 1) y = 38 L(0 - 40) B(10) \"y\"\n", " 2) x = 9 L(-10 - 10) B(10) \"x\"\n", "Binned Dataset dh (binned version of d)\n", " Contains 100 bins with a total weight of 1000\n", " Observables: 1) y = 38 L(0 - 40) B(10) \"y\"\n", " 2) x = 9 L(-10 - 10) B(10) \"x\"\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x.setBins(10)\n", "y.setBins(10)\n", "dh = ROOT.RooDataHist(\"dh\", \"binned version of d\", {x, y}, d)\n", "dh.Print(\"v\")\n", "\n", "yframe = y.frame(Bins=10, Title=\"Operations on binned datasets\")\n", "dh.plotOn(yframe) # plot projection of 2D binned data on y" ] }, { "cell_type": "markdown", "id": "ade8133e", "metadata": {}, "source": [ "Examine the statistics of a binned dataset" ] }, { "cell_type": "code", "execution_count": 12, "id": "7f62b4aa", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:31:35.428908Z", "iopub.status.busy": "2026-05-19T20:31:35.428786Z", "iopub.status.idle": "2026-05-19T20:31:35.557251Z", "shell.execute_reply": "2026-05-19T20:31:35.556642Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">> number of bins in dh : 100\n", ">> sum of weights in dh : 1000.0\n" ] } ], "source": [ "print(\">> number of bins in dh : \", dh.numEntries())\n", "print(\">> sum of weights in dh : \", dh.sum(False))" ] }, { "cell_type": "markdown", "id": "dffe8782", "metadata": {}, "source": [ "accounts for bin volume" ] }, { "cell_type": "code", "execution_count": 13, "id": "cf9c93ec", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:31:35.559254Z", "iopub.status.busy": "2026-05-19T20:31:35.559131Z", "iopub.status.idle": "2026-05-19T20:31:35.662422Z", "shell.execute_reply": "2026-05-19T20:31:35.661838Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">> integral over histogram: 8000.0\n" ] } ], "source": [ "print(\">> integral over histogram: \", dh.sum(True))" ] }, { "cell_type": "markdown", "id": "a6b43d93", "metadata": {}, "source": [ "Locate a bin from a set of coordinates and retrieve its properties" ] }, { "cell_type": "code", "execution_count": 14, "id": "a8a983f0", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:31:35.664224Z", "iopub.status.busy": "2026-05-19T20:31:35.664085Z", "iopub.status.idle": "2026-05-19T20:31:35.767247Z", "shell.execute_reply": "2026-05-19T20:31:35.766832Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">> retrieving the properties of the bin enclosing coordinate (x,y) = (0.3,20.5) bin center:\n" ] } ], "source": [ "x.setVal(0.3)\n", "y.setVal(20.5)\n", "print(\">> retrieving the properties of the bin enclosing coordinate (x,y) = (0.3,20.5) bin center:\")" ] }, { "cell_type": "markdown", "id": "30a66396", "metadata": {}, "source": [ "load bin center coordinates in internal buffer" ] }, { "cell_type": "code", "execution_count": 15, "id": "cb2740e1", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:31:35.775042Z", "iopub.status.busy": "2026-05-19T20:31:35.774915Z", "iopub.status.idle": "2026-05-19T20:31:35.885454Z", "shell.execute_reply": "2026-05-19T20:31:35.884872Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " weight = 76.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 1) 0x55682b1d37d0 RooRealVar:: y = 22 L(0 - 40) B(10) \"y\"\n", " 2) 0x55682cff2660 RooRealVar:: x = 1 L(-10 - 10) B(10) \"x\"\n" ] } ], "source": [ "dh.get({x, y}).Print(\"v\")\n", "print(\" weight = \", dh.weight()) # return weight of last loaded coordinates" ] }, { "cell_type": "markdown", "id": "16243af5", "metadata": {}, "source": [ "Reduce the 2-dimensional binned dataset to a 1-dimensional binned dataset\n", "\n", "All reduce() methods are interfaced in RooAbsData. All reduction techniques\n", "demonstrated on unbinned datasets can be applied to binned datasets as\n", "well." ] }, { "cell_type": "code", "execution_count": 16, "id": "4794cefa", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:31:35.886959Z", "iopub.status.busy": "2026-05-19T20:31:35.886763Z", "iopub.status.idle": "2026-05-19T20:31:35.999623Z", "shell.execute_reply": "2026-05-19T20:31:35.998967Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">> Creating 1-dimensional projection on y of dh for bins with x>0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "DataStore dh (binned version of d)\n", " Contains 10 entries\n", " Observables: \n", " 1) y = 38 L(0 - 40) B(10) \"y\"\n", "Binned Dataset dh (binned version of d)\n", " Contains 10 bins with a total weight of 500\n", " Observables: 1) y = 38 L(0 - 40) B(10) \"y\"\n" ] } ], "source": [ "print(\">> Creating 1-dimensional projection on y of dh for bins with x>0\")\n", "dh2 = dh.reduce({y}, \"x>0\")\n", "dh2.Print(\"v\")" ] }, { "cell_type": "markdown", "id": "814d38f9", "metadata": {}, "source": [ "Add dh2 to yframe and redraw" ] }, { "cell_type": "code", "execution_count": 17, "id": "2468c9b2", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:31:36.000861Z", "iopub.status.busy": "2026-05-19T20:31:36.000745Z", "iopub.status.idle": "2026-05-19T20:31:36.117534Z", "shell.execute_reply": "2026-05-19T20:31:36.116850Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[#1] INFO:Plotting -- RooPlot::updateFitRangeNorm: New event count of 500 will supersede previous event count of 1000 for normalization of PDF projections\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dh2.plotOn(yframe, LineColor=\"r\", MarkerColor=\"r\")" ] }, { "cell_type": "markdown", "id": "510609a6", "metadata": {}, "source": [ "Saving and loading from file\n", "-------------------------------------------------------" ] }, { "cell_type": "markdown", "id": "b1a37d1f", "metadata": {}, "source": [ "Datasets can be persisted with ROOT I/O" ] }, { "cell_type": "code", "execution_count": 18, "id": "8a612f11", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:31:36.119172Z", "iopub.status.busy": "2026-05-19T20:31:36.119052Z", "iopub.status.idle": "2026-05-19T20:31:36.360597Z", "shell.execute_reply": "2026-05-19T20:31:36.360179Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " >> Persisting d via ROOT I/O\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "TFile**\t\trf402_datahandling.root\t\n", " TFile*\t\trf402_datahandling.root\t\n", " KEY: RooDataSet\td;1\td\n", " KEY: TProcessID\tProcessID0;1\tb974c63b-53c1-11f1-964b-0200590abeef\n" ] } ], "source": [ "print(\"\\n >> Persisting d via ROOT I/O\")\n", "f = ROOT.TFile(\"rf402_datahandling.root\", \"RECREATE\")\n", "d.Write()\n", "f.ls()" ] }, { "cell_type": "markdown", "id": "61a7f32f", "metadata": {}, "source": [ "To read back in future session:\n", "> ROOT.TFile f(\"rf402_datahandling.root\")\n", "> d = (ROOT.RooDataSet*) f.FindObject(\"d\")" ] }, { "cell_type": "code", "execution_count": 19, "id": "575eca4e", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:31:36.390525Z", "iopub.status.busy": "2026-05-19T20:31:36.390331Z", "iopub.status.idle": "2026-05-19T20:31:36.609919Z", "shell.execute_reply": "2026-05-19T20:31:36.609523Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Info in : png file rf402_datahandling.png has been created\n" ] } ], "source": [ "c = ROOT.TCanvas(\"rf402_datahandling\", \"rf402_datahandling\", 600, 600)\n", "ROOT.gPad.SetLeftMargin(0.15)\n", "yframe.GetYaxis().SetTitleOffset(1.4)\n", "yframe.Draw()\n", "\n", "c.SaveAs(\"rf402_datahandling.png\")" ] }, { "cell_type": "markdown", "id": "30594642", "metadata": {}, "source": [ "Draw all canvases " ] }, { "cell_type": "code", "execution_count": 20, "id": "f03e5c61", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-05-19T20:31:36.626454Z", "iopub.status.busy": "2026-05-19T20:31:36.626284Z", "iopub.status.idle": "2026-05-19T20:31:36.814941Z", "shell.execute_reply": "2026-05-19T20:31:36.814365Z" } }, "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 }