{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "30649461",
   "metadata": {},
   "source": [
    "# rf711_lagrangianmorph\n",
    "Morphing effective field theory distributions with RooLagrangianMorphFunc.\n",
    "A morphing function as a function of one coefficient is setup and can be used\n",
    "to obtain the distribution for any value of the coefficient.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "**Author:** Rahul Balasubramanian  \n",
    "<i><small>This notebook tutorial was automatically generated with <a href= \"https://github.com/root-project/root/blob/master/documentation/doxygen/converttonotebook.py\">ROOTBOOK-izer</a> from the macro found in the ROOT repository  on Tuesday, May 19, 2026 at 08:35 PM.</small></i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bd87f19b",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:09.128849Z",
     "iopub.status.busy": "2026-05-19T20:35:09.128717Z",
     "iopub.status.idle": "2026-05-19T20:35:10.108420Z",
     "shell.execute_reply": "2026-05-19T20:35:10.107829Z"
    }
   },
   "outputs": [],
   "source": [
    "import ROOT\n",
    "\n",
    "ROOT.gStyle.SetOptStat(0)\n",
    "ROOT.PyConfig.IgnoreCommandLineOptions = True\n",
    "ROOT.gROOT.SetBatch(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33165e87",
   "metadata": {},
   "source": [
    "Create functions\n",
    "-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "749e384b",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:10.110288Z",
     "iopub.status.busy": "2026-05-19T20:35:10.110149Z",
     "iopub.status.idle": "2026-05-19T20:35:10.225637Z",
     "shell.execute_reply": "2026-05-19T20:35:10.224490Z"
    }
   },
   "outputs": [],
   "source": [
    "observablename = \"pTV\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82bf1cc1",
   "metadata": {},
   "source": [
    "Setup observable that is to be morphed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cd2675cf",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:10.227158Z",
     "iopub.status.busy": "2026-05-19T20:35:10.227024Z",
     "iopub.status.idle": "2026-05-19T20:35:10.378563Z",
     "shell.execute_reply": "2026-05-19T20:35:10.377382Z"
    }
   },
   "outputs": [],
   "source": [
    "obsvar = ROOT.RooRealVar(observablename, \"p_{T}^{V}\", 10, 600)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c73c46d4",
   "metadata": {},
   "source": [
    "Setup two couplings that enters the morphing function\n",
    "kSM -> SM coupling set to constant (1)\n",
    "cHq3 -> EFT parameter with NewPhysics attribute set to true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e5d640a8",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:10.380189Z",
     "iopub.status.busy": "2026-05-19T20:35:10.380051Z",
     "iopub.status.idle": "2026-05-19T20:35:10.492289Z",
     "shell.execute_reply": "2026-05-19T20:35:10.491541Z"
    }
   },
   "outputs": [],
   "source": [
    "kSM = ROOT.RooRealVar(\"kSM\", \"sm modifier\", 1.0)\n",
    "cHq3 = ROOT.RooRealVar(\"cHq3\", \"EFT modifier\", 0.0, 1.0)\n",
    "cHq3.setAttribute(\"NewPhysics\", True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81070f27",
   "metadata": {},
   "source": [
    "Inputs to setup config\n",
    "-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f42e7b28",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:10.494288Z",
     "iopub.status.busy": "2026-05-19T20:35:10.494154Z",
     "iopub.status.idle": "2026-05-19T20:35:10.601775Z",
     "shell.execute_reply": "2026-05-19T20:35:10.601131Z"
    }
   },
   "outputs": [],
   "source": [
    "infilename = ROOT.gROOT.GetTutorialDir().Data() + \"/roofit/roofit/input_histos_rf_lagrangianmorph.root\"\n",
    "par = \"cHq3\"\n",
    "samplelist = [\"SM_NPsq0\", \"cHq3_NPsq1\", \"cHq3_NPsq2\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff5b6849",
   "metadata": {},
   "source": [
    "Set Config\n",
    "-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ab2905b8",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:10.603733Z",
     "iopub.status.busy": "2026-05-19T20:35:10.603579Z",
     "iopub.status.idle": "2026-05-19T20:35:10.990190Z",
     "shell.execute_reply": "2026-05-19T20:35:10.989609Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config = ROOT.RooLagrangianMorphFunc.Config()\n",
    "config.fileName = infilename\n",
    "config.observableName = observablename\n",
    "config.folderNames = samplelist\n",
    "config.couplings.add(cHq3)\n",
    "config.couplings.add(kSM)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "436daaa6",
   "metadata": {},
   "source": [
    "Create morphing function\n",
    "-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1e6b64d0",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:10.992304Z",
     "iopub.status.busy": "2026-05-19T20:35:10.992175Z",
     "iopub.status.idle": "2026-05-19T20:35:11.135127Z",
     "shell.execute_reply": "2026-05-19T20:35:11.134550Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#0] PROGRESS:InputArguments -- initializing physics inputs from file /github/home/ROOT-CI/build/tutorials/roofit/roofit/input_histos_rf_lagrangianmorph.root with object name(s) 'pTV'\n"
     ]
    }
   ],
   "source": [
    "morphfunc = ROOT.RooLagrangianMorphFunc(\"morphfunc\", \"morphed dist. of pTV\", config)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9c6c7a5",
   "metadata": {},
   "source": [
    "Get morphed distribution at cHq3 = 0.01, 0.5\n",
    "-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "24da442d",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:11.136576Z",
     "iopub.status.busy": "2026-05-19T20:35:11.136451Z",
     "iopub.status.idle": "2026-05-19T20:35:11.377742Z",
     "shell.execute_reply": "2026-05-19T20:35:11.377289Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#0] PROGRESS:Caching -- creating cache from getCache function for 0x55c263df8f10\n",
      "[#0] PROGRESS:Caching -- current storage has size 3\n",
      "[#0] PROGRESS:ObjectHandling -- observable: pTV\n",
      "[#0] PROGRESS:ObjectHandling -- binWidth: binWidth_pTV\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#1] INFO:DataHandling -- RooDataHist::adjustBinning(morph_dh_cHq3=0.01): fit range of variable pTV expanded to nearest bin boundaries: [10,600] --> [0,600]\n"
     ]
    }
   ],
   "source": [
    "morphfunc.setParameter(\"cHq3\", 0.01)\n",
    "morph_hist_0p01 = morphfunc.createTH1(\"morph_cHq3=0.01\")\n",
    "morphfunc.setParameter(\"cHq3\", 0.25)\n",
    "morph_hist_0p25 = morphfunc.createTH1(\"morph_cHq3=0.25\")\n",
    "morphfunc.setParameter(\"cHq3\", 0.5)\n",
    "morph_hist_0p5 = morphfunc.createTH1(\"morph_cHq3=0.5\")\n",
    "morph_datahist_0p01 = ROOT.RooDataHist(\"morph_dh_cHq3=0.01\", \"\", [obsvar], morph_hist_0p01)\n",
    "morph_datahist_0p25 = ROOT.RooDataHist(\"morph_dh_cHq3=0.25\", \"\", [obsvar], morph_hist_0p25)\n",
    "morph_datahist_0p5 = ROOT.RooDataHist(\"morph_dh_cHq3=0.5\", \"\", [obsvar], morph_hist_0p5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97757b33",
   "metadata": {},
   "source": [
    "Extract input templates for plotting\n",
    "-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e1727f42",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:11.380291Z",
     "iopub.status.busy": "2026-05-19T20:35:11.380165Z",
     "iopub.status.idle": "2026-05-19T20:35:11.515838Z",
     "shell.execute_reply": "2026-05-19T20:35:11.515215Z"
    }
   },
   "outputs": [],
   "source": [
    "input_hists = {sample: ROOT.TFile.Open(infilename).Get(sample).FindObject(observablename) for sample in samplelist}\n",
    "input_datahists = {\n",
    "    sample: ROOT.RooDataHist(\"dh_\" + sample, \"dh_\" + sample, [obsvar], input_hists[sample]) for sample in samplelist\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fbc61f1",
   "metadata": {},
   "source": [
    "Plot input templates\n",
    "-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dcaf3b3e",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:11.517712Z",
     "iopub.status.busy": "2026-05-19T20:35:11.517561Z",
     "iopub.status.idle": "2026-05-19T20:35:11.687842Z",
     "shell.execute_reply": "2026-05-19T20:35:11.687254Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#1] INFO:InputArguments -- RooAbsData::plotOn(dh_SM_NPsq0) INFO: dataset has non-integer weights, auto-selecting SumW2 errors instead of Poisson errors\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#1] INFO:InputArguments -- RooAbsData::plotOn(dh_cHq3_NPsq1) INFO: dataset has non-integer weights, auto-selecting SumW2 errors instead of Poisson errors\n",
      "[#1] INFO:Plotting -- RooPlot::updateFitRangeNorm: New event count of 24931.9 will supersede previous event count of 10852.3 for normalization of PDF projections\n",
      "[#1] INFO:InputArguments -- RooAbsData::plotOn(dh_cHq3_NPsq2) INFO: dataset has non-integer weights, auto-selecting SumW2 errors instead of Poisson errors\n",
      "[#1] INFO:Plotting -- RooPlot::updateFitRangeNorm: New event count of 29789.2 will supersede previous event count of 24931.9 for normalization of PDF projections\n"
     ]
    }
   ],
   "source": [
    "frame0 = obsvar.frame(Title=\"Input templates for p_{T}^{V}\")\n",
    "for sample, color in zip(samplelist, \"krb\"):\n",
    "    input_datahists[sample].plotOn(frame0, Name=sample, LineColor=color, MarkerColor=color, MarkerSize=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71fdfb93",
   "metadata": {},
   "source": [
    "Plot morphed templates for cHq3=0.01,0.25,0.5\n",
    "-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1e9d3334",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:11.689303Z",
     "iopub.status.busy": "2026-05-19T20:35:11.689143Z",
     "iopub.status.idle": "2026-05-19T20:35:11.805118Z",
     "shell.execute_reply": "2026-05-19T20:35:11.804555Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cppyy.gbl.RooPlot object at 0x55c264ccdb80>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#1] INFO:Plotting -- RooPlot::updateFitRangeNorm: New event count of 18947.1 will supersede previous event count of 11104.6 for normalization of PDF projections\n",
      "[#1] INFO:Plotting -- RooPlot::updateFitRangeNorm: New event count of 30765.5 will supersede previous event count of 18947.1 for normalization of PDF projections\n"
     ]
    }
   ],
   "source": [
    "frame1 = obsvar.frame(Title=\"Morphed templates for selected values\")\n",
    "plot_args = dict(\n",
    "    DrawOption=\"C\",\n",
    "    DataError=None,\n",
    "    XErrorSize=0,\n",
    ")\n",
    "morph_datahist_0p01.plotOn(frame1, Name=\"morph_dh_cHq3=0.01\", LineColor=\"kGreen\", **plot_args)\n",
    "morph_datahist_0p25.plotOn(frame1, Name=\"morph_dh_cHq3=0.25\", LineColor=\"kGreen+1\", **plot_args)\n",
    "morph_datahist_0p5.plotOn(frame1, Name=\"morph_dh_cHq3=0.5\", LineColor=\"kGreen+2\", **plot_args)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd19ded8",
   "metadata": {},
   "source": [
    "Create wrapped pdf to generate 2D dataset of cHq3 as a function of pTV\n",
    "-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "33c93304",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:11.806759Z",
     "iopub.status.busy": "2026-05-19T20:35:11.806629Z",
     "iopub.status.idle": "2026-05-19T20:35:13.692132Z",
     "shell.execute_reply": "2026-05-19T20:35:13.691577Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#0] PROGRESS:Caching -- creating cache from getCache function for 0x55c264f54910\n",
      "[#0] PROGRESS:Caching -- current storage has size 3\n",
      "[#0] PROGRESS:ObjectHandling -- observable: pTV\n",
      "[#0] PROGRESS:ObjectHandling -- binWidth: binWidth_pTV\n",
      "\n",
      "[#1] INFO:NumericIntegration -- RooRealIntegral::init(w_SM_NPsq0_morphfunc_Int[cHq3]) using numeric integrator RooIntegrator1D to calculate Int(cHq3)\n",
      "[#1] INFO:NumericIntegration -- RooRealIntegral::init(w_cHq3_NPsq1_morphfunc_Int[cHq3]) using numeric integrator RooIntegrator1D to calculate Int(cHq3)\n",
      "[#1] INFO:NumericIntegration -- RooRealIntegral::init(w_cHq3_NPsq2_morphfunc_Int[cHq3]) using numeric integrator RooIntegrator1D to calculate Int(cHq3)\n",
      "[#0] PROGRESS:Caching -- creating cache from getCache function for 0x55c2650120d0\n",
      "[#0] PROGRESS:Caching -- current storage has size 3\n",
      "[#0] PROGRESS:ObjectHandling -- observable: pTV\n",
      "[#0] PROGRESS:ObjectHandling -- binWidth: binWidth_pTV\n",
      "\n",
      "[#1] INFO:NumericIntegration -- RooRealIntegral::init(w_SM_NPsq0_morphfunc_Int[cHq3]) using numeric integrator RooIntegrator1D to calculate Int(cHq3)\n",
      "[#1] INFO:NumericIntegration -- RooRealIntegral::init(w_cHq3_NPsq1_morphfunc_Int[cHq3]) using numeric integrator RooIntegrator1D to calculate Int(cHq3)\n",
      "[#1] INFO:NumericIntegration -- RooRealIntegral::init(w_cHq3_NPsq2_morphfunc_Int[cHq3]) using numeric integrator RooIntegrator1D to calculate Int(cHq3)\n"
     ]
    }
   ],
   "source": [
    "model = ROOT.RooWrapperPdf(\"wrap_pdf\", \"wrap_pdf\", morphfunc)\n",
    "data = model.generate({cHq3, obsvar}, 1000000)\n",
    "hh_data = data.createHistogram(\"x,y\", obsvar, Binning=20, YVar=dict(var=cHq3, Binning=50))\n",
    "hh_data.SetTitle(\"Morphing prediction\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a058017",
   "metadata": {},
   "source": [
    "Draw plots on canvas\n",
    "-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3c2f1e9d",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:13.694010Z",
     "iopub.status.busy": "2026-05-19T20:35:13.693884Z",
     "iopub.status.idle": "2026-05-19T20:35:14.054242Z",
     "shell.execute_reply": "2026-05-19T20:35:14.053678Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TCanvas::Print>: png file rf711_lagrangianmorph.png has been created\n"
     ]
    },
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').then(json => {\n",
       "   const obj = Core.parse(json);\n",
       "   Core.draw('root_plot_1779222914045', obj, '');\n",
       "});\n",
       "\n",
       "      }\n",
       "      const servers = ['/static/', 'https://root.cern/js/7.11.0/', 'https://jsroot.gsi.de/7.11.0/'],\n",
       "            path = 'build/jsroot';\n",
       "      if (typeof JSROOT !== 'undefined')\n",
       "         execCode(JSROOT);\n",
       "      else if (typeof requirejs !== 'undefined') {\n",
       "         servers.forEach((s,i) => { servers[i] = s + path; });\n",
       "         requirejs.config({ paths: { 'jsroot' : servers } })(['jsroot'],  execCode);\n",
       "      } else {\n",
       "         const config = document.getElementById('jupyter-config-data');\n",
       "         if (config)\n",
       "            servers[0] = (JSON.parse(config.innerHTML || '{}')?.baseUrl || '/') + 'static/';\n",
       "         else\n",
       "            servers.shift();\n",
       "         function loadJsroot() {\n",
       "            return !servers.length ? 0 : import(servers.shift() + path + '.js').catch(loadJsroot).then(() => execCode(JSROOT));\n",
       "         }\n",
       "         loadJsroot();\n",
       "      }\n",
       "   }\n",
       "   process_root_plot_1779222914045();\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c1 = ROOT.TCanvas(\"fig3\", \"fig3\", 1200, 400)\n",
    "c1.Divide(3, 1)\n",
    "\n",
    "c1.cd(1)\n",
    "ROOT.gPad.SetLeftMargin(0.15)\n",
    "ROOT.gPad.SetRightMargin(0.05)\n",
    "\n",
    "frame0.Draw()\n",
    "leg1 = ROOT.TLegend(0.55, 0.65, 0.94, 0.87)\n",
    "leg1.SetTextSize(0.04)\n",
    "leg1.SetFillColor(\"background\")\n",
    "leg1.SetLineColor(\"background\")\n",
    "leg1.AddEntry(\"SM_NPsq0\", \"SM\", \"LP\")\n",
    "leg1.AddEntry(0, \"\", \"\")\n",
    "leg1.AddEntry(\"cHq3_NPsq1\", \"c_{Hq^{(3)}}=1.0 at #Lambda^{-2}\", \"LP\")\n",
    "leg1.AddEntry(0, \"\", \"\")\n",
    "leg1.AddEntry(\"cHq3_NPsq2\", \"c_{Hq^{(3)}}=1.0 at #Lambda^{-4}\", \"LP\")\n",
    "leg1.Draw()\n",
    "\n",
    "c1.cd(2)\n",
    "ROOT.gPad.SetLeftMargin(0.15)\n",
    "ROOT.gPad.SetRightMargin(0.05)\n",
    "\n",
    "frame1.Draw()\n",
    "\n",
    "leg2 = ROOT.TLegend(0.62, 0.65, 0.94, 0.87)\n",
    "leg2.SetTextSize(0.04)\n",
    "leg2.SetFillColor(\"w\")\n",
    "leg2.SetLineColor(\"w\")\n",
    "\n",
    "leg2.AddEntry(\"morph_dh_cHq3=0.01\", \"c_{Hq^{(3)}}=0.01\", \"L\")\n",
    "leg2.AddEntry(0, \"\", \"\")\n",
    "leg2.AddEntry(\"morph_dh_cHq3=0.25\", \"c_{Hq^{(3)}}=0.25\", \"L\")\n",
    "leg2.AddEntry(0, \"\", \"\")\n",
    "leg2.AddEntry(\"morph_dh_cHq3=0.5\", \"c_{Hq^{(3)}}=0.5\", \"L\")\n",
    "leg2.AddEntry(0, \"\", \"\")\n",
    "leg2.Draw()\n",
    "\n",
    "c1.cd(3)\n",
    "ROOT.gPad.SetLeftMargin(0.12)\n",
    "ROOT.gPad.SetRightMargin(0.18)\n",
    "ROOT.gStyle.SetNumberContours(255)\n",
    "ROOT.gStyle.SetPalette(ROOT.kGreyScale)\n",
    "ROOT.gStyle.SetOptStat(0)\n",
    "ROOT.TColor.InvertPalette()\n",
    "ROOT.gPad.SetLogz()\n",
    "hh_data.GetYaxis().SetTitle(\"c_{Hq^{(3)}}\")\n",
    "hh_data.GetYaxis().SetRangeUser(0, 0.5)\n",
    "hh_data.GetZaxis().SetTitleOffset(1.8)\n",
    "hh_data.Draw(\"COLZ\")\n",
    "c1.SaveAs(\"rf711_lagrangianmorph.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9030d167",
   "metadata": {},
   "source": [
    "Draw all canvases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f7e6115f",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:14.055595Z",
     "iopub.status.busy": "2026-05-19T20:35:14.055473Z",
     "iopub.status.idle": "2026-05-19T20:35:14.177523Z",
     "shell.execute_reply": "2026-05-19T20:35:14.177013Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<div id=\"root_plot_1779222914175\" style=\"width: 1200px; height: 400px; position: relative\">\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "<script>\n",
       "   function process_root_plot_1779222914175() {\n",
       "      function execCode(Core) {\n",
       "         Core.settings.HandleKeys = false;\n",
       "         \n",
       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').then(json => {\n",
       "   const obj = Core.parse(json);\n",
       "   Core.draw('root_plot_1779222914175', obj, '');\n",
       "});\n",
       "\n",
       "      }\n",
       "      const servers = ['/static/', 'https://root.cern/js/7.11.0/', 'https://jsroot.gsi.de/7.11.0/'],\n",
       "            path = 'build/jsroot';\n",
       "      if (typeof JSROOT !== 'undefined')\n",
       "         execCode(JSROOT);\n",
       "      else if (typeof requirejs !== 'undefined') {\n",
       "         servers.forEach((s,i) => { servers[i] = s + path; });\n",
       "         requirejs.config({ paths: { 'jsroot' : servers } })(['jsroot'],  execCode);\n",
       "      } else {\n",
       "         const config = document.getElementById('jupyter-config-data');\n",
       "         if (config)\n",
       "            servers[0] = (JSON.parse(config.innerHTML || '{}')?.baseUrl || '/') + 'static/';\n",
       "         else\n",
       "            servers.shift();\n",
       "         function loadJsroot() {\n",
       "            return !servers.length ? 0 : import(servers.shift() + path + '.js').catch(loadJsroot).then(() => execCode(JSROOT));\n",
       "         }\n",
       "         loadJsroot();\n",
       "      }\n",
       "   }\n",
       "   process_root_plot_1779222914175();\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%jsroot on\n",
    "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
}