{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8984a3dd",
   "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": "20041379",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:07.209952Z",
     "iopub.status.busy": "2026-05-19T20:35:07.209814Z",
     "iopub.status.idle": "2026-05-19T20:35:07.225513Z",
     "shell.execute_reply": "2026-05-19T20:35:07.225020Z"
    }
   },
   "outputs": [],
   "source": [
    "%%cpp -d\n",
    "#include <RooAbsCollection.h>\n",
    "#include <RooDataHist.h>\n",
    "#include <RooDataSet.h>\n",
    "#include <RooLagrangianMorphFunc.h>\n",
    "#include <RooPlot.h>\n",
    "#include <RooRealVar.h>\n",
    "\n",
    "#include <TCanvas.h>\n",
    "#include <TColor.h>\n",
    "#include <TFile.h>\n",
    "#include <TFolder.h>\n",
    "#include <TH1.h>\n",
    "#include <TLegend.h>\n",
    "#include <TStyle.h>\n",
    "\n",
    "using namespace RooFit;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f526666c",
   "metadata": {},
   "source": [
    "Create variables for\n",
    "morphing function\n",
    "---------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ad044615",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:07.227183Z",
     "iopub.status.busy": "2026-05-19T20:35:07.227068Z",
     "iopub.status.idle": "2026-05-19T20:35:07.547598Z",
     "shell.execute_reply": "2026-05-19T20:35:07.546891Z"
    }
   },
   "outputs": [],
   "source": [
    "std::string observablename = \"pTV\";"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af147524",
   "metadata": {},
   "source": [
    "Setup observable that is morphed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "351ffbc9",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:07.549689Z",
     "iopub.status.busy": "2026-05-19T20:35:07.549551Z",
     "iopub.status.idle": "2026-05-19T20:35:07.758900Z",
     "shell.execute_reply": "2026-05-19T20:35:07.758114Z"
    }
   },
   "outputs": [],
   "source": [
    "RooRealVar obsvar(observablename.c_str(), \"p_{T}^{V}\", 10, 600);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24152a9b",
   "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": "c6b6080d",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:07.760623Z",
     "iopub.status.busy": "2026-05-19T20:35:07.760486Z",
     "iopub.status.idle": "2026-05-19T20:35:07.970152Z",
     "shell.execute_reply": "2026-05-19T20:35:07.969281Z"
    }
   },
   "outputs": [],
   "source": [
    "RooRealVar kSM(\"kSM\", \"sm modifier\", 1.0);\n",
    "RooRealVar cHq3(\"cHq3\", \"EFT modifier\", 0.0, 1.0);\n",
    "cHq3.setAttribute(\"NewPhysics\", true);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3416c101",
   "metadata": {},
   "source": [
    "Inputs needed for config\n",
    "---------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ec575e2c",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:07.972246Z",
     "iopub.status.busy": "2026-05-19T20:35:07.972116Z",
     "iopub.status.idle": "2026-05-19T20:35:08.178384Z",
     "shell.execute_reply": "2026-05-19T20:35:08.177640Z"
    }
   },
   "outputs": [],
   "source": [
    "std::string infilename = std::string(gROOT->GetTutorialDir()) + \"/roofit/roofit/input_histos_rf_lagrangianmorph.root\";\n",
    "std::vector<std::string> samplelist = {\"SM_NPsq0\", \"cHq3_NPsq1\", \"cHq3_NPsq2\"};"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f47cce8",
   "metadata": {},
   "source": [
    "Setup Config\n",
    "---------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d688d765",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:08.180365Z",
     "iopub.status.busy": "2026-05-19T20:35:08.180242Z",
     "iopub.status.idle": "2026-05-19T20:35:08.510330Z",
     "shell.execute_reply": "2026-05-19T20:35:08.509741Z"
    }
   },
   "outputs": [],
   "source": [
    "RooLagrangianMorphFunc::Config 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": "4f324cca",
   "metadata": {},
   "source": [
    "Create morphing function\n",
    "---------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e47e1ca2",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:08.512353Z",
     "iopub.status.busy": "2026-05-19T20:35:08.512231Z",
     "iopub.status.idle": "2026-05-19T20:35:08.721819Z",
     "shell.execute_reply": "2026-05-19T20:35:08.721137Z"
    }
   },
   "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": [
    "RooLagrangianMorphFunc morphfunc(\"morphfunc\", \"morphed dist. of pTV\", config);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa96149c",
   "metadata": {},
   "source": [
    "Get morphed distribution for\n",
    "different cHq3\n",
    "---------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ba90476f",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:08.723345Z",
     "iopub.status.busy": "2026-05-19T20:35:08.723224Z",
     "iopub.status.idle": "2026-05-19T20:35:08.932641Z",
     "shell.execute_reply": "2026-05-19T20:35:08.932136Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#0] PROGRESS:Caching -- creating cache from getCache function for 0x7f0cc4333000\n",
      "[#0] PROGRESS:Caching -- current storage has size 3\n",
      "[#0] PROGRESS:ObjectHandling -- observable: pTV\n",
      "[#0] PROGRESS:ObjectHandling -- binWidth: binWidth_pTV\n",
      "[#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",
    "auto morph_hist_0p01 = morphfunc.createTH1(\"morph_cHq3=0.01\");\n",
    "morphfunc.setParameter(\"cHq3\", 0.25);\n",
    "auto morph_hist_0p25 = morphfunc.createTH1(\"morph_cHq3=0.25\");\n",
    "morphfunc.setParameter(\"cHq3\", 0.5);\n",
    "auto morph_hist_0p5 = morphfunc.createTH1(\"morph_cHq3=0.5\");\n",
    "RooDataHist morph_datahist_0p01(\"morph_dh_cHq3=0.01\", \"\", {obsvar}, morph_hist_0p01);\n",
    "RooDataHist morph_datahist_0p25(\"morph_dh_cHq3=0.25\", \"\", {obsvar}, morph_hist_0p25);\n",
    "RooDataHist morph_datahist_0p5(\"morph_dh_cHq3=0.5\", \"\", {obsvar}, morph_hist_0p5);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a88b2e3c",
   "metadata": {},
   "source": [
    "Extract input templates\n",
    "for plotting\n",
    "---------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c80aac1e",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:08.934358Z",
     "iopub.status.busy": "2026-05-19T20:35:08.934235Z",
     "iopub.status.idle": "2026-05-19T20:35:09.143731Z",
     "shell.execute_reply": "2026-05-19T20:35:09.143363Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#1] INFO:InputArguments -- RooAbsData::plotOn(SM_NPsq0) INFO: dataset has non-integer weights, auto-selecting SumW2 errors instead of Poisson errors\n",
      "[#1] INFO:InputArguments -- RooAbsData::plotOn(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(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": [
    "TFile *file = new TFile(infilename.c_str(), \"READ\");\n",
    "TFolder *folder = 0;\n",
    "file->GetObject(samplelist[0].c_str(), folder);\n",
    "TH1 *input_hist0 = dynamic_cast<TH1 *>(folder->FindObject(observablename.c_str()));\n",
    "input_hist0->SetDirectory(NULL);\n",
    "file->GetObject(samplelist[1].c_str(), folder);\n",
    "TH1 *input_hist1 = dynamic_cast<TH1 *>(folder->FindObject(observablename.c_str()));\n",
    "input_hist1->SetDirectory(NULL);\n",
    "file->GetObject(samplelist[2].c_str(), folder);\n",
    "TH1 *input_hist2 = dynamic_cast<TH1 *>(folder->FindObject(observablename.c_str()));\n",
    "input_hist2->SetDirectory(NULL);\n",
    "file->Close();\n",
    "\n",
    "RooDataHist input_dh0(samplelist[0], \"\", {obsvar}, input_hist0);\n",
    "RooDataHist input_dh1(samplelist[1], \"\", {obsvar}, input_hist1);\n",
    "RooDataHist input_dh2(samplelist[2], \"\", {obsvar}, input_hist2);\n",
    "\n",
    "auto frame0 = obsvar.frame(Title(\"Input templates for p_{T}^{V}\"));\n",
    "input_dh0.plotOn(frame0, Name(samplelist[0].c_str()), LineColor(kBlack), MarkerColor(kBlack), MarkerSize(1));\n",
    "input_dh1.plotOn(frame0, Name(samplelist[1].c_str()), LineColor(kRed), MarkerColor(kRed), MarkerSize(1));\n",
    "input_dh2.plotOn(frame0, Name(samplelist[2].c_str()), LineColor(kBlue), MarkerColor(kBlue), MarkerSize(1));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dbb9610",
   "metadata": {},
   "source": [
    "Plot morphed distribution for\n",
    "different cHq3\n",
    "---------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b884605b",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:09.146861Z",
     "iopub.status.busy": "2026-05-19T20:35:09.146730Z",
     "iopub.status.idle": "2026-05-19T20:35:09.356218Z",
     "shell.execute_reply": "2026-05-19T20:35:09.355549Z"
    }
   },
   "outputs": [
    {
     "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": [
    "auto frame1 = obsvar.frame(Title(\"Morphed templates for selected values\"));\n",
    "morph_datahist_0p01.plotOn(frame1, Name(\"morph_dh_cHq3=0.01\"), DrawOption(\"C\"), LineColor(kGreen),\n",
    "                           DataError(RooAbsData::None), XErrorSize(0));\n",
    "morph_datahist_0p25.plotOn(frame1, Name(\"morph_dh_cHq3=0.25\"), DrawOption(\"C\"), LineColor(kGreen + 1),\n",
    "                           DataError(RooAbsData::None), XErrorSize(0));\n",
    "morph_datahist_0p5.plotOn(frame1, Name(\"morph_dh_cHq3=0.5\"), DrawOption(\"C\"), LineColor(kGreen + 2),\n",
    "                          DataError(RooAbsData::None), XErrorSize(0));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af82d88c",
   "metadata": {},
   "source": [
    "Create wrapped pdf togenerate\n",
    "2D  d a t a s e t  o f  c H q 3  a s  a  f u n c t i o n  o f\n",
    "observable\n",
    "---------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "319f8ae7",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:09.357662Z",
     "iopub.status.busy": "2026-05-19T20:35:09.357528Z",
     "iopub.status.idle": "2026-05-19T20:35:11.291924Z",
     "shell.execute_reply": "2026-05-19T20:35:11.291446Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#0] PROGRESS:Caching -- creating cache from getCache function for 0x7f0c75c65e10\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 0x7f0c760db540\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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "input_line_70:3:1: warning: 'data' shadows a declaration with the same name in the 'std' namespace; use '::data' to reference this declaration\n",
      "std::unique_ptr<RooDataSet> data{model.generate({cHq3, obsvar}, 1000000)};\n",
      "^\n"
     ]
    }
   ],
   "source": [
    "RooWrapperPdf model(\"wrap_pdf\", \"wrap_pdf\", morphfunc);\n",
    "std::unique_ptr<RooDataSet> data{model.generate({cHq3, obsvar}, 1000000)};\n",
    "TH1 *hh_data = data->createHistogram(\"pTV vs cHq3\", obsvar, Binning(20), YVar(cHq3, Binning(50)));\n",
    "hh_data->SetTitle(\"Morphing prediction\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dbe3838",
   "metadata": {},
   "source": [
    "Draw plots on canvas\n",
    "---------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b6c008a8",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:11.293339Z",
     "iopub.status.busy": "2026-05-19T20:35:11.293185Z",
     "iopub.status.idle": "2026-05-19T20:35:11.610874Z",
     "shell.execute_reply": "2026-05-19T20:35:11.610294Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<div id=\"root_plot_1779222911600\" style=\"width: 1200px; height: 400px; position: relative\">\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "<script>\n",
       "   function process_root_plot_1779222911600() {\n",
       "      function execCode(Core) {\n",
       "         Core.settings.HandleKeys = false;\n",
       "         \n",
       "Core.unzipJSON(73441,'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').then(json => {\n",
       "   const obj = Core.parse(json);\n",
       "   Core.draw('root_plot_1779222911600', 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_1779222911600();\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TCanvas::Print>: png file rf711_lagrangianmorph.png has been created\n"
     ]
    }
   ],
   "source": [
    "TCanvas *c1 = new TCanvas(\"fig3\", \"fig3\", 1200, 400);\n",
    "c1->Divide(3, 1);\n",
    "\n",
    "c1->cd(1);\n",
    "gPad->SetLeftMargin(0.15);\n",
    "gPad->SetRightMargin(0.05);\n",
    "\n",
    "frame0->Draw();\n",
    "TLegend *leg1 = new TLegend(0.55, 0.65, 0.94, 0.87);\n",
    "leg1->SetTextSize(0.04);\n",
    "leg1->SetFillColor(kWhite);\n",
    "leg1->SetLineColor(kWhite);\n",
    "leg1->AddEntry(\"SM_NPsq0\", \"SM\", \"LP\");\n",
    "leg1->AddEntry((TObject *)0, \"\", \"\");\n",
    "leg1->AddEntry(\"cHq3_NPsq1\", \"c_{Hq^(3)}=1.0 at #Lambda^{-2}\", \"LP\");\n",
    "leg1->AddEntry((TObject *)0, \"\", \"\");\n",
    "leg1->AddEntry(\"cHq3_NPsq2\", \"c_{Hq^(3)}=1.0 at #Lambda^{-4}\", \"LP\");\n",
    "leg1->Draw();\n",
    "\n",
    "c1->cd(2);\n",
    "gPad->SetLeftMargin(0.15);\n",
    "gPad->SetRightMargin(0.05);\n",
    "\n",
    "frame1->Draw();\n",
    "\n",
    "TLegend *leg2 = new TLegend(0.60, 0.65, 0.94, 0.87);\n",
    "leg2->SetTextSize(0.04);\n",
    "leg2->SetFillColor(kWhite);\n",
    "leg2->SetLineColor(kWhite);\n",
    "leg2->AddEntry(\"morph_dh_cHq3=0.01\", \"c_{Hq^{(3)}}=0.01\", \"L\");\n",
    "leg2->AddEntry((TObject *)0, \"\", \"\");\n",
    "leg2->AddEntry(\"morph_dh_cHq3=0.25\", \"c_{Hq^{(3)}}=0.25\", \"L\");\n",
    "leg2->AddEntry((TObject *)0, \"\", \"\");\n",
    "leg2->AddEntry(\"morph_dh_cHq3=0.5\", \"c_{Hq^{(3)}}=0.5\", \"L\");\n",
    "leg2->AddEntry((TObject *)0, \"\", \"\");\n",
    "leg2->Draw();\n",
    "\n",
    "c1->cd(3);\n",
    "gPad->SetLeftMargin(0.12);\n",
    "gPad->SetRightMargin(0.18);\n",
    "gStyle->SetNumberContours(255);\n",
    "gStyle->SetPalette(kGreyScale);\n",
    "gStyle->SetOptStat(0);\n",
    "TColor::InvertPalette();\n",
    "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": "e6facdfd",
   "metadata": {},
   "source": [
    "Draw all canvases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f8fea886",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2026-05-19T20:35:11.612179Z",
     "iopub.status.busy": "2026-05-19T20:35:11.612060Z",
     "iopub.status.idle": "2026-05-19T20:35:11.827455Z",
     "shell.execute_reply": "2026-05-19T20:35:11.826832Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<div id=\"root_plot_1779222911730\" style=\"width: 1200px; height: 400px; position: relative\">\n",
       "</div>\n",
       "\n",
       "</div>\n",
       "<script>\n",
       "   function process_root_plot_1779222911730() {\n",
       "      function execCode(Core) {\n",
       "         Core.settings.HandleKeys = false;\n",
       "         \n",
       "Core.unzipJSON(73441,'WkwIOVEA4R4BeAHtvelyHEeWoPsqtLz9Y8bMFePHj68ZNj+orVX3aqGVVCWy61bLkmSCzBEIsAFQoqpM7z72HY/IDSQFLlUtlQghKLjH4vvZl78vvrv66en6bPVkvVguvvlodfbD6vLb9f2vz1ZPLx+fXy3c4uRPZ5v/erb+w8eLpXeLkw83V5f9r6/u/5/1gyvqFzz21dOrzfnZVPj/NmcPF0t1i5Ptl5Z/f1FbL2tANQaN0S1OPt+crT86Pz2/WCxlKn599dPpelf8dvPw6nEvfro5PZ0eprMU54e9t9fXJ1dfrC4ebc4WSz9Q88fNo8dHVR+eX12dPzl87Jvzp4cVd082dCK4xcm93Z+3+598+O7l1eqKVlrjmYPS7V7i5U8vVk/Wx/2m7mjg2+cOB7R9dK6eP8nr+zPDJz88v3i4vvh687dp9vYqvzh/uO7relf6/+9N/78b+tzem/5/9+r89v3LO5vn69Pvpzeuzl9UXCxFWmbsB29osTnbvXNYsVh+0Cv+dPAWy/mn3Tv7xW07fz54o3/lz7t3Diu27cyDuTq/O43HBnO9uFjWQbMEH72oltJ0/QE7dPeBe9/3qZo+QJGOzsXF8gM/eB9yrVKCj+rFl8RWOT3/8cuPP5rmfb9w909P7QbLem/v72+3tZ9t/7p9//LgQ7fvXx586/b9y91rt+9f7t780/MndiCY4592fz5/snreB/Snn7Z/fvN4fbVaLNUG9ngz/XX78un6wdUfV1eb8z6KL589ub++6H9/s3nw/fPdnz/1Pz8/fzRVfn7+aFf3t373zurhndXmjAPkFicfXZxfXj5ebaYPbot3zidgtL+z2V69vNvUX5w/3Jxs1g8Xy5PV6eXaLU7+/WLz8Plh8add8fb9y4/Ozy/2nv/k4eZqdZ8jf3XxjA98unm+fngw7vnTdy42TzZXmx/Wl9eg3uebS4DqDHCn4uriYrH8y1/d4vzpFX/87BYnnzxfP7hcLM+enZ66xcmXHUKfbB4pIPObzRU92Za/fPbkzup0fXU1g0om7cv186vrtR//4es7n9++t1gu/m3+0y1OPj5/dv90/eGzk5N50f64vlptzpixabx3Lzd/W//pcr5/77Bod/+4Xp0uloHG7XYv5yHzU9zi5NvN2cPzH785f3q3r+u2fG+/PIEtPtMf+GwNkJ62wo/b8/7R48V0qD9aXV1dm+vbV1cdoTHAux+ur35cr88miH1Qstn89OL8yTfnTxdLGdhAdx+urgCIVrg3F8Bot3tBfnaL7784/2H91dPVfz3bbovv/7hmYg4rTz7bPHr8OWOYkJNt0dXVg8fz7H7/9ePzHz/5YX129fXV6urZ5XYnfn/72dU5e2H75Bfrs2cfri56mZ1y+wF7bfvGyR/Xq4dfnZ3+NL9x8u3m6vH5s6v9bTlv1c9Wl9NGm2v2n/rLEc5+Z5QBYPOllMG36/t29Ddnj15GHhhMOF1dXk7nguc6PbJf8ZS9uvDLkJKbrlGW3nnnx2C1/KVLb3f9GO1eSGlM23f8mKcnqS/Ts/xdl7W5IOJqHBt/1+iC5FFoMLrpGkWW0oKTGpyUMEpYluzsdxRdig9uukaJSwnFTdcoaSlJ3XSNkpdSqpuuUcoy+Oima5S6DKG56RqlLUMMbrrG4O1habzvxyCHxbCUpk6aOMl1DLqUmp1UijqG2NvNyUnyY0hLqcodJymNgV5NnYx+DGUpKTmJwYn6MdSlaLIB1jyGtpSSXPCZnozql6LBWdNZR5WlaKEHTrIfNSxFvZNUHR9W7e3U2t9lqkJvJ+moaSlSnL0f06h5KT7SA+uylqX45uwFDaPWpYRo8xp8G7XtZj2lMfqlFO9suE3GKEuW2PqsaYxhGYI4mx+KTFXtnVQ/xri0KWbVQhxjWtqc0g1pY8xLPtOia3mMpTfDRMUyxrq0FsW6Oca2lFicDULCmLx1wrbYmGQpMTvbs2MKNg9TQZetzn/HZYnz32mZZP47L0OZ/y5Lnf+sbOzpM23pXWQDpzH7pXe59r85Ok3632HJoOxYjZkTJNr6HQ4Ru8NeTxRq6YVMG9IbyXaSdGrFWrdDKmOmeSuEMhbat4KPY6EDFKT6sdADK6Q8lvkMs76FHtgdX8dCDyjUNBbrQEouy1jmk6xlLHPzomNpy95D58fql72D/C3LZMPg77CsNg38zRHu0MWPlV1p00ChH1/a82PlmOS5wNHtvfJj5eDaNFDg2NpsOz+2CXBppSAdIkmk0IGXNMBXU7sjOVOIvRADhQ7F2FR+bB2MNftysaeKUF/t71j4u9nfQZ0fxfuliLiYDCiIF9uhMbgYRvGhA4XG8sooXpe1OLFdw+1o45CUnVJMyxCq0+JSHMXnpSTvCge0jeIBGdmelcynOJ7BqboQKDIlLjOrMorB1sQZCqMIYEyciguUgs0qwCi3UYCt/M0V0igAV1aTiee7knqP+34WoKsX15oT70eRsgTAMR6powBd2Xe5OQGit6XURsMySvBLicCs6CQD34Fjfd4qxbC013xxtY0SdBnU2vGjAFprAi04jrkAW0WdgbLAw9mOepDa+wx03UMBwcuyCUeyyRhYEkBiv8bggU3qpmsMrIkwHrvG4FOHt4D1MbAoVQ0E+DH4suRsFnGFL9dl0Na7JWUMvi15C2CdxsCSsAt4eQwiyyKuvzwGCQaXp7fHwKLQHi+PQSJv9pfHIIk3+8tjYD2A6FJ4ewxSem/nRut2cNZqs/7OzYLuAEe902OwJaG3vV2WpI9Vqo7BlsQGGzSOIcTtaK1lFmUaLZ0OQPHdrIYyj7e/XLcD5mWdzzCwzcdR52PMZgJvcZInLN7L/TDbfR9G5TzP9628RxNYuZ9qnk8yKgd7etyKdUtBWBHY1smNJGP0O8LIj9F3moDXKYXtm5R2dAqlCDoBo4zRMxsdejG+aIfZYL+NJ3KYKYONk46RrePDdnxRfC9z34cxQizRRTColQNtTcOLolby0fov1g8eZTRiSI6XrTSDej9GMVDfxyWAel6hHkTDC36MATzThxSmJek04xghh6YhgdIih3Yakg2Z/TENqd8HkgQbcr8P9TENiSkIdR5Sf7zNY7Kn1VOkAXtYxYp91caoM9KzV9WwXl/vMaoh3u2Lhnm37zEdtmhjYktOe4SvpCbbpe7l3dLTo9R2i9/vxz6ePsYxsSV7O9bl1DJd7qs2plbmkn3Lxt4HOKZmQ6fkw5g9S2BYccy+U+m8kz1jtkUbs2fEtmhj9p1ah2JgF/YTZl3MgK9+wqZytSFPyzZmcAr3+7KN2UCY7cDeJLuw70AbUga12AnrnxO1zzFEOghmsU04PdznYx6W7LMPeaLagXq82U/HNDiwCruxT0ewjcCqjZktOY+P94BY0/jYJRmQ1U/YVJ4Q/7T82WBWH59NtVHpdsKmckdvfYPk0Obh0TZUeh+dlfr5nJ7UHROVtU8KFEmGOueM2d99OvrftjX4s8yAsHd7LBMgnKZ9LOy6qYtJxsKmm4q8nLbjtZu75Wd4ZSJvrAtjmQgcWrKH++j6zTrDQIMOFRg4zWuSsfoOCWxRxjoBQQMS1W/XnDtTdzrsqdNunPo71nk3GqSp7MVp71kb28mGxuwQuTcxbUIe5kFQ6QRWeNIg4fTJHSDkzgSO+nAreHQGyD6MFTzat709XLcla2MLiyBybQPSPIUJO/TZGSsodIbrfBYgyW7rwLgCI6ciL0+U8NSlaTdOszDWaTfOLfWzSokuwTNODUFsT7Crd4rNOI2NY1FhGacp4lhU7ZPU981YdwCSHqvhC/rUn52g9oSoq878QX94B7OtIWNR5lfjPo9So0FsvmPfjQax++EbazROadoncQbYjDPuukPv4q47/TPb7jCwuO1Of3juDh9Kc2+sMM1QR9FjhV2c1sUGkrbbyL6b6NEEnWvnGqcJHWvqPZomfKyJHtldRCHJerQr0qMJPlbjISd8UHNHaH3Zxtr5yL5oYzVGcgKctTOSvbdjzR29960w1ryHZGnf2MndV/v8zKM2jnL7amcp53tlEoXMHSoT9pu7VPrGniel9I3dIXYtfWPPAy2dGNrOQoGzmTnsWvrGnicCRrPfZEfVvrE7+qu1L9v8aO2r1pFGrbZmHUvUajTQ1B04TttbfXHhOSeYaXNU+9Gf2+hHf9uGHf2piTbhHnttgtTzDEyAemp/Ig4MTdUJSE+FDhTn1Zxg9HY9Jxg9f2aLgGxDTzDaPtQmED0VdhCaeWt7IJod3ADSEy72YWwTmO6ttAlOTyNpM5yelrpNcLpPZptohrnUKYZppdsEqKebk3TN9hSdgt1g4vs1NoAl+7hfYwu2gh2HthCXSWGrRXnVtnmENS5jC3mZvHHgEaa/LGODUZfIR+syVgfjGfPYQlvG7JJ3EuvY1C8j4NNJkrGpLGN01pk4Ng3LqM7AUxmb6jIGB/udkSTEZfQOiVsOY9O01OZSQxY2Ns1LrS53EWHTstTiYJELLdSlJgcDXGihITbJ2UnJY4t+qepycVLa2KIsNcDNwxu3GJZqLJ/UNLaoy9AcvHAtY4txGaoJCRpijYTYqiSkhWNDkpZdKU4aLRQkBKU5abQAs+oqO1nGFtsyBFc7XddMjuaM9StjAzg2VxMyqbEBGqur2QXRsSWkB65WFySNLcWlZNeQXtWxJWMIEYQFGRtwMbkWphJH3sQTgW9C3yFy6SVjm1vupQxd5VqZSmwZ1+DzEdUgM0AWEaSNLSslQXBhReuM7Vorpn5Xp7u5F7vgouXSiwyyji1XhiWeUVJEIOsQTVixIPp0iEqDlLEVsSLiMCsi8nGIhHrRZkgkTEWkxA7pBSLwBmSsTkzWQBHpQpcXWrH0Iu1yF2beCVIOZhvISJFupLFVb0UkBVaUXqQb3EV2bUKPXtRepBtxbAjkuMvwKSKQdRIYPsXci3SDYulFuqFjA0DyMN2g2KyodEPH1nqvkD1JGBtMVEX6OBX7XCFvEhkbUBLpNN2g2FdQ6QbFvoJKN5DB2YYSpRsUbUeZdNWKtqUksr/b2GDpoxOkWB5ZGkI7ymz4ShlhFABjLiNVdxIRR9h9XQoS6OyCLzyPBBOJ9FxOvVznMmIZJ7EhEef5YuVEd6wMVgHsuOAT902cKon+UBa/bBMqtqJY0dQY3A1LhOldViJe1Ir0xe5GK9IV5W5aItKhJ1bMFDMdQeKIAA954lysVqQbdrctkeYxK4gYg7d3TTpIUWgIcNaY0xDoJOqPxpQZqWviVmlMGbRumMqMCWoXGSTQiW6bEG8q01Ejd7uao9EXREb0HPhlnenb32AkzcOANdQ2TirtKxTLVKZ9mDBUS6aVoIyEscsUK/1RZLFTmf4ozGGXiyKf9GoaAZOTdkluAfAK4tnKbGgFLKO4AYKLB7zTPmX6A4Cn/Q7vxQPiaZ8y7QPkaR8MQPtRQRZdjsl8xAgq6WX6ExOIRoDnhfZjBg1JZc5pPxaQVC9b+xUU1rVVzEdsILheZj6SB/2Z+i3TnyQgRxOLZtpPAUTay7Q/0cSm42I9Ulwm3geh0z50cXFSYRxoP+UlONLK1l5ZZvrDfWuvLtk7Vmb8qS1RcSH3NVF59kgQe5n2siyRWXM/0l42hWEv017WJXODhi7SXo5LVItWpr2clrRlZdrL2QTdVmZ8uSxNag8dYe3VJco/yiaOz23Z0P5Rpr3i0Sr1MvMLWvB0GHhHgyAGGByroEWjmZkig4hUxCVopFfQJuiBQ1ObqQnFgyCQslIRrFUAC81SYc2iFqBZKqxZ9HacUW/KNvEgioBqkQpWFlTB1rUKmgVZmMaRJ9hboAs2I5pP5PwehMFu5AmhWVAG229XgUwerSVPMP+gDTaYVdAxEEec+oG2wIM62GL2BB0DeUCdWQUTNMvgrIKemhBumg+hpyYPmebDIL0hEeYUjQVdNzQyzanBchCJ7TyeoKegEsCSVdBTkEmHM6gaqYAgmNaWClMCGSSoTgCzpgbiaFsFWpdZE2QVpqFB7zB/A1UKOMUUEdUBT9EG2W6mjG7FVA8MrToAqimEmIhaUJSKKYRMgV4cABaNkDT2W3EAWHRC0hhWcQBY1EKCHoeyaYkQ49ChudwlVVamw6aGYEiQeJThXOhPNo0NqiG0ljwPAEY3hErYyvQP5ZBt5oqSUdAOoWRhPABcFETB9nZ1lf4hSwHio86lfaR701YH4JqKCAjfvAPgoiUynTNl2ke6B8Rv4grzhTzFDoI4AC5aogCEp2zto2FkTwcHwBXke0B4yswPQhU7JuoAuAKCQbXV1HVFWlgGOzXRAXDFEAwbPjoAroBg7BBB9FNOy6Dsf1gCynmJDtPKtA+CsSOVHQBXQDCR9jKKWRHttgusKQBXol8GO2DFAXAFlTwQnDLto5O381YdAFjgI+y4VQcAFjiJaNyBS7QPL2GnrzkAsMBNGIFiLKYI/ITJGbxLzAcchZWDAyALPIWV1UXah6uwcnIAZEnwqJAW2QGQJSElNQLMAZAlIb2FEmku0p+EzhbCUUy1Kon+UVYHgBZU9laODgAtif5xPzsAtCT6R7mgzBZJ9I9yc0p/Mv2DW/EOeC2Z/lEODnAtmf5Rjk7pT6Z/lJMDWEumf5SLU+Yj9/ljD6GklEz/vAuQxvQn0z/K4oDckukfZXXB+kP/KKM2R9VK/yhnB9gWRDJWhhuiTP+4DyVOmfVtjj0DEJfS1xdbF6E/JU33OYOU81SGIqEMv8j7UF2U2X+UJ+VuQXiFkFoElOJMyytVlsEFUzvXsFSnjr5WXUYXHdOIKMYlx4hqWmaXHR+vkKXF0W4ty+qqo0u1Lptrjt6CHJBPOMC8gBnARQ4sIKAFNLoOJCHgBHX2S0k7pT8ppJFHm4KT9ieOAgKYErYHzn4pdd4L+EapMzmcDkoIoycdsAQEL7A3NuvomgNMrq0JymZ4LH55khVx9kuJ/eLsl1KCV7dfSkZswnEVSkZqso8qJSM04bdQfXsjM9XbnkPJrMHZLxp2IzFVbb+iZNbo7Jd7Rl5qsr2Oilmzs1/uJUhN+6VkEg2tdopQMGtz9su9CpFpv5TaMoqzXxT6HoJzMoRAtxzV2S/3AsSm/VJSSE37pRSR0dgvpbSMBWkOMAStcpfmQOKhVEbSwy+lihTIfinNSjOIxYC6MDj7pSTIkuyXUoBwtV9KukSkzi+luEwZuQ8QMyALB5gaPA1oaSpSIKBt0AJ5a7+U6jJ7Z7+U2jILMiHgdogewtd+KQkWFPZLKSxzREIERghRIYntl1Jc5oy8COwRYlrmgrwI3BJihli2X0plmRvSI/BSiHVZvLNfSm0yHyj0JfllAb0ZhgvJSOqihv9CCphG2S9P6rIkJEvgzoDZ1NY6L6S0LAU5E3g2pLws1dkvpQIZbr+U6rJ6pE5g8JAaBkL2ixGHh0C3X0qyrIoMClog5ACxbr+UdFmxVDI6IkDYZyRSUBkBsr44+6WUl5AT/FIqZn7YuoUJBL1HWgU1EyDnZxsRCRDzAWkVlFEoAulvv5TCEts0finpspnsCporlAhTYL+U0rIVZFfQa6FkGAT7pVSWrSG7gtYLUO0e2GYXFYA7NL52jRIAsXvCUQlQ7R7lpF1UTPyFKVHo2mRzhd2VQMeGilQeEZldVCAJ75Ito4UDVDs0Xb94YrIJxC4QejpAtQOF+0VFM/snBF4CNA4Gm81SZ7LWATwDjvvFE5i7RBN7GeUfANLA5X7xBJxQhnPo3EMAVAOa+8UTcEJdGmYsSQBgA6D7xRNwQsbBdC4mQLXDGvVrFEUKtGcKJIoYCKFAv6jAWCjCOHXmSU0QlExUZuyVmiQom7BMQNtqoqA961SFboeO7BdPlM5vQUwCwxVpELKCflHRzDYKsZlAWyi0O7Rlv6hAYAWWs4sKsB7ycLuomHg2SE7oGTXLruyQpwkEj6IChYnrFxV5iTxtuqjAmgje0y4q4NmQnttFBTwbki67RlEz85osTuGjFSk/goZ+URE6IwjdAaGnSIogVPtFReycIZQIpKIiKzKBPsw4PTVhETJ9u6iAu4QwsYsKBICI2OyiArGyifpN3C9qGnt0sXZRcaiPEEVRikyjX1SgNoF+sIsK6InJKA8UoajxEXv0iwrElJPpK4hCsbJFENIvKuAukdrYRUVbIpGbrlEUW1tY1n5RgZHhZFwMr6ARSTjSM7uoQBiO+MwuKhCmQunYRQXiVEg7u6iAD0agZxcV8MGI9OyiAhHcJFODBVLsb+GD+zWKJkTjZsDdjbgV5cFsXghXpegP4JT7RQVCXwRddlGBiNykZKYZEUWRYGIxqDN6ii5hz75PFHWCCcIQhtFTNApIwvpFBdJCmFG7RlH0CiYPQhTAnKJa2LMDFEW7YBIi6GN6ioIBcVO/qIBj7xyxgHwUJQMCoX5RgUC/8+wmlVQUDcgV+sUTUJadi0WDI4qyATlLv0ZR1A3w9f2iAsYdO3S7qEC43zlNk6UqkqUG62cXTyDgh9e0iwpE/DC7dlGBkN+Mxrv8VpEsIR7oF0+gmIXstYsKyGCYC7tGUdPOmuy4y48VZgCNS794YmfHZ8JqrQgZEGTbxRPY6CBmt4sKxAwmZ++yda2oj5Fu28UTWPMh37aLCiQNKCDsogJRAwS6XaOYvaGpZOD/mFO0t6aUQSNCT9Hf7pmBimJyiCF3v6jAUBTdhF1UJBNYwGCi+hFFmWvaGnQU9BRtrulrYNHoKQpdWIl+UYHQASbSrlHM+tC4CTgKfB+MwcDo1y4qEGxH40tDiFTocQWSEbRrdvEEohEUbHZRgWwEHZtdVCAcgbG0i4ojxwlMFI2VhZ2F38VG0XhZ+FmQFkaKCESmiwq4IXghu6hABh/NHhWzUMFW0fhdeF6QFuaKneGF6aWnSJlQAfWLCsQk1U0XFd0Md7JspQJBiXfTNQpWjMYVwxkjOoiImvbcQMSMG1EG9YsKWDd4N7uogJtObrqogJ3ObrqogJ8uxj8HkFZE4ISGqF9U7DjqAAsTETnBwvdrFMwcTaaAXAFGBkNHE1oguEAqg6mjSU2QnCC2wdpxz60Gh58Pn53g8/Vv5j23+PT0fHWlYeEWp+bllZJb/LBY/oU3wdLgbuiOCDUSoVEilEuEpolQOhH6J0IRReikCAUVoaki1FaE/opQZhFaLULBRei62BVwvAF1GKEXI1RkhLaMUJwRWjRCnUZo1gglG6FvI5RvhBaOUMgKTFKglQK/FKimwDoFCipwUYGWCgxVoKsCbxVIrF1gzxvAcQW6K3BfwQQKflCwhoJLFCyj4B0FIyk4SsFcCk7DTxZozD+8AZ5UsKeCUxVMq+BfBSsr+FrB4ApuV7C9QgMo1IFCLyiUhEJbKBSHYoqgUCcK3aJQMgp9o1A9Ci2kUEgK3YQPOFiff3gDekyh0tRoN6PqjM4z6s9oQqMUO/3IG0ZrGgXKmitrrqy5subKmitrrqy5subKmitrrqy5subKmitrrqy5suZqohjWXFlzZc2VNVfWXFlzZc2VNVfWXFlzZc2VNVfWPLDmgTUPrHlgzQNrHljzwJoH1jyw5oE1D6x5YM0Dax5Y88CaB9Y8sOaBNQ+seWDNA2seWPPAmgfWPLDmgTUP5iLBmgfWPLDmgTUPrHlgzQNrHljzwJoH1jyw5oE1D6x5YM0Dax5Y88CamxUchxY2m394gzUHEMCy8w9vsOYmwzXBrkl7AVHIDfiHN1hzExubLBkwiaSCf+gVaw74RerBP7zBmptg26TdJgIHMSB64R/eMH6oc0q8YVwVa27yeBPSgwURHPEPb7DmJu03FYDpBcDPSK/4h16x5uB9JGH8wxusuSkhoDIQtPGPGTnA+7HmRtMYtWP0j1FFRisZFWV0lVFcRoMZdWb0mlFyRt8Z1Wd0oFGHUtpff/75Z/eP8uDEbf2lHpw9FsErYkdMXpr4Z1+c8eDs0Nxrbm0/cBQEgeJeoIPjGAe76A9CzIXr4R++WF18v77YCyfRK/Y+OVVsQyR8s35+dfvsEd7WOJ1S7Df94JkDu3+6eXSGV/JU3vs+tz89x409m1fx6vnmulP47aur29Tjs/1w88PmcnN+drlYJqFF7ux98PPV/fUc4oL2rNxbiLRg5a9OTi7XFnvCWyetcttttX5vHnz/+frsEZEz/OBxbLY1mF+1seBlfvza1ensY759ZG4eZ+R7v5kR2nK+wQj/4zczwu0CveYafri62Isc8uHqYt4U5nZNLBaO6emdr/uZ+Phi9WOPt9DLXz292sV26IUpvEMvTBEevnp69XH3se/RY3B8335gggeM4KunV59aqJXpuU8302PXXOZ5wCofbq6IFzOXvzk/PzWPeSp6YIqPzs+uzp9dXC7MGn9xcvtq6swRvLx9dcURNhD1CkgQXhMUcFIYPGEQBgtMQ4kgCL30ydnDTy4uzqdQLZxrK9rjNPXps7MHE1DgJsU9GEZxWkDuEuliepgJoDg9zKmnuLfan68frc8e7ofTYA167R585UO7yrntOcYOn5iBwvZB5m/aiG5x8hnRD9aXRxB8qv366eoB3v/W9jbwzd4YtlFvpjr6uH3usDfbR+fq+dGjpu2543HvVe5iiXy2uWRH7veHKr43dSd7hj0/NzfcZ2d+dK6dHjzqDU99sTnbPHn25D/WF+e74A7cOIhFZAC+x7m4c7E+WV/8++e7p3v93sT1iv1h0tP92t04e+3H65PPFsvkWbRtzbeLZTmsubtAj7n3yL2p4s5qf/PdWR3sLRrfVu1atqrrMZjurB4eDJ25u7N6eD2m053VwxeEdbqzeshmv7ubnqnm3kENmHEKT0KDmwffT8FJ7qye9uBJd6cYJ9uKewuUnIuTrx9crNdnn64eGPihewC1vemnyDnY27ZU7a/H/Nbe+eERirvTQ828gfq26s9cPAGGLvIQLXQGlQYzWp36YiCmGQXAzW/nCCcUPqMgNo6ri83Tj9cPNk9Wp5fbMCIGkydaJ2wJhr3R2QNHw7O6/fGxSaxyb4Db8hZRdRIF8qPH+tq+tQWB9k4fTts+zXjm7zOcDnzO/3B2tr74I8PjSY6affZysfwLoUNu3eIfCbcE8cGteKvO5Xwr3pJMKXgeiNMju5JV7dcHf/j49l71073qb0V/K96K9qS1/Q/6568Al/Xq4foCXG1RUmzatqVPN1efzpsmTZvGYq+wits7tpds1h6sTu1lVv//Pd+cUTmTAR+tnu4Xv9k82RKTpdYmFRnH4uQPT1aP1nxoC+A/Wp09PF1/+3hz+f364o+rs0dTdLJe/+H586mur16vtZ7sRWf68+b8dHM2104Ri/qjH20uHpweQ/vpFmGl6PQeArwLwf3J86d394meufLefuW9Fz05Vx48yYNfrJ5/vHlk0fPYhF9dXD0+/2j1ZH2xmqDPdWbtzurhO4u4w4q9lF+7s3r4Mm4NPooZ2oNYFGfgM93dm78jzHidBzsC10zGEbC20/8+CN8HImFI3nsfStOWgeEE5fsgxTSkWEMsNRT0WgsL0pe1GHwjUJ+WpEOUpCGXUht49jBuX64DMei8b60ln9gee2H8Uh4CsgokPxWB2O4uUDS2lnMIAVvf4gnsdRTiL4TBS06+JPXI5+yBOUagSh1C425G5jdFCLS7PUJejdVL1YSxvK8M7zAWoMjQUmutFYanDO6loQG1xKFEwo8Fj5p5cRwlMNchVCbZV0Xvag9su5rjkDI3JWcEdLu7xA+8fncXC5Bggh+I5iFqlhISPeXrUzDAfvv6As+3F8sweI+c1IfmSzMOffd1QgtqaWUINRBGMLTISZ3e5u6Ldsl8m57JUFqQVnIMJQsBuXbBAwfbFjbDJceKWG9xsosl+MLbc6RCP2jQ/pNSrom9M0cuJBamTbX3IWgSCwnXAx8Oihqan1I90ubFFNnQD60vj/cxli68OIh0+MLeHMQ+fNkTL296Gx3xRa1fj5b4gUgacpKaY4mhr/MUPTEb0pviJ6oGHZLUFjW3aCv2muEUmc334RS3AWtfO5ziEXt/8/f3AjB+J/sSS0Iy9pqbBmF8dzH7JjrnnxKyj533UgLC+OSXkRCc5XdLQuyHqH3R2SNkbT94hguvn7p9hoSB9fLEgF4nxN6SCvv0T19+xOT0qMivnMjP5OOXTWOyuJrzPiSc73dPv/nzd+XEPyjpwf2U1/ATncBfLBd/OHv67OrW1frJ09PV1fry1sn5xa2n3/39m5//8+9//pkH95akyyvekq57fdn6lw/Wp/CXhpZfIhqf5OIvC0I9Tcdzk8nujf5gpL9poToiy/sb9AEWcvfuNjzvXQvJ27f53f4EIWRPPt1cXE4S0s9X81+E7Q4zc/xk/fHm8unpai/eLdzalt+z3YGeYRd/9ovzh5+v7k/lV8j5b7ZcPx0vl0U+vbz1//+vW/8D3vp/0oNfzaptJQpb6cSkQXmFKmS3ahz340Wzun/6kr1EcXGzJfvb8ZL9qpaICTXJxm98iY50LXuKGG905CdnVxcb5FZssG8unz35cTrT/Dmfb/6ew23b39ONL1bPkScvXkSGTrJmYwyukbJfnl88mYWaHIZJedJDVp983Zs22HOQBAA1BDkBris8b0529YjUO8HnHKHaWvtwc4bc8pOLi6+Im03XKH/1w/ri5PT8x2mabl9cAOkODYUyFkRmKATyIeqzWQsdkwrvXoP+lHPzy4TAH8/PkfTz8ItQH27fgJgeFfzrL7778s7lfx2gf94+f3SxenLr/OTW/MB3T0/Pr76DbODDewQA5+ct0D8z/2r0Dw14rFr/8un55oxsEh2xvWKJ/GTKhTcQnjDF/BvMVh8rD6xJsFqxCB1mttdNzYp3WMxgmYMFEJZGWDRhOYWFFpZgkQArxbvY8P7zLtEAvjbFu9S8RWK/d4OeDTVk31otIdWGOVBuQ5AWfClZM44ozQ/Fp5Rz9BkLlFJkqNlXbTVigBVCSUOVXFtMSsgKKVIH9T600HI1cx1VxDKSavAYHpWWhlSStP5CanlIoeFhmaly0ceh1JJ8xu0fe87ahhJDCipNsdIKPg4pRC9FlY5guT7Ugpd50GAmcJKHWnxuxQeJeKqUwccYpLXii1dxOQ2YPNVWW0iliotxiJo884C9lThtQ8opNl9CyIkoDHVApNJCjiFYpA8dirbQok+hOyUMpYbcSi4lm9uCH1rKqdVQpWYfXR1SkNKaryWFiG/WwAogWfCpNeyDjtdEBx81ttZiTg1Dv2sV177hh+xrRtJUtJWGbdAQpChCs1haqtF2yLuAdNsDu435vw+TzbZkC8A/wMu5q8Z3OVc+QX7zi1sVJd0ZRw4i7pO7jzePHr/mO8iBfvGVv8jgUwytKStesKHzg8TmU5Fai5mXytC0pliqtoR8S8NQo2RfNGnDVFDLEILX1mqstUSCCQ8asYaMUhIma6EM2mrIIbaa2BYIHln/pspmc9IGn5KG6JvPCcO3NOSWm6elFs1uEflfyBnTucQxk0FSySmpD77U5NqgzaecgyYN2DvWIWrUFjN2bTiSDaFUmsTMNFVxGfmmdbJFrURYGoJnXL60UmKpLg7Nl9hyaNhwqro4YL/nY8i+0BGnQyXNiA9VstnJDqW2iDlnStI4REOqIbakleFjSTuEUlJE5qZmODhkwmdUJIQxYYJ2bU0GzeJbTKU0b+ar1yuOvmEnojjfYZeUliKCXu05K+7daD+93xy/w83x3U9wGsZWLpYvYvi+65lnPggA8uArRrc5lbT+oDpukosmqOjgtQTOlZfgFt+dnV88+fPqlAwc3opPNmer0w83Z7MFjNV+vXn0ZGVU3nfrmYYmugdnpsaYi0a3+O5i9eOWwv5A3OK784vNIz73rSUgMUJ239x8j4qcSZRjpPOeEPjdEALvXG74j+AVHnz2X2rcwoEQe0t8wC3sHnkpv5CN/XiXHEP/4sQidJuPf3WeIQxBamut5pCLOXnVocWYlMRgJREuxg+1ZYHwRFeHO1kaAgyFwBCoeB1yiSoTUxFqzQN8A/xEzBHqVSDDMqEMfdEhtZA6Q2GpZGRIKdSUaRX/UZ+HLBC3hOHArL3VoUKXR9gFQkhrHFqCGI7Zm694GkoqXpqnGTzi4xC0xhw1pUiFlwFaLGmUwhMpxqHl7P2uQozPmZiS6GIRtGm5sw/EIYESjKqxSLZvwMik2pKvEGb4O4U6xBZrKKH4ZC50ftCGEwTuD/iUMjM15Bo1ZNw5fBsanI5xQjiSCDwFvFiTlEJxWQbfklcpCtsRXGpDLYprR6uwSxCotVRIOBT0McHpiPdVYp0ZrDzUKtWn2JfwPbuAm9Z1vKlDzdpq8SW01HBLSUMrNUqqqSjOSAq7rCFpLgmjBJf8YGvbvI8+2nKlKjX4GFOOBA8atMYaQ9WiJZN8ZKgwlE2qVFxxkx8Ukj/WXLVqcTEPcCtRm0fAEsPQfMJyKYeCH4u2oaqx61qSb+o0DSnjWlM4YpW0JUNrEaMHqU1rptuSQkytSMWvJ7QhFE5HCDXgehPSUH1snNLQ8KIJaShSvbTaNMCyhDh4n1NIKOsbDpN+CK2VULRxWvH0HSpRxHAZSxW3kzwU34LiTAIDZmwPDGvyBEgyRn8IseXqA/IIohgMqUnL+L7UYh5Hg9fImcQaIxZXh0ziw1JSU4GpqQOclZkJ2I/LQ/UaEm5FLQaXh4abDFxUTLFEl4aiRFPxkluoIb0Wz/B+h/xOd8hNGQfo/M4lqOQ0hJTxWkx4Ar0FkxBiUxmax7HTp5zeDZfwHvXPtMDvCfX/thgEbKG31gQvZhDCyxkE0wm8UwZhT8vwO2EQhuIV4XPMEZVzCkOVWHyE8MVfNFQZiI85qxByzVDFsZSkzSPbFJVhJ+7HHZVXKlGFQzHf6oj4vsaIOan5vibvh5ZD5QOEg8kNMksRJCIczTFAzrcQq4e8l9Tq4EvqhDi+rxmiKE0kMu7bJYQhxhoh3yCjJYnJ/xHbQ3zw0Tj4knP2knqroQ6hKwtwnm4ytNCJcIqZFiWpdjUIzrwwJaZpMSdZ33QoBZhtXyQIlA7QjhMp71qoQ9obVhH6LC361gFTjlDyNUdTaYTkkk8DvFoppo6pLmoeJJZWWlEG5rTqoKrwTJ1dULGpbZpT0Egsh1gGrzuGjTAlQw5NfWilJsnvuYMXcwd2EoprQ6ypei8RsTcSbxlqjTGEEmNAyaBxCF6S1gqZq9lFBXeH0t+Cps9DkdYktuIroapiY7uHJomzJTAFKdcsBcYZv+tkrdeU4UdLJdzeUFF+hQoTS2i8AGuJKkNDIe5BQlhfAmEXkMcLrIcUTlHKWH+ynYaaYvWhttJSFl5pIXKua8v41icZfEgloh6AJ65DbiVI9YovRaYiFFjLwjZvwcU05KpKhGpVXwNjl1xqkRIk11zgZ0oOSX2WLJVoim0o9Dm3wlYmIOjQ6LJH60IIAzSOKVYsraU2Uic0UxFoJHKGhbcpQ0jkP+AEEbtdMSyvNJOwz4ZbSZJCjjBZFdajDhpbhJzSql5eiw14vxX+1bfCTen9D9KgqeXYsqIK9mVfUSClmCgL94jmccd8Gx6glYo632ANp/fdaAreY3fD9b8j7P7OGYDbd//w9de3v/jk5UZF/3axPlkspb3zpl/e5HdXP+1Z6JszOA+/yI7pyDQcO6K34BnMrFIWy9AatBrUGmQBHmb4TYUQ61BK9QgQc8FfBkPx1AIOTTyOeh+7KrMalxaHlLzPwaOJ52mZfFWILXVyby4hFDi5G6Z7DVeae3Op4uJybG3+hzNCGNDbrx+vHp7/uOdb99H5Ba6qq4ebZ5fdom1rUnf/ghZ2Vl/f3Fn9YDb3+Gq+LEQJnbH7U4gSsxs8dEDurp6EKLEAIvvOBK/hLXH8qC07ytSfXrb2LPbLuz7f7P2ei/vOxtvIKj3uybvxUaSh17dk++r+/1k/uDKjGDtuIWFdM+u6v/6CKdiu4+d3FtfC8vzrTpf5Dh5b/s3zdfbs9HRvog6m6dc1SW+n8Ly2q26o7Zzn6e8TGA91f189+O7vn/3Xf/4P/Z8//28Z/K3V1a3/5/PVk/sPV//59w+CuXi833Pdcths5aYz+hvac28lQ7u+524mQJvnad5zKjfdc/H6nvurW5xjmv0Xm/UX/NN5P9DE1+unq4tVt2f0Ftrji9XFo82ZRagAw3750fnpsydYk7OcvXDw0jsnce6fThj3l+21DRmDkl6G7d6hHz0rC5mT6lBziWgCE3KnicxR1TokRG3ZR8LodTInogDXEFuJ1SPomMicgnoj+ITxbbRIGlsyJ4QazaI2+VKL0TUzzdO8xhaiRo/Wdp/+KUUSTuUltkA+pj1iCE9zfmLJlSCTh5QRw3pDymhep8ke/grPHBbipuRF+GWyaEbmfPbz87NHazyrAqu626Y91trmDAeNY3qoG/OfdYP92QHCIpdeI51WV+vnL9tGTNI0ShugjfQGzn43nQm+z7MvJLRs43WqdAqJc2Oqiwn+fPNkc9X9SDpYZmt+ZRZ2W7cdTFGIYBoQg5V4CEAIaPjXa7TT+ygZT3ZbkGN9LUbS3ROLn2YAYvfn7f4nb9y97LHXhsaGvndQut1LvGxevsfxPXBEPdoG2+fmKCE9RNH20bl6/iSv70cQ4ZPHPNNe5S5OFFDw5VEytA2x1VoIaItk8nqUjFhTGlCnYOide4TGq/M5ysL3CwOaVWvOKCeIAL+Lg/H94q2jZGDSErQhXdJkH793tQs9EepAtkUUDDiQ7EJo9CAZuSTskqKZrySA82GQjCh+yCHnnBHlTvctAMT3/QjPITMIJPHfGSOjaRzUV8TvWLKDMeYgGN+/eHnn2zeIkRGb+CFhVy+xESd1+/EeI+P6HtmPkRGHgnVeSylkn5nDvRgZGqeAFSTSs7BZN46QkRFA8FObJ4LrPzpCxvWu/nPjY4A4XhQPI6Iia81n1AEoDxevGw+Dc/E+HsZ/czyMA0sBi4dhNe/jYdxI6AkaBAPuYcVjhHgYbPJ6SK39eBhGpB3Ev3jBKTvGrr/5+Bf3H5LocJ8k/uL84unj9cOjCBiX69P1g6v1w1s/4DJiIZf3iJf3cTB+vcGlYXzex8H47wwJbrwhDOeWW3sfB6MH1fzVhCox9u9fYIluHAcDgt6CXHSexv68cRyMFyDGaz7Xv5/YF8ez8e611Q/Az78sS32t2BdPQPPfPXz8HV5r/5vw6ftEwIHZ6vVHX2q+Snqft6LKgJWvr0f8l4qI0WKN2YdA1hqSwxQhxmPT7n9F9Io24O+eNZVuelrQxNcgpZRuL6k+4yqGcw22pyWWIeA9MsWVIN/MYOJk379QJQ945ceAdARHfIlD8pJCwk+LjNyBLVImhzaS/3qs+UhuU3Mj3WOQhIcxeZgsngVpEonCWmvyvhXLhIMDW8Svq+VW1OHyZvEsrRu1uuIHAmqUlPAeyNGMDGvB0450T3gaxSFKrWLeQzgSyYAPfxDvCcpJquyh4MiUWtYSLcHjICW2UkPKwdLr6OCTKgaFoWDiJH4gNoDmWFvNGCoOMaQUyOpUYi2VgBdZWtSE52Bm9ENu1eeEogC3Piqw5I05SkuEHYhDLiS2iaFgo2pBBDDcldJKsuQ7Q/KpVd9yjSXpbykgxs1CYNhTvxT0oj/0S9EPfsZF5WZu8DtvlqNT8DaGbCJCwJeSMcf078zl/f1J/52e9Heucf1nUAgh3ZhCCOkVFAJaz7eQ2/zuKYQ4tFpUfSMtIGkvQxwKJqFztCrxJh1uHu8JPNpRiaRac/dGqU4FzxKtJVgkKmzQpQ1NMt7yRjG06AkyZLGszDMk+jKUqjGWHokKl9kBbDm70NcaB998j7CVXJE4aEyJWOgWUytlNEapbImUWOvQsmrEuh1FDYoX9SjgtVMUiotNDL7Okb4aTuGE/kkePx0Xch5KSlpwXDHX/TAQ6SpHLa1a8t5KEK5KsB+IJTxmhthKaA103jNfDxAsIHwtlrO8Db5mLUHREFWIlFKrZp9asrhdJQwa8LYmBoEP6rIOvjJx+BrX0vAoAFdgL1nN/T/K0IoXtCklEWJIdbDoAnQ6oczCXWkKHoCXcm25Bq0VX4DfkHPLb4MwuLb934o0wG9sID5V05gI7/EOouG8P+K/tyP+m6QIbk4QvIoemG3Z31CP87unByqOZYTKqNJ8I7d4zUNtafbiJIVqIZ0GUS5iNYfKVmQwjnqKiIOT/hAznHb3JNVYZch175WK1QVJYFNNCABC8GUgxuTs4VoLsWXgq40mwAUW3ztP/o7uv4qBXfHV+1ItUKdYnB6LZNnTPPtog8ngZsLm1KaYmexImYonAgz/FKynSBtybmmOD+oyyDhnyWGiCbKUYfJOBdsmxBaZnMEm+wguGm7OqVnsIHj9lphQcykkPqjTaJEOYdaFeD8uFDK5VKJ51IhgIwQZ4Omb9xbWxkktEFyh439i5MQwZNKlTB/BoXioopoi5n4EFfIyNKKQEOInx+SqDI3QJ/MiuBwHTxTQ6pMgpXlPF7w0NPGbCQyuHYK3oQvUl5wGgjd5iRUTqXdAF7w/6r/Xo/7O6YObesAlfedN30ybMfn28PA/zwOOgGnm00ZQqmjOa+YB17IFUktVE4z3ZBL5Eg+4iORbIsmlqsfFe88Dzuy6Z2Pw9w5wi+tW3P+NDnBvp736ZUL0lT5ds99IanDRs9l891X6O85KP2+1dDv3pF+Xs9fbWGS95ez9dryT4lsJQN9ymuZNli2T9ks2WRf0vt9km79NCVVn167f0iZ7G676XW2yA2/eI0hmsoP3e+w199gv+gWar+B758CfTqeje2OLYPa8ucUQpiuSjwReXVNef9BptsUyxiaD+FSR7BsUtzAIWDXYT6q+mSsbYRBitVBZMfuYkhyGQZgiK8yBDxTZDUIVn6O5cWxjIlSLlzDHRPiXcwO0BLc7H6z/PjfAG9s8/1bcAaMkaQGNHsFw8yHc+B27A8KLHSXNNkz5Pmn2B4JkNBWJQX0rltjakmb7gYCG5n6M+1XPBQ3gKykOoaaCBFibZYAG8BEG7uB5AOuBc2DlRcL3BQKmZcDrXgpti3JOmmolq9Gcgdu8yyyFthYiVUdfK9LW7t6353n4Fs6BqeqAXrcJumL79qFrYPVtCA3vNU8cdjo+OwP+ynwD0bnjnd4qyXvMy3F2/jPfwOtrPd/GNzCA/lokQ1Y0V+15fq/M+88C/dVSkLoHDO2OnANfuGP23APJKy1FojYi5qkF3j7wEDSvUX5iVLFszzf2ELyeJ/sfm0P7eld/HR6C3jLav2aSbDMK3zz4fk6CuHnw/U/dPvzz80dT5efnj3Z1f+sxJe6sHt5ZbYiEBBX10cX55eXj1ebiqHjnfIrMtO9EBUA+cKLC/vf84eZks364WJ6sTi/XbnHy7xebh88Pi3s5WG/fv/zo/Pxi7/lPHm6uVvehAadMz59unq8f3r58un5w9UdiZey+9YZhm7aEeKe1n68fvF7sgsP3p8gE5gKo+1ZWu5r3ToE3kgO/jQhqcrd/tVNgP1j7W5gmD7bwOxeYf/TV5/9xQ6H5Z+HTV0nMp4329Js/3/rhsiessWTYOAHs7zsjiDdnj249vVg/3Fg2Um7/elz+RKK5lVt23eOgITdMzfu7SH5tyc+P8yj3kEb//EzK9157uYI3wnLat9fyX+8LldigvxqHMvDYmzuUJZNGHGUsN63T9eTXBvEsX7lFe3rnCcvfbfbr/YTl6m89v+UHH35lectlQHj65qtnC3WzxWOd/4HJ5m/sDkia7J4pe3IKPKoIi+Oa54slFj9k19GhkdqVYdvb5M3GqoUoMTGXQaTBp13Lwnpzn8GXGXoEotP8sFj+Bcth9K2tJeK2VHEep6mcRGo1Z6pETSAfkQUX99EqtFoImUgIlRp5JFk881yIDS6VmhyFeMleE+G4qSl4SQk5fJRURX4g1jjW0TkolkfOD9FbOjA1EUyirSgxk0osiK++NmpCCVFaSD6SNpgabTUrPkI1pkS20JgCJlviWwgEGsdKKxGq3GvN4gOtx1JKyLnl0lqvIMpYqUQfJ6OQ80PyJBnz1fy7AsNKklLzoZDtpWUaT6EGEdKgJlypqIm+KhleY/NRGVZKGLnWFGOoOTOslLMvyceiRGVhWKnU3HKIXn0StdYbOSmjKrnIMq1nr2R5CmYs5hlWlpwwjmoirSrDwtC7ipfgU/V0J0epPgQfW8vqaTzTESXDMN2i8ZwLeaxIbxxKYVh4weVUqlYR37/TQqiWI5lQbAyr+Nh8lYy/XCw0XrqhNhHlU7FH1EvyRGrP5NHlkRhKkRJr8kEKo8IWnaRvEqNlhfVDyeSAEuzwK7m1/FCqz+TDLSq52j4tpKvKKXic0GyfVp9qKUUssVtkWFUqFmc+i0qqDKuqSPCeIDCt2kYlv1aU0IInuxtzWlPWzP4hYa9t1JoJRp9LqTX2RyzaHTby5NFmTmuLgag9WrJvtk+bx0UtpIx/gO1TrOdzjYQQL9H2aVPs9C3tQ8qNCWsxFnJs4FnXtyWOchp8oGlhkltuLWkIos0Tq99jvZ8K6S001Gz7FLP4lkjumzNmVDKY+WCulrBaKAePvX8ji7AqFaqWfcyS+GUqYsrVa2lkCaCcCJ+fiiCytzdI+hUKDoA5kAGbfiOf4FAnyo39KcmXEkiyRo6LUjPrKr7xhgR8JJJmUo1R1uiRo7RUC8k8BoncaiGkkLK9kVooIqQNAHiKJSBsQX2NpAynosYkmjzfwohwkFZEY24xlJp5JSCqSySGFuCXmD9lwcZSEtn8ZAhk7q6hqCfPMhWx4MuYs1dJjI2ssdGb5WEr9goZGoQMUHyHJ0i5oJ5cJBZ5fAit4syghRywvKLiSfSmZCovDF+DaiT1c86+0QoGnblUycjXSAeu5KRo3jdSQdsr5FDzwROaK9srRStGq4nIdvZKzREXi0rgenulNV8Ua9LmceYYomA82koMoeI7OxDIUbLkIpb7ToaouWnR7MUnsq4M5MRIlazVUYO9kkmQXiLQKLE7IpEffSN/RCMT9hCJjx1EpSYGj3DUgFtplhFuSBJyjklwx428kALZIkmaWGx7JfZips9YqfJA8ingC5piI8fikDKSr1AbbVEuqZDBBkeVbBW1glzYcVp5gxFJICOfDywaWbw0erx68VgUgprh1ZqilJrsDa21ZI6pkGvDckdGcuV58Iu9kiMIlGR1ZGoRxF0leg/SFzt8uTZbtwpA5RVSWmI6RYINO2wFHZhmHwsTyxOhhEACGCAI61zIw5OAZCFkWiEzPYbJmjLGVjLgTFOrksavib1SSvEtYShc7GSUxqkvGa8dgxLVB4McSQmLSFJ7SamoxBpS8KwzRsktahRpJC2UoUayfmKyGUDZMlRSirJk3sKek0gx+1SAsL7nBQcIlCZCXmWypuMSrR7MVyuSSzKgKaRQq1WanYwmOcWQGza8jVdMT6O1xkxWQipshXwpWWNkd7QUyfdTfAIKUpGLJxuiJrGhtIJldyYIZBN7o4WQW41Na9HsSO4ea7Mziwt0wJc7Cb7XgVwiVChZ02OogbyDVMRAUnRIAm8P4CCVKlCupsgD0BvFExM1F3ujevA5RFjiftOgTdnEGngB1WlGfVqVvIhhEKmxSinVEqlQoaQ9bIWsFM1eQdwfBb9qco+GQVIG1aeSasTpfCBPRc52fkK2V6qAPaPG7MneOiDE9mxTbU0s27vnkAjUW1F6GqQBaIOhQF4JGoBngg94tVdiBGywPyx3yxBsLzUvvvlgr+TWlNSuPuDtjRA9pAQkxRHfWmlJSo2lNSgYFwb1pbSG0T1ZLakInq5CrUjkFTQNEfqnCBlgAy51mWSektmGVKQayEIVExiKioISOisu5WqvAC0ZMK579krLQgYdTJMrr0TPAgTJGhvpdIdoGDn6FnA0pEKjJ05UwPzfXol0o7QkeMOHIZIfV9nkPthuiZZMF/otQqQSn6CTC4o3POVWREgoABCjjeRb0VpSqyk2ljqxPVtLpKGl30ljIz2RzTwrnWLJPkCCQ/tSQQ9x0s++MA6mH4/FmGIWe6OmmFODaDPPyQEH/Frw7g4hs9BZfPGVPKTZV17JQTW02HIA11ChqSWfwdRF7RVza6zEX0h2MEh4RBIg1P6F2cEDQmIgQkDw9gouiig/8GxgNnJj0TLkY428AkwGu2ZiaPJKCZEeaLOkp1RobsEHDeQxtldIhirgBxrjiRzESOwsLdsrJXY6OKViR4PoxD55z9LgBjpU733I0jg+djSqhBwL9BkqIZ4I7KZkCZOqvaLGaZQcovlkDjV5wBLsUwn2SlZRuiLgH75RUkmqKRmVSQXAPZLXCbbJkT7Wt5ZKtPPE7miiWUg5XJkUKkKGkwt4oLIsTc2EQlohdgQViV0OKiaUBBUZW/zC4bDNYOFBQ201ZRggXEebRnxFLGwzQR88E2fnKwtliQkyvpDLlpgPnlROJRevISR7Q/E/JWFb9hY2Ar8cT/YqTj1v4BYqATANKaADYL6FGIMvKVkjzcO5GtlXeAWIqGRoBp/xighciAeogep1IDtdsbRZ9JWKCPKN3rRT9EPAaqg24XTslZw9D4VM+BqeIJCrtJpSFQtwISSBVmgFsts6HQJOtxC8MXGEdQiCw2vKUVsfLdnAosVXJVgJT0QpubTEPCV7JZGXC/JevMXVIIO3cNrgMe2V0rJKCsbB2StGMpcAfxSb00F9rIgbjPvgFXjLlsQbfZ+pUKCVeihpsVdiMFbYCFZ7JSVNleRekrO9kvHgJB0xWbv4RvVQVOx6FdZaGwgiZKMoeCX6VKPGBOJOvBJJeAx7DorkFfj2CqICF9krUQsu1wbyeSCRAS+oAVaryLWRA9hAGGsdq6RC3jHfrIkWpQl2RjgoOx2ARFActu+7q/DXz54gr3mZzKRLzp8ulkG77CQVlzIUwvZHCBnDP5B4oLfsOQzbH3EKDYrXUvSu4vDsNDoIINy+nLrsgotOnXfeRXH4xUrJ3kkhHo0P3mU9alKTdwFCFZpQxNEtMoXbZ5vLLjn+7d9MwaGt3/aIQMkgG0fOdwc8d+W4zwbQ4JqgqZJ3RRxLrw7s4h2+38kaSNZt0tGRyXz3QxOWhg2aXkpxJRzdj+SapwPWDB2JLlcXs4veQfySCd5V11y0NnIy/7RdE7TR4FmnxOrNlZQP78O3K1wr1DVxZXx2LbkU51XwTqrLDjqQ6fcuNZfD0WQFDzlFeh4hdHAp3oF1bRGg9C2RdXES1JXkOHd4/DlLeO2SK/3D6lJDm7/9ERcAScFDciMrqKIuAaRAlUBythQzE4qr1WVxUdk7wl5iDaYvZ5e1HX5ZGiShxP2RiBOAOzF6UkwOcjlAwdk+6nupFUfK8ujAZ2TVpJVq/a/NZV8OWwnsvYB8QtRXRxSBVI7GGMlaabNRnJDLE0o/NFezDQgPTBeYXobFCtTgMpiPWE7buWKygIpwuJwJnqkumsehOFYFYkEg+KW5WlyN7PvoHbuMWAb27eJdLvtzwncl7XdZXCC7oiQEADDKWa9tquzILGCUBFMEV8oE+uCKOlJGsin2hpSby0qC9e0Pw4Fw00J2U1h6OP9yvPOSiIOdV89q0jXAhj3tOLexuuTt3Eea478irlxbJwRaIcKmpsbp1eJKtPyo8w9wJzmcOmBeA/CFUTDlDCsxBo5Ns9b6qS/NlXi08WxEEJFOMvuaw1Fac4mQF7sfcTGTGtLb8Qwcdxg+QF+IbAEwYLVt2HcFEET2NwRTyN4KqRokszebq+zTcDS02ICszSlTSNcQAjC0FB17NgN1AtPcp5DRpsPukqDSQVYAOgEEhOTSFzwYGwlBkwuweRxCxkR4Df7vO4DxNrB+ehkYxCL7LUDdS2H7hSoO4Jbr0aCvoZ/IDogAZ85ycsIKs/G9oZuUDJ5OK1Zdbke4JGT19NYgdqGf6lprrhDFbPcjLvvjaS3shuIUshwezJfpeAegbFYH5zqd6uJK8zRGSmEkQw15ERtOvIor5Wh/pHrUWgL7QFzBuTK5ACjAfzSYnhut6dSauJKOx8m8koDEIRUHq3HAU3WVmBS7H3EkF96VORrRuxi9Q4Fh7DmAuQL11CGFLspJRBDvvMvBVR8c6WAdklMX5AB9A9PZRpKKq0VdLketEU/uoPlYOZnRGWlegZWe08XEuRIBOMjYHQfbO2NWKvCgwdh4uog4QIAbCIRKBpUcHVsSMh+2CboHDMPSIxxFyqjimhiGmPZudEjHTHyoqC8Cvtu77xhEJiE0p9PTW/bVMXRKWlw6rkT1YfsKiAfxxDg8uxIUzWAbbt8Mk55ykkNg5xdIItgvpA2uhuZyO5rPa3iYHkQoalgwXmV9lHA2YIzUySjgYM3RKZIS5GdgTs42ux5gLkjGalJXDtbbTs7R7JLwejdNQEOAHaclVxcQtUZWrAPevraNjXr0FqFvnALXO3A3yKTmIF+TQxhaanH5mOzI/hijaZtgMXgUbEla7wzOR8EDUi4OcQd6FKcx7PcDkMqGrCB3w2dC45X+HrVzbemzHkG3LVaACwVvg0M0OvZN33NNHS1B9DtknnvzCEaAoISBFxPKomoS1suWJR1h/hybS5a/OTpF/mOUN3sNoFQ5Wzb2RmwFp6wNWbgPW0z01EPcJKAnmBNNjkNwX+R4AnpS8vmHRNBHT8QiTjlToEMEzWwx4Zz1vpBWHApRxWk52FbiQjHqnLcwNBX0foI7RWvqSjmCcoX8z+14/os3IGsCHvAc2hcPGupnLvbm7bAUMKIJwSTvj4JTbzgIFqOvAPQe81H3l8sg7VFFsmCb2x9xkYTbnDWkvVDxkL29M0A7AgYQf2N/bJBMaR+mQpAhVZSDj0NyQLmZuiSAESDBmoqrKb8I/UAyg2q1dwYcFirncoJGLZXJuaX/OE0HyFs4Y/vjZcUaIcfQzxTl+HRCgWEGjh9nSFyO+6Nh1xzMN7MEDRC8EcKkEwdSy0RPc5BbhaT3+4tNbw52o7jQAPgKDWGBzQCLAhJAiOIa4BT2KPsXUPkFQAQZALkNYmYqbWbEF8eJ1HzAndF+3kfxAraEXioFiJ+cZDg3hU4LuTk4mmas6PEx1qOKBJBC9Ao7wJ4BgPTzI/AZbEe01QcHmdwgBxUdofEtY6a6zjBCoCESEx/FIUllD+WyDxBZj1adCcORrsEaAFB1mo9gNHV2WvVw28ZwtD3U6HAkOQHsKYVDAIfYN4ohCR+LqwEov7+6Rs83F3m1ZKPnCRHLXrDdygAUguZ4FsLBiQTSwIhmY8kNykMnZiCDQneLMp8puZphPY67EMXFMHFKAEaWFtsIo1WYhpI4FIcgNZo8TC0+LdQHcei2EIFjaxq5DGA0lU8wgYTPDhMB1ET5uB8RQR/ksdGdkMczvQQJCGyvR4gkxgyDfzQ76CVhVgDNkBkZoiTBwgVENbC37HRo6XywtjOGhwLlPNVgUpU+D/CsvrnoD/ATHNIRFNN0wLtyYFFHKcoNjwCiQDn0/rTWeS9kI/WAwTV6ZH/vs1eAohXiJwMm9zoHKIK09keEXSwHRDJk+cGI6RzqEJY/eHgu452wUxCF8ofWwvaIDQW/H5ECIp1G2E4XsLNhn0AFcHaiHBAL4mI5oJzpQjsGKFDLyQP7j2CNNJswgK4xq4B18RlU/wKKKdX9LUrbnF84NlBsmvCR7UPJsJqH/cBmZG8LB47+fgWgz+Q9HLPAmnoYG4g3icYXm3xMAaSQl8fwrh1V2JlDO8XemhBUJyRKoeuHbcd2IB2ELQ+HgEkB6SGjn2GJhF4I7psYTiAjZPMb0yeu+urSAT62zR+7EIjFbB0GmewSmipC+GIXdDhF7QDOC8Aww1VBGFYkhIT23v4AFjiDmZkyKtC4UdifqghA91fEDpcayUvuSlTcttUYB7KIGHNFErffADE3j8CB2pHB7CPYXpCyv0sgREHFqKQwT0A+OMl5oIWItbXtvfUIpQgBsjANmHEnQA40FtHTpHDAkgcQxv6kiUPrt/fVmcCIqLUBCQFdn1joTjNOMWIfiFybd/m6iAYSGwIFpmSaI2OvQC0pHYLlpAd8BjwNdHoiyNj2x8ACcAbeFGlF4MAQKx10D+ECIYbE2biXa3DK7y8JA26MkW917sCkLBqji8Vf697hXMVOQyM5Ie0aWhfWEQCY95cGOhGyDDc/0QLSawVizx02YdQ86Ay5Dz3y5u6yddvH5PrTZ2fmH/B6/i9HaT7vrE7XV1frXzLc3/M/MLPnm2Z/wqKSrm5Oj1Ir4uuRffdjRCuVkST0rPcfYDfmazBdfAht/YF5ilqKqDi5SvYXupO2eT6aE/nk112DRRJCnZjB1Hx2ujVZ13rMSuNhFKBKJNh28Nbs/00gPH7IHYtkc3Y76UEEcId4wzSwF2Y631OsPu0rQY2txrFvxb+bCf9LnHHMnPl1Esq/yNeGj5gjKfbE6+dXt88e4ck1F7dJdBiw3e+p7sXsj3cpWOfip+c4plmu+2+3RtnfPllN/my4ln2+Pnt09XhqYvPg+20TOMRZkJSvTk4u1/i3Db57qZPwafcYa2H24vNzLzUhx0GhN2SfmNLF2mbmk3ud/fKL84efr+5PPnMfPbZ80HbippViEazRxXLxCrP6bzZP1p+eXzxZXU1Jd+YDu/eZbVNEOLHwQ+ePFkt/7DbOjnt2crKedhwr0suLJQ5tJx9uzr6+Wl19cnHxFcmruU/5qx/WFyen5z9eLszd7+sHq9P1yerB1flFdx80o/GfFkuMVhIxJS15p8c1sN/CnrykFlBsF8yRzdOh25r/tFhKSFlTSoLBMuhmcXL74mL107EOVMmud7o+W7xMB/oCtee70XS+iXLzBfrMN1NhvkBr+YaKyhfoJt9YHfkuNZDvVOm40zPC172lbvEl6sS30iC+Q62hKQpfoBx08Q31gS9TAb651g/K+EWKvjfW7b1KnbeT1WLEdlMN3quUdm+mp3uVau4foI1DAfcGOrdXqdnQrLl3rEyD43kDlZl7lZYMxdg70IVd13+h8vrHaLlQbLk312VhUfAC/dWssnrHWioUU/8AXZTDgzUhxLmhkunmeiVUSe71tUev0hihJEIvtAMvN1YF/aL65001PljivFLL44pgiZFfV33ziyqbWUsDP/tmiplXKWMcqv9khjQ307KYZmWnTfklDUrHO7+gNMHKo3bdyM31IahAbqz1ME3H62k33A0VGqbEuLniwiExvql6wlQSr6mGuLnmwbQNr6thQMhzI6WCQ5HwusoDd2N9wYt0BDfSC9xMFbAT/7+myL+D2F+S8ptk36T5ryHBN1B7Izm9yeZNHv8PkcGb3N1k7ZhC7MnXf1Gmjhj9lyXnJi03CfnrSsVdavrLsm+Td5uM+03k2kirbyDKBuRgMnBNZP2LYmoXbySZNmn0iyTQvyh1djcTNJtw2QTKE1S+sRDZxV+SG5useCcfZpu+jkzYJUxRXiX5pQEE0GYl8WIJ76ukug6V0YEg952Hqbl/injzRqlqv7mz+mGNsO5lkkTEta8lSUS2O0nTkDvtiXopImcUX4aacIbBDbki2yPAHa7wlXxe+OVJrhaOLiyWKeqQcmsZYW2zyIFTeLtEsjHvi/nP09G7s1yXQJ6aenbRFnsLc5RPojHixeCLwBstdlE+C+3io521+mQh7WaR779cyE9hvn41IT+vRzh6ucD5UNo8F/e2HK92+a3tuC7/vDcJe28qFEfo+fnmyebqUxOQdhEzG+mri82jzdlW5JzIRocDi0XgkGNJ7c8//5Wqk68fXGyeXl1Okt/PNo8en24ePb766PzsbP3gahcFzeKj9Y9bzLWf/y+ghiwX').then(json => {\n",
       "   const obj = Core.parse(json);\n",
       "   Core.draw('root_plot_1779222911730', 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_1779222911730();\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%jsroot on\n",
    "gROOT->GetListOfCanvases()->Draw()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ROOT C++",
   "language": "c++",
   "name": "root"
  },
  "language_info": {
   "codemirror_mode": "text/x-c++src",
   "file_extension": ".C",
   "mimetype": " text/x-c++src",
   "name": "c++"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}