{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "QA plots for the generic tracking performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:32.633699Z",
     "iopub.status.busy": "2025-03-25T17:00:32.633052Z",
     "iopub.status.idle": "2025-03-25T17:00:32.639990Z",
     "shell.execute_reply": "2025-03-25T17:00:32.639054Z"
    }
   },
   "outputs": [],
   "source": [
    "# imports to write dynamic markdown contents\n",
    "import os\n",
    "from IPython.display import display, Markdown, Latex\n",
    "from IPython.display import HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:32.644254Z",
     "iopub.status.busy": "2025-03-25T17:00:32.643718Z",
     "iopub.status.idle": "2025-03-25T17:00:32.658429Z",
     "shell.execute_reply": "2025-03-25T17:00:32.657613Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "*For the result HTML page:* "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<script>\n",
       "code_show=true; \n",
       "function code_toggle() {\n",
       " if (code_show){\n",
       " $('div.input').hide();\n",
       " $('div.jp-CodeMirrorEditor').hide(); // for newer versions of nbviewer.org\n",
       " } else {\n",
       " $('div.input').show();\n",
       " $('div.jp-CodeMirrorEditor').show(); // for newer versions of nbviewer.org\n",
       " }\n",
       " code_show = !code_show\n",
       "} \n",
       "$( document ).ready(code_toggle);\n",
       "</script>\n",
       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# turn off/on code for the result HTML page\n",
    "\n",
    "display(Markdown('*For the result HTML page:* '))\n",
    "    \n",
    "HTML('''<script>\n",
    "code_show=true; \n",
    "function code_toggle() {\n",
    " if (code_show){\n",
    " $('div.input').hide();\n",
    " $('div.jp-CodeMirrorEditor').hide(); // for newer versions of nbviewer.org\n",
    " } else {\n",
    " $('div.input').show();\n",
    " $('div.jp-CodeMirrorEditor').show(); // for newer versions of nbviewer.org\n",
    " }\n",
    " code_show = !code_show\n",
    "} \n",
    "$( document ).ready(code_toggle);\n",
    "</script>\n",
    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:32.730480Z",
     "iopub.status.busy": "2025-03-25T17:00:32.729728Z",
     "iopub.status.idle": "2025-03-25T17:00:32.739846Z",
     "shell.execute_reply": "2025-03-25T17:00:32.738904Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "[//]: # (This section starts with a 2nd level heading and get embedded in the result QA report at https://github.com/sPHENIX-Collaboration/QA-gallery/tree/QA-tracking-low-occupancy)\n",
       "\n",
       "## Tracking QA at low occupancy\n",
       "\n",
       "Low occupancy tracking QA concists full tracker + reconstruction of events with 20 $\\pi^+$,  20 $\\pi^-$ and one $\\Upsilon(1S) \\rightarrow e^+ e^-$. Please note the calorimeters are disabled to improve execution speed, which also removed some of the correlated albedo background for the tracker. \n",
       "\n",
       "The source code of the macro can be found at https://github.com/sPHENIX-Collaboration/macros/tree/QA-tracking-low-occupancy or [comparing it to the master branch](https://github.com/sPHENIX-Collaboration/macros/compare/QA-tracking-low-occupancy?expand=1).\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os.path\n",
    "\n",
    "# readme file of the macros, available if run under JenkinsCI\n",
    "# https://github.com/sPHENIX-Collaboration/utilities/blob/master/jenkins/built-test/test-tracking-qa.sh \n",
    "\n",
    "macro_markdown = 'Fun4All-macros-README.md'\n",
    "\n",
    "\n",
    "if os.path.isfile(macro_markdown) :\n",
    "    with open(macro_markdown, 'r') as file:\n",
    "        display(Markdown(file.read()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `pyROOT` env check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:32.743319Z",
     "iopub.status.busy": "2025-03-25T17:00:32.742740Z",
     "iopub.status.idle": "2025-03-25T17:00:38.855754Z",
     "shell.execute_reply": "2025-03-25T17:00:38.854834Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "via sPHENIX software distribution at `/var/lib/jenkins/workspace/sPHENIX/Build-Master-gcc14/build/new/install.1`"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ROOT\n",
    "\n",
    "OFFLINE_MAIN = os.getenv(\"OFFLINE_MAIN\")\n",
    "if OFFLINE_MAIN is not None:\n",
    "    display(Markdown(f\"via sPHENIX software distribution at `{OFFLINE_MAIN}`\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting source code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:38.860396Z",
     "iopub.status.busy": "2025-03-25T17:00:38.860095Z",
     "iopub.status.idle": "2025-03-25T17:00:38.986477Z",
     "shell.execute_reply": "2025-03-25T17:00:38.985442Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "View the source code repository at https://github.com/sPHENIX-Collaboration/QA-gallery.git"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import subprocess\n",
    "\n",
    "try:\n",
    "    git_url = \\\n",
    "        subprocess.run(['git','remote','get-url','origin'], stdout=subprocess.PIPE)\\\n",
    "        .stdout.decode('utf-8').strip()\\\n",
    "        .replace('git@github.com:','https://github.com/')\n",
    "\n",
    "    display(Markdown(f\"View the source code repository at {git_url}\"))\n",
    "except: # catch *all* exceptions\n",
    "    #     well do nothing\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## JenkinsCI information (if available)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:38.991311Z",
     "iopub.status.busy": "2025-03-25T17:00:38.990780Z",
     "iopub.status.idle": "2025-03-25T17:00:39.116804Z",
     "shell.execute_reply": "2025-03-25T17:00:39.115358Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Some further details about the QA run, if executed under the Jenkins CI:"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "* The commit being checked is sPHENIX-Collaboration/coresoftware/cdd9aff0a42f7fc7812a7e49161bae9e18b1bfb3"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "* Link to the pull request: https://github.com/sPHENIX-Collaboration/coresoftware/pull/3493"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "* Link to the build: https://web.sdcc.bnl.gov/jenkins-sphenix/job/sPHENIX/job/test-tracking-low-occupancy-qa/7018/"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "* Git repo for macros: https://github.com/sPHENIX-Collaboration/macros.git , which merges `master` and the QA tracking branch"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "* Download the QA ROOT files: https://web.sdcc.bnl.gov/jenkins-sphenix/job/sPHENIX/job/test-tracking-low-occupancy-qa/7018/display/redirect?page=artifacts"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Automatically generated by [sPHENIX Jenkins continuous integration](https://web.sdcc.bnl.gov/jenkins-sphenix/) [![sPHENIX](https://raw.githubusercontent.com/sPHENIX-Collaboration/utilities/master/jenkins/material/sphenix-logo-white-bg-72p.png)](https://www.sphenix.bnl.gov/web/) &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; [![jenkins.io](https://raw.githubusercontent.com/sPHENIX-Collaboration/utilities/master/jenkins/material/jenkins_logo_title-72p.png)](https://jenkins.io/)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown('Some further details about the QA run, if executed under the Jenkins CI:'))\n",
    "\n",
    "checkrun_repo_commit = os.getenv(\"checkrun_repo_commit\")\n",
    "if checkrun_repo_commit is not None:\n",
    "    display(Markdown(f\"* The commit being checked is {checkrun_repo_commit}\"))\n",
    "        \n",
    "ghprbPullLink = os.getenv(\"ghprbPullLink\")\n",
    "if ghprbPullLink is not None:\n",
    "    display(Markdown(f\"* Link to the pull request: {ghprbPullLink}\"))\n",
    "\n",
    "BUILD_URL =  os.getenv(\"BUILD_URL\")\n",
    "if BUILD_URL is not None:\n",
    "    display(Markdown(f\"* Link to the build: {BUILD_URL}\"))\n",
    "\n",
    "git_url_macros =  os.getenv(\"git_url_macros\")\n",
    "sha_macros =  os.getenv(\"sha_macros\")\n",
    "if git_url_macros is not None:\n",
    "    display(Markdown(f\"* Git repo for macros: {git_url_macros} , which merges `{sha_macros}` and the QA tracking branch\"))\n",
    "\n",
    "RUN_ARTIFACTS_DISPLAY_URL = os.getenv(\"RUN_ARTIFACTS_DISPLAY_URL\")\n",
    "if RUN_ARTIFACTS_DISPLAY_URL is not None:\n",
    "    display(Markdown(f\"* Download the QA ROOT files: {RUN_ARTIFACTS_DISPLAY_URL}\"))\n",
    "\n",
    "JENKINS_URL  = os.getenv(\"JENKINS_URL\")\n",
    "if JENKINS_URL is not None:\n",
    "    display(Markdown(f\"Automatically generated by [sPHENIX Jenkins continuous integration]({JENKINS_URL}) [![sPHENIX](https://raw.githubusercontent.com/sPHENIX-Collaboration/utilities/master/jenkins/material/sphenix-logo-white-bg-72p.png)](https://www.sphenix.bnl.gov/web/) &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; [![jenkins.io](https://raw.githubusercontent.com/sPHENIX-Collaboration/utilities/master/jenkins/material/jenkins_logo_title-72p.png)](https://jenkins.io/)\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:39.122015Z",
     "iopub.status.busy": "2025-03-25T17:00:39.121390Z",
     "iopub.status.idle": "2025-03-25T17:00:39.648407Z",
     "shell.execute_reply": "2025-03-25T17:00:39.647318Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%cpp -d\n",
    "\n",
    "#include \"QA_Draw_Utility.C\"\n",
    "\n",
    "#include <sPhenixStyle.C>\n",
    "\n",
    "#include <TFile.h>\n",
    "#include <TLine.h>\n",
    "#include <TString.h>\n",
    "#include <TTree.h>\n",
    "#include <cassert>\n",
    "#include <cmath>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:39.653014Z",
     "iopub.status.busy": "2025-03-25T17:00:39.652517Z",
     "iopub.status.idle": "2025-03-25T17:00:40.414921Z",
     "shell.execute_reply": "2025-03-25T17:00:40.414286Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sPhenixStyle: Applying nominal settings.\n",
      "sPhenixStyle: ROOT6 mode\n"
     ]
    }
   ],
   "source": [
    "%%cpp\n",
    "\n",
    "SetsPhenixStyle();\n",
    "TVirtualFitter::SetDefaultFitter(\"Minuit2\");\n",
    "\n",
    "// test sPHENIX lib load\n",
    "// gSystem->Load(\"libg4eval.so\");\n",
    "\n",
    "// test libs\n",
    "// gSystem->ListLibraries();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:40.418265Z",
     "iopub.status.busy": "2025-03-25T17:00:40.417913Z",
     "iopub.status.idle": "2025-03-25T17:00:40.563957Z",
     "shell.execute_reply": "2025-03-25T17:00:40.562655Z"
    }
   },
   "outputs": [],
   "source": [
    "%jsroot on"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inputs and file checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:40.568747Z",
     "iopub.status.busy": "2025-03-25T17:00:40.568216Z",
     "iopub.status.idle": "2025-03-25T17:00:40.679136Z",
     "shell.execute_reply": "2025-03-25T17:00:40.677692Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "qa_file_name_new = os.getenv(\"qa_file_name_new\")\n",
    "if qa_file_name_new is None:\n",
    "    qa_file_name_new = \"G4sPHENIX_test-tracking-low-occupancy-qa_Event100_Sum10_qa.root\"\n",
    "    display(Markdown(f\"`qa_file_name_new` env not set. use the default `qa_file_name_new={qa_file_name_new}`\"))\n",
    "\n",
    "qa_file_name_ref = os.getenv(\"qa_file_name_ref\")\n",
    "if qa_file_name_ref is None:\n",
    "    qa_file_name_ref = \"reference/G4sPHENIX_test-tracking-low-occupancy-qa_Event100_Sum10_qa.root\"\n",
    "    display(Markdown(f\"`qa_file_name_ref` env not set. use the default `qa_file_name_ref={qa_file_name_ref}`\"))\n",
    "elif qa_file_name_ref == 'None':\n",
    "    qa_file_name_ref = None\n",
    "    display(Markdown(f\"`qa_file_name_ref` = None and we are set to not to use the reference histograms\"))\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:40.684191Z",
     "iopub.status.busy": "2025-03-25T17:00:40.683573Z",
     "iopub.status.idle": "2025-03-25T17:00:40.874137Z",
     "shell.execute_reply": "2025-03-25T17:00:40.873269Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "Opening QA file at `G4sPHENIX_test-tracking_Event400_Sum28_qa.root`"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Opening QA reference file at `reference/G4sPHENIX_test-tracking_Event400_Sum28_qa.root`"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# qa_file_new = ROOT.TFile.Open(qa_file_name_new);\n",
    "\n",
    "# assert qa_file_new.IsOpen()\n",
    "# qa_file_new.ls()\n",
    "display(Markdown(f\"Opening QA file at `{qa_file_name_new}`\"))\n",
    "ROOT.gInterpreter.ProcessLine(f\"TFile *qa_file_new = new TFile(\\\"{qa_file_name_new}\\\");\")\n",
    "ROOT.gInterpreter.ProcessLine(f\"const char * qa_file_name_new = \\\"{qa_file_name_new}\\\";\")\n",
    "\n",
    "if qa_file_name_ref is not None:\n",
    "#     qa_file_ref = ROOT.TFile.Open(qa_file_name_ref);\n",
    "\n",
    "#     assert qa_file_ref.IsOpen()\n",
    "    display(Markdown(f\"Opening QA reference file at `{qa_file_name_ref}`\"))\n",
    "    ROOT.gInterpreter.ProcessLine(f\"TFile *qa_file_ref = new TFile(\\\"{qa_file_name_ref}\\\");\")\n",
    "    ROOT.gInterpreter.ProcessLine(f\"const char *  qa_file_name_ref = \\\"{qa_file_name_ref}\\\";\")\n",
    "else:\n",
    "    ROOT.gInterpreter.ProcessLine(f\"TFile *qa_file_ref = nullptr;\")\n",
    "    ROOT.gInterpreter.ProcessLine(f\"const char *  qa_file_name_ref = nullptr;\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:40.878954Z",
     "iopub.status.busy": "2025-03-25T17:00:40.878333Z",
     "iopub.status.idle": "2025-03-25T17:00:41.007368Z",
     "shell.execute_reply": "2025-03-25T17:00:41.006493Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%cpp\n",
    "\n",
    "if (qa_file_new == nullptr) \n",
    "{\n",
    "    cout <<\"Error, can not open QA root file\"<<qa_file_name_new<<endl;\n",
    "    exit(1);\n",
    "}\n",
    "\n",
    "// list inputs histograms if needed\n",
    "// qa_file_new ->ls();\n",
    "\n",
    "//TFile *qa_file_ref = NULL;\n",
    "//if (qa_file_name_ref)\n",
    "//{\n",
    "//    qa_file_ref = new TFile(qa_file_name_ref);\n",
    "//  \n",
    "//    if (qa_file_ref == nullptr) \n",
    "//    {\n",
    "//        cout <<\"Error, can not open QA root file\"<<qa_file_name_ref<<endl;\n",
    "//        exit(1);\n",
    "//    }\n",
    "//}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Truth matched efficiency and $p_T$ resolution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Match truth track to reco tracks\n",
    "\n",
    "Reco track matched with truth track. Please note the efficiency only require truth-reco matching in unique pairs, no cut on the reco quality is required, as defined in [`QAG4SimulationTracking::process_event(PHCompositeNode * )`](https://sphenix-collaboration.github.io/doxygen/de/dbf/classQAG4SimulationTracking.html#ac2aeab5238fcf828f70c61b4976b57fe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:41.012786Z",
     "iopub.status.busy": "2025-03-25T17:00:41.012221Z",
     "iopub.status.idle": "2025-03-25T17:00:43.326609Z",
     "shell.execute_reply": "2025-03-25T17:00:43.325731Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "<div id=\"root_plot_1742922043280\" style=\"width: 900px; height: 500px; position: relative\">\n",
       "</div>\n",
       "\n",
       "<script>\n",
       "\n",
       "function display_root_plot_1742922043280(Core) {\n",
       "   let obj = 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       "    try {\n",
       "        var base_url = JSON.parse(document.getElementById('jupyter-config-data').innerHTML).baseUrl;\n",
       "    } catch(_) {\n",
       "        var base_url = '/';\n",
       "    }\n",
       "\n",
       "    // Try loading a local version of requirejs and fallback to cdn if not possible.\n",
       "    script_load_root_plot_1742922043280(base_url + 'static/build/jsroot.js', function(){\n",
       "        console.error('Fail to load JSROOT locally, please check your jupyter_notebook_config.py file');\n",
       "        script_load_root_plot_1742922043280('https://root.cern/js/7.7.2/build/jsroot.js', function(){\n",
       "            document.getElementById(\"root_plot_1742922043280\").innerHTML = \"Failed to load JSROOT\";\n",
       "        });\n",
       "    });\n",
       "}\n",
       "\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%cpp\n",
    "{\n",
    "    const char *hist_name_prefix = \"QAG4SimulationTracking\";\n",
    "    TString prefix = TString(\"h_\") + hist_name_prefix + TString(\"_\");\n",
    "    \n",
    "    TCanvas *rawResCan = new TCanvas(TString(\"QA_Draw_pTResolution\") + TString(\"_\") + hist_name_prefix,\n",
    "                                     TString(\"QA_Draw_pTResolution\") + TString(\"_\") + hist_name_prefix,\n",
    "                                     900,500);\n",
    "    rawResCan->Divide(2,2);\n",
    "    \n",
    "    TH2F *h_QAG4SimulationTracking_pTRecoGenRatio_pTGen_new =\n",
    "        (TH2F *)qa_file_new->GetObjectChecked(prefix + \"pTRecoGenRatio_pTGen\",\n",
    "                                             \"TH2\");\n",
    "    assert(h_QAG4SimulationTracking_pTRecoGenRatio_pTGen_new);\n",
    "    \n",
    "    h_QAG4SimulationTracking_pTRecoGenRatio_pTGen_new->SetDirectory(nullptr);\n",
    "    \n",
    "    TGraphErrors *newScale = FitResolution(h_QAG4SimulationTracking_pTRecoGenRatio_pTGen_new, false, 1);\n",
    "    TGraphErrors *newRes = FitResolution(h_QAG4SimulationTracking_pTRecoGenRatio_pTGen_new, false, 2);\n",
    "    newScale->SetMarkerStyle(20);\n",
    "    newScale->SetMarkerColor(kBlack);\n",
    "    newScale->SetMarkerSize(1.0);\n",
    "    newRes->SetMarkerStyle(20);\n",
    "    newRes->SetMarkerColor(kBlack);\n",
    "    newRes->SetMarkerSize(1.0);\n",
    "    \n",
    "    rawResCan->cd(3);\n",
    "    newScale->GetXaxis()->SetTitle(\"Truth p_{T} [GeV]\");\n",
    "    newScale->GetYaxis()->SetTitle(\"#LTp_{T}^{reco}/p_{T}^{true}#GT\");\n",
    "    newScale->GetYaxis()->SetRangeUser(0.9,1.1);\n",
    "    newScale->Draw(\"ap\");\n",
    "    \n",
    "    rawResCan->cd(4);\n",
    "    newRes->GetXaxis()->SetTitle(\"Truth p_{T} [GeV]\");\n",
    "    newRes->GetYaxis()->SetTitle(\"#sigma(p_{T}^{reco}/p_{T}^{true})\");\n",
    "    newRes->GetYaxis()->SetRangeUser(0,0.08);\n",
    "    newRes->Draw(\"ap\");\n",
    "    \n",
    "    rawResCan->cd(2);\n",
    "    gPad->SetRightMargin(0.2);\n",
    "    gPad->SetLogx();\n",
    "    gPad->SetLogz();\n",
    "    h_QAG4SimulationTracking_pTRecoGenRatio_pTGen_new->SetTitle(\"New pT Spectrum\");\n",
    "    h_QAG4SimulationTracking_pTRecoGenRatio_pTGen_new->Draw(\"colz\");\n",
    "    \n",
    "    TLatex newl; \n",
    "    newl.SetTextSize(0.05); \n",
    "    newl.SetNDC();\n",
    "    newl.SetTextColor(kBlack);\n",
    "    newl.DrawLatex(0.45,0.96,\"New\");\n",
    "    \n",
    "    if (qa_file_ref) {\n",
    "      TH2F *h_QAG4SimulationTracking_pTRecoGenRatio_pTGen_ref =\n",
    "          (TH2F *)qa_file_ref->GetObjectChecked(prefix + \"pTRecoGenRatio_pTGen\",\n",
    "                                               \"TH2\");\n",
    "      assert(h_QAG4SimulationTracking_pTRecoGenRatio_pTGen_ref);\n",
    "      h_QAG4SimulationTracking_pTRecoGenRatio_pTGen_ref->SetDirectory(nullptr);\n",
    "        \n",
    "      TGraphErrors *refScale = FitResolution(h_QAG4SimulationTracking_pTRecoGenRatio_pTGen_ref, false, 1);\n",
    "      TGraphErrors *refRes = FitResolution(h_QAG4SimulationTracking_pTRecoGenRatio_pTGen_ref, false, 2);\n",
    "      refScale->SetMarkerStyle(24);\n",
    "      refScale->SetMarkerColor(kRed);\n",
    "      refScale->SetMarkerSize(1.0);\n",
    "      refRes->SetMarkerStyle(24);\n",
    "      refRes->SetMarkerColor(kRed);\n",
    "      refRes->SetMarkerSize(1.0);\n",
    "        \n",
    "      TLegend *ptresleg = new TLegend(0.4,0.77,0.5,0.9);\n",
    "      ptresleg->AddEntry(refScale,\"Reference\",\"P\");\n",
    "      ptresleg->AddEntry(newScale,\"New\",\"P\"); \n",
    "        \n",
    "      rawResCan->cd(3);\n",
    "      refScale->Draw(\"psame\");\n",
    "      ptresleg->Draw(\"same\");\n",
    "       \n",
    "      rawResCan->cd(4);\n",
    "      refRes->Draw(\"psame\");\n",
    "      ptresleg->Draw(\"same\"); \n",
    "        \n",
    "      rawResCan->cd(1);\n",
    "  \n",
    "      gPad->SetRightMargin(0.2);\n",
    "      gPad->SetLogx();\n",
    "      gPad->SetLogz();\n",
    "      h_QAG4SimulationTracking_pTRecoGenRatio_pTGen_ref->SetTitle(\"Reference pT Spectrum\");\n",
    "      h_QAG4SimulationTracking_pTRecoGenRatio_pTGen_ref->Draw(\"colz\");\n",
    "      TLatex refl; \n",
    "      refl.SetTextSize(0.05); \n",
    "      refl.SetNDC();\n",
    "      refl.SetTextColor(kBlack);\n",
    "      refl.DrawLatex(0.45,0.96,\"Reference\");\n",
    "  \n",
    "    }\n",
    "        \n",
    "    rawResCan->Draw();\n",
    "    \n",
    "   \n",
    "}\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:43.353216Z",
     "iopub.status.busy": "2025-03-25T17:00:43.352776Z",
     "iopub.status.idle": "2025-03-25T17:00:43.467585Z",
     "shell.execute_reply": "2025-03-25T17:00:43.466415Z"
    }
   },
   "outputs": [],
   "source": [
    "%jsroot off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:43.472843Z",
     "iopub.status.busy": "2025-03-25T17:00:43.472210Z",
     "iopub.status.idle": "2025-03-25T17:00:44.026838Z",
     "shell.execute_reply": "2025-03-25T17:00:44.026042Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KSTestSummary::PushKSTest - Warning - received pValue = 0. Reset to an arbitary small non-zero value (e^(-15))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%cpp\n",
    "\n",
    "{\n",
    "  //base histogram from the reco module name \n",
    "  const char *hist_name_prefix = \"QAG4SimulationTracking\";\n",
    "  TString prefix = TString(\"h_\") + hist_name_prefix + TString(\"_\");\n",
    "    \n",
    "  // obtain normalization\n",
    "  double Nevent_new = 1;\n",
    "  double Nevent_ref = 1;\n",
    "\n",
    "  TCanvas *c1 = new TCanvas(TString(\"QA_Draw_Tracking_TruthMatchingOverview\") +\n",
    "                                TString(\"_\") + hist_name_prefix,\n",
    "                            TString(\"QA_Draw_Tracking_TruthMatchingOverview\") +\n",
    "                                TString(\"_\") + hist_name_prefix,\n",
    "                            900, 400);\n",
    "  c1->Divide(3, 1);\n",
    "  int idx = 1;\n",
    "  TPad *p;\n",
    "\n",
    "  {\n",
    "    static const int nrebin = 5;\n",
    "\n",
    "    p = (TPad *)c1->cd(idx++);\n",
    "    c1->Update();\n",
    "    p->SetLogx();\n",
    "    p->SetGridy();\n",
    "\n",
    "    TH1 *h_pass =\n",
    "        (TH1 *)qa_file_new->GetObjectChecked(prefix + \"nReco_pTGen\", \"TH1\");\n",
    "    TH1 *h_norm =\n",
    "        (TH1 *)qa_file_new->GetObjectChecked(prefix + \"nGen_pTGen\", \"TH1\");\n",
    "    assert(h_norm);\n",
    "    assert(h_pass);\n",
    "      \n",
    "      h_norm->SetDirectory(nullptr);\n",
    "      h_pass->SetDirectory(nullptr);\n",
    "\n",
    "    h_norm->Rebin(nrebin);\n",
    "    h_pass->Rebin(nrebin);\n",
    "\n",
    "    TH1 *h_ratio = GetBinominalRatio(h_pass, h_norm);\n",
    "\n",
    "    //    h_ratio->GetXaxis()->SetRangeUser(min_Et, max_Et);\n",
    "    h_ratio->GetYaxis()->SetTitle(\"Reco efficiency\");\n",
    "    h_ratio->GetYaxis()->SetRangeUser(-0, 1.);\n",
    "\n",
    "    TH1 *h_ratio_ref = NULL;\n",
    "    if (qa_file_ref) {\n",
    "      TH1 *h_pass =\n",
    "          (TH1 *)qa_file_ref->GetObjectChecked(prefix + \"nReco_pTGen\", \"TH1\");\n",
    "      TH1 *h_norm =\n",
    "          (TH1 *)qa_file_ref->GetObjectChecked(prefix + \"nGen_pTGen\", \"TH1\");\n",
    "      assert(h_norm);\n",
    "      assert(h_pass);\n",
    "      h_norm->SetDirectory(nullptr);\n",
    "      h_pass->SetDirectory(nullptr);\n",
    "      h_norm->Rebin(nrebin);\n",
    "      h_pass->Rebin(nrebin);\n",
    "      h_ratio_ref = GetBinominalRatio(h_pass, h_norm);\n",
    "    }\n",
    "\n",
    "    h_ratio->SetTitle(TString(hist_name_prefix) + \": Tracking Efficiency\");\n",
    "\n",
    "    DrawReference(h_ratio, h_ratio_ref, false);\n",
    "  }\n",
    "\n",
    "  {\n",
    "    static const int nrebin = 4;\n",
    "\n",
    "    p = (TPad *)c1->cd(idx++);\n",
    "    c1->Update();\n",
    "    // p->SetLogx();\n",
    "    p->SetGridy();\n",
    "\n",
    "    TH1 *h_pass =\n",
    "        (TH1 *)qa_file_new->GetObjectChecked(prefix + \"nReco_etaGen\", \"TH1\");\n",
    "    TH1 *h_norm =\n",
    "        (TH1 *)qa_file_new->GetObjectChecked(prefix + \"nGen_etaGen\", \"TH1\");\n",
    "    assert(h_norm);\n",
    "    assert(h_pass);\n",
    "\n",
    "      h_norm->SetDirectory(nullptr);\n",
    "      h_pass->SetDirectory(nullptr);\n",
    "    h_norm->Rebin(nrebin);\n",
    "    h_pass->Rebin(nrebin);\n",
    "\n",
    "    TH1 *h_ratio = GetBinominalRatio(h_pass, h_norm);\n",
    "\n",
    "    h_ratio->GetXaxis()->SetRangeUser(-1.1, 1.1);\n",
    "    h_ratio->GetYaxis()->SetTitle(\"Reco efficiency\");\n",
    "    h_ratio->GetYaxis()->SetRangeUser(-0, 1.);\n",
    "\n",
    "    TH1 *h_ratio_ref = NULL;\n",
    "    if (qa_file_ref) {\n",
    "      TH1 *h_pass =\n",
    "          (TH1 *)qa_file_ref->GetObjectChecked(prefix + \"nReco_etaGen\", \"TH1\");\n",
    "      TH1 *h_norm =\n",
    "          (TH1 *)qa_file_ref->GetObjectChecked(prefix + \"nGen_etaGen\", \"TH1\");\n",
    "      assert(h_norm);\n",
    "      assert(h_pass);\n",
    "      h_norm->SetDirectory(nullptr);\n",
    "      h_pass->SetDirectory(nullptr);\n",
    "      h_norm->Rebin(nrebin);\n",
    "      h_pass->Rebin(nrebin);\n",
    "      h_ratio_ref = GetBinominalRatio(h_pass, h_norm);\n",
    "    }\n",
    "\n",
    "    h_ratio->SetTitle(TString(hist_name_prefix) + \": Tracking Efficiency\");\n",
    "\n",
    "    DrawReference(h_ratio, h_ratio_ref, false);\n",
    "  }\n",
    "\n",
    "  {\n",
    "    p = (TPad *)c1->cd(idx++);\n",
    "    c1->Update();\n",
    "    //    p->SetLogx();\n",
    "    TH1 *frame = p->DrawFrame(0, .9, 50, 1.1,\n",
    "                              \"Mean and sigma, p_{T,reco}/p_{T,truth};Truth p_{T} [GeV/c];<p_{T,reco}/p_{T,truth}> #pm #sigma(p_{T,reco}/p_{T,truth})\");\n",
    "    //gPad->SetLeftMargin(.2);\n",
    "    gPad->SetTopMargin(-1);\n",
    "    frame->GetYaxis()->SetTitleOffset(1.7);\n",
    "    //TLine *l = new TLine(0, 1, 50, 1);\n",
    "    //l->SetLineColor(kGray);\n",
    "    //l->Draw();\n",
    "    HorizontalLine( gPad, 1 )->Draw();\n",
    "\n",
    "    TH2 *h_QAG4SimulationTracking_pTRecoGenRatio_pTGen =\n",
    "        (TH2 *)qa_file_new->GetObjectChecked(prefix + \"pTRecoGenRatio_pTGen\",\n",
    "                                             \"TH2\");\n",
    "    assert(h_QAG4SimulationTracking_pTRecoGenRatio_pTGen);\n",
    "\n",
    "      h_QAG4SimulationTracking_pTRecoGenRatio_pTGen->SetDirectory(nullptr);\n",
    "    h_QAG4SimulationTracking_pTRecoGenRatio_pTGen->Rebin2D(16, 1);\n",
    "    TGraphErrors *ge_QAG4SimulationTracking_pTRecoGenRatio_pTGen =\n",
    "        FitProfile(h_QAG4SimulationTracking_pTRecoGenRatio_pTGen);\n",
    "    ge_QAG4SimulationTracking_pTRecoGenRatio_pTGen->Draw(\"pe\");\n",
    "    ge_QAG4SimulationTracking_pTRecoGenRatio_pTGen->SetTitle(\n",
    "        \"Mean and sigma, p_{T,reco}/p_{T,truth}\");\n",
    "\n",
    "    TGraphErrors *h_ratio_ref = NULL;\n",
    "    if (qa_file_ref) {\n",
    "      TH2 *h_QAG4SimulationTracking_pTRecoGenRatio_pTGen =\n",
    "          (TH2 *)qa_file_ref->GetObjectChecked(prefix + \"pTRecoGenRatio_pTGen\",\n",
    "                                               \"TH2\");\n",
    "      assert(h_QAG4SimulationTracking_pTRecoGenRatio_pTGen);\n",
    "\n",
    "      h_QAG4SimulationTracking_pTRecoGenRatio_pTGen->SetDirectory(nullptr);\n",
    "      h_QAG4SimulationTracking_pTRecoGenRatio_pTGen->Rebin2D(16, 1);\n",
    "\n",
    "      h_ratio_ref = FitProfile(h_QAG4SimulationTracking_pTRecoGenRatio_pTGen);\n",
    "      ge_QAG4SimulationTracking_pTRecoGenRatio_pTGen->Draw(\"pe\");\n",
    "    }\n",
    "\n",
    "    DrawReference(ge_QAG4SimulationTracking_pTRecoGenRatio_pTGen, h_ratio_ref,\n",
    "                  true);\n",
    "            \n",
    "      SaveGraphError2CSV(ge_QAG4SimulationTracking_pTRecoGenRatio_pTGen, \"QAG4SimulationTracking_pTRecoGenRatio_pTGen\");\n",
    "  }\n",
    "\n",
    "  //SaveCanvas(c1,\n",
    "  //           TString(qa_file_name_new) + TString(\"_\") + TString(c1->GetName()),\n",
    "  //           true);\n",
    "    \n",
    "  c1->Draw();\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# $p_T$ resolution and lineshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:44.030855Z",
     "iopub.status.busy": "2025-03-25T17:00:44.030448Z",
     "iopub.status.idle": "2025-03-25T17:00:44.138975Z",
     "shell.execute_reply": "2025-03-25T17:00:44.137900Z"
    }
   },
   "outputs": [],
   "source": [
    "%jsroot on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:44.143264Z",
     "iopub.status.busy": "2025-03-25T17:00:44.142665Z",
     "iopub.status.idle": "2025-03-25T17:00:44.575837Z",
     "shell.execute_reply": "2025-03-25T17:00:44.575013Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KSTestSummary::PushKSTest - Warning - received pValue = 0. Reset to an arbitary small non-zero value (e^(-15))\n",
      "KSTestSummary::PushKSTest - Warning - received pValue = 0. Reset to an arbitary small non-zero value (e^(-15))\n",
      "KSTestSummary::PushKSTest - Warning - received pValue = 0. Reset to an arbitary small non-zero value (e^(-15))\n"
     ]
    },
    {
     "data": {
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       "<script>\n",
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       "   Core.settings.HandleKeys = false;\n",
       "   Core.draw(\"root_plot_1742922044563\", obj, \"\");\n",
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       "    script.onload = function() { display_root_plot_1742922044563(JSROOT); };\n",
       "    script.onerror = function() { script.remove(); on_error(); };\n",
       "    document.head.appendChild(script);\n",
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       "       paths: { 'JSRootCore' : [ 'build/jsroot', 'https://root.cern/js/7.7.2/build/jsroot', 'https://jsroot.gsi.de/7.7.2/build/jsroot' ] }\n",
       "    })(['JSRootCore'],  function(Core) {\n",
       "       display_root_plot_1742922044563(Core);\n",
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       "\n",
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       "\n",
       "   // JSROOT already loaded, just use it\n",
       "   display_root_plot_1742922044563(JSROOT);\n",
       "\n",
       "} else {\n",
       "\n",
       "    // We are in jupyterlab without require.js, directly loading jsroot\n",
       "    // Jupyterlab might be installed in a different base_url so we need to know it.\n",
       "    try {\n",
       "        var base_url = JSON.parse(document.getElementById('jupyter-config-data').innerHTML).baseUrl;\n",
       "    } catch(_) {\n",
       "        var base_url = '/';\n",
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       "    // Try loading a local version of requirejs and fallback to cdn if not possible.\n",
       "    script_load_root_plot_1742922044563(base_url + 'static/build/jsroot.js', function(){\n",
       "        console.error('Fail to load JSROOT locally, please check your jupyter_notebook_config.py file');\n",
       "        script_load_root_plot_1742922044563('https://root.cern/js/7.7.2/build/jsroot.js', function(){\n",
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       "\n",
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   ],
   "source": [
    "%%cpp\n",
    "\n",
    "{\n",
    "    const char *hist_name_prefix = \"QAG4SimulationTracking\";\n",
    "    TString prefix = TString(\"h_\") + hist_name_prefix + TString(\"_\");\n",
    "    \n",
    "  // obtain normalization\n",
    "  double Nevent_new = 1;\n",
    "  double Nevent_ref = 1;\n",
    "    \n",
    "    \n",
    "  TH2 *h_new = (TH2 *) qa_file_new->GetObjectChecked(\n",
    "      prefix + TString(\"pTRecoGenRatio_pTGen\"), \"TH2\");\n",
    "  assert(h_new);\n",
    "\n",
    "  //  h_new->Rebin(1, 2);\n",
    "  //h_new->Sumw2();\n",
    "  //  h_new->Scale(1. / Nevent_new);\n",
    "\n",
    "  TH2 *h_ref = NULL;\n",
    "  if (qa_file_ref)\n",
    "  {\n",
    "    h_ref = (TH2 *) qa_file_ref->GetObjectChecked(\n",
    "        prefix + TString(\"pTRecoGenRatio_pTGen\"), \"TH2\");\n",
    "    assert(h_ref);\n",
    "\n",
    "    //    h_ref->Rebin(1, 2);\n",
    "    //h_ref->Sumw2();\n",
    "    h_ref->Scale(Nevent_new / Nevent_ref);\n",
    "  }\n",
    "\n",
    "  TCanvas *c1 = new TCanvas(TString(\"QA_Draw_Tracking_pTRatio\") + TString(\"_\") + hist_name_prefix,\n",
    "                            TString(\"QA_Draw_Tracking_pTRatio\") + TString(\"_\") + hist_name_prefix,\n",
    "                            950, 600);\n",
    "  c1->Divide(4, 2);\n",
    "  int idx = 1;\n",
    "  TPad *p;\n",
    "\n",
    "  vector<pair<double, double>> gpt_ranges{\n",
    "      {0, 1},\n",
    "      {1, 5},\n",
    "      {5, 10},\n",
    "      {10, 20},\n",
    "      {20, 30},\n",
    "      {30, 40},\n",
    "      {40, 45},\n",
    "      {45, 50}};\n",
    "  TF1 *f1 = nullptr;\n",
    "  TF1 *fit = nullptr;\n",
    "  Double_t sigma = 0;\n",
    "  Double_t sigma_unc = 0;\n",
    "  char resstr[500];\n",
    "  TLatex *res = nullptr;\n",
    "  for (auto pt_range : gpt_ranges)\n",
    "  {\n",
    "    //cout << __PRETTY_FUNCTION__ << \" process \" << pt_range.first << \" - \" << pt_range.second << \" GeV/c\";\n",
    "\n",
    "    p = (TPad *) c1->cd(idx++);\n",
    "    c1->Update();\n",
    "    p->SetLogy();\n",
    "\n",
    "    const double epsilon = 1e-6;\n",
    "    const int bin_start = h_new->GetXaxis()->FindBin(pt_range.first + epsilon);\n",
    "    const int bin_end = h_new->GetXaxis()->FindBin(pt_range.second - epsilon);\n",
    "\n",
    "    TH1 *h_proj_new = h_new->ProjectionY(\n",
    "        TString::Format(\n",
    "            \"%s_New_ProjX_%d_%d\",\n",
    "            h_new->GetName(), bin_start, bin_end),\n",
    "        bin_start, bin_end);\n",
    "\n",
    "    h_proj_new->GetXaxis()->SetRangeUser(.7, 1.3);\n",
    "    h_proj_new->SetTitle(TString(hist_name_prefix) + TString::Format(\n",
    "                                                         \": %.1f - %.1f GeV/c\", pt_range.first, pt_range.second));\n",
    "    h_proj_new->GetXaxis()->SetTitle(TString::Format(\n",
    "        \"Reco p_{T}/Truth p_{T}\"));\n",
    "\n",
    "    f1 = new TF1(\"f1\", \"gaus\", -.85, 1.15);\n",
    "    h_proj_new->Fit(f1, \"mq\");\n",
    "    sigma = f1->GetParameter(2);\n",
    "    sigma_unc = f1->GetParError(2);\n",
    "\n",
    "    TH1 *h_proj_ref = nullptr;\n",
    "    if (h_ref)\n",
    "      h_proj_ref =\n",
    "          h_ref->ProjectionY(\n",
    "              TString::Format(\n",
    "                  \"%s_Ref_ProjX_%d_%d\",\n",
    "                  h_new->GetName(), bin_start, bin_end),\n",
    "              bin_start, bin_end);\n",
    "\n",
    "    DrawReference(h_proj_new, h_proj_ref);\n",
    "    sprintf(resstr, \"#sigma = %.5f #pm %.5f\", sigma, sigma_unc);\n",
    "    res = new TLatex(0.325, 0.825, resstr);\n",
    "    res->SetNDC();\n",
    "    res->SetTextSize(0.05);\n",
    "    res->SetTextAlign(13);\n",
    "    res->Draw();\n",
    "  }\n",
    "\n",
    " // SaveCanvas(c1, TString(qa_file_name_new) + TString(\"_\") + TString(c1->GetName()), true);\n",
    "    c1->Draw();\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:44.584517Z",
     "iopub.status.busy": "2025-03-25T17:00:44.584145Z",
     "iopub.status.idle": "2025-03-25T17:00:44.691421Z",
     "shell.execute_reply": "2025-03-25T17:00:44.690280Z"
    }
   },
   "outputs": [],
   "source": [
    "%jsroot off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:44.696887Z",
     "iopub.status.busy": "2025-03-25T17:00:44.695962Z",
     "iopub.status.idle": "2025-03-25T17:00:45.127581Z",
     "shell.execute_reply": "2025-03-25T17:00:45.126821Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%cpp\n",
    "\n",
    "{\n",
    "const char *hist_name_prefix = \"QAG4SimulationTracking\";\n",
    "    TString prefix = TString(\"h_\") + hist_name_prefix + TString(\"_\");\n",
    "    \n",
    "    \n",
    "  // obtain normalization\n",
    "  double Nevent_new = 1;\n",
    "  double Nevent_ref = 1;\n",
    "\n",
    "  if (qa_file_new)\n",
    "  {\n",
    "    //cout << \"Open new QA file \" << qa_file_new->GetName() << endl;\n",
    "\n",
    "    TH1 *h_norm = (TH1 *) qa_file_new->GetObjectChecked(\n",
    "        prefix + TString(\"Normalization\"), \"TH1\");\n",
    "    assert(h_norm);\n",
    "\n",
    "    Nevent_new = h_norm->GetBinContent(h_norm->GetXaxis()->FindBin(\"Event\"));\n",
    "  }\n",
    "  if (qa_file_ref)\n",
    "  {\n",
    "   // cout << \"Open ref QA file \" << qa_file_ref->GetName() << endl;\n",
    "    TH1 *h_norm = (TH1 *) qa_file_ref->GetObjectChecked(\n",
    "        prefix + TString(\"Normalization\"), \"TH1\");\n",
    "    assert(h_norm);\n",
    "\n",
    "    Nevent_ref = h_norm->GetBinContent(h_norm->GetXaxis()->FindBin(\"Event\"));\n",
    "  }\n",
    "    \n",
    "    \n",
    "  TCanvas *c1 = new TCanvas(TString(\"QA_Draw_Tracking_RecoTruthMatching\") +\n",
    "                                TString(\"_\") + hist_name_prefix,\n",
    "                            TString(\"QA_Draw_Tracking_RecoTruthMatching\") +\n",
    "                                TString(\"_\") + hist_name_prefix,\n",
    "                            950, 600);\n",
    "  c1->Divide(2, 1);\n",
    "  int idx = 1;\n",
    "  TPad *p;\n",
    "\n",
    "  {\n",
    "    static const int nrebin = 5;\n",
    "\n",
    "    p = (TPad *) c1->cd(idx++);\n",
    "    c1->Update();\n",
    "    p->SetLogx();\n",
    "    p->SetGridy();\n",
    "\n",
    "    TH1 *h_pass =\n",
    "        (TH1 *) qa_file_new->GetObjectChecked(prefix + \"nGen_pTReco\", \"TH1\");\n",
    "    TH1 *h_norm =\n",
    "        (TH1 *) qa_file_new->GetObjectChecked(prefix + \"nReco_pTReco\", \"TH1\");\n",
    "    assert(h_norm);\n",
    "    assert(h_pass);\n",
    "      \n",
    "      h_norm->SetDirectory(nullptr);\n",
    "      h_pass->SetDirectory(nullptr);\n",
    "\n",
    "    h_norm->Rebin(nrebin);\n",
    "    h_pass->Rebin(nrebin);\n",
    "\n",
    "    TH1 *h_ratio = GetBinominalRatio(h_pass, h_norm);\n",
    "\n",
    "    //    h_ratio->GetXaxis()->SetRangeUser(min_Et, max_Et);\n",
    "    h_ratio->GetYaxis()->SetTitle(\"Tracking Purity\");\n",
    "    h_ratio->GetYaxis()->SetRangeUser(-0, 1.1);\n",
    "\n",
    "    TH1 *h_ratio_ref = NULL;\n",
    "    if (qa_file_ref)\n",
    "    {\n",
    "      TH1 *h_pass =\n",
    "          (TH1 *) qa_file_ref->GetObjectChecked(prefix + \"nGen_pTReco\", \"TH1\");\n",
    "      TH1 *h_norm =\n",
    "          (TH1 *) qa_file_ref->GetObjectChecked(prefix + \"nReco_pTReco\", \"TH1\");\n",
    "      assert(h_norm);\n",
    "      assert(h_pass);\n",
    "      h_norm->SetDirectory(nullptr);\n",
    "      h_pass->SetDirectory(nullptr);\n",
    "      h_norm->Rebin(nrebin);\n",
    "      h_pass->Rebin(nrebin);\n",
    "      h_ratio_ref = GetBinominalRatio(h_pass, h_norm);\n",
    "    }\n",
    "\n",
    "    h_ratio->SetTitle(\"Tracking Purity (matched truth-reco pairs)\");\n",
    "\n",
    "    DrawReference(h_ratio, h_ratio_ref, false);\n",
    "  }\n",
    "\n",
    "  {\n",
    "    p = (TPad *) c1->cd(idx++);\n",
    "    c1->Update();\n",
    "    //    p->SetLogx();\n",
    "    TH1 *frame = p->DrawFrame(0, .9, 50, 1.1,\n",
    "                              \"Mean and sigma p_{Tmatched}/p_{Treco};Reco p_{T} [GeV/c];<p_{T,matched}/p_{T,reco}> #pm #sigma(p_{T,matched}/p_{T,reco})\");\n",
    "    // gPad->SetLeftMargin(.2);\n",
    "    gPad->SetTopMargin(-1);\n",
    "    frame->GetYaxis()->SetTitleOffset(1.7);\n",
    "    // TLine *l = new TLine(0, 1, 50, 1);\n",
    "    // l->SetLineColor(kGray);\n",
    "    // l->Draw();\n",
    "    HorizontalLine(gPad, 1)->Draw();\n",
    "\n",
    "    TH2 *h_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco =\n",
    "        (TH2 *) qa_file_new->GetObjectChecked(\n",
    "            prefix + \"pTRecoTruthMatchedRatio_pTReco\", \"TH2\");\n",
    "    assert(h_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco);\n",
    "\n",
    "      h_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco->SetDirectory(nullptr);\n",
    "    h_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco->Rebin2D(16, 1);\n",
    "\n",
    "    TGraphErrors *ge_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco =\n",
    "        FitProfile(h_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco);\n",
    "    ge_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco->Draw(\"pe\");\n",
    "    ge_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco->SetTitle(\n",
    "        \"Mean and sigma p_{Tmatched}/p_{Treco}\");\n",
    "\n",
    "    TGraphErrors *h_ratio_ref = NULL;\n",
    "    if (qa_file_ref)\n",
    "    {\n",
    "      TH2 *h_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco =\n",
    "          (TH2 *) qa_file_ref->GetObjectChecked(\n",
    "              prefix + \"pTRecoTruthMatchedRatio_pTReco\", \"TH2\");\n",
    "      assert(h_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco);\n",
    "\n",
    "      h_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco->SetDirectory(nullptr);\n",
    "      h_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco->Rebin2D(16, 1);\n",
    "\n",
    "      h_ratio_ref =\n",
    "          FitProfile(h_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco);\n",
    "      ge_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco->Draw(\"pe\");\n",
    "    }\n",
    "\n",
    "    DrawReference(ge_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco,\n",
    "                  h_ratio_ref, true);\n",
    "\n",
    "  }\n",
    "\n",
    "    c1->Draw();\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:45.132102Z",
     "iopub.status.busy": "2025-03-25T17:00:45.131686Z",
     "iopub.status.idle": "2025-03-25T17:00:45.540289Z",
     "shell.execute_reply": "2025-03-25T17:00:45.539644Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: QA_Draw_Tracking_RecoTruthMatching_QAG4SimulationTracking\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%cpp\n",
    "\n",
    "{\n",
    "const char *hist_name_prefix = \"QAG4SimulationTracking\";\n",
    "    TString prefix = TString(\"h_\") + hist_name_prefix + TString(\"_\");\n",
    "    \n",
    "    \n",
    "  // obtain normalization\n",
    "  double Nevent_new = 1;\n",
    "  double Nevent_ref = 1;\n",
    "\n",
    "  if (qa_file_new)\n",
    "  {\n",
    "    //cout << \"Open new QA file \" << qa_file_new->GetName() << endl;\n",
    "\n",
    "    TH1 *h_norm = (TH1 *) qa_file_new->GetObjectChecked(\n",
    "        prefix + TString(\"Normalization\"), \"TH1\");\n",
    "    assert(h_norm);\n",
    "\n",
    "    Nevent_new = h_norm->GetBinContent(h_norm->GetXaxis()->FindBin(\"Event\"));\n",
    "  }\n",
    "  if (qa_file_ref)\n",
    "  {\n",
    "   // cout << \"Open ref QA file \" << qa_file_ref->GetName() << endl;\n",
    "    TH1 *h_norm = (TH1 *) qa_file_ref->GetObjectChecked(\n",
    "        prefix + TString(\"Normalization\"), \"TH1\");\n",
    "    assert(h_norm);\n",
    "\n",
    "    Nevent_ref = h_norm->GetBinContent(h_norm->GetXaxis()->FindBin(\"Event\"));\n",
    "  }\n",
    "    \n",
    "    \n",
    "  TCanvas *c1 = new TCanvas(TString(\"QA_Draw_Tracking_RecoTruthMatching\") +\n",
    "                                TString(\"_\") + hist_name_prefix,\n",
    "                            TString(\"QA_Draw_Tracking_RecoTruthMatching\") +\n",
    "                                TString(\"_\") + hist_name_prefix,\n",
    "                            950, 600);\n",
    "  c1->Divide(2, 1);\n",
    "  int idx = 1;\n",
    "  TPad *p;\n",
    "\n",
    "  {\n",
    "    static const int nrebin = 5;\n",
    "\n",
    "    p = (TPad *) c1->cd(idx++);\n",
    "    c1->Update();\n",
    "    p->SetLogx();\n",
    "    p->SetGridy();\n",
    "\n",
    "    TH1 *h_pass =\n",
    "        (TH1 *) qa_file_new->GetObjectChecked(prefix + \"nGen_pTReco\", \"TH1\");\n",
    "    TH1 *h_norm =\n",
    "        (TH1 *) qa_file_new->GetObjectChecked(prefix + \"nReco_pTReco\", \"TH1\");\n",
    "    assert(h_norm);\n",
    "    assert(h_pass);\n",
    "      \n",
    "      h_norm->SetDirectory(nullptr);\n",
    "      h_pass->SetDirectory(nullptr);\n",
    "\n",
    "    h_norm->Rebin(nrebin);\n",
    "    h_pass->Rebin(nrebin);\n",
    "\n",
    "    TH1 *h_ratio = GetBinominalRatio(h_pass, h_norm);\n",
    "\n",
    "    //    h_ratio->GetXaxis()->SetRangeUser(min_Et, max_Et);\n",
    "    h_ratio->GetYaxis()->SetTitle(\"Tracking Purity\");\n",
    "    h_ratio->GetYaxis()->SetRangeUser(-0, 1.1);\n",
    "\n",
    "    TH1 *h_ratio_ref = NULL;\n",
    "    if (qa_file_ref)\n",
    "    {\n",
    "      TH1 *h_pass =\n",
    "          (TH1 *) qa_file_ref->GetObjectChecked(prefix + \"nGen_pTReco\", \"TH1\");\n",
    "      TH1 *h_norm =\n",
    "          (TH1 *) qa_file_ref->GetObjectChecked(prefix + \"nReco_pTReco\", \"TH1\");\n",
    "      assert(h_norm);\n",
    "      assert(h_pass);\n",
    "      h_norm->SetDirectory(nullptr);\n",
    "      h_pass->SetDirectory(nullptr);\n",
    "      h_norm->Rebin(nrebin);\n",
    "      h_pass->Rebin(nrebin);\n",
    "      h_ratio_ref = GetBinominalRatio(h_pass, h_norm);\n",
    "    }\n",
    "\n",
    "    h_ratio->SetTitle(\"Tracking Purity (matched truth-reco pairs)\");\n",
    "\n",
    "    DrawReference(h_ratio, h_ratio_ref, false);\n",
    "  }\n",
    "\n",
    "  {\n",
    "    p = (TPad *) c1->cd(idx++);\n",
    "    c1->Update();\n",
    "    //    p->SetLogx();\n",
    "    TH1 *frame = p->DrawFrame(0, .9, 50, 1.1,\n",
    "                              \"Mean and sigma p_{Tmatched}/p_{Treco};Reco p_{T} [GeV/c];<p_{T,matched}/p_{T,reco}> #pm #sigma(p_{T,matched}/p_{T,reco})\");\n",
    "    // gPad->SetLeftMargin(.2);\n",
    "    gPad->SetTopMargin(-1);\n",
    "    frame->GetYaxis()->SetTitleOffset(1.7);\n",
    "    // TLine *l = new TLine(0, 1, 50, 1);\n",
    "    // l->SetLineColor(kGray);\n",
    "    // l->Draw();\n",
    "    HorizontalLine(gPad, 1)->Draw();\n",
    "\n",
    "    TH2 *h_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco =\n",
    "        (TH2 *) qa_file_new->GetObjectChecked(\n",
    "            prefix + \"pTRecoTruthMatchedRatio_pTReco\", \"TH2\");\n",
    "    assert(h_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco);\n",
    "\n",
    "      h_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco->SetDirectory(nullptr);\n",
    "    h_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco->Rebin2D(16, 1);\n",
    "\n",
    "    TGraphErrors *ge_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco =\n",
    "        FitProfile(h_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco);\n",
    "    ge_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco->Draw(\"pe\");\n",
    "    ge_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco->SetTitle(\n",
    "        \"Mean and sigma p_{Tmatched}/p_{Treco}\");\n",
    "\n",
    "    TGraphErrors *h_ratio_ref = NULL;\n",
    "    if (qa_file_ref)\n",
    "    {\n",
    "      TH2 *h_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco =\n",
    "          (TH2 *) qa_file_ref->GetObjectChecked(\n",
    "              prefix + \"pTRecoTruthMatchedRatio_pTReco\", \"TH2\");\n",
    "      assert(h_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco);\n",
    "\n",
    "      h_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco->SetDirectory(nullptr);\n",
    "      h_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco->Rebin2D(16, 1);\n",
    "\n",
    "      h_ratio_ref =\n",
    "          FitProfile(h_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco);\n",
    "      ge_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco->Draw(\"pe\");\n",
    "    }\n",
    "\n",
    "    DrawReference(ge_QAG4SimulationTracking_pTRecoTruthMatchedRatio_pTReco,\n",
    "                  h_ratio_ref, true);\n",
    "  }\n",
    "\n",
    "    c1->Draw();\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tracker hit checks\n",
    "\n",
    "Hits per tracker and layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:45.544407Z",
     "iopub.status.busy": "2025-03-25T17:00:45.544042Z",
     "iopub.status.idle": "2025-03-25T17:00:45.651516Z",
     "shell.execute_reply": "2025-03-25T17:00:45.650298Z"
    }
   },
   "outputs": [],
   "source": [
    "%jsroot on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:45.656432Z",
     "iopub.status.busy": "2025-03-25T17:00:45.655819Z",
     "iopub.status.idle": "2025-03-25T17:00:46.054034Z",
     "shell.execute_reply": "2025-03-25T17:00:46.053071Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KSTestSummary::PushKSTest - Warning - received pValue = 0. Reset to an arbitary small non-zero value (e^(-15))\n",
      "KSTestSummary::PushKSTest - Warning - received pValue = 0. Reset to an arbitary small non-zero value (e^(-15))\n",
      "KSTestSummary::PushKSTest - Warning - received pValue = 0. Reset to an arbitary small non-zero value (e^(-15))\n",
      "KSTestSummary::PushKSTest - Warning - received pValue = 0. Reset to an arbitary small non-zero value (e^(-15))\n"
     ]
    },
    {
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    "%%cpp\n",
    "{\n",
    "    const char *hist_name_prefix = \"QAG4SimulationTracking\";\n",
    "      TString prefix = TString(\"h_\") + hist_name_prefix + TString(\"_\");\n",
    "\n",
    "  // obtain normalization\n",
    "  double Nevent_new = 1;\n",
    "  double Nevent_ref = 1;\n",
    "    \n",
    "    \n",
    "\n",
    "  if (qa_file_new)\n",
    "  {\n",
    "    TH1 *h_norm = (TH1 *) qa_file_new->GetObjectChecked(\n",
    "        prefix + TString(\"Normalization\"), \"TH1\");\n",
    "    assert(h_norm);\n",
    "\n",
    "    Nevent_new = h_norm->GetBinContent(h_norm->GetXaxis()->FindBin(\"Event\"));\n",
    "  }\n",
    "  if (qa_file_ref)\n",
    "  {\n",
    "    TH1 *h_norm = (TH1 *) qa_file_ref->GetObjectChecked(\n",
    "        prefix + TString(\"Normalization\"), \"TH1\");\n",
    "    assert(h_norm);\n",
    "\n",
    "    Nevent_ref = h_norm->GetBinContent(h_norm->GetXaxis()->FindBin(\"Event\"));\n",
    "  }\n",
    "\n",
    "  //MVTX, INTT, TPC\n",
    "  vector<TString> detectors{\"MVTX\", \"INTT\", \"TPC\"};\n",
    "  vector<int> eff_ncluster_cuts{2, 2, 40};\n",
    "  vector<double> ncluster_spectrum_pt_cuts{2, 2, 2};\n",
    "  vector<TH2 *> h_pass_detectors(3, nullptr);\n",
    "  static const int nrebin = 5;\n",
    "\n",
    "  h_pass_detectors[0] = (TH2 *) qa_file_new->GetObjectChecked(\n",
    "      prefix + \"nMVTX_nReco_pTGen\", \"TH1\") ;\n",
    "  h_pass_detectors[1] = (TH2 *) qa_file_new->GetObjectChecked(\n",
    "      prefix + \"nINTT_nReco_pTGen\", \"TH1\") ;\n",
    "  h_pass_detectors[2] = (TH2 *) qa_file_new->GetObjectChecked(\n",
    "      prefix + \"nTPC_nReco_pTGen\", \"TH1\") ;\n",
    "\n",
    "  TH1 *h_norm = (TH1 *) qa_file_new->GetObjectChecked(\n",
    "      prefix + \"nGen_pTGen\", \"TH1\") ;\n",
    "  assert(h_norm);\n",
    "    h_norm->SetDirectory(nullptr);\n",
    "  h_norm->Rebin(nrebin);\n",
    "\n",
    "  vector<TH2 *> h_pass_detectors_ref(3, nullptr);\n",
    "  TH1 *h_norm_ref = nullptr;\n",
    "  if (qa_file_ref)\n",
    "  {\n",
    "    h_pass_detectors_ref[0] = (TH2 *) qa_file_ref->GetObjectChecked(\n",
    "        prefix + \"nMVTX_nReco_pTGen\", \"TH1\") ;\n",
    "    h_pass_detectors_ref[1] = (TH2 *) qa_file_ref->GetObjectChecked(\n",
    "        prefix + \"nINTT_nReco_pTGen\", \"TH1\") ;\n",
    "    h_pass_detectors_ref[2] = (TH2 *) qa_file_ref->GetObjectChecked(\n",
    "        prefix + \"nTPC_nReco_pTGen\", \"TH1\") ;\n",
    "\n",
    "    h_norm_ref = (TH1 *) qa_file_ref->GetObjectChecked(\n",
    "        prefix + \"nGen_pTGen\", \"TH1\") ;\n",
    "    h_norm_ref->SetDirectory(nullptr);\n",
    "    h_norm_ref->Rebin(nrebin);\n",
    "\n",
    "  }\n",
    "\n",
    "  TCanvas *c1 = new TCanvas(TString(\"QA_Draw_Tracking_TruthMatching_NumOfClusters\") + TString(\"_\") + hist_name_prefix,\n",
    "                            TString(\"QA_Draw_Tracking_TruthMatching_NumOfClusters\") + TString(\"_\") + hist_name_prefix,\n",
    "                            950, 600);\n",
    "  c1->Divide(3, 2);\n",
    "  TPad *p;\n",
    "\n",
    "  for (int i = 0; i < 3; ++i)\n",
    "  {\n",
    "    TString detector = detectors[i];\n",
    "    TH2 *h_pass_detector = h_pass_detectors[i];\n",
    "    TH2 *h_pass_detector_ref = h_pass_detectors_ref[i];\n",
    "    assert(h_pass_detector);\n",
    "\n",
    "    {\n",
    "      p = (TPad *) c1->cd(i + 1);\n",
    "      c1->Update();\n",
    "      p->SetLogy();\n",
    "\n",
    "      const int bin_start = h_pass_detector->GetXaxis()->FindBin(ncluster_spectrum_pt_cuts[i]);\n",
    "\n",
    "      TH1 *h_pass_detector_ncluster = h_pass_detector->ProjectionY(\n",
    "          TString(h_pass_detector->GetName()) + \"_nCluster_new\",\n",
    "          bin_start);\n",
    "      TH1 *h_pass_detector_ncluster_ref = nullptr;\n",
    "      if (h_pass_detector_ref)\n",
    "      {\n",
    "        h_pass_detector_ncluster_ref = h_pass_detector_ref->ProjectionY(\n",
    "            TString(h_pass_detector_ref->GetName()) + \"_nCluster_ref\",\n",
    "            bin_start);\n",
    "      }\n",
    "\n",
    "      h_pass_detector_ncluster->SetTitle(TString(hist_name_prefix) + \": \" + detector + Form(\" n_{Cluster} | p_{T} #geq %.1fGeV/c\", ncluster_spectrum_pt_cuts[i]));\n",
    "      h_pass_detector_ncluster->SetYTitle(\"# of reconstructed track\");\n",
    "      DrawReference(h_pass_detector_ncluster, h_pass_detector_ncluster_ref, false);\n",
    "    }\n",
    "\n",
    "    {\n",
    "      p = (TPad *) c1->cd(i + 3 + 1);\n",
    "      c1->Update();\n",
    "      p->SetLogx();\n",
    "      p->SetGridy();\n",
    "\n",
    "      const int bin_start = h_pass_detector->GetYaxis()->FindBin(eff_ncluster_cuts[i]);\n",
    "      TH1 *h_pass = h_pass_detector->ProjectionX(\n",
    "          TString(h_pass_detector->GetName()) + \"_nReco_new\",\n",
    "          bin_start);\n",
    "\n",
    "      assert(h_pass);\n",
    "        h_pass->SetDirectory(nullptr);\n",
    "      h_pass->Rebin(nrebin);\n",
    "\n",
    "      TH1 *h_ratio = GetBinominalRatio(h_pass, h_norm);\n",
    "      h_ratio->GetYaxis()->SetTitle(\"Reco efficiency | \" + detector + Form(\" n_{Cluster} #geq %d\", eff_ncluster_cuts[i]));\n",
    "      h_ratio->GetYaxis()->SetRangeUser(-0, 1.);\n",
    "      //\n",
    "      TH1 *h_ratio_ref = NULL;\n",
    "      if (h_pass_detector_ref)\n",
    "      {\n",
    "        TH1 *h_pass = h_pass_detector_ref->ProjectionX(\n",
    "            TString(h_pass_detector->GetName()) + \"_nReco_ref\",\n",
    "            bin_start);\n",
    "\n",
    "        assert(h_pass);\n",
    "        h_pass->SetDirectory(nullptr);\n",
    "      h_pass->Rebin(nrebin);\n",
    "\n",
    "        h_ratio_ref = GetBinominalRatio(h_pass, h_norm_ref);\n",
    "      }\n",
    "      //\n",
    "      h_ratio->SetTitle(\"Tracking efficiency | \" + detector + Form(\" n_{Cluster} #geq %d\", eff_ncluster_cuts[i]));\n",
    "      DrawReference(h_ratio, h_ratio_ref, false);\n",
    "    }\n",
    "  }\n",
    "\n",
    "  // SaveCanvas(c1, TString(qa_file_name_new) + TString(\"_\") + TString(c1->GetName()), true);\n",
    "    c1->Draw();\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:46.060926Z",
     "iopub.status.busy": "2025-03-25T17:00:46.060415Z",
     "iopub.status.idle": "2025-03-25T17:00:46.353717Z",
     "shell.execute_reply": "2025-03-25T17:00:46.353036Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KSTestSummary::PushKSTest - Warning - received pValue = 0. Reset to an arbitary small non-zero value (e^(-15))\n",
      "KSTestSummary::PushKSTest - Warning - received pValue = 0. Reset to an arbitary small non-zero value (e^(-15))\n",
      "KSTestSummary::PushKSTest - Warning - received pValue = 0. Reset to an arbitary small non-zero value (e^(-15))\n",
      "KSTestSummary::PushKSTest - Warning - received pValue = 0. Reset to an arbitary small non-zero value (e^(-15))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: QA_Draw_Tracking_TruthMatching_NumOfClusters_QAG4SimulationTracking\n"
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       "   Core.settings.HandleKeys = false;\n",
       "   Core.draw(\"root_plot_1742922046342\", obj, \"\");\n",
       "}\n",
       "\n",
       "function script_load_root_plot_1742922046342(src, on_error) {\n",
       "    let script = document.createElement('script');\n",
       "    script.src = src;\n",
       "    script.onload = function() { display_root_plot_1742922046342(JSROOT); };\n",
       "    script.onerror = function() { script.remove(); on_error(); };\n",
       "    document.head.appendChild(script);\n",
       "}\n",
       "\n",
       "if (typeof requirejs !== 'undefined') {\n",
       "\n",
       "    // We are in jupyter notebooks, use require.js which should be configured already\n",
       "    requirejs.config({\n",
       "       paths: { 'JSRootCore' : [ 'build/jsroot', 'https://root.cern/js/7.7.2/build/jsroot', 'https://jsroot.gsi.de/7.7.2/build/jsroot' ] }\n",
       "    })(['JSRootCore'],  function(Core) {\n",
       "       display_root_plot_1742922046342(Core);\n",
       "    });\n",
       "\n",
       "} else if (typeof JSROOT !== 'undefined') {\n",
       "\n",
       "   // JSROOT already loaded, just use it\n",
       "   display_root_plot_1742922046342(JSROOT);\n",
       "\n",
       "} else {\n",
       "\n",
       "    // We are in jupyterlab without require.js, directly loading jsroot\n",
       "    // Jupyterlab might be installed in a different base_url so we need to know it.\n",
       "    try {\n",
       "        var base_url = JSON.parse(document.getElementById('jupyter-config-data').innerHTML).baseUrl;\n",
       "    } catch(_) {\n",
       "        var base_url = '/';\n",
       "    }\n",
       "\n",
       "    // Try loading a local version of requirejs and fallback to cdn if not possible.\n",
       "    script_load_root_plot_1742922046342(base_url + 'static/build/jsroot.js', function(){\n",
       "        console.error('Fail to load JSROOT locally, please check your jupyter_notebook_config.py file');\n",
       "        script_load_root_plot_1742922046342('https://root.cern/js/7.7.2/build/jsroot.js', function(){\n",
       "            document.getElementById(\"root_plot_1742922046342\").innerHTML = \"Failed to load JSROOT\";\n",
       "        });\n",
       "    });\n",
       "}\n",
       "\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%cpp\n",
    "{\n",
    "    const char *hist_name_prefix = \"QAG4SimulationTracking\";\n",
    "      TString prefix = TString(\"h_\") + hist_name_prefix + TString(\"_\");\n",
    "\n",
    "  // obtain normalization\n",
    "  double Nevent_new = 1;\n",
    "  double Nevent_ref = 1;\n",
    "    \n",
    "    \n",
    "\n",
    "  if (qa_file_new)\n",
    "  {\n",
    "    TH1 *h_norm = (TH1 *) qa_file_new->GetObjectChecked(\n",
    "        prefix + TString(\"Normalization\"), \"TH1\");\n",
    "    assert(h_norm);\n",
    "\n",
    "    Nevent_new = h_norm->GetBinContent(h_norm->GetXaxis()->FindBin(\"Event\"));\n",
    "  }\n",
    "  if (qa_file_ref)\n",
    "  {\n",
    "    TH1 *h_norm = (TH1 *) qa_file_ref->GetObjectChecked(\n",
    "        prefix + TString(\"Normalization\"), \"TH1\");\n",
    "    assert(h_norm);\n",
    "\n",
    "    Nevent_ref = h_norm->GetBinContent(h_norm->GetXaxis()->FindBin(\"Event\"));\n",
    "  }\n",
    "\n",
    "  //MVTX, INTT, TPC\n",
    "  vector<TString> detectors{\"MVTX\", \"INTT\", \"TPC\"};\n",
    "  vector<int> eff_ncluster_cuts{2, 2, 40};\n",
    "  vector<double> ncluster_spectrum_pt_cuts{2, 2, 2};\n",
    "  vector<TH2 *> h_pass_detectors(3, nullptr);\n",
    "  static const int nrebin = 5;\n",
    "\n",
    "  h_pass_detectors[0] = (TH2 *) qa_file_new->GetObjectChecked(\n",
    "      prefix + \"nMVTX_nReco_pTGen\", \"TH1\") ;\n",
    "  h_pass_detectors[1] = (TH2 *) qa_file_new->GetObjectChecked(\n",
    "      prefix + \"nINTT_nReco_pTGen\", \"TH1\") ;\n",
    "  h_pass_detectors[2] = (TH2 *) qa_file_new->GetObjectChecked(\n",
    "      prefix + \"nTPC_nReco_pTGen\", \"TH1\") ;\n",
    "\n",
    "  TH1 *h_norm = (TH1 *) qa_file_new->GetObjectChecked(\n",
    "      prefix + \"nGen_pTGen\", \"TH1\") ;\n",
    "  assert(h_norm);\n",
    "    h_norm->SetDirectory(nullptr);\n",
    "  h_norm->Rebin(nrebin);\n",
    "\n",
    "  vector<TH2 *> h_pass_detectors_ref(3, nullptr);\n",
    "  TH1 *h_norm_ref = nullptr;\n",
    "  if (qa_file_ref)\n",
    "  {\n",
    "    h_pass_detectors_ref[0] = (TH2 *) qa_file_ref->GetObjectChecked(\n",
    "        prefix + \"nMVTX_nReco_pTGen\", \"TH1\") ;\n",
    "    h_pass_detectors_ref[1] = (TH2 *) qa_file_ref->GetObjectChecked(\n",
    "        prefix + \"nINTT_nReco_pTGen\", \"TH1\") ;\n",
    "    h_pass_detectors_ref[2] = (TH2 *) qa_file_ref->GetObjectChecked(\n",
    "        prefix + \"nTPC_nReco_pTGen\", \"TH1\") ;\n",
    "\n",
    "    h_norm_ref = (TH1 *) qa_file_ref->GetObjectChecked(\n",
    "        prefix + \"nGen_pTGen\", \"TH1\") ;\n",
    "    h_norm_ref->SetDirectory(nullptr);\n",
    "    h_norm_ref->Rebin(nrebin);\n",
    "\n",
    "  }\n",
    "\n",
    "  TCanvas *c1 = new TCanvas(TString(\"QA_Draw_Tracking_TruthMatching_NumOfClusters\") + TString(\"_\") + hist_name_prefix,\n",
    "                            TString(\"QA_Draw_Tracking_TruthMatching_NumOfClusters\") + TString(\"_\") + hist_name_prefix,\n",
    "                            950, 600);\n",
    "  c1->Divide(3, 2);\n",
    "  TPad *p;\n",
    "\n",
    "  for (int i = 0; i < 3; ++i)\n",
    "  {\n",
    "    TString detector = detectors[i];\n",
    "    TH2 *h_pass_detector = h_pass_detectors[i];\n",
    "    TH2 *h_pass_detector_ref = h_pass_detectors_ref[i];\n",
    "    assert(h_pass_detector);\n",
    "\n",
    "    {\n",
    "      p = (TPad *) c1->cd(i + 1);\n",
    "      c1->Update();\n",
    "      p->SetLogy();\n",
    "\n",
    "      const int bin_start = h_pass_detector->GetXaxis()->FindBin(ncluster_spectrum_pt_cuts[i]);\n",
    "\n",
    "      TH1 *h_pass_detector_ncluster = h_pass_detector->ProjectionY(\n",
    "          TString(h_pass_detector->GetName()) + \"_nCluster_new\",\n",
    "          bin_start);\n",
    "      TH1 *h_pass_detector_ncluster_ref = nullptr;\n",
    "      if (h_pass_detector_ref)\n",
    "      {\n",
    "        h_pass_detector_ncluster_ref = h_pass_detector_ref->ProjectionY(\n",
    "            TString(h_pass_detector_ref->GetName()) + \"_nCluster_ref\",\n",
    "            bin_start);\n",
    "      }\n",
    "\n",
    "      h_pass_detector_ncluster->SetTitle(TString(hist_name_prefix) + \": \" + detector + Form(\" n_{Cluster} | p_{T} #geq %.1fGeV/c\", ncluster_spectrum_pt_cuts[i]));\n",
    "      h_pass_detector_ncluster->SetYTitle(\"# of reconstructed track\");\n",
    "      DrawReference(h_pass_detector_ncluster, h_pass_detector_ncluster_ref, false);\n",
    "    }\n",
    "\n",
    "    {\n",
    "      p = (TPad *) c1->cd(i + 3 + 1);\n",
    "      c1->Update();\n",
    "      p->SetLogx();\n",
    "      p->SetGridy();\n",
    "\n",
    "      const int bin_start = h_pass_detector->GetYaxis()->FindBin(eff_ncluster_cuts[i]);\n",
    "      TH1 *h_pass = h_pass_detector->ProjectionX(\n",
    "          TString(h_pass_detector->GetName()) + \"_nReco_new\",\n",
    "          bin_start);\n",
    "\n",
    "      assert(h_pass);\n",
    "        h_pass->SetDirectory(nullptr);\n",
    "      h_pass->Rebin(nrebin);\n",
    "\n",
    "      TH1 *h_ratio = GetBinominalRatio(h_pass, h_norm);\n",
    "      h_ratio->GetYaxis()->SetTitle(\"Reco efficiency | \" + detector + Form(\" n_{Cluster} #geq %d\", eff_ncluster_cuts[i]));\n",
    "      h_ratio->GetYaxis()->SetRangeUser(-0, 1.);\n",
    "      //\n",
    "      TH1 *h_ratio_ref = NULL;\n",
    "      if (h_pass_detector_ref)\n",
    "      {\n",
    "        TH1 *h_pass = h_pass_detector_ref->ProjectionX(\n",
    "            TString(h_pass_detector->GetName()) + \"_nReco_ref\",\n",
    "            bin_start);\n",
    "\n",
    "        assert(h_pass);\n",
    "        h_pass->SetDirectory(nullptr);\n",
    "      h_pass->Rebin(nrebin);\n",
    "\n",
    "        h_ratio_ref = GetBinominalRatio(h_pass, h_norm_ref);\n",
    "      }\n",
    "      //\n",
    "      h_ratio->SetTitle(\"Tracking efficiency | \" + detector + Form(\" n_{Cluster} #geq %d\", eff_ncluster_cuts[i]));\n",
    "      DrawReference(h_ratio, h_ratio_ref, false);\n",
    "    }\n",
    "  }\n",
    "\n",
    "  // SaveCanvas(c1, TString(qa_file_name_new) + TString(\"_\") + TString(c1->GetName()), true);\n",
    "    c1->Draw();\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hits per layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:46.360642Z",
     "iopub.status.busy": "2025-03-25T17:00:46.360248Z",
     "iopub.status.idle": "2025-03-25T17:00:46.482635Z",
     "shell.execute_reply": "2025-03-25T17:00:46.481541Z"
    }
   },
   "outputs": [],
   "source": [
    "%%cpp -d\n",
    "\n",
    "\n",
    "namespace\n",
    "{\n",
    "  // Normalization\n",
    "  double Nevent_new = 1;\n",
    "  double Nevent_ref = 1;\n",
    "\n",
    "  void GetNormalization(TFile *qa_file_new, TFile *qa_file_ref, const TString &prefix, const TString &tag)\n",
    "  {\n",
    "    if (qa_file_new)\n",
    "    {\n",
    "      TH1 *h_norm = (TH1 *) qa_file_new->GetObjectChecked(prefix + TString(\"Normalization\"), \"TH1\");\n",
    "      assert(h_norm);\n",
    "      Nevent_new = h_norm->GetBinContent(h_norm->GetXaxis()->FindBin(tag));\n",
    "    }\n",
    "\n",
    "    if (qa_file_ref)\n",
    "    {\n",
    "      TH1 *h_norm = (TH1 *) qa_file_ref->GetObjectChecked(prefix + TString(\"Normalization\"), \"TH1\");\n",
    "      assert(h_norm);\n",
    "      Nevent_ref = h_norm->GetBinContent(h_norm->GetXaxis()->FindBin(tag));\n",
    "    }\n",
    "  }\n",
    "\n",
    "  void Draw(TFile *qa_file_new, TFile *qa_file_ref, const TString &prefix, const TString &tag)\n",
    "  {\n",
    "    auto h_new = static_cast<TH1 *>(qa_file_new->GetObjectChecked(prefix + tag, \"TH1\"));\n",
    "    assert(h_new);\n",
    "    //h_new->Sumw2();\n",
    "    h_new->Scale(1. / Nevent_new);\n",
    "\n",
    "    TH1 *h_ref = nullptr;\n",
    "    if (qa_file_ref)\n",
    "    {\n",
    "      h_ref = static_cast<TH1 *>(qa_file_ref->GetObjectChecked(prefix + tag, \"TH1\"));\n",
    "      assert(h_ref);\n",
    "      //h_ref->Sumw2();\n",
    "      h_ref->Scale(1.0 / Nevent_ref);\n",
    "    }\n",
    "\n",
    "    DrawReference(h_new, h_ref);\n",
    "    HorizontalLine(gPad, 1)->Draw();\n",
    "  }\n",
    "\n",
    "}  // namespace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:46.487611Z",
     "iopub.status.busy": "2025-03-25T17:00:46.487044Z",
     "iopub.status.idle": "2025-03-25T17:00:46.667001Z",
     "shell.execute_reply": "2025-03-25T17:00:46.666156Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KSTestSummary::PushKSTest - Warning - received pValue = 0. Reset to an arbitary small non-zero value (e^(-15))\n"
     ]
    },
    {
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       "\n",
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       "function display_root_plot_1742922046653(Core) {\n",
       "   let obj = 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       "       paths: { 'JSRootCore' : [ 'build/jsroot', 'https://root.cern/js/7.7.2/build/jsroot', 'https://jsroot.gsi.de/7.7.2/build/jsroot' ] }\n",
       "    })(['JSRootCore'],  function(Core) {\n",
       "       display_root_plot_1742922046653(Core);\n",
       "    });\n",
       "\n",
       "} else if (typeof JSROOT !== 'undefined') {\n",
       "\n",
       "   // JSROOT already loaded, just use it\n",
       "   display_root_plot_1742922046653(JSROOT);\n",
       "\n",
       "} else {\n",
       "\n",
       "    // We are in jupyterlab without require.js, directly loading jsroot\n",
       "    // Jupyterlab might be installed in a different base_url so we need to know it.\n",
       "    try {\n",
       "        var base_url = JSON.parse(document.getElementById('jupyter-config-data').innerHTML).baseUrl;\n",
       "    } catch(_) {\n",
       "        var base_url = '/';\n",
       "    }\n",
       "\n",
       "    // Try loading a local version of requirejs and fallback to cdn if not possible.\n",
       "    script_load_root_plot_1742922046653(base_url + 'static/build/jsroot.js', function(){\n",
       "        console.error('Fail to load JSROOT locally, please check your jupyter_notebook_config.py file');\n",
       "        script_load_root_plot_1742922046653('https://root.cern/js/7.7.2/build/jsroot.js', function(){\n",
       "            document.getElementById(\"root_plot_1742922046653\").innerHTML = \"Failed to load JSROOT\";\n",
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       "\n",
       "</script>\n"
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       "<IPython.core.display.HTML object>"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "%%cpp\n",
    "{\n",
    "     const char *hist_name_prefix = \"QAG4SimulationTracking\";\n",
    "      TString prefix = TString(\"h_\") + hist_name_prefix + TString(\"_\");\n",
    "\n",
    "  auto c1 = new TCanvas(TString(\"QA_Draw_Tracking_nClus_Layer\") + TString(\"_\") + hist_name_prefix,\n",
    "                        TString(\"QA_Draw_Tracking_nClus_Layer\") + TString(\"_\") + hist_name_prefix,\n",
    "                        950, 600);\n",
    "\n",
    "  c1->Divide(2, 1);\n",
    "  c1->cd(1);\n",
    "  GetNormalization(qa_file_new, qa_file_ref, prefix, \"Truth Track\");\n",
    "  Draw(qa_file_new, qa_file_ref, prefix, \"nClus_layerGen\");\n",
    "\n",
    "  c1->cd(2);\n",
    "  GetNormalization(qa_file_new, qa_file_ref, prefix, \"Reco Track\");\n",
    "  Draw(qa_file_new, qa_file_ref, prefix, \"nClus_layer\");\n",
    "\n",
    "  // SaveCanvas(c1, TString(qa_file_name_new) + TString(\"_\") + TString(c1->GetName()), true);\n",
    "    c1->Draw();\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Upsilon reconstruction\n",
    "\n",
    "One $\\Upsilon(1S) \\rightarrow e^+ e^-$ is embedded. Here is its reco results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:46.672446Z",
     "iopub.status.busy": "2025-03-25T17:00:46.671953Z",
     "iopub.status.idle": "2025-03-25T17:00:46.801236Z",
     "shell.execute_reply": "2025-03-25T17:00:46.800295Z"
    }
   },
   "outputs": [],
   "source": [
    "%%cpp -d\n",
    "\n",
    "// square\n",
    "inline double square( const double& x ) { return x*x; }\n",
    "\n",
    "// christal ball function for momentum resolution fit\n",
    "Double_t CBcalc(Double_t *xx, Double_t *par)\n",
    "{\n",
    "  // Crystal Ball fit to one state\n",
    "  double f;\n",
    "  const double x = xx[0];\n",
    "\n",
    "  // The four parameters (alpha, n, x_mean, sigma) plus normalization (N) are:\n",
    "   \n",
    "  const double alpha = par[0];\n",
    "  const double n = par[1];\n",
    "  const double x_mean = par[2];\n",
    "  const double sigma = par[3];\n",
    "  const double N = par[4];\n",
    "\n",
    "  // dimensionless variable\n",
    "  const double t = (x-x_mean)/sigma;\n",
    "\n",
    "  // The Crystal Ball function is:\n",
    "   \n",
    "  if( t > -alpha)\n",
    "  {\n",
    "    return N * exp( -square(t)/2 );\n",
    "  }\n",
    "  else\n",
    "  {\n",
    "    const double A = pow( (n/TMath::Abs(alpha)),n) * exp(-square(alpha)/2.0);\n",
    "    const double B = n/TMath::Abs(alpha) - TMath::Abs(alpha);\n",
    "    return N * A * pow(B - t, -n);\n",
    "  }\n",
    "\n",
    "}\n",
    "\n",
    "// christal ball function for Upsilon fits\n",
    "Double_t CBcalc2(Double_t *xx, Double_t *par)\n",
    "{\n",
    "  // Crystal Ball fit to one state\n",
    "  const double x = xx[0];\n",
    "\n",
    "  /* The parameters are: \n",
    "   * N the normalization\n",
    "   * x_mean, sigma\n",
    "   * alpha_left, n_left\n",
    "   * alpha_right, n_right\n",
    "   */\n",
    "  const double N = par[0];\n",
    "  const double x_mean = par[1];\n",
    "  const double sigma = par[2];\n",
    "  const double t = (x-x_mean)/sigma;    \n",
    "\n",
    "  // left tail\n",
    "  const double alpha_left = std::abs(par[3]);\n",
    "  const double n_left = par[4];\n",
    "\n",
    "  // right tail\n",
    "  const double alpha_right = std::abs(par[5]);\n",
    "  const double n_right = par[6];\n",
    "    \n",
    "  // The Crystal Ball function is:   \n",
    "  if( t < -alpha_left )\n",
    "  {      \n",
    "    const double A = pow( (n_left/TMath::Abs(alpha_left)),n_left) * exp(-square(alpha_left)/2.0);\n",
    "    const double B = n_left/std::abs(alpha_left) - std::abs(alpha_left);\n",
    "    return N * A * pow(B - t, -n_left);\n",
    "  } else if( t > alpha_right ) {\n",
    "     const double A = pow( (n_right/TMath::Abs(alpha_right)),n_right) * exp(-square(alpha_right)/2.0);\n",
    "     const double B = n_right/std::abs(alpha_right) - std::abs(alpha_right);\n",
    "     return N * A * pow(B + t, -n_right);  \n",
    "  } else {\n",
    "      return N * exp( -square(t)/2);\n",
    "  }\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:46.806538Z",
     "iopub.status.busy": "2025-03-25T17:00:46.805962Z",
     "iopub.status.idle": "2025-03-25T17:00:47.218277Z",
     "shell.execute_reply": "2025-03-25T17:00:47.217680Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****************************************\n",
      "         Invalid FitResult  (status = 3 )\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      1557.86\n",
      "NDf                       =          302\n",
      "Edm                       =       127439\n",
      "NCalls                    =          942\n",
      "alpha                     =    -0.643375   +/-   1.00003     \n",
      "n                         =      1.28936   +/-   1.00021     \n",
      "m                         =     0.997789   +/-   1           \n",
      "sigma                     =    0.0189798   +/-   1           \n",
      "N                         =      388.082   +/-   6.83573     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      718.691\n",
      "NDf                       =          367\n",
      "Edm                       =  3.38725e-07\n",
      "NCalls                    =          597\n",
      "alpha                     =      1.21811   +/-   0.0232098   \n",
      "n                         =     0.953047   +/-   0.0185185   \n",
      "m                         =     0.997167   +/-   0.000238494 \n",
      "sigma                     =    0.0163169   +/-   0.000223389 \n",
      "N                         =      863.786   +/-   12.7091     \n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      413.563\n",
      "NDf                       =          351\n",
      "Edm                       =  5.95627e-06\n",
      "NCalls                    =          558\n",
      "N                         =       117.28   +/-   5.38846     \n",
      "m                         =      9.43477   +/-   0.00706503  \n",
      "#sigma                    =     0.107821   +/-   0.00784131  \n",
      "#alpha_{left}             =     0.520053   +/-   0.0301615    \t (limited)\n",
      "n_{left}                  =         1.05   +/-   0.00415039   \t (limited)\n",
      "#alpha_{right}            =      1.99312   +/-   0.0862669    \t (limited)\n",
      "#sigma_{right}            =         1.05   +/-   0.0158688    \t (limited)\n",
      "****************************************\n",
      "Minimizer is Minuit2 / Migrad\n",
      "Chi2                      =      478.721\n",
      "NDf                       =          331\n",
      "Edm                       =   2.2803e-07\n",
      "NCalls                    =          496\n",
      "N                         =      204.728   +/-   1.87329     \n",
      "m                         =      9.42249   +/-   0.00594171  \n",
      "#sigma                    =     0.126503   +/-   0.00412685  \n",
      "#alpha_{left}             =     0.659405   +/-   0.0384828    \t (limited)\n",
      "n_{left}                  =      1.15196   +/-   0.0372615    \t (limited)\n",
      "#alpha_{right}            =      5.47513   +/-   7.47196      \t (limited)\n",
      "#sigma_{right}            =       9.9397   +/-   7.86707      \t (limited)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <Fit>: Abnormal termination of minimization.\n"
     ]
    },
    {
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       "   Core.settings.HandleKeys = false;\n",
       "   Core.draw(\"root_plot_1742922047208\", obj, \"\");\n",
       "}\n",
       "\n",
       "function script_load_root_plot_1742922047208(src, on_error) {\n",
       "    let script = document.createElement('script');\n",
       "    script.src = src;\n",
       "    script.onload = function() { display_root_plot_1742922047208(JSROOT); };\n",
       "    script.onerror = function() { script.remove(); on_error(); };\n",
       "    document.head.appendChild(script);\n",
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       "    // We are in jupyter notebooks, use require.js which should be configured already\n",
       "    requirejs.config({\n",
       "       paths: { 'JSRootCore' : [ 'build/jsroot', 'https://root.cern/js/7.7.2/build/jsroot', 'https://jsroot.gsi.de/7.7.2/build/jsroot' ] }\n",
       "    })(['JSRootCore'],  function(Core) {\n",
       "       display_root_plot_1742922047208(Core);\n",
       "    });\n",
       "\n",
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       "\n",
       "   // JSROOT already loaded, just use it\n",
       "   display_root_plot_1742922047208(JSROOT);\n",
       "\n",
       "} else {\n",
       "\n",
       "    // We are in jupyterlab without require.js, directly loading jsroot\n",
       "    // Jupyterlab might be installed in a different base_url so we need to know it.\n",
       "    try {\n",
       "        var base_url = JSON.parse(document.getElementById('jupyter-config-data').innerHTML).baseUrl;\n",
       "    } catch(_) {\n",
       "        var base_url = '/';\n",
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       "    // Try loading a local version of requirejs and fallback to cdn if not possible.\n",
       "    script_load_root_plot_1742922047208(base_url + 'static/build/jsroot.js', function(){\n",
       "        console.error('Fail to load JSROOT locally, please check your jupyter_notebook_config.py file');\n",
       "        script_load_root_plot_1742922047208('https://root.cern/js/7.7.2/build/jsroot.js', function(){\n",
       "            document.getElementById(\"root_plot_1742922047208\").innerHTML = \"Failed to load JSROOT\";\n",
       "        });\n",
       "    });\n",
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       "</script>\n"
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   ],
   "source": [
    "%%cpp\n",
    "\n",
    "{\n",
    "  const char *hist_name_prefix = \"QAG4SimulationUpsilon\";\n",
    "  TString prefix = TString(\"h_\") + hist_name_prefix + TString(\"_\");\n",
    "\n",
    "  // obtain normalization\n",
    "  double Nevent_new = 1;\n",
    "  double Nevent_ref = 1;\n",
    "\n",
    "  if ( qa_file_new->GetObjectChecked(\n",
    "        prefix + TString(\"pTRecoGenRatio_pTGen\"), \"TH2\")\n",
    "     == nullptr )\n",
    "    {\n",
    "        cout <<\"QAG4SimulationUpsilon is not enabled. Skip....\"<<endl;\n",
    "    }\n",
    "    else\n",
    "    {\n",
    "\n",
    "      TCanvas *c1 = new TCanvas(TString(\"QA_Draw_Tracking_UpsilonOverview\") + TString(\"_\") + hist_name_prefix,\n",
    "                                TString(\"QA_Draw_Tracking_UpsilonOverview\") + TString(\"_\") + hist_name_prefix,\n",
    "                                950, 600);\n",
    "      c1->Divide(2, 1);\n",
    "      int idx = 1;\n",
    "      TPad *p;\n",
    "\n",
    "      {\n",
    "        p = (TPad *) c1->cd(idx++);\n",
    "        c1->Update();\n",
    "        p->SetLogy();\n",
    "\n",
    "        TH2 *h_new = (TH2 *) qa_file_new->GetObjectChecked(\n",
    "            prefix + TString(\"pTRecoGenRatio_pTGen\"), \"TH2\");\n",
    "        assert(h_new);\n",
    "\n",
    "        //  h_new->Rebin(1, 2);\n",
    "        //h_new->Sumw2();\n",
    "        //  h_new->Scale(1. / Nevent_new);\n",
    "\n",
    "        TH2 *h_ref = NULL;\n",
    "        if (qa_file_ref)\n",
    "        {\n",
    "          h_ref = (TH2 *) qa_file_ref->GetObjectChecked(\n",
    "              prefix + TString(\"pTRecoGenRatio_pTGen\"), \"TH2\");\n",
    "          assert(h_ref);\n",
    "\n",
    "          //    h_ref->Rebin(1, 2);\n",
    "          //h_ref->Sumw2();\n",
    "          h_ref->Scale(Nevent_new / Nevent_ref);\n",
    "        }\n",
    "\n",
    "        TH1 *h_proj_new = h_new->ProjectionY(\n",
    "            TString::Format(\n",
    "                \"%s_New_ProjX\",\n",
    "                h_new->GetName()));\n",
    "\n",
    "        h_proj_new->GetXaxis()->SetRangeUser(0, 1.3);\n",
    "        h_proj_new->SetTitle(TString(hist_name_prefix) + TString::Format(\n",
    "                                                             \": Electron lineshape\"));\n",
    "        h_proj_new->GetXaxis()->SetTitle(TString::Format(\n",
    "            \"Reco p_{T}/Truth p_{T}\"));\n",
    "\n",
    "        TF1 *f_eLineshape = new TF1(\"f_eLineshape\", CBcalc, 7, 11, 5);\n",
    "        f_eLineshape->SetParameter(0, 1.0);\n",
    "        f_eLineshape->SetParameter(1, 1.0);\n",
    "        f_eLineshape->SetParameter(2, 0.95);\n",
    "        f_eLineshape->SetParameter(3, 0.08);\n",
    "        f_eLineshape->SetParameter(4, 20.0);\n",
    "\n",
    "        f_eLineshape->SetParNames(\"alpha\",\"n\",\"m\",\"sigma\",\"N\");\n",
    "        f_eLineshape->SetLineColor(kRed);\n",
    "        f_eLineshape->SetLineWidth(3);\n",
    "        f_eLineshape->SetLineStyle(kSolid);\n",
    "        f_eLineshape->SetNpx(1000);\n",
    "\n",
    "        h_proj_new->Fit(f_eLineshape);\n",
    "\n",
    "        TH1 *h_proj_ref = nullptr;\n",
    "        if (h_ref)\n",
    "        {\n",
    "          h_proj_ref =\n",
    "              h_ref->ProjectionY(\n",
    "                  TString::Format(\n",
    "                      \"%s_Ref_ProjX\",\n",
    "                      h_new->GetName()));\n",
    "        }\n",
    "        TF1 *f_eLineshape_ref = new TF1(\"f_eLineshape_ref\", CBcalc, 7, 11, 5);\n",
    "        f_eLineshape_ref->SetParameter(0, 1.0);\n",
    "        f_eLineshape_ref->SetParameter(1, 1.0);\n",
    "        f_eLineshape_ref->SetParameter(2, 0.95);\n",
    "        f_eLineshape_ref->SetParameter(3, 0.08);\n",
    "        f_eLineshape_ref->SetParameter(4, 20.0);\n",
    "\n",
    "        f_eLineshape_ref->SetParNames(\"alpha\",\"n\",\"m\",\"sigma\",\"N\");\n",
    "        f_eLineshape_ref->SetLineColor(kRed);\n",
    "        f_eLineshape_ref->SetLineWidth(3);\n",
    "        f_eLineshape_ref->SetLineStyle(kSolid);\n",
    "\n",
    "        h_proj_ref->Fit(f_eLineshape_ref);\n",
    "\n",
    "\n",
    "        DrawReference(h_proj_new, h_proj_ref);\n",
    "        f_eLineshape->Draw(\"same\");\n",
    "\n",
    "        char resstr_1[500];\n",
    "        sprintf(resstr_1,\"#sigma_{dp/p} = %.2f #pm %.2f %%\", f_eLineshape->GetParameter(3)*100, f_eLineshape->GetParError(3)*100);\n",
    "        TLatex *res_1 = new TLatex(0.2,0.75,resstr_1);\n",
    "        res_1->SetNDC();\n",
    "        res_1->SetTextSize(0.05);\n",
    "        res_1->SetTextAlign(13);\n",
    "        res_1->Draw();\n",
    "\n",
    "        char resstr_2[500];\n",
    "        sprintf(resstr_2,\"#sigma_{dp/p,ref} = %.2f #pm %.2f %%\", f_eLineshape_ref->GetParameter(3)*100, f_eLineshape_ref->GetParError(3)*100);\n",
    "        TLatex *res_2 = new TLatex(0.2,0.7,resstr_2);\n",
    "        res_2->SetNDC();\n",
    "        res_2->SetTextSize(0.05);\n",
    "        res_2->SetTextAlign(13);\n",
    "        res_2->Draw();\n",
    "      }\n",
    "\n",
    "      {\n",
    "        p = (TPad *) c1->cd(idx++);\n",
    "        c1->Update();\n",
    "    //    p->SetLogy();\n",
    "\n",
    "        TH1 *h_new = (TH1 *) qa_file_new->GetObjectChecked(\n",
    "            prefix + TString(\"nReco_Pair_InvMassReco\"), \"TH1\");\n",
    "        assert(h_new);\n",
    "\n",
    "        // h_new->Rebin(2);\n",
    "        // h_new->Sumw2();\n",
    "        // h_new->Scale(1. / Nevent_new);\n",
    "\n",
    "        TF1 *f1S = new TF1(\"f1S\", CBcalc2, 7, 11, 7);\n",
    "        f1S->SetParameter(0, 50.0);\n",
    "        f1S->SetParameter(1, 9.46);\n",
    "        f1S->SetParameter(2, 0.08);\n",
    "        f1S->SetParameter(3, 1.0);\n",
    "        f1S->SetParameter(4, 3.0);\n",
    "        f1S->SetParameter(5, 1.0);\n",
    "        f1S->SetParLimits(3, 0.120, 10);\n",
    "        f1S->SetParLimits(4, 1.05, 10);\n",
    "        f1S->SetParameter(6, 3.0);\n",
    "        f1S->SetParLimits(5, 0.120, 10);\n",
    "        f1S->SetParLimits(6, 1.05, 10);\n",
    " \n",
    "        f1S->SetParNames(\"N\", \"m\", \"#sigma\", \"#alpha_{left}\",\"n_{left}\",\"#alpha_{right}\",\"#sigma_{right}\");\n",
    "        f1S->SetLineColor(kRed);\n",
    "        f1S->SetLineWidth(3);\n",
    "        f1S->SetLineStyle(kSolid);\n",
    "        f1S->SetNpx(1000);\n",
    "\n",
    "        h_new->Fit(f1S);\n",
    "\n",
    "        TH1 *h_ref = NULL;\n",
    "        if (qa_file_ref)\n",
    "        {\n",
    "          h_ref = (TH1 *) qa_file_ref->GetObjectChecked(\n",
    "              prefix + TString(\"nReco_Pair_InvMassReco\"), \"TH1\");\n",
    "          assert(h_ref);\n",
    "\n",
    "          // h_ref->Rebin(2);\n",
    "          // h_ref->Sumw2();\n",
    "          // h_ref->Scale(Nevent_new / Nevent_ref);\n",
    "        }\n",
    "\n",
    "        h_new->SetTitle(TString(hist_name_prefix) + TString::Format(\n",
    "                                                        \": #Upsilon #rightarrow e^{+}e^{-} lineshape\"));\n",
    "        h_new->GetXaxis()->SetRangeUser(7, 10);\n",
    "\n",
    "        TF1 *f1S_ref = new TF1(\"f1S_ref\", CBcalc2, 7, 11, 7);\n",
    "        f1S_ref->SetParameter(0, 50.0);\n",
    "        f1S_ref->SetParameter(1, 9.46);\n",
    "        f1S_ref->SetParameter(2, 0.08);\n",
    "        f1S_ref->SetParameter(3, 1.0);\n",
    "        f1S_ref->SetParameter(4, 3.0);\n",
    "        f1S_ref->SetParameter(5, 1.0);\n",
    "        f1S_ref->SetParLimits(3, 0.120, 10);\n",
    "        f1S_ref->SetParLimits(4, 1.05, 10);\n",
    "        f1S_ref->SetParameter(6, 3.0);\n",
    "        f1S_ref->SetParLimits(5, 0.120, 10);\n",
    "        f1S_ref->SetParLimits(6, 1.05, 10);\n",
    " \n",
    "        f1S_ref->SetParNames(\"N\", \"m\", \"#sigma\", \"#alpha_{left}\",\"n_{left}\",\"#alpha_{right}\",\"#sigma_{right}\");\n",
    "        f1S_ref->SetLineColor(kRed);\n",
    "        f1S_ref->SetLineWidth(3);\n",
    "        f1S_ref->SetLineStyle(kSolid);\n",
    "\n",
    "        h_ref->Fit(f1S_ref);\n",
    "\n",
    "        DrawReference(h_new, h_ref, false);\n",
    "        f1S->Draw(\"same\");\n",
    "\n",
    "        // cout << \"f1S pars \" <<  f1S->GetParameter(3) << \"   \" << f1S->GetParError(3) << endl;\n",
    "\n",
    "        char resstr_3[500];\n",
    "        sprintf(resstr_3,\"#sigma_{1S} = %.1f #pm %.1f MeV\", f1S->GetParameter(2)*1000, f1S->GetParError(2)*1000);\n",
    "        TLatex *res_3 = new TLatex(0.2,0.75,resstr_3);\n",
    "        res_3->SetNDC();\n",
    "        res_3->SetTextSize(0.05);\n",
    "        res_3->SetTextAlign(13);\n",
    "        res_3->Draw();\n",
    "\n",
    "        char resstr_4[500];\n",
    "        sprintf(resstr_4,\"#sigma_{1S,ref} = %.1f #pm %.1f MeV\", f1S_ref->GetParameter(2)*1000, f1S_ref->GetParError(2)*1000);\n",
    "        TLatex *res_4 = new TLatex(0.2,0.7,resstr_4);\n",
    "        res_4->SetNDC();\n",
    "        res_4->SetTextSize(0.05);\n",
    "        res_4->SetTextAlign(13);\n",
    "        res_4->Draw();\n",
    "          \n",
    "        ofstream fcsv;\n",
    "        fcsv.open (\"Upsilon_mean.csv\");\n",
    "        fcsv<<\"Upsilon mean (GeV)\"<<endl;\n",
    "        fcsv<<f1S->GetParameter(1)<<endl;\n",
    "        fcsv.close();\n",
    "        fcsv.open (\"Upsilon_width.csv\");\n",
    "        fcsv<<\"Upsilon width (GeV)\"<<endl;\n",
    "        fcsv<<f1S->GetParameter(2)<<endl;\n",
    "        fcsv.close();\n",
    "        fcsv.open (\"Upsilon_count.csv\");\n",
    "        fcsv<<\"Upsilon reconstructed\"<<endl;\n",
    "        fcsv<<f1S->GetParameter(0)<<endl;\n",
    "        fcsv.close();\n",
    "      }\n",
    "\n",
    "      // SaveCanvas(c1, TString(qa_file_name_new) + TString(\"_\") + TString(c1->GetName()), true);\n",
    "\n",
    "      c1 -> Draw();\n",
    "    }// if checks\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:47.225421Z",
     "iopub.status.busy": "2025-03-25T17:00:47.225058Z",
     "iopub.status.idle": "2025-03-25T17:00:47.372297Z",
     "shell.execute_reply": "2025-03-25T17:00:47.371417Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook contains 28 KSTets: combined Chi2/nDoF = 2257.25 / 56, and combined __p-Value = 0__\n"
     ]
    }
   ],
   "source": [
    "%%cpp\n",
    "\n",
    "KSTestSummary::getInstance()->make_summary_txt(\"QA-tracking.txt\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T17:00:47.376542Z",
     "iopub.status.busy": "2025-03-25T17:00:47.376008Z",
     "iopub.status.idle": "2025-03-25T17:00:47.516965Z",
     "shell.execute_reply": "2025-03-25T17:00:47.515910Z"
    }
   },
   "outputs": [
    {
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       "\n",
       "    // We are in jupyterlab without require.js, directly loading jsroot\n",
       "    // Jupyterlab might be installed in a different base_url so we need to know it.\n",
       "    try {\n",
       "        var base_url = JSON.parse(document.getElementById('jupyter-config-data').innerHTML).baseUrl;\n",
       "    } catch(_) {\n",
       "        var base_url = '/';\n",
       "    }\n",
       "\n",
       "    // Try loading a local version of requirejs and fallback to cdn if not possible.\n",
       "    script_load_root_plot_1742922047500(base_url + 'static/build/jsroot.js', function(){\n",
       "        console.error('Fail to load JSROOT locally, please check your jupyter_notebook_config.py file');\n",
       "        script_load_root_plot_1742922047500('https://root.cern/js/7.7.2/build/jsroot.js', function(){\n",
       "            document.getElementById(\"root_plot_1742922047500\").innerHTML = \"Failed to load JSROOT\";\n",
       "        });\n",
       "    });\n",
       "}\n",
       "\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%cpp\n",
    "\n",
    "KSTestSummary::getInstance()->make_summary_TCanvas() -> Draw();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python (sPHENIX)",
   "language": "python",
   "name": "sphenix-env"
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   "codemirror_mode": {
    "name": "ipython",
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