{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "acc4d7fa",
   "metadata": {},
   "source": [
    "# Exercise\n",
    "\n",
    "Write a python macro ExerciseHist.py.\n",
    "\n",
    "Open the Zmumu.root file and load the tree called physics.\n",
    "\n",
    "Create two histograms with 40 bins ranging from 0 to 0.2 GeV to plot the muon masses.\n",
    "\n",
    "Fill the histograms with leading and subleading muon mass from branches lep1_m and lep2_m.\n",
    "\n",
    "Calculate the mean values and RMSs.\n",
    "\n",
    "Calculate the integrals.\n",
    "\n",
    "## Bonus questions:\n",
    "\n",
    "Are the integrals the same and why?\n",
    "Write the histogram to a file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8c4eb0e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in /Users/yvonne/miniforge3/lib/python3.10/site-packages (3.7.0)\n",
      "Requirement already satisfied: numpy>=1.20 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib) (1.24.2)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib) (1.0.7)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib) (23.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib) (9.4.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib) (4.38.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: six>=1.5 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "Requirement already satisfied: mplhep in /Users/yvonne/miniforge3/lib/python3.10/site-packages (0.3.26)\n",
      "Requirement already satisfied: mplhep-data in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from mplhep) (0.0.3)\n",
      "Requirement already satisfied: matplotlib>=3.4 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from mplhep) (3.7.0)\n",
      "Requirement already satisfied: uhi>=0.2.0 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from mplhep) (0.3.3)\n",
      "Requirement already satisfied: packaging in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from mplhep) (23.0)\n",
      "Requirement already satisfied: numpy>=1.16.0 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from mplhep) (1.24.2)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib>=3.4->mplhep) (3.0.9)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib>=3.4->mplhep) (4.38.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib>=3.4->mplhep) (1.4.4)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib>=3.4->mplhep) (0.11.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib>=3.4->mplhep) (9.4.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib>=3.4->mplhep) (2.8.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from matplotlib>=3.4->mplhep) (1.0.7)\n",
      "Requirement already satisfied: six>=1.5 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib>=3.4->mplhep) (1.16.0)\n",
      "Requirement already satisfied: uproot in /Users/yvonne/miniforge3/lib/python3.10/site-packages (5.0.3)\n",
      "Requirement already satisfied: numpy in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from uproot) (1.24.2)\n",
      "Requirement already satisfied: packaging in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from uproot) (23.0)\n",
      "Requirement already satisfied: awkward>=2.0.0 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from uproot) (2.0.8)\n",
      "Requirement already satisfied: typing-extensions>=4.1.0 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from awkward>=2.0.0->uproot) (4.5.0)\n",
      "Requirement already satisfied: awkward-cpp==9 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from awkward>=2.0.0->uproot) (9)\n",
      "Requirement already satisfied: scipy in /Users/yvonne/miniforge3/lib/python3.10/site-packages (1.10.1)\n",
      "Requirement already satisfied: numpy<1.27.0,>=1.19.5 in /Users/yvonne/miniforge3/lib/python3.10/site-packages (from scipy) (1.24.2)\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install matplotlib\n",
    "!{sys.executable} -m pip install mplhep\n",
    "!{sys.executable} -m pip install uproot\n",
    "!{sys.executable} -m pip install scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "16c8c6a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import uproot\n",
    "import numpy as np\n",
    "import awkward as ak\n",
    "import mplhep as hep\n",
    "from scipy.optimize import curve_fit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32962104",
   "metadata": {},
   "source": [
    "## Open the Zmumu.root file and load the tree called physics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "991d7635",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<TTree '' (15 branches) at 0x0002f994a7d0>\n",
      "[{lep1_pt: 59.9, lep1_eta: -2.18, lep1_phi: -1.42, lep1_E: 268, ...}, ...]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub data rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_data_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tree=uproot.open(\"Zmumu.root\")[\"physics\"]\n",
    "print(tree)\n",
    "branches=tree.arrays()\n",
    "print(branches)\n",
    "branches_dict=branches.to_list()\n",
    "print(branches_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1c4d89b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "lep1_m=branches[\"lep1_m\"]\n",
    "lep2_m=branches[\"lep2_m\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f095ad01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x3997c57e0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(lep1_m, bins=40, range=(0, 0.2), alpha=0.5, label=\"leading lepton\")\n",
    "plt.hist(lep2_m, bins=40, range=(0, 0.2), color=\"r\", alpha=0.5, label=\"subleading lepton\")\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14d3bec0",
   "metadata": {},
   "source": [
    "## Bonus: (beautifying using hep/root style)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f77c68f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(exptext: Custom Text(0.05, 0.95, 'ATLAS'),\n",
       " expsuffix: Custom Text(0.05, 0.955, 'Simulation Example'),\n",
       " expsuffix: Custom Text(0.05, 0.95, '$\\\\sqrt{s} = \\\\mathrm{13\\\\ TeV}$'))"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hep.style.use(hep.style.ATLAS)\n",
    "fig, ax = plt.subplots()\n",
    "plt.hist(lep1_m, bins=40, range=(0, 0.2), alpha=0.5, label=\"leading lepton\")\n",
    "plt.hist(lep2_m, bins=40, range=(0, 0.2), color=\"r\", alpha=0.5, label=\"subleading lepton\")\n",
    "plt.legend()\n",
    "ax.set_xlabel(\"Mass (GeV)\")\n",
    "ax.set_ylabel(\"Entries\")\n",
    "hep.atlas.label(\"Example\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "343b64c7",
   "metadata": {},
   "source": [
    "## Calculate mean values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "68f47cee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lep1 mass mean: 0.106\n",
      "lep2 mass mean: 0.107\n"
     ]
    }
   ],
   "source": [
    "print(\"lep1 mass mean: %.3f\"%np.mean(lep1_m))\n",
    "print(\"lep2 mass mean: %.3f\"%np.mean(lep2_m))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9788f9c2",
   "metadata": {},
   "source": [
    "## Calculate RMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c55f4274",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lep1 mass rms: 0.107\n",
      "lep2 mass rms: 0.106\n"
     ]
    }
   ],
   "source": [
    "print(\"lep1 mass rms: %.3f\"%np.sqrt(np.mean(np.square(lep1_m))))\n",
    "print(\"lep2 mass rms: %.3f\"%np.sqrt(np.mean(np.square(lep2_m))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4e0944b",
   "metadata": {},
   "source": [
    "## Calculate integral"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1fed55c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lep1 mass integral:  266019.06\n",
      "lep2 mass integral:  266833.34\n"
     ]
    }
   ],
   "source": [
    "print(\"lep1 mass integral: %.1f\"%np.sum(lep1_m))\n",
    "print(\"lep2 mass integral: %.1f\"%np.sum(lep2_m))"
   ]
  }
 ],
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