PKU_Training_l2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8698e811",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: uproot in /cvmfs/sft.cern.ch/lcg/views/LCG_102swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages (4.2.0)\n",
      "Requirement already satisfied: tqdm in /cvmfs/sft.cern.ch/lcg/views/LCG_102swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages (4.61.2)\n",
      "Requirement already satisfied: numpy in /cvmfs/sft.cern.ch/lcg/views/LCG_102swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages (from uproot) (1.22.3)\n",
      "Requirement already satisfied: setuptools in /cvmfs/sft.cern.ch/lcg/views/LCG_102swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages (from uproot) (57.1.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install --user uproot tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9ec3b089",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Default principal: yian@CERN.CH\n"
     ]
    }
   ],
   "source": [
    "username = \"yian\"\n",
    "import getpass\n",
    "import os\n",
    "if os.system('klist | grep Default | grep ' + username + '@CERN.CH'):\n",
    "    os.system('echo %s' % getpass.getpass() + \" | kinit \" + username)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d890f6ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\n",
    "import glob\n",
    "import pandas as pd\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8c644db1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: awkward in /cvmfs/sft.cern.ch/lcg/views/LCG_102swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages (1.7.0)\n",
      "Requirement already satisfied: numpy>=1.13.1 in /cvmfs/sft.cern.ch/lcg/views/LCG_102swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages (from awkward) (1.22.3)\n",
      "Requirement already satisfied: setuptools in /cvmfs/sft.cern.ch/lcg/views/LCG_102swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages (from awkward) (57.1.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install awkward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cdb27a65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.26/04\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import awkward as ak\n",
    "import ROOT\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cffc8cf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_files = 20\n",
    "root_files = glob.glob(\"/eos/user/y/yian/SWAN_projects/*.root\")[:n_files]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d7a4275f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ntuplizer;1', 'ntuplizer/tree;1']\n"
     ]
    }
   ],
   "source": [
    "f = uproot.open(root_files[0])\n",
    "print(f.keys())\n",
    "tree=f[f.keys()[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "24304741",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62\n"
     ]
    }
   ],
   "source": [
    "unneccesary_columns = ['ele_index',\n",
    "       'Spring16GPV1Vals', 'Spring16GPV1RawVals', 'Spring16HZZV1Vals',\n",
    "       'Spring16HZZV1RawVals', 'Fall17NoIsoV2Vals',\n",
    "       'Fall17IsoV2Vals', 'Fall17IsoV1Vals',\n",
    "       'Fall17NoIsoV1Vals', 'Spring16GPV1wp80', 'Spring16GPV1wp90',\n",
    "       'Spring16HZZV1wpLoose', 'Fall17noIsoV2wp80', 'Fall17noIsoV2wpLoose',\n",
    "       'Fall17noIsoV2wp90', 'Fall17isoV2wpHZZ', 'Fall17isoV2wp80',\n",
    "       'Fall17isoV2wpLoose', 'Fall17isoV2wp90', 'Fall17noIsoV1wp90',\n",
    "       'Fall17noIsoV1wp80', 'Fall17noIsoV1wpLoose', 'Fall17isoV1wp90',\n",
    "       'Fall17isoV1wp80', 'Fall17isoV1wpLoose', 'nEvent', 'nRun', 'nLumi']\n",
    "\n",
    "branch=tree.keys()\n",
    "for c in unneccesary_columns:\n",
    "    branch.remove(c)\n",
    "print(len(branch))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b6f2d6e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "f = uproot.lazy(root_files[0]+':ntuplizer/tree')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0180e3a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(np.array(f),columns=branch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fbaad5c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       genNpu  vtxN  ele_q  ele_3q  matchedToGenEle        rho     ele_pt  \\\n",
      "0          70    55   -1.0       1                1  39.336685  75.166786   \n",
      "1          70    55    1.0       1                1  39.336685  25.170097   \n",
      "2          55    41    1.0       1                1  34.542377  40.437748   \n",
      "3          55    41   -1.0       1                1  34.542377  39.272686   \n",
      "4          65    54    1.0       1                1  28.301094  56.119308   \n",
      "...       ...   ...    ...     ...              ...        ...        ...   \n",
      "38613      63    53    1.0       0                1  43.432579  27.979042   \n",
      "38614      63    53   -1.0       1                1  43.432579  26.223799   \n",
      "38615      83    83    1.0       1                1  52.417740  23.142691   \n",
      "38616      64    53   -1.0       1                2  49.768986  20.793974   \n",
      "38617      64    53    1.0       1                0  49.768986  13.645608   \n",
      "\n",
      "       ele_oldsigmaietaieta  ele_oldsigmaiphiiphi  ele_oldcircularity  ...  \\\n",
      "0                  0.009603              0.010184            0.168566  ...   \n",
      "1                  0.009036              0.012087            0.176306  ...   \n",
      "2                  0.009075              0.014045            0.222403  ...   \n",
      "3                  0.009918              0.010296            0.243003  ...   \n",
      "4                  0.008885              0.015579            0.406262  ...   \n",
      "...                     ...                   ...                 ...  ...   \n",
      "38613              0.027920              0.037330            0.256048  ...   \n",
      "38614              0.027341              0.042599            0.283034  ...   \n",
      "38615              0.037951              0.042338            0.353547  ...   \n",
      "38616              0.012032              0.011581            0.444886  ...   \n",
      "38617              0.019079              0.020440            0.616030  ...   \n",
      "\n",
      "       ele_isEB  ele_isEE  ele_isEBEtaGap  ele_isEBPhiGap  ele_isEBEEGap  \\\n",
      "0          True     False           False           False          False   \n",
      "1          True     False           False           False          False   \n",
      "2          True     False           False           False          False   \n",
      "3          True     False           False            True          False   \n",
      "4          True     False           False           False          False   \n",
      "...         ...       ...             ...             ...            ...   \n",
      "38613     False      True           False           False          False   \n",
      "38614     False      True           False           False          False   \n",
      "38615     False      True           False           False          False   \n",
      "38616      True     False           False           False          False   \n",
      "38617      True     False           False           False          False   \n",
      "\n",
      "       ele_isEEDeeGap  ele_isEERingGap  Fall17NoIsoV2RawVals  \\\n",
      "0               False            False              6.022810   \n",
      "1               False            False              6.400648   \n",
      "2               False            False              8.018516   \n",
      "3               False            False             -0.038783   \n",
      "4               False            False              8.790177   \n",
      "...               ...              ...                   ...   \n",
      "38613           False            False              3.599527   \n",
      "38614           False            False              2.512864   \n",
      "38615           False            False              5.388796   \n",
      "38616           False            False              0.742088   \n",
      "38617           False            False             -5.241233   \n",
      "\n",
      "       Fall17IsoV2RawVals  EleMVACats  \n",
      "0                7.528009           4  \n",
      "1                7.456933           3  \n",
      "2                8.095548           3  \n",
      "3                0.998242           3  \n",
      "4                9.188561           3  \n",
      "...                   ...         ...  \n",
      "38613            4.159488           5  \n",
      "38614            0.907157           5  \n",
      "38615            6.325344           5  \n",
      "38616            2.401831           3  \n",
      "38617           -4.827538           4  \n",
      "\n",
      "[38618 rows x 62 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3fb56776",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "def get_df(root_file_name):        \n",
    "    fin = uproot.lazy(root_file_name+':ntuplizer/tree')\n",
    "    if len(fin) == 0:\n",
    "        return pd.DataFrame()\n",
    "    df=pd.DataFrame(np.array(fin),columns=branch)\n",
    "    return df        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b1cc0798",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [00:14<00:00,  1.34it/s]\n"
     ]
    }
   ],
   "source": [
    "df = pd.concat((get_df(f1) for f1 in  tqdm(root_files)), ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "683795fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['genNpu', 'vtxN', 'ele_q', 'ele_3q', 'matchedToGenEle', 'rho', 'ele_pt',\n",
       "       'ele_oldsigmaietaieta', 'ele_oldsigmaiphiiphi', 'ele_oldcircularity',\n",
       "       'ele_oldr9', 'ele_scletawidth', 'ele_sclphiwidth', 'ele_hadronicOverEm',\n",
       "       'ele_oldhe', 'ele_kfhits', 'ele_kfchi2', 'ele_gsfchi2', 'ele_fbrem',\n",
       "       'ele_gsfhits', 'ele_expected_inner_hits',\n",
       "       'ele_conversionVertexFitProbability', 'ele_ep', 'ele_eelepout',\n",
       "       'ele_IoEmIop', 'ele_deltaetain', 'ele_deltaphiin', 'ele_deltaetaseed',\n",
       "       'ele_psEoverEraw', 'ele_pfPhotonIso', 'ele_pfChargedHadIso',\n",
       "       'ele_pfNeutralHadIso', 'scl_E', 'scl_Et', 'scl_eta', 'scl_phi',\n",
       "       'ele_sclRawE', 'ele_sclNclus', 'ele_olde15', 'ele_olde25max',\n",
       "       'ele_olde55', 'ele_ecalE', 'ele_ecalErr', 'ele_trackErr', 'ele_combErr',\n",
       "       'ele_PFcombErr', 'ele_pfSumPUIso', 'ele_SCfbrem', 'ele_eClass',\n",
       "       'ele_nbrem', 'ele_oldhebc', 'ele_hebc', 'ele_isEB', 'ele_isEE',\n",
       "       'ele_isEBEtaGap', 'ele_isEBPhiGap', 'ele_isEBEEGap', 'ele_isEEDeeGap',\n",
       "       'ele_isEERingGap', 'Fall17NoIsoV2RawVals', 'Fall17IsoV2RawVals',\n",
       "       'EleMVACats'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cfca87a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def get_label(name):\n",
    "    if name == 0:\n",
    "        return \"background\"\n",
    "    else:\n",
    "        return \"signal\"\n",
    "\n",
    "def plot_electrons(df, column, bins, logscale=False, ax=None, title=None):\n",
    "    if ax is None:\n",
    "        ax = plt.gca()\n",
    "    for name, group in df.groupby(\"matchedToGenEle\"):\n",
    "        group[column].hist(bins=bins, histtype=\"step\", label=get_label(name), ax=ax, density=True)\n",
    "    ax.set_ylabel(\"density\")\n",
    "    ax.set_xlabel(column)\n",
    "    ax.legend()\n",
    "    ax.set_title(title)\n",
    "    if logscale:\n",
    "        ax.set_yscale(\"log\", nonposy='clip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "216af21a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.query(\"matchedToGenEle != 2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9403df27",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df[\"matchedToGenEle\"] != 1, \"matchedToGenEle\"] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "557b31e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.query(\"abs(scl_eta) < 2.5\")\n",
    "df = df.query(\"ele_pt >= 10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b47321d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "plot_electrons(df.query(\"ele_isEB\"), \"ele_oldsigmaietaieta\", np.linspace(0,0.03,100), ax=axes[0], title=\"Barrel\")\n",
    "plot_electrons(df.query(\"ele_isEE\"), \"ele_oldsigmaietaieta\", np.linspace(0,0.06,100), ax=axes[1], title=\"Endcap\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "51eee79b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "plot_electrons(df.query(\"ele_isEB\"), \"ele_pfChargedHadIso\", np.linspace(0.001,20,200), ax=axes[0], title=\"Barrel\")\n",
    "plot_electrons(df.query(\"ele_isEE\"), \"ele_pfChargedHadIso\", np.linspace(0.001,20,200), ax=axes[1], title=\"Endcap\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9518a3f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "plot_electrons(df.query(\"ele_isEB\"), \"ele_hadronicOverEm\", np.linspace(0.001,0.15,100), ax=axes[0], title=\"Barrel\")\n",
    "plot_electrons(df.query(\"ele_isEE\"), \"ele_hadronicOverEm\", np.linspace(0.001,0.15,100), ax=axes[1], title=\"Endcap\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e360fa97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "plot_electrons(df.query(\"ele_isEB\"), \"ele_deltaetaseed\", np.linspace(0,0.02,100), ax=axes[0], title=\"Barrel\")\n",
    "plot_electrons(df.query(\"ele_isEE\"), \"ele_deltaetaseed\", np.linspace(0,0.02,100), ax=axes[1], title=\"Endcap\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a25dbf8e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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