spin1Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spin 1 mediator\n",
    "## Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<contextlib.ExitStack at 0x7f91f41486d0>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import awkward as ak\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import vector\n",
    "from coffea.nanoevents import NanoEventsFactory\n",
    "from coffea.nanoevents import NanoAODSchema, DelphesSchema\n",
    "import mplhep as hep\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import DM_HEP_AN as dm\n",
    "from math import pi\n",
    "from funcs import *\n",
    "hep.style.use(\"CMS\")\n",
    "plt.ioff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unify csv\n",
    "csvDir = \"sim_outputs/spin1/csv/\"\n",
    "nFiles = 2\n",
    "dataSignal = []\n",
    "for j in range(3):\n",
    "    csvFiles = [csvDir+f\"DMspin1_gSq_only_{j+1}_{k+1}.csv\" for k in range(nFiles)]\n",
    "    dfs = [pd.read_csv(csvFile) for csvFile in csvFiles]\n",
    "    data = pd.concat(dfs)\n",
    "    data = construct_variables(data)\n",
    "    dataSignal.append(data)\n",
    "    data.to_csv(csvDir+f\"DMspin1_gSq_only_{j+1}.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "csvDir = \"sim_outputs/spin1/csv/\"\n",
    "data = [pd.read_csv(csvDir+f\"DMspin1_gSq_only_{j+1}.csv\") for j in range(3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numbero de Eventos:  [100000, 100000, 100000]\n"
     ]
    }
   ],
   "source": [
    "print(\"Numbero de Eventos: \", [data[i].shape[0] for i in range(3)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Background\n",
    "csvFile = \"./background/ZpJets.csv\"\n",
    "data_ZpJ = pd.read_csv(csvFile)\n",
    "csvFile = \"./background/WpJets.csv\"\n",
    "data_WpJ = pd.read_csv(csvFile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "CSs = [[] for j in range(3)]\n",
    "CSerrors = [[] for j in range(3)]\n",
    "\n",
    "htmlDir = \"sim_outputs/spin1/html/\"\n",
    "nFiles = 2\n",
    "for j in range(3):\n",
    "    htmlFiles = [htmlDir+f\"DMspin1_gSq_only_{j+1}_{k+1}.html\" for k in range(nFiles)]\n",
    "    for k in range(nFiles):\n",
    "        with open(htmlDir+f\"DMspin1_gSq_only_{j+1}_{k+1}.html\", 'r') as htmlFile:\n",
    "            htmlText = htmlFile.read()\n",
    "        indexS = htmlText.find(\"<b>s= \")\n",
    "        indexPM = htmlText.find(\" &#177 \")\n",
    "        indexPB = htmlText.find(\" (pb)\")\n",
    "        CSs[j].append(float(htmlText[indexS+6:indexPM]))\n",
    "        CSerrors[j].append(float(htmlText[indexPM+7:indexPB]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "massPoints = [r\"$m_{Y_{1}} = 100GeV, m_{\\chi} = 10 GeV$\",\n",
    "              r\"$m_{Y_{1}} = 1000GeV, m_{\\chi} = 100 GeV$\",\n",
    "              r\"$m_{Y_{1}} = 5000GeV, m_{\\chi} = 1000 GeV$\"]\n",
    "\n",
    "for i in range(3):\n",
    "    ax.errorbar(x=range(2), y=CSs[i], yerr=CSerrors[i], label=massPoints[i])\n",
    "\n",
    "ax.legend()\n",
    "ax.set_ylabel(r'$\\sigma$ [pb]')\n",
    "ax.set_xlabel('run')\n",
    "ax.grid()\n",
    "ax.set_yscale('log')\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "CS = []\n",
    "for sigma in CSs:\n",
    "    CS.append(np.mean(sigma))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[22834.0, 233.345, 0.0654595]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>jet_pt0</th>\n",
       "      <th>jet_pt1</th>\n",
       "      <th>jet_pt2</th>\n",
       "      <th>jet_pt3</th>\n",
       "      <th>jet_eta0</th>\n",
       "      <th>jet_eta1</th>\n",
       "      <th>jet_eta2</th>\n",
       "      <th>jet_eta3</th>\n",
       "      <th>jet_phi0</th>\n",
       "      <th>...</th>\n",
       "      <th>jet_tautag1</th>\n",
       "      <th>jet_tautag2</th>\n",
       "      <th>jet_tautag3</th>\n",
       "      <th>missinget_met</th>\n",
       "      <th>missinget_phi</th>\n",
       "      <th>Delta_phi</th>\n",
       "      <th>Delta_rapidity</th>\n",
       "      <th>Inv_mass</th>\n",
       "      <th>Rapidity_prod</th>\n",
       "      <th>Hadronic_energy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>80.981860</td>\n",
       "      <td>26.637465</td>\n",
       "      <td>21.995190</td>\n",
       "      <td>10.768453</td>\n",
       "      <td>0.148990</td>\n",
       "      <td>2.638907</td>\n",
       "      <td>3.103195</td>\n",
       "      <td>3.662650</td>\n",
       "      <td>-0.065423</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.625180</td>\n",
       "      <td>2.954881</td>\n",
       "      <td>1.146353</td>\n",
       "      <td>2.489917</td>\n",
       "      <td>136.825744</td>\n",
       "      <td>0.393171</td>\n",
       "      <td>140.382968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>45.562565</td>\n",
       "      <td>44.971930</td>\n",
       "      <td>16.141575</td>\n",
       "      <td>9.697227</td>\n",
       "      <td>-1.789925</td>\n",
       "      <td>-2.439331</td>\n",
       "      <td>1.558544</td>\n",
       "      <td>0.545162</td>\n",
       "      <td>2.536021</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.706993</td>\n",
       "      <td>-2.525519</td>\n",
       "      <td>2.316530</td>\n",
       "      <td>0.649406</td>\n",
       "      <td>126.440357</td>\n",
       "      <td>4.366220</td>\n",
       "      <td>116.373297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>62.351960</td>\n",
       "      <td>17.451275</td>\n",
       "      <td>6.007831</td>\n",
       "      <td>4.015706</td>\n",
       "      <td>0.934584</td>\n",
       "      <td>2.110173</td>\n",
       "      <td>1.365179</td>\n",
       "      <td>4.491027</td>\n",
       "      <td>1.732550</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.103287</td>\n",
       "      <td>-1.201248</td>\n",
       "      <td>2.896751</td>\n",
       "      <td>1.175588</td>\n",
       "      <td>126.135588</td>\n",
       "      <td>1.972134</td>\n",
       "      <td>89.826771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>35.790657</td>\n",
       "      <td>27.536840</td>\n",
       "      <td>17.099865</td>\n",
       "      <td>14.294047</td>\n",
       "      <td>2.781854</td>\n",
       "      <td>1.682870</td>\n",
       "      <td>-0.550996</td>\n",
       "      <td>-0.774408</td>\n",
       "      <td>-1.681304</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.329530</td>\n",
       "      <td>2.774610</td>\n",
       "      <td>2.406023</td>\n",
       "      <td>1.098984</td>\n",
       "      <td>87.933722</td>\n",
       "      <td>4.681498</td>\n",
       "      <td>94.721409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>21.460012</td>\n",
       "      <td>14.271392</td>\n",
       "      <td>11.405839</td>\n",
       "      <td>9.244654</td>\n",
       "      <td>-0.661400</td>\n",
       "      <td>2.245356</td>\n",
       "      <td>2.107273</td>\n",
       "      <td>4.037921</td>\n",
       "      <td>-0.731168</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.447433</td>\n",
       "      <td>1.569699</td>\n",
       "      <td>1.109161</td>\n",
       "      <td>2.906755</td>\n",
       "      <td>67.756946</td>\n",
       "      <td>-1.485077</td>\n",
       "      <td>56.381897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>49995</td>\n",
       "      <td>111.564800</td>\n",
       "      <td>87.844290</td>\n",
       "      <td>16.626852</td>\n",
       "      <td>7.307886</td>\n",
       "      <td>1.575887</td>\n",
       "      <td>2.584260</td>\n",
       "      <td>1.536552</td>\n",
       "      <td>1.025132</td>\n",
       "      <td>0.260976</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.890425</td>\n",
       "      <td>-2.583841</td>\n",
       "      <td>3.068220</td>\n",
       "      <td>1.008373</td>\n",
       "      <td>425.157282</td>\n",
       "      <td>4.072502</td>\n",
       "      <td>223.343828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>49996</td>\n",
       "      <td>39.768066</td>\n",
       "      <td>28.867702</td>\n",
       "      <td>11.178156</td>\n",
       "      <td>7.348786</td>\n",
       "      <td>2.749969</td>\n",
       "      <td>0.604587</td>\n",
       "      <td>0.388633</td>\n",
       "      <td>-2.224380</td>\n",
       "      <td>2.214520</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.914394</td>\n",
       "      <td>-1.997417</td>\n",
       "      <td>2.325148</td>\n",
       "      <td>2.145382</td>\n",
       "      <td>43.702056</td>\n",
       "      <td>1.662595</td>\n",
       "      <td>87.162710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>49997</td>\n",
       "      <td>98.863810</td>\n",
       "      <td>54.996160</td>\n",
       "      <td>29.577047</td>\n",
       "      <td>25.651470</td>\n",
       "      <td>2.321034</td>\n",
       "      <td>0.980548</td>\n",
       "      <td>-0.293191</td>\n",
       "      <td>-1.694065</td>\n",
       "      <td>2.689833</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.642700</td>\n",
       "      <td>0.016057</td>\n",
       "      <td>0.109578</td>\n",
       "      <td>1.340486</td>\n",
       "      <td>106.104247</td>\n",
       "      <td>2.275886</td>\n",
       "      <td>209.088487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>49998</td>\n",
       "      <td>91.328830</td>\n",
       "      <td>56.056103</td>\n",
       "      <td>30.979584</td>\n",
       "      <td>25.716864</td>\n",
       "      <td>2.064066</td>\n",
       "      <td>0.681887</td>\n",
       "      <td>0.145015</td>\n",
       "      <td>1.773796</td>\n",
       "      <td>1.796386</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.369680</td>\n",
       "      <td>-2.826333</td>\n",
       "      <td>2.350363</td>\n",
       "      <td>1.382179</td>\n",
       "      <td>180.304222</td>\n",
       "      <td>1.407460</td>\n",
       "      <td>204.081381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>49999</td>\n",
       "      <td>58.527756</td>\n",
       "      <td>29.290298</td>\n",
       "      <td>27.707325</td>\n",
       "      <td>18.123049</td>\n",
       "      <td>-1.523305</td>\n",
       "      <td>-3.147759</td>\n",
       "      <td>-1.803583</td>\n",
       "      <td>-2.879926</td>\n",
       "      <td>-2.198417</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>142.797530</td>\n",
       "      <td>1.428489</td>\n",
       "      <td>1.248361</td>\n",
       "      <td>1.624454</td>\n",
       "      <td>50.727347</td>\n",
       "      <td>4.794998</td>\n",
       "      <td>133.648428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0     jet_pt0    jet_pt1    jet_pt2    jet_pt3  jet_eta0  \\\n",
       "0               0   80.981860  26.637465  21.995190  10.768453  0.148990   \n",
       "1               1   45.562565  44.971930  16.141575   9.697227 -1.789925   \n",
       "2               2   62.351960  17.451275   6.007831   4.015706  0.934584   \n",
       "3               3   35.790657  27.536840  17.099865  14.294047  2.781854   \n",
       "4               4   21.460012  14.271392  11.405839   9.244654 -0.661400   \n",
       "...           ...         ...        ...        ...        ...       ...   \n",
       "99995       49995  111.564800  87.844290  16.626852   7.307886  1.575887   \n",
       "99996       49996   39.768066  28.867702  11.178156   7.348786  2.749969   \n",
       "99997       49997   98.863810  54.996160  29.577047  25.651470  2.321034   \n",
       "99998       49998   91.328830  56.056103  30.979584  25.716864  2.064066   \n",
       "99999       49999   58.527756  29.290298  27.707325  18.123049 -1.523305   \n",
       "\n",
       "       jet_eta1  jet_eta2  jet_eta3  jet_phi0  ...  jet_tautag1  jet_tautag2  \\\n",
       "0      2.638907  3.103195  3.662650 -0.065423  ...          0.0          0.0   \n",
       "1     -2.439331  1.558544  0.545162  2.536021  ...          0.0          0.0   \n",
       "2      2.110173  1.365179  4.491027  1.732550  ...          0.0          0.0   \n",
       "3      1.682870 -0.550996 -0.774408 -1.681304  ...          0.0          0.0   \n",
       "4      2.245356  2.107273  4.037921 -0.731168  ...          0.0          0.0   \n",
       "...         ...       ...       ...       ...  ...          ...          ...   \n",
       "99995  2.584260  1.536552  1.025132  0.260976  ...          0.0          0.0   \n",
       "99996  0.604587  0.388633 -2.224380  2.214520  ...          0.0          0.0   \n",
       "99997  0.980548 -0.293191 -1.694065  2.689833  ...          0.0          0.0   \n",
       "99998  0.681887  0.145015  1.773796  1.796386  ...          0.0          0.0   \n",
       "99999 -3.147759 -1.803583 -2.879926 -2.198417  ...          0.0          0.0   \n",
       "\n",
       "       jet_tautag3  missinget_met  missinget_phi  Delta_phi  Delta_rapidity  \\\n",
       "0              0.0      98.625180       2.954881   1.146353        2.489917   \n",
       "1              0.0      48.706993      -2.525519   2.316530        0.649406   \n",
       "2              0.0      44.103287      -1.201248   2.896751        1.175588   \n",
       "3              0.0      13.329530       2.774610   2.406023        1.098984   \n",
       "4              0.0      47.447433       1.569699   1.109161        2.906755   \n",
       "...            ...            ...            ...        ...             ...   \n",
       "99995          0.0      27.890425      -2.583841   3.068220        1.008373   \n",
       "99996          0.0      46.914394      -1.997417   2.325148        2.145382   \n",
       "99997          0.0     180.642700       0.016057   0.109578        1.340486   \n",
       "99998          0.0      87.369680      -2.826333   2.350363        1.382179   \n",
       "99999          0.0     142.797530       1.428489   1.248361        1.624454   \n",
       "\n",
       "         Inv_mass  Rapidity_prod  Hadronic_energy  \n",
       "0      136.825744       0.393171       140.382968  \n",
       "1      126.440357       4.366220       116.373297  \n",
       "2      126.135588       1.972134        89.826771  \n",
       "3       87.933722       4.681498        94.721409  \n",
       "4       67.756946      -1.485077        56.381897  \n",
       "...           ...            ...              ...  \n",
       "99995  425.157282       4.072502       223.343828  \n",
       "99996   43.702056       1.662595        87.162710  \n",
       "99997  106.104247       2.275886       209.088487  \n",
       "99998  180.304222       1.407460       204.081381  \n",
       "99999   50.727347       4.794998       133.648428  \n",
       "\n",
       "[100000 rows x 32 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Baseline selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Number of jets > 1\n",
    "- $\\eta(j_{1})\\cdot\\eta(j_{2})$ < 0\n",
    "- Leading jets $p_{T}$ > 30GeV\n",
    "- Leading jets $|\\eta|$ < 5\n",
    "- $H_{T}$ > 200GeV\n",
    "- $p_{T}^{miss}$ > 50GeV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,12))\n",
    "\n",
    "variable = 'Inv_mass'\n",
    "nBins = 40\n",
    "rango = np.linspace(min(data[0][variable]), max(data[0][variable]), nBins)\n",
    "prop_cycle = plt.rcParams['axes.prop_cycle']\n",
    "colors = prop_cycle.by_key()['color']\n",
    "colorsIter = iter(colors)\n",
    "\n",
    "ax.hist(data_WpJ[variable], bins=rango, density=True, color=next(colorsIter), label=r'$W+Jets$')\n",
    "ax.hist(data_ZpJ[variable], bins=rango, density=True, color=next(colorsIter), label=r'$Z+Jets$')\n",
    "\n",
    "for j in range(3):\n",
    "    color = next(colorsIter)\n",
    "    ax.hist(data[j][variable], bins=rango, density=True, color=color, edgecolor=color, fc='None', lw=2, label=massPoints[j])\n",
    "\n",
    "ax.legend(fontsize=15)\n",
    "ax.set_xlabel(r\"$m_{jj}$ [GeV]\", fontsize=15)\n",
    "ax.set_xlim(0,3000)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = 'jet_pt1.notna() &\\\n",
    "         Rapidity_prod < 0 &\\\n",
    "         jet_pt0 > 30 &\\\n",
    "         jet_pt1 > 30 &\\\n",
    "         abs(jet_eta0) < 5 &\\\n",
    "         abs(jet_eta1) < 5 &\\\n",
    "         Hadronic_energy > 200 &\\\n",
    "         missinget_met > 50'\n",
    "\n",
    "for j in range(3):\n",
    "    data[j] = data[j].query(query)\n",
    "\n",
    "data_WpJ = data_WpJ.query(query)\n",
    "data_ZpJ = data_ZpJ.query(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Númbero de Eventos\n",
      "Modelos:  [6678, 22305]\n",
      "BG(W+j,Z+j): [5763, 9467]\n"
     ]
    }
   ],
   "source": [
    "print(\"Númbero de Eventos\")\n",
    "print(\"Modelos: \", [data[j].shape[0] for j in range(2)])\n",
    "print(\"BG(W+j,Z+j):\", [data_WpJ.shape[0], data_ZpJ.shape[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_hist(yvalues, hist):\n",
    "    for item in hist:\n",
    "        item.set_height(item.get_height() / sum(yvalues))\n",
    "    return yvalues.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,12))\n",
    "\n",
    "variable = 'Inv_mass'\n",
    "nBins = 40\n",
    "rango = np.linspace(min(data[0][variable]), max(data[0][variable]), nBins)\n",
    "prop_cycle = plt.rcParams['axes.prop_cycle']\n",
    "colors = prop_cycle.by_key()['color']\n",
    "colorsIter = iter(colors)\n",
    "\n",
    "yvalues, bins, hist = ax.hist(data_WpJ[variable], bins=rango, density=True, color=next(colorsIter), label=r'$W+Jets$')\n",
    "maxValue = normalize_hist(yvalues, hist)\n",
    "yvalues, bins, hist = ax.hist(data_ZpJ[variable], bins=rango, density=True, color=next(colorsIter), label=r'$Z+Jets$')\n",
    "maxValue = normalize_hist(yvalues, hist)\n",
    "\n",
    "for j in range(3):\n",
    "    color = next(colorsIter)\n",
    "    yvalues, bins, hist = ax.hist(data[j][variable], bins=rango, density=True, color=color, edgecolor=color, fc='None', lw=2, label=massPoints[j])\n",
    "    maxValue = normalize_hist(yvalues, hist)\n",
    "\n",
    "ax.legend(fontsize=15)\n",
    "ax.set_xlabel(r\"$m_{jj}$ [GeV]\", fontsize=15)\n",
    "ax.set_xlim(0,3000)\n",
    "ax.set_ylim(0,0.25)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}