W-boson sum-up.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"http://kati.web.cern.ch/kati/SingleMuonMet.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Run</th>\n",
       "      <th>Event</th>\n",
       "      <th>type1</th>\n",
       "      <th>E1</th>\n",
       "      <th>px1</th>\n",
       "      <th>py1</th>\n",
       "      <th>pz1</th>\n",
       "      <th>pt1</th>\n",
       "      <th>eta1</th>\n",
       "      <th>phi1</th>\n",
       "      <th>Q1</th>\n",
       "      <th>iso1</th>\n",
       "      <th>chisq1</th>\n",
       "      <th>dxy1</th>\n",
       "      <th>type2</th>\n",
       "      <th>E2</th>\n",
       "      <th>px2</th>\n",
       "      <th>py2</th>\n",
       "      <th>pz2</th>\n",
       "      <th>pt2</th>\n",
       "      <th>eta2</th>\n",
       "      <th>phi2</th>\n",
       "      <th>Q2</th>\n",
       "      <th>iso2</th>\n",
       "      <th>chisq2</th>\n",
       "      <th>dxy2</th>\n",
       "      <th>met_et</th>\n",
       "      <th>met_phi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>173389</td>\n",
       "      <td>489861810</td>\n",
       "      <td>G</td>\n",
       "      <td>81.20700</td>\n",
       "      <td>-19.73920</td>\n",
       "      <td>-13.390400</td>\n",
       "      <td>77.62490</td>\n",
       "      <td>23.85240</td>\n",
       "      <td>1.895960</td>\n",
       "      <td>-2.545530</td>\n",
       "      <td>1</td>\n",
       "      <td>0.757915</td>\n",
       "      <td>0.859481</td>\n",
       "      <td>0.001574</td>\n",
       "      <td>G</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.87528</td>\n",
       "      <td>-1.477850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>173389</td>\n",
       "      <td>489865843</td>\n",
       "      <td>G</td>\n",
       "      <td>20.89240</td>\n",
       "      <td>-14.55570</td>\n",
       "      <td>-14.942700</td>\n",
       "      <td>1.15372</td>\n",
       "      <td>20.86030</td>\n",
       "      <td>0.055279</td>\n",
       "      <td>-2.343080</td>\n",
       "      <td>1</td>\n",
       "      <td>7.996410</td>\n",
       "      <td>0.861278</td>\n",
       "      <td>0.017863</td>\n",
       "      <td>G</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.22140</td>\n",
       "      <td>-1.851490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>173389</td>\n",
       "      <td>489914455</td>\n",
       "      <td>G</td>\n",
       "      <td>12.95950</td>\n",
       "      <td>3.13972</td>\n",
       "      <td>-3.732570</td>\n",
       "      <td>-12.00610</td>\n",
       "      <td>4.87749</td>\n",
       "      <td>-1.632850</td>\n",
       "      <td>-0.871451</td>\n",
       "      <td>-1</td>\n",
       "      <td>2.153700</td>\n",
       "      <td>0.856005</td>\n",
       "      <td>0.093071</td>\n",
       "      <td>G</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.47978</td>\n",
       "      <td>-1.580460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>173389</td>\n",
       "      <td>489963747</td>\n",
       "      <td>G</td>\n",
       "      <td>29.63810</td>\n",
       "      <td>6.25372</td>\n",
       "      <td>28.679100</td>\n",
       "      <td>-4.09978</td>\n",
       "      <td>29.35300</td>\n",
       "      <td>-0.139221</td>\n",
       "      <td>1.356100</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.845578</td>\n",
       "      <td>-0.060012</td>\n",
       "      <td>G</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.66700</td>\n",
       "      <td>-2.130810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>173389</td>\n",
       "      <td>489925803</td>\n",
       "      <td>G</td>\n",
       "      <td>7.01111</td>\n",
       "      <td>-1.61130</td>\n",
       "      <td>0.006709</td>\n",
       "      <td>-6.82262</td>\n",
       "      <td>1.61131</td>\n",
       "      <td>-2.150000</td>\n",
       "      <td>3.137430</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.643718</td>\n",
       "      <td>2.970230</td>\n",
       "      <td>-0.041494</td>\n",
       "      <td>G</td>\n",
       "      <td>11.2786</td>\n",
       "      <td>1.43852</td>\n",
       "      <td>2.48368</td>\n",
       "      <td>-10.9068</td>\n",
       "      <td>2.87019</td>\n",
       "      <td>-2.04503</td>\n",
       "      <td>1.04582</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.36497</td>\n",
       "      <td>8.03214</td>\n",
       "      <td>-0.045094</td>\n",
       "      <td>13.75300</td>\n",
       "      <td>-0.996907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      Run      Event type1        E1       px1        py1       pz1       pt1  \\\n",
       "0  173389  489861810     G  81.20700 -19.73920 -13.390400  77.62490  23.85240   \n",
       "1  173389  489865843     G  20.89240 -14.55570 -14.942700   1.15372  20.86030   \n",
       "2  173389  489914455     G  12.95950   3.13972  -3.732570 -12.00610   4.87749   \n",
       "3  173389  489963747     G  29.63810   6.25372  28.679100  -4.09978  29.35300   \n",
       "4  173389  489925803     G   7.01111  -1.61130   0.006709  -6.82262   1.61131   \n",
       "\n",
       "       eta1      phi1  Q1      iso1    chisq1      dxy1 type2       E2  \\\n",
       "0  1.895960 -2.545530   1  0.757915  0.859481  0.001574     G   0.0000   \n",
       "1  0.055279 -2.343080   1  7.996410  0.861278  0.017863     G   0.0000   \n",
       "2 -1.632850 -0.871451  -1  2.153700  0.856005  0.093071     G   0.0000   \n",
       "3 -0.139221  1.356100   1  0.000000  0.845578 -0.060012     G   0.0000   \n",
       "4 -2.150000  3.137430  -1  0.643718  2.970230 -0.041494     G  11.2786   \n",
       "\n",
       "       px2      py2      pz2      pt2     eta2     phi2   Q2     iso2  \\\n",
       "0  0.00000  0.00000   0.0000  0.00000  0.00000  0.00000  0.0  0.00000   \n",
       "1  0.00000  0.00000   0.0000  0.00000  0.00000  0.00000  0.0  0.00000   \n",
       "2  0.00000  0.00000   0.0000  0.00000  0.00000  0.00000  0.0  0.00000   \n",
       "3  0.00000  0.00000   0.0000  0.00000  0.00000  0.00000  0.0  0.00000   \n",
       "4  1.43852  2.48368 -10.9068  2.87019 -2.04503  1.04582  1.0  1.36497   \n",
       "\n",
       "    chisq2      dxy2    met_et   met_phi  \n",
       "0  0.00000  0.000000   4.87528 -1.477850  \n",
       "1  0.00000  0.000000  24.22140 -1.851490  \n",
       "2  0.00000  0.000000   7.47978 -1.580460  \n",
       "3  0.00000  0.000000  30.66700 -2.130810  \n",
       "4  8.03214 -0.045094  13.75300 -0.996907  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option(\"display.max_columns\",None)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "newE = data[(data[\"met_et\"]>30) & (data[\"chisq1\"]<1) & (np.abs(data[\"eta1\"])<1) & (data[\"pt1\"]>35)]\n",
    "MTE = np.sqrt(2*newE.pt1*newE.met_et*(1-np.cos(newE.phi1-newE.met_phi)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jenny"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "newJ = data[(data.pt2==0)&(data.pt1>40)&(np.abs(data.eta1)<1)]\n",
    "MTJ = np.sqrt(2*newJ.pt1*newJ.met_et*(1-np.cos(newJ.phi1-newJ.met_phi)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taneli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "newT = data[(data[\"met_et\"]>30) & (data[\"chisq1\"]<5) &(np.abs(data[\"eta1\"])<1) & (data[\"pt1\"]>35)]\n",
    "MTT = np.sqrt(2*newT.pt1*newT.met_et*(1-np.cos(newT.phi1-newT.met_phi)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.hist(MTE, bins=500, range=(20,120), alpha=0.5, label=\"Eero´s cut\")\n",
    "plt.hist(MTJ, bins=500, range=(20,120), alpha=0.5, label=\"Jenny´s cut\")\n",
    "plt.hist(MTT, bins=500, range=(20,120), alpha=0.5, label=\"Taneli´s cut\")\n",
    "\n",
    "plt.title(\"\\n W ->  μv \\n\", fontsize=15)\n",
    "plt.xlabel(\"Transverse mass (GeV)\", fontsize=10)\n",
    "plt.ylabel(\"Number of events\", fontsize=10)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}