Exercise1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing a file & plotting a histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's practice a little the use of Jupyter Notebook. <br>\n",
    "For this you only need the packages pandas and matplotlib.pyplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The idea is the plot a histogram based on the data in the file *Zmumu_Run2011A_massoilla.csv* which is located in the folder _data_. Save the data into variable _dataset_ and check the content of the first 5 rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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>...</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>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165617</td>\n",
       "      <td>74969122</td>\n",
       "      <td>G</td>\n",
       "      <td>59.9226</td>\n",
       "      <td>-46.15160</td>\n",
       "      <td>29.4115</td>\n",
       "      <td>-24.4070</td>\n",
       "      <td>54.7055</td>\n",
       "      <td>-0.432396</td>\n",
       "      <td>2.574210</td>\n",
       "      <td>...</td>\n",
       "      <td>G</td>\n",
       "      <td>52.4465</td>\n",
       "      <td>30.10970</td>\n",
       "      <td>-16.39890</td>\n",
       "      <td>-39.68760</td>\n",
       "      <td>34.2464</td>\n",
       "      <td>-0.988480</td>\n",
       "      <td>-0.498704</td>\n",
       "      <td>-1</td>\n",
       "      <td>89.9557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>165617</td>\n",
       "      <td>75138253</td>\n",
       "      <td>G</td>\n",
       "      <td>97.1011</td>\n",
       "      <td>-23.61440</td>\n",
       "      <td>6.6660</td>\n",
       "      <td>-93.9497</td>\n",
       "      <td>24.5872</td>\n",
       "      <td>-2.052200</td>\n",
       "      <td>2.866570</td>\n",
       "      <td>...</td>\n",
       "      <td>G</td>\n",
       "      <td>30.5992</td>\n",
       "      <td>-11.61340</td>\n",
       "      <td>-25.98480</td>\n",
       "      <td>11.23470</td>\n",
       "      <td>28.5389</td>\n",
       "      <td>0.385163</td>\n",
       "      <td>-1.991170</td>\n",
       "      <td>1</td>\n",
       "      <td>88.6081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>165617</td>\n",
       "      <td>75887636</td>\n",
       "      <td>G</td>\n",
       "      <td>152.9720</td>\n",
       "      <td>7.46565</td>\n",
       "      <td>-30.7098</td>\n",
       "      <td>-149.6710</td>\n",
       "      <td>31.7386</td>\n",
       "      <td>-2.259450</td>\n",
       "      <td>-1.332290</td>\n",
       "      <td>...</td>\n",
       "      <td>G</td>\n",
       "      <td>33.5835</td>\n",
       "      <td>-9.28778</td>\n",
       "      <td>28.74570</td>\n",
       "      <td>-14.67190</td>\n",
       "      <td>30.2344</td>\n",
       "      <td>-0.468419</td>\n",
       "      <td>1.883310</td>\n",
       "      <td>1</td>\n",
       "      <td>88.2438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>165617</td>\n",
       "      <td>75833588</td>\n",
       "      <td>G</td>\n",
       "      <td>181.8770</td>\n",
       "      <td>44.14270</td>\n",
       "      <td>-14.9498</td>\n",
       "      <td>175.8040</td>\n",
       "      <td>46.7085</td>\n",
       "      <td>2.037940</td>\n",
       "      <td>-0.326565</td>\n",
       "      <td>...</td>\n",
       "      <td>G</td>\n",
       "      <td>170.0210</td>\n",
       "      <td>-34.63010</td>\n",
       "      <td>12.32480</td>\n",
       "      <td>166.00000</td>\n",
       "      <td>36.1024</td>\n",
       "      <td>2.212810</td>\n",
       "      <td>2.799550</td>\n",
       "      <td>-1</td>\n",
       "      <td>83.0943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>165617</td>\n",
       "      <td>75779415</td>\n",
       "      <td>G</td>\n",
       "      <td>50.2440</td>\n",
       "      <td>37.80720</td>\n",
       "      <td>-12.2044</td>\n",
       "      <td>-30.7590</td>\n",
       "      <td>39.7394</td>\n",
       "      <td>-0.712338</td>\n",
       "      <td>-0.312266</td>\n",
       "      <td>...</td>\n",
       "      <td>G</td>\n",
       "      <td>49.2396</td>\n",
       "      <td>-47.60640</td>\n",
       "      <td>8.23376</td>\n",
       "      <td>-9.50613</td>\n",
       "      <td>48.2790</td>\n",
       "      <td>-0.195625</td>\n",
       "      <td>2.970320</td>\n",
       "      <td>-1</td>\n",
       "      <td>90.3544</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Run     Event Type1        E1       px1      py1       pz1      pt1  \\\n",
       "0  165617  74969122     G   59.9226 -46.15160  29.4115  -24.4070  54.7055   \n",
       "1  165617  75138253     G   97.1011 -23.61440   6.6660  -93.9497  24.5872   \n",
       "2  165617  75887636     G  152.9720   7.46565 -30.7098 -149.6710  31.7386   \n",
       "3  165617  75833588     G  181.8770  44.14270 -14.9498  175.8040  46.7085   \n",
       "4  165617  75779415     G   50.2440  37.80720 -12.2044  -30.7590  39.7394   \n",
       "\n",
       "       eta1      phi1   ...     Type2        E2       px2       py2  \\\n",
       "0 -0.432396  2.574210   ...         G   52.4465  30.10970 -16.39890   \n",
       "1 -2.052200  2.866570   ...         G   30.5992 -11.61340 -25.98480   \n",
       "2 -2.259450 -1.332290   ...         G   33.5835  -9.28778  28.74570   \n",
       "3  2.037940 -0.326565   ...         G  170.0210 -34.63010  12.32480   \n",
       "4 -0.712338 -0.312266   ...         G   49.2396 -47.60640   8.23376   \n",
       "\n",
       "         pz2      pt2      eta2      phi2  Q2        M  \n",
       "0  -39.68760  34.2464 -0.988480 -0.498704  -1  89.9557  \n",
       "1   11.23470  28.5389  0.385163 -1.991170   1  88.6081  \n",
       "2  -14.67190  30.2344 -0.468419  1.883310   1  88.2438  \n",
       "3  166.00000  36.1024  2.212810  2.799550  -1  83.0943  \n",
       "4   -9.50613  48.2790 -0.195625  2.970320  -1  90.3544  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pandas.read_csv('data/Zmumu_Run2011A_massoilla.csv')\n",
    "\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the column containing invariant masses (M) into variable *invariant_mass* and plot the histogram. <br>\n",
    "What happens if you change the number of bins? <br>\n",
    "What if you change the range? Or take it away totally?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f70cf2887d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "invariant_mass = dataset['M']\n",
    "\n",
    "plt.hist(invariant_mass, bins=500, range=(60,120))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make the histogram a bit prettier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEoCAYAAABLtMayAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGGZmVooDhpmZlfL/AS5PO941z7O5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f70cea6c510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('Invariant mass [GeV]')\n",
    "plt.ylabel('Number of events')\n",
    "plt.title('The histogram of the invariant masses of two muons \\n')\n",
    "\n",
    "plt.hist(invariant_mass, bins=500, range=(60,120))\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}