studio_processo_uscite.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.6.1 |Anaconda 4.4.0 (64-bit)| (default, May 11 2017, 13:25:24) [MSC v.1900 64 bit (AMD64)]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(context=\"paper\", font=\"monospace\")\n",
    "\n",
    "import sys\n",
    "print(sys.version)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Dialogo direttamente con il databaas\n",
    "import MySQLdb\n",
    "db = MySQLdb.connect(host=\"localhost\",    # your host, usually localhost\n",
    "                     user=\"root\",         # your username\n",
    "                     passwd=\"\",  # your password\n",
    "                     db=\"densita_popolazione\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_sql('SELECT * FROM sniff', con=db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>id</th>\n",
       "      <th>mac</th>\n",
       "      <th>mac_num</th>\n",
       "      <th>locally</th>\n",
       "      <th>pow</th>\n",
       "      <th>data</th>\n",
       "      <th>chan</th>\n",
       "      <th>seq</th>\n",
       "      <th>ht_cap</th>\n",
       "      <th>ht_ampdu</th>\n",
       "      <th>vht_cap</th>\n",
       "      <th>sup_rates</th>\n",
       "      <th>ext_sup_rates</th>\n",
       "      <th>mcs_1_7</th>\n",
       "      <th>mcs_8_15</th>\n",
       "      <th>mcs_16_23</th>\n",
       "      <th>mcs_24_31</th>\n",
       "      <th>mcs_32</th>\n",
       "      <th>mcs_33_38</th>\n",
       "      <th>mcs_39_52</th>\n",
       "      <th>mcs_53_76</th>\n",
       "      <th>ssid</th>\n",
       "      <th>uuid_e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>78:25:ad:11:67:af</td>\n",
       "      <td>None</td>\n",
       "      <td>8</td>\n",
       "      <td>-86</td>\n",
       "      <td>2017-04-27 19:45:30</td>\n",
       "      <td>10</td>\n",
       "      <td>321</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>130,132,139,150,140,146,152,164</td>\n",
       "      <td>176,72,96,108</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>SAMSUNGSMART</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>78:25:ad:11:67:af</td>\n",
       "      <td>None</td>\n",
       "      <td>8</td>\n",
       "      <td>-86</td>\n",
       "      <td>2017-04-27 19:45:31</td>\n",
       "      <td>10</td>\n",
       "      <td>347</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>130,132,139,150,140,146,152,164</td>\n",
       "      <td>176,72,96,108</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>SAMSUNGSMART</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>18:4f:32:f2:e7:81</td>\n",
       "      <td>None</td>\n",
       "      <td>8</td>\n",
       "      <td>-77</td>\n",
       "      <td>2017-04-27 19:45:31</td>\n",
       "      <td>11</td>\n",
       "      <td>1187</td>\n",
       "      <td>0x0000182d</td>\n",
       "      <td>0x00000017</td>\n",
       "      <td></td>\n",
       "      <td>2,4,11,22</td>\n",
       "      <td>12,18,24,36,48,72,96,108</td>\n",
       "      <td>0x000000ff,0x000000f</td>\n",
       "      <td>0x000000ff,0x000000f</td>\n",
       "      <td>0x00000000,0x0000000</td>\n",
       "      <td>0x00000000,0x0000000</td>\n",
       "      <td>0x00000000,0x0000000</td>\n",
       "      <td>0x00000000,0x0000000</td>\n",
       "      <td>0x00000000,0x0000000</td>\n",
       "      <td>0x00000000,0x0000000</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>18:4f:32:f2:e7:81</td>\n",
       "      <td>None</td>\n",
       "      <td>8</td>\n",
       "      <td>-82</td>\n",
       "      <td>2017-04-27 19:45:31</td>\n",
       "      <td>11</td>\n",
       "      <td>1188</td>\n",
       "      <td>0x0000182d</td>\n",
       "      <td>0x00000017</td>\n",
       "      <td></td>\n",
       "      <td>2,4,11,22</td>\n",
       "      <td>12,18,24,36,48,72,96,108</td>\n",
       "      <td>0x000000ff,0x000000f</td>\n",
       "      <td>0x000000ff,0x000000f</td>\n",
       "      <td>0x00000000,0x0000000</td>\n",
       "      <td>0x00000000,0x0000000</td>\n",
       "      <td>0x00000000,0x0000000</td>\n",
       "      <td>0x00000000,0x0000000</td>\n",
       "      <td>0x00000000,0x0000000</td>\n",
       "      <td>0x00000000,0x0000000</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>78:25:ad:11:67:af</td>\n",
       "      <td>None</td>\n",
       "      <td>8</td>\n",
       "      <td>-88</td>\n",
       "      <td>2017-04-27 19:45:32</td>\n",
       "      <td>11</td>\n",
       "      <td>361</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>130,132,139,150,140,146,152,164</td>\n",
       "      <td>176,72,96,108</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>SAMSUNGSMART</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                mac mac_num  locally  pow                data  chan  \\\n",
       "0   2  78:25:ad:11:67:af    None        8  -86 2017-04-27 19:45:30    10   \n",
       "1   3  78:25:ad:11:67:af    None        8  -86 2017-04-27 19:45:31    10   \n",
       "2   4  18:4f:32:f2:e7:81    None        8  -77 2017-04-27 19:45:31    11   \n",
       "3   5  18:4f:32:f2:e7:81    None        8  -82 2017-04-27 19:45:31    11   \n",
       "4   6  78:25:ad:11:67:af    None        8  -88 2017-04-27 19:45:32    11   \n",
       "\n",
       "    seq      ht_cap    ht_ampdu vht_cap                        sup_rates  \\\n",
       "0   321                                  130,132,139,150,140,146,152,164   \n",
       "1   347                                  130,132,139,150,140,146,152,164   \n",
       "2  1187  0x0000182d  0x00000017                                2,4,11,22   \n",
       "3  1188  0x0000182d  0x00000017                                2,4,11,22   \n",
       "4   361                                  130,132,139,150,140,146,152,164   \n",
       "\n",
       "              ext_sup_rates               mcs_1_7              mcs_8_15  \\\n",
       "0             176,72,96,108                                               \n",
       "1             176,72,96,108                                               \n",
       "2  12,18,24,36,48,72,96,108  0x000000ff,0x000000f  0x000000ff,0x000000f   \n",
       "3  12,18,24,36,48,72,96,108  0x000000ff,0x000000f  0x000000ff,0x000000f   \n",
       "4             176,72,96,108                                               \n",
       "\n",
       "              mcs_16_23             mcs_24_31                mcs_32  \\\n",
       "0                                                                     \n",
       "1                                                                     \n",
       "2  0x00000000,0x0000000  0x00000000,0x0000000  0x00000000,0x0000000   \n",
       "3  0x00000000,0x0000000  0x00000000,0x0000000  0x00000000,0x0000000   \n",
       "4                                                                     \n",
       "\n",
       "              mcs_33_38             mcs_39_52             mcs_53_76  \\\n",
       "0                                                                     \n",
       "1                                                                     \n",
       "2  0x00000000,0x0000000  0x00000000,0x0000000  0x00000000,0x0000000   \n",
       "3  0x00000000,0x0000000  0x00000000,0x0000000  0x00000000,0x0000000   \n",
       "4                                                                     \n",
       "\n",
       "           ssid uuid_e  \n",
       "0  SAMSUNGSMART         \n",
       "1  SAMSUNGSMART         \n",
       "2                       \n",
       "3                       \n",
       "4  SAMSUNGSMART         "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_sort = df.sort_values(['mac','data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "time_between_probes = []\n",
    "for i in range(0, df_sort.shape[0]-1):\n",
    "    if(df_sort['mac'][i] == df_sort['mac'][i+1]):\n",
    "        time = df_sort['data'][i+1] -  df_sort['data'][i]\n",
    "        time_between_probes.append(time.total_seconds())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Q5KlVtTuz25hJsrmqzkmyKw+F4+aqOj/Jx6fzjmrnzm3ZunXLYn6adWhlZftajzCXLVs2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x287de590780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "plt.hist(time_between_probes)\n",
    "plt.title(\"Time between probes Histogram\")\n",
    "plt.xlabel(\"Value\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "fig = plt.gcf()\n",
    "# Dal grafico possiamo notare come sia davvero difficile che due probe siamo inviati a distanza maggiore di 8 secondi\n",
    "# Supponendo un intervallo di 10 secondi: \"Se non mi arrivano più probe entro 10 secondi allora l'utente se ne è andato\"\n",
    "# otterremmo una previsione dell'uscita della persona aggiornata nel giro di 10 secondi dalla sua effettiva uscita\n",
    "\n",
    "# /!\\ Bisogna anche considerare la probabilità di assegnare una probe request in un determinato momento\n",
    "# ad un cluster piuttosto che un altro, ciò significherebbe rimuovere l'utente che è stato assegnato male\n",
    "# per maggiore stabilità sarebbe probabilmente meglio utilizzare tempi maggiori (es 8*3 = 24 sec -> devo assegnare in maniera \n",
    "# sbagliata 3 pacchetti prima di far sparire l'utente)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-3.4562777039298771, 5.4562777039298771)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NOTA: Non ha senso calcolare l'intervallo di confidenza di tale valore, per ottenere un valore massimo corretto in quanto\n",
    "# le misurazioni che abbiamo non sono misurazioni di un singolo valore ma sono misure su più telefoni, ciascuno con un\n",
    "# proprio valore diverso\n",
    "# CREDO (o forse comunque si perde un po' il significato di intervallo di confidenza, non essendoci un solo valore \"puro\")\n",
    "\n",
    "# qui di sotto il calcolo è riportato comunque\n",
    "from scipy.stats import t\n",
    "t.interval(0.95, 10, loc=1, scale=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}