05_Etude_donnee_reel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Séance 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import gmaps\n",
    "import gmaps.datasets\n",
    "from datetime import datetime\n",
    "from datetime import timedelta\n",
    "import matplotlib.pyplot as plt\n",
    "import statistics\n",
    "import glob\n",
    "\n",
    "from ipywidgets import interactive\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.image as image\n",
    "import matplotlib.pyplot as plt\n",
    "gmaps.configure(api_key=\"AIzaSyDm-b-Wg34YmbNWba0I7WJqE9fOhdQ7VZk\") # Your Google API key\n",
    "from geopy.distance import vincenty\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utilisation d'une base de donnée\n",
    "\n",
    "Étant donné le grand nombre de lignes du fichier de données, nous ne pouvons pas tout mettre dans une datafrme directement.\n",
    "\n",
    "Nous avons choisi de mettre le fichier dans une base de donnée, et ainsi juste charger un sous-ensemble de la base à étudier à chaque fois dans une dataframe.\n",
    "\n",
    "Auparavant, nous avons retiré les lignes qui peuvent être en doublons du fichier avec la commande shell \n",
    "\" sort | uniq -u \"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Création de la base de donnée "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "connex = sqlite3.connect(\"./Data/2e_partie/gps/bdd/database.db\")\n",
    "cur = connex.cursor()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importation du fichier texte dans la base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "for chunk in pd.read_csv(\"./Data/2e_partie/gps/privamov-gps-sorted\", chunksize = 100000, sep=\"\\t\", header=None,  names = [\"Id\", \"DateTime\", \"Longitude\", \"Latitude\"]):\n",
    "    chunk.to_sql(name=\"gpsData\", con=connex, if_exists=\"append\", index = False)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
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      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql = \"SELECT DISTINCT Id FROM gpsData\" \n",
    "cur.execute(sql)\n",
    "cur.fetchall()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Première études\n",
    "\n",
    "Nous allons tout d'abord voir le nombre d'identifiants des personnes ayant participé à l'étude, et le nombre de données que ces personnes ont envoyé"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SELECT Id, count(*) FROM gpsData GROUP BY Id\n"
     ]
    }
   ],
   "source": [
    "# COUNT\n",
    "sql = \"SELECT Id, count(*) FROM gpsData GROUP BY Id\"\n",
    "print(sql)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Affichage de l'identifiant des personnes et du nombre de lignes correspondant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.execute(sql)\n",
    "cur.fetchall()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Intéressons nous d'abord à quelques personnes tirées aléatoirement, afin de voir si nous pouvons sortir quelques observations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ID 21"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mettons les données de l'ID 21 dans une dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SELECT DISTINCT * FROM gpsData WHERE Id ='21'\n"
     ]
    }
   ],
   "source": [
    "sql = \"SELECT DISTINCT * FROM gpsData WHERE Id ='21'\"\n",
    "print(sql)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_sql_query(sql, connex)\n",
    "df = df.sort_values(by=\"DateTime\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Id                DateTime  Longitude   Latitude\n",
      "0  21 2014-11-18 16:24:51.631   4.879682  45.786253\n",
      "1  21 2014-11-18 16:24:51.645   4.879682  45.786253\n",
      "2  21 2014-11-18 16:24:52.996   4.879728  45.786215\n",
      "3  21 2014-11-18 16:24:53.006   4.879728  45.786215\n",
      "4  21 2014-11-18 16:24:53.997   4.879737  45.786203\n",
      "        Id                DateTime  Longitude   Latitude\n",
      "183412  21 2015-01-06 14:31:02.038   4.879832  45.785982\n",
      "183413  21 2015-01-06 14:31:03.032   4.879843  45.786023\n",
      "183414  21 2015-01-06 14:31:03.039   4.879843  45.786023\n",
      "183415  21 2015-01-06 14:31:04.032   4.879850  45.786048\n",
      "183416  21 2015-01-06 14:31:04.036   4.879850  45.786048\n",
      "183417\n"
     ]
    }
   ],
   "source": [
    "print(df.head())\n",
    "print(df.tail())\n",
    "print(len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regardons les date du début et de fin de l'étude de cette personne."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durée totale de l'ID 21 : 48 days 22:06:12.405000\n",
      "Dernière date de l'ID 21 : 2015-01-06 14:31:04.036000, première date de l'ID 1: 2014-11-18 16:24:51.631000\n",
      "Longueur totale de l'ID 21 : 183417\n"
     ]
    }
   ],
   "source": [
    "df21 = df\n",
    "df21['DateTime'] = pd.to_datetime(df21['DateTime'])\n",
    "df21.DateTime.min()\n",
    "maxi = df21.DateTime.max()\n",
    "mini = df21.DateTime.min()\n",
    "deltaT = maxi - mini\n",
    "print(\"Durée totale de l'ID 21 : \" + str(deltaT))\n",
    "print(\"Dernière date de l'ID 21 : \" + str(maxi) + \", première date de l'ID 1: \" + str(mini))\n",
    "print(\"Longueur totale de l'ID 21 : \" + str(len(df21)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L'étude se déroule donc sur 48 jours, de novembre 2014 à janvier 2015\n",
    "\n",
    "Nous allons nous intéresser au mois de décembre, qu'il parait intéressant d'étudier vu le nombre de données:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "181783\n",
      "430\n",
      "1204\n"
     ]
    }
   ],
   "source": [
    "print(len(df21.loc[(df['DateTime'].dt.month == 12)]))\n",
    "print(len(df21.loc[(df['DateTime'].dt.month == 11)]))\n",
    "print(len(df21.loc[(df['DateTime'].dt.month == 1)]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "df21January = df21.loc[(df['DateTime'].dt.month==12)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014-12-08 16:54:37.191000\n",
      "2014-12-12 07:49:42.096000\n"
     ]
    }
   ],
   "source": [
    "print(df21January.DateTime.min())\n",
    "print(df21January.DateTime.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Malheureusement, nous n'avons que 5 jours de données en décembre."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>DateTime</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>21</td>\n",
       "      <td>2014-12-08 16:54:37.191</td>\n",
       "      <td>-0.571103</td>\n",
       "      <td>44.849857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>21</td>\n",
       "      <td>2014-12-08 16:54:37.207</td>\n",
       "      <td>-0.571103</td>\n",
       "      <td>44.849857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>21</td>\n",
       "      <td>2014-12-08 16:54:38.180</td>\n",
       "      <td>-0.571103</td>\n",
       "      <td>44.849857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>21</td>\n",
       "      <td>2014-12-08 16:54:38.193</td>\n",
       "      <td>-0.571103</td>\n",
       "      <td>44.849857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>21</td>\n",
       "      <td>2014-12-08 16:54:39.180</td>\n",
       "      <td>-0.571103</td>\n",
       "      <td>44.849840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id                DateTime  Longitude   Latitude\n",
       "430  21 2014-12-08 16:54:37.191  -0.571103  44.849857\n",
       "431  21 2014-12-08 16:54:37.207  -0.571103  44.849857\n",
       "432  21 2014-12-08 16:54:38.180  -0.571103  44.849857\n",
       "433  21 2014-12-08 16:54:38.193  -0.571103  44.849857\n",
       "434  21 2014-12-08 16:54:39.180  -0.571103  44.849840"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df21January.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "dfToFig = df21January[[\"Latitude\", \"Longitude\"]]\n",
    "fig = gmaps.figure()\n",
    "heatmap_layer = gmaps.heatmap_layer(dfToFig)\n",
    "fig.add_layer(heatmap_layer)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7f7b31c911f14ba5b74c56c11058b172",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](./Images/map_id21_décembre.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On remarque que les données qui ont été envoyées concernent Bordeaux et non Lyon.\n",
    "De plus, la quantité insuffisante de données concernant les autres mois permettent d'éliminer cette personne de l'étude.\n",
    "\n",
    "Continuons d'étudier de la même façon d'autres IDs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ID 18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SELECT DISTINCT * FROM gpsData WHERE Id ='18'\n"
     ]
    }
   ],
   "source": [
    "sql = \"SELECT DISTINCT * FROM gpsData WHERE Id ='18'\"\n",
    "print(sql)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_sql_query(sql, connex)\n",
    "df = df.sort_values(by=\"DateTime\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Id                 DateTime  Longitude   Latitude\n",
      "0  18  2015-03-19 16:05:02.615   4.865967  45.782033\n",
      "1  18  2015-03-19 16:05:02.842   4.865967  45.782033\n",
      "2  18  2015-03-19 16:05:04.411   4.865887  45.782113\n",
      "3  18  2015-03-19 16:05:04.426   4.865887  45.782113\n",
      "4  18  2015-03-19 16:05:05.414   4.865890  45.782110\n",
      "        Id                 DateTime  Longitude   Latitude\n",
      "446294  18  2015-05-17 19:38:45.802   4.865873  45.755952\n",
      "446295  18  2015-05-17 19:38:46.783   4.865873  45.755952\n",
      "446296  18  2015-05-17 19:38:46.805   4.865873  45.755952\n",
      "446297  18  2015-05-17 19:38:47.782   4.865873  45.755952\n",
      "446298  18  2015-05-17 19:38:47.799   4.865873  45.755952\n"
     ]
    }
   ],
   "source": [
    "print(df.head())\n",
    "print(df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durée totale de l'ID 18 : 59 days 03:33:45.184000\n",
      "Dernière date de l'ID 18 : 2015-05-17 19:38:47.799000, première date de l'ID 1: 2015-03-19 16:05:02.615000\n",
      "Longueur totale de l'ID 18 : 446299\n"
     ]
    }
   ],
   "source": [
    "df18 = df\n",
    "df18['DateTime'] = pd.to_datetime(df18['DateTime'])\n",
    "df18.DateTime.min()\n",
    "maxi = df18.DateTime.max()\n",
    "mini = df18.DateTime.min()\n",
    "deltaT = maxi - mini\n",
    "print(\"Durée totale de l'ID 18 : \" + str(deltaT))\n",
    "print(\"Dernière date de l'ID 18 : \" + str(max) + \", première date de l'ID 1: \" + str(min))\n",
    "print(\"Longueur totale de l'ID 18 : \" + str(len(df18)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cette étude dure donc 59 jours, de mars à Mai.\n",
    "Regardons donc par rapport au mois d'Avril."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015-04-30 15:39:19.286000\n",
      "2015-04-08 22:40:59.006000\n"
     ]
    }
   ],
   "source": [
    "df18April = df18.loc[(df18['DateTime'].dt.month==4)]\n",
    "len(df18April)\n",
    "(print(df18April.DateTime.max()))\n",
    "(print(df18April.DateTime.min()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfToFig = df18April[[\"Latitude\", \"Longitude\"]]\n",
    "fig = gmaps.figure()\n",
    "heatmap_layer = gmaps.heatmap_layer(dfToFig)\n",
    "fig.add_layer(heatmap_layer)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](./Images/map_id18_avril.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous trouvons des données qui concernent à la fois Paris, Lyon et Amiens.\n",
    "\n",
    "De ce fait, avec l'étude rapide faite sur l'ID 21, il faudrait donc nettoyer les données, afin de faire en sorte qu'il ne reste que les coordonnées aux alentours du grand Lyon, ce qui nous intéresse.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "181837\n",
      "200350\n",
      "64112\n"
     ]
    }
   ],
   "source": [
    "print(len(df18.loc[(df18['DateTime'].dt.month == 3)]))\n",
    "print(len(df18.loc[(df18['DateTime'].dt.month == 4)]))\n",
    "print(len(df18.loc[(df18['DateTime'].dt.month == 5)]))\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regardons par rapport à Mars, qui possède une quantité de donnée assez élevée."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "364409ac865344a3b1957330b966d05d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df18March = df18.loc[(df18['DateTime'].dt.month==3)]\n",
    "\n",
    "dfToFig = df18March[[\"Latitude\", \"Longitude\"]]\n",
    "fig = gmaps.figure()\n",
    "heatmap_layer = gmaps.heatmap_layer(dfToFig)\n",
    "fig.add_layer(heatmap_layer)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](./Images/map_id18_mars.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous remarquons toujours un point d'attente vers l'est de la région Rhône-Alpes, ce qui ne nous intéresse pas non plus.\n",
    "\n",
    "Cela nous confirme donc la nécessité de nettoyer les données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Échantillon ID 10\n",
    "\n",
    "Regardons par rapport à une personne ayant assez peu de données, pour voir si nous pouvons quand même caractériser un déplacement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SELECT DISTINCT * FROM gpsData WHERE Id ='10'\n"
     ]
    }
   ],
   "source": [
    "sql = \"SELECT DISTINCT * FROM gpsData WHERE Id ='10'\"\n",
    "print(sql)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>Id</th>\n",
       "      <th>DateTime</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>2014-11-21 14:40:46.951</td>\n",
       "      <td>4.855740</td>\n",
       "      <td>45.759810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>2014-11-21 14:40:46.961</td>\n",
       "      <td>4.855740</td>\n",
       "      <td>45.759810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>2014-11-21 14:40:47.95</td>\n",
       "      <td>4.855762</td>\n",
       "      <td>45.759887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>2014-11-21 14:40:47.966</td>\n",
       "      <td>4.855762</td>\n",
       "      <td>45.759887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>2014-11-21 14:40:48.95</td>\n",
       "      <td>4.855737</td>\n",
       "      <td>45.759910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id                 DateTime  Longitude   Latitude\n",
       "0  10  2014-11-21 14:40:46.951   4.855740  45.759810\n",
       "1  10  2014-11-21 14:40:46.961   4.855740  45.759810\n",
       "2  10   2014-11-21 14:40:47.95   4.855762  45.759887\n",
       "3  10  2014-11-21 14:40:47.966   4.855762  45.759887\n",
       "4  10   2014-11-21 14:40:48.95   4.855737  45.759910"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_sql_query(sql, connex)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>Id</th>\n",
       "      <th>DateTime</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3011</th>\n",
       "      <td>10</td>\n",
       "      <td>2014-11-21 15:09:46.979</td>\n",
       "      <td>4.871645</td>\n",
       "      <td>45.784343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3012</th>\n",
       "      <td>10</td>\n",
       "      <td>2014-11-21 15:09:47.95</td>\n",
       "      <td>4.871640</td>\n",
       "      <td>45.784343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3013</th>\n",
       "      <td>10</td>\n",
       "      <td>2014-11-21 15:09:47.963</td>\n",
       "      <td>4.871640</td>\n",
       "      <td>45.784343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3014</th>\n",
       "      <td>10</td>\n",
       "      <td>2014-11-21 15:09:48.953</td>\n",
       "      <td>4.871637</td>\n",
       "      <td>45.784342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3015</th>\n",
       "      <td>10</td>\n",
       "      <td>2014-11-21 15:09:48.967</td>\n",
       "      <td>4.871637</td>\n",
       "      <td>45.784342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id                 DateTime  Longitude   Latitude\n",
       "3011  10  2014-11-21 15:09:46.979   4.871645  45.784343\n",
       "3012  10   2014-11-21 15:09:47.95   4.871640  45.784343\n",
       "3013  10  2014-11-21 15:09:47.963   4.871640  45.784343\n",
       "3014  10  2014-11-21 15:09:48.953   4.871637  45.784342\n",
       "3015  10  2014-11-21 15:09:48.967   4.871637  45.784342"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.sort_values(by=\"DateTime\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durée totale de l'ID 18 : 0 days 00:29:02.016000\n",
      "Dernière date de l'ID 18 : 2014-11-21 15:09:48.967000, première date de l'ID 1: 2014-11-21 14:40:46.951000\n",
      "Longueur totale de l'ID 18 : 3016\n"
     ]
    }
   ],
   "source": [
    "df18 = df\n",
    "df18['DateTime'] = pd.to_datetime(df18['DateTime'])\n",
    "df18.DateTime.min()\n",
    "maxi = df18.DateTime.max()\n",
    "mini = df18.DateTime.min()\n",
    "deltaT = maxi - mini\n",
    "print(\"Durée totale de l'ID 18 : \" + str(deltaT))\n",
    "print(\"Dernière date de l'ID 18 : \" + str(maxi) + \", première date de l'ID 1: \" + str(mini))\n",
    "print(\"Longueur totale de l'ID 18 : \" + str(len(df18)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous n'avons qu'une demi-heure d'étude sur cette personne."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Id                 DateTime  Longitude   Latitude\n",
      "0  10  2014-11-21 14:40:46.951   4.855740  45.759810\n",
      "1  10  2014-11-21 14:40:46.961   4.855740  45.759810\n",
      "2  10   2014-11-21 14:40:47.95   4.855762  45.759887\n",
      "3  10  2014-11-21 14:40:47.966   4.855762  45.759887\n",
      "4  10   2014-11-21 14:40:48.95   4.855737  45.759910\n",
      "      Id                 DateTime  Longitude   Latitude\n",
      "3011  10  2014-11-21 15:09:46.979   4.871645  45.784343\n",
      "3012  10   2014-11-21 15:09:47.95   4.871640  45.784343\n",
      "3013  10  2014-11-21 15:09:47.963   4.871640  45.784343\n",
      "3014  10  2014-11-21 15:09:48.953   4.871637  45.784342\n",
      "3015  10  2014-11-21 15:09:48.967   4.871637  45.784342\n",
      "taille du dataframe : 3016\n"
     ]
    }
   ],
   "source": [
    "print(df.head())\n",
    "print(df.tail())\n",
    "print(\"taille du dataframe : \" + str(len(df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6202cea1fe4942cb867ccca6aa12dfbc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfToFig = df[[\"Latitude\", \"Longitude\"]]\n",
    "fig = gmaps.figure()\n",
    "heatmap_layer = gmaps.heatmap_layer(dfToFig)\n",
    "fig.add_layer(heatmap_layer)\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](./Images/map_id10.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La heatmap trouvée est idéale, car elle nous permet d'observer un déplacement.\n",
    "\n",
    "De plus, il semble que ce déplacement se fasse grâce au tramway T1, ce qui peut nous permettre de faire une première caractérisation de la vitesse d'un tramway.\n",
    "\n",
    "Utilisons les algorithmes que nous avons utilisé précédemment pour le nettoyage et les déplacements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanMethodMinutes(frameParam, delta):\n",
    "    listeLongitude = []\n",
    "    listeLatitude = []\n",
    "    listeTemps = []\n",
    "    listeLongitude2 = []\n",
    "    listeLatitude2 = []\n",
    "    localDf = frameParam\n",
    "\n",
    "    #current = datetime.strptime(localDf.DateTime.iloc[0], '%Y-%m-%d %H:%M:%S.%f') + timedelta(minutes = delta)\n",
    "\n",
    "    current = localDf.DateTime.iloc[0] + timedelta(minutes = delta)\n",
    "    for i in range(0, len(localDf)):\n",
    "        #if datetime.strptime(localDf.DateTime.iloc[i], '%Y-%m-%d %H:%M:%S.%f') <= current:\n",
    "        if localDf.DateTime.iloc[i] <= current:\n",
    "            listeLongitude2.append(localDf.Longitude.iloc[i])\n",
    "            listeLatitude2.append(localDf.Latitude.iloc[i])\n",
    "            currentLongitude = localDf.Longitude.iloc[i]\n",
    "            currentLatitude = localDf.Latitude.iloc[i]\n",
    "        #elif datetime.strptime(localDf.DateTime.iloc[i], '%Y-%m-%d %H:%M:%S.%f') >= current + timedelta(minutes = delta):\n",
    "        elif localDf.DateTime.iloc[i] >= current + timedelta(minutes = delta):\n",
    "            #listeTemps.append(localDf.DateTime.iloc[i])\n",
    "            listeTemps.append(current)\n",
    "            listeLongitude.append(currentLongitude)\n",
    "            listeLatitude.append(currentLatitude)\n",
    "            current = current + timedelta(minutes = delta)\n",
    "        else:\n",
    "            #listeTemps.append(localDf.DateTime.iloc[i])\n",
    "            listeTemps.append(current)\n",
    "            current = current + timedelta(minutes = delta)\n",
    "            listeLongitude.append(max(set(listeLongitude2), key = listeLongitude2.count))\n",
    "            listeLatitude.append(max(set(listeLatitude2), key = listeLatitude2.count))\n",
    "            listeLongitude2 = []\n",
    "            listeLatitude2 = []\n",
    "\n",
    "    localDf = pd.DataFrame(\n",
    "        {'DateTime': listeTemps,\n",
    "         'Longitude': listeLongitude,\n",
    "         'Latitude': listeLatitude\n",
    "        })\n",
    "    \n",
    "    return localDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanMethodSeconds(frameParam, delta):\n",
    "    listeLongitude = []\n",
    "    listeLatitude = []\n",
    "    listeTemps = []\n",
    "    listeLongitude2 = []\n",
    "    listeLatitude2 = []\n",
    "    localDf = frameParam\n",
    "\n",
    "    #current = datetime.strptime(localDf.DateTime.iloc[0], '%Y-%m-%d %H:%M:%S.%f') + timedelta(minutes = delta)\n",
    "\n",
    "    current = localDf.DateTime.iloc[0] + timedelta(seconds = delta)\n",
    "    for i in range(0, len(localDf)):\n",
    "        #if datetime.strptime(localDf.DateTime.iloc[i], '%Y-%m-%d %H:%M:%S.%f') <= current:\n",
    "        if localDf.DateTime.iloc[i] <= current:\n",
    "            listeLongitude2.append(localDf.Longitude.iloc[i])\n",
    "            listeLatitude2.append(localDf.Latitude.iloc[i])\n",
    "            currentLongitude = localDf.Longitude.iloc[i]\n",
    "            currentLatitude = localDf.Latitude.iloc[i]\n",
    "        #elif datetime.strptime(localDf.DateTime.iloc[i], '%Y-%m-%d %H:%M:%S.%f') >= current + timedelta(minutes = delta):\n",
    "        elif localDf.DateTime.iloc[i] >= current + timedelta(seconds = delta):\n",
    "            #listeTemps.append(localDf.DateTime.iloc[i])\n",
    "            listeTemps.append(current)\n",
    "            listeLongitude.append(currentLongitude)\n",
    "            listeLatitude.append(currentLatitude)\n",
    "            current = current + timedelta(seconds = delta)\n",
    "        else:\n",
    "            #listeTemps.append(localDf.DateTime.iloc[i])\n",
    "            listeTemps.append(current)\n",
    "            current = current + timedelta(seconds = delta)\n",
    "            listeLongitude.append(max(set(listeLongitude2), key = listeLongitude2.count))\n",
    "            listeLatitude.append(max(set(listeLatitude2), key = listeLatitude2.count))\n",
    "            listeLongitude2 = []\n",
    "            listeLatitude2 = []\n",
    "\n",
    "    localDf = pd.DataFrame(\n",
    "        {'DateTime': listeTemps,\n",
    "         'Longitude': listeLongitude,\n",
    "         'Latitude': listeLatitude\n",
    "        })\n",
    "    \n",
    "    return localDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def showSpeedGraph(localDf, delta, unit):\n",
    "    liste = []\n",
    "    for i in range(1, len(localDf)):\n",
    "        #deltaT = datetime.strptime(localDf.DateTime.iloc[i], '%Y-%m-%d %H:%M:%S.%f') - datetime.strptime(localDf.DateTime.iloc[i-1], '%Y-%m-%d %H:%M:%S.%f')\n",
    "        deltaT = df.DateTime.iloc[i] - df.DateTime.iloc[i-1]\n",
    "        if deltaT.total_seconds() == 0:\n",
    "            continue\n",
    "        iterloc1 = (localDf.Latitude.iloc[i], localDf.Longitude.iloc[i])\n",
    "        iterloc2 = (localDf.Latitude.iloc[i-1], localDf.Longitude.iloc[i-1])\n",
    "        speed = (vincenty(iterloc1, iterloc2).kilometers) / (deltaT.total_seconds() )\n",
    "        #speed = (vincenty(iterloc1, iterloc2).kilometers) / (deltaT.total_seconds() / 3600)\n",
    "        liste.append(speed)\n",
    "\n",
    "\n",
    "    plt.figure(figsize=(12,8))\n",
    "    plt.xlabel('Echantillons')\n",
    "    plt.ylabel('Vitesse (km/h)')\n",
    "    plt.plot(liste)\n",
    "    plt.title(\"Vitesse après nettoyages, échantillons par intervalle de \"+ str(delta) + \" \" + unit+ \"(s)\" )\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regardons dans un premier temps les vitesses sans nettoyage des données."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "liste = []\n",
    "for i in range(1, len(df)):\n",
    "    deltaT = df.DateTime.iloc[i] - df.DateTime.iloc[i-1]\n",
    "    if deltaT.total_seconds() == 0:\n",
    "        continue\n",
    "    iterloc1 = (df.Latitude.iloc[i], df.Longitude.iloc[i])\n",
    "    iterloc2 = (df.Latitude.iloc[i-1], df.Longitude.iloc[i-1])\n",
    "    speed = (vincenty(iterloc1, iterloc2).kilometers) / (deltaT.total_seconds() / 3600)\n",
    "    liste.append(speed)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.xlabel('Echantillons')\n",
    "plt.ylabel('Vitesse (km/h)')\n",
    "plt.plot(liste)\n",
    "#plt.title(\"Vitesse après nettoyages, échantillons par intervalle de \"+ str(delta) + \" \" + unit+ \"(s)\" )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cela confirme la nécessité de nettoyer les données, vu que nous avons des vitesses très rapides dont une pointe à 100 km/h en ville."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = cleanMethodMinutes(df, 1)\n",
    "showSpeedGraph(result, 1, \"minute\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Après nettoyage avec des intervalles de 1 minute, nous nous retrouvons avec des vitesses qui ont l'air plus réalistes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6336fe6ccdd047ae96b9dfc89eb41f66",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=15, description='x', max=29, min=1), Output()), _dom_classes=('widget-in…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coord11 = result[[\"Latitude\", \"Longitude\"]]\n",
    "\n",
    "%matplotlib inline\n",
    "from ipywidgets import interactive\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.image as image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "im = image.imread('Images/map11.png')\n",
    "\n",
    "def fct(x):\n",
    "    coord11_dyn = coord11.iloc[0:x]\n",
    "    lat_coord11_dyn = coord11_dyn['Longitude'].values\n",
    "    long_coord11_dyn = coord11_dyn['Latitude'].values\n",
    "    plt.figure(figsize=(10,10))\n",
    "    #plt.xlim(0.0035, 0.0070)\n",
    "    #plt.ylim(4.867,4.873)\n",
    "    plt.xlabel('Longitude')\n",
    "    plt.ylabel('Latitude')\n",
    "    plt.plot(lat_coord11_dyn, long_coord11_dyn)\n",
    "    plt.xlim(4.855,4.88)\n",
    "    plt.ylim(45.760, 45.785)\n",
    "    #plt.imshow(im, aspect='auto', extent=(4.866, 4.873, 45.7725, 45.7776), zorder=-1)\n",
    "    plt.show()\n",
    "    \n",
    "interactive_plot = interactive(fct, x=(1, len(coord11)))\n",
    "#plt.imshow(im, aspect='auto', extent=(4.866, 4.873, 45.7725, 45.7776), zorder=-1)\n",
    "output = interactive_plot.children[-1]\n",
    "interactive_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Essayons avec un intervalle de temps plus petit, par exemple 20 secondes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous nous retrouvons avec des vitesses comparables à un tramway classique (15 km/h en moyenne sur le site TCL pour la ligne T1).\n",
    "\n",
    "SI on regarde la trajectoire en faisant correspondre, le numéro de l'échantillon avec le slider:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc92b6cddc5d4398b6b8a02378849a8e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=44, description='x', max=87, min=1), Output()), _dom_classes=('widget-in…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfToFig = df[[\"Latitude\", \"Longitude\"]]\n",
    "fig = gmaps.figure()\n",
    "heatmap_layer = gmaps.heatmap_layer(dfToFig)\n",
    "fig.add_layer(heatmap_layer)\n",
    "\n",
    "figcoord11 = result[[\"Latitude\", \"Longitude\"]]\n",
    "\n",
    "%matplotlib inline\n",
    "from ipywidgets import interactive\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.image as image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "im = image.imread('Images/map11.png')\n",
    "\n",
    "def fct(x):\n",
    "    coord11_dyn = coord11.iloc[0:x]\n",
    "    lat_coord11_dyn = coord11_dyn['Longitude'].values\n",
    "    long_coord11_dyn = coord11_dyn['Latitude'].values\n",
    "    plt.figure(figsize=(10,10))\n",
    "    #plt.xlim(0.0035, 0.0070)\n",
    "    #plt.ylim(4.867,4.873)\n",
    "    plt.xlabel('Longitude')\n",
    "    plt.ylabel('Latitude')\n",
    "    plt.plot(lat_coord11_dyn, long_coord11_dyn)\n",
    "    plt.xlim(4.855,4.88)\n",
    "    plt.ylim(45.760, 45.785)\n",
    "    #plt.imshow(im, aspect='auto', extent=(4.866, 4.873, 45.7725, 45.7776), zorder=-1)\n",
    "    plt.show()\n",
    "    \n",
    "interactive_plot = interactive(fct, x=(1, len(coord11)))\n",
    "#plt.imshow(im, aspect='auto', extent=(4.866, 4.873, 45.7725, 45.7776), zorder=-1)\n",
    "output = interactive_plot.children[-1]\n",
    "interactive_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = cleanMethodSeconds(df10, 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les vitesses ont l'air de correspondre avec le déplacement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8f4d96b12e04456c8c1b802455d119a4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfToFig = result[[\"Latitude\", \"Longitude\"]]\n",
    "fig = gmaps.figure()\n",
    "heatmap_layer = gmaps.heatmap_layer(dfToFig)\n",
    "fig.add_layer(heatmap_layer)\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Données nettoyées\n",
    "![](./Images/map_id18_cleaned.png)\n",
    "Données de Base\n",
    "![](./Images/map_id10.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les données nettoyées ont toujours l'air de posséder une trajectoire correcte"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On va maintenant essayer de prendre un ID avec un nombre de ligne similaires à l'ID 10, afin de voir si on obtient les même vitesses au niveau des déplacements."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ID 98"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SELECT DISTINCT * FROM gpsData WHERE Id ='98'\n"
     ]
    }
   ],
   "source": [
    "sql = \"SELECT DISTINCT * FROM gpsData WHERE Id ='98'\"\n",
    "print(sql)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durée totale de l'ID 18 : 230 days 05:05:27.070000\n",
      "Dernière date de l'ID 18 : 2015-06-25 17:12:34.994000, première date de l'ID 1: 2014-11-07 12:07:07.924000\n",
      "Longueur totale de l'ID 18 : 5223\n",
      "Taille de la frame : 5223\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_sql_query(sql, connex)\n",
    "df = df.sort_values(by=\"DateTime\")\n",
    "df98 = df\n",
    "df98['DateTime'] = pd.to_datetime(df98['DateTime'])\n",
    "df98.DateTime.min()\n",
    "maxi = df98.DateTime.max()\n",
    "mini = df98.DateTime.min()\n",
    "deltaT = maxi - mini\n",
    "print(\"Durée totale de l'ID 18 : \" + str(deltaT))\n",
    "print(\"Dernière date de l'ID 18 : \" + str(maxi) + \", première date de l'ID 1: \" + str(mini))\n",
    "print(\"Longueur totale de l'ID 18 : \" + str(len(df98)))\n",
    "print(\"Taille de la frame : \" + str(len(df98)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous avons 5000 lignes sur 230 jours, ce qui est très insuffisant. Mais nous voulons voir si nous pouvons confirmer la vitesse trouvée précédemment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6b6e3e576c604883b3739b1f4c826e9c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfToFig = df98[[\"Latitude\", \"Longitude\"]]\n",
    "fig = gmaps.figure()\n",
    "heatmap_layer = gmaps.heatmap_layer(dfToFig)\n",
    "fig.add_layer(heatmap_layer)\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il faut d'abord qu'on trouve le mois dans lequel on a le plus de données."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014 1 0\n",
      "2014 2 0\n",
      "2014 3 0\n",
      "2014 4 0\n",
      "2014 5 0\n",
      "2014 6 0\n",
      "2014 7 0\n",
      "2014 8 0\n",
      "2014 9 0\n",
      "2014 10 0\n",
      "2014 11 886\n",
      "2015 1 0\n",
      "2015 2 0\n",
      "2015 3 0\n",
      "2015 4 0\n",
      "2015 5 0\n",
      "2015 6 3962\n",
      "2015 7 0\n",
      "2015 8 0\n",
      "2015 9 0\n",
      "2015 10 0\n",
      "2015 11 0\n"
     ]
    }
   ],
   "source": [
    "len(df98.loc[(df98['DateTime'].dt.month == 12) & (df98['DateTime'].dt.year == 2014)])\n",
    "year = 2014\n",
    "for year in range(2014, 2016):\n",
    "    for month in range(1, 12):\n",
    "        print(str(year) + \" \" + str(month) + \" \" +\n",
    "              str(len(df98.loc[(df98['DateTime'].dt.month == month) & (df98['DateTime'].dt.year == year)])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On remarque donc un seul mois où il y a un nombre de lignes suffisant pour étudier un peu l'utilisateur 98, en Juin 2015, trouvons maintenant le jour qui possédera le nombre le plus important de données."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015 6 1 0\n",
      "2015 6 2 0\n",
      "2015 6 3 0\n",
      "2015 6 4 0\n",
      "2015 6 5 0\n",
      "2015 6 6 0\n",
      "2015 6 7 0\n",
      "2015 6 8 0\n",
      "2015 6 9 0\n",
      "2015 6 10 0\n",
      "2015 6 11 0\n",
      "2015 6 12 0\n",
      "2015 6 13 0\n",
      "2015 6 14 0\n",
      "2015 6 15 0\n",
      "2015 6 16 0\n",
      "2015 6 17 0\n",
      "2015 6 18 0\n",
      "2015 6 19 0\n",
      "2015 6 20 0\n",
      "2015 6 21 0\n",
      "2015 6 22 0\n",
      "2015 6 23 0\n",
      "2015 6 24 0\n",
      "2015 6 25 3962\n",
      "2015 6 26 0\n",
      "2015 6 27 0\n",
      "2015 6 28 0\n",
      "2015 6 29 0\n",
      "2015 6 30 0\n"
     ]
    }
   ],
   "source": [
    "for day in range (1, 31):\n",
    "    print(str(2015) + \" \" + str(6) + \" \" + str(day) + \" \" +\n",
    "         str(len(df98.loc[(df98['DateTime'].dt.month == 6) & (df98['DateTime'].dt.year == 2015) & (df98['DateTime'].dt.day == day) ])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "C'est donc le 25 juin 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Id                DateTime  Longitude   Latitude\n",
      "1261  98 2015-06-25 14:09:17.997   4.833322  45.767515\n",
      "1262  98 2015-06-25 14:09:18.032   4.833322  45.767515\n",
      "1263  98 2015-06-25 14:09:19.006   4.833317  45.767525\n",
      "1264  98 2015-06-25 14:09:19.029   4.833317  45.767525\n",
      "1265  98 2015-06-25 14:09:19.236   4.833315  45.767530\n",
      "      Id                DateTime  Longitude   Latitude\n",
      "5218  98 2015-06-25 17:12:32.996   4.827508  45.760877\n",
      "5219  98 2015-06-25 17:12:33.986   4.827513  45.760875\n",
      "5220  98 2015-06-25 17:12:33.996   4.827513  45.760875\n",
      "5221  98 2015-06-25 17:12:34.986   4.827515  45.760877\n",
      "5222  98 2015-06-25 17:12:34.994   4.827515  45.760877\n",
      "Id                                0\n",
      "DateTime     0 days 03:03:16.997000\n",
      "Longitude                0.00649333\n",
      "Latitude                 0.00704167\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "df98_2015_6_25 = df98.loc[(df98['DateTime'].dt.month == 6) & (df98['DateTime'].dt.year == 2015) & (df98['DateTime'].dt.day == 25) ]\n",
    "print(df98_2015_6_25.head())\n",
    "print(df98_2015_6_25.tail())\n",
    "print(df98_2015_6_25.max() - df98_2015_6_25.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous avons donc un peu moins de 4000 lignes pendant 3 heures, regardons un heatmap pour voir si on peut observer un déplacement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c13e308ce785445398267f19197b9e80",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfToFig = df98_2015_6_25[[\"Latitude\", \"Longitude\"]]\n",
    "fig = gmaps.figure()\n",
    "heatmap_layer = gmaps.heatmap_layer(dfToFig)\n",
    "fig.add_layer(heatmap_layer)\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df98_2015_6_25['Longitude'].values\n",
    "y = df98_2015_6_25['Latitude'].values\n",
    "\n",
    "plt.scatter(x,y,s=100)\n",
    "\n",
    "plt.title(\"Nuage de point de l'ID 98\")\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On observe deux points d'attente sur la heatmap. Mais étant donné que la heatmap ne montre pas forcément tous les points si on a une grosse densité sur une coordonnée précise, nous avons utilisé un nuage de point pour observer un déplacement. Mais nous tombons sur le même résultat.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = cleanMethodMinutes(df98_2015_6_25, 1)\n",
    "showSpeedGraph(result, 1, \"minute\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En observant les vitesses on peut aussi dire qu'il n'a pas de déplacements, on peut donc supprimer l'utilisateur 98 de nos données car il ne nous est pas utile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ville1 = (46.25, 4.850000)\n",
    "ville2 = (45.75000, 5.50000)\n",
    "ville3 = (45.75, 4.2)\n",
    "ville4 = (45.25, 4.85)\n",
    "lyon = (45.750000, 4.850000)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nettoyage des données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous avons vu précédemment que certaines données ne nous étaient pas utiles pour la suite, par exemples les lignes qui ne sont pas à proximité de Lyon.\n",
    "\n",
    "Nous allons donc supprimer les données GPS où la latitude n'est pas comprise entre 45.25 et 46.25 et où la longitude n'est pas comprise entre 4.2 et 5.5\n",
    "\n",
    "Cela nous permet d'enlever tout ce qui n'est pas dans une distance de 50 km de Lyon, (c'est assez approximatif, car ce n'est pas un cercle, mais un carré autour de Lyon).\n",
    "\n",
    "\n",
    "Appliquons d'abord l'algorithme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DELETE FROM gpsData WHERE Latitude NOT BETWEEN 45.25 AND 46.25 AND Longitude NOT BETWEEN 4.2 AND 5.5;\n"
     ]
    }
   ],
   "source": [
    "sql = \"DELETE FROM gpsData WHERE Latitude NOT BETWEEN 45.25 AND 46.25 AND Longitude NOT BETWEEN 4.2 AND 5.5;\"\n",
    "print(sql)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sqlite3.Cursor at 0x7f597a959c00>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.execute(sql)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regardons maintenant si l'algorithme a bien fonctionné, en prenant les données d'une personne dont on sait qu'elle est allée en dehors de Lyon.\n",
    "\n",
    "Regardons donc l'ID 21, qui est allée à Bordeaux, en sélectionnant toutes les lignes le concernant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SELECT DISTINCT * FROM gpsData WHERE Id ='21'\n"
     ]
    }
   ],
   "source": [
    "sql = \"SELECT DISTINCT * FROM gpsData WHERE Id ='21'\"\n",
    "print(sql)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_sql_query(sql, connex)\n",
    "df = df.sort_values(by=\"DateTime\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durée totale de l'ID 21 : 48 days 22:06:12.405000\n",
      "Dernière date de l'ID 21 : 2015-01-06 14:31:04.036000, première date de l'ID 1: 2014-11-18 16:24:51.631000\n",
      "Longueur totale de l'ID 21 : 1634\n"
     ]
    }
   ],
   "source": [
    "df21 = df\n",
    "df21['DateTime'] = pd.to_datetime(df21['DateTime'])\n",
    "df21.DateTime.min()\n",
    "maxi = df21.DateTime.max()\n",
    "mini = df21.DateTime.min()\n",
    "deltaT = maxi - mini\n",
    "print(\"Durée totale de l'ID 21 : \" + str(deltaT))\n",
    "print(\"Dernière date de l'ID 21 : \" + str(maxi) + \", première date de l'ID 1: \" + str(mini))\n",
    "print(\"Longueur totale de l'ID 21 : \" + str(len(df21)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si on compare le nombre de lignes de la dataframe par rapport aux données non nettoyées, on observe un gros changement.\n",
    "\n",
    "En effet, il y avait de nombreuses lignes (183417) sur cette personne, dont beaucoup de données qui sont à Bordeaux. Là, il ne nous reste que 1634 lignes de données, ce qui peut montrer que l'algorithme a marché."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fef00a87df9043b49031c3240e1c113c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfToFig = df[[\"Latitude\", \"Longitude\"]]\n",
    "fig = gmaps.figure()\n",
    "heatmap_layer = gmaps.heatmap_layer(dfToFig)\n",
    "fig.add_layer(heatmap_layer)\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](./Images/map_id21_cleaned.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si on vérifie tous les points de cette personne, on n'en trouve que dans le campus de la doua, donc l'algorithme a dû fonctionner comme prévu."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "Nous avons étudié quelques petits échantillons des données, où nous avons pu appliquer quelques algorithmes de nettoyage qui ont eu l’air de fonctionner correctement.\n",
    "Nous avons aussi remarqué certaines données inutiles que nous avons du supprimer, afin de nous concentrer sur les données à proximité de Lyon.\n",
    "On a aussi pu obtenir avec plus ou moins de précisions la vitesse d’un tramway avec les données d’un utilisateur, qui correspond à peu près à ce qui est donné avec les données TCL. Bien sûr, il faudra confirmer avec les études des semaines prochaines.\n",
    "\n",
    "Pour les prochaines séances, il nous faudra nous concentrer sur un nettoyage plus poussé encore ; un personne comme l'ID 98 (les deux nuages de points) ne nous apportent rien. Il nous faudra trouver un moyen de faire émerger certains pattern et certaines règles en conséquence.\n",
    "Nous allons aussi continuer la représentation interactive des données nettoyées dans une page web avec D3."
   ]
  }
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