Douillet_BDD.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc7b9843",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:90% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "704b2b91",
   "metadata": {},
   "source": [
    "# BDD Iter3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e70ad998",
   "metadata": {},
   "source": [
    "C3.1.3 : Diagramme\n",
    "\n",
    "Le diagramme manque d’une information clé: la multiplicité des associations. En équipe, retrouvez les multiplicités des associations suivantes:\n",
    "    a. Agency - Routes -> 1..1 ------- 1..* (une route appartient forcément à une agence, une agence peut prendre une muliciplicité de routes et au moins une)\n",
    "    b. Trips - StopTimes  -> 1..1 ------- 2..*\n",
    "    c. StopTimes - Stops  -> 1..* ------- 1..1\n",
    "    d. Trips - Calendar  -> 1..* ------- 1..* (ou 1..1 ?)\n",
    "    e. Trips - Frequencies  -> 1..1 ------ 0..*   (ou 0..* ------- 0..1 ?)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f621d8c",
   "metadata": {},
   "source": [
    "C3.2 - Création d’un schéma\n",
    "\n",
    "DROP TABLE IF EXISTS agency;\n",
    "CREATE TABLE agency\n",
    "(\n",
    "    agency_id INT,\n",
    "    agency_name TEXT,\n",
    "    PRIMARY KEY  (agency_id)\n",
    ");\n",
    "\n",
    "DROP TABLE IF EXISTS routes;\n",
    "CREATE TABLE routes\n",
    "(\n",
    "    route_id INT,\n",
    "    agency_id INT,\n",
    "    route_short_name TEXT,\n",
    "    route_type INT,\n",
    "    PRIMARY KEY (route_id),\n",
    "    FOREIGN KEY (agency_id) REFERENCES agency(agency_id)\n",
    ");\n",
    "\n",
    "DROP TABLE IF EXISTS trips;\n",
    "CREATE TABLE trips\n",
    "(\n",
    "    trip_id INT,\n",
    "    trip_short_name TEXT,\n",
    "    PRIMARY KEY (trip_id)\n",
    "    -- cette ligne ne marche pas : FOREIGN KEY (route_id) REFERENCES routes(route_id)\n",
    ");\n",
    "\n",
    "DROP TABLE IF EXISTS stoptimes;\n",
    "CREATE TABLE stoptimes\n",
    "(\n",
    "    trip_id INT,\n",
    "    stop_id INT,\n",
    "    arrival_time TIME,\n",
    "    departure_time TIME,\n",
    "    stop_sequence INT,\n",
    "    PRIMARY KEY (trip_id, stop_id),\n",
    "    FOREIGN KEY (trip_id) REFERENCES trips(trip_id),\n",
    "    FOREIGN KEY (stop_id) REFERENCES stops(stop_id)\n",
    ");\n",
    "\n",
    "DROP TABLE IF EXISTS stops;\n",
    "CREATE TABLE stops\n",
    "(\n",
    "    stop_id INT,\n",
    "    stop_name TEXT,\n",
    "    stop_lat REAL,\n",
    "    stop_lon REAL,\n",
    "    PRIMARY KEY (stop_id)\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45c9e132",
   "metadata": {},
   "source": [
    "C3.3 - Insertion des données via Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "757c9294",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ce22d7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a DataFrame with all .txt\n",
    "agency = pd.read_csv('./SEM-GTFS/agency.txt')\n",
    "agency = agency.iloc[:,[0,1]]\n",
    "agency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2330a688",
   "metadata": {},
   "outputs": [],
   "source": [
    "routes = pd.read_csv('./SEM-GTFS/routes.txt')\n",
    "routes = routes.iloc[:,[0,1,2,4]]\n",
    "routes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "672c2187",
   "metadata": {},
   "outputs": [],
   "source": [
    "stops = pd.read_csv('./SEM-GTFS/stops.txt')\n",
    "stops = stops.iloc[:,[0,2,3,4]]\n",
    "stops.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3562e1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "stoptimes = pd.read_csv('./SEM-GTFS/stop_times.txt')\n",
    "stoptimes = stoptimes.iloc[:,[0,1,2,3,4]]\n",
    "stoptimes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37453f98",
   "metadata": {},
   "outputs": [],
   "source": [
    "trips = pd.read_csv('./SEM-GTFS/trips.txt')\n",
    "trips = trips.iloc[:,[0,1,2]]\n",
    "trips[\"trip_short_name\"] = trips[\"trip_headsign\"]\n",
    "trips = trips.drop(\"trip_headsign\",axis=1)\n",
    "trips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4230379f",
   "metadata": {},
   "outputs": [],
   "source": [
    "gtfs = sqlite3.connect('./gtfs_tag.db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50a6b7e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "agency.to_sql('agency', con=gtfs, if_exists='append', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d7b7c96",
   "metadata": {},
   "outputs": [],
   "source": [
    "routes.to_sql('routes', con=gtfs, if_exists='append', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d406eb31",
   "metadata": {},
   "outputs": [],
   "source": [
    "stops.to_sql('stops', con=gtfs, if_exists='append', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd1c960d",
   "metadata": {},
   "outputs": [],
   "source": [
    "stoptimes.to_sql('stoptimes', con=gtfs, if_exists='append', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f85924fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "trips.to_sql('trips', con=gtfs, if_exists='append', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "526f78d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "gtfs.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efa6c902",
   "metadata": {},
   "source": [
    "C3.4 - Insertion des données (bulk insert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a30abf5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bulk insert en python\n",
    "#create dictionnaries from pd from txt\n",
    "# agency_dict = agency.to_dict(orient='list')\n",
    "# routes_dict = routes.to_dict(orient='list')\n",
    "# stops_dict = stops.to_dict(orient='list')\n",
    "# stoptimes_dict = stoptimes.to_dict(orient='list')\n",
    "# trips_dict = trips.to_dict(orient='list')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a16d15e",
   "metadata": {},
   "outputs": [],
   "source": [
    "agency_dict = agency.T.reset_index().to_dict(orient='list')\n",
    "routes_dict = routes.T.reset_index().to_dict(orient='list')\n",
    "stops_dict = stops.T.reset_index().to_dict(orient='list')\n",
    "stoptimes_dict = stoptimes.T.reset_index().to_dict(orient='list')\n",
    "trips_dict = trips.T.reset_index().to_dict(orient='list')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc75b4f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "agency_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaff1e85",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sans passé par Pandas\n",
    "# a_dictionary = {}\n",
    "# a_file = open(\"data.txt\")\n",
    "# for line in a_file:\n",
    "#     key, value = line.split()\n",
    "# Split line into a tuple\n",
    "#     a_dictionary[key] = value\n",
    "# Add tuple values to dictionary\n",
    "# print(a_dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9639564",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # return a single query from a dict\n",
    "# def gen_insert_query(table_name:str, a_dict:dict) -> str:\n",
    "#     columns = list(a_dict.keys())\n",
    "#     features = \"\"\n",
    "#     for col in columns:\n",
    "#         features += f\"{col},\"\n",
    "#     features = features[:-1]\n",
    "#     values = \"(\"\n",
    "#     for col in columns:\n",
    "#         values += \"?,\"\n",
    "#     values = values[:-1]\n",
    "#     values += \")\"\n",
    "#     return f'''INSERT INTO {table_name}({features}) VALUES{values}'''"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a9f8f75",
   "metadata": {},
   "source": [
    "Étape 3.4.1 - implémenter une fonction qui génère une commande insertion SQL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ac2763f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# return a single query from a dict\n",
    "def gen_insert_query(table_name:str, a_dict:dict) -> str:\n",
    "    columns = a_dict['index']\n",
    "    features = \"\"\n",
    "    for col in columns:\n",
    "        features += f\"{col},\"\n",
    "    features = features[:-1]\n",
    "    v=\"\"\n",
    "    for i in a_dict:\n",
    "        if i!='index':\n",
    "            values = \"(\"\n",
    "            for j in range(0,len(a_dict[i])):\n",
    "                if isinstance(a_dict[i][j],str):\n",
    "                    values += \"'\"\n",
    "                    values += f\"{a_dict[i][j]}\"\n",
    "                    values += \"',\"\n",
    "                else:\n",
    "                    values += f\"{a_dict[i][j]},\"\n",
    "            values = values[:-1]\n",
    "            values += \")\"\n",
    "            v+=values\n",
    "            v+=','\n",
    "    v = v[:-1]\n",
    "    return f'''INSERT INTO {table_name}({features}) VALUES {v}'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fee0bd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "gen_insert_query('routes', routes_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95f18f2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# on crée un agency_test\n",
    "agency_dict_test={'index':['agency_id','agency_name'], 0:['TKM','Travel Kernel Mobility']}\n",
    "gen_insert_query('agency', agency_dict_test)\n",
    "# ça marche !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50af7713",
   "metadata": {},
   "outputs": [],
   "source": [
    "gtfs = sqlite3.connect('./gtfs_tag.db')\n",
    "c = gtfs.cursor()\n",
    "c.execute(gen_insert_query('agency', agency_dict_test))\n",
    "gtfs.commit()\n",
    "# ça marche !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b4dad2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "gtfs.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4defdf4d",
   "metadata": {},
   "source": [
    "Étape 3.4.2 - implémenter une fonction qui génère des commandes d’insertion SQL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a57252c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# return queries from a df\n",
    "def gen_insert_queries(table_name:str, df: pd.DataFrame) -> list:\n",
    "    return f\"INSERT INTO {table_name}{tuple(df.columns.to_list())} VALUES {','.join(str(i) for i in tuple(zip(*df.to_dict(orient='list').values())))}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e557f889",
   "metadata": {},
   "outputs": [],
   "source": [
    "gen_insert_queries('trips', trips)\n",
    "# ça marche !"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ed8d941",
   "metadata": {},
   "source": [
    "Étape 3.4.3 - implémenter une procédure qui crée un fichier SQL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c19d120b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_insert_file(filename, tablename, df):\n",
    "    with open(filename, \"w\") as fichier:\n",
    "        fichier.write(gen_insert_queries(tablename, df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3e67323",
   "metadata": {},
   "outputs": [],
   "source": [
    "gen_insert_file('insert_trips.sql','trips',trips)\n",
    "# ça marche !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a98d930",
   "metadata": {},
   "outputs": [],
   "source": [
    "# on recrée un agency_test\n",
    "agency_test=pd.DataFrame(data={\"agency_id\":[\"Data+\"],\"agency_name\":[\"Directional Aerial Transportation Agency Plus\"]})\n",
    "agency_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c2a30a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "gen_insert_file('insert_agency_test.sql','agency', agency_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c6e71ab",
   "metadata": {},
   "source": [
    "Étape 3.4.4 – Exécutez votre fichier SQL sur la BDD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a930f563",
   "metadata": {},
   "outputs": [],
   "source": [
    "gtfs = sqlite3.connect('./gtfs_tag.db')\n",
    "c = gtfs.cursor()\n",
    "with open('insert_agency_test.sql', \"r\") as fichier:  \n",
    "    c.execute(fichier.read())\n",
    "gtfs.commit()\n",
    "gtfs.close()\n",
    "# ça marche !"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "id": "4374af33",
   "metadata": {},
   "source": [
    "C3.5 - Réflexions\n",
    "\n",
    "Vous avez inséré des données avec deux méthodes différentes (pandas, bulk Insert):\n",
    "1. Quelle méthode est plus rapide et facile à implémenter ? <br/> **Pandas**\n",
    "2. Quelle méthode est plus rapide pour insérer l’information ?  <br/> **Pandas**\n",
    "3. Dans quel scénario préconisez-vous l’utilisation d’une méthode ou l’autre ?  <br/> **python pour tester pas à pas, Pandas '.to_sql' + sqlalchemy 'event' + python 'cursor.fast_executemany = True' pour deployer**\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c976577a",
   "metadata": {},
   "source": [
    "C3.6 -  (🕹 Bonus) Créer un calculateur d’itinéraire\n",
    "\n",
    "À partir de :\n",
    "\n",
    "• A - Un point GPS de départ, en format tuples de float; <br/> \n",
    "• B – Un point GPS d’arrivée, en format tuples de float; <br/>\n",
    "• H – une heure de part en format datetime; <br/>\n",
    "\n",
    "→ Calculez l’itinéraire des lignes TAG à prendre."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c3ec504",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyroutelib3 import Router\n",
    "import folium\n",
    "import webbrowser\n",
    "from datetime import datetime\n",
    "def calculateur_itineraire_ss_changement_plus_court(A,B,H):\n",
    "    router = Router(\"car\")\n",
    "    # find closest stop x from A\n",
    "    \n",
    "    # rajouter A to x à l'itineraire\n",
    "    depart = router.findNode(A[0], A[1])\n",
    "    arrivee = router.findNode(B[0], B[1]) #pour tester\n",
    "#(x[0], x[1])\n",
    "    status, route = router.doRoute(depart, arrivee)\n",
    "    if status == 'success':\n",
    "        routeLatLons = list(map(router.nodeLatLon, route))\n",
    "        \n",
    "    # find closest stop y from B, in route that contains x and y\n",
    "    \n",
    "    # rajouter y to B à l'itineraire\n",
    "\n",
    "    depart = router.findNode(A[0], A[1]) #pour tester\n",
    "#(y[0], y[1])\n",
    "    arrivee = router.findNode(B[0], B[1])\n",
    "    status, route = router.doRoute(depart, arrivee)\n",
    "    if status == 'success':\n",
    "        routeLatLons = list(map(router.nodeLatLon, route))\n",
    "    \n",
    "    # find all stops betwteen x and y via route_ident\n",
    "#   list_stops = []\n",
    "#     for stop in route_ident:\n",
    "#         list_stops.append(x to y)\n",
    "    \n",
    "    # find when next bus pass avec calendar avec H\n",
    "    \n",
    "    # display text (route a: list of stops from x to y)\n",
    "# horaire_next_bus = un DateTime\n",
    "# print(\"L'itinéraire TAG passe par :\", route_ident, list_stops)\n",
    "# print(\"Le prochain bus passera à :\", horaire_next_bus)\n",
    "    \n",
    "    # display stops on map\n",
    "    c= folium.Map(location=[A[0], A[1]],zoom_start=15)\n",
    "    for indice,coord in enumerate(routeLatLons):\n",
    "        if indice%10==0:\n",
    "            coord=list(coord)\n",
    "            folium.Marker(coord).add_to(c)\n",
    "    c.save('maCarte.html')\n",
    "    webbrowser.open('maCarte.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00fb9924",
   "metadata": {},
   "outputs": [],
   "source": [
    "A=(45.18810254439141,5.720416539957304)\n",
    "B=(45.185046370907855,5.731498880680741)\n",
    "H=datetime.now()\n",
    "\n",
    "calculateur_itineraire_ss_changement_plus_court(A,B,H)\n",
    "# ça marche ! (ou pas...)\n"
   ]
  }
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