_doc/notebooks/api_velib_jcdecaux.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# R\u00e9cup\u00e9rer les donn\u00e9es Velib (JCDecaux) et les visualiser\n",
        "\n",
        "La soci\u00e9t\u00e9 [JCDecaux](http://www.jcdecaux.com/fr/pour-nos-partenaires/velos-en-libre-service) met \u00e0 disoposition les donn\u00e9es Velib pour toutes les villes dont il g\u00e8re le service (Besan\u00e7on, Nantes, Lyon, ...). Les donn\u00e9es sont accessibles en temps r\u00e9el via une API assez simple : [JCDecaux Developer](https://developer.jcdecaux.com/#/home). Le module [manydataapi](http://www.xavierdupre.fr/app/manydataapi/helpsphinx/index.html) inclut d\u00e9sormais la classe [DataCollectJCDecaux](http://www.xavierdupre.fr/app/manydataapi/helpsphinx/manydataapi/velib/data_jcdecaux.html?highlight=velib#velib.data_jcdecaux.DataCollectJCDecaux) qui permet de r\u00e9cup\u00e9rer ces donn\u00e9es et d'effectuer quelques traitements simples."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Tout d'abord, il est n\u00e9cessaire d'obtenir une cl\u00e9 en vous enregistrant sur le site de JCDecaux : [Votre compte d\u00e9veloppeur](https://developer.jcdecaux.com/#/signup). Le code suivant permet de r\u00e9cup\u00e9rer des donn\u00e9es pendant 5 minutes :"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "from manydataapi.velib import DataCollectJCDecaux\n",
        "import os\n",
        "import datetime\n",
        "folder = os.path.abspath(\"data_velib\")\n",
        "if not os.path.exists(folder):\n",
        "    os.makedirs(folder)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "import keyring\n",
        "key = keyring.get_password(\"velib\", \"manydataapi,key\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "datetime.datetime(2019, 6, 8, 11, 41, 42, 472087)"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "stop = datetime.datetime.now() + datetime.timedelta(minutes=2)\n",
        "stop"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DataCollectJCDecaux.collecting_data: nb=1 2019-06-08 11:39:42.712536 delay=0:00:20\n",
            "DataCollectJCDecaux.collecting_data: nb=2 2019-06-08 11:40:03.329307 delay=0:00:20\n",
            "DataCollectJCDecaux.collecting_data: nb=3 2019-06-08 11:40:23.218483 delay=0:00:20\n",
            "DataCollectJCDecaux.collecting_data: nb=4 2019-06-08 11:40:43.474764 delay=0:00:20\n",
            "DataCollectJCDecaux.collecting_data: nb=5 2019-06-08 11:41:03.346586 delay=0:00:20\n",
            "DataCollectJCDecaux.collecting_data: nb=6 2019-06-08 11:41:23.215681 delay=0:00:20\n"
          ]
        }
      ],
      "source": [
        "DataCollectJCDecaux.run_collection(key, contract=\"besancon\",\n",
        "            folder_file=os.path.join(folder, \"besancon\"), stop_datetime=stop,\n",
        "            delayms=20000)  # remplacer par None pour r\u00e9cup\u00e9rer les donn\u00e9es sans s'arr\u00eater"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "On liste les fichiers dans le r\u00e9pertoire :"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['besancon.2019-03-09_11-24-11.683780.txt',\n",
              " 'besancon.2019-03-09_11-24-32.348985.txt',\n",
              " 'besancon.2019-03-09_11-24-52.274777.txt',\n",
              " 'besancon.2019-03-09_11-25-12.248416.txt',\n",
              " 'besancon.2019-03-09_11-25-32.187105.txt',\n",
              " 'besancon.2019-03-09_11-25-52.203976.txt',\n",
              " 'besancon.2019-04-04_01-31-03.090776.txt',\n",
              " 'besancon.2019-04-04_01-31-23.694889.txt',\n",
              " 'besancon.2019-04-04_01-31-43.533015.txt',\n",
              " 'besancon.2019-04-04_01-32-03.824015.txt',\n",
              " 'besancon.2019-04-04_01-32-23.730851.txt',\n",
              " 'besancon.2019-04-04_01-32-43.578783.txt',\n",
              " 'besancon.2019-06-08_11-01-55.687069.txt',\n",
              " 'besancon.2019-06-08_11-02-16.310015.txt',\n",
              " 'besancon.2019-06-08_11-02-36.256942.txt',\n",
              " 'besancon.2019-06-08_11-02-56.211805.txt',\n",
              " 'besancon.2019-06-08_11-03-16.204509.txt',\n",
              " 'besancon.2019-06-08_11-03-36.229623.txt',\n",
              " 'besancon.2019-06-08_11-21-09.267587.txt',\n",
              " 'besancon.2019-06-08_11-21-29.748227.txt',\n",
              " 'besancon.2019-06-08_11-21-49.927647.txt',\n",
              " 'besancon.2019-06-08_11-22-09.988129.txt',\n",
              " 'besancon.2019-06-08_11-22-29.754535.txt',\n",
              " 'besancon.2019-06-08_11-22-49.904857.txt',\n",
              " 'besancon.2019-06-08_11-39-42.712536.txt',\n",
              " 'besancon.2019-06-08_11-40-03.329307.txt',\n",
              " 'besancon.2019-06-08_11-40-23.218483.txt',\n",
              " 'besancon.2019-06-08_11-40-43.474764.txt',\n",
              " 'besancon.2019-06-08_11-41-03.346586.txt',\n",
              " 'besancon.2019-06-08_11-41-23.215681.txt',\n",
              " 'resume_besancon.txt',\n",
              " 'resume_besancon.xlsx']"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "os.listdir(folder)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "On construit un DataFrame \u00e0 partir de ces donn\u00e9es :"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\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>name</th>\n",
              "      <th>lng</th>\n",
              "      <th>lat</th>\n",
              "      <th>collect_date</th>\n",
              "      <th>available_bike_stands</th>\n",
              "      <th>available_bikes</th>\n",
              "      <th>file</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>16 - BEAUREGARD</td>\n",
              "      <td>6.032438</td>\n",
              "      <td>47.245026</td>\n",
              "      <td>2019-03-09 11:24:11.830996</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>besancon.2019-03-09_11-24-11.683780.txt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>30 - XAVIER MARMIER</td>\n",
              "      <td>6.006690</td>\n",
              "      <td>47.241256</td>\n",
              "      <td>2019-03-09 11:24:11.830996</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>besancon.2019-03-09_11-24-11.683780.txt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>02 - SQUARE BOUCHOT</td>\n",
              "      <td>6.022070</td>\n",
              "      <td>47.242990</td>\n",
              "      <td>2019-03-09 11:24:11.830996</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>besancon.2019-03-09_11-24-11.683780.txt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>26 - BERSOT</td>\n",
              "      <td>6.029520</td>\n",
              "      <td>47.238861</td>\n",
              "      <td>2019-03-09 11:24:11.830996</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>besancon.2019-03-09_11-24-11.683780.txt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>14 - JACOBINS</td>\n",
              "      <td>6.033465</td>\n",
              "      <td>47.234142</td>\n",
              "      <td>2019-03-09 11:24:11.830996</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>besancon.2019-03-09_11-24-11.683780.txt</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  name       lng        lat               collect_date  \\\n",
              "0      16 - BEAUREGARD  6.032438  47.245026 2019-03-09 11:24:11.830996   \n",
              "1  30 - XAVIER MARMIER  6.006690  47.241256 2019-03-09 11:24:11.830996   \n",
              "2  02 - SQUARE BOUCHOT  6.022070  47.242990 2019-03-09 11:24:11.830996   \n",
              "3          26 - BERSOT  6.029520  47.238861 2019-03-09 11:24:11.830996   \n",
              "4        14 - JACOBINS  6.033465  47.234142 2019-03-09 11:24:11.830996   \n",
              "\n",
              "   available_bike_stands  available_bikes  \\\n",
              "0                      5                6   \n",
              "1                     11                1   \n",
              "2                      1                9   \n",
              "3                      7                3   \n",
              "4                      7                3   \n",
              "\n",
              "                                      file  \n",
              "0  besancon.2019-03-09_11-24-11.683780.txt  \n",
              "1  besancon.2019-03-09_11-24-11.683780.txt  \n",
              "2  besancon.2019-03-09_11-24-11.683780.txt  \n",
              "3  besancon.2019-03-09_11-24-11.683780.txt  \n",
              "4  besancon.2019-03-09_11-24-11.683780.txt  "
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = DataCollectJCDecaux.to_df(folder, \"^besancon.*[.]txt\")\n",
        "df.to_csv(os.path.join(folder, \"resume_besancon.txt\"), sep=\"\\t\", index=False, encoding=\"utf8\")\n",
        "df.to_excel(os.path.join(folder, \"resume_besancon.xlsx\"), index=False)\n",
        "df[[\"name\",\"lng\", \"lat\", \"collect_date\", \"available_bike_stands\", \"available_bikes\", \"file\"]].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "On visualise les donn\u00e9es pour une date donn\u00e9e :"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "dt = df[\"file\"][0]\n",
        "subset = df [ df[\"file\"] == dt ]\n",
        "fig,ax,plt = DataCollectJCDecaux.draw(subset, figsize=(16,6))\n",
        "ax.set_title(\"Besan\u00e7on - {0} - {1} stations\".format(dt.replace(\"besancon.\",\"\") \\\n",
        "                    .replace(\".txt\",\"\").replace(\"_\", \" \"), len(subset)));"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "On cr\u00e9e une petite animation avec les donn\u00e9es velib :"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "scrolled": false
      },
      "outputs": [],
      "source": [
        "import matplotlib.animation as animation\n",
        "anim = DataCollectJCDecaux.animation(df, interval = 100, figsize=(16,6))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": [
        "from IPython.display import HTML\n",
        "# Ne marche pas vraiment sur une aussi petite dur\u00e9e\n",
        "# HTML(anim.to_jshtml())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "On essaye autrement."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "scrolled": false
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "pygame 1.9.4\n",
            "Hello from the pygame community. https://www.pygame.org/contribute.html\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "anim = DataCollectJCDecaux.animation(df[-1000:], interval = 100, figsize=(16,6), module=\"moviepy\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [],
      "source": [
        "# anim.write_gif(\"anim1.gif\", fps=20)\n",
        "# from IPython.display import Image, display, HTML\n",
        "# Image(url='anim1.gif') # does not work all the time\n",
        "# HTML('''<div style=\"display: flex; justify-content: row;\"><img src=\"anim1.gif\"></div>''');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Ca ne bouge pas trop. Sur deux minutes, c'est plut\u00f4t attendu. On essaye sur plus de donn\u00e9es."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['besancon.df.txt']"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from pyensae.datasource import download_data\n",
        "import pandas\n",
        "download_data('besancon.df.txt.zip', website = 'xdtd')  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(22650, 15)"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pandas.read_csv(\"besancon.df.txt\", sep=\"\\t\", encoding=\"utf8\")\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from manydataapi.velib import DataCollectJCDecaux\n",
        "anim = DataCollectJCDecaux.animation(df[-1000:], interval=100, figsize=(16,6), module=\"moviepy\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [],
      "source": [
        "# anim.write_gif(\"anim2.gif\", fps=20)\n",
        "# Image('anim2.gif')\n",
        "# HTML('''<div style=\"display: flex; justify-content: row;\"><img src=\"anim2.gif\"></div>''');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# premi\u00e8re image des donn\u00e9es\n",
        "dt = df[\"file\"][0]\n",
        "subset = df[df[\"file\"] == dt]\n",
        "fig,ax,plt = DataCollectJCDecaux.draw(subset, figsize=(16,6))\n",
        "ax.set_title(\"Besan\u00e7on - {0} - {1} stations\".format(dt.replace(\"besancon.\",\"\") \\\n",
        "                .replace(\".txt\",\"\").replace(\"_\", \" \"), len(subset)));"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# derni\u00e8re image\n",
        "dt = df[\"file\"][len(df)-1]\n",
        "subset = df [ df[\"file\"] == dt ]\n",
        "fig,ax,plt = DataCollectJCDecaux.draw(subset, figsize=(16,6))\n",
        "ax.set_title(\"Besan\u00e7on - {0} - {1} stations\".format(dt.replace(\"besancon.\",\"\") \\\n",
        "                            .replace(\".txt\",\"\").replace(\"_\", \" \"), len(subset)));"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Vous trouverez un exemple d'utilisation de ces donn\u00e9es ici [Coding Party 22 mai 2014 : inf\u00e9rer des trajectoires de v\u00e9los](http://www.xavierdupre.fr/app/ensae_teaching_cs/helpsphinx/coding_party_1.html) et l\u00e0 [D\u00e9terminer la vitesse moyenne des v\u00e9lib](http://www.xavierdupre.fr/app/ensae_teaching_cs/helpsphinx/notebooks/expose_velib.html)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "scrolled": false
      },
      "outputs": [
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