TD2_webscrapping.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Web-Scrapping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This notebook includes:\n",
    "    1. Web-scrapping with beautiful soup the weather website from Paris\n",
    "    2. Web-scrapping with beautiful soup donations given to Telethon and visualization\n",
    "    3. Web-scrapping with Selenium to retrieve photos from Instagram Hashtags"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using '!' you can make command terminal calls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Synchro with Github"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import git_credentials as cd # another python file in which i've put my GitHub credentials to push"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Luc-Bertin'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cd.GIT_USERNAME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "def push(file_to_transfer, message, repos):\n",
    "    \"\"\" This function push one file to your github specified repository \"\"\"\n",
    "    \n",
    "    ## Load credentials from external file\n",
    "    import git_credentials as cd\n",
    "    \n",
    "    ## Message should contain escape whitespace character (to be interpreted in command)\n",
    "    message = message.replace(' ', '\\ ')\n",
    "    \n",
    "    ## Adding the remote\n",
    "    !git remote add origin https://$cd.GIT_USERNAME:$cd.GIT_PASSWORD@github.com/$cd.GIT_USERNAME/$repos\n",
    "    ## Name of file + comment for commiting\n",
    "    !git add $file_to_transfer\n",
    "    !git commit $file_to_transfer -m $message\n",
    "    ## Push to Github repo\n",
    "    !git push origin master\n",
    "    \n",
    "    # When synchronizing again (re-adding the origin)\n",
    "    !git remote rm origin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[master 16413f6] Added ammendment of committing to push function\n",
      " 1 file changed, 69 insertions(+), 121 deletions(-)\n",
      "Enumerating objects: 5, done.\n",
      "Counting objects: 100% (5/5), done.\n",
      "Delta compression using up to 8 threads\n",
      "Compressing objects: 100% (3/3), done.\n",
      "Writing objects: 100% (3/3), 1.34 KiB | 137.00 KiB/s, done.\n",
      "Total 3 (delta 2), reused 0 (delta 0)\n",
      "remote: Resolving deltas: 100% (2/2), completed with 2 local objects.\u001b[K\n",
      "To https://github.com/Luc-Bertin/TDs_ESILV.git\n",
      "   0b58a97..16413f6  master -> master\n"
     ]
    }
   ],
   "source": [
    "push(file_to_transfer=\"TD2_webscrapping.ipynb\", \n",
    "     message=\"Added ammendment of committing to push function\", \n",
    "     repos=\"TDs_ESILV.git\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.0 Let's push a list of files along with some commits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def push_multiple_files(files_to_transfer, commit_messages, repos):\n",
    "    \"\"\" This is an improved function to push multiple files and specify commits along with them \"\"\"\n",
    "    \n",
    "    if not isinstance(files_to_transfer, list) or not isinstance(commit_messages, list):\n",
    "        return 'This fonction only accepts a list of files to be commited to GitHub along with a list of commit messages'\n",
    "    \n",
    "    if len(files_to_transfer) < len(commit_messages):\n",
    "        return 'Error - there should be equal or greater number of files to be transferred than commits'\n",
    "    \n",
    "    import itertools\n",
    "    for file, commit in itertools.zip_longest(files_to_push, commit_messages, fillvalue='Additional\\ changes'):\n",
    "        push(file, commit, repos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "files_to_push = ['TD2_webscrapping.ipynb', 'data', 'chromedriver', 'insta_credentials.py']\n",
    "commits       = ['Jupyter Notebook', 'Where scrapped images are', 'Chromedriver is used here by Selenium', \n",
    "                 'Credentials for the Fake account']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TD2_webscrapping.ipynb\n",
      "Jupyter Notebook\n",
      "TDs_ESILV.git\n",
      "On branch master\n",
      "Untracked files:\n",
      "\t\u001b[31m.DS_Store\u001b[m\n",
      "\t\u001b[31m.ipynb_checkpoints/\u001b[m\n",
      "\t\u001b[31m__pycache__/\u001b[m\n",
      "\t\u001b[31mgit_credentials.py\u001b[m\n",
      "\n",
      "nothing added to commit but untracked files present\n",
      "Everything up-to-date\n",
      "data\n",
      "Where scrapped images are\n",
      "TDs_ESILV.git\n",
      "On branch master\n",
      "Untracked files:\n",
      "\t\u001b[31m.DS_Store\u001b[m\n",
      "\t\u001b[31m.ipynb_checkpoints/\u001b[m\n",
      "\t\u001b[31m__pycache__/\u001b[m\n",
      "\t\u001b[31mgit_credentials.py\u001b[m\n",
      "\n",
      "nothing added to commit but untracked files present\n",
      "Everything up-to-date\n",
      "chromedriver\n",
      "Chromedriver is used here by Selenium\n",
      "TDs_ESILV.git\n",
      "On branch master\n",
      "Untracked files:\n",
      "\t\u001b[31m.DS_Store\u001b[m\n",
      "\t\u001b[31m.ipynb_checkpoints/\u001b[m\n",
      "\t\u001b[31m__pycache__/\u001b[m\n",
      "\t\u001b[31mgit_credentials.py\u001b[m\n",
      "\n",
      "nothing added to commit but untracked files present\n",
      "Everything up-to-date\n",
      "insta_credentials.py\n",
      "Credentials for the Fake account\n",
      "TDs_ESILV.git\n",
      "On branch master\n",
      "Untracked files:\n",
      "\t\u001b[31m.DS_Store\u001b[m\n",
      "\t\u001b[31m.ipynb_checkpoints/\u001b[m\n",
      "\t\u001b[31m__pycache__/\u001b[m\n",
      "\t\u001b[31mgit_credentials.py\u001b[m\n",
      "\n",
      "nothing added to commit but untracked files present\n",
      "Everything up-to-date\n"
     ]
    }
   ],
   "source": [
    "push_multiple_files(files_to_push, commits, 'TDs_ESILV.git')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXAMPLE 1: Visualiser les donations faites au téléthon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definition from the official website:\n",
    "* Beautiful Soup is a **Python library for pulling data out of HTML and XML files**. \n",
    "* It works with your favorite parser to provide idiomatic ways of **navigating, searching, and modifying the parse tree**. It commonly saves programmers hours or days of work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "import requests as req"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Querying a website using request library "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://www.afm-telethon.fr/telethon/bref/parrains-resultats-telethon-1379'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests as req"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reponse = req.get(url)\n",
    "reponse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some reponse status\n",
    "* Reponse 200 : SUCCESS of the get request\n",
    "* Reponse 404 : Could not find \"error 404\"\n",
    "* Reponse 503 : Access to ressource forbidden "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'<!DOCTYPE html PUBLIC \"-//W3C//DTD XHTML+RDFa 1.0//EN\"\\r\\n\"http://www.w3.org/MarkUp/DTD/xhtml-rdfa-1.dtd\">\\r\\n<!--[if IE]>\\r\\n<html xmlns=\"http://www.w3.org/1999/xhtml\" class=\"ie\" xml:lang=\"fr\" version=\"XHTML+RDFa 1.0\" dir=\"ltr\"\\n  xmlns:og=\"http://ogp.me/ns#\"\\n  xmlns:article=\"http://ogp.me/ns/article#\"\\n  xmlns:book=\"http://ogp.me/ns/book#\"\\n  xmlns:profile=\"http://ogp.me/ns/profile#\"\\n  xmlns:video=\"http://ogp.me/ns/video#\"\\n  xmlns:product=\"http://ogp.me/ns/product#\"\\n  xmlns:content=\"http://purl.org/rss'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reponse.content[0:500] # The retrieved unparsed HTML "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "soup = BeautifulSoup(reponse.content, 'html.parser') # the parser that suits to the html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bs4.BeautifulSoup"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(soup) # it's an object of type bs4.BeautifulSoup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using CSS Selectors\n",
    "You can also search for items using CSS selectors. These selectors are how the CSS language allows developers to specify HTML tags to style. \n",
    "Here are some examples:\n",
    "\n",
    "* p a — finds all **a** tags inside of a **p** tag.\n",
    "* body p a — finds all **a** tags inside of a **p** tag inside of a **body** tag.\n",
    "* html body — finds all **body** tags inside of an **html** tag.\n",
    "* p.outer-text — finds all **p tags with a class of outer-text**.\n",
    "* p#first — finds all **p tags with an id of first**.\n",
    "* body p.outer-text — finds any **p tags with a class of outer-text inside of a body tag**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<td class=\" txt-petit\">Année</td>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variable = soup.select('tr td')[0]\n",
    "variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Année'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variable.getText()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liste = [1,2,3,4,5]\n",
    "liste[::3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "infos = [element.getText() for element in soup.select('tr td')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "year, parain, donations = infos[3::3], infos[4::3], infos[5::3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1987',\n",
       " '1988',\n",
       " '1989',\n",
       " '1990',\n",
       " '1991',\n",
       " '1992',\n",
       " '1993',\n",
       " '1994',\n",
       " '1995',\n",
       " '1996',\n",
       " '1997',\n",
       " '1998',\n",
       " '1999',\n",
       " '2000',\n",
       " '2001',\n",
       " '2002',\n",
       " '2003',\n",
       " '2004',\n",
       " '2005',\n",
       " '2006',\n",
       " '2007',\n",
       " '2008',\n",
       " '2009',\n",
       " '2010',\n",
       " '2011',\n",
       " '2012',\n",
       " '2013',\n",
       " '2014',\n",
       " '2015',\n",
       " '2016',\n",
       " '2017',\n",
       " '2018']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame({\n",
    "    'year' : year,\n",
    "    'referrer' : parain,\n",
    "    'amount_collected' : donations\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>year</th>\n",
       "      <th>referrer</th>\n",
       "      <th>amount_collected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1987</td>\n",
       "      <td>Jerry LEWIS</td>\n",
       "      <td>29 650 000 €</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1988</td>\n",
       "      <td>Mireille MATHIEU</td>\n",
       "      <td>28 490 000 €</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1989</td>\n",
       "      <td>Alain DELON</td>\n",
       "      <td>40 930 000 €</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990</td>\n",
       "      <td>Claudia CARDINALE</td>\n",
       "      <td>46 510 000 €</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1991</td>\n",
       "      <td>Jerry LEWIS, Mireille MATHIEU et Ornella MUTTI</td>\n",
       "      <td>38 650 000 €</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1992</td>\n",
       "      <td>Barbara HENDRICKS</td>\n",
       "      <td>47 870 000 €</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1993</td>\n",
       "      <td>Michel SARDOU</td>\n",
       "      <td>55 600 000 €</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1994</td>\n",
       "      <td>Pierre PERRET</td>\n",
       "      <td>57 520 000 €</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1995</td>\n",
       "      <td>Serge LAMA</td>\n",
       "      <td>56 763 000 €</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1996</td>\n",
       "      <td>Thierry LHERMITTE</td>\n",
       "      <td>56 872 000 €</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1997</td>\n",
       "      <td>Robert HOSSEIN</td>\n",
       "      <td>63 043 286 €</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1998</td>\n",
       "      <td>Michel BOUJENAH</td>\n",
       "      <td>70 449 000 €</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1999</td>\n",
       "      <td>Pierre PERRET</td>\n",
       "      <td>71 417 798 €</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2000</td>\n",
       "      <td>Patrick SEBASTIEN</td>\n",
       "      <td>79 640 119 €</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2001</td>\n",
       "      <td>Jane BIRKIN</td>\n",
       "      <td>81 188 013 €</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year                                        referrer amount_collected\n",
       "0   1987                                     Jerry LEWIS     29 650 000 €\n",
       "1   1988                                Mireille MATHIEU     28 490 000 €\n",
       "2   1989                                     Alain DELON     40 930 000 €\n",
       "3   1990                               Claudia CARDINALE     46 510 000 €\n",
       "4   1991  Jerry LEWIS, Mireille MATHIEU et Ornella MUTTI     38 650 000 €\n",
       "5   1992                               Barbara HENDRICKS     47 870 000 €\n",
       "6   1993                                   Michel SARDOU     55 600 000 €\n",
       "7   1994                                   Pierre PERRET     57 520 000 €\n",
       "8   1995                                      Serge LAMA     56 763 000 €\n",
       "9   1996                               Thierry LHERMITTE     56 872 000 €\n",
       "10  1997                                  Robert HOSSEIN     63 043 286 €\n",
       "11  1998                                Michel BOUJENAH     70 449 000 € \n",
       "12  1999                                  Pierre PERRET     71 417 798 € \n",
       "13  2000                              Patrick SEBASTIEN     79 640 119 € \n",
       "14  2001                                    Jane BIRKIN     81 188 013 € "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### nettoyons rapidement la donnée"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       29650000\n",
       "1       28490000\n",
       "2       40930000\n",
       "3       46510000\n",
       "4       38650000\n",
       "5       47870000\n",
       "6       55600000\n",
       "7       57520000\n",
       "8       56763000\n",
       "9       56872000\n",
       "10      63043286\n",
       "11     70449000 \n",
       "12     71417798 \n",
       "13     79640119 \n",
       "14     81188013 \n",
       "15     91546548 \n",
       "16     97657833 \n",
       "17    104678697 \n",
       "18    104078464 \n",
       "19    106696532 \n",
       "20    102315233 \n",
       "21    104911383 \n",
       "22     95200117 \n",
       "23     90450486 \n",
       "24     94091902 \n",
       "25     88156400 \n",
       "26     89327268 \n",
       "27     92920108 \n",
       "28      93850778\n",
       "29      92740769\n",
       "30      89189384\n",
       "31      85844117\n",
       "Name: amount_collected, dtype: object"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## not very good but still doing the job\n",
    "def enleve__euros_et_espaces(x):\n",
    "    return x.replace('€', '').replace(' ','')\n",
    "df['amount_collected'].apply(enleve__euros_et_espaces)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other solution ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      29650000\n",
       "1      28490000\n",
       "2      40930000\n",
       "3      46510000\n",
       "4      38650000\n",
       "5      47870000\n",
       "6      55600000\n",
       "7      57520000\n",
       "8      56763000\n",
       "9      56872000\n",
       "10     63043286\n",
       "11     70449000\n",
       "12     71417798\n",
       "13     79640119\n",
       "14     81188013\n",
       "15     91546548\n",
       "16     97657833\n",
       "17    104678697\n",
       "18    104078464\n",
       "19    106696532\n",
       "20    102315233\n",
       "21    104911383\n",
       "22     95200117\n",
       "23     90450486\n",
       "24     94091902\n",
       "25     88156400\n",
       "26     89327268\n",
       "27     92920108\n",
       "28     93850778\n",
       "29     92740769\n",
       "30     89189384\n",
       "31     85844117\n",
       "Name: amount_collected, dtype: object"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['amount_collected'].str.replace('\\D', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['year'] = df.year.astype(int) #on cast -> integer\n",
    "#Take the digits only in the price then convert to int then divide by 1M to get the price in Millions\n",
    "df['amount_collected'] = df.amount_collected.str.replace('\\D', '').astype(int)/10000000\n",
    "#on supprime les espaces avant et après les noms\n",
    "df.referrer = df.referrer.str.strip()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax =plt.subplots(figsize=(14,7), nrows=1, ncols=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# DISPLAYING FEATURES\n",
    "fig, ax = plt.subplots(figsize=(15, 10), ncols=1, nrows=1) #fig containing a subplot 'ax' using seaborn package\n",
    "\n",
    "#color theme\n",
    "flatui = [\"#7F7FFF\",\"#CCCCFF\"] # some great colors \n",
    "sns.set_style(\"whitegrid\") # to have a style in the background grid\n",
    "\n",
    "#the plot is a barplot, x for each year in absciss, y in ordinate\n",
    "#we add the palette of colors and assign the the defined ax\n",
    "sns.barplot(x=\"year\", y=\"amount_collected\", \n",
    "                 data=df, palette=flatui, ax=ax)\n",
    "\n",
    "#rotation of the x labels\n",
    "ax.set_xticklabels(labels=ax.get_xticklabels(), rotation=90)\n",
    "#title and subtitle\n",
    "ax.text(x=0.5, y=1.05, s='Prix investi dans le Telethon', fontsize=18, weight='bold', ha='center', va='bottom', transform=ax.transAxes)\n",
    "ax.text(x=0.5, y=1.01, s='En France depuis 1987', fontsize=14, alpha=0.75, ha='center', va='bottom', transform=ax.transAxes)\n",
    "#grid labels\n",
    "ax.set_xlabel('Year',fontsize=16)\n",
    "ax.set_ylabel('Amount Collected', fontsize=16)\n",
    "\n",
    "#adding something fancy: the actual digit in donations 2/3 centered in each barplot\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.text(p.get_x()+p.get_width()/2., height*2/3, '{}'.format(round(height,2)), \n",
    "            color='white', ha=\"center\", rotation=90, weight='bold')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXAMPLE 2 : Scrapping de données météo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Paris-Montsouris (Paris - France) | Relevés météo en temps réel - Infoclimat'"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://www.infoclimat.fr/observations-meteo/temps-reel/paris-montsouris/07156.html'\n",
    "reponse = req.get(url)\n",
    "soup = BeautifulSoup(reponse.content, 'html.parser')\n",
    "soup.title.string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(soup.find_all('p'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<td style=\"border-left:2px solid red\"><span class=\"tipsy-trigger\" title=\"Heure réelle d'émission :&lt;br /&gt;21/09/2019&lt;br /&gt;&lt;b&gt;17h00 UTC&lt;/b&gt;\">19h</span><a class=\"autorep-img\" href=\"/stations-meteo/static/meteofrance/0/05b4f2ab9aa29f30f3566c45c42704e762d345a1/0435634cc08a3807006d32dea79e3872.png\"><img alt=\"MF\" class=\"tipsy-trigger\" src=\"//static.infoclimat.net/images/mf.png\" style=\"cursor:pointer;float:right;border:0\" title=\"Cliquez ici pour accéder aux relevés complets de cette heure, issus du partenariat avec MétéoFrance\"/></a></td>,\n",
       " <td style=\"background-color:rgba(0,0,0,0.1)\"></td>,\n",
       " <td><span class=\"\" style=\"font-weight:bold;margin-top:10px;display:inline-block;font-size:16px\" title=\"\">26.1</span> <span class=\"tab-units-v\">°C</span><span class=\"color-heatmap\" style=\"background-color:rgb(238,61,8)\"></span></td>,\n",
       " <td style=\"background-color:rgba(0,0,0,0.1)\"><span style=\"margin-top:10px;display:inline-block\">29</span><span class=\"color-heatmap\" style=\"background-color:rgb(202,16,29)\"></span></td>,\n",
       " <td>0 <span class=\"tab-units-v\">mm/1h</span><span class=\"color-heatmap\" style=\"background-color:rgb(255,255,255)\"></span></td>,\n",
       " <td style=\"background-color:rgba(0,0,0,0.1)\"><span style=\"font-weight:bold;margin-top:10px;display:inline-block\">45</span><span class=\"tab-units-v\">%</span><span class=\"color-heatmap\" style=\"background-color:rgb(171,242,176)\"></span></td>,\n",
       " <td><span style=\"font-weight:bold;margin-top:10px;display:inline-block\">13.3</span> <span class=\"tab-units-v\">°C</span><span class=\"color-heatmap\" style=\"background-color:rgb(239,255,38)\"></span></td>,\n",
       " <td style=\"background-color:rgba(0,0,0,0.1)\"><span style=\"font-weight:bold\">5</span> <span class=\"tab-units-v\">km/h</span><div alt=\"vent\" class=\"tipsy-trigger\" style=\"float:left;margin-left:3px;margin-top:10px;width:20px;height:20px;background-image:url(//static.infoclimat.net/images/pictos_vent2/sprite.png);background-position:100px 0px;\" title=\"Vent de direction 160°\"></div><br/>(<span style=\"font-weight:bold\">25.9</span> <span class=\"tab-units-v\">km/h</span>)</td>,\n",
       " <td>1009.2<span class=\"tab-units-v\">hPa</span><span class=\"color-heatmap\" style=\"background-color:rgb(25,255,113)\"></span><br/><img alt=\"baisse\" class=\"tipsy-trigger\" src=\"//static.infoclimat.net/images/v5.2/down.png\" style=\"height:12px\" title=\"-2 hPa/3h\"/></td>,\n",
       " <td style=\"background-color:rgba(0,0,0,0.1)\">20 <span class=\"tab-units-v\">km</span></td>]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soup.select('tr.cdata-hour17 td')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### at 5pm, from the tab, let's take the text within the html tags to see what do we get "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19h\n",
      "\n",
      "26.1 °C\n",
      "29\n",
      "0 mm/1h\n",
      "45%\n",
      "13.3 °C\n",
      "5 km/h(25.9 km/h)\n",
      "1009.2hPa\n",
      "20 km\n"
     ]
    }
   ],
   "source": [
    "for element in soup.select('tr.cdata-hour17 td'):\n",
    "    print(element.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's wrap that in a function and loop on different years and months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "months = ['Janvier', 'Février', 'Mars', 'Avril', 'Mai', 'Juin', 'Juillet',\n",
    "'Août', 'Septembre', 'Octobre', 'Novembre', 'Décembre']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "import unidecode as uni\n",
    "months_formatted = list(map(lambda x: uni.unidecode(x).lower(), months))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scrap_weather_data(months_range, years_range):\n",
    "    for year in years_range:\n",
    "        for month in months_range:\n",
    "            url = 'https://www.infoclimat.fr/observations-meteo/archives/1er/'+\\\n",
    "            month +'/' +str(year)+ '/paris-montsouris/07156.html'\n",
    "            requesting = req.request('get', url)\n",
    "            print(requesting.status_code)\n",
    "            print(url)\n",
    "            if requesting.status_code in {503, 404}:\n",
    "                break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200\n",
      "https://www.infoclimat.fr/observations-meteo/archives/1er/janvier/2010/paris-montsouris/07156.html\n",
      "200\n",
      "https://www.infoclimat.fr/observations-meteo/archives/1er/fevrier/2010/paris-montsouris/07156.html\n",
      "200\n",
      "https://www.infoclimat.fr/observations-meteo/archives/1er/mars/2010/paris-montsouris/07156.html\n",
      "200\n",
      "https://www.infoclimat.fr/observations-meteo/archives/1er/avril/2010/paris-montsouris/07156.html\n",
      "200\n",
      "https://www.infoclimat.fr/observations-meteo/archives/1er/mai/2010/paris-montsouris/07156.html\n",
      "200\n",
      "https://www.infoclimat.fr/observations-meteo/archives/1er/juin/2010/paris-montsouris/07156.html\n",
      "200\n",
      "https://www.infoclimat.fr/observations-meteo/archives/1er/juillet/2010/paris-montsouris/07156.html\n",
      "503\n",
      "https://www.infoclimat.fr/observations-meteo/archives/1er/aout/2010/paris-montsouris/07156.html\n",
      "503\n",
      "https://www.infoclimat.fr/observations-meteo/archives/1er/janvier/2011/paris-montsouris/07156.html\n",
      "503\n",
      "https://www.infoclimat.fr/observations-meteo/archives/1er/janvier/2012/paris-montsouris/07156.html\n",
      "503\n",
      "https://www.infoclimat.fr/observations-meteo/archives/1er/janvier/2013/paris-montsouris/07156.html\n",
      "503\n",
      "https://www.infoclimat.fr/observations-meteo/archives/1er/janvier/2014/paris-montsouris/07156.html\n",
      "503\n",
      "https://www.infoclimat.fr/observations-meteo/archives/1er/janvier/2015/paris-montsouris/07156.html\n",
      "503\n",
      "https://www.infoclimat.fr/observations-meteo/archives/1er/janvier/2016/paris-montsouris/07156.html\n",
      "503\n",
      "https://www.infoclimat.fr/observations-meteo/archives/1er/janvier/2017/paris-montsouris/07156.html\n",
      "503\n",
      "https://www.infoclimat.fr/observations-meteo/archives/1er/janvier/2018/paris-montsouris/07156.html\n"
     ]
    }
   ],
   "source": [
    "scrap_weather_data(months_range=months_formatted, \n",
    "                   years_range=range(2010,2019))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## oups..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seems the server has detected suspicious redondant calls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='img/forbidden.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "## better function adding random timed requests\n",
    "def scrap_better_weather_data(months_range, years_range):\n",
    "    import random, time\n",
    "    \n",
    "    YEAR, MONTH, HTML   = [], [], []\n",
    "\n",
    "    for year in years_range:\n",
    "        for month in months_range:\n",
    "            waitfor = 0\n",
    "            print(month + ', ' +str(year))\n",
    "            url = 'https://www.infoclimat.fr/observations-meteo/archives/1er/'+\\\n",
    "            month +'/' +str(year)+ '/paris-montsouris/07156.html'\n",
    "            requesting = req.request('get', url)\n",
    "            while requesting.status_code != 200:\n",
    "                print(\"Retrying...\")\n",
    "                waitfor += random.randint(2,4)\n",
    "                time.sleep(waitfor)\n",
    "                requesting = req.request('get', url)\n",
    "                print(requesting.status_code)\n",
    "            YEAR.append(year)\n",
    "            MONTH.append(month)\n",
    "            HTML.append(requesting.text)\n",
    "        \n",
    "    return YEAR, MONTH, HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "janvier, 2017\n",
      "avril, 2017\n",
      "Retrying...\n",
      "200\n",
      "juillet, 2017\n",
      "Retrying...\n",
      "503\n",
      "Retrying...\n",
      "200\n",
      "octobre, 2017\n",
      "Retrying...\n",
      "503\n",
      "Retrying...\n",
      "200\n",
      "janvier, 2018\n",
      "Retrying...\n",
      "200\n",
      "avril, 2018\n",
      "Retrying...\n",
      "503\n",
      "Retrying...\n",
      "200\n",
      "juillet, 2018\n",
      "Retrying...\n",
      "503\n",
      "Retrying...\n",
      "200\n",
      "octobre, 2018\n"
     ]
    }
   ],
   "source": [
    "YEAR, MONTH, HTML = scrap_better_weather_data(months_range=months_formatted[::3], \n",
    "                           years_range=range(2017,2019))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_ = lambda x: [x.text for x in BeautifulSoup(x, 'html.parser').select('tr.cdata-hour17 td')[0:4:2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "HTML2 = list(map(f_, HTML))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['18h', '-0.6 °C'],\n",
       " ['19h', '16.7 °C'],\n",
       " ['19h', '20.4 °C'],\n",
       " ['19h', '15.3 °C'],\n",
       " ['18h', '8.6 °C'],\n",
       " ['19h', '11.1 °C'],\n",
       " ['19h', '30.3 °C'],\n",
       " ['19h', '13.4 °C']]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>hour and temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>janvier</td>\n",
       "      <td>[18h, -0.6 °C]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>avril</td>\n",
       "      <td>[19h, 16.7 °C]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>juillet</td>\n",
       "      <td>[19h, 20.4 °C]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>octobre</td>\n",
       "      <td>[19h, 15.3 °C]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>janvier</td>\n",
       "      <td>[18h, 8.6 °C]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>avril</td>\n",
       "      <td>[19h, 11.1 °C]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018</td>\n",
       "      <td>juillet</td>\n",
       "      <td>[19h, 30.3 °C]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018</td>\n",
       "      <td>octobre</td>\n",
       "      <td>[19h, 13.4 °C]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year    month hour and temperature\n",
       "0  2017  janvier       [18h, -0.6 °C]\n",
       "1  2017    avril       [19h, 16.7 °C]\n",
       "2  2017  juillet       [19h, 20.4 °C]\n",
       "3  2017  octobre       [19h, 15.3 °C]\n",
       "4  2018  janvier        [18h, 8.6 °C]\n",
       "5  2018    avril       [19h, 11.1 °C]\n",
       "6  2018  juillet       [19h, 30.3 °C]\n",
       "7  2018  octobre       [19h, 13.4 °C]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'year':YEAR,\n",
    "              'month':MONTH,\n",
    "              'hour and temperature':HTML2})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>hour and temperature</th>\n",
       "      <th>hour</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>janvier</td>\n",
       "      <td>[18h, -0.6 °C]</td>\n",
       "      <td>18h</td>\n",
       "      <td>-0.6 °C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>avril</td>\n",
       "      <td>[19h, 16.7 °C]</td>\n",
       "      <td>19h</td>\n",
       "      <td>16.7 °C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>juillet</td>\n",
       "      <td>[19h, 20.4 °C]</td>\n",
       "      <td>19h</td>\n",
       "      <td>20.4 °C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>octobre</td>\n",
       "      <td>[19h, 15.3 °C]</td>\n",
       "      <td>19h</td>\n",
       "      <td>15.3 °C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>janvier</td>\n",
       "      <td>[18h, 8.6 °C]</td>\n",
       "      <td>18h</td>\n",
       "      <td>8.6 °C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>avril</td>\n",
       "      <td>[19h, 11.1 °C]</td>\n",
       "      <td>19h</td>\n",
       "      <td>11.1 °C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018</td>\n",
       "      <td>juillet</td>\n",
       "      <td>[19h, 30.3 °C]</td>\n",
       "      <td>19h</td>\n",
       "      <td>30.3 °C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018</td>\n",
       "      <td>octobre</td>\n",
       "      <td>[19h, 13.4 °C]</td>\n",
       "      <td>19h</td>\n",
       "      <td>13.4 °C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year    month hour and temperature hour temperature\n",
       "0  2017  janvier       [18h, -0.6 °C]  18h     -0.6 °C\n",
       "1  2017    avril       [19h, 16.7 °C]  19h     16.7 °C\n",
       "2  2017  juillet       [19h, 20.4 °C]  19h     20.4 °C\n",
       "3  2017  octobre       [19h, 15.3 °C]  19h     15.3 °C\n",
       "4  2018  janvier        [18h, 8.6 °C]  18h      8.6 °C\n",
       "5  2018    avril       [19h, 11.1 °C]  19h     11.1 °C\n",
       "6  2018  juillet       [19h, 30.3 °C]  19h     30.3 °C\n",
       "7  2018  octobre       [19h, 13.4 °C]  19h     13.4 °C"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['hour', 'temperature']] = df['hour and temperature'].apply(pd.Series)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Selenium (dynamic content from websites)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From wikipedia:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selenium is a portable framework for **testing web applications**. Selenium provides a playback tool for authoring functional tests **without the need to learn a test scripting language (Selenium IDE)**. It also provides a test domain-specific language (Selenese) to write tests in a number of popular programming languages, including C#, Groovy, Java, Perl, PHP, Python, Ruby and Scala. The tests can then **run against most modern web browsers. Selenium runs on Windows, Linux, and macOS**. It is open-source software released under the Apache License 2.0.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selenium WebDriver is the successor to Selenium RC. Selenium WebDriver **accepts commands** (sent in Selenese, or **via a Client API**) and sends them to a browser. This is implemented through a browser-specific browser driver, which sends commands to a browser and retrieves results. **Most browser drivers actually launch and access a browser application (such as Firefox, Chrome, Internet Explorer, Safari, or Microsoft Edge)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## You first have to download a webdriver"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "better to put it in the current working directory..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/lucbertin/Desktop/TDs_Python_ESILV_5A'"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='img/ChoosingYourWebdriver.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='img/chromedriver.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from selenium import webdriver #the webdriver package\n",
    "from selenium.webdriver.common.by import By #use the method to select\n",
    "from selenium.webdriver.common.keys import Keys \n",
    "\n",
    "from selenium.webdriver.support.ui import WebDriverWait #enables to wait until condition is fullfilled or just until a timeout exception is returned\n",
    "from selenium.webdriver.support import expected_conditions as ec #enables to create the conditions\n",
    "from selenium.common.exceptions import TimeoutException #enables to retrieve the exceptions type\n",
    "from selenium.common.exceptions import NoSuchElementException #enables to retrieve the exceptions type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Options for the web driver (here incognito mode for Chrome, better to be prompted again the password each time we open a new web-browser)\n",
    "options = webdriver.ChromeOptions()\n",
    "options.add_argument(' - incognito')\n",
    "## Get the webdriver from its location path\n",
    "driver = webdriver.Chrome(executable_path='./chromedriver', options = options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Go to Instagram main connection page\n",
    "driver.get(\"https://www.instagram.com/accounts/login/?source=auth_switcher\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"f368b70c-4681-4aa5-b85f-98940303ebd5\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"79ac4477-fb3b-4ee8-8fde-31333238acd3\")>]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Define a callable functino which waits element to load\n",
    "WebDriverWait(driver, timeout=1000).until(ec.visibility_of_all_elements_located((By.TAG_NAME, 'input')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 2 tags matching with tag name 'input'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### creating a fake account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import insta_credentials as insta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'alejandroboteglia@gmail.com'"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insta.USER_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Enter ID/password\n",
    "driver.find_element(By.CSS_SELECTOR, \"input[name='username']\").send_keys(insta.USER_ID)\n",
    "driver.find_element(By.CSS_SELECTOR, \"input[name='password']\").send_keys(insta.USER_PASSWORD)\n",
    "driver.find_element(By.CSS_SELECTOR, \"button[type='submit']\").click()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='img/pop-upInsta.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handle this pop-up while opening Instagram\n",
    "Using CSS Selector for example "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "ButtonPopup = WebDriverWait(driver, timeout=5).until(ec.visibility_of_all_elements_located((By.CSS_SELECTOR, \"button[class='aOOlW   HoLwm ']\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"54051db8-7d44-46fb-a759-b1d28453bb44\")>]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ButtonPopup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "it exists, so we can click on it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "ButtonPopup[0].click()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's take an hashtag to retrieve images from"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "hashtag = '#kimkadarshian'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "search_bar = driver.find_element(By.CSS_SELECTOR, \"input[class='XTCLo x3qfX ']\")\n",
    "search_bar.send_keys(hashtag)\n",
    "search_bar.send_keys(Keys.ENTER)\n",
    "time.sleep(3)\n",
    "## sometimes we have to press multiple times Enter...\n",
    "search_bar.send_keys(Keys.ENTER)\n",
    "search_bar.send_keys(Keys.ENTER)\n",
    "search_bar.send_keys(Keys.ENTER)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All photos that appeared or will appear are located in div[class='v1Nh3 kIKUG  _bz0w']m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"1225e9e7-172f-4b5f-9697-280b00ddb5ff\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"52adafa6-ef36-4d03-b0c2-03a480e4c17a\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"50ac3be4-b484-491c-b4c7-c509cc27824c\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"94633ca3-c582-472e-aa0a-50b021bfaaa6\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"f57cbd83-9700-4574-a6de-2caa1e184a5d\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"132a2e66-ad0b-49b8-90d1-9e1cf2aaa927\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"d9cc75ab-4bfe-4ea5-82b9-5771af917c47\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"eba4a3cc-c94c-4ff4-9ba8-c9af58f789de\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"624a13a0-a3e6-45c2-928c-44d25f5f5890\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"929bfe68-9379-4753-b993-935016e4e943\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"adcc286a-3667-4708-b981-32ef9ad48bf0\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"491ae9ec-932d-4380-b1e7-b93189ca2867\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"ba1c86ae-d769-453f-ada8-12517052401c\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"a6024ef7-abfe-4111-a99c-a301fbe41454\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"108bd8c6-8eb2-427b-a108-b30c31bbf6ee\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"077a2ff1-11a1-4fc2-a533-5cd0eb9f4107\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"e838e1f2-56ee-43cd-a04d-01936e0c01d1\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"f26b7e40-01cc-4be3-9a23-19b198ab3ae5\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"44db9aee-9beb-407a-b5f2-8aab4a3d2e22\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"7b41f32f-e5b7-4259-b07a-b5af3bf69844\")>,\n",
       " <selenium.webdriver.remote.webelement.WebElement (session=\"d04c91223098767ff6f09639f7137de0\", element=\"d6642ae0-4ecd-4f16-8999-ff385563eebf\")>]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WebDriverWait(driver, timeout=10).until(ec.visibility_of_all_elements_located((By.CSS_SELECTOR, \"div[class='v1Nh3 kIKUG  _bz0w']\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's scroll over all images to download them all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NbCallFunction:\n",
    "    \"\"\" This is a decorator to count the number of times a function has been called\n",
    "    It will be used to retrieve the image number to put it in working directory\"\"\"\n",
    "    def __init__(self, function):\n",
    "        self.callNumber = 0\n",
    "        self.function = function\n",
    "    def __call__(self, *args, **kwargs):\n",
    "        ## onCall\n",
    "        self.callNumber += 1\n",
    "        return self.function(*args, **kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### example of use of a decorator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "@NbCallFunction\n",
    "def helloworld():\n",
    "    print('hello-world')\n",
    "#helloworld.callNumber"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello-world\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "helloworld()\n",
    "helloworld.callNumber"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "@NbCallFunction\n",
    "def download_img_from_link(string_url_img, hashtag_name):\n",
    "    \"\"\" This function retrieve all the photos from the visible window\"\"\"\n",
    "    import os\n",
    "    import requests as req\n",
    "    if not os.path.exists('data/'):\n",
    "        os.mkdir('data/')\n",
    "    string_path = 'data/'+ hashtag_name.lstrip('#') + str(download_img_from_link.callNumber) + '.jpg'\n",
    "    with open(string_path, 'wb') as file:\n",
    "        response = req.get(string_url_img)\n",
    "        print(response) if (not response.ok) else file.write(response.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scrolling number : 1 on limit : 10\n",
      "number of photos downloaded : 21\n",
      "scrolling number : 2 on limit : 10\n",
      "number of photos downloaded : 42\n",
      "scrolling number : 3 on limit : 10\n",
      "number of photos downloaded : 63\n",
      "scrolling number : 4 on limit : 10\n",
      "number of photos downloaded : 84\n",
      "scrolling number : 5 on limit : 10\n",
      "number of photos downloaded : 105\n",
      "scrolling number : 6 on limit : 10\n",
      "number of photos downloaded : 126\n",
      "scrolling number : 7 on limit : 10\n",
      "number of photos downloaded : 147\n",
      "scrolling number : 8 on limit : 10\n",
      "number of photos downloaded : 168\n",
      "scrolling number : 9 on limit : 10\n",
      "number of photos downloaded : 189\n",
      "scrolling number : 10 on limit : 10\n",
      "number of photos downloaded : 210\n"
     ]
    }
   ],
   "source": [
    "##==== the while loop idea using the last_height and new_height is from @Artjom B. on Stackoverflow \\\n",
    "##==== i find it quite straightforward and useful ====##\n",
    "SLEEP_EACH_SCROLL = 3\n",
    "last_height = driver.execute_script(\"return document.body.scrollHeight\") # Get scroll height executing js script\n",
    "count, limit = 0, 10 # if we want to stop\n",
    "\n",
    "all_images_so_far = set()\n",
    "while count < limit:\n",
    "    ## Scroll down to bottom\n",
    "    #driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n",
    "    driver.execute_script(\"window.scrollTo(0, 1000);\")\n",
    "    time.sleep(SLEEP_EACH_SCROLL)\n",
    "     \n",
    "    ## Retrieve the divs list\n",
    "    liens_images = [ element.get_attribute('src') for element in driver.find_elements(By.CSS_SELECTOR, \"div[class='v1Nh3 kIKUG  _bz0w'] img\")]\n",
    "    \n",
    "    ## important: in case the visible window overlapp with the former one, we don't want photos to get scrapped twice (so we say not in 's')\n",
    "    string_url_imgs = [download_img_from_link(x, hashtag) for x in liens_images if x not in s]\n",
    "    \n",
    "    ## Keep note of stored images\n",
    "    all_images_so_far = all_images_so_far | set(liens_images) # Saving this list to avoid downloading again the same photos\n",
    "    count += 1\n",
    "    \n",
    "    ## Some printing \n",
    "    print(\"scrolling number : \" + str(count) + \" on limit : \" + str(limit))\n",
    "    print(\"number of photos downloaded : \" + str(download_img_from_link.callNumber))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12 photos more each 3 secunds => 240 photos / min => 14,400 /hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
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NwJp12VMBHUnGKnWGNVKAcHqKYltCjblXmgCZc7RnrS0UUAFFFFABVa7/AOPdvw/nVmmuiyKUcZBoAqvzPD9D/KlcD7Yn+6asGNCysRyvSlKKXEhHzDjNAFf/AJfP+Af1qOOSWVWdWVFB9P51c2Lv8zHzYxn2qI20BbeV5oApKCbFsev9atSyRG2OCMFcAVLs8uMrEB9D0qltJUqsGHIxnsKAEfPkwHOB6mpZEclPNkHUEcVZWICIRPyAMGkS3hjbcq80ATVGs0bgkH7vWpKge2hdtzLyaAKsf/HifxqRIZXjXMp2kDgCrRjQp5ZHy9MU4AKAo6DigCncosdtsXoCKf8AZbf/ACaneNJF2uMiofslv/d/U0AMnPlzRyn7vIP41JMFI8wyFQB2PWpyqsu1hkVXFpADnb+tAC2u8wguSSfWrFFFABRRRQAUUUUAFFFFABRRRQBwFFFFABS0lLQB/9Pm6XijikoAKXvSUYPWgArb0j/lr/wH+tYlbekf8tf+A/1oA2aKKKYgooooAWpAMVHUgOa5q26NIC0xhT6YxqIfErFPYbRRRXYYhWbaELNck9A3+NW0uIpHaMHDIcHNULeTZ9qnXkBiR74zQBJ9tmKmRISYx3zj9Knku1W2+0oNwOOOlUszyW5nknCgg8AD8qYRnSB/n+KgDWkk8uJpcZwM4qvJeCOCOcrw5HGemaLiWP7Gzbh8y4H41Ul/487b/eX+VAFhr2SPDSwlUPfP9KsXFwsAHBZm4UDvUWo/8ej/AIfzqrchhPA27YCuN3oaALSXbiRY7iMx7uhzkVerKlh+ZFmuMncCox3/AArVoAWiiigApaSloAKWkpaAClpKWgApaSloAKWkpaAClpKWgAooooAWiiigAooooAWhfvr+NFA++v40AWM0ZptFIY7NGabRQA7NGabRQA7NGabRQA7NGabRQA7NIcEYIpKafvKPWgA2xf3Vo2xf3VqCGQyjkKCPaq0lzMhbaiELn6nHtQOxobYv7q08YAwBVSKUyQiVlVTjOMVYH3mHpQIkzRmopZY4UMkhwo70+gB2aM1B50Xm+Rn58Zx7VLQA7NGabUcsscK7pDgE4oAmzRmm0UAOzRmm0UAOzRmoYpY5k8yI5X1qSgB2aM02igB2aM02o45Y5d3lnO07T9RQBNmjNRu6xoZHOFHJNCOsiB0OQRkGgCTNGabRQA7NGabTPNj3+VuG70zzQBLmjNQySxQjdKwUe9Ec0UwzEwbHpQBNmjNNJAGT0FRxSxzp5kRypoAmzRmm0UAOzRmm0UAOzRmm0UAOzRmm0UAOzRmm0UAOzRmm0UAOzRmm0UAOzRmm0UAOzRmm0UAOzRmm0UAOzRmm0UAOzRmm0UAOzRmm0UAOzRmm0UAOzRmm0UAOzRmm0UAOzRmmkgDJ4FQtMowI/nJ6AUAWM0ZqtuuF5KqR6A81KjrIoZehoAkzRmm0UAOzRmm0UAOzRmm0UAOzRmm0x5EjxvOMnAoAlzRmm0UAOzRmm0UAOzRmm0UAOzRmolkRgWU8DrQZYwocng96AJc0ZptFADs0ZptMWRGYoDyvWgCXNGabTI5FlXcvY4oAlzRmm0jMqDLEAe9AD80ZpuQRntUQnhJ2hhmgCfNGabQSAMngUAOzRmmKysNynIphmiDbSwzQBNmjNNooAdmjNNooAdmjNNooAdmjNNooAdmjNNooAdmjNNooAdmjNNooAdmjNNooAdmjNNooAdmjNNooAdmjNNooAdmjNNooAdmjNNooAdmjNNooAdmjNNpCyghSeT0oAfmjNNooAdmjNRPLHH99gKcrKw3KcigB+aM0wkKMk4ApcgjI6UAOzRmmKyuMqcj2paAHZozTaKAHZozTaKAHZozTaRmVRuY4FAD80ZptFADs0ZptFADs0ZptFADs0ZptFADs0ZptFADs0ZqMOhXeCMetNE0J4Dj86AJs0ZpjMqjLHAqPz4f74/OgCfNGabkYz2qITwk7Q4zQBPmjNNooAdmjNNooAdmjNNooAdmjNNooAdmjNNooA4OiiigApaSloA//1OcIwcA5pKKKACijFFAB9a2tI/5a/wDAf61i81taP/y1/wCA/wBaANqiiimIKKKKAClBIpKKTSejGmO3GkpKKUYJbA22LRRRVCKstnbztvdefUcVMkUcaeWigL6VJRQBUWxtVbcE5/Gp1hjSPyVHyenXrUlFAFRbG1XOE68dTUpt4mRYyvyoQQMnjFTUUAMkjSVDHIMqe1DxRyJ5brlfSpKKAK0VnbwtvjXB9etWaKKAFooooAKWkpaAClpKWgApaSloAKWkpaAClpKWgApaSloAKKKKAFooooAKKKKAFoH31/GigffH40ATUUUUhhRRRQAUUUUAFFFFABRRRQAU0/fX6n+VOpp++v1P8qAKkUewjaeSKikjK9DwetTwtkNJ2AwPw61WSYOwOevGKTLRLgvGwVhgjBq6Pvt9R/Ks6AFRID0BxWiPvt9R/KmSylqf/Hk/4fzFK1y7yi3tgGI++x6L/wDXpNT/AOPJ/wAP5iqyqdNkB5MEmM/7J9aBFwS/6d5O1f8AV53Y569PpVWO7vZ4zJEiYUnOc849KlBB1TI/55f1pNN/49D/ALzUAMF5eSw/aII1CAc7upx1xSXkonsY5QMbmXin2P8AyDfwb+ZqpIM6VEPVh/M0AaJuXln8m2AIX77HoPYe9LNJdmXy7dFwByz9PwxVVM6bN5bf6iQ8E/wn3qGd0+1ut67KgxsAzg/lQBegu2ZZRcAK0P3sdMVElzeyJ9oWNfL6hcncR/KqtrGJPtccYKhgNoPXkHGangvo44FhYHzUG3Zg5JFABYu8en741Ltk4A+tPa5vIGRrlU2OQPlzkZqmjTJpYMWR8x3EdQM81FN9kIQ2xdzuGWOePz70Aatxczx3C28Khiy55ps1zcQJHGwUyyEj/ZFEn/ITj/65mnXzQBVW5QshP3h/DQA+GS78zy7hFxjIZen05qjatdbp0tlX/WMSW6fTiltGH2rZauzw4+bd0B9s1Y0//lv/ANdWoAEu2ktpmkQB4shh1HFSedMLWN4YwzOBwOAMj+VUx9y++p/lTZ3dbS25ZYyBvK9cYFAFpbi6inSG6VcSdCuev40Pc3JuntoVUkAEE5wPrVAfZzdQG23EbuWOeT+NX4v+QlN/urQAsU9xK0lrLhJVGQy9Oe9Vkt0t9QjVSSShJJ6k81YT/kJv/wBcx/OiT/kJxf7h/rQBDdmOK8Sa5G6Mrgd8H6UlsYpr4y2o2oFw3bJ+lLIyW+oNLcj5GUBGxkCkV47i/jktRwoO9gMA+1AGueATWWLt004XKqoOegHHXFXEuEmaWNQQY+Dmsg/8gUfX/wBmoAuvc3qJ9oMa+X125O7H8qmnuxHCjxDe0uAg+tVpr+N7doQD5rDbswcgniiaCWGC3kUbjDjcB+tACzXV7bIGmRDk4BXOB9a1aw767W4gCxK2NwJJGAPatygAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAKrHz5QgGUUnd6ZqdY0ViyqAT6VXgkSJfKkIVlJ69/ep/Oh/vr+YoAkqsjBJnQ8bjke/HNS+dD/AH1/MVBO6SARxkMxIxjt70AW6KKKACiiigCu7XBcrEoAHdu9NWdjE7EYZOCO1QMyGZxcFhg/KBnGKIl3RTKoIz0B60AXPMPkeb325/SomlOyJiASxH4Z9Ki+0IbfyxndtxjHtQ3+pg/3loAnklfzPKiALYySegojlfzPKmADYyCOhqN28i4Mj/dYYz6GhW8+4WRPuoDz6k0ATQyGQMW7MRTBMxSRsD5CQPwqKKVIC8cmQdxI4601CTBMSMZJoAnikmcCRwFTGfemrLcSDzEUbewPU1LGN1uq+qAfpVeGdYoxE4IdeMY60AJCGkt5AnVmPX3p9yojt1UdiKWzz5bZ4O40t3/qh/vCgBHmnjw8ijb7dRUrSFZkjHRgf0pl5/qG/D+dNnPlyxyt90ZB/GgCWSQrKiDo2c/hSRvumkXAG3HPc1A0qy3ERToM81JF/wAfEv4UAWHJVSQMn0qnvmh/euigMecdanuS4hJj6+1Z8vkmLMZZm7k54oA16pXMOVeVjnHQdhV2oLr/AFDfSgBdnmQBM4yBVWY24QwoBu6Djv8AWrRZ0gDIMkAcVXlngkjIAyx6DHOaALDJI0SqrbWGMmklh8w5YnaB09TTQs6wKqY3DGc1YPQ0AUoiwsiV64NOjS3NuCQMY5PvS2uRbAqMnnioA9rjcUw/93nrQBYs3LQjPY4q1UFsjRxAN1PJqegAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAa7FVLKNxHas94XaVDI3zPnp2x0rSqtL/wAfEP8AwL+VAD4TIQVlHK9/WpqKKAM2KRGLOymRmPYZwKs23lfM0WRk8qe1RROlsWjl45yD6in2+XleYDCtjHvQBLcf6h/pVVX88LbocAKNx9fYVauP9Q/0qAw5hR4uHUAj3oAnkdbeMbR7AVEZZ4sPKq7e+OopjuZollQcowJFE0yTp5UWSzY7dKAL1FIBgAelLQAUUUUAFV7v/j3b8P51Yqvd/wDHu34fzoAa8roY0QA7hTfOnV/KZQWPTHSlb/Xw/Q/ypW/4+0/3TQAsUshlMUoAOMgigvcknYoAHr1NIf8Aj8H+5/WqysjFvtBYvn7vP6UAW1uAYPOIxjt71GZblU8xlXGOg6ioo42ksyq9c1I9xvhZQp3Y5GOnrQBIZyIkfGWfoPek82aNlEwGGOMjsagdMwRPgkL1x6UAWzsqxhmOfU8UAaNFFVPtaruWQbWHQetAEMf/AB4t+NTrHB5ALhR8oyfwqJVKWRDcHBNSRW0BRWK8kA96AK5LGyG71/Sp/MsvRf8Avn/61LeACDA6ZFL9qtvX9KAEuPnkSDoDyfwp8kdvt2NtX07GmzhgyToM7eo9jTJJ7ZxnG9uwxQBZhGIwN27HepKgt4zHCFbr1NT0AFFFFABRRRQAUUUUAFFFFAHB0UUUAFLSUtAH/9XnM0lLnjFB/KmAlApfrSUgFz2rZ0f/AJa/8B/rWN0rZ0j/AJa/8B/rQBtUUUUxBRRRQAUUUUAFLSUtABRRRQAUUUUAFFFFABRRRQAtFFFABRRRQAtFFFABS0lLQAUtJS0AFLSUtABS0lLQAUtJS0AFLSUtABRRRQAtFFFABRRRQAtA++v40UL99fxoAmop+BRgUhjKKfgUYFADKKfgUYFADKKfgUYFADKKfgUYFADKY52kNgnHpU2BSHAoApIVjQIqSYHsKri3iVt4WXPbpxWnkehoyPQ0DuUlCqu3bKc+oFWkO4lsEZ9afkehpwwaBDaKfgUYFADKKfgUYFADKKfgUYFADKKfgUYFADKKfgUYFADKKfgUYFADKKfgUYFADOnSin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAwgHg0AAcDin4FGBQBXnSSSMpE2wnvjNVXsQbMWitjHcj3zWlgUYFADKguInlTEbmNgcgj+tWsCjAoAymtbqcqLqRSinOFHXFaVPwKMCgBlFPwKMCgBlFPwKMCgBlFPwKMCgBlFPwKMCgBlFPwKMCgBlFPwKMCgBlFPwKMCgBlFPwKMCgBlFPwKMCgBlFPwKMCgBlFPwKMCgBlFPwKMCgBlFPwKMCgBlFPwKMCgCJkR/vAH603yYf7i/kKnwKMCgCDyYf7i/kKcqIn3VA+gqXAowKAGUU/AowKAGUU/AowKAGUU/AowKAGVC8RkkVi3yqc496s4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAyjAzmn4FGBQAyin4FGBQAyjAzmn4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAzAPWin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQAyin4FGBQBE67lKgkZ7iqxiuWXy2cbemcc1ewKMCgCNVCKFHQDFLT8CjAoAZRgGn4FGBQAyin4FGBQAyin4FGBQAyjA60/AowKAGUU/AowKAGUU/AowKAGUU/AowKAGUU/AowKAGUU/ApMCgDgaKKKAClpKWgD//1ucyaTrS9RSCmAvTmkp52hcY5ptFgAitnR/+Wv8AwH+tYtbWj/8ALX/gP9aANqiiigQUUUUABOBk1B80nJOB2FPl/wBWcUDpxQAzywPu5Bp6MSSrdRS0z/lqv0NAE9FFFAFVJ2a6e3wMKAc1arOi/wCQjL/uj+lVrS1juEcykkBiAM9PegDaorGtbYXCMJ2LBCVUZ6U6AsLO4iJyE3AflQBr0Vk29lHNbq8xJJHHPQdsUiXEsVpKpOWibaD+lAGvS1lrp8bRBiT5hGd2e9RPK02lln5IwD+BoA2faoIpxK8iAY8s4qGGziVd7ZZmXBJPr1qraW0JuJeP9Ww289KANfOOtLWROLMzMbmQueyjPH5U/T3z5saklFI259DmgDUoJwM1jWVpHPbhpsnkgDPSpbVBLBJby5ZUYgfhQBft5hcRCUDAPap6y9NhjEQnx85yM1qUAFLSUtABS0lLQAUtJS0AFLSUtABRRRQAtFFFABRRRQAtC/6xfxooX/WL+NAFmiiikMKKKKACiiigAooooAKKKKACmN95fr/Sn0xvvL9T/KgDkhdXJc/vXwP9o1YS5lJ5lb8zVVFVlOD1NSxqkbjPINItI1Y7liuVJbPfJrVX7zfX+lYHl7ceUMAZyPet9fvN9R/KhEsiup/s0DTY3Yxx06mrAORms/VP+PJ/w/mKijle/kxExSGPqQcFj/QUxF7zZPtHleWdu3O/tn0qeqAd/wC0jHk7fKzjtnNUraK4uoGkad1IJC4Pp60AblV7q4+zRiTbuywHXHWsuNbm6tPtLTMrAHAXgcev1pbiVpdNilk6lhk/SgDborLhkkv5fMRikKHAAOCx9/ai7x537648pMcKpwfqaANSise0u2WO4y/mrFyrHqc5p0MF1LCLkzt5jDcB/D7DFAF61uPtUIl27ckjGc9KsVjWZf8As35HEZyfmPQc1A0ywOjQXLSksAyscgigDoKKyrrznvo4I5CgZCTj/PWm3nm20UUYkfYSd79WA+tAGvVe3uPtHmfLjY5Xr1xVW0H7wtDOZY8chjkg1WtYJJ2nHmMiiRvu8En60AbErMkbOi7iBwB3ojZnjV2XaSMkHtWVHLMLW5jdyWiyA3epmaRrGJvNEeVXcx69O3vQBpUVhRTLFdRpBO0qucMGOcVOwmmv5IRIyIFBODz+HpQBrEgDJqlHfwS3H2ePLH+924qvCri4lsZXMiFcgsecH3pWRI9ShRAABGcAfjQBZnvEhcRKrSSHnatLb3aTsYypR15Kt1qk8gs795pwdkigBsZxjtUZuBNqMEkQO3BXJ4zxzQBtE4BIGaigkeWMPIhjJ7GpW+6fpWGZpv7IEu9t+fvZ5+960AbtFZM0F0kJuRM3mKNxH8PrjFE1xLOtvFCdhmGSR2AHOKANaisW5jntfL2TOyM4B3Hn8/StqgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAGSSLGMt34AHU1D5ryHy4wVPcsOgpE3TyCboikge/bNWqAK/kyDlJGz78ipIpDIpyMMDgj3qSqpLQzM7D5HI59DQBaooooAKKKKACiqU2PMPmS7R2C9fxpiSuYJPmzt6NQBoVHI7JjapbJwfamFj9l3Z52Zz+FQM7+VCcnJYZ96AL1FVHLyzmFWKqoycdaELwziJmLKwyM9RigC3RVa2ZmV9xzhyKiV38qc5OQxx7UAXqKqRK4QTyOT8ucdulMijllTzjIQT0A6flQBOk4aNpCMBSf0oacLEsu372OPrUFunmQOjHGWOcU+7GIQF7EYoAt0VRmWSFfOEjEg8g9PyqV2YXMag8EHIoAs1GrszspUgDofWopWYTxAHg5zREzGeVSeBjFAFmoYpGfIcYZTg0+TOw7SFPqe1ZzyCIB0mLMOoPQ0AalQS3EcPDcn0FT1UuY0WKRwOT1NAFguoTzDwMZqv9rAG4owX1qZVV4lVhkECq1w8m0pswnTd14+lAF3cuN2eDTJJUiXLn8O9N8qKWJUPKgDFOaNCd5HIGBQARyrJH5o4Hv7VB9rGNwRivrimQqXstq9SD/OljuY1jEZB3gY24oAuKwdQy8g0tVLLPk4PYkVboAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACoHuYo5BGx5PX2qVw5QhDhuxqi0CJLEh+bduyT34oA0KKhhjaIFS2V/h9QKmoArvcBXKIpcjrjtT4pllBK5BHUHqKpWxmZW8oAZJJLetWYH3MyuoVx1x3oAndxGhc9BTTIoj81uBjNMuf9Q/0qlG3mvGswwmPlHYkUAaMb+YofBGfWn1XuJGRAqfeYgCoZEkt181XZsdQehoAvUUgORkd6WgAooooAKZLIIkMjcgU+q13/wAe7fh/OgCyDkZ9aKpSlzJEiMV3A5/KmlZUmEIkO1hnJ6igC/RVOLfHcGIsWUrkZpkmNzeZMVPYL2oAv0VRW4cWhkPLDjNK0UqRGQSMWAyc9KALtFUHmZYIxuwX6saZ5qxOpjlLgnBBoA0qKKKAIFnVoTMAcCo/tYxuKOB64qGP/jxb8ael1EIlXkkADGKALDzokQl6g+lR/aT/AM8n/Kqzo0dkA3XOcVa86f8A54n86AJnkSNN78Cq/wBqx8zRsF9cUTDdcxI3Tk1LLKqfKys2R2GaAJQQwDLyDS1DbmMxDygQvvU1ABRRRQAUUUUAFFFFABRRRQBwFFFFABS0lLQB/9fm/elzSfWlIxxTAUgcUh9qSlPHIoAQDnFbekf8tf8AgP8AWsTmtrR/+Wv/AAH+tAG1RRRQIKKKKAAgEYNQfNHwRkdjU9FAEHmA/dyTT0Ugl26mpKWgAooooApJFIL2SYj5WUAH8qWyikhjZZBglifwq5RQBTs4pIlkEgxuckfSoBFJFBc7xjcWI+ladNdBIhRujDBoAy7dryO3VY0DgjKnOMZ9amjsz9meKQ/PIck+9XY0WNBGvQDAp9AGaG1BY/J8sZxjfn9adJautj9nj+ZuPzzk1o0UANQEKAewqikdxDdOyqGSQgk56VoUUAZaR3NtLJ5cYkDnIOcVNaQzxyTPMBl8EEfjV+igCpYxPDbhJBg5NJawvGZd4xvckfSrlLQBQskuIP3EijaMkMD/AErQpKWgApaSloAKWkpaAClpKWgApaSloAKKKKAFooooAKKKKAFoX/WL+NFKv+sX8aALFFFFIYUUUUAFFFFABRRRQAUUUUAFMb7y/U/yp9McMcFMZHrQBySW7RsSDwalSIuQckVv+Q/9yL/vmgQSDokX/fNA7lJECHbnkitZfvN9R/Kq/lS5zti/KrCBhkvjJ9KAuVr+KSe1aOIZY4wPxqCS2khlW5tF54Dp0BFadFAikIpPt/n4wnl7c8dc9KSxhkhtzHIMHJP51eooAoWkEsVj5LjD4bj65qu1rO2nxwbfnVgSMjpk1r0UAZr28tvcCe0XKvw6cD8Rmo2huIbt50iEwkxjJAI/OtaigDKt7WYtcfaAAJgOnTof5URf2hFELYRg44EmeMfTrWrRQBifYrg6eIMYdW3YJ60s0V3cIiiBYlVgcAjJraooApPDIb9JgPkCEE+9PuTdLta3AcD7ynjP0NWqKAMqC3la7Fy0QhA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0qoImgRLiPJ4+YexoAuDbbxZdicdSaYt1GSAQy56Ejio7h1eOOQcpuBNOuypgI6k4xQBbopFzgZ60tABRRRQAVXuiVgYqcHj+dWKrXf8Ax7t+H86AHNOkQUPnLCmi6j5BDAjsRyaY/M8P0P8AKlcD7Yn+6aAJYp0lJUAgjsaY10isQAzY6kDgUn/L5/wD+tRxySyqzqyooPp/OgC2JUaPzQfl9agF3HjJDAepHBqqoJsWx6/1q1LJEbY4IwVwBQBKZkEQlPANMS5RmCEMpPTcMZqo+fJgOcD1NSyI5KebIOoI4oAvUUVGs0bgkH7vWgCnG7GyZiTnnmnrblow6yOGIB68VHH/AMeJ/GpEhleJcynaQOAKAI3ld7QPnDZxkVP9lH/PR/zplyix22xegIp/2W3/AMmgBZ3cFYYzhn7+gpptSBlHbd6k0k58uaOU/d5B/GpJgpHmGQqAOx60ASxFygMgw3en1Xtd5hBckk+tWKACiiigAooooAKKKKACiiigDgKKKKAClpKWgD//1Oboope3SgBMUUo5pKACtrR/+Wv/AAH+tYtbWkf8tf8AgP8AWgDaooopiCiiigAooJwMmq5Yv7CgCxRVbGOnFSo5Pyt1FAySlpKWgQUVTuLoxOIYl3yN2/xqFrm8gG+eNSnfb1FAGlRTVZXUOvIIyKSTfsby8bscZoAfRUcXmeWPOxv74qTOaAClpKWgAooooAWiiigApaSloAKWkpaAClpKWgApaSloAKWkpaAClpKWgAooooAWiiigAooooAWhf9Yv40UL99fxoAs0UmaM0hi0UmaM0ALRSZozQAtFJmjNAC0UmaM0ALUbkAqScAU/NIcEYIoAwvssuMebF+f/ANaozYyEH97GD7N/9at7bF/dWjbF/dWgDBFi20KZY+P9r/61b6EEsQcg0m2L+6tPGAMAUANlijnQxyjKnqKbLBFOoSVcgcipc0ZoAjEMYl87Hz7ducnpSxQxwp5cYwvXH1p+aM0ARxwRRReSgwnPH1phtYDEsBX5FOQMmp80ZoAilginx5q52nIPII/Ko5rK2nfzJUy3rkj+VWc0ZoAiit4YSzRLt3Yzj26VD/Z9n5nmeWM5z3x+XSreaM0AQi1gEPkbfkPaoV0+zUYEffPU9quZozQAwwxmUTEfOBgH2ps0ENwu2ZdwFS5ozQBBBaW9uSYlwT1PU/rUkcMcW7yxjcdx+pp+aM0ARfZ4cSDb/rfvcnmke1gkiELrlV6D0x71NmjNAFWOxtYypRMFTkHJ61OIY1laYD52GCfpT80ZoAYIYxKZgPnIwT7VUOmWJOTH+p/xq9mjNAEUFvDbArCu0Hk8k/zp3kx+b5+Pnxtz7U/NGaAK7RrBvuIY90jdQDjNURazrprwkZkY5wPc1rZozQBRXT7U7XeMbsDPpn6dKluxJ5OIkWT1U9x7VZzRmgDCMBnAhgtjECQWZvQVvUmaM0ALRSZozQAtFJmjNAC0UmaM0ALRSZozQAtFJmjNAC0UmaM0ALRSZozQAtFJmjNAC0UmaM0ALRSZozQAtFJmjNAC0UmaM0ALRSZozQAtFJmjNAEL26M28EqT1KnFN+zf9NJP++qsZozQBX+zf9NJP++qctuisHJZiOm45xU2aM0ALRSZozQAtFJmjNAET28Mjb2XmnrGiZ2jGetOzRmgCAWsAJO3rTJI3Z440XCIQc59KtZozQAySKOUYcZojijiGEGM0/NGaAIWtoXbey808QxhWUDhutPzRmgAUBQFHQcVCbaAtvK81NmjNACIixghBjJzQ6LINrjI60uaM0AI6LIuxxkGkkiSUbXGRTs0ZoAiW3hQgquCOlSBFVi4HLdaXNGaABlVwVYZBqAWsABG3r7mp80ZoAWmsqupVhkGlzRmgA2rt2Y4xjFVxaQA52/hmrGaM0ALRSZozQAiIsa7EGBUP2SDdu2/4VPmjNAC9OBRSZozQAtFJmjNAC0UmaM0ALRSZozQAtFJmjNAC0UmaM0ALRSZozQAtFJmjNAC0UmaM0ALRSZozQAtFJmjNAC0UmaM0ALRSZozQAtFJmjNAC00orMHI5Xp+NLmjNAC0UmaM0ARSW8Up3OOfWnpGka7UGBTs0ZoAGVXUq3INAUBQo6DijNGaAGLDGqlAOD1FMS2hRtyrzU2aM0ALRSZozQAtFJmjNAC010WRSjjINLmjNADTGhZWI5XpSlFLiQj5hxmlzRmgBNi7/Mx82MZ9qiNtAW3leamzRmgCPZ5cZWID6HpVLaSpVYMORjPYVo5ozQBGsSiIRPyAMGkS3hjbcq81LmjNAC1A9tC7bmXk1NmjNACFEKeWR8vTFKAFAUdBxRmjNADXjSRdrjIqH7Jb/3f1NWM0ZoACqsu1hkVXFpADnb+tWM0ZoAWikzRmgBaKTNGaAFopM0ZoAWikzRmgBaKTNGaAOBooooAKWiigD//1ebpxJxt7U2l6DJFMBKKPegY70gCtrR/+Wv/AAH+tYtbWj/8tf8AgP8AWgDaooopiCiiigBkn3DUVWCMjBquVKcdR60DClX/AFgpuc9OalRCPmbqaAJKWkpaBGbb8307N1GAPpUs9wyFkMLOoHJ7Yps8EyzC6tsFsYZT3qOQ3tyvleWIweCSc8UARXLCW1haIbAXGB6dastbRW9vKUySUOST14pJ7ZvKiihGdjA/gKtzqzwui8kqQPyoAzZBIbCLYCRxu29cUW62bSq1s5Rh1U9/bmrAS6jto1iwHXqp7/jUTRXFzKjSRiMIck5yTQBqUtJS0AFFFFAC0UUUAFLSUtABS0lLQAUtJS0AFLSUtABS0lLQAUUUUALRRRQAtFFFABRRRQAtA++v40UD74/GgCaiiikMKKKKACiiigAooooAKKKKACmn7yj1p1NP31+p/lQBzzavKD8qIfwP+NWDqR8kSKqFj7cZ/OubHzM248c1IgZhhRnAzSGaf9tz5wI4/wAj/jXTD7zD0rgsfNn3rvR99vqP5UxCSyxwoZJDhR3p9UNT/wCPJ/w/mKVrl2lFvbAMR99j0X/69AFnzovN8jPz4zj2qWqYl/07ydq/6vO7HPXp9Kqx3d7PGZIkTCk5znnHpQBrVHLLHCu6Q4BOKzheXksP2iCNQgHO7qcdcUl5KJ7GOUDG5l4oA1qKpG5eWfybYAhfvseg9h70s0l2ZfLt0XAHLP0/DFAFyiqMF2zLKLgBWh+9jpj2qJLm9kT7Qsa+X1C5O4j+VAF+KWOZPMiOV9akrJsXePT98al2ycAfWnm5vIGRrlU2OQPlzkZoA06KoXFzPHcLbwoGLLnmmy3NxAkcbBTLISP9kUAaNRxyxy7vLOdp2n6iq8Ml35nl3CLjGQy9PpzVG1a63TpbKv8ArGJLdPpxQBsO6xoZHOFHJNCOsiB0OQRkGqCXbyW0zSIA8WQw6jipPOmFrG8MYZnA4HAGR/KgC7RWctxdRTpDdKuJOhXPX8aHubk3T20KqSACCc4H1oA0agkubeJtsjhT6VDbXEzyvb3CgOozlehFIlrBbo7z4ckkszCgC6rBgGU5B7ioDd2wfyzIufrVOwjdrN1BKBy2z2BouLe1trEoygnGAcclqANSommiSRYmOGfoKZaK620ayfeCjNRzS7buGPaDv3ckcjA7UAXKKzTdXL3ElvCikrjBPQD3qW3uZWla3uVCuozx0IoAu0VmJc3lxmS2RPLBwN2cn6VPZ3L3Icuu3a2AP8aALlFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAEgDJ4FQtMowI/nJ6AVGx8+UIBlFJ3emanWNFYsqgE+lAEe64XkqpHoDzUqOsihl6GnVWRgkzoeNxyPfjmgCzRRRQAUUUUAFFV3a4LlYlAA7t3pqzsYnYjDJwR2oAtUx5EjxvOMnApvmHyPN77c/pUTSnZExAJYj8M+lAFqiq8kr+Z5UQBbGST0FEcr+Z5UwAbGQR0NAFiioYZDIGLdmIpgmYpI2B8hIH4UAWaKrRSTOBI4CpjPvTVluJB5iKNvYHqaALCyIwLKeB1oMsYUOTwe9U4Q0lvIE6sx/Wn3KiO3VR2IoAuUVUeaePDyKNuecdRUrSFZkjHRgf0oAmpiyIzFAeV60ySQrKiDo2c/hSRvumkXAG3HPc0AT0yORZV3L2OKVyVUkDJ9Kp75of3rooDHnHWgC9SMyoMsQB70tUrmLKvKxzjoOwoAu5BGe1RCeEnaGGaTZ5kATOMgVVmNuEMKAbug47/WgDQoJAGTwKhZJGiVVbawxk0ksPmHLE7QOnqaAJlZWG5TkUwzRBtpYZqrEWFkSvXBp0aW5twSBjHJ96ALlFVbNy0Iz2OKtUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABSFlBCk8npSOxVCyjcR2rPeF2lTzG+Z89O2OlAGlRUMJkIKyjle/rU1AEfnQjguPzpyuj/AHCDj0qN0hRS7IvHPQUy1j2oXIwX5x7dqALBIUZJwBS5BGR0qG4/1D/Sqqv54W3Q4AUbj6+woAvqyuMqcj2pahkdbeMbR7AVEZZ4sPKq7e+OooAt0UUUAFFFFABSMwUbmOBS1Xu/+Pdvw/nQBYoqq8roY0QA7hTfOnV/KZQWPTHSgC5RVaKWQymKUAHGQRQXuSTsUAD16mgCzRVdbgGDziMY7e9RmW5VPMZVxjoOooAuUVWM5ESPjLP0HvSebNGyiYDDHGR2NAFqiiigBodCu8EY9aaJoTwHH51Tj/48W/Gpljg8gFwo+UZP4UAWWZVGWOBUfnw/3x+dUSSbIbvX9Kn8yy9F/wC+f/rUAXMjGe1RCeEnaHGahuPnkSDoDyfwp8kVvt2NtX07GgCxRUcIxGBu3Y71JQAUUUUAFFFFABRRRQAUUUUAcHRRRQAtFFFID//W5ulpKKACijFFABW1o/8Ay1/4D/Wsc47Vq6S4EjxnqwB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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='img/data_retrieved.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### End"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}