scipy_notebook_1_gini.ipynb

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    "<img src= \"https://datascientest.fr/train/assets/logo_datascientest.png\" style=\"height:150px\">\n",
    "<hr style=\"border-width:2px;border-color:#75DFC1\">\n",
    "<h1 style = \"text-align:center\" > Calcul Scientifique avec Scipy </h1>\n",
    "<hr style=\"border-width:2px;border-color:#75DFC1\">\n",
    "\n",
    "### Contexte et objectif\n",
    ">`NumPy` et `SciPy` sont deux bibliothèques Python largement utilisées dans le domaine du calcul scientifique. Dans ce notebook nous allons introduire la bibliothèque `SciPy` pour le calcul mathématique.\n",
    ">\n",
    "> **SciPy** (Scientific Python) a été construite sur `NumPy` et dépend fortement de ses fonctionnalités. Cependant `SciPy` étend ces fonctionnalités en proposant des modules spécialisés pour des domaines scientifiques plus avancés. Cela inclut des domaines tels que l'optimisation, l'intégration, la résolution d'équations différentielles, l'algèbre linéaire avancée, les statistiques, et bien plus encore. En résumé, `SciPy` vise à offrir une boîte à outils complète pour les scientifiques et les ingénieurs.\n",
    ">\n",
    "> Dans la pratique, les bibliothèques `NumPy` et `SciPy` sont souvent utilisées ensemble pour le calcule scientifique et s'appuient sur `Matplotlib` pour la visualisation. \n",
    "> \n",
    ">\n",
    "> Nous allons commencer par importer les librairies nécessaire : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a5094487-a6da-40f9-b240-0aa65ef46cd5",
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy as sp\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import mstats\n",
    "import requests\n",
    "from scipy.integrate import quad"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e291b725-fdea-4958-8309-c6d7a67a61a1",
   "metadata": {},
   "source": [
    "> Notre objectif sera d'étudier les revenues des joueurs de baseball de la MLB en 2023 et calculer **l'index de Gini** \n",
    ">\n",
    "> L'indice de Gini est un outil statistique utilisé pour mesurer l'inégalité de répartition des revenus ou des richesses au sein d'une population donnée. Il est généralement représenté par un chiffre compris entre 0 et 1, où 0 représente une répartition parfaitement égalitaire (tout le monde a le même revenu ou la même richesse) et 1 représente une inégalité totale (une seule personne possède tout).\n",
    ">\n",
    "> Pour calculer l'indice de Gini, on utilise souvent **la courbe de Lorenz**, qui représente graphiquement la répartition cumulative du revenu ou de la richesse par rapport à la population cumulative. Plus la courbe de Lorenz s'éloigne de la ligne d'égalité parfaite (une ligne diagonale reliant le point de départ [0,0] au point d'arrivée [1,1]), plus l'indice de Gini est élevé, ce qui indique une plus grande inégalité.\n",
    ">\n",
    "> En pratique, pour calculer l'indice de Gini, on mesure **l'aire située entre la courbe de Lorenz et la ligne d'égalité parfaite, puis on la divise par l'aire totale sous la ligne d'égalité parfaite**. Cette division donne l'indice de Gini, qui est souvent exprimé en pourcentage.\n",
    ">\n",
    "> Nous allons récuper une table à partir du site [Spotrac](https://www.spotrac.com/) et notamment de la page dédiée au [baseball](https://www.spotrac.com/mlb/contracts/sort-value/limit-2000)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "aed0f4f6-7845-49bf-85a0-69b9c1531ec7",
   "metadata": {
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   "outputs": [
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 576 entries, 0 to 575\n",
      "Data columns (total 7 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   Rank           576 non-null    int64 \n",
      " 1   Player         576 non-null    object\n",
      " 2   Signed Age     576 non-null    int64 \n",
      " 3   Yrs            576 non-null    int64 \n",
      " 4   Value          576 non-null    object\n",
      " 5   AAV            576 non-null    object\n",
      " 6   Signing Bonus  576 non-null    object\n",
      "dtypes: int64(3), object(4)\n",
      "memory usage: 31.6+ KB\n"
     ]
    },
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     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Player</th>\n",
       "      <th>Signed Age</th>\n",
       "      <th>Yrs</th>\n",
       "      <th>Value</th>\n",
       "      <th>AAV</th>\n",
       "      <th>Signing Bonus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Shohei Ohtani  Starting Pitcher | 2024-2033 (F...</td>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>$700,000,000</td>\n",
       "      <td>$70,000,000</td>\n",
       "      <td>$0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Mike Trout  Center Field | 2019-2030 (FA: 2031)</td>\n",
       "      <td>27</td>\n",
       "      <td>12</td>\n",
       "      <td>$426,500,000</td>\n",
       "      <td>$35,541,667</td>\n",
       "      <td>$20,000,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Mookie Betts  Shortstop | 2021-2032 (FA: 2033)</td>\n",
       "      <td>27</td>\n",
       "      <td>12</td>\n",
       "      <td>$365,000,000</td>\n",
       "      <td>$30,416,667</td>\n",
       "      <td>$65,000,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Aaron Judge  Center Field | 2023-2031 (FA: 2032)</td>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "      <td>$360,000,000</td>\n",
       "      <td>$40,000,000</td>\n",
       "      <td>$0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Manny Machado  Designated Hitter | 2023-2033 (...</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>$350,000,000</td>\n",
       "      <td>$31,818,182</td>\n",
       "      <td>$45,000,000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank                                             Player  Signed Age  Yrs  \\\n",
       "0     1  Shohei Ohtani  Starting Pitcher | 2024-2033 (F...          29   10   \n",
       "1     2    Mike Trout  Center Field | 2019-2030 (FA: 2031)          27   12   \n",
       "2     3     Mookie Betts  Shortstop | 2021-2032 (FA: 2033)          27   12   \n",
       "3     4   Aaron Judge  Center Field | 2023-2031 (FA: 2032)          30    9   \n",
       "4     5  Manny Machado  Designated Hitter | 2023-2033 (...          30   11   \n",
       "\n",
       "          Value          AAV Signing Bonus  \n",
       "0  $700,000,000  $70,000,000            $0  \n",
       "1  $426,500,000  $35,541,667   $20,000,000  \n",
       "2  $365,000,000  $30,416,667   $65,000,000  \n",
       "3  $360,000,000  $40,000,000            $0  \n",
       "4  $350,000,000  $31,818,182   $45,000,000  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://www.spotrac.com/mlb/contracts/sort-value/limit-2000/'\n",
    "df = pd.read_html(url)[0]\n",
    "df.info()\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2190eaeb-f802-4655-9ef3-aa8464ce65f1",
   "metadata": {},
   "source": [
    "> Nous allons créer un fichier csv à partir de notre `DataFrame`. Cette opération n'est pas nécessaire mais elle peut être utile pour stocker de manière permanente les données. \n",
    ">\n",
    "> Nous constatons que nous avons unpetit travail de pre-traitement à réaliser si nous voulons explorer d'avantage nos données. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e2d64cec-5575-4978-9d01-dfbd89b719d6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.to_csv('salaries_2023_top570.csv')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 96,
   "id": "561e95cb-a9ad-432e-9c72-c6c777ae3a21",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 576 entries, 0 to 575\n",
      "Data columns (total 7 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   Rank           576 non-null    int64 \n",
      " 1   Player         576 non-null    object\n",
      " 2   Signed Age     576 non-null    int64 \n",
      " 3   Yrs            576 non-null    int64 \n",
      " 4   Value          576 non-null    object\n",
      " 5   AAV            576 non-null    object\n",
      " 6   Signing Bonus  576 non-null    object\n",
      "dtypes: int64(3), object(4)\n",
      "memory usage: 36.0+ KB\n"
     ]
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Player</th>\n",
       "      <th>Signed Age</th>\n",
       "      <th>Yrs</th>\n",
       "      <th>Value</th>\n",
       "      <th>AAV</th>\n",
       "      <th>Signing Bonus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Shohei Ohtani  Starting Pitcher | 2024-2033 (F...</td>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>$700,000,000</td>\n",
       "      <td>$70,000,000</td>\n",
       "      <td>$0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Mike Trout  Center Field | 2019-2030 (FA: 2031)</td>\n",
       "      <td>27</td>\n",
       "      <td>12</td>\n",
       "      <td>$426,500,000</td>\n",
       "      <td>$35,541,667</td>\n",
       "      <td>$20,000,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Mookie Betts  Shortstop | 2021-2032 (FA: 2033)</td>\n",
       "      <td>27</td>\n",
       "      <td>12</td>\n",
       "      <td>$365,000,000</td>\n",
       "      <td>$30,416,667</td>\n",
       "      <td>$65,000,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Aaron Judge  Center Field | 2023-2031 (FA: 2032)</td>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "      <td>$360,000,000</td>\n",
       "      <td>$40,000,000</td>\n",
       "      <td>$0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Manny Machado  Designated Hitter | 2023-2033 (...</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>$350,000,000</td>\n",
       "      <td>$31,818,182</td>\n",
       "      <td>$45,000,000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank                                             Player  Signed Age  Yrs  \\\n",
       "0     1  Shohei Ohtani  Starting Pitcher | 2024-2033 (F...          29   10   \n",
       "1     2    Mike Trout  Center Field | 2019-2030 (FA: 2031)          27   12   \n",
       "2     3     Mookie Betts  Shortstop | 2021-2032 (FA: 2033)          27   12   \n",
       "3     4   Aaron Judge  Center Field | 2023-2031 (FA: 2032)          30    9   \n",
       "4     5  Manny Machado  Designated Hitter | 2023-2033 (...          30   11   \n",
       "\n",
       "          Value          AAV Signing Bonus  \n",
       "0  $700,000,000  $70,000,000            $0  \n",
       "1  $426,500,000  $35,541,667   $20,000,000  \n",
       "2  $365,000,000  $30,416,667   $65,000,000  \n",
       "3  $360,000,000  $40,000,000            $0  \n",
       "4  $350,000,000  $31,818,182   $45,000,000  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('salaries_2023_top570.csv', index_col =0)\n",
    "df.info()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "2c872203-6800-4299-9a8f-51ae0ed59a09",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Shohei Ohtani  Starting Pitcher | 2024-2033 (FA: 2034)'"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Player'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c9f6630-b6ca-4c45-8665-a5cb9b557f9b",
   "metadata": {},
   "source": [
    "> Nous allons préparer nos données de la manière suivante :\n",
    ">\n",
    ">> Nous allons créer une colonne contenant le nom du joueur.\n",
    ">\n",
    ">> Nous allons créer une colonne contenant le rôle du joueur.\n",
    ">\n",
    ">> Nous allons transformer les colonnes `Value` et `AAV` en colonne numériques. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "e092c617-bd1b-473a-a7ec-5a23cb1b0774",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Shohei Ohtani\n",
       "1            Mike Trout\n",
       "2          Mookie Betts\n",
       "3           Aaron Judge\n",
       "4         Manny Machado\n",
       "             ...       \n",
       "571           Darin Ruf\n",
       "572      Dallas Keuchel\n",
       "573        Carson Kelly\n",
       "574     Zach McAllister\n",
       "575    Elehuris Montero\n",
       "Name: Players, Length: 576, dtype: object"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Players'] = df['Player'].apply(lambda x : x.split('|')[0].split('  ')[0])\n",
    "df['Players']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "32e01034-dad8-494b-ad2a-0e04be1212f6",
   "metadata": {
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   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Position'] = df['Player'].apply(lambda x : x.split('|')[0].split('  ')[1])\n",
    "df = df.drop('Player', axis = 1)\n",
    "df.Position.value_counts().plot(kind = 'bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45e56357-06bb-48a6-9476-f750333e194f",
   "metadata": {
    "tags": []
   },
   "source": [
    "> Très bien, nous pouvons attaquer les variables décrivant le salaire annuel ainsi que les revenues totales du contrat signé par chaque joueur. \n",
    ">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "1594c03c-b4bb-4111-9a24-07288322fd7d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      700000000.0\n",
       "1      426500000.0\n",
       "2      365000000.0\n",
       "3      360000000.0\n",
       "4      350000000.0\n",
       "          ...     \n",
       "571       720000.0\n",
       "572       720000.0\n",
       "573       720000.0\n",
       "574       720000.0\n",
       "575       300000.0\n",
       "Name: Value, Length: 576, dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Value'] = df['Value'].str.replace('$', '')\n",
    "df['Value'] = df['Value'].str.replace(',', '').astype(float)\n",
    "df['Value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "666ba0de-bc2c-4da9-908c-8a2b845ccf4d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      70000000.0\n",
       "1      35541667.0\n",
       "2      30416667.0\n",
       "3      40000000.0\n",
       "4      31818182.0\n",
       "          ...    \n",
       "571      720000.0\n",
       "572      720000.0\n",
       "573      720000.0\n",
       "574      720000.0\n",
       "575      300000.0\n",
       "Name: AAV, Length: 576, dtype: float64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['AAV'] = df['AAV'].str.replace('$', '')\n",
    "df['AAV'] = df['AAV'].str.replace(',', '').astype(float)\n",
    "df['AAV']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a3433460-9770-4412-97fe-3d36c44f0180",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20, 10))\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "plt.hist(df['Value'], bins = 100);\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.hist(df['AAV'], bins = 100);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "643ac369-c078-48d9-a9d8-a384662ef0cb",
   "metadata": {},
   "source": [
    "> Nous pouvons étudier maintenant s'il existe une différence en termes de revenues à travers les rôles.\n",
    ">\n",
    "> Nous pouvons visualser cette relation et par la suite effectuer un test statistique. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "eddbf3cb-6270-4563-8f47-53855988f43d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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v7kiMaWlp8vX1VVBQkFNtlSpVlJaW5qipXLlygZ9XuXJlp5oqVao4rQ8KCpKvr2+RNfmPr1Vz8eJFnTx5ssia/J9RmGHDhikjI8PxdejQoavWAgAAAMCVSmx0f05Ojh5//HHl5eXp3XffvWa9YRiy2WyOx5f/d3HW5J/1WRw1hT1/Pj8/P6cjegAAAABwI0rkyFpOTo66du2qffv2afny5U7nYVatWlXZ2dlKT093+p7jx487jl5VrVpVx44dK/BzT5w44VRz5ZGt9PR05eTkFFlz/PhxSbpmjbe3typWrFhkzZVH2wAAAACguBT7kbX8oPbLL7/o+++/dwSefBEREfLx8dHy5cvVtWtXSdLRo0eVmpqqt956S5LUpEkTZWRkaMOGDbr33nslSevXr1dGRoaaNm3qqBkzZoyOHj2qatWqSZISExPl5+eniIgIR80rr7yi7Oxs+fr6OmqCg4NVq1YtR83XX3/t1GNiYqIaNWokHx8fR83y5cs1aNAgp5r8XgAAAOA6Zg+XMHu4BjzHDR9ZO3PmjFJSUpSSkiLp0uj7lJQUHTx4UBcvXtRjjz2m5ORkzZ8/X7m5uUpLS1NaWprj/md2u129evXS4MGDtXLlSm3ZskU9evRQvXr11LJlS0lS3bp11aZNG8XGxiopKUlJSUmKjY1V+/btVadOHUlS69atFRYWpujoaG3ZskUrV67UkCFDFBsb6ziS1717d/n5+SkmJkapqamKj4/X2LFjFRcX5ziFsU+fPjpw4IDi4uK0c+dOffzxx5o5c6aGDBnieM0vvPCCEhMT9eabb2rXrl168803tWLFCg0cOPC/3vAAAAAAUJQbDmvJyclq0KCBGjRoIEmKi4tTgwYN9Oqrr+rw4cNavHixDh8+rLvvvlvVqlVzfF0+OXHSpEnq1KmTunbtqmbNmql06dL6+uuv5eXl5aiZP3++6tWrp9atW6t169aqX7++5s6d61jv5eWlJUuWyN/fX82aNVPXrl3VqVMnjR8/3lFjt9u1fPlyHT58WI0aNVLfvn0VFxenuLg4R01oaKiWLl2qH374QXfffbf++c9/asqUKerSpYujpmnTplqwYIFmzZql+vXra/bs2fr888+dplUCAAAAQHH6n+6zhut3vfdSAAAAQNE4DZJtYPbrl9zkPmsAAAAAgBtHWAMAAAAACyKsAQAAAIAFEdYAAAAAwIIIawAAAABgQYQ1AAAAALAgwhoAAAAAWBBhDQAAAAAsiLAGAAAAABZEWAMAAAAACyKsAQAAAIAFEdYAAAAAwIIIawAAAABgQYQ1AAAAALAgb7MbAAAAwI2pNXSJqc+//412pj4/4Ck4sgYAAAAAFkRYAwAAAAAL4jRIAADwl8IpgAA8BUfWAAAAAMCCCGsAAAAAYEGENQAAAACwIMIaAAAAAFgQYQ0AAAAALIiwBgAAAAAWRFgDAAAAAAsirAEAAACABRHWAAAAAMCCCGsAAAAAYEGENQAAAACwIMIaAAAAAFgQYQ0AAAAALIiwBgAAAAAWRFgDAAAAAAsirAEAAACABRHWAAAAAMCCCGsAAAAAYEGENQAAAACwIMIaAAAAAFiQt9kNAACA61dr6BKzW9D+N9qZ3QIAeASOrAEAAACABRHWAAAAAMCCCGsAAAAAYEGENQAAAACwIMIaAAAAAFgQYQ0AAAAALIiwBgAAAAAWRFgDAAAAAAsirAEAAACABRHWAAAAAMCCCGsAAAAAYEHeZjcAAMCNqDV0ianPv/+NdqY+PwDAc3BkDQAAAAAsiLAGAAAAABZEWAMAAAAACyKsAQAAAIAFEdYAAAAAwIIIawAAAABgQTcc1lavXq0OHTooODhYNptNX331ldN6wzA0cuRIBQcHKyAgQM2bN9fPP//sVJOVlaX+/furUqVKKlOmjDp27KjDhw871aSnpys6Olp2u112u13R0dE6ffq0U83BgwfVoUMHlSlTRpUqVdKAAQOUnZ3tVLN9+3ZFRkYqICBA1atX16hRo2QYhlPNqlWrFBERIX9/f9WuXVszZswo8LoXLlyosLAw+fn5KSwsTPHx8Te45QAAAADg+t1wWDt79qzuuusuTZs2rdD1b731liZOnKhp06Zp48aNqlq1qlq1aqU///zTUTNw4EDFx8drwYIFWrNmjc6cOaP27dsrNzfXUdO9e3elpKQoISFBCQkJSklJUXR0tGN9bm6u2rVrp7Nnz2rNmjVasGCBFi5cqMGDBztqMjMz1apVKwUHB2vjxo2aOnWqxo8fr4kTJzpq9u3bp7Zt2+qBBx7Qli1b9Morr2jAgAFauHCho2bdunXq1q2boqOjtXXrVkVHR6tr165av379jW4+AAAAALguN3xT7EceeUSPPPJIoesMw9DkyZM1fPhwde7cWZI0Z84cValSRZ9++ql69+6tjIwMzZw5U3PnzlXLli0lSfPmzVNISIhWrFihqKgo7dy5UwkJCUpKSlLjxo0lSR9++KGaNGmi3bt3q06dOkpMTNSOHTt06NAhBQcHS5ImTJigmJgYjRkzRoGBgZo/f74uXLig2bNny8/PT+Hh4dqzZ48mTpyouLg42Ww2zZgxQzVq1NDkyZMlSXXr1lVycrLGjx+vLl26SJImT56sVq1aadiwYZKkYcOGadWqVZo8ebI+++yzQrdFVlaWsrKyHI8zMzNvdFMDAAAA8GDFes3avn37lJaWptatWzuW+fn5KTIyUmvXrpUkbdq0STk5OU41wcHBCg8Pd9SsW7dOdrvdEdQk6b777pPdbneqCQ8PdwQ1SYqKilJWVpY2bdrkqImMjJSfn59TzZEjR7R//35HzeW95NckJycrJyenyJr8Xgozbtw4xymcdrtdISEh19h6AAAAAPAfxRrW0tLSJElVqlRxWl6lShXHurS0NPn6+iooKKjImsqVKxf4+ZUrV3aqufJ5goKC5OvrW2RN/uNr1Vy8eFEnT54ssib/ZxRm2LBhysjIcHwdOnToqrUAAAAAcKUbPg3yethsNqfHhmEUWHalK2sKqy+OmvzhIsVRU9Rr8vPzczqiBwAAAAA3oliPrFWtWlWSChxxOn78uOPIVNWqVZWdna309PQia44dO1bg5584ccKp5srnSU9PV05OTpE1x48fl6Rr1nh7e6tixYpF1lx5tA0AAAAAikuxhrXQ0FBVrVpVy5cvdyzLzs7WqlWr1LRpU0lSRESEfHx8nGqOHj2q1NRUR02TJk2UkZGhDRs2OGrWr1+vjIwMp5rU1FQdPXrUUZOYmCg/Pz9FREQ4alavXu00zj8xMVHBwcGqVauWo+byXvJrGjVqJB8fnyJr8nsBAAAAgOJ2w2HtzJkzSklJUUpKiqRLQ0VSUlJ08OBB2Ww2DRw4UGPHjlV8fLxSU1MVExOj0qVLq3v37pIku92uXr16afDgwVq5cqW2bNmiHj16qF69eo7pkHXr1lWbNm0UGxurpKQkJSUlKTY2Vu3bt1edOnUkSa1bt1ZYWJiio6O1ZcsWrVy5UkOGDFFsbKwCAwMlXRr/7+fnp5iYGKWmpio+Pl5jx451TIKUpD59+ujAgQOKi4vTzp079fHHH2vmzJkaMmSI4zW/8MILSkxM1Jtvvqldu3bpzTff1IoVKzRw4MD/esMDAAAAQFFu+Jq15ORktWjRwvE4Li5OkvT0009r9uzZeumll3T+/Hn17dtX6enpaty4sRITE1WuXDnH90yaNEne3t7q2rWrzp8/r4cfflizZ8+Wl5eXo2b+/PkaMGCAYwpjx44dne7t5uXlpSVLlqhv375q1qyZAgIC1L17d40fP95RY7fbtXz5cvXr10+NGjVSUFCQ4uLiHD1Ll44GLl26VIMGDdL06dMVHBysKVOmOMb2S1LTpk21YMEC/eMf/9CIESN0yy236PPPP3eaVgkAAAAAxemGw1rz5s0dAzgKY7PZNHLkSI0cOfKqNf7+/po6daqmTp161ZoKFSpo3rx5RfZSo0YNffPNN0XW1KtXT6tXry6yJjIyUps3by6y5rHHHtNjjz1WZA0AlLRaQ5eY+vz732hn6vMDAOBJivWaNQAAAABA8SCsAQAAAIAFEdYAAAAAwIIIawAAAABgQYQ1AAAAALAgwhoAAAAAWBBhDQAAAAAsiLAGAAAAABZEWAMAAAAAC/I2uwEAuBG1hi4x9fn3v9HO1OcHAACegyNrAAAAAGBBhDUAAAAAsCDCGgAAAABYEGENAAAAACyIsAYAAAAAFkRYAwAAAAALIqwBAAAAgAUR1gAAAADAgghrAAAAAGBBhDUAAAAAsCBvsxsAcP1qDV1i6vPvf6Odqc8PAADgSTiyBgAAAAAWRFgDAAAAAAsirAEAAACABRHWAAAAAMCCCGsAAAAAYEGENQAAAACwIMIaAAAAAFgQYQ0AAAAALIiwBgAAAAAWRFgDAAAAAAsirAEAAACABRHWAAAAAMCCCGsAAAAAYEGENQAAAACwIMIaAAAAAFgQYQ0AAAAALIiwBgAAAAAWRFgDAAAAAAsirAEAAACABRHWAAAAAMCCCGsAAAAAYEGENQAAAACwIMIaAAAAAFgQYQ0AAAAALIiwBgAAAAAWRFgDAAAAAAsirAEAAACABRHWAAAAAMCCCGsAAAAAYEGENQAAAACwIMIaAAAAAFgQYQ0AAAAALIiwBgAAAAAWRFgDAAAAAAsirAEAAACABRHWAAAAAMCCij2sXbx4Uf/4xz8UGhqqgIAA1a5dW6NGjVJeXp6jxjAMjRw5UsHBwQoICFDz5s31888/O/2crKws9e/fX5UqVVKZMmXUsWNHHT582KkmPT1d0dHRstvtstvtio6O1unTp51qDh48qA4dOqhMmTKqVKmSBgwYoOzsbKea7du3KzIyUgEBAapevbpGjRolwzCcalatWqWIiAj5+/urdu3amjFjRjFsLQAAAAAoXLGHtTfffFMzZszQtGnTtHPnTr311lt6++23NXXqVEfNW2+9pYkTJ2ratGnauHGjqlatqlatWunPP/901AwcOFDx8fFasGCB1qxZozNnzqh9+/bKzc111HTv3l0pKSlKSEhQQkKCUlJSFB0d7Vifm5urdu3a6ezZs1qzZo0WLFighQsXavDgwY6azMxMtWrVSsHBwdq4caOmTp2q8ePHa+LEiY6affv2qW3btnrggQe0ZcsWvfLKKxowYIAWLlxY3JsPAAAAACRJ3sX9A9etW6dHH31U7dq1kyTVqlVLn332mZKTkyVdOqo2efJkDR8+XJ07d5YkzZkzR1WqVNGnn36q3r17KyMjQzNnztTcuXPVsmVLSdK8efMUEhKiFStWKCoqSjt37lRCQoKSkpLUuHFjSdKHH36oJk2aaPfu3apTp44SExO1Y8cOHTp0SMHBwZKkCRMmKCYmRmPGjFFgYKDmz5+vCxcuaPbs2fLz81N4eLj27NmjiRMnKi4uTjabTTNmzFCNGjU0efJkSVLdunWVnJys8ePHq0uXLoVuh6ysLGVlZTkeZ2ZmFvemBgAAAODGiv3I2v3336+VK1dqz549kqStW7dqzZo1atu2raRLR6nS0tLUunVrx/f4+fkpMjJSa9eulSRt2rRJOTk5TjXBwcEKDw931Kxbt052u90R1CTpvvvuk91ud6oJDw93BDVJioqKUlZWljZt2uSoiYyMlJ+fn1PNkSNHtH//fkfN5b3k1yQnJysnJ6fQ7TBu3DjH6Zl2u10hISE3sBUBAAAAeLpiD2svv/yynnjiCd1xxx3y8fFRgwYNNHDgQD3xxBOSpLS0NElSlSpVnL6vSpUqjnVpaWny9fVVUFBQkTWVK1cu8PyVK1d2qrnyeYKCguTr61tkTf7ja9VcvHhRJ0+eLHQ7DBs2TBkZGY6vQ4cOFVoHAAAAAIUp9tMgP//8c82bN0+ffvqp7rzzTqWkpGjgwIEKDg7W008/7aiz2WxO32cYRoFlV7qyprD64qjJHy5yozWX8/PzczpaBwAAAAA3otiPrL344osaOnSoHn/8cdWrV0/R0dEaNGiQxo0bJ0mqWrWqpP8ctcp3/Phxx9GrqlWrKjs7W+np6UXWHDt2rMDznzhxwqnmyudJT09XTk5OkTXHjx+XpGvWeHt7q2LFitezWQAAAADghhR7WDt37pxKlXL+sV5eXo7R/aGhoapataqWL1/uWJ+dna1Vq1apadOmkqSIiAj5+Pg41Rw9elSpqamOmiZNmigjI0MbNmxw1Kxfv14ZGRlONampqTp69KijJjExUX5+foqIiHDUrF692mmcf2JiooKDg1WrVi1HzeW95Nc0atRIPj4+/92GAgAAAIAiFHtY69Chg8aMGaMlS5Zo//79io+P18SJE/W3v/1N0qXTBgcOHKixY8cqPj5eqampiomJUenSpdW9e3dJkt1uV69evTR48GCtXLlSW7ZsUY8ePVSvXj3HdMi6deuqTZs2io2NVVJSkpKSkhQbG6v27durTp06kqTWrVsrLCxM0dHR2rJli1auXKkhQ4YoNjZWgYGBki6N//fz81NMTIxSU1MVHx+vsWPHOiZBSlKfPn104MABxcXFaefOnfr44481c+ZMDRkypLg3HwAAAABIKoFr1qZOnaoRI0aob9++On78uIKDg9W7d2+9+uqrjpqXXnpJ58+fV9++fZWenq7GjRsrMTFR5cqVc9RMmjRJ3t7e6tq1q86fP6+HH35Ys2fPlpeXl6Nm/vz5GjBggGNSY8eOHTVt2jTHei8vLy1ZskR9+/ZVs2bNFBAQoO7du2v8+PGOGrvdruXLl6tfv35q1KiRgoKCFBcXp7i4OEdNaGioli5dqkGDBmn69OkKDg7WlClTrjq2HwAAAAD+V8Ue1sqVK6fJkyc77klWGJvNppEjR2rkyJFXrfH399fUqVOdbqZ9pQoVKmjevHlF9lOjRg198803RdbUq1dPq1evLrImMjJSmzdvLrIGAAAAAIpLsZ8GCQAAAAD43xHWAAAAAMCCCGsAAAAAYEGENQAAAACwIMIaAAAAAFgQYQ0AAAAALIiwBgAAAAAWRFgDAAAAAAsirAEAAACABRHWAAAAAMCCCGsAAAAAYEGENQAAAACwIMIaAAAAAFgQYQ0AAAAALIiwBgAAAAAWRFgDAAAAAAsirAEAAACABRHWAAAAAMCCCGsAAAAAYEGENQAAAACwIMIaAAAAAFgQYQ0AAAAALIiwBgAAAAAWRFgDAAAAAAsirAEAAACABRHWAAAAAMCCCGsAAAAAYEGENQAAAACwIMIaAAAAAFgQYQ0AAAAALIiwBgAAAAAWRFgDAAAAAAsirAEAAACABRHWAAAAAMCCCGsAAAAAYEGENQAAAACwIMIaAAAAAFgQYQ0AAAAALIiwBgAAAAAWRFgDAAAAAAsirAEAAACABRHWAAAAAMCCCGsAAAAAYEGENQAAAACwIMIaAAAAAFgQYQ0AAAAALIiwBgAAAAAWRFgDAAAAAAsirAEAAACABRHWAAAAAMCCCGsAAAAAYEGENQAAAACwIMIaAAAAAFgQYQ0AAAAALIiwBgAAAAAWRFgDAAAAAAsirAEAAACABZVIWPv999/Vo0cPVaxYUaVLl9bdd9+tTZs2OdYbhqGRI0cqODhYAQEBat68uX7++Wenn5GVlaX+/furUqVKKlOmjDp27KjDhw871aSnpys6Olp2u112u13R0dE6ffq0U83BgwfVoUMHlSlTRpUqVdKAAQOUnZ3tVLN9+3ZFRkYqICBA1atX16hRo2QYhlPNqlWrFBERIX9/f9WuXVszZswohi0FAAAAAIUr9rCWnp6uZs2aycfHR99++6127NihCRMmqHz58o6at956SxMnTtS0adO0ceNGVa1aVa1atdKff/7pqBk4cKDi4+O1YMECrVmzRmfOnFH79u2Vm5vrqOnevbtSUlKUkJCghIQEpaSkKDo62rE+NzdX7dq109mzZ7VmzRotWLBACxcu1ODBgx01mZmZatWqlYKDg7Vx40ZNnTpV48eP18SJEx01+/btU9u2bfXAAw9oy5YteuWVVzRgwAAtXLiwuDcfAAAAAEiSvIv7B7755psKCQnRrFmzHMtq1arl+G/DMDR58mQNHz5cnTt3liTNmTNHVapU0aeffqrevXsrIyNDM2fO1Ny5c9WyZUtJ0rx58xQSEqIVK1YoKipKO3fuVEJCgpKSktS4cWNJ0ocffqgmTZpo9+7dqlOnjhITE7Vjxw4dOnRIwcHBkqQJEyYoJiZGY8aMUWBgoObPn68LFy5o9uzZ8vPzU3h4uPbs2aOJEycqLi5ONptNM2bMUI0aNTR58mRJUt26dZWcnKzx48erS5cuxb0JAQAAAKD4j6wtXrxYjRo10v/93/+pcuXKatCggT788EPH+n379iktLU2tW7d2LPPz81NkZKTWrl0rSdq0aZNycnKcaoKDgxUeHu6oWbdunex2uyOoSdJ9990nu93uVBMeHu4IapIUFRWlrKwsx2mZ69atU2RkpPz8/Jxqjhw5ov379ztqLu8lvyY5OVk5OTmFboesrCxlZmY6fQEAAADA9Sr2sPbbb7/pvffe02233aZly5apT58+GjBggD755BNJUlpamiSpSpUqTt9XpUoVx7q0tDT5+voqKCioyJrKlSsXeP7KlSs71Vz5PEFBQfL19S2yJv/xtWouXryokydPFrodxo0b57iWzm63KyQkpNA6AAAAAChMsYe1vLw8NWzYUGPHjlWDBg3Uu3dvxcbG6r333nOqs9lsTo8Nwyiw7EpX1hRWXxw1+cNFbrTmcsOGDVNGRobj69ChQ1d9XQAAAABwpWIPa9WqVVNYWJjTsrp16+rgwYOSpKpVq0r6z1GrfMePH3ccvapataqys7OVnp5eZM2xY8cKPP+JEyecaq58nvT0dOXk5BRZc/z4cUm6Zo23t7cqVqxY6Hbw8/NTYGCg0xcAAAAAXK9iD2vNmjXT7t27nZbt2bNHNWvWlCSFhoaqatWqWr58uWN9dna2Vq1apaZNm0qSIiIi5OPj41Rz9OhRpaamOmqaNGmijIwMbdiwwVGzfv16ZWRkONWkpqbq6NGjjprExET5+fkpIiLCUbN69Wqncf6JiYkKDg52DEZp0qSJUy/5NY0aNZKPj89/t6EAAAAAoAjFHtYGDRqkpKQkjR07Vnv37tWnn36qDz74QP369ZN06bTBgQMHauzYsYqPj1dqaqpiYmJUunRpde/eXZJkt9vVq1cvDR48WCtXrtSWLVvUo0cP1atXzzEdsm7dumrTpo1iY2OVlJSkpKQkxcbGqn379qpTp44kqXXr1goLC1N0dLS2bNmilStXasiQIYqNjXUc6erevbv8/PwUExOj1NRUxcfHa+zYsY5JkJLUp08fHThwQHFxcdq5c6c+/vhjzZw5U0OGDCnuzQcAAAAAkkpgdP8999yj+Ph4DRs2TKNGjVJoaKgmT56sJ5980lHz0ksv6fz58+rbt6/S09PVuHFjJSYmqly5co6aSZMmydvbW127dtX58+f18MMPa/bs2fLy8nLUzJ8/XwMGDHBMauzYsaOmTZvmWO/l5aUlS5aob9++atasmQICAtS9e3eNHz/eUWO327V8+XL169dPjRo1UlBQkOLi4hQXF+eoCQ0N1dKlSzVo0CBNnz5dwcHBmjJlCmP7AQAAAJSYYg9rktS+fXu1b9/+quttNptGjhypkSNHXrXG399fU6dO1dSpU69aU6FCBc2bN6/IXmrUqKFvvvmmyJp69epp9erVRdZERkZq8+bNRdYAAAAAQHEp9tMgAQAAAAD/O8IaAAAAAFhQiZwGCZSEWkOXmN2C9r/RzuwWAAAA4CE4sgYAAAAAFkRYAwAAAAALIqwBAAAAgAUR1gAAAADAgghrAAAAAGBBhDUAAAAAsCDCGgAAAABYEGENAAAAACyIsAYAAAAAFkRYAwAAAAALIqwBAAAAgAUR1gAAAADAgghrAAAAAGBBhDUAAAAAsCDCGgAAAABYEGENAAAAACyIsAYAAAAAFkRYAwAAAAALIqwBAAAAgAUR1gAAAADAgghrAAAAAGBBhDUAAAAAsCDCGgAAAABYEGENAAAAACyIsAYAAAAAFkRYAwAAAAALIqwBAAAAgAUR1gAAAADAgghrAAAAAGBBhDUAAAAAsCDCGgAAAABYEGENAAAAACyIsAYAAAAAFkRYAwAAAAALIqwBAAAAgAUR1gAAAADAgghrAAAAAGBBhDUAAAAAsCDCGgAAAABYEGENAAAAACyIsAYAAAAAFkRYAwAAAAALIqwBAAAAgAUR1gAAAADAgghrAAAAAGBBhDUAAAAAsCDCGgAAAABYEGENAAAAACyIsAYAAAAAFkRYAwAAAAALIqwBAAAAgAUR1gAAAADAgghrAAAAAGBBhDUAAAAAsKASD2vjxo2TzWbTwIEDHcsMw9DIkSMVHBysgIAANW/eXD///LPT92VlZal///6qVKmSypQpo44dO+rw4cNONenp6YqOjpbdbpfdbld0dLROnz7tVHPw4EF16NBBZcqUUaVKlTRgwABlZ2c71Wzfvl2RkZEKCAhQ9erVNWrUKBmG4VSzatUqRUREyN/fX7Vr19aMGTP+940DAAAAAFdRomFt48aN+uCDD1S/fn2n5W+99ZYmTpyoadOmaePGjapatapatWqlP//801EzcOBAxcfHa8GCBVqzZo3OnDmj9u3bKzc311HTvXt3paSkKCEhQQkJCUpJSVF0dLRjfW5urtq1a6ezZ89qzZo1WrBggRYuXKjBgwc7ajIzM9WqVSsFBwdr48aNmjp1qsaPH6+JEyc6avbt26e2bdvqgQce0JYtW/TKK69owIABWrhwYUlsNgAAAACQd0n94DNnzujJJ5/Uhx9+qNGjRzuWG4ahyZMna/jw4ercubMkac6cOapSpYo+/fRT9e7dWxkZGZo5c6bmzp2rli1bSpLmzZunkJAQrVixQlFRUdq5c6cSEhKUlJSkxo0bS5I+/PBDNWnSRLt371adOnWUmJioHTt26NChQwoODpYkTZgwQTExMRozZowCAwM1f/58XbhwQbNnz5afn5/Cw8O1Z88eTZw4UXFxcbLZbJoxY4Zq1KihyZMnS5Lq1q2r5ORkjR8/Xl26dCmpTQgAAADAg5XYkbV+/fqpXbt2jrCVb9++fUpLS1Pr1q0dy/z8/BQZGam1a9dKkjZt2qScnBynmuDgYIWHhztq1q1bJ7vd7ghqknTffffJbrc71YSHhzuCmiRFRUUpKytLmzZtctRERkbKz8/PqebIkSPav3+/o+byXvJrkpOTlZOTU+jrz8rKUmZmptMXAAAAAFyvEglrCxYs0ObNmzVu3LgC69LS0iRJVapUcVpepUoVx7q0tDT5+voqKCioyJrKlSsX+PmVK1d2qrnyeYKCguTr61tkTf7ja9VcvHhRJ0+eLHQbjBs3znEtnd1uV0hISKF1AAAAAFCYYg9rhw4d0gsvvKB58+bJ39//qnU2m83psWEYBZZd6cqawuqLoyZ/uMiN1lxu2LBhysjIcHwdOnToqq8LAAAAAK5U7GFt06ZNOn78uCIiIuTt7S1vb2+tWrVKU6ZMkbe3d4GjVvmOHz/uWFe1alVlZ2crPT29yJpjx44VeP4TJ0441Vz5POnp6crJySmy5vjx45J0zRpvb29VrFix0O3g5+enwMBApy8AAAAAuF7FHtYefvhhbd++XSkpKY6vRo0a6cknn1RKSopq166tqlWravny5Y7vyc7O1qpVq9S0aVNJUkREhHx8fJxqjh49qtTUVEdNkyZNlJGRoQ0bNjhq1q9fr4yMDKea1NRUHT161FGTmJgoPz8/RUREOGpWr17tNM4/MTFRwcHBqlWrlqPm8l7yaxo1aiQfH5/i2GwAAAAA4KTYp0GWK1dO4eHhTsvKlCmjihUrOpYPHDhQY8eO1W233abbbrtNY8eOVenSpdW9e3dJkt1uV69evTR48GBVrFhRFSpU0JAhQ1SvXj3HwJK6deuqTZs2io2N1fvvvy9JevbZZ9W+fXvVqVNHktS6dWuFhYUpOjpab7/9tv744w8NGTJEsbGxjiNd3bt31+uvv66YmBi98sor+uWXXzR27Fi9+uqrjlMc+/Tpo2nTpikuLk6xsbFat26dZs6cqc8++6y4N1+Rag1d4tLnu9L+N9qZ+vwAAACAJymx0f1Feemll3T+/Hn17dtX6enpaty4sRITE1WuXDlHzaRJk+Tt7a2uXbvq/PnzevjhhzV79mx5eXk5aubPn68BAwY4JjV27NhR06ZNc6z38vLSkiVL1LdvXzVr1kwBAQHq3r27xo8f76ix2+1avny5+vXrp0aNGikoKEhxcXGKi4tz1ISGhmrp0qUaNGiQpk+fruDgYE2ZMoWx/QAAAABKjEvC2g8//OD02GazaeTIkRo5cuRVv8ff319Tp07V1KlTr1pToUIFzZs3r8jnrlGjhr755psia+rVq6fVq1cXWRMZGanNmzcXWQMAAAAAxaXE7rMGAAAAAPjvEdYAAAAAwIIIawAAAABgQYQ1AAAAALAgwhoAAAAAWBBhDQAAAAAsiLAGAAAAABZEWAMAAAAACyKsAQAAAIAFEdYAAAAAwIIIawAAAABgQYQ1AAAAALAgwhoAAAAAWBBhDQAAAAAsiLAGAAAAABZEWAMAAAAACyKsAQAAAIAFEdYAAAAAwIIIawAAAABgQYQ1AAAAALAgwhoAAAAAWBBhDQAAAAAsiLAGAAAAABZEWAMAAAAACyKsAQAAAIAFEdYAAAAAwIIIawAAAABgQYQ1AAAAALAgwhoAAAAAWBBhDQAAAAAsiLAGAAAAABZEWAMAAAAACyKsAQAAAIAFEdYAAAAAwIIIawAAAABgQYQ1AAAAALAgwhoAAAAAWBBhDQAAAAAsiLAGAAAAABZEWAMAAAAACyKsAQAAAIAFEdYAAAAAwIIIawAAAABgQYQ1AAAAALAgwhoAAAAAWBBhDQAAAAAsiLAGAAAAABZEWAMAAAAACyKsAQAAAIAFEdYAAAAAwIIIawAAAABgQYQ1AAAAALAgwhoAAAAAWBBhDQAAAAAsiLAGAAAAABZEWAMAAAAACyKsAQAAAIAFFXtYGzdunO655x6VK1dOlStXVqdOnbR7926nGsMwNHLkSAUHBysgIEDNmzfXzz//7FSTlZWl/v37q1KlSipTpow6duyow4cPO9Wkp6crOjpadrtddrtd0dHROn36tFPNwYMH1aFDB5UpU0aVKlXSgAEDlJ2d7VSzfft2RUZGKiAgQNWrV9eoUaNkGIZTzapVqxQRESF/f3/Vrl1bM2bM+B+3FAAAAABcXbGHtVWrVqlfv35KSkrS8uXLdfHiRbVu3Vpnz5511Lz11luaOHGipk2bpo0bN6pq1apq1aqV/vzzT0fNwIEDFR8frwULFmjNmjU6c+aM2rdvr9zcXEdN9+7dlZKSooSEBCUkJCglJUXR0dGO9bm5uWrXrp3Onj2rNWvWaMGCBVq4cKEGDx7sqMnMzFSrVq0UHBysjRs3aurUqRo/frwmTpzoqNm3b5/atm2rBx54QFu2bNErr7yiAQMGaOHChcW9+QAAAABAkuRd3D8wISHB6fGsWbNUuXJlbdq0SQ8++KAMw9DkyZM1fPhwde7cWZI0Z84cValSRZ9++ql69+6tjIwMzZw5U3PnzlXLli0lSfPmzVNISIhWrFihqKgo7dy5UwkJCUpKSlLjxo0lSR9++KGaNGmi3bt3q06dOkpMTNSOHTt06NAhBQcHS5ImTJigmJgYjRkzRoGBgZo/f74uXLig2bNny8/PT+Hh4dqzZ48mTpyouLg42Ww2zZgxQzVq1NDkyZMlSXXr1lVycrLGjx+vLl26FPcmBAAAAICSv2YtIyNDklShQgVJl45SpaWlqXXr1o4aPz8/RUZGau3atZKkTZs2KScnx6kmODhY4eHhjpp169bJbrc7gpok3XfffbLb7U414eHhjqAmSVFRUcrKytKmTZscNZGRkfLz83OqOXLkiPbv3++oubyX/Jrk5GTl5OQU+rqzsrKUmZnp9AUAAAAA16tEw5phGIqLi9P999+v8PBwSVJaWpokqUqVKk61VapUcaxLS0uTr6+vgoKCiqypXLlygeesXLmyU82VzxMUFCRfX98ia/IfX6vm4sWLOnnyZKGvfdy4cY5r6ex2u0JCQgqtAwAAAIDClGhYe/7557Vt2zZ99tlnBdbZbDanx4ZhFFh2pStrCqsvjpr84SI3WnO5YcOGKSMjw/F16NChq74uAAAAALhSiYW1/v37a/Hixfr+++918803O5ZXrVpV0n+OWuU7fvy44+hV1apVlZ2drfT09CJrjh07VuB5T5w44VRz5fOkp6crJyenyJrjx49L0jVrvL29VbFixUJfv5+fnwIDA52+AAAAAOB6FXtYMwxDzz//vBYtWqTvvvtOoaGhTutDQ0NVtWpVLV++3LEsOztbq1atUtOmTSVJERER8vHxcao5evSoUlNTHTVNmjRRRkaGNmzY4KhZv369MjIynGpSU1N19OhRR01iYqL8/PwUERHhqFm9erXTOP/ExEQFBwerVq1ajprLe8mvadSokXx8fP7rbQUAAAAAV1PsYa1fv36aN2+ePv30U5UrV05paWlKS0vT+fPnJV06bXDgwIEaO3as4uPjlZqaqpiYGJUuXVrdu3eXJNntdvXq1UuDBw/WypUrtWXLFvXo0UP16tVzTIesW7eu2rRpo9jYWCUlJSkpKUmxsbFq37696tSpI0lq3bq1wsLCFB0drS1btmjlypUaMmSIYmNjHUe6unfvLj8/P8XExCg1NVXx8fEaO3asYxKkJPXp00cHDhxQXFycdu7cqY8//lgzZ87UkCFDinvzAQAAAICkEhjd/95770mSmjdv7rR81qxZiomJkSS99NJLOn/+vPr27av09HQ1btxYiYmJKleunKN+0qRJ8vb2VteuXXX+/Hk9/PDDmj17try8vBw18+fP14ABAxyTGjt27Khp06Y51nt5eWnJkiXq27evmjVrpoCAAHXv3l3jx4931Njtdi1fvlz9+vVTo0aNFBQUpLi4OMXFxTlqQkNDtXTpUg0aNEjTp09XcHCwpkyZwth+AAAAACWm2MNa/uCNothsNo0cOVIjR468ao2/v7+mTp2qqVOnXrWmQoUKmjdvXpHPVaNGDX3zzTdF1tSrV0+rV68usiYyMlKbN28usgYAAAAAikuJ32cNAAAAAHDjCGsAAAAAYEGENQAAAACwIMIaAAAAAFgQYQ0AAAAALIiwBgAAAAAWRFgDAAAAAAsirAEAAACABRHWAAAAAMCCCGsAAAAAYEGENQAAAACwIMIaAAAAAFgQYQ0AAAAALIiwBgAAAAAWRFgDAAAAAAsirAEAAACABRHWAAAAAMCCCGsAAAAAYEGENQAAAACwIMIaAAAAAFgQYQ0AAAAALIiwBgAAAAAWRFgDAAAAAAsirAEAAACABRHWAAAAAMCCCGsAAAAAYEGENQAAAACwIMIaAAAAAFgQYQ0AAAAALIiwBgAAAAAWRFgDAAAAAAsirAEAAACABRHWAAAAAMCCCGsAAAAAYEGENQAAAACwIMIaAAAAAFgQYQ0AAAAALIiwBgAAAAAWRFgDAAAAAAsirAEAAACABRHWAAAAAMCCCGsAAAAAYEGENQAAAACwIMIaAAAAAFgQYQ0AAAAALIiwBgAAAAAWRFgDAAAAAAsirAEAAACABRHWAAAAAMCCCGsAAAAAYEGENQAAAACwIMIaAAAAAFgQYQ0AAAAALIiwBgAAAAAWRFgDAAAAAAsirAEAAACABRHWAAAAAMCCCGs34N1331VoaKj8/f0VERGhH3/80eyWAAAAALgpwtp1+vzzzzVw4EANHz5cW7Zs0QMPPKBHHnlEBw8eNLs1AAAAAG6IsHadJk6cqF69eunvf/+76tatq8mTJyskJETvvfee2a0BAAAAcEPeZjfwV5Cdna1NmzZp6NChTstbt26ttWvXFvo9WVlZysrKcjzOyMiQJGVmZv7XfeRlnfuvv7c4/C+9FwezX7/ENjD79UtsA09//RLbwOzXL7ENzH79EtvA01+/xDYw+/VL/9s2yP9ewzCKrLMZ16qAjhw5ourVq+unn35S06ZNHcvHjh2rOXPmaPfu3QW+Z+TIkXr99ddd2SYAAACAv5BDhw7p5ptvvup6jqzdAJvN5vTYMIwCy/INGzZMcXFxjsd5eXn6448/VLFixat+T0nKzMxUSEiIDh06pMDAQJc/vxV4+jbw9NcvsQ08/fVLbAOJbeDpr19iG3j665fYBlZ4/YZh6M8//1RwcHCRdYS161CpUiV5eXkpLS3Nafnx48dVpUqVQr/Hz89Pfn5+TsvKly9fUi1et8DAQI/cKS/n6dvA01+/xDbw9NcvsQ0ktoGnv36JbeDpr19iG5j9+u12+zVrGDByHXx9fRUREaHly5c7LV++fLnTaZEAAAAAUFw4snad4uLiFB0drUaNGqlJkyb64IMPdPDgQfXp08fs1gAAAAC4IcLaderWrZtOnTqlUaNG6ejRowoPD9fSpUtVs2ZNs1u7Ln5+fnrttdcKnJrpSTx9G3j665fYBp7++iW2gcQ28PTXL7ENPP31S2yDv9LrZxokAAAAAFgQ16wBAAAAgAUR1gAAAADAgghrAAAAAGBBhDUAAAAAsCDCGgAAAABYEGENAAAAACyIsAYAAAAAFkRYAwAAAAALIqwBAAC4qdzcXK1atUrp6elmtwKY4q++DxDW3NDFixf1+uuv69ChQ2a3Yhq2gbMLFy6Y3QJgOvYDeCIvLy9FRUXp9OnTZrdiuuTkZM2dO1fz5s1TcnKy2e3ARf7q+4C32Q2g+Hl7e+vtt9/W008/bXYrpmEbSHl5eRozZoxmzJihY8eOac+ePapdu7ZGjBihWrVqqVevXma3WCKCgoJks9muq/aPP/4o4W5cb8qUKdddO2DAgBLsxBrYD67NHfeDK/344496//339euvv+pf//qXqlevrrlz5yo0NFT333+/2e2VuHr16um3335TaGio2a2Y4vDhw3riiSf0008/qXz58pKk06dPq2nTpvrss88UEhJiboMuMGfOHFWqVEnt2rWTJL300kv64IMPFBYWps8++0w1a9Y0ucOS9VfeBwhrbqply5b64YcfFBMTY3YrpvH0bTB69GjNmTNHb731lmJjYx3L69Wrp0mTJrntm9TJkyc7/vvUqVMaPXq0oqKi1KRJE0nSunXrtGzZMo0YMcKkDkvWpEmTnB6fOHFC586dc3qDUrp0aVWuXNkjwhr7gWfuB5dbuHChoqOj9eSTT2rLli3KysqSJP35558aO3asli5danKHJW/MmDEaMmSI/vnPfyoiIkJlypRxWh8YGGhSZ67Rs2dP5eTkaOfOnapTp44kaffu3erZs6d69eqlxMREkzsseWPHjtV7770n6dL+P23aNE2ePFnffPONBg0apEWLFpncYcn6K+8DNsMwDLObQPF7//33NXLkSD355JOF/qPs2LGjSZ25jqdvg1tvvVXvv/++Hn74YZUrV05bt25V7dq1tWvXLjVp0uQve+72jejSpYtatGih559/3mn5tGnTtGLFCn311VfmNOYin376qd59913NnDnT6Q1KbGysevfurSeffNLkDkse+wH7QYMGDTRo0CA99dRTTv8GUlJS1KZNG6WlpZndYokrVeo/V71cfsTVMAzZbDbl5uaa0ZbLBAQEaO3atWrQoIHT8s2bN6tZs2Y6f/68SZ25TunSpbVr1y7VqFFDL7/8so4ePapPPvlEP//8s5o3b64TJ06Y3WKJ+ivvAxxZc1PPPfecJGnixIkF1ln9H2Vx8fRt8Pvvv+vWW28tsDwvL085OTkmdOR6y5Yt05tvvllgeVRUlIYOHWpCR641YsQI/etf/3IENUmqU6eOJk2apMcee8wjwhr7AfvB7t279eCDDxZYHhgY+Je9huVGff/992a3YKoaNWoUur9fvHhR1atXN6Ej1ytbtqxOnTqlGjVqKDExUYMGDZIk+fv7e0RY/SvvA4Q1N5WXl2d2C6bz9G1w55136scffyxwHvqXX35Z4NNFd1WxYkXFx8frxRdfdFr+1VdfqWLFiiZ15TpHjx4t9A1Kbm6ujh07ZkJHrsd+wH5QrVo17d27V7Vq1XJavmbNGtWuXducplwsMjLS7BZM9dZbb6l///6aPn26IiIiZLPZlJycrBdeeEHjx483uz2XaNWqlf7+97+rQYMG2rNnj+PatZ9//rnAvuGO/sr7AGHNA1y4cEH+/v5mt2EqT9wGr732mqKjo/X7778rLy9PixYt0u7du/XJJ5/om2++Mbs9l3j99dfVq1cv/fDDD45rdZKSkpSQkKCPPvrI5O5K3sMPP6zY2FjNnDnT6Q1K79691bJlS7Pbcwn2A/aD3r1764UXXtDHH38sm82mI0eOaN26dRoyZIheffVVs9tzmfwhK7/99pu+/PJLjxqyEhMTo3Pnzqlx48by9r701vfixYvy9vZWz5491bNnT0etuw7cmT59ukaMGKGDBw9q4cKFjg9qNm3apCeeeMLk7lzjr7oPcM2am8rNzdXYsWM9bgLa5dgGl05/Gjt2rDZt2qS8vDw1bNhQr776qlq3bm12ay6zfv16TZkyRTt37pRhGAoLC9OAAQPUuHFjs1srcSdOnNDTTz+thIQE+fj4SLr0BiUqKkqzZ89W5cqVTe7QNdgPPHs/kKThw4dr0qRJjts3+Pn5OYYNeILLh6zMnTtXO3bsUO3atfXuu+/qm2++cfshK3PmzLnuWnecIn3x4kWNGTNGPXv29IjJl4X5K+8DhDU3NWrUKM2ZM0ejRo1SbGysUlNTVbt2bX3xxReaNGmS1q1bZ3aLJY5tAFyyZ88e7dq1S4ZhqG7durr99tvNbglwuXPnzmnHjh3Ky8tTWFiYypYta3ZLLsOQFZQtW1apqakeccpjYf7K+wCnQbqpTz75RB988IEefvhh9enTx7G8fv362rVrl4mduY6nb4NDhw7JZrPp5ptvliRt2LBBn376qcLCwvTss8+a3F3JyczMvO5aK4/qLU633367xwY09oNr85T9oHTp0mrUqJEyMzO1YsUK1alTR3Xr1jW7LZdgyMqls22++uor7dy5UzabTWFhYerYsaO8vLzMbs0lPP12Rn/lfYCw5qaYgMY26N69u5599llFR0crLS1NLVu2VHh4uObNm6e0tDS3vVajfPny17wZ8F9hVO9/Ky4u7rprC5uU6m7YD67OnfeDy3Xt2lUPPvignn/+eZ0/f1733HOP9u3bJ8MwtGDBAnXp0sXsFkucpw9Z2bt3r9q2bavff/9dderUkWEY2rNnj0JCQrRkyRLdcsstZrdY4h555BENGzZMqampHnk7o7/yPkBYc1NMQGMbpKam6t5775UkffHFF6pXr55++uknJSYmqk+fPm77JvWvPJ63OGzZsuW66q71Rt5dsB9g9erVGj58uCQpPj5eeXl5On36tObMmaPRo0d7RFjz9CErAwYM0C233KKkpCRVqFBB0qWbxffo0UMDBgzQkiVLTO6w5Hn67Yz+yvsAYc1NMQGNbZCTkyM/Pz9J0ooVKxyfmt1xxx06evSoma2VqL/yeN7iwJt0Z+wHyMjIcLxBT0hIUJcuXVS6dGm1a9euwO0M3NVLL72kjIwMtWjRQhcuXNCDDz7oGLJy5c3S3dGqVaucgpp06ZYWb7zxhpo1a2ZiZ67j6bcz+ivvA6WuXYK/og4dOujzzz/X0qVLZbPZ9Oqrr2rnzp36+uuv1apVK7PbcwlP3wZ33nmnZsyYoR9//FHLly9XmzZtJElHjhzxiHsr5fvxxx/Vo0cPNW3aVL///rskae7cuVqzZo3JnbnO3r17tWzZMseNTz1prhT7wSWevB+EhIRo3bp1Onv2rBISEhxTQNPT0z3qli5jxozRyZMntWHDBiUlJenEiRMeMw3Tz89Pf/75Z4HlZ86cka+vrwkdmSt/Kqqn+cvuAwYAt/T9998b5cuXN0qVKmU888wzjuXDhg0z/va3v5nYmev861//MgICAoy///3vhp+fn/Hrr78ahmEY06dPNx555BGTuyt5J0+eNB566CHDZrMZpUqVcrz+nj17GnFxcSZ35xrsB+wH06dPN7y9vY3y5csbd911l5Gbm2sYhmFMmTLFaN68ucndwRWio6ONO++800hKSjLy8vKMvLw8Y926dUZ4eLjx9NNPm92eS1y8eNEYNWqUERwcbHh5eTl+D/zjH/8wPvroI5O7K3nPPPOMkZmZWWD5mTNnnP42WBGj+91cdna2jh8/XuDwd40aNUzqyPU8eRvk5uYqMzNTQUFBjmX79+9X6dKlPeIeW3/lUb3F4amnntLx48f10UcfqW7duo7Xn5iYqEGDBunnn382u0WXYD/w7P1AunTj34MHD6pVq1aOkf1LlixR+fLlPeI0uLNnz+qNN97QypUrC/17+Ntvv5nUmWucPn1aTz/9tL7++mune0527NhRs2bNUvny5c1t0AU8/XZGXl5eOnr0aIHf+SdPnlTVqlV18eJFkzq7Nq5Zc1O//PKLevbsqbVr1zotNzxk+pfENpAu/XK6/A2qJI+6x8pfeVRvcUhMTNSyZcscY+vz3XbbbTpw4IBJXbke+4Fn7weSFBERoYiICKdl7dq1M6kb1/v73/+uVatWKTo6WtWqVfOYAUP5ypcvr3//+9/au3ev043hC5sY7a489XZGmZmZMgxDhmHozz//dDr1OTc3V0uXLrX8h3aENTcVExMjb29vffPNNx75i1liG0jSv/71L33xxRc6ePCgsrOzndZt3rzZpK5c5688qrc4nD17VqVLly6w/OTJk46hG56A/cCz9wNJOnz4sBYvXlzovwFPuIXFt99+qyVLlnjEUcTCjBo1SkOGDNGtt97qFNDOnz+vt99+2/LTAIuDp97OKP82JjabrdD7jdpsNr3++usmdHb9CGtuKiUlRZs2bdIdd9xhdium8fRtMGXKFA0fPlxPP/20/v3vf+uZZ57Rr7/+qo0bN6pfv35mt+cSf+VRvcXhwQcf1CeffOK4gNpmsykvL09vv/22WrRoYXJ3rsF+wH6wcuVKdezYUaGhodq9e7fCw8O1f/9+GYahhg0bmt2eSwQFBTlNQvQ0r7/+uvr06VPgw6tz587p9ddf94j9wFNvZ/T999/LMAw99NBDWrhwodN+4Ovrq5o1ayo4ONjEDq+DaVfLoUQ1atTI+PHHH81uw1Sevg3q1KljfPrpp4ZhGEbZsmUdFxOPGDHC6Nevn5mtudQrr7xiBAQEGDabzbDZbIa/v7/xj3/8w+y2XOLnn382brrpJqNNmzaGr6+v8dhjjxl169Y1qlSpYuzdu9fs9lyC/eAST94P7rnnHmPEiBGGYfzn38Cff/5pdOzY0Xj33XdN7s415s6dazz22GPG2bNnzW7FFDabzTh+/HiB5StXrjQqVapkQkeut3jxYsNutxtvvPGGUbp0aePtt982/v73vxu+vr5GYmKi2e2VuP379xt5eXmFrjtw4ICLu7kxDBhxI5mZmY7/Tk5O1j/+8Q+NHTtW9erVc1xQmy8wMNDV7bkE2+A/SpcurZ07d6pmzZqqXLmyli9frrvuuku//PKL7rvvPp06dcrsFl3m3Llz2rFjh/Ly8hQWFuYYMOAJ0tLS9N5772nTpk3Ky8tTw4YN1a9fP1WrVs3s1lyC/eA/PHU/KFeunFJSUnTLLbcoKChIa9as0Z133qmtW7fq0Ucf1f79+81usUQ0aNDA6fT/vXv3yjAM1apVq8DfQ3c9HTgoKEg2m00ZGRkKDAx02h65ubk6c+aM+vTpo+nTp5vYpessW7ZMY8eOdfp78OqrrzpuZ+HOrjZg5NSpU6pcubKl5xhwGqQbyT8vN59hGHr44Yedagw3H67BNviPqlWr6tSpU6pZs6Zq1qyppKQk3XXXXdq3b59H3WdLuvSGvVGjRma3YYqqVata/nz8ksR+8B+euh+UKVNGWVlZkqTg4GD9+uuvuvPOOyVdun7TXXXq1MnsFkw3efJkGYahnj176vXXX5fdbnes8/X1Va1atdSkSRMTO3StqKgoRUVFmd2GKa72+/7MmTOWv98iYc2NfP/992a3YDq2wX889NBD+vrrr9WwYUP16tVLgwYN0r/+9S8lJyerc+fOZrdXYjp37qzZs2crMDDwmq9z0aJFLurKdbZt26bw8HCVKlVK27ZtK7K2fv36LurKPOwHnrkfXO6+++7TTz/9pLCwMLVr106DBw/W9u3btWjRIt13331mt1diXnvtNbNbMN3TTz8tSQoNDVWzZs3k7c3bXk8TFxcn6dI126+++qrTdYu5ublav3697r77bpO6uz78q3UjkZGRZrdgOrbBf3zwwQeOe+n06dNHFSpU0Jo1a9ShQwensb3uxm63O46uXv4pqqe4++67lZaWpsqVK+vuu++WzWYr9BNFTzi6LLEf5P+3J5s4caLOnDkjSRo5cqTOnDmjzz//XLfeeqsmTZpkcneusXHjRuXl5alx48ZOy9evXy8vLy+3P+Jarlw57dy5U/Xq1ZMk/fvf/9asWbMUFhamkSNHytfX1+QOS0b+aaDX448//ijhbsyxZcsWSZeOrG3fvt3p/7Wvr6/uuusuDRkyxKz2rgvXrLmpWbNmqWzZsvq///s/p+Vffvmlzp075/i0yZ2xDTzXd999pwcffNAjP0U9cOCAatSoIZvNds17qV05FQzuxZP3Azi799579dJLL+mxxx5zWr5o0SK9+eabWr9+vUmducY999yjoUOHqkuXLvrtt98UFhamzp07a+PGjWrXrp0mT55sdoslYs6cOY7/PnXqlEaPHq2oqCjHqZ/r1q3TsmXLNGLECA0aNMisNl3imWee0TvvvPOXnFdAWHNTderU0YwZMwqM5161apWeffZZ7d6926TOXIdt8B9nz57V559/rvPnz6t169a67bbbzG6pRF15IfF9992nhQsXqnr16iZ35hoPPvigFi9erPLly0uSFi9erFatWikgIMDcxkzGfuBZ+8HV/Pbbbzp//rzq1q2rUqVKmd2OS5QtW1bbtm0rcF+9ffv2qX79+vrzzz9N6sw17Ha7Nm/erFtuuUVvvvmmvvvuOy1btkw//fSTHn/8cR06dMjsFktcly5d1KJFCz3//PNOy6dNm6YVK1boq6++MqcxXBMft7mpAwcOKDQ0tMDymjVr6uDBgyZ05Hqeug0OHjyo6Ohobd68Wffdd59mzpypVq1a6ZdffpEkBQQE6Ntvv9WDDz5ocqcl58rPoH7++WfHgAFPsGbNGqcb//bo0UMpKSkecwNkif1AYj/IycnR6NGjHf8Ghg4dqh49euiLL76QdOkDvaVLlxa4Wbg78vPz07Fjxwr8Djh69KhHHHk1DMNxOvSKFSvUvn17SVJISIhbD5m53LJly/Tmm28WWB4VFaWhQ4ea0FHJc5drdz3jIyUPVLly5UKHC2zdulUVK1Y0oSPX89RtMGTIEGVnZ+u9995T6dKlFRUVpdtuu01Hjx7VsWPH1LZtW40cOdLsNuFCnngCBfsBhg4dqvfee09VqlTRxx9/rM6dO2vLli369NNPtWDBAnl7e2v48OFmt+kSrVq10rBhw5SRkeFYdvr0ab3yyitq1aqViZ25RqNGjTR69GjNnTtXq1atUrt27SRdOrJYpUoVk7tzjYoVKyo+Pr7A8q+++spt3xNdee1uUV+W5tK7usFlXnzxRaNmzZrGd999Z1y8eNG4ePGisXLlSqNmzZrG4MGDzW7PJTx1G1SpUsVYv369YRiGcerUKcNmsxlr1651rE9JSTEqVqxoVnsuUapUKacboJYrV8747bffTOzItWw2m3Hs2DHH48tvBu0p2A/YD2rUqGEsWbLEMAzD2L17t2Gz2YylS5c61v/www9G9erVzWrPpQ4fPmzUrl3bsNvtRvPmzY3mzZsb5cuXN+rUqWMcPHjQ7PZKXEpKihEeHm4EBgYaI0eOdCx//vnnjSeeeMLEzlxn1qxZRqlSpYy2bdsa//znP41//vOfRrt27QwvLy9j1qxZZreHInDNmpvKzs5WdHS0vvzyS8cpDnl5eXrqqaf03nvvyc/Pz+QOS56nbgMvLy8dOXLE8WnhldcqHDt2TMHBwW49CbBUqVIKDw93/H/ftm2b7rjjjgITv9z1RrClSpXSnDlzHJ8WPvHEE5o8eXKBT5A7duxoRnsuwX7AfuDj46P9+/c7rtELCAjQtm3bHNcqHj16VCEhIbp48aKZbbrM2bNnNX/+fG3dulUBAQGqX7++nnjiiQI3yPYkFy5ckLe3t0ecCipdmv45ZcoU7dy5U4ZhKCwsTAMGDCgwJRTWQlhzc7/88otSUlIUEBCgevXqeeT0N0/bBqVKlXKMbpcujSzeunWrR71Jvd6bQLvrfYiuZ2iCu4/uZz9gP+DfwH+sXr1aTZs2LRBKLl68qLVr17r1tZuSVLt2bW3cuLHA6X6nT59Ww4YN9dtvv5nUGUpagwYNruv2BVb+0MozPkrwQKNGjdKQIUN02223OU08O3/+vN5++229+uqrJnbnGp68DS6/8WN2drbGjBnjOMpy7tw5M1tzCXd983m98i+k93TsB569H0iXhirk/z/Py8vTypUrlZqaKunSG3VP0aJFC6fJoPkyMjLUokULtw+s+/fvL/Q1ZmVl6fDhwyZ05BqZmZnXXftXHGl/PTp16uT4b8MwNG7cOMc9N/8qOLLmpq4c2Zzv1KlTqly5stv/YpY8dxs0b978uj5F+v77713QDWAO9gNwhPk/SpUqpWPHjummm25yWr5nzx41atToht7U/5UsXrxY0qU37JefGi5Jubm5WrlypZYvX+62t/IpVarUNX8PGobhMfuBVPAI+18BR9bcVP7Od6WtW7f+pT5N+F946jb44YcfzG4BMB37ATjCLMe4cpvNppiYGKdrtXNzc7Vt2zY1bdrUrPZK3OVHVZ5++mmndT4+PqpVq5YmTJjg4q5chw+j3ANhzc0EBQXJZrPJZrPp9ttvdworubm5OnPmjPr06WNihyWPbQAAgBxHkgzDULly5RQQEOBY5+vrq/vuu0+xsbFmtVfi8gN7aGiokpOT3XZE/dVERkaa3QKKAWHNzUyePFmGYahnz556/fXXnQ75+/r6qlatWmrSpImJHZY8tgEAANKsWbMkSbVq1dKQIUNUpkwZkztyvZycHNWqVUunTp3yuLB2pR9//FHvv/++fvvtN3355ZeqXr265s6dq9DQUN1///1mt4erIKy5mfzD/KGhoWratKlHjuRlG0C69Ae6devWev/993X77beb3Q5gmk8++UTdunUrcLuS7OxsLViwQE899ZRJncFVPHnYjI+Pj1JTU6/rGlZ3tnDhQkVHR+vJJ5/U5s2blZWVJUn6888/NXbsWC1dutTkDkvGlClTnB5fvHhRs2fPVqVKlZyWDxgwwJVt3RAGjLiRzMxMxzSfa10s7K5Tf67m/PnzysnJcVrmadvAE910001au3at0zRQT8K4akieO2zJ0zVs2FArV65UUFDQNceXW3lseXEYPHiwfHx89MYbb5jdimkaNGigQYMG6amnnnIaspGSkqI2bdooLS3N7BZLRGho6DVrbDabpf8ecmTNjQQFBTn+IJcvX77QX8yeNPXn3Llzeumll/TFF1/o1KlTBdZ7wjbwdE899ZRmzpzpsX+gixpX/fvvv5vQkesdPHhQISEhBX4fGoahQ4cOqUaNGiZ15jpXG7Z0+PBhp9PE4V4effRRx9HURx991KOPLGVnZ+ujjz7S8uXL1ahRowKng06cONGkzlxn9+7dhd5PLzAw0K1vY7Fv3z6zW/ifEdbcyHfffeeYcsgEIOnFF1/U999/r3fffVdPPfWUpk+frt9//13vv/++275537Zt23XX1q9fvwQ7sQZP/QOdP65acr7PlPSfcdW1atUyoTPXCw0NLfSo0h9//KHQ0FC3/tAm/2iKzWbTww8/7HRD5NzcXO3bt09t2rQxsUOUpMtPfRw5cqR5jVhAamqqGjZsKOnS7Qou5ykhtlq1atq7d2+B3/1r1qz5S42x90SENTeSP/XHMAwFBwcrJydHt99+u9MfaE/y9ddf65NPPlHz5s3Vs2dPPfDAA7r11ltVs2ZNzZ8/X08++aTZLRa7u+++Wzab7aqfpF/Ond+k5vPUP9CePq76clfbF86cOSN/f38TOnKd/H8HKSkpioqKUtmyZR3r8octdenSxaTuSlb+VODr8ccff5RwN+Y5d+6cXnzxRX311VfKyclRy5YtNWXKlALX67g7PsCWevfurRdeeEEff/yxbDabjhw5onXr1mnIkCF69dVXzW4PRfDMd/FubP/+/Xr00UeVmpoqSQoJCdGiRYscb1g9Sf4n59Klw/z5f5Dvv/9+Pffcc2a2VmIuP9y/ZcsWDRkyRC+++KJj+uW6des0YcIEvfXWW2a16FKe+Ad627ZtysnJkZeXl0JDQ7Vx40aPe2MmSXFxcZIuhfIRI0aodOnSjnW5ublav3697r77bpO6c438Iyu1atVSt27d3D6cXm7y5MmO/z516pRGjx6tqKgop9+Fy5Yt04gRI0zq0DVee+01zZ49W08++aT8/f312Wef6bnnntOXX35pdmumOXz4sGw2m6pXr252Ky710ksvKSMjQy1atNCFCxf04IMPys/PT0OGDNHzzz9vdnsoAgNG3Ey3bt2UkpKi1157Tf7+/nr77beVm5urDRs2mN2ay9WvX19Tp05VZGSkWrdurfr162v8+PGaMmWK3nrrLR0+fNjsFkvUvffeq5EjR6pt27ZOy5cuXaoRI0Zo06ZNJnXmenv37tWvv/6qBx98UAEBAdd15PGvysvLS2lpabrpppuuOmDEE7Ro0UKStGrVKjVp0kS+vr6OdflHlYYMGeJRw2eys7N1/PjxAjeLdvfr9rp06aIWLVoUeEM6bdo0rVixQl999ZU5jbnALbfcojFjxujxxx+XJG3YsEHNmjXThQsX5OXlZXJ3rpOXl6fRo0drwoQJOnPmjCSpXLlyGjx4sIYPH65SpUqZ3KHrnDt3Tjt27FBeXp7CwsKcjrjDogy4lWrVqhk//PCD4/GhQ4eMUqVKGefOnTOxK3NMnDjReOeddwzDMIzvvvvOCAgIMHx9fY1SpUoZkydPNrm7kufv72/s2LGjwPIdO3YY/v7+JnTkeidPnjQeeughw2azGaVKlTJ+/fVXwzAMo2fPnkZcXJzJ3ZWMChUqGElJSYZhGEapUqWM48ePm9yRuWJiYoyMjAyz2zDVnj17jPvvv98oVaqU01f+fuHuypQpY/zyyy8Flu/Zs8coU6aMCR25jo+Pj3H48GGnZf7+/sbBgwdN6sgcQ4cONW666Sbj3XffNbZu3WqkpKQY06dPN2666SbjlVdeMbs9oEgcWXMzpUqV0tGjR1WlShXHsrJlyyo1NdVjBgpczcGDB5WcnKxbbrlFd911l9ntlLiGDRuqbt26mjlzpuP0p6ysLPXs2VM7d+50+1HN0qVpkMePH9dHH32kunXrOkYVJyYmatCgQfr555/NbrHYPfvss/rkk09UrVo1HTx4UDfffPNVP0G38qjikpKZmanvvvtOd9xxh+644w6z23GJZs2aydvbW0OHDlW1atUKHFV299+HNWvW1PPPP68XX3zRafnbb7+tadOm6cCBAyZ1VvIuP9Ker1y5ctq2bdt1jTR3F8HBwZoxY4Y6duzotPzf//63+vbt67bTcTt37qzZs2crMDBQnTt3LrJ20aJFLurKda51G6vLWfl2Tlyz5mZsNluBw/mlSpUSmfzSqT7ufrrP5WbMmKEOHTooJCTE8WZs69atstls+uabb0zuzjUSExO1bNky3XzzzU7Lb7vtNrd9g/bBBx+oc+fO2rt3rwYMGKDY2FiVK1fO7LZM07VrVz344IN6/vnndf78eTVq1Ej79++XYRhasGCB2w7YuFxKSoo2bdrkMeH0Sq+//rp69eqlH374wXHNWlJSkhISEvTRRx+Z3F3JMgxDMTExTjdEv3Dhgvr06eM0Hdcd36hf7o8//ij03/8dd9zh1gNm7Ha748OZwMBAtz39/2qudhurwlh56Bphzc0YhqHbb7/d6R/nmTNn1KBBA6cQ586/nL777js9//zzSkpKKvBJSUZGhpo2baoZM2bogQceMKlD17j33nu1b98+zZs3T7t27ZJhGOrWrZu6d+9eYIS9uzp79qzTYIl8J0+edHrz4m7yx7Fv2rRJL7zwgkeHtdWrV2v48OGSpPj4eBmGodOnT2vOnDkaPXq0R4S1sLAwnTx50uw2TBMTE6O6detqypQpWrRokQzDUFhYmH766Sc1btzY7PZK1JXTYCWpR48eJnRirrvuukvTpk3TlClTnJZPmzbNrY8s/+1vf3OcWTN79mxzmzHB5UPG9u/fr6FDhyomJsZp0NCcOXM0btw4s1q8LpwG6WbmzJlzXXWF/QJ3Fx07dlSLFi00aNCgQtdPmTJF33//veLj413cGVytXbt2atiwof75z386Tv2pWbOmHn/8ceXl5elf//qX2S26hCcNWLlSQECA9uzZo5CQED311FMKDg7WG2+8oYMHDyosLMwxbMDdXH76T3Jysv7xj39o7Nixqlevnnx8fJxqrXz6z/8qJydHzz77rEaMGMG9pDzYqlWr1K5dO9WoUUNNmjSRzWbT2rVrdejQIS1dutRtP7y9/DRYLy+vQu856Skefvhh/f3vf9cTTzzhtPzTTz/VBx98oB9++MGcxq4DYQ1up2bNmkpISFDdunULXb9r1y61bt1aBw8edHFnrrdnzx798MMPhU6A84T7quzYsUPNmzdXRESEvvvuO3Xs2FE///yz/vjjD/3000+65ZZbzG6xRP3xxx/6v//7P33//fey2Wz65ZdfVLt2bfXq1Uvly5f3iHut3X777Ro9erTatWun0NBQLViwQA899JC2bt2qhx9+2G2POJUqVcopkBcW0POXWfn0n+JQvnx5bd68mbDm4Y4cOaLp06c7zjQJCwtT3759FRwcbHZrJaZq1ar68MMP1aFDB5UqVUrHjh1zun7Rk5QuXVpbt24tMAF4z549uvvuu3Xu3DmTOrs2ToOE2zl27FiBT44v5+3trRMnTriwI3N8+OGHeu6551SpUiVVrVrV6Y2azWbziLAWFhambdu26b333pOXl5fOnj2rzp07q1+/fqpWrZrZ7ZW4gQMHysfHRwcPHnT68KJbt24aNGiQR4S1gQMH6sknn1TZsmVVs2ZNNW/eXNKl0yPr1atnbnMlyBPvMXg1f/vb3/TVV1857r0HzxQcHKwxY8aY3YZL9enTR48++qhsNptsNpuqVq161Vp3/9AmJCREM2bMKPB37/3331dISIhJXV0fjqzB7dxyyy0aP368/va3vxW6ftGiRRoyZIjbT8KrWbOm+vbtq5dfftnsVkxz8OBBhYSEFHrK38GDB91+4EzVqlW1bNky3XXXXSpXrpxjGua+fftUr149tz0F8EqbNm3SwYMH1apVK8c9hZYsWaLy5curWbNmJneHkjZmzBiNHz9eDz/8sCIiIgpcsztgwACTOoMrnT59Whs2bCj0TJOnnnrKpK5K3q5du7R371517NhRs2bNUvny5Qute/TRR13bmIstXbpUXbp00S233KL77rtP0qVBQ7/++qsWLlxY4J60VkJYg9vp37+/fvjhB23cuNFxYW2+8+fP695771WLFi0KXGjsbgIDA5WSkuLRp/5c7Rz9U6dOqXLlym7/SWK5cuW0efNm3XbbbU5hbePGjWrTpo1OnTpldotwgW3bthW63Gazyd/fXzVq1HDrgTtFjai32Wxu/8EdpK+//lpPPvmkzp49q3LlyhU408Sdh67le/311/Xiiy8WOnTLUxw6dEjvvfee06mwffr04cgaXCczM9OtLxS/XseOHVPDhg3l5eWl559/XnXq1JHNZtPOnTs1ffp05ebmavPmzU73onNHvXr10j333KM+ffqY3YpprnaO/oEDBxQWFqazZ8+a1JlrMGDl6g4dOqTXXntNH3/8sdmtlLgrr1+7ko+Pj7p166b333+/wAdcgDu4/fbb1bZtW40dO9Zjw8pDDz2kRYsWFTiylpmZqU6dOum7774zpzFcE2HNjVx+FOFqO6WnOHDggJ577jktW7bMcY85m82mqKgovfvuux5xg/Bx48Zp4sSJateuXaET4Nz51J/8a1PeeecdxcbGOv1xzs3N1fr16+Xl5aWffvrJrBZdwtMHrBRl69atatiwodsfXZUu3fj35Zdf1osvvqh7771XhmFo48aNmjBhgl577TVdvHhRQ4cOVbdu3TR+/Hiz20UJWLx4caHL84+u3nrrrW59k+wyZcpo+/btnGlSyJkmx48fV/Xq1ZWTk2NSZ67z448/6v3339dvv/2mL7/8UtWrV9fcuXMVGhqq+++/3+z2rooBI26kbNmyjtO7fvjhB4/Y8a6mZs2aWrp0qdLT07V3714ZhqHbbrtNQUFBZrfmMh988IHKli2rVatWadWqVU7rbDabW4e1LVu2SLo07W779u3y9fV1rPP19dVdd92lIUOGmNWey3jygJWrvTnN50mnvo0ZM0bvvPOOoqKiHMvq16+vm2++WSNGjNCGDRtUpkwZDR482O3C2s6dO5WUlKQmTZrojjvu0K5du/TOO+8oKytLPXr00EMPPWR2iy7RqVMn2Ww2Xfn5fP4ym82m+++/X1999ZVb/p2MiopScnKyR4a1/NOgDcPQjh07lJaW5liXm5urhIQEVa9e3az2XGbhwoWKjo7Wk08+qc2bNysrK0uS9Oeff2rs2LFaunSpyR1eHUfW3EiXLl30008/qW7dulq1apWaNm3q9Cb1chzuhid45pln9M4773B68BU84RTA/FP/ivoT5wlj66VL95rbsmWL7rjjDqflu3btUoMGDXT+/Hnt379fYWFhlh5ffaMSEhL06KOPqmzZsjp37pzi4+P11FNP6a677pJhGFq1apWWLVvmEYFt5cqVGj58uMaMGaN7771XkrRhwwb94x//0IgRI2S329W7d281btxYM2fONLnb4nH5BzYnTpzQqFGj9MwzzxR6pknHjh1d3Z7LXH4adGG/DwMCAjR16lT17NnT1a25VIMGDTRo0CA99dRTTtdwp6SkqE2bNk4h1moIa27k/PnzmjNnjn799VdNmDChwOlfl5s0aZKLuwNgFZ5wCmD16tU1ffp0derUqdD1KSkpioiIcOttkK9Bgwa666679MEHHzg+wMvJyVFsbKy2bt2qLVu26KefflKPHj20b98+k7stPk2bNtVDDz2k0aNHa8GCBerbt6+ee+45x/j24cOHa+PGjUpMTDS505IXHh6uDz74QE2bNnVa/tNPP+nZZ5/Vzz//rBUrVqhnz55ucw/SUqVKXVedu39oc+DAARmGodq1a2vDhg1O13D7+vqqcuXK8vLyMrFD1yhdurR27NihWrVqOYW13377TWFhYbpw4YLZLV4Vp0G6kYCAAMcwieTkZL355psee82apzt//rw2bdqkChUqKCwszGndhQsX9MUXX7j1qOJ8Z8+e1RtvvKGVK1cWOq7Zk06F8zQRERHavHnzVcPatY66uZPp06erY8eOuvnmm1W/fn3ZbDZt27ZNubm5+uabbyRd2hf69u1rcqfF6+eff9Ynn3wiSeratauio6PVpUsXx/onnnjCbY4iXcuvv/5a6BkGgYGBjt+Dt912m1vdJP7K3/eeqmbNmpLYHtWqVdPevXsLzCxYs2aN5U+PJay5qfwbomZnZ2vfvn265ZZb5O3N/25PsGfPHrVu3VoHDx6UzWbTAw88oM8++8xxjVJGRoaeeeYZjwhrf//737Vq1SpFR0erWrVqRU7Eg3t58cUXi5z2eeutt3rMjaObNm2q/fv3a968edqzZ48Mw9Bjjz2m7t27q1y5cpKk6Ohok7ssWaVKlZK/v7/TB5jlypVTRkaGeU25UEREhF588UV98sknjiMrJ06c0EsvvaR77rlHkvTLL7/o5ptvNrNNlKD8Dy6uxt3fE/Tu3VsvvPCCPv74Y9lsNh05ckTr1q3TkCFD9Oqrr5rdXtEMuKVz584ZPXv2NLy8vAwvLy/j119/NQzDMPr372+MGzfO5O5Qkjp16mS0b9/eOHHihPHLL78YHTp0MEJDQ40DBw4YhmEYaWlpRqlSpUzu0jXsdruxZs0as9uwnJSUFI/5NwDPVb9+fePbb791PN6+fbuRk5PjePzjjz8aoaGhZrTmcrt27TLq1Klj+Pr6Grfccotx6623Gr6+vsYdd9xh7N692zAMw4iPjzc++eQTkzstXklJScbSpUudls2ZM8eoVauWcdNNNxmxsbHGhQsXTOrOtcqXL+/0VaZMGcNmsxl+fn5GUFCQ2e25xCuvvGIEBAQYNpvNsNlshr+/v/GPf/zD7LauiUMtbmro0KHaunWrfvjhB7Vp08axvGXLlnrttdc0dOhQE7tDSVq7dq1WrFihSpUqqVKlSlq8eLH69eunBx54QN9//73KlCljdosuExQUpAoVKpjdhst17ty5yPWnT592TSMwzeLFi/XII4/Ix8fnmpMx3XW4wnPPPed0LVJ4eLjT+m+//dYjhotIUp06dbRz504tW7bMcXT1jjvuUKtWrRzXdl3tlOG/spEjR6p58+Z65JFHJEnbt29Xr169FBMTo7p16+rtt99WcHCwRo4caW6jLpCenl5g2S+//KLnnntOL774ogkdud6YMWM0fPhw7dixQ3l5eQoLC1PZsmXNbuuaGDDipmrWrKnPP/9c9913n9OFlHv37lXDhg2VmZlpdosoIYGBgVq/fr3q1q3rtLx///766quv9Omnn6p58+ZufUF1vnnz5unf//635syZ41E3Qn3mmWeuq27WrFkl3AnMUqpUKaWlpaly5cpFDlpw9+EK8GzVqlXT119/rUaNGkm6NFRm1apVWrNmjSTpyy+/1GuvvaYdO3aY2aapkpOT1aNHD+3atcvsVkpUz5499c477zhO/c539uxZ9e/f39LTkTmy5qZOnDhR4MaH0qV/lFy3497uuOMOJScnFwhrU6dOlWEYbvspemEmTJigX3/9VVWqVFGtWrUKjGvevHmzSZ2VLEIYLh8m4OmDBXDJypUrrzpsycpvVP8X6enpqlKliuPxqlWrnM42uueee3To0CEzWrMMLy8vHTlyxOw2StycOXP0xhtvFAhr58+f1yeffGLpfYCw5qbuueceLVmyRP3795ckR0D78MMP1aRJEzNbQwn729/+ps8++6zQgQHTpk1TXl6eZsyYYUJnrueOp/UAxen333/3iBvierrXX39do0aNUqNGjTxq2FKVKlW0b98+hYSEKDs7W5s3b9brr7/uWP/nn38W+BDPXV15OrRhGDp69KimTZumZs2amdRVycvMzJRhGDIMQ3/++af8/f0d63Jzc7V06dJCD25YCadBuqm1a9eqTZs2evLJJzV79mz17t1bP//8s9atW6dVq1YpIiLC7BYBACZJS0vTmDFj9NFHH+n8+fNmt4MSVq1aNb311ltuP/XzSr1799b27dv15ptv6quvvtKcOXN05MgRx/0G58+fr8mTJ2vjxo0md1ryrjwd2maz6aabbtJDDz2kCRMmOCZGu5vLbwpeGJvNptdff13Dhw93YVc3hiNrbqpp06b66aefNH78eN1yyy1KTExUw4YNtW7dOtWrV8/s9gCXOX36tP71r3/p119/1YsvvqgKFSpo8+bNqlKlCkcU4NZOnz6tfv36KTExUT4+Pho6dKief/55jRw5UuPHj9edd95p6VN/UHyys7ML3BDbE4wePVqdO3dWZGSkypYtqzlz5jiCmnTp9M/WrVub2KHreOrp0N9//70Mw9BDDz2khQsXOg0d8/X1Vc2aNRUcHGxih9fGkTUAbmvbtm1q2bKl7Ha79u/fr927d6t27doaMWKEDhw4cM37zgB/ZX379tXXX3+tbt26KSEhQTt37lRUVJQuXLig1157TZGRkWa3CBd5+eWXVbZsWY0YMcLsVkyRkZGhsmXLysvLy2n5H3/8obJlyzoFOHd38uRJ2Ww2VaxY0exWXOrAgQMKCQkpcuCSVRHW3EhmZqYCAwMd/12U/DrAnbVs2VINGzbUW2+95TQVde3aterevbv2799vdotAialZs6Zmzpypli1b6rffftOtt96qAQMGaPLkyWa3Bhd74YUX9Mknn6h+/fqqX79+geu0Jk6caFJncIXTp09r+PDh+vzzzx0j/IOCgvT4449r9OjRTjeLd3fnzp3TwYMHlZ2d7bS8fv36JnV0bYQ1N+Ll5aWjR486RjUXdo6uYRiMaobHsNvt2rx5s2655RansHbgwAHVqVNHFy5cMLtFoMT4+PjowIEDjlN8SpcurQ0bNhS43xjcX4sWLa66zmaz6bvvvnNhN3ClP/74Q02aNNHvv/+uJ598UnXr1pVhGNq5c6c+/fRThYSEaO3atQoKCjK71RJ14sQJPfPMM/r2228LXW/l98Vcs+ZGvvvuO8e5uN9//73J3QDm8/f3L/Qo8+7du3XTTTeZ0BHgOnl5eU5HULy8vFSmTBkTO4JZeE/guUaNGiVfX1/HbWyuXNe6dWuNGjVKkyZNMqlD1xg4cKDS09OVlJSkFi1aKD4+XseOHdPo0aM1YcIEs9srEkfWALitZ599VidOnNAXX3yhChUqaNu2bfLy8lKnTp304IMPcjoY3FqpUqX0yCOPyM/PT5L09ddf66GHHioQ2BYtWmRGewBcoFatWnr//fcVFRVV6PqEhAT16dPH7S8LqFatmv7973/r3nvvVWBgoJKTk3X77bdr8eLFeuuttxw3Srcijqy5kW3btl13rZXPzQWKy/jx49W2bVtVrlxZ58+fV2RkpNLS0tSkSRONGTPG7PaAEvX00087Pe7Ro4dJncAMnTt31uzZsxUYGKjOnTsXWUtgd19Hjx7VnXfeedX14eHhSktLc2FH5jh79qzjfmoVKlTQiRMndPvtt6tevXravHmzyd0VjbDmRu6++27ZbDZd62Ap16zBUwQGBmrNmjX67rvvtHnzZuXl5alhw4Zq2bKl2a0BJW7WrFlmtwAT2e12x7XrgYGBHnMjbDirVKmS9u/fr5tvvrnQ9fv27fOIyZB16tTR7t27VatWLd199916//33VatWLc2YMcPy95jjNEg3cuDAgeuurVmzZgl2AgAAALP16tVLe/fu1fLlywvcoiArK0tRUVG65ZZbNHPmTJM6dI358+crJydHMTEx2rJli6KionTq1Cn5+vpq9uzZ6tatm9ktXhVhDYDb+e677/T8888rKSmpwG0qMjIy1LRpU82YMUMPPPCASR0CgOs89NBDWrRoUYER7ZmZmerUqRPTIN3Y4cOH1ahRI/n5+alfv3664447JEk7duzQu+++q6ysLCUnJyskJMTkTl3r3Llz2rVrl2rUqKFKlSqZ3U6RCGtubO7cuZoxY4b27dundevWqWbNmpo8ebJCQ0P16KOPmt0eUGI6duyoFi1aaNCgQYWunzJlir7//nvFx8e7uDMAcL1SpUopLS3Ncc1OvuPHj6t69erKyckxqTO4wr59+9S3b18lJiY6LpWx2Wxq1aqVpk2bpltvvdXkDlEUrllzU++9955effVVDRw4UGPGjHFco1a+fHlNnjyZsAa3tnXrVr355ptXXd+6dWuNHz/ehR0BgOtdPnhsx44dToMkcnNzlZCQoOrVq5vRGlwoNDRU3377rdLT0/XLL79Ikm699VbH7Z48QW5urmbPnq2VK1fq+PHjysvLc1pv5aPLhDU3NXXqVH344Yfq1KmT3njjDcfyRo0aaciQISZ2BpS8Y8eOOd1f6kre3t46ceKECzsCANfLHzxms9n00EMPFVgfEBCgqVOnmtAZzBAUFKR7773X7DZM8cILL2j27Nlq166dwsPD/1IDdwhrbmrfvn1q0KBBgeV+fn46e/asCR0BrlO9enVt3779qqd2bNu2zfLTnwDgf7Vv3z4ZhqHatWtrw4YNuummmxzrfH19VblyZXl5eZnYIeAaCxYs0BdffKG2bdua3coNI6y5qdDQUKWkpBSY+vjtt98qLCzMpK4A12jbtq1effVVPfLII/L393dad/78eb322mtq3769Sd0BgGvUrFlTOTk5euqpp1ShQgUmQcNj+fr6/mWvzWPAiJuaNWuWRowYoQkTJqhXr1766KOP9Ouvv2rcuHH66KOP9Pjjj5vdIlBijh07poYNG8rLy0vPP/+86tSpI5vNpp07d2r69OnKzc3V5s2bVaVKFbNbBYASFxQUpE2bNql27dpmtwKYYsKECfrtt980bdq0v9QpkBJhza19+OGHGj16tA4dOiTp0qlhI0eOVK9evUzuDCh5Bw4c0HPPPadly5Y5Tb+KiorSu+++q1q1apnbIAC4yDPPPKN69eopLi7O7FYAU/ztb3/T999/rwoVKujOO+8scF37okWLTOrs2ghrHuDkyZPKy8tzjOz9/fffmf4Ej5Genq69e/fKMAzddtttCgoKMrslAHCpMWPGaPz48Xr44YcVERGhMmXKOK0fMGCASZ0BrvHMM88UuX7WrFku6uTGEdY8SFpamsaMGaOPPvpI58+fN7sdAADgAqGhoVddZ7PZ9Ntvv7mwGwA3ggEjbub06dPq16+fEhMT5ePjo6FDh+r555/XyJEjNX78eN155536+OOPzW4TAAC4yL59+8xuAcB/iSNrbqZv3776+uuv1a1bNyUkJGjnzp2KiorShQsX9NprrykyMtLsFgEAAACXadCgQaGDRWw2m/z9/XXrrbcqJiZGLVq0MKG7onFkzc0sWbJEs2bNUsuWLdW3b1/deuutuv322zV58mSzWwMAACY5fPiwFi9erIMHDyo7O9tp3cSJE03qCnCNNm3a6L333lO9evV07733yjAMJScna9u2bYqJidGOHTvUsmVLLVq0SI8++qjZ7TrhyJqb8fHx0YEDBxQcHCxJKl26tDZs2KDw8HCTOwMAAGZYuXKlOnbsqNDQUO3evVvh4eHav3+/DMNQw4YN9d1335ndIlCiYmNjVaNGDY0YMcJp+ejRo3XgwAF9+OGHeu2117RkyRIlJyeb1GXhSpndAIpXXl6e0zhSLy+vAlOfAACA5xg2bJgGDx6s1NRU+fv7a+HChTp06JAiIyP1f//3f2a3B5S4L774Qk888USB5Y8//ri++OILSdITTzyh3bt3u7q1a+I0SDdjGIZiYmLk5+cnSbpw4YL69OlTILBZ+X4SAACg+OzcuVOfffaZJMnb21vnz59X2bJlNWrUKD366KN67rnnTO4QKFn+/v5au3atbr31Vqfla9eulb+/v6RLBzzy3z9bCWHNzTz99NNOj3v06GFSJwAAwArKlCmjrKwsSVJwcLB+/fVX3XnnnZIu3YsVcHf9+/dXnz59tGnTJt1zzz2y2WzasGGDPvroI73yyiuSpGXLlqlBgwYmd1oQ16wBAAC4sU6dOqldu3aKjY3VSy+9pPj4eMXExGjRokUKCgrSihUrzG4RKHHz58/XtGnTHKc61qlTR/3791f37t0lSefPn3dMh7QSwhoAAIAb++2333TmzBnVr19f586d05AhQ7RmzRrdeuutmjRpkmrWrGl2iwCugrAGAAAAABbENEgAAAA3Vrt2bZ06darA8tOnT6t27domdASUvAoVKjiuyQwKClKFChWu+mVlDBgBAABwY/v371dubm6B5VlZWfr9999N6AgoeZMmTVK5cuUc/22z2Uzu6L/DaZAAAABuaPHixZIuDRiZM2eO7Ha7Y11ubq5Wrlyp5cuXW/LeUgAuIawBAAC4oVKlLl3tYrPZdOXbPR8fH9WqVUsTJkxQ+/btzWgPcJnNmzfLx8dH9erVk/T/7d1bbBRlA8bxZ7K0bNslPWi7CFkQWoKAihwiKSJQS2C3HmqCRiMB13AwJNUQAs0SD0EkKsYL4KZglZaCplGpFZuSRm2kigQlm1IiTSkJbU0saWKLNJCuCzvfBV82begWEdnZb77/727feXfm6V71yfvOjPTVV1+poqJC06dP19atW5WcnGxxwti4Zw0AAMCGIpGIIpGIJkyYoJ6enujnSCSiUCiktrY2ihr+L7z88ss6e/aspOtPR33uueeUmpqqzz//XKWlpRanGxkrawAAAABsKz09XcFgULm5udqxY4caGxvV0NCgY8eO6fnnn9dvv/1mdcSYWFkDAACwoRMnTujIkSNDxqqqqjRp0iTl5ORo3bp1CoVCFqUD4sc0TUUiEUnSt99+q6KiIkmSx+OJPjEyUVHWAAAAbGjr1q1qaWmJfj59+rRWr16tJUuWKBAI6Ouvv9a7775rYUIgPubOnavt27frwIEDOnr0qB5//HFJ0vnz5+V2uy1ONzLKGgAAgA01NzersLAw+rm6ulrz5s1TeXm5Nm7cqN27d+uzzz6zMCEQHzt37lQwGFRJSYlee+015eXlSZK++OILzZ8/3+J0I+OeNQAAABtyOp1qb2+Xx+ORJC1YsEBer1evv/66pOvvX3vggQfU399vZUzAMgMDA3I4HEpKSrI6SkysrAEAANiQ2+3W+fPnJUl//fWXgsGg8vPzo8f7+/sT+p9U4N908eJFffTRR9qyZYt6e3slSWfOnFFPT4/FyUY2yuoAAAAA+Pd5vV4FAgHt2LFDtbW1Sk1N1aOPPho93tLSotzcXAsTAvHR0tKiwsJCZWRkqKOjQ2vXrlVWVpa+/PJLdXZ2qqqqyuqIMbGyBgAAYEPbt2+Xw+HQokWLVF5ervLy8iEv/923b5+WLl1qYUIgPjZu3KiXXnpJ7e3tcjqd0XGfz6empiYLk90c96wBAADY2J9//imXyyWHwzFkvLe3Vy6Xa0iBA+xo8HvWxowZo1OnTmny5Mnq7OzU1KlTNTAwYHXEmNgGCQAAYGPp6enDjmdlZcU5CWANp9OpS5cu3TDe1tam7OxsCxL9fWyDBAAAAGBbxcXF2rZtm8LhsCTJMAx1dXUpEAho+fLlFqcbGdsgAQAAANjWpUuXVFRUpF9//VX9/f0aN26cLly4oPz8fNXX1ystLc3qiDFR1gAAAADYXmNjo4LBoCKRiGbPnq0lS5ZYHemmKGsAAAAAkIB4wAgAAAAAW4pEIqqsrFRNTY06OjpkGIYmTZqkZ555RitXrpRhGFZHHBErawAAAABsxzRNPfnkk6qvr9fMmTN13333yTRNtba26vTp03rqqadUW1trdcwRsbIGAAAAwHYqKyvV1NSk7777TgUFBUOONTY26umnn1ZVVZVWrVplUcKbY2UNAAAAgO0sXbpUjz32mAKBwLDH33nnHR09elQNDQ1xTvb38Z41AAAAALbT0tIir9cb87jP59OpU6fimOjWUdYAAAAA2E5vb6/cbnfM4263W319fXFMdOsoawAAAABs59q1axo1KvYjOhwOh65evRrHRLeOB4wAAAAAsB3TNOX3+zV69Ohhj4dCoTgnunWUNQAAAAC28+KLL950TiI/CVLiaZAAAAAAkJC4Zw0AAAAAEhBlDQAAAAASEGUNAAAAABIQZQ0AAAAAEhBlDQAAAAASEGUNAIA7rKOjQ4ZhqLm5ecR5ixcv1oYNG+KSCQCQ+ChrAAD8l9/vl2EYMgxDSUlJmjx5sjZt2qTLly/f1nk9Ho+6u7t1//33S5K+//57GYahixcvDplXU1Ojt99++7auBQCwD16KDQDAIF6vVxUVFQqHw/rhhx+0Zs0aXb58WWVlZf/4nA6HQ2PHjr3pvKysrH98DQCA/bCyBgDAIKNHj9bYsWPl8Xj0wgsvaMWKFaqtrVUoFNKrr76qnJwcOZ1OLViwQL/88kv0e319fVqxYoWys7OVkpKiKVOmqKKiQtLQbZAdHR0qKCiQJGVmZsowDPn9fkk3boPs6+vTqlWrlJmZqdTUVPl8PrW3t0ePV1ZWKiMjQw0NDZo2bZpcLpe8Xq+6u7vv/A8FALjjKGsAAIwgJSVF4XBYpaWlOnTokPbv369gMKi8vDwtW7ZMvb29kqQ33nhDZ86c0ZEjR9Ta2qqysjLdfffdN5zP4/Ho0KFDkqS2tjZ1d3dr165dw17b7/fr5MmTOnz4sI4fPy7TNFVUVKRwOBydc+XKFX3wwQc6cOCAmpqa1NXVpU2bNt2BXwIAEG9sgwQAIIaff/5Zn376qQoKClRWVqbKykr5fD5JUnl5ub755ht9/PHH2rx5s7q6ujRr1izNnTtXknTvvfcOe06HwxHd7piTk6OMjIxh57W3t+vw4cM6duyY5s+fL0n65JNP5PF4VFtbq2effVaSFA6HtWfPHuXm5kqSSkpKtG3btn/rJwAAWIiVNQAABqmrq5PL5ZLT6VR+fr4WLlyoV155ReFwWI888kh0XlJSkh5++GG1trZKktavX6/q6mo99NBDKi0t1U8//XRbOVpbWzVq1CjNmzcvOnbXXXdp6tSp0WtKUmpqarSoSdI999yjnp6e27o2ACAxUNYAABikoKBAzc3Namtr08DAgGpqapSeni5JMgxjyFzTNKNjPp9PnZ2d2rBhg37//XcVFhbe1nZE0zRjjg/OkZSUNOS4YRgxvwsA+N9CWQMAYJC0tDTl5eVp4sSJ0SKUl5en5ORk/fjjj9F54XBYJ0+e1LRp06Jj2dnZ8vv9OnjwoHbu3KkPP/xw2GskJydLkq5duxYzx/Tp03X16lWdOHEiOvbHH3/o7NmzQ64JALAv7lkDAOAm0tLStH79em3evFlZWVmaMGGC3n//fV25ckWrV6+WJL355puaM2eOZsyYoVAopLq6upilauLEiTIMQ3V1dSoqKlJKSopcLteQOVOmTFFxcbHWrl2rvXv3asyYMQoEAho/fryKi4vv+N8MALAeK2sAAPwN7733npYvX66VK1dq9uzZOnfunBoaGpSZmSnp+mrZli1b9OCDD2rhwoVyOByqrq4e9lzjx4/XW2+9pUAgILfbrZKSkmHnVVRUaM6cOXriiSeUn58v0zRVX19/w9ZHAIA9GSYb2wEAAAAg4bCyBgAAAAAJiLIGAAAAAAmIsgYAAAAACYiyBgAAAAAJiLIGAAAAAAmIsgYAAAAACYiyBgAAAAAJiLIGAAAAAAmIsgYAAAAACYiyBgAAAAAJiLIGAAAAAAnoP3/bbHzbGtCRAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 7))\n",
    "ax = df.groupby('Position')['AAV'].mean().sort_values().plot(kind = 'bar')\n",
    "ax.get_yaxis().get_major_formatter().set_scientific(False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29f0349b-2fce-4815-82f7-9d71d5e56dea",
   "metadata": {},
   "source": [
    "> Les `Designated Hitter ` sont les joueurs qui en moyenne sont le plus payés. Faisont un petit focus sur ces joueurs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "72e6a5e7-44de-4338-9a1d-e3dc1858ae97",
   "metadata": {
    "tags": []
   },
   "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>Rank</th>\n",
       "      <th>Signed Age</th>\n",
       "      <th>Yrs</th>\n",
       "      <th>Value</th>\n",
       "      <th>AAV</th>\n",
       "      <th>Signing Bonus</th>\n",
       "      <th>Players</th>\n",
       "      <th>Position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>58</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>100000000.0</td>\n",
       "      <td>20000000.0</td>\n",
       "      <td>$0</td>\n",
       "      <td>Nick Castellanos</td>\n",
       "      <td>Designated Hitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>94</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>65000000.0</td>\n",
       "      <td>16250000.0</td>\n",
       "      <td>$0</td>\n",
       "      <td>Marcell Ozuna</td>\n",
       "      <td>Designated Hitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>124</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>42000000.0</td>\n",
       "      <td>14000000.0</td>\n",
       "      <td>$9,000,000</td>\n",
       "      <td>Jorge Soler</td>\n",
       "      <td>Designated Hitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>247</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>$0</td>\n",
       "      <td>J.D. Martinez</td>\n",
       "      <td>Designated Hitter</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Rank  Signed Age  Yrs        Value         AAV Signing Bonus  \\\n",
       "60     58          30    5  100000000.0  20000000.0            $0   \n",
       "93     94          30    4   65000000.0  16250000.0            $0   \n",
       "124   124          31    3   42000000.0  14000000.0    $9,000,000   \n",
       "249   247          35    1   10000000.0  10000000.0            $0   \n",
       "\n",
       "              Players            Position  \n",
       "60   Nick Castellanos  Designated Hitter   \n",
       "93      Marcell Ozuna  Designated Hitter   \n",
       "124       Jorge Soler  Designated Hitter   \n",
       "249     J.D. Martinez  Designated Hitter   "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Position'] == 'Designated Hitter ']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a5faf8b-7031-4fa7-88fd-bf372a1cd864",
   "metadata": {},
   "source": [
    "> Passons à l'analyse statistique. \n",
    ">\n",
    "> Nous pouvons par exemple étudier dans quelle mesure le salaire varie en fonction de la \"Position\". \n",
    ">\n",
    "> Pour ce faire nous allons regrouper les salaires par position et les stocker dans un liste contenant des Séries de pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b4049835-4d0c-482c-b87b-4e892819f3e8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Groupe 1 (Position: Starting Pitcher ):\n",
      "7      25384615.0\n",
      "29     21000000.0\n",
      "31     27000000.0\n",
      "41     26000000.0\n",
      "73     11428571.0\n",
      "99     19500000.0\n",
      "126    20000000.0\n",
      "140    17000000.0\n",
      "142    16500000.0\n",
      "169     8000000.0\n",
      "170    12000000.0\n",
      "178    20500000.0\n",
      "183    19900000.0\n",
      "193     5500000.0\n",
      "240    10900000.0\n",
      "254     9300000.0\n",
      "284     7500000.0\n",
      "295     6775000.0\n",
      "298     6500000.0\n",
      "316     5250000.0\n",
      "327     5000000.0\n",
      "334     4650000.0\n",
      "346     4137500.0\n",
      "354     4000000.0\n",
      "367     3500000.0\n",
      "374     3250000.0\n",
      "376     3200000.0\n",
      "495     1500000.0\n",
      "497     1350000.0\n",
      "499     1350000.0\n",
      "504     1250000.0\n",
      "571      720000.0\n",
      "Name: AAV, dtype: float64\n",
      "\n",
      "\n",
      "Groupe 2 (Position: Center Field ):\n",
      "15     25454545.0\n",
      "27     25000000.0\n",
      "30     23357143.0\n",
      "44     25000000.0\n",
      "55     15214286.0\n",
      "63     15000000.0\n",
      "79     15200000.0\n",
      "114    12500000.0\n",
      "136     5000000.0\n",
      "137    11666667.0\n",
      "154     4773750.0\n",
      "162     5150000.0\n",
      "166     4000000.0\n",
      "192     8500000.0\n",
      "211    14200000.0\n",
      "241    10600000.0\n",
      "260     2958333.0\n",
      "261     4400000.0\n",
      "286     2433333.0\n",
      "333     4700000.0\n",
      "339     4500000.0\n",
      "345     4210000.0\n",
      "366     3500000.0\n",
      "378     3200000.0\n",
      "430     2125000.0\n",
      "456     1950000.0\n",
      "464     1810000.0\n",
      "546      900000.0\n",
      "Name: AAV, dtype: float64\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "groupes = []\n",
    "\n",
    "for groupe, salaire in df.groupby('Position')['AAV'] :\n",
    "    groupes.append(salaire)\n",
    "\n",
    "for i, groupe in enumerate(groupes[:2]):\n",
    "    print(f\"Groupe {i+1} (Position: {df['Position'].unique()[i]}):\")\n",
    "    print(groupe)\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fcc6949-b8a8-489c-b2e7-e61ce61a1e49",
   "metadata": {},
   "source": [
    "> Le test de **Shapiro-Wilk** est un test statistique utilisé pour évaluer si un échantillon de données suit une distribution normale ou non. Il est largement utilisé en statistique pour vérifier l'assumption de normalité avant d'appliquer certains tests paramétriques.\n",
    ">\n",
    "> **H0 : Les données suivent une distribution normale.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "4bfdf0c3-b881-4948-a22d-597ca0f48bd8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test de Shapiro-Wilk pour le groupe 'Starting Pitcher ': p-value = 0.00233553908765316\n",
      "Test de Shapiro-Wilk pour le groupe 'Center Field ': p-value = 0.0003565112710930407\n",
      "Test de Shapiro-Wilk pour le groupe 'Shortstop ': p-value = 0.0006252459716051817\n",
      "Test de Shapiro-Wilk pour le groupe 'Designated Hitter ': p-value = 6.4261826082656626e-06\n",
      "Test de Shapiro-Wilk pour le groupe 'Right Field ': p-value = 5.66720227652695e-05\n",
      "Test de Shapiro-Wilk pour le groupe '1st Base ': p-value = 0.998475193977356\n",
      "Test de Shapiro-Wilk pour le groupe '3rd Base ': p-value = 0.0064527359791100025\n",
      "Test de Shapiro-Wilk pour le groupe '2nd Base ': p-value = 0.9854952692985535\n",
      "Test de Shapiro-Wilk pour le groupe 'Left Field ': p-value = 1.7598668019831037e-14\n",
      "Test de Shapiro-Wilk pour le groupe 'Catcher ': p-value = 0.004507182631641626\n",
      "Test de Shapiro-Wilk pour le groupe 'Relief Pitcher ': p-value = 0.00012429706112015992\n",
      "Test de Shapiro-Wilk pour le groupe 'Starting Pitcher #4 ': p-value = 2.6520237828703275e-12\n"
     ]
    }
   ],
   "source": [
    "for i, groupe in enumerate(groupes) :\n",
    "    stat, p_value = stats.shapiro(groupe)\n",
    "    print(f\"Test de Shapiro-Wilk pour le groupe '{df['Position'].unique()[i]}': p-value = {p_value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9237754-8e2c-4873-aa7d-90b0333575d2",
   "metadata": {},
   "source": [
    "> Utilisons maintenant le test de Levene pour évaluer l'homoscédasticité des groupes, c'est-à-dire pour déterminer si les variances des échantillons sont égales. \n",
    ">\n",
    "> H0 : Les variances des échantillons sont égales. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "34ffe8a4-5a92-4ca7-97f1-9fce6854022e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test de Levene pour l'homoscédasticité: 1.7714689631336793e-11\n"
     ]
    }
   ],
   "source": [
    "stat_homoscedasticity, p_value_homoscedasticity = stats.levene(*groupes)\n",
    "print(\"Test de Levene pour l'homoscédasticité:\", p_value_homoscedasticity)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f2e7f13-842d-4823-b960-65f609f1774b",
   "metadata": {},
   "source": [
    "> Faisons un test Anova"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "07e44777-10cf-4544-bf75-54282046f0f2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             df        sum_sq       mean_sq         F        PR(>F)\n",
      "Position   12.0  6.937265e+15  5.781054e+14  8.744052  2.094279e-15\n",
      "Residual  563.0  3.722226e+16  6.611414e+13       NaN           NaN\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>AAV</td>       <th>  R-squared:         </th> <td>   0.157</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.139</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   8.744</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 29 Mar 2024</td> <th>  Prob (F-statistic):</th> <td>2.09e-15</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:21:51</td>     <th>  Log-Likelihood:    </th> <td> -9975.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   576</td>      <th>  AIC:               </th> <td>1.998e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   563</td>      <th>  BIC:               </th> <td>2.003e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    12</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                  <td></td>                    <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                        <td> 1.188e+07</td> <td> 1.48e+06</td> <td>    8.005</td> <td> 0.000</td> <td> 8.97e+06</td> <td> 1.48e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Position[T.2nd Base ]</th>            <td> -3.56e+06</td> <td> 2.08e+06</td> <td>   -1.710</td> <td> 0.088</td> <td>-7.65e+06</td> <td>  5.3e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Position[T.3rd Base ]</th>            <td>-1.866e+06</td> <td> 2.07e+06</td> <td>   -0.903</td> <td> 0.367</td> <td>-5.92e+06</td> <td> 2.19e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Position[T.Catcher ]</th>             <td>-6.217e+06</td> <td> 1.97e+06</td> <td>   -3.149</td> <td> 0.002</td> <td>-1.01e+07</td> <td>-2.34e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Position[T.Center Field ]</th>        <td>-2.631e+06</td> <td> 2.28e+06</td> <td>   -1.153</td> <td> 0.250</td> <td>-7.11e+06</td> <td> 1.85e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Position[T.Designated Hitter ]</th>   <td> 2.576e+05</td> <td> 2.42e+06</td> <td>    0.106</td> <td> 0.915</td> <td> -4.5e+06</td> <td> 5.02e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Position[T.Left Field ]</th>          <td>-1.823e+06</td> <td> 2.28e+06</td> <td>   -0.799</td> <td> 0.425</td> <td>-6.31e+06</td> <td> 2.66e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Position[T.Outfielders ]</th>         <td>-9.384e+06</td> <td> 5.94e+06</td> <td>   -1.580</td> <td> 0.115</td> <td> -2.1e+07</td> <td> 2.28e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Position[T.Relief Pitcher ]</th>      <td>-8.048e+06</td> <td> 1.61e+06</td> <td>   -5.009</td> <td> 0.000</td> <td>-1.12e+07</td> <td>-4.89e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Position[T.Right Field ]</th>         <td> -1.47e+06</td> <td> 2.16e+06</td> <td>   -0.682</td> <td> 0.496</td> <td>-5.71e+06</td> <td> 2.77e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Position[T.Shortstop ]</th>           <td>-6.935e+05</td> <td> 2.05e+06</td> <td>   -0.338</td> <td> 0.735</td> <td>-4.72e+06</td> <td> 3.34e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Position[T.Starting Pitcher ]</th>    <td> 2.406e+04</td> <td> 1.63e+06</td> <td>    0.015</td> <td> 0.988</td> <td>-3.18e+06</td> <td> 3.23e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Position[T.Starting Pitcher #4 ]</th> <td>-7.384e+06</td> <td> 8.27e+06</td> <td>   -0.893</td> <td> 0.372</td> <td>-2.36e+07</td> <td> 8.85e+06</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>228.694</td> <th>  Durbin-Watson:     </th> <td>   0.526</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>1115.350</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.725</td>  <th>  Prob(JB):          </th> <td>6.38e-243</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 8.879</td>  <th>  Cond. No.          </th> <td>    26.9</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}                    &       AAV        & \\textbf{  R-squared:         } &     0.157   \\\\\n",
       "\\textbf{Model:}                            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.139   \\\\\n",
       "\\textbf{Method:}                           &  Least Squares   & \\textbf{  F-statistic:       } &     8.744   \\\\\n",
       "\\textbf{Date:}                             & Fri, 29 Mar 2024 & \\textbf{  Prob (F-statistic):} &  2.09e-15   \\\\\n",
       "\\textbf{Time:}                             &     15:21:51     & \\textbf{  Log-Likelihood:    } &   -9975.6   \\\\\n",
       "\\textbf{No. Observations:}                 &         576      & \\textbf{  AIC:               } & 1.998e+04   \\\\\n",
       "\\textbf{Df Residuals:}                     &         563      & \\textbf{  BIC:               } & 2.003e+04   \\\\\n",
       "\\textbf{Df Model:}                         &          12      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}                  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                                           & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}                         &    1.188e+07  &     1.48e+06     &     8.005  &         0.000        &     8.97e+06    &     1.48e+07     \\\\\n",
       "\\textbf{Position[T.2nd Base ]}             &    -3.56e+06  &     2.08e+06     &    -1.710  &         0.088        &    -7.65e+06    &      5.3e+05     \\\\\n",
       "\\textbf{Position[T.3rd Base ]}             &   -1.866e+06  &     2.07e+06     &    -0.903  &         0.367        &    -5.92e+06    &     2.19e+06     \\\\\n",
       "\\textbf{Position[T.Catcher ]}              &   -6.217e+06  &     1.97e+06     &    -3.149  &         0.002        &    -1.01e+07    &    -2.34e+06     \\\\\n",
       "\\textbf{Position[T.Center Field ]}         &   -2.631e+06  &     2.28e+06     &    -1.153  &         0.250        &    -7.11e+06    &     1.85e+06     \\\\\n",
       "\\textbf{Position[T.Designated Hitter ]}    &    2.576e+05  &     2.42e+06     &     0.106  &         0.915        &     -4.5e+06    &     5.02e+06     \\\\\n",
       "\\textbf{Position[T.Left Field ]}           &   -1.823e+06  &     2.28e+06     &    -0.799  &         0.425        &    -6.31e+06    &     2.66e+06     \\\\\n",
       "\\textbf{Position[T.Outfielders ]}          &   -9.384e+06  &     5.94e+06     &    -1.580  &         0.115        &     -2.1e+07    &     2.28e+06     \\\\\n",
       "\\textbf{Position[T.Relief Pitcher ]}       &   -8.048e+06  &     1.61e+06     &    -5.009  &         0.000        &    -1.12e+07    &    -4.89e+06     \\\\\n",
       "\\textbf{Position[T.Right Field ]}          &    -1.47e+06  &     2.16e+06     &    -0.682  &         0.496        &    -5.71e+06    &     2.77e+06     \\\\\n",
       "\\textbf{Position[T.Shortstop ]}            &   -6.935e+05  &     2.05e+06     &    -0.338  &         0.735        &    -4.72e+06    &     3.34e+06     \\\\\n",
       "\\textbf{Position[T.Starting Pitcher ]}     &    2.406e+04  &     1.63e+06     &     0.015  &         0.988        &    -3.18e+06    &     3.23e+06     \\\\\n",
       "\\textbf{Position[T.Starting Pitcher \\#4 ]} &   -7.384e+06  &     8.27e+06     &    -0.893  &         0.372        &    -2.36e+07    &     8.85e+06     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 228.694 & \\textbf{  Durbin-Watson:     } &     0.526  \\\\\n",
       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } &  1115.350  \\\\\n",
       "\\textbf{Skew:}          &   1.725 & \\textbf{  Prob(JB):          } & 6.38e-243  \\\\\n",
       "\\textbf{Kurtosis:}      &   8.879 & \\textbf{  Cond. No.          } &      26.9  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    AAV   R-squared:                       0.157\n",
       "Model:                            OLS   Adj. R-squared:                  0.139\n",
       "Method:                 Least Squares   F-statistic:                     8.744\n",
       "Date:                Fri, 29 Mar 2024   Prob (F-statistic):           2.09e-15\n",
       "Time:                        15:21:51   Log-Likelihood:                -9975.6\n",
       "No. Observations:                 576   AIC:                         1.998e+04\n",
       "Df Residuals:                     563   BIC:                         2.003e+04\n",
       "Df Model:                          12                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================================\n",
       "                                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------------------\n",
       "Intercept                         1.188e+07   1.48e+06      8.005      0.000    8.97e+06    1.48e+07\n",
       "Position[T.2nd Base ]             -3.56e+06   2.08e+06     -1.710      0.088   -7.65e+06     5.3e+05\n",
       "Position[T.3rd Base ]            -1.866e+06   2.07e+06     -0.903      0.367   -5.92e+06    2.19e+06\n",
       "Position[T.Catcher ]             -6.217e+06   1.97e+06     -3.149      0.002   -1.01e+07   -2.34e+06\n",
       "Position[T.Center Field ]        -2.631e+06   2.28e+06     -1.153      0.250   -7.11e+06    1.85e+06\n",
       "Position[T.Designated Hitter ]    2.576e+05   2.42e+06      0.106      0.915    -4.5e+06    5.02e+06\n",
       "Position[T.Left Field ]          -1.823e+06   2.28e+06     -0.799      0.425   -6.31e+06    2.66e+06\n",
       "Position[T.Outfielders ]         -9.384e+06   5.94e+06     -1.580      0.115    -2.1e+07    2.28e+06\n",
       "Position[T.Relief Pitcher ]      -8.048e+06   1.61e+06     -5.009      0.000   -1.12e+07   -4.89e+06\n",
       "Position[T.Right Field ]          -1.47e+06   2.16e+06     -0.682      0.496   -5.71e+06    2.77e+06\n",
       "Position[T.Shortstop ]           -6.935e+05   2.05e+06     -0.338      0.735   -4.72e+06    3.34e+06\n",
       "Position[T.Starting Pitcher ]     2.406e+04   1.63e+06      0.015      0.988   -3.18e+06    3.23e+06\n",
       "Position[T.Starting Pitcher #4 ] -7.384e+06   8.27e+06     -0.893      0.372   -2.36e+07    8.85e+06\n",
       "==============================================================================\n",
       "Omnibus:                      228.694   Durbin-Watson:                   0.526\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1115.350\n",
       "Skew:                           1.725   Prob(JB):                    6.38e-243\n",
       "Kurtosis:                       8.879   Cond. No.                         26.9\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "\n",
    "model = ols('AAV ~ Position', data=df).fit()\n",
    "\n",
    "\n",
    "anova_table = sm.stats.anova_lm(model)\n",
    "\n",
    "print(anova_table)\n",
    "\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "14359d9d-6823-41b9-a317-ab5bfd5e9c57",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11884161.766666668\n",
      "8323700.774193549\n",
      "8323700.77\n"
     ]
    }
   ],
   "source": [
    "print(df[df['Position'] == '1st Base ']['AAV'].mean())\n",
    "print(df[df['Position'] == '2nd Base ']['AAV'].mean())\n",
    "print(round(model.params[0] + model.params[1], 2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ef1c31d-7dc4-4bc9-a54e-1157dfa6d015",
   "metadata": {},
   "source": [
    "> Prenons un instant pour étudier la relation le salaire et l'âge auquel le joueur à signé le contrat. \n",
    ">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "713eb928-1901-4501-9af5-df3c5c901db8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient de corrélation de Pearson : 0.016737209322173506\n",
      "Valeur p : 0.6880175568339278\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 8))\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.hist(df['Signed Age'], bins = 50)\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(df['AAV'], df['Signed Age'])\n",
    "\n",
    "\n",
    "correlation_coefficient, p_value = stats.pearsonr(df['AAV'], df['Signed Age'])\n",
    "\n",
    "\n",
    "print(\"Coefficient de corrélation de Pearson :\", correlation_coefficient)\n",
    "print(\"Valeur p :\", p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f36c69ef-cea2-4bb5-a34e-831deb9c7b23",
   "metadata": {},
   "source": [
    "> Ok, nous pouvons attaquer maintenant notre travail sur l'index de Gini. \n",
    ">\n",
    "> Commençons par diviser notre population en déciles en fonction de la variable `AAV`\n",
    ">\n",
    "> Interpretons nos résultats. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "281da5ae-cd65-4ed9-a82c-5fc86c550ad8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Les 5 quantiles des salaires sont :\n",
      "Quantile 0: 300000.0\n",
      "Quantile 1: 1750000.0\n",
      "Quantile 2: 3500000.0\n",
      "Quantile 3: 7500000.0\n",
      "Quantile 4: 14000000.0\n",
      "Quantile 5: 70000000.0\n"
     ]
    }
   ],
   "source": [
    "quantiles = mstats.mquantiles(df['AAV'], np.array([0, 0.2, 0.4, 0.6, 0.8, 1]))\n",
    "\n",
    "\n",
    "print(\"Les 5 quantiles des salaires sont :\")\n",
    "for i, quantile in enumerate(quantiles):\n",
    "    print(f\"Quantile {i}: {quantile}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b661ab6e-ea14-46a4-bfd3-94773e7678c9",
   "metadata": {},
   "source": [
    "> Pour calculer le coefficient de Gini nous aurons besoin de définir **la courbe de Lorenz** en utilisant nos données. La courbe de **Lorenz** est un outil graphique utilisé en économie pour représenter graphiquement **la distribution cumulative de la richesse (ou d'autres variables économiques) au sein d'une population donnée**.\n",
    ">\n",
    "> La construction de la courbe de Lorenz implique deux axes : **l'axe horizontal représente la proportion cumulée des individus classés par ordre croissant de richesse**, tandis que **l'axe vertical représente la proportion cumulée de la richesse totale** ou du revenu total correspondant à chaque tranche de population. \n",
    ">\n",
    ">Nous voulons donc maintenant calculer le pourcentage de salaire cumulé décile par décile. Autrement dit pour chaque décile je veux connaître le pourçentage de revenues cumulés par rapport aux revenues totales. \n",
    ">\n",
    "> Par la suite nous pouvons comparer la courbe décrivant la distribution des pourcentages de revenues cumulées par quintiles (**la courbe de Lorenz**) par rapport à la droite décrivant l'égalité parfaite.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "2adc8749-4ad7-4cd2-9da6-8442533e1a0b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 0.03, 0.09, 0.22, 0.46, 1.0]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pourcentages = []\n",
    "salaires_totaux = df['AAV'].sum()\n",
    "\n",
    "for quantile in quantiles :\n",
    "    somme_salaire_quartile = df.AAV[df.AAV <= quantile].sum()\n",
    "    pourcentage_par_quartile = (somme_salaire_quartile / salaires_totaux).round(2)\n",
    "    pourcentages.append(pourcentage_par_quartile)\n",
    "    \n",
    "\n",
    "list(pourcentages)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "85b9d1f2-1797-4635-8efa-438cd70d8b97",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array([0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "plt.figure(figsize = (12, 7))\n",
    "plt.plot(x, pourcentages, '--o', color = 'blue')\n",
    "plt.plot(x, x, '--o', color = 'red');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfbec7e1-1e1f-4869-9021-1a5c4bc60877",
   "metadata": {},
   "source": [
    "> **Le coefficient de Gini est le ratio entre l'aire comprise entre la courbe de Lorenz et la droite d'égalité, et l'aire totale sous la droite d'égalité**.\n",
    ">\n",
    "> Pour calculer ces aires, nous avons besoin de trouver une fonction qui va approximer le modèle des salaire. Nous avons déjà une fonction pour la courbe de l'égalité telle que x = y. \n",
    ">\n",
    "> Pour trouver une fonction qui approxime le modèle des salaires nous pouvons ajuster un polynôme de degré 4 aux données représentant la distribution des revenus ou des richesses cumulées. Pour ce faire nous pouvons utiliser la fonction `polyfit` de `Numpy`.\n",
    ">\n",
    "> La fonction `polyfit` prend en arguement un tableau de valeurs représentant la proportion cumulative de la population, et un tableau représentant les pourcentages cumulés des revenus ou des richesses correspondants. Ce polynôme peut ensuite être utilisé pour représenter **la courbe de Lorenz**.\n",
    ">\n",
    "> La fonction `polyfit` nous permet d'ajouster le polynôme suivant : $P(x) = a_n x^n + a_{n-1} x^{n-1} + \\ldots + a_1 x + a_0$\n",
    ">\n",
    "\n",
    "> Vous avez une courbe qui représente le pourcentage de revenus par quantile, ce qui signifie qu'elle illustre la répartition des revenus dans différents groupes de la population.\n",
    ">\n",
    "> En ajustant un polynôme de degré 4 à cette courbe, vous obtenez une fonction mathématique qui modélise la distribution des revenus selon une forme spécifique. L'ajustement d'un polynôme de degré 4 vous permet de capturer une certaine complexité dans la distribution des revenus, qui pourrait ne pas être bien représentée par une simple fonction linéaire ou quadratique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "4e33f504-c9f3-430f-ae15-8fa1770e481d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.95312500e+00, -2.20486111e+00,  1.30729167e+00, -5.67460317e-02,\n",
       "        5.95238095e-04])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefs = np.polyfit(x, pourcentages, 4)\n",
    "coefs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0346005e-b5cb-4c9d-a271-a31827a647f3",
   "metadata": {},
   "source": [
    "> Une fois que nous avez estimé les coefficients de notre polynôme à l'aide d'une méthode d'ajustement, comme `polyfit`, nous pouvons utiliser la fonction `np.polyval()` pour évaluer ce polynôme à différents points de notre choix (pour nous chaque quantile). Cela nous permet d'obtenir les valeurs de la fonction polynomiale pour ces points spécifiques.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "bb82cab0-070a-4880-ad7e-8969fe876e65",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.95238095e-04 2.70238095e-02 9.59523810e-02 2.14047619e-01\n",
      " 4.62976190e-01 9.99404762e-01]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(np.polyval(coefs, x))\n",
    "\n",
    "plt.figure(figsize = (12, 7))\n",
    "plt.plot(x, np.polyval(coefs, x), '--v', label = 'approximation')\n",
    "plt.plot(x, pourcentages, '--o', color = 'red', label = 'vraies valeurs')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "58b3df6d-fe23-4402-a0c1-6437129633e4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.56250000e+00, -1.50462963e+00,  9.44444444e-01, -3.10846561e-03,\n",
       "        3.96825397e-04])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b5d8038-b49d-40bb-bf87-df4be3c05e08",
   "metadata": {},
   "source": [
    "> Définissons une fonction qui nous retourne pour chaque valeur représentant un pourcentage de la population, le pourcentage de richesse détenue par celle-ci.\n",
    ">\n",
    "> Définissons par la suite la fonction Egalité. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "af1d061c-af7b-4286-ad7b-fb726d6586c5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.8, 0.1, 'A2')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def L(x) :\n",
    "    return 1.56*x**4 -1.50*x**3 + 0.94*x**2 -0.003*x\n",
    "\n",
    "def E(x) :\n",
    "    return x\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "plt.plot(x, L(x), '--o', label = 'Lorenz')\n",
    "plt.plot(x, E(x), color = 'black', label = 'Equality');\n",
    "plt.fill_between(x, L(x), E(x), color = 'gray', alpha = 0.6)\n",
    "plt.fill_between(x, L(x), color = 'lightblue', alpha = 0.6);\n",
    "plt.text(0.4, 0.2, 'A1', size = 15)\n",
    "plt.text(0.8, 0.1, 'A2', size = 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ab4b2c7-1be5-4542-b620-f0c61a2f4e0f",
   "metadata": {},
   "source": [
    "> Nous pouvons définir le coefficient de Gini ainsi : $G = 1 - 2 \\times \\int_{0}^{1} (L(x) - x) \\, dx$\n",
    "\n",
    "> Nous allons créer une fonction a1(x) qui calcule la différence entre la valeur de la droite d'égalité et la valeur de la courbe de Lorenz pour une valeur donnée de x. Autrement dit, elle mesure la distance verticale entre la courbe de Lorenz et la droite d'égalité à chaque point x.\n",
    ">\n",
    "> Par la suite nous devons intégrer la Différence Entre les Courbes :\n",
    ">\n",
    "> En utilisant la fonction quad(a1, 0, 1), on intègre la fonction a1(x) sur l'intervalle [0, 1]. Cela revient à calculer l'aire entre les deux courbes pour la tranche de population allant de 0 % à 100 %.\n",
    "Cette intégration numérique nous donne l'aire entre les deux courbes sur cet intervalle.\n",
    ">\n",
    ">Par la suite nous devons multiplier l'Aire par 2 et Calculer le Coefficient de Gini :\n",
    ">\n",
    "> En multipliant l'aire entre les courbes par 2, on obtient l'aire totale entre les deux courbes sur l'intervalle [0, 1], car l'intégrale calcule l'aire sous la courbe et on veut l'aire au-dessus et en dessous de la courbe.\n",
    "En soustrayant ce résultat de 1, on obtient le coefficient de Gini. Plus précisément, on soustrait le double de l'aire entre les courbes de 1, qui est l'aire totale sous la droite d'égalité (qui représente une distribution parfaite et équitable), pour obtenir un indice de Gini compris entre 0 (parfaite égalité) et 1 (inégalité totale)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "7e78af52-db76-44e0-9eea-9d8d6b5da546",
   "metadata": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4976666666666667\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def a1(x) :\n",
    "    return E(x) - L(x)\n",
    "\n",
    "print(1 - (2*quad(a1, 0, 1)[0]))  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcab0baf-17ab-4189-ad58-b9f8c7721954",
   "metadata": {},
   "source": [
    "> Dans le calcul du coefficient de Gini, la multiplication par 2 vient du fait que nous sommes intéressés par la mesure de l'aire entre la courbe de Lorenz (qui représente la distribution cumulative réelle des richesses ou des revenus) et la ligne d'égalité parfaite (qui représente une distribution parfaitement égalitaire).\n",
    ">\n",
    ">Considérons les deux situations possibles :\n",
    ">\n",
    ">Lorsque la courbe de Lorenz est au-dessus de la ligne d'égalité, cela signifie qu'il y a un excès de richesse par rapport à une distribution parfaitement égalitaire.\n",
    ">\n",
    ">Lorsque la courbe de Lorenz est en dessous de la ligne d'égalité, cela signifie qu'il y a un déficit de richesse par rapport à une distribution parfaitement égalitaire.\n",
    ">\n",
    "> En multipliant par 2 dans la formule du coefficient de Gini, nous prenons en compte simultanément ces deux aspects :\n",
    ">\n",
    ">La partie positive de la différence entre la courbe de Lorenz et la ligne d'égalité (l'excès de richesse au-dessus de l'égalité).\n",
    ">\n",
    "> La partie négative de la différence entre la courbe de Lorenz et la ligne d'égalité (le déficit de richesse en dessous de l'égalité)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e02d87dd-3cdd-4795-a86b-21d47bffc58d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a174b09e-d7de-4cb4-a139-fc5e414ec34f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "feb6c502-ec03-4b71-bf95-ec6da5fee52d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e172fb2c-227d-408f-8c26-792a8e329b78",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: html5lib in c:\\users\\lenov\\anaconda3_b\\lib\\site-packages (1.1)\n",
      "Requirement already satisfied: six>=1.9 in c:\\users\\lenov\\anaconda3_b\\lib\\site-packages (from html5lib) (1.16.0)\n",
      "Requirement already satisfied: webencodings in c:\\users\\lenov\\anaconda3_b\\lib\\site-packages (from html5lib) (0.5.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install html5lib"
   ]
  }
 ],
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