TheCustomers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4dae2cc4",
   "metadata": {},
   "source": [
    "# Costumers Clusters\n",
    "## Author : HoomanKaseban ( Mr.Programmer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0a8bf76",
   "metadata": {},
   "source": [
    "#### In this project, we are going to categorize the store's customers into different clusters based on their spending score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1453c694",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "17820ae6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebaafdd7",
   "metadata": {},
   "source": [
    "# Read The Csv File"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ed2c344",
   "metadata": {},
   "source": [
    "### This File Contains the information of  customers of a shop\n",
    "<ul>\n",
    "    <li> <i>First Column : </i><b>CustomerID :</b>\n",
    "        <i style=\"color:red;font-family:arial;font-weight:bold\">We use ID to anonymize people's names</i>\n",
    "    </li>\n",
    "    <li><i>Second Column : </i><b>Gender :</b>\n",
    "        <i style=\"color:red;font-family:arial;font-weight:bold\">Customer's gender</i>\n",
    "    </li>\n",
    "    <li><i>Third Column : </i><b>Age :</b>\n",
    "        <i style=\"color:red;font-family:arial;font-weight:bold\">Customer's age</i>\n",
    "    </li>\n",
    "    <li><i>Forth Column : </i><b>Annual Income :</b>\n",
    "        <i style=\"color:red;font-family:arial;font-weight:bold\">Customer's annual income</i>\n",
    "    </li>\n",
    "    <li><i>Fifth Column : </i><b>Spending Score (1-100) :</b>\n",
    "        <i style=\"color:red;font-family:arial;font-weight:bold\">The score given by the seller based on customer's purchases</i>\n",
    "    </li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "491c7275",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
       "0           1    Male   19                  15                      39\n",
       "1           2    Male   21                  15                      81\n",
       "2           3  Female   20                  16                       6\n",
       "3           4  Female   23                  16                      77\n",
       "4           5  Female   31                  17                      40"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main=pd.read_csv('1632560262896716.csv')\n",
    "df_main.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50cfb728",
   "metadata": {},
   "source": [
    "# Create Costumized DataFrame\n",
    "### We don't need ID & Gender (it's useless)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c500a5a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   Age  Annual Income (k$)  Spending Score (1-100)\n",
       "0   19                  15                      39\n",
       "1   21                  15                      81\n",
       "2   20                  16                       6\n",
       "3   23                  16                      77\n",
       "4   31                  17                      40"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df_main[['Age','Annual Income (k$)','Spending Score (1-100)']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "039fa008",
   "metadata": {},
   "source": [
    "# Cleaning The Rows That Have NaN Column ( If Exist ! )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "afda34ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8cb1ea33",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mr.Programmer\\AppData\\Local\\Temp\\ipykernel_7808\\1379821321.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df.dropna(inplace=True)\n"
     ]
    }
   ],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6ec46364",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0fb4c8d",
   "metadata": {},
   "source": [
    "# Convert PandasDF to NumpyAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1dfa7150",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[19, 15, 39],\n",
       "       [21, 15, 81],\n",
       "       [20, 16,  6]], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=np.asanyarray(df)\n",
    "x[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaf0bc1d",
   "metadata": {},
   "source": [
    "# Normalizing Datas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8870937d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0e53cd3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.42456879, -1.73899919, -0.43480148],\n",
       "       [-1.28103541, -1.73899919,  1.19570407],\n",
       "       [-1.3528021 , -1.70082976, -1.71591298]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler=StandardScaler().fit(x)\n",
    "norm=scaler.transform(x)\n",
    "norm[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6814af85",
   "metadata": {},
   "source": [
    "# \n",
    "# \n",
    "# **** Machine Learning ***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daa6018e",
   "metadata": {},
   "source": [
    "# \n",
    "# Clustering with K-Means (For this project,it's the BEST way)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6172ae4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the main tools\n",
    "from sklearn.cluster import KMeans as km\n",
    "from sklearn import preprocessing as prp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "224affcd",
   "metadata": {},
   "source": [
    "# Use Elbow Method to Find The Best K\n",
    "<p style=\"color:blue;font-family:timesnewroman;font-size:17pt;font-weight:bold\">\n",
    "    The \"Elbow Method\" is a well-known method for finding the best k in K-Means\n",
    "</p>\n",
    "<a href='https://predictivehacks.com/k-means-elbow-method-code-for-python/#:~:text=K-Means%20is%20an%20unsupervised,optimal%20for%20the%20specific%20case' target='blank'><br>Click to Get more Informations!</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "778f9e7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mr.Programmer\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1036: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[600.0,\n",
       " 389.3861889564371,\n",
       " 295.45624492135573,\n",
       " 205.22514747675922,\n",
       " 168.24758017556837,\n",
       " 133.86842085478852,\n",
       " 117.17517978708025,\n",
       " 104.02116700198584,\n",
       " 92.2664773736451]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get the Distortions Matrix to use in elbow plot!\n",
    "distortions=[]\n",
    "K=range(1,10)\n",
    "for k in K:\n",
    "    kmeanmodel=km(n_clusters=k)\n",
    "    kmeanmodel.fit(norm)\n",
    "    distortions.append(kmeanmodel.inertia_)\n",
    "distortions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "97e5050d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Ploting Distortions-K and find the elobw\n",
    "plt.figure(figsize=(9,8))\n",
    "plt.plot(K,distortions,'bx-')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Distortions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "190baed5",
   "metadata": {},
   "source": [
    "### English Description\n",
    "## As shown in the Plot, we reach the elbow in K=4\n",
    "## and we can guess with high accuracy that the best number of clusters is 4.\n",
    "### Persian Description\n",
    "## همانطور که در نمودار مشخص است است در همسایگی برابر با 4 به آرنج می رسیم و میتوانیم\n",
    "## حدس با دقت بالایی بزنیم که بهترین تعداد کلاستر ، 4 عدد است\n",
    "# \n",
    "# \n",
    "# Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fa87b6f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 3 0 3 0 3 0 3 0 3 0 0 0 3 0 0 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3\n",
      " 0 3 0 3 0 3 0 3 0 3 0 0 0 3 0 0 3 3 3 3 3 0 3 3 0 3 3 3 0 3 3 0 0 3 3 3 3\n",
      " 3 0 3 3 0 3 3 0 3 3 0 3 3 0 0 3 3 0 3 3 0 0 3 0 3 0 0 3 3 0 3 0 3 3 3 3 3\n",
      " 0 1 0 0 0 3 3 3 3 0 1 2 2 1 2 1 2 3 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2\n",
      " 1 2 1 2 1 2 1 2 1 2 1 2 3 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1\n",
      " 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2]\n"
     ]
    }
   ],
   "source": [
    "knum=4 #number of cluaters\n",
    "# create model\n",
    "kmean=km(init='k-means++',n_clusters=knum,n_init=10)\n",
    "kmean.fit(norm)\n",
    "labels=kmean.labels_\n",
    "# get overview of labels\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5034f62",
   "metadata": {},
   "source": [
    "### Find The Center of Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "784e76d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.96249203, -0.78476346,  0.39202966],\n",
       "       [ 0.03720536,  0.99011499, -1.18875705],\n",
       "       [-0.42880597,  0.97484722,  1.21608539],\n",
       "       [ 1.08616125, -0.49056525, -0.39717443]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centers=kmean.cluster_centers_\n",
    "centers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e725c214",
   "metadata": {},
   "source": [
    "## Create Column of K-Mean Model's Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5f426d4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mr.Programmer\\AppData\\Local\\Temp\\ipykernel_7808\\3778044142.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['K-means Label']=labels\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <th>K-means Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Annual Income (k$)  Spending Score (1-100)  K-means Label\n",
       "0   19                  15                      39              0\n",
       "1   21                  15                      81              0\n",
       "2   20                  16                       6              0\n",
       "3   23                  16                      77              0\n",
       "4   31                  17                      40              0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['K-means Label']=labels\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6cb45b7",
   "metadata": {},
   "source": [
    "## Get the approximate Information of centers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "425d41d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K-means Label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.438596</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>60.298246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.368421</td>\n",
       "      <td>86.500000</td>\n",
       "      <td>19.578947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32.875000</td>\n",
       "      <td>86.100000</td>\n",
       "      <td>81.525000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53.984615</td>\n",
       "      <td>47.707692</td>\n",
       "      <td>39.969231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Age  Annual Income (k$)  Spending Score (1-100)\n",
       "K-means Label                                                       \n",
       "0              25.438596           40.000000               60.298246\n",
       "1              39.368421           86.500000               19.578947\n",
       "2              32.875000           86.100000               81.525000\n",
       "3              53.984615           47.707692               39.969231"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('K-means Label').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baa76d93",
   "metadata": {},
   "source": [
    "# Describe Labels:\n",
    "\n",
    "### English Description\n",
    "### We clustered our data into 4 clusters\n",
    "## Cluster 0: young people with low income and average to high spending score\n",
    "## Cluster 2: Adults with high income and excellent spending score\n",
    "## Cluster 3: middle-aged with high income and very low spending score\n",
    "## Cluster 1: Elderly people with low income and average to low spending score\n",
    "## Approximate conclusion\n",
    "<p style='color:red;font-weight:bold'> It can be generally assumed that people under the age of 35, who are generally single, have a high spending score with any income level.</p>\n",
    "<br>\n",
    "<p style='color:red;font-weight:bold'>And on the other hand, people over 35 years old, who are generally married, do not have a good spending score with any level of income.</p>\n",
    "\n",
    "\n",
    "#  \n",
    "### Persian Description\n",
    "### ما دیتای خود را به 4 خوشه ، خوشه بندی کردیم\n",
    "## خوشه 0 : جوانان با درآمد پایین و نمره خرج کرد متوسط به بالا\n",
    "## خوشه 2 : بزرگسالان با درآمد بالا و نمره خرج کرد عالی\n",
    "## خوشه 3 : میانسالان با درآمد بالا و نمره خرج کرد خیلی پایین\n",
    "## خوشه 1 : افراد مسن با درآمد پایین و نمره خرج کرد متوسط به پایین \n",
    "<p style='color:red;font-weight:bold;font-size:15pt'>می توان به طور کلی حدس زد که افراد زیر 35 سال که عموما مجرد هستند، با هرسطح درآمدی نمره خرج کرد بالا دارند\n",
    "</p>\n",
    "<br>\n",
    "<p style='color:red;font-weight:bold;font-size:15pt'>و در مقابل افراد بالای 35 سال که عموما متاهل هستند ، با هرسطح در آمدی نمره خرج کرد خوبی ندارند\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12d87e6c",
   "metadata": {},
   "source": [
    "# \n",
    "# \n",
    "# \n",
    "# Create The Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "943aead8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PTz+fiPfNvQg74y18d9VtpK0sFb4wRnfCmrKy/G3bM2zrauJj087uOV7mDfKteVewvGUzj9Svoj3TxchgBeeNms/iqkl4jf6/TB3TPFKlUEeoETeUVNgJIYQ4OpJkCyGO6E+bn+DFlo1U+6IHtBNc17Gbn627l2vnvh2lFFprfrvxIXK2fUj5R8D04jM8PNe8niXVk1lSPaXnMb/p5ZQRMzllxMy8xDsqWImtNVr33blkn4ydw2d4KPcGmV0+FqUgZ1sHlLbsqxk/rmpSXuITQghR+mRZRghxWM2pGM81b6BqvwQbnBKKKl+EjZ31bOlqAGBLVwO7Eq19jkI3lMJveLlvz6uDGvOYUBWTIiPoyqWOeG5nNsm5I+fhNTyU+0K8bezxtGXjdOVS2FqTsjI0pWMcVzW5Z6OmEEIIcSSSZIthoTXdxTON63iyYc0x91werjZ21qOUwuhlRVgpha1t1nTsBo7c9xqc7iebOusPPzY+D64YfyJpO0fWzvV5TjyXImB6OXPk3J5jbx17HNdMfwu1/ijt2Th+08uVk07jP6afd0ytBIUQQgxPUi4iSlrOtvj7tmd4rGF1d1KnobuW+FPT39Lniqs4NsWUgs6tGMeHJi/jpq1PYqAo84Z6aqqztkUslyBgePnS7EsO6F6ilOKEmmmcUKSj6IUQQgwNkmSLknbrjud5eO8bVPujPQnWvlri/117D9+cd7lsZjuCaWWj0Fpja33IarbWGkMZzC4fA8D4cC0Kddha6M5cimnRUQVZFT5z5FzGhWu4b/ervNK2FcMZkYShDM6tm895o+czokinYQohhBjaJMkWJasrm+LhvSup9EUOSKT31RJvizeyLraH2eVjB/R1UlaG1e276MqlCHn8zCkfS8jjH2j4RaPGH+WU2hk83bSOal+0J9HWWtOS6WJm2WgmR+oAmBwZwbhwNXsT7ZT7Dm1raWtN2s5y4ehFBYt/anQkn5l5AbFskrZ0F0opRgTKCJi+gsUghBBi+JEkW5SsTV31gO514p9TSwwr23Ycc5JtaZs7d77MfXteJWPnyNk2HsPAa3g4d+R8rhh/wmGnDQ4lH5xyBrbWPN+8AaBnpXpO+Vg+Nf0tPavSSik+Oe08vrPyNlrTnQe18MvQmU1y2ohZLHahS0eZN3jU5UG2ttnYWU9zupOoJ8Cs8jHH1EpQCCHE8CW/NUTJso+wsU4BNse2+U5rze83PcaTjWtIWhk6MomeK5V7g9y9ezn1yTY+PfOCQwabDEU+w8Mnpp/LZeOPZ23HbrS2mRJ1htEcbEyoiv+Z/w7+ueMFlrduwUBhoynzBLly0umcM2peUW8g3NbVyC/XP0BLpmtfCT9B08tHppzNcdWT3Q5PCCHEECFJtihZUyJ1aJwV594SXWcl9tim963u2MXTTetoT8dJ2Bm8yoNyyn3pyCZJWVmWt27hldatLN2vH/RQVxcop+4oapjrghVcM+N8OjIJWjNdeJTJ6FBl0b/haE7F+P7qf2NrTZUv0nM8ZWX4xfr7+a+5lzGjbLSLEQohhBgqivs3nhADUO4LsWzEbFrTXQesamutaU13MTpYyZyKYysVuX/Pq2SsLEk7i29fgg2gnDHdKTtL1ra4b/creXgmQ1e5L8Sk7jrtYk+wAR6tX0XKyhI9qKwkYPrwGCa37XjRpciEEEIMNbKSLUra+yedStrK8nzzBrR2BoKbymBCuIbPzbrwmBO/dbE9pO2c07Kul8oHE4NkLs2mzoY+V9IHS2u6i8ZUB2FPgLGhqqIuzSg2L7ZsIuwJ9PpYmSfo/LtbWfymt8CRCSGEGGokyRYlzdtdS/y2cUtZ1bGTnG0xOVLHtOjIASWfWmuOVM6t0aC6zy1AnhvLJvnj5sd5pXUrpjKwtWZksIKPTD2LqdGRgx9ACbC0fcR/Kn2MdfxCCCGGF0myxbAwMljByGBF3q43KTKCrmyqz3TLQhM0/YwJVhakw0jGznH96n+zO9Ha3bLQ6VXdku7k+6v/zbfmXdHrJkVxoIWVE3iyYW2vK9XxXJrx4Rpp/SeEEOKoFH+RpCga8ViC333pz3zjrdfzzO0vuB2Oqy4YvRC/6cGrTLK29eaqtnamTHow8JomF41ZXJB4Xmndyu5EK1W+CGkrSyybIG6lCXsC2LbNnbuWFySOoe68UQswlCJpZQ44nrUtknaGt4873qXIhBBCDDWyki2O2v2/f5TXHl9FpCLMLT/4N9OOm0LdhFq3w3LFwsqJzK0Yz+vt2+nMJIlbaVR3BUnI9FHmDTGjbPQho7k7MgmeblrLs43rSds5JoZrOXfUfGaWjR5Q+crzTRtIWVnWx/aQsrM9x01lUOOP8nLzJvT0vqcwCseYUBWfm3khv9rwIC3pzp7SEFMZXDnxNFf6ewshhBiaJMkWRy3VlUIZCl/QS6IzSSaVOfInlSiPYfK5mRfyp81P8HzzRqJ2gFz3Bkef4WFJ1WSunnoWvv0GmGzubOBHa+4iaWUImj5MZfBq2zZWtG7h9BGz+NCUM455xPv2eDP1qXY8ysCjjJ5k2tY29cl2/KaHrLbwKfmRP5L5lRP4xZIP8mrrNhpSHZR7gyyumtzrBEshhBCiL/IbVxy18z54Jute2kRrfTunXnY8Y6cP737BftPLx6efy+UTTuSV1i20ZxKUe4MsqprEiIN6SSdyaX689m5sNNX+6AHXsLXmiYY1jAtVc97oBf2OozXdxY54Ewp1SP23oZxRMJa2Wd68mZNHzDi2JzvMBEwfJ9VOdzsMIYQQQ5gk2eKojRhXw7fv/ArZdBZ/0O92OEWjxh/lvFGHT45fatlEMpemar8Eex9DKaLeIHftXsFZI+f2e6Pk003rCHv8dOZSTj34fp+vtUYrRZ2/gnv3vMJJtdOLtmRkV6KFFS1b6MymKPeFWFI9mVHBSrfDEkIIIY6JJNmiXwzDkAT7GLzYvAmv0fePW8D00pbpoj7VzthQdb+u/WrrVkKeAFOjQbZ0NZC1LTQahUIB40PVVPki7Eq0krAyhD3F9e/Xluni/zY8zPrYHrTWGMrARvOvHS8wt2IcH592LmUHDYcRQgghip0k2UIUQE5bR1xBVigs2+73tff1dg6aPmaXj6UrmyJtZzGVSZk32DMIRymnRruYdGQSfGfl7bSmu6jyRQ74O9Jas7p9J99bdTvfmHdF0b05EEIIIQ5HWvgJUQAzy0aTtrJ9Pp6znST84FruozE5MoKU7WxCVTilJzX+Mip94Z4EO21liXgChIosUb171wqa0zGq/JFD3oQopajyR9mTaOOBPa+5E6AQQghxjCTJFqIATh8xG6UUWTvX6+Pt2TjLRswi6On/oJOz6uaiNdi670mEndkk549aWNDx7keSzGV4onE1Fd7wYc8r84Z4uP6NPv/uhBBCiGJUPL9xhShhtYEyPjDpdDqyCWLZhDNqHWdSY3MqxthQNZePP/GYrj0+XMNpI2bRko5hHVQOorWmLdPFiGA5Z4ycM+DnkU+7ki3YWh9xo6fP9JCxcuxNthcmMCGEECIPpCZbiAI5a+RcRgTK+ef251kf24MCAh4fbx27hAvGLDrmmmOlFB+cvIyg6ePh+jewtY1CoQEFTIuO5JoZ5xPxBPL5dAbM1toJ8KjPL656ciGEEOJwJMkWogC01mzuauCpxrXsSDTj93hBO8dfaN5AlT/CiTXTjrlm2mOYvG/SqVw0ZjHLWzbRlO4kbPpZWDWR8eGaPD+b/Kj2R7FsG1trjMNsCrW1jY3utf2hEEIIUawkyRZikNna5pbtz/HgntcxUJT5Qj210VprunJpbtryJHftWs6XZ1/K6NCx94au8IU4Z9T8fIU+qGr8UeaUj2NdbDcVvr7rsjsyCZZWT5Y2fkIIIYYUqckWIg/2JNpY07GL3YnWnnprcJLoW7Y/x/27X6XCF6bSHzlg86FSipDHT7U/Slc2xXWrb6c5FXPjKbji8vHHY6NJWZleH0/mMhhKccnYJQWOTAghhBgYWckWYgB2J1q5cdOjbO1q7BmiMjZUxdVTzmJSZASbuxp4cM/rVPmjR+zsUeYL0Zrp4k9bnuRLsy8p0DNw15ToSD4z4wJ+veFB4rk0YY8fjzLJaotELo3P8PCFWRcXbcmLEEII0RdJsoU4Ro2pDr6z8jaydo7K7kEqWmvqk+18b9Ud/Pf8d/Dw3jcwUEfdOq/CG2Zl+w6aUjFqA2WD/AyKw+KqSfx48ft5unEdTzauIZ5NE/UGuGTMYk6pnUm5L+R2iEIIIUS/SZItxDG6b/erJK0M1fttyFNKUeYN0Zru5NYdz/NG23bK+pEk7tsA+HTjOt4+/vi8x1ysKn0R3jp2CW+VshAhhBAlQmqyhThGzzSto9zbewJd7gvzUvMmgH4PgPEZHrbFGwccnxBCCCHcI0m2EMdAa03GzvWZQBsoLK3pewZj3xSKjEw3FEIIIYY0SbKFOAZKKcaHa4nn0r0+nrQyjPBHenph90dWW0ccNS6EEEKI4iZJthDH6JIxi0naGWxtE2vpZNeGPXQ0xbC1Jp5L8fbxJ1ITiJLsoz1dX2xtc1Lt9EGKWgghhBCFIEm2EMfo+OqpvGXUAhpi7WzevouW9hhbdu5mb3sLp4+YzRl1s7l4zOJ+JdkpK0PUG2RO+dhBjFwIIYQQg02SbCGOkVKK9008lffaiynfqImmvJRt0lyenMdHpp6FoQyOr55KpS9MRyZxxOtZ2qYzl+Id40/EY5gFeAZCCCGEGCySZAsxAEopzjlxKcdtH8HYf2RYsK6S8085EdXdii/k8fPl2ZcS9HhpzXRi91GfnbIytGa6uGTMcZw+YlYhn4IQQgghBoHq76asYrdkyRK9fPlyt8NwzauPreTOXz9AKBrkvddezthpo9wOaVjIZrK07m2ncmQFPr/3kMebUzH+vPUpXm/bjsZp02egyGoLrTURb4B3jD+R00fM6knQhRBCCFHclFIrtNa9DnmQYTQlpGlXCzf8559p3NGM4TFoa2jne/ddK0lbAXh9Xuom1Pb5eE2gjC/MupjmVIynm9axrauJjJ2j3BfipJrpzCkfKyUiQgghRAmRJLuEtDd2kEllyWZykIHWve3ksjm8vkNXVoU7agJlXDZu+ExyFEIIIYarokmylVKfBz4CaGAl8CEgBPwDmAhsA96ptW5zKcSiN3bGaMZMHUUmmcHwmCy9YKEk2MI1Wmsa0zFi2QQeZTIyUEHQ43M7LCGEEKIgiqImWyk1BngGmK21TiqlbgXuA2YDrVrr65VSXwUqtdZfOdy1hntNdrwjzutPrMEf8rHwzLmYHilBEIVla5vlLVu4Z/cr7Eg0OVMxNaAUy0bM5i2j5lMXrHA7TCGEEGLAhkpNtgcIKqWyOCvYe4CvAWd0P34T8ARw2CR7uAuXhzn50qVuhyGGqZxt8duND/NSyyb8hpdKb6RnT0DOtni0fiVPNa7lC7MuYrb0AhdCCFHCiqKFn9Z6N/BjYAewF+jQWj8E1Gmt93afsxcY0dvnK6U+ppRarpRa3tTUVKiwhRAH+fOWp3ixZRPVvihRb/CATbcew6TaH8WjDH6y5h52xltcjFQIIYQYXEWRZCulKoFLgUnAaCCslHr/0X6+1voGrfUSrfWS2tq+OzwIIQZPQ6qDJxvXUO2LHLajTcjjx9Y2d+56uYDRCSGEEIVVFEk2cA6wVWvdpLXOArcDJwMNSqlRAN0fG12MsSRprWlr7MDKWW6HIoa4JxvWAGCoI7+slPtCLG/ZQnsmPthhCSGEEK4oliR7B3CiUiqknCWws4G1wF3AVd3nXAXc6VJ8JeuWH9zBf11wHT//5A3Ytu12OMNG1s4Rz6Upho3H+fJq61ZCpv+ozjWUAQq2dDUMclRCCCGEO4pi46PW+kWl1L+AV4Ac8CpwAxABblVKXY2TiL/DvShL06uPriQQ9rPxla2k4mlC0aDbIZW03YlWbt/5EitaNqOBKn+Ei0cv5syRc45qBbiYpe0sRn8GH2lIW7nBC0gIIYRwUVEk2QBa628B3zrocBpnVVsMkiu+cAl3/eZBzn7faZJgD7LtXU18d/Xt5CyLCl8YQxkkcxn+tOUJtsabuHrKmUN6OmeZN8jeXDt+jq43u1IQ9hzdyrcQQggx1BRNki3ccfwFizn+gsVuhzEs/Gnrk9i2ptIf6TkW9Pjwm16eblzLsrpZTIuOcjHCgTltxGz+vOVJwp7AEc/N2jm8hocZZaMLEJkQQghReEP7/rQQQ0RjqoMtnQ2UeQ+9W2AohQKeblxX+MDy6MSaaXgMg4x95BKQjkyCs+rm4DdlIqkQQojSJEm2EAXQmU1iKqPPchCv4aEl3XnI8Zxt8VrbNv64+Qn+suUp1nTsKtrNkmGPnw9MWkYsmyB7mES7LROnNlDGRWPkDooQQojSJeUiQhRAtT+KjcbWdq8bHDN2jrGh6gOOJXJpfrz2bjZ3NqBQaDSP1K9kUeVEPjXjLXiN4vvxXVY3G0vb/GXrU9haE/EE8BkeNJquXIqsbTMmVMkXZ11MtJdVfSGEEKJUFN9vaVFSspksAF7f8C4LqPCFWVg5kVdbt1Ltjx7wWNa2UMCyEbMPOP6P7c+zqbOeal+0ZwVca82K1i08uPd1Lh5zXKHC75ezRs5lQeUEnm5cxyP1K2nJdGIqk2nRkVwweiHzKsbjMUy3wxRCCCEGlSTZol/u/PUDvP7Eaj74nXczfuaYw567e9Nefvqx3wLw+Rs+wZipQ3dTXz5cNfl0dsSbaU7HCJl+PIZJPJfC1pr3TTqV0aHKnnPTVpZnGtdS4Q0fUGKilKLMG+LBPcWbZIOzcv+2cUt527ilaK2LpmvKvlKbYolHCCFE6ZIkWxw1rTUP/ulxErEkyx967YhJ9vqXNhFr6XL+++XNwz7JrvRF+PaCd/J0w1qeaFxDysqwuGoS549eeEhXkXgujY3udcXXZ3hoTnf2WXpSbIohoV3ZvoO7di5nfeceTGWwtHoql4w5jnHh6iN/shBCCHEMJMkWR00pxXv/6zJWPr2OUy874Yjnzz9jDs/c8aLz38tmH+Hs4SHiCXDBmEVcMGbRYc+LegN4ldnT6m5/SSvDiEDZkEiwi8FDe1/nr1ufxmd4qPY5tfEvNW9iResWvjL7rUyXNoJCCCEGgSrWTgXHasmSJXr58uVuhyHEgP1z+/PctWsFNf43a7JtbdOS7uTDU8/izLo5LkdY/NoyXXxhxZ+JeIJ4D7or0JVLEfEE+PHi98sbFiGEEMdEKbVCa72kt8dkJXuYe/3J1fzzx3dxxrtO5pz3L3M7nKKgtWZLVyN7kq14lMms8jFU+MIFj+Nt45ZSn+pgectmbK1ROD213zJqIWeMKL07A/v+3rtyKcq9QSaEawdcavJi8yZsbR+SYINzV6E108XmroYhPQRICCFEcZIke5h79G9PU7+1kft//5gk2cDOeAv/t/Eh9iRa0Tijv0FxSu0MPjDp9IIOT/EaHj4943x2xJtZ27EbQynmV4ynLlhRsBgK5fXWbfxl61M0p7swlMLWmrpAGR+cciazyg9f+384LelOFAZaa2K5JJ3ZJAaKcl+IsMePQtGeSRzyeV3ZFC+2bGJHvJlaf5QTa6ZREygbyFMUQggxzEiSPcxdcPXZJDuTLHvnSW6H4rqGZDvXrbqdrG1R6YscUKLxdONa2jNxvjjr4oKXFowP1zA+XHNU56asDBtie4nn0vgMDxMitdQc1DKw2Lzeuo3/XXcvAdNHpc/ppqK1pi2b4Adr7uQrsy895kR7RKCcnLZY37mHlJXtPqppTMco94Yo8wapOuguxYbYXn6y9m6SVhZTGdja5radL/KhyWdyet2sAT5bIYQQw4Uk2UVu5/rd3HL9v+ls6+LM95zKGe88Oa/dGmadMI1rb/583q43lN21ewVJK3NIH2tDGVT7oqxq38m62B5ml491KcK+taa7uH/PazzesApL2z3Hba1ZUDmBS8YeV5QlEba2+dOWJwiaPkIef89xpRQRT4CubIqbtjzJ9xe+55i+74+vnsr/rr2XpJXBb3hg3yW0s8pd7g0yOVLXc37KyvDTdfcC6oA3Jxk7xx+3PM6UaB1jQlXH+nSFEEIMI7Lbp4gl4yl+8R83smvDHpKdKW794Z2sfnad22GVpLSV5fmmDX3WXiulMJTBkw1rChzZke1OtPLNN27lob2vEzT9VPoiB/xZ1b6T7668naca1rod6iE2dTbQnk0ckGDvL+zxU59sZ2ei5Ziur9GETB8KyGoLrTW21mTsHB7DwGd4DkjeX23dRiKXJnxQPD7Dg601TzYW37+/EEKI4iRJdhFrb+gglUhTVh0lEPajlGL72l1uh1WSklYGrTXmYUpBfIaHpnSsgFEdWUcmwfWr/03KylDtj5C0MmzurO9edd9Na6aTMm+QqDfI7zc/xhtt290O+QCxbII3l5cP5by5UcSyyWO6fmumi3JfiGlloyjzBrG7uynV+MuYGR1DovvffZ+mVKznnIP5DA+7E63HFIcQQojhR8pFilhFXTmBkI9Yaxe+gBeNZvzM4itVKAVB0wcKLG33mWhn7BzVvuKqb36iYTWxbJJqf4Q9yXYaUu0YKExlkLFz7Ey00JruYkp0JH7Dyy3bn2NexfiiGBADUOYNoum7jaiz8mwT9QaP6fpVvgi2tgmZoQPKQsApDdm/PSJAbaAMo4+/m4ydY7SUigghhDhKspJdxILhANf86iOMnlxHIOTnHV+8hLmnznQ7rJLkN72cVDOdjmy818f3JXtnjiye3tQ52+KBva8T8QSI59I0pjrwKQ8ew0QpJ9H2KQ9xy3ks7PGzO9HK1nij26H3mBodSYU3RCKX7vXxeC7NyGAF40PHNpmxwhdmYdUk2jJdBxzXWtOZTXLh6AOHAi2qmkjQ9B0ST8bOoZQqydaJQgghBoesZBe5CbPG8pU/f9rtMIaFS8cuYUXrFmLZBFFP8IDuIq2ZLuaVj2dmEU0H3JloIWVlqPRFqI+3OwcPXoRV4MWkKR1jZLACjWZ1+85DVnXdYiiDqyYv42fr7gPoqc3WWhO30mTsLB+YvGxAK+8fmnwGjakOdidaUaielfMTaqZx1si5B5wbMH18ftZF/GTtPTSnOzGV0X13Q/GhyWfIpkchhBBHTZJsIbrVBSu4du7b+c2Gh9ibbENBd69sxSk1M7hqyhkDbt/XmU3yRMNqnmhcQyqXZVp0JG8ZvfCYWtSlrCyqO6tOW1kMpdAasnYOS9sYSuE1PE7fadvuHmijiOVSA3oO+baoahKfn3kRN219krZMFwqFjabWX8aHZp4x4G4u5b4Q357/Tl5r28bK9p34TQ9LqqYwLTqy1+R9RtlofrL4Sp5v2sDORAs1gTJOqpnGiED5gOIQQggxvEiSLcR+xodr+P7C97Cpq549iTY8hsmssjFU+SMDvnZruovvrLqNlnQnEU8AUxm80b6DV9q28Z6JJ3PBQaULR+I3PT3VzD7DQ1cuRcZOv1nhrCFt5/AZHjzKwMDpPx0xe+/k4aaFVROZXzmeLV2NxLJJKnwhJoVH5K123GOYLKmewpLqKUd1ftQb5LzRC/LytYUQQgxPkmSXmGQ8xcqn1hII+5l76kwMY+iU3Wut6WiOES4P4fUVbrLiwZRSTIuOyntf6T9veZK2dJwqT4Tm3S1k0lmq6iqIREL8Y9vzzK+Y0Gc5QltDO4ZpUF7z5tTBsaFqfIZJxs5R6QuzJ9nmdOPYr2ZE46xyV3ZPiVRKDWiC4mAylMHU6Ei3wxBCCCHyQpLsEmJZFr/4j//HttU7UcDZ7zuNyz9/idthHbV//uQunvjHc1TWlfPlP11zQEI51LVn4rzWto0KX4Sda3cRa+lEKUV7Q4ypiyaivU4P5vdOPPWQz33mjhe5+ft3YJgGH//Rlcw91Zk66DM8nDNyHvfsfgUAj2FgaY2N3n/mCoZSWNomaWUYESgvyqE0QgghRKkZOsuc4ohiLV3sWLub6lGVRKuivPzg626HdNS01jxxy3NU1JbRVt/B+pc3ux1SXrWkOzGUgaEUna1deP1evH4vWmsSnUl8qu8ezE/+4zkCIadP+tO3v3jAY2eNnEvQ9BHLJPAqD0HT59Rmdz/uN7yEPQFSuSzxXJp3jj+pYO374rk0j9av4jcbHuSmLU+yIbb3gJ7UQgghRCmTJLuERCvDVNZV0Lq3jY7mGNOPm+x2SEdNKcW802fR0dRJIOxn4pzS6gce9QaxtI3WmmAkQDadI5ezAAiE/GTt3AFjvPc3b9kskvEUVtZi3mmzDnis2h/lP2dfgtf0YGkLjzKIeAI9A2h8hknOttBK856Jp7C05uhqkgdqW1cjX3rlL/x5y5OsaNnK4/WruG7V7fx248PkbKsgMQghhBBuUqW2srRkyRK9fPlyt8NwTWt9G8/c8SKhaJDTrzgJX8DndkhHLZfNsW31TqpHV1E5ovQ6OXxn5W1s7Wokgo89WxrIpLLUjK6irDZKWybON+ddzpReapK11mx6dSsen4dJc8f3eu0NsT18bvlNJK1MT5s6pVTPFMurp57Je3opRRkMWTvHF1f8haSVOWCIjK01LelOrpx0mmwqFEIIURKUUiu01kt6fUySbCEKY0e8metW3U4qlyGxs4tkZ5KaqbVYPs3ZI+dx1QD7QT/btJ7fbniYpJXGo8ye6ZXL6mbz8WnnDLj94NF6pXULP193P9W9rMynrAxew8PPjruqaKZOCiGEEMfqcEm2bHwUokDGh2v47/nv4C+vP859nS+DAcFdnVxz3qWcPmJWr0mn1potXY2si+3GxGBe5fg+O5CcUjuDcaFqHqlfydauRiq8Ic4aOZcFlRMKlmADNCQ7sLTd62MB00dzOkZWW/iUvPwIIYQoXfJbTogCGhWs5HMLLyb9i+3s2d7AR/7nQo6r631Ud8rK8qv1D7CyfQe2tgGFsV1xSu0MPjzlTDyGecjnjA/X8OEpZw7yszi8Ml8Is4+kPmvnCJg+vOrQ2IUQQohSIkm2EAXmC/i49q+fJZfJHbZm/uZtz/BG+3aqfdH9Rrxrnm5cy8hgBW8d2+vdKdctrJyIxzBJW1n85oH9zjuyCS4cvUhKRYQQQpQ86S4ihAsMwzhsgt2VTfFU4zoqfZEDElJDKcq8Ie7f81rRdukIe/x8dOpZxHNp2tJdZOwciVya5nSMsaFqLinSNwdCCCFEPslKthBFqCkdQ0GvZRd+00trpouuXIoKX7jwwR2FE2umUxeo4P49r7E+tpuIL8xldUs5fcRsgp6h0/FGCCGEOFaSZAtRhKKeALbWaK0PKa2wtI3C2URYzCZFRvAf089zOwwhhBDCFVIuIkQRqgmUMbVsJB3Z+CGPtWfinFQzncBB9c77aK1JW9nuzZJCCCGEcIOsZIthIWPn2BFvxtY2o4KVBwxJKVZXTzmT61bdTku6k6DpQ6NJWhlGBMp554STDjm/MdXBY/WreLxhdc9QmkVVkzh/1AJmlI2WzYZCCCFEAckwGlHScrbF3btX8MCe18jYFgrQaE6smc57Jp5CWZEn2+2ZBE82ruHlls14lOKkmumcOmIWYY//gPNWtu3g5+vvI2tblHmDeA0PtrbpyCaxtc1FYxbxjvEnSaIthBBC5JFMfBTDkq1tfrPhIV5s3kS5L4TPcG7cWNqmPROnLlDON+ddQcQbcDnSgdmbbOPrr/0Dn+HpdVOhpW1aM118ePIZnDlyrgsRCiGEEKXpcEm21GSLkrWqfScvtWymxh/tSbDB6dhR7Y+yN9XGA3tedTHC/Hho7xvktNVn1w5TGUQ9Ae7Y+XKfkxiFEEIIkV9Sky1K1kN738CrzD5LJMq9YR6uX8nbxh3fMz0xZ1u83radB/e+xu5EG6ZhsKhyImeNnMeEcE0hwz8qWTvH041rKfeGDntewPTRmuliQ2wvs8rHFCg6IYQQYviSJFuUrF2JFoKHaXPnMzx05VLEc2nKfSE6Mgl+svYetseb8BoegqYP29Y82bCGJxrWcMmY47h8/AlFVdccz2XIabvXEeuH0E5nEiGEEEIMPkmyRckKmj7arDi+Pr7N9/Wh9hkeLG3zv90JdtVBUxarzCiWtrlz13LKfEHOG7WgUE/hiLyG2Wc/7b7OF0IIIcTgk5psUbJOGzGLhJXu8/FYNsHcinEEPT7WdOxiWy8J9j6mMij3hrh950tk7dxght0vYY+fyZERdOVShz3P0jYomBYdVaDIhBBCiOFNkmxRsk4dMZOIJ0BnNnnIY2kri6VtLh3rbAh+dO9KTGUcdjXYZ3pI5TKsbN85aDEfi4vGLCZlZ7EP0ymoPRPnhOqplPsOX7sthBBCiPyQJFuUrDJvkK/MeRsB00tLupOOTIJYNklLupOkleET085letlowGmD19cExf3ZaFozXYMder8cVzWJk2qm05LuPGSV3e5u31ftj/Leiae6FKEQQggx/EhNtihpE8I1/HjxlbzSupUVrVvI2RYzykZzcu2MA1Z1faYXu5cV74MpFF5VXHXNhjL42NSzGRko5/49r9GZTWKjUSgUsKhqEldNXiar2EIIIUQBSZItSp7f9HJS7XROqp3e5zlLq6dw244XCR00SXF/+wY3zexe/S4mHsPk7eNP4MIxi1nVvoOObAK/4WVW+Riq/VG3wxNCCCGGHUmyhQBOGzGTf+98mYyVw2f2/mPRno0zu3wsdcGKwgbXDwHTy5LqKW6HIYQQQgx7UpMtBFDpi/DhKWcSyyWJ59I9q9bQXdec7iLiCXL1lDNdjFIIIYQQQ4WsZAvR7dQRMwl7/Ny87Vka0zH29RnRwMKqibx/4qnUBMrcDFEIIYQQQ4Qk2ULsZ1HVJBZWTmRLVwONqRimYTAlUid1zUIIIYToF0myi5yVs7jz1w/QuKOZyz9/MbVjq90OqeQppZgSHcmU6Ei3QxFCCCHEECVJdpFb/dx6Hv7zk87YbEPx8R99wO2QhBBCCCHEEcjGxyJXWVeOx+dBGYq6CbVuhyOEEEIIIY6CrGQXuXEzxvCVm66hvSnGrBOnuR1OvzTuaOLp214E4LTLT2DEeHmTIIQQQojhQZLsIWDs9NGMnV58A1AOp2VvGz/84K9JdCYAeP7u5Xztb5+lelSly5EJIYQQQgw+KRcRg2LtCxtIxBJUj6qielQViViCtS9scDssIYQQQoiCkJVsMSj8QR+aN0eR6+5jQgghhBDDgaxki0Gx4My5TF00ifbGDtobO5i2aBILzpzrdlhCCCGEEAUhK9liUPj8Xj77m4+y5Y3tAEyePwGPV77dCiVnW5jKQCl15JOFEEIIkXeS9YhB4/F6mH7cFLfDGDaSuQxPNKzmgb2v0ZqO4zM9nFY7kwtGL6QuWOF2eEIIIcSwIkm2ECWgK5fie6vuYGe8hag3QI0/iqVtnmhYw7NN6/nKnEuZKhMshRBCiIKRmmwhSsAt255lV6KF2kAZAdOHUgqPYVLlj2Aoxc/W3UvWzrkdphBCCDFsSJItxBDXmU3ybNN6Kn2RXh8PewJ0ZlO83ra9wJEJIYQQw5ck2UIMcbsTrSgUpjr8j/PGzvoCRSSEEEIISbKFGOKMo+ggogHPEZJwIYQQQuSP/NYVYogbH67FVAZZ2+rzHAOYUzGucEEJIYQQw5wk2UIMcQHTy7mj5tORjfdM2NxfeybOyGAFM8tGuxCdEEIIMTxJCz8hSsDbxx3PnkQrr7Ztw6NMAqaXnLZIWVmq/BG+MOtiDCkXEUIIIQpGkmwhSoDHMPn0zAtY3b6Th/a+wd5kG2FPhDPr5nBCzTRCHr/bIQohhBDDiiTZQhS5juYYbzy1FtM0WHjWXELRYK/nmcpgfuUE5ldOKHCEQgghhDiYJNlCFLH2pg6uv/KXdDTFQMHDf3mSL990DcFwwO3Qjsozd7zInb96gLqJtXzk+vdRUVvudkhCCCFEQUiRphBF7JVHVtLRFKNmTBU1o6to3N7E2hc2uh3WUels6+KW6/+NMhRbXt/Gff/vEbdDEkIIIQqmaJJspVSFUupfSql1Sqm1SqmTlFJVSqmHlVIbuz9Wuh2nEIWklEKj9z/gXjBAvCPO8ode58X7XqG9qeOw5xqGQinQtvMMDKNoXm6EEEKIQVdM5SI/Bx7QWl+hlPIBIeC/gEe11tcrpb4KfBX4iptBClFIx507n0f++iQte9tAw+ipI5l90vS8XT+TyvD0bS/SsqeVacdNZuGZc1F9JPKrnl3HjV/5K7lMrjtpVrzna5dx8qXH93p+uDzM+79xBXf++gFmLp3KhR89J29xCyGE1hqdfgayK8CoQgUvRRlSkiaKh+qtr27Bg1CqDHgdmKz3C0gptR44Q2u9Vyk1CnhCaz3jcNdasmSJXr58+eAGLEQBdbZ1seqZdZgek3mnz8pbPbaVs/jFp25kw/JNmB4PVs7iss9cyHlXnXHIucmuJF+74Ht4fR4CYadTSTadJd6R4Fu3fYmaMdV5iUkIIY6Wnfw3xP8Eygc6C+YoVPmPUUbI7dDEMKKUWqG1XtLbY8Vy/3Yy0AT8USn1qlLqRqVUGKjTWu8F6P44ws0ghXBDtDLCSZcs4fgLFuV1w+OujXvZ/No2qkdXUVlXTnlNlPv/8Fiv525YvoVcJteTYAN4/V5yOYtVz6zLW0xCCHHUkreBUQFGJZgjwGqA7Eq3oxKiR7GUi3iAxcCntdYvKqV+jlMaclSUUh8DPgYwfvz4wYlQiFKjNexX762UQtt2H6f2ccdLd19GiCKnrUZ06kHIrgGzFhU4H+Wd7XZYYiC07mWfirwgDTZtt6JTT4HdAJ4ZKP+JKDU0Ol4VWrEk2buAXVrrF7v//184SXaDUmrUfuUijb19stb6BuAGcMpFChHwPsl4ijt+di+rn1tPqDzEZZ++gNkn9V3RUr+tkX/84N80bG9i9NSRvPurl1EzuqqAEQvhGDt9NBPmjGPL69vx+j1k0zku+cS5vZ47fckUTK9JOpnBH/QBkMvkMEyDOacctoJLCNfp3BZ0x9dBp0AFwdqITj+DDn8QI/hWt8PLO519Ax2/GXQCfCegQu9AKa/bYeVf8G2Q+Csov1MuYtSCd67bUZU0nV2Njn0HdAaUAamH0Ml/QPl1KENymYMVRU02gFLqaeAjWuv1Sqn/BsLdD7Xst/GxSmv95cNdp5A12Vprfv3ZP7DmufVEqyJk0zkyqQyf+93Hmbpw0iHndzTH+O67f0oqkSYUCZKIJSirjvJfN3+OcJnUkInCS8ZTPHHLszTvamH60qkcf8GiPjc+vv7kan7/tb9j5Sy01hiGwRVfvJgz3nlKgaMW4uhprdEdXwZrh1Na0PNAFuwYqvJ3KLN09hTo7Fp07Bs4a2he0J0QeAtG5JNuh5Z3zsbHRyGzHIwaVOjtkugNIq1z6LaPgU6DEXnzAasJ/KdjRD/nWmxuOlxNdrGsZAN8Gvhbd2eRLcCHcGrGb1VKXQ3sAN7hYnyHaG/sYO0LG6kaVYlSCl/ARzaT45nbX+w1yV77wkYSHQmqRjmdCH2Bclrr29n0ylYWnDGn0OELQTAc4IKrzz6qcxcsm8N37/4qq59bj5WzmHXidKpHSVdNUeTsVshtBqPmwOPKC9iQfQ3Mo/sZGAp06kFAwb4uG9oPqYfR4atxfr2WDqUUKnAOBKRzUUHkNoMdg4PflBpVkHkGrT+DUsWy1a84FE2SrbV+DejtnUDRvvrZtj6kHEwphZXrva7Vtm046HzDUM5xIYaAsuooJ13S6xv2YStr53i0fhWP1K+kM5tkanQkbx27hBllo90OTQBgccgLbw/d/XgpOfj5Kpzn2fdda61TkNsAeMEzHaXMwQ1RDFFWHz9KCrSN1MMfqmiS7KGosq6c8bPGsn3NTipqy8lmcuQyOY6/YFGv508/bjJev4+u9jihsiDx9gT+kJ8pCyYWNnAhRF7kbIv/XXsvK9t3EPUE8Rte1nXsYVX7HfzH9PM4oWaa2yEKoxbMUc6Ktoq+eVx3J6Pe+a6FNih8Z0D6WbATzmq93Qq+k1HK3+vpdvpl6PqpU2MLzqpk2ddRHmkiIA7imQoEQCedvQ372G3gWypvznoh6/oDYBgGn/jfq5h14gxiLZ1orbnyv9/BvNNm9Xp+zZhqPvubj1BeU0Z7Y4zqMVV87rcfo6w62uv5Qoji9nrbdtZ07KTWX0bQ48NjmJT7QoQ9Af645Qmyds7tEIc9pRQq8gkgC1aLU09qx8BuhuBbUeZIt0PMK8N/HEQ+B0YQyELgbFT0072eq60W6PoRKI9TAmBWg+5Ax65Da7nDKg6klA8i1zgbaq0m5+fIagIjggp/0O3wilLRbHzMF7eG0Wit+9wwlo/zhRDF5xfr7ue1tm1U+MKHPNaWifOV2W9lZvkYFyITB9O5rejkHZBd62ySC14CvpOG9euwTj2I7vqt02N6f1YLqvx7KG/+psuK0qFz29Gph8DaC97ZqMDZKGP47s8ZKhsfh7T+vlAP1gu71ppYSycen0c6lggxyHLawujzZ1ljyWpg0VCeSajoF47qXK01ZN9Ap+4HuxE8M1GBC1GesYMcZWFpnaXXOlqlALkLI3qnPBNQkY+6HcaQIEl2Canf1sifvnkLO9ftQSnFwrPm8r5r304wEjzyJwsh+m1J1WReb9t+yPGs7STfkyIypHYo0ombIfkvp4wCP+S2o9OPQNk3UN55boeXN8q7AI0JOtf9XHFqufF3198KIQZCarJLRDaT5ZfX3MieTfU9I7JfeeQN/nbd7W6HJkTJOr5mKnWBclrSnT2r1ikrQ3smzqVjlxLy9L7ZTBQvndvVPa67svtPCMwawIvu/Dlal043EuUZB6H3g90OVqNTX0sWIl8suXZ/QrhBVrJLxKZXt9HR3EnlCKc3qjIVlSPKee2xVcRjCSkdEWIQBEwf1859O7dsf44XmjeitU2ZL8SHppzBmXXS+34o0pmXAfvNld19jIizcdLaWlKrvEboMrT/eMi+AfjAdxxq/6E9QohjJkl2icikMoe0r1SGQmtNLjO8a+usnMVrj6/i+buXk0lmWHTWPE64+DhCUSmjEQNX7gvx8Wnn8MHJy0hZWaLeAIYMZCg6WtuQW4XObkWZleBdgjJ6W3zI9H0RZbzZ6q6EKHMMmLJBV4h8kyS7RExdNAnDNEkn0vhDzi3qWEsn42aMHtYtAq2cxe++9GdWPbMWX8CHYRpsem0bj938DF/6w39QXlPmdoiiRPhNL37T63YYohfaTqBj34HcesBGYzgr02XfRB20Kq2889DcClpzwLQxnQFM8EwuaOwif3RuEzr+N7C2gXcuKvS+kmvhONxonUIn7wJrO5iTUMFL+uwJ7wZZbikR4bIQH/7ee0gns7Q3dtDe2EGkMsIHv/PuYd2iasXDb7DqmbVUjaokWhUhXB6ielQlLfVt3P1/D7odnhCiAHTiFsg5rfsw68CsBZ1Fx35waI21Zxb4FjhdRXTGSbbtuDNwI/Q+lAq48yTEgGirHt3xdcitdg6kn0N3XIu2E+4GJo6Z1jl0x/9A4mbILIfEX9Gx76B18dy9l5XsErJg2Ry+d/9/senVrfgCPqYtnoTHO7z/iZ/990v4Q/5D3mhU1JTx4n2v8t5rL8cw5L2mEKVKaw3ph51Jhvu/DhhlTo11biN4Z/YcVkpB9Cvo5G2QvA90m1NKEfwYyn+aC89A5INOP+68aTJrnQNmDVjNkF0B8u86NOXWQW6DM9VVKecNcXYt5DYd8DPtpuGdgZWgcFmIBcuOfsNVMp6ieVcLFSPKiVZGBjGy/Mpmsqx9YSPtjR3UTRzBtMWTek2WU/EUpnnoccM0sLIWtmVLkj2MZFIZDNMY9m8+hx2dBtXb65tyHjv4qPKjQu9FB98DZAHvsL4jWBLsLjhk55LtjAgXg0rrnJMMkwPPjPyVc+iMs09i38+mUjgFGsWzb0J+0xRYJpXhiVufo2FrI2NnjOG0y09w5Re+1ppH/voU9/z2IbStsbXm5EuX8q7/vBTTYxY8nv5o3NnMzz95Ax1NMWxLYxiKcbPGcM0vrz6ki8qCM+Zw7w0PEzxok2NXW5wpCydKsjVMtOxt4+bv386a5zfg8Zic+NYlXPH5i/EFpE1ZqVNKob2LIPcGqKo3H9j3C9ozpe9PtraD3QLmRGfkuBiylP9EdOo+0FlQXtApwATvfLdDK2k6uxHdeT3oDtAKlBcd+RyG//iBX9wzBTAgu+7NXu/mGDAnDfzaeSJLeAWUy+b45ad/zx0/v4+XH3yNW390J7/7z79g24WfCvfGU2u44+f3EooGKa8to7w6ytP/ep4H//hYwWPpD601N371r8Rau6isq6B6dCUVdeVsW72TO35+7yHnn3rZCUQro7Q1tGNbNlprOtvi5LIWl15zvgvPQBSaZVn88pobWffiJirrKohUhnnmthe4+fo73A5NFIgKXwX4nPIAnXTqq+0OCH0AZfR+B89O/hvd/gV05/Xo9mvQ2XWFDVrkl2eu0xNcd4Hd6iTbkc/KxsdBpHUK3fkd52fOqAazynmD0/kjtFWfh69gAgboOOiE8xGj+3hxkCS7gDa/to0tr2+nenQl5TVlVI+uZM1z69m5fk/BY3n8lmfxBf14fM5KrmEalFWX8fg/nit4LP1Rv62R3ZvqKd+vY4pSTk/wl+57lWwme8D5ZdVRvvSHTzJ/2Rzam2K01rczekodn/vtx5i6sHje7YrBs/m1bbTsbqWyrhzDUJgek8q6CpY/8BqJTrlVPBwoz3hUxU8gcD6oSvAuQJV9CyN4Ua/na7sT4n/pHkbjrH7r+A2FDFnkmVIKI3Q5qvIPqPIfo6r+iBGQWuxBlXnNmSBq7NfhTAUAC53OQ66Redkp9/IdB77jnY864dTZFwm5V15A6aTTy7qztYtMKos/6MMwFZlk4euH4m1xPN4D3+15vCbtTfGCx9If6UQG0zCcW8Ba93TZMkyDXNYil7Xw+g5so1YzppqP/fBKMukstmUTCBVPex8x+JJdqUOO7eshn05mpF/6MKHMkajIR4/uZL2v40T3a6Tygd05KHGJwlJGxGnfKAafTgK6j8e6Bn55HQe6KwF6yu1194p2cehXkq2UWgR8AzgdqACO11q/opT6HvCU1vqB/IdYOsLlQfZsbiCXzTm/5C2NL+QjVF74aYyLzpnH3b99iGDkzXZU7c0x5p0+q+Cx9MfoKXV4fB6adrbQvKeVbDpLIBygYkQ5E+eMO2wC7fMPzR7GHc0x1r6wEcM0mHvqTEkK+2nKgokopciksvgCzvdAV1ucERNqqaiVPumiF0YteKY53QtUwKnfDb3H7aiEGFq83fmEtkB1v2HVTlKsvAsGfHnlnY/G7N7Y7O/+aBRVnf1Rl4sopU4FngdmAn8/6HNt4BP5Da20aK3510/uoWZsFf6Qs9kqEA1QNbKC2392aC3xYDvjXacwZuooWve20dbQQWt9O5GKMG//bO+3T4uFL+Bj6fkL2bVhD9l0Fo/XJNmZZM/mes567ykl1wGgcWcz173np/zlf27lT9+6he+//+fEWmVFrT8iFWHe940rSHQmaa1vp62hHa/fywe/Pbx7yIu+KWWgyr4OgUvBOw/CH0UF3+F2WEIMKcocCcHLnM3Ddkv3Xohm8J3o/FwN9Pqe8RD5D+dNsNXqJNnha1Dm6DxEnx/9Wcm+HngQeBvOPbRr9nvsFeAD+Qur9DTuaGbHut2MGF9D3YRatK2d1Wxbs/6ljXQ0xwo6fTAUDfLlP32K159YzbbVOxkxoZYl584nXB4uWAzHqn5rI6OnjSLRkSCTyhCpjOALeNizKR8bKYrLff/vEeIdSapGVQLQsruVp//1Ahd97FyXIxtaTrhwMdOXTGHDy5vw+r3MOmk6wbAMFRF9U0YEFfmQ22EIMaSp0PvAOw+dfgLIonyngG8pSuVnS6AROBvtPwWsJjBHFNW0R+hfkr0YeLvWWiulDi6yaQZq8xdW6dnXr3nfypky3vyolCIVT1NeU9iYnFXhRSw9f1Fhv/AAxVq6KK+OUjf+zb+wjuYYHc2lt8Lb2drVU+IAYJgmnW0Dr2UbjipHlHPCRce5HUZJ0FrLXQAhxBEppcC3AOUbeHlI318jAJ5xg3b9gejPW4kU0Ffx8CigY+DhlK5Rk+swPSbZ9IHdL1KJNMFokJoxVX18pjjYwrPm0tURdya5AdrW5DI55p8+2+XI8m/xOfNJxdOkkxmSXSlsy2Z+P4YNCZFP2mrGjv0Q3foO7NYPYCf+WVQjjIUQopj0J8l+BvicUmr/lhT7VrSvBoq7wbLLfAEfl376fDqaO4l3JLByFp1tceIdCS7//MVFPwCmmJz7gWWMnzmG9oYOmve00tbYzsKz5rHgjNJLPk++dCmXf/5iPF6TUDTIVd9+J7NPnO52WGIY0jqLjn0TMi+CqgAMSPwNnbjF7dCEEKIoqX2rgUc8UakFwLPANuBfOF1GfgksAI4Dlmqt1w9OmEdvyZIlevny5W6H0adXH1vJA398nMbtTYyeOpILP3IOc06e4XZYQ46Vs1j74kZa9rQxZtrIng4SQojBoTOvoGPfO3Dyoc6CTqCq/oxSMj1TCDH8KKVWaK2X9PbYUddka61fV0qdBvwYuBanK+E1wNPAsmJIsIeCRWfNY9FZA99VOxRYOYtdG5xBO2Onj87rar3pMZl7ysy8XU8IcQQ6Rk9P2h4eZzy5zji9pIUQRUHbrZBdDTgt7dT+A2FEwRxVkq2U8gIXAm9orc9WSgWAKqBd656u/aKEaa158b4V3Pf/HqWjKcbME6dx+WcvYsT43ve7bnptKzd+9W/EO5xvj0hFmKu//16ZsijEUOWZCSjQOVDdvzp0B3gmgSr+rkQA2moBazsYFSjPZLfDEWJQ2Mm7IP5neip6lYkOfwIjcFZerq/tBDr9GFhNKO908J0sd5L7cFQ12VrrLHArMLH7/1Na6z2SYA8fL963gj9/61YSsSTh8hCrn1nHTz76W+KxQ78FYq2d/PozfyCTylBRW0ZFbRnpZJpff+YP0uNZiCFKmSMh9F6w28FqAKsR8KIinxwSv2B1dhW6/VPozu+j27+EHf+r2yEJkXc6uxrifwKjDMwa548KQ9dv0LkdA7++TqE7roX4jZC6B935I3T8dxxt6fFw05+Nj1uAEYMViChu9/2/RwmVhQhGApgek8q6Crra4rz++OpDzn310ZWkkxnCZW82owmXhcikMrz62KpChi2EyCMjdDmq4icQ/ggqcg2q8jcoz1S3wzoquvMXgAlGlfMneQc6t8vtsITIK516wJmuqPabcKx8gHZWnwcq/RxYW8GscxJ4owZSD4NdenMq8qE/fbJ/CFyrlHpMa900WAGJ4tTRFCN80Ph3bdu0NbYfcm6sj37V2u77MSHE0KA8E1GeiW6H0X+6DVS589/KdP7omLsxCZFvVgvQy/4I5XGmLg6QtjtxtuTtu273z5LdCeaoAV+/1PQnyT4Lpw57q1LqBWAvb7bwA9Ba66vyGZwoHrNOms6qp9dSWVcBgG07wyimLT60rnHygoko1AEDK7TWoGDy/AmFDLtHrLWTN55cQy6TY8bSqYyaXOdKHEIIl3iXQOZ5ZxVbx0H5wSzOARalxE4/D8l/gNUMvkWo0Puc0qNhTuscOvWYs0fAHIUKnJufaYW+hZBYB0QO+oJZ8ORhlLl3GhrlXE95we4CfFBEo8yLSX+S7FOBLNAETOn+sz8pyClhb//sRWxduYPW+jZnJLxSLL1gUa9J9szjpzJ18SQ2LN/cs/odjyWYftwUZp5Q+FvLbY0d/PCqX9Le5KxaeX0ePvWLq5mx5OBvYSFEqVKRa9BxL2RfBXMcKvIp6bgwyOzUc9D1Q6cmWPkh/ZxTM1zxC5QROfIFSpTWFrrzesgsx0nDsuj001D+nQG3wlSBc9Gp+503NUYloMFuc0aO+08dcOzKOxsd/jAk/gS2AiOIin51WP97Hs5R98keKoq9T/ZQluhM8tpjq2hv6mDqoklMWzy5zw1PmVSGp257gefvfBmAky5dyumXn4gvUPg2XzdffzvP3P4iVSMrASfhj1ZG+J87vlzwWIQQYriw2z4Ndivsn4BZTRD+JEbwXPcCc5nOvoHu+G+nnlkp0BrsJlT0Syj/KQO/vlWPTvzNuXODAv/pzh0EI3+TpbXdBboTjKr8rMAPYXnpky3yw8pZrHtpE20N7dSOrWbq4kmY5tCY9hiKBjn50qVHda4v4OOc953OOe87fZCjOrK2+g68fh+ZVAbb1viDPqkNF0KIwWY3gwoddNBJKIc1uxMwnAQb3vyo8/N7SZkjUdEv9nT8OJruP9qqB2uHs2/BM/2In+OsXMvq9ZH0K8lWSoWADwPLcOqzW4AngD9JO78ja9zZzK+u+T2t9W09Nc11E2r59K8/QuWIcrfDK1kjJ9fxyF+fxLZs6K4VP/6CRW6HJcSg0DqNTj0Jdr2zQdF3Kkr1p5GUOBZap7uHf1jgmSW3zwG88yGz4s0poVoDCuWd5WpYrjPHOx/3DXHSOUCBmd89S0eVXOssuuv/IP0EYALa2cBYdq3UzufBUb/yKqVGAq8AvwCWACFgKfArYIVSSnaSHYbWmt9+8SbamzqorKugelQlVSMraNrZzO+/9je3wytZuWyOVx55HV/Qx77KKF/Ay94tDTTubHY3OCHyTOsMuuObEP8NJP+N7vxfdNfPpYftINN2F7r9K+jYdejY9ej2z6KtBrfDcp0KfwCMsFMfbDU5K9i+E8C7wO3QXKU84yDySdBdTr207oDwB1x586GTt0H6MTCqwexub2nXo2PfReuDJ7yK/upvC79K4DSt9bP7DiqlTgZuA34AfDCv0ZWQbat30ri9mYoRZQccL68tY+vKHdRva2TkRGlDnm/rXtpEe0MH0xZNJpvJoW2N1++hraGD5+58mbddc4HbIQqRP5nnILcejBHdtZ42pJ+BwIXgneF2dCVLp+7q7hLR/RpuNaPjN6HKhve+D2WOgYpfotPPgN2M8s4B70K5swIYgbPRvkXOUCejGmX2Pj15MGltQ/IeZ4Pkvn8TpUBVgVUPuXXgnV3wuEpJf5LsC4Cv7J9gA2itn1NKfR24Pq+RlZjO1i6UcejtG6UUpsegs7WrzyS7eXcLqUSGkRNr8XiljL4/Ys2daNtZxfP63vy78/g8NMlKtigx2upw/qOn1tMAjJLtB631vvpeE7WvJMENVsNBwz+CYMtKNoAyylDBC90OoyipfYORXJPtbmfZR4Jvtxc0mlLUn4wtAuzp47FdSAX8YY2aXIdtaaf9nfFmom1bNratqZtw6Dd5855W/vj1m9m2aieGoQhEArznq5ex+Jz5hQx9SBszbSSoA3t2A2TT2V7bDwoxlCnP+O4etlb3sJUMKA3mGLdDyzud24Tu/CVYuwCN9kxHRT/trJ4WmmcOpJ90/t5RThmAd/h2zyhW2u50ph7mtoBnLipw+jDvjOFz6q/tmNNmcR9tA/abtePimPXnns164Mo+Hns/sG7g4ZSu2rHVLDp7Lq31bVg5C4Bc1qK1vp1T3raUsuoD+7VaOYtffupGdqzZRWVdORXdGyP/cO3f2bpqR8HjH6rGzxrLvNNm0rK3jVQiTSadpWVvG9WjKjn+wsVuhydEfnkXQvDtTp2n1eoMigh/ElVigyK03ea0QLMbnFpSowaszeiObzobEAtMBc6BwCVOuzq7BXynoELvLngcom/a7kJ3fBnif3Ja28V/g459G61zbofmGqUUhK50VrPtuLMxVWe7a+dPRHnGuh3ikNeflewfA3/u3uD4d5yJjyOBdwPn0HcCLrp94FvvJFwe4vm7lqM1GIbi3A8s463/8ZZDzl3/8iZa9rRRWfdm15FAyE+yK8UTtzzLpO/KO8yjoZTi6u+/j8dvfoan/vUimVSG0684iQuuPotQNOh2eELklVIKFb4S7T/TGSNujHSl1nOw6fQzoBOw/3NTVc7musxyyEOv4f5QykBFrkaH3w/aQhkHt60TbtPpJ5yynn1181pDdh1k3wDf8F1wMfwnYesvQfIvTn248kPwMlToPW6HVhKOOsnWWv+1u4Xft4Eb93uoAfiE1vrv+Q5uqGje3cKOtbsJRAJMWzwJr8/b63m+gI/3fPXtvO2aC4i1dFFWEyUYDvR6bntTrNeOAIGgj4Ydw7zHaD95fV7Ou+pMzrvqTLdDEaIgnBWoEl6Fsva8uVHrwAfQVjN9NS7TdgLsPaAqUGZN3sNSyk+fX1y4y9p54PeMUoANdqNrIRULI3AKtnc25DY5kyHN8UfV/k8cWb920Wmtb1BK3QjMwOmT3Qqs18O0z4uVs7j5+jt4/s7lPT+7obIQH//xB5iyYGKfnxeMBAlGDr+KOmqy0xHx4FriZDzFlIWTBhy7ECJ/dHYNOnG78wvbtwQVvBRlSO/7QeOZAakHDzymNeBBeQ7tNay1Rif/Ccnbus+z0L7FqMhnZLT6cOGZA6mHnH9/pd6snzeH994crS10/CZI3YfzDtFGe2dA5It520ysrRbILkfrHMo732lhOEz0u4+O1trWWq/VWj/b/XFYJtgAD//5CZ694yUq6sqorKugsq4CK2vx68/8gXhHfEDXnjhnHDOOn0rL3jaymRy2bdPeFMMf8nPmu07O0zMQQgyUnX4V3fENyL3hlGgk70B3/JcrtcHDhfKf6GzYshqdGlKdcd7geKaCd94h5+vUQ5D4u7O5y6x0OjpkVqA7f+RC9MINyn8S+I5zplBajU79fOB88ExzOzRX6eTdkLoLjIruPtnVkF2P7rwuL/31dW4nuuNzzsCb+I3oji+is28MPPAhoj/DaH6qlPpLH4/9RSn14/yFVfxs2+aRvz5NWXUUw3jzrzFUFiSVTPPKoysHdH2lFB//0ZWcd9UZZJIZ2hs6mHn8VL544yepGeNiqyohxIGSf3HqGI1KZ4S0OQKsvej0C25HVrKUCqDKr3OSJJ0BLAi+HVX2TZQyD/2E5L/AKHuzzZ4ynM2SudXonGwkHw6U8qKi16LKv4OKfApV8SNU+KPDuixCaw2p250Ee9/PjVJOop3bDrmNA/8a8d+DToNZ110P70N3/mLA1x0q+lMu8lbgv/t47MHux740wHiGjGw6S6IzSfWoykMeUyiad7cO+Gv4g34u+/SFPQNThvOLgRBFy9oN6uCSAw32XlfCGeq0Tju/lFX0sK95yqhART4OkY8f4XrdvbSNg+YQKAXadFY0kY3kw4FSCrxznD8CyIDd+eZm0H2UApSz6s/0gX0Ju97pG99z7RDYTWhtD4uhRP1JsscAO/t4bFf348OGL+CjoraMVDxNIHxon83RU0bm7WtJcp0fVs7Csmx8/t43ppaKTa9tZc+meiKVEeafPksGGA02z3RnyqLqfsOtNaBQnimuhjXUaDuBTtzkjHjWltPbO/JRlHdgcwGUUmhzvNNaT+03zkHb3V9n1AAjF27Rdhc6dR9kXgajFhV8K8o70+2whhAfGLVgJ2D/jjha4/TJzkNaZ06CzEtgdjd50B1gjhsWCTb0rya7DZjax2NTgc6BhzN0KKW46GPn0tUeJ5tx+mxqrWlvjlFeE2XhmfJOuZg8/o9n+dypX+dzp36dm771j55e5aXmgT8+xk8/+ltu+cG/ufGrf+UXn7qRbCbrdlglTYWvAgynfZzd7tQGe2eDd5HboQ0puutnzsY0VeaUctgt6Nh30LktA7946H1Oyz870f3FunsB+09DmXUDv74oOK0zzl6IxN/B2gvZl9Ed12JnXnM7tCHD6ZP9btCdoFPOQW1198le3Osm4n5/jchHwRwJVkv3G10/KvqFAV93qOjPEtcjwLVKqbu11j3zYrv7Zv8X8HC+gyt2J1+6lERnkntveIRERwLbthk3YzQf+u578AeH8xSp4rJt9U7+9ZO7KauKYHgMnr97OeNnj+XMdxW2l+5ga9rVwj2/fYjy2jJMj4nWmo2vbOGFe17htLef4HZ4JUt5pkLFT9Gph53hKN7FKP9pKCV3EI6WtvY4/a2N2v1GwkfBakEn70JFPzeg6xv+E7D1FyDxF7Candrs4NtQofcOPHjhjsxysLY7tb4AhJ3Sh8SfwbfQzciGFOU/E62zkPy7kwRjQOBsVOjD+bm+UQUVP4bseiAHnmnDqvNSf34LfAN4GdiolLqHN0tELgbSwNfzH15xU0px7pXLOO3yE6nf2kgoGqB2XM0Ryzta69torW+nZkwVFbXD55vNLU07m1EKPD7n290X8LJr3W6Xo8q/9sYODNPA9DgbWJRSGIZB445mlyMrfcochQp/wO0whi6rEZTnzQR7HxV0Eqk8MAKnof2nOiPPlR+lfHm5brHSVr2zOmmOLsnnqq3dwEHdL1QYLNnI2h9KKVTwLejA2c7+BBXJ+zAlpYLD9o1Pf4bRbFNKLcUZRnMuUA00A3cA39Ja5+eVcAgKhPxMnHPkvo/JriR//p9/8saTazA9BrZlc9x5C3nftW/HFyi9F8FiMWpyHVpDOpnB4zXJpnNMmjfw22DFpmpUJdrWZDM5vD4PWmtsy2L0FLkdLoqcORJ0zqmT3r9WUyf6bLGmte1Mfkw/DsqDCpwH3iWH3yypVC+bVHu7tkann4Lkrc6tc89MVOhKlNe9dm86t6N7A2fdYcdda2svuvNnzmARZYAKoEMfxgiU1jAu5ZmIRr3Z9xqcsgdT9kIcC6U8h26AdInW6e4N5SHn+30I70vr7zCabYAs1xyjP33zH6x6ei0VdRUYhsK2bF6+/1VMj8EHvvVOt8MrWWOnj+aqb7+Tf/zwLlJdKc65chknv22p22HlXfWoSt7xn2/l1h/eCSi01iw4Yw7HXyC1waK4KXMk2n86pJ9wWiHidTZIKQ8qcEmvn6Pjv4fUvaACgEZnlkPo/ajQ5QOOR6fuh/gNzkZJVQa5dejY16H8h3mpU+0vO3Fbd59vA7SNDn8II3jxoXHrDLrjm6DbnTZsSjmr2V2/RBtVKN+Cgsc+aLyLwDsLsqud7wGdBWV275EQQ5W29jrfw3Y7YEPgLTCEWy2qY202rpQqB6YB9VrrXXmNagCWLFmily9f7nYYh2je3cK3LvsRlSPKUcab3yy2ZRNr6eS6+/6LsiqZPDbYDp6gWYp2b9pL/dZGIhVhph03+YA+7kIUK60z6MS/IHUP6CR4Z6FCH+p19VhbTei2T3T3Ju/u76uzoLtQlX9EGeEBxJFDt30YMJz+5/tYLeA/FWOA9eH9jie3Hd3+he5exp7uTZsxVOUvUQd1RtHp59CdP4GDR8bbHeCZhlH+ncIFXgBap9GpxyHr1POrwAUoj7RjHMrs9q+AtcUZGKUtsFtQZf+F8hXvwphSaoXWeklvjx12JVsp9RbgTK31Vw86fi3wzX2fr5T6B/ABrXUuPyGXnpY9bZimcUCCDWB0H2tr6JAkuwBKPcEGGDN1FGOmSlsyMbQo5UOF34sOvQewex8qs4+1BycJ3u8c5XVKdO1mGECSjY47f4yDElUjBPnodNJfdiPOc+3+da28zoq21XhI+0Ft7QV6GcKsgmD11YF36FLKjwqeD8Hz3Q5F5Iu1w7l7BN0/3xqd28NQ3VZwpCWuT3BQJ3Kl1LnAd4B1wOeA3wHvAj47CPGVjOrRlViWjbYPvHNgWza2ramskw2QQgihlDp8gg1gjga6+1zvo7OAcjqUDCiAsPNHp5zr67RTK24nwI3e50YdznPtXsPSWSeeXupnnZXtXn6t6wSYR943JITrPBNAx5z/1hbOzIHRroY0EEdKshcB9x507ENACniL1vqXWuv/wEm0pRfSYdSMqWbe6bNobWjHtp2VBtuyaWto54QLF8sqthBCHCVl1kLgAmfV2u7o7k/eBsF3DbgzglIeCL4HcnsgswIyb0DmFSCHCl6Wl/j7FY9nvNPn2253uj/YMQh/6JBSEQB8S5zb7FZr90ARut8sZFHBKwoatxDHQkU+6wz2sludn+nAReDttRJjSDjSxscRwOaDjp0LPKO1rt/v2L3AlfkMrBRd9d/v5C/f+RdvPLEawzSwLc0JFx/Hu758qduhCSHEkKLCHwbvLHTqMWfDW+A88B6Xn4t7F/JmTXYO8IEqd63e1wi9He1b6nQXMetQfUziU8oH5d9Bd/0cshu6u4sEIfLZ0tr0KEqWMkdB5S+dAUMqOOSHRR0pye4EeorblFLTcFr3vXDQeTHgCPf3RDAS5GM/uJK2hnbaGjqoHl1JeU2Z22EJIcSQo5QB/lNQ/vwPlVJ2I9osB2Oic0BrsJvQOuNaz2nlGQccueRDmSNR5d9HWw379cn2Dn6AQuSJUn7wTHQ7jLw4UpK9DriUN0tGLsXZWvLQQedNAhoQR6WyroLKugq3wxBCiEGl7U506hHIrQfPFFTgHJRR6XZYR2bW4tRBZ0D5nP7LRi0wdJLVob4CKEQpOFKS/VPgdqVUFU4S/UFgJfDsQeddBrye9+iEEEIMSdqOodv/0ylxUD7IvIRO3QflP0aZ1W6Hd1jKHI0OfRQSN4JWzm3rsi8Pi+5EQoj8OWySrbX+t1Lqc8AXgSqcMpFP6P2aayulxgJnAl8exDiFEEIMITr1kNN+bv8uGFYjOnkXKvIh9wI7SkbwLWj/Cc7GSnOEMxpaCCH64YgTH7XWvwB+cZjHdwEVeYxJCCHEUJdd0z2NcT8qDLlV7sRzDJRR4QyBOQpaZyC3AbDBnDrgLiei9Gm707nTY1Q532slTNtdgAWqbFjdEerXWHUhhBDiqHgmQfa1A4/ppHO8xNjp5yH+6+52eYAy0aErUYGLhlVCIY6O1hl0/I+QfgRQoG20/xRU+OMl9+ZMW/Xort9CdqVzwDMJwh9Deacf/hNLhMxbFkIIkXcqcL4zedFqcpJrqxmUDxV4m9uh5ZXOboTOHwMGGNVgVoMKQfxGdObgRlxCgI7fAKn7QZWDUencLUk/he76qduh5ZW2u9Ad10J2ldO/3agGaxc69k20tcft8ApCkmwhhBB5p8xaVPmPIHCOM1zCfzqq/Acoz1i3Q8srnbrrzX7U+yifk2gnb3UvMFGUtN0GqcfBqOkeG47z0aiFzAp0bpe7AeaRTj/jDJQxq7t/RhQY5aCz6OTBcw5Lk5SLlCArZ6EMhWHIeygh9tFa0x5P4TENokG/2+EMC8ociYr8h9thDK7cRiehPpgKQ2574eMRxc3a6yTV6qDRIkoBJli7oVTeiOY2Hvo8wXlDmltX+HhcIEl2CdmzuZ7bfnYva1/YgMdjcuIlx3Hpp84nXB4+8ieLkrBj3W7u+d1DbH51G5Ujy3nLh85iyXkLhn1d6Jrt9fzxwZfZ29oJwJwJdXz4/OOpq4y6HJnYn9ba6amtY+CZgTLK+z7XakEn/w7pZwAT/GehQu9CGb3/m2qdgczL6OwbYIxCBZblp2e3UefEfPAmT9LO7XEh9mdUgbZA287q7j5aA7ZTPlIqzFHOcz1EGszRBQ/HDZJkl4jmPa385Or/I5POUllXgbZtnrnjJbat2slX/vxpTI8M5Bws8Y44f7vudra8sZ0Js8bw3msvz+skz2duf5EH//Q4psfkss9cyIIz5vR63s71u/nJ1b9B25pwRZi2+g7+8F9/p72xg3OvXJa3eIaarfWtXH/L43g9BlVR55b+mu2NfPsvD/PDj11MOODOBD9xIG0n0J3XQXYdoEAZ6MgXMfwn9HJuF7rja2A3dyclGlL3orOroeKHh0w41DqLjn3X2XylTGejWep2KP9+nyPKj5YKXoyOveEkE/tW7bQNdgzCVw/o2qL0KHMk2ju3u065unsFm+6yirHgmeZugHmk/MvQyX+CHXf2ZwDoNGgLFbjI3eAKROoJSsSTtz5PKpGmorYMw1CYHpOqkRXs3lTPmuc3uB1eSfvDtTfz2uMrQWtWPbue337xprxde9Wz6/jb924jFU/T1dbF//vKX9ixbnev5977/x7BtjXltWV4vCahsiDltWXce8MjpJPpvMU01Nz9/GoAIkE/SimUUlSVBemIJ3luzTZ3gxM9dOoBp+1fz+bBIHT9HK1Th56bftpJsM1aUB5QXqem1doOmVcOvXjm5e6kprb7+rVgx9HxmwceuHcJBN/hJElWI1gNYLeC/wxU4IKBX1+UHBX9HHgmg93ibAi2msGsQ5V9raTuOiqzFlX2defNp90CVouTZEeuQXlnuh1eQchKdonY9MoWguED60yVUmhbs3P9buadNitvX0trXVIvBAOhtWbdixuprKvEMBSVdeVsX72LTCqDLw8rpBtXbMYwDALd/7aJrhTb1+xi/MxDV982v7qNSPmBtaFen4d4R4KmXa2MnTZqwPEMRRt2NRMOHDoO2zQNNu5u5tzFw6OVVNHLLHcS632vLSoAdgKsXeCZeuC5ubVOcr2/7s/TuU2og1a/dXYtzur4fq9bRhnkVg44bKUUKvxedOBMyLyK1hbKNxdVgq0KRX4ooxLKf+iUGdn1ziZIz2yUKr11T+WdB5W/7+4hb4FnGuqQ0qrSJUn2AHU0x3jkr0+z8qk1RKsjnP3e01hwxpyCJ6G146rZtWEPweiBU8mUARUj+q5rPFpWzuKRvz7FY39/hs7WLqYsnMil15zP1IXD+xeJUoqasdV0NMeIVkZIdqaIVIbx+g9N6o5FzdgabMtC29qpV9WayhG9l6JUj66kcUczHt+bP9a2ZaO1pqw6kpd4hqKa8hA7GtvxeQ98ubMsmxHlw/fvpeh4JnTXY0dxmk3bzkej6tBzzZF91HoCxohDj5kju6+5H50Cc/zAYt6PMkdBcBSy/CCOhlIKvDOBobeiq+0up3OItRflnQW+JaiD3/TuRykPeGcXMMLiUXpvmwoo1trJD6/6FY/+7SnisQS71u/hd//5Zx780+MFj+WMd52CbWsyqWzPsa72OIFwgIVnzh3w9f/23dv49y/vQ2tN1cgKdq7bzc8/cQNb3pDd8x+5/n34Aj7aGztQhuLjP/5A3t5knXTJcSw6ez5tjR20N8U4/R0nM+eU3l+Uz/vgmSTjKTJp53vAtmzaGto57twFlFUN3w1+F50wm1QmR86ye46lMllM0+C0ecP7TWIxUcFLgRxkXoLMi5BdDYHzUL0k2cp/lrOSbcedA1o7NdBGCOU/sZfzT3d6EVtNzu1quwN0GhV6z+A+KSFKjLYa0e2fhfgNkLoH3fkDdOy7aJ098icPQ0prfeSzhpAlS5bo5cuXF+Rr3f/7R7nntw9RNerN3cC5bI54e4LvP/h1wmWFndz0/N3LufVHd5LLWqA1ZTVlfOyH72fC7HEDum7jzmb+5/IfUzGi7IC2gLGWTqYsnMhnfv3RgYY+5GXSWWLNMcqqo3kpE9mf1pr2phimaVBW3XeyrLXmiVuf4+7fPEg2k0NrzZLzFvDur15GIDR8W9Zprbnt6ZXc+dwqnJIB8HlM/uOSkzhu+sB+NkT+6Nx255e3TnV3XgD852GUfan387NvoDt/5nQi0YBZi4p+EXVwacm+860GdOIfkH3dqX8NvgvlWzBoz0eIUmR3/dLp823WOge0BrsJFf0Syn+Ku8G5RCm1Qmu9pLfHpFxkANa9tAn/QcmLx+tBA/VbG5myYGJB4znpkiUsPmceO9buxuPzMH7WGExz4F1F9myqx+il73a4PMTm17YN+PrFJp7K8PyabazZ3kjA5+Gk2ROYM2EkhtH36rTP76VmzOC061JKUXkUJT9KKc581ymcculSWva0EakME62UcgilFFecPp8zFkzh+TXb8Ps8nDp3EiG/dBUpKrnNgOmUjQDoLORe7/N05Z0PlTeAtcP5PHP8Ye8gKbMOFf1MfmMWYrjJvObsZ9hHKVAmOvvGsE2yD6eokmyllAksB3ZrrS9WSlUB/wAmAtuAd2qt29yL8EA1Y6vY/NpWwvttNtNaY1s20Sp3kht/0M+0xZPzes1IZfjgakYAMqks5bX5a1VXDLbVt/L9Wx4jnsrgMRWWrXlq5RZmT6jji1csw+8tqh+ZXvkCPkZNrnM7jKLy9Mot/O3RV0ims9ha8/CKjXz8ohOZOqZmwNfWWkP2ZXTyTmcHvXcBKngZyhyZh8iHEaMC0G/2D9YJp8/uYSjlcbo0CCEKw6yD3BZQ+y0wahsMeb3rTbHVZH8WWLvf/38VeFRrPQ14tPv/i8bpV5wESpGKOy2mbNumtb6dWSdMY8S4gf/yLhaT50+gdmw17U0d7CsvsiybeEeipPovZ7I5fnjr42RzFtVlIcrDQaqiIaqiQVZtq+cfT7zmdojiGKzYsJPf3vM8Gk1FNEhlNEhLLM51Nz9KQ1vngK+vU3eiY99zfvHoJKQeRnf8J9qqz0P0w4h3IfhPddrf2a2ABxX5lNtRCSH2o4LvAp0Bu9O522S1gBFGBUonF8inokmylVJjgYuAG/c7fCmwr+nwTcDbChzWYU2YNZaP/uD9mB6T9sYOOpo6WXjGHD50XWltpjEMg0/9/EPUja+lvTFGe1OMWEsn51x5Oqdcdrzb4eXNq5v3EEukiYYObYVYGQny2GubSKYLv7kjk85y56/v58E/PY5t20f+BHGAW598g4DPQ8DndHxRShEN+cnmLB5aMbAe8tqOQ+JmpwOGUea0ndvXgzl5R6+fs7u5gz8/vJyv3XgvX7vxXm56aDm7mtoHFEcpUMpART6PKr8OFf0qqvKXfdZXCyHcoXwLUGXfcAbnkAXfUlTZ93vdoCyKq1zkZ8CXgf13dtVprfcCaK33KqV66c3krgXL5jD3lJm07G0jEPaXbBeHmjHVXHvL59mxdhdd7QnGTh+V16mGxWBnYzt9bQT2mAZaa5pjccbVVhQ0rlVPr+Xe//copmkwZeHEYd82sT+ylsXOpnZqyg/dhBz0e1mzvWFgX8DaCdjOMJT9GVFng91+tNbc/Pir3PfSOtAQDHhRwCOvbOCRVzbwlqUzee+Ziw5b+1/q3mxrJoQoVsq3COVb5HYYQ0JRJNlKqYuBRq31CqXUGcfw+R8DPgYwfnz++p4eLdNjllR5SF+UUgPuVFLMIsG+N8JprbFsTdCXn/7X/TFm2ijKqiL4gz5GjC/977N88hgGQb+XnGXj9Ry4CTibs6iMBPv4zKNklDv1iFofOOhEZ+Cgcd3/fm4197ywlqqyIOZ+m4iDfi+WbXPfi2sI+jxcftr8gcUkhBCiKBRLucgpwFuVUtuAW4CzlFJ/BRqUUqMAuj829vbJWusbtNZLtNZLamtrCxWzKDHHTR+LUuqAfsr7xBIppo6upqY8XPC46ibU8oOHvsG37/xKyd4pGSxKKc5ZNI32rtQBdyls2yabszh78bSBXd8c5QxZsJudRBucOkWdRAXf1nNeIpXhzudWURENHJBg72MaBpXRIHc/v4Z4KjOgmIQQQhSHokiytdZf01qP1VpPBN4NPKa1fj9wF3BV92lXAXe6FGJeNe9u4dG/PU1bY4fboYj91JZHeNvJc2jrTBJPZZxOMbamtTOBwuCq85a6FpvpMTE9A2/HOBy97ZS5zBhXS0ssQWssQXNHnLbOFOceN53FUw8dT99fKvpF8M51OovYbaDjEP4geN/8fnll025yORvvYVpqekyTnGWzfMPOAcckhBDCfUVRLnIY1wO3KqWuBnYA73A5nrz423W3s+Kh19m6ajsf+f773Q5H7Ofy0+YzsrKMO55dSUNbFyjN4qljueL0+YwfUXnkC4iiE/R7ufa957By615e37wHv9fD8TPHMXlUdV4mcyqjAlX+bbS115kkaI5DGQfe8Who7UT32gjzQBpNY1vXgGMSQgjhvqJLsrXWTwBPdP93C3C2m/EMhjknT2fTq1uZdeJ0t0MRB1FKceq8SZwydyKJdBaPaQyJ3tji8DymwaKpY1iUh5XrvihzVJ99nQM+L0czXFdrjd8n329CCFEK5NXcBee8fxnnvF96ShYzpRThPI9HF8PXnIl1GIZCa93n6rnWGsMwmDtRhjoIIUQpKIqabCGEKGUT6iqZNLKKju7BVb3p6Er1nCeEEGLokyRbCCEGmVKKay49hZDfR0ssgbXfUCHLtmmJJQgFfHz60lPzUicuhBDCfVIuIoQQBVBXGeU7Hzyffz71Os+v2d7TVltrOGX2RK5YNp/a8oi7QQohhMgb1deEu6FqyZIlevny5W6HIYQQfepMpNjTEgNgdHUZ0VDA5YiEEEIcC6XUCq31kt4ek5VsIYQosGgowAxJrIUQoqRJki2EEGLIsWybVdvqWb2tHsNQLJwyhhlja6WmXQhRNCTJFkIIMaS0dSW5/pbH2N3cwb6Sx3teWMuMsbV84Ypl0n5TCFEUpLuIEEKIIUNrzf/+60n2NHdQHVXUhFuoCbdSFTFYt6uJG+59we0QhRACkCRbCCHEELJpTwtb97ZSGVGQXQt2I9gNqNw6qiIeVmzYSWO7jKYXQrhPykWEEKLENLV30RFPUVMepiISdDucvNre0IqtNUp3AjYov/OATmPQhWEE2VbfyogKaYc4GLTVhE4/CdYOMKpR/mUoz0S3wxKiKEmSLYQQJaKpo4vf3fMC63Y2YhoK29YcP3M8Hz7/+JKpU/aaJoZSODditdNovIdzc9brMd0IreTZqYeh63c4b268oHPo5F3owFtQ4Y/KplMhDiJJthBClIBkOst3/vIw7fEUVdEgSils2+aFtdtp60zwjfefWxJJ0LxJo1AKLCoxVTPohPOAUU7WimAaOWaOG+FukCVIZ9dB1/+BUQ5qvzds2oLU/WhzPCp4vnsBigM4dxyeBkxUYBnKqHA7pGFJarKFEIPmxXtf4SNzP883L72edDLtdjgl7fk122npTFDZnWADGIZBdVmIDbubWb+ryeUI86OqLMR5S2bQ2pkmyzTwTAfPDNL2RNrjaS4/bT5Bv9ftMEuOTt4JyjwwwQbnmBGF5L/Q2nYnOHEAbbeiO74EiZsg8Ud0+1fQtuxTcIMk2aJoxOIpNuxqYldTO6U2iXS4+vknb6B+WxMrHlnJPb972O1wStprm3f3WibhrGhr1u1odCGqwfG+sxbzjmXzSWUt2hMe2uImtlZ86C1LueiEWW6HV5pyK0FFe39MBcFuB7utoCGJPmReATsG5kgw68Buhuxqt6MalqRcRLgunc3xl0dW8NQbW1AKbFszurqMj198EpNHVbsdnhiAcEWIjpZOlFJUjax0O5yS5vOa2Id5c+rzlk6dsmEoLjtlHhceP4sdje0YCsaPqJRa7EHlAfpYqdbaeUxJSlEUejYD73s90KBkwqwbZCVbuO43dz3HY69uoizspyISpDIapLG9i+/+7REa2jrdDk8MwHfv/hrnf+hMPvaj93PGu052O5ySdsqcSViWfchdIMu2MQzFoqljXIps8Pi9HqaNqWHK6BpJsAeb73Sw+3g91jHwTEUZ5YWNSfTOdwJ4Z4Pd4vzxLQXvXLejGpbkbadw1a6mdpZv2EVNeainjlQpRVk4QEsswf0vr+OD5y11OUpxrOom1PLpX33E7TCGhfmTRzF/0ihe37KHaMiP3+shkc6SSGW48PhZjKoqcztEMYSp4EXo9KNgd4Aqg32baHUSdAYVfr+7AYoeSvmg7H8gtwkwnDdAStZU3SB/68JVG3Y3A7rXrgeRoI9XNu4ufFBCDEGmYfCFK5bx3rMW4zVNWmIJKiNBPnnJybzv7MVuhyeGOGXWocq/A0aVszpqNYPVAmiI/ifKO9/tEMV+lPKgvDNR3umSYLtIVrKFq0xD9dlWzLY1XlNeHIQ4Wl6PycUnzubiE2ejde9vXoU4VsozGSp+Dbl1YDc5GyG9c5yVUyHEISTJFq6aN3EUCqdu1DQOTKjjqQwXHj/TncCEGOIkwRaDQSkF3lmAdHER4kgkyRauqioLcdEJs7jr+dVEgn6Cfi85y6a9K0lNWZizF01zO0RRpPa0dLB+ZxOWbTOiIsrsCXV45M6HEEKIIiFJtnDdu85YSG1FhH8/u4qWjjimaXD6/Cm8c9kCoiFpOyQO1DM6fEcDGqdLlWkooiE/HzxvKcfPHO92iEIIIQSq1IZ+LFmyRC9fvtztMMQx0FqTSGfxeUxpxyV61RpL8I2bHqAzkaYiEjigJCKZzhJPZfjUW0/h5DkT3QtSCCHEsKGUWqG1XtLbY3JvVRQNpRThgE8SbNGnfz79Bh1dyQNGh+8T9HuJhvzceP+LJDNZlyIUQgghHJJkCyGGhM5kmmdXbaUiEuzzHL/XQyZnsXz9zgJGNrzYqaewWz+A3f55tFXvdjhCCFG0JMkWQgwJe1tiKAXmETY3Gmpf/3WRb1qnoeuXgILcdnT8JrdDEkKIoiVJthBiSDjalnQakOZ1g0U5k/60BWhQsndeCCH6Ikm2EGJIGF1dBihyln3Y87SGmeNGFCaoYUYpH0S+BEYYvLNRoavcDkkIIYqWLEMIIYaEcMDH6fMm8dhrm6gpD/d6TiqTJeDzcNz0sQWNTdut6NgPwdoGgctQoXeW7DAYw388+I93OwwhhCh6spIthBgyrjh9AbXlYVpiCWz7zfajWmviqQzxZIZPXnwSfm9h1w904jZn1LQKQfJWsHYU9OsLIYQoPpJkCyGGjPJwgG994C0snjqGtq4kLbEELbEEbZ1JokE/X37XmRw3fZwLke2rBFfd/11a8weEEEL0n5SLCCGGlMpIkC9csYymji427m7GsmzqKqNMG1PjWomGCl2Bzm0EazsELwdzgitxCCGEKB6SZAshhqTa8gi15RG3wwBAGVWoih+5HYYQQogiIuUiot+0llvhQgghhBCHIyvZ4qgkUhnueXEtj766ka5kmsmjqnn7qfNYNHWM26EJIYQQQhQdSbLFEaWzOb5386Ns2dtKedhPdVmI3c0d/OifT/Dh85dyzqLpbocohBhkWcvi9c17eOzVTTR1xPGYBlNHV3P2omlMqKss2ZaFQghxrCTJFkf0wprtbNnbSk15qOcXaTTkx5/z8LdHXuGUOZMI+rwuRymEGCyvbdrN/93zPPFUBo9p4POaaA27mtt5/PXNTBtTw2fediqV0ZDboQ4Z2m5FJ+8HcyzKf7q8SRGiBElNtjiip1dtJeDzHPJLwOcxsWyb9TsbXYpMCDHYXl6/gx//8wm01lSXhSgPBwj6vIT8XqqiIaqiQTbvaeGbNz1IW1fS7XCHDN35I0j+A7p+CtlX3Q5HCDEIJMkWR5SzbPpeZFFYtmyEFKIUtcYS/Pqu5wgHfQT9vd+tUkpRGQ3S0ZXkN3c9W+AIhzA7gXMzWYNOuR2NEGIQSJItjmjpjHGkMrlDjluWDcC00TWFDkkIUQBPr9xCLmcROIpysIpokHU7Gtnd3FGAyI6NtprQmZfQuU1uh4KKfh58x0HwCvDJmHohSpEk2eKITp8/mapoiNb9RlmnszlaOxNceMIsysIBlyMUQuSbbWvuf3kdkZD/qM5XSqGBx1/fPLiBHSOd3YBu/ww69iN0+5exE/90NR7lmYhRdi1G+EqUku1RQpQi+ckWRxQN+vnmlefyl4dX8Oqm3SilCPg8vPesxVx4/Cy3wxMlIGfZvLFlD4+/tomWzgS15RHOWjSVuRNHYhqyFuCGRDpDVypDddnRb2b0e0221bcOYlTHTif+DNhgVoHOQeIWdOAClFEcA42EEKVHkmxxVGrLI3zhimV0JdMkM1kqIkG8pul2WKIEJFIZfvTPJ9i4uxmPqfB6POxu7mDFhp3MnjiSL1y+jIBPXqoKTWtNf/tdKBS2bQ9KPAOmk7z5K88ANJB1Lx4hRMmTJSLRL5Ggn9ryiCTYIm9uuPcFNu5qoioapDwcJOT3UhEJUlUWYvW2em56eLnbIQ5LQb8P0zTIWUefNGeyFiMqooMY1QAE3uok2lYT2E3gOwFUhdtRCSFKmCTZQgjXNLZ3sXzjLirLQoe0iNzXteKZVVukNZwLPKbBsvmTicWPrvOF1hobzZkLpwxyZMfGCCxDlX2L/9/efYfHdV93/n+fOw2D3tl7Eyl2UaQqJUqiJdkqliw5ku1IdhJ73TayV3bW641/sdeJnyTeVZLHmyiruMSOWyzZsmS5qPdOkWIvYu8kiA5Mn/v9/TEgxQKQFAnODIDP63n4EHPvnZkz+AKDM9977vkSvRnKPoNV/Df1phaRc0rnYEWkYNbvOIBzDq+PZCfgeeCMjbsOctH0cXmOTq6eN5WnV2wmk/UJBk4+J9MVTzGsupypoxvyFN17Z+E5WHjOKY9zzrFlbzOvrt9BR3eC0fVVXDJzPA1Vqt8WkdOnJFtECiZzWvW77ki7SMmvsY3V3HzJ+Tz80hpqKqJ9JtrdiRS+c3zu5ksH/OxwMp3hO79+ibc37wUgEDBeXefz0IuruP2KOdx40YwB/xpFJD+UZItIwYxpqM61fnMul7i4LJACImAezjkcjjGN1QWOdOi67fLZeObx8MurwUFlWYRQMIBzjlgyTSKZobQkxFfvvJqJI+oKHe5Z+97v32D55j3UH1fClMn6/PzZt6mvLOOS88cXLkARGTCUZItIwUwZVc/w2goOtXdTVepDehOQBQIQmkZ7N0wcXsfYxppChzpkmRkfunwWl5w/jmff3swzKzbT3p3AORhRV8lHr5rOovPGUloSLnSoZ62pvYtX1m2nruLEawSCAY+ykhAPvbCKi2eM02y2iJySkmwRKRgz488/eBnf/PGTHGrdTnXUJxgMk86kae/aTUX5BD5z0yWFDlOAEbWVfOSq+dy5ZB7pTBbP805Zpz3QrN3ec42A13sCHY2EONjexYHWLobXFmkXFREpGoPrHVJEBpyxjTX89SeuZ8mMNrqSYZo7A8RSIZbOauOvP3E9I2orCx2iHMXMCIeCgy7BBshkszjX934zI+AZmWw2f0GJyIClmWwRKbhhNRX8yXWLuOvSH5FIBygJZQlWfhovWlbo0GQIGVVfhecddY3AcdKZLJ551KvLiIicBiXZIlIULPpBQoGRBLPbsOAULHxBoUOSIea8MY0Mqy6npTNGZVnJMfucc7R3J7j+wvOKfgXS5o5uNu9tJpP1aagqY8qoetWQixRAcb9TiMiQYWYQWYSxqNChyBBlZtxz6+V888dP0twRo7I0QjDgkUhn6IqlmDSijg9dPrvQYfbpUHs3P3pyGcs378EwckvHQ31VGR+7ej4XTB1T2ABFhhhzJytAG4AWLFjgli3TMswiInJmmtq6+P2bG3h25RaSqTR1lWW8f+F0rpw7iWg4VOjwetXU3sXXf/gEHbEE1eXRIxdvHm61GE+m+bPrF7Fk7uQCRyoyuJjZW865Bb3t00y2iIjIURqqy7lr6QL++JoL8J3LrTxa5L77u9fpjCeprSw9ZruZUVYSJhQM8IPH32TOxJEnHCMi50bxv3OISFFwzrF5zyGeXvEOTy3fxLod+/H9wXUmTORouW4ixf9ncn9LJ2t3HKC6vKTPY8LBAL5zPLdqSx4jExnaNJMtIqe0cddBvvv7N9jX0sHhEjPPjOryKB9/3wLVeooU0MbdB8Fxyosbo5Egb2zYya2XzcpTZCJDW/F/RBeRglq7fT9/89Onae7sprYiSn1VGfVVZdRWlpJIp7nvly/w4uqthQ5TZMhKZ7I4Tn1WyTOPVEY9vkXyRUm2iPQpncnyTw+/RCQcoCIaOWGmrDQSprI0wnd//zptXfECRSkytNVVluGdRou+RCrNyDot7iSSL0qyRaRPyzfvIZZMURoJ93lMOBQkm/V5ac22PEYmIofNHD+caCRMMp3p8xjnHFnfsfSCqXmMTGRoU5ItIn1atnEXAe+oGTIHqXSWVPrY5adLIiFe37Az/wGKCKFggA8vnk1Hd5JM1j9hv3OOls4444fVMnP88AJEKDI06cJHEelTIpU+0m+3rSvO3uYOkukMhhEMeAyvraC+sgzPs5POoonIuXX1/Cm0xRL8+uU1GFAWDeOZEU+mSWd9xjVW8+UPXzkguqWIDBZKskWkTyPqKlmxeS/N7TF2HGwlEPAIBQMYkPUdOw+2kc5kiUZCDBtdUehwRYYsM+O2y2ezcNpYnl6xieXv7CGT9Zk0oo7rF05n9qQRhAKBQocpMqQoyRaRPl0+ayKPvbaePYfaCAW8I7PaAAHP8CzA/tZORtVVcc38KQWMVEQAxjZW84lrF/KJawsdiYjovJGI9GlMQzWjGqpIZ316a15gBpmsT0k4qFpPERGRoyjJFpGTuu6CaZSEgqQyPpmsj3Mu16kg65PKZAkGPK6eN0W1niIiIkdRuYiInNTYYTWMqKvEDJrauokn0zggEg4ysroc52Dq6IZChykiIlJUlGSLyElNHlnHmIZq9rV0ct7YxtxMNrll1bvjKYIBj3mTRxU6TBERkaKi87siclJmxp/fcjllJWEOtXfTnUgRT6Zp7ogBcO/tVxIKqmvBuZLOZHnrnd38/o0NPPP2Zpo7uvv9OeLJNJ3xJM6demluERE5PZrJFpFTGllXyd998gO8vGY7r67bju8c8yaPYsmcSdRUlBY6vEFr1dZ9/POjLxNLpMhkfTzPMDOumDWRj1974Vl/uNm0u4kHn1/Jup0HMKCxuoJbLpvJZTMnYKexTLeIiPTNimHmwszGAD8ChgM+8IBz7p/MrBb4T2A8sB34sHOu9WSPtWDBArds2bJzG7CIyDm2aXcT3/zxk5REQpRGQke2+76juSPGZTMn8NmbLjnjx1+1dR/f/sWzBAIelaURIDej3Z1Ic8ulM7n9ijln/RpERAY7M3vLObegt33FUi6SAe51zk0HLgI+Z2YzgK8ATzvnpgBP99wWERn0fvH8SgIB75gEG8DzjLqqUl5Zt529ze1n9NhZ3+f//fZVSsJBqspKMMvNkJeWhKmtiPLIq2s50NrZHy9DRGTIKook2zm3zzm3vOfrTmA9MAq4Gfhhz2E/BD5YkABFRPKorSvOhp0Hj8wwH88zwznHGxt2ndHjb95ziM5YktKS8An7AgEP5xyvrd9xRo8tIiI5RZFkH83MxgPzgNeBYc65fZBLxIHGPu7zKTNbZmbLmpqa8harSDFyzpFMZ/D9wpeCyZmJJVMEAnbSumjPM9q7E2f0+F3x1En3e57R2hk/o8cWEZGcorrw0czKgV8CX3DOdZzuhTfOuQeAByBXk33uIhQpTs45Nuw6yOPLNvLWpt34zuEcjB9WwwcWTWfBtDFEQkX16y4nUVUWxblcWUdfi/w4H0bUVpzR4zdUl/X8jLheE3nnYHRD1Rk9toiI5BTNX10zC5FLsH/inPtVz+YDZjbCObfPzEYABwsXoUhxau2K838efJ7tB1rwDKrLS/C83Cn/A22d3P/Yq5Q+uYwvfmgx08cOK3S4chrKSsJcPH0cL6/bTl3lid1b0pksnmcsmj7ujB5/TEM144fVsOtgG9UV0WP2JVJpggHvjB9bRERyiqJcxHJTKd8D1jvn7jtq16PA3T1f3w08ku/YRIpZe3eCb/zocXYebKW2IkpNRSlez8ynmVEejVBXWYpzjm/97BnW7dhf4IjldN12xWwqohFaOmLHlP7Ek2nauuLcuWQuVWUlZ/TYZsbnbr6U0miu93k8mSaZytDSESOezPBfb76Uimjv9eAiInJ6iqWF32XAi8Bqci38AL5Kri77F8BYYCdwu3Ou5WSPpRZ+MpT83X8+y5pt+6jtZbbzeLFkCufDP372ZsqVQA0IB9u6+NGTy1i5ZS+eZzgHVWUlfPiKOVw+a+JZP357d4Ln3t7MS2u3k85kmTVhONcumMbohuqzD15EZAg4WQu/okiy+5OSbBkq9rV08OX/9xi1ldHTXjikuT3G3dcuYOn8qec4OulPLR0xmtq7iISCjG2swfO0UIyISDE4WZJdNDXZIvLePLNiM8B7WpkvWhLisdfWcc28KVrRr4A640n2NXcAMKKu8pSlGbWVpad1tkJERIqHkmyRAWrNtn1ES0KnPvAo0XCQ5o4YHbHkGdfzypk70NrJwy+t5pV1Ozj8Gcc5uHj6OG65bBbDz7BbSG/66hwiIiL5oSRbZIBKpDME3mMSZWZ4ZqTSmXMUlfRl58FWvvnjp4inUlSXR4+05sv6Pi+v3c7yzbv5y48uZdywmjN+ju5EiseXbeTJ5Zvo6ErQWFPO+xdN58o5kwgFAv31UkRE5DQURXcREXnvopEQWd8/9YFHcc7h+46SsD5f51M6m+V//+I5Mn6WusqyY3pfBzyPuqpSsr7j2794lnQ2e0bP0RVP8o3/eIKHXliFc466qlK6Ekl+8Ic3+cdfvkgm+95+VgrBOUd3IkV7d4J05sy+DyIixUJ/aUUGqAVTx/CrF1f1ujR2X2LJNCPrK9VdJM9Wbd1HS1e8157Xh1WURmjuiLFyy14WTB1zzD4/8TIkn4TAaKz0DswrP+H+j722jt2H2mmoLjuyrTQSJhoOsWLzHl5fv4NLZ07ovxfVj2KJFK+t38FvXltHU1sXZrnVLi+ZMY73LZjGxBF1hQ5RROQ900y2yAC1eNZEMHtPy6cnUxluvGiGanXz7MVVWwmcRkeQYMB4YdXWY7a59Bro+t+Q2QCJx3Bd3znhfr7veHL5O73W2ZsZ0UiQPyzbeOYv4Bzac6idv/jub/n+42/SlUgeucizsizCy2u38//9++P853NvM9g6YYnI4KeZbJEBqr6qjEXnjeX19Tuoqyo75fGd8STl0QgLpo055bHSv9pjidOqiQ4GArTHEsdsc+mtgAOvClwZpNeecL9kOkMilaY82vtZjUgoyKH27jOK/Vxq6Yjx1z95ikQqTWVpCU1tXWzpaCbrHKWREMNqKiiPhHnklTWEgwFuuWxWoUMWETltmskWGcD+7PpFjGmsobkjdtKZvo5YAhx8+cNXEg2/t44kcvaqy6OnVWudzmSpPm422kKTAQ/8NvCbITT7hPtFQkFKI2FSfdQxJ9IZGqpPLDEptEdeXUNHLEFJOMSGXQc50NoJQNDziCfTbNnbzMH2LmoqSnn45TW0dsULHLGIyOlTki0ygEUjIf7yo9cwb9JIWjrjNHfESKYzZH2fdCZLW1duW1VpCV+/632qbS2QxbMmnlZZj+87Fs+edMw2C82Aiq9AaC5Eb8HKP3/C/TzPuOaCKbT3koQ650gkM1x/4XlnHP+5EEukeH7lVqrLo+xqaiOTzRIOBfA8wwyCAY9wMMD+lk7SmSy+c7y0euupH1hEpEioXERkgCsrCXPv7Vey51A7T694h9fX76Q7niYcCjBz/HCuXzidGWOHaZXAApo1YQS1laV0dCepLOv9otOO7gQ1FVHmTBx5wj4vshAiC0/6HDcumsHKLXvZvr+VitIw4WCAeCpDLJHiwmljuDDPZULt3QleWLWFp1e8Q3t3kvJomCVzJ3Pl7EnUVpbyzp5D+M7hnKMjliAUPLGcxgwwaO7opraylJfXbufGi8/P6+uQ/Nrb3M7WfS0455g4oo5R9VWFDknkjGlZdRGRPNjd1MY3f/IU3fEkVeVRgoHcicRM1qe9K0FpSYivfWwpYxqqz/g54sk0T694h8eXbaS9O8GwmgpuWDSdS2dOOPJ8+bBl7yH+9ufPEk+mKI3mEv50xqc7kSIcDPCl26+gI5bknx99hZJQkE27mwgFe48vk/UpKwkztrGaYCDAdz5/S95eh+RPa1ec+x99hXU7Dxyz/bwxDXz2xku14qkUrZMtq64kW0QkT5rau3j0lbW8uHobjsPvvcbiWRO46eLzi7Ju+r1q7YrzFw88hu/8XltFxpIpMhmfP156Af/++DLKo2HWbN9PKBigt3MtqUyW+soy6qrKqCmP8vefvOHcvwjJq+5Eiq/94A80tXdRUxE90v3IOUdbV+4Mz19/4noq1HpUitDJkmyVi4iI5ElDVTl/ev0i7lwyj/09F/kNq6mg7D30Oi92L67eSjyV7rMneGkkTHMyxrb9rUf6YVdEI3TFUyfMZjsHOKirLCOeTBddXbn0j+dWbuFAayf11cd2STIzaiqiNLV188yKd7j5kpkFilDkzCjJFhHJs9KS8KC9CPXJtzZRVnLyDjaVpRFeXrONy2aO5/lVWxnTUM3G3U2k0lmCQQ8zI5v1yfqOYTXlREIBEqk0Vx53UagMDo8v20hZac8HTZeCzKbc18EpYBEqSyM8vmzjCUn2ofZuXl67jd2H2omGQ1w4bQznjxuu60+kaCjJFhGRftPenaCqj4s7DwsFA7R3J/jAwum8uXEXyUyG88Y0crCti+aObnzfJxoJMby2gopohJbOGDdfMlN1uYNUa2eM6vJo7obfBn4czOW+DgwjHApwqL0b5xxmhnOOXzy/kt+8tg6AgGdkfcezb29meG0FX/7wEhoHQemVDHxq4SciIv2mLBomnfVPekwm6xMMeIyoq+QvP3oNJaEQnfEk9VWlzJk0knmTRzF1dD3O5Wq8r11wHrcvnpOnVyD5Vl4aebePvFcBFgJC4FUCkM74lJVEjtRq/+ql1Tzyylqqy0uoqyylujxKXc9KoQfbuvjmj5+kK54s0KsReZeSbBER6TdXzp5EVzx10mM6YgkuO38CZsbYxhr+/pMf4M4l8wgGArR0xGjtitPWlWD+lFF87WNLuWvpBf1SAuDSG/C7f4SfeA7nTv5BYKBzmR241EqcSxc6lFNaOn8qnbGepNiiEJqT6wtvudntzliCa+ZPAaArnuTRV9dSUxEl4J2YwlSXR2npjPHimm35Cl+kTyoXERE5S5msz9Z9zSRSGRqryxleW1HokArmqrmT+cObG4gn00QjJ9ZmJ9MZDLj2qIsYK0pL+MCi6Vx34TQ6Ykky2SzlJZFe73+mXGYzruNr4LJAFpfdj5Xd0W+P/17tampjf0sn4WCAqaMb+vW1+omXoes+wEF4LlR87cgscDG6au5knly+ifauBJVlkWNi7ehOUhaNsHT+VABWbN5DtudMSF/KSsI8vmyjLpQ9h/zuH0PqZSj9E7zIhYUOp2gpyRYROUPOOZ5a/g4PvbiKeDKNGWR9x9RR9fzJdQsZfRY9rweqhupyvvihxdz30AvEk2kqyyIEAwEyWZ+O7gRmxmdvvJSxjdUn3DfgedQcrs3tZy61NndRXWA4+DFIvQoFSLJ3NbXxb799ja37W/B6kknPM6678Dxuu3x2//QzTz6dK7mwSki9Da4z93WRqi6P8rWPXsM//PIF9rV0kvVzZxmCnkdjz8/T4Xr85o4Y/ilaD0dCuTMicm44vwviD4KFIf4TUJLdJyXZIiJn6OGX1/DQC6uoLItQU5FLDp1zbNnXwtf/4wm+cde1Q3LFutkTR/KtP72ex5dt5PlVW0lnEgQ8j8tmTeT6C6cxtrEm7zFZcCwOD/xOcDEIXZT3GPY2t/ON/3iCdCZL7VH9oDPZLI++soaWjhifufHis591Ds+G9HJwcQiMASs79X0KbGRdFX//yRtYv/Mg7+w5hHOOKaPqmX7carUV0cgpvz/prE9ZdPC0xSw6VgbhRZB6AyJXFzqaoqbFaEREzsCh9m6+cP8jVJWV9Dr72NoZZ9aE4Xz5w0sKEF3x8H1HMpMhHAz0WkOb11gST0HyOQhMwMo+gtm5mTXvy30PPc+KzXuprTzxeX3naO2I8/W73sfkUfVn9TzOOUi9ivNbscilmFd9Vo9XTJo7urnnXx6hpjzaZ51+c0eMGy+ewR9dMTe/wQ0hudwxjZk+zGgxGhEZdJxL4jr/AbK7sYovYsH89lB+Zd12nHN9nt6vKith1dZ9tHbGqKkYuq3nPM+Ihvuv3vhseCXXQMk1BXnu9u4EKzbvebdV3XE8M8zgmbc3n3WSbWYQuaTXFTQHurrKMi47fzwvrN5GfVXpCbPa3YkU4WCAq+ZOLlCEQ0Pu+64E+1SUZEtR2NvcztMrNrN5zyEqSiMsmTOZOZNG9k99ogxO6fWQeh0AF38Yq/hSXp9+76GOk/58ep7heR6tXfEhnWRLTmtnrOdnou/UNxIOsudQex6jGpg+cd1COuMpVm7ZixcwouEgWd8RT6YpjYT4yh1X0VClPtlSeEqypeBeXL2Vf/vda/gOSsJBMgd9Vmzew/njh3PvbVcQCenHVHoRHAdebW7BitCivD99VVkJmZP0g3bOHVlURaS0JIzvuyMLqvQmk81SWVaS58gGnkgoyL23XcH6nQd44q1N7G5qoyQS4rKZE7js/PFUlOp7KMVB2YsU1IHWTv7td69TVhImfFQy7Zxj9bZ9/PrlNfzRlXMLF6AULfNqoOZfwCUxL/+dEy6aMY7fvrG+z6SpO5FiVH0Vw2uGbjs/eVdDVRljG2vY19pBRbT3FTEzWceVc7R0/OnwPOP88cM5f/zwQoci0iedi5eCem7lFrK+f0yCDbl6r+ryKE++tYl0Jlug6KTYmUUKkmADjB9Ww7xJo2juiHH8BeTJdIZEKsMdV84t6v7Ekj9mxh1L5pJKZUimM8fsc87R0hFjdH0VsyeOKFCEItLflGRLQW3b10Ik3PsJlXAwQCqTpSOWyHNUIqdmZnzu5ktZOG0MLZ1xmtq7OdTeTXN7N8lUls/ddClzJ48qdJhSRGZNGMFnbrqERCpDc0eMls4Yh9q7aemMM2F4LV+54ypCgUChwxwwnHO5RYYSz+CSr+Oc/lZIcVG5iBRUdXm0z5lq3/dxzhVNZwKR45WEg9xz62L2Nrez/J09xJJpRtRWsGDqGNViS68uPX8CcyeN4vX1O9lxsJVoOMj8KaOZMqpeZz3eA+d34Dr/LncBNAAGFsGV34MXyf81GiK9UZItBbV49kReWrO117rWtq4EF0wdTWmJ2gRJcRtZV8XIuqG36IycmbKSMFfNU4u5M+Wcw3V+G9IbwKuHw387XAK6vo0LfBsLTihskCKoXEQKbPrYRi6aPo5D7d0kUmkAsr5Pc3uMaCSkix6HmJaOGNv3t9DaqSWRRaQP2e2QXgte3bsJNoCVgAOXeKxgoYkcTTPZedYZT7Js4y6aO2I0VpezYIjP1JoZn77xEiaNrOM3r62juSOGGVx8/jg+dNlshteqM8NQsHnPIX727Ao27m4i4BlZ3zFz3DDuWDKP8cNrCx2eiBST7C5y5SG9lNd4ZUeVkIgUlpZVz6M3N+7knx99hUwmiwMMIxIO8oVbL2fWBF1R7vuOWDK3Wtfx3UZk8Fq34wB/+/Nn8DyoLC3BzPCdo707QcCM//mRa856BTwRGTxcajmu41sQqDtxp98BwQl4Vd/Kf2AyJJ1sWXWVi+TJ7qY2vvPrlykJBamrKqO+qoy6qlICnvF/HnqepvauQodYcJ5nlEcjSrCHkKzv8y+/eYVwKEBVWfRIXb5nRk157va/PvbqCS3yRGQIC80ELwr+cWVlzoGLQ+S6wsQlchwl2Xny5FubcM6d0K4uGgmRyWZ5fuXWAkUmUjjrdhygvStOWR8lU+XRMPtbO9m8tznPkYlIsTILY+VfApKQPZRLtv128JsgfBEWuaTQIYoAqsnOm427m/ps6RUJBdm462CeIxIpvIOtXfgnmaU2M4zcyqBTVDIy6KUzWTbtbiKeSjO2sYbG6vJChyRFysJzoOo+XOJ3PRdBVmEl10J4EWZKbaQ46CcxTyqiEQ60dRLlxEQ7nfWpKCspQFQihRUJB8ml0SdhuQ+iMri9vXkP9z/2KrFkGgN851g4bQyf+sDFlPSxYJUMbRYcjZV/qtBhiPRJ71x5cvX8Kax7+AAuemw/aOcc2azPFbMmFjA6Gar2t3SycddBkpkMlaUlzJowos/SjXPh/PHDMcvVZge8E6vXMlkfzzxmjBuWt5gk/7bvb+G+X75ASThIbUUUyCXZr63fiQPuueXywgYoInIGlGTnyQVTR3P+hOGs2bafitIwkVCQRCpDVzzJhdPGqruI5NXOg638x1PLWb/zADiHDwTM8DyPxbMmcMeSeXlJtmvKoyyZO5mnVrxDfWXpMR9Afd/R2hnjxovPz2viL/n3uzc25FZ3PaqkzjOjrqqUNzfu4mBbl0pHRGTAUZKdJ6FAgC/ddgVPvLWJP7y5keb2GPVVZXzo8tlcPW8KnqfldCU/Nu85xN/89Gmyzqe2InpMYpvJ+jzz9mY27T7E1z52DeXRyDmP54+vuYBYMs1r63ZgBoGAkcnm6rSXzJ3C7YvnnPMYpLDW7ThAefTED1KeGZ4ZOw60KskWkQFHSXYehUNBbrhoBjdcNKPXZcRFzrV4Ks23H3wu15M6Gj1hfzDgUV9Vxt7mdr73+9e559bF5zymUDDA52++lJsunsFLa7YdWajp0vPHM7qh+pw/vxReeTTMoY5uwr1fG05pHxeNi4gUMyXZBaIEWwrhzQ276E6kqKssPelx1eVR3ty4m6b2Lhqq8jODOLaxho9cVZOX55LicvW8yfzg8TcpjYSOeW+MJ9NEIyGmjmkoYHQiImdGfbJFhpAn3tpIJBQ45XGeZzgcr63fkYeoZKhbPHsSU0c3cqgjRnciRSqdoaUjRiKV4dM3XEwocOqfWRGRYqOZbJEhpKm9+7Tb4QU8jwOtWol0sOtOpFi/8wCZrM+kEXU0FKD2ORIK8j/uvIoXVm3h6RWb6YqnuHjGeN6/8DzGD6/NezwiIv1BSbbIEBL0DP80Vyj3ndMS94OYc47fv7GBXzy/kqzvg+VWpb5kxnj+7PqFeR/7SCjI0gumsfSCaXl9XhGRc0XlIiJDyIzxw+mKJ0/7+PNGqxZ2sHp13Q5+/PRySktC1FaWUltRSk15lJfWbOMHTywrdHgiIgOekmyRIeTaC6bhnMOdZClzgGQ6QzQcYt7kUXmKTPLJOceDL6ykrCREKPhuvbPnGXWVUV5avZWWjlgBIxQRGfiUZIsMIZNG1jF/ymia22N9JtqZbJaO7gQfuWreMQmYDB5d8SRNbd3HLP5ymOd5mBnbD7QUIDIRkcFDSbbIEGJmfPamS5k7eSTNHTHauxNHku1M1qelI0Z7d5KPXjWfK+dMKnC0cq4EA4EjNdh9Od0LZEVEpHd6FxUZYkrCQf7bbVewets+fvf6Btbu2A/kEq8r50xi6QVTGduoftWDWTQSYs7EEazato/aimN7pidTGcKhAFNG1RcoOhGRwUFJ9gCwfe0umve2MPeqmQTUL1b6QcDzmDtpFHMnjSKT9UlnskRCQTyvfxdJcn43LvEEFhyPhef162PL2blzyTw27mqipSNOdXkEM6MrniKZzvCZGy9RZxkRkbOkd9E8cs7xzp5DPLdyC01tXQyvrWDJ3MlMGF7b5wqQbc0d/K8//zeSyQx/Gkty1c0L8xy1DHbBgEcwcG4qx1z8QYg/iCMKtQ9gnnoeF4vRDdV84+5refCFlby1aTe+c0wcUcdti2czZ+LIQocng0Q8lWbNtv3EkilG1VUxaWSdVjyWIUNJdp5kfZ8HfvsaL6/ZjhmEQwE27DrIsyu3sHT+VO5aesEJbzyvrdvB937/OtvGVZFNZ7j/rbVsCzvuWrpAF6TJwOA1AGHwynP/S1EZVV/FF25dTCbr4/u+Zq+lX720Zhvf/8MbpDPZI/X/Y4dV88UPLaahKv+LHonkm95R8+QPb27khdVbqa8se/eUfBR83+fxZRsYN6zmmAvNVm/bx/995CXKomFmzpuAn/WxoMfTK94hnfX59A0XF+iViJw+K3k/hKaBV4d5+qNarIIBD87R2QwZmtZu38+//uYVyksjVJRGgNzZ3D2H2vnWT5/m7z95gyaLZNDTu2oeZLI+v3ltHVWlJSfUvHqeR1lJmEdeWXNMS7WHXlhFKBSgJBzCC3gEw0ECnkddZRkvrdlGU7uWu5biZ2ZYcDLm6ULKocg5hx97CL/1v+B3/+iU/dll8PjVS6sJBgPHdKkxM6rLozS1d7N8854CRieSH0qy86ClM0YskSIS7v3EQTQSoqmtm65ECoB0JsvmvYeoiEZOONbzDDPYsrf5nMYsInLWsnsg9lNwcYj/GjIbCx2R5IHvO9bvPEhl6Yl/wwA8y52tFRnslGTngWd2ylX2HI5AT022mWFmJ+1hG/A0dCJS5KwECICLAQZWeqp7yCBgBgHP8Pv4I+Y7R1ilIjIEKFPLg7rKUobVVBBLpnvd3xlPMmVUPaUluQvDggGP+ZNH0dYdP+HYdDaLZx4zxg07pzGLiJwtC9RDxVchfBlU3IsFxxY6pAHL+V24zGacX/ylgmbGwvPG0t6VOGFfbrLJWDB1TP4DA5zzc99LlS5JHijJzgMz48NXzCGWSJPOZI/Zl0pnSKWz3LZ4zjHbb79iDqFAgNau+JE3g3gyTVtngtsWz6asRJ0aRKT4eZF5eBVfwItcUuhQBiyX2Ylr+yyu7au4ts/hsnsLHdIp3XLpTELBwAmryja3x5gxbhjTxzbmPSbnt+Da/hzXcheu42s4l8x7DDK0KMnOk4XnjeUT111IdyJNc3s3TW1dNLfHSKQyfPqGi5k5fvgxx49pqObrd72PGWMaaemM09IRoyQc4r/ccBE3LJpeoFchIiL55uIPgx+DQA34nbj4bwod0imNbqjmax9bypiGalo747R1xemKp7hm/hTuve2KgvTKdoknILsbvHpIr4HU63mPQYYWtfDLo6Xzp3LR9HEsf2c3rZ1x6qvKmD951JEykeONbazhK3deTXciRSqTpbqsRE38RUSGGosAh8+COgZKz/kJw2v55sev42BbF7FEiobq8gKfhQ2R+/5lAWOgfB9l4FKSnWcV0QhXzJ506gOPUlYSpuwcxSMiIsXNorfj0qshuw8CY7DSDxY6pPeksbo4euRbyfW4zAZIr4WSpRC+sNAhySCnJFtERKSIWaAOqr8DrgusHDNVep4J80qxyv9Z6DDOmEuvg+wuCF+OeerUMxAoyRYRESlyZh5YZaHDkAJx2UO4jr/K9Zwv2YaVf7rQIclp0MdhERERkWJmHrmUzSNXWy4DgWayRURERIqYebVQ9beQ3ata8gFESbaIiIhIkbPgBAhOKHQY8h4oyRaR09bU3sWGnQfxnWPKqHpG1lUVLJb27gRrtu8nnckytrGaCcNr1eJSRESKhpJsETmldCbL9x9/kxdXb4XDyxGbMWfiSD570yV57X3r+44HX1jJY6+vz60k17NM86SRdXzh1supqdBV9yIiUni68FFETunfn3iT51duoaY8Sl1VGXVVZdRWRHl7yx7+6eEXjyybnA+PvrqWR15ZQ1VphLrK0lwslVG27WvmWz97mnQ2e+oHEREROceUZIvISbV0xHhh1VbqKqN43rvlGGZGXWUp63YcYNv+lrzEkkhlePTVtVSXRwkE3n37MjNqKkvZ39LJqi378hKLiIjIySjJFpGT2rD7IA7wvBPfLswM3/dZu31/XmLZfqCFjO8TCgZ63W8Gb23anZdYRERETmZAJNlmdp2ZbTSzzWb2lULHIzKUOAcnv5zQ8PNULeL77uSxmOHnsXRFRESkL0WfZJtZAPhn4HpgBnCnmc0obFQiQ8fUUfU4cgnu8ZxzeJ4xfWxjXmI53EEk00fdte875k4emZdYRERETqbok2xgIbDZObfVOZcCfg7cXOCYRIaMhupyFp03lpaO2DEXODrnaOmIM2lEHVNG1ecllmgkxHUXnkdrZ/yEpL+tK059ZSnzJ4/KSywiIiInMxBa+I0Cdh11ezewqECxiAxJn3z/RWSyPss27cY5h8MR8DymjK7ni7cuzmt/6tsun013IsWzb2/Gdw7nO4IBj+G1FXz5w0sIhwbC25qIiAx2A+GvUW9/vY+ZwjKzTwGfAhg7dmw+YhIZUkrCQb74ocXsampj3Y4D+L7PlFENTBpZl/cFYIIBjz+9biE3XjSDVVv3kkxnmTC8hvPGDDum+4mIiEghDYQkezcw5qjbo4G9Rx/gnHsAeABgwYIFuupJ5BwZ01DNmIbqQocBQGN1OdfMn1roMERERHo1EGqy3wSmmNkEMwsDdwCPFjgmEREREZE+Ff1MtnMuY2afBx4HAsD3nXNrCxyWiIiIiEifij7JBnDO/Q74XaHjEBERERE5HQOhXEREREREZEBRki0iIiIi0s+UZIuIiIiI9DMl2SIiIiIi/UxJtoiIiIhIP1OSLSIiIiLSz5Rki4iIiIj0MyXZIiIiIiL9TEm2iIiIiEg/U5ItIiIiItLPlGSLiIiIiPQzc84VOoZ+ZWZNwI5Cx3EO1AOHCh2E9CuN6eCjMR18NKaDj8Z08CnkmI5zzjX0tmPQJdmDlZktc84tKHQc0n80poOPxnTw0ZgOPhrTwadYx1TlIiIiIiIi/UxJtoiIiIhIP1OSPXA8UOgApN9pTAcfjengozEdfDSmg09RjqlqskVERERE+plmskVERERE+pmS7CJkZmPM7FkzW29ma83snp7ttWb2pJm90/N/TaFjldNjZiVm9oaZrewZ02/0bNeYDmBmFjCzFWb2WM9tjecAZ2bbzWy1mb1tZst6tmlcBzAzqzazh8xsQ8/f1Ys1pgOXmU3r+f08/K/DzL5QjGOqJLs4ZYB7nXPTgYuAz5nZDOArwNPOuSnA0z23ZWBIAlc55+YAc4HrzOwiNKYD3T3A+qNuazwHhyXOublHtQTTuA5s/wT8wTl3HjCH3O+sxnSAcs5t7Pn9nAtcAMSAhynCMVWSXYScc/ucc8t7vu4k94YwCrgZ+GHPYT8EPliQAOU9czldPTdDPf8cGtMBy8xGAx8AvnvUZo3n4KRxHaDMrBJYDHwPwDmXcs61oTEdLK4GtjjndlCEY6oku8iZ2XhgHvA6MMw5tw9yiTjQWMDQ5D3qKS14GzgIPOmc05gObP8I/AXgH7VN4znwOeAJM3vLzD7Vs03jOnBNBJqAH/SUdn3XzMrQmA4WdwA/6/m66MZUSXYRM7Ny4JfAF5xzHYWOR86Ocy7bc3prNLDQzGYWOCQ5Q2Z2A3DQOfdWoWORfnepc24+cD25Ur3FhQ5IzkoQmA/c75ybB3RTBGUEcvbMLAzcBDxY6Fj6oiS7SJlZiFyC/RPn3K96Nh8wsxE9+0eQmxGVAabnVOVzwHVoTAeqS4GbzGw78HPgKjP7MRrPAc85t7fn/4Pk6jwXonEdyHYDu3vOHAI8RC7p1pgOfNcDy51zB3puF92YKskuQmZm5OrH1jvn7jtq16PA3T1f3w08ku/Y5MyYWYOZVfd8HQWuATagMR2QnHP/wzk32jk3ntzpymeccx9D4zmgmVmZmVUc/hp4H7AGjeuA5ZzbD+wys2k9m64G1qExHQzu5N1SESjCMdViNEXIzC4DXgRW826951fJ1WX/AhgL7ARud861FCRIeU/MbDa5CzEC5D7c/sI597/MrA6N6YBmZlcCX3LO3aDxHNjMbCK52WvIlRn81Dn3NxrXgc3M5pK7QDkMbAU+Qc/7MBrTAcnMSoFdwETnXHvPtqL7PVWSLSIiIiLSz1QuIiIiIiLSz5Rki4iIiIj0MyXZIiIiIiL9TEm2iIiIiEg/U5ItIiIiItLPlGSLiAxSPUtIOzO779RHi4hIf1ILPxGRQahn0aP9QCW5lc9GOecyhY1KRGTo0Ey2iMjgdAu5BPt3QCNwXWHDEREZWpRki4gMTncDrcDHgThw1/EHmNmdZrbBzBJmttrMbjKz58zsueOOqzez+81sj5kle+7zqXy8CBGRgSpY6ABERKR/mdlI4BrgAedck5n9GrjVzGqcc609xywFfgI8CtwL1AP/CJQAm456rErgZSAKfB3YBlwL3G9mEefcd/L0skREBhQl2SIig88fkztT+aOe2z8E7gT+CPjXnm3fANYBt7iei3PMbDXwFkcl2cA9wDhglnPunZ5tT5lZNfBXZna/ar1FRE6kchERkcHnLuAd59yrPbefAvb2bMfMAsAC4JfuqKvfnXPLyc1UH+064HVgm5kFD/8DHgfqgBnn9JWIiAxQmskWERlEzOxCconv3/XMNh/2K+DzZjYVaAdC5LqOHO/AcbcbgclAuo+nrDurgEVEBikl2SIig8vdPf//955/x7sL+CtySXNjL/uHATuPut1MLhm/p4/n23hmYYqIDG7qky0iMkiYWZhcWchm4Cu9HPIPQC0wntzFjJXkaq0P12RfACwDnnfOXdmz7evAfwWmO+d6m/kWEZFeKMkWERkkzOxW4JfAx51zP+xl/6eB+4GryJ3JfAJ4BHiAXHeRr5PrIrLeOXdVz32qgNfIXcPzD+RmrsuA84DLnXM3n9tXJSIyMOnCRxGRweNuoBN4sI/9PyPXM/tu59yTwEeB6cDD5EpL7iW3SmT74Ts459qBS8gtavPfyV3w+H3gZuDZc/IqREQGAc1ki4gIAGY2mlypyd84575Z6HhERAYyJdkiIkOQmUWB+8i19zsETAT+gtyFj+c75/YVMDwRkQFP3UVERIamLDAc+L/k2vB1Ay8CtyvBFhE5e5rJFhERERHpZ7rwUURERESknynJFhERERHpZ0qyRURERET6mZJsEREREZF+piRbRERERKSfKckWEREREeln/z+1iKz5T9MdZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "# we use 'area' to highlight the persons who have higher income\n",
    "area=np.pi*(x[:,1]**2)\n",
    "\n",
    "# Create Age-Score Plot\n",
    "plt.scatter(x[:,0],x[:,2],s=area/300,c=labels.astype(float),alpha=0.7)\n",
    "\n",
    "plt.xlabel('Age',fontsize=16)\n",
    "plt.ylabel('Score',fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d095811",
   "metadata": {},
   "source": [
    "# Plot Describe:\n",
    "### English Description\n",
    "## It can be seen in the Plot that most of the high spending scores were given to people under 30 years old (excluding income!)\n",
    "## Between the ages of 30 and 50, the scores of spending are average and low\n",
    "## People aged 50 and above also mostly have average spending scores\n",
    "## Of course, there is a defect in the diagram: the problem of mixing two clusters between the ages of 40 and 60.\n",
    "### But In general, the clustering is done well\n",
    "# \n",
    "### Persian Description\n",
    "## می شود در نمودار دید که اکثر نمرات خرج کرد بالا به افراد زیر 30 سال تعلق گرفته (فارغ از درآمد!)\n",
    "## بین سنین 30 تا 50 سالگی نمرات خرج کرد متوسط و پایین است\n",
    "## افراد با سنین 50 به بالا نیز بیشتر دارای نمرات متوسط خرج کرد می شوند\n",
    "## البته نقصی در نمودار به چشم می آید :  مشکل قاطی شدن دو کلاستر در سن 40 تا 60 \n",
    "### میتوان به طور کلی گفت که کلاسترینگ خوب انجام شده "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c3ae812",
   "metadata": {},
   "source": [
    "# \n",
    "# \n",
    "# \n",
    "# Create 3D Plot\n",
    "## Get a better and more accurate view of the Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3cd09a1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mr.Programmer\\AppData\\Local\\Temp\\ipykernel_7808\\1922838910.py:4: MatplotlibDeprecationWarning: Axes3D(fig) adding itself to the figure is deprecated since 3.4. Pass the keyword argument auto_add_to_figure=False and use fig.add_axes(ax) to suppress this warning. The default value of auto_add_to_figure will change to False in mpl3.5 and True values will no longer work in 3.6.  This is consistent with other Axes classes.\n",
      "  ax=d3(fig,rect=[0,0,1,1],elev=20,azim=200)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D as d3\n",
    "fig=plt.figure(1,figsize=(8,6))\n",
    "plt.clf()\n",
    "ax=d3(fig,rect=[0,0,1,1],elev=20,azim=200)\n",
    "plt.cla()\n",
    "ax.set_xlabel('Age')\n",
    "ax.set_ylabel('Score')\n",
    "ax.set_zlabel('Income')\n",
    "ax.scatter(x[:,0],x[:,2],x[:,1],c=labels.astype(float))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9782be3c",
   "metadata": {},
   "source": [
    "## Author : HoomanKaseban\n",
    "#### Note:All this project is built and planned by Hooman Kaseban. No piece of code has been taken from any site or person.\n",
    "#### Credit:All credits of this code are for Hooman Kaseban and are free for public use.\n",
    "# The END"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}