FEATURE_TRANSFORMATION.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "syWqj-Xm4GsU"
   },
   "source": [
    "#**Feature Transformation**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1XUjl5_MN19V"
   },
   "source": [
    "Consider a machine learning model whose task is to decide whether a credit card transaction is fraudulent or not. Based on your application background knowledge and data analysis, you might decide which data fields (or features) are important to include in the input data. For example, transaction amount, merchant name, address, and credit card owner's address are important to provide to the learning process. On the other hand, a randomly generated transaction ID carries no information (if we know that it really is random), and is not useful.\n",
    "\n",
    "Once you have decided on which fields to include, you transform these features to help the learning process. Transformations add background experience to the input data, enabling the machine learning model to benefit from this experience. For example, the following merchant address is represented as a string:\n",
    "\n",
    "\"123 Main Street, Seattle, WA 98101\"\n",
    "\n",
    "By itself, the address has limited expressive power – it is useful only for learning patterns associated with that exact address. Breaking it up into constituent parts, however, can create additional features like \"Address\" (123 Main Street), \"City\" (Seattle), \"State\" (WA) and \"Zip\" (98101). Now, the learning algorithm can group more disparate transactions together, and discover broader patterns – perhaps some merchant zip codes experience more fraudulent activity than others.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Pn3s0Nm2OXX-"
   },
   "source": [
    "Feature Engineer/transformation is the process of determining which features might be useful in training a model, and then creating those features by transforming raw data found in log files and other sources."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rUbo2iz-RG8b"
   },
   "source": [
    "We will talk about the following Feature Transformation techniques:\n",
    "1. Log Transformation\n",
    "2. Power Transformation: Box-Cox Transformation\n",
    "3. Yeo-Johnson Transformation\n",
    "4. Polynomial Transformation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EZkUGYHDRuMi"
   },
   "source": [
    "#**Log Transformation**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3z4o2tjfeoYC"
   },
   "source": [
    "![image.png](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mY_lydi8TRPp"
   },
   "source": [
    "Quite often data arising in real studies are so skewed that standard statistical analyses of these data yield invalid results. Many methods have been developed to test the normality assumption of observed data. When the distribution of the continuous data is non-normal, transformations of data are applied to make the data as \"normal\" as possible and, thus, increase the validity of the associated statistical analyses. \n",
    "\n",
    "The log transformation is, arguably, the most popular among the different types of transformations used to transform skewed data to approximately conform to normality.\n",
    "\n",
    "Log transforms are useful when applied to skewed distributions as they tend to expand the values which fall in the range of lower magnitudes and tend to compress or reduce the values which fall in the range of higher magnitudes. This tends to make the skewed distribution as normal-like as possible. \n",
    "\n",
    "We can use it (natural logarithmic ln or log base 10) to make extremely skewed distributions less skewed.\n",
    "\n",
    "note that this function is defined only for strictly ***positive numbers.***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kSy8kQFoTtxA"
   },
   "source": [
    "Let's see an example below:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "-1cffHuhVQBW"
   },
   "outputs": [],
   "source": [
    "#import the required libraries\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.stats as spstats\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "tGugA97gl5C3"
   },
   "outputs": [],
   "source": [
    "fcc=pd.read_csv('C:/Users/Aabha Gupta/Downloads/workbench-dataset/fcc_2016.csv',encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 428
    },
    "id": "hjfgLkU1VHjh",
    "outputId": "6b6e38ea-0046-40fb-fd65-1fcd12859254"
   },
   "outputs": [
    {
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID.x</th>\n",
       "      <th>Age</th>\n",
       "      <th>ChildrenNumber</th>\n",
       "      <th>CountryCitizen</th>\n",
       "      <th>EmploymentField</th>\n",
       "      <th>EmploymentStatus</th>\n",
       "      <th>ExpectedEarning</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Income</th>\n",
       "      <th>JobPref</th>\n",
       "      <th>JobRoleInterest</th>\n",
       "      <th>LanguageAtHome</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>SchoolDegree</th>\n",
       "      <th>StudentDebtOwe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cef35615d61b202f1dc794ef2746df14</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>office and administrative support</td>\n",
       "      <td>Employed for wages</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>freelance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>English</td>\n",
       "      <td>married or domestic partnership</td>\n",
       "      <td>some college credit, no degree</td>\n",
       "      <td>20000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>323e5a113644d18185c743c241407754</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>food and beverage</td>\n",
       "      <td>Employed for wages</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>male</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>work for a startup</td>\n",
       "      <td>Front-End Web Developer</td>\n",
       "      <td>English</td>\n",
       "      <td>NaN</td>\n",
       "      <td>some college credit, no degree</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b29a1027e5cd062e654a63764157461d</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>finance</td>\n",
       "      <td>Employed for wages</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>start your own business</td>\n",
       "      <td>NaN</td>\n",
       "      <td>English</td>\n",
       "      <td>NaN</td>\n",
       "      <td>high school diploma or equivalent (GED)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04a11e4bcb573a1261eb0d9948d32637</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>arts, entertainment, sports, or media</td>\n",
       "      <td>Employed for wages</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>female</td>\n",
       "      <td>43000.0</td>\n",
       "      <td>work for a startup</td>\n",
       "      <td>Front-End Web Developer</td>\n",
       "      <td>English</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>7000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9368291c93d5d5f5c8cdb1a575e18bec</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>education</td>\n",
       "      <td>Employed for wages</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>female</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>work for a medium-sized company</td>\n",
       "      <td>Full-Stack Web Developer</td>\n",
       "      <td>Khmer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>some college credit, no degree</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                               ID.x   Age  ChildrenNumber  \\\n",
       "0  cef35615d61b202f1dc794ef2746df14  28.0             NaN   \n",
       "1  323e5a113644d18185c743c241407754  22.0             NaN   \n",
       "2  b29a1027e5cd062e654a63764157461d  19.0             NaN   \n",
       "3  04a11e4bcb573a1261eb0d9948d32637  26.0             NaN   \n",
       "4  9368291c93d5d5f5c8cdb1a575e18bec  20.0             NaN   \n",
       "\n",
       "             CountryCitizen                        EmploymentField  \\\n",
       "0  United States of America      office and administrative support   \n",
       "1  United States of America                      food and beverage   \n",
       "2  United States of America                                finance   \n",
       "3  United States of America  arts, entertainment, sports, or media   \n",
       "4  United States of America                              education   \n",
       "\n",
       "     EmploymentStatus  ExpectedEarning  Gender   Income  \\\n",
       "0  Employed for wages              NaN    male  32000.0   \n",
       "1  Employed for wages          50000.0    male  15000.0   \n",
       "2  Employed for wages              NaN    male  48000.0   \n",
       "3  Employed for wages          65000.0  female  43000.0   \n",
       "4  Employed for wages          45000.0  female   6000.0   \n",
       "\n",
       "                           JobPref            JobRoleInterest LanguageAtHome  \\\n",
       "0                       freelance                         NaN        English   \n",
       "1               work for a startup    Front-End Web Developer        English   \n",
       "2          start your own business                        NaN        English   \n",
       "3               work for a startup    Front-End Web Developer        English   \n",
       "4  work for a medium-sized company   Full-Stack Web Developer          Khmer   \n",
       "\n",
       "                     MaritalStatus                             SchoolDegree  \\\n",
       "0  married or domestic partnership           some college credit, no degree   \n",
       "1                              NaN           some college credit, no degree   \n",
       "2                              NaN  high school diploma or equivalent (GED)   \n",
       "3                              NaN                        bachelor's degree   \n",
       "4                              NaN           some college credit, no degree   \n",
       "\n",
       "   StudentDebtOwe  \n",
       "0         20000.0  \n",
       "1             NaN  \n",
       "2             NaN  \n",
       "3          7000.0  \n",
       "4             NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QzuV89ekVvjU"
   },
   "source": [
    "Let's select some features to work with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "WBZRt-_RVzpY",
    "outputId": "127c61e0-675d-4a12-e9c7-aaaae758a396"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CountryCitizen</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>32000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>15000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>48000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>43000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>female</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15615</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15616</th>\n",
       "      <td>India</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15617</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>male</td>\n",
       "      <td>37.0</td>\n",
       "      <td>48000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15618</th>\n",
       "      <td>Spain</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15619</th>\n",
       "      <td>Trinidad &amp; Tobago</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>18000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15620 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 CountryCitizen  Gender   Age   Income\n",
       "0      United States of America    male  28.0  32000.0\n",
       "1      United States of America    male  22.0  15000.0\n",
       "2      United States of America    male  19.0  48000.0\n",
       "3      United States of America  female  26.0  43000.0\n",
       "4      United States of America  female  20.0   6000.0\n",
       "...                         ...     ...   ...      ...\n",
       "15615                     Kenya    male  39.0  10000.0\n",
       "15616                     India    male  27.0  10000.0\n",
       "15617                   Nigeria    male  37.0  48000.0\n",
       "15618                     Spain    male  26.0  10000.0\n",
       "15619         Trinidad & Tobago  female  22.0  18000.0\n",
       "\n",
       "[15620 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_fcc=fcc[['CountryCitizen','Gender','Age','Income']]\n",
    "selected_fcc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 319
    },
    "id": "UxGX_2N3VHPL",
    "outputId": "b8c4f56b-0eb2-41ec-beba-a79038cc7e7d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "selected_fcc['Income'].hist(bins=10, color='#A9C5D3', \n",
    "                             edgecolor='black', grid=False)\n",
    "\n",
    "\n",
    "ax.set_title('Developer Income Histogram', fontsize=15)\n",
    "ax.set_xlabel('Developer Income', fontsize=15)\n",
    "ax.set_ylabel('Frequency', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w9Ot1ih2WNlm"
   },
   "source": [
    "We can see that our income feature is right skewed and does not follow normal distribution.\n",
    "\n",
    "Some Machine Learning algorithms such as Naive Bayes assume that your data is normally distributed and hence it is a good practice to transform your data to approach normal distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "efd1rclBQPI7"
   },
   "source": [
    "##Log transform the Feature to Normal Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "d0USzHa6V8_J",
    "outputId": "37df361e-bc22-43b4-ce30-c8b5dbbd2eeb"
   },
   "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>CountryCitizen</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Income_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>10.373491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>9.615805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>10.778956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>43000.0</td>\n",
       "      <td>10.668955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>female</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>8.699515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15615</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9.210340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15616</th>\n",
       "      <td>India</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9.210340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15617</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>male</td>\n",
       "      <td>37.0</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>10.778956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15618</th>\n",
       "      <td>Spain</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9.210340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15619</th>\n",
       "      <td>Trinidad &amp; Tobago</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>9.798127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15620 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 CountryCitizen  Gender   Age   Income  Income_log\n",
       "0      United States of America    male  28.0  32000.0   10.373491\n",
       "1      United States of America    male  22.0  15000.0    9.615805\n",
       "2      United States of America    male  19.0  48000.0   10.778956\n",
       "3      United States of America  female  26.0  43000.0   10.668955\n",
       "4      United States of America  female  20.0   6000.0    8.699515\n",
       "...                         ...     ...   ...      ...         ...\n",
       "15615                     Kenya    male  39.0  10000.0    9.210340\n",
       "15616                     India    male  27.0  10000.0    9.210340\n",
       "15617                   Nigeria    male  37.0  48000.0   10.778956\n",
       "15618                     Spain    male  26.0  10000.0    9.210340\n",
       "15619         Trinidad & Tobago  female  22.0  18000.0    9.798127\n",
       "\n",
       "[15620 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_fcc['Income_log'] = np.log((selected_fcc['Income']))\n",
    "selected_fcc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N4jiolOgZ9iI"
   },
   "source": [
    "The **Income_log** field depicts the transformed feature after log transformation. Let’s look at the data distribution on this transformed field now.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "id": "hkUSnLLHYYpL",
    "outputId": "159b7908-fd69-4867-fb3b-0ccdb61f679c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(9.8, 1380, '$\\\\mu$=10.43')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#plot the Income_log\n",
    "fig, ax = plt.subplots()\n",
    "selected_fcc['Income_log'].hist(bins=10, color='#A9C5D3', \n",
    "                                 edgecolor='black', grid=False)\n",
    "\n",
    "#let's add the income_log_mean on our plot to see where the mean of the new income distribution is.\n",
    "income_log_mean = np.round(np.mean(selected_fcc['Income_log']), 2)\n",
    "plt.axvline(income_log_mean, color='r')\n",
    "\n",
    "#set title and x,y labels\n",
    "ax.set_title('Developer Income Histogram after Log Transform', \n",
    "             fontsize=12)\n",
    "ax.set_xlabel('Developer Income (log scale)', fontsize=15)\n",
    "ax.set_ylabel('Frequency', fontsize=15)\n",
    "\n",
    "#disply the mean on the plot(NB: (9.8, 1380) shows where to indicate the mean on the plot. It can be adjusted to be put anywhere on the plot)\n",
    "\n",
    "ax.text(9.8, 1380, r'$\\mu$='+str(income_log_mean), fontsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SBnicLI3aziQ"
   },
   "source": [
    "We can now see that our income feature has been transformed to approach normal distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vmJgD_0CfMnK"
   },
   "source": [
    "We can also use the [**FunctionTransformer**](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.FunctionTransformer.html) in the *scikit-learn preprpcessor* to achive the same thing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_t7B1FyEsXDG"
   },
   "source": [
    "#**Power Transformation:** Box-Cox Transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PeqBxJPcsjDA"
   },
   "source": [
    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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CeBQJCLYs5MB"
   },
   "source": [
    "In **Box-Cox transformation**, we search and evaluate all the other transformations and choose the best one.\n",
    "The exponent is a variable called lambda (λ) that varies over the range of -5 to 5, and in the process of searching, we examine all values of λ and finally choose the optimal value which results in the best approximation to a normal distribution for our variable.\n",
    "\n",
    "Note that this function is defined only for positive numbers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N7u53vq2tuRk"
   },
   "source": [
    "The optimal value of λ is usually determined using a maximum likelihood or log-likelihood estimation.\n",
    "\n",
    "First we get the optimal lambda value from the data distribution by removing the non-null values as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Tp31VdBZknxq",
    "outputId": "c6ec2bbe-2d41-4771-a6e3-5bd478d47f1b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal lambda value: 0.11799122497648248\n"
     ]
    }
   ],
   "source": [
    "income = np.array(selected_fcc['Income'])\n",
    "#use the '~'sign to help remove all the null income values\n",
    "income_clean = income[~np.isnan(income)]\n",
    "l, opt_lambda = spstats.boxcox(income_clean)\n",
    "print('Optimal lambda value:', opt_lambda)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jdwqdjzrvQF0"
   },
   "source": [
    "With our optimal λ value, we can now use the Box-Cox transform for two values of λ such that λ = 0 and λ = λ(optimal) and transform the developer Income feature.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "1I8l7a4hvGh8"
   },
   "outputs": [],
   "source": [
    "selected_fcc['Income_boxcox(lambda=0)'] = spstats.boxcox(\n",
    "                                  (selected_fcc['Income']), \n",
    "                                          lmbda=0)\n",
    "\n",
    "\n",
    "\n",
    "selected_fcc['Income_boxcox(lambda=opt)'] = spstats.boxcox(\n",
    "                                            selected_fcc['Income'], \n",
    "                                              lmbda=opt_lambda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "sB40bzvTwBgo",
    "outputId": "bdf7a769-64f8-4fb2-fb52-6c64e3564e7b"
   },
   "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>CountryCitizen</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Income_log</th>\n",
       "      <th>Income_boxcox(lambda=0)</th>\n",
       "      <th>Income_boxcox(lambda=opt)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>32000.0</td>\n",
       "      <td>10.373491</td>\n",
       "      <td>10.373491</td>\n",
       "      <td>20.346418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>9.615805</td>\n",
       "      <td>9.615805</td>\n",
       "      <td>17.881577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>10.778956</td>\n",
       "      <td>10.778956</td>\n",
       "      <td>21.758798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>43000.0</td>\n",
       "      <td>10.668955</td>\n",
       "      <td>10.668955</td>\n",
       "      <td>21.368923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>female</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>8.699515</td>\n",
       "      <td>15.180667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CountryCitizen  Gender   Age   Income  Income_log  \\\n",
       "0  United States of America    male  28.0  32000.0   10.373491   \n",
       "1  United States of America    male  22.0  15000.0    9.615805   \n",
       "2  United States of America    male  19.0  48000.0   10.778956   \n",
       "3  United States of America  female  26.0  43000.0   10.668955   \n",
       "4  United States of America  female  20.0   6000.0    8.699515   \n",
       "\n",
       "   Income_boxcox(lambda=0)  Income_boxcox(lambda=opt)  \n",
       "0                10.373491                  20.346418  \n",
       "1                 9.615805                  17.881577  \n",
       "2                10.778956                  21.758798  \n",
       "3                10.668955                  21.368923  \n",
       "4                 8.699515                  15.180667  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "selected_fcc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eTfkXOgNwjFF"
   },
   "source": [
    "We see the transformed features shown in the above data frame. As expected *Income_log* and *Income_boxcox_lamba_0* have the same values. \n",
    "\n",
    "Let’s look at the distribution of the transformed Income feature after transforming with the optimal λ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "id": "kZVORYOYw-99",
    "outputId": "2520ade7-6659-440c-bd18-43ecce68b908"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(21.5, 1380, '$\\\\mu$=20.65')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#plot the Income_log\n",
    "fig, ax = plt.subplots()\n",
    "selected_fcc['Income_boxcox(lambda=opt)'].hist(bins=10, color='#A9C5D3', \n",
    "                                 edgecolor='black', grid=False)\n",
    "\n",
    "#let's add the income_log_mean on our plot to see where the mean of the new income distribution is.\n",
    "income_log_mean = np.round(np.mean(selected_fcc['Income_boxcox(lambda=opt)']), 2)\n",
    "plt.axvline(income_log_mean, color='r')\n",
    "\n",
    "#set title and x,y labels\n",
    "ax.set_title('Developer Income Histogram after Box–Cox Transform', \n",
    "             fontsize=12)\n",
    "ax.set_xlabel('Developer Income (Box–Cox transform)', fontsize=15)\n",
    "ax.set_ylabel('Frequency', fontsize=15)\n",
    "\n",
    "#disply the mean on the plot(NB: (21.5, 1380) shows where to indicate the mean on the plot. It can be adjusted to be put anywhere on the plot)\n",
    "\n",
    "ax.text(21.5, 1380, r'$\\mu$='+str(income_log_mean), fontsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p5KwTSmK0LYS"
   },
   "source": [
    "#**Yeo-Johnson  Transformation**\n",
    "\n",
    "Return a dataset transformed by a Yeo-Johnson power transformation.\n",
    "\n",
    "Unlike the Box-Cox transform, it does not require the values for each input variable to be strictly positive. It supports zero values and negative values. This means we can apply it to our dataset without scaling it first."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d5Ev4Otf9hFn"
   },
   "source": [
    "<br>\n",
    "\n",
    "**Before Yeo-Johnson Transformation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 775
    },
    "id": "1IYdIHkm20n1",
    "outputId": "076cb84f-257d-4241-8bd7-7da31f076ffb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PowerTransformer\n",
    "from matplotlib import pyplot\n",
    "# Load dataset\n",
    "sona=pd.read_csv('C:/Users/Aabha Gupta/Downloads/workbench-dataset/sonar.csv',encoding='utf-8')\n",
    "# sona = sona.values[:, :-1]\n",
    "# # convert the array back to a dataframe\n",
    "# sona = pd.DataFrame(sona)\n",
    "# # histograms of the variables\n",
    "sona.hist(figsize=(20,15))\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HtdoIZAl9p_w"
   },
   "source": [
    "<br>\n",
    "\n",
    "**After Yeo-Johnson Transformation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 779
    },
    "id": "EZYHPhg96mGS",
    "outputId": "6cbc446b-5506-429a-91dc-2b66402d47c6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eNZXMvAYYNa/irGXWovaa1xpRW7ZsWejIgUWO7FjWaA3o1IgNj12S1HJdO3GXZmHHjxbKk3dJdXGNKE3LY1c9unCS5sLy0oN5VyapfofUXYAkSfNWYaoOOFVH0vz1FpZ/LPA9wE9GxOkUC8lflZmnAVeVjyVJWgpH/EiSusipOpKWrm0LyzsSQ5JWw9iOH4esSpLaZtFTdbowJUXSYk2zsLwkSYtQZcRPb8jqDRHxcOD6iLgSOJ9iyOruiNhFMWS19isXkiRJUp2mXVh+8I6CVe9qN+0d8JZ1x79l3F1w1jsA1nUXQUlahrEdP20bslqVQ1slSZI0b7MsLD94R8Gqd7Wb9g54i7yjYL9l3F1w1rsK1ngXQUlauIn2wNMMWZ3mysUsPe7LunIxaN5XMpZ1xcGrG5IkSfNRYWH53biwvCRpySp3/Ew7ZHWaKxez9Lgv68rFoHlfyZj1qkVVXt3oJtcfkSSpFi4sL0lqnEo9FbMMWdV0PHGXJElql0UvLC9Jw3hDJo1T5a5eDlmVJGkKricnSZKWwBsyaVOHVHhNb8jqUyPiI+W/p1N0+JwdEbcAZ5ePJUmSJEkTiohTI+LqiLg5Im6KiJeVzx8XEVdGxC3lx2PrrlXNkpkHMvOG8vN7gP4bMu0tX7YXeHYtBap2Ve7q5ZBVSZ3jSIzRnGqqRauasYvPOWrBlUhSozhqQzNb1g2ZBi3qpkHzvnnTvG/K9IZLqk182n7yMRP/7Hlu08XeV1GSJEmSNFZ5gt47Sb8nIvpHbayVL9sLrGPHz9x06YLXMm/INGhRNw2a982b5n1TpqqmuXnTPLepHT+SJEmSWq1LJ+9Q36iNQfMYcdD0ERtVjdsOixrxUpU3ZNJm7PiRVljXGkmSJEltV+eojUHzGHGwKiM2FjXipQpvyKRx7PiR1FgR8RbgmcAdmXlG+Zy3pZQkaROuY9dejtrQlHo3ZNoXER8pn/t5ig6fSyPiJcBngOfWU57qZsePFsoTd83oYuCNwFv7ntuFCxxKjdSVk02PXZLq4KgNTcsbMmkcO360aBfjibumlJkfKOe493OBQ43libtmdDELPHZ1pYNM0tw5akPSQtjxo4XyxF0LMNMCh72F9+pYFHBWdS1mOMokt6+sYcHDi7HTWVPy2CWpDo7akLQodvyoDpVO3GH8yfu+2++u9At3bp+15Plq2gn8OP0n7HXfsaCqUQsc9hbem/dCg8tQ12KGs9r/wrWlL3joibsWYG7Hrjbt/6F5x6yqnc6PPubQVhyvJElatPadQWildPHkHdp3At9/F4M671hQcoFDTWvqE/fBDs8mnQSP07ST9km0paN5UNeOXW07ZvVcfM5RdR+vpE5z2qrUHu07iusBWno7bk/cW6Y/Zzu3Hxx50rKknLnAoRZu8MR9y5YtDziBbNOJe1tP2qFxJ+4euyRJmhM7DpfrkLoL0ErqnbiDJ+7aRET8AfBXwHdExG3looa7gbMj4hbg7PKxVMXnyxN2PHHXFDx2SZKkVmrnJUC1RnnivgYcHxG3Aa/COxOoosx8wYgvucChpuFoMVXisUuSJHVJazp+HAo2m1Hbb3Dazryn6njiLqkOnrhrFh67JElSl7Sm40eS1E7bdl2x6dpQPfPsePbEXZI0TF0XQyWpTq7xI0mSJEmS1FGO+JEkSZJaoOpoFUlSOy3qbsqO+JEkSZIkSeooO34kSZIkSZI6yqlekiRJkiRpqCp32O5NS3Jh9OHqvku5HT+SJEmSJKD6GiPSMHV3cGg4p3pJkiRJkiR1VO0jfob1CNqzLEmSpFnsu/3uSu1JpyVIqmLcSBanOqnJHPEjSZIkSZLUUbWP+JEkSZIkqQuqrnHjyCAtkx0/kiSpkZyqI0nz46K70upyqpckSZIkSVJHOeJHkiRJkvo4XUdSl8w04icizomIj0fErRGxa15FaTWYH03L7GgW5kezMD+altnRLMyPZmF+NHXHT0QcCrwJ+CHgdOAFEXH6vApTt5kfTcvsaBbmR7MwP5qW2dEszI9mYX4Es434eRJwa2Z+MjO/BvwhcO58ytIKMD+altnRLMyPZmF+NC2zo1mYH83C/GimNX5OBj7b9/g24MmDL4qIC4ALyocbEfHxcT/4p+B44Asz1LZ0bawZHlx3XDj0ZY9awK+eNT+t3N49bc0LbF77kvKzstnpam56mpyfpzzlKV+kw9u+qarW7rFrsdqaoYblZ2HtZmj+36jp9cH0NXYhP5tp4t+uiTXB5HU16dg1j+w09e8yqC11wnzPu2bp+Ikhz+WDnsjcA+yZ6AdHXJeZO6YtrA5trBlqrXum/LR1e/e0uf4G1L6y2bH2uZgqPw2qf2LWPlcruf+x7rlYWLsZGvdeH6Tp9UHja1xofjb9xQ3cLk2sCZpbFxXyM4/sNPj9P0Bb6oT51jrLVK/bgFP7Hp8CfG62crRCzI+mZXY0C/OjWZgfTcvsaBbmR7MwP5qp4+da4LSIeHREPAR4PnD5fMrSCjA/mpbZ0SzMj2ZhfjQts6NZmB/Nwvxo+qlemXkwIl4KvBc4FHhLZt40p7rmOkRxSdpYM9RU9xzy09bt3dPm+mutfcWzY+0zmiE/jah/StY+Jyu8/7HuGS243QwNeq8jNL0+aHCNS8jPZpq4XZpYEzS0riXmp5Hvf4i21AlzrDUyHzQ9VJIkSZIkSR0wy1QvSZIkSZIkNZgdP5IkSZIkSR3V2I6fiHhNRHwsIm6MiHdFxCPqrmmUiDgnIj4eEbdGxK666xknIk6NiKsj4uaIuCkiXlZ3TdNoU0Z62paVfl3JDZidZTI39TI3zdOmHLUxP13OzjhNzVbTc7TKmamqSdlqWp7MzwM1KSvDNC0/wywqU41d4ycifgB4f7kY1YUAmfmKmst6kIg4FPgEcDbFrfKuBV6QmX9ba2GbiIiTgJMy84aIeDhwPfDsJtc8TFsy0tPGrPTrSm7A7CyTuamPuWmmtuSorfnpcnbGaWK22pCjVc5MVU3JVhPzZH4eqClZGaaJ+RlmUZlq7IifzPyzzDxYPvxr4JQ669nEs4CjKf4gnwG+AvybWisaIzMPZOYN5ef3ADcDJ9db1eRalJGeJwG3ZuYnM/NrwB8CPxYR74+Iu8ue58Zmpyu5gdZm56vApcA9wEHg3N4XI+Ks8urKV8orBI+qqc4HMTe1um+fA1wAfAtwY0Rc3HtBRDwkIv44IvZHREbEWi2VDuhSbga1KEdPAm4Fng78X+C7gP/V+2JEfE9EXBkRX4qIf4iIPyobq7XqcnbGaWi2LgSOp/g77KFo+5wbEadHxHURcWf5730RcXodBa5yZqqqK1sR8dIyJ18tj13D2tKvL49fT1tGTYPMzwM1ZT80JDtQ5Oc24O+ALwHfCXw4In6pjhpHWVSmGtvxM+A/AO+pu4gRfgm4EzgJeDxFuH+wzoImERHbgCcAH6y5lFk1OSM9JwOf7Xv8OeDfA+8GjqM4MXtbRHx7DbVNpEO5gfZk53PArwNvAe4tnyMijgfeSbEvOg64Dnh7PWVuztwsXf8+53PAH1E0HgZdA7wI+Psl1TWRjuVmUJNz1MtPb9/z58DD+r5+LMWJ/DbgURSd0r+33BI31/HsjNOUbCXwIYpjFxQnXb1j2r+lOG4dD1xOcRJfqxXPTFXLzFZ/2wce3Jb+GvDdwIEl1bMp8/Mgde6HBrMDRX56WXkE8GPA/8zMX1tuadXNM1OHzVzNDCLifcAjh3zpFzLzsvI1v0BxdfuSZdY2gROBv8nMfwL+PiJupGgANV5EbAHeAbw8M79cdz3DdCQjPTHw+J8BRwH/PYs5l++PiL8EXkxxEt9IbcgNdDI7+zPzTyJiR/lcb57ujwA3ZeYfAUTEq4EvRMR3ZubHll/qcOamFvftczLznRFxFPCv+19QXjF9HUBEfGOp1VXQltwM6kiOAorsAETEv+//YmY+oDEfEW+k6BxqhLZmZ5wWZutDFJ2EX+T+K/+ZmXcBdwFERADfAB5TQ3336Wpmqmpitvr2Pzso8jPYlv5R4C+Bxy2jns2sUn6amJVBQ7IDD84P3N+ebpx5Z6rWjp/M3HRIXkScBzwTOCubuhhREeYXRcTDKA5sTwSurLek8SLicIogXdL7j9FEHclIz23AqX2Pt/LgnU0AZyytogm1JTfQ+ewcRXElA4rpFx/tfSEz742Ivyufb0THj7mpzWBuTqEYldEKbcrNoI7kaDA/D6eYzj7K9wE3LbSiitqcnXFamK1ejnojCk/h/uMXEXEXsIViFsIvL7u4vjo6m5mqWpKt+/ZLEfFc4HDgr6i542fV8tOSrAxzG8UsHYBPU+x7bomI4zPzC/WV9WCLyFRjp3pFxDnAK4BnZeZmDY26/S+KIapfpgjTQ4DdtVY0Rnll5c3AzZn52rrrmVaLMtJzLXBaRDw6Ih4CPAW4A/jZiDi8XAzt+3ngUPrG6EpuoN3ZodhvP5piWDwUB627B15/N8VJWu3MTa0G9znPp1jUsPG6lJtBLcrRYH6+iwdOsbhPRDyO4qT9Z5dY31Bdzs44Dc3WtcBpwDEUx6/nc//xi8x8RPm1lwIfrqG+lc5MVQ3KVm+/9F3Af6W4YHr55t+yWObngRqUlWGuBb6VYp3Mx1Ccu3+JZoyOvM+iMtXYjh/gjRQnLldGxEci4nfqLmhQRBwC/CnFnORPAfsprmicV2NZVZxJMZ3oqeW2/UhEPL3uoqbQ+Iz0Kxc6eynwXop1Ni4FnlH++3tgZ/ncbXXVOEZXcgPtzs5/opj21buyvkGxwHy/o2nOyA5zU5MR+5xGXdHaRJdyM6gVORqSn7+lnJrTLyIeQ7GGw8sy8y+WWeMIXc7OOI3LVl+O/h3wbODSvuNX7zX3Ar8DvDUiTlx6kaudmaoaka2+PP0FxQ0L3jaYpxqYnwdqRFaGKfPzk8BvAn8D/D7FdMEfiIjBtnSdFpKpxt7OvQ2iWFT1H4BHZObd5XPPBn49Mxs7XUfNFRH/F9ibmf//umtRM0XErwOnZOb55eMLgPMy88zy8VEU+6XvbtIaP2qGwfwMfO024EWZub7sutR8w7ITxR0E/xzYnZmNadyreTbb95RfP4zigsW/ysxaRv6ouYa0fT5CMW2wd/eoEyhGO1+YmRfWUaOaqcK+ZyvFBfj7zue7qskjfhqvnAv4KeA/RcRhEfEIitE+H930G6VSRDwuIo6IiIdFxM9QzDu9uOay1EDlPuYI4FDg0DI3hwHvAs6IiOeUX/9l4EY7fdRvk/wQEQ8tvwbwkPJrwxZA1AoalZ2IOBl4P/AmO300yib5OTsinhARh5ZX2l9LcZfcYXce1Ira5Nh1FsWamI8v/30O+HHgTTWVqobZZN/z5Ij4jog4JCK+BfgtYL3rnT5gx888/AhwDsUV9lspep7/S60VqU1eTHFbwTsoDmJnZ+ZX6y1JDfWLwD8Cuyhuvf2PwC9m5j8AzwF+g6LR/GSKNRSkfkPzU37t4+Xjkymm9fwjLbk7pZZiVHZ+DPjnwKsiYqP3r74y1VCj8vMI4A8oRmn8HcV6G+eUd8mVeka1fb6YmX/f+0dxV7g7M9N9kHpG7Xv+OfB/KEYY/g3wVeAFNdW4VE71kiRJkiRJ6ihH/EiSJEmSJHWUHT+SJEmSJEkdZcePJEmSJElSR1Xu+ClX3f9wRLy7fHxcRFwZEbeUH49dXJmSJE3OY5ekOrjvkSQ1SeXFnSPip4EdwNGZ+cyI+H+BL2Xm7ojYBRybma/Y7Gccf/zxuW3btokKvPfeeznqqKMm+p66tKXWSeu8/vrrv5CZJyywpEqq5qctf4eettULk9XchPyMy07b/gZtqnfWWmfNzyKOXW3a/j1trBlmq7sJ+x6YvO3Tpr9VW2qdps4m7ns204a/RdNrnGd9Tdj/dK3tA+2sGbrXbu7X1r9JT9fq3zQ/mTn2H3AKcBXwVODd5XMfB04qPz8J+Pi4n/PEJz4xJ3X11VdP/D11aUutk9YJXJcVcrLof1Xz05a/Q0/b6s2crOYm5Gdcdtr2N2hTvbPWOkt+FnXsatP272ljzZmz1d2EfU9O0fZp09+qLbVOU2cT9z3zfo/L1vQa51lfE/Y/XWv7ZLaz5szltpuBQ4EP9+17jgOuBG4pPx477md0bd+zma7Vv1l+DqvYmfQ64OeAh/c9tzUzD5SdRwci4sRh3xgRFwAXAGzdupX19fWKv7KwsbEx8ffUpS21tqVOSZrR61jAsauN+9A21gztrVsr73Usud3chv8rTa+x6fVJFb0MuBk4uny8C7gq7x9tuAvYdLShumlsx09EPBO4IzOvj4i1SX9BZu4B9gDs2LEj19Ym+xHr6+tM+j11aUutbalTkqa1yGNXG/ehbawZ2lu3Vldd7eY2/F9peo1Nr08aJyJOAZ4B/Abw0+XT5wJr5ed7gXXs+FlJVUb8nAk8KyKeDhwBHB0RbwM+HxEnlVctTgLuWGShTbdt1xXs3H6Q83ddsenr9u9+xpIqUptsG5ObHvOjQVWzAyuXH49dFbjv0Sz23X732HYPrFx+3PdU4L5ntfj3XprX0cHRhvtuv7vS67affMzEP7vtI/0mqX9sx09mvhJ4JUB55eJnMvNFEfEa4Dxgd/nxsunKlaTRIuJQ4Drg9iwWyDwOeDuwDdgPPC8z76yvQjWRxy5JdXDfI6kOXR5tWOUCA8D+F05eR9tH+k1Sf9U1fobZDVwaES8BPgM8d4af1ViTXE2XtBDOVdY8rcSxS1LjuO+RtEiONtSmDpnkxZm5npnPLD//YmaelZmnlR+/tJgSJa2qvrnKv9v39LkUc5QpPz57yWWpZTx2SaqD+x5Jy5KZr8zMUzJzG/B84P2Z+SLgcopRhuBow5U2y4gfSVq017GEucptm9/bX+/O7Qcrf18d77Ft21bqcZqpJKkDHG0owI4fSQ21zLnKbZvf219v1XnPMN3c51m1bdtKfZxmKklqncxcp7h7F5n5ReCsOutRM9jxI6mpnKssTWlwfboqd53U/bwlrjSdzdbGdD8kSfWx40dSI3lnFEk1eh1TTjOF6W+LC+2aHrn1yGrTTet+P23appIkLYIdP5LaxrnKkhZm1mmmMP1tcaFd0yPfcMllXLRvfFOyjmmm/dq0TSVJWgQ7fiQ1nnOVJS2R00wlSVKnTHQ7d2lSEXFERHwoIj4aETdFxK+Uzx8XEVdGxC3lx2PrrlWSJG+JK6kutpslLYojfrRoXwWempkbEXE4cE1EvAf4EVp8d5TNFi/st3/3MxZciSRpSZxmKmnROtlullQ/O360UJmZwEb58PDyX+LdUSRJDec0U0nLZLtZ0qLY8aOFi4hDgeuBxwBvyswPRkSlu6NMc2eUZdy9o8pdTKDanUw2NjbYuf0blX7eGy6pNrNg+8nHVHrdtLxDiiRJ0vwtq928zLbcvNrNbW1/trVudYsdP1q4zPwG8PiIeATwrog4Y4LvnfjOKMu4e8f5Vad6VbiTyfr6Ohddc++MFU3+e2fhHVIkSZLmb1nt5mW25ebVbm5r+7Otdatb7PjR0mTmXRGxDpzDitwdpcpaQMVVEP8rSpIkqbCK7WZJi+PZphYqIk4Avl4evI4EngZcyP13R9mNd0eRJKkxqt7AYOf2BRcirRjbzZIWxY4fLdpJwN5yvvIhwKWZ+e6I+Cu8O4okSZLUY7u5Au+uK03Ojh8tVGbeCDxhyPPeHUWSJEkq2W6WtCh2/Eh9ql5BkCRJkiSpDez4kSRJkjSSF8Ykqd0OqbsASZIkSZIkLYYjfiRJkiRJE5n3SLBxP2/n9oOc7+gzaSqO+JEkSZIkSeooR/xIkrQg3nJWUpO5do/ksVqrwY4fSZIkSZLUWnV14LWl43BlO36afoWjLQGSNJl57HumnePufkWS1CaTHDM9dknSaCvb8SNJkiRJkpav6QMxusaOH0mSauZoLC2DjWxJklaTHT+SJEmSJEkLUvdFPjt+lsyrbZKktqm7sSJJ0qQ875Lud0jdBajbIuLUiLg6Im6OiJsi4mXl88dFxJURcUv58di6a5UkSZLqYrtZ0qJ0bsSPPbuNcxDYmZk3RMTDgesj4krgfOCqzNwdEbuAXcAraqxTkiQtgCPGpMpsN0taCEf8aKEy80Bm3lB+fg9wM3AycC6wt3zZXuDZtRQoSZIkNYDtZkmLMnbET0ScCrwVeCTwTWBPZr4+Io4D3g5sA/YDz8vMOxdXqtouIrYBTwA+CGzNzANQHOQi4sQR33MBcAHA1q1bWV9fH/t7NjY2Kr1umJ3bD071fbPYeuT8f++077+qWbaxtAweuyTVwX2P5mXR7eZ5tOWW3W5eRJt5EtNuL9vNaoIqU70ccqiZRcQW4B3AyzPzyxFR6fsycw+wB2DHjh25trY29nvW19ep8rphzq9hquDO7Qe5aN98Z13uf+HaXH/eoFm2sbQkHru0Mpzm3ijuezSzZbSb59GWW3a7eRFt5klM275eRrvZTmeNM3aql0MONauIOJzi4HVJZr6zfPrzEXFS+fWTgDvqqk9S93jsklQH9z2ale1mTanX6fxY4HuAn4yI0yk6ma/KzNOAq8rHWkETdZlOM+RQqy2KSxRvBm7OzNf2fely4Dxgd/nxshrKU4M15cqFV9Lbz2OXpDq479GkbDdrWuX+pbePuSci+jud18qX7QXWcbThSqrc8TPtkMNp1mjpN+mcyDrnfdYx73Sa+aJLnmd6JvBiYF9EfKR87ucpDlyXRsRLgM8Az11WQWoNh8trZos4dk2yD61r/a7B37us49O8jy2ui6C2Wna7edr/K8tsty56P/SGS6r1hWw/+Zihzzdkf2O7WTOz01nDVOr42WzIYRmgkUMOp1mjpd+kcyLrWKOlp455p9PMNV3m+iyZeQ0wqrVz1lKKUCt55UKzWtSxa5J96LyPSVX3+YO/d1nHp3mvL1bXemJNGXGodqqj3Tzt/5VltpvrXp+lZ9R+qgnrF9pu1qza0ukM9Q3YmPZi3ih13qRnkvqr3NXLIYeSalXnnS3qHEU4yqKvms7zimddV1C7euxy6uHSOOJQU+nqvkdS87Wp0xnqG7DR3/nbxEXOJ7mINkn9VbrdHXIotUzVk8P9u5+x4EpmV/edLeocRTjKoq+aznPURo1XUD12aWqOONQM3Peo9bzI0D52OlfXn++d2w82sq2/CGPPHBxyKKkus1y50Grz2KV5WfSIw0HTjJKra2TivEcfLmp04DJHHjZl3+OJu7Ry7HTWpuqfaCtJQ3jlQlLdljHicNA0o+Tqulo579GH814jqqcJa7dI0iI1pdNZzXVI3QVI0gi9KxdPjYiPlP+eTtHhc3ZE3AKcXT6WpLnabMRh+XVHHEqSpFZwxI+kRvLKhaS6OOJQkjSoS2toavXY8SNJkvRArpUgSZI6w44fSZKkPo44lCRJXWLHjyRJWqqqw+UvPueoBVciSZLUfXb8SJIkqXaunyFJ0mLY8SNJkiRJklSzRV0E8XbukiRJkiRJHWXHjyRJkiRJUke1ZqpX1SFPapaIeAvwTOCOzDyjfO444O3ANmA/8LzMvLOuGiVpUh6TpPq4FpC6ynazpEVxxI8W7WLgnIHndgFXZeZpwFXlY0mSJGmVXYztZkkLYMePFiozPwB8aeDpc4G95ed7gWcvsyZJkiSpaWw3S1qU1kz1UqdszcwDAJl5ICJOHPXCiLgAuABg69atrK+vj/3hGxsbD3rdvtvvrlTYzu2VXjZXW4+EndsPLv8XA2+45LJKr9t+8jEPeDxsG0uSJGnuFtJu3qwtV1e7dJw628yTGNyutpvVBHb8qNEycw+wB2DHjh25trY29nvW19cZfN35DV6PY+f2g1y0r9n/Ffe/cO0Bj4dtY3XHPNev2bn9IGtz+2mSJGmUSdrNm7XlmtpubkObGWw3q5ma/z9HXfT5iDipvGpxEnBH3QVpc4MdATu3HxzaKHAhTUmSpLmy3SxpZnb8qA6XA+cBu8uP1eYbSXPU35k1qiNLkiSpZrabJc3Mjh8tVET8AbAGHB8RtwGvojhwXRoRLwE+Azx32p8/bEqKJ/GSJElqm0W3myWtLjt+VkTV0Q3znqqTmS8Y8aWz5vqLJEm1m+f6UHJ7SqvGdrOkRfF27pIkSZIkSR3liJ+W82qgJEmSJEkaxY4fSZIkSeo4b2yxHKt+N1wHJjSTU70kSZIkSZI6yo4fSZIkSZKkjnKqlyRJkjqnN91g3JSWrk63kCSpx44fSdJCVZ3r7cmXVsVm/ydcd0OSJM1b7R0/404IbABJkiRJo9vNtperm/c29KKFpDZwjR9JkiRJkqSOqn3EjyRJkiRJaiZHG7afI34kSZIkSZI6yhE/kiSpkfbdfnelK4musSFJkjTaTCN+IuKciPh4RNwaEbvmVZRWg/nRtMyOZmF+NAvzo2mZHc3C/GgW5kdTd/xExKHAm4AfAk4HXhARp8+rMHWb+dG0zI5mYX40C/OjaZkdzcL8aBbmRzDbiJ8nAbdm5icz82vAHwLnzqcsrQDzo2mZHc3C/GgW5kfTMjuahfnRLMyPZlrj52Tgs32PbwOePPiiiLgAuKB8uBERH5/kl/wUHA98Ydoil6kttW5WZ1w49FsetYAyFpaftvwdetpWL4yueUn5mXt22vY3aFO9k9Taovy0Zvv3tCkz/arW3YVjV0+b/lZtqXVcnS3a94zUhr9F02uctr625qfpf49h2lgz1N5uhgr56fK+ZzNtrr/Mz2D9I/MzS8dPDHkuH/RE5h5gz9S/JOK6zNwx7fcvU1tqbUidC8tPQ95fZW2rF2qvee7ZadvfoE31NrDWmfPTwPc0VhtrhkbWvfC2TwPf80htqbUhdS40Ow15j5tqeo0Nr2/l2z7QzpqhEXWPzU+X9z2bWaX6Z5nqdRtwat/jU4DPzfDztFrMj6ZldjQL86NZmB9Ny+xoFuZHszA/mqnj51rgtIh4dEQ8BHg+cPl8ytIKMD+altnRLMyPZmF+NC2zo1mYH83C/Gj6qV6ZeTAiXgq8FzgUeEtm3jS3yu439TSxGrSl1trrXHB+an9/E2pbvVBjzQvKTtv+Bm2qt1G1zik/jXpPFbWxZmhY3Utq+zTqPY/Rllprr3MJ2an9PVbQ9BobW59tn/u0sWaouW7Puza1MvVH5oOmh0qSJEmSJKkDZpnqJUmSJEmSpAaz40eSJEmSJKmjWtHxExGviYiPRcSNEfGuiHhE3TX1i4hzIuLjEXFrROyqu55hIuLUiLg6Im6OiJsi4mV117RITc9MTxuy09P1DLUhM+alOdqQl5425Qa6n50qmp6vtmRq1bLU1Nw0PS+rlpOepuZllKbnaJhVyVbbstTTxkz1TJOtVqzxExE/ALy/XJjqQoDMfEXNZQEQEYcCnwDOprhV3rXACzLzb2stbEBEnASclJk3RMTDgeuBZzetznlpcmZ62pKdnq5nqOmZMS/N0vS89LQtN9D97FTR5Hy1KVOrlqUm5qYNeVm1nPQ0MS+jtCFHw6xKttqUpZ62Zqpnmmy1YsRPZv5ZZh4sH/41cEpdtUTESyPiuoj4akRcDDwJuDUzP0lxl7R/BK6NiLsj4gN11TkoMw9k5g3l5/cANwMn11vV4jQpM/3680NxG8Vedp4LfCfw4YjYiIivRERGxBNrLbhP1zPU1Mz0uRA4nmK77wH+EDgXICKeV/b43xMRfxsRz66vzIJ5aYwnAbcCTwf+L/BdwP/qf0FE/Fh5tWsjIv5PRPyzGuq8T9ezU0WT8hURD42IN0fEpyPiHuBvgbsy85OZ+TWKxvJ6edy6OiIeVVetg1YtS03KTZ8zgYcCVwNfBE4AfhYgIh4SEX8cEfvLNs9aHQWuWk56GpqX+/Tve4AvU2TnO8r9zl8Cl0XElyLiHyLij8oT4UZZlWw1MUuDx66I+HBE/FD5tdOBvwG+laLD5E+B91O2q9tgmmy1ouNnwH8A3lPj7/8c8OvAW8rHJwOfLT/fAyTwB8BxwH9ZenUVRMQ24AnAB2suZVnqzky//vw8jDI7mXkJ8GPA/8zMLcBPAJ8Ebqipzk2tQIaalJmeBD7E/fue24CTI+Jk4G3ATwNHUzSofz8iTqylyiHMS616x6jevufPKfY9AETE9wP/laKxcxzwKYpjWCOsQHaqqDtfh1Fk6PuBY4B3AtsjYltEHA/8O4qG83HAdcDb6yp0MyuYpbpz03MqcAf35+cPgBeWfw+Aa4AXAX9fS3UDVjAnPU3JS7/+fc/5wF8Bl5Z/o3spLmpsAx4F3AP8Xh1FVrVC2WpKlgaPXb/E/fn5HPBbwF6Ki6qXA8+hpZ1yVbN12FKqqSAi3gc8csiXfiEzLytf8wvAQeCSZdbWLzPfWdayg6I3M8rH3wE8C9gJPC4zv0HREGqUiNgCvAN4eWZ+ue56ZtGWzPQbyM/Ql5QfzwPemg2ci9nmDLUxM30+BBxLccW0dyUly8/vyszeQfaKiLgX+DaKxnatzEvtAh6w7/n3A1//YeCPMvOm8uu/BtweEd+WmX+31EoHtDk7VbQlX5l5L/Dq3uOIuAHYAJ4IfAtwO8Xo1X+KiFcDX4iI78zMj9VQ7lBdylJbctPna8ANmbkfICI+QjF644nlc68rn/9GPeXdr0s56WlhXu7Tv++JiH9JcRL/KYp9z43AEb2/U0S8keLCRiN1IVtty9LgsQt4d0R8imLf846I+EL5fADfALZy/3lYa0ySrcZ0/GTm0zb7ekScBzwTOKthJ8O3UVzNeDLwaYoT9ieUw1VfnZnvqK+0B4qIwymCcUnvJKDNWpyZnq9QZKfnFOBz5TD576PoMW+Utmeo5Znp7Wt6V0VPobhicR1wc0Q8C7iC4kT+qxSNolqZl0bo5abn4RT7np4o//U/BjgDqK3jp+3ZqaLF+bqX4urpTcB/ouiM/hwUDe2I+DuKKYWN6PjpWpZamJvBfdB3cn9+GqNrOelpYV5GuY3igta3U2Tn31Dud0rfR8My1dOVbLU9SxGxlfvzA0WmzqMYTXYI8D4emKnGmzRbrZjqFRHnAK8AnpWZXxn3+iW7FjgN2E7RUH4M8K+AlwJ7I+KxNdZ2n4gI4M3AzZn52rrrWbSGZ6bnC8BpEfHoiHgI8HyKoYY/CvxFZn6q1uoGdD1DLchMb19zDMW++/nA5eXowrcCv0/R4fP7wI+XVzpqY14a41oeuJ/5Lu6fngzFvPbnRcTjIuJI4Jcprng97ME/ajm6np0qmpqvspG5k6Lz56sU00sfTXHs6rmbooOxdquWpYbmpn8f9DDgp4B3NWxE2ErlpKeheRnlwxQX2d9JsRRCr81MRDyO4tj1s7VVN8KqZKvpWSqPXZcAe/v2PddStIceB7yM4hz+8uE/oXmmyVYrOn6AN1I0Iq6MiI9ExO/UXVBPuZDVSyl6DAHelJkfzcw/p1jI7gdqK+6BzgReDDy13IYfiYin113UAjU2M32SIjvvpViQ69JyusWPUsw5bZquZ6jRmenb1/w74NmUeYmIpwH/L7AGPIRiLvPvRsTj66n0PualAfpy09vP/C1wV9/XrwJeRXHF6NPAfoq1Em5bcqn9up6dKhqXr4g4hGJh8K8CL6TI1L8BPtWbKlg6miJDTbBqWWpcbgb2QZ+nuKL+wlqLerBVy0lP4/IyTLnvuZhipMYO+trMEfEYivVkXpaZf1FflSOtSrYam6W+Y9fXKPZFwAP2TX8CvJxiGts/LL/CqU2crVbczr2JIuLXgVMy8/zy8VkUO56H9VY1j4j/DbwvM19fW6FqpMH89D1/JvBnwCPLFdqlBxiy7/kZ4MzM/Dd9r/kT4JrM/M1ailRjjdr39H392ymurJ6SmXcuszY1V3ll8S0UC6k+PTP/sXz+AuC8zDyzfHwURcP5u5s0okP1GpWfgdfcBrwoM9eXW52abLPslEsj/DmwOzMb09Gg5qiy7ylfdxjFBYt/lZkfXl6Fy9WWET+NERGHRcQRwKHAoRFxRBmWDwCfAV5ZvuZMiivw762vWjXNJvnpOQ94h50+GrRJdq4Fvrc3wicingB8Lw1Y40fNMSo/5cczovCtFHenfL2dPhrwP4DHAj880HB+F3BGRDynzNcvAzfa6aMBo/JDFLdcPqJ8+JBynxQP+glaVUOzE8UdTd9PMdPCTh+NMio/Z0fEEyLi0Ig4GngtcCfFaLLOcsTPhKK4Y8WrBp7+lcx8dUR8F/C7FHMFP02xyvm7llyiGmxMfo6gWLj3OeX0C+k+Y7LzUophqlsprra/KTMvWm6FarJR+aG4o84HKBbN7N0O9xfLtaOk3lX1/RRTvA72fenHM/OScrrpGyluqfxB4PzeHZykCvnZT5Gdfo82Q9osOxTrsbyaYr2x+2TmliWVp4Ybk5+vAb9GcaOUf6S4iLorMzt90dSOH0mSJEmSpI5yqpckSZIkSVJH2fEjSZIkSZLUUXb8SJIkSZIkdZQdP5IkSZIkSR112PiXzM/xxx+f27Zte9Dz9957L0cdddQyS1mKrryv66+//guZeULddYzKT7+ubPOeLryfJuRnXHa6sJ0HdeU9NSk/Xdmmo3Tt/TUhO/Dg/U/XtvOgrry/JuRn2LGrK9t3lK68vyblpyvbdDNdeo9Nys4wXdrW/bryvjbLz1I7frZt28Z11133oOfX19dZW1tbZilL0ZX3FRGfrrsGGJ2ffl3Z5j1deD9NyM+47HRhOw/qynuaNj8RcQTFbcofSnGs++PMfFVEHAe8HdhGcZvP52XmnZv9rF5+urJNR+na+2vCvgcevP/p2nYe1JX314T8DDt2dWX7jtKV99ek/HRlm26mS++xSdkZpkvbul9X3tdm+XGqlySpi74KPDUz/wXweOCciPgeYBdwVWaeBlxVPpYkSZI6y44fSVLnZGGjfHh4+S+Bc4G95fN7gWcvvzpJXRURR0TEhyLioxFxU0T8Svn8cRFxZUTcUn48tu5aJUmrY6lTvZpk264rKr1u/+5nLLgSdZk50yAzsTwRcShwPfAY4E2Z+cGI2JqZBwAy80BEnDjiey8ALgDYunUr6+vrbGxssL6+vqTql2+Z72/f7XdXet32k49ZcCVqko7sH3ujDTci4nDgmoh4D/AjFKMNd0fELorRhq+os9Cu6Uh+VAOz0w3+HTe3sh0/kqRuy8xvAI+PiEcA74qIMyb43j3AHoAdO3bk2tpaZ+Z/j7LM93d+1cbZC9cWW4g0Z5mZwKjRhmvl83uBdez4kSQtiVO9JEmdlpl3UZxknQN8PiJOAig/3lFfZWoqp+toFhFxaER8hGL/cmVmfhB4wGhDYOhoQ0mSFsERP5KkzomIE4CvZ+ZdEXEk8DTgQuBy4Dxgd/nxsvqq7J6qw6xbwOk6mtosow2HTTPt55TT0XZuP1jpdV3efpLGG9ZW2bn94INGI3dtSpgdP1qoed5SWavF7GhGJwF7y3V+DgEuzcx3R8RfAZdGxEuAzwDPrbNINZPTdTQPZcfzOn2jDcu1xUaONhw2zbSfU05HcwqpJI1mx48WrZNXTbtyVbvhi6B1Mjtajsy8EXjCkOe/CJy1/Irmo+H/Zztl3ouD9zhiY7QujNhwtKEkqYns+NFCedVU0zI7kuo078XBexyxMVpHRmw42lCS1Dh2/GjhFnXVdJhlXUmtelWyqlE1L/r9NP3q6rKys8wr8Mva5l0fVSAtyzTTddrOkWXT6+poQ0lSu43t+HGdDc1qUVdNh1nWldSqVyWrGnX1ctHvp+lXV5eVnS7exrrrowqkRXK6jiSN1/RO4q4szSDNQ5URP66z0WBN3+H2W8WrppoPsyNpyZyuI0mSxpr3+fiizu8PGfeCLIxaZ2Nv+fxe4NkT/WathIg4oRytQd9V049x/1VT8KqphjA7kuqSmTdm5hMy83GZeUZm/mr5/Bcz86zMPK38+KW6a5UkSRqn0ho/i15no461KJaxzsYy3lfT12jBq6aantmRJEmSpBlV6vhZ9DobdaxFsYx1NpbxvlqwRouLHGoqq56dNk3jlCRJkrpk3mtE1b3m1NipXv0y8y6KWyfft84GgOtsSJIkSZIkNc/Yjh/X2ZAkSZIkSWqnKlO9XGdDkiRJkiSphcZ2/LjOhutsSJIWq+5535IkSequSos7S5IkSZK0KNNeBNm5/WDlG97MoskDAiLiVOCtwCOBbwJ7MvP1EXEc8HZgG7AfeF5m3rn0AmfgxbH5sONHkiRJWkFNPpGtm9tGLXMQ2JmZN0TEw4HrI+JK4HzgqszcHRG7gF3AK2qsUzWx40fSWDZ+NMy8r8CYH0nz5LFLbdPlURtarMw8ABwoP78nIm4GTgbOBdbKl+2luEO3HT8ryI4fSZIkSUszqlNuWVN2GsxRG5pZRGyjWKP3g8DWslOIzDwQESeO+J4LgAsAtm7dyvr6+tCfvbGxMfJri7Jz+8GF/46tRy7n98zTpH8HO34kSeoYRzpIUvs4akOziogtwDuAl2fmlyOi0vdl5h5gD8COHTtybW1t6OvW19cZ9bVFWUZn8M7tB7loX7u6Rva/cG2i17fr3UmSJGmludCnVsG8Rm0scoRG1RESVX//tCMumjZaY9kjYnoi4nCKTp9LMvOd5dOfj4iTytycBNxRS3GqnR0/UgNUHfLs1XmpGtdJkCS11TxHbSxyhEbVkRhVRyZMO7KjaaM1Jh2JMQ9RhOTNwM2Z+dq+L10OnAfsLj9etvTi1AjN+R8yJ14FGs5h/5JWjOskSFo6O501K0dtaEpnAi8G9kXER8rnfp6iw+fSiHgJ8BngufWUp7p1ruNHkiTXSZBUEzudG67JF4kdtdENdVxwz8xrgFFDw86a2y9Sa9nxI0lz0OSG5KS69F6gW+skzFv/+5nH+5v3+6hxnQRHbWgqdjprRo7akLQQdvxooWw8a1pmR/PQtXUS5q1/HYJ5vL95v4861kkotWrUhtO5m2kRt1Sedwd01c7aN1xSbYDJ9pOPmen3Nm2R3irm+fdw1IakRbHjR4vWqsazGsXsaCauk6BpOWpDs1rULZXn3QFdV2ftqN/btEV6q6ixg3pidhJLq6tde1a1jo1nTcvsaBauk6B5mfeojUVMGazrlsrDft6w97fv9rsr/t5KL5u7ef897HSWJDXN2I4fp1toXhYx5HnQItfg6LesYciDQ56rvre6GtmL2vaLzk4T1zapalTds7ynpr2XKblOgma2iFEbi5gyWNctlYf9vGHvr66pjFXNc8SGnc6SpCaqMuLH6Raa2aKGPA9a5Boc/ZbViB0c8jzvRvu8LWK48zKy08S1Taoatc1neU9Ney/TWPQ6CQ6XX446t7OjNjQlO50lSY0ztuPH6RaalY1nTcvsSKqDozY0LRfnlSQ10URr/CxqusU8p+c0aYrCMqYdNf3WuW1rPHftNtZt1rbstIH51qD+TOzcfnDkaK8VHLnkqA1JktQZlTt+FjndYp7Tc5o0RWEZ045acOtcG8+altmRVIumjNqYd2etnb+SpEVzOnwzVer4cbqFptWUxrPax+xIkjQdO/kkSf0OGfeCCtMtwOkWkiRJkiRJjVNlxI/TLSoYdmVl2HoJDmmTJEmSJEnLUuWuXk63kCRJkiRJaqGxU70kSZIkSZLUThPdzl2S1CyjFvDc7NbckiRJUp1chH657PiRJEmSJAHzPyH3BF+qnx0/DeUOUpIkSW1i+1WSmsk1fiRJkiRJkjrKjh9JkiRJkqSOsuNHkiRJkqQWi4i3RMQdEfE3fc8dFxFXRsQt5cdj66xR9XGNH0mSJE1s2Hou3lFQml5EvAV4JnBHZp5RPncc8HZgG7AfeF5m3llXjWq0i4E3Am/te24XcFVm7o6IXeXjV0z6g3v7e/fx7eWIH0mSJEmq38XAOQPP9U7cTwOuKh9LD5KZHwC+NPD0ucDe8vO9wLOXWZOawxE/S+bdDiRp8bxqKklqm8z8QERsG3j6XGCt/HwvsM4UIza0srZm5gGAzDwQESfWXZDqYcePJKmLLmZBw51XkRctpOrseNaceeKuhYuIC4ALALZu3cr6+voDvr5z+0EAth55/+dd0sb3Nfg3GseOH0lS53jVVLPwxF0zuhg7nlWDYSfvGxsb950gtu3Etqo2nrTD5CfuU/p8RJxUdhqeBNwx7EWZuQfYA7Bjx45cW1t7wNfP71vj56J93etCaOP72v/CtYle35p359XGdmpK49n8tFMT8mN2OqXyVdN5NZ7fcMlllV63c3ully1UWxvOsJDG88V44q4p2fGsOat04g7DT97X19fpncR3dVHeNp60w+Qn7lO6HDgP2F1+rNYwUeeM/R/ShBMvtdrF2HjW9C7G/KgGq9h4bmvDGebfePbEXQtQqeN53HSLO750d6UO5SZ0Jk+jjR3QSxi14Ym7KomIP6A4Th0fEbcBr6LIzaUR8RLgM8Bz66tQdarSwrsYT7w0JRvPmoX50ZxVvmoqDTHTiLGe/pFj47TtBBg8cZ/VuOkWb7jkstZ20FbRxg7oeXY8e+KuWWTmC0Z86aylFqJGGrtn9cRLCzCXxvMwoxrUbWuE9gw2oJs+bWRJjee5XDXtt9mJWFey0wZeNVVXbHby3j9ybJw2jixb9RP3TdjxrLE8cZe0KNMemed64l7l6lfbTmDAE695GHfla9CoBnUbG8/Qvgb0khrPlUySnc1OxMzO8njVVA03txN31w5bSXY8S5Jqs/CzgionX1WufrXx5GvVT7w24VUvzcL8aCyvmmoBPHFXJXY8S5KaZtpeCU+8NAsbz5qF+ZG0UJ64axZ2PEuSmmbajh9PvFSJjWfNwvxIqoMn7pIkqUuq3M7dEy9NzcazZmF+JEmSJGk2Ve7q5YmXJEmSJElSCx1SdwGSJEmSJElaDDt+JEmSJEmSOqpd9xqXpDnZtuuK+z7fuf0g5/c9liRJkqSuqL3jZ9uuKzzpkiRJkiRJWgCnekmSJEmSJHWUHT+SJEmSJEkdZcePJEmSJElSR9W+xo80i20Da0O5XpQkSZIkSfdzxI8kSZIkSVJH2fEjSZIkSZLUUXb8SJIkSZIkdZQdP5IkSZIkSR1lx48kSZIkSVJHzdTxExHnRMTHI+LWiNg1r6K0GsyPpmV2NAvzo1mYH03L7GgW5kezMD+auuMnIg4F3gT8EHA68IKIOH1ehanbzI+mZXY0C/OjWZgfTcvsaBbmR7MwP4LZRvw8Cbg1Mz+ZmV8D/hA4dz5laQWYH03L7GgW5kezMD+altnRLMyPZmF+xGEzfO/JwGf7Ht8GPHnwRRFxAXBB+XAjIj4++JqfguOBL8xQSyO18X3FhUOfftQCftXc8tOvjdt8M217P0vKz9yz07btXEUb31ML8tO6bTqJNmamp2XHrtZu5yramKMW7Hv6tW77TsL8jOSxaxNtzA0069hVtd3c1m09Thvf16T5maXjJ4Y8lw96InMPsGfTHxRxXWbumKGWRurq+5qTueXnAT+0Y9u8a+9nTuaenS5u5y6+pzmZOj9d36Zdf39zMvP+p+vbuevvbwZzOXZ1fft2/f3NwGPXJlbhPc5obH6qtpu7uq27+r76zTLV6zbg1L7HpwCfm60crRDzo2mZHc3C/GgW5kfTMjuahfnRLMyPZur4uRY4LSIeHREPAZ4PXD6fsrQCzI+mZXY0C/OjWZgfTcvsaBbmR7MwP5p+qldmHoyIlwLvBQ4F3pKZN0354ypP5WmZrr6vmc05P/26ts279n5mtqDsdHE7d/E9zWzG/HR9m3b9/c1sTvufrm/nrr+/qczx2NX17dv19zcVj11jrcJ7nJrn7ZV09X3dJzIfND1UkiRJkiRJHTDLVC9JkiRJkiQ1mB0/kiRJkiRJHdWYjp+IeE1EfCwiboyId0XEI+quaVoRcU5EfDwibo2IXXXXs2q6kiVztFzmRuN0JSODzMzymSXNoov5MTuL1cXMgLmpS9fytCo5akzHD3AlcEZmPg74BPDKmuuZSkQcCrwJ+CHgdOAFEXF6vVWtnNZnyRzVwtxonNZnZJCZqY1Z0iw6lR+zsxSdygyYm5p1Jk+rlKPGdPxk5p9l5sHy4V8Dp9RZzzAR8dCIeHNEfDoi7omID0fEDw287ElAAn8HfB/wh8C5y651lTU1S5vlJyK2RURGxEZEbAD3AN/MzE9m5tcwRwvX1Nz0jNv/RMTDgEuBU4EPA+/D3MxV0zMyypjs/CLwKOBG4EvAY4GbIuKJddW7CtqUpQr7nudFxM3Al4GtwOM8bi1Wx/LzY8CnKfZDvw0cj9mZuzZlpl9EvC0iDkTElyPiE2Veen6CYp/zNxR3q/o/mJulaEueRuUnIh4SEX8cEfuBg8AXV+GcqzEdPwP+A/CeuosY4jDgs8D3A8cAvwRcGhHb+l7z3cBxwIHy8W3AyUusUQ/UpCxVyc8jMnML8KPAn/c9b46Wq0m56RmXnz0UDaBLKfZB/wVzs0hNzMgom2XnJuDizNxS7nsupjiBv6GeUldS07M0Mj8RcTLwNuCngfOAvwR+PyJOxP3PsrQ5P98P/FfgN4G3Ap8C/gCzs2hNz0y//wZsy8yjgWcBvx4RT4yI44HdwAcp2jzXAc/B3NShyXkamp/ya9cALwLuAv6h73s6u/85bJm/LCLeBzxyyJd+ITMvK1/zCxQ9b5css7YqMvNe4NV9T707Ij4FPBHYXz73Y8CHgO/s/9Zl1LdK2pilMfm5fuDlMexHLKi0ldHG3PRslp+IeCjFAe2lwPdl5jeA68uhquZmAm3OyChj9j2D+5p/DXwsM83NjLqSpTH5uQ24KzPfExHPpTjBvxf4tt63L7PWLlmR/Pw/wB8BtwNnAL9Wfn45ZmdiXclMv8y8qf9h+e/buH//86nM/KeIeDXwMuARy66xq7qQp1H5yczrgdcBRMQ3h33r4qtbvqV2/GTm0zb7ekScBzwTOKsNjc6I2Ap8O8UVU8pGzz08sCF9CvC55VfXbV3I0mB+Sp+OiAQ+Ajys73lzNAddyE3PQH6eTDFU/unAj0TEkyka2uZmQl3KyCgD2TmWYnogEfEoiosWv1lfdd3R1SwN5OcW4OaIeBbFvuaJwFcppg6u4f5naiuSn39F0Wa+jWI/1Gs//wvg5loKbLEOZ+a3gfOBIymmsv8p8BsUGToVig7GiPhSXTV2UVfyNCI//b4BnND3uLNt58ZM9YqIc4BXAM/KzK/UXc84EXE4Re/m3sz8WERsoRiu+h+A0yg61Q4Dnk9x5UJL0oYsDeYH+ALwLynmuPcazt8TEY+OiIdgjhauDbnpGZKfUyiuln6CogH968BeiqkX5mZO2pSRUYZk51rgtIh4NPDvga9QZEcL1NYsDeanHF34VuD3KaYn/wvgl4Gv43FrYbqSH4oTsOcB/0TRGfQaiivt34/Zmau2ZgYgM38CeDjwvcA7KdrIWyg6nk/rays/HPjb2gpdIW3K04j89PsacMoqnHNFUzroIuJW4KHAF8un/joz/2ONJY0UEYdQNHKOBs7NzK9HxEXA3Zn5qxHxdOAyivmCb8rM36ix3JXT9CwNy8+Q1zySYp2ov6O4AvYWc7RYTc9Nz4j9z38BLqQYJfYDFMNXTwauzsxn1lVr17QlI6OM2veUx6zXAduA/52Zz6mrxlXRxiyN2Pc8DXg78IMU60L9Z+AiiuPX73jcWoyu5Kd8/ieBl1Nccf86xXotb87MC2oqtZPamJlhIuJ3KDp3vg04HHg3xfHrUOAo4Ccz8x21Fbgi2pqnXn4y87f6nrsNeAPwEoocdfacqzEdP20REQG8haKB/PTM/Mfy+Y9QXHXvrXB+AnA3cGFmXrj8StVEo/Iz5HVbgb+nWOz57uVVqCbbZP9zFsXCeg/r3WUhIv438L7MfH1N5apBxu17IuJM4M+AR2bmPcuvUE22yb7nZ4AzM/Pf9L32T4BrMtMpgwImavt8O8VUjFMy887lVai2iIjfpVhH7CbgvMw8s3z+KIoL7t9djiaTHqSXn8x8Wd9ztwEvysz12gpbksZM9WqR/0Fxu9sfHjhwnUUx1eLx5b/PAT8OvGnJ9anZhuYnIp4cEd8REYdExLcAvwWs2+mjAaP2Px8APgO8MiIOK0/i1yhubyrB6Oz0nAe8w04fjTAqP9cC3xsRjweIiCdQDKe/cekVqslGtX2OiIgzovCtFHenfL2dPgKIiBMj4vkRsSUiDo2IHwReALwfeBdwRkQ8JyKOoJhieqOdPuoZkx8i4qFldgAeUu6Pht1cpzMc8TOBcuHL/RRzAw/2fenHM/OSgdfuB34sM9+3tALVaJvlB/gmxRpRJ1LcSvlK4Ocy8++XXKYaatz+JyK+C/hd4HEUCz3/Qma+a+mFqnEqZOcIihGGz8nMq2ooUQ1WIT8vpZiqs5X7p7hftOw61Uxj2j5XUFy4+DaKm6P8HvCL5dpRWnERcQLwxxRrhx1C0bb5rcz8n+XXnwa8kWJ9zA8C52fm/nqqVdNUyM9+iuz0e3SXM2THjyRJkiRJUkc51UuSJEmSJKmj7PiRJEmSJEnqKDt+JEmSJEmSOsqOH0mSJEmSpI46bJm/7Pjjj89t27bd9/jee+/lqKOOWmYJS9WV93f99dd/ITNPqLuO/vx0ZduO04X32YT8DO57erqwfatq63ttcn562rptq2rr+2tCdmA189OF99SE/IzLTr+2b/M21z+s9rblZ5g2/k26UHNbstPGbd2vq/Vvmp/MXNq/Jz7xidnv6quvzi7ryvsDrssl5mTUv/78dGXbjtOF99mE/Azue3q6sH2raut7bXJ+etq6batq6/trQnZyRfPThffUhPyMy06/tm/zNtc/rPa25afq+2q6LtTcluy0cVv362r9m+XHqV6SJEmSJEkdZcePFioijoiID0XERyPipoj4lfL54yLiyoi4pfx4bN21qpki4tCI+HBEvLt8bHYkSVIn2e6RtAhLXeNH87dt1xWVXrd/9zMWXMlIXwWempkbEXE4cE1EvAf4EeCqzNwdEbuAXcAr6ipSm6s5Zy8DbgaOLh/vwuy0Sgv2U2ooszNfbk81xWAWd24/yPlD8rmiWbTdMyfu8zTMtl1XjNzn9OtaLhzxo4UqpxtulA8PL/8lcC6wt3x+L/Ds5VenpouIU4BnAL/b97TZkSRJnWO7R9KiOOJHCxcRhwLXA48B3pSZH4yIrZl5ACAzD0TEibUWqaZ6HfBzwMP7nquUnYi4ALgAYOvWrayvrz/oNRsbG0Of76I63+vO7QcrvW5V/haSJI3wOqZs90C1tk9VbWwjDdbchvZHG7ez2smOnzEcIji7zPwG8PiIeATwrog4o+r3jjqArcpOsinvs44DZ0Q8E7gjM6+PiLVJvz8z9wB7AHbs2JFraw/+Eevr6wx7vovqfK/jhtL27H/h2mILkSSpoWZt90C1tk9VbWwjDdbchvZHG7ez2smOHy1NZt4VEevAOcDnI+Kk8srFScAdI75n6AFsVXaSTXmfNR04zwSeFRFPB44Ajo6It1ExO5I0rYg4AvgA8FCKttIfZ+arIuI44O3ANmA/8LzMvLOuOiV1iu0eTc3jlsax40cLFREnAF8vO32OBJ4GXAhcDpwH7C4/XlZflWqizHwl8EqA8srXz2TmiyLiNZgdSYvljQlq4ChrrTLbPZqRxy1tysWdtWgnAVdHxI3AtcCVmfluioPX2RFxC3B2+ViqwuxIWihvTCCpQWz3aCyPWxrHET9aqMy8EXjCkOe/CJy1/IrURpm5DqyXn5sdSQs3y40JJllgdZlruS1rvbZp31PV+t5wSbUBD9tPPmbiGqQmsN2jaSzruAXLPXbtu/3uSq+rus/fuf0gW48cf8xpwjqro0yz/e34kSRJGjDLjQkmWWB1mWu5LWu9tmnfU9X6qnLBeEmrZFnHLWj3sev8XVewc/tBLtq3eVdIk48h02x/O36WzPnrkiS1xzQ3JpAkqS4etzSMa/xIkjonIo6IiA9FxEcj4qaI+JXy+eMi4sqIuKX8eGzdtap5IuKE8oopfTcm+Bj335gAXGRVktQQHrc0jiN+5mTYSJ6d2w9OPWy56sggSdJQ3t1CszgJ2Fuul3AIcGlmvjsi/gq4NCJeAnwGeG6dRap5vKWypJp43NKm7PiRJHVOZiYw6u4Wa+XzeykWz7TjRw/gjQk0AzudJS2dxy2NY8ePJKmTunhXpjrU+f6WdRcqaV7sdJam51qo0uLY8SNJ6qQu3pWpDnW+v2XdhUqap1k6nSVJWgQ7fqQV5lpSWgXe3ULSMs3S6TzJaMN+bRt5ODiab+uRw0f4veGS6uvQbj/5mJnrmkbbtr2k1WTHjySpcyLiBODrZadP7+4WF3L/3S12490tJC3QNJ3Ok4w27Ne2kYeDo/l2bj/IRftmOy2pa+Rf27a91FVe0N7c2D1sRJwKvBV4JPBNYE9mvt67E7SLc2bny+0pNZ53t5C0dF3tdLbdI0ntVqVr/SCwMzNviIiHA9dHxJXA+Xh3AklSA3l3C0k1sdNZktQ4Yzt+yoXoeovR3RMRNwMn490JJEmSpPvY6SxJ3dC1kY4TTaaNiG0UB7MPAjPfErfOxdD23X53pdft3D797xi1UF2TuThdNzjHVZIkSZIEE3T8RMQW4B3AyzPzyxFR6fs2W6SuDbeIncU8FqpbNm+JK0nt17WrVJIkSZpepV6JiDicotPnksx8Z/m0t8SVlsyRPJIkSZKkSRwy7gVRDO15M3BzZr6270u9uxNAC+9OIEmSJEmS1HVjO36AM4EXA0+NiI+U/55OcTvKsyPiFuDs8rH0ABFxakRcHRE3R8RNEfGy8vnjIuLKiLil/Hhs3bVKkiRJktQ1Ve7qdQ0wakEf706gcQ4COzPzhoh4OHB9RFwJnA9clZm7I2IXsAvvCidJkiRJ0ly1ZuVhF6psp/LOb727v90TETcDJwPnAmvly/YC69jxI7WKa05pkMfqbvDvKGmebC9I9WtNx4/aLyK2AU8APghsLTuFKBcIP3HE91wAXACwdevW+243v7GxUeut53duP1jpdW+4ZLalr7Ye+cCfsXP7TD9u4er8m0jSvETEqcBbgUcC3wT2ZObrI+I44O3ANmA/8LzMvLOuOiVJAo9bGs+OHy1FRGyhuDPcyzPzy8Wa4eNl5h5gD8COHTtybW0NKDoYep/X4fwlXbnYuf0gF+1rz3/T/S9cq7sESZqHVk1TbvrV9KbXJ6ldRu1Tdm4/uLQ2egO16ril5WvPGaVaKyIOp+j0uSQz31k+/fmIOKkc7XMScEd9FUqSdD+nKUvTsZNPqofHLY1jx48WKoqhPW8Gbs7M1/Z96XLgPIq7wZ0HzDYnSpI0sSonaTu3H7yvxbjM39sU85ymPMw8pi5XnX48b6PqHnxPTatPkrps0cctmM+xa9/td1d63SKWuth65PyOTbMu7TFo+8nHjH3NNNvfjh8t2pnAi4F9EfGR8rmfp+jwuTQiXgJ8BnhuPeVJ6iLnumse5j1NeZh5TF2ua2rDqOm9g++pafVJUlct47gF7T52QbOX06hy7Jpm+zfz3aozMvMaYNQe56xl1iJppTjXXTNxmrKmYaezpLp43NJm7PiRJHWOc901C6cpd0NNt6W307nhasqFtFAetzSOHT+SpE5rwxot8zbPNVW2Hll9/voi5uFXsYDt7zRlTcVOZ0k18bilTdnxI0nqrLas0TJv85w33+R58D3zXsvFacqah2UssNqz6A7oRS/QPY+FVqu+/6q/pynbXqrC45bGaXZLTtLK6uo6CW0YYt6mOy5txrnukuqyrAVWexbdAb3oRVjn0cFctQO46nup+vPmue272vaRVL/aO366coKhZmvDybYexHUSNDXnukuqi53OmoFtH0kLcUjdBUjSMJl5IDNvKD+/B+hfJ2Fv+bK9wLNrKVBN15vr/tSI+Ej57+kUHT5nR8QtwNnlY0maiwqdzmCns0aw7SNpUWof8SNJ4yxqnYR5zsvfd/vdlV5XdfHbea8XMMl7XfRaDqPM8z23ba67o1+lznCBVc3FMteIGmaebaRltSumXSuq6vus2tbbfvIxlX+3a0RpWez4Uat5stR9i1wnYZ7z8ue9/sG8F6ud5L0uei2HUeb9niVp2drW6axmWvYaUcM0uY00yrRrRdW1PhQ08wYR6iY7fiQ11iqvkzDvTs2d2w/edx9hSZLUTKvc9pG0OHb8SGokF+eVJGk63tSinWz7SFoUO34kNZXrJEiSpFVi20fSQtjxI6mRXCdBkppt1KiSndsP1rZWmFZXF9Z9tO0jaVHGdvxExFuAZwJ3ZOYZ5XPHAW8HtgH7gedl5p2LK1OSJEmS1BRd6GxbBKdaqomqjPi5GHgj8Na+53YBV2Xm7ojYVT5+xfzLm5w7IEmSJEmzqHpOcfE5Ry24Ekma3diOn8z8QERsG3j6XLjvBjF7gXUa0vEjSVJTeDFCkqRm81g9X27PZpp2jZ+tmXkAoLyt4IlzrEmSJEnSgnmCJkmrYeGLO0fEBcAFAFu3bmV9ff2+r21sbLBz+zcWXUJtth5ZLHDYJv1/n3lwjShJkiRJkuozbcfP5yPipHK0z0nAHaNemJl7gD0AO3bsyLW1tfu+tr6+zkXX3DtlCc23c/tBLtrXrhun7X/h2rx/5MW4RpQkqUW8aCFJahuPXdrMtL0SlwPnAbvLj5fNrSJ1imtESc1hx6ZU2cW06KKFJEl47OqEKu31ndsP3ncyXVWV27n/AcVJ+vERcRvwKooOn0sj4iXAZ4DnTvh7tdoqrxE1aqrgxsYG6+vrrZtKN6m2TRec91RBaVpe9dIsvGghSWobj13aTJW7er1gxJfOmnMt0oOMmiq4vr7O2toa53d8BEPbpgsuYKqgNK2L8aqX5ssbW6gSO54lNYjHLgFLWNxZGqLyGlFS3Zwe1U5e9VKdNruxxaDeCNZh9t1+d6Xft3P7pBUulqNV7XiW1C6THLdg82NXG/b/bTtODdp65OTHLjt+VAfXiJJUB696aRZzubHFoN4I1mHaOqp11Uer2vEsqUEqHbsmOW5B+49dbTtODdq5/SDPG/M3GtTed6tWcI0oNdW2XVewc/vBVhyctHzzGrHR5qtJPW24Krak9cW8aKFZVOp4nvSqe8/gfqjp/2cHtWE/M8pmxwCpAeZy7BocAW8bun3s+NFCuUaUpAZxxMYU2nBVbN4jNrxoobpMetW9Z3A/1LZ9Txv2M6NcfM5RI48B0jJ57NJm2rmHlSRpco7YUCVetNACuL6hpIXy2KXNHFJ3AZIkzVt51euvgO+IiNvKK127gbMj4hbg7PKxJC1Dr+MZ7HiWJC2ZI34kSZ3jVS9JdXG6hSSpaez4kSRJkubEjmdJUtPY8SNJ0oT6727hnS0kSZLUZHb8SJIkLZEdh5IkaZlc3FmSJEmSJKmj7PiRJEmSJEnqKKd6SZIkSZKAYjqq01ClbrHjR5IkSWqBbSNOxD1JlyRtxqlekiRJkiRJHWXHjyRJkiRJUkfZ8SNJkiRJktRRrvEjSZIkSR03ao0oSd3niB9JkiRJkqSOsuNHkiRJkiSpo2bq+ImIcyLi4xFxa0TsmldRWg3mR9MyO5qF+dEszI+mZXY0C/OjWZgfTd3xExGHAm8Cfgg4HXhBRJw+r8LUbeZH0zI7moX50SzMj6ZldjQL86NZmB/BbCN+ngTcmpmfzMyvAX8InDufsrQCzI+mZXY0C/OjWZgfTcvsaBbmR7MwP5rprl4nA5/te3wb8OTBF0XEBcAF5cONiPh435ePB74wQw2N9lMtfH9x4dCnH7WAXzVrflq3bafRtgwtKT/z2PcA7du+s2jDe21bfnrasG1n0Yb316Jj14O0YftOqm3vqa37nn5t2+aD2lz/Uy4cWnur8jNMG/8mbau53PcM1lzLsWvS7LRtWw/qQv0/9aKh9Y/MzywdPzHkuXzQE5l7gD1Df0DEdZm5Y4YaGq3r729GM+VnVbbtqrzPCc2877nvB63Q9l2l9zrG3PJz3w/s+Lbt+vubkPmpoIvvaQ7mnp0H/PCWb/M217+k2hean6G/sIV/E2se/WuGPPeA/EyanTZu636rWP8sU71uA07te3wK8LkZfp5Wi/nRtMyOZmF+NAvzo2mZHc3C/GgW5kczdfxcC5wWEY+OiIcAzwcun09ZWgHmR9MyO5qF+dEszI+mZXY0C/OjWZgfTT/VKzMPRsRLgfcChwJvycybJvwxcxmK2GBdf39Tm0N+VmXbrsr7rGxO+56eVdq+q/ReR5pzfnq6vm27/v4qMz+VdfE9zWRB2enX9m3e5voXXvsS8jNMG/8m1jyEx66hVq7+yHzQ9FBJkiRJkiR1wCxTvSRJkiRJktRgdvxIkiRJkiR1VO0dPxHxmoj4WETcGBHviohH1F3TrCLinIj4eETcGhG76q6nq7qYnX7maDnMkeahqzkyP8vRlfyYl3q1MUdtzkxEnBoRV0fEzRFxU0S8rO6a5q0tmWpjjrqQn7bkY1Ab89IzS25q7/gBrgTOyMzHAZ8AXllzPTOJiEOBNwE/BJwOvCAiTq+3qs7qVHb6maOlMkeah87lyPwsVevzY14aoVU56kBmDgI7M/OxwPcAP9my+qtofKZanKMu5Kfx+RjU4rz0TJ2b2jt+MvPPMvNg+fCvgVPqrGeciHhbRByIiC9HxCci4sfK578nIq4E7gROBS4EvgX4Q+Dc+irurrZlBzbNz+kRcV1E3BkRd1K8nwOZ+cnM/BrmaGHalKNR+Rl4zasiIiPiacCTgFvN0eI1PUeb7Hu2lXnZ6Pv3S+W3mZ8laWt+yq89LCJ+G/gi8K3AxealHk3N0Sb5+UXgUcCNwJeAxwI3RcQT66p1Epl5IDNvKD+/B7gZOLnequar7kyN2fc8LyJuBr4MbAUe16Z9TxfyU3c+xhmRnycBtwJPBf4W+E7gTyLin9VZa1Wz5Kb2jp8B/wF4T91FjPHfgG2ZeTTwLODXywPUsRS3VftJ4BLgHuD3gNto2X/ilmpDdmB0fj4H/FvgOOB4ikZQf++tOVqOpudoVH4AiIhvo8jRgfKpk4HP9n2/OVqOJuZo0+wAj8jMLeW/XyufMz/1aFt+9lAcu34aeCvwX8rnzUu9mpSjUfm5iaKjcEtmbgEupjiJv6G2SqcUEduAJwAfrLmURaojU0OzExEnA2+j2O+cB/wl8PsRcSIt3Pd0JD9N2uf0PCg/wPcCXwf+K0UH4Y8DXwX+oK4ipzVpbg5baDWliHgf8MghX/qFzLysfM0vUAxdumQZNU0rM2/qf1j++7bMvBQgIp4LfAP4beDPKd5PLrvOruhSdmDT/FwP3AUQEVE+f/Tgty+jxi7qSo5G5Qe4vnzujcArKPY/ADHsxyyswI5rc442yc4XN/k28zNHXcxPRGxQNKZPAX6weGleP/BazVEbc7TJ/mcwH/8a+Fhmtio3EbEFeAfw8sz8ct31TKrJmdokO4cBd2Xme8pzr88C95Zf6722FZqenybnY5wR+XkkxejUP8rMmyLiu4FrgX8fEd+WmX9XQ6kTmyY3S+n4ycynbfb1iDgPeCZwVht29uWQ5vOBI4EPA3/a9+XbKKZ6fR/FlYxTKEZzaApdyw5snp+IuAvYQjEar3/HY45m0KUcjcpP2fD5Wmb+adF3CNy/P+oxRzNoe45GZOf48sufjoikmK//s5n5BczPXHU0Pz8CfBr4FeDfA4dGxHsy8x2Yl4Voa45G5Gc75T4mIh5FMeXiN2sqcSoRcTjFydclmfnOuuuZRtMzNSI7/wjcHBHPotjPPJFi1MaNwBot2fe0IT9Nz8c4Q/LzbopjV6+xfArw+fLzM3jg+VcjTZ2bzKz1H3AOxfy6E+quZcK6D6W4MvGLwOF9zx9G0Vi+C3gK8FHgu+qut4v/2pqdsvah+Sm/dhTwUorpOo8GHmKOFvq3aF2OBvND0Vl4C/Do8uv7gaeV+6NPmiNzNCY7O8qsbAX+GHhv+VrzY37G5efnKa6gvpqiUf05iqvujzMv5qhCfvr3Ma8GNtqUGYoTx7cCr6u7lgW+x0Zkali7GXhJmZmDwDcpphq15ljVhfw0JR8T5ueI8lj1JeC7KToLLy0z9IK6a11kbqL8AbWJiFuBh3L/cPO/zsz/WGNJE4mI3wH+NjN/q3z8GIrFrb4OfAV4S2b+Ro0ldlbbswMPzk/f84dQdB7+Q/mUOVqQNueolx+KxTHvzsxfLZ/fD/xYZr4vIp4OvI7ioGeOFqRtOdpk3/NIik7nYzLzy+ZnOdqaH4pcXAg8LDMPlnm5FPgn4L+bl+VqS4769z99+5htwP/OzOfUWdskIuJfA38B7KM4aQT4+cz809Hf1S5Ny1TfvudvgbdTTDG9AfjPwEUUx6/facO+pwv5aVo+xunLz60Ua/EeC3wN+A1gF/DMzPyL+iocb5bcLGWq12Yy8zF11zCjwyjnk5bDVN8H/GJm/k6tVa2ADmQH+vIz4BCKq2H/NjM/vNySVkvLc9TLz/cDp0TET5TPnwBcGhEXZuaFPHA6qhaghTkate/pXQ0KgLIhYX4WrMX5ubz/ySymml4NvC8zX19LZSusRTm6b/9TZuZu4M8opmO0RmZew/C10DqjgZnqZechwAcy87ry+ddHxFOAazKzFdMFu5CfBuZjnMMo1lb9LYpRzgBExLdTjAb6m7oKq2qW3DTtrl6NFhEnRsTzI2JLRBwaET8IvAB4f7m6/PuBN9npo2HG5OfsiHhC+fzRwGuBOylu0Sdtmh/gLIp5yY8v/32O4i4Fb6qpXDXImH3PkyPiOyLikIj4FuC3gPXMvLveqtUUY/Y9HwA+A7wyIg6LiDMp1td4b30Vq0nG5KfnPOAdWdyaWALGZuda4Hsj4vHla59AcbemG2srWI0ypu1zREScEYVvpbg75esz8856q16s2qd6tUlEnECx/sG/oOg0+zTwW5n5PyPiVRTzk+/t/54sbk8pjcvPc4Ffo1hg7B8pDmi7MtMDmIDN8zPktfspp3ottUg10ph9zwsobml6IsVtlK8Efi4z/76uetUs4/Y9EfFdwO9SrOvzaYo7vbyrpnLVMBXycwTw98BzMvOq2gpV41TIzkuBl1OM3PgHiovvF9VTrZpmTNvnERQXLr4NuIdi2tcvZuY3aip3Kez4kSRJkiRJ6iinekmSJEmSJHWUHT+SJEmSJEkdZcePpEYqF177UER8NCJuiohfKZ8/LiKujIhbyo/H1l2rmsf8SJIkSQXX+JHUSBERwFGZuRERhwPXAC8DfgT4UmbujohdwLGZ+Yo6a1XzmB9JkiSpcNgyf9nxxx+f27Zt2/Q19957L0cdddRyCpqTrtd8/fXXfyEzT1hwSWONy08b/w6Duvgeps1PFr3SG+XDw8t/CZxLcbtggL3AOrDpifvxxx+fJ5xwQuu37ShdyM0w9957Lx/72McakZ9t27Z1ejt38X215djVVl3NDcy275mnzbLT9u3f9fqbsP8Zlp+ub/cmq1p7U7MzSpv+JqtQ62b5WWrHz7Zt27juuus2fc36+jpra2vLKWhOul5zRHx6sdVUMy4/bfw7DOrie5glPxFxKHA98BiK23R+MCK2ZuYBgMw8EBEnjvjeC4ALALZu3cprXvMatmzZMm0pjbaxsdHJ97axscEP//APNyI/v/mbv9np7dzF9/WUpzylFceuturC8WqU9fX1RuRns+y0fft3vf4mtJ2H5afr273Jqtbe1OyM0qa/ySrUull+ltrxI0mTyMxvAI+PiEcA74qIMyb43j3AHoAdO3bkli1bWrOzn1SbDmSTWF9fn+n755mftbW1Tm/nLr4vSZIkFVzcWQvlAquah8y8i2JKzjnA5yPiJIDy4x31VaY2MD+SJElaZSs74mfbrisqvW7/7mcsuJLO+yrw1P4FViPiPRQLrF7Vt8DqLsass9FG5mx6EXEC8PXMvCsijgSeBlwIXA6cB+wuP15WX5X382/dLF3NT1XmbLW4/1Hb7Lv9bs6vkFsz2w3uo7QM43K2c/tBzt91xcrmbGU7frQc81xgVSvnJGBvuU7LIcClmfnuiPgr4NKIeAnwGeC5dRapxjI/kiRJEhU6fiLiCOADwEPL1/9xZr4qIo4D3g5sA/YDz8vMOxdXqtpqlgVWtboy80bgCUOe/yJw1vIrUpuYH83Cto+kOrjvkbQoVUb8rPRUHc1ulgVWB++ss9lirxsbGzMvBlvVvtvvrvS6ndur/bxe3ct8D4vShfcgaeXZ9pFUB/c9khZibMePU3U0L+VaG+v0LbBajvYZucDqsDvrjLLMO9NUmZc+if0vXAO6cXedLrwHSavNto+kOrjvkbQoldb4mWWqziQjNmB5owV2bj9Y6XVVamnjCIdl1dy2BVYlSYvXhoU+l9n2mdU82zRVtbHtU9XGxsb4F0kLssh9T13/b+e1j+rVX3Xk/faTj6n0umXo8j5T7VCp42eWqTqTjNiA5Y0WqDpiozcSYzNtHOGwxJpdYFWS1DrLbPvMap5tmqra2PapypMz1WmR+566/t/Oax/Vq7+Ofd6surzPVDtMdFevaabqtF2Vq5I7tx+8b+zlPH4edOd2hi6wKklqs1Vs+0iDVq392gSruO+pejtuSZOrclcvp+pIkrTCVu2kz7aPpuVdmTQL9z3S4q1am6anyogfp+pIkqRVYttH0/KuTHNU9QQNOnOS5r5H0kJUuauXU3UkSdLKsO2jaXlXJs3CfY+kRZlojR9J0mxWdXipJK2KZdwRbtl3CJr3neO2Hln9Z1a1zO3hHZrUNE4z1Th2/EgN0OsMGLdonZ0BkqS2WbUO72XcEW7Zdwia912U3nDJZVy0b76nIcu8g5N3aFIDOc1Um7LjR5IaaNVOlCSpa1bxrkyS6tHGaaa2dZfLjp85mWTxOUmSJHWPd2WSVJdZppmq++z4kTrIHnRJkmrhXZkk1WKWaaZV1xcbNMt6V/NeO2zcz5t0bbE61/FaxDpidvxIkh5g3iMY7WCUtCq8K5Okuk0zzbTq+mKDZlnvat5rh437eTu3H5xobbFlrhs2aBHriNnxI0mSJEmaiEtdNIfTTDWOHT+SJEmSJLWX00y1KTt+Gso1WiRJkiRJ43R5mqkjy+bDjh+pT9N3LE2vT5IkSZLULHb8SGqkiDgVeCvwSOCbwJ7MfH1EHAe8HdgG7Aeel5l31lVn3bbtuoKd2w9WWiBvlUYImh9JkiSp0LmOH0dESJ1xENiZmTdExMOB6yPiSuB84KrM3B0Ru4BdwCtqrFPNZH4kSZIkioWfJKlxMvNAZt5Qfn4PcDNwMnAusLd82V7g2bUUqEYzP5IkSVKhcyN+1CxOt9A8RMQ2igXrPghszcwDUJzcR8SJI77nAuACgK1bt7KxscH6+vrEv3vf7XdXet3O7RP/6LnZeiTs3H5w7Ouqvv8qP2sS02x3gI2Njbn8/nnkZ319faoM1ZWfSeqs8r6qZmLeGZs2O13hKGYtmhlTl3mzHOl+dvxo0ZxuoZlExBbgHcDLM/PLEVHp+zJzD7AHYMeOHbllyxbW1tYm/v1V1s6p287tB7lo3/jd+f4XrlX6efN+z1V/76B5nPTPKz9ra2usr69PnKG68jPJNq/yvqq+j3lnbNrsSJIk6X5jp3pFxKkRcXVE3BwRN0XEy8rnj4uIKyPilvLjsYsvV23jdAvNIiIOpzhpvyQz31k+/fmIOKn8+knAHXXVp2YzP5qWbR9JdXDfI2lRqoz4ccSG5mJe0y1GmXYqT795T3GZVNUpO/OyiGkU8/g7AEQxNOPNwM2Z+dq+L10OnAfsLj9eNvMvU+eYH83Ito+kOrjvkbQQYzt+ypPz3gn6PRHRP2JjrXzZXmAdd0AaYZ7TLUaZZhrGoLqn9VSdsjMvi5hGMY+/Q+lM4MXAvoj4SPncz1OcsF8aES8BPgM8dx6/TJ1jfjQ12z6S6uC+R9KiTHSGuegRGzD7aIE6Rmwse5RGv1kWTV3WopmbTbcos+N0Cz1IZl4DjOohPGuZtah9Vj0/Lmg5P8to+4wy77bFGy6pNsBt+8nHjH3NxsZG5Z9XdfHypizmPa+F5VdR1X1PnTdEaItF7Hvm3f5f9vnPos655rlvHGWZ516aj661pSp3/CxjxAbMPlqgjhEbyx6l0W+WRVPnNCpjU063kCS11bLaPqM0eXHw9fV1Lrrm3qX/3mWY5eTMu5lqHha175l3+3/Z+6g6z7lgtn3Uss69pFEq/c9pwogNbzfZWk63kCS1ThPaPnWp0uYqrrrP9wRs3ldXa7pa6xotmskq73skLc7YI7YjNjSLVZ9uITWJHehatK5kzLaPpuUaLZqF+x5Ny9GGGqfKpRpHbEiSpFVi20czW+T6UPNaL6SuNSoXsVbLMtdPWeB6Le57NC1HG2pTVe7q5YgNSZK0Mmz7aFaLXh9qXuuF1LWO1CLWalnmGlGLWq/FfY+m5WhDjVPf6liSWqNrq9pLagf3PWoj12iRVKdl3o1yltFv3hVutEWMKrTjR5IkSZoD12iRVKdl341yltFv3hVutEWMKrTjR5K0UI7aaK5tu65g5/aDtU33kDrINVok1cLRhtqMHT+SJElqjSbfPc41WqTuavKFLEcbahw7fiRJkiRJai9HG2pTdvxIkiRJktRSjjbUOHb8tFyThxxKkiRJapcmT6eUNB07fqQV5oFdkiRJkrrtkLoLkCRJkiRJ0mI44keSJEnSSnCZBEmryBE/kiRJkiRJHeWIH0mSpCVyfTVp/vx/JanJ6h5taMePVoKNgXaKiLcAzwTuyMwzyueOA94ObAP2A8/LzDvrqlHNZHYkSZKkgh0/kprsYuCNwFv7ntsFXJWZuyNiV/n4FTXUpma7GLMjaUV5wUvD7Lv9bs43G9JKco0fLVREvCUi7oiIv+l77riIuDIibik/HltnjWquzPwA8KWBp88F9paf7wWevcya1A5mR5IkSSqMHfHjcHnN6GK86q752pqZBwAy80BEnDjsRRFxAXABwNatW9nY2GB9ff2+r++7/e5Kv2zn9lnLXbytR8LO7QfrLmNm/X8fgI2NjXn/ikrZgQfnZ319/QEZMj/NMpidWdn2kVQH9z2SFqXKVK+L8cRdU8rMD0TEtoGnzwXWys/3AuuYH81ZZu4B9gDs2LEjt2zZwtra2n1f79JQ553bD3LRvvbP3N3/wrUHPJ73yfwkBvOztrbG+vr6fRkyP80ymJ05uBjbPtJK22y63M7tB+87Dsx5IdaLcd8jaQHGtvQ8cdcCzHTVfZTBER392nI1u+1X3gdHRSzI5yPipDI7JwF3LPKXqVPMjiqx7aNZOGpD03LfI2lRpr3Et5ATdxh98t7kk+E2nKwPm0JR59X0qoZddR+l/2r8oLZcnW/7lff9L1zb9O8wJ5cD5wG7y4+XLfKXqVPMjmYxt7ZP09sMVbSh7VPVAqaZXoyjNjQ/c9v3tP3/bVvqH3aOtYxzLzudtZmFn2FOcuIOo0/em3zi3oaT9WFTKBZ8cr4Zr7qrkoj4A4qrXMdHxG3AqyhO2i+NiJcAnwGeW1+FaiqzozqNa/s0uU1TVRvaPlXNe5qpozZUl3H7njdcclmr/9+2Zb8zbPrxks69LsZOZ40w7f8cT9w1C6+6q5LMfMGIL5211ELUOmZHC2DbR7OY6sYEozqhxo0eaPqoiLaM3Bilv/4ljKB336NK7HTWZqbt+PHEXZV41V2S1BG2fbRwVUfKjxs90PRRZW0ZuTFKf/0LWFx+kPsezWKunc6DZpnCtuzO37Z0OC9qzdQqt3P3xF1T86q7JKltbPtoAeYyaqN3p6n+u0qpO9z3qC6TLs/SM8sUtmXvw9rS4byoNVOr3NXLE3dJkrQybPtoARy1obHc92gBnCooAA6puwBJkiSpK8pRG38FfEdE3FaO1NgNnB0RtwBnl48ladF6nc5gp/NKq32s07aBIV4OXZUkSVJbOWpDUh0WOVVw8Jy9x3P39qi940eSJEmSJE3PTmdtxo4ftVp/77M9zpIkSZIkPZAdP5IkSZIkzcGwaVHDLlDv3/2MZZUkubizJEmSJElSV9nxI0mSJEmS1FF2/EiSJEmSJHWUHT+SJEmSJEkdZcePJEmSJElSR3lXL0mSJEmSpJpt23XF0LvADZr0rnB2/KyIwdsKjgqTtxWUJEmSJKk7nOolSZIkSZLUUXb8SJIkSZIkdZQdP5IkSZIkSR01U8dPRJwTER+PiFsjYte8itJqMD+altnRLMyPZmF+NC2zo1mYH83C/Gjqjp+IOBR4E/BDwOnACyLi9HkVpm4zP5qW2dEszI9mYX40LbOjWZgfzcL8CGYb8fMk4NbM/GRmfg34Q+Dc+ZSlFWB+NC2zo1mYH83C/GhaZkezMD+ahfnRTLdzPxn4bN/j24AnD74oIi4ALigfbkTExzf7oT8FxwNfmKGupetSzXHh0Jc/agElzD0/bfw7DGr7eyjzM/ge5p2fqbLzlKc85Yu0eNtupu256Rmy/zmehuSn3Pd0YjsP6kJ+Rhy7vmMBv2ohbZ826kJuelqw73mQtm//LtW/pLbzvPLTme3eNsNqb9J517THrTb9TbpW66T5maXjJ4Y8lw96InMPsKfyD424LjN3zFDX0lnzdCUMeW6m/DTgPc3M91DtVwx5bmx2urBtR+nqeyvf17Z5/9ghz1Xa93R8O3fyfS3ixw55bua2Txt1NTfQvH3P0B/W8u1v/ZP/yiHPTZwft3t9aq59bH6mPW616W+y6rXOMtXrNuDUvsenAJ+brRytEPOjaZkdzcL8aBbmR9MyO5qF+dEszI9m6vi5FjgtIh4dEQ8Bng9cPp+ytALMj6ZldjQL86NZmB9Ny+xoFuZHszA/mn6qV2YejIiXAu8FDgXekpk3zaGmNg6NtuYJLSg/bfw7DPI9jDFDdrqwbUfp6nub+/uacd/jdm6XpuWna7qaG2hHdtq+/a1/AnPMj9u9PrXVvuBjV5v+Jitda2Q+aHqoJEmSJEmSOmCWqV6SJEmSJElqMDt+JEmSJEmSOqpxHT8R8ZqI+FhE3BgR74qIR9Rd0ygRcU5EfDwibo2IXXXXM05EnBoRV0fEzRFxU0S8rO6a5q1N+RnUtjwNakO+2pyPYdqemWGanqMuZaiL+YHmZ6gLzE4ztHV/1Ob8tC0jw7QxN2am2dqQqbZkaJF5adwaPxHxA8D7y0WoLgTIzFfUXNaDRMShwCeAsylukXct8ILM/NtaC9tERJwEnJSZN0TEw4HrgWc3ueZJtSU/g9qYp0FtyFdb8zFMFzIzTNNz1JUMdTU/0PwMtZ3ZaY427o/anp+2ZWSYtuXGzDRf0zPVpgwtMi+NG/GTmX+WmQfLh38NnFJnPZv4EWAb8KuZ+TXgD4GfLns7v1L21D2q1goHZOaBzLyh/Pwe4Gbg5Hqrmq+25CciTouIf4qIt5VP/SvgKOD9wFeBDwLn1lXfNNqQr7bkYzO97ABXALcCJ5afnwZ8KCL+qDxotFLTc9T2DA3JzxHA/wW+Hbg+It4XEafXWeOsmp6hNouI04CvAEdm5if72j/nRsSrIiIj4mn1Vjm9tmWnTfujIfueb1K0d74T+HBEbETEL9VZYxVty8gwbcpN6UnA54C/Bd7C/fuch0XEb0fEFyLi7oj4QK1VjtCFzIzTgkwNnrffzP37nY3y3D0j4om1Vsli89K4jp8B/wF4T91FjPALwD/0Pb4LeDHwS8BxwHXA25dfVjURsQ14AkUHQ1c1OT9vouht7jkZ+BTwIuDvKbLV2oNCS/LV5HxsppedhwGfBY6luOXjy4C3AfcAv1dbdXPUghy1MUOD+fkc8G+BCyga1JdTNKo7oQUZaps3AZ+k6PzpuQ14LEWODtRR1CK0MDtN3x8N7nt6LgD+Z2Zuycxfq6WyKbUwI8M0PTdQtIcfzf3t5tvK5/ZQnHM9tvz4X2qpbgIdycw4TczU4Hn7Fdy/39kC/ATFse2GOoobZd55OWweP2RSEfE+4JFDvvQLmXlZ+ZpfAA4Clyyztioi4vkUjZ7P9T29A/hSZv5R+ZpXA1+IiO/MzI8tv8rRImIL8A7g5Zn55brrmVRH8nMXxVX2x5RPfwO4KTOviYhvlM81ax5mRXXnq+352MxAdp4KkJnvKb/2YuDrFA2hP6+pxLmpM0ddzdCI/NwF3BUR30uxz/kG9++XWq3ufVHX9OXnLuC7B778fRQN599eblWL0aTsdGF/NGzfM6B17Z0mZWSYLuSmz5nA14CruP/4dAzwLOCUvu1/fQ21Vdb0zIzT1kyNOG+HB+53zgPemg1aA2cReaml4yczNx0GHBHnAc8EzmrSHwAgIo4GfhX4WeCivi99J/Dp3oPMvDci/g74LqAxHT8RcThFiC7JzHfWXc80OpKfs4CX9H3pNuDUvscnUIwAapUm5KvN+djMkOx8hQdm5hSKg9r3ATctvcA5qjtHXcxQhfzsAR4CBPDLSy9wzurOUNcM5OdVFKM2es4BvpKZfxoRdZQ3V03LTtv3RxX2Pb9Vvm4L8LOZ+YWlFzmhpmVkmLbnpqfMz3N4YJv4FOChFOddv1Je+DoAvDoz37H8KsdrQ2bGaWOmNjlv77WZKZdm+T6KkUqNsKi8NG6qV0ScA7wCeFZmfmXc62vwa8CbKYaIfQtwVEQ8hKLj528GXns38PDlljdaFC2yNwM3Z+Zr665nEdqSn8z87MDz1wKnRcSjy8dPpZhy0RptyFcL8rGZwex8kTIz5T7o+RSdzL9McYBrpabnqMUZGpefTwBPBl4KfLimGuei6Rlqqf78fA54eJmdYymmeL28zuLmpW3Zacn+aOi+h2Jdw/8H2A/8MEV7uTGjBEZpW0aGaUluen4N+B8UnYWPoDh3fT7FfugMinOtf0Zx7NobEY+tp8zRupCZcRqcqVHn7c/n/vOsHwX+IjMbccF9kXmpZcTPGG+k6MW9srxy9NeZ+R/rLakQEY8HngY8IYtVy/+U4mB1M/BximGI/Y6mWG+jKc6kWIdoX0R8pHzu5zPzT+srae5akZ/Br5V5einwXophlBdnZttGbbQhX43Nx2ZGZCcpGjrvBQ4F/oTi/b0sM/9iySXOU9Nz1LoMVczPWzLzuoi4AfiHiHhsZt6x9GLno+kZapUh+fkmxcWK91IsLv8Xmfln9VQ3d23LTqP3R2P2Pe/i/n3PByLiE8CBiDi64dNg2paRYRqdm56B/HwU+F8UI1N3U4wc+zrw6+Wiwn8eEVcDP0BxXtYkXcjMOI3L1Jjz9rf0nWf9KPBf66lyqIXlpXEdP5nZ5LUF1ihWBP9MGeotFAetu4GLKeYHAhARRwHfRoOmXGTmNRTD+DurjfmJiNMz87vL/9B/GhG3USzS2yptyFfD87GZNYbve07PzG8vh6n+OfBrmfm/6ipyHpqeo5ZmaI1N8jPw2kMopvGcDLSy46fpGWqhNYbn52aKk6/HR8Tfl689Abg0Ii7MzAuXX+ps2padFuyP1qi+7+lNDWn09m9bRoZpQW561ijzUz5+KEV+nkOLRjZ3ITPjNDRTa4w4b8/M3wCIiDMpRoz9cT0lPtgi8xINmYLXChHxMIpRPD0/QxGo/1Q+vpVifuAVwK8A35+Z37PMGtVcm+UnM/8hIh5K8R+9l6MPAF9tyjxZ1WfMvuchFFn5ncx8zfKrU9ONyc/jgS8AN1JMvfh1iqk7/zwz/2mphaqRxuTnm8DhfV+7Fvhp4D2ZubGsGtVMY7LzzykWfL6F4u6Uvw2cmJlPWW6Vaqox+bmLovN5L/DfKKYq/x/gX2bDbqqjeow77ypfswc4IjN/dPkVLl/jRvw0WTln8b55ixGxAfxTX3ieQzHU7W0Ut117fh11qpnG5YdiuuCjys/fW358NMX8d62wzbITEa+iaEC/qvy89z1bll+pmmhMfh4BvIFiocN/pDhxP8dOH/VUOHbR97VvAHfa6SMYu+95GsX0ihOBLwNXAi+opVA1UoXzrnOB3wV2USz0/KN2+qinQn6OAJ5HMYJsJTjiR5IkSZIkqaMad1cvSZIkSZIkzYcdP5IkSZIkSR1lx48kSZIkSVJH2fEjSZIkSZLUUUu9q9fxxx+f27Zte8Bz9957L0cdddQyy5iLVar7+uuv/0JmnrCgkioblp9Bbfy7dL3mJuSnSnaGacPfpuk1zlpfm/MzTNf/XovWtn0PTJefpv8dRulS3U3Iz2bZaeu27mlz/VVqb3p+qmjD36iLNbYpO23Y/oPaVvNc85OZS/v3xCc+MQddffXVD3quDVapbuC6XGJORv0blp95vL+6db3mJuSnSnZmfZ91aXqNs9bX5vwM0/W/16K1bd+TU+an6X+HUbpUdxPys1l22rqte9pcf5Xam56feb3PunWxxjZlpw3bf1Dbap5nfpzqJUmSJEmS1FF2/EhqtIg4NCI+HBHvLh8fFxFXRsQt5cdj665RkiRJkppqqWv8tNG2XVcMfX7n9oOc3/e1/bufsayS1CKj8tNv5/aDrC2+lDZ7GXAzcHT5eBdwVWbujohd5eNX1FVc3fbdfvcD9kWbcT+1Oqrse8BMaDjzo0FVM1HVxee0Z40NLY/7Hs2i6nnXqrabHfEjqbEi4hTgGcDv9j19LrC3/Hwv8OwllyVJkiRJreGIH0lN9jrg54CH9z23NTMPAGTmgYg4cdg3RsQFwAUAW7duZX19feJfvrGxMdX3LdPWI4urF1XU8V7q3oYRcShwHXB7Zj4zIo4D3g5sA/YDz8vMO2srsGaDV8dGXQnr2lUvSe0wyUgj91Oro5eLcaM3VikTEXEE8AHgoRTn+H+cma+y3aMeO34kNVJEPBO4IzOvj4i1Sb8/M/cAewB27NiRa2sT/wjW19eZ5vuW6Q2XXMZF+6rtyve/cG2xxQzRgG3oVEFJktR1XwWempkbEXE4cE1EvAf4EWz3CDt+JDXXmcCzIuLpwBHA0RHxNuDzEXFSOdrnJOCOWqtUY/VNFfwN4KfLp8+F+5bV2gusYwNImoojxh7Mq+6S6lDeynujfHh4+S+x3aOSHT+SGikzXwm8EqAc8fMzmfmiiHgNcB6wu/x4WV01qvFeR41TBYdZ1tS3qtP/Bo2aOtiUKY91Tx2UKvCqu6RalNPbrwceA7wpMz8YEQtr9yzrmLzv9rsrvW7n9vGvmWSJhDdcUu0UY/vJx1R63TTmuY3t+JHUNruBSyPiJcBngOfWXI8aqAlTBYdZ1tS3qnesGLRz+8GhUwfrmCY4TAOmDkqb8qq7pLpk5jeAx0fEI4B3RcQZE3zvxO2eprdphhnVzpnFIttI89zGdvxIarzMXKdoJJOZXwTOqrMetYJTBSXVYhlX3Zc9+m3aUYSjVK1/kt+7rO3hyEM1XWbeFRHrwDnY7lHJjh8tlHPdpelUvZNJlWGtq8ipgpLqsoyr7sse/TbPK+4AF59zVKX6J/m9yxqZ6MhDNVFEnAB8vez0ORJ4GnAhcDm2e4QdP1q8Ts51n+T2ovP8eau0QKa0IE4VlLQUXnWXtEQnAXvLEYeHAJdm5rsj4q+w3SMqdPw4YkOzcK67pLo5VVCq1ypd3PCqu6Q6ZOaNwBOGPG+7R0C1ET+dHLGh5VnmCvNNv2POMJOsLt+UOeXOb5fUZV700gy86i5JapyxHT+O2NCslrnCfNdXl/fOOpK0FF700lS86i5JaqJKZ5uLHLHR9JEDo0ZiDI7SeMMl1Ubsbj/5mLnUNa06t7dz3SVJbeBFL0l1cLShpEWp1PGzyBEbTR85MGpkxySjNPrVPWJj2dvbue6SpDaa5aKXJE3J0YZaGfO+WY42N1HPhSM2NAXnukt9PMhJ7TDLRa9J16cbVPdo6GnXsZtkzbph6nrPdW9vqcfRhpIWpcpdvRyxoak5112S1GbTXPSadH26QXWPhp52HbtpR0P31DUquu7tLfVb5k1RNlNHh+ikHcfjOpub0KFrx7KaosrR2REbkiRpZXjRS1JdlnlTlM3U0SE6aafzuM7mupfYADuW1RxV7urliA1JkrRKvOglqVYusSFpnqYfjytJktRBXvTSqnDduWZxtKGkRbHjR2qReTfQ9u9+xlx/nqRuqrrvcZ8iSTPp5GhDOxil+q1sx09dOyAbz5IkSVpl+26/e+pFxLvM0YaSFmVlO34kSZIkSd3kBXfpfofUXYAkSZIkSZIWw44fSZIkSZKkjrLjR5IkSZIkqaM6t8aPq8ZL0nDOdZckSeqeiDgVeCvwSOCbwJ7MfH1EHAe8HdgG7Aeel5l31lWn6uOIH0mSJGkOIuLUiLg6Im6OiJsi4mXl88dFxJURcUv58di6a5XUKQeBnZn5WOB7gJ+MiNOBXcBVmXkacFX5WCuocyN+JEmSpJr0Tr5uiIiHA9dHxJXA+RQnX7sjYhfFydcraqxTmpkzLZojMw8AB8rP74mIm4GTgXOBtfJle4F13PesJDt+JEmSpDnw5EtS3SJiG/AE4IPA1nK/RGYeiIgTR3zPBcAFAFu3bmV9fX3s79nY2Kj0ulF2bj849fdOa+uR8/+9s2yDcWbdxv3s+NFCOd9UkiStokWefM3rZKCOEy9o38lXv3meiKkZurQGYkRsAd4BvDwzvxwRlb4vM/cAewB27NiRa2trY79nfX2dKq8b5fwaRozt3H6Qi/bNtwtk/wvX5vrz+s26jfuNfdeeuGtGDnmWJGmBnG7RPIs++ZrXyUAdJ17QvpOvfvM8EZPmKSIOp9jvXJKZ7yyf/nxEnFR2OJ8E3FFfhapTlcWdXShKU8vMA5l5Q/n5PUD/kOe95cv2As+upUBJkga4QK9msdnJV/l1T74kzVUUvctvBm7OzNf2fely4Lzy8/OAy5Zdm5phbFe7c5U1L8uYb7qs4bfzHJ68iOHOVU27rRzmLKnjHK2qqVQ4+dqNJ18awZkWmsGZwIuBfRHxkfK5n6fY51waES8BPgM8t57yVLeJxlgu4sR93ieQyzqBXvTJ+qJOqus6YV/WfNNlDb+d59DoRQx3rmraYdEOc5bUZV700gw8+dIs7HTWVDLzGmDUCdZZy6xFzVT5bHNRJ+7zPoFc1lzlRZ+sL2qech0n7G2ab+o6CZKkfsu6O0q/SS/S1DVqdNCsF8XmfWFq3+13V3rdo485dG6/25MvzcJOZ0mLUqnnok0n7moWhzxrWg53lh7MzunlWubdUfpNepGmrgV6B816UWzeF72qbpeLzznKUaxqnDo6nftV6YCuu9N52cslTLM9XR5BTVHlrl6euGsWDnnWtBzuLKk2XvSSVJe6Op37VemArrvTednLJUzTOe3yCGqKKv9TPHHX1BzyrGm1bbizIzGaxRFjmoUXvSTVxU5nqV2qngPs3/2MBVeyuSp39fLEXVKt6hruPMnw3LqGOy9imPMbLql2Lrv95GPGvqbGIc6OGGuwFjSSvOglaensdJa0KPXcSkiSKqpzuPMkw3PrGu7c9LvC1TXEuW0jxtQsXvSSVBM7nSUtRGs6flZtGkULroZKC+dwZ81D3Qtk9pt1BNSiR5Yt665MVX/H4M9zkUxJXWans6RFaU3Hj6TV4nBnzUMTFsjsN+sIqEWPLFvWXZmqvo/Bn+cimZIkSZM7pO4CJGmE3nDnp0bER8p/T6fo8Dk7Im4Bzi4fSw+y2Yix8uuOGJMkSVLnOeJHK2HVpgp2gcOdNQtHjEnd5TFdw7hMgiSNVnvHz77b765tUVRJUme5QKYkSZJEAzp+JEmaN0eMSZIkSQU7fiRJkvT/tXfvwZKc5Z3nv48ugIwAoREc2pJMsyBjsNqLoAMcgz0+Y4ERSGNhe1GgxbZYs6F1jAnDusOrNoQXZjy2W+HBA8ZMzPR6ZIlA5mIuI425CsExsDFgJBmjEUJGMA0IehEXATpiuDQ8+0dWNdXVdTuVVZVvZn0/ER3nVFb16afy/Drfet988011iJfDjeclYZLWkQM/kiQ1zE6aJEnqAj/TjNb0oLMDP5IkSZI0YFwnbd+eI8esT+rMIElt4O3cJUmSpAWJiKsi4u6I+G8D206PiBsi4lO9rw9tskZJ3eOxR5M48CNJkiQtztXABUPb9gM3ZuY5wI29x5K0SFfjsUdjTL3UKyKuAi4C7s7Mc3vbTgfeCOwGDgGXZOY9yytTbWV+JGl9eF2/BJn5gYjYPbT5YmCz9/01wBZwxeqqktR1Hns0ySxr/FwN/Dnw2oFt/ZHDAxGxv/fYADWg6UWiZnA15keS1CKetNASbGTmYYDMPBwRD2+6IJXHY0/ZWtDvGmWmY09EXA5cDrCxscHW1tbUH7y9vT3ydfv2HKlR7nJtnFJ2fcAx+3TcPp7H1IEfRw5Vh/lR2zmDQVpLV+NJCzVg1s7XtM5A6R2bNnS+xhmufVGdsp6r8dijBmTmQeAgwN69e3Nzc3Pq39na2mLU655f8GfnfXuO8Ipby76/1aHnbR79ftw+nse879qzFqpj5vzsdPTZkeedmffDyiJHnyWpNJ600BJ8KSJ29T737ALuHvWiWTtf0zoDJXe8oB2dr3GGax/spNXlsUdLMNOxR+UYPOk8fBfBQTudWbb0I+60jntbR/zbVnd/v7etw77T0WdHnndm3g8rixx9lqSWWNpJi2E7batL+TxS97PRq6+9bqbX7dsz9z8x0oo+G10PXAYc6H2d7c1KnnBXPR57BMw/8DPzyOG0jvurr72ulSP+bTtT0e/gF9Jhd+S5EC29VlmSijbPlPlBO22rSzm50bbPRn1XX/DAhX42iojXU83QOCMi7gJeRtXpelNEvAD4HPCchf2DUk/dQedBswyINj3oXOqJ+GWt0TKNxx5NMm/r7Mih6jA/kqS2WflJC9cYa6fMvHTMU+fX/dn9TEya/q/OWdgJ952YZQC66QyWOti8rDVaplnFsWeQx6F2meV27o4cam7Lzs/wQcgDkCRpSTxpIakJHntaZllrtEh1zHJXr6WNHKr7zI8kqW086SWpCR57JC1LeXPjJEmSGuRJC0lN8NgjaVkc+JE01ayX1Dlldb3Msv7Ivj1Hjt6DVpIkSdLqOfAjSdKSuA6ZJKltdu9/u+2V1DEO/EiSpCI521CSJKm+E5ouQJIkSZIkScvhjJ810T9rOm3apmdNJUmSJEnqDmf8SJIkSZIkdZQDP5IkSZIkSR3lpV6S1tKstyL3MFnfLPsavNRU62Pa/wnvpiNJkhbJGT+SJEmSJEkd5cCPJEmSJElSRznwI0mSJEmS1FEuXiFJkiRJHTfrmnuSuqfWjJ+IuCAi7oiIOyNi/6KK0nowP5qX2VEd5kd1mB/Ny+yoDvOjOsyP5p7xExEnAq8Bng7cBXw0Iq7PzE8sqjh1l/nRvMyO6lhUfjxrup48/mheZkd1mB/VYX4E9Wb8PBm4MzM/k5nfBd4AXLyYsrQGzI/mZXZUh/lRHeZH8zI7qsP8qA7zo1pr/JwJfH7g8V3AU4ZfFBGXA5f3Hm5HxB1DLzkD+EqNOhrx2x2tO64cufmRSyhlUfk5Rht/L12qeUX5WUp2RmnD76b0GndSX9fyM0qXfl9NaPjYAyvKT+m/h3HaWvc/v3Jk3UUfe9q6r/vaXP9w7V1tu9rwO2p7jSW1XfNkpw37f1jbal5kfuoM/MSIbXnchsyDwMGxPyTipszcW6OORlh3bQvJz3E/tJz3NzNr3vk/P2Jb7eyM/Ida8LspvcYC61tZfkb+4+Xtj2NY3/QSRmxbeH4KeJ9zse7J/8yIbXNnp637uq/N9TdU+8rbrjb8jqxx9jJGbDsmP/Nkp5D3tiNtq3mR9da51Osu4OyBx2cBX6xXjtaI+dG8zI7qMD+qw/xoXmZHdZgf1WF+VGvg56PAORHxqIi4H/Bc4PrFlKU1YH40L7OjOsyP6jA/mpfZUR3mR3WYH81/qVdmHomIFwLvBk4ErsrM2+b4UQufSr8i1l3DAvMzrIj3t0PWvANLzM4obfjdlF5jUfWtOD+jFLU/RrC+CVaYn9J/D+NY9xhLyE5b93Vfm+tfee0NtV1t+B1Z4wzsdx2jbTUvrN7IPO7yUEmSJEmSJHVAnUu9JEmSJEmSVDAHfiRJkiRJkjqqiIGfiPiTiPhkRHw8It4WEac1XdM4EXFBRNwREXdGxP6m65lVRJwdEe+PiNsj4raIeFHTNS2LeVqOdcrQoFLzVHJ21jUrsyoxU+apPCXmZJKSMzRO27PVtoxAO3PS1/a87FTp+So9S+uSl9Jz0ld6XoYtIz9FrPETEb8AvK+38NSVAJl5RcNlHSciTgT+EXg61W3xPgpcmpmfaLSwGUTELmBXZt4SEQ8Cbgae3Ybad8o8Lcc6ZWhQiXkqPTvrmpVZlZYp81Sm0nIySekZGqft2WpTRqC9Oelre152quR8tSFL65KXknPS14a8DFtGfoqY8ZOZ78nMI72HHwbOarKeCZ4M3JmZnwEeCTweuLb/ZERc0huVuzciPhERz26ozuNk5uHMvKX3/b3A7cCZzVa1HG3IU0ScA3wLOCUzP5OZ3wXuBD4YEdsR8a6I+NFmqzzWOmVoUEl5ioitiPg2cC/VMeidvey8Afid3hmXb/XOEDyyqTrXNSuzaiJT/ez0ji/bEXFHb/v9gBuAHwU+DfxTqjxdvOyaZrWueSrx2DMiPz8dETcA9wBnA1cC/4TCMjRO27NVUkYmmdB2vQ94T0Tc0/vz3oh4fKPFTtD2vOxUCfkad+zh2D7Z7wE/BfzOquubZF3yUkJOxplw7HkXcNtArrYj4vcbLXbIMvJTxMDPkN8A3tl0EWOcCXy+9/1rgM8APwIQEWcCr6M66DwY+F3gryLi4Q3UOVFE7AbOAz7ScCmrUGqe+vn5FkBE/BzwNKrbLJ4O/Hfg9Y1VN8WaZWhQCXl6IfDrwNWZ+djetq8Dvwb8PlV+bgLe2Eh1Q9Y4K7NaZaZemJmn9v48dmD7Z4H3Av9f7/FdFPrhdI3zVMSxZ0R+Hkp1q9nfojoRdi/wlxScoXE6kK0SMjLJqLbrk8CNVO3WGcD1VIOGxetAXnaqyXyNOvacCXw+Ih4N/C9Un4POaKi+qdYoLyUeh0Yde/qfd04byNYfNFPedIvKz0kLqWYGEfFe4BEjnnppZl7Xe81LgSMMzKIpTABExHOpDjDbVLN+oBrd/Hpm9sP+9oi4D3g0cPeK6xwrIk4F3gK8ODO/2XQ982pzngby83Xgib3N/wL4O+CrmfndiPgD4AsR8ejM/HQjhY7RlQwNamGeYujxXuBrmfnXABHxcuArEfETmfnJVRfX18WszKotmeodb94BPAP4/uBTDZU0Vhfz1JacjNP/zBMRz6HKz78H/paq1uIyNE7J2Wp7RoYMt13fAr6ZmRkRQZWhx6y+rJ0pOS871eJ89bP058AVwDUUeszpQl5anJO+4WNPaywyPysb+MnMp016PiIuAy4Czs8sYOGh0e4CdgP/Gjif6qzW/+g9dxNwe0T8IvB2qo78d4CPr77M0SLiZKrgXJuZb226njramqeIeDA/zM/L6M0YozogPQj44sBjgHOpLr8oQpcyNKhlefpj4GQgI+J1mbkF/ATVrA0AMvO+iPg08JNUZ1RXrqtZmVWhmfrjiDgA3EH1YW2rt/0uqst0+s7ih8eiInQ1T4XmZJxx+YEfZuifAbdRYIbGKT1bLcvIJKParrOAL0bE14FTqa5E+L8bq3AGpedlp1qSr1HHnruoTnp9NjPfEREnAV9tqL6xupKXluRknFHHnv4g1mcjIqkuef/dzPxKQzWOtPD8ZGbjf4ALgE8AD2u6lil1ngR8AzgA3I9qmth/GXj+BVSzgI5QncW4sOmaB2oL4LXAK5uuZQXvtdg8Aa8Cruh9/6+ppsU/iups+xHgl4BTgP8I/IBq4bHG6163DA2972LyBDyFaoDwR6hmEm5TDfp8FfiLodf+v8DzzUp5f5rI1EB27g9c1jv2PLr33ElUl54eprrk9B+An2x6Pw3UvpZ5KvTYc1x+BjJ0F9VM1n9eWoYmvK9WZ6ukjMyYn+G262hOgAcC/7Kkz85dy8sc77fxfI079gAPAb4H/CxVn+y7wAua3mfrmJcScjJDfoaPPbcCl/Targ3gzcC7m6532fkp5a5ed1L9h+6P1H44M3+zwZJGiognANdRzeQ5ETgEHM7MX42Ip1GtqfEM4BbgSVTXKj8zMz/WRL2DIuJngA9SBf0Hvc0vycx3NFfVcpSap15+rgXOy+ryipdTnR09iypPtwGPo2rM/h2wH7goMz/YSMFD1ilDgwrO07OAv6YaZP4U8LHM/JcDz98KvDwz39JAbWuZlVmVkKmIeBfw9sx8de/xs6jat7uBf5+Zf7jKeiZZ1zyVkJNxRuTnMVSLen6P6ph0VUkZGqft2So5I+MMtV2vHMxJRJwAfBl4XGYWs0xCX9vzslMl5qt/7KG6AuPhVB37E6nWiXpOZr63ueqOtS55KTEno0w59jyC6uTXQ7KQy/GWkZ8iBn7aIiJeDPwh1WgzVNNST6RaZfuvgKdm5i8NvP4/Ax/KzH+72kpVokn5ycwnDr32x4G/B87KzHtWWafaIyLeSbWI3reByzLzqb3tD6T68PzEbHCNH5Wrn53M/LOBbXcBv5rHXsIjHWcwP1HdQfBvgQOZ+R8aLk0tMOr409t+EtVnpH+amX/fSHEq2sDnnt+gOnHav5vUw6iuyrgyM69sqDwVbsKxZ4PqSp7TMvMbjRS3AiXe1atkB6mmFz6h9+c/UI06PwP4KPCzvVkdRMR5VNMPi1njR40bm5+IeEBEnBuVH+u99lUO+qgvIk6LiH5WToqI51HNGHs38Dbg3Ij4lYh4ANUaCR930EcwNTtExP17uQG4X+91rV0IUYs1KT9R3dH0fcBrHPTRKFPy8/SIOC8iTuytgfinwD1UJ1S15qa0XedTrYP5hN6fLwL/B9Vdc6Vpx56nRMRjI+KEiPgnwJ8BW10e9IEVLu7cBZn5LXq33waIiG3g25n5ZeBve5fuvLk3avhl4I8y8z2NFKviTMpPRJxGNWvs0fzwdri/30SdKtbJwL+hujb5+1SLNj87M+8AiIhfobq7xeuobvf43IbqVHkmZodqwcxH9r5/d+/ro6guZ5bG5iciXgb8T8DLet8DkJmnNlKpSjQpPz8FvJpq5sb/oDqJekFmfrupYlWUaW3XURHxfeCezNxebYkq2KRjz6XAH1FdLvhNqsWdL22q0FXxUi9JkiRJkqSO8lIvSZIkSZKkjnLgR5IkSZIkqaMc+JEkSZIkSeooB34kSZIkSZI6aqV39TrjjDNy9+7dE19z33338cAHPnA1BS1I12u++eabv5KZD1tySVPNkp9J2vB76mKNJeRnWna6uN+bsIwa25Cfvjb8jga1qd55ai0hO3B8ftq034e1tfa25mcwO23d94O68B5gtvdRWn6g3fu/zbVD+/pdXfjcPKhN9datdWJ+MnNlf570pCflNO9///unvqY0Xa8ZuClXmJNxf2bJzyRt+D11scYS8jMtO13c701YRo1tyE9fG35Hg9pU7zy1lpCdHJGfNu33YW2tva35GcxOW/f9oC68h8zZ3kdp+Zm17lK1ufbM9vW7uvC5eVCb6q1b66T8eKmXJEmSJElSRznwI0mSNCAiHhARfxcR/xARt0XEv+ptPz0iboiIT/W+PrTpWiVJkqZZ6Ro/XbZ7/9tnet2hAxcuuRK1UT8/+/Yc4fkTsrSO+YmIE4GbgC9k5kURcTrwRmA3cAi4JDPvaa7C5Rg+pozLxjpmQtPN0ibt23OEzeWX0lbfAX4+M7cj4mTgQxHxTuCXgRsz80BE7Af2A1c0WWiT/OyjYZMyMdiOmYlu8BigVVh0ztY1t874kVS6FwG3DzzeT9XxOge4sfdYkhamd6n8du/hyb0/CVwMXNPbfg3w7NVXJ0mStDPO+JFUrIg4C7gQ+EPgd3qbL4ajExWuAbZY4zPuUh3retZrFr3ZhjcDjwFek5kfiYiNzDwMkJmHI+LhY/7u5cDlABsbG2xtbR19bnt7+5jHq3TrF74x0+v2nPmQkduHa9+358hMP6+p99vX5D6XJK3GuM80066oWBcO/Egq2SuB/wt40MC2mTpeklRHZn4feEJEnAa8LSLO3cHfPQgcBNi7d29ubm4efW5ra4vBx6s06wffQ8/bHLl9uPa6P29VmtznkiSVwIEfLd26rtGieiLiIuDuzLw5Ijbn+Ptjz7gPK/Fs8PCZ9I1TRp9dL6nuEvejVFdmfj0itoALgC9FxK7eoPMu4O5mq5MkSZrOgR+tQn+Nlgf3HvfXaHFxTE3yVOAXI+JZwAOAB0fE65ix4zXpjPuwEs8GD59J37fnCK+49fhDdtNn0geVuB+leUTEw4Dv9QZ9TgGeBlwJXA9cBhzofb2uuSolSZJm4+LOWqqBNVr+YmCzi2Nqqsz8vcw8KzN3A88F3peZv8oPO15gx0vScuwC3h8RHwc+CtyQmX9DNeDz9Ij4FPD03mNJkqSire2MHxe0XJlXUmONlp1crjNNyZeh9C/hGXc5T18J9RewHw8Ab4qIFwCfA57TZDGSuiczPw6cN2L7V4HzV1/RZLN+ppGkks26OK/9M2nnpg78RMQDgA8A9++9/s2Z+TLXadE0dddogZ1drjNNyZeh9BuzcZfz9JVwWU8T+zEzt6ju3lVsx0tlse2SJLWNbZfmZXY0zSwzfr4D/HxmbkfEycCHIuKdwC/jOi2arNYaLZKm28mZ/jU7Q9bJtsuZHZLUaZ1su7QSZkcTTV3jJyvbvYcn9/4krtOiKVyjRVJTbLskNSEiHhARfxcR/xARt0XEv+ptPz0iboiIT/W+PrTpWlUe2y7Ny+xompnW+Ondjvtm4DHAazLzIxEx0zotO12jZVXrh0xaR2XQq6+dPiZRrcsy279bwhot0Pg6La7RImnpVtl2wWqOq7O2XbOYtqbYoKbbrgLWFpNm5Vl31bKstmsZx9FFtyHjft5we9W29mBVbdiqPvesuk2u+9lnJ593BjWRs2Xu25kGfjLz+8ATIuI04G0Rce6s/8BO12hZ1fohw7dKrmPauiyDSlijBVa/TotrtEhatVW2XbCa4+q6tl0lr9EmDcrMBMaddd/sbb+G6jORAz86zrLarmUcR2dtk2ZtQ8b9vOH2quk2aadW1Yat6nPPqtvkup99dvJ5Z1ATOVvmvt3RHsjMr0fEFnABrtMiSWoB2y5Jq7SMs+5tmfU26az64Fn3NryXcVb1u7Dt0rzMjkaZ5a5eDwO+1wvQKcDTgCv54TotB3CdFklSQWy7JDVlGWfd2zLrbdKZ+cGz7m2bsTFomb8L2y7Ny+xomllm/OwCrumdvTgBeFNm/k1E/Fdcp0WSVCbbLrVeU3dxG/fv7ttzZKGXG3adZ901B9suzcvsaKKpAz+Z+XHgvBHbXadFklQk2y5JTfCsu+qw7dK8zI6m2fkqR5IaM+vZ30MHLlxyJZIkaQTPukuSiuPAjyRJkrQAnnWXjtfUZauSfsiBH0mSJGkNlT6TeCcDBs52lqTxHPiRpBXyrJfaqPTOobrBnElaJI8p0g858CNJkiRpZTwJIkmr5cDPijnyLEmS1omdfEmSmnVC0wVIkiRJkiRpORz4kSRJGhARZ0fE+yPi9oi4LSJe1Nt+ekTcEBGf6n19aNO1SpIkTePAjyRJ0rGOAPsy83HATwO/FRGPB/YDN2bmOcCNvceSJElF69waP15HrlVwrSZJi2TbVZbMPAwc7n1/b0TcDpwJXAxs9l52DbAFXNFAiZK0tmwzu8Hf42p1buBHkiRpUSJiN3Ae8BFgozcoRGYejoiHj/k7lwOXA2xsbLC1tXX0ue3t7WMeT7Jvz5EalS/exill1DTr/uvbyT6XJKmLHPiRlsiRbElqr4g4FXgL8OLM/GZEzPT3MvMgcBBg7969ubm5efS5ra0tBh9P8vzC2pB9e47wilub/+h46HmbO3r9Tva5JEld1HzrLUmSVJiIOJlq0OfazHxrb/OXImJXb7bPLuDu5iqUJEnL0rWlPaYO/ETE2cBrgUcAPwAOZuarIuJ04I3AbuAQcElm3rO8UiVJmo1tl+qIamrPfwJuz8w/HXjqeuAy4EDv63UNlCetnDOYV8O2S/MyO5pmlhk//Ttb3BIRDwJujogbgOdT3dniQETsp7qzxdIWOLTB0SqYM6kzimi71FpPBX4NuDUiPtbb9hKqAZ83RcQLgM8Bz2mmPJXKzpdqsu3SvMyOJpo68OOdLSRJbWPbpToy80PAuAV9zl9lLWodO1+am22X5mV2NM2O1vhZ9J0tRhl354US7iIxzjLucrHsu094hwtJ62IVbRfUO6420ca1qe2yzVJb2PnSojR5R8FZrartWvYdBbvW71r2555FvR/zc7xlZmXmgZ9l3NlilHF3XijtzhaDlnGXi53esWKnvMOFpHWwqrYL6h1Xm2jj2tR22WapjRbZ+VpWZ2CVg85t6nxNsopOfNN3FJzVqtquZd9RsEv9rlV87lnU+zE/x1tmVmbaA97ZQpLUNiW0Xeu2bljX7oAhzWvRna9ldQZWOejcps7XJMvuxJfQdqmdSsnOun32aYsTpr1ghjtbgHe2kCQVxLZLUlMmdb56z9tx10i2XZqX2dE0swy7e2cLSStXyp1RnMHQWrZdklZuhs7XAex8aTzbLs3L7GiiWe7q5Z0tJDWhVXdGacO01nUaxLLtktQQO1+am22X5mV2NM3yLrSVKGfWhtrHO6NIktrGzpckqUQO/BSqQ2fmWzVrQ2Vq8raUTdxme5Rl3w1l0Lx3K/GW25IkSVonbem3O/CjpXLWhupq+raUTdxme5Rl3w1l0Lx3RvGW25K0XG3pYEiSyuLAj1Zm2bM2ppllNkLTszsWNatjmbMuVjmro5TbUkqSJElSWznwo5VYxayNaWaZjdD07I5FzeqYd8bGLFY1q8M7o0iSJElSfQ78tFwbpvw6a0Nz8s4okiRJK9aGO5UuUhv6U1JdDvxoqZy1oXl5ZxRJkuazbh13SdJkDvxo2Zy1IbWMZ74kSZKk7nDgR0vlrI1m2HGXJHWVbZxGMReSNJ4DP5IkSZLUcV4CWI+Di2ozB34kSZIGRMRVwEXA3Zl5bm/b6cAbgd3AIeCSzLynqRolzcfOu6R1dELTBUiSJBXmauCCoW37gRsz8xzgxt5jSZKk4jnjR1pjnvWSpONl5gciYvfQ5ouBzd731wBbwBWrq0qSJGk+Uwd+nO4sSWob2y4twUZmHgbIzMMR8fBxL4yIy4HLATY2Ntja2jr63Pb29jGPJ9m350iNchdv45Tyapqkv593ss8XweOP5mV2VIf50SSzzPi5Gvhz4LUD2/rTnQ9ExP7eY896SZJKcTW2XWpIZh4EDgLs3bs3Nzc3jz63tbXF4ONJnl/YQqz79hzhFbe2Z7L4oedtAjvb5wtyNR5/NJ+rMTua39WYH40xdY2fzPwA8LWhzRdTTXOm9/XZiy1LkqT52XZpCb4UEbsAel/vbrgeFcrjj+ZldlSH+dEk8562Wch051HGTccteWpxG6Y+D+/TVU97lqQCLK3tgtHH1ZLbhibbrldfe91Mr9tz5kOAYtqs64HLgAO9r7O9Caky0/Fn3LGn/3+g5GPKNG34vDxo3HFq45Rjn+sfp5ZoYZeZ7tvz/SWXuhxty05h/a5ax55Rpr2f0n5XpeRn3s+Si7L0+bqTpjuPMm46bmnTnQe1Yepzf7pzXwPTniWpNXbadsHo46ptVz1NXaoTEa+nWsj5jIi4C3gZ1YDPmyLiBcDngOesrCCtjXHHnv7/gZKPKdO04Zgzi+H3MfwZu0nTLjN9xYfua6iyetqWnTb2u3byuWfa+yntOFVKfmY5ViwzK/PugS9FxK7eqKHTnSVJbWDbpZlk5qVjnjp/ET9/1jsqqlM8/mheZkd1LCQ/g+3Wvj1Hihvc0XRT1/gZoz/dGZzuLElqB9suSU3x+KN5mR3VYX4EzHY7d6c7S+ocz1x0m22XpKZ4/NG8zE43DM/qHPc589CBCxf675qfss0y23ffniNsLunfnzrws+zpzpIkLZptl6SmePzRvMyO6jA/mqT5VY60Ek2NPEuSNK9+2zVtVp5tlyRJ0njzrvEjSZIkSZKkwjnwI0mSJEmS1FEO/EiSJEmSJHWUa/xoLeze/3bv3CRJkiRJWjsO/EiSJEmSJDVsltu+w85vbOGlXpIkSZIkSR3ljB9JUw2OPE+6ZM5bKkuSJElSWRz4kSRJUuf0T1pMW+PPkxaSpK5z4EeSNJfha5DHda662Kkadf21C8hLkiSpRK7xI0mSJEmS1FGNz/iZ9YyxJEmSJEmSdqbxgR+pjllvdyepOcu6LaUkSZKk6Wpd6hURF0TEHRFxZ0TsX1RRWg/mR/MyO6rD/KgO86N5mR3VYX5Uh/nR3AM/EXEi8BrgmcDjgUsj4vGLKkzdZn40L7OjOsyP6jA/mpfZUR3mR3WYH0G9GT9PBu7MzM9k5neBNwAXL6YsrQHzo3mZHdVhflSH+dG8zI7qMD+qw/yo1ho/ZwKfH3h8F/CU4RdFxOXA5b2H2xFxx6Qf+ttwBvCVGnWtXJdqjitHvvyRSyhhKfmZpA2/p7bXuKL8LDw7bd/vpahbY1vz09eG39GgNtU7rdaWtV2t2e/D2pSZQYXkp252WrnvB7U1P8OG30dL8gMt3v9tz04b+l1d+9w8qE317qTWneanzsBPjNiWx23IPAgcnPmHRtyUmXtr1LVy1jxfCSO21c7PxH+w+fc8lTXOVsKIbbWyU8B7msoaF2Zpx56WvP+j2lRvQbXWzk9B72XH2lp7IXXXyk4h76GWLrwHaOx9eOxpae1QRP1T89O1z82D2lTvMmutc6nXXcDZA4/PAr5YrxytEfOjeZkd1WF+VIf50bzMjuowP6rD/KjWwM9HgXMi4lERcT/gucD1iylLa8D8aF5mR3WYH9VhfjQvs6M6zI/qMD+a/1KvzDwSES8E3g2cCFyVmbctoKaFXNazYta8Q0vMzyRt+D1Z4xRLyo77fTGKr3HJx57i3/+QNtVbRK0Lyk8R72VOba298boXkJ3G38MCdOE9QAPvw2NPq2uH7n12btvvo031Lq3WyDzu8lBJkiRJkiR1QJ1LvSRJkiRJklQwB34kSZIkSZI6qsiBn4j4k4j4ZER8PCLeFhGnNV3TOBFxQUTcERF3RsT+puuZJiLOjoj3R8TtEXFbRLyo6ZpWpeRclZ6jdciN+ZjfOuRjViXnqK/0PPV1OVdtyMmgtmRmUNfy07bMDGtjhgZ1IU9tzVBbs9OFzEzShjy1KTuryEuRa/xExC8A7+stRHUlQGZe0XBZx4mIE4F/BJ5OdZu8jwKXZuYnGi1sgojYBezKzFsi4kHAzcCzS655UUrNVRtytA65MR/zW4d8zKrUHPW1IU99Xc5V6TkZ1KbMDOpaftqUmWFtzdCgLuSpjRlqc3a6kJlJSs9T27KzirwUOeMnM9+TmUd6Dz8MnNVkPYMiYisivh0R28C9wCMy8zOZ+V3gLcBVEfGViPhGRHyg2WqPl5mHM/OW3vf3ArcDZzZb1WqUkKvB/PT+3AE8GbgP+DjwNeAngI9FREbEk1Zd4yjrkJsS8jHGk4E7M/MzwHuAxwN/P5AfACLikt5Zgnsj4hMR8exVFbgO+ZhVqTnqH3uo2q1HAu/stVtvAP6gdzZsOyLeFRE/2mixPV3OVak5GePoMWggMxc3XNNUXctPyzIzrJUZGtSFPLU0Q63NThcyM0kL8tSq7KwiL0UO/Az5DeCdTRcx5IWZeSrw68DrB7Y/AzgdeFzv6//ZQG0zi4jdwHnARxoupQlN5uqFmXlq789jqf5T/11/G/C/Ax8APgPc0lCNY61Jbko67pwJfH7g8TXA/zOQHyLiTOB1wO8ADwZ+F/iriHj4qotdk3zMqqQcAbyQqt26up8d4FTgmVQfhk4H/jvHtmtF6HiuSsvJsOFj0F20rPPSwfyUnplhrc/QoI7kqS0Z6kR2OpKZSUrMU2uzs6y8nLTIH7YTEfFe4BEjnnppZl7Xe81LgSPAtausbQfi6DcRjwWeCFyTmV/ubb65kapmEBGnUs1QenFmfrPpehalpbmKEdt+gqpzX9S1mG3PTYfyMZyLs4CvZ2a/0X17RNwHPBq4e5nFDWp7PmbV0hz1DefpPKozYrcBRMQfAF+IiEdn5qdXXt0Ibc1Vy3MyaJZjULHalJ8OZWZYqzM0qPQ8dTBDrc9O6ZmZpOV5amV2lpmXxgZ+MvNpk56PiMuAi4DzS+v8An8cEQeALwLf7m17CvAN4NyI+ApwGHh5Zr6loRrHioiTqQJ1bWa+tel6Fqkluern5w7gpVQj0GcPPH8u8KPAaxuobawu5KYl+Rg2nI/nAhERT6RqeLeAm4DbI+IXgbcD/wL4DtXlgyvRhXzMqqU5Avhj4GQgI+J1vew8CPjWwGv6H5TOBRof+Glzrlqck2HDx6CzqD7/FK9t+elQZoa1NkOD2pCnDmao1dlpQ2YmaXmeWpedpeclM4v7A1wAfAJ4WNO1jKjtKVQflO8P/G/AD4CfA36fahTxNcD9etu2gcc1XfNQ/UE1oPDKpmtp4L03nquh/FxGtd7Gj1Nd1vWoXnYOU1361fg+W6fclJCPMXWdNJCPpwK3Ak8YyM+je697Qe+Yc4SqI3+h+TBHA3X1jz0/QjULbJtqZuGngXuAnwJOAf5jr127tICaO5urUnMyptbBY9D9gH8AfrLpumaou1P5aVNmRtTeygwNvYfW56mNGWpzdrqQmSnvr+g8tS07q8hLqXf1upOqY/zV3qYPZ+ZvNljSWBFxE/BjVOslnQY8IHsLXUXEfwHem5mvaq7CY0XEzwAfpOo8/qC3+SWZ+Y7mqlqNEnMVEe+imqHxaeCVwInAQ4Dfzcy/bLC0Y6xDbkrMR19EPIsf5uOqzPzD3vZ+fm4H3ki1ztgtwJOA64FnZubHVlBf5/Mxq5Jz1NfL019TDRC+Evg68GKqY8+/A/YDF2XmB5upsNLlXLUhJ4PGHYNK1rX8tC0zw9qYoUFdyFNbM9TW7HQhM5O0IU9tys4q8lLkwE+bRMQ7qRazuq339UdKHvhRWfr5ycw/6z1+KtWdmx6R1Yru0lgDx5/7AU/NzF8aeO4/Ax/KzH/bUHkq2PCxZ2D7jwN/D5yVmfc0UpwkSZIWqg139SpGRJwWEc+IiAdExEkR8TzgnwHvproL0+eA3+s991Rgs/ecNC0/fZcBb3HQR8Om5OejwM9GxBN6rz0P+FlWuMaPyjUpO71t50blx4CDwKsc9JEkSeqOxhZ3bqmTgX9DtS7C94FPAs/OzDsAIuJi4C+opsl/Fvj1zPxkQ7WqPNPy8wDgEuBXGqtQJZuUnzsi4uXAmyNiA/gy8EeZ+Z6milVRxmYnIk4D/orqDnD3An9JtWadJEmSOsJLvSRJkiRJkjrKS70kSZIkSZI6yoEfSZIkSZKkjnLgR5IkSZIkqaMc+JEkSZIkSeqold7V64wzzsjdu3ePff6+++7jgQ984OoKWpCu133zzTd/JTMftoKSJhrOT1v3O6xX7SXkZ9KxZ51+F6XYSd2l5qeN+76NNcP8dZeQHUmSpBKsdOBn9+7d3HTTTWOf39raYnNzc3UFLUjX646Izy6/mumG89PW/Q7rVXsJ+Zl07Fmn30UpdlJ3qflp475vY80wf90lZEeSJKkEXuolSZIkSZLUUQ78SJIkSZIkddRKL/Vqo9373z71Nfv2HGFz+aWoILPkAuDQgQuXXInW3axZBPO4TkblYt+eIzx/aLuZkCRJ6j5n/EiSJEmSJHVU52b87OTst5YvIh4AfAC4P1Xe3pyZL4uI04E3AruBQ8AlmXlPU3VKkiRJktRFzvjRsn0H+PnM/J+BJwAXRMRPA/uBGzPzHODG3mNJkiRJkrRAU2f8OGNDdWRmAtu9hyf3/iRwMRxdGukaYAu4YsXlScWadX2xDk7clCRJkrRAs/QY+jM2tiPiZOBDEfFO4JepZmwciIj9VDM27LjrOBFxInAz8BjgNZn5kYjYyMzDAJl5OCIePubvXg5cDrCxscHW1tbR57a3t495vEpVh3u6cfU1WXtdba5dkiRJktbN1IEfZ2yorsz8PvCEiDgNeFtEnLuDv3sQOAiwd+/e3NzcPPrc1tYWg49XafjOOOMcet7myO1N1l5Xm2vX+nC2qiRJklSZ6RqBZc3YGLaImQSzzsRYpI1Txs/sKNmqZ25k5tcjYgu4APhSROzqZWcXcPfKCmkxbyMvzczZqpIkSRIzDvwsa8bGsEXMJJh1JsYi7dtzhEtaOANiFTM3IuJhwPd6gz6nAE8DrgSuBy4DDvS+XrfUQtQ6zthQHc5WlSRJkio7WhXUGRuawy7gmt6ssROAN2Xm30TEfwXeFBEvAD4HPKfJIlUkZ2yolmXOVi19ratRs183Tjl+e8nvoa/0fS1JklS6We7q5YwNzS0zPw6cN2L7V4HzV1/Rao27NGvfniPHzE7z0qzjOWNDdS1ztmrpa12Nmv26b88RXnHrsc3+uHXISlL6vpYkSSrdLDN+nLEhqRGrWF+s1NkEs6xXNmoGxySlvM91Wl+s9HW5Sq9PkiRJ9c1yV6+1nrGxaH7ILtusvx+txirWFyt1NsEs65WNmsExSSmzO1xfTJIkSVqdHa3xI0lNcH0xzcHZqpIkSRIO/EgqlDM2VIezVSVJkqSKAz+SStWqGRttuIyzDTVKkiRJWiwHfiQVyRkbkiRJklTfCU0XIEmSJEmSpOVwxo8kSQ3zjoKSJElaFgd+tBbsVEmSJEmS1pGXekmSJEmSJHWUM34WxBklkiRJkiSpNA78SNIKOUgsSZIkaZUc+JEkSRPNOmB56MCFS65EkiRJO+UaP5IkSZIkSR3lwI8kSZIkSVJHeamXlioizgZeCzwC+AFwMDNfFRGnA28EdgOHgEsy856m6mya675IkiRJkpZh6oyfiDg7It4fEbdHxG0R8aLe9tMj4oaI+FTv60OXX65a6AiwLzMfB/w08FsR8XhgP3BjZp4D3Nh7LEmSJEmSFmiWGT/9jvstEfEg4OaIuAF4PlXH/UBE7KfquF+xvFLXS1cW0szMw8Dh3vf3RsTtwJnAxcBm72XXAFuYH0kL4mxDSZIkqTJ14MeOuxYlInYD5wEfATZ62SIzD0fEw8f8ncuBywE2NjbY2to6+tz29vYxjyfZt+dIjcoXb+OU5dY0636Zx072u9QgT1pIkiRJ7HCNn0V33IctokPZRAd/2Z34Sersr1V24CPiVOAtwIsz85sRMdPfy8yDwEGAvXv35ubm5tHntra2GHw8yfMLW0Nn354jvOLW5S2xdeh5m0v72TvZ73U4Y0N1eNJCkiRJqszc81xGx33YIjqUTXTwl92Jn6ROB3+FHfiTqbJzbWa+tbf5SxGxqzdouAu4e+mFqG2csaGFmOekhSRJktQVM41W2HHXvKIaIfxPwO2Z+acDT10PXAYc6H29roHyVDBnbGgR5j1pMW226qwzJku6zHQVs1OXMYvUy0slSZLqmTrwY8ddNT0V+DXg1oj4WG/bS6hy86aIeAHwOeA5zZSnNljmZaaL6lR26TLTZXeyV9WRr3PSYtps1VlnTJZ0mekqZqcu4zLTVc1OlSRJ6qpZPgHacdfcMvNDwLhT7Oevsha107IvM11Up7JLl5kuc40oWE1H3pMWkiRJUmWWu3rZcZfUCC8zVQ2etJAkSZLY4V29JLXD7hlnnxw6cOGSK5mfMzZUhyctmtGFY48kSVLXOPAjqVTO2JAkSZKkmhz4kVQkZ2xIkiRJUn0nNF2AJEmSJEmSlsOBH0mSJEmSpI7yUi9pjbkQqyRJkiR1mzN+JEmSJEmSOsqBH0mSJEmSpI7yUi9JmmDWy+EkSZIkqUTO+JEkSZIkSeooB34kSZIkSZI6yoEfSZIkSZKkjnLgR5IkSZIkqaNas7izC6yONut+OXTgwiVXIkmSJEmSSjN14CcirgIuAu7OzHN7204H3gjsBg4Bl2TmPcsrU21lfqT2WfRAuwPPkiRJUnNmudTrauCCoW37gRsz8xzgxt5jaZSrMT+SViwiroqIuyPivw1sOz0iboiIT/W+PrTJGiVJkqRVmDrwk5kfAL42tPli4Jre99cAz15sWeoK86M67Lyrhqtx0FmSJEmae42fjcw8DJCZhyPi4QusSd03c34i4nLgcoCNjQ22traOPre9vX3M40n27TlSo9zF2zilvJommXe/L8DVwJ8Drx3Y1u+8H4iI/b3HV6yqILVDZn4gInYPbb4Y2Ox9fw2whdmRJElSxy19cedJHfdhkzqUJXeS29CJH7VfV9yBn0tmHgQOAuzduzc3NzePPre1tcXg40meX9ji4Pv2HOEVt7ZmbXUOPW/z6Pc72e912XnXgi1k0BlmP36W1DaU1FbtpO1pQ1slSZJUsnl7nl+KiF29D867gLvHvXBSx33YpA5laR33QW3oxA923PtW2YEfMnN+pvFub2tpps77rIPO0zqVpXSURympIz/Jq6+97pjHG6ccvw1gz5kPWVVJU01ru2Y9fpbUdpXUVo1qk8ZpsK2SJEnqhHk/AV4PXAYc6H09/hO8NJ750dLNOug8rVNZUsd9WEkd+Z0YV/dOBgPmtLBBZ9Uz66C9d4STJEmqb5bbub+e6rKKMyLiLuBlVB32N0XEC4DPAc9ZZpFqL/OjJVhI573f8dy350jRgztaKAedJUmStHamDvxk5qVjnjp/wbVoiUadXR3V4V302VXzoyWw866pHHSWJEmSKu27RkDS2rDzrnk56CxJkiRVHPiRVCw775IkSZJUjwM/kiTt0K1f+IZrQ0mSJKkVTmi6AEmSJEmSJC2HAz+SJEmSJEkd5cCPJEmSJElSRzW+xs/gbcZH3V5cUvNm/X966MCFqypJ0hrYvf/tM3028NgjSZI0njN+JEmSJEmSOsqBH0mSJEmSpI5y4EeSJEmSJKmjHPiRJEmSJEnqKAd+JEmSJEmSOsqBH0mSJEmSpI5y4EeSJEmSJKmjHPiRJEmSJEnqqFoDPxFxQUTcERF3RsT+RRWl9WB+NC+zozrMjyRJktbJ3AM/EXEi8BrgmcDjgUsj4vGLKkzdZn40L7OjOsyPJEmS1k2dGT9PBu7MzM9k5neBNwAXL6YsrQHzo3mZHdVhfiRJkrRWTqrxd88EPj/w+C7gKcMviojLgct7D7cj4o5xP/C34QzgKzVqakSX6o4rR770kUv45xeRn1bud2hvZmBy7SvKz0KPPV39XZRsXN0ty0/r9n3X8jJohW2XJElS69QZ+IkR2/K4DZkHgYMz/cCImzJzb42aGmHd8/3zI7btKD9t3e9g7XVLGLFt7mNPAe9nbm2tve3HHmj8PcyljTVDe+uWJEkqRZ1Lve4Czh54fBbwxXrlaI2YH83L7KgO8yNJkqS1Umfg56PAORHxqIi4H/Bc4PrFlKU1YH40L7OjOsyPJEmS1srcl3pl5pGIeCHwbuBE4KrMvK1mPTNdElYg696hBeWnrfsdrH1uSzj2+LtYvbYfe6Cd+76NNUN765YkSSpCZB63tIEkSZIkSZI6oM6lXpIkSZIkSSqYAz+SJEmSJEkdVdzAT0T8SUR8MiI+HhFvi4jTmq5pnIi4ICLuiIg7I2J/0/XMIiLOjoj3R8TtEXFbRLyo6ZrqaFNe+tqYG+hedga1LUdmqHlmZrm6lBVJkqSmFbfGT0T8AvC+3gKcVwJk5hUNl3WciDgR+Efg6VS3B/4ocGlmfqLRwqaIiF3Arsy8JSIeBNwMPLv0usdpS1762pob6F52BrUpR2aoDGZmubqUFUmSpKYVN+MnM9+TmUd6Dz8MnNVkPRM8GbgzMz+Tmd8F3gBc3HBNU2Xm4cy8pff9vcDtwJnNVjW/FuWlr5W5ge5lZ1DLcmSGCmBmlqtLWZEkSWpacQM/Q34DeGfTRYxxJvD5gcd30bIPpRGxGzgP+EjDpSxKyXnpa31uoJPZGVR6jsxQeczMEnUsK5IkSSt3UhP/aES8F3jEiKdempnX9V7zUuAIcO0qa9uBGLGtrOvmJoiIU4G3AC/OzG82Xc8kHclLX6tzA+3KzqAO5cgMrYiZaV5bsiJJklSyRgZ+MvNpk56PiMuAi4Dzs7RFiH7oLuDsgcdnAV9sqJYdiYiTqT5IX5uZb226nmk6kpe+1uYG2pedQR3KkRlaETPTrDZlRZIkqWQlLu58AfCnwM9l5pebrmeciDiJarHM84EvUC2W+b9m5m2NFjZFRARwDfC1zHxxw+XU1pa89LU1N9C97AxqU47MUBnMzHJ1KSuSJElNK3Hg507g/sBXe5s+nJm/2WBJY0XEs4BXAicCV2XmHzZb0XQR8TPAB4FbgR/0Nr8kM9/RXFXza1Ne+tqYG+hedga1LUdmqHlmZrm6lBVJkqSmFTfwI0mSJEmSpMUo/a5ekiRJkiRJmpMDP5IkSZIkSR3lwI8kSZIkSVJHOfAjSZIkSZLUUQ78SJIkSZIkdZQDP5IkSZIkSR3lwI8kSZIkSVJH/f+9FNxD3EoMIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1080 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load dataset\n",
    "sona=pd.read_csv('C:/Users/Aabha Gupta/Downloads/workbench-dataset/sonar.csv',encoding='utf-8')\n",
    "sona = sona.values[:, :-1]\n",
    "# perform a yeo-johnson transform of the dataset\n",
    "pt = PowerTransformer(method='yeo-johnson')\n",
    "sona = pt.fit_transform(sona)\n",
    "# convert the array back to a dataframe\n",
    "sona = pd.DataFrame(sona)\n",
    "# histograms of the variables\n",
    "sona.hist(figsize=(20,15))\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NiUbCdQnlwx2"
   },
   "source": [
    "#**Polynomial Transformation**: Exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HS4NXhjlKQKC"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from matplotlib import pyplot\n",
    "# load dataset\n",
    "sona=pd.read_csv('C:/Users/Aabha Gupta/Downloads/workbench-dataset/sonar.csv',encoding='utf-8')\n",
    "sona = sona.values[:, :-1]\n",
    "# perform a polynomial features transform of the dataset\n",
    "trans = PolynomialFeatures(degree=3)\n",
    "sona = trans.fit_transform(sona)\n",
    "# convert the array back to a dataframe\n",
    "sona = pd.DataFrame(sona)\n",
    "# histograms of the variables\n",
    "sona.hist(figsize=(20,15))\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q9pbgm8m4TT6"
   },
   "source": [
    "We will be seeing other feature transformation as we progress in the course.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_AkR9J_vM_7p"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "FEATURE TRANSFORMATION.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}