Feature Engineering for Machine Learning in Python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c8f31244",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      SurveyDate                                    FormalEducation  \\\n",
      "0  2/28/18 20:20           Bachelor's degree (BA. BS. B.Eng.. etc.)   \n",
      "1  6/28/18 13:26           Bachelor's degree (BA. BS. B.Eng.. etc.)   \n",
      "2    6/6/18 3:37           Bachelor's degree (BA. BS. B.Eng.. etc.)   \n",
      "3    5/9/18 1:06  Some college/university study without earning ...   \n",
      "4  4/12/18 22:41           Bachelor's degree (BA. BS. B.Eng.. etc.)   \n",
      "\n",
      "   ConvertedSalary Hobby       Country  StackOverflowJobsRecommend  \\\n",
      "0              NaN   Yes  South Africa                         NaN   \n",
      "1          70841.0   Yes       Sweeden                         7.0   \n",
      "2              NaN    No       Sweeden                         8.0   \n",
      "3          21426.0   Yes       Sweeden                         NaN   \n",
      "4          41671.0   Yes            UK                         8.0   \n",
      "\n",
      "      VersionControl  Age  Years Experience Gender   RawSalary  \n",
      "0                Git   21                13   Male         NaN  \n",
      "1     Git;Subversion   38                 9   Male   70,841.00  \n",
      "2                Git   45                11    NaN         NaN  \n",
      "3  Zip file back-ups   46                12   Male   21,426.00  \n",
      "4                Git   39                 7   Male  £41,671.00  \n",
      "SurveyDate                     object\n",
      "FormalEducation                object\n",
      "ConvertedSalary               float64\n",
      "Hobby                          object\n",
      "Country                        object\n",
      "StackOverflowJobsRecommend    float64\n",
      "VersionControl                 object\n",
      "Age                             int64\n",
      "Years Experience                int64\n",
      "Gender                         object\n",
      "RawSalary                      object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Import pandas\n",
    "import pandas as pd\n",
    "\n",
    "# Import so_survey_csv into so_survey_df\n",
    "so_survey_df = pd.read_csv('Combined_DS_v10.csv')\n",
    "\n",
    "# Print the first five rows of the DataFrame\n",
    "print(so_survey_df.head())\n",
    "\n",
    "# Print the data type of each column\n",
    "print(so_survey_df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c57364fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>SurveyDate</th>\n",
       "      <th>FormalEducation</th>\n",
       "      <th>ConvertedSalary</th>\n",
       "      <th>Hobby</th>\n",
       "      <th>Country</th>\n",
       "      <th>StackOverflowJobsRecommend</th>\n",
       "      <th>VersionControl</th>\n",
       "      <th>Age</th>\n",
       "      <th>Years Experience</th>\n",
       "      <th>Gender</th>\n",
       "      <th>RawSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2/28/18 20:20</td>\n",
       "      <td>Bachelor's degree (BA. BS. B.Eng.. etc.)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Git</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6/28/18 13:26</td>\n",
       "      <td>Bachelor's degree (BA. BS. B.Eng.. etc.)</td>\n",
       "      <td>70841.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sweeden</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Git;Subversion</td>\n",
       "      <td>38</td>\n",
       "      <td>9</td>\n",
       "      <td>Male</td>\n",
       "      <td>70,841.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6/6/18 3:37</td>\n",
       "      <td>Bachelor's degree (BA. BS. B.Eng.. etc.)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Sweeden</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Git</td>\n",
       "      <td>45</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5/9/18 1:06</td>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>21426.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sweeden</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zip file back-ups</td>\n",
       "      <td>46</td>\n",
       "      <td>12</td>\n",
       "      <td>Male</td>\n",
       "      <td>21,426.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4/12/18 22:41</td>\n",
       "      <td>Bachelor's degree (BA. BS. B.Eng.. etc.)</td>\n",
       "      <td>41671.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>UK</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Git</td>\n",
       "      <td>39</td>\n",
       "      <td>7</td>\n",
       "      <td>Male</td>\n",
       "      <td>£41,671.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SurveyDate                                    FormalEducation  \\\n",
       "0  2/28/18 20:20           Bachelor's degree (BA. BS. B.Eng.. etc.)   \n",
       "1  6/28/18 13:26           Bachelor's degree (BA. BS. B.Eng.. etc.)   \n",
       "2    6/6/18 3:37           Bachelor's degree (BA. BS. B.Eng.. etc.)   \n",
       "3    5/9/18 1:06  Some college/university study without earning ...   \n",
       "4  4/12/18 22:41           Bachelor's degree (BA. BS. B.Eng.. etc.)   \n",
       "\n",
       "   ConvertedSalary Hobby       Country  StackOverflowJobsRecommend  \\\n",
       "0              NaN   Yes  South Africa                         NaN   \n",
       "1          70841.0   Yes       Sweeden                         7.0   \n",
       "2              NaN    No       Sweeden                         8.0   \n",
       "3          21426.0   Yes       Sweeden                         NaN   \n",
       "4          41671.0   Yes            UK                         8.0   \n",
       "\n",
       "      VersionControl  Age  Years Experience Gender   RawSalary  \n",
       "0                Git   21                13   Male         NaN  \n",
       "1     Git;Subversion   38                 9   Male   70,841.00  \n",
       "2                Git   45                11    NaN         NaN  \n",
       "3  Zip file back-ups   46                12   Male   21,426.00  \n",
       "4                Git   39                 7   Male  £41,671.00  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "so_survey_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5d3372b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['ConvertedSalary', 'StackOverflowJobsRecommend', 'Age',\n",
      "       'Years Experience'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Create subset of only the numeric columns\n",
    "so_numeric_df = so_survey_df.select_dtypes(include=['int64', 'float64'])\n",
    "\n",
    "# Print the column names contained in so_survey_df_num\n",
    "print(so_numeric_df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cabde74",
   "metadata": {},
   "source": [
    "## Dealing with categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "14305e30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['SurveyDate', 'FormalEducation', 'ConvertedSalary', 'Hobby',\n",
      "       'StackOverflowJobsRecommend', 'VersionControl', 'Age',\n",
      "       'Years Experience', 'Gender', 'RawSalary', 'DM_India', 'DM_Ireland',\n",
      "       'DM_Russia', 'DM_South Africa', 'DM_Spain', 'DM_Sweeden', 'DM_UK',\n",
      "       'DM_USA', 'DM_Ukraine'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "dummy = pd.get_dummies(so_survey_df, columns=['Country'], drop_first=True, prefix='DM')\n",
    "\n",
    "# Print the columns names\n",
    "print(dummy.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7029d135",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "South Africa    166\n",
      "USA             164\n",
      "Spain           134\n",
      "Sweeden         119\n",
      "France          115\n",
      "Russia           97\n",
      "UK               95\n",
      "India            95\n",
      "Other            14\n",
      "Name: Country, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-5-4d327b8ef0c2>:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  countries[mask] = 'Other'\n"
     ]
    }
   ],
   "source": [
    "# Create a series out of the Country column\n",
    "countries = so_survey_df['Country']\n",
    "\n",
    "# Get the counts of each category\n",
    "country_counts = countries.value_counts()\n",
    "\n",
    "# Create a mask for only categories that occur less than 10 times\n",
    "mask = countries.isin(country_counts[country_counts < 10].index)\n",
    "\n",
    "# Label all other categories as Other\n",
    "countries[mask] = 'Other'\n",
    "\n",
    "# Print the updated category counts\n",
    "print(pd.value_counts(countries))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55327d20",
   "metadata": {},
   "source": [
    "## Numeric variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a65ed133",
   "metadata": {},
   "source": [
    "### Binarizing columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ba3d122c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Paid_Job  ConvertedSalary\n",
      "0         0              NaN\n",
      "1         1          70841.0\n",
      "2         0              NaN\n",
      "3         1          21426.0\n",
      "4         1          41671.0\n"
     ]
    }
   ],
   "source": [
    "# Create the Paid_Job column filled with zeros\n",
    "so_survey_df['Paid_Job'] = 0\n",
    "\n",
    "# Replace all the Paid_Job values where ConvertedSalary is > 0\n",
    "so_survey_df.loc[so_survey_df['ConvertedSalary'] > 0, 'Paid_Job'] = 1\n",
    "\n",
    "# Print the first five rows of the columns\n",
    "print(so_survey_df[['Paid_Job', 'ConvertedSalary']].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e404238",
   "metadata": {},
   "source": [
    "### Binning values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c83e7acc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          equal_binned  ConvertedSalary\n",
      "0                  NaN              NaN\n",
      "1  (-2000.0, 400000.0]          70841.0\n",
      "2                  NaN              NaN\n",
      "3  (-2000.0, 400000.0]          21426.0\n",
      "4  (-2000.0, 400000.0]          41671.0\n"
     ]
    }
   ],
   "source": [
    "# Bin the continuous variable ConvertedSalary into 5 bins\n",
    "so_survey_df['equal_binned'] = pd.cut(so_survey_df['ConvertedSalary'], bins=5)\n",
    "\n",
    "# Print the first 5 rows of the equal_binned column\n",
    "print(so_survey_df[['equal_binned', 'ConvertedSalary']].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "267c8b0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  boundary_binned  ConvertedSalary\n",
      "0             NaN              NaN\n",
      "1          Medium          70841.0\n",
      "2             NaN              NaN\n",
      "3             Low          21426.0\n",
      "4             Low          41671.0\n"
     ]
    }
   ],
   "source": [
    "# Import numpy\n",
    "import numpy as np\n",
    "\n",
    "# Specify the boundaries of the bins\n",
    "bins = [-np.inf, 10000, 50000, 100000, 150000, np.inf]\n",
    "\n",
    "# Bin labels\n",
    "labels = ['Very low', 'Low', 'Medium', 'High', 'Very high']\n",
    "\n",
    "# Bin the continuous variable ConvertedSalary using these boundaries\n",
    "so_survey_df['boundary_binned'] = pd.cut(so_survey_df['ConvertedSalary'], bins=bins, labels=labels)\n",
    "\n",
    "# Print the first 5 rows of the boundary_binned column\n",
    "print(so_survey_df[['boundary_binned', 'ConvertedSalary']].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0af60f5a",
   "metadata": {},
   "source": [
    "## Dealing with Messy Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "80dbe734",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_df = so_survey_df[['Age', 'Gender']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "669962b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age       999\n",
      "Gender    693\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Print the number of non-missing values\n",
    "print(sub_df.notnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2a0c3d16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Age  Gender\n",
      "0   21    Male\n",
      "1   38    Male\n",
      "2   45     NaN\n",
      "3   46    Male\n",
      "4   39    Male\n",
      "5   39    Male\n",
      "6   34    Male\n",
      "7   24  Female\n",
      "8   23    Male\n",
      "9   36     NaN\n"
     ]
    }
   ],
   "source": [
    "# Print the top 10 entries of the DataFrame\n",
    "print(sub_df.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7a72add5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Age  Gender\n",
      "0  False   False\n",
      "1  False   False\n",
      "2  False    True\n",
      "3  False   False\n",
      "4  False   False\n",
      "5  False   False\n",
      "6  False   False\n",
      "7  False   False\n",
      "8  False   False\n",
      "9  False    True\n"
     ]
    }
   ],
   "source": [
    "# Print the locations of the missing values\n",
    "print(sub_df.head(10).isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2efb1f6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Age  Gender\n",
      "0  True    True\n",
      "1  True    True\n",
      "2  True   False\n",
      "3  True    True\n",
      "4  True    True\n",
      "5  True    True\n",
      "6  True    True\n",
      "7  True    True\n",
      "8  True    True\n",
      "9  True   False\n"
     ]
    }
   ],
   "source": [
    "# Print the locations of the non-missing values\n",
    "print(sub_df.head(10).notnull())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fdec3cd",
   "metadata": {},
   "source": [
    "### Listwise deletion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "70f00884",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(999, 14)\n"
     ]
    }
   ],
   "source": [
    "# Print the number of rows and columns\n",
    "print(so_survey_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6d0c5cc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(264, 14)\n"
     ]
    }
   ],
   "source": [
    "# Create a new DataFrame dropping all incomplete rows\n",
    "no_missing_values_rows = so_survey_df.dropna(how='any')\n",
    "\n",
    "# Print the shape of the new DataFrame\n",
    "print(no_missing_values_rows.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "73dbf28b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(999, 8)\n"
     ]
    }
   ],
   "source": [
    "# Create a new DataFrame dropping all columns with incomplete rows\n",
    "no_missing_values_cols = so_survey_df.dropna(how='any', axis=1)\n",
    "\n",
    "# Print the shape of the new DataFrame\n",
    "print(no_missing_values_cols.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1e6a49f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(693, 14)\n"
     ]
    }
   ],
   "source": [
    "# Drop all rows where Gender is missing\n",
    "no_gender = so_survey_df.dropna(subset=['Gender'])\n",
    "\n",
    "# Print the shape of the new DataFrame\n",
    "print(no_gender.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d0aa675",
   "metadata": {},
   "source": [
    "### Replacing missing values with constants "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "06767228",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male                                                                         632\n",
      "Female                                                                        53\n",
      "Female;Male                                                                    2\n",
      "Transgender                                                                    2\n",
      "Male;Non-binary. genderqueer. or gender non-conforming                         1\n",
      "Female;Transgender                                                             1\n",
      "Non-binary. genderqueer. or gender non-conforming                              1\n",
      "Female;Male;Transgender;Non-binary. genderqueer. or gender non-conforming      1\n",
      "Name: Gender, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Print the count of occurrences\n",
    "print(so_survey_df['Gender'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5787cd73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male                                                                         632\n",
      "Not Given                                                                    306\n",
      "Female                                                                        53\n",
      "Female;Male                                                                    2\n",
      "Transgender                                                                    2\n",
      "Male;Non-binary. genderqueer. or gender non-conforming                         1\n",
      "Female;Transgender                                                             1\n",
      "Non-binary. genderqueer. or gender non-conforming                              1\n",
      "Female;Male;Transgender;Non-binary. genderqueer. or gender non-conforming      1\n",
      "Name: Gender, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Replace missing values\n",
    "so_survey_df['Gender'].fillna(value='Not Given', inplace=True )\n",
    "\n",
    "# Print the count of each value\n",
    "print(so_survey_df['Gender'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2dca2ca",
   "metadata": {},
   "source": [
    "### Filling continuous missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d5fabc6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    NaN\n",
      "1    7.0\n",
      "2    8.0\n",
      "3    NaN\n",
      "4    8.0\n",
      "Name: StackOverflowJobsRecommend, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Print the first five rows of StackOverflowJobsRecommend column\n",
    "print(so_survey_df['StackOverflowJobsRecommend'].head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "848c08cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    7.061602\n",
      "1    7.000000\n",
      "2    8.000000\n",
      "3    7.061602\n",
      "4    8.000000\n",
      "Name: StackOverflowJobsRecommend, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Fill missing values with the mean\n",
    "so_survey_df['StackOverflowJobsRecommend'].fillna(so_survey_df['StackOverflowJobsRecommend'].mean(), inplace=True)\n",
    "\n",
    "# Print the first five rows of StackOverflowJobsRecommend column\n",
    "print(so_survey_df['StackOverflowJobsRecommend'].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ecd01694",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    7.0\n",
      "1    7.0\n",
      "2    8.0\n",
      "3    7.0\n",
      "4    8.0\n",
      "Name: StackOverflowJobsRecommend, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Fill missing values with the mean\n",
    "so_survey_df['StackOverflowJobsRecommend'].fillna(so_survey_df['StackOverflowJobsRecommend'].mean(), inplace=True)\n",
    "\n",
    "# Round the StackOverflowJobsRecommend values\n",
    "so_survey_df['StackOverflowJobsRecommend'] = round(so_survey_df['StackOverflowJobsRecommend'])\n",
    "\n",
    "# Print the top 5 rows\n",
    "print(so_survey_df['StackOverflowJobsRecommend'].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70729aea",
   "metadata": {},
   "source": [
    "### Dealing with stray characters (I)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c92bec0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "object\n"
     ]
    }
   ],
   "source": [
    "print(so_survey_df['RawSalary'].dtype)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f661cb0c",
   "metadata": {},
   "outputs": [],
   "source": [
    " # Remove the commas in the column\n",
    "so_survey_df['RawSalary'] = so_survey_df['RawSalary'].str.replace(',', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c0010dc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-25-2c94d2f82c80>:2: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will*not* be treated as literal strings when regex=True.\n",
      "  so_survey_df['RawSalary'] = so_survey_df['RawSalary'].str.replace('$', '')\n"
     ]
    }
   ],
   "source": [
    "# Remove the dollar signs in the column\n",
    "so_survey_df['RawSalary'] = so_survey_df['RawSalary'].str.replace('$', '')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04991083",
   "metadata": {},
   "source": [
    "### Dealing with stray characters (II)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1d96ef90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0            NaN\n",
      "2            NaN\n",
      "4      £41671.00\n",
      "6            NaN\n",
      "8            NaN\n",
      "         ...    \n",
      "989          NaN\n",
      "990          NaN\n",
      "992          NaN\n",
      "994          NaN\n",
      "997          NaN\n",
      "Name: RawSalary, Length: 401, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Attempt to convert the column to numeric values\n",
    "numeric_vals = pd.to_numeric(so_survey_df['RawSalary'], errors='coerce')\n",
    "\n",
    "# Find the indexes of missing values\n",
    "idx = numeric_vals.isna()\n",
    "\n",
    "# Print the relevant rows\n",
    "print(so_survey_df['RawSalary'][idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "48b9fb70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0            NaN\n",
      "1        70841.0\n",
      "2            NaN\n",
      "3        21426.0\n",
      "4        41671.0\n",
      "         ...    \n",
      "994          NaN\n",
      "995      58746.0\n",
      "996      55000.0\n",
      "997          NaN\n",
      "998    1000000.0\n",
      "Name: RawSalary, Length: 999, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Replace the offending characters\n",
    "so_survey_df['RawSalary'] = so_survey_df['RawSalary'].str.replace('£', '')\n",
    "\n",
    "# Convert the column to float\n",
    "so_survey_df['RawSalary'] = so_survey_df['RawSalary'].astype('float')\n",
    "\n",
    "# Print the column\n",
    "print(so_survey_df['RawSalary'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49bc53ff",
   "metadata": {},
   "source": [
    "### Method chaining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7e56e274",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use method chaining\n",
    "#so_survey_df['RawSalary'] = so_survey_df['RawSalary']\\\n",
    "                              #.str.replace(',','')\\\n",
    "                              #.str.replace('$','')\\\n",
    "                              #.str.replace('£','')\\\n",
    "                              #.astype('float')\n",
    " \n",
    "# Print the RawSalary column\n",
    "#print(so_survey_df['RawSalary'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1737a0f6",
   "metadata": {},
   "source": [
    "## Conforming to Statistical Assumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b0229aa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "aa0ed082",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a histogram\n",
    "so_numeric_df.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c9ec2247",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Create a boxplot of two columns\n",
    "so_numeric_df[['Age', 'Years Experience']].boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8d1a6eda",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Create a boxplot of ConvertedSalary\n",
    "so_numeric_df[['ConvertedSalary']].boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d19169b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7Y+wbAMA5DzHGwqNsW+05vKrmcM4fAfAIADQ0NIxOuwgii5BuZg6tsk7MaF9jJtLNQhr7dPqZjtyF1MexoJj6m67dzHff0mk/F9MPxrL/+R7rfJPqk2IPY6wMw04rY+y9iCy2Gw3tAM6LOp4O4PQo6yQIokigVdaJyeVrzEIa+3T6mY7chdTHsWA89zfffUun/Vxct2PZ/3yPdb5J9UnxWgC7AMwZjk9cDuDGUba9C8CdjLE/ILLAzsU57xxlnQQx4SmWlcOFEs2gUMnlZhmFNPbp9DMdubPRx2K5loDCOqfZJt99SyciRC6u27Hsf77HOt+k6hS3AvgAgPmITHs4giRPmRljvwdwBYCpjLF2AOsB6AGAc/5TAE8hEo7tGCIh2W5LX3yCGL9kckMutpXD+Y5mUMjk+jVmoYx9pv3kKUxWGk0fi+1aAgrnnOaCfPct1YgQ8ytLVfW5vMQ0qpBqY9n/fI91Pkl1+sRLnPMQ57yVc97COQ8CeClRAc75xznn1ZxzPed8Ouf8F5zznw47xBiOOnEH53wO57yOc9402s4QxHhBNLhXb30BH//ZK7h66wvY29qVdMHGRF85PJ6YKK8x0+lnptdFJtC1RCQinn5oNVDV57f7BsdEb4nRkfBJMWOsCpGIEGbG2EUYWRxnA2DJsWwEMWGJZ3AvWH1Zwl/vuXzlTowtE+U1Zjr9zPS6yAS6lohExNOPLrdPoc8aBizb8sKY6C0xOpJNn/gwgFsRWQT3AEacYjeAb+ZOLIKY2GR6Q57oK4fHGxPlNWaq/RxLR5WuJSIRifQjVp9feusM/cAqEhJOn+CcPwZgKYDPcc6XcM6vHP6s5Jz/aWxEJIiJh2hwo0nlhjxRXrkTE5NMr4tMoGuJSEQ6+jGWekuMjqQL7TjnAmPsswB+OwbyEASBEYMbu4gj2Q15orxyJyYmmV4XmUDXEpGIdPRjLPWWGB2pRp94hjH2FQDbAEirDDjnibZxJggiQ0ZzQ54or9yJicdYO6p0LRGJSFU/6AdW8ZCqU/yp4b93RKVxALOzK0524JxjaGgIFosFjJHSEcUJ3ZAJQgldF0QxQnpbHKQUko1zfr7KpyAdYgAYGhrCTVv2YmhoKN+iEARBEARBEEVASk4xY8zCGLubMfbI8HEtY2x5bkXLDM45PB4PdAaawE4QBEEQBEGkRqqbd/wSQADAJcPH7QA25USiUTI0NIRbfvwMwhQUmyAIgiAIgkiRVJ3iOZzz+wAEAYBz7sVIzOKCg54SEwRBEARBEOmQ6kK7AGPMjMjiOjDG5gDw50wqgiAIgiBSQhA4TvR50O32odJGkQ3GI3SOx4ZUneJvA9gL4DzG2G8BvB+Rne4KFnFuMUWgIAiCIMYrgsCxt7VLEQN3maOKnKZxAp3jsSPV6BNPA7gOEUf49wAaOOfP5k6s0RMO+nHrw89RBAqCIAhi3HKizyM5S0Bk++C12w/gRJ8nSUmiWKBzPHakGn1iF4CrADzLOd/DOT+TW7Gyg9ZozrcIBEEQBJEzut0+yVkS8QUF9Az48iQRkW3oHI8dqS60ewDAZQAOM8aeYIzdwBij1WwEQRAEkUcqbSaY9PJbuUmvQUUp3aLHC3SOx45Up088xzn/H0R2sHsEwCoAPbkULBuI84o5p/BsBEEQxPhjVpkVm1ctkpwmcb7prDJrniUjsgWd47Ej1YV2GI4+cQ2AmwAsBvBYroTKFuK84ifWXg2rlZSHIAiCGF9oNAzLHFW4YPVl6BnwoaKUIhOMN+gcjx0pOcWMsW0A3oNIBIqHEJlbLCQuVRjQvGKCIAhiPKPRMMwuL8Hs8pJ8i0LkCDrHY0OqT4p/CeBmznk4l8IQBEGkCsXtzC40nsR4hvSbSIWUnGLO+V7G2CWMsVnRZTjnj+dKsEwQ5xATBDG+obid2YXGkxjPkH4TqZJqSLZfA/ghgEsBXDz8acihXBkRDvrx2Uf/ibBAC+sIIl0EgeN47yBeeusMjvcOQijg64jidmaXsR7PYtI1ovgpBHtBOl8cpDp9ogHAhbwIwjjQHGKCSJ9ie5KSKG4nzblLn7Ecz2LTNaL4ybe9IJ0vHlKNU9wCoCqXghAEkT8K4UlKOlDczuwyluNZbLpGFD/5thek88VDqk7xVEQ27vgbY2yX+MmlYARBjB3FtmMSxe3MLmM5nsWma0Txk297QTpfPKQ6feLbuRSCIIj8Ij5JiTbchfzkleJ2ZpexHM9i0zWi+Mm3vSCdLx5SjT7xHGOsEpEFdgDwb855we9oRxBEaohPUmLnvBXyk9eJELdzLMNIjdV4ziqz4sGbL0JzuwsCB7QMqJtuL2hdI4qf0er3aK7FYrSvE5VUN+9YBeB+AM8CYAB+xBi7i3P+xyTllgHYAkAL4Oec8+/HfH8FgJ0A3h5O+hPnfGMa8hMEkQXy/SSFUDKeF+cEQhyPPH9c1q9ChmLcjk9SPa+jvRbJvhYPqU6f+BaAi8Wnw4yxcgB/BxDXKWaMaRHZ/e5DANoB/Icxtotzfjgm6wuc8+VpS04QRFaZCE9ei4l4i3MuWH1ZUZ+jYuvXeP5xMpFJ57xmQ2fJvhYHqS6008RMl+hLoey7ARzjnB/nnAcA/AHAygxkJAiCmHCM18U5xdYvihwwPknnvBabzhKZk6pTvHc48sStjLFbAfwFwFNJytQAeCfquH04LZb3McYOMsb+yhhzqFXEGLudMdbEGGvq7e1NUWSCyD2km0SuGG0YqULVzXyHx0oXcoiyTyHoZjrntdh0lsichE4xY2wuY+z9nPO7ADwMoB7AQgAvAXgkSd1q75ViN/94DcBMzvlCAD8CsEOtIs75I5zzBs55Q3l5eZJmCWLsIN0kcsVow0gVqm7mOzxWupBDlH0KQTfTOa/FprNE5iSbU/x/AL4JAJzzPwH4EwAwxhqGv7smQdl2AOdFHU8HcDo6A+fcHfX/U4yxHzPGpnLOz6Qof1I45/B4PLBYLGCM5n8RxESnWBZNFcvinHTHs1j6JUKRA8Yn6ZzXYtPZYrFxhUgyp3gW57w5NpFz3sQYm5Wk7H8A1DLGzgfQAeBjAG6OzsAYqwLQzTnnjLF3I/Lkui9V4VMhHPTj1oefwxNrr4bVSkaMICYyxbZoqtAX52Q6noXer2iKzSEiUiPd81osOltsNq7QSDanONH7IXOigpzzEIA7AfwNwBsAtnPOWxljn2OMfW442w0AWhhjBwFsBfAxznnsFItRozUmFJUgiAkCLZrKLhNlPEWH6L2zp2J2eQk5F+OE8XheJ8o1mSuSPSn+D2PsvznnP4tOZIx9GsCrySrnnD+FmAV5nPOfRv3/IIAHUxeXIAgicxItrin0J0CFCI0nQRQWdE2OjmRO8RcB/Jkx9gmMOMENAAwArs2hXARBEFmHtlvNLjSeBFFY0DU5OhJOn+Ccd3POLwGwAcCJ4c8Gzvn7OOdduRePIAgie9Aq8uxC40kQhQVdk6MjpR3tOOf/APCPHMtCEASRU2jRVHah8SSIwoKuydGR6jbPBEEQ44JiWUVeLNB4EkRhQddk5qS6ox1BEARBEARBjFvIKSYIgiAIgiAmPBNi+gTtakcQBEGMFtopjIiG9GH8MSGcYtrVjiAKE7qpjB8K8VxmUybaKWzsKERdiiUb+lAM/ZxoTAinGKBd7Qii0CAnY/xQiOcy2zLF2ynsgtWX0YKmLFKIuqTGaPWhWPo50aA5xQRB5IV8bUcqCBzHewfx0ltncLx3EIKQ9Z3lxy3xxq4Qt5bNtkyJdgojskc65y2f1/Jo9aEQrxliAj0pJgiisBjtdqSZvHostqczhfR6NdHYjeXWsmpjAkCRlm2ZaKewsSHV85bLazmV6260+kDbMRcm5BQTBJEXRnNTyfSGWEyvwAvNgU80dpmey3SdfrUxefDmixAIccU4za8szaoTK+4UFtsO7RSWXVLVpVxdy6led6PVh4pS9X6Wl9CPrHxC0ycIgsgLo9mONNNXj8X0CvztM+p9fPtMfl6vJhq7TM6l6HxcvfUFfPxnr+DqrS9gb2tXwlfgaue9ud2lOk5aDbK63a24U9hTqy/DH25/D55afVnBvmEoZlLVpVxdy6led6PVB60GWLO0VtbPNUtroSWvLK/Qk2KCIPLCaLYjzfTV42ifTo/lVIaTZz2YbDHgusXTIUaSfPLVdpw668GcitE/1Y7tz4zJFpzqH4rbv0Rjl8m5jPfD5sI1l0HgUJVD7bwLHKq60OX2ZX27W9opLPekqku5ms5y8qxHVZ86XUNgTKmXqepD7PXW7fbh8ZdO4tOXzgZjAOfA4y+dxEUzJmHW1OLQr0Ka3pUtyCkmCCJvZOpkZHpDzPSVpyBw7D/SjeZ2FwQOaBlQN92OJfMrc3YTsJv0uOV9M7FlX5sk65qltbCZ9KOuW+0V8caVTjz0jzac7POqvjJONnbpOgi9A3585rLZACLOfqfLh8kWA147dQ7f/PMh1VfXauddy5DQWc9Ev8bjzb6YSOW85Wo6i9WgU+jTzDIzegYC+Mzjr2Y0lUntevvZJxvQPxTAQ/84JuVLZMNSnUs/VnpaaNO7sgU5xQRBFB2Z3hA1GoarFlRi2+3vRafLh2q7GY5qW1IjfrLPg7buQTzy/HGZgzpnagnOz9FTQ72OSQ4xEHlatWVfG7Z/9r2jrlvtKe26nS2474aFONo9AAD4wd43cEFVqeSYjObJfjRqN9NvLLsAA/4QykuNONkXeULe6fIp5omqnfe66faMf+ioORTj9WY/3siWPsZSaTNizdJa2Y/Rry9bgC+NYv7yiT4PfrD3DempMABs2XcE37m2Dt+K+gG4qdGJGZMtivLpzKVf5qiS2symsxx7vXCOolmfkQ7kFBNEDsnHE6diesoVCglo7XTJHFSdLvmkukxviILA8fQb3Wk7PKddXlUHtX66PWdOcd9gQPU1bt9gYNR1d7t9mFdRgs9cPgdefwgWow4/e/4tHOsZwIP7j8Gk12D1klqc9fhlN7hsTB+IdcgnWwwYCobx4D+OSedk9ZJa7G3pxGXzKsAY0Dvol86v2nkXBC790CkvMUKjibQTrRPR10VFqQlv9w3izt+9rtCDQlmMWUzX8VgQbzxGo49q9mfGFCtqK0tw++WzIXBAwwCBc9Vr8azHD0A5pSJWVpc3gE9dcj76hgLSm6Zr6msQCoZw3w0L4Q2EYDHo8NiLx7F4xmRFf+LNpRd/pItpa7cfwPwvXIYj3QNZ/VGn5pT/8IaF4zJ6BjnFBJEj8vHKfTRPuUZzE86kbCgk4M8HOnDPzhZJ1ntXOnHtopqUHGMRnkZo0kwdngFfSPUGMOgLp954mliNOswsM2N5fY30dGn3wQ5YDaM329MmmfDx98zEV/94UBr79dc4EAxF+uMLCti6vw3bbld/Kh19vqdNMuGcJ4hOt/yHTbzXvdHTJp4/0oPPXD5HkiO67Qdvvki6dl48dgYDviCWzK+UZODDDsvx3kG82TWAtp4BbG9qR/9QAKuX1GJb0yl8bdkC6clZ7HXxzY9cgDuvnAudlqFmkgXHewdx4J1+uLzBvN/s072Ox7sDnYvxCIUE7DjYgbt3jNifTY1ONC6swRW1FSgvMUrOcolBq7gWX36rF2cGA3i+7YzMvl9RW6H84X3jQvjDguJN04U1k/FCVPlr6mtw1uPHjMkWmbMeCIcVP2I7+r2qenrqbOo2LlW9UbObbT0DcactFbM+ThinmHOOwcFBcM5htVrBWHGcIKJ4OXXWgy6XfCV0l8uHU2c9OVtIofaaLvY1uBqjceAzdcRbTrskhxiIGNp7dragtqIEi2ZMzkmb3W6f6uK1ZA5PzSSz6g2gepIxoZzR8qZ7k+BcwJc+OA/Hz3ikc/KlD84Dh5CwXCr0e4LYsLtVNvYbdrdiy02LpDy+oIChQFghf/RT1nkVJfj4e2ZKdYmOxYq6afj7kR7Z+fnB9fWwGLTwhwSUGLXwBsK47f3n41SffEGhWa+BRa+Fxx/GvMpSDHgDqJlsgcsbxKGOc2jv9+HLTxzAZItBMedanIbhC4XxlasuwA/2voGaSSaUGPXSdWHUaTC3woqwwDEUCMOs0+KuqB8H/3vTItVzbdZrIQg8qz8U4/1wONRxDm92ufGZy2ZL860TOTbjfbpHPLt2YXWpYlEmoPwBtHnVIly1oFK2kHTQH5QcYiCi73fviNif3kG/ZAvf6HTjPbMn45tXL0AwxOHxh2A16XDFvDL858Q5haNbVWpSOJBvdg8onupu2deGRz75LsyrKJXqHPIHYTVoFc76966tw6cvO1/2I/bHNy9W1VOrUYf3nT8Ft156Pvo9QUyx6vHLf76teKo9Y7Il7lszQeAyp9zjVz4U2N7Uju9eW6eY/z9jsqWo9XHCOMXhoB+ffPBp6AwmPLH2alitFFuSyC1nPQEM+EIKo9nvCWDW1Ny02efx4+Z3z8T//v2o1OaXPjhP8Ro8llNn1efMzi0vSerAZ+qId5xTf9LRcc6b1CnO9IlvVakRn7nsfNz/tyNSP+/68HxUlCR2bq0GLb75kQtwxjPy+rPMakBJCk9tM3VaTHodOl0uxTk5f+robdfpOGPvC8lvsBrG0NJxDke7B2U3v3uWX4gvfrAWMyZbsPaJgwrHYuYUi+L8fO3JZnz60tn4xT+PY/WSWuw40IGPXTwDjhqb6oLCX7xwHMvrq1FlN6PpZD8EDjAwfGXYMbhu8XTZlBa1aRgbVjgQDAs41juI2y+fg3v3HJZN0djWdAofu3iGbA7z9//6BtZf45A5+t+6egFefKsPJ/uGMGuqFRdWReahiw6txaBDIByGQatFIBxGeYkRhzuVr7CjHbNqu0k1j0HHZNM6Vi+pxa9fPolOl0/1x1uhTPfIJmrTD2Lt2leumo/W0258+YmDsvGbX1mqOh6P/lcDznqC8PhD6PMEoNMw1Wugy+1Dd8zDjHNDIQz6Qli3K/rHXx3+8J9TCkfXMc2mqDdehJSzniC+/qdm2duaQX9Y4ax/48+HcPvls+U/Yve04juNdfjWjkOy8pMtWnykrhqf/fXIosCNKxzwBkO4euu/pbRHPtmgOk7z7rwUB9rPyZzyn3xisepbq8UzJuGpmKlMxa6PE8YpBgCt0QytgQJjE2PDUCCsOg+17paGnLVp1GqkG4fY5v/+/Si2x3kNLtLt9qvKunjG5KROcaaOeFmJQfVJxxSrIWk/482J7XYnfuJ7zheUHGKxn/f/7QgWz5iUsL3TLi/CHDIH9WvLLkCny4vZFaUJy8aLezr/C5clDK024Aup60+NPWF7qVBWalQd+0qbCdV2E/qHAliztBbf+cth3H75HAz5g3j4k+9Ct9sPs16L3QffwYqF5yEUZ65ll9uvms7YyPSIhz/5Lvxo31E4p9kVzq03GMZnPzAHeq0Gb/WO/FhbvXSulE+sSyTWSfYFBazf1Yof3rAQJ/o8krNcbTfhusXTpafJP3z6TVy3eLoUBSAQ4jDomDSn1GbUQq/VyJz2TY1OTLLoFc7rtqZTuKlhBhiDQpa12w/gkU824PZfN0l9UZsTGuv8bN3fJv2YUItMMN52RlON1HBLg8Ku/fDpI1iztFYxfj++eXGc8QjIHNAff0L9SeskswHHez2ya/2e5Rfiz6+9I/vh/6P9R7G8vkYWPcIXFOAfLhNdb7wIKZ0ur0z+Dbtb8X83LVKVPzZ898k+L/RayEK6/fS5Y7j/hoWS8y6WXberFY/ddrFM/jc7Xapvzd7u8yic8of+0YY7r6xVTHWbZjOj3eWVTWFLRx8LcZrFhHKKCWIsGVR55eQLCvD4U5uHmonBOBNvcZYn8eIstddjvqCAoUAoqZyGOI54vPmoIiVGHb53XR3ejpoeMGuqFaXG5GZpkkWnOid2kjlx2d4B9fHpHUg8Pma9Dj/Y+6asjz/Y+yZ+++n3JJX1VJy4p8niDcfTn0F/8nOSjHA4jPXLHdiwZ+TJ1/rlDvhDIaz90DycP9WCh/Yfw/WLp6NnwIcpViNeO9kvzdm97/o69Az4YDXosGbpXGxvirziB0THQq/qBIg3T19QwKsn+/GJ98wCY5DNMb5+8XT0DQXwVq8HF8+aDJNOg/tvWIiOc0OomWSR6jXrNbKnV/MrS6UnvtHjdbRnAOdNtkgO8SffOxNb9484uKuX1KJmkgl3LpkLIPJW4Ft/HnEK7rhyLjb/Xe5k3L2jJa7zunV/GzZc41A9d83t5yTHpMZuVv1hF+v8+IKCtBGJhgEvvXVGZg/G2/bTam+eegfUf2TVTDbjjivnypw6s0GjOh7HzwwqHNB7ll8oe3uwZmktAK74QXPvnsOKCCerl9TCZtLK2t99sAN2ix73rnTKHMj5laXYsMKB9VFPmtdf48AUixaPfepi2TSHSRb1a8dq0Mr6b9JrwJhG5pQDkQccsWM12WLAO/1e/OKfI47+vSsd+PwHZuO7f31T1v9Sk05R/or5lapT3SptRtz+a3mYugur1XeSLC8x4XjvYErTN/LpGJNTTBA5YppdfR5qlT35PNRMX7mXmJQxNk16DUqSOJpT4zw5LEvhqW1PnBtW74A/YblwmCMscNkTmU2NToQT7GgmMuANq86Jffy2dycsV2pSX7xWakriTA+q9/HMYOI+ApEFc/Hm/iViilWvKusUy+jjFOu0WvzjSCce/uS7cG4oiEkWPX778tv49KVz8E7/EEpNOvz3B2bjVJ8X346aRiBGheg451NMd3j8pZPoHwpgwwoHTvYNysJazSwz42vLFuBYzyDuXDIXuw92wKzX4uxQAN+Impbxg+vrMOAbcfr7PAGUlRrR0T+EaXYjDFoN7ru+fniFfxif+8Bc2TQHUY5oB/39c6YiGI7I8OWrLlBd1PfLWy/GybNehAUBs8qssnNl1GlUz72a8zpjihmTLQZY4pzzC6ttONB+DhaDFrMrLKo/7LSMKxytpfMr0OvxY9mWFxT2IFl4wkJ8GpcItTdPaq/vX36rF4EQlzl6az80D2a9cqrTnIoS/Pz54woHusxqkEWZKDXp4PKGVJ+gtp/1yBz1/W924ePvmYVf/HPEqVy/3IFQOAzGOH54w0J4AiFYDTqUlRqw48A7kevNE8Qkqx4HTpyBlpVizbYRp3LDCgdKjDpsXHEhLAa9bK5x9I8fUdddQ36FrlSo2PIbG6Yrnv7es7MV9668MBL9YvhH2WMvHkfDzMmK8lV2k+o10HSyX/Gkfs+dl6rqY2y0l3jTN/I9zWLCOcWcc3g8HlgsFlpsR+QUu0WHH1xfh7d6R56Ezi63YlIKTk2m87JKTVrVNktM2rhlAMAfCuNbVy9A76BfKje1xIhAOPmiLpNeq+oAGPWJ2xwKhlQXuvzy1uTTS3oH/apP2XqTOKkGHccdV9Ri3a6RpzgbVzhhSCwqSuJFgkjhqbY3EMI3ll0gC8c0xWKAN8lTeJtJpyqrLcnT8FQw6zVYvnA6Xh2eq6tlwPKF01Fi0uGJ4afBP/rYRZJDDIw4kPfdsBAPPP2mzEH4w39O4dsrHAiGBIQ5xzS7GW/3DeL+GxaifygAi0Eru0luWOGABsA9w0/OxCkNwTBHWYkRP3vhuLSJyOoltTjwTh8mL6jGV/80clP98c2L8dUnX5PJt2VfG26/fDa27jsmjdeRLjfmVlrxuQ/Mxak+9af2XS4ffv/vU+gfCmDd8gsxs8yMk31eAEBtZYn6k9gSg8whefmtXlgNOnz5qnkoK9Hj7o8uwKa/vCH9KPj6sgXwhwVcWG3DWY8fZwbUFzs+euvFWLdr5OnlpkYnJln1uOlnL8e1B/HCExbjIjyDRvnm6aF/tGHth+bJ7NrqpfOkp5Rivs3PHMVjt12MQFj+Y/t719bhlktm4tRZr1T+jivnoNJmRCgctdDNF0RFqXIB5zc/cgH0Oq30xkAcx1gbvWFPKx7/1Ltx3mQjGHToHuCosBlhMTA0LjoPA94QfMEwBnwMH7igCh/72Suy8ut3teKXtzUgzEfmzos/lhiDzIGfPdWCAX9Y5pR/+xoHppZosXGFU2Y35pSXqNpLo04n/1E2/LYoNk7ztDgLjWNvD76ggHf6hxSx4CdZdPjw/70g62vTybMZLXrONRPOKQ4H/bj14edosR2Rc3oG/OgdCMiM81eumo/eAT9mliW+6ONFSUg2Z9YfjMzxjG5zwwoHAsHET1/LS4x4g7tl5b75kQswNckCNCDyRFNtEVqyJ5ruOGHOBrzJp5fUTDLhtkvPx7GeAanN2y49H9MmJX5lzLlGulmI7a3b1YLffCr5E+bYp5Lrr3GkNNXDbNAixOU36bs+PB+mJJ642xdWlfXxJLKmQlgAOvq9inmT/qCAz10+Gz99/jjODqlPNQmEwripYYZsCsI3ll0Atzcoe2W8ekktfvHPN/Gtqy/E6j+8rrj5/+yWBskhVpvSIC4uE+cffzbGATrQfk5VvnmVpfjhDfU41T+E//37UfQPBfDjTyzGht2tuP+Ghao3d71Wg0++dyZ+/fJJbNxzGI988l3S4r5JZp3CSfjmRy6A1aTHL6JePW9Y4cD9T78pOfObVjqxbvkCuH0hlJr00uYP4lM+q0H5mtoXFNB+dkj2Q+HU2SHMmGKRTQ0RvxM3WhE3fNBqGAZ8Qbzydh8qbSZoWPFtsNCt8uZp6YIqnD7nU+ir2nSZYJjjh0/L1w38376juP3yObLyaz80D++cHcJXnzwkSxO4cj74GU9AMf/7zS636vlz+4I4OxjAul2vSfVuuWkRBvwh2fSJjSudmFdRguYOt7z8kPpbsF/ddjHCQmQufVgAjp8ZUsj57d2t+PWn3o3tTSdlT4AFIaT6VsLtDcjb2dOKx257t2L76UGf8of9jDILtu5rk/XfpNfAZtIrpkV899o6xbmyGLS47f2zsPmZo7Lxr7Lld9pP6sFAxxFaoznfIhATAINWg70tp3HfDQvxg+vqcP8NC7G35TT02uSXXalJh9vePwu/+OdxPLj/GH7+wnHc9v5ZSV/z+0JhyfACIw6IN5TY0QyGuDS3TCz33b++iWAo+VQGq0EHo16LR56PyPrw88dh1GuTPkU1Dz9hjsak18BkSD4+nEfmGUa32TvgB5KI2xtnzvWZJHOu3b6Q6o3KncL8XgamurhPg8RP6nK5eceAX7mI7949h/Hs0V4MBcO45X0zYRne7jaayFxVo+TASjINBRRzDrfub8NXrroAJ6Lmckb3o/OcFw0z7fj+9XWK+rbub8N1i6dLx/0eZexggUNVPg2Au3e2YOu+Y1JEiQPvRBzojnNDWL2kVionOuDt54akNidbDOh2j+jWS8fPSk7CnUvm4tOXzobbF5J2IhNlXL+rFcvra6Tju3e2YHZ5CWZOsUrzVsXvtuxrQ8Xw6/BY+U0GnfRD4Rf/PI6t+47hlkf/jVveNxPVdpPsuw27D2PHgQ78paUTzx3tweEON2565GV8/Gev4OqtL+C1U+cw2SKfAuULRhY9FSomvUYxLudNtqjq640N0xVlPQHlj+3l9TWKc7D5maPocvmk8/qZy2bjt6+cVJ0OphY9Ip7+2Ux6xUK3YJgr7PK6nS24/fI5ivJWo1bdRg1EtoR+cP8xPPSPY/AEwqr5+r1BLLmgCl/940F87U+HcNcfD6KsxKxqv6ZYjcp2Bv0w6EZsE2OAxaCR4iyL9vbcUBBfuWq+7Fpas7RW9YfYN/98SHGuDFqN5BBHn5NQOI3A83EQBI7jvYN46a0zON47CCGFKXkiE+5JMUGMFaGwgOsXz1C8ngqnMCXBFwyrGozFMxI/JYy70C6JI/V2nNfKJ/o8qK1KHF2h2+1XdcQfu+3dCSNXWI3KJ3DiE7RkeALq4/PzJJE9SjKc3zukcqP1BQV4A8mfavcPqW8GcW4omFjWePPDk/wwSoXBOE/pxadkv7z1YpyKmRcsPp1T29wiXsiptp4BVNtMsukIYj/8QQE3NszAoXaXalnxDYlJr8EUq3Lx0e6DHYptctd+aB5O9Q/FdWAG/WHsPtghewq2rekUltfXSG3e2DBd5uALHOgfCsgWNEVHwVCTWTw+1TcETZzQX4c6XIrFVxtWOPD4i8dx3eLpih8KW/a14Yc3LAQH8MOn38SdV85Fpc2E9v4hfP+vb0oRQ0Qn+LrF03Giz4NvfXQBvvOXN2TzrAt5EZ7drFfoHZi6fs0pH5naItrXUpXrRqtRlp9sMcBq0mPz3+VvKOxmlfIq0SN2H+xQLNRbvaQW51Suj3gLmcOcK+YJWwzq09EmWfSy6Tol8fKZ9QrdiRf+MnbRbmRBnBFfW3YBBAHStBKjTquwt/f/7Qh+8V8Nsmvp8ZdO4rzhtxqxbznnV5XK+lptN6vK9E7/EOZWJr7nJGK0U4Zy6hQzxpYB2AJAC+DnnPPvx3zPhr+/GsAQgFs556/lUiaCGCvCHNLqfkA+5ywZ8Ryp/iSO1NQMw5yZ4xjYZK/4gfgOY7LIFcFwCNMmmWTz5KZNMiEoJH/6Gr/NxE6qXstUHXG9NrGxjLci3J7C/N7JKg6deJNLWM6i/qMhWYSNVCgvVdcTPuzcvnS8D3MrSnCeaWQh0gWVpbj/6TdxzcKalJwGcc7hhj2H8dDNi3HH70ZeJ69eUosKuwlr/vA6Nq9Sn9Jg0mmweulcnDfZgnfODuG+6+tkr7pvapiB379yAv+7ahEADg5g0BtEh8un6sBsanTiR/vbFFM/xKkaJr0GGgbMKS+RlX3y1XasXlIrK7OgyhZ3/KKPzQYdjsXZ+cvlDeHp1k788taLcWbQD7tZjx2vtWPVu86D2ahX1e+jPQOwGLRx+7BlXxs237gQnkAY7f1D2N7UDoOOYd3yC9HWM4iwIKBuul1ahFeoWA1amV2wxrFPU6x6xTbJ3/jIhYrrxjnNrih/Y8N0xdPjrfvb8JtPv1tRvsxqUGxUsWbpPPzulROKH1j3XV+vWH8wOY5d1muYwqmcW16imBO8caUTHn9QtqjwvhvqVSNauFTuHeJbn9j2ayaPzBU26SNh1kpMWrx1JiSbKvaD6+tV9bHb7Zf9WDTpNaixm1Tjjs+cbJHFNO4d8KvKZBnljp2jjZOcM6eYMaYF8BCADwFoB/AfxtguzvnhqGwfAVA7/HkPgJ8M/yWIokctNI4vKKDHnTxigc2k7kjZTIkdKbNOo9h4YP01Dlj0iack6DRQdcBS2W05U1kBDX790gnccslseAMhmA06PP7icXzlwxckbTOekbckceLNeo36DTdJR3Us8hQydv6bTpPaDDS1sU0yewKcM9UfDUkLpkCZVbkYZ/1yB37/75OSM/vVPzbjoZsvAgCcX2bBNLsJn3jPTPz2lZMKJ3GKxYAvfXCeLGKA6Kj5ggKCYUGapxsWgF+/fBJfHI4xGwwJivrWX+OA1ajFV/84Elf2a8suwG8//R4819Yr1dHp8qHl9AH87JYG/PfjTVj7oXm4tLYM0ydbZPObP3v5HPzh3yexvL4GGg0i2+6GBLzd58GvX45Ezdi40gmPLwijTh7Sq9Plizg7NyzE0e4BcA784p9vKcJurb/GgZ8+F3EQxHMsOqYbVzhkGz9Ez5l+6XjfyMLAay6EABbXkTZoNaiZZJF24APk4eAe+scxvNk9INX3pQ/Og0mnkc1n3rxq0aj1J5d0uX34yXPHpSeNYQHoHfCpPlXXaZjsTdzqJbUYDARRZZNfN3aVH5gzplhU7fNZT0BhIwCgymaIuRaNWLqgSvmmy6hcf7BxhQM/uL4OX4v6UbdxhROP/us4mk66pPYjP6S0ePj5Y5Kzbzbo4B7yY03M9KSv/rEZaz9YqxqnOFZ3HnvxuKoDbdJrZH3yh8LwBgTFVIvjvYOq+jhtkjwixqZGp+qc7C372tAwczKc0ydJjqmGqdvFSltqu4TGY7Rxu3P5pPjdAI5xzo8DAGPsDwBWAoh2ilcCeJxzzgG8zBibxBir5px35lAughgTKm3qYc4qUrjoK23GjAxGvzcILeThgIb8QfR7Ez9hNuq0qs6iUZf8SbHLF1A4NauX1MLtSzxlY8AbQtNJF5pOvq5IT4Y2jhOfbLp2OM6DZCHJjJZOtw+//Jf8qdAv/3UCNZOSr0/odvsVC1cef+kkZkyxJCx3biiIzc8clZ44hQVg8zNHsamxLmmbyegZCOOhZ9vkN9Tnj2Hlohosc1ZLzuxQQJAcrE2NTvzyXydw3eLp0GiA+25YiHBYgEmvxff3voFAKKJ3R3sGZE6r+ARVp2Uw67XSObMOv+bWaDTY1nRKJsuAN4ANu+U31h/sfRM//X/vwtZ98risoiPjC0am0Pzivxrw65ciT5A9gRBOnR2C2xcc1rURB6TabsL3r69DzSQzzAYdfv78W2jucKO+xqb4wXDHFXPxQNQiug0rHHiq+bQks0mngRYcKxfVSNePRa/FT58/jv6hAOyWiENVW1GKN7sGZGMjzqbyBQVYjHp85Y8HMdliUFxTkRi6kc1g1G76jEFR3//+/aginnKhL7Qz67WK6SoPfvwi3B8V8YRz4MfPHsNdV12g+HHw+Kfejf/b1yq7bvo9QcU12O1WvlEQn+7HOuU/ee44bmyYHqN7c/FEU7viunZMsymcynXDC0vlYdp0WLqgCi2nBxRvrWLt4p1L1KfruP1hRZxizrnCNi5dUIUZU0zy+0IgiFdP9Mv6ZNJr8PAn36Voa3tTu+pUEX8wjG23vxddLh+q7CY4qu14/livqqyxUzVmTLGitrJEds+prSzBjCmje4sx2rjduXSKawC8E3XcDuVTYLU8NQBkTjFj7HYAtwPAjBkzsi4oQWRKIt2sq1LeXDeucKKuypa03kwNhkmvw0PPvSXdEDgH9jR34L7rFyYsFwiHUWrWyxaclZr1CMbzIqMos5qwremw4jXi1o9dlLCc3aL+tDeVKQmZOvGdKk+hfvLccdz90QUJy1WUGhU3anH+XTKmlhhUy5aVJJ7SMugP4WSfV3HTG/Ql/9EAJNbNngG/at0XVNlw757DksMmbhjgCwqosplU59Y+0dSOb169AG09A3infwhWg072xHjN0lq8c9aDjXtO4nOXz5bO2TmPHxtWONB5bggfu3iGYsc4tRurNxBOOBXFFxTQ3u/F0Z5BtHa68eSr7bhu8XTZph8i/UMBuL0h2Xx4ADjaM4jaSisev+3d6HT7MNkSmdbw5asukJ7cnRvy46W3z+IfR89I5WaWmfHDGxbi5NkhnDo7JDnEa5bW4uSZQZj1WlgMGtkrcPGJsYg497TT5cOvXx5x4hafNwnf2tGC6981Xepz7BhoGBT1+YLq8ZTzHfYqkW5aDFqFU6fRQFVfPTGOli8ooHdQqdu/vLVBobszy8yKOenfvsYBq0HplEf/2BBRm2tu0mswFGcB3IAviKPD0XI0DJjHSlBi1MlsmEWvBY+aZywSb3pS7BRZk16Dsyo/AB5/6SSC4RnwhwQp7U+vtUv6FC2nLyCoXiseX1Bh4797bR0WnjcZC88bqWPmFKuqrLH3Lo2GYcn8SsyeWqIIJTgaksXtTkYunWK1nsUuAUwlDzjnjwB4BAAaGhpGvzSRILJEIt00m/W4xlmFWVMt6Hb7UWkzoq7KBrM5eZziTA1Glc2ocDBSecJcXmLGD/92RDGV4QfXL0oqq6Pahi8sqZViDotOjaM68XbEJUYdvr7sAnx/70hYq68vuwDWFBaSLagoRXu/V+bET7IYsCDJlstVdqVjZ9JrkoYBqijRK16Bb1zhQEVp8nNZWWpUndJSWZrknMSZ91uexJkWSaSb8d5iTLboJYd4zdJanOzzjHxn1WNTo1N2ni86bxIeef44vvvUG/jkeyPzCCcPPxWdMdmC3kE/pk+OPE3vHwrgp89HnrjNryxBpc2Ezn4Pzptux5mBgMw5iLcjnlGnUZyHDSsc+NU/35by9A74h7f/DUvnutpuUn2b8diLx1V/uGrAUWrWosPF4faF8FRrN/50YORZzcwyMzY11uHuHSMO1R1X1KLUpIV2+Bq9/l3ToWFAzSQzzg0FMLXEhJ88e0xyLOZXluKHT78pC1NljVok1uny4aF/HBuePxqJ96xhwBNN7aoLIMtLjPjOU4dl9cVznPK90C6Rbk4tMaLKLp/+MDnOnP6zQ/K3USa9BhUlSt1+5+yQYsw+dvEMaCHIpik8/uJx3LPcoZhq8N1r67Bl31FZW7sPduA7jXX41g75Qk+1zTNMeuVGSHaTHvf/7YjsifajL76NLTctUp3THCvTpkYnhvwh2fSFNUtrUV6q/iM8JAiKNDXdKC81KOzVhhUOGHQabP77IVlb0+zKN2XnT1V3Ss+fqnRKNRqG2eUlWf2BptGwuHG7U4FxnhsfkzH2PgDf5px/ePj4GwDAOf9eVJ6HATzLOf/98PERAFckmj7R0NDAm5qaFOkejwc3bdmb0m5YWr0Rj972HpSXl9MGHgQ45xgaGkq2oUtSRYmnm2OJIHDsP9KN5naXdEOpn27HkvmVCY3CaFfshkICWjtdstdouiTzdEMhAX873IUj3SNPT+ZXluLDF1YlLQsAXm8Qh7rcaf3gCATC2NF8Gut2yhexNNZPgyHBfGRB4Hj5eC9CQmQR5GSLHjoN8N7Z5UnHRxA4XmjrwYAvLL26LDVpcVltRcKy57w+PNPaK5u3eu9KJz7kKMcks8ypSVs3PV4//tLSo3AGzQbgSPcQNCzydPyhf7yF/qEANjU60bgwEm4s+jwvqLTh70d6sHb7AUy2GHBjw3ScX2bF1BIjhoIhWPQ6dLqHIotnOJP67w2G4A+GYdTrcM/OFqnsnPISVNmM6B3wwRvksr5vXOHAQ88ew+wyK2679Hy4vEGUl5rw6D+P4enDZyRHd1vTKaz90Dy4hoLQaTXYOPzKd2aZGRtWOOENhlBmNcIbCKHEqAfTCAiFGLqHb6B6Hcei6WUAgBeO9cAf5Dg3FJSN1aZGJ2orrHB7wzg7FIjogxZ498yp+NfxXgx4w/AGw6i0GSFwjvMmW3HeJDN2HTot/aiYWWbGHVfWynTxJ5+4CL2DQVna+uUOPPnaKXzq0jm4sLoUXW4fqmwmhAWgZ8AHi0GLYFjA1BIjDncOKOLDWgxafHFb3jbvSFs3BYHjhWM9GPCOXC9TrDp0uQOyhW7fvbYOei3DXVHzzjc1OrHcUY09rZ2yH2/33VAPHWN4M8rWLKgqRTDM8dUnm2XXwHJnJf719ln4g4LU/rRJBhw/41X88C8vMeDVU+dG3uZVlGDpvHL8pbVbprubGp04b4oR/uCI/dBoOLpcAUWdyy4sx7NtZ3E0StYLKkuh1TC0drqltItm2DHgDeNY76CUNre8BFfOL8PeGLuxqdEJg04jm6O/qdEJo04jG797VzpxjbMar5zqk41/qUkLMODVk+dSuq+Iuyhm8wlwDlAVKJdOsQ7AUQBLAXQA+A+AmznnrVF5PgrgTkSiT7wHwFbOecKl+dlwigGAcT6hNvBI0fGbkIi6s23NskT6UBROMZC5QcqHIcvEmR4tgUAYzadd0ra39dPsCR1ikdGMT6Zlz3l9ONrlkRz/eVXWWIcYyFA3PV4/WrsGpbpLTRoM+CLbc1fZTDAZNGg/6016XqK3EdZrNRj0B1Fi1MOgYwiEOM4M+jF9khkCOLpdftgteri8QUy2GOCssuH0gA/d7hHnrrzUiHOeIHzhEBg0knx2sxYubxg94o+gajs0Gobm0y70DPhQXmIER2Rb5siiIQ63LwCbyYBgWIi7xXGicyN+1+fxg4HhrCeASpsRjuG21colqi9W3xdU2tDu8sryCgJHa6cLnS4fppYYodMANrMhJZ1RaxtAPh2UjHRTrR/iuETbCgCq9kPNrqjlDYUEHOp0jfywrrbDZNKplvcFAzgcdS1eWGWFQatXtSVqNkYQuKItzrnqD/tUywtcQEvXgJTmrCqFxWxQLQ8gpTSDQVuIepQLxtYpBgDG2NUA/g+RkGyPcs6/wxj7HABwzn86HJLtQQDLEAnJdhvnPKFXkS2nWKs34he3vltyEq1W67h2FlN0/CYkHo8Hn/jJs/jt568YF04xMeEg3SQKFdJNolBR1c2cxinmnD8F4KmYtJ9G/c8B3JFLGeIRDvrxyQefBgDoDCZs/9JHRJnAGIPFYsHQ0BAAJHWY1Z7Ccs7h8XgU5cW8ZrMZXq837pPbdJ/sqtULQKpD7GcqctMTZYIgCIIgJhrjake7UMAHIYXdwtTo7e3F7T/bDyHMoTUY8fCnLsXtP9sPpjXgN1/4cMKnqx6PB//142fw2P98SMrn8Xjwif/brSgv5v3xf12C/3nsRVmZZHUmQq1eAFIdQGR8REc9kdzptDse8Hg8CAUKd9tTgiAIgiByT06nT+QCxlgvgJNxvp4K4Eyc7/IByZOYYpLnDOd8WaLCpJujguRJTC51M522CpVik3miyDve7GY2oD4VBqq6WXROcSIYY02c84Z8yyFC8iRmIskzkfqaCSRPYsZSnkLreyoUm8wkb2G3m0uoT4VNbpd5EwRBEARBEEQRQE4xQRAEQRAEMeEZb07xI/kWIAaSJzETSZ6J1NdMIHkSM5byFFrfU6HYZCZ5C7vdXEJ9KmDG1ZxigiAIgiAIgsiE8fakmCAIgiAIgiDShpxigiAIgiAIYsJDTjFBEARBEAQx4Sk6p3jZsmUcAH3oM9afpJBu0idPn6SQbtInT5+kkG7SJ08fVYrOKT5zptg2TSEmCqSbRKFCukkUKqSbRCFRdE4xQRAEQRAEQWQbcooJgiAIgiCICY8u1YyMsR8hwTwMzvnqJOUfBbAcQA/n3DmcNgXANgCzAJwAsIpz3p+qTCKCwPFOvxvdrhC6B/yomWSCQcfQPxTCoC+EshID9FogLABnPQGY9FqUGnWwm7XocPlh1GrhCYRgM+nh8gVQZjXBUW2DTpf5bwZB4DjR50G324dKmwmzyqzQaFjG9RVb+8VEKCSgtdOFTpcP1XbzqM89QRQD57w+HO3yoNvtR6XNiMkWLfqHwuh2+1FtN6LEpMU7fT7YLXq4vEHYzXqEwmHotFq4vAHYzQb0DwVRbTMiEBbQ5fajotQInYYjJDCpXkBAiVGPoYCAbrcP5aVGeAMhWAw6CAiDQYszg35MLTHC7QuizGqABgwuXwAmvQ69A36UlxoREsLQabTSsT8UQqXNiHPDMlfajODD9fUPBTDZYsCgP4gSox7nvEFMtRpQYtTB5QuCgaF/KIBJZj0YEyBwDbrdflTZjagoNeKsJ4BQmKNnINInLWPggOr9QbQffR4/So0jY+UJBFFqNEAAR5nViFllVgDAiT4P+jx+GLQaeHxhGPUaWb0AMrZHanZfEHjR2bdAIIzm0y50uX2otplQN82OcFjAoS63dK7rqmzQ67WqfVMrD0CRFgqH0dI1INXprCqFxWxQXBvzqiLnLjZNC+CNqLQFVVaUmk3w+UI41OlCl9uPKpsRddV2+HkIR6Lyzq+ygqvUOclsgtcbVPQ1hLCiLcRpf8gbUPTLoNcpxioUEhRymkw6Vfk55ymPvxrFcp9N2SkG0DT89/0ALkTEmQWAGwG8mkL5XwF4EMDjUWlfB7CPc/59xtjXh4+/loZMEASO19/pw1s9Q1i3qxWTLQbcceUcDPhC2LKvDb6gAJNeg40rHHjo2WM42eeFSa/BmqW1qLKbEAyGsW73YSnf6iW12NZ0GF9YUovGhTUZnTRB4Njb2oW12w9I9W5etQjLHFVj4pjmu/1iIhQSsONgB+7e0SKN1aZGZ8bnniCKgXNeH55u6cW6XS0yG7m96RSaTrpg0muw/hoHfv/KSRztGcTqJbXY/2YXbmyYgfW7WqUy37p6Ac4NBaS0hpl2rGqYgXVReTascECr8eLuHa0yO6tWX8T+nsJ/XzYbOo0G3979GnxBATPLzPifK+bK8n77Ggc8/jC+uO1g3DxifZ+65HycOOPBb185iZsaZmDr/vj3hvtuqIc3EJbVs365A0++dgpLLqiS3R8AYMfBDvxof5uiXrHtmxpmYFvTKXxt2QIYdAz37jkcJ+9hfPPqBXB7Q7hnZ/r2SM3uP/zJxegdCBSVfQsEwtjRfBrrosbg3pVO2Ew6rNl2IOq8OVFhM+Bzv3lN1rePXliF3a1dsvKbGp0w6DT46h+bpbT7bqhHICTIxmbjCieucpYrr42VTthj2r/v+nqEBI5v/vlQ0vJbP34RznmCsrR7Vzph1MtlSqf9+2+ohy8oyHQlbvkVTky26vGF37+etP9XO8vxVEz5LTctgssXko3plpsWwe1LTVeL6T6bsjSc88c4548BqAVwJef8R5zzHwFYCmBRCuWfB3A2JnklgMeG/38MQGOq8oic6PMgFGaSEb5u8XT0DPglhxgAfEEB63a1Ynl9jXS8ZV8b3j7jgcWol+Xbur8Ny+trcPeOFrR2utIVR5JJNExivWu3H8CJPk9G9RVb+8VEa6dLulCByFiN5twTRDFwtMsj3fSAERt5yyWzpeMNu1vxmcvnSHbxlktmS46imKd30C9Lu+WS2ZItFvOs39UKk06nsLNq9Yn2t2fAj2/vHvlueX2NIu+3d7ciFEbCPGJ9fUMBbH7mKJbX10jOaHS/o+8Nx3oGFfVs2BMZm9j7g2g/1OoV84p/124/gOZ2V8K8wRCXnAzxu1TtkZrdH/CGi86+NZ92Sc4XAMnxC4Z5zHlrgYYxRd8OdbkV5e/e0YJjPYOytGM9g4qxWberRf3aUGn/WO+g5BAnKx8Y/i62T7EypdN+W8+gQlfilt/VAn9QSKn/h1XKB8NcMabBcOq6Wkz32Uxc9GkASqOOS4bTMqGSc94JAMN/K9QyMcZuZ4w1Mcaaent7Zd91u33oGfBJg80YIPARQyniCwpgTH4scMATCKnm8wUFdLl8GXWq2+1Tbb9nILP6iq39YqLTpT5WqZ77RLpJEPkksd30q+q9N8oeRh/7ggK8/pCiTKytVcvjCwqqdjZeXjUbLtrkRPXGyxNdX6I88foUPRax9wfRfiSqN/qvwBPL6YkzJqnYIzW7P5r6ckki3eyKc/9S06H+oaAiLZ5uCzGTP+Od53jlY9tPp3y88xArU67az4X86ejWaO+zY0kmTvH3AbzOGPsVY+xXAF4D8N2sShUD5/wRznkD57yhvLxc9l2lzYSKUhNM+pGuaBlkx0DkmHP5sYYBVoNONZ9Jr0GV3ZSRvJU2k2r7FaWZ1Vds7RcT1Xaz6lileu4T6SZB5JPEdtOoqvfmKHsYfWzSa2Ax6hRlYm2tWh6TXqNqZ+Pl5Ty+DU+l3mT1Jbs3xGvbbNAp7g/R9iNevdF/xdlr8fJaTepjkoo9UrP7o6kvlyTSzeo49y+1cz3ZolekxdPt2JmD8c5zvPKx7adTPt55iJUpV+3nQv50dGu099mxJG2nmHP+SwDvAfDn4c/7hqdVZEI3Y6waAIb/9qRbwawyK3Rajo0rHDDpNXjy1XaUlxqxZmmtzFhtXOHAnuYO6XjN0lqcP9WKIX9Qlm/1klrsae7ApkYnHNX2jDo1q8yKzasWyerdvGqRtNAi1+S7/WLCUW3DpkanbKxGc+4JohiYV2XFxhVOhY18/MXj0vH6axz4+fNvSXbxsRePY8OwnRXzTC0xytIee/G4ZIvFPBtWOOALhRR2Vq0+0f6Wlxrx7WtGvtt9sEOR99vXOKDTImEesb4pFgPWfmgedh/swOolie8NcypKFPWsXx4Zm9j7g2g/1OoV84p/N69ahPrp9oR59VqGe1dmZo/U7H6pSVt09q1umh0bY8bg3pVO6LUs5rw5IXCu6FtdlU1RflOjE3MrSmRpcypKFGOzcYVT/dpQaX9OeQm+e21dSuUNOo0i7d6VSpnSaX9uRYlCV+KWXxGZv5xK/y9UKa/XMMWY6jWp62ox3WcZ5zx5rthCjNUAmImohXrDc4aTlZsFYE9U9In7AfRFLbSbwjn/aqI6GhoaeFNTkywtUfQJjz+EKRYD9Dox+kTECRajT5x2+WGIij7h9gUwxWqEo9qelegTPQM+VJTmL/pEvtovJsRVsV0uH6rspnjnPungqekmQYwBGelmvOgTPcPHpeZE0Scix/1DQVTZjAiGI69dy0uN0Gk5QmF59AmrUQ9vdPSJYAgWvQ4CBDBo0DfoR9lw9IkpFgO0jMHlD8Ck00mRKcToE+KxPxRCZakR57wRmSuiok+cGwpgksUAjz8I63BEiLKY6BPnhgKwR0WfEPtdYVOJPqFhEDgwoHJ/iI0+4fYFYTPp4QmEUGLUg6tEnzjr8UOv1cDjD8Og08jqBZCKPVJFze6L0ScyqS8LZKSbYvQIMYpGfZLoE7F9UysPQJGW6+gTkqwFFH0ieqzE6BPRckZHn4hOTxR9IhXdSvE+O5ao6mbaTjFj7AcAbgLQCkCcJMI55yuSlPs9gCsATAXQDWA9gB0AtgOYAeAUgBs557GL8WSQ40HkCXKKiUKFdJMoVEg3iUJFVTfTCckm0ghgPufcn04hzvnH43y1NAMZCIIgCIIgCCJrZPLs+jgAfdJcBEEQBEEQBFEkZPKkeAjAAcbYPgDS0+JkO9oRBEEQBEEQRKGSiVO8a/hDEARBEARBEOOCtJ1izvljjDEzgBmc8yM5kIkgCIIgCIIgxpS05xQzxq4BcADA3uHjRYwxenJMEARBEARBFC2ZTJ/4NoB3A3gWADjnBxhj52dRpowIhQS80eXGOW8AIUGAWafDgC+EshID/KEwAiEBJUY9+jx+VNrMcFTb8h0jjyAIIm+IcUM7XT5U282YX16CI70D0nG0jYzNG/2dGBtXjP+aKCZ6vLyJ6o8t2+fxw6DVYCgQRrXdhLAA9Az4YDHoEAiHUWY1YsZkC071D8HlHYk3XGU3ocxqQO9gpLzHH4bVOFJGTe5U+hYdp9hmMiAQElBlV8+bzlgRclLRERExzm6X24+qqPi7qeZTO09ivOfo9jUalvL5FGMnd7l9qLaZUDfNDoNBm3JfBYEryms0THVMUtUz0kclmTjFIc65izHZwKW/A0gWCYUEPNXaiY5+L/7wn1O4qWEGtu5vgy8owKTX4OvLLgAD8L29b0ppmxqdaFxYQ44xQRATjlBIwI6DHbh7R4tkEzeucGJ700k0nXTJbCQARV7xO42GYW9rF9ZuPyB9t3nVIixzVKk6hGp5Pzi/ArsOnVatP9rx3tvahR/sfUOy75MtBtzyvpnYsm/E1q9eUottTafwhSW1eLr1NK6cX40Ne1ql7793XR36BgP44dNHFGW+tmyBTO548kbnEcfxR/vbFPed2Lyp1Eeoo6av8e7hPl8Iuw51Yt0uuW6vqKuWOcbx8l3jrMI/jp2RnaeHP7kYvQMBRfuTLHrc+bvXk57PQCCMHc2nsW5nVFsrnWisn6ZwjNX6+sMbF8IbCOOeqPL33VCPUJjjm38+JJNpRd00/P1IT1I9I31UJxOPsIUxdjMALWOsljH2IwAvZlmutGjtdOFYzyC27GvD8voayTABgC8o4Pt730TfUECWdveOFrR2uvIpNkEQRF5o7XRJN10gYhPX7WrBLZfMlo5FG6mWV/zuRJ9HuqmK363dfgAn+jyKNuPlbT4dv/7YstH2/brF0yWHWCy3dX/kHnD3jhZ84r3nSw6x+P3bZzySQxxbJlbuVPomjo3afSeT+gh1EulgLIc6XZKjK+Zdt6sFh2Lyxs3X5VacpwFvWLX95nZXSuez+bRLcoiltna2oPm0Un61vh7tHpAcYjHtWM+g5BDLZDrtSknPSB/VycQp/gIAByLh2H4PwA3gi1mUKW06XT4IPHJSGYN0kkV8QQFCzLNsX1BAl8s3hlISBEEUBp0un6qd9AZCsuMuly9u3i6XD91u9e96BpS2NV7ernjpUfZZLBtt3+PZejG93xNUfC/eJ+KViZY7lb6JYxNPlnTrI9RJpIOxdLn9qnm73f6U88Wme/yhlP0KtfMZT8e73cq8an1V09t4uhyvrVi5SB/VSdsp5pwPcc6/xTm/mHPeMPx/Xkex2m6GlgEmfaQ74l8Rk16D2LcBJr0GVXbTWIlIEARRMFTbzap20mzQyY6r7Ka4eavsJlTaTKrfVZQqbWu8vNVx0qPtc3TZ6Lxq5TiP/J1i1Su+j75PqJWJljuVvkWPTbK86YwVISeRDsZSZTOq5q20GVPOF5tuNelS9ivUzmc8Ha+0qeRV6aua3sbT5aoU9Yz0UZ1Mok80MMb+xBh7jTHWLH5yIVyqOKptmFNRgjVLa7H7YAdWL6mVGaqvL7sAZRaDLG1ToxOOans+xSYIgsgLjmobNjU6ZTZx4wonHn/xuHQs2ki1vOJ3s8qs2Lxqkey7zasWYVaZVdFmvLx10+xx648tG23fn3y1HWuWym396iW12NPcgU2NTvzm5bexfrlD9v2sqVZ85ar5qmVi5U6lb+LYqN13MqmPUCeRDsZSV23HxhVK3a6LyRs3X5VNcZ5KTVrV9uun21M6n3XT7Ni4MqatlU7UT1PKr9bX2spS3BtTfk5FCb57bZ1Spmn2lPSM9FEdxnl6a+QYY0cA3AXgEADp2Tvn/GR2RVOnoaGBNzU1KdJHok8EEQoLMOm1GPSFMMVqQCAcHX0igEqbEY5qOy2yI9Ih6cqDeLpJEDkmI90UV7h3uXyospswv7wUR3oHpONoGxmbN/o7cQV7z4APFaWpRZ+IzZuo/tiyZz1+6FWjT2gRDAuYEi/6hM2EshIDzgyOlI8ukyhaRKK+ibKf9fhRajIgGBbiruRPZ6zGCVmzm6noiIgYVaLb7UdlCtEnYvOpnScx+kR0+2L0iVTOpxh9Qoz0UJ9C9InotsToE9HlxegTsWOSqp5NQH2MRrWjmTjF/+ScX5oVkTKAHA8iT5BTTBQqpJtEoUK6SRQqqrqZSUi29YyxnwPYh8hiOwAA5/xPGQpGEAQxIag5bwZOt7+Tdrlp089DxzunciARQRAEIZKJU3wbgAsA6DEyfYIDIKeYIAgiAafb38FND6cfwXLbZy/JgTQEQRBENJk4xQs553VZl4QgCIIgCIIg8kQmK81eZoxdmHVJCIIgCIIgCCJPZPKk+FIA/8UYexuROcUMAOec12dVMoIgCIIgCIIYIzJxipdlXQqCIAiCIAiCyCNpO8Wc85OMsckAzospn1GcYsbYlwB8BpHFeocA3JbJDnkerx+tXYPoHfCjvNSIYDgMg1aL/qEgSkxa6LUa9HuCmGTRwajVwjMc43LAH8Tpcz7YzDpU28yYOYHi9IkxCsW4hxMsRiFBTGhcXh+OdHmkGK0VpVqcGRRwZtCPaXYzODje6fei2maCo8qG0wM+dLl8MOo0GPQHYTHoMOAPoqLUBJNOi35vAIbh+L+x9kSMu9rp8qHMaoCAMDTQon8ogEqbGXOmmnB4WJbzJkdiD3e5fSgvNcLlDWKSWQ9HVSnaznjQ6fKhvMQIjQaYbDFExSnWIRAOoywqTvGgPyht3VtlM6JqkhFnBgIIC4DbF4DNZEAgJKDKbpI2LTjR54HLG8lz1uNHmTXSloYx1b7FEm1Xo2VKFLe42x2JE6vVRLb5VWtjotvraB2qtpvhqLbFjVOslheAIk2M/dvl9qHaZkLdcOxgMX5x17DeiBt/xKYJXEBL14B0DTmrSmExG6SYxNH1hsMCDnW5R2IiV9kgQEBr16CU5qgqgdVsxJA3oKjXZNQrzr/PH1RtX/SHous1Gw0plx8rXctVO6OpN22nmDF2L4BbAbyFiCOL4b9LMqirBsBqABdyzr2Mse0APgbgV+nU4/H68ZeWHqzb1QJfUMDMMjPuvLIW9+yMHJv0GqxZWovHXzqJ/qEA7vrwfOx8vQMfqavGln1tsjy1lSVYMr9y3BsbQeDY29qFtdsPSP3fvGoRljmqxn3fCWKi4/L68LeWXslmRnbzcuBo1zn8/F/vyGymQcdwxxW1sryrl9RiW9Mp3PzumWg/68Uv/vU2bmqYga372xT2RBA4dhzswN07Ruzz/1wxF+t3tUa17cRDz7YhEOK45X0zZXZZbOuOK2qxvekkmk66YNJr8L1r6/Da0Dnc/7cjsrz73+zCx949E0+3nsbSBdUx7ThQaTNi3a5WVXkNOoafPnsM1y+egQ17WiV5P3f5XOk4ka1Us6ui/F9btkBWRi1v9H0quo2Jbq9DIUGmQ+LubY0LaxSOsVreLR9bBLc3JPMJ7l3phM2sw5o/jIzpxpVOfNRRIfMnxLwmvQZ3/bFZSvvutXXQaRi++mSzTI8/4izHX1t7sS6mLaNeg69Glb/vhnoEQoJMzo0rnLjKWY6nFdemE5U2Az77m9ektN/998U41u1V5FMrf9/19dBqGL78xEEp7YEbFyIscIX8H3VW4LljZ3Oua7nS6dHWm8lCu1UA5nDOr+CcXzn8SdshjkIHwMwY0wGwADidbgWtXYOSAgDA8voaSfkBwBcUsGVfG65bPB2+oID7/3YEn7l8jmR4o/M0t7twos8ziu4UByf6PJLSAJH+r91+YEL0nSAmOke6PDKb6QsKWLerFR901EjHos1cXl+jyLt1fxuW19fgf/9+FGc8ASyvr5EcTDGPaE9aO13SjR+I2GfRUR1puwXL62tw3eLpCrsstrVuVwtuuWS2lP52n0dyiKPz3nLJbNy9owWfeO/5Ku20wqDTxpW3ud2FWy6ZLTnAorzRx4lspZpdFeWPLaOWN/o+FZ1/otvrWB3yBSPOZGunK6W8wRBX+AT37GxBMMTl+rGzReFPiHnbegZlad/88yEc6x2M0a8WvNHlkRzi6PLHYsof6xlUyLluVwuOql6bLRjwhWP6xFTzqZU/1jsoOcRi2pefOKgqf2vX4JjoWq50erT1ZuIUtwCYlEE5BZzzDgA/BHAKQCcAF+f86dh8jLHbGWNNjLGm3t5eRT3dbr80AJH8kB0DkWPGRv73BkKqeQQeeRU33ul2+1T7PxH6nk2S6SZB5ItEuhlrM4HI9d8bdf2LNjORPRVtZrw8PQM+dLp8KdvnZG15AyEpXeDqeb3+iG3v9wRVvz8z6I/bjsAhlU8mr5qtjGdXxTqiyyTKG9vGeLPX6drNWB0CIv3vcin7r5bX41e/33ui9ElMi3dtCDGb/8ZLS7V8PP2NVz5W1p4B9TFRKx+vrXTkz7au5UqnR1tvJk7x9wC8zhj7G2Nsl/jJoB4Mz01eCeB8ANMAWBlj/y82H+f8Ec55A+e8oby8XFFPpc0Ik17eFbVjcUdrk14Di0GnmkfDgIpSUybdKSoqbSbV/k+EvmeTZLpJEPkikW7Gs5nlUdd/rM2Mzcv5iM2Ml6ei1IRquzlt+xyvLbNhZMaflqnntRgjtn2KVa/6/dQSY9x2NAxS+WTyqtnKeHZVlD+6TKK8sW2MN3udrt2Mp0NVdmX/1fJaTern1GrQKdLiXRuxb97jpaVaPp7+xisfK2tFqbpOqJWP11Y68mdb13Kl06OtNxOn+DEAPwDwfQAPRH0y4YMA3uac93LOg4jsipf21k2OqhJsXOGUBmL3wQ5pDhAAaa7Wn15rj8wL+vB8/Oz5t7Bmaa0iT/10u7TgYjwzq8yKzasWyfq/edWiCdF3gpjozK+yymymON/2760d0rFoM3cf7FDkXb2kFnuaO/ClD87DVKsBuw92YPWSWlV74qi2YVOj3D5vWOGIaduJPc0dePLVdoVdFtvauMKJx188LqXPKrPirg/PV+R97MXj2NToxG9eflulHQcCoXBceeun2/HYi8exfrlDJm/0cSJbqWZXRfljy6jljb5PReef6PY6VofEOcWO4QVwyfLqtUzhE9y70gm9jsn1Y6VT4U+IeWsrSmRp3722DnPLS2L0y4kFVVZsVGlrbkz5ORUlCjk3rnBinuq16USpSSvvk46r5lMrP6e8BA/cuFCW9sCNC1Xld1SVjImu5UqnR1sv45wnzxVdgLHnOOcfSFtS9breA+BRABcD8CKywK6Jc/6jeGUaGhp4U1OTIl0WfaLEiJAQhl6rxbmhICxGLQw6Dc55grAPR58YCoZRZYtEn+g850eJSYtp9okZfaJnILLyeaKtZk6TpAMTTzcJQoQxlvE2zwlsdUa6mSj6RLXNBDCgvd+LSpsJzuHoE91uH/RaDTwq0SfOeQPQpxx9QoAGGvQPBVFpM2LOVLMUfWL6JBMEDnQP+DC1RD36xNQSI3QaYJIs+oQWwbCAKSrRJ3qG+1g1yYi+gQBCUdEngmFBkheIzEl0eyN5znoCmGI1QJtx9IkRmRJFn+gZ8KG8JBJ9osutbo+L1F5nzW6KOtTl8qHKboKj2p40+kR0XgCKNDH6hBiloD4m+oQUKSIq+kR0WrLoE9H1Ziv6RPT5zyT6RCrlx0rXctVOivWqNpSJU7wZkU07dg3/BQBwzl9LT2ypvg0AbgIQAvA6gM9wzv3x8pPjQeQJcoqJUVNITjFBjAGkm0ShoqqbmWzecdHw3/dGpWUUkg0AOOfrAazPpCxBEARBEARBZINMNu+4MheCEARBEARBEES+SHuhHWPMzhjbLIZTYYw9wBhTznYnCIIgCIIgiCIhk+gTjwIYQGQTj1UA3AB+mU2hCIIgCIIgCGIsyWRO8RzO+fVRxxsYYweyJA9BEARBEARBjDmZPCn2MsYuFQ8YY+9HJJwaQRAEQRAEQRQlmTwp/jyAx6LmEfcDuDVrEhEEQRAEQRDEGJNJ9IkDABYyxmzDx+5sC5UugsBxrMeNE31e2Mw6mHQaDPjDCIbDsBh0cHmDmGo1IigIcHmDqLIZMW9qKToH/VJw7ejA7dFpqQSSjg7Wnk45giCIfCFuENA/FMRkix4DviBsJj08gSCmlkQ25Ogd9KPSZpI2w4hsSKGDwAVpM4tqu0naQKPSZsK0UhNautzocfswtdQIty+Icmtka+WuAR8mWwwICWHoNFr0ewKYWmJESIjU1+8NYprdBG8gjG63HxU2I3SMQavVQOACwgLQPxTAJLMeA/4gyktMYADaz3lRbTdjfnkJjvQOoNPlQ3mJESaDBt5AZEOSKrsZWgb0efyRjUd8QZSa9OjzBFBmNWAoEEKJUYepVgM63X5ZPwXOERYQ2djEboaj2iZt/BDZbMMIDRMAaBEICaiyq98HxE0l+jx+2EyGhHkTMd7vOWr9A5T350AgjEOdLnS5/aga3lDDZNJJm2d0uX2otplQN82OUDis2KjCoNdJm8qI5zXehiBqG2pwcNXNNwa8PrwRtTHOgiorrEZjyvKLm4dEp6ttFBKvfTVZ1foarcPiOBkMWtlmO4nGJZ4eplq+EEnbKWaMfRfAfZzzc8PHkwF8mXN+d5ZlSwlB4PhrSye+/MRBTLYY8PkPzIYnEMYf/nMKNzXMwNb9bfAFBWkLzcdfOon+oQC+e20dtuw7ipN9XmkbQIOO4c7fvS7l37xqEZY5qhIaG0Hg2NvahbXbD6RVjiAIIl8MeQPY09KNh55tU9jJL31wHk7pvfjuX9+U0jY1OvGj/W2SvVy/3IGfPn8MgRDHLe+biS37IuWvunAqPrhgGtbtapHVdzqqvpllZvzPFXOxflerlGf1klpsazqFz1w6G/2egOy79csd+MeRTlw5vxob9sjLbGg6jI9dPAOPv3QSBh3DHVfUSm03zLRjVcMMrIuq63vX1SEYEvDQs62Kfq9f7sCTr7Xh+nfNwO9fOYmjPYNx2/7OtXXQaxm++sdmKW3jCge2N51C00mX6n0gFBKw42AHfrRfOebp3DPG+z0nXv9i788P/7/F6HYHZLq2cYUTH3VW4C+tPVi3cyT9/hvq4QsKuCcq7b7r6xESOL7550MyPW9cWKNw4MTrJbYtu0WHNX84IEu7ylmOp1t6FXmnTzbiU4+9KqU9euu70H7Wr8h3tbMcT8WUv++Gevhj5Be3rb5LpoPq7d+70gmzXouv/PGgrK8GnUauwyuduMZRhb8c7sLdO1oSjku88/TB+RXYdeh00vKFSiYSfkR0iAGAc94P4OqsSZQmJ/o8+PITkRN93eLpOOMJYMu+Niyvr5GMDgD4ggK27GvDdYunwxcU8M0/H8Ly+hrpu7XbD6C53SXLv3b7AZzo8yRtX1SKdMoRBEHki5auAazb1aJqJ//370dxxhOQpd29o0VmLzfsacXy+hpct3i65BADwCfee750M45X3/L6GsnpFfNs3R+x2b2DfsV3G/a04hPvPV9ySmPLiHZ9eX2NrO1bLpktOcRimbfPeLBuV6tqvzfsacUtl8zGht2t+MzlcxK2/a0/H8KxnkFZ2rpdkfLicex9oLXTJY1jbNvp3DPG+z0nXv9i788DvrBC19btakFr16DkEIvpbT2DkkMpph3rHZQcYjHt7h0taO10KWQSr5fYtoIhrkg72uVRzRsSIEsLhaGa77BK+WMq8t+zswVtCh1Ub/+enS042jOg6KtCh3e24FCXW3JoE41L3PN02pVS+UIlE6dYyxgzigeMMTMAY4L8OaXb7ZMGnzFA4JGTwNiIAoqI6bH/i8dCzC6qvqCAngFfyu2nU44gCCJfdLv9Ce2kmi2MtZeMQVG+3xNMWl8i2yza79jvzsWpV6xLTRavP6Qok+z+4A2EpL9iWip9ii4ffRx9H+h0+RK2neo9Y7zfc+L1L3a8PSrn1xcUJN2ORk2v4ulal0s5jmp1+oICPFHnO1H7vqCA/qGgLO1sHL1KVf54OhivfbW86ZSPHZd456krXrrKuBYimTjFvwGwjzH2acbYpwA8A+Cx7IqVOpU2E0z6kW5oGaTj6HTxmHPl/+Jx7Jsnk16DilJTWu2nWo4gCCJfVNqMCe2kmi2MtZfRtlRkilWfcn1q9Ufb7+jvJsWpl/P4sliMOkWZZPcHs0En/RXT0umTWE48jr4PVNvNCdtO9Z4x3u858foXO95Wk/L8mvQamW6LqOlVPF2rsivHUa1Ok14Dq0GnSIuXd7JFL0uLp1epyh9PB+O1r5Y3nfKx4xLvPFXHSVcb10IkbaeYc34fgE0AFgBwALh3OC0vzCqz4oEbF8Kk1+DJV9tRZjVgzdJa7D7YgdVLamVGaM3SWvzptXaY9Bp899o67GnukL7bvGoR6qfbZfk3r1okTfBP1P7mVYvSLkcQBJEvnFWl2LjCqWonv/TBeZhqNcjSNjU6ZfZy/XIH9jR34MlX27Fm6Uj537z8NjaucCasb/fBDmxY4ZDlWb2kFnuaOzC1xKj4bv1yB3778ttYv1y9jGjXdx/skLX92IvHsTGmrllTrdi4wqHa7/XLHXj8xeNYf40DP3/+LSntNyptf+faOsytKJGlbVwRKS8ex94HHNU2bGpUH/N07hnj/Z4Tr3+x9+dSo1ahaxtXOOGoKsHGlfL0uRUl0hxcMW1OeQm+e22dQs8d1coNesXrJbYtvY4p0uZVWVXz6jTyH2Q6LVTzXahSfo6K/PeudKJWoYPq7d+70ol5FaWKvip0eKUTdVURPU02LvHOU900e0rlCxXGOU+eK7YQYzMB1HLO/84YswDQcs4Hsi6dCg0NDbypqUmWphZ9YtAfRmA4+oTbG8QUqxFhQYBrKIRKmwHzyiPRJ3oGfKgola9ujU5LJ/pEuuWIoiLpCVXTTYKIhjGGmx5+Me1y2z57CRLY6ox0Mzb6xOBwNAZPIISpJUaYdFqc8fhRURobfUILzjlYTPSJ3sGI/ZOiTwz4MLXEiAFfEGVWIxiA7gE/Jln0UvSJc0NBTLEaIAgCGGM45w2i2maCNxhG94AfFaVG6DQMWs1IBIhzQwHYh6NPTC0xQgOGjnNeVNlNmF9eKkWfmFpihMWgwZAYfcJmglbDcNbjh9mgw6A/iBKjHmc9AUwZjj5hNehQXiJGnxjppzz6hAmOarsi+oSWCeDQIhgW4kaEEFfln/X4UWoyJMybiCK652Skm2r9A5T3ZzF6gxhlITb6hBgVoT5J9Ikulw9Vw+c119EnUpFfjD4RnZ6t6BPRfRV1OHqcoqNPJBuXeHqYavk8o6qbaTvFjLH/BnA7gCmc8zmMsVoAP+WcLx29jMkhx4PIE+QUE6OmkJxighgDSDeJQkVVNzNx3e8A8H4AbgDgnLcBqMhcLoIgCIIgCILIL5k4xX7OeUA8YIzpAKQ/B4MgCIIgCIIgCoRMnOLnGGPfBGBmjH0IwBMAdmdXLIIgCIIgCIIYOzJxir8OoBfAIQCfBfAU5/xbWZWKIAiCIAiCIMaQTEKyCZzzn3HOb+Sc3wDgJGPsmRzIRhAEQRAEQRBjQspOMWNsCWPsKGNskDH2G8bYhYyxJgDfA/CT3IlIEARBEARBELlFlzyLxAOIhGJ7CcBHALwM4B7O+ZbRCsEYmwTg5wCciCza+xTn/KV06giFBLT1unFuKIRBfwh2sx59Hj8mWwww67ToGfRjskUPvUaDvqEAZk6x4vyp8tiOYsw9MWZfLmM/jmVbBEEQsYixULuGY5laDFr4ggLOevyotJlh0AEn+7woLzFCr2PgHBgKhFFpM2G63Yw3ut3odPkwzW6GVgOc8/qh1+rQOxCJCVw3HPMUGInP2+nyodpuxoLKUrS7vJL9i46DXGUzYcAXxNkhP6wGPfqHAqi0meGotkGn08hsp5i3y+3DZIsBnkAQky0mRf3RcW5jbW6sbGI7aqjZbbHePo8fBq0GHn8YVqMOgXAYZVajzLaT3U8NtXOi0bC4Y5/KeKYz9mp5QyFBiktdPazfGg1T1R0xTnKyvNGxrqujrhmP16+IP6zX6lTzpjp+oZAgXe9VUTGRU0WtTzqdRnVMi1nP03GKOef82eH/dzDGerPhEA+zBcBezvkNjDEDAEs6hUMhAfuOdOOsJ4ifPncMNzXMwNb9bfAFhciuRNc4oAXHpr8cxuc/MBe/e+UkjvYMYvOqRVjmqJJO4t7WLqzdfkAqF/19NhnLtgiCIGLx+ULYdagT63a1wBcUMLPMjDuumIt1u1pldvP3r5yEyxfE/1wxF+uHv5tZZsYdV9Zi3c4WKe9dH56PKRYDvvHn16S0jSudaKyfBo2GYcfBDty9o0X23UP/aMPJPq+049WP9rchEOK45X0z8Yf/nFLY8U2NTqyom4a/H+nB2u0HMNliwC3vm4kt+0byrF5Si21Nh3HHlbXY/p+TaDrpkuyrQcdw5+9el9ncD86vwK5Dp2WybWp0onFhjcIxjme3DTqGe/ccVsgbkeUUvrZsAZY5qgCA7H4KhEKCQl/uv6EeGqbBl58YGbsHb74IgRBPaTzTueeq5X34k4vR7Q7IdF7cYe6uPzbLdOejF1Zhd2uXIq/ZoMVXnjgo65MvKOCenfLrYpmjHHtbeqVr06TX4L7r6xEIC4prqLF+msIxjjd+irZWOLGirjolxzgQCGNH82lZnzaudGL6ZCM+9atXZWN61YJKPP1Gd9HqeTpziicxxq4TPwBYzHFGMMZsAC4H8AsA4JwHOOfn0qmjtdOFYIhjw+5WLK+vkQwTAPiCAjbsboXFqMfy+hp8e3crPnP5HPiCAtZuP4ATfR4AkV+b4kkUy0V/n03Gsi2CIIhYDnW6pJsuACyvr5EcYmDEbn7m8jlYXl8jOcRS3p0tsrz3/+0I3u7zyNLW7WxB82kXWjtd0g06+rvl9TXS8d07IsfXLZ6OLfvaVO343Tsi9Ym2U8wbnWfr/jZJvlsumS2lr91+AM3tLoXNbT6tlO3uHS1o7XQpxiye3W5ud6nKK8oi2nay+6mhpi9tPYOSQyymNbe7Uh7PdMZeLe+AN6zQ+Xt2tqCtZ1ChO4e63Kp5j3YPKPp0z07ldfFml0d2bfqCAo71DqpeQ82nlXoab/wUbe1qwSEVPVej+bRL0ad1O1sQCkMxpq2dqZ+XQiQdp/g5ANdEfaKPl49ChtmIRLP4JWPsdcbYzxljsk3cGWO3M8aaGGNNvb29igo6XT54/CH4ggIYGzlJIr6gAE8gJH3nDYSk9J4BHwCg2+1TLSd+n03Gsi0ityTTTYLIF4l0s8vtl9mgeHbTG2U3k+UVYqLV+4ICut0+dLrU7R1jymOx7nhtdEXZznh5Yu18Ivm64tjiLpfSFsez2wJPLkvPgI/sfhSJdFNNXwSuHF+1tHjjmc7Yq+UV/YvY8uo6708pbzz51crHz6uUP9XxE9tKhXjXSf9QUJEW73ovFj1P2SnmnN+W4POpUcigA7AYwE845xcB8CAS9i267Uc45w2c84by8nJFBdV2M6wmHUz6SHfEvyImvQZWgw6cR/43G3RSekWpCQBQaTOplhO/zyZj2RaRW5LpJkHki0S6WWUzqtqg2ONoW5ksb+ybUZNeg0qbCdV2s2r+6F2ro48T2fHqGNsZr95o2RPJF1ufmF5lV9rieHZbrDeRLBWlJrL7USTSTTV90TLl+KqlxRvPdMZeLW+0fxFdXl3n1a+t2Lzx5FcrHz+vUv5Ux09sKxXiXSeTLXpFWrzrvVj0PO2QbIyxNYwxG4vwc8bYa4yxq0YhQzuAds75K8PHf0TESU4ZR7UNei3D+msc2H2wA6uX1MoM6/prHBjyB7GnuQPfvsaBnz//ljTPRZysP6vMis2rFsnKRX+fTcayLYIgiFjqqu3YuMIp2aDdBzuwcYVDYTd//vxb2H2wAxuivtt9sAMbVzplee/68HycX2aVpW1c6UT9NDsc1TZsanQqvtvT3CEdb2qMHD/5ajvWLK1VteObGp2om2aXbKeYNzrP6iW12NMcke/xF49L6ZtXLUL9dLvC5tZNsytk29TohKParhizeHa7frpdVV5RFtG2k91PDTV9mVtRggdulI9d3XR7yuOZztir5S01aRU6f+9KJ2orSpQ6WmVTzTuvslTRp3tXKq+LC6qssmvTpNdgTnmJ6jVUP02pp/HGT9HWCifqVPRcjbppdkWfNq50QqeFYkwd1bai1nPGeXo7NDPGDnLOFzLGPgzgDgD3APgl5zwtRzamzhcAfIZzfoQx9m0AVs75XWp5GxoaeFNTkyJdEX3CpMfZoQDsZj0senn0ibNDAcxIEH2iZ8CHitKxiT4xFm0RWSHpyYmnmwQhwhjDTQ+/mHa5bZ+9BAlsdUa6KUaf6Hb7UWEzwmrUwhsQcNYTQEWpEUY9w8k+L6aWGGHUMQgq0Se6XD5U2U3QaRjOeQPQa7XoHfCj0mZCvUr0CTH/gkob2l1eyf6J0Sd6BnyoLBWjTwRgNejQPxSMrMCvtsuiT0Tn7XJH7LsnEMJki0FRf3SkglibGyub2I4aanZbrPesxw+9VoOhQBgWgxbBsIApcaJPTCC7n5Fuqp0TMfpEKudUjXTGXi2vGH1CjKhQHxVRIlZ3xEgNyfKK0Sei8yWLPhGbVw218ROjT4h1Zhp9Irp9MfpE7JgWiZ6rCpSJU9zMOa9njG0B8Czn/M+MsdeHpz5kJhljixAJyWYAcBzAbZzzfrW85HgQeYKcYmLUFJJTTBBjAOkmUaio6mY6IdlEXmWMPQ3gfADfYIyVAhCSlEkI5/wAgIbR1EEQBEEQBEEQmZKJU/xpAIsAHOecDzHGygDcllWpCIIgCIIgCGIMSdsp5pwLjLFZAP4fY4wD+Cfn/M9Zl4wgCIIgCIIgxohMok/8GMDnABwC0ALgs4yxh7ItGEEQBEEQBEGMFZlMn/gAACcfXvXBGHsMEQeZIAiCIAiCIIqStJ8UAzgCYEbU8XkAmrMjDkEQBEEQBEGMPSk/KWaM7QbAAdgBvMEY+/fwV+8GkH6MoSwz6PXhSM8Q+gYDKDXpIHABJp0OgXAYYQHwBEKwmXWw6LTwBMIY9IdhM+tRZTNixhRlDD0xzp4Yk69A4+ylxHjqC0EQ2WHQ68PhLo8Ut1SrEQCuRV1UTNVutw+TLQZwCAA0cHmDmGTWQ+AcWo0GZz1+VNrMcFTbpDiyfR4/GBg8/iDMBh3ODPpRWWpCuc2Afk8Q/pAgtWk3a+H2htEz4Ed5qRHBcAiTzEb4Q5HtYitKjbAMx092+wKwmQzwB8Mw6DTo9wZRWWoEONDp9qHabsa8qVa0dg/A5Q2g1KSHyxuE3axHMCSgepIZs8qsEASO1k4X+jx+2EwGCJzDqNNIMZij49+KNlOMo6xmQ2Pt67RSE1q63OgZ8GFqiRHeQAiTLEYYdMDJPi/KS4zQaAC72ZCwnkzstBhLtsvtQ7XNhLoEsWwLGbV+AFCkiXFyY8dMbSwDgTAOdbrQ5fajajhOr0bDVMfL6w3iUJd7JKZvlQ0hhPFG1PWyoMoKLTRo6RqQ0pxVpbCYDYpr68IqK7TQKupkjClkMpl0qnGKGZiiLQ3TqJYX4xR3uiLXRfT1mYp+iTHMo+s1GLRZ9yMK0TdJZ/rED3MmxSgZ9Pqw93Av7t7RAl9QwMwyM764dB4GfB54AmFs2dcGX1CASa/BhhUO/PjZYzjZ54VJr8GapbWorSzBkvmVMuO0t7ULa7cfkMptXrUIyxxVeT9h6TKe+kIQRHYY9PrwVEsv1u1qkezCxhUONJ04g+Pnl8Ns0OIrTxyU7On/XDEXP372GG5qmIFtTadwU8MMbN0/Ylc3NTpRXmrAup2tcfPcu9IJo16Dr/6xWdbmQ1H2+HvX1uHNLg/u/9sRWZ7tTaew5IIqWX3fWHYBTpzxYPMzR6PyOvH3N05j0XllqjI8cOMiCFzAD58+ElfOB2++CIEQl2zmzDIzvrCkVrq/RNtQADL7OrPMjDuuqJWN6+oltdjW1IrPfWAufv/KSRztGcT65Q48+dopfOrSOar1ZGKnA4EwdjSfxrqdUed0pRON9dOKyjFW68eWjy2CyxuSpd13Qz20TIMvPyEfs6sWVOLpN7plY/nore9C+1m/7LxsuWkRXL6QYryudpQrro3vXVsHrYbhq082y3St3KbH53/zuiztKmc5no4pf98N9fAHBdwT05bdpMOabQdk5T/sLMffYsp/59o66GPaF3eou+uPcpmWOyvx1OFumb7ef0M9NCpjpaZfPl8Iuw51yuW/vh5aDcOXh21CNvyIQvVNUp4+wTl/TvwAeBNA6fDnjeG0vHG4yyMpAAAsr6/B230enPEEJIcYAHxBAet3tWJ5fY10vGVfG5rbXTjR55HqO9HnkU6UmG/t9gOyPMXCeOoLQRDZ4XCXR7rpARG7sG5XKxoXz8A9O1twtHtAZk9Fu7l1f5v0N7rs3TtaMOANJ8xzz84WHOsZVLQZbY/f7htxiKPz3HLJbEV9fUMBySEeyduCT7z3/LgyfPmJA2jrGUwoZ3O7S2Yzl9fXyO4v0TY01r4ur69RjKvYzobdrfjM5XPgCwrYsCfSp3j1ZGKnm0+7JAdPGo+dLWg+7Uq5jkJArR/BEFekHesZlJw8MW3t9gNo7XQpxjIUhuK8BMPKOtftbFG9Nr7x50M41huruy3QMo0i7ahK+WM9g5JDHN1WMMwV5Y+olP+WSvv37GxBm+J6akFL14BCX9vijJWafh3qdCnl7x2UHOJk5VOlUH2TTKJPrALwbwA3AlgF4BXG2A3ZFiwdut1+aWABgDFA4JFPdDoQOWZMfixwoGfAF1WfT7VcdJ5iYTz1hSCI7BBrM4FhR3PQL9lEEcZG7Gb039iynkAoaR4hZlO+WHscz2Z7/SFFery854aCSWVIJGdsvfHq6RnwKexrvLxiujcQGulTIBS3nug2UqUrTh3d7uKy9Wr98KRx/jtdyvJnPcGU6oyMl/q1oaa7/UPBlMrHk9UzrA+Ztq+Wlk77avrVNcryqVKovkkmC+2+BeBizvl/cc5vQWRO8T3ZFSs9Km1GmPTyrmhZ5BObbtJrEL1bqkmvgYYBFaWmqPpMquWi8xQL46kvBEFkBzWbadJrUFZilGxi7Hdqf6O/txp0SfOo1Rttj+PZbItRp2rj1fJOsuhTkiFennj1xh5XlJri2le1Ppr0Gpijxshs0CWtJx07XR2njkpbcdl6tX5YTamf/2q7svwUqz6lOiPjpX5tqOnuZIs+pfLxZBWvmUzbV0tLp301/aoaZflUKVTfJBOnWMM574k67suwnqxxYZUVmxqd0gDvPtiBWWVWlFkNWLO0Vmb8NqxwYE9zh3S8Zmkt6qfbpcUVADCrzIrNqxbJym1etUiWp1gYT30hCCI7XFhlxcYVTpld2LjCgR2vncK9K52YV1kqs6cbVjiw+2AHVi+plf5Gl93U6ESpWZswz70rnZhbUaJoM9oezyqz4q4Pz1fkeezF44r6plgMWPuheTF5nfjty2/HleGBGxehtqIkoZx10+0ym7n7YIfs/hJtQ2Pt6+6DHYpxXb2kFnuaO7D+Ggd+/vxbMOk1WL/cgcdfPB63nkzsdN00OzaujDmnK52oH16kViyo9UOvZYq0ORUleOBG5Zg5qu2KsdRpoTgveo2yzo0rnarXxveurcPc8ljddSLMBUXaPJXycypKpDnA0W3ptUxRfr5K+e+otH/vSidqFdeTE86qUoW+zo0zVmr6VVdtV8pfXoIHblyYVT+iUH0TxjlPniu6AGP3A6gH8PvhpJsANHPOv5Zl2VRpaGjgTU1NivTY6BOcCzBGRZ8YCoRRatLBotfCEwhh0B+G3axHZZLoEz0DPlSUFsaqyEwZT33JI0kHLJ5uEoQIYww3PZx+sJ5tn70ECWx1RrqpFn2Ccy3qZdEn/Jhs0SuiT3DOodFocNYTiKyOH17JHy/6REWpCRU2gzS1QWxzkkUL11B09Ikw7GYDAilEnzjnDaJiOPpEl9uHKrsJ86aWRKJP+AIoNY5EnwiFBFTFRJ846/Gj1GQAB4dBqx59QrSZYvQJNRsaa1/Vok/YzQYY9Qwn+7yYWmKETgPY4kSfGI2dFqM2iKv56/MffSIj3VTrBwBFmhh9InbM1MZSjD4hRX+Iij4RO15jHX0iWqZMok/ElhejT3S5ItdF9PWZin6J0Sei6xWjT2TTj8izb6LaUNpOMQAwxq4H8P7hSp8fy22eyfEg8gQ5xcSoKSSnmCDGANJNolBR1c1MdrQD5/xJAE+OShyCIAiCIAiCKBDS2bxjAJHNO9jw32j8AN4C8C3O+b7siUcQBEEQBEEQuSdlp5hzXhrvO8aYFoATwG+H/xIEQRAEQRBE0ZBJnOKNjLEPMsakJYKc8zDn/CCAH2VVOoIgCIIgCIIYAzIJpXYCwM0Amhhj/2aMPcAYWwkAnPOHsykcQRAEQRAEQYwFaTvFnPNHOeefAnAlgN8gsrPdb7ItGEEQBEEQBEGMFWlHn2CM/RzAhQC6AbwA4AYAr2VZLoIgCIIgCIIYMzIJyVYGQAvgHICzAM5wzkMJSyRheKFeE4AOzvnyTOoQg1V3unyYPtkMo06Ld/qHUGLUwaDV4JzXj1KjAW5fEBaDLu6mHQRBEBMBMXC+uHHBdLsZb3S70enyodpuhqPaBp1Oo5o3W0H21eoFkLCt6DLVdhPCAtAzEMkrbrKhVlYs5/IGEBaAM4N+RT9HKzvdTwqD0Z4bcfOQLrcP1TYT6qI2CklFV9XaFzeNUbu+UiHaxxHLixtyjIUOThR9T9sp5pxfCwCMsQUAPgzgH4wxLed8+ijkWAPgDQC2TAqHQgJ2HOzA3TtaMNliwC3vm4kt+9rgC0a2YFz7oXkwajX43t7XpbQ1S2tRW1mCJfMrx+WJJQiCiIcgcOxt7cLa7Qckm7hxpRMP/aMNJ/u80tbNjQtroNEwRd7NqxZhmaNqVLZTTYYHb74IgRCP21Z0GTVbv6nRiR/tH+mDWBYA9rZ24dF/voXrF8/Ahj2tsjKNC2vSclDUZM/GmBCjZ7TnJhAIY0fzaazb2SKVv3elE5Msenzh96/L6jToGO78nTztqgWVePqNbln7D9y4CAIXcNcfmzPSu2gfRyx//w310DANvvxE7nVwIul7JtEnljPGfgDgUQCfA7AfwLpMBWCMTQfwUQA/z7SO1k6XpCzXLZ4uGUkA8AUFbH7mKPqGArK0Lfva0Nzuwok+T6bNEgRBFCUn+jzSDQ6I2MR1O1uwvL5GOr57RwtaO12qedduPzBq26lWb3O7K2Fb0WXUbP3dO+R9EMuK5W65ZLbkEMf2c7SyZ2NMiNEz2nPTfNolOcRi+Xt2tuBQh0tRZ3O7Mq21U6nDX37iANp6BjPWu2gfRyzf1jMoOcSZ9DMdJpK+ZxJ94iOIzCG+nnN+Aef8Ns75o6OQ4f8AfBWAEC8DY+x2xlgTY6ypt7dX8X2nyyedLMYg/S/iCwoQYrYbEdN6BnyjEJ2Y6CTTTYLIF4l0s9vtU7WTjMmPu1y+uHlHazvV6hW4uv0W24ouE8/Wx/ahZ2CkD15/SLVMlyu9vuRqTCYKubSboz03XXHKx/MhYtOi/ZFkeVPVO7U6k10r2WQi6Xsm0SfuAPAsgMXDT40rMm2cMbYcQA/n/NUkbT7COW/gnDeUl5crvq+2m2HSj3Ql+n/xOPYJv5hWUWrKVHyCSKqbRGrUnDcDjLGMPjXnzci3+AVJIt2stJlU7STn8uMquylu3tHaTrV6tUzdfottxZZJpQ8VpSN9sBh1qmWq7On1JVdjMlHIpd0c7bmpjlM+ng8RmxbrjyTKm6reqdWZ7FrJJhNJ3zOZPnEjgH8jEoptFYBXGGM3ZNj++wGsYIydAPAHAEsYY2mHd3NU27Cp0QmTXoMnX23HmqW10gkU5xSXWQyytDVLa1E/3S5NlicIIn+cbn8HNz38Ykaf0+3vZNRmpo74eHDCZ5VZsXnVIplN3LjSiT3NHdLxpkYnHNV21bybVy0ate1Uq7duuj1hW9Fl1Gz9pkZ5H8SyYrnHXjyO9csdijKOavuoZc/GmBCjZ7Tnpm6aHRtXOmXl713pRF2NXVFn/XRlmqPapmj/gRsXobaiJGO9i/ZxxPJzK0rwwI1jo4MTSd8Z5zx5rugCjB0E8CHOec/wcTmAv3POF45KEMauAPCVZNEnGhoaeFNTkyJdXJnZ5fKhZjj6RHv/EKxS9IkASo16ij5BZEpSRYmnm0RyGGO46eEXMyq77bOXIF07Npo2M20vh21mpJviavKeAR8qSkeiT3S5fKiym+CotiuiT4h5sx19IrpeAAnbii5TZYtEn+gdjOQVo0+olRXLub0BhKToE/J+jlZ2up8oyIvdHO25EaNPiJEW6qOiT6Siq2rti9En1K6vVIj2ccTyYvSJsdDBcajvqsJnEpJNIzrEw/Qhs7nJWUWn02DheZOx8LyRtNrK0vwJRBAEUcBoNAyzy0swu7xESou1oYny5koGAAnbUiszp2Lk/3hlxXLZIldjQoye0Z4bg0GLhllTFOmp6qpa+xoNi3t9pYKajxOv/VwwUfQ9E6d4L2PsbwB+P3x8E4CnRisI5/xZROYqEwRBEARBEMSYkkmc4rsYY9cjMh+YAXiEc/7nrEtGEARBEARBEGNEJk+KwTl/EsCTWZaFIAiCIAiCIPJCyk4xY2wAAEfk6XD0ig8GgHPOM9qNjiAIgiAIgiDyTTpPihdyzo/nTBKCIIhiQKMDY0W96pogCIJQIR2n+AkA72KM7eOcL82VQARBEAWNEBpV+DiCIAiiMEnHKdYwxtYDmMcYWxv7Jed8c/bESp9QSMCb3W54gyEIAhAIcXiDIVTbzbCZdTh9zodpk0w45wmi0+1Dtd0MR7Uto/iUo0GM9SfGPxwHsf4IgiBSItr+WQw6BMJhlFmNMjsoxojtcvtQbTOhbpodBoM24/pExDivnS65/U9kk6PLlJcYodEAdrOh4Oz2eLuvjLY/uRgPNb0EkJGuijIBSFnOdK6LYqEQ9TYdp/hjABqHyxRUAOBQSMBfWzsx6AsiGObwBMLYsq8NvqAg7Ubz0rFeXDx7KjbsbpXSNzU60biwZswcY0Hg2NvahbXbD0gybF61CMscVXlXBIIgiFyiZv9WL6nFtqZT+NqyBVjmqEIoJGBH82ms29ki5dm40onG+mkKByCV+qKd2x0HO3D3jhaZ/V9RNw1/P9KjapMFgSvKrF/uwJOvncKnLp1TMHZ7vN1XRtufXIxHIBBW1Uu7WYc1fziQka4+ePNFCIR4SnLGa1+trWKhUPU2ZW+Qc34EwP0A1nLON8R+cidiclo7XWjrGUSn248znoDkEAOALyjgnp0tuOHiGZJDLKbfvaMFrZ2uMZPzRJ9HUgBRhrXbD+BEn2fMZCAIgsgHavZv6/42LK+vkexg82mXdOMX86zb2YLm00o7nUp9Iq2dLsm5FfPevSNSbzybrFZmw55W3HLJ7IKy2+PtvjLa/uRiPOLpZTDEM9bV5vb4updq+2ptFQuFqrdpPSLlnAsAPp8jWTKm0+WDwCF9xEEW8QUFnBn0q6Z3uXxjJme326cqQ8/A2MlAEASRD+LZP8ZG7GBXnDzdbqWNTKU+kU6Xet547fUM+OKW8QZCBWW3x9t9ZbT9ycV4xNMTTyCkSEtVV+P5KmpypnNdFAuFqreZzBt4hjH2FcbYeYyxKeIn65KlQbXdDC2D9DHp5d0y6TUoLzGqplfZTWMmZ6XNpCpDRenYyUAQBJEP4tk/zkfsYHWcPJU2pY1MpT6RartZ3f4nsMnxypgNuoKy2+PtvjLa/uRiPOLppdWgU6SlqqvxfBU1OdO5LoqFQtXbTJziTwG4A8DzAF4d/jRlU6h0cVTbMLeiBFU2I8qsBqxZWisNtjin+In/nML6axyy9E2NTjiq7WMm56wyKzavWiSTYfOqRdKEe4IgiPGKmv1bvaQWe5o7JDtYN82OjSudsjwbVzpRP01pp1OpT8RRbcOmRqfC/tdPs8e1yWpl1i934PEXjxeU3R5v95XR9icX4xFPL/U6lrGu1k2Pr3uptq/WVrFQqHrLOOfJcxUQDQ0NvKlJ6YOrR58Io8puhN2sH15xHIk+0eX2ocpugqPanrfoEz0DPlSUFsZqSyIlkp6keLpJJIcxNqowZ5nYsUzb3PbZS0Yla6ZtJuhj0eimPFqEFsGwgClxok+IK9LrU44+oV6fiBhJosslt/+JbHJ09ImpJUboNICtgKNPFOB9JSPdHG1/cjEeanoJIG1djZYJQMpypnNdFAt51lvVhtLe5pkxZgGwFsAMzvntjLFaAPM553tGKeCo0Ok0cNZMivv9zLKS4b/AwjGSSQ2NhmF2eQlml5fkUQqCIIixJxX7ZzBo0TArtRl56dhTnU6DhedNxsLzUq8jXplCY7zdV0bbn1yMRzy9HK2upipnOtdFsVCIepvJY9JfAggAEKPQtwPYlDWJCIIgCIIgCGKMycQpnsM5vw9AEAA4516k8IqEIAiCIAiCIAqVTJziAGPMDIADAGNsDgB/VqUiCIIgCIIgiDEk7TnFAL4NYC+A8xhjvwXwfgC3ZlEmgiAIgiAIghhT0naKOedPM8ZeBfBeRKZNrOGcn8m6ZARBEARBEAQxRmQSfWIXgN8D2MU5L859JAmCIAiCIAgiikymTzwA4CYA32eM/RvANgB7OOcZ7c3HGDsPwOMAqgAIAB7hnG9Jtx5B4Dh11oMzgwGEBQFhgWPAH4JFr4PNpIM3FIJeo4VOy3DWE4DFoEOlzYgZUwomnuOoiY7ZWWkrqFiVBEEUGD5fCIc6Xehy+1FlM6Ku2g6TKbVbgpqtARAVM1iHQDiMsjgxg2PLz5hswan+IXS7I/FKtRrg9DkfjDoNXL4AyqwmOKptKceVTyZfqvYxkU0lezu2jHa81coLApfiUFfbzQl1LDpmtZhXo2GqMokxhbvcPlTbTKibFomJnQ2dTGVMcjFWo5W1WMhk+sRzAJ5jjGkBLAHw3wAeBWDLUIYQgC9zzl9jjJUCeJUx9gzn/HCqFQgCx/4j3Tjd7wUA+EICNj9zFL6gAJNegzVLazFzigWd7gHc/7cjsvTayhIsmV9Z9CdXEDj2tnZh7fYDUv82r1qEZY6qou8bQRDZxecLYdehTqzb1SLZi40rnFhRV53UMVazNQ/efBECIS5LW72kFtuaTuFryxbI7FBs+ZllZnxhSS3u3tEis82Pv3QS/UOB4XoO4wtLatG4sCapY5yqfMnsYyKbCoDs7Rgy2vubWvkHblwEgQu464/NUtqmRqeqjoVCAnYc7JDp6KZGJ8pLDfjsr1+TybSkthy7WjqxbmfUtbXSiUqbMq9Bx3Dn717PWp82r1qEqxZU4uk3urM6VqOVtZjIaDu34egT1wP4HICLATyWqQCc807O+WvD/w8AeANATTp1nOjzoLndhTOeAM54ApJDDAC+oIAt+9ogcEgOcXR6c7sLJ/qKfxbIiT6PpMRApH9rtx8YF30jCCK7HOp0SQ4xELEX63a14FCnK2lZNVvT3O5SpG3d34bl9TUKOxRbfnl9jeRsiGW37GvDdYuny+q5e0cLWrMoXzL7mMimkr0dW0Y73mrlv/zEAbT1DMrS4ulYa6dLoaN372jBgDeskOlQp0tyiMX0dTvV8za3u7Lap7XbD6C1M31dT6Xe0chaTKTtFDPGtgF4E5GnxA8hErf4C9kQhjE2C8BFAF6JSb+dMdbEGGvq7e1VlOt2+yBwSB/xxIn4ggI8gZBqusCBnoGMZn4UFN1un2r/xkPfCplkukkQ+SKRbna5/ar2otudPLqmmq2JZ3cZU9qh2PJiHrWysfV0uZLbs3TkS2QfE9lUsrejI127OdrxjldeiNk5PZ6OdbrUy3sCIWX5ONeWWl619kfbp3iy5mKsxqO+Z7qj3WzO+ec45/s550LSEinAGCsB8CSAL3LO3dHfcc4f4Zw3cM4bysvLFWUrbSZoGaSPSS/vlkmvgdWgU03XMKCi1JSNLuSVSptJtX/joW+FTDLdJIh8kUg3q2xGVXtRaTMmrVfN1sSzu5wr7VA8W6VWNraeKntye5aOfInsYyKbSvZ2dKRrN0c73vHKx775j6dj1XZzXL9CUT7OtaWWV6390fap2j52YzUe9T0tp5gxVoHI9s7bGGNPMMY2MMYqRysEY0yPiEP8W875n9ItP6vMirrpdpRZDSizGrD2Q/OkkyrOT9Mw4K4Pz1ek10+3S5PIi5lZZVZsXrVI1r/NqxaNi74RBJFd6qrt2LjCKbMXG1c4UVdtT1pWzdbUTbcr0lYvqcWe5g6FHYotv/tgBzY1OhW2+U+vtcvq2dTohCOL8iWzj4lsKtnbsWW0461W/oEbF6G2okSWFk/HHNU2hY5uanSi1KxVyFRXbcfGlTHX1kr1vPXT7Vnt0+ZVi+CoTl/XU6l3NLIWE4xznjwXAMbY+wH8DsCvALyKSIzixQD+C8AnOOf/ykgAxhgic5LPcs6/mCx/Q0MDb2pqUqSL0Sf6BgMIDUefGPSFYTZqUWrUwR8KQTdBok/0DERWcI/X1aF5IulAxtNNIjmMMdz08IsZld32+csBIZQ8owqZtLnts5dkLmuGZbd99hIksNUZ6aYYfaLb7UdlhtEnom0NEB19QotgWMCUJNEnxPJi9ImeAR/KSyLRJzpdPui1Ggz4AphiNcJRbU87+oSafOnYx0Q2lextSmTNbo52vNXKi9Enulw+VNlNCXVMjD4RnVeMPhErkxh9QozUUB8VfWK0OpnKmORirEYrawGiKnw6TvHLAD7POX89Jn0RgIc55+/JSCrGLgXwAoBDiIRkA4Bvcs6fUstPjgeRJ8gpziGjcopH4WhOZKeYIMYA0k2iUFHVzXRCstliHWIA4JwfGA6llhGc83/GE44gCIIgCIIgxoJ05hQzxthklcQpadZDEARBEARBEAVFOs7s/wJ4mjH2AcZY6fDnCgB/Hf6OIAiCIAiCIIqSlKdPcM4fYYydBnAvAAcADuAwgE2c8905ko8gCIIgCIIgck5a2zxzzvcwxl7inPdFpzPGzuecv51d0QiCIAiCIAhibMhkLvAuxphNPGCMXQiAnhQTBEEQBEEQRUtaT4qH+S6A3YyxjwKYD+BxAJ/IqlQZIsYq7nb74fYFYTfrUWkzIhSObOVcaRsXsfVUEeMKinERx2s/CYLIDmIs1S63D9U2E+qm2WEwaPMtVlJSsXVinj6PHwatBkOBMNnFIiLV+1mu7ntq9QIYVVtinONOlw/VdjMc1baU424TY0faTjHn/C/DO9A9DaAUQCPnvC3rkqWJIHDsP9KNtu5BbNnXBl9QgEmvwYYVDvz42WM42eeVdmFZ5qgaV4ZREDj2tnZh7fYDUr/HYz8JgsgOgUAYO5pPY93OFslmbFzpRGP9tIJ2jFOxdWKeH+x9Azc1zMDW/W1kF4uIVO9nubrvqdX74M0XIRDiGbcVCgnYcbADd+8Yud42NTrRuLCGHOMCI+WzwRj7EWNsK2NsK4AlAGwA3gbwheG0vHKiz4PmdpfkEAOALyhg/a5WLK+vkY7Xbj+AE32efIqadU70eaSLFRi//SQIIjs0n3ZJDjEQsRnrdrag+bQrz5IlJhVbJ+ZZXl8jOcTx8hKFR6r3s1zd99TqbW53jaqt1k6X5BCL5e/e0YLWzsK+3iYi6fxEaUJke2fxcz+AJ6OO80q32weBQ1I6EV9QAGPy454B3xhLl1u63f+fvTOPj6q6+//nzJbZMpOQPYQQY8KWDTAibdXHgrXUIkEFtLZ20Zb2eaRQrUsXlbJo60Yr4tNWaxe1tqA8FaQ+VAV91F+1FheWiEBEwEA2QjJJZjLrPb8/JjPMnXvuLHcymSXn/XrlBbm5555zt+8993u/3+/HydzvbNtPDoczOnTK2IyugfS2GbHYusA6hLCfB9wupjexPs+S9dxjbVdubhFrXx029lg7bfxaTDfiKcn2JwAghJgAOCmlvpHf1QBykjO82Cmx6KEmgF6rEl18eq0Koeqoeq0Kxbn6FIwweZRY9Mz9zrb95HA4o0OZjM0osaS3zYjF1gXWCfyN28XMItbnWbKee6ztys0tYu2rzGpgti+18msx3VASzLILgCHkdwOAV0ZnOMqpKjChocKKVfNrRQZxzaI67Nh3Mvj7hmUzg0Hz2UJVgQkbls0U7Xc27ieHwxkdGsqtWNtSL7IZa1vq0VhuTfHIIhOLrQus88Lek1g5r5bbxQwj1udZsp57rO02VFgT6quuzIL1i8X32/rF9agrS+/7bTyipPqEnlI6FPiFUjpECDGO4pgUoVIRzJtagpoiM2ZX5mPA6UGeQYtiSw6aJ89Bz5ATxbnZmX2sUhEsqCvFtJUXoXswe/eTw+GMDjqdGosby1FdaApm0zdmQPWJWGxdcJ3SXJyxu7B5+VxefSKDiPV5lqznntx2ASjuS6NRYXHTRNQWm9Fpc6LUqkddmZUn2aUhSibFdkLIbErpewBACDkPwPDoDksZKhVBVaEZVYVmyd/OLZYuyyZUKoLqIjOqi7J7Pzkczuig06nRXDUh1cOIm1hsHbeHmU2s5y9Z51luu4n0pdGo0DQpH02TRmOEnGShZFL8AwDPjkg+A0AZgGtGbUQcDofD4XA4HM4Yo6RO8b8JIdPgF+4gAD6ilHpGfWQcDofD4XA4HM4YocRTDPgnxDMA6AHMIoSAUvrk6A2Lw+FwOBwOh8MZO+KeFBNCVgO4BP5J8YsAvgTgTfjlnjkcDofD4XA4nIxDSerjEgDzAXRSSr8FoAlpUKeYw+GImTipEoSQuH8mTqpM9dA5HA6HwxlzlIRPDFNKBUKIlxBiAdANoHqUx8XhcBLkVPunuOa3/4y73eb/vBiEKCtrVF4xCSc/PaGoLYfD4XA4qUTJpHgPISQPwOPwyzsPAXhnNAfF4WQbEydV4lT7p4rajvlEU/AqmkwDiU2oORwOh8NJJUqqT/zXyH9/QwjZCcBCKd2ndACEkAUAHgagBvA7SukvlG5LECg+OT2EDpsTQy4vrAYNzHotnB4fisw58PooPu1zwJSjwbDbC4NOg+LcHFRO4AXdOclFqdcWADZ/97OjPJokonBCnVH7mGV4vQJaO2zosDlRZjWgrswSVVRAECiO9dqDwh+hQgaBv3XanMjRqGBzulFg0se0Xda2K/ONONHnkP09UcEGJfufTEL336jTwO3zocCUM+6FRyJdc+GwzqlKRSTtAcS8zUTHyuornv5Z2/R6Bew7ZUPngBNlFj0aIgjwxHP8xjNKEu12UUrnAwCl9Fj4sji3pQbwKIAvAGgH8G9CyHZK6YfxbksQKHZ91IW27iE8vOsInB4Beq0Kt3xhCgxaNQ6RQaz/+8Hg8pXzarF5zwl89YLJqC4yYd7UEn6BcDiccYXXK+D5vSdx5/MHgrZx/eJ6LG6aKDsxFASKna2duGXLB8E2G5bNxIK6UgCQ/M1vaz/E9+fVRtyu3LbXL67HI7uP4HjvMPP3QN9K7LeS/U8mrP0PPKvuWDBd8X5mOpGuufDjwTqnDyxphIqo8MNnz7bfdN0suL00pm0mOla5vnQaghXPvB+1f9Y2f3v9bHQNuHH3trP7ubalHosbyyUT43iO33gn5rueEKInhEwAUEgIySeETBj5qQJQrrD/OQDaKKVHKaVuAH8F0KJkQ8d67dh/0hacEAOA0yNgw8uH0TPkQvegS7R84+4jWNg4ERtePox97TYc67Ur3AUOh8PJTFo7bMHJA+C3jXc+fwCtHTbZNsd67cGHa6DNLVs+wLFeO/NvAVsbbbty277z+QNY2DhR9vdA32O1/8kk0vFLZD8znUjXXDisc3qkeyg4IQ4s29dui3mbiY5Vrq997baY+mdtc3DYF5wQB5bdve0A9p2SXrvxHL/xTjyvwt+FP4Z42si/e0Z+tsHv7VXCRAChgZbtI8tEEEKWE0L2EEL29PT0MDfUNeCEQBE86QGcHgECBQQKyXJCzv69e9CpcBc445lYrk0OJxXEcm122JxMm9lpk7eHXQPsNt2DTtm/BWxtpO1G2nZomDrrd6X2W8n+J5Noxy9bnlPx2s1I11w4rHPKmhvIzRcSPcassUaam8TSP2ubdpeXuc2ugdjaZ9P1NJrEMyn+J4DPAriVUloNYA2AAwD+D8AzCvtn+e2pZAGlj1FKmymlzUVFRcwNlVj0UBNArxXvkl6rgooA4V8I9FoVKD379+JcvcJd4IxnYrk2OZxUEMu1WWY1MG1mqVXeHpZY9Mw2xbl62b8FbG2k7UbaNqWRf1dqv5XsfzKJdvyy5TkVr92MdM2FwzqnrLmB3Hwh0WPMGmukuUks/bO2adJrmNssscTWPpuup9EknknxbwG4KKWPEEIuBvBzAH8CYAPwmML+2wFMCvm9AsApJRuqKjChfqIVq+bXBk9+IKa4yJyD4twc0fKV82qxY99J3PKFKWissAaD3jmctEOlUVRvmMOJRl2ZBesX14ts4/rF9agrs8q2qSowYcOymaI2G5bNRFWBifm3gK2Ntl25ba9fXI8d+07K/h7oe6z2P5lEOn6J7GemE+maC4d1TmuKzXhoqbh9Q4U15m0mOla5vhorrDH1z9pmrl6NtS3i/VzbUo/Gcum1G8/xG+/Ek2inppSeGfn/NQAeo5RuBbCVEPKBwv7/DaCWEHIOgJMArgVwnZINqVQE86eVoLrQhIaJVgy5vLDoNcjVa+Hy+lBozsGOFRf6q0/oNBj2ePHgkiYU8eoTnHSHV3TgJAmNRoXFTRNRW2xGp82JUqsedWXWiElmKhXBgrpSTFt5EboHnSjOFWeyB/7WNeCEVq3CoNONjdfOirpduW1X5hsxuzJf9vdEsuiV7H8yCd1/f/UJNTw+AQvqS8d1tYBo11wocudUpSKYXiZuDyCmbY7GWFl9AcCLMfQvt02vV0B1oSlYUaJRpvpEPMdvvBPXpJgQoqGUeuFXtFuucDtBKKVeQsgKAP+AvyTb7ymlrUq2BfhP/LnFuTi3OFd2nZoS+b9xOBzOeEOjUaFpUj6aJkVfN4BKRVBdZEZ1kTmuvynddrTfE0HJ/ieTRI9fthLPcZE7p6z2yTjWcmNNpH/WNnU6NZqrJiQ0Jo6YeCazfwHwf4SQ0wCGAbwBAISQGvhDKBRBKX0RwItK23M4HA6Hw+FwOIkS86SYUnoPIWQXgDIAL1EaTHVQAfh+MgbH4XA4HA6Hw+GMBYRSSbGHtIYQ0gPguMyfCwGcHsPhRIOPJzKZNJ7TlNIFkRrzazMh+Hgik8xrM56+0pVMG/N4GW+22c3RgO9TesC8NjNuUhwJQsgeSmlzqscRgI8nMuNpPONpX5XAxxOZsRxPuu17LGTamPl407vfZML3Kb1JncA7h8PhcDgcDoeTJvBJMYfD4XA4HA5n3JNtk2KlIiLJgo8nMuNpPONpX5XAxxOZsRxPuu17LGTamPl407vfZML3KY3JqphiDofD4XA4HA5HCdnmKeZwOBwOh8PhcOKGT4o5HA6Hw+FwOOOejJsUL1iwgALgP/xnrH+iwq9N/pOin6jwa5P/pOgnKvza5D8p+mGScZPi06czrT40Z7zAr01OusKvTU66wq9NTjqRcZNiDofD4XA4HA5ntOGTYg6Hw+FwOBzOuEeT6gFwOBwOh8PhcOJDECiO9drRNeBEiUWPqgITVCqS6mFlNHxSzOFwOBwOh5NBCALFztZO3LLlAzg9AvRaFTYsm4kFdaV8YpwAPHyCw+FwOBwOJ4M41msPTogBwOkRcMuWD3Cs157ikWU2fFLM4XA4HA6Hk0F0DTiDE+IATo+A7kFnikaUHfDwCQ6HwxnHzF/wZXR0y5fFKisuxK6dfx/DEXE4nGiUWPTQa1WiibFeq0Jxrj6Fo8p8+KSYw+FwxjEd3afR+L1fyf59329+MGZj4XA4sVFVYMKGZTMlMcVVBaZUDy2j4ZNiDofD4XA4nAxCpSJYUFeKaSsvQvegE8W5vPrEaDBmk2JCyM0Avg2/vN5+AN8CYASwGUAVgGMAllFK+8ZqTBwOh8PhcDiZiEpFUF1kRnWROdVDyRrGJNGOEDIRwEoAzZTSegBqANcC+BGAXZTSWgC7Rn7ncDgcDofD4XDGlLGsPqEBYCCEaOD3EJ8C0ALgTyN//xOAxWM4Hg6Hw+FwOBwOB8AYTYoppScBPAjgBIAOADZK6UsASiilHSPrdAAoZrUnhCwnhOwhhOzp6ekZiyFzODHBr01OusKvTU66wq9NTroyVuET+fB7hc8BUA7ARAj5WqztKaWPUUqbKaXNRUVFyRomhxM3/NrkpCv82uSkK/za5KQrYxU+cSmATyilPZRSD4D/AfBZAF2EkDIAGPm3e4zGw+FwOBwOh8PhBBmrSfEJAHMJIUZCCAEwH8BBANsBfGNknW8A2DZG4+FwOBwOh8PhcIKMSUk2Sum/CCHPAXgPgBfA+wAeA2AGsIUQciP8E+elYzEeDofD4XA4HA4nlDGrU0wpXQ1gddhiF/xeYw6Hw+FwOBwOJ2WMZUk2DofD4XA4HA4nLeGTYg6Hw+FwOBzOuGfMwic4HA6Hw+FwsgVBoDjWa0fXgBMlFj2qCkxQqUiqh8VJAD4p5nA4HA6Hw4kDQaDY2dqJW7Z8AKdHgF6rwoZlM7GgrpRPjDMYHj7B4XA4HA6HEwfHeu3BCTEAOD0CbtnyAY712lM8Mk4i8Ekxh8PhcDgcThx0DTiDE+IATo+A7kFnikbEGQ2yJnyCFdsDgMf7cDicjMXrFdDaYUOHzYkyqwF1ZRZoNNyXweGMNeFzjOJcPfRalWhirNeqUJyrH5P++XwmOWTFpFgutkenIVjxzPs83ofD4WQcXq+A5/eexJ3PHwjasPWL67G4aSKfGHM4YwhrjrHpulnYsGymZN4RcMglu38+n0kOWWFZ5WJ79rXbeLwPh8PJSFo7bMEJMeC3YXc+fwCtHbYUj4zDGV+w5hgrnnkfM8py8eLKi/DX5RfgxZUXJW2SyuOXx46s8BTLxfZoVCrJsu5BJ6qLzGM5PA6Hw4mbDhvbrnXanGialKJBcTjjELk5RueAE3OrC5M+p5Dr/4zdFfw7D6kYHbJiUlxiYcf21BabUWbVo8PmDC5LVrwPh8PhjCZlVgPTrpVauQ3jcMYSuTnGWM0nWP1PLjDgZL8TX3viHR5SMYpkRfhEVYEJv7pmJlbOr8GKeTVYNb8GP14wDb/YeRA/vXw6yqz6pMb7pDuCQHG0ZwhvfXwaR3uGIAg01UPicAAAbrcPe46dwY59p/DusTNwu32pHlLaUFdmwfrF9dBr/WY6EFNcV2ZN8cg4nOwm/JlZmW/EhmUzRffihmUzUZlvHJNna1WBSdL/upYG3LF1X1JCKrxeAXs/7cPOAx3Y+2k/vF6BuV42zi2ywlMMAA63D4+9fjT4xnTzpVPg9lIc7h7EbV+citmVeaicMP4+LfAAfU664nb78Py+U7h729lEsrUt9VjcWA6dTp3q4aUclYqgKFeHB5c0we72wqTTINeg5vcth5NE5J6Zl00vwYsrL0L3oBPFuXpU5hvx0sGuMXm2qlQEC+pKMS2k/0gl4RIJ54g1wTdb5xZZ4Sk+1mvHT/62X/TG9MtXDmNpcwV8AvCTv+1H4AUmE95qRvPtiwfoc2JlrN/6952yBSfEgP/avHvbAew7xRPJAP+9+92n3sOKv7yPO7bux4q/vI/vPvUev3c5nCQi98w80edAdZE5GEN8os+R8LNVic2lI6sEvoCHMhohHbEm+Gbr3CIrPMVyb0yV+UY89PJhOD0Cugac+KhzMO3fakb77StZb5Oc7CIVb/2dMtdm1wAvfg/we5fDSQWx3ndy63UNxHZ/xmNzWes+tHQmHljSiNue2yfy6FbmGxPY+9gTfLPVPmXFpDgQhJ5v1OGq2RUgBFATYNDpQYfNCb1WBa1axXyrmbbyorQ6gXJvX0rHmeoEAU5mMNrXXSyUyVybJRZ+bQL83uVwUkGs951Rp8HkAgMWNk4EGZnDvrD3JIwxhn7FY3NZ6/7w2Q9w+xen4v4lTRh2e2HUafCnfx7F7Mr8hGx2rAm+2WqfsiJ8ojLfiAeWNOLrn5mMJ948ik272/Db14/C7aOYXGDAynm1GHC6M0KScbSlI1kB+uM14ZAjTyokSxvKrVjbIk4kW9tSj8ZynkgG8HuXw0kFsd53hFD81yU1wTnH7944iv+6pAaxfliLx+bKrWvRa3H7c3txx9b9uO25vZg3rTRYpk0psSb4Zqt9ygpP8Yk+B9r7hvHXf5/AjRdWB9/annnnONa21OORXYfxuZr6jHirGe23L1aAPq9lyAknFW/9Op0aixvLUV1oCtbZbCy3Jj3JLlPkUvm9y+GMPSoVwWXTS7B5+VyRvHr4fef2Uqze3iry3q7e3oqnb7wgpn7isbly657oc4j637j7CDYvnxvzvrJsoUajwuKmiagtNqPT5kSpVY+6MqtERTNb7VNWTIq7BpwgBLimuRIbdx8JxtesnFeLQx0DuHbOZEwvyR0zScZECLx9jeY4VSqC6iJzWoWJcNKLZFx3saDTqdFcNSGpfYSSaRnT/N7lcMYWQaAxVZU4PeRiem9PD8XmqY3H5rLWvffKBjzwj0OS/h0xlrWMZAs1GhWaJuVHFQnKRvuUFZPiEoseFXlG3PrcXslb0/1LmnD7c3sxuzI/I95qsvXti5PejJfrLhWx0xwOJ3OI1UbIxd6WxSiuE4/NZa2rIkCfwy1aL56cDG4L2WRFTHFVgQmEgPnWduy0HU6PgOMjZUJCS6qk6wM/8PaV7uPkZBfj4bpLJHY6GwvVczgcMbHaiNEQ15GzuSxbE75u5YTEYnpTkUeSCWSFpxgA8oxa5luby+v/LPD+p/0Y9ghp+5mUw+EkH6Wx05kWdsHhcJRRnMu2EUVmsY2INfY2XmK1NYl+3cvW6hGJkhWe4oB4x8p5taK3ppXzarFj30msnFeLZ/e0Z0VhaQ6HoxylGdPZWqiew+GIUauAVfPFc4lV82uhZsyWArG3X6wvQ9Ok/IQnxEB8tiaRr3vZWj0iUbLCU9w14MTx3mE89fZx3HhhNXI0KlQVmkCpgIWNE/HU28fRYfN/Esj0wtIcDkc5Sr0r2VqonsPhiOmwOfHkW8eDlawoBZ586zhmVeahqjD59/pY2ZrxkkcSL1kxKS6x6INFtHP1akzMM+J4rx1VhSb8z3vtwQlx4NNAppRk4mQ+/FpLP5RkTPNPjRxOakmGLWVts8SiR5/DjUdfbQuuN5b3+mjYmliPVTZWj0iUrJgUV+Yb8f15tXhk9xFc01yJ20aqUAQ+ezz51nH0OdzYsGwmKvONPDaQMybwONTsIVUl6zgcTnJsqdw2L5tektJ7PVFbw587iZEVk+ITfQ78dUSo47tPvSuKxXl41xE8/vVmWPRaTC/JRWuHDR91DuDbF1Vj67t+LzIvQ8JJBsd67bhv50GRoMx9Ow9iWmkuv9YyjFR9avR6BbR22EQiAqMRt8jhZBLJKB8mt80XV17EFO8AgKM9QyLva2A7o+m9TtTWyO1X7YoLYRv2oHPAiTKLHg1jIJSUiWTFpNg27MbVsyvx7vE+ZizOvz45g9+9cRTrF9fjkd1HcLx3OJiIF4g35rGBnNGm1+5iCsqcsbv4tZaBjPWnRq9XwPN7T+LO5w8Er5/1i+uxuGkinxhzxhXJiLOV2+YZuwsfdQ6KPK2brpsFt5dKvK86DcGKZ94fdY9sIrZGbr8+7BjAHVv3Bce6tqUeixvL+cQ4jKywrD4BWLOjFQJFMJMygF6rAqX+i+LO5w9gYeNEAGfFPa6aXcFjAzlJQadWBSfEwNlrTstKYx6nZFLt37Eea2uHLTghBs7asNYOW1L75XDSjUCcbSiJPrfltqlVqySe1n3tNqb3dV+7jVklIpV2TW6/Pu4ZEo317m0HsO8UtyXhZMXTOSC3uPXddmZZtv95rx2A/0IgIS9wTo8AtQo8NpCTFBxuH/ONPVYZzmwnEPt2+cY38JXH/4XLN76Bna2daTkxTsVYO2xsj0+nbXwX1+eMP5JRPkxumyy7LVC2OFj47e/0COgacKbUrrH2a11LPZ7d084cK0dMVoRPBOQWO2zOYFk2tQponpyPO7buF1WfoCHXpV6rwvxpxWiYmMcD0DmjjlwWcawynNlOJsmMpmKscjKypTHKyHI42UIyYvrltnms1y6579QEzHsxvHu9VgWjTo1v/fHfKbNrrP0aHPYkJAk9nsiKSXFdmQVrW+px97YD6LA58cSbR/GzK+rQ2T+MiXk5+PHl0zHs9qJyghGfnrFjxbwaqAnQUGHlE2JO0uAVCyKTSbV/uwacyDfqcNXsiuDXpq3vtid1rHVlFqxrqcdd287GFK9riU9GlsPJFpIZ0x/qLGPZ7ZpiM26+dAp++crh4LKbL52Csryzjo+AfXf7BKatOGN3AUBMiXqJlp8LP1Zuty84RwqNKa4vtUiSB0ej/0wmKybFAGA1aPDgkibY3V6YdBo4XB68fbQXS5srcXtIibbVV9Th9UPdONw9hA3LZqZ62JwshhdHj0wm1f4ts+rx9c9MxsO7ziZNrppfi9Ike1osYXZNq+HXDoczGkQqyabTECy/uBoCBVQEmGDS4cGXDokEPZ555zgevmYWXgyz7yfO2CW24pYvTMHpITe+9sQ7URP1LptegpcOdo1qSTWNRoWK/Bw8dv156HN4kG/UQq8j2H2kh7n/o91/JpEVk+LWDhtW/fUDycP1sevPw/KwEm1rXmjF/UuasPIv76ftp1pO9sCLo8uTKk+6Ei+IT0DwIQecLfd42YzSpI2ztcOGe188iIWNE4Mepxf2nkSpRY+mSflJ65fDSUdG23spFxK1efnc4EQ1wOQCA74/bwrufH6/yNM6o9QCnU4tsu8sW7Hh5cNYfnG1pK9V82slJTsn5uljDtWK9Zgc67Xjhj++K9qnlfNr8NjrR5n7nylhbckgKybFcgkptmEPc/mw2xv8fzp+quVwxgOp8KQrLWzfPci2MT1DTpxbnBz7EamkH4cznkiGIIVc+BZrPnG8dxiTCwx4+sYLghPQRpk6v3K2gpWUV2TOwcO7Doju7167K6awsniOCWtf5ZIH5eZT42WuNGbVJwgheYSQ5wghHxFCDhJCPkMImUAIeZkQcmTkX0Xuj0BCiv//etz0+RqsnF+DQnMOszSJQacJ/j8dP9VyxpZMKguWrVAFh1zJeZPzDh3rtUdsl2hJKCVjteh1zJJ+uXpdTH1yONmC0vs2lPB7sDiXfU+XWdnLC0w5aK6agC83lqO5aoJsfV+jTsNsz0rKO9HnkNzfFr0uJlsTzzFh2a9A8qB0/w3M5UVm/bh4To5lSbaHAeyklE4D0ATgIIAfAdhFKa0FsGvk97iZWmTG2kX1mFxgwPVzJ+OJN49i4642PPTSR1izqE5UmmT1FXX43esf86QnDoDMKguWbSRy7JW27ZTxgkQrTZRISSilY3V7BOZY3V5BpgWHk51ESsqNBdY9+EnvEPOeriuzJlT+ze3zSUrD3nzpFNQUm0XL1i9ml0lzefxJcaHrrm2pR4XVENMxYdkylv1qqGDvZ12Zhbn8k96hcfGcHJPwCUKIBcDFAL4JAJRSNwA3IaQFwCUjq/0JwGsA7oh3+4d6BvHoa0dEMs9lVj0uqC6CzeHBH755ProHndCp1TDoVPjqBZNx0jaMGWW54yJwnCNPJpUFyzaO9drx+zc/xv1LmjDs8sKYo8Hv3/w4JhlspectR6NiJvdFE1RJJNRD6VhNeg1zrKac7FSgmr/gy+joPi3797LiQuza+fcxHBEnVYTHyga8urEm5Ya3VxFI7sEVz7yPnasukiTKxXOvs2J6dWo1Nu85IUnK++qcStGyiXl6Zpk0rUaFR189Ilr30VePYGqJWZRLEPBIhx8TI8ODLbdPAJj7Gb6uigALHn5jXDwnxyqmuBpAD4A/EEKaALwLYBWAEkppBwBQSjsIIcWsxoSQ5QCWA0BlZaXk7x02J473DqOz3xmcEF8/d7IoFm/V/FqUWvVYu+NDXH1eBTbtbsNnzy1AVWF2nVBOfCRaFizatcmRJyDPLqoOs7AOA8PuqG2Vnjeb042V82olcbqDzuh9Kk2aVDrWgMcpfKweX2ye4ky7Nju6T6Pxe7+S/fu+3/xgzMbCSS6Rrk1WrOym62bFnJTLan/vlQ3IN+qCmgXAiBDOgBNzqwsl92Es97pcTG9Fvh7fu7gGa3a0nk3KW1SPX75yWNT/lOIm5v3d5/DgeO8wHn21TdRfp82Jpklnf4/XPsjtE2tZ+LpvfXx63MQZj9WkWANgNoDvU0r/RQh5GHGESlBKHwPwGAA0NzdL/PVlVgMmFxgwqcCATV+ZBaNOjf965j1JpvhvvzYbS5srMNFqwKr5NUkvp8RJfxItCxbt2uTI4xOAre+dEHmK//TPo/jRl2ZEbav0vBWY9Ni850ORF2bznhPYeO2sqH0qzX5XPtYc7P6oU3J8FtTHVvGCX5ucdCXStcn6shLJqxsOq/1P/rYfd14+DRZjjuhekrsHvV4BrR02dNicKLMaUFdmgUYj/pok9wXo6RsvwG9ebxPZmH6HS+IVVqtVEo/y5j0n8OCSpphEeyLZh9Gu1JFJ5TMTZawmxe0A2iml/xr5/Tn4J8VdhJCyES9xGYBuJRufXpKLH142FXs/teHhXUfw7YuqmW81/cPeYAkSvVaFqaUWVE7gdWPHM1xgI3UMezxMT/GwxxO1rdLzVldmwffn1eLO589mfK9fHF0QI5Hsd6Vjrcw34to5k0XHZ/3ielTmGyO243AyGbkvK3Je3VjaTyk2Q6fRiO4lVpwu4J8QP7/3pMRGLG6aKJoYy43z9JBL4ukts+px75UN+MnfzpZ0y9WrmbaoocyK9Yvro9ooOftQYTWMeqWO8fScHJNJMaW0kxDyKSFkKqX0EID5AD4c+fkGgF+M/LtNyfbbbcPot7uhJsADS5pg0qmZbzUf9wyNi5gYTuxwgY3Uoddo8JvXW0V1On/zehseXNIUta1KRXDZ9BJsXj5X5M2Jdt40GhUWN01EbbEZnTYnSq161JVZJV6gcI712nHfzoOSmqKxxD8rvcZO9DnwyO4joj4f2X0Esyvzuc3iZC2JeiVLLHpMLjCI6nvXlVlwc5hX9+5tByRxuoC/PnhgQhpY987nD6CuPBduLw3am+KR6lbh4wxUrwhd3udwY3ZlnsTTLQiUaYsWNZSjqsCEzgEnyix6NJT7J8R7P+0L9m/Ra5jjrC02j3qejFJ7m4mMZZ3i7wP4MyFEB+AogG/BX/1iCyHkRgAnACxVsmHbsBt5Rh1O9g/jtuf2It+ow6r5tSJFmbsWzsCm3eIYnWyNieHEBxfYSA12t5dZh9cxUkc8EoJAFasuaTQqNE3KF8XnRSNSzeBYrhsl15hcn70x9snhZCKJeiUr840SD+z9SxrZ3uewOF2ArXswpdiM/ScHRTLJDyxpxENLZ+KHz4rHGaheET7+wFfp0HtXpSISWyQIFK8c6ha1/+31s9Ez6Bbt07qWemacdDLqDCdibzONMZsUU0o/ANDM+NP8RLft9VG09QwFQyM6bE48+dZxLL+4GrXFufiocxCDTg8z0zMbY2I4nEzAqNMw6/A+ecOcqG3HumqITq1ijnXz8rmj3lcAAsLs8+kbL0hanxxOqkn0692JPofEg6oiJKY4XeCs7kHoussvPhe3joQpBLZ523P78Nz3PpNQ9QoWLNs2OOyT7NNd2w5g+cXV2LjrrLMvtM7waMb/jqcqTVmhaNc96Aqqs5RZ9fj6ZyajIt8Ih8uLPIMGbxzuhk5DsH5xg0imMVtjYjicTOD0EFu56fRQdMW2rgEn8o06XDW7IviJdOu77TF5Q5QkoTjcPuZYHW5f1LEq7fOM3cXcxzP26JUyOJxMJpGvd0zbsOdTrG2pF3l65XIJ6soskpheAZSdp+RwwSdQ9Ds8MGg1EAQKlYowx8+yAYJAJQl9rFhlu8vL7L+myBycAIfWGQ73YD+0NLG5TrSayKOV0JcOZMWkuNSix+GuQUwuMOCGz54Dh8eH20KCz9e11MOUo8Z9Oz/CjRdWQ60CmidPwGerCzL+BHI4mUpJnLVHQwm8/IaGSK2aXxu1oozShDm5OMeSGCrYKO2zPM/A3MdyhneLw+H4kbMNc6vzsXn53Ki5BKy8A0DqaW6ebEWHzY1vP/lexIQ8gG0DHlo6EwL1e5xD259XmS/pS65meVWBUeKp9noF+KiA5RdXQ6CAigA+KsDrFWRV+KIhZ/88PorLN76RVSEVY6lolzQayq04t9iMHy2Yjl6HO3gzAGc/M+Tqtbj2/EoQ4tf8vmvbfpzoc6R45BzO+KWh3Ip1YcpN61rq0VgeuRIE4C/nFn6fP7zrCKKV8FUqF5uIop3SPgVKmfsogFdX44w/YpVKl7MNHi/QNCkfX6wvQ9Ok/IjJtSoVQa5eC4tBi1y9FtNLcrF+sdhW3fyFqbhrmzTRrbXDJhnrJ6elNuCHz36A9r5h3HhhNVbMq8G3L6rGI7uPYNDpkdiaXL2aaStnlFlRXWQOVuVQqQj2nbLh9uf2YeOuNmza3YaNu9pw+3P7sO+UTfGxZ9m/+65uxF3b9sdt19KdrPAU63RqXDa1BK9/fDoYRhGK0yPg38fOwKBVY+u77ehzuONKkuFwOKOPRqNCnlEr8mjkGbVRK0EAQPegfDLJucXy93QkmedItiCROEGlfXbZ2OElXTYXUBG1Ww4na4jna4ucbegZimwbovW1qKFc5D0+1c/up8PmxMl+cfsHlzQx1y0y5+DhXWfDNFbOq8Vpuws6DRHZRQCwGDR4cEkT7G4vTDoNtBq27YlkG5XCsn+9dn/pOVY/mTyvyopJMQB0Dg4jz6iFmkBSjuWFvSehU6sw7PHhB/Nr8Wn/MDbvOYHP1RSkdtAcThIY7cLtyerzWK8d3//L+5JPci/GkLwRj8RpKDkaFZonW/H1z1aLCt5Hk3kGlMc5KpWWzjNqme3yjNq4+udwMp14Er3kPvWXWvQ42jMU1UbJ9fX371+EXL0WDrcPuXotinIFZj8TTDrc9txeUSnF9j4Hc90TfQ5movFtz+0NzmEEChzqGMSGV45I2m9ZPhdmvVa0T0VypeIselFJN5YgSSRY9i8bBT2yYlLsdHrxzrF+PPraEdz0H+fie/9RgzUvnJVY/NkVdVCB4u4XPhS9kbljlEvlcDKFREQmxrrPRCS2lUogU/iwrFksGLJ2UR0IYkuYU4JSaelBl4fdzhVd3ITDySbisRWskm6brpuFDzsGY7JRcn0d7BzArc+etRu//tosrFlUh9Xbz8411iyqA4UgKaX408unY11LfTDcIiAe8vRbxyT99NndkvZ3LZzBLL92rNeB27fuE+1ToUknsRs/XjANH5+2RxUkiYdsFfTIiknx/g4b7t7uP9ndQ25serVN9Pb1sxdasfziatGyWMoppcLjxuEkQipK5yjtM5Ei/SKJU7cXRl1sEsgCVeHukYdYYKx3b2+NqQycUpRKSyciSc3hZBMsQY4X9p6U9f6Gf+qnFLjhT+/EJL4jZ5cOdw2K7Mb7J2zY9sFJ0f3536+14YElTZJSive8eBC3XForWvfRV4+gZeZE7Ds5IOpngkmHVZvF9nTdjg+Z5dfaGIJkm5d/RiIf7fD48POdH0nin2uLpeIlsZKtwldZMSnuHDgbe+f0Csy3vPCYfKcncjmlVHjcOJxEScT7OtZ9JuJpUCqB3D3IjtPtHoxeBk4pSqWllbbjcLINliDHA0sa0XpqUCKeEXhGh37q//ex3pjFd1h26d4rG/DAPw6J1hMoJHLOgHypyQGXT7LuuWEl1dYuqodA2eXfwtdd11KPDS8flqzn8flwx4LpovHff3Xs4iXxkI3CV3FPigkhJQDuBVBOKf0SIWQGgM9QSp8Y9dHFSKnlbAxNvoEdh1dXZsGKeTUAEEy2i1ROKRGPG/cwc1JFIt5Xpdet0j4TkQ5lFei/8/kDUSWQyyJ4nKKh9PgolZbWaFRYWFeGyROM6BxwodSSg4YY2nE42Qbrfj/SfVawK7As8IyuKjCJ7lWdii2+s2X5XKan+dKpxXj6xguCMssFZp1E/Esuf6lQJqY33FTotSoMOT347fXnod/uQZ5Jiz+//Qm+c1ENc7v15RY8dcOcoC0oMuug0xDc9Pka0XoTTDmYWZEfYlf1cYmXJItMmRcp8RT/EcAfAPx05PfDADYDSNmkeEapGWsX1WPLnuOYYNLhli9MwYaXDwffklZfUYdf7DyI473DwZqFFfmGiF4lpd4v7mHmpBKl3tdErttE+lQqHar0/pxRkoubLqkNhlsFvDN1JblRx5rIfa1EWtrrFbCjtWNU4wA5nEyEdb/LVZrqGnDio05x/PBDS9nVH06cGcatz70tuqcvnVqM7ftPie67e65swANLGkU1hWdWWlGeZ2DGFIfH9N586RRU5BtEnt57FjdARYDvPvXu2bnKQn971heiDzsGRP0/sKSRuV6F1SCxq498ZRYzpnl6iSXp5w7IrHmREstaSCndAkAAAEqpF0hilkoMHOyy49HXjuD786fgx3/bD59Asfxif+2/+5c04Tf/1xYsHeL0+GsWtvcNR6xTHPB+hRKL90tpTVIOZzQIxHm9uPIi/HX5BXhx5UUxGZ5ErttU9Kn0/jx8eig4IQ70eff2Azh8eihpY1VKa4eN6Q1v7VBeb5TDyUQC1WZCURMwbYBWrZLcq4e7BpnrHu4elNzT+05J77uf/m2/qKbwjRdWQ6/RBCfEgfVWb28FgSoY0xtY95l3jqOu3CKykecUGvHjv4nr/K7Z0QpAxbzvj3SL44ePdA8x1zvYNSDZ/+//5X302V2iMT366hG028Ql1ZJFJs2LlHiK7YSQAsBfQZ4QMhdASq1054ATx3uH0TlSN9Du9mHTbn/szop5NcxaehNMuoh1ipV6v1IR0xkgUz5PcMYGGofGQ9eAE1OKzfj2xecGS5U9/vrHMV+3SmLLErlXlN6fHTI1g6PF1iV6Xyu5NztsbCnrROMAOZx0x+32Yd8pWzB8Qa9TSbyvlQVG3HzpFPzylbNfhVfOq8WA0y25V7fsacfqK+pEVanuXjgDj+wWx/g6PQI6Ze51j++sQSUEsnWKz9jdkpjeDctmonKCKWgnAWDn/g7Z/AbW8vC8KDlPuZyNY8U0y9mv0Z5LpHJeFC9KJsW3ANgO4FxCyP8DUARgyaiOKk7K8/xeI2PO2bfJ0PgZVizNyf5h6LVqzB7RKg9HaWZlIjGdiZBJnyc4yUO5pLAeX7lAnLi2+oo6lCUx5iyRe0Xp/VlmNTD7jBZTXCwjSV1kTp7Mc0U+W+Z5Yr4hap8cTqbidvvw/L5TuHubOMTp2OkBUUWF3kEXnnnnBLM6S/i9qtMQiSBGiTVHEiccqOfLutenlJjxg81n7+HHv94sWw+4oSIvqm0qz2fbIrn+w01FwFOutL2crU3GXCJV8yIlxD0pppS+Rwj5DwBTARAAhyilKS2cqVP76/j96Z9H8fOrGtAz4MRdC2dg3Y4PsfXddqyaXyt6sNx86RT88Z/HoNMQVBUY4XD7mG9DSrxfqardl4pSXJz0Q+l10G/3BL0ogXZrXmjF5u/MxeQkadwkeq8ouT/zjBqJx2j1FXXIM0UWxFCrIMlVuOULUxCD5ofic6LXqLHr4EjZuRChkS9ML4l5fzmcTGPfKVtwQgycDXH607fm4P+NqNZqVMDUslymR7auzCqxK+taGrD8qT2iSdnkAgPuu7oRd4TV+c3RqvCTL03DabsbAvVPPisLjPjdGx+LSro99dZRSZ3igC1h2aZw76tFr5HMTVbNr4UpRyVZvmZRHQgoVs6vCY6ptsSM1Quno2PAFVxWlJuDPJNWsv8PLZ2JHO3ZZLug9zrfKEk0TMZcIpNqGiupPnFV2KIphBAbgP2U0u7RGVZ8tPcNY9DpwU3zanF60I0NrxxBvlGH5RdXo6bIjOLcHPxq2Uwc7h6CVxCg16hQZNZhQX0Zrnns7VF7GwJSV7svkz5PcJKH0uugQ6Zd54ATTUkZqZ9w741ORrp0tDjV78Rf/nU8WN/YoNPgd69/jKoCIyYXyB+fniEXctQq0Vhz1Cr0DLlQVRj5/lJ6TgZdHlw9Wyw0snphHRfv4GQ1cuEL3YOuYLWJwPP6sukleJHxrA1/BrPuQbeXQqOS2p9euwvDHkHU15pFdbiicWKw1m8gVIMQKrEl5xRKbQnL+3rvlQ343/0dIk/3k28dxzmFJjz51nHRcgKgz+EVjem+qxugVqlEy352RR3O2N0Su5qjJbiktlh0rCrzjcxE56Jc3ajPJTKpprGS8IkbAXwGwKsjv18C4G34J8drKaVPjdLYYqbMasDBjgGoCQlmV3bYnNi4qw16rYpZ9Pr+JU3Bhw0wup7VVNTuy6TPE5zkofQ6kA0rSGL4xLFeO1Y8o0zmWSlGnQY2pweHOgeDHh+b0xNVHpqAiIrfB8b69I0XRO1T6Tnx+ijW7Ajz3u9oxVNJFBrhcFKN3Od/q0EjeV4HbEW4vYhFknhpcwV++OxeST9P3jAnGKcc6Gv1drYA2C+XzcSBUwMiW1Kcq5d4hSmFxPv6k7/tZ85Nyqx69Dncovjfv3znAty9/d+i9h/32CUl6X72gl+IKJJdDRyToz1DTI/w5uVzE55LyMUkZ0JNYyXVJwQA0ymlV1NKrwYwA4ALwAUA7hjNwcVKXZkFNcVm9Ds8MQWoOz0Cht1e2behTCTweSI0pjpdP09wkofS66CuzIL1i+tF7ZItFBHJg5osBCrgexfX4Ik3j2LT7jb87o2j+N7FNaBRshLP2NnJL2fskaWaAeXnJBVCI5nO/AVfxozZF8j+zF/w5VQPkROFhnIr1raIbdHaRfX4w5ufiNaLx1aw7sEpxbnM+6snxkQ3/zIqsiXfn1eLCqsBO1s7cfnGN/CVx/+Fyze+gYMdA8xtBgQ5AmNav9hfJi18rGfs0uRBuUQ7ebshPlZy9tfh9iU0lwh4xUP3f2drJ4TwA5imKPEUV1FKu0J+7wYwhVJ6hhCSku96Go0Kn59SgA877cw3nGkluVgxrwZb321Hh83pz1ydYMwqz2omfZ4YT3i9Alo7bCJximTWmFV6HWg0KixqKEdVgSmY8d1QnlyhiES/big5tipCsPW9E5I43bsW1kVsV2BiF+SfYNJFHafSc1Kcy+6zODcnap/jlY7u02j83q9k/77vNz8Ys7FwlKHTqbG4sRzVhaagp7HYosOjrx2RCFXI2Yrw6hUN5VaJIMcEo1ZWyCfWRDUaMjF1es5KJ4d7YI90DzK32Tvkktii2ZX5ElEjr0+QtJdLtCvJzUHzZCu+/tlq0XbDJbHLrGwhoxKLHhecU6B4LiEXkzxj1UUQKNK+OpaSSfEbhJAdAJ4d+f1qAK8TQkwA+kdrYPHgGHbjxQM92LLnOFYvrAt+cgwErt/z4kH0OdxYOa8Wm/ecwHVzJuOhlz6SrJvpntVM+TwxXvB6BTy/9+SYiy8ouQ4EgeKVQ91jWr2kMt+I9YvrJccnmlQzoPzY+nyUGafri+LFUKkgsRerF9Yh1tOo5JyY9WqsXVSHu0MSedYuqkOuIXKoB4eT6eh0ajRXTQj+7vWyBS1YtoJVvWLdiOc5VPzinisbcOtlU0XL1i+ux4ySXKxtqZe0d3p8okS1lfNq0d4v1joIhG6Ge2Bf/ahbkpS3tqUeLo9PZItWzqtFr90lER/5/TfOw9pF9SLRoXOLzVJBjkX1mFZqwrLzxZWE7mdIYj+0dCZz/yvzjQnNJVge6HyjDu+d6MdPRuoyj8XzRSlKJsU3AbgKwIUjv78DoIxSagfw+dEaWDwc6BwMXixurz+JxqBVweURcPS0HVefV4Gt77Zj4+4jeHBJEz7tc2DPcRt6htrw4JIm5GhVqC4045zC9Hxz4WQmcuILtcVmNE3KT/HoxKSiesmJPgce2X1ElFDyyO4jUaWaAeXH1ksp01N8+4LpEfuzGnR49VCHX5LV4UGe0S/JekF1ZA9zACV1PwedPjz6Wpvo+Dz6WhseWJLM1EcOJ/WwYnJZ9/usSfkgROx9ZFWvuGvbAUlM8E//th+r5teKKko8svsIaovNePRVsV3a9OoRfHVOpaT821fnVEq814VmqafW5nDhv8Pu5X67CxtekUpPP3XDHIkt9grAlj1nE4SNOr/dWjxzoijR78l/HkVVoVGy/20MSewfPvuB5Jjc+fyBmOxvJEosUg+0JUcdnBAH+krX6lhKSrJRQsjH8McQLwPwCYCtoz2weOgaOBtDs+/kAP727qe4vLFc9Aa1cl4tnnr7OD7qGgy2O947DLdPwAMvfYQ7FkzHOYWZ6yXmpB9KhSJSQSqql3SNiO6EF5TvGojep9Jj63B7mZ5ih9sbsb/KfCMuqysXSbLG6tVWWveza8DFPD7dA5kXUzx/wZfR0X064jqftp9E4xiNh5O+sO6XB5ewZZoPdg7g1mf3iu4rSmnMMcFF5hw8vEs8T+i1s++7CaYcbHjl7Lo/vXw69Fq1aNn6xfUw61VY2iy2MWsX1cOq14q2uWJeDTumeUgaE+xw+zBvWqnEqzzk9uHObe+L1g2dDwWPqUz8MeuYJGrzK/ONEq/+upZ65Bt16LCdjWtO9vNFKTFPigkhUwBcC+ArAHoBbAZAKKUp8Q6HUmIRx95988Jzgg8v4Owb2PKLqwEAvpFrQ69VQa9R4XjvcNq+tXAyl1RUdFBKKqqXBKRbw/uMVgkCUH5sjToN1ux4T2Qb1uzwZ2xH4kSfg+mpisWrotQLH27XAvtYbMm8mOJosb4A8MmPw6t9jj6fnjiOGbMjVwwpKy7Erp1/T/pYOGxY94tcTO7hLqlM81M3zIk5JvhEn0MyT3hSpn3PkEvk6R1yeXHPiwclNuGpG+ZI5J/v3n4ADy5pwoq/nJ3AysUEs4SC8o06bNz9gWSsD4Z9NdJrVUy7IddXrIIe8cCylQFPfXiljXTM4YrHU/wRgDcAXEEpbQMAQsjNSRlVnBh1alFsTZ/dI5JHNeeo4fVRVBWYYNZrcOz0ECYXGHBNcyVO9PljgtL1rYWTuQQqOoTHwSWzooNSEi2uriTpze3zSaRbV86rhccnRGwHKD+2cpnlPVEqOiTiSe8aYMs1R2srUB/uu7oBH/fYg8X5q4tMEKgvYn8cebyURJ2c82S81MK617bsace9VzaIYlLvvbIBD/zjkGg9p0eARk0ktuGexQ0YcnpEMcFyMs9n7C6JLfz5lQ0Y9nhFghr3LG5g2oQuGRtDAVH/9ROtTBvWWG7FputmYV+7LXjf9zncTBtCQUXbXNtSD4H68OMF09DrOCs+MtGqx4NLm3C4azC4rH6iFYSIxxSw+YnIPMvZyiklucy+0o14JsVXw+8pfpUQshPAX+GvKZ1yzCPyzoFi1RPz9UF51HyjDl//zGRserUteDJWza/Fyvm1+Mu/juOC6iIA6fvWwslcNBoVFjdNRG2xGZ02J0qtetSVJbeig1ISqV6iNOltgkGHzXukMq2XzYiu2Kb02AYk4cM9JtHkrIvNbK9toSm617bMqmfKNUeTljbn6PCR3S4qzv+jBdNwbhSxEA4nk2F5SvscbsyalCcSn1CNTBZD0WtVyDfqoNMMi8UrNCr8Zd9Jka3x+gRm+yKzHn3DblF7o06NHK1YvKc8j20T5KpXFJp1ovZOjw+VBXo8uKQJdrcXJp0mmETb7/CI7vsnbzyfaUPOKTDh6RsvCE5eG8utONwzCJdvUNR+/eI6OD2+sGX1KM/LkYiXCAJlinrEmhQn99VxeqmFKbSSbsT8dKaU/o1Seg2AaQBeA3AzgBJCyK8JIZclaXwx4ROAB186FAyLGHKefaO7anZF8P+A/43l4V1HkKNWY+X8KXjjcHdav7VwMhuNRoWmSfn4Yn0Zmiblj8mEWBAojvYM4a2PT+Noz1DM9SEFgWLQ6UG/w4NBpzfmdnJJb60dtojthr0+XHt+ZbDO5xNvHsW151fC6Y3NE6pSEeTqtbAYtMjVa2MysBqV/2Gi156twblqfi20UfSah70+ZrtYxuoTwLRB0RziAqX4RYhgiNMj4Bc7P4Lf58ThZCcBSfXQe+2WL0yBauQWDZQUr8gzMuvp9g65cftz+7BxVxs27W7Dxl1tuPW5vbjp81NEtqa62My8p9UqghXPvC9qv2rzB2g/4wjeswIFNGqCHy2YJmr/owXTYND65Z5Dl6++og4n++yi9g++dAj9dh8Odg7i075hfNQ1iLu3tWLfKVswAXnFvBp8+6JqHO0aYs9jtGpMMOmCPxqNCnqNOihHH1hXr9EwbfSZIa9oP1c88z5aO2zMcK9jvfaYzp9cXfZzCk2oLjJjbnUhqovMUKmI4mdVMlGSaGcH8GcAfyaETACwFMCPALw0ymOLmTMOF65prgx+hr31sinBE0oIO8D8cPcgSi16fPc/zkVduQWVE9LzrYXDiQelSV2JlI+TS3rriJL01t43LJEzffKt46guNKF+Yl5S9vOkTJ/nFJrQUCHfp9xYz4lhrN2D8qEX5xbLe307ZZMJXWisiNglh5OxsCTV8/QavHtcWtKLJfP8jw87mPeNx+fD5uVzg1+W5O7pinyjpH2+UQeTXhusFqHXqnBOoQmlVrGnNd+kRceAVEr+b+9+ikvrSvHEm2c9tTdfOgVOj1e0bOW8WthdHtF8Rq9V4a6FM5iJaoe6BiWJhiyZZruLLVY27PFJlrES9eIJL431q6NSG55slJRkC0IpPQPgtyM/KSNHrQpeQAAwucAkct+zXPk+AVi740Msv7ga08sszBOmNKYm3cimfUkG6Xp8lIxLaVJXIuXjiuRCC8yRQwvKrAboNGf3h4x8voslEVHpfk4w6yQSqnptdBGOInMOLqrJx+dqCnB60IWi3Bz0DNij7iOgPKEwEcGQsSZadYnRqCwRS5Icr2CR+ejUKryw76SopFnPoBP3v/Ch5H5nyTzL3TcFphy4vQLcPgq3V0BFvoFpC0osOZKSYiadGut2iPsPlHTzCX7bJVBgw8uH8cCSJomU/JzqAkn7X75ymCkd/eQNc0TzGadHwLqRuUp4ohor0ZAl02zSs21QqUUvKSk3waRjinqU5+mx59gZkSCKTsaGxVLnOJllQBN5pic0KU4HBIHi+Jlh0ck+2e8IJvBsfbcdq+bXimJxAuXZnB5/SZLwN6B0fYNRQjbtSzJI1+OjvIyXsoSwRMrHKRW2mF6Si5s+Xysqkr+2xS9zGg2l+0khMAUx/Or18uQaVGiuKsQNf/y3qJ3VED0chhAqKdy/ZlFd8IEjh1cQmImIPiF6IuJYE626xGhUloglSW4sKlhwkotApQI7v7iqMeb73Tbskdw3t142FSf7h3H71rNCFeta6vHAkkaReMXaRfWYUWqSlBR7YAm7/xKLHg+HeK9XzqvFsNuL711cI7KH913Nbs8qiSaXDByQhA4d/4aXD0vWC8g0hz47cnPUTBtEIYg81asX1kFFINn/h6+dibeP9kls9eLGctmJcTSSVQY00Wd6xk+Kj/Xa0RZWrmXI5cMLe0+KinKvml+LApMOx88M46m3jwflnlUEkgS7VAgZJIts2pdkkK7HR3kZL2Wl1RIpH2c16M4KYoQUkb9/ycyI7dptw5Ii83dvO4DmydHLnLEKxEeSfQ1AoFIkiNHv8AUn0sGxbo9eyg0AXB5BUrj/v19rk5RTkoyVEGYiYnPV2Aq/cA8tZyzxCQhOKAH/vfbJ6SGmfSoyi6WLqwpMmGDUYfdHnSKhi1y9Gt97WlyK8a5tB/DHb52PP31rDroHnSjJ1eOtti58fNqMv75zXCTwY9CqmP3r1CrRNgOe3vCyj0d72ONnlUQrkpF3H3J6/OJBdg/yTFp09juYiYJGnRrnTcoXSVprNQSP7D4isdHf/KzYU71mRyueumGO5Kuhx0uZtrq60CRSHoyHZJUBTfSZnvGT4q4BJ7bsaRe9Gb79cQ/+65Ia0VvR6ivqAFDRW9FdC2egxJIjSbBLhZBBssimfUkG6Xp85MYVTdhCqXRyIuXjqgpMuOHCc+Mu59bRP8zcx87+4ajHnlUgPpb97B1yMwvzn7G7ZVr4kY2zi0FIo3tQRoQjShk4B8PjtHphHYajCI2MNtxDyxlLehniFVv2tEvs04ZlM/FJ7xBWPPO+aNnnawqxrFkscywnHtEz6BJ5ilcvrIPd5ZF4qu+72i8J/eBLh0ReYZbMM8vTu2VPu0SmeV1LPRwur8j7G/A0h6+7fnE91CoiEg/6+ZUNkmOycl4tVATYvv+UaPl9Vzdiz3Eb9hwXC31cPEWcPOf0sMVD5GKSuwacUEqiZUDlSPSZnvGT4hKLHn0ON5562x8wXznBAItei1/sPCjysGx99wR+cOlU/HLZTORoVGjrHsTUEjMzJjAVQgbJIpv2JRmk6/FRGoeqVDo5kfJxKhXBZdNLsHn5XFGd4mifqrQatvcllj6VimkUy3hhosUGJyKkIddncW7ktkadBlvfOxK3JDWHk8nkm3SS+6XP4UZ1oUlSkm3Bw29IPIKbl88NTigDy+XEI6wGrcRT6vf0ij3Vd2zdj43XzpJ8tWmZOVE0djlPb5/DjX6HWPyjz+7Cn9+Rfgl6YEkTHn2tVbTcotfinhc/FH39/tWuw3hwaZOk/ZxzJkg83TaHi2mDvGGhWIFnX6wxySVRykpGQvrc8D9zEg1bTPSZnvGT4qoCU/Bt6dFX2zC5wICbL50q8syUWfW4fu5kfPvJPSGxQ3V4dHcb3vrkjCTeJFlvMKkgm/YlGaTr8VEqbCEnnRzLW3KgfFy8EtRK61qyYv9WzquFbdgTtU+l3gC9TsWMKTbqIk/EAzF44TF5em30CbxGTdgx1+rIxn/I5WFKu9pd0Y8Ph5OpDDjZduGMw43ZkycE7++3Pj7NtAFy+RHhMblrF9Xj+ffaJet1y3wVcnp9oi/Na1vq4fL4mJ7e8PGvbanHL18+LPJUl1n1uOULU4KiY0FPtdsrseEbvzJTUpFi5bxa9Dk8ojFtWDYTwx6pp3v1FXV4+JomrNp8dtnNl05BWUjd9oBHurHcKo1J1quxtqVeElPcWK5ciCrReshyJPpMz/hJsUpFUF1owi+XzcTBzgH4BP/nl9A3hatmV0iyOe/e3or7lzTh1cOnJfEmiQgZpBvZtC/JIF2PT4EphxlPuqC+NGK7VHi+lcZwWQ1a5j5Gi+8FlO/nsFvAlj3S+OfqohkR25lztNh1sMMf0+fwIM+oxZ/f/gTnxxBPpzTm2pyjlditQMwih5OtWPVsuxAegy9nA+TyI4IxuSH377SyPNE2/d5P9pcdrUolzkV49Qi+OqdSMs6Hls6UjD/PqJXE//Y53KgqNErswu0Lpkv6LzLn4PaRMA9AbAvCS9K9e/yMVMr+hVb84Zvni8b0zDvH8cdvzhGVqQt8HWQ9E71eAdWFJpFQiNIkOyB5+TyJPtMzflIcgFKKcqsBP3uhFflGHVZfUYc1L/g9M2oVu1YxFShWzKsJSq5WFZgkZTyyIe42lvIo45l0PD5VBSbcsWB63G+7ibwlKy1jo9RrO+iU1uNcOa8WgzF4QqsKTPjt9bMxOOyD3eWFSe9Ppom2n6eHXMzYutNDkeN7qwpMWDyrUhTTF+txrSow4XuX1IhkW793SU3UtqzYSqfHL0PL4WQrOVqVJCeI9VVGztbVlVkky++9sgEvfHASV543CU63DwOEoNPmxlWzLVg5vyZ4X9YUm9FQZsW6lnqRB3dtSz1+/Vob9p0cEI2hxGrAhlfOxiQ/tHQmGsutuPWyqTjSPeQX+VABWjVEc5KAV9jp8aKt+6z08hWNE6HXqnD/kka0jbRXE6Df4Za1BXPOKRDZ2V47e91eu1viVZ48YuPDvw6ynok6nVpxUh2LrgEnU7p6NPJ5EnmmZ8WkON+ow/uf9uPJt47hxgurYdWrUTnBECyqXVucy3zzO9w9hCfePIpV82tRZtWnZWkuzvhE6duu0naJlLFR6rXNk5F5fmjpzIjtAuPtGXRLEu0EgUYcr5wXKZrMM+CvoRwuiRorbi8VSaxuWDYzaptSa3rGu3M4ySTWrzJytg6Q3qtFuVpcVl8mCilYs6gOAET35UNLZ0KlIrAYNCL55SKLDoe7h0T9B+xGaD8CFSAIFC6vINruvVc24NWPOkRe4R17P8VldeWi9W75whRYcjTw+sT24jdfO49pC4rMUlsgZ+Mm5RnSSma5zKpnSleXJhCnPBqM6aSYEKIGsAfASUrpwhFFvM0AqgAcA7CMUtoX73YHnR48+daxYHmm6iIzPD4BFXkGPPpaG/LmTsbPr2rAsdN2bNnTjj6HG3ctnIHN75yA0+OXS/xsdUFalubijF+Uvu0qaXes1477QpJTAeC+nQcxrTQ36naqCkzYdN0skSe0ocIa1RPaUG7FTy6fDo+XBr29jRXTY4pTa+2wiRIKAX9CYTSxEaVVNo712rFux4dBGyNQYN2OD1FdGP04Kz22BAS3fXEqHvjH2Yz32744FapoBY45nAymqsCEZedPxrvH+4L2ZNn5k1GZb5SUXwsQkH4G/PdboCJFgE1fmRX00gL+5/vq7a0S8YwfPvsBnr7xAtz74kFRucc//rMHG78yC26PELRVWjXBW0e6cF5VIfrsHkwwafH025+g1KKXJAH/5G/78d/XzcZ/PfNe8F4O/T2w3oaXD2PWpDw8vOuwyF5s2n0Y91zZgJ+G1ERes6gOxVadRFCjrswi8XSva6nH9FIL2m3DomMlh9croLXDJkmcHk2RK58ApnT1ZTMihwgmm7H2FK8CcBBAoDr/jwDsopT+ghDyo5Hf74h3o/3Dbsln2FXza/HO0V587z9qRJ8s7lo4AwPDHjz2+se4prkSPUNudNic6EjT0lwczljQa3cxQxnO2F0xXf9KPKEqFcHAsFdivGMxtH0O9njD4/ZY5GjEErI5MVS7SOT4KG076HJjglEnGusEow6Druj7yOFkMuH2ZNN1syRJWZuumwW3l0q+bsUjc8wSzzjjkM4n1l4xA/0OjzjRbFE9Zp9TgO88+a5oototI74x7PGJvM8Oj4+5Xt+wNKzs5kunwJyjFtkCo06N94/bRIIka1vqsbCuROLpNulVePmjbvzw2ehfAr1eAc/vPSkRL1ERVUztY6V7kD3n6hly4tzi1M25xmxSTAipAPBlAPcAuGVkcQuAS0b+/ycAr0HBpFiv1WDznhP4yZemYXKhCX12D8w5ajRWWHHjn/aI3kTW7fCXNTneO4yNI56mJ948irI0Lc0VK5HiQdNVxng8kMixH8vzpguTSg8kcmxePjdq20Skpe/aJi2dNKUkurS0ScdOQnsqShJaa4cND750SOTxffClQ6icYIzYZyLHR2nbHI0GP/7bexKbxBPtONkMy57sa7cFJ8mRlkWSOWaJ/bDEM/LDEoABIM+Ug5V/fV/U193bD+BP35qD315/nshT/O2Laph9nexzwOb0BZdZctTMOUeeQWrbfvnKYdxyqVhS+sGXDqFl5kTxmLYdQFWBEav++oFouyvn12DbBydj+lrF+grX3jcs8eom+iU9XcuhjqWn+FcAbgeQG7KshFLaAQCU0g5CSDGrISFkOYDlAFBZWSn5e/+wBzf9x7nwQVzcev3ieuabSOBEOz0CDCNvPP1Ot0QOOh1Kc8VCpHhQADxWOolEujYTidMda/lph5vttXC4fVHbKhUa6ZRtFz2RrN/hYbbtd0RO0pPz2vZGSV6zu5QfH6VtExEMAaLbTQ4nVUS6Nln2RKNSSZYJlJ1Az5I5nmDSMCXlSyxnayIHbKxAqdRTvKiOKf7RPegUiX+sWVQHnZpK+lrXUg+nx4cNr4i9v/dcWY+f/u1AmC2SJsrlG3Uw6bWi9ivn1UIV9pErYD/D2xt16pi/VrFspH/8o/slPV3LoY7JpJgQshBAN6X0XULIJfG2p5Q+BuAxAGhubpZExOQZtHB7BNw6EkQP+E/Y6UF2wepATI1eq8Lc6gIUmnVY8PAbyDfqgm9HKgLMKMvNiIljJE8dgJi9eNyjHD+Rrs1ESs6Mtfy03Ft7LMXZlQqN5BulRfr1WhXyjNqofU5gFPjXa1XIN+kitsvNkXqBWOWewjHlKNvHRNqWWHLQPNmKr3+2WiTeEYtgCBDdbnI4qSLStcmyRdVFJskyNYHsfXXBOQWiBDxKgRv++K7Eq/rCigtFcsgN5VbsPdl/ViY65L5b2lwhEf8gIKJtrh6Rfg+XRA6Ih4R7f3/91dki6eY/v/0JmqvyJfu1tLkC63Z8KPnatPHaWbjp8zUijzSrpFxFnlEyP9q4+wie+95nJHHaFr1O4qlu73OMulc3WeIdiTJWnuLPAVhECLkcgB6AhRDyNIAuQkjZiJe4DEC3ko27vD5JzFCZVQ+DVi3x/t72xan43RufBN+UABos9t1hc4oKZn/23AJUFaZ/PHGkklhU5m06/A1vrD2T44FE5CbHWn46kbd2pUIjwx4vU9Ri2BNdxlgAZfZJEXnu53B7mR4TRxTpZEIoU7wjlltD7vi4oxyfHA2wrFlchH/tojrkZEXNIA6HDcsW6dQqyT00wajDz66ow89CcoYCdic82VhO6OPDjgHcERKTu35xPSryc6TiFwvrUGIVe5XXLqrHY69/LNmmXEyxJsyt6/QIGHB6Rf2vXVQHgUrtReUEI9N7bBsWi3esXVSPCSa1xFYJoMz2h7uG8JOQ5L0Ny2ai0CyNyd6ypx33XtkgWTcRr26yxDsSZUzMK6X0xwB+DAAjnuJbKaVfI4Q8AOAbAH4x8u82JdvP0aglMoRXza7Az3d+JPH+NlRY8ZU5lXB5BZEYQjrGtsRKtNicWPZtrD2T6USyPOQlFj0ztiyW62qs460SKXiuVGjEqNXgN6+L5Ux/83pbVK9tQn3qNIpikd1eiv9+rU3U33+/1hZT+TidWs0c6+dqCiK2c3kRVN4LjPXuEU8Uh5OtsGzRkMuLx15vC5Y0M+r83tvlF9fEZAPkvmZ93DMkur/ufP4AU+Y5IP/8x2/NQc+gE0W5ehi0KmaZNjlZ9+pCk2TdLtuw6KvVo6+14YElTRJ7wfrqvbS5QpKTcff2A9i8fC7ePXYav//m+egdcqHAnAOvz8dsH5jkBtrLxWT3Odw4b3LeqHp103XOkWqfwy8AbCGE3AjgBIClSjbS5/AAoEzBjnDvb0WeARtePhx8K6zMN0KlImkZ2xIr0bx8sezbWHsm04Vkesgr8434/rxaSfmvynxj1LapiLdSWgJOqdCI2yswJand3sge1ET67B9mxyJHk5Y+PeRijjWa6Afg9xSzvNPRPOk9Mh6nWPrkcDKZcFv0/okzTO+tTkMkghQsGyBQQfJVKiC9HIrTw47JdXr8sfy3hvT/26/NxtpF9bh7u7gihcsjlXkOfMUK9TTff3UDOmwu0fgDMvc3XVIjkqL/9ddmSby/1YVm5jjtLh/mVBfhhj/++2xfSxpx86VT8MtXDgeXTZ5gkm0f/uzZdN0stJ4aHNXnZLrOOcZ8UkwpfQ3+KhOglPYCmJ/oNvONWnxwYghGnToYn1NizRFlpgL+N7NSqx73XdWAk7ZhnBly4USfA9VF5rSU+o2VaF6+WPYtXTNBk00y31ZP9Dkk9SrvfP4AZlfmR912uspPs1A6Vr2WnX2do40ep6u0zwmyccyRY5ETEf1Q6tUukvE4FZpjiynmcLIFFSFM7+3m78yNSZBCRQh+87r4S0+/3SUp4ejPo2DfdwUhYQVOj4DvPv0e1rfMkIiMfOeiGub9fu35YknoCaYc3L5V7KkNSDev//uHonWdbumXqk7bMHOcOo1K8ty5/bl9uOXSWlF7uf3UMWSeKQW+/Mgbo/qcTNc5R6o9xaNCjppgcqEJgoBgwe//19aFtS31ogzQ1VfU4cF/HMLFU4uxaXcbVsyrCWbIp6PUbzxEGn8s+5aumaDJJplvq4luW+k1ySq8romhFm8iKBmrzelmelQGnbHV4RUEikGnB/0ODwxaTVQ1O8AfixzuMbn50ilRY5HryiyS4v06jSqq6Aeg3KvtcHslMZM/u6Iuavwzh5PphIe0uX0CUxLY7vYBBBIbEN7e7vJJvvSUWfW4a+GMYALbWY+uwMwf+LTPLhqj0yPg2Bknbn3ugGj5tXMmM+sM15WbIVCCMyPl24ZkaifbHB64vWftESGA28sef7hIx8p5tRhwunF5XQmWnF+J04MuFOXm4Nl/n8CU0lwMOX1B++UThKj2N1CUQK6msNyzLJaQxHSdc2T8pNjrFeAVBIkIwNqWerzy4Sncv6QJTrcXZVYD+uxO2JweUOp/I1ERgILG9DDNdjLJMzmaJPNtNRVvwqzC6+sX12Nx08SkT4zjpcCkx+Y9H0o8KhuvmRW1rdL9LDLnwKAVi3cYtCoURfG+er0C+u0eyadSr1eIelyV3lv5Rh3a+4ZFY/UKAvINkb3aHE4mwwppu//qRvznf1Tj3v/9KLhs1fxaCFTANY/9W2QDFjWU45VD3aL2j1/fzIyTtTs9Evszt3omU2Z6WlmeaJx6rQqmsAoyeq0KBcYciV37uNuGfKNWFBLx39fNZj4fShnyx+ta6jG5wIDjvcOi8Vfk6yXj/+31szH33EJR+MT6xfUYcHpxe0j5uHsWN2D3R52S9o9cO0ty/FnHT69ly0zHGpKYrnOOjJ8Ut3bY4HBTacD5tgNY8fkaHOocBCHAyf5hFJh0uGPBdNy38yBWza9FsSUHQ04f9p/sh8PtG/dlyDLdW66EZL6tKpU/TgSl8scA4Hb7sO+UTVSeSBdDyTFAWbJinlGDH1w6BZ+ctkOggEYF/ODSKcgzRS/J1tphY4amRNtPnwBs33tSUubsczVFEfvb32HDo68dCUuKOYJzCo04/5zICXOAsnvL46NY//eDkgfR0zdeEPM2OJxMgxXSdvvWfZKSZg/vOoKffGmaxNZVFZgksuoP7zqE+69uFKm/3X91I/odblHt31Xza6FWEcyqLBBpHqy+og4e79lktcC6deW5WDm/JmjfC0w6UAA3XXIujDpt0Ct76fQifOXxf0nCP8Klm2/5whS4vYJEKOOubQfw31+djf/681mZ6LUt9TDmqHDBOflB7/PnavIxOOyTJOje+fwBrJpfKzomG3cfxs8W1eM/nxZrO+Tqtbhv5/uS4xeasxUYq5rhD4gnJDEd5xwZPynusDnh8gpM136pRY87w2QZDTqCn11Rh2On7ei3e+D1URw4ZcPGXW1pUxKEM3Yk+21VifxxIkSSFI44TrcPz+87JSluv7ixPOrEWGmy4hm7Gx6vIDo+axfV4YzdjclR5pnygiGR99M27GYm7AxECdnoZ0ivrpxXi/4oCXqJECgVGYrTI6AzRECAw8k25O7tcEnmfKMOGrVKkqg25GLLJBt0YplktYrguXfbRZ7SJ986jkkTjHjyreOi5Q6XF7978xPRsv/d34F8o05kv26+dAqG3B74KBEl5a1dVI8pxWbsOzkQHP/x3mFY9Bqx5LxaJXvfD7t9EunmI10O0deytS31sBo0zPYlFj0eDpmAr5xXCwKKzcvnotPmROlIRYn32/uYxy9Xr5aM9fSQS1K2Nl0T6GIlvb6nKqDMagiWQDm7TI+V82vgoxTfvqgaZVa933u8/QBURIX//PN7cHr9F0mZVY/a4tzgOrds+QDHeu0ReswMBIHiaM8Q3vr4NI72DEEItyicIIG31bnVhcH48tFA7o05mdeXRa8LJnmsmFeDb19Ujc17TiBXH/mT+75TNknB+bu3HcC+U7aofSrdT6+PMkuOeX3Rr9WA8Ecoeq0K+VGEP7wCZSbseKL0aWVIr27cfQRWQ3SvNuAPv9j7aR92HujA3k/74Y2hwkYgESYUvVYVs3gHh5OJBMLOQtFrVbDkqHHT52uwYp7/51ufnYy1DEELVtnFX75yGG3dQwgUfBEocP8/PsIl04rx6Ktt2LS7DY++2oY+hxvFuTnoc7hFy0usesmyS6YVB+P9Q/vRqdVBj2pg+d3bD2D5xedK9smcoxaN6ff//ATFMvf9oa5BrPjL+7hj636s+Mv7eP+E9GvZ3dsOyNpGnVolOVYmnQa5ei0sBi1y9VqoVAQEhHn8DncNYeMu//5v3NWGn+/8CFqVSjLPkDt/qU6gi5WMnxTXlVngFXxYv7g+mA3+9c9MxmOvH8UdW/fjd28cxfVzJwcnvd0j5VbKrAYYdCo8+I9DuO25veJ1BjPbExPw3F2+8Q185fF/4fKNb2BnayefGI8xkd6Yk0VAovSJN49i0+42/O6No7imuTJqIpm85HL0sSrdz54h5SXHHG5/2aOA8Q14PqIlockV1u8ejNznGTu73Rl79KTAQPzzNY+9je89/R6ueewtPL/3ZNSJsUB9WLOoTrSPaxbVgdLo0tIcTqZSmW8MPs8B/3X/wJJG5JtyRHYt35SD/LCqMU6PP/afda8WmXMkdnFGmUXUz/rF9aDwYfVC8X2nVROJvakqYJc065Yp6eajVNT+xwumob3PKRlTjoZibdh9v3ZRPfZ/2i/appzMdZ/Dw7SN7f0O6VgHXZJ5Qr9DKjPt9LDFR9ptw5L2lflGbFg2U9R/OiTQxUrGh09oNCoYtBpUFfg/Q9QW5+I2hpzhjRdW44k3jyJnpAzU0dNDqCuzBD9nhK5j0KozOvkuXYtijzdSkWiXo1ExPZqbl8+N2K4sAZlnpftZaJYrfRTdE2oOk2sOJIlEE9OQK6xfnBu5zwkmdrsJUWSlAeXxzyqixrN7TkgEC25fMD1qnxzlfHriOGbMlo/bLisuxK6dfx/DEY0vTvQ5RHkRlAJalQq3bdsnibNdfnG1RHp5gplddvFEn0NiFx+9bpZEzvnHX5qBVw91iOSXDVo1dn/0sWhdY46KbUtkSp0ZtWpR+55BpyQXauPuI2iumoNHw8qvPfraEfz08hmYMTEvGOdryVEzxaHyjWzb2DJzoug4B7zP4fOEJ2+Ywxw/S3zkUKe0/YsrL0rLBLpYyfhJMeCXeT5j92LjLn+ZNdZbjloFrF1UjzcPd2LlvFo89fZxaOZUStZZOa8WK//6Pu5YMD1jY4szPaYnW0hFyRmH28c89w53ZO9iQ7lVUsJwbUs9GsujlxwLeHbiFylhlz4CoocWCKDMGN9oHnGv4GP26RUiH5/+YQ+zfFE00Q8gcmxw0yT5dkMuD+ZNKxXFPwdiJjnJw0sJGr/3K9m/7/vND8ZsLOORrgGnpPzYfVc1MO+hynyjKPktYAPC7e69VzbggX8cErXPN+rQNeAKhmAE2mtUwKXTy0WJdr/+2ixJLsK6lnr8eME0/HznR6L2bp9PIhSyZlEdhr0+UfWH+65ulPU0s8qvhUs6P7CkESs+XyuuurWoDkYdwX9dUiOyceta/J730GO1dlE9nn77mKR/OVunVRNRe9YxDZ1npFsCXaxkxaRYp1bDaoDIXR/+ltM8OR8bdx3G9+dPwY+27kefww2XV7zOtJJcrPv7QXTYnAl7VuWy8ZMlKRxKuhbFHm+kouSM3LmP5vHV6dRYVF+GcwqM6BxwodSSg4ay2KpPsDw7j+w+EoNIiYopnfxAjDLPuz/qlHh5ogliaFVs7+sdUbyveQbt2f5C2jVXRffaygl/lEYR/jDnsOOYucwzJ5th2TCTni3T3DPkYorinFc5QWR3VQQSoY6lzRXMmOTmqjnB0ouB5XqNBr95/UNRRYZNrx6RCHJs3nMCzVVN2PreWRtj0GnQb3cFJ6mBbR7tGYrZ08ySdD7SPSQSKPPHLvvlqFk2LlwS+9HXjqBl5kRR8p9eq0KBUYd7GJ7mR66dJRJKYR3TbJhnZMWk2O0VcLLPgdUL6/Cb19skbzl3LZyBO7buR4fNiU6bE30ON352RR1+/X/+N7FAeZWekLjCRDyrctn4l00vwUsHu5IiKRxKuhbFHo+MdckZpV5bQaDYfaRH0bXJ8uwAiHr/9AyypZN7osT3Av79vHbOZJHnJqb9pJTpfaU0sod5RqkJy5rF/a1dVI8ZpdHvqboyC/OcRBP+OC0Tc93LZZ45WQzr+ZWrV0vuoXuubIBOTXBbiPc1YAPC7a4gSL3HNUVsmWRW3sGpfgfzy1R1oQmrNp/d5ppFddCpqcSr/IurpF7hLXvameIhakIl8tHnMiSd5WKKuwddTBv36RmHxNZOKckVeX83LJuJhnIrvj+vVmKvZpRZodGoIh7TbJhnZMWk2KzXItegwx//31H88LJpoFTAb68/D4c6B1FuNeDTPgc6bE7otSpU5Buw4vM1eOZfx9EycyImWg040TeMJ986jj6HGzdeWI1HX21L6I1HLqZ38/K5YxLrm65FsTnJR6nXNpE4dKVfJpTG9wb2U4mEtlrFjrmOVvv349PDEu/R3dsPYGrpXDRNiryfGo0Ki5smorbYLCp9FE30I5GYaw4nU5F7fgkCFd1DFr0W3/zjOzHZOtY2z9hdsvYnfHl5nhE/fb5VYjee/NYcicjHuUW1Eknp0NCFAHLiIc1V+diy57jI09w75JS0VxP2V/Hi3Bzc+qw0r+rBsC9weq0K00stTJnsWOxVts4zsmJS7Pb5IFCKBfXlorejVfP99VmffOt4MK7GqFOjpsiEqv84Fw6nB7/cdQQdIXU/CUk8W1IuplcutjAZsb7pWBQ7XRmLkJaxQqnXtmvAyZRRjeXaVPplQqMmuP/qBrT12IPF788tMkGjjn7slcbNK60ioTQuOIBKRZCr18Lh9gVLH0XD5WXLPLu8XOaZk92wnl8qFUHTpPzg/fbWx6dlbV1VgYlp00O3+XHPEO5YMA33hcQE37FgGobdXolXutfOrshw2u7CR52DQfu17PzJcHsFiUxz96BT8gV79RV1cHt8eOJNsfe5z+HByX5XUHgMAF4/1C2RdD632CxZtnZRHZwetnw0AIlX+JzCs8clFI1GJTrWgPxzMtvmGVkxKc4z6HC0exA6NcHtX5yKYoseoEDPoBNOjw8//tI0GHVqbNx1BIe7h7B6YR22vncCV59XiSKzLjgp1mtVuKimEFfNmpjQxEjOc1Zm5bG+6YZS4Yl0RanXtowhLbpqfi1KY6g+odRjUGDWoa3bLip+v66lHgXm6BUdFHunLez43mje6XKFccGA8mtsgikHx3ulMs8TjNxTzOEU57JtQKEpJ6b7rSJPD4fbK7q/Jpi0KLbkwOYULy+VqShh0WtF9mv94nrUTDZLbOndC2dg90cnRV7hwWE3/vwOu4IO2xbrRN7jXD1BjkYlEvTQagisBi1zrJPyDUyvcCxk23MyEhlfpxgA7C4vJheace//foReuwe3PrsXK/7yPh574xPYnD609QxheOQCufHCanQMDOP786fgN//XFiyoHTjJ51dNSFjAIeA5C03827BsJurKrBldvy8bOdZrD0qCBsQu7tt5MGMFXOSuvWjXmE+ARFr04V1HgoXlo6FEAKXT5pIkj9y17QA6bdFjZgMS2ivn+wv5r5pfg03XzYq6n2oCZu1fdZTxqlXAbV+cKmp32xenQhPDfiYibvL4G0dFxf0ff+MovLzeOCfLiUV8Sq0CbvnCFNE9ecsXpsDp9THvtxNn7KJt2t0C7ti6XyRIccfW/Rh0+vDkPz9BTXEuJuUZUFuci2GPj9mX2+sTPTse2X0EvUNuiS1du+NDrJp/VhZZowIqJhixav6UYJ3iJ948ipXzpyDfqGXa4hydGm3dg/i0bxgfdw/CmpODx1//2F+rhwIUwOOvfwyfANx3dYPINt53dQNytH5bFZo+EavIVyqEqFJFVniKbcNu9A/7PxkQ4j9hZVY9rp87Wfy5YiQR73jvcPBThV6nwl+XXzCq8TCRPGfZGIOTyUSSRc7ET0JKr7HuQXZ4QM+QE+cWJ+c4dMkUuY8m1Qz4jXm/wyPx0kSrL9416ITbK4i8QG6vgK5BJxoi9HfG4cIEo07sVTLq0OeIPlaloR79DjdbWtoRXTCEw8lUYvVK9gy5kKNWSaSHT/UPS+63fKMO753ox09CZI4fWNIkm6gWnij38DUzmX05vYJEZtrmlIZa5Bt1OGVziuzVnZdPR1FujmibWjVBJyNUK9+oQ/sZcfuaYjOWnCeVrPcI/rKcoeuuWVSHDpsT//nn94PLNl03C24vjcn7O57KvGbFpNig0+C9E/2it7irZldIEmrW7GjF/UuacLhrEMDZTxXNVRNGfUxysTbZGIOTyejUysQuYsXrFdDaYUOHzYkyqwF1ZZaoCVaJouQaS0UZvxJLDrP4fEkMMsZKBTEsei1WvPi+ZD+jlTnTazT48d/ek7R7KobyaEqPrYEhV8tLsnGyndCvdwG7cN/Og5hWmiuyaTq1KlgjOIBeq8Kfb7yAWdIsMCEG/PeS1cAu88ZKVDPo1My+HgyZWIfen6z+Q8tPAsDjbx7FtedXwif4444FCjz00iE8sKSJ2f7hXYdF7dv7hiUe5TU7/CXZwsu/rd7eiseuP0+0bF+7TVLSLZBcHR6TLRf6WWrR42jPUFbk4wTIiklxz6ALW/a0Y+W8WmzecwIr59XC6WWLGLR1D2LT7razpZiiFPvnZDdKxS5iISDvG17aZnHTxKRPjOMlFWX8rAY1brqkBneHFJlfu6gOecbotZGVJr7JSZj2OyILYsgl2vTGIPOs9NjKlWSLRQabw8lUYv16J2e7BYZ4x5TiXMm6f3jzE6xdVCeyP2sW1WHYLU1U+/DUILOvo6ftkmWnh1ySpLqaYjNznyZPMOKWZ8Wl0+wjEvbR2q9rqZf90hbLFziNSsW2of3D+KhzUGKvNl03CyueEXuaP+yQrpfpccZZMSkuys2BTkMgUIq7Fs6AXqPGkMuLVfNrsGVPuyiRLhCfN9oeQU5molTsIhZaO2yi8miAv2RQNG9moiipppGK0J6BYV/wgQScLT4fi/dVqSBGnlHH9E7nGbWK+iuLIdFO6bEtNOegebIVX/9stUigpJCXZEsp0WSgAS4FnQixfr2Ts90FphzMqsjH5uVzg1/oLAzxj7c+OYNrL6jAH781Bz2DThTl6vFK60mcUzhRsu6wx8u8F/Uagps+XyOyJYXmHNy38yOJTDVrnx6/vln0fNi85wQeWNIkkWlmtW/vc8g8u9hJgQat2NlQXWRirqfRqJie+j99aw6evvECdA44UWbRw2LQ4opNbzI9zZn8JTwrJsVunw83XVKLR187InmbWjW/NliDOCDvHGC0PIKczCWZHtJIHo9kkUiW8FiH9vTIeEJ7YvCEKhXEcHq9+N7FNSIJ1tULo5c5U9pfACXHVqumWNZcGSYY4hcH4KSOaDLQAJeCToRYv97J2e7KfCNTJEvi6fzKLJyxe7Dqr++I7q8Ck1oieT9zkhUT84wSmWe9VoUNIeIhaxfVwePzSez+vVeyZar7HG5JTDKlVCKe8eBSafzzlj3tkpJsAfGQcJnp1VfUIUcrlnnWqAhTztnu9krGf+tlU7HvpC02meoMjzPOikmxTq3G3dsPYMXna4Inssyqx1WzKzDs8eG+qxug16lx27N7RTWJI3kEs6l2LUeeZHpILXqdIqGIRIg1Hi8dkBOniMUTqtGo8OUZpaicYETXgAsllhw0lEaP19ZrNFiz4z3ROQnE4UXrb1FDOaoKTEFPSUN5dAGORPD4CNOTzmOKOdlMpBj88PhVlu2Wq5Tw9+9fJCpJ1md3Y8Vf3pfcX0/dMAePvioWQHJ6qES8565tB7D84mrm/RkuQX+aIb6h16rQ1jPEjEl+qfUUfnv9eeizezDBpIVGTSTt+xxuFJp1EvGQaaVT8eqhDsnyn365TuQ9Byh+/r8HJSXhHljSJHluPfjSIcm+yspU5+ozev6UFZPiQacH+UYdSiz64IQ4vPLEupZ63Pi5c3Dv/54t1C3nERxPNfk4yfOQur0C803aE2udMwVkUjWNYUbs3Mp5tRh2RxencLt9eKG1U+TNWdtSj8WN5dDp5GOSTzMkXGOJ0xUEilcOdY+pTUikOgeHk6nISdV/0jsk8vQG7r9wvXEGlgAArONJREFU2y1XKaFnyBksGQkAO7s62PcXQ37+/iVsr2h4BTOnR0C/wy2RhL//6kbcc2UDfhpS/WJdSz02vHxY0n7Q6cHnp5bhu0+9G1z3l8tmMm2lR6BY+ef3RMdkekkuLqsrF7V/YEkjDpwcxA+fPWu/Hlo6Ez+8bKrI+7u2pR4emedW+L5u2dMuOU8BT30mz5+yYlKcq9diaXNFMMaGVXnirm0H8MtlM7Fqfi2qCkwoys3B7Mp85klKRPKWwwlQKpOxOxrxynIku5rGaGLO0Upi5wIVYaKx75QtOCEGRrw02w6gutAUsZqM0koQqbAJcrGBsVTn4HAyFZZU/Zkhl6TajNz9F+s9LpcnUMKQeZYT7wifPui1KuQZdVj5V7GtuH3rPmy8dpZon/rsLvSFlVfUa1XI1Wtx0zNiD/ZHnQN4/oOTElv5+2/MkQhyHOu1S47Vke4hSaWJHz77AV5YcSGevvGCoEe3sdyKdttwTPva53Dj3EITs/9Mnj+lVwq8QmzDHkyeYApWoFCrIDqhgP/3j3uGYNSqYdZrMOj04JPTdmax6kg1+VjEWgCbM75QKqSRCMmspjHaCKC4prlSVLz+mubKmCrCdMrco10D7Hs0QEO5FWtHYgEBBL0jjeWRY4PjtQnhKLERXp8Pa8OERtYuqoNXSL9zyeGMFqFS9Zt2t+HRV9sw4GLbNdb9J2d3K/ONontwekku1i8W24L1i+uRM+KFDV0+7PFh9UKp6E9tsVm0bF1LPWzDHtn44dB9+sM/jwfjkgPt1y6qY1ad2bKnHd+fVyuylXcsmI6JFj3O2N04PeRGn90Nr9dvA/ONOtz0eb94x4p5NbKVJk4PuTDBpAv+aDQq5vG758oGnFNgEi1bvbAOdrdHItoUj61Mx7lTVniKi3NzQOB/c9l5oAO3fXEa802nfqIVH/cM4TtP7ono1o/Hm8RDLThypKKiQzKraYw2BaYcpqd4QX1p1LZlCvdTo1GhIj8Hj11/HvocHuQbtdCoETU2WE5StsicPJlnc44WFoNXJOOqUQNmXeRKGRxOJsO619QEcX3h0WmISBRDpyF47Ui3JPxiUUM5aovN6LQ5UWrVo67MihN9DoldOtU/jL/vOyWSWd6x91MsmjlJ0k8xw9Os16pg0omnW30ON0osYeIdGhWzJnCfw43JBUbRunlGNbYf6JCEkF1QlSeRid70lVnMMfkoxeUb35DYpfDnlsPtxc+2HxDt/5P/PIq7FtZJjn2s86d0nTtlxaQYFFj39w+D9Ynv3LYfN186Bb985bAoJqn9jB0P/ONQVLd+PBUJMv1TAWdsoGP0ApyKesNKqSowYW1LHQaHfbC7vDDpNZhzTl1MYzXlqPDzqxr8X3uo/6FZVWiCOSfy5PZYrx03/PFdicF+Mcr9qlYBq+bXih40q+bXBmVbo/WpxEa4fQJ+sHmvZKxP38gT7TjZi1oF/ORL03Da7g7e2xPz9Lj/6kbcvnVfTM/kFc9IBXpWza+VJCDPKMtFrl4Lh9uHXL0WKhVh2qWJef58pdA44V8um4mbQ+7rQD9bls/F/Usa0dY9FBz/ucVmCNSHlfNrRMs27jqMC6qLguIdv3rlMH61bCYzpvrx19tQW5IXXHfA4WOGkD15wxyJqMfRniGm/frwpI1plyrzjRh0etDv8MCg1WBqkRnXzhHHSctV34n1GZSuc6esmBR3jHxueert4/jB/Fq4vRRGnVr0VuUYubjzjTpRBYrAJ9fQkxCPh288yR9y4iNVb8IsL0k6IggUPYNuifGPJtUM+MMneofcIinTWy+bis4BF6aVybeTu1/DbUA4p/qdePKt4yLv0ZNvHcfMSXmoKox8nyu1EZ080Y4zDukZcmHYI5YpvvnSKbjgnFxJ/Go8z+Qicw4e3nVAtM3WUwP4YYh4xoZlMzGvtghdA26xB3ZRPd452ivxHsuFSbi94vE/sKQRp+1eiSz9FY0Tg0p5geS5XocbORqxpHSuXoPzJheKHH0/v4pd5o2VoDvg8mHru+0S+3X1eRWM9k68d6JPYpcX1pVJvOpyX9hieQal69wpKybFgYD5DpsTn/YPY2lzBe558aDkDW75xdVY2lyBjbvaRMuNI9nqrDIi0U5OKuRxR4tMLpuSCaTiTVjOSxLNE5oKlEo1A4BJp8WDL4m/+jz40qGowh9GHVva1RihYgUA5GhUIsNORgy9NgZXsVIbIfcZtiiXJ9pxshedWhWc/AH+e/uXrxzGc9/7DADxVzfWM0zufjvR55BsM7zM2C1bPsBTN8yRemC3H8DGa2dhb7sNgP/+n1qay+zHoNPgzufFZR9ZiW53Pi8t6RYoyRZuw1fOr5G0P3baLpuIGy5QZNapgzHNoeuyEgW1alVEuxxJMRSI/RmUrnOnrEi0m16SGwxYf/1QN6oLzcw3EIECkyeIg8VvvnQKPD4h6NW7fOMb+Mrj/8LlG9/AztbOqIHfqUimGg2U7i8ndhJNzsqUPpUi77WN7gntk5Fr7osi1+z2+SRJNCvn1cIdpUyew+PB9y6uCSa6/O6No/jexTUY9kTuD1BuI9w+H9Yskib3eHw80Y6TvdgZSXX5Rh0Odw1Jnle7D3VJllXmGyX3271XNuDZPe2ibQbmBOHLWF9o8o062IY9ovv/jMONmy+dIrEl3YyyjwJlJ/+z+u9lJNqx2m/Z0461i8SJeqsX1kFNBNz0+VrRWMvzDXhoqdQGNVZYJcsGnGzb2mmL7RkS6zMoXedOWeEpbrcNY9jtxfKLq1FbnIuTMvKHKgI4PV788Vvno71vGCadBr/YeRBfmFGi2KuXimSq0SBd43myiVS8Cafq7VvJV4cCE9sTOsGki9pfiUWmnFKUcmU6tVpSWP9P/zyKz9UURGyn12jwm9dbRTGJv3m9DQ8uaYo6VpWK4NKpxSKJ1IZya9Tjo1Or8d+vtYk+ef73a214IIY+OaklmhQ0l4GWx5Qj/ZqztLkCPxmp8QucfV6xPL0vrrxIdL+VWvQotuiY5c8sOWqJTDOrFOLS5oqgclygr9uf24dbLq1lil+Ee2otOWrZOUn4mArMUk8vq73fMeCVJL/dvmC6xNN9+3P78L8rLwoR79AH44HDl7V2DEj6f2HvSZTGIGkPxP4MSte5U1ZMirsGnOgecmPT7jasmFeDre+2SxLtVs2vhUmnhsWgRVe/A90DbmhULtyxYDqqCkz41ye9iuNbxloedzRI13iebCIVSW+p6FNp7LRXEJgF6QUhurjJ9JJciRTr2pZ6TC+xRB4rFXD1bLF08uqFdaBRMiEdDOnTlfNq4YhBaMTrFbB9/ylJjN7ipokRq17Yht0SEQH/8ujeaU5qiSYFzWWg5Ql8zQm91yonGJnPK41KJVk2MOzG9rCY2HUt9Xj42plY9dezNuqBJY1w+yg2vLJfdF+actQSmeRzi9hfn0usBmx4JVTmuR55RjVuuqQmqEYZ6P/uhdOxdsfB4LI1i+rgdPuCE8izX628+K9LarA6pP36lnrcv6RRIrQBQZz8t3JerexXtNaOAdwaFj+t0xBJRY55tUW46fO1cdvWAPE8g9Jx7pQVk+ISiz5YsgXwv0H98Z/H8Oh1s0BAYHf7oFET/PrVNhzuHsIfvnk+7vhbK56+8YKggEe6xrcki/G2v6kgFW/CqehT8VcWQpgl2c6vihxPDPi/DoVLsT766hE0T86P2mfgYRcY65odrVHFTUw5WsWS3Upjp60GHfMetRp4STZO9sIq1WhmeI/1WhVqi80ST69XgOR+u2vbATx63SzRF6I+uyuocBtY787nD2Dz8rlBmeQ+uwf5Ji0MWhWzf5fHK5JjfvrtT1BVWCORZ79r2wFR9QtKgXyTDve89qHE/jVXNWH1dnFM8p2M9o++egTXnl8pab9hJEwifKyHuwYlNppVkWNinp5Z1SKabQ2Qrh7gWMmKSXFVgQkNFVbctXAGHnv9Y6ycV4vNe06gc8CFdTs+FL1F9bx9HD1DLuQbdfD4hOCJyqRSVqPBeNvfVJGKN+Gx7lPpV4f+YQ/T+9ofgyc0tMB/+PJIfSoVN0lEsrvDxj4+nTZnxKQV70hMcajHaM2iOnh5TDEni6kqMOGOBdNFz6aN185iflUiBHjiTXFFh147Oya41+4ReT/vWjiDWY3K7RMkMsnrF9fjxwumiSpFPLi0EYNOn2i91QvZ4htOj7T6xf1LGpn2r0dGit7u9knsXXmeAXeElKlbv7geKhUkx+quhTOwaXebZJvhY1o5rxbdslVvYv+KnI4e4FjJikmxSkUwb2oJPuywoWXmRKhUwANXN+Hrf3hH4tlZfnE1VCBY2lwhyjjP9LebeBlv+8tJHkq/OlgNbJnnWGJmlVaRUCpukohkt5ycbLQYPQ2PKeaMQ1jPJkqBe16UelVbZk6UeHqfumEOMyY43Pu5bseHWH5xtaQalU6m+kK4p9aq1+HWZ/dIvjo9yehfr1WhZ8glLunWNyxr/2KNPz7VPyxq/8juI9h47WzJdu1ODzOmOrwix8bdR5jHLxbbmi1kxaRYECg+7bNj2O3DuUVmdPQPo2uQ7Z05t8iMx17/GF//bJXEy5PJbzdKGG/7y0kOSr86eHxe/Od/1OBnL5z1hP7sitiqK7h9Ptx62dRgWTa91l+nOJrntqrAhE3XzcK+dluwiH5DhTXqWKsKTHho6Uz88Nmz+/jQ0ti+rNSVWZjF+FmF70PpGXTB7T0b60wI4PZS9AzyOsWc7Cb82SQIVOI9vvfKBjzwj0Oidn6PqkfiKZ2Uz45JnlqaKxLUaKiwyn5NKrXo8UmvXyxIo/J/6WKt1z/skeQ0/eRL05Bn1InER+5f0ohV86cEEwgDdkGgPvzsijqRXVy7qA4G3dlku9D97wirCuHx+XDXwhlBG6dRAVPLciU2+t4rG/DH//eJKPxk67vtGHRJj9/KebVQEWDvp30jSXkG1JVZoFKRrCvrmvGTYkGgeKOtG6f6XVgTchE98Y1m5tuOQaPG4e4hWPVaFJp5vU8OJ1GUfnXIM+bgk9PDoiLvXkFAniF69Yni3BwUmHWitgVmXUw1fN1eKiqiv2HZzKhtvF4BPiqI+vNRAV6vAF0UD4pGo8LipokxF74PUJFvkMi1rppfi4o8Q9TxcjjZBMvGqAiY3s98ox6b94i9yr1DLuZ8IDdHK7EF5xSwvwpZDeJ1f/9N9hwj36BFp1YsvgEAMyutePrGC4ITyPpSC/51olck455rUMOco4NXcIjau30CavPNYYIYKokohl6rQqE5B92Dbsl+XTa9RCR+oiJA10CZxL4Um/VYE3b8Pvi0F0W5OcEKHIEJfJ5RK0nUS7VMc6KMyaSYEDIJwJMASgEIAB6jlD5MCJkAYDOAKgDHACyjlPbFs+1jvXYMDvuw9d0T2LC0CW4fxQSTFkNONzM7/bE32rByXi1++3ob7vpyHd76+DSMOg3cPh8KTDlj9qbDhTM42YQgUJEsaCyqdF4fxfq/S0V2oiW9AUC/3YM/v30MX/9stai0WnVBHSZHqK6mNClw3ykbHnrpULBMkUCBh146hHKrAc1VE6KON4Df7xvbfU4A7DrIKB93buTycRxONhMoFFORZ2R+oaors0i8yr+6ZiZ+tGAafhESE7xmUR027jokSTT707fmSL4K/fzKBkmC7oaXDknnGIvqYdSpRQl8gN+uzZyUJ7IVR3uG8Oe3j+Grc8+BV6CwGDR4+u1PsPziWjz+xlGRrXn8jaO498p6nDc5X5TUt2FZE+wuH86MLNOoAZ8Apo3735UXiWy0Rg2JHPTDu47gczUFkuP3h2+ej2/98d+SkBJWSbxML+s6Vp5iL4AfUkrfI4TkAniXEPIygG8C2EUp/QUh5EcAfgTgjng23DXgRI5WhavPq8QtIeVGVl9RhwlGNTYsbcKQy4tSqwGECJg/vRT/u78DC+rLcM3jb4s+D2zecwJ3LJie9DedVMn/cjjJwOsV8Pzek3GXHFOagAYAAy43s7TaoMsdsV2nTJ/Rkkj6h90ySYGR+wOUH58hl4e5j0MuXpIt04lWxxjgtYxDkXtmhns/A86lcK/ykMuDYbdP5Gk16NRMmeXuQReEsK9Cxhw1jvcOi8a057gN//V5rcjT6/Z50SGTeNw54EJjyLIhlwfzp5eJEvXWLKpDn0Nqa368YBo6bC6RDbn/6gZ8ctohWra2pR4alZqZaLjneJ/I07uupZ6ZaNjR75Qcv7buQeY+sUriZXpZ1zGZFFNKOwB0jPx/kBByEMBEAC0ALhlZ7U8AXkOck+ISix7qQRe+/xe/C7/MqsdVsyvQYRvGxLx8DHsEnOgbxqOvtWFdSz1mVeZhVmUevvkH8VvPxt3+8k5j8abDhTPGD16vgNYOmygOK9pn80xDacmxMqtBcZF4vUaDNTvEZYvW7GiNKvOco1Ex+4wm12zRs0uyPRmlP0D58dGo1czycbH0yUlvotUxBngt41CO9dpx386DEq/utNLciHkxAa+yx0vxq12HJV96whP1Avf0bSP1gANsum4WM1Tig09tkkQ9uUS7cFEil0fAs3tOSL4E3b5gukRgyOZwSWxIW49dIv189zb5RMNw8ZG7th1gJhqWWvWSmO4+u5u5T9WF4pyKbCjrOuYxxYSQKgCzAPwLQMnIhBmU0g5CSLFMm+UAlgNAZWWl6G9VBSa0nhoIToivnztZUorkhb0ncU1zJY50DeGBkU8erDckQsbmTYcLZ2QPka5NpR7CTEOpx3dKoQk3XVKLu7eLPz9OKYx+D5xRKPPs9PrlmkML869eWAeXN7IIR5+d3V9/lP4A5cdHrjRSd4yJdpGuTU7mM3/Bl9HRfVr27+nsaY732uy1u5hfas7YXZJnJsur/PC1s5jtwxydcHrYMsvtfQ5J8tndC2fgEUaZs34HO1FNCBMIcni8zC9BAnyS5WsW1WFKsRn7Tg6c3U8Z6eieIZcksVdOfOTcIrMoeU9OpEMAZe6TLqR+c7aUdR3TSTEhxAxgK4AfUEoHCIktVIBS+hiAxwCgublZdGWpVAQT8/R4cEk9KiaY8c+PT+MHl9bC66NwegV0Dzhx39WNECiFXq3Gw9fMhFmvwV0LZ0CvUaFrYBj9w14Me3yoLc7F5AJD0t90xlo4g8cvJ49I16ZSD2EAt9uHfadsImngaEldAOB0erG/w4bOARdKLTloKLNCr0/era7U49vaNYg9x3rw+2+ej9ODLhTl5uBv751AdZEpapxuoUKJaJ1amYe50JyDb39uEi6tm4iekU+KL7eejEmSusxqQPNkqyT+OdrxKbbkMNsVx5BMCES+NjmZT0f36YxVzYt2bYbbMKtBjQ8+7ZUIZXzu3AL8+5Neka07NejE79/8WORpNenUojJlgL/82T2L6/HMdy4Q2Z8Cs9S2DLt9yNUBT35rDroG/c9RnZpCpyES8ZA8oxYejxt/+tYcdI+s+9aRLuQb1Xjnk150DbhQYsmBVa/Fdxm26Mkb5uA3r7eJxvrfr7XhtsumYcVf3g+OKSBYFm4Di8w5+OeRLlH/PQNOpo3WqSAZZ7ttGGXmHOzvHAiOtSRXJ/Fe/+mfR/Gl+pkhMtFnK1Kw5hwAMmIeMmaTYkKIFv4J8Z8ppf8zsriLEFI24iUuA9Ad73a9XgFDLg98lOCbf3gH+UYdvv6Zydj0alvw7aXYosdjr3+MGz93Dtw+KirjtGp+LXL1Gjz51nH0OfzJeZX5xtHcdQljKZzB45dTRyIxs263D8/vOyVJFF3cWB5xYux0erF9f4fE+7qooSxpE+PpJbmKZEEJ8aG5qhA3jCRwBEoPERK9JFvOyLqhUqr+skWRPfADTnYZpUFnZI9vgVmFKaV5+OZI7fNAf4Xm6B7/qUVmLGueLPL8rF1Uj6lFuRHb5RvVWNZcGdauDvnG8VEvdLwTLe740/aTohjVbIFlwx6+ZiYuZcTfDrjc+M6T74nuq5mVuRJP68avSD3Ft142FWccHrF08qI6GLSQ2JZ5UwtwqGs4qH0Q6OtnV8zAf/75fVH7PKMaZflmfCNk3XUt9fio0x4My9BrVbjv6kamLTo9xPaKazVE5JU9t9iMdS31ojjhtYvqUWKV9v/wNTMlNnpdSz08FKL11i6qA4EXLxzoEx3/dS31+MZnz8FtIcd0XUs92nqGgqGroXHeLx3sksw5WJLS6TgPGZNvuMTvEn4CwEFK6YaQP20H8I2R/38DwLZ4t93aYQNAgqpPV82ukGRUrtvxIRY2TsRpuzs4IQ787eFdR9A96MJVsyuCMTkn+hwJ7G10AokAL668CH9dfgFeXHlR0i4OufjlY732Ue+LIyYg2hBKIGYrGvtO2ZhSm/tO2SK2299hCxqzYLvtB7C/I3K7RGi3DTPH2m4bjtjOJ6gkcqh3b2+FT4hulhxuHx4dEbZYMa8GN15YjUdfa4PdFXlCnavXMs+JWR9ZOrl70Mcca/dg9An8oZ5B5jk51DMYsV2fg91nn4Mr2o0HAnHHcj/ZqmzIsmEeH5XcC6u3t4qSygL3Vb/DJ4nF12tUkpyAB186hLbuIcn9NeyBxLa4vIR5D6tVKkn7fsZ9e9e2AzgS1tfRniGmLSo05zDzF/INOty/pAn3Xd2A+5c0odicg00jUvdnbeARdNqk/bd2DEhs9F3bDjD3v2fIJ9lX1vjv2nYA+0/aJHOL1g4bc86xr126bjrOQ8bKU/w5ANcD2E8I+WBk2U8A/ALAFkLIjQBOAFga74YD3rjAwQ7EBYfi9AjB4HrW3wSK4CcFp2dsYnvHSjiDxy+nDqWiDQDQKXPeugacMi0C7eQkOpMn+KD0GutKYKxdAy6mzHN3lLZ9MvF+0WKRExmr0i8GifTJ4WQqLBtmd3llbIxLsox133TLSCcLYYEbgfbhtmVaKTsmN9xuyPXP6mvLnnbctXAG1u34UGSL+h3sr1lnHC6R9/uBJY0yUvfS/iPNfZSOX26ZnL1jrZuO85Cxqj7xJuSLc85PZNtlVgNsw+LMSFacDR1RdmH9TUX8tf0Cv2d69mQoYx2/zDmLUtEGAChTKkdsYcfalliSJ1Sj9BorSWCsSvcz36hMWjqRsSqVeU6kTw4nU2Hd2yY9W9Y93yj+whO4P6TrqmWf/bG0l7Nxsfcv7avP4Ybd6YlZ5tmUo/XH9Lq9MOg0KMrVydqH8OVy8cex7r/cuqxlZVb2sWKtm47zkIxPga8rswCgWLOoDnqtClvfbceq+bXBzxKBt68d+06iwKTDrZdNFf1t1fxaFOfm4H/ea8+a7MlQAvHLofucbfuYzmg0KjRNyscX68vQNCk/5qoTDeVWrG2pF523tS31aCyP7GVuKLNi7aKwdovq0RCDd1opSq+xGaUm5lhnlEa/NpXuZ31pLm66pBZPvHkUm3a34Yk3j+KmS2pRXxo5vneqzFinxjDWwBeD0LaxfDGYItPnlBj65HAyFda9rVURqT1cVA+fIEiW1ZfmSu43nVol2eb6xfWoKTYz76/wvtQqgXkv+qi0f9Z9u66lHrVhfa1rqUdZnkFii+TuezWhuP25vbhj637c/txeqAiVtQ/hy88tMkuOyb1XNqCmSGb/GeOfWpIrOX6NFVaJ3a8rszKfB6x103EekvEyzxqNCp+tLsInvQMjWZQuTMzTY8t3L0CXzQ2TXgO314eHljaBgGDI7cFTN8xBr92NXL0GZRZ/Tb6aYnPM8rSZhFIJXk5q0enUWNxYjupCUzBbtzGG6hN6vQaLGspwTqExmDmc7OoTSq8xs0GPy+uLUFU4JzjWGaUmmA0x1ClWuJ9Ggw4L60tQFdKuvjQXxijS0laDHl8MG+vUUhOsMYxV6ReDPIMel4X1OaXUhLwY+uRwMhW5e1ulIhJ7KAgUT90wR2IDWPeb0+OW2BoBwJMh7aeVmmAx6CW2t6HMitpij6j9tFITtFCL2jeUWmAwaPHl+mLRunWlZlBQ0bozSk3whvUfuL+vqC8V2aiGUgtycjQSoZL6Ug/TlrFsHAFB5QTxNn3wScZklulfq1WjYrlBdExVKhKTeEpg8staN93I+Ekx4H/o1JbkSf9QEfs2qmKojZqpjFX8Mmd00enUcUkIB9DrNTj/nLGVAlZ6jZkNesw5R9kkT+l+Gg06zFHQzprAWANfDKJVHQknL4E+OeObTFbNk7u3WfaQtR7rfjNr2PcSaxnL9uqglmkv7d9kyMGcc6RhTrH2bzBomdsNt7FytkxuuXSZNq7+WTaMZfflngeZMA/Jikkxh8PhcDics3DVPA4nfvikmMPhcDgcjoRoqnlA+nqbORwlEEozS+iIENID4LjMnwsBRL6DxxY+nshk0nhOU0oXRGrMr82E4OOJTDKvzXj6SlcybczjZbzZZjdHA75P6QHz2sy4SXEkCCF7KKXNqR5HAD6eyIyn8YynfVUCH09kxnI86bbvsZBpY+bjTe9+kwnfp/Qm40uycTgcDofD4XA4icInxRwOh8PhcDiccU+2TYofS/UAwuDjicx4Gs942lcl8PFEZizHk277HguZNmY+3vTuN5nwfUpjsiqmmMPhcDgcDofDUUK2eYo5HA6Hw+FwOJy44ZNiDofD4XA4HM64h0+KORwOh8PhcDjjnoybFC9YsIAC4D/8Z6x/osKvTf6Top+o8GuT/6ToJyr82uQ/KfphknGT4tOnM000hTNe4NcmJ13h1yYnXeHXJiedyLhJMYfD4XA4HA6HM9rwSTGHw+FwOBwOZ9yjSfUARhtBoDjWa0fXgBMlFj2qCkxQqUiqh8XhcDIcblsyD37OOBxOPGTVpFgQKHa2duKWLR/A6RGg16qwYdlMLKgr5YaQw+EohtuWzIOfMw6HEy9ZFT5xrNceNIAA4PQIuGXLBzjWa0/xyDgcTibDbUvmwc8Zh8OJl6yaFHcNOIMGMIDTI6B70JmiEXE4nGyA25bMg58zDocTL1k1KS6x6KHXindJr1WhOFefohFxOJxsgNuWzIOfMw6HEy9ZNSmuKjBhw7KZQUMYiCGrKjCleGQcDieT4bYl8+DnjMPhxEtWJdqpVAQL6koxbeVF6B50ojiXZxtzOJzE4bYl8+DnjMPhxEtWTYoBvyGsLjKjusic6qFwOJwsgtuWzIOfMw6HEw9ZFT7B4XA4HA6Hw+EogU+KORwOh8PhcDjjnjGZFBNCJhFCXiWEHCSEtBJCVo0s/xkh5CQh5IORn8vHYjwcDofD4XA4HE4oYxVT7AXwQ0rpe4SQXADvEkJeHvnbLymlD47RODgcDofD4XA4HAljMimmlHYA6Bj5/yAh5CCAiWPRN4fD4XA4HA6HE40xjykmhFQBmAXgXyOLVhBC9hFCfk8IyZdps5wQsocQsqenp2eshsrhRIVfm5x0hV+bnHSFX5ucdGVMJ8WEEDOArQB+QCkdAPBrAOcCmAm/J/khVjtK6WOU0mZKaXNRUdFYDZfDiQq/NjnpCr82OekKvzY56cqYTYoJIVr4J8R/ppT+DwBQSrsopT5KqQDgcQBzxmo8HA6Hw+FwOBxOgLGqPkEAPAHgIKV0Q8jyspDVrgRwYCzGw+FwOBwOh8PhhDJW1Sc+B+B6APsJIR+MLPsJgK8QQmYCoACOAfjuGI2Hw+FwOBwOh8MJMlbVJ94EwBKcf3Es+udwOBwOh8PhcCLBFe04HA6Hw+FwOOMePinmcDgcDofD4Yx7xiqmeEwRBIoTZ+zoGnDB7vZicr4RajVBh82JEoseVQUmqFSsaA4OZ/wiCBTHeu3oGhi7+yQVfSolk8Y6lozVcYnUT+jfjDoN3D4fCkw5suvw88fhcFhk3aRYECh2H+rCka4hPLzrCJweAXqtCrd8YQr+8P+Ooc/hxoZlM7GgrpQbRA5nBEGg2NnaiVu2fBC8Z5J9n6SiT6Vk0ljHkrE6LpH6ASD528p5tdi85wTuWDBddh1+/jgcTjhZFz5xrNeOfe224IQYAJweARtePoyrZlfA6RFwy5YPcKzXnuKRcjjpw7Fee3DCAGBM7pNU9KmUTBrrWDJWxyVSP6y/bdx9BAsbJ0Zch58/DocTTtZNirsGnBAogsYvgNMjgJCz/+8edKZgdBxOetI14GTeM8m8T1LRp1IyaaxjyVgdl0j9yP2NkOjrjPfzx+FwxGTdpLjEooeaAHqteNf0WhUoPfv/4lx9CkbH4aQnJRY9855J5n2Sij6VkkljHUvG6rhE6kfub5RGX2e8nz8OhyMm6ybFVQUmNFRYsWp+bdAIBmKK/+e99mAsWVWBKcUj5XDSh6oCEzYsmym6Z5J9n6SiT6Wkaqxer4C9n/Zh54EO7P20H16vEL1RjAgCxdGeIbz18Wkc7RmCINC4tzFWxyVSP6y/rZxXix37TkZcJ/C3WI7DaBwrDoeT/hBKM+vmbm5upnv27Im4jiBQHO+149M+BzptTgw4PRAEivJ8I6aXWnBOIc865sRN1AsmlmsznQlk53cPOlGcO7bVJ8ayT6WM9Vi9XgHP7z2JO58/EEwOW7+4HoubJkKjEfkz4r42RzNBbqyOS6R+xNUn1PD4BEyQqT4R2h6InoDHkywTIuvtJidjYV6bWTkpBoCjPUO4fOMbojgyvVaFF1dehOoiczKHyMlOuHHnjCl7P+3DNY+9LbFhm5fPRdOk/NBV4742uX30E8tx4McqIbjd5KQrzGsz68InAvDECg6Hk8l02Ng2rNOWuA3j9tFPLMeBHysOZ/yQtZNinljB4XAymTKrgWnDSq2J2zBuH/3Echz4seJwxg9ZOynOpCQeDofDCaeuzIL1i+tFNmz94nrUlVkT3ja3j35iOQ78WHE444esUrQTBIpPTttx/Iwd5hwNKvMN+PVXZ8OYo0FJbg4q8oxc5pPDyRKUyvZ6vQJaO2zosDlRZjWgrswSnriWFmg0KixumojaYjM6bU6UWvWoK7OOylhVKoLLppdg8/K5ouMQqz1kHXsAo2pfky3LHNh+vlGLzcs/A4/Ph3xjDtQq4F+f9Ir6XFBXimkrL8qIhFAOh6OcrJkUszKEV82vxZNvHQ9KO7f1DGHFM+/zDGIOJ8NRWhEgjooOaYFGo0LTpHw0TRrd7QoCxUsHuxRVVGAd+03XzYLbS0etQkOyKz7Ibb9/2CP7jKguMvPEOg4ny4n7KUAIuZAQ8q2R/xcRQs4Z/WHFD0vG8+FdR0TSzvvabVzmk8PJApTK9rZ22IIT4kC7O58/gNYOW9LHnE4kInvMaruv3TaqMsrJlmWW2z5/RnA445u4JsWEkNUA7gDw45FFWgBPj/aglBBJ6jPw//B66zyDmMPJTJRWBEhmRYdMIpGKCqy2AsWoVmhIdsUHue3zZwSHM76J11N8JYBFAOwAQCk9BSB3tAelhEhSn4H/h3914xnEHE5morQiQDIrOmQSiVRUYLVVE4xqhYZkV3yQ2z5/RnA445t4J8Vu6lf7oABACIkp/ZYQMokQ8ioh5CAhpJUQsmpk+QRCyMuEkCMj/+ZH25YcrAzhuxbOQIlFhzsWTMX9Vzfi/KoJmFxgCP59w7KZqMw3cvlODifDUFoRIJkVHZJBsmSeqwpM2HTdLKycX4MV82qwan4NNl03K3j8Iskas459Q4WVeT5UBDHZ1vD+KvONSa34IHf9NFZYFfeZDlLQ6TAGDieTiTfRbgsh5LcA8ggh3wFwA4DHY2jnBfBDSul7hJBcAO8SQl4G8E0AuyilvyCE/AjAj+APz4ibQIbwlBUX4mDnAI50D2HzOyfwpYYyPLzrSDBx4p4rG1A5wYACUw4q842Kk004HE5q0WkIll9cDYECKuL/PRoqFUGeUStql2fUpuX9nuykQLeX4rHXj4psHxA9yU2uGgOA4LIisx6f9A5hwcNvRLWtcv1dNr0ELyap4kOkfVDSZzpIQafDGDicTCdumWdCyBcAXAa/RN4/KKUvx90pIdsAbBr5uYRS2kEIKQPwGqV0aqS20SQhQyU5b/p8DZ5486isTCqX7+TEAZcrTSOU3ruZdM+nSuYZQMLHKJ7jnEnnRI502Id0GAMDbjc56UriMs8jlSbeoJTeRim9FcCbhJCqOLdRBWAWgH8BKKGUdgDAyL/FMm2WE0L2EEL29PT0RNx+aAIFIezkj0BSDZfv5CRKPNcmZ/RQeu9m0j2faFJgpGsz0nEYjWMUzzYy6ZzIkQ77kA5jiBVuNznpSrzf4J4FEHrX+UaWxQQhxAxgK4AfUEoHYm1HKX2MUtpMKW0uKiqKuG54AkWkpBou38lJlHiuTc7oofTezaR7PtGkwEjXZqTjMBrHKJ5tZNI5kSMd9iEdxhAr3G5y0pV4Y4o1lFJ34BdKqZsQooulISFEC/+E+M+U0v8ZWdxFCCkLCZ/ojnM8QbxeAa2nbDhlc+IP32zGkNOHYY8Pv/7qbPzshVa4vRRLmytQU2SGWaeB1ytARYB7r2zAT/62XxSDxeU7xxfJVs4aa1KxP0r7VNquqsCE33/zPHh9wBm7BxNMWmjUiHrvBhKswuMu0/GeryuzYF1LPe7adjameF3L6Mk8b7puFva12yBQf/WI+olWqIjfQ/3Hb56P9z/th93tg5oADRXWqMcooBQ44HTDrNNi/eJ6UTx0aGJz6PkOPyeTCwxY19KATpsTg04vBCpARQgcbp/kGol2/bjdPuw7ZUPngBNlFj0ayq3Q6dQJHz/W8QzsQ75Rh6XNFZhSnAtK/WOMdbyjNYZ0v7Y5nHQl3klxDyFkEaV0OwAQQloAnI7WiBBCADwB4CCldEPIn7YD+AaAX4z8uy3O8QAQJ6TkG3X4+mcmi5LrVl9RB52G4Kd/EyesPLL7CNxeiuUXV2NKSS6ml1pwTmFmT4g48ZFtySmp2B+lfSYyVq9XQHufC3eHTBjXttTDO0mIOOnJJMlej8cHi16DB5c0we72wqTTQKsm8Hh8CSfaCQJFv8MjSrRb11KPb/zhHbi9VGJD1y+uF03uwgnY4L++cxxXz67Emh3vId+ow/KLq3FukRl1ZRZUFZhkE5sD5+SM3YWT/U4sf2pPcIL8vYtrsGZHq6QNgIjXj9vtw/P7TkmukcWN5aM+MQ5cVzNWXYT3TvRLHC2xjHe0xpAJ1zaHk67ElWhHCDkXwJ8BlMMfpPwpgK9TStuitLsQwBsA9uNs+MVP4I8r3gKgEsAJAEsppWcibYsVlB+akCKXXLf84mps3NUmWnbjhdV49NW24O+ZlNjBGR3iSE7JiISRVCTbpCLpbc+xM/jaE/+StH36xgvQXDUhgb1JH975pBdf//07kn188oY5mHNOQeiqcV+bckl8N15YDQARE5RZBLZ3/5Im3P7cXmbbXL026vkOvybk7HksCYGpuEaSncCYgWSE3WSxfOWtONUrVbosL7DisY0PpmBEnFGGeW3G5SmmlH4MYO5IbDChlA7G2O5NuQEAmB/PGFiEJqTIJdexlIoIEf/ePejMZuPEYRApOSUTr4VU7I/SPhMZa6dM266B9EsqUkrXgEtmH10Jb1suiS9UATT8b502J5omRd7esMsr29bh9kU93+HXhJw97x50gkZQ0asuMqfkGol0TUcbLye9ONVrg/k/bpAu/7/fp2A0nLEirkkxISQHwNUAqgBoyIgFpZSuHfWRxUEgISVgcEL/H/idpVQU6iRP14QETnIJJKeEXy+Zei2kYn+U9pnIWMtk2pZYMvO8sSix5MjsY07C2w63mYFtU+qfiLL+FinBL7A9Y45Gtm2uXhv1fMtdE3JtIv0tFddItGs6m2wNh5ONxBuYtg1AC/xiHPaQn5QSUKmaXGCASafGXQtniFSJbvnCFBQYdaJl61rqsWPfyeDv2ZyQwFWO5FGqjJauRFMqS1afSo5hVYEJDy1tErV7aGlTTGNtKLdibYtYmW5tSz0ay6MnoSlViRvr+6ih1MLcx4ZSS8Lbriuz4KGlTaLr5IEljdix7yS2vtuOVfNrRf3ev6QRJESdzu32iY7h1CIz1i+ux5/+eRQ/v7JBtN37lzQiz6iNqFIXOCdt3YP4/TeacceCqVgxrwaWHLXkGASU8nrtLtx3VWPY9XP2upO7RupLLTGfx3jPeaR7gfW3TdfNAqWxqf7FQrIUEDmc8UK8iXYVlNIFSRlJAqhUBOXWHHx/Xm0w2S6Q4FFu1YOComvAhT9883zsP2mDbdiLzf8+jp9dUQe9Vp0VFQfkyLZEstEmG5NT5JTKkoXSY+h2++ATqEhdzidQuN0+6PWRTZNOp8bixnJUF5qCmfyNMVQWUKoSl4r7iBACKyPRjpDE+/N4fHC4fZJEuye/dT46BlwoydXjs+cW4MSZYeRoVDhxxoHbn9snSlh79NUjON47HDyGC+vKMK00F4e6hkTbvfWyqfj679/BHQumM1XqBIFGTJT+6eXTsWp+LSZNMGJaSS6OnbEHlfImFxjwy2UzcaR7CF5BgECFYEKgRqNCiUUnOn65ejXe/OQ0VjzzftTzqOScR7sXQv9WatHjw45BfPmR6Kp/sZBsBUROesBjnZNLvIl2jwF4hFK6P3lDigwrKP9ozxA+PDWAWxkJHg8uacLBTn/oc7zJI9lAmqocZSIZkTCSSef735/04npGItlTN8zB+eJEslEjDpU4Eak4rnEcn7ivzViS+AL7fOOF1UzbGZ6oHCmZLrAN1vGKJVE60H7z8rmyCYKPvtomOpdy54yVdJ0Kpb3R3r7SazvJZITdZLHwq99hxhQP/d/vsePPj6dgRH7SdVwZSOKKdgAuBPAuIeQQIWQfIWQ/IWRf4mNLjK4BJ+wyCR52txeERFe3y1YySeWIkziZdL47k5hIJodSlbhUHNdkHp9YkvgC+yxnO8MTlTtt8mp4gW2wjlcsidKB5bEkCEZTLGUlXadCaW+0t5+oAiKHw4k/fOJLSRlFgpRY9Oi1u5lJDCadRnHySDaQbYlknMhk0vkuTWIimRxyCWbR7EAqjmsyj08sSXyhCmlySXmhv0dKpqNU/njFkigdaB8pQTB0HKHjjyXpOpLSXrLO+WhvX+m1zeFwzhKXp5hSehzAJADzRv7viHcbyaCqwIRcvRqrr6gTGfHVV9RBrYJs8sj6xaOjDpXOZFsiGScymXS+G8qsWLsoLBFqUT0aknhPBpJy47UDqTiuyTw+DaUW9rZDkvgC+/zC3pNYOU9sO9eGJSoHjiHrOK2cV4sd+07KHq/Qc8Ky06Ht68osku2vml+L/3mvXXIu5c5ZY4U1pvOY7HM+2ttXem1zOJyzxBtTvBpAM4CplNIphJByAM9SSj+XrAGGIxd/5Bh248POQfQMuWHWa2DSqqHWAG4PRfegCxNMOhi1ahAC2JxeON0+VBeacE6ROaOTqmIhIC2aLYlkKSJjYuMy6Xw7nV7s77Cha8CFEksOGsqsUZPsEmV42IP9nQNn+yy1wGDQRm1nH3ahtXMo2K6u1AyTITavrVJ536FhJz7stAf7nFFqgtkg8fwpujb7h504HLLtIrMaFJpg8ltrhw3dAy7kGbVwer3QazTod3hQbMnB1KJcHOoZRKfNiYn5Bug1avQMuVBi0aMy34gTfQ509g9Do1Ghz+FBsVkHc44Wp+2uqHLMFXkGqFRAp81vt9UqwCcAQy43zDk6uD0CTHoNPD4f8gw6OL0+nOwbRqlVj7oyqyipLHS75VY98gxa9DrcICA4Y3ehxGLA9JJctNuGRecGQPB8GXX+viaYckb9XgpIY3fYnChjjF8OuespsL1Om5N5PFJAxtjNcNI1djddx5WBJC7eAeBKALMAvAcAlNJThJDcBAeWMMPDHuw40IW7t5/Nur3v6gY4PQJWbz8rD7r6ijpYDRrct/OjYNb0eKjEoFIRVBeZ0y7RipMcMul86/WapCXVsXC7fXihtTNu6V/HsBt/P9AtsjFrF9VjYX0JjAZdxD6VVq5wOr148UCPpM9FDWUJvzgMDTvxkmTbdTjc2Y+5NcXod3hEVQxWzqvF5j0ncE1zJda/+CHuWDAdC+pK0TCRLV08r7YIe4734e5t7IoSofsvCBSvHOpmHp9AZYpHdh/BNc2V2LhbvI3ZlROgUhHUT8yT7KPXK2D7/lOi/bjlC1OQo1bh5zs/ClavCFQtCt2uTkMkFSoCfY0WgkBlZa+VyqNrNCo0TcqXFVnhcDiRifcV0k39rmUKAISQtPgmu79zIGjcAX9ywcc99uCEOLBszQutEARgYePE4LJbtnyAY70pL7XM4XDGgH2nbMEJMeC3AXdvO4B9p6QljkI50DkosTF3bz+AA53RRT2P9dqDE5hA21jszv4OG7PP/R2RxxoLH3baGdtuxaV1E7Gv3RacJAb+tnH3ESxsnBj8NzB+uX3b33H2OF81uyI4IWbtf6Tj09rhH0ug73iOYaBtaJsNLx9Gr8MdXLawcaJknVu2fIB97ba4z1e8KL0ulLbjcDjRiXdSvIUQ8lsAeYSQ7wB4BUDK/fWsTGpBRlIzUI0idFk6ZuZzOJzRR6n0byKSy0qrDKSi+kTPoFPWdgYqQIRWkpDbt9CxR5Jq9o9F/vgEKipE2wYLuWoModUn5LYba4WKRFB6XWRShRkOJ9OIN9HuQQDPAdgKYCqAuymljyRjYPEQyKQORT1SbSKU0GoUocvSMTOfw+GMPmUhFRUC+KsuRKs+IbUxsVaCKJHpM5rdKU2gz+hjYm+7KFcvazsDFSBCK0nI7Vv42CPtf6TjE6ioEG0bLELbhrZhVZ+IZZ3Rfk4ovS6UtuNwONGJOzCNUvoygJeTMBbFNJRacP/VjWjrGYJAgdJcHaaUWjC11AKBUpwedMLtFVBiMcBq0OBfR3sApHdmPic7UJpgNV5I5PgEkvQ6B1wojTFJr6HcivuXNKKt228r1AQ4t9gcVR66vjRXZGPUBDi3yIz60ugpFYEqA+ExoNHsTkOZFesX10sUykaj+sSMUpN0f4rN6B50oKY4F7/52nlYvf1AMPfi5kungIBiXUs9eodcIunwTdfNwr52G4w6NSryjCAEyDPocP+SRtz+3L5gRYnwmOJAQh+lwINLmnCkexBb9rRDpyFYs6geH/cMId+oxa+umYkTvXbcfOkU/PKVw8Ft3HtlAwiAY6eH0GETXz+CQGHRa0Tn2qxTY0qpGT4BePKG89E54IJFr8GvvzYL75+wBY9DQ4X/+AbKm+m1YjnmQD+BZER/kpwBdWWWuJLa5K6L8lw99hw7g84BJ8osejSEKTUqvZ44HE50YpoUE0LepJReSAgZxEg8ceBPACil1CLTdMxw+wQ89vpRTCk246tzJwfVmgIle0w6NX6x8yP0Ody4e+EMXDajFNPKrPhsdQGfpHCSApfYjkwix8fp9GL7/o64k9BUKgK3VxDJEK9fXB+1P32OFmoVEbV7aGkT9DnRq1YolcHWaFTQaVQiGWydRjUq1QTUUMMrUInM86YQ6eZ7Fjeg0KyFWa9Fp82JHz67V3SeAri9FNs+OIlrmiuDqqL+4zMTf/3O/2/vzOPrqMr//z53z540e1u6p4Um6UYpqIBAAUFLW7Ati6JItfr9ia0s4sLagqissrmgICCiZZGtIqAsoiJCKXQJhba0pbZNmjSk2e9+fn/MzO1dZu6a5Oa283m98sq9M2d55pznnDl35nyez3Hs7eynJM/Oirl19HoD6nUIXaLZbYun4/EH+dYj78SQ/C4/fQq/u2g2ew94+PiTXm558UM6+rysmFvHw//5mI4+L7cvmcHpR1Xz0uZ9/OyFzVwwZ2zENa6aP5WAFKx8rinCb555b08E+TpcjlpPjvmeC2bGkBHTkVR22ERE/1YU2Xh2U3NcIuihKE1vwsRwQVKjV0p5vPq/SEpZHPZXNBwWxBtbukKT09dPnBhDsLvz5a3s7/VyzqzRuH1BVq15n053gGW/X8uujr4sW2/iUIVJiImPTNonXRKaHvnq6qc30ZQg38723tCiUMt3+ePrk+5LLSLIcRMqmJBkGMim5k6ufGIDd728jXte2cZdL2/jyic2JLQ1GWxs6eJHT22MuJ5rntkUQUK+6umNlOY7KMt3xFx7NNFOjwh3+ePvUZxn58iaYpY+tJafvfBh6DouefRdmpo7Y/r/w33dMXZp5L4rn9iANyC56umN3PXyttCe4Ttf3hqa2y977L1QufOmjQo9WdbKynfYQwti7di1z26KIV/v6ugL9VdQEmOnHhkxGT8Kx872Xi559N2I/nV7ZVJE0HT8yYQJE4mR9E9aIYRFCLFpMI1JF+GkkX4DueegqmqnfY8nO2rCxEDAJMTERybtky4JLZdkngdTtteIaJeKdHM40S4eEc4ov971JSL5dfT64todXq6eTb0G94d45Gs9+43sTKVv9Mr9xOD6EhFBTZgwMTBIelEspQwC64UQYwbRnrQQThrJd9oMiRPhUqDxZEdNmBgImISY+MikfdIloRmRr5KVeU7H1nSRrq3JwIhopyfdHO/ao6WgE6UJP6d3fYlIfmUF9rh2K+Ua21Tg0r8/xCNf69lvZGcqfaNX7giD60tEBDVhwsTAIFWiXS3QJIR4Cwi9N5RSzk+UUQjxADAPaJVSNqjHrge+AbSpyX4kpXw+RZtorCnmlkXT2NraQ4/by01nN4ZewYXvKf7lP7Yre+XObsRphZ+fO4P2XuXJUiZ7skwyVW5jsPovW4SYXPHHceUF3LZ4Bpc/Hr6nNLn2aawt4YYFDVwTtvfyhgWJSWj1tcXcumg6W1q7Q8SquqqipGSeb1s8PWJf7W2Lpyfdl+n0SX1tMXefPxOPL0ivx0+By4bTZhkQ2d7GmuIYEt+qBQ3c++pWgNA86Q0EkVIh013y6LuU5TtYPHs0k6uKkBLGjsjntsUzuPnFzSw/pS5CXOO2xTOwCGjr8fCLC2bS5w3S5wtQU+zEaQNfIMgti6YjBOzrdFNd4iLfYeH2xdP5YF93iLjnDQRp7XJz86JptHX1xxDutD3FLruFX395Voi4V1HkoHFkEStWH+yzPo+P68+q5/qoPcX3vnbwum9bPIMxZflsb+thX5eiNKeRCTWfmTW2VJcEGQxKnt+4l+oiF/U1xexVn5Tr9bne/GCzwqoFDSHRk8WzRzOxspCSPDvBoByW49iEiUMJqS6KV2ZQ14PAPcDDUcfvUEO9pQ2JxOMPhsgeq9du55KTJ1FV5KSyyMn+bg8jCpUJJhCEu17ewnnHjKG8wM7K594PETTSIUCZZKrcxmD2XzYIMbnkj8GgJCiDEUSjoAwmdfO32Sy47JEkNJc9MQnN6/PT7wvEEMy8Pj82m7EyXb/HSyAoI+oLBCX9Hm9Cqed0+8Tr89PR64shEyayNRlIJA5rZPuVuGxcOrcOm9VKkcvGtWHRJ25c2MDVnz8SYbFww5r3Ixa+NissmDEKi0VZjHoDyuv+oAzy1d+9RYnLzvnHjo0it9Vz72vbQuUvP6WOW178gK8fPwGnzaJL3LtxYQNv7Wjn+MnV/HzJDFq73YwpL2BLSxdfPHo0NUUO9nV5+eYj60J5rjurnqs/fyRFeQ6m1hbjsAm27Ovh1kXT6fX6KXAoT47PO2ZMiAQYlEH+vb2Nb/5eKUdTvQv3mduXzGB+40gmVxWys72PPQf66XX7Of+3/w3l+fZJdRF9F93nRvOD/4ggk6sK2draE/Gjb7iOYxMmDiUIGf7eKJkMQowF6qSUfxdC5ANWKWViWScl7zhgTdST4p5UFsV6Oulv7WjnKw+8xdLjJ3D/v7ZH7Mly2S3cvGg6Vz6xPub4shMnEAjCva9uw2W38PzyE1KWxt3e1sPn7/pnTNnplGVi6JFC/yW8E+n55lAjl/xx/f86OPe+N2NsXb3sOKYfUTYoebW5IjrfwxfPYU4cuel080H6fZJCnSn7plHZD35tDm98tD+0AAw/F28evevlbRHHtLl46fETmFJTpJtv6fETuPfVbRHfrRa47/XthnP5Axcdw8UPvs3S4ycwZ3wZ3/z9O6E0d50/U7ceze7Vy46jyGXX7YtoW25dNJ1L/vguAN8+eZKuLc8vPwGAz9/1zxh74+VJZhzm0jhOgJyYN/Uw70vfoPCzF8cc7/nHA6z5Q/Y0y4arXTkIXd9MKbaPqmL3BPBr9dAo4OmMzIJLhBAbhBAPCCF072ZCiGVCiLVCiLVtbW0x5zXSiBHZo9+bHPkuHdKMSabKbWTaf4l8c6iRS/6YCZEsfcJcegS9bCjaZVInxPfNdBTt4pGYo4+Fq98Z5YsmtwlxkMBmNJe39xyc66NJd0b1aPN/PNJgtC29Xn/oezIkwug06SjwhSOXxnE6GG7zpgkTGlINePlt4DNAF4CUcitQlUH9vwQmAjOAZuA2vURSyvuklLOllLMrKytjzoeTRvRICvmO5Mh36ZBmTDJVbiPT/kvkm0ONXPLHTIhk6RPm0iPoZUPRLpM6Ib5vpqNoF4/EHH0sXP3OKF80uU3KSAKbXp7yQmcobTQpzaiePHX+j0cajLalwGGLSRP9PRmioV6eZJBL4zgdDLd504QJDakuij1SSq/2RQhhI1LMIyVIKfdJKQNqZIvfAHPSKaehpogbFjTw3Po9LD+lLmKSWjG3jt+8/hEr5sYeH1Xi4s/rdmdEgNLIEuFlm+pCuYNDrf9y6XrqaxWyV7itNy5sSIpIlm7ehpoiVs2PzLdqfkNCZbr6mkLdfPU1iV9lp9sn6dqaDPTLrufvTXtoHF0SY++NCxt46I3tMfPrbYtnUFdVGHFs+Sl1rNmwhxsXNrBmwx5+8/pHXHdWfUxdazbsiclTUehk5fx63bl81fx6nl63K1Tu7/61g5XzD5b70BvbY67punn1PPzG9pBv6PXFdfMibblxYQNFedZQmufW74nxNa3/tPKi7X1u/Z4YW1IZh7k0jk2YOJSQ0p5iIcTNwAHgK8B3gP8HvC+lvCrJ/OOI3FNcK6VsVj9fChwrpTwvXhlG+48O9LvZ0tJLR5+Psjw7nf0+ygrsWIRgb6ebUSUuEMprw3yHlWKXjRGFDpoPeOj1+hk7ooDxFemRoDRmuakulJtIsv/S2hvn9QbYsLfTULJ1MOD3BzOSn00H6Ua86Ov3sqmlm31dHqqLnTTUFJGflxyJTLvOlk43NSUu6mtLkrpOba7Q6pxcU0BpXuIncOnmg/T9IMk60/LN6LJrSqzs6wywr9vDyBIXpXl29nUr82VQSixCEJSSQBA+6fFSWmCny+1jdJmLrv5AqByX3UKh087okjw27+uiudNNXVUe7b0H01gtkkBQ4A8EsFmttHZ5qCp2IglS5LTT4wkoc3m+nQN9XkrzHUAQi7Di9QUpcNnwBQLUljrZ0+EJlTu1poCP9vfT3OmmvMCB2+8nz27HaoGyfAeBILR2u8l32PAGAggEECAoLaEyGmuKEUKwsbmTfapd+Q4rbl+Q/T2e0JiyWAQ723tp7/XgsFrw+IMIlGgbVUUu6quL2KJKUNeq/hmdp88bMBwvycxL4eNOu6byAidjyvLZ1dE3qBFokhzz5p7iAcZwtSsHoeubqUaf+AGwFNgIfBN4HvhtUrUL8UfgJKBCCLEbuA44SQgxA+Vp8061zJTR1e/mpU1tEUzfn5zTqEg6P3uQ8XzdvHp+9brCeE6GHZwsNHWhHCNAmFAxWP3n9QZ4esPeuJKtAw096dzBZq2nG13B7fazZtO+lKWaNdhsFqYfUcb0I5K3tbffEzNXrJrfwBcaquJGkejr9+rmm9dQnXAR7/cHeXbj3pQlgbt15rVV8xs4s6GSoiQX48lej958uGpBA6PLnJx739sRURjufmUr584ew12rtzK5qjA2ssSCBuY31PL3D1u57LH3+NT4EZzRWBtSGtXm4nd37Wf2uIqIOfqGBQ04bR6ufHJjxDX//f1mxlUUx4R829vp5sonNsSMr+lHlIX88hsP/5eyfAdf+dRY7nx5a4SPnlJXGSOrfOd5M+js90ccWzm/nl+ERcu4fckMHDbBJY++q+vzRmPCYRPcsOZ9pf1e2Rp3vCSal/TqWH5KHa980MJ5c8ZG+NtAzwG5FOXGhIlUkNLjI3Wbw0PADSjh2R6SST5qllKeL6WslVLapZSjpZT3SykvlFI2SimnSSnna0+NU8UHLb0xkq879veGJlvt2Mo1TSE5z3nTRsXkMSV4TQwkNuztTEqydSCRDWnpdOtMV6o5EzS19OjW2dTSEzffppZu3XybWhIH3klXWnqzzrx27bOb2NySeV9GX4/efHjtM5vwB4hIc/XTmyIknb9+4sRY2eRnlD7UfOKi48eHFsRampVrmlg4a0zMHH3NM5vY1tYbc80LZ43RlZHe1tpjOL7C/fKcWaNDC2It7WWPvaf4YNQY9fljpZave7YpRgp6w+5OQ583GhMbdnfqSmKnM0b16rjrla185dMTYvxtoOcAU8LexKGKVKNPfAH4CLgLJebwNiHEmYNhWCrQY1LHkwuFzNnBJkwkQosBg3wwJVuzwVpPt850pZozQTaiTwx1pIxkEF220XzY0eeLSROe1ijiQ3jfGkkz7+/Wvz69aBb7e5JPq42vcL80uj49H0xWClqvbs3njcaEFvFoIMaoUR1GfTKQc8ChHh3DxOGLVDca3gacLKU8SUr5WeBk4I6BNys16DGp48mFhn+PPn+osHtNZB+1BgzywZRszQZrPd0605VqzgTZiD4x1JEykoFR2dHfy/Ltumm0/0YRH8L71ki6uLJI3wa9aBaVhcmn1cZXtF8mslNDslLQenVrPm80JrQ8AzFGjeow6pOBnAMO9egYJg5fpEq0e11KeWLYdwH8I/zYYENvU35Xv5sXNrVx72tbmTdtFFYLzB5bRmu3J2Jf1U1nN1LotNHR68XjD5DnsA1LxaCBlulNtrxckQfOElImjGRrT3G6e/3SJYMFg5K/bmqJkWs+syHxnuK/fbiPra09IfncSVWFnDalOqk9xenY29vv4ZUt+2PqPGVyRcI9xXr7n5PeU7xhb4T0/E1nNzJ/2siEe4pfaGqLkbI+oz5mT3HKvhl9PWPL87jk5LqIum5c2EBNsYO3dx7g1Q9aObOxlpGleTQf6GdEvp3dnW5KXHZK8u0R8+zNi6ZxVHUxO9p78fqDdLu9OGw2rnr64PVff1Y96z7ezzHjK2PqdNosfC9sn/CPz25k3Ig8tu/vi0k7qtTFxt2ddHkCWAVMHVlMeYGTth435QVODvT7KHLa6Pf6ae/1cU2YfHJdVRGjSl209XjwBSS7O/ro9waYOaaUtm5vRF3RCny3LZ6B0y4ipK8nVRYyrqKAyRWFbGnrZmd7Hx+19fDY2t0h1dRU9hQnQvhY12w4oiyfHo+PikJnRBv+7IvTGFnqorzAOSDzegrzjEm0G2AMV7tyEANCtGsSQjwPPIZCjlsMvC2EOAdASvnnjExME3asjK1wsmLu5Igbz81fnMaKuXX4ApKjaopo7uyPOH/Lomlc+bkpfNLnwyLAYcv+AnCgCQzJlmcSJwYeNpuF6mJHhKRsUZ51UCNBpCstnckCPl255qAM4vYFYySXgzJomCdTewVCt06R4N7tctqZVJ3HQ1+bE2pXu03ictrj5gOlfWxWEdE+NqtCxkoElz1Wynog4HLaGVfh4ncXHcP+Hg+VhU5aezwRdTmsgvv/uZPt7b0RJLyx5Xl8++SDssdjy/O494JZdPR5KXbZ6fb4+frv345Y+I0tz+OOJTPY2tqDPxikwGnlS8eOZ/eB/sg6bRbsYW01It+O3SL40v1vUZbvYMXcOkaW5vFRWw+3vbSFjj4vK+bW8eQ7u3HYBKNK81nxp0ji2eq1u/jWiZN49cNmfvuVo2nt9kbcB7Q0584ew9Pv7WGiGmIuQgI73x4jBX3ixBr+uvwE1n7cEVpAzx5bwpLZYyN+PN2woIHZY8sYq4ZTm1BRyCe9HlYvOy5u9IlE0Mb61BUnsG7XgYhrum3xDF5YcQLNnW58Ack1z2yMIAlmOq9nQ8LehImhQKpPin8X57SUUsb+fBlgGMk8t3Z5uMJAShQIyYdGn7950XSWq3KeLnv2ZTQHWt4z2fIOIVnRwULKTzxyqU3X7vyEL9//3xhbH1l6LLPHjYibd6gllzOxN906M+nLIbA1bd/UJJCNJJx/feHRvLWjIykJY22u1eSdjdLc++q2UNnhMs1amnDZ6Gjp5mTq1jt3/7+2c/Oi6Wxr7da9D4TLUhvdK6KloFcvOw4gwveNpKaTkS5PF/F8E8j2HGQ+KR5gDFe7chCZPymWUn4tplQhHOGCHtnAvi4PfQnIEYbSpWFynhpRIJsLlngEhnTsSra8ga7XRG61aSakwHhEsnjh0jIhkqVrb/pEu/T7cqhtTQba9WikLCNy1oE+X9ISxtpcq31OlMaIgBf+AD3armTK1TunzfXxCNja/0Qkbe17S6cbGVWnIfEwwVjIBPF8Uxpcy3Ccg0yYGA5INfrEa6oAh/b9GODtgTYqVVQXOxOSI4yId3lhcp7DgSgw0ASGZMsziRMDj1xq00xIgdkgkqVrb/pEu/T7cqhtTQba9WikLCNyVqlKtNM7F/1dm2u1c4nSGBHwwt/AG9mVqO7oc9pcH4+Arf1PlqRdU+KK8X1D4mES0uXpIp5v5tIcZMLEcECq2yc+B9yJEpJtFPB5YKmUct3gmBcLvVct/f0+3tzVzv5uX8TerktPO5L2Xg/VRS76vD7yHDbe39vFJ30+rAImVhXy8Bs7WPtx57DZQzuUe4qBELGutsTF+83d5p5iY6T8GjCX9mlnsqfY7w/y9Po9KYtT9PV7+fuHbWxrO0h6m1hZyKlTKhOS17zegK4gxvzG+Pb29Xv5S9O+GPLaF+rjE+aCQck/t7bS7Q7Q6/FT4LJR5LRyQl1VUvu1n9mwN6bOBQnatrvfzRsfdeALyFCddovg0xPLMibaBYOSN7e3EZCCHreP8kInHX1efAHJff/4iC2tPfz47EYK7FbKCuwc6PfRtLeLoIRCh5WRZXkRohmXnTaZ/2zbz/nHjqWz38c9r26NIZOtmFvHw//5mI4+LzcubGBceT472/si+nDVggbGludxywsfsOeAh2+cMJ7yAifb9yuENYdN8P9OmhQhBHLNvKl0u30gYWRpHlc+edCu0J7iz07i1Q+a+dJx4+ns80UQ4ML3FK9eu4vLT5uMxy8j+usnZzfS0uWm1xsIkTM/d1QNuzr6aGruYrta3qhSZ8ye4vCxEK02eVR1Ebs7+5MiNxsRoRPN8Vmeg8ztEwOM4WpXDmJAtk+8KIT4FvA3YD8wU0rZMgDGZQQ/AbrdAYJBhexTUejAabOy9KG3QxPBdWfVYxUebn7xw9Cxq79wFJeffiTAsIm2MNAEBqPyIHayvOeCmfzlOyfQ1mMSJwYCuURGcTisLJw2kgkVBaGb7rQko0/YbBYWTh9FXVVhSpLLEokvKCNIbz8+uxFJ4h/qvoAfh9USRQyzKMcxttkiLDijSFROuwWLiG+r2+NjX5c3JvqE2+NLuIC3WAR5DmtEnXkOa0I/sGGl0+2P+aFii3N9ycLj8bO7wxNxPdri8P+dNAmfP0Cv28etL34YWoiG99ONCxq47NQ6ujwK8Wx8RQFl+Q6W/+lgNIbxFQX89iuz6XL7KHLZ2dHWw+WnTWZXR1+IJPfDM47kmi8cRaHLzkdtPdzxN+X4zYum4fUHIxfM8+spL3RgscCyEydgs1iYXF3IT/66OUQiu+L0KVx2ah21pXlUFDpp6/HwvdOPpMBl4cyGUaE9zNo9YVx5Hn2+ADd/cTodfV6FdCkkJXkOfn/xHFq7FZnnnft7I9Twbls8g9c/aotQtLtm3lQCgSCVxfYYcq3FImJ+PGqExWuTiICU6Ad2vHkmV+YgEyaGA1J9UnwNsARYBkwDLgUul1L+ZXDMi4UR0e5f2/aHJm0jssOti6ZziUqq044tO3ECC2eMOuz2V+USCWyYIGefeAxXZEK0Szfv2zvauVAn3+8vnsMxcfJlYusQEBFT9k2jssNJaVc+sT4hgU0jni2fO8mQyKyR24zId3qEu3jlaXN7PNKdRppLZJ9e2miSmpHd4YRAPfvCjz+//AS63b4IPzCyX28OzuH5OmfnzeH6RHa42pWD0PXNVOP7VABzpJT/kVL+Gvgc8N0MDcsY+7o8EeQII7JDbxipTjsWlByWKjymIpGJbCMTIlm6edNV0TvUFe20ssNJadr3ROQ2iE9k1s4ZqubpEO7ilacdj2dXsvbppdXmQW2ONKpHT9Eu3L7o8qL9IBVlO3O+NmFiaJDSolhKuUJK2R/2/WPgCwNuVYqoLnZGkCOMyA4FDlvMMYvgsCQdmAQME9lGJkSydPOmq6J3uCjahZPSkiGwaYhHZA4/p5dGj3BnVF6+I3JuT0SaS1SeXlo9kloiQqCRfeHlGfmBXtpomPO1CRNDg6QWxUKIf4V9/n3U6TcH1KI0cFRNARMrC/ne56bgslv4zesfcd1Z9RET2nVn1dPn9UUcu+y0yUwbXRLaY3s4YVx5AbcvmRHRHrcvmXFYtoWJ7KC+ppBV8xsifHDV/AbqaxK/Dk43b2NtiW6+xtqSuPkaaop08zXUFCW2tbaYGxdG5r1xYQP1CepsrCnWt7WmOGGdiaBX9vJT6lizYQ/Xzavn4Te2s2JuHX9et5vn1u9h5fzI+XTl/HrWbNgT+j6pqjDmGrVyGkeXcPuSGTy3fg/LT6mLqfORN3fE2DKxspBVC+pj0j70xnZWqbY8+c5uVsyNLW/Nhj2smt8QYd/EqkJuOrtRP+2CyLTaPKjNkXp2375kBtNGl+jaF90OWnnRfvDceqXuZOZgc742YWJokNSeYiHEu1LKmerndVLKWXrnhgJ6+4+CQUlrVzet3QE8gSD93gAeX5DyQgd93gC9Xj9FLhsFdht+KWntcjOiwInLIbAKC/2+AOUFTsaU5bOroy8UjSEQVLZWDBcS3kBDYzMf7gSMJOWt09oblw3p7HTrzIatnf1uPmzpZV+Xh+piJ1NqCijJS+7pV1e/mw/C8h5ZU0BxEnl7+t28H5Zvak0BhUnkO9DvZktYvsk1BZQmaasWdSAVImIKdablm1rZB/p9lObZ2d/joaLQSUAGsFts2CyCPQf6qSxyYhVBAtJCW7eHyiInnoAfp9VGW7eHmmIXNotgX7eH0nw7Hb1eygocdLl9FLvsIKEs30GPx0drj5eyfDtSSoQQdPb7KMmzYxWSgBSh6yxyWWnt9pBnt9He46G80Emf10e+wx6yt8/rpyzfgc8fpFW1XSuvyGml2xMIkd1auz2MKcvDGwjS0uWhqshJv9dPgdPO1OpC3t/XQ0uXm5ElLkrz7Ozr9lBd7GJ0SR6b93XR4/FityrlVBU58QUCFDrs5DttHOj3goT2Xi8jChz0en0UOx0EkTGyym63n43NnXzS56Ms347PH8Rlt9Lp9lJe4KK+ttjQL+LN19kYu0nC3FM8wBiuduUgMoo+EW/lnDxTbxAQDEre+187uw94ONDrpdcb4M6Xt1KW7+ArnxobwRheMbeO0jwbd73yER19XjUihaTfG+S5DXs4b85Yrn56k27e4RpKKxNYLIIJlYXDnagxqBjMsGnZCMmWbp3ZsNXt9vPipraYiA7zG2txueJPTV5vgBea2lIOIed2+3k+jTp7+z28pJPvCw1VFOQl3s5gs1mYfkRZSgIOB/rdunWe3lCZ9GI8Udn3vhYbOu26efU8uW4Xi2eP4YWNzXR7fCw5Ziz3qmHWbn7xA8Nwaw6b4NsnTWLF6lip5fOOGROWpi5UtxYOzai8b504iUsfW68bJWN/j4e7X9nGubPHcFlYmuvm1fPqh82cPKWWlWvW6c7py0+p473/tXPq1JERfhRe93dOqePuV4zaaAtf+fT4mCgZmn3fOaWOmaPLQuPH7w+ypqlZtzwlz/t8/4yjDMec0XydS6EfTURi2fIr2NveGXN8ZHkJ9911axYsMpHsk+LtwOUo2y1uAa7QTgE3SyknDpqFUdCTK23r9vDvj/YDB+U5jZi9y06cQCBISGr01kXT2dLazaSqooSs5hxg+ppIESmwunNC5jndOrNha7qRICB96eRsRJ9IF0MRfcIosoIWReHXFx5NZ78/FIkikYQzxJda1pOCzqS8WxdNZ3NLd8KoFkZzupHUdDKS1cnIRodHGNGikMS73vv/tT3lMTfMI1Mcck+K37znUhoaG3XzpLqYTeepr/mkeMCQ0ZPifwDzwz6fFXbu9QyMyhj7utzs7/GEmMCJmMlBSYQkaK/Kjg6PWBGPFTwMJhkTA4jBlGLOhsxzunVmw9Z0I0EoedOTTs5G9Il0MRTRJ4zmOi2KwoE+H25vICZSQ7xoFMlEeRio8nq9/qSiWhilOWAgNZ2MZHUystHh8s5a9IlEUTNSHXO5JCd/KMArLbqLUoC9/3hgiK0xMdBIalEspfzaYBuSLqqLXViEYFtrD6D8QtYmiPDP2neLgEDw4PcChw2LOBixIl5ek+l76EFjdQ9GXw9m2QNdZzZs1SJBRNeZisxzbN749qZbZ3UGtqaLwawzPPqEXh1a9InSfDtdQkSkNcoj1QcORuei0wxEeVpEIb00WlSLeHN6aVQavbrjtZE1gX3hEUbCo0/Ey5PqmMvG2DVh4lBFSuIdGVUkxAPAPKBVStmgHhsBrAbGATuBJVLKjnjl6MmVvve/dvYc8NCh7in+09u7uOhT4xhXUUBbt4e2Hg+Prf0f5x0zhlFleRzo89LrCVBbkseIAjv5Disf7evGZrdx1VMbB3VP8WARIoYx0WJYI4X9eGlJ6b7y4T427O4MyRg3ji7hlCnVSfVNtCRsPBJOpnVmui8xHVvdbj//2NYWI2P82UmVSe0pTkc62e328+zG5rT2FP/1/dYYWekzpya3p9jrDbBhbyctXW5qi100JqEWeKDfzetb2tnWGiaDXVXIiZPLo/cUp+ybB/rd/HNLO80H+ilw2blhzftR+2Uj9xSfN2dsaC/s6rW7uPjT42nv84bsKi9w8MQ7u5k3rZayAifXPLMpQtmuyGnDbhWs392FwyoYWZbHbS99yMWfHo/HH4ix4YYFDRQ5rZTk29nd4Y7o50tPnYxAUl2SR0Whgx1tPQiLJSL/jQsbmFhZwLbW3pAt//fZCezv9YZJihdQVmBnX5c3ol8vO20ya9bv5czGWkaW5tF8oJ88u5WfvPBBRBu9+qEiG32gz8f2th5e/aCVk46s4oiyfNp7PIwqy+PM+trQONAU7cL3FGttpOWZVF2Y9PygYZjvKT7ktk+8fvflnPid23TzpLqFwdw+kVVkLvOcIR4E7gEeDjv2A+BlKeVPhRA/UL9/P5VCLRbBpKpC2rp9TK4pACkYVTaFPR39/N8f1oUmiZvObsRhBYsQ+AIyhoCXb7fy3Lqd3HfhbOxWQW2Ji9On1gyo5PFgTV7DfFIc1hhsKWavP1LG+PYlM5LKFy0Jq93oF04flXCxmW6dDpuIlE22JdcG6doqpdSVMU7mh7rFIkIcAc1el92SsN8cDiujRzi578Kj6VAjANisJFygWrDgsEXJStssWJKIaun1Bnh6w96USYEC8PiCEX15w4KGxKuMJGBDecV++9+VhdmyEycwsbKQsSPy6HT7+MGZU6kqdjC2PJ+RpS7e29XJeceMYXRZHld9fiodfV7ueyFSnvvrJ4znyieVhwqXnVoXWhyHz7N/fGtXSE75G8ePx2pVFpuaDePKCxhR4OCeV7ZwypE1oYWjZl9VkZO2bjdXPrkxoty/btzNlZ+bQnmhk/YeD1YhOP83/w3lrasqpM8biGjLS0+dzMP/2clXPjU+ol9Hl+Vx/rFjWflcU8T940/fOI5WNcKGPxDgjDDZaD3J5tsWz4jwR4tFUFnk4HunH0lABvndRbPZc8AdMW6SHavhyCU5eRMmhjuG7EkxgBBiHLAm7Enxh8BJUspmIUQt8JqUckq8MuLJPE+uKmJLazeALvnh1xcezTsfd+ie0wh46RAdksVgESKGOdHiUMGQEu3SlQbOBtFuCGSMB6zOdK8zE1vTJQVmQ+ZZ73o0+xMR48Jlj+NJMGskZyNJ5PC5OPrcAxcdw8UPvq1b7pQahSytV24ystGJ0ob7SLQfJUPOTifPIQDzSXEadZhPiocEmcs8CyEWCyGK1M9XCyH+LISYlShfHFRLKZsB1P9VBvUuE0KsFUKsbWtrizmvyTz3ehTigxH54UCfz/CcRsDTCAqDgcGS6jQlQLOHeL6ZSb+kLw2cXp3ZsTUb0snpts/QkwIzJdrF983ky26JkjtORvY4GfKckSRy+FwcfW5/j7E8tUaWDidNa0hGNjpR2nAfifajZCSb08lzqCLRPd2EiWwhpUUxcI2UslsIcTzwOeAh4JcDb1YkpJT3SSlnSylnV1ZWxpzXZJ4LXArxwUjSszTfbnjOIkib6JAsBkuq05QAzR7i+WYm/ZK+NHB6dWbH1mxIJ6fbPhnYalBnIlJgpjLP8X0z+bJrdeSOjebQ6GPR38Nlo40kkbVy9M5VFhrLU2tkae1/OJKVjY6XNtxHjPxooPMcqkh0TzdhIltIafuEpl4nhPgJsFFK+WgqinaDtX1CUbbqIRCE9h4vhS4ru9r7uOmvB4kRN53dyAub9nBmwyg6+338NIw0cdlpk3FaLTzwxo64wdMzxVDuKb7ngpmMLy88pBX5hhhpEe3S7e909+kGg5J/bmuluz8QIq8VuaycMKlq0Ih2fn+QvzY1szWMEDapqjCCZKSH/n4fa5paYshy8+pryMuzJ6zzhfdb2LKvO1RnXXURZ0ytSdg+b25vwx+ET3p9jCiwY7PAcRMq415nf7+Pv21pjSG9nTa5KqGt6e4p7ux364qbfK6hMlr1L2Xf7Ol38+qW9pg+O7KmgLZuP7UlLrrdPlq63FQVOdnX5WH7/l4q1D27xXk2Vq3ZHLLrB2ccyYh8GxaLlV6Pn7JCB91uP1c9Fbn39+H/fBzaU+zx+en1Brnj71tCaVbOr6eyyIHHJ/EGglgtgvv+8RFbWnu4+YvT8Pj9OO02LAi27+8lEAyGSH4LZ46ivMDJJ30eSvMd/PDPG0NEtklVhfR6AhH7hFfOr2fsiHzaejwIcbCen5zTiNsXjEirjQWAne29tPd6aO/x8n5zF0EJxU5rzB7q8PETDEp2fdLLul0H+JFK5v7ap8dSU5oX4VNH1RZTXuiIUcLLYZjbJzAW6Wja/CHHfutnSZcTzy5z+0TKGBCi3R4hxK+BU4GfCSGcpP60ORzPAl8Ffqr+fybVAoJBybu7Otnb6YmYxH58diN3nTeTjl4vn/R5sVkEl546hU63F3sUYWZ0aR4jS1387qI5gzoRDRYhIrrcmmIX7zd384W7/2kS77KMdMlrNpuFhdNHUVdVmJI0sN8fZF+XN2YB5vcH4y7AMvFNi0VgEZYIElM0yUgPAQI4o8hyTruFAAEg/kIzGJT0RxGnbljQQDAY/0d+r8fD7g5PzEKz1+OhKI5KnNVq0SW9Wa2Jpz+Hw8rCaSOZUFEQig4zLYnoE05ho7TAzq2Lpofkih12C06ROT9aIHDrXM/qt3bxl01tEdF3xpbn8X+fnRRBTr5hQQOXnVpHlyeARUB5oQMJXPHEQVW5m784jRVz6/AFJEfVFNHc2c/lp01mV0cft/9tCx19Xn58dgP3nD+TXm+Aj9p6eGFjM59rqOX6sLl81YIG6qry2bi7i/v/vSNGDe6n50zj6ydO4MonNkT8gPzVl2fR2n1wLIwtz+Pu82bS6/XzSa8Xh9XCRer+ZG2RXFvi5JpnmvD6JctOnMDk6iKOqilmfEUBQOiHY1m+g699ZlxE+131+aNYMbeOI0bkh/JoC+LwfJedWkdpvpN7X9vKBXPGRpRx2WmTWfnc+3T0ec05+xDC3vZO3YWsZ8PlWbDGRDykOrsuAc4AbpVSHlCf7n4vmYxCiD8CJwEVQojdwHUoi+HHhBBLgV3A4hTtYWd7L93ug08AQNmXddVTG0OkDlBeSz30tTl4/HDF47HkjqEiNwyWtHJ4udvbekJP/EBpj8see48jDy0Cx7DHzvZeLnn03bR9LR1p4A17O0OLAFD6/tpnNjGhoiAuqQvS982d7b1c/nikv13++HscVRv/Ot9v6Q0tZDQcJHvFf4W8YW9n6KmcVuc1z2xifILr3NzSG1oQa/mufXYT4yri15lufRocDmtS6cKxsbmT5X+M9Z9kFP8SoamlR/d6HvzaHJwOZ2gBDDBv2qjQIjU8bfT8uuzECRFprnxyQ0gZ7jt/elcl6H0QcT1XPbWJmxdN5/tPKn5w1/kzI4hvmv/++sKjuemvH7D0+AmhBbF2/gd/3hBT99VPK+WGj4WP2/v5zp/eZdmJEyIUTLU81z3bxK2LpvNxez8Ad728LTReLRYRMa+eM2s0t/9tS0T+Hz+/maXHT+CKx9eH8oAyPrR8zZ1uOt0Bbv+70n7aU3KtjNv/tiXUruacbcLE0CPpRbEQwgK8pW19gBA5rjmZ/FLK8w1OzU3WBj3s63LTq0OsCCd1aN9bu90Egoe+Wp2pcDQ8kB2VuPRIXZkgfRW93CGvZaNdM1H8SwSjdmjrdscQwBKR5rTv0Q/po4l1idTzAF2SnNt3UHkuGZKfXrnRaY3q6fX6Y45pfhzu56mo0hkR7JIhI5pztgkTQ4ukF8VSyqAQYr0QYoyUctdgGpUKqotdtPd6cdn1FYLCv1cVuXD7A7ppDyVyg6lwNDyQjX5IV+ktE6Svojf0inbp1pmNds1E8S8RjNqhssjFh/t6dM8lml/1iHbJKMNpZDe3LxijLKql0ZTnjMrQqzu83Oi0RvVoCnnhxzQ/jvZzozYxIthFp03Uruacffhi08aNzPvSN3TPNW3+kGM/O8QGHUZIdftELdAkhHgL6NUOSinnD6hVKWBceQH/+6SX686qj9hTvGp+A4+t/ZhvnzwJqwVmHlFKcZ4VX0+Q25fMYFd7Lw+ppI/bl8xgXHlBTNm5phKn2dve6+FnX5wWeiWp7SnWu0YTg4dx5QXcvmRGDHltMPuhcWQJqxY0xOwpnjayJGHedFTXIP3rnFpTwA0LGmKIdlNrErdP48gS3byJrvOomgJWzW+I2VN8VII6061PQzpzSWNtCXefPxOPLxgiTTptFhprk6szHuprCnXbwW6VFDps3HHuDH76182UuOxMGxV77avmN3Dva1sBQhyOAoeVFXMnhZTdJlYU0tzZz/Mbm/nRmUfS6w1ww4IGdnf08dja3XT0ebn01Mk89MZ2Vs6v57pnm/jN6x/FzuULGrBZ4Np5U/n16x+x/JS6mD3FFsvBBabWN519npiybjq7kXyHlf3dbq4/qz5i7/L1Z9UTlAGWz50UoQZpEfDurk8IBuHWRdMpL3TQ7/Vz86JpESS5+lHF5NlsHFlTRLfbj98fxGazMK68gHsumBlSmSx0WPnJ2Y38/OUtMdeiCJE0s3zuJCZXFSGl4jtGvhKuJFlZ6MRuE0gJfd6AoZ/l2n3tcIRXWnT3IIO5D3mwkeqieOWgWJEhqkoceAIyREgpcdmx2wRnzzoiQvpz5fx6fvHaNj5u78dlt3DdWfWMK8/nmLEjdCeOXFKJi7Z3bHleSJ3PnPiyg2woTdlsFkaXxSq2JVTBSzNCgoZ0CIUWLKG9qBGqdElwd9NVtLMgqCyyR7RPIBjEkoAk7w/okwL9gQAO4rdPunOJT/rp6PXFLFx90o8rQzFSCxbGljt56GtzQr4JAS57bENofrzzvJl09vtY/qd3Q8pwEyoK6fX4qCyy873Tj8TtD1Ca7+CGNU183N6vKLudVBdh840LG8i3W7np8YMkvOvOqqe730tNqYvLTpvM/l4fy06cQL7DSpHLGkEutAj40VOb8Poll5w8kfJCJ7cums72/b34g0GEkLy2uTWkeleSZ6Pb7eeeV3dw8acj1ep63D5uefFDHDbBj848KqIep91CMBipBnnjwgZuer6Jk6fUsnJN5AJayoNExbHleYwszWP5s+9F5F04fRQWi4hRmfzJ2Y3ce8Es+n0BVi87jj5vgKoiFzar8mT5R2FRO4x8RS9CTfQ9Ljpvrt3XTJgYaqQ0s0op/zFYhqSLne29dPcFIwgp2tPhcFUijUihkRjcPiXkzrITJ1Bd7IrZtxVOjtDyD2fiQ7S9H7f3s+z3aw81daScw2ARK42ws72Xix98J+aVbCI/yISgly6hcFNLN98zJNrFJ5I1NXfq5h0zIj+uol1TSw//94dYW5U6jbclbGrpjkMKjG9runNJuqTAZLCxpYuv/i7WT8LnR58/GPKJ5k53iHj26wuPDskbK6psB22cN21UjM1XP70phgi38jllLr79iQ08cNExobb99smTuOLx2HbW7Bo9Ij9Ud/j5X194NF994G1cdkX17ntPrGPp8RP4yQsf6JYFsGL1ezHn9Ah74derHb9evXeEX/d1zzbF5K2rKqTIZY/p/x8+tZHnl59Aw6jSiH7Z3tYTWhBraY18pam5M7Qg1tJG3+Oi8+bafS3XYLTtwdzykDtIVdHuOCHE20KIHiGEVwgREEJ0DZZxyWBfl5t93bFEBiNVIj1yiJ6CUK6pxOWavSYGB+n6QSZEsmyoxA21il4mtmajfRLbZKwMp8GIwHygzxc6niwpz4iE5/YF2d/tMSwv2q6OXp+hTdpnTfUuXlmp2GlUZzIKfi2d7pT6P5W0RmNAj2CeTvkmUoe27SH6z+PzJ85sYlgg1RjD9wDnA1uBPODr6rGsobrYpasUZKRKpEcO0SMz5JpKXK7Za2JwkK4fpKu6lkmd2VG0S6/OTGzNRvsktslYGU5DgUtfca403x5xXC9N9HcjEp5C7nMmLE+za0SB3dAm7XO46l28spK106jOZBT8akr0709G/Z9KWqMxoEcwT6d8EyYOR6QsvCGl3AZYpZQBKeXvUGIPZw3jygvIdwpuWNAQGuzPrd/DuPICVsyti5gcV86vZ82GPaHvl502mcZRJbqEII08FJ5/OJPVcs1eE4ODdP1AI+iF50uWoJdunQ01RayaH1Xn/AYaaooS1llfW8yNCyPz3riwgfoEJDSNYBZdZ31N/FfHmdiabvtopMDoOhORApNBY02xTtmR86PP749Jc928ev7w5o7Q8Sff2R0xzz63fg+r5tfHzLsTKwsiji0/pY41G/awakEDT63bxUo1T3R5Wp2aXY+E1R1e/oP/2hH6/NS6Xaxa0MBz6/ew/JTIslbMrePP63br2nn7khlMG10S41OPvLmD6+ZFpr3+rHqqwhbzz63fE7qG8Lz1tSUp9X8qafXGQPQ9LjqveZ8wYSI+UpV5fh1Fze63QAtKjOKLpJTTB8e8WOhJQvr9QVp7utjdEaC120NVkRMIYrNY6fH46fcGqCh00uXxYhNWHDaBEJBvtzG5qojdnf26TFyNpZsOSSobDF8je0228YBgyOVK0+23dKNI9PV72dTSzb4uD9XFThpqisjPcyRlazgLvrYkj/ra4oTkPoAD/W62tPSG6pxcU0BpHGW5cPT3+9jY0hXK21hTnFByOZM6FTn5g/mOrCmgOElb0+2TJG1Nyzejyy7Js9LVH6Cjz8fIEhfeQJBuj488u422bg+VRU58gQB2q5Uet49Cl50D/T6qC50EpKSly01FoROXHdw+QuVaRJB8h51eT4C2Hg8VhU76vX7yHDY+6fMyIt+B2+fHZbfR0eejqtBBUCpbeiqLnIq0s81GR6+XmpI8Jla4eF+1e8yIPLz+IC1qXeUFVtp7A7T3eCgvdOIPBrBZrLR2eRhd5iIQhL2dbqqLnVgtkkBQ0OP2UZznQCKpKnJyoNdHe5+HYpcDjy+Ay27FL/0IrKF28Pj9VBQ46fMGQ3ZaRBApLbT3eikvcOD2+yl2OamvLcZiEQnvJZqP9Hh85DtsfNLrTTiWwsddRaETp00QjIo+AUTMI2PK8tnV0TdU5N/DSubZ6NxAHY93zpR5ThkDIvN8IcrT5UuAS4EjgC9mZldmCAYlW9s62binJ0LO8/+dNClEfNCeEDyshmBbMbeO2hIXj7y5k/PmjOXuV7bqsnXTJUlli+GrZ6/JNs5NpNtvfn+QZzfujWCkayz4eItUrzfAmqZ9aUWfCAYlL23el7Kt3f1uXtrUFhNd4cyGyriSy5q9zzW1pGxvX79Xt855DdVxfwC43X5e0Mk3v7EWlyv+NJpunxwwaJ/TGyqT/uFghC6Dsps7uvnTO80RMs/aE8jf/Xs7pxxZExFC7MaFDVgE3LDmfc6dPYYH/rWdL84aExGpYeX8eqyWfq5+uik0P3/rxEmsXLMulGb5KXWsXruLC+aMZV9nf0jO+eYXP4iRdb5xYQN3v7KVo48o5biJFVz7bGwozrUfd4bV00RZviPmmpafUscrH7SwePYY/t+j74bS/OntXTF1avZpNl0zbyo72vtjfP7UKVU8u3Ev310dG4Ui3r1Ei/5y76tbY+qON5YSqV7Gm0dMYp0JE7FIafuElPJjlNV1rZRypZTyMnU7Rdaws72X7v5gBHNejwl858tbOWfW6NDn7ft7+cqnJ3D105uYN21UKN1lj73HzvZew/qStUmP4Ztpublui4nkkW6/6THSr356E03NnXHzGUWf2LA3fr5MbDWKrrC5JbFvpmvvppZu3To3tXTHzbexuVM338YE7Qrp98kWg/bZkkT7JMIHBmUfN6mac2aNjpB51qIafOXTsRLLVz+9iQ27O5k3bRR3vbKVr3x6QmhBHJ7XZbNFzM/Rae56ZSvzpo3ijr9vYX+vN1Se9j+6znnTRrHomDGhBXH4NXzl0xNi6tG7Js1e7V6hpdGrM9yWedNGsWF3p67Pb9ib2fjTqzuT+dqc/w8faJEvov+WLb8i26blFFKNPnEW8B7wgvp9hhDi2UGwK2kYRZ+Ix8rVmMOaDGg8tm66Ng0Xhu9wssVE8ki339KNypBr0SeGXuY5dyJlJAOjslt1ZJ61c0bSyEF5cM5NRj45kURyeHnx0oZHrYiwU60rPG8yEtPJ1Kn9N4puZOSXyY4/o7rTna/N+f/wgVHki73tiX+4mziIVIl21wNzgAMAUsr3gHEDaVCqMIo+kYh1bBGQp8qAxmPrpmvTcGH4DidbTCSPtKNIpBmVIeeiT6Rpb7p11uRQpIxkYFS21md65zRp5Ojj2lv9eGn05JOjv2vRKMLLi5c2OmqFdi4vrK7w87rX5LDppolnn5TG0Y2M/DKV8TeQ87U5/5swkRpS3VPsl1J2ChG7tylbGFdegMfvY9WCBu59VXm1Vey0ctPZjRGqQDcubKDP62fF3EmUFzgoK3Dw3Pr/hfanARHpNInOdG0aannfXLDFRPJIt980Rnr0/tVEURkykYceV17A3efPZOOezpDkbYNBVJdwHFVTwM1fnMa2toNSuRMrC5OKrpCuvVoUiei9tImiSDTWlnDLomlsDZP1nVRVmJTkcrp9MrmmgDvPnYEvIEMyz3aLYPIARJ840kDu+s1t+3jynWZWzK3jzpe3UpbvYPHs0UyoKGR/j5sfnnFkSBDDZVfklMdV5POb17ex/JQ6HnpjO9fNq4/YU7xqfj2BYACXXZFhfm79npg02p7dS0+dTJ7dwv3/3hE69sMzjqS9z8voMhdHlBWwv8dLdbETX8DPLy6YxUpVTU+7hoff2M60UcXU1xZz8xenYbEInlz7v9A1aXVeM28qHb0efn7udN5v7sZmsXDHuTO4/58fherU+npUiYv2Ph83Lmxgf7eHo0YWhcan1kaTq4oYkW+P6etVCxpw2ITuPUUj0wak5MaFjdz9yhYuPXUyd/x9S8y4Dyfe1pYopMHW7kgSbjQ5d0xZvjn/mzCRApJaFAshnge+DWwSQlwAWIUQdcBy4I1BtC8hLBbBmNJ8hIAVcyfzo6c2Upbv4NJTJ3HHkhlsbukiEIS7X1EIDE+/t4dvfXYSD72xg/OPHccXptYwY3QpTc1dfNTWw20vbaGjz5sUESaeTUMt75sLtphIDelIJ9tsFhZOH0VdVSEtnW5qSlzU15Yk9GOHw8rCaSOZUFEQuqFOSzJCgt+viCeEy9jesKABvz8YN78NK4FgrPytLYFssnad1cWOCJneojxrwuvMz3Mwr6GacRX5KUfZcPuCMdeYDCwWQWVRrK3JjMFOtz9m4T8QcFntTKxy8fDX5rCv2011kQunTbL7QB9fPHo0VgEPXDSbvQfcMYv5hy4+hpZODx+19XD735T5ctWCBkaVOhlfUYDNKrjvwqPZ1+Uhz67INne5/aqMs42ptUUEZJCHvjaHjj4PJXkOmg/089NzptHl9lGpyji393q56vNT+aTXywfNBxhVWsvXHnw7ZMt18+p5ct0uLjm5DodNYBUW+n1+zjvmCILSwqVhC8GV8+uxCslvvzqbzj4fH+7r5p5XtuGwCS45uS6iX3++ZAa93kDE4n/l/Hr+9Pau0OL7xoUNzG8cyQsrTmDdrgMRD2DuuWAmq79xHLs6+hAI7nv9I7a09sTcU6JJcGPL87jp7EY+6fXGjPtwMqseafD2JTM4/ahqXcLr6UdV83yOzv/Lll+h+/p/ZHkJ991166DWYarQHZ5I9knxg8CLwO+BBsADPKoeu2FQLEsBTS09uH2B0MR0zqzR7D7g5r7X34/YT3XXK1tZevyEkMToVU9tZFKl8ov5+09GSotqEp3xJGPjYajlfXPFFhPJIV3pZEjMSDeCw2FNKOmshw17O7kmivR2zTObGJ9AInpjSxc/jJK0/eFTGxlVlpeUdPI3f78urfbJz3MkLD/G1mb9axxXns8xg2TrlpZeXTLhuPLMZZ437O3ky/evDZWtyDVvj7Bx+dxJocWiVr8mexw9X177zCZuXTSdzS3dCcvRrv/XFx7Nd1evjzm+7MQJTKoq4son1rP0+Anc/6/tulLLK9c0cfOi6aF0974aK0Otpb3uWSXt1x9ay7ITJ3DXy9tC1x3dr++3dMVcd7R8criEc7Qs8yWPvssjS4+NkSGPvqdEk+A+bu/nvzs+0W2r1cuOC6XVIw1e9th7EWnCj2t+lovz/972Tt2waHv/8cCg1+HZcPmA1WEid5DUY1Ap5WPATKAQ+AKwGvgT0IHyBDmr2Nfl4ZPeSOnReDLP4f9bOt1pE2FMmBgs5BJBZqhJb0reoW2fTIh2w1HmObrP9AheRnPoAQPZ416vf0DKCUpChD2tPCOp5WiydDJpE8kzx7t3hH+PJ+GcDOFOL69R3eH3KCMyntF9bDjOGSZMDFeksjfAB/QCTpTFsfaXWNJpkFFd7IyR4own8xz+v6bElTYRxoSJwUIuEWSGmvSm5B3a9smEaDccZZ6N+iwcRnNoqYHssUamy7QciyCCsOeyG0stR5Olk0mbSJ453r0j/Hs8CeeaJAh3enkNCXwlkWmTSaMdH45zhgkTwxXJ7ik+A7gdeBaYJaXsG0gjhBA7gW4ggELmm51K/oaaIja3drFqfgP3vrYVl83CyNI87jh3Bj/96+bQPjAtWPvtS2awva2HX335aKZUFmGzWdIiwhjBVJDLLQxmf6VbdiYEyXTV5dJFuqS3xhp9AlpjTXHCOoe6fRprS7hhQUPoVbu2pzgZot248gJ+/eVZdLsDIcJckdOa0NbJNQW67ToQRLvGkSWsXnYMHp9gX7eyt/rkKeU0H/CyfX8vDqtg5pgSxpYXROyXvWFBA21d/Vx/Vj3XP9cUYVdRnlWRTz5rKvlOe8S1/vjsRq56aiOTqwpZduJEgkhcNgu//eos3tpxIIK8aBGC3/17O9efVc8v/6EQ+B55c0dM+19/Vj0Pv7E9JMykkedau/pjSITXzVPS3rZ4BmX5ViafPxMhoLLQyZEqyXJ3Rx/93gCzxpaG7A2v65f/ULZchN8fLBah64fTRpYkvKeMKy/gngtmsmH3QYLqrLGlMeXddHYjxS4791wwk0sefTckhR29p7i+tsQk1ZkwkSGS3VN8FbBYStk0iLacLKXcn05GgWBPh4eSPAvL506OmMxuXNhISZ6NQqcNXyBAZZEzYtLQyA/pkJP0YCrI5RYGs78yKTtdgqTfH+Tp9XtSVk/LBOmS9KxWCw6bJYpUZMFqTWznULePxSJC+101W112S9Ky2/u6vDGRHrzeQFw1PAcWSly2CIKe3SpwpBxJMxbugI8PW/qjbKpn7c79PPZOMy67ogL6143NLDtxAhMrC9l7oD9ErLvzvBncsWQGXW5fiDj4mQmVPHzxHP67o4MrnlgfsaCsKHKwav5UwBI6N7Y8j2+fNCmC5HbpqZP52/vNXHraFDp6fZx3zBjGjMhn5pgS9vdEEtAKnFau+UI9hS4bR5Tls6VVIc+F29fj8auRKoJ8vnEktaV2tu3r597XFOL15Y8ftHP5KXU8/d4eJlcXU1FoD9VV4LBSVmBnwYxRobpL8+0h1VMjP0zmnuL1RxJNNWLcX75zAptbutiyr5tbXvyQjj4vty+ZwQsrTqCly01NsYvTp9bQ1hNZp0mqNmEiMyS1KJZSnjDYhmSCjS1dbGvrAdAhhmwMkTVuXTQ9hlQRTn5Ih5wUDSMFoSOTIACZGHoMZn9lWnY6BEkj9bRMSKPJIB2S3oa9nVwZRUZy2S2MXJqXVFlD2T4b9nbGEKdcdgu1SdhqpIY3viI+SW9TSzcrVr8XU+fDF89JmSgYDX1FuyYeuOgYHnunGbdPUf5cerxCSnPZLSw9fgLN6p7YFX96L0Q80+x6fvkJdLt9MeTA659r4tZF06kuyYsgwM2bNipGke6Ov29h6fETQoSzpcdP4PLHDxLu9AhoQojQQluDnn03L5qOxwfXPrtJuS4d1TqlvvdiyHgr/hTbDxqBzcgPExFejeaH55efgBBwxeOR16SdO25CRejYxKrIOk1StQkTmWHw3qmmBgm8JIR4RwixLPqkEGKZEGKtEGJtW1tbTOZ9XR6CMjG5rtdAbWkgCXW5RJAykXl/xfPNbPhCLpFGM1HRSxfZUPxLl6SXKdEuvm/ql93e44n4Hk5giyaa6SmBGrVvr9cfQ4CLpxynzeWJVObikd2i7ev3+mlTVfCSUdVLZOdgKp8e6veRRPd0EyayhVTFOwYLn5FS7hVCVAF/E0J8IKV8XTsppbwPuA9g9uzZMjpzdbGTba3dAKEA8RrCSXUFLpvu+YEk1Gnkieg6TLLD8ESm/RXPN7PhCxppdDB9fKBQa9A+yajopV1nmu2Tia0aSS82b3zCXHWa+TTE9039sssLnRHfwwls0UQzPSXQPLv+HFvgsFGcp39Ob762WWLV5Yz6rchlNywn/Huew0ZloSNuedGqevHsHCjlU6NyD+X7SKJ7+kDDKBYxmPGITURiWDwpllLuVf+3Ak+hSEknjcaaYiZWFlJe4OCy0yZHTHrLT6ljzYY9fO9zU7AAK+bWRZzPhFCnB40AFF6HSXYYvhjM/sqGL2jqaYPp4wMFjaAXbmuyKnrpIt32ycTWxtoSVs2Pyjs/MUlPU9+LzpdIfS8ZaIp2kWXX8/S6XaHvK+bW8ed1u0NttGbDntC56O+aX+u17/Vn1WOxwCNv7mDl/PrQuefW72FV2Pfw+bpxtEIae279HpafUhf6r9dveuNs1YJI+zSindUqWTW/Qbc8re7bl8xg2uiSCDujr2kgxnG8+cG8jwwstFjEen8enz/b5pkYRsj6k2IhRAFgkVJ2q59PB1alUobTaWNKTSHNnW4KnDYe+OpsejwB8hxWHFbB0WNLcVotdLp9zB5XFjpfU+xkapqEOiMkS3YwI1QMD1gsgtOPqmb1suPUaASuEKt8OJdtBJvNwvzGkYwrL6Cly01tsYvGkQPr4wMFh8PKF+qrGFc+J6QuV19TmJSKHqQ3hmw2C2dMrWTMiIN1Tq0pSErx76z6GsaVH1TCa6wpTspWl8vG/MZaxoep6DXWlsQl2YEiMnJmQyXjKg7aelRNQVLqe4lQnOfi9KiyhQgyrmIsp04dSVmBnSKXlSk1hRS7HHh8AW5fPINOt5fyAidTKouoqyoMRfAozbfx3x3tVBe7mN84MnSuotCJzQIjCh1UFdXh8ft5+OI5tHZ7qCpyAkEevngOXW4fJS473R4fd58/iyKnjZYuN3edNwuLkMwZP4Iut5eHL55Dt8dHsdOOzy/Z1dHHuPICTp1SxSNLj6Wly011kROHzcLti2cgCRCUFvZ1ebjyjKNorC6msRrGVeTT4/GF6i522dnf4+H2xTNC4+X55Sewr8tNvsNKUEoeWXosPR4fhU47Xn+QHft7sVqULTl6/qcX5cRiERE+G09tziTNmTAx9Mj6ohioBp4SygYwG/ColPKFZDMHg5K/bmrm5hc/4NzZY1i9dhfnzh4TIlFoTwBWr93FBXPG8uhbH3PeMWMYVZrHZDUc20AjEdnBjFAxfBAunzoY0ScGq+x4df79w9ac8C23289fNrXGRGWY31ibcMGY7hjq6/fy/Ka2mDrnNVTHXWz6/UH+8n5L2lE9XC5bQuW7aHi9Af7a1BYTkm3htJFJ/3AwQk+/m5ei2kGbJ7V59LunTqa9x8utL70bkeaVD7Zx3pyxEW2hhUXToiScUV/D9COUfjDqq9ljR2CxiND5Cx94K6Y8h03wnVPqQnWNLc/jWydO4ttrDtp02+IZBGUwRITUngx/1HqAyTVluv4164gynl6/h5XPvR9zv9D6dVx5AR+0dIekpbW6V65ZF/e6LRZhGOWkssgRUjcM91m9e4VJmjNhYuiR9cdHUsrtUsrp6l+9lPLHqeTf2d7L5Y+vZ960Udz1ytbQ/2hW8bxpo7jj71uYN20Ud768lW1tPWxs1t9jNNgwYh3vbO/Nij2HMwazL7LRz7nkW0ZRGZIZl+le56aWbt06N7V0x81nFLWiaRDnkA17O3VlnjfszbzO93WiT4TPn/OmjWLH/l5ufenDmDRf+fSEmLa48+WtnDNrtG4/JOorvfNaefOmjYqoa960UaxcExmx4vLH32Nra0/EsZVrmji1fpShf2n9qXe/0Po12i69uo2u28hfuvsDOTE2TZg4XJH1RXGm0Fi6iVjK0eeDkgGRS83E5mgbDxVmcS5hMPsiG/2cS76VS9LJ2YjqMZjROYzaIXyeNIrmo8kl6+XVPof3Q6K+ihc9Ino+N5rfg1FULbcvSFu3Uft5Qv2ZSlSLePeW6GuKF4XDqB1MmDCRfeT8ojhcKjP6v4ZwVnE4u3gg5FLTQS5J+B7qGMy+yEY/55Jv5ZJ0cjak4NOVz04GRu0QPk8aSQ7nO2yGebXP4f2QqK+MzoeXF30u+rtetIiqIqP2c0b0p1G/GtmVzHUb+Ysmha3XDiZMmMg+cn5RPK68gNsWT4/LUr5m3lT+u72NS0+dzH+3t3HHkhnUVRVSmucgGP2IYYhsNpnFwwODHX3itsWRZd+2eHD7OZPrCQYl29t6+M9H+9ne1pPS2Egnb7pRGSD962yoKeKGqCgSNyxIHNGhvrZYN1+yUT3cbj9v72jnufV7eXtHO253YsZ748gSbl40jeVzJ3HJKZNYMXcSNy+aNiDROabqRJ/Qoi9o/8dVFPD9M46MiepQ4LRw53kH235seR53LJmB02ZhxdxJ/PrCWUhJyBfGlOXH9NVNZzdiEYrfRPdleHmFDis3L5oWEQniunn1MWOqrqow4thPzmmk+UAfPztnGivmTqK2xBWy3xcM4PUHuGXRNAodVladNTWijW9ZNE03qsVz6/dERM/Q9hRrETpuXzIDi1Cu22ZF118KXdYIe8x538RgY9PGjcz70jdi/pYtvyLbpg1LDAeiXUawWASnHVlNoctGV7+Pn54zDbfPzz0XzGLD7gMEgnDf6x+xYu5kakscVBQ5uTTLJCRTjnP4YDD7IhiUBGUwQpo2KIMEg3LQ+jrd68mE/JluXofDyugRTu678Gg6+nyU5duxWUmKRJbuddqsVlz2WLlmmzVxncV5UZLLtuT60O328+zG5rQIhV5/MEIG+MaFDUnVmQguu4PSfDu3LpqO2x+gqsiJxx/kxwsb6fUGuPPcmYwotLNxdxcr5tZRWehkV0cfd6gyzzcsaODF755AW7eHPQfcoTl1bHkeR4zIjyGTxZMuPqO+JtSXn/RGlueyW7ji9ClcdmodI8vyGVOWR1BKVi87jj5vgOpiF2PK8vn39rZQ35TlO+hy+yJIbjcsaGBMeT73/3MbM44oD+0jHluex7dProto4xsWNBAMSiVSSX0NU8Ls/sObH7PsxAlMri7iyOoibFbBzDGlVBa62NHewxl3/jNUzlWfP4qfL5mBLyipKHJw24sfsPbjztCPglljShkzwpz3TQwuvNJC4Wcvjjm+9x8PZMGa4Y+cXxQDbN7XFZIP/fbJk7BaIuWeAX701EZuXjSdq57aGEN0yIYEs8ksHj4YrL5oataXBh4zIn9QJZfTuZ5MJKnTzbuzvZeLH3wnpn2eH0QZ7HTlmpuaO3WlflcvOy5hX6Yr87xhrz5Za1x5QcqS2nrXs/xP78Zcz82LpnPlE+t5fvkJ+AOEJJbvfHlTRNprntnE6mXHMaLAyZfvfyuCjBZtcyLpYs1PtH4ML8/tC3LrSx+y9PgJ3P74el3f2N7WE1qEgyLLHC4J7fYFueaZTSw7cQJ11aURxLp500bFkBmveWYT4yuUNrZYRIzdG/Z0hfx0XEUh4yoK2d7WwyWPvhtRzo+f3xySp1524gTWftwZOvejpzby/PITzAWxiaxBe4Ksh5HlJdx3161DbNHwwCGxKA4nNaRDEGntdpuLUxMDjnjkrOlHZMkoA8QjQyUaG+nmzaTOdJEueS2TvkyXUDiYRDuj69HmyNZuNzJKajk6bUunmz5vIOJcPElkaTAvh/d3PNKdkW8kS4gLyuSJe+FtnIyfJrJbjwho3ndMZBNGT5Dh8H6KnPN7iiGW1JAqQcQkOpgYDGSDnJUuMiHopU96G3pSYLrktUz6Ml1C4WAS7YyuJ0+dI6uKXLok5vC0qZDRosuLPqchHunOyDeStUF7KJtM2vA2Hgi7jYiAJkyYGF44JBbFR1UXhQgpZXl2JlYVxsg5XztvKh29Hn72xUZDck4mRCMTJqKRqeTyUPpjJgS9dPOOKy/gngtmRpCc7rlgZtLEo7TIfWnKNWfSl+kSCgdTBluPOLhyviKFHC01rEdeXrWggdJ8ewyJzkgSeUxZPhYBN52tzL+1JS6Wz53ErYumI6UijrK9rYd9XW4evOgYvn/GlJBP/PCMI1mzYU+IpBre7zv3K/1+66LpIQKbng23LZ7OpKrCmGtRpKbjt3G4n/7gzCncc/5Mblus2K35nN4YMJKNNgl2g4dly6/QJZU1bf4w26aZyBHk/PaJYFDy+kdttHV7QvuIx5bncc28qdx17kz6/QE+auvh7le20dHn5QdnHMmKuXX0+wLMHjuCT08oj1BVygUlMBO5AZvNwsLpo6irKqSl002NKvOcjALaUPtjJoTDTPJ6/TKC5HT7khlJ2Ztu+wSDkhIdwlyiBXUmfZmuzLPFInRtHYj+9/uDFLuiyrYK7jhvBqNKCkJzosMmWDBjFE6bhfsuPJoutx+rEPzytW1c+8ymEIkuXKp4TFk+s8aURXzXlB3L8h1cdmodpfnO0D7rseV5IdW6snwHX/nUWO58+aDC3HVn1fOlOWMIyiB+fzCk2KiX9qazGzl6bClbW3siyJROu4Xjx1cwsiSPjj5FLnp/j4dgEF7Z3KIszpGMLMmjcWRJDNnT65c8894ezp09hiueWK/rc9oY0KShfYEgZzTUhBa/RnLOJgYOe9s7dbcEeDZcngVrchdG+40Ph73GOb8o3tney4bdnRHEuo/b+7nk0Xe5edF0vv9kJKnmpy98wNLjJ3Dvq9siSD2ZEI1MmDCCzWZh+hFlKe8hzoY/ZkI4zBVy30YDwtzvL56TUIY53b6E9GSeMyH3JcLG5k5WrNZvhyPKlPbb2d4bQR7T0iw9fgIb9nQBhEh00X0f/n17W0+or5o73XS6A9z+90iVOo2cd86s0aFFLqjqdM81KUS7JzZQuzQvVJZe2h89tZHVy47Ttfv55SeECIrb23r4ygMHCX1/fq85lCZ6Qaz52tLjJ8So34X7XKIxYBKrTeQKDueIFTm/fWJfl9uYWOdJXnkpl5TATBz6OBz8MZNrTDdvJip6Q43BVNFLph3ikcfCvydWEYxPhAv/nkiRNJx8aJTWqN1SUdnTsz8eidCECROHBnJ+UVxd7DIm1jmTV17KJSUwE4c+Dgd/zAa5LxMVvaHGYBI1k2mHREpz2vd0CZVG3+MR1mqiytJLqwljRB9PRWXPyP5DfUyaMHG4I+e3T4wrL6BxdAk/O6cBq8VKr8dPWaEDu1XQ1e/jl186muuf28TH7f0qOaYRh03wgzOmcMSI/BhVpeg9iocCGSIYlOxs72Vfl5vqYnM/21Ai3bbPxB9zpb81xb/LHz94jckq/qXbPhrpLVpIIxkVPb8/SFNzJ82dbmpL8qivLU5qT3G6eetri/nVl2dhEYJPen2MKLATlDJpomY8NNaW8MsvzcRqsdDR56OqyIk/GMBhs/L2znbKC5whEp22f3fx7NFMrS1GSrjstMkEgkEaR5dEEJX1/C66rzRVupVrmkLfb1k0ja2tPdgsFu44dwY//evm0Jx93Vn1lBc6uPO8GdisFn715Vn0uAPs63Jz3Vn1rHyuKcIH6mtLdH1jTFl+iMxXVeTingtmcsmj74aubXJVUQR5TruW2hIXty2ewc0vbubSUydzx9+3GJab7HhLZozmyjjOBoz2vDZt/pBjP5sFgw4THA57jXN+UWyxCD49bgRrNu3j2mfX65Ivrpk3lV63j4lVRfzyta0hVaErTp/CzS9+wPfPOCpCVelQIkOYBMLsIZO2z4Yy3VDD7w8SiFL8C6hkqmRU7Rw2EZHXkYTCnMNhpbrYEUEwK3JZE9bn9wd5ev2eCJW0Gxc2sHD6qISL23Tz+nwBWru8MQt4ny+Q9GLcCBaLoK3bF1H2yvn1/H7zTmYcUc7qtbv4/hlHcfpR1byw4gTW7TrAnS9vwWWzhvbVhhMjE/ldNAlNhqnS1RS7WPe/A1HKfY04bYKtrT3c9fJWOvq8LD+ljpue38y3TpzEr17fhtcv+b/PTojxAb2xE072C7fvxe+ewDsfH+BHqqiTdtxhE6F9yS67hVsWTWPpZ8ZT4LLF1Pfa1taItInGWzJjNJfGcTZgtOfVJNQNLg6HvcY5vygG2NTSHZrc9cgXN6x5n6XHT+Dbj65j6fGKspDbd1ApKVpV6VAiQ5gEwuwh07YfavLaUGPD3k6u1FGXG5lAXQ6MSWCJ1PB2tvfyzUfWpZyvqVlfXa6uqjAh6S3dvBtbunSV8MZV5DMnRdJeNDbsjVXZu+7ZJn594dF88/fvhObF55efACiKoPGIZkBcv4vny+v/1xGjNHr10xtZduIE7np5WyjdXa9sZenxE1i5RiHeAdz01w8M+9KI7Bdu3+plx4UWxOHHl504IeLY957YEFL7i64vOm2i8ZbMGM2lcWzCxKGEnN9TDLAvjDSSiKgRTRIJV0o6FHE4ELaGK7LR9rnU35kotqV7nenmy4T0lm7efYNICjRq+wN9vph5MRmiWSZ+Z9Q+eipw4fN4KsQ3I/tSqduIuG2kVmeEZNoql8axCROHEg6JRXF1FGkkHlEjmiQSTynpUMDhQNgarshG2+dSf2ei2DbUKnqZkN7SzRs9r2n5BoIUaNT2pfn2mHkxGaJZJn5n1D56KnDR83g6ZLnwtEakPL26jYjbqarVZaKQNxzHsQkThxIOiUVxY01xSJXoyXd2x6jZacpC151Vz5oNe0LHLz11ckhx6FAg1OkhE6UyE5khG22fS/2diWJbJip66eTLRNEu3bzh85qWb9X8BhprihPWmQh6bb9yfj1/eHNHhBJbImW76DTp+J1e+6ycX09loVN/Hp+nzON6c71RnUb2aaS86OPRCnQ3LmzgoTe2615/qmp1ybRVLo1jEyYOJWR9T7EQ4gzgTsAK/FZK+dNUy8jLs/P5hkrGVcyhtcvDyFIXD35tNt3uACUuOz1eHz9e2EiR08pPzm7E7QtSUejAGwhy2tTqQ5r5m4namInMkGnbp+ODmdQ51D7vcFiZV1/NuPKDSm8NNUVJkewsFsGpU6p4ZOmxtHS5qSl2MW1kSVLtc/pR1axedlxEJIhE+TJRtEs3b/i8prXP1JoC8vLsCetMBIfDyhfqqxhXfrDs0nwrR9YURSixae1yRn0NR9YU0d7r4ZGlx9LR66WswIFEsmN/L1YL1JY4eWTpsezv8VBb4uKo6uKQP1UVubBalK0S0b5ls1mY3ziSceUFob6sKnbwSY83VF5loROLBT4zqRwpJXedNzNE0vvMxHL2droj6uzx+PD4gnS6fRS77HT0eRkzIo/Hvnkcew+4qSh04rAJmpo7qSxyhEh/mm2gKND1epTtJPu6PXz/jKMocFr5zKRy3bTxxlv02IpWAYzOY87bJnIJh1JUiqwuioUQVuBe4DRgN/C2EOJZKeX7qZTj9QZ4vqmNe1/dGpLhLMt38H+fncA2b09EJIrLTpvMhMoCGkaVZsQOziVkolRmIjOk2/aZRq5Itc5s+LzXG2BN0z6ufSYsusKCBhZOG5lUNIhnN+5NOaJDMCh1oxAkc52ZKNqlk1eb19Jpn0Rwu/38ZVNrTGSL+Y21uvLTWmi1D1q6+dkLmzl39piIKBSXnTYZp9XCT15QiG/h0s1amhVz63j4Px/T0eeNaPNgUIakm7W091wwE69fphyh4caFDby1vY2jx1Xwi9e2xdh53Vn1/PG/H9Pp9vGtEyeFwsJp5R87vjxU/uiSPJ7e0JFU+8cbb/HGVrwxas7bJnIFh1JUimxvn5gDbJNSbpdSeoE/AQtSLWTD3k6ufWYT86aNCk2A58wazf5eb0wkitv/toUNuzvZ2d5rWJ4R8zdeHhMmBhJD7YPZ8Hlt3EZEV3hmExv2dibMaxTRoak5ft5cGtuZtE8ibGyOjT5x7bOb2Bin/bS2C59ntby3/20L7X3e0LFw6WYtzZ0vb+WcWaNj2lyvTzbs7kzYT3r5rn56EwtnjeG6Z5t07Vz5XBNfP3Ei86aNCi2IjcofqPbPJZ8zYeJwR7YXxaOA/4V9360ei4AQYpkQYq0QYm1bW1tMIS067GghMJR/DkoyZgebMAGJfTNdDLUPZsPnM4k+kX5Eh9wZ25m0D8T3zXTkrhNFoQiPwhAvCpD2WWtzvT4xmruTidCwv9sT185+rz+pyBWZtn8iO4ejzw0VBmveNGEiU2R7Uaz3vlLGHJDyPinlbCnl7MrKypgMtQbsaCP5Z4sgY3awCROQ2DfTxVD7YDZ8PpPoE+lHdMidsZ1J+0B830xH7jpRFAq9KAzR38OjRmhtrtcnRnN3MhEaKoucce3Mc9gMz4WXn2n7J7JzOPrcUGGw5k0TJjJFthfFu4HwXXajgb2pFqIxqcPZ0U++s5vyAkcMO/my0yYzLUyaVA8m89dEtjHUPpgNn88k+kS6ER1yaWxn0j4Jy1blrmMiW8Rpv3hRKC47bTLl+Y7QsefW74npnxVz6/jzut0xba7XJ42j9aNCJIrQcOPCBp5at4uV8+t17bzurHp++/pHIanpeOUPVPvnks+ZMHG4I9vRJ94G6oQQ44E9wHnABakW4nBYWThtJBMqCvikz8vDX5tDp9tHZaEDgaB+ZDFuX5DSfDtVRU7GjIjP4jWZvyayjaH2wWz4fPi41Vj500aWJEUiSzeiQy6N7UzaJxFcLhvzG2sZX3Ew8kdjbYkuyU5DqO1qivik1xOK2KBFltjf44mI4jCmLJ9ZY8po7XZTWaikmTmmNKbNjfoEiNtPRnLOs8aU0evxceui6XS6fTx88RwO9PmoLnJSWmBnXHl+jNS0XrSVgWr/XPI5EyaGCsuWX8He9tj9+dmOWJHVRbGU0i+EuAR4ESUk2wNSyqZ0ynI4rAmlYVOByfw1kW0MtQ9mw+czGbfpRoPIpbE90PNaOFwuG8ekKBcdr+3GVcQei06rlyZeuYn6SS9fon4dW558vw9U++eSz5kwMVAwCtUG0LT5Q4791s9ijmc7YkW2nxQjpXweeD7bdpgwYcKECRMmTJgYGBiFagPwbLh8iK1JDllfFJswYcKECRMmTJgwkSqMtmFAelsxhJQxwR6GNYQQbcDHBqcrgP1DaE4imPbERy7Zs19KeUa8zKZvZgTTnvgYTN9Mpa7hilyz+XCx91CbNwcC5jUND+j6Zs4tiuNBCLFWSjk723ZoMO2Jj8PJnsPpWtOBaU98DKU9w+3ak0Gu2WzaO7zrHUyY1zS8ke2QbCZMmDBhwoQJEyZMZB3motiECRMmTJgwYcLEYY9DbVF8X7YNiIJpT3wcTvYcTteaDkx74mMo7Rlu154Mcs1m097hXe9gwrymYYxDak+xCRMmTJgwYcKECRPp4FB7UmzChAkTJkyYMGHCRMowF8UmTJgwYcKECRMmDnvk5KJYCHGGEOJDIcQ2IcQPdM4LIcRd6vkNQohZWbbnJCFEpxDiPfXv2kG05QEhRKsQYpPB+aFum0T2DFnbqPUdIYR4VQixWQjRJIRYoZMm7TYaTr6Z5LUOdfvvFEJsVOtaq3N+KNtnSth1vyeE6BJCfDcqzaC2j974EEKMEEL8TQixVf1fZpA3rq8lWb+ujyRrQ7YghLAKId4VQqxRvw9be4UQpUKIJ4QQH6jt/Klhbu+lqi9sEkL8UQjhGmp7B8K3hwMyGd/DFbk6ZyQNKWVO/QFW4CNgAuAA1gNTo9J8HvgrIIDjgP9m2Z6TgDVD1D4nArOATQbnh6xtkrRnyNpGra8WmKV+LgK2DJT/DEPfTOZah7r9dwIVcc4PqX9G9V0LMHYo20dvfAA3Az9QP/8A+Fk6vpaJjyRjQzb/gMuAR7W+Gc72Ag8BX1c/O4DS4WovMArYAeSp3x8DLhpKewfKt4fDX7rjezj/5eqckexfLj4pngNsk1Jul1J6gT8BC6LSLAAelgreBEqFELVZtGfIIKV8HfgkTpKhbJtk7BlSSCmbpZTr1M/dwGaUG0E40m2jYeWbSV7rcMOQ+mcY5gIfSSmTVX0bEBiMjwUoCynU/wt1sg7IvBPHR5KxISsQQowGvgD8NuzwsLRXCFGMsjC6H0BK6ZVSHmCY2qvCBuQJIWxAPrCXobV3WN1TM0EG43vYIhfnjFSQi4viUcD/wr7vJvZGn0yaobQH4FNCiPVCiL8KIeoHyZZkMJRtkyyy0jZCiHHATOC/UafSbaPh5pshxLlWGNr2l8BLQoh3hBDLdM5nyz/PA/5ocG6o/bNaStkMyg0IqNJJM+DtFOUjydiQLfwcuBIIhh0brvZOANqA36nbPX4rhChgmNorpdwD3ArsApqBTinlSwytvcPxHjWQGJZ9nw5yaM5IGrm4KBY6x6LjyiWTZqCQTF3rUF7LTgfuBp4eJFuSwVC2TTLIStsIIQqBJ4HvSim7ok/rZEmmjYabbyoVxr/WoW7/z0gpZwFnAt8WQpwYba5OnsFuHwcwH3hc5/RwGrvhGNB2SuAjwwZCiHlAq5TynWzbkiRsKK/PfymlnAn0orxaHpZQ94EuAMYDI4ECIcSXh9oMnWNm7NhhhlyZM1JFLi6KdwNHhH0fjfJ6J9U0Q2aPlLJLStmjfn4esAshKgbJnkQYyrZJiGy0jRDCjjKY/yCl/LNOknTbaLj5ZsJrHer2l1LuVf+3Ak+hvCoNRzb880xgnZRyX/SJLI3dfdqWEfV/q06aAWsnAx9JxoZs4DPAfCHETpTX6qcIIR5h+Nq7G9gtpdTe0DyBskgervaeCuyQUrZJKX3An4FPM7T2Dqt71CBguPZ90sixOSMl5OKi+G2gTggxXn3Ccx7wbFSaZ4GvCAXHobwCas6WPUKIGiGEUD/PQWn39kGyJxGGsm0SYqjbRq3rfmCzlPJ2g2TpttGw8s1krnUo218IUSCEKNI+A6cD0VFJsuGf52OwdSJLY/dZ4Kvq568Cz+ikScbXEiKOjyRjw5BDSvlDKeVoKeU4lGt+RUr5ZYavvS3A/4QQU9RDc4H3Gab2omybOE4Ika/6xlyUPaNDae+A+PYwxnDt+6SQa3NGyjBi4A3nPxSG+hYUhupV6rFvAd9SPwvgXvX8RmB2lu25BGhCYdG+CXx6EG35I8peMB/KL+6lWW6bRPYMWduo9R2P8ipuA/Ce+vf5gWqj4eSbSV7rUPrmBLWe9Wqdw2Hs5qMsckvCjg1Z+xiMj3LgZWCr+n+EmnYk8Hw8XxtAH9G1YTj9ERYZZDjbC8wA1qpt/DRQNsztXQl8gPKD9feAc6jtHQjfHg5/qYzvXPnL5TkjmT9T5tmECRMmTJgwYcLEYY9c3D5hwoQJEyZMmDBhwsSAwlwUmzBhwoQJEyZMmDjsYS6KTZgwYcKECRMmTBz2MBfFJkyYMGHChAkTJg57mItiE4c8hBAPCCFahRDR4b+M0i8RQrwvhGgSQjw62PaZMGHChAkTJrIPc1E8gFBjmv5JCPGRuqh6XggxOQt2XCSEGJlinnHaolGNUfkHIcRGIcQmIcS/VPWaePl3ZlGQJBEeBM5IJqEQog74IYryWj3w3XQqFEJcpS6qNwgh3hNCHCuE+K4QIj/N8i4SQtxjcG6ZEOID9e8tIcTx6dRhUPaRqv3vCiEmCiF6BqDMuGUIIU4SQqxJobwHhRA7VDvXCyHmZmrjcEU8PzCRHoQQZwshpBDiyGzbYiL7UGOk/0sIcWbYsSVCiBcGud7weew9IcQbg1mfWueg15FrMBfFAwQ1oPVTwGtSyolSyqnAj4DqIbbDClyEEtM0XawA9kkpG6WUDSixFX0DYF4Iqp1DAinl68AnUfVPFEK8IIR4Rwjxz7Ab4jeAe6WUHWrelFV5hBCfAuYBs6SU01BUov6HssBOa1Ecp655wDeB46WUR6LE2H1UCFEzAGVbgYXAM1LKmVLKjzItcxDxPSnlDJQ2/lV2TTGRYzgf+BeKSISJwxxSiVP7LeB2IYRLFRr6MfDtdMpL8V73PSnlDPXv0+nUl4pNg1lHrsJcFA8cTgZ8UsrQDVlK+R7wLyHELeoT141CiHMh9DTsNSHEE+oTvj+ov1DPFEI8ppWhpntO/Xy6EOI/Qoh1QojHtae36lPaa4UQ/0KZ4GcDf1B/beYJIY4WQvxDXQC+KA5KMR6tPln7D5EDvhbYE3YdH0opPWqep9VymoQQy/QawiiNEKJHCLFKCPFf4GohxFNh504TQuhJLg8W7gO+I6U8GrgC+IV6fDIwWQjxbyHEm0KIpJ4wR6EW2K+1mZRyP7AI5YfKq0KIVwGEEL8UQqxV22mlllkIcYwQ4g21b94Sqgpc2PkvqH5QAXwfZSLdr9a1DngI+PYA+NK5KIvMr2s2h5UlDPz6F0KI+ernp4QQD6iflwohbkymDBXFav73hRC/EkJYhBBWoTxN0dJfqtP2/wFGqeVb1fLfFsoT+2+G1X2lWsZ6IcRP1WMz1D7foNZdph5/TQhxhxDidSHEZrV//iyE2Kpdk1DetHwghPitat8fhBCnqn60VShqeJqq3wOqTe8KIRaoxy9Sy3xBTX9zmK1fE0JsEUL8A0Xm2MQAQfX7z6D88D9PPWZR/bhJCLFGKG/8FqnndOdSE4cWpJSbgOdQ5tfrgEeAq3TG7TihPFRZp/59Wj1+khDiVaFsv9uojvu/qPPNpqi5Li6EEHcJIa5VP39OnYcs6lz4K7X+LUJ5QGI470XbpB7rCavne2F5VoZd32YhxG/U8fCSECJPPTdJCPF39ZrWCSEmGpWTU8i2esih8gcsB+7QOf5F4G+AFeWp8S6URdNJQCeKrrsF5WZ+PGBT0xSo+X8JfBmoAF4PO/594Fr1807gyrA6X0NVAgPswBtApfr9XOAB9fMG4LPq51uATernGSi65f8BbgTqwsrW1LXyUBSPysNsqEiQRgJL1M8CRTVJs+tR4KxB7J9xYddXCPRzUI3nPRTJSoA1KE/87cB4FBWi0hTrKlTL3IKy2NbaONRGUe1kVftsGuAAtgPHqOeKVZ+4CLgHOBv4J1Cmnv+EMDU29dgC4M8D5EvXA1eEfe9J4NfnAbeoad4C3lQ//w74XJJlnAS4URTwrGqaRcDRwN/CbClV/z8ILFI/LwQeVT8vA65WPztRVMXGA2eijIn8qH4IHw+rgJ+HjaefqZ9XAHtVO50o/lGO4l9+oBFlPL8DPIDi5wuAp9X8NwFf1uxH8ZECtX+3AyWAC/gYOEKtZxdQieIb/wbuyeZcdyj9qePhfvXzG8As1deeV/uxBuhQjxnOpebfofenjssPURaQPzEYt/mASz1eB6xVP58E9ALj1e9fBH4TVnaJTn0PAjs4eE/6g3o8H0VV82TVnolh6V9Q/bROnYtccea9CJvU89pcfDrKgyKhlrcGODFsXpuhpnssrB3+C5ytfnapduqWk+2+TOXPhonBxvHAH6WUAWCf+rTnGKALeEtKuRtACPEeME5K+S+h7F06SwjxBPAF4Ergs8BU4N9CCFBukP8Jq2e1Qf1TgAbgb2o+K9AshChBWVT8Q033e5TFAlLK94QQE1Ac/FTgbSHEp6SUm4HlQoiz1TxHoAzG9qg6jdIEgCfVOqQQ4vfAl4UQvwM+BXwlflMOGCzAAam8bo/GbpSFnA/YIYT4EMX+t5MtXErZI4Q4GjgBZSJbLYT4gU7SJUJ5km5DWfxMRfnh0CylfFstqwtA7buTUd4CnK4dN4BQskr/APtSOIz8+p/Ad4UQU4H3gTL1adqnUH44JlOGNja2q9f+RzXty8AEIcTdwF+Al8LKukV9uloFHKceOx2Ypj3lQ1lw1qH49O+klH0oDfWJznh4CHg8rPxn1f8bgSYpZbNq23YUHz8A7JBSak9gmoCXVT/fiHJz0WyaL4S4Qv3uAsaon1+WUnaq+d8HxqL8gHlNStmmHl+N8jbDxMDgfODn6uc/qd/twONSyiDQIg6+JdGdS4fUWhNDBillrzreeoAlKPNo9LjdC9wjhJiBcn8LH5tvSSl3qJ83ArcKIX6GIk3+T4NqvyelfCLKjj4hxDdQHmRcKiO3sT2m+ulWdS46EuN5zxtlUzhOV//eVb8Xqnl2ocxr76nH3wHGCeXt5Sgp5VOqjW5Q3kAalPO6wfUOO5iL4oFDE8rThGiIOHk8YZ8DHOyP1SjbGT4B3pZSdgtlFv6blPJ8g7J6DY4LlJv4pyIOClGKsgDThZSyB+Vp45+FEEHg80KIapQFxafUgfoayuQQXu5JcdK41QWQht+hvKJyo9yE/Eb2DCSklF1CITQsllI+rrbtNCnleuBplBvjg0LZnjAZ5QleqnUEUJ4wvqYuir4afl4IMR5l28YxUsoOIcSDKO0kMO6X7ShPTyej/PoHZeF5NPBKWLpZ6nEYWF+KuAS9g1LKPULZdnAGykQ4AuWG0iOl7E6mDK2o2KJlhxBiOvA5lGtaAlysnv8eir8uR1nQHq2W/x0p5YsRlSpbYlLVt9fGapDIcRvk4LiNPu7RSSOAL0opP4yy6ViM54NUbTWRBIQQ5cApQIMQQqIsciXKmyLdLOjMpSYOaQTVP6Nxez2wD5iO8rDFHXY6NI9KKbeoD0o+D/xECPGSlHJVCnY0ojxYiuYKxcyTGM97JxF/nfATKeWvo/KMI3ZeysN47tYtJ5dg7ikeOLwCONVfdICyNxTl1du56j6fSpRXEm8lKOs1lIXNNzj41O5N4DNCiElq2fnCOLJFN6DtQ/0QqBQK+QshhF0IUS+lPAB0ioORCr4UZvdnxMH9lA6Up4ofo/zi7FAXu0dy8IlcOJJJA4CUci/KL+2rUV4FDQrUJ43/AaYIIXYLIZaiXO9SIcR6lB80C9TkLwLt6pO6V1F+uUc/CU9U3xShRLHQMAOl/cL7pRhlgupUf2xoTOcPgJGq7yCEKBJCaIujj4FzgIeFEPXqsZuBn6k3eNQnFhdxcI/0a2TmS0Z4HWO//g/KXuTXUZ4cX6H+T6WMOUKI8UIIC8pr6n+pP1IsUsongWvU6wpBfWJyJ2ARQnwOpS//TwhhV69zslBIMy8BFws1EogQYoT6hLZDCHGCWtyFwD8YeLwIfEf9YYIQYmaC9P8FThJClKvXsXgQbDpcsQh4WEo5Vko5Tkp5BMrr6/3AF4Wyb7Ma5bUzGMyl2TDcxJDDaNyWoLzZC6LMGbqkOqFEg+qTUj4C3ErU3BUPQoixwOXATOBM9Qe0hsWqn05EeWDyIcbzXqLru1gc5JaMEkJUGSVW31TuFkIsVNM71fk0pXKGI8wnxQME9TXp2cDPhfKq3I2yP/O7KK8Q1qP8irtSStki4oT/kVIGhBKS6iLUJ4xSyjYhxEXAH4UQTjXp1Sh7m6LxIPArIUQ/ymvrRcBdQnlFbEN5XdgEfA14QAjRh+LMGiYCv1QnAAvKq+onUV6zf0sIsQFl8L2pU/cLSaQJxx9Q9ui9nyBd2ojzRDSGRCellMBl6l+6KATuVp/G+4FtKPu8zgf+KoRollKeLIR4F6UftqPsFUVK6RUKCeNuoRAa+lGevGv2fSiE+BLwuBDiLCnls0KIUcAb6tOubpQ9X81q+kx9yQhPofhWhF+r5/6JssVjmxDiY5Snxf8EUBf4nnhlqGPjP8BPUZ6QvK6mbQR+py6UQQmdFwF1HN6Isk3kNJRtC+tUX24DFkopX1B/PKwVQnhR9o/+SG2fX6mT+3aU8THQuAFl/G1QbdqJEqlEF1LKZvVp1H9QXtWvw+DGayJlnI/iY+F4EjgKZRvVJpQx8V+gUx2bRnOpiUMbRuP2F8CTQojFKA9RjJ7ENqJs8QqiRHL6P4N0twghrg77fixwPwqvY6/6QOdB7aEJyj32HyicjG9JKd1CiN+iM+/Fuzgp5UtCiKOA/6jr/h6U/faBONkuBH4thFilXtPiOOWkHMUpWxDKGsCEiexAKDFX35VS3p9tW0wMPoSy/eE3Uso52bbFhAkjCCEKVW5AOcrbi8+E/egzYSLrEMqWuzXRe5BNZAbzSbGJrEEI8Q7KL+vLs22LicGHEOJbKHt+v5tlU0yYSIQ16pseB3CDuSA2YeLwgPmk2IQJEyZMmDBhwsRhD5NoZ8KECRMmTJgwYeKwh7koNmHChAkTJkyYMHHYw1wUmzBhwoQJEyZMmDjsYS6KTZgwYcKECRMmTBz2MBfFJkyYMGHChAkTJg57/H8OUboiXAyopgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import packages\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Plot pairwise relationships\n",
    "sns.pairplot(so_numeric_df)\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c4ff6d9",
   "metadata": {},
   "source": [
    "### Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cf2491ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Age_MM  Age\n",
      "0  0.046154   21\n",
      "1  0.307692   38\n",
      "2  0.415385   45\n",
      "3  0.430769   46\n",
      "4  0.323077   39\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1597: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[key] = value\n",
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1738: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(loc, value[:, i].tolist(), pi)\n"
     ]
    }
   ],
   "source": [
    "# Import MinMaxScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Instantiate MinMaxScaler\n",
    "MM_scaler = MinMaxScaler()\n",
    "\n",
    "# Fit MM_scaler to the data\n",
    "MM_scaler.fit(so_numeric_df[['Age']])\n",
    "\n",
    "# Transform the data using the fitted scaler\n",
    "scaler = MM_scaler.transform(so_numeric_df[['Age']])\n",
    "so_numeric_df.loc[:, 'Age_MM'] = scaler\n",
    "\n",
    "\n",
    "# Compare the origional and transformed column\n",
    "print(so_numeric_df[['Age_MM', 'Age']].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3549fc26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ConvertedSalary</th>\n",
       "      <th>StackOverflowJobsRecommend</th>\n",
       "      <th>Age</th>\n",
       "      <th>Years Experience</th>\n",
       "      <th>Age_MM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>0.046154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70841.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>38</td>\n",
       "      <td>9</td>\n",
       "      <td>0.307692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>45</td>\n",
       "      <td>11</td>\n",
       "      <td>0.415385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21426.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46</td>\n",
       "      <td>12</td>\n",
       "      <td>0.430769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41671.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>39</td>\n",
       "      <td>7</td>\n",
       "      <td>0.323077</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>994</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45</td>\n",
       "      <td>10</td>\n",
       "      <td>0.415385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>58746.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>27</td>\n",
       "      <td>16</td>\n",
       "      <td>0.138462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>55000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "      <td>16</td>\n",
       "      <td>0.553846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>0.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>1000000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>999 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ConvertedSalary  StackOverflowJobsRecommend  Age  Years Experience  \\\n",
       "0                NaN                         NaN   21                13   \n",
       "1            70841.0                         7.0   38                 9   \n",
       "2                NaN                         8.0   45                11   \n",
       "3            21426.0                         NaN   46                12   \n",
       "4            41671.0                         8.0   39                 7   \n",
       "..               ...                         ...  ...               ...   \n",
       "994              NaN                         NaN   45                10   \n",
       "995          58746.0                         8.0   27                16   \n",
       "996          55000.0                         NaN   54                16   \n",
       "997              NaN                         NaN   23                 9   \n",
       "998        1000000.0                         NaN   18                 7   \n",
       "\n",
       "       Age_MM  \n",
       "0    0.046154  \n",
       "1    0.307692  \n",
       "2    0.415385  \n",
       "3    0.430769  \n",
       "4    0.323077  \n",
       "..        ...  \n",
       "994  0.415385  \n",
       "995  0.138462  \n",
       "996  0.553846  \n",
       "997  0.076923  \n",
       "998  0.000000  \n",
       "\n",
       "[999 rows x 5 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "so_numeric_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d73eb30",
   "metadata": {},
   "source": [
    "### Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d465013d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Age_SS  Age\n",
      "0 -1.132431   21\n",
      "1  0.150734   38\n",
      "2  0.679096   45\n",
      "3  0.754576   46\n",
      "4  0.226214   39\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-36-c8058629ed8a>:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  so_numeric_df['Age_SS'] = SS_scaler.transform(so_numeric_df[['Age']])\n"
     ]
    }
   ],
   "source": [
    "# Import StandardScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Instantiate StandardScaler\n",
    "SS_scaler = StandardScaler()\n",
    "\n",
    "# Fit SS_scaler to the data\n",
    "SS_scaler.fit(so_numeric_df[['Age']])\n",
    "\n",
    "# Transform the data using the fitted scaler\n",
    "so_numeric_df['Age_SS'] = SS_scaler.transform(so_numeric_df[['Age']])\n",
    "\n",
    "# Compare the origional and transformed column\n",
    "print(so_numeric_df[['Age_SS', 'Age']].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2d1c49a",
   "metadata": {},
   "source": [
    "### Log transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ab976aa7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-37-09e21efa5f14>:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  so_numeric_df['ConvertedSalary_LG'] = pow_trans.transform(so_numeric_df[['ConvertedSalary']])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import PowerTransformer\n",
    "from sklearn.preprocessing import PowerTransformer\n",
    "\n",
    "# Instantiate PowerTransformer\n",
    "pow_trans = PowerTransformer()\n",
    "\n",
    "# Train the transform on the data\n",
    "pow_trans.fit(so_numeric_df[['ConvertedSalary']])\n",
    "\n",
    "# Apply the power transform to the data\n",
    "so_numeric_df['ConvertedSalary_LG'] = pow_trans.transform(so_numeric_df[['ConvertedSalary']])\n",
    "\n",
    "# Plot the data before and after the transformation\n",
    "so_numeric_df[['ConvertedSalary', 'ConvertedSalary_LG']].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d628454b",
   "metadata": {},
   "source": [
    "### Removing outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aa30b1a",
   "metadata": {},
   "source": [
    "### Percentage based outlier removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "284cbb8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "# Find the 95th quantile\n",
    "quantile = so_numeric_df['ConvertedSalary'].quantile(.95)\n",
    "\n",
    "# Trim the outliers\n",
    "trimmed_df = so_numeric_df[so_numeric_df['ConvertedSalary'] < quantile]\n",
    "\n",
    "# The original histogram\n",
    "so_numeric_df[['ConvertedSalary']].hist()\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "# The trimmed histogram\n",
    "trimmed_df[['ConvertedSalary']].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31d9e6b5",
   "metadata": {},
   "source": [
    "### Statistical outlier removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3f4b2a70",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "so_numeric_df[['ConvertedSalary']].boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e7569159",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Find the mean and standard dev\n",
    "std = so_numeric_df['ConvertedSalary'].std()\n",
    "mean = so_numeric_df['ConvertedSalary'].mean()\n",
    "\n",
    "# Calculate the cutoff\n",
    "cut_off = std * 3\n",
    "lower, upper = mean - cut_off, mean + cut_off\n",
    "\n",
    "# Trim the outliers\n",
    "trimmed_df = so_numeric_df[(so_numeric_df['ConvertedSalary'] > lower) & (so_numeric_df['ConvertedSalary'] < upper)]\n",
    "\n",
    "# The trimmed box plot\n",
    "trimmed_df[['ConvertedSalary']].boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dac826c",
   "metadata": {},
   "source": [
    "### Train and testing transformations (I)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "784fc531",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9f2dbe9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ConvertedSalary</th>\n",
       "      <th>StackOverflowJobsRecommend</th>\n",
       "      <th>Age</th>\n",
       "      <th>Years Experience</th>\n",
       "      <th>Age_MM</th>\n",
       "      <th>Age_SS</th>\n",
       "      <th>ConvertedSalary_LG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>0.046154</td>\n",
       "      <td>-1.132431</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70841.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>38</td>\n",
       "      <td>9</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.150734</td>\n",
       "      <td>0.312939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>45</td>\n",
       "      <td>11</td>\n",
       "      <td>0.415385</td>\n",
       "      <td>0.679096</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21426.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46</td>\n",
       "      <td>12</td>\n",
       "      <td>0.430769</td>\n",
       "      <td>0.754576</td>\n",
       "      <td>-0.652182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41671.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>39</td>\n",
       "      <td>7</td>\n",
       "      <td>0.323077</td>\n",
       "      <td>0.226214</td>\n",
       "      <td>-0.135589</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ConvertedSalary  StackOverflowJobsRecommend  Age  Years Experience  \\\n",
       "0              NaN                         NaN   21                13   \n",
       "1          70841.0                         7.0   38                 9   \n",
       "2              NaN                         8.0   45                11   \n",
       "3          21426.0                         NaN   46                12   \n",
       "4          41671.0                         8.0   39                 7   \n",
       "\n",
       "     Age_MM    Age_SS  ConvertedSalary_LG  \n",
       "0  0.046154 -1.132431                 NaN  \n",
       "1  0.307692  0.150734            0.312939  \n",
       "2  0.415385  0.679096                 NaN  \n",
       "3  0.430769  0.754576           -0.652182  \n",
       "4  0.323077  0.226214           -0.135589  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "so_numeric_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ba4c8a7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X= so_numeric_df.drop('StackOverflowJobsRecommend', axis='columns')\n",
    "y = so_numeric_df['StackOverflowJobsRecommend']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "61cc7a27",
   "metadata": {},
   "outputs": [],
   "source": [
    "so_train_numeric, so_test_numeric, y_train, y_test = train_test_split(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5efa43ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ConvertedSalary</th>\n",
       "      <th>Age</th>\n",
       "      <th>Years Experience</th>\n",
       "      <th>Age_MM</th>\n",
       "      <th>Age_SS</th>\n",
       "      <th>ConvertedSalary_LG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>140000.0</td>\n",
       "      <td>70</td>\n",
       "      <td>8</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2.566103</td>\n",
       "      <td>0.940549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>NaN</td>\n",
       "      <td>33</td>\n",
       "      <td>5</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>-0.226668</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>85000.0</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.358872</td>\n",
       "      <td>0.474917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>10584.0</td>\n",
       "      <td>69</td>\n",
       "      <td>8</td>\n",
       "      <td>0.784615</td>\n",
       "      <td>2.490623</td>\n",
       "      <td>-1.149139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>45</td>\n",
       "      <td>11</td>\n",
       "      <td>0.415385</td>\n",
       "      <td>0.679096</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>9984.0</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>0.092308</td>\n",
       "      <td>-0.905990</td>\n",
       "      <td>-1.188076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>110000.0</td>\n",
       "      <td>48</td>\n",
       "      <td>9</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.905537</td>\n",
       "      <td>0.711391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>0.492308</td>\n",
       "      <td>1.056497</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>19092.0</td>\n",
       "      <td>44</td>\n",
       "      <td>19</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.603616</td>\n",
       "      <td>-0.736894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>70000.0</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "      <td>0.276923</td>\n",
       "      <td>-0.000227</td>\n",
       "      <td>0.302468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>749 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ConvertedSalary  Age  Years Experience    Age_MM    Age_SS  \\\n",
       "420         140000.0   70                 8  0.800000  2.566103   \n",
       "934              NaN   33                 5  0.230769 -0.226668   \n",
       "518          85000.0   18                11  0.000000 -1.358872   \n",
       "462          10584.0   69                 8  0.784615  2.490623   \n",
       "2                NaN   45                11  0.415385  0.679096   \n",
       "..               ...  ...               ...       ...       ...   \n",
       "106           9984.0   24                 6  0.092308 -0.905990   \n",
       "270         110000.0   48                 9  0.461538  0.905537   \n",
       "860              NaN   50                13  0.492308  1.056497   \n",
       "435          19092.0   44                19  0.400000  0.603616   \n",
       "102          70000.0   36                12  0.276923 -0.000227   \n",
       "\n",
       "     ConvertedSalary_LG  \n",
       "420            0.940549  \n",
       "934                 NaN  \n",
       "518            0.474917  \n",
       "462           -1.149139  \n",
       "2                   NaN  \n",
       "..                  ...  \n",
       "106           -1.188076  \n",
       "270            0.711391  \n",
       "860                 NaN  \n",
       "435           -0.736894  \n",
       "102            0.302468  \n",
       "\n",
       "[749 rows x 6 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "so_train_numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "906c8018",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Age    Age_ss\n",
      "453   29 -0.526719\n",
      "793   25 -0.831362\n",
      "209   46  0.768014\n",
      "309   18 -1.364487\n",
      "740   26 -0.755201\n"
     ]
    }
   ],
   "source": [
    "# Import StandardScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Apply a standard scaler to the data\n",
    "SS_scaler = StandardScaler()\n",
    "\n",
    "# Fit the standard scaler to the data\n",
    "SS_scaler.fit(so_train_numeric[['Age']])\n",
    "\n",
    "# Transform the test data using the fitted scaler\n",
    "so_test_numeric['Age_ss'] = SS_scaler.transform(so_test_numeric[['Age']])\n",
    "print(so_test_numeric[['Age', 'Age_ss']].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fac04ad",
   "metadata": {},
   "source": [
    "### Train and testing transformations (II)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2bee7389",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_std = so_train_numeric['ConvertedSalary'].std()\n",
    "train_mean = so_train_numeric['ConvertedSalary'].mean()\n",
    "\n",
    "cut_off = train_std * 3\n",
    "train_lower, train_upper = train_mean - cut_off, train_mean + cut_off\n",
    "\n",
    "# Trim the test DataFrame\n",
    "trimmed_df = so_test_numeric[(so_test_numeric['ConvertedSalary'] < train_upper) \\\n",
    "                             & (so_test_numeric['ConvertedSalary'] > train_lower)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2c7da9a",
   "metadata": {},
   "source": [
    "## Encoding text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fdd8b46",
   "metadata": {},
   "source": [
    "### Cleaning up your text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "92ec8a8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "speech_df = pd.read_csv('inaugural_speeches.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "658969e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Inaugural Address</th>\n",
       "      <th>Date</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>George Washington</td>\n",
       "      <td>First Inaugural Address</td>\n",
       "      <td>Thursday, April 30, 1789</td>\n",
       "      <td>Fellow-Citizens of the Senate and of the House...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>George Washington</td>\n",
       "      <td>Second Inaugural Address</td>\n",
       "      <td>Monday, March 4, 1793</td>\n",
       "      <td>Fellow Citizens:  I AM again called upon by th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John Adams</td>\n",
       "      <td>Inaugural Address</td>\n",
       "      <td>Saturday, March 4, 1797</td>\n",
       "      <td>WHEN it was first perceived, in early times, t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thomas Jefferson</td>\n",
       "      <td>First Inaugural Address</td>\n",
       "      <td>Wednesday, March 4, 1801</td>\n",
       "      <td>Friends and Fellow-Citizens:  CALLED upon to u...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thomas Jefferson</td>\n",
       "      <td>Second Inaugural Address</td>\n",
       "      <td>Monday, March 4, 1805</td>\n",
       "      <td>PROCEEDING, fellow-citizens, to that qualifica...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name         Inaugural Address                      Date  \\\n",
       "0  George Washington   First Inaugural Address  Thursday, April 30, 1789   \n",
       "1  George Washington  Second Inaugural Address     Monday, March 4, 1793   \n",
       "2         John Adams         Inaugural Address   Saturday, March 4, 1797   \n",
       "3   Thomas Jefferson   First Inaugural Address  Wednesday, March 4, 1801   \n",
       "4   Thomas Jefferson  Second Inaugural Address     Monday, March 4, 1805   \n",
       "\n",
       "                                                text  \n",
       "0  Fellow-Citizens of the Senate and of the House...  \n",
       "1  Fellow Citizens:  I AM again called upon by th...  \n",
       "2  WHEN it was first perceived, in early times, t...  \n",
       "3  Friends and Fellow-Citizens:  CALLED upon to u...  \n",
       "4  PROCEEDING, fellow-citizens, to that qualifica...  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "speech_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2ff321f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    Fellow-Citizens of the Senate and of the House...\n",
      "1    Fellow Citizens:  I AM again called upon by th...\n",
      "2    WHEN it was first perceived, in early times, t...\n",
      "3    Friends and Fellow-Citizens:  CALLED upon to u...\n",
      "4    PROCEEDING, fellow-citizens, to that qualifica...\n",
      "Name: text, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Print the first 5 rows of the text column\n",
    "print(speech_df['text'].head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4c527ae5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    fellow citizens of the senate and of the house...\n",
      "1    fellow citizens   i am again called upon by th...\n",
      "2    when it was first perceived  in early times  t...\n",
      "3    friends and fellow citizens   called upon to u...\n",
      "4    proceeding  fellow citizens  to that qualifica...\n",
      "Name: text_clean, dtype: object\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-51-6cef27110c6b>:2: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  speech_df['text_clean'] = speech_df['text'].str.replace('[^a-zA-Z]', ' ')\n"
     ]
    }
   ],
   "source": [
    "# Replace all non letter characters with a whitespace\n",
    "speech_df['text_clean'] = speech_df['text'].str.replace('[^a-zA-Z]', ' ')\n",
    "\n",
    "# Change to lower case\n",
    "speech_df['text_clean'] = speech_df['text_clean'].str.lower()\n",
    "\n",
    "# Print the first 5 rows of the text_clean column\n",
    "print(speech_df['text_clean'].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a623a1d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                          text_clean  char_cnt  word_cnt  \\\n",
      "0  fellow citizens of the senate and of the house...      8616      1432   \n",
      "1  fellow citizens   i am again called upon by th...       787       135   \n",
      "2  when it was first perceived  in early times  t...     13871      2323   \n",
      "3  friends and fellow citizens   called upon to u...     10144      1736   \n",
      "4  proceeding  fellow citizens  to that qualifica...     12902      2169   \n",
      "\n",
      "   avg_word_length  \n",
      "0         6.016760  \n",
      "1         5.829630  \n",
      "2         5.971158  \n",
      "3         5.843318  \n",
      "4         5.948363  \n"
     ]
    }
   ],
   "source": [
    "# Find the length of each text\n",
    "speech_df['char_cnt'] = speech_df['text_clean'].str.len()\n",
    "\n",
    "# Count the number of words in each text\n",
    "speech_df['word_cnt'] = speech_df['text_clean'].str.split().str.len()\n",
    "\n",
    "# Find the average length of word\n",
    "speech_df['avg_word_length'] = speech_df['char_cnt'] / speech_df['word_cnt']\n",
    "\n",
    "# Print the first 5 rows of these columns\n",
    "print(speech_df[['text_clean', 'char_cnt', 'word_cnt', 'avg_word_length']].head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "447959f2",
   "metadata": {},
   "source": [
    "### Counting words (I)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "81a48fd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['abandon', 'abandoned', 'abandonment', 'abate', 'abdicated', 'abeyance', 'abhorring', 'abide', 'abiding', 'abilities', 'ability', 'abject', 'able', 'ably', 'abnormal', 'abode', 'abolish', 'abolished', 'abolishing', 'aboriginal', 'aborigines', 'abound', 'abounding', 'abounds', 'about', 'above', 'abraham', 'abreast', 'abridging', 'abroad', 'absence', 'absent', 'absolute', 'absolutely', 'absolutism', 'absorb', 'absorbed', 'absorbing', 'absorbs', 'abstain', 'abstaining', 'abstract', 'abstractions', 'absurd', 'abundance', 'abundant', 'abundantly', 'abuse', 'abused', 'abuses', 'academies', 'accept', 'acceptance', 'accepted', 'accepting', 'accepts', 'access', 'accessible', 'accession', 'accident', 'accidental', 'accidents', 'acclaim', 'accommodation', 'accommodations', 'accompanied', 'accompany', 'accomplish', 'accomplished', 'accomplishing', 'accomplishment', 'accomplishments', 'accord', 'accordance', 'accorded', 'according', 'accordingly', 'accords', 'account', 'accountability', 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'spiritually', 'spite', 'splendid', 'splendor', 'split', 'spoil', 'spoils', 'spoke', 'spoken', 'spokesmen', 'spoliation', 'sponsors', 'spot', 'spotless', 'sprang', 'sprawl', 'spread', 'spreading', 'spring', 'springing', 'springs', 'spun', 'squadron', 'squander', 'squandered', 'squandering', 'square', 'squared', 'st', 'stability', 'stable', 'staff', 'stage', 'stages', 'staggering', 'stagnant', 'stagnation', 'stained', 'stairway', 'stake', 'staked', 'stakes', 'stale', 'stamina', 'stamp', 'stamped', 'stamping', 'stanch', 'stand', 'standard', 'standards', 'standing', 'standpoint', 'stands', 'staple', 'star', 'stars', 'start', 'started', 'starting', 'starved', 'state', 'stated', 'stately', 'statement', 'statements', 'states', 'statesman', 'statesmanship', 'statesmen', 'stating', 'station', 'stations', 'statistics', 'stature', 'status', 'statute', 'statutes', 'staunchest', 'stay', 'stayed', 'stays', 'stead', 'steadfast', 'steadfastness', 'steadily', 'steady', 'stealing', 'steam', 'steamers', 'steamship', 'steeled', 'steer', 'stennis', 'step', 'steps', 'sterile', 'stern', 'sternest', 'sternly', 'stewardship', 'stifle', 'stifles', 'still', 'stilling', 'stimulate', 'stimulating', 'stimulation', 'stimulus', 'stipulated', 'stipulations', 'stir', 'stirred', 'stock', 'stolen', 'stone', 'stonewall', 'stood', 'stop', 'stopped', 'stopping', 'stops', 'store', 'storm', 'storms', 'story', 'stout', 'straight', 'strain', 'strained', 'strange', 'stranger', 'strangers', 'strangle', 'strangled', 'stream', 'streams', 'streets', 'strength', 'strengthen', 'strengthened', 'strengthening', 'strengthens', 'strengths', 'strenuous', 'stress', 'stretched', 'stretching', 'stricken', 'strict', 'strictest', 'strictly', 'stride', 'strides', 'strife', 'strike', 'strikes', 'striking', 'stringent', 'strings', 'stripped', 'strive', 'strives', 'striving', 'strong', 'stronger', 'strongest', 'strongly', 'strove', 'struck', 'structure', 'structures', 'struggle', 'struggled', 'struggles', 'struggling', 'stubbornness', 'stuck', 'students', 'studied', 'studies', 'studiously', 'study', 'studying', 'stuff', 'stunning', 'stupefies', 'stupendous', 'stupid', 'sturdiness', 'sturdy', 'style', 'subdivision', 'subje', 'subject', 'subjected', 'subjects', 'sublime', 'submission', 'submit', 'submits', 'submitted', 'submitting', 'subordinate', 'subordination', 'subsequent', 'subserved', 'subserviency', 'subsided', 'subsidiary', 'subsidies', 'subsidized', 'subsistence', 'substance', 'substantial', 'substantially', 'substantive', 'substitute', 'substitutes', 'substituting', 'subterfuge', 'subterfuges', 'subtlety', 'subversion', 'succeed', 'succeeding', 'success', 'successes', 'successful', 'successfully', 'succession', 'successive', 'successively', 'successor', 'successors', 'succumb', 'succumbed', 'such', 'sudden', 'suddenly', 'suffer', 'suffered', 'suffering', 'sufferings', 'suffers', 'suffice', 'sufficiency', 'sufficient', 'sufficiently', 'suffrage', 'suffrages', 'sugar', 'suggest', 'suggested', 'suggesting', 'suggestion', 'suggestions', 'suggestive', 'suggests', 'suicide', 'suit', 'suitable', 'suitably', 'suited', 'sum', 'summary', 'summer', 'summit', 'summon', 'summoned', 'summons', 'sums', 'sumter', 'sun', 'sundered', 'sung', 'sunk', 'sunlight', 'suns', 'sunset', 'sunshine', 'superbly', 'superficial', 'superfluous', 'supergovernment', 'superintend', 'superior', 'superiority', 'superiors', 'supersede', 'superseded', 'superstructure', 'supervising', 'supervision', 'supplanted', 'supplement', 'supplemented', 'supplicating', 'supplication', 'supplications', 'supplies', 'supply', 'supplying', 'support', 'supported', 'supporters', 'supporting', 'supports', 'suppose', 'supposed', 'suppress', 'suppressed', 'suppressing', 'suppression', 'supremacy', 'supreme', 'sure', 'surely', 'surer', 'surest', 'surface', 'surge', 'surging', 'surmount', 'surmounting', 'surpass', 'surpassed', 'surpasses', 'surpassing', 'surplus', 'surpluses', 'surprised', 'surrender', 'surrendered', 'surround', 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'tobacco', 'today', 'together', 'toil', 'toiled', 'toilers', 'toiling', 'token', 'told', 'tolerance', 'tolerant', 'tolerate', 'tolerated', 'toleration', 'tomb', 'tombstones', 'tomorrow', 'tomorrows', 'tone', 'tones', 'tongues', 'tonnage', 'too', 'took', 'tool', 'tools', 'topic', 'topics', 'toppled', 'torch', 'torment', 'tormented', 'torn', 'torture', 'total', 'totalitarian', 'totally', 'touch', 'touched', 'touching', 'touchstone', 'touchstones', 'toward', 'towards', 'tower', 'town', 'towns', 'traced', 'traces', 'tracing', 'tracts', 'trade', 'traded', 'trading', 'tradition', 'traditional', 'traditions', 'traffic', 'tragedy', 'tragic', 'tragically', 'train', 'trained', 'training', 'trait', 'traitors', 'traits', 'tranquil', 'tranquillity', 'transaction', 'transactions', 'transcend', 'transcended', 'transcendent', 'transcendently', 'transcending', 'transcends', 'transcontinental', 'transfer', 'transferred', 'transferring', 'transfers', 'transform', 'transit', 'transition', 'transitory', 'translated', 'translating', 'transmit', 'transmitted', 'transpire', 'transportation', 'transported', 'trapped', 'trappings', 'travail', 'travel', 'traveled', 'traveler', 'travels', 'traversed', 'traversing', 'tread', 'treading', 'treason', 'treasure', 'treasured', 'treasures', 'treasury', 'treat', 'treated', 'treaties', 'treating', 'treatment', 'treats', 'treaty', 'tree', 'tremble', 'trembling', 'tremendous', 'trend', 'treptow', 'trespass', 'trial', 'trials', 'tribe', 'tribes', 'tribulation', 'tribunal', 'tribunals', 'tributary', 'tribute', 'tried', 'trifling', 'trillions', 'trim', 'triple', 'triumph', 'triumphant', 'triumphantly', 'triumphed', 'triumphs', 'troops', 'tropic', 'troubled', 'troubles', 'troublesome', 'truce', 'truckdrivers', 'truculence', 'true', 'truest', 'truly', 'truman', 'trumpet', 'trumpets', 'trust', 'trusted', 'trustees', 'trustful', 'trusting', 'trusts', 'truth', 'truths', 'try', 'trying', 'tumults', 'tumultuous', 'tunnels', 'turbulence', 'turmoil', 'turn', 'turned', 'turning', 'turns', 'tutor', 'twelve', 'twentieth', 'twenty', 'twice', 'twilight', 'two', 'type', 'tyrannical', 'tyranny', 'tyrant', 'tyrants', 'ultimate', 'ultimately', 'umerable', 'umpire', 'unabashed', 'unabated', 'unable', 'unaccounted', 'unadjusted', 'unafraid', 'unaided', 'unalienable', 'unalloyed', 'unanimity', 'unanimous', 'unanswerably', 'unassailed', 'unattended', 'unauthorized', 'unavoidable', 'unavoidably', 'unbalanced', 'unbending', 'unbiased', 'unborn', 'unbounded', 'unbroken', 'uncanny', 'unceasing', 'unceasingly', 'uncertain', 'uncertainty', 'unchanged', 'unchanging', 'uncharitableness', 'unclench', 'unclimbed', 'uncomplaining', 'uncompromising', 'unconnected', 'unconquerable', 'unconstitutional', 'uncontrollable', 'uncontrolled', 'uncorrupted', 'uncounted', 'uncovered', 'uncrossed', 'unctions', 'undauntedly', 'undelayed', 'under', 'undercurrent', 'underdeveloped', 'undergoing', 'underlies', 'undermine', 'undermines', 'underneath', 'understand', 'understandable', 'understanding', 'understandings', 'understands', 'understood', 'undertake', 'undertaken', 'undertaking', 'undertakings', 'undertone', 'undeserved', 'undeveloped', 'undeviating', 'undiminished', 'undisturbed', 'undo', 'undoing', 'undone', 'undoubted', 'undoubtedly', 'undue', 'unduly', 'uneconomical', 'uneducated', 'unemployed', 'unemployment', 'unequal', 'unequaled', 'unequivocal', 'unerring', 'unessential', 'unexaggerated', 'unexampled', 'unexceptionable', 'unexpected', 'unexplained', 'unexplored', 'unfailing', 'unfair', 'unfairly', 'unfaithful', 'unfaltering', 'unfavorable', 'unfeeling', 'unfinished', 'unfitness', 'unflinching', 'unfocused', 'unfolded', 'unfolding', 'unfolds', 'unforeseen', 'unfortunate', 'unfortunately', 'unfounded', 'unfriendly', 'unfulfilled', 'unfurl', 'unfurling', 'ungrateful', 'ungrudging', 'unhallowed', 'unhampered', 'unhappily', 'unhappy', 'unheeded', 'unhesitatingly', 'unhonored', 'unification', 'uniform', 'uniformly', 'unify', 'unifying', 'unimagined', 'unimpaired', 'unimpressive', 'uninjured', 'uninspiring', 'unintelligible', 'uninteresting', 'uninterrupted', 'union', 'unique', 'unite', 'united', 'unitedly', 'unites', 'unity', 'universal', 'universality', 'universally', 'universe', 'universities', 'unjust', 'unjustified', 'unkept', 'unknowing', 'unknown', 'unlawful', 'unlawfulness', 'unlearned', 'unleash', 'unleashed', 'unless', 'unlike', 'unlikely', 'unlimited', 'unlock', 'unmade', 'unmake', 'unmatched', 'unmindful', 'unmistakable', 'unmolested', 'unmoved', 'unnatural', 'unnecessary', 'unnoticed', 'unobtrusive', 'unparalleled', 'unpatriotic', 'unpleasant', 'unplowed', 'unpracticed', 'unprecedented', 'unprepared', 'unpreparedness', 'unprofitable', 'unprovided', 'unquestionable', 'unquestionably', 'unquestioned', 'unquestioning', 'unrealized', 'unreasonable', 'unreasonably', 'unreasoning', 'unreclaimed', 'unrecompensed', 'unregarded', 'unremittingly', 'unrepealed', 'unrequited', 'unreserved', 'unrestrained', 'unrestricted', 'unrivaled', 'unscrupulous', 'unselfish', 'unselfishness', 'unsettled', 'unshaken', 'unsolicited', 'unsolvable', 'unsought', 'unspeakable', 'unspoken', 'unstable', 'unstoppable', 'unstrained', 'unsubstantial', 'unsuccessful', 'unsung', 'unsurpassed', 'untamed', 'untarnished', 'untended', 'until', 'unto', 'untouched', 'untrammeled', 'untried', 'untroubled', 'untrue', 'untrusting', 'untruths', 'unused', 'unusual', 'unvexed', 'unwanted', 'unwarrantable', 'unwasted', 'unwavering', 'unwholesome', 'unwilling', 'unwise', 'unwisely', 'unworthiness', 'unworthy', 'up', 'upbraidings', 'upbuilding', 'upgrade', 'upheaval', 'upheavals', 'upheld', 'uphold', 'upholding', 'upon', 'upper', 'uppermost', 'upright', 'uprooted', 'uprooting', 'upward', 'urban', 'urge', 'urged', 'urgent', 'urgently', 'urging', 'us', 'usage', 'usages', 'use', 'used', 'useful', 'usefulness', 'useless', 'uselessly', 'uses', 'ushered', 'ushering', 'using', 'usual', 'usually', 'usurp', 'usurpation', 'usurpations', 'usurped', 'usurper', 'usury', 'utilities', 'utmost', 'utter', 'utterance', 'uttered', 'uttering', 'utterly', 'uttermost', 'vacant', 'vain', 'vainglory', 'valiantly', 'valid', 'valley', 'valleys', 'valor', 'valuable', 'value', 'valued', 'values', 'vanish', 'vanquished', 'variance', 'variations', 'varied', 'varieties', 'variety', 'various', 'varnish', 'vary', 'varying', 'vast', 'vastly', 'vaults', 've', 'veil', 'venality', 'venerated', 'veneration', 'venture', 'ventured', 'ventures', 'verbal', 'verdict', 'verified', 'very', 'vessel', 'vessels', 'vested', 'veto', 'vexation', 'vibrant', 'vibrations', 'vice', 'vicious', 'vicissitude', 'vicissitudes', 'victim', 'victims', 'victories', 'victorious', 'victory', 'vietnam', 'view', 'viewed', 'viewing', 'viewpoint', 'views', 'vigilance', 'vigilant', 'vigilantly', 'vigor', 'vigorous', 'vigorously', 'village', 'villages', 'vindicate', 'vindicated', 'vindication', 'vindictiveness', 'violate', 'violated', 'violates', 'violation', 'violative', 'violence', 'violent', 'virginia', 'virility', 'virtual', 'virtually', 'virtue', 'virtues', 'virtuous', 'visible', 'vision', 'visionary', 'visit', 'visitation', 'visited', 'vista', 'vital', 'vitality', 'vitalizing', 'vitally', 'vitals', 'voice', 'voices', 'void', 'volume', 'voluntarily', 'voluntary', 'volunteer', 'volution', 'vote', 'voted', 'voter', 'voters', 'votes', 'voting', 'vouchsafed', 'vow', 'vowed', 'vows', 'voyage', 'vulnerability', 'vulnerable', 'wage', 'waged', 'wages', 'waging', 'wait', 'waited', 'waiting', 'waketh', 'walk', 'walked', 'wall', 'wallet', 'walls', 'wander', 'wane', 'want', 'wanted', 'wanting', 'wants', 'war', 'wards', 'warfare', 'warm', 'warmed', 'warming', 'warmly', 'warms', 'warmth', 'warned', 'warns', 'warrant', 'warren', 'warriors', 'wars', 'wartime', 'was', 'washington', 'wastage', 'waste', 'wasted', 'wasteful', 'wastefulness', 'watch', 'watched', 'watchful', 'watchfulness', 'watching', 'watchman', 'water', 'watercourses', 'waters', 'waterway', 'waterways', 'wave', 'waved', 'waver', 'wavering', 'waves', 'way', 'ways', 'we', 'weak', 'weaken', 'weakened', 'weakening', 'weaker', 'weakest', 'weakness', 'weaknesses', 'weal', 'wealth', 'wealthy', 'weapon', 'weapons', 'weary', 'weaving', 'webster', 'weed', 'week', 'weeks', 'weigh', 'weighed', 'weight', 'weightiest', 'weighty', 'welcome', 'welcomed', 'weld', 'welfare', 'well', 'wellspring', 'went', 'were', 'west', 'western', 'westward', 'wh', 'what', 'whatever', 'whatsoever', 'wheat', 'wheels', 'when', 'whenever', 'where', 'whereas', 'wherein', 'whereof', 'wherever', 'whether', 'which', 'while', 'whilst', 'whip', 'whirlwind', 'whisper', 'white', 'whitened', 'whites', 'whither', 'who', 'whoever', 'whole', 'wholesome', 'wholly', 'whom', 'whomsoever', 'whose', 'why', 'wi', 'wicked', 'wickedness', 'wide', 'widely', 'widen', 'widened', 'widening', 'wider', 'widespread', 'widow', 'widows', 'wield', 'wields', 'wife', 'wild', 'wilderness', 'will', 'willing', 'willingly', 'willingness', 'wills', 'wily', 'win', 'wind', 'winding', 'window', 'windows', 'winds', 'wings', 'winning', 'winston', 'winter', 'wipe', 'wisdom', 'wise', 'wisely', 'wiser', 'wisest', 'wish', 'wished', 'wishes', 'wishing', 'wit', 'with', 'withal', 'withdraw', 'withdrawal', 'withdrew', 'wither', 'withered', 'withheld', 'withhold', 'within', 'without', 'withstood', 'witness', 'witnessed', 'witnesses', 'wives', 'woe', 'woman', 'womanhood', 'women', 'won', 'wonder', 'wonderful', 'wonders', 'wonted', 'wood', 'woodsmen', 'word', 'words', 'work', 'worked', 'worker', 'workers', 'workforce', 'working', 'workingman', 'workingmen', 'workmanship', 'works', 'workshop', 'world', 'worldly', 'worlds', 'worldwide', 'worm', 'worn', 'worry', 'worse', 'worship', 'worst', 'worth', 'worthily', 'worthless', 'worthy', 'would', 'wouldn', 'wounded', 'wounds', 'wreckage', 'wrecked', 'wrest', 'wretchedness', 'wright', 'wringing', 'writ', 'write', 'writers', 'writing', 'written', 'wrong', 'wrongdoing', 'wronged', 'wrongfully', 'wrongs', 'wrote', 'wrought', 'year', 'yearn', 'yearning', 'years', 'yes', 'yesterday', 'yesterdays', 'yet', 'yield', 'yielded', 'yielding', 'yields', 'york', 'yorktown', 'you', 'young', 'younger', 'youngest', 'your', 'yours', 'yourself', 'yourselves', 'youth', 'youthful', 'zeal', 'zealous', 'zealously', 'zone']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Usuario\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# Import CountVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "# Instantiate CountVectorizer\n",
    "cv = CountVectorizer()\n",
    "#cv = CountVectorizer(min_df=0.1, max_df=0.9)\n",
    "\n",
    "# Fit the vectorizer\n",
    "cv.fit(speech_df['text_clean'])\n",
    "\n",
    "# Print feature names\n",
    "print(cv.get_feature_names())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c297e14",
   "metadata": {},
   "source": [
    "### Counting words (II)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "151f7885",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 1 0 ... 0 0 0]\n",
      " ...\n",
      " [0 1 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "# Apply the vectorizer\n",
    "cv_transformed = cv.transform(speech_df['text_clean'])\n",
    "\n",
    "# Print the full array\n",
    "cv_array = cv_transformed.toarray()\n",
    "print(cv_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "65050d5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(58, 9043)\n"
     ]
    }
   ],
   "source": [
    "# Print the shape of cv_array\n",
    "print(cv_array.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1775ea83",
   "metadata": {},
   "source": [
    "### Limiting your features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3ae8cb6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(58, 818)\n"
     ]
    }
   ],
   "source": [
    "# Import CountVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "# Specify arguements to limit the number of features generated\n",
    "cv = CountVectorizer(min_df=.2, max_df=.8)\n",
    "\n",
    "# Fit, transform, and convert into array\n",
    "cv_transformed = cv.fit_transform(speech_df['text_clean'])\n",
    "cv_array = cv_transformed.toarray()\n",
    "\n",
    "# Print the array shape\n",
    "print(cv_array.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc61b2d1",
   "metadata": {},
   "source": [
    "### Text to DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8d305750",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Name         Inaugural Address                      Date  \\\n",
      "0  George Washington   First Inaugural Address  Thursday, April 30, 1789   \n",
      "1  George Washington  Second Inaugural Address     Monday, March 4, 1793   \n",
      "2         John Adams         Inaugural Address   Saturday, March 4, 1797   \n",
      "3   Thomas Jefferson   First Inaugural Address  Wednesday, March 4, 1801   \n",
      "4   Thomas Jefferson  Second Inaugural Address     Monday, March 4, 1805   \n",
      "\n",
      "                                                text  \\\n",
      "0  Fellow-Citizens of the Senate and of the House...   \n",
      "1  Fellow Citizens:  I AM again called upon by th...   \n",
      "2  WHEN it was first perceived, in early times, t...   \n",
      "3  Friends and Fellow-Citizens:  CALLED upon to u...   \n",
      "4  PROCEEDING, fellow-citizens, to that qualifica...   \n",
      "\n",
      "                                          text_clean  char_cnt  word_cnt  \\\n",
      "0  fellow citizens of the senate and of the house...      8616      1432   \n",
      "1  fellow citizens   i am again called upon by th...       787       135   \n",
      "2  when it was first perceived  in early times  t...     13871      2323   \n",
      "3  friends and fellow citizens   called upon to u...     10144      1736   \n",
      "4  proceeding  fellow citizens  to that qualifica...     12902      2169   \n",
      "\n",
      "   avg_word_length  Counts_abiding  Counts_ability  ...  Counts_women  \\\n",
      "0         6.016760               0               0  ...             0   \n",
      "1         5.829630               0               0  ...             0   \n",
      "2         5.971158               0               0  ...             0   \n",
      "3         5.843318               0               0  ...             0   \n",
      "4         5.948363               0               0  ...             0   \n",
      "\n",
      "   Counts_words  Counts_work  Counts_wrong  Counts_year  Counts_years  \\\n",
      "0             0            0             0            0             1   \n",
      "1             0            0             0            0             0   \n",
      "2             0            0             0            2             3   \n",
      "3             0            1             2            0             0   \n",
      "4             0            0             0            2             2   \n",
      "\n",
      "   Counts_yet  Counts_you  Counts_young  Counts_your  \n",
      "0           0           5             0            9  \n",
      "1           0           0             0            1  \n",
      "2           0           0             0            1  \n",
      "3           2           7             0            7  \n",
      "4           2           4             0            4  \n",
      "\n",
      "[5 rows x 826 columns]\n"
     ]
    }
   ],
   "source": [
    "# Create a DataFrame with these features\n",
    "cv_df = pd.DataFrame(cv_array, \n",
    "                     columns=cv.get_feature_names()).add_prefix('Counts_')\n",
    "\n",
    "# Add the new columns to the original DataFrame\n",
    "speech_df_new = pd.concat([speech_df, cv_df], axis=1, sort=False)\n",
    "print(speech_df_new.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba22ac68",
   "metadata": {},
   "source": [
    "### Tf-idf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ccabdd81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   TFIDF_action  TFIDF_administration  TFIDF_america  TFIDF_american  \\\n",
      "0      0.000000              0.133415       0.000000        0.105388   \n",
      "1      0.000000              0.261016       0.266097        0.000000   \n",
      "2      0.000000              0.092436       0.157058        0.073018   \n",
      "3      0.000000              0.092693       0.000000        0.000000   \n",
      "4      0.041334              0.039761       0.000000        0.031408   \n",
      "\n",
      "   TFIDF_americans  TFIDF_believe  TFIDF_best  TFIDF_better  TFIDF_change  \\\n",
      "0              0.0       0.000000    0.000000      0.000000      0.000000   \n",
      "1              0.0       0.000000    0.000000      0.000000      0.000000   \n",
      "2              0.0       0.000000    0.026112      0.060460      0.000000   \n",
      "3              0.0       0.090942    0.117831      0.045471      0.053335   \n",
      "4              0.0       0.000000    0.067393      0.039011      0.091514   \n",
      "\n",
      "   TFIDF_citizens  ...  TFIDF_things  TFIDF_time  TFIDF_today  TFIDF_union  \\\n",
      "0        0.229644  ...      0.000000    0.045929          0.0     0.136012   \n",
      "1        0.179712  ...      0.000000    0.000000          0.0     0.000000   \n",
      "2        0.106072  ...      0.032030    0.021214          0.0     0.062823   \n",
      "3        0.223369  ...      0.048179    0.000000          0.0     0.094497   \n",
      "4        0.273760  ...      0.082667    0.164256          0.0     0.121605   \n",
      "\n",
      "   TFIDF_united  TFIDF_war  TFIDF_way  TFIDF_work  TFIDF_world  TFIDF_years  \n",
      "0      0.203593   0.000000   0.060755    0.000000     0.045929     0.052694  \n",
      "1      0.199157   0.000000   0.000000    0.000000     0.000000     0.000000  \n",
      "2      0.070529   0.024339   0.000000    0.000000     0.063643     0.073018  \n",
      "3      0.000000   0.036610   0.000000    0.039277     0.095729     0.000000  \n",
      "4      0.030338   0.094225   0.000000    0.000000     0.054752     0.062817  \n",
      "\n",
      "[5 rows x 100 columns]\n"
     ]
    }
   ],
   "source": [
    "# Import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "# Instantiate TfidfVectorizer\n",
    "tv = TfidfVectorizer(max_features=100, stop_words='english')\n",
    "\n",
    "# Fit the vectroizer and transform the data\n",
    "tv_transformed = tv.fit_transform(speech_df['text_clean'])\n",
    "\n",
    "# Create a DataFrame with these features\n",
    "tv_df = pd.DataFrame(tv_transformed.toarray(), \n",
    "                     columns=tv.get_feature_names_out()).add_prefix('TFIDF_')\n",
    "print(tv_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e62e4314",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TFIDF_government    0.367430\n",
      "TFIDF_public        0.333237\n",
      "TFIDF_present       0.315182\n",
      "TFIDF_duty          0.238637\n",
      "TFIDF_country       0.229644\n",
      "Name: 0, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Isolate the row to be examined\n",
    "sample_row = tv_df.iloc[0]\n",
    "\n",
    "# Print the top 5 words of the sorted output\n",
    "print(sample_row.sort_values(ascending=False).head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63440493",
   "metadata": {},
   "source": [
    "### Transforming unseen data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "aa80ad47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 58 entries, 0 to 57\n",
      "Data columns (total 8 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Name               58 non-null     object \n",
      " 1   Inaugural Address  58 non-null     object \n",
      " 2   Date               58 non-null     object \n",
      " 3   text               58 non-null     object \n",
      " 4   text_clean         58 non-null     object \n",
      " 5   char_cnt           58 non-null     int64  \n",
      " 6   word_cnt           58 non-null     int64  \n",
      " 7   avg_word_length    58 non-null     float64\n",
      "dtypes: float64(1), int64(2), object(5)\n",
      "memory usage: 3.8+ KB\n"
     ]
    }
   ],
   "source": [
    "speech_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "0b260f26",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_speech_df = speech_df.iloc[0:46]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a49dd48b",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_speech_df = speech_df.iloc[46:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "34efc51f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   TFIDF_action  TFIDF_administration  TFIDF_america  TFIDF_american  \\\n",
      "0      0.000000              0.000000       0.536937        0.036504   \n",
      "1      0.000000              0.000000       0.123423        0.132160   \n",
      "2      0.036579              0.065076       0.255644        0.030416   \n",
      "3      0.000000              0.000000       0.212346        0.151584   \n",
      "4      0.066046              0.000000       0.269257        0.054917   \n",
      "\n",
      "   TFIDF_best  TFIDF_business  TFIDF_citizens  TFIDF_commerce  TFIDF_common  \\\n",
      "0    0.036504        0.000000        0.015014             0.0      0.000000   \n",
      "1    0.132160        0.000000        0.000000             0.0      0.047127   \n",
      "2    0.060831        0.049217        0.075060             0.0      0.000000   \n",
      "3    0.086620        0.000000        0.106880             0.0      0.023166   \n",
      "4    0.027459        0.000000        0.045175             0.0      0.000000   \n",
      "\n",
      "   TFIDF_confidence  ...  TFIDF_subject  TFIDF_support  TFIDF_time  \\\n",
      "0          0.017464  ...            0.0       0.019526    0.092032   \n",
      "1          0.042151  ...            0.0       0.000000    0.074043   \n",
      "2          0.000000  ...            0.0       0.097614    0.204484   \n",
      "3          0.020720  ...            0.0       0.023166    0.181982   \n",
      "4          0.000000  ...            0.0       0.000000    0.161530   \n",
      "\n",
      "   TFIDF_union  TFIDF_united  TFIDF_war  TFIDF_way  TFIDF_work  TFIDF_world  \\\n",
      "0     0.000000      0.000000   0.052392   0.065853    0.079912     0.276096   \n",
      "1     0.000000      0.078975   0.042151   0.052981    0.096437     0.222128   \n",
      "2     0.000000      0.054526   0.029102   0.036579    0.233039     0.230045   \n",
      "3     0.130216      0.038821   0.020720   0.078130    0.118512     0.291172   \n",
      "4     0.033023      0.073839   0.078819   0.099070    0.210385     0.230756   \n",
      "\n",
      "   TFIDF_years  \n",
      "0     0.124968  \n",
      "1     0.043089  \n",
      "2     0.059499  \n",
      "3     0.148265  \n",
      "4     0.053715  \n",
      "\n",
      "[5 rows x 100 columns]\n"
     ]
    }
   ],
   "source": [
    "# Instantiate TfidfVectorizer\n",
    "tv = TfidfVectorizer(max_features=100, stop_words='english')\n",
    "\n",
    "# Fit the vectroizer and transform the data\n",
    "tv_transformed = tv.fit_transform(train_speech_df['text_clean'])\n",
    "\n",
    "# Transform test data\n",
    "test_tv_transformed = tv.transform(test_speech_df['text_clean'])\n",
    "\n",
    "# Create new features for the test set\n",
    "test_tv_df = pd.DataFrame(test_tv_transformed.toarray(), \n",
    "                          columns=tv.get_feature_names_out()).add_prefix('TFIDF_')\n",
    "print(test_tv_df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34662828",
   "metadata": {},
   "source": [
    "## N-grams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f53ac0e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ability preserve protect' 'agriculture commerce manufactures'\n",
      " 'america ideal freedom' 'amity mutual concession' 'anchor peace home'\n",
      " 'ask bow heads' 'best ability preserve' 'best interests country'\n",
      " 'bless god bless' 'bless united states' 'chief justice mr'\n",
      " 'children children children' 'citizens united states'\n",
      " 'civil religious liberty' 'civil service reform' 'commerce united states'\n",
      " 'confidence fellow citizens' 'congress extraordinary session'\n",
      " 'constitution does expressly' 'constitution united states'\n",
      " 'coordinate branches government' 'day task people'\n",
      " 'defend constitution united' 'distinction powers granted'\n",
      " 'distinguished guests fellow' 'does expressly say' 'equal exact justice'\n",
      " 'era good feeling' 'executive branch government'\n",
      " 'faithfully execute office' 'fellow citizens assembled'\n",
      " 'fellow citizens called' 'fellow citizens large' 'fellow citizens world'\n",
      " 'form perfect union' 'general welfare secure' 'god bless america'\n",
      " 'god bless god' 'good greatest number' 'government peace war'\n",
      " 'government united states' 'granted federal government'\n",
      " 'great body people' 'great political parties' 'greatest good greatest'\n",
      " 'guests fellow citizens' 'invasion wars powers' 'land new promise'\n",
      " 'laws faithfully executed' 'letter spirit constitution'\n",
      " 'liberty pursuit happiness' 'life liberty pursuit'\n",
      " 'local self government' 'make hard choices' 'men women children'\n",
      " 'mr chief justice' 'mr majority leader' 'mr president vice'\n",
      " 'mr speaker mr' 'mr vice president' 'nation like person'\n",
      " 'new breeze blowing' 'new states admitted' 'north south east'\n",
      " 'oath prescribed constitution' 'office president united'\n",
      " 'passed generation generation' 'peace shall strive'\n",
      " 'people united states' 'physical moral political' 'policy united states'\n",
      " 'power general government' 'preservation general government'\n",
      " 'preservation sacred liberty' 'preserve protect defend'\n",
      " 'president united states' 'president vice president'\n",
      " 'promote general welfare' 'proof confidence fellow'\n",
      " 'protect defend constitution' 'protection great interests'\n",
      " 'reform civil service' 'reserved states people'\n",
      " 'respect individual human' 'right self government'\n",
      " 'secure blessings liberty' 'south east west'\n",
      " 'sovereignty general government' 'states admitted union'\n",
      " 'territories united states' 'thank god bless' 'turning away old'\n",
      " 'united states america' 'united states best' 'united states government'\n",
      " 'united states great' 'united states maintain' 'united states territory'\n",
      " 'vice president mr' 'welfare secure blessings']\n"
     ]
    }
   ],
   "source": [
    "# Import CountVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "# Instantiate a trigram vectorizer\n",
    "cv_trigram_vec = CountVectorizer(max_features=100, \n",
    "                                 stop_words='english', \n",
    "                                 ngram_range = (3,3))\n",
    "\n",
    "# Fit and apply trigram vectorizer\n",
    "cv_trigram = cv_trigram_vec.fit_transform(speech_df['text_clean'])\n",
    "\n",
    "# Print the trigram features\n",
    "print(cv_trigram_vec.get_feature_names_out())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36c7c5b4",
   "metadata": {},
   "source": [
    "### Finding the most common words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e9f8e493",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counts_constitution united states    20\n",
      "Counts_people united states          13\n",
      "Counts_mr chief justice              10\n",
      "Counts_preserve protect defend       10\n",
      "Counts_president united states        8\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Create a DataFrame of the features\n",
    "cv_tri_df = pd.DataFrame(cv_trigram.toarray(), \n",
    "                 columns=cv_trigram_vec.get_feature_names_out()).add_prefix('Counts_')\n",
    "\n",
    "# Print the top 5 words in the sorted output\n",
    "print(cv_tri_df.sum().sort_values(ascending=False).head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "206d3168",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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