HandsON.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_series = pd.Series([1, 3, 5, np.nan, 6, 8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(my_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_series = pd.Series([[1, 3, 5, np.nan, 6, 8,7,8],[1, 3, 5, np.nan, 6, 8],[1, 3, 5, np.nan, 6, 8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [1, 3, 5, nan, 6, 8, 7, 8]\n",
       "1          [1, 3, 5, nan, 6, 8]\n",
       "2          [1, 3, 5, nan, 6, 8]\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_dataframe = pd.DataFrame([[1, 3, 5, np.nan, 6, 8,7,8],[1, 3, 5, np.nan, 6, 8],[1, 3, 5, np.nan, 6, 8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
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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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1  2   3  4  5    6    7\n",
       "0  1  3  5 NaN  6  8  7.0  8.0\n",
       "1  1  3  5 NaN  6  8  NaN  NaN\n",
       "2  1  3  5 NaN  6  8  NaN  NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(my_series[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(my_dataframe[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dataframe[2][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Pandas loading dataset from Excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_birthdata = pd.read_excel(\"Birth_dataset.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15547, 5)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_birthdata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'month', 'day', 'gender', 'births'], dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_birthdata.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>year</th>\n",
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       "      <th>day</th>\n",
       "      <th>gender</th>\n",
       "      <th>births</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1969</td>\n",
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       "      <th>1</th>\n",
       "      <td>1969</td>\n",
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       "      <td>M</td>\n",
       "      <td>4440</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1969</td>\n",
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       "      <td>4454</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>M</td>\n",
       "      <td>4548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   year  month  day gender  births\n",
       "0  1969      1  1.0      F    4046\n",
       "1  1969      1  1.0      M    4440\n",
       "2  1969      1  2.0      F    4454\n",
       "3  1969      1  2.0      M    4548\n",
       "4  1969      1  3.0      F    4548"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_birthdata.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <th>day</th>\n",
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       "  <tbody>\n",
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       "      <th>15542</th>\n",
       "      <td>2008</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>183219</td>\n",
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       "    <tr>\n",
       "      <th>15543</th>\n",
       "      <td>2008</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>173215</td>\n",
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       "      <th>15546</th>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>181235</td>\n",
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      ],
      "text/plain": [
       "       year  month  day gender  births\n",
       "15542  2008     10  NaN      M  183219\n",
       "15543  2008     11  NaN      F  158939\n",
       "15544  2008     11  NaN      M  165468\n",
       "15545  2008     12  NaN      F  173215\n",
       "15546  2008     12  NaN      M  181235"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_birthdata.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>births</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1969</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1969</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1969</td>\n",
       "      <td>4454</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1969</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1969</td>\n",
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       "      <th>5</th>\n",
       "      <td>1969</td>\n",
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       "      <th>6</th>\n",
       "      <td>1969</td>\n",
       "      <td>4440</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1969</td>\n",
       "      <td>4520</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1969</td>\n",
       "      <td>4192</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1969</td>\n",
       "      <td>4198</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1969</td>\n",
       "      <td>4710</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1969</td>\n",
       "      <td>4850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1969</td>\n",
       "      <td>4646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1969</td>\n",
       "      <td>5092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1969</td>\n",
       "      <td>4800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1969</td>\n",
       "      <td>4934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1969</td>\n",
       "      <td>4592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1969</td>\n",
       "      <td>4842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1969</td>\n",
       "      <td>4852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1969</td>\n",
       "      <td>5190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1969</td>\n",
       "      <td>4580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1969</td>\n",
       "      <td>4598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1969</td>\n",
       "      <td>4126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1969</td>\n",
       "      <td>4324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1969</td>\n",
       "      <td>4758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1969</td>\n",
       "      <td>5076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1969</td>\n",
       "      <td>5070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1969</td>\n",
       "      <td>5296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1969</td>\n",
       "      <td>4798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1969</td>\n",
       "      <td>5096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15517</th>\n",
       "      <td>2007</td>\n",
       "      <td>180912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15518</th>\n",
       "      <td>2007</td>\n",
       "      <td>189157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15519</th>\n",
       "      <td>2007</td>\n",
       "      <td>173513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15520</th>\n",
       "      <td>2007</td>\n",
       "      <td>180814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15521</th>\n",
       "      <td>2007</td>\n",
       "      <td>173787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15522</th>\n",
       "      <td>2007</td>\n",
       "      <td>181426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15523</th>\n",
       "      <td>2008</td>\n",
       "      <td>174255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15524</th>\n",
       "      <td>2008</td>\n",
       "      <td>182789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15525</th>\n",
       "      <td>2008</td>\n",
       "      <td>165669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15526</th>\n",
       "      <td>2008</td>\n",
       "      <td>173434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15527</th>\n",
       "      <td>2008</td>\n",
       "      <td>172053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15528</th>\n",
       "      <td>2008</td>\n",
       "      <td>179129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15529</th>\n",
       "      <td>2008</td>\n",
       "      <td>169585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15530</th>\n",
       "      <td>2008</td>\n",
       "      <td>177399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15531</th>\n",
       "      <td>2008</td>\n",
       "      <td>173141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15532</th>\n",
       "      <td>2008</td>\n",
       "      <td>182294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15533</th>\n",
       "      <td>2008</td>\n",
       "      <td>169958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15534</th>\n",
       "      <td>2008</td>\n",
       "      <td>179267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15535</th>\n",
       "      <td>2008</td>\n",
       "      <td>183391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15536</th>\n",
       "      <td>2008</td>\n",
       "      <td>192714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15537</th>\n",
       "      <td>2008</td>\n",
       "      <td>182713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15538</th>\n",
       "      <td>2008</td>\n",
       "      <td>191315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15539</th>\n",
       "      <td>2008</td>\n",
       "      <td>179696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15540</th>\n",
       "      <td>2008</td>\n",
       "      <td>188964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15541</th>\n",
       "      <td>2008</td>\n",
       "      <td>175314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15542</th>\n",
       "      <td>2008</td>\n",
       "      <td>183219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15543</th>\n",
       "      <td>2008</td>\n",
       "      <td>158939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15544</th>\n",
       "      <td>2008</td>\n",
       "      <td>165468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15545</th>\n",
       "      <td>2008</td>\n",
       "      <td>173215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15546</th>\n",
       "      <td>2008</td>\n",
       "      <td>181235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15547 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  births\n",
       "0      1969    4046\n",
       "1      1969    4440\n",
       "2      1969    4454\n",
       "3      1969    4548\n",
       "4      1969    4548\n",
       "5      1969    4994\n",
       "6      1969    4440\n",
       "7      1969    4520\n",
       "8      1969    4192\n",
       "9      1969    4198\n",
       "10     1969    4710\n",
       "11     1969    4850\n",
       "12     1969    4646\n",
       "13     1969    5092\n",
       "14     1969    4800\n",
       "15     1969    4934\n",
       "16     1969    4592\n",
       "17     1969    4842\n",
       "18     1969    4852\n",
       "19     1969    5190\n",
       "20     1969    4580\n",
       "21     1969    4598\n",
       "22     1969    4126\n",
       "23     1969    4324\n",
       "24     1969    4758\n",
       "25     1969    5076\n",
       "26     1969    5070\n",
       "27     1969    5296\n",
       "28     1969    4798\n",
       "29     1969    5096\n",
       "...     ...     ...\n",
       "15517  2007  180912\n",
       "15518  2007  189157\n",
       "15519  2007  173513\n",
       "15520  2007  180814\n",
       "15521  2007  173787\n",
       "15522  2007  181426\n",
       "15523  2008  174255\n",
       "15524  2008  182789\n",
       "15525  2008  165669\n",
       "15526  2008  173434\n",
       "15527  2008  172053\n",
       "15528  2008  179129\n",
       "15529  2008  169585\n",
       "15530  2008  177399\n",
       "15531  2008  173141\n",
       "15532  2008  182294\n",
       "15533  2008  169958\n",
       "15534  2008  179267\n",
       "15535  2008  183391\n",
       "15536  2008  192714\n",
       "15537  2008  182713\n",
       "15538  2008  191315\n",
       "15539  2008  179696\n",
       "15540  2008  188964\n",
       "15541  2008  175314\n",
       "15542  2008  183219\n",
       "15543  2008  158939\n",
       "15544  2008  165468\n",
       "15545  2008  173215\n",
       "15546  2008  181235\n",
       "\n",
       "[15547 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_birthdata[['year','births']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>year</th>\n",
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       "      <th>births</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>15547.000000</td>\n",
       "      <td>15547.000000</td>\n",
       "      <td>15067.000000</td>\n",
       "      <td>15547.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1979.037435</td>\n",
       "      <td>6.515919</td>\n",
       "      <td>17.769894</td>\n",
       "      <td>9762.293561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.728340</td>\n",
       "      <td>3.449632</td>\n",
       "      <td>15.284034</td>\n",
       "      <td>28552.465810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1969.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1974.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4358.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1979.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>4814.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1984.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>5289.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>199622.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               year         month           day         births\n",
       "count  15547.000000  15547.000000  15067.000000   15547.000000\n",
       "mean    1979.037435      6.515919     17.769894    9762.293561\n",
       "std        6.728340      3.449632     15.284034   28552.465810\n",
       "min     1969.000000      1.000000      1.000000       1.000000\n",
       "25%     1974.000000      4.000000      8.000000    4358.000000\n",
       "50%     1979.000000      7.000000     16.000000    4814.000000\n",
       "75%     1984.000000     10.000000     24.000000    5289.500000\n",
       "max     2008.000000     12.000000     99.000000  199622.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_birthdata.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <th>...</th>\n",
       "      <th>15537</th>\n",
       "      <th>15538</th>\n",
       "      <th>15539</th>\n",
       "      <th>15540</th>\n",
       "      <th>15541</th>\n",
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       "      <th>15544</th>\n",
       "      <th>15545</th>\n",
       "      <th>15546</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>1969</td>\n",
       "      <td>1969</td>\n",
       "      <td>1969</td>\n",
       "      <td>1969</td>\n",
       "      <td>1969</td>\n",
       "      <td>1969</td>\n",
       "      <td>1969</td>\n",
       "      <td>1969</td>\n",
       "      <td>1969</td>\n",
       "      <td>1969</td>\n",
       "      <td>...</td>\n",
       "      <td>2008</td>\n",
       "      <td>2008</td>\n",
       "      <td>2008</td>\n",
       "      <td>2008</td>\n",
       "      <td>2008</td>\n",
       "      <td>2008</td>\n",
       "      <td>2008</td>\n",
       "      <td>2008</td>\n",
       "      <td>2008</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>births</th>\n",
       "      <td>4046</td>\n",
       "      <td>4440</td>\n",
       "      <td>4454</td>\n",
       "      <td>4548</td>\n",
       "      <td>4548</td>\n",
       "      <td>4994</td>\n",
       "      <td>4440</td>\n",
       "      <td>4520</td>\n",
       "      <td>4192</td>\n",
       "      <td>4198</td>\n",
       "      <td>...</td>\n",
       "      <td>182713</td>\n",
       "      <td>191315</td>\n",
       "      <td>179696</td>\n",
       "      <td>188964</td>\n",
       "      <td>175314</td>\n",
       "      <td>183219</td>\n",
       "      <td>158939</td>\n",
       "      <td>165468</td>\n",
       "      <td>173215</td>\n",
       "      <td>181235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 15547 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0     1     2     3     4     5     6     7     8     9      ...  \\\n",
       "year    1969  1969  1969  1969  1969  1969  1969  1969  1969  1969  ...   \n",
       "month      1     1     1     1     1     1     1     1     1     1  ...   \n",
       "day        1     1     2     2     3     3     4     4     5     5  ...   \n",
       "gender     F     M     F     M     F     M     F     M     F     M  ...   \n",
       "births  4046  4440  4454  4548  4548  4994  4440  4520  4192  4198  ...   \n",
       "\n",
       "         15537   15538   15539   15540   15541   15542   15543   15544  \\\n",
       "year      2008    2008    2008    2008    2008    2008    2008    2008   \n",
       "month        8       8       9       9      10      10      11      11   \n",
       "day        NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "gender       F       M       F       M       F       M       F       M   \n",
       "births  182713  191315  179696  188964  175314  183219  158939  165468   \n",
       "\n",
       "         15545   15546  \n",
       "year      2008    2008  \n",
       "month       12      12  \n",
       "day        NaN     NaN  \n",
       "gender       F       M  \n",
       "births  173215  181235  \n",
       "\n",
       "[5 rows x 15547 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_birthdata.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>year</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1969</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>18.593750</td>\n",
       "      <td>4687.768229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>18.593750</td>\n",
       "      <td>4866.927083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>6.518325</td>\n",
       "      <td>18.532723</td>\n",
       "      <td>4664.329843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>6.512418</td>\n",
       "      <td>18.546405</td>\n",
       "      <td>4269.588235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>6.520997</td>\n",
       "      <td>18.498688</td>\n",
       "      <td>4128.772966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>6.511749</td>\n",
       "      <td>18.563969</td>\n",
       "      <td>4139.204961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>6.507208</td>\n",
       "      <td>18.336828</td>\n",
       "      <td>4133.100917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>6.512484</td>\n",
       "      <td>18.391590</td>\n",
       "      <td>4174.081472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>6.534823</td>\n",
       "      <td>18.215506</td>\n",
       "      <td>4378.658344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>6.529566</td>\n",
       "      <td>17.943495</td>\n",
       "      <td>4386.727989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>6.540830</td>\n",
       "      <td>17.615797</td>\n",
       "      <td>4685.133869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>6.526104</td>\n",
       "      <td>17.428380</td>\n",
       "      <td>4843.348059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>6.530201</td>\n",
       "      <td>17.397315</td>\n",
       "      <td>4879.885906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>6.526738</td>\n",
       "      <td>17.724599</td>\n",
       "      <td>4927.081551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>6.506008</td>\n",
       "      <td>17.833111</td>\n",
       "      <td>4863.579439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>6.503347</td>\n",
       "      <td>17.428380</td>\n",
       "      <td>4917.761714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>6.503392</td>\n",
       "      <td>16.511533</td>\n",
       "      <td>5108.635007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>6.531250</td>\n",
       "      <td>16.399457</td>\n",
       "      <td>5109.639946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>6.511596</td>\n",
       "      <td>16.061392</td>\n",
       "      <td>5202.204638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>6.502030</td>\n",
       "      <td>16.545332</td>\n",
       "      <td>5296.066306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168570.541667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173454.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>171472.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>169559.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>166855.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>164871.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>162625.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>162286.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>161847.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>164383.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165144.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>169325.958333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>167980.458333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>167807.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170670.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>171621.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>172734.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>178051.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180167.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>177298.166667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         month        day         births\n",
       "year                                    \n",
       "1969  6.500000  18.593750    4687.768229\n",
       "1970  6.500000  18.593750    4866.927083\n",
       "1971  6.518325  18.532723    4664.329843\n",
       "1972  6.512418  18.546405    4269.588235\n",
       "1973  6.520997  18.498688    4128.772966\n",
       "1974  6.511749  18.563969    4139.204961\n",
       "1975  6.507208  18.336828    4133.100917\n",
       "1976  6.512484  18.391590    4174.081472\n",
       "1977  6.534823  18.215506    4378.658344\n",
       "1978  6.529566  17.943495    4386.727989\n",
       "1979  6.540830  17.615797    4685.133869\n",
       "1980  6.526104  17.428380    4843.348059\n",
       "1981  6.530201  17.397315    4879.885906\n",
       "1982  6.526738  17.724599    4927.081551\n",
       "1983  6.506008  17.833111    4863.579439\n",
       "1984  6.503347  17.428380    4917.761714\n",
       "1985  6.503392  16.511533    5108.635007\n",
       "1986  6.531250  16.399457    5109.639946\n",
       "1987  6.511596  16.061392    5202.204638\n",
       "1988  6.502030  16.545332    5296.066306\n",
       "1989  6.500000        NaN  168570.541667\n",
       "1990  6.500000        NaN  173454.875000\n",
       "1991  6.500000        NaN  171472.583333\n",
       "1992  6.500000        NaN  169559.500000\n",
       "1993  6.500000        NaN  166855.125000\n",
       "1994  6.500000        NaN  164871.875000\n",
       "1995  6.500000        NaN  162625.500000\n",
       "1996  6.500000        NaN  162286.416667\n",
       "1997  6.500000        NaN  161847.041667\n",
       "1998  6.500000        NaN  164383.000000\n",
       "1999  6.500000        NaN  165144.375000\n",
       "2000  6.500000        NaN  169325.958333\n",
       "2001  6.500000        NaN  167980.458333\n",
       "2002  6.500000        NaN  167807.333333\n",
       "2003  6.500000        NaN  170670.500000\n",
       "2004  6.500000        NaN  171621.125000\n",
       "2005  6.500000        NaN  172734.125000\n",
       "2006  6.500000        NaN  178051.041667\n",
       "2007  6.500000        NaN  180167.000000\n",
       "2008  6.500000        NaN  177298.166667"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_birthdata.groupby(['year']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1969</th>\n",
       "      <td>1753634</td>\n",
       "      <td>1846572</td>\n",
       "      <td>3600206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>1819164</td>\n",
       "      <td>1918636</td>\n",
       "      <td>3737800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>1736774</td>\n",
       "      <td>1826774</td>\n",
       "      <td>3563548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>1592347</td>\n",
       "      <td>1673888</td>\n",
       "      <td>3266235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>1533102</td>\n",
       "      <td>1613023</td>\n",
       "      <td>3146125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>1543005</td>\n",
       "      <td>1627626</td>\n",
       "      <td>3170631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>1535546</td>\n",
       "      <td>1618010</td>\n",
       "      <td>3153556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>1547613</td>\n",
       "      <td>1628863</td>\n",
       "      <td>3176476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>1623363</td>\n",
       "      <td>1708796</td>\n",
       "      <td>3332159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>1626324</td>\n",
       "      <td>1711976</td>\n",
       "      <td>3338300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>1705837</td>\n",
       "      <td>1793958</td>\n",
       "      <td>3499795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>1762459</td>\n",
       "      <td>1855522</td>\n",
       "      <td>3617981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>1772037</td>\n",
       "      <td>1863478</td>\n",
       "      <td>3635515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>1797239</td>\n",
       "      <td>1888218</td>\n",
       "      <td>3685457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>1775299</td>\n",
       "      <td>1867522</td>\n",
       "      <td>3642821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>1791802</td>\n",
       "      <td>1881766</td>\n",
       "      <td>3673568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>1834774</td>\n",
       "      <td>1930290</td>\n",
       "      <td>3765064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>1833708</td>\n",
       "      <td>1926987</td>\n",
       "      <td>3760695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>1860111</td>\n",
       "      <td>1953105</td>\n",
       "      <td>3813216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>1909210</td>\n",
       "      <td>2004583</td>\n",
       "      <td>3913793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>1973712</td>\n",
       "      <td>2071981</td>\n",
       "      <td>4045693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>2030966</td>\n",
       "      <td>2131951</td>\n",
       "      <td>4162917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>2011601</td>\n",
       "      <td>2103741</td>\n",
       "      <td>4115342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>1985118</td>\n",
       "      <td>2084310</td>\n",
       "      <td>4069428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>1953456</td>\n",
       "      <td>2051067</td>\n",
       "      <td>4004523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>1932234</td>\n",
       "      <td>2024691</td>\n",
       "      <td>3956925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>1904871</td>\n",
       "      <td>1998141</td>\n",
       "      <td>3903012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>1902664</td>\n",
       "      <td>1992210</td>\n",
       "      <td>3894874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>1896928</td>\n",
       "      <td>1987401</td>\n",
       "      <td>3884329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>1927106</td>\n",
       "      <td>2018086</td>\n",
       "      <td>3945192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>1934510</td>\n",
       "      <td>2028955</td>\n",
       "      <td>3963465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>1984255</td>\n",
       "      <td>2079568</td>\n",
       "      <td>4063823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>1970770</td>\n",
       "      <td>2060761</td>\n",
       "      <td>4031531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>1966519</td>\n",
       "      <td>2060857</td>\n",
       "      <td>4027376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>1999387</td>\n",
       "      <td>2096705</td>\n",
       "      <td>4096092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>2010710</td>\n",
       "      <td>2108197</td>\n",
       "      <td>4118907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>2022892</td>\n",
       "      <td>2122727</td>\n",
       "      <td>4145619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>2084957</td>\n",
       "      <td>2188268</td>\n",
       "      <td>4273225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>2111890</td>\n",
       "      <td>2212118</td>\n",
       "      <td>4324008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>2077929</td>\n",
       "      <td>2177227</td>\n",
       "      <td>4255156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>74035823</td>\n",
       "      <td>77738555</td>\n",
       "      <td>151774378</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender         F         M        All\n",
       "year                                 \n",
       "1969     1753634   1846572    3600206\n",
       "1970     1819164   1918636    3737800\n",
       "1971     1736774   1826774    3563548\n",
       "1972     1592347   1673888    3266235\n",
       "1973     1533102   1613023    3146125\n",
       "1974     1543005   1627626    3170631\n",
       "1975     1535546   1618010    3153556\n",
       "1976     1547613   1628863    3176476\n",
       "1977     1623363   1708796    3332159\n",
       "1978     1626324   1711976    3338300\n",
       "1979     1705837   1793958    3499795\n",
       "1980     1762459   1855522    3617981\n",
       "1981     1772037   1863478    3635515\n",
       "1982     1797239   1888218    3685457\n",
       "1983     1775299   1867522    3642821\n",
       "1984     1791802   1881766    3673568\n",
       "1985     1834774   1930290    3765064\n",
       "1986     1833708   1926987    3760695\n",
       "1987     1860111   1953105    3813216\n",
       "1988     1909210   2004583    3913793\n",
       "1989     1973712   2071981    4045693\n",
       "1990     2030966   2131951    4162917\n",
       "1991     2011601   2103741    4115342\n",
       "1992     1985118   2084310    4069428\n",
       "1993     1953456   2051067    4004523\n",
       "1994     1932234   2024691    3956925\n",
       "1995     1904871   1998141    3903012\n",
       "1996     1902664   1992210    3894874\n",
       "1997     1896928   1987401    3884329\n",
       "1998     1927106   2018086    3945192\n",
       "1999     1934510   2028955    3963465\n",
       "2000     1984255   2079568    4063823\n",
       "2001     1970770   2060761    4031531\n",
       "2002     1966519   2060857    4027376\n",
       "2003     1999387   2096705    4096092\n",
       "2004     2010710   2108197    4118907\n",
       "2005     2022892   2122727    4145619\n",
       "2006     2084957   2188268    4273225\n",
       "2007     2111890   2212118    4324008\n",
       "2008     2077929   2177227    4255156\n",
       "All     74035823  77738555  151774378"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_birthdata.pivot_table(index='year',columns='gender',values='births',aggfunc=sum,margins='True')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f2b794ece80>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2b794b2400>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f2b794d8978>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2b7947fef0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TtE7StnQ/OZVL0rK0zw9KOq3kPfPT8tskzS8pf7Okzek9yyRppG20i6Tpku6RtFXSFkkfG6me3dwW5XTL8V4rSTvSZ/eApI2prPCfW6M06u9ERRHRFTeyi3q/BF4FvAj4OXByu+tV4778GXAa8FBJ2T8Ai9PjxcBV6fG5wPfIxs3PBu5P5ccBj6b7yenx5PTaT4A/Se/5HnDOSNtoYzucAJyWHh8N/D/g5PHYFt18vNfRBjuAKcPKCv25NXj/6/47MeL6272DDWyoPwHWljy/Arii3fWqY396h33ojwAnpMcnAI+kx18FLhy+HHAh8NWS8q+mshOAh0vK/7hcpW0U5QbcRZaLyG3RZcd7jW1QLjgU+nNrQhvU9XdipHV3U7dSuXQEU9tUl2boiYhdAOn++FReab9HKh8oUz7SNtpOUi9wKnA/47wtkm4/3scigB9I2pTSjEDxP7dmq/a7UVEhfufQIGNKR9CFKu13teWFJeko4Hbg8oh4Ol0WKLtombKuaosSnVz3RjkzInZKOh5YJ+nhdleowKo+XrrpzKHb0xHslnQCQLrfk8or7fdI5dOGl0v6VLbqsttoG0lHkAWGmyLijlTc1LYYZRtF0e3H+6giYme63wPcSZbptuifW7NV+92oqJuCQ7enI1gNDI2ymU/W/z5UfnEajTAb2J9OJ9cCcyRNTiMW5pD1Ue8CSKMcBFxcsq6BCttoi1S/64GtEXFNyUuNbItnJM0u0xaVtlEU3X68j0jSJElHDz0m+0wfovifW7NV+92orN0XVBp8ceZcshEtvwT+vt31qWM/bgZ2Ab8j+4N9GfBSYD2wLd0fl5YV2aQxvwQ2A7NK1vNBYHu6XVpS/qG07l8CX07r+BSwqtw22tgOf0p26vsg8EC6ndvgtphF9kflj22Rystuo0i3bjnea9z3V5GN0Po5sGVo/6v93IAJ7d6XOtqgIX8nKq6/3TvoW+4D3wH8bfqDeJDsP+cesmFozwD/zKFhmO9KX4yngH7gpGHr+Zu0nv3ArcCRwCTgOeAPwIF0e3lJcLgxbWfLWA4g33xr1i19D24fVnYt8EXgJem7sQv4FfBZ4PC0zKuBHwK/JpvP4Cbg2JJ17AA+nr4bz3dygGjmrZu6lbrJe8iGbL4WeCdZYPgE2eQdhwEflfRasv8cLgdeBqwBvpO6GIb8NTAXOBF4I3BJRBwEzgF2RsRR6TbU9/gu4BbgWLLT0C83dS/NRva/gLmSjgWQNAF4L/BNYCUwCLyGbBTbHOC/pvcJ+DzZPz0nkfW1f2rYui8EziMLGoNN3YsO5eBQTNdGxO6I+BXwL2Q/WPlZRDxPduHtVLIvyd0RsS4ifgf8IzAR+M8l61kWETsjYi/wHeCUUbb7o4hYExG/J/sCvqnB+2U2ZpH1id8LnJ+K5pKdCQyQ/YNzeUQcjOyC9BfIrrsQEdvT9+L5iPh34Brgz4etfllEPBERz7ViXzpRNw1l7Sa7Sx4/V+b5UWT/FT02VBgRf5D0BC8cu/xvJY+fTe8ZyfDlj5Q0wf9ZWRutBD4MfA14P9k/La8EjgB2lQxrPow0jj8NbV0GvIXsl/WHAfuGrfcJbEQ+c+hcOymZCzaNtplO1v86mvE2Ht4617eBN0p6A/AOsusHT5BdK5gSEcem2zER8fr0ns+THeNvjIhjyILK8HH+/g6MwsGhc60CzpN0VvotwCKyL8z/HcN7dwMvlfSSZlbQrF4R8RvgNuBbwE8i4vHU3fQD4GpJx0g6TNKrJQ11HR1NNtDiKUlTyS5sW5UcHDpURDxC9h/RtWT9sO8E3hkRvx3Dex8mu5j9qKSnJI3W3WTWTiuBmWRdSkMuJks4+AuyLqPbyHIJAXyaLCHdfuBu4A6sap4JzswKTdIrgIeB/xgRT7e7PuOFzxzMrLAkHQb8d+AWB4bW8mglMyuklBZjN9movLltrs64424lMzPLcbeSmZnldGy30pQpU6K3t7fd1Wi4gwcPMmnSpHZXo6Xatc+bNm16MiJe1vIN16jTjvnxeCyPpCjtMdbjvmODQ29vLxs3bmx3NRquv7+fvr6+dlejpdq1z5IeG32p4ui0Y348HssjKUp7jPW4d7eSmZnlODiYmVmOg4NZFSQdKeknkn4uaYukT6fyEyXdL2mbpFuHUqdLenF6vj293luyritS+SOSzm7PHpmV17HXHGz86V18d03v27H0vEZW43ngbRFxIOW0+pGk75H9UOsLEXGLpH8im5XrunS/LyJeI+kC4CrgvZJOJksx/XqybLn/LOm1KV161Wptm2o1uC2twBwczKoQ2Q+DDqSnR6RbAG8D3pfKV5JNLnMdMI9DE83cBnw5ZdCdR/ar3+eBf5W0HTgd+HHz96J21QShRTMHuWTx3Q4oHarrgkNB/ru0LibpcGAT2SxkQ/PyPlUy78UAh+bVmEqaOyAiBiXtJ5vndypwX8lqS99Tuq0FwAKAnp4e+vv7y9Zp0cziTbnRMzGr17U33TX6wsPMnNp9CYMPHDhQ8fMroq4LDmbNlrp+TknTV95JNhVlbrF0P3wegaHXKpUP39ZyYDnArFmzotJQyEta1K1UjUUzB7l6c41/YjYfrOltRf4nryhDWcfKF6TNahQRTwH9wGzg2DTHMcA0ssmYIDsjmA5/nAP5JcDe0vIy7zFrOwcHsypIelnJhPcTgb8AtgL3AH+VFpsPDPWlrE7PSa//MF23WA1ckEYznQjMAH7Smr0wG527lcyqcwKwMl13OAxYFRHflfQL4BZJnwV+Blyflr8e+Ga64LyXbIQSEbFF0iqyyWoGgYW1jlQyawYHB7MqRMSDwKllyh8lG200vPw3wPkV1vU54HONrqNZI7hbyczMchwczMwsx8HBzMxyHBzMzCzHwcHMzHIcHMzMLMfBwczMchwczMwsx8HBzMxyag4OkqZLukfS1jQj1sdS+XGS1qUZsdZJmpzKJWlZmvnqQUmnlaxrflp+m6T5lbZpZmatUc+ZwyCwKCJOIstKuTDNbrUYWB8RM4D16TnAOWTJxWaQ5ae/DrJgAiwBziBLP7BkKKCYmVl71BwcImJXRPw0PX6GLDPlVLIZrlamxVYC706P5wE3RuY+shTHJwBnA+siYm9E7APWAXNrrZeZmdWvIYn30qTppwL3Az0RsQuyACLp+LTYH2fESoZmvqpUXm47o86KVeuMWEWZoanTZotqhLHuc6d/tmadpO7gIOko4Hbg8oh4Opset/yiZcrGPCMWjG1WrFpnxNpxUX5d7dBps0U1wlj3udM/W7NOUtdoJUlHkAWGmyLijlS8O3UXke73pPJKM195Riwzs4KpZ7SSyCYy2RoR15S8VDrz1fAZsS5Oo5ZmA/tT99NaYI6kyelC9JxUZmZmbVJPt9KZwAeAzZIeSGWfAJYCqyRdBjzOoYlO1gDnAtuBZ4FLASJir6QrgQ1puc9ExN466mVmZnWqOThExI8of70A4KwyywewsMK6VgAraq2LmZk1ln8hbWZmOQ4OZmaW4+BgVgWnjbHxwsHBrDpOG2PjgoODWRWcNsbGi4akzzAbj1qRNmYsKWOg9tQizdQzsfX1KnKqlE5LjePgYFaDVqWNGUvKGKg9tUgzLZo5yNWbW/snpsipUjotNY67lcyq5LQxNh44OJhVwWljbLxwt5JZdZw2xsYFBwezKjhtjI0X7lYyM7McBwczM8txcDAzsxwHBzMzy3FwMDOzHAcHMzPLcXAwM7Mc/84h6a0hN82Opec1oSaNUcv+1KqWdiit36KZg4XMDWQ2njk41KEZf4A78Q9lKwORmbWGu5XMzCzHwcHMzHIcHMzMLMfBwczMchwczMwsx8HBzMxy6goOklZI2iPpoZKy4yStk7Qt3U9O5ZK0TNJ2SQ9KOq3kPfPT8tskzS+3LTMza516zxxuAOYOK1sMrI+IGcD69BzgHGBGui0AroMsmABLgDOA04ElQwHFzMzao67gEBH3AsOnNpwHrEyPVwLvLim/MTL3AcemidjPBtZFxN6I2AesIx9wzMyshZrxC+meNIE6EbFL0vGpfCrwRMlyA6msUnmOpAVkZx309PTQ39+fW2bRzME6q99ePRM7fx+q1ex9LnecmNnIWpk+o9y8uzFCeb4wYjmwHGDWrFnR19eXW6bTUk8Mt2jmIFdvHl9ZTZq9zzsu6mvaus26VTNGK+1O3UWk+z2pfACYXrLcNGDnCOVmheNBGDZeNCM4rAaGDvb5wF0l5RenL8xsYH/qfloLzJE0OX2p5qQysyK6AQ/CsHGg3qGsNwM/Bl4naUDSZcBS4O2StgFvT88B1gCPAtuBrwH/DSAi9gJXAhvS7TOpzKxwPAjDxou6Onoj4sIKL51VZtkAFlZYzwpgRT11MWujtg7CgGIOYmjH4IoiDz44cOBAoes33Pi68mnWWi0ZhAHFHIjRjsEVRR580N/fT6XPr4icPsOsfh6EYV3HwcGsfh6EYV3H3UpmVUiDMPqAKZIGyEYdLQVWpQEZjwPnp8XXAOeSDcJ4FrgUskEYkoYGYYAHYVgBOTiYVcGDMGy8cLeSmZnlODiYmVmOg4OZmeU4OJiZWY6Dg5mZ5Tg4mJlZjoODmZnlODiYmVmOg4OZmeU4OJiZWY6Dg5mZ5Tg4mJlZjoODmZnlODiYmVmOg4OZmeU4OJiZWY6Dg5mZ5Tg4mJlZjoODmZnlODiYmVmOg4OZmeVMaHcFhkiaC3wJOBz4ekQsbXOVzJrKx3zj9S6+u91VqGjRzEEuaVH9diw9r+51FOLMQdLhwFeAc4CTgQslndzeWpk1j495K7pCBAfgdGB7RDwaEb8FbgHmtblOZs3kY94KrSjdSlOBJ0qeDwBnDF9I0gJgQXp6QNIjLahbS30UpgBPtrserdTsfdZVFV96ZbO2OQZdf8yPx2N5JK1sjxGOeRjjcV+U4KAyZZEriFgOLG9+ddpH0saImNXuerTSeNxnxsExP04/14o6rT2K0q00AEwveT4N2Nmmupi1go95K7SiBIcNwAxJJ0p6EXABsLrNdTJrJh/zVmiF6FaKiEFJHwHWkg3rWxERW9pcrXbpyC6EOo27fR4nx/y4+1xH0VHtoYhcN6eZmY1zRelWMjOzAnFwMDOzHAeHApG0Q9JmSQ9I2tju+jSapBWS9kh6qKTsOEnrJG1L95PbWUerrMLn9yZJP07H7XckHZPKj5C0MpVvlXRFyXvmSnpE0nZJi9uxL40gabqke9L+bZH0sVRe9phWZlna7wclnVayrvlp+W2S5rdrn0o5OBTPWyPilE4aD12FG4C5w8oWA+sjYgawPj23YrqB/Of3dWBxRMwE7gT+NpWfD7w4lb8Z+JCk3i5LGzIILIqIk4DZwMK0L5WO6XOAGem2ALgOsmACLCH7EeTpwJIi/JPk4GAtExH3AnuHFc8DVqbHK4F3t7RSNmYVPr/XAfemx+uA9wwtDkySNAGYCPwWeJouShsSEbsi4qfp8TPAVrJfvlc6pucBN0bmPuBYSScAZwPrImJvROwja8fhQbjlHByKJYAfSNqU0iaMBz0RsQuyLxtwfJvrY9V5CHhXenw+h37YdxtwENgFPA78Y0TspXzakKmtqWrzSOoFTgXup/IxXWnfC9kmDg7FcmZEnEZ2+rlQ0p+1u0Jmo/gg2bG6CTia7AwBsjOE3wMvB04EFkl6FWNMG9JJJB0F3A5cHhFPj7RombIYobytHBwKJCJ2pvs9ZP23p7e3Ri2xO51ak+73tLk+VoWIeDgi5kTEm4GbgV+ml94HfD8ifpeO5/8DzKLL0oZIOoIsMNwUEXek4krHdKV9L2SbODgUhKRJko4eegzMITtl73argaHRGfOBu9pYF6uSpOPT/WHAJ4F/Si89DrwtjdCZRHbB9mG6KG2IJAHXA1sj4pqSlyod06uBi1ObzAb2p26ntcAcSZPTheg5qaytCpE+wwDoAe7MjjcmAN+KiO+3t0qNJelmoA+YImmAbITGUmCVpMvI/qCc374a2kgqfH5HSVqYFrkD+EZ6/JX0+CGybpNvRMSDaT3dkjbkTOADwGZJD6SyT1D5mF4DnAtsB54FLgWIiL2SriQLnACfSddn2srpM8zMLMfdSmZmluPgYGZmOQ4OZmaW4+BgZmYl4HqSAAAAGElEQVQ5Dg5mZpbj4GBmZjkODmZmlvP/AajDXLYCRol+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "df_birthdata.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b79371be0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pY1hEZADkRvzW15bWVCo6ExARKVJ7OkNHOl7H7oH2NJD8bR/6SklARKQIe97s4INf/SXtMZMARJPBNPTDFT59oSQgIlKEtv0dtKez/NfmKZw8cVSsOo4bNzzx6/z7SklARKQIqTC/74JTJ/KRkycMcjTJKa3zEhGREpWb5L2hNtnbNgw2JQERkSKkw0jfUru6p6/UHCQiVcHd+ejXf822PQf7VM+Q+so6E1ASEJGq0NGZZdueg5x94rtofvfYWHWMHFLHKcfG6xQuVcXMLLYSuBBoc/dTQ9lsoiklRwDbgaXuvt/M6oFbgLmh7tvd/Z/DPtuBA0CGaNrJ5sTfjYhIN3Idu388s4k//9DxgxxN6SimT+BWYEGXsluA5e5+GnAPcE0oXww0hvJ5wF+a2fS8/f7Y3ecoAYjIQMu16ZfaYK3B1uPRcPe1wN4uxTOBtWF5DXBxbnNguJnVEc0lnAL2JxOqiEh8uTOBpOfoLXdx+wRagIXAvUTf/qeG8ruARcAuYBhwtbvnEogD/2lmDtzs7itiRy0iVenK7z/JI1v2xNo3m43u3dOoM4EjxE0ClwM3mdmXgfuIvvEDnEHU5n8sMAZ42Mx+4e7bgLPdfaeZNQFrzGxzOMt4BzNbBiwDmDZtWswQRaTSPP7iXiaMauQDJ4yLtX9jXQ3nzGxKOKryFisJuPtmYD6AmZ0EXBBWfRL4ubungTYz+w3QDGxz951h3zYzu4coYRRMAuEsYQVAc3Ozx4lRRCpPqjPL+49/F19ZeMpgh1IxYp0XhW/zmFkNcC3RlUIALwMfschw4Cxgs5kNN7ORYZ/hRAmkpa/Bi0h1SWey6thNWI9H08xWA48CM82s1cyuAJaY2fPAZmAnsCps/k2iy0ZbgCeAVe6+EZgAPGJmTwGPAz91958n/m5EpKLlJluX5PTYHOTuS7pZdWOBbd8k6ijuWr4NmN3r6ESkonzme+t5dlf8CwY7s64zgYRpxLCIDJj/fPZVZjSNZFbMUbfvmz6W80+blHBU1U1JQEQGRCbrZB0ueO8k/vajMwY7HAl0XiUiAyLVqcFapUi/DREZELkRu2rTLy1qDhKRoqQzWT777+tpO9ARc/9oyE9Dhd2Pv9wpCYhIUdoOdPCLTW2cPHEkk0YPiVXH1DETOPvEeKN9pX8oCYhIUXJt+ss+fDwfnztlkKORpKhxTkSKklabfkXSb1NEipI7E2jQ1T0VRc1BIlXizY5O/uGujexvT8fa/0B7J6AzgUqjJCBSJZ579QA/fXoXJ4wfzqih9b3e3ww+eOK42KN9pTQpCYhUiVxzzj9ddGrs+/FL5dF5nUiVyA3W0sxakk9/DSJVIq3bNkgBag4SKRP729P8759s4mCqM9b+r+5rB9SxK0dSEhApExtf2ccP173C5GOG0lgf74N83rvHMGXMsIQjk3JWVBIws5XAhUCbu58aymYTTSs5AtgOLHX3/WZWD9wCzA313+7u/xz2WUA0GU0tcIu7X5/s2xGpXKlMBoB/WzqX2VOPGeRopFIU+3XiVmBBl7JbgOXufhpwD3BNKF8MNIbyecBfmtl0M6slmn7yPGAW0RSVs/oYv0jVSHVGN2BTm74kqai/JndfC+ztUjwTWBuW1wAX5zYHhptZHTAUSAH7gTOAre6+zd1TwA+ARX0LX6R6vH0rZt2FU5LTlz6BFmAhcC/Rt/+pofwuog/3XcAw4Gp332tmk4FX8vZvBc7sw88XKSt7D6b45kNb6ejMxNp/2+6DADTU1iYZllS5viSBy4GbzOzLwH1E3/gh+safAY4FxgAPm9kvgEJfX7xQxWa2DFgGMG3atD6EKFI6Ht6ym+8+8iKjh9ZTVxPv2/zMCSMZP7Ix4cikmsVOAu6+GZgPYGYnAReEVZ8Efu7uaaDNzH4DNBOdBUzNq2IKsLObulcAKwCam5sLJgqRcpMbsfuTv/kgU8fqCh0pDbF7mMysKTzXANcSXSkE8DLwEYsMB84CNgNPADPM7DgzawAuITqDEKkKGrErpaiov0YzWw08Csw0s1Yzu4Lo6p7niT7gdwKrwubfJLpstIXog3+Vu290907gr4EHgE3Ane7+TKLvRqSEacSulKKimoPcfUk3q24ssO2bRB3Fheq5H7i/6OhESkjbgXbu+N3LdGazsfbf8MobANTrTEBKiEYMixTppxt3ceODW6itsYJXORTj+PHDGVqvq3ukdCgJiBSpPR2dAbR85WMMbdAHuVQGnZeKFCk3x259rQZrSeVQEhApUqozS41BnTp2pYKoOUiqxvqXXufJl16Pvf+6l/bqNsxScZQEpGp86cctPLtrf5/qOHniyISiESkNSgJSNQ6nM5x36kS+tnh27DqG6ExAKoySgFSNVGeWYQ11jGjUn71Ijr7WSNVIZbK6DbNIF/pKJGXB3fl5y6scaI83vy7AwY5OGnRlj8gRlASkLDy7az+fvePJPtczYfSQBKIRqRxKAlIWDnZEE7HceMkcmqePjVVHjcHEUUoCIvmUBKQs5O7FP2n0UCYfM3SQoxGpHGoglbKQfmt+Xf3JiiRJZwIyIN44lOLxF/cWnk+0CG/dhln37RFJlJKADIgbH9zCqt9s73M9Y4c39D0YEXmLkoAMiP2HO2ka2ciqT78vdh2jh9YzabT6A0SS1GMSMLOVwIVAm7ufGspmE80pPALYDix19/1mthS4Jm/39wJz3X2Dmf0KmAQcDuvmu3tbUm9ESlsqk2V4Yx2nHDt6sEMRkTzF9LLdCizoUnYLsNzdTwPuIXzwu/sd7j7H3ecAlwLb3X1D3n5Lc+uVAKpLujOr9nyREtTjmYC7rzWz6V2KZwJrw/Iaosnjv9RlmyXA6j7GJyXixT0HeW1/e+z92w6068oekRIUt0+gBVgI3Es0qfzUAtt8AljUpWyVmWWAHwH/y93jXiwiAyidybLghrV0dMabYD3ngyeOSygiEUlK3CRwOXCTmX0ZuA9I5a80szOBQ+7ekle81N13mNlIoiRwKXB7ocrNbBmwDGDatGkxQ5SktKczdHRm+W9nTeP80ybFruekCboXv0ipiZUE3H0zMB/AzE4CLuiyySV0aQpy9x3h+YCZfR84g26SgLuvAFYANDc362xhkOVG685oGskHTtC3eZFKEquR1syawnMNcC3RlULklS0GfpBXVmdm48JyPdHVRvlnCVLC0pkoD6tNX6TyFHOJ6GrgHGCcmbUC1wEjzOzKsMndwKq8XT4MtLr7tryyRuCBkABqgV8A3+l7+FKMTNZ5YfebZGN2wby6L+oQrtdtmEUqTjFXBy3pZtWN3Wz/K+CsLmUHgXm9DU6S8Z2Ht3H9zzb3uZ6RQzS2UKTS6L+6Cuw50EFjXQ03fGJO7Doa62v40IzxCUYlIqVASaAKpDNZhjbUcl4fruwRkcqkRt4qkMpk1Z4vIgXpTKAMtKczvH4o1fOG3dh3OK25dUWkICWBMnDBTQ/zwu6DfarjpAkjEopGRCqJkkAZ2PlGOx+aMY4L+tCmf+pk3b1TRN5JSaAMpDJZ3jtlNJecoVtoiEiy1FBc4jJZJ5N1GmprBzsUEalAOhMYAO3pDHHvl9rRmQGgvk734heR5CkJ9LOHNrdxxW1PkO3jbfCG1utMQESSpyTQz17cc5Csw1V/MoMhMT/I62qMi+ZMTjgyERElgX6XzkS3YV724eMZ1qDDLSKlRR3D/Sx3L36N2BWRUqRPpn6WzmQxi5p0RERKjdonevCZ763nl8+1xd6/M5Olsa4GMyUBESk9SgI92Nj6BsePG845M5ti1zFzom7ZICKlqZiZxVYSTQfZ5u6nhrLZRFNKjgC2E00iv9/MlgLX5O3+XmCuu28ws3nArcBQ4H7gc+5xr54fOKlMlnnvHsPy804e7FBERBJXTJ/ArcCCLmW3AMvd/TTgHsIHv7vf4e5z3H0OcCmw3d03hH2+BSwDZoRH1zpLUqpTt2EWkcrV46ebu68F9nYpngmsDctrgIsL7LoEWA1gZpOAUe7+aPj2fztwUdygB1Iqk9UE6yJSseL2CbQAC4F7gcXA1ALbfAJYFJYnA61561pDWb9a/9Je/nb1BlLhWv042tNZ3YtfRCpW3CRwOXCTmX0ZuA84YsYTMzsTOOTuLbmiAnV02x9gZsuImo6YNi3+nTNbduxnxxuH+fjcyTTWxRutW1sDF51+bOwYRERKWawk4O6bgfkAZnYScEGXTS4hNAUFrcCUvNdTgJ1HqX8FsAKgubk5dudxbqDWPy48hZFD6uNWIyJSsWK1c5hZU3iuAa4lulKIvLLFwA9yZe6+CzhgZmdZdMH8p4iakvpVrhlIbfoiIoX1+OloZquBR4GZZtZqZlcAS8zseWAz0Tf6VXm7fBhodfdtXar6LNFVRVuBF4CfJRD/UeXu21NfoyQgIlKIlfql+qOmzvQzr14Ra98/HEyx/3Carf/n/ISjEhEpbWa23t2be9qu5EcMD6mvZUbMSdJnAO+ZOCrZgEREKkjJJ4FpY4fxb0vnDXYYIiIVSY3lIiJVTElARKSKKQmIiFQxJQERkSqmJCAiUsWUBEREqpiSgIhIFVMSEBGpYiV/2wgzOwA8N9hxFGkcsGewgyiSYu0firV/KNbee7e7j+9po5IfMQw8V8z9L0qBma1TrMlTrP1DsfaPcooV1BwkIlLVlARERKpYOSSBePeRHhyKtX8o1v6hWPtHOcVa+h3DIiLSf8rhTEBERPpJySYBM1tgZs+Z2VYzWz5IMUw1s4fMbJOZPWNmnwvlY81sjZltCc9jQrmZ2U0h5o1mNjevrsvC9lvM7LJ+jLnWzH5vZj8Jr48zs8fCz/2hmTWE8sbwemtYPz2vjs+H8ufM7GP9FOcxZnaXmW0Ox/f9pXpczezq8PtvMbPVZjakVI6rma00szYza8krS+w4mtk8M3s67HNTmCM86Xi/Fv4ONprZPWZ2TN66gsesu8+H7n4vScWat+7vzczNbFx4PejHNjZ3L7kHUEs0D/HxQAPwFDBrEOKYBMwNyyOB54FZwL8Ay0P5cuCrYfl8ormTDTgLeCyUjwW2hecxYXlMP8X834HvAz8Jr+8ELgnL3wY+G5b/Cvh2WL4E+GFYnhWOdyNwXPg91PZDnLcBfx6WG4BjSvG4ApOBF4Ghecfzz0rluBLN6T0XaMkrS+w4Ao8D7w/7/Aw4rx/inQ/UheWv5sVb8JhxlM+H7n4vScUayqcCDwAvAeNK5djGfp+D8UOLOPjvBx7Ie/154PMlENe9wLlEg9cmhbJJRGMZAG4GluRt/1xYvwS4Oa/8iO0SjG8K8CDwEeAn4Y9rT94/2FvHNfwRvz8s14XtrOuxzt8uwThHEX2wWpfykjuuREnglfBPXBeO68dK6bgC0znyQzWR4xjWbc4rP2K7pOLtsu6/AHeE5YLHjG4+H472955krMBdwGxgO28ngZI4tnEepdoclPvHy2kNZYMmnNafDjwGTHD3XQDhuSls1l3cA/V+bgD+B5ANr98FvOHunQV+7lsxhfX7wvYDEevxwG5glUVNV7eY2XBK8Li6+w7gX4GXgV1Ex2k9pXlcc5I6jpPD8kDEnHM50bdieoirUPnR/t4TYWYLgR3u/lSXVeVwbAsq1SRQqG1s0C5jMrMRwI+Aq9x9/9E2LVDmRylPjJldCLS5+/oi4jnauoE49nVEp9nfcvfTgYNEzRbdGczjOgZYRNQccSwwHDjvKD93MI9rT3ob24DGbGZfBDqBO3JFvYyrX+M1s2HAF4EvF1rdy5hK4e8BKN0k0ErU7pYzBdg5GIGYWT1RArjD3e8Oxa+Z2aSwfhLQFsq7i3sg3s/ZwEIz2w78gKhJ6AbgGDPL3R4k/+e+FVNYPxrYO0CxtgKt7v5YeH0XUVIoxeP6J8CL7r7b3dPA3cAHKM3jmpPUcWwNy/0ec+gwvRBY6qF9JEa8e+j+95KEE4i+DDwV/s+mAE+a2cQYsQ7Yse3RYLRBFdEOV0fUgXIcb3f8nDIIcRhwO3BDl/KvcWTH27+E5Qs4snPo8VA+lqgNfEx4vAiM7ce4z+HtjuH/x5EdZX8Vlq/kyA7MO8PyKRzZGbeN/ukYfhiYGZa/Eo5pyR1X4EzgGWBY+Pm3AX9TSseVd/YJJHYcgSfCtrkgwttjAAABEElEQVTOy/P7Id4FwLPA+C7bFTxmHOXzobvfS1Kxdlm3nbf7BEri2MZ6j4PxQ4s8+OcTXY3zAvDFQYrhg0SnaBuBDeFxPlHb44PAlvCc+6Ua8M0Q89NAc15dlwNbw+PT/Rz3ObydBI4nugpha/gHaQzlQ8LrrWH98Xn7fzG8h+fopysWgDnAunBsfxz+QUryuAL/CGwGWoDvhQ+lkjiuwGqivoo00bfLK5I8jkBzeN8vAP+XLp35CcW7lajdPPc/9u2ejhndfD5093tJKtYu67fzdhIY9GMb96ERwyIiVaxU+wRERGQAKAmIiFQxJQERkSqmJCAiUsWUBEREqpiSgIhIFVMSEBGpYkoCIiJV7P8DnKIVaNhPrrMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_birthdata['year'].plot.line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_birthdata[['month','births']].plot(kind='area',title='Some graph',figsize=(10,8),table=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>gender</th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M</td>\n",
       "      <td>4440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>M</td>\n",
       "      <td>4548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>M</td>\n",
       "      <td>4994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>M</td>\n",
       "      <td>4520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>M</td>\n",
       "      <td>4198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>M</td>\n",
       "      <td>4850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>M</td>\n",
       "      <td>5092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>M</td>\n",
       "      <td>4934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>M</td>\n",
       "      <td>4842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>M</td>\n",
       "      <td>5190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>M</td>\n",
       "      <td>4598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>M</td>\n",
       "      <td>4324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>M</td>\n",
       "      <td>5076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>F</td>\n",
       "      <td>5070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>M</td>\n",
       "      <td>5296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>F</td>\n",
       "      <td>4798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>M</td>\n",
       "      <td>5096</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>15517</th>\n",
       "      <td>2007</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>180912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15518</th>\n",
       "      <td>2007</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>189157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15519</th>\n",
       "      <td>2007</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>173513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15520</th>\n",
       "      <td>2007</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>180814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15521</th>\n",
       "      <td>2007</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>173787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15522</th>\n",
       "      <td>2007</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>181426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15523</th>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>174255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15524</th>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>182789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15525</th>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>165669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15526</th>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>173434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15527</th>\n",
       "      <td>2008</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>172053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15528</th>\n",
       "      <td>2008</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>179129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15529</th>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>169585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15530</th>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>177399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15531</th>\n",
       "      <td>2008</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>173141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15532</th>\n",
       "      <td>2008</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>182294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15533</th>\n",
       "      <td>2008</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>169958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15534</th>\n",
       "      <td>2008</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>179267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15535</th>\n",
       "      <td>2008</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>183391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15536</th>\n",
       "      <td>2008</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>192714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15537</th>\n",
       "      <td>2008</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>182713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15538</th>\n",
       "      <td>2008</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>191315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15539</th>\n",
       "      <td>2008</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>179696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15540</th>\n",
       "      <td>2008</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>188964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15541</th>\n",
       "      <td>2008</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>175314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15542</th>\n",
       "      <td>2008</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>183219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15543</th>\n",
       "      <td>2008</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>158939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15544</th>\n",
       "      <td>2008</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>165468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15545</th>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>173215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15546</th>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>181235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15547 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  month   day gender  births\n",
       "0      1969      1   1.0      F    4046\n",
       "1      1969      1   1.0      M    4440\n",
       "2      1969      1   2.0      F    4454\n",
       "3      1969      1   2.0      M    4548\n",
       "4      1969      1   3.0      F    4548\n",
       "5      1969      1   3.0      M    4994\n",
       "6      1969      1   4.0      F    4440\n",
       "7      1969      1   4.0      M    4520\n",
       "8      1969      1   5.0      F    4192\n",
       "9      1969      1   5.0      M    4198\n",
       "10     1969      1   6.0      F    4710\n",
       "11     1969      1   6.0      M    4850\n",
       "12     1969      1   7.0      F    4646\n",
       "13     1969      1   7.0      M    5092\n",
       "14     1969      1   8.0      F    4800\n",
       "15     1969      1   8.0      M    4934\n",
       "16     1969      1   9.0      F    4592\n",
       "17     1969      1   9.0      M    4842\n",
       "18     1969      1  10.0      F    4852\n",
       "19     1969      1  10.0      M    5190\n",
       "20     1969      1  11.0      F    4580\n",
       "21     1969      1  11.0      M    4598\n",
       "22     1969      1  12.0      F    4126\n",
       "23     1969      1  12.0      M    4324\n",
       "24     1969      1  13.0      F    4758\n",
       "25     1969      1  13.0      M    5076\n",
       "26     1969      1  14.0      F    5070\n",
       "27     1969      1  14.0      M    5296\n",
       "28     1969      1  15.0      F    4798\n",
       "29     1969      1  15.0      M    5096\n",
       "...     ...    ...   ...    ...     ...\n",
       "15517  2007     10   NaN      F  180912\n",
       "15518  2007     10   NaN      M  189157\n",
       "15519  2007     11   NaN      F  173513\n",
       "15520  2007     11   NaN      M  180814\n",
       "15521  2007     12   NaN      F  173787\n",
       "15522  2007     12   NaN      M  181426\n",
       "15523  2008      1   NaN      F  174255\n",
       "15524  2008      1   NaN      M  182789\n",
       "15525  2008      2   NaN      F  165669\n",
       "15526  2008      2   NaN      M  173434\n",
       "15527  2008      3   NaN      F  172053\n",
       "15528  2008      3   NaN      M  179129\n",
       "15529  2008      4   NaN      F  169585\n",
       "15530  2008      4   NaN      M  177399\n",
       "15531  2008      5   NaN      F  173141\n",
       "15532  2008      5   NaN      M  182294\n",
       "15533  2008      6   NaN      F  169958\n",
       "15534  2008      6   NaN      M  179267\n",
       "15535  2008      7   NaN      F  183391\n",
       "15536  2008      7   NaN      M  192714\n",
       "15537  2008      8   NaN      F  182713\n",
       "15538  2008      8   NaN      M  191315\n",
       "15539  2008      9   NaN      F  179696\n",
       "15540  2008      9   NaN      M  188964\n",
       "15541  2008     10   NaN      F  175314\n",
       "15542  2008     10   NaN      M  183219\n",
       "15543  2008     11   NaN      F  158939\n",
       "15544  2008     11   NaN      M  165468\n",
       "15545  2008     12   NaN      F  173215\n",
       "15546  2008     12   NaN      M  181235\n",
       "\n",
       "[15547 rows x 5 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_birthdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = df_birthdata.pivot_table(index='year',columns='gender',values='births',aggfunc=sum)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(temp,'ro')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b74a8f320>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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s0rswWlsIXXMmtDZnKp5Ifvr/fesDAfD5iF1yJ/FL7+pyhbaZWA2u43WMjFOxLSIiGZM85HjiV0zFreyb0n3u4GHEf3cLRs1CQjecB4l4hhKK5JHmRgL33kToytPBdXG22I7Y7U/gDN/D62Q/EGp4h4p5l1K07G9eR8k4FdsiIpIZjgOBIM42O2/a7dvvSuLMqzC/+gTz83+nOZxIHrEt/P94nMikX+Cf9SRO9WBIJtqvmV2v1As2fUTR0odwfMWE698k2Pi+15EySmu2RUQkI0KXT8TZZkSnDqqx9xxLbPNtcPtUpzGZSP7wvfEigcf+hLl8CfZ2u5I4/jzcgUO9jrVegZb/ULzkPqzIYJoGTqJk0RSKah6mITwAJ5Sfz2d0vbc7IiKS88x5n+H74mPcsh6dbsvtUw2ui/+Z+/E/c38a0onkuMY6aKwHwGiswy0uI37RbcQn392lC21/27eULJqKHexN08AzcH0FNFefCIafkiX3gpP0OmJGdFhs19fXM3HiRMaOHcuECROYNGkSdXV1AJx//vnsueeeDBs2jNbW1nXu+/jjjznooIMYO3YsJ5xwArW1tZ5dExGR7PLPfAI3Uog1+mdpa9Nc/B3BR6bge+35tLUpkkuMmoUEpl5L5JSfEXjmAQCscUcQv/5B7F1+0qW39PPFllCy8I84gVIaB52N6ysEwAlU0NLvOPyxxRSueMrjlJnRYbFtGAYnnXQSs2bNYvr06fTv359bbrkFgMMOO4znnnvuB/e4rssFF1zA5ZdfzqxZsxgxYsTae7J9TUREsqyxDt9bL2GNHg+RwvS0aRgkTp+MvcNIQlOuJPDIH/+744JIPnNdzC8/JnjDeYTPOhT/7BlYo8dj7X9w+3Wfr0sX2QBmfAWlC/6A6wvTOPAcXH/JOtcTxdsT7bEfkbp/Emz60KOUmdNhsV1WVsbIkSPXfr3jjjtSU1MDwO67706PHj/8iHDu3LmEQiFGjBgBwJFHHsmLL77oyTUREcku/yvPYlhJrAMPT2/DgQDxC2/D2u9gAs88QOiqM9o/ThfJY8bq5YQuPQnfFx9jHXoi0akzSJ56SZfbxm99zEQdpQvvAKBp4Nk4wR9fWtba6xckI4MoWvpXzMSqbEbMuJTWbDuOw6OPPsqYMWM2+Lply5ZRVVW19uuKigocx6GhoSHr10REJLuMWBRrxN6ZKQZCYRKnXUp80hUYdau6/IyeSMriUfwvPkHomrPAcXAr+xK/+A6i97zQ/rBxGp6DyBbDaqJk4R8w7BiNA8/EDvVZ/4tNP83VJwFQvORecPLnk6uUdiO5+uqrKSgo4JhjjslUHk/16FHkdYSUVVYWex0hp2i8UqPxSo3Ga41zLsJ1XYwOCuFOjdcvf4V7yOEU+wO49bVYr8/Ef/DRHfaZy/TzlZpcGy+3vpbk3x8m+fdHoLEBc6vtKfLFMXv2ggPHZbz/dI+Xm2yFj/8IVgPscCEVpcM24q5i3OApmJ/fQc/mFzA2z496c6OL7RtvvJGFCxcydepUzA72bOzbt+/apSYAdXV1GIZBWVlZ1q+lora2BcdxU7rHS5WVxaxapZPVNpbGKzUar9RovNqZH76Fs+2IDo9mT994xfA//TDBv02h7Z03SJwxGQpzq8jaGPr5Sk2ujZdRt4rw+UdhNNVj7fITrJ8fi7PljrS4BmThz5H28XLilC74A/7YUpoGnEEyUZXCn2NLCiv2IbJkJo3GIBIlO6QvVxqYppHy5OxGLSO5/fbb+eyzz5gyZQrBYLDD12+77bbEYjE++OADAB577DHGjRvnyTUREckOY9E3hK89C/+LT2S1X+uQ40kcdy6+9+cQ/t2xGAu+zmr/Ip1lfvUJ2BbRmx8mcdFtOFvtlLtLpJwkJYum4o/Op7n6RJJFW6fcRGvvQ7DCAyiq+QtmIvefyzBc193gVO68efMYP348gwYNIhwOA1BdXc2UKVOYNGkSn376KStWrKBXr15sscUW3HfffQB8+OGHTJ48mXg8Tr9+/bj55pvp2bOnJ9c2lma285vGKzUar9RovCAw7Xr8rz1PdNo/oKR8g6/NxHiZX3xE8NaLMFqbSUy8CHvMQWlt30v6+UpNTo5Xa7Nnn8qkbbxcm+LF9xJq/ojmql8TLx+1yU2Z8ZWUfXcddqiKxsHng+HrfL402JSZ7Q6L7e5ExXZ+03ilRuOVmm4/Xq3NRE4eh737fiQmXdHhyzM2Xg21hO64BGfotiSPnpT+9j3S7X++UpQr42UsnIfvX7OxDjkefN4d6p2W8XIdimoeItzwLi19DifWY99O5wo2fkDJkntp6zmWtt4Hd7q9dNiUYlvHtYuISKf5Z8/AiEVJjjvC2yBlPYhfNgVonzjxvf0yzpCtdNy7dD2JOKE7LsVorMMae1iHnwZ1aa5L4fInCTe8S2vlhLQU2gCJ0hFEW7+iYPUskgVDSRZvm5Z2s03HtYuISOc4Dv4Xn8TeYjvczbbyOk37IR8+P8SiBO+7mfAFR+N7/59epxJZR+CRKZiLviF+xuTcLrSBglUziNS9TrTHvkQrf5rWtlv7HI4V6kfx0gcxk/VpbTtbVGyLiEinJX99NsmjTvc6xrrCEWLXPYDbp5rQDecR+OudOnVSugTzk/cIzHiE5IG/xNl5T6/jdEp49SsUrHqBWNketPY+LP0PdppBmvtPxHCTFC+5H1w7ve1ngYptERHpHNPE3uUnONvv6nWSH3B79yN27f0k9z+EwLN/IXTl6RBt8zqWdGfNDQT/OBmn3yCSvz7L6zSdEmj9iqIVTxEvGU5L1dEZ20HFDvWhpe9RBNrmUbDqhYz0kUkqtkVEZJMZK2sI3nQBRs0ir6OsXzBE8tRLiE+6AnPpAozli71OJN2ZZeEO2oL4OddAKOJ1mk4J176G4yumud/xYGS2pIyX7UasbBSRVTMJtHyR0b7STcW2iIhsMv+sp9rXQ2/EGQxes/eZQPTu53AHb8xJdiIZ4LpQ3pP4JXfiDukCzzd0gpmsJ9j8KbHyUWAGstJnS98jsEN9KF76AEayMSt9poOKbRER2TTxGP5XnsXedTRuzz5ep9k4oQi0NhO87fcY3+XW7JjkNmP54vZTIvPk5y5c/yYAsfIsrjk3QzRXT8SwoxQvfQBcJ3t9d4KKbRER2SS+t17CaGnEGvdLr6OkxrIwv/iI0C0Xth8kIvkj2or/H4/hxmP4X3gU8/MPvU7UzrYI3nk5xqplUFzmdZrOc21C9W+SLNoaJ1iZ1a7tcBUtfY8k2Pol4fo3str3plKxLSIiqXNdAv94DKf/Zjjb7Ox1mtSUlpM4/waM1csJTrmy/aN9yQv+mY8TvO9mnC/n4n/xSUJXnIL/qXvB9nYHC//T9+P76lMSJ1+MW9nX0yzpEGyei89qJFa+tyf9x8tG0dr7UOxgb0/6T5WKbRERSZmxejnG8iXts9oZ2oEgk5wtdyB5zJn433sd/4y/eR1H0iHaSuC5h7GH74Fvh12I3fRX7FH7E3z0T4SuORMaaj2JZX49l8CT92LtdSD2Xgd6kiHdwnVzsP3lJLw6ZMYwiPbcn2TRlt70nyIV2yIikjK3si/RP8/EGj3e6yibzJpwDNauown89Q8Y3+bHOtruzD/zcYyWRpK/PLn9G5FCEudcS/y0SzG//JjI+Udhzvssu6GSSYJ/uAy3opLExIuy23eGmIlVBFv/075W2/B5HScn6Lh2ERFJTSza/s9Iobc5OsswSEy6gsD0R3AHbOZ1GumMaCuB59tntZ2h35ttNQzs/Q4mNnRbgvfdjNsjy8sOAgGSx52DW1QKhcXZ7TtDwnVv4GISLx/ldZScoZltERFJif/VZ4mcNBbqV3kdpfMKi0keeSoEghjLl4CTG7sbyLp877yK0fy9We3/4Q4cSvyqabgVle270dx5OUZdZn9+jdqVANi7jsbZeqeM9pU1TpJww9skinfACeT2EfPZpGJbRERS4n99Ok7VACjP7i4EmWTULCJ83hH4n3nA6yiyCex9JhC96eF1Z7XXw/zuS3zvvkr4/KMwP3o7M4HqVxE+/0gCj9+TmfY9Emr6CNNuIVaxl9dRcoqKbRER2WjGgq8x53+FPXqC11HSyu3bH3vX0QQen4o5932v40gqGuvAMHA327hDYpztdiF208O4ZRWErzmTwMN3gZVMXx7HIfTHKyEew9rjgPS12wWE6+dgBytJFubGg4ldhYptERHZaP7XZ+D6/Vh7jfU6SnoZBolTLsGtGkjojkvyY4lMdxBtJXL2YSnPILvVg4nd8BDWfgcT+PuDhC4/BRLxtETyv/gEvo/fIXncubjVg9PSZlfgi9UQaPuGWPleGT+aPd9otEREZONYSfxz/oE94if5cTDH/4oUEP/tTRBtJXTb78G2vE4kHfDPfAKjuRF7+B6p3xwKkzjtUuLnXNu+pjoY6nQeY9G3BB76A/bOe2KNPazT7XUl4fo5uIafWJkejEyVdiMREZGN09KEvfVwrH1/7nWSjHH7DyFx8u8J3nMt5vyvcTbf2utIsj7RVgLP//WHO5CkyP7e/te+2TPwff4hTu9+gNt+4JHrYo/aH7d6MOZXn+L78K0111hzIJKLPWx7nBF7E3jyzxApJH765Tm5//x6OXFCDe8SLxmO6y/yOk3OUbEtIiIbp6wHiQtu8jpFxtmjf0ZsuxHZ3yZOUvL/s9rr24FkU5iLv8M3ezr+/9mVJj5wKHb1YMx5nxF46l5cw1hTTBtgAOOOxBmxN4lJkzFqFkFZj7Rl6gpCjR9gOjHPTozMdSq2RUSkY80N+L78BHunUeAPeJ0m49wevSGZJPDEPVj7H4rbK/eP2M4raZrV/l/JY88iedRp7bPWBv8tqM32VbfW+F9hjf/V+hsIRXAHD0tbnq4iXPdPrFAVVoH2o98UWrMtIiId8s+ZSeiG8zBqFnodJWuMupX4X3yC4G0XQjKNu1VIGhgkf3okySNOSX/T/gAEAu3/9PnB58uvJSEp8kcXEogtap/V7sbj0BkqtkVEpEP+16djb7Y17oDNvY6SNW7vfiTOuALfvM8JPHS713Hk+yIFWL88GWfzbbxOkvfCdXNwjSDxspFeR8lZKrZFRGSDjPlfrdlbe7zXUbLO3m0MyfG/IvCPx/G9/bLXcYT2tdr+J6bptM8sMOw2Qo3vEy/bFdcX8TpOzlKxLSIiG+Sfnad7a2+k5DFnYW+xHcG7r+5Wy2i6pGgbgcen4vv6s7XrqCVzQg3vYbgJouU6MbIz9JMqIiLrl8zzvbU3RiBA4rzrsYfvgZtHR9TnIv+L/78DyUSvo+Q/1yVcP4dkZCB2ZKDXaXKadiMREZENShx/Pm7vaq9jeMqt7EvivOsBMJYvAb8ft2cfj1N1M9E2As89hL3TKJwttvM6Td7zt32DP76M5qpjvY6S81Rsi4jI+gUC2Hv/1OsUXYdtE7r+HHAcYldNg/KeXifqNv47q52+fbVl/cL1c3DMCPHSXbyOkvO0jERERH5cYz3Bu69qP6RD2vl8JE67DKNuJeGrTofmBq8TdRvmom81q50lhtVEqOlD4mW7gRn0Ok7OU7EtIiI/yv/GTPyvPgdWwusoXYqz5Q7EL7oNY9liQldPgtZmryN1C4mzryZ+wc1ex+gWwg3vYLg2MT0YmRYqtkVE5Ef5Z8/odntrbyxnu12JX3Az5sJ5hK49W4feZFK0DfOT98B1IRT2Ok3+cx3CdW+QLBiKHa7yOk1eULEtIiI/sHZv7X0meB2ly3J23pPEuddjjxzdfuKgZIR/1pOErzodY+E3XkfpFgKtX+BLriZasbfXUfKGHpAUEZEfaN9bO4C15wFeR+nS7N3GrP1385N3cbYeDgGtcU2b7+1A4g4a6nWabiFcNwfHV0yieEevo+QNzWyLiMi6HAff269g77J3991bO0XG0gWErjmT4O2/B9vyOk7e8M96EqOpQTuQZImZrCfYPJdY+Sgw9WlNuqjYFhGRdZkmsdseJXns2V4nyRluv0EkjzsX/3uvE7xrMti215Fyn/bVzrpw/ZuAS6x8T6+j5BUtIxERkXU5DhSX4WpWOyXW+F9BIkbwkSkQDJE49VIdKd4J/lf+rlntbHJtQvVvkizaGieok1LTScW2iIj8V2NeSe4uAAAgAElEQVQ94d/+iuTJF2Hv8pOMdBFo+Q+uLwIMzkj7XrIOOQEjFiPw9H24JeUkjznT60g5yzrgENyevTWrnSXB5rn4rEZay4/yOkreUbEtIiJr+d+YiVm3Eqd3v4y0b9hRihdPg4UxCnvsS2vvQ8DwZaQvrySPOg1ME2vXzLxZyXuu2753eVEJ9u77eZ2m2wjXz8H2l5Mo1pubdNPnWyIislam99YONbyN6cSgclcita9SOv9WzGR9RvryjGGQPPJU3CFbQTKJ793XvE6UG5IJfP/8B6GLjiMy8UB8s1/wOlG34UZXEGz5T/ta7Tx789sVqNgWEREgC3truw6R2tdJFmyGsc3ZNFWfhC++lLJvryXQ8p/M9Okx/0tPEbr5AvzPP+x1lK6rfjWBx+8hcup4QndehtHWQvLX56yzraJkWM1ruJjEy0d5nSQvaRmJiIgAmd9bO9j8Cb7kalr7HEIQSJSOoDFcTfHiaZQsvIu2yvFEK8eBkT/zQNaBh2N+8THBv9wOoTDW2MO8jtR12Bb4/Phff57AE9Owh+9B8qdH4uywmx4szSYnCcv/SaJ4e5xAuddp8pKKbRERAcBorMPedXTG9taO1L6KHeixzmEZdqgPDUMupKjmbxSumk4g+i3N/U7A9RdlJEPW+fwkzr4GIxEn8OcbcEMR7NE/8zqVd5JJfO++gv+Fx3B2GEnyqNOxDjgMe7f9cKsGeJ2u+3GSFKyaCclmYlU6MTJTVGyLiAgAiXOuzdj+0P7oQgJt39DS5/AfzlybIVr6/YZkweYULX+csm+vpbn/RKyCIRnJknWBAPHf3kjourMJTrmCeHlPnB1Gep0quxpq8b/8DP5ZT2HWr8apGojTp3/7taIS3KISb/N1N3aMSP0cIrWvYFpNULEDycItvU6Vt1Rsi4gIxqJvcasHgy8zD0eFa1/BMcPEy9azJtQwiFfshRUZQMniaZQuuJXW3ocRqxgNhpGRTFkVDBG/8DYCT0zDGba912myq341kdMmYCQT2DvtQeyMyVoq4hHDbiNc+zqRutcw7VYShVsSrT6R0oE7w+oWr+PlLRXbIiLdXWM94d8eRfLwiViHT0x782aynlDjv4n12GfN/trrZ0cG0rDZ7yla+heKlj9OoG0eLVXHdnhfTogUkDzuHACMZYsx6lfjbL2Tx6EypLWZwMN3kTz5YijvSfLYs7B33B233yCvk3VLhtVMpPZVwnWzMZ0YiaLtaKsct/bTIyMf3tB2YSq2RUS6Of8bMzFsG3vkPhlpP1w3G3CJVmxc+66vkOb+p2LVvkzBimcpjS2luf/J2OHM7P3theC06zG/nkt88t35d2iLbRO64xLMT97FOuR43Mq+WD/TQSleMJP1RFa/TLj+DXAtEiXDaaschx2u9jpat6LPcEREurmM7q3txAnXv0GiZEecYM+Nv88wifYcS+OgczGdKGXf3UCo4d305/NI4swrccsqCF1zJsaCr72Ok1aBR/6I78O3SJ74O9zKvl7H6ZbMxCoKax6hfN5lhOtmEy/dmYbNJ9Pcf6IKbQ+o2BYR6cYyvbd2uOFdTLuNaI99N+l+q3AL6odcghUZRPHSBymseaR9q7Ic51ZUEp88FTdSQPjK0zGWzPc6Ulr4Zs8g8NxDJA88XNscesAXX0bRkgconzeZcMM7xMpGUT/0Klr6/QY71MfreN2Wim0RkW4so3truw7h2tdIRgZiRTbb9GYCpTQOOoe2nmOJ1L9B6YJbMOy2NAb1hturL/HJfwLTJHTl6e1HlOcw85vPCf7pGuxtR5A8/nyv43QrvugiihdPo+ybqwg1fUSsxxjqh15Da9WvUvtESTJCa7ZFRLoxZ/CWWD//dUb21g60fI4/sYKm6hM7v6OI4aOt98HtM9xL7qVk4R9pHHgW+MLpCesRt2ogscun4PvPR1BY7HWcTnH6DsDaZwLJo88Af8DrOPnPdQi0fEZk9SsE277GMcNEex5ItMe++bNPfZ5QsS0i0o3Zo39GZnbWhkjtK9j+chIlw9PWZqJkJ5qrT6J48Z8pWfwnmgacAWYwbe17wR04FGvgUAB877yKvfVOUFrhcaoUxKMYLc24PXqRPPUSr9PkPydBuOFdwrWv4k+swPaX09r7EGLle+XHrj15SMtIRES6Kd8rz2Is+iYzbceWEGz9as0+2enduztRshMt/Y4j0Po1JYungWOltX3PNNQS/ONkwledAS1NXqfZOK5LcMpVhC76NURbvU6T1wyriYKVz1Px9cUULfsbrhmiqfpE6re4hmjPA1Rod2EdFtv19fVMnDiRsWPHMmHCBCZNmkRdXR0AH3/8MQcddBBjx47lhBNOoLa2du19XemaiIj8j8Y6gtOuw//6jIw0H6l9FdcIEqvYMyPtx8tG0tL3VwRbPqN46X3gZmp+PovKehC/4GaMJfMJXXNmThSv/qfvw//WS+1b+0UKvY6Tl3yxGoqWPkTF178nsmomyYLNaBh0Po1DLiZRukva38xK+nVYbBuGwUknncSsWbOYPn06/fv355ZbbsF1XS644AIuv/xyZs2axYgRI7jlllsAutQ1ERH5ocCMR8FxsPb9edrbNpKNhBrfJ1a2O64vcwVYvGIvWnofRqjpI4qWPgSuk7G+ssXZcXcS59+A+e0XhK4/F+JRryOtl++91wg++iesvX/avu5f0sd1CbT8h5KFd1H+7VVrfp9GUb/5FTQPOB2rcGh+nKzaTXRYbJeVlTFy5Mi1X++4447U1NQwd+5cQqEQI0aMAODII4/kxRdfBOhS10RE5H+0NOGf+Tj27vu1H9GeZpH6ORiuRbTHmLS3/b9iPfejtddBhBvfo3DZY+C6Ge8z0+xdR5M460rM/3xIcMrVXsf5UcaCeQTvvBx76DYkTrtUhV+6OElC9W9T9u01lC68E39sMa29DqJui+vbdxYJ9fY6oWyClB6QdByHRx99lDFjxrBs2TKqqqrWXquoqMBxHBoaGrrUtbKy9D9hLyKSywIvPIoRbSV52Inpb9xJEq6bQ6Jou6wVBtGe4zCcOAWrZ+GaQdp6H5rzxZ+91zgSto2z5sHJrsb39ae4RSXEL7wVgiGv4+SFcO3rFKyeiWk1YYWqaK76NfHSXcDUzi65LqVi++qrr6agoIBjjjmGl19+OVOZPNOjR+5tlVNZmdtbRWWbxis1Gq/U5MJ4uVaS6CvPYO61Pz1HpG+XkLXtL5sNdjPBzSZQWb7h8UjneLmVx8I3LgVLX6KgqARj8KFpa9szh7cfce4mE8TvvJbSip74dhiBOWw7jKA3O7C4rothGHD0b3APPpzigq67TjsXfh//n7vsn7D8cSjbGgZMwF++HSVZfsOYS+OVaza62L7xxhtZuHAhU6dOxTRN+vbtS01NzdrrdXV1GIZBWVlZl7qWitraFhwndz6CrKwsZtWq3D4EIZs0XqnReKUml8bLuOGvYCVpTnde16VswQsQ6kdDsj9soP2MjFfpLyhqbSG88BlaYhDtmYGDejxgfvQ2kX+9gbvoO5KAGwjibL419vA9sQ45PntBXJfAtOtxqwZiTTi6/Xtd9CCeXPp99MWWUPbdAyQLh9FUdQbYPljdktUMuTReXjNNI+XJ2Y3a+u/222/ns88+Y8qUKQTXvJvedttticVifPDBBwA89thjjBs3rstdExGRNeIxSCZwe/TC7d0v7c0HWr/AH68h2nM/b5ZxGCYtVccQLxlB4YpnCNfNzn6GDHB2GkXBwy/Sdv8rxC+8FWvcL8GyML/7ov0FiTjhC44mcM91+Ob8A2PlsoysXffPfJzAS09jNDWkve3uyrCjlCy+B8dXQHP1idpZJE8Zrrvh38h58+Yxfvx4Bg0aRDjcflJXdXU1U6ZM4cMPP2Ty5MnE43H69evHzTffTM+e7ceCdqVrG0sz2/lN45UajVdqcmG8/H9/EP/MJ4jd+igUl6a9/ZKFd+GPLaZu6LUdrjPN6Hi5NsWL7yHU/CnN/Y4jXrZ7ZvrJoh8dL9cFw8CoXUnw7qswv/oUY812gU6P3tgj9iJ58sXtr7Ut8G36OXbmJ+8SuuYs7J33JPG7W8Ds2sd05MLvI65L8eKpBJvn0jjoPKzCzT2LkhPj1UVsysx2h8V2d6JiO79pvFKj8UpNlx+vWJTIaeNxNtua+KV3pb15X3wZ5d9cSWvlBKK9ftbh6zM+Xk6SkkV3E2j9kubqE0mUjshcX1mwUeNl2xiLvsH3xceYX34MkQISp10GyQSR34zB7TsAZ/CWOIOH4QzeAmfQFhu1N7ZRs5DwRcfh9uhF7LoHcmI/7S7/+whEVr9E4YpnaOl9GLGe+3maJRfGq6vYlGJbx7WLiHQD/peewmhqIHn4xIy0H659DdfwE6vYOyPtp8wM0DTgVEoX3kXxkvtpMoMki7f3OlVm+Xy4g4dhDR4GPz3iv99PxLEO/CXm/K/wfTAH/2vPAeCW9SB630vtt77yLG5FJc7gYVC+7qfCwbuvBp+P+EW350ShnQv8rfMoWPEs8ZKdiPXY1+s4kmEqtkVE8l08RuC5v2JvtyvOsPQXnIbVQrjhXeKlI3H9XWhHAzNE04AzKFlwByWLp9E04AySRVt5nSr7CotJHntW+7+7LkbdKoz5X2K0rnkIz7YI3ncTRiLe/pKyHmtmv4eRPHwiibOuwmiozcg6/+7ISDZSsuTPOMGetFT9Oue3qZSOqdgWEclz/lf+jtFQS/K8GzLSfrj+DQw3SbQLztC5vghNA8+idMFtlCz6E42DzsIq8G5trOcMo/0B2R69/vs9n5/ovbMwF3yNOf8rzPlfYs7/Gv93X5L81Rm4vapwe1Wtv03ZeK5NyZJ7MewojQPPxvVFvE4kWaBiW0QkzznVQ0ge+EucbdK/rzaORbhuNonCrbHDXbMgc/2FNA46m9L5t1Ky8I80Dv5dl83qmcJinG12xtlm5/9+z7ZyctbVTKzGrf0O3EFgdK0HOQtWPk+gbR7N/X6DHdYnBd1F1/opFBGRtHN2GEly4oUZaTvU9G98VmOXnNX+PtdfQtOgs8EMUrJ4KoYd9TpS19eJ3Us847oUL54Gc2+i9LsbCLR84XWitYJNn1CwehbR8r2Il+3mdRzJIhXbIiL5KpkkePdVGIu+yUz7rkuk9lWsUB+SRVtnpo80cgIVNFVPxEyspnjJ/eA6XkeSNAs2f0ogtgj6/ATTbqF04R8oWXAH/uhCT3OZiVUULX0QKzyA1j6/9DSLZJ+KbRGRPOWbPR3/q89h1K7MSPv+tm/wxxYRrdg3Z5YbWIVDae37S4ItcylY9YLXcSSdXIeCVdOxg5WwxQnUb34lLX0Oxx9bQtl311O8eBq++PLs53ISlCyeBoZBU/+TO9yDXvJPDn5GJCIiHbKSBJ55AHvoNjg7ZuZQl0jtKzi+QuJlIzPSfqbEyn+CP7qIglUvYIX7kyjZ0etIkgbB5k/wx5bQ3O83lJh+MAPEeuxLvGwUkdpXCNe+QlnTx8TLR9FW+TOcQHlWchUtexx/bDGNA87ACaZ22J7kB81si4jkId+cmZgra9r31c7ArLOZWEWw+VNi5XuDGUx7+xllGLT0PYpkZCBFSx/EF1/mdSLpLNehYOV0rGBv4qW7rHvJF6Gt1wTqh15NrOInhBreoXze5RQsfwbDbs1orFD924Qb3qKt54Eki7fLaF/SdanYFhHJN7ZF4On7cYZsiTN8z4x0Eal9DQyTaMXojLSfcWaA5v6nghGgeJEemMx1waYP8cdraOs1Hgzfj77G9ZfQ2vcI6je/knjJcCK1L1P+9WVEVr0ITiLtmXzRxRQte5RE4TDaeh2U9vYld6jYFhHJM8aib9v31c7QrLZhxwg1vE28ZARuoDTt7WeLEyinqf/J+BKrKFr6gB6YzFWuQ8GqGVihviRKdu7w5U6wJy3Vx9Ow2SVYBZtRuPJZyuddRrhuDrh2WiIZdpSSJdNwfAU0V5/Y5bYglOzSf30RkTzjDh5GdOoM7BGZOTo92Pg+phMnlquz2t9jFQ6ltc/hhJo/1QOTOSrU+AH++HLaKsenVNTa4WqaBp5Bw6DzcYI9KVr2N8q/uZJQw7uYidWb/ubLdSla+iBmopbm/hNx/SWb1o7kDT0gKSKSR4wl83F79IbizM04hxvewgpVYUUGZayPbIpVjMYfW7jmgckBJEp28DqSbCzXXjOr3Y9EyU6b1IRVOJTGQb9t36FmxbMUL32wvWkzhBXsgx3uix2qwgq1/9MJVGzwE6NI7cuEmj+hpfdh3fu0UllLxbaISL5wHEK3XIhbXEr86j9npAtfbAmB6AJa+hyeM9v9dcgwaOl7NL7YMoqWPkBj6CLsUB+vU8lGCDX+C19iJU39T+ncUg3DIFG8PYmibfFHF+CL1+CP1eCL1xBo+YJww7trX+qYIew1hbcd6osVqsIO98Xxl+Nv+4aCFc8SLxlOrIsf9CTZo2JbRCRP+N57HXPxt8TPuTZjfYTr38I1/MRL8+wEPDNA84BTKPv2eooXTaVxyIW4vojXqWRDXJuClWu2byxO0/aNholVMASrYAjx73/bbsUXW4Y/XoMvvgxfvIZgy2eYDW+vfY1jhgG3fU141bH582ZUOk3FtohIPnBdAk/di1M1EHvU/pnpw0kQaniPeMlOuP7CzPThISdQQVP/iZQuuIOipQ/S3NnZUsmoUMO7+JKraep7esYLW9dXiFW4OVbhustCDKsFX/z/i/AaTKuZtsrxeqMm61CxLSKSB3wfzMFc8DXxM68C349vfdZZoaaPMJ024uWZ2U6wK7AKt6C1z2EULX8Ca9VMor1+5nUk+TGORcGqF0hGBpIo8m7/atdfhOUfilU41LMM0vXpLbuISK5zXfxP/hmnTzX2XmMz1k2o/i3sYCXJgvwuLGIV+xAr3Y3CVdMJNH/qdRz5EeGGt/El62irPEjLNaTL08y2iEiuMwySx50LiTj4MvPXuhlfQbDta1p7/SL/l1YYBi1Vv8IXr6F4yf00DtEDk12KkySyaibJyBCSRVt7nUakQ3n+N6aISPfgbLMzzk6jMtZ+uOFtXExiZbtnrI8uxQyuOWHSrxMmu5hw/Vv4rHraek3QrLbkBBXbIiI5zPzkXUKTT8GoXZm5TlybcMM7JIq3y+kTI1PlBCto7j8RX2IlRUsf1AmTXYGTILJ6JsmCzUkWbul1GpGNomJbRCRXuS6BJ/+MsWwxbklZxroJNs/FtJqIle+RsT66qmThMFr7HEqo+RMiq1/0Ok63F65/A5/VqFltySkqtkVEcpT50dv4vvgY6+DfQCCYsX7C9W9i+8tIFm2TsT66sljFGGKlIylYOZ1Q/dsd3yCZ4SQoWD2LROEwkoXDvE4jstFUbIuI5CDz67mEbrsYp98grH1/nrl+knUEWj4nXjYKjMxsKdjlGQYtVUeTLBxGcc1DFCx/RktKPBCpm41pNdFWOcHrKCIpUbEtIpJrGusJXXMmbmk58cl/gmAoY12F6t/BwCVWnrmHL3OCGaRp4JlEy/emoPYlihffA3bM61Tdhx0jsvolEoVb/+BgGZGuTlv/iYjkmtJyEr8+G2f73XB79MpcP65DuOEtEoVb4QR7Zq6fXGH4aO17FHaoD4XLn6RswS00DTgdJ1DhdbK8F6mbjWm30NZrvNdRRFKmmW0RkRxhLJmP75//AMDe72DcXn0z2l+g9Ut8ybpu+WDkehkGsR5jaBpwBmZiNWXf3YC/bb7XqfKaYUeJ1L5MomhbrIIhXscRSZmKbRGRHGAsX0zoytMIPnQHtLVkpc9w/Zs4vkISxTtkpb9ckizelsbBv8M1gpQuuI1g4/teR8pb4brXMe3W9h1IRHKQim0RkS7OWLmM0ORTMZIJYpPvhoKizPdpNRFs/oR42e5gBjLeXy6yw1U0DLkQKzKQkiX3UbByOriu17HyimG3EVn9CvHi7bEiA72OI7JJVGyLiHRhRu1KQlecghFtJXb53bgDsvNwWLjhPQzXJlbWzR+M7IDrL6Zx4NnEynajYNULFC+5D5yE17HyRqT2VUynjbZKrdWW3KUHJEVEuirXJXjrRRhNDcQvn4I7JEsn5rkuofo3SUaGYIerstNnLjMDtFQdhx3sS8HKZylNrqap/2nd6rTNTDCsVsK1rxIv3gk7MsDrOCKbTMW2iEhXZRgkTr4YI9qCs8V2WevW3/Yt/sQKmqt+nbU+c55hEK0cix3qTfGS+yn77gaaBpyOHenvdbKcFal9GdOJaQcSyXlaRiIi0tW0NBH4652QTOAOGoqz1U5Z7T7c8CaOGSZeunNW+80HiZIdaRj8WwDK5t9MsOljjxPlJsNqIVL3OvGSnbHD/byOI9IpKrZFRLqSthZC10zCP+NvmPO/ynr3ht1GqPHfxEt3ATNzh+XkMzsygIYhF2GFqyhefA+RVbP04GQKzGQ9hSueAiehWW3JC1pGIiLSVUTbCF17FuZ3X5L47U1ZXTry/0KN72O4SWLle2a973ziBkppHHQexUsfonDl3/ElltHS92jt7PJjXAd/dD7B5rkEWz7DH1sCQLR8b+xQZveSF8kGFdsiIl1BPEbohnMxv55L4tzrsHcd7UmMcP1bWOH+2GE9kNZpZpDm6hOxV/WmYNULmFYLTQNOA8PndTLPGVYrgdb/rCmwP8e0W3ExsQo2o7X3wSSKtlOhLXlDxbaISJoEpl6L4dg4vfrh9m7/n9O7H5SUg2Fs+N6n78f8/N8kzrwKe9T+WUq8Ll90Ef7YIlr6HNlhXtlIhkFbrwnY/jKKlz1C0dIHael3PBjdbBWn6+KL1/x39rrtWwxcHF8RiaJtSRRvR7JoK1xfoddJRdJOxbaISCeYH78DtoU7dhzmqmWYC77G31C7zmuiU2fgVvbF/8KjGCuW4vaqwu1djdO7CrdXPwhHSB56Avaw7XF29m75Rrj+TVwjQLxsV88y5Kt4xV6YdiuFK5/F9RXS2ueI/H9D4yQItn5JYE2B7UvWA2CF+xPtOY5E8bZYkUHd742HdDsqtkVENpVtEbz3JggE4YADiV/2x/bvx6IYK2swVi7FXLEUt6IXAOaCefjefhkj1ra2CTcYInbzI7jVgz0ttHEShBr/RbxkOK6vwLsceSzacyyG3UJB7Ss4viKiefzwny++nNL5t2DaLbhmiEThlrRV/oxk0bY4gTKv44lklYptEZFN5Ht9BuayRcQvug3D/N7sXDiCO2Az3AGb4Xzv9YkzLofTL4PmBswVS9sL8hVLMFYtw60enPX83xdq+jemE9ODkZlkGLT1PhTTbqNw1QxcXyGxHvt4nSrtDKuFkkVTAIPGAWeSLNxCD4ZKt6ZiW0RkUyQTBJ6chj10G+wRe2/8fYYBJeU4JeUwdNvM5UtRuP4trGBvrILsHAffbRkGLVVHY9itFC1/HNdXQLxspNep0sexKFl8D2aynsZB52EVDPE6kYjntFBKRGQT+F96GnP1CpK/mpTza2998WUE2r4hXr5Hzv9ZcoLho7n6JBIFW1C09C8Emud6nSg9XJeiZY8SaJtHS9WxKrRF1lCxLSKSKtfFN2cm9rYjcLbP/YcJQ/Vv4WISK9vN6yjdhxmgecBpWOFqShZPw9/6jdeJOi1c+yrhhrdo6zkuv2brRTpJxXYuaqglfN6R2F/myWyISK4xDOJX/5nEWVd5naTzHItww7skSnbA9Zd4naZbcX0RmgaeiR2ooGTRFHxrDnPJRYHmTylc8TTxkp1o6zXB6zgiXYqK7Rzkf+05zIXzSD7xgNdRRLqf1maM2hUQDOH26O11mk4LNn+CabcQK9ODkV5w/cU0DTwb1wxTuvBOzPhKryOlzBdbSvGS+7DD/Wnu9xtt5SfyP/QbkYN8776OW1xK6LJbvY4i0u0Env8r4UkHw//spZ2rwvVvYQcqSBZt5XWUbssJVtA06CxwbUoX/gEz2eB1pI1mWE2ULJqCa4bbT8c0Q15HEulyVGznGKNmIb5v/0PykBMw9CCTSHY11uGf8TfsXX4CZT28TtNpZmI1gdYviJWN0mykx+xQX5oGnolht1Cy8E4Mq9XrSB1zkpQsmoppNdM84HScQLnXiUS6JP3tmmN8b87CNQzsHUYSPf1IfK9P9zqSSLcReOZBSMRJHnGK11HSItzwNgDxslEeJxEAKzKI5v6n4UusbN+n2ol7HWn9XJeimocJRL+jud9vsCIDvU4k0mWp2M4x1v6HkDj3OtwBm+MuX4Lvw7e8jiTSLRi1K/DPehJ79ATcfoO8jtN5rkOo/m2SRVvjBCu8TiNrJIu2pLn6RPzR+ZQsvgccy+tIPyqyehbhxvdorZxAonRnr+OIdGkqtnNNeU/sPQ4Aw8C3y574Pv0X2LbXqUTynv+p+8B1SB4+0esoaRFs+gif1aATI7ugRMlOtFQdQ7DlPxQvfRBcp6NbsirY9BGFK58lVroL0cqfeh1HpMtTsZ1D/M/+hcCjd6/92rfLnhgtjZjffeFhKpHuwZpwNInTLsPt1dfrKJ3nOhSsmo4V6kOieAev08iPiJfvQWvvgwk1fUDhssdwXdfrSAD4oosoXvIAychgWqqO1SFIIhuhw2L7xhtvZMyYMQwbNoyvv/567fdnz57NwQcfzIQJEzjmmGNYvHjx2mvz58/niCOOYOzYsRxxxBEsWLDAs2t5w7bxv/AoxsL/HnzgGzEK1zAwP3nXw2Ai3YDr4lYNxB493uskaRFqfB9/fDltlRP0YGQXFu05lrYeBxCpnwOf3UbBimcJ175KqOFfBFq+xBdbimE1ZW3m20w2ULLobhx/IU39TwUzmJV+RXJdh3/L7rvvvjzyyCP069dv7fcaGxu58MILue2225g+fTqH/x97dx4nV1Xmf/xzl9p6X7J2J2SBAGFLgBAIa1gDGkRBDJNxRsdxXH8j6jiIoKAjowZnxF1R0ZlRHGUZBRJIIhB2CBBIAgQIZCH70vtW2733/P6oJOD2AaAAACAASURBVBBJL9VdXVXd/X2/XnmlU/fWPU+fVHc/dfq5z7nySr7+9a8fOH7jjTeycOFCli1bxsKFC7nhhhsKdmy4sF99EbtpL/5ZFx94zKqqIZh6NPYbrxQwMpHhzdryJtHPfwhr8/reTx4KjE/J3sV40YmkKk4sdDTSi66xH6Cr9kJo30SsYTllu+6kfPuvqXzr+1Rv+Ca1r19D7brPUvPav1L15r9RsfkWyrf+itKdfyS2934izU8Q6nhl4N1NghTlW36GHcRpO+yzmFBlbj5BkRHA7e2EWbNmveuxt956i1GjRjFlyhQAzjnnHK655hqampowxrBu3Tp+85vMhivz58/nm9/8ZkGO1dQMn5t+3MeXYqIx/FlnHfR48vofQoXaLYkMltAffo7VtHtYbGADEGl5Gie1l7bDPqNV7aHAsugadwWlx3+Uhj2tWH4c22/H8tqwvfbMH78Na//HXhtO+q3Mv4PEQZfyIuPxYlNJlxyOVzIVPzy2b2UgJqB8+3/jJrbQPvFT+NEJg/TJigxPvSbbhzJlyhQaGhpYu3YtJ5xwAvfdl2k/t3PnTowxjB07FsdxAHAchzFjxhTk2LBJttNpnGcewp99LkRiBx+r3Pc5GqPaOZEcs998BXflClJXfQrKh8FKXpCmZO/9pGOTSZUdX+hoJFuWjXFL8d1SiIzr/fwghe114KT24MY3EuraSLjtRaItmS5WgVN6IPlOl0zFi00+ZGlIyd4lRNpW0Tn2A6QqVOMvkq1+Jdvl5eXccsstfPvb3yaZTHL22WdTUVGB67qk0+lcx5g3tbVlhQ7hkPzVz5LoaKP0ve+ncnT5QcdGjSojec3HsSZMJnL11woU4dAx+q/mT3o20ucr8Z1f4FdWU/XRT2CV9P79odjny2xfDukmnOmfYHRNRaHDKfr5Kjb9m69aYBJwCgDGBNC1E9rewG5dT7h1PeE9L2VOtRwomwQVR0LlNKiYBq3rYe8SGHc2pUddQdkQWtTR6ys7mq/B069kG+D000/n9NMzGyE0NDRw2223MXHiROLxOLt378b3fRzHwfd99uzZw/jx4zHG5PVYthobOwiC4rjj+yD107F+ei9dtWNgb/uBh0ePLqehoYOIB9YTD9O28PMFDLL4jR5dzt53zJ/0bKTPl/3KKqLPPUHqI1+gq9NAZ89zUfTzFaSo2fQn/JJptHqTDvpeUghFP19FJrfzVQHuyVB7MtSC5XUcWPkOdW3A3fEQ1valB85OlxxBa/WV0NCRo/EHn15f2dF89Z1tW1kvzva7YG/v3r0ABEHA9773Pa666ipKSkqora1l+vTpLF68GIDFixczffp0ampq8n5sWAiCTCeEsfXghg55ij9zDvbu7Vi7th7yuIj0g+fhH3sy3rwPFjqSnIg1PYrttdE55n0qOZODGLeMdPkJdI19P61T/oXG6d+nZepX6Bj3IeI159I28ZNgH/rnj4j0zjK9NO+86aabWL58OQ0NDVRXV1NVVcWSJUu4/vrreeGFF0in05xxxhlcd911RCIRADZs2MC1115LW1sbFRUVLFq0iKlTpxbkWDaKcWXbeex+Qnf+iuTXf46pHXPQsf3vRK0dbxH758tJ/dO1eBdfWaBIi5/euWdH85WdYp4vy09Q/cZX8aKH0Tb5c4UOByju+SpGmq/saL6yo/nqu/6sbPeabI8kxZhsR/79c1hbNpD42X1gH/yLiANfHMYQ/cz7CCZNI3Xt9woUafHTN5PsjNj5CgJCf/g53nmXYsZN7PPTinm+YnsfoHTPPbRM+TJeyZRChwMU93wVI81XdjRf2dF89V1ey0gkD9qasdc8g3/mvHcl2gexLPwZp2Fv25TpSiIi/easfJjQ3bdhv7a20KHkhOV3EWv4C8nyE4om0RYRGUn6fYOkDD736YewfB/vHRvZdCf9kc+TjsRUiylDmr3uRex1L+Bd9ncQKsDudL5P6H9/RjBh6kEbSA1lscYHsYOuzG6RIiKSd0q2i5jz+FKCCVMxk6b1fnKsNPO378O+nuMiQ4n94lNEFv0LVjqF8+KTJP/1u1BVm9cYnMfux96+meSXbh4WX0eW10G08SGSFSfhx/peEiMiIrmjMpJilUpiJboyq9p9XK0O/XIRka/90yAHJpJ71rZNRL7zRcyEKSQ//VXsja8R/cZnMt148iWdJnTHL/APn45/2nn5G3cQxRqWYQUpusZoVVtEpFC0sl2swhES//F78L0+P8VUVmOvXwvtLVBeNYjBieSWqZ9M+qNfxDtzHpRXkpg6Hau9NXOvQhD0fM9CjthvvozVtJfUJ74yLMqxrHQrsaZHSFbOxo9kv++AiIjkhla2i5S1a1vmA6fv74eCmXOwjMFZ++wgRSWSW85Tf8F55mGwLLxLPnRgS3Qz9WiCGaeCMYR/dAOh3/90cFe502mC6ScS//l9BDPnDN44eVTSsBSMT9eY+YUORURkRFOyXYSsrRuJffYynEeWZPW84PDpmNJynNXPDFJkIrnjPL6U8C3X4y69o/suOoEPoTChu28jfPOXIN6Z0xisvTsJf/96It+6OhND9ehhsaptp5uINj9OsmoOQXh0ocMRERnRlGwXIfeJpRjbxp9xanZPdFz8E2Zjr35aLQClqDmPLCb8w68RTJ9J8svf6z7BdVxSn/4aqX/8V5xVTxD9yj+8/VufgYh3Evr9T4l+7gqclSsIph2XSeyHidjeBwDoGv3eAkciIiJKtouNMZkuJMefAtWjsn66P+M0LC+N1dwwCMGJDJzz0D2Ef/x1guNOIXn9DyBW0vMTLAvvPVeR/NqPsZr3Ev3y32NtXt//8R+7n9j/+wChu2/DP+08Ej+8m/TCz2RVslXM7NReos1Pkqg+kyBcU+hwRERGvOHx02UYsd94GXv3dpIf/Hi/nu/PvZT4+e/Pyw1lIllrbSb8m/8kmDkn09ovEu3zU4MTZpNY9D+E/vBzzPh+tLHb1xbTatxDMG4C6Wu/l1nRHmZK9i4By6Fr1CWFDkVERFCyXXScx5diQmH8U8/t3wVCoczf6fTbH4sUA2OgsprEv/0CM3FqvzatMeMmkvr8vwNg7diCu/QO0n93dY+vdWvbJsL/8wOCKUeS/pvP4L3vw3jv/8iwqM3+a05yF5GWlcRrL8CEKgsdjoiIoDKSomPG1uPN+yCUlvf7Gu7i3xP72AWQSuYwMpH+c+/9LeGffAOCADP16JzsDumsfprQkv8l8o1PQWvTu09oayb0q5uJfmEB9qsvYCqq9z3RHZaJNkDJnsUYO0x81EWFDkVERPbRynaR8eYvHPA1gvETsbo6sF9dnWmfJlJA7v/9hvDtP8abc37mJsQclTh571mAqagi/JNvEL3mwyS//L1MIm8M7n2/I3TnryDRhXfh5aQXfAoqq3MybrFyEtuItD1P16hLMG7/36yLiEhuaWW7iNgvPIm1d+eArxMcczLGdXHWqAWgFJZ7xy8yifZZF5P6wrfAzW1pk3/mPBL/fhsYiF7/MZwnl4NlZd5oHjWDxH/+gfQnvjLsE22Akj33Edgx4qMuKHQoIiLyDkq2i0UqSeSW6wj98daBXytWQnD0TJzVTw/8WiL9YQyh//0p4T/eijf3vaT++d8GrduHmTqdxM2/JZhyNFZnOwCpz/87ya/+EHPY4YMyZrFx428RaV9DfNSFGKe00OGIiMg7KNkuEs6LT2J1dWS2q84Bf8Zp2G+9Ac17c3I9kaykktgvPo13/mWkPnMjOM7gjldVS/Kbv8C76IrMv7PocjIclOy5l8ApJVFzXqFDERGRv6Ka7SLhPL4UU1mT6a+dA/7MOZh7f4u9YytBtXaQkzxIJnDWrsS4IYITTyf5jZ9DJJa/NpTDpE92ttzONwl3vELn2Msxzsh6kyEiMhSMzJ9OxaarA+f5x/Eu/EDOEgYz5Sjiv35Q/bZlcHW04ax6AufZR3BWP4WViOPPOI3kiadDTOUMg84YSvfcQ+BWEK+ZW+hoRETkEJRsFwFn5QqsdAr/rBxuQmFZmT/xrsyv1JV0S67s6+FubdtE9IsLsHyfoHoU3tnvwT/1XIJjZxU6whEj1PESoa436Bi3AOyBt1MUEZHcU7JdBIJpx5Fa8Mmc72Znv/gUke98gcS3/zvTEk2kn6xtm3CeXYHz7CMQiZH8xq2YukmkP/hxgplzCI44Vm/o8szt2kjFttvwInUkqs8sdDgiItINJdtFwEyYgvehT+T8usHkaVieh7P6aTwl25Il09VJ6Hc/wlm5AnvHWwD4hx+DP3NOZjdI2x6U1630zolvoeKtHxG4lbROuhps7RYrIlKslGwXmPPMw1hNezO7Rua6Y0P1aIJJ03DWPIN3+T/k9toy/EWiOI/ej6mfTOqSBfizz8GMGlfoqEY8J7Gdyrd+gHFitE7+vLZlFxEpckq2C8y953+wkgm89ywYlOv7M+fgLvl9pnY7VjIoY8jwYr/yAlZbE7z3fSR+ek9OtlaX3LCTuzOJtuXSOvkLBKGaQockIiK9UJFlAVm7tuGsfwnvrIsHbQx/xmlYnoe9btWgjSHDS+jOXxL6r1v2/UOJdrGwU41UvvV9MIa2yZ8nCKulp4jIUKBku4CcJ5cD4J+Rm41sDiWYPpNg1Fis1uZBG0OGD2vHWzgvPYt34eVYg70RjfSZnW6hcvMtWH6S1slX40fGFzokERHpI5WRFIoxuI89gD99JmbMIP7gDEdI/HxJpg2gSC/cZXdjHAfv/MsKHYrsY3ltVGz+PpbfQdukq/GjEwodkoiIZEEr2wVibXkTe9tG/DMHr4Tk7cEsSMQzf0S6k0zgPnIf/qnnQfWoQkcjgOV1UvnWD3HSjbQd9lm8kimFDklERLKkZLtAzMTDSXzzV3hnXDToY1l7dhL7yLk4Tywd9LFk6HKefgirow3v4isLHYoAlh+nYsuPcJK7aDvs03il0wodkoiI9IPKSArFtgmOOTEvQ5nR4zAV1Tirn8a/4AN5GVOGHv+Mi0iWlBIcc1KhQ5EgRcWWn+DGt9A+8ZOky44pdEQiItJPWtkeCSyLYOYcnLXPgu8XOhopRsZAKIQ/e67q+wstSFOx5We4XRton/AxUhUzCh2RiIgMgJLtEcKfeRpWZzv2hnWFDkWKUOhXNxO69VuFDkOMT/m2XxLufJWO+r8nVTmr0BGJiMgAKdkeIfwTZmMsC3v104UORYpNZzvuw/dgBfqtR0GZgPJtvyHSvpaO8VeRrJpT6IhERCQHlGyPFOVVBDPnYBlT6EikyLiPLMZKJUnP+2ChQxm5TEDZjt8SaXuezrGXk6iZW+iIREQkR3SD5AiS/OqPCh2CFBtjcJffjT/tWMzU6YWOZshyO9/EJHcT6QzAjmDsMMaO7PsTxliZf7Pvcax3rHMYQ+nOPxJteZrO0fOJjxr8DkUiIpI/SrZHmuS+ftuVNYWORIqA/coq7G2bSH72xkKHMmQ5yV1UvnVLpt66j88xlrsvCY+A5eCkG+iqvZD46PcOaqwiIpJ/SrZHkiAg9qlL8U47j/Qnryt0NFIE7B1vEdSMwc9Dv/dhyRhKd92BsUJYp9xMU0sSK0hBkMIKMh9bZt/f+x47cMy8/Vii5izitRepE4yIyDCkZHsksW38o07AWf00aWP0g13wLroC77z3gRsqdChDUrh9LeGOdXSMu5LyknEEne2FDklERIqMbpAcYYKZc7D37MDaubXQoUiBWVvehFRSiXZ/BWlKd92JFxmvGxpFRKRbSrZHGH/maQA4agE4svk+kX//HOHvf7XQkQxZsca/4KQb6By3ACyn0OGIiEiRUrI9wphxEwlqxmC/8XKhQ5ECcl54ArthN/7ZlxQ6lCHJTjdRsvcBkhUnki47utDhiIhIEVPN9ggUzDwNEystdBhSQO6yuwhqRuOfcnahQxmSSnfdDUDn2CsKHImIiBQ7JdsjUEpt3kY0a9dWnBefIrXgk+DoW0C2Qp2vE2lbRefo+QThUYUOR0REipzKSIaqge4EmUpCOp2bWGRIcZf/H8Z28M9/f6FDGXqMT+nOP+KHarT5jIiI9ImS7SHITu2l5rXPY5r6V3dtbXyV2N+eib1GN0mORP4Js0lf9SlM7ZhChzLkRJsew03uoHPclWCHCx2OiIgMAUq2h6Bo8xPYQRJ2P9Gv55uxE7CCAHvbphxHJkNBMHMO3hUfK3QYQ47ltVOy5z5SpUeTKp9Z6HBERGSIULI91BifSMszmY8bXwTjZ3+N0nKCmtHYWzfkNjYpeqHbf4L92ppChzEkle65BytI0Dl+gTaEEhGRPlOyPcSEOl7B8VpJVJ4CXgdu18Z+XcdMmIq1VSvbI4m1+Q1C//dr7PUvFTqUIceNv0Wk+UkStefiR8YXOhwRERlClGwPMdHmJwncCjrHXQWWQ7i9f6uUwcSp2Ns3QRDkOEIpVu6yOzHhCN65lxY6lKHFBJTu/CPGLadr9PxCRyMiIkOMku0hxPLaCLe/RKLyVIxbClXHEmlf06/OJMGEqRAtgbbmQYhUik5XB+5j9+OffiGUVxY6miEl0vosofhGOse8H+PECh2OiIgMMUq2h5BoyzNYBCSrz8g8MOoknNRenNSurK/lX/B+4rcth6raHEcpxch97H6sRBxv3gcLHcqQYvlxSnf/H+nYFJJVpxU6HBERGYKUbA8VxhBpfop0bCp+ZFzmsdqTAAi39aOUxNZ//UjiPLaUYOrRBNOOK3QoQ0ps7/3YXtu+myL1NSMiItnTT48hwo1vxE3tIrF/VRuworWko4f1u2478vVPE7r1W7kKUYpY8ms/Inn1TeqikQUnuYtY40Mkqk7Hi00udDgiIjJE9ZpsL1q0iPPOO4+jjjqK9evXH3h8xYoVvP/97+eyyy7j0ksvZfny5QeObdq0iQULFjBv3jwWLFjA5s2bC3ZsuIg2P4mxIyQrTj7o8VT5DNz4Zqx0az+uarA3vZabAKV4eWmIlWImTCl0JEOHMZTuvANjR+gcq502RUSk/3pNts8//3xuv/126uvrDzxmjOGaa67h5ptv5p577uG73/0uX/7ylwn2dba48cYbWbhwIcuWLWPhwoXccMMNB56b72PDgeUniLStyiTaTvSgY6mKGVgYwh3Zt3MLJk7F3rpp4Fu/S/FqbSb2TxfjPP1goSMZUsLtawh3rqNrzHyMW1HocEREZAjrNdmeNWsW48e/u6+sbdu0t7cD0N7ezpgxY7Btm8bGRtatW8f8+ZkWWfPnz2fdunU0NTXl/dhwEW5bhRUkDyoh2c+P1OOHagi3r836umbCVKxEF1ZD9jdYytDgPnwPVlsLwcTDCx3K0BGkKN11F15kPImauYWORkREhji3P0+yLIvvf//7fOYzn6GkpITOzk5uvfVWAHbu3MnYsWNxHAcAx3EYM2YMO3fuxBiT12M1NTVZfV61tWX9mY5BZ7Y+AyV1VB02A+uvam5Hj6nAtM7C2bmCSE0I669WvnviH3ccCaCqfRfuMUfmOOriNHp0eaFDyBvj+8Qf/BPWiacy6sQT+nWNkTRf+5nNf4J0A8y4jtHVVVk9dyTO10BovrKj+cqO5is7mq/B069k2/M8br31Vn76059y8skns2rVKr7whS+wZMmSXMeXV42NHQRBcZVUOMmdVLe9QefYy4k3dBx0bPTocvbubSfkHkNlsJy2zc+RqpjZ94tXjKMEaF/3Kt7UE3MbeBHaP18jhb3qCaK7tpFY+Fk6+vF5F2K+oo0PEe54hY66vyMIVed1bAA71UT1W/eQqjiJdu8wyOLzH2mvr4HSfGVH85UdzVd2NF99Z9tW1ouz/Uq2X331Vfbs2cPJJ2du1jv55JOJxWJs2LCB+vp6du/eje/7OI6D7/vs2bOH8ePHY4zJ67HhINL8FAabRA89ftOl0wjsGOH2tdkl2+VVdP1qKVSNykGkUmxCy+7CVNXizz630KH0SWzvUkr3/BmDReXGm2mb9Fn86IS8xlC6+y4AOsdekddxRURk+OpX679x48axa9cuNm7cCMCGDRtoaGjgsMMOo7a2lunTp7N48WIAFi9ezPTp06mpqcn7sSHP+ERbniFVfnzPN2lZDqny4wi3vwQmy+3Xq0erHdxw5PuZrdkvugJCoUJH06vY3gco3fNnEpWn0DL1K4ChctN/EOpYl7cYQp2vE2l7ga5RFxOEtdmTiIjkhmVMz60obrrpJpYvX05DQwPV1dVUVVWxZMkS7r33Xn75y18eqCH+3Oc+xwUXXABkku9rr72WtrY2KioqWLRoEVOnTi3IsWwUWxlJuG01FVt/TuthnyFd/u6a23f+2ifc+hwV226jZcqX8EqO6PMYzqP34z70Z5LfuHXYJ90j8tdkxvT7/zVf8xXbez+le+4lUTmbjvqPgmVjp5upeOvHOMmddNT97du7pg4Sy2uncvMtWEGS5iNuBDuc9TVG5OtrADRf2dF8ZUfzlR3NV9/1p4yk12R7JCm2ZLvirZ/gJLbQfOS3wHLedfydXxyWH6fmtX8hXnsBXeMu7/MY7tI7CP9yEfFf3I+pHZuz2IvRSPpmYr+5jmDStAGtaudjvt5OtE+lo/4jB+3SaPlxyrf9knDHOrpGXULXmPfl/g2hMYTbnqds5x+xgjhtEz9Fuvz4fl1qJL2+ckHzlR3NV3Y0X9nRfPVdf5Jt7SBZpOx0C6GOl0lWzTlkov3XjBMjXXpk1rtJ7m8JZ23d2K84pQh1thO57qOE7vxloSPpUU+JNmRe022HfZZE1RmUNDxA2fZfQ5DO2fh2uoXyrT+jYttt+OFaWqZe1+9EW0REpDv9ukFSBl+k5RksDImqOX1+Tqp8BmW7/oiT3IUfGden5wQTMuU29taNBDP7PpYUL+eFJ7B8H//kMwsdSrdie5ZQuvc+EpWn0VH/9+9KtA+wHDrqPowfHk3pnj/jpJtpm/hpjFva/8GNIdLyFKW77sIyXqbTT+35fXpTKyIiki2tbBcjY4i2PEm6ZBpBpO+lHanyGQDZbXBTWY2pqMLeppXt4cJ59lFMVS3BtOMKHcoh9TnR3s+yiI++mLYJ/4gb30zlppuxU3v7NbadaqDirR9SvuO3+NF6mg//KvFRFynRFhGRQaNkuwi5XW/ipPYecsfIngThGrzoxH6VkqiMZJhIp3BefAp/1llgF9+Xd2zP4kyiXdXHRPsdUpWn0Drpamy/g6qNN+N2ZfGaNQHRxhVUb/gmbnwjHeP/htbJX8zqzayIiEh/qIykCEWbnySwoyQrTsr6uanyE4jtvR/La8e4fdsNKvXxayBakvVYUnzsl5/HinfinXJOoUN5l5I991GydwmJqjl01P1dVon2fl7pNFqmXEPllh9TufkW2id8jFRFzxsyOcldlO34LaGuDaTKjqVj/N8ShIdBa1ARERkSim/pa4Sz/DiRtlUkK0/pV/uxZPkMLEym53YfmcOOwIypy3osKULRGN7suQTHzy50JAd5O9E+vd+J9n5BZCwtU67Bi06kfOsviDY8mGlx+NeMT2zvUqo23IST3El7/UdoO+z/KdEWEZG80sp2kYm0Podl0iSrTu/X8/3oRHy3mnD7GpLVfbuG1bgH965f4Z9/GcERx/ZrXCkOwfQTSU3veaU3r4yhZO/idyTaHx5Qon3gsm45rZM/T/n2/6Js91046b10jvvQgdprJ7GN8u3/g5vYQrL8RDrGX4UJVQ54XBERkWwp2S4ykZan8CJ1eLHJ/buAZZEqP4Foy9MQpPq0Om4ch9DyuzH1k5VsD2HW7u1Ye3YQHHMiOEXwpT1IifYBdpj2CR/H3/0nShr/gpNqor3+o8QaHyLWsBTjlNI24Z9IVZ6cuzFFRESypDKSIuIkthOKb87cGDmAzTtSFTOwTIpQ52t9e0JlDaasEmvbpn6PKYXnPnQPkX/7LHR1FDqUfYn2fYOXaO9n2XSNu4KO8VcR6niZmvVfpqThfpKVp9B8xI1KtEVEpOCKYPlL9os2P4mxHJKVpw7oOumSIwnsKJG2NYfc5v1dLItg4lS1/xvinOceIZg+E8qrChuIMZka7Yb7SVSdQUfd3w5Oov0OiZq5+KFaShqW0zXqIm1OIyIiRUPJdrEI0kRaV5Iqn4Fxs9sG9F1sl3TZsYQ7XgIT9CnRCSZOxX3qL5kbzXK9JbYMOmvXNuwtG0h99IsFjcNJbCPW+DDRlqfylmjvly4/nlYl2SIiUmSUbBeJcPtabL+TRFV2vbW7kyqfQaRtFW58M17J1F7PNxOmYHW0QUsjVI/KSQySP85zjwLgF6Dln+V1EGl9lmjL07iJrRjLJV57Pp1jr8hboi0iIlKslGwXiWjLU/ihatJl03NyvVT5sRhswu1r+pRs+yefSbJmjPptD1HOc48SHHY4ZtyE/AxofEId64i2PEW4fS2W8fGih9ExbgHJylMG/tsZERGRYULJdhGw002EOtYRH31JzlYCjVNKunQa4fa1dI39QO/nj5uIP25iTsaW/PPOvBjC2fdlz5aT3EWk+SkirStxvFYCp4xE9Tkkqk/Hj+Yp0RcRERlClGwXgUjz01gYEv3srd2dVPkJlO26Ezu5hyAyptfzncfuBzeEf/qFOY1DBp9/0eWDdm3LjxNufZ5oy1OE4psw2KTKj6Ozag6psuPB1rcRERGR7uinZKGZgGjLU6RKjyII57ZWOlU+A3bdSbh9LYnIBb2e7y69CxxHyfYQ4zyyONMjfdpxubuoCTDNL1O27SEibS9imTReZDydYy8nUXmqNogRERHpIyXbBRbqXI+TbqRrzGU5v3YQHoUXqSfSvobEqN6TbTNxKs7Kh9WRZChJJQn/8jt4Z78nd8m28ancuAgSWwjbMRJVc0hWzclstKTXhYiISFaUbBdYpOVJAruEZMXMQbl+qnwGsYYHsLyOXm9aCyZOxX3wT9DWDJU1gxKP5Jb90nNYiXhOu5BEWp8nlNgCh/8tTeFT+7QLqYiIvNYqTQAAIABJREFUiBya+nIVkOV3Eml7kWTVKYOW0KQqZmBhCHe83Ou5wYQpANhbtbnNUOE89ygmWkJw/Cm5uaAxxBofwguPgwkXK9EWEREZICXbBRRpeQ7LeDnrrX0oXnQivltJuH1Nr+eaiYcDSraHjCDAfe5R/BPnQCg3SbHb9QZuYgvx2vOx1CNbRERkwPTTtICiLU9mkuHYYYM3iGWTKp9BuGMdBOkeTzU1o0lf+U8ERxwzePFIzthvvoLV0oh/ytycXTPW+CCBU0ay6tScXVNERGQkU7JdIE5iG25ia87b/R1KqnwGVpAk1Pl6zydaFumrPpXbrhYyaIKx9aQ+9iX8k3LzmxE7uZtI+1oSNeeofERERCRHlGwXjE2q9BiSVbMHfaR06ZEEdqRPpSTW7u04jy8d9JgkBypr8N77N1CemzZ8scaHMlut1+R/y3cREZHhSsl2gfjROtomfw7jlA7+YHaIdNmxhNvXggl6PNVZuYLI96+H1ubBj0v6zdq9ndDvfoTVuCc31/M6iLY8TbJyNsatyMk1RURERMn2iJEqPwHHa8VNbOnxvGDiVADsbZvyEZb0k7PyYUJ/+i/wvZxcL9r8GJZJE6/tvR+7iIiI9J2S7REiVXY8BptwW8+lJOZAsq2OJMXMee4xgknTMGPqBn6xIE2s6RFSZcfgR3NwPRERETlAyfYIYdxSvJLDM6UkPZ1XOxYTLcFS+7/i1daM/dpq/FPOzsnlIm3PY3ttWtUWEREZBEq2R5Bk+Qzc5HbsVEP3J1kWwcQpWtkuYs6qJ7CCIDct/4wh1vAgXqSOdOn0gV9PREREDqJkewRJVcwA6LUriX/6RfjHnpyPkKQfnOceJagZQ3D4wJPjUOdruMntmVVty8pBdCIiIvJObqEDkPwJwqPxInWE29eSqD2/2/O89304j1FJttJ/fzXW7h05SY5jjQ8SuBUkK3O03buIiIgcRCvbI0yq/ARCnW9g+Z3dn+R7WFs2qP1fkTLjJhLMGPgOj05iB+GOV4jXzAU7NPDARERE5F2UbI8wqfIZWASE21/u9hyrcQ+xL3wIZ+WKPEYmfeHedRvun/4rJ9fKbGITIlGdmxstRURE5N2UbI8wXmwSgVPa49btZtQ4TDSGvXVDHiOTXgUBoQf+iL3xtQFfyvLaiLSuJFF1GsYty0FwIiIicihKtkcay8aLTsBJ7uj+HNsmqJ+ijW2KjP3Gy1gtjfizB76deqzpUSzj9Vi7LyIiIgOnZHsE8iP1uMmdPW7dbiZOxVL7v6LiPPsIxnHwTzpzYBcKUkSbHiNVdjx+ZFxughMREZFDUrI9AnnReqwgiZ1u7PacYOJU7Ka90Nmex8ikJ85zjxIcczKUlg/oOpHWZ7H9duKjtImNiIjIYFOyPQL5kcyW3G5ie7fnBFOn4x97MlZ7a77Ckh5Yu7Zib9888BISExBrfBAvOpF0yZG5CU5ERES6pT7bI5AXGQ+Ak9gOFTMPeU5wwmySJ8zOZ1jSAzNuIvEf3IWprB7QdUId63CTu2iv/wdtYiMiIpIHSrZHIieKHxqFm+x+ZRuAIICuDiiryE9c0iMzYcqArxFrfBDfrSRZoR1CRURE8kFlJCOUF63vuSMJEPnqPxL53lfyFJF0q7WJyDUfxn5l1YAu4yS2Ee58jUTNeWDrfbaIiEg+KNkeofxIPU5yDwTpbs8x4yZibVVHkkJznn8cZ8OrmFjpgK4Ta3wQY4VJ1Aywm4mIiIj0mZLtEcqL1mER4CR3dXtOMGEKdtMedSQpMOe5RwlGjcNMOarf17DSrURanyNRfTrGGVjSLiIiIn2nZHuEOtCRpIe6bTPxcABtblNIyTjOmmfwTzlnQDc0xpoeARMQrzkvd7GJiIhIr5Rsj1B+ZCzGcjMdSboRTMzckKdSksJx1j6LlUpmku3+CpJEmx8jVT6DIDImd8GJiIhIr5Rsj1SWgx8Zh9vDTZJmdB2mqhYr0ZXHwOSd7HUvYkrKCI49qd/XiLY8g+13Eq/VJjYiIiL5ppYEI5gXqSfUub77ExyH+K+WqR9zAaX//mq8+QvBDfXvAiYg1vgQ6dgkvJLDcxuciIiI9Eor2yOYH63D8Zqx/M7uT7IsMCZ/QY1kxkB7K9bGV3GefgjnwT+B52Fq+1/6EW5/CSe1h3jthXrTJCIiUgBa2R7BvH03STqJHXil0w55jrPiPsK/+U/iP18MJWX5DG946mjD2rMDe+8OrD078c69FMoqCP3uR7hL78SKH/zGx1uzktQXvgV2/94XxxofxA/VkKo4MRfRi4iISJaUbI9gfrQeyHQk6S7ZprQcq7Mde/tmgmnH5TG64SX83Wtw1q7E6uo46PHgqBMIjjyeYNIReOdeihlThxkznmB0HWZsPZSW93tMJ/4Woa436Bx7BVjOQD8FERER6Qcl2yNY4FYT2DGcRPc3SQYTpgL7OpIo2c6eMWBZBJOOwFSPwowZjxlTTzBmPGZ0HZRVAOCfdQn+WZfkdOhY44MEdpREtTaxERERKRQl2yOZZeFH63vuSDK2HhMKY2/diJ/H0IYDq3E3ka/8A6nPfA3vQ5/I69h2uoVI6yoStedinFhexxYREZG39VoIumjRIs477zyOOuoo1q/PdK7Ytm0bl1122YE/5513HrNnzz7wnE2bNrFgwQLmzZvHggUL2Lx5c8GOSc+8SF2m13Z3N0E6DqZ+MvY29drOlrPyEezG3ZjR4/M+drjtRSwCEtVn5X1sEREReVuvyfb555/P7bffTn19/YHHJkyYwD333HPgz/nnn8/8+fMPHL/xxhtZuHAhy5YtY+HChdxwww0FOyY986P12EEc22vu9pxgwhSsXdvyGNXw4Dy7gqB+MqZ+ct7HDrevxouMx4+My/vYIiIi8rZek+1Zs2Yxfnz3K3OpVIr77ruPK664AoDGxkbWrVt3IPmeP38+69ato6mpKe/HpHdvdyTpfifJ1Ce+QuL7d+YrpOGhvQX7lRfwTz0370NbXgehzjdIlc/M+9giIiJysAHXbD/88MOMHTuWY489FoCdO3cyduxYHCfT/cBxHMaMGcPOnTsxxuT1WE1NTVafS23t0GttN3p0/7tVAJiqo2AzVLqNWN1da4BjFJOBzldfpZ//C6nAp3zee3HyPH9m5wtAQMmkMygtH9jY+Zqv4ULzlR3NV3Y0X9nRfGVH8zV4Bpxs33333QdWtYe6xsYOgmDobOAyenQ5e/e2D/g61W416cZNdMS6uVZrM5HvXYt38ZX4c4bult+5mq++CL3+Os6ocTTVToY8jblf+Y5ncEM1NMdrIdH/sfM5X8OB5is7mq/saL6yo/nKjuar72zbynpxdkA7SO7evZvnnnuOSy+99MBj48ePZ/fu3fh+pneF7/vs2bOH8ePH5/2Y9I0frcNNdl9GQlk59mtrsN9cl7+ghrj03/4/Ej+8O/+7NvoJwh3rMiUk2jFSRESk4AaUbP/pT3/inHPOobq6+sBjtbW1TJ8+ncWLFwOwePFipk+fTk1NTd6PSd94kXqc5C4w3TT3c1xM3STsrepI0ifxzkx3l0g070OHO17BMh5J7RgpIiJSFCxjuuv5lnHTTTexfPlyGhoaqK6upqqqiiVLlgAwb948rr/+es4+++yDnrNhwwauvfZa2traqKioYNGiRUydOrUgx7IxUstIIi3PUL79v2g+/Ab8aN0hzwl/7yvYb7xC4mf3Dni8QsnXr8nCP/ga9raNJG7+Xd5Xl8u23Ua44zWajloE1oDeS+vXilnSfGVH85UdzVd2NF/Z0Xz1XX/KSHpNtkeSkZpsO/GtVG/8d9omfJxU5axDnuPe8QtCd/yC+O8eh+jQ3CQlL99MvDSxj12If8o5pP75G4M71l8L0tS8/q+kKk6mo/7vBnw5ffPNjuYrO5qv7Gi+sqP5yo7mq+/yXrMtw4MfGYfBxu2h/Z+ZeDiWMVjbN+cvsCHIfmUVVmd7QVr+hTpfxw4SJCvU8k9ERKRYaLt2ATuEHxmD08O27f4Js4l/7w+Yusn5i2sIclauwESi+DNOy/vYkfbVBHaUdOnReR9bREREDk3JtgDgR+px4291f0JpOaZUPTh7FAQ4zz6CP/P0/N8caQLCbWtIlx0Hdii/Y4uIiEi3VEYiAHjRepx0A/iJbs9x77sd945f5DGqIaatGTNqLP5p+S8hcbs2YPvtKiEREREpMkq2BQB/37btbnJnt+fYr6/FfXRJvkIaeqpqSX7nf/DPuiTvQ0faXsRYbmZlW0RERIqGkm0BMivbAE4Pm9uYiVOxdm+HZPer3yOWMVjbNmX6a+d7MxljCLevJl06HePkv7e3iIiIdE/JtgAQhGoxdgQ30f1NksHEqZmOJDt6qO0eoaytG4ld/UGcRxbnfWwnsRUn3aQSEhERkSKkZFsyLBsvMr7Hle1gQmajIHvrhnxFNWQ4z64AIChEF5K21RgsUuUz8j62iIiI9EzJthzgR+p7XNk24w/D2A721k15jGpocFeuwD/yeEzN6LyPHW5fTbpkGsbNrsm+iIiIDD4l23KAF63D9tuxvLZDnxAKkfqX7+CdOz+/gRU5a89O7I2vFWQjGzu5Gze5g5RKSERERIqS+mzLAX4kc5Okm9hOuqzi0Oecdl4+QxoS9peQ+LPzn2xH2lYDKNkWEREpUlrZlgO8aKb9X087SeKlCf32hziPqAXgfqaiCu/0CzF1h+V97HD7i6SjkwhCNXkfW0RERHqnZFsOMG4FgVOOm+j+JkkcF/ulZwnd89+ZNneCf/Z7SP3Ld/I+rp1uJhTfTKrixLyPLSIiIn2jZFsO4kXre+xIgmXhzbsSe8sG7FdX5y+wImVtfgNr++aCjB1WCYmIiEjRU7ItB/EjdbiJnWCC7s858yJMWQXu0jvyGFlxCv/+J0T+7bMFWeUPt6/GC4/Dj4zL+9giIiLSN0q25SBetB7LpLBTDd2fFInhnXspzjMPQXMP5w138U7stSszXUjyvGuk5XUQ6nxDq9oiIiJFTsm2HMSPZG6SdHsqJQG8iz4IxuCsfTYfYRUlZ/XTWOlUQVr+hdvXYhGoXltERKTIqfWfHMSL1mGwcBI7oIdEztQdRuLW+wuyiUuxcFauwFRUERyd/9XlcPtq/FA1XjT/HVBERESk77SyLQezIwThUb2ubAOZRNsYaG/JQ2BFJp3GWfUE/qxzwHHyO7afINyxjlT5zLyXr4iIiEh2tLIt7+JF6jIr230Q+eZnITAkv/6zQY6qyKSTeO+5Cv+EU/M+dLjjFSzjqV5bRERkCNDKtryLH63HSe2BIN37ucfOwnnpWaxtm/IQWREpKSP9N58mOPakvA8dbl9N4JSRLjki72OLiIhIdpRsy7t4kTosApzkzt7PPf8yjOviLrsrD5EViSDAXfK/WI27CzC2R7j9JVLlJ4CV5/IVERERyZqSbXkXP1oPgNvTtu37VdXiz7kA95H7IBEf5MiKg71+LeFf/wf2uhfzPnao83XsIEFSJSQiIiJDgpJteRc/PAZjuTg9bdv+Dt7FV2J1deI89sAgR1YcnJWPYFwX/6Qz8j52pP1FAjtCunR63scWERGR7CnZlnezHPzI+L6tbAPBUTPwzrgIKqoGObAiYAzOsysIjp8NpeV5Hjsg3LaGdNlxYIfyO7aIiIj0i7qRyCF5kTpCna/37WTLIvXFbw9uQEXC2vIm9q5tJC/7SN7Hdrs2YPvtJLWRjYiIyJChlW05JD9ah+O1YPmdfX6OtW0Tzor7BjGqwnNWrsBYFv7sc/I+dqR9NcZySZcdm/exRUREpH+0si2H5EUyN0k6iR14pdP69Bx32Z24y+8mftIZUFkzmOEVjD/73Ey5TFVtfgc2hnDbatKlR2OcWH7HFhERkX7TyrYc0tsdSfp2kySAN+9KLM/DfejPgxVWwZnJ0/Au/lDex3USW3HSjSohERERGWKUbMshBW4VgV3S550kAcyEKfjHn4K77G7w/UGMrjCcZx7GXfK/EAR5HzvSvhqDlemvLSIiIkOGkm05NMvCj9ZltbINmdVtu2EXzgtPDFJgheMu/j3uQ/eAnf8vm3DbarySIzBunjugiIiIyIAo2ZZueZH6zMq2MX1+jj/7HIKa0cNvR8nWJuzXVuOfem7eh7aTu3GTO7SRjYiIyBCkGySlW360Drs5jp1uJgj38YZHxyV19U2Y8RMHN7g8c557FMsYvAIk25G21QCkypVsi4iIDDVKtqVbBzqSJLf3PdkGguNmDVZIBeOuXEEwph4zqW+dWXIp3L6adPQwgnCeO6CIiIjIgKmMRLrlR+sAcLO4SXI/59lHiH5pISTjuQ4r/7o6sNc+i3/qXLCsvA5tp5sJxTeRUgmJiIjIkKRkW7plnBL8UDVOljdJApjScuxNr+M8+ZdBiCzPIjGS3/g53rwr8z50uG0NACm1/BMRERmSlGxLj/xIPW4i+2Q7OOYkgomH4y69I6sbLItOvBNr+yaCo2cWpA493L4aLzwWPzI+72OLiIjIwCnZlh55kTqc1C4wWfbNtiy8iz+Is+FV7DdfGVgQxmA//9jbvbvzkLxbO7cS+s1/EvvEJUT+89qCvGGwvA5CnetVQiIiIjKE6QZJ6ZEfrccyPk5y94Ea7r7yzn4Pod/+CHfpnaSmHdev8a2NrxL+9X/gvLqa5Be+hX/mPMK3XAeeh3fepQQnng5Ojl7GxmCvXUloyR+wX3gCbBv/9Avx3nPV4NRqG4Pld2Cnm3DSTfv+bsbe/3GqEYtAXUhERESGMCXb0iMvur8jyY6sk21KyvDmvhd768bMrovZbAbT2kT49z/BeegeKK8i+anr8edcAIAZU4f70D24Kx/GVNXizZ2Pd+6lmAlTsotvv3QKQmEAwr/+D6z2VrwPfhzvoiswNaP7d839giShrk1vJ9D7/rbTzTjpJiyTPuh0Y4XxQzUEoRpS5fV40Ql4sckDi0FEREQKRsm29MgPj8Vg4ya2k6rMvqVf+qP/AqFQVs+xtmwgev0/QDKBN38h6Sv/CUrf3jkx/eF/Jn3Vp3BeeBLn4Xtx7/0d7rK7iP/6LxCOgJcGt/cxrV1bcR+4E3fFvSS+/V+Y+skkv/w9zOhxB5LvgSrb8Tuirc8BYLAI3AqCUA1+dAKp8hMIQjUEoeoDCbZxSvPe8UREREQGj5Jt6Zkdwo+M7VdHEuBAom1tfA0z+cjuV7eNwdryJmbSNMyEKXjnvR/vossx9ZMPfb4bwp89F3/2XGhuwN68PpNoJ+LEPnsZ/ozT8M69lODYkw8e0xjstc/i3v8HnFWPZ0pFTrsArMw5pu6w/n2ehxKkiLStIVF5Cl1jLiNwq8DWl5yIiMhIop/80is/Uo8b39zv59svPkX0pn8mcd0PCE4+813HrW2bCP/mP7HXPEPiP36PmXwk6X/4Yt8HqB5FUD0q83Eyjjd7Lu4TS3EfXUIwph7v3Pn4cy+F0eW49/yW8G9/gKmoxrviH/HmfXDgpSLdCLe/hGVSJKvPJAiPGpQxREREpLgp2ZZeedE6Im3PY/kJjBPN+vnB8adgqmoJLbuT5DuT7Y42Qnf8AveBOyAaI/3RL/a/7nq/yhrSn7yO9Ee/iPPsCtyH7iX8x1vx3noTbv4Z/pkXkayqwT/9wsxK+CCKtL1A4JSTLsn/rpMiIiJSHJRsS6/8SObGSCe5E6+kH8mwG8K78HLcu36FtXs7Zmw99iuriHz3X6GzHe+CD5C+6tNQWZ27oCNR/LMuwT/rEqw9OyGdBMCMGoc/d37uxulOkCLc/hKJqtMOlKiIiIjIyKMsQHr1dkeSftZtA96FHwDLJnTXbQAEE6biHzWDxHdvJ/3J63KbaP8VM2Z897Xfg2R/CUmq8uS8jisiIiLFRcm29CoI1WLsSL92ktzP1I7Fn3UW7sP3QFcHVFaT+sotmZsmhyGVkIiIiAiojET6wrIzO0kmdwzoMumPfQn/5DNztwlNsVIJiYiIiOyjTED6xI/UZVa2B7BtuRk9Hv+CD0Ak+5sshxKVkIiIiMh+SralT7xoHbbfgeW1FTqUohdpW0XgVqiERERERJRsS9/4kcxNku4AS0mGvSBJuP1lkuUnqoREREREek+2Fy1axHnnncdRRx3F+vXrDzyeTCa58cYbueiii7j00kv52te+duDYpk2bWLBgAfPmzWPBggVs3ry5YMckNw50JBnATZIjQbj95X0lJCcVOhQREREpAr0m2+effz6333479fX1Bz3+3e9+l0gkwrJly7jvvvu4+uqrDxy78cYbWbhwIcuWLWPhwoXccMMNBTsmuWHccgK3QivbvVAJiYiIiLxTr8n2rFmzGD9+/EGPdXZ28uc//5mrr74ay7IAGDUqsx11Y2Mj69atY/78zMYh8+fPZ926dTQ1NeX9mOSWF6nTynZPgiTh9pdUQiIiIiIH9KsH29atW6mqquLHP/4xK1eupLS0lKuvvppZs2axc+dOxo4di+M4ADiOw5gxY9i5cyfGmLweq6mpGfAEydv8SB3R5sfBBEomDyFTQpJWCYmIiIgc0K9k2/M8tm7dyjHHHMOXv/xl1qxZw6c+9Sn+8pe/5Dq+vKqtLSt0CFkbPbo8b2MZ73BoephRZXGsknF5GzeXBnO+zJ61EKqkctJJWMPkzUg+X1/DgeYrO5qv7Gi+sqP5yo7ma/D0K9muq6vDdd0DpRszZsygurqaTZs2UVdXx+7du/F9H8dx8H2fPXv2MH78eIwxeT2WrcbGDoKg/32k82306HL27m3P23huupYqoG3nelIVpXkbN1cGdb6CJLUNL5CoOp3Ohs7BGSPP8v36Guo0X9nRfGVH85UdzVd2NF99Z9tW1ouz/Vp+q6mp4dRTT+XJJ58EMt1AGhsbmTRpErW1tUyfPp3FixcDsHjxYqZPn05NTU3ej0lueZE6DBZOQjdJ/rX9JSRJbWQjIiIi72AZ0/OWgDfddBPLly+noaGB6upqqqqqWLJkCVu3buW6666jpaUF13X5/Oc/zznnnAPAhg0buPbaa2lra6OiooJFixYxderUghzLhla2e1f9xtfwohNpn/iJvI6bC4M5X+Vbf0Go602ajvzOsKln10pHdjRf2dF8ZUfzlR3NV3Y0X33Xn5XtXpPtkUTJdu/Ktv2acPvLNB31bbAjeR17oAZtvoIkta99iUT16XSO/5vcX79A9M03O5qv7Gi+sqP5yo7mKzuar77LWxmJjFyJ6rOwgy4iLc8WOpSiEW5/KVNCUqESEhERETmYkm3JildyBF50IrGmFaBfigAQaXuBwK3AKzmi0KGIiIhIkVGyLdmxLOI1c3GTOwh1rS90NIW3fyObCm1kIyIiIu+m7ECylqw8hcApJdr4SKFDKTiVkIiIiEhPlGxL9uwwieozCbevxk41FjqaglIJiYiIiPREybb0S6I60+Yx2vxYgSMpIJWQiIiISC+UIUi/BOEaUuUziTY/AUGq0OEUxNslJLMKHYqIiIgUKSXb0m+J2rnYfieR1ucKHUpBRNpW7SshObzQoYiIiEiRUrIt/ZYuORIvUjcy2wD6CcLtL5OsOEklJCIiItItZQnSf5ZFvOZc3MQ23K43Cx1NXoU7XlYXEhEREemVkm0ZkGTVqQROSWZ1ewRRCYmIiIj0hZJtGRg7TKLqDMJtq7HTzYWOJj9UQiIiIiJ9pExBBixRcw5giDY9WuhQ8kIlJCIiItJXSrZlwILwKFLlJ+xrA5gudDiDLtK2Ct+tVAmJiIiI9ErJtuREouZcbL+DSNvzhQ5lcO0rIUlpIxsRERHpA2ULkhPp0qPwIuOJNg7vNoAqIREREZFsKNmW3LAsEjVzCSW24MY3FjqaQRNpVQmJiIiI9J2SbcmZROWpBHaMWOMwbQPoJwh3vExKXUhERESkj5QxSO44UZLVpxNuewE73VLoaHIu3PHSvhKSkwodioiIiAwRSrYlp+I1cwFDtPnxQoeSc5HWF1RCIiIiIllRsi05FYRHky47jmjTY8OrDaBKSERERKQflDVIzsVrz8X224m0vVDoUHJGJSQiIiLSH0q2JefSpUfjhccSbRo+N0qqhERERET6Q8m25J5lZ9oAxjfjdm0qdDQDpxISERER6SdlDjIoklVzCOzosFjdfruERBvZiIiISHaUbMugME6UZNUcIm2rsNKthQ6n3yyvg1jTY/tKSKYWOhwREREZYpRsy6CJ18zFMj7R5icKHUrW7HQzpbvupGb9dYS63iBee6FKSERERCRrbqEDkOEriIwlVXYs0ebHiI+aB3bxv9zs5B5KGpcTaXkGTECycjbxURfhR+sKHZqIiIgMQcWf/ciQFq+ZS+WWnxBuf5FU5Sn9uoad3EO05SlCna/hxSaTKj+edMmRYIdyFqeT2E6sYSmR1ufBckhUnU581EUE4VE5G0NERERGHiXbMqjSZcfih0cTa1yRXbIdpIm0vUik5UnCna9jsPFik4g2P0ms6RGMFSZVNp1U2XGky48nCFX1Kz63axOxhgeItK8lsCPEay8gXnsBJlTZr+uJiIiIvJOSbRlclk28Zi5lu+7Eib+FH5vU4+lOYjvR5ieItK7E9rvwQ6PoHHNZprtJqAqCFKHO9YTbXyLc8RKR9jWwE7zoRFLlx5MqOx4vNqnn+mpjCHW+TqzhAcKdrxM4pXSOvpREzVyMW5rjCRAREZGRTMm2DLpk1emU7rmXWNMKOuo/+q7jlp8g3PY80eYnCMU3YyyXVPlMEtVnki498uDE2Q6TLj+OdPlxdJqrcJI7CLe/TLjjJWJ7H6Bk7/0ETjmp8mNJlR1PuuwYjBMDwJiAcNsaYg1LCcU34buVdIz9IInqM8GJ5mk2REREZCRRsi2DzjgxEpWnEW15ks6xl2PcCjAGN745s4rd9jxWkMSL1NEx7kqSladi3LLeL2xZ+NF64tF64qPnYXmdhDteIdTxEuH2tURbnsFgky6dhldyBGxeQ0V4Y/+OAAAMeklEQVTnNvzQKDrGLyRRNSendd8iIiIif03JtuRFonYuseZHiTX8hSBURbT5SdzkDowdIVkxi0T1GXixKWBZ/R7DuKUkq2aTrJoNxv//7d19TNV1/8fx10F24IeF3O0oaKl5DcaiSfOUf6A5iYkYSjfXGusnLf2jtgy3UsvQqSFOsIys0Y259RfTrVkIQupV6SI3J4y8aVaUtqjQBgdJbgZ4OJ/fH/0814WaFwfO9xzhPB//8X1/z5fP57U3873jB47Ce3+Wvfus7F1nFdVWK0VNU9fUFeqf5JRsE/y4OwAAgJtj2EZADEYkamBiqqJc/5IkXf2fGepK/F8NTHJ6j3n4lW2C3BP/IffEf6h38mOyuXsUP8Wh/vYe/38vAACAv8GwjYDpmfJPuf9sUP8kpwYjpwX0e5vwibLxoTQAACDAGLYRMIORU9UbOTXYywAAAAgY3uoDAAAALMKwDQAAAFiEYRsAAACwCMM2AAAAYBGGbQAAAMAiDNsAAACARRi2AQAAAIswbAMAAAAWYdgGAAAALMKwDQAAAFiEYRsAAACwCMM2AAAAYBGGbQAAAMAiDNsAAACARYY1bJeVlSkzM1MpKSlqbm72Xs/MzNTixYuVl5envLw81dfXe2unTp3SsmXLlJ2drZUrV8rlcgWtBgAAAATDsIbthx9+WJWVlZo6deoNtbffflsHDhzQgQMHNH/+fEmSMUbr1q3Tpk2bdPjwYTmdTr3xxhtBqQEAAADBMqxh2+l0KjExcdgPPXv2rCIiIuR0OiVJ+fn5OnToUFBqAAAAQLCEj/YBa9eulTFGc+bM0UsvvaTo6GhdvHhRSUlJ3nvi4uLk8XjU2dkZ8FpMTMyw9xIWZhtpDEEzFtccTOTlG/LyDXn5hrx8Q16+IS/fkNfwjCSnUQ3blZWVSkxM1MDAgLZt26bi4uIxfXwjNnZisJfgs/j4O4K9hDGFvHxDXr4hL9+Ql2/Iyzfk5Rvyss6o/hrJtaMldrtdTz31lJqamrzXW1tbvfd1dHTIZrMpJiYm4DUAAAAgWEY8bPf29qqrq0vSX7+gWFdXp9TUVElSWlqa+vr61NjYKEnat2+fcnJyglIDAAAAgsVmjDH/7aaSkhIdOXJE7e3tio2NVUxMjN5//30VFhZqcHBQHo9Hs2bN0saNG+VwOCRJTU1N2rx5s/r7+zV16lS9/vrrSkhICEoNAAAACIZhDdsAAAAAfMcnSAIAAAAWYdgGAAAALMKwDQAAAFiEYRsAAACwCMM2AAAAYJFRf1w7/KOsrEyHDx/W77//rpqaGiUnJ0uSjh07pl27dsntdmvSpEnavn277rrrLv32229atWqV9/VdXV3q7u7WyZMnJUk///yz1q9f7/3I+rKyMs2YMSMYW7OEv/PKzMyU3W5XRESEJGnt2rWaP39+4DdmEV/zkqSjR49q165dMsbI4/GosLBQixYtkkR/+ZoX/XVjXreq0V++5TXe++vy5ct6+eWX1dLSIrvdrunTp6u4uFhxcXE6deqUNm3aNOTP/sbHx0vSiGtjnRV5paSkKDk5WWFhf71Hu2PHDqWkpARtj2OOwW2hoaHBtLa2moULF5offvjBGGNMZ2enefDBB82FCxeMMcZUVVWZlStX3vT1JSUl5rXXXvN+XVBQYKqqqryvKygosHgHgeXvvP7zOeORr3l5PB7jdDq993733XcmPT3dDA4OGmPoL2N8y4v+GprXf/tZpb98y2u899fly5fNiRMnvF+XlpaaV1991Xg8HpOVlWUaGhqMMcZUVFSY9evXG2PMiGvjgb/zMsaY5ORk093dHcBdjC8cI7lNOJ1OJSYmDrn2yy+/KCEhQTNnzpQkLViwQF9//bU6OjqG3DcwMKCamho98cQTkiSXy6Vz584pNzdXkpSbm6tz587d8LqxzJ95hYKR5BUWFub9lNiuri45HA6FhYXRXz7mFQp8zetWNfrLt7xCQUxMjObOnev9Oj09Xa2trTp79qwiIiLkdDolSfn5+Tp06JAkjbg2Hvg7L4xeaPxLMEbNnDlT7e3tOnPmjCSppqZGknTx4sUh93355ZeaPHmy7r33Xm998uTJmjBhgiRpwoQJcjgcN7xuvBlpXtesXbtWS5cu1ZYtW3TlypXALDqIbpWXzWbTW2+9peeff14LFy7UqlWrVFpa6q3TX8PP6xr669953apGf/mW1zWh0l8ej0d79+5VZmamLl68qKSkJG8tLi5OHo9HnZ2dI66NN/7I65qCggLl5eVp586dGhgYCOg+xjqG7dvYnXfeqfLycm3fvl2PP/64XC6XoqOjFR4+9Kj9/v37Q+pd2r8zmrwqKytVXV2t/fv3yxij4uLiQC49KG6Vl9vt1gcffKB3331XR48e1XvvvacXX3xRPT09wV520IwmL/praF7D/VkNJaPJK5T6a+vWrYqKitLy5cuDvZQxwV95HTt2TJ988okqKyv1008/qaKiwk8rDBFBPsaC69zq7F1bW5tJS0szPT093muXLl0ys2fPNh0dHd5r7e3tZs6cOcbtdhtjjHG73WbOnDnG5XJZu/gg8Ede1/v+++/NwoUL/b7W28Fw8zpz5ozJyckZUl+8eLE5ffo0/fX/hpvX9eivnlvW6K+/DDev643n/iotLTUrVqww/f39xhhjTp8+bR555BFv3eVymdmzZ4+qNp74K6/rffHFF2b58uUWrnz84Z3t21xbW5ukv/4r6M0331R+fr6ioqK89U8//VQLFixQbGys91p8fLxSU1N18OBBSdLBgweVmpqquLi4wC4+CEaSV29vr/esrTFGdXV1Sk1NDezCg+Tv8poyZYouXbqkCxcuSJLOnz+v9vZ23X333fSXfMuL/rr5z+Pf1egv3/IKlf4qLy/Xt99+q4qKCtntdklSWlqa+vr61NjYKEnat2+fcnJyRlUbL/yZ159//qm+vj5Jktvt1uHDh8dlj1nJZowxwV4EpJKSEh05ckTt7e2KjY1VTEyMamtrtWHDBjU1Nenq1avKyMhQUVGR9887SVJ2drY2bNighx56aMjzzp8/r/Xr1+vKlSuKjo5WWVmZ7rnnnkBvyzL+zOvXX39VYWGhBgcH5fF4NGvWLG3cuFEOhyMYW7PESPKqrq7Whx9+KJvNJklavXq1srKyJNFfvuRFf908r1vV6K/h5xUK/fXjjz8qNzdXM2bMUGRkpCRp2rRpqqioUFNTkzZv3jzkz9UlJCRI0ohrY52/8/rmm2+0adMm2Ww2ud1u3X///SoqKtLEiRODuc0xhWEbAAAAsAjHSAAAAACLMGwDAAAAFmHYBgAAACzCsA0AAABYhGEbAAAAsAjDNgAAAGARhm0AAADAIgzbAAC/cLvdwV4CANx2GLYBIATs2bNHhYWFQ65t3bpV27ZtU1dXl4qKijRv3jzNnz9f5eXlGhwclCS1tLTo6aef1ty5czV37lytWbNGV65c8T4jMzNTu3fv1tKlS5Wens7ADQDXYdgGgBCwbNky1dfXewdlt9uturo65eXl6ZVXXlF4eLiOHDmiqqoqHT9+XB9//LEkyRij5557TvX19frss8906dIlvfPOO0OeXVtbq927d6uxsVHh4eEB3xsA3M4YtgEgBDgcDjmdTh06dEiSVF9fr9jYWE2ZMkVfffWVioqKFBUVpfj4eD3zzDOqra2VJE2fPl0ZGRmy2+2Ki4vTihUr1NDQMOTZBQUFSkxMVGRkZMD3BQC3O96CAIAQ8dhjj2nv3r168sknVV1drby8PLW2tsrtdmvevHne+zwejxITEyVJLpdLJSUlamxsVE9Pj4wxio6OHvLca/cCAG7EsA0AISIrK0tbtmxRc3Ozjh07pnXr1ik8PFx2u10nTpy46RGQnTt3ymazqbq6WrGxsfr8889VXFw85B6bzRaoLQDAmMMxEgAIEREREcrOztaaNWt03333KSkpSQ6HQxkZGSotLVV3d7c8Ho9aWlp08uRJSVJPT4+ioqIUHR2tP/74Q3v27AnyLgBgbGHYBoAQ8uijj6q5uVl5eXneazt27NDVq1e1ZMkSPfDAA1q9erXa2tokSS+88ILOnTsnp9OpZ599VosWLQrW0gFgTLIZY0ywFwEACIzW1lbl5OTo+PHjuuOOO4K9HAAY93hnGwBChMfj0UcffaQlS5YwaANAgPALkgAQAnp7e5WRkaGkpCTOXQNAAHGMBAAAALAIx0gAAAAAizBsAwAAABZh2AYAAAAswrANAAAAWIRhGwAAALAIwzYAAABgkf8Ddvuv7v103lEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set(style=\"dark\")\n",
    "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
    "\n",
    "data = temp\n",
    "\n",
    "sns.lineplot(data=data, palette=\"YlOrRd\", linewidth=1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>STATION</th>\n",
       "      <th>STATION_NAME</th>\n",
       "      <th>DATE</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>SNWD</th>\n",
       "      <th>SNOW</th>\n",
       "      <th>TMAX</th>\n",
       "      <th>TMIN</th>\n",
       "      <th>AWND</th>\n",
       "      <th>WDF2</th>\n",
       "      <th>WDF5</th>\n",
       "      <th>WSF2</th>\n",
       "      <th>WSF5</th>\n",
       "      <th>WT01</th>\n",
       "      <th>WT05</th>\n",
       "      <th>WT02</th>\n",
       "      <th>WT03</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>GHCND:USW00024233</td>\n",
       "      <td>SEATTLE TACOMA INTERNATIONAL AIRPORT WA US</td>\n",
       "      <td>20140914</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>117</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>58</td>\n",
       "      <td>63</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "      <td>-9999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               STATION                                STATION_NAME      DATE  \\\n",
       "256  GHCND:USW00024233  SEATTLE TACOMA INTERNATIONAL AIRPORT WA US  20140914   \n",
       "\n",
       "     PRCP  SNWD  SNOW  TMAX  TMIN  AWND  WDF2  WDF5  WSF2  WSF5  WT01  WT05  \\\n",
       "256     0     0     0   300   117    18    20    30    58    63 -9999 -9999   \n",
       "\n",
       "     WT02  WT03  \n",
       "256 -9999 -9999  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seattle = pd.read_excel(\"Seattle_dataset.xlsx\")\n",
    "df_seattle.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "\n",
    "def convertdatetime(date):\n",
    "    return dt.datetime.strptime(str(date), '%Y%m%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_seattle['new_date'] = df_seattle['DATE'].apply(convertdatetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b65d4b9b0>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Fhx02DytWvIKrrromr/kDwK5dO/HRRx/i0EMPS3r81lsTLYyPPXYuHnjgMbhcrqQGBeyxdLDn3n//XfzmN7/EqlWvGRcIdXX1eOSRBzF//oKki4b081lqiOCf/vTnxvO53uvVq1fg+eefxR133Iu6ujoEAgH86lc/g6ZpuOCCiwfMf968eWn388EH76GmphYff7wRPT3dqKqqTrtdNnbs2I6enh7YbDZs2fIFZs7cP+O2bE47dmzDkiXn46ijjjEuvKZOnW6I7j/96Y/405/uwi23/CHvuaZ7T/L9bgHAoYceho8++tAQwRs3bkB9/UQ0Ne3A9On74Msvt8Dt9hgCGBj859LM66+/ivXr/41HH30SdrsdoiiipaU579ePykxwCeIQiqpBAzB5QkVcBAcxZ79aNHcE8dxrX+Lysw6EQ6A/ewRB6FAcogT09HTBauUNN4njuIwOUDpWrHgF3//+RZg+fQbeeefNoRpmyVizZhWOPvoYnHXWuVix4hXj8fXr/43W1j244oqfwmbTa5Pa7Q6jffETTzyG008/0xCfFosFP/7xldi9+yt8/PFHxn4WLz4Dq1evAACEw2Fs2vSxIcwZ27dvQ19fL375y99g3bo1EMX8C+MvWXIJHnro/sFNPg/mzDkMkUgY/f2Jbjz77z8LkydPwfLlS9O+xu/vw/r1/8H119+ETZs+MZz+Qnj00Ydx+eVXoa6uDgDg9Xrxs5/9Ek8++dek85Nr/itWvIJvfvMsHH/8CVi9emXB49D3sRSLFp2ORYtOx4oV6eecyvTpM1BR4UVnZ0fa5+fOPRy7dn0FIP+5MtK9J/kwZ85cfPTRhwD0uxyxWBQLFiw0HtuwIfliqpjPpZnOznZUVlYad1MEQcC0adPzfj0TlKPJCS7FwjiWB3Y7ebjsPAJhCQDwZYsfn+3sxbYWf7aXEwQxxiiLS+J3N7XinU8GNigoBcceVI9jDqzPus2MGfvha187AGeffToOPfQwHHTQITj11NMMUQwA1133c+M2ttVqNdytL7/cikAggMMOm4eenm6sWPEKTjzx5CGZCwDcffcdeOSRB4x/33XXfRg3rrBamitXvoLLL78as2cfiHvuuRNdXZ2oq5uArVt1xy+T07l9+zZ8//sXJT3G8zz2228mtm3bioMPPhQA4rf8Bezc2YTNmzfhuONOgNVqTXrd8uVLsXjx11FXV48ZM/bD22+/iZNOWpjX+OfPX4AXX3web7/9Bo477oSC5n7ppUuMOMSECRNw2213DdjmzTffwGGHzcO4ceOSHv/hDy/DNddcicWLTx/wmjVrVuKYY45DVVU15s8/EatXr8B3vvP9vMcVDofQ2tqCAw44MOnxqVOnwWazobl5l+Egs/m/9dYbOPro45O27+3txYYN63HddTdg8uSpuP32m/Htb38373EAgCzLWLt2NR588DHwPI8lS76Dyy+/2hB0mfjkk43w+SoxY8Z+A55TVRVvvPEa9ttvZkFzZWR6T3Jx4IEHobV1D3p6urFly+c48MCDccghh+Lpp5/A2Wd/Cxs3fmjcCQGK+1yaOemkU/Hyy//Aeef9Fw4++FAcdtg8nHTSKeD57OeQwQRldFSUSCudE8waZfBWC+yC1bgIYD+b9gQwe1rhdzYIgihPykIEDzcWiwW33nonduzYho8+2oC3334DzzzzJJ544jkj15rpNjZzzDiOw/z5J+Kuu/6Azs4O1NaOH5Kx5opD5GLr1i/Q39+POXPmGmNetWoFLrxwCbQcK7FzPW9m0aLTsWrVcmzevAn/+7//D6+//qrxnCRJ+Oc/V+Ohh/4KADjttDOwYsUrBYmNH/7wctxzzx045pjjc29sIlsc4tJLlyAcDqOnpxv33//IgOf32WcGDjlkDv7+9/8bcOGxYsUyXHHF/wIAFi/+On7/+98VJIKzke68//CHl+Pee+/EkUcem/T4mjUrcMwxx8HlcuOggw6BLCv49NNNmD37wAH7yMS7776FyZOnGBGBffedibfeeh0nn3xq2u2vu+7n0DQNe/a04Oab/2DcRQD0HPQFF3wbmqZhxowZ+MlPri5orrnek1zY7Q7MmnUAPvroQ2zZ8gUOPfQw7Lff/vjyyy1QFAWffLLRiDwU8rnkOC7t8djjNTU1ePLJ57F58yZ88slGPPHEY1izZhXuvTf3HQxFVaGo+nkYDU6wXMISaUxQ26wWOASrcREQFfUFgk2thd0JIAiivCkLEXzMgbnd2r3B9OkzMH36DJx99rdw/vnn4qOPkl2iVNgfTUGwG7f/ZVnGqlXLjYVkI43ly5ciGOzHuefqmUpJEuFyuXHhhUswc+YsvPjiC5BlOa0bPGPGvti8eZORdwX0+W7dugXnnXd+0rYLFizE+eefi+rqakyfPiNJBL/zzpsIh0O48spLAeguYW9vD9rb2zBhQl1e85g793BUVdVgzZrB3e5PBxPIzzzzJK699v/hmWf+Abs9uW3rxRf/CD/60RKcf35C4H7xxWfYuXMHbr31RuOxrq5ObNr0cdKixWy4XG7U1zdg8+ZNOPLIo43Hd+5sgizLaGiYlLS9Pv/qAfNfuXIZ+vr6cM45ZwAAgsEgVqx4pSARvGLFK9i5c4exj2g0ghUrXskogtkF4muvrcPvfvdrPPvsi0YO2ZwJNpPvXPN5T3LBcsFbt27Bt771P7BarWhomIS1a1fB46nAxIkNAAr7XFZWVsLvT7417/f3JV0c8TyPgw8+FAcffChOP/0b+MY3ToXf74fbXZF1vKKpysKoyASX1AnW98FbLbDbTE5wXAw3tQagaVrGixCCIMYWlAkuAZ2dHfj000+Mf3d0tKOvr9dYxZ6JN998HZMnT8VLL63E3/++DH//+zLcddd9WLly2VAPeVCIooh169bikUeeMMa7dOkacByHjRs/wty5h2PChAm47767IUl6Fi8Wi+Gpp/4GADj//AuwfPnLRp5SVVX8+c/3oLFxEg45ZE7SsVwuFy677EpcdlnyojxAF1lXX/3/jDG8+OIKLF78daxatbyg+Vx66eV47LHC3cFc/M//nI+qqiosXfrigOcmTmzACScsMKoTAMDy5a/gO9/5vjGfv/99GS666IdJeet8WLLkB7j//rvR3t4GQK86cscdt+L88y9IK/wuu+yKpPl/9tmnCAaDWLp0tTGOJ5/8P7z++jpEo9G8xtDV1YWNGz/CCy+8YuzjH/9YgS+++BxtbW1ZX7tgwcmYN+9I4/NSyrlme09yMWfOXPzrX+8jHA4bC/YOOeRQPP74Y0l54EI+l7NmzUZT0w5s374NABCLRbFy5TIcfvgRAIAvvvjcqFQCAFu2fIGKCi8qKrILYCC53u6o6BgXF65SCUqkGSKY5+AQrIjFHWAmhv0hEb39saKPQxBEeVAWTvBwoygKHn30IbS1tcJud0DTVFx88aXYb7/EinhzJhgAfvGL67B8+Ss45ZTFSfuaPfsgqKqKjRs3DBCGhXDVVZcl5Wgff1wXXamZ4Isv/qFRyisXb7/9BhoaGjFp0uSkxxcuXITly5di9uyDcccd9+LBB+/Hd75zDhwOR/x5fY6TJ0/BTTfdjoceuh99fX1QFBmHHDIHN910W9rjsRX5Zrq6OrFx4wZcf/3NSY+fcspi3HLLb/H971+Ut8uz//5fw8yZM9Ha2pLX9kByJhgA7rjjHkMYMTiOw2WXXYXrr/8VzjzzrAH7uOCCiw1hFIvF8Oqra/HAA48mbbNw4SJ8//vn4corfwan05nX2BYv/jpisSh++tOfQNM0KIqCRYtOz3hXYdas5PmvXLkMJ598atL5q60dj/32m4nXX1+HCRPqsH37l/iv/zrNeH7u3MNx7bU3GP9evXo5jjzyaLhciTJ8drsdxx03H6tWLcOiRQPz0GZ+9KPLcdFF5+eMghQ619T3pBA3+IADDkR3dxdOPTUx70MOmYOHH/4zvve9CwEU/rmsrKzEb397C26//WbEYjGoqoL58xfgtNP0Oyx+fx/uvPP3CIdDsNkEOBwO3HrrHbBYLFDV7GLRHCtgInAkw0R7rAQl0lgmWI9D8Ojp1y/ezNnoptZ+VHkdRR+LIIjRD6cVEtQcJrq7g1DVxDDb2r5CXd2UYRxRaTCX3hrtlNNcUinXuZXrvBjlOL90c0r9fdjSFcKv//IvAMA+E7249ntz9+oYC0HVNFx82+sAgMNnjcevLz4KnZ2Dz+02tQbwu8fX48pzDsIHn7WjqTWA3//wKPzpH5+gtTuMzr4ITj18Ms45YZ9STaEgamsriprfSKQc52SmXOdXjvOyWDhUV3sKeg05wQRBEGUEazphtXBZq0NERRmhiIxq3/C5omZBX4q2yWwfqZngqKjA47LBLljR1Boo+jgEQZQHlAkmCIIoI9jCOI/LlrU6xMoPvsLNT67fW8NKC8sDA6WtE8xbuZTqEAocNium1lVgd0ew6OMQBFEekAgmCIIoI1jL5AqnkLU6RE8ghr6gmBQ129uYneCSVofgdSdYFBWomoaYpMAuWFFVYUcwIpXEdSYIYvQzSkUwB02jX2IEQYxt9N+DyQtBmRPsdduyVoeIxPRFc9FhXDxnFqNiCapDSLJpYZzdCg161YmYKMNhs8Ln0RdEBkKD6+RHEER5MSpFsCA40NfXBVmWCmrAQBAEUQ5omgZZltDX1wVBSM70MifY6xIgySqUDNUkwlE56edwkBSHKEl1iEQm2GHTq+NERVmPQwg8vG69456fRDBBEBilC+PGjatFMOhHT087VHXk18HMRD7ljkYL5TSXVMp1buU6L0Y5zs88J4vFCqfTA4/Hl7QNc1QrXLrgi4kKXI6Bfkc47gSzn8MBc4IdgrUkTnBSHEKIi2BJMeIQlZ64CA5SrWCCIEapCOY4DhUVlaioqBzuoRRFOZUoKae5pFKucyvXeTHKcX75zIkJS69bbz8dFRW4HLYB27E4xLA6wfGxuh02o6pFMTARbLNaYLfpf95CERmyosEuWOFz63EIcoIJggBGaRyCIAiCSA+rsmA4wRlywUz8RkaAE+x28oiVZGGcHo+zxqtDAIn8r8NmRYXLBg4kggmC0CERTBAEUUYw0VvhTDjBqaialnCCh1MEKyYnWFKLXuPBRLXNmohD9IX06INdsIK3WuBx2SgOQRAEABLBBEEQZYUkq/rCMLseB0hXKzgaU8Dk5siIQ/BQNc1wcgdL0sI45gQH405w/N8+t0BOMEEQAEgEEwRBlBWipMJuS4jAdF3jzBGI4XSCmWhlmeVsJd0K2R9v5YzqEH1xwWu3kQgmCCIZEsEEQRBlhCgrsPEJEZzOCTYL3xGRCXbornVq17iWrhB+8dD7edf1lRQVvJUDx3FGHIJFHwwn2GMvaRxClBRc/9i/8cn27pLtcyj426ov8PLbO4Z7GAQxoiARTBAEUUaIsgrBZjWcz3TNMMJRyfT/wy+CXY700Y1d7f3o6I2gozeS1/4URQNv1f+sDVgYJ+jHYE5wqWrM72oPYndHEDtbAyXZ31CxuakbW3f3DfcwCGJEQSKYIAiijBAlBUIBTvBIqBPsji/iS3WCmUCPSvmNUXeC9T9rvNUCC8cZ0Qe7KRMsK1rJ5t0UF7/DeR5zoWka/CFxRI+RIIYDEsEEQRBlBHOCBVvuTLDPLSS5wnsbVh3CkyETzMaZTsinQ5b1OAQAIxLRF0zJBMdbJ7PHi2U0iOBQVK+VPJyuP0GMREgEEwRBlBFS3Anmrfp/aZ3guBiq8TlGhBPsZHGIVCc4PrZ0Zd7SIZucYECPRLDFcubqEAAQKFEumIngyAgWmMwNJxFMEMmQCCYIgigjYnEnGNCFXzonmInLap9j2BfGWS2JSg6pgp2JtnyrRkiKBhufLIIZCSc43jq5BBUiQlEJ7fG88kh2gpngj8RkqCXKQhNEOUAimCAIooyQZNUQgnabNa0THInJEGwWeJy2YXUHZUUfKxvvgExwoU6wnOwEM+Er8BZYLHpMgrVOLkUcYmer3sLaLlhHtAhmZeI06DWiCYLQIRFMEARRRugL40xOcIY4hMvOw+XgEY7JJauUUChMsDOxmikTXEwcAkgsigMAp90KG2/Ju+xaNnbEoxAzJ1WO7DiESfCHY8OXASeIkQaJYIIgiFGc28afAAAgAElEQVSAqmnYsKUjp2AVJQV2W6JMWCxdibSYDJfDBqedh6blLzJLxdbdfYiKsiGCzU7wrvZ+9MVv3xtxiCzjkxUVn+3sMf7fFl8YByScYHMsguO4eJk0/RiapuHfn7fjzY0t2Litq6B57GwNoK7Khephzlbnwiz4KRdMEAlIBBMEQYwCPt/Zi+sffh872/qzbifKKmx8wgHN6gTHWyvvzVxwb38Mtz29Ae980gpJUWGzWhKVLEQFdzy3EUvfadLHyapDZCmR9vG2Ltzx3Ea094QhKxp4UyaYOcB2G5/0msoKO7r9UQDA9pYAHly6GY+v3oJ7//5JQVnh5s4gJk/wwGXnERlGRz0XTPADJIIJwgyJYIIgiFFAKF7KrLc/e1UDUVIhxJ1gl51P61BGYjKcdt5oV7w3XczmziA06PNgTrAQF66tXSEEI5Ixx0h8ztmc6mBE3yYQFpPqBAOJBhlmJxgAGmrcaOkKQdM07OrQLyq+cczUpP3lgz8kotJjh8vOQ1E1iJKa92v3Jn1B0TjHI9mxJoi9DYlggiCIUQArJxYIZ3YqZUWFqmmG4GGZ31T0OETCCd6b7mBzZxBAXLTGF7KxOMTWXb36c6ykV3wRV7Y4BHsuHJXzygQDuggORWX0BUW0dIbgtPPYp8EHIP9SZzFRgSip8LoFo8TbSBWYgZCIuioXAHKCCcIMiWCCIIhRAKuc0J/ldj1zIlm8wGnn04o6Fodw2ve+eGvuCAEA+sMSJFmBjbeA4zgINgua9vjjz4mQZMWo8ZutRBorAReOyUnNMgBTJtiWLIIbaz0AgJauIJo7g2iodRutm/NdOMYuRipctsTFxAgVwf6QiPoaN4CRO0aCGA5IBBMEQYwCYnGBGwhlFmmSrAtCwwm28xBl1RCTgL4QLMKcYMfezwS3dMWd4JAeX2AusMBbISt6ptYfkhAyifdINidYSnaCbfk4wbW6IGzuCKGlM4TGGnfBrjgTwV6XkMhWj0CXVVZUBCMS6qpc4IBh7RBIECMNEsEEQRCjACZws8UhYnKyE5wu8yvKKhRV0zPBezkOoagq9nSFAejzkGXNEK0sxwzowq0noOeCOeQXh4jEZEiZFsaliOAKlwCfW8Dmpm6EYzIaaj0FLxJkkY3kOMTIE5hsnJUeAQ47T3EIgjBBIpggCGIUIMYFbn8WESzFXVGbyQkGkh1KJoKGIw7R0RuBrKjwumwIhCSI8TiEPmZdqHpdunBv79XFstcjZI1DxAbEIQY2y0hdGAfobvBnX+kZ5MakOER+56I/LMXHKwxLtjpfWLULn9uecaEkQYxVChLBt912GxYsWICZM2di69atxuMLFizAokWLcOaZZ+LMM8/E22+/bTy3ceNGfOMb38Cpp56KJUuWoLu7u3SjJwiCGCOwvG+2El6iPDATDCQLO/b/Lgdv1OjdW7fxWzr1PPCsqVWQFRX9YSnR3S7+c9bUKgBAe48ugqsq7FmrQyQtjFNT4xDx6hC2gSK4sdYDVtGsodYDG28Fb7XkH4cIDcwED2cL6kywRhk+j6AvlByBQp0ghouCRPBJJ52Ep59+Gg0NDQOeu/fee7F06VIsXboUxx13HAA9e3bNNdfgN7/5DdasWYO5c+fijjvuKM3ICYIgxhBiPA7BHMi020gpmeA07iYTaky47U13sLkzCI4DZk6uBKCXIzOc4HgcYtaUcQCAtrgIHlfhQExUMtbgZQvjIjEZsqyB5xML4xKZYH7A61gu2OcR4HHq7nOmahrpCIREOAQrBJu1YBd5b9IXrxHscwvkBBNECgWJ4Llz56K+vj7v7Tdt2gS73Y65c+cCAM477zysXr26sBESBEEQhhMcjEhQ1PT1aFOd4GxxCOYSO+08AiERgZCYs9mDqmmDagihahr8wRi+auvH+HEu1HgdxnMsviDwVnAcsH9cIBsi2GOHqmmQFRVamuOLcSc4GJGgahp4y8BMcLo4BKsQwX4C8Woa+YrgsAivWwCAAS5yICzCH4wlLUjMh6FothEIJrLLuhOcfBGljtAGHwSxNyhZJvhnP/sZzjjjDNxwww0IBPR+6q2trZg4caKxTVVVFVRVRV9fX6kOSxAEMSZgTjCQ2Q3Oxwlmi7eYCPa4bNi4rQtX/ekd/HN9c8bja5qGa/78Ht76eE/BY3/u1S9x9X3v4uPt3ZhU60aFSzCes5nG2lDrQVVcILf3RgAA47x2AHqFiL+t+gIPvPxp0r6ZE8wWDJoXxjGH1+0Y6ARPrHbDauEweXxCBLsKWDjWH5bgNc3DZbciEpPx1sd7cNW97+Dq+97FXc9/nNe+AF3EX3bXW9jc1JP3a/KhLxiDx2kDb7UMcLo3bO3EVfe+MyJjHASxNxj4m2EQPP3006ivr4coirj55ptx4403ljT2UF3tyb3RKKW2tmK4h1AyymkuqZTr3Mp1Xoxymh9ncjh5uy3t3PgmfaFXQ70PtdVuuCt0QclZrcb2gl1fl9E40YdqnxNXnjcHnzd144mVn6O7P5bxnMUkBb39MTS1BXFOgee1JyhifJUL55w4A4fOHG8IXwDweZ2ora3Aj84+GDFJQX2dFx6nDcGIBKuFQ8MELwDA7XGgpTsMSVaTxqioupMZiuhCrtLnNJ6vra3Aby85CgfNqElaMMe45cfHoHF8heHoVlbYEY7KeX1uwjEZ9TVuY9sKtwAFHHZ1huB1C5hcV4Hm9uCAfWXad++uXkRFBYE8j58vnYEYJk2oQG1tBarHuRDZ2mXsv39TG4IRCf2iismNgz9mOX3P0lGu8yvXeRVCSUQwi0gIgoBvf/vbuPTSS43H9+xJuAY9PT3gOA6VlZUF7b+7OwhVLb9bNrW1Fejs7B/uYZSEcppLKuU6t3KdF6Pc5hcMxWCxcFBVDV8198FjGyjq2uM1eKPhGDpVvXscxwGdPSHjXHR164vTgoEoVFGGm+cwd98aLPfa0d4dynjOWNvm7S19BZ9Xf38UNV475u5bA6gqxEjCeZRiEjo7+2EFMKXOi87OflS4dBHstPOQ4s51a1sA3f4ILByXdPxQvM2xP559jUXEpOcnVTnR2xNKO65aj4BYOIbOsP5a3sLBH4zlNb+eQBRT6xKfMYG3otcfQSAsYtJ4D6bXVWDz9m60tfthjV/AZPtMftWs3yHtzPIeFIqmadi5x495+49HZ2c/OFWvEc3G1B2vwLH5yw6MrxBy7C095fY9S6Vc51eO87JYuIJN06LjEOFwGP39+onUNA0rV67ErFmzAACzZ89GNBrF+vXrAQDPPfccFi9eXOwhCYIgxhyirKKm0gkgUZkgFZb3dMYXglk4Dq6UrnGs0oJdSP7173PbjUoC6WBtm9u6wwVnXcNR2YhfAHoOmEUUWGk0Mywu4bLzRp43HJMRCIkD4gqsRBqLtqZzfPMl30ywqmoIpolDhKIS9nSF0Vjrhs8tQEP2hYxmmIgv5cK1vqCIUFSvg6yPkVWx0M9ZVNSP1dyV/iKBIMqdgpzgm266CWvXrkVXVxcuvPBCVFZW4sEHH8RPfvITKIoCVVWxzz774PrrrwcAWCwW3H777bj++usRi8XQ0NCAP/zhD0MyEYIgiHJGlBRMGOdCR084Y8OMcEyGQ7DCYklUSHDa+aQmDlFJgcBbDHeS4fMI2N2R2RliIlhRNbT1hJMWlOUiHJMNAcaocAkIReWkaASD1Qp2OnhjcVu3PwpN00WvrOj1gDVNQ0xUYeE4Y4EXn2Z/+ZJvdYhgRIIGGDEKfaw2bNnth6yoaKjxGHlsf1BEpceec5/sAqSUJcxaOvU7A43xShhGRjwqweO0GeXlWjqCJTsmQYwmChLB1113Ha677roBj7/88ssZXzNnzhwsW7as8JERBEEQBqKkYlyFHbyVyyiCWTtkM6mLvWKiMqCDGqCX0AqE9AoLFo4b8DwTwYBe6qwQEZxuXF6XDW09AG8deKwKd8IJZg0vWPMMQHezPU4LZEWPfIyrsKO3X3dSbUU4wS47D0lWIclKWoeawc5/RVyss9cyh7xxvBuK0QI6BiB39pLVfy7lIrXmeF1mwwlOWSjJXPTmzhA0TQOX5n0niHKGOsYRBEGMAkRZF68VLiFLHGKg4+pyJN/ij4qKISzN+NwCVE2/zZ8OswhmTS/yQVZUiJI6YFyJ8mID/wz50sQhOuLVIoBE7CMWLxtXVZFwWq1pRHW+JERi5uYcQCKO4jM5wey1HID6arfxXLaIiRkmgksZh2jpDCbXQU7pbMeiMeGYjL48x0kQ5QSJYIIgiFGAKKmwC1Z4XULGnGkkTezAmdIgISYpaevmslv2mTrSScrgRDA7tjM1DmGqsZsKe87p4I2ub2YnmO2TZVorTSK4GCfYmWfnt4QTbIpDxF87fpwTdpsVPo/+XF+WDn9mjExwCeMQzZ2hJMfe5bAlHSMmKoYT39xJkQhi7EEimCAIYhQgyirsNisq3LasTnCq2EztEhYT5bRxCObMMjGWCnOCfR6hIMFkdKgbEIeIi+A0opVlgl32RCa4vcfsBCdEHACMM4ngYhbGpTqlmegP6Rch5kwwey0TnTbeCpedN5pV5MLIBMfyW0iXC1XVsKc7hIYa94AxGhcRkoIpE/SoRiEXNgRRLpAIJgiCGOGoqt4xzW5jTnDmhXGpYtOZGoeQFDjSxSE82W/fMxE8rc6LLn807+wqE5Quuy3pcSZ008UhjOoQDh4CbwGH9K2fWRwiSQQXuTAOyC1EA2FRr7xhOtfs/1k7ZkA/p5kuKsxommbKBGePYuRLR18EkqymOMED4xDVPkfBFzYEUS6QCCYIghgGlr23E+9vbgOgRxDuf3FTxgVvkqkdstctwB+S0rbY1eMQyWLTZecRiSlGrfWoqBgRAzNGhjUkoqUrhAeXfgrJ1KWO/f+0et053JNnWa1wJic4frx0C+PYWJx2HhzHGW4w64SXcIL1n+M8pY1D5HKCAyERFW5b0gJCZ4oTzOaRTxwiElMgySoEXm+9XIr2yc3xig9mUe4Q9NbUTOTHRBl2mxWNtR5s2NqJ3z3+H/zu8f/g5ifW48tm6uxKlD8kggmCIIaB1zY0Y128TfHH27rw4dZObNrenXbbmMxq+1oxvtIJWVHR6Y8mbaNqGsIxGc7U6hDxHGhETEQI0sUhHIIePfAHRXywuQ3//rwDO/YEjOeZEGeVBroD0QH7SAerUZwa09h/yjiccGgDptZ7B7ymttKJU+ZNwiEzaox5A8CEKhcAc3WDNE5wMQvj8s0Eh8SkGsEAsF9jJU44ZCIOmFZlPObz2POKQzC3uK7KBVXTjKoNxfBVe7/ecc8Uh+DidaONiwhJ/yycNKcR+zZWwuMU4HEK2NURxAeb24seA0GMdErSMY4gCILIH1XVEAiJCEUkyIqKplZdbDa1BnDMgfUDtpekhBNcFxeNTXsCGB9vngHo4lbTkGZhXLzZRFSG22HTq0OkEcGA7lz6QzGjC1tTaz9mTh6njyEugmt8eivmfKsJGE5wyrjcDhu+d+rMtK+xWDicd9K+xr8dNiv8ACaMc6K5I5i4nS/pP71uAVYLB0XVShSHyC6C+0KiER8xv/Z7i/ZPekw/n3mI4Pi5rKt2YVd8func+kJoag2godYNISX6wmoha5oWvytgxSH71uCQfWuMbW5/ZoPxmSSIcoacYIIgiL1Mf0SCpgGyomF3RxBNexIiOB0ic4JtVjTUusFbLQO2zbQAjcUjIqbasOkywQBQ6RbQFxSxs61/wHhYdQifR69VnE/WFTBlgh2DF3VMtFdW2JO6urGFcXab1XCaecvg/6zZbVZYOC6vOIS5PFomfG4BMUkxqlhkggnlurjTXWytYFXTsLO1H9PSuOwuhw3hqAxJVqFpSFsub9pEL3Z3BJPK4hFEOUIimCAIYi/jDyYE5NbdfWjuDIG3chmFh2hygnmrBVMmeAaI4MQCtIF1gtnziqpCktWMTrDXY8fOtgBCURm8lUsWwSyXzFv0xhoFOMEch4zHzAfmivrcQlJXNxaHcAhWY97FOMEcx8Fpt2Z1glVNd/Hz6QKXa7Ehg4ngifHoQrG1gjt6IwjH5PQiOB6HiMYjF+kc52l1XiiqfoFGEOUMiWCCIIi9jPkW+Zsb90DVNMzbfzxkRUu7Sl80ZYIBYFq9F1+190NRE4LZqMebpmMcez4mxkVjBifY5xYMwT1v//Ho8keNxXpMBNt4C7xue971byMxGU6BT9uFLl9YXWOfW3eCUxfGCTarMW9bEZlgYGBzkVSCEQmKqiWVR8uEz5299jLDH4yBt3KojkdNiq0VzC5e0jvB+kUEa5SRrmb09InepP0QRLlCIpggCGIvw5zB+moX2nr0JhAL5jQCSC88mDBlt66n1XshSipau0wNJDI4wU6TE8wWXGVyZSvjzqXAW4xs8s74eCRZBQfAauH0rGu+TnB0YNm2QmHzrvQI8WoXCSeYt3LgrZaEE1xEdQhAj49kE6HMAc83DgHkIYLj8Yp8F+blomlPAILNgok1rgHP6U6wlBQlSWVchR1et0AimCh7SAQTBEHsZViellU/GFdhx/SJXnhdNiMfbMacCQb0zCYA7DCJlEiGBWhmYcWyqZkWXTF3c0pdBabVe8FBXxwH6JlgG28Bx3Go9AgI5JkJTtfFrlCYaPemxiFMLaBLEYcAkLT/dDBBW0gcoi+Y/Vz5QyK8bnuio1uxIrgtgCkTKmBNk49m84tlcYI5jsP0ei+JYKLsIRFMEASxl/EHRTjtVsycXAkAmFpXAY7jMK3ei6b4ojQzhhMcFyzjxznhtPPY3NSDnW0BSLKSMQ5hVIcw3QLPXB1CF3bT6r1w2nlMrHEbQkiSVKOxhdett242xzEyka6BR6Gw+IbPkxyHiEqJ7ndOBw+rhSsqdgHopdwiaZxgluNmgjYfJ9jttMFq4ZI6/KVzhf1BMe5yJyp5DBZZUbGrPZg2CgHoC+NESUUwqlcAyfRZmFpfgbbucNGutHlcmZq8DIaIScgTxGAhEUwQBLGXYc7ftHovrBYO+zbqYnhavRetXaEB1QSYE8zKXVk4Dvs0ePGfLzpw49/W44U3tiMcFzWprqvVYoFDsCIUSdwCz5QJrq3UM6n7NvoA6I4wWxwlKYrhsvo8dmgAAqHcLX7TtXIuFJ9HgEOwosJpS2oDHZNUwwmuqrDD47Rl201epFsY91VbP66+711sa/YbgjafTLCF4/TmJvEIxWdN3fjfP72D1u7kRiOBsIgKlwAbb4WNtxTlBLf1hCHJKqbWVaR9nn0+evt1MZ8uDgHoF2YaULLFcWv+vQvX/eVfJWkEAgAPLP0Uj6/+oiT7IsYuVCeYIAhiL+MPiah0C6hwCfjtksNRG6/32zjeAw3Anq6wsTgJSM4ES4ouZC86bRaa2vrx0ls7sLO1H/s0eCHwlrSZWK9bQCAsGhUBMrl/9dVu/HbJ4WiMdxmrcCXysZKsGt3YKuMCMBASkxpVpCMSk+Cye7Juk4uT507CvP3Hw2LRWxVHY7LeVMIUh1h0xOS0NZYLxW6zQkxpVrFjjx8AsH2PH/6QCMFmSRsjSIfeNU4XnLvbg9AAtPdGUF+daGIRkxRjf+YScIOB1XjO5FQzV763X292kmkerHV1sdEMxrZmP/rDEkJRuSQXK93+aJLDThCDgZxggiCIvYw/GDPyohNr3EbMgInP1AoRibbJiV/ZPo8dh8yowYxGH1q69AYLqVEIY9u4G5ktB8qYNN4DLh4psNusiEkKVE3T2/rGBac3z6wrAIRjSsZx5YvdZsX4cfoiL5edhwYgGlOSxKND4I2LiWIQbFaIKWXqmjtD8Z9B9AVjqHTbjXOUC3M5ub648DSXyNM0DaKkGO+tuaPbYEjUZU4vNJkT3BOIO8EZ8uHs4qJUkQN2Dv15fGbyISoqeTUiIYhskAgmCILYy+hxiIFOXU2lE4LNMkAEM2cy3a3rxho3IjEFzZ2hjAvQfB47/CExUR0iwy3wVNgCOlFSkpzgfKseqJqGaAkWxplxGiXfpCQnuFQIvAWSrEI13bZvib8fLZ0hvWWyJ3cUguHzJLrGsQiC+bwpqgZNAwQ+vsAvx8K8XBjZcHv685JwgvWxZLogYo+XooVzJCYbbbZLJVxjooL+kARVLU28ghibkAgmCILYi8REBVFRSVtdwMJxaKhxo6UzOTMqyiqsFg7WNFGHhlo9arCrvT/jAjTWvjdbbdh0sNhETFSM6hBsf0BuQRONydAwMKdcDObmH1EpcwvowcLcbua+a5pmuJh7ukLo7Y/ltSiO4XXbEQiLUFXNiCCYzxu7wBFMVS6G1AmOP97THwMHXfSng53XaAmc4JauxOc539J62dA0zbhD0R/JnUsniEyQCCYIgtiLsPJomYRUQ63HcB4Z+u3y9GKPRSgUVcu4AM3nFhCJycbq/HyFI1tAF2VOcFww2Xgr3A4+561tJsiKjUOYMZd8E6XSO8FsjkwE9wVFhGMyptRVQJRVtPdGUOnOXR6NUekRoGlAf1hEbzyCYBaCrOsdE6PFZoIjuZxgtjAuEIVdsGaMdSTiEMVngs2f51I4wbKiQYk7wKWKVxBjExLBBEEQexEmAjKJ4MYaNwJhKWnRjyirGR07l8NmLE7LGIeIH6u9NwIbb0lbPzYdDrMTbBLBgL7YLpegCRu1i4tfCMUwmn/ES76VWgSz/TGHlkVTjpg1wdimoDiEyTVPOMEJ4SYZlT/imeASxCHsgjXje8ycdDFL+2xAbzrCWy3GYspiaO4MwW6zQuAtSXMfLOaIBi2OI4qBRDBBEMRehLmAvgzNFhrG6/EGs3smykrSorhUGuORiEy3wNmxOnrDBYlG8y1xSUlkgoFExCIbRgOPIXCCw1G9Tmyp4xBM6LPFcSyacvis8WCeaSFxCFZ7uS8oJjLBQXMcgjnBpYtDZIufCLwFVos+k0yl8hgOwVqaOERnEA21bj0fXYI4hLmEYF8J9keMXUgEEwRB7EXycYIBoNmUo5QkNWMcAkhEInI5wR29kbzzwECKCJaSneBKjz3vOERpM8G60O8PS1A1raD55AMTo2YnuNIjoMrrMKpPVBa4MA4A2nvDiImK0TyD1csVUyp/uBw8ZEU1HOJCydWhj+M446Ik1wWE3WYtujoEy1Q31rrhc9tLEocwC/NSOMvE2IVEMEEQxF7EH4rBwnHwuNK7tl63AI/TluQEx2QlYxwCABriIjhTDpQJsWiBzilzCmOS7gTzaeIQ2ZofZOpiVwxM9PbEowXZLg4GAxOjZieYLT5k59lXQCaYVQHZ1a53AmyocUOUVURiupBjYtvGJ+oEA3ppucEQjmUulcdgFxL5OMHFiuBAWEIwIqGhxpPX3YN8iCWJYHKCicFDzTIIgigpTa0BPLvuS1z9rYOL7hQ21LR2h/DXVV/gqnMOhsvB45V3mqABOPPYaUN2TH9QRIXblrG9L8dxaKx1GxUJAP2WOXMo05ErDuF1CeA4QNNyCx8zCSdYHpAJ9nkEiJKK/73vXbCcwClzJ2HxkVOMbRKZ4NJ9DnirBXabFW9u3AOgsPnkA7vYEOPVB/Z0h3Di5AYA+qLFj77syqtbHMNus8Jpt2J3u35RM3lCBXZ1BOEPxeBy8AOd4Pi5+s2j/4IlHls4enYdzj1hRl7HC0dl46InE+wYjhzvi12wFp0JZhdzDbVutPeG8cWu3qL2ByBpTKWIVxBjF3KCCYIoKeu/6MC2Fj+2x7tsjWR2tvZjW7MfLV36H+oPt3biwy2dQ3rMcEyGJ4NYZUyeoLcrlhVdIEmyAlu2TPB4D7514gwctl9t2uctFs7oAFZIfIDVCTYWxpkywYfvPwEnzmnAwTNqcPA+NRB4C97f3Jb0+v6wCIvp9nup+O8FM3DUAXU4aU4jDtqnuqT7FoyFcSqiMX3ebOHhCYdMxP+ctG9BcQhAd45ZmbDJE/QLFragyyiRFr/IOWBaFU4+rBGH7ltrnNdPtnfnfaxwTMp50WHEIfaCE8waqlR7HfC5BYSislF5Y7CYm76QE0wUw8i2aQiCGHU0tQbiP/sxe1ppBUqpicVzl8xN8odE3S4dymPmEUmYVu/F2v/sRktnSC/NJamoqsj8GgvHYdERk7Pu0+cWEAiJGTuEpcNujkOkOMHVPge+e8pM498vvbUDy9/fmTQ/f1CEN4vrPVhOOLShpPszYzjBspJoLhKfT5XXgYXzJhW8T59bQFtPGIB+gQMkFnSJKdUhKlwCvr1wP+O1T6zZgvVfdOR9rEgeHfqYSM4nExwIFVeH11wmjy3QDIREVPscg94nE8HjxzlJBBNFQU4wQRAlQ9U07GzTs49NewLDPJrcsJX5/pAIRVXRHxLRH5agqMU5VdmI5lHbdlq9LpSa2vRzKOZwgvOBLY4rpDqELV5JIByVoWpa1lzytIleaBrwVTz7CujntZD87EggyQmOVyEodvEdiyfwVg4T4wsfmXgz4hAZ4i6VbgHBiGTcFciGpmk5q0MACSc4v+oQxdUJNi+OZDGSviIXs7E4xIRxLqoTTBQFiWCCIEpGW3fYqN3a1BrIumhqJMBuRftDMfSHJWgANKBo9ysb0ZiSU1TVVjrhdvDGhUSuTHA+MCFWqKBzCFb0h/XzYcsyhmn1XgCJOwGA7gTnyqeONASjWUbCCXbYirtpyi4EKisccDt48FbOqGpglEjLcJHDahLnUw+XdVHLFT/JuzqEwBfdNjkU1esW81aLESMJFJnjZU7whConoqJSdGSDGLuQCCYIomQwAXTU7Lp4c4CR7dKIpjiEeYHNUBbgj0lyTvHBcRym1XvR1NofH2fmZhn5woRYoXV17YIVwQgTwZnH4HMLqPbak0VwqLAWwyMBwYiAqIa4KrYWMbsQGFdhB8dxejQl/nkzmmVkuMDIt0U1AKPiRK4FqcbCuBzzcpSgRJo5o8w+g8VGGJg7XevTS9b5wxSJIAYHiWCCIEpGU2sAdsGKow+oi8lzguEAACAASURBVP+7P8crhhdzHMJcb7RvCG+xxkQlr4oG0+q9aOkKIiYqWdsm5wsTU4VWU7DbrOiP6CIjmwgGgKn1XkMEq6qGQEgadU6wzeQER0wLsIqBnftxFXoO1uu2oy8uBGOSCg56VCIdlfEcbT5VEMJR/WIldxxCX5iZKxpjF6wQZRWqOvg7OuGoDHfcea5w2cCh+O8Xu9tUWcHOzci+2CZGLiSCCYIoGU2tAUyrq8CUOg+sFi7JFRyJGHGIFCd4KBfbRKX8avVOq9cztk2tASiqlrVjXD4wMVqoq5kUh7BmH8P0ei86+6LoD4sIRvRmFqMtE8xb9Ry0KJuc4GIvQJgT7I3HIkyd0yRZv8DhMiweTDjBuYVeOM8OffkujDPaZhcRiQhFZUN081YLPC5b0XdaYvHvkHFuqEwaMUhIBBMEURJkRcXujiCm1nth461orPUMWgR/1da/V/LEMcMJjiUJ36ESwaqqQZRUo/RYNtjiuHc2tQLIfLs8XwwnuNA4hM2KYDh3HAJI5IJ3tvXn7Iw3krHxFoiSmsgEF+0E6+KXOcF6pY5EJjjbefWmxCG27OrFhq2d2Lq7b8C2iUVo2UvwMZHszPE5NHcMHCypC/VK0TCD3U1h1SaoQgQxWEgEEwRREtp7I5AVDZPH63VQp9TptW4LZceeAH77t/9ge8vQu8gsExwISegNxuCy83A7+CG7vWqU3MrDWfR57JhQ5cJ7n+q1d4sVk+PHuWC1cAWXpnIIvOEw8jlE8JQ6XbjvbA0Y53C0xSEAPRcsyooh/orNBFd77RBsFkyK1wj2ugW97bOqQZQU2LO4/LzVAo/TBn9QxJ6uEG575iPc9+Im/P7pDegJRJO2jbAOfRk6BzJqK53ggJyfBRadKaZCRDgmGXEIQJ97sU4w63xY4bTBauEGnAeCyBeqE0wQRElg0QK2KKfKazdKO/E5bqOb6eyLABjaXC6DFe1XNQ0tnSFDsA2Vs1Sos3jtdw9DTyAKqyVRWmuwjKuw466fHJskSPLBPNZcTrDTzqPG50BzZwhV3oTrOdoQmBMcF3/FxiFcDhvu+PExmNI4Dt3dQbgcNmjQxaUoq1mrbgAJ95S1Xl4wpwGvbWhBKCqjypvYLhGHyO4E11W5cM+Vx8HjzL6dvcRxCDa2YhfMsjiExcKhrtplNCIhiEIhJ5ggiJLABCUTSkz8FOr6MAeR/UEfSkTTH/fdHf3wuYWS3K7NRKHVBjxOGyZPqEBDrSdjZrQQPE5bwfsxjzVXJhjQWzi3dIVMcYjRlQkGTE6wpIC3Wgq6iMuEx2kz2iCzeEA4KscXPWbfv9ctwB+KoaUrBKuFwwFTqwAkf37Z/vT95/585RLAQMIJHmyFCEXVc9XmjLLLzhvjHCxRMVFmsLHWg5ZOEsHE4CARTBBESRgoggeX12PbF/uHMh9ikmqIvEhMgc9jR6XHXnQd00yw2+uFVmgYTswuaC4nGAAax7vR1h1Gtz8Kh2AtOkowHAi8BVJ8YVyxeeB0MFEYjsnx8nfZj8EW0jV3BFFX5TLutogp7YcjMRk23pLTWc4X1l1wsJlgQ5SbRbCDL/oCNyYlKqw01LjRHYgaURCCKAQSwQRBlIQBItgzuJXbhgjeG06wrBi1RgHdvfa6BfSFYkOyMI9lK0eTMCwkDgEADTUeqJqGL3b1jsooBMDiEIpRiqvUOM1OsJzbCfa57fCHRDR3htBQ6zZ1tUtxgmNyzhrBhVBsdQgmgs0RHKedhySrxu+LwRAVE7W2G2v1nDVFIojBQCKYIIiSIMXburJb5r5BtkhlcYjIXnCCRUlBbWVicZDPLcDnEeItc0vfhcpYGDeKRLC9QBHcWKtnl1u7w6NXBNusRrOMIXGC40I1EpMh5dEN0OsWIMkqugNRNNZ6DNGcKiTzaZlcCI4iq0Okyyib5z5Y9OoQ+n4a4p+35s7CF+ESBIlggiBKghz/g8wqCLDSToVGCxJO8NC1LmaIkgqvWzCEns8joDIe4xiKrnGjMQ5hHms+ZdomVOlVKAAYJaxGGzbeAimeCR6KCxZzHCImqzmd4EpThY2GWrfRPTDVoQ3H5Jw1gguh2BJpoTTNO8xzHyysOgSgV7iwC1bKBRODgkQwQRAlIdUJNko7DTITzFrADiWirEDgE0X3fW47vHHBMRTVKWJGB7LRU5inUCeYt1pQX+0CMDorQwB6DlqMO8FDEYcwhCBbGJdHdQiG7gTr26c6wZFYaZ1gu7EwbnCCNVMcwvxcociKCkXVjM+lhePQWONGCznBxCAgEUwQRElIzQQDurNaiJiUFdXoTsZawA4loqS7cAkRLJg6dA2BEzwK4xBmwZ5PdQggkdMcjTWCgXizjHid4KGIQ7AmFZGYDElWYctVHSLuqNsFK6p9DsMJTlcdopSZYL0yBmd8bgslFC19HCKappV1Q60bzZ2hvdJghygvSAQTBFES0orgAgvjMwEMDP3COOYoCabOUz7P0IrgWJo/4CMds2Dn+fzKq7Gc5mgsjwbES6RJKmKSPCTvlcXCwSFY9eoQkgJ7nk5wQ40bFo4znOBYaia4xHEIQHeDB1sijV3IplaHAAb//Y6liRQ11HoQjEhDEmEiyhsSwQRBlAQp3n0tVQQXIib98UV0LnvxZZRyIcZbJtt5Cyo9AngrB7fTBne8CxWrarGt2Y//98B7JXGmo6ICq4UrSd3ZvQUTG1YLB6ulMCe4smJ0OsECb4EYL5FmH6LoisvBIxSV4s0ysp9Xt4OHwFuM82q1cOC4xHcO0Bu+lHphHKDfCYiJCl58azv+/NImALoD/cuHP8CmHd1pX/PH5zdi1b++Qjgqg7dyhnMNmGskD+77lO5uSmO8kcxuikTggZc/xYtv7RjuYYwaRs9vYoIgRjSSosLCJQsln8eOvqCY921KJjzrq11DXveTtUwWbFYsOnwyrjj7IFg4DhaOM5oTAMD2PX50+aNo740UfcyhqjYwlDCxkatlspkDp1fjotNnYdaUcUM1rCFFsFkhKyrCMWXIFjE67TwCISl+vOznluM4XH7Wgfj60VOMfzO3mtHeE4asqKiL57FLhUOwIiop+HBLJ7bs7gMAdAeiaO8JY3uLP+1rmvYEsGFLp9EtztygxcgEDzoOIRvjYkyOt+v+qq1/UPssJ5paA/hwS8dwD2PUMHpWZxAEMaKR0jhaPrcAWVH1BTs5WrkCiQhCfbUb2/cEoKqa0WWr1Iim+EaV12G0+WXjZmMxfpaggUZUkkdVHhhIOMH55oEB/Xb/MQfWD9WQhhzmXMqKOmTvl8vOG+UA86m6MXt6ddK/mVvNaGoNAACm1XtRSuyCFX39MbR1hwHo54TFDjLFDyRFxa6OILxuYYAz7RCs4LjBZ4KNroumixO3w4YJ45xoaiURLEoKuv1685BS5sPLFXKCCYIoCbKspRXBQP75WiYKmJsVGeSq9Hxgi4rSrf43d41j4tdfYL3jdAxVtYGhhInAfCpDlAuC6T0aqvfLZeeNRaO5nOB0CLw1aWFc055+2G1WTKx2l2yMgD7/nW390ABo0HP77Pvcl+bCUNM0oxnGthZ/UmUIQHexi2mdnKnCyrR6r3EhMJYRZRUagJ3kiufF2PmtRhDEkCIpCnhrsmtrNMzI00X1h0S4HTwqXLprPJQNM9it5HQCRO8alyx+S+MEK6OqPBqQuO08pkSwaa5DFV9xOXhjIagwCKEt2FKc4LYAptZVlPzOiUOwQlETcSZ/KGZ8n9Nd3CqqBpZ+6g9Lae8AFdM6OVOFlWn1XvT2x9DbX/rShqMJ9nttJ10Q5MXY+a1GEMSQkjYOEa+6kK+L6g+K8HnscNn1P5xDuTiOuWjpbkX73AL6wyJUVRsQiyiG0ZgJ5q0WWC3cmBLB5pJlQxWHcNp5MGkpDOLcmp1gWVGxq72/5FEIIDF/Jq39QdH4PgfSfK9Taxenq1bhtPODvsDNVGGFzX0siz9ZUaHGr0DIFc+PsfNbjSCIIUUXwcl/mFid2Hy7xvlDInxuIamZwFBhXhiXSqVHgKYB/WHRFIcojQgebXEIjuNgt1kLygSPdswly4bSCWYM1glmgrO5MwhZ0TBtYulFMLtzsU+DD4D+PQiYvhOpi15Z0xxGOhFcTPWXaJpMMABMmuCBhePQ1DZ2xZ95oSSJ4PwYO7/VCIIYUiRZHSCUXHYevNViRAty4Q/FdBFc5AryfMgeh9Ad7O5ADMGIfsvaX4IOckPVfGGosQvWQbmVoxWzEzxU1SHY3Q5gsE6wxXCC2YKwafEqCaWEzf/gGfrCPH8wZnyfZUUzvh8MKf69Glehf4dSM8GA3jxj0HWCM2T57TYrGmvdY3pxHLuwHz/Oie5AbEhqnZcbY+e3GkEQQ4qkDIxDcBwHn1vA1t19eG1Dc9Zi9pqmxeMQApyO4rpK5QP7Y5rOhWMO9u4O/Q8qh4QT3NIZRFtPOOu+d3cE0dE7cJuopIy66hCA7oaOpTiEOSIzlHWCjeMNygm2Gpngpj0BVLhsqPY5cryqcNjndd/GSrgdPPwh/e4Ii0f0BqJJ2zMneN9G3Tk2i32G025NussTkxS8ubEF69bvxuadPcbjm5t6sG79bry5scUQ/FFRhmCzpM0+T633YseeANat342t8XJu+bJ5Z8+Ql2UcatjnYf/JlQDIDc6HsfNbjSCIISVdJhgAJo33YMeeAJ5auxWvftic8fVRUYEoq/C57aaC+kMZh0g0y0iFLejb1aEX359Q5TJu/T687DM8u+7LrPt+4OVP8fzr2wc8PhrjEIDekauuxFUHRjLCXsoEMwZzgWEzOcF7ukOYNN6TVI+3VNRXu+DzCJhSVwGfx67HIUIxTKjSK7ikLkRjEY39p4yD28Eb3QPNuOy2JMG58csuPL56C55Z9yXue1FvyKFpGv70j0/wzLov8fjqLfjPF3rt2/6wBHeGcosHTKtCJCbjmXVf4i/LP8t7juGojD8+txGvbcj8+2k0wD4PLB/NytoRmSERTBBEScgkgi8/60Dce+Vx8HkE9GaJFDCn1ecW4LTrwmOvLIxL5wQzEdyuO8GT/z97bx4kSXqWeT5+x5URWUdWVVZVH9Wt7lbrQkcjltMYaQSLrWwP2EGMmNUa+8cajEwMw4hjzWQSw4IJAQsLA1qwZXZ2sNEsSDYShsQlDhlCSEhIINFSC7VaXdXdVZVVlZmVmZFx+b1/fP5+/rmHe4R7hEdEZtb3M5OpOjMyMiIywr/3e77nfd7zLbhegMOhi5s7fRwO8hVt2/Fx++5g7DZBGMJ2j6cd4l/+9y/D97/h0VU/jKUhKsGLs0NUpwT3Rx5a9ek53LPw2sfP45fe+s2wDA2dpom7XRuHAxf3n2fT68aU4OgxnWpZ+NV/9a14eSrfGGAq+Mjx4QfstmSp+PZXXoTt+HC9AE70vzd+04PQNRXXo2lwN7b7uHg2e0P29S8+h1/7kW/Dt7xik3uHizC0PYRgJzjHGXrtaZNAr68kH1kESySSSvD8IHMcsKoqaNUNlr07wQ5BnttOy4SmqqiZ2kKPJ6kIzircTUND3dL5onj/eea1fPr5ffhBOLE4v7nbR4jxAt7JiXaSHD1Ej+7ChmWIdogZlGBLSIcYjtzKxyWLkMLcaZq4sdNDiPgzMa4EU+qKmqtM02Md2uy2NEKZ1OWh7fFToNNrFi6ebeDGdh9BEOLmbh+XcopggL2uDUsfa9CbBFmjbmz3C//MUYTeD7TRDooN6rynkUWwRCKphDwlmOg0TT4cIAtRCQbYcfGi7RCaqmQW7vQ4qHnuvnNM9SK/4qTinBSr9G3yQv4lRw9RmV2Uci/aIYpMjEtjCDnBA9vnPvpF0mnFn4kLpxswdHW8CPZpEmP+c6pbSc//0PZhGiraDZN/nb7XqOm4dLaF69s93NkfwvUCXN5oTXychq7C88oXwbei0dPHFW7xit6zoayCpyKLYIlEUgmuP54OIbLeMid2K1MUGWULzxOoXwTHDSZO6lqPmuNadQNnopHKX7rKiuDByBuLhiJITUoX8BTyv6jjdUl10GZO1/I3SfOStEPMlg7hegFs14fnBwtVgolOlJoCsIK40zRz7RCTNsTpCMSB7aJu6bw4Htge/+zXLR2XzzWx33Pwlef3ACDTZyxiaCr8ICxsByDrhB+E2DrGPtr0FMwg5xoliZFFsEQiqYRpSnC7aeGw7yLIUSf2+zY0VeGRSmy0qpt52ypwPH+iAteOFOlO0+RpETsHbMH3gzAxrUvkRqQEi55HABjZ0g5xXKCidJFNjFTwqYoCbYYpb6RW08ZyOUWwmfh3p2Vi7zC7CNYnFcGpCMTByEPD0hPFMRXIDYspwQDwmS/fgQLkeoIJug55XrEi0BbGT9Pn9zjClWBDg6LIIrgIsgiWSCSVUMQOEYQhDofZhW03ikcjH2HD0rlncBE4rj9RgSPVqx3lFqcVwTyrxvWd2FcoPn5beoKPDZqqQteUhTYxGroKU1dhGvne2UmQj5i89MuwQ7RbqSK4aeWmQ5RRgoe2x7289N8D2+W3vRwpv//43B7OnapP3ZxQAV7UF2wLTXTXj7EvWOxzUBUlMe5ako0sgiUSSSUUKYKB/KETNC2OqNd0vhAuAmaHyF9MyQ6xHhXm9NjoKDbLF9wbujjoOZm3oSNXaYc4Hhi6trCMYKJu6TMPIeFKcI+U4MWkQ4isR5+BZk2HoWtMCc7JCZ5kjUp7gge2h7qoBNse30DWLR2n1iw+ZvrSFD8wEBfg6RHOedAGtWHpJ0IJNg0NmqogPL725qUhi2CJRDI3QRjCD8IpnmCmrOYlROz3nITnsLHgxji7sB2CPSayRDxymQXRZ/mVaQHlt0kNBACkEnxcMA114ZnOjZo+UzwaECvB1Gy6FDtEKz4dAdjG9nDgJopNmhhXSAlO2SG4J3jkcStUw9KhKApXgy9P8QMDcQFeVgl+6GL7RCjBpq5CURVphyiALIIlknuQIAgr7YL2ChyB0lHqfi+7CO72bV5oAmyhHNo+b0Dz/GDsoh6Gk5/HQd/BzsEwU7V13ADWJDtE9FjEBR8AHo0mYWUV6LSA8tsklGD2b6kEHw9MXV14pnPD0meexEfFM32elmGHaNZ0aGp8KkL/L2Zix+kQE5Rgk4pdVugyO4QBy2ReVmqM0zWF3w8pwItQgqlp9aGLbex2R7npL0c9OcIRNiCqouT2X0hiZBEskdyD/PaffAX/9rf+prL7o4VvUic9t0P0x+0QfhDgcOAm7BANy0AQxpm8P/e+v8P7/+KZxM994sktvP29n8zsAn/yazv41//uE/jx/+tT+N9+81NjBTTzBOcXOZQIcTYaRXumXUPd0nhcWtZCeXOnj4alY/PMuB2CiubaEhQ7yfw0a8bCBlAQ7aaJ5oy/I+0JXoYSrCgKzrRrfDwznZKIG1vuCZ5wLVBVhY9ODqPPeMPSoSoK6wUYeRhG6jD5pWk4x/3nihfBRWPSHNeHpio8+zgrIWLkePjhX/krfDaaXHcUcb2A5zOrsjGuEPJqLJHcg9y6O8CzNw+m+niLUqQZxjI01C0tMyat23cRItl9Tr7a52/38MB5Nnq5nlLmXrjdQ7fvYOT4aNaSv/sfvroDRQG+4fHz+JunbqM/dLHWiO/fiRaMPDbPNPHj//xVeOQ+puq+8ZsfxLe8YpMXLVl2iL1DG6fbtbHGH3oenabJ0y8kR5v/9b996cx+3aK8+Z8+OrO6yJXgJaZDAMDbvufl/DNApyXixtaN8rfVKYkXaw0T3YED1wvg+SGfElm3WC+AH4SJLOVvefkmNk83+ECNSZRWgqNx5rTpGTrZfv+R4+OLV3fxxIvPFbrfZWN78cZeVRU5LKMA8moskdyDDEYePD/EC3d6eOhie+77K1IEAywm7SDDDkE+YfIcAsCDF5gqc22ryxWN/VQBTQW17fh8VCjx1Rf2cPFsE6985Cz+5qnbOOg7ySJ4ihIMAC9+4FT82Bsm2g2T++6y4tsOIktH2vMIANdudXFlsz1TEoBk+VwoUGzNCymqs2AInmBNVWbKGp4F0Y4Qn+4kleAij6XTNHHQc/hnpBF9fikVxguCxFQ9XVPx2P2nMu8rDfcEe8XSZWzHh2Vq/DV13PGf83x2Dbq6dVjoPleBK2Sfq6qCQI5Nnoq0Q0gk9yDDKHXh6la3kvsrWgR3mtkDM0hJEpXgtYaJjfUant3q4upN9jjTBTTd18hJLlphGOLp5/dxZbOduVADxYrgLAydxWdlKcEHfQfrTXPM8zgYedjaHeDK5lrp3yeRZCGmQ9QF28AyafPEl6QneJIVgui0LBz0nXgyXKT6NmosH5zsELNQOiLN9VEzNf6aZinIZK24sd1P5AofJRzP55P6mCd4xQ/oGCCLYInkHmQQxQ9VXgRPWfxyi+BecmQycWWzjWtbXf44e0M3cXxMfsj0orRzMMLhwGFFcKQup6PZ7Cl2iDwURUE9I8M4DEMc9By0WyZUlWXM0m2eu9Xlz0ciqQJ67/aGbkIxXSa6pqKd+ky7nl/IYkXXArIMkfWB2SE8Hps2C7ESXHxYhmlosKLHnVXkUkEdhCGev3001WDHDfhzUBWZDlEEWQRLJPcYQRhiFKkvlRXBBTrCAeYhzMoJ3ud2iGQR/OCFNna7Nv7x+T0+VUuMWMtTgul5PZSjBIdhOHVYxiSyptn1Rx78IOTNQg0h5/jZ6PE8KItgSUWIpxizFotVcGrNSnymXS+APiF6kOg0TQxtjzfVUSFP49IH0QCNWeCeYL+4HaJmaDCi15RSFkR8Py4o6WTqqOF4Pgxuh5CNcUWQRbBEco8xsj2EANYaBm7tDnLjgMpQxg4xcvzEhCaATYtjcVHJxZP8ykPbx6P3sexd0QdMxW/6/q5udWHoKi5tNKNjTjVxZOsHIcIQE3OCJ0ELtQgVAjRkQ8w5vrZ1iHPr9YWnDUjuHcRTjGU1xWVxaq025gkuZodgn5Nbd1msILdDWAaGNqVDzPZ5mSUizTI1/ppm/Zxorbh664gqwV7Ar2kyIq0YsgiWSO4xqHh7xSMbCAFcq+CC7hWISAPiSKWDQdrba4+pwADwwPk1kNXxlS86G93W4T9DjNxkQXr1ZhcPXepA11Q+7U1cqHmo/IyZvfUoxin5HJKWDtbgw27z7FYXVypoQJRICHHDuSo7BACcalvjnuBCdgh2LbgVxZHRc6hbzEbkeMHM2ce0mS4akWZH6RB0MpTdGMfuq9Myj64S7Pq8kGeNcbIInoYsgiUL5e+f3sbtu+OZi5LVQerkqx7dAFCNJaKoEkwqadoSsZ8amUxYpoZLZ5vQVAUvf/gMgNgOIRa1oh3CDwJcu32IRyLlGIiacITfabs0XnQOO0RaCY4eDzULkbdxv2dj79DGlQuyKU5SHbqmcovQau0QTAmmoTauW7QIZp+TrWh9qPPGuFj9nVXhjj3BxRvjLFODprLX1JnQGPfo5XXc2R/id//iq/jAx57BBz72DL509e5Mj7NqWDLH4iLStveH+NsjnJM8C7IIliyUf/8HX8aHP3lt1Q9DIkDq5IUzTXRaJldi5qFoEXwqGkCxsz9KfH33YIRTa9lxUa99/DyeePE5PryCxsSK6pNoh7h9dwjHDfCiy0IRnFKCqZBu1WY7bs22Q7D7pPHQrMvd4000D8giWFIxtIlbqR2ibcHzA/55KKoE04Z4a3fAIt708ecycxFcNh0iUoIB9ppmKcF0X6965CyaNR1/8Xc38Gefu44//vTz+C9/+bWZHmfVJJTgBdghPvb3N/Abv/dFPv3yJCBzgiULxXZ9XN/urfphSARICW7WDLRqRmbUV1mKpkOcP1WHpiq4vhO/J/ojF3uHNi5HwzHSvPGbHuT/btZ0wQ6RXQTf2GEeQ7EJrdM08eVre/y/6T15Ked3TqNhGRl2CDsxape8jTeiUcqXC0y6kkjKYOrMOrBKO8R6tHk96Dlo1gy4XoB2Y3oRvNYwoShsU77WMHjEm/hcZrVD6Bq7rzJKMH1uTV3LVoKjIvjKxTb+3Y98G//6r3/oyUqEhCqwXUEJXkA6RH/Ihhrd3BlUki9/FJBKsGRheH4APwhxc2eQOdZWshqo6G3WDdRrejWNcQXTIXRNxeaZBi8MAfB/iyH8eXRaFrq92BOsKGzBGwnKzfU7PSgKcN/5tcTPDWyPh+ff2OlD11ScO1Uv+AyT1C22UCbi2voO2k2TL+b1SC1+YbuHU2vW2DAPiWRejAz1dNmcbkc+/2hTWnQKpaoqfHiN+PjrFSjBiqJA19RCRTCtU6QEG7oKJ2PIBg3L0NXkczP0Yr9nGYiv/SLSIWituHGChC1ZBEsWBl0YPD/Anb3hih+NhBCLYDHBYB6K2iEAVuyKF1FSZfOUYJFO08R+P7ZDtJsmaqY+pgSfP9VINL2lY9Kub/dw8UwDmjqjJ7g2Pjr5oOdwKwTAFvAwBJ690Z1ZcZZIJkGF26o9wUDs8y8zin1d8M8TVdghgKg4LWCHoH4Cei0tQ4ObEZHGm39Tz83Qiv2eRRPHPi4uHYKud9cFEeO4I4tgycIQfVU3TtCH5rgz5HYIPWrwGh//WxZSWIssfpc3mtjt2oKq0Efd0nFqzZryk5QzHNsh1psWaqaWaIy7vt0bK6g7qclWN7b7cxWmtDiLloiDVHMfHeve2R/icgGVWyIpC1eCV5kOsZZSgv3iQ2jaFCcoPP68f5fF0NVC6RC0TllmrATbWUowt3wlJ/MdFSXY80OEwELTIUgwubEjlWCJZCqir0r6go8OA9uDZWjQNDWyQ8w/ApQfFRbIB710lhWEtDG6vt3DpY1mobGvnaaJbtSJftBz0GmZqJkan/BkOz6294Zj1gpSaA/6juBBnr0wJa9iUgm2+aIOJFWsS2elEiypHlL9VmmHaNYN6JqasEOk1dI8xDhBIuEJnkcJLmiHoA009wTnKMFuTgzkUSmCycKR9ARX+zuGUgmWSIoj1ZsdYwAAIABJREFUleCjiTiJiewQ4ZzeMdcLoGtKoUKWVNrrOz2EYYgb2/3CBWmnacHxAowcHwd9G+2mCcvUeLfyzd0+QoxbK9qCHaKMBzkPWrR5R7wXoD/y+PEukFzApRIsWQQ8HWKFfnOew90r5wkG4s2p+Fmpm3p0v3FhOgtF7RB2KjPcNKZ4gtN2iKNSBFPso6gEV+wJHtgeFIWl63RTWe/HlVJF8Hve8x687nWvw2OPPYann36af/3q1at405vehO/8zu/Em970Jly7dq3Q9yQnG1KCTUOVSvARgk1iiovgIAz5QjArZRa+050aLFPDjTt97B3aGNheYaWUBmrsHdro9l10miZqhsY9wXHqQ7LoXGsYUMDU2hslPMh5pO0QXT72WfAE1+LFfPNMY+bfJZHkQdPB6tbsxWIVdFomDvo2wjCE5xebGAfEm1NR/VVVBTVTQ8PSC22q8yhanNK1o2YI6RA5nmBNVaCmHpOhqQjCcOXN37ESTBFpQFihFByGIQYjD/dHDccnRdgqVQS//vWvx/ve9z5cunQp8fV3vetdePOb34w/+ZM/wZvf/Ga8853vLPQ9ycmGlOAHzq/hzt4wM3tRsnwGtseP8/mx/pzNcW6JhU9VFFw+28SNnR6PMytakNLx6Y2dPoIwxHrLgmXqPB3ixnYfhq7i3Hoy9UHXVLQaBg76Dq7vFPcg59FI2SHSgzKAuFBON+lJJFUR5wSvNnmEcri9gikx4s8B43aORk2fu9mvaBE8SnmC85Rgdto1/rxoOt2q1WCXK8Hs8SgVK8GOx1I0HrncAXByLI6liuAnnngCm5ubia/t7u7iqaeewhvf+EYAwBvf+EY89dRTuHv37sTvrYogCLF3aE+/oWRuSAm+stlm2YK7J2PneNwZ2EklGMDcMWmu5xde+ACWz/vCnR6e/Npu9N8F7RCR0ko/12masAQl+MZ2DxfPNKGq4wpSp2lha6ePZ292C3uQ86AFmjYP1Bm/LniCaYMhkyEki8LUVSgAaitXgtnoZIenxBR7PPFgmWQR37D0uZv9ikak8cY4rgSruUqwro1fM/hgjhUXwXZKCdYUBX6FSjCtEZtnmmjVjUqV4IOenYibXCZze4K3trZw/vx5aBp7A2mahnPnzmFra2vi91bF557exk/8xqcwGM3fES+ZDF1IrkRDC27uyCL4KJC2QwCYe2AGa4YpvhA/cKGN/sjDn33uOs52amjViylZp1oWNFXBJ55k15AznVoiHeLm7gAXc6wVG+s1PH39AM/dOsSD5+eb3maZGlRFQT+6jpAS3GkmI9IsU8NDmycjVF5y9Og0LZxqW2NH9Mt/HCZ6Q5cXSkU3xGc7NSgKcDp1KnO6XcPpnAmSRSkbkTZ9WEaYowQfjSI4rQSzdIjq7p82/A1Lx8UzDWxVJGoFYYh3/Nan8bG/v1HJ/ZXlWEyMO3OmuqYS7x/vwPMDWA0LG2dWr9BsbJycUarp51J7fh8A8OKHzwIAvFA5ts/3uD7uLIaOj9ORXeDiBVagGZYx13NUNRV1Sy98H9/9+kfx+MNn4fsBNs+2sHG6uGf2V37027F3OELd0vHo/afw1PP7cFwfZ860cNB3cPnCGn8c4uP5N//iCVy9eQAFCh65f33uZqL1NROOH2JjYw1eyLy/Dz1wOrFQ/vqPvQ6n21ZhZawIJ+m9mIV8fsV5yxtfiu/5p4/iTGe2oS9VcTm6jgQqe5+fXm8Uep4bG2v4jZ94PS6kTm9+/C1fD0VREvaisrQaJoa2P/VxGFEj3sXNDtYaJjrtGlwv+XMbG2vQDQ2WqY3d3+lT7Nq11qlj4+zqGmCf22FT685ttLCxsYZazYCq2ROff5n34m6fbfg3z6/hzKkGrt/pVfJetl0f/ZEHN1jNZ3/uInhzcxO3b9+G7/vQNA2+7+POnTvY3NxEGIa53yvD7m6vsry7g4MRAGDrdhfaio3sGxtr2N4+XOljqIqs57K7xz6Uvu3CMjTcvHN4LJ/vSfo7hWGI/tCFEnnF7CFTMLfuHGJ7jmP7/sCFApR6nS5Ek6bg+6V+rqEraEST3nZ2evA9H44X4GvP7SIIQhgKexxZf7dL0c/1D0foH44K/84sWnUDt3f72N4+xM07h2jVDezdTaojKoD9vepGqp6k92IW8vnNxipfs42NNaghW0ufeY7ZlEZDp/BjMsDW+Cy2B7NbFwM/wNB2pz4OWqd63SFGfRu+68PzQ9y6fQBNVfnfrNe3oSjK2P2NopSE23cOYVScxlCG7Si7d9Czsb19CNf14br519ay78Wbt7sAAGfkQlOAw75dyfuOTtO6h6O5709VldKi6dx2iDNnzuDxxx/HRz7yEQDARz7yETz++OM4ffr0xO+tCvKdrPro4l5AjJ7pNE3s96QXe9U4boAgDGM7RKSGVuIJzvDLLQPq6r59ly1mYkLDIlmPfJDA+KAMieRegmxAO/tsY1mmP2BRFE6HcH1oqsJPcIzIU5v2BbtekHmNo59bdU1BzXyGkA5R5bAM0Q7RsKrJlwfi121VU/dKKcE/8zM/g49+9KPY2dnBD/zAD2B9fR1/8Ad/gJ/6qZ/CT/7kT+K9730v2u023vOe9/CfmfS9VUBFsEwqWDziKN1Oy+QxUpLVQd7fOs8JZgVkFekQtRUlIFBXN43mXlYx2m6aeP42Uy5kESy5l6H3/k500lp0YtwiKZwO4fi8KQ6IPbWuF6Au7KePuifYyfAEV9kYR2tHo8aaFm3Xj5oF5/tb0+vmH4ci+B3veAfe8Y53jH394Ycfxgc+8IHMn5n0vVVAbwo7o/tTUi200TB0FZ2myeOwJKuDGkJJCTZ0DbqmVtIYt1ZfTRHIi+D9qAhuLedxsOl1LoJoet35+9aX8nslkqNGmxfB7DN4JJTggukQtuvzawgQpyukhTLPz56Ex4vgFRVxhJjLD7B0iHmHIInQ2lG34vi6oe1hrTHf9ZYrwd5qrCSrf6cumdgOIZXgReO4bICCqijoNOOjY8nqoCMsMX6oUdMrsEMUH5VaNTWDPRduh1iSIrveshCEIQ4HLg76diIeTSK5lzB0Fc2azpXgVV0LRApPjHP8xGQ6UlLtVAHt+gH0jOjFo6MER/bDRE5wdfc/tJltxNTVyqI1gfh1O7YRaccNGn2YlQMoqRbH8/mxWKdlYmB70oayYgZ2vJsn6tHo5HlgfrnVXE5EO4RlaqiZywm9oWL71m4fnh9KO4TknqbTsnC3y/o+VnUtEKGc4GlqqO36iWE2pKSmhTI/TwmOnqu36iLYC6AAPMtYVZRqPcG2h0aNTfFLDwuaB9qoyCJ4SZDvJGsijKRaHDfgFxcqEKQveLWIzQ1Ew9Lnt0P4xccmVw2pOLf3h0stROkI+PnbrCu7LZVgyT1Mp2nyCWVHwg4RPQYSvvKwHT/Rz0BK6nhjXJhZ3B8lJdg0ND4ESK14Ytxg5I7ny88pngCrb4xb/Tt1yUgleHmklWAgHiogWQ1xc0Ockduoza8Ee97qi2Db8bG+xCKY7A/UHLfeXE4qhURyFBG9+EerCJ681o/yPMFehid4UhG8Yk+wm7oGV54OYXv8BDE9MXMeuB1iRZuI1b9Tl4wXSCV4WSSVYFYg7Au+4A9+/Fn83l89u5LHtgg++PFn8V/+8murfhiZ3Lo7wL/59b/G+//iGQBxKgT7d9IT/Jkv38Yvvf/ziZ8f2h7+9//4t7zgS7NSO4Sg4rSXFI8GxErwc5ESvKyGPInkKCKewlQ5HGZWiiq0jpudDpEWyvKKYP1IKcFCEVyxEjyM7BBA3FNSjSeY1WLTFPtFce8VwVIJXhpuhhLc7cdZwV+6ehdffm5vJY9tEXzhmR18/pmdVT+MTLZ2+9g7tPHKR87i+9/waGKRqqfsEJ9+6ja++OxdjJz4a7sHI1zdOsS1W+NFsO2yYRXN+moGUIpNLcu0Q9RMNhaZxodKT7DkXkYcGX4UPMFGwfxeO10E5yjBzPKV0Rh3RHKCXT8pRDBPcHX3Pxh5gh2CnSRW4glecWPcsRibXCUej+OQRfCisb1YCV5rGFCQtEO4no8wXP3FsioO+k6lx09V4kebv//mGx/EfeeSE3XSdoirW2wyULfv8CazUdTQOHLGT1Dob9pZkR1AbGpZdkLDetPE7b0hDF1NNBtKJPcaR9UOMc2mwE4s48dLPzemBHtT7BArPl32gxCatjglWLRD1CwNCiq2Q0hP8HIgO4Qt7RALx3Vjj5KmqlhrGIki2HGDE7MZCYIQhwMHvaG7sg/zJCgfW8uI+GlYOjw/gOv52Du0uWVFtK6QKiyqw0Q3ut2q7AC6pvLFqb1kNZbU307T5A0pEsm9CH0WxISCVVLYDuGl0yHiYRkiecMyNFWBoqzeE+z7YeL6XnU6hGiHUBUFtQoaqoH4dXOlHWI5+NwOIYvgRZO+uHRayaxg2/NPTBHcHTigTfdRTMCgwjxrcYrjbnyuAgPJ52FHCrCdqQQzi8sq7QBkiVi2Gk0eZGmFkNzr0GfA0NUjsSEsUgSHYciUYEG5trgnuFhjnKIohafTLRIvCBLXd1VFZUqw5wdw3GAsVajSnGDZGLcc4mEZJ6P4Oso4rp+4uHSaJi+Y2PeDle+eq0Is7o9iAkasBI9/5ONOXzdRBIvPg2wQo4zN4z5XgleXjkBF8CrsEMBqn7tEchSgz8BRsEIA4AXrJJsC1QGiWGNwT3C8NgVhCD8IcxXuotPpFglTgpOe4DBEJVPjSPEVLV9VpAoB0g6xdGRj3PJwBE8wQEWwaIc4OUqw+LyO4mQ8KoIzlWArDj6/utXFfedaUBUF+714w2K7k5RgB4oCrNWNse8tC8tM5lEvC7KASCVYcq/TrOnQVOVITIsDinmC+ahhPVk86pqaUIJpvkBegX8UlGA/SNkhon9XoQYPKV++Vm2+PCCL4KUjh2Usj/QxE9khwjCE5wfwg3DlF46qOBAKRlHtPirQBUbLOM7jcTcjD9e2DvHQxTbazaR/e5Idotu30W6a/KK7CmqGxgrxOefYl6XdlEWwRAIwW0CnZR6JZAigmB2Cjxo2kpFupq4mhDLXIxFhQhG8ck9wyg4RWVKqSIjg+fJWLHTUq7JDcE+wLIKXghdIJXiRDG0P17d7zGvlJXMLO00TfhCiP/LiKTEnpQjuz2+H6A1dHrdVNeSFz2qMoyOuv3nqNga2hyubbXSaVsITPM0Oseoi0DI1tBvLL8TXyRMsM4IlEnSa1pGxQ/Bxxn6I524dZm7gbV4EJx+zaagJoSzuqcgrgrWVr2VeRjoEUI0SHA9ZWpwdwpeNccvBk0rwQvnzz13Hz/725+B4AcIwDh4HgLUm20V2+w7fgTOv1fEvhA/6DuqWjmZNn9kO8aGPP4tffv8XKn5kDHqNs+wQ6y0Lmqrgk1+8BQXAo/eto9MyS9khVhWPRmys13Fpo7n033vhdAOaquDS2db0G0skJ5xLG02c6dRW/TAAxEpwt+/gZ377s/joZ18Yuw2JYZaeVoK1hCfYm2aH0NSVNXYRWekQQDVT48gOIWayV22H8IOw0ki3otxzwZbcDiGV4IUwsD3Yro+73RGApNeqTpmzjp/wjbleAM083vuxg56N9ZYJVVFmVoJ3DkaJWLIqiZXg8de5VTfwi2/9ZgxGLmqmjlNrFjpNE88J0+EmRqT1nbHs4WXz/W94tJIGkLJsrNfxq//qW2VGsEQC4C3f+RhW8DHMhAYCXdvqwg/CzGmXJIaN2SGMpCeY28lyTpqOhB0iCJJKcPRQq7gu0uskDhWpWzpGtocgDHnBPQti46KX6iNaBvfclZsa41YdbH1SoV3n9n5UBAtvaNpF2o6XOH5yvQC1Y36azNRQlhU7qyf4oG/zvN6qx456QQgFyLULdJpmwtLQaZk47LsIghCqqsR2iJQSHIQhuv3V2yHyjimXgSyAJRLGKj+HaUi1vRpNuby+PW41IzEsbYcwUkowZdge6cY4P4Se0RjnV6AEOxkpGo2ajhDAyPYTNomyuCnFfdlF8NF5xy4J2tHZUgleCPSB2zkYAkheNKiDf+T6qaaD4/+3OOg7aDdNdFrmzHYIUpD7Ffis0qRVgml0mhaCMMTh0AUgNMalPMG9oQs/CFdeBEskEokIeYK3dljxe2dvMJb9yxvjUqKDZahw3aRCCUxpjFt1ERwEOekQ8983rdfieh6nCrlz3bebsdlYJvdeERyQEnz8C6+jCBXBd/ZYEWwllGD2obEdP6HEn4S/xUHfwXrLwnrTwkHfKX0EFQQhb0Srotkgje+H0EpMcaKillIv8jzB3SOQESyRSCRpdJ1d70KwKXZhCGztDhK3iRXOcSXYzvAE5xbB2urtEJ6faoyr0BPscjuEUATTkKU51yvxdVuFr/qeK4JjT7C/Eg/hSSeIGrC29zOUYCNWgu0TpASPHA+246PTNNFumnC9AEO7nN3mcOhyL10VzQZp0kdl06C0AyrMyQbheEHiorp/BKbFSSQSSRpNVXkh+Oh96wCA69u9xG1ICbYyPMHpY3oAMPKGZRwJJTgpdJASXEWdY7tBNA5b6PGJlOB5Y9KyXudlck8VwSyflnVQhlhdOPNJhhqwdg4meYL9xLHUqnfQ80I2BrJDsK+V8wWLOcMLUYLL2iEiZZca9UQbhPjvA64EyyJYIpEcLUiEefWjG9A1FTdSvmBSgtNeX1PXMteovEEg+pEoggPoQuMz9apVpQQbRnIcNleCZRF8fKCjevrjOcdcgTyK+CE1xjEl2MxQgm3HT0TUrfriMS9UCDI7hJn4WuH7EBIl5vVYZeGl4nOm0Wkki3mxIU78NynFUgmWSCRHDSpu7z/fwsUzjVwleGo6RJFhGavOCU5Z3kgF96tIh3CDMd809wRXYIegx+pJT/BioV0GyfgyJq16aNdJhZJ4cVFVBaauYuScrMa4A6EQbEcKatmYNLFoXpgSXKIItkwNNVPjz2Pk+GhFY5HFmLT9nhPdViYkSCSSowUVwZc2Wri00RorgulUK1MJPmae4HROMP27CiU4PfgKABo1th5UoQTXLVYnrOI1vMeK4EgJpiJYxqRVTnrqi5m6uFimFqVDnCQlmKml7ZY51lBW+D76i7ZDhKXjizpNk4+5th2fWx4Sdoi+LVVgiURyJDE0FZ2WiVbdwOWNJvZ7DnrD+KTN8QKYujqWc8uU4KwiON8TvMphGUHIBk3omRPj5r//LCWY7I3DeZVgL+DCpGyMq5h//5Gn8L4/fZr/ty+V4IWTziRMHzNZhgbb8RKdt5N2f7/1kafwO3/+1WofZMXs9WxoqoJW3UCzpkPXyg/MYBPnNBi6upDGuPRRWRE6LZZ04fkBgjCOQbNTdghZBEskkqNIzdRw3wYb5HMp+v8bghrsuH5mLq2pqwjCkAs0Lm+My7dD+MHip5/+5u9/Ce//i2fGvh4PQxq3Q4SVeIKDMUFL11RYhjZ1vfqp//AZfPwLNyfeNwmTq/AEn+gzzOdu9xILP1eCa1IJXhTpsYfpI5SaqUd2iPi1T2c3ijx/u4d2NG75qPLC7R42zzT5RadZM9AflfP1HvQctJsWRra3oIi0cnYIgCnBz9/pcWsLFbuiJ3i/5+DyiqfFSSQSSRb/83e9mCuWD5xn16mrW4d47P5TACKF0xgvbFtRTwSpxlQ75DXGkZ3C80JoC9QEnrt1iIPW+CkjFd9ivUNNbFWMIrZzNgtrDQPdQb7g43oBnr/dwwt3ehNvQ8LkKuwQJ7oIdjwf7kg40gikErxo/NSbOL17rJka7PSwjAlvfNcPVj6TfRJhGOLqVheveWyDf61R00sPvKCJc6qyoIi0me0QNld+KTEiaYdw8FKpBEskkiPIlc02/3enZeFM28K1W13+Ncfzx475AcQNzpFNbeqwjOjrrh/AwuImnjmen7m28CJdFe0Q7P+rKIJdL8icljdtONRglBy2lHff3A4hG+OqxXF9dPsOfxOkPcFydHL1iHaIdK4gwDzBlA5ByuQkT7Dn+SuZIlOU7f0h+iMvcbFt1ozSai4VwY2azi8cVeLNogS3TIwcHwfRTn89pQQ7ro+h7Uk7hEQiORY8uNnG1S2hCM5RgttR/wNFRHoF7BDA4vtbHDfIXFto3RWVYK3KscmuP5alDIAPh8qDBJ1RzmmvHzCrXX2FdogTXQS7XgA/CNGPjjRIpeR2CKkEV44fhGhGr286VxAAaobG7RDNKG1gktLresGRznN+NrqgikUwK2RLFsE9G52WiYZVvoAugh+Ui0gD2OhkANiOpv/RwkC7ep6KITOCJRLJMeChzTa290c4jDb2LPVgvLijjT1FQNJpZV5fRVwEL1ZYczw/M0KTaptsT/D8v9f28jcLk5rASbXOU4Jp09CQjXGLgaaSHfDdnEyHWDRBEOJ0uwZgfB47EKVDOD4cL+DF8qTds3PEi+BrW4cwdRUXzzb515gdoriaazs+RtHEuUZNPzp2iKi4vR0VwZQdTBFpcTScHJkskUiOPg9GYsW1W4cAmBBmZR3zR9c0KvA8P4QC5AoJRrTWLVIJDsMQjsumkaZjz7xgPMdYUavzBLuen22HaJroj7zc5x3bIbLXNPq5eo29flIJrpAgCPkLSou1zAlePH4QRt5WJXPnaAme4JqpQ1OVyZ5gLzjSEWpXt7q4//xa4uLTtIxSoyTJbtBpWmhY5VXkIsxkh2hSETwAADTrBnRN4UdbfFqctENIJJJjwIMX1qAAuHqTneA5rs8LWBFDV9Gs6dgXagddHz/Z5LcXPMGLQlwH00LJJCW4kpxgN8hUzNejPpFujiWC1rI8OwQvgnljnPQEV4ao8pK5fdwOIZXgqiHFca1pZCrBNTO2Q5i6OnHSThCE8IXNzFHDDwI8d+swYYUAYjtE0R14l0+cY0rw0PYqmfeefKxhqbHJQNwIdydSgmumFkXckR2Cfa7WpR1CIpEcA+qWjgtnGtwXnHfMDwDtpsmvzZ4XTDxJW4YnWBzeke4biT3BQmMcjU2uYmKc5481uQPsNQLyh0NNtUOkhEmpBFeIqPKSYuWO2SGOZnE1D8/dOsTWbn/6DRdEELA82k7TzPzQ1AwNnh9gaHswDW1iEUwfkFV0jBbh5s4AjhfgyuZa4uuNmo4QwChDDX7+9iFu7CT/Pvs0bCOyQ/hBmEhgELmzP8TXbhyUfqy+H+YGveexVjegKMDtu0wJtkydpXs4sRKsKMBaQxbBEonkePBQ1BzH7AXZnmCAqZziKbIx4fqZLoJdL8DnvnKnUjFDFO3SCRE+T4cQG+PYY5pVCX76hX3sHAy5DSNL1OqkUjTSULE+muIJrpvSE1w5TirGCYiVYMvUoCgn0xP8m7//JXzgY19b2e/3ogasF13q4L7z4/mxVvRmPxy6MI3JSjB9/agqwTejYjadk0snDVlRNv/pT5/G76aGfxxGjZtrDXPqPPbf/8RV/N8fear0Y53FDqGqCtoNkz+PmqHBMvXYDtF3sNYw+WQiiUQiOercd34N3YGL3tBlqQcZxR0QRURGxZ0b2SHySBfB//C1Hfz6h76Ird1BZY9bFEbS64OXlRM8Z0Tar33wSfzhp57j62+WYk52iLyYtGnpEPR6UU3mLXjYSBYnNidYnEjGd3OCedzUtRPnCXZcH7fvDtCqr264RBCwYutffMdjmd+n4PJu34GpazA0NXczctSLYPqAr6Ve7ybNVM8oZIe2NzZampTVmqkl5rGfzvqdI6+U35hg6RDl97ydlomDvgMFLO0jqQTLkckSieR4cWotLtycKXYIGhvv+ZMbi7knOFqzSPmssr9DrFfGPcE0MU60Q5AnuPzvcj0fvSHbKFDAQJYSvNZg69U0O4Tj+AjCcGw8Nb1ehq7C0FR4nvQEV0ZCCeYdnnHgtWmoJ84OcXO3jxCLGbZQlCAIJyqDlDXoByGsaUqwYIeo2iNbBXTUQ34mIlZzxxMiXDcYszpQ2oJlalOV4JHjzfS+ZV7t8ootdUmbpgZVUWAZWkIJlvFoEonkOEEb9/2ezcYBT7BDOB6z7hX2BPtJ4WbkVlgEe6IdIuUJ5rVNRmPcDGsnFbUDO05+MDI2C7qmolU3cotgWsdCsLUvDb9vTYWuqSuZGHfii+BmTR9Lh9BVBaauwj1hjXE3ttnx/CKGLRTFm5JHa5nxBYd7gnPe+GJxfBR9wQPbg64pY9Exk+wQtuePxcXYURC5qij8Z/M2MixZwy+9KWBjk2dQgqMFgxT8hBIcDfiQSCSS4wJt3Lf3WcNvnhJM17a9Q5ulQ5TwBNN6NWlSWlkSSnDaExxkKMHq7OkQvAgeebyWyrONrE/IChZrkSxLhKgE67oqG+OqhNSyjfU696vwIwNNhWloCcvESeD6NpvPPbRXV9wHU47da4ZYBNMRSPbfwUsUwUfvbzW0fTQsfSw2pylYGtK4bjDWJGA7Pt8c8CI4ZyMzcnyEYfkpQF7UsFgWWjDo78ZynlnyRbfvyIxgiURyrKDidnt/BCD7mB+IhwPd7Y6ixrj8dU3PaIwD8hvCZkFUgsc9weMT4+ZJh6BUjIEdnzxm5QQD5J2erAQD2VnBJIAxO4QiG+OqhHYvG+v1SNL3BTsEU+9OqhJsu/7Kikbfn2yHqFlCEaxPSYcQvr6KY5JpDEYu6rVx/3VcyI5/6B3Pz7BD+LzInGaHoJ8tG+/n+zMWwdGCQUV6LYpI6w9dlgkt7RASieQYUTN1WIaG7YPJSjCNid/v2nBLeoJp/c1L+ZmFpBKcbYdI5ATPoQRTPvLQjpXgPNtIu2nlNsb1Rx5fO7I2BDRhz9ClHaJy6A2zsV4HwEzwdEShR0rwSfMEX9/ugT4CszRPVYEfTrFDGGk7hJZbBIs733Qz2VFgYHtoWNlZyKqijPm22AAX+l/8nEeCEkz+4lw7RHQhKfvendkOEXX/JpRg1xemxckiWCKRHC86TZOPg7dyiju69u0djviwjDzyPMHV2iHignEsIi1+C8JUAAAgAElEQVQrJ3iOiXFkb0jYIfI2C1HzdJZFb2C7OB01ImZtCGjtN3UNuqauxPZ4Yotg2yMlmI3wPeg78INYCTZ19UQNy+gNXez3HFzaYHFdq2qOC6Z4gmtm3ETGh2UU8AQfRSV4OPK4ciuiRN7e9N9ALOrFC4LtxkWwrqmwDG1CY1x5JTgIQoTAjI1xpASz52kZLFVl/9BOfF8ikUiOC52WiTuRJzjvmL9ZYxNN73ZH8LzJdghNVaAo457gYaV2CHbfp1rWmBIs9jsR86RD0AQ4PwjRi9airMl6AFsDPD8YW++CIMTQ9nkRnK0EC55gTXqCK8VNK8H9WAnWKCLtBCnBNyI/8CP3dQCsUAmeYoew0p7ggnaIVXiFpjGwvUw7BBBPjRMR328jwbc9cnzeeMZ/NstP7AV8x1/mvUubv7I5wUDsCeZ2iKgYpgWEciIlEonkuNBpmrwoyzvmVxQFnZaJvUOb5QRPEBEUhVksvbQdYgFK8HrLzI9IqygdYl+wN9Awp9wouWiNSFsi6DGebjMhMuu14J5gTYWuK7IInpcwDHnclOPFnmCAimCWYasqCotIW5IS7PnBQscpAsD1yA/8yGVWBFeZT1iUMAwRTLNDmPFbjjzBeQWdqP4u8sPh+QG2dvvY2u3zgrEIeXYIgHl703YI8f02SinBYsNgVgFNt4vvq/jj9DIyJIuSlQ4BANe2DgHEYzMlEonkuCA29OalHrDbmbh9dwDb9Sd6ggFWyPHGOO4Jrm4dpuv/+pqVb4fISoeYIyINiIvbrAmwALAevZbp5jhSq6kIFpVgP2BRofR66boysUl+kZyoYRlfvHoXv/bBJ/GL//KbYLsBFAU43baggHlcxOagZSrB//GP/hHdgYt//b1ft7DfsbXbR93ScPFME8BqiuD4g5hfBGtqrP6ahpa4cKRZlh3iP/zhl/GpL90GAPzXr70f3/u6FxX6OWaHyFaCmzUdw9TfQHw+4q7YdrxEdFzD0jPTIUZCd22ZDZzPh8SUV4Jrpo5W3eCh6DSI5RNPbqFu6QkFWyKRSI4DYkNvnsIJsALuc1/ZBgDUMqxvIrquwvXZdZmKuWrTIQIYuopmzRhPh6DGOK2axrhu30a7YaA7cAUlOK8xzox+JlsJPpXhCf7Qx6/ic1+5gydefA6aqkBTmR2iX+GmoSgnqgi+szeE6wW427X5THBNVVGzdL5z0qOdUp7atgiu7/RxOMjunqyKw4GLdtOaGM+1aIIMc34WNZM1w00dlrEkO8RXXtjHY/et4/beAHcPR4V+xvMDOF6Aei37I9SoGdjtJrMTRfVWjIth6RDx/TQsHXsZuYti4VzKDsEvkLMd/PzE978a69Gi8XUvOou3/g8vh+v7OH+qMRYPJ5FIJEcdsZchr7gDgH/++kfwT564H4eHI7z4gVMT71MUdBZlhzB1ldcuYRjy6y8XOhIT49j/ly2CwzDEQd/BQxc76A72eZNcnhJMFse0MEM11+k2eYLjNe+pa3dxe2+I23tD3nC4Kk/wiSqCaadB2XZW9OI2a0xZswyNq2ENS+dRYtOOOebloGfjcOAm3rRVw47mdR7PtQpPMH0Q06MR01iGhkO4fFiG5weZr80yhmUc9Gzc7dr4jifuw6eeul14506bjKzGOCB+z4mIjXGiHUJMhwDYBu3GTn/sPkcJO0Txi2tsh5jtvXfpbJP/29BVvOaxjZnuRyKRSI4CCSV4QurD6XYNjz28ge3tw6n3KQo6Lg3LqDgizTQ0NGo6gjDEyPF5mpCflRPM7RDlfk9/5MHzQ2yeaeDpF/Z5XFreZiGdjEGQyLjetKAqCn8tXC/AC3dYD9PTL+zzhkNdk57guYnndbtcCQZi1dfzQ66GTZvMVRVsqADLVM2aIFYVg5GLZk2HZWpQcHTtEEDsK6V0CCDb87sMT/DVyNt65WIbdWEa2jTI6pBXBNdr7PRBjI1JKsHRsZnPmt2SdggjcxMjPrYyHnMxFUUikUjudURP8CQluAxiEUynb1UPyzANLT7tFdZ4cQYCEadDlKuCydu7ebrB/rvnQFHy1/X0tDyCRKBGVJfQa/HCnR6vFbp9h/+8PuFUeJGcqCKYioR+lG1nCgMI+rYHL4g7PLPeSIugN3S5MT1vtGAV9EceGjUdqqKgbmWnCyyaIGM3mgUVfJQTDGQXdUkleDEfjme3ulAVBfefX4NlaKWV4Dw7RLNmwA/ChG0hqzGOfl9WY1y6oSHhI/bKe4JnaYyTSCSSk4aoBOfl35ZFjPt0F1EEu+x0m4QXsfGa0iHEU1j6d1Z+7yS6UZ2yGZ0A9obs1DbvFJuK2LRFj2qrRo31jtBrcXWrCyD2ChuCHaLsJNQqOFGrInlOBiNmhzC5HcLgSrCeVoIXXAR3hdiQvNGCVTAYebywX6bfWYTbIaYpwYZYBGfvIoGkD3hRjXFXt7q4tNGEZWh8EEQRptkhst5fWRFpVNimI9JCJGPUgOQF1Z0pHUIqwRKJRLLWMKAAUIDK7JAJT7C3gIlxXAketzz6UT6/WKiS5lE2HYLqlLOdGl8zJllG0hnJRH/kQVMVtrYa8Snr1a0u2k0Tr3zkLIC4CJ7UJL9ITlQRHHuCs+wQbmJqVlykjHfhV8l+P1Z/80YLzksYhhhESjDACrPVeIKjBqxpnuAoa9bU1bFxkyKih9bzqt8hhmGIa1tdXNlcA8AK0az55lkMhV1uFlm7dScViwbEinA6HQJg72ORhCe4lBJMR2Un6uMukUgkM6GpKtYaxkSFsyzi9FMSHqpWgk1DRSMSu/opO0T6+k5iVFl1lTKCO02Lr2/mhBi5dEYyMRi5aNR0KIoCy9T4mnd1q4uHNtt4aLMd3fdqG+NO1Ko4EuwQtpunBEeNcRlvpEVwsAQleOT4CMKQv2GPuh2Ce4INNfcoBWCFMd3TIj4cd/aH6I88XIk+jJahF965T2+MG7fbiM+RdsV5SnD6Z8XbAuVygrOC1CUSieReptOycqfFzUKWHcJ2/NJ2hDxYOoTG14eEHSJjUuusnmDy6dYtja9vk2LkgGwVl5r1AaAe2SEGIw+3dge4srmGB6N1lzfGyWEZ80NFwnDk8aMDgPk2HS/AyPH4bqm5JCWYsvM0VcFBfzGeYCqWjosdwjI1ng04yQ7h+gHvfq3KDvHxL9zkuYfkTeJFcPRBFS9a2/tDfOxzL4zdD72+9Sl2iD/+9PP48F9fRRCGXL01dVXwBLP7ESfpcSU49Tek27IBI2XSIWafGCeRSCQnkU7TrMwPDCQb4+iaG4RhZYWd7TElmNshRik7RErkUBQFCsqnQxz0bXSaJhRFKaQEA7QBGI9II7GR7BDP3eoiBFtzN083UDO1hB3C88PKNg1FOVFFMBUWcWNcsuDtDtxERBqw+HSIg74Dy9Bwum0tzA6RViVXZ4cYzyrM4tHL6/i6FyX9QFlFruvFRXAVF5LByMX/+0f/iL9+cgsAsLUzgKIAF6MGgJqpIQyTBfkn/mELv/Sf/w5uqugc2B4UBbnDIs6fquPceh1PPXcXH/qrq7h9d8B9vGsNk9suYiVYyAnOyXq2XZ/7q8qNTaZhGSfq4y6RSCQz8/KHzuClV85Udn/JnOC4kKvKEkFKcM1iDfCHw1jAy4t6VVWldFHZG3p8KFK9qBKckexgOz5fH6nf5rnbLBrtgQtrUFUF3/zyTTx6P8tfpuSuRcWh5nGycoITEWkB373Qbqbbd7DRYSP8TENjE0oWrJju99iuqt00F2aHEKNIAKZ8r9IOMU0J/oaXnMc3vOQ8AEz0BLMimP0Nq/hgUOFIm5GDvoN2w+QXD/rAjpz4FIGK74O+g7OdOr8vNi1Oz/WTNWoGfu4HvxH/8LVd/J8f+AIGIw+250PXWHoHXRgzPcET7BCWybKuZ5kYJ5VgiUQiYbzh6++r9P4SSnBk5QvBrttrjfnvn4Q9VVHQbhqJesL3x+0QAFODy9oh0slawOTGOCDphyY84SSX+m1ubPfQaZlYa7B0ju9/w6PxffAiOKjUpjKNEyUNpYdl0O6FRtsObS+xW8oaaFA13b6DTstEZ4FFcD9th7B0jOzxiK1FU9QOITLRDuEFXCGtYmIc3QeFfx9EGxSCLAliAxoV32kVf2C7uVYIETGP2o02ZjWhSWCiJzi1kRm5PmqGxkZ+l0qHGB+pKZFIJJLqED3Bnh/w63hlSrAX8OK007QSY4r9IMgsgjVVKd0Yl6idyA4xJUs5yxMs+pSp3+b6dh+XheFLInRKv2xf8IkqgskzSXYIKmqaQge/WAgswzt70HfQaZrotMyF5QQPUkkFDYsitparBsd2iIqK4GhHWFXXKF2gKAfxoO+gLeRFUiEqNqB5QawEi4hpHJMQ/b2O58Mw1ERwOP2/6Amum9l+9ZHNlGCzZKg4NcZNs6lIJBKJZDbEhATPD7iloGjs5iSCMIQrxL52WibvbQHYNT7bDlE+Is3x/PgUPRIQpymzhqGOWRqZRSMqgk0NI9vHzd0+Lm20Mu+Dxicv2w5xYlbFMBojCAD9IZvQRm8YsVgR3yiNaKrXIjnoOeg0LXSaJvojbyE5eFQsNQU7BLB4v3MampIzkxLsj18oXI8VwYauVNIYx1XdfmyHSCjBGUUwPaf0BmYodL5OgtTiIR/lraEmZCaSIiwWwaqqoG5p43YINyqCDa1U/mTWSE2JRCKRVIcRDXvwgwCuUAQXnUI6CeonsbgSnDxZzkqHAFhCRFhy6XTdWAkmO+LMSrBgNQzB1vRLG9lKMLdGSiV4NlwvQBiyooMWfZMrwQa/nahSUnTaonBcHwPbY3aIFpuO0l2AJaI/8qAAqPHGuOVMw0sTzOA9nTYsI1aC598dkpq833eicdYO1lvx+MyaER1fuWL+YrJwJga2V9oO4bhBhhLsMZ9X6jVrWMa4HcJhdoisJoRJyHQIiUQiWSy0lnleCM8L0SQluIIimCcLURHcMnHYd/ma6wVBpsihKEppJdgWleCodpruCVbHmrVFn7Jo97ucpwSTJ3jJAzNOTBFMRw6n2+MzwRsJO0RaCV6cJ5gK3k7T5IrjInzBgxEryCgXkJ7vshMi/LD8eF4am5z1xnc8Py6Cq/AEC9mNdw9G8IMQ7SlKsNgYJzKwi9khTF2FpirMDhF191opT3AtY5edZdUhJdgytJka42Q6hEQikSwGOs63XZbbT+Kb7c6/DlMPCLdDNC0EYcgTInw/TNQ2hKqWL4IdYcZCnBM8PSItvUZ7gWCHiH5eQZzGlEZ6gueECpfTazX+NXESCcn7urBbalrGQgtFKpyYEhwVwQvwBQ9sN1GQ5eXMLho+v7yMEqxNHpZhaGqUH1hdYxwAvHCHRbVk2SFGCTtEdmMcs0MYmAZlLVKzpmWoiTnqo6iwTdPIGHhiOx7PVZwlIk0qwRKJRLIYSAkeRr1JVdohxpTgZrKe8P0gsxdHVcoPy3BTMxaAYkrwmB3CD7kgRkrwxql6wvonoks7xHzwIjhDCQZiS4SohtUjtW1RKQri+MFOkz2uRSjB/ZGXsHysyhNctR2CPMG6XlVjXPx3fj4qghN2iIwiOFaC481LEIQY2j73S02jHuU2O64PI/IEe34Azw9Y7JkxrihnKcHMDqHDNGYcliGVYIlEIlkIJOiQsNZqVNcYx5VgI26MA+LTZi/HE6yp5SLSgiCE54fllWBtvDEukQ4Rra15VghAaIxbsh3iWOQE73ZH6DRNftyfBbdDrAlFsLB7aVg69g7thG+mWYtTFBq16apeWbpR4dRpmWjVDShYnB0iUwleUTpEmSKYlPmJ6RCqUqknGACev30IIKkEky1BbDrzgqQn+M7+EDe2WQFd9D3TsFhB60bRM1aU/uC4PitsM4pppgQnrTpkhwjCkF8Ub+z0cdCzYRkaHrrYTuQW70df52OTpRIskUgkC4ErwaN4eJUCluozL7QmkVeX1i0S2vLsEGU9wWnFuXhOcJYSHA/woH6bSzlWCEDICS474m5OjkUR/Av/+e/xz779Ybzq0Y3c21A82inRDiGOoo2KRDEmKp7BvZgi+HDAipi1hgFNVbHWNLF7MKr89wxsD5utOI27bmlQwFIylsksKQSKosDMGAMcRpEwpARXkw4xXgSLnmBDV6EoaTtEPGAjCEL87G9/lv9dTwkbrkkwO4QL2/Vh6mpCcbZdPzNlopFq2mTKcQjL1OAHARzPx8jx8FP/z2f46/4Tb34VHoum7wDAz/2nv8NLHzrNN4a6TIeQSCSShUBF8CAqeg1dTfR/zENcnMaeYCA+oczLCWae4BK/JxJX6Ll0WiY0VUmcmGaRWQQLo5w7LRMKgIcvtSfeB3sM1eQqF+VYFMFBGOJgMFlBJTvEmYQdQhyMQXYIISd4wSkKrs/emOSLuf9cC9duHVb+e/ojN5mFrKpoNQx0B8stgotOjEvD7ALJN74fhAhD1jhXVWOc+CHd7TKVVEx4UBQlmmwzPizDD0I8u9XF4cDFG7/pAbzyRRt48MJaod9LpxBuFHbOh3I4TAnOKqYbNTZVjl3cVF6Y1ww2mcdxA+wd2vCDEN/1Dffjjz79PL56/YAXwXuHNu7sD3Gxa6MTTecp07AokUgkkuJwJTg6gTW0ZBLQPMSNcfEY4pqp8RNKL68xruTEuHSxvdYw8Z4f/EasTxF80kVwGIYJO8TGeh3v+aFvxJl2Le8u0I7WqcMl1y3HZlWcFglFbzTxj2XpwgCCqNhJT4wDxocSVEV6nveVzTZu7vQrMcqLDEbjTVqd5uKGc+RBgyW0CbaVLOoZTWD092aNcUolnmBSTOnhiVYIwjI0fqoAJNXjLzyzAwB47YvP46GL7cLFPvcER2kXPIXC9VmzW1Y6BM8XTk6WY+kQ7D2122WnCi+5chrnTzdwdavLf57+PbA9eEEIRSm/OZFIJBJJMbgnOFo/dF1lmfCVeIKTxSlAa3xkhxCSGETKpkOki20AON2uTbSiAuy5ByHLSGaPh06F48d7tlNP2PXStLnFY7l1y7EpgqcpgfRGa9QMftycVIJZUZGOSAOwsIEZnh8m3phXNtsIwhDP3a5ODXY9H64XjMV1dZrmQjKJJxFkvPGL0KjpYykdZH+giLQq7BBUWJPyKk6LIyxTT3qC/ZB3+X7+qzswDRWbZ8sNgufpEG4Ay9B40UtKcGY6RKq5kTzvNVPjF6jtfVYEd5omrmyuZRfBI4+ryRKJRCJZDBT3SWuZrirMDlFJOkRyWAaQHJgxaVjGPEpwUei50xobTykt1yTfrOkL6ZuaxLFZGacpwbZwXEwFb5Yn2Eg0xkV2iAU1kPljSjA7Pr8mFCvzQlaOZqoIbjctbppfFv6MdghqHBOhTQ95gv0KG+PoSCZLCa4ZyeMrPwiwcaoOgDWhPXh+rXRB2bB0OG7ApxiKecR2XkRa6pSCK8GGBiO6QG3vDwGwhIsrm23s9xzsHbJdNL3HhrYXjdSUKrBEIpEsirQdgpRg8WRxVnhjnFgEt6ykHSJjXVLVchFpcQpFseQjIp3yxE+FSwpinZaF7pLrluNTBE9RAkeODwVshnU85WQ8Ii1LCV6UJzitBHdaFk63LTxbYRFMKna6sW+9xXaJ4YLi37KYNY+WlFIRVyiCs+JXZoEXwR1WBK83x31O457gABvrsfL74Ga+sT8P0Xds6Bo/qeiPXHh+iJqZ0RiXynq2owtpzdS4zWd7fwhNZTnEV6LHdXWrizAMcXWLnTYMoiJYJkNIJBLJ4hgrgjUVNUuvxA5B66GY0sBOe4Wc4Cw7hFKyMY6U4ClpEGnSRfCsiUSdpon9vrRDZDJVCXZ9mKYGVVGEbLvxgld8o9Si2y9qahwbZZh8ia9stnFtqzo7xIAXweN2CM8Pljo1bpacYCDyzKb+Bo7gCdYrGpZB7yFSgrPtEFoi19HzQ6w1DX5RuDJDESz+bdiwDPbfZFfJnhiXbNqM7RA6v+Ds7I/QjqID7z/XgqYquLrVxZ29IQa2h1NrFka2xxo0ZUawRCKRLAzyBPN0CE2Nekyq8wQbYhHcMjG02Wkis0PMPzHOzfAEF2GsCJ4hKQpI+pyXxbFZGadNyBpF07QAVnRoqpKwIsRFcPw1Ps1rUUqwF4yNqr2y2cad/SF6FcWXUZZsugimAm+Zloi57BCpdAhRCdY1paKxyWEi7iXTDpFSgn0/gKFrPJz8ysUZimChadHQ43QI+tvkTYwDBE+w0BhHR1Xb+0OsR4/LNDRc2mji6laXnzS85MFTCMGi8qQdQiKRSBbHuB1CqTQdwjTURGOZOIArfepMlPUE27N6grWUHSISrfSS1sFOi/UyLfME+9gUwe6UCVlsmhYrDpo1I7Fjoq8B4zFRWUfxk3+Ph9/5868WOuLw/HDMGC4eW1dBn3uCU3aIghPqbMfH//dnXy3kW7qx3cOHP3kt9w06jx3C84PE35j+HTfGVeMJ1nWVF7/rWUpwysPlB+zi0mlaaNUNbHTyI17yEDcobFgGew/+3dN3AMST6rJ+5s8+ex2//sEn8dHPvMAfn8nzKD1+IQTYe+uZGwf4yCevwdRVPHypAwA4HDjSDiGRSCQLhGqOkWiHMKppjLM9f0yd5VPjeg5rfs4sglGqoJxbCfbnVYItOF4wFpm6SI5RETy9MY4UtVc9ehbf9nUXE9+/71wLr3zRWd6cRrTqBnpTMohFnrlxgI/+7Qt49sbB1Ntm2SEunGb+0qqGZvCGQDP7A3IwxV/z1ev7+NPPvoCnX5j+fD795dv40Mefzd3ZzpMTDCS92Yl0iIrGJnt+AENT8cjlDl7x8Bk8dLEzdpt0uDnbYav4xpeex3d8/X0TI17yED3Bpq5BU1W89vFzqFk6rmyuZeYN10wNr3l0AyFC3NobwAsCvPyhM2g3jUTTgjjs4796yXmcP9WAqip43asvYy1KtTgcujIdQiKRSBaIIYgTAFNHTUMrNeI+D9cNxoQ9ytXd69kIw+wceFVVeEFaBG5DLJ0OkfYER41xM3iCgel1S5Uci2EZQDFPMCnBr3pkA696JDldrm7p+OH/8RVjP9dumDxvtQiex95Q0+wZADK78umYocjPF0G0DYjwN9MUOwR9YIt4h6lI7Q6cRGFH+EHA8mhLFori0X8nsiok7RBsWEYYhjMVoYQXNQ90WhZ+5J99XeZtatHxFf0uLxrd/E9efXnm3ytOhKO//w/+dy+b+DOKouCt3/3yzO+JTQuimv3Y/afwb/+X1/L//vK1uwCY97jodDuJRCKRlEdTFSiI11JNU6BrCnw/nHvtcv2MIjha4ykRKC8nuExTORXs1pyeYBoylbaDToOr230Hm2fyRyxXybGRh4qkQ1gZXfbTWGsY6JZQgkmRnFaU023TbwKT5+lVI/dzxTT1e+qWDl1Tp9ohqAguYgmhD/dhP9vPnGfOn0Y6ExcYH5YRAqUM/lm4Xjj1Q2kZGsKQ/X6aelP2g5wmaYcod3HJIh2YnkddyMGWSrBEIpEsDkVRYOgqP8o3NBWapla0do0XwWsNdtK3HxXBmUqwoiAoobfxscnzKsEzWiNjJXh5vUzHZmUsMiwjq8FoGu2mid7ALfwmpaKzqCc47YnRNbZbtN1qlWA99QFRFIXFpE1Rgoej2ZTgLGaN4qLGseEoowg2NP7cSIWflaxNSRpKbhhFHbdA+d1sGsvU+JS6stEzWRi6aIfIV3hFBbqsN0sikUgk5TB0ldcGetTUDcTK6Ky4XjAmdOkaGy6x16MiuIqJcT50TS19mssb47gnmHKCSxbB0UnwMhv6j00RXGRsclaD0TTaDRN+EBZOiCijBPt+MNYdqSgKTEOrTgn22BF/1puWTZSZ7K3hSnCB50+3zZtEF+RMrZlG3dIS9w8kFW499QGblWJFcDzIgne4zllAqoqCujk+wGVWLCPbDpFGzI4uM7lHIpFIJOURxShdU/n678+5drmeP6YEA8Baw8ReZOfMS4cIS3qCrZIqMMDEKnqcgGCHKHkC2YySvZbpCT5ZRfAMBcZakxUKRUcM0x/XKaIEB9mxJYau8mOHeck6JiHawljFPKj4LWKH4EVwnhIchqWb4gAhEzfLDhENywAwd3Mc81VNfnyWMNJ4Vl9TFmSJqEIJNlOjM/OgzQVQfnKPRCKRSMohqrVMwKlGCfb8MHOdbzdN3CU7RMY1XlUV+CWV4Lx6YhLpiLRZlWBFUVhM2hKV4Moa4173utfBNE1YFpOz3/72t+Nbv/Vb8fnPfx7vfOc7Yds2Ll26hF/4hV/AmTNnSt//JBUwDMNEOkQZqMPycOAAmG7EJltGkca2POXRMtRKOkbpd6SPSYj1loWvXp+c+jAs0Rg31RM8sx2CvQ1FO4Q4Npk+SPNmBfv+dH+vqATT7j1tNZmFeIDL/EqwpipQFCAM40aC7NuxsHbb9WVEmkQikSwYKiBVRYGqKrwwnVvA8QI0a+PlWrth4NmbbI3PtEMo5cYmu14w0xqVPzGu/NrJpsYdwyIYAH71V38Vjz76KP/vMAzxYz/2Y3j3u9+NJ554Au9973vxi7/4i3j3u99d+r4nKcGeHyAIw9nsEJGS1h0UG15Bb+YiRazvZ2f3Gbq2FCW40zTRG7oTbQCl7BDRbQ5ylOAgGPdAF8E0mAdJVIKdRGNcNXYId8KGgaCN1Mj1KlWCKU2jCiWYLDWaoiT8wVk0amxsZxXPQSKRSCT50FqsRyeOVJh6ZWYXZ5CVDgEAa02Tr1OZOcFqubHJtuvPtEblp0OUrwc6TQs7FUXIFmGhK+OTTz4Jy7LwxBNPAAC+7/u+D3/8x388031NUl75NK0ZdjCkBBe1Q1AhVqSI9fwws+gyDbWQp7jo48krcNqt6c8ttkNM3gQEQchf58Oc+/ODoLShHhAm96XsEJrKdtM6303PeaQ0YcNA0HvIdnx4AXmCq7NDzHLUlIWpqxNVYP57o+JbKsESiUSyWHgRHCmgtHZU4QnOOpHsNOI1IMt/W8VBuYwAACAASURBVNYTPLMSnGeHmGHdYVPjjmlO8Nvf/naEYYjXvOY1+NEf/VFsbW3h4sV4aMXp06cRBAH29/exvr5e6r4nFY22MFK2LK26AQVlPMHl7BBZPh1T1wqlSxRhmhIMsLiR0+140tnWbh+WoeF0u8YtDtOU4KEwRS3XExyEM3tPG5Y+lg4R76qrOVLy/GDqh5JOE0RP8DT1uAgNS4epq3NlRYqYujbRD0xQTJpMh5BIJJLFQmsFrVmLTIcAmBJMZK1tilI+HWIWJVhVFWhCJnE8MW42O8ThwGVT8JYQ7VlZEfy+970Pm5ubcBwHP/uzP4uf/umfxhve8Iaq7h6eH2BjY3yyFgAMojfYxplW7m0m0W6ZcEMU+lnDZE1cqqZOvb0fhFhrWWO3azVMPpp3lscroqgK6jUj834eHEZFpaYlvv+O3/o0Hr68jh//n57AKCrGbTf/9QUAf7cPgG00ekMv87aGocM0tJn/Bp7wN1B1FTVTx8bGGs7eHQIAWmu1uV6vEECrOf73ENGjJj3D1NFu19nXdGXuv9PD95/CCzv9ue+HuHSuhQc321Pvb32tBuAAzYaZeduqHs9R5SQ+v5P4nETk8zt+nMTnJFL0+TUjZdYy2Tp4eputm2vt+dYuPwjRzlj/Ll9o83+fPt0c+36zYQJK/vqV/noAFrs5y2M1DQ26wdbsRoMNajq3sYaNaEpuUS5daCMEYNRMnOnUSz+OslRWBG9ubgIATNPEm9/8ZvzQD/0Q3vKWt+DmzZv8Nnfv3mX5tSVVYIDthO7c6WYqadvbPQBAv2dje/uw9H236gbu7PYL/ezhIfOqdAv8LtcL4Dre+O3CEIOhGz328o9XpD9woIRh5v2ELiuCn7+5jyvnWNPf0PZwc6ePuqlhe/sQvcgL3Rs6Ex/L9Vvse+fX63j+Tg9btw4SNoGNjTUMhg7CIPuxTMPQVOwfjvjP7h0MYRkqtrcP0e+x13xnp4ftAhaAPEa2D9/zJz4+Usbv7g+xvcPeV7qmzv13+raXX8C3vPT83PdDvO27XwYFytT7ozAM1x1/3hsba5U9nqPISXx+J/E5icjnd/w4ic9JpMzzI+uBCqTWrj7WMxrbimK7Pjx3vJYIhd6kXm809n3HceHmrHlZz2sw8tBpBjP9PXVNQTdaw/cOmHB1sD+A6pc79dYi5frq83sILhSLriVUVcGZM61yP1Pq1jkMBgMcHrIXLQxD/OEf/iEef/xxvOxlL8NoNMJnP/tZAMDv/M7v4Lu+67tm/j15x+Ek98965NtumOXtEFPsDDRtLOuIwjRU2BV6gidFpAHJ6Ss3d/r8a34QRE1TChw3mGg3IL/uhTNsV9cbjnuI/Rkb44BxO8Rg5HEfLR0tufN6goPpOcHxWGsxJ3j+j4mqKJU2p2mqWiiOjuwQMidYIpFIFkvawkc+3bmtfF52RJpoicu6xpfOCXZ9PtW2LIauZqRDzNIYR3XLcnzBlSjBu7u7eNvb3gbf9xEEAR5++GG8613vgqqq+Pmf/3m8613vSkSklYVeSOYTHf8D+TyYebaFfq1h4NqtYjsf8rxMa2ybNG2MvVkqikjzQhj17Detrqlo1Y3E1LjrkWp+0He4D/h0u4Y7e0MMbI83CqYhhfTcKVYEd/sO1lvJaWV5RX8R6qnGuKHt8aauqnKCvRxflYimqtBUtimoMiJtVfDGOJkOIZFIJAuFe4JT/+/PkQ7hByz9KtMTLKzXWdd4pWQ6hOMFXAgqi6GpsSd4jkFTVAQva2pcJUXwfffdh9/7vd/L/N6rX/1qfPjDH57r/mvRQp5XeM7TiQgwJfgwp9krTdFhGZNURNOoMCLNDyYWaZ3UwIwbkUfJ9QLsRpNmznZYETycUARTwXzhNPPoZDXHzToxDmDFmticN7A9nIqa+arKWvT8kEfXTMKMcpwp1qaKxrhVIdMhJBKJZDmQWktrBs+4n2PtigdHjYtddUuDrinwcjL6VUUplRM8nxKsCekQc+QEt8ZPsBfJsVjdG1OKYG+OTkSA2QaGtl9InS06LGNSdp+la5UNy3A9H8aE3VY7NTqZlGAA2NodAGBFMDA5IYJU2vORyT1rYMY8SnDDYnm2tKEZjDw0ooln9PzmjZUrMjYZYMkLoj3kOGfsynQIiUQiWQ6xAqwk/nuedAhxemoaRVG47TG3CC6RDuHOowQLdghaO2dZdwxdQ8PSlzY17lis7rwIztlNzeM/AYSBGTmT0ETcgp5gf0IBRWOTwxJvztzHMyX7dr1lJuwQN3b6vOi9FRXBZyLFddLoZLJDnCc7RIYS7AfBTGOTgbhYG9o+/30NiyU1VHGkFEQe7SIFLdlVuM3mBNghys5wl0gkEkk5uCc4VQyTuDMLVFjmWQvIEpG1tmlqcSXY8wP4QTjzQCfR5hkrwbPVA52Wif0leYKPxcpIBdJUO8SMatdagxVbefm3IkVzgr0JPmXaaVUxMIPlB+YfX3SaFg76DsIwxEHfweHAxUuvnAbA8oIB4ExUFA8nKcEjDzVTQ7Omsy7QXDvE7DnB7Pe4cL0AjhfETV36/K+XN+VCImJFdpV4WMbxVVEbUgmWSCSSpZAugquw8pHwltsAHxXBWdd4RUVhJZjW11mGZdDjE+0QmqrMnIuftnEukuNRBEcFUl7hSTudWaaVAbESXMQXzO0Q0zzBE6aNkeemioEZk9IhAPbcXC/A0PZxI7JCvPTBqAi+S3YI5vOdpAQPbBeNmg5FUbCWk6bh5/iSikBF8ND2uepcZWMc/WwRfy/zBAexEnyc7RDSEyyRSCRLYTwdYv5hGZM8wQDQjkS8fE9wsd9DNc3MSrCWtEPMI7x0WtbS7BCVToxbFNM8wfNMJwHinVSRnQf5j6epkpM8wTyGq4oi2JvSGNeK40auR01xj9y3Dl1TcTsaQlHIEzyK0xraTRP7hzaPSaMPnx+GM9shSLEcjFw++Y8f5Ud/1/7Iw8jxUDPLv229EtYGQ9fguHFEmqGrCJxqGhmXTfo1lEgkEsliIJHFSHmC5xmbzIvgnGs4iXhZ1/gynmC7CiWYp0PMfioMMCV4v28jDMPKpqzmcSxWxnrUIJVbBM8ZkUZF8OFguidYbIyb9Oaa5AnmSrAzXxHsBwHCMP+YBADWKXOv5+D6dg9rDQOdpolO04TnB1AArLcsKJjuCaaCar1p4kvX9vDDv/JX+OFf+Su89Zc/js88dWuudAhSLPsjjyvB3A6hsZGMH/nkNbz1lz+Oq1vdwvf7wY8/i//jdz9fqsmNlGDvBCjBNBZ8lpHiEolEIilOnid4HiXYm2KHWF9j63eWgkuiVJFC2I1EuUn1xCSy7BCz0mmacNwAo1SNdGdvgB/6pb/kVs4qOCZKMJP789Mh5otIMw0VuqagnzEAIo3YnOd6AaycXZM7MSKNfc12fRgzdmLS7wcmH/G3oyzfg76Da1uHeOACG4fYaZnY7Y5Qs3SoqoK6pfPiM4uB7eFUdF/f+7oX4SWRr9j3Q7z/Y8/gxp3eXHYI0ZJCBTEV3Yqi4G3f8wp87cYBPvzJa7h9d4Arm+3c+xLZ2u3jhTs94e9RICJN17Dn2YmNzHIOZqpnrWHiR7/vlXj4YrHX6/9v787D5CrLvPF/z1JLV/WWpDtJZ0GSYFogsoSQDK+8IO0AEbNCMFGMvDqCgV9YHHacC2JEZhBhRkFENMjlhYALCQwg4BUDijIEIiA4gIEIIQlJZ+9OL7Wc5ffHqefUqe7az0lXnarv5x/opbrO6apO3eeu73M/RERUnqFxCDsT7MHCuFzF6amf7MCEMVFEwoFhX7OLYMOEXOC1L+FhJtiaxOQmDiGGFaTrAQDYfXAQ8YSOnfsG0DEmWvbPd/JFi6shnOoE59h+z20cQpIkRMKBvJ1QQRtSBOeSzpMOfyIEPMoEF/rjANKDp3cfHMSOvX2YktprXHxeFJqRsFo4DpHqzHaMieLMWZNx5qzJOGv2ZEiStYOcm6u/pojVsezpT9iPQ8Tx5D9u2hh85qRJAKxucbGSmoGBWNKxMK7ITnAyPSfYz9MhACsDXk6EhIiIijd0TrAXneBCr/MNIdVe7D6UeDkuZkKEl5lg3cUiecBa0A8AB/syJ0SI/RX6Y4UblsXyxat7oU6w2xFp1n2oRRVXmm5C3Eu+TG++t99DohPsMg5RTBEspjm8uWUfTBOYMmFIERxOd13zdYKdI8ucZElCJKSibyABw0UmWJFlNEWDONiXSC+MG7LXujM3XKxkKtYgCuuiFsapIg6Regy5qIyIiAoYukmGWKzvRSa4nFheKXEI153ggCMTbJjuFsZFs6/TEjVXvoZdqfxRBKeKH63gwrjyf+nRsFpUcZXUDHsHu3xj0vItjBOdYLcL45JFTDyQJAkt0SC27OgBADtG0JKKNohua0Mo9/mbplVENoSzdxOj4QD6BzXouuE6B9Tr2M7Z+TYIYP0jEAzIJXeCAdizkovp6qY3y7AueMot7ImIqH4M7QRLkmTv6FauQiPS8hFFeDFpDNFlddsJNk2z6I2pcsm1a5youUqpAQrxRRHcUGA6hNsRaQCsOERRnWAD0VQxmK+ItfOkWd4ScGaC3SimEwwAzdEQTABjmkP2FZb4b4MzDhHPfjyxhA7TzIwnODWEVfQNJjx4C8Ta3W4gnoQkAeEsi7miRcZWBLsITv0xFbVZRkBslmFAUeTDvjqViIj8b+jCOMCKaXqybXI5nWCplE5wKg7hIhMMWA1AN+uDACDaEIAiS8NGsYqaKd+eBqXyRRFccMc4DzY1iBbIxAqabtrHk7cTbOTOBIsrLdedYPE2SYEiWBS8RzoWk2WPQ2TvBOeKJwjRsGpngt10TVsarTiEGMeWrfgslF0eSjxnxB9TMf+QhFQFmm4iobkL9xMRUf1QsxTBqizZkc1yFNvsyqak6RCay06wmp7i5XY6hJzaDnpoJlgcY3+OWqWs+/LsJx1G4aACCcVkgss/nYawWjBsLdr8kSI6wfkyweJKy20muNDoFKE19dbCVGcRPDQOEVZzdlizLVRzioQD6B9MWiPSXOWAQnYcYmgUQojmiW1kI7ZxFDvcqWrh4xMTOwbjmq/HoxER0cix5wQ7XpNVRXY1HaLY1/lsnNMhChFdVred4KSmu45DANl3javbTLAiSwikFitloxnus5vRVBFo5pv9m3oiiVEk+TPBubdyFlda8aS7DRiKfZukuehOsG7/sfz9wwP2LD47o5ujExwJqa6nQ4hj0g0Te3oGc3adI+GAu0xwERdKYo7zQEzjdsNERFSUdBwi/bphZYIP34i0fEZ6OgTgTScYSK0R6htaBNfpdAhJkjJm0A2lG+626AOsCRSmiWHDmZ3E/dtxiLyZ4NwbLdid4KS7q5li/zimTmjB2FENmNLRZH+upTGIiW1RfGyc9bloqrAXT66fPPkWfvP8FgDpMSVNDcOnQ1i3VdE3kIpDuMjPijD8zr0DebrOJcYhhmaCi1kY5+wEu3h3gYiI6kdrYwjtrWFMbGu0P6cosus4hITypl+VkgkuNl6Zi73WSTM8qclaGoM4ODQTrHnfCfbN8FA1XxHscos+IN0R7Y8lc74VL67m0nGIwp3gbEWwIkuQJcm7EWkFOsHHTRuD46adkvE5VZHx7a/NsT92DqeONgRw8FACitwHANixpx+SBIwfHcn68yNhNW/nu1iiOz0Qzx2HsBbwlRCHsDPBViFf7GYZ4jiYCSYiomI0hFTctuL/ZHzOikO4mw6hquUt0E5ngou7H1GblENsHJZI6tB0E9Gwu5qsORrCoYGEtdFH6jzqNg4BZA5iHsqL1nvUnkGb+5crxpyIIljkTfN9b7bjkiQJgYCct4guhpvRKUM55/L1DSRhmCb2HIwhntCxY28/xo2K5MwKRR071biKQ6RyykDuRXhDYxv5mKaJZFJ0gq3Cuag5wcwEExGRB6yFce7iEOVMhgDSnWCziNdLXTddbQwlpjnFErrr6RCAtZbJNK1dZIV0HKIei2BVzjkdwnA5lQBI53zzFcHi/kXRly/Tm68TDAAhVR6xEWnFaHYUwc4w+o69/di+pw+T2nNvUegsWN2OSLN/ZpaNOYD0776YMWm6YUL86duPR5FzggFmgomIyB1rRJq7OES5r/ElTYfQyy+2ASAUTC/41w13ewYA6XrgYJ+zCLZqJk03XE/XEnxVBOfeLMN9/iRqxyFyF1fias4e2Za3E5w/HhBQlREbkVaM1lQXtqcvgZ7+9FiS93f2Ys+BQUxsb8x104wi2M3FSDio2F3YhlD2rnMpu8Zle+eg2G2TAWAwwU4wERGVz4uFcW6LYL2ITrDmciRoOGi9NscSmtVVdj0dIlWTOJpyzmEEpewXkI9vXuGDqpKz6NR10/XWtqKwzVdc2XP0AgoUWco7HUJENHLla4IB2bsRaR4UauGggqAqo6c/bk9SAIBX3u6GCeTtBHsVhxC72wHpzvxQkSIuVoTsRXDxmWDT5JbJRERUPlWRXcUhNN1FEVzCdAi3Y81EJjiW9KgTbO8al27KORuHXkUifFME558O4cXCuMJvs2t6egOMYIFMb6EnVFBVqioOIaWGUzvjEB1jIti83dpuudhOsPsnfub84qGiRcRWhKHPF0kqLq4hOsGA9VYWERFRORRFcrcwzotMcFEL40xXtUTYEYfQdHd7BgCOiKajKRdPGvYIt1L2C8jHN6/w+TLBmstNGgAgHFIgSfmvLpy50qCq2NsMZv9eM2/X0Sqi3RfBVmHnTbeytTFkxSH6EggFFUydYM0VDqgyxrY25Lyds2B1m80eup3zsPsScYgi3gpJDsllF/sPydBB50REROVQZZfbJrvoBEslZII1zXDVTAwFnJlg03UDKRRQ0BBShsQhdDu66dWECN+8wuedDqG7b73LkoRIgd3Iko74QUDN3wnWdSPvkyDoxcK4VJC9nNEp2bTYneA4WqNBe9bhhDHRvMWtp53gIZt4DLuvUHqUXSHi+SJ+ZrEFrXMKBkekERFRuVTF/bbJ5TZj7DnBRcYhAkXsqJrzvmQJQVX2LA4BWLlgZxGc1Ay0NtVrEZxnxzgv4hCAmEGb/sUmNQODjo/FwjxVkREKuO0EK57MCfYiCiE0NwbR02dlgluiQUwaa+WA8+WBAStiIDq3IxmHSGoGPuw+hA+7D2XNi4siWGwbXewCQueOOYxDEBFRuVTFZSfYxeu8UsrCOA+2Og4FFTsO4cVGU9aucZmZYPF67tWucTWxWYbhQRwCsHLBzquLXz//HjZ/eBCrvjobgHPsWf4d7ABAM4y8TwKrk+y+CPZiMoTQEg2iP6ZhX28MR3Y0Y/LYJsiShI+Nbyp428ZIAINxzXUcor0lDAnpPNBQwYAMRZbQH0vikd+/i+de2wEA+NSM8fiXecdkfK8ojEVhHSjyOSIWxgHsBBMRUfkUWarciLTUy5dZTBzCZSYYsCIMYjqEFzVZS2MQH+w6ZH8cTxqexyF8UwQXWhjntvgCrDFpzquL/b1xfLi7D32DSTQ2BDI2pwgG8o84KxQMDwa8WRjnxWQIQTy59vbEcMJRbWiJBnHzV07OuVOcUzQcwB4Muu7In3z0WExoi2JUUyjr1yVJQjSsYjCm4d3tPZg2oRmDCR37emPDvteOQzSWFoeQZcm+eue2yUREVC5FkaEZlckE23OCi+gEJ3UjZwyxWOFgajMr0/1mGYCIQ+wDYJ2DphuIhFSEgkr9jUgLKLkXxum64ckoKysTnP7FiiL3g529qfsR0yHkVKY3fyY4/3QI9yPS3PxxZOPsvorCcfLYxqLuozFixRTcPvEVWcYR4/J3nhvCAew/FMdHe/tx7JTRaG8JZ70qtOMQJWaCgXQkgp1gIiIql9tMsOZmOoSIQxTVCXYfhwgHFXtdlReLylsag4gndMQSmh0/DQYUREKqZ3EI/xTBeTfL8OaqY2gcQhTB/0gVwc5pA8FA7rnFQHGZYLdxCM3jTLBzx7ZccYRcGhusItiLjnwh0bCKv287CBPA1AnNiIQDWad6iMdLxCFKKoJTY9K8yJoTEVF9cp0J9qQTXPh73W6WAViZ4P5Uh9aTOIRjJ1sxiCAYkBENq/W3MC6oytANE3qWR1PzYBwHIOIQ6V9sPFV0f7DTyqSkF8ZZqyALzQkuNB0ioRlFjS7JxetOsIhDAOndWooVTRXBI7HNcCSs2l30Izuahy1oFIYvjCv+2EQumNsmExFRuRTFfSa43LU/pU6HcN0JDih2cepFA6nFMStYNA2DqpKz8VUO3xTBgVRRomnDH0xd96oTrGbsSe3sBJum6dgsQ7bm/BbYNjlfREOM4cq3uK4QrzPBTZH0Lm2icCxWY4P1/YpH49ryEZMj2lrCaI4ErYxwXBv2h54ekSYWxpXeCeacYCIiKpcqWzvGFbM4LRs3r/NKCXOCkx5sdRwKKugfFHEILxbGWa/dvf0JuylZt51g0fHMlgv2aibd0F3jEklrM4re/gQOHIpnLoxTlQKd4PxPKJE5dROJ8Ho6hKrIdqyhpdQ4RGTkOsFiTNqRHdZmHrl2+yt3YRyQvuhiJpiIiMqlKhJMFFeIDmU139xMhyi+E6zr7ptqoaBij7L1ZmGc9dp9sC+e2QkOqRiI12EmGMjeOTU8jEMA6V3jEpqOI8Zai7T+8VGvHYdQZAmBQO65xUARC+OqsBMMWAWjJAFNkerNBIsVrFNTRXA0xy5y4qIlElIRUOWSiuCQyASzE0xERGUSrzvlRCI0R+OtHHJJnWCjpMhgNmHHRlNexCEaIwHIkpTKBIuFcXKdxiEUUQQP75zqhmlf8bhhb8mbWnWY0AxMm9gMRZbw/q5eOzMjSRJCqgJNN3JeYRXayll0gt2MSfM6EwxYkxSaI8GSi1k7EzyCRfCUDusCRcQjBmJJaLqBJ1/8AImkbl9gqKqMSEgtqavLTjAREbklGinlTIgQr2Gup0MUkwnW3MchwkFvZ+zLkoTmaAA9/Qn7dxEMKIim1gW5WXAo+GpOMICsY8l0jzbLELueDcY1mKaJRFJHJBzA+DER7No3gLaWBntbQdHJjSd1+3ZOWoG3FkKO25dL03TPi+Djj2rD+DH5d4jLpvNjozBtQjPGtjZ4ejzZTJ/Uis7JrY44RLqD//cPD2LtH/+BI8Y1pbablCBLEmZ1jsWEAjvfOdmZYE6HICKiMolisJxZwXYRfJjnBBuGCcM0PYhDpGshr95FbYmGrEywmA6hpneojSV0NDa4ux/fFMHiCiNb0ejVnOCwXZga0HQTpmm9Ld4aDaKnP4HWxpB9pSQKr8G4lrUI1nUjb2Fu395FSz+pm3bH0iv/PGtyWbeb0NaIb355lqfHksu0iS247oKZ9sfOrZTFWyYD8SQSjouEC86aXtJ9BNkJJiIil1Q3nWA9/W5mOURZVKgIdns/grMT7NW7wi2NQSsTnEoBhAIKQqIeTOh2FLNcvmlzhVNXGNk2mLDmBLs/FfGLjcUdg5lVBS2NIfT0WQvj7CLYfgs+exFbaGGcKJzd7HpyODLBfuSMsfT2JwBYFxfWHOXyLhKCzAQTEZFLohgs5617t51gURcVygSLY3PbTAwFvI1DANbiuMxMsGIX27GE+1ywb17hQ3lOulD+tlii0I4l9YzBzOJBcA6TbsixGMs+pgJb7hYqoovhZk/xWpIugjX0pIrggbjm6iKBnWAiInIrvTDORRGslNfMKbYTbI9/dVlPhILeLowDrE7wof4kYon0wji7CHa54RjgyyI4WxzCmznBYUeL3XnV0RINQtNN9PQniu8EFyjMIwWK6ELE6BQWadbVpyJL6I9pONgXB2A9Lm4WDnJOMBERuSVeo93EIQJlTm1IT4fI/33pjcDcb5YheBaHiIZgmCb29cYAWA0qe01VlnqwVL55hQ/nWUhmeNQJVhUZiiwhntTt+xFxCADY1xuzO4vOTHA2hUakhUMqJCn37QtxOzqllkiSZO8aZ8chRCe43CJY5bbJRETkjojUlbMwThuh6RB2PeHBnGDBqwaSmBW892AMkmRdVOSLx5bKN6/wziC0k2laqxq9GJEGWN3gWEK3ZwCLOAQA7O+N2W8XRPJkesXucvm6tLIkWQOfy4xDpLNC3i6M8yvrd5kcHocoswjmiDQiInLLng7hZkRama/zxW6WcVgWxnmVCU5tdrXn4CCCAcUaUVuPcYigKkPC8DiEuMLxagFTKKhkxiFU2X4QnIvdGkKZM4XLOaZIQ6DsOITbwHytEcOze/pSRXDMXSY4xDgEERG5JN5N1CuwMK7YzTLshXEuC9ewc0SaZ3GIdBEcSv0e6jIOIar/oXEIkbPxYkQaYP1yYwnNsTBOydhCOJB6kqiKjGBAxmB8+INQ7BMqGg6U3wn26O2LWhENq+jpi9sXFQNxd5lgdoKJiMgtV51glx1apcg5wZpmfd11HMKZCfZwTjBgbV4m9mcI51kjVipfVVBWVCGzaNQN7/aptu8jqadHpAUUNKS23QUyH9hc+1fbKy0L5EmjDYGyM8HsBGeKhFXs2j9of+w6E8wRaURE5JIn0yHKnhNcWhzC7etdKJi+vWeNyWB6JFpgSCe4rkakAdZuJEMrf83jOEQ4qKbiENaTIqRa2ySLbrDzSqkhR6ZXL7IT3Mg4hGci4YD9j8yoppC1cYaLInh0cxgSgGbHuwBERESlSM8JHvltk8VSqUJxCN2jd5YVWU43DD0qgoF0JEJ0gmVZQjAgu9pxV/BVBRUOKMMyICIO4dUvPJS6D2cnGEg/CM63JcREgqHEk71gJjhc/sI4cTHgXI1Zz6LhdBapY0wEA3HN1bbSR01swZ2XnToi20ATEVFtsneMK2M6hFigL9aolEqSJMiSVHA6RDp24b6OEl1aL99FFfVXyPF6nq0eLIeviuBsmWDR5vc8DuHYLAOAPSbN2d2NhLLHGcQolIKZYBdxCPF7cM7lq2cRZxE8OoqkZmAgXGyLegAAIABJREFUrruantHCLjAREbmQzgSXUQSnXufdvI7JslTEwrhUhNODwlVEF7yaDgGk66+go94JpWo1t3xVBIvxZU7i6kr2MH+SOR0isxPsfLsgVye32CeUKIILPUGzibMTnCEatvYPlyRg7GirezsY17hwkIiIKiadCS79dT6h6fZs3HLJcjEL47xbaC+KYK8ywcDwOAQAhAJqHXaCA1mmQ9iZYC+nQ+iIazpURbaLazsOMTQTnKWTm84E5//1NjYEYAKIZZkwUYi4GGAn2CLmNjdFgmhsCNifZ2aaiIgqRcQCyhmRlkga9mzcsu9fllAoiZEssmYpRih4GOIQjaIIdsQhsjRFy+GrCiGUrRNc5CSGYoVTkYt4Qrd3DQPSD0JGJji1MM4c0slNd4LzP3Ejqe5ltgkThYiLAXaCLSIO0RIN2gUxwCKYiIgqx82ItERSz8jBlkOWpMKdYI82ywDSjTlvF8al4hBqZhyi/hbGZY1DeJ0JtgqovsFkxlWHeBAyMsFhFbph2is4Ba3IcSPRVMcy26zhQsRoEOdw6nom4hAtjUF7IxOARTAREVWOaNAVWpyWTTxpZEQAyiHLEvRCmWDNm80yAGuKF+BxEcxOsCWcyus6O69iEZpncYhUZ7W3P5Hx5LM7wUPiEMDwrZPtOESBJ0Gj6ARn2XWukFhChyJL3MwhJaMT7Fgkx0wwERFViuJmYZymuy+Ci+kEG94tjAsFFCiy5CrCMZQ9HcLxu7CmQ9TbnOCAAsM0M55M6RFpHsUhUr/kQwPJjNZ71oVx9tbJmQ9EsU8o0QkuZ1ZwPKEj5DIrVEvSRXCIcQgiIqoK6TnB5W2WEXQbh5CLKII9Xhjn5WQIwLEwzvG7yBaPLYev3ksXb/3HEunRV16PSLM7wQOJjBmxrY0hnHpcB445crT9OVF4DS1iRVEcLpDXjTRkL6KLEUvqzAM7REIqTj9hAk6c3pbZCWYRTEREFSJJ1ju25cQhEkmPOsEF4hBJ3YAsSZ5M2Zo5vd3z2qQpGsRpx0/AsVPG2J/zKhPsqyJYtMLjCR1NEetz4onl5Yg0wMoET2pvtD8vyxK+es7RGd8rOo5DZ/329CcApGfb5SJyrOXMCo4n9IJFdj2RJAkXzv0EAMA0TfsPn0UwERFVkqLIZXWC40kD0QZ3ZZpSTCdYNzzZKAMAjp0yGsdOGV34G0sgSxL+32c/kfG5cECBplvJADcxDl9VCKLocw5I1j3OBIs4hGmi4NsQDTniED39cSiylLGLWTau4hBJPSMfQ2mSJKEhlLnXOBERUSWoslT2nOCQi40yAEAqZrMMzfTd+hmxAM9tN9hXZy26tM4ciNcj0pxt/EJvQ+SKQ/T0JdDSGCyY11UVGaGAUl4cIq6xE5yHeGxYBBMRUSWpilzmnGA9YyJCORS5uG2TvVgUN5LspmgZ07WcfHXW4qSdu4R4PyLNWQTn//Xki0OIkWqFNISUsjrBsaTO8Wh5REJWl91vV7dERFRbFKXMTrAXI9KKnBPst0lT2ZIB5fBVhSDe/nd2gr0ekeYsLIMF3oYIqDIUWRoeh+hL2KsZC4mEA2VngrkwLrd0HIK/IyIiqhxVlu1apRQJTXf9bmZR2yb7sBPsXCPmhq/O2u4EJ9NFY3pEmnfbJguFOsGSJCESHr51cm9/3J4rXIjYda5UMWaC8xK78TEOQUREleSmE+z2dV6RJRQaTKHppie7xY2kdDLA3axgX521HYR2VP7pEWnenIqqSHZBXagTDIgiNr3ZhW4YODSQLKETPLyILganQ+QnoiosgomIqJLKyQRrugHdMN3PCZYkGAW60L7sBNdjHEJMbsicDuHtiDRJkuwrr2IC6ZGwmrHtcW9/EiYKj0cTGkJqyXEI0zTtzTIoO3thnM/+sImIqLaoZXSCE0mrcPVi2+RCneCkZvjutbIu4xCBgAwJmasB7YVxHoa6xRVGMU++hpCK7v0DeP61HdjbM4heMSO42E5wGXGIRNKAicKbcdSzBnaCiYioCpQzJzipWXWOFwvjCk2H8OfCuNTmafXUCZYlCcEhu4SItxhUjzrBQLq4LKbTOm50BLsPDuLnz/4dv3puC3r64wBQfCY4bBXBheb4OYkHnQvjcpvYFkVjQ8AuhomIiCohqMpIlFisxVNbGXuybXKhOcG64dtMsNsRab6rEMJD9ovWPc4Ei/sAinvyXXDmdMz/P0fiwd9txvsf9WBGaqeUYjvBLdEgDNNE32ASzZHibiOC4OwE53ZSZztO6mwvOKuZiIjocGqOBvHegZ6SbiOKZrexR1mWYCQLzAnWTM/2WhgpdhyinjrBgJULdp60djjiEIHi4xCyJKG1MYSjJrZgX28c23b3ASihCE5lh3v7EkUfn7gICAV8dw0zYiRJYgFMREQV1xoNobc/AbOEd3zTmWD3m2UUGpGmG/7rBMuyhKAq11cmGLAiANmnQ3gZh7CKy1Lehpg6oRkA8Nf39iIaVoueTyuK5YOpGEUxxEUAO8FERETVrTkaREIzMhbRFyI6wcVMqcqnmM0yrIVx/msahYJKfWWCAasTHHPMhdMNA7LHXb9SFsYJHxvXBEkC9vbE0FxkFxhIZ4d7SugEi4sAZoKJiIiqm/06X0KzK+HVwrhiM8E+mw4BWO/a19WcYMCaFZy5MM70NAoBoKQRaenjUjCxLQqg+CiE83vFVIliiDhEmCPSiIiIqlprGa/zXsUhZAlFTIfw32YZwPA1YuXw3VmHsiyM82pGsJBeGFdakXlkhxWJKHZGsHVfKkIBBQfLyQSzE0xERFTVmlM1QSmv86LZNyJzgnX/zQkGUvHY+oxDZHaCvRyPBjiK4BKvwKaIIriETjBgvVVSytskcY5IIyIi8gVRE/SU0glOjUgLeTEirdCOcZrh+TvqIyEcVOuvEzx8RJrh6aI4wJEJLrETPNXuBJdYBEeDJcYhrAxMA4tgIiKiqhYNq1AVCT39ccQSGu569A3sPjCQ9zZiYVyxi+xzUSQJ+WpgwzShG6YvO8FDm6Ll8N2MrWhDAINxzQ5ya4YJxeMHb+b0dsQTOpoigZJuN3lsI875p4/hpM6xJd2uJRrEjr39RX9/PKlDliRfBtmJiIjqiSRJaIkG0dOXwJYdvXjt3b04qbMdY0dFct7Gng7hMhMsFVgYp6e2c/ZjPREMKCVvQjKU785avK1waCAJwBqR5nUneNyoCBb936klT5yQZQlLPj0NY1sbSrpdS2Oo5ExwKKhwDi4REZEPNEdD6OlPYMceay8BsfAtl4RmvcvttjgtNCdYbOfsxyI4FJDt2Ei5fHfWQ0eN6IehCB5pLdEgBuNa0Vc0sYTOGcFEREQ+0dpodYK377He9S30ep9IGq67wIA1JzjfdIhkqggO+HA6RJ12gjNXWeq64XkcYqSVOiYtntBdb6VIREREI6Mlai2A357qBMcLdDATmu56owzAeoc63051miY6wf5rJgZUGYmkUdJOfEP5rnocWjDqhgnZ57EAMVLtYLFFcFLnZAgiIiKfaI4G0TeQxEd7i+0E6yPSCfZzHCIYUOyFfeXy3VmL3dh6+hxxCB9ewTjZ41OKzAXHEjo3yiAiIvKJlsYQTKRHnxXMBCcN1zOCgcKZ4KSPF8aJ8XFuIhEjctbvv/8+li5dirPPPhtLly7FBx98UPbPCqgyomHV7prquuH5nOCRJnLOvUXOCo4zE0xEROQbrUP2DxDbIucS9ygOIckoMB3C351gAIgXuKDIZ0TO+uabb8YXv/hFPPvss/jiF7+Im266ydXPa2kMobcvHYfw+8K45kgQklT8bjIxxiGIiIh8o9mxf0BzJFDUwriQB3EIRS52YZz/6igRFyl0QZHPYS+C9+3bh7feegvz5s0DAMybNw9vvfUW9u/fX/bPtALmjiLYh1cwTrIsoSkSLHo3mVhCYyeYiIjIJ0Tssb01jKZIsIg4hO5JHEKWJJgmci4eSy+M818dJTrlhX6X+Rz2s965cyfGjRsHRbEOVlEUjB07Fjt37iz7Zzq3Ga6FTjBQ2q5x1nQI3+1zQkREVJfEZKtJ7Y0IBmTEC3QvE5qBoAdjy+RUfZQrEqH5OBMsLhLcZIJ9UUmNGdOY8fH4tka8unkv2toaIckSGhoCaG9vqtDRuSOOe0xrAwbjWsHz6B9MIpbQMb69serOudqOx0u1em61el5CLZ5fLZ6TE8/Pf2rxnJy8Or/jjmrDp06YhOdf3QbTzP9zNcNEc1PY9X03NYYBWHXU0C2Y29ubEEnNLW5vq76aopCxvVYztCEaKvvYD3sR3NHRge7ubui6DkVRoOs6du/ejY6OjqJ/xr59fRmrG4OyhERSx7YdBxGPa9CSOvbsOXQ4Dv+wam9vso9bAdDXnyh4Hm9/YMVI2puDVXXOznOpNbV6brV6XkItnl8tnpMTz89/avGcnLw8vyuXHAcA+MNftqF/MP/rfSyuwdDc1zaxQesd5u7uQxlricR57dtnFcH9fTHfPY6DqUTA7r192NMahixLw5qmhRz2/veYMWNw9NFH48knnwQAPPnkkzj66KMxevTosn9mete4RE1kggEgFFQQSxRu6f9jZy8A4MjxzYf7kIiIiMhjwYA8YplgScofhxCLyny5Y1zqmJMutk4ekTjEqlWrcP311+Oee+5Bc3MzbrvtNlc/r8UxK1jXTd+PSAOAcFBBLKEV/L4Pdh7C2NYGNDYERuCoiIiIyEtBVUY8T47VNE3Ptk0Wa6ZyTYgQBWTAh83EgF8ywdOmTcOvf/1rz36e2GHN6gQbNbEwLhxUi+4ET5/cOgJHRERERF4LBhR704xsdMOEYZqebZsM5OsEG/Yx+Y1vNsvwmnOHtVrYMQ6wOsG6YeZt6x/si+PAoTimjPdXeJ2IiIgsQVXJW7iJr3kyIk0UwTk6wZqPO8G+2SzDa9GwClWR0plg2ZenkUEE1vO9RfJ+Kg88ZQLzwERERH4kMsG5ZveKos7LOESuItiOQ/gwEyyOuao3yzgcJEmyNsxIZYJrIw5hFcGxeO5c8Ps7D0GWJBwxjp1gIiIiPwoGFBimmTOnK4q6kBfbJqfKo1xFcEKzIqWyD+soVZGhyJKrzTJ8MSc4m+ZoCC+91V0zcYiGoPVQ5MsFf7CrFxPaogj5MLtDREREmVnWbJtUiKLOi+6sUiATnNQMX3aBhWAgf7SkEN8WwYtPm4K/vrcPkgT83+MmVPpwXBNxiFieB7N/MInWpmDOrxMREVF1c2ZZI+HhXx9MvSPcEHZfoslSgekQujc701VKMCC7ikP4tgieMWUMZkwZU+nD8Iwdh8gzJk3TTag1kH8mIiKqVyLrm6t4648lAVjrn9xKT4fI/vVkUvd1JzikKq7iEP498xoTFnGIeO4rGk03oNZA9IOIiKheidFnuYq3gZjVDIuEvOsE51wYpxtQPcgeV0owkH/mciEsgqtEuhOc+8HUdTNrfoiIiIj8QcQhcnWC7SI47H5TrGKmQ/g7DpF/5nIh/j3zGlPMiDTNMGpiESAREVG9CtoL47IXbyIO4UUnWCpisww/xyGCqowkO8H+11BsJpidYCIiIt8KFtjudyCuoSGkeDK2rJhOsB83yhCCAQVxdoL9T8y7yx+HMFgEExER+Vh6YVzuTHAk5D4KARQxHUIzEPBgU45KCapy/W2bXIskSUI4qOQtgq1OMOMQREREflWwExzTPJkMAaSnQ+TanS6p6b7vBHM6RI0IBZUCcQh2gomIiPzMuVlGNgOxJCJeFcGpvlneTrCfM8EBpf62Ta5V4aCasxNsprZYrIUtoomIiOqVc7OMbPrjmieTIQBASe0tkHPHON2wR7b5kRWHYCe4JuSLQ2i69QRmJ5iIiMi/ROc134g0rzrBUqpkyLUwLpGsgU5wUs8Z9yjEv2deg8JBBfGcRbB1pcMimIiIyL/EQvh8I9K8ygSnp0Nk/3pS93cRHArIMJGukUrl3zOvQaFA7kywyPNwTjAREZG/iQ7mUJpuIJE0PJkRDBQxHcLvnWA1f7SkEP+eeQ3KlwlmJ5iIiKg2BANy1jiEl7vFAenpENkywbphwDBNfxfBgfyLDAvx75nXoHAoXyY4VQRzYRwREZGvBVU565xgsVucZyPSpNybZSRT9+/rIlgVW1CzE+x7+RbG6VwYR0REVBNyzbcdiItOsNeZ4OFFsCgcfT0dgp3g2hEOKNB0I2vAW3yOmWAiIiJ/C6rZM8FexyGkPHEIrRY6wQF2gmtGOGhd+WXrBnNEGhERUW0IBbJv9+t1HELJszCuNuIQ7ATXjHAwtcoxWxFsiIVx7AQTERH5WTCgIK4ZeGbjh/jWz16x59wOik6wV9Mh8nSCRffU79smAyh7wwz/nnkNCqWK4Gxj0pgJJiIiqg3WTmc6Xn9vL7Z2H8KBQ3EAQP/hmg5Rq51gOw7BTrDv5Y9DcEQaERFRLQgGrIXwW3cdAgC8v7MXgJUJDqqyZ4WpGCiVvQi2ao2gj4vgUOrY44xD+F/Y7gTnzgRzYRwREZG/BQMKDhyK28Xb+zutYrg/lvRsMgSQfzpEuhPs5+kQjEPUjHxFsG7PCeZDRkRE5GfO7mtjQyDdCY5rnkUhAEASc4KzbBhXG3GI1MI4xiH8L5wnE5zUuTCOiIioFogOZkNIwazOdnywqxeGaWIgph2WTrBuDO+UirrC10Wwyk5wzciXCebCOCIiotogOsFHjm/GlAnNGIzr6N4/gP5YElGPJkMAzukQw78mCkc/F8GyLEFVJI5IqwX2iLQsDyY3yyAiIqoNohM8paMZUzqaAViL47zuBOedDlEDnWBAbDzCTrDvBVQZsiRljUNoBjvBREREtUBkWad0NGHCmChCAQV/emMnegcSnmaCZUmChPwL4/w8HQKwfpfMBNcASZIQCsockUZERFTDJoyJojkaxMcntUKWJcyYMhrvfHgQiaSBie1RT+9LlqWsm2WIEWl+7wRHwwEcGkiWdVvveu7kiYCq2OPQnEQmWITciYiIyJ+mT27Ff112qv3xJYtnIJHUrWZYwNuRZbIs5e0E+7251tEWxdZdvWXd1t9nXoMCimxfnTmxE0xERFSbZElCOKh6XgCLn61nKYITmoGAKttj1PxqUnsUew7GysoFs6KqMqoq21dnTlwYR0RERKXKHYcwEKiBxtrEtkYAwK79AyXf1v9nX2OsTvDwIlg3TCiyBNnnV2xEREQ0cmQp98I4v+eBAWDSWCtD3b2/v+Tb+v/sa0xAle2xJU6abjAKQURERCVRZCnHjnF6TRTB7S0NCKoydu4fLPm2/j/7GhNQZWhZ4xAmd4sjIiKikkiyBCPbjnE10gmWZQkT2qLsBNeCXJ1gXTegsBNMREREJVBkCVlqYCQ1w9522O8mtkfZCa4FuTLB7AQTERFRqQpNh6gFk9ob0TeQKPl2tXH2NSSQZzqEKvPhIiIiouLJsgQz23QIvbaK4HLUxtnXEDVnJ9jgeDQiIiIqSa5OcK1kggFg0tjGsqZn1cbZ15Dc0yFMTocgIiKikij55gTXSBHcEg3i8iXHlXy72jj7GpJzOoRhMBNMREREJZGkXNsm18aINGFCW7Tk29TO2deIXJlgXTc5HYKIiIhKosi54xDBGiqCy1HfZ1+FxHSIoSF2a2EcO8FERERUPFlGnm2Ta2NEWrlYBFeZgCrDBIZdtTETTERERKWS5VxxiNrJBJervs++Cokn5NBIhM5tk4mIiKhEcpZMsGmaNTUnuFz1ffZVSBS6QydEaAY3yyAiIqLSKFk6wVqqxqj3Ilit9AFQJvGEHDohQmMnmIiIiEokSZkj0l5+uxtbd/cDYBHMIrjK5ItDcLMMIiIiKoUiS4g5OsG/3PAeDg0k0RQJ4IhxTRU8sspjEVxlAkr2IpgL44iIiKhU8pDNMvpjScw7dQoWnPKxCh5VdWBVVWXsTvDQTLBuQJX5cBEREVHxnAvjNN1AImmgsSFQ4aOqDqyqqkyuOISmm4xDEBERUUmcneCBuAYAiLIIBsAiuOrk7QQzDkFEREQlcM4JHkwVwZEwi2CARXDVydYJNk0TOkekERERUYlkCXYRPBCzimDGISwsgquM6PY6R6SJ3eMUdoKJiIioBIos2XUE4xCZWFVVmWydYDHUmp1gIiIiKoUsSzBTmeDBGItgJxbBVSaQZcc4TbeevJwOQURERKWQpeGd4EiYE3IBFsFVJ1snWGcnmIiIiMqgyBLEXhnMBGdiEVxlsschmAkmIiKi0kmO6RADcQ2SBISD7AQDLIKrTroI1u3PaYZVEAdYBBMREVEJFClzRFokpEKW+c4ywCK46iiyDFmSkNTTWxymO8F80hIREVHxZFmCbqZHpDWE2AUWWARXIVWVMkek2ZlgPlxERERUPFmWYA7pBJOFVVUVCihy1kwwF8YRERFRKTKmQ8SSnAzhwCK4CgVUGUndkQlOdYK5MI6IiIhKIcsSDBGHiOuMQziwqqpCAXVoJzgVh2CQnYiIiEogS4BpAqZpYjCeZBzCgUVwFQqoSo44BB8uIiIiKp6SaqAZpomBuIYGxiFsrKqq0NBMMBfGERERUTnEODRNNzEY19kJdmBVVYUCqmxHIABAMzgijYiIiEoXTe0O171/AABYBDuwCK5CqiJlzwSzE0xEREQlmNTWCAB4d3sPADAO4cCqqgoFVAVJnQvjiIiIyJ2J7VEAwOZtBwEAkVCgkodTVVgEV6Gh0yF0e8c4PlxERERUvIaQijHNIby7PVUEsxNsY1VVhXKOSGMmmIiIiEo0sb0RB/sSAJgJdmIRXIUCijwkDsERaURERFQeEYkAmAl2YlVVhYbFIQwujCMiIqLyTGpvtP+fneA0VlVVaHgcgiPSiIiIqDwT2xyd4JBSwSOpLq4vB66//nq8+OKLGDVqFABg7ty5uOSSSwAAe/fuxbXXXosdO3YgFArh29/+No4//ni3d1nzVGXInGDdgCJLkCUWwURERFSajjFRyJKEQECGIrP/KXjSE7/44ovxpS99adjn77jjDsyaNQv3338/Nm3ahKuvvhq/+93vILGYy8vaLMOEYZqQJQm6brILTERERGUJqDLGjW5ALKFX+lCqymG9HHjmmWewbNkyAMCsWbMQCoXw5ptvHs67rAkB1XpYtFQkIp7UEWAemIiIiMr0iSNGYfzoSKUPo6p4Uln97Gc/w/z583HppZdiy5YtAIADBw7ANE2MHj3a/r6Ojg7s2rXLi7usaaLgFRMievsTaGkMVfKQiIiIyMe+eObH8a9LGUl1KhiHWLx4MT766KOsX3vxxRfxjW98A+3t7ZBlGY899hi+9rWvYf369Z4e5JgxjYW/yafa25uGfW7UKOtKrbklgtHNYQwkdLS1NmT93mpS7cfnRq2eW62el1CL51eL5+TE8/OfWjwnp1o9v1o9r1IULILXrVuX9+vjxo2z/3/RokX493//d+zatQsTJ04EAOzfv9/uBu/cuRPjx48v+SD37euDYZgl367atbc3Yc+eQ8M+Hx+0Blrv6u6FHk9iz4EBHDWxJev3Votc51ILavXcavW8hFo8v1o8Jyeen//U4jk51er51eJ5ybJUctPUdRyiu7vb/v8XXngBsizbhfHcuXPxyCOPAAA2bdqEWCyGGTNmuL3LmicywUnNgGmaqThEsMJHRURERFQ7XE+HuO6667Bv3z5IkoTGxkb86Ec/gqpaP/aqq67CNddcg8ceewyhUAjf/e53IXM0R0F2JlgzEEvoSGgGWqLMBBMRERF5xXUR/MADD+T8Wnt7e96vU3aqmA6hGzjYFwcAtETZCSYiIiLyCtuyVcjZCe7tt/LBzYxDEBEREXmGG0hXITsTrBsYjGsAgFZ2gomIiIg8w05wFXIujDvYZ3WCOSeYiIiIyDssgquQKILjCR09/XEosoRomE17IiIiIq+wsqpCbS0NUBUJ2/f02ePRJEmq9GERERER1QwWwVUooMqYPLYR7+/shaLInAxBRERE5DHGIarUlI5mfLDrEA72xTkjmIiIiMhjLIKr1JSOZsQSOj7a08/d4oiIiIg8xiK4Sk3paAYAmOBGGUREREReYxFcpcaPiSAcVACwCCYiIiLyGovgKiVLEo4c3wSAM4KJiIiIvMYiuIqJSAQ7wURERETeYhFcxY4/qg2jmkIYNzpS6UMhIiIiqimcE1zFpk9uxR3/36cqfRhERERENYedYCIiIiKqOyyCiYiIiKjusAgmIiIiorrDIpiIiIiI6g6LYCIiIiKqOyyCiYiIiKjusAgmIiIiorrDIpiIiIiI6g6LYCIiIiKqOyyCiYiIiKjusAgmIiIiorrDIpiIiIiI6g6LYCIiIiKqOyyCiYiIiKjusAgmIiIiorrDIpiIiIiI6g6LYCIiIiKqOyyCiYiIiKjuqJU+gGLIslTpQzhsauncaulchqrVc6vV8xJq8fxq8ZyceH7+U4vn5FSr51dr51XO+UimaZqH4ViIiIiIiKoW4xBEREREVHdYBBMRERFR3WERTERERER1h0UwEREREdUdFsFEREREVHdYBBMRERFR3WERTERERER1h0UwEREREdWdqiiCu7q6sHnz5kofhqe6urowd+5cLFy4EAsXLsStt96a9/uXL1+O5557boSOrnhdXV049dRToeu6/blHH30UnZ2dePDBByt4ZN7q6enBJz/5SXznO9+p9KG4Vi+PGVCb/3YIxZybn86/lv7Gsnn66aexaNEiLFy4EHPnzsVVV11V6UPyTCKRwH/8x3/gn//5nzF37lwsWrQI69evz3ub7du345e//OUIHWHpurq6MG/ePBiGkfE5v/w95SJqjwULFuDMM8/EJZdcgldffbXSh1W1fLFtsl/94Ac/wPTp0yt9GK61t7fjT3/6E04//XQAwGOPPYZjjz22pJ+haRpUtXqfbk888QROOOEEPPXUU7jmmmsQDAaLvq1hGJAkCZJUPVtQevGYEXnJzd9JYBYoAAATRUlEQVRYtdu9eze+9a1vYd26dejo6IBpmnjnnXcqfVieWbVqFQYGBvDUU08hFAph8+bN+NrXvoaWlhacfPLJWW+zY8cO/PKXv8TSpUtH+GiLNzAwgMcffxyLFy+u9KF4yll7/O53v8PFF1+MNWvW4Pjjj6/wkVWfqugEC/fffz/OO+88LFq0CEuXLsXbb79tf62zsxP33nsvzjvvPHzmM5/Bs88+W8EjLU9fXx+++c1vYsmSJZg/fz5uueWWjG7diy++iOXLl+PMM8/EnXfeWcEjzbR48WKsXbsWALBt2zYMDg7af2D/8z//g6VLl2LRokWYP38+nnrqKft2y5cvx5133okLL7wQl156aUWOvViPPvooLr30UkyfPh0bNmwAANx111244oorcNFFF+Fzn/scLrvsMhw6dMj+2tVXX41LL70UCxcuRG9vbyUPf5hyHrM33ngD8+bNy/g5CxYs8EUXYWgHx/lxV1cXvv/972Pp0qXo6uryXTc837n5Sba/seuvvz7j8XB+3N3djQsvvBCf+9znsGLFCqxYsaJqH7u9e/dCVVW0trYCACRJwtFHHw0A+Otf/4rly5fj3HPPxbnnnovnn38egNUpnTNnDm677Tb7NWHTpk2VOoWcduzYgaeffhqrVq1CKBQCAEyfPh0rVqzA3XffDQD48Y9/jPnz52PBggVYtmwZDMPA6tWrsWXLFixcuBCXX355JU8hp5UrV+Kuu+5CIpHI+PzWrVtx4YUXYv78+Vi8eDH++Mc/AgB++MMfZryre+DAAcyZMwcDAwMjetylOOuss7Bs2TKsWbMGiUTCfr4tXLgQ11xzDfr7+wEAhw4dwg033GA/jqtXr67wkY+MqmrNLVq0CF/96lcBWAXhzTffjF/96lf21xsbG/Hoo4/iL3/5C6688kqcffbZlTrUolx++eX2PxpXX301nnnmGZx88sn4zne+A8MwcPXVV+PRRx/F5z//eQDAli1b8LOf/QzxeBzLli3DiSeeiDPOOKOSpwAAmDNnDh566CH09PRg3bp1WLRoEf72t78BAI455hg89NBDUBQFe/fuxbnnnotTTz0VLS0tAIDNmzdjzZo1Vd0Ffuedd9DT04N/+qd/wp49e/Doo49i7ty5AIC//OUveOyxx9DW1oYbbrgB99xzD6677joAwKZNm7B27VqMHj26koefVTmP2XHHHYdIJIKXX34Zs2fPxqZNmyDLMmbOnFnhs3EvFovhl7/8JbZv326/sEWj0UofVt3I9zeWyy233II5c+bg0ksvxY4dOzB//nyceuqpI3TEpfnEJz6B4447Dp/+9KcxZ84czJw5EwsXLoSiKLj55ptx3333YezYsdi9ezeWLFmCJ598EgBw8OBBdHZ24rrrrsPLL7+Mf/3Xf8X69eurqku+efNmHHHEEXaBL5xwwgn4/ve/j3Xr1mHDhg14+OGH0djYiAMHDkCWZdx000247bbb7IvxajRjxgzMmDEDDz/8MC688EL781dffTU+//nP4/zzz8d7772HCy64AE8//TQWL16Mz3/+87j22muhqiqefPJJdHV1IRKJVPAsCjv++OOxYcMG/PSnP0VTUxN+85vfAABuv/123HffffjGN76BW2+9FZFIBI8//jhkWcb+/fsrfNQjo6oqk7/97W/48Y9/jJ6eHkiShA8++CDj6+eccw4A649v9+7diMfjdpFZjYbGIa699lq88cYb+NnPfgbAemEeN26c/fVFixZBVVWoqopzzjkHL730UlUUwZIk4bOf/Syeeuop/Pa3v8XDDz9sF1T79+/HjTfeiK1bt0JRFPT09OD999/HCSecAACYP39+VRfAAPCb3/wGCxcuhCRJOOuss3DLLbegu7sbAPDpT38abW1tAIAlS5bglltusW932mmnVWUBDJT/mC1fvhwPPfQQZs+ejV/84he44IILKnwm3hD/dkyaNAnNzc3YtWsXpk2bVuGjqh/5/sZy2bhxI/7t3/4NADBx4kSccsopI3GoZZFlGffccw82b96MV155BevXr8eaNWtw7bXXYvv27bjooovs75UkCVu3bsWoUaMQCASwYMECAMDs2bMRDofxj3/8A5/4xCcqdSrDmKaZ9+vPPfccvvCFL6CxsREAMGrUqJE4LM9ceeWV+PKXv4wlS5YAsM737bffxnnnnQcAOOqoo3D00Ufj9ddfR1dXF6ZNm4Y//OEP+MxnPoN169bhxhtvrOThF0U8hhs2bEBfX5/9TnoikbCfa8899xzWrl0LWbYCAtX62ua1qqlODMPAFVdcgQcffBDHHnssuru7cdppp2V8jyh4FUUBYOVMq7kIHso0Tdxzzz2YPHlyUd9bTRnTc889F+effz5mz56d8Y/cqlWr0NXVhbvvvhuSJOHss89GPB63v17tV8iJRAJPPPEEQqEQHn/8cQBAMpnEunXrhn3v0Mek2juJ5Txmc+fOxZ133om33noLGzduLLigs1ooipKxwMX5HASQ8e+EoigZMaRqV+jcql2+vzG/n9tQ06dPx/Tp03HBBRfgnHPOgWma6OzsxC9+8Yth37t9+/Zhn6u2f/cB65w+/PBDHDx4MKMb/Prrr6Ozs7OCR+aNqVOn4vTTT7ebU7mKfvG4LF68GI899hgmT56MQ4cOYdasWSN2rOV688038fGPfxzbt2/HzTffXNUXlCOtqjLBmqaho6MDAPDQQw9V+Gi819XVhfvuu89+Ad6/fz+2bdtmf/3xxx+HpmkYGBjAM888gzlz5lTqUIeZPHkyvvGNbwzL9h46dAgTJ06EJEn485//jK1bt1boCMuzfv16TJ06FX/84x+xYcMGbNiwAffff7/9Ft7zzz9vvy20bt26qnpMCinnMQsEAjjvvPNwySWXYP78+WhoaBjpwy7LEUccgTfffBOAlXneu3dvhY/IO34/t3x/Y85z2717NzZu3Gjfbvbs2fbF6M6dO/HSSy9V5PiL0d3djddee83+eNeuXdi/fz+OOuoobN26NePY33jjDbvQSiaTeOKJJwBY8ap4PI4pU6aM7MEXMGnSJMydOxerVq2yL1I2b96Me++9FytXrsQZZ5yBhx9+GH19fQCsnCxgxRfF56rdZZddhoceegj9/f2QZRlHH320/dzbsmUL3nnnHXtR2dlnn41XXnkF999/vy8W1K1fvx4PP/wwvvKVr6CrqwsPPPAAYrEYAGud0pYtWwAAZ5xxBtasWWM/NxmHGEGapqGhoQGXX345lixZgo6OjmFd4Fpw44034vbbb7ffFgwEArjxxhvtzvCxxx6Lr3zlK+ju7sbcuXOrIgrhlG2V71VXXYVvfetb+MlPfoLOzk7fdQbWrl2L+fPnZ3zuxBNPhGEYeOWVV3DKKafgxhtvxLZt2zBlyhRcf/31FTrS8pTzmJ1//vm4++678YUvfGGkDrNs4t2gK664Atdffz1+/etfY+bMmZgwYUKlD821Wjm3fH9jJ554Iv70pz9hwYIFOPLII3HcccfZ3/PNb34T1157LX77299i6tSpmDlzpv2We7XRNA133XUXduzYgXA4DMMwcOWVV+KYY47BPffcg9tvvx233norkskkJk+ejHvvvRcA0Nraiq1bt+L8889HLBbDnXfeWVV5YGHVqlW44447cM455yAQCCAUCuGb3/wmZs+eDdM00d3djaVLl0JRFESjUfziF79AZ2cnpkyZgnnz5mHq1Kn4wQ9+UOnTyGn8+PFYuHAh7r//fgDA9773Pdx000144IEHoKoqvvvd79rxgIaGBnzmM5/B2rVr8fvf/76Sh53T5ZdfjmAwiMHBQUybNg333XcfTjjhBBx77LG4++67sWTJEnui0cqVKzFt2jTccMMNuPXWWzFv3jwoioLZs2fbcaRaJpmFAj+H2e7du/HZz34Wf/7znxEOhyt5KEQZ7rrrLgwMDNgL4erF448/jqeeegr33XdfpQ8lr1r+t6OWz61YsVjMXiMhFpQ98MADmDp1aqUPzRPbt2/Heeedl9H9JqKRVdFO8M9//nM89NBDuO666+r2H3qiavIv//Iv+PDDD/GjH/2o0oeSVy3/21HL51aKDz74ANdddx1M04SmaVi5cmXNFMBEVB0q3gkmIiIiIhppVbUwjoiIiIhoJIxoEXzgwAFcdNFFOPvsszF//nysXLnSXoH4+uuvY8GCBTj77LPx1a9+Ffv27bNvd9VVV+HUU09FZ2envbvJUDfccEPerxMRERERCSNaBEuShK997Wt49tln8cQTT2Dy5Mn43ve+B9M0cc011+Cmm27Cs88+i1mzZuF73/uefbslS5bY8yWz2bBhQ9XNViQiIiKi6jWiRXBra2vGnNUTTjgBH330Ed58802EQiF76PSyZcvwzDPP2N93yimnYMyYMVl/5oEDB3D33XfjhhtuOLwHT0REREQ1o2KZYMMw8PDDD6Orqws7d+7MmH05evRoGIaBgwcPFvw5q1evxmWXXYampqbDebhEREREVEMqVgR/+9vfRiQSwZe+9KWyf8bTTz+NQCBQdZtKEBEREVF1q0gRfNttt2Hr1q34r//6L8iyjI6ODnz00Uf21/fv3w9JkjL2Kc9m48aNeOmll9DV1YWuri4AwLx58/Dee+8d1uMnIiIiIn8b8c0y/vM//xN/+9vfcN9999nbQ86YMQOxWAybNm3CrFmz8Mgjj+Czn/1swZ+1atUqrFq1yv64s7MTTz75JKLR6OE6fCIiIiKqASO6Wca7776LefPm4cgjj7R3Qpo0aRJ++MMf4tVXX8XNN9+MeDyOiRMn4vbbb0dbWxsAYOXKlXjjjTfQ3d2NsWPHYvr06VizZs2wn9/Z2YlXX32VRTARERER5cUd44iIiIio7nDHOCIiIiKqOyyCiYiIiKjusAgmIiIiorrDIpiIiIiI6g6LYCIiIiKqOyyCiYiIiKjusAgmIqoRy5cvx69//etKHwYRkS+wCCYiqkNdXV148cUXK30YREQVwyKYiIiIiOoOi2AiIpe6urqwZs0azJ8/HyeddBKuvPJKxONxAMBzzz2HhQsXYtasWVi2bBneeecdAMCjjz6KFStW2D/jzDPPxBVXXGF/fPrpp+Ptt9/Oe79//vOfMXfuXJx00klYvXo1nBuAfvjhh/jyl7+MOXPmYM6cObjqqqvQ29sLALjmmmvw0UcfYcWKFTjxxBPxk5/8BADw+uuvY9myZZg1axYWLFiAjRs3evMLIiKqQiyCiYg88PTTT+OnP/0pfv/73+Pvf/871q5di//93//FjTfeiNWrV2Pjxo1YunQpLr30UiQSCcyePRubNm2CYRjYvXs3NE3Dq6++CgDYtm0bBgYG0NnZmfP+9u/fj8suuwxXXnklXnrpJRxxxBH27QHANE18/etfxwsvvICnn34au3btwl133QUAuP322zFhwgTce++9eO2113DRRRehu7sbX//613HJJZfg5ZdfxnXXXYfLL78c+/fvP7y/OCKiCmERTETkgeXLl2PcuHFobW3FGWecgbfffhu/+tWvsHTpUhx//PFQFAWLFy9GIBDA66+/jsmTJyMajeLtt9/GK6+8glNPPRXjxo3Dli1b8PLLL+Okk06CLOf+J/qPf/wjjjrqKMydOxeBQAAXXngh2tra7K9/7GMfw6c+9SkEg0GMHj0aX/nKV/DKK6/k/HmPP/44TjvtNJx++umQZRmf+tSnMGPGDPzhD3/w9PdERFQt1EofABFRLWhvb7f/v6GhAbt370ZPTw8ee+wxPPjgg/bXkskkdu/eDQA4+eST8fLLL2Pr1q04+eST0dTUhFdeeQWvv/46Zs+enff+du/ejfHjx9sfS5KEjo4O++N9+/bhlltuwaZNm9Df3w/TNNHc3Jzz53300Ud45pln8Nxzz9mf0zQNc+bMKf6XQETkIyyCiYgOk46ODqxYsQKXXHJJ1q/Pnj0bGzZswI4dO7BixQo0NzfjiSeewGuvvYYLLrgg789ub2/Hrl277I9N08TOnTvtj++44w5IkoT//u//xqhRo7B+/XqsXr0677EuXLgQt9xyS4lnSUTkT4xDEBEdJueffz4eeeQR/PWvf4VpmhgYGMDzzz+Pvr4+AFYneOPGjYjFYhg/fjxmzZqFF154AQcPHsQxxxyT92effvrpePfdd/G73/0Omqbh5z//Ofbu3Wt/vb+/H5FIBM3Nzeju7sZPf/rTjNu3tbVh27Zt9scLFizAc889hxdeeAG6riMej2Pjxo0ZhTYRUS1hEUxEdJh88pOfxLe//W2sXr0aJ598Ms466yysXbvW/vqUKVMQjUYxa9YsAEBjYyMmTZqEmTNnQlGUvD979OjR+P73v4877rgDc+bMwdatWzFz5kz76ytXrsRbb72FWbNm4eKLL8ZZZ52VcfuLL74YP/rRjzBr1iysWbMGHR0duOeee/DjH/8Yp5xyCk4//XSsWbMGhmF4+BshIqoekumcqUNEREREVAfYCSYiIiKiusOFcUREVWrTpk246KKLsn7ttddeG+GjISKqLYxDEBEREVHdYRyCiIiIiOoOi2AiIiIiqjssgomIiIio7rAIJiIiIqK6wyKYiIiIiOrO/w9qrYYcHPSD3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_seattle.pivot_table(index='STATION_NAME',columns='new_date',values='TMIN').transpose().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATION           1\n",
       "STATION_NAME      1\n",
       "DATE            365\n",
       "PRCP             74\n",
       "SNWD              2\n",
       "SNOW              4\n",
       "TMAX             59\n",
       "TMIN             42\n",
       "AWND             69\n",
       "WDF2             37\n",
       "WDF5             38\n",
       "WSF2             29\n",
       "WSF5             37\n",
       "WT01              2\n",
       "WT05              1\n",
       "WT02              2\n",
       "WT03              2\n",
       "new_date        365\n",
       "dtype: int64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seattle.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'01/01/2014'"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}