Price of lumber 1979-2021.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Price of lumber 1979-2021"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In my project I explored and compared the rise of gold prices and silver prices to the rise of lumber prices from 1979 to 2021. I became interested in seeing how badly the lumber prices spiked because there were many news stories earlier this year (2021) about the price increase. I'm using the prices of gold and silver for comparisson because these precious metal resources have significant historical data. This project was completed in December 2021.\n",
    "\n",
    "Articles about the price of lumber:\n",
    "\n",
    "https://fortune.com/2021/10/11/lumber-prices-rise-another-bubble/\n",
    "https://www.npr.org/2021/07/08/1013819703/what-the-rise-and-fall-of-lumber-prices-tell-us-about-the-pandemic-economy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data sources:\n",
    "\n",
    "   Lumber data: https://data.nasdaq.com/data/FRED/WPU081-producer-price-index-by-commodity-for-lumber-and-wood-products-lumber\n",
    "   \n",
    "   Gold data: https://www.kaggle.com/andriimakarchuk/monthly-gold-prices-1990-2021-eda\n",
    "   \n",
    "   Silver data: https://www.kaggle.com/lbronchal/gold-and-silver-prices-dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 0: Importing and cleaning the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>United States(USD)</th>\n",
       "      <th>Europe(EUR)</th>\n",
       "      <th>Japan(JPY)</th>\n",
       "      <th>United Kingdom(GBP)</th>\n",
       "      <th>Canada(CAD)</th>\n",
       "      <th>Switzerland(CHF)</th>\n",
       "      <th>India(INR)</th>\n",
       "      <th>China(CNY)</th>\n",
       "      <th>Turkey(TRY)</th>\n",
       "      <th>Saudi Arabia(SAR)</th>\n",
       "      <th>Indonesia(IDR)</th>\n",
       "      <th>United Arab Emirates(AED)</th>\n",
       "      <th>Thailand(THB)</th>\n",
       "      <th>Vietnam(VND)</th>\n",
       "      <th>Egypt(EGP)</th>\n",
       "      <th>South Korean(KRW)</th>\n",
       "      <th>Australia(AUD)</th>\n",
       "      <th>South Africa(ZAR)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31-01-1979</td>\n",
       "      <td>233.7</td>\n",
       "      <td>144.8</td>\n",
       "      <td>45160.3</td>\n",
       "      <td>117.4</td>\n",
       "      <td>267.1</td>\n",
       "      <td>379.3</td>\n",
       "      <td>1840.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>745.2</td>\n",
       "      <td>139739.3</td>\n",
       "      <td>856.1</td>\n",
       "      <td>4511.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>108235.8</td>\n",
       "      <td>197.2</td>\n",
       "      <td>194.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28-02-1979</td>\n",
       "      <td>251.3</td>\n",
       "      <td>154.6</td>\n",
       "      <td>50209.1</td>\n",
       "      <td>124.2</td>\n",
       "      <td>295.5</td>\n",
       "      <td>413.6</td>\n",
       "      <td>2011.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>827.8</td>\n",
       "      <td>154683.1</td>\n",
       "      <td>946.6</td>\n",
       "      <td>4985.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>119929.3</td>\n",
       "      <td>219.6</td>\n",
       "      <td>209.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30-03-1979</td>\n",
       "      <td>240.1</td>\n",
       "      <td>148.0</td>\n",
       "      <td>50274.3</td>\n",
       "      <td>116.2</td>\n",
       "      <td>278.2</td>\n",
       "      <td>406.2</td>\n",
       "      <td>1940.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>796.7</td>\n",
       "      <td>148844.2</td>\n",
       "      <td>910.4</td>\n",
       "      <td>4812.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116123.6</td>\n",
       "      <td>214.6</td>\n",
       "      <td>202.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30-04-1979</td>\n",
       "      <td>245.3</td>\n",
       "      <td>152.8</td>\n",
       "      <td>54144.6</td>\n",
       "      <td>118.8</td>\n",
       "      <td>278.5</td>\n",
       "      <td>420.0</td>\n",
       "      <td>2013.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>819.4</td>\n",
       "      <td>152453.6</td>\n",
       "      <td>930.2</td>\n",
       "      <td>4891.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>117423.3</td>\n",
       "      <td>221.1</td>\n",
       "      <td>206.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31-05-1979</td>\n",
       "      <td>274.6</td>\n",
       "      <td>172.0</td>\n",
       "      <td>61057.1</td>\n",
       "      <td>132.7</td>\n",
       "      <td>321.6</td>\n",
       "      <td>478.0</td>\n",
       "      <td>2253.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>933.3</td>\n",
       "      <td>171656.1</td>\n",
       "      <td>1048.4</td>\n",
       "      <td>5560.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>133889.6</td>\n",
       "      <td>250.7</td>\n",
       "      <td>233.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>31-03-2021</td>\n",
       "      <td>1691.1</td>\n",
       "      <td>1438.8</td>\n",
       "      <td>186861.0</td>\n",
       "      <td>1225.7</td>\n",
       "      <td>2125.4</td>\n",
       "      <td>1591.5</td>\n",
       "      <td>123639.0</td>\n",
       "      <td>11084.8</td>\n",
       "      <td>13995.6</td>\n",
       "      <td>6342.1</td>\n",
       "      <td>24562502.0</td>\n",
       "      <td>6211.5</td>\n",
       "      <td>52845.3</td>\n",
       "      <td>39020979.9</td>\n",
       "      <td>26579.1</td>\n",
       "      <td>1913845.9</td>\n",
       "      <td>2220.3</td>\n",
       "      <td>24970.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>30-04-2021</td>\n",
       "      <td>1767.7</td>\n",
       "      <td>1468.4</td>\n",
       "      <td>193213.0</td>\n",
       "      <td>1276.7</td>\n",
       "      <td>2174.6</td>\n",
       "      <td>1612.2</td>\n",
       "      <td>130934.3</td>\n",
       "      <td>11439.4</td>\n",
       "      <td>14657.4</td>\n",
       "      <td>6628.9</td>\n",
       "      <td>25533702.8</td>\n",
       "      <td>6492.8</td>\n",
       "      <td>55040.2</td>\n",
       "      <td>40753168.5</td>\n",
       "      <td>27681.4</td>\n",
       "      <td>1966245.4</td>\n",
       "      <td>2288.4</td>\n",
       "      <td>25644.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>31-05-2021</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>1554.0</td>\n",
       "      <td>207845.0</td>\n",
       "      <td>1336.6</td>\n",
       "      <td>2295.3</td>\n",
       "      <td>1708.7</td>\n",
       "      <td>137979.1</td>\n",
       "      <td>12084.8</td>\n",
       "      <td>16105.4</td>\n",
       "      <td>7125.4</td>\n",
       "      <td>27131285.3</td>\n",
       "      <td>6978.3</td>\n",
       "      <td>59344.9</td>\n",
       "      <td>43786246.6</td>\n",
       "      <td>29772.2</td>\n",
       "      <td>2110939.4</td>\n",
       "      <td>2454.6</td>\n",
       "      <td>26067.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>30-06-2021</td>\n",
       "      <td>1763.2</td>\n",
       "      <td>1486.8</td>\n",
       "      <td>195692.0</td>\n",
       "      <td>1276.3</td>\n",
       "      <td>2183.3</td>\n",
       "      <td>1629.8</td>\n",
       "      <td>131054.9</td>\n",
       "      <td>11392.5</td>\n",
       "      <td>15317.4</td>\n",
       "      <td>6612.5</td>\n",
       "      <td>25565673.6</td>\n",
       "      <td>6476.3</td>\n",
       "      <td>56509.0</td>\n",
       "      <td>40580658.2</td>\n",
       "      <td>27672.6</td>\n",
       "      <td>1985571.3</td>\n",
       "      <td>2348.5</td>\n",
       "      <td>25175.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>30-07-2021</td>\n",
       "      <td>1825.8</td>\n",
       "      <td>1539.7</td>\n",
       "      <td>200376.1</td>\n",
       "      <td>1313.2</td>\n",
       "      <td>2279.2</td>\n",
       "      <td>1654.7</td>\n",
       "      <td>135863.2</td>\n",
       "      <td>11787.6</td>\n",
       "      <td>15376.5</td>\n",
       "      <td>6847.3</td>\n",
       "      <td>26404909.4</td>\n",
       "      <td>6706.3</td>\n",
       "      <td>60007.8</td>\n",
       "      <td>41910091.3</td>\n",
       "      <td>28673.4</td>\n",
       "      <td>2100068.9</td>\n",
       "      <td>2483.8</td>\n",
       "      <td>26708.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>511 rows × 19 columns</p>\n",
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      "text/plain": [
       "           Date  United States(USD)  Europe(EUR)  Japan(JPY)  \\\n",
       "0    31-01-1979               233.7        144.8     45160.3   \n",
       "1    28-02-1979               251.3        154.6     50209.1   \n",
       "2    30-03-1979               240.1        148.0     50274.3   \n",
       "3    30-04-1979               245.3        152.8     54144.6   \n",
       "4    31-05-1979               274.6        172.0     61057.1   \n",
       "..          ...                 ...          ...         ...   \n",
       "506  31-03-2021              1691.1       1438.8    186861.0   \n",
       "507  30-04-2021              1767.7       1468.4    193213.0   \n",
       "508  31-05-2021              1900.0       1554.0    207845.0   \n",
       "509  30-06-2021              1763.2       1486.8    195692.0   \n",
       "510  30-07-2021              1825.8       1539.7    200376.1   \n",
       "\n",
       "     United Kingdom(GBP)  Canada(CAD)  Switzerland(CHF)  India(INR)  \\\n",
       "0                  117.4        267.1             379.3      1840.8   \n",
       "1                  124.2        295.5             413.6      2011.7   \n",
       "2                  116.2        278.2             406.2      1940.2   \n",
       "3                  118.8        278.5             420.0      2013.1   \n",
       "4                  132.7        321.6             478.0      2253.4   \n",
       "..                   ...          ...               ...         ...   \n",
       "506               1225.7       2125.4            1591.5    123639.0   \n",
       "507               1276.7       2174.6            1612.2    130934.3   \n",
       "508               1336.6       2295.3            1708.7    137979.1   \n",
       "509               1276.3       2183.3            1629.8    131054.9   \n",
       "510               1313.2       2279.2            1654.7    135863.2   \n",
       "\n",
       "     China(CNY)  Turkey(TRY)  Saudi Arabia(SAR)  Indonesia(IDR)  \\\n",
       "0           NaN          NaN              745.2        139739.3   \n",
       "1           NaN          NaN              827.8        154683.1   \n",
       "2           NaN          NaN              796.7        148844.2   \n",
       "3           NaN          NaN              819.4        152453.6   \n",
       "4           NaN          NaN              933.3        171656.1   \n",
       "..          ...          ...                ...             ...   \n",
       "506     11084.8      13995.6             6342.1      24562502.0   \n",
       "507     11439.4      14657.4             6628.9      25533702.8   \n",
       "508     12084.8      16105.4             7125.4      27131285.3   \n",
       "509     11392.5      15317.4             6612.5      25565673.6   \n",
       "510     11787.6      15376.5             6847.3      26404909.4   \n",
       "\n",
       "     United Arab Emirates(AED)  Thailand(THB)  Vietnam(VND)  Egypt(EGP)  \\\n",
       "0                        856.1         4511.3           NaN         NaN   \n",
       "1                        946.6         4985.7           NaN         NaN   \n",
       "2                        910.4         4812.9           NaN         NaN   \n",
       "3                        930.2         4891.0           NaN         NaN   \n",
       "4                       1048.4         5560.8           NaN         NaN   \n",
       "..                         ...            ...           ...         ...   \n",
       "506                     6211.5        52845.3    39020979.9     26579.1   \n",
       "507                     6492.8        55040.2    40753168.5     27681.4   \n",
       "508                     6978.3        59344.9    43786246.6     29772.2   \n",
       "509                     6476.3        56509.0    40580658.2     27672.6   \n",
       "510                     6706.3        60007.8    41910091.3     28673.4   \n",
       "\n",
       "     South Korean(KRW)  Australia(AUD)  South Africa(ZAR)  \n",
       "0             108235.8           197.2              194.5  \n",
       "1             119929.3           219.6              209.4  \n",
       "2             116123.6           214.6              202.4  \n",
       "3             117423.3           221.1              206.9  \n",
       "4             133889.6           250.7              233.7  \n",
       "..                 ...             ...                ...  \n",
       "506          1913845.9          2220.3            24970.5  \n",
       "507          1966245.4          2288.4            25644.2  \n",
       "508          2110939.4          2454.6            26067.3  \n",
       "509          1985571.3          2348.5            25175.6  \n",
       "510          2100068.9          2483.8            26708.4  \n",
       "\n",
       "[511 rows x 19 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import the gold data\n",
    "gold_raw = pd.read_csv(\"1979-2021.csv\")\n",
    "display(gold_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1968-01-02</td>\n",
       "      <td>2.173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1968-01-03</td>\n",
       "      <td>2.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1968-01-04</td>\n",
       "      <td>2.171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1968-01-05</td>\n",
       "      <td>2.157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1968-01-08</td>\n",
       "      <td>2.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>13470</th>\n",
       "      <td>2021-03-30</td>\n",
       "      <td>24.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13471</th>\n",
       "      <td>2021-03-31</td>\n",
       "      <td>24.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13472</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>24.315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13473</th>\n",
       "      <td>2021-04-06</td>\n",
       "      <td>25.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13474</th>\n",
       "      <td>2021-04-07</td>\n",
       "      <td>24.985</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>13475 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date   price\n",
       "0      1968-01-02   2.173\n",
       "1      1968-01-03   2.225\n",
       "2      1968-01-04   2.171\n",
       "3      1968-01-05   2.157\n",
       "4      1968-01-08   2.163\n",
       "...           ...     ...\n",
       "13470  2021-03-30  24.340\n",
       "13471  2021-03-31  24.000\n",
       "13472  2021-04-01  24.315\n",
       "13473  2021-04-06  25.040\n",
       "13474  2021-04-07  24.985\n",
       "\n",
       "[13475 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import the silver data\n",
    "silver_raw = pd.read_csv(\"silver_price.csv\")\n",
    "#silver_raw = silver_raw.dropna()\n",
    "display(silver_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>290.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>275.1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>282.6</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-06-01</td>\n",
       "      <td>444.3</td>\n",
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       "      <th>1146</th>\n",
       "      <td>1926-04-01</td>\n",
       "      <td>8.2</td>\n",
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       "    <tr>\n",
       "      <th>1147</th>\n",
       "      <td>1926-03-01</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1148</th>\n",
       "      <td>1926-02-01</td>\n",
       "      <td>8.3</td>\n",
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       "    <tr>\n",
       "      <th>1149</th>\n",
       "      <td>1926-01-01</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1150 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  Value\n",
       "0     2021-10-01  290.0\n",
       "1     2021-09-01  275.1\n",
       "2     2021-08-01  282.6\n",
       "3     2021-07-01  350.0\n",
       "4     2021-06-01  444.3\n",
       "...          ...    ...\n",
       "1145  1926-05-01    8.1\n",
       "1146  1926-04-01    8.2\n",
       "1147  1926-03-01    8.3\n",
       "1148  1926-02-01    8.3\n",
       "1149  1926-01-01    8.3\n",
       "\n",
       "[1150 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import the lumber data\n",
    "lumber_raw = pd.read_csv(\"FRED-WPU081.csv\")\n",
    "display(lumber_raw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, I need to make the data line up as well as possible. I'll need to filter the gold data for only United States prices since the lumber is only US data. Then, I'll need to filter all data to have the same years and months as each other so the comparison is on the same timeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top of the gold data\n"
     ]
    },
    {
     "data": {
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       "      <th>Date</th>\n",
       "      <th>United States(USD)</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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      ],
      "text/plain": [
       "      Date  United States(USD)\n",
       "0  1979-01               233.7\n",
       "1  1979-02               251.3"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
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     "text": [
      "\n",
      "End of the gold data\n"
     ]
    },
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       "      <th>510</th>\n",
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       "        Date  United States(USD)\n",
       "509  2021-06              1763.2\n",
       "510  2021-07              1825.8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# grab only the date and USD price format\n",
    "gold = gold_raw[['Date','United States(USD)']]\n",
    "\n",
    "# convert to datetime type and reduce to year/month\n",
    "gold['Date'] = pd.to_datetime(gold['Date'])\n",
    "\n",
    "#extract only year and month to align data better\n",
    "gold['Date'] = gold['Date'].dt.strftime('%Y-%m')\n",
    "\n",
    "# view the gold data and check for date range\n",
    "print(\"Top of the gold data\")\n",
    "display(gold.head(2))\n",
    "print(\"\\nEnd of the gold data\")\n",
    "display(gold.tail(2))\n",
    "\n",
    "# gold has the latest start date of all the date stamps, so use it to filter earliest date\n",
    "earliest_date = min(gold['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top of the silver data\n"
     ]
    },
    {
     "data": {
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       "      date  price\n",
       "0  1979-01  6.083\n",
       "1  1979-01  5.989"
      ]
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     "text": [
      "End of the silver data\n"
     ]
    },
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      "text/plain": [
       "          date   price\n",
       "10681  2021-04  25.040\n",
       "10682  2021-04  24.985"
      ]
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date range:  1979-01 to 2021-04\n"
     ]
    }
   ],
   "source": [
    "# convert to datetime type and reduce to year/month\n",
    "silver_raw['date'] = pd.to_datetime(silver_raw['date'])\n",
    "\n",
    "#extract only year and month to align data better\n",
    "silver_raw['date'] = silver_raw['date'].dt.strftime('%Y-%m')\n",
    "\n",
    "# filter to be in the same date range as gold\n",
    "silver = silver_raw.loc[(silver_raw['date'] >= str(earliest_date))].reset_index(drop=True)\n",
    "\n",
    "print(\"Top of the silver data\")\n",
    "display(silver.head(2))\n",
    "print(\"End of the silver data\")\n",
    "display(silver.tail(2))\n",
    "\n",
    "# silver has the earliest end date of all the date stamps, so use it to filter latest date\n",
    "latest_date = max(silver['date'])\n",
    "print(\"Date range: \", earliest_date, \"to\", latest_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top of the lumber data\n"
     ]
    },
    {
     "data": {
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       "      Date  Value\n",
       "6  2021-04  390.3\n",
       "7  2021-03  365.8"
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     "text": [
      "\n",
      "End of the lumber data\n"
     ]
    },
    {
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       "      <th>513</th>\n",
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       "        Date  Value\n",
       "512  1979-02  109.4\n",
       "513  1979-01  108.3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# convert to datetime type and reduce to year/month\n",
    "lumber_raw['Date'] = pd.to_datetime(lumber_raw['Date'])\n",
    "\n",
    "#extract only year and month to align data better\n",
    "lumber_raw['Date'] = lumber_raw['Date'].dt.strftime('%Y-%m')\n",
    "\n",
    "# filter the dates to match the gold and silver data\n",
    "lumber = lumber_raw.loc[(lumber_raw[\"Date\"] >= earliest_date) & (lumber_raw[\"Date\"] <= latest_date)]\n",
    "\n",
    "print(\"Top of the lumber data\") \n",
    "display(lumber.head(2))\n",
    "print(\"\\nEnd of the lumber data\")\n",
    "display(lumber.tail(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the lumber data is sorted in the opposite direction from the gold and silver data. That just requires resorting the lumber data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <th>2</th>\n",
       "      <td>1979-03</td>\n",
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      ],
      "text/plain": [
       "      Date  Value\n",
       "0  1979-01  108.3\n",
       "1  1979-02  109.4\n",
       "2  1979-03  112.8"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "lumber = lumber.sort_values(\"Date\").reset_index(drop=True)\n",
    "display(lumber.head(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if there are any missing values in the data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking for any missing values in gold data: \n",
      " Date                  0\n",
      "United States(USD)    0\n",
      "dtype: int64\n",
      "\n",
      "Checking for any missing values in silver data: \n",
      " date     0\n",
      "price    2\n",
      "dtype: int64\n",
      "\n",
      "Checking for any missing values in lumber data: \n",
      " Date     0\n",
      "Value    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Checking for any missing values in gold data: \\n\", gold.isnull().sum())\n",
    "print(\"\\nChecking for any missing values in silver data: \\n\", silver.isnull().sum())\n",
    "print(\"\\nChecking for any missing values in lumber data: \\n\", lumber.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date     0\n",
      "price    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# remove missing values identified above and verify their removal\n",
    "silver = silver.dropna()\n",
    "print(silver.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Now the range of time matches between the two data sets and the data has been filtered for missing values. Now, combine the data into a single dataframe for easy comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Gold_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1979-01</td>\n",
       "      <td>233.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Date  Gold_Price\n",
       "0  1979-01       233.7"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Silver_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1979-01</td>\n",
       "      <td>6.083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Date  Silver_Price\n",
       "0  1979-01         6.083"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Date</th>\n",
       "      <th>Lumber_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1979-01</td>\n",
       "      <td>108.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Date  Lumber_Price\n",
       "0  1979-01         108.3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rename the columns to prepare for clear desctiptions in the merge\n",
    "gold.rename(columns={'Date': 'Date', 'United States(USD)': 'Gold_Price'}, inplace=True)\n",
    "silver.rename(columns={'date': 'Date', 'price': 'Silver_Price'}, inplace=True)\n",
    "lumber.rename(columns={'Date': 'Date', 'Value': 'Lumber_Price'}, inplace=True)\n",
    "display(gold.head(1), silver.head(1), lumber.head(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Date</th>\n",
       "      <th>Gold_Price</th>\n",
       "      <th>Silver_Price</th>\n",
       "      <th>Lumber_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1979-01-01</td>\n",
       "      <td>233.7</td>\n",
       "      <td>6.083</td>\n",
       "      <td>108.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1979-02-01</td>\n",
       "      <td>251.3</td>\n",
       "      <td>5.989</td>\n",
       "      <td>109.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1979-03-01</td>\n",
       "      <td>240.1</td>\n",
       "      <td>5.982</td>\n",
       "      <td>112.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1979-04-01</td>\n",
       "      <td>245.3</td>\n",
       "      <td>5.984</td>\n",
       "      <td>114.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1979-05-01</td>\n",
       "      <td>274.6</td>\n",
       "      <td>6.009</td>\n",
       "      <td>114.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>1887.6</td>\n",
       "      <td>16.500</td>\n",
       "      <td>291.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>1863.8</td>\n",
       "      <td>16.208</td>\n",
       "      <td>329.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>2021-02-01</td>\n",
       "      <td>1742.9</td>\n",
       "      <td>15.590</td>\n",
       "      <td>343.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>1691.1</td>\n",
       "      <td>15.500</td>\n",
       "      <td>365.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>1767.7</td>\n",
       "      <td>15.800</td>\n",
       "      <td>390.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>508 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Gold_Price  Silver_Price  Lumber_Price\n",
       "0   1979-01-01       233.7         6.083         108.3\n",
       "1   1979-02-01       251.3         5.989         109.4\n",
       "2   1979-03-01       240.1         5.982         112.8\n",
       "3   1979-04-01       245.3         5.984         114.3\n",
       "4   1979-05-01       274.6         6.009         114.2\n",
       "..         ...         ...           ...           ...\n",
       "503 2020-12-01      1887.6        16.500         291.5\n",
       "504 2021-01-01      1863.8        16.208         329.9\n",
       "505 2021-02-01      1742.9        15.590         343.9\n",
       "506 2021-03-01      1691.1        15.500         365.8\n",
       "507 2021-04-01      1767.7        15.800         390.3\n",
       "\n",
       "[508 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# concatinate the dataframes together\n",
    "gsl = pd.concat([lumber[['Date']], gold[['Gold_Price']], silver[['Silver_Price']], lumber[['Lumber_Price']]], axis=1)\n",
    "gsl = gsl.dropna() # removes all the extra rows from the silver set so we end up with 1 price set per month\n",
    "# put the date back in datetime format to prepare for the analysis below\n",
    "gsl['Date'] = pd.to_datetime(gsl['Date'])\n",
    "# make a copy of gsl for later use\n",
    "gsl_regression = gsl.copy()\n",
    "display(gsl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: Comparing the prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now begin to ask questions and compare the historical price of gold to the price of lumber."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# initial plot of the prices of each resource\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "sns.set(rc={'figure.figsize':(12,8)})\n",
    "sns.lineplot(data = gsl).set_title(\"Resource prices from 1979 to early 2021\", size = 20)\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At first glance, it seems like the price of gold is the one that has spiked more than silver or lumber. To better compare, I set up some calculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "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>Date</th>\n",
       "      <th>Gold_Price</th>\n",
       "      <th>Silver_Price</th>\n",
       "      <th>Lumber_Price</th>\n",
       "      <th>$ Diff Gold to Lumber</th>\n",
       "      <th>% Diff Gold to Lumber</th>\n",
       "      <th>$ Diff Silver to Lumber</th>\n",
       "      <th>% Diff Silver to Lumber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1979-01-01</td>\n",
       "      <td>233.7</td>\n",
       "      <td>6.083</td>\n",
       "      <td>108.3</td>\n",
       "      <td>125.4</td>\n",
       "      <td>0.435644</td>\n",
       "      <td>-102.217</td>\n",
       "      <td>-1.697027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1979-02-01</td>\n",
       "      <td>251.3</td>\n",
       "      <td>5.989</td>\n",
       "      <td>109.4</td>\n",
       "      <td>141.9</td>\n",
       "      <td>0.463725</td>\n",
       "      <td>-103.411</td>\n",
       "      <td>-1.703950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1979-03-01</td>\n",
       "      <td>240.1</td>\n",
       "      <td>5.982</td>\n",
       "      <td>112.8</td>\n",
       "      <td>127.3</td>\n",
       "      <td>0.429342</td>\n",
       "      <td>-106.818</td>\n",
       "      <td>-1.712321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1979-04-01</td>\n",
       "      <td>245.3</td>\n",
       "      <td>5.984</td>\n",
       "      <td>114.3</td>\n",
       "      <td>131.0</td>\n",
       "      <td>0.433129</td>\n",
       "      <td>-108.316</td>\n",
       "      <td>-1.715652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1979-05-01</td>\n",
       "      <td>274.6</td>\n",
       "      <td>6.009</td>\n",
       "      <td>114.2</td>\n",
       "      <td>160.4</td>\n",
       "      <td>0.483569</td>\n",
       "      <td>-108.191</td>\n",
       "      <td>-1.714351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>1887.6</td>\n",
       "      <td>16.500</td>\n",
       "      <td>291.5</td>\n",
       "      <td>1596.1</td>\n",
       "      <td>0.784961</td>\n",
       "      <td>-275.000</td>\n",
       "      <td>-1.694915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>1863.8</td>\n",
       "      <td>16.208</td>\n",
       "      <td>329.9</td>\n",
       "      <td>1533.9</td>\n",
       "      <td>0.756081</td>\n",
       "      <td>-313.692</td>\n",
       "      <td>-1.731593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>2021-02-01</td>\n",
       "      <td>1742.9</td>\n",
       "      <td>15.590</td>\n",
       "      <td>343.9</td>\n",
       "      <td>1399.0</td>\n",
       "      <td>0.730606</td>\n",
       "      <td>-328.310</td>\n",
       "      <td>-1.750613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>1691.1</td>\n",
       "      <td>15.500</td>\n",
       "      <td>365.8</td>\n",
       "      <td>1325.3</td>\n",
       "      <td>0.707204</td>\n",
       "      <td>-350.300</td>\n",
       "      <td>-1.765625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>1767.7</td>\n",
       "      <td>15.800</td>\n",
       "      <td>390.3</td>\n",
       "      <td>1377.4</td>\n",
       "      <td>0.701735</td>\n",
       "      <td>-374.500</td>\n",
       "      <td>-1.775302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>508 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Gold_Price  Silver_Price  Lumber_Price  $ Diff Gold to Lumber  \\\n",
       "0   1979-01-01       233.7         6.083         108.3                  125.4   \n",
       "1   1979-02-01       251.3         5.989         109.4                  141.9   \n",
       "2   1979-03-01       240.1         5.982         112.8                  127.3   \n",
       "3   1979-04-01       245.3         5.984         114.3                  131.0   \n",
       "4   1979-05-01       274.6         6.009         114.2                  160.4   \n",
       "..         ...         ...           ...           ...                    ...   \n",
       "503 2020-12-01      1887.6        16.500         291.5                 1596.1   \n",
       "504 2021-01-01      1863.8        16.208         329.9                 1533.9   \n",
       "505 2021-02-01      1742.9        15.590         343.9                 1399.0   \n",
       "506 2021-03-01      1691.1        15.500         365.8                 1325.3   \n",
       "507 2021-04-01      1767.7        15.800         390.3                 1377.4   \n",
       "\n",
       "     % Diff Gold to Lumber  $ Diff Silver to Lumber  % Diff Silver to Lumber  \n",
       "0                 0.435644                 -102.217                -1.697027  \n",
       "1                 0.463725                 -103.411                -1.703950  \n",
       "2                 0.429342                 -106.818                -1.712321  \n",
       "3                 0.433129                 -108.316                -1.715652  \n",
       "4                 0.483569                 -108.191                -1.714351  \n",
       "..                     ...                      ...                      ...  \n",
       "503               0.784961                 -275.000                -1.694915  \n",
       "504               0.756081                 -313.692                -1.731593  \n",
       "505               0.730606                 -328.310                -1.750613  \n",
       "506               0.707204                 -350.300                -1.765625  \n",
       "507               0.701735                 -374.500                -1.775302  \n",
       "\n",
       "[508 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate the difference in dollar value and then use that to\n",
    "# calculate the percent difference between the precious metal and lumber\n",
    "gsl['$ Diff Gold to Lumber'] = gsl.apply(lambda row: row['Gold_Price'] - row['Lumber_Price'], axis=1)\n",
    "gsl['% Diff Gold to Lumber'] = gsl.apply(lambda row: row['$ Diff Gold to Lumber'] / (row['Gold_Price'] + row['Lumber_Price'] / 2), axis=1)\n",
    "gsl['$ Diff Silver to Lumber'] = gsl.apply(lambda row: row['Silver_Price'] - row['Lumber_Price'], axis=1)\n",
    "gsl['% Diff Silver to Lumber'] = gsl.apply(lambda row: row['$ Diff Silver to Lumber'] / (row['Silver_Price'] + row['Lumber_Price'] / 2), axis=1)\n",
    "display(gsl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average % difference of gold to lumber 58.96388 %\n",
      "Average % difference of silver to lumber -153.39385 %\n"
     ]
    }
   ],
   "source": [
    "print(\"Average % difference of gold to lumber\", round(gsl['% Diff Gold to Lumber'].mean()*100,5), \"%\")\n",
    "print(\"Average % difference of silver to lumber\", round(gsl['% Diff Silver to Lumber'].mean()*100,5), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How much did the % difference gold to lumber change over the 42-year period?  26.60912 %\n",
      "How much did the % difference silver to lumber change over the 42-year period?  -7.82757 %\n"
     ]
    }
   ],
   "source": [
    "print(\"How much did the % difference gold to lumber change over the 42-year period? \", round((gsl['% Diff Gold to Lumber'][507] - gsl['% Diff Gold to Lumber'][0])*100,5), \"%\")\n",
    "print(\"How much did the % difference silver to lumber change over the 42-year period? \", round((gsl['% Diff Silver to Lumber'][507] - gsl['% Diff Silver to Lumber'][0])*100,5), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DDISfAZg1axZeeuklVFdXx5wIKv9uoR80QkV7TiSSfEyffPLJsB798Yh0TOX7u+2225SuQIkmPy+bmpr6ve1Q/taJRhvWhBONUllZWQAQtS1bPOSOCJs2bYLValXqw0NLUQBg0qRJyM7Oxq5du+D1ege/6DjJAW+kaYoTJ06E0WjEwYMHI2a75bZzvWtvh0oOZCKViJw6dUrJ0KdCpC4gO3bsgN/vD+smM2/ePOW6dHjrrbfwzjvv4KGHHoLBYAi7LrQGPN56cLPZjPHjx6OpqQnV1dV9ro+3O8pQyMd0+/btCbm/uXPnQqVSJez+IpH/fg8dOtRvIJ7qv3Wi4YxBONEItWHDBmzZsiViT96Wlha8+OKLAKCc0h+slStXwu124/nnn8dHH32EadOm9QlgNRoNbrrpJrS0tOC3v/0tXC5Xn/tpbm6OuMFvMOQNoQ0NDX2u0+l0WLFiBex2O1avXh12XXV1Nf7xj39Aq9UqGx8TZdKkSTCbzdi0aVNYJtblcuG3v/1tQh+rP0899RS6urqU791uN/74xz8CkDbmya6++mpkZ2fjz3/+c8T+1YFAIGbP9qHo7OzEb37zG9xwww1hz1G59lyeVil/rdPpItZ593b11VcjEAjgv//7v8P+NuS+5sl20UUXYdy4cXj++efx4YcfRrzNzp074XQ647q/goICrFixAnv37sUTTzwR8YNndXU1ampqBr1mtVqNG264AS6XC7/4xS/6fOjxeDxob28HkPq/daLhjOUoRCPU7t278eyzz6KoqAgLFy7EmDFjAEhDcD788EO4XC5cdNFFuOyyy4b0OFdddRUee+wxPP744/B6vX2y4LLvfOc7OHjwIF544QW8//77OPPMM1FSUoK2tjacOnUKX3zxBb7//e8nZAri/PnzYTQa8X//93/o7OxUamlvvvlmWCwW3Hfffdi+fTvWrFmDqqoqnHHGGUqfcLvdjp/97GcD6uwBSAOMeneKkd18882YNWsWbrnlFjz55JNYuXIlli1bBp/Ph08++STqRMZkmTRpEq688sqwPuHV1dW44IILwj585OXl4bHHHsN3v/tdXHfddTjrrLMwZcoUCIKAxsZG7Ny5E52dnTFrkQfrt7/9LfR6Pe67776wy1esWIHHH38cv/zlL7F7925UV1dj27Zt+PrXvx6zHlx2++23Y+PGjXj77bexatUqnHvuubBarXjzzTexePHiIXcN6o9Wq8Xjjz+Ob3zjG/jmN7+JBQsWYMaMGTAYDGhsbERVVRVqamqwefPmuH4fAPj5z3+OU6dO4bHHHsNrr72GhQsXorCwEM3NzTh27Biqqqrwxz/+ccDP6VDf/e53sXv3brz//vu49NJLccEFFyArKwsNDQ3YsmULfvzjHyudVlL5t040nDEIJxqhbr/9dkyYMAGffPIJDh06hM2bN8Pj8SA3Nxenn346li9fjhUrVgy5dre8vBxnnHEGtm7dCo1GgxUrVkS8nVarxZNPPon169fjlVdewQcffACHw4G8vDyMGTMG9957b9SfHaicnBw89thjeOKJJ/DKK68om9y+9KUvwWKxIDc3F//85z/xl7/8Be+++y7+9re/wWAwYO7cufj617+ubGIdiM2bN0e97uKLL8asWbOUjYPr1q3DunXrUFhYiCuuuAJ33303rrzyykH/vgO1evVqPPHEE3j99dfR3NyMkpIS3H333fjmN7/Z5/lw1lln4bXXXsP//u//YvPmzdi+fTu0Wi2Ki4tx5pln4tJLL034+j744AO8/vrr+Nvf/qaUTckMBgOeeeYZPPTQQ3jppZdgMplw66234nvf+15c963T6fD3v/8djz/+ON544w08++yzqKiowLe//W0sW7Ys6UE4IHX8Wb9+Pf72t7/hgw8+UNomFhUVYebMmbj77ruVsznxMJvN+Mc//oF169Zhw4YNeOedd+B2u1FYWIjx48fjgQcewNlnnz2kNet0OjzzzDN44YUX8Oqrr+LVV1+FKIooLi7GsmXLsGjRIuW2qfxbJxrOBHEoBaFERERERDRgrAknIiIiIkoxBuFERERERCnGIJyIiIiIKMUYhBMRERERpRiDcCIiIiKiFBuVLQo7OuwIBIZPU5iCAjPa2mzpXkbG4vGJjccnNh6f2Hh8YuPxiY3HJzYen/4N52OkUgnIy8uKev2oDMIDAXFYBeEAht16U43HJzYen9h4fGLj8YmNxyc2Hp/YeHz6N1KPEctRiIiIiIhSjEE4EREREVGKMQgnIiIiIkoxBuFERERERCnGIJyIiIiIKMUYhBMRERERpRiDcCIiIiKiFGMQTkRERESUYgzCiYiIiIhSjEE4EREREVGKMQgnIiIiIkoxBuFERERERCnGIJyIiIiIKMUYhBMRERERpRiDcCIiIiKiFGMQTkRERESUYgzCiYgoKQ6e6sCh6o50L4OIKCMxCCcioqT4w9qd+P3zO9O9DCKijMQgnIiIksrl8aV7CUREGYdBOBERJdWpRmu6l0BElHEYhBMRUVLodWoAwIkGBuFERL0xCCcioqQwaKUg/HhDd5pXQkSUeRiEExFRUnh9AQBAXYstzSshIso8DMKJiCgpPMEgXA7GiYioB4NwIiJKOFEU4fNLwbc/IKZ5NUREmYdBOBERJZwcgAPMhBMRRcIgnIiIEs4TEnj7AwzCiYh6YxBOREQJJ2e/jXo1fH6WoxAR9cYgnIiIEsbh8mH1i7vR3OEEABj1mrDSFCIikjAIJyKihKlrtWH3sTYcqu4AABh1GogiEODmTCKiMAzCiYgoYdxePwDA6vQCkDLhAJgNJyLqhUE4EREljNsjBds2R+8gnJlwIqJQDMKJiChhPHIm3OEBIG3MBJgJJyLqjUE4ERElTO9yFIOO5ShERJEwCCciooTpyYRLQbhJLkfhxkwiojAMwomIKGHcvYJwQ7Acxc9MOBFRGAbhRESUMG6vFGzL5SfcmElEFBmDcCIiShg5Ey4zsUUhEVFEDMKJiChhegfh3JhJRBQZg3AiIkoYT68gvKdFIctRiIhCMQgnIqKE8Xh7Mt5qlQCdlhsziYgiYRBOREQJE1qOotGooFELAJgJJyLqjUE4ERElTGgQrtOooFFJbzOsCSciCscgnIiIEiY0CNdqVNBogkF4gEE4EVEoBuFERJQwbk9IEK5WQaOSylH8LEchIgrDIJyIiBLG4+vJeGs1aqjV0tuMl+UoRERhGIQTEVHCuL1+ZBmk3uDakI2ZzIQTEYVjEE5ERAkhiiI8Hj+ys3QA5CCcGzOJiCJhEE5ERAnh9QUgAsgJBuG6sBaFDMKJiEIxCCciooSQO6OEZsLlmnCWoxARhWMQTkRECSFPy8w29QThKkGAWiWwRSERUS8MwomIKCEiZcIBQK0WODGTiKgXBuFERJQQfYNwNQBAo1LB52MmnIgoFINwIiJKCE/vIDxYD65RC/AFmAknIgrFIJyIiBJCzoRbjFroNCqYgv3C1WoVu6MQEfWiSfcCiIhoZJA3Zup1avzohgUoyTMBkDLifgbhRERhGIQTEVFCyJlwvVaNMUVm5XJuzCQi6ovlKERElBByTbhOqw67XMNyFCKiPhiEExFRQriD5Sg6Tfhbi4aZcCKiPhiEExFRQnh8ciY8/K2FGzOJiPpiEE5ERAnh9QWgUQtQq3plwlUCN2YSEfXCIJyIiBLC7fUrA3pCadQq9gknIuqFQTgRESWExxvoU4oCcGMmEVEkGRGEr1+/HitWrMDMmTOxZs2amLddt24dli1bhosvvhi//vWvEQjwhZ2IKBN4fH7oI2bCBfi5MZOIKExGBOEzZszAo48+iuXLl8e8XU1NDf785z/jn//8J9555x2cOnUKr732WopWSUREsTATTkQUv4wIwisrKzFlyhSoVLGX8/bbb+Piiy9Gfn4+VCoVrr32WrzxxhspWiUREcXi8UWuCZeG9TAIJyIKlRFBeLwaGhpQXl6ufF9eXo6GhoY0roiIiGQebwD6qJlwlqMQEYVKydj6VatWob6+PuJ1n3zyCdTqvpmTZCooMPd/owxTVGRJ9xIyGo9PbDw+sfH4xBbv8QkAyM7S97m9JUuPgCiO2OM8Un+vROHxiY3Hp38j9RilJAh/5ZVXEnI/ZWVlYcF8fX09ysrKBnw/bW02BIZRu6yiIgtaWqzpXkbG4vGJjccnNh6f2AZyfBxOL3JN2j6393p98PgCI/I48/kTG49PbDw+/RvOx0ilEmImfodVOcqll16KjRs3or29HYFAAC+++CIuv/zydC+LiIgAeLx+6LSR+4RzWA8RUbiMCMI3bNiAJUuW4K233sLq1auxZMkSHD16FACwevVqrF27FgAwduxYfOc738F1112HSy65BGPGjMGXvvSldC6diIiCPL4AdJq+bytqlQCfX4QoDp8zkEREyZaScpT+LF++PGp7wnvvvTfs++uvvx7XX399KpZFREQDECsTDgD+gAiNWkj1soiIMlJGZMKJiGj4i9UnHADbFBIRhWAQTkREQ+bzBxAQReii9AmXbsNyFCIiGYNwIiIaMo9XynJHqgnXBi/z+pgJJyKSMQgnIqIh8/j8ABCxJlzLchQioj4YhBMR0ZB5vHIQzkw4EVE8GIQTEdGQeXxyOUr07ijMhBMR9WAQTkREQ6bUhDMTTkQUFwbhREQ0ZEo5SoRMOGvCiYj6YhBORERDFmtjpoaZcCKiPhiEExHRkMUsR1EPPQhv7XLiREP3oH+eiCjTMAgnIqIhUzLhEfqEK5nwIZSjvLblJFa/uBuiyIE/RDQyMAgnIqIh68mER6gJT0A5itPlQ7fDi06bZ9D3QUSUSRiEExHRkCV7Y6Y7mGmvbrIO+j6IiDIJg3AiIhoyty+5LQrlTHtNs23Q90FElEkYhBMR0ZB5fX6oBEEZzBNKoxYAAD7/4Ou55Ux7NYNwIhohGIQTEdGQebyBiFlwIDQT7h/8/Qez6DUsRyGiEYJBOBERDZnH64+4KRMA1CoVBAHwJiAT3tzhhNPtG/T9EBFlCgbhREQ0ZC6vH/oomXBAyob7hlQT7keeRQ8RQF2LfdD3Q0SUKRiEExHRkLncfhh0mqjXa9WqIfUJd3sDmFyRAwCoaWZJChENfwzCiYhoyFweHwy6yOUogDSwZ7DdUURRhMfrR2m+CVkGDTdnEtGIwCCciIiGzOWJIxM+yCDc5w9ABKDXqjC22IzqJgbhRDT8MQgnIqIhc3n8MOqjZ8K1GtWgh/W4Q6ZxjiuxoLbFBn9g8KUtRESZgEE4ERENmbO/cpRgJryp3THgVoVyZxS9Vo2xxebg/TiHtF4ionRjEE5EREPWbzmKRgWXx4df/O9neO+LugHdt9wjXKdRYUyRGQBQ28KSFCIa3hiEExHRkAREEW6Pv99MeIfNA48vgMZ2x4DuX86E67RqlBaYAGDA90FElGkYhBMR0ZC4PVKQ3F8mvMvmBgC0d7sHdP8epSZcBb1WjYJsAxrbGIQT0fDGIJyIiIbEpQThMTZmqlXK7dqtrrDrIm3Y7HZ48KcXd6PT5oY7WEOu00j3X1ZgQgODcCIa5hiEExHRkLg80hh5Q4zuKBpNz9tNaCa8ucOBbz/yIU41hg/g2XGwGXuOteHVj0+EbcwEgNICExra7QiIYsJ+ByKiVGMQTkREQ+KKpxxFLShfO90+ON1S4F7f6oA/IKKuNXyjZY5ZD0DagBlajgIA5QVZ8HgD6BhgWQsRUSZhEE5EREPiCgbUxljlKJrwt5sOqxRAd9ql/3fbvWHXyyUqtS02uHtlwsuCmzMb2u1DXToRUdowCCcioiGJJxOuUYe/3ch14d02j/R/hyfsenm6pscbgMMlBfk6pRwlCwBYF05EwxqDcCIiGpK4Nmb2yoTLdeGddin4ttp7BeEhmzWP1XUBkPqEA0C2SQuTXsMOKUQ0rDEIJyKiIVE2ZvbTJxwA8ix6CADau6VMuNy2sNsRXo4iZ8IB4FBNJ4CeQF4QhGCHFJajENHwxSCciIiGxClnwvWx+4QDQLZJh2yzDu3BmvCuYAa8u1cm3BcMwtUqATanFzqtCoLQs7lT6pDCTDgRDV8MwomIaEhcHh8EoadcJBJtMBNuMmiQbdLBFsx892TCI9eElwXrv+Ue4bKygix02TxKvTgR0XDDIJyIiOLmDwTCSkUAwOX2w6DThGWqe5P7hGcZtTDq1HC6fRBFUcmEWx0eiCF9v73+ANQqAaX5RgCAXhv+dlWWz/H1RDS8MQgnIqK4/euDY3jkn7vCLnN5/DHrwYGeTHiWQQOjXgOnxwe7ywefX0SeRQ+fX4TT7Vdu7/UFoNWoUJQnBeFyZxRZWaHcIYV14UQ0PDEIJyKiuLV1udDW5Qy7zOXx9R+Ea3rKUYwGDZxun1KKMrbYDADY8MlJ7DjUAqAnCC/JkzLevYPwwhwD1CqBmXAiGrYYhBMRUdz8AbFvOYrHH7NHONAThGcZtDDqNHC6/Up7QjkIf+uzarz3RS2AniC8ODdYjtKr3lyjVqG0wIR9J9o5vp6IhiUG4UREFDefX4TXHx70Oj0+GPWxM+GakI2ZRr2UCZcH9chBONDTJcXrD0CrVqE4SjkKAFx2+jicbLTio931g/+FiIjShEE4ERHFLeLGzAFkws0GLYx6NfwBEa3BXuEVRT1BuLxRU86E51r00KhVEYPws2eXYvq4XLz84XFlzD0R0XDBIJyIiOLm94vw+QNhnUyk7iixM+GmYA/x7CwdjMGvWzqcUKsElOQZoRIECADsTq8S6Gs1KqgEAdPH5aI8uBEzlCAIuPT0cbA5vdh7vD1xvyQRUQowCCciopgOnupQ6q59ASnjHJp5jmdj5qTybPzw+vmYOiZHCcKbO50wG7XQqFW4+8tzsPzsCRABWB1eeH1+paPKD74yH1cvmRTxfmdNzIfZqMXWfY1D/TWJiFKKQTgREUVV3diNP6zdiX0npEyzP1gP7vWFZMLjKEcRBAEzJ+RDEISeTHinExaTFgAwb0ohxpVIZSnddo9UEx5j+I9Mo1bh9BnF2HW0FW6vv9/bExFlCgbhREQUlTyRUt4w6ZOD8GAm3OsLwB8Q+82Eh5JLUzqsbpiNWuXy7CwdAKkuXCpHie8+x5VY4PUFYHd6414DEVG6MQgnIqKo5LITl0fKMvsDcvAtfe/0SEH6QILw0NtaTDrlazkI7w4G4Zo4MuEAoFFLkzq93JxJRMMIg3AiIopKDsIdbinY7ilHCQ/O5RKTeJhCbms2hWTCTeFBuFwT3h+5/aHPxyCciIYPBuFERBSVXH7ilINwZWOmdLnLPfBMuNHQE4RbQspRDDo1dBqVVI4SZ004ACVY9/k5tIeIhg8G4UREFJVSjhIMtn2ByJnw/jZmhopWjiIIArKzdOh2eODzxR+Eq4NBOMtRiGg4YRBORERRyeUnfctRpOC7JwiPPxOuVqmgDw7fCd2YCQA5WbqecpS4M+FSTTjLUYhoOGEQTkREUXmjbcxULh94OQoAZcy9xRQehGeHBuHx1oRr5HIUBuFENHwwCCcioqj8vTZmynXXvmCf8MGUowA9GzkjZcLbut0Qgbgz4RqWoxDRMMQgnIiIoupdE+7v1SdcvlzObMdLDsJDa8IBINeiVzaBcmMmEY1kDMKJiCiq0O4oAVFUxtf3rgnXD7gcJXImPN9iUL7WDLQchTXhRDSMMAgnIqKoevqE+5UsOBDeHUWnUUGtGtjbiVGvgVGv7pPtzs/WK1/HX47CYT1ENPwwCCcioqj8IWUnoRsflT7hHt+AN2UCwJTybMwYn9/n8vzsnkz4QMtR/P0E4c+9cxibdtQCAERRxM7DLcpGUyKiVGMQTkREUcnBtgjA4fIpl8uZcKfHP+BNmQBwyenjcNfVc/pcnmcJyYQPsBzFG6MmvKHNjk1f1OK5dw8DAE41WfH4y1XYcahlIMsmIkoYBuFERBRVaPbb5vQqXys14e7BZcKj0WvVSp34QLujxGpRKGfAC4LlLq2dLgBATbNt0GslIhoKBuFERBRVaGBrdXqUr0P7hxv0A8+Ex5IfzIYPuDtKlI2ZXl8AW/Y2ApCmcgJAh80NAKhlEE5EacIgnIiIogpt+2d19GTCQ/uEJzITDvSUpMQbhKtUAlSCEHVjZlu3C26PH1kGDewu6XfotAaD8BZ7AlZMRDRwDMKJiCiq0M2ONkdoOUrPxMxEB+Hy5sx4g3AA0GiEPuUoXp8ftS02tHY5AQDjSy1wuv3w+QNKJryt26X0JSciSiUG4UREFJU3Wk24v6dP+GA2ZsYitymMd2OmfFs5Oy/buL0Wv/rb5zjVaAUgBeGANP2z0+qGELxdHbPhRJQGDMKJiCiq0N7g4Rsz5e4oic+EjyuxQKNWwZKl6//GQRq1Cl5/AJv3NODFD47iVKMVR2q74A+I+OJwK9QqAWMKzQAAu9OLDqsbE8qyAQC1LawLJ6LUS2z6goiIRhSfPxCspfbBGpYJFxEIiPB4AwkPwudMKsDqe85VpmrGQ6NWwecP4P/eOgh/QMSRmi40dzgAACcaulGcZ4TFJHVdsTm96LC5MXdyIU42dKM9WB9ORJRKDMKJiCgqnz8AUzAId7pCN2YG0GWXuqXkmPXRfnzQBhKAA1KvcKfbB39AhE6jwtG6rrDrC3MMyAq2PmztdMHjDSDPoodOq4bH60/YuomI4sVyFCIiisrnD0CrUUOjVsHhDh3W40e7Veq1HTpgJ120agH2YKb+7DllyuU5ZqmkpTDHqAThNcHykzyLHnqtCm4G4USUBgzCiYgoKr9fhEYlQK9VwR6cmKnVqOD1BdDRLZVx5GdAEK5Rq2ALrm9imQVlBSZo1CqcMaMEAFCUa4DZIAXhcm9wORPOIJyI0oHlKEREFJXPH4BaLUCnVStj6406Nbx+Ee3dUiZcbimYThqNSqntNug0uHrJJDS2O1CYYwQAFOQYYNSroRIEZUpmrkUPvU4Nt4dBOBGlHoNwIiKKSgrCVdBr1egO1oAbdBp4fQG0W93QaVXIMqT/rUSrVinlKAadGnMmFQCQNmEumFqIGePyIAgCTAYNuuweGHRqFGTroWdNOBGlSfpfOYmIKGP5lHIUNfwBqV2hXqeGJxiE51kMyij4dNKoVT3r0/Z0azEbtbj7y3PDvrc5vZhSkQO1Svpw4fZGnrRJRJRMrAknIqKo5Ey4TtvzdmHUqeHz+dHR7cqIenAA0Kh7PgjEapmYZZRyT5VjcwEgGIQzE05EqccgnIiIovL7A1AHM+Eyg14j1YRb3cp0y3QLHXGvjxGEy5sz5SBcx+4oRJQmLEchIqKofH4RGrUKQkhcawhuZvT4/Mi3pH9TJiCVo8gMuuhvbWajFhq1ChOD0zIN3JhJRGnCIJyIiKLyBTPhvYNcOXuclyGZ8LD1aaNnwi89fRzmTSlUMudsUUhE6dJvOYrf78fq1avh8XhSsR4iIsogPn8AGrXUJ1wWWnNdkAHtCQGpOwoACAC02uhvbWOKzVg8vVj5Xq4JF0Ux2UskIgrTbxCuVqvx/PPPQ6Nh0pyIaLTx+UWoVSroQmvCg0F4lkGj1Fanm0YjbczU6aRe4PHSa9UQRenDBhFRKsW1MXPlypVYu3ZtstdCREQZRh7WE7oxU56cedkZ48IuTye5HCVWZ5RI5PWzTSERpVpc6e09e/ZgzZo1+Otf/4rS0tKwnrDPPfdc0hZHRETp5fcHoFH1tCgUBGDpggqIoohLThuX5tX1kMtRYtWDRyJ3UnF7/DAbtQlfFxFRNHEF4ddddx2uu+66ZK+FiIgyjM8vhmXC1SoVyguzcNMl09K8snCa4EbLWO0JI5E/XHBzJhGlWlxB+KpVq5K9DiIiykC9y1FCh+JkEs1gM+FKOQqDcCJKrbhqwkVRxLp163DLLbdgxYoVAIDPP/8cb7zxRlIXR0RE6SUN61EpGWa1KjODcG3ww4FBP7AmAnIQ7mEQTkQpFlcQvnr1avzrX//CV77yFTQ0NAAASktL8cwzzyRkEevXr8eKFSswc+ZMrFmzJurtNm7ciKuvvhrLly/HlVdeif/93/9NyOMTEVFfgYCIgChlv3UaOROemYOW5XUNdKMoM+FElC5xpQxeeeUVvPLKK8jPz8cvf/lLAMCYMWNQU1OTkEXMmDEDjz76KJ5++umYtysqKsJTTz2FkpISWK1WXH311Zg7dy4WL16ckHUQEVEPf0DqGCKNrZeCXHWmlqMMsiac3VGIKF3iCsL9fj+ysrIAQOmMYrfbYTKZErKIyspKAIBKFTvDMm/ePOVri8WCyZMno66ujkE4EVES+PzSABuNWgVdMLjV9PM6nS6D7Y6iC+mOQkSUSnEF4eeffz4efvhh/PSnPwUg1YivXr0aS5cuTeriYjl27Bh27dqFX/3qVwP+2YICcxJWlFxFRZZ0LyGj8fjExuMTG49PZN12aVJyTrYRZcXZAKSgNROPV0GzHQCQl2sc0Pq0Bh0AQKfXDPr3ysTjkUl4fGLj8enfSD1GcQXhDzzwAO6//34sWrQIPp8PCxYswDnnnIPf//73cT3IqlWrUF9fH/G6Tz75BGr1wDIXzc3N+M53voNf/OIXKCkpGdDPAkBbmw2BwPAZUVxUZEFLizXdy8hYPD6x8fjExuMTXafNDQBwOT2w2VzShaKYkcfLHlxfwOcf0PrkDHhbh2NQvxefP7Hx+MTG49O/4XyMVCohZuI3riDcbDbjiSeeQFtbG+rq6lBWVoaioqK4F/HKK6/Efdv+tLW14Wtf+xq+8Y1v4PLLL0/Y/RIRUTh/sBxFqgkPdkfJ8I2ZBt3AuqNog7XuLpajEFGKxf1q1d3djS1btqC5uRnFxcU4//zzkZOTk8y19dHR0YGvfe1ruPHGG3Httdem9LGJiEYbX3BjpkatUjZmajK0RaGyMXOANeEqQYBOq2J3FCJKubhSGlu3bsWFF16If/zjH6iqqsKaNWtw0UUXYevWrQlZxIYNG7BkyRK89dZbWL16NZYsWYKjR48CkNojrl27FgDw9NNP4+TJk/jnP/+Jq666CldddRVeeumlhKyBiIjCKZlwtQBdhmfCDcENlibDwDLhgBS4s084EaVaXK9Wv/nNb/DrX/8aV1xxhXLZm2++iV/96ld46623hryI5cuXY/ny5RGvu/fee5Wv77//ftx///1DfjwiIuqfPyCXo6igEgRoNaqMHdZTUZiFb6+cjbmTCwb8s3qtmplwIkq5uFIazc3NuPTSS8MuW7ZsGVpbW5OyKCIiSj+fP9gnPNgbXK9VZ+ywHkEQcNr04kGtTwrC2SeciFIrrlerq666Cs8991zYZWvXrsXKlSuTsSYiIsoAciZcowThmZsJHwq9Tg23x5fuZRDRKBO1HOWGG25QBvMEAgG88MILeOaZZ1BSUoKmpia0tbWFDc8hIqKRxS9nwoMDesxGHYz6gddcZzq9Vg0Xy1GIKMWivpr27j5y3XXXJX0xRESUOXyBnhaFAHDnylkD7j4yHBh0anQ7POleBhGNMlGD8FWrVqVyHURElGHkTLhcZ12SZ0rncpLGoNPA5WYmnIhSK+7zitu3b8f+/fvhcDjCLr/zzjsTvigiIkq/0GE9I5lBr4aLNeFElGJxtyh88803sXjxYuj1euVyuWaciIhGHl+vjZkjlUGn5sRMIkq5uILw119/Ha+//jpKSkqSvR4iIsoQysbMDG1LmCgGnQb+gAivLwCtZmT/rkSUOeJ6tSktLYVOp0v2WoiIKIP4guUomTqqPlHkaZssSSGiVIorE/7QQw/hZz/7Ga688koUFhaGXXfaaaclZWFERJRe/sBoyYTLQbgflpG595SIMlBcQfi+ffvw0Ucf4fPPP4fBYFAuFwQBH3zwQbLWRkREaeQPjI6NmUad9FbIunAiSqW4gvBHH30U//M//4Ozzz472eshIqIM4QmOch/pddIsRyGidIjrldVoNGLx4sXJXgsREWUQr0/KDOu0Iz0IZyaciFIvrlfWe+65B//5n/+JlpYWBAKBsP+IiGhk8vgC0KgFZWz9SBVaE05ElCpxlaP89Kc/BQD885//VC4TRRGCIODAgQPJWRkREaWVxxuAbgSOqe9NCcLdLEchotSJKwjftGlTstdBREQZxuvzj44gXM9yFCJKvbiC8IqKimSvg4iIMox7tGXCuTGTiFIoriD8Rz/6UdQR9X/4wx8SuiAiIsoMXp8f+lEQhGvUKmjUAjPhRJRScQXh48ePD/u+paUFb7/9NlasWJGURRERUfp5fAHoR3hnFJlBp2EQTkQpFVcQftddd/W57JprrsETTzyR8AUREVFm8PpGRzkKIJWksByFiFJp0CmOGTNm4LPPPkvkWoiIKIN4vKOjHAWQg3BmwokodeLKhG/dujXse5fLhX//+9+YMmVKUhZFRETp5xlVmXCWoxBRasUVhD/44INh35tMJkyfPh2PPPJIUhZFRETpJ9WEj5YgXA27y5vuZRDRKBJXEP7ee+/1uUwURXz66acYO3ZswhdFRETp5/H6odeNniC8rduV7mUQ0Sgy6Jpwr9eL22+/PZFrISKiDDK6NmayHIWIUmtIvadEUUzUOoiIKMN4RsnETIDdUYgo9YYUhEcb4ENERMObKIrweEdPTbhOq4bHG0j3MohoFIlZEx4IRH9B8vt52o6IaKTy+aXXf90oGdaj06jgD4jwBwJQq0bH70xE6RUzCJ85c2bUbLcoisyEExGNUB6fFISPpkw4AHi8ARj1DMKJKPliBuGbNm1K1TqIiCiDyKUZo6UmXKuRAm+vLwCjPs2LIaJRIWYQXlFRkap1EBFRBvH4pJLD0dKiUC678XhZaklEqcFzbkRE1Id3lGXCdZpgOYqPmzOJKDUYhBMRUR9uORM+WoJwORPuYyaciFKDQTgREfUhZ8JHTRCu6dmYSUSUCnGNrZfV19ejqakJJSUlKC8vT9aaiIgozeSyjFHTopCZcCJKsbiC8ObmZvzgBz/Arl27kJubi87OTsybNw9//OMfUVJSkuw1EhFRinmDwehoqwn3MhNORCkSV4rjl7/8JaZPn47PPvsMmzdvxmeffYYZM2bgF7/4RbLXR0REaSCXZYyW7ihyi0I3M+FElCJxBeE7duzA/fffD5PJBAAwmUz48Y9/jJ07dyZ1cURElB6eUboxk5lwIkqVuILwnJwcHDt2LOyy48ePIzs7OymLIiKi9OqpCR8lQThbFBJRisVVE/6Nb3wDt912G6655hqUl5ejvr4eL7/8Mu69995kr4+IiNJAHlqj16rhSvNaUoEbM4ko1eIKwq+77jqMHTsWGzZswKFDh1BcXIxHHnkEZ511VrLXR0REaeD1BSCgp1Z6pOPGTCJKtbiC8DfffBOXX355n6D7rbfewmWXXZaUhRERUfp4fAFoNSoIgpDupaSESiVAoxa4MZOIUiauFMeDDz4Y8fKf//znCV0MERFlBo/XP2rqwWVajZqZcCJKmZiZ8JqaGgCAKIrK16HX6XS65K2MiIjSIhAQ0e3wjppSFJlOq2JNOBGlTMwgfNmyZRAEAaIoYtmyZWHXFRYW4u67707q4oiIKPUee2kP9hxrw+SK0dUBS6dRsTsKEaVMzCD84MGDAICbbroJa9asScmCiIgovepb7Zg9KR/fvmp2upeSUjqtWhlSRESUbHGda2QATkQ0erg8fhTnGmHUx7V3f8SQMuEsRyGi1BhdBX9ERNQvp9sHg250BeCA1KaQmXAiShUG4UREpPD6AvAHRBj1o6szCgBotSp4mQknohSJKwgPBJgZICIaDZweHwAwE05ElGT9BuF+vx/z58+Hx+NJxXqIiCiNXB4pE2zQjb5MOFsUElEq9RuEq9VqTJgwAR0dHalYDxERpZHLPZoz4Sq4vQF8ur8RXrYqJKIki+tVdsWKFbjzzjtxyy23oLS0NOy63qPsiYho+JIz4aOxJlynUaPb7sHTr+3HLZf6ccGCinQviYhGsLiC8LVr1wIAHn/88bDLBUHApk2bEr8qIiJKC+cozoRrtT0nh9VqIY0rIaLRIK5X2ffeey/Z6yAiogww2jPhMn9ATONKiGg0iLtFodfrxfbt2/HGG28AABwOBxwOR9IWRkREqTeau6M0dziVrz0ebtAkouSKKwg/dOgQLr30UvzHf/wHHnzwQQDA559/jp/+9KdJXRwREaWWyz16u6NMLLMoX7u5MZOIkiyuIPyXv/wl7rnnHrz11lvQaKTsyGmnnYYdO3YkdXFERJRarmAmXD8Kg/ALF43B4987D2qVAI+XmXAiSq64gvCjR4/iqquuAiBtxgQAk8kEt9udvJUREVHKuTx+GHRqqITRtzFRJQjIMmih06rgZjkKESVZXEF4RUUF9u7dG3bZnj17MG7cuKQsioiI0sPp9o3KUpRQOq2aQ3uIKOni2nlz77334lvf+hauv/56eL1e/OUvf8ELL7yA3/zmN8leHxERpZDL44dRP/o2ZYbSa9Vwc3w9ESVZXJnwpUuX4plnnkF7eztOO+001NXV4fHHH8e5556b7PUREVEKOT3MhOs0apajEFHSxZXuaG9vx8yZM/HLX/4yycshIqJ0kmrCR3kmXKeCmxsziSjJ4s6E33HHHXjttdfYG5yIaARzsSYcetaEE1EKxBWEv//++7jggguwdu1anHPOOfjBD36A9957Dz6fL9nrIyKiFGJNeLAm3MOacCJKrriC8Pz8fNx4441Yu3YtNmzYgOnTp+PRRx9lTTgR0QjD7ijB7igsRyGiJIt7bL2sra0Nra2t6OjoQHZ2djLWREREaSCKIjPhAPRaFdwsRyGiJIvrlfbo0aPYsGED/v3vf8PlcuHyyy/Hk08+iblz5yZ7fURElCI+fwD+gMhMODPhRJQCcQXhX/3qV3HJJZfg17/+Nc444wyoVANOoBMRUYZzuKR9PqZRnwmXasJFUVSmRBMRJVpcr7RbtmyBTqdL9lqIiCiNbMEgPMuoTfNK0kunVSMgivAHRGjUDMKJKDmiBuH/+te/4rqDa665JmGLISKi9HG4vAAAk4GZcABwe/3QqHnml4iSI+or7fr16/v9YUEQGIQTEY0QdmcwE24Y3ZlwvVYKvN0e/6g/FkSUPFGD8H/84x+pXAcREaWZPZgJZzlKTyaciChZogbhoRtSAoHoQwu4SZOIaGSwO6Ug3MxyFACAx8uBPUSUPFFfaRctWoQvvvgCADBz5sw+O8TlIP3AgQPJXSEREaWE3eWDAMDA7igAmAknouSK+kr773//W/l606ZNKVkMERGlj93lhcmggWqUt+XTBWvC2SuciJIpahBeVlamfF1RURF2XVdXF3JycpK3KiIiSjm7yzfq68GB0Ew4y1GIKHliFnS/+uqr+Pjjj5Xvq6qqcP755+PMM8/EpZdeiuPHjyd9gURElBp2l5fdQBBaE85MOBElT8wg/K9//SuKioqU73/+85/j7LPPxmuvvYazzz4bf/jDH5K+QCIiSg2704esUb4pE2B3FCJKjZivto2NjaisrAQANDQ04PDhw/jb3/6G3Nxc3HfffbjkkktSskgiIko+u8uL4jxjupeRdkqfcAbhRJREMTPharUaXq/Usmrnzp2YNGkScnNzAQBGoxEulyshi1i/fj1WrFiBmTNnYs2aNf3e3u1248orr8TVV1+dkMcnIiKpRSEz4T2ZcJajEFEyxQzCTz/9dDz66KM4ePAg/vGPf2Dp0qXKdcePHw8rVRmKGTNm4NFHH8Xy5cvjuv2jjz6KefPmJeSxiYgICIgiHG4fTKwJh0atglolcGMmESVVzCD8wQcfxP79+/HVr34VRqMRd9xxh3Ld+vXrcd555yVkEZWVlZgyZUpcg3+2b9+OkydP4qqrrkrIYxMREeBy+yCKHNQj02nVLEchoqSK+WpbUlKCZ599NuJ1P/zhD5OyoFgcDgf+8z//E0899RROnjw56PspKDAnblEpUlRkSfcSMhqPT2w8PrHx+ACNbXYAQGmxpc/xGI3Hx6jXQKVWxfW7j8bjMxA8PrHx+PRvpB6jlKQ8Vq1ahfr6+ojXffLJJ1Cr1XHdzx/+8AfccMMNKCkpGVIQ3tZmQyAgDvrnU62oyIKWFmu6l5GxeHxi4/GJjcdHUt3QDQAIeP1hx2O0Hh+tWkCX1dXv7z5aj0+8eHxi4/Hp33A+RiqVEDPxm5Ig/JVXXknI/ezYsQMfffQRnnzySbjdbnR1dWHFihV4/fXXE3L/RESjVXOHEwCQn61P80oyg06rhoc14WHau13YuL0W11wwGSrV6J6qSpQIw6r4LzTY3rZtG37/+9/j5ZdfTuOKiIhGhupmK9QqAeWFWeleSkbQsya8j51HWvHWZ9W4YEE5ivNM6V4O0bAXdSfk73//e+XrrVu3JnURGzZswJIlS/DWW29h9erVWLJkCY4ePQoAWL16NdauXZvUxyciGu2qm2yoKMyCRt3/BvnRQK9VsUVhL1aHBwDYNYYoQaJmwtetW4f7778fAPDd734XX3zxRdIWsXz58qjtCe+9996Il59xxhnMghMRJYAoiqhusmLe5MJ0LyVj6LRqdNk96V5GRrE5pbkhPENAlBhRg/Dp06fjnnvuweTJk+HxeLB69eqIt4sWJBMR0fDQZffA6vBibMnw6xyVLCxH6YtBOFFiRQ3CH3vsMfzzn/9Uupo0NjambFFERJQ61U1S54FxxQzCZdyY2ZfVIQXhHg+DcKJEiBqE79q1C9/5zncAAE6nEw8//HDKFkVERKlzvL4bAoCxxSOzF+9gMBPel5wJd/G4ECVE1B04P/rRj5Sv33///ZQshoiIUudUoxVtXS5UHW/HxPJsmDgtU6HTquD2+iGKw2emRLKxHIUosaK+4hYVFWHNmjWYPHky/H4/Pv3004gvRmeddVZSF0hERMnx+Mt7oNeq0djmwJfOnZju5WQUvVYNUQR8fhFaTd+e2J02N9q6XSN2kl9voiiyHIUowaIG4Q8//DAee+wxPPvss/B6vfjpT3/a5zaCIGDTpk1JXSARESWey+NDe7db+X7u5II0ribz6LXSJGe31w+tpu9J4ze2nsKn+5tw5rwxqV5aWni8Afj8Uo08y1GIEiNqEL5w4UL8/e9/BwAsW7YM7777bqrWRERESSZPyBQAmE1ajC8dHRndeOl1UhDu8foBo7bP9d0OD2xOL/yB0VGuYnX2tGtkOUpyBUQR735eg0/3NeH2K2dgLDdMj1hxFQAyACciGlka2x0AgK9dMQNFuQaoBI4hD6ULZr+jBZx2lw8A4HR5U7amdJLrwQHA42HXmGTavKcB/3xPGlh4tLaTQfgIFjUI//rXv46//vWvAIAbbrgBQpQX6Oeeey45KyMioqRpCgbhp00vVrK+1EMuR4nWptARDL5tTi9Gw9GzOXqCcGbCk2vr3kYU5RrQ0umC1Tk6PuSNVlGD8JUrVypfX3vttalYCxERpUhThxN5Fj0D8Ch0up6a8EjsTl/w/15k60f+MZSDQbVKYE14ErV3u3CophMrz5uItz+rhs3hxYZPTqLL7sGNyyrTvTxKsKhB+IoVK5SvV61alZLFEBFRajS1O1Cab0r3MjKWXtNPEB7MhNtdoyMIlzPheRa9VCdPSfHZgWYAwJkzS7ClqgE2pxcnG61o6XIyCB+B+q0Jd7lceOmll7Bjxw50dXUhJycHixcvxtVXXw2DwZCKNRIRUYI1dTixeFpRupeRsXRaqSY8UsAZEEU43D2ZcOSM/PdCq9MLQZCCcDdbFCZNS6cTZqMWxXkmmI06WJ1edNnd6LJ54PH6odOO/A98o0nUYT0AYLPZcO211+Kpp56CVqvFzJkzodFo8OSTT+Laa6+FzWZL1TqJiChOHVY3PtpdH3XQTIfVDZvTixJmwqPSxyhHcbn9kA+tfZTU7NqcXpiNWhh0GpajJJHT44MxeGbFYtLC6vCgyyZ1pmnrdqVzaZQEMTPhTz/9NPLy8vDCCy8gKytLudxut+Ouu+7C008/jR/84AdJXyQREcXvw111eG3LSYwrMWNCaXaf67dUNQAA5k8pTPXSho2ePuF9N2baQzqi2EZZEK7XqliOkkQutx9GnRSamY1aHKntgscnPQdbOl0oK8iK9eM0zMTMhL///vv48Y9/HBaAA0BWVhbuu+8+jrMnIspAcvvBrXub+lwXEEV8tLse08flMhMeg06uCY9QeuEIticERlEQ7vDAYtRCr1OzO0oSOd0+GPQ9QbjT3fNca+2SevtHO8NFw0/MILy+vh6VlZE3AlRWVqKuri4piyIiosGTg/BtB5rgD4Rncg9Xd6K1y4Ul88vTsbRhQ68L1oT7+gacoZnw0VSOkmXUQq9VsyY8iVweP4y6nnKUUK2dLjhcPtyz+mPsOdaajuVRgsUMwgFAp9NFvTxa73AiIkoPURTR1O5EUa4B3XYPDld34p3Pa7B5j1SCsutoKzRqAQumcFNmLGqVChq1EDHrG5oJHy1BuNXphcUUDMKZCU8aqSa8JxMu06gFtHQ60drlhN3lQ9Xx9nQtkRIoZk242+3G6tWro17v8XiiXkdERKlzvL4bY4uzYHP64Pb6ccH8CXj5o+PYdbQNH+6qgz8gYmyxGVXH2zBtXB77g8dBp1FHnA5pC2bCzUat0i98JBNFETaHF2ajDjqtCj6/CH8gALWq3zweDZArrBylJwk6vtSCli6n0q/9VKM1LeujxIoZhK9YsQKNjY1Rr1++fHnCF0RERAPT3u3CQ89uxzVLJysbMceXWjCxPBsf7qqDxxeARi3gzy/vQVu3GxfMr0jziocHvU4Nd4RyFDkTXphjCCtNGalcHj/8ARFmoxbyCXC3JwCTgUF4ojkjlKNo1CqMK7Fg274mWO1S8rO62YpAQIRKxYqE4SxmEP7www+nah1ERDRIx+u7IQLYf7IDhmBnhdJ8E6aPy8PR2i7otCrc/eW5+PNLVQCAuZML0rja4UOnVffpBPI/6/fieH031CoBeRY9Omwj/4ywnH21mLTwBjt1uL1+mAz9jhqhAfD5A/D6AmEbMwEgJ0uHwmwDHG4fmjukzZkebwANbXZUFJnTtl4aOn6MJSIa5o43dAMAjtR2orbFBp1WhVyLHjPG5wEAZo7Px6wJ+bj/xgW46ZJKdkWJk16jCtuEGBBFfHagGa1dLmQZNDAZNKOiO4o8LdMc3JgJRJ8kSoPnCj7X5Ey4OZgJzzXrkJ8tDYQ61dRThnKSJSnDHoNwIqJh7kR9N1SCAI83gI921WPa2DyoBAFTKrIxvsSCc+eWAQAmlGbjwoVj0rza4UOnVSs9moHw6ZndDi9Meu2o2Jgpf9AwB1sUApFbN9LQyO0I5Y2ZWQYNBAA5Zj3ys/UApMA7O7hBNjQgp+GJQTgR0TAWCIg42WjF4ulFyvdfPn8SAECrUeMXXzsNCyvZCWUwtBqVUn4BSCUAoUwGDZxuX582kCONzSmV3JhNzIQnkxyEyyVlapUKBTkGlOQbkW+RMuEdVjeys/QoyjWgtZMTNIc7FnQREQ1j9a12uL1+zJtcqIyiH1diSfeyRgStRhW28VIOPOdOLsD588rR2iUFQU63H2bjyM1pyeUoFqMWVrv0NYPwxFPKUfQ9nYsevGUxDDo1NGoBggCIIpCdpYVKJaDT5k7XUilBBhSE22w2/OUvf8Hhw4cxduxY3HHHHSgpKUnW2oiIqB/yYJ6Koiz88PoFaV7NyKLrkwmXgqSzZ5diQWURtlRJvdcdLm9YT+eRxur0QiUIMOo1MATLUR5/aQ9uWFbJTjsJ1LscBZA2ZcpyzXopE27SQaNRoabZlvI1UmIN6KP7r371K5hMJtx8880wGo249957k7UuIiKKQ7tVyobJG7cocbQadXgQHvxaFyzJkMsGnO6RnRW2Ob0wm7QQBAHlRVm49oLJKMwx4p3PavDWtmr8/rkvRnxJTio4PXI5SuQe/nJduNmkRZ5Zj267h8d9mIsZhD/00EOw2+3K9w0NDfjmN7+Jc889F9/+9rdx/PjxpC+QiIiia+92QadRIYvt4hJOp1WFbcyUNyPKddGmYNmAyzOyB/bYHF5Ygpl+lSDg8jPH47IzxqGx3YF/fXAMh2o6sW1/U5pXOfy53HI5SuS/5YLgB+1skw65Fj1EEei2j/yNwSNZzCB87ty5uPnmm/HGG28AAC655BKsXLkSP/zhD7Fq1SqsXLkyFWskIqIo2q1u5GUbIAgc2pFoWrUK3pBhPZ7g1zqt9NYp93Me6Zlwq7Nvuc1p04uh06ogCEBxnhGvbznJrOwQyZlwoy5yEC5vzszO0iHPLGXFWRc+vPU7MfOCCy7An/70J7z00kt48MEHcfbZZ+Pw4cO45ZZbMHfu3FStk4goY9icXjyzYT+uOHM8KsfmpnUt7d0uFARPU1NiabXhNeHuYHcUORMuZyydIz0T7vSirFdveaNegy+fPxmA1ErvmQ0HUN1kw8Sy7CE/ntvrV47xaOJ0+yEIPR/yessL/p1bjFrkWqRa8Q6rGxPLem7T7fBAFMNrySlz9VsTbrFY8LOf/Qzf//738dOf/hSvvfYaLrroIgbgRDQq+QMB/GX9Xuw51obPDzSnezlo73YpGTJKLJ1GDZ9fRCAgAujZmCnXhMtDVeQNdSOVXBPe27LFY7Fs8VhMKJUC74Y2e5/bxCKKIv704m58srdBuay1y4l7Vn+MPcfahrboYUIURby2+QSaOhxwuX0w6jRRz2oV5xoBSMF4pEy4KIp46Nnt+P7jm/HXDfuTv3gasphBeFNTE37729/iW9/6Ft588008+eSTKCkpwVe+8hVs2rQpVWskIsoYB6s7se9kB3QaFU42dqd1LT5/AF02j7JhixJLp5HeIuVsuByE6zW9y1FGbhAuiiJsjtjdX4rzjFCrBNS3OvDout1K15j+NHc4sedYGz7e3QCHy4e6VjuqjrXB6wvgeH1Xon6FjNZpdePVzSfw8D92wOnxhbUn7G3O5ALcd/18jC+xwGLSQSUI2H6wGc+/exheXwAdVjdaOqUzY1v2NqK1y5nC34QGI2YQfu+990Kn0+Gmm26SPmE99BBuvPFGPPPMM3jzzTdx5513pmqdREQZoTPYjWT2pAJUN9vg86evDrbT5oYIdkZJFk0w2JZrweVyFDkTrtOooFYJSn/nkcjp9iEgisrGzEg0ahWK84zYc6wNVcfbsO1AfJs0D1Z3AACO1nXh728dxK/+9jk+2dcIoKf15kgn96HvdnjhcvuVD3aRqAQBsybkQxAEqFQCcsw6HKzuxMYdtXj27YM4Xi8lBa67cCoA4JO9jcn/BWhIYgbhx48fx/e//32cd955uPfee3H06FEAQGFhIf77v/8bt912WyrWSESUFqIo4vUtJ9De3TOZrtsuTQ+cO7kAXl8A9a0DOwWfSO3dwfaEFmbCkyFqJjwYhAuCoEzNHKms8sj6COUoocoLs1DbIvWtPtlghSiK/d73wepOAIA/IGL7wWb4/AEcq5MCyYa2URKEO3u6m5xqsiLLEH+/+Y5gQmB8qQVbqhqxYetJaNQqLJhaiOnjcvFJVSNEUUR7twt/eP4LbPjkZKKXT0MUMwi/6qqr8LWvfQ2PPvoobr/9dqxatSrs+jPPPDOpiyMiSqemDide+fgE3t9Zp1zW7fBAp1VhWnBD5slGa5pWB+XDQR4z4Umh00jBthyEu71+aNQqqFQ9NbtGg3ZEd0eRp2WajbE3+pUVZPX8jNOLlq7YI9VFUcTB6g4smFoIjVo6nhPLpEmvhTkGNLU7EIgjkB+Odh9tVUp27M6eD3CtXS4sWzwm7vspzJH+7n90/XzkZOlQ3WTD+BIzNGoVzphZguZOJ042WvHQP3bgYHUn3v6sOq1n7qivmN1RHnzwQezZswe1tbVYvnw5pk6dmqp1ERGlXXOHVFN5KJixA6S+vNkmHYryjDDq1ag61obz5palpUWgMqiHmfCk0CrlKHImPAB9r84VJr1mRPcJVzLh/UwELS+UuqcUZBvQ1u3CifpuZSNhJM2dTnTZPJgzqQD+gAib04tvXzUbG7aeRFm+CS+8dxTt3S4U5kS/j+Fq0xe1aO104Zw5ZWGZ8NNnFGPRtOK47+fHNyyA0+2HyaDFBQsqsH7zCUwslzbJzpqYDwB4YdMRdFjduHjxGGzcXos9x9qwsLIosb8QDVq/3VHmzp2LK664ggE4EY06LZ1SEH6ioRvuYCmC1eFRNkVdML8COw63YO3GI2lZX6fVDYNOHXW4Bw2NtndNuM+v1IPLRno5ipIJ768cJZgJXzK/HFqNCicaYm9armmSSlcmlFlw51WzcN9X5qMgx4BbL5uO8aVSRnwwdeGHqjvS9vcYL6fbB1sw+LYFa8J/cdtp+MbymQO6n8IcI8YWmwEAF8wvR1GuAfOnFCrXleSbcKS2C2ajFtdeMBnZJi22sk48owxobD0R0WgiB+H+gIjjdVK3hm6HB9nBgOSaCybjnDml+GBXXVoCsU67BzlmZsGTRa4J94XUhPcNwkd4OUowWIy1MRMAxhabcfOl03DRwgqML7Xgi8Mt6Arun4iktsUGAVLwbtBpwj5IlgYD+sZB1IW/v7MO726vCdvHkWlcbj/sLi8CoqhkwksLTNCoBx+S5Zj1+P2dZ2PmhHzlstnBr+dPLYRWo8bi6cWoOt6m7G2g9GMQTkQURUunEwXZeggCcKimEwBgdXhhCQ7CEAQB580th88vpqWvcZfNjVwO5UgabTDgDitH0fQqRzFoRvSwHpvTC7VKgEEXe3iOIAhYuqACJoOUde12ePCnF3dH3aBZ12pHcZ6xz4caAMg2aWHSa1A3iE3Px4Iflo/WZW6LQ6fHB1GUMuL24PHVaRIfjs2ZXAAAWBwscZk/pRAeX0DpSkPpxyCciCiK5k4nxhZbMKE0G3tPtEMURXTbPcg29QS+UypykJ2lw47DLSlfX5fNg1zWgyeNHBh5vD0bM3W6vplw10guR3F6YDZpB7TnYeqYXFx/4VScarQqQXFvdS12VBSZI14nCAImlWdH/dlo2rtdaAt2DJLb9WUi+cyJzemF3elFliH6gJ6hmDMpHz+7dTHmTJIy4tPG5UKvVWPX0dExCGk4YBBORBSBKIpo6XSiKNeI+VMKcLy+Gw1tDvgDolKOAgAqlYCFUwtRdawtpZ0HRFFEp93N8dRJpFVaFEpBk8fr75sJ12vgHMF9wq0Ob7+lKJEsmlYEQQA+jTC4x+vzo6nDgYrCrAg/KZk6Jgd1LXalj3Y8jgUD7yyDZsABfKqIoqh8aJODcOMA2hIOhCAImFiWrQT4Wo0asybmY/fR1rhaSFLyMQgnolHnswNNMU9XO90+7DzSCo83gOI8IxZMlboJfLynHgCUchRZRZEZbq8/rNNBsjndfni8AeSyJjxptL36hLu9gYgbM72+wIht/WZzxp6WGY3FpMO0sbnYurdvEF7f6oAoAmOKI2fCAWDKmFyIwICC6aO1XdBpVDh7dhlONVmVf7dM4vb6IYe/dqcXNpcXphRurJ4+LhcdVjc6bdHr9Sl1GIQT0aiz5p3DeGPrqajXP7NhP/78chUAoCjXgIqiLBTmGPDBTikIDy1HAXrat9lSGIR32aXT7jlmZsKTRe4T7gnZmKnvFYQbDVIANVKnZg42CAeABZVFqG60orkzfHx6Q5tU611eYIr6s5PKsqFWCThSG18Q3tzpxCd7GzBlTA6mjcuFzy9mZDY8dBOvnAk3GVIXhBcG20a2WzN34+powiCciEYVm9MLm9OLtijdE/Yca8POI62YOSEPFYVZmFAqnc49d06Z0qbQ0qtdWzqCcDmTxY2ZydMnE+7zQ9enT7j0b+8YoXXhVsfgg/AJwVaDTb1aDXYH2x7G2s+g16kxrsQSdxD+l/V7AQA3XzoNMyfkQadR4fNDzYNZdlKFdlGyOaWNmanMhBcEB3u19TNMiVKDQTgRjSpNHVJAEO1NaOP2GhTmGHDvNfPwm2+cgexgkHvJ6WOV22RnRc6E212pC8S6bFImnBszk6d3n/BILQqzjMFM+AgMwgMBqYWexTS4D3rRAj6b0wOVIPTb337qmBycaOjut6zE5fHhRIMVl5w2FiV5Jhh0GsydUogdB5sRCGRW7XNoJ510ZMLlf5P24AZWSi8G4UQ0qjS3S6fGHW4fHBGC5uZOJyaWZSsBmMyg0+DrV87ApPLsPplwORBLRyY8J4tBeLJo1CqoBEEJAqWJmb1qwoOZ8JE4sMfm9EJE3w+d8co166FWCX3OOknZdQ1U/XQEmTomB15fAKearMplOw+3YOveRlgdPTXNTcG/6fKQjZ6nTy9Gt8OLvScyqxOIK6Qcxe7ypjwTbjJoYNSro54JpNRiEE5Eo4qcCQfQ540oIIpo73Yr2aLezplThv+4ZTHUqvCXTiUTntIg3A2dRgWjPnb/ZhoarValbLz0B8Q+/ZzlmvCR2CFFDnR7f+iMl0oloCDX2OfvzObwwhxHdn3KmFwAwJHaTgCA2+PHE6/sxf/bsB9//fcB5Xb1wRpzecgPAMybUoDiXCOef/cI3Bn0bxP6Ya3T6obHF0hpJhwA8rMNLEfJEAzCiWhUaero2STW+43I6vDC5w+gICdyEB6NXquGRi2keGOmBzlmXVL6C1MPnUYFjy+gTBnskwmXg/ARmAmXa7cHW44CAMV5xr5/Z8742h7mZOlQkmfEkRqpLvxUkxUBUYRJr0F9yCCfhjYHVIKA4uCmQ0Bqx/e1K6ajudOJd7bXDHr9iSaXo+SYdcpE3lRmwgGpJCWTJ4qOJgzCiWhUaWp3YGywNVrvDJ0cLORnD6zEQxAEZBm1qQ3CbW6OrE8BrUYFr9cPd3BgT++acINOCqBG4ijwoWbCAaAomAl/5/Ma1LbYlPs1x3mflWNzcaC6A102tzKAZ/H0YrR3u5W2kI1tdhTlGvqUkE0bl4eSfBNqmm2DXn+iyd1RinKMStcYY4oz4QXZBpajZAgG4UQ0aoiiiKYOJ6ZU5ECrUaG1K7x1mpwdilaOEos5xUF4p83DHuEpoNWo4fVHz4T37qAykliDmfDeLTkHojjPhPZuN17YdASbdtQCkGrN4x0AdNkZ4+D3B/DP947iREM3CnMMmFyeHSwdk/5eG9odKCuIPPgn36JHR5SAc807h/DUq3sH8VsNnryBtzDHoExilfcVpEp+th52lw8uz8g7ezPcMAgnolHD5vTC6fahJM8oZYN6nSZvDX4/0HIUADAbtCmtCe+yu9meMAV0GhU83oDSK7x3tlXOjHtH4LCebrsHAjDoFoUAUJTX0wu8usmGgChKvcfjDOzLCrJwxZnj8en+Juw+2oqJZdkozpPKTvaf6sCTr+5FU7sDpVF6judn69FujdwJZO+Jduw53oZACqdHOj0+6LSqsM2uQznTMBhykuGP63b3SURQajEIJ6JRoyP4ZpyfbUBBTt9Tsu3dLhh06kHVaJqNWthS1KLQ7fHD6fZzUE8K6DQqeH1+pU1h7z7hupGcCXd6kWXUQqUa/L6DoryeOu3aFhtsDi9EEXFnwgFg+dkTMKk8Gx5fABPLslEUrP1+fctJbD/YDJ9fxJiiyJnwgmwDOq3uPhNNPV4/WjqccHv8aO5IXSDqdPth1GkwZ1IBZozPw13XzsP4YD/1VJHPGhyt7cLOw60pfWwKxyCciEaNLnuwrZ9ZB4tJC7szPGhu63ahINswqM2OqawJ7wxOy2Q5SvJpgxszfXImXB3+tqnu1cZwJLHaPYNuTyiTs9Y5Zh28vgCOBqdYxlsTDkitIr991WzMnVyAhZWFyLXooVGr0GF1Y1yJGT+4bh5On1ES8Wfzsw0QIXUTCtXQ5lDGx1eHtEBMNpfHB6Neg1kT8/Gjry7ApWdO6LdVY6KNL7XgN984A4LQk5ig9GAQTkSjhvxGnGPWw6jThA3OAIJB+CBKUQApE253eiGm4NR2lzwtk0F40mk1anh9IeUo2r4tIbUa1YgMwrsdngFlrCMpLzTjsjPG4ZZLpgEA9p1sBzCwTDgglYh979p5KM4zQSUIKMqV/k7nTCrA7EkF0KgjhzP5wWFWvYfThHZXqW5K3cZNp9ufEW1FKwqzUJhjQIeNQXg6pXZLLhFRGoWOejfo1EqnAll7txuTyrIHdd9ZRg38AREuj7/fSYBD1fNhguUoyaYLBtjyJrremXAgGISPoJrw5g4HXvzgGFq7XJhckTOk+1KpBFy3dAp8/gA0ahX2nwgG4UPY7AlIXVca2hyYOSE/5u3ylQmRLnTa3Hh9y0kU5BjQ2uWCWiWgNN+U0ky40+NTOuqkW57FkJJMeHWTFaKIlJfdDAeZ8UwgIkqBLpsbJr0GOq0aBr0GPn9AyWD6/AHYnN5Bt/0zG3oG9iQ7CGcmPHW0WhU8Pj+8/sg14cDIy4RvO9CMHYdaAADZCdo0qFGrMKYoCycbpYB3KJs9ASmTe6S2E1P6+ZAgtxttt7qxdW8j3t9Zp1xXXpiFiWXZ2H2sFaIopqTnvtPtQ3Ze5E2kqZZn0eN4fVdSH+P5dw9j445a5Gfr8d/fOSepjzUcsRyFiIa9ti5XXPXYXTYPcoOnp4066ZSwPGRFacc2yBpYucbVmoK68E67Gxq1gKwU9xcejeRhPV5v5O4ogJQd942gIFwf8jsONWMdasm8cuXrgdSER7L87An4xddOj/jvEcqg08Ck16C924WD1Z0oKzDhmytmApAC+fLCLFgd3pQMWxJFEQ6XT3ntSbc8sx4dVk/SSuh2HmnBxh21KMo1oL3bDbsrdd2jhgsG4UQ07D3+8h68+P7Rfm/XaXMjJxhky9lqR/CNoVvetDnIIFzOSsulIl6fP2lv7J1WD3KyOC0zFXQaNTzegFJuotWM/JpwR8jzNlGZcAA4d26Z8nXvfusDZdRrwiZkxpKfbUBThxOHazsxfVwezpxViu+snI0vnTMBhcE9IG3dyS/LeO7dw+iwujOmLCPPolfOACaaPxDA2o1HUFGYha9eVAkAqI0wNMnr84/q6Z0Mwolo2LM6vErnk1ikATdSkG3olQmXf36wmfCe2lPpzfyZDQfwh7U7B3Vf/emyu1mKkiI6rRoer1+pCddFyLxq4qgJ31LVgP9Zn9rBMIPlcPmg16lx/YVTsGh6ccLuV6NW4fvXzcOq8yYm7D7jUTk2B/tOtMPt8WPauFwA0tTNiiKz8neb7AmSzR0OvPdFHZYurMBFi8Yk9bHilRc8K5iMuvDWThdau1y45LSxGFciTSiubbGH3aau1Y5f/u1z/OQvn2bUVNNUYhBORMOezx9QJtFFI4piWPBqUDLhchAe3Ow4yCDcYtJCo1ahrduF9m4Xth9qRk2TrU9/4kTosnk4sj5F9FpVcMOt9DyJWI4SRya86ngbvjjcmpLuOUPlcPtgMWpxyenjhjQtM5I5kwqw4pzUBuFXL5mMgmBt+LSxuWHXyZf3HtyVaHKQee6csow5gyUH4b3bN0biDwTw2pYTaOpwxHXf8u1KC0zIs+hh0mtQ1xoehD/71kFYHV4Y9Wr8v9f3w+cP4PUtJ7DzSMsAf5Phi0E4EQ17Pn8ALo8/5m3sLh98flEJXo3BDgVyJrx7iJlwlSAg36JHe7cLH+9pgCgCAVFEU3t8b1rxcnv9aOpwxn0qnoZGnohpd/ogCIA6wuAarbr/ILwjODDGM8CylW6HBz9+6pOUZgodLt+gBlZlKpNBg7u/PBdfvXhqnw+vliwdNGoh6SURtS12CJA2g2YKOQiPNlE01PrNJ/Dqxyfw3o66fm8LAE3BAUgleSYIgoCKoizUtvQ8h7sdHhyt7cKFCytwy6XTUdtiw1vbqvHKxyfw1rbqQfw2wxODcCIa9rw+sU/P71CiKOJ4fTcAKOUocq9euSa8y+6BQaceUq1qfrYe7d1ufLK3QenK0NAWHoTbnF40DiEwP1zTCZ8/gJkT8gZ9HxQ/+flgc3mh1agiZjHjyYTLp/ztA6y/bW53orXLldI2eg63D6YRtul3XIkFyxaP7XO59OG57/TcRKttsaEozzjkWvhEys7SQRCAzghB+M7DLTgV7GRT32rHv7eeAhD5Q2gkze1OGHRqWIJ7CsYUmVHXYlfOBO052gYRwIKpRVhYWYiSfBNe+fg4AOBEQze8vthJlZGCQTgRDWuiKPabCX/vizr86cXdAKCMvJZ79crlKN12z6BLUWQF2QbUttjQ0unCkrlSJ4jqZis27aiF1xdAt92Dh57djkdeGHyt+L4T7dCoVajsdVqdkkNuSWh3eqGLsCkT6L9PuCiKSo/6gW6CkzdJpmoaKyB9ME12m81MIn94TqbaFjvGFJmT+hgDpVGrkG8x4HhDNwIBEV3BshSP14+/vL4Pr4YExXIVVbwdTpo6HUoWHJDOADjdPnTaPPB4/fjsYBPyLHqMKzFDEARcML8coij9vfn8PUmTkY5BOBENa/6A9O7Qe/BOqOYOJ3RaFX568yJMCHYmkDdmJjIIz8s2KB8Gpo3LRUG2Ae98XoPn3j2MHYeb8fTr+9DU4URbt1t53Ni/WwAvfnAUz2zYH2xv5sXuo62YNjZHKZOg5JIDb5vTG7UdnjTQJ/rzTyqFkoL0gWbC5Vr0VLZ3G4mZ8FgKspObCXd7/Whud2BMUeaUosjOm1eGvcfb8fCaHfjJXz5Ft92D/Sc74PEGlLN4zR1OCAJQnGcM65wTS3O7E8V5PSVzJfnS16carbjviS3Ye7wdZ84sUYL0c+aUIcesw7UXTAEgnfEbDUbPXxkRjUihw3bkqXy92ZweZJt0YYM99Do1BIR3R6kYYr2mvMlLgHT6u6zQhLbj0pv7joMtOHCyA+NLLDjVZEVjuwOTymNP5/x/r+/HZweaAUi1yZ8faILd5cOXzk3txrbRTK/rPwjvrxwltPuEPY4PX6Hk56fdmfw+1jKpJjxxrQkzXX62AZ02d9TXj6Gqb7VDBDIuEw4AFy0agze3VeNYMPO8paoBDcFyuZZOJzxeP1q6nMi3GJBt0sWVPPD5A2jtcuH0mT2ddYqDA4o+Oyi9ht24rBIXLAjpG2/U4tG7zgUAfLirbtQE4cyEE9GwFtp9RM5CBwIint94GK1d0uYgq9Or1CbKVIIAg14Nq8ODD3bVobnDiZysoXUcKQi2OysrzIJRr0F5gRTUazUq7DjcAhHAxYul9mQNbfZodwMAaGx34LMDzbj8zHGYXJ6ND3bWoSDHgF9+7TScNat0SOuk+MktCe0ub8T2hACgVavjDsJtA8xoy2d4UpUJ9wek0q5RlQnPMUAU4+sSMhhybfXYkswLwrMMWtx62TR89eKpqByTg/e+qMOuI60w6TUQIb0OtXRIWW2TQRNXEN7W5UJAFFGc2zMZtCBbD7VKwK4jrQCA06YXQ62K/Pc0qTwHp5psw6KT0FAxCCeiYc3n73mhltsUtnQ6sXF7LXYfbQMglQBkRRiTbdBp8OneBjz71iH4AyIsWUPL/uUFg3C55GX2pHyMKzbjijPHAwCyDBqcPqMYapXQ7+bMj3bVQ60ScMnisbhjxUysOm8iHrhxEcaVZMagj9FCLvtxuv2xM+ExasJDg7uBlqM4U1wTLgf9I6k7Sn8KggN7WoIdPRLtcG0nsrN0GdvR6MyZpVi2eCwuXDQGbd0u+AMBXLN0MgApi9/c6URRrgEmgyauD4PNndJxDC1HUatUKMyRyvVyzbqYXagqCrNgc3qVKcYj2ej5KyOiEckbIRMud0qRAx6rw4vSfFOfnzXo1GHdSywRAvWBKMwxwGLSYs6kAgDA7IkFmD2xAKcarVi/+QRmTsiHVqNGcZ6xT9eUUP5AAJurGjB/SqHSUi3VvZVJEtrNItK0TCA4rMcXgCiKEbunyN0nNGphwGUlqS5HkWt+R1MmvCz42tDY4cSMCYm//8M1nagcm5sx/cGjOW16McYWm1GUa4QoAmvePozjDd2wOrwoyjWi0+qJawqw/KEz1xJ+ZrE4z4SmDme/iQS5jWNdq33QLWOHi9HzV0ZEI5IvpAxADr5dwWyefOrf5vTCbOz7Yi53gNDr1LjzS7MwfdzQ2v7ptWr86e5z+1w+tsSMs2aVYsk8aWx3ab4pZia8rsUOm9OLhdOKhrQeGjq5OwoQeVCPfLkoSpuENeq+gVaHza0McxpoRtuZ4o2ZzmC5wWjKhOda9NBpVGiM8cF4sFq7nGjvduOy03P6v3GaCYKAsoKefTHFeUbsCZ5NLM4zwe0NwOHyISCKUMX4QCHPXOi90V3OjMcbhNe32jFj/MhuxcpyFCIa1iLVhMv/tzu9SvtCsylSOYqU2SzNN2HelEJlE95QCILQJ+OlEgTcsWImpgWD/NICE5raHVGnaR5vkDZJTe5n4yYlX2gmPHpNuHS5XBfu8vjCpv51WN3IM+uRFefp/FByeUiqylHkvvmjKROuEgSU9PPBeLCO1HQBwLBsKTq+1KKUlhTlGpBlkOrEXTE6UQHSRF+jvu/MBSUIL45dG59r1sGo16C+Nfa+mZGAQTgRDWuh5SiHqjvx1rZqpa2bzelTghdzhFITeWpmSV5qazWnVOTAHxCx+sXdcHv7vqEdr++G2ahVeppT+ujCylGiZ8KBnufi5wea8fhLVcoUxm67B9lmHcxG7aBrwl0ef9QPbYnwm//7HB/vqQ8pRxk93VEA+exU5KBPFEWs3XgEz797uN8N1b2daOyGTqvKyM4o/bn+oqkYV2yGShBQnGtUzo44+vkg2Wn3IDvCJvcZ4/JQVmDq9wOJIAgoLzQxCCciynSh5Sgbt9dg3ftHwwac2IKbeyLVexv0PZnwVFowtQi3XDYN+0524L0vavtcf6K+GxPLsjO+hnQ0CA28+wvC5eeiHDjLG8tsTi8sRh2yDFrYBtmiEEBcnSkGw+cP4ESDFUdqu5THGE3lKID0GtDa5YrY5eZ4Qzfe3V6DTTtq8bvnvkC3wxP3/bZ3u1GYY4QqzkmTmSQnS4ef3LQQD96yCCaDVvlg1l+v8G6bG7kRarnHFJvx0B1nxlXnXVGYhbpW+4jvkMIgnIiGtdBMuCf4BipPv7M7vbDGkwlPcRAOABfMr8Csifl489NquEOmfTrdPtS32vvtIU6poRIEpS482sZMJRMefP65g/8P35OgRZZRM/BMuMcP+bNYskpS5PKtDqt7VG7MBKQSMVHs6ewR6v0v6qDXqfHAzYvgcPmwduORuO+3vduFfMvQWp+mk0GnwcQy6bVIfk701+u+y+5BjnloGyonV+TA5vRi2/6mId1PpmMQTkTDms/XN1MiT7+zOb1K0BMpCE9XJlx21bkTYXN68cm+RuWyg9UdEMF68EwiT82Mtybc4428JyHLqIXN6R1Qds/l9iHfIrXQS9bmTLl8q8Pqht3lgwAkZH/EcCK/BvTenOlw+fDZgWacPbsUUypycNkZ47Btf1NY7/dY2rtdyM8evkF4qCyDXI7SfxA+1K4m58wuw+TybDz37mFlo+dIxCCciIa1SHWychDucPvQFXwBj7QxM9esh0atQkleeoLwKRU5yLPocai6Q7nsvR21yDXrMH2EdwUYTuQNZv2Vo3iUILxnRL2cvbYYtTAbtPAHxIj7AKJxun0oyg0G4UlqU9iTCXehrcuJ/Gx9zO4XI1FZgQlajQpVx1vDLm8MbqCePTEfAHD6jBIAwN4Tbf3ep9cXQLfDi/zg/IDhLp6acLfHH+wFPrQPHiqVgKvPnwy7y4fDIa+PIw2DcCIa1iINSZE3xAFAU4eU2YqUCT9vbhkeu++CtJ56nzomB4drpA2lv3vuC+w72YGLFo1JyvhsGpyecpR+Nmb6pGDW45NbZPqUPQlSOYr0HPx4dwMCcWTDff4APL6AMkwmeZnwYH99tx8nG61pKc9KN4NOg7Nnl2LrviZYQ2q+5Q/0hTnSJukxRVnIMeuw70R7v/fZYZV+Vj6TMdzFUxPeZZfOEPRuTzgY8vO+K0mTTDMBX+WJaFiTN8OF9mcOPVXc1O6EUa+OGNRqNWqMTfMEyqljctFp8+Dlj47jeH0XsgwanD+/Iq1ronByhxRdfzXh/r7lKKF7EhZWFmFyeTbWbjqCj3bX9/u4cnBcFAwAdx9rQ3NH4tvoyeUoANDQ5khbeVa6Xbx4LLy+AD7c1fNv09YlBdIFwWy2IAiYPTEf+060IxCI/UGqLbg3ZaSUoxj0agiIXRPeFaVH+GBkB89eMggnIspQcjlKaKY7NMnY2G5HVga3W5s6Rhri4fMH8MBNi/Df3z0nYtae0ifechRvhHKU0D0JZqMWP715EcaXWPDu5zX9ZsPlzih5wSBu+8Fm/OW1/UP8bfrq3fd5tAbhFYVZmFBqwYFTPeUPrV1OGPWasLNlcyYVwO7y4WhdV8z7k8/IjZRyFJUgwGTQKAOdejtc04kPdtYBgDLpdyj0WjW0GhU6bawJJ6IRbvfRVmzbL52KfeWj40kZXJEMXr8UyFhM4ZkXeRBPa5cro0cfjykyw6jXYEpFDiaWZfcZcEHp1285irp3d5SeATtKJjyY1RMEActOG4OGNgf291PSIAfhJr0GX71oKmZPzMfJhu4BtciLh8vTKwgvGJ1BOCBNc6xusiqbZ9u6XEoWXDZnUgG0GhU+P9gc877ag2fkhnN3lN5MBg3s7shlUa9/chJb90ndTBKRCRcEAdkmLTPhNLoERBFOt6/fU200sryw6Qie2bAfz2w4gNc/OYn/+H/b8H6EHtaZRs6EW0xaCOgJlAqD9YSimNnT6lQqAfdeMxffWDEz3UuhKPSaQWbCXZGHRZ02vQR6rRp7jsXe3CcH4Ua9BstOG4tVSyZBBPoN3gcqtBwFAErTtFE5E4wtNsPu8iklbW3dLuW1RGbUazB3UgG2H2qO+T7Z3u2C2agNG/g03GUZtFFbZXba3CjJM+LyM8bBEmEj/GBYTDoG4TS6/PmlKnz30Y/wH89sS9pwCMoszR0ONHU44Q+IqDrehiXzyjB7Uj7+8c5hfBrSPi8TyTXhOVl65GXrlWBH3kgFSJmrTFY5NhfFnI6ZsXpqwqMF4dL1fWrCXdKwKIMufE+CVqNCrlnXb0bbGcxQG4NdKcaXWGA2arE34UG4/DjS6f/8nJFRPjEYY4Mj1WuabQCkILwgwvE4bUYxumwe/Ph/PsHG7TUR76ttBLUnlJlNWmWzcW+dVjdmTsjHtUunJGzQWHYWg/CkW79+PVasWIGZM2dizZo1MW974MAB3HjjjbjiiitwxRVX4MMPP0zRKkc2URSx83ALGtrs2H20FbMn5qOp3YFXNx9P99IoBaqOS2/qFy0ag+I8I665YAq+u2oOyguz4tpAlk5efwAqQcCqJRPx3VVzlF62uWYd1CoBBp1aqbsmGgx93N1RgkG4PKzH6YXN6YlY42/J0vXb/1i+Xg7CVSoBMyfkYf/JxAfhapWA4lwTSvKMo649YSg5CK9utsHh8sLp9vcpRwGAeZMLMW1sLjzeALbsjZyoaByBm1wtRh2sDqn/fVfI89frC8Du8g15SE+fxzNpR3RNeEaMxJoxYwYeffRRPP300zFv53A4cNddd+GRRx7B/Pnz4fP5YLVaU7TKka2uxY7HX65Svr/l0ml4c1s1Nu2oxcwJ+Zg/pTCNq6Nk23u8DcW5Rty4rBKiKCpZjOnjcrFlbyMCATFjxy77/AFoNAIKc4wozDEqmzCNeg2ys3SYVJbNdn80JDplY2aU7ijq8LH1Pd1RfLAGp2X2lmPS9bvvYuveRhTmGMLOkhTnmfD5gWYERDFhwbLL44NBp8bys8cn5P6GM6Neg6JcA2qarGjtktsT9g3C9To17r9xIdZvPoHXNp+A3eUN2wDu9vrR1uXCuXPKUrb2VLCYtLA6Pdi0oxYbPjmJ1fecB5VKUFoTDrU/eG/ZwXKU0PelkSQj3pkqKysxZcoUqFSxl7NhwwYsWrQI8+fPBwBoNBrk5XGgRSI0hbS9mj4uF4W5Rly7dDLGl1jwP6/uRUObPY2ro2QKBEQcqunEzOAwitAXuoll2XB7/Bn97+/ziUoQBPSMVjbo1Ljny3Nxw7LKdC2NRoh+y1G0KgjoKeuQg/CAKKK10xVxUJQlK3Y5Sm2LDYdqOrF0QUXYB2CTXgMR0lCURHF5/DDo1Fg0rRiLphUn7H6Hq2nj8lB1vB0nG6UkX6RyFNn0cbkQARyu7gy7vLHNARFAeWFW8haaBhaTFh5vAMfqu6U9D8He9XK2OjfhmXAdvL5An83DI0VGZMLjdfToUWg0Gtxxxx1obm7GrFmzcP/99yMnZ2CnmgsKzElaYfIUFSW3l7EzuKP58rMn4OLTximP98tvnoWv/eYd7DvVibnTSwEAx2o7seatg7j/lsUw6DLjKTSQ49NpdWPD5uP4yrJpUU8vjzSxjs+pxm64PH4smF7c53aLZgF//fcBtNo8mD8zvf20o9Ho1NBp1craC4Obygrzs7B4Tnlc95Hsv6/hbrQfn/xgJrq4yBLxWJQUZ8Nk0EBUCSgqssDjC0gZQ4cXTR0OTJ+Q3+fnSgvNsO2qQ36BGeoIZ5le/7QaGrUKVy2dGtburTgY1JnMBhT2s4/A6vDg0KkONLTaIQjAZWdNiHhWSBQEZJl0Sft3Hm7Pn1VLp2LzngY8/+5hFOYaMX9GadTNlbl5JujW7cbJFjsuOWeScvm+YFA+a2pRv7//cDo+5SXZAKQPiQCg1WtRVGTBkQbpA8uEMXkJ/X0qSqX70hi0KCocfrFbf1ISQa1atQr19ZHrSj/55BOo1fHtHA4EAvj000/xwgsvoLCwEA8//DB+97vf4eGHHx7QetrabMOq80dRkQUtLcktuzlR1wmTXoNrl0gvIqGPN6ksG1urGnDxQmmAyIaPjmH7gSZ8tqceMzJgtPZAj887n1Xjn+8dRWmuAfMilNlUN1kxpsisZJ9EUUSnzYO8ftpMtXW5IAiZ1xM20vFp7XKipsmGBZVF2LGvAQBQaNb1uZ1OEGHUq7H7cAvmBTPlmcZqc0MlCMraVZD+tn0eX1zPi1T8fQ1nPD6AN9g9xGZ1oqUlPIiVj49Rr0FbhwPNzd1we/wozDHC6vBCFAGNCn2OoRoiRBE4Ud0esZ3b0eoOlBea4HF60OLsyZj7gmupre+E6I29cf53a3bgcG1PL+vDJ9tx86XT+tyuy+qCViUk5d95OD5/8owaTCi14GSjFVefNxFdnbHLhqaMycH2/Y1Ydc4E5bKDJ9qgEgRoIcb8/Yfd8Qm232xsk45JdV0njGoB1fXS80z0+RP7+wQ3O5+q6YRWFOEPBOBw+fq0pM1UKpUQM/GbkjTgK6+8gm3btkX8L94AHADKyspwxhlnoLi4GCqVCitWrEBVVVX/P0j9au10oShKVmXu5AKcbOjGY//ag1c+Oo6DwU/4x+tjDyrIVCeCpxirjvdtD9bQZscv//Y5nn37oNIndv3mE7jviS3Yc6wVgLTZqvdpZK/Pj989twN//feBJK8+MV7fchJ/frkK3Q4PTjRYYdSrI46qVgkCKsfk4ovDLQk9/Z1IXl8AGk1oOYp06t+gz4yzNDT8WUxaCELPcysSk0EDu8sHnz8AEUBBsCuGWiVg9sS+3Xnk3vXWKJszmzscETvmmILPa6fbD58/gF/9/XNs29/U53b+QAAnGq04Z04pHr3rHFx+xji8v7MOO4+09LmtXI5CPa5bOgWXnDYWZ8ws6fe286cUoqHNoZTt+fwBNLQ5UJJvHHH7UXoHv1aHXI4iJUMS1ZpQlh18PGvwPffDXfV44C+fKq1ph7th9ey4/PLLsWfPHths0mmQjz76CNOm9f1UTwPX0ulEYW7kDO7cyYUQAew62oo3t1Urp6GO1HbhV3//HD9+6hO8/Vl1Clc7NCcaugFIQfj2g82oa+2pd65rkb7+aHcDNu6oxeGaTrz+yUkIAvB/bx3ClqoG/PTpT/GLv36G1i6n8nMbt9eirdsdNuQhkx2u6YQIYN/xdpyo78aE0uyom7yuPGsCuu0ebNwRuQ1Xuvn8AWhDRtabgzXhRgYVlCCLpxXj57eeFnMAiUmvgcPtgzvYI3zG+DzcfOk0PPTNMzF3coQgPBisRKoL9wcCaO1yoThCv275w6XD7cOh6k6carRG7Dfe0OaA1xfAzPH5yDHr8eXzJyPXrMNHu/qelZaCcH5oDTV9fB6uv2hqXJsB5Tr67YdaUNdqx12PfoSdR1pQXjCy6sEB9Amy5WFUXTYPsrO0Ce+sY+n1d1LfaofD7RsxNeIZEYRv2LABS5YswVtvvYXVq1djyZIlOHr0KABg9erVWLt2LQCgvLwcd9xxB66//nqsWLEC+/btwwMPPJDOpY8IAVFEa1f0TPi4EjMuO30crl06Wfn0WZJnxJ5jbTjVaIXb68eHEV7YM5Hd5UVzhxOFOQa0dLrw5Kt78dSre5XyJLlbwawJeXjx/WN48pUqFOUYcd9X5sPq8OKv/z6ALKMWHl8Af3pxD7rtHjhcPvx76yloNSrYXb6wtk2ZqMvmRlOH9AHi84PNqG2xYWJZdtTbTxmTg3mTC/DWtuqMzD54/YGwbJOSCWdQQQmiUaswvjR2nWuWQQuHy6dsytTr1Fi6oCJq/3c5Ex4pCG/vdsMfEFGc1/dnjUom3IcvDktZ7bpWG7ZUNeB/Q87EVTdJZ/zGlUinwlUqAWfNKkXV8fY+r1FydxQanDyLHpMrsvH5gSa89MExqFQCyguyIpY7Dnd9gvDg87fT7k54ZxTp8eS/k55gHwBc7pExwyQj3qWWL1+O5cuXR7zu3nvvDft+5cqVWLlyZQpWNXp02Tzw+QMoirIDXBAEXHfhFADAF4daUNtix9IFFXjhvaMoyTPi9Bkl2LD1JDxef8ZPBjsZ3Dyy4pwJeO6dwxhTbMbx+m5s3deIc+aUoandgTyLHt9YPhP/8cw2eP0ifnzNXJQXZuG/v3s2WjtdqCjKwsmGbjy6bjd+//wXmDOpAA63D9cunYwX3z+GulZ7Ul6MEkWuES0vzMKuo61QCQLmT439ZrFkfjl2H2vD4ZpOzJyQWbXhvl7lKNPH5eLMmSUYUzzyslCUuUwGDRwur9IjvL/XQjm4sNq92LSjFtv2N+F7186F1y+iIVhvG6scxeHy4otgaUl9qwObdtTiZKMVK8+biPxsA6qbbNBqVGEj6M+eU4Y3t1XjyVeqsOLsCZgdHGLlcjMTPlTnzCnDs28dQm2LHauWTMKKsyeke0lJYdRroFYJ8AcTV/Lgnk6rJ2Irx6HSalQw6NSwBzPuncFWiCMlE86/OkJLp5QVjZYJD3XbFTPQ0e1SsjgXLRqDXLMeogjUt9kxoTR6RjUTyHXsiyqLsHhaMQw6NX777Hase/8oZk/MR2O7NFwhx6zHT29eBAAoC55SzDbplPq0aePy8P3r5uFPL+7BO5/XYNaEPJw9u0wKwlvsmJWgQNXu8uK/nt8Jrz+A5WdPwFmzSuP+2YY2O9q6XDgvPzwYPVzTCZ1WhWsumIwnX9mLr185A1MqYncYmjkhH1qNCjuPtGZeEO4Xw7rc5Jj1+OaXZqVxRTQaSUF4TyZcF6WneOjt1SoBL35wFD6/FNC89VkN3v28RmmzGTkTLt3v4doudNk8weE9HUo7vb0n2rFkXnlwg3kW1CGtfysKs3DlWeOxaUct3vj0FGZPKoAoilI5ij6zEyiZ7vx55cg167HvRDsuWTw23ctJGkEQYDZp0WXzIMesg9XphSiKaO92JW0omtmkg93VE+wDIycIz4hyFEqv5mBpQn/trgDpRXz2pAKMK7HgwZsX4cKFYzAmOGGstjlze0nLdh5pxcQyC0wGLYx6DQRBwO1XzoTL48f/vnEQje0OZYNiWUGWEoBHIgfiY4rMWHneJGSbtDAbtTha26ls4hyqj3c3oLrZBqvDi48HMLkyIIp47KUq/HHdbnzt1+/gxfePKiU3x+u7MKksG/OnFOKJ7y+Ja+ORXqvGrAn52HWkNWrNe7pKVbz+wKhpNUmZy2SQytTsLuk0uTxlMxqVIECnVcPnFzF7Uj7GFGVhwycn4fb60WF1Q6NWITdCRya9Vg2VIKAuuDdn8fSevt6CIO118Xj9ONVkxbiSviU0Xz5/MmZOyFc21Hl9AQREkeUoQyQIAuZPKcSNyyqhH+HH0mLUwWzUojDbAKvDg6YOJxxuX78lW4N+PJMWdqcPoigqQ4FcnpFRjsJ3LsKpJiv0WnXU2sVoJlfkQKUSUJxrhE6jUjZsZqrmTidONlrD3rQA6YPFqvMmoep4G+wuH0ojZJ+iqRybi19//XRMrsiBIAioKMzC9kMt+NOLe3CqcWhtmgIBEe99UYtpY3OxeFoRTjXZ4t70uftIK5raHbjsjHGYMjYXb26rxp7jbQiIIupa7RhbLL1YDiR4nTu5AG3dLjR3OsMuF0URH+ysw3f++BE+O9C3S0Oy+XrVhBOlQ1Ywe91pk4KEeErznMG61ivPHI/z5ko97ccFkxpFuYaIm9wEQYBRr1b2dcwYlwcBUt36GTNLsP9kO97aVg2n24/TZ0T+gC31MA/PKLIcheKVn61HSZ4RFpMONocXx+qkM8yTy5NzJtwSzIRL3Yek90BmwmnEONHQjQmllkGPJVepBJQVZmV8EL7jYDMA4LQIE+EuWFCuvImG1lAO1NSxucpUvRON3YO+n7pWO3777Ha0drlw0aIxGF9qgdPtU0qH+vP2Z9UozDHgy+dPwo9ukspqapttaOl0wuMNoKJo4PXSZcHjIo9yllUdb8Ozbx+Czx/A1r2NA77fofL5AtCoR944Yxpe5FrtTqsUhOvjCMLloKVybC7OnVuGixaOwfeum4fSfBPGFkfvLWzQaeAN1p4X5BhQWmDC1DE5WLqgAh5vAK9uPoFpY3OjznGwmKQygoAoKhlFZsIpXjdfMg13rJgJs1ELq9OLY/XdMOrVKEvSdFCzSSs1PQh+wAUAJzPhNBL4/AFUN1kxcYifYMcUZaG2Of5MbTp8frAZE8ssEctuDDoNLlggDSOKVYLSny+dMwGr7z0PJr1G2QQKABs+OYkHnv407iFRb2w9iaYOB265bBoWTStSau1PxpFd77S5cbi2C+fNK4dapYLJoEVhjgG1LTalBeOYooFPHisIDiFq6xWE7zjUAqNeOn77T3XA7U1thkJqUciXMkovuStPh1XOhPf/nPzRVxfgqR+cH8xua3DjJZXINevxwE0Lccul06P+nNwhJcuggUatwndWzsbXLp+OqWNy8b3r5mFMURauWTo56s9bTFqIImB3epVx49kx2i8ShSrIMaA4z6ScUTlWJ5U4Jro9ocxi0knP1ZCuPsyE04hQ02yDzy/GbFEXj/ElFnQ7vMobUKaJVooS6kvnTMSPrp8f1wbVaDRqFfRaNSaUWZRyFKfbhze3VaOp3YEjtZ393ocoith/sgNzJhXggvkVUplLURbUKiGuEpeqYM/g+SHtscYWm1HTbENtiw0CpBKcgcq16CEAaO/uCcJFUcSe422YNTEfiyqL4PUFcOBkx4Dveyi8fjGsOwpROvQpR+lnYyYglaxEqh+2mHTK5sxITMFNlHLgXFFkVpILsybk49dfPwOTy6NvkpPbzFkdXuXvOdMm/VLmM5u08PlF1DTbMLmfzf1DehyjFnaXVznLBDAIpxFCHlwzsWxoGyomBIP4odZBJ0usUhSZVqPCjAR1/hhfakFtiw1en9RD3en2QSUI+OJw/xs269sc6LJ7wrqQaNQqjCmS2in2Z/exNuRZ9BgTUnIypsiMxnYHTjZYUZRrHNTGIXmjWFtIEF7dZEOXzYN5kwswbVwu9Do19p7oOzhkqERRxJaqBmWHfCipHIUvZZRectDcYYs/Ez5YciY81vCgWEKnEMp/z4UMwmmAzCETZM+aHX/nroGymHTw+UWliYQAbsykYcbq8IRlMGW1zTZkGTRKqcFgjS02QxB6RsInW2O7Q9nUFI9YpSjJMLE0G/6AiFc/PoGXPzqGWRPzMXtSPr443IK6Fhve+PSU0t2gt/0n2wEAM3vVcy6YWohDNZ0x664dLi/2nWzHvCmFYZPexhabIYpS/fZg6sFl+dl6tHdLQcb7X9Ti0XW7IAjAnEkF0KhVKC/IUgYeJVJzpxN//fcBrHvvaJ/rWI5CmUAuR5FbqCVzZoIxGPAPtoRE6VHu8KKtywWzUTviO3pQ4k0sy0Z5YRZ+etMilESY7poo5uDztb7VDoNOjSyjlplwGj4+3FWHHz+1Fb977os+17V1u1GYY4xrNG8seq0aFYVZODmEzYjx8AcC+PubB/HTpz/FMxv2x/UzNqcXJxutmD+1KKlrCzW5IgcatYA3t1WjosiMO6+ahUXTitDW7cLP/voZ/vXBMbz68Yk+PyeKInYcakFRrqHPB4YrzhqPqWNysObdw/AH+rYDdLp9+O8XdsHnC+CcXlmJSeXZ0KgF6LQqLI5xNqA/BdkGJXP2/s56GPUafO/aeUowUJJnRFN7fJtHB0KuQ99S1YimXkG+1x+ARsONmZReysbMYCY8mW0z5Uy4nNEeqJ5yFA9au11DTsLQ6DSm2IzffuMMTElSf3CZ/HytabYh16yHQaeGyz0ygnD2JBrhRFHEvz44BrfXD3eXv087t/ZuV8SBEIMxoTQbu49JfaSHGtRHU3W8HR/trse4YjN2HmnFiYZuFBXFLqWR67Cnjc1NypoiybPo8ce7zkVrlxMVhVnQatQ4a1apMtq66ngb9hxrg9fnhzakdlSeSnnDxVP73KdGrcLShRV4+rX9qG229+nJurmqAScbrbjr6jl96vPysw144vtLoFGrhvRvU5BtwBeHW+EPBNDY7sBFiyowJzh1D5CGi2zb39Tn94rmg111yDHpsKAy9gckOfsuQsRzGw/je9fOg0oQ4Pb6IYpgOQqlnVajgk6jgscXgE6jStomNQAwBtsJ5pgHF4SbjT014W1driFtRidKNvnMTXOnE4unF6Oxzc5yFBoeWjqdsLt8ysbLTlv4xsm2blfCNuSML7UEN/rEtznT4fJFLJGJ5fMDTcgyaPDDry6A2ajF61tOhl3/4vtHcbimM+yywzWd0KhVQ958OlBmoxYTSrOVYFSjVmFhZRHOnVuG8+aWwe31Y3/IJkafP4B17x1FSb5J6dTS29SKXACIuMHzswNNGFNkxsIoAa1Wox7yh6P8bAN8/gBO1Fvh8wf6vHmX5JsgAmju7P/f1e7yYs3bh/Hnl6vw4a66mLeVnydfWToFe4+34+1t1TjVaMW3H/kQAFiOQhlBrgtPZikK0DM1c7CZcI1ahSyDBt3BmnBmwimTmU09tecTSi0w6DQsR6Hh4USwTd6iaVJg1tLpwvrNJ2B3eeFw+eDy+BP2Aiz3tY2nX/g7n9fg7j99hB8++Qne31mHA6c6Im66C+Xx+vHFkVYsmlYEs1GLs2eXYu+JNqU2vLHdgTe3VeOjXpMlD9d0YlJ5dkZNVZw2Lg96nRo7j/Rs1PxwVz0a2x34ytIpUTO7BTkG5GfrcaS2K+zy1k4njtV144yZgy81iYf8XKk6Lm2+LO8dhAfrApvjqAvffbQVAVFEaYEJ694/GnPiZrvVhewsHZadNhZzJhXgnc9rcCjkw5Z6kD3uiRIpK1gXbgkJGpJBLn0ZSltBs0mHhjYHPN4ACnIYhFPmsoR82JSCcDUz4TQ8nGjohk6jwuyJUqeNT/c1Yv3mE/hgZ11Ia6q+o5EHQ+7GUdfa//j6T/c1orwwC7Mm5OEfbx/Cf63difWb+9ZIh/p4TwPcnp4pcPOnFMLnF7HrsNT5ZFcwoK1t7vkQ4HT7cKrRhsoUlqLEQ6tRYcHUQnx2oAkNbXY8v/EwXvnoOKaPy8W8KQUxf3bqmFwcqe2EGJx+aXN68Xmw+0u0CXmJIj9X9gTbIJYVhm/GKcmXSpvkaX6xfHG4FXkWPa45fzKcbj8OVXdGvW17txv5Fj0EQcDsSfnosntQdawV2Vk6fOtLs3D2nLJB/kZEibPinAlYdd5E/PD6BUl9HHkT6GDLUQAg26RV5g4wE06ZLDQTPl4JwkdGJpw14SPc8YZujCu1oDCY6dh7Quq88fmBZmVgS6LKUUwGLfIs+n4z4Q6XF6earFhx9gRcfsZ4vP15Nbbtb1LaJUZS32rHi+8fxawJeZge7BoyZUwOjHoNPtvXhCmlFuw5JgXh9W12pfb9WH0XAqKY0nrweF12+jh8uq8Jv/77dvgDAUwuz8HNl07rt2Rk6pgcbNvfhH0n2/Hnl6owf2ohGtscmFSePaQe5/EoL8xCtkmLU01W5GTplMyfLMughdmoRXNH7Ex4dZMVe4+34Zy5ZZg1MR86rQo7j7Rg1sTILSLbul1K1n1CsBZ+/8kOzJqUjzNmJveDB1G8kv0hWDZvSgFuuHgqxpUMvrWsxaSD0y2dUSvISUwihigZ9Fo1NGoV8i16ZBm0LEeh4WHv8TYcq+vCtLG5MOo10GvVyjCd6mYbDpyS6pETmQUZU2RWpjJGc6imE6IIzBgvlWR86ZyJmDUxHzVNtogTJbtsbqz+127odWp8fflMZcOTRq3CnEn5+HRvAz7d14jDNV0oyjXA5xeVNnmHazqhEgRMrkhtPXg8xpVYMG9yATxeP7591Wzcf+PCuDZILZ5WDKNejcf+VQWPL4DtB1tQ3WxLSQCgUatCJotGbklVkmeMmgl3un146cNjePi5L5Bl1OKS08ZCp1Vj1oR87DzSGnHiqiiKaLe6kRfMwo8rtkAQADH4NdFoY9BpcPHisUPa/CnXlZcXZvUpKyPKJIIgICdLh0nByd4sR6G0sru8/T4BHS4v/vLaPlQUZmH5WRMgCALyLFIQU1ZgggCpBlmtEgY98CGSiqIsNAQz0dEcONUBrUaFSSET3caXWODxBdDQq5bY4fLhj+t2o9vuxb3XzEOuOTxjs/zsCTAaNHj69f0w6tW49oIpAHpKUg5XdwZPX2XmSZ87VszEf9y6uN/uIKGys3RYcfZE+PwBzJyQh4AoQgBwWoxpoIl0wYIKqFWCcialtzHFZpxstEb8QPXGp6fwxtZTmDUhH/9xy2KlhnzG+Dx0WN3oChlLDEh144/8cxfcHj/yLdKHRb1OrQQN40oir4GIYrvs9HH46kVT8fNbFyd9IynRUN1zzVxcd6H0/m7QS+UokZI2w01mRiYU06PrdkOvVeNHX41ed1jbYofd5cMdK2YqQxjyLHo0tjswY3weJpRasHVfEwpzDFAlcFPbmKIsZbJVeZTR6MfqujC510ZJud1edaNVGanu9fnx+Et7UN9qx73XzFU+BYc/nhmP3bcUG7eewIKphdBp1VCrBNS02LDI58fxBisuWhS500gmMBm0mFg28E1cFy8eA71WhdNmlCj90uUPWcmWa9bjpzcvirqZa/q4PHy4qx6nmqx9OtKcbOjGuBIL7rp6TtjlpfnBDZ0dzrAPWh/vaVA6yIQ+3vhSC+pa7cpmYCIamIoiMyqifJAmyjShr/UGnQaiCHi8gWE/ZIqZ8GHG5w/gVKMVB0514GivDhmhWrukcoDikClWcpBWUWTGqiWToFELyE9w4FZRGLtDiiiKaGx3oqxXgF5WYIJWo8KpJmmjUEAU8fRr+3GophO3XzkDsydF36xoNmpxzpwymAxaaWpjYRaqm2w40SC10cu0TZmJIPUMHwOzUYu7vzwHd395Tv8/lEATy7KjtkebPi4XAHCwuqPPdTXNtoiBs9yrvvcgHn1Ihq44pN799BklmD0pP6lT2oiIKPMYgoH3SChJYRA+zDS2OeAPnubfsPVk2HX/7/V9+PubBwEArcEJgwUhnU+UILwwC4U5RnxzxSysOHdiQtdXUZQFvVaNIzWRPyDYnF443T6U9NpAqFapMLbYrGzOPFrbhR2HW/Dl8yfhrFmlke4qqoll2ThR360EgVPH5A78FxlG1CoV1KrM+VPOMetRVmBS9hzIumxudDu8EYPwghwD1CoBzZ3hteQOl3T7n9+2OGw40dzJBfjBdfMTehaHiIgyX08QPvw3Z2bOOzfFpSaYYZ47uQD7TrTD65OehP5AADsOt2BLVQO67R60drmQY9aFTS0cX2KBSa9RankXTy/GrAmRu1EMlkatwtQxORGzoEBP67ri/L4ZzMoxuTjR0A23149jdVIQf9688gGvYXJFNhxuHzbvacCYoixlOhylzvTxeThS0xW2N6AmWKcfKQhXq1QozDH0yYTbXT5l6BEREZE8MZZBOKVcbYsNapWAs2eXwh8QUd0kBTZ1LXZ4vAH4AyI+2duIti6X0pZQtmhaEf50z7nKVLdkmTYuF3WtdnT32mQHQGldV5LXt5Xe9PF58PlFHK3rwtG6LhTnGgc1EW5KcGR7a5drRJaiDAdTx+TA7fWjPqRnvBKER9lMWZJvQnOvrip2lxdZSX6+EhHR8JEVTKzJZ/yHMwbhw0xdix1lBSalxOJ4sHzjWL30/8IcAzZXNaCl04nCnPBAVxCEqJMYE2n6OKmP9/6T7X2ua2p3QhAQsZ/11DE5UKsEHDjZgWP13YNuK1iSb1ICNwbh6TEpuCHzeEjv91NNVuRn6/v0FpcV50qtDUN3vNtdPmUwCRER0aTybJiNWmw70JTupQwZg/BhprbFhjFFZuRZ9Mgx63AyGOQcr+uCxaTFZWeMQ32rHa0RMuGpMr7UgqJcA9a8c7jPAJ7mTicKsg0RPwwY9RpMLMvG1n2N6LZ7MLkip89t4qESBKX94UivB89URblGZBk0yvMzIIo4cKoj5r9HcZ4Rbq9fOYMiiiIcLi+yjMyEExGRRKNW4cyZJdh1pBV2lzfdyxkSBuHDiNvrR3u3W+ksMqksG8cbpG4ix+q7MaksGwum9vSbjtZCLtk0ahV+9NUFMBk0eOrVvfB4e+q2mtodEUtRZEsXVihB2JRBBuEAcMH8clwwvzxlbfsonCAImFiejeP1Vri9fpxqtMLq8GLOpOh7EEqC+wQa2qSSJY8vAJ9fjJo5JyKi0ensOaXw+QPYebg13UsZEgbhw4g87VLueDKxLBtN7Q68/slJNLY7MHtSAfIseqWMI12ZcOmxjbjt8ulo7XLhrc+qAUjtFZs6nBE3ZcrOmlWK3995Fu758twhjWReUFmEWy6bPuifp6GbWJqNuhbb/2/v3oOjLu89jn92N2SzSTaXTUgIQeSaEKoRBIRGhEPQEmwS1KkHTgtzZnRqbbWWqn8AOtppRclgVUZRqXI6c44O/FEVASsyHEFsvQAHNHKpXEUkuCE3cmFz2ezv/LFkASFLEpLfXvJ+/Zf8frvzfb48Q7777Pf3PHrgue16bYN/L/Prhne+1WTH7idHKvwP5TZ5/Csc9IQDAC40NNMpm9USOB07UlGER5Caev9DCB0nB96Sn6VUp13vbD+q7IEJmj7Ov5PIhBz/yYmh3kN57DCXJuYO1IZ/fqN/Ha/V+n8ek6fFqxtGpgd9nSspTuNGB78H4S9/ZJrssTYNGZig72vOatggp5KCnM6aFB+rQa74wP73Z5v9e8CyEg4AuJD13CngHXVRpGKJKYJ0rISnnlsJT060a+HdN+i/P/iX/mNmTqDP+taJQzQ8y3nZhx/N9p+zx+hk1f9p+Zo9MiTdfP0g5Y/sfDUU0WNkdrJefni6Wtva9eq7+zQ+58ofrEYPSdbug6flM4xAr19f7+YDAIg8rqS4i4pwT4tX/7P5a80rHB10wSec8NctgpxfCT/f53xNRqIeWzDxovtibFblntuhJNQS4gbokbnjtO2LCjnsNs0YH75HyKNvxA6w6aGf5Xfp3lFDkvVx+Smdqj6rJlbCAQCdSEuy6+AFBwN+c6pen+1zK39EmqZ085C/UKEIjyA1DS1yxg+46ACeSOBKitNd00aEOgxEgJxzu6cc/q5OVov/NEx6wgEAP+RKilNtQ6V8PkNWqyWwcFMdQS0q9IRHkJr6Fnb7QFTLSHVoQIxV7hrP+ZVwTjwFAPxAWlKcfIahM+d2VOtoYayOoEN8KMIjSG1Dc+ChTCAaWSwWuZx2Vdc3q6m5TVaLRXGxkfXNDwCg77nOPR/XsfLd8TB/FSvh6As19S2BhzKBaOVKilNNQ7PONnsVHxcjy7m2FAAAOriS/IuSHc/LBdpRWAlHb2tu9epsi/eihzKBaORKsqumvkVNzW30gwMAListUIT7d467sB3FMIyQxdUdFOERomOSdXzyA6KVyxmnusYWuWs9Sk7kQycA4FIOe4wc9phAO0rHSnir16cGT2QcZ88yU5j7ZO8pVdQclc/rkyQNzUgMcURA33Il2WUY0vHvGzR7ytBQhwMACFMpibE603huJfyCwrv6TLOS4sN/r3CK8DC259BprX7vgDq+VZk8NlPZAynCEd3SLvi2Z3R2SugCAQCENYc9Rs2t7ZL8D2amOu2qbWhR9ZlmDc9KCnF0V0Y7ikla29q73aP09kdHlZ2eoHtLf6SMFAd7baNfSL2gCB81JDmEkQAAwpkj1iZPq78Npam5LdAtECl7hbMSbpIn/muHpl6fpeKCYV263+cz5K49q9smXqM7po/SzWMz+zZAIEx0PHyclRavRPYIBwB0Is4eo7rGjn3CvUpPcchmtajhbGT0hLMSbpIsV7z+9/++k7fd16X7axta5G03NDDV0ceRAeHFYY9RSmKs8q5NDXUoAIAw5oiNkafVK5/PkKfFq4S4GCU6BqgxQh7MpAg3yfRx2TrT1KryI9Vdur+y9qwkKTOFIhz9z5IFE/SzfxsZ6jAAAGEszm6Tp6VdZ1vOn7BMEY5LXD/SpVSnXR9/WdGl+yvrPJLESjj6pfRkh+Ji6ZYDAHTOERuj5hZvoOhOiItRAkU4fshmtWpC7kDtP16rlrb2K95fWetRjM3CMfUAAACX4bDHyJBUe+5BzIQ4/0p4E0U4fuiGUelq8/p04HjtFe+trPMoPdkhq5UjuwEAAH4ozm6TJFVdVITHsBKOS+VekyJ7rE3lh6uueG9lrUcZtKIAAABcluNc22L1mXNFuON8O0okHF1P06WJYmxW5Y9I06f73Zryo0E6ebpR7lqPxo1K15gLdoIwDEOVdR7lXpMSumABAADCmOPcSnhHER5/rh2l3WeoubVdDnt4l7nhHV0UmjdztA6eqNOyN3dLkqwWizbvPKF/nzFKRZP9R3RXn2lWS2u7stITQhkqAABA2Op4gN9d65FFUqIjJnC+RJOnjSIcF0t12rXw7hv0cXmFpt0wWJmueL309ld679NvVHhjtmIH2PRtZaMkaWgmR9QDAABcTkeRfaq6SUkJsbJZrYEivMHTpvQw3+aZnvAQuHaQU/N/kquhmU7ZB9h0++Shamr2aue/KiVJ37obZJE0JJ0iHAAA4HIcsf52lKZmr1IS/actX7gSHu4owsPAmGtTNcgVr21fnJQknahsVKYrXvZzkwsAAAAXi7ug3SQlMVbS+SI8EnZIoQgPAxaLRVPzs3TkZL2qznj0rbuRVhQAAIAg4i5YrExx+lfCEyjC0V0Tx2RIkrZ/WaHq+mZdk0ERDgAA0JkYm1UDYvylbEc7SkKcf3WcIhxdlpHi0LWZTm36/FtJ0ojBySGOCAAAILx19IV3tKPYrFbF22PU5PGGMqwuoQgPIzeNzZC33VBJwTCNGZoS6nAAAADCWkdfeMdKuOTvC2/wtIYqpC5ji8Iw8pNJ12jstS5dO8gZ6lAAAADCXsepmRcW4SlOu2obWkIVUpexEh5GbFYrBTgAAEAXdZya2dGOIkkZqQ65az2hCqnLKMIBAAAQkeJiY2S1WOSMP1+EZ6Y6VN/UKk9LePeFU4QDAAAgIiUlDJAryS6r1RL4XWZqvCSpMsxXw+kJBwAAQES685YRum3SxSvemS5/Ee6uPRvWbb4U4QAAAIhIyYl2JV/wUKbk7wmXJHfN2VCE1GW0owAAACBq2AfYlOq0h/3DmRThAAAAiCqZqQ65a1kJBwAAAEyT6YqXu4aVcAAAAMA0WWkJavS0qf5s+J6cSREOAACAqDI43b9DyqmqphBH0jmKcAAAAESVwWkJkqSTFOEAAACAOVKddjnsNlVQhAMAAADmsFgsGpyWQBEOAAAAmCkrPUEV1eG7TSFFOAAAAKLO0IxE1Te16nRdeG5VSBEOAACAqPOj4S5J0t6j1SGO5PIowgEAABB1BrnilZ4cp6+O1oQ6lMuiCAcAAEDUsVgsun5Emg4cr1Wb1xfqcC5BEQ4AAICoNG50ulra2lXT0BzqUC5BEQ4AAICodP2IND193xRlpDhCHcolYkIdAAAAANBXBrniQx3CZbESDgAAAJiMIhwAAAAwGUU4AAAAYDKKcAAAAMBkFOEAAACAycKiCH/33XdVUlKisWPH6o033uj0Pp/Pp6eeekq33367SkpKdO+998rtdpsYKQAAAHD1wqIIz8vL0/PPP6/i4uKg93344YcqLy/X+vXrtWHDBo0aNUqvvPKKSVECAAAAvSMs9gnPycmRJFmtV/5M0NraqpaWFlmtVjU1NWnIkCF9HR4AAADQq8KiCO+qwsJC7dixQ1OnTlVcXJxGjBihJ554ItRhAQAAAN1iShF+5513qqKi4rLXPvnkE9lsti69z759+3TkyBFt375dCQkJWrp0qZYtW9btQjwtLbFb94eDgQOdoQ4hrJGf4MhPcOQnOPITHPkJjvwER36uLFpzZEoR/s477/Ta+0yZMkVOp/8fo7S0VEuWLOn2+1RXN8rnM3olJjMMHOjU6dMNoQ4jbJGf4MhPcOQnOPITHPkJjvwER36uLJJzZLVagi78hsWDmV01ZMgQffbZZ2pra5MkffTRRxo9enSIowIAAAC6JyyK8I0bN2ratGnatGmTVqxYoWnTpunw4cOSpBUrVmjNmjWSpF/84hfKyMhQaWmpSkpKtHfvXi1evDiUoQMAAADdZjEMI3L6MnoJ7SjRhfwER36CIz/BkZ/gyE9w5Cc48nNlkZyjqGpHAQAAAKIBRTgAAABgsojaJ7y3WK2WUIfQbZEYs5nIT3DkJzjyExz5CY78BEd+giM/VxapObpS3P2yJxwAAAAIJdpRAAAAAJNRhAMAAAAmowgHAAAATEYRDgAAAJiMIhwAAAAwGUU4AAAAYDKKcAAAAMBkFOEAAACAySjCAQAAAJNRhAMAAAAmowg3WVlZmQoLC5Wbm6uDBw8Gfr9t2zbdeeedKikp0fz583XixInAta1bt+qOO+7QnDlzVFpaqs2bNweuHTt2THPnztWsWbM0d+5cffPNN2YOp9f1dn4KCwtVVFSkOXPmaM6cOfr4449NHU9v60l+gl2Ltvkj9X6OomkO1dbW6pe//KVmzZqlkpISPfjgg6qpqZEkffHFFyotLdWsWbN0zz33qLq6OvC6nl6LNH2Rn9zcXJWUlATmz9dff236uHpLT/PzyCOPaOrUqcrNzVVTU9NF78n8CZ6f/j5/jh07pgULFqioqEjFxcVavHixmpubA+/54YcfqqioSLfddpsWLlwoj8cTkrH1mAFT7dy506ioqDBmzJhhfP3114ZhGEZdXZ1x0003GUePHjUMwzDWrVtn3HPPPYZhGIbP5zMmTpwYuPfAgQPGuHHjjPb2dsMwDGPBggXGunXrAq9bsGCB2UPqVb2dnwvfJxp0Nz/BrhlG9M0fw+j9HEXTHKqtrTU+++yzwM/Lli0zFi9ebLS3txu33nqrsXPnTsMwDGPlypXGokWLDMMwenwtEvV2fgzDMHJycozGxkYTR9F3epIfwzCMTz75xKiqqrokF8wfv87yYxjMnxMnThj79u0zDMM/X373u98ZL730kmEYhtHY2GgUFBQYx44dMwzDMJYsWWK8+OKLJo7o6rESbrKJEycqKyvrot8dP35c6enpGj58uCRp+vTp+sc//hH4hGi1WtXQ0CBJamhoUEZGhqxWq6qrq7V//34VFxdLkoqLi7V///7A6yJRb+YnGnU3P8GuReP8kXo3R9EmJSVFkydPDvw8btw4VVRUaO/evbLb7Zo4caIkad68edq0aZMk9fhaJOrt/ESbnuRHkn784x8rLS3tkveLttz1dn6iTU/yM2TIEI0dO1aS/299fn6+KioqJEnbt2/Xddddp2HDhgVe9/7775s4oqsXnZVKhBk+fLiqqqpUXl4uSdqwYYMk6dSpU7JYLHrhhRf0m9/8RjNmzNADDzygsrKywPXMzEzZbDZJks1mU0ZGhk6dOhWagfSRnuanw6OPPqqSkhL94Q9/UH19venx97Vg+Ql2rb/MH6nnOeoQjXPI5/NpzZo1Kiws1KlTpzR48ODANZfLJZ/Pp7q6uh5fi3S9kZ8OCxYs0Jw5c/TnP/9Zra2tZg6jz3Q1P8Ewf+q69F7MH7/m5ma99dZbKiwslHTp/Bk8eHDE/f2iCA8DTqdTzz//vJ555hndddddqq6uVlJSkmw2m7xer1atWqWXX35ZW7du1SuvvKKFCxde0jcWza4mP2+++abWr1+vt956S4Zh6I9//GOIR9P7guUn2LX+5GpyFK1z6E9/+pPi4+M1f/78UIcSlnorP9u2bdPbb7+tN998U4cPH9bKlSt7KcLQYv4Ex/wJrrv58Xq9+v3vf68pU6Zo5syZfRydeWJCHQD8CgoKVFBQIEmqqqrS6tWrNXToUB04cECVlZWaMGGCJGnChAlyOBw6cuSIsrOz5Xa71d7eLpvNpvb2dlVWVl7yVXw06El+8vPzA7mIjY3Vz3/+c/36178O2Rj6Umf5CXbN4/H0m/kj9SxHkqJyDpWVlen48eN69dVXZbValZWVFfiKV5JqampktVqVkpLS42uRrLfyI52fP4mJibr77rv117/+1dSx9IXu5CcY5k/KFd+L+SO1t7fr0UcfVXJysh5//PHAfVlZWfr8888DP1dUVETc3y9WwsPE6dOnJfm/onnuuec0b948xcfHa9CgQfr+++919OhRSdKRI0dUXV2toUOHKi0tTXl5edq4caMkaePGjcrLy5PL5QrZOPpKT/Jz9uzZQK+4YRj6+9//rry8vJCNoS91lp9g1/rT/JF6lqNonEPPPfec9u7dq5UrVyo2NlaSdN1116m5uVm7du2SJK1du1ZFRUVXdS1S9WZ+zpw5E9jJwev16oMPPuh38ycY5k9wzB///8mLFi2SzWbT0qVLZbFYAu91yy236Kuvvgrs6rV27VrNnj3b3AFdJYthGEaog+hPnnrqKW3evFlVVVVKTU1VSkqK3nvvPT322GPavXu32tradPPNN2vJkiWy2+2SpPXr1+u1114LTL6HHnpIt956qyR/0blo0SLV19crKSlJZWVlGjFiRMjGd7V6Mz8nTpzQb3/7W7W3t8vn82nkyJF6/PHHlZGREcohXpWe5CfYtWibP1Lv5ija5tChQ4dUXFysYcOGKS4uTpL/waeVK1dq9+7devLJJ9XS0qLs7GwtX75c6enpktTja5Gmt/OzZ88ePfHEE7JYLPJ6vRo/fryWLFmihISEUA6zx3qanwcffFDl5eVyu93KyMhQTk6OVq9eLYn5I3WeH+ZPurZt26Zf/epXysnJCWy4cOONN+rJJ5+UJG3ZskXLly+Xz+dTXl6eli1bFlhciQQU4QAAAIDJaEcBAAAATEYRDgAAAJiMIhwAAAAwGUU4AAAAYDKKcAAAAMBkFOEAAACAyTgxEwD6mcLCQlVVVclms8lms2nUqFGaM2eO5s6dG9iLtzPfffedZs6cqX379ikmhj8hANBT/A8KAP3Qq6++qoKCAjU0NGjHjh1aunSpysvL9cwzz4Q6NADoF2hHAYB+zOl0aubMmXrhhRf0zjvv6ODBg9q2bZvuuOMO3XjjjZo+fbpefPHFwP3z58+XJE2aNEnjx4/Xnj17JEl/+9vfNHv2bE2aNEn33nuvTp48GZLxAECkoAgHACg/P1+DBg3Srl275HA4VFZWpl27dmnVqlVas2aNtmzZIkl64403JEk7d+7Unj17NH78eG3ZskWrVq3SSy+9pE8//VQTJkzQI488EsrhAEDYowgHAEiSMjIydObMGU2ePFm5ubmyWq0aM2aMfvrTn2rHjh2dvm7t2rW67777NHLkSMXExOj+++/XgQMHWA0HgCDoCQcASJLcbreSk5P15Zdf6tlnn9WhQ4fU1tam1tZWFRUVdfq6iooKPf300yorKwv8zjAMud1uZWdnmxE6AEQcinAAgMrLy+V2uzVhwgQ98MADmj9/vl5//XXZ7XYtXbpUtbW1kiSLxXLJa7OysnT//fertLTU7LABIGLRjgIA/VhjY6O2bt2qhx9+WKWlpcrNzVVTU5OSk5Nlt9tVXl6ujRs3Bu53uVyyWq06ceJE4Hfz5s3TX/7yFx06dEiS1NDQoPfff9/0sQBAJLEYhmGEOggAgHku3CfcarVq1KhRKi0t1bx582Sz2bRp0yaVlZWprq5ON910k7Kzs1VfX69nn31WkrRixQqtWbNGXq9Xr7/+usaNG6d169Zp9erVOnnypJxOpwoKCtjuEACCoAgHAAAATEY7CgAAAGAyinAAAADAZBThAAAAgMkowgEAAACTUYQDAAAAJqMIBwAAAExGEQ4AAACYjCIcAAAAMNn/A9GniN2GMTplAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x = 'Date', y = '% Diff Gold to Lumber', data = gsl).set_title(\"Gold to Lumber % difference\", size = 20)\n",
    "sns.set(rc={'figure.figsize':(12,8)})\n",
    "plt.show()\n",
    "sns.lineplot(x = 'Date', y = '% Diff Silver to Lumber', data = gsl).set_title(\"Silver to Lumber % difference\", size = 20)\n",
    "sns.set(rc={'figure.figsize':(12,8)})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Over the course of this dataset the price of lumber (not accounting for inflation) rose $ 282.0\n",
      "In the last year of data the price of lumber rose $ 185.8\n",
      "That is a 52.396 % increase in only 1 year!\n"
     ]
    }
   ],
   "source": [
    "print(\"Over the course of this dataset the price of lumber (not accounting for inflation) rose $\", gsl['Lumber_Price'][507] - gsl['Lumber_Price'][0])\n",
    "oneyear = gsl['Lumber_Price'][507] - gsl['Lumber_Price'][495]\n",
    "print(\"In the last year of data the price of lumber rose $\", oneyear)\n",
    "percent = (gsl['Lumber_Price'][495]/gsl['Lumber_Price'][507])*100\n",
    "print(\"That is a\", round(percent,3), \"% increase in only 1 year!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: building a predictive model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've seen a comparison of the prices of gold to lumber and silver to lumber, I'll use an ARIMA model to predict the next 2 years of prices. To train the model, I'll use approximately 85% of the 42 year span, which is 430 rows or almost 36 years of data. Then I'll test the model on the remaining 15% of data, which is 78 rows or 6.5 years. Because this is a time series I will not be using a random sample to assign data to the training and test sets. The data needs to be sequential in time.\n",
    "\n",
    "I tried other percentages of splitting the data such as 80/20, 90/10, and 67/33 but all resulted in worse or similar predictions to this set. I chose to use the 85/15 split because the price was on an upward trend prior to my cutoff for the training set and I hoped that would help the prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# split the data into training and testing sets\n",
    "train = gsl_regression[:431]\n",
    "train.set_index(train['Date'], drop=True, inplace=True)\n",
    "test = gsl_regression[431:]\n",
    "test.set_index(test['Date'], drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Date</th>\n",
       "      <th>Gold_Price</th>\n",
       "      <th>Silver_Price</th>\n",
       "      <th>Lumber_Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1979-01-01</th>\n",
       "      <td>1979-01-01</td>\n",
       "      <td>233.7</td>\n",
       "      <td>6.083</td>\n",
       "      <td>108.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-02-01</th>\n",
       "      <td>1979-02-01</td>\n",
       "      <td>251.3</td>\n",
       "      <td>5.989</td>\n",
       "      <td>109.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Date  Gold_Price  Silver_Price  Lumber_Price\n",
       "Date                                                         \n",
       "1979-01-01 1979-01-01       233.7         6.083         108.3\n",
       "1979-02-01 1979-02-01       251.3         5.989         109.4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Date</th>\n",
       "      <th>Gold_Price</th>\n",
       "      <th>Silver_Price</th>\n",
       "      <th>Lumber_Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-01</th>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>19.726</td>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>1260.3</td>\n",
       "      <td>19.780</td>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Date  Gold_Price  Silver_Price  Lumber_Price\n",
       "Date                                                         \n",
       "2014-12-01 2014-12-01      1206.0        19.726         210.0\n",
       "2015-01-01 2015-01-01      1260.3        19.780         210.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(train.head(2))\n",
    "display(test.head(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am choosing to use an ARIMA (Autoregressive Integrated Moving Average) model because it uses the moving average of the data. This is non-linear data and the ARIMA model will keep up with the shifts in the data mean with each new cycle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# set up to train the model\n",
    "import statsmodels as sm\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "import statistics\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              ARMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:           Lumber_Price   No. Observations:                  431\n",
      "Model:                     ARMA(1, 1)   Log Likelihood               -1154.269\n",
      "Method:                       css-mle   S.D. of innovations              3.502\n",
      "Date:                Wed, 08 Dec 2021   AIC                           2316.538\n",
      "Time:                        15:29:15   BIC                           2332.803\n",
      "Sample:                    01-01-1979   HQIC                          2322.960\n",
      "                         - 11-01-2014                                         \n",
      "======================================================================================\n",
      "                         coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "const                157.2035     24.307      6.467      0.000     109.562     204.845\n",
      "ar.L1.Lumber_Price     0.9925      0.006    175.114      0.000       0.981       1.004\n",
      "ma.L1.Lumber_Price     0.3655      0.043      8.554      0.000       0.282       0.449\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1            1.0075           +0.0000j            1.0075            0.0000\n",
      "MA.1           -2.7359           +0.0000j            2.7359            0.5000\n",
      "-----------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:527: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    }
   ],
   "source": [
    "# build the ARIMA model\n",
    "model = ARIMA(train.Lumber_Price, order=(1,0,1)).fit(disp=-1) # .fit(transparams=False)?\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Actual vs Fitted\n",
    "model.plot_predict(dynamic=False)\n",
    "plt.title(\"Model fit on training data\", size = 20)\n",
    "plt.legend(loc='upper left')\n",
    "plt.figure(figsize=(12,8), dpi=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting from 2014-12-01 00:00:00 to 2021-04-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "# test the model on the remaining 20% of the data, which is the remaining 102 rows.\n",
    "test_date_early = min(test['Date'])\n",
    "test_date_late = max(test['Date'])\n",
    "print('Predicting from', test_date_early, 'to', test_date_late)\n",
    "test_predictions, std_err, confidence = model.forecast(77, alpha=0.05)\n",
    "\n",
    "# make the data a series for plotting\n",
    "test_pred_series = pd.Series(test_predictions, index=test.index)\n",
    "\n",
    "# Find the upper and lower boundaries of the prediction confidence\n",
    "# note that the boundary will expand as time is farther from the\n",
    "# training data by nature of predictions\n",
    "lower_bound_series = pd.Series(confidence[:, 0], index=test.index)\n",
    "upper_bound_series = pd.Series(confidence[:, 1], index=test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "plt.plot(test_pred_series, label='predictions')\n",
    "plt.plot(test['Lumber_Price'], label='actual data')\n",
    "plt.plot(train['Lumber_Price'], label='training data')\n",
    "plt.fill_between(lower_bound_series.index, lower_bound_series, upper_bound_series, color='k', alpha=.15)\n",
    "plt.title('Predictions', size=20)\n",
    "plt.legend(loc='upper left')\n",
    "plt.figure(figsize=(12,8), dpi=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model was unable to predict the spike in price of lumber that happened in 2021. See calculations below to measure the quaity of this model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error (RMSE):  51.56\n",
      "Mean Absolute Error (MAE) 31.042\n"
     ]
    }
   ],
   "source": [
    "# Calculate mean squared error as a measure of the model\n",
    "errors = test['Lumber_Price'] - test_pred_series\n",
    "squared_error = errors**2\n",
    "rmse = math.sqrt(statistics.mean(squared_error))\n",
    "print(\"Root Mean Squared Error (RMSE): \", round(rmse,3))\n",
    "mae = statistics.mean(map(abs, errors))\n",
    "print(\"Mean Absolute Error (MAE)\", round(mae,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3: Looking into the future"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What will the model do if I feed it all the data and use that do predict the next 4 years?\n",
    "\n",
    "I'm going to rebuild the model using all the original data because the first model was unable to predict the spike in price for 2021. The reason for the spike was due to factors outside of control of the supply and demand of the lumber industry. Supply chain and labor shortages have affected and are still affecting many industries after the impact of the pandemic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             ARIMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:         D.Lumber_Price   No. Observations:                  507\n",
      "Model:                 ARIMA(1, 1, 1)   Log Likelihood               -1557.018\n",
      "Method:                       css-mle   S.D. of innovations              5.216\n",
      "Date:                Wed, 08 Dec 2021   AIC                           3122.035\n",
      "Time:                        15:29:15   BIC                           3138.949\n",
      "Sample:                             1   HQIC                          3128.668\n",
      "                                                                              \n",
      "========================================================================================\n",
      "                           coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------\n",
      "const                    0.5662      0.330      1.718      0.086      -0.080       1.212\n",
      "ar.L1.D.Lumber_Price    -0.1060      0.075     -1.422      0.155      -0.252       0.040\n",
      "ma.L1.D.Lumber_Price     0.5744      0.056     10.177      0.000       0.464       0.685\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1           -9.4323           +0.0000j            9.4323            0.5000\n",
      "MA.1           -1.7411           +0.0000j            1.7411            0.5000\n",
      "-----------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# prepare the data\n",
    "gsl_regression.set_index(gsl_regression['Date'], drop=True, inplace=True)\n",
    "# build the ARIMA model from ALL the data\n",
    "model2 = ARIMA(gsl.Lumber_Price, order=(1,1,1)).fit(disp=-1) # .fit(transparams=False)?\n",
    "print(model2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zzjsv00e5ra2NWbNmsW3bti7HdCySc/nll2f237hxI2vXruWcc87psXvL4X7XxsI9EWK0k0BZCNGrhQsX8o9//INf//rXvPHGG7z44ovk5ORw/vnn8+Uvf5lp06Z12T83N5e//vWv/OQnP+HFF19k48aNTJ06le9973uUlZX1GCgXFxfz0EMP8ec//5l///vfPP7445imSX5+PjNmzOCKK66gvLx8qJ4ykG4v1trayosvvsi6deswTZMPf/jDfQbKEydO5KGHHuJXv/oVr7zyCm+//TZer5cVK1bwxS9+8bAdJ46VYRj88pe/5K677uLpp58mHA6jlGLp0qX9CpSLior4y1/+wp133snatWt57bXXmDZtGjfffDMnnXTSqAnKvve97zFt2jQeeOABHnjgAQKBAGeddRbf+MY3WLlyZY/t1nrz6U9/moKCAu69914eeeQRvF4vp556Ktdffz3XXXddt/1XrlzJb37zG37961/z1FNPYRgGCxYs4P777+fAgQM9BsqH+10bK/dEiNFMU6qHNUCFEEKIMWTv3r2cc845nH/++fzkJz8Z7uEIIUYJqVEWQggxZtTX13erH45Go/zgBz8AGLQacSHE2CSlF0IIIcaMP/7xjzz55JMsX76cgoICGhoaeOONN6ipqWHlypWcd955wz1EIcQoIoGyEEKIMeOUU05h69atrF69mmAwiM1mY8qUKVx55ZVcddVV/Z7MJ4QQIDXKQgghhBBC9EhqlIUQQgghhOjBiC69aG4OY1lDk/DOy/PR2BgakmuJoSf3d+yTezy2yf0d++Qej20j9f7qukZOjrfX7SM6ULYsNWSBcsf1xNgl93fsk3s8tsn9HfvkHo9to/H+SumFEEIIIYQQPTiiQPmXv/wls2bNYvv27QCsX7+eCy+8kHPOOYfPfOYzNDY2ZvY93DYhhBBCCCFGun4Hyps2bWL9+vWZpVAty+L666/nu9/9Ls888wzLli3jzjvv7HObEEIIIYQQo0G/apQTiQS33nord911F5/85CcB2LhxI06nk2XLlgFw6aWXcsYZZ/DDH/7wsNuOhWmmaG6uJ5VKHNN5elJXp3dbzUkMPJvNQU5OAYYxosvjhRBCCCH6Fyj/7Gc/48ILL2TChAmZx6qrqyktLc38nJubi2VZBIPBw24LBAL9Hlxenq/Lz7t378br9eLzlUrT+FFIKUVbWwuRSDPTpk0b8usXFPiH/JpiaMk9Htvk/o59co/HttF4f/sMlN999102btzIddddNxTj6aKxMdRlhmQ4HKGoKB/TVMDAzpy02XRSKckoDza3209tbTP19W1Det2CAv+QX1MMLbnHY5vc37FP7vHYNlLvr65r3RKznfUZKL/zzjvs2rWLM844A4Camho++9nPcuWVV1JVVZXZr6mpCV3XCQQClJSU9LrtWEkmeXST+yeEEEKI0aLPyXyf//znee2113jhhRd44YUXKC4u5t577+Xqq68mFouxZs0aAB544AHOPfdcAObNm9frNiGEEEIIIUaDo55Rpes6d9xxBzfffDPxeJyysjJ+/OMf97ltrHnllZe4555f4nA4uOWWHzBp0pThHlLGjh3b2L9/P2eccdZwD0UIIYQQYtQ54kD5hRdeyPx9yZIlPP744z3ud7htY8ljjz3MZz/7RVatOrPfx5imiWEYgziqtB07tvP6669KoCyEEEIIcRRGdY+u1Ruqee396gE5l6aB6jQ/8NQFJZwyv+Swx/z853fx/vvvsn//Ph555EH+3/+7invu+SWWZREI5HD99TcyYcJE1q1bw89+diezZs1h+/ZtfO5zX2LixIn87Gc/oaUlSDKZ5JJLLuP88y8EYOPG97n77p8RiUQA+MpXvsry5Sfyy1/+lPXr15FMJgkEAvzXf32X4uISmpub+N73vk1zc3pRl2XLlnPVVZ/l97//DZFImE996nIWLVrM1752/YC8VkIIIYQQ48GoDpSH23/8xzfZvn0bl112JccdN5crr7yEX/zit0ydOo0nnniUW275Nr/73R8B2LNnN9dffyPz5i0glUrx+c9/iptv/j6TJ08hEgnz2c9eybx5C8jJyeHGG6/nttvuYP78hZimSTgcBuCKKz7FNdd8DYDHH3+UX//659xyyw/597//RVlZGT/72a8AaG1tJSsri6uv/iKvv/4q3//+HcPy+gghhBBCjGajOlA+ZX7fWd/+Otb2cJs2bWT69HKmTk33B/7gBy/krrtuJxJJB7kTJkxk3rwFABw4sJ99+/Zw8803Zo5PJpPs3buHysoKpkyZyvz5CwEwDIOsrCwA3nxzNQ8//CDRaATTNDPHzp07n7/97f+4++6fsWjREk444aSjfh5CCCGEECJtVAfKo4nb7cn8XSlFdnaAP/zh/7rt9/rrr/V4fE1NNb/4xU/43e/up7S0jA0b3uOWW74NwLx5C7jvvr/wzjtv8cwzT/HnP/+BX//63sF5IkIIIYQQ40Sf7eFE/8ydO59du7azb99eAP71ryeYOXMWHo+3276TJk3G5XLx9NNPZh7bt28v4XCIefPms3fvHjZufB9IT/xrbW0lHA5js9nJy8vDsiweffShzLFVVZV4vT7OPPMcrr3262zbthXLsvB6vYRCocF94kIIIYQQY5RklAdITk4O3/72rdxyy02YpkkgkMN3v/vfPe5rs9m4/fb/4ec/v4u//vVPmKZFbm4ut976IwKBALfddge/+MX/EItF0TSdr3zlqxx//AmcfvqZXHHFJWRnBzjppFN47713AXj33bX87W9/QdcNlLK4/vr/Qtd1li5dzl//+meuuuoyFi9eIpP5hBBCCCGOgKaUGti1oAfQoUtY19Tso7h48qBcS5awHjqDeR97M1KXzhQDR+7x2Cb3d+yTezy29XV/U/veRfMXYOROGMJR9b2EtZReCCGEEEKIYRV79Y8kNz0/3MPoRgJlIYQQQggxrFQqDsbIqwiWQFkIIYQQQgyvVALN5hjuUXQjgbIQQgghhBg2yrLAMsGQQFkIIYQQQoiDzAQAms0+zAPpTgJlIYQQQggxbFQqHSgjpRdCCCGEEEJ00h4oa1J6IYQQQgghxEGSUR7DPvaxC9i9e+eAn/fee+/hl7/86YCft8NTTz3Oueeexqc+dTlXXPFxbrrpelpbW3rc99FH/8Hf/vaXQRuLEEIIIcYxc+QGyiOvYd0RSG5fTXLbKwNyLk3T6LxIoX3WSuzlpwzIuYdLKpXCZuv9Fi9btpzvf/8OLMviu9+9gT/+8V6uvfYb3c5x8cUfG+yhCiGEEGKcUqkkwIhsDzeqA+WRaN26Ndx998+4994/dft53bo1/Oxnd3HccXPZtGkDNpuNb3/7Vu6773fs2bOLwsIibrvtx7jdbgBqa2v4j//4Ig0N9UydOo3/+q+b8fl8JJNJfvvbX7F+/VoSiSQzZszgm9/8LzweD7fd9j0Mw2D//n1EIhH+8If/63PMuq6zZMnxvPHGa1RXV3H11Vdy3nkXsG7dO1x44YdpbGwkGo1yzTVfA+BPf7qPZ599Gk3Tcbvd/OpXv0fXdf71ryd4+OEHMU0Tn8/HddfdwKRJUwbrpRZCCCHEWJCKp/+UQHlg2ctPGbCsr82mk0pZA3Kuw9m7dzff/vb3+M///DZ33XU73/zmtdxzz30UFhZx3XX/wXPPPcMFF1wMwPvvv8t99/0fubl5/OAHt/CHP/yea675Gn/5yx/xer387nf3A/CrX/2cP/3pPr7wha8AsGPHdn75y99mAu6+JBIJXnvtFWbPngNAS0sLc+YclwmM7733nsy+//rXE7z22iv85jf/i8fjpaUliK7rvPfeu7zwwrPcfffvcDgcvPHGan74w1v59a//d4BeOSGEEEKMSR0Z5RE4mW9UB8qj0aRJk5k5cxYAs2bNora2msLCovaf51BRcSCz78knryA3Nw+AD33oIn760x8DsHr1K4TDYV566QUAkskEM2bMzBx32mln9CtIXrPmbT71qcsBmD9/IVde+WmCwWYcDierVp3V4zGrV7/KxRd/FI/HC0B2diAzpp07d/D5z38KAKUUbW2t/XpNhBBCCDF+KalRHj8Mw4ZSBzPTiUSiy3aHw5n5u64bOByOTj/rmKbZ5zWUgm9+8waWLj2+x+0eT/8yyR01yp0Fg8243S40TevXOTqP6fzzL+Tqq794RMcJIYQQYpxLyYIj40ZZWRlVVZW0trailOK555456nO9/vprNDc3A+kuFUuWpAPjU09dyd/+9hfi8RgAkUiYvXv3HPvg++GUU1bw6KMPEYmEAWhpCWYef/rpJ6mrqwXANE22bt0yJGMSQgghxOiVaQ8npRdj09e+9hUMw8j8fOmlV/DZz15Jbm4uixYtYc+e3Ud13oULF/O9791IfX0dU6ZM45prvg7AFVd8invvvYerr/4kuq4DGp/5zOeYMmXqQDydwzr33POpr6/j85//NDabDbfbzd13/45Fi5bw+c9/mRtu+AamaZFKJTn99DMzdc9CCCGEED3KLGE98gJlTXXuiTbCNDaGsKyDw6up2Udx8eRBudZQTeYTg3sfe1NQ4Ke+vm1IrymGltzjsU3u79gn93hsO9z9ja97jMSaR/BdfS+abvS4z2DRdY28PF/v24dwLEIIIYQQQnSVSoJuDHmQ3B9SejGGNTc38fWvX9Pt8Q984HQ+/enPDcOIhBBCCCG6Uqn4iKxPhlEYKCuljrgjw3iVk5PbrwVHhtIIrvQRQgghxHBIJYlbOjt31LN4ZsFwj6aLUVV6YbM5CIdbJdgapZRShMOt2EZgsb4QQgghhocyE4RTOu/vahzuoXQzqjLKOTkFNDfXEwoFB/zcuq5jWTKZb7DZbA5yckbWp0UhhBBCDKNUgqQysBsjL387qgJlw7CRn18yKOeW2bZCCCGEEENPpRIklIHdNvIC5ZE3IiGEEEIIMX6kEsQtCZSFEEIIIYTowkolSGJgG4GlFyNvREIIIYQQYtxQqQRJZZOMshBCCCGEEJ2pjsl8EigLIYQQQgjRSSpBEtuI7Hox8kYkhBBCCCHGDzNJQhnYJKMshBBCCCFEJx01ypJRFkIIIYQQIk0phWYlSSA1ykIIIYQQQhxkJgFIKpuUXgghhBBCCJGRSgCM2CWsR96IhBBCCCHEuKDaA+UE0kdZCCGEEEKIg1JxABKy4IgQQgghhBAHZTLKUnohhBBCCCHEQUoyykIIIYQQQvSgU42ydL0QQgghhBCinUp2yihL6YUQQgghhBDt2ksvklJ6IYQQQgghxEEHJ/PZMHRtmEfTnQTKQgghhBBieLRnlJVhR9MkUBZCCCGEEAI4WKOsbM5hHknPJFAWQgghhBDDI5XAQkc3bMM9kh5JoCyEEEIIIYaFSsVJaSNzIh9IoCyEEEIIIYZLKk5Ks0ugLIQQQgghRGcqlSCFDdsI7KEMEigLIYQQQojhkoyTRDLKQgghhBBCdKFSCZKMzFX5QAJlIYQQQggxTFQqTgKZzCeEEEIIIURXqQRJNXJrlPvVtO7LX/4yFRUV6LqOx+PhO9/5DnPmzGHVqlU4HA6cznST6Ouuu44VK1YAsH79er773e8Sj8cpKyvjxz/+MXl5eYP3TIQQQgghxKiiUnHiyj9iM8r9CpRvv/12/H4/AM899xw33ngjjzzyCAA///nPKS8v77K/ZVlcf/31/PCHP2TZsmX86le/4s477+SHP/zhAA9fCCGEEEKMWqkEccsYsYFyv0bVESQDhEKhPtfi3rhxI06nk2XLlgFw6aWX8vTTTx/DMIUQQgghxFijkvERHSj3e73Am266idWrV6OU4ve//33m8euuuw6lFEuXLuUb3/gGWVlZVFdXU1pamtknNzcXy7IIBoMEAoF+Dy4vz9fvfQdCQYG/753EqCX3d+yTezy2yf0d++Qej2093d82M0Fc2fD7nCPy/vc7UL7tttsAePTRR7njjjv43e9+x1/+8hdKSkpIJBLcdttt3Hrrrdx5550DNrjGxhCWpQbsfIdTUOCnvr5tSK4lhp7c37FP7vHYJvd37JN7PLb1dH+VZYKZImYaWElzWO6/rmuHTcwecZ774osv5q233qK5uZmSkhIAHA4Hl19+OevWrQOgpKSEqqqqzDFNTU3oun5E2WQhhBBCCDGGpRIARC199PZRDofDVFdXZ35+4YUXyM7Oxul00taWjvyVUjz11FPMmTMHgHnz5hGLxVizZg0ADzzwAOeee+5gjF8IIYQQQoxCKhUHIKFGbh/lPksvotEoX/3qV4lGo+i6TnZ2Nr/5zW9obGzk2muvxTRNLMti+vTp3HzzzQDous4dd9zBzTff3KU9nBBCCCGEEEAmo5wYzX2U8/Pz+fvf/97jtkcffbTX45YsWcLjjz9+1AMTQgghhBBjl0qO/IzyyByVEEIIIYQY2zpKL7Dh99iHeTA9k0BZCCGEEEIMOZUpvTAoCLiHeTQ9k0BZCCGEEEIMvU6lFxIoCyGEEEII0U4lIuk/7R68rn4v7TGkJFAWQgghhBBDriNQ9mRloWnaMI+mZxIoCyGEEEKIQVfZEMa0rMzPKh4GICuQPVxD6pMEykIIIYQQYlC1hOLcfO/bvLu9IfOYFY8QUzbyc7zDOLLDk0BZCCGEEEIMqkgshaUUoVgy81gi3EbUcozYiXwggbIQQgghhBhkllLpPy2VeSwRbiOqHBQEXMM1rD5JoCyEEEIIIQaVaaZrkzsHylY8QkQ58bsdwzWsPkmgLIQQQgghBpVpdc8o68koUWUfsctXgwTKQgghhBBikHUEyqbqFCinokSUE5sEykIIIYQQYryyesgoG6koUeXAbozccHTkjkwIIYQQQowJHQFyR2ZZWSaGFSdqOaT0QgghhBBCjF/dapQTUQDJKAshhBBCiPGtY0W+jjZxHctXR5QDm21kLl8NEigLIYQQQohBZpqHlF7E04FyFAeGPnLD0ZE7MiGEEEIIMSYcWnqh4mEAkrpz2MbUHxIoCyGEEEKIQdVtMl976UVSG7mr8oEEykIIIYQQYpD1mlE2JFAWQgghhBDjWLc+yu0Z5ZThHq4h9YsEykIIIYQQYlB1dL04WHoRRQHKcAzjqPomgbIQQgghhBhUmdKLjiWsLRMTA7vNGMZR9U0CZSGEEEIIMag62sNlapTNFBYGthG82AhIoCyEEEIIIQZZRya5I7OMlcJEH9HLV4MEykIIIYQQYpB1W8K6I1A2Ru6qfCCBshBCCCGEGGSWechkPjNdoyylF0IIIYQQYlzrNaMspRdCCCGEEGI8O9j1ouOBFCmlY5NAWQghhBBCjGcHFxxJl2AoKx0o26X0QgghhBBCjGcdGeWDXS9MUkhGWQghhBBCjGNWWz0ztt6Hk+TBGmVTMspCCCGEEGKcM+v2EGjZTqHRgtneT1lZKVKWTOYTQgghhBDjmZUCwKGlumWUpT2cEEIIIYQYv1R6Ap9TS3Xqo5wiJe3hhBBCCCHEeKYsEwAHKdqbXqCsFKZklIUQQgghxLjWEShrKSx1MKMsS1gLIYQQQojxrVOgLO3hhBBCCCGE6NBeb5GezNe+4IiZwlSGtIcTQgghhBDjV0eNsrNz14v2jLJM5hNCCCGEEOOXag+UOVh6oclkPiGEEEIIMe51Kb04mFE2pUZZCCGEEEKMa50XHFGglEJT7aUXklEWQgghhBDjljqYUTYtlemCYSpDapSFEEIIIcT4lVlwREumSy/aM8ySURZCCCGEEOObdchkPjMdKJtKapSFEEIIIcR41nllPkuh2jPKJjo2WZlPCCGEEEKMW527XiiFas8op5SO3WYM58j6JIGyEEIIIYQYNKpTRhlApToyygZ2ySgLIYQQQohxq33BEQftAXIqCSAr8wkhhBBCiHGuvfTC2Z5RtlIHJ/MZ0vVCCCGEEEKMW+2T9wxNYWBimemMsoWBrknphRBCCCGEGK/aFxyB9gl97ZP5lD6yJ/KBBMpCCCGEEGIQdUzmg3T5RUfphdIkUBZCCCGEEONZp0DZQQrVPplPMspCCCGEEGJ86xwody69kIyyEEIIIYQY1zrVKDs7BcrotmEaUP9JoCyEEEIIIQaNskziyg6kM8pqFE3m61co/+Uvf5mKigp0Xcfj8fCd73yHOXPmsGfPHm644QaCwSCBQIDbb7+dKVOmABx2mxBCCCGEGCcskxgOnCTTNcpmR43yGMko33777fzzn//k0Ucf5TOf+Qw33ngjADfffDOXX345zzzzDJdffjnf/e53M8ccbpsQQgghhBgnLIuYcgBdM8qMlRplv9+f+XsoFELTNBobG9m8eTMf+tCHAPjQhz7E5s2baWpqOuw2IYQQQggxjlgmifYiBptmHgyUR0FGud8jvOmmm1i9ejVKKX7/+99TXV1NUVERhpH+NGAYBoWFhVRXV6OU6nVbbm5uvweXl+c7wqdzbAoK/H3vJEYtub9jn9zjsU3u79gn93hsiumKhGoPlLFwOdJ5WpvTMeLveb8D5dtuuw2ARx99lDvuuIOvfvWrgzaoDo2NISxLDfp1IP2Ps76+bUiuJYae3N+xT+7x2Cb3d+yTezx2pZJJ4soJgKFZREIR/EDK0ob9nuu6dtjE7BF3vbj44ot56623KC4upra2FtNM98YzTZO6ujpKSkooKSnpdZsQQgghhBhHLJN4JqNsQnvphTYKul70GSiHw2Gqq6szP7/wwgtkZ2eTl5fHnDlzeOKJJwB44oknmDNnDrm5uYfdJoQQQgghxhHLJG6lg2IbFsoysdDQjZEfKPdZehGNRvnqV79KNBpF13Wys7P5zW9+g6ZpfO973+OGG27gV7/6FVlZWdx+++2Z4w63TQghhBBCjBPKIqV0THQMzQIzhYWOoWvDPbI+9Rko5+fn8/e//73HbdOnT+fBBx884m1CCCGEEGJ86MggW+gYmCgrhYmBYYz8de9G/giFEEIIIcToZZmY6Fiaga09o2yio2sjP6MsgbIQQgghhBg8loWl0oGygdUeOBsYhgTKQgghhBBiPGsvvVCagU0zwUphqtFRoyyBshBCCCGEGBRKKVCdSi+w0oHyKJnMJ4GyEEIIIYQYHMoCwFQ6SrNlSi9S6Bj6yA9DR/4IhRBCCCHE6GSlF5+z0FC6gaFZaB2lF1KjLIQQQgghxq32QNkknVG2tWeUk8pAl9ILIYQQQggxbrWXXlhKBz09mU+3kiQxpEZZCCGEEEKMXyqTUdZAT9coa1aSpGVgkxplIYQQQggxbmVqlHUwOmqUkyRGSelFn0tYCyGEEEIIcVQyXS/SGWUbJpoFyVHSR1kCZSGEEEIIMTg6ZZS19tIL3TJJKFmZTwghhBBCjGedul5g2LBpFrqVIiWT+YQQQgghxHjWMZnPUhqakS690FWKpLLJgiNCCCGEEGIc6yGjbKjUqGkPJzXKQgghhBBicBxSo+zQkgCkRknXC8koCyGEEEKIwdHR9QINDBsOLR04J0fJZD7JKAshhBBCiEFxsEZZR+tUkyylF0IIIYQQYnzrvDKfYWQeTipZmU8IIYQQQoxnmdILHc04mJ9NIjXKQgghhBBiPLNS6T9UejJfh6QaHaUXEigLIYQQQohBoayDk/l028FAOcXomMwngbIQQgghhBgcndrD6TZ75mHJKAshhBBCiPGtYzKf0jHsnQNlWZlPCCGEEEKMZ5mMstYlo5waJe3hJFAWQgghhBCDo73rhYWOzd619EK6XgghhBBCiHFLdS696FyjLJP5hBBCCCHEuNYeKCtd79L1QhYcEUIIIYQQ41t76YVu2MCQ0gshhBBCCCHS2hccMQwD9EP6KEugLIQQQgghxquOBUd0mw1NNwAwlYaFLjXKQgghhBBiHGuvUTZsBhjpjHKSdMBsaBIoCyGEEEKI8aojUDZsmdKLpLK1Pzbyw9CRP0IhhBBCCDE6WSYWGna7Da09o5xS6fBTapSFEEIIIcS4pSwzvdiITc9klBOk/5SuF0IIIYQQYvyyUpgY2A09U6OcUu01yhIoCyGEEEKIcSuVJIUNu03PdL3omMxnk64XQgghhBBivFJmkhQGdtvBjHKyPaMspRdCCCGEEGL86hwo611LL3RpDyeEEEIIIcatTKBsoOkGStNItq/Kp0mgLIQQQgghxitlJkmq9owyoOk2kmp0LF8NEigLIYQQQojBYiZJKj0TKKPbSGGMiuWrQQJlIYQQQggxSLpllN1+Wi03hj46QtDRMUohhBBCCDH6mEkSlo7dlp7A57ngv3gptXBUdLwACZSFEEIIIcRgSXXNKOveHDS7S2qUhRBCCCHE+KYOrVEGHDaZzCeEEEIIIca5TI2y0SlQtusYxugIQUfHKIUQQgghxOiT6rTgSDuHffRklG3DPQAhhBBCCDE2KSvVpUYZwGk3sCw1jKPqPwmUhRBCCCHEgFNKoZndM8o+tx2UBMpCCCGEEGK8slIAJJWBrb09HMDlZ84kJRllIYQQQggxbplJAFKHlF7kZrmGa0RHTCbzCSGEEEKIAadS7YEyXdvDjSajc9RCCCGEEGJka88oH9oebjQZnaMWQgghhBD98srbO/j3G7tIpsyhvXBH6cUhk/lGk9E5aiGEEEII0S9Fa+4m+c7f+dUjG4f0uqpzRlkCZSGEEEIIMZKoRJRSW5AJRhO7qlqH9uJmuutFOqNs9LHzyCSBshBCCCHEGJVqqgQg32gjFE2STFlDdu2xkFHusz1cc3Mz3/rWt9i/fz8Oh4PJkydz6623kpuby6xZsygvL0fX00/+jjvuYNasWQC88MIL3HHHHZimydy5c/nhD3+I2+0e3GcjhBBCCCEy4nX7AMjSYzhI0hKKkx8YonisI1DuqFG2hi5IHyh9hveapnH11VfzzDPP8PjjjzNx4kTuvPPOzPYHHniAxx57jMceeywTJIfDYb7zne/wm9/8hmeffRav18u99947eM9CCCGEEEJ0k2qsyPw93wgRDCWG8OI991EeTfocdSAQ4IQTTsj8vGjRIqqqqg57zCuvvMK8efOYMmUKAJdeein/+te/jm2kQgghhBDiiKjmSmIqXUCQr7fSHIoP3bW7dL0YBzXKlmXx17/+lVWrVmUeu/LKK7nooou46667SCTSn1Kqq6spLS3N7FNaWkp1dfUADVkIIYQQQvSH1lrFlmQZkK5TDrYNXaDMeKhR7uy///u/8Xg8XHHFFQC89NJLlJSUEAqFuP7667n77rv5+te/PmCDy8vzDdi5+qOgwD+k1xNDS+7v2Cf3eGyT+zv2yT0eWGa0jbZEiL2p2SzyN1IYD5Gw1JC9zq0HdGIcLL0Yjfe334Hy7bffzr59+/jNb36TmbxXUlICgM/n4+Mf/zj33Xdf5vG33norc2xVVVVm3yPR2BjCstQRH3c0Cgr81Ne3Dcm1xNCT+zv2yT0e2+T+jn1yjwee2ZzueNFiubE8+RTFwrxV1zZkr3OiJQSkl7A2dG1E3l9d1w6bmO1XHvwnP/kJGzdu5O6778bhcADQ0tJCLBYDIJVK8cwzzzBnzhwAVqxYwYYNG9i7dy+QnvB33nnnHcvzEEIIIYQQR0BF04Fp2HKh+fPI0cNDWnqh2ifzYdjRNG3IrjuQ+swo79ixg3vuuYcpU6Zw6aWXAjBhwgSuvvpqvvvd76JpGqlUisWLF/PVr34VSGeYb731Vr7whS9gWRZz5szhpptuGtxnIoQQQgghMlQsHSiHlAvD5cWpJWkeyq4XVjpQNuyOobvmAOszUJ45cybbtm3rcdvjjz/e63FnnnkmZ5555tGPTAghhBBCHLWOQDlsObG5PDhUguAQdr0glcRCx263D901B9jonIIohBBCCCEOq6P0IuXwoTnc2FSSZCJJPGEOzfXNJKZm4HSMztZwIIGyEEIIIcSYpGKtJDQnTqcdzZ5ejc+pJYklhyZQxkxiYuC0j95wc/SOXAghhBBC9EpF24hpHtwOG5ojHSi7tCTxRGpoBmAmSWHgtI/ejPIR9VEWQgghhBCjg4q1EdFcuBwG2F1Ae6CctIbm+maSlDJwjOJAWTLKQgghhBBjyOoN1Xzujhexom2ElQu389CM8hCVXqSSJEd5RlkCZSGEEEKIMeRfb+3HtBSpcAttlrNL6YVbSxBLDk3phTKTJC2ZzCeEEEIIIUaIKUU+nCTREmFaTSdupwGdJvMNWUbZTJJQ+qjOKEuNshBCCCHEGDIztIZP5L4IQDDpxHXoZL4h6nqhkjGiln1UB8qSURZCCCGEGENKo9szfw+mHHiGqUZZJSLtgfLoDTdH78iFEEIIIUQXKhGlIFWd+TmmHOmuFzYnoOEawj7KKh4lpiSjLIQQQgghRgCzeisGFm+7V6LlTuSEFcs5eX4JmqaBwzW0GeVklKiy4xjFk/mkRlkIIYQQYoxIVWwioWzszV7GGRd+hlM7bdPsbjxGkoYhyCgrK4VmJtMZbckoCyGEEEKI4WYFq6mxcnC6nN22aQ43Xj01NBnlRAxASi+EEEIIIcTIoGJt6d7Jzh6KBhxu3PrQ1CirRBRIB8qjufRCAmUhhBBCiDHCioUI9RIoa/ahq1FWyYOBsmSUhRBCCCHEsFOxtnSg3EMWV3O4h6yP8sGMskMCZSGEEEIIMbxUKo5mJomo3jLKbpwkhqhGuXNGefSGm6N35EIIIYQQIkPFQgCElAtXLzXKDhJDk1GW0gshhBBCCDFSdATK4cOUXthVkngiOfhjac8oR5UDp0zmE0IIIYQQwykTKPdWeuEJAOBNBQd/LJ27XkhGWQghhBBCDCcVawPaM8o9BMpGSTkAZVYV72ytozWcGLzBJKJY6Gg2O7qmDd51BpkEykIIIYQQY0BfGWU9u4SE4WGKXsOvH93In57ZNnhjSURJ6Q4c9tG9CLQEykIIIYQQY4CKpwPliHLi6qlGWdNo9U1huq0OANNSgzeWZJSE5hzVE/lAAmUhhBBCiDFBxdpI6C5sNhs2o+cQL5I9lTwjRI4eojDHPXiDSURJMLon8oEEykIIIYQQY4KKhUjo7p5bw7VLZpUBUKi3YpqDmFFORInjGNU9lEECZSGEEEKIUStlWqzZWoelFCoWIqq5eqxPzrB7AXDrCeKpweunrJJRopYNr9s+aNcYChIoCyGEEEKMUut3NPCrRzeybls9KtZGMGEnP9vV6/5xPb3NoyVIDOLCIyoRozVpIz+r97GMBhIoCyGEEEKMUrXNETQUr66vINnWRFPCzonHFfW6/5J5kwAo9ikSSWvQxqUSEUIpg9xRHiiP7p4dQgghhBDj0L/fOUAskSK7+m1+EHiOTfUTMZwhtljL+dysgl6PszlcoNvw6UkSA1x6kUxZbNzTyKIZ+ahElJiyUzzKA2XJKAshhBgTmlpjNLfFh3sYQgyJ1Ruq2bf+bRY1PY1DMzneuZs9qXx8M5bhcvSeB9U0Dc3pwa0lBjyj/Jdnt/GLhzZwoLoJzUoRUU5ys5wDeo2hJhllIYQQY8K9T25B1+Cbly4e7qEIMaiUUtQ1RznHuYE25eaF/Mv5sO89Cqat4oqp5X0erzk8uM2Br1Hesq85/Zf25aujykHeKM8oS6AshBBiTAiG4sQSgzc5SYiRovWtRymnhVn2at6Iz8SdW4z/9FPx9/cETi+uaJx4auAyymbNDubH1vAC87DiYQBiyk7AP7ozylJ6IYQQYkwIR5ME2+IkB/DNX4iRRqUSaO//k8/4XsahmWxITDxsl4ueaE4PThUf0IxyYutLnOd+DwArmg6UdZev14VPRovRPXohhBCC9FfR4VgKRbpWWYixygpWo6HQNUXEcrArVUR+4MhW2NMc3gEPlM3WehyaiY6FlUgHyk6vb8DOP1wkUBZCCDHqxRImppVeZaw+GB3m0YiRLBpPcdcD71JZH6IlFO82AfR3j2/m9Y3VwzS6vlnNlQA8F5vPG64VWOhHlVG2WzESA1l60VIHgFNLouIRAFy+fheDjFgSKAshhBj1ojveYrqtFoD6Fskoi95tPxBk095mNuxu4t4nt/CrRzdkttXUNfPGpmrW72wcxhF2F4kl2VXVglIKq6kCE501rhMJl52ABkc8YU5zeLBZMZIpE0sdXMZaKcWf/r2N3VWtR3Q+ZSbRoi0AuDoFym5f1hGdZySSyXxCCCFGNWVZ2F//Hf+RBU9FFtIQnDTcQxIjlIqFsN5/Ep0y6oJRDtSHCEWSJFMWRqQB12M3scB+Ms1t2cM91C5eeeltKre8z2NlJ/EF3wGaCFCQ4+Oc5RMpnxjAYTeO6Hya04uOwkmSZNLC6Ugf3xZN8uK6Spx2g2ml/Q9yVagRjXTA7dKSkEx/WNVc3iMa10gkGWUhhBCjkqUUDcEoKh7KPHaco1IyyqJXia2vMKPhRWbbq9hf20ZLKIFpKSrqQyQ2Poehksyw1xIcYf24p1b/m0u8b1G/dzfRugMcSGRTmuclP9vN8bMLj/yETg8AHj1BvNOiI62hBHDkdf5WW8PBU2spVDxCXNmw2+1HPrYRRgJlIYQQo9LabfX812/fpK3x4NfkPiMpNcqiV2bVFgAW2PdzUegBLvOuxobJ/op6optfBmCSrZlgKIFlqcOdashYoSZKkvsAuMC/AWciSGUqwLKjCZDbaY50oJxedORgoBwMpz8gHOnCPVZrXebvTtKlF1FlP+JM90gkpRdCCCFGperGMKalaGtuJBuoNbPIsiVolIyy6IEyU6Sqt6EBJzp3omkwxdZAsdFCyzt+bLY49bZiJupNWJZFayRBwDf8PYCTO15HAxocZcxP7CGubNR4ZjK15OgnymnOdEmE55DV+aINNQA0tR5ZoKw6ZZRdWhItGSFqOXHYRn8+dvQ/AyGEEONSsP1r4nhbEIA6MwunihOOJVBqZGQDxchh1u9BMxNsTExA02BfKo//bfsAZbYgC217eNdzChNOPg+blSBPbxsxy6En96xlT7KA3RMuIJ43k5+3nsPMuXPQNO2oz6k5O2WU20svzPq9zH73x8yz7ycYineZ5NcXq62epEpnj51aEj0Vbc8oj/4wc/Q/AyGEEOOOUopgex1lMhQEoIkAOhZ2lSI+wEvzitHPrNqCAlY7TiXpL+GfkaVUuGfx3oyreTC+gnkXXIGRNxmACUbTiAiUVSqB1biPnakinIWTyfvIjVx04WmcvfzYJqxqju4ZZbMxXd5xinM7pqVoDSf6fT6zpZZqM5A+p2FiM2NElQOHbfSXXkigLIQQYtRJvPs4HwreD4AVacVCo81IdypwawmicQmURVfRyu3UpLKZPW82tgtvYWeqmOJcNx847QQu/dxV5Ga70XPLUJrOBNvICJTfeX0NmrLYmyogN8uJpmksnVWA8xhrfzu6UXj1WKZG2Qqme0fPtleRo4e494nNfP/+Nd2OVUp1qd9WysIKVrMvlQ+A20hht2JElFMyykIIIcRwsOr3UKAaydIiEG8jprmx7O1ZMj1BLJEa5hGKkUQphVm3m/1mPifOLcLvsZPlsTOx0I+ua3hc6SlbmmHHKJzOcY7KYQ+ULaXYvm4dAPtS+UfcK/lwNIcHy+mnyGgl3p5RtoLVhEmXZHzE8w5b9jayu6qVUDSJUooHnt/BzooWHn99L9+77+3MuVSoEc1MUmnmoDQDj57CoeLp0gvJKAshhBBDzwqlO11MsDWhx0NEcHeZoBSJS6AsDlKhRhxmhEZ7CfnZbjRN49tXLePCU6d029c+dSllRjPJ5uFdnS8SSzHJVk+D6aNNucnxD+zEQpVdRqnRnKlRtoI17FPFvOFayQLHAT7qSQfD+2rbiCVM/v3OAd7YVMPOyhYq6sOZ8iar/XWqNbNRNiduPYlTJYgqB3bJKAshhBBDz2xrD5SNJuypECHlQm8PlN1anJiUXohOzPrdAIQ8ZZnH8rPduBzdm3/ZpixN/+XAev77j2tIDuAyz0eitS3KNFsd+1L5+NwD32pNy5lAsRFkV0Uz/1q9A6u1jqqEn+ayFbwWn81Jzh3k6W3sq2kjGEpn12uaItQ1p9svNrS3YbSCVUA6UNYdbrL0CLqmiCoHTskoCyGEEENLJWNoiTCQzig7zAgtpgvD4wPSGeWoZJRFJ2bdHlJKh0BZn/vqWQVEPCWc7N7Fgepm1myt48V1FbQcweS2gZDa+TrZepT1iSnkZg18mzojbwIOzWTGjj9z3Ps/RUNRlfQT8DtY61iGpelcmLWJvTVtmQVYCpvepSS8DYD6YHoybaKxkrjuIWF40B0uskn/24xYYyOjLH2UhRBCjCpWqAmAhDKYbG/CbcUJJh04vRIoi56Z9XuoNHMIZPdvSeW8lZcSffp/+HzOajY8v5vV0em0hBNcvGLaII80TZkpfDueZl8qj5knfYAJhUffM7k39vzJJIC5jsrMY7VmNvNddi46azGprTs4rvJdnqhpIV4dwa9F+ZBtNTHDzobEBOqbw7z33Nu4d2wkbPnSPZMdbnJJLz7SptxSoyyEEEIMNRVKL26wLVlCQAvh1JK0Wi5Ki/MBDbeeIJroWnpR1RDmlfeqhmG0YrgppTAbD1CZyu33hDjbpIU4llzELG0vH3O/wWLHXnZWtgzySA+yGvdjjwd5MXYcpy4sY+GM/AG/hj2vDEtpWEqjpvAkUnYfdWYWk4p8zJ+WR2DKLBwqQVl4G9M3/IpvZj2JQzPJ0mPMs1cQ2vQK03b/nRKjmVozmxy/C83uwkESgHoza0x0vZCMshBCiFGlI6P8fGwe8x0VAESUkyml2eBw44nFiR2SUX5pfSXPralg6awCvC77kI9ZDDwrFmbDlgNsC9r42Aemo+s9L8ChIkG0RJgqM8DSI+gc4Vz2YfR559D69+9wnnMvP66aiWlZGPrgB39W+0p3tWYAn3twQjXd7mBfKp8Gy8+U5ZcRKPkcP0sqnI50FljPnQjAya7tAOQYETYnSim1NXOWdzP+WIgDKpeJx5/GgtzjWBgoQVuXngBoKY0myysZZSGEEGKoqVAjFhqt7jK2zPki1als6m3F5GW50JxevEayW9eLUCSd5dpT1TocQxaDYNcT95L95i95+q39NLREe93Pakp/mKo2c8jLPrIWa3aXB//CMyiIV5BjNlJRFz6mMfeX1VYPQMwRGNTA/BdtZ/N/4ZOZWOhF0/RMkAxg5JQCMMteQ7Pp4eHUafwtchIvJhYyUa8jx4jwjnsF/uMvpHT6DEryvGBPv77NlhdLM7AZR7964EghgbIQQohRxQo1EsJLUZ4Po3AaP2q9CFfRVDRNQ3N48BnJbn2U2yLpiVi7OgXKj766m7/8e/uQjl0MHHfLHvKMEAYmbe0fhHrSEShXmQFyj6LFmm3WCpSms8Sxhx0VwaMdbo/iSZNHX92d+f3soNrqiWkuXO1194PFxMBCx95D5ldzetG8OQDsN/Opy1mI5s1lt38Jz0/+Kj9tPZfsGQu7HtMeKDdYPhw245iW2R4ppPRCCCHEqGK1NdKQ8lCa78XnTpdRTC1JT3bSnB68elO3lfl84QOsdB5gV2VO5rGt+5ppPUyAJUYulYiQbTYDkK1HaIv2fh/Npgpihg/N6cPtPPKwR3f5sRVOZ15NDf/e28yZyyYe9bg7U/Ewa575N49v9pNIWlyyakZmm9XWQIuWRZZncMuEVi4sPeyHBz13Ama4mX2pfAI+BzPKsvG4bNgMnT2pZi6bmtdl/0ygPEbqk0ECZSGEGPEO1IW4/S/ruPWzy8kdwNW5RiuztZ5GM0BZvpeSPC/ZXgfzp6ffsDWnF7dW06XrRap6Gx9L/ROHN8UfagNUN5ZTlOshHE/JwiSjlFm/N/P3HD3SLSPbQSlF3Z4dNCSyj+nfjjFxHqW1j7BnbxXR+HFHFXAfqvn5/2VBzVo+6ZvKP94zWDgjj/xsN3nZLqy2eposH36P45ivczifOm/2YbfrOWWYBzawP5XHDJ+TD69Md/2IxFI47QbTy7K6HuDolFF2jI1AeWw8CyGEGMP21bQRiaeobYoM91CGnTJTEGmm0fJRmu8ly+vgf649leml2UA6o+wiTrRT6UV8zcOElJNGy8+59rf5zu/eYN22eiKxFJFYenleMbp0LCACENDDmRr0Q9WveY5Asp56o4hT5hUf9fVsE+ajATP0Ct7b1XDU5+mQrNiEvWItB8x8ljj28FXXI/zlb8/x84fex7JMVFsj9UkvWYMcKPfFNmkhYXdxe0b5YObZ47Jx6oKSbqUVXTPKo38iH0igLIQYIq++X8XmvU3DPYyj9tL6Sv7v2eGpZ20Jp5v9R2S1OVSoEQ1Fo5kOlA+lOX24VJxY7GCgbLU2sCtZRO3EMyg2WigzmqhviRKJpUiZisQwrbwmjp5Zt4cWyw1Anq3n0gsrWI373b+wLVXMCR+7irOXTzrq6+kFU8DlZ7lnP29vrjvq8wBY0TZanv0tDaaPuuO/gvucr5Hjgi/mrqamLsiBvRVgpahJePAPculFX2ylc2he8S0S2Pu1hLbmTNdU11lZ2G1jI8QcG89CCDHiPfjiLp5bUzHcwzgqiZodvPbael7fWDMs1w+2pb9WjsSlnrajG0DcmdNjmzfN5cfAxEymuyAoy0JFmglaHmx56drSfFuE1nCCeDL9wSMSk/KL0cZsqmB3qpCU4aLAHuux9CJVk/5g+4Z7FTm52cd0PU3Tccw9g9n6fur3bGfb/uajOs/Wt95k15//GyPRxsv+81m5ZAq2yYvIPvuL+M0gV/hfZ/UzzwHQaPnwe4c3owwwa1IOnzxnFgtn5PW5r23KEt6beBk1ZkAyykII0V+RWJJQNElbdGiXgB0ISllE/vUzzuANIvEUieTQZnVTNTuYVfMkoLpNUBuPOvrL2gNFPW7XXOmMltG+xLWKtqApi2bLizM7vWhDoTNGfVMIjXTJRSQmH0BGE6UUKtxE0PSSdAbItUV67HoRr9pB2HIwecbArKbnmHcWONx8KGsT9z+zDesIS3baKndRsv4evFaI/TM+zicvPzfT+9lWdhyOJReywL6fC4xXAUh5CphWknW4Uw4JXdc4bXFZj50xDqXZHLTmpOuenWMkoyyT+YQQg0opi7bVD1BseGkLu4d7OEfMaqrESIYoaX+PCIYTZHscXfqNDqbU7reZFXsPjzZLlmUGVGsdptJxZuX2uF1zpbtf2JIRrPaACiBoefFmB8Cwk69FOb7pforcE/lXdBHhXjLK8aTJi+sq+cCi0szkrfU7Gnjk1d1856pl2IyxEQiMOvEwmpmk2fJguS0CsUZCPZReJGt2si+VT/mknB5OcuQ0pxf7jJOYtfU1ahpCNLbEKAj07/80pSxir/2JpHLSeuZNLJxR1m0f57KPwIyVRKp24s/J4caSWQMy7qHWUXLRn8B6NOjzX3lzczOf+9znOOecc7jgggu45ppraGpK/8ezfv16LrzwQs455xw+85nP0NjYmDnucNuEEOOHamvEs+t5jnfsGpUZZbNqMwB5RggHSbbua+Yr//MKe6qHZuEK1b4KXa4elhIBaO8G4MXv67mDgeZOB8pePUY8YWZW8Wu2PPg9DjRfLiV6EwU0M8deCfReerFxdxPPvbye3z62EctKZw/f3FzDgbpQj4GZGBpWpw8/ujcXH+HufYgTUWyhWvalCijK8QzYtY2iGRhWgmKjhaqG9LcWdc0RWnvpupEZc+0uXC17eSq6iMkTe/42BMAZyCfnuBOxjdIgGQ4GymOlPVyfz0LTNK6++mqeeeYZHn/8cSZOnMidd96JZVlcf/31fPe73+WZZ55h2bJl3HnnnQCH3SaEGBve3lLL/tq2PvezWtJ1vcVGC9G4STSeoqIuNNjDGzCx/Zsyfy8yWli/owFLKfbW9P3cB4IVSicZ8vSQZJSBVEs9DZav124AHRllrxYnGk9lPmgELS9+jwPdm0upWQVAmdGMDbPX2m9t92puDjzM/Np/csPdL7PtuUeZWPksQK9ZaDH4VObDjxfDn4fLihCLdl2Zz6zfg4bigCogJ+vIFxnpjVE4HYAptnqqGsK0tbTw/T++wwPP7zjscWbDPgCa/DMHpLXcSGZv/6ZlLCxfDf0IlAOBACeccELm50WLFlFVVcXGjRtxOp0sW7YMgEsvvZSnn34a4LDbhBBjw5+e2cY/V+/tcz+rpRaAYiMIwJNv7OOWP7wzKoI+ZZmomm1sT6bbShUbLZmVueqbe18ydyB1ZERzDQmUIb1iWZPpJ7uXSU4dgbJPjxGKJrHCTaQ0OynDhdNhoPlyMUjXets0iwm2pkzQG42nMq3iVCzEpMpnCFpeljn38HHXq+TteoLF+rb0vhIoD5uDGWUPzkC67tyZbCPZqXuJWZduHxf1TUQfwNXhtKxCNKePclcTzTXVpP5+HfPVlj6XtjabDhBRTgpLSwZsLCPVWMsoH9HHGsuy+Otf/8qqVauorq6mtLQ0sy03NxfLsggGg4fdFggE+n29vLzBXbrxUAUF/iG9nhhacn8HjmUpIvEU+2vb+nxdGxJNxIFcPYSdFLuqWzEthcvjpCB34L4ShYG/x7HK7YTMOG/EZ1LubGCxcx8FqVaeZiEt0eSg/06pVJK2WLrEI1cP0Wipcf17nBdw0JYI02R5mVea3eNroZSPNt3Ap8XAZuBIttJkyyLb76KgwE9TQQnB7ZBSOjbNYrJRj6brONwOvvyTl7n2kkWsXDyB4BvP47Si/MN9KV9eHGX2m4+BBk4thYGJ3WUf1/disPTnNW2yQkTRCWtuiiZNpGY1BPQITo+DvOx0zXBNyz5qCFBQXDDg96lm4iym7dmDWfcaNpLMd1SwNjiHvDxfZnLeofY0HqAilcPC2cVj/vcmvzb9jWF2lrvbcx2Nz/2IAuX//u//xuPxcMUVV/Dss88O1pgyGhtDmbqwwVZQ4Ke+fmi+ShVDT+7vwApFkygFDS0xdu5pINvX+1ebkZr9AOhaunRhV0U6E1hZ04JmDlwXh8G4x/GN7wBQZZuIHtjD3KYK5tor2J/Ko6I2a9B/p6zWg/1a820R1oTi4/b3uKDAT/3+dHvBFssDKbP318LhwxuJs68iSF5TLS2WB4/TRn19Gwk9nYCpNHPI0qJMtTdQ2RThrfcqiSVM3tlUw5wJ2UQP7KYND2ZWGeZx5ejb19LSFMRPiGw9QlVtK/X5A/tBb7zr77/haH0NUcOH1+2kzUzfg4AeZu+BZqxE+luB6IFt7E4UEPDaB/zfjJk/g8DOtSxVTVholDvrSbUl2ba7nvzs7pP7lGVhNh6gypzBPLdtzP8bjkTSfd/NZKrLcx2p78O6rh02MdvvvPjtt9/Ovn37+OlPf4qu65SUlFBVVZXZ3tTUhK7rBAKBw24TQox+4ViSKbY6srUIe/qo1TWDtVSk0rPOT3DuZLaertWLJUZ+qzOzagv1Wj45BQXoeZNJYKfNcnGKczv1weigr+jWUXYRU3YK7eFx/3W/FQkC0Gq5yTpMf1nd7cerxwmG4qRaGqiMOJg7Jd0lQ/em/6wxA+wzC5hib8DWVk3x63eQp7dxoL3u3mqpoTblpyDbjWZz4vnILThXfhpIZy9lYuXwUaEmwpoPv9uO5kv/35KjH1x0RIUaUdFW9iTzKBzAiXwd7PPOomLiuVSYeWzwn4LdijPBaKKmseeVM1VrLbqVpDKVQ/EAf4s2EnXUKI+rBUd+8pOfsHHjRu6++24cjvR/TvPmzSMWi7FmzRoAHnjgAc4999w+twkhRr/U9tV8PetpPux9h72H6f6gzCQq1MCWZBmWZrDStY3P+V8EIJYYeYFGLJHK1DkmYjGS1dvZHCukrMCL66TLeCL3k7wWL2eOvQpPKtjjamADqaO12Z5kAdm0jfsFR1Q4CECrcuNz975ime72k23ESTTXosdbqSOf805Mr8qm+ToC5Wyq9SJytBCTWtfhS9RzpmsDB+rT32SaLbXUpfwUBNLdNTTdIFCYrlUP6BEiUi8+bKxwE0Hlw+u2o9mcKIeXgB6mqTUGQP3OdKeavakCinIGviWlptvIPeFC/mR8jGkrzwdgpr2G6vYl5pVSPPnGXqob03XLZuMBAFqchWN+Ih8cbAs3VhYc6fOO7dixg3vuuYcpU6Zw6aWXAjBhwgTuvvtu7rjjDm6++Wbi8ThlZWX8+Mc/BkDX9V63CSFGNxUL4X//rwBkaVHWHSajbLXWo6GoNgNYRXPQazYCoKGIjcDFM3781/XMKMvmsjNnsn/1UxQpkxrHZFbOKkRz+bBlFbFu91TOc7/PTFsN9cFor90XBkJHRrlCK2GOqkJLRFBKoQ3g5KTRREXSq6FZruxea0EhPaHPb1ThbdoKQPaspZlV/PScUsIzz2XN2z7KPekPHjPj6d/L5c7dPB1dRG1tA754iHori4Wd+uTq7dnLPFtUFikZJkoprFATldFCikvT90b35ZITilAVTAfKVZvWMEkZ1KpcynpY5nwgFOV6+NEXTgIgFChhZqqebe2BcmV9mIde3k1bJMmKhaVoW9/FiR0j0L138ljUkUl2jpdAeebMmWzbtq3HbUuWLOHxxx8/4m1CiNHLal/pDCDbnqT2MN0fVHuNbaPlx/fBz/DQPb/jfPsbeLUY0RGYUa4PRvG6bZi1OynY9TibUxP51Bc+is1I/1cZ8Duot7KwDCcTbE3UN0eZXnpsS+Mejgo3EcVFylcMccimjUTKGjNvQEdKRVowMbC7Dz8hSHP58WhxckM7qNP8FE6acnCbpqMtupDWt96kzenGiuvYSbEpUcZcRyVLnHuo21+CD6g3s8jP7tSv2e4Gu4s8M0qllF4MC6utAc1K0apn8+GV6VZtui+XPHsF7wWjWOFmpkQ2scMxm7uuXXnYbx4GipE/mbLgRl5ujGCFm1HP/ZxcfREH6kL844UdfKTpPbZbpRTmjb6JbEfD77GjaxoB3/Avvz0QxkYBiRBi6CTSgXGd6SdLC9Majve6a8dyw5Y3H7tNJ+VMv1Fk6dERWaMcS5i0hZMkd7+DpXTW5l+QCZIBls8p4sJTp2HkT2airYmqxsO3hDpWVlsDzaYHZ276K/98o21ct4izws2E8ZDdxxuw5vLhUjHKjSo2JycwoaBrVrEjeLK73bTYCwBYk5yB8uQy2dZIW236q/JGq2ugrGkaujeHXCMqpRfDJFaXvjeTZ83KtAjUfXkE9DB1zVHi7z2FpiyqiocmSAYw8iaRRYim+gaSu94iENrDCuc2qupaiNXsJqBHeC9WNi7qkwECPid3fOkk5k/LG+6hDAgJlIUQR0S1B8o1ZgCHSmDFY8STPQe9VqiBJDb8gfRX1sqZzr6OxEA5ZVqkTIvWSIJ43X6qzWxmTO3a87Qg4OaiU6diK5jCBFszm3cP7oqjqWANtaafrKL0V7YF+vgOlFW0hRblOexEPkgvNQxgaIr1qWkUHlKn6nba0DUNr8tOlZZeJW3S/CXYi6YxzdFIrLEaBThyi7stw6t5c8jSZDLfcInXt3fRyZ2QeUzz5uJSMYLBNpK73ub95CQCxRN6O8WA0/PS9e9ZiTrCO9cBcIJrN9c6HuILzsexFGxOllGcNz4CZYDcLNeYKRGTQFkIcURUMh0oN5IOfrP1CK3hnpdvtdoaaLa8FOalAxdndnoiVY4RG3GT+ToC97ZIArOpgmozwHGTc3rc18ifjJ0UkbqKPpeuPVrKSkGogXozi+LCAClHFvlG67gO0KxwkOaUq89AWc+bCMAfQitIBSZj6F3f6nRNoyjXTUHAjWvRB3lUreKsFXMxCqeRTRu50X0ELS+Ty3K7nVvz5uAnNK7vw3CymipoNj14fAfLGPT2CZoFyUq0aAu7koWUDGFQ2hEoT7fVoTfs5EAqF68WI0uPsj4xmQ3OJYSUm+K8wamXFoNLAmUhxBHpyCgHbemv1QJ6mJZeAuVUSz0NKS9F7S2aPrhqAQC59tiIm8zXkal1WlHsyTZqrRxKC3p+Y9PzpwAwwdbE5j1NgzIe1dqApizqzCxK871Y3gLyx3lG2YoECZoucv2uw+5nK53DjpU/4t3E1G5lFx1uunIZF54yhUWLZnPlFz6ZzjIXTAVgpq2GDYkJTCvJ6nac7s3Fo8JE472XHInBo7VUUW3m4O1UVqF50x9olzr3ALAnVUjJIE3i64nuzkLzZHOqaxsGFi9wItqcM/h92+k8mFrF/I9/kU+dN5uC7MP/3oqRSQJlIcSRaQ+UQ450bWdAj9AS6jlQVm0NNFm+TIsmn88LDjeBEZxRLmlfajvkKup16Vs9UAyGjanOIBsHKVC2WmoAaLPlpDOo/gIKjLZxWxtrJeNoiQitlof8QN8BRyAr/eGsrKDnhQQ8Lhs2o+tboJE/BYVGXNl4NjqfqaU9BMr+AnQUrnjzUTwLcSyUZWIL11JtBvC6Ds4dMPIno2wuljt2k1A2Wh2Fg9qNpid63mT8eow9yQKKZi3Et+JKWvzTmFGWTbbXwcqFpWOmFGG8kUBZCHFEOjLKCXc+kA6Ue5rQpxJR9GSYJstHXqdMiu4JkK3HMoHpW5tr2bJv+IKOA3Uh/uNnr1JRn152tdRIjyXlL+31GE030HPKmOJupbI+TDSeYu22ul73PxpWSzWQDso1TcMIFJGtRwk2d2/H19wWxxrkxU+GmxlK35cWy01BD6ufHaqswMuC6Xksnpnf72toDjf2mSfzqracuN1PaQ9flXeUdeSYDWP+NR9prNZadGWmA+XOGWWHB332aeiaYl8qj+LDrLI2WJwnXsr60o/xs7ZzWD43/X/HtR9dwFXnzhrysYiBJYGyEOKIqESUOHacbg+4fOmMcg+lF1YoPdGtyfIS6LTEteYJ4NcjxBImsdYW/vivTTz15r4hG/+h3tvZQDwaZepbP+Js1/uU26sJWU48gcPP2NZzJ5CvGqltjvDKe1Xc/chGKhsGrguGFawlopwEctP1l5789MTCde9u7pKNbwknuOGeN/j7CzsH7NojUao1/fvUYnm6fPDqjdNu8LWPL6TkCOtC3ad/jpzjz+ec4yf22KtZzylDoVFqNBMfYRNSxzrV3kWnwfJ3ySgDeBadi6nZoWgWnzhj5pCPzcgpZeHpZ/O5C+YxqSgdqJfme8nNknKL0U4CZSHEEVGJKDHlwOu2o3tzybNHewyUO97UWuj6pqa5s/ERIRWLEHvwWxyvb6ElNDz1nsntq1my+ScUGK14U0HO96xnvqOC1+KzyAscPmtp5E7AZYYxkmE2tZdf7KgIDtjYUsFqalJZFLaPw8hOt4hzxZp4dk0FAMqy2P/mc5xme493127ina11pExrwMYwkqSCtQDEHIFBX93stMVlXLxiWo/bNJuDuCufUqOZsCw6MqQ6VmaM6N5u3Uh0T4Csy+9g8YevZGoPteVDIcvr4MS5xVJiMcZIoCyEOCIqGSVq2fC6bGjeHHKMaI81yh09lE13bpc3Ds2TjccKk5usxjDjTLE1EOylxnmwpSo24bdamWZLl03Um36eiizkX9GFXRea6IGem/4K/gTnTrKr3wIUuypaAIjEksc06U4phdlcRb2VlWltpmcXAjAjK8qeqlbaIgn2Pv8gU3b9nQ953uX67Cd54Yl/85O/rT/q645kyWAdCrD5+19KMVgS/hJKjWbpfDHErEgw/aez50BY9+agGUPTO1mMHxIoCyGOiBWLELUc+Nx2dHcWPq3njLIVaiCFgd0b6PK47s3FRorpyXSpQIktSCia5L2dDdz0uzeHtKtDR0/WGfZ0tvIXrefwXGIRoPUjUE73ab3Is45LPG9wufd1dlQGAfifv73Ld367mlfeq2L1hmrUEday7t26HT3exu5UIYXtHUM0hwfNE6DU3kpTW4zX396Mf/ezvJ+YyPMTv4QzO5cr89azdX8zTa2xI7reSGQp1eV1S7XU04aXnJyhrz/tJjCBfCPEz//6FhsHuZe2OEhFgsQ1J05X3zXqQgwUCZSFEEfEjEeIKTtupw3NnYVbRWkNdw/MVFsDQfwE/M4ujxuT5gOwzLYNgGKjFR2LNzbVUN0YYV9N98lqg0FZFlprurPETFsNptJoVa5MgJzfx4QxzZ0NTh+W0lgTn8oJzl342/ZS+fZzfCr6v9zk+CPmK7/j3ie3UN0Y6fe4apsivPT0cwBsT5Z0WSxDzykljyBNrXGKKl5Ex+LR6PHMWzAb1+IP4U82MN1Wy4ZRHryZlsU3f7ma1zfWZB5LBmupT3lHRIut/CkzAMi1Gtk8jBNRxxsVaSGEF597cEtvhOhMAmUhxBFRySgxZcflMNDcWehYJMKhbllTK9RIY6rrRD4AI1BKq6sUQ1NELTsGJvl6G9v2BwHYVztEgXJrHbpKZ699epxmy4vDbifb68DQtb6XSdY0HPPP5nntJB4In4SpO7nQs46s9X+m0fKhimazzLkHl5agrjna73Ht3bWXWfZqGkwfMXvXNlh6oISsVCPEWpkc2ch2+xy+d+15zCjLxjb9BHB6WeXbyYbdR9+y7qk39/Hvt/cf9fEDIRRJ0hJOZDqRACSaa2k0fX1m+oeCvWAyADO9bTS2jP7s/WhhRZppVR68LimvEENHAmUhxJFJRIkqO057OlAGcKtot5IJq7We+pS3W0YZoCF3IQDrkukJU8uduyhPbgHUoAbKlqX407+3sb+2DbPpQPox0vXTzZYXl8MgL9tFca6n1x7KnTmXXMiBnBMwNTuOGcuZYmugzXLxmOMispaeC8AEo4n6YP8C5dT+9znu3TuY76hgW3s2uXN9tx4oxWYlON+9Hhsm+3NOzAQNms2BbfISZtqq2by3CdM6ukl9b2+u5cV3K4/q2IHSFk1PkgtF0n8qM4kVaqbR8pHfxyTLoaB5c8HhYaIjOCbKXEYLFWkhaLrxSkZZDCH5bRNCHBEtFSOuHOmMsp4OlP16jNZIEk970KaSMYiHaLJ8TO0hM9tStIxXdu9mk2c5p1jbOcu9EYBJsQZerz4NgN89vgm/x8GlA9jqqaElSnDja/zo3TJu/0AYBdTZSihOVaUDZaeNT6yaSTzZ/7ZfJ80rpijXg3PORCLbX+XZ5FKWLJyInp9u6zbN2f9A2azeiolOC1msTUzrUnYB6dILgJNdO9iUKMNZMKHLdiOnFKcVRUtGaGqNU3AUQWUknqKhJUY0nhr07hK96QiQQ+0Bswo1oqFoMg8uXjOcNE3DyJtIUWMTjRIoDwmlFCoSpClZKhllMaQkUBZC9JuyTHQzQVTZcdgNtPbZ5z4tRms4QXFueuKZ1Xawh/JiX/eMssPr5aHICZwwpQgtUkBbsIVNyQmsdG3jjZZywrEka7bVk+0d2EA5VruPT/le5YnIIqp3RrBbWUTdRdBWRZPlw+Uw0qvgHYHlc4pYPqcIAO9ld/IJRwCHw0DXNDRvLtO0IK/3M1BO1u6mMpXDjvlf4SSvg0lF/i7b9UBJ5u8vxObygUPKEPRAuoVcod5KQ0vsqALljm8GKupDzJwQOOLjB0JHgNzxZ0cHlaDy96uH8lDQcycSqHmZ1lCc2uYIkVhq2NqSjQcqHgLLJGi6KHRLoCyGjpReCCH6L5nOnmVqlF0dGeUobZGDnS9UqB6AJtPXrUYZwOVIf0YvzffiXvVF7rcu5MnIYgBm2yupePJ3TKKahpYY67bX85173xqQbhiqejMA5Y5assP72JUsQPOl2401m17cDuNwh/dJ9+fjctoyZRtGwRTKtMZ+ZZSVsjAb9rI/lceMsmxWLZnAjLLsLvto7mxweDiQymVnqqhbIKy391ouMFppaOl/XfTBMSjyzVpKjWb214b6PmAQKDOJb9e/cZCkLZqkqiFMxc50hxTLX4Chj4y3LT1vIjaVJEcPcffDG/j1oxuHe0hjWkcP5RbL022xESEG08j4H0cIMSqoRLp7Q0w50jXKLh8KDb8eoy1ycPGFjgxgk+Uj0EPpha/9q9MJBV6MwmmY/mJalAfTX8wZrk2UNr7NJd430bH463M7qKwPU9PU/84RvbE3bAdgpq0ajxZne7IEeyCdDU7XKA/sG7CeP4Usq5m2YGufyx2r1jr0VIwDqTwmF/t73EfTNNxnfpmHU6fRUws7zV8Amk6h0XpUk8zi8ThXe5/nI563h2xS5aHM6m2UVT7HQsd+QpEkT7y+l/rN79BMNp7A8PdQ7mC099GeZz9AVX1bl99/MfBUew/lVuWW0gsxpCRQFkL0m0oczCg7HQaang6WfVqM1k4ZZast3UPZm5ObqVvubFpZFl++eB4Lp6cDn4DPgcOm45y8AJ8eJ6EMio0Wbsj+J59QjwPQ3Bbn4Vd2sbOy5ejGbqbwtOym2fTQMT1ue7IY59RFVE86jx2pYtzOY8soH6qj13IOQVpCCd7f1cD6nQ097mvW7wGg2VmM39N7+YdtwjxSvmJshtZtoqRm2ND8BUxwhmg4mkB5x1tk6TFy9TAHhimj3PEha5qtjkg8RXNTkBlGNe9GyyjKPbLlqAeTnluGsrn4iHcNH/e8RTxpjtlVEUeCjkC5xXJTmj9yfg/E2CeBshCi3zoyyh1dLwB0dxYBW5y28MGMWqqxkkbTx/xpPWcAdU1j2exCdD0dsp66oJQLT52KfcI8AJ6OLqTOMZFcPcwsezU+LUpVQ5gnXt/Hm5tqejxnX8z63RhWkmdj6T7O1alsWpWHgrxsIjPOwELHNcCT13R/AQB5Roi65ggPv7yb+57a0mNAZdbtIYmBI39in+ctyHFTlNNzZw49UEyRkW5btmF3Y6bOtz/UthcByNYj1DS2HfFCKQOhY+nzqe2rJfqC27FpFhuSEynKHf6JfB00mxPnx29nbyqfEiMIQFhW6hs04boKUkpnzuypEiiLISWBshCi/5Lputek5sBmpP/70NxZZNvimYyyFWvDqtrExkQZC6bn9eu0C6bn8cETJ2NMnIfz9C+gzzkTc9U3eCHwUQCm2JvYuj+9sMPRLnetWtOB115tIkbZXDaomTgdBl6XDX/75CDXMdYoH0pvX245X2+jPhijtTVELBLlvZ3dFwRJ1e5kfyqPCUV9Twi77IyZfPnD83q+ZnYxARWkoqaZ//n7e7ywtqJfY1VWCqNpL22WC5tm4TQjRIZwlcQOVigdKJfYWljq2M1FrrdpsdzsSRVQ1L5K4Ujh9GfTRA45Rjr7Hon170NJMmXS3BYfzKEdk0Qswvs7atm2f+QsptJasZtaM5sLVg7c5F4h+kMCZSFEv3WUXljGwdpYzeXHr8Uyk/lSu99BUxbvWTMonxg4ovNrmo5j5klcds5xlE/K4fhTlqPQmOUN0lhZAaijDjCs9slApjMLz/nXU1PyAUrzvGiahs+TDpTdA1yjrDnc4PKTb4Sorm7gS87HuMr3Cqs3VHfZT6USWA372JssYGJB30s052a5KMnrOatmK5uLjRRfdj+BW0tQ29y/2m4VSwd7+1PpDzc5enhYgjmrrYFEe0OmT/peI2w5eUg/H4VOSd7ICpQBfAXFZOtRDEzC0YMfLPZUt1LVEO62v7JMfvf4Zm79wzt91q0Plz3/90Pqn75nRE1QdEdqqLJyRsTKjGJ8kUBZCNFvHaUXlv3gV+CaJxsPUVrbJzNFtrxGtRlgUvls7LZj+y9m8oR8jJxSluhbudH/EPPtBwiGji54U5EgcZy43OmxX3XebP7jo+kyjIDXidtp69a3eCDoWQUUOSLM2P8QxUYLk+1NbNzT2CVIshr3oymTvakCJhb2HSgfjm3SAnbPvJwJtmYW2PdRH+xfrbKKpifvVZjp/s85epjgMATKKtTIDmsSYdxsSEzgJ60f5PQzTuS2L51MbtbIC5IWzJ+JRrpcJdwpo3zvk1v4w9NbqagL8ZvHNpJImiilaHz4R8yr/Act4SNbsXGoqFSComQFxUYLbdHksJTfHMqKteEy22jU87sswCPEUJBAWQjRbx0ZZeydMsqebBwqTiwcwoq2ojfuZqM5lY+eNmNArqkXTMOn0pm52fYqWkKJo8rEqUiQsObB215m4XPbyW5vXed0GPzkmlM4fnbhgIy5M91fyAStnhlqL0HLQ5YWwTATXYJQszbd/qyCQopyjz1r6pq2jFbLxRxXHXX97OGsYu2BcntGOWBEhjyjrMwUKhykKpXFYwVf4PehVSSxUZzrYcGMgiEdS39pvoMZ+Eh7jbKlFHXNUfZUtfL02/t5e0sd67bXY9buxNm0gwX2/fi1KLurjm5i6mCyGvdjaAq/FkUpiCX6v/jOQEqZFrf9aQ3v72rEakyvotnqKBqWsYjxTQJlIUT/JSJYaOi2g90WOnr3ehNNJPa9hwY4py4+4oU7emMUpwPuZtPDDHstllJH1YrLigRptdz4elmswGk3BiVbpWcV4CBdlvJybDYAhUZLl64UqdqdNCsfkyZPyNR+H4vySTnoJbM5zlVHazhOLNF3rXFHoFxv+VE2Z7r04iiz90cr0VIPKOoSHgrz0rXaGozITHIHvVOgHGrPKLeEEphmijMc7zF77wOUGs2s3lhDeN2TxJUNQ1Mc79nPnqrhacF3OMma9Ic2vx4D1ID0L++vNVtqeal9+fRgW5xdla28u6M+EyhH3MVDNhYhOkigLIToN5WMksCBs1N3CD07vVpcodFCaMdagpYbb+m0AbumvfwUNs69llfisyk2WvBr0R5LAirrQ1Q3dq8JzYw90kKz6R7yxQq09gl9NWY2W1LpjhaFhywIkqjeye5EPotnDkyfYE3TyCtfhDPVRqHe2q/yi47Si5DlQvflUWDv+XU+1J/+vY1fDVAt67vrtwHp/ts5WU6cDoNsn2NAPjwMFs2XLlXJbc8oKysFj32b23Me4HzPeubYK7ku60n8VW+hVaznpdgcrMAETnTvZXd16zCPvrt4zS4AbJqFW0tmsuRD4bFXdvG3F3ZiWlbmQ1pFXQiz6QAh5cbhDwzZWIToMHL/9xFCjDgqESWOI9MaDkDPLkShUWIEMWq3sDlR1utEs6Oh6TbchZPZmUxnk6bbazNvoomkya8f3Uhdc4Sf/+1d7ntqa8/jVgoVCdKcdPaaUR4sela6nGNTogzTmw+aTpHRQkN78GqFmjBiQfaZBSwcoEAZwFY6B4Bye3X/VgZszyin7B50Xy55RnqRl3uf3EzDYY7fuLuRtdvqjqgNXW+s6vSCME2WF48z3Y0kP3vktITriWZzoLmzyLdHCEeTWM1V2CL17E4W8o/kB/he8KPg9HKZ9w3ClpNdWSfgnn0KRVYtsYYKkqmR1XtZ1e8hpdKhgV+LDmnnk9qmCPGkyf7aUKbsp6I+jNVSS62VnelOI8RQkkBZCNF/HYFypzZqmmHH8uZzvHMXNivO5uQEigcwUAYoynVTYeaSMlxdJvQdqAvxztY61m1vYF9NW+8BYSICZpIW5RnyVb2M/CkkC2bzZnwmudletKwCyjotCGLWpb/qTuRMIeswC40cKS2rEPwFzLVX9GvSmIq1kdBcOB12dF8eubRQW1HB6g01vNFL7+pYWwup1kZQivU7el5Ipb8SG59lfvh19moTKJ44kRll2SyeWcCS8pFZm9yZ5ssjz4gQjqUyC8c8HFnOzBXnsnzRdLwrPwnAW46T+MAJM7BNPxGFxiLbblZvrD7cqYeUSsYwIg3sTqU/3Pn1aJcJioPJjIZJBevQsNi5u5q8jX9lqWM3iWSKVLCGuqQPv0cCZTH0ZMF0IUS/qUS0fbGRrp+xHbmlBMLvkVI6+/WJZA3wG1pJnpdbrj4J5+ZKFmx5hfs37iMSS2WWcN635wCJaJQwNpIpq1u3Datj+VvLzcwhzkppDjeu866jbtsrzMxyoduKKW6t4NX20guzdhdJZeAqnDKw19U07FOWUN76LE81NgOTDru/irUR1d24nTbsc8+AbW9yjf/f3NV6Ptsruk86U8oi/sh3+V6ghc2JUt7ens+pC0qOeryJ7W+wP5XH7nmf4rqV6br0y84cHT1zdW8uufouIrEkVv1eEpoD05d+PTpeE+P//Q8Xe3Myx9jK5nBS1V7uenU3Jx5XNODLpx8Nq73X+O5UIeX2Gvx6bMhKL8L/vI1vZ1WxI1lE1d7FFIbX80kfZEci6PE2GqwZlA7gB0kh+ksyykKIflPJKFHLjsve9U1dz0kHAztSReTlZQ/KpLiyfC/O4z6AQzPJrl/PQy/voq45ikeL8+GWP/Ahz7sANLd1r8dVkXSg12K5M10vhpLHZWPRjHzmT8/DyJ1Irmom1BIEIFG9gwOpXIrz+15o5EjZpizBplk0bV3X54qGKhYigguP04aRN4kNk68gRw9zsWctOytbMK2uJQJW4wH0WAu7koUc56gitf893utlee6+KDOF1bSPHckiJhVnH9U5hpNeOJ1cghANYjbspVYrpCDQtXuJ3ilIBrDPOIls1Up2rJo1W+uHcLS9s1rT49iTSmfxh6r0QsXDaC1VRCwH0211eFp2k8JGUPk42bkDgHozSzLKYlhIoCyE6DeViBIxbTgcXf/r6Oh8sTkxgeIBaG/WGyN/ChWpHJY69pCrtVG67QEucK/DrSWZbz8AKBpbu09AU5mMsgeve3gyd//xsQUsn1OEbdrx6FhMju0gFY9A4z52pwoHZVleo2gGOH2cml3BH5/eRjxh9vpVuoq2EVGuzDLetuIZvBCbywnOnRSbNeyvDXXZ36zaAsD/RVag/IVc5H+PXz78fr/qoQ9lNVWgWSb7zXwmFh1bH+nhYJu8EID8ls0k6/exMxogP3D42mpj8iLQNBZ5qthRERz8QfZDx+qV+1PpWvosPUZ0CDLKZuN+AN5KTEfXFAuM3VSaudTbSykw0rXzDZYfv2SUxTCQQFkI0W8qESVi2XHZuy71bJswl2TedNYnJg9qoAyQNXMJU+yNrHJtYmZyKye7dhBTdvKMEEV6C02t3TPKHavyKZefSYX+QR1fX/S8ScRcBSxx7OKxB59CUyZbkmWDsuqcphs45nyA6eZuAmYjf3l2O//xs1fZtKep274q1kqblV54BeD4OYVMO/PjAEyz17H9QLDL/qmqLbToOThzC3EtPJcC1Ug+wcx+0XiKeLLnHryJpMn+2oOt0cz63QDUG0XkjeBWcL3RA6WEjACn2d/DwGJvMo8pxYf/PdNdfozCGSx0VbGjh9KWznZVtvCPl3YN+uIfVmsdCd2NcnjRXH4CttiQZJSthnSg/Hp8FgoNp5ZiXzKHNs+EzD4Npl8yymJYSKAshOi/RJSYsnfpegGg+wvI+ci3ueisRaw4hjrV/iiaswQdixOdO6lM5bDFnMR9oZUAHOeopLGHQDl6YAvNpocTF0095tUCj5WmabjnnMIMex0zWt8hpuxU6SXk+J19H3wU7AvORbM5+Er28yzf93v8RLpNIFNKoWIhWlNOPM70vXXaDRbNnYzmzmKis42KuoMZZTOVInZgC5vjhZTle7FNXADAfFcNe2vaeOnvf+XHv3iIH/55bY/B3dNv7ee//7iG1vZlz8263YSVi6IJE0flymuaptHgn0mWHuOAMYnPf+kyTltc1udxxuRF5Jt1RJsbMkvA9+TNTbU89eY+9lQPbt/lZLCWNiPdXULzZJFtxPtdo1xZH+KV96qO6rpm4z6iuhfLX4SRUwrAgVQeiezJALRZLuLYJaMshoUEykKIflFmEqwUMdW160UHTdM4fcmEzGp3g8UongG6DZtm8XZiOuvLPsHWZBkt9gLmu6q7ZZRVIoJWs5n3klM4vR/By1Dwzj8TzZ3NNFstW5MlFOX5Bi1A1F1+nMd/BM3lo8QIcnXO66zfUdc125uIgLJoTjm6TSrTs4spsbd1WeGvbscm7CpBg2sypy0qQ/fnowdKWeCpwdz5OkuDz3B11ivU1jZ3y5ZarXXU7NyMaSl2HGjh1XcPEN2/ib3JPBbMGLj2eENtnW0JT0QW07jks7jcLvR+3E/bpHTJxmx7FTsre88qt7aFKNBbeW3D4HXIqKwP0VCxn70hFz6PHc2dTZbed0Y5mTKxlOLRV/fwh39tpTXce8Dfm3jtHqqsPIpyPelyIeCAmYeRPwl0g3rLj65peIa4B7oQIIGyEKKfVCIdKPWUUR5Kms2JUTQdgM2JMsoKvJyzfCKuSXOZqNfT1NK1Rja19110ZVKXNWfQsrZHSnP5cH3gMwBsSEwc0L7TPXHMP4fcS2+jde7HmEwVlzpe4nu/X83jr+9l675m7v3fRwBoTTmYPz2vy7F6oIQ8gl0C5dT+9ZhK44QzVjF7cnqSmjFxPhOtCs7XX6UqFcBHhPN9G3hpfWWX84Wf+w0fiT9Cthbh+bUH2PHS49hjzbweL2f+tK7XHk1WnDiX2rLTWD5vQt87t9NzSsHhYZq9np29lF8opTip+Z9cn/0E6zZXkuilnOVYNQbD5OhhGi0/Pnc6UPZrYSKHaQ9nWhY3/vZN3nj4AbIqXgNg6/7mI7vu/j3QUsPOaDbTyrKxlZ9KU+58as1sAtk+bNNPoMY1HZ/b1q8PH0IMNAmUhRD90x4oR5W9x4zyULLP/gAHXOXUWVnk+l18YtVMJs5fhIMkttauX//Gd75Ns+UhMGXOMI22Z7ZJC/Fe9mMWnvlBzj3h8K3bBoLbaWPqqefhOOFSFjv2cYL1Lm9uqqFp7TN8wniOJtNL6bylzJ2S2+U4PVCC04qSDLcST6SDNGfdJnamisjJDWT2s09fjjLsrE9M5p/uD2MvP5VTnVvYvm1PJsi2gtXQsBunluLzWS9xfvAvXOxZw/ZkMa2B2SPmg8zRmFaaxdc+vvCIPkRqmo5RNIOZrkZ29JJRTm17lenWXpxaimKzqlut+EAx2xoxNEWD6cPvtmMUTMGnwtjDdfzrrX3dlrJWSrF/3etMiW1hXsMzXOx6i3JbFVv2dQ2ULaUyvzeHstoa0J69g4hysuScC/jshfOwFc8k57xrWVxexMwJ2bhP/zwlH/g4F5wydVCetxB9kUBZCNEvKjkyMsoA9pkns2v6ZYBGblY6uHKWzQIgK1qR6eygEhGsyo28l5jMnEMCwJFA9xdwyoIyJhQMXacH58JzMUpmsdxXhdVczZyGZ9mjTSJ5/i1ceObi7mMMpDuaFBqt1AejWK11uKN1bE5OIMt7sGbUKJxO8qM/5S/hU5k7ZwrOJReiozjDtYkHnttBSyhO3drnsdBYnTyOMqORhDLY5Z5PZPH/46Onzxiy12AkMYqmk6uaqKluYEdFkGfXHOiyPbH1FarNABY6sxy1bDsQ5JFXdh/zAi/dxtGYXrq60szF57Fjm7oMBZSGt/Dgi7tYs7Wuy/5mzXby1/2eT/peo9HyU2tl88nst9i6t7HLfk++vpdv/eZ1Umb3FQiTO9/EbkZ5wvcxps0ux9DTGeMsr4OvfGR+piZ5wfQ8zlja/0y9EANJCn6EEP1ysPSi5xrloTZrUg5ZnopMWzVbdgGWM4vJ8VqefecAF6+YRmrfejRl8n5qCueUjb7+vIPFNmkR/uq/8UnfK8Qtgy0TL+QTk3quD9YD6clVRXordcEoheZmAA7YpmAzuuZaCgJubrxiKVNK/OiGjmPWqZyy9RU81VE2/MFinv0A28wJZH/gSipc8It/bOHas+ezYubIX31vsBhFM9GA6XoVdz/0Hm1Rk9MWlWUmnZottexOFuPLymKuVsfvN1XR0JqkMMfNghl5A1aOkNWwgUbTS8HUcuZNy0P35tDknMCi1D6eiS2kujHSZf/kzjdJKBtrPaeilc7FH69hbsVDeEP7qQsuoTDgJr7rHRrWb6QtMpmK+hBTirv2Cg/tepe6VA5zFhw3IM9BiMEgGWUhRL90rlH2OIf/M3b5xAA//Y8VmayTpmk4SmYy293Ic2sOEIomCW97k6DlwTthFo5hzoKPJEZ7398Jtmaeii6isLT3TiWaLx8MO3MclTz3zn62rF1LTHNh+Yp63H/GhOxMAO088TLs885hoaeG2b5WWiacypxLv8pJ80qYM72Y735qGYtG8QS+gWAUTEVpGp/xv8wlxrMooKG9zl4lImjxNhosP4n8mRSrWm4y/perfS8QDjazuYc2f0dDJaIEQrvYmJrMtR9bmCm/qc+eR6ktSLERpKoxfHB/K0Vi59tsSEzAteBszjrjeE4480yUbmOhYz+vb6hGxcNEX7qXC2yrcWtx9lS1dr1mPIy9aQ+bkhNYOH18/w6IkU0CZSFE/3QKlAOD3NniaNmmLcNntXKivoFnH3kCo+p93k1N5/+dPWu4hzai6NklaNnF1FvZvB4vZ9JhFvnQdB3HovNZ7NhHdu1aXK0H2JfKJ7cf/Y41hxv3yZcR+OxvyP/UT5l8/mfx56aDIk3TmFKcNSrbwQ0kzeHGffoX2KtKmWWvxsCkrrm9prslXe7QYGahTzmepKeINYmpHOeo4iL/Bl5af3Tt2A6VqtiIoUx2atO6PL7LPhNTaSx37KK6U6BsVm5GT4bZaE3j+NmFmedhmzCPpe4KXn2/iuZ3nsQwYzi1FB/w7WF3VSu1zZHMCo+pik1oWFQ7p3Yp4RFipJFAWQjRLx0ZZcvmxjUCSi96Ypt+IrbJi7nIvYZVbY9RYeYxYdVlFPSxStp4o2kanvO+wbOBS1Ca3meNtGPxhVRoJVzoWUuxEWR3IpecrJH5YWk0ss84EdeCs3FoJpNtDZ0C5fSy4/WWH3/pVHyX/ID1BRdgBSYyyRPu0tv6WFjBdNu5ZmfXbxZKJ5SwKTmBk717aQpGMh03wlvfIKrs5M9ZllmgBsA+7Xj8qo0r+Cf6pn+xMTWZWM40Vjo2U7FzB/91z5u88l41L6+vpOLNf9Om3LhKZw7IcxBisEigLIToF5VI1yg6vd4RmwXUNA3XBz6LfcnF1JSdTt7F17PkuNLhHtaIpGcVsnxpOeedMLnPyZmaruM+/sP49Di6BvtSBeT6R98KeiPZjMXLAI3ZzrqDXULal5RutPxkedPdZr556WLcuUVkqTZajqJncU9UpIU4Tpzurvd0xYISFpz1IdxWmGWOXfzmsU08+doOrH3reC8xidOWTemyv23mSdiXfZSpjkYaAvOY9tFryV15OXbd4lr3Y5QYzWzc3cjrr79Hfngnr0bLmVaWMyDPQYjBMvyFhkKIUUHFwySx4feO7Oys5vLhWnYxUmzRt8UzC1jcz4l0kxceT2TXJKzG/exL5bFyFLdyG4k0lw89bwKzm+p5NpNRriVi+HF7PRj6wbyW7s/HnVpLIpkilkh1WyTmSKlIkBAevIfMPdA0Dd/MZQQ3zuDj6i1+ticHW1WMla44u52zOeuQ5eo1Tce15AKci84joHecK5/QGf+F55lb+UTOBhqqNzPDqCaldF6Pl/P1sq4T/IQYaSSjLIToFxVpoU15CEiANC5pmobzlCvZXXA6EeUa1T2PRyqj9DgmqGrCzfVAuuNFvekn75B6cM2Xh65M/Fp0QLLKVrSFVsuNx2Xvtk3TDbxnX0NIufmU/zXOtK0laLnxT5nf6/k0vWvAPXnKRLKWnMtUay9L7buoMnPYXXI22fn5Q9oaUYijIYGyEKJfVKSZoOkm2ysB0nhlK55J4cqPMWdyDhMLJcAZaI7jVqFhsSi+lj/c9wiJur1UxHycf+LkLvvp/vSEyFwjREvo2ANlFQkStFx4e1ki2uEPEFl8JXl6G0VGC38JncLcaUfWqcIx/xxUUTl/CZ/KU+4LWXrhpdz62RO6tRgUYqSR0gshRL+Y4SBB003AJzPUx7OSPC/XX9Z9YRJx7PTsIoL5i/lAwzpIbqHOCpCafRaLy7uWx2i+9kBZD9N6jBllpRQqEqQpWYinl0AZYPYJJ5LIivHSezXsbi1m9qQjqy3WXD6yLroRxxObWTVBepqL0UMCZSFEn5RSqHCQFpVLwQhtDSfEWFC06nIqX7JTPG0G02afynRH9zkBuj8PgFw9RDAUP7YLJiJgpmhVHkp6KL3ozDHnNI6fEGdKMHrUiw5d/SFZXESMLhIoCyH6loyhmXFaLA8zJKMsxKBxBfKZfvGXDruPZneB00dePHzMNcpWJAhAq+Vm+mEyyh1y/E6pTxfjihQHCSH6ZEWagfSb6UhdbESI8UT351NgjxxzoKwiLUD637bXffiMshDjkQTKQog+qXAQgBbLIzXKQowAeqCEMr2BtlDsmM6jOmWUe5vMJ8R4JoGyEKJPHW+mYd3bZSUuIcTwsE1ZgocY/rY9x3Sejn/bLb20hxNivJNAWQjRJ6s9o6x7skfsqnxCjCe2SQtIaXZKw1v55t2rqWuOHNV5aiuqSCgbcez4PRIoC3EoCZSFEH1SkWYS2HH7/MM9FCEEoNmc1HtnssCxn9a2CO/uaDjic8R3vom/YjVVWhFf+/gisjxSViXEoSRQFkL0SUWCtOEl4JU3UiFGisJlZ+HXY5ydu5/Ne5uP6FilLCKv/okDZh7q1C+yYHreII1SiNFNAmUhRJ9UOEhzyiUdL4QYQbJnLkIvmsFK4z12HmggmbL6faxVvxdbMsx7xgLmzZkwiKMUYnSTQFkI0Scz3L58tXS8EGLE0DQN58Lz8ZitTFLV7Kps6fMY07J45b0qYnvXYynwTFuILvMOhOiVTF8XQhxWxxK3LVaRrMonxAhjlJQDMNHWyI7KFmZP7nlp6fU7G6htipCX5eIP/9rKtNI1NJn5lJQUDeVwhRh1JFAWQhxePIxmpWix3MyUQFmIEUVzetH8BUxXQdbWhXrd76V3K9mxp5YzpkGBHiE7VsWriUWsKPIN4WiFGH0kUBZCHFbHErctyiOlF0KMQEbBFCaGt/NobVuv+zQGo3zS8xLHNVUx1xcgoQzeSc3ikjzPEI5UiNGnXzXKt99+O6tWrWLWrFls37498/iqVas499xzueiii7jooot49dVX/397dx5nVX3ff/x1zt1mu7PvCzMwLA77Moi4IqAYg4TUVAxqWzWmja35PZLaxvxqNQ+rvwbTpDGNrWmTZnvY2CQ2aMAoKohIBZFFGAYYGIZhhtnv7Mu9c+855/cHZiIyDAMMzvZ+/uWcc+4533u/nuF9v/M532/fvn379rFq1SpWrFjBfffdRyAQGPrWi8hl99GVu/Qwn8jIY6YW4Lfb6GxtJdRrnbXfDnVxXe8WpntraLOjyXa3siM0mcTUVFymHlUSGcig7pBly5bx/PPPk5OTc9a+733ve7z00ku89NJLXHfddQDYts3f/M3f8Nhjj/Haa69RXFzMP/3TPw1ty0XkE+F0nZ52qpM4LXErMgK5UgsAyHEFqG48u/yi69VnWOw5wtuhIr7TfiuNacW83jOLCSq7EDmvQQXl4uJisrKyBn3SkpISfD4fxcXFANx55528+uqrF9dCEbnsKus6zjm11O9LLwytyicyIrnSCnBMF7O8VVR9rE7ZsW2cxuO8FSzCWbCGaxZNZ8KqBymYlMfCK/Qgn8j5XPLw0MMPP4zjOCxYsICvfvWrxMfHU1tbS3Z2dt8xycnJ2LZNa2sriYmJgz53Sson+203LU2rjo1l6t/+fXCohm//9G3yJ+by6L1XEvex1bmanG46DB9JSf4R/xmO9PbJpVH/nosfZt3A4n1b+fH+copnZjExOwGAcFsDnXaEOiuBz83LZeqE07NiPPXgtcPZ4HNSH49to7F/LykoP//882RlZdHb28tTTz3FE088MaQlFoFAJ7btDNn5BpKW5qex8dwPQsjopv7tX+RUKdEbv81TSRY/qFrOzzbG8sdLJp9xTE9TA212DImx3hH9GaqPxzb178CcaTfh/mALkzv38I8/8fLUA1cBEDlVAUCjFY/Ltkf0Z6g+HttGav+apjHgwOwlVfH/vhzD6/Wydu1a9uzZ07e9pqam77jm5mZM07yg0WQRufw63nuJTtuHjcmc+GZqGrvOOsbqaqU5EkVqYvQwtFBEBsNMzMKdNZVifz11gW56w6cf6rPb6gBoNRLwx3iGs4kio9JFB+Xu7m46Ok5/M3Ach1deeYWioiIAZs6cSTAY5P333wfghRde4JZbbhmC5orIULFb63A3HmF7aCpGUg557mbqW3r69kcsm5+/doRwR4A2O4a0xKhhbK2InI8raxr+YB1eeqkNdNMTimC3NxDBjcufrGcMRC7CoEovnnzySTZt2kRTUxP33nsviYmJPPfcczz00ENYloVt2xQWFvL4448DYJomTz/9NI8//jihUIicnBy+9a1vXdY3IiIXJlz2DhYmdUnz8aYfJbV9N42t3di2g2kanKjtYM++o6xOaqXemsSCBI0oi4xkrsxpGLzMRE8jb++vYeveGtYVVdNuxJPq1xddkYsxqKD86KOP8uijj561ff369ed8zfz58/ntb3970Q0TkcsrVF9BbSSBwsI8zMRefEe2Eed00dweJDUxmuqmToo8pwAoDedwi0ovREY0V8ZkMFwUeWp584Pj2I6P3uY6anvjyE7VVHAiF0OTooqMU+HAKeqsJGYXpuIyTtcz/r78IslqouCDH5Aa1UOLFUODk0SSX4uNiIxkhseHmZbPkoZSrvYd4XvtK/CFWmiIZDBjYvJwN09kVNKSPCLjkBMO4u1tJWAkkZcRh5mch2MYTPXUcvhkCw07f0dKqJp8d4DScA4pCdGYpuobRUY635V/zIGYRYQcD1+J/x0ew+KYncW0CYnD3TSRUUlBWWQcsltrAej2pWEaBobHh2fy1VzrO0LJe+9jndzLSSuVBiOVnaHJpKnsQmRUcGcX0TzpU7zQvRhcHn7RtRgncwY+j2u4myYyKikoi4xDdsvp6RtD0el926KuuYsuI5YH498gwexhS08Rx+Z8mQZ3FpnJMcPVVBG5QMuLc/ns5z+L6/PfY489jflT04a7SSKjlmqURcYhu7UGyzGx41L7thneGBrnPUDewf8k0ttFaTiHZRl+/u/d80mIU32yyGgR5XUzMSsegKe/dDVx0Zo/WeRiKSiLjEN2Sw2NTjz+uDNLKuYunIM960kCdXUsKAlTmB2PV3+yFRm14j+2JL2IXBgFZZFxyGpvoCHi7/cfUTPKT1qBn/sLPvl2iYiIjCSqURYZh+yuFtrsGOJjNdokIiJyLgrKIuOME+nF6O0+HZT1Z1kREZFzUlAWGWecrhYAWjWiLCIiMiAFZZFxxu5uBaDdjiY+Vk/Di4iInIuCssg4c8aIskovREREzklBWWSM6Q6G+ZcX99PcHux3/++Dcsjt19RvIiIiA1BQFhljjte0s/doE+8dauh3v93dStjw4I2J/YRbJiIiMrooKIuMMS2dIQAOn2zpd7/T1UIXscTHarU9ERGRgWjBEZExpq29m3xXI0eqTCKWjdt15vdhp6uFViua5HgFZRERkYFoRFlkDLE7Gplf9m98NeF35NvVVNZ1nHWM1dVCUziKvPS4YWihiIjI6KGgLDKGhI/twB8JAJDm6qCsuvWM/Y5tnR5RtmMUlEVERM5DQVnkE1BZ10FHd+9lv47TGSCIDxuDVE8Pze2hM/bbzdUYjkW1lUxumoKyiIjIQBSURS6zlvYgT/38fV5489h5j3Uch237ay46VNudAZrtOIKuOFK9Qdq6zjyP1VAOQIMrkyS/apRFREQGoqAschlZgSrCv/ob5rjK2Xu0kXDEHvD4wxVN/PiVw7z2XtVFXc/uCNAYiaXXm0Cy2U37x4NyfTldxBCXkolhGBd1DRERkfFCQVnkMik/VknVb54h2urgztgdJEQCHKxoPufxkeqDpL3590x21/HBsaYLvp7jONidAVrsWOyoROKNrn6C8jEqwqnkZvgv+PwiIiLjjYKyyGVwYvOLpG9+nBS7iZ3xN+OJiuIO/y52lNYRjlgcPNGM4zh9xzuOQ9d7L+Jzgtzj305zoIWG1p4Lu2ioCyMSosWKxYhNJsbuoK3rDzXKTrATp72einAKcyenDtVbFRERGbMUlEWGmBMOEVf+OsftLIyVj7H8zrVEzfs0k121mOXb+fFPN/DtF/ZR1dDZ9xqrrgyz6Tg7e6eQYHRzY9RB9h1toqG1h7f2nhrUde3O06PQzXYsnoQU3E4Eo7eLUNgiHLGJVJcA0OTNpSg/aejfuIiIyBijBUdEhlj4yDZ8TpCylCXMyZ4EgKdoKaF9r/B53iUSMdnNndQ1dzMhw49jW7Rs+RkhO5rwvDtwd2zkupNH+NGugxw56GVvHUwvSCI9KWbA69qdp6eFa7bjiE5MAyDR7OanvztMZX0Hf5u7i047irwZczBN1SeLiIicj0aURQbJdhw6e8LnPS546C0qIyn4867o22Z4fETf/GWcaUtxGzZ57gANLadLK3pLXieq8xSbzWu56apCvDNvIoYe/tL1ArcHf4mXMAdP9L8c9Uc5HaeDctAdT1xqOgBJZjd7yhppDHRgV+/nQO8ErpyeeTFvX0REZNxRUJZxq7k9yJZBljXUHD/Gj3/yMn/97Haa24PnPM7ubMZoqWZfbz4FWfFn7HNnTiHuytUAXBFzugbZ7gwQ3PU/HOzNYeKiJbhdJq6c6bhyphOInoDfDHJrQhmlAzwE+IdrBwjjJjU9FTMuBYAZniri7HaKvcdxO2GOuwvJTo0d1HsWEREZ7xSUZdx6Y3c1P3/tCG2doX73N7X28P6hesre2kjU6//IZ3tfJsrqYn954JznjFTtB6A0nEt+5tkzS5jR8Rjx6Uz2NdHY3E3Zi/9KOGLzunEdi2dmAWAYBjGf/lsm/ckTuPLnc637ANUnq7HsgaeWswJVNFh+8rPiMaITcKITuCbqKH+XsJ47YndwPJxGTP4MTQsnIiIySArKMm6drO8A6Hd2icpDJVT/1+PEbvkmWWW/otZJwW3Y3JpQNuDUbV3le2kjDhKyiPb1/wiAK72QbKeeuEAJOaFySvzX8MXPX4/bdfbtGLX4TlwG3O55m4ef3c7hyv5LMBwrglV/lGPhDPIz/BimSdQdT/P/WlexM1RIpauA5zqWMbMwfTAfjYiIiKCgLONU8N1fcGPLrwH6aoV/z2o+Rew73yfN7CAjPZmWaZ8h964n8ExayELXIcor6wmFrbPO2VNfiX2qhIPhXG67euI5r+1KLyTa7uKOqG1UR5KYsvx2MpL7f1DPjE/Hd9UapnlqybNOsvtIY7/HWY0VGFaYY+HMvpFsr89HhyeV9b3X4Fn+EGlpSUwvSB7U5yMiIiIKyvKh7//PAbbtrxnuZnwiHMemt+x/meo6RbLZSUNLD7WBLiKWjWVbdG75IWELduT+Gcmf+3sm3PBZEvxReGevwOP0Mtt1/KxRZSccpOvVZwg6HgpvupPFM8/9wJy78EqaM66kPJzBRm4kO23gxT98V1wHLi+L4us5Ud/e7zFWzSEAqoysM0J3QpyX3PQ4phck88T9i4iJ0kQ3IiIig6WgfIHOtwTxaBQKW+wpaxz0fL2jnd14AiN0uuxituckx0618diP3mP9tgre+fUvMAMVrO9eQHHx9DNeZ6ZNwkjK5bqYcl7ZUXnGgiGR47vwhZrZYC6ncEr+gNc3o+OJLLyLf+9cRtqkqeetGTbcXty5M5hMJSfrO/qtVQ6ePEiNlcT0qXmYHznf55YU8rkbCs/7mYiIiMjZFJQvwIm6dv7yn98e8GGu0ajxwxrdE7UdZy15PBbV7N+BAzRZcSyOO0lD1UlinS78Jb9ifusbnPLkc+Wtt531MJ5hGHiLbiDbaMQbOMaWvaewHQfbceg69A6Nlp/cmQsG9bBcbmock3MSuHZW1qDa7Jowh5hIG6lOMzVN3QC0dIT495cP0lZXjdFwhNJIHp+9ftIZr5s3JY0rtLiIiIjIRdHfYQcpfKoU9ys/IJ3reWN3FbMLU4a7SUPCsS3s93/NF+KOsT00jZKKAFfP7D+8tXaG+OWWY/zR9ZNITYj+hFs6NForj2Ad+19OWGlU+yZxnb2TxxJ/A4DtwNZQEdfc8VckJfY/hZpnytX0lrzOn7OFrdtq+Mab+UQnJPGXHGF372yWneOz+zif18X/vWfBoNvtzp9L8B2TW6I/4FdbJjEtrgNfoIydNQUs7t5MrmPiTFlCcnzUoM8pIiIiA1NQHqSW3ZtIcNr48/i3eLoihkDbFaQkjP5QYjdWkFT1Fn6PSbq7k01lc84ZlA+X1xJVvoX/qjlGJDGfxTMyB6zFHWns1lqM19bhxYt/8e1cN2U2JTuL2Lf/KDMyXZx05ROdWXDOkAxg+GKJue3rGJv+heWNpSyJKaOiNxnHDW1p8y9bUDVjEvFd+Tnm7vwlCU3/RV5rALdh4/XXkN1Wye7eiSycP/WyXFtERGS8UunFIDiRXjz1B6mMpJJodDDLc5IdpXXD3awhYTWdAODN8DwyzFbaj++npKL/0pLY8jf5bMz73Gv8Blf1XnYeqr+ka3cHw9i2c/4Dh0jne+uJOAZvZN7LxPlX4ffH4Jswg129hZgzP8Xda5Zx+yDqec3YJGI/+xhxa7+FNyqKqZ46Xuy+ktnzii5r+72zP0Vj/k1kJkcTyZ5DY8IM5nmOE7D87I6+lrz0uMt6fRERkfFGI8qDYJ06iNsJs8N9JfnuN5gU28Gxhs7hbtaQsJsq6TGiORxbzApXGcs5zk9/d5inv3T1GbW2TrCTnMBODln5FMU0ca2vmd+0nXuFuvMJhS2+9ty73HZ1ATdfOWEo3sqAemrK4cR7bA9N5+brZ/VtnzExmduuLqD4irQLPqcZl0L0p79GS3UF3ro05k258HNcCMMwmLTirr6fU8Mhukrf5odvGXxqsUaTRUREhpqC8iBETuwl6HixM6Zhcpi85ja2BbqHu1lDwmqqpMZOISXJj9s/g/yqwwSaQwR7rTMWzOg9vBWP08vumKuZmbqfjKYGGlt7cBznolZ6qzxUSlcwzPHa/qc7GyptjQ1s27SZOR1bgWjsopvJ/Mj0aT6P66wH4C6EKzmH1OQc7p49BI29QIbHR9ycm/jHWfYZM12IiIjI0FDpxXk4jkO4qoTD4UxyMxNxJeeRajdR39yF7XxyZQOXg2OFsZtPURFKIj0pGjMhHV9vGy4sWj+2rLNVe5gGJwlXUi6u1Hz8vU3YkTBtFzFLhtVQTtaObzPXW0lNU9dQvZ2zhGqPEvzNY9zQ9SoxHjhQcDcrrp9x2a43XFymqWWpRURELgMF5fOw22qhu5nD4Wzy0uMwk/Pw2j1EWV20tIfOf4IRLBKoAsfiZDiZzOQYzPh0DBySzS5aO/8QgLt6QkTqjnGsN42UhCjMtAJMxyLb1UJT64WXX1h1xwCY7z1BXXPPoOuUWzpC9PazIl5/nGAnnRu/Q7flpqb4y0z96x9x6y1Xn3NZaREREZGPU1D+GNtxKKtqZeu+U+wsrefE7h0AHAlnnQ7KKXkAZLtbqGse3eUXVe9uwnYM0qdM58qiDIz4dABSXR20fWREef1vt2OEezgeTiMtMRpX6unlmfPczX1zMF+I3objAEz3nMK0QjS2nf8ctuPw+H++x6+3lg/qGp271uO2etiT9cdMmz8f0+O74HaKiIjI+KbhtY858NYbpB55kSw7iggmMWYXjY4fIy6NhFgveHIByHadDsozJiYPc4svjtVUSUr9TvZQxJ23XYlhGNi/D8pmxxkjyq7m42DC8Ug6NyREYfiTwBfL1FAtDa099IQiFzRSG647TqcdQ6LZzQxvNTVNXWQkxQz4mqa2IJ09YXYfaeTzy6acUWrw01cPM6MgmeIrTrff7m7FPryFnaHJXHn14OcqFhEREfkojSh/hNPbQ1b5esJ4SM8vIDM3B1/2FGIX3c437luIYRgYvliM2GTyvG3UBi5ffe3l1B0M0/n2z+i2vbQWfqovdBrRCeD2keHp6KtRDjbXMdM+RLsdRcCOIzUh6sMV6pYwz1fJgfd28/C/bic02JKI3m7c3Y1sD07F9sQw1V1L7XkejHRsi+5dv2Get4K2jh4q6zv69tUFOtm6r4atH9Tw9gc1/P0PdxIqexfTsTgUt1BTpomIiMhF04jyR4QPbSHK7mJTwue5+9MrznmcmZxLXvcp3hulM1+8+etfs6S7nN92L2bZ9D9MzWYYBmZ8GpnhLiragvz0lYPc1vADMl3dvGFcS5TX3beghnfuSlr3beYW327+teMmqhs6KcxJOO+1raZKAGpIx5NpURiq5q3zPNBnNZSTUrGJP4uDzcEA+45OoiAznnDzKXy/eYK53kUcqXFjWTanmrpoO/A2TZEUps+bfgmfkoiIiIx3CsofEUkv4qed1zNh+sBz0rpS8kipKqGmoe2ip0cbLk5vNwu63qbKSaM1o5iJWfFn7DfjM0hpOc6B4wEKqMYT385/dt7A6rtu5+YYD27X6T9CGN5oWnKuZtqpN0kwuqis7xhcUK4twwHCCXm40mNJrzrAiaoGHKfonJ+jHagCoNpJ56qoCp45VEd+pp/QpueY4Qnx6ei9TAw3EhsI4fPkEdtTy6beK1k9I+PSPiwREREZ11R68RFV4UT29hYwMcs/4HFmch4mNjG9gb5a3mPVbUQs+5No5iXp2PEisfTQUnQ7f3vXgrPm3zWTskl0Wlni3scC3wlCjptDkRyyU2PPWp556rU3A3BVXBVlVa088ZNd7C8/c1W/3rDFcy+VsOX9Cv7tN/up3rudajud5NRkXBmFGEBc1ylqBhidt5urCOLjSNxCYuihuOd/4Y1/ZoanirJwJumuDpZEHWKB9zj3+bfSYPkJ5iwgPsY7NB+aiIiIjEsaUf6IitrTta8FHxtl/Tgz+cOZL1wtnKzvoKqhk+/+6gOWzc/lrptH7gppjm1B2VZ29RaSmTel32O8s2+hoqycT7MPgD2hAhIT/XjcZ3+nMhMyMNMmclXgGC8cjedEJIvtB2qZXZgCgN3VwvGXf0RxcxMT6xpJjiST7mlkQ3AeWSmxuNJOr2SX727kQHmAnNTYfttkBao4FUnEypoJlW9yEyU0Wn5q0q/BmXIrZt1v2VBqURZKYfXUCM+WpvHnyy5+ERERERER0IjyGcIRi/xM/3lHIs3EDDDdZLta6Dq6i9w3v8YDcZt5f28Zx6rbPqHWXjinvRHDjnA0nElWSv+h1PDFUlG4hh913ECLK5VtoWlkn+NYAM+060i2m3kw/g1ujCql9EQzlm3j2BZNG/+FtPZDpMVAT9JkJnkaATgYziUrJRbDF4uZlM2MmAD7y5v6b7NjYzWf4lQkkez0JHzFf8TJ5MX8wP4cBZ++j+IZOcQu+wsCudcTlTONok+v5YHPzmfulNRL/8BERERkXNOI8kfcds1EPnVV/nmPM0w3ZnIOc6xTmFXVBG03M73VHLZr2Xmonsm556/V/bj2rl7+e/Mxli7IITslFp/HhWkObe2z3VoLQIuRRHL8uecVTozzsT+cz5IbbmPi8WamTkg857He6Us5EM4nuO0n3Bazl7K2TCpq2oje99/Etx7nVfcyVq79PFFeFz2vPUNLzUlqrESyUk5PB+fKmcmE1jeprG6iOxghJurM/yWdjgBGJEiNlcT8LD/elJuZPtPhKdvpq5cGeOC26dgOuF0mC6alXcKnJCIiInKaRpQ/5qPhayC+hbeTYrSRQhtvmNcBkOIN0RUMn3VsVUPnGdtt2+G32yto+shiHQf37GPaif/mwK/+na9/91X+Y0PpJb6Ts1kfBmUjMXPABxDnTE5h1TUFzChI5q6bp7Lww/mJz2XW9AJ65t2FGR3Pn8Zto/nV54iv2cF75nxuvetOon1uDMMg+uaH8K56nFXXTCTzw6Dszp+Ly7EodNVQeqL57DYHTs+S0WymkpF8+jWGYZzVTx63C5/HNfgPQ0REROQ8FJQvkjtvNi1z/pRjCYu4/Z47wBtNojtEdzDSd4wd6eXAL/+V7/3kTZ5/vQwAx3E4uPcA67cd79tmNRxn2uH/oMhby43Rh/jr1M0cOHSSsqrWIW2z3VpDhxNDaurAi6TERHlYfd2kfuuS++P1uFh+zRXELP0i6a52iuwj7PJdxaK7/oLYKE/fcYbpIi01gdXXTep7iNCVORU80cyNruGDfsovIpV7CeLFl1Fw1oOHIiIiIpeTSi8uQcGiJRQsWgJAZ3QC8V3BM0aOm/a9TUHre/xRXD4/P5pIOGLDifco2P0cX41P4ScV11NWlU/uoQ2EbBdbMu/ljoXxmK98my8nvsHPX3Kx7JoZLJmXMyTtjbTUUBuJJzt14FXwLpY7ZzrRy/8SwxfL0pzBzWFsuNy482Yxq+IAvztej+0U9QXiiuoASeXv80Eoj4Kc0bkCooiIiIxeGlEeImZMAnFGT9+IsuM4cGQLtgMz3ZUkW40cqmymo2QrHXYUWd4u/jT+f3nhd/uInPyAXaGJTJg4AXd2EdE3P0Smu4N7fG/ys9eO0NwevOT2OY6D3VpLvZVwzgf5hoJn0kLcgwzJv+eddTNRTg/zrP2s31YBQMn//JDWl9bhskLsDk1kUvaF132LiIiIXAoF5SFiRMcT43TT9WFQthsriO46xSvB+eCJ4qaYQxworcDdcJh3Q1NwL7qTfLOeP4r8FsO2eD80iSs+fGjOPWEOvgWfIcupJ8HoPmPJ5ovl9LRhhnuot+L7HqQbKVwZk3FNmMuK2FI2v3uYI2+9Qn7TO+R7Wgh5E4maMIOpeQrKIiIi8slSUB4iRnQCUXYX3cEwjuMQqTkMQEXsHDyTr2K2t5K4ym0YODQkzsY/8wY8RUuY4GmlxZ3G4msXkpoY3Xc+d95sAK7w1lBZd+lB2W6uBqDBSSI9Kfo8R3/yfIv+GC8RvpC4ndQjL3I0nEH7ynWk3PMt/s8d84jyqkpIREREPllKH0PEiI7HY4fAihCO2NhNJ2hx4khNT8UzNYfwobdY6t3Pwd4cJsyagmEYRF33Z/gW3YEfgwneM8OrmZyLEZPIPOp5dwiCstV0AoBefy4uc+R9P3Il5eCZuZzCA6/RaPl5PepT/G1W0qhaHlxERETGFgXlIWLEnC4N8Js9dAUjuBtPcDKcTE5aLGZ6PmZCJsG2AL/qXsRXCv+wGIbh7b8MwjAMXLmzKDz6Hr+sH9wiJqXldWx89V3MlALuvbXojCWn7aZKWhw/KWlJl/AuLy/fgtU4hsnWk1ksn3OFQrKIiIgMq5E3tDhKmdGng3K80UN3Rzt0NFAVSSY3Le706PGyL9G26EvMn3cFOWmDe5jOnT8HrxMiNVhFW2dowGMd28a9/T/4C89LtFYdY+eh+jP2W42VVIaTLuuDfJfK8EYTfdUa7r3jehZMG3juZhEREZHL7bxBed26dSxdupRp06ZRVlbWt72iooI1a9awYsUK1qxZw4kTJwa1b6wyouMB8JtBwo0nAai2UsjP9APgSs1n0pz53HXT1EGPlLrzZmG7vMz1VrK7rPGcx72+q4ryTS+QFSzHAZYmnORgxR8W73BCXTgdDVRHUgYd0kVERETGu/MG5WXLlvH888+Tk3PmXL6PP/44a9eu5bXXXmPt2rU89thjg9o3Vn209ML5cDW5kD+HxLhzLxV93nO6fXjz5zIvqoqN248T6rXYsvcU//7yQSKWDUDjoT28s3UHiZWb2dubT3PSTGabxyivauZgRTMn6zuwAqeD+ykrhekFmo9YREREZDDOG5SLi4vJyso6Y1sgEKC0tJSVK1cCsHLlSkpLS2lubh5w31jWN6JsBHE1V9Bqx5I3IfuSz+suvJIYesjtPc7Wt3dzdPsbVB05xH9vPkbrkfeJ2vY9Ho7fiBuLjd3ziC66Hq/dw/0xr/PjX73DD14+SKTqADYG3qxJxEV7zn9REREREbm4h/lqa2vJyMjA5XIB4HK5SE9Pp7a2FsdxzrkvOfnCRjNTUuIupnkXLS3Nf0mv745LJqe3mei2ACXhdBbOyLrkczrJ11G953/4vGsPnvJtLPZa4IU9hw/QXV5Pp52INymdBjMdl5nJ9BtupDm6m/w3nufP4rfz3cBNBI9sp7Q3h8ULp15ye0az8fzexwv18dim/h371Mdj22js3xE960Ug0IltO5/ItdLS/DQ2Xto0bGbubGZ1bMVlORyLZDA3MeqSzwngLr6D2Ne+S6PtZ72znC/NbmfO0XcJuhOx59zNhJkzyLEdZkYsAoEuKFyCr8smf8fP+NO4t3EF29gdmc+fZF36exythqJ/ZWRTH49t6t+xT308to3U/jVNY8CB2YsKyllZWdTX12NZFi6XC8uyaGhoICsrC8dxzrlvrPNMKiZy+C0AuvwFJPkvvj75o1wT5hC19C+oC/hZnppJdGEK0dfeA0Dmh8eYpoHH/YdKGv+sJfRU7WbuqYO02dFMv/p6Ei6hXlpERERkvLmo6eFSUlIoKipiw4YNAGzYsIGioiKSk5MH3DfWubKvoNv20mFHUXjF1CE7r2EYeCZfxdWLZjC7MGWQrzGJvvWvOXnN37Or8EvcuLBgyNojIiIiMh4YjuMMWNvw5JNPsmnTJpqamkhKSiIxMZGNGzdSXl7OI488Qnt7O/Hx8axbt45JkyYBDLjvQoy20guAn/zzs5g43PQn947oOYvHm5H6Jx8ZOurjsU39O/apj8e2kdq/5yu9OG9QHk6jMSh/+ZltdPaE+c9Hlg5Bq2SojNQbVIaO+nhsU/+OferjsW2k9u9lqVGWc3vygUWM3K8eIiIiIjJYCspDLD7GO9xNEBEREZEhcFEP84mIiIiIjHUKyiIiIiIi/VBQFhERERHph4KyiIiIiEg/FJRFRERERPqhoCwiIiIi0g8FZRERERGRfigoi4iIiIj0Q0FZRERERKQfCsoiIiIiIv1QUBYRERER6YeCsoiIiIhIPxSURURERET6oaAsIiIiItIPBWURERERkX4oKIuIiIiI9MM93A0YiGkaY/p68slS/4596uOxTf079qmPx7aR2L/na5PhOI7zCbVFRERERGTUUOmFiIiIiEg/FJRFRERERPqhoCwiIiIi0g8FZRERERGRfigoi4iIiIj0Q0FZRERERKQfCsoiIiIiIv1QUBYRERER6YeCsoiIiIhIPxSURURERET6Me6DckVFBWvWrGHFihWsWbOGEydODHeT5AKtW7eOpUuXMm3aNMrKyvq2D9S36vfRo6WlhQceeIAVK1Zw22238Vd/9Vc0NzcDsG/fPlatWsWKFSu47777CAQCfa8baJ+MPA8++CCrVq1i9erVrF27lkOHDgG6j8ea73//+2f8rtY9PHYsXbqUW265hc985jN85jOfYdu2bcAY6GNnnLvnnnuc9evXO47jOOvXr3fuueeeYW6RXKhdu3Y5NTU1zo033ugcOXKkb/tAfat+Hz1aWlqcHTt29P38zW9+0/n617/uWJblLF++3Nm1a5fjOI7z7LPPOo888ojjOM6A+2Rkam9v7/vv119/3Vm9erXjOLqPx5KSkhLn/vvv7/tdrXt4bPn4v8GOM3A/jpY+HtcjyoFAgNLSUlauXAnAypUrKS0t7RutktGhuLiYrKysM7YN1Lfq99ElMTGRRYsW9f08d+5campqKCkpwefzUVxcDMCdd97Jq6++CjDgPhmZ/H5/3393dnZiGIbu4zGkt7eXJ554gm984xt923QPj31joY/dw92A4VRbW0tGRgYulwsAl8tFeno6tbW1JCcnD3Pr5FIM1LeO46jfRynbtvnFL37B0qVLqa2tJTs7u29fcnIytm3T2to64L7ExMRhaLkMxt/93d+xfft2HMfhhz/8oe7jMeSZZ55h1apV5Obm9m3TPTz2PPzwwziOw4IFC/jqV786Jvp4XI8oi8jo8g//8A/ExMRw9913D3dT5DJ46qmneOutt/jKV77C008/PdzNkSGyd+9eSkpKWLt27XA3RS6j559/npdffpkXX3wRx3F44oknhrtJQ2JcB+WsrCzq6+uxLAsAy7JoaGg468/4MvoM1Lfq99Fp3bp1VFZW8t3vfhfTNMnKyqKmpqZvf3NzM6ZpkpiYOOA+GflWr17Nzp07yczM1H08BuzatYvy8nKWLVvG0qVLqaur4/7776eyslL38Bjy+3vP6/Wydu1a9uzZMyZ+T4/roJySkkJRUREbNmwAYMOGDRQVFenPdmPAQH2rfh99vvOd71BSUsKzzz6L1+sFYObMmQSDQd5//30AXnjhBW655Zbz7pORp6uri9ra2r6fN2/eTEJCgu7jMeKLX/wi77zzDps3b2bz5s1kZmbyox/9iC984Qu6h8eI7u5uOjo6AHAch1deeYWioqIx8XvacBzHGe5GDKfy8nIeeeQR2tvbiY+PZ926dUyaNGm4myUX4Mknn2TTpk00NTWRlJREYmIiGzduHLBv1e+jx9GjR1m5ciUFBQVERUUBkJuby7PPPsuePXt4/PHHCYVC5OTk8K1vfYvU1FSAAffJyNLU1MSDDz5IT08PpmmSkJDA1772NWbMmKH7eAxaunQpzz33HFOnTtU9PEZUVVXx0EMPYVkWtm1TWFjIo48+Snp6+qjv43EflEVERERE+jOuSy9ERERERM5FQVlEREREpB8KyiIiIiIi/VBQFhERERHph4KyiIiIiEg/FJRFRERERPqhoCwiIiIi0o//DxwaPCwiWmjcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Actual vs Fitted with ALL the data\n",
    "model2.plot_predict(dynamic=False)\n",
    "plt.title(\"Model fit on training data\", size = 20)\n",
    "plt.legend(loc='upper left')\n",
    "plt.figure(figsize=(12,8), dpi=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a list of dates to index the forecast\n",
    "future_dates = pd.date_range(start='2021-05-01', end='2025-04-01')\n",
    "future_dates = future_dates[future_dates.is_month_start]\n",
    "\n",
    "# make the predictions\n",
    "predictions, se, conf = model2.forecast(48, alpha=0.05)\n",
    "\n",
    "# make the data a series for plotting\n",
    "pred_series = pd.Series(predictions, index=future_dates)\n",
    "\n",
    "# Find the upper and lower boundaries of the prediction confidence\n",
    "# note that the boundary will expand as time is farther from the\n",
    "# training data by nature of predictions\n",
    "lower_bnd_series = pd.Series(conf[:, 0], index=future_dates)\n",
    "upper_bnd_series = pd.Series(conf[:, 1], index=future_dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "#plt.figure(figsize=(8,5), dpi=100)\n",
    "plt.plot(pred_series, label='predictions')\n",
    "plt.plot(gsl_regression['Lumber_Price'], label='historic lumber price')\n",
    "plt.fill_between(lower_bnd_series.index, lower_bnd_series, upper_bnd_series, color='k', alpha=.15)\n",
    "plt.title('Predictions', size=20)\n",
    "plt.legend(loc='upper left')\n",
    "plt.figure(figsize=(12,8), dpi=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The model predicts that the price of lumber is likely to continue to rise over the next 4 years and is unlikely to fall to the prices pre-pandemic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The price of lumber certainly spiked in 2021 an extreme amount. However, it seems less concerning than the huge price increase of gold. It was interesting that the price of silver stayed nearly steady compared to either gold or lumber. Maybe there are other factors contributing to the increase in gold price such as the demand for computer components increasing sharply for the last 20 years. One could also explore how the lumber prices willl affect new home prices and availability for the next few years."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 [3.7]",
   "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}