{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>label</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IEXE1601.CL</td>\n",
       "      <td>AUD_CLOSE</td>\n",
       "      <td>Australian dollar (close)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IEXE0301.CL</td>\n",
       "      <td>DKK_CLOSE</td>\n",
       "      <td>Danish krone (close)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EUROCAE01.CL</td>\n",
       "      <td>EUR_CLOSE</td>\n",
       "      <td>European Euro (close)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IEXE1401.CL</td>\n",
       "      <td>HKD_CLOSE</td>\n",
       "      <td>Hong Kong dollar (close)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IEXE0701.CL</td>\n",
       "      <td>JPY_CLOSE</td>\n",
       "      <td>Japanese yen (close)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>IEXE2001.CL</td>\n",
       "      <td>MXN_CLOSE</td>\n",
       "      <td>Mexican peso (close)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>IEXE1901.CL</td>\n",
       "      <td>NZD_CLOSE</td>\n",
       "      <td>New Zealand dollar (close)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>IEXE0901.CL</td>\n",
       "      <td>NOK_CLOSE</td>\n",
       "      <td>Norwegian krone (close)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>IEXE1001.CL</td>\n",
       "      <td>SEK_CLOSE</td>\n",
       "      <td>Swedish krona (close)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>IEXE1101.CL</td>\n",
       "      <td>CHF_CLOSE</td>\n",
       "      <td>Swiss franc (close)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>IEXE1201.CL</td>\n",
       "      <td>GBP_CLOSE</td>\n",
       "      <td>U.K. pound sterling (close)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>IEXE0102</td>\n",
       "      <td>USD_CLOSE</td>\n",
       "      <td>U.S. dollar (close)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>IEXE0103</td>\n",
       "      <td>USD_HIGH</td>\n",
       "      <td>U.S. dollar (high)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>IEXE0104</td>\n",
       "      <td>USD_LOW</td>\n",
       "      <td>U.S. dollar (low)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>IEXE0106</td>\n",
       "      <td>USD_CLOSE_90_DAY</td>\n",
       "      <td>U.S. dollar closing,90-day</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id             label                  description\n",
       "0    IEXE1601.CL         AUD_CLOSE    Australian dollar (close)\n",
       "1    IEXE0301.CL         DKK_CLOSE         Danish krone (close)\n",
       "2   EUROCAE01.CL         EUR_CLOSE        European Euro (close)\n",
       "3    IEXE1401.CL         HKD_CLOSE     Hong Kong dollar (close)\n",
       "4    IEXE0701.CL         JPY_CLOSE         Japanese yen (close)\n",
       "5    IEXE2001.CL         MXN_CLOSE         Mexican peso (close)\n",
       "6    IEXE1901.CL         NZD_CLOSE   New Zealand dollar (close)\n",
       "7    IEXE0901.CL         NOK_CLOSE      Norwegian krone (close)\n",
       "8    IEXE1001.CL         SEK_CLOSE        Swedish krona (close)\n",
       "9    IEXE1101.CL         CHF_CLOSE          Swiss franc (close)\n",
       "10   IEXE1201.CL         GBP_CLOSE  U.K. pound sterling (close)\n",
       "11      IEXE0102         USD_CLOSE          U.S. dollar (close)\n",
       "12      IEXE0103          USD_HIGH           U.S. dollar (high)\n",
       "13      IEXE0104           USD_LOW            U.S. dollar (low)\n",
       "14      IEXE0106  USD_CLOSE_90_DAY   U.S. dollar closing,90-day"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boc_forex_names = pd.read_csv(\"/Volumes/GoogleDrive/My Drive/Forecasting/bootcamp_datasets/boc_exchange/column_names.csv\")\n",
    "boc_forex_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_name_map = boc_forex_names[['id', 'label']].set_index('id').to_dict()['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>IEXE1601.CL</th>\n",
       "      <th>IEXE0301.CL</th>\n",
       "      <th>EUROCAE01.CL</th>\n",
       "      <th>IEXE1401.CL</th>\n",
       "      <th>IEXE0701.CL</th>\n",
       "      <th>IEXE2001.CL</th>\n",
       "      <th>IEXE1901.CL</th>\n",
       "      <th>IEXE0901.CL</th>\n",
       "      <th>IEXE1001.CL</th>\n",
       "      <th>IEXE1101.CL</th>\n",
       "      <th>IEXE1201.CL</th>\n",
       "      <th>IEXE0102</th>\n",
       "      <th>IEXE0103</th>\n",
       "      <th>IEXE0104</th>\n",
       "      <th>IEXE0106</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-05-01</td>\n",
       "      <td>0.9200</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>1.5100</td>\n",
       "      <td>0.141970</td>\n",
       "      <td>0.009271</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.8200</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.1700</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>2.2199</td>\n",
       "      <td>1.1105</td>\n",
       "      <td>1.1116</td>\n",
       "      <td>1.1048</td>\n",
       "      <td>1.1075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007-05-02</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>1.5100</td>\n",
       "      <td>0.141740</td>\n",
       "      <td>0.009232</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.8200</td>\n",
       "      <td>0.1854</td>\n",
       "      <td>0.1700</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>2.2055</td>\n",
       "      <td>1.1087</td>\n",
       "      <td>1.1115</td>\n",
       "      <td>1.1066</td>\n",
       "      <td>1.1057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007-05-03</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>1.5000</td>\n",
       "      <td>0.141496</td>\n",
       "      <td>0.009190</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.8100</td>\n",
       "      <td>0.1847</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>2.1999</td>\n",
       "      <td>1.1066</td>\n",
       "      <td>1.1086</td>\n",
       "      <td>1.1054</td>\n",
       "      <td>1.1036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2007-05-04</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>1.5100</td>\n",
       "      <td>0.141616</td>\n",
       "      <td>0.009218</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.8100</td>\n",
       "      <td>0.1854</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>2.2075</td>\n",
       "      <td>1.1075</td>\n",
       "      <td>1.1077</td>\n",
       "      <td>1.1032</td>\n",
       "      <td>1.1046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2007-05-07</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>1.5000</td>\n",
       "      <td>0.140908</td>\n",
       "      <td>0.009177</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.8100</td>\n",
       "      <td>0.1843</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>2.1957</td>\n",
       "      <td>1.1018</td>\n",
       "      <td>1.1042</td>\n",
       "      <td>1.1007</td>\n",
       "      <td>1.0988</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2499</th>\n",
       "      <td>2017-04-24</td>\n",
       "      <td>1.0218</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>1.4684</td>\n",
       "      <td>0.173684</td>\n",
       "      <td>0.012310</td>\n",
       "      <td>0.07215</td>\n",
       "      <td>0.9477</td>\n",
       "      <td>0.1579</td>\n",
       "      <td>0.1525</td>\n",
       "      <td>1.3568</td>\n",
       "      <td>1.7280</td>\n",
       "      <td>1.3511</td>\n",
       "      <td>1.3521</td>\n",
       "      <td>1.3425</td>\n",
       "      <td>1.3493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2500</th>\n",
       "      <td>2017-04-25</td>\n",
       "      <td>1.0224</td>\n",
       "      <td>0.1994</td>\n",
       "      <td>1.4837</td>\n",
       "      <td>0.174374</td>\n",
       "      <td>0.012210</td>\n",
       "      <td>0.07183</td>\n",
       "      <td>0.9426</td>\n",
       "      <td>0.1586</td>\n",
       "      <td>0.1548</td>\n",
       "      <td>1.3661</td>\n",
       "      <td>1.7407</td>\n",
       "      <td>1.3565</td>\n",
       "      <td>1.3626</td>\n",
       "      <td>1.3565</td>\n",
       "      <td>1.3547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2501</th>\n",
       "      <td>2017-04-26</td>\n",
       "      <td>1.0176</td>\n",
       "      <td>0.1995</td>\n",
       "      <td>1.4847</td>\n",
       "      <td>0.174965</td>\n",
       "      <td>0.012260</td>\n",
       "      <td>0.07098</td>\n",
       "      <td>0.9382</td>\n",
       "      <td>0.1585</td>\n",
       "      <td>0.1554</td>\n",
       "      <td>1.3707</td>\n",
       "      <td>1.7493</td>\n",
       "      <td>1.3612</td>\n",
       "      <td>1.3623</td>\n",
       "      <td>1.3543</td>\n",
       "      <td>1.3594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2502</th>\n",
       "      <td>2017-04-27</td>\n",
       "      <td>1.0176</td>\n",
       "      <td>0.1992</td>\n",
       "      <td>1.4815</td>\n",
       "      <td>0.175103</td>\n",
       "      <td>0.012250</td>\n",
       "      <td>0.07151</td>\n",
       "      <td>0.9369</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.1543</td>\n",
       "      <td>1.3704</td>\n",
       "      <td>1.7584</td>\n",
       "      <td>1.3624</td>\n",
       "      <td>1.3670</td>\n",
       "      <td>1.3574</td>\n",
       "      <td>1.3605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2503</th>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>1.0222</td>\n",
       "      <td>0.1999</td>\n",
       "      <td>1.4870</td>\n",
       "      <td>0.175485</td>\n",
       "      <td>0.012250</td>\n",
       "      <td>0.07253</td>\n",
       "      <td>0.9373</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.1541</td>\n",
       "      <td>1.3719</td>\n",
       "      <td>1.7679</td>\n",
       "      <td>1.3650</td>\n",
       "      <td>1.3697</td>\n",
       "      <td>1.3635</td>\n",
       "      <td>1.3631</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2504 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  IEXE1601.CL  IEXE0301.CL  EUROCAE01.CL  IEXE1401.CL  \\\n",
       "0     2007-05-01       0.9200       0.2000        1.5100     0.141970   \n",
       "1     2007-05-02       0.9100       0.2000        1.5100     0.141740   \n",
       "2     2007-05-03       0.9100       0.2000        1.5000     0.141496   \n",
       "3     2007-05-04       0.9100       0.2000        1.5100     0.141616   \n",
       "4     2007-05-07       0.9100       0.2000        1.5000     0.140908   \n",
       "...          ...          ...          ...           ...          ...   \n",
       "2499  2017-04-24       1.0218       0.1974        1.4684     0.173684   \n",
       "2500  2017-04-25       1.0224       0.1994        1.4837     0.174374   \n",
       "2501  2017-04-26       1.0176       0.1995        1.4847     0.174965   \n",
       "2502  2017-04-27       1.0176       0.1992        1.4815     0.175103   \n",
       "2503  2017-04-28       1.0222       0.1999        1.4870     0.175485   \n",
       "\n",
       "      IEXE0701.CL  IEXE2001.CL  IEXE1901.CL  IEXE0901.CL  IEXE1001.CL  \\\n",
       "0        0.009271      0.10000       0.8200       0.1860       0.1700   \n",
       "1        0.009232      0.10000       0.8200       0.1854       0.1700   \n",
       "2        0.009190      0.10000       0.8100       0.1847       0.1600   \n",
       "3        0.009218      0.10000       0.8100       0.1854       0.1600   \n",
       "4        0.009177      0.10000       0.8100       0.1843       0.1600   \n",
       "...           ...          ...          ...          ...          ...   \n",
       "2499     0.012310      0.07215       0.9477       0.1579       0.1525   \n",
       "2500     0.012210      0.07183       0.9426       0.1586       0.1548   \n",
       "2501     0.012260      0.07098       0.9382       0.1585       0.1554   \n",
       "2502     0.012250      0.07151       0.9369       0.1590       0.1543   \n",
       "2503     0.012250      0.07253       0.9373       0.1590       0.1541   \n",
       "\n",
       "      IEXE1101.CL  IEXE1201.CL  IEXE0102  IEXE0103  IEXE0104  IEXE0106  \n",
       "0          0.9100       2.2199    1.1105    1.1116    1.1048    1.1075  \n",
       "1          0.9100       2.2055    1.1087    1.1115    1.1066    1.1057  \n",
       "2          0.9100       2.1999    1.1066    1.1086    1.1054    1.1036  \n",
       "3          0.9100       2.2075    1.1075    1.1077    1.1032    1.1046  \n",
       "4          0.9100       2.1957    1.1018    1.1042    1.1007    1.0988  \n",
       "...           ...          ...       ...       ...       ...       ...  \n",
       "2499       1.3568       1.7280    1.3511    1.3521    1.3425    1.3493  \n",
       "2500       1.3661       1.7407    1.3565    1.3626    1.3565    1.3547  \n",
       "2501       1.3707       1.7493    1.3612    1.3623    1.3543    1.3594  \n",
       "2502       1.3704       1.7584    1.3624    1.3670    1.3574    1.3605  \n",
       "2503       1.3719       1.7679    1.3650    1.3697    1.3635    1.3631  \n",
       "\n",
       "[2504 rows x 16 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boc_forex_df = pd.read_csv(\"/Volumes/GoogleDrive/My Drive/Forecasting/bootcamp_datasets/boc_exchange/LEGACY_CLOSING_RATES.csv\")\n",
    "boc_forex_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>AUD_CLOSE</th>\n",
       "      <th>DKK_CLOSE</th>\n",
       "      <th>EUR_CLOSE</th>\n",
       "      <th>HKD_CLOSE</th>\n",
       "      <th>JPY_CLOSE</th>\n",
       "      <th>MXN_CLOSE</th>\n",
       "      <th>NZD_CLOSE</th>\n",
       "      <th>NOK_CLOSE</th>\n",
       "      <th>SEK_CLOSE</th>\n",
       "      <th>CHF_CLOSE</th>\n",
       "      <th>GBP_CLOSE</th>\n",
       "      <th>USD_CLOSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-05-01</td>\n",
       "      <td>0.9200</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>1.5100</td>\n",
       "      <td>0.141970</td>\n",
       "      <td>0.009271</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.8200</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.1700</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>2.2199</td>\n",
       "      <td>1.1105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007-05-02</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>1.5100</td>\n",
       "      <td>0.141740</td>\n",
       "      <td>0.009232</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.8200</td>\n",
       "      <td>0.1854</td>\n",
       "      <td>0.1700</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>2.2055</td>\n",
       "      <td>1.1087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007-05-03</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>1.5000</td>\n",
       "      <td>0.141496</td>\n",
       "      <td>0.009190</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.8100</td>\n",
       "      <td>0.1847</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>2.1999</td>\n",
       "      <td>1.1066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2007-05-04</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>1.5100</td>\n",
       "      <td>0.141616</td>\n",
       "      <td>0.009218</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.8100</td>\n",
       "      <td>0.1854</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>2.2075</td>\n",
       "      <td>1.1075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2007-05-07</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>1.5000</td>\n",
       "      <td>0.140908</td>\n",
       "      <td>0.009177</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.8100</td>\n",
       "      <td>0.1843</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>2.1957</td>\n",
       "      <td>1.1018</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2499</th>\n",
       "      <td>2017-04-24</td>\n",
       "      <td>1.0218</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>1.4684</td>\n",
       "      <td>0.173684</td>\n",
       "      <td>0.012310</td>\n",
       "      <td>0.07215</td>\n",
       "      <td>0.9477</td>\n",
       "      <td>0.1579</td>\n",
       "      <td>0.1525</td>\n",
       "      <td>1.3568</td>\n",
       "      <td>1.7280</td>\n",
       "      <td>1.3511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2500</th>\n",
       "      <td>2017-04-25</td>\n",
       "      <td>1.0224</td>\n",
       "      <td>0.1994</td>\n",
       "      <td>1.4837</td>\n",
       "      <td>0.174374</td>\n",
       "      <td>0.012210</td>\n",
       "      <td>0.07183</td>\n",
       "      <td>0.9426</td>\n",
       "      <td>0.1586</td>\n",
       "      <td>0.1548</td>\n",
       "      <td>1.3661</td>\n",
       "      <td>1.7407</td>\n",
       "      <td>1.3565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2501</th>\n",
       "      <td>2017-04-26</td>\n",
       "      <td>1.0176</td>\n",
       "      <td>0.1995</td>\n",
       "      <td>1.4847</td>\n",
       "      <td>0.174965</td>\n",
       "      <td>0.012260</td>\n",
       "      <td>0.07098</td>\n",
       "      <td>0.9382</td>\n",
       "      <td>0.1585</td>\n",
       "      <td>0.1554</td>\n",
       "      <td>1.3707</td>\n",
       "      <td>1.7493</td>\n",
       "      <td>1.3612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2502</th>\n",
       "      <td>2017-04-27</td>\n",
       "      <td>1.0176</td>\n",
       "      <td>0.1992</td>\n",
       "      <td>1.4815</td>\n",
       "      <td>0.175103</td>\n",
       "      <td>0.012250</td>\n",
       "      <td>0.07151</td>\n",
       "      <td>0.9369</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.1543</td>\n",
       "      <td>1.3704</td>\n",
       "      <td>1.7584</td>\n",
       "      <td>1.3624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2503</th>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>1.0222</td>\n",
       "      <td>0.1999</td>\n",
       "      <td>1.4870</td>\n",
       "      <td>0.175485</td>\n",
       "      <td>0.012250</td>\n",
       "      <td>0.07253</td>\n",
       "      <td>0.9373</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.1541</td>\n",
       "      <td>1.3719</td>\n",
       "      <td>1.7679</td>\n",
       "      <td>1.3650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2504 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  AUD_CLOSE  DKK_CLOSE  EUR_CLOSE  HKD_CLOSE  JPY_CLOSE  \\\n",
       "0     2007-05-01     0.9200     0.2000     1.5100   0.141970   0.009271   \n",
       "1     2007-05-02     0.9100     0.2000     1.5100   0.141740   0.009232   \n",
       "2     2007-05-03     0.9100     0.2000     1.5000   0.141496   0.009190   \n",
       "3     2007-05-04     0.9100     0.2000     1.5100   0.141616   0.009218   \n",
       "4     2007-05-07     0.9100     0.2000     1.5000   0.140908   0.009177   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "2499  2017-04-24     1.0218     0.1974     1.4684   0.173684   0.012310   \n",
       "2500  2017-04-25     1.0224     0.1994     1.4837   0.174374   0.012210   \n",
       "2501  2017-04-26     1.0176     0.1995     1.4847   0.174965   0.012260   \n",
       "2502  2017-04-27     1.0176     0.1992     1.4815   0.175103   0.012250   \n",
       "2503  2017-04-28     1.0222     0.1999     1.4870   0.175485   0.012250   \n",
       "\n",
       "      MXN_CLOSE  NZD_CLOSE  NOK_CLOSE  SEK_CLOSE  CHF_CLOSE  GBP_CLOSE  \\\n",
       "0       0.10000     0.8200     0.1860     0.1700     0.9100     2.2199   \n",
       "1       0.10000     0.8200     0.1854     0.1700     0.9100     2.2055   \n",
       "2       0.10000     0.8100     0.1847     0.1600     0.9100     2.1999   \n",
       "3       0.10000     0.8100     0.1854     0.1600     0.9100     2.2075   \n",
       "4       0.10000     0.8100     0.1843     0.1600     0.9100     2.1957   \n",
       "...         ...        ...        ...        ...        ...        ...   \n",
       "2499    0.07215     0.9477     0.1579     0.1525     1.3568     1.7280   \n",
       "2500    0.07183     0.9426     0.1586     0.1548     1.3661     1.7407   \n",
       "2501    0.07098     0.9382     0.1585     0.1554     1.3707     1.7493   \n",
       "2502    0.07151     0.9369     0.1590     0.1543     1.3704     1.7584   \n",
       "2503    0.07253     0.9373     0.1590     0.1541     1.3719     1.7679   \n",
       "\n",
       "      USD_CLOSE  \n",
       "0        1.1105  \n",
       "1        1.1087  \n",
       "2        1.1066  \n",
       "3        1.1075  \n",
       "4        1.1018  \n",
       "...         ...  \n",
       "2499     1.3511  \n",
       "2500     1.3565  \n",
       "2501     1.3612  \n",
       "2502     1.3624  \n",
       "2503     1.3650  \n",
       "\n",
       "[2504 rows x 13 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boc_forex_df = boc_forex_df.rename(column_name_map, axis=1)\n",
    "boc_forex_df = boc_forex_df[['date'] + [col for col in boc_forex_df if col.endswith('_CLOSE')]]\n",
    "boc_forex_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7Dby9fXj44YerTGGcOHEi33zzDTt3WqPJy5Ytq7JJ9IQJE1i4cKHltXJzc5k+fTrPPvssAGvWrEGvl9bSMjIyyM3NpW5dhyh0jXM5Hnon4CGghyAIByr+9RcE4TFBEMwdVVcDKcBp4HPgidq53GvD7CUDeFVkvDQJkbzur8e05fFuUXbj70wIs3teeTEzoa6fw2vIKjyKd/88SeSMVfxQEb4J85WMv97giqG7+O+h0RtJyS7BeIkyftuq7voVZftm/fCmTZvSq1cvevfuzUsvveRwbNu2bfn6668ZNGiQJS4d4uOOIAh8+NmXpKenWwxuZUJCQli8eDHTpk2jSZMmxMXFsXbtWry9JfuwcOFCOz10o9HIokWLeOSRR4iNjaVjx46MHTvW0hpv3bp1NGvWjBYtWtCnTx/mzZtn0VavHEO/kk5Jl+KW1kO/GsyLokdf6YPaTUGxRs/5vHLiw6XG03vP5qMzmNienMMD7eoT7mfvhSdnl9Dznb95qmc0T98R43D+zCIN7V//y2H79L6xvLXmOOuf7kpUsGNox4WLm5Ga0kM/nlGEzmAiyEvl8Juy5VxuKQUVWWLNI/yu+XVFUeTQhUJCfNwJ8bn19JquVA/9lhbnuhpCfFRkFmnxqFg993ZXEh9u9dzNOegdGgc6Pb5xHS+2Tu9OuK/zL2VVXxpzeEZXQx660SSyOzWP9g0DXM2wXdzUFJbrLHnl5fqqFRNzS7QUlOtRuyloXKdm+gALgoBcEKqUASjXGUnOLiE6xAuV4tZPVvjPGfQVEzpxLL3omoxghH/1Cm7e7gqKNdYFGKVcwFMlfVl+2nOecp2R5/rH4atWVnWKS7Iy6SKTFh9g/rAW3NMy4tIHuHBxAyjTGjiba42Jl2qlcn0vd/vvflJageWxt7uiRp0UuUygoEyPm1zG3u2bmD59umWfRm8kvF4DPvr6B+LCfGrsNW8U/zmDHubrYYln1xaTekbz2qpjlufB3u4oK+LvC7enAlLx0acPXX7ea2X+SckDYMqSg9yZEI6bwlWs5OLmo9SJhnlKTqldOKWy96x2q1lPWRAEDEYjFwvL6dnrDg4csFaLmm8keqOJiwXllGoNRIfcuiFRlxWoBU5WEvm6UFBuMehm1hzJYEfy1XdJ2XXGemxldUcXLm4WbI21l02KsMlm+4kM+9+LbSpxTWArI5BWqWG8j81MIadEW21I6FbAZdBrgYEtwu2eN6rj6WDQAR74/J+rfg3bL6ZLDMzFzYq5OYW7Uk6jOl64V8SpD18stKT0msc0j/CjeYSfJVOsphCx3jyKNHryS220lmr0lW48LoNeC3RqHARAu4YB7HyuJyuf7IxbDeq3XCgoR2vjdTjryOTCRVUUlOko1RoskrO1hSiK5FUYT3NldkObxU5z/0+5TCDQ063WrkPtZu/xn88vQxRFUrJLKNb8u5yh/1wM/Xogkwmkvnmn3Taloua8jqeXHAAgoa4vhy4U2omBuXDhjL1n8wCBV1ce5cD5Asv2E6/1JWH2OnQGE8/3j+ORro2cHl+i0eOulF+RsJw53CITBMtxlWeq5/PKMJrEWhWsiwqWSv5tF151RpNFcvffhMtDv05U1eZOozfyyabkKzLK5jzeZ/o0AVwxdBfVcyKjmCGf7GDIJ9vtjDlAk1lrLDHmOauPETljFcv2pdFo5ir+99cpcku0JKUVkJJT6qB5dCnMYfJwP/tUXn+11RvPr6iodqbZYkYul9sV4rz5pqQpExkZSU6OVQhv06ZNDBgwAJAKgerUqUNiYiKxsbHMnz+fBoHW2YHt781cwPT70sUM7tmBhIQEWrZsydtvvw3A6NGjnRb/LFiwwKKF3q5dO7Zu3WrZt3LlSlq2bEmLFi2Ij4/ns88+A2D27NnUrVvX7v3YCnddKy4P/ToR6GX9Et/ZPIwIPw8+25zC4l3neGvNccr1RodCJY3eSGpuKbGh9ulURpNI/QA1HSty5d/44ziP3t6YzCINwd4qV166Czv2nM27ovFP/ySJSL3z50k+HxSG7bevsvyzySRFqI0mE24V8fGsYg1uchnuFbUelWPi9QLUBPuo7BZDFdUYdA8PD7ty/stl2LBhfPjhh+Tm5tKkSROGDh1KfHhdjqYXWfqSNghU4+vhxg+//MqiLz/h0++X0atNHFqtlm+//bbKc69cuZLPPvuMrVu3EhQUxL59+7j77rvZtWsXgYGBjB8/nl27dhEREYFWqyU1NdVy7JQpU5g2bdoVv5/LwWXQrxNhvh50bBzI9uRc+jQNZX2Fhswv+yQNs8IyHScyijmeUcSkxQf45fEOLN17gR93neOLkW3oGRdsMdTpheWE+brb/bDMFbBvDE7ggXb1ceHCjFJmPxGfekcMg1tH8PHG0xZl0X0v3EGrV/+s9jzfnfyAs3tPoVYpEEURk0kKXZirzT3c5MgEgdKKUIZMJmAyibgr5U498FKtgQbe0YyOnWSXAVPTBAYGEhUVRXp6OvXq1bPb562Ssly+/HA+T896leBQSe5DpVLxyCOPVHnOt956i3nz5hEUJK2XtWrVilGjRvHRRx8xZcoUDAYDgYGBlnM1adKkNt6aA66Qy3XEHD9UK+WkF0oLUocuSNrqa45k0Oe9zUxafACAIZ/s4Mdd0o9t3Ld7+GpbKnmlOjafzGZ3aj5hFW30RneMtHuNL7eesUvTcuFCX0k/ZWKPKOr6efDyoKYMblmXXx7vSICnGzsr9PwBPn5QatygdpMTG+pNnM0sUW8wUa4zojUY7cSuTCbRopJofg5VZ5Io5TLUbnKahvtWG0M3a7mY/y1ZsuSy3zvAuXPn0Gg0NG/eHJB6JJj/b1ZKPX3iGPEJLS77nNXpoQcEBDBo0CAaNGjAAw88wPfff4/J5m8wf/58y3vp3r37Fb2XS+Hy0K8jnaOC+PtkNrFh3rx7XyJd5lqlPjOLqs9UeXXlUf634ZQlRbFuhZRAqwb+lmIlkDIHYmb9wW8TO9WIFoaLW5usYg3PL5dkWyMD1fRpGmqZ6SnkMt4dlmgZG+Ljzoapt5NRqKFjVBCpb97JsWPHLKGU17o8z4lM5y3ZzAR7u5NVbL+mExvqc02Fb1WFXJyFFm23LVmyhM2bN3P8+HE+/PBD3N0lJyjCX42/2g0PmwImuUzAx0OJiJSdc61hyy+++IJDhw6xfv163n77bf78808WLlwI1G7IxeWhX0ce7tyQvbN6EeGvdiq9eyls881DK6pdQ6vQjnnptyOWxxcKynlu+SGX5/4fxGzMATY9052Z/asX2mpUx4uOUUFO912OUTYbc7NWUpCXqtaqmAMDA8nPz7c8z8vLs4RAQIqhJyUlsX37dmbMmEFGhrWlpKdKYRfbb9q0KScPJwGXl5t+OXroCQkJTJkyhT///JNffvnlSt/eVeEy6NcRmUwg0EvqtnK5HkCHRs5FwkIquraYQy+O+63bO725gR92nuOl32pfYN/FzYW5iKZewLXLXQiCYCc+F+7nQUyIt8OiPUDDIE88VQpLeKM26NatG9999x2ARc7WWQijTZs2PPTQQ7z//vtVnmvmzJm88uJz5GRlggg6nY4vvviiyvHPPvss06dPJzdXqtg+cOAACxcu5IknnqCkpIRNmzZZxh44cIAGDRpYnhtMJrtK2ZrEFXK5yRjVoQHf7Dhred45OojMIg0pOaV2425vUgeQMgbevz+RLtF17Ba1jqYXYTSJXLCpKP1x13keaFffFYr5j3A+r4w9ZyUPdv3Tt9fIOc06K3W8VAR5WVvB+XooLamAsaE+KOQyGtepmZZv5hi6mb59+/Lmm2/ywgsv8Pjjj9OiRQtEUaRv376MGDHC6TmmT59Oq1ateO655ywa57b079+f02fTGP/A3agUMgRBYOzYsZb9jz76KJMnTwagXr167NixgwsXLtCxY0cEQcDb25tFixYRFhZGcXExc+fO5dFHH8XDwwNPT09LuEUURea/+x7ffbfIsqa2YsUKIiMja+Sz+s/pod9MNJq5Ctsb9cx+sTx6e2NOZxVTWK6nrp+aOt4qtAYj8S+uBeDlQU2JDnY+LT6bW8rqQxl8sSWF3FIdT98RQ/MIX0Z/vdsypmGQJxum3u5KbfwXYzKJfLMjlZd/P2rZVrnQ7XKprMctiiJFGj3eKqVd60W9wcSxjCLclXJiblFxq5wSLRcLyokP80EQQKM3WXRl9EYTggAK2bUFNfRGE8fSiwj387C7IVbFleqhu0IuN5B37rNfVTd/eaKCvWndIIBQX3fkMgG1m4IvRrbhodsaMKpjZJUxzgaBnjzerbGlzV1SWoGlY5KZMzml/HbwYi28Gxc3Cz/uPmdnzGsSQRDw9XBz6KOrVMhoHuF3yxpzsGblaPRGjlwsIjm7BIPRZDHCRy9eWWGVM8zrWLW1ruAy6DeQuxPr8sekLpbn+87mVzm2V3wIr97d7LLOe28bKdd2/bEs1lXku+96vifDKrZPWnyANYdvqpavLmqQvZW+R9tm9LhBV3LzMWfOHLsUyMTERObMmQNAcUX+vG14U28SOXaFFbLVUVQRllLVktSBy6DfIFalrKL5t83Zlbfcsq2dkybVV8NzTjIZAtRuvHaP9YYwZ/UxRFEkcsYqRn61q0Ze18XNQVqedd3k+f5x1K2m5dt/jeeff54DBw7Y/Xv++ecBnIZAiipJcuReoxCeWR9eWUseumtR9AaxIGkBAG/veRuQtCl6Nw2tkXM7q8ozF27IBEljo2GQl0XtbvPJ7Bp5XRc3Hp3BxK7UPDpHBTF/WGKtqhj+27BtIm+msk7ShYJyS6ba1WAwmvBXu9W4RLAZl4d+g6ijrmN5bBYv8rP5Qp0vOk9WWdZVn3/x+Nssj/va3Ch+f7IzIBnxpyqqUkHSiXZx62LujbnvnBRuCfN1p463yiHW7eLqMCs2gjUOXq4zoDUYMdjIH1wKoyjW6t/E5aHfINzl1nzehWMTyCmW2f2h+y/vD8ChUYeu6vxNbBan1hyxFlQ0DfelR2wwG45n2cUG1xzK4L629joXLm4dnll6kJVJ1nWRcV2cy+C6uDziwny4kF9OkUaPl0qB2k1BoyBPUnJK0RiMuClknKqY4QLU8VZV29pSbzBBxey4Nu+xLg/9BlGoLbQ8Vnvo6NjYeebKoqOLrur8fjYNqF8aGG+3z9k0/Nlfkq7qdVzcWFbsv0DiK+vsjDnUTCHRfxGBClkEmYA5KmJWjTRLIBSXGyyNO8xkF1cfWz+WUcSx9CJEUUReiynDLoN+gyjWWTUx9mbuxSQ6L8t/a/dblz2ds0UQBJRy6YvTpoH9YqttDNDH3TVJu5WZvOSAQwvCKb1iHLr03Mp4edkXKC1cuJCJEycCkr64Wbdco9Fwxx13MHv2bMCqo960aVNatGjBO++8YyeS5Yzc1CM8NmwgsbGx9O/WkdnPPIVo0LJw4UKenvwUIOXim+V39+3awfABPbmrWzsaRsXw2WefUao1YBJFTpw4Qbdu3WjeogV3d2/PK9MnA7Bz2xZ8fX3tMm3Wr19fI5/Vv+evfotRrCsmwD2APE0es7bNIrs8m3EJ4wBYf9b+j5tSmEJjv8ZX/BpymYDeKKJW2XdRvzMhjE//TgZgyaMd+PXART79OxmN3mjxRlzc/Bys1KwC4I74EJ7qGXX9L+YGo9PpGDJkCK1bt7YYdFtRr6ysLIYPH05RUREvv/yy03NkZmYy6sEHWLx4MR06dEBnMPL1osXIDNaFUQ+lHL1JxGAykZOVyXNPjmf+F4uIS2hBfl4uEx8ait7dj649+/D0xCcZ/8REOvboR5FGz6ljkr6S2k1Oly5dWLlyZY1/Di6DfgPQGrVklWcR4x9DnkZqPrAnc4/FoE/ZNMVufG557lUZdDe5DI3eZBFKMpMQ4cuHw1uyYHMKDYM8Ca7QhZm57BDzbdT3LocV+y8weckB/NRK2jcM4LOHrAVsWoORUq2RDcezaBvpb9cxpjYwmkRKdQa7Tu7/RqYvTeJoepFFetnMhqm306iGyu2dkfH662iPHa/Rc6riYgl97rlrOofBYGDYsGFER0dbuhlVJjg4mAULFtC2bVtmz57ttFL6o48+YtSoUXTo0AGQQiyPjn7QbozkJEle/uJvvmD4Qw9xb5+u5JRqgUCemjmbT959k649+3Au7QIyryBLwkG/29tjMIrk1WK7PVfI5TqxLnUdq1NWA3DXirsAaOJvFb3fdmEbAAaTtc/h651fB+CTg584nC+jNIOEbxJou6gtepPzDJU59yTgVYVA0oDm4fw2sTPuSjkP3iY1xFi+/8IVvSdRFJlc0d+0oEzP2iOZ7DqTR+SMVWw9lcPYhbtp9eqfTPv5oJ38QE1RWK4ncsYqJi/eD8CsFYdoPnsdKdklvPL7UeJfXMNLvx6moEx3iTPdOpTpDCzZc97OmKe+eSeHZveuVWN+I6msh/7iiy/a7Z87dy5ubm6899571Z6nUaNGGI1GsrKcZ48dPnzYQeO8MoIgoNFLueTJJ4/T+bZ2yGQCwd7uKGQC8c0TST4p3fRGjHuCR+4fxBMPDeW7zz9GV1aCT0Um25YtW+zeU3Jy8uV8FJfE5aFfgm+OfMPbe94m1DOUB2IfYEzTMVXqoKQUpJCryaVtaFu77RqDhql/TwWgY3hHLpRIhrOy16036vn2qLXtVae6nQDJe6+s0Tx6zWjp3EYNacVpNPRt6HA9A1uEM7BF+CXfo0ph9eDP5pZe0pPOLtaSkl1CWr5j1/hXVkrTyhFf7rTbfqaSuNiVYDSJDrn1WoORFi+vA2DFgYu8d39Lftx1HoAe7/xtGffNjrOkF2qYPagpCrn0w7scZv92hN8OXuTXCZ2oV9Fz8nIQRZHcUp1dkcq+c/nsSM5lQvcrD4U89OVOdAYTSx7tgCiKfL0t1W7/9ooqUO/rMCu5Vk/6aqmsh75w4UJsdaA6d+7M9u3bOXnyJDExMU7OUHOU6awOl5dKYfebrOPtTn6Bdezdwx6k4+09SD2wnd9+/43bbvuWgwel9n6ukMsNQir8kTzi+Xvn0y+yH2FeYaxLXcf54vN4Kb14bedrtA5pzd5MSR/54MiDyATr5GfT+U2Wx12WWEv9Kxv0tt+3xShKd/8xTcfg6+Zr2Tfk9yEsG7TM8tx8UzA/rmzQc8tz8VB4oFZevjECOJ5RfEmD/sKKw3apkLYcvuC8TNpZteJvBy+iVsrxcJPTPMKX5OxS8st0dG8SbBlTrjMS9+IaAFY+2ZlmdaXPJCnNPtygNRhJrOfn0AQZYN3RTNYdzcTXQ8nBl3pX+95EUWTRP2ctTUO6zN3IR8Nb8dzyQ6yY0ImGQdV/No98u4f1x7LYPqMHAZ5uFJTpGfzxdgDGd23k0PW+OpKzS9hySmqCnFWhuDlv7QkA1kzu4lS29r9I165dGTVqFP369WPr1q2EhYU5HZeSkoJcLic4ONjp/qZNm7J3717uuuuuKl/LaKOml9DMfrxCLnD00EEax8RaFU0j/KBNHOPGPUyzZs04fLh2JaxdIZcrJF8rFW5M/Xsq7+17j9d2vgZgMeYgGX9bntn8jMN5YvxjCHS31zo3G3OASa0mIZdZPedT+ad4cNWD7M/a73CuAm2Bw7ZuP3Vj3DopJv978u+MXTuWzWmb2Z+132nWzMuDJGH+3JJLhycqG/OnekTRLtK5bMHLg5rSNaYOFwrKiZyxinfXSQbpk03JPPXjfsZ9u4cHv9jJiC93cfdH2xhTKTRzNs/q2Q/4n7WreonWYDfuqR/3OxjzVvX97J4XlusddKiLK+KbeaU6+szfTMOZq3nh1yN2Yyb8sI/Ccj3d397k9D0aTSK/HrhAVrGG9cek6XzHNzcQ+8IabnvjL8u4lOxSktIKnJ6jMquS0ulpM9N46bcjlOus348mt7AIVm0wZMgQpk2bRt++fSkoKHD4jmdnZ/PYY48xceLEKmfYEydO5JtvvmHnTuvsctmyZWRmZlqem2dCQV4qJkyYwMKFCy2zB11JIe+/PpvRj0vZMGvWrEGvl75fGRkZ5ObmUrdu3Rp7z85weejVUNkwA5wtOkt8YLyT0Va2XtjKfU3uq3bMyfyTJNRJ4PEWj+Ol9GLennl2+22NuZmknCRG/jGS/Q9JRn1ozFCWnlzKuSJ7RcV5u6VzHco5xO/Jv/PcVmmqvDtDMpavdXqNu6LsvZB7WtXlpd+OWBr8XglP95bWAsyNqnc935P5f55kxG0NKvpFChZ5gQ82nGZCjyjeWmO/uGabsaE1GDmdVcKdH2ylMhuPZ9E9Npi8ihvP6I6RLNyeytoj0o9OIRMwmETiwnz4YlRbNh7PYurPBy3H3/vZDhaOacvba0/g7a7kw42n+Wp0G8YutJdyblXfj7lDW9Dr3b/ttpvfY/Lr/ZHLpHhq7AtrLutz6vPeZgDCfd1ZPqGTXbOIyqw7av/d++NwBn8clrZ1aBTokj92wuOPP05mZiYDBw3kfz/+zxJ71+v1KBQKHnroIZ5++ukqjw8JCWHx4sVMmzaNrKwsZDIZXbt2pW/fvoAU6lmxYgWiCIIA//zzD4sWLeKRRx6huLgYURS5d9R4ut3RD4B169YxadIkS+u7efPmERoayvHjxy0xdDOzZs1i6NCh1/wZuPTQq6H30t6kl0oFGzJBZskVPzTqEAnfJFjGtQ9tz96svUxuNZm397xNv8h+zL19LiBN4Zt/KzWnVclVaI3WAgRzFajOqKP1IutizOIBi2kaKHnMm9M2M+GvCXbXNabZGL4+/DX3N7mfxScW253L9rqq4r6Y+3ihwwt224wmkcbPrWZyr2gm96o+Dmmr475k/G20r+iq9OfRTHan5jmIg+kMJmJm/WF5/tHwVkz4YV+V55/WO4a315202zakVQS/7EsD4NScfsxbe4IFm1M4+kofi1Y8wPv3J3JXotULKtboSZi9rtr344wzb/RHEASyijWczizhfH4Z03+xVu2+c28LXv79CEUaxxvguM4N+WLrGfo2DSW9SMOD7eo7FG51janDt2PbWZ5nFmno8fYmQn3dWfVUFzq/tYGcEh1rJneh73tb7I49+GJvfNXXJ5PHmR73zY7WoOV0wWkAVAoVnkpPwjydh2FqGvOCaU2l/7r00GsQszEHWHHXCuSC9EeqbDTf7f4u+x/az6imowD4I/UPjudJHmi5QVo4VMqUdsb8tjCr1oqbXMpC8VJ6sW7IOosxB+ga0ZUdD+ywe72vD38NwJCYIZZt68+uvyxjDrDs9DKHbeZFx/fWn7JsW3skg4PnC5jw/T5L6pUoinZpkO1tWuTdER/iVOmxsvaz2Zg/3z+Of2b25LeJnez2VzbmYK8dfza3lAWbUwBQuyl4tq81W6hyj1VvdyXP9Y/l94md8b8MI3hbowD2zupl8YCDvd3pGBVE/4QwS7cegKk/H7Qz5t0rOkg90K4+swbEk/rmnXz6UGt+ndCJ+9rW48wb/Rnc0nqj2Xwym7Zz1nMio5jFu87R/vW/KNUZSc4uJfaFNeSU6Kjr5+E0Tu5dA8VgJtGEzvjvyf6xRWeyvi+tQUteed51e213pfyG1nK4Qi6XwbYHtuHj5sPUNlOZu3uuZfsbXd5gQKMBTo+59/d7SRqZZCnxD1GHkFYieZhN/JvwQY8P7MZXp9ni5eY8HS02IJb3ur3H5E2THXLXVw9eTf9lkh7MqPhRhHlJHsqbu97EYDJQqC3EV+XrcE6AH3aeY0CLMB79zrou0DUmiMGtIkjLL7dIgCbW86vymiuzbkpXPtp4ml8PWJtrjOvSEEEQCPV154dx7fl+5zlWHbLeROcObU52sZYR7aV+jENbR7B0bxrDPvsHgAaB0oLvg+0bMHeNFJt3ZgDHd5UWnzdN606LVyRv/cwb/dl7Np995/J5ffVxBAHOvFF1Vx9vdyVHX5Gm3uawi5kGgWreu78lv+xNY3THSKfHC4LAu8MSqevvwf82SN5jdrHWEoZxxqJx7QHYOK0bF/LLKSzXcyanpEbEnR764yGSspPY/sB2vN3+XfF4jUFT7f61a9cyffp0u20NGzZk+fLlVRxx6+Ay6JeBj5tkJIZED7Ez6P4qf4exr3d+3RKzPp533JIjHuJpNej3NbkPD8WVaW181+87PBQeDP1dirO90eUNAIuhtuWrPl9Rz9sqtLUrYxc/DfwJkBZv/zz7J1subKnyZvTc8kM8t9z+BjP9l0NM/+UQw9tLOevzh7XgzoRLp0SaiQnx5v37W9oZdNs4cMeoIDpGBTHhYhHFGr2d52+5hr6xLN2bRm6FjoZZ0sAseyqXCdWGInzVSvbO6oWxIgW0TWQArRv4U6o1XpEW/eZnutN13kbub1uPN4c0t2wf29kxdbQyU3s3wcddyZzVxxz2/TOzJ0t2n2f++pPc2zrCklHTMMjzktk1V8Kvp38lKVsKAf2W/BsPxj14iSNuPLnluXi7eVtms9WO1eRWu79Pnz706dOnpi7tpuKSBl0QhK+AAUCWKIoOLXMEQegG/Aqcqdi0TBTFV2rwGm8ItuJZZtRKNQceOsCL21/kt+TfCFY7pj8NaDTAYtDvW2ldGA1Rh1geX2rB1BmJwYmAZKz9VH5E+0cDjqmPk1tNdsiDH9ZkmOXx3K5zabOoDYdzDjsY9D+ndOWO+VV7jIClpV1MiPdVtdH6c0pXJi0+wOxBTZ3ujw+vOhWvjre9DvW0PtZY/8nX+mG6jPWgylrWgiAw5Y4ry12uH6i+6h6dAI90bcRdLcNpN+cvu+2hvu5M6hXNw10a4qWqWV/rQNYBtEYtO9N38vmhzy3bj+YeZVXKKpILkhnVdFSVs7YbidFkJKM0g5zyHJoENKl2bLGuGKPJ6LC9VF+Kp7J2K5VvBi7nW7MQ+BD4tpoxW0RRdO7u1TB6ox6DaLhiD/dSbL+wneZ1mlvCG58e/BSAYA97oy2XyZndYTb3xtxrMaq2CILA4jsXc/+q++22h3hKBt2s5na1VDbWKrmKLnW7sOXCFnY/uBt3hTWG/OfQP1mbupbB0YMt2xQyBU2DmvLr6V+Z2mYqSpnVo40O8eaLkW0Y9620WP1Uz2gS6vryyLeOi9eBnlcn8h8d4s1qm7Z7V8qvEzqxPTmXcD93O7nS2urRWFsEe7vz9ei27D9fQK+4YLsbTU0b88zSTB764yGn+35L/o3fkn8D4PNDn5M0Mummy6ARkW7UtlXUzjCYDJaML0EQ7FIX8zR5LoMOIIriZkEQIq/DtVwWa8+uZeaWmUT6RPL7Pb/XyDnzNHk8uv5RAJJGJqE36Vl0TJKtXTxgscN4pVxp8Zid0cCngcM2s4fubN+18r8e/wMcUx1DPUMtC7W2ZJZmUqIvodV3rVg3ZJ1d2KZXfAhv39sCf7WSnnEhDseaCfK6MZ1wWtTzo8UVxO5vZrrHBtM91nmRS01yOMexmOWeqHs4nnecY3n2oZ9cTS5BHs6lnG8Utkqkp/JPoTPqCPEMcbjOE3knLI9Vcim7RS7IySrLokhbBP+upQKn1JRb00EQhIOCIPwhCILzuXQNYS7GSS1KteRVXy1J2UkczT3KxRJrXPel7S/ZpRDadha6XJwtYppnFM68+mtFLpM7zVuvClvPpfcvvVlxeoXd/qGtI5wac/OC3wsD4i0t7Vzc3OhNeiZvmmy37fv+3/NKp1dY2Hchdb3qMip+FDPazQCg+0/dWXNmDeeLz9+Aq3WO7ffVnJmTWZqJzqirUlraX+VPqGcoddR1EAQBd4U7pfpS8jX5HMk5YpdxdrkYTAZLOMdgMlCgKbjyNwNOQ0I1RU38KvcBDURRbAH8D1hR1UBBEMYLgrBHEIQ92dlX18eyQ3gHnmkjVV5uOLfhqs4B8NOJn3hw9YMMWzmMLWnWPN/lp60r3ffFXHms28yfQ/9k7RBrfnSHsA6EqEN4uNnDV33OmmLNEPtCmBe2vVDFSHu6xgRx8KXePHwZi38ubg5+OPaD5XHL4Jb0btCbZkHSUphaqWbNkDVMazuNoTHWopZnNj/D1E1Tr/u1VkWgXyAXzl2gdb3WDOk2hEGdBvHytJf5Y8cfRMdEU15ebvHin3rwKQ6uO0iAh3WR20vphcagIbUwlSUrlnBfr/to3qw5iS0TmfK0lB1mq6tuy4oVK2jevDkxsTHEN4vno0UfkVyQzJnCM6zetJp27dqRmJhIXFycRbZ34cKF1KlTx0586+jRo4AUMj6ed5y04rRa+ayuOVgnimKRzePVgiB8LAhCkCiKOU7GLgAWgFRYdLWvObLpSD5L+ozc8upXs6tCZ9Tx6j+vWp5/fPBjp+P83P2u6vwghTsAXurwEnmaPMK8wlh/b82I2F8rSrmS97q/x+SNky3bZm+fzeyOs52OP/ZKX37ac55uMcGuHpW3EPmafEvosGf9nrzX/b0qx6rkKn4a8JNlIf9Y3jF+OvETCUi1DUXaInYtO0vxxZrtPRtUz4su913eonS9yHr8sukXDAYDD9/zMGdOn6H3wN7MmTOHF2a/wF+r/0I0igwfPtzuOPM60aljp5gzcw4f//Axt7W4jfTidH7+9mfSS9PRGDR4itYYu96oZ+e+nUybNo2169ai8dGQdjaNR4Y+QkSDCJo0bcLzE5/nh8U/0KFNB4xGIydOWEM+w4YN48MPP3R4D8V6qbFNbcXzr9lDFwQhVKhYRREEoV3FOa/O0l4B8YHx/JH6h0Onn/Vn17PmTPWl2Laph2ZaBbey6JGbqSqt70oYGjOU8c3HX/N5apqe9Xuy+M7FPNbiMQB+OfVLlWM93OSM6hjpMua3EC/veJmuS7pa5Cve7fbuJY+JC4zjy95fUtdLKoB69Z9XyS3PJbM0k/PF5ynRlWISLx0uECv+qylsz+Xt5o1CoSCxbSLnz5xn7OSx/Pzzz2zfs533Xn2P+R/MlwYadGAObVR8bb/68CvGTx5Po+hG6E165HI594+5n7zyPIp0RZQZyiwhnDOFZ3hr3luMenIUGh8prz2iQQTjJo3j64+kwr68nDz0ntINTi6XEx9fvSQIWNcDzKnQNc3lpC3+CHQDggRBSANeApQAoih+CgwFHhcEwQCUA/eL10FPwPyl252xm/Zh7TlffN5SSAPwWdJnGEwGov2jeavrW3bZHM7SDUM9Q5nUahJfHPoCkBZDnUnS/ptoGtSUpkFNLRk9JtFkpxLp4tZBFEXKDeWolWqyyrJYenKp3f7L/bu2C2vHmiFr+N/+/7EgaQFao5accmmyHX2ntKoYHxhvyYQxmoyIiChkVlNyJOcICpnikimGl8IkmijRlViMrJvcjXre9SgoLmDn1p1MeHYCHmoP3n77bQbeMZCRj48krkkcJpMJWdYRtCpfVIGN8FZ6k1eex+ljp5k8ZTJucjfyNfkOr1esK+Zo7lG83bzRm/QkH09m9BOjLft9VD50vq0zi7+SEiUeeuwhBnYYSI/uPejbty+jRo2y6LYsWbKErVutWkQ/rPkBb09vy+dUW7+zS55VFMUHRFEME0VRKYpihCiKX4qi+GmFMUcUxQ9FUWwqimILURRvE0Vxe61caSUeipfSsMatG0fCNwl2xhzgdMFpUotS+fPsn+xMt9fmNhtt25L6nvV7AjCz3UziAuKID7j03fbfwnPtpbz5D/d/iN5Ys1PqmqJMX3ajL+Gm5ruj39H+h/akFKbwyg6pDOSeqHsAaBboUD5ySSYmTqSxr32Ng7+7VEiXXppOmb4Mk2jieN5xu+wSs/E1pxgW6Q3kaQqr7JlbHRmlGXaLsynJKbRs2ZIet/dgyKAhdOklpb+27t4aXz9f7h9zP25yN/QGacFTpS3EKIqolZ7EBcbhrnAn0CPQIuEBUM+7nkOlrG2/X1vqetVFJbemlz773LP8suEXet3Rix9++MEi4gVSyOXAgQPs37+f79d/j6gQKdIWUaIrAai11NBb1h1r5Nvossd+c+Qbu9Vws76KbTZKbEAsAMPjhvPTwJ9uulzc2sScOfD5oc/56vBXNXLOs0Vn6fVzLz7Y98FVG+MSXQlGk5EvD31J+x/a8/f5vx3G/G///5i0YdK1Xu4tjzlktuPiDv5Okz6nlzu+zKb7NvFV3yv/mwqCwIq7VxDuFU60fzRxgXGW1Nt8TT5nCs+gNVgzRY7kHKFQW2hpqQhgNJk4U67jvNbEsdxjXCi+cEUNz229aKXCg8aNG1uM5OzZsy21HfmafBBAqZBm4Uad9ft2oqSc1HIdMkFm0Ts3HwdSXUZ9n/pO60MSExLJOZVDXGAcMf4xyAQZx5KOER0XTX2f+ijlSsLqh9Htvm78vOpnDh48SHZONiW6EssNTGO0lyHQGXW1mg9/yxp0QRB4tdOrdtt6N+jN+Objmdxqst32f9L/YczaMXbbKuewmhcx/4vc2cha9bgn89oVMM8UnmHA8gFklmXy+aHP+fCA4+LQpdAZdXT4sQPz987nQPYBAA5kH+Bs0VnrtWbsYUHSAjac30BKQYple4GmwGnu9b8Zc0jxzV1ST82nWj6FIAgEegRecxGem9wNmSBzSI1NKUyxe55WnGYnOX0iv0LoTZC82gJtgYOBq4pSvX2Hq3IhCH2lm0Hl37DRZMRgEikrtzZZkRu1FBukWPozzzzD66+/TlqKlGFiMpn4+vOvLecKUYcQ5RdFQ9+GNPZrzLPPPMvbc9/m3NlzKOVKUlNTefONN3l55st4u3mz86+dlhvUP0n/gAw0Sg2F2kKLnoxtSrSZK206cyXc0loud0fdjVyQo5QpERHpG9nX4lnX865Hh/AOvL3nbZadWsbezL0cyDpAizotUMqUDGo8CIAN927geN7xy9KI+LcS5BHEsCbDWHJiCf+k/0NueS6BHo5aKpdDniaPQSsG2W0zx2CvBHPLvl9O/UKfSEl344tDX/DFoS9Ydc8qgjyC7G7SZwrPsOzUMr45+o1l2+I7F3O++Dy9GvSyi/He6vyW/Bv5mnxGNR3F+aLz9F/e32FM57qda+W1A9wD7Lzw6jA5WRgt1BZWeYMp1BZiFI34q/xJLUwFwGg04uEujTc68e5jA2ItyqYgcKSknDhDiWV/qDabVI8IAJo3b857773H8OHDKS0rRSbIGDBASnwQBIE5c+bw/vvvW45NS0vjrbfeYuDAgej1epRKJXPnzrXomC/+YTF7p+1F7iZHrpDz1idvUaSXbiYrflnBzh07LTnnn3/6OYFxgYiiWKtiaP96PfQyfRkfHfjI0qtz5T0rGbB8AJNbTebhhBufE34zYSu/W536Y3XsTN9p6ZQ0q/0sS0en7/p9V211LcBjfz5GtH80vipf3t8n/bAifSJJLUq1Gze7w2yaBDThgVUPXNY1TWo1ySGD6VZlb+ZeSz/Zr/p8xdi1Y52O2zl8Z414gpX1uHVGHQXaArQGLQbRQJBHEB4KDzLLMh0KbUwyL0wKqTAvzlPBqQqPvWmQ89rDIzlHHLaVni3liccm8uVfmwBo4eP8PeVr8snRyYkukZotp7sFEaaTHIlzqlAiAkKR1VIY9Xje8WqLhfzd/Qn3CsckmjCJpityLlx66JVQK9U8kfiE5fmA5dId+WYUIbrRbLxvo+Xx1RY+mOO33/X7jmGxw/BWSt7ItL+nVXucKIpsu7iNhUcWWow54GDMQercZJ7a/zTgp0te06Hsq7s53QzojXrLQvXmtM0WYw7YGfPpbaeTNDKJXwb9wqFRh2ptWu8mdyNYHUw9n3o09G0opRHKFNT1qou3mzf+7v40CWhCXGAcJoXNLE9Q4KGsOvTjbNF045KNjH5oNC++8rLd9kK9Ab3Jfry/uz8qgzVMUya3ahrV12ZwqNgaV68JJ7bcaCJNI1WqVlZdDfQItCwgA5bewDJBhkKmqJHXr4p/zzy0GjyVng6enjPp2/86QR5BPJH4BB8f+Jh+y/rxeufXaV6neZX6M2X6MgRBQGPQWL7A3x39DoAWdaSGFOZCisyyTLtjRVHkowMf0bluZxKDEy95A/nsjs9QK9T8b///OJV/imWnlmFQhKJ0C2Zgo4H8niLp+oR7hrNkwBK7Ztzm7jW3GkaTkVaLWgGSvv3mNOdKmHtH7LWEDGP8a7frfXXU96lv91wQZJhtV5pGb1l8N3viMf4xKOVKcstznbZ7nDhhIhMnTKRIb+BMuXTswSLJMLvLBULdlHgr5MgEAZMoEqCXFFK/XvIr8xcuB6MOWcWN4ra2rfh04Y+ka3Rk6Qw0VqvwUlxZIwqDSUQQQC4InCyVYuSBSgUhniH4u/sjIKCUKyvGGiyLurY3MlEUSSstxkulxl9Z8+b3Xx9ysSWlIIW7fpV6adq2eXNhRW/S0+q7VpbnDXwasPKelQ7jbEMrAPtG7EMpV9Lz5554K71ZcfcKQCr0mrJpCgICSaOsbdje3fuupfNSt3rd2J2x224hzNvNm4a+DUnKTqJtaFu+6iNlary16y1L9WN2/e9QCgKf1D/PzC0zAfjt7t9o6NuQMn0ZIiLfHv2Wjw98jKfSk3+G/1NDn9LVIYoivX/pTde6XXk88fFqRbDK9GW0/6G9031BHkE8GPcg7+97n3duf4fekb1r65KvqQWd2fiaqe+m52KJ/Y07WB1MVlmW5XmEdwQm0YS7woMioxwBqU/s+XLn3ZWCVQrCVG5ojSZUmQcRASG8JQBHCouoq83Ez1CCERnG0ASOlUiGONhNQZh71etmepNImdGIb4XR1ZlMlmMbqVWklGmRi0ZkMoF474psOb0GFCqp4WglNEYTAmAwGVFlH0Xv7o9HQH2HcZVxhVyqoZGfNdUxyP3mUpS7WVDKlDza/FHL87NFZ53qeuzP2m/3vPvP3Un4JoGssiy7WHmvBr24N+ZeRERG/TGKbRe2AZJcsZlN5zdZjPmWYVs4NOoQ2x/Yzhe9v+D59s/zRe8vLGNtp7IAelG0a+Zhnk2olWo8lZ4WLfhSfSk/n/zZ/liTnr/P/12rU2BbNp7fSEZpBj+d/InuP3Wnx089OJgtNbDOKc/hfJE15/rB1c6bTjzb9lk23reRcQnjODTqUK0a82vFr1IrNq3gGHKxNeYg6a74u/tzssxEhlZPulZfpTEH0FY0t9Vrpe+Pweb7EePthXedKPQyFXJMHCspt+wTBNCaTBgqjhdFyYBbKkXLtaSW6yg2GCk3GjlZak3RTCmTHjcrPU188SnIPgGaQsg+BuWVCpYMOri4n6yCTE6UlqErOIcCE26el99Q5Ur4Txl0sKZ32Yr3uLDHds0BYN3ZdaxKWWWZQhbpitiVsctujG1DkMqr+ObK3H1Z+3hs/WMU64odfsggKVLa6ud4KDy4P/Z+u6q6IdFDHI5rGtgUX5UvD8Q+4FCBF+AewOimowF4/Z/X7fYtPLyQiRsm2jV8qE0qh36yy7MZsXoEAH2W9qH/8v7sTN9JWnGaZezXfb6mU7jUc/XQqEOWgrpbAQ+Z/d9Cb7LeOM03ZtsiH3f3epws05FU7Fi34GEqx0MwEiQXaVFygkC99F0s1BtBU4SX+bP1tKqjKmUy5IKA6CatJ7QoOYkMk+VajpdoOFJh5IuNJk6VakkqLidXZ6DcKI1LKdNyslRrl2HjbSihRYm1mAp9GeRVpHCWZIJBiznWJGZL8sT1tRk0LzmFf0UWjFzlvK3ktfKfiKHbsmTAEvZm7rWTAnBhj0yQ8XiLx/nk4CeWbTO2zCDaP5plg5bx1q63qpUutlXuAxjVdBQfHfjI8rzjjx0BeLT5o3yW9BkgiZiZ0xOrI9AjkJ3Dd6I16ojbkQpIxSFb799a5TFT20zFKBr54dgP5Gvy0Rl1FOmKOJIrxXLzNfk8+deTbErbxMp7VtaKZj045m2bsc0uGrdunKVo7vXOr9MmtA2tQ1pfVaXljcZsAiPc3UjT6MjTG4n0ro8oGpEJMvI1+RhFI55KTxr4NCCpuNzmKClOrjFKz2PKztmdO1ybg05QEqm5aHeM0snCq5tvXahwRuJLkznsGU2e3pqVklqulW4MFaRpdLiZdOhkSsxCMAImGst0KMtzcdOXUCUGDWQdpcyvEW7u3iic/N3KVAHUVib6f86gR/tH14om+b+NJxKf4MG4B+m82JrPbE47K9AWWLYljUyi+bfN7Y6tbBA9FB48FP8QR3OPsjfT2nhab9Lz97C/cZO5VdkI2xlqpRpRdmUdkwY1HsR3R7/jnT3v8Gvyr3b7zDF5kLKgFvVfZFnUrQnm7Z7H3sy9HMk9QnxgPEsGLAGkcJY568qWlMIUPJWeDGw8EJBypG092VsFs5kNUMpJq6gnEmUeeMnlFBms3YeKRQ+OlzoWHMWo3dEVnEflRFVVpnCjkeaC5blWUHJOXY9omZOgg9zqvMlFEy1KTnDIKwoTcuSiEa+STPQKb8rkakDEz1BMA43UrDzTqwF5JhlxZWfsTmmUuSHzCeWwQUVCifS7KJOpUJukcIy6IIVSmTsKIEfph5ebilJtOb6GUgxeVTeOuVb+cyGXWwJNIWir8QKuE74qXzu9G8t2N2vKpyAI7Buxj53Dd9Kzfk/6N3QscgEp9ruw70JeuM2qvd46pDUB7gFXZMzNlBmvzGM1SztUNubOGLF6BAnfJDB391yO5B6xSEVcKbnluaxOWc23R7+1zAZsQ0YNfBpwR4M7LM/3jLAmCdRWYdD1xeo5h6oko3pOo+NoaTnnNXqMylBMMg9MMm90FeEYs6BnhLsbQmkOqvJchLqtGPHk89IOpQcGUU6duI4MGPkUAG8sWMKwZ+dSJkiv8f3333PnnVL18+zZs1Gr1WTJrQ3NvaI7EayViqPqadIJ0hcQXX6eGIWeFiUnUZ07xP2Pz6Bxx0H079yJqUMGcDL5LKnnL9Ksx70AyNUBCOpAYr3U7MiFQWOeJq7TQBp3HMSkF+eh0+nxNGkoKy/n8YnTadu5D7f3uIuu9z6OTC/dzORyuZ1m+ptvvnnNn/h/zkO/YRScBzdPOLkGlGpoerfzcaW5MK+RNOb59Ot6ic6obGxNoskiXvRJLykko5QrLRrrl6JT3U6Wx10jul71dV2pQQeI8ouyxKbvjbmXc8XniPWPtasuteW7o99Z0jAVgoJVg1cR7hXudCxI6Xj3r7qfAPcAVg9eTbefujmMqRyOGtR4EH+e/ROQNMmTRibxd9rfN6VB37hwAVlnnYeNnKEziehMIscr+r2WGU2IInb1o971Imly/2gAfBVyItUVMy+DFoqkjBhPtQeHjydT7tcED7WaP5cvpm6oVTF15ORnWdi1G0d3/UODNq2YNWsWf/1lbcAdFBTEO+++y1uTrH1+jYKcAH0BvkZrZpVHQQqiKHLPw1MZde9AFn8iGdiDR06SmZNLvYh6krcfmgAVMyaFIPDYmPEMHvswr44YibuumLlPPsrzb33InBef4aWFqwgLq8vPS6R6iRMnTqBUSjceDw8PDhw4cNmf5+XgMujXi/eagZs3mJXcgndDHSc5w39X3KX1ZdLCyk0mEvb+vvfZlLYJuDovsqby/0uuwqD/eOePtP2+LZE+kbzY4UXL9syyTJoGNqVXg14U6grZn7mft3a/ZXesQTTQ55c+Tito92TsoVlQM0tj8DxNHrf9cJvDuEGNBzks2t4ecTuf9vqUdqHtAGnG061etyt+b1eK1mSi1GgioBZyoatCKQjoRSMyUcRYKYQUolJavHhAikWbEQT6D7yHVatXM3ToUH5cvpoH7u7Llp37ICyRuoLA/z78kGeeeoqN7doxduxYGjWyZrSNHTuWhQsXMn3qZAIqeuGG65x3TNu4bTdKpYLHHh4JXiGQf4YWTaXfaWq5t2TIbSo9N2zYgLeHB3ePGEkjtQpRreLtT78hqlEjnn3rIzQFRURGRlrGN2lybZLCl8Jl0K8ntrKcH7WFF/NBJoOL+8GnLngFQ55NrC51CzS8ei+2NrhWNUZzBePtEbdf03lsU9AuF3eFO7se3OUQj553+zzL4wgiaBrYlEjfSB5f/7jDOcr0ZXZVmPsy9zkIv1VmRrsZDI4ejLtN9aIZQRDsZi3Xg2KDkegt0o0pqWNTglWXlyDQffSVNWq5qNGRq9OTYMoD71DKkKHOlGoRdCo/jimDMS862hlzUbRmjYQ0AwTuHz6cV155hQEDBpCUlMTYh95gy/4TFoend9eufB0Xx/r16zl2zL7xtZeXF2PHjuX9jz7l5Zftq04BCIyGXCkOfvhEMq3bd4KgGDA381CqpewZXZHDoUeOHKFtm9b2kgS+vtSvX5/01DM8/PDD9O7dm6VLl9KzZ09GjRpFdLS0hldeXm7RhQGYOXMmw4YNu8xP1zmuGPr1wEZm1I69X0tf3gXd4POesONjOP2ndf83A2Hj686PvY7Maj/LYZu5qfDVcGjUIT7seeUKjLbYhlzOa6rOU66Mh8LjsoTYOtftzLYHtvFo80fZ/eBu3uoieeynCk7Zjdt6wT67pl/DftwddTcAa4es5c+hfzI8djgeCo+bQpK5QG+wGHOA9XlFlBprr2mxv74IynIg8zAeuSct2920BbQoOYnctgOSySj9Hmy984oFzebNm5OamsqPP/5I//79Qelh5ymXlJSwZ88e9Ho9zvoVP/XUU3zzzTcUFxeD7SzJO0wKhbp5g0+49FymkG4UMgUEx0kGX311ac6JiYmkpKTwzDPPkJeXR9u2bS03HHPIxfzvWo05uAz69SH7hPPtq56G0xWxvsJzsHamdZ+iwpv7+y3H464zw2KH8efQPy0LiwDDY4dXc0TtU2iwGoK2O46yv6jmG2D4uPkwseVE3BXulsydEatHIIoiBpOBubvnWnLYWwa3JDYglufbP8+rnV7l0KhDhHuFE+oZelMY8t+zClicnkvsVntZ4aePn6fx5kOEbjzA08fPVXH01SECXjYxasHgmMniaSyTvHN9OWQkQcYhKdwIUMe+QnLQoEFMmzaNBx5wFGV76aWXGDFiBM8//zxTpkxx2O/n58fw4cP56KOK9Fnz2pBcKRnvoCjwCqFpQnP27rVmYqFwl2bRVRAfH28/HigqKuLcuXNERUUB0gxh8ODBfPzxx4wYMYLVq1dXeb5rxWXQrweVVOjsSNvlfLvtl3+2Lxxa6nzcdSLUM5R3u73L0JihbH9g+w03UsUGe6+y396TVYysGeICrcal+bfNafldS8uCKcC3/b7l54E/35Sib0ZR5JEjqUw+bq1E9ZI7/vR/SM8jV2dw2H4t+BmcdP+xqQFpqJIRolJCUUUKomiEgoobi8I+NXXs2LG89NJLJCQk2G0/dOgQq1atYvr06YwfP57U1FT+/PNPKvP000/z2WefYTAYIDBK+udhv6bTo0cPtFotCxYssGxLSkpiy5YtTt9fz549KSsr49tvJTVXo9HI1KlTGT16NGq1mm3btpGfL+XA63Q6jh49SoMGtVPnAC6Dfn3QVyPqX+QogM+M847bfrnxUr/1vOvxUoeXalXP+XIpuopF0WtBJsj4696/nO77c6ij8biZqBySeqFxOLs7xLOujbTY19DDjXa+Uhed985m8H5qJoedVGteKXaNopUe4O4HAY0htBmEVeT5F6ZJYRatE8NfyWmIiIjgqaeesn8NUeTxxx9n/vz5uLu7I5PJ+OSTT5g0aRI6nf37DgoK4p577kGr1UrnVnnbh1+Q1jSWL1/O+vXrady4MU2bNmXmzJmEhkoNcE6cOEFERITl39KlS1m+fDk///wz0dHRxMTE4O7uzuuvS6HS5ORkbr/9dhISEmjZsiVt2rRhyBApddUcQzf/mzHj6sOYluv/L4lz3TCO/gY/XWbJ9h2vQKdJsGgInF5vv++lgpsu6+VG8cTRs+wtLGVC/WCePSmlt2V0T6z1191+YTuPrrdq3ex6cNc1dwSqbR4/ksryrALL81NdEvCuUBrcW1hKgrcHJhEiNyfZHXe4UzOyk08RFxeH1mSS+nPKL13gVGIwIhcE8rQa6hacsC74V+aivR4QMgVU9CIlON7BQ/8v4hLnuhnR2bfTYupJcK80NY/uA/F3Q+vR0vPhP8OsSnonmkJcSBQZjPgq5Ciu8w2uY92OHHjoAMEewcxsN/OmNuaHi8t4MyXdYsy3tY9ld4d4izEHaO3riZtMhrtcxoqWUXbHN9t2mBydgTydgeMlGk6Vah2yi0RRdBA3Sy7TcrJUg8xsnKtq6OBX3/F5WKLkvbuM+VXhSlu8HmTbp1HhHQKhzaW0RDMPVmrUIJOBTAV3fwIrKtLnijPAw69WL/VWodhgxFshd9LkrPaRy+T8dZ/z8MvNwu27jnPCppxeBjTyUFW79nGbnxcb2jbhr9wi5qRIRW3lJpNdyEZnEsnS6glQKpAJcKhYMvAxnu54yGUW9UKA0KLkihevwsyoA6G8ALQV6YBKj4oZaO3fpHNzc+nZs6fD9r/++ovAwKtrv3gz4DLotY0owrb3Hbff9y3s+Ai2vF398YnDoU4T+LwHrH8J+r8NfvWqP+ZfSmq5lsXpefQJ8iVNoyPRR83dwX5MPXGeOm6ur7KZNI3OzpgDnLu9xWUtZMd7eRDv5cFj9YJJ2HYYKtQJlTKBaLWKoyUa0itkbRuprV70yUqvpzaWW81yddIOgY3BqJNSe69jX9/AwMAar9K8GXCFXGqbgkqpYAEVFWzqAOhWkabYqHv15wiuaMRxco1UcWom/ywcXFIz13kTszW/mNCNB7jtn2O8dzaTfntPckGrp5WPJ54KOb0CfQi7zOKYfytb84u5Y/cJDCaRr9KkXppvxkjNkT1kAgrZlXm9SpnA8S4J1HN3I8HbgzhPd5Qymd0SjlkXvDIqk9YibnXMs2G1aX+AZMhVN36h/d+Ay62pbUyV0sDU1ulcqShQ9lwOddwuYYyUjhWGnNsJX1U0N6jfHvwjr+06b2IePXLW6fYELyl+LRek1LwMrZ4DRWUk+qg5VaqhS8B/x0hMOnaOC1o9Cy/m8PF5ae3lofBA2vt6EnSNsxfb5sr13N04V6nhRIhKQabW/D0XiS1LtewzyK6f1+3CZdBrH/OCaPxdcPRXO8Pbd+9JTpVpryw7o3FF3O8rm041Bef/1QY9V+88N7p1Rbm1QhAo0BsZtO8U52zivXVVSvZ2/Pe1GRRFEa1JxF0uI12ro9X2o5a1hFmnpHzu8RF1kAsCcV41u2jrr1Tgr1RQajCiMYnIBGlbqFwk3QB+xdaU2yKvCOI8b95F438jrpBLbWM26K1Hw92fwoD5ll2nKqasl5U6+mjFAmpxhqTIaEuZo170v4Vsnd7yOMCmpdnf7WLxrMjWiFa7c0GrtzPmABe0enrtPsG6nH9PdlCh3sAde04SuTmJYoORljbG3JaHI2q3xaKnQk6gmwJ/OVCWB1lHCcs7ikdFU3BCEvDxqXPFoR4X14bLoNc2ZoPu5gWJDziNFVaWgr2o0dkZMgDCmkPiCMg6An9WaIp3fabiBDk1fdU3DVkVlYtxnu4c7GhdP2jiaQ1DRamrTnE7XFJuydi4FVmXU8jOAkkb/3hpOU22HuZwRergQ0n2UravRIXjo5DxVP1gGnhcIu3v4n5JO+ha6lByTkml+gWVQmIKd5DX3ORfEASmTrX2tX377beZPXs2AH369LErzgkPD6d9e6m59ujRo2nYsCEtWrQgJiaGkSNHkpaW5uwlLJSUlPDoo4/SuHFjWrduTbdu3di5cycglfBXprCwkJEjRxIVFUXjxo0ZOXIkhYWSA2EymXjqqado1qwZCQkJtG3bljNnJPG9yMhIEhISLNdduWDqarn1Qi66Uik7xDMIWo+5+QttKprGfpGjw0+Zx9BQSeTHNguh8ZZDrGwVTZuKar1WO44CcPb25sgQUJq9HHN614Hvpf93fRY2z4NVU6HtuOvwZq4vW/KK2VUo3RA/jm+AUiZwuksCHpXK1jv5O/7QPm8aySNHUgGpUrLYYMRDJrPzGJOKy+i95yRr28TQwru2moJdPZlaPSMPSQYguWsCW/Lsm578U/HZnOqSwJlyLc291Yyv56SAB+CzrqDygYR7YeVkMLdGq98e6ra+rOsp+D0Z3cVSQJQyU4xViKK5yYEk5/sqDw33xG9g42rHqFQqli1bxsyZMwkKsp95rF271vK4tLSU1q1b89prr1m2zZs3j6FDhyKKIu+99x49evTg8OHDuLk5j+2PGzeOhg0bcurUKWQyGWfOnOHo0aNVXtvDDz9Ms2bNLKX/L730EuPGjePnn39myZIlXLx4kaSkJGQyGWlpaXh6elqO3bhxo8P7uVZuPYN+ZAVsnCM91pZAp5q5s9UahVKWy+u5csoKzlkM+qfn7YuG/sgppI2vp10z2gZ/J9HaR82q1hW66fd8Bsd+sx6ksPlSbnhNMu7e4dB+PHR2FCi62SnUG/BWyJEJAhqjiXsPJlv2Na7wwr0UjpWKYSo3Poirz66CUp5tGIpaLsNLIefp0hD+dzaLMqOJ6C2HeCg8kHlN6qE3iYiIfHdRClV9fj6bYDclH5/P4lCnppdepL4G1ucW4SOXcbC4nIZqFb0CfQB44VQa63KK2H5bHHJBIFunp8X2I9b3v9lRhx3gzjq+eCvkNK/uhpSyCdIPSo9TK2mSfN4DmtwpaQfd/TF4hzo/h8kk3QREo1U8y4zCXSqhr6U2eQqFgvHjxzN//nzmzJlT5bhJkybRv39/7rjjDod9giAwZcoUli9fzh9//MFdd93lMCY5OZmdO3fy/fffI6vIzGnYsCENGzZ0+nqnT59m7969LFlizTR78cUXiYqKIjk5mfT0dMLCwiznioiIuKL3fTXcegY9cTj8WtGV/s8XbgGDnoboEUCZ3H5xKLBSYwFz2OXd1Ay77XuLyhBFUcohdrP50YZUhB96zLIac4Dii7B+Nig8ICgaohyLJ25G9CaRJlsPMyIskLdj67El317bw+0SqW/3hQZwX6i9xOmzDcMYFR5kMYzfXcxlbkwE9f4+aDduaWa+5XHCtiM08lCxJLExXnIZ/jXQAGJrfjEZWj13BfszolKYZFpkKFFqFZ9XpBp+eDaLSZEhzDhZdWjgtei63B3szx85BTwYdokiGE0hfOtovABrqf2JVdLzd5qAf0PJuLcaaU2rNegg6wh+lmLziu+hm5eUhiur/X6nEyZMoHnz5jz77LNO9y9btow9e/ZYwiNV0apVK44fP+7UoB85coTExETklyFvAHD06FGH8ea2ckeOHOG+++6jc+fObNmyhZ49ezJixAhatmxpGdu9e3fLsaNGjXKqEnml3HoGXRBgdiG8EycZr6zj4FtXMmDmuF1ZHqTtgZje1Z+rNinLk1QWC85j9HG8M2tM9nHzry/kIAO+vOAYD/8jp5D+dfzsth19aD1FBSWUB9yG0yz2NdOl/086CB+2hb5vQtsqBL70GulHKb9xudw5emnNYFF6Lq9E12WKjTLglAZX31S3csFR2KaDVYy0klKupW1F2MtNEAhQKnihcRhDKt0wLoXOZCKlXMvQA9JMo/KiLcDblW7gb5xJp4u/l6VQ54umkYyrCB3tvC3OLjb+UPhlTNf/esX6+IUcaXZ72wRQeUmVmbMrSVDkVzRY+fstSQEx6nFp3aYySrWUWXUdjDmAj48PI0eO5IMPPsDDw945unDhApMmTWLt2rWoVNWvHVxP7aqIiAhOnDjBhg0b2LBhAz179uTnn3+2VKi6Qi629Jgleeoft7duezFf8jbWzITC83DPAmhx7aLxl01ZnhSfTLgXloyQtqkDKQ9vZzdsS14xX6Q5Gm5nxhxg7OFUvmwWSf8gX4T7f4CMw/TYLWmsy0Q1TvQarSwbL8U6Vz0tSfAO/RI2vw2xd0re++m/YNFgUPnCzGr0sD+6TfosO0+R3mf6AWjcw7q/OFMyBvWl1msmUeon6V4p3v31hRz0JhPj6wWjNZlQVXje2Tayrf32niRHb6CbvzduMoGnrsKg640mlHIZMkEgo3sioijaGXPZxTJEtQKh1ICpruRxTosMdTCwOlEkQ6dnwrFzzDiZxqmuzat8zX1FpfTfewo3QeCbhIY8UMkbn3smAw+ZwIKmkcR6eXD/gWSSy63FOS3V7uwv09B/n9REo0eANwOC/VjuFsXxUs2lFzotb74cfnsSso5BZoX++fOZ0g2712z7sTMq/uYGLfzxrCSk5e4rGf79iyB4EPhXxOXDEiVVRJlcagpxnZk8eTKtWrVizBhrhyhRFBk1ahQzZswgPj7+kufYv3+/05J/gKZNm3Lw4EGMRuNleenx8fEcOHAAk8lkCauYTCYOHDhguRaVSkW/fv3o168fISEhrFixosrXrwlu3SyXlg86bvuhwpAWVnh3y8fDuhccx9UW296Xcs3NxhygLJcim8bCyzLz7WLDzngtui51VUo2t7M2lHj4cCpRWw6xvk4X6Dbdst0kyNk0dh80HwbTTsOsbCmObua8zRT03HZ4Nw72fCkZ8bI86f8A2kLJWyvOdLyg3GRJj2b9bKmD0jtN4Lt7YM/XYDRI0/p3YuCrPuiNJmacTCN800EiNyeRpbVm65wr1zLzZBovnr7I0ow8GvydxP/OZjJ0/2nGHLK23jMvGHcL8Obb5o0cFkGrxGSCU+uZv+440c//wZxV1sUsQRB4JlKKDwuFOtwO5aPamY3b4Xxk2Ro6+nkxrWEoGd0TyeieyJzoug6nLzaaKDMY+WjjaX7cZb35iaJIscFI/72SIdaJooMxNxPhpuSOIF9Ony2g/M803HZJ3XVkmeUcW27/vegR6AMmEx22vsAYjU0M3WiQjHVVnbBeD4dDP1uNOTgvTgPJeLv7SmqI9y6EPnPg9mdh5G/240KaSbNjd58bYswBAgICuO+++/jyyy8t295++23c3d2ZMGFCtceKosgHH3xAeno6ffv2dTqmcePGtGnThpdeesniyaemprJq1Sqn46OiomjZsqXdIuxrr71Gq1atiIqKYt++fVy8KLlbJpOJpKSkWtVCh1vZoIOkawLQoqKDSWW5WYDtHzg3UrWBk44sAAuKrT+mJ47ap3i9F1uPP8yLnsC4iCDGRdRhb8emxHja/whLjSbePpPB2XL7H/I5uS8MXgBedcg1yXiu9y/EdFp56eud62SxZ/cX9s9PrYf/tbI+//sta3bDysmwbBzMs6r0fbfmIxbazDTa7DhKulYaP9LGaE88JhnEOSnpbC0o4YLWPk2zlY+a0WGBiKLIu+tO0OGNvzifZ12Me3fdCWb/doRynZHIGauInLEKcecn8P0QTm+SsoC+2XISZvsy7/lHyCvV8b/P9qJaewG3f7J5vFtjts3oQaCnG277ctm35AQ7kq35/A9H1OFIp2Y8FhFEcpcEXqi4GTT53ybmrT3BzGWHSM4u4Vy5lrBNB+3aupmZ3SiMVt4efBJTn8WxkQCkbk/nk03JjPl6N3mlOmT5OlQb0nE7kAeAMkn6/9AQfx6uGyT1nt3zFXx3t5ThlXMKPukAH98GH1TEY0VR6oo121f6J9qE88JbwjPOby7V0uh2mHJUapQclnhDw3G2TJ06lZwc6/dr1qxZHDt2zC51sXt3axDymWeesaQt7t69m40bN1aZ4QLwxRdfkJmZSVRUFM2aNWP06NEEB0szlLKyMjst9HfffZcvv/ySkydP0rhxYxo3bszJkyctN5ysrCwGDhxIs2bNaN68OQqFgokTJ1peq3v37pZrHjlyZI18Pv8ePfQdH8Ha56zPB7wnGfO8FHhwKUQ7rnzbUZoD5/6BuAF2m7N1evYXldE7yEknGqNBmn6aUye/GQhnNkuPW4+GvQsBGBf/MivrdHM4XC7AhW6JgNT0uPvuE2xvH2cnejTt+HkWpTsvHBpQx5eV2VLOa3o3qWGAbVjhlwOT6FR4AIBkjwgal0sLbYf9EmhWYGOARv5qXTiLHQD3f2/dt342bLUWQ10OG/zbMSP6aVQmLac8IwE4f3sLh8XI6pgoePLFGuddiJ7o1piPNznOcg42+wnf0ytYY2zLY/opdJPtZ6GbtQH0p4YBvGkYzpx7mvFge8lT0hlMxMz6wzImKtiLeUObk1jPD0FbDO8lcME7gYdzBlDaOpRTnpEoThQiy9Oi6+CYIjjc3YulG1IQtEYEXdVNODwpp7XsJHOmPEG9ivWRTm9u4EJBOZ802U6/s5fZc3XAe9KNtTJPH5N6ZF4CrcHIE4v2MbV3E+LDfRz2O9PjdiGFFPNLdfir3ZBdonjKaBIp0erxcVdecaevK9VDv6RBFwThK2AAkCWKYjMn+wXgfaA/UAaMFkVx36UutFYaXOQmw64FUnxd5S3F+95tCk36weDPHMebjJLHExxrXRx6cp+kAAdg0HHwvU4E6gvIaXo/Le54GsGglVK7so5L8fsuUyGyM4QkwNtRUvz8ns8kQ7+gG1zcz23tvifVw3Fh9GSXBHycpOHZojOZ2FtUxj37TzvsS+/WwmLAbfOuzez+Zxj1tBkkqxvQqe23tPFRs6ei92bqwTG4F1R4bi/mQfZx+HWCVHDi7isVMfV9XQqxmPuaevhb8uovl+PqhqSrguiev5thCW9zoE57Cp10G3otui6tvNVEebqTXKblntc3XtHreFHGYXdrLv6hB/aQ8KPT7zw8td8qkgb8fvAiT/5o32yhR5M6fHW+P5jsZw5Dms/nrEc4ae726X3xSYXc2asRH3yXdFnir6nuNj1Z73gVTq2D1C08onuaz93etR/c80X7xU2QKo5XVsqK8AqRVDzDWkgLntVgMok8/dMBVhywrsD8OqETYb7u7EjJZcnu83w5qi2pySeJjY2lTGdEKRfQ6E3IZQJqN/kNb0N4ozCaTBy5KNWEBPu4E+pTRTgLyCvVkpYvFYIFeqoI9XXndFYJEf4eeKouvYRZGwa9K1ACfFuFQe8PPIlk0NsD74ui2L7yuMpct45FH3eArKNSZowteg3MqVhsG/g+/D5JetxuPCQtgaAmbGo+gW6rRzmec/DnsOIJhx87APd9B/GDpMeiyJGiYnruS6GuSmkXVrgn2I9PmkZe9ts4r9GhEgSaV6TgzY+txwNhgewsKOGuSsb+ofBA5sZE0Hn173xw/A2W3fEFc5pLmiZvpqTz3tlM5KKBC5t7clzdkLX3rWVSZAjknYEPEq0niukrKTyCNO1+ZCO8UtGD8aUC6Yb4qpQ216/lJ2hkKjbuHVvt+9CISoYlvsNOvxasbhVtWQC01bNZ9M9ZZq2wxn+f6dOEMZ0i2ZmSx+9JF1m274LdOecPa8HCn37hV9WLl/FJVjDjvBQPrkAURXrP38ypLKl4p5mQwkrVrCoPj+jyFwaZAsXRAuRppQg2P6MmId78OrETbnIZMpnAmsMZRAapiQ31obCklE+W/8mMZCffq6qYuFe6mc5rJIVQus+C6F6wdCwc/gXu+kgKuzToaHVGLsHLvx/h622pDttDfdzJKLKGDn9/KBLR13E9oVGQJ17uN0cYxpYynYFSrZEAT8kbllVx02nfvr3Uis6G7777zq5fqckkIghYblwlGj0Gk8g5m9Cf2k1BwyBP5DZeujnNOLtYS3qhfUMQM4FeKur6XVrnpsYNesUJIoGVVRj0z4BNoij+WPH8BNBNFMVq662vm0H/tIvUTTxxBNz1oTU8cmIN/Fh9BswnEcN4PO0K5Wkr3Ti255cw+MBpnmsUxusVJeib28XSwMPNkuFxJZzX6Cg3mizx9QK9wa6T+6tRdXmkXh0ARialsC63iK3tY4lSW72IxG1HyKgkLZDorWZNmxj4eTQcWe74ws+ekSR/TUZyNBo+Sy9kf1EZn3meZ+TRC+z1bcbQEH+WZuaz6sgcWuesq/Z9rBy8EfHnmbwf/iAy7yb8NqwNhy4Ucu+nOyxjZt0Zx7gujRyO/fTvZMp0Rj7ZdJqn72jC490aM++N53lG+yFbjM3oIrd+HuvDxtMrfYFUDZl/1l4mwb+hFBtu/xh4hyJu/xDh3HaebfAzw89MJ1GWQivNpwiI7O6bhmyTtahlj3c8b3RayN4V9jfTBCGFX9seQZZzAvrNhYg2YNRL+d5nt8H3Q62Dnz4uzYx2firdOOUqMEpGpof2baZEZTGweWjV6aY2rD2Swb5z+czoG1ul5yyKIp9tTmHhtlSL0R7TKZIhrSIY8L+tTo/5fFAYIfUd/wZyQaBpXV/LeQ0mEaWTxWuN3oibQlalYa1JTCaRwxetvz+VQk6TUElqQ6s3kl+mQyYTcFfK8bnEzcgkihy5WISnm5xGdbwQRZFDF+x/2z7uSoo00u8ooa4vgiCg0Rs5lVmCIEjnMBPhryYt33ojiA/zQXEZi/03wqCvBN4URXFrxfO/gOmiKDpYa0EQxgPjAerXr9/67Fnnsqg1ytnt8HU/6fGIXyCql1Rh+oaj11EVX4bfw8MXl5PiUZdG5VbvcEzTV1kX2JGB2Zv49NirLGg7h5zYwTzX2Bq7XJtTyKhDZ3gjJoKZJ9OIVqvY0r5mY5KNNydRajTZGXMzRlFENIkczyimWcUPsNRg5JEjqWzIsy/eyeieKHl6+nJ4Pcy6Y+Rv0iJZBY8cTuX37AKH60jv1oKdZ/K4f8EOhsk3sd8UxTqVlJEzWZzAaHENiTLnGT79ta/zQuBGXs7tyXGxPtHBXqye1MXeSOjKpKbCdWKkjBbzbCEkATKlNYGWxm/Z0fgb3FP/ksIqT+2XQm9KtbUh8Mt+1X+gFWw2JjBSP5PfJnaieUTFMckbpQVKG4yTjzJ/6XoMZ7YzQ7nY/iQqH6tkgy0R7WCcTXNpUQR9GbrtH/PCn5ksMUoLe8mv97fz/pxxNreU2+dtsjxfO7mrxZCZ0RqMNJm1xm7bA+3q8cbg5nZjHvx8J/UD1MwaEE+rV/+0GPRm4b7IZIKdYWsa7mMJPQB4qRQ0qmOVYbANTTQN90VW4e3qDEYUMtklY89XSlJagdPtgiA45J+r3RREBqodjKreaCIluxStwWjZ5u2upFhj7wBFBnqiNRhJL5RujKE+7gT7uJNdrLFsA/BQyokK9kIQBLKKNWQUamhcx+uywi1wkxt0W65rk+hdn8PqadLje7+Bn22mu4kj4MAiiBso6aEsfwxa3G+3EPjx2FO8kizFGleElXPbYintqVmHFeS4+Tu83OZ2sRYP+qeMPJ46do5N7Zrw0qmLvNA4jGY1rBuSrdOTrzc6ZMWA5CG1evVPynTSF/T9+xPpnxCGUi7DJIqUGU28k5rBJ+ezCXFTsKJlNA3Ni7JVFByNOpTC2hxHI5XRPZEhn2xn71kpzr5nVi8MRpEhn2znQoE09WzoXsxGHnU41pbdg7fTtiJERNFFKbyVYhNTn34Wzu+S0lQrM7sQitKlvPt+bzn2rQRpMfv3p6TmI5VL4W0wPH0KhY8TbZTKoSlnNOkPJ1Y7bg9qIomqJQytUoeoXGck7kXJ+I64rT6z7ozHXVn1WkvkDMe0upOv9cNNISOvVEfntzZY/v62pLzev1qjevhCIYbc8yQ2t//Z55fp7DKO7K4l0BMfD+n7ciqzmHK9/euqFHKLsbTcJKvBbJ8uFa+/UFBObok0u3GTy9A5WaepTKCnG3X97X+LuSVay3fVGc3q+kp6ZmV6jAVa0jBRUqF3Wdffg+wireW1Y0K87f5uoihSrjeivgJ9elfIxRmi6Nwrm3JESvH68yW48x0ppACSPO28RqwJ7MQzMVM51Kcnf2cXMuywlHb3g/IIj5Y3oFhR4Y2YRKj0w3i+URhzUtLp4OfJjoJSjnZuRkANlJFfKc5+7AD7XrgDlUKGp0rBhtwihlfkTbfxUfNp00jKbMI6lbl73ymLMJSZU10SyCnQ0P3tTchlAodn98HDTfoym73DkR0a8Mpdzbj7gw1kXTzPpMHd6BZUSMi3XRxfZNxfcG4HrHMSx1Z4SAuFa2fab2/3KPSfe4lPpBJFFyvUAd2k6klRlBaHG/eQjG5V6Mulf85SP23Dbskbpcwnv/qSzHGHCZdcsAQ4kVFMn/c2W57PHdqc+9pYWw/qDCZm/JLEsv3WGeOCh1oz/ru9luddY+owpmMkYxbutmz7fGQbesUFS16rSUS4hJfszKAYjCaOpjuZdVQQHeyFu1LuEKKojL/ajWBvFaU6I2n5ZXi7KwnwdMNbpcAoiihkguUc3u5KGgSqnYZudAYjxzOk2WZUHem1RSQDanudDYM88VIpOJ9XTkG5DqVcRlyYdR2lsncdG+rN+fxySrUG5EBjL3dU3m4Ichn6nHJEjQGZpxKDl5KTmdbZbj1/NR5KGSrltS8c3wiDficwEeui6AeiKLarPK4y19Wgg6NXFX+XlBFQBU027qZQVOAhl3Hm9haM/3YPK930mEKsP0ZZZjkmHyWq7Vl4+rhRIoro2tVxer70bpfX07EmySrS0O51582MR3eMZOH2VEBaVHyuJJ98g703NaVBCMPCAoisVKF4+67jRKlVfNmsIYP3n6aZlwf3engx8EMpDjt7YDyjOzkXNHJKRfelXPwIpODyjwOYeUHKi1eqqy6eqU0OLYVfHrauMdQQZToD8S+uddh+4rW+qBRyhxv1nlm9CPJSsTMll2EL/rFsH9Mp0m7x0+yVF208R9HasyiC1fgPjkIZ7gUGEzK1/WysqrRFg9FEVrEWb3cF3hXxaNuQRx1vFdnFWgK9VBbP2V0pR6M3ElpR/pKJ6bKafLeo589Dj0xg7rx5hPi4884771BSUsL0514ARN6c/xHffv4RMgEC/f1455136NJFchK6devG49NnE5eQiFtZDgPv7MeHH35IXJsuFJTrUCnkxIR4kVuq42JBOYf272X+nBcpzs9BrVbTunVr5r/+NksWLWZv0j7ef+0dFCFqDJnSDGXbnh08++pzFBQXYzCJjH5kAjMflBIDTp5LZsL0pygsKkKr09KlSxcWLFjApk2buOuuu+xEv95++2169erl8N6v1KBf0mUUBOFHoBsQJAhCGvASoAQQRfFTYDWSMT+NlLY4xvmZbjABDe3zret3rHZ4IUoQYF/Hpny7I5V1RzORB6nsDLo8rZRpLerz9GvSZxs5YxVuO7Kc5idfb2P+/c6zPL9cWhx8tm8ThrWph1wmcOcHW7lQUG4x5gBTlhxk2/TuTD1+nk0a63Rz/tlM5p/NpLGHipWto/FXKtAYTaRpdHTwk2Ynr4bU4e+T2bxgkzL54G1XWA1Xvz3MLiQQJI/5XZsvsEcAPJtiDU/8ONwqJuVbX/KqbyQJQ6v35K8StZuCT0e04rFF9hnAlePgoztG0q1JHYK8pJtu+0aBvDcskUX/nGXP2Xy+3pZK20h/RndsSK/4YEscvHiDVE1tyCoj+1Or1G3ojHbIvZTkfHUYr9sjMJUbMGkNyCrFfBVyGeGVsjQa1/EiOVvKEsouloy4r4eSun4eiAYT+oxSBHcVokaSefBDzjlMlF3CrLupVPy15ndOTJxCVkAgOSVaykp1nMoq5u/1a/j5+69ZuOwPOjZtyL5127j3/uHs2LKdsHrSWlaDQDWK8jzuGngn77zzDn369CGrWAPl0uzRPAvIzc5i+uNj+OGjr+jYtTOizsjyv1ZScMG+DsRszDOyMhk14WF+/uIHWiYkUqgsp2/fvrQIDad/z75MmTmVJ0c/waB+A1D4qzh8/CjGEh2GAg1dunRh5crLKP67Qi5p0EVRfOAS+0Wg+rrbm4VIm6l9paKLsYfOsDqnkFWtolm8LRU8IN5dxdAPtpKcLYUXZDlalHtz0LeWBHX+eLgDzfytZdDv3teCp386yMKIcEanWfN776zjpCjpGkkvLGfy4gO8MTjBbiGqVGtg1orDLLeZit+dWJfAih/8thk9nIZhOr0lxahVZun13tZF4+RyLR+dzWJWVDhJxWWUGk108vPi8IVCh+yIvbN6Oc12uGx8wqWQxd5vJImCMX/Yx5of+AH2fStpldz22NW/zi1A32ZhHHulL6sOpTPtZ8eirPvb1mP2IMcWe3e3rEvrBv50mSv9TT8d0dry9xdNIpnv70PUm3CPC0BzLM/u2Iw3d+HdrR7alEK0KYWYBnlhyC5HEaJm7fp1ZGRkOLyeLaIozS4AFAjsAQQ3OaKTGD5ASGAwCd17EhGgJi1fCoWYEQQBGaBUKBg2YgyLPv+YJ6e/gN5gQl8Rp/764/eZ8vwrxDQIR24QaZmQyIihD/DR+/9j9jMvSO/3/EXGjh7NnDlzGDRISikO9nZHbxQtsweAxd98wahhD3Jb6/aYSqVF0Lu79q+4GJB5WmcvgruCBUu/5qF7h9MyIREAX70Hc6a/zGvvvkH/nn3JyMokIiwcTCKGXA2xdRphLNAi6k2I+kvH+K+GW06cK72wnGX7LhDh70GfpqF2iw7ncstYvv8C47o0dL6KbKsMF2qNHuXoDKyuaFN2575TUOF4nDyYhSLbGite9WRnVAoZO1Jy6dkijLqVOuUk1vMD4LEvd7Nqxu2c0ei4O9i/cni9Rvh88xl2nsljzMLd/P2MlBGh0Rtp+pL9NP3R2xsR5msfivBSKSjRGvj5sQ6E+3nQ6c0Nln3mfGp5chHGxtb44ofns9haUIJXhbFu5ulO91ftwzlfj25rMRzXTOtR0j9ntBoJzYZIYZZ/OR5ucoa2jqBrTBD9399KTomWvk1DWXMkgwndo6o8rl6AmtQ37wQkI57z3VG8OoRT+s9Fi4cZ+FA8os6IaBQpWH6K8sOSJ1q86bzD+QyZZZhK9GAUpRLnKhAE8FQppIXDCiPu1JgrZGCQjFqYEWQygfqBauqjtstIMc9sH3vscbp3asvox61y2SqFnOSTx4lPaEGIjzumAsk4t2reikVLf5AG6U2MeXgMr855jaFD7WdSdf08UCvl5JfpCPB0IzPlJHfcdb/je1LKkPuppBuMlxumEh1yLyVHjx9j1KhRKMO90F+UZiatm7fk6KnjKMO9mPTEk/R5YCC3tWpHr649GHXfCPx8/QDY+s82EhMTLa/xyy+/0Ljx5dUQVMctZ9BXp+TwxvE0MIo8nVPK071i+HZHKr/su8DB8wUA/J50kfVP3+78BL1mw1+vIPo1YHV2AX2DfDla5HzFXtBav4gz+sVa0v6iQ5x3k7f1lNduP89z/WunZDq9sJyvtkkLtGdzJb30hjPtMyqe6NaYZ/vGOjuclwbGM3ftCZpH+KJSyHn//kQiAz0J9lGRW6IjJaeUp37cT4JW4ES0F+VK6Ud1oNj6OXV79S9LReTj3RrjoZTTPbaKbjm1wQ0SiLpRBHu78+LAeH7ec57/DW952bMgUW/kwgvbAdAcsYYO6jzaHEEmILhLJiBwhKQOmL0gCW1KIe5xAQQOj6Po1HHJSosid3SQHAe5rwqZV9Vl7KIoYizWYSqylwtWhnmiTy9FUMpRhqgtYRhRb7JboHV23qiIYAYMuZ+fFn5OaIAPhaKOmBAv5DKBpuG+CICp3IDMQ4Hc116rpUeX7ixa+B2jR4+26xgE4O/phr+nG6JJhEpeszzAHZlSDgrBck0KPxWitxLB5vMXZALyAHeMeRULqoK0bdyTj9J/yED+WLWa337/nS8Xf8PutdtQBLjfuJDLzYbgo8QQ6wfA2vxifpyz3hKvM3M6q4RTmcXODW/nKZR1mESjv6W4YWsfNaml0vFCgQ7Rz/plkOVoeGtIAl2i6zh4uVUxb2hznlmaxILNKTVu0HNLtLgpZHR4Y4Pd9srG/K0hCQxr6yRdr4J729TjXpuMibsSreGVMF8PmtX1ZVALa0iq/q+70flYp5uyHI3FmJsX6VzUPoNahNv9XS4HfYajs+J/bwyqhs7DgAHDY9FfLEUV7YcgSIbMra7kqJgNsLFQi7FQi7KuF8ZiHTKlHMFNJhlFrDFmkIy4IVeD3F+FIJehCFFbDbdChiLIA0NOueThCgLKcE+nBl0mE3hxxjQ63daOMWPGSJksBhPxcXHs27ePbp1uB5OI4CZnf9IBmrW0VnxOfWwS3y9bzNBBg/n1t19RKJXI3GzSCU0i+oslxMfEsv/wAYaMvs9yc3B2LWZjHh8fz969e7nrrruQq5XI1UoObdhN06bWMFh4eDgPPzKOhx8ZR7NmzTiZm2p3M6hpbjm1xYcahrCjreR5HjLqHYx5u+710XQPZczifQw/mMysU2n8cyaX+BfXUFguxcWeOWGdUu4tKiPXKHnibjuzURyRcqh/bliPs6/2Z1jb+oT7eVz2oubgVlbNlsrFCFdKcnYJA/63hTWH0yks19P6tfUkzLZWYH7yYCu78XclhvNc/9hqjfnV8H6jusgvWENP8jNSitaKCZ1cxvwmp/yY5JXLfaVQWMjTrfFsXbW+vNzLDfcYf+eGTCFDEWwNc+nTSzEV6TDklqNPL8WQWWZnzAU3OYJchjJYLXm6IBl/W+9WZfP9EUWrl+uEuqHBFvlcURQxZJYx5eGnmD59Opknpd/0waNJLFy4kAlPTkQR5CFdg1LOO7PfwsfLm4dHj0WfWYourRiTxoBJb7SESx4f/SiLlv3Irj27kXu5IQgCy5YtIzPTuVrrhAkTWLhwIQcOHAAgNzeX6dOnW7oqrVmzBn1F45aMjAxyc3OpW/fyCxqvhlvOQ1fKBBp6uaPQmzAEuWOs405AiZGDL/W2aWIg53RzX07nFbMhr5hvt2Ui0xlp8fI6OjQKZGuMo7etOFmIACjSyng6JpzODa4uBU0uE5h1ZxyvrTrG55tT6NsszKmK3eXQ852/ARwyHUBaue+XEMbE7lF8uPE0SrnA+/e3dBhXE8TV8UJ5uADl4QJEQAA+eKClZc3AxbUjGk3X5LmVHchCGeGNIBMw5JaT86VV/sCtnjd1nmiBsVCLwu/aUjtlbnJr2p6p6uwUc1jmUgiCIMXkjdK5TOUGTDqjnQcNoM8sRR7gztSpU/nwww8xlUkLrwN79yejOJvb774DQRDw8fdl0aJFhIVVVDoLIPOSPO0v53/G3aPvZeacF3hz1msYcuwLiMJj6rN48WKmTZtGVlYWMpmMrl27WvTTFy5cyIoVKyzj//nnHxYtWsQjjzxCcXExoigyefJkBg4cCMC6deuYNGkS7u7SZz5v3jxCQ0M5fvw4W7ZssYuhz5o1yyHGfzXcsvK5P6fl8OQpSQ7WWy6juJrKMFl6GW5JVpVATR/pLinLLLekISr355L0aBd8PJSXLLW+FEcuFnLnB9bsD/PiVGVEUaSo3ICv2vkXv6qiIIC/pt5O44qY/YHzBTQN97m27JJq0OiNxL5gTZdbPP42bmt0iV6WLi4bk8bAxdmSho2ynjchExIv6zhRFDEWatGdKSJvyYkqx3l1DMdv0NUtuFWVh65LL7EYYTOCUo6oN0qefIj6sme1otEEIugzpFmg3N8duafS8v5MJZKXK6jkKOuoLSGSysi8lFXesExaA4bscmQeClDIMBXbx/cFpQxlyM23LnOleei3XMjFzL0R1l58VRrzikVNU5iavnfF2O8zmCxNBQAeSqiLv6fbNRtzwEFO01aUx5Yvt56hxSvrOGIjKGRm5Fe7HLa9MTiBJ3tE8fcz3SzGHKTsmtoy5iAVhNjelFrW96u11/ovYMjTkDZjCxdf+wfNyXwuvmotBNKfL6Zoo2OWiTNK/0kn483dVRpzn76RyLyU+PSu+S45ZsOpCPJAGeaJMtwLZYgatwhvlKHO4+BVIchlCAqZVNwEGPM1UhjnQonFmAOI5t9zRUqhbRohgNyn6gwrmUqBW4Q3ikAPFL4qqPi9yAPcUQSrUQRdunr3VuCWC7nY8lfbJsxPzaCuyo3jpRqGBvuTkVdGmAGmLZbydhv1qsdRuYkVmlJm3ZeAnwGmleYRkKHhpyc7M+DkGTTA01dS2XgJAr1U9IoLZv2xLACSs0sJ9FRZSuFBEgF690+pgcMj3+xhw7RudimYm09Krck+frAV/mo36ni7ERXsPLvmevHjI7dRpNHfFHFz0SBlRlSemt/MmLQGCledoXSXlMttKtGT85U1NKKK8kN7uoCitamoGvpIsecqZm8ApTut6hr+Q6MxlRkwlupR+LjhFumLW10vfLrVq/L4a0HmIRnImkSQWcMvpnKD0zG6NGuJvdzHDYW/uzRWIVxSxsDM2rVrmT59Opb4IdCwYUOWL3eiMnqLccuGXCqjSysm56vDmMoMqHs3YL2PQLcmdXD3UPLokVQ25hUT7+nOsLAAXjp9kY/i6jMkNIBcnYFTZRpu86vZikOTSeTJxftZlWT90X0xsg294kPYdy6fwR9vtxvfJTqIN4c0p66fBz/sPMdzyw/xVI8onu7dpEav699Cxvy9GLLLCX/pNtJf24nvnQ3xuu3yMkBMGgOFq62Gtc7jLVA1uLp1jkuhOZWPIV+DqqEvOV8exljgvA9o6Iy2KPzcSZthFQtThnsS8lQrKS9bBG1KIfqMUor/TkOdEETJ9osoI7yoM7ZZtYb/WrjeHYtMeqPdwqoiRFpQNWunmJGplSgCboDUw3WmVrRcaoOrNeiG3HI0pwuQqeR4JNRBkAsU/51G4R/WfpWKYDWhT7e2PC82GB16Pv7eKpq2vs5jZqLRhEljRO6pRHuuCLmPCoXflRfMVG5vBpIc6qsrj9qV3tvywoB4Xl0pNTc++GLvKuPr/2VEg4kLs7Y5bA9/peMlPXZRFLkw0766VRXlR+CDcegullC86TwB9zVB7l1130l9dhmi3oRbuKMToDmZT85Xh3GPC8CrQ7idB25L0LhmqBr5YcgpR2mTOaK7WELWB9buSf5Do8lfeqrKawmd0e6qvpuXy41qQScaTAgKaxjRHAMHrjhGfytT41ouNxuaUwUUVDQV8CszIJpEO2MOkj6FsVSPvCLG5q2Q09HPi+0F1oWU4GokLNPf2IWpRE/ojLZkf1xRci0X8IgLIOCB2MvORnBTyHigXT1+3GWNiR5MK6jSmAMWYw64jHkVXJyz0/n2F7cT9EgCOZ9LN2/P9qH43xNt2W+7+AgQOCqeorWpaE8XcPFl6/b0OTsJm9UeuZdk1MuP5JD300mp4lFmzcgImdwKRbCakm0XKVyVgnfP+hT/JTW/1hzLcyirBwh/uaOUeVFx47E15gBu4V6EvXAbWR/sx1iordaYq6L8atWY30hsjTlYY+AuqueWM+geCUEWg160/qwlfQmkHNvMdyX50OwFSYROsXrpvyQ2xihCREWj4jpuSvRZZZTtz8K7ez3SX9uJ/z1RqKL8LAsxGW9aZUcxipQfzqX8SC7q5nUsbaYuhWelG4dtqCX1zTtJSitg0IeO3uapOf0uee7LwViiQ3+xFPcYR93264HmVD7KUM9qKwsvRcmudAqWnQaFAIbqZ5RmYw5QujMDr45SGEYZ4knON0cs++q+2kkq6fZRkfW//Q7nyfrkIGHPtEUURfIWn7Bqb9hkdmS+tw9FoDuGXCl32mzMZd5uliyK4AmJGCsqJj2aXl5mkNxTSdDYpmTOt09X9e3fEFO5Aa9O4eR8fQT/odFVnMHFf5VbzqDLPZVEvNmFC7N32BnziDcl4S2zUZep7KfegiCgEODT+AYsychDLZeRVmH8lcFqRJ2x2tQvM3k/HEcZrCbzvX0OHqAz1FV0JvGv8L6bR/ix+/letJ2zHpD0JRaMbH3NWSuiSQQB0l+TvFm/e6KQuctRt7h+5fnGYp0lH9r79gh8+135wrOxRCcZc7Az5j59I3EL90KmVqCs64WpzEC6TbaImcpGEaDua50sHqBbXS8CR8ZjKtWjbh2CPrOMrPf3YczVoD1XRPmBbES9Cd9+kXg0DSLvpxPIfVVojuch6k0WY66K8kN3rgiPhDr4D4665mpARR01Xl3qYszX4DeosUMGR8iTtVNz4OLW5pYz6GYUQe7o06QQSvjsDpbtymA1bg18LDmtlbk7xJ+7Q/wxaa03A21q1UL8gWOaUrLtIqYiraWMOvM9yUiU7sxAESTdDNStg53mwB5z0gggNtSbHx+5zfoantZ47bYZPaq8lstFNImkz9lpSe8CKFguGUVVlL8lFFXbaM9YP9fiv9Pw7dcQ0WCS4sahnhhyypH5uFUb99ZdkP7GZmVARYgahZ8Krw7hdjdtuaeS8Bdvo2x/Fp4dJYW7C887znzqjE9wmM57xFs9Z7cwT+o80YLsjw9aw22AV+cIBLlA8BOJgPQZl2y5QOEfZ1CGqqkzLoGaRJAJ+N3p2Mvzv8icOXP44YcfkMvlyGQyPvvsM6ZPn056ejoeHlK6YVRUFEuXLmX27Nl4eXkxbdo0NBoNAwcOpFOnTsyePdvpuTMyMpg8eTK7d+/Gz8+PkJAQ3nvvPdzc3BgwYACHD9uvgaSlpTFhwgSOHj2KyWRiwIABzJs3Dzc3N8rKynjkkUdISkpCFEX8/PxYs2YNXl5eyOVyuwbU999/PzNmzKjxz+qWNeh1xiVwcfYOAkfGI3O3fxtyHzd0Z4vsFlYqd2cpXGmNu5futJcE9R8ag7plMMYCDYpADzyaSFWj+pxyMt+2X8gtXCV1+in68yx+dzd2yLSwbdU1d0hz7mvrmEYmkwk81TOajo1rplhHm1xgZ8xtyfr4AGK5AZ+e9SndnYnfXY2r1PW4WvTZksZ25WvI/HA/Cl8V5UdyUTX2RZssGfyQqa1R1rEpKc8otdw0zQQ8EHvJBU+ZWolXp4rSarlAwPBY8n44jkfzIIxFOvzvjkIZeuniEVV9H5QRXhaHIfjJlgiVFAYFmYBX57rI/VR4JAQ5O42LGmDHjh2sXLmSffv2oVKpyMnJQaeTQljff/89bdo4XRtEp9MxZMgQWrduXaUxF0WRe+65h1GjRrF4sdQL9uDBg2RmZlKvnuPvVBRFBg8ezOOPP86vv/6K0Whk/PjxPP/888ybN4/333+fkJAQDh2Swn4nTpxAqZScJw8PD4tEQG1yyxp0mbvCEmapjCrSh/JDOVyYtY26czpR8HsKpTvTCXteWugSRZHS3Y66zv7DmqBOrGNVVgu0LzZQBnmgrOuF/kIJMk8FplL7XNmCFckUrEhG3TIY/3tjEGQCdf08OJ5RTGyot1NjbubpO2Kq3OcM0SSiPVOIqpGvXWy6/EQeuV8fsRvr3sQfzcl8EMFYESIo+F26EWV/loRHQhAB9ze5ojCB9lyRxYMNfCgeVZQvF1/a4TDOq1M47nGB5HxxCH1aicVImo05SN57wFDr+8/+wj4jSe6nuqp8c3XzOpL3Lbv8HGUzwY+2IOvjA/jd1dgiTlUZQS6gbuG8Q9W/kZMnX6W45FiNntPbK46YmBeq3J+enk5QUBAqlRRyCgq69M3TYDAwbNgwoqOjefPNN6sct3HjRpRKJY89ZtXVb9GiBQCpqakO4zds2IC7uztjxkg9fORyOfPnz6dhw4a8/PLLpKen06CBtYirSZPrn3J8yxr06hBsfvy20+7013YS+kwbCv88a9nmPyQafVYZ3rdHWLIaqiN4QiKixoBQSYnt4us7LXKhZfuzkPup8O0TycNdGpJ3PJcPipRSWXQ1zX6vhMI1qZRsTiPggSaoWwRjyNOQMXe33ZiQqa0p25+Fzx0NEASBgt+SKdl+0eFc5YdyKPRV4TfAcYqvu1BC9qcHCX6ypbTWIIrozhbZdbnJ/e6ow3EgpREKShmCIKBq5Is2xT605dGiDuUHsynbk4lnq2BUjfzIX3bKrjoQJA/5aqkcXrns45QyQia1uvRAF7VK7969eeWVV4iJiaFXr14MGzaM22+XpLEffPBBS8jljjvuYN68eQDMnTuXO+64g/fee6/acx8+fJjWrVtXO8aWI0eOOIz38fGhfv36nD59mrFjx9K7d2+WLl1Kz549GTVqFNHR0hpbeXm5nXbLzJkzGTZs2GW/9uXyrzTosmpyiDPm2YdMPNuGXtG5BZmA4CSdMGxmO/J/OUXZHkmZrXjjeXzuaEDHxkH8D08oM5D7w3GCRjl2mKkOZ+2/AEo2Szo2eT+ewCM+yM6Yu8cGEDRaeh3f3pGW7X6DGqNuHULhH2fQni4g5OnWlOy4SOmOdEq2XqBk6wVCJreyC0tkL0hC1JvIX3aK4MdaoDtTSPYCew/a7vNRyQl+vIVDaKPO+Obk/XySsr2Z1J3TGRAR5DIKfFWUbE6j7FAOpjKDpdjH7+4ovG4Lu6LPykXtUp0nXVt4eXmxd+9etmzZwsaNGxk2bJjF664q5NK5c2e2b9/OyZMniYm5spnvtZCYmEhKSgrr1q1j/fr1tG3blh07dhAXF+cKuVwL7jH+loUtM/73xpD/80m7ceGvVN9X9EoQBIGAoTH4D47mwnNS4Ur+slN4xFpVG53lJVdH3uLjlB3IJnBUPB5x1vi6PtN+wffCC9ZZSJ3xzVE1qjom7lbXi4AHYtGnFaMMVuN/VxS6c8XoKxYfzbFrVSNf3OMCLPoZutQidBdL7Iy53z1RqFsGk/X+PmTeblLThGpSE/0HR+F3V+OKeLQ0zq9/Q0xlekp3pFO6Q6qq9RvU2GXMXViQy+V069aNbt26kZCQwDfffFPt+K5duzJq1Cj69evH1q1brcqLlWjatClLly697OuIj493GF9UVMS5c+eIipK6R3l5eTF48GAGDx6MTCZj9erV17Uw65YV56oOQSagqu+DW0NfFEEehL/SEXUr+3S9gOGXXmS72tcOekRazS7bk0nuIvuYo1jRdsukMVCwMoULL2zDpHGuW1F2QNJzyf3GGtIoS8q2pOL532fvfdR5tHpjbkbuqcS9ifVGE/JkS0Ketp9KalMKKVwlLRzLA6XsHdsKxrqvdsSrfRgyNzmhz7Ql+LEWl8wzF+Qyp5+59+0Rds/NueMuXJw4cYJTp6zFVQcOHLCLU1fFkCFDmDZtGn379qWgoMDpmB49eqDValmwYIFlW1JSElu2bHE6vmfPnpSVlfHtt98CYDQamTp1KqNHj0atVrNt2zby8yVVV51Ox9GjRy/rWmuSf6VBNxP8aHNCp7VB5ibn/+29eXgcxbno/ateZtXMaLEky7ZkZHnfIhuzJmGHBG7YEk4g4VwwWcjhg5DLl5ATIAFyP8K5LCck+eAkJCEnJ1ySkOTGLCEk7CQGwm4bbPAiW8KSZe3S7DO91P2jRyPJkmzZ2NgW9Xueeaanurq73q6at996u+otIQQz/tfHCR89lfCxNYSWHriXWWNNCR+k44eeMu66dx3J1W1Iy6X91tGRFVOvjQyqn9nQQ+u3/k7vr98tpoWWVVFz3dFD1z1i3+ORmFWh4miNiv8+ZFGYNWHKLxz5cqdi5aL99i4AwKwMUXq257+Pna2G6imGSCaTXHrppSxcuJClS5eyYcOG4qiViy++mMbGRhobGznttNNGHXvFFVdw/vnnc84555DNjl44QwjBqlWreOqpp2hoaGDRokVcd911TJ3quWE3btzIjBkzip8//OEPrFq1it///vfMmTOHuXPnEggEuPXWWwFoamrixBNPZMmSJSxbtowVK1bwmc98BhjyoQ9+DsSQRTgMY7kcLmSb+ok/9R75baPHuFesXETPL0eORNl1xM5gkCYt6hu1NiOAURlk6tc9/6Gbd5CW+77Hl0tXgvR829nNffhmRosWtd2TKb5/GJxlqZj8HKxYLgqPSR/L5XAh0FBKoKGUgSdbSDz9HhUrF+H0Zul/pGmEMhd+HZlzSLzQRqQwhnr4Qza0rIrk860jzh1cOoXosJedmk+H/eA+8ob2eW6TwJyRoQKMiiDTv/cx3LSllLlCcYiiFPoBJnpKLebUEIF5Zci8Q/8jTcV9U764mPz2BPEnWhh4dGtRoee3ezGfS46fRuy0mdgdabLv9k4o1MCBROhit1EIFYpDkZ6eHk499dRR6U8//TQVFZNr5S2l0A8wQtcILfH89cJvMO3m44oR/wJzygjMKSP+hDcuvvVbf6fyX5ZidXqzS8PH1SBMjSkrF+HmHIRPWcYKxd5SUVHxgQwZPBRQCv0DRgsYlF0wF3Pa2FPQh0/YGT5TdddgYwqFQrEryuQ7CIRXVI8YCVN63tgL+O7tdHWFQvHhRlnohwDBJZX0P+T51kPLq0B6k3YUCoVib1AK/RDAm+hThhY2Kf+sWkNUoVDsG8rlcogw5bLFSpkrFGOwc+dOLrroIhoaGjjyyCM566yz2LRpE4sXLx6R7+abb+bOO+8EYOXKldTX1xcn8vzoRz8a9/zJZJKvfOUrxfOfdNJJvPyytzBMScnoSYIDAwNccsklzJ49m4aGBi655BIGBrz5Jq7rcvXVV7N48WKWLFnCUUcdxbZt3ozrI444giVLlhTLdPXVV++X+zMcZaErFIpDlt3FLN8Td9xxBxdccMEe833pS1+ivr6ezZs3o2ka27ZtY8OGsSOIAnzxi19k8eLFxRAAN910E1/60pf4/e9/z4MPPsiOHTtYt24dmqbR2tpKODw0AOLZZ5+dUAjgfUUpdIVCMSG+s7mVt5OZ/XrOxSVB/r85M8bdP17M8rHile8LTU1NvPzyyzzwwANomuewqK+vp75+7OUSt2zZwuuvv86DDz5YTLvxxhuZPXs2TU1NtLe3U1NTUzzXjBnjy3YgUC4XhUJxyLK7mOVNTU0j4qP85Cc/GbH/2muvLe4bXEVoV9avX09jYyO6PrFhwRs2bBiVX9d1GhsbWb9+PZ/97Gd59NFHaWxs5Otf/zpvvjlyAfKTTz65WKa77rprQtfcG5SFrlAoJsTuLOmDQUNDw4gJQ7suNTdRl8v+ZMaMGWzcuJFnnnmGZ555hlNPPZXf//73xZmqyuWiUCg+tOxtzPJ9Of/atWtxHGdCVvrChQtZs2YNrusW3Squ67JmzRoWLlwIgN/v58wzz+TMM8+kurqahx56aMzQAwcC5XJRKBSHLOPFLN++fft+OX9DQwMrVqzgpptuKgbFa25u5rHHHhsz/+zZs1m2bBm33HJLMe2WW25h+fLlzJ49mzfeeIMdO7xlHl3XZd26dR9oTPQJKXQhxCeFEBuFEFuEEKMC+QohVgohuoQQawqfL+3/oioUig8be4pZvj/4+c9/TkdHB7Nnz2bx4sWsXLmSqipvQZx0Oj0iJvr3v/997rvvPjZt2kRDQwMNDQ1s2rSJ++67D4DOzk7OPvtsFi9ezNKlSzEMg6uuuqp4reE+9EsuuWS/yTDIHuOhCyF0YBNwOtAKvAp8Tkq5YVielcAKKeVVY55kDCZ7PHSFYjKg4qEfXPY2HvpELPSjgS1Syq1SyjzwW+Dc911ShUKhUOxXJvJSdDow3GHVChwzRr7PCCFOwLPmr5FSjnJyCSEuBy4HqKur2/vSKhQKxT5yzDHHkMvlRqTdf//9LFmy5CCVaP+zv0a5PAr8RkqZE0J8Bfgv4JRdM0kpfwr8FDyXy366tkKhUOyRwen8k5mJuFzagNphv2cU0opIKXuklIOPvp8DY88EUCgUCsUBYyIK/VVgjhCiXgjhAy4CHhmeQQhRM+znOcA7+6+ICoVCoZgIe3S5SCltIcRVwF8BHfiFlHK9EOJ/Aq9JKR8BrhZCnAPYQC+w8gCWWaFQKBRjMCEfupTyz8Cfd0m7cdj2dcB1+7doCoVCodgb1ExRhUJxSNPR0cHnP/95Zs2axZFHHslxxx3HqlWreO6554jFYjQ2NrJ06VJOO+00Ojs7AfjlL39JZWUljY2NLFy4kJ/97Ge7vcbjjz/OihUrWLhwIcuWLePrX/86MDLG+nAeeughli5dyoIFC1iyZAkPPfRQcd8//vEPjjnmGBobG1mwYEExxszwMg1+dhemd19QsVwUCsUhi5SS8847j0svvZRf//rXALS0tPDII49QVlbGxz/+cf70pz8BcN1113HPPffw3e9+F4ALL7yQu+++m87OThYtWsQ555xDdXX1qGu8/fbbXHXVVTz22GPMnz8fx3FGhBrYlbVr1/KNb3yDJ598kvr6erZt28bpp5/OrFmzWLp0KZdeeim/+93v+MhHPoLjOGzcuLF47GCZDhRKoSsUignx3UfXs2FHfL+ec+G0KDedvWjc/c888ww+n29EPPSZM2fy1a9+leeee66YJqUkkUgwe/botXirqqpoaGigpaVlTIV+++23c8MNNzB//nzAC4d7xRVXjFumO++8k+uvv74YM72+vp7rrruOO+64g/vvv5/Ozk5qamqK5xoM2vVBoFwuCoXikGX9+vUsX7583P1///vfaWxspK6ujqeeeoovfOELo/Js3bqVrVu3jqnsYfcx18cr0675V6xYwfr16wG45pprmDdvHueffz733nsv2Wy2mO/BBx8c4XLJZPbvgiHKQlcoFBNid5b0B8WVV17J6tWr8fl83HHHHSNcLrfddhvf/OY3iwtdPPjgg6xevRq/38+9995LeXn5B1LGG2+8kYsvvpgnnniCX//61/zmN78p9iYOtMtFWegKheKQZdGiRbzxxhvF3/fccw9PP/00XV1do/Kec845/O1vfyv+vvDCC1mzZg0vv/wy559//m6v8frrr0+4TAsXLhyV//XXX2fRoqEHXkNDA1dccQVPP/00a9eupaenZ8Lnfz8oha5QKA5ZTjnlFLLZLD/+8Y+Lael0esy8q1evpqGhYa+vce2113LrrbeyadMmwItjvutydsP5xje+wb/9278V1zVtbm7m1ltvLY6Meeyxx4qx1Tdv3oyu65SWlu51ufYF5XJRKBSHLEIIHnroIa655hpuv/12KisrCYfD3HbbbcCQD11KSSwW4+c///leX2Pp0qX84Ac/4HOf+xzpdBohBJ/61KeK+2+55RZ+8IMfFH+3trZy2223cfbZZ2NZFqZpcvvtt9PY2Ah4Ab+uueYaQqEQhmHwwAMPFFdDGnQDDfIf//EfHH/88ftwZ8Zmj/HQDxQqHrpCceij4qEfXA5EPHSFQqFQHAYol4tCofhQ8J//+Z/88Ic/HJH20Y9+lHvuuecglWj/oxS6QqH4UHDZZZdx2WWXHexiHFCUy0WhUCgmCUqhKxQKxSRBKXSFQqGYJCiFrlAoFJMEpdAVCsUhTXNzM4sXLx6RNhinfE+xx5ctW8acOXP4xCc+wYsvvrjHa915553Mnz+fxsZGjjrqKH71q18BcNJJJ7HrvBkpJbfccgtz5sxh7ty5nHzyycUAXQC/+MUvWLJkCUuXLmXx4sU8/PDDAKxcuZL6+vpigK79ObFIjXI5WGz7Ozx8JTReDCd+E4QYub9jPSQ7YNpyCJYelCIqFIc6E409/uyzz/LpT3+aZ599dtyJUj/5yU948skneeWVV4hGo8TjcVatWjXute+55x5efPFF1q5dSygU4oknnuCcc85h/fr1dHd3873vfY833niDWCxGMpkcEX/mjjvu4IILLthPd2EIpdD3xOanYO1voP89WHIBHH25p3z7t0OiHTQDdr4FgSgkdkJsBiw4e+Q5urfAgxfDkZdB3TGw/iF44Qfevudu9T5mGD51F/Q2waa/QPvaoeNjtXDKt2H6kRCdDr6Qd45//Bgq58EnbgV/yVD+XBJ6tkB0GoSmwPZ/AML77dpQsffxLhQKHv+W19b3J1OXwJn/a58PHzf2uOvAsFnwJ598Mpdffjk//elPueuuu8Y816233spzzz1HNBoFIBqNcumll4577dtuu43nn3+eUCgEwBlnnMHxxx/PAw88wLJly4hEIpSUeP/LkpKS4vaBRCn0QXqaQLpQMdtTegOt8KNGXCBDkK3UUtb6fWY8/k3QDPIuWJgEyQISDXAQWJgEyMMRH/es7DNvh0evBisNf/nXkdc860748ze8bSsFqy4f2mcEwS7ESh7YDqu+Mna5t/8D3vgviEyDxI6JyRqdAR/9Gsw+FUpngtBAU963SYuUsOmvoBf+7v3boeNtzwCZdRIEyyDVDbVHw7Rlo3uLhwKuA1YWNC8myqCyHow9ftJJJ/HJM87g0gvPJUDW+/+mu2Hn297/uXoRy5cv59577wXXHdXe4/E4iUSCWbNmTag48XicVCo1Kv9gXPSVK1dSXV1NfX09p556Kp/+9Kc5++whQ+/aa6/llltuAbxojw888MC+3pkRHH4Kff0q+MMXPOXri8Dlz4I/MrT/7qMhNwBffApKa0ceK6VnybavhV+d61W00JEzP0pTczPT6SBIjjR++onxU64Zuwzu7otYQwcNzS2cSi/ij1/yEqPTId7mbc//lGdxVy3wFH9JFbz4I8+yDsRgxRdY39pPMpnk6KXzERtWQfNqcPLQu9Wzkj7yOTjjFtj4Z/j7v0Nfs3fu0plQdoRnjfc1w3svIf0xqD0GUb3A62k0PQuPXztUYM2E8llw0r/CnE+MtPb3BSnBykDPZvCVeOfeFyWR7oVAqVdP3ZugetHI87gO9Ld4CilYNsIiw3WGFNiHgUQHtKyGef/NayNbnoSWF6Hr3aG2MRbv/mnkb93vtcHIVKhZCjM+DwNtXh0c+y+eoWH4QTe93mk+5W0jvN9CQC7utSl/BJwc5NOFY/xg+CYuk2NBpg/R3wKuBV3vDO1LdSJkghv/n8u4+J/O92KP/+o+fnP/fTz3h2Hrh7qW993xNrKnyfuP7VwHZmCovWgGxAc8nZJo92QRwiu3pnttKZ+Cro3Q7vNkSaRAOp5b1AyDGfSOlxJcG13T+Mvjj/PqK6/w9BN/5pqvXc3rLz7PzddfC5k+7vju9VxwyZcnfi8myGHX4tvcSp6Ifo0G2cKc+N+ZevcK+ojxNvMIkqWPpQD477sBP3neZRb1bAdgKl3U0o4EXuNI3gsuIptJYzTbtHAkGg4uOgYWNuaI606fPp22tjZ0XUfXdfL5fHHf1KlTicfjpNNpYrEY7QPQTjWrOZqFUwMkci7b+/IsnRHhzE9/DhEqp7+/n+SWLcyePZ+mpiZ6omfh9/vZsmULvr+9UYy3/PjjjxMOh5k+/RSOPPJI6urqCAaDSClpbW0l2nAusWX/HcfKsXlrM21tbbS0tKDHdZKZJcT9J5LL5WALsAVKShZTOfVElvvfo2PnDsojQbSB96jrfo2yP3wBT10KmHMGVM33/pQLz/MeFLrpNXQpIdkJb/0Otv0Nao+BhpM9S797Mzx508jegj8Gs06ApRfClLlQVu+5ljY+7llSs06C+A5oex1yCejeSMaGtrhDfalOt17F1p4cC9hCb+AIZi45Hs0fZvPqP6Lh0sRMptDHMtbTR5RmatlIAx/jNcrLywjPPBIx9wyoOw7CFUPlch3iTa8QaTgGIYT3wDT8+7vJ4rouUkqsXJb1z/yOdDpN2jWZN7Oa6oaPEKycicwlEfE27/4IAR1vYzf9DT3bh4i3gtC9B3XvVs+6ttJQOd970HVvGvvC/ijk4iRKF9IcPZbKI+bRn3FZ2+OjO54lHPQxxW+ja4KcIzDsFNH8TrCzlOa6WPD2n6DqHEh1etcHpOzjfdnvQgPTc1EQm+Ep02SH9zuXADs76pCKsEHfwMil73r7+qmfUQOuTUMMrvinU/jy+SdQufQ0OhMGlh3CsX24FQsQdgIx0Mqbb29kwbw53jWtYSsFaQbRgE5JKMjWt19j1swZhbIO9gbcYj6kA1aaaMgkHAyy9a1XhvIDr69+ihOPOxLa1yCAo+v8HP2l8zn9qHlc9v/ezM1fvdjLeADaGRyGCj3pMxFlaZ5pruUZ/nlCxzRTN/aODEAMoPgSBMDGpLS0lOXLlzN9ejv9Aw8TCubQ9RDTpn2WYLCWRGIDA/E1BAMzsJ0kfv9isplWhNCYUvkV/vToX1i3bh0bdg410HWtCdb9aPzFZ3clFosxMDBAKpVi06ZNxXjNJSUlJJPJYr6ysjL6+vpGHV9WVkYwGCQSiZBIJKivr+fdd98lmUyyDYAaGAAohcKDMGq61NlbmbF5O/rmdlKEqH3mVzTwHpQ3eO8QnrwR6eToI8YaFpLf/DzznvlPeimlgym8K87G0gMYhk7YTTI/t5aPvPM83e+sI0EJ1XTzFvNIEyKLH/s178XTABGkFiPvziBJoZfQPyTPXzkJsqC96uAigZGLFjzK6SN+b2IW9AK94H9zNcfy/1NJL3FjCinXT7M7lTamAn+lnAFM8lTQRyAQpCwgENKmOmDTJ0rJYzKtZiozT/kCOT1MLpejt7eXztZt+Aa2YkmN9pYt9OYMbHRCwQCpTJa87ZKxJBl39F/tpXd3AG9SQR+9xAiQw8KkRvTQKWPkCACgM4UZtBNiDQkRZYc8mTIzT6A/hWF8nPKSY0gZZWxP6lQEXGw9QHn1DFzNR2trK8LZxtTAenLNLWSzJZSV7aC6xkbTAry3czrxgenYtoUQPiwrDISBCqLRoznJX0l/8Agc18WyLFzpYmrgSonf0NA0DV3TCIoc2Dk03EJvyYV0j6egA2UQjHkKO90D+ULb7Xp3/MZvhpCaHyl8BP0GNVOn88QLTZxy2qn0dHTyl+df5aqvXccjz63jzFNOAsdhw4YN6EIjlHVxnRzStclt3gJCsPrN9fz0f6/iqT//GTfWgBAWQmhIM4TM5hA+g2/d8G2uvPlufnP/L4kEwyQTSVb95S9c8s//DEYQ26zECc9C2hbSsrnmy1/hqhvu4rc/uZtgwOSp51ez+pW13P29W9nenWVnexvLFy/CsQ1ee6uVuul1WE4Zjm3g5JzxZX8fHHbhc9vafsu7G2+gtPQkBgZq2bw5gqHvoG7mdmpnnEIylUTXdQb6p1FT04Bt/4H6+utJp/N0b9/Cm6+8QGd2E8uPDDBlynxsSyeTmcOUyrcJh+rR9QbS6Xfo6/8rfX3/wHGSey7ULuh6iPLyj2EYpaSSjfT0/AbT9xbbty+iedtyPJ+NwDQ9ZW9ZQXxSclw2i9Hbi/vuRqp1nfD8+diVU8jOno2cM4ftiQQvvfQS4CnrwdVcNE2joqKC2tpagsEgdXV1lJfrhELVCKEjpfQs0AKJRILu7m4qKipIp9N0dHTw6quv0tPTQzAYpLe3d5RMBhbT2YmfPEGyrGX3y5GFw2Gy2SyOs7uGK6Fg71XGwlhYGGaYcDhKJjNAaWkvPT1TmD9/KlJupbt7OqFQhHXr1uG6LoFAgPPOO49oNMzjjz7C9vYu6urqOOGEE+jr6yEeT9LS0kI6laK3txd3RFuXlAWgLzvS3jSxsfaDnRNjgHLimOQJmAamcCAQZdb8GsyIIJHvJJXdQTKbpPW9WqrLsvTnTXZ2TsNyBHOn+rBMQU9yK9lsGE1zqaiQhMNBsrkBcrk+bNtHJNKN6xqYhkEk2kXr9iPQdIeKih1owqIk0oOmjfYRGkYE206MkV6Gz1dFLmfS2RFk5syrqK8/Ak2TGIaOlBqZzPDzuYU69O6jpmlEIhH8fj+aptHX04OVzeHqns9ac13CySQEA/hkHnyCpB7GlRo4DpquY2gaWjaLO2wtzvWtTfzrt79Lf3wAgKv/5TIuOPc8vnz511j7zjsEAwEMv8G3v/M/OO2MY3jggYf5zne+T01NFZlMlpnTp/Ota/6F45YtQ+oCPa+D0HGkhXBBCoGedfn+L+7jV39chWkYGIbBV1eu5KJzzuHMSy9l49atGKbXc1+xbBk/v+cefnDXXfz24YcxNI3qigq+f/31LFiwgOYd7Vx5w/W0d3YS8PuZUlbGD79zIw11tXz5ppt44dVXKS0rK8r3yiuv4PONdkftbfjcw06hZzJtvPjSCcXful4yIaV7xExvFe98vocd7b/bq2vOm/tdotGl5PPd9PT8DVfmkdIlGllCLtdOIDCddKaFgL8Gn7+Knu5nad/5f/ZOMCDwukbkMR19AHb+u1VMjzys45RJ3IikZPlHmX/87eSaWmjecBclsp7K0z/Pjs4/MjDwBonE0CgEn6+KQGA68fhaNM1PKFiH0AymTbuIUKgev68SiVf/pllGJtNCNLKErq4+2traiEQi6LrOjh07eO3VV+kfGBgqUyRCbW0tc+fOoaeni1Aogs+nkUqtAfELLKsbXQ/h81UhxEfp7QkyEO9gdkMlUpaRSP4WIdoQwsAwYth2AinzaFoAKS2kHPtBUFKykFi0kZ0dD+M4qWGyVlJVdRZtbQ9gGFEsqxddD1FaehQAuhbC719GKpXB53OIxarJ5dpIpTYTKz0Sv7+GVGoz0ehSkCa2Haanx08o5CcQyOM4vWx+8ylkKgk0I1IpAhGBU5IgHJxDODYN4Q7glkTx6VECZiUD2S0E/DWkMy1kc+24bp54/M09tgOfr4p8vnNvms4oQqFZ5PO92HY/lVNOp6HhWjTNJJfvIuCvwe+vwcr10Rd/mWy2DYFGPt9NNreDgYE3cJwsltVDedm9NDRUj3kNz1hwAAFSRyKREqQUuK6G6xrouo1h5HFdHds2EUJi2z4QEsf2AQIhHISQ6LqFYeQRwkVKDSkFuu4UrjX2iyspBVLqxXN419aRUmAYFlIKhNg7Hec4ZuGaBdkK1xdC4koN19EBDdfVcByzeE1NsxFCouk2slAGAE2z0TQXIVxcV8c0cwgRIxqtHacEQ0x6hZ566SXa77oNwiadn2kjE+oo7jty1q/QUjrNPT+jy3oehlWkEJ7VJaVdTDvu2Kd49bVPo2kBGmZ9HcdNk0isJxyeTVnZcURKFiLExEd/yHyezFpvuOHA+hfo8r2ApSfJbduC+Z4gc6TEqt/DG9X3gwWYENwZI5yaQaK6lVyJp4QD/WUYlJAs3b7H04RCDcRiy3CcNFa+lxJ9DrH+WSTe7UQ/9nRCwSRd3f+bXutVLMdz9fhEBXm59+smxmLLse0kwWAduVw7mhYgkViPzzeFSGQRyeQ7VFV+kmRqI5lMG7nczhEP8HB4DsFALd09zwCgiyClZUeTTG3E56skm2nFske7oyaKQEfy/rvHwUAdCI2KihMJBqYTDs8lGl1MNtdBMrEeIQz6B14jm2kFwO+bSig8i2yyjXTvRiLBRZRULUG6NkG3BqerB6aGiE47CmGBk0ni9sZJZDaipwXa9hxCN3AGBsi+tY58axtOfz+4Lm4igXRd3FQK38yZ6NEo0rbRY1FEKAQStLIY+fmSxPz/xtwjasGRYDtIbByfg8RB2BIcQAepgzQpWrsYE9Erg72jPeUVCOHDMKOYRoRsNomUNpI00nUYnB8pZQC/vxIAn8+Hrnu903w+Ry7XBegIEUDTtGIb0vUAUuZx3Tyumym+DwXh9TuE4ZWzkC7d/AgdMnE0hkZTaAQCM/D5Yns8atIr9PRrr9Hzs59jd3WRfeedkSMbxiF01FH4587FV1dLel6WzszTTP1rA9bOnSDAXz+L3KZN5JqaMGfMwKioQK8oB8cl/sQT+GprQdfQYzHsri5KTjwRu7OL/NatCL8fN5VCLy8j9+5GnDF82SUnnsiMn/wYbBthmkjpFh8Urmtj23EcJ0ne6qOp6U76+l6ktvYy5s75NqlUEzvaf0dZ6XEEU1PY+sfrGTA2EKldTuWST9O5YRXW2ib8zyYw2woPH9MEy0IKiRsBPT7kVpC6JH+EJLfIRe8WiDzYUyVaRmBXSjTLwJ6pkZ9m4/od0EfeX60P3DLAAS3lfewaMFsExg6B2W0QXu3HV1aNtX07vjmzSAc6saM5RP0UzHgAe45OQKsh1t2AZpgYlZWY02pw+gfQohHMadPQYzFk3sLu7MQZGPAGUfj9EPCTCXUQmNpA/43/QWDBAvRYlHTPVlLP/h13c3uxrMLnQ9o20nVwo6DPqIKAjuErRYuVYARLMQJR+uq2ErSqCKQryJv9uD6XtNnGgG8TMp3G12JgdGq403wYvij4DMplI0aglGRVOyWRhZh9Jgl9K4GOEMLScAJ5NIIITQPLQc/5EIZBvrkZ4feTWbsWu6MDLRDwlKjrIi0Ls3YGTn8/Vst7ngx+PzKXG79x6zrs1q0FRlUVvoZZGGXlOPE4emkpwjTRo1Hyba24yRRCE1idnQghkHkLJ5nE6enBuudu5lRXI3QdYRje9VzX+xYCYZpevQjhpVkWWjQKpoGUOUBD0/x4SlsipYvtJJGujeOkcZwkhhFB10sQwkDXgwXL30XTBo2wkS7D98uVV17JCy+8MCLta1/72oRD60rpIKWD61reg0VaOE4aTfMjhA9dDwLgujmkdDCMKJpm4Lo24KJpEx/pM+kV+nDsnh5PqQYCOH195Fu9xqn5fQifD72iguxbbxN/7DGsri6wrBHHC9NELy3F7urCnFmH5g+Q27QJraQEd9hLx8BHliIMEzeZJL91K9Ky0MvKCCxahN3ZiTBNhGFgTp9Gyamn4vT3o/n9aCUR7N4eSs8/Hy0YfF+y7gnpup7yKGzLfJ7cpk30PfggsXPPBVdi7dgBQuCbWUe+5T1ymzfjb2gguHQJueZmnJ5e8tu2Ynd1kW9tw965E6urg/TxLu6xFcichawyCVkzqEgtw08lVvsOsB38C+Zjt+/0/J4CMmvWYnd3YZR7o0qkbXsKQAisnTtxxvDTv1/0yimUfOzjxB97DFkYhRQ79xyszk60QNBT8I6NzOZwBga8T18fbibj3TtNAyGQloXw+QguWoR/wXxwXKzWVtx8DiEEdl8/VksLbj4P9i7WmmkiTBNsG+k4nrIdPLdtY06f7tXBEUfgnzcXN51GZnPYPd1ofj9OIonm9+Orr8dJxBE+H+FjjsVJxHFTKdxEEj0awayrI7d5C24yiVZSghbwo0WjaH4/IhjEP2sW0nbQAn6Mmpp9UohOMsmmlhbmLViApuYpfOBIKXn33Xcnt0J3bBvHyu85Y2F4neH3o2k6bjZLZs1a0q+/Ru7djVR88QsEliwBTcPp78cY9oICwO7rQ/P5EMFgUVEO4mazCJ9vRLrrOGxb8zqdzU1EKiqJVVYxdc48TN+BGZ50uCNtu2jROfG497CREnQDq3U7bioFmo5RXoZR7flwZT6Pk0hg79xJvrnFe4CefLLX8wkG0aNRz4p8v2XzHMGj6n0sBstuVFaCpqFHIiPKMGhdSinBdRGFxYIPF7Zt20YkEqGiomK/WsmK3SOlpKenpzg6bTiTSqFvfGk1f/rBxKcKRyurOe+b3/Gs1sJ4YCkL2670fImuQ29bK7l0CjMQJJdKYvr9hGKltG3cQCYep2NbE6bfj26ahGKlpHp7iHd3eV1RwLYsrGxmxLWF0JBypM/86HMvwHVdNF1n6uy51C5cQjaZJBSL4QsMWfFWPodrOzS9/jK9ba0sPOEUIuUVmIHAXt8zhWJfsSyL1tZWstnR48MVB5ZAIMCMGTMwzZFzYiaVQu/d0crW11/ZbZ54dxdv/uXRfS3aCHTDIFo1Fek4mIEAyd4eSsorCEYiRCoqMXyef9CxHWYtX8HMJY2kBvrpfq+Z1g1v88bjj0zoOr5giEjFFMKlZZSUV7D5lZdGPSAAymqmUVI+Bcey6G3bTuXMemrmzEPTdbrea6F0ag3V9Q34gkHCsTKyyQSWlUfXDZK9PSR6u8kmE1TW1WP4fGQScVzHwRcMks9mCZZ4s25z6RS5dBopXQLhCNL1hpQJXUc6DkLX0Q2vobm2jWYY+ENhhCaId3Zg+Pxkk96wOCklumlSUlaO49g4eQvXdchnMiAlZiCA6Q+AAMeyCg9ZFyubJZdOYWWzmMEgvkAAx7IwA0EC4RJPxrJy/KEwoWiMaGUVg+15d9akdF1S/X04toUvFEY3DHTDQNONA2aFuq5DJh4n0d2FY9vYVp5cKkmyr5dcKkWqvxfHttF0Hd00iVVWUzp1mldH8QEGOju8exwM4Tg21fWz0TSNbDpFpLyCeFcnuUyaYCRK/852XMcml0qhmyY9rdvxBYNYuSy+QBDHtrFyWfKZNJruye44Dpqm4bouuC6h0lKCkRiGz1c0elzH9d5HDPPZ21Ye3TCxshkiFZWUTZtOtKIShGCgs4PtG94i0d1JIFzi1XPAmxSXz6SJVlQy8yPL0HSDTCJOLpWkpKwCK+fVez6TIVBSAhIqausQmoaVzXp1ZRjk02msbBbN0Mkk4ghNI5dK4QuGMP0Bps6eM8JImixMKoU+UXY2bSY6pZLWd94GQGgaQmgITQzb1hBCIIRGafVUfMEQjm3hD5cQ7+rEzueIVlYRCO/7VHjbsnj2l/dS37iCafMWoOk6fe1tRKdU0f1eC2ueeIxoZRXd7zV7Vn4uS2agHzMYomb2XOYe+zH8wRBvPfNXEIKulm10tWwjFCtl5pJGmte9SaYwNjdWVc1AZ8ceSnR4Yfj9+AJB8uk0tpVH0/URCmVEXtOH49gIoWH4fESnVGIGAti5HJphYPoDZJMJ+ne2Y4/jtgtEooRjpQRKIp4C8vmRUjLQuZNMIo5tWfgCAaSU+AJBTL8fK5fDzuew83nsfB4EmIEgrm1j53M4luUpQzn+CKdASQTD58N1HOx8znvY7U8KLkhfMIim6ZiBIL5gEOm62FYew/SuLQrvEdL9feTSqVGn0XQdTdOLw10d28YwfRg+X/EBPhzD5ydWVY2Vy2JlvYeIlBJNN7w6OMD6J1pZhXQlwUgU1/XaTShWSqS8At0wCUaj+ENhfMEgZiBINplEug666UO6DvlsllAshj8YYkpdPdlkgq6WrSR6etB0neVnnuM9dMYhm0zS2dxE9/b3POPJtuhr38GyM8+mduGSfZLpQ6nQJzO2ZaHrOkLTcF0HITQcyyr8qZKk+nvJZzMkursIRqLohS5bSVkFZjCI6Q8w0NFetKp1wyhYOkaxV+APlxSsOYt8JoPh8+O6DtL1lJLreJaaEAJN13Fsm3wmjZ3PE6mYgnRdgtFowe0kcaw8qf4+dMPEdV38oRC+wotiK5fDymVBgm6aaJqO0ASmP4A2zOcsXReEwLFtcqkkqf4+sskEqYF+Et1dNL32MmU103Fsi/6OdsKlZeTTaXSfz3sZm8viD4UpnTqN0uoaDNMkn83g2rb3bsa2SPX1kY4PkE0myCYT5NJpzECAWFU1oVgphukjl04VrVYrm8X0BzD8fgyfr/gAyGczGKaJbvrQTRPdMDxFUlFZSDfxh8KUlFcQCJeMlFNKMok4fTvaSPR0UVYznZLCy+VMfICObU24rkOwJFq04EOlZQTCJaQH+impmII/FCZYEiGXSROKxYqjqvamB+LYFm5hsk/RCNrN8flMmr72HQx07kRoGqFoabH3OFy2QZK9PXRsawIp8YfD+ENhUv19+AJB/OEwpt9Psq+PfCZNvKsDKSEYiWLnvVE/hs+PGfAjEF66bWEYJlJKene0srNpE53btqLpuvcg0w1Akujp9h7O+TyZeHy3D9qJEIxEsS0LpKR20RKCkRi9bdsZ6OogPdA/Iq/QNHTT5NjzL+SY8z+7T9dTCl2hUCjGQLouVj5HPp0mk0wQ7+qkfNp0fMEQruvgC4TIJOJkE3F2Nm3GFwoxfd4C/KES1j//NOmBPrLJJJphkEsl2fLayziWRc2ceUQqphCrqmb6vIVU1M4kFI2hm+b7duspha5QKBQfAPt7zPxY7E6hT2hwqRDik0KIjUKILUKIb42x3y+EeLCw/2UhxBHvs8wKhUJx2HGwh3bucdCuEEIH7gFOB1qBV4UQj0gpNwzL9kWgT0o5WwhxEXAbcOGBKPAHiZQSx3a9+BSuBFmY7+bK4v5B99vQthzKPyw9Hc+BELi2i3TBcVxcRxY+LgjQda3gkx7+0bxJeGbhBa421GhcVyJdien3fM6D54NhY5+BoXgUQz/FsCCorusFWBKaly4HBWX4OytPruGztAejIriOxLG8+IfeS+Zh1yhMmR4ss3fMyDyD5xFCFGeDS7dwb1xZmHI9dC+976H77A1FLRxTSBcCNE3gFuoNMXR+wcjyDaUNuyvDpoCP2D/WPSzmHfaHHj6FXPPqVtMFuqGhm8PsKDl8U46dvptO9Lg97HGPH5l/3MPl2GXZ/bFy7H0TucbuzjuBe7Tr8dKV3gAIAY7toulixH4kQ+1GFv7bxe1COxsso0sxTo0YrHfNa1+GqaMZYlRZdpWt+LPwHYr5iJTv/yHIE5mFcTSwRUq5FUAI8VvgXGC4Qj8XuLmw/QfgbiGEkAfAn7P1zS6e+uWGwmiVggIpVFxRiQ3qozEqrJg2bD8j8hSazMHxRCkUig8Byz9Rx3Hnz97v552IQp8ODI/o1AocM14eKaUthBgAKoDu/VHI4USmBFj4sWmjLbRhllnRkip8CwBtMNjOMKusYJGNshqHWXJFy3gwbfj+wfMPv9Yu+Qb3BUpMXNvF9OtoBWtt0GLTdOEtdOK4hW9PHsd2h7YdOUxW715ound+K+8gHVk8LwWLdkRcMTlkJez6nNW0IYt/kKJFPRQZdciiHX5OvOvqhiha96Mekgz1WEZZRrukDRZS6KLYY5GFHkfxwT3ie+T24D3xei8F2cSw8jDswV649q69j+LDfvBHQc4hQ2G0BTvCQGDkMW6htyFdiW25OJY74j6O7KWLcdJHMmLfOBnHO36UW2DcfBMolxhzc+LlGnH87k42/Pg9n3ewxwqgG6LYtkf0qrTd/2fH+q9LVxbr2nW8+nRtOXZ9Dv6HxpApVnlgxsd/oAtcCCEuBy4HqKsbZ9GJPVBZG6GyNrLnjAqFQvEhYyIvRduA4YF7ZxTSxswjvHiTMWBULFUp5U+llCuklCsqKyv3rcQKhUKhGJOJKPRXgTlCiHohhA+4CNh1PvsjwKWF7QuAZw6E/1yhUCgU47NHl0vBJ34V8FdAB34hpVwvhPifwGtSykeA+4D7hRBb8FZxvOhAFlqhUCgUo5mQD11K+Wfgz7uk3ThsOwv80/4tmkKhUCj2BhW1XqFQKCYJSqErFArFJEEpdIVCoZgkKIWuUCgUk4SDFm1RCNEFtOzj4VM4ALNQD3GUzB8OlMyTn/cr70wp5ZgTeQ6aQn8/CCFeGy985GRFyfzhQMk8+TmQ8iqXi0KhUEwSlEJXKBSKScLhqtB/erALcBBQMn84UDJPfg6YvIelD12hUCgUozlcLXSFQqFQ7IJS6AqFQjFJOOwU+p4WrD6cEUI0CyHeEkKsEUK8VkgrF0I8KYTYXPguK6QLIcSPCvdhnRBi+cEt/cQQQvxCCNEphHh7WNpeyyiEuLSQf7MQ4tKxrnUoMI68Nwsh2gr1vEYIcdawfdcV5N0ohPjEsPTDpt0LIWqFEM8KITYIIdYLIb5WSJ/M9TyezB9sXXtLfx0eH7zwvU3ALMAHrAUWHuxy7Uf5moEpu6TdDnyrsP0t4LbC9lnA43gLWx0LvHywyz9BGU8AlgNv76uMQDmwtfBdVtguO9iy7YW8NwPfGCPvwkKb9gP1hbauH27tHqgBlhe2I8CmgmyTuZ7Hk/kDrevDzUIvLlgtpcwDgwtWT2bOBf6rsP1fwHnD0n8lPf4BlAohag5C+fYKKeXf8GLmD2dvZfwE8KSUsldK2Qc8CXzygBd+HxhH3vE4F/itlDInpdwGbMFr84dVu5dStksp3yhsJ4B38NYdnsz1PJ7M43FA6vpwU+hjLVi9u5t2uCGBJ4QQrxfWXwWollK2F7Z3AtWF7cl0L/ZWxskg+1UF98IvBl0PTEJ5hRBHAMuAl/mQ1PMuMsMHWNeHm0Kf7HxMSrkcOBO4UghxwvCd0uurTepxph8GGYEfAw1AI9AO/PtBLc0BQghRAvwf4H9IKePD903Weh5D5g+0rg83hT6RBasPW6SUbYXvTmAVXverY9CVUvjuLGSfTPdib2U8rGWXUnZIKR0ppQv8DK+eYRLJK4Qw8RTbA1LKPxaSJ3U9jyXzB13Xh5tCn8iC1YclQoiwECIyuA2cAbzNyAW4LwUeLmw/AlxSGCFwLDAwrDt7uLG3Mv4VOEMIUVbowp5RSDss2OVdx/l49QyevBcJIfxCiHpgDvAKh1m7F0IIvHWG35FSfn/Yrklbz+PJ/IHX9cF+O7wPb5PPwnuD3ATccLDLsx/lmoX3RnstsH5QNqACeBrYDDwFlBfSBXBP4T68Baw42DJMUM7f4HU9LTz/4Bf3RUbgC3gvkrYAlx1sufZS3vsL8qwr/FlrhuW/oSDvRuDMYemHTbsHPobnTlkHrCl8zprk9TyezB9oXaup/wqFQjFJONxcLgqFQqEYB6XQFQqFYpKgFLpCoVBMEpRCVygUikmCUugKhUIxSVAKXaFQKCYJSqErFArFJOH/AqKZw0cADKT0AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boc_forex_df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "boc_forex_df.to_csv(\"/Volumes/GoogleDrive/My Drive/Forecasting/bootcamp_datasets/boc_exchange/dataset.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>AUD_CLOSE</th>\n",
       "      <th>DKK_CLOSE</th>\n",
       "      <th>EUR_CLOSE</th>\n",
       "      <th>HKD_CLOSE</th>\n",
       "      <th>JPY_CLOSE</th>\n",
       "      <th>MXN_CLOSE</th>\n",
       "      <th>NZD_CLOSE</th>\n",
       "      <th>NOK_CLOSE</th>\n",
       "      <th>SEK_CLOSE</th>\n",
       "      <th>CHF_CLOSE</th>\n",
       "      <th>GBP_CLOSE</th>\n",
       "      <th>USD_CLOSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-05-01</td>\n",
       "      <td>0.9200</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>1.5100</td>\n",
       "      <td>0.141970</td>\n",
       "      <td>0.009271</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.8200</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.1700</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>2.2199</td>\n",
       "      <td>1.1105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007-05-02</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>1.5100</td>\n",
       "      <td>0.141740</td>\n",
       "      <td>0.009232</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.8200</td>\n",
       "      <td>0.1854</td>\n",
       "      <td>0.1700</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>2.2055</td>\n",
       "      <td>1.1087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007-05-03</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>1.5000</td>\n",
       "      <td>0.141496</td>\n",
       "      <td>0.009190</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.8100</td>\n",
       "      <td>0.1847</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>2.1999</td>\n",
       "      <td>1.1066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2007-05-04</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>1.5100</td>\n",
       "      <td>0.141616</td>\n",
       "      <td>0.009218</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.8100</td>\n",
       "      <td>0.1854</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>2.2075</td>\n",
       "      <td>1.1075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2007-05-07</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>1.5000</td>\n",
       "      <td>0.140908</td>\n",
       "      <td>0.009177</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.8100</td>\n",
       "      <td>0.1843</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>2.1957</td>\n",
       "      <td>1.1018</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2499</th>\n",
       "      <td>2017-04-24</td>\n",
       "      <td>1.0218</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>1.4684</td>\n",
       "      <td>0.173684</td>\n",
       "      <td>0.012310</td>\n",
       "      <td>0.07215</td>\n",
       "      <td>0.9477</td>\n",
       "      <td>0.1579</td>\n",
       "      <td>0.1525</td>\n",
       "      <td>1.3568</td>\n",
       "      <td>1.7280</td>\n",
       "      <td>1.3511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2500</th>\n",
       "      <td>2017-04-25</td>\n",
       "      <td>1.0224</td>\n",
       "      <td>0.1994</td>\n",
       "      <td>1.4837</td>\n",
       "      <td>0.174374</td>\n",
       "      <td>0.012210</td>\n",
       "      <td>0.07183</td>\n",
       "      <td>0.9426</td>\n",
       "      <td>0.1586</td>\n",
       "      <td>0.1548</td>\n",
       "      <td>1.3661</td>\n",
       "      <td>1.7407</td>\n",
       "      <td>1.3565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2501</th>\n",
       "      <td>2017-04-26</td>\n",
       "      <td>1.0176</td>\n",
       "      <td>0.1995</td>\n",
       "      <td>1.4847</td>\n",
       "      <td>0.174965</td>\n",
       "      <td>0.012260</td>\n",
       "      <td>0.07098</td>\n",
       "      <td>0.9382</td>\n",
       "      <td>0.1585</td>\n",
       "      <td>0.1554</td>\n",
       "      <td>1.3707</td>\n",
       "      <td>1.7493</td>\n",
       "      <td>1.3612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2502</th>\n",
       "      <td>2017-04-27</td>\n",
       "      <td>1.0176</td>\n",
       "      <td>0.1992</td>\n",
       "      <td>1.4815</td>\n",
       "      <td>0.175103</td>\n",
       "      <td>0.012250</td>\n",
       "      <td>0.07151</td>\n",
       "      <td>0.9369</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.1543</td>\n",
       "      <td>1.3704</td>\n",
       "      <td>1.7584</td>\n",
       "      <td>1.3624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2503</th>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>1.0222</td>\n",
       "      <td>0.1999</td>\n",
       "      <td>1.4870</td>\n",
       "      <td>0.175485</td>\n",
       "      <td>0.012250</td>\n",
       "      <td>0.07253</td>\n",
       "      <td>0.9373</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.1541</td>\n",
       "      <td>1.3719</td>\n",
       "      <td>1.7679</td>\n",
       "      <td>1.3650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2504 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  AUD_CLOSE  DKK_CLOSE  EUR_CLOSE  HKD_CLOSE  JPY_CLOSE  \\\n",
       "0     2007-05-01     0.9200     0.2000     1.5100   0.141970   0.009271   \n",
       "1     2007-05-02     0.9100     0.2000     1.5100   0.141740   0.009232   \n",
       "2     2007-05-03     0.9100     0.2000     1.5000   0.141496   0.009190   \n",
       "3     2007-05-04     0.9100     0.2000     1.5100   0.141616   0.009218   \n",
       "4     2007-05-07     0.9100     0.2000     1.5000   0.140908   0.009177   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "2499  2017-04-24     1.0218     0.1974     1.4684   0.173684   0.012310   \n",
       "2500  2017-04-25     1.0224     0.1994     1.4837   0.174374   0.012210   \n",
       "2501  2017-04-26     1.0176     0.1995     1.4847   0.174965   0.012260   \n",
       "2502  2017-04-27     1.0176     0.1992     1.4815   0.175103   0.012250   \n",
       "2503  2017-04-28     1.0222     0.1999     1.4870   0.175485   0.012250   \n",
       "\n",
       "      MXN_CLOSE  NZD_CLOSE  NOK_CLOSE  SEK_CLOSE  CHF_CLOSE  GBP_CLOSE  \\\n",
       "0       0.10000     0.8200     0.1860     0.1700     0.9100     2.2199   \n",
       "1       0.10000     0.8200     0.1854     0.1700     0.9100     2.2055   \n",
       "2       0.10000     0.8100     0.1847     0.1600     0.9100     2.1999   \n",
       "3       0.10000     0.8100     0.1854     0.1600     0.9100     2.2075   \n",
       "4       0.10000     0.8100     0.1843     0.1600     0.9100     2.1957   \n",
       "...         ...        ...        ...        ...        ...        ...   \n",
       "2499    0.07215     0.9477     0.1579     0.1525     1.3568     1.7280   \n",
       "2500    0.07183     0.9426     0.1586     0.1548     1.3661     1.7407   \n",
       "2501    0.07098     0.9382     0.1585     0.1554     1.3707     1.7493   \n",
       "2502    0.07151     0.9369     0.1590     0.1543     1.3704     1.7584   \n",
       "2503    0.07253     0.9373     0.1590     0.1541     1.3719     1.7679   \n",
       "\n",
       "      USD_CLOSE  \n",
       "0        1.1105  \n",
       "1        1.1087  \n",
       "2        1.1066  \n",
       "3        1.1075  \n",
       "4        1.1018  \n",
       "...         ...  \n",
       "2499     1.3511  \n",
       "2500     1.3565  \n",
       "2501     1.3612  \n",
       "2502     1.3624  \n",
       "2503     1.3650  \n",
       "\n",
       "[2504 rows x 13 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"/Volumes/GoogleDrive/My Drive/Forecasting/bootcamp_datasets/boc_exchange/dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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