notebooks/cleaning_data.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning Data\n",
    "\n",
    "## About the data\n",
    "In this notebook, we will using daily temperature data from the [National Centers for Environmental Information (NCEI) API](https://www.ncdc.noaa.gov/cdo-web/webservices/v2). We will use the Global Historical Climatology Network - Daily (GHCND) dataset; see the documentation [here](https://www1.ncdc.noaa.gov/pub/data/cdo/documentation/GHCND_documentation.pdf).\n",
    "\n",
    "This data was collected from the LaGuardia Airport station in New York City for October 2018. It contains:\n",
    "- the daily minimum temperature (`TMIN`)\n",
    "- the daily maximum temperature (`TMAX`)\n",
    "- the daily average temperature (`TAVG`)\n",
    "\n",
    "*Note: The NCEI is part of the National Oceanic and Atmospheric Administration (NOAA) and, as you can see from the URL for the API, this resource was created when the NCEI was called the NCDC. Should the URL for this resource change in the future, you can search for \"NCEI weather API\" to find the updated one.*\n",
    "\n",
    "In addition, we will be using S&P 500 stock market data (obtained using the [`stock_analysis`](https://github.com/stefmolin/stock-analysis) package and data for bitcoin for 2017 through 2018. For the first edition, the bitcoin data was collected from CoinMarketCap using the `stock_analysis` package; however, changes in the website led to the necessity of changing the data source to Yahoo! Finance. The bitcoin data that was collected before the CoinMarketCap website change should be equivalent to the historical data that can be viewed on [this](https://coinmarketcap.com/currencies/bitcoin/historical-data/) page.\n",
    "\n",
    "## Setup\n",
    "We need to import `pandas` and read in the temperature data to get started:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>datatype</th>\n",
       "      <th>station</th>\n",
       "      <th>attributes</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-10-01T00:00:00</td>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-10-01T00:00:00</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>25.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-10-01T00:00:00</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>18.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-10-02T00:00:00</td>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-10-02T00:00:00</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>26.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date datatype            station attributes  value\n",
       "0  2018-10-01T00:00:00     TAVG  GHCND:USW00014732      H,,S,   21.2\n",
       "1  2018-10-01T00:00:00     TMAX  GHCND:USW00014732   ,,W,2400   25.6\n",
       "2  2018-10-01T00:00:00     TMIN  GHCND:USW00014732   ,,W,2400   18.3\n",
       "3  2018-10-02T00:00:00     TAVG  GHCND:USW00014732      H,,S,   22.7\n",
       "4  2018-10-02T00:00:00     TMAX  GHCND:USW00014732   ,,W,2400   26.1"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('../data/nyc_temperatures.csv')\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Renaming Columns\n",
    "We start out with the following columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'datatype', 'station', 'attributes', 'value'], dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to rename the `value` column to indicate it contains the temperature in Celsius and the `attributes` column to say `flags` since each value in the comma-delimited string is a different flag about the data collection. For this task, we use the `rename()` method and pass in a dictionary mapping the column names to their new names. We pass `inplace=True` to change our original dataframe instead of getting a new one back:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(\n",
    "    columns={\n",
    "        'value': 'temp_C',\n",
    "        'attributes': 'flags'\n",
    "    }, inplace=True\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Those columns have been successfully renamed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'datatype', 'station', 'flags', 'temp_C'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also perform string operations on the column names with `rename()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DATE', 'DATATYPE', 'STATION', 'FLAGS', 'TEMP_C'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(str.upper, axis='columns').columns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Type Conversion\n",
    "The `date` column is not currently being stored as a `datetime`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date         object\n",
       "datatype     object\n",
       "station      object\n",
       "flags        object\n",
       "temp_C      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's perform the conversion with `pd.to_datetime()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date         object\n",
       "datatype     object\n",
       "station      object\n",
       "flags        object\n",
       "temp_C      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,'date'] = pd.to_datetime(df.date)\n",
    "df.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we get useful information when we use `describe()` on this column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                      93\n",
       "unique                     31\n",
       "top       2018-10-01 00:00:00\n",
       "freq                        3\n",
       "Name: date, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.date.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use `tz_localize()` on a `DatetimeIndex` object to convert to a desired timezone:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-10-25 00:00:00-05:00', '2018-10-26 00:00:00-05:00'], dtype='datetime64[ns, EST]', freq=None)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start='2018-10-25', periods=2, freq='D').tz_localize('EST')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This also works with `Series`/`DataFrame` objects that have an index of type `DatetimeIndex`. Let's read in the CSV again for this example and set the `date` column to be the index and stored as a datetime:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>datatype</th>\n",
       "      <th>station</th>\n",
       "      <th>attributes</th>\n",
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       "    <tr>\n",
       "      <th>date</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10-01 00:00:00-05:00</th>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01 00:00:00-05:00</th>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>25.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01 00:00:00-05:00</th>\n",
       "      <td>TMIN</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>18.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-02 00:00:00-05:00</th>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-02 00:00:00-05:00</th>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>26.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          datatype            station attributes  value\n",
       "date                                                                   \n",
       "2018-10-01 00:00:00-05:00     TAVG  GHCND:USW00014732      H,,S,   21.2\n",
       "2018-10-01 00:00:00-05:00     TMAX  GHCND:USW00014732   ,,W,2400   25.6\n",
       "2018-10-01 00:00:00-05:00     TMIN  GHCND:USW00014732   ,,W,2400   18.3\n",
       "2018-10-02 00:00:00-05:00     TAVG  GHCND:USW00014732      H,,S,   22.7\n",
       "2018-10-02 00:00:00-05:00     TMAX  GHCND:USW00014732   ,,W,2400   26.1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eastern = pd.read_csv(\n",
    "    '../data/nyc_temperatures.csv', index_col='date', parse_dates=True\n",
    ").tz_localize('EST')\n",
    "eastern.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use `tz_convert()` to convert to another timezone from there. If we convert the Eastern datetimes to UTC, they will now be at 5 AM, since `pandas` will use the offsets to convert:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>station</th>\n",
       "      <th>attributes</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10-01 05:00:00+00:00</th>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01 05:00:00+00:00</th>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>25.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01 05:00:00+00:00</th>\n",
       "      <td>TMIN</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>18.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-02 05:00:00+00:00</th>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-02 05:00:00+00:00</th>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>26.1</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                          datatype            station attributes  value\n",
       "date                                                                   \n",
       "2018-10-01 05:00:00+00:00     TAVG  GHCND:USW00014732      H,,S,   21.2\n",
       "2018-10-01 05:00:00+00:00     TMAX  GHCND:USW00014732   ,,W,2400   25.6\n",
       "2018-10-01 05:00:00+00:00     TMIN  GHCND:USW00014732   ,,W,2400   18.3\n",
       "2018-10-02 05:00:00+00:00     TAVG  GHCND:USW00014732      H,,S,   22.7\n",
       "2018-10-02 05:00:00+00:00     TMAX  GHCND:USW00014732   ,,W,2400   26.1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eastern.tz_convert('UTC').head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can change the period of the index as well. We could change the period to be monthly to make it easier to aggregate later.\n",
    "\n",
    "The reason we have to add the parameter within `tz_localize()` to `None` for this, is because we'll get a warning from `pandas` that our output class `PeriodArray` doesn't have time zone information and we'll lose it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2018-10', '2018-10', '2018-10', '2018-10', '2018-10', '2018-10',\n",
       "             '2018-10', '2018-10', '2018-10', '2018-10', '2018-10', '2018-10',\n",
       "             '2018-10', '2018-10', '2018-10', '2018-10', '2018-10', '2018-10',\n",
       "             '2018-10', '2018-10', '2018-10', '2018-10', '2018-10', '2018-10',\n",
       "             '2018-10', '2018-10', '2018-10', '2018-10', '2018-10', '2018-10',\n",
       "             '2018-10', '2018-10', '2018-10', '2018-10', '2018-10', '2018-10',\n",
       "             '2018-10', '2018-10', '2018-10', '2018-10', '2018-10', '2018-10',\n",
       "             '2018-10', '2018-10', '2018-10', '2018-10', '2018-10', '2018-10',\n",
       "             '2018-10', '2018-10', '2018-10', '2018-10', '2018-10', '2018-10',\n",
       "             '2018-10', '2018-10', '2018-10', '2018-10', '2018-10', '2018-10',\n",
       "             '2018-10', '2018-10', '2018-10', '2018-10', '2018-10', '2018-10',\n",
       "             '2018-10', '2018-10', '2018-10', '2018-10', '2018-10', '2018-10',\n",
       "             '2018-10', '2018-10', '2018-10', '2018-10', '2018-10', '2018-10',\n",
       "             '2018-10', '2018-10', '2018-10', '2018-10', '2018-10', '2018-10',\n",
       "             '2018-10', '2018-10', '2018-10', '2018-10', '2018-10', '2018-10',\n",
       "             '2018-10', '2018-10', '2018-10'],\n",
       "            dtype='period[M]', name='date')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eastern.tz_localize(None).to_period('M').index"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now get a `PeriodIndex` object, which we can change back into a `DatetimeIndex` object with `to_timestamp()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-10-01', '2018-10-01', '2018-10-01', '2018-10-01',\n",
       "               '2018-10-01', '2018-10-01', '2018-10-01', '2018-10-01',\n",
       "               '2018-10-01', '2018-10-01', '2018-10-01', '2018-10-01',\n",
       "               '2018-10-01', '2018-10-01', '2018-10-01', '2018-10-01',\n",
       "               '2018-10-01', '2018-10-01', '2018-10-01', '2018-10-01',\n",
       "               '2018-10-01', '2018-10-01', '2018-10-01', '2018-10-01',\n",
       "               '2018-10-01', '2018-10-01', '2018-10-01', '2018-10-01',\n",
       "               '2018-10-01', '2018-10-01', '2018-10-01', '2018-10-01',\n",
       "               '2018-10-01', '2018-10-01', '2018-10-01', '2018-10-01',\n",
       "               '2018-10-01', '2018-10-01', '2018-10-01', '2018-10-01',\n",
       "               '2018-10-01', '2018-10-01', '2018-10-01', '2018-10-01',\n",
       "               '2018-10-01', '2018-10-01', '2018-10-01', '2018-10-01',\n",
       "               '2018-10-01', '2018-10-01', '2018-10-01', '2018-10-01',\n",
       "               '2018-10-01', '2018-10-01', '2018-10-01', '2018-10-01',\n",
       "               '2018-10-01', '2018-10-01', '2018-10-01', '2018-10-01',\n",
       "               '2018-10-01', '2018-10-01', '2018-10-01', '2018-10-01',\n",
       "               '2018-10-01', '2018-10-01', '2018-10-01', '2018-10-01',\n",
       "               '2018-10-01', '2018-10-01', '2018-10-01', '2018-10-01',\n",
       "               '2018-10-01', '2018-10-01', '2018-10-01', '2018-10-01',\n",
       "               '2018-10-01', '2018-10-01', '2018-10-01', '2018-10-01',\n",
       "               '2018-10-01', '2018-10-01', '2018-10-01', '2018-10-01',\n",
       "               '2018-10-01', '2018-10-01', '2018-10-01', '2018-10-01',\n",
       "               '2018-10-01', '2018-10-01', '2018-10-01', '2018-10-01',\n",
       "               '2018-10-01'],\n",
       "              dtype='datetime64[ns]', name='date', freq=None)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eastern.tz_localize(None).to_period('M').to_timestamp().index"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the `assign()` method for working with multiple columns at once (or creating new ones). Since our `date` column has already been converted, we need to read in the data again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date        datetime64[ns]\n",
       "datatype            object\n",
       "station             object\n",
       "flags               object\n",
       "temp_C             float64\n",
       "temp_F             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../data/nyc_temperatures.csv').rename(\n",
    "    columns={\n",
    "        'value': 'temp_C',\n",
    "        'attributes': 'flags'\n",
    "    }\n",
    ")\n",
    "\n",
    "new_df = df.assign(\n",
    "    date=pd.to_datetime(df.date),\n",
    "    temp_F=(df.temp_C * 9/5) + 32\n",
    ")\n",
    "new_df.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `date` column now has datetimes and the `temp_F` column was added:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>datatype</th>\n",
       "      <th>station</th>\n",
       "      <th>flags</th>\n",
       "      <th>temp_C</th>\n",
       "      <th>temp_F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>21.2</td>\n",
       "      <td>70.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>25.6</td>\n",
       "      <td>78.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>18.3</td>\n",
       "      <td>64.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-10-02</td>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>22.7</td>\n",
       "      <td>72.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-10-02</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>26.1</td>\n",
       "      <td>78.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date datatype            station     flags  temp_C  temp_F\n",
       "0 2018-10-01     TAVG  GHCND:USW00014732     H,,S,    21.2   70.16\n",
       "1 2018-10-01     TMAX  GHCND:USW00014732  ,,W,2400    25.6   78.08\n",
       "2 2018-10-01     TMIN  GHCND:USW00014732  ,,W,2400    18.3   64.94\n",
       "3 2018-10-02     TAVG  GHCND:USW00014732     H,,S,    22.7   72.86\n",
       "4 2018-10-02     TMAX  GHCND:USW00014732  ,,W,2400    26.1   78.98"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use `astype()` to perform conversions. Let's create columns of the integer portion of the temperatures in Celsius and Fahrenheit. We will use **lambda functions** (first introduced in *Chapter 2, Working with Pandas DataFrames*), so that we can use the values being created in the `temp_F` column to calculate the `temp_F_whole` column. It is very common (and useful) to use lambda functions with `assign()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>date</th>\n",
       "      <th>datatype</th>\n",
       "      <th>station</th>\n",
       "      <th>flags</th>\n",
       "      <th>temp_C</th>\n",
       "      <th>temp_C_whole</th>\n",
       "      <th>temp_F</th>\n",
       "      <th>temp_F_whole</th>\n",
       "    </tr>\n",
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       "      <td>70</td>\n",
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       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
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       "      <td>25.6</td>\n",
       "      <td>25</td>\n",
       "      <td>78.08</td>\n",
       "      <td>78</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>18.3</td>\n",
       "      <td>18</td>\n",
       "      <td>64.94</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-10-02</td>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>22.7</td>\n",
       "      <td>22</td>\n",
       "      <td>72.86</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-10-02</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>26.1</td>\n",
       "      <td>26</td>\n",
       "      <td>78.98</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date datatype            station     flags  temp_C  temp_C_whole  \\\n",
       "0 2018-10-01     TAVG  GHCND:USW00014732     H,,S,    21.2            21   \n",
       "1 2018-10-01     TMAX  GHCND:USW00014732  ,,W,2400    25.6            25   \n",
       "2 2018-10-01     TMIN  GHCND:USW00014732  ,,W,2400    18.3            18   \n",
       "3 2018-10-02     TAVG  GHCND:USW00014732     H,,S,    22.7            22   \n",
       "4 2018-10-02     TMAX  GHCND:USW00014732  ,,W,2400    26.1            26   \n",
       "\n",
       "   temp_F  temp_F_whole  \n",
       "0   70.16            70  \n",
       "1   78.08            78  \n",
       "2   64.94            64  \n",
       "3   72.86            72  \n",
       "4   78.98            78  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.assign(\n",
    "    date=lambda x: pd.to_datetime(x.date),\n",
    "    temp_C_whole=lambda x: x.temp_C.astype('int'),\n",
    "    temp_F=lambda x: (x.temp_C * 9/5) + 32,\n",
    "    temp_F_whole=lambda x: x.temp_F.astype('int')\n",
    ")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date            datetime64[ns]\n",
       "datatype              category\n",
       "station               category\n",
       "flags                   object\n",
       "temp_C                 float64\n",
       "temp_C_whole             int32\n",
       "temp_F                 float64\n",
       "temp_F_whole             int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_categories = df.assign(\n",
    "    station=df.station.astype('category'),\n",
    "    datatype=df.datatype.astype('category')\n",
    ")\n",
    "df_with_categories.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datatype</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>31</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       datatype            station\n",
       "count        93                 93\n",
       "unique        3                  1\n",
       "top        TAVG  GHCND:USW00014732\n",
       "freq         31                 93"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_categories.describe(include='category')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our categories have no order, but this is something that `pandas` supports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['med', 'med', 'low', 'high']\n",
       "Categories (3, object): ['low' < 'med' < 'high']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Categorical(\n",
    "    ['med', 'med', 'low', 'high'], \n",
    "    categories=['low', 'med', 'high'],\n",
    "    ordered=True\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reordering, reindexing, and sorting\n",
    "Say we want to find the days that reached the hottest temperatures in the weather data; we can sort our values by the `temp_C` column with the largest on top to find this: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>datatype</th>\n",
       "      <th>station</th>\n",
       "      <th>flags</th>\n",
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       "      <th>temp_F</th>\n",
       "      <th>temp_F_whole</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018-10-07</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>27.8</td>\n",
       "      <td>27</td>\n",
       "      <td>82.04</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2018-10-10</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>27.8</td>\n",
       "      <td>27</td>\n",
       "      <td>82.04</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>26.7</td>\n",
       "      <td>26</td>\n",
       "      <td>80.06</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018-10-04</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>26.1</td>\n",
       "      <td>26</td>\n",
       "      <td>78.98</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-10-02</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>26.1</td>\n",
       "      <td>26</td>\n",
       "      <td>78.98</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>25.6</td>\n",
       "      <td>25</td>\n",
       "      <td>78.08</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2018-10-09</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>25.6</td>\n",
       "      <td>25</td>\n",
       "      <td>78.08</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25</td>\n",
       "      <td>77.00</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018-10-05</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>22.8</td>\n",
       "      <td>22</td>\n",
       "      <td>73.04</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018-10-08</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>22.8</td>\n",
       "      <td>22</td>\n",
       "      <td>73.04</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date datatype            station     flags  temp_C  temp_C_whole  \\\n",
       "19 2018-10-07     TMAX  GHCND:USW00014732  ,,W,2400    27.8            27   \n",
       "28 2018-10-10     TMAX  GHCND:USW00014732  ,,W,2400    27.8            27   \n",
       "31 2018-10-11     TMAX  GHCND:USW00014732  ,,W,2400    26.7            26   \n",
       "10 2018-10-04     TMAX  GHCND:USW00014732  ,,W,2400    26.1            26   \n",
       "4  2018-10-02     TMAX  GHCND:USW00014732  ,,W,2400    26.1            26   \n",
       "1  2018-10-01     TMAX  GHCND:USW00014732  ,,W,2400    25.6            25   \n",
       "25 2018-10-09     TMAX  GHCND:USW00014732  ,,W,2400    25.6            25   \n",
       "7  2018-10-03     TMAX  GHCND:USW00014732  ,,W,2400    25.0            25   \n",
       "13 2018-10-05     TMAX  GHCND:USW00014732  ,,W,2400    22.8            22   \n",
       "22 2018-10-08     TMAX  GHCND:USW00014732  ,,W,2400    22.8            22   \n",
       "\n",
       "    temp_F  temp_F_whole  \n",
       "19   82.04            82  \n",
       "28   82.04            82  \n",
       "31   80.06            80  \n",
       "10   78.98            78  \n",
       "4    78.98            78  \n",
       "1    78.08            78  \n",
       "25   78.08            78  \n",
       "7    77.00            77  \n",
       "13   73.04            73  \n",
       "22   73.04            73  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.datatype == 'TMAX'].sort_values(by='temp_C', ascending=False).head(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, this isn't perfect because we have some ties, and they aren't sorted consistently. In the first tie between the 7th and the 10th, the earlier date comes first, but the opposite is true with the tie between the 4th and the 2nd. We can use other columns to break ties and specify how to sort each with `ascending`. Let's break ties with the date column and show earlier dates before later ones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>datatype</th>\n",
       "      <th>station</th>\n",
       "      <th>flags</th>\n",
       "      <th>temp_C</th>\n",
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       "      <th>temp_F</th>\n",
       "      <th>temp_F_whole</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018-10-07</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>27.8</td>\n",
       "      <td>27</td>\n",
       "      <td>82.04</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2018-10-10</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>27.8</td>\n",
       "      <td>27</td>\n",
       "      <td>82.04</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>26.7</td>\n",
       "      <td>26</td>\n",
       "      <td>80.06</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-10-02</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>26.1</td>\n",
       "      <td>26</td>\n",
       "      <td>78.98</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018-10-04</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>26.1</td>\n",
       "      <td>26</td>\n",
       "      <td>78.98</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>25.6</td>\n",
       "      <td>25</td>\n",
       "      <td>78.08</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2018-10-09</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>25.6</td>\n",
       "      <td>25</td>\n",
       "      <td>78.08</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25</td>\n",
       "      <td>77.00</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018-10-05</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>22.8</td>\n",
       "      <td>22</td>\n",
       "      <td>73.04</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018-10-08</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>22.8</td>\n",
       "      <td>22</td>\n",
       "      <td>73.04</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date datatype            station     flags  temp_C  temp_C_whole  \\\n",
       "19 2018-10-07     TMAX  GHCND:USW00014732  ,,W,2400    27.8            27   \n",
       "28 2018-10-10     TMAX  GHCND:USW00014732  ,,W,2400    27.8            27   \n",
       "31 2018-10-11     TMAX  GHCND:USW00014732  ,,W,2400    26.7            26   \n",
       "4  2018-10-02     TMAX  GHCND:USW00014732  ,,W,2400    26.1            26   \n",
       "10 2018-10-04     TMAX  GHCND:USW00014732  ,,W,2400    26.1            26   \n",
       "1  2018-10-01     TMAX  GHCND:USW00014732  ,,W,2400    25.6            25   \n",
       "25 2018-10-09     TMAX  GHCND:USW00014732  ,,W,2400    25.6            25   \n",
       "7  2018-10-03     TMAX  GHCND:USW00014732  ,,W,2400    25.0            25   \n",
       "13 2018-10-05     TMAX  GHCND:USW00014732  ,,W,2400    22.8            22   \n",
       "22 2018-10-08     TMAX  GHCND:USW00014732  ,,W,2400    22.8            22   \n",
       "\n",
       "    temp_F  temp_F_whole  \n",
       "19   82.04            82  \n",
       "28   82.04            82  \n",
       "31   80.06            80  \n",
       "4    78.98            78  \n",
       "10   78.98            78  \n",
       "1    78.08            78  \n",
       "25   78.08            78  \n",
       "7    77.00            77  \n",
       "13   73.04            73  \n",
       "22   73.04            73  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.datatype == 'TMAX'].sort_values(by=['temp_C', 'date'], ascending=[False, True]).head(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the index was jumbled in the past 2 results. Here, our index only stores the row number in the original data, but we may not need to keep track of that information. In this case, we can pass in `ignore_index=True` to get a new index after sorting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>2018-10-07</td>\n",
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       "      <td>2018-10-10</td>\n",
       "      <td>TMAX</td>\n",
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       "      <th>2</th>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
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       "      <td>26</td>\n",
       "      <td>80.06</td>\n",
       "      <td>80</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-10-02</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>26.1</td>\n",
       "      <td>26</td>\n",
       "      <td>78.98</td>\n",
       "      <td>78</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-10-04</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>26.1</td>\n",
       "      <td>26</td>\n",
       "      <td>78.98</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>25.6</td>\n",
       "      <td>25</td>\n",
       "      <td>78.08</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-10-09</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>25.6</td>\n",
       "      <td>25</td>\n",
       "      <td>78.08</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25</td>\n",
       "      <td>77.00</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-10-05</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>22.8</td>\n",
       "      <td>22</td>\n",
       "      <td>73.04</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-10-08</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>22.8</td>\n",
       "      <td>22</td>\n",
       "      <td>73.04</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date datatype            station     flags  temp_C  temp_C_whole  \\\n",
       "0 2018-10-07     TMAX  GHCND:USW00014732  ,,W,2400    27.8            27   \n",
       "1 2018-10-10     TMAX  GHCND:USW00014732  ,,W,2400    27.8            27   \n",
       "2 2018-10-11     TMAX  GHCND:USW00014732  ,,W,2400    26.7            26   \n",
       "3 2018-10-02     TMAX  GHCND:USW00014732  ,,W,2400    26.1            26   \n",
       "4 2018-10-04     TMAX  GHCND:USW00014732  ,,W,2400    26.1            26   \n",
       "5 2018-10-01     TMAX  GHCND:USW00014732  ,,W,2400    25.6            25   \n",
       "6 2018-10-09     TMAX  GHCND:USW00014732  ,,W,2400    25.6            25   \n",
       "7 2018-10-03     TMAX  GHCND:USW00014732  ,,W,2400    25.0            25   \n",
       "8 2018-10-05     TMAX  GHCND:USW00014732  ,,W,2400    22.8            22   \n",
       "9 2018-10-08     TMAX  GHCND:USW00014732  ,,W,2400    22.8            22   \n",
       "\n",
       "   temp_F  temp_F_whole  \n",
       "0   82.04            82  \n",
       "1   82.04            82  \n",
       "2   80.06            80  \n",
       "3   78.98            78  \n",
       "4   78.98            78  \n",
       "5   78.08            78  \n",
       "6   78.08            78  \n",
       "7   77.00            77  \n",
       "8   73.04            73  \n",
       "9   73.04            73  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.datatype == 'TMAX'].sort_values(by=['temp_C', 'date'], ascending=[False, True], ignore_index=True).head(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When just looking for the n-largest values, rather than wanting to sort all the data, we can use `nlargest()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>27</th>\n",
       "      <td>2018-10-10</td>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>23.8</td>\n",
       "      <td>23</td>\n",
       "      <td>74.84</td>\n",
       "      <td>74</td>\n",
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       "      <th>30</th>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>23.4</td>\n",
       "      <td>23</td>\n",
       "      <td>74.12</td>\n",
       "      <td>74</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-10-07</td>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>22.8</td>\n",
       "      <td>22</td>\n",
       "      <td>73.04</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-10-02</td>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>22.7</td>\n",
       "      <td>22</td>\n",
       "      <td>72.86</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>21.8</td>\n",
       "      <td>21</td>\n",
       "      <td>71.24</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2018-10-09</td>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>21.8</td>\n",
       "      <td>21</td>\n",
       "      <td>71.24</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-10-04</td>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>21.3</td>\n",
       "      <td>21</td>\n",
       "      <td>70.34</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>21.2</td>\n",
       "      <td>21</td>\n",
       "      <td>70.16</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018-10-08</td>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>20.9</td>\n",
       "      <td>20</td>\n",
       "      <td>69.62</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018-10-05</td>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>20.3</td>\n",
       "      <td>20</td>\n",
       "      <td>68.54</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date datatype            station  flags  temp_C  temp_C_whole  \\\n",
       "27 2018-10-10     TAVG  GHCND:USW00014732  H,,S,    23.8            23   \n",
       "30 2018-10-11     TAVG  GHCND:USW00014732  H,,S,    23.4            23   \n",
       "18 2018-10-07     TAVG  GHCND:USW00014732  H,,S,    22.8            22   \n",
       "3  2018-10-02     TAVG  GHCND:USW00014732  H,,S,    22.7            22   \n",
       "6  2018-10-03     TAVG  GHCND:USW00014732  H,,S,    21.8            21   \n",
       "24 2018-10-09     TAVG  GHCND:USW00014732  H,,S,    21.8            21   \n",
       "9  2018-10-04     TAVG  GHCND:USW00014732  H,,S,    21.3            21   \n",
       "0  2018-10-01     TAVG  GHCND:USW00014732  H,,S,    21.2            21   \n",
       "21 2018-10-08     TAVG  GHCND:USW00014732  H,,S,    20.9            20   \n",
       "12 2018-10-05     TAVG  GHCND:USW00014732  H,,S,    20.3            20   \n",
       "\n",
       "    temp_F  temp_F_whole  \n",
       "27   74.84            74  \n",
       "30   74.12            74  \n",
       "18   73.04            73  \n",
       "3    72.86            72  \n",
       "6    71.24            71  \n",
       "24   71.24            71  \n",
       "9    70.34            70  \n",
       "0    70.16            70  \n",
       "21   69.62            69  \n",
       "12   68.54            68  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.datatype == 'TAVG'].nlargest(n=10, columns='temp_C')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use `nsmallest()` for the n-smallest values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>datatype</th>\n",
       "      <th>station</th>\n",
       "      <th>flags</th>\n",
       "      <th>temp_C</th>\n",
       "      <th>temp_C_whole</th>\n",
       "      <th>temp_F</th>\n",
       "      <th>temp_F_whole</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2018-10-22</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5</td>\n",
       "      <td>42.08</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>2018-10-26</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5</td>\n",
       "      <td>42.08</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2018-10-21</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>6.1</td>\n",
       "      <td>6</td>\n",
       "      <td>42.98</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>2018-10-25</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>6.1</td>\n",
       "      <td>6</td>\n",
       "      <td>42.98</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2018-10-18</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>6.7</td>\n",
       "      <td>6</td>\n",
       "      <td>44.06</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date datatype            station     flags  temp_C  temp_C_whole  \\\n",
       "65 2018-10-22     TMIN  GHCND:USW00014732  ,,W,2400     5.6             5   \n",
       "77 2018-10-26     TMIN  GHCND:USW00014732  ,,W,2400     5.6             5   \n",
       "62 2018-10-21     TMIN  GHCND:USW00014732  ,,W,2400     6.1             6   \n",
       "74 2018-10-25     TMIN  GHCND:USW00014732  ,,W,2400     6.1             6   \n",
       "53 2018-10-18     TMIN  GHCND:USW00014732  ,,W,2400     6.7             6   \n",
       "\n",
       "    temp_F  temp_F_whole  \n",
       "65   42.08            42  \n",
       "77   42.08            42  \n",
       "62   42.98            42  \n",
       "74   42.98            42  \n",
       "53   44.06            44  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nsmallest(n=5, columns=['temp_C', 'date'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `sample()` method will give us rows (or columns with `axis=1`) at random. We can provide a seed (`random_state`) to make this reproducible. The index after we do this is jumbled:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([2, 30, 55, 16, 13], dtype='int64')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5, random_state=0).index"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use `sort_index()` to order it again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([2, 13, 16, 30, 55], dtype='int64')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5, random_state=0).sort_index().index"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `sort_index()` method can also sort columns alphabetically:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datatype</th>\n",
       "      <th>date</th>\n",
       "      <th>flags</th>\n",
       "      <th>station</th>\n",
       "      <th>temp_C</th>\n",
       "      <th>temp_C_whole</th>\n",
       "      <th>temp_F</th>\n",
       "      <th>temp_F_whole</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TAVG</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>21.2</td>\n",
       "      <td>21</td>\n",
       "      <td>70.16</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TMAX</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>25.6</td>\n",
       "      <td>25</td>\n",
       "      <td>78.08</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TMIN</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>18.3</td>\n",
       "      <td>18</td>\n",
       "      <td>64.94</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TAVG</td>\n",
       "      <td>2018-10-02</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>22.7</td>\n",
       "      <td>22</td>\n",
       "      <td>72.86</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TMAX</td>\n",
       "      <td>2018-10-02</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>26.1</td>\n",
       "      <td>26</td>\n",
       "      <td>78.98</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  datatype       date     flags            station  temp_C  temp_C_whole  \\\n",
       "0     TAVG 2018-10-01     H,,S,  GHCND:USW00014732    21.2            21   \n",
       "1     TMAX 2018-10-01  ,,W,2400  GHCND:USW00014732    25.6            25   \n",
       "2     TMIN 2018-10-01  ,,W,2400  GHCND:USW00014732    18.3            18   \n",
       "3     TAVG 2018-10-02     H,,S,  GHCND:USW00014732    22.7            22   \n",
       "4     TMAX 2018-10-02  ,,W,2400  GHCND:USW00014732    26.1            26   \n",
       "\n",
       "   temp_F  temp_F_whole  \n",
       "0   70.16            70  \n",
       "1   78.08            78  \n",
       "2   64.94            64  \n",
       "3   72.86            72  \n",
       "4   78.98            78  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis=1).head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can make selection with `loc` easier for many columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp_C</th>\n",
       "      <th>temp_C_whole</th>\n",
       "      <th>temp_F</th>\n",
       "      <th>temp_F_whole</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.2</td>\n",
       "      <td>21</td>\n",
       "      <td>70.16</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.6</td>\n",
       "      <td>25</td>\n",
       "      <td>78.08</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.3</td>\n",
       "      <td>18</td>\n",
       "      <td>64.94</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22.7</td>\n",
       "      <td>22</td>\n",
       "      <td>72.86</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.1</td>\n",
       "      <td>26</td>\n",
       "      <td>78.98</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   temp_C  temp_C_whole  temp_F  temp_F_whole\n",
       "0    21.2            21   70.16            70\n",
       "1    25.6            25   78.08            78\n",
       "2    18.3            18   64.94            64\n",
       "3    22.7            22   72.86            72\n",
       "4    26.1            26   78.98            78"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis=1).head().loc[:,'temp_C':'temp_F_whole']"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We must sort the index to compare two dataframes. If the index is different, but the data is the same, they will be marked not-equal:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.equals(df.sort_values(by='temp_C'))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting the index solves this issue:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.equals(df.sort_values(by='temp_C').sort_index())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's set the `date` column as our index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>date</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>21.2</td>\n",
       "      <td>21</td>\n",
       "      <td>70.16</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
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       "      <td>25.6</td>\n",
       "      <td>25</td>\n",
       "      <td>78.08</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>TMIN</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>18.3</td>\n",
       "      <td>18</td>\n",
       "      <td>64.94</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-02</th>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>22.7</td>\n",
       "      <td>22</td>\n",
       "      <td>72.86</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-02</th>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>26.1</td>\n",
       "      <td>26</td>\n",
       "      <td>78.98</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           datatype            station     flags  temp_C  temp_C_whole  \\\n",
       "date                                                                     \n",
       "2018-10-01     TAVG  GHCND:USW00014732     H,,S,    21.2            21   \n",
       "2018-10-01     TMAX  GHCND:USW00014732  ,,W,2400    25.6            25   \n",
       "2018-10-01     TMIN  GHCND:USW00014732  ,,W,2400    18.3            18   \n",
       "2018-10-02     TAVG  GHCND:USW00014732     H,,S,    22.7            22   \n",
       "2018-10-02     TMAX  GHCND:USW00014732  ,,W,2400    26.1            26   \n",
       "\n",
       "            temp_F  temp_F_whole  \n",
       "date                              \n",
       "2018-10-01   70.16            70  \n",
       "2018-10-01   78.08            78  \n",
       "2018-10-01   64.94            64  \n",
       "2018-10-02   72.86            72  \n",
       "2018-10-02   78.98            78  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('date', inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have an index of type `DatetimeIndex`, we can do datetime slicing and indexing. As long as we provide a date format that pandas understands, we can grab the data. To select all of 2018, we simply use `df.loc['2018']`, for the fourth quarter of 2018 we can use `df.loc['2018-Q4']`, grabbing October is as simple as using `df.loc['2018-10']`; these can also be combined to build ranges. Let's grab October 11, 2018 through October 12, 2018 (inclusive of both endpoints)&mdash;note that using `loc[]` is optional for ranges:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datatype</th>\n",
       "      <th>station</th>\n",
       "      <th>flags</th>\n",
       "      <th>temp_C</th>\n",
       "      <th>temp_C_whole</th>\n",
       "      <th>temp_F</th>\n",
       "      <th>temp_F_whole</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10-11</th>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>23.4</td>\n",
       "      <td>23</td>\n",
       "      <td>74.12</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-11</th>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>26.7</td>\n",
       "      <td>26</td>\n",
       "      <td>80.06</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-11</th>\n",
       "      <td>TMIN</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>21.7</td>\n",
       "      <td>21</td>\n",
       "      <td>71.06</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-12</th>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>18.3</td>\n",
       "      <td>18</td>\n",
       "      <td>64.94</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-12</th>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>22.2</td>\n",
       "      <td>22</td>\n",
       "      <td>71.96</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-12</th>\n",
       "      <td>TMIN</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>12.2</td>\n",
       "      <td>12</td>\n",
       "      <td>53.96</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           datatype            station     flags  temp_C  temp_C_whole  \\\n",
       "date                                                                     \n",
       "2018-10-11     TAVG  GHCND:USW00014732     H,,S,    23.4            23   \n",
       "2018-10-11     TMAX  GHCND:USW00014732  ,,W,2400    26.7            26   \n",
       "2018-10-11     TMIN  GHCND:USW00014732  ,,W,2400    21.7            21   \n",
       "2018-10-12     TAVG  GHCND:USW00014732     H,,S,    18.3            18   \n",
       "2018-10-12     TMAX  GHCND:USW00014732  ,,W,2400    22.2            22   \n",
       "2018-10-12     TMIN  GHCND:USW00014732  ,,W,2400    12.2            12   \n",
       "\n",
       "            temp_F  temp_F_whole  \n",
       "date                              \n",
       "2018-10-11   74.12            74  \n",
       "2018-10-11   80.06            80  \n",
       "2018-10-11   71.06            71  \n",
       "2018-10-12   64.94            64  \n",
       "2018-10-12   71.96            71  \n",
       "2018-10-12   53.96            53  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2018-10-11':'2018-10-12']"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use `reset_index()` to get a fresh index and move our current index into a column for safe keeping. This is especially useful if we had data, such as the date, in the index that we don't want to lose:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>datatype</th>\n",
       "      <th>station</th>\n",
       "      <th>flags</th>\n",
       "      <th>temp_C</th>\n",
       "      <th>temp_C_whole</th>\n",
       "      <th>temp_F</th>\n",
       "      <th>temp_F_whole</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>23.4</td>\n",
       "      <td>23</td>\n",
       "      <td>74.12</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>26.7</td>\n",
       "      <td>26</td>\n",
       "      <td>80.06</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>21.7</td>\n",
       "      <td>21</td>\n",
       "      <td>71.06</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-10-12</td>\n",
       "      <td>TAVG</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>H,,S,</td>\n",
       "      <td>18.3</td>\n",
       "      <td>18</td>\n",
       "      <td>64.94</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-10-12</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>22.2</td>\n",
       "      <td>22</td>\n",
       "      <td>71.96</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-10-12</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>GHCND:USW00014732</td>\n",
       "      <td>,,W,2400</td>\n",
       "      <td>12.2</td>\n",
       "      <td>12</td>\n",
       "      <td>53.96</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date datatype            station     flags  temp_C  temp_C_whole  \\\n",
       "0 2018-10-11     TAVG  GHCND:USW00014732     H,,S,    23.4            23   \n",
       "1 2018-10-11     TMAX  GHCND:USW00014732  ,,W,2400    26.7            26   \n",
       "2 2018-10-11     TMIN  GHCND:USW00014732  ,,W,2400    21.7            21   \n",
       "3 2018-10-12     TAVG  GHCND:USW00014732     H,,S,    18.3            18   \n",
       "4 2018-10-12     TMAX  GHCND:USW00014732  ,,W,2400    22.2            22   \n",
       "5 2018-10-12     TMIN  GHCND:USW00014732  ,,W,2400    12.2            12   \n",
       "\n",
       "   temp_F  temp_F_whole  \n",
       "0   74.12            74  \n",
       "1   80.06            80  \n",
       "2   71.06            71  \n",
       "3   64.94            64  \n",
       "4   71.96            71  \n",
       "5   53.96            53  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2018-10-11':'2018-10-12'].reset_index()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reindexing allows us to conform our axis to contain a given set of labels. Let's turn to the S&P 500 stock data in the `sp500.csv` file to see an example of this. Notice we only have data for trading days (weekdays, excluding holidays):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>day_of_week</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>2263.879883</td>\n",
       "      <td>2245.129883</td>\n",
       "      <td>2251.570068</td>\n",
       "      <td>2257.830078</td>\n",
       "      <td>3770530000</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>2272.820068</td>\n",
       "      <td>2261.600098</td>\n",
       "      <td>2261.600098</td>\n",
       "      <td>2270.750000</td>\n",
       "      <td>3764890000</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>2271.500000</td>\n",
       "      <td>2260.449951</td>\n",
       "      <td>2268.179932</td>\n",
       "      <td>2269.000000</td>\n",
       "      <td>3761820000</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>2282.100098</td>\n",
       "      <td>2264.060059</td>\n",
       "      <td>2271.139893</td>\n",
       "      <td>2276.979980</td>\n",
       "      <td>3339890000</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>2275.489990</td>\n",
       "      <td>2268.899902</td>\n",
       "      <td>2273.590088</td>\n",
       "      <td>2268.899902</td>\n",
       "      <td>3217610000</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>2279.270020</td>\n",
       "      <td>2265.270020</td>\n",
       "      <td>2269.719971</td>\n",
       "      <td>2268.899902</td>\n",
       "      <td>3638790000</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>2275.320068</td>\n",
       "      <td>2260.830078</td>\n",
       "      <td>2268.600098</td>\n",
       "      <td>2275.320068</td>\n",
       "      <td>3620410000</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-12</th>\n",
       "      <td>2271.780029</td>\n",
       "      <td>2254.250000</td>\n",
       "      <td>2271.139893</td>\n",
       "      <td>2270.439941</td>\n",
       "      <td>3462130000</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-13</th>\n",
       "      <td>2278.679932</td>\n",
       "      <td>2271.510010</td>\n",
       "      <td>2272.739990</td>\n",
       "      <td>2274.639893</td>\n",
       "      <td>3081270000</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-17</th>\n",
       "      <td>2272.080078</td>\n",
       "      <td>2262.810059</td>\n",
       "      <td>2269.139893</td>\n",
       "      <td>2267.889893</td>\n",
       "      <td>3584990000</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   high          low         open        close      volume  \\\n",
       "date                                                                         \n",
       "2017-01-03  2263.879883  2245.129883  2251.570068  2257.830078  3770530000   \n",
       "2017-01-04  2272.820068  2261.600098  2261.600098  2270.750000  3764890000   \n",
       "2017-01-05  2271.500000  2260.449951  2268.179932  2269.000000  3761820000   \n",
       "2017-01-06  2282.100098  2264.060059  2271.139893  2276.979980  3339890000   \n",
       "2017-01-09  2275.489990  2268.899902  2273.590088  2268.899902  3217610000   \n",
       "2017-01-10  2279.270020  2265.270020  2269.719971  2268.899902  3638790000   \n",
       "2017-01-11  2275.320068  2260.830078  2268.600098  2275.320068  3620410000   \n",
       "2017-01-12  2271.780029  2254.250000  2271.139893  2270.439941  3462130000   \n",
       "2017-01-13  2278.679932  2271.510010  2272.739990  2274.639893  3081270000   \n",
       "2017-01-17  2272.080078  2262.810059  2269.139893  2267.889893  3584990000   \n",
       "\n",
       "           day_of_week  \n",
       "date                    \n",
       "2017-01-03     Tuesday  \n",
       "2017-01-04   Wednesday  \n",
       "2017-01-05    Thursday  \n",
       "2017-01-06      Friday  \n",
       "2017-01-09      Monday  \n",
       "2017-01-10     Tuesday  \n",
       "2017-01-11   Wednesday  \n",
       "2017-01-12    Thursday  \n",
       "2017-01-13      Friday  \n",
       "2017-01-17     Tuesday  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp = pd.read_csv(\n",
    "    '../data/sp500.csv', index_col='date', parse_dates=True\n",
    ").drop(columns=['adj_close'])\n",
    "\n",
    "sp.head(10).assign(\n",
    "    day_of_week=lambda x: x.index.day_name()\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to look at the value of a portfolio (group of assets) that trade on different days, we need to handle the mismatch in the index. Bitcoin, for example, trades daily. If we sum up all the data we have for each day (aggregations will be covered in chapter 4, so don't fixate on this part), we get the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>day_of_week</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>1003.080000</td>\n",
       "      <td>958.700000</td>\n",
       "      <td>963.660000</td>\n",
       "      <td>998.330000</td>\n",
       "      <td>147775008</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>1031.390000</td>\n",
       "      <td>996.700000</td>\n",
       "      <td>998.620000</td>\n",
       "      <td>1021.750000</td>\n",
       "      <td>222184992</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>3307.959883</td>\n",
       "      <td>3266.729883</td>\n",
       "      <td>3273.170068</td>\n",
       "      <td>3301.670078</td>\n",
       "      <td>3955698000</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>3432.240068</td>\n",
       "      <td>3306.000098</td>\n",
       "      <td>3306.000098</td>\n",
       "      <td>3425.480000</td>\n",
       "      <td>4109835984</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>3462.600000</td>\n",
       "      <td>3170.869951</td>\n",
       "      <td>3424.909932</td>\n",
       "      <td>3282.380000</td>\n",
       "      <td>4272019008</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>3328.910098</td>\n",
       "      <td>3148.000059</td>\n",
       "      <td>3285.379893</td>\n",
       "      <td>3179.179980</td>\n",
       "      <td>3691766000</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-07</th>\n",
       "      <td>908.590000</td>\n",
       "      <td>823.560000</td>\n",
       "      <td>903.490000</td>\n",
       "      <td>908.590000</td>\n",
       "      <td>279550016</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>942.720000</td>\n",
       "      <td>887.250000</td>\n",
       "      <td>908.170000</td>\n",
       "      <td>911.200000</td>\n",
       "      <td>158715008</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>3189.179990</td>\n",
       "      <td>3148.709902</td>\n",
       "      <td>3186.830088</td>\n",
       "      <td>3171.729902</td>\n",
       "      <td>3359486992</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>3194.140020</td>\n",
       "      <td>3166.330020</td>\n",
       "      <td>3172.159971</td>\n",
       "      <td>3176.579902</td>\n",
       "      <td>3754598000</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   high          low         open        close      volume  \\\n",
       "date                                                                         \n",
       "2017-01-01  1003.080000   958.700000   963.660000   998.330000   147775008   \n",
       "2017-01-02  1031.390000   996.700000   998.620000  1021.750000   222184992   \n",
       "2017-01-03  3307.959883  3266.729883  3273.170068  3301.670078  3955698000   \n",
       "2017-01-04  3432.240068  3306.000098  3306.000098  3425.480000  4109835984   \n",
       "2017-01-05  3462.600000  3170.869951  3424.909932  3282.380000  4272019008   \n",
       "2017-01-06  3328.910098  3148.000059  3285.379893  3179.179980  3691766000   \n",
       "2017-01-07   908.590000   823.560000   903.490000   908.590000   279550016   \n",
       "2017-01-08   942.720000   887.250000   908.170000   911.200000   158715008   \n",
       "2017-01-09  3189.179990  3148.709902  3186.830088  3171.729902  3359486992   \n",
       "2017-01-10  3194.140020  3166.330020  3172.159971  3176.579902  3754598000   \n",
       "\n",
       "           day_of_week  \n",
       "date                    \n",
       "2017-01-01      Sunday  \n",
       "2017-01-02      Monday  \n",
       "2017-01-03     Tuesday  \n",
       "2017-01-04   Wednesday  \n",
       "2017-01-05    Thursday  \n",
       "2017-01-06      Friday  \n",
       "2017-01-07    Saturday  \n",
       "2017-01-08      Sunday  \n",
       "2017-01-09      Monday  \n",
       "2017-01-10     Tuesday  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bitcoin = pd.read_csv(\n",
    "    '../data/bitcoin.csv', index_col='date', parse_dates=True\n",
    ").drop(columns=['market_cap'])\n",
    "\n",
    "# every day's closing price = S&P 500 close + Bitcoin close (same for other metrics)\n",
    "portfolio = pd.concat([sp, bitcoin], sort=False).groupby(level='date').sum()\n",
    "\n",
    "portfolio.head(10).assign(\n",
    "    day_of_week=lambda x: x.index.day_name()\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It may not be immediately obvious what is wrong with the previous data, but with a visualization we can easily see the cyclical pattern of drops on the days the stock market is closed. (Don't worry about the plotting code too much, we will cover it in depth in chapters 5 and 6).\n",
    "\n",
    "We will need to import `matplotlib` now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt # we use this module for plotting\n",
    "from matplotlib.ticker import StrMethodFormatter # for formatting the axis"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can see why we need to reindex:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6TKfTCVFRUW6/RxyJ9zUvL0+67NixYwIAYdasWU7b7t69WwAg/Otf/5IuE99bN2/e7PFY4vv41Vdf7fRcnj17VggICHD5GDi+/sTX6ssvv+y0nfga+fzzz50u79atm9C3b1/BZDI5XX7zzTcLbdq0kd7TxOd16tSpTtudP39eUCqVwuzZs50u1+l0Qnx8vDBhwgTpsmnTpgkAhP/9739O244dO9bpvTIvL6/G/aqNq/cn8Tl76aWXnLadNWuWoNFopMf2xx9/FAAIr7/+utN2ixYtqnEO1b+3vX3defsYp6amCrfddptX95mIqDVipR4REV0xq1atwt69e7F37178+OOPmDZtGh5++GG88cYbddrfzp07UVpailmzZrltFztx4gSysrIwZcoUpxbV4OBg3Hnnnfjtt99QXl5e63Hi4+Nx7bXXOl3Wu3dvr1pShw8fjoCAAOlrxIgRAIBOnTo5Xe6p7QywrYl3/vx5rF27FvPnz0fPnj3x9ddfY/z48U5TS0+fPo2bbroJc+fOxd69e9GlSxeMGjUK+/btk7Z5/vnnoVKppPYu0T333IOAgAAEBQXhhhtugMViwRdffOGyYs7R888/j4ceegjDhg3D2LFjsWzZMrzwwgv45Zdf8M033zhtW1trX/XrfNnWkVKpxOTJk/HVV19JbWUWiwWrV6/GrbfeiqioKGnb/fv3Y/z48YiKioJCoUBAQACmTp0Ki8Xist3MV5WVldi8eTNuv/12BAUFwWw2S19jx45FZWVlrdWUHTt2RHJyMjZt2gQA2LhxI3r16oXJkycjMzMTp0+fhsFgwI4dO6Tvr7oaNmwYQkJCpH/HxcUhNjZW+l7X6/XYvXs37rrrLgQHB0vbKRQKTJkyBRcvXqx3y7c3vHnOvHl/OHnyJE6fPo0ZM2ZAo9G43KYh73OfPn2QmJgo/Vuj0aBLly71bnf35nn8/fffcdttt0GlUknbBQcHO61v6qstW7YAQI220GuvvRbdu3fH5s2bnS6PiIjAjTfe6HG/4vv4pEmTnJ7LpKQkDBo0qM7n68qpU6dw/Phx/OUvfwGAGq/X7OzsGs/3nXfe6fTvDRs2wGw2Y+rUqU6312g0GDp0aI0JtjKZrMbj7u3PmLoYP358jWNVVlYiNzcXQNXzKD4Gotqq6kXevO58eYyvvfZa/Pjjj/jnP/+JrVu31qj+JiJq7Tgog4iIrpju3bvXGJRx7tw5PP7445g8ebLLSam1ycvLAwCnKYfV1bYWXUJCAqxWK4qKimpdnN0xABKp1WqvPly8/fbb0Ol00r/37duHBx98EOvWrXM6p+joaI/7AmzDKG677TbcdtttAIDz589jzJgx+O9//4uHHnoIPXv2xNKlSyGTyfDII49AqVRi48aNGDVqFEaOHInNmzejb9++2Lp1K0aMGFFjYuWLL76IG2+8EQqFAtHR0Wjfvr1X5+XK5MmTMX/+fPz222/SZN6oqCgcOHCgxrZ6vR5Go1EakhEREQGZTOayxaqwsBAApG3dmT59Ol555RV89tlnmDlzJjZs2IDs7Gyn1tvz589jyJAh6Nq1K15//XUkJydDo9Fgz549ePjhh/3yAbKgoABmsxnLli3DsmXLXG5TvS2xuuHDh0vtd5s2bcLIkSPRq1cvxMXFYdOmTejcuTMqKirqHep5+l4vKiqCIAhuX08AGrwtztvnzJv3B2+2acj7XJ/3lvrsV7xPcXFxNbZzdZm3PL3fVg+pvJ2YKu63etu+eJmn5Qh8IbZdz58/H/Pnz3e5TfXXa/X7Ie7jmmuucXn76mugBgUF1QiV1Wo1KisrvT9xH1T//hB/DojfHwUFBVAqlTW2c/X4V+fNa8qXx/g///kP2rVrhzVr1uDFF1+ERqPB6NGj8fLLL6Nz584ez4eIqKVjqEdERI2qd+/e2LBhA06ePFmjGs4TcZ236guwOxI/lIjrkTnKysqCXC5HRESET8f1RdeuXZ3+XVZWBgDo1auXT9Nv3UlMTMRf//pXzJkzB0eOHEHPnj1x+vRpBAUFQam0/ZgPCwvDxo0bMXr0aIwYMQL//ve/8fPPP2P79u019tehQwen4NUfHD/A9urVC5999hlycnKcPiAeOnQIgG1CMmALLzt16iRd7ujQoUMIDAxEhw4daj1ujx49cO211+KDDz7AzJkz8cEHHyAhIQGjRo2Stvn666+h1+vx1VdfOQ3kcBU8Vid+CK9e7Vg94ImIiJCquh5++GGX+0pJSan1WMOHD8eKFSuwZ88e7N69WxpScOONN2Ljxo04d+4cgoODG3zaZUREBORyudvXE1AVUDs+Po7hsacA0xNvnzNv3h+82aYp3Gd/E0Nzx3UDRTk5OXXer+P7bfVQJysrq8YfL7ydECvu19W51ed8XRHP8YknnsAdd9zhcpvq7+vV74e4jy+++MLpe7S5iIqKgtlsRkFBgVOw581j7c1rypfHWKvV4plnnsEzzzyDy5cvS1V7t9xyC44fP+71fSIiaqnYfktERI1K/CBe2yCG6lUEokGDBiEsLAzLly+HIAgub9u1a1e0bdsWn3zyidM2er0eX375pTQRt6nT6XRSIFjdsWPHAFRVDaWmpiIrK8up1S00NBQbNmxASkoK5syZg6lTp2Lw4MENes7igvuOQdOtt94KmUxWYzH+lStXIjAwEDfddJN02e23346ff/4ZFy5ckC7T6XT46quvMH78eCm0rM19992H3bt3Y8eOHfj2228xbdo0aTIwUPVh3DGAEQQB7777rsd9x8XFQaPR4M8//3S6vHq7cVBQEIYNG4b9+/ejd+/e6N+/f40vV5VVjoYPHw6ZTIannnoKcrkcN9xwAwDbEI0tW7Zg48aNuOGGG2pMkq7O3WvJW1qtFgMGDMBXX33ltA+r1YqPPvoI7dq1Q5cuXQBACq2rPz7idN7q5+XtOXn7nHnz/tClSxd07NgR77//fo1wVtRQ99lb9X3OXNFqtejfvz++/vprGI1G6fKysjKXU3K9JbbSVh8itHfvXhw7dgzDhw+v0367du2KNm3a4NNPP3V6Ls+dO4edO3fW+XzdHatz5844ePCgy9dq//79nVqbXRk9ejSUSiVOnz7tdh++aojvA3eGDRsGAPj444+dLv/kk0883tbbn8t1eYzj4uKQnp6Oe++9FydOnPC4dAYRUWvASj0iIrpiDh8+DLPZDMBWzfTVV19h48aNuP3222utVBKrt9555x2EhIRAo9EgJSUFUVFReOWVV3D//fdjxIgReOCBBxAXF4dTp07h4MGDeOONNyCXy/HSSy/hL3/5C26++WbMnDkTBoMBL7/8MoqLi/HCCy9ckfteXydOnMDo0aMxceJEDB06FG3atEFRURG+//57vPPOO0hLS5PWlnr88cfxxRdf4LbbbsPcuXMxZMgQlJWVYcuWLTh8+DDat2+Pzz//HNOnT5fCofrYvn07Fi1ahNtvvx0dOnRAZWUlfvzxR7zzzju48cYbndaK6tmzJ2bMmIGnn34aCoUC11xzDX766Se88847eP75551aaufPn4/Vq1dj3LhxePbZZ6FWq/HCCy+gsrISCxcu9Orc7r33XsybNw/33nsvDAZDjbW+Ro4cCZVKhXvvvRePP/44Kisr8dZbb6GoqMjjvmUyGSZPnoz3338fHTt2xFVXXYU9e/a4/OD7+uuv4/rrr8eQIUPw0EMPITk5GTqdDqdOncK3337rcYJnbGwsUlNT8dNPP2HYsGFSED1ixAgUFhaisLAQS5cu9XjOvXr1AmBrsx4zZgwUCgV69+7ttK6aJ0uWLMHIkSMxbNgwzJ8/HyqVCm+++SYOHz6MTz/9VArdxo4di8jISMyYMQPPPvsslEolVq5c6RTSOp7XZ599hjVr1qBDhw7QaDTSuVbn7XMWHBzs8f0BsE2VveWWWzBw4EDMnTsXiYmJOH/+PDZs2CCFGg1xn71V2/tffTz77LMYN24cRo8ejUcffRQWiwUvv/wygoODpRZ3X3Xt2hV//etfsWzZMsjlcowZMwZnz57FU089hfbt2ztNWPWFXC7Hc889h/vvvx+33347HnjgARQXF2PhwoVetYT66u2338aYMWMwevRopKeno23btigsLMSxY8fwxx9/4PPPP6/19snJyXj22WexYMECnDlzBjfddBMiIiJw+fJl7NmzR6o+80VISAiSkpLwzTffYPjw4YiMjER0dLRfKr6rGzVqFG644QY8/vjj0Ov16N+/P3799VesXr3a4229fd15+xgPGDAAN998M3r37o2IiAgcO3YMq1evbjZ/kCMianCNNaGDiIhaD1fTb8PCwoQ+ffoIS5cuFSorK522h4sJf6+99pqQkpIiKBSKGhP9fvjhB2Ho0KGCVqsVgoKChB49eggvvvii0+2//vprYcCAAYJGoxG0Wq0wfPhw4ddff3V5ntWn3/bs2bPGfXI1ddIb4rRFx2N4o6ioSHj++eeFG2+8UWjbtq2gUqkErVYr9OnTR3j++eeF8vJyp+1zc3OF2bNnC0lJSYJSqRQiIyOFsWPHCj/++KOg1+uFAQMGCMHBwdJj4G4KpDcyMjKEsWPHCm3bthXUarWg0WiEXr16CYsWLarx3AqCIBiNRuHpp58WEhMTBZVKJXTp0kX4z3/+43Lfp06dEm677TYhNDRUCAoKEoYPHy7s27fPp/ObNGmSAEAYPHiwy+u//fZb4aqrrhI0Go3Qtm1b4e9//7s0/dFx4qir57ykpES4//77hbi4OEGr1Qq33HKLcPbsWZffw5mZmcL06dOFtm3bCgEBAUJMTIwwaNAg4fnnn/fqfsydO1cAICxatMjp8s6dOwsAhD///NPpclfTbw0Gg3D//fcLMTExgkwmc/peBCA8/PDDNY6blJQkTJs2zemy7du3CzfeeKOg1WqFwMBAYeDAgcK3335b47Z79uwRBg0aJGi1WqFt27bC008/Lbz33ns1XgNnz54VRo0aJYSEhAgAPL62vH3OBMG794ddu3YJY8aMEcLCwgS1Wi107NhRmDt3boPe56SkJGHcuHE1bj906NAak4Ddvf+5m37r7fO4du1aoVevXoJKpRISExOFF154QXjkkUeEiIiIGrevztX0W0GwTap98cUXhS5duggBAQFCdHS0MHnyZOHChQs17qer99bavPfee0Lnzp2l943333/f7WNQn+m3giAIBw8eFCZMmCDExsYKAQEBQnx8vHDjjTcKy5cvl7YRf2bs3bvX5fl+/fXXwrBhw4TQ0FBBrVYLSUlJwl133SVs2rRJ2mbatGmCVqutcVtXk5Q3bdok9O3bV1Cr1QKAGs+no9qm31Z/zlz97CsuLhamT58uhIeHC0FBQcLIkSOF48ePe5x+K/LmdefNY/zPf/5T6N+/vxARESGo1WqhQ4cOwty5c91OMCciam1kguCmLpqIiIiIiFoNk8mEPn36oG3btvjpp58a+3SIiIjIA7bfEhERERG1QjNmzMDIkSPRpk0b5OTkYPny5Th27Bhef/31xj41IiIi8gJDPSIiIiKiVkin02H+/PnIy8tDQEAArr76avzwww8YMWJEY58aEREReYHtt0RERERERERERM2MvLFPgIiIiIiIiIiIiHzDUI+IiIiIiIiIiKiZaTKhXnp6emOfAhERERERERERUbPQZEK96kwmE/7xj3+gV69e0Gq1SEhIwNSpU5GVlSVtU1hYiNmzZ6Nr164ICgpCYmIiHnnkEZSUlNS674ULF0Imkzl9xcfHO20jCAIWLlyIhIQEBAYGIi0tDUeOHHHaxmAwYPbs2YiOjoZWq8X48eNx8eJFn+6nIAgoLS0FlzYkIiIiIiIiIiJvNWqol5+fj2nTpiExMRGffvopOnXqhAkTJsBoNKK8vBx//PEHnnrqKfzxxx/46quvcPLkSYwfP166fVZWFrKysvB///d/OHToEFauXIn169djxowZHo/ds2dPZGdnS1+HDh1yuv6ll17C0qVL8cYbb2Dv3r2Ij4/HyJEjodPppG3mzJmDtWvX4rPPPsOOHTtQVlaGm2++GRaLxevHQKfTISwszGm/REREREREREREtWnU6bdTpkzB3r178fbbb+O1117DI488gvXr1+OZZ56BRqOpsf3evXtx7bXX4ty5c0hMTHS5z88//xyTJ0+GXq+HUql0uc3ChQvx9ddf48CBAy6vFwQBCQkJmDNnDv7xj38AsFXlxcXF4cUXX8TMmTNRUlKCmJgYrF69Gvfccw8AW8jYvn17/PDDDxg9erRXj0FpaSnCwsJQUlKC0NBQr25DREREREREREStW6NW6u3fvx9TpkzB0KFDERYWhmHDhuHFF190GegBQElJCWQyGcLDw93uUwzH3AV6ooyMDCQkJCAlJQUTJ07EmTNnpOsyMzORk5ODUaNGSZep1WoMHToUO3fuBADs27cPJpPJaZuEhASkpqZK27hiMBhQWlrq9EVEREREREREROSLRg31Bg8ejA8++ADfffedx20rKyvxz3/+E5MmTXJb0VZQUIDnnnsOM2fOrHVfAwYMwKpVq7Bhwwa8++67yMnJwaBBg1BQUAAAyMnJAQDExcU53S4uLk66LicnByqVChEREW63cWXJkiUICwuTvtq3b1/7HSciIiIiIiIiIqqmUUO9pUuX4p577sHcuXOxatUq9OnTB8uXL6+xnclkwsSJE2G1WvHmm2+63FdpaSnGjRuHHj164Omnn671uGPGjMGdd96JXr16YcSIEfj+++8BAB9++KHTdjKZzOnfgiDUuKw6T9s88cQTKCkpkb4uXLhQ6/6IiIiIiIiIiIiqa9RQT6vVYtGiRcjIyMD48ePx0EMPYd68eXjnnXekbUwmEyZMmIDMzExs3LjRZZWeTqfDTTfdhODgYKxduxYBAQE+n0evXr2QkZEBANIk3OoVd7m5uVL1Xnx8PIxGI4qKitxu44parUZoaKjTFxERERERERERkS8aNdRzFB4ejpkzZ2LMmDHYvn07gKpALyMjA5s2bUJUVFSN25WWlmLUqFFQqVRYt26d2/X4amMwGHDs2DG0adMGAJCSkoL4+Hhs3LhR2sZoNGLbtm0YNGgQAKBfv34ICAhw2iY7OxuHDx+WtiEiIiIiIiIiImoIjRrqzZ07F9u2bUNJSQksFgu2bNmCbdu2oV+/fjCbzbjrrrvw+++/4+OPP4bFYkFOTg5ycnJgNBoB2Cr0Ro0aBb1ejxUrVqC0tFTaxmKxuD3u/PnzsW3bNmRmZmL37t246667UFpaimnTpgGwtd3OmTMHixcvxtq1a3H48GGkp6cjKCgIkyZNAgCEhYVhxowZeOyxx7B582bs378fkydPllp6iYiIiIiIiIiIGkrtI2IbWGJiIubNm4eMjAzo9Xps3boV06dPx+zZs3HhwgWsW7cOANCnTx+n223ZsgVpaWnYt28fdu/eDQDo1KmT0zaZmZlITk4GAKSlpSE5ORkrV64EAFy8eBH33nsv8vPzERMTg4EDB+K3335DUlKSdPvHH38cFRUVmDVrFoqKijBgwAD89NNPCAkJkbZ59dVXoVQqMWHCBFRUVGD48OFYuXIlFAqFnx8pIiIiIiIiIiKiKjJBEITGPgkASE9Pl0I3f0tOTsbChQuRnp7eIPuvj9LSUoSFhaGkpITr6xERERERERERkVeazJp6DeX48eMICQnB1KlTG/tUiIiIiIiIiIiI/KLJVOq1VqzUIyIiIiIiIiIiX7X4Sj0iIiIiIiIiIqKWhqEeERERERERERFRM8NQj4iIiIiIiIiIqJlhqEdERM0Kl4IlIiIiIiJiqEdERM3I0o0ncc2izbhYVN7Yp0JERERERNSoGOoREVGz8dORHOSXGfDLyfzGPhUiIiIiIqJGxVCPiIiajQK9EQBwOq+skc+EiIiIiIiocTHUIyKiZkEQBBQx1CMiIiIiIgLAUI+IiJoJncEMs9U2JONULkM9IiIiIiJq3RjqERFRs1BYZpT+/1JxBSqMlkY8GyIiIiIiosbFUI+IiJqFwvKqUE8QgMx8fSOeDRERERERUeNiqEdERM2CY6UeAJziunpERERERNSKMdQjIqJmwbFSDwBOc109IiIiIiJqxRjqERFRsyBOvhWxUo+IiIiIiFozhnpERNQsFNpDvQ7RWgCs1CMiIiIiotaNoR4RETULYqh3TXIkANugDItVaMxTIiIiIiIiajQM9YiIqFkQQ71e7cKgUsphMFtxqaiikc+KiIiIiIiocTDUIyKiZkEclBEToq5qweW6ekRERERE1Eox1CMiomZBrNSL1KrQMSYYAHCK6+oREREREVErxVCPiIiaBTHUiwhSoWMMK/WIiIiIiKh1Y6hHRERNnsliha7SDACI0qrQMdZWqcdQj4iIiIiIWiuGekRE1OQV2av05DIgLDCA7bdERERERNTqMdQjIqImTxySERGkglwuk0K9onKT1JZLRERERETUmjDUIyKiJq+wzB7qaVUAgECVAm3DAwGwBZeIiIiIiFonhnpERNTkiZV6kfZQDwA6xbIFl4iIiIiIWi+GekRE1OSJa+pFBlWFemIL7mmGekRERERE1Aox1CMioiavQO/cfgs4VOqx/ZaIiIiIiFohhnpERNTkiZV6UVrHSj0tAK6pR0RERERErRNDPSIiavJqq9S7WFSBSpOlUc6LiIiIiIiosTDUIyJqZU5e1uHj3edgtQqNfSpeKyqvWakXqVUhPCgAggCcydM31qkRERERERE1CoZ6REStzFNfH8aCtYex/VR+Y5+K1wr1JgDOlXoymQydxGEZbMElIiIiIqJWhqEeEVErk6szAACOZpU28pl4r1BvO2fH6bdA1QTcU5yAS0RERERErQxDPSKiVkZXaat6a+wgTG8wY8ORHJgt1lq3EwQBRfZKvchg51Cvc5wt1Dt5WdcwJ0lERERERNREMdQjImplSivNAIBTuY0bhL2+OQMzV+/Dyp1na92uzGCG0R78Va/U694mFABwLLv5VB0SERERERH5A0M9IqJWxGC2wGi2BWSn8/QQhMYblrH3bCEAYNvJvFq3E6v0AgMUCFQpnK4TQ71zheXQG8wNcJZERERERERNE0M9IqJWRFdZFXyVGczIKa1slPOwWgWcyLFVCu47V1RrC26hffJtpFZV47pIrQpxoWoIAnA8hy24RERERETUejDUIyJqRRxDPQDIuNw46+qdLyxHudECACg3WnCklqEd4pCMCG2Ay+vFar2jbMF1yWi2Ir/M0NinQUREREREftZkQr309PTGPgUiohZPHJIhaqxhGdXXwBNbcV0pFIdkaNUur+e6erV78KN9uG7JZlwqrmjsUyEiIiIiIj9qMqFedSaTCf/4xz/Qq1cvaLVaJCQkYOrUqcjKynLazmAwYPbs2YiOjoZWq8X48eNx8eJFj/t/8803kZKSAo1Gg379+mH79u1O1wuCgIULFyIhIQGBgYFIS0vDkSNH/HJsIqLGUqNSz0+hniAIOHChGOVG79a1O2ZvlQ1QyAAAuzNrC/VsVWaRQbVX6jHUc+33s4UwWQScyOHjQ0RERETUkjRqqJefn49p06YhMTERn376KTp16oQJEybAaDSivLwcf/zxB5566in88ccf+Oqrr3Dy5EmMHz/eaR9z5szB2rVr8dlnn2HHjh0oKyvDzTffDIvF4va4a9aswZw5c7BgwQLs378fQ4YMwZgxY3D+/Hlpm5deeglLly7FG2+8gb179yI+Ph4jR46ETle1ZlNdjk1E1JiqV+qd9lOot2bvBdz231/xwo/HvdpeDODGpLYBYAuerFbXQzs8Ver1aBMCADiRo3O7j9ZKbzBL044LyoyNfDZERERERORPjRrqzZ07F7t378bq1asxduxYvPvuu0hJSYHVakVYWBg2btyICRMmoGvXrhg4cCCWLVuGffv2SeFbSUkJVqxYgVdeeQUjRoxA37598dFHH+HQoUPYtGmT2+MuXboUM2bMwP3334/u3bvjtddeQ/v27fHWW28BsFWcvPbaa1iwYAHuuOMOpKam4sMPP0R5eTk++eSTeh3bYDCgtLTU6YuI6EoRA56YEFtAdiqv/qGe1Spg+bbTAIDtGfle3UYM9e7u3w6BAQoUlZvcnotUqedmTb2U6GBoAuQoN1pwrrDc19Nv0bJLqlpui8oZ6hERERERtSSNGurt378fU6ZMwdChQxEWFoZhw4bhxRdfhEajcbl9SUkJZDIZwsPDAQD79u2DyWTCqFGjpG0SEhKQmpqKnTt3utyH0WjEvn37nG4DAKNGjZJuk5mZiZycHKdt1Go1hg4dKm1Tl2MDwJIlSxAWFiZ9tW/fvpZHiIjIv8T226vahUMmAwr1RhTUc4jCz8dzcbbAFqZl5utR7CE8Kq004WKRLWzq1TYMfRPDAQB73LTgeqrUU8hl6Bpnq9ZjC66zS8VV040L9Az1iIiIiIhakkYN9QYPHowPPvgA3333ncdtKysr8c9//hOTJk1CaKht/aScnByoVCpEREQ4bRsXF4ecnByX+8nPz4fFYkFcXJzb24j/9bSNr8cGgCeeeAIlJSXS14ULFzzddSIivxHbb2ND1WgbHgig/sMyVuzIdPr3nxdLat3+hH09vTZhGoQHqXBtSiQA96GeWGHmrlIP4Lp67mQ7DMcoYqhHRERERNSiNGqot3TpUtxzzz2YO3cuVq1ahT59+mD58uU1tjOZTJg4cSKsVivefPNNj/sVBAEymazWbapf7+o23mzj67HVajVCQ0OdvoiIrhSxUi9Eo0Tn2GAA9WvBPZJVgl1nCqCQy3Btsi2cO3ihuNbbiMGbGMSJt9uTWQhBqLkmXqE9jIoIUrndJ0M917IcQr1ChnpERERERC1Ko4Z6Wq0WixYtQkZGBsaPH4+HHnoI8+bNwzvvvCNtYzKZMGHCBGRmZmLjxo1OIVh8fDyMRiOKioqc9pubm1ujyk4UHR0NhUJRo5rO8Tbx8fEA4HEbX49NRNTYxEq9UE0AOtlDvYzLdQ/13t9xFgAwJjUeN6Xa3jsPXiyu9TbHsm2Vet3ibS2zfRMjoJTLkFNaKbXlOhLDqKhgz6He0SyGeo6ySqrabxnqERERERG1LI0a6jkKDw/HzJkzMWbMGGzfvh1AVaCXkZGBTZs2ISoqyuk2/fr1Q0BAADZu3Chdlp2djcOHD2PQoEEuj6NSqdCvXz+n2wDAxo0bpdukpKQgPj7eaRuj0Yht27ZJ29Tl2EREjc25Us8Wqp2uY6Verq4S3x7MAgDMuD4FV7UPBwAcuFDisuJOVL1SL1ClQK92YQBqtuCaLFaUVNiCyNoq9brZJ+BmlVR6XNOvNXEclMFQj4iIiIioZWn06bfbtm1DSUkJLBYLtmzZgm3btqFfv34wm82466678Pvvv+Pjjz+GxWJBTk4OcnJyYDTaPpiEhYVhxowZeOyxx7B582bs378fkydPRq9evTBixAi3x503bx7ee+89vP/++zh27Bjmzp2L8+fP48EHHwRga7udM2cOFi9ejLVr1+Lw4cNIT09HUFAQJk2aVK9jExE1JsdQr6PYflvHNfU++u08jBYr+iaGo29iBHomhEIplyG/zOBUIebIYhWkNfXEUA+AtK7e3rPOoV5xuS3Qk8mA8FpCvVBNANpF2NYIFCsBCcjioAwiIiIiohZL2ZgHT0xMxLx585CRkQG9Xo+tW7di+vTpmD17Ni5cuIB169YBAPr06eN0uy1btiAtLQ0A8Oqrr0KpVGLChAmoqKjA8OHDsXLlSigUCmn7tLQ0JCcnY+XKlQCAe+65BwUFBXj22WeRnZ2N1NRU/PDDD0hKSpJu8/jjj6OiogKzZs1CUVERBgwYgJ9++gkhISHSNt4cm4ioKRHbb0PUVe232SWV0FWaEKJxP4iiukqTBR//dg6ArUoPADQBCnRrE4LDl0px8EKxNIjD0fnCclSYLFAr5UiOCpIuvzY5Em9vO1OjUk8ckhEeGACFvPY1Tbu3CcXFogocyy7FdR2jat22NRAEwWlNPV2lGSaLFQGKJlOkT0RERERE9dDolXr79u1DaWkppkyZggsXLuCll16CQqFAcnIyBEFw+SUGegCg0WiwbNkyFBQUoLy8HN9++y3at2/vdJyzZ8863QYAZs2ahbNnz8JgMGDfvn244YYbnK6XyWRYuHAhsrOzUVlZiW3btiE1NdVpG2+OTUTUlDhW6oUFBiA2RA0AOJ2n92k/3xy4hAK9EW3DA3FTz3jp8qvahQNwPyxDbL3tGh8CpUO41D8pEjIZcCZfjzydQbq8oMw+JEPrvkpPxGEZzgr1RhjMVgC2SkeAE3CJiIiIiFqSFv/n+uPHjyMkJARTp05t7FMhImp0pVKoZ6vK61THFtxNx3IBAJMGJDqFc1Xr6hW7vJ20nl688+TvsKAAdI2zVUI7tuCKlXpRXoR6Pezr6h3LYagH2CowASAmRC2tR1jI9QaJiIiIiFqMJhPqia2x/tatWzccOnQIcnmTuatERI1Gar/V2FZfkCbg5vq2Dp04vCLJoYUWAPrYQ71Dl0pgsdYcliFNvm0TUuM6cV09xxZccR242oZkiHq0sQ3bOHm5DCaL1eP2LZ3YepsQpkGkPRQtLGOoR0RERETUUjDpIiJqJQxmi9SOGWqv1OtsD/VO+1ippzfYKv60auelWTvGBCNIpUC50eKy+q/65FtHAzvY1sH79mAWSuwDMsR20ahgz6Feu4hABKuVMJqtOONjO3FLJIZ6bcICq0I9VuoREREREbUYDPWIiFoJcT09AAi2V+rVdQKuGOoFVwv1FHIZerW1VcxVX1evpMKES/agqXr7LQCM7BGHTrHBKNAb8eqmkwBs68IB3lXqyeUydIu3t+ByXT2p/TYhPBCRYvst19QjIiIiImoxGOoREbUSYqinVSmkSbJi++35wnJUmixe76vMYLHvq+YQdbEF98DFYqfLT+TYWm8TwjQIC6o5aTdAIccz43sCAFbtOoujWaVSCBXpxZp6AIdlOBID1IRwDSKDGeoREREREbU0DPWIiFqJqvX0qgK1mGA1wgIDYBWAzHzvW1bdVeoBVcMyqlfq1dZ6KxrcKRrjerWBVQCeXne4zqHeUYZ6rNQjIiIiImrhGOoREbUSOmnybVUQJ5PJfJ6Aa7EKqLBX9QVr3Id6x3N0TtV/YqjnakiGowXjuiMwQIG9Z4vw25kCAECEl6FezwRbqPfnRdeDOlqTbGlNvapBGQUM9YiIiIiIWgyGekRErUT1ybeiTjHiBFzvQr0yQ9XafFq1osb1CWEaRAerYbEKOJJVAgDILqnAztO2gK62Sj3AVln2txs7AQDM9mAuyodQL0StREmFCUezWm+1ntliRU6pQ6We/fErYqhHRERERNRiMNQjImolSu2VeqGBzuvZtY0IBADklxm82o/YehugkEGtrBnqyWQy9GlvG5Zx4EIJ1u6/iFGv/oLzheUIUSsxICXK4zHuH5KClGit9G9vBmUAgFIhx4AOkQCAnafzvbpNS5SrM8Aq2J6jmGB11fRbhnpERERERC0GQz0iolaiqv3WOdTT2tfFK3OYjlsbMdTTulhPT3RVu3AAwKsbT2LumoPQVZpxVbswrH14MGJC1B6PoVYq8PQtPQAASrkMUcHehXoAcF3HaADAr/bKwNYoy956GxeqgVwuY6hHRERERNQCuf9ERkRELYq79tsQezinN3gX6ontt64m34rEdfXKDGYo5TI8OrwzHkrrCKXC+78lpXWNxZI7ekETIEdQLceqbnAnWyXg3sxCGM1WqJSt7+9XWQ5DMoCqQSNF5UYIggCZTNZo50ZERERERP7BUI+IqJVwNSgDqKq403kZ6ukN9iEZtVTq9UuKQGJkEEIDlXjxzt7omRBWl1PGvdcm+nybrnEhiNKqUKA34sCFYlybElmnYzdn4pCMhDANgKpQz2QRoDOYEVqtWpOIiIiIiJofhnpERK2EWKlXPdARh134XKnnYkhG1T6V2Do/DXL5la8Ik8lkuK5jFL77Mxu/nspvlaGe2H7bxl6ppwlQIEilQLnRgsIyI0M9IiIiIqIWoPX1JBERtVLuKvXEf5d5XanneU09AI0S6IkGd7Ktq7erla6rV739Fqiq1ivgunpERERERC0CQz0iolbCU/utr5V61ffTlAy2D8vYf6EI5Ubv7ldLklWt/RZwWFePoR4RERERUYvAUI+IqJWQBmWonVsvxbXxdF5Ov/VmUEZjax8ZiLbhgTBZBOzJLPT7/gVBQJ7O4Pf9+kt2LZV6nIBLRERERNQyMNQjImol3FXqiaGewWyFyWL1uB9v228bk0wmk6bgNkQL7oc7z+KaRZuwdONJv++7vipNFim4SwhzEeqVM9QjIiIiImoJGOoREbUSpVKoV31QRlU4500LrrhNbdNvmwJxXb1fT+f7fd9HskoBAP/ZnIFvD2b5ff/1IbbeBqkUCA2seo4ig1ipR0RERETUkjDUIyJqJaT222qVegEKOdRK248Db4ZllBksAJp2pR4AXNfBVql3JKsUxX6uTis3WaT///sXB3HoYolf918fjq23MlnVsJLIYIZ6REREREQtCUM9IqJWwGi2wmC2tdaGVqvUA3ybgFtVqafw4xn6X2yoBp1jgyEIwG9n/NuCW+4wLKTSZMVfV/+OXF2lX49RV5fslXptHIZkAKzUIyIiIiJqaRjqERG1AmKVHgAEu5ha68sEXL2x6a+pJxrU0Vat9+spP4d6Rlul3r/GdkfHGC2ySyrx4Op9MJgtHm7Z8LKLbeFiW4chGQAHZRARERERtTQM9YiIWgFxSIZWpYBCLqtxvbg+nthaW5uyZjAoQzSogdbVE0O9uFA13p3aH6EaJf44X4yX1p/w63HqIrtErNRzDvWi2H5LRERERNSiMNQjImoFdG6GZIjEgK6s0os19cR9NYNQb2CHKMhlwJk8PfJ0Br/tt9xerRgYoESHmGAsvqMXAGDj0ct+O0ZdSe234c7ttxFsvyUiIiIialEY6hERtQLuhmSIgn1pv21GlXphgQFSxdqFonK/7bfCKA4Lsa0rKA7lOF9YLgV+jUUclFG9/TZKqwZgq7RsCm3CRERERERUPwz1iIhagVIvQz2dV9Nvm0+oBwDx9oERl0v8N8hCbw/1glS2UC8qWI3oYFtodvJymd+O4ytBEJDlZlBGiEYptV4X6U01bktERERERM0LQz0iolag1Mv2W0+VeoIgSIFWcHMJ9UJt4Va2H0O9CinUq3oMusWHAABO5uj8dhxfFeqN0np/CdUq9eRyGVtwiYiIiIhaEIZ6REStQNWaeq6DOPHyMg+hnsFshcUqAKhqPW3qpEq9Uv+EeiaLFUaLFUBVpR4AdImzhXonLjdeqPfRb+ft5xIMTUDN5ydSawt1GeoRERERETV/DPWIiFqBqjX13FTqqbwL9Ryv16paZ6WeWAkHAIEOoV7X+GAAwIlGqtQrLjfive1nAACzb+zscptIrb1Sr5yhHhERERFRc8dQj4ioFRAr9ULdramn8W76rdieG6RSQG5fn62pEyv1cvwW6tkeA6VcBpWi6sdo1/hQAI1Xqbd82xnoDGZ0bxOKcb3auNxGHJZRWOa/ScBERERERNQ4GOoREbUCnqff2irOPK2pJ4aDzWVIBlA1MCLHT+23YqVeoEoBmawq2Owca6vUy9MZrnh7a66uEit3ZgIAHhvZxW3gGsH2WyIiIiKiFoOhHhFRKyBV6gW6br8NVtsu9zT9Vgz9QppRqBcXWhXqCYJQ7/2VG2yhXvX2Y61aifaRtuEUV7oF980tp1FpsqJP+3AM7x7rdrtIsVKP7bdERERERM0eQz0iolbA06AMrZeVenpj86vUE0M9o9mKonJTvfcntt86DskQdY2zteCevIItuJeKK/DJbtuAjL+P7upUPVhdZBAr9YiIiIiIWgqGekRErYDUfqt2V6nn7aAMe5VaM5l8CwAqpRzRwbYBEdklFfXeX7nJ9hgEuXgMpGEZVzDU+8+mDBgtVgzsEIlBHaNq3TYy2F6px1CPiIiIiKjZY6hHRNQKeKrUEwdleKzUs18f3Iwq9QD/DssQ22+DAmo+Bl3iQgBcufbbjMs6fPHHRQCeq/QAIDLIPv3WQ6i35IdjeOrrw7Ba69+uTEREREREDYOhHhFRK1AqhXquK/XE9eF0Xk6/bU7ttwAQH+q/YRlS+62LSr1u9gm4J3N0flm/rza5ukpM/3AvLFYBN3aLRb+kSI+3idSKoZ77NuSCMgPe/uUMVv92DnvPFvrtfImIiIiIyL8Y6hERtQKept+KlxvMVpgtVrf7KWuuoZ4/K/Xs029dramXEq2FUi6DzmBGth+O5Y6u0oT09/fiQmEFkqKC8NJdvb26XZS9Dbmo3Oi2Ci8zXy/9/xf7Ltb/ZImIiIiIqEEw1CMiauGMZisMZltQF+quUs8hpNPb20tdKatsnu23bcJsU2n9GeoFumi/VSnl6BCjBdBwLbgGswUPfrQPR7NLER2swqrp1yLavlaeJ+H2QRkWq+C2KtMx1PvhULZUmUhERERERE0LQz0iohZOrNIDqtbOqy5AIYdaafuRoDO4b80Up982t1AvrgHab90NC+lqb8FtiGEZVquAx/53EL+eKoBWpcDK+65FUpTW69urlQqE2J+7Ar3B5TaOoZ7eaMGGIzk1tjlwoRi3vrEDv5zM8/EeEBERERGRvzSZUC89Pb2xT4GIqEUSK7K0KgUUcveDFMSgrtZKPWn6bfMK9do0QPttoIv2WwDoGmebgHuyASr1VuzIxHd/ZiNAIcPyKf2Q2jbM531EaGsfliGGenGhtuq/L/ddcrrebLHiH1/8iYMXS7Bm7wWfj09ERERERP7RZEI9V7766iuMHj0a0dHRkMlkOHDggNP1Z8+ehUwmc/n1+eefu93vwoULa2wfHx/vtI0gCFi4cCESEhIQGBiItLQ0HDlyxGkbg8GA2bNnIzo6GlqtFuPHj8fFi1x/iIiaFp2HIRkisYqvrLZKPWn6retAq6mSKvX8EuqJIanrYFOcgHu8AUK93ZkFAIA5I7pgSOeYOu0j0stQ7+FhnQAAv57OR1ZxhXT9mt8vSFWI5wr1NXdARERERERXRKOGevn5+Zg2bRoSExPx6aefolOnTpgwYQKMRtsHDb1ej8GDB+OFF15wefv27dsjOzvb6euZZ56BVqvFmDFjaj12z549nW536NAhp+tfeuklLF26FG+88Qb27t2L+Ph4jBw5Ejpd1Ye0OXPmYO3atfjss8+wY8cOlJWV4eabb4bF4r7KhYjoSvM0JEMkhlRltVbqNe9BGTqDWboPdVXboAygagLuqbyyWoeO1IV47klRQXXeR22hntUq4GyBLai7oXMMBqREQhCAtftt1XqllSYs/emktP25/PIGn/JLRERERESuNWqoN3fuXOzevRurV6/G2LFj8e677yIlJQVWq+1D0JQpU/Dvf/8bI0aMcHl7hUKB+Ph4p6+1a9finnvuQXBwcK3HViqVTreLiamqeBAEAa+99hoWLFiAO+64A6mpqfjwww9RXl6OTz75BABQUlKCFStW4JVXXsGIESPQt29ffPTRRzh06BA2bdrkp0eIiKj+SqVKvdqDOKlSz80ABaCqUq+5hXrBaqW0llx9q/WqQj3Xj0G7iEAEBihgNFtxrrC8Xseqzh+hqhjqFbgI9XJKK1FpskIpl6FdRCDu7NcOAPDlvosQBAH/3XIKBXojku2hos5gRlG5+8pOIiIiIiJqOI0a6u3fvx9TpkzB0KFDERYWhmHDhuHFF1+ERqOp0/727duHAwcOYMaMGR63zcjIQEJCAlJSUjBx4kScOXNGui4zMxM5OTkYNWqUdJlarcbQoUOxc+dO6Vgmk8lpm4SEBKSmpkrbuGIwGFBaWur0RUTUkKoq9Ty030pr6nkO9ZrboAygqlqv/qGe7TFwV6knl8vQxb6unr8n4IqBa0g9Hv+EcNskYMeBGCLxssSoICgVcozt1QaBAQqcyddj3cEsfLDjLADgqZt7IN7e0nyugC24RERERESNoVFDvcGDB+ODDz7Ad99955f9rVixAt27d8egQYNq3W7AgAFYtWoVNmzYgHfffRc5OTkYNGgQCgpsaxXl5Ngm/cXFxTndLi4uTrouJycHKpUKERERbrdxZcmSJQgLC5O+2rdv7/P9JCLyhc7LSj2x+ktXS6gnDcpwU6XWlEmhXj0n4HpqvwWArvG2dfX8Hur5YVBJL/twjUMXS2pcd8Ye6nWItk3UDVYrcVOqbc3Z+Z8fhNFixfWdonFjt1gk2qv1zvu5GpGIiIiIiLzTqKHe0qVLcc8992Du3LlYtWoV+vTpg+XLl9dpXxUVFfjkk0+8qtIbM2YM7rzzTvTq1QsjRozA999/DwD48MMPnbaTyZynRAqCUOOy6jxt88QTT6CkpET6unCBkwOJqGF5PSjDi0o9cYhGs6zUk4ZlVHjYsnblhtrbb4GqYRknLzuHevVdf84fj3/vdrZQLyNXJ1UdijLzbKFecpRWuuwuewuuySJALgOevLk7ZDIZkiJtod65AoZ6RERERESNoVFDPa1Wi0WLFiEjIwPjx4/HQw89hHnz5uGdd97xeV9ffPEFysvLMXXq1DqdR69evZCRkQEA0iTc6hV3ubm5UvVefHw8jEYjioqK3G7jilqtRmhoqNMXEVFDEttvQz2tqWefaOtukITZYkWlybbmabCHfTVFbeyVetn1bb812R6fQC8q9fadK8LCdUcw6d3f0P/5TRiweLPTJFlfOD3+9Qj14kI1iA1RwyoAx7Kdl4AQh2SkxFSFetd1iEKC/bGbeG2iNAhEHNbBUI+IiIiIqHE0aqjnKDw8HDNnzsSYMWOwfft2n2+/YsUKjB8/3mnghbcMBgOOHTuGNm3aAABSUlIQHx+PjRs3StsYjUZs27ZNau3t168fAgICnLbJzs7G4cOHPbb/EhFdSd623warbZV87kI9vbFqKq5W7T7Qaqri7MHU5fq230otsJ5DvVydASt3nsXO0wXILzMgV2fA7+eK3N6uNnqD4+Nfv1BVrNb7s1oLrrimXkp0Vagnl8uw5M7euPfa9vjH6G7S5Yn2ar7zhVxTj4iIiIioMTRqqcXcuXNx2223oU+fPrBYLNiyZQu2bduGJ598EgBQWFiI8+fPIysrCwBw4sQJAJAm1opOnTqFX375BT/88INXx50/fz5uueUWJCYmIjc3F88//zxKS0sxbdo0ALa22zlz5mDx4sXo3LkzOnfujMWLFyMoKAiTJk0CAISFhWHGjBl47LHHEBUVhcjISMyfP19q6SUiaipKvRyUIYZU7qbfim25AQoZ1MrmF+r5rVJPXFMvwP2P0NgQDR65sRP+vFSCzrHB6BwXgs/2nMcf54trbW+uTZm9VVallEOlrN/f5Hq1DcemY7lO6+qZLFZpfbwO0c4T5Id2icHQLs5/NBMn4J5lpR4RERERUaNo1FAvMTER8+bNQ0ZGBvR6PbZu3Yrp06dj9uzZAIB169bhvvvuk7afOHEiAODpp5/GwoULpcvff/99tG3b1mkSraO0tDQkJydj5cqVAICLFy/i3nvvRX5+PmJiYjBw4ED89ttvSEpKkm7z+OOPo6KiArNmzUJRUREGDBiAn376CSEhIdI2r776KpRKJSZMmICKigoMHz4cK1euhELR/D7sElHL5W2lnni9u9BJvLy+VWKNJS60/pV6VquACpM91PNQrThvVFenf/96Kr9+oZ4fJt+KpEq9S1Wh3oXCclisAgIDFIgLVXvcR1KkrVIvT2dAudFc6xqDRERERETkf41eqTd37lwAQHp6uhS6idLT05Genu5xP4sXL8bixYvdXn/27Fmn/Xz22Wce9ymTybBw4UKn8LA6jUaDZcuWYdmyZR73R0TUWHReV+rVPv1WbMttjpNvAaBNWCAAIL/MCIPZUqdqQzHQA2qffuuKGHo5ttH6osyPoWqqfQLu6bwylBnMCFYrpdbb5Gitx6FQABAWFICwwACUVJhwvrBcWmuPiIiIiIiujCazpl5DOX78OEJCQuo0QIOIqCXwfk09T5V6FqftmpuIoACpbTW31FCnfYittzIZoPExFBQHkVSfOOstMdTzx+MfE6JGmzANBAE4mmUbliGGeh0c1tPzhMMyiIiIiIgaT5MJ9apX6flLt27dcOjQIcjlTeauEhFdUaV+CvXKDLaKv+Y4JAOwVWDH21twc+rYgisGcoEBCsjlnqvZHEmVenUM9fR+DPUAoFdbcVhGMQDXQzI8SYy0hXrnGeoREREREV1xTLqIiFo4sf021Mv2W3fTb8ukqa/Ns1IPAOLrOSxDGpJRhxbkqtC0ju239nA22EM46y1xXb1D9nX16hLqSZV6nIBLRERERHTFMdQjImrBjGYrDGYrAM+hXrCHUE+sFPNU8deUiZV6l+sc6tkeA1/X0wOqBmvUeVCGnweV9GoXDgDSBFzHNfW8JQ7LYPstEREREdGVx1CPiKgFK6mwVenJZJ4rvMRQr9JkhdlirXF9cx+UAQBt/Fap53uop61n+60/19QDqtpvz+TrkVtaKT0mXFOPiIiIiKh5YKhHRNSC5ZfZBkJEBqmg8LAGnGMFmKsWUb2fK8Uag9h+e7mOa+qJj0udQr36tt9KoZ5/1jSM1KrQNtw2Efi7P7MBAOFBAYjQqrzeR1KULQC8VFwBk4sgmIiIiIiIGg5DPSKiFkwM9aKD1R63VSnl0nRYnX0ohiN/D2poDGL7bXZJRZ1uX2ES2299fwy0Kv+03wara2+j9oW4rt66g1kAfFtPDwBiQ9RQK+WwWAVkFTs/pt8cuIT/bjnlnxMlIiIiIqIaGOoREbVgUqgX4l31VUgt1WQtaVBGTh3bb/1RqSe28PpKHJThz+nDveyh3oELxQB8D/Xkcpk0AdexBbdIb8Rj/zuIlzecwKncMv+cLBEREREROWGoR0TUguXrjAC8q9QDHCfg1lap579Q6UoTQ71cnQEWq+Dz7Svqs6aeOCijjmvqNcSgkt5tw53+nRLlW6gHOE7ArQr1vj+UDbP98a1rqzMREREREdWOoR4RUQvmS/st4DgB11WlXvNfUy8mWA25DDBbBRTYHxtfSIMy6vAYiC27dW2/1TVA+604LEOUEuN7qJdon4B7vkAvXfb1/kvS/+fX4XEmIiIiIiLPGOoREbVgefZAJSrYu/ZbKdSrrBk8tYRQT6mQIybEFnDm1KGCrNxeZRcUUPf2W5NFgNHs+1CJqkEl/quUDAsKkCrtAN/bb4GaE3AvFJbj93NF0vX5ZcZ6niUREREREbnCUI+IqAUTAxWvK/U07qvJpPbPZhzqAUB8mG3ia3Yd1tWrT6We1qFlty7VemUN0H4LAKkO1XrJ9Wi/PW9vv/3mwCWn61mpR0RERETUMBjqERG1YPk6W6AS4+OaerpaQr3mXKkHAG3ECbjFvk/AFdfDq8uaekqFHGr7dOG6rKvXUI9/b3uoFx+qqdO+k+xB4LmCcgiCgLX21lux6q8ubc5EREREROQZQz0iohbM9zX17MMcXIR6LaH9FgASwuteqVefQRlA1WPnarqwJ7pKcU09/z7+w7rFQqWQY0jn6Drdvm14IOQyoMJkwZYTuTidp4daKcdfBiQCYPstEREREVFDad6fzIiIyC2rVUCh3t5+G+LjmnrVQj1BEKC3B1r+DpWutIRwW6XepTpV6omhXt0eA61agUK975V6JosVBvs6fP5+/LvEhWD3v4bXOaxVKeVICA/ExaIKvL4pAwAwokec1MrLSj0iIiIioobBSj0iohaqpMIEs1UAAERpfWu/rR7qGcxWWOz78ueghsYgVupl1SHUq6hH+y0AaO1hYLmPlXqOlZMNUSkZoVVBpaz7rwTiunoHL5YAAG7v01YazsJKPSIiIiKihsFQj4iohRJbb8MCA7wObILVrgdl6Bym4WrrWKXWVNSn/ba8nu234u2qh6aeiNurlXIEKJrej+7EyKoBGxFBAbihS4zU8p1XZoAgCI11akRERERELVbT+2RARER+kSetp+dd6y3g0H5b6Rw6iSFfkEoBuVzmpzNsHGL77eXSSpgsVp9uW17v9lt7pZ6P7bcNNfnWX8RKPQAY17sNVEq5FOoZzVafQ0wiIiIiIvKMoR4RUQsltj16OyQDAII1rttvW8qQDACI1qqhUshhFWzBni/K/dR+62oQSW2a+uThZIdQ7/a+bQEAgSoFtPbHiS24RERERET+x1CPiKiFytfZK/VCvA/13K2pJ4ZKIU00VPKFXC5DG3u1Xlaxj6GewU/Tb42+ranXUJNv/aVnQhgCFDJ0jQvB1YkR0uXi914+h2UQEREREfld0/x0QERE9SYGKTE+VOqFuFlTT5zW2lQrxXyVEBaIcwXlPg3LEAQB5ab6T78FfK/Ua+qVku0jg/Djo0MQHqSCTFbVnh2lVeFcQTkn4BIRERERNYCm+emAiIjqLb8Oa+q5q9Qrs1eoNffJtyJxWMYlH0I9xwnAQXV8HKRKvTpOv23KlZKdYkNqXFY1LIPtt0RERERE/sb2WyKiFqpOa+p5aL9tqu2fvmortd96H+pVOLTMBgXUdU092+18HZQhtt821Uo9d8T2W1bqERERERH5H0M9IqIWSqzUi6pDqFdpssLsMBm2rJmGSu6IlXq+hHpiC7JKKYdSUbcfn2Lbrq/TYMXKvuAmOv3WnWitrUqUa+oREREREfkfQz0iohaqQKrU8739FnBuEW3qa7r5qirU835QhlipV9chGUBVaFru46CMMoPJ6fbNhTQoQ8f2WyIiIiIif2OoR0TUAgmCgDxpTT3vK/VUSjlUStuPBp09SAJaXvttXSr1xCBOW8chGUDVWny+VuqJaxo2t8c/Smtvv9WzUo+IiIiIyN8Y6hERtUA6gxlGs619NibE+1APqAqOHCv1pOm39Qi0mpIE+5p6OoMZpZUmD1vbiI9BYD0q9bRSpV7dpt82t1BPrBLN56AMIiIiIiK/Y6hHRNQC5etslVHBaiU0Pg51qBqWURV2lTXTNd3cCVIpEREUAMD7ar0KqVKvHqGeqn7Tb5tdqCe233JNPSIiIiIiv2OoR0TUAuXXYT09kVYK9Rwq9aRQqe6BVlPjawuu3h7q1adST1yPT+9r+619UElzC1Wj7e23ukozKk2+BZlERERERFQ7hnpERC1Qfh3W0xOFiKFeZVXw1NIGZQBVod4lL4dlVNhbZoPq0YJc90EZzfPxDw1UIkAhAwAU6NmCS0RERETkTwz1iIhaoPqEelp1zWoyfTMNlWrT1tdKPUP9p9+KgzL0RjMEQfD6ds11TT2ZTFY1LIMtuEREREREfsVQj4ioBRLX1IsO8b39NlhjW2vOcUJrcw2VaiMOy8j2dk09U/1DPfHxE4Sq/Xmjua6pB1R9D3JdPSIiIiIi/2KoR0TUAuVJa+r5XqknrptX5qpSr4VMvwUc19Tzrv1WfAzq034bGKCAzNaN6vT4eqIzNM819YCq70FOwCUiIiIi8i+GekRELVC92m+lCa22IOl8QTmKy22TcEOaYajkTtWaet5V6onr4NWnUk8mk0mPb7mXE3CNZiuMZisAILgZhqpi+y0r9YiIiIiI/IuhHhFRC1SfUE+sBtMZbBNLH/p4H8xWAf2SItAuItCv59mYEsJs9yWntBIWq+f17SrsoV591xUUQ0FvK/Uc1zbUNsPpw1L7rY6VekRERERE/sRQj4ioBSqQ2m/rsKaeuqpS75lvj+JIVikitSosu7cvZGLvaAsQE6KGUi6DxSogV+e5BVdvn34bGFC/YE3r4wRcMfzTBMihVDS/H9vR4qAMPSv1iIiIiIj8qfn18RARkUf1qtSzh05bT+ShpMIEmQx47Z4+UrtqS6GQyxAfpsHFogpkFVegTVjt96+qUq++oV7VBFxvVA0pCajXcRsLB2UQERERETWM5vcnfyIiqlW50SxVgUWH1GFNPXuoV1JhW0fv0eGdcUOXGP+dYBNSta6eD5V69VzXLqjamoWeVIV6za/1FqgKlgs4KIOIiIiIyK+aTKiXnp7e2KdARNQiiGuXaQLk0NZhqIPjhNUbusTgkRs7++3cmpq20gRcz8MyxEq9oHq234qVkN4OyihrxpNvAQ7KICIiIiJqKE0m1HPlq6++wujRoxEdHQ2ZTIYDBw7U2CYtLQ0ymczpa+LEiR73/eabbyIlJQUajQb9+vXD9u3bna4XBAELFy5EQkICAgMDkZaWhiNHjjhtYzAYMHv2bERHR0Or1WL8+PG4ePFive4zEVF95Tm03tZlDbykyCAAQEKYBq/d0wdyectZR6+6hHANAO9CPb0Y6tWzYs7XQRlllbbttM1w8i1Q1X5bqDd6NZCEiIiIiIi806ihXn5+PqZNm4bExER8+umn6NSpEyZMmACj0VZlotfrMXjwYLzwwgu17ueBBx5Adna29PX222/Xuv2aNWswZ84cLFiwAPv378eQIUMwZswYnD9/XtrmpZdewtKlS/HGG29g7969iI+Px8iRI6HT6aRt5syZg7Vr1+Kzzz7Djh07UFZWhptvvhkWi3fVF0REDaE+6+kBQIeYYKz722B898gQRGp9H7TRnCTUpVKvnuGaGM6Ve7mmntimG9JMK/Uig1SQyQCrABSVswWXiIiIiMhfGjXUmzt3Lnbv3o3Vq1dj7NixePfdd5GSkgKr1QoAmDJlCv79739jxIgRte4nKCgI8fHx0ldYWFit2y9duhQzZszA/fffj+7du+O1115D+/bt8dZbbwGwVem99tprWLBgAe644w6kpqbiww8/RHl5OT755BMAQElJCVasWIFXXnkFI0aMQN++ffHRRx/h0KFD2LRpkx8eHSKiuqlvqAcAvduFt/hAD6jbmnp1aWl2JK5ZWOZj+614u+ZGqZAjIojDMoiIiIiI/K1RQ739+/djypQpGDp0KMLCwjBs2DC8+OKL0Gg0Pu3n448/RnR0NHr27In58+c7VdNVZzQasW/fPowaNcrp8lGjRmHnzp0AgMzMTOTk5Dhto1arMXToUGmbffv2wWQyOW2TkJCA1NRUaRtXDAYDSktLnb6IiPxJXFMvJqTlh3L15cuaeuLwkcB6h3oK+/58HZTRPEM9AIgOtn0vclgGEREREZH/NOonhMGDB+ODDz7AVVddVed9/OUvf0FKSgri4+Nx+PBhPPHEEzh48CA2btzocvv8/HxYLBbExcU5XR4XF4ecnBwAkP7raptz585J26hUKkRERLjdjytLlizBM88849udJCLygT8q9VqLNmG2PyKVVJhQZjC7Dc7MFiuMZlsVeX3XthMr7vTeVupVNv9QzzYso4yVekREREREftSolXpLly7FPffcg7lz52LVqlXo06cPli9f7tM+HnjgAYwYMQKpqamYOHEivvjiC2zatAl//PFHrbervni8IAg1LvNmm+o8bfPEE0+gpKRE+rpw4UKt+yMi8hVDPe+FaAIQal+rLruWar1yU1UAV+9KPfvt9V4OyhDbfptzqBcdYvtezNMx1CMiIiIi8pdGDfW0Wi0WLVqEjIwMjB8/Hg899BDmzZuHd955p877vPrqqxEQEICMjAyX10dHR0OhUNSopsvNzZUq8+Lj4wHA4zZGoxFFRUVut3FFrVYjNDTU6YuIyJ/EFkeGet6pWlfPfagnDslQyGVQK+v3o1Oq1POy/VYnVuo100EZABBlX5+xQM/2WyIiIiIif2nUUM9ReHg4Zs6ciTFjxmD79u113s+RI0dgMpnQpk0bl9erVCr069evRnvuxo0bMWjQIACQ2nkdtzEajdi2bZu0Tb9+/RAQEOC0TXZ2Ng4fPixtQ0TUGKoq9bimnjeq1tVzPyxDrKoLClB4rNj2RJye622lXnMflAEAMfZKvXxW6hERERER+U2jfkKYO3cubrvtNvTp0wcWiwVbtmzBtm3b8OSTTwIACgsLcf78eWRlZQEATpw4AQDSlNvTp0/j448/xtixYxEdHY2jR4/iscceQ9++fTF48GC3x503bx6mTJmC/v3747rrrsM777yD8+fP48EHHwRga7udM2cOFi9ejM6dO6Nz585YvHgxgoKCMGnSJABAWFgYZsyYgcceewxRUVGIjIzE/Pnz0atXL4/TeomIGlKePdSLYqWeV8RKveySWtpv/TQkA3AclOHdmnpi+BfSjEM9aVAGK/WIiIiIiPymUT8hJCYmYt68ecjIyIBer8fWrVsxffp0zJ49GwCwbt063HfffdL2EydOBAA8/fTTWLhwIVQqFTZv3ozXX38dZWVlaN++PcaNG4enn34aCkXVB6+0tDQkJydj5cqVAIB77rkHBQUFePbZZ5GdnY3U1FT88MMPSEpKkm7z+OOPo6KiArNmzUJRUREGDBiAn376CSEhIdI2r776KpRKJSZMmICKigoMHz4cK1eudDo2EdGVVGmySO2aMQz1vCKGeheLPId6/qiWE/dR5mWlnvh8NudKPdugDHBQBhERERGRHzV6pd7cuXMBAOnp6VLoJkpPT0d6errb27dv3x7btm3zeJyzZ8/W2M+sWbMwa9Yst7eRyWRYuHAhFi5c6HYbjUaDZcuWYdmyZR7PgYjoShAroVQKOUIDm28IdCV1i7f9sWbv2UK3w47K7evfBQb4oVLP3n7rdaWesfmvqRfN9lsiIiIiIr9rMmvqNZTjx48jJCQEU6dObexTISJqcGJoEhWsqvfab63FgA6RCFDIcLGoAmcLyl1uU1Wp57/2W6/X1Kts/tNvxUEZ+XojBEFo5LMhIiIiImoZmkyoV71Kz1+6deuGQ4cOQS5vMneViKjBnMkvA8DJt74IUinRPykSALA9I8/lNlVr6vmh/da+D4PZCrPF6nF7vcF27OYc6omDMoxmK3RehplERERERFQ7Jl1ERC3E6bwyLFx3FABwbUpkI59N8zKkSzQA4JeT+S6vr7C3wGr9MCgjyKHaT++hBddgtsBoD/6a85p6mgCFFEoWlHFYBhERERGRPzDUIyJqAQr1RkxfuRclFSZcnRiOv4/u2tin1Kzc0DkGALDrdD6M5prVc3o/Tr9VKxUIUNhaoz214IpVekDzrtQDbC3hQMMPy9iTWYjfzhQ06DGIiIiIiJoChnpERM2cwWzBg6v34VxBOdpFBOKdqf2h8cNAh9akR5tQRGlV0Bst2H++qMb10pp6fmi/BWwtv7b91h7qievpBQYooJA37zUSxZbwhhqWUVppwvzPD2LC27swdcUelFaaGuQ4RERERERNBUM9IqJmTBAEPPHVIew5W4gQtRIfpF/D9fTqQC6X4frO9hZcF+vqldsr6oL8UKkHVFXdlRlqb78tMzT/ybeiaHul3qXiCr/ve0dGPm569Rd8se8iAMBosSIzT+/34xARERERNSUM9YiImrE1ey/gqz8uQSGX4c3JV6NzXEhjn1KzJbbgbs+oua5eucl/7bdAVThY7qH9Vgr1mnnrLQBpGMn6wzle3+ZCYTm2ncxzOzHXZLHi6W8OY/KK3cgqqURSVBCSooIAAGcLGOpR6/P1/ksY9eo2nMkra+xTISIioiuAoR4RUTO287Rt7bAHh3bAEHsoRXUzxF6pd+hSCQr1zsMcxPDNX+234tALT4My9C0o1BvfJwEyGfD7uSKcLyj3uL3ZYsXkFbsx7f09WLXrnMttXt5wAh/ar5t6XRJ+fHQIrk22hYfnvDgGUUvz5R8XcfJyGT7dc76xT4WIiIiuAIZ6RETNWHGFbd2wDtHBjXwmzV9sqAbd4kMgCMCOU87VeuV+HJQBAFr7BNzqgzJKK02oNFUFfToxTFQ3/zUS40I1GNzRFpx+feCSx+2//TNLCuae//4o/qi21uGmo5fxzi9nAAD/ubcvnr01FUEqJZKjtQD8W6m3Zu959HtuI/755Z9+rYCyWF1XIBLVVVG57Q8SriqOiYiIqOVhqEdE1IyV2D/AhQcFNPKZtAw3dLG34J50XldPGpThp3BNHJShdxiUkVNSiYGLN+O+D/ZK7abioIxgdct4fm/v2xaArUXQXUstAFitAt7cchqAbS0+k0XAwx//gQL75NyLReV47PODAIDpg1Mw/qoE6baJkbb2W2+qAb1hMFvw8oYTKNAb8dneCxi+dBseXL3P5UAVX+SUVKL/8xvx9DeH/XKe5L2tJ3L99v3R1BTpbX/oOZ6jQ66uspHPhoiIiBoaQz0iomZMrNRjqOcfYgvu9ox8p9BJnFIbGOCfNlixndaxUm93ZgHKjRbsOlOA388VOV0f3AIq9QBgdGo8NAFynMnX48+LJW6323TsMjJyyxCiVuLb2dejQ7QW2SWVmLPmACpNFjz8yX6UVJhwVftw/HNMN6fbJkeJlXr+CW2+PZiN/DIj4kLVGNE9FoIArD+Sg9vf3Ilh/7cV/1p7CN8ezEJ+mW9TffefL0JRuQnf/plda8BJ/nUsuxTpH+zF/av2NvapNAixUg8Afj3Faj0iIqKWjqEeEVEzVmRf+y0sUNXIZ9IyXJMcCbVSjpzSSmTk2tosTRarFJ76r1JPbL+tarU9kaOT/n/lzrMAqtpvW8L0W8AWZo7uGQ8AWLvfdQuuIAj471Zbld6U65LQJiwQb03uh8AABbZn5GPcf7bj4IVihGqUeOPevlApnX+VSbQPysgvM0iDRupKEAS8vyMTADBtUDLem3YNNs69AXf3a4cAhQyZ+Xp8svs8Zn+6H/2f34TZn+73et9iCFioNyK/zOhha/IXsaX75OWyFjdMotJkkaqKAbbgEhERtQYM9YiImimLVUCpvT0zgpV6fqEJUGBAhygAwMajl7Fq11mkvbwVZ/Js67NFBPknPHVVqecY6m04nIOckkrpem0LGJQhus3egvvtwSyYLNYa1+88XYCDF4qhVsox/foUAEDX+BAsviMVAHDa/ly8MqEP2ttbbR2FBQYgUmt7ns7Vc129PZmFOJpdCk2AHPdekwgA6BwXgpfvvgq/PzkS703tj+mDU9AtPkS6T45rItYmzyHIc3zuqWEVO1SybTmRV8uWzU9xucnp3zuqVRwTERFRy8NQj4iomSqtqPoAFxbIUM9fbrC34L684QT+/c0RXCquQHSwCk+O646eCaF+OUbVmnpVAdBxe7ATHhQAs1XAJ7vPSWvqhbSgUG9Ip2hEB6tQoDdie0bNUOW/W04BACZe0x7RwWrp8tv7tkP6oGQAwKy0jhjZI87tMZLs1Xr1nYD7wa9n7cduiwitc6AbFhiAET3i8O9beuDHR4dAKZcBcG5/rI1ju+6Jywz1rpQih+Bry/HcRjwT/xOndodqlNAEyJGrM0gVx0RERNQyMdQjImqmxPAgRK2EUsG3c39J6xoj/X+7iEA8d2tP7PjHjbh/SAfIZDK/HENs4xXX6iutNOFScQUA4PHRtjXiPtlzHoX25zi4BYV6SoUct9gHW6zdn+V03f7zRdh5ugBKuQwP3NChxm0Xju+JPf8ajsdv6lbjOkdV6+rVvVLvQmE5fjqaAwBIH5RS67YymUwK/Qq8bKXN1zmEejmldTxL8pVjpd7uzIJ6t2g3JeJ9iw3V4JrkSADALydbVjUiEREROeOnQCKiZkoakqFllZ4/dYoNwdtT+uGNSX2xZX4aplyXDE2AfwdVaKu13560V+m1CdPg7v7tEB+qQX6ZEdvs7YEtqf0WAO7o2w4A8NORHOgqTRAEARcKy7F040kAthbddhE1W2sBW2DhiVipV58Jp6t/OwerAFzfKRpd7e21tYmyh3pitZQnzpV6rKa6UhwrKU0WoUUNkxD/CBAZpMINnW1/nNjRgu4fERER1dSyPiUQEbUiJfY2snAOyfA7cZhDQ6k+KENsve0aH4IAhRyTBybi/346CaN9zbmQFjIoQ5TaNhQdY7Q4nafHlBV7kFVcgVx75ZpMBjw4tGO99i+Get5W6p3N18NksaJTbDBkMhn0BjM+3XMeAHDf4GSv9iGu4+d9+23VdhmXdbBaBcjl/qkEJffEdedC1EroDGZsOZ7b4K/3urpQWI6v91/CXf3boU1YoMftxdbiCG0ArrcvI7D7TCEMZgvUypYxQZuIiIictaxPCURErUhxhS0UCOeQjGZHGpRhb7894RDqAcDEaxPxn82npFCvpVXqyWQy3N63Lf7vp5M4cKEYAKCUy9AzIRRTr0tGp9jgeu0/yd5+682aejkllRj16i8wWqyIDVHj+k7RCFDIoas0IzkqCMO6xnp1TJ/bbx0q9cqNFlwsqpAm91LDEUO9cb3b4LO9F7DlRC4EQfBba70/CIKAL/ZdxMJ1R6A3WlBUbsK/b+nh8XbiNPSIIBW6xYcgOliN/DID/jhXjOs6RjX0aRMREVEjqPOnhNWrV2P58uXIzMzErl27kJSUhNdeew0pKSm49dZb/XmORETkQpHe9uGUQzKaH2lQhsE51BOnqEYHq3HzVW3w1R+XALSsNfVE069Pgc5gRnigCv2SItC7XZjf2pzFNfWySypRabLUut+Tl3VSeJqrM+Cr/Zek66YNSva6es6X9ttyoxnl9iEpSVFBOFdQjuM5pQz1rgCxknJ0z3h8cyALl0sNOJpdip4JYY18ZjZFeiOe+OoQ1h/JkS7Lsq+36fG29vsWoVVBJpPh+k5R+PpAFnacymOoR0RE1ELVaU29t956C/PmzcPYsWNRXFwMi8X2i2l4eDhee+01f54fEVGrIQgCfj9bKAU9nohr6kUEsf22uZEq9QwWCIKA4/ZBCV3jqqbripNeHbdvSYJUSjwxpjseSuuIa1Mi/bpuYURQgNSyfL6w9mo9se13QEokPrl/AB5K64je7cIwsEMkJvRv7/UxxfbbAi9CvXydbRtNgBz9EiMA2MJFanhii2psqBqDO9laVJvKFNzfzxZi9Gu/YP2RHAQoZBhmH9qT51DVWZuqSj3bH3quF9fVy+C6ekRERC1VnUK9ZcuW4d1338WCBQugUFT9Et6/f38cOnTIbydHRNSafLHvIu5avguvbTrp1fYl5Wy/ba6C7NNv9UYzsksqUVpphlIuQ8dYrbRN73bhmDwwEcO6xqBDTP3aUVsbmUxWNQE3v/Z19XJ1lQCAthGBGNQpGv+4qRvW/e16fPbX63xqe66q1PMcwIghTXSwWmq5FtdVpIYjCII0ITYiSIUbu9laq39uAqGexSpgzpoDyNUZ0DFGi7WzBuPhYZ0AAHk670K9wnLnP/QMsa+r9+elEinwIyIiopalTqFeZmYm+vbtW+NytVoNvd67RamJiMjZ94eyAXj/4V6sOGH7bfOjtbfflhstUutthxhtjcXsn7+tFz6471ooOEDBZ9IEXE+VeqW2wCQ2xPNU3dpEatUAvGu/zXcI9brYQ70TDPUanN5ogdkqALAFX8O62SrZ9l8o9npqcUPZdjIXF4sqEBYYgHV/ux6pbcMQHWz7nvI21HMMLAEgLlSDLnHBEARg5+mChjlxIiIialR1CvVSUlJw4MCBGpf/+OOP6NHD80K+RETkrMJowS77hy5vP1yK7bfhbL9tdrT2Sj2LVcDBi8UAgK7xobXcgnwlVep5mIArBiaxIep6Hc+n9luHUE9cRzEzXw+D2VKvc6DaidVqKqUcmgA52oQFolt8CAQB+OVkXoMeWxAEbM/IcxqQ4uij32zTlu/q106qEI2xf09WmCxeLcsg/uwQh7YAwPWd7C24pxr2/hEREVHjqFOo9/e//x0PP/ww1qxZA0EQsGfPHixatAj/+te/8Pe//93f50hE1OL9dqYABrNtsX5vp2eWlDuvn0TNhzgoAwD2nSsCUDUkg/xDHDrhaQKu2H4bF1q/Sr2oYO8HZYhr6sWEqBAfqkGIRgmzVcCZPHY7NKRiqT01QJp2e6VacFftOocpK/Zg6oo9MNsHs4guFJZjywnb8f8yIFG6XKtWIkhl+wOAN9V64v2LdAj1xBbcX0+xUo+IiKglqlOod9999+Hpp5/G448/jvLyckyaNAnLly/H66+/jokTJ/r7HImIWrytJ6o+UBbqjRAEweNtxPZbrqnX/CjkMgTaB0McOF8MAOgax1DPn7yt1BMHZcSG+qdSr7jcVCO0qc6xUk8mk0mBLodlNKyiau2pQFWot+1knsfnra4uFJbjhR+PAwCOZpfikz3nna7/ZM95CAJwfafoGutnitV6noZlGM1WlNmr+Rz/0HNNSiSUchnOF5bjgodWdCIiImp+6hTqAcADDzyAc+fOITc3Fzk5Obhw4QJmzJjhz3MjImoVBEHAlhNVrVFGixU6L1qtxPWTwgLZftsciS244nPdlZV6fpVsr9S7VFQBo9l1WCMIAi6X2ir16tt+G+6wtqXYGu+OY6gHAF3iOCzjSihyMVyob2IEwoMCUFJhkqpm/UkQBPzjyz9RYbJIw1T+b8MJFNi/BwxmC/639wIAYPLAxBq3j/FyXT3x54FcBoRqqu5fsFqJq9qHA4C0xAMRERG1HHUelJGRkQEAiI6ORmys7a+cGRkZOHv2rN9OjoioNTiTr8f5wnKoFHKolba35UIPLbgWq4DSyppVGdR8OE5WDVYr0S4isBHPpuWJCVEjMEABqwBcKq5wuY3OYEalyRb41XdQhlIhl8IiTy241UM9qVKPoV6DKqlwng4L2KpmxWq9jUcv+/2Yn+29gJ2nC6AJkOPzB69DjzahKK004+UNJwAA6w/noEBvRFyoGiO6x9W4vfg94m4tPlGhFFiqIK82WGdwxygAwK+n8+t9f4iIiKhpqVOol56ejp07d9a4fPfu3UhPT6/vORERtSpb7Gs5DegQKbVaeVpsv9ShEojTb5snx3X1usaHSGt8kX/IZDJpAq67Flxx8m2IWolAlcLlNr6QhmV4COXz7ddH29fhY6XelVGkd71kwage8QCAn45e9mrpA29lFVdg0ffHAAB/H90NHWKC8eytPQEAa36/gAMXivGxfUDGvdcmQqmo+Wu51H7roVJPvG+u/shzXUfbuno7Txf49f4RERFR46tTqLd//34MHjy4xuUDBw50ORWXiIjc22pvvU3rGosoe1VGgYeqDLGNLEStdPlBkJo+rUOIxNbbhiGuq3cu302oZx+SEVPP9fREYnulx0o9e0ATbQ9sxOf/UnEFdJW1t+5S3RU5VLM5uqFLNNRKOc4XluOEn9Y1FAQBC9YeQpnBjKsTw5E+KBkA0D85Enf0bQtBAB79bD/2nC2EQi7DxGtqtt4CPoR6LtYLFF2dFA5NgBx5OgNO5ZbV414RERFRU1OnT4IymQw6Xc1fekpKSmCxWOp9UkRErYXeYMaezEIAQFrXGK9DAXHNrjC23jZbju23nHzbMKoq9VwPCBCDkvqupyeKlF6/7gOYSpNFWkdRbK0MD1Ihzh4snrzM0KWhFLuZGB6kUkpTYn864p8W3O/+zMaWE3lQKeV46a6roHBoif3nmG4IViulycwju8chPsx1+7e3oZ74MyNCWzPUUysVuCY5EgDw6ym24BIREbUkdQr1hgwZgiVLljgFeBaLBUuWLMH111/vt5MjImrpdp4ugNFiRWJkEDpEa6VQz1P7bUl5zbWhqHkRB2UAnHzbUJLESj0P7bf1XU9PFKn13D4vro2mUsgRqnFswQ4FwAm4DalqYnjN902xBddf6+qJFdjpg5LRKdZ5om1sqAZzRnSW/j3luiS3+xGDX0/Tb90FlqLrpHX1OCyDiIioJVF63qSml156CTfccAO6du2KIUOGAAC2b9+O0tJS/Pzzz349QSKilmzLCdt6esO6xkAmkyEy2Ls1uVxNcaTmRatyrNQLbcQzabnECbjn3FTqie23cVew/dZxPT3HdRS7xYfgl5N5OGFfV6+kwoTl207jz4vFWDqhD+JC/RM8tmbFLgZliG7sHguZDDh0qQRZxRVICK8aXHMkqwQL1h7GrLSOGNUz3qtjldrbqMUW8OqmDUrG72eLoFLKMcgeuLkiVurle6zUs983F5V6ADC4YzSAE/jtTAHMFiuXbSAiImoh6vQTvUePHvjzzz8xYcIE5ObmQqfTYerUqTh+/DhSU1P9fY5ERC2SIAjYah+SkWafvhjlRfseABTbK044JKP5Ettv24Rp2EbdQJKibYHKhaJyWKw1BwRc9nulnudK27xq6+mJxGEZhy+VYMWOTAx9eQve2noav54qwLcHs/xyfs2dxSrgTF4ZKk11W+qluJY/hkQHq9E/KQKAc7WeyWLFvDUHceBCMb7Yd9HrY4nDjEI0rv9+HqCQY/mUfvjPvX1rHZIjtd+WGWodciHet0g31dupbcMQqlFCV2nGkaxSr+4DERERNX11qtQDgISEBCxevNif50JE1KqcvFyGrJJKqJVyXNfBVqkR5UX7HlB7xQk1D0H2QRkcktFw2oRqoFLKYTRbkVVcgfaRQU7Xi5V6sX6q1JPW1Kul0lZsvxXbKkXiuoq/nyvC7+eKAABqpRwGsxWHL5X45fyaI5PFit/OFODHwzn46chl5JcZcO+1iVhyRy+f91Wkr71FdWSPOOw9W4SNRy9jmn2wxXvbM6XhGSUV3g8xKa20rZsYWs8/vIgTkk0WASUVJpetwwBQWMugDABQyGUY2CEKPx29jF9P5+Oq9uH1Oi8iIiJqGrwO9f7880+kpqZCLpfjzz//rHXb3r171/vEiIhaOrH1dlDHKGgCbAGPt+23tVWcUPPQNzECCrkMw7vHNfaptFhyuQyJkUE4lVuGswV6F6GeLWCL8fugjFpCPbFSL9g5fOkUG4wAhQwmi4DYEDXmjeyCmBA1Znz4O/5spaHe5mOX8djnB6XKZNG+c4U+78tssUpBm7tgbGSPeCz+4Th+O1OAknITSipMeH3zSel6X0I9cYpxqJtKPW+plQqEBQagpMKEPJ3B7bmL6wW6a78FbD9rfjp6GTtPFWBWWqd6nRcRERE1DV7/ptGnTx/k5OQgNjYWffr0gUwmc9kGIJPJOAGXiMgDo9mK/+29AAAYZm+9BYBoe6Wex+m3bL9t9kb2iMPhhaMRqFJ43pjqLDlKi1O5ZcjM12NI5xin6/IaqP1WrJpyxV2lniZAgVfv6YPcUgMmXtseQSqltG1mvh66ShNCNK3r9b5822kUl5sQpVVhVM849EwIw5NfH0Z2caXP+3IM5MLdvG+mRGvRJS4YJy+XYcuJXHy1/xIqTVbEh2qQU1optdR6o6r9tv7PWXSwyhbqlRnQ2c1QHU9ViAAwuJNtwu/es4UwmC1QK/neQ0RE1Nx5HeplZmYiJiZG+n8iIqq7VbvO4ky+HtHBKtzWt610uVSpp7etn+RurSWx/dZd1QY1Dwz0Gl7nuGBsOnYZGZfLnC6vMFqgM9gqt/zVfhtlf/0W6Y1uX79VgzJqHvPm3glO/44OVqNteCAuFVfgSFYpBnZwP1ChpSnUG7HP3oa8bvb1aBseiHKjGU9+fRg6gxmllSaE+hCYie+ZIWplrUMiRvaIw8nLZXhx/XFkl1RCpZRjyR29cN/KvVKlnydWqyB9b4UG1q9SD7BVkp7O00vrMboiDk+qrVKvU2wwYkLUyNMZ8Me5YmkiLhERETVfXg/KSEpKgkwmg8lkwsKFC2GxWJCUlOTyi4iI3MvTGfD6pgwAwOOjuzl9MBUHZZgsVR8KXSkp91yVQURAp5hgAEBGrs7pcnE9PU2AHCHq+gcvQFWlntkqoLTC9es3r8z1oAx3UtvaJiMfuti6WnC3HM+FVQC6twlFW/sk2iCVUqpO9rVaT1qyQFv7e+aoHrbpttkltv3/bVgnaf25MoMZZovV47H0RjPEZhZfgkd3YuyVpO5CPZPFCp09cKxtnVWZTCZN2t15Or/e50VERESNz+fptwEBAVi7dq3fTyQ9Pd3v+yQiaiwGswVvbT0trZvn6JWfTkBnMKNX2zDc1a+d03WaAAW09uqt2tbVE9dP4pp6RLXrHGcL9U7lOlfqievpxYZoap0+6gu1UoFge0BY4GaCdVX7rXdVtr3bhQMADrWydfU2H7dNoB3RPdbp8gR7wJdVUuHT/or03g0X6tU2DHH2ys2OMVrMHNrBaV08b6r1xG1UCjnUSp9/1a4hJrhqAq4rYpWeTOZ5SYbBHW0tuL+eYqhHRETUEtTpN43bb78dX3/9tZ9PpaavvvoKo0ePRnR0NGQyGQ4cOOB0fWFhIWbPno2uXbsiKCgIiYmJeOSRR1BSUvsvvgsXLoRMJnP6io+Pd9pGEAQsXLgQCQkJCAwMRFpaGo4cOeK0jcFgwOzZsxEdHQ2tVovx48fj4sWLfrnvRNS8LVx3FC+uP477PtiLF9cfh8VqK9s4dLEEa363raW3cHwPyOU1wwSxBbfQTSgAVFWdhAWy/ZaoNh3tlXr5ZUZp3TEAyLWvpxfnp9ZbkadhGeKgjBgX7beu9GobBqB1hXpGsxW/nLSFTtUHySSE2arWsop9DPWk4UK1v2fK5TI8MKQDYkPUeOmuq6BWKqBUyKWw1pthGdKQjEClXwLj6BDbObur1HNcY1Xh4meKo8GdbaHegQvFyCnxfW1CIiIialrqFOp16tQJzz33HO666y4sWbIE//nPf5y+vJWfn49p06YhMTERn376KTp16oQJEybAaLT94qXX6zF48GC88MILLm+flZWFrKws/N///R8OHTqElStXYv369ZgxY4bHY/fs2RPZ2dnS16FDh5yuf+mll7B06VK88cYb2Lt3L+Lj4zFy5EjodFXtO3PmzMHatWvx2WefYceOHSgrK8PNN9/MQSFErdwnu8/j0z3npX+/tfU0Zny4FyXlJjzz7REIAnBrnwT0S4p0efso+7AMd5V6FqvgMMWRlXpEtdGqlVL75qm8qmq9y6W2QMNfQzJEYqhX4CLUM5gt0mvX1Zp6roihXma+HqWV3g9qaM52ZxagzGBGTIgave33X9Qm3PZ8+d5+K1bqeX7PvH9IB+xZMAL9kiKky8RqPW9CPbH12l+DTcQAON/NzwQxQI70Yo3VtuGBuDY5ElYB+PIP/iGaiIiouavTIjLvvfcewsPDsW/fPuzbt8/pOplMhkceecSr/cydOxd79+7F6tWr8dprr+GRRx7B+vXrYbXa1iuZMmUKAODs2bMub5+amoovv/xS+nfHjh2xaNEiTJ48GWazGUql+7unVCprVOeJBEHAa6+9hgULFuCOO+4AAHz44YeIi4vDJ598gpkzZ6KkpAQrVqzA6tWrMWLECADARx99hPbt22PTpk0YPXq0V48BEbUs+84V4el1hwEAfx/dFe0iAvGPL//E1hN5GPbKVhTqjQgMUOCfY7q53UdULaEA4N0URyKq0jkuGJeKK5BxuQzXJNvCdLH9NsbLte28FVVLpZ4Y1CvlMq8nV0doVWgXEYiLRRU4fKkEg+ztky3ZpqO21tvh3WJrVDPXtf22uMJeqVfH98zQwABklXg3AVfcxrFttz7E71H3lXqeh2Q4uqt/O+w5W4jPf7+AWWkd/dZ+TkRERFdenSr1MjMzpa8zZ87gzJkzTv/21v79+zFlyhQMHToUYWFhGDZsGF588UVoNHX/q3lJSQlCQ0NrDfQAICMjAwkJCUhJScHEiROdzjszMxM5OTkYNWqUdJlarcbQoUOxc+dOAMC+fftgMpmctklISEBqaqq0jSsGgwGlpaVOX0TUMlwurcRDH+2DySJgbK94zErriFv7tMUXDw5C2/BA6UP+rLSOaBMW6HY/ntr3xA9wnqY4EpFN59iawzLEQRn+mnwriqjl9SuupxcVrHLZeu9O73b2FtxWMCxDEARsOmZbi7R66y0AJNjfO31vv63fxHAxhPWq/dZgn7Trr0o9D6Feod77KkQAGNerDYJUCpwtKMfv9gnDRERE1DzV+dPgihUrkJqaCo1GA41Gg9TUVLz33ns+7WPw4MH44IMP8N1339X1NJwUFBTgueeew8yZM2vdbsCAAVi1ahU2bNiAd999Fzk5ORg0aBAKCgoAADk5OQCAuDjnXybj4uKk63JycqBSqRAREeF2G1eWLFmCsLAw6at9+/Y+308ianoMZgse+mgfcnUGdIkLxst3XSVVP6S2DcO6vw3GuF5tMLxbLB64oUOt+4qSWq3cVGXYP1SGsfWWyCudY0MAOA/LyHMYlOFPUqWti1bJqiEZvgWJqfYW1D9bwbp6Jy7rcKm4AmqlHNd3qlmVKFbqZfu4HlxxPSeG+xLqie23oYH+rdQr1Buk9VkdFUn3zbvAUqtWYlyvNgCA/+294JdzJCIiosZRp1DvqaeewqOPPopbbrkFn3/+OT7//HPccsstmDt3Lp588kmv97N06VLcc889mDt3LlatWoU+ffpg+fLldTkllJaWYty4cejRoweefvrpWrcdM2YM7rzzTvTq1QsjRozA999/D8DWYuuoejuCIAgeWxQ8bfPEE0+gpKRE+rpwgb9MEbUEn+4+jz/OFyNUo8Q7U/pDq3b+MBcVrMZ//3I1VqRfA02AotZ91da+BwAl5d5NcSQim45ipd7lqlBPHJQR6+f226pK25qhfL7O9pr2NdTr3TYcAHC4FYR6m+1Vetd3ikagquZ7ZZuwqjX1rC4CLnek6bdetqhW51Olnjgow0+VepFBKshkgFVw/XNBHADjy327u7/tj8rfH8qG3uB5oi8RNT0mixVbT+Riy4lc7D5TgEMXS3Amr8yn90Yiav7q9CfEt956C++++y7uvfde6bLx48ejd+/emD17Np5//nmv9qPVarFo0SIsWrQIt912G8aMGYO5c+dCLpfjr3/9q9fno9PpcNNNNyE4OBhr165FQIBvv0RptVr06tULGRkZACCttZeTk4M2bdpI2+Xm5krVe/Hx8TAajSgqKnKq1svNzcWgQYPcHkutVkOt9u8HCCJqfKfz9ACAyQOTkBytrde+ooJrD/WqpjiyUo/IG53soV5OaSVKK00I1QQ0WPutFOqV1wx/8upYqScOyzhXUI6SclOLrtLdKK6n56L1FgDiwzSQyQCjxYoCvdHrNRHFCue6tt+G2kM9r9bUqxQHZfinUk+pkCNKq0J+mRF5OkON+1xUhz/0XJMcgeSoIJwtKMcPh7KlkI+Iatp3rgiVJgsGu6gebkzLfj6F/2zOqHH5LVclYNm9fRvhjIioMdSpUs9isaB///41Lu/Xrx/M5rr9tS88PBwzZ87EmDFjsH37dq9vV1pailGjRkGlUmHdunV1Wo/PYDDg2LFjUoCXkpKC+Ph4bNy4UdrGaDRi27ZtUmDXr18/BAQEOG2TnZ2Nw4cP1xrqEVHLJFZvRNaxCsSRuA93kw7FKY7eLrRP1NqFBQYgzh7enc4tg8FskYIQv7ffBtdSqSeGeiG+vU+EBQUgMTIIAHCoBVfr5ekMOHixGAAwvHusy20CFHKpujLbh2EZYvttXQdl+NZ+699KPaAqCM5zsSxDUR1ai2UymRTkff47p+ASuVNmMGPye7sx7f09Pr3nNLRKkwUf/XYOgO0PVx1itNL7xL6zhY15akR0hdUp1Js8eTLeeuutGpe/8847+Mtf/uL1fubOnYtt27ahpKQEFosFW7ZswbZt29CvXz8AQGFhIQ4cOICjR48CAE6cOIEDBw5Ia9bpdDqMGjUKer0eK1asQGlpKXJycpCTkwOLxeL2uPPnz8e2bduQmZmJ3bt346677kJpaSmmTZsGwPaLzpw5c7B48WKsXbsWhw8fRnp6OoKCgjBp0iQAQFhYGGbMmIHHHnsMmzdvxv79+zF58mSppZeIWpf6VoE4itJWrZ9U+7EY6hF5S1xXLyO3TFpPL0Ahq/Maa+5Eiq9fl2vq2S6L8bFSDwB6icMyWnCot+V4LgTBVpkYF+o+bG1Th2EZvq47V50Y6pVWetN+K66p57/vrdqGZRT5OP1WdMfVbSGXAXvOFiIzX1//kyRqgX49lY8KkwVmq4Bdpwsa+3Qk3/2ZjUK9EQlhGqx/dAh+fiwNa2fZCkvy9UYIAltwiVqLOvcFrFixAj/99BMGDhwIAPjtt99w4cIFTJ06FfPmzZO2W7p0qdt9JCYmYt68ecjIyIBer8fWrVsxffp0zJ49GwCwbt063HfffdL2EydOBAA8/fTTWLhwIfbt24fdu3cDADp16uS078zMTCQnJwMA0tLSkJycjJUrVwIALl68iHvvvRf5+fmIiYnBwIED8dtvvyEpKUm6/eOPP46KigrMmjULRUVFGDBgAH766SeEhIRI27z66qtQKpWYMGECKioqMHz4cKxcuRIKRe3rZRFRyyNWb9S1CsSRY/utq3U6i+v54ZSoNeoUG4wdp/JxKrdMaseNDdF4XCvXV9KgDBev33xd3dpvAaB32zB8/2c2Dl0q9st5NkUbj4mtt66r9EQJ4RocuABkFXs3LKPSZEGlyQoACNdegUq9SnH6rX/ab4GqUM/VACVpTT0ffya0CQvEkM4x2HYyD1/su4C/j+5W/xMlamG2HM+V/v+3MwW44+p2jXg2NoIg4MOdZwEAk69LglJhq9MRf380mq0oM5j9NoGbiJq2Ov22cfjwYVx99dUAgNOnTwMAYmJiEBMTg8OHD0vbefpFee7cuZg7dy4AID09XQrdROnp6UhPT3d7+7S0NK/+CnH27Fmn/Xz22WcebyOTybBw4UIsXLjQ7TYajQbLli3DsmXLPO6PiFq2EnvQ5o+1rsT2W5NFQGmluUabLdtviXzXOU4clqHD1Ym2tXC9XY/NF+Lr12C2otxocRqaU9fpt0DVunp/XmyZlXoWhyqY4d1cr6cnSggTJ+B6V6knVrIp5TKEqOsWtDV2+61Y3emqUk9cfzWyDoHl3f3bYdvJPPzv94sID1QhSK2AVqVETIgaAztEQSH3b+hN1JwIgoAtJxxDvabR1rr/QjEOXSqBSinHxGsSpcuDVEoEqRQoN1pQUGZkqEfUStTpN5stW7b4+zwazPHjxxESEoKpU6c29qkQUQtW7MdKPU2AAsFqJcoMZhTqjTVDPT+2+hK1Fs7tt/YhGQ0Q6gWpFFAp5TCarSjUG12Hej6uqQcAPe2h3sWiChTpjXWe4tpUncjRocxgRrBaiR4JobVu2yZcbL/1rlJP/ENIeFBAnSszQ32afuvfQRmA+/Zbs8UqDeaoS/X2yB5xCA8KQJ7OgEU/HHO6bskdvXDvtYlubknU8h3L1uFyqQGaADlMFgHnC8txqbgCbe3vQY1llb1Kb/xVCTXWco7UqlBurECB3ljvwW1E1DzUaU29hlC9Ss9funXrhkOHDkEubzJ3lYhaGKtVkCoz/FU9J/6SVuCi1aq4DouiE7V2YsvtxaIKnC0oB+D/ybeArdLfsQVXZLJYpeEcdanUCwsMQIr9A1pLXFdv3/kiAEDfxHCP1WFtw23r7WX5WKlXn/fnsEBbQFfiYqpxdWL77ZVYU6/YIWSsy/1TKxV48y9X4y8DEnF737YY1SMO7SNtgcWZvLJ6nDFR8ydW6V3fKRqp9j+s/NbI6+rl6irx/aFsAMC065JrXB9l//ni6vdHVy6XVmLumgP45sAltx1wBWUGnOW6m0RNlv/+hEhE1ErpDGZY7b8H+etDXKRWhfOF5U6hgMix6oSIvBOpVSFKq0KB3ojfztg+lPl78q3jsbJLKp2G3YgtknJZ3dfDTG0bhsx8PQ5dKsENXWL8cq5NxR/nbKGe2BpdG18HZYjvmfVZh1R8b9cZzLBaBchrCR5LG2JQhpvpt8UOgaW4rpavBnWMxqCO0dK/3/g5A//300mUVpi93sfRrFJ892cWYkPUSIwKQvuIILSPDIImgOtMU/MlrqeX1jUWF4sqcPBCMX47U4A7+zXeunqf7bkAk0VA38RwaYCSo2gXf1SqzbcHs7B2/yWs3X8J3xzIwqLbU6X3WF2lCW9vO4P3dpyB0WzFO1P6Y0SP2pdHIKIrj6EeEVE9iVV6gQEKv32AiXYYllFd1Ye4ltV+R9TQOsUGoyCzEEezSwE0TPst4FhpW/X6FSusIrXqOq9T1rd9OL49mIVvDlzCg0M7tqj1zn4/Z1urql+SF6GevVIvV2eAyWJFgIcwS6zUq8+SBWIVnCDYgj13VXGVJguMZttQDn+230a7GZRRqBcDS/8FiL6sHyh69rsjNdYbU8pleGXCVbi1T1u/nRvRlVKkN+IPewXxsG6xyLisw/Jtp/FbZs1KvQuF5Xjm26NIH5SM6ztH17jeX0wWKz7efQ4AkD4o2eU2UbX8/uhKrkP178/HczFq6S94Ymx3mCxWvL45w2k/j362H1/OGoRu8bUvkUBEVxZ7UomI6qkhBle4a7+1WAWpCoSVekS+EYdliB1GDdF+C1RNwBXDJMBxSEbdg6U7+7VDWGAATl4uw9f7L9XvJJuQ3NJKXCisgExma7/1JFqrhkohhyDYWsc8qarUq/t7plqpgCbA9mtzaS1hl9h6K5MBwSo/rqlnr9QrLjfBYLZIl4vfY/5cY9GX9QNF4vqG1yZHonubUGhVCpitAr78o+V8n1Lr8ktGHqwC0DUuBG3DA9E/ORIKuQwXCitwsajcadsX1x/HpmOX8dDH+3ChsNzNHmtXabLgza2n8MvJPLdtsBuO5OByqQHRwWqMSW3jcptIrftJ2a6IU9kn9G+HPu3DoTOY8a+1h/D0uiMo1BvRIVqLt/5yNa7rEAW90YL7P/zd69ZeIroyGOoREdVTcYVYBeK/kE1aE6XaX1odP2T5YygHUWsiDssQNVz7bc3Xb769aq8+E3fDAgPwUFpHAMDSjSedwp3mTKyG6RoX4tW0Rrlchvgw+7p6XgzLKPZT8OVNBZs4JCNYray1Rbcuxw5Q2PbnWAFapBfXWG3cUE8MEF6+uzd+fHQI1sy8DgCw/1wRrFbXAQVRU7b1RB4AW5UeYHtNi1PIdztUpZ4vKMcP9jXudJVmPPrZfpgsVp+Pt/lYLl5afwJT39+DSe/uxsELxdJ1l0srseTHY3jiy0MAgEnXtodK6fpjvPiHI8f3idqILf3XJEfiy4cG4clx3aEJkCM6WIXnbkvFhrk3YEyvNnjzL1cjKSoIF4sq8NBHf0gVyUTU+BjqERHVk/jBx5/rJ0W5aN8Dqj6chqiVdV4/iai16mwfliFqsEo9sf2pzFWlXv2OOe26ZMSFqnGpuAKf7D5fr301Fb+ftYV63rTeitrYQ71sL4ZlFPmpmjpU4znsEqv4Qr0IJ30hl8uk7x3HYRlFflgvsDrxcRKrDj0pN5pRbrQFzOIfpLrFhyBIpYDOYEZGLgduUPNisQrYah+SMaxr1fql13WMAgDsOlPVgrtixxlYBeCq9uEI0Sjxx/livL4pw+djOr6X7TpTgFv/+yse/vgP/P3zg7j+xZ/x9rYz0BnM6NEmFOmDU9zuR/z5U6D3rppO/D0zOsS2NMT9Qzpg74IR+PWfN2LKwCRpeYMIrQorpvVHiFqJPWcL8eTXh9xWFBLRlcVPhERE9SQNrmiA9tvqa6KIkw7D2HpL5LNODqGeXAZEaRt2TT3x9SsIAn49lQ8AiAutX3VgoEqBR4d3AQC88fMplBm8H2bQVImTb/snex/qtQ0Xh2X4UKlXz+DLm0q9hhiSIYpxsa5eUQNMQ/d1TT0xFNAEyKFV2daVVSrk6NM+HACwzz4Ehai5OHixGEXlJoRolLja4Y8NAzvYQj1x2FKh3og1v18AADw+uiuW3NELAPDfraew0/6e7y2xsntsr3jccXVbyGTA94ey8fm+izBZBFybHIkV0/rju9nXSz9jXBF/rnlbqSe+n8Q4/MEpRBMAtbLmGtGdYkOwbFJfyGXA/36/iHUHs7y+f0TUcBjqERHVk/jB50q03xaX+7/Vl6i1iAlRI9Q+vCA6uO4DKzwRwyPx9fvj4Rxsz8iHSiHHhP71n5p4d/92SInWokBvxIrtmfXeX2OqNFlw+FIJAKBfYqTXtxOHZXgzAbfID2vqAQ4VbLW239qu8+eQDJGrSj0xOPbrmnr2KkNdpRkWL1pnxfa9KK0aMlnVa0qsvBTbq4maC3Hq7Q1dYpwG8fRPioBCLsPFogpcKCzH6l3nUGmyomdCKAZ1jMLNvRMw8Zr2EARgzpoDXg+rAKrWUO7RJhRLJ/TBD48MwW19EnBrnwR8+dAg/O/B6zC8e5zHtv5IH6bfWq2CtJ23VeRpXWPx8LBOAIBP97SManGi5o6hHhFRPYmhnj8HZUS5GZRR3ACtVkSthUwmQ+c427p6DdV6CzhPHywzmPHst0cBAA+mdUSHmODabuqVAIUcj42yVeu9u/1Ms160/NClEpgsAmJC1GgfGej17RLslXretd/Wf/ot4GWlXoW9Us/P7bdAVSWNY6gn/qGntsodXzn+LCur9FwJ6ti+5+jqRHuodwUr9QxmC9YdzMKKHZn4z+YMLPnhGP79zWHsPlNzYimRO1uk1ttYp8u1aiV6t7Otq7ftZB4+3HUWADBzaEcp0P73LT3QMUaLXJ0BT3192Otjiq8j8Y+63duE4rWJffH6xL4+LU0ghnOFeqPH9SyLK0xScO/Le8jEaxMBALszC716DyaihsVQj4ionor99IHRkWMo4LhmSUNM2iVqTcR19RpqSAbg3H776saTyCmtRFJUEGbZh1z4w9jUNkhtG4oygxn/3XLab/ttKCaLFRuO5NRoFxZbM/slRjhVeXmSEOZ9+21JuX+qqb0ZICFW6oU2QKWe2H6bV+aiUs+P1dsqpRyBAbbWO29acKX1IquFAuIk4zP5ep8qlurKahXw4Op9eOTT/Xjuu6NYuvEk3v7lDFbtOofpK/ci47Kuwc+Bmr/c0kocvlQKAEhzWE9PdJ29BfflDSdQqDeiXUQgxqbGS9cHqZR46a6rAFSFg97It79GouoZ0Is/fyxWweO6mOIfhMICA9wO3nClbXggrk2JhCAA6w6wBZeosTHUIyKqp4YYlCH+Uma2ClLlB8D2W6L6EtdH6hof4mHLuhM/lJUZzFi58ywA4NlbU6EJqLlGUV3J5TI8NrIrADSLdY2++zMLM1fvw30f7HGqHpFCPR8qUQCH9lsPVSKCIEhrkda3wtmbUE/8EN2Qa+o5V+o1TPV2aKAtlPQm1CtwMwQmPEglrWN5Jar13ttxBltO5EGtlOPm3m0w8Zr2mHF9Cq5qFwa90YKZq/d5PfyDWg5fhzkcybIFel3jQly2pIrr6omvjRnXp9QYXNbN/vOl3GiB3st1T8XXUVQ9hymplHKp/T/fw7p6edJr1/f3j9v6tAUAfM1Qj6jRMdQjIqqnhhiUoVYqEKy2/VLmOMHMXx9OiVqrO69uh69mDcKjwzs32DFCNQHSen0Wq4Bxvdv8f3v3Hd5Gff8B/H2SLHlvx86wsyfZgywgCZCEQNiFQFgpUKC0FJIChab0F6BltaQpUCg7bCh7QwIZhJFBSEISspcz7Hgv2da83x/S9yTZGifrzivv1/PkAdsn3Z0tne4+9xmYMqB5xkeshnnLwMqtNjhdbs2fX0uF5Z7g24aDlUrJmizLSrBnTBRDMgBf+W1VvQP19tAXzTV+feFivRkSXfmtfj311h+owNxn1+L0f67CgXIrAG176gHRTcAtU8oGm2/DGG8J7kad++r9VFiJR77YBQD4v3NPwhNzR+Ohi4fjntlD8Py8ceiaFo/9ZVb88X9bmpUkyrKMBu/0Xupc6u1OnLVkDS5/Zq3q4J7IKg3VomFMzwyYvMf39MQ4zBmX32yZJItJyXYtU9keQZTf5sQY1AN8x4pIrRnEe7clU9nPHpaHOKOEHUU12M0sWKI2xaAeEVGM9BiUAQSW4AosvyWKjdEgYXRBhqZZc00ZDJISeE+2mPDX2UN0WU9GohkGCZBloKJe//LGWPgHhx7+YicOlVtxsLwe5VY7zCYDTuqWGtXzpcbHKTc+wpXgiuzmhDhjzH9zX6ArdBDRNyhD+2O06DlYbrXj+33l2F9mhSwDPTISUJCZqOm6opmAWxYiUw/wZWDqOQG3ut6BW17fBKc3gH75yYFBluxkC566cgzMRgOW/3IcT632lKs32F14de0hnP7oaoy8bxl2FTMw0dl8vOUYdh2vxQ/7y1GvMnAbqQdnksWkTHa+akJPJJqDB/CDTasOpd7uRIPDs33BguPRylI5LKOsNvR7N5L0RDOmDPD0HPxg09GoH3+ksh4L39+KX7yZkUTUctrfRiQiOsHoMSgD8JTgHiqvDyifqFICiMzUI2rPemQkoKzOhj/OGIDcVH369xkNEjKTzCirs6O8zq5rn8BYiYmxBglodLhx5zs/4+IxnknAw7unwWKKPuDWLT0eu4/Xoai6QSnzbEqrybeAykw9b8BPlK9qaVj3NDx88TBUWB3IS7MgLzUBeWnx6J6eEFU/LDXEoI+ognopzQMDo3umAwB+PlIFh8sdMElUC7Is40/v/oyjVQ0oyEzEgxcNC9qbcWR+Ou47/yTc9d5W/HPZLhypbMAX24qU1wfgGdqiZ1k+tb7X1/mms1ZY7UiyRH5fipunmWGOGfeefxKWbT+Om6aE7pOanWxGYUV9QLl8KCJLLz7OgERz7DecRGAwUlBPVIK0pPwWAC4Y1Q1f7TiODzcfw+0zBkaczOvvrQ2H8dq6Qny6tQhv3TCR7z2iGDCoR0QUI1/5rbaBtqwk3wQz37q8d5CZqUfUrj188XBsO1qNC0d113U9WUkWlNXZVZd4aaGq3o4F/9uCMwZ3wRXje6p6jMjU+82pffDyD4ew7kAFDpR5SkejLb0VuqYleIJ6KjL10jS4EaJk6oUtvxWDMrQ/RkuShDnjCjR/3mDU7KuglPAFKQHuk52MtIQ4VDc4sKOoBsN7pGu6na+uK8QX24sRZ5TwxNxRYX/vl51cgC1HqvDG+sN4Y70n2JOfmQCz0YB9pVZUtsIwD2o9245WY8uRauXrCqsd+SoyWitUDD87qVsaTuqWFvZ5fINtIr+uxPE7K8kS1cCgUDKTVJbf1ra8/BYAzhyci2SLCUerGrCxsBLjemWqfqwIdlbVO3Dl8+vw9o0T0Ss7qUXbQXSiY/ktEVEMbE6XUjKRpnX5rSif8J6UVVrtOFxRDwDISGJQj6g9G5iXgovH9Igqc6ElslPEcaL1AhKv/HAIK3aW4Lk1B1Q/ptabwTakWyrumjUIAFDivagTfdei1c07LONoVehhGVWtnKkn9lOP8tvWpGYoiFAeJlPPYJAw2jsFV48S3KXfeV6Dd8wcqCpguOi8k3D2sDyM752JJ68YjVW3T8Op/T39LivbeQk7Ref19YUBX6udwCxuBGTG2KdSBMqiydRracZc83Wr+1wIl2WrRnycETNP8kz+jbYEV/w94owSSmttuOK5dTgW5lhORKExqEdEFANxwSNJQIqKso5oZPqVT9icLtz46kZU1jvQPT0h4h1iIjoxiIze1srUc7rcysWy2otkIHAq7FUTeuLk3r6MjtFRTr4VuqV5eswVhZmAKwI1WgwX8p8IG6rpvm8/O3YxjNqeeg6XWylhzQoRBNGzr57IEpw2sIuq5S0mI568YgzeunEizh7WFUa//pcM6nUeVpsTH3qDTOK1HKkUVRDHtVj7JAebVh2KKIONdfKtIN6LkY7RvgzBlh8fLxjVDQDw6dYi2J3qBzaJ99s9s4egT3YSjlY14Mrn1rVq1jlRZ8GgHhFRDKr9BldonZHj3+j47ne3Yv2BCqRYTHjx1+N0bfJPRB2HyAYpa6VMva92HEdRtafctbrBoXrqrm8qrOdY+cjFw5GVZMbEPlktLv3qmi6CeqHLb8UFohaDjERwwOWWYQ3RdP9Ey9QTJasGKXTgVARtf9I4qOd2y0oQNZZM+Uxv5nulNXJWInUMH205BqvdhT7ZScrkcbXl1b7sXm0y9dQEqZQJ0hpNsc5UuW6ldL6FmXoAMKlvNnJSLKiqd+Cb3aWqHycCjv27pODV68eje3oC9pdZcf4T3+HrHcdbvD1EJyIG9YiIYqDXkAzA1+j4y+3FeG/TURgNEv5zxWgMyGUzYSLyEMeJ1spueGXtoYCv1ZRnAn4ZbPGeDLZe2Un47q7T8dr141u8Ld3SIpfffrunDAA0yW5OiDMizui5eROs15zT5UadTQQvO0emXrhJvwBQ6n3dZSZZQt7YGtEjHQYJOFbdqGl5Xa3NCZEwGctnsOid1t4nSJN6YkDG5ScXqB4aIYhgU6zlt9Fk6ikZcxpl6mWrmH4ry7Ky3pwY1ms0SDh3uCdb7+Ofj6l+nMjwzUwyo1t6Al69fjx6ZCTgaFUDrnvpR9zw8o9hj+0t9cuxGtz65iYc9PZ0JeoMGNQjIoqBb0iGDkE9b1mdKGe47/yTcJr3jjMREeC7GIvUEF0Le0vq8N3echgkKNNW1ZQsyrKsZLCl+h0r4+OMMWU4dxOZelWNcLubl8MWVTdgy5FqSBIwfUhui9cjSJIUtixVBPSAjp+pp7b8Vk0vsCSLCYO7pgIAfipsnq3ncsvYfbwW7248gld+OAibM3gWZFMiUz4hztii6cmCCN5UMajXKWw9Uo2tR6thNhpw8ZgeyBRBW2vkY6Qsy77zOo3Kb9XccNG6p16Wis+FOpsTNu/5ZVaM653UNwsAsL9UXaDM5ZaV95voEd07OwnL5p+Gm6b0hckgYdkvx3Hmo6vxwrfqe7eq8fIPB/Hh5mO4892fQ7ZRIOpoOvZtRCKiNlYlMvU06NfUlP9J1m9O7a16yiQRnTh8mXr6ByRe9WbpnTE4F3tL6nCgzIoKFSWL9XYXXN6gm5ZTYbulJyDZYkKdzYl1Byow0XthKSzb7inhGlOQoVxgxyo1IQ5ldfagwS4RuIyPMyhBz45KZBpGmn6rZPpE+P2O6ZmB7cdqsPyX4zBIEgor6nGovB67j9fil2M1ysApAEg0m3DxmB4Rt1GrTHkRvBGZQ9Sxvb7ec5w6a2geMpPMSn9iNT1A6+0u2L0tBWLO1PMblCHLctiptr6eeloF9byBam+LBJOx+fFIfGYkmo1INMcWEsiK4ncMeI4r4j6Mf5lzotmEu2YNwoWjuuOeD7Zh/cEK3PfJL0i2mHDpuPyYtlEQmZPrD1Tgqx0lmtzwIWprHfuMg4iojelZfjswNwVnDu6Cayb2xF2zBmv+/ETU8WW3Uqae1ebEuxuPAACumtBTmSar5iJOlN7GGSXEx2l36mk2GTB7eFcAwNsbDzf7+ZfbiwFAmc6oBRGUDBbUE9/TMnDZVkSPOrVBvUi9wMSwjA83H8PNr/2Ehz7fiTfWF2LjoUo0OFxIMhuV4Fq4wSf+qho8r71YP3+VQRlWOzN3Orjqegc+3OwpAZ07vgCA+qER/suYTQYkxNi7WBybbU53QBZvMOVKTz1tbj5kJJohSYAshw5WK1OrNSj5zYzidwz4St1T4k2ICxJwHJiXgrdunIBbTu8HALjnw23Yfqw65u30XzcAPPT5DtV9YYnaMwb1iIhiUO09OdCj/NZkNOC5a8bh3vOHwqjxEA4i6hz8M/X0DEh8sPkoam1O9M5Owin9spWLODXlt2JIRkp8XNhslZa4ZKwno+vzrcUBF85V9XasO1ABAJhxknaZGOHKUkXwMqWD99MDAvcz3OvKVzYYPjAwZUAO+mQnISvJjFEF6bhgZDf84fR+WDJnJL5aMAU/L5qJS7zZeWr7NCo31WIskxRBPadbjhh8ofbJ4XLjlbWHcMbi1ai3u9AnJwnjvRO2M72BMjUBJ1F6m5lojvlYlWA2ItniORZE6qunDMrQKFPPf6pzeYiy4zIlqBf7OsXnQYPDhYYQQ4T8VaroWyhJEuafOQCnD+oCm9ONm1/7KeDYIMsyXvnhICY9+DX+92PzmzqhiNeBJAH7Sq14c4P6xxK1Vx3/rIOIqA3pmalHRBSJCKbYXW7U2py6ZIl5Lp48JW1XjC+Awe+CMZpMPT2GR4wuyECfnCTsL7Xi05+PYc44T3bO1ztK4HLLGJSXgp5ZSZqtTxkgEab8NrUTfB6I15HTLaPe7kKSJfjfrlRlg//0RDNW3D414jKA+qBeVb02n78JZiPi4wxodLhRaXWo7ocYqaSS9CfLMr7YVox/fLkL+72DD3pmJeKfl4xQ/jZiurGaQRkii0uLadmApyy9zuZEWZ0dfUK0RHa7ZaXfnxZZc0JWkhkVVjsqQrRmKFUZkFcj2WJCnFGCwyWjot6O7uaEsMuLz41IE4YNBgmLLx2Bcx77FofK63HH21vw9FVjUFpnw53v/IxVuzzTdr/YVoxLx6orzxXrvnpCT7z0wyEs+Wo3LhjVXQnAEnVEzNQjohOKLMt4bd0hPPT5zpDZBxVWO65/aYNSuhWO6Kmn1QkgEVE04uN82SBlKqYstsSPhyqxs7gW8XEGXDLGc+GkZOqpuFCuFUE9HYJdkiThV94Mr7d/PKJ8Xxy/Z2hYeguED+rVdKLy20SzESZvhrgIygajZYP/VJXDOQSxnBaZ8mKYgprMUwC45Y1NmP6vb1S9/kk/93y4Db997SfsL7MiK8mMe887CcvnT8HoggxlGZGpV9voVAaPhSKGN8TaT08Q74twmXpVIfrLxUrsQ1mI16j4vNBi4q4kSQFl7JFURvF7Tk8046krR8NsNGDZL8dx+9s/46wla5SAHqC+7NfudCs3X353ej/0ykpEWZ0dz6zep+rxRO0Vg3pE1Km43TLW7S8PelFgd7px+9s/Y+H72/Df1fvwS1FN0OdYubMEX+0owbPf7I+4Pq0yBYiIWkpcOKrJRGmJ17wDMs4b0U0pdcwQPZSiKL/VK9h18egeMEie4OP+0jo02F34Zo/ngm+mhqW3QPjyW3Gx2BnKbyNN+hXKNOzLpXbirqBlprzIElTzena7ZXy2tQh7S+rwzJrI5wmkj9fWHcKrawshScDvp/XDqjum4ppJvZoNqUlPiIPoYBJpwrHaDDK1xACZ0trGkMuI3nZpCXGaDtiJ1G9VlOXmaFTyG01fPTFgSe3veXiPdPz13CEAgHd/OoIKqx2Du6bigQuHAVAfjBfLGQ0SspMs+NNZgwAAz645gOM1of9GRO0dg3pE1Kl8sPko5jyzFqc9shL/Xb1P6e1RabXjyufX4d2ffJkcoaZFihOSYhUf8Cy/JaK2JjIt9MjUq6534LNtnqy3uX4TuMWgDDVZGXr3mstNjcdpAzy1be9sPIJv9pSi0eFG9/QEDOmaqum6UhM8+xCup15nKL8F/IJsYabC6hPUU9fXTmyXFpnyIiARKegDeP72YprzS98fVJ0lRNFbu78cu4prm31/w8EKLPpoOwDg9hkDcfvMgSHLpg0B/eXC/60qNXxNAb73Rbjp5Fr30xPE85WHWHdZrTfLVqPJ4NEE9XyZeup/z1eML8DlJ+fDaJBw45Q++OB3kzC+T6bqdfovl5EYB4NBwllD8zCmZwYaHC4s+WqP6m0ham8Y1COiTuWAt6dKdYMDD32+E1P/uRLPfrMfFz75HdYfqECKxYTu6Z5eH6FO3sXJRkmNLWLjeaX8R8OSCSKiaIhMvVBlVrH4YPNR2J1uDMpLwYgeacr3lVKrMAEfoTXKUkVZ8Hs/HcXnW4sAeKbeat3zLOygDJ0zEltbiig1bgweZJNl2Te1U4OAhC+IqO51rG2mnghSR349l/llPtXbXXhGRVY/Re9wRT0ue2Ytzvr3N1j4/lblnK2ougG/ffUnOFwyzhnWFTdP7RvxuTJUBpy0Lr/NSRaZeqFvuIiMuWyNJt8KYpJuqECmlgF5INpMPW9wLYrfsyRJePCi4dh+70zcPWswLCajUjZf2+iEQ8UU24omAzokScJdszzZeu9uPIKSMBmVRO0Zg3pE1KmID+wJfTLRPT0Bx2ts+PtnO3CwvB7d0xPw7s2TMDI/HUDoDBNxkWp3uZXy2lDECSAz9YioreiVqSfLMt5YXwgAuGxcfkCALJrpt74BEvqVpZ45pAvSE+NQXNOID7ccA6B96S0Qqfy280y/BSKXw1Y3OOD0ZqxpGtRTOyijwfv5q8FNtWhez2I4iOg5+PIPB0OWOFLLFVbUAwBkGXhtXSFOf3Q13lxfiJte2YiyOhsG5aXgH5cMVxW4F3/fSJl64hxSqxu1ovy2LMzrQ8vAuL9MJVMv/PTbLI0CmFFl6ongWgt+z/FxRuX/U/1Kq9W8d8uDlFeP65WJ0QXpsLvceNU7EIqoo2FQj4g6FRGEO+ukPKy4fQoWnTsEuakWTOiTiQ9+NxkDclOQ4U33rwgRsPMP9oUrwXW7Zb9MPQb1iKhtKL2TrNoGFrYercbO4lqYTQZcMKp7wM/UZr4A/tNv9TtOWkxGnD+iGwBPECAzyYyxvTI1X09qmOy1Tlt+GyLIJsoGU+NNsJiMQZdpyfqsdpeqrBtRpqtlTz01gQGx36N7ZmB4jzRm6+lEBJ16ZydhQG4yKqx23PXeVmw5Uo2MxDg8e/VYJJrVBdBF4KoiQvBVnENGUxYajjg2l4YN6omBFdoG9bIjBDKVITdal9+qeA+JZaLJ1AvGaJB8710VWbbi/L7p7/o3p/YBALyy9pDStoeoI+kctxKJiLz8U/otJiPmTe6Nayb1AgDlbq64Qxep/BYAjtc0YnCInkx1dqcysYyZekTUVpTy21pty2/f3HAYADBraF6zzJWmZU9xxtD3iUVZqt4ZbJeMzcdL3kyLMwd3gdGgbektoG5QRmonydQT+xFs0i+gffme/++tpsERcSpntYaZ8hnRlN96M2JzUiy4eHR3XLv0R7z8wyH85rQ+yE62wOWW8eHmo3h/01HMm9QLZwzWPmO0vWt0uHDvx9tRVN0ICZ7zLwnAuN6ZuGlK5HJZwBc8Hdo9DYsvHYGl3x3Ekq92w+Z044m5o5Gfmah6e3wBp/B/X90y9cJkUYu2CVlal9963z/Bbrw0OlyotXmOV1qX36qaftukDDYWGYlxqLDaVd1gCpapB3impOdnJuBwRQPe23QEV/j1jyXqCDrHWQcRkZcIyPl/YDctzUiP0AvKv+S2pCb0iZho0m0xGQLKAYiIWpMemXr1dic+2uwpY50zLr/Zz0XZk1v2HHe7pMSHfK7WymA7qVsqhvdIw89HqnGuN2tPa2F76rVCRmJripSpp2T6aBQUMBkNSLGYUGtzolpNUE9kymvwuoqm/FYEM3OSLZg2sAtG5Kdjy+EqPL16Hyb2zcIjX+zCTu9wh9Ja2wkZ1Fv+y3G8sf5ws+9/vbMEvxrTQ9Vrxhc0NiPOaMBvTuuDS8b2QJ3NiR4Z6gN6gF+mXoRjpNJTT6OgXnaKb1CGLMtBS4XL/fZTSyIbLVjpr/ie2WjQ7CaE2mEkgLZThjOTzNhXalX13lUy9ZoEE40GCddO7o17P/4Fz685gMvHFcCgw00hIr0wqEdE7ca2o9VYvHw3rp7YE1MHdmnRczRtghuMKKsIdTfRv3QgXPktS2+JqD0QFyjhJiyG8+raQ5ABzD25QMlu+/TnItTZnOiZlYgJvbOaPUaUPVVY7ai0OiIE9VpngIQkSXju6rHYW1qHSX2zdVmHCEzanW40OlwBN3SUQRk69g5sTSKoFylTT8uywdSEOCWoF47D5YbVWyanZfmtmmwf/15kkiThtjP749cvbsCzaw7g2TUHAHiyUq02J3YW16KougFd0xJi3saOZOOhSgDAtIE5mDWsKyAD93/6C2obnSittakL6tU2zwRNTzS3KItObbsAcbNXi2AT4AvU2V1u1DQ4kRbkfNHXU0/rQRm+bGqb0xVQIu8/cVerYUJZKjP1HC638pmgTaZe9AM6gq33krH5WLx8N/aXWbFyV8kJE4yXZRnFNY04XNGA4ppGHK9uRHFNI4wGCcO6p2Fkfjp6ZCRoPnSKtNU5zjqIqFP41/LdWLGzBCt3leCP0wfgd9P6RfUhIsuyL1MvzIlCuN45siwHlOUeDxPUExl9LL0loraUraIZeyiHK+rxlw+2AQA+2HQUj14yAr2yk/CWt/T20rH5ITMW1JY91Ta0Xq+5Lqnx6JIaOsAYq2SzSclQrG5wBAT1fIMyOsdnQmrEnnralt8Cns/To1UNEYN6/j/X4nWVqbTlUDP9NrAX2dQBORhVkI5NhVUwmwz49aRe+O3Uvvj10g3YVFiFb3aXYs64gpi3sSP5qdAT1LtwdA+c582afXbNftQ21qkKvgC+jC8tMtiUQRlhbnw0OlxocHgCxeka9dSzmIxIjTehptGJ0rrG4EG9ENljsUqNj4PJIMHpllFhtQcElst1eO+qnzDseY9Jkjbnz9GU/Yps9mDXCMkWE+aOL8DTq/fj2TX7O21Qz+ly49u9ZdhUWIWtR6vx85GqiDcEM5PMOLlXJu6/YKhSUk7tC4N6RNQulNbasGp3KQBPk/N/LtuNbUdr8M9LRyDZou5Q5Wmu7WlyF650IiPMybv/cwDA8XDlt0rpj7YnYkRE0cj29mKqbXQ2yx6L5Id95cr/bzxUiVn/XoPrTumNHw9VwmiQ8KsxPUI+Vm3ZU00nmgprMEhITYhDVb0DNQ0O5HoDiLIst1pGYmtRMvUaww/K0DJTT+0EXPHzlHiTJr0TRcZ9RX3oMkmhaTBTkiQ8ecVofPpzEc4Z3lUJnkwZkINNhVVYfYIF9RrsLvxyrAYAMKZnhvL9zKTQ5aDBaBk0Fv3qwh2rxM9MBgkpKs871chJsaCm0YmSWhv6dUlp9nNfxqu2wRKDQUJmkhkltTaU1wUG9cp0KPnN8ithd7vlkDeDxO85PSFOk/duRhQDOkTPzFD9C+dN6oXn1xzA2v0V2Ha0GkO7p8W8fe3Jt3vKcP8nv2DX8dqA7xsNEnpkJCA3NR65qfHIS7WgweHCz0eqsaOoBhVWO77YXoyJfbOUPuXUvnT8sysi6hQ+2nIMLreMEfnpuHxcPv764XZ8sb0Ye/9Th+euHote2UkRn0PcpYuPMyDBHPqiNjNMqn7TO31hM/UavE26WX5LRG0oNcGEOKMEh8uTkdEtXX2p3w/7PUG9S8b0wJHKBvywvxxPrNwLAJg2sIsStApGbdmTryy1cxwr07xBPf/AU73dBZd3clJnCF4Caqbf6pOpF26dgrgpp1X7CxFwsjvdaHC4wk5V9ZWF+gIiXdMScL13gqYwZUAOlny1B2v2lMHpcsMUZphMZ/LzkSo43TLyUuPRLc13/BDBX9WZehqWpWaqyCLzH5KhZalhdrIF+0qtQbOhbE6XMmBH6556AHxBvSb7XaZxP0zAVwXjlj03AkKVSfsPtNNCZmI0mXrhW/R0TUvAOcO74sPNx/CXD7ahX5dkFFc34lh1AwyShHmTemHOuPywg6H0JMsybE531H2895fW4YHPduCrHSUAPMfN0wd2wfAeaRien44hXVNDPmejw4W73v0ZH2w+pmR4UvvTOc46iKjDe++nIwCAi0d3x2UnF2BgXgpuenUj9pbU4S8fbMOr14+P+BxqG++KsooGh6tZVkvTu7gsvyWi9k6SJGQlWVBc04iyOpvqoJ4sy1jrDepdMKo7JvbJwovfH8QjX+yEzenGlRPCZxepKXtqdLhgd7kBdJ6psMECT+LC3GiQkBjmplJHEnlQho5BvQhlsKLPn1afv4lmI8xGA+wuNyrrHSGDerIsqw6IDO+RjvRETwB40+EqjOuVqcm2tncbvaW3o3umBwTH1ATWBFmWUaphNplvEIojZBaZOKfL1Kj0VhDliqVBJuCK34XJIOmS4et5jdY2C8aIbdEyO9BsMiAl3oTaRifKrfaQQT1l8q1GfQt9mXrhjxlut69FT7heftef0gcfbj6GzYersPlwVcDP/vLBNjz/7QH8ccYAnD20a6sN0zhYZsX7m47ig81Hcai8Hs9dPRZnDlFXHvz6ukL89cNtcLplmAwSrprYE7edMUB1QkJ8nFGZNB1qwCC1vc5xdkVEHdrO4hpsP1aDOKOEc4d7eq+MKsjAk1eMwcVPfY+9JXWqnqciyOTbYFIsJqXPSFW9A3lp/kE9zwdWdrIFZXU2lNXZQt5hr9Fw8h4RUSyyU8wormkM2zOqqUPl9SiqboTZaMDoggwYDBKuO6U3ZgzJxbGqBozv03xAhr9Ik8QBX+mmQQKSwmQ/dSTi4ts/2OWbfGvqNA3FxX6KTMumfMEt7TKMROZdxEw9kSmv0eevJElIT4xDSa0NlVY7uocIjNc0OpUgdaTeUkaDhFP75+DjLcewelfpCRPU+8k7JGN0QUbA9zOTxJTuyMeoWpsTdqfn96xF0DjDG6hzuWVUNziCZon5Z+ppKSdMz1NxvM5MMusSIAqVHallv0J/mUlm1DY6PYG7nODLVKjofR3dOsMPvxNqG51KNnVGmMDtsB5puP+CodhRVINuafHompaArunx2FVciydW7MWBMit+//omDOu+H0suG4m+Ocma7EdTRdUN+GJbMT7acgybCqsCfvbD/nLVQb23NhTC6ZZxSr9sLDrvJPTrEv32hutFTu1Duzm7mjdvHpYuXdrWm0FEbeD9n44CAE4f1CXgQ76rt2yjwhq5xw3gd/cvwomC5+TdjLI6GyqsduT5lYeI5+jXJQmV9Xa43J678v7LCMzUI6L2QvQIKo2iPEZk6Y3MTw9oWZCfmajcmQ9HuZgKc6IvAkIp8XGtltWgt+CZep1rSAbg288Ghwt2pxtmU+DNLT0y9SIN5xBEJp+WPW1FqWK417MIzCRbTKpK4KYO8Ab1dpfi9pkDNdvW9kqWZfzkDUD499MDfAGkChU3HkSwS+3vORKLyYgUiwm1Nicq6u1BA0piSJpWGWSCeH8Ey9TTq5+eID4Xmpb+ihJyrYceZCSacai8PmzgVvNMPZVtIMSQjGSLKWAScDBXTejZ7HuT+mbjkrH5eG7Nfjz7zX5sPVqNpd8dxP0XDG3Rdh+rasBtb22GySChT04Semcno3d2IvYcr8Pn24oDsgQNEnBK/xyYjQZ8teO46hJ2wPe3XzBjQIsCeoBnKBYQ+bhMbaddN3d47733MHPmTGRnZ0OSJGzevLnZMjabDbfccguys7ORlJSE8847D0eOHIn43E8++SR69+6N+Ph4jBkzBmvWrAn4uSzLWLRoEbp164aEhARMnToV27dv12TdROTjdLnx/iZPUO+i0YEN2ZUeNy43am3BMwX8iWwRNXf/xAdUVZOTd3Eyn5VsQY73JCtUCa4yKIM99YiojYmMjGgy9URQb0KflmUQqbmY6kxDMoRggSdf38DOs58p8SaIe2lNL+Ya7C5Y7Z5JoW0xKKNKh4nKyrCMMK/n8iizE08dkA0A2Hq0ukXTqTuag+X1qLDaYTYZcFK3wCED0ZTf+oJdGgZtI/T0851D6lN+Gy5TT49+eoD/50LguvXohwn4DcsI27tQ/bm6GplJ6rLI1JTeRpJsMeG2MwfgDm+AXgQKW+LL7cVYf6AC3+8rx6trC3H/J7/g2qU/4sHPd2Lz4SpIEjC2ZwbumT0Ea/98Bl6+9mTMOMmTnRdNUE9sY3aI4SBqZDBTr91r06BeWVkZrrnmGhQUFOCNN95Av379cOmll8Ju97xgrFYrJk+ejIceeijkc9x22214//338eabb+Lbb79FXV0dZs+eDZfLFfIxb731Fm677TYsXLgQmzZtwqmnnopZs2ahsLBQWeaRRx7B4sWL8cQTT2DDhg3Iy8vD9OnTUVvrmxbTknUTtUc/7CvHoo+2R+xho4fv9pWjpNaG9MQ4TBvYJeBn8XFGZfKtmgtV392/yCdkGSHKxpSTusQ45KaGD+r5BmVw+i0RtS1xE0Jt4ECWZWVIxoS+4ctsQ1FzMVXbySbCAn5TYf3KUpXgpaXz7KfBICmfwU0n4IrXmcVkUD2hXo1op99qeVNNvJ6rwpwLRRsM6ZISj5O6pQIAvtldGuMWNvfvr/Zg4oNfY1+pujYletvoLb0d3j2tWWanMv1WRSDEN4xEu6CTOO8LdT6pW/ltmEw9EXDJ0ijA1ZR43uaDMrQPmgLqJtH6gmvavHfFOuvtnj7ZofiXOmu1TjFNtyXEcebk3pm4eWpfzBqah0F5KTilXzbuP/8krLv7DLzz20m47pTe6JLiqRbKiiIwDgD1dicaHZ4y9swY/tbiOBvL/pK+2vR24vz587Fhwwa88sorWLJkCf7whz/giy++gNvtefFdddVVAICDBw8GfXx1dTWef/55vPLKKzjzzDMBAK+++iry8/Px1VdfYebMmUEft3jxYlx33XW4/vrrAQBLlizBl19+iaeeegoPPvggZFnGkiVLsHDhQlx00UUAgJdeegm5ubl4/fXXceONN7Z43TabDTab76BeU1MT5W+NSFslNY248ZUfUdPoRFW9HUsuG9Wq6xcDMs4b0a3ZCSDgOeGoszlRXmdD7wgTcMVJhJoTMnEntumJh39ZgGfqY3XooB7Lb4monQiVkRHKwfJ6HK+xKf30WiJDxQVGjZJR1Xky2IIFnmoaO1+mHuDZ19pGZ7Mgm39wS8segtEG9bT8/E1XkXnakgynKQNysP1YDVbvLm1WkRCLg2VWPL5iD5xuOaYyQC2JoF7T0lvAVwqqKlNPh55vkQIiepXfqsnU0638Nrl5H0OndxgMoF+mXrgSa7VD7dTy75NdWW9H17Tg/TArVLboUUOLzDVxDBvXKwN3zBykbr1RBvXE68tiMiAphgFOYn+bVjdR+9GmmXqbNm3CVVddhSlTpiAtLQ3Tpk3Dww8/jPj45r2rgtm4cSMcDgdmzJihfK9bt24YOnQovv/++6CPsdvt2LhxY8BjAGDGjBnKYw4cOIDi4uKAZSwWC6ZMmaIs05J1A8CDDz6ItLQ05V9+fr6qfSXSgyzL+PP7W5WLkQ82H8OKncdbbf21jQ58ub0YQPPSW0GcIDTtBxKM2p56gN8HVJMPxkq/wKAnqAccrwl+kcxBGUTUXmQnB++dFIrST68gvcU9q8TFb7hSK98Aic5znBSBu8Dy2863n0DoIJseQzLCra8pX089DTP1VFy4KhlkKer3e8oAz8SAb3aXKo36tfCvr3bD6X2+j7Ycg83Z9pVCm5TJt82Dev6ZkE7vsJFQynSYzhops1hMT9W6pYr/sdnd5O9fpgT19MnUE/vsf7NHBIQMknaBNSG6TD1t1i1JkqpgV4WG69UiqCeOM9H0BY02U08Ec7OSzDHdfBH7a7W7lAE21L60aVBv8uTJePHFF/HJJ5+06PHFxcUwm83IyAj84MjNzUVxcXHQx5SVlcHlciE3NzfkY8R/Iy0T7boB4O6770Z1dbXy7/Dhwyr2lEgfH2w+iq92lMBsNGDW0DwAwJ/f26Y0/Nbb59uK0ehwo29OEkb0SAu6TGYUd3aVu38qPrCVO/JNPpCr/HqqRC6/ZaYeEbUPWVGW3/6wzxPUmxhhwm044lhrDVP25D8oo7Pwld82n37bmfYT8J+AG3heUK5Tg/+2zdQTg19Cr7tUCWaq3+/RPTOQYjGhst6BbUerY9tIrx1FNfhoyzEAnonL1Q0OrNhRoslzt1RNowO7jnvaFAXL/s1IjFN6NIb7HQN+fcC0DOpF6DtapXGwSRABO5c3k8yfFv3OwskOss9imFJmkgVGjYcXqembGM25uur1KjeYQr+uKjQsv/U/VshyywL1yjVEFEFkse0NDhca7JGD+OI4HUvpLeDprypeKszWa5/aNKi3ePFizJkzB/Pnz8fLL7+MkSNH4r///W/Mz6tmSmbTnwd7jJplol23xWJBampqwD+itlBS04hFH/0CALj1zP5YfOlI9MpKRHFNIx78fGerbIOYenvR6B4h3zfZUZSUiYCcmtIJ36CMpj31fGUBIlOvOEhQz+50o977gcpBGUTU1sSxUk2mnizLfkMyWh7US403KReFofqQKZl6nagsNfj0285bfgs0D+r5ylC1DYCIz9N6uwuOMNlcLbkgjkRNj8iWlN/GGQ2Y3M8zMGO1Rn31Hl22C7IMzB7eFVd4J3W+s7Fth/VtLqyCLAMFmYlBp6qajAYlszLSjdqyWu0zQTOV8urg55N69dSLMxqUc86mx+dynTP1RNC9weFSbtjrlWULqMve1nr6LRC6pY4/TTP1xCA/pxsNYfr4hVPVgmzjZIsJcUbPZ66aIR2+TL3YgsYGg6R8FkQKyFPbaNOgXlJSEv7+979jz549OO+88/Db3/4WCxYswDPPPKPq8Xl5ebDb7aisrAz4fklJSbMsOyE7OxtGo7FZNp3/Y/LyPBlLkZaJdt1EsZJlGcu2F+PHgxUxP8+f39+K6gYHhnVPw42n9UGC2YgHLxoOAHh9XaGSxaGX8job1h3wrOO8Ed1CLqf0iVKTqScCciqa72aEOHmvtDYP6pUEKb8VF3OS1PkyM4io4xFBhgqrrVmJV1MHyqwoqbXBbDJgVEF6i9cpSVLECbi1nbD8VlzclNbZlKDliVt+q22Gkf/nabhsPT0y9dSU1LV0auiUgZ4S3FW7Ys+m23ioEl/tKIHRIGHB9AG42Nu+ZNXu0qDDGFpLuH56QrBy0GD0mM6qrDtkTz1HwHJaEkHOpn8fvTJehSSzUak6ufm1n1Bvdyrr1Pq9C/jOrUP9jm1O39RsTTP1VE3d1S6ol2Q2KsG1lga5fMN+1G+PJEl++xp5vRV+5bex4gTc9q1Ng3r+0tPTceONN2LWrFlYs2aNqseMGTMGcXFxWL58ufK9oqIibNu2DZMmTQr6GLPZjDFjxgQ8BgCWL1+uPKZ3797Iy8sLWMZut2P16tXKMi1ZN1EsDpZZMffZdbjhlY349dINLU73BgLLbv95yQiYjJ5DwcS+WZg7vgAAcNd7P6tK7W6pr3Ych1sGhnZPRX5mYsjlxN2lSCVlsiwHBOQiyQhxN9E3/davp15t80y9au/k2xSLSfPyBSKiaIkTfbcc+aRbTL0dHUM/Pd96xd374OsU5bepnahNQc/MJCRbTKiw2jFryRqs21+uZOqlxHeyTL3EUEE9fYIRRoOk/A5DBfVkWVZ66ulSfhvmYlnsd04UPfUAYJJ3wvTPR6pjOn+TZRn/+NJTTfGr0T3QJycZ/bokY0R+OlxuGR9uPtri547VT6KfXpgbBeKcLtKN2nINgxHKupNDByXsTjfqbJ73cIYO1RfBhmXIsqwMBNFr+q0kSVgyZxQSzUas2VOGa15Yj4NlVgD6ZOplRQiuicCp0SAhVcNjZaSbS/4/0yJDUJIkJRgXLpAYjtJTL8rXW6byHlKRqafhlON0pcKJQb32qE2DevPnz8fq1atRXV0Nl8uFlStXYvXq1RgzZgwAoKKiAps3b8Yvv3hKBHft2oXNmzcrGXRpaWm47rrr8Mc//hFff/01Nm3ahCuvvBLDhg1TJtIGs2DBAjz33HN44YUXsGPHDsyfPx+FhYW46aabAHjeqLfddhseeOABvP/++9i2bRvmzZuHxMREzJ07N6Z1E0XL6XLjmW/24ax/f6NciAWbRKdGTaMDD362A396ZysAT9ntwLyUgGXunjUIXdPicai8Hk+u2hv7DoTwxTbP+/isk/LCLpcVoQeKUGtzKg2j1QX1mqeRNzpcShp9elIc8rxBvap6R7N+US25w0ZEpJc4o0E56Y50wbx2vyfbO5bSWyE9wt17X6+5zhPsSkuMw0vXjkNBZiKOVjXgsmfXYoM3g74zBS8BKBfeIjgr6FV+C0Tuq9focMPuLc3V8jNYVfmtUhYaXTBT3CR0umXU2pwRlg7t271lWLu/AmajAbee2V/5/q9GdwcAvPtT2wT1XG4ZmwurAAQfkiGo6bkG+A8k0TJTz5vNHOR8UgQqDJI+2bbi9eKfqVdncypDB/QqvwU8N+xfuW48UuJN2HCwEk+s3BuwTVqK1GfVf/KtllOz1bx3laCeRr/rUG181HC7Zd91RJSfGZFupPkrV7ITY/9b+zL11O1vhdWOncU1Ma+X1GnToF5BQQEWLFiA/Px8vP7667j66qtx7bXX4pZbbgEAfPTRRxg1ahTOOeccAMBll12GUaNGBfTd+9e//oULLrgAl156KSZPnozExER8/PHHMBp9d56nTp2KefPmKV/PmTMHS5YswX333YeRI0fim2++wWeffYaePXsqy9x555247bbbcPPNN2Ps2LE4evQoli1bhpSUlKjWTRSLktpGXPTU93jgs51odLgxqW8WEr0jydVOOAQ8J1uvryvEtH+swtPf7Ifd5caMIbm48bQ+zZZNiY/DX84ZAsBThqvHlKOaRge+2+sJUJ41NEJQT+WgDHGnLCHOiAQVY9uDld+K/zcZJKRYTEhNMMFi8hwmm5bgVumQJUBEFAtlymKYEjxZlpX2CloE9SL1UOqsZaljembis1tPxZyx+ZBlKD1WO1PwEggdYCvXqfw2YJ0hLh7FthgNEpJUfN6rJQKE9SECElabU7nxF22GYnycEfFxnvOJUPulxqPLdgMArpzQE93SE5TvnzuiG8xGA3YU1eCXY61/Ib2npBa1NieSzEYMzE0JuZyaliqNDpcS+NTy9ZXlVxraNFuyst53o9agQ/VFTpBBRuI9lGg2ItGs73FjTM8MvPGbCchIjIPozqBlwFRIjTfBZBBlqc3/xko/PRVtcqIRTaaeVlmRsZSj1tqcyt8h2htBSqaeiutApWejJsNBotvf37/+E85asgbLtoceIEraafNMvY0bN6KmpgZXXXUVDh8+jEceeUQJis2bNw+yLDf7t2jRIuU54uPj8fjjj6O8vBz19fX4+OOPkZ+fH7CegwcPYurUqQHfu/nmm3Hw4EHYbDZs3LgRp512WsDPJUnCokWLUFRUhMbGRqxevRpDhw4NWEbNuolaSpZl3P72z/j5SDVS4k14+OJheO368crdXrUTDqvrHTjviW/x5/e3otxqR5/sJLwwbyyevmqMUnbb1MyTcpGbakG51Y4vdTgYr9xZArvLM/W2X5fQJ3+A/wlg+P2tjLIXivgwrm10Ks24RclNuvcOoiRJIYdlKA1uOSSDiNoJceJeFubCZl+pFWV1NlhMBozMT495nRlK5k3wQEVnHSABeJqWP/yr4Xj6qjHITDLDZJDQJzu5rTdLU6khe+rp15crUqZelbf9RXpCnKbZPpEGv4h9jo8ztCiYGGtPqnq7E5sPVwEAbpoSeFM2PdGMMwZ3AQC8+1PrDsxwu2V8sqUIADCyID3kuSXgO0aFGlYB+AJ+ZqNB2xJN77ptfoPOBN+QDH3O6bKD9NQT57V6Zun5G9o9DW/dOFEpBe4ZpvVNS0mS5PeZ0Px1XuE3jE5LkTL1Gh0u5W+uVS8/sQ8tKUcVgf2EOGPULTCyVGa7+i+jxWss2szE3cfrAAALP9jGkt1W0PnOsJrYuXMnUlJScPXVV7f1phBF5dV1hfhmdyksJgPe++0k9Pfe+cxONuNAmVXVHRoAWHegHNuP1SDJbMQfZwzEVRN7Ii7MCRfgmVA2Z1wBHvt6D15fV4hzwwyyaIll248DAGZGKL0FfB9EFVY7XG45ZP86pZ+eyrt/aQlxkCRAlj0fUDkpFuVDx7+fSl5qPAor6nG8SVBPjybdRESxEBeO4TL1RN+rkfmx99MDVPTU64SDMpqaeVIeJvfLRk2DA3lp8W29OZoSQT3xdwQ8/cfEjTQ9AhKRgnp69NMDxOCXOJTV2VFhtTf7W/oHMlsSTExLiENRdWOLG+uLC3SzyRB0uuzFo3vg823F+HDzUdw1a1DEcz0t/HiwAvd/ugNbvMHGKQNywi7vG5QRrsTZF+zSMmibZDbCbDLA7nSjwmpHksV3GSzO/7ScyOpPZOqV+t2QL1OyqPQZkhHMgNwUfHLLKdhwsALTh+gz2DEryYzSWlvQoJMvU0/b33Okm0tiW+KMnkocbdbZ8mmwyo2JFgSRo7k5oOVwkAwVw0gEWZaV91RprQ33ffwLFs8ZGfM2RMvtllFmtaGkxoY6mxNWmxN1Nica7C4M6ZaKod3SdMnMbQvtJqi3dOlSXZ530KBB2Lp1qy7PTaSXA2VWPPDpDgDAn84apAT0APWDI4Tj3pOjSf2yce0pvVVvw2Xj8vHEij34YX859pXWoW+ONtkHjQ4XVnqnv0UqvQV8J1hu2XPSFarkxb9PhxpG73j2qnoHqurtyEmx+E3P9T1HF+/UsKZBvSoG9YionclOipzZvLfEc/d8cNdUTdYZqexJGZTRiYN6gCdrL1mji8X2JFiA7Udv/8CsJLMuQZD0EMM5BOXzV4esqoxEM8rq7EEzS0pb2E/P/7mBljear1IGeQXPUJwyMAdZSZ7t/9fy3RjXKxNd0+PRLT1B8/ffwTIrHv5iJz739kdOMhtx87R+uHZy+PPMzOTIgzL0ymCTJAlZSWYUVTeiwmoPGNJWoQwt0CeoFzRTTylhb93ezLmp8Zg9XNub9f7CfSaIoJuWk2+ByG0g9Ojll66i5DeUWFr4ZKrsNS7LsqYZ1elBepGHYrW7lD7nkgS8t+kozh7WFWfqFEj29/aPh/HOxiM4Vt2A49U2pf9qMF1SLDhjcC7OHNwFk/tla3Kjs610vrMPog7O6XJj/lub0eBwYXK/LMyb1Cvg59kp4mCuLqgnTiC6RNk7o1t6AqYN7IKvd5bgjXWF+MvsIVE9PpQ1e8pQb3ehW1o8hnVPi7i8ydv8varegXJr6KBeZQtS+jMSzaiqdygfyJV+J8yCMgG3SVCvpoHlt0TUvvh66oU+2RdBvb5dtLlRE67syeFyKz3IOmP57YkgWFDv652eG3PTBnXRJcshVMmvoGemfLhm8CLY1OKgXlLLG+t7tin8eU6c0YALRnXH898ewJOr9gHYp/xs+pBcPH75qJgvWgvL6/H4ij14b9NRuNwyDBIwZ1wBFkwfEDR7sCk1pYMtHUaiRqZfUM9fldLCRZ9zuuA99bzBy1bM1GsNmWEyuip1yogU762Kek+/xKaBOy0z1pR1xjANtiqGawi15bf1dhds3r7ommTqRXFTQvztLSYD5k3qhae/2Y8/v78V43plhr0Z8+raQ2h0uHDt5N4t/mz526c7Aj47JMnzHktNMCHFYkKSxdNm4adDlSipteGN9YV4Y30hBuWl4LM/nNphM/d4hkXUzjy1ah82H65CSrwJ//jViGYHF/HhX6qy/La01hOMUnOy1dTc8QX4emcJ3vnpCG6fOVCTOxiiR9/MoXmq75ZlJXmCb+V1diDETZ6WfGA3vetUFSTbL08J6jUdlOFZlpl6RNReZAW5cGxKBPX6aZR9Ha5/kuinB6BTZrGdCESGV53NCbdbhiQBX+/wtNA409vDTWttVX4LBAYHmhLBppyUll0gpyXE1lNPTUXC76b1AwAcKrfiaFUjiqobUFXvwPJfjmP+W5vxxNzRIduYhHO0qgFPrNiDt388omTgTBuYg7tmDcbAvPC9kf35t1QJpSzG4Gk4Svlvk/VXRlntES1xQ77CaofV5sSaPaX48hfP+XBr9dRrLeEmHCuvYa0z9bzPZ/f2S0xq8nmjZW85IT3MDYBIqkVmaEL026NkQkY4johMPovJoAxZjIXvminy8cuXVWzG/OkDsHzHcewvteK+T37Bo5eOCPqYZduL8ZcPtgEA9pdZ8fcLhkadVelwuZXPjRd/PQ79uyQjNzU+aCsCm9OFtfsr8NUvx/G/Hw9jZ3EtNh2uwpgw07vbM55hEbUjW49U499f7wEA3Hf+SQGTzQSRwq82U09Mbe2SEn2fn6kDu6BbWjyOVTfii23FuGBU96ifw5/D5cZXO9T30xOyki3YV2oNW1JW6fcBolZmk7tOLSm/bckHMhGRHkQZV6hBGY0OFw5X1gMA+mmVqRem7ElkNCdbTGGb51P7JQJnsuwJ0pZZbThYXo84o4RT+ofvnxbrOiNl6qXrmKlXFeT1HGspW7SN5ptSLpTDZJNlJplxT5PKiu/3lmHeixvw+bZi/PXDbfhblBfLDXYXzv73GuX3ftqAHMw/sz9GFUR/8euf2RuqT7IInuoR7AqVRRbs/E9LWUkWGCRPK5kxf1uORoevJFBN1UpHotzoCTb9VmTqaZwRmRBnhMVkgC1Iv0TAF8TVMmgbSzl9LMP21ATGgcDMYi1Kjn37G/n4VamUs8chPs6If14yAr966nu8+9MRTB/SBWcN7RqwfE2jA/d8uE35+vV1hYg3GXHP7MFRbXuN32fGqf2yw553WExGTBmQgykDclDT6MCHm4/hs61FHTaoxzMsonai0eHC/P9thtMt4+xhebhgZPAAmuiZpLannmjK25JMPaNBwpxxBQA8B9hYrT9Qgap6B7KSzBjXK1P147JV9I/wNd9V/wGZ3uRuV1UU5bfiwzTaUfRERHpRMvVCDMrYX2qFLHuCJlr1ccoMcwEnhiukaDjBklqX2WRAgjdLv7rBoWTpTeiTpVv2pdrpt3pk6jU9L/AXa1AvPYZyPcD/Qjm69+6kftlYctlISBLw2rpCLPlqT1SPP1JZj+oGBxLijHjnpol4+dqTWxTQA3yBAVkO/XsQv+ecVszUC3b+pyWjQULXNM+N+kaHG93TE/CbU3vjw99NxqxhXSM8umMJVx7qmzKsfb/EcK0gxDVClg7lty0blNHyvqBiP6vqHXCG6RendcmxEtRrcECW5bDLNi0vHl2Qgd+c6pnYPf+tLcpgHeGRL3bieI0NvbIScf/5JwEAXvjuAB5dtjuqbRTrTY2P7kbi2d734Odbi+B2h9+39opBPaJ24pEvdmFvSR1yUiz4+wXDQt6ZUDL1VDZm9WXqtezkaM64fBgNEtYfrMCe47Uteg5BlN5OH5IbVfmHb1pa6EBmS+6yNr1rHuyE2b/8VnyIHamsx9aj1QCAIRo1myciilWO0oTeFvSke2+pt/S2S7JmzcLFMbfR4UaD3RXwsxNlSEZnl+Y3AffrHZ5+emcM0qf01n991SEulqu9r6s0HUolM8P0vYs9qNfycj3/bWpJP7Kzh3XFfecPBQD8++s9eGXtIdWPFTeHu6XHY2wUN2SDiTMalL9vqPNYvQZlAP4Bp8DzSb2CTf7+cclwzD9zAD743WR8+6dpWHjOEIzIT9dtfW0lXEsG5Qa8Dr/ncAM6ynUo+/W9n2PI1GtBtY9/hnJViBsfgC8RQqv3kQjQudwyavxaawRTFaT/5+0zB+K0ATlocLhw3UsbcLjCUzXw48EKvLrWkzjywEXDcNXEXrj3PE9g74mVe/GflXtVb6MvAzK6fZ4yIAdJZiOOVTdi85GqqB7bXjCoR9QOfL+3DC98dwAA8Mivhof90BEnJKEyMfy53b7JR6KMNFp5afE43Xvy/vr6lmXrVTc48N5PR/Dpz0UAPP30oiH6CIYLZLakH0rT8ezBnkNk6jU4XKi1eT7E/vfjEcgyMKlvFgqyEkFE1B6Ik/dGhxvWJgE2QPt+eoBn6qXZe0e86cVNrTdTj0MyOjbx9yusqMePhyoBAGcM1m+KodryW10z9YKW38Y2rdQ/06UlfIGnlu33VRN64tYz+gMAFn20HSW1jREe4eHbb20y57KUG7UhMvV0HJQRKuBUVa9tVlMwk/pm49Yz+2NkfrpmN1Xaoyzl3Lr567xCx99zuEw9EcTVMlNPrK+20Rk2Yy6Y6oaWv5fFAEEgfAluucaZevFxRiVrO1K2sfjb++9fnNGAJ68YjSFdU1FWZ8e8F9ejpLYRf3r3ZwDAnLH5mNQ3GwBwzaReuGvWIADAP77chU2Flaq2saW/1/g4o/KZ9pn3WrWjYVCPqI1VNzhw+9tbAHgGU0wbGP7ut8jUs9pdzbIimqqstysNjWOZrnX5yfkAgC+3Fat+jCzL+HDzUVy7dAPG/m05FvxvC8qtdmQmmTGpb1ZU61dVftvC6bf+j60MMv0swWxEqrd07Hh1I1xuGW//eBiAJ4uRiKi9SLKYlJPuYDd+9pX4MvW0IklSyAsMUX7LTL2OTQTPPt5yDC63jAG5ycjP1O+GlsheCT0oQzSZ1/511bTXrj/xnspuYeWDVuW3sfQFu+3M/sjPTIDLLeNgWb2qx8S6301F6gmmZOrpMBU2VGmobwgJj1WxEq/PpjfiG+wupZegHr0LfQHb5scNEWTK1PA1lZYQBxGbjTZQ78vUa9nrLTNCYNzzM+0DmWpLjkO1Cki2mPDir8eha1o89pVaMeNf32BfqRXZyRb8+ezBAcveNKUvJvfzXC/uKlZXKVYVwxAlpQR3W3HE8uL2iEE9ojZ270fbcay6ET2zErGwyQEtmBSLScmKiNRXr8R7IpaZZIbZ1PK3+8A8T4lpaV3wkq5gVuwswa1vbsaKnSVwuGT075KMW8/ojw9/NxkWU3RTmLL8SsqCkWXZLyAXffltZZjyW8C/r54N3+wuRVF1I9IT46Ia9kFE1BoKvMGWnUFOgvfqENQDQmdIiPJb9tTr2MQFkii9PX2Qfll6/utrcLhgdzbPgKmOoR9VJKGm3zb6ZevHOigj2FAZNdQMyohEkiTkegenqe3NrHWPu8wQJbCAp7RPBNiyWzhlOPy6Ld51+/4GTpdbKSfUa/rticT/88D/mkG8p8xGA5I0mMbabL1h3l/i+iGW905TRoOk3LCKNlAfS089wG9AVZj1+ib+ahfIVFtyLI7RwYLkuanxePHX45BiMSnHtHvPOyno70L0oYw06VdoafktAEwd6CnBPVrVgM1Nev51BAzqEbWhz7cW4b1NR2GQgMWXjmw2rSkYSZJ8mWsRTgxFUK+l/fQEcVB2uOSgJV3BHKlsAACM6JGG5fNPw/IFUzB/+oAW3d2PVKpR0+iEy5uRGE3KdYbfiYfD5UZtiJM6/2EZb3hLkC8a1QPxcdqflBARxWJML08D+x8PVgR83+ly40CZFYD2Qb1QvYyUTD0OFOrQxIWr3VtidsZg/frpAZ4gsMiACZatV9Uq028D1ysCW2ajQcnej5a40PQ/Z4lGRQvajAQjBqeVqmjjAvj3EtRquE7olioVVjvcMiBJ+vRdCzYow/81pkdJ94lGBM5cblm5sQP4tbhJitOl/Dj81F3P31jr7M8MJUu9pZl6LXuNhxr44k/r8lvA97cN1e9UiDTUZ1BeKp66cgwyEuNw6dgeOHtY8CQJ5QZAmIxEf7F8NsTHGXG6twT38ygq09oLBvWI2kil1Y6FH3jGd/92at+oRmiLEohIffXECVtLJt/6S4gzKpl+au8wi5OkId1S0T83Jab1i1KNUHeVxTYlmY1RBdqU8lurXfmAlaTmJ3UiqLf1aDW+3unJVLjsZJbeElH7c7K3kf2GJkG9w5UNsLvciI8zoHt6gqbrzGzSn1QQN0pYftux+Qdl0xPjMLqFk0/VMhgkpHhvcjYN6rndMmp07KknzgtqbU44/Ppk+ffTa2lAwn97Q5UWhxOs+XxLiExD9Zl6rddTT8moSjRHNb1SLf8+aCILVAQg0hLidFnnicZiMiqTsf0DbFoFpUMJ9TnkcsvK31jrXn4tGZYhy3JMPfUAvxL2cOW3Vm2D8YD6/a1UUV58Sv9sbFh4Jh751YiQx9TMMIHaYJTWDC38vZ7jDS5++nNRhyvB5ZGLqI08+PkOVFjtGJCbjFvPGBDVY5UTohDlqIJoghxrUE+SpGaTYiMRJ6xaZGiIO2s1fidh/ioi3BEKRexTdYNDOdlIS4hrNpk31ztk5M0NhXC5ZYwuSMeAGAOVRER6GOvN1Nt2rAb1dl+WhCi97ZOdDEMU08fV8JUsBn4+1CifAyy/7cj8g1HTBnaJanp9i9fp9/nsr9bmhEhy0yMDNNWvT5b/hasWfeXijAYlWBntxEyb06VUSrR+UC+2qb9NZYboawf4D8nQJ/CTnhAH8fKtatJPmf30tBOsxFqvwJoQKmO8qt4OEZ9pabAn9DqjL7+tt7vgcEVfXRS4XhXlt3Xi961ddqLannrKDYgIf+tIQfTMxOCB2pDrjfGGz5QBXZAQ5ynB/flIdVSPfXTZLvzpnZ9VH1eDaXS4lAqHaDGoR9QG1u4vx/9+PAIAePCiYVH3u/OdkEUov60R5bfxLdjKQGo+QPxpOZ3OP9AWbP2VLUwxF0FAtwwcLPeUpQU7WRaZeqLB72UnF0S1HiKi1tIjIxHd0uLhcsvYVFilfF+vfnpA6BNvcXKawky9Ds0/eHb6IH1LbwXfBNymfRo9r6n4OIMuLTCMBknJLvG/ialVYCs9KbobpIJY3miQYu5RGXX5rU6DMoLdmNZzSAbgyQIV53niHNo3VZj99LQSbGiFkqmnU1AvVG9X/0zMOI0zMX3XRurfzyLwZDYalMFW0YpUfivLMspETz1NB2WEHiTkr0qjQLmvpFrd77cyhp56gGc44une9hKfbVU/BbfR4cLjK/birR8P46wl32DFzuNRr7ve7sT5T3yHSQ+uaNEwJQb1iFqZzenCwve3AvBMux3TMzPq58hSeZe1tE6b8lugbYN6BoOkfIAF22flLmuUH1xmk0EpERC9pkI1dRVSLCbMHt41qvUQEbWmcb2bl+DqGdQL1ctI9FNi+W3HJj7HTQYJpw3IadV1Ns3U0/LcIpRgGT/lGl0gix5a0V60KT2qEuJizrQVWXClKvpUybIcUHqshawgwyqEUo0DiMGIwOy1SzfgPyv3Kud/emWQnYiygpTCKjfgdQqe+t63gceM8rqW3fhXoyXlt6IfXVpiy3sL+iZIB78OtNp9Q4ayNMx6FcfdcEFMl1tWbujFGigPN1QnGC0mo5/jnYL76Vb1JbgBWd11dly79Ef85YOtaLC7UG934qfCSry69hAe+GyHci7W1CNf7MKu47WoszlDLhMO6yGIWtnTq/cr47v/NHNQi54jOznwLmMopTXaDMoAfOVV0fbU0+rEOyvJjNJaW9AeLL4ThejXlZ4YhzqbE/tLPQfQ4Jl6vt/feSO7IdHMQycRtV9je2Xiw83HAoN6pTpm6oXoZeQblMFjZkc2KM/TbuL0QV1abZCACH41bcgea4N5NTKSzECZNSDwplWwKV1l+VpTlVZxkRz7719tX2bAE5gXA1JapfxW4wBiMLec0Q+LPvoFxTWN+MeXu5Tva12aeSIT59L+mWQVKksyW6rp1F0RMKvQYWCEoJTfRjEoo6oh9sBTqACmIEpv4+MMml6zqMnUq25wKOXOsX5e+M4t1P1+lUEZMbyXpw30lOAeqWzAjqJaDOmWGvExYvsyk8y4cFR3PP/tAby6thAfbj4Gq1/LCAD4YNNRvPvbSQGDI9fuL8fS7w8qX0cahBkMM/WIWtGBMiueWLkXAPDXc4e0eJS5OLEqj5CpJ3rqaRHUS48yxVzrRtbhyjViOVEQHxj7Sz13aoPdVcpL82XqXc7SWyJq58SwjJ8OVcHhckOWZezTM1MvxOcDB2V0DkO7e6bYL54zstXWmapk6jkDvt86mXrNJ1pqVX6rtnytKa2GZABAjncfSutsETNRRMVHisWkWbmzL8vIDneTKcDlGvfvC2b28G747q5pWHzpCJzkd8GuRasa8sgUiQD1/pl63sCHTsFTEcjxZIr5jhsVOvbyywhR8htOdX3sgSdftmvw68AyncrYM4L8XZsSx6oUiynmcmeR1Vlnc8LmdEVcvkqD322C2YiB3htZRyrrVT1G/D6yksy4Z/YQvHrdeOSmWlDb6Ano5aRYMGVADvpkJ6Gk1oZrXlyv3AS12py4852fA54vWAJLJLx1StRKZFnGXz7YCrvTjdMG5ODcGEo41TY51mr6LRB9M1jtM/VEIDNcpl70H9giiOcrv2i+vV3TEvCbU3sjzmjA0O5pUa+DiKg19e+SjLSEOFQ3OPDLsRrkpsajzuaE0SChV1aS5usLmanXIHrq8XSzo4t1in20QpXfiiyXlt4UVSNYuxFfUC/G8tsoh44JWmY5iXNCu9ONWpszbNBd2W8Ny2Ez/PoZVzU4AoItWv2eI7GYjLhodA9cOKo71h+owJo9ZbhyQk9d13kiyWxyzi7LshIg0StTLz7OiCSzEVa7C5VWu3IMUQZG6FD26wvSR99TLy2GbGNlOJU1MCtREPusZekt4JfgESZzTulrF+R6Klop8SYYDRJcbhlV9Q7kpoa+seBf9hvL7xYI3Z8xlMomx+dT+mfj6z9Oxc6iGhRkJSo3DIqrG3HRk99hf6kV1760Aa9fPwEPf7EThRX16J6egJH56fh0a5HqcmN/zNQjaiW7j9fhu73lMJsM+Nv5Q1vcRwHwy1oLE8m32pzKpLQuqVoOylCZAl2vV6Ze831Wmhy34ERBBCvLIzRKXnjOENx5VsvKpYmIWpPBIGFsT88U3A0HK5T+LD0zE6MezKSGCFRUeMueAM8Jdq3Nm6nXSiWb1Hm0aU8977nE0aoG5XuiLDQn1kEZIgjQEG2mnnYTWuPjjEo/4UgluHoE2cwmA1K9gf6mF6++3oX6Zer5kyQJ4/tk4faZAwOqMig2/pl6sizjvk9+wZYj1ZAkBGRHai1Yf1clU0+HQLFvGqz697MW2WTi/eFwyaizOZv9XFQ1aZ2dqCbTWMusYv/BNpGy12obtS/7VVsGK25o+h+fky0mjO2VGZABnJcWj5euPRlpCXHYVFiFy575AS//cAgA8PDFw1GQ5SnJjdReKxgG9ajDszldaHRETslta0XVnpPDfjnJypu2pUSmXkW9HU5vr5OmSrwnaolm38lbLKJpBmt3utHg/Zto2VMPCF5yLD4gW3IXrumHjhYfQkREbW1sL9+wjL0ltQCAvjqU3gK+E2C7063cTKrzK39iph5FK2RQT+MbhsGIHoKvryvEI1/shMsta5axltHinnraXSgD6ifgKpNvNS6HzUoOXn2h9aRdahtKpp7Vjoe+2IkXvzsIAHj4ouHo10W/rN9gWeMVOg7oiLY1EaBNT70Es1GZnBusN6VewXFx/PIfxNFUpcbH6GCl3MGIa8Fkiynmm5ehqg9CEfusJojaPzcFz18zFhaTAVuOVAMArhhfgFP6ZyvXusH+ppEwqEcdWoPdhQv+8z0mPbQi6J2K9qRcw1TojMQ4SBIgy6E/SLQsvRXrBNSlmPufhKdo1Esp1Akg4F+WEv26mgf1mFFCRB3fyb09mXo/HqzEHh376QFAQpxR+axZvasUgG9IRnycARaTNr246MThC+oFfuaL84tYLogjOX9kd9w4pQ8A4MlV+3D9SxuUc59Yg1vpUbYyEcR5TqzTJAW1A9d8gyu0DQ4EG5bhP2k31inD1LZEIGbrkSo8vXo/AOBvFwzFpePydV1vsMnVug7KSPK9n9VOStWipx4QPptMy2tOf6nxcRDDt0Mdw7TM1PN/nkiBrioNs7gjDSJpqiJCtVdTY3tl4vHLR8FokNA7Owl3nz0YQPj+8ZEwqEcd2iNf7sSOohpUWO3K9FI1DpVbcbymUccta07LDxWT0aDccQrVV0/LIRlAdJl61X59lIyGlpcZ+xMneGVBDuqVMfxumwYC9er1QUTUmoZ2T4PFZEC51Y4VO0sAeDLF9SBJEi73Xqwt/f4AAF9QT6sbO3RiiVh+q+MNOKNBwt2zBuPfl42ExWTASm+g2miQYg4mpregB5f/8sH6/raE2t7MWg0IaSozyDldrc03aVerG9LUNkSmnpiDcs/sIa3SszBYLzQR4NKn/NbznE6/dhORKO2JYgx6hcsmq7DqExw3GCTl2BwqqUTLVgGA+v52VcqNj9jX68uYUxdcE+uOJht0xkl5+P6u0/HpH05RKuqa9qKMBoN61GGt3V+upHMD6uveaxodOOexb3HOY2uanSyGcrSqAXe+swVPrdqH2sboTsSabp9WqdCR+uqV1HgORFpN84omFVmPnjciU6/pAdbtlpUDPctviYg8LCYjRuSnAwCKqj03efTK1AOAKyb0hMkgYcPBSmw7Wo2aBjH5lqW3FD1xYdZsUEYrlN8K54/sjndumoSu3l5rOckWGGK8UdmSxvqA74JWq0w91eW3StmxtudGykWz3zmsKL1N1nDSLrWN7GSzclP/T2cNwnWn9G6V9foyQD3vr5W7SrCjuAYAkJ+RoPn64uOMiI/zhFOqVGZ1aVF+C0TI1NMzOzFCkof4fqxBS0HZzwiBLiWLW4Ognq83o8pMvRZmX+amxiPR7DtHYvktnXCCjX+uUBnVPlLRgDqbE2V1djzzzb6Iyy/bXoyz/70G//vxCB7+YidOeXgllny1W0mfVkv0gtMqFTrSXdbSOn3Kb8P1URBqdAjqZYcIYtY0OpQ7gS052WX5LRF1Vid7++oJevXUAzwnp+d4p7q/+N1BJVOPQzKoJdpyUIa/YT3S8NHvT8ElY3pgwYwBMT9fupLl0tJBGa1zDimU6lR+K86F/W/UKje/dZ58S/pLiY/Dvy8biccvH4XfTu3bauv1T0A4WGbFrW9sgiwDl59coFsvv0hBrqa0GJQBBC9hF7S+5vQXqYVA22XqidYMse+z0sdPZXBNydSLMYjqOy6qL+cWGNSjDsl//POZg3MBqK8/91/uhW8PoiREGa7N6cKij7bjhlc2orrBgaHdU9EnJwnVDQ4s+WoPJj+8Ak+tihwUFLTu6ZAV4YRMZOppFdRT00dB0OOkW/ze6u0u1Nt9Ke6VMTZGbfqhqtVdcCKitja2V4by/13T4jUZmhTOryd7sjE+3nIMB8qsADyfHUTREsHgRocbNqdvGJovG6P1PqtzUiz4xyUjcOnY2PuBiQBAvd0VsF+R+M4hW7n8VqdBGf6DFPReF7WN2cO74dwR3Vp1neL9daSqHje88iNqGp0YXZCORecN0X2daoN6vr6grVF+q/17KSPCcBCRidjqPfXqtWvNoHadQrQ99UIRf1OnW1aqHdRiUI/alNstRx2J/m5vWcD4557eSbJqy2/9M70aHC48vmJvs2UOV9Tj4qe+x9LvDwIAfnNqb7z328lYPn8Knpg7CoPyUlBnc+LhL3aqbnZcrnF/g0hNjrXuqaemj4KgR1DPP2jn/zcUB9KWDMkAAoOsWkxMIiJqL8b0zFBuxuhZeiuMzE/HyPx02F1uvPidp7ceJ99SS6RYTJC8r13/bL3WztTTWkq8SXlPqq34cLllJfO1NctvPYMrRCsXjTP1gmQZiTYBHJJBLSWC3t/tLcfu43XISbHgqSvH6DqsyTcsQ2X5rc6DMmRZ9vUR1OG9FKnHeqVVuzJYIIpMPY3KmgFfMLTO5lR186Uqium34VhMRqR4b76WRTksg1ev1GZkWcaFT36HyQ+twP9+PAyXO3Jwb1NhJe54ewsA4MoJnvHPmUH6coQjTlAKMj3BwDfWF+JQuVX5eWF5PS57Zi22Ha1BRmIcXpg3FgvPGQKzyQCjQcLs4d3w2R9OVfoERbrLKYgMQa3Lb8tDld9qPP0WUH83So+TbkmSkB3kA0wZktHCE13/O0lafQAREbUHKfFxGJSXCgDoq9OQjKZ+PbkXAOC4N1uc5bfUEgaDpGR5ipYeDpcbdd5m9HpOv9VTNDdIheoGB8T9b632W830W88Frdu7vL7TbxvsLjz/redGwNDuaZqui04c/uf0cUYJ/71yNHJTtektHko0gwQbHS40ODxBolgzykKV39b5DZzRo/w2IzF8ELNK4/6fanvqaRUsBQIHPVZG6JVod/o+l7QoOfYvwY0Gg3qkmaNVDVi7v1z18pX1Dmw5Uo1j1Y24852fMfvxb/HtnrKgyx6uqMctb2zChU9+j2PVjSjITMTds7zjn6NsKikCQmcM7oIpA3LgdMt4dNluACKg9wOOVjWgT04SPv3DqTh9UG6z5zAYJL/BDSqbaCp3TbQ5MfKdkIUP6mk1KAOI3EdB0Gs6XbBhGRVidHoL744kmI2weLPz9LijRUTUli4c1R0AlFYVeps1tGtAVg/Lb6mlRPBLXKz5X0R25GCxb1hGdKVdKfEmmIzaXLqJIF1pnS1kxYwI+CWZjUgwa5vppEy/9a7j31/vwdGqBnRPT8D1p7bOUAXqfLL8gs+LzjsJY3pmhllaGyKQoyZIL25QGA2SkpHVUqGCeuLrhDhjwBAGrWSEKfsFfL+HNpt+q0FPPYNBUrY/ctmv5+cGSZvzHV8QM7pMPdZEkCY2HqrAvBc3oLbRia8WnKaqGakIzJiNBljiDNhRVIMrn1+H8b0z0Ts7CakJcUiNN6Gszo7X1xfC7nRDkoCLR/fAnTMHIkkZ/+w9MVBdfuvr2XHx6B5YvbsUH205hrOH5eG+j3/BsepG9MlJwpu/mYAuYe7uZCTG4QCaT2MNptHhgtXuuTOj1V2TrCD9SASHy618v0uqHpl6rV9+CzQ/CQR8Hyqx9G7ITDKjqLqR/fSIqNO5/tTeuGJCgS4n98GYTQZcNaEnHl3uuVmWmsBTTWqZpsMy/rPS0y6lV1aikkXREaVFEQQAtGvC7k9UcdidbtQ0OoOer/km32rfl0sEFSvr7dhZXIPn1uwH4AnEtNaxijqfvjlJuO6U3shNtWDuyQWtss5ogvRVftdHkhTbMSxUUkuZjqW3gC/BI9jxyz8TUatrKl8Q0QFZlkP+3qo0TijJTDKjrM4eMZhYWe9rjRDrdHTAF5hW21ZM4FGTYvb93jJc//KPqPcGrfaVWlUF9UQabfeMBLz720l47Os9eHXtIaw7UIF1ByqaLT+pbxYWnjMYJ3ULTMsPlr2lZr1ZSWYM7Z6Gc0d0w8dbjuGmV38C4PlAeCNCQA8I3uQ35Dq9y8QZY78zI4iTrLIg/VDEPhoNUovLUoPJUHm3RK+gXlaQCbiVGkyES0/0BPU4+ZaIOhtJklr9Ivny8QV4fMVe2F1upDBTj1pIXDxWNziwZk+p0uf43vOHtuFWxS7aTD3/i0atxMd5ejfV2pwoq7MFD+rpOLhC9CFzuWXc9uZmON0yZgzJxfQhrZNRTJ2TJEm4Z7Z+QzGCSY9iqIJvQqsGwxwiZOpl6zRFOtzxq9ovEzFVo3664jrW7nLDaneFHPhVreHvFvDtZ6TrfKW3u0bXkFkqy42bYlCPYrJi53Hc9OpPsHt7bgDqX4T+02Azk8xYdN5JuGZSL3yzuxTVDQ7UNjpQ2+iE3enGOcO74vRBXYJG57Oi7aknBlZ4T1L+OH0APt9aBKdbVh3Qi3a9FX53TWK9M9N0/WXesdf+zyuGZGQna3PXQFBSzCMc4PQK6gXrI6j01IthIpzYL60mNRERnciyky247tTeeG7Nfozzm8BLFA1RYnu4ogGPfLELAHDVhJ6YMiCnLTcrZkorkwZ1mXriRqrWNx5zUiyeoF6tLWjPTSVTT4fggGgIX2tzYmdxLRLNRiw67yTN10Okt0g95vyJQJgW2WTiOlAMcxDDQMQ1kv6Zes2vBcX3tMhEFBLMRiTEGdHgcKGizh4yqFel8WT0cNOFA9arHJ81qsRrYU89BvWoxT79uQi3vrkJTreM6UNykWIx4b1NR6MYHNE8Pbh3dhJ6ZydFtR2Z3he/1e5Co8OF+LjwfT8qmgys6JWdhAcuGobv95bhz2cPVhXQ81+vukw97zo1HC0uAlx2pxu1NmdAHb8e/fQA/2aw4T+4avTK1GtyV8rtlrG3tA5Ay3vq+T+WgzKIiLRx58yBWDB9AOI06gFGJx5xDvHkqr2wOd3onZ2Eu88e1MZbFTvR80lNY30g9oFgoWQnW7C/zIrSUL2Z6+zKcnrITDaj1ttgfsH0AeiWnqDLeoj0pHaIIBBYfhur1Pg4GA0SXG4ZlVYH8tK8Qb0mySta82XqNb8W1HJYhb/MJDOOVjWgot6OgqzEZj93u2W/AR3a9vKLFFyLtbd78/V6K/Gi7KnHMy1qkWNVDbjtLU9A7/yR3fDkFaOVD2O1L0LxJol1dH2KxQSz96JBVYCtrvl6Lx2bjyWXjVId0PN/vJpIurJODe92JpiNSDKLuzKB21Ciw+RbQH3JiH7lt94Dnfd3/o9lu7DxUCXijBJO7tXyZrizh3VF7+wknBFkKAoREUVPkiQG9Cgm4hzC5nTDaJCw+NIRnaLfmpLZo3LQmh7ltwCQneKt+AjSxgXwz9TTKajnPY8e3DUV8yb10mUdRHpLjyJTT8sSUf9hDuX+AwQ1ur4ORbkWbHA0G7Kjddaass6k8JVidXYn3N5N0eraU21Qr0rjwSDRDgAVeLZFLfLxlmNwuGSMLkjH4ktHIs5oiDiNtamKIJl6LSFJku+NF6EUtt7uVHr/xXoHQ+00HkC7fW1K6avX5HdeUiMy9bQO6qlr7qxbUM9vItAb6wvx1Kp9AICHLhqO/rmR+ziGMmtYV6y8fSqG9UiLvDARERHpzv8c4nfT+mFUQeco5U5PEhfF6i7aqvQqvxU3SkOcOys99XQYlAEAs4d3Q4+MBDxy8XDNpvoStbZorgfFe177ElHfdVlrld+63LKSaStoPflWyIjQt1AES+PjDBEr9qJeZ4S/q9JTT6Pfd7D+8WrwCEot8vHPxwAAF4/poUwg8w1uUDmFVsNAl28CbviAoniDWEwGJcst1nWqedMpqdAalt96ni/42OvSOk9PPa2DeukqUswdLrcSONVrUMa+0jr85YNtAIA/nNEfF4/poel6iIiIqG318pZZDeuehltO79fGW6MdkaWjdvqtuGhM1/rGsDeoVxohUy9Hp4b7153SG9/+6XTeUKUOTVwb1dtdsDldYZcVWV1aZ5P5Z+rpXX4bH+fpcQc0zzb29dTT9pgRKXutSoOhic3Wmayup16lxtmJvr8pe+qRzvaX1mHb0RoYDRJmDe2qfD87Oboa8EoNg3pKU8kIATbxBslOtsTcwFNtWi7gC7ppWX4L+J2QNS2/rdGp/DYpcop5tV/jZ62nHooPqEaHZzDLhaO6Y/6Z/TVdBxEREbW96UPy8OK8cRjTK6NTlXJHO/1WnHNp3lMvRLWHUKZzTz2iziA13qT0tquqdyA3NXTSiG+Yg34losHaTGktIzEODdUuVDbpcVetV6ZeUvisOZEBqWUySaTsQEHrnqfieFtZb4fbLaseeNl5PiGp1XzycxEA4JR+2QEBOV+AqeWDMlpKbf25lsE1/wNp054CTelVfpsVZBos4N9TT9tBGf4nom538H0WQb0U74eclvw/oE7unYmHLh6m2XQlIiIiaj+MBgnTBnUJGATWGUTTgwvQcfpthJvxevfUI+oMJEnyy76N0HNc40ESwSa0Kj31dMqwBYC0EJVblRoPjRBEwCxU1pweAzrUJu9Uarxuca3tcssBiTKRMKhHUZFlGR9t8ZTenjuiW8DPxMlBbaMTjY7w6ceA3xRaDUpSxaSYSKmqWt69ENttd7lR16SnQLP16tS0NCdEH8NSnQZliAOWWwZqGoMfaPTqpwd4Ur4vGt0dE/pk4pmrxijj24mIiIg6Av+gXqSbwoCegzJCl9/696DWq6ceUWch3tOVEYbfKD31NCpP9b/+dbtl/LCvXCnF1aunHuA37Ke+afmtPteAGZHKb0UGpIZlv/69EsMdp0UgU6vft9lkQGq8p5g2mhLcDhXUmzdvXltvwglvZ3Et9pbUwWwyYMZJgZNCUxPUT6GVZdmXvabBnQRfU8nwWYKi554WfQYSzL6eAhEzBK36lN/6MvV865dlWTlB07qnnsXkm7gbqheMnkE9AFh86Ui8ecNEzU9uiYiIiPQmMjHsfj2IQ5FlWSnT1XzYmnJjuPlFq+iPHR8Xew9qos5ObUm90lNPq0w97/Os2lWKyQ+vwOXProXDJSMhzqhrhm1GiEw9vabfRqrIq64XA0i0L791uJoPBPEnsge1vC4NVYkXTocK6gUjSVLQf//4xz9CPmbp0qVBH9PY2Biw3JNPPonevXsjPj4eY8aMwZo1awJ+LssyFi1ahG7duiEhIQFTp07F9u3bddnP9uJjb5betIE5zcohJElSglZlIZruCrU2JxwuzwmENllz6lJkKzTuM6C2maVYb6bGgzKC9TGsbnDA7vL0nNM6Uw+IPCyjRuegHhEREVFHlWg2KjfBqyKUV9XanHB6251oecEK+M4h7S43ahoDL1rFwDUtelATdXbpKielKuW3Wg3K8L6Hj1Y1oKi6ESnxJlx+cj7evmmiZlNgg1EyE5skeFS1UU+9So2DpUBg8k6osl+n37FTy5suauMa/tp9UK+srAzXXHMNCgoK8MYbb6Bfv3649NJLYbd7drKoqCjg3wsvvABJknDxxReHfd7U1NRmj42P9/Ufe+utt3Dbbbdh4cKF2LRpE0499VTMmjULhYWFyjKPPPIIFi9ejCeeeAIbNmxAXl4epk+fjtraWn1+GW1MlmVl6m3T0ltB7bAMEeRKNBs1OeioDa6Va9xnQM1knEaHC1bvnVjtM/WaT+AVWXqp8SZdDui+YRkh7pYwqEdEREQUlCRJysVnpMmKYrpkQpw258v+4uOMSPGWeTUtwS2t5ZAMIrVClaP6c7rcSsaXVlldUwbkYHzvTEwfkounrhiNDQvPxIMXDcfQ7vpOlA6VmahXq4BgvQP9KT31NJ66G6mvnrgpI0naXveK9ZZ1pqDe/PnzsW7dOrzyyis4++yz8eyzz6J3795wuz2ZSHl5eQH/PvzwQ0ybNg19+vQJ+7ySJDV7rL/Fixfjuuuuw/XXX4/BgwdjyZIlyM/Px1NPPQXAE+BasmQJFi5ciIsuughDhw7FSy+9hPr6erz++uv6/DLa2ObDVThc0YBEsxFnDMoNukx2iB5vTYngmlbpuUpwyxqh/LZOuz5+gLpgovhZnFFCikXbgdPBhpOIIRldUrUdkiEoKdch+kZU69RPgYiIiKgzUBMEAHyZKVpnvgihhmVwSAaRehkRgk4AArJhRc+0WKUlxOGtGyfi2avHYtawrrpm5/kLlqnn3ypA66xiJYjY4IAryKDG6gZ91hspqCf+3mkJcZoOhxTltxV1nSiot2nTJlx11VWYMmUK0tLSMG3aNDz88MMBWXXC8ePH8emnn+K6666L+Lx1dXXo2bMnevTogdmzZ2PTpk3Kz+x2OzZu3IgZM2YEPGbGjBn4/vvvAQAHDhxAcXFxwDIWiwVTpkxRlgnGZrOhpqYm4F9H8fEWz9Tb6UNykRCiv4YvUy9COarGGXOirDXSi18ZlKHVelWMu/aV3po1L2EQQdTaRidsTk82YEmtp2RC6356QqTyW2bqEREREYWWrlykRpqsqM80SUEMwQgV1MtJYf9iokh8PeZCB+lFwCsl3gSTsd2HYMLKz0wEAGw/Wq18r86vVYDWPfXETQ1ZDl4pVqVxWbOy3khBPaXcWJ8egpGSlfy1+1fU5MmT8eKLL+KTTz6JuOxLL72ElJQUXHTRRWGXGzRoEJYuXYqPPvoIb7zxBuLj4zF58mTs2bMHgKfk1+VyITc3MBstNzcXxcXFAKD8N9wywTz44INIS0tT/uXn50fcr/bA5ZbxiSi9HR689BbwRZaDTdLyV6HxZB4RpLPaXWEn74o3h1Z3HtWMuy7XcMpvU2kJcTB57wwcLKvHtqPVWH+gAoA+/fSAyHeXRVAvlUE9IiIiombExWe4IACgX+N5ISfEeTsz9YjU810bhb4eVCa06pR125om9s2CySBhf5kVheX1AHzXhRaTIWTyT0uZjAYlWSRYUon43WrZUw/wDSIJlchSYdUnQ9BXgag+U0/bWkAdLF68GA888ADmz5+Pffv2YfPmzbjppptw0003NVv2hRdewBVXXBE0i8/fhAkTMGHCBOXryZMnY/To0Xj88cfx2GOPKd9vmlUly3Kz76lZxt/dd9+NBQsWKF/X1NS028CezenCtqM12HioAt/vK0dJrQ2p8SacNiAn5GPUlt9WeEs3tQrqpVhMiDNKcLg8U3W7pSc0W0aWZe0z9YL0tGtK63X6E8NJjtfYMHPJNwE/65rW/HeghVATjwRm6hERERGFppSTRRq0ZtU3EBDqvL2MPfWIVBOZt7uO12Lx8t2oa3SittGBAbkpuPaU3jAaJL8hGR0/+zU1Pg6je2Zg/YEKrNpdgqsn9vIbkqHP/mUmmVHd4FCOif7066nnnUIbspeftxpPpx6C0Uy/bfdBvaSkJPz973/H3//+d1xwwQWYNWsW5s+fD4PBgBtuuEFZbs2aNdi1axfeeuutqNdhMBgwbtw4JVMvOzsbRqOxWcZdSUmJkpknevAVFxeja9euQZcJxmKxwGJpfx+QX2wrxmdbi1BZb0dVvQOV9XaU1Npgd7oDlrt4TA+YTaETPHNCpPE3VaFkr2nzJpAkCZlJnuBWeV3woF5Ngy8tWLMMQSVTL/T+iii+llNx/I3Kz8AX24uV7emRmYi+OUm4ckKBLuvLiHDXgkE9IiIiotDSxdCxCNNvdc/UE+fttYHndMzUI1JPlKkfqWzAY1/vCfjZ/jIrHrhwqFJq3xky9QBg6sAcT1BvVymunthLuS7Ua/8yk8w4UGZtVh0ny7KOPfXCDzSqUPZZ2+OzOO5GM/223Qf1/KWnp+PGG2/EsmXLsGbNmoCg3vPPP48xY8ZgxIgRUT+vLMvYvHkzhg0bBgAwm80YM2YMli9fjgsvvFBZbvny5Tj//PMBAL1790ZeXh6WL1+OUaNGAfD04lu9ejUefvjhWHazTdzxzhbUNhlnD3jeQGN7ZmBsrwyM6ZmJUfnpYZ9HbU+9ciXQpd3JQlaSxRPUCxFgK/N+PyXeBItJm7RgpZdfmPIJZeKuDuW3APDE3FEorKhHbmo8kjQexBGMrxls+PJbBvWIiIiImhMZJaFukAq699QLMnAN8O+px6AeUSSj8jNw42l9UFTdiNQEE1Li4+B2y3h2zX68sb4QWUlmpWKrs1wfTR3QBY98sQvf7ytDo8Ole3lxRog+9vV2FxwuWZd1+3rqBb/mFRmCIvinFV+mXicK6s2fPx8XXHABRo4cCZfLhZUrV2L16tX4y1/+oixTU1ODt99+G48++qiq57z33nsxYcIE9O/fHzU1NXjsscewefNm/Oc//1GWWbBgAa666iqMHTsWEydOxDPPZgYelAAANHVJREFUPIPCwkKl7FeSJNx222144IEH0L9/f/Tv3x8PPPAAEhMTMXfuXG1/CTprsLuUgN5DFw1Dl1QL0hPNyEm2oEdGQlTDHbJDTNFqShmUoeFJijhYhopq67HOTBWZeiJ1Vo/yW8DTZ6BPTrIuzx1MpEEZNQzqEREREYWkdvqtrxG7XuW3oQZliPLbjl8qSKQ3g0HC3WcPbvb9nllJ+PP7W/HEyr3ok5MEoPNk6g3umoLcVE9CzfoDFbpnFStZc02uP0Uw0Ww0IEHj6b+RKvJ8PfU0HpSRrO6mj792H9QrKCjAggULsGfPHlitVqxatQrXXnstbrnlFmWZN998E7Is4/LLLw/6HPPmzcPBgwexatUqAEBVVRVuuOEGFBcXIy0tDaNGjcI333yDk08+WXnMnDlzUF5ejvvuuw9FRUUYOnQoPvvsM/Ts2VNZ5s4770RDQwNuvvlmVFZWYvz48Vi2bBlSUlL0+WXoRKSOmo0GzBmXH9OEVvHhX1XvgMPlRlyI6T56lKRGimr7gmtaZgd63+xhIul6l9+2NrWDMhjUIyIiImouXUVjfcBX9qV/+a3vorXR4UKdzXOzP5uZekQtNnd8ASqsNvxz2W7sL7UC6Bw99QBPgtOUATn4349HsGpXqXLdp3WASwg1iVYcQ9MS42KKYQRdZ4SpxkpPPY2v8cV6vV3DVGn302/nz5+PjRs3oqamBldddRUOHz6MRx55BEajLxJ7ww03oL6+HmlpaUGf4+DBg5g6dary9b/+9S8cOnQINpsNJSUl+PLLLzFx4sRmj7v55ptx8OBB2Gw2bNy4EaeddlrAzyVJwqJFi1BUVITGxkasXr0aQ4cO1WbHW5FywpAU+5shI9EMo3caq5rhEVqWEyhBvRCZeuKuo5aZemL7w03dLdMhQ7AthRuU4XC5YbV7fg8M6hERERE1Jy58VWfq6VV+m+JrmyPLnitIMQnXbDIgpRXauhB1Zr+b1g/zJvVSvu4smXoAMHVgFwDAqt0luvfUy1LaPwVef1brmM2cGSKQKFQoN120XXec0RD177HdB/ViVVtbi3379uH2229v601ptyo0vAtoMEjKGyBcCa4epbC+ppLB1+ubQqvdXcfUeM/UXc96Q73h9S2/bW3iIGNzutFgDwxk1vg1fE5lUI+IiIioGWX6repBGfpeKNtdbtQ0eLLzlH56yRbNM1+ITjSSJOGvs4fgkjE9YDYaMLpnRltvkmYm98uG0SBhf6kV245WA9DvWCWOmU2Td5RefjpkQIqYRnWDpwKxKT0n/kab/dehgnpLly6N+jEpKSk4fPgwkpNbr+dYR1OpcepoqKa7QoPdhQZvVlumhoGuiOW3VjHJS7t1SpIUsnGnIEpztRwK0paSLSaYvNmYTbP1ROltisWkZGwSERERkY9/+a07TI2Vljfeg4mPMyI13pONJ87bfymqAcB+ekRaMRgk/OOSEfh50QyMLug8Qb20hDiM8e7Pj4cqAehXfiuu85v11PMG1tJ0CCamJcRB3NcIllVdoeMgo+wo4wYdKqhH+lBOGDQL6nkz9WpDZMx5g2txRknTtP5I5bflOpTfRlpvo8OllKN2lkw9SZJC9zXwBvWYpUdEREQUnAjquWWgttGJmkYHXl17CHe/9zPW7CkF4LkJbnO6A5bXgyjBXbOnFL9+cT0Wvr8NAFCQlaTbOolORPEaD3JoD6YMzAn4Ol2na8BQ155K2a8O6zUZDUo7qabBRJdbVpJZ2kOmHhsltDN/fn8rlv9yHOeN6Ia54wvQV+VUU7dbxuHKevxyrEa5w/aHM/qHHFThT9SmZ2r0gsxJ9vXnCL4+Mf7ZrGlaf3aE6bdlOgzK8Dxf8Bp/wBfo0zqA2dYyEuNQWmtrdteCQzKIiIiIwrOYjEg0G1Fvd+GPb2/Bt3tL0ejwBPDeWH8Ys4d3xXWn9AbgOYdM1vEcMifZgv2lVtz78S8AAKNBwqVj83H7jAG6rZOIOocpA3Lwjy93KV/r1f8zZE89UX6r042PzCQzquodnuSg3MD1etuQ6rLuaJOBOk+UoZP4clsxyq12PP/tATz/7QFM6JOJueN7YuZJubCYmkf3tx+rxpKv9uD7vWVKRpgwokc6zhyS2+wxTWmdOupruhs+U0/rclTxfKGCeuU6DawQ6w2WqecrvdU2gNnW0kMMy6hhUI+IiIgoovSEONTbXfhqx3EAQP8uyTipWyo+2nIMn/xchC+2FXuWS9T3HLJrWrzy/+eO6IYF0wegdzaz9IgospO6pSInxaIM2NGtp16T4ZQi67FKGdChU9lvohn7YW12zSu+Tok3qUqiila08QoG9doRt1tWXiCT+2Xhh33lWLu/Amv3VyAzyYxLxvTA5ScXoFd2Eg6VW/Host34aMsx5fFmkwGD8lJQWmtDUXUjimsaVa1XyZzT6E0oMubKQwT19BiSAfjSVOtszoA3e7P1ap2pp6QDN9/fMjEko5P00xMy/HrB+GOmHhEREVFkUwbm4KPNx3DO8K6YM64AowvSIUkSrj+1D+75cBs2FVYB0O8iWbh5Wj9kJVtw4ajuGNo9Tdd1EVHnIkkSpgzIwTsbjwDQL7iW4u3p7vTGS7qmJQDw66nX2mW/Ovc7jTZewaBeO1LV4IDolbv01yejtNaGtzYcxlsbDqO4phFPf7MfT3+zHyPy07H9aDWc3oXPG9ENN5zWB4PyUmAyGnD3e1vxxvrCkAMjmhKZc9r11AtffiveFFoN5hDEJFqHS0aF1Y5u6QnKz5wutxIw1bq3XbhBGRV1+qyzrWUomXpNym91PrASERERdQYPXDgMD1w4rFkW3tDuaXj3pkn434+H8cTKvThnWDddt2NAbgrumT1E13UQUec1daAvqKfXNaDo6V5aa0OF1S+op3P5bVbIXn7efno6lRuzp14HJjLbUr1pnN3SEzB/+gDccno/rNhZgtfXF2L17lJsOVwFwFPDfsfMgc3uqimZckEyx4Lx73GnBV9QL1T5rT5BPUmSkJlkxvEaW7OgXmW9p+5dkrSPqGcqmYlBgno67WtbC1V+q2Tq6XxXmYiIiKgjC1dSazBIuOzkAlx2ckErbhERUfROG5CD7ukJ6JoWr0spqpDlF9QTREJJeoI+19qRM/X0DSaqxaBeOyKCXdlN0i1NRgNmnJSHGSfl4XBFPVbtKsGA3BSM75MV9HnEi0Btpp7SU0+jYFekoJ5/nzmtZSZZcLzG1mzdSh+/RDOMBm37koSK4AMnQvktB2UQERERERGdiFLj47Di9imIM+gX0AOCV8dVNYieejoNygiRyCK+1mrQaFMsv+3AylUEu/IzE3HVxF5hnycrQlDNnyzLSqRZqxLR7BTfG87llpsF0fTK1ANCB9jKdSyDFftRUX8Cld8mhc/US2VQj4iIiIiIqNMLNtBTa+Ka238Crt499TJDxBYq9B7QEWWcRN9wKkVFDFqINQCkZOqFmALrr9bmVHrzaZWpl5lohiQBbjlEnzkle02HoF5y8DeeCHDqkTEXLlOvs5bfKj31QowVZ6YeERERERERaSEjyXN9WeEN5DU6XLA53QB0zNQLcZ1fpbQv02lAR2Icohl6zky9dqRMydSLLfAkMvWCBZmaEkGZRLOx2bTYljIZDchMNKPcakdZnQ05KYH7o2egKzNEQLM1MvWq6h1wutww+fUSKNNp0m9bE+W3zQZlMKhHREREREREGhIxkv+u2ocf9pVhSNdUAIDRICHZok9YKyNIdiCgf6aeyWhAehTX08zUa0cqlJ56MWbqJftKI50ut6p1aj08IlxfvQqNy339+foJBu+pp0dwLd2bmQg0D3JplX3Z3oQalFHDoB4RERERERFpaMaQXHRNi4fd5caGg5V46YdDAID0hLiwg4diIXrmlVvtkGVZ+X5Vvf7VeNH01WOmXjuiDHOI8cWR4Q0yybInyNQ0U85fpU4vyOwUM3Ydbx7Uc7jcqGl0etepfSmseM7QPfW0X6fRICE9IQ6V9Q5UWO0Bv+8KjbIv2xuRqVfb6AzITmSmHhEREREREWlpaPc0fH/X6ThYXo8NByqw7kAFth6twuzh3XRbZ6Y3McfmdKPB4UKi2RM+E7EGvcp+gejiMwzqtSNaBZ6MBkkpfy23Ni9/9VfhrQfP0DqoJzL1aoOPfzZI+gR+REZcs/JbHbMDAc+brrLe4Q3MpgDw1Plb7S5d19tW/P92VQ0OZCdb4HC5lf1lUI+IiIiIiIi0IkkSemcnoXd2Ei4dl6/7+pLMRpiNBthdblRY7UpQTwzo0DNTL5rqTZbftiMVGvZfU3rL1YXvqyeCbJkaR5lDld+WW331502n4mohK8R+l+s4KMP/ef0zBMW+xhklpOhU599WTEaDErg7UGYF4Cu9BYDU+M61v0RERERERHTikCTJN6DDe23vdstKtaPWLcz89cpKUr0sg3rtiJbZZKEy1poSTR71ytQrbRLU03sabKgJNeUa9SuMtF7/Jpq+0luzbnX+ben0QV0AAE+s2AvAV3qbYjEFDAshIiIiIiIi6miatveqbXTC7W2vp2f57Z1nDVK9LK+82wmXX8RXi4CXKOFtOjCiKV+mntZBPc/zlTXNmNM5qCf2u87mhM3p8q1Xx556gK/e3j+IWmbVNzuwrd16Rn8YDRJW7y7FhoMVSlAvlaW3RERERERE1MFlejP1RKxG/DfJbITFZGyz7fLHoF47UVVvhxiookUaZ7bK8lsRhNI8Uy9F9NRrkqlXp98UWsBT9hln9GTFiWh6o8OFOptnOIdeve2ygmQIVtTp28evrfXKTsKlYz29DP7xxS4OySAiIiIiIqJOQ8RmPv25GP9avhuPfb3H830d++lFi42v2gkR8U1PjEOcBqWLSqaeVV2mntZBtpwQPfX0Lr+VJAkZiWaU1NpQXmdH17SEVultp/Qw9AvqFVbUB/ysM/rDGf3w7k9HsP5gBQp+TgTAoB4RERERERF1fF1S4gEAX+04jq92HFe+3z09oa02qRkG9dqJ8jptg11ZIcpfm9K7p1651Q63W4bBOxSjXKcgor/MJG9Qz7su/yEZevW2U3r51Ylaewde/uEgAGBy32xd1tkedE1LwFUTeuL5bw/gnY1HADCoR0RERERERB3fVRN7eqoqASRZjEiymJBiMeGc4d3aetMUDOq1EyJTL1uj/mu+KbAqe+ppHGQTQUWXW0ZVg8M3SELDvoGheAKKtfjPyr34fm8Zahr1Lb0Fmg/oeG7NAVTWO9AnOwkXje6u23rbg5un9sWb6wthtXt6GDKoR0RERERERB1d7+wkLJ4zsq03Iyz21GsntC5LFeW3TafA+hMBN0D7ccxxRoMyDca/BFfJSNRpYAUA9M3xjH9ef6ACT3+zH2+sLwTgyx7Ug3/5bVmdDc+t2Q8AuH3mwE4/CTYr2YJrT+mtfJ2m4xQgIiIiIiIiIvJgpl47UWH1BNe0yibLUjEoo7rBoQzn0GMcc3ayBVX1DpTV2jAgNwWAX/BS4yCiv7vPHozxfbJwtLIBRdWNKK5pQHWDA9ef2jvyg1tITLitrLfjiRV7YbW7MKx7GmYNzdNtne3J9af2wcs/HEJ1g4OZekREREREREStgEG9dqKyXtupsCJTr9bmRKPDhfi45uOWRYDNMzFW+2yy7GQz9pYApX6ZenoPygCA+Dgjzh7WVbfnDybDO+ra5ZbxytpDAIA7zxqoWw+/9iYtIQ73nncS/v31HpwxuEtbbw4RERERERFRp8egXjshgl1ZGpWIegJ1EhwuGRVWO7oFmc6id387Ue5aWO6ZAut2y8o69exv1xYsJiNSLCbU2pxwuWVM6puFU/p13gEZwVwwqjsuGNW5+wcSERERERERtRedu9lXByLKb7UKsEmS5OvzFqIEVwQStZ58KwzumgoAWPzVbvzzy10os9rg9pb7at3Drz3I9AtU3nnWoBMmS4+IiIiIiIiIWh+Deu1EhdVbfqthBpvo81ZuDT4Bt1Ln/nbXndIbc8bmQ5aBJ1buxWVPrwUApMSbYDZ1vpeeCFSedVIeRuant+3GEBEREREREVGn1vkiKx2UCLCJQJwWRIAwZKZevb6ZevFxRjz8q+F4Yu4opMSbsL/M6tkuHfvptaXLT87HyPx03H32oLbeFCIiIiIiIiLq5NhTr52oanDCYDFr2t9O9LQLlalXUaf/0AoAmD28G0bmp+O2Nzfjx0OVyM9M1HV9bWXOuALMGVfQ1ptBRERERERERCcABvXaEUkCMhLjNHu+rEg99XQelOGvR0Yi3rxhAr7acRzDe6Trvj4iIiIiIiIios6MQb12JD0hDiajdhXRYpJuWYignt499ZoyGQ04a2jXVlkXEREREREREVFnxp567YgIwmn2fCJTL1T5bb1n4q5ePfWIiIiIiIiIiEgfDOq1I1qXwYpBGRXWCJl6SdqV/BIRERERERERkf4Y1GtHspO1Dup5B2VEKL/NaKXyWyIiIiIiIiIi0gaDeu2I5pl63ucrq7NBluWAn9mdbtTanLqsl4iIiIiIiIiI9MWgXjuSlaRxTz1v5p/N6YbV7gr4WZV38q1BAlLjWX5LRERERERERNSRMKjXjmRpXH6baDYhIc4IACivCxyWUVHvK701GCRN10tERERERERERPrqUEG9efPmtfUm6ErrTD3AFygsa9JXTwzP4ORbIiIiIiIiIqKOp0MF9YKZN28eJEkK+DdhwoSIj3v33XcxZMgQWCwWDBkyBO+//36zZZ588kn07t0b8fHxGDNmDNasWRPwc1mWsWjRInTr1g0JCQmYOnUqtm/f3uJ90aO3nRiW0XQCbqXV4Vknh2QQEREREREREXU47T6oV1ZWhmuuuQYFBQV444030K9fP1x66aWw231BqrPOOgtFRUXKv88++yzsc/7www+YM2cOrrrqKmzZsgVXXXUVLr30Uqxbt05Z5q233sJtt92GhQsXYtOmTTj11FMxa9YsFBYWKss88sgjWLx4MZ544gls2LABeXl5mD59Ompra1u0r1qX3wJAtjdQ2Kz81ur5mkMyiIiIiIiIiIg6nnYf1Js/fz7WrVuHV155BWeffTaeffZZ9O7dG263W1nGYrEgLy9P+ZeZmRn2OZcsWYLp06fj7rvvxqBBg3D33XfjjDPOwJIlS5RlFi9ejOuuuw7XX389Bg8ejCVLliA/Px9PPfUUAE+W3pIlS7Bw4UJcdNFFGDp0KF566SXU19fj9ddfb9G+ZumSqecN6lmblt96MvVYfktERERERERE1PG0+6Depk2bcNVVV2HKlClIS0vDtGnT8PDDDyM+Pl5ZZtWqVejSpQsGDBiA3/zmNygpKQn7nD/88ANmzJgR8L2ZM2fi+++/BwDY7XZs3Lix2TIzZsxQljlw4ACKi4sDlrFYLJgyZYqyTDA2mw01NTUB/wBAkoB0HUphM719+sqaZOpVegdlZCZx8i0RERERERERUUfT7oN6kydPxosvvohPPvkk6M9nzZqF1157DStWrMCjjz6KDRs24PTTT4fNZgu6PAAUFxcjNzc34Hu5ubkoLi4G4Cn5dblcYZcR/w23TDAPPvgg0tLSlH/5+fkAgIyEOBh1mEKbLTL1Qg3KYE89IiIiIiIiIqIOp90H9RYvXow5c+Zg/vz5ePnllzFy5Ej897//VX4+Z84cnHPOORg6dCjOPfdcfP7559i9ezc+/fTTsM8rSYEBNFmWm31Pq2X83X333aiurlb+HT58GIB+ZbCi/LbZoAwlU49BPSIiIiIiIiKijqbdB/WSkpLw97//HXv27MF5552H3/72t1iwYAGeeeaZoMt37doVPXv2xJ49e0I+Z15eXrNsupKSEiXrLjs7G0ajMewyeXl5ABB2mWAsFgtSU1MD/gH6BdeyQpTfKpl6DOoREREREREREXU47T6o5y89PR033ngjZs2ahTVr1gRdpry8HIcPH0bXrl1DPs/EiROxfPnygO8tW7YMkyZNAgCYzWaMGTOm2TLLly9Xlunduzfy8vIClrHb7Vi9erWyTDQydSqDDTUoo9L7tV7rJSIiIiIiIiIi/bT7oN78+fOxevVqVFdXw+VyYeXKlVi9ejXGjBmDuro63H777fjhhx9w8OBBrFq1Cueeey6ys7Nx4YUXhnzOW2+9FcuWLcPDDz+MnTt34uGHH8ZXX32F2267TVlmwYIFeO655/DCCy9gx44dmD9/PgoLC3HTTTcB8JTd3nbbbXjggQfw/vvvY9u2bZg3bx4SExMxd+7cqPczM1mf4Fp2sidTr8Jqh9stK9+vYPktEREREREREVGHZWrrDYikoKAACxYswJ49e2C1WrFq1Spce+21uOWWW2C327F161a8/PLLqKqqQteuXTFt2jS89dZbSElJUZ5j3rx5StAPACZNmoQ333wTf/nLX3DPPfegb9++eOuttzB+/HjlMXPmzEF5eTnuu+8+FBUVYejQofjss8/Qs2dPZZk777wTDQ0NuPnmm1FZWYnx48dj2bJlAetWS6+BFeJ5XW4Z1Q0OZCSZ0WB3odHh9vycQT0iIiIiIiIiog5HkmVZjrxY+zBv3jwsXbo06sdNnToVU6dOxaJFizTfpljV1NQgLS0NT3+1FTecMVSXdQxf9CVqGp34asFp6NclBUerGjD5oRUwGw3Y9bezwg72ICIiIiIiIiKi9qfdZ+rFqra2Fvv27cMnn3zS1psSlp697bKTLahpdKK8zo5+XYCvfjkOAMhIimNAj4iIiIiIiIioA+pQQb2WZOmlpKTg8OHD2m+MxvQqvwU8wzL2l1lRVmfHo8t24fEVewEAF4zsrts6iYiIiIiIiIhIPx0qqNeZZSbH6fbcWUmeYRl/+/QXFFU3AgBuOb0fFkwfoNs6iYiIiIiIiIhIPwzqtRMZiRbdnltM1i2qboTJIOGBC4fh0nH5uq2PiIiIiIiIiIj0xaBeO5GeoF+mXtfUeABAisWEp64cg1P6Z+u2LiIiIiIiIiIi0h+Deu2EwaDfwIrLxxfA6ZZx7ohu6NclWbf1EBERERERERFR65BkWZbbeiNOZDU1NUhLS0N1dTVSU1PbenOIiIiIiIiIiKgDMLT1BhAREREREREREVF0GNQjIiIiIiIiIiLqYBjUIyIiIiIiIiIi6mAY1CMiIiIiIiIiIupgGNQjIiIiIiIiIiLqYBjUIyIiIiIiIiIi6mAY1CMiIiIiIiIiIupgGNQjIiIiIiIiIiLqYBjUIyIiIiIiIiIi6mAY1CMiIiIiIiIiIupgGNQjIiIiIiIiIiLqYBjUIyIiIiIiIiIi6mAY1CMiIiIiIiIiIupgGNQjIiIiIiIiIiLqYBjUIyIiIiIiIiIi6mBMbb0BJzpZlgEANTU1bbwlRERERERERETUHqSkpECSpLDLMKjXxsrLywEA+fn5bbwlRERERERERETUHpSUlCAnJyfsMgzqtbHMzEwAQGFhIdLS0gAA48aNw4YNG1p9W7jetl9vTU0N8vPzcfjwYaSmprbaevXE9UanJa+BjrqvXG9keh4TIjmRfs8dab1avSY6yv525PXyb8v1xirYa6Iz7y/XG7je1jwHaA/725nXqcV6W/p66Kj7e6KvV/y9zWZzxGUZ1GtjBoOnrWFaWpry5jQaja1+8cb1tq/1pqamar5t7Xl/ud7monkNdPR95Xoj0+OYEMmJ9nvuaOuN9TXR0fa3I66Xf1uuVyv+r4kTYX+53sD1tsY5QHva3864Ti3XG+3roaPv74m+3kiltwAHZbRLv/vd77herpfr5Xrb9Tq53s7vRPs9c71cb2dYJ9fL9XK9XC/X2/7WyfVyvXqSZDGpgdpETU0N0tLSUF1d3WYZINR+8PVAfA2QP74eqCm+Jjov/m2pKb4mTmz8+5M/vh5OLNH8vZmp18YsFgv+7//+DxaLpa03hdoBvh6IrwHyx9cDNcXXROfFvy01xdfEiY1/f/LH18OJJZq/NzP1iIiIiIiIiIiIOhhm6hEREREREREREXUwDOoRERERERERERF1MAzqERERERERERERdTAM6hERERF1YJIk4YMPPmjrzSAiIiKiVsagns4OHz6M6667Dt26dYPZbEbPnj1x6623ory8XNXjV61aBUmSUFVVpe+Gkm7mzZsHSZLw0EMPBXz/gw8+gCRJbbRV1JrEa0CSJMTFxSE3NxfTp0/HCy+8ALfb3dabR21o3rx5uOCCC9p6M6gd8D9O+P/bu3dvW28axUD8XW+66aZmP7v55pshSRLmzZvX+htG7cL3338Po9GIs846q603hVoBjwcUDs8JqaUY1NPR/v37MXbsWOzevRtvvPEG9u7di//+97/4+uuvMXHiRFRUVLT1JlIriY+Px8MPP4zKysq23hRqI2eddRaKiopw8OBBfP7555g2bRpuvfVWzJ49G06ns603j4jaAXGc8P/Xu3fvtt4silF+fj7efPNNNDQ0KN9rbGzEG2+8gYKCgpie2+FwxLp51IZeeOEF3HLLLfj2229RWFgY03O5XC7eKOwA9DweENGJiUE9Hf3ud7+D2WzGsmXLMGXKFBQUFGDWrFn46quvcPToUSxcuBAAYLPZcOeddyI/Px8WiwX9+/fH888/j4MHD2LatGkAgIyMDN696cDOPPNM5OXl4cEHHwy5zLvvvouTTjoJFosFvXr1wqOPPqr87O6778aECROaPWb48OH4v//7P122mbRlsViQl5eH7t27Y/To0fjzn/+MDz/8EJ9//jmWLl0KAKiursYNN9yALl26IDU1Faeffjq2bNkS8DwfffQRxo4di/j4eGRnZ+Oiiy5qg70hPXzxxRc45ZRTkJ6ejqysLMyePRv79u1Tfn7w4EFIkoT33nsP06ZNQ2JiIkaMGIEffvihDbeatCSOE/7/jEYjPv74Y4wZMwbx8fHo06cP7r333mY3A4qKijBr1iwkJCSgd+/eePvtt9toL6ip0aNHo6CgAO+9957yvffeew/5+fkYNWqU8j21x4D//e9/mDp1KuLj4/Hqq6+26r6QdqxWK/73v//ht7/9LWbPnq2cCwC+Sp1PP/0UI0aMQHx8PMaPH4+tW7cqyyxduhTp6en45JNPMGTIEFgsFhw6dKgN9oSiodXx4PTTT8fvf//7gOcuLy+HxWLBihUr9N8R0lWvXr2wZMmSgO+NHDkSixYtUr6WJAnPPfccLrzwQiQmJqJ///746KOPWndDqV1gUE8nFRUV+PLLL3HzzTcjISEh4Gd5eXm44oor8NZbb0GWZVx99dV488038dhjj2HHjh3473//i+TkZOTn5+Pdd98FAOzatQtFRUX497//3Ra7QzEyGo144IEH8Pjjj+PIkSPNfr5x40ZceumluOyyy7B161YsWrQI99xzj3KCd8UVV2DdunUBH+bbt2/H1q1bccUVV7TWbpDGTj/9dIwYMQLvvfceZFnGOeecg+LiYnz22WfYuHEjRo8ejTPOOEPJ6v30009x0UUX4ZxzzsGmTZvw9ddfY+zYsW28F6QVq9WKBQsWYMOGDfj6669hMBhw4YUXNsu8WLhwIW6//XZs3rwZAwYMwOWXX85sz07syy+/xJVXXok//OEP+OWXX/D0009j6dKl+Pvf/x6w3D333IOLL74YW7ZswZVXXonLL78cO3bsaKOtpqZ+/etf48UXX1S+fuGFF3DttdcGLKP2GPCnP/0Jf/jDH7Bjxw7MnDmzVbaftPfWW29h4MCBGDhwIK688kq8+OKLkGU5YJk77rgD//znP7FhwwZ06dIF5513XkB2Zn19PR588EE899xz2L59O7p06dLau0EtoMXx4Prrr8frr78Om82mPOa1115Dt27dlKQQ6vzuvfdeXHrppfj5559x9tln44orrmA14IlIJl2sXbtWBiC///77QX++ePFiGYC8bt06GYC8fPnyoMutXLlSBiBXVlbqt7Gkq2uuuUY+//zzZVmW5QkTJsjXXnutLMuy/P7778viLTh37lx5+vTpAY+744475CFDhihfDx8+XL7vvvuUr++++2553LhxOm89acH/NdDUnDlz5MGDB8tff/21nJqaKjc2Ngb8vG/fvvLTTz8ty7IsT5w4Ub7iiiv03lxqReFeGyUlJTIAeevWrbIsy/KBAwdkAPJzzz2nLLN9+3YZgLxjx47W2FzS0TXXXCMbjUY5KSlJ+ferX/1KPvXUU+UHHnggYNlXXnlF7tq1q/I1APmmm24KWGb8+PHyb3/721bZdgpNvMdLS0tli8UiHzhwQD548KAcHx8vl5aWyueff758zTXXBH1sqGPAkiVLWnEPSC+TJk1S/pYOh0POzs5WrgfE+f+bb76pLF9eXi4nJCTIb731lizLsvziiy/KAOTNmze3/sZTi2h5PGhsbJQzMzOV14Msy/LIkSPlRYsWtcaukA78zwl79uwp/+tf/wr4+YgRI+T/+7//U74GIP/lL39Rvq6rq5MlSZI///zzVthaak+YqddGZO+duAMHDsBoNGLKlCltvEXUGh5++GG89NJL+OWXXwK+v2PHDkyePDnge5MnT8aePXvgcrkAeLL1XnvtNQCe188bb7zBLL1OQJZlSJKEjRs3oq6uDllZWUhOTlb+HThwQMnQ3Lx5M84444w23mLSy759+zB37lz06dMHqampSi+1pn2Whg8frvx/165dAQAlJSWtt6Gkm2nTpmHz5s3Kv8ceewwbN27EfffdF3Bc+M1vfoOioiLU19crj504cWLAc02cOJGZeu1IdnY2zjnnHLz00kt48cUXcc455yA7OztgGbXHAGZod3y7du3C+vXrcdlllwEATCYT5syZgxdeeCFgOf/3dWZmJgYOHBjwvjabzQGfCdQxaHE8sFgsuPLKK5XXzObNm7Flyxa2ajrB+L//k5KSkJKSwnPCE5CprTegs+rXrx8kScIvv/wSdIrNzp07kZGRgcTExNbfOGozp512GmbOnIk///nPAR+6IrDjT25SgjF37lzcdddd+Omnn9DQ0IDDhw8rJ4PUce3YsQO9e/eG2+1G165dsWrVqmbLpKenA0CzUn7qXM4991zk5+fj2WefRbdu3eB2uzF06FDY7faA5eLi4pT/F8cNNkfvHJKSktCvX7+A77ndbtx7771B+2fGx8eHfT5OWG9frr32WqUH1n/+859mP1d7DEhKSmqV7SX9PP/883A6nejevbvyPVmWERcXF3Gomv/7OiEhge/zDkqL48H111+PkSNH4siRI3jhhRdwxhlnoGfPnq22D6Qfg8HQ7Fow2GAk/3NCwHN84DnhiYdBPZ1kZWVh+vTpePLJJzF//vyAi/Hi4mK89tpruPrqqzFs2DC43W6sXr0aZ555ZrPnMZvNAKBka1HH99BDD2HkyJEYMGCA8r0hQ4bg22+/DVju+++/x4ABA2A0GgEAPXr0wGmnnYbXXnsNDQ0NOPPMM5Gbm9uq207aWrFiBbZu3Yr58+ejR48eKC4uhslkQq9evYIuP3z4cHz99df49a9/3bobSrorLy/Hjh078PTTT+PUU08FgGbHBDoxjR49Grt27WoW7Gtq7dq1uPrqqwO+9m+6Tm3vrLPOUi7Im/bC4zHgxOF0OvHyyy/j0UcfxYwZMwJ+dvHFF+O1117D0KFDAXjex2IiamVlJXbv3o1Bgwa1+jaT9rQ4HgwbNgxjx47Fs88+i9dffx2PP/64/htOrSInJwdFRUXK1zU1NThw4EAbbhG1Zwzq6eiJJ57ApEmTMHPmTPztb39D7969sX37dtxxxx3o3r07/v73vyMzMxPXXHMNrr32Wjz22GMYMWIEDh06hJKSElx66aXo2bMnJEnCJ598grPPPhsJCQlITk5u612jGAwbNgxXXHFFwAfvH//4R4wbNw73338/5syZgx9++AFPPPEEnnzyyYDHXnHFFVi0aBHsdjv+9a9/tfamUwxsNhuKi4vhcrlw/PhxfPHFF3jwwQcxe/ZsXH311TAYDJg4cSIuuOACPPzwwxg4cCCOHTuGzz77DBdccAHGjh2L//u//8MZZ5yBvn374rLLLoPT6cTnn3+OO++8s613j2KUkZGBrKwsPPPMM+jatSsKCwtx1113tfVmUTvw17/+FbNnz0Z+fj4uueQSGAwG/Pzzz9i6dSv+9re/Kcu9/fbbGDt2LE455RS89tprWL9+PZ5//vk23HJqymg0KqWT4oadwGPAieOTTz5BZWUlrrvuOqSlpQX87Fe/+hWef/555RzvvvvuQ1ZWFnJzc7Fw4UJkZ2cHrQCijker48H111+P3//+90hMTMSFF16o+3ZT6zj99NOxdOlSnHvuucjIyMA999zT7HVCJLCnno769++PH3/8EX379sWcOXPQt29f3HDDDZg2bRp++OEHZGZmAgCeeuop/OpXv8LNN9+MQYMG4Te/+Q2sVisAoHv37rj33ntx1113ITc3t9nocuqY7r///oCU6tGjR+N///sf3nzzTQwdOhR//etfcd999zXri3HJJZegvLwc9fX1PKnrYL744gt07doVvXr1wllnnYWVK1fisccew4cffgij0QhJkvDZZ5/htNNOw7XXXosBAwbgsssuw8GDB5WMzKlTp+Ltt9/GRx99hJEjR+L000/HunXr2njPKBZutxsmkwkGgwFvvvkmNm7ciKFDh2L+/Pn4xz/+0dabR+3AzJkz8cknn2D58uUYN24cJkyYgMWLFzcrsbr33nvx5ptvYvjw4XjppZfw2muvYciQIW201RRKamoqUlNTm32fx4ATx/PPP48zzzyzWUAP8GTqbd68GT/99BMAT3XHrbfeijFjxqCoqAgfffSRUsVDHZ8Wx4PLL78cJpMJc+fOjdiSgdo3cU4IAHfffTdOO+00zJ49G2effTYuuOAC9O3bt423kNorSW5arE1ERESt4qyzzkK/fv3wxBNPtPWmEBFRO7Fq1SpMmzYNlZWVSl9domAOHz6MXr16YcOGDRg9enRbbw7FgOeE1FLM1CMiImpllZWV+PTTT7Fq1aqg/VSJiIiIQnE4HCgsLMSf/vQnTJgwgQG9DoznhBQr9tQjIiJqZddeey02bNiAP/7xjzj//PPbenOIiIioA/nuu+8wbdo0DBgwAO+8805bbw7FgOeEFCuW3xIREREREREREXUwLL8lIiIiIiIiIiLqYBjUIyIiIiIiIiIi6mAY1CMiItLJgw8+iHHjxiElJQVdunTBBRdcgF27dgUsI8syFi1ahG7duiEhIQFTp07F9u3bA5Z55plnMHXqVKSmpkKSJFRVVTVb1+7du3H++ecjOzsbqampmDx5MlauXKnn7hERERERURtiUI+IiEgnq1evxu9+9zusXbsWy5cvh9PpxIwZM2C1WpVlHnnkESxevBhPPPEENmzYgLy8PEyfPh21tbXKMvX19TjrrLPw5z//OeS6zjnnHDidTqxYsQIbN27EyJEjMXv2bBQXF+u6j0RERERE1DY4KIOIiKiVlJaWokuXLli9ejVOO+00yLKMbt264bbbbsOf/vQnAIDNZkNubi4efvhh3HjjjQGPX7VqFaZNm4bKykqkp6cr3y8rK0NOTg6++eYbnHrqqQCA2tpapKam4quvvsIZZ5zRavtIREREREStg5l6REREraS6uhoAkJmZCQA4cOAAiouLMWPGDGUZi8WCKVOm4Pvvv1f9vFlZWRg8eDBefvllWK1WOJ1OPP3008jNzcWYMWO03QkiIiIiImoXTG29AURERCcCWZaxYMECnHLKKRg6dCgAKKWxubm5Acvm5ubi0KFDqp9bkiQsX74c559/PlJSUmAwGJCbm4svvvgiIKOPiIiIiIg6D2bqERERtYLf//73+Pnnn/HGG280+5kkSQFfy7Lc7HvhyLKMm2++GV26dMGaNWuwfv16nH/++Zg9ezaKiopi3nYiIiIiImp/GNQjIiLS2S233IKPPvoIK1euRI8ePZTv5+XlAUCzYRYlJSXNsvfCWbFiBT755BO8+eabmDx5MkaPHo0nn3wSCQkJeOmll7TZCSIiIiIialcY1CMiItKJLMv4/e9/j/feew8rVqxA7969A37eu3dv5OXlYfny5cr37HY7Vq9ejUmTJqleT319PQDAYAj8WDcYDHC73THsARERERERtVfsqUdERKST3/3ud3j99dfx4YcfIiUlRcnIS0tLQ0JCAiRJwm233YYHHngA/fv3R//+/fHAAw8gMTERc+fOVZ6nuLgYxcXF2Lt3LwBg69atSElJQUFBATIzMzFx4kRkZGTgmmuuwV//+lckJCTg2WefxYEDB3DOOee0yb4TEREREZG+JFmW5bbeCCIios4oVF+8F198EfPmzQPgyea799578fTTT6OyshLjx4/Hf/7zH2WYBgAsWrQI9957b9jn+fHHH7Fw4UL8+OOPcDgcOOmkk/DXv/4Vs2bN0ny/iIiIiIio7TGoR0RERERERERE1MGwpx4REREREREREVEHw6AeERERERERERFRB8OgHhERERERERERUQfDoB4REREREREREVEHw6AeERERERERERFRB8OgHhERERERERERUQfDoB4REREREREREVEHw6AeERERERERERFRB8OgHhERERHpburUqbjtttvaejOIiIiIOg0G9YiIiIioXVm1ahUkSUJVVVVbbwoRERFRu8WgHhERERERERERUQfDoB4RERERacpqteLqq69GcnIyunbtikcffTTg56+++irGjh2LlJQU5OXlYe7cuSgpKQEAHDx4ENOmTQMAZGRkQJIkzJs3DwAgyzIeeeQR9OnTBwkJCRgxYgTeeeedVt03IiIiovaCQT0iIiIi0tQdd9yBlStX4v3338eyZcuwatUqbNy4Ufm53W7H/fffjy1btuCDDz7AgQMHlMBdfn4+3n33XQDArl27UFRUhH//+98AgL/85S948cUX8dRTT2H79u2YP38+rrzySqxevbrV95GIiIiorUmyLMttvRFERERE1DnU1dUhKysLL7/8MubMmQMAqKioQI8ePXDDDTdgyZIlzR6zYcMGnHzyyaitrUVycjJWrVqFadOmobKyEunp6QA82X/Z2dlYsWIFJk6cqDz2+uuvR319PV5//fXW2D0iIiKidsPU1htARERERJ3Hvn37YLfbAwJvmZmZGDhwoPL1pk2bsGjRImzevBkVFRVwu90AgMLCQgwZMiTo8/7yyy9obGzE9OnTA75vt9sxatQoHfaEiIiIqH1jUI+IiIiINBOpCMRqtWLGjBmYMWMGXn31VeTk5KCwsBAzZ86E3W4P+TgR+Pv000/RvXv3gJ9ZLJbYN5yIiIiog2FQj4iIiIg0069fP8TFxWHt2rUoKCgAAFRWVmL37t2YMmUKdu7cibKyMjz00EPIz88HAPz4448Bz2E2mwEALpdL+d6QIUNgsVhQWFiIKVOmtNLeEBEREbVfDOoRERERkWaSk5Nx3XXX4Y477kBWVhZyc3OxcOFCGAye+WwFBQUwm814/PHHcdNNN2Hbtm24//77A56jZ8+ekCQJn3zyCc4++2wkJCQgJSUFt99+O+bPnw+3241TTjkFNTU1+P7775GcnIxrrrmmLXaXiIiIqM1w+i0RERERaeof//gHTjvtNJx33nk488wzccopp2DMmDEAgJycHCxduhRvv/02hgwZgoceegj//Oc/Ax7fvXt33HvvvbjrrruQm5uL3//+9wCA+++/H3/961/x4IMPYvDgwZg5cyY+/vhj9O7du9X3kYiIiKitcfotERERERERERFRB8NMPSIiIiIiIiIiog6GQT0iIiIiIiIiIqIOhkE9IiIiIiIiIiKiDoZBPSIiIiIiIiIiog6GQT0iIiIiIiIiIqIOhkE9IiIiIiIiIiKiDoZBPSIiIiIiIiIiog6GQT0iIiIiIiIiIqIOhkE9IiIiIiIiIiKiDoZBPSIiIiIiIiIiog6GQT0iIiIiIiIiIqIO5v8BfgFb1q4qJoEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the closing price from Q4 2017 through Q2 2018\n",
    "ax = portfolio['2017-Q4':'2018-Q2'].plot(\n",
    "    y='close', figsize=(15, 5), legend=False,\n",
    "    title='Bitcoin + S&P 500 value without accounting for different indices'\n",
    ")\n",
    "\n",
    "# formatting\n",
    "ax.set_ylabel('price')\n",
    "ax.yaxis.set_major_formatter(StrMethodFormatter('${x:,.0f}'))\n",
    "for spine in ['top', 'right']:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to align the index of the S&P 500 to match bitcoin in order to fix this. We will use the `reindex()` method, but by default we get `NaN` for the values that we don't have data for:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>day_of_week</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>2263.879883</td>\n",
       "      <td>2245.129883</td>\n",
       "      <td>2251.570068</td>\n",
       "      <td>2257.830078</td>\n",
       "      <td>3.770530e+09</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>2272.820068</td>\n",
       "      <td>2261.600098</td>\n",
       "      <td>2261.600098</td>\n",
       "      <td>2270.750000</td>\n",
       "      <td>3.764890e+09</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>2271.500000</td>\n",
       "      <td>2260.449951</td>\n",
       "      <td>2268.179932</td>\n",
       "      <td>2269.000000</td>\n",
       "      <td>3.761820e+09</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>2282.100098</td>\n",
       "      <td>2264.060059</td>\n",
       "      <td>2271.139893</td>\n",
       "      <td>2276.979980</td>\n",
       "      <td>3.339890e+09</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>2275.489990</td>\n",
       "      <td>2268.899902</td>\n",
       "      <td>2273.590088</td>\n",
       "      <td>2268.899902</td>\n",
       "      <td>3.217610e+09</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>2279.270020</td>\n",
       "      <td>2265.270020</td>\n",
       "      <td>2269.719971</td>\n",
       "      <td>2268.899902</td>\n",
       "      <td>3.638790e+09</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   high          low         open        close        volume  \\\n",
       "date                                                                           \n",
       "2017-01-01          NaN          NaN          NaN          NaN           NaN   \n",
       "2017-01-02          NaN          NaN          NaN          NaN           NaN   \n",
       "2017-01-03  2263.879883  2245.129883  2251.570068  2257.830078  3.770530e+09   \n",
       "2017-01-04  2272.820068  2261.600098  2261.600098  2270.750000  3.764890e+09   \n",
       "2017-01-05  2271.500000  2260.449951  2268.179932  2269.000000  3.761820e+09   \n",
       "2017-01-06  2282.100098  2264.060059  2271.139893  2276.979980  3.339890e+09   \n",
       "2017-01-07          NaN          NaN          NaN          NaN           NaN   \n",
       "2017-01-08          NaN          NaN          NaN          NaN           NaN   \n",
       "2017-01-09  2275.489990  2268.899902  2273.590088  2268.899902  3.217610e+09   \n",
       "2017-01-10  2279.270020  2265.270020  2269.719971  2268.899902  3.638790e+09   \n",
       "\n",
       "           day_of_week  \n",
       "date                    \n",
       "2017-01-01      Sunday  \n",
       "2017-01-02      Monday  \n",
       "2017-01-03     Tuesday  \n",
       "2017-01-04   Wednesday  \n",
       "2017-01-05    Thursday  \n",
       "2017-01-06      Friday  \n",
       "2017-01-07    Saturday  \n",
       "2017-01-08      Sunday  \n",
       "2017-01-09      Monday  \n",
       "2017-01-10     Tuesday  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.reindex(bitcoin.index).head(10).assign(\n",
    "    day_of_week=lambda x: x.index.day_name()\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now we have rows for every day of the year, but all the weekends and holidays have `NaN` values. To address this, we can specify how to handle missing values with the `method` argument. In this case, we want to forward-fill, which will put the weekend and holiday values as the value they had for the Friday (or end of trading week) before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>2263.879883</td>\n",
       "      <td>2245.129883</td>\n",
       "      <td>2251.570068</td>\n",
       "      <td>2257.830078</td>\n",
       "      <td>3.770530e+09</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>2272.820068</td>\n",
       "      <td>2261.600098</td>\n",
       "      <td>2261.600098</td>\n",
       "      <td>2270.750000</td>\n",
       "      <td>3.764890e+09</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>2271.500000</td>\n",
       "      <td>2260.449951</td>\n",
       "      <td>2268.179932</td>\n",
       "      <td>2269.000000</td>\n",
       "      <td>3.761820e+09</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>2282.100098</td>\n",
       "      <td>2264.060059</td>\n",
       "      <td>2271.139893</td>\n",
       "      <td>2276.979980</td>\n",
       "      <td>3.339890e+09</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-07</th>\n",
       "      <td>2282.100098</td>\n",
       "      <td>2264.060059</td>\n",
       "      <td>2271.139893</td>\n",
       "      <td>2276.979980</td>\n",
       "      <td>3.339890e+09</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>2282.100098</td>\n",
       "      <td>2264.060059</td>\n",
       "      <td>2271.139893</td>\n",
       "      <td>2276.979980</td>\n",
       "      <td>3.339890e+09</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>2275.489990</td>\n",
       "      <td>2268.899902</td>\n",
       "      <td>2273.590088</td>\n",
       "      <td>2268.899902</td>\n",
       "      <td>3.217610e+09</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>2279.270020</td>\n",
       "      <td>2265.270020</td>\n",
       "      <td>2269.719971</td>\n",
       "      <td>2268.899902</td>\n",
       "      <td>3.638790e+09</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   high          low         open        close        volume  \\\n",
       "date                                                                           \n",
       "2017-01-01          NaN          NaN          NaN          NaN           NaN   \n",
       "2017-01-02          NaN          NaN          NaN          NaN           NaN   \n",
       "2017-01-03  2263.879883  2245.129883  2251.570068  2257.830078  3.770530e+09   \n",
       "2017-01-04  2272.820068  2261.600098  2261.600098  2270.750000  3.764890e+09   \n",
       "2017-01-05  2271.500000  2260.449951  2268.179932  2269.000000  3.761820e+09   \n",
       "2017-01-06  2282.100098  2264.060059  2271.139893  2276.979980  3.339890e+09   \n",
       "2017-01-07  2282.100098  2264.060059  2271.139893  2276.979980  3.339890e+09   \n",
       "2017-01-08  2282.100098  2264.060059  2271.139893  2276.979980  3.339890e+09   \n",
       "2017-01-09  2275.489990  2268.899902  2273.590088  2268.899902  3.217610e+09   \n",
       "2017-01-10  2279.270020  2265.270020  2269.719971  2268.899902  3.638790e+09   \n",
       "\n",
       "           day_of_week  \n",
       "date                    \n",
       "2017-01-01      Sunday  \n",
       "2017-01-02      Monday  \n",
       "2017-01-03     Tuesday  \n",
       "2017-01-04   Wednesday  \n",
       "2017-01-05    Thursday  \n",
       "2017-01-06      Friday  \n",
       "2017-01-07    Saturday  \n",
       "2017-01-08      Sunday  \n",
       "2017-01-09      Monday  \n",
       "2017-01-10     Tuesday  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.reindex(bitcoin.index, method='ffill').head(10)\\\n",
    "    .assign(day_of_week=lambda x: x.index.day_name())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To isolate the changes happening with the forward-filling, we can use the `compare()` method. It shows us the values that differ across identically-labeled dataframes (same names and same columns). Here, we can see that only weekends and holidays (Monday, January 16, 2017 was MLK day) have values forward-filled. Notice that consecutive days have the same values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>date</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2282.100098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2264.060059</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>2276.979980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.339890e+09</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2282.100098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2264.060059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2271.139893</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2276.979980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.339890e+09</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2278.679932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2271.510010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2272.739990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2274.639893</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.081270e+09</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2278.679932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2271.510010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2272.739990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2274.639893</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.081270e+09</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2278.679932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2271.510010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2272.739990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2274.639893</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.081270e+09</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2276.959961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2265.010010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2269.959961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2271.310059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.524970e+09</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2276.959961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2265.010010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2269.959961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2271.310059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.524970e+09</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2299.020020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2291.620117</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2299.020020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2294.689941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.135890e+09</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2299.020020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2291.620117</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2299.020020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2294.689941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.135890e+09</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2298.310059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2287.879883</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2288.540039</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2297.419922</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.597970e+09</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           high               low              open              close  \\\n",
       "           self        other self        other self        other  self   \n",
       "date                                                                     \n",
       "2017-01-07  NaN  2282.100098  NaN  2264.060059  NaN  2271.139893   NaN   \n",
       "2017-01-08  NaN  2282.100098  NaN  2264.060059  NaN  2271.139893   NaN   \n",
       "2017-01-14  NaN  2278.679932  NaN  2271.510010  NaN  2272.739990   NaN   \n",
       "2017-01-15  NaN  2278.679932  NaN  2271.510010  NaN  2272.739990   NaN   \n",
       "2017-01-16  NaN  2278.679932  NaN  2271.510010  NaN  2272.739990   NaN   \n",
       "2017-01-21  NaN  2276.959961  NaN  2265.010010  NaN  2269.959961   NaN   \n",
       "2017-01-22  NaN  2276.959961  NaN  2265.010010  NaN  2269.959961   NaN   \n",
       "2017-01-28  NaN  2299.020020  NaN  2291.620117  NaN  2299.020020   NaN   \n",
       "2017-01-29  NaN  2299.020020  NaN  2291.620117  NaN  2299.020020   NaN   \n",
       "2017-02-04  NaN  2298.310059  NaN  2287.879883  NaN  2288.540039   NaN   \n",
       "\n",
       "                        volume               day_of_week  \n",
       "                  other   self         other              \n",
       "date                                                      \n",
       "2017-01-07  2276.979980    NaN  3.339890e+09    Saturday  \n",
       "2017-01-08  2276.979980    NaN  3.339890e+09      Sunday  \n",
       "2017-01-14  2274.639893    NaN  3.081270e+09    Saturday  \n",
       "2017-01-15  2274.639893    NaN  3.081270e+09      Sunday  \n",
       "2017-01-16  2274.639893    NaN  3.081270e+09      Monday  \n",
       "2017-01-21  2271.310059    NaN  3.524970e+09    Saturday  \n",
       "2017-01-22  2271.310059    NaN  3.524970e+09      Sunday  \n",
       "2017-01-28  2294.689941    NaN  3.135890e+09    Saturday  \n",
       "2017-01-29  2294.689941    NaN  3.135890e+09      Sunday  \n",
       "2017-02-04  2297.419922    NaN  3.597970e+09    Saturday  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.reindex(bitcoin.index)\\\n",
    "    .compare(sp.reindex(bitcoin.index, method='ffill'))\\\n",
    "    .head(10).assign(day_of_week=lambda x: x.index.day_name())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This isn't perfect though. We probably want 0 for the volume traded and to put the closing price for the open, high, low, and close on the days the market is closed:\n",
    "\n",
    "The reason why we're using `np.where(boolean condition, value if True, value if False)` within `lambda` functions in the example below, is that <b>vectorized operations</b> allow us to be faster and more efficient than utilizing `for` loops to perform calculations on arrays all at once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>day_of_week</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>2263.879883</td>\n",
       "      <td>2245.129883</td>\n",
       "      <td>2251.570068</td>\n",
       "      <td>2257.830078</td>\n",
       "      <td>3.770530e+09</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>2272.820068</td>\n",
       "      <td>2261.600098</td>\n",
       "      <td>2261.600098</td>\n",
       "      <td>2270.750000</td>\n",
       "      <td>3.764890e+09</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>2271.500000</td>\n",
       "      <td>2260.449951</td>\n",
       "      <td>2268.179932</td>\n",
       "      <td>2269.000000</td>\n",
       "      <td>3.761820e+09</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>2282.100098</td>\n",
       "      <td>2264.060059</td>\n",
       "      <td>2271.139893</td>\n",
       "      <td>2276.979980</td>\n",
       "      <td>3.339890e+09</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-07</th>\n",
       "      <td>2276.979980</td>\n",
       "      <td>2276.979980</td>\n",
       "      <td>2276.979980</td>\n",
       "      <td>2276.979980</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>2276.979980</td>\n",
       "      <td>2276.979980</td>\n",
       "      <td>2276.979980</td>\n",
       "      <td>2276.979980</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>2275.489990</td>\n",
       "      <td>2268.899902</td>\n",
       "      <td>2273.590088</td>\n",
       "      <td>2268.899902</td>\n",
       "      <td>3.217610e+09</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>2279.270020</td>\n",
       "      <td>2265.270020</td>\n",
       "      <td>2269.719971</td>\n",
       "      <td>2268.899902</td>\n",
       "      <td>3.638790e+09</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   high          low         open        close        volume  \\\n",
       "date                                                                           \n",
       "2017-01-01          NaN          NaN          NaN          NaN  0.000000e+00   \n",
       "2017-01-02          NaN          NaN          NaN          NaN  0.000000e+00   \n",
       "2017-01-03  2263.879883  2245.129883  2251.570068  2257.830078  3.770530e+09   \n",
       "2017-01-04  2272.820068  2261.600098  2261.600098  2270.750000  3.764890e+09   \n",
       "2017-01-05  2271.500000  2260.449951  2268.179932  2269.000000  3.761820e+09   \n",
       "2017-01-06  2282.100098  2264.060059  2271.139893  2276.979980  3.339890e+09   \n",
       "2017-01-07  2276.979980  2276.979980  2276.979980  2276.979980  0.000000e+00   \n",
       "2017-01-08  2276.979980  2276.979980  2276.979980  2276.979980  0.000000e+00   \n",
       "2017-01-09  2275.489990  2268.899902  2273.590088  2268.899902  3.217610e+09   \n",
       "2017-01-10  2279.270020  2265.270020  2269.719971  2268.899902  3.638790e+09   \n",
       "\n",
       "           day_of_week  \n",
       "date                    \n",
       "2017-01-01      Sunday  \n",
       "2017-01-02      Monday  \n",
       "2017-01-03     Tuesday  \n",
       "2017-01-04   Wednesday  \n",
       "2017-01-05    Thursday  \n",
       "2017-01-06      Friday  \n",
       "2017-01-07    Saturday  \n",
       "2017-01-08      Sunday  \n",
       "2017-01-09      Monday  \n",
       "2017-01-10     Tuesday  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "sp_reindexed = sp.reindex(bitcoin.index).assign(\n",
    "    volume=lambda x: x.volume.fillna(0), # put 0 when market is closed\n",
    "    close=lambda x: x.close.fillna(method='ffill'), # carry this forward\n",
    "    # take the closing price if these aren't available\n",
    "    open=lambda x: np.where(x.open.isnull(), x.close, x.open),\n",
    "    high=lambda x: np.where(x.high.isnull(), x.close, x.high),\n",
    "    low=lambda x: np.where(x.low.isnull(), x.close, x.low)\n",
    ")\n",
    "sp_reindexed.head(10).assign(\n",
    "    day_of_week=lambda x: x.index.day_name()\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we create a visualization comparing the reindexed data to the first attempt, we see how reindexing helped maintain the asset value when the market was closed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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D1c+dO8fp06dN66/l5OSYHt27dycqKoozZ84U2YZKpaJXr15m2+rXr8/FixdNrzdt2oSzszO9e/c2Kzdw4ECz16Xtz+uvv06PHj146qmnWLJkCbNnz6ZevXqm/WvWrKFu3bo0bNjQrK4uXbqYTQXctGkTQIF16G7tX3EGDhxoNsUsMDCQVq1ameo/c+YM165dY/DgwajVN/8L4uTkxJNPPsnu3btJT083q/PJJ58sVR8s+ffff3F0dDSNRsuTNwpn48aNJa4rLS2NPXv20LdvX5ycnEzbNRoNgwcP5sqVK0W+Z+rVq0eTJk1YtGiRadupU6fYu3cvzzzzjGlbs2bNOHLkCCNHjuSff/4hOTm5xH0sTIcOHfDx8THrc//+/Tl37pxp+ty///5Lhw4dCAgIMDs2LCyM9PT0AtOky+P+5L0/bh0V26xZM2rXrl2q+wPGcwgNDaVZs2Zm28PCwlAUhX///bfQYx0cHGjZsiUbNmwAYP369bi6uvL666+TnZ3N9u3bAePovcJG6ZVUw4YNTSPWAOzs7KhZs6bZd0dRunfvbvY5ql27NgA9evQwK5e3/dKlS4XW1axZMxITE3nqqaf4/fffiY2NLbTsrVl7i2r31nP5999/6dixI1qtFo1Gg7W1Ne+++y5xcXEFpghb8tdff1GzZs0SXfsePXqg0WhMr+vXrw9g6lNpvm83bdpU6OfndpTkPXA7bf/111/Y2dmZfbfcas2aNahUKgYNGmR2DXx9fWnQoIHp34nq1avj5ubG+PHjmTdvnsVRnEIIIf57EtQTQghx31q6dCn79u3j33//ZcSIEZw6dYqnnnrKtD9vbb1x48ZhbW1t9hg5ciRAkb/cgjEIYGdnZ7bN1taWzMxM0+u4uDizX8jy+Pr6mr0ubX9UKhVhYWFkZmbi6+tbYK2269evc/To0QJ1OTs7oyiKqa64uDisrKzw8PAosn/FsVTe19eXuLg4UzuAaSpifv7+/hgMBhISEsy2WypbWnFxcfj6+hZY08rb2xsrKytTv0oiISEBRVEKPYe89oryzDPPsGvXLtMaYIsWLcLW1tbsvTlhwgSmT5/O7t276datGx4eHnTo0IH9+/eXuK+3Kuz+5O9zXFxcqc6tvO5PYXX5+/uX6v7k1Xc796djx47s3r2btLQ0NmzYwKOPPoqHhwdNmjRhw4YNREREEBERcdtBvVs/b2D87sjIyCjR8bdmdraxsSlye/7vpFsNHjzYNF33ySefxNvbm+bNm7N+/frbajd/m3v37qVz584AfPvtt+zYsYN9+/YxceJEgBKd940bN0qccOLW65s3hTevndJ83+Z9h9yqtN+RxfUxr5/5r8XttH3jxg38/f3Ngr+3un79Ooqi4OPjU+A67N6923QNtFotW7ZsoWHDhrz11lvUqVMHf39/Jk2aVOK1BYUQQpQ/WVNPCCHEfat27dqm5Bjt27dHr9czf/58fvnlF/r27Wtap2vChAk88cQTFuuoVavWbffDw8ODvXv3Fth+a6KM0vYnKiqKl156iYYNG3LixAnGjRvHF198YVafvb19oYvv57Xn4eFBTk4OcXFxZr9kFpbIozCWykdHR5vqzPsZFRVVoNy1a9dQq9W4ubmZbS8um2pJeHh4sGfPHhRFMasvJiaGnJycUq3X5ubmhlqtLvQcgGLre+qppxgzZgyLFy9mypQpfPfdd/Tp08fs3K2srBgzZgxjxowhMTGRDRs28NZbb9GlSxcuX75cbBZgSwq7P4DZPSrNuZXX/QHj++LWgM21a9dKdX/y6rud+9OhQwfeeecdtm7dysaNG5k0aZJp+7p16wgODja9vp8MGzaMYcOGkZaWxtatW5k0aRI9e/bk7NmzBAYG3nb9K1aswNramjVr1pj9IWTVqlUlrsPLy6vckjKU5vvWw8OjyM/PnXQ7bXt5ebF9+3YMBkOhgT1PT09UKhXbtm0zBT7zy7+tXr16rFixAkVROHr0KIsXL+b999/H3t6eN998sxRnJYQQorzISD0hhBAPjGnTpuHm5sa7776LwWCgVq1a1KhRgyNHjtC0aVOLD2dn59tut3379qSkpBRYqP7Whc5L0x+9Xs9TTz2FSqXir7/+4uOPPzYtyp+nZ8+enD9/Hg8PD4t15WWUbN++PYApuURh/SvO8uXLzbJYXrx4kZ07d5qyMdaqVYtKlSrxww8/mJVLS0vj119/NWXELc6tI26K06FDB1JTUwsED5YuXWraX1KOjo40b96c3377zax9g8HAsmXLqFy5slkiFkvc3Nzo06cPS5cuZc2aNURHRxc5Pc7V1ZW+ffvy0ksvER8fT2RkZIn7m9/GjRvNMj/r9Xp+/PFHqlWrZgqmdejQgX///dcUAMuzdOlSHBwcaNGiRbHtlPb+PProowAsW7bMbPu+ffs4depUqYNnHTp04OTJkxw8eNBs+9KlS1GpVKb3e2GaNWuGi4sLs2bNIjo6mk6dOgHGEXyHDh3ip59+IjQ01DTyrzClvQ53C0dHR7p168bEiRPJzs7mxIkT5VKvSqXCysrKbEpsRkYG3333XYGyhY1Y7NatG2fPni1yCnVJleb7tn379oV+fu6022m7W7duZGZmFsjenV/Pnj1RFIWrV69avAb5l3TIo1KpaNCgAZ999hmurq4FPmtCCCH+OzJSTwghxAPDzc2NCRMm8MYbb/DDDz8waNAgvv76a7p160aXLl0ICwujUqVKxMfHc+rUKQ4ePMjPP/982+0OGTKEzz77jCFDhjBlyhRq1KjB2rVr+eeffwqULWl/Jk2axLZt21i3bh2+vr6MHTuWLVu28Oyzz9KoUSOCg4MZNWoUv/76K23atGH06NHUr18fg8HApUuXWLduHWPHjqV58+Z07tyZNm3a8MYbb5CWlkbTpk3ZsWOHxV+2ixITE8Pjjz/O8OHDSUpKYtKkSdjZ2TFhwgQA1Go106ZN4+mnn6Znz56MGDGCrKwsPv30UxITE/nkk09K1E7eL5lTp06lW7duaDQa6tevb5oGaOn6z5kzh6FDhxIZGUm9evXYvn07H330Ed27dy/1NMqPP/6YTp060b59e8aNG4eNjQ1z587l+PHjLF++vESj15555hl+/PFHXn75ZSpXrlygD7169aJu3bo0bdoULy8vLl68yKxZswgMDKRGjRqAMQtvhw4dePfdd02ZQ4vi6enJo48+yjvvvGPKfnv69GlWrFhhKjNp0iTWrFlD+/bteffdd3F3d+f777/nzz//ZNq0aaasoUWpW7cuAN988w3Ozs7Y2dkRHBxscaohGIMrzz//PLNnz0atVtOtWzdT9tuAgABGjx5dbJv5jR49mqVLl9KjRw/ef/99AgMD+fPPP5k7dy4vvvhisUFXjUZD27Zt+eOPPwgODqZatWoAtG7dGltbWzZu3Mirr75abD/y3qeff/45Q4cOxdramlq1apXLHwrK2/Dhw7G3t6d169b4+fkRHR3Nxx9/jFar5aGHHiqXNnr06MHMmTMZOHAgzz//PHFxcUyfPt3i6LC8EWE//vgjVatWxc7Ojnr16jFq1Ch+/PFHHnvsMd58802aNWtGRkYGW7ZsoWfPnsUGbG9V0u/bt99+m9WrV/Poo4/y7rvv4uDgwJw5c0hLSyuXa1OU22n7qaeeYtGiRbzwwgucOXOG9u3bYzAY2LNnD7Vr12bAgAG0bt2a559/nmHDhrF//37atGmDo6MjUVFRbN++nXr16vHiiy+yZs0a5s6dS58+fahatSqKovDbb7+RmJhoCnwLIYSoABWVoUMIIYS4U/KyNN6a9VVRjNkFq1SpotSoUcOUGfHIkSNKv379FG9vb8Xa2lrx9fVVHn30UWXevHmm4wrLfuvo6FigDUuZDq9cuaI8+eSTipOTk+Ls7Kw8+eSTys6dOwtkzCxJf9atW6eo1eoCWWnj4uKUKlWqKA899JCSlZWlKIqipKamKm+//bZSq1YtxcbGRtFqtUq9evWU0aNHm2VTTExMVJ555hnF1dVVcXBwUDp16qScPn26VNlvv/vuO+XVV19VvLy8FFtbW+WRRx5R9u/fX6D8qlWrlObNmyt2dnaKo6Oj0qFDB2XHjh0Wr2H+LKl5srKylOeee07x8vJSVCqVWQZIS9lv867NCy+8oPj5+SlWVlZKYGCgMmHCBFM20zwlyX6rKIqybds25dFHH1UcHR0Ve3t7pUWLFsoff/xR5HXKT6/XKwEBAQqgTJw4scD+GTNmKK1atVI8PT0VGxsbpUqVKsqzzz6rREZGmsrkXffi7o+iGLPfvvTSS8rcuXOVatWqKdbW1kpISIjy/fffFyh77NgxpVevXopWq1VsbGyUBg0aFDj/vLZ//vlni+3NmjVLCQ4OVjQajdn1K+y+6vV6ZerUqUrNmjUVa2trxdPTUxk0aJBy+fJls3IlyX6rKIpy8eJFZeDAgYqHh4dibW2t1KpVS/n000/NssUW5fPPP1cAZfjw4WbbO3XqpADK6tWrzbYX9j6ZMGGC4u/vr6jVarPvj8DAQKVHjx4F2r01e6kleW19+umnZtsLuyeWvg9vbWfJkiVK+/btFR8fH8XGxkbx9/dX+vXrZ5ZNu7Dv1cLuqaXvx4ULFyq1atVSbG1tlapVqyoff/yxsmDBggJZXCMjI5XOnTsrzs7OCmB2zxMSEpTXXntNqVKlimJtba14e3srPXr0UE6fPl3k9VEUxeLnpSTf/4qiKDt27FBatGih2NraKr6+vsrrr7+ufPPNN7eV/bak74GStm3p2IyMDOXdd99VatSoodjY2CgeHh7Ko48+quzcudOs3MKFC5XmzZubvtOqVaumDBkyxPQdfvr0aeWpp55SqlWrptjb2ytarVZp1qyZsnjx4iLPXQghxJ2lUpR881+EEEIIIUpp8+bNtG/fnp9//rlAhllxd1CpVLz00kt8+eWXFd0VIYQQQghRTmRNPSGEEEIIIYQQQggh7jES1BNCCCGEEEIIIYQQ4h4j02+FEEIIIYQQQgghhLjHyEg9IYQQQgghhBBCCCHuMRLUE0IIIYQQQgghhBDiHiNBPSGEEEIIIYQQQggh7jES1KtgiqKQnJyMLG0ohBBCCCGEEEIIIUpKgnoVLCUlBa1WS0pKSkV3RQghhBBCCCGEEELcIySoJ4QQQgghhBBCCCHEPUaCekIIIYQQQgghhBBC3GMkqCeEEEIIIYQQQgghxD1GgnpCCCGEEEIIIYQQQtxjrCq6A0IIIYQQQgghypder0en01V0N4QQQlhgY2ODWn374+zumqBeWFgYixcvruhuCCGEEEIIIcQ9S1EUoqOjSUxMrOiuCCGEKIRarSY4OBgbG5vbqueuCerdSqfT8fbbb7N27VouXLiAVqulY8eOfPLJJ/j7+wMQHx/PpEmTWLduHZcvX8bT05M+ffrwwQcfoNVqC6178uTJvPfee2bbfHx8iI6ONr1WFIX33nuPb775hoSEBJo3b86cOXOoU6eOqUxWVhbjxo1j+fLlZGRk0KFDB+bOnUvlypXL+WoIIYQQQgghRPHyAnre3t44ODigUqkquktCCCHyMRgMXLt2jaioKKpUqXJb39MVGtSLjY1l7NixbNq0ievXr7N9+3YaN27MsmXLyMjI4ODBg7zzzjs0aNCAhIQERo0aRe/evdm/fz8A165d49q1a0yfPp3Q0FAuXrzICy+8wLVr1/jll1+KbLtOnTps2LDB9Fqj0ZjtnzZtGjNnzmTx4sXUrFmTDz/8kE6dOnHmzBmcnZ0BGDVqFH/88QcrVqzAw8ODsWPH0rNnTw4cOFCgPiGEEEIIIYS4k/R6vSmg5+HhUdHdEUIIUQgvLy+uXbtGTk4O1tbWZa5HpSiKUo79KpXBgwezb98+vv76a2bNmsWrr77K33//zXvvvYednV2B8vv27aNZs2ZcvHiRKlWqWKzz559/ZtCgQaSlpWFlZTlmOXnyZFatWsXhw4ct7lcUBX9/f0aNGsX48eMB46g8Hx8fpk6dyogRI0hKSsLLy4vvvvuO/v37A8YgY0BAAGvXrqVLly4lugbJyclotVqSkpJwcXEp0TFCCCGEEEIIcavMzEwiIiIICgrC3t6+orsjhBCiEBkZGURGRhIcHGwx/lVSFZr99tChQwwePJi2bdui1Wpp3749U6dOLfSEkpKSUKlUuLq6FlpnXnCssIBenvDwcPz9/QkODmbAgAFcuHDBtC8iIoLo6Gg6d+5s2mZra0vbtm3ZuXMnAAcOHECn05mV8ff3p27duqYylmRlZZGcnGz2EEIIIYQQQojyIlNuhRDi7lZe39MVGtRr3bo1ixYtYs2aNcWWzczM5M0332TgwIGFjmiLi4vjgw8+YMSIEUXW1bx5c5YuXco///zDt99+S3R0NK1atSIuLg7AtLaej4+P2XH5192Ljo7GxsYGNze3QstY8vHHH6PVak2PgICAok9cCCGEEEIIIYQQQohbVGhQb+bMmfTv35/Ro0ezdOlSGjZsyLx58wqU0+l0DBgwAIPBwNy5cy3WlZycTI8ePQgNDWXSpElFttutWzeefPJJ6tWrR8eOHfnzzz8BWLJkiVm5WyOniqIUG00trsyECRNISkoyPS5fvlxkfUIIIYQQQgghhBBC3KpCg3qOjo5MmTKF8PBwevfuzYsvvsiYMWP45ptvTGV0Oh39+vUjIiKC9evXWxyll5KSQteuXXFycmLlypWlXmTQ0dGRevXqER4eDoCvry9AgRF3MTExptF7vr6+ZGdnk5CQUGgZS2xtbXFxcTF7CCGEEEIIIYQonXbt2jFq1KjbqiMyMhKVSlXoeuv/lbCwMPr06VOhfbiVoig8//zzuLu7l/gabd68GZVKRWJiIgCLFy8ucvms8nL69GlatGiBnZ0dDRs2vOPtCcv+i/fx3fKZvVtUaFAvP1dXV0aMGEG3bt3Ytm0bcDOgFx4ezoYNGyxmcEpOTqZz587Y2NiwevXqMi0wmJWVxalTp/Dz8wMgODgYX19f1q9fbyqTnZ3Nli1baNWqFQBNmjTB2trarExUVBTHjx83lRFCCCGEEEIIcWf89ttvfPDBBxXdjXverYG4PH///TeLFy9mzZo1REVFUbdu3VLX3b9/f86ePVtOPS3cpEmTcHR05MyZM2zcuNFimZiYGEaMGEGVKlWwtbXF19eXLl26sGvXLrNyy5YtIyQkBDs7O4KCgiy+x/ICS3kPNzc32rRpw5YtWwrt463H5D3+/vtvs3JbtmyhSZMm2NnZUbVqVYuzGX/99VdCQ0OxtbUlNDSUlStXluQyldmtfbexsaF69ep8+OGH5M+9+vnnn7N48WLT6/IIvN8qICCgzO/H+1GFBvVGjx7Nli1bSEpKQq/Xs2nTJtMbOCcnh759+7J//36+//579Ho90dHRREdHk52dDRhH6HXu3Jm0tDQWLFhAcnKyqYxery+03XHjxrFlyxYiIiLYs2cPffv2JTk5maFDhwLGabejRo3io48+YuXKlRw/fpywsDAcHBwYOHAgAFqtlmeffZaxY8eyceNGDh06xKBBg0xTeoUQQpSOwaCwLfwGUUkZFd0VIYQQQlSgvN/3iuPu7o6zs/Md7s39TafTFbrv/Pnz+Pn50apVK3x9fYtNRmmJvb093t7et9PFEjl//jwPP/wwgYGBFgcDATz55JMcOXKEJUuWcPbsWVavXk27du2Ij483lYmMjGTIkCH06dOHU6dO8dNPPxEcHFxouxs2bCAqKootW7bg4uJC9+7diYiIKLKvecfkPR599FHTvoiICLp3784jjzzCoUOHeOutt3j11Vf59ddfTWV27dpF//79GTx4MEeOHGHw4MH069ePPXv2lPRysXjxYtq1a1fi8rf2PTw8nPfee48pU6awcOFC036tVnvHR2ZqNJoyvx/vS0oFmjlzptK4cWPF2dlZUavVSuXKlZXXX39dycnJUSIiIhTA4mPTpk2KoijKpk2bCi0TERFhaqdt27bK0KFDTa/79++v+Pn5KdbW1oq/v7/yxBNPKCdOnDDrm8FgUCZNmqT4+voqtra2Sps2bZRjx46ZlcnIyFBefvllxd3dXbG3t1d69uypXLp0qVTXICkpSQGUpKSkUh0nhBD3m1nrzyqB49cojd9fp8QkZ1Z0d4QQQoh7TkZGhnLy5EklIyOjortSKm3btlVeeuklZfTo0YqHh4fSpk0bRVEU5cSJE0q3bt0UR0dHxdvbWxk0aJBy48YNs+Nee+010+vAwEBlypQpyrBhwxQnJyclICBA+frrr83a2rNnj9KwYUPF1tZWadKkifLbb78pgHLo0CFTmaLa3bRpk2Jtba1s3brVVH769OmKh4eHcu3aNUVRFOXKlStKv379FFdXV8Xd3V3p3bu32e+nOTk5yujRoxWtVqu4u7srr7/+ujJkyBDlscceK/QaLVq0SNFqtcrKlSuVGjVqKLa2tkrHjh0L/P45d+5cpWrVqoq1tbVSs2ZNZenSpWb7AeWrr75SevfurTg4OChDhgwp8Lv00KFDlaFDh5ptCwwMVBRFUTIzM5VXXnlF8fLyUmxtbZXWrVsre/fuNdWf9zt6QkKCWb9L08db6fV65b333lMqVaqk2NjYKA0aNFD++usvs3PK/5g0aVKBOhISEhRA2bx5c5FtRUZGKmq1Wjlz5kyR5fLiFfnfN1euXFEAZd68eSU+5lZvvPGGEhISYrZtxIgRSosWLUyv+/Xrp3Tt2tWsTJcuXZQBAwYU2ef8Fi1apLRt27bE5Qvr+6OPPqqMHDnS9Hro0KGm9/Gt76H8cZrjx48r3bt3V5ydnRUnJyfl4YcfVs6dO6coSvH3+9a+5L3nNmzYoDRp0kSxt7dXWrZsqZw+fbrE51cRyuv7ukJDm6NHj2b06NGAce51/mGaQUFBZsM4LWnXrl2xZcAYbQ8LCzO9XrFiRbHHqFQqJk+ezOTJkwstY2dnx+zZs5k9e3ax9QkhhCja7gvGDORxadl8vPYUM/s3rNgOCSGEEPeBXrO3cyMl6z9v18vZlj9eebjE5ZcsWcKLL77Ijh07UBSFqKgo2rZty/Dhw5k5cyYZGRmMHz+efv368e+//xZaz4wZM/jggw946623+OWXX3jxxRdp06YNISEhpKWl0bNnTx599FGWLVtGREQEr732mtnxxbWbN50wb5RUZGQkEydOZPny5fj5+ZGenk779u155JFH2Lp1K1ZWVnz44Yd07dqVo0ePYmNjw4wZM1i4cCELFiwgNDSUGTNmsHLlSrMRW5akp6czZcoUlixZgo2NDSNHjmTAgAHs2LEDgJUrV/Laa68xa9YsOnbsyJo1axg2bBiVK1emffv2pnomTZrExx9/zGeffYZGo+Gxxx7jySef5MyZM7i4uGBvbw9AtWrV+Oabb9i3bx8ajQaAN954g19//ZUlS5YQGBjItGnT6NKlC+fOncPd3b3Y+1zSPub3+eefM2PGDL7++msaNWrEwoUL6d27NydOnKBGjRpERUXRsWNHunbtyrhx43BycipQh5OTE05OTqxatYoWLVpga2trsa1KlSrRtGlTXn755VIv7+Xg4AAUPfoRoHfv3mRmZlKjRg1Gjx5N3759Tft27dpF586dzcp36dKFBQsWoNPpsLa2ZteuXaY4Sv4ys2bNKnFfy8P+/fs5ePCgacbjrT7//HPOnj1L3bp1ef/99wHw8vLi6tWrtGnThnbt2vHvv//i4uLCjh07yMnJMR1X1P0uzMSJE5kxYwZeXl688MILPPPMM6bPxv3svh+vePr0aZydnRkyZEhFd0UIIUQRribenHb726Gr9HsogBZVLU+fEEIIIUTJ3EjJIjo5s6K7Uazq1aszbdo00+t3332Xxo0b89FHH5m2LVy4kICAAM6ePUvNmjUt1tO9e3dGjhwJwPjx4/nss8/YvHkzISEhpmWdFi5ciIODA3Xq1OHKlSu8+OKLpuO/+uqrYtv98MMP2bBhA88//zwnTpxg8ODBPP7444BxAIlarWb+/PmoVCoAFi1ahKurK5s3b6Zz587MmjWLCRMm8OSTTwIwb948/vnnn2KvkU6n48svv6R58+aAMRBau3Zt9u7dS7NmzZg+fTphYWGm8x8zZgy7d+9m+vTpZgGzgQMH8swzz5he500X9fb2Nps66ezsbJrqCJCWlsZXX33F4sWL6datGwDffvst69evZ8GCBbz++uvFnkNJ+3jrMePHj2fAgAEATJ06lU2bNjFr1izmzJljmorp5ORk6uutrKysWLx4McOHD2fevHk0btyYtm3bMmDAAOrXr28qN3z4cBRFoWrVqnTt2pXVq1ebklv27NmT4OBgi4N60tLSmDBhAhqNhrZt21rsg5OTEzNnzqR169ao1WpWr15N//79WbJkCYMGDQKMyTpvTbzp4+NDTk4OsbGx+Pn5FVrm1kSfd0KrVq1Qq9VkZ2ej0+l4/vnnC421aLVabGxscHBwMLsvc+bMQavVsmLFClOS0/yf5+Lud2GmTJliuvZvvvkmPXr0IDMzs0x5F+4ld01QL/8ovfIUEhLCsWPH7kjdQgghyofBoBRYS++dVcf589VHsLG6a3I6CSGEEPccL2fLI5LutnabNm1q9vrAgQNs2rTJ4qir8+fPFxrUyx+gUalU+Pr6EhMTA8CpU6do0KCBaUQVQMuWLUvdro2NDcuWLaN+/foEBgaajZA6cOAA586dK7DWX2ZmJufPnycpKYmoqCizdq2srGjatGmxs9DyyuUJCQnB1dWVU6dO0axZM06dOsXzzz9vdkzr1q35/PPPzbbdeq1L6vz58+h0Olq3bm3aZm1tbWq7JEraxzzJyclcu3bNrM28Y44cOVKq/j/55JP06NGDbdu2sWvXLv7++2+mTZvG/PnzCQsL4+TJkyxevJgTJ05Qu3Zthg0bRrt27fj777/x9vY2BXDzywtypaen4+fnx+LFi6lXr57F9j09Pc1G2DVt2pSEhASmTZtmCuoBpmBwnrz3Rf7tlsrcui2/S5cuERoaanqdk5ODTqcze58PGjTIYlKO/H788Udq166NTqfj2LFjvPrqq7i5ufHJJ58UeVx+hw8f5pFHHjEF9PK7nfud/7OflwQ1JiaGKlWqlLhv96K7JqgnhBDiwRWTkoVOb/4f2fCYVBbuiOCFttUqqFdCCCHEva80U2ArkqOjo9lrg8FAr169mDp1aoGyeb+wW3JroEClUmEwGABKtHRTSdvduXMnAPHx8cTHx5v6bzAYaNKkCd9//32B4728vIptvziWAjelDfbceq1LylJwqbA2ilKW42+3zTx2dnZ06tSJTp068e677/Lcc88xadIkwsLCTNOj84JfCxYsoH///rRu3ZrXX3+dlJQUevfubVbfjz/+SGhoKK6uroUm6ChKixYtmD9/vum1r69vgRF3MTExWFlZmeovrMyto/fy8/f35/Dhw6bXv/32G7/++qvZ+zRvRGJRAgICqF69OgC1a9fmwoULvPPOO0yePLnEI+LypncXpSz3O/9nP69s3mf/fibDH4QQQlS4q4nppucPBbmhzv03+/MN4WbTcoUQQgjxYGjcuDEnTpwgKCiI6tWrmz3KGpQKDQ3lyJEjZGTc/L/F7t27S93u+fPnGT16NN9++y0tWrRgyJAhpuBB48aNCQ8Px9vbu8DxWq0WrVaLn5+fWbs5OTkcOHCg2P7n5OSwf/9+0+szZ86QmJhISEgIYAyybN++3eyYnTt3Urt27SLrtbGxAUCv1xdZrnr16tjY2Ji1odPp2L9/f7Ft5CltH11cXPD39y/TeZVEaGgoaWlpgHE9vezsbFMWWY1Gww8//ED16tUZMWIEEydOLBCQCggIoFq1amUK6AEcOnTILFjcsmVL1q9fb1Zm3bp1NG3a1BS0KqxMq1atCm3HysrK7L3o7e2Nvb19gW2lpdFoyMnJKTRjtY2NTYH3Vf369dm2bZvFtQfv9P2+H0lQTwghRIW7knDzP9cda/swqEUgABk6PW/9dozt4bEcuZzIhRuppGblVFQ3hRBCCPEfeemll4iPj+epp55i7969XLhwgXXr1vHMM88UG3wqzMCBA1Gr1Tz77LOcPHmStWvXMn369FK1q9frGTx4MJ07d2bYsGEsWrSI48ePM2PGDACefvppPD09eeyxx9i2bRsRERFs2bKF1157jStXrgDw2muv8cknn7By5UpOnz7NyJEjSUxMLLb/1tbWvPLKK+zZs4eDBw8ybNgwWrRoQbNmzQB4/fXXWbx4MfPmzSM8PJyZM2fy22+/MW7cuCLrDQwMRKVSsWbNGm7cuEFqaqrFco6Ojrz44ou8/vrr/P3335w8eZLhw4eTnp7Os88+W2z/y9rH119/nalTp/Ljjz9y5swZ3nzzTQ4fPlwgyUlR4uLiTAlSjh49SkREBD///DPTpk3jscceA+Dhhx+mVatW9O/fn1WrVnH+/HnWrl3LhQsXcHR05IcffiA9Pb2Ylgq3ZMkSfvjhB06dOsWZM2eYPn06X3zxBa+88oqpzAsvvMDFixcZM2YMp06dMiVUyX99XnvtNdatW8fUqVM5ffo0U6dOZcOGDYwaNarMfSupuLg4oqOjuXLlCn/99Reff/457du3L3SUX1BQEHv27CEyMpLY2FgMBgMvv/wyycnJDBgwgP379xMeHs53333HmTNngPK53w8SmX4rhBCiwuUfjVfJzZ4Bzaqw9lgUsanZbDl7gy1nb5j221qp+XJgYzqFFj7FQAghhBD3Nn9/f3bs2MH48ePp0qULWVlZBAYG0rVrV9Tqso1NcXJy4o8//uCFF16gUaNGhIaGMnXqVFPCipK0+8EHHxAZGckff/wBGKdCzp8/n379+tGpUycaNmzI1q1bGT9+PE888QQpKSlUqlSJDh06mAIfY8eOJSoqirCwMNRqNc888wyPP/44SUlJRfbfwcGB8ePHM3DgQK5cucLDDz/MwoULTfv79OnD559/zqeffsqrr75KcHAwixYtol27dkXWW6lSJd577z3efPNNhg0bxpAhQwpd8/6TTz7BYDAwePBgUlJSaNq0Kf/88w9ubm4luANl6+Orr75KcnIyY8eOJSYmhtDQUFavXl1kJtRbOTk50bx5cz777DPT2oABAQEMHz6ct956CzBO2fz777+ZPHkyY8aM4erVq1SvXp0XX3yRfv360bx5c55++ml+/fXXErd7qw8//JCLFy+i0WioWbMmCxcuNFtPLzg4mLVr1zJ69GjmzJmDv78/X3zxhdl7tFWrVqxYsYK3336bd955h2rVqvHjjz+aEqjcSR07dgSMI/T8/Pzo3r07U6ZMKbT8uHHjGDp0KKGhoWRkZBAREUFQUBD//vsvr7/+Om3btkWj0dCwYUPTOnrlcb8fJCqlJAsLiDsmOTkZrVZLUlJSieawCyHE/WjiymN8v+cSACtHtqJRFTd+P3yV11Yctli+fS0vFg1r9h/2UAghhLj7ZWZmEhERQXBw8H2f8fFBs3jxYkaNGlWiEX1CiLtfeX1fy0g9IYQQFe7WkXoAjzWshNbemlNRKaRm6UjNzOHo/q3E5dgRHVO2aTdCCCGEEEIIcb+QoJ4QQogKdzV3TT0bKzWejram7e1qedOuVr5Few+1Bg2cSAskI7sH9jaa/7qrQgghhBBCCHFXkEQZQgghKpSiKKaRepVc7VGrC0lXn51meuqmSuH8DcuLOAshhBBC3G/CwsJk6q0QogAJ6gkhhKhQiek60rON02krudoXXjAt1vTUiyQuXI+/010TQgghhBBCiLuWBPWEEEJUKLP19IoK6qXfDOpZq/TEXTp9J7slhBBCCCGEEHc1CeoJIYSoUFcSbgb1KrsVNVIvzuylLlqCekIIIYQQQogHlwT1hBBCVChLmW8tSjcP6lknhN+pLgkhhBBCCCHEXU+CekIIISrU1YTST78FcE+PRKc33KluCSGEEEIIIcRdTYJ6QgghKtTVxHTT8yJH6lVqCg+PYbu2F+v1TThgqM7FuPTCywshhBBCCCHEfUyCekIIISpU3pp6GrUKXxe7wgsGtoSOk9hf712G68ayVN+FczEp/1EvhRBCCHE/OX36NC1atMDOzo6GDRuW6JiwsDD69Oljet2uXTtGjRp1R/qX3zfffENAQABqtZpZs2bd8faEZSqVilWrVt3RNiZPnlzi9+P94NbPVFmV970pr379FySoJ4QQokLlrann62KHlab4f5aqezuZnp+LSb1j/RJCCCHEva+wX84nTZqEo6MjZ86cYePGjWWq+7fffuODDz64zR4WLTk5mZdffpnx48dz9epVnn/+eYvlNm3aRPv27XF3d8fBwYEaNWowdOhQcnJyTGUyMjJ47rnn8PLywsnJiWbNmrFz584CdU2ePBmVSoVKpUKj0RAQEMBzzz3HjRs3Cu1n/mPyHr6+vmZlFEVh8uTJ+Pv7Y29vT7t27Thx4oRZmaysLF555RU8PT1xdHSkd+/eXLlypTSXrNRu7btWq+WRRx5hy5YtZuWioqLo1q0bAJGRkahUKg4fPlyufRk3blyZ34+3IyMjAwcHB06f/m8T0X3++ecsXrz4P22zJO7WflkiQT0hhBAVJi0rh8R0HVDMenoAMacgPoLqHjaAgheJXIu6euc7KYQQQoh7jl6vx2AofO3d8+fP8/DDDxMYGIiHh0eZ2nB3d8fZ2bmsXSyRS5cuodPp6NGjB35+fjg4OBQoc+LECbp168ZDDz3E1q1bOXbsGLNnz8ba2trsGnz66af88ssvLFu2jKNHj/LOO+9gZWVlsd06deoQFRXFpUuX+Oqrr/jjjz8YMmRIkX3NOybvcezYMbP906ZNY+bMmXz55Zfs27cPX19fOnXqRErKzZkXo0aNYuXKlaxYsYLt27eTmppKz5490ev1Jb5m7dq1K3VAJn/fd+3aRY0aNejZsydJSUmmMr6+vtja2paq3tJycnIq8/vxdqxfv56AgABCQkLKpb7s7OwSldNqtbi6upZLm+Xpbu2XJRLUE0IIUWFKnPkWYMVA+KIhVbPOMMN6HvvsRlLtyu93uIdCCCHEfSA7rfCHLrMUZTNKVraU2rVrx8svv8zLL7+Mq6srHh4evP322yiKYiqTkJDAkCFDcHNzw8HBgW7duhEeHm7av3jxYlxdXVmzZg2hoaHY2toybNgwlixZwu+//24ahbV582ZUKhUHDhzg/fffR6VSMXnyZACOHTvGo48+ir29PR4eHjz//POkphY+K+DW6bfF9dGSS5cu8dhjj+Hk5ISLiwv9+vXj+vXrpnOqV68eAFWrVkWlUhEZGVmgjvXr1+Pn58e0adOoW7cu1apVo2vXrsyfPx8bGxtTObVaTWhoKF26dKFq1ar06tWLZs2aWeyXlZUVvr6+VKpUiZ49e/Lqq6+ybt06MjIyLJbPf0zew8vLy7RPURRmzZrFxIkTeeKJJ6hbty5LliwhPT2dH374AYCkpCQWLFjAjBkz6NixI40aNWLZsmUcO3aMDRs2FHkdb1f+voeGhvLee++RmprK2bNnTWXyT/EMDg4GoFGjRqhUKtq1a2cqt3DhQurUqYOtrS1+fn68/PLLpn1F3W8oOP02b6Tp9OnT8fPzw8PDg5deegmdTmfxPJKSktBoNBw4cAAwXnd3d3ceeughU5nly5fj5+dndtzvv/9O7969Ta+/+uorqlWrho2NDbVq1eK7774r8vrl9fPjjz/G39+fmjVrAnD16lX69++Pm5sbHh4ePPbYY2bvYUtT2l999VXeeOMN3N3d8fX1NX0+84SHh9OmTRvs7OwIDQ1l/fr1BfpTVLunT5/GwcHB9L4D46hbOzs7UyC6LP06ffo0Dz/8sKlfGzZs+E+mbEtQTwghRIUpceZbgLQ4AGxcvEmxqwSANu0CBoNS1FFCCCGE+Mi/8MdPg83Lflq98LLL+pqXnVXPcrkyWLJkCVZWVuzZs4cvvviCzz77jPnz55v2h4WFsX//flavXs2uXbtQFIXu3bubBTfS09P5+OOPmT9/PidOnOCLL76gX79+dO3a1TQKq1WrVkRFRVGnTh3Gjh1LVFQU48aNIz09na5du+Lm5sa+ffv4+eef2bBhg1lApjgl6WN+iqLQp08f4uPj2bJlC+vXr+f8+fP0798fgP79+5uCWXv37iUqKoqAgIAC9fj6+hIVFcXWrVuL7F+vXr3Ys2cPCxYsKPE55bG3t8dgMJhN571VeHg4/v7+BAcHM2DAAC5cuGDaFxERQXR0NJ07dzZts7W1pW3btqYpwAcOHECn05mV8ff3p27duhanCd8pWVlZpiBxrVq1LJbZu3cvABs2bCAqKorffvsNMAbDXnrpJZ5//nmOHTvG6tWrqV69OlD8/S7Mpk2bOH/+PJs2bWLJkiUsXry40JGIWq2Whg0bsnnzZgCOHj1q+pmcnAzA5s2badu2rekYg8HAmjVreOyxxwBYuXIlr732GmPHjuX48eOMGDGCYcOGsWnTpiL7uXHjRk6dOsX69etZs2YN6enptG/fHicnJ7Zu3cr27dtxcnKia9euRY7kW7JkCY6OjuzZs4dp06bx/vvvmwJ3BoOBJ554Ao1Gw+7du5k3bx7jx483O764dkNCQpg+fTojR47k4sWLXLt2jeHDh/PJJ5+Yguhl6VefPn1wcHBgz549fPPNN0ycOLHI61VeLI+1FUIIIf4DV0o6Ui8nG7Jypz84eJDlWh1uQDBXuZqYQYB7wakoQgghhLh3BAQE8Nlnn6FSqahVqxbHjh3js88+Y/jw4YSHh7N69Wp27NhBq1atAPj+++8JCAhg1apV/O9//wNAp9Mxd+5cGjRoYKrX3t6erKwss/XdfH19sbKywsnJybT922+/JSMjg6VLl+Lo6AjAl19+Sa9evZg6dSo+Pj5F9r+kfcxvw4YNHD16lIiICFOw7rvvvqNOnTrs27ePhx56yDQV08vLq8AadXn+97//8c8//9C2bVt8fX1p0aIFHTp0YMiQIbi4uABw/fp1unbtyvjx45k6dSopKSmmUYZxcXF4enqyf/9+mjRpUqD+06dP89VXX9GsWbNCpxs3b96cpUuXUrNmTa5fv86HH35Iq1atOHHiBB4eHkRHRwMUuI4+Pj5cvHgRgOjoaGxsbHBzcytQJu/4O+XYsWM4ORnXbU5PT8fZ2Zkff/zRdP1ulTcK0cPDw+y+fPjhh4wdO5bXXnvNtC1vlFxJ7rclbm5ufPnll2g0GkJCQujRowcbN25k+PDhFsu3a9eOzZs3M3bsWDZv3kyHDh24cOEC27dvp3v37mzevJnRo0ebyu/evRuDwWB6306fPp2wsDBGjhwJwJgxY9i9ezfTp0+nffv2hV5DR0dHs9GhCxcuRK1WM3/+fFQqFQCLFi3C1dWVzZs3mwVv86tfvz6TJk0CoEaNGnz55Zds3LiRTp06sWHDBk6dOkVkZCSVK1cG4KOPPjKtdQiwYsWKYtsdOXIka9euZfDgwdjY2NCkSROze1bafq1bt47z58+zefNm0/thypQpdOrUqcg6y4ME9YQQQlSYEo/USzeO0kOlATtXrHxD4AbUUF3lQEyKBPWEEEKIorx1rfB9Ko3569fPFVH2loleo45ZLlcGLVq0MP0CDtCyZUtmzJiBXq/n1KlTWFlZ0bx5c9N+Dw8PatWqxalTp0zbbGxsqF+/fpnaP3XqFA0aNDAF9ABat26NwWDgzJkzxQb1StrHW48JCAgwG30XGhqKq6srp06dKjTIcyuNRsOiRYv48MMP+ffff9m9ezdTpkxh6tSp7N27Fz8/P2bMmEFAQAAfffQRI0aM4JFHHuHGjRtMmTKFY8eO4ezsbDZKKS/IpdfrycrKol27dnzzzTeF9iF/UKVevXq0bNmSatWqsWTJEsaMGWPal/8eg3H02q3bblVcmY8++oiPPvrI9DojI4Pdu3ebjbL866+/eOSRRwqto1atWqxevRqAlJQUfvzxR/73v/+xadMmmjZtWmT/8sTExHDt2jU6dOhgcX9Z73edOnXQaG5+Tv38/AqsV5hfu3btWLBgAQaDgS1bttChQweqVKnCli1baNy4MWfPnjUbqff777/Ts2dP1Gq1qZ+3JmRp3bo1n3/+eZHnX69ePbPp3gcOHODcuXMFAsGZmZmcP3++0Hpu/Qz7+fkRExNj6luVKlVMAT0wflfkV9J2Fy5cSM2aNVGr1Rw/frzY92FR/Tpz5gwBAQFmAd7CpraXNwnqCSGEqDAlXlMvPdb408Ed1GrcA0LRH1Xhokrn6uVICCn6P9pCCCHEA83Gsfgyd7rsbci/tt6t2/P/Im5vb1/sL+ZFtVHYsSWps6R9LMm+kgS6LKlUqRKDBw9m8ODBfPjhh9SsWZN58+bx3nvvcfToURo1agRAYGAgGzZs4JFHHiE2NpakpCQGDRpkFpDJC3JpNBr8/f1LnSDC0dGRevXqmdYUzAt2REdHm63nFhMTYwqY+vr6kp2dTUJCgtlovZiYGNMoMkteeOEF+vXrZ3r99NNP8+STT/LEE0+YXZui2NjYmKbJgnGtvFWrVjFr1iyWLVtWklPG3r7opWTKer+tra3NXqtUqiKTwLRp04aUlBQOHjzItm3b+OCDD0wB3YYNG+Lt7U3t2rVN5VevXs3HH39coI3S9BEwC4iDcUpqkyZN+P777wuUzb/e4q2KOl9Ln7Nb+1XSdo8cOUJaWhpqtZro6Gj8/YteOqC4fpX1u+d2yZp6QgghKszVhHTT8yJH6qXlBfU8Aajq58ElxRuA9Ksn71j/hBBCCPHf2L17d4HXNWrUQKPREBoaSk5ODnv27DHtj4uL4+zZs2bBCUtsbGxKlDk1NDSUw4cPk5Z2M9HHjh07UKvVpkX/izu+tH0MDQ3l0qVLXL582bTt5MmTJCUlFXtexXFzc8PPz890PpUqVWLnzp2ma1GzZk3WrVvHTz/9xKpVq3jnnXfMjs8LcgUHB5cp42tWVhanTp0yBfCCg4Px9fU1S2qQnZ3Nli1bTAG7Jk2aYG1tbVYmKiqK48ePFxnUc3d3p3r16qaHvb093t7eBbaVlkajKTQxSF4ANP97y9nZmaCgIDZu3GjxmDt5v/PLW1fvyy+/RKVSERoayiOPPMKhQ4dYs2aN2Si98PBwIiMjzabC1q5dm+3bt5vVuXPnzlL3sXHjxoSHhxe4F9WrV0er1Zbp3PKu4bVrN0cf79q1q9TtxsfHExYWxsSJExk2bBhPP/10kUlgihMSEsKlS5fMkp7s27evzPWVhgT1hBBCVJi8kXqeTjbYWWsKL5g3/dbRGNSr5u3EOcX4F1dV3NnCjhJCCCHEPeLy5cuMGTOGM2fOsHz5cmbPnm1a46pGjRo89thjDB8+nO3bt3PkyBEGDRpEpUqVTIv7FyYoKIijR49y5swZYmNjC01a8fTTT2NnZ8fQoUM5fvw4mzZt4pVXXmHw4MHFTr0tax87duxI/fr1efrppzl48CB79+5lyJAhtG3btsRTPgG+/vprXnzxRdO6XidOnGD8+PGcOHGCXr16AfDqq69y7tw5BgwYwMGDBzlx4gRr1qwxJSwoLrtpccaNG8eWLVuIiIhgz5499O3bl+TkZIYOHQoYRzWNGjWKjz76iJUrV3L8+HHCwsJwcHBg4MCBgDEY9eyzzzJ27Fg2btzIoUOHGDRoEPXq1aNjx4631b/i5OTkEB0dTXR0NOHh4Xz44YecPHmy0Hvn7e2Nvb09f//9N9evXycpybj28+TJk5kxYwZffPEF4eHhHDx4kNmzZwPld79Lol27dixbtoy2bduiUqlwc3MjNDSUH3/80SxT7++//07Hjh1xcLi5lM3rr7/O4sWLmTdvHuHh4cycOZPffvuNcePGlaoPTz/9NJ6enjz22GNs27aNiIgItmzZwmuvvcaVK1fKdF4dO3akVq1aDBkyhCNHjrBt27YCCSlK0u4LL7xAQEAAb7/9NjNnzkRRlFKfX36dOnWiWrVqDB06lKNHj7Jjxw5Tv+70CD4J6gkhhKgQ2TkGYlKygBJkvvWsCY+MhbrGaRROtlbssW3J/JxubEvxLXTKixBCCCHuDUOGDCEjI4NmzZrx0ksv8corr5it67Vo0SKaNGlCz549admyJYqisHbt2gJT4m41fPhwatWqRdOmTfHy8mLHjh0Wyzk4OPDPP/8QHx/PQw89RN++fenQoQNffvllic+htH1UqVSsWrUKNzc32rRpQ8eOHalatSo//vhjidsE49pdqampvPDCC9SpU4e2bduye/duVq1aZRqV1aBBA3bu3ElKSgqdOnWiRYsWbN++nQ0bNjB//nwmTJjAr7/+Wqp287ty5QpPPfUUtWrV4oknnsDGxobdu3cTGBhoKvPGG28watQoRo4cSdOmTbl69Srr1q0zW/vss88+o0+fPvTr14/WrVvj4ODAH3/8Ybam3J1w4sQJ/Pz88PPzo2HDhvz000989dVXDBkyxGJ5KysrvvjiC77++mv8/f1Nwb+hQ4cya9Ys5s6dS506dejZs6dpCnJ53e+SaN++PXq93iyA17ZtW/R6fYH19G4NXPbp04fPP/+cTz/9lDp16vD111+zaNEis7pKwsHBga1bt1KlShWeeOIJateuzTPPPENGRkahCUiKo1arWblyJVlZWTRr1oznnnuOKVOmlKrdpUuXsnbtWr777jusrKxwcHDg+++/Z/78+axdu7ZM/dJoNKxatYrU1FQeeughnnvuOd5++20A7OzsylRnSakU+U2oQiUnJ6PVaklKSirzG1sIIe5FF+PSaPvpZgC61/Nl7tMFs60VZfCCPWwLN07L3TuxA97Od/YfTCGEEOJul5mZSUREBMHBwXf8F8ny1K5dOxo2bMisWbMquitCPDBiY2Px8/Pj8uXLhWZWFmW3Y8cOHn74Yc6dO0e1atUK7C+v72tJlCGEEKJClDjzbSGqezuZgnrnYlIlqCeEEEIIIUQJxcfHM3PmTAnolZOVK1fi5OREjRo1OHfuHK+99hqtW7e2GNArTxLUE0KIB4yiKGTo9DjYVOw/AVcSSxHUizsPKhU4+4O1MXhX3dsJLalUV10lMqoqrap53snuCiGEEEIIcd+oWbNmiZLAiJJJSUnhjTfe4PLly3h6etKxY0dmzJhxx9uVoJ4QQjxAFEWh39e7OHI5iTlPN6ZTaPELP98pZiP13ByKKAmsfgUu7oC+C6HukwBU93LiL9s38VfF8+0lf6D8soYJIYQQ4r+zefPmiu6CEELcliFDhhS6BuOdJIkyhBDiARIRm0anK1/yoeorNt2Sqv6/drU0I/XSjNNscbg5Gq+6txPnDMYMuIaYM+XePyGEEEIIIYS4m0lQTwghHiBJGTq6qvfSz2oLcXExFdoX85F6xQT10nODeo43g3oeTrZcsQoAwCk5vNz7J4QQQtyrJBeiEELc3crre1qCekII8QBJzszBWWUMpp1P0ZCenVNhfckbqedsa4XW3rrwggY9pMcbnzuYr5uX4lIDgCq6CG6kZN2RfgohhBD3Cmtr47+n6enpFdwTIYQQRcnOzgZAo9HcVj2ypp4QQjxAUjKycVOlAvCGZgUXbvSnbiXtf96PTJ2eISnf4msdy0aXvkUXzkgEcv+S5eButkvj3wCSIFR9kRPXkvCq5X1H+iuEEELcCzQaDa6ursTEGEfjOzg4oFKpKrhXQggh8jMYDNy4cQMHBwesrG4vLCdBPSGEeIBkpKWYnnfWHOD3qLgKCeqdi0mlveog1dRRXHUtJqiXN/XWzhU05iP6PIIbknNSjYcqhYsXz4ME9YoUEZvGwu0R7L+YwMvtq9Ojvl9Fd0kIIUQ58/X1BTAF9oQQQtx91Go1VapUue0/vEhQTwghHiCZqQlmr+MunYKmVf/zfpy5FM3jqmgAnAIbFV04reB6enlCArw4p1QiRHWZtIuHgJbl3NN7n6Io7L+YwLdbL7D+1HXylu/4aO0pCeoJIcR9SKVS4efnh7e3NzqdrqK7I4QQwgIbGxvU6ttfEe+uCeqFhYWxePHiiu6GEELc13LSzIN62VGngB7/eT/iI4+gVhmjS4/GLIHILAh62HJhZ194ZBzYOhXYVc3LiUmG7mj02VxI8uaFO9npe5DeoDD9629RXdnHen1vlHxL6V5NzCAmJRNvZ7sK7KEQQog7RaPR3PZaTUIIIe5ud22iDJ1Ox/jx46lXrx6Ojo74+/szZMgQrl27ZlYuKyuLV155BU9PTxwdHenduzdXrlwptv65c+cSHByMnZ0dTZo0Ydu2bWb7FUVh8uTJ+Pv7Y29vT7t27Thx4kS5tC2EEBVFn55k9tou8Wy51Z2alVPiLE5K1FHTc7+z38GZvwov7FENOrwDD48usMvGSs1R714s03did5w9Gdn6Uvf7fnbiWhLjr7/OG9Y/0Vl9AB8XWxoEuJr2H72cVPjBQgghhBBCiLtahQb1YmNjGTp0KFWqVGH58uVUr16dfv36kZ2dTXp6OgcPHuSdd97h4MGD/Pbbb5w9e5bevXub1TFq1ChWrlzJihUr2L59O6mpqfTs2RO9vvBf7H788UdGjRrFxIkTOXToEI888gjdunXj0qVLpjLTpk1j5syZfPnll+zbtw9fX186depESsrN9ajK0rYQQlSkE5oQQjMX8lVOLwC8MyPI1N3+d9YP63ax+cPuzJq/qNiyiqLgknwGgGhyp9Re3FHmtkP9XAAwKHDmekoxpR8sl+MzTM97BenZ9sajPP/IzenWR68kVkCvhBBCCCGEEOWhQoN6o0ePZs+ePXz33Xd0796db7/9luDgYAwGA1qtlvXr19OvXz9q1apFixYtmD17NgcOHDAF35KSkliwYAEzZsygY8eONGrUiGXLlnHs2DE2bNhQaLszZ87k2Wef5bnnnqN27drMmjWLgIAAvvrqK8D4C+esWbOYOHEiTzzxBHXr1mXJkiWkp6fzww8/3FbbQghRkVKyckjHjt2GUACqq65y4UbabdWZlKGj/o6R9NTsYfCVScSkZBZZ/kpCBjUMEQBsdXvcuDHqCGQmWz4g4SLEnYfsdIu7Q/1dqKu6QD/NJs5ciirzedyPriVm8Lf+IQACvFyxsVJTv/LNxChHrshIPSGEEEIIIe5VFRrUO3ToEIMHD6Zt27ZotVrat2/P1KlTsbOzvL5PUlISKpUKV1dXAA4cOIBOp6Nz586mMv7+/tStW5edO3darCM7O5sDBw6YHQPQuXNn0zERERFER0eblbG1taVt27amMmVpG4xTdpOTk80eQgjxX0nOyAHgrKEyAD6qBMKvJ95Wnb9vO0Bd1QUAPFXJHL4YV2T5k1HJ5KAhW9GQWqUjuAWBYoDLeywfsPF9mN0YDlgeBVjHX8s3NjOZZv0tSRcO3M6p3HeuJmYQrzgD4KkyjmKs7GaPu6MNYBypV9Ip00IIIYQQQoi7S4UG9Vq3bs2iRYtYs2ZNsWUzMzN58803GThwIC4uxqlW0dHR2NjY4ObmZlbWx8eH6Ohoi/XExsai1+vx8fEp9Ji8n8WVKW3bAB9//DFardb0CAgIKO7UhRCi3NRN3so0q695SH2Gh7M+p2HWN4THZBR/YCGycwxE7DH/Dr989kiRx5yKSqZ/9rvUyVqEb7V6EJibIKOwKbjpudlvHQpmvwUI8XPmpCHQ+OL68RL3/UEQE59EIsYEI1rFOCpPpVKZRuslpOu4klD2+y+EEEIIIYSoOBUa1Js5cyb9+/dn9OjRLF26lIYNGzJv3rwC5XQ6HQMGDMBgMDB37txi61UUBZVKVWSZW/dbOqYkZUrb9oQJE0hKSjI9Ll++XGR9QghRnoKzTtPPagsN1ee4onihoCY8puzr0P157BqL0lrSPmsGyYoDADmX9hV5zMlrxhHKOqyo7e8Kga2MOyILCeql5Y78c/CwuNvFzpqrttUBcE8+jd4gI8/yNIn5mZFWqwFw0N3MfFy/sqvp+RFZV08IIYQQQoh7UoUG9RwdHZkyZQrh4eH07t2bF198kTFjxvDNN9+Yyuh0Ovr160dERATr1683jdID8PX1JTs7m4SEBLN6Y2JiCoyyy+Pp6YlGoykwmi7/Mb6+vgDFlilt22Ccxuvi4mL2EEKI/4q1PhUAlb0WWyvjPwHhMallqktRFL7dalwbL0LxY5nSheOGIM7H64oMrJ28Zhwx5mCjIdDdAYJaG3dkJIA+p+ABeSP1HC0H9QAyPOoAUJNIIuNub43A+4l9xs1/x9Qu/qbnDfKtq3dU1tUTQgghhBDinlShQb38XF1dGTFiBN26dWPbtm3AzYBeeHg4GzZswMPD/Be6Jk2aYG1tzfr1603boqKiOH78OK1atbLYjo2NDU2aNDE7BmD9+vWmY4KDg/H19TUrk52dzZYtW0xlytK2EEJUJINBwS43qGewcaan22W+tP6cAYnfkpVT+gy4u8JjSI4+D0D9ylqOVn+Zntkf8VN2S84VEihMytAxMe1j1ti8xf/cz6NWq8A1EEafhFf2g8bK/ABFgbSip98C2AU0BKCW6gqnrhS9pt+DIiNbj0dODADfurwEXT8y7auXP1nG5cT/umtCCCGEEEKIclDh2W+3bNlCUlISer2eTZs2sWXLFpo0aUJOTg59+/Zl//79fP/99+j1eqKjo4mOjiY7OxsArVbLs88+y9ixY9m4cSOHDh1i0KBB1KtXj44dOxba7pgxY5g/fz4LFy7k1KlTjB49mkuXLvHCCy8Axmm3o0aN4qOPPmLlypUcP36csLAwHBwcGDhw4G21LYQQFSUtOwcXjBlk9TYu1NTq6anZwyOqI0TGWs4sW5RD675ji80oPrRawHOPVKVR4M01Rg9fTrB4zOmoZBqqz1NXHUklT1fjRpUKtJUsN5KVDAad8blj4UG9ysEhJCv22Kp0XI+QdfUAriVlUEllDIgqLubX19vZDj+tMSnV8atJMmVZCCGEEEKIe5BV8UXunCpVqjBmzBjCw8NJS0tj8+bNPPPMM7zyyitcvnyZ1auN6wA1bNjQ7LhNmzbRrl07AD777DOsrKzo168fGRkZdOjQgcWLF6PRaEzl27VrR1BQEIsXLwagf//+xMXF8f777xMVFUXdunVZu3YtgYGBpmPeeOMNMjIyGDlyJAkJCTRv3px169bh7OxsKlOStoUQ4m6RnJmDsyo3eGfngn2lOnAFqqqiWB+VQC1f56IryCf8egotY5ajUSvk2LrTva4vBy4aA3m2ZHMy8ho8VKXAcRcuXqS5Kh4A1+DGBStWFGOQL0/eKD1rR7C2L7Q/dSq7ckoJpLnqNLqrR4AeJT6X+9W1xAzq5Ab1rNwK3ov6lbVEJWWSlq0nIjaV6t4lv/9CCCGEEEKIilfhI/UOHDhAcnIygwcP5vLly0ybNg2NRkNQUBCKolh85AX0AOzs7Jg9ezZxcXGkp6fzxx9/FMgoGxkZaXYMwMiRI4mMjCQrK4sDBw7Qpk0bs/0qlYrJkycTFRVFZmYmW7ZsoW7dumZlStK2EELcLVIydTiTm+nUVotPQHXSFVtsVTncuHS6VHWt/2c1jdXnyFKssG75PFYaNfUqa3nTegXHbJ8l4Pxyy324eBiASIMPNQJ8b+7ISoHv+8H0mqDLvLndxgkeGQfNny+yP74udizU9GNQ9gRWJNYp1bncr67HxuGuMk6D7h8+Dj6tYVy3MJdZsozLsq6eEEIIIYQQ95q7Zk29O+X06dM4OzszZMiQiu6KEEJUqJTMHFxUxiQSGnstNXxcOKcYkydkR58scT1ZOXp8zv0IwJ88Qu+HGwHgYGOFlZMHNio9ldNOkJZVMOmFOuYEAKeVKoT45ksUZOMEUYchLQau7r+53dkHOrwDHScX2SeVSkVqpdZsN9QjMs2KmJTMIss/CFJiLhl/KvbY6lON1zbt5nqDDfIF9Y5KBlwhhBBCCCHuOXdNUC9vamx5CwkJ4dixY6jVd82pCiFEhUjO0NExazotMmeT6R5CFXcHzmMcXWwTH17iei7FpeOjGKd1pvi3wtnO2rRP79cEgAbq8xy7aj76S6c34Jl6BoAo+xrY2+RbqkClgsDcLLgXd5b63ABC/W4GCU9eSy5THfeT6ynZ/K5vxQZDYwz2uYmm8jIJc0uyDMmAK4QQQgghxD1HIl1CCPGASMnMIR07ovHAwcEBK42aeIdgANzTI9DpDSWqJyI2DSeVcRqvq6u72T7PGs3IUdT4qeIJP3fWbN+FG2nUIhKAbM/QghUH5mYOP7UaDLnZeJOjIO48ZFnOpptfqL8LXdV7ecNqBZGR50t0LvezoxmevKZ7mdG6l1A7eRk3pt0M6mntrQn2dATgZFQy2Tklu/9CCCGEEEKIu4ME9YQQ4gGRnKkzPc8bXZfjUYssxQoNOUTGppWonsi4NJwwTm91dzfPSFuvqj9nFePov7Tzu832nbqWxBklgAsGX+yrNCxYcWgfsHWB6GNwYLFx264vYXZj2Pxxsf0K9dPymtVvjLRaTebFAyU6l/vZtURj4NXZ1gqrvKBevpF6YEyWAZCdY+Ds9ZT/tH9CCCGEEEKI2yNBPSGEeEDkJF9nqtU3vGG1Ahc7Y/JzXdCjhGYt4mXda4THFD8aDiAiNp2v9T35VNcPj4BaZvuqeTlxXFUDAKcbh832nYxOYZTuZR7NnkmVYPPjjAd4waNvG59vfA9Sb0B67hpwDh7F9quqlyOnCQLAJvZEic7lfmUwKKQkxaFBj7+rPTjmXr+0W4N6rqbnR2RdPSGEEEIIIe4pEtQTQogHhJISTX+rzfTVbDWN1Kvm64oe49p24ddLGtRL5Wd9O+bo+1ApsLrZPo1aRYJbfQBq5JwmOulmwopTUTfXucu//p2Zh54D3/qQmQR7v74ZhHL0tFw+H2uNmjjnmgD4Z4STnl0wUceDIi4tmy9UMzljO5Re1vvAIff6pceZlWuQb129o5IBVwghhBBCiHuKBPWEEOIBoU9PBIzZUF3sjSP1avg4mfaHx5Rs+mVkbDoA7o42aO2tC+xXB7bkT30z1uhbcPhyAgCKonDpahSg4Olkg5ezreXK1RroOQu6ToW2b96cLupQfFAPwOBdD4D66vMcvZxYomPuR9cSM/BXxWKlMmCr9Qa3QPAKAQfzNRDr+GvRqFWAjNQTQgghhBDiXiNBPSGEeEAomcaRWMk44pI7Ui/Qw5Eh1htYY/MWdS8tK7aO9OwcYpNTaao6TTvtdVCUAmUCa9bnJd0ovtN35lBuYO3f45f5MmcSR22fY4DrKVQqVeGNVG4CLV4AjRWk5Y4sK8FIPQDvkFZkKVb4q+I5efxgiY65H11LSMNfFQ+ArWcgNH0GXtoDbV43K2dvo6GGtzGwGx6TSka2/j/vqxBCCCGEEKJsJKgnhBAPCFWmcSReimKPc+6aetYaNTXs06mrjsQl7SJ6Q8EgXX6Rsem4k8Ivtu8zPf4Vi2UaVnE1Pd99Po4vliwn8Ocu1FNHYk823kF1S97p5CvGnyVYUw+gRUgVDhqMU3BzwjeVvJ37THzMVWxVOgyKCq13lSLL1qtknIKrNyglHq0phBBCCCGEqHgS1BNCiAeEOjtvpJ4DTrZWpu32zm7Gn2RwJSG9yDoi49JwUhmzqmZbOYKFEXfeznZU0tpRU3WZCTHjeOnCi1RXXyNGcWW292T+17ltyTqceAkUg/F5CUfq+WrtOOXQGABt0imzjL8PkozYiwBcxw0/90LWL8xVy9fZ9PxsCddVFEIIIYQQQlQ8q+KLCCGEuB9odMZRWBlqJ6w0N/+m46x1gzhwIoPzN1IJ9HAstI6I2DScMAb1FGunQssNcj/Ni1lvmV6vUR5G1/kTRreqW/TU2/xcKkPLl8HGCey0xZfPFV+zP232N+GS4o37hXg6hfqU+Nj7hSHhMgDXFA/8Xe2MmYSX9ILsVBh1zCwYW90737qK12WknhBCCCGEEPcKGaknhBAPCBudcRRWlsY8GOfqZkye4EQm52PSiqwjIjYNR5Uxo63KzrnQcn5125KkOHBD0fKZ52Qaj/6Fx1vXK3lAD0Cthi5ToP2Ekh8DNKhdi0uKD6Bix7nYUh17v9CkXgWMQT0fFzuwcYQbpyDpsjGwl09Nn5v3MTxGRuoJIYQQQghxr5CRekII8YCYruvLbF0nArxcGZRvu5eHcWqrkyqd8zeKDupExqbhjnGKrrV94dM6e7Wsw2r1RpydnBlVt3Lpgnm3qXlVdzRqFXqDwvYHNKh3PMOT3/WtOGNbl14aNWgcwNoBdOmQFgu2NwN5flo7nGytSM3K4ayM1BNCCCGEEOKeIUE9IYR4AOj0BhJ0VoAHlRzczPb5euUG9XKn3xYlMi6NAIwj9TRFBPU0ahWPtwy9vU6XkYudNb18E+h6YxEkQHRSc3y1dhXSl4qQqdOzMr0+K6lPE/9899rBE5IuQXocuAebNqtUKqp7O3H4ciJXEjJIy8rB0Vb+eyCEEEIIIcTdTqbfCiHEAyAlM8f03MXOPGDj4OJOEk6k4MD5G4VPv03O1BGbmm2afotN4WvqVbQGgV501eyjvfoQu09fruju/KeikjJNz/1d7W/ucMzNIJxWcPRiTZ+b97K4wK4QQgghhBDi7iBBPSGEeACkZOp4WbOSiVbLqKqONt/pU4cXK/1K7+wpxKdlE5+WbbGOyFhjwO+IoRrrfJ6Duk/c6W6XWWjdxlxT3LFV5RB9fHO5138xNpXvN+4nJiWz+ML/sWuJGfgQjwY9lfIH9RxyMwinWwrqSQZcIYQQQggh7jUS1BNCiAdASmYOj2u2M9xqLT7q5AL7q3ndHKl1oZCRWhG5Qb1jSlUuhI6EOo/fmc6Wg0aB7uxW6gHgeHUbiqKUa/1/L3qfp7d14K9ZL951gb3ouAT22L3MGduhBDnqbu5wKHyknmTAFUIIIYQQ4t4jQT0hhHgAJGfocFEZE1xYOWgL7K/m5Wh6Xtj0y8jYdNPzIA9Hi2XuFjZWam54tQSgUc4RzpVjVled3sDy+FpcU9wZqv+NH76ZRqZOX271366UmEgAMrHBy9Pr5g73quBVG2wLTpuWDLhCCCGEEELceySoJ4QQD4DkzBxccrPWWjm6Ftjf68RoVtm8jS9xha6rFxFrDPYEqaIIUV+EjMQ71d1y4VS7IwB11ZHsOxlebvXeSMkiUvFlpf5hAF5M/pyvvvuh3EcDllV23CUArike+Ls53NzRbjy8tBseeq7AMXkZcAHJgCuEEEIIIcQ9QoJ6QgjxAEhLS8NWZZyKaePoVmC/a+IxGqov4KpK43whI7Ui4oxBwTetVhD0U2c4/sud63A5aFynFqcMAQAkn9xYbvVGJxun207P6cff+oewVeUw+OJbLFm7rdzauC1JVwC4pniaJ8ooQl4GXMCUAVcIIYQQQghxd5OgnhBCPAAy0xIBMCgq7J0KTr9V27kA4EgGF2ILjtRTFIWI3Gm5Hta5iTRsnAuUu5vU8nFmn6Yx+ww1OR6TjU5vKJd6rydl8qzmT960Ws5W70GcMATiqUqmxZ6R/HOw/EYElpVt6jUAYtReBTIdF0Uy4AohhBBCCHFvkaCeEEI8AHRp8QCkYoeLvW2B/SpbY4DOWZXBpfh0snLM14hLSNeRnGkcveVmlWXcaHt3B/XUahX7a4zif9mTWZPVkKNXEsul3uvJmTym2ckIqz/pUc2GA63mEaO4EqK+TOrvb1ToKDdFUXDKMmY3TrPzRaVS3dwZcxrmNIdv2lk8toa3ZMAVQgghhBDiXiJBPSGEeADkpCcBkIwjzpZGb+WOunMiA71B4VJcutnuiHyj91xUudleLSRcuNu0ruFper4vMqFc6oxOzsIR4zVw0boyuEtLlvu9wRFDVW7oHTkZVTC78H8lPi0bH+UGADonf/OdVjZw4zTcOGvx2Bo+kgFXCCGEEEKIe0nJ5+UIIYS4Z4WrgmmRORtblY5v7a0LFsgddeeYG7A7fyOVGvkyokbmC+o5kGF2zN0sL6urLdlExyWVS53XkzNxUBlHK7pqXVGpVHg07MljkUEAfBCVzENB7uXSVmldS8xkk6EhcWjJcg8x3+mQG+DUpYEuA6zN19uTDLhCCCGEEELcW2SknhBCPACSsiAaDy4qvpZH6tnmjdQzjtC7NQNuZNzN13aG3Od3+Zp6AH5ae761ns4ZuzD8ojaUS53RSZmmkXru7sakIyG+N6/F6eiKG+V2NTGDBfoevKZ7GbV/A/Odts6gzg3opsUWOFYy4AohhBBCCHFvkaCeEEI8AJIzdabnLnYWRurZu6K3uZlA49YMuDeTZyhocnKn5t4DI/W8nG1JxzgizSotqlzqvJ6UgT3GkXqOuUlHauYG9azJ4cq1a+XSTlmcyjf119/VznynSgWOuaP10m4UOFYy4AohhBBCCHFvkaCeEEI8AKon72Wi1TK6afbjYKMpWKD7p+hej2ChoQdQMPtp3vRbG7WCoc2b0OoVsCuYRfduo1GrSLbxAsA+4/pt16coCnEpqVirchOJ2DgCxkDpGKf1nLQdRveYb1EU5bbbKq2kdB0pO76huuoKahU0ruJWsFDeFNz0OIt1SAZcIYQQQggh7h0S1BNCiAdAtcxjDLdaS1vrE+YZUfOxs9ZQ2c04qu38jTRTYEpRFFNQz8/NCU378dD5Q7C2s1jP3SbL3hcA15wbBbL6llZqVg6q7HxTk60dTU/tXH2xVumpplzkamLGbbVTFj/9s4m3lAX8YzOe5+tAoIdjwUKOHsafFqbfgmTAFUIIIYQQ4l4iQT0hhHgAWOcYAzTZVkVnrK3mZdyfmpVDTIpxiumNlCzSso3BsCBLgaK7nMHZmAXWTxVPTHLWbdV1PTmTZBx5OGsW04IXgebm+oRW/vUAqKm6zJn/OANuTHImlQ7NwEplYIvSiME9H7Vc0KMGeIeCla3F3ZIBVwghhBBCiHuHBPWEEOI+pygKdnpjUC/HupB18CK2wpLevJAx37Qpb129I1duZo2t5aaC6ycg6eqd63A5U7tWAsBXFc+12xxBF52UhQE1VxRvFJ9Qs32eQXXIVjS4qDK4ejH8ttoprV/W/EF39W4MioqzdUZTydXecsEe02HkLqj7hMXdNSQDrhBCCCGEEPcMCeoJIcR9LivHgJNinDKqLyyol5EIEVuoqjtr2nT+RioxyZm8tfKYadsjjpfgq1bw3eN3ssvlyt6jCgDeJHA98fYCVdHJmabnvi7m049r+XtwXjGOCsy4cvS22imNyNg06p3+AoA1PMz/enQpc13+kgFXCCGEEEKIe4YE9YQQ4j6XnKHDGeMINUNhyS1yM9k6KemmTaejU3j5h0PcyJ2G27KqB60q25qVvxdovfzZqq/Hr/o23EhIKv6AIlxPzqSq6hpvWi2n6Y2VZvuqejlyFmMA0Tr25G21Uxp//P4jj6iPkq1oiH1oDB5OlqfWloRkwBVCCCGEEOLeIUE9IYS4zyVn5uCsMgbrVHYulgvZGrfbGm4G9Vbsu8zeyHgA/LR2zB7YCI0uN0mEbdFr891N/FwdGKKbwBs5I7icZiHzbylEJ2USrIriBas/CLpsHtSz1qiJdagOgFf6ebJzDLfVVkmcuJrIwxfnALBS3Yl+ndoUfUDkDviyGXzfr9AiNbydsCcTZ9IlA64QQgghhBB3MQnqCSHEfS4lU4cLxmCcutCResYgnTo7Ba29NQB6gzH7rbVGxdynG+PpZAvZuVMybe6hoJ725vpyUUm3t6be9eRMHDGOXLSyL3gNUr2bsFrfkm2Gunc8IJacqWP8z4dZq2/GVcUD/cPjTFNnC6VSQewZiCt8zb9aXvastZnA37bjOXXxWjn3WgghhBBCCFFeivnfvxBCiHtdcmYOL2RPQqtKo6dbbcuFcqfTqrJSqObpwMHLN6epTupVh0ZV3IwvsnKDeraFjPi7C3k526JWgbWSTVJC/G3VdT05k9oq47p61nYFpyDbVH2YV896AtAyOpnafnfmOmXq9Axfsp/j0WkcpycbXJ7krzaNiz/Qwdg30uIKLdLKK4Ng9XUAEvaugNbvl0eXhRBCCCGEEOVMRuoJIcR9LiVTx3XcOasEYO9YyFp4eWvkKXpqe91ck61vk8o83bzKzXJZuaPP7qHpt9YaNRPtf+OMXRi9EpfeVl3RyZk45I7UU1u4BiG+N6/v6eg7k2giR2/g80VLuRRhHG3n5mDNt8NaYGddgqnFjrlBvawkyMm2WKS2dbTpeeOEv4mMTbvtPgshhBBCCCHKnwT1hBDiPpeccTPZgYt9IQO0rR1BbQV2Wp5t5kVNHyd6NfDnwz51UalUN8uZRurdO4kyAAz27gC46mLKvNZdjt7AjZQsHMjNgGvjUKBMiJ8zagwEqaK4ceV8mftbGEVRmPPDL7x09U1+sZ1MdZs4Fg9rZkpuUSw7V1DlBv/SLY/WU8WdMz1vpj7Dhu27CpQ5dS2JOUuWsefM1dKeghBCCCGEEKKc3DVBvbCwsIrughBC3JeyU2KZaLWMkZrfcbaztlxIrYZ3YuHNS1QNCmLd6LbMfqpRwdFfVdtCy5ehSqs73/FyZHDyB8BXFU9MSmaZ6ohNzcaggKMqL6hXMJDm62LHB3bL2Gw7lsZRP5e5v4VZ9MdGnj43FidVJpcUX94b2IEGAa4lr0CtBgdjgJO0G5bL2Dih86pLguLE/7LeZcHJm+srAmRk6zmy4CVeiniJEz++g6IolusRQgghhBBC3FF3TVDPkt9++40uXbrg6emJSqXi8OHDZvsjIyNRqVQWHz//XPgvU5MnTy5Q3tfX16yMoihMnjwZf39/7O3tadeuHSdOnDArk5WVxSuvvIKnpyeOjo707t2bK1eulNv5CyFEeVBSohlutZbnrP7E2a6IpVTzj8grTEgP6DIFanQsvw7+B9SulQBjUC86qWxBvehk43H2udNvsXEsUEalUpHuUgOAyroIktJ1ZWrLkkydHu3+L/BUJXPUEExynyW0DqlU+ory1tVLj7W8v8lQrF/awbjgVexTQohKzmLHuZtll/+zhQH6PwDoq/+LhHI8RyGEEEIIIUTJVWhQLzY2lqFDh1KlShWWL19O9erV6devH9nZxnV+0tLSaN26NZ988onF4wMCAoiKijJ7vPfeezg6OtKtW7ci265Tp47ZcceOHTPbP23aNGbOnMmXX37Jvn378PX1pVOnTqSk3FwjadSoUaxcuZIVK1awfft2UlNT6dmzJ3q9/javjBBClB99eiIAKYoDLoWN1LvP2XsGAOBDAlGJZVsj7npuUO/LnMdZ3ng5NH3GYjnFpw4AtdSXOR2dXKa2LDl/IxVPJQGAo5UG0KVxjbJV5FUTvOuAquj/AvyvaWXT85/2XwbgamIGW/YeMG1PUhyJjJM194QQQgghhKgIFZr9dvTo0ezbt4/vvvuOWbNm8eqrr/L3339jMBjXOxo8eDBgHJFniUajKTDCbuXKlfTv3x8np6LXF7KysipwbB5FUZg1axYTJ07kiSeeAGDJkiX4+Pjwww8/MGLECJKSkliwYAHfffcdHTsaR6wsW7aMgIAANmzYQJcuXUp8HYQQ4k4yZBoDS8k4FD1Sb8N7cO0QtH0DAguZXhsfYVx7z8kbrGwtl7kLuXhVJkdRY6UykHzjKhBQ6jrygnqxaLHyrwcu/hbLaQPrQzj4qeLZcvkKzat63E7XTc7FpFJZlQGAh/tt1NmviGQhuf/+olbzaIgPdR0S+F/2KmxOKySlr+DjtafYogulfc4MNtmOxV8Vy4EbCTTOy44shBBCCCGE+M9U6Ei9Q4cOMXjwYNq2bYtWq6V9+/ZMnToVOzu7MtV34MABDh8+zLPPPlts2fDwcPz9/QkODmbAgAFcuHDBtC8iIoLo6Gg6d+5s2mZra0vbtm3ZuXOnqS2dTmdWxt/fn7p165rKWJKVlUVycrLZQwgh7qjMJMA4Uq/QNfUArh2EC5sg8XLhZZYPgFl14dLucu7kneXn6kgMrgBkxhVxfkXIP23XV1v4v1PVA/y4ohinuCZfPFKmtiw5H5OKE8agnpenZ7nVa+b6MfjIDxb1wMZKTa9aTgy1Ws8Tqs18snIna45GARCh+JKm2KJRKSRcLf+EIEIIIYQQQojiVWhQr3Xr1ixatIg1a9aUS30LFiygdu3atGpV9ALuzZs3Z+nSpfzzzz98++23REdH06pVK+LijJkAo6OjAfDx8TE7zsfHx7QvOjoaGxsb3NzcCi1jyccff4xWqzU9AgJKP1pECCFKQ5Vl/ONBSnEj9fIy2mYV8ceGrNTcsiXMtnqX8HWx4x/9Q/yU05Yr6QX/6StJsoe8NfUGajZSO/xriLMczKrp48xpg/G7XRVz8jZ6be7cjVR0WJGpWOPn5XX7FeotrIUXGw45mWAw7mvT5lGOG4KwVeXQ7fREeqt3AArDWgcTrlTivMGP+LhC1uYTQgghhBBC3FEVGtSbOXMm/fv3Z/To0SxdupSGDRsyb968MtWVkZHBDz/8UKJRet26dePJJ5+kXr16dOzYkT///BMwTrHNT3XLovGKohTYdqviykyYMIGkpCTT4/Llso0YEUKIktJkG4N0qSrHgtls87N1Mf7MTi28TFaKedl7hI+LHe/lDOWNnBEcyjBfeuHPo1E0+XAD7/1xopCjja7nC+p57v0U4i9YLOdsZ81Vm6oAaFPCyy077LmYVHpmf0T9nO/wCWlZ9oqSrsJXD8OMWjen2+aJO2f86WFcr6+2nws7nIzLSbTRHOMLmzm84HGEcZ1r0Sf7Azpkz2B7RpWy90UIIYQQQghRZhUa1HN0dGTKlCmEh4fTu3dvXnzxRcaMGcM333xT6rp++eUX0tPTGTJkSJn6Ua9ePcLDwwFMa+3dOuIuJibGNHrP19eX7OxsEhISCi1jia2tLS4uLmYPIYS4k6x1xiBdlqaY0XWmkXoplvcrCmTn7rO5t0bq2Vip8XQyrgF4a/bbzzeeJT4tm0U7IrmWmFFoHXnHOaoKz36bJ8r7Yb7I6cNKXXNm/PIvX8+ZxpIpw1ny0QgORsaUuv85egMRscaEFMGejlhZFRGcLY6TN8SFQ3pcwcBkXlDPs7ppk2vzgWQpxhGepw0BtH/8ORxtrfDT2gNwMS697H0RQgghhBBClFmFBvXyc3V1ZcSIEXTr1o1t27aV+vgFCxbQu3dvvMowJSkrK4tTp07h5+cHQHBwML6+vqxfv95UJjs7my1btpim9jZp0gRra2uzMlFRURw/frzY6b9CCPFf+lrfgy5Zn7Da/vGiC+YF6goL6ukyQMkd2XWPTb8F8NPaYUs2hpTr5OiN5xGbmsXZ6zdHJm49e6PQ42OSjcE8J3XxQT1NUCtm5vRjl6EOyYd/Z8SNKQzV/cTQ7BXs+vunUvf9Ynw6Or1xxF9179u89hpr8K1nfH7toPm+WOMft/C4GdTr8lAov6vak6lY80/gOJpXN/7hKtDDAYD4tGySMixM5RVCCCGEEELcURUa1Bs9ejRbtmwhKSkJvV7Ppk2b2LJlC02aNAEgPj6ew4cPc/KkcU2iM2fOcPjw4QIj6M6dO8fWrVt57rnnStTuuHHj2LJlCxEREezZs4e+ffuSnJzM0KFDAeO021GjRvHRRx+xcuVKjh8/TlhYGA4ODgwcOBAArVbLs88+y9ixY9m4cSOHDh1i0KBBpim9QghxNzAYFK5m2XNGqUKGg1/RhU0j9QqZfmsK9qnAuvCA1t2qm9V+ztiF8aX1LG6kGgNzeyPizcpsKSSol5aVQ0pWDgCO5I70K+IaPFz95h+YThoC2W+oyXXFFYDMpNKP1DsXk4o7yayw+YDRce+V+vgC/Bsbf17NF9RTlJvrBOZOvwVwdbCh0QsL+KfHTl4cenM0fDPHGFbZvMNKm3e5JKP1hBBCCCGE+M8VsWL6nVelShXGjBlDeHg4aWlpbN68mWeeeYZXXnkFgNWrVzNs2DBT+QEDBgAwadIkJk+ebNq+cOFCKlWqZJaJNr927doRFBTE4sWLAbhy5QpPPfUUsbGxeHl50aJFC3bv3k1gYKDpmDfeeIOMjAxGjhxJQkICzZs3Z926dTg7O5vKfPbZZ1hZWdGvXz8yMjLo0KEDixcvRqO5jWlRQghRjlKzc8hb0s3FvojMt5Ab1FOZkiQUkLfWno0TqO+agd4lZq31hevgRzxRSZn4ae3ZfSHOrMz2c7Hk6A1YaczPLy9JBijYKbnPixip17KaB/OHNCUyLo1q3g/h6/0iJ758Eh/9TrLTk0u0Rmt+52JScVWl0kJ9iuyUqyU+rlCVcoN6+UfqpV43Tq9WqcE92Kx4DV8tNXy1Zts8PTxpqD5PtqJh3Y0k6lU233+rpAwdJ64m0TTIHRure+/9I4QQQgghxN2mQoN6o0ePZvTo0QCEhYWZgm55wsLCCAsLK7aejz76iI8++qjQ/ZGRkWb1rFixotg6VSoVkydPNgse3srOzo7Zs2cze/bsYusTQoiKkJKZw1DNP7iQRrL6saILNwmDps9AYcEmG0do+XK59/G/YutuzEjro4rnaEI6VHErENRLyczh8OVEmga5m22/nrueni061OROQS4iqAfQMdR8fdVzts6QDpqcNJIydLg62JS47+diUnHCuN6fyta5mNIlkDdSL+oo6HNAYwU5WVC7t3GatZVtsVV4+QeRpVhjq9IRd+0CNCo8YUamTs+TX+3kXEwqA5tX4aPH693+OQghhBBCCPGAu+//VH769GmcnZ3LlEBDCCHudSmZOgZqNjLW+hcqqWKLLqzWFB7QA3D2hS5TjI97kNarEnpFhY1KT+KNq2br6eU/bUtTcPNG6jmQL8lGMUG9W1nbG4NxjqpMriQUnpDDknMxqTipjMdo7MshwZJHdWMG45wMuHHKuM0tEPp/B4N+KVEVgZ7OXFK8AciMOVdk2fnbLnAuxnitf9l/hcT07LL3XQghhBBCCAHcRUG9W0fplZeQkBCOHTuG+h6cKiaEELcrOSMHZ5VxvTMrh6KnR97vfNycuYErAJlxl83W03u8YSXTc0tBveu5STJScGBbh5XwzD/GIGgpxPh34EPd0/yrb8Tl+JKvQWcwKJy/kYpz7kg9dXmM1FOrIaQn1O0LqrItGVHFw4FIxTgaUZ1wodBy15Mzmbv5vOl1tt7AH0ejytSmEEIIIYQQ4iaJdAkhxH0sJVOHC8YAksbBrejCiZfhpyHwyzOW92cmQdKVwrPj3uX8tHZEKR4A5CRdNZt626uhP6F+xhFwx64mEZebSCPP9dyRejlY4VClEVRpUer21dXaMl/fgwNKLS4nlDyoF5WcSXq2HsfcoB7lEdQDePwr6LsAfEKNr9PjMS3AWAJOtlbEWPkD4JB6qdByn/5zhvRsvdm23w5eKX1/hRBCCCGEEGYkqCeEEPexlIwsnFTGgJSNYzEj9Qw6OPk7nP3H8v5jP8NndWDlC+Xcy/+Gj4sdUYpxrTxNyjVTUE+jVtE00I02NY0ZaxXFmDAjv+ikm9NufbV2ZWq/spuD6Xlppt+GXzcGUfOm35ZbUO9W8zvCR/5wZX+JD0l3MiaY8tZdIy03O3B+R68k8ssBYwDPxc6Kql7GKcuHLiVy/kYhWZaFEEIIIYQQJSJBPSGEuI9lJt+cYmrrVMxIPZvcYFF2KhgMBfdn5QZh7lRQ6Q6zs9Zw2KoBP+W0ZW+Ku2k9vbqVtDjbWdM2N6gHsOWM+RTcvDX1qqiu43P4Szj6U6nbr2KfTSNVOHVUEYVOv03PzsFgMB8tl7cWnTV69Gqb8r3+BgPcOAPZaZAQCbp0cPYr8eF6t+pEGnyIUVy5dMs5KYrC+3+cNL0e1bEmA5vdTKax8mA5ZPEVQgghhBDiASZBPSGEuI9lJBrXLktSHPDQFhMMyh8syrYwiipvm41TOfXuv7fD7THeyBnBusw6pm0tqhpH7zUJdMPRxri+3NbwG2bBtbzpt03to7Ha/CHs+brUbXvG7mGl7STet17MZQsj9Xadj6PxB+vpMmsrKZk60/a8EW0L9N05GnYWen1R6rYLNbsxzGkGp/4ARQ/WDqUK6ukCH6Fd9mdMzHmWi3FpZvv+PBbF/osJAFT1cmRwy0B6N/RHozZmJVl56GqBAKYQQgghhBCi5CSoJ4QQ9zFd0nUAbiiueDrZFF3YyhbU1sbnltbNy9t2j47UA+O6erdqUdW4zp6NlZpW1T0BiE3N5mRUMgB6g0JMinGNPV/73LXhSpn5FkBlawyGOpDJlYR0lFvWr/v5wGUydQbCY1L5cd9l0/a8kXoA1bydjEkuyotHdePPoz/mvq5WqvoDPW9eh8i4myP1MnV6Vq9ZRRf1PgDe7lEba40ab2c72tQwXuOriRnsjohDCCGEEEIIUTYS1BNCiPvYIUN1OmVN4zXdS3g52RZdWKWC3MCT5aBe3vTbe3eknq/WDluyqawyTq/NW08vj9kU3NwsuKejk9HnjijzsctdN64MQb286c1OZJKpMxCbmm22+3TUzWu+eGckeoOCoiiE5wb1fFxscbGzLn27RanU2Pjz/L/Gn3lBvhIK8ri5TuDF2JvBx78Pnueb7Al8bfMZT1bNoX0tb9O+JxpXNj3/9YBMwRVCCCGEEKKsJKgnhBD3sWvpKsKVypwkGHfHYkbqwc1ReJaCetm522zu3ZF6NW0TOWMXxkabsYBiWk8vT/6g3h9HrvHyDwfpNXu7aZuX7W0E9XKDoY65CS+u5MuAq9MbzEbkXUnIYMOp68SlZZOYbpyKO8H2F1j+FERsLX3bhfFvbP7ao0apDg90d+Rtq+/Yb/sCwZd+M20/eGC36fnwelaoVCrT606hPjjbWQHw1/Eo0rMLJtgQQgghhBBCFE+CekIIcR+LzZ026u5gg5WmBF/5ti6ACnRpBffdB9Nvnb0qYVBU2KpycCfFtJ5engB3B6rmTik9HZ3CmqNR5C375mxrRUPf3MBomUbq5Qb1MK7Pl39dvYjYNLL15slJFu2IMAv0NVDOwJm1kGaexOO2VLo1qFe6kXpaB2ucrBQ8VcnYp14EICopg+8ue3DAYAwQ1nLKNDvGzlpDz/rGdfvSs/X8ceQahy8nsnB7BGN+PMzMdWfI0VtI1CKEEEIIIYQwY1XRHRBCCHFnKIpCy7QN+GpiuGD/cMkOenY9WNlZXletRhfQVgbPmuXb0f+Qj5sLsWjxJhE/VbxpPb382tT04kLszaCmh6MNw1oHMbhFENodO4wbrcsS1DMeY6vKwZocswy4p3LX78tv94V4/jhyzfTaRZ0bBLR1KX3bhXHyBpfKkHwFbLXgW7fUVaQ4VoE08Mi+SqZOz++Hr6EoEKUYA6YqC0HIJxpXZvle47qB4389VmD/Q8HuPFLDq8B2IYQQQgghxE0S1BNCiPtUSlYOPdhOe+sjfGMdULKDbBwK39fihfLpWAXy09oTpbjjrUqkkjrObD29PGGtgthw6jp21hqGtgrif00qY2dtzIpLdm6wr0zTb2+OcDQmy7g5Uu909M3pzu1rebHpjDEQ9sPeSzePUfKCeuU8UrJSI2NQr81Y8KlTfPlbGNyCIQ2CVNe5FJ/O5v1HUGFNrKI1FkiLKXBM00A3qrg7cClfYDO/i3HpPFK6mcBCCCGEEEI8cCSoJ4QQ96kbKVl4qpKMLxxl1BMYs9+ewwO4QBO3dLP19PIEeTqyffyjlito9SrUeQJc/EvfuMaa9Nbj+WzzZXRYma2pl3+k3sQetdl/MYGUzBzyJ8i11ecFFMs5UUndJ8ErBIJKOJrzFjZe1eEKBKqimX80islJ7+Jkm8E1uxqQBaQWDOqpVCre6RnKhN+O4mRrRaMqbthZa1ieG8S8kTttXAghhBBCCFE4CeoJIcR9KjYli8DcoJ7GxbdkBx3+Ac7+DbV7Q72+5vuSo4wj1GydjZly70F21hoqB9WAS/voHVSGddvcAo2PMrLvOIEftv1Dul5vPlIvN/Oti50V1byceKpZFb7ZesG039XBGlV2Xvbhch6pV+dx46OM3PyrYziowlGVxckdK3lNfZksxZqLIb1B7w+VH7J4XKdQHzqFdjK9PnYl6WZQL1WCekIIIYQQQhRHEmUIIcR96kZKBh4YR4DZaksY1Lt+Ak7+DlFHzLcrCnxWBz4JgJSocu7pf6t2SCgAfsT+522rVCoquxmnOF9NyMBgUEhIyyY62ZhMIsTPBZVKxZCWgajzxU1redqhyrlD029vU4C3G9cwrk34ouFHAP5VGlO3y3DouxCaDC1RPV7OtqbnsTJSTwghhBBCiGJJUE8IIe5TKfE3sFbpAXBwL2FQLy9glJVivl2XAYrevMy9yr8xNBoE1QqZYluUIz/Cnm8g4WLZ2o6PoI3jRbSkkq03cD0l02w9vdq+xmtb2c2BzqE371mopwbUuVOF77LrH+ThwB5DCDv1oTRUnwcgolIvtA4FpzYXxcPJxvRcRuoJIYQQQghRPJl+K4QQ96nMROOIugTFCU9tCQNBhQX18qZ+Qtkyv95NglobH2WxazZEHwOPqmWbhvvb80y8tpdL6tH8Y3iIKwkZnI6+uZ5eiN/NzLbD21Tln5PRKArUqx4I/WIhJws0pQuW3WnujjZMVr9KE91+WmlOEqc4U7N17nRefQ6kxxmz7BYzZdtao8bNwZqEdJ2sqSeEEP9n777jm6rXB45/TtK96aZQ2jILtOy9EQQBRUVEFEEcKA4UnNefeuXe67juvfWKC3CBoiACMmTvUfZqgZbulu6RJuf3x0nSlq60TSktz/v16isl55tzTkqaJk+eIYQQQthAgnpCCNFMGbKSAUhTvfH3cK5htZklqFc2iAelQT4nD9BdwUnelum3dQ1sOmtDLtzRSmnPZeRb++kBRAaXBl97h7Xgu7v7k5ZXzIToltqVDjb+P15CiqIQ5u/GjSmbAFilG8JNkSFgKIQXgwEVnjoDrj417ivA09ka1FNVFaWJ9m4UQgghhBDiUpCgnhBCNFN7TB0YXfQqzpSwwLOWQb2LM/Us/77MSj/rzFAIWfHaFFsnN9tvV2yeWOtUx6CeeXKtu6L10Cubqaco0DGo/M93UHv/uh3nEmvn40iv1BMAZHe4CScHHeCi3d/iHMhLtTmodzw5l6ISE7lFJZVOJxZCCCGEEEJoruB0CyGEaN7O58NJtTVHCMfX3anmGwA41VB+aw5KNXkfD4b3e0PC7trdzpKpV9egnjko6oEW1ItLz+NYsvazDvN1w925is/a4nfBolth7Yt1O24D6xniggrsN7Vl8LDSibZ4BGiXuSk27SegTEaplOAKIYQQQghRPcnUE0KIZsoyQdTX3Qm9zsYyxhoz9ZpJUM+7NaSfhKxztt9GVcsEN+uaqafdzs2cqbf5ZBqFBhMAncv006vgwhk4tqLi/8tl4pah0fxo/I6QQD9Gt/Yp3eARBBmnIc+2oJ7/RUG9tgHN5PEmhBBCCCFEA5CgnhBCNEOqqjI4bw0t9SmcdBlq+w1b9YJ/nKuYkefZEnpOB+9Q+55oY7Hcj6x4229jKABU7ft6lt/6OhRBCSRnl2ajRQZXE9S7zMufXZ30zBjTv+IGd0umXqpN+wkoUyYuE3CFEEIIIYSongT1hBCiGcouKGGCspkRDvv51LEWU1r1jpVPVw3pAde/b7fza3Q+bbTLC2dtv40hv/R7x1r04SvLnOkY4GTAXIFrFdmymoBdURMtf/YI1C5tzNQrG9RLk/JbIYQQQgghqiVBPSGEaIZSc4sIUC5o/7AEVkQp79baZW0y9Zw94Y7ftYw9nb5ux20zEIY9yanjnpBdflPnJpypVyV382PP1p56kqknhBBCCCGEzSSoJ4QQzVBqThFtlSwA9F5Btt9QVWHZHC2INPFdcPHWri/K0bY5eYCuGcxYspbf1qKnnoMzRNSilLkyYYMgbBAZBYchLtZ6tbuTntYtXKu+XVMN6rXsDlE3Qes+Ni2/uKeeEEIIIYQQomrN4J2ZEEKIi6XlFOBnTgVz9gm2/YaKAgd/hsO/QEFm6fXrXoL/hsLaf9v3RBtL2Uw9Vb3kh2/donz5bqdgT3TVDTMpMqf1NbWgXqdrYPL/oNcMm5aXy9SToJ4QQgghhBDVkkw9IYRohnIyk3FQtKmqHr61COqBFjgy5JeftGr5vqn1dKuKVyvocTv4hILRAA5ONd8m8wycWKUFBDuNq9txDQVw4RyR+vLlqJHVTb4FKM7TLptaUK+WWrhpk5qNJpW03GK77ddoUm2fAC2EEEIIIUQTIUE9IYRohgozEwHIUD3w86plIM7JA0iuPKjnXEPwqalwcIIbPqjdbZJiYMXj0Lpv3YN6iQfgf2Po6RUGvGy9unNwDcG6yf+DGz6q2zEbm7EE8tPAI0jLBK2GXqfg5+5ESk6RXTL1VFVl7vf7WHskhTdv6cHVXWpRii6EEEIIIcRlTspvhRCiGSrJTgYgTfUu16fMJpZsMMvEVYBi8/fOzSRTry4s02+d3Ou+D/PPz7Ekr9zVnWvK1FMUcHTRvpqS4nz4jz+80al8kLgalsdrWm4RJlP9SqPjMwv4dd95copK+N+m2JpvIIQQQgghRBMiQT0hhGiGdpkiGVX0GvMMD5brU2YTa1CvzHjW5lZ+C1opbNpJuGDjsAxLYLM+PwNzQFApzsPfo7Tkt2NNmXpNlZNbaRA0L9Wmm1geryUmlawCQ70OfyypNJB4IiW3mpVCCCGEEEI0PRLUE0KIZigxT+WU2oojhNPCzYZ+cWVZg3ply29zy29rDta/DO/3hq3v27be0tfO0a36ddVxMv/8SgoY2s4HgH4Rvni5OFZ/u2VzYOlsuHC27sduLO4B2mVuSvXrzMoNy8itXwnu8ZTSx3BabhGZefbr0yeEEEIIIURjk6CeEEI0Q2nmYIifh3PtBwRUGtTLKb+tOfAO1S5tztSzX/ktwMvXtmPBnX35bEafmm93+FfYvwhKmuBEWI9A7TKvDkG9evbVO5FcPjvveLJtJcBCCCGEEEI0BTIoQwghmhlVVRmSt5oQfQqnXIbVfgfjXoXxr5UvM40cDzmJpQGa5sAS1MuqbfltPYJ6Ds6gcwSTARdTPiM6ta75NqratIOqtczUK9sDsr5BvYuDeMdTcunf1q9e+xRCCCGEEOJyIUE9IYRoZrIKDFyrbGa4wwE+dYyo/Q5cfSpeN+6Vep/XZcentkE9c/ltfYJ6ltsXXijdX00M+aCazLdtgj0NrZl6teupB6UZp3VhNKmcvKiP3knJ1BNCCCGEEM2IBPWEEKKZScstIkDJ0v5R38y6wmxwqWEya1Plbc6SK8jUegbWNNm33yxoPxr82tXvuP1ng6kEXLxtW28tg1bqH1BsDO7mx6CtPfXslKl3LiOfohJTueuOJ8uwDCGEEEII0XxIUE8IIZqZlJwi2puDeg6eQbXfwZmtsH8heATBgR+gy/Uw6nnQN7M/GS7e4OwNRVmQFQ+BkdWvD+ysfdXXyKdrt946pMQLlFr2R7wchPSA6JuhtQ29A7FfT73K+uedSJFMPSGEEEII0Xw0s3doQggh0nIK6I8W1HPyaVn7HWTGwZ6vS/99ZBkMfazystymzrs1pNgY1GssRdnaZVPspwfQaZz2ZaNymXr1KL89Uab0VlG01oRpucVk5BXj617LidBCCCGEEEJchmT6rRBCNDM5GSnoFRWTquDpG1z7HZQNHjm4wi3fNs+AHkCv6TD8H+DTpua1R36D/YshO7F+x8xLh7QTkJ9h2/qmPCSjDrxcHXDSay9P7JWp1z/C1/r9CemrJ4QQQgghmonLJqg3c+bMxj4FIYRoFooyzwOQgSd+XnXowVa2h95170BwtJ3O7DI04H6tHDagY81r178CS++DlMP1O+ZvD8P7feDQEtvWRwyDZ5Lhrj/qd9zGZCyBnCSbliqKYi3Brc+gDEv/PAedwpgupcHt4ynSV08IIYQQQjQPl01QrzJLlixh7Nix+Pv7oygK+/btq7BmxIgRKIpS7mvq1Kk17vvDDz8kIiICFxcXevfuzcaNG8ttV1WV+fPnExISgqurKyNGjODQoUPl1hQVFTFnzhz8/f1xd3dn4sSJxMfH1+s+CyFEfZVkJwOQpnqX609ms9b9oNN4uPrf0P0WO59dE1ZsDgbVdwKt5fa2Tr9VFHB0AdcW9TtuYynKhf/4wRudSvsD1sDf/LhNzyumxGiqYXVFRpPKqVTtWOH+7nQNKQ1US6aeEEIIIYRoLho1qJeWlsYdd9xBmzZtWLRoEe3bt2fKlCkUFxcDkJeXx+DBg/nvf/9b7X5mzZpFYmKi9euTTz6pdv3333/P3LlzeeaZZ9i7dy9Dhw5l3LhxnD171rrm1Vdf5c033+T9999n586dBAcHc/XVV5OTU/pmYO7cuSxdupTFixezadMmcnNzufbaazEajfX4qQghRP3sVCMZVfQa8wwP4O9Rh95hji5w6yIY/Ij9T+5yYzRA2klI2FPzWksQzsmtfse0TNm1McDV5Dm5a2XcAHm2TsDVHreqChl5xbU+5Jn0PIrNk287BHrQMai0dLmyARpCCCGEEEI0RY0a1Js3bx7bt2/nm2++Yfz48Xz22WdERERgMmkvxKdPn84///lPRo8eXe1+3NzcCA4Otn55e3tXu/7NN9/k7rvv5p577qFz5868/fbbhIaG8tFHHwFalt7bb7/NM888w6RJk4iKiuKrr74iPz+fhQsXApCVlcUXX3zBG2+8wejRo+nZsyfffvstMTExrFmzpspjFxUVkZ2dXe5LCCHsKTFP5ZTaiuNKOC3cZCBAtc7vhfd7ww8zal5ryNcunepQ0lxWbTP1Di+DpbO1ScRNkaKAR4D2fW6qTTcpNwG3DiW4ltJbgA5BnrRwd8LfPIDjpJTfCiGEEEKIZqJRg3p79+5l+vTpDB8+HG9vb0aOHMkrr7yCi4tLrfbz3Xff4e/vT9euXXn88cfLZdNdrLi4mN27dzNmzJhy148ZM4YtW7YAEBsbS1JSUrk1zs7ODB8+3Lpm9+7dGAyGcmtCQkKIioqyrqnMyy+/jLe3t/UrNDS0VvdVCCFqYhku4OfuhE6nNPLZXOa8zc/B2ee1vm9VMZnKZOrVs/zWkqlXbGPG2Pk9sH+RFoBsqtwDtUubM/XKBPXqMCyjbIltxyDt590hULu0TMAVQgghhBCiqWvUoN7gwYP58ssv+f333+u8j2nTprFo0SLWr1/Pc889x88//8ykSZOqXJ+WlobRaCQoKKjc9UFBQSQlaU28LZc1rXFycqJFixZVrqnM008/TVZWlvXr3Llztt9ZIYSogcmkMjx/NXMdfqKfi/T4rJFHEOgcQTVCTjVTbUsKAFX73rGe5bdOtSy/bQ7Tbz3MQb1cG4N6nvUL6pUdhmEpvbUE90BKcIUQQgghRPPg0JgHf/PNN3nppZeYN28ep06dYt++fcyePZvZs2fbvI9Zs2ZZv4+KiqJDhw706dOHPXv20KtXrypvpyjls1dUVa1wnS1rLlbTGmdnZ5yd69C4XgghbJBVYOBaZTPD9DF84ti+sU/n8qfTgXcryIyDrHPgU0X2dNlSWXsF9Wwtv7UE/5pDUC/PtvJbf4/6ld9aMvUcdArhflq5dIcyffVOJOcwoK1frfcrhBBCCCHE5aRRM/Xc3d158cUXOXHiBBMnTuT+++/n0Ucf5dNPP63zPnv16oWjoyMnTpyodLu/vz96vb5CNl1KSoo1My84OBigxjXFxcVkZmZWuUYIIS611NwiApQsABRLIEVUz1KCm1VNZqOzJ0xdCDd9oQUC6yOoC/SfDZETbFtvydSrb9lvY3Kve6ZeWk7tSmVLjCZOp2oB0wh/d5wctP+vssMyTkhfPSGEEEII0Qw0alCvLB8fH+677z7GjRvHxo0b67yfQ4cOYTAYaNmyZaXbnZyc6N27N6tXry53/erVqxk0aBAAERERBAcHl1tTXFzMhg0brGt69+6No6NjuTWJiYkcPHjQukYIIS61tJwi/M1BPb2XBPVsYgnqXThb9RpHVy0IFz25/sdr1RvGvQK977BtfZF5oFJTztQL6QnRU6B1H5uW12dQRlx6PsVGbeBW2UCepaceSPmtEEIIIYRoHhq1/HbevHnccMMN9OjRA6PRyLp169iwYQPPPvssABkZGZw9e5bz588DcOzYMQDrlNtTp07x3XffMX78ePz9/Tl8+DCPPfYYPXv2ZPDgwVUe99FHH2X69On06dOHgQMH8umnn3L27Flr2a+iKMydO5eXXnqJDh060KFDB1566SXc3Ny47bbbAPD29ubuu+/msccew8/PD19fXx5//HGio6NrnNYrhBANJTUnn/5oQSAXn8o/3BAXsZTcZl2mPU6tPfW8Gvc86iNyvPZlo3LltzmFtTpU2SEZHcr00bNMwE3LLeJE8qXP1Cs0GNkRm0HvsBa4Ozfqyy8hhBBCCNFMNOqryjZt2vDoo49y4sQJ8vLyWL9+PXfddRdz5swBYNmyZdx5553W9VOnTgXg+eefZ/78+Tg5OfHXX3/xzjvvkJubS2hoKBMmTOD5559Hr9dbbzdixAjCw8NZsGABALfccgvp6en8+9//JjExkaioKFasWEFYWJj1Nk8++SQFBQU88MADZGZm0r9/f1atWoWnZ+mn/m+99RYODg5MmTKFgoICRo0axYIFC8odWwghLqXc9GT0iopJVfD0lVYANmndD4bMgy7XV70mKwHObAbPYIgYVr/jmYxabzlDAfhG1Ly+uBn01Ksld2cH3Jz05BcbScutXfnt8eSKQzJK/+1BWm4R6XnFpOcW4edxaXrclhhN3PrZNvaevcCwjgF8fVe/S3JcIYQQQgjRvCmqqqqNfRIAM2fOtAbd7C08PJz58+czc+bMBtl/fWRnZ+Pt7U1WVhZeXk04C0MIcVn430/LuOvgdFJVL45P38vg9v6NfUrNw5Hf4ftpENof7l5Vv31lnIZ3e4KjOzxzvub1xflatp6rDzg04UFLRgPkpYGXbRmkw19bx5n0fLxdHdn//BibD/Pgwj0sP6BNMl7z6DDaB5YG9uYvO8SCLXEALL53wCUblvH5xtO8sPyI9d+/PjiY7qE+l+TYQgghhBCi+bpseuo1lKNHj+Lp6cmMGTMa+1SEEKLB5WVowYw01btcXzJRT5ZJtfWdfAvgZA4yGfLAZLJhvRt4BjXtgF5hNvzHH96M1DIUbRBgzqLLKjBQVGK0+VCW8ltHvUKYefKtRdly3BOXqK9efGY+b6w6Xu66r8yBRSGEEEIIIerjsgnqNVSWXmRkJDExMejqO61QCCEuc0v3xvP+6UCuKnqdp0wPEeLj2tin1HQU58PxVbB/ceXbDeagnpN75dtrw7nMFFvLfps7Z0/Qm4OSdZmAa2MJrsFoIjZN+5m29ffAUV/+b3/Zctzjl6Cvnqqq/PPXQxQYygclfz+QSFotB4AIIYQQQghxMYl0CSFEM7DlVBpP/nSAIpw4rYZw7Zir8ZBm/LZL2A0Lb4ZVz1WePWfJ1HPyqLitthxcQDH/+S2qIbBUlANL74cVT9qW1Xe5UhTwME9jzku16SZlh2Wk5dgWAItLy8PHmEkQGeWy8izKTsA9kdLwmXp/HExi7VEtiBno6cy0/m0AKDaaWLS9mmnLQgghhBBC2ECCekII0cSdSMom8ZtZTFA3AjCtfxtmDW3byGfVxIT21wJ2eSmQHFNxuzWoZ4fyW0UpLcEtriGoV5AJ+xfCnq+gqWecuwdol3XI1Eu1Iai3Jy6Nv/73LJucH2aV85N08XessMbHzcm632NJOZhMDddWOLvQwPxlh6z/nj+xK/ePaIdO0f797fYzGIxNOFArhBBCCCEaXRN/hyCEEFe2lJxCNn32KDexltccP2FKOyP/mtgVRVEa+9SaFgen0qm2J9dU3F5sx/JbKC3BrSmoZ8nks0eGYGOzZOplJ9i0vFxQr5pS1UKDkU+W/AH/G8vsogU4KyV4K/lMCq+8ZLeHeUBFZr6B3WczbTv3Onh15VFSzMHIUZGBjIsKpnULN0Z31qZSJ2cX8eehpAY7vrgyFZeYSM4ubOzTEEIIIcQlIkE9IYRookqMJr78/D3uNP4IwIfuD/D8jPE46OWpvU7aj9IuT/5VcZs9y2/L7seW8lvQetI1dS27a5fHV9q0PMCj5ky90yk5fPn648zcP51eupNkq6787HoTJ+8+QnDH3pXeZnx0sPV7y5Rce1t9OJlvt2nltW5Oev59Q5Q10D5zULh13YLNcQ1yfHHlSbhQwKsrjzLvpTc48No4vl61rbFPSQghhBCXgDRcEkKIJup/m2PpkLEe9LBEN5aps5/FXfro1V07c1Dv3HZtWquLV+m2nrdDaD8I6mqfY0VNgvx08Aiqfl1zCup1uwU2vAKn1kJOEngGV7u8bKbeumMp3D+iXbnBFzmFBlZ//gz3F38FCvxt6k7swJeYNmZQtYHt0Z2DcHLQUVxiYkVMIs9d2wW9rvrM1kKDkYQLBbTxdaswfONiJ1NymPf9Puu/Hx/TiVZlhtYMbOdHxyAPjifnsutMJgcTsohq5V3tPoUoq9BgJDm7kKSsQhKzClkek8hfR5IxqRDn8gLoYf/WR1Cv3iZZ20IIIUQzJ+/+hBCiCYpNy+ONVcf5WNGCPr0GjSLIy6WRz6qJ840Av/aQfhJi/4bO15Zua9VL+7KXEf+o/HqTeUqqTq9dFmVrl85ela9vSvzaQZ+7tcCoDWXMXQIcaOXjSsKFApRzO/jsp3QeuGUioE2VfernA2TmtmaSoxdLXW5kxJ0vMCy45p+Tp4sjwzsGsPpwMik5ReyKy6B/W78q15tMKnct2MmWU+l08SpiWr9WTBzSC0+Xij37sgoMzPp6N7lFJQBM6NaSOweHl1ujKAozBobz7C8HAfhqSxyv3dy9xvMWIiY+iwcW7uZcRkGNa7urRzmVlke7gGZQui+EEEKIKkmNlhBCNDEmk8pTPx2gqMREC0Ur3wxvHdrIZ9VMtB+tXZ7bfumPnZsCr7WD728vva45ZeoBXPsm9L275vujqjh+NYEVAe9zt+OfLHb6D+MOPc7yHUcA+N/mOFbEJLHV1JUblLcZO+slOloCemkn4PdHYeX/VX0a3Vpav18eU30J7oYTqWw5lQ7Ajfk/MnnjeFa/fDOf/ryC48k5qKo2bMNoUpm7eC+xaVqpdmSwJ69N7lZpptSNPVsR5pLHBqe5tI15k6wCQ/U/D2E3x5NzeOfLb1m0pmmVp5pMKv+3NKbKgF6wlwuPXt0Rg1Ka4br94PFLdXpCCCGEaCSSqSeEEE3Md9vPsCMuAwB/fR6ogGuLxj2p5qL/bC2bzL9D+etPrYWSImjdF9z9638cQ6GWhad3Alcf8zHWadNuj62A+N3QuneZoN4Vlm1zcg0k7sPb0Y0eIxaT8vdyInTJxC5/kIUln/Ltio2ANnhj/pQhhPmX+fkUZcOuL7TS5mteqnT3o8qV4Cbx/HVdqyzB/WnDHhRMqCi0VxJwVkqYxDqIWcfB/eEs1EeR3XIgcW7dWHdM+//ycXPksxl9cHOq/GWWu7MDT7fcQ1hiCvfzC1sS3mBQezs8rkS1ftx1jm9+Xc4y/VOY4hQOdj7TZEqfl8ckEpOQBUCgpzN9I3wJ9nIh2MuF9oEeDO3gj4Neh3G7ExRrPShPHz8II+2YYSyEEEKIy45k6gkhRBMSn5nPf/84av232nUSdJsK3q0b8ayaEd8ICOgIZbOrjCWw4klYNBWSDtjnOH8+Da93gG0flV6Xdqz0++3m6/veA48dg7Ev2+e4l4OCTNjxGWz/tPLtqgobXtW+73MX1141jCXt/0uR6shVym56rryBZY5PM1a3k9nD23F1l4v6Evq20y5zk6scROLh7MDITgEApOUWsSM2o9J1RxOzuDf+aVY7PcnVPom0emg5H7b9iJWmfphUhShdHNPU37n//DPMPP4gAHqdwge39SLU163aH0Owd+n22LNnql0r6ie/uIRHf9jHEz8doKPpNAA6RWXH7t2NfGa2MRhNvL6q9PnhtZu788FtvXju2i7MGtaWkZGB1j6SuqdiucXhLdoVfsN38YEUlRgb67SFEEIIcQlIUE8IIZoIk0nl6SUx5BVrb9Ju7deG0Mkvw6RPwEfKb+2uOF8LPL3XE9JPaNe5+tpn35bpt8Vlgk6h/aFlD+37Q0shOxEcXbSBEh4B9jnu5eDcDljxOPz9KhgrKTuN2wjxO0DvDIMeRlEU7p06iY89taBZZ905PJUCIoPceHxMx4q3d/UBN3OPvIzTVZ7GhG4h1u+Xx5yvdM3aVb/SXXeaUCWVq/r3omOQJw/MuI3eT/zOoqGr+Nj/GX5QR3PK1JJtps4A/N/4zgy2Iesu2LXE+n3e2f01rhd1czo1l4nvb2bJngQA/jT2JVfV+o+WHP2jMU/NZot3nOVMej4Ag9r5MaxD1Y8vRe9Iq469MKKnwGBkd1zmpTpNIYQQQjQCCeoJIUQT8fqqY2w8kQZAS28Xnh4f2chn1EwlH4ZPR8BLLeGPJ+DCWXDzh6v/DcHd7HOMyoJ6HcfCfRugzUAwlcDOz+1zrMtNu6vAPQDyUuHkXxW3//2adtn7DvDUsvBcHPVMvucfLFbGAfC17gam3fVw1VNufdtql9UE9UZFBuLsoN1+5cEkSoymcttTc4pof+prAH5ThjFxUOkwiwBPZ6aN7sfsh55k0j9/JO/ebahjXuTDab2466LBGFXx9fEpPVZ6uk23EbX3yOJ9nEzRfs/cnfS8MHUQi9y1vpWdc7aQnF3YmKdXo7yiEt7564T1309dE1njRNvhHbUPARRMbD6W0KDnJ4QQQojGVeeg3jfffMPgwYMJCQnhzBmtbOTtt9/m119/tdvJCSGE0CzcfpYP158CQKfAKzd1w8vBpGVzlRQ18tk1MzoHOL9P+94nDMa/DvMOwuBHQGenz8IsPfIqKw8d+CB0nggdr9ECe388Bed22ue4lwO9I0TfrH2/f1H5bWe3a5OHdY4w6OFym1r5uNL3gc/5pM8Khj/4IYGe1Ux7tpTgZpyqcom7swNXRWp9+dJyiyuU4C5bt5nRyi5tN9H34O5ceX88B72Obq19uGtYB8ZHt6wx4GLhNPxRRrn9THjhQr65EIXRpNp0O1He8eQcTqVWXmZ9KjXX2oeuja8bv80ZwvU9WqGP1ILD/XVH+Dum6sDv5eCLTbGk5RYDMCG6Jd1DfapenLgffrqb0enfca/+N3Y534/PwQWX5DyFEEII0Tjq9O7ko48+4tFHH2X8+PFcuHABo1ErBfPx8eHtt9+25/kJIcQVb92xFJ779aD13/MndmVYxwDtDdybkfB+n0Y8u2YooCPc9j3c8i3M2QP9ZoGjq32PYc3U0yalkpOs/X8aCqHzdXDLNxDaF479Ads/Li3/bS6636pdHluh9dizsGQn9ri10pLydgEe3HftYML83KvfvyVTL736gM2EMlNwfy8zBbfQYMRt72foFJUNpu5cO2pk9ccDWHo/vNdbC0raqEPLFubjmTiTnmfz7YRm95kMxrz1N2Pf+psD8RcqbF95MMn6/YyBYbQN8IDDv3KNg9ZLz0kxkrb/8i3BTc8t4pMNWmBar1N4fGynGm5wEg7+hPvZ9fh7e+Cn5NApdyepOfLBjxBCCNFc1Smo99577/HZZ5/xzDPPoNfrrdf36dOHmJgYu52cEEJc6Q4mZPHgd3usWTyzhkYwY2C4tjHfnFlkrz5volTHsVpwTd9AQ+KdLyq/PbIMPhkGP95Rfp1l+q1TM5t+GxwNgV3BWAwLp5ZeP/E9LTNyyLz67d/PnKmXl1rtsqsiA3Fx1NFTOUHqgVX8uuskKdmFLN9xlOtMawGIaTOdEB8bgrrZCVpQJTux5rVmnYI9zd+pHEvMtvl2QvPbfu1nXWJSWbA5rsL2skG9sV2DtW/2LyZkx0vE0ooPSybyW6IPhYbLb5hEXlEJz/5y0NpDdWrfUCL8awhmW/4muPmib38VAP10R9ly7FxDnqoQQgghGlGdgnqxsbH07NmzwvXOzs7k5cknzUIIUV8mk8qfh5K4a8FO8s1v6sZHB/P0uM6liywZTm4S1GtyLu6pl2zOxAzqWrom/RSc26597+xJs6IoWjYewLltkBWvfe/oomVGWjLt6qrTOHjqDEz7odplbk4O3NxO5Sen+Xym/hvTrw/T76W/2P3HAjyUQo6aQhkyZrJtx3Q3Dy/IT6t5rarCe7256/Cd/OL0LHuc7yMx7pBtxxFW206X9iJccTCR7MLSwSvxmfnW0tuoVl6l04jNga9NoffxaslUDhuC2Xrq8uppuOt0Cre//St/mIOSro56HhnVoeYbWoN6fnTp1o/zqi8uioHE/Wsb8GyFEEII0ZjqlIIQERHBvn37CAsLK3f9H3/8QZcuXexyYkIIcSUqNBhZsieBzzee5nRa6YckvcNa8OaUHuh0Zfp1FUimXpPl0wa6TS3NKEs2B3TKBvW2flD6vbPXpTu3S6XffYCiTau1dyaiUw0ZTWXc2akYfZyWCbvVpL2GWVgynDiTP5GB7vyzTQvbduRmDurl2RDUK8qB9JN4A660xlfJxZBwALja5vO+0l3IL+ZYco7134UGE8v2nef2Adpr07JZetdYsvTA+rwZ2TYMzFXtfx1NZqS5v2JjKiox8tWyNfTb9zT/wcgsHqOPYxy3D+1IoFc1PSQt8s3BSTc/eof7sozuTGYdHvF/YzLNKv/3QwghhBDNQp2Cek888QQPPvgghYWFqKrKjh07WLRoES+//DKff95Mp/UJIUQD23A8lcd+2Gdtim7RPdSHz2b0wcVRX/4G1vJbG4MO4vIR2BkmfaJ9bzJpE3cBgqJK1/SfDbu+0L5vbpl6AA5OMOihxj4L2jprgaHMkKEEtbmTHqcucCD+Art03Zh7fX/bd+SuTRy1KVPPXBasOnlwsLADnYjHOeNYbU/9irYjNgP1otkiP+w6Zw3q/XmoTFAvqrR3ouV5s2v7tniuiaWvegDDwQOo10fZPOSkIZQYTXzw/uvcn/k6rrpislU35vltZUreQjjTB5ha4z7KBvWcHHSkBQ6G1HX0Ne7lSFI2XUO8G/Q+CCGEEOLSq1NQ784776SkpIQnn3yS/Px8brvtNlq1asU777zD1Kk2vOgQQghRwYvLD5cL6A1s68e9w9syomNA5W82C0r7J4km7EIcGPJA71w6tRW0gR2j/6X1avO3ofROlLf1Qzi5BgY8AB1GV70uVwv+tAgK47FruvIYkFtUgk7RynNt5u6nXdqSqZebDIDiEUiGYwfIW0fLwlPkFZVUOWVXlLe9zLRiJwcdxSUmDsRncSQxGz8PJ3ad0doTtA/0oH2gORPUZLI+b7p5B/BA8HLuT3+dI8WhHEl8kC4hjZcR+1fMWe7IfA9XpZjNpihODXqVaV0c4X8LITfFtp2UCeoBeEeNwbj2RTrp4vn6wCG6hgxqoLMXQgghRGOp8yvHWbNmMWvWLNLS0jCZTAQGNn7ZghBCNFXnMvIJSN3Ke07fkOTYhhZ3LqJba5/qb2TpqSflt02PqkJJoTb9NsncTy8wsuJgjiFzL/mpNRuJ++HUXxA+uPqgXo45o8uzNJvLoy6BtdqU31qCNO6BqF5dIA86Kec4npxDT1vLfa9wln56igJzRrbnjdXHAfh+5znaB3pYs/jGRZUpvS3KAtWkfe/mS4vocRjXvUJn3Tm+2rePLiHDLuVdKOfU+m8Yq+SQoPpROOUHZkS1ggtntY25SdpzRk2ZhBcF9QZ0bc/Pq4eRgg+HYjOZ0YDnL4QQQojGUaegXmxsLCUlJXTo0AF/f3/r9SdOnMDR0ZHw8HB7nZ8QQlwRNhyJxxkDnXTx+Ds741dTQA8gfAg4uJTvwyaaBkM+vBSifT9ojnYZFN1459McWfoVpp+ufp01qBdc/bqaeAaDZ0jpwIzqWIJ6HoF4BHSDOGijpLArPlmCejbIKjBw2DwtODLYixmDwnl/3UmKSkz8si+BjkGl5epjy/bTs7QscHQHB2cGd+vI7rUd6acco/DwHzC+4YJ6247Fc2TJSxSGjWD2rTeXy74+npzDoIyloIMVzuO5u4v5ucHd/IG5sRgKL9TcamHWOu3DHvN07XA/N6Z7ziU+swDHeIX84pLaZZ8KIYQQ4rJXp+m3M2fOZMuWLRWu3759OzNnzqzvOQkhxBUnZ88S/uf0OgBearZtN+p7D9z4MUQMbcAzEw3C0Q0wv6kPH6aV2Xa9sVFPqdmxTNDNqCGoFz4Uom6CwHoGx0P7wWNH4NZFNa/NKw3qhYeFkaL6oFNUMs8cqN85XCF2xZX20+sf4Yu3q6M1I+9CvoEd5tLcUF9XupYtqfVqBbPWwm2LzdvdiHEbAEDHrM2k5RY1yPmm5RZxdvGjTCxcxsGDMSzZk1Bu+9q/VtJDd4oi1QH3AXeWDrRwdAEXcx88W0pw9Q7gEQCOrgAoisLQDlqvR4NRtf5chBBCCNF81Cmot3fvXgYPHlzh+gEDBrBv3776npMQQlxR8otLaJX6t/XfDoXpWu8n0XwpSunEV792WpltdSWiovasQb1T1a8bMBsm/w/CBjb8OVk4ukGLCPAOJTLYi62mLmwyduVMeu6lO4cmrGw/vQFttfYDU/qGVlh3Tdfg8v1IHV2gVW+IKM3IUzteA8Ag3WH+PhjXIOf77aKvmaL+iZ+SQwaevPzHEbIKDADkFBpYfNTARyXX8T1XM2Fgt/I39gjSLs19GGtrSHt/3ClgpG4vMYdi6nM3hBBCCHEZqlNQT1EUcnJyKlyflZWF0Wis90kJIcSVZMvxZIYp+6z/VlRTab+86mQngqGw4U5MNCxziRzFEshpEJagXm4yFFV8zdKohj4Kj+yDIXPxdXfiRZfHuN3wDL9ntEa9eKSrqGC7uZ8eQL8Ic/+4CD/a+LqVW3dNVM0l1T179eeMKRBnxUDSnuX2PVHgj90nuOncKwB8WzKKraaupOUW88Yqbdrxkj0JxBV780rJrRzt8Qzero7ld2AN6tWQqZebAj/dBX8+U+7qQe38eNfxfb50eg3XE7/b5T4JIYQQ4vJRp6De0KFDefnll8sF8IxGIy+//DJDhgyx28kJIcSV4OTedfgoeWSqHpQ4mgM9eanV36ikCN6MhBeDbAsAisuPk7t2ufdbSK8hm0zUnquPdWAAGbGVrykp1oLjxhL7HHPRbfBuL0g+VKubdQrWesBdyDeQktMwJaDNRU6hgZiELAA6Bnng6+4EgE6nMKVPa+u6QE9neoZe1IPu7DbY/C7EbbJe1bONLxsdtBJc16Sd5Bfb6bEApOYUkfvb04TqUjlnCqDkqn/h5qRjvG4bA3bNI+ZcJl9vjbOunzEwrOJOBs+FyV9CmwHVHywnEQ7+rH2V0cLdidOefQDokLurwUqMhRBCCNE46tQt99VXX2XYsGF06tSJoUO1Xk4bN24kOzubtWvX2vUEhRCiOVNVFbfYNQBsVLtzrWcyZJyE/BomaFoCeYoOnL0b+CxFg7CU3+74VOv7dvvP1a8XtefbFkzG0qmgF0s9Cp8M1bKhHj9e/+NlxmnlvjlJtRpg07mlFxtPpOFFHkfOZxHk5VL/c2mmdp3JxGTtp+dXbtvk3qF8sO4UBQYjN/RsVdqbzuLEatj4OvS7Vxs0hBYMPN9pBqP2DeWU2orgY6mMi25JfamqyrcLFzCP1QD8HPoP5l4VDcYiJm/6FA+lkJcWvMysomMs1w2guM0wIoO9Ku7I1rJ8y9RlN78Km5R2IyHmM/rpjvLX8USu7RVex3slhBBCiMtNnTL1unTpwoEDB5gyZQopKSnk5OQwY8YMjh49SlRUlL3PUQghmq3Didn0K9kFQELgMHQBnSCwC9RUgmeZ4ujaAnR1eioXjc25dEKnTDBuIHf8Bv84A+1GVr7d0qfMI9A+x7NMvq0qiAja7/Y7PeCT4dZATKdAN/52eoQDLrM4d7aGwR5XuO2ny/bTKx/ACvZ2YfG9A3jxxijmje5Y8cYFludN33JXD+jZg1NqKwBWHkqyy3mu2HWcm8+/CsD3yjVMv3U6ALeN7MW3LrcC8HjJ50x1WM+7ju9zR/96BhItfxPcfCtsiozuR6rqjatSTMKBDfU7jhBCCCEuK3Weax8SEsJLL71kz3MRQogrzq59+7lDdw6jqtAiehwM/4dtNywoE9QTTVPbERC3Ufs+SD4QaxDmKaBVyknULj1q7r1mE0tQL6+aTNvCLMg0lwObszUjQ3woQisjzT+3H7iEQzuamO2xZfvpVQxgdQ/1oXuoT+U3riLwNaCtH54uDuQUlrDuaBLFJSacHOr+YYnJpPLSqjhuLBnJDfrNtLjxRfw8nAFwctDRY/I/OPrNGiJ15wD4XT+Kqd3CK99Zbiqc2waKHiLHV31QSyC5kky9PhG+/KlGc72yCedzf6OqM8oPEBFCCCFEk2VzUO/AgQNERUWh0+k4cOBAtWu7detW7XYhhBCa9adzSDbcQpCSwVXRHWy/YX7lGSeiCRn6mNbfqyhLMvUaS445U8/TTkE9N0tQr5qemJaBB87e2jRWoH2gB6vVNnQgAYe0I4BWvvnXkRSOJmVza7821qDQlSyvqISYeK2fXrsAdwI8a/kzqSJTz8lBx/XtHRlw7A26muLYevJvhkeWPibOXyhg+Zo19OzRhz7ta86oO5CQRUJOCe9zI4fa3smXPduX2z6gQzDvhj9B5NmHMaoKhl534qivIoiYHAPf365lcNcxqOfiqCfJrz9kbqK7YR9n0vMJ93ev8X4IIYQQ4vJnc1CvR48eJCUlERgYSI8ePVAUpdIJbYqiyARcIYSwQXpuEesTYJ16PR2DPLjjosmN1bL01Kuk1Eo0EVnntICezhH8ahHQFbbLSoDf52rTb+9aWXG7JVPPs/491ABwD9Auq+uJmWcO6nkEWK9ydtCT6tYOirbil3eSTSfSeH3VMQ6eS6OTEk92Ylv+b9o4+5xjE7b7TCYl5oZ6/dtWDF7VyJqpVzHDeWi3dgw8EYOXks+fO/9ieOQ0AAoNRlZ/OJdZxYv5X8x1tH/qf/i4OVV7mLVHSyfVjo0OrXTN7bfcygdfZWDSOXLn1UOr3pl1+m1ytcesLqgH4NJpFGx7g27KKX46cprwodHV708IIYQQTYLNQb3Y2FgCAgKs3wshhKifDcdTra3zroo0v3E78jusfQFa94brP6j6xlVknIgmJOmgdukTCg7VBwlEHTm5wYlV2vdFueDsUX67JVDiGWSf47mbAyrVld9aMvU8yh+zyK8znIcbdJuI/OJvCnHmJYcF3Oawlq9P3wRcuUG91JwiNhw4yemtvzDfYR+/GgfTP6JH7XdUTYbz0MhWrFF7MVHZhMfpPzCabkOvU/huxTruLl4MwE38xXc7zvLAiPYVbl/W2qPJdFDiMaJjZLtBla7xdXfiwQfm1XzOltLw/HRtWnNVzxWWoF4VfxN6RnXl0b9ns1ftQKfYIm6pJo4ohBBCiKbD5qBeWFgYAAaDgfnz5/Pcc8/Rtm3bBjsxIYRo7o7v28hE3UE2mLpxVaS5Ub/JAKlHwNWn+hsHREL3WyFMem81Wfu+0y6zzzfueTRnri20IEdBhjZhuOVF7UHsnann2RK8WlXf69IS1HMPKHe1W2g3MD8U7tb/wQfGGzioRgAQXnyctNwi/K+wEtzNJ9N4e81xRsZ/xD365TgpRnCASN05ItrOrv0OC6oeJuHm5EB8y9GQvIlhxm3sPZOBh4sDnXf/0zpWLsYUwVdb4rhnSNsqe+4lZxdyMCGb1U7v0EGXANmdwG9Y7c/VwrUF6BzAVKKVdXu3qnzdTV/AhDdAX3nQr2uIN385jyKrwED66XSMJhX9xdOBhRBCCNHk1LoLsKOjI0uXLm2IcxFCiCtGTqGBjme/512n93na5Wd6tfHRNlje6FfXkwug0zi48WPoNaNBz1M0oHGvQJ+74F6ZRtmg/Nppl+knK27rNA6iJoNf9ZlXNus0Dh49DDd8WPUaa/lt+Ym7XTt35aQphHzVGTdXF964uTth3YYA0E13mphzF+xzjk1EcYmJBxfuYWdcJmmqN06KkSRVC5ZGuV0gyMuldjtUVbhzBUz7ucogbkivCRSqjrTRpbJ7xyZWLXyTQbpDFKkOjFff4XbDMyRnF7E8pupA/Dpz6a2Xkqdd4eJdu/O8mE4H7ubHSnUluHoHbVCLi1flm3UKg9trmaTZhSUcTMiq33kJIYQQ4rJQp9FeN954I7/88oudT0UIIa4c3/32JxNVLZiTHjYBB0uTdFuDeqLp824N174FgZGNfSbNm38n7TLteMVtw56AyV9AQKdLdz5O7uDbFnzalLu6d7gfZyavZO11m7nnH29zU+/WtOzQkyLVER8lj7iTBy/dOV4Gtp1O50K+AYCt7qP4vPti0m/9EwCPohQwltRuh4oCrXpDh9HgUHnG44jocDaq3QHQH1zM9OzPAVjgMo2nb59gXff5xthK+0oD/GUJ6pGvXVHfoB6UBoBzU6pfV4PB7f25Rb+ODxzfZt/BK+vxJIQQQjRXNpffltW+fXv+85//sGXLFnr37o27e/kJWg8//LBdTk4IIZqjE0nZdIt5CQedidWmvky8/ubSjZagXmFWDf2TMsDRzTo9UwhRBUvQNOVI456HxdDHtK9KjOoWVu7f3doEckRtQw/lFMVndgGjLsEJXh727NvNCN1etpq68tC1A5jQrSWYTNpgGZMBcpO0wLgd+bg5ccpvJFdn7mKwEsMDhke4Xb+anlOepW9bf7q19uZAfBaHzmezPTaDARcN6yg0GNl0Ig0nDLgqxdqV9gjqeQZDItp9royqwpJ7tbYNo/4Jzp6VLhvS3p/O+nX00p3k42NrYdyQ+p+bEEIIIRpVnYJ6n3/+OT4+PuzevZvdu3eX26YoigT1hBBXPIPRxB+7jtM6OIBeYaVv/FRVZcUPH/OI7hCFqiPn+j3D1WWn3rr4gKIH1ag1PveqotfXl+O13nszlkHb4Q17Z4RoygLMQb3Uo+WvL87XgufuAVrpor18OQFyzsPMFVX//toozM+Nbbr29OAUHukHUFUVRWn+fdBUVaXFse9Z4LSEX01DGN7pOm2DTgftR2nPkUZD7XaafgqOLtfKsSMnVLmsRY/r2PPXUtYYe7Pd1Jm2fcfxYngL+OlOPleSGMEs8nHh842xFYJ622MzKDAY8bNk6QE4V14OWysD7ofuU7VMw8oU50LMD9r3o/9V5W7a+LqxzqknvUpO0jJjOwXFRlyd9PU/PyGEEEI0mjqV38bGxlq/Tp8+zenTp8v9uy5mzpxZp9sJIcTlxmRSeffzLxi9YhhuXwzn06WrMJq0Uq3V+04zOf1jABY6TuK2sRc1UNfpwM0yQbOaElzr9NtqGvILIbSgnpOH9rtStmTyzGZ4MxI+G2nf46Wf0IZy5NWvVBK0D0qzfKMB6GA8QWJWYb332RQcOp/NQMN2AM4HDMXDuUzQ9bbv4daF4BtRu50m7oPVz8HWaqaKAyN6dmKy4d98aLyeAC9XnhoXqQ2qOPI7galb6eilBRP/OprM6dTccrdde0TreeepmIN6zl6gs0PQrO0I6HpjhZJtK8vkW0c3beJzFRRFoShUG3s7WDnAluNVZP4JIYQQosmoU1AP4IsvviAqKgoXFxdcXFyIiori888/t+e5sWTJEsaOHYu/vz+KorBv375y2zMyMpgzZw6dOnXCzc2NNm3a8PDDD5OVVX3z3/nz56MoSrmv4ODgcmtUVWX+/PmEhITg6urKiBEjOHToULk1RUVFzJkzB39/f9zd3Zk4cSLx8fF2ue9CiKbry+XruOv8fNyUIiJ155i6bwbvfPgO5y8UcP73l2ilpHPOFEDoxP/DxbGSN3wBnSCwqzbtsDKqqpXfggT1hKiJd2t4Oh7uWqn1VbOwTr4Nrvx2dWXti5lWcZvJBG9HwyfDIC/dtv2FD+FVwy28WXIzB+Iv2O00L2c7du+ioy4Bg6rHr0fVWXW1YuNzZpCXCy/cEM2wjgF8NqMPXi6O2uPGPJF8WnetnFZV4cvNcdbbqapq7afnqy/QrrRH6a0tLEE9N7/q1wHte48iVfXCX8nm6KafG/jEhBBCCNHQ6hTUe+6553jkkUe47rrr+PHHH/nxxx+57rrrmDdvHs8++6zN+0lLS+OOO+6gTZs2LFq0iPbt2zNlyhSKi7U+JHl5eQwePJj//ve/ld7+/PnznD9/ntdff52YmBgWLFjAypUrufvuu2s8dteuXUlMTLR+xcTElNv+6quv8uabb/L++++zc+dOgoODufrqq8nJybGumTt3LkuXLmXx4sVs2rSJ3Nxcrr32WoxGo80/AyFE87Jq70kG73yYFkouB0xt2WnqhJdSwKNpz/PhG8/xTX5//jZG83Pgg4yODqt8JzN/hwe2QKtelW8vztN6SgG4+TbMHRGiuVCU8sE8ixzzJFF7B/UsgZX8SoJ2hRfgwllI3A/OHjbtLqxtZz40Xs82Uxf2x18ZE0uNR34HYIcpkmHdOlRcoKpgKKjdTgsytUsbAl+39W/D13f1o1trn9IrzcHAce1ccDOXrP64+xw747Rg4cmUXOIztXMKbR2mDWHpc2ftzrEq+RlweBkc/rXq7WDT34PhnUNYqdeyUyPPLyU1p8g+5yiEuCyl5xZx/kItny+FEE1KnZrIfPTRR3z22Wfceuut1usmTpxIt27dmDNnDi+88IJN+5k3bx47d+7km2++4e233+bhhx9m5cqVmEwmAKZPnw5AXFxcpbePiori559LP2Vs164dL774IrfffjslJSU4OFR99xwcHCpk51moqsrbb7/NM888w6RJkwD46quvCAoKYuHChdx3331kZWXxxRdf8M033zB69GgAvv32W0JDQ1mzZg1jx4616WcghGg+Dp/P5vElR3lK7YivksOBoR/SISKCRd/NY6zpb9YZokgggLtNT7NyyrC698aylN7qnbVyKyGEbVS1NMBnydTzsHemnr92WVn5fK45kOjiU+UE1ot1Dy3N9oq5AoJ68Zn5dMvbAjo45DWEwd4XDQOK+Ql+fUjrJXrb97bv2JrNVscPQlx8APBQc5nSpy0LtsRRaDAx5ZOt3Du0LW5Opa85u0dFw5CJdTtOZdKOww/TwScMulxfcbvlvrnWfN8c9DoKo2+D/b8yXNnHoq17mT5mgP3OVQhxWTCZVD7beJrXVx3DaFJ5dkIX7hpSy7YFQogmoU6ZekajkT59+lS4vnfv3pSUVFEuVom9e/cyffp0hg8fjre3NyNHjuSVV17BxaXu0xyzsrLw8vKqNqAHcOLECUJCQoiIiGDq1KnlegHGxsaSlJTEmDFjrNc5OzszfPhwtmzZAsDu3bsxGAzl1oSEhBAVFWVdU5mioiKys7PLfQkhmr6MvGJmfb2LbIPCMyV3817HL5k2egD92wczcu6XPBrwCQloZXl3DW5Lu8DKpxPaxJpx4lt5BpIQoryjK+D9vrBkVul1OeZ+Ypey/DbX3GfPI8jm3QV7udDBo5Bxuu34xa9CLdsXsBnauO8ofZRjADh1ubbiAhcfKCmArFq2O7GW39YxqGcuv6XgAvOu7kjvMC1zT1Xhk79P89aa49alV0UG1u0YVbE8XnJTyveFtLA81mzIQgQYPXQo202R7DRFsn7f0Wb/mBLiSpOSU8gdX+7g5T+OYjCqmFT49++HeXvNcfl9F6IZqlNQ7/bbb+ejjz6qcP2nn37KtGnTbN7P4MGD+fLLL/n999/rchoVpKen85///If77ruv2nX9+/fn66+/5s8//+Szzz4jKSmJQYMGkZ6ufdKZlKS90A8KKv+iOygoyLotKSkJJycnWrRoUeWayrz88st4e3tbv0JDQ2t9P4UQl58nf9pPgrm8oXtrb56ZMtyaiRfs7cIns8fx1DWRPDyqA4+O6Vj9zvZ/Dx/0h5VPV75d+ukJUTt6Ry3bKalMq41cS1CvfhNqK3AzZ+rlVxfUsz3ooygKk1uc4iOnd7jTtIS49Pyab9SEpcasRq+oHDaFMaB3j4oLvFtrl1nnarfjAttLVCtlztSj8ALero78cN9AnromEid9+ZfSbQPcCXfOgbQTUHChbse6mCWoV1IARTkVt9eipx5AhL8774W8yq2GZ/krI4BdZzJrfUqFBiM5hbWcQCyEsKu8ohL+OpLMqkNJbDqRxu4zmSw/kMi4tzey8UTFv0FvrznBf34/IoE9IZqZOpXfgjYoY9WqVQwYoKXsb9u2jXPnzjFjxgweffRR67o333yzyn28+eabvPTSS8ybN49Tp06xb98+Zs+ezezZs2t9PtnZ2UyYMIEuXbrw/PPPV7t23Lhx1u+jo6MZOHAg7dq146uvvip37heXxqmqWmO5XE1rnn766XLHyM7OlsCeEE3c0aRs2h3/gnud9rDc4Wrun/5chQEYzg567h/RzrYdlhRC6lFoEV75djc/6H6r/TOMhGiuAiK1y/STYDRoQT5rpp7tWXM28WoJXq3BqZJs3LzaB/UAnML6Qip0Vs7y59kUIvybZwlVVoGBd5OiWKa+SicvA+8HVfIz9G6lXRZmQWE2uHjZtnO7ZOopUKwFVfU6hftHtGNkZADzvt/PkUSt8mJMl2DY/glsehP6z4Zxr9TteGU5uWmTdIuytRLui+/zVc/CwIdqlbl9U/92bIrbD8D3O8/RN9z2n0tcWh7Xvb8JB53Cd/cMoEuIjf8HQjRRq/adJrfQwI39O9a9dUoDeHDhHtYfS+U9x3fprTtEAc64q66MNl7D94wk0NOZt27pwZHEbF5YfgSA/22OJbfIwMuTuqHXXT73RQhRd3UK6h08eJBevbQG7qdOnQIgICCAgIAADh48aF1X05Oeu7s7L774Ii+++CI33HAD48aNY968eeh0Ou69916bzycnJ4drrrkGDw8Pli5diqOjY63uj7u7O9HR0Zw4cQLA2msvKSmJli1LP8FPSUmxZu8FBwdTXFxMZmZmuWy9lJQUBg0aVOWxnJ2dcXa2rY+OEKJp+GJjLP11CfTTHUPXbhzBF/eAqi1r+V4lPbkAWnaDGz+u3zGEuJJ4m4NsxTmQfgoCI6HbFK2E07uNfY/V83btqzKWnnrutQvqhbfvQuZOD1oouaSc2AO9mmdQb/2xFEpMcJLWDIuKqPx1pLOnljVXeAGyE2wP6l3/PmQnQsvudTu5MS/ANf8FXfkPbCKDvfj1wcF8vTWOxKxCHhjZDtaYex/ac/qtR2BpUM//ouEhOj2425alZzEuqiX//PUQzoXp5B3YR851XfB0se3188IdZ8kp1Nrt/PPXg/w4e+BlFegQwp62nEoj7afHGKY/wAqHlUzoU8nwnkYQE5/F+mPa68QW5OCn5AA5oMD9LCO1wy28Nrkbfh7ODG7vj5eLI/9YcgCTCj/sikenKLw8KVp+d4VoBuoU1Fu3bp29zwMfHx/uu+8+Vq1axcaNG20O6mVnZzN27FicnZ1ZtmxZnfrxFRUVceTIEYYOHQpAREQEwcHBrF69mp49ewJQXFzMhg0beOUV7RPX3r174+joyOrVq5kyZQoAiYmJHDx4kFdffbXW5yCEaJpScgr5dd95xuhyAYhuH17/nVob7VdSvieEqD1FgYBOkLALUo9oQb3R8y/9eTh7gW878KldILF7aAtiTBEM08dgStgD3NQw59fIVh1Otn4/uks1gU/vUC2olxUPgZ1t23lQV+2rrqoZbOLkoOOeoW1LryhsiKBekJZpmptc81obuDjquauzykOHH8KIjt92Xc/NQ6Jsuu2aMv9Pu85k8tuBRCZ2D7HLeQlxuVl+IJEJShKe5BO/dxVcJkG9LzeX9oNf1fFfnPAoxi0njltO/x+tnHL54o4+5QJ2U/qG4uHiwCOL92IwqizeeY7o1t5M6x/WGKcvhLCjOvXUs5d58+axYcMGsrKyMBqNrFu3jg0bNtC7d28AMjIy2LdvH4cPHwbg2LFj7Nu3z9qzLicnhzFjxpCXl8cXX3xBdnY2SUlJJCUlYTQaqzzu448/zoYNG4iNjWX79u1MnjyZ7Oxs7rjjDkDLMJw7dy4vvfQSS5cu5eDBg8ycORM3Nzduu+02ALy9vbn77rt57LHH+Ouvv9i7dy+333470dHR1mm4Qojm79utZyg2mvBRtKCes2ftsiUqVVNQrzAbDIX1P44QVxJLCW7K0cY7h2GPw8N7YOADtbqZr7sTsc6dAPC7cIgSo6khzq5RFZeY6HvsDd5zfJdBLmfoV105aF376l0qReYhaPYO6gHkVBLU+/MZWP44ZMbVapdXDx7ASTUEF8VA+tZvbbrNqdRcTqfllbvu5RVHKCiu+nW3EE2VqqqsPZrCGTUIbyUft/PbLot+dKk5RYw89H/81+FTOrtk8vQtV3HnjRO4ZYqWJe5YkodSUvF14vjolrx+c2m28vxlh9h9JuOSnbcQomHUuaeePbRp04ZHH32UEydOkJeXx/r167nrrruYM2cOAMuWLePOO++0rp86dSoAzz//PPPnz2f37t1s374dgPbt25fbd2xsLOHh4QCMGDGC8PBwFixYAEB8fDy33noraWlpBAQEMGDAALZt20ZYWOknFU8++SQFBQU88MADZGZm0r9/f1atWoWnZ2l/l7feegsHBwemTJlCQUEBo0aNYsGCBej15UszhBDNU0GxkW+2nQGghWJ+k2OP4RWW8ltDHhTngZN7+e0rnoADi7VysEFz6n88Ia4EgeagXupRLZOqKFcradTXrmVHjYrz4OsbtEEZD2yrNsOrNgoDukPiT3ThJCdTc4kMbl59zHbHZTCWLbTUZ5AQOhkHfTWfO7cZoJWcetqYHVaYDbu/1IaY9LR9oFs5SQfh79e0x8z412o4XgNk6vWbBV2uh5AeFbcd+F5r19B7Zq12GdXah088rqFz/mcMz1nOR990x9ndE8XVFyffVkyIbomPm1O525TN0nN11FNgMJKYVcgnf59i7ugahkAJ0cQcScwhNyuDrbou3Mo6ehoPcCo1l/aBlfT7vIR+27CFmcpWdA4q2dH34epkfu/p7AV6JzAWax8M+1Ts2359j1YciM/ii02xGIwqs7/dw+9zhhDkVc/WMUKIRtOoQb158+Yxb948AGbOnGkNulnMnDmTmTNnVnn7ESNG2PRpSVxcXLn9LF68uMbbKIrC/PnzmT9/fpVrXFxceO+993jvvfdq3J8QovlZsjeezHxt+l+gQz4YsU9Qz8kDHFy0gRl5aRWDepYpjvZ8wyhEcxccDUHR4BsBh3+FZXOgw1iY9oN9j+PgCgm7QTVqv7+WwQ715BrRDxKhoxLPL3GJzS6od/zYQQYqGRSreoKjRlS/eMjc2u08+zys/qfWi6+uQb2ibDj8i1Y+XZOGCOqFVdGv2WQqHQJi4/Tbsnz6307R2i/prDtL51PaoLq/jdHMMDzNb/vPs/jegeXWrzlSGtR779aezP52NyUmlY83nGJKn1BCfFxrfQ5CXK527tvHXud7iVO1futdlDP8cOQ07QPr2JsTLfvv0PlsVh5M4mRKLjMHhzOgre2/u8UlJpx2f4FOUdloimb8VSNKNyqK9oGvo1u1/UafHhfJofNZbDudQWpOEQ98t4dFswbg5NCoRXxCiDpq9r+5R48exdPTkxkzZjT2qQghmhGTSeWLTbHmf6l4qFr5rTYhsZ4UResTFdgVSooqbi/INB+rjlMchbgStR0B92/Seuk11ORbAJ2utIQ+v0wJvckEb0XBJ8NKgzC10L5de+4rnsfw4rfYm1jJ80ITV3B6MwAH1Qh6trdzf7aCuge9rFx8tMvCCzWvbYigXlWKsrQAMoBb7f8mjO/flU+d7+CIqQ1nTIGkql5komUhbTudwa640sdqem4Ru89of3/aBbgzuksQ0wdqVS6FBhP//aMRS9uFaACFh//AQTGRjhcnTSHoFJULh+vWWz42LY+XVxxh+Gvrufa9Tby/7iQrDyUx/YvtLD+QaPN+Vu49wUTTXwAcaHUrrVu4lV/Q/z7oNb3a5x8HvY4PbutFiLcLbhTSI/5b3vhmCTmFhgprVVVlV2w6a2LOYTI1fumxEKKiRs3UK+viLD17iYyMJCYmpkH2LYS4cq0/nsLpVK3kdmi4O7pcPy3YZo9MPYB711e9zZqVIUE9Ieokx/wGyrNl9evqys1fG2hQti9mQYbWAy7rnDbBtZaiW3mzSu2LqsKBhGw7nmzjM5pUfNP2gAKHHbowzdet5hupqhZgs+U5Nz9du6zPc6blA5uCC9qxq5sY2eduyEux7+OrIBNi/wajAaInl15v+Xvg5FmnUm9PF0dmP/U6cWl5ZBYbSSgu4dSpdFh7EoCPN5zmc3N/w3XHUrG8px/dRQuIzx3VkV/2JuCQn4ISs4mPC/biGhiBT8sIwgJ96NbKG51OpmuKpictt4iOWZtBDwdc++NZlEh7zuOVvA2TSa3V4/p4cg43frCZvEp6TxqMKg8t2sOFgqgah1aoqkrC+i/xUvKJNQXR9+pban2/LPw8nPlkeh9+/ORfPKf/jkOxm7j6zRa8cEOU9fd739kMNi75hOsyvqStkscH+9/hoWk3ycRcIS4zl01QTwghmpLPN8Zav58xrAt0Oab941I0ULZkndgrgCjElcRkhAzz769ncMMcw92cEVY2qGeZWurmV6c+fp4ujrQL8OBkSi5HE9I5mZxN+6Dy5VUmk8rHf58iPrOAZ8Z3xt25abzMO5aUQ3f1KCiQF9Sn5jeMOcnwbg8twPVsstZfrzqWwFd9spstz7eqEYpyqi1tY/gTdT9OVbLi4YcZWs/VckG9+gcsHfU6OgSVBpr7uiXjuH0lJwo8WXZkMCdTcmkf6MFfZUpvr+6sven3dnPk0TGdCF7+Mlfr98BZ4CyUqDrOqQG83+YJHr5nVp3PTYjGsvHQGcbrtGGNSqdruJB0BFJW0dsUw5GkbLqGlGbCrdx5mPSV/6Wgyy3cPWl8uecwVVV57peD1oCeXqcwoK0v10S15MC5C/y4Ox5VhWeWHuRCvoEHRrSr8jlwz5kMxuT8Ajr40/167ouoJPv4wllIOwFeITVOB49u7Y1bFx0cg1i1JUnZhdzz9S6ujQqkU/5uRsZ/xBxdnLW27+YTj/PNuvbMuKqHzT9HIUTDa/blt0IIYW/HknLYckp7IxXu58aoyMDSjQ396aXJpGWKgJTfClFb616CF1vCaXP5lEdDBfXMw27yKwnquQdWXG+jG3u2YoxuJysdn+SPJV9V2P7t9jO8uvIYC7ef5eMNp+p8nEvtwIlYOuniAfDoMKTmG7j7a60JTAbITal5fYEdspsdXUFvzoSzpQTX3izTb/PSwFhSer01qGeHyetmjikHmWP8min69QB89vdpCg1GNhxPBbRpzD3blH6odGvfUKKdtcf3edWXItURB8VEhC4Z17h1ZFdS0ifE5S5p30qcFQNnTQH06Nkfry6jeNxwH3cVP8FW82tAgNyiEnKW/5Npxl8Zf+AhlmwuXyG2bP95tsdqz0Fhfm7s+L9RfHfPAKYPCOPVyd24b1hbAPQYObJ6AW8vWkZGXnGF81FVla2rfqSdLpEc1ZWgYXdVHvzb8Sl8Own22jbRup1rPgAFvqUBwOnHH2LO+aeI0sWRo7rymX4qR02hfFYynudXx7P6cCVTuIUQjUaCekIIUUsLt5+xfn/HoPCGKS3a+Tm831cLQpRVeAEwZwNKpp4QtePsBcYy/egaKlPPzdxTr1ymnhYQwaPuQb27Bkcw1OU0bXVJjEn8mJ2nU63bkrIKeXXlMeu/N59Mq2wXl6VTsbHsN7XluKkV0R1tGESh04OXeQBJVnzN6+sxSKKcsiW4VTEUaFkyualVr6kLNz9QdICqTbq1aICgnuV++uq1N/tL9yawbN958s2ZRldFBqIv83fPQa8jWJ8DQO6Un1l7036eDP6cvoUf8mLJNPaevWC/cxPiEiguMeF/fj0Am3R96BnmS+/IdvxkHE4CAeWCer9u3M1EVfugKETJwGHV0xxN0lok5BQaeGH5Eeva+dd1xc+jtExeURSeHt+Zf4yLZIRuH+87vce84zPY9OpNfPn7enKLSjAYTSzdG8+Edzfx5Wkv3jRM5hvd9YzrU8W0acsHR3k2PgeZPxiZPKwnb93SnT6uifTXHaVIdeA75Vr+GLWSmf/3EasGL+YL4wRUVeGRxXs5dD7Ltv0LIRpc06jLEEKIy0RBsZElexMAcHHUMalXazj5F/z9GrQZoDXht4fifEg7XlomWFb328CQBw5O9jmWEFeKwMjy/26onnpeIeAdqmV3WVgy9eoR1HN10uM26gkurFpDJ1087y99nz6PzkdRFJ5fdpDcotIMrgPxWRQUG3F1qqE09TKwPNGTz4pfwNMJ9ra0caqvd2vIOqv1KAztW/3afDu1LHDx0d4AF+VUvSblMHx2FXi1hkcP1e94Zen02pv13CTtseRlfux2vxU6jtOyFu3FPBQkxLkICqHYaOL5ZaX3ZXTnSgbM3PUn5KXQMbQLHR1dKDYN4oe4fQDsjstgeMcA+52fEA1sZ2w6w9gNQHboKPQ6hY5BHvi5O5GeV8z22AxKjCaMqkrJlg9xVko4ZWpJgurPS4apeHy3h2UPDeHtNSdIzdE+SLq6SxAjIyt//p89vB3ti3qRtdkdbyWPicrfFO/czJLdYyjWubC7oCWHTUMAb941TuK5UV1wcaziud2SKW5rUC9PC+opHkHc2Kk1wzpM539/tcHF3YsbhvaxtnGYM6YrJzMMLNt/Hn1xDku+eAX/R/5JkJeLbccRQjQYCeoJIUQt/HbgPDmF2hvn67qF4O3qqPUvObvVvplzlZXvgVY+duNH9juOEFeSgDJBvS43lP6e2duQudpXWWe3aZf1zA68fkBXvvh7CvcVfclN2V+z+sBMcHDlz0Ply6FKTCp7z2YyqL1/vY7X0BIuFHA+qxCA6FA/HPQ2FpF41yJTb+ijEDUJWoTX7SQtZq0FRzdtwnFVGnLyrYclqFem5FinL+3haC/mTD0v8nDS6yg2migwaFl6Tg46hnao5DEV1AXoYv1nn/DSUueyE3SFaArWHUnkt5LJDNMdILTn1YCWVTci3Bmfo7/QzpjAwfP9iT13nhuNf4ICvwXN5s+S3iQnZpOcmsfsb3dbW7U4O+j457Vdqjsko8dcS1L/03z7+zK6H3uHIbqDTFX/ACMMcghhWfEguoX6ct+wtoyLqubvSG2DetYscu12fh7O3HX9mArLFEXh1cndSMnI5F/JT9LJGM973wQy58G5th1HCNFgpPxWCCFq4bvtZ63f39a/jfZNQaZ2adegnqV8z84lXEJcybxaaSW4AMOfAv0l+mxTVSF8MDh7Q7e6TysErdSx/bWPEq/601LJIHb5mxdlUZVmguxoAsGU3adTcEHLZCkbCKqRd2vt0pagnl87aD9Ku6wPZ4/qA3rQwEE9c4ZcbpL9912WOVNPV5TNTT3LZ7MObudn0wCWVj6uTPY4yC9OzzIx4Q1KjKaGOFMh7E5VVVYfS2ex8SrmGOcxpHMr67b+Eb78n8N33Oawjv0HD3Jqw7d4KQUcN7Vi2LXT+eC2nribs6MdT63C3aRl9T40sj2hNkz1DvZ24cFpU2j98Creb/0Gfxuj+dsUzcqWs/nhvoH88sAgxke3rH6YkOX1oy0tAEwma6ae9fmlGi6Oet6/YzC7HXoA0CP5JxKzCmo+jhCiQUmmnhBC2OjQ+Sz2n7sAQOeWXvQI9dE2NGhQ76JMPYP5xVPZsj4hhG0UBQI6QfxOSD1qzi66RMcd+CD0mgHOnjWvr8FVUaG8v+YO5mS9wa1FP/FR0WDAk+EdA5g/sStrjmhv0nbEXv5BvaQjW4lxfoANpm44h/9o+w1rE9S7lBoyqOdpCeqVycrc/I6WLd5jGrTqZZ/jWHoHojKrnz+LdiVYN43uUskb/+RDcHQ5BHaBztdar24f6E6P86fxNBVwJDGH6NYN8DO5yN9HEli2eT9xBh/yio14FSRwffFvZISNY/btt9meCSquWKfT8jiTrvWT7BveQqvIMOsbGUHMqgh6KKc5vv0PviscxF6dF52DPXk2TMuYfWlSNEd//DdPOS7mF+Mg3vZ6klnmYRjVyknSXts5exHu785D99zDuYzb0OsUhvnU4jVf2UoPk6nmDyJm/Ko9p9g4xMnfw5nCnnfD7t8ZpBxk4fb9TB8zwPbzE0LYnfxlE0IIGy28KEvP+kmpNajnY7+DWcsn0rQsH4vdC+DFYPh5lv2OJcSVxPLGJflgwx0jOxE+Hw0fD9HeVFnYIaAHWhnUoBse4IipDV5KPtfqt+HiqOOFG6Jo3cKNVuY3gHvOZlJccnlkSB2KT+OTT95l9Z4T5a7XxW/DUTFiRF9uomqNArtCpwnQpn/Na7d/Anu+hsLsWp71RQ7+DD/cUf1UScsxGiKo12smTPkaom4qve7ocm2wkj2Dmw7O4KA9htp6GBjbVQvkOTnoKu+nF78T1r2o/YzL8O4wGIB2ukRiTjT8NOYj5y9gXDQNv9hl7DqTyZHEbPKzUrnN9Dt3nH6cz5eubPBzEJe39NwiVh9O5rf958kqqNiHMj23iI9/38Yd+j9praQwKrL84z3cz40DDt0B6FFyAFDYbIpmyNibrWuu79EK747aY/9q3W6en9i16v53ZS2+Df7bBo6tsF4V6utGSG0CelD6obCppOZJ3TodhA/RnlNq0af5qoH92WnqiF5RKdyzuHbnJ4SwO8nUE0IIG+QVlfDrvvMAuDnpuaFHSOnGhsjUs0zPNBm0zA9LwNDS8N1OwQEhrjht+sOx5aVTQxuCg7MW6AD4cACMfw3aDrfrIXpH+PNa68d4KS6RjaZu/N/Yjtbyrv4RvizZm0ChwURMQha9wxp/Uvbq7z9kbs4bpP36Gnt999Iz3J/sQgNhuQdAD+c8uuFhQ1mnVdhA7asmqgornwbVCO2vBhcbB3FUJvU4HP5F623a8/bK1zRkpl5lA0EaYvotwO0/af0DPVvy+s2t6RQcS5+wFpU3xb+oJ5dFdMe2nFwXQnvdebJPbIaRdsokrEROoYG/FzzHfbq9FKiOOBuLUR1cKHQKItXoTYCSxZgD81jeJoIJ/To32HmIy4uqqqw8EM/aY+nsPnuB02l51m1uTnpu6tWaOwaFEe7nzsIdZ3n9z2P0Kt7JAqevmGb6C8cuU8rtT1EUCloNgnNLuVq/G0pUOrf0rjAI5u6br4dX5+CuFDEyvOayW6C03YqNGXNVcnCGMS9qrxsdnGtcXhfh/u6s9B5D35zjDMn/i8Pns+kSUo/nViFEvUhQTwghbLBs/3nrZMnre4Tg6VJajkHBBe3SnkE9Rxfwa69lSxTnlQb1LAFEt1r0nhJClBr4EIQOgJbdG+4YLj6g6LVAUtoxLcPWzkE9gJm33sr8ZYeY5ePCXYMjrNf3Mwf1QCvBbeyg3oX8YjwyD4MD+CvZ/LToP3R+/E32nsmkt+4YAGqoDRl3dVGYpf0/QP2fN63PwxeqPx40TFAPtMm7vzwAOYlaRmi2OUPP3kG98CHWbz0d4dGrO1a91tKT66JgRGSwJ78onWjPedyTdqGqavW9wOpIVVU+//Y75hR9Cwqc9OzH3rnX4eakvc354e8ODPprMm11SZz7fTYxwUuJbqM9FkwmlY3HzpOUksr4/l3L/20XTd6K3SeJXHYdgXjwY/F8oPTxl19s5JttZ/hm2xkCPZ1JMU+p9dVpffC8A9sQ5O9eYZ/+XYfDOfBR8njR4X94jni/wuPa0dVLC4ob8rXfD2eP6k9UVUuD4+52GG406CHb1iUf1ga9BXax7UOSMlr0m0rRmo/prDvLZ5vX0+XmidWuN5lUTqbmogCOeh0OegUPZwd83GzPEBRCVE6CekIIYYNypbf9wspv1OlB72zfoB7AnN0VryswZ+q5SlBPiDrR6W0r2azXMXTaG7PcZG04xjX/bZDDBHg688G0itlP/SJKnx92xKZz/4h6Doiop22n09lp6sQ9/AHAnYXf8eUvY3B2dGC4kkuh6kjLznXoyaSqWvayk7v2QUhlLM+Zju71z1oxD5CotqQtfIjWQzG0X/2OVRVHN63k1hKoBPDrAL4RVd+moVmm8XqUD+o56HWk+/aCzHVElhwm4UIBrVvYmLVUCz/9vZdbz83HQTHxmzqU6+962hrQA7h5aA/eP/tfZp24n+G6fXz11WP4PPgB64+lsOHvdbyQ/y8Gkc28XW/x79m34usuQYbmImXdx0zQJQLgrS+mbasg+oS1oMBgZMmeBPKLtd8jS0APYHioHpIhKDik0n327xTK8RWt6KhLwMXFmfGVTaJVFO33ITNO+/3wraGnXnEelJh7JnvUM1OvNk6vhz+fhq431jqoN6ZXJ9av7sVodpJ2ZBNG03XodZUH7c9l5HPHlzs4nVqaKRmuJDLP4Wfi20/jPul3KUS9SFBPCCFqcCD+AjEJWvZFdCvvis2+71imXZbtfddQLOW3kqknxOXNt60W1Lt6fumAg4aw+V3IOAWD51oDOxH+7vh7OJOWW8SuuEyMJrXKN1t1oaoqe89doKW3Cy29a+73tPlkOn+a+hFe+B1fOr7KSP1+BsY8y59OVwOwX21H77Z1+Bl9NhLO74Xbl2jTbStjz+dMWzL1ut6gfTUUnR6ue1sLMHqFgGdL8AzWrrenU2shKQbChkDr3tWvtQT13AMqbHKMGASZb9BNiWXV6WRa97Zv8DHmbAYt/3qYYF0mJ00huEx6mzD/8llRiqJw79RJfPjuMeZlv8YdxiU8+HprlpsG4IwPwS5aBrxLxiFu/XQb397TnwDPhilbFJfOwfgLDMhZZe0gv/3xgbi0KJ3m/MTYSH7cdY6vt57hbEY+XUO8+Pf1Xel9cjckU2X2a+sWbnzT5y3+PvIHPSY/VXUwyiPYHNRLrnx7WZZsV0c37UOK+sqI1f4u+ISBf4eaj2vD5NuLtXB3YmObB3juZB4ptGDwyTSGdaz4HHAuI5+pn24j4UL5Kbk36zdwvX4Lead3895XKg/PvN2uf6eEuJJISFwIIWpw8YCMKjVAWVEFDdG/Twhhfzd8CFMXQe87G/Y4+xdr5b0ZpYMIFEWhvzlbL6eohCOJ9RwQcZHFa3fi+PkIFr72MM8sjSE5u7Da9ZtPaVO8HXQ6TvR/iSzVje660zxZ8gkARx272hQcrMASRKpuSIQlqGeP50xLpp7lebix9JoBXSZC6z7g3cr+AT2AAz/C6n9C3N81r82rPFMPoH2nbhw0hbPa1JtDp8/Z9RTzikrY8s1zDNHFUKA6sSrqVa7u0b7Stc4Oeqbd8zjf6iaSpzpTYn4LVIQTZxy0QGMLcjiWnMPUT7fW+JgWl7+N65bTWXeWAtWJH0ZtKhfQA/B2deSeoW1Z9/gINj01kt/nDKF3mK9NfSqnTxzLPU+9TZ92LatcY/19sAS9q5OnPUdWFhivky3vwrc3QUwNE8WtJb91O+6QAQNIQXtuXbo3ocL2+Mx8bv2sNKAX4e/Ozb1bc2PPVvT01QaVuCtF3HnmKf71w2ZMpkvw4bgQzZBk6gkhRDVyCg0s268NyPBwdmBi98rLMRrE5ne0KYu9ZsCgOVp2SPIhbZu9+ycJIezLt23NJVf24BEAKZS+OTPrG96C5TFa2dmO2AyiWtmnx5vBaCJs05NE6+KI1sURvv0Gft4Tz52DI5g9rB3ebuV7kiVlFXI6NQ9vcuncOoi7xw/mveMP4pJxhFi1JUN1B8gJHVLF0Wrg3Vq7rC6oZym/tcdzpiUwWF35beYZLdvGzbdhgm2Xii1ZiRa5VTf47xnWgu6GlzCp0Pm8wj/sdoLwwbqTXFW0gyLFgW887+KeSROqXR/o5ULUHW9x24LlHFYCmNQrhLsGRxAWMx62fUAblwLIg1OpedzyyVYWzhpQ+8mj4rKQXWig1YlFoIM/GMT4aoaj6HVK+bJwa1Cvntm93adC2CBoY0NZazXZrnVi+V3MS61+XTUBeVuMjAzE29WRrAIDfx+MJe+GKNzNA48SLhRw62fbiM/UAnrtAtxZdO8AAj3NrRIWKZANWao7zxvu4Nd9Waguh/j39V0bpPemEM2ZBPWEEKIav+w7b+25ckPPEOuLFavcFPh+utY/a+p39j14YTakHdfeJIL2Jmv8q1pWTsse9j2WEKJpsr55K58N0i+iNIi1My6Du4bYp+zxz0NJnDWEMshhr/W6QoOJj9af4sdd5/hp9iDCyzSX32LO0lvo9CJdU87A6Z+ZOH0u49/dSGGJie+NI3mha1TdTsaWoF5DlN8W5YDJpPVOvNj/roGc83DvBgjpUf9jNhZb+gda3L1Ke/y1CKuwydPFkU7BXhxJzOZYUjY5hQa7DKOIS8vj842xfGicj5Ne4c87huHkUHMBUo8wfxb+Yxo6RcHVyRx0PaUFa2/s5Monsa7EZxYQl57PjR9u5uPbe9OzjWTGNzXLtx9mkrIVgORO02o3WTvfTh8ERFYfZC7HK0TL6vYJrd8xLSzDNmoK6lmDiXUL6jk76JncxZ0RB56ku+4Uq/Z1pW1IIGuPpvDjrnOcz9IyXtv6u7NoVpmAHlizE08NfInfNmjBzG+2ncGZYp64tjvODvX/UMRkUvlg3UmOJecwvGMAY7oG4+0qw3BE8yPlt0IIUQVVVasfkAHai5Jz27TpYfZm+cQ2P630uj53wT1rQS+fyQghqLLEq1OwJ14u2vPEjtgMVDv1/PxqSxyflFxr/fc9A1vjZO4plZZbzFtrjpdbv/mklvXiRb52hYsPbQM8eHZCFwA8nR0Y1bmOjeG9zW+As6op6+x6g9Zzb6CN0yCr4x4IT8XBs6mVB/Sg4affXiq1ydQLjISIYeBYeVZbn7AWgEoYiew9Y5/S5X//fphiowmAe4a2JSKghumiZbg7O5QG9MAavPEwZvHDfQMJ89OytpKzi7jlk20s2nG2st2Iy5SqqnyxK5Opxc/xbskN3Ox5CN6Khq0f2raD4U/Bde9Cqxp6SdpTq15ar8yhj9lnf5bXj3lp1a+zDrmpe4bguD6RhCqpeCkFbFj2Jdd/sJl3/jphDehF+Jsz9LwuGmZkzojs1bkjb07pgaJAIJncu/dGvn71ETYeqVjOW1tv/3WCN1Yf5/cDiTzx0wH6vrCGe77axa/7EjBKqa9oRiSoJ4QQVdh37oK1F1WPUB+6hHhVXNSQPe4sn7QeWlr6yTFIQE8IUcqj8jIrvU6hb7iWnZaeV8ypMlMH6+rQ+Sx2xmWSjRtG80vIZ68KYu3jw60TQ5cfSCTJ/GZOVVVrpp63Yj6+Odh1+4AwVjw8lJXzhtWtnx7YlqnnFaIN0WhVcUpwrel02nN9VQE9owEM5e9nk1WbTL0a9AnzZrPzw6xzfoyTx/ZXuU5VVUrMgbrqrD2azNqjWjAi2MuFB0dW3kfPZpaMrPx0Qnxc+Wn2IPqZf3eKjSaeXhLD00tiKCoxVrMTcbnYHpvBydQ89qod2Nj6PvydSiDrLGTbGCRqOxx631Fp5mmtFOfBuZ0Qa0NfSnuzBPWq6+enqqV/N+owKMOid7gva51GADBet73ctv4RviyaNYCgiwN6AHethPu3Qsvu3NCzFa9M6sZUh3UEKheYVfwNIYtG8c5nn3L+ogEbtvojJpF3/zpR7rpio4k1R5J5ZPE+Hv1hX532K8TlSIJ6QghRhe9sGZDRoEG9Mp+cfjlee8MohBBluVfdjL1fRGnJ6Y7YjArba2vNX38yWb8BJ0owOJmDVvnptG7hxjTzc2SJSeWrrXEAxKXnk5hViIIJD8X8xqxMsKtLiBet6tOzzBLUy06AkqK678deCssMJHGu5EOgpsTWTL3kw7D+FTjyW5VL+kT4k6BqH1IZYrcA2pCLo0nZ/LovgZdWHOHBj3/n+3/dwq/zr+enjQeq3Fehwci/fjsMQCtSWe75Au6/3Wfz3apU+FC45y+46TMAAjyd+W5Wf2YOCrcuWbTjLLd+uo2sfPk7fLn7dtsZ6/e3DwgrF7S9pFKPwRejYYkNj8+cJO13zU4Z1TZl6qkqzPgVJn9Zr6CeoihE9rkKgDa6NK7uEsTLk6LZ9vQovr9vIMHelQT0QPtAKqiLddrvlL6hXHP/m7zr9TipqhftdIk8kvAEu9+azK7TNkwQLuPw+Wwe/aH0A4T7hrXlzsHhBHo6M1K3l5v169m7fy+bTtSQyShEEyFBPSGEqERWgYHfD2gDMjxdHLiuWxUDMhoyqOdZZqraoIdAL31AhBAXsZRNVdI7qXxQr35vaDPziulw4gted/yEf7osxskzABSdNegzfUAYjnqtufnC7WfJLy5h80ntDZMnBegwv1m1ZwabV2sIG6yVy1X2oceOz2DFE5AUY79jrn8FfrgDEivJOLNktTl5Nv2Malsz9RJ2w/qXYPdXVS5p5ePKUUet3Lp3+nIW/us2trwwhiXvPckji/fx6d+n2R6XzVT+5Cb93xxZ/SUZecWV7uuLTbGcSddKuYe3UvFL3wPndtT23pXn5qtNEvYp/fDOUa9j/sSuvDmlO87mXn17zl7g9i+2S2DvMpaaU8TVR5/lRYcviHK7wDVRwbaXogIYCiDmJzi1tv4BNkugLC9F68FZnZ/uglfC4ODP9TumhaXSoyir6g88dDoIHwxRk+r9+nJQt04AdPIq5rMZfbi1X5uqg3nV6NLKm4fmPsuWcX+ySBmPUVW4TtnE8W8fJcXGidTpuUXM+noXBQYts3ZSz1b8Y1wkz1/Xla1Pj+I/LbfwmuOn9NMd5YXlh6UMVzQLTfwVhxBCNIyle+IpNGgvwm7q1bp8/52yGjKo598BRj6rNU7uPtX++xdCNH2hA2DOnkqnJka18sbZQUdRiYkDCVn1Osxvm3Zzm6IFT3I6T0N3/Qgtw8I84TXQy4XruoWwZG8CWQUGft6TwFZz6a2XYu6n5+AKDs71Oo9ydDq4c0XV23d/Bckx0LI7BEfb55in1mp9VLveqO23rObSTw+0PnnTl4Kbf/XrbJyemR/UF84vpY/uGH3UY2D+k/qpuaI1jdKfmYcxi082nOLp8eUnlp6/UMD7a08CoFNgdl9fWElpVmEDmNSrNR2DPJn55Q7ScouJSchi2hfb+Pbu/vi4OTXYca9EC7ef5b9/HOHGnq14/rqu6HS1m4CamFXAt9//wKPKFvQOKnlR92rDFiwBrnwbgnrZ5+Hnu8HJA/6vnj3dLM/JphLttaJ7NYM3rGWwdewvejEXHxj9L/tN062JORtSyUvTgqE1Ta+9cA62vq8F0gc+WG6TTqdw/YAuZHX/mrc/fYfHMv/DbabfeeWLD5g3Z165gTixqbms2byF80ower0Der3CtlPpJJhLdru39ualSdHWabp6nUIrJ61FQkclnhvT/8Gv2z5h0iA7/X0QopFIUE8IIS6iqioLd9hQegsNG9RTFBj+hP33K4RoPpw9tK9KOOp1RPi7czQph7Pp+RiMJhz1tS/SMJpUTDu+wEExsd0UyTWjRoGLe4V1dw2JYMle7Y3wl5tiyczXsq2Cnc2ZIpcy2JVxWgvoKXroNN5++7U811eWwdacgnou3tDuqprX5ZqDETUED/pePZmfvlyNu5pLumNLCjzaUOjbmcdCOxLVypuuIV7kbpyDx8738Fby+GRrHHcPibA21zeZVJ786YA1++b2AWG0cTui7by+f39VFbZ9qJVnDp4LLuVLp6NaebNo1gBu/Ww7ablFHEzIZtrn2/nuHgns2cvJlByeX3YQg1Hlq61ncHN24KlrIm267YX8Yn74Yw3h+9/kCd1OUGCjKZpxI4ZpCyyB6TwbspXtOS3bwQlcfaEgA3KTbQvq2SsIp9PBkLnVr0k+DGe3QEBnLWOvPiw/Y5MBirJrfg7MjIXtH4N/xwpBPQtvV0fuuHsOX711CL/iBL5KDqdgxRHmT+yKyaTy09978F/3BLOU3XxSMoGXS6aVu32gpzOfTO+Di2P5D+UVc8bmvQ7LAfhuzSvk9l5QuwnJQlxm5NErhLjiJFwoIC2niG6tva2f3pW1+0wmZ5IzACf6hLWgY5Bn1TszlYCDS8ME9YQQop7aBXhwNCmHEpNKfGYBEf4Vg3E1+evgOSaU/AkK7Aq8mQer2EdUK2/6R/iyPTaD02mlgzk6hgZDi6lVBh/rraQIjv6uBWeiJ2vXWXq8hQ+xzxt0i+p6zXkGQ797L112zOUg19zrqoYMo94RQXR7fglGk1rhTbaVj/Zz81ZyKTSY+HD9KeZP7ArA/zbHsslczh3k5cxjV3eCGK0/X70z9RRFK6suyoJuUysE9QA6BHmy+N7+TP1UC+wdOq8F9hbOGoC3q7TGqA+TSeXpJTEYjKVlkB+tP0XrFq5M61/9sIpfN+/HuPp57lbXo9epGFWF3xiO/pp/E+qrTTG2BtMqaVFQgaXvnls1Abja8AgqDeoFdal8jdFQ+gGxu50y9WwRuwFW/kPLOq5vUM/JDdqOBGdPMJbUvN7Gn7O/hzPd73iDKZ9soxiVBVviaN3ClZz9vzI99U38FfMwO92pcrdzddTz8fTeFUuAVdWasfmd/1ympb3NzcYVfLNyPXdfP9q2+yrEZUiCekKIZkdV1UqDdQA//b2XgDWP0IpUXu75NU/f0Kfc2pxCA1//soIDznP50ngNgf3/W/3BxvxH+6qpX4oQQjSUTW9rmWlDH6swsbFtQGkA7nRqbp2CeifXfcMYJZvzqi9Ro27Trjz2B+xfDG0GwoDZ1rV3D4lg+0VDOTpGdoPB19f6uDY7vAyW3KOVcnWdpGWpHF6mbet8nX2PVV2vucDOMP41+x6vMe1bpJXX9pxedWDUmmFUczDCUa+jqngeYP3Z+uq0cu2F288ya1hbsgsMvLrymHXZGzf3wNvN0b6Z8m6+WlCvoOqBMu0DKwb23lp93Bp4FHWzeOc5dsZp/5deLg5kF2pBoed+OUiItysjIyt/bK06lIRx5f8xSb8JFFht6svpbnOZMm4MLdzLZFC6B0CLcC2brKRYy6CriuX/325BvUBIPVL9FFpLrz9Fb98PiNNPQUYs+LUF37YVt1vOyV6BxBm/2L62FsHTHm18mT8xiv9bGoMTBiatGYqvkgsKlKg6jrYYTkCvKfwcNhCDUcVoUmkX4FF5T7/iPCjRevMNmfQgGz5ex3DdflrvfoXzwwcTUp/BTUI0IhmUIYRoNkqMJj74bTOL59/Ce++/zv6zGeW2ffj9MvqvuZnhuv20151n145N/PPXQ6jmZsi5RSXM/HIn49MX4KwYmO3wG+OjW1Z1uPJ08nQqhGgk+xfBnq+0wN5FygbxTqfmVdhek9i0PPqm/wLAH87jGNrJ/JyYEQuHf4Fz28utH9U5iDA/t3LXDW5fQ1+2+up8LTh7w4WzWvZJVgIk7AIU+wf1bJ0K2xz89S9Y/U+4cKbqNZbAgIcdshPNAY0OnlpQp9ho4o1Vx5i7eB/FRu2Ds3uGRDCkg/nxZNegnm0TUrXA3gBcHfV4kk/2joWcN/fvErWXkl3Iy38csf77o9t7c+8wLQBlUuHBhXs4WEk/0KNJ2cz7fh/PGu7i+5IRvNPmPbo++hv3Tb62fEAPtOyxR/bDrL+qD+iB/TP1PIO1y9xqprda+lK6+9v3teSGV+C7m6qeTJ1nx9/d2sqr3c/51n6h3Ny7Nf91/EwL6AFf625k69QYoub+Qttht9E7zJcBbf0Y3N6/6iEdlr6KDi6EtQzgSPSTGFWFsbod/Lz0h/reKyEajbwLFUI0Cyk5hdz+xXYytn3HrcqfzEn7Dw6fj+D1Tz5nV1wGb370ETMOzyJUV1p+0ULJ4ZttZ3j2l4PkFpVw55c72H0mEyOlmXtVlgkJIcTlwr3qCbhtA0pLXsuWxNrqp23HKVIdKVb1OPWdUdq83tp8vnwQRK9TuHNQuPXfAZ7OtG+hA4NtkwvrxNG1tOx27zdaKS5AaL/SN9X2YsnUswSUysrP0HrMlVQ+ubXJsd7XC1WvybNjto9/R+g/G9+B0/A097dasieBY8k5AEQGe/LENZ1K16tG0Dtd0qAeQHsvE++03UmMyz286fA+S5dXETQRNfrXb4fJMWfm3dSrNYPb+/OPayKZEN0SHSaeNX3C9s/n8tuOo9YPYDPyirnnq13kFRvJx4WNXZ7n4Tun2yfLyvL/72qnkv2oyXDNf6HtiKrX2LufnkU1fxcA+2fqgVa1Utkk8ovVMniqKAr/uSGKs4GjWGPsxTtt3uX6xz9jaOfWtTs/azDRHxSFWyeM5WdFK7sdGPs+J8zPNUI0NRLUE0I0eTtiM7j23U1sO51BgFL6iW5X3Rliz5xh8sdbWZugQ0Flq6kLaT7axEI/Rfvj/d32s4x4bZ21/MOkL/MJn1rDqPvvp8PCqVrWihBCNAZLP7NKSrzKZ+rl1mq3RSVGFu1N43bDMwwv+YDxA3uWbrSUY1YSBLm5TyiBntqU2/FRwSib34UXg2BFAw7+6TVDuzzyu9ao3as1dJ5o/+NYMvUKK2YPse4leL09/N1MSnCt9/VC1Wvu+hPu+A18I+p/vMBIGPcKboNnc/fQ8vtzctDx7q09tWmmFuNfg2dTYOBD9T92NY/nCjJOM+bM69Z/Rh97j7Pp+fU/hyvMmsPJLI9JBMDX3YlnJmjTjnU6hTemdGdgqCsH1QiuNm6k//KxvP/Wf9h3NoMvP3uH67K/B1SiW3nz2uTuVbZcqTV7Z+p1HAMD7oeW3ape4xEEfe6yf1axNahXxdTfXNsmV9tsxRPwH39tqm1NLBlz7rZncbs46nnkoXkMfHY1j9x1h1aCr6qQfAhi/7btwxT/9nD7z3DtmwB4uzlSPPhJilQH+uiO8+efEqAXTZP01BNCNGlfbYnj378fxmjSgm+tnPLBBIfDZ7D6nMIKUz8AjqptuEf/Hx6ddgP+Sd9DUieu9xrAT2u1Eo+0XO3FgJeLA0M6BMAJYPS/tAba1Tm1DopzYOyLDXk3hRCiapZMi7yKQT1vV0f8PZxIyy2udabe6sPJZORpz419oiLx83Au3VhNZpO7swM/3z+ImIQsrooMhDXfahucGmhQBkBIDwiOhqQYcPaCeQdtyxiprc4ToeNYrdz3Ys1p+i3YlqkX0En7srO7hkSwYEscF/K1/8Onx0VWPrRKUbReZPVVi0w9SzCkUO+BvqSAYboDvP/bzzw0c3r9z+MKEBOfxTfb4vh133nrdc9d2xnfMmWzLo56PrhzGB9/m0BJ/HIClVTmZL/B3s9/5X7lHG6OReS5BHL/jP/D1cmG//8VT8KxFTDqeeh2c9Xret0BrftCy+71uYu1ExwN175l//3WlKlnud5eQT29k5Y9a8vvUB2Dp4qi4H7xlNpPR4KxCB45UKGnbAUu3tC+/ECMG4b14oPNt3Cy2I+1x7yZkl1onbotRFMhQT0hRJP1w65zPL/skPXfg9r5McbFEU5Bl+jetJ02HfdtZ/hqaxwtvVx5dcpIbRpau4cBGAy8HXieed/vw2hS8XR24Ju7+9Pi+A7IidaarlfHaNACeiDTb4UQjcfSEym38jdvbf09SMvNIDWniJxCA54utk3r3LhpAwGYSMWHW/uFlt9YNgiiqhU+AAn1dSudPnmpgl09Z8AfT8Ceb7QptDX1z6oLJzfArfJt1vtZcXpqk2RLpp49qaoWMCu8gJdvO168IZrnfj3I2K5B3DEwvGGPbc3Uq6Ss+mLm4Lm+TX9+jVWYzBp6n/6QU6k30s5c7n4w/gKbt2+lW3Q3BnZs1VBnfdkqNBhZtiuWgrQzoCgoioJJhd/jFHbFl/9wYWgHf27oUfFn5OPmxD/uvYMNh69izS+vMq3oe3rqTgKwyRTNDTMfpqW3jSW3hRcg6xzknK9+Xes+2pe9FOdD8kEozoV2V9lvv7aoLqhn+V0D+5XfWrLu8mwI6t30P+33yCOofsdUFO242Qna36KagnqV8HB2oLj/w6zYoE3QXbAljieviazfeQlxiUlQTwjRJG04nsrTS2Ks/75veFueHBuJ/n+lk8tcHPXcM7Qt9wytZOqX2cTuIQR5OrPqcDK39A3VMgFCn4NRz2kvekxG0FXxKXDZ7IXmkpkhhGh6qsnUA20C7o447bkxNi2Pbq19atxlbFoeNyW9xQvOJ3nB5VEGth1ffoGb+Q2csVh7w+pcSRaVxaUK6nW7GVY/pwXeirIv/fNyc8vUs3xYVVWmXsoRbcpwYCR0sdN047e6gKkE5h1mQrdWTOhWzbCqhVO1N/XjXwfvegbOut0CESNs24956IGjdzB5/W+jaNt6BuoO896v3zNu4i18u3wNk2Lnc58ulu8PjCb6/77H4+Lsombuw/WnWLX2L1Y6/6Pc9VeZAhjFGxhwwNPZgcl9WvPYmE6Vl88mH4KcRIaHdGTgk+/w/drbcNv8Ct5qNsXXvs+Q8Fr0oLM8X1VVitpQLpyBL67Wfpeeiqt8TU6y9gGEi0/N1SG14V7NfVZVuGOZ9liub2DNwvIzzrfhZ+zup33Z5bi+pUG9msRuhMw4LXAb2Nl69Z2Dw/li02kMRpXvtsXywMj2V9zvrGja5NEqhLhsnEjOYcneBCZEtySqVdVvig4mZPHAt7utJbczB4Xzj2sitReFt/+svYCpqeGw0aCNtnf1oX9bP/q3vejFxYonYO+3MOqfWj+Uylgapbt4Vx34E0KIhlZNTz2oOAH34qDehiMJOOgdGNQh0PrmevWGDdyrO0aJqqN979EV33Q7uYGDq1b2lJ9RQ1DvgnbZ0MEu1xZaCZannd6kVqY4D/78Py3QNfnL8tMqm1tQz1J+W1WmXsIeWP+SloFkj6CeomjHzE/TGb0Y0QAAn5ZJREFUjlldgE1V4eQaMBm0oF59ebfWvmyRWzrYYPKwASzdcTVT1T8YdPYjxrwVhLtq4BFnbc01bGHr8SSujq5lQ/8m7q8jyZhQyFZdUQAFFQ+lkDa6VAYHFDFmSE9u6BmCm1M1b0V3fwU7PoEhj+I0+nmmjxlI4cifySsqKd8KwBbuNpZXH/5VaxMQNkgbwFNfloBZQSaUFIFDJef9y/1w6i+4/kPoOa3+x7Qom6l3cTa1TqfdR3uqLojYkGpTOr9/Eez7TnttXyaoF+Tlwg3dg/Hd/ym3mf7i102LmDbKjhmbl8j5CwX8eSgJZwc9t/QNRa+zLUisqipZBQaSs4tIzi4kObsQRVGIauVF+wAPHPQyhuFyJ0E9IcRlwWRSufeb3ZxPy6Tl5uc4POof3Dy8V4U3kvGZ+dy5YCd5xUYArukazHPXdild5+Jd8xuqI7/B97dD6AC4+8/K1yh6MORDdjWlGpagnpTeCiEaU9hgmLOnyt5I5SbgXjQsY8Ohc4R/fxUxagSz2r/ES5Oi8HF1wv2g1gdvrdqLcYN6UqnHjmi95XQ1vOC/lMGuhgzoAegcYPcC7fuid0pLVKH5BfWiJ0NoX/CpoqTNnpNvLVxbaEG9yqYLl1WcpwX0LLe5lMyZengE4e7sgHHwPHI3riNODcZJLSYHN+aZ5vGW7i1aKLmc2reeq6Nvv7Tn2Ihyi0o4kpiNSQ3les/FPDuhM6oKg38fiWt+Al9OCUcJraG9CZQ+vso8r7k46nFxrMOHqLZk6plM8ONMUE3w2HH7BPVcfEDnqD1W81IrDxw31PRbj0C4+t/aflWTfXpPVscaXKshqFecD2vma0HAoY/V/0Px2mRhWtZYblPGrGHtyTu4kzBdCuu2fIRhxGc4NoFg1oX8YlbEJPHLvgR2xGZYr193LIX3bu1Z6e9LXFoeB86mkHJiL6aEPfhkHSbYlEKgcoGPSmaw1dQVgA5KPLMdV5Du3RU1dACDBw+vNulCNB4J6gkhLgvbYzOITctjvsNCZuhXkbhuF+/EvcB9t03F1UlPblEJK2IS+XDdSVJzigDoHdaCt6f2sPmTKCtr8++MituMBni9Y+m2nKSq9yNBPSHE5cDZQ/uqQtuAMpl6Fw3LOLJjNcN1KRwwtmXNkWR2vZXBdV18ecy0HhQ4ETqZMVVlxdj63GcJdpUNgDVVDs5ahmJJgfY3oDkH9fzaaV9VsWSsedgxGGH5edYU1LNs1zvZJ/hSmAV7vtYCDiOeqn7tRQMGbhreh8l7PuNghh4vFwfmDG/HLX1Hs/HVtUxUNuMa9xeqOs1+E1ovc/vOZOKqFpCHKwPb+TGqsznQvikI8hNQbMmognIZkfXmbkNpaOEFLfgF9ntdp9Npj5PsBC0YXF1Qz56/R6A9Vw1+pPJtyYfgzBYI7ALhg+1zPGtQr5LX1mXlpWoZmA6uMPxJOx7XlgEdVU/d7RjsxfuBt9Ez7d/cUPIHf+49xbV9OtT//BrIqdRcPt1wmqV7Eyg2mipsX304mWmfb+fzGX1oYR5CczAhi99+/oZxaf9jrHIGZ6VEW6wA5thfa6W0B2N/3RFu0q2HnPVw+ANm7nuS3NCR3DEonGuigptE0PNKIUE9IcRl4cfd5wD4xng1Q3QHaa87zwOxc/j0rQOcDpvKH4eSKTAYresj/N35bEaf8p9AFWTCmn9pLwCveqbqg1X3AqAgs3ywLyex6v0Y8sHBBVx9bbmLQgjRKEJbuKHXKRhNKqdTywf1XOI3A2AwvyS8kG/g1gN34aPLI171p8fwSfU/gQ5XQ3ai/Xo3NTbXFpBTUL4sVVW10rnCrFpPdGyyLBlr9s7Ug+on7kL5D9XsESwrKYZVzwKKlj2kr+Yt0shnIOO0NiUVLXvsqwfGsj/+Ar3b+OLtpg2iSQgYCumb6WvYzbHkHCKDm8kAlRqcOBbDAed7OKC2I7bN0tINgx+GolwIirJtR5Vk6tWZNZurikmwUBqMcvay75Ada1CvkvYIqtpwmXrVif0bVv4Dut5ov6CeewCEDdFKnavrR20JrNnredKWgK2F5WddSaYeQO+x04n95hMidMmcX/0+XyZMID5Px/ECTxRFYUqf1oyPaomutskEdhQTn8WH60+y8lASqlp+W7sAd66KDGTh9rPkFRvZfSaTyR9v4dXJ3fhu+1mW7k2gH4U87awNBbmgunPA1JY4pw4UercFz2DC/bvwb/9W5BcbuXAyhwWJuYws3kCYLoXOylk+OpPJrjOZBHk589Q1kUzqdWW1FrhcSVBPCNHocotKCDv4Abfr3VnnOIwj43/h1PI5jFW281DBx7wZk0aBsfSNZZeWXnx8e2983S960ZWdCLu/1IJstgT1CjIrvvC4ONBXXflt1CTty2iw8Z4KIUQD2fgmZMbCsCfBp/ykWicHHW183YhNyyM2LQ+TSUWnUziXkU+U4QDoIN6nDxNatiQxZgNddGcA+NPpau5sX80b6r3fwolV2hvDrjdWve7at+xxDy8frj7aFM2ygSdFgfGvNdYZNYz8DK1dhWqEPndV3G7PoIuFNZPexkw9e2VUWfejasHaSjJ5rMIGal9l+Hk4c1Vk+aC1d9RYTOtfoYvuDF/vP0RkcPnbNFdFsVvQKyomVaFPeJmfY3XPEZWx53RWj0BoEQE+1ZT9Wl7/udn5g1rLhxmWIHhZBZnaYBhomKBe2gnIPAMBHcvfd3tPvgVt6vedy2teZwme2uvnHDFMe/4NqaJNRFmWybxV/H4PaB/Ax543cX/eh9xbtAD2LuCdkhvZWHIzAFuPJ3LSdxc9JtzH8C6tLln2bWpOEcsPnOeXfefZd+5CuW2eLg5M6RPKjT1b0TXEC0VRuL5HK2Z+uZO03CLyU8/y+Sdr+cPUH4DtdOZfzo8RFj2U8PZdiG7tw7CqsvGHtwOmUvD707DrQ9q6F0K2tik5u4infj7AqMgg6wcZovFIUE8I0ehW7TnBvcovuDoW83GH/lzXrxMnw37i6y8eZ0bx99yo38gCxylM7BHCTb1a0yPUp/I/pNYXZDV8+md58a6azFkVZV5YWPah6LTtOYkVGwxfTC9/zIQQjWzfQkg/AdE3VwjqAbT1dyc2LY8Cg5HknEJaeruy49gZJiraJ/benUfxwfhe/LHHk2+X7STEmIDfmAerz0hIitEay/u2rf0b9qaspgESzUVuCvz2sHZ/Kwvq2bM80sJSflvTz9beQT29g3Y/Cy9orwOqC+rZaFB0Jz77azxn1SDOn85lRr33ePkzmlR80/eCAocdOnO7bx1Lo0uKSx8D9gga+0bAI/uqX2Pra8jaqm6QkaXHm7N35UM06mvNfDj6O0x4E/reXea4loD8JcwOtB676hLYOmkzQPuqiaEADOZM9Sr+jxVFocPY+4j96WcidMlkq67oKS1tvUW/jrn5XxL/w0I+8J7BgBseoE/bhvkZZuYVsy4mluSdSzmQVMgfxr54k0sXJY1cXCnwaMPdQyKY1r8Nni7l34dEtfJm6QODuON/O3gu6xX6645yoKgtua4hPDyqA9MHjMPJwfbSWVdv7T5O7uJGq6j+vLbqGHvPXsBgVNl4MpVru4XY9b6L2rtsgnozZ85kwYIFjX0aQohGkLxtMa5KMadMLRkw9BoA2gd50XLmPPj0e1o75bHjqdE1N0fOr/4TOCsHJ628oihbu025oJ75E8SgrtobVkO+FvhrDr2ghBDNl0egFtSrorzs4gm4Lb1dST20AUfFyFlTAFFdowEY16sDhdHfkJZbROsWbtUf01LCVF0vI5NRy2Z2dKnV3bmsWfu+XSi9zlAIRTlaPz17lu41JmuALUsbInDxQJSGyNQLH6p9qNa6X/XrSgq1fnr27Gnr5lca1KtKYRYc+R28WmpTf6sR7u/OYp97iU3LQ3/OQHahAS+X5v0h4LGkHLqrR0GBvMA+5T+AzU3RXlc5udcchLE8j+kcSoPoDc3y/27vlipdb9RKjkP7V9xmHTZjpwDXxdyrKDu2BuTt+LtrYTJpH4pXVcLeUMHTmliCiTrHavueju4WzkbnDexMzSTA359R3i7c7u3C/vgsdizbQ3KOD62VNB7KfpP/fnGer6Jm849xkbTyqV0Ae8/ZTN5cdRxXJz1t/d0J93MlzM+DM+fOkrX/N9pnrGeCEoOzYuCIvg1/GPtyh34Vjzr+xInQyYTOuKPa90Whvm78dP8gCt7KwK2kiCcj0xg+5TZ83Orw98n8PknJz2RQe38eU+H2L7YDsPZoigT1LgOXdXfDJUuWMHbsWPz9/VEUhX379lVYU1RUxJw5c/D398fd3Z2JEycSHx9f474//PBDIiIicHFxoXfv3mzcuLHcdlVVmT9/PiEhIbi6ujJixAgOHTpkl2MLIUqdSc+jd+YfAKx1HUP3UB/rNvcWLaFldxzDB+Jiy3Cs2rxQsATyLm7oa9mHd6j25qLTBCgpqnwf61+BhbfAiTU2nJwQQjQgS7ZUbuVBvYsn4Kqqitv5LQDsUKLo1rr0TY6Lo77mgB5U/TxaVlIMvBgEb0fXvL+morIS0bNb4fX28OnwRjmlBmENpqjah2AXu3Ml3PE7+FYzTKO2ukyEca9Ap2uqX9d9KjybAlO+tt+xbWm4n3Eafn0AfnnQpl2O6KT9XhpNKptO2NDzq4mLORlHJ532Xsijw0W92k6ugW8nwYZXat6Rsydc/yGMfbnm6dr2YumnbO9gU7uroP99ENKj4jY3Py0LtusN9j2mhSVoVyGoZ5ngbOeg3g8z4D/+cPDnqtfkVz2Btk6MBm3wR9zm6te5+cHtS+Cmz2vswzm0U0umDOnCyMhAOrf0wsfNieEdA3j8sWfZd8M6VjmMBKCjLp7f9p/nqtfX8+aqYxxPzmHv2Uz+Pp7KipjEctNoy0rNKWLWV7vYdDKN1YeT+eTv0xT89hTtv+nNlPVXMfvCm4zW7cFZMRBrCmKbY38eHB7GtKt6AdDBvdCmKdC+7k60ctQm3l8/fkLdAnoALcKhzSAIjASgb0QL3Jy04284lorJpFZzY3EpNGqmXlpaGo899hjr1q0jOTmZTZs20atXL7799lucnJzIy8tj8ODB3HzzzcyaNavSfcydO5fffvuNxYsX4+fnx2OPPca1117L7t270esrf7B///33zJ07lw8//JDBgwfzyf+3d9/hTZVfAMe/SfcupdAWKHvvvafsoQIqIAgioiAqAu6fCxy4EXHiYgniVkREkCXKnrL3pmV27zb398fbpCtp0/amaeF8nidP2+TmvjdtenPvuec9Z+5c+vfvz8GDB6laVdUbeOutt5g1axbz58+nbt26vPrqq/Tu3ZsjR47g5+dX5LGFKI1WH7zEv8evMrFbLUIDSjabYs0/mxlnPEKGZsCz1d05r+p6BcKEv+1fWWHqdNTtp5b38Mu1jmw1Ve7+Jv91nNsKJ9ZAw9vt30YhhHAE88lZgpUpXuTsgHviSgInriRQLu0SuMDVCu2L1sXOfPKbkF9HycyOsG52BAnLir6vQb/XVca32Y3W+RZUdqWrp8qKS47Om7Feoa66OYvBoG/5C3u6d8YXbtpi93oVWb1pO92NeziyO4kBTe4s5kZm0TSNT75eiu+Z1dQd9irt6zo/Wyb6qAqsnDKF0Lhu7ZwPmi885NeswszTXzWe0dOP4+HMZrj9Q6jVI+/jdfqozM/AavqOm5+KDRxbc9TW7zxXB2fdGF1VDc78mlbonamXFAWfdAQM8OI12w063L2hds9iDWUwGOjboiYm7Q5Yto4Ql3hIg5R0E3PWHmfO2uN5njOlVx2m9MraT2qaxpM/7GVg8m+Mdv+LnzM68XHGYGoaIqhoiAbggKkaWz06klFvIC1bd2JstXLq/OhA5vrt7SCdkZZ18ak4ZRJqdle3TB6uLnSuHcyqg5e4lpDKfxdiaJ4tKUOUPKcG9aZOncr27dtZtGgRs2fPZvLkyaxcuRKTSc1dHz16NACnT5+2+vyYmBi+/PJLFi1aRK9evQD4+uuvCQ8P56+//qJv375Wnzdr1izuv/9+xo8fD8Ds2bP5888/+eSTT3j99dfRNI3Zs2fz3HPPMXSoKs6/YMECQkJCWLJkCRMmTCjy2EI4Qmq6iYiYJM5HJXE+KpG45HQGNg0jLKDgVPCdZ64zYdEOTBpsO3WdXx/pVGItyk0mDeN/KnC2UWtK3/Z2FLnNj+VAwY6rf/1tXCn2KgehTVSNqILoXdNHCCGKyiefuk3kDOqduprA5hNXeSFtMjPS7mVSwyJm0fnYMf32Rgx2WbtwdCO+TlDZevGRaqpxSXzUZaSr91N6ksoOKUn2ZOoVssFAuxpBPOH2I4ONf7Po9DVMpjt065y5cvcpJp2YCMC8n3xo9/ScEivcb4tnxHYA9lCPQZVydfu1TAV1UsZiwlWIPW+9YQWoAFvFBvqPm5akMpaTY6FOL/3Xnx9rv/McHXd1Dup52/E37v0ytJ/kmCY3SVGOm8qcjTFzjHZhBsZXrsH8TadJt5GtNvuvY/h7ujGucw0AFm4+w/ojV3jW9Sp1jBcY1zyQ9o3bcjHyQ764eAQ3/4q0a9mC+0L88v4/F/Z/yHyBwmDU/VylR/2KrDqo/pfWHb4sQT0nc2pQb/fu3YwePZpu3boxb948evToQY8eVq6c2LBz507S0tLo06eP5b5KlSrRuHFjNm3aZDWwlpqays6dO3nmmWdy3N+nTx82bVLTUE6dOkVkZGSO9Xp4eNCtWzc2bdrEhAkTijQ2qCm7KSlZU/liY61MZxDCTpfjknnmx32sP3KZ3J8l32w7yx+Pdc23EGpiajovffsvJs2IARMtLv/Eoj9MjBtUMtOHthy/TJ/0taqgcsVb6e6fT5ZgQc0qIFtKfzGu/rW5P2cxYU1TV7qs1UiSoJ4QorTwzT8LpoKvB74ersSnpHPyajybTqhshqsE0LZePh0h82NPEORGDXbldqO+Tq/AzKBerm60lw/DwV+gQn19pw5e2Alf9VEBvcf22l5u7atqyl27CTkySIql02NqKmS5fDK1LNMWQ2wvk42nmwuRIV3gyt+0S9/JwYhYGlcu/nskLcPEuZWzLD93T/yT/edjaOLEE+vImGQ2JlUnwKUjp8t3znuB2JI1drXgY7pLByH2IgTXyf/vURi26ss5WlwEfNlbZSv/72LO1x1/WWWbegYWfIxbFNYy9TQNxixTWd12vo/tH898oSefoJNXOX2Pm82/v+Ro9d6yFdS7sFPtM0Kb2NcpNz+ZdRddk67z/KCGjGxXlYWbzxCXnI6fpyt+nq5EJaby9ZazALy8/CD+Xm40rRLAaysOAVAONS02uEIoXetWgLoVgKb5j+tdyPeweTnv8rYzGAtDyzzZNBjoUS8rILzuyGWm9nZi1rZwbk29Tp06MW/ePJYvX16k50dGRuLu7k65cjl3DCEhIURGRlp9ztWrV8nIyCAkJMTmc8xfC1qmsGMDvP766wQEBFhu4eF5O9QJYY+dZ6K49YN/WHs4b0AP1PSqr7ecyXcd839azjcJD/Cgy2886/oNr7l9Rf1tz3LwQoyDtjqnFdsPcdBUjWuaHzU63WV9oV8ehplVVGfHggx6Dx7dBc3utm8D0lMhJd7245s+hNfCYOUz1h+XoJ4QorQoIFPPYDBYsvXORyWx6bg66fL3dKVBmL/V5xTIfIKREqcaYlhzIwa7Iv6D3x6DDW9l3WeuOXcjvU7I+nzL3Y02Yg+sfx12ztN5vED1NXsTEmvOboEjK/TN+qpQF6q0yj/TxzJt0f6pbOWa9idDM1DXeIGde/MJVBbCL5v2MyJF1S2L0nx5JG0y3+88p8u6i2rHmev8ZWrFlLRHSG9gpSyJeX9hSsvaL9iyexEsvgN2fKnfBhaURXZ8jbrlDmAXl3nfnJYIqbmOOZdPhTer6/s6c4xtJahnNEK1Dqp0jN5NfcxZzAl2Tg/VbVw7LjAdWg7LHoW9S4s/XoW6MGwRDP0MUDVrp9/WiHeHNWP6bY14vE89Xh3chMm3ZE1Bf+qHvdw3bzup6WpGYtPymV11C3MOYd43JUerhIOCWDIyi9mh15QB79SFVypY/j9CAzwtxw7/nY/hSpyN+uOiRDg1qDdr1iyGDx/O1KlTWbhwIc2bN+fTTz8t9no1TSsw/Tz349aeY88yhR372WefJSYmxnI7d865H8CibFqy9SwjPtvMpVi1Aw329WBgkzAmdK3Jk33rWZab/ddRohJSra5j05GLdD/4In6GJNq6HuNq/ZEkae50NB5g7eI3SMswWX2eXuJT0vnhcBLj055kgOETejS2EeA2pUNqnH1XpTz8oHwt8LEjU+/f9+HVCrDyadvLuHmqKUBxEVa2KyProFSCekIIZ6vRBSbvhnt/s7lIzcwOuJoGC0zPsMhtJrdXScSlqNMBvcvDU6dUwwJbWQA3YlAvLhJ2zofD2S5K34ivE6DnSzD6Z6jWOef95ow1vafvmZtzmDvu2mIO+pV0Z/pCZuoBdGxUi52aymJJPvRnsTchMTWd5HVv429I5JCpKu3S53JAq8GveyNITrMRXC8BO05nBcNaV7MyRd3NE9wz6xgXFIwt5DRnuxSURfbHU6qRx6WD+o0J4OEL7pmNinJfdHHUNFizgMpqumvf1x2z/ty87cjUW/2iajSnZ/DUngxBPRt0eJVTTX0K6OI8tXddxnasDoBJgwvRSQDUC/Gjrn961roKMy6Zn9f51f408wuFdhOh0VD7x7DG6KKmkZvScozbo15WsHD9EesXFEXJcGpQz8fHh9dee41jx45x22238dBDDzFt2jQ+++wzu54fGhpKamoqUVE5dwqXL1/Ok2VnFhwcjIuLS55suuzPCQ0NBShwmcKODWoar7+/f46bEPZKTE3nlW838MbPW0jLUOl57WoEsXJKFz4a1ZJnBzTg4R61uaNlFQBik9N5f82xPOuJTU7j6Hcv0NB4hmuaH9e6v8XjI/ozz2sMAGMTvuLrlRvzPE9Paw5dIjlNHbD3bl7ddhcnR02XMBc4z/2hOLcbzGkBkfvBL7PodOzFvM9PjgEyUyQtHQKFEMJJPPxULVAPX5uL1AhWjwUTQ3PjSbq47KdhrWJMbTMaVWZGft0pb8RglzmQlHg9azqS+XV63GDHddU6qO6duTPTLA0jdA5GWIJ0GqTkk83liEz5mPOw6QPY9rntZeILn/kSHuTNfq82ANSK3mTzYqu9lq7bxbCMPwD4q9JEBjZVx3wxSWmsPmijXlwJuHJiN3UM5zFgomVVG38Xe4IvkNXwR8/3l+V40kY2V/ZGaXozB4Fz1/OzBC+LmUlli4efmlbePNsMlksHYOtncMoBx/kF1VnNSId/58D6mfZlmtnLnkw989/dngv/OjEYDLw4qCFDW1S23OfuauT9u5tjTDbvwwrxfjO6QNcnodd0+7IsKzZQNcS7PVm4DbfG0u0+63d8S/2s/8/1R0p4WrvIwalBvewCAwOZMGEC/fv3Z+NG+3YyrVq1ws3NjdWrV1vui4iIYP/+/XTs2NHqc9zd3WnVqlWO5wCsXr3a8pwaNWoQGhqaY5nU1FQ2bNhgWaYoYwtRHOuOXGbEu7/w5ME7+NX9eUDjvk7V+Xp8O4J9PXIs+1S/enhlBskWbTnD8ctxOR6f/+0PjE5X0zYWlX+Mu7q1VJ2MRj3HNlN9fA3J1Nv6LIcuOm4a7s5dO6mEOqgb2CSfjm2Wjo52TLH5/QlY84oqRlwQWwcA10/B9ZPg6gH+Yeo+a5l6KbHg6qWuvuo9fUEIIRzAPP22vVFlohw0VaNVwzqOHbRifajbHyo2dOw4Jcm/EmCAmHOwdJQ6MQ9vB02GQUgjZ29dydBrWldurh5ZnZLzm4LrqKDequdhy8e2l7nleRj8CYS3LdSqTbVUg4R2xkO8tuIQmjkYXEhRCam8tymKcWlPsCijN/2HjOauVlVoZjjO266fcmrjN0Vab3ElpqYzMGohqz2e4n+BqwnwttGVuPszcPtHUK5G/it0RLArv3pkpoys95teXVmzMwf14nKVaDIf2+odHLcmJQ62fAKn/oY/noTtX+g/hn8lqN7FdgZbUhSWC+KFCWYVxJ6gnp6ZegCHV6gLADZKXpgZjQbeurMpw1pXoZy3G2/e0YT6of5F34fd8hx0nlrys4TMf6+krGSI5uGBBHip//W/j15x+CwvYZvTu98OHjyY5s2bk5GRwbp169iwYQPPP/88ANevX+fs2bNcvKiyZI4cOQKoLLnQ0FACAgK4//77efzxxylfvjxBQUE88cQTNGnSxNKR1ppp06YxevRoWrduTYcOHfjss884e/YsEyeqDlIGg4EpU6Ywc+ZM6tSpQ506dZg5cybe3t6MHDkSoMhjC1FYV+JSeGX5QZbtvYg3Bjw906hhuMQHQ+twa9t6Vp8T4u/JQ91rMWv1UTJMGq/+foj597XlcEQMq374jLuufoyLQWO51pk7Rz9s6cTWNDyIz1u+QpPdo+hoPMCHS96h/uMv695NLTE1nVan5/Ky5z+8ZxhD2xoDbC9sqQdSQFp3aiJsz7y63nlKwRthueKULVMvIy0rM8C7fFbGRfxl9ZhLtoPUctXh+UhISy54LCGEKAl/vw3RZ6HbM2raVS7moF5H4wEAdrs0YWRF25l9dvl3DlzYAW0nQPVOeR9vPU7dbiQBVaDf67DqBTjyO5zbomq63pFPhldZdfkwnNuqXnPtnln3OypTD9TJalpi5kmvleBPeiqkJWQtqxd7AgPVOqhbIfXs1AEOgp8hieU7T9A8PJB72hc+S/bj9ceJS0nnX5pQpeUARof4U7OCxu0++7kr/W82Xo4iImYSYQFehV53cew5G0UrgzpPM1VqY3vBZiPsW6Ej3l9+YSqbOaBK3seSoskKNjkgWGJ+HdkDQGlJqrwMOL5jq8kES0eqgJ45OOOI/92gmjA2n1r55v8tz0Bw0TEMUX+QaqhSvavtZSwXInT6Xa9+Ea4dU9lwBfwuXV2MvHVns5x3+lcCg4tjMkPNYi+C0VX9zYv7+7Zy3uTqYqRb3Qos23uRuJR0dp6Jon1NtR/dcfo6l2JT6N84VLeO38I2p2bqVa1alWnTphEeHs6SJUsYM2YM48aN49FHHwVg2bJltGjRgoEDBwIwYsQIWrRokaPu3nvvvcfgwYMZNmwYnTp1wtvbm99++w0Xl6ypfN27d2fs2LGWn4cPH87s2bN5+eWXad68OX///TcrVqygWrWsD9ennnqKKVOmMGnSJFq3bs2FCxdYtWoVfn5+hRpbiOLYfPg8698dxf7/dgCQiCdpqMDSrbXy3zk/0KUmlQJUN9n1R65w//ztjJnzO+Ovvk2Y4TpnTBVJ7/smVcp553jemEE9Weg5CoABsd+x87T+xW43HrxAD8MuALxqts+/npO1Ir/WmK/Aubhn1S7Jj7WDd8sHlUFNFfOpoD4M0fJOmTBzy6djrxBClKQ9S2DXQog6bfXhGsE5M/XiwjoW/6LN2c1w8Fe4eqR46ylr2j8ED66HkMbqc+TniRDnvKmPDnNyPfw2WTUuyM7SMMIBgQFLXb1o649b7jeAh47Tus3BjuQYfacGAjUqhbC2xy+0Sf6IFNyY8dsBdp4pXE2x/85H88PmwwB4uBqZ0ltl2RqNBrRmKumgk2E/qzbtKNa2Zpg0DkXEFqo+37EjBwgxRJOmuRBSL/86YwVvQFpWNpCeteaqtFJ1R4ctyPuYJdgUkPMCrl6sTb81/w+5eDh+2r7RqJrIGYyO+d3ay3ysrncQs14/NS21ajvby5in3+qVqWdlOmqhPLgeHj9kPcicn4RrELFXZRYXZPk0eKdO3v13UXhZf7096mdl0647fJnjl+MYO28bd366mWlLtjDr+5yzI4VjODWoN3XqVHbu3ElsbCyjR4/m3LlzvPXWW5ag2NixY9E0Lc9t+vTplnV4enrywQcfcO3aNRITE/ntt9/ydJQ9ffo03bt3z3HfpEmTOH36NCkpKezcuZOuXXNG9g0GA9OnTyciIoLk5GQ2bNhA48aNcyxjz9hCFNWl2GTOL53CXazmNdevCPRy5Z27muEamDlVtYAgl5e7C0/3r2/5ec3hy1zWApiTPoSvXIZxaPAfDO7YOM/zPFxdCLnlIX7O6MRz6fezKLMdu55Obl+BvyGRS1ogjdoVkNlqqYFSwPRbSy2U8mDPSao5qJcUldW10Xyg41VO1a0wGsFX1djMM2VCCCFKG/NJmo3PB293V5r5x1PTGEm6ZiSoYY/ij1nQiY3OwZFSJbQxPLAOujwOfV4BP/ubJ5QZtrrROqKRgVnjIaq4u1+Y9cdT4lQgxDMg/3qOheUViKUIvbUi/knRsPtr1SG1sAwGbunWg1s7tUTDSFqGxqTFO7kSl0JyWgbL9l5k9JdbafzSnzz5/V6uxufsJLn8v4vcM3cDG1weZrLLT4zrUCVHNl6fTu3YnNEQo0EjfdeSIk3v1TSNP/ZF0G/23/R/fyN9Z//NpdiCZyNcjU8h7qA6cT+gVadF7XxKqsRehON/wYWdtpcxH+8ZjI7NYsou+zGkIzS8Dfq/DQ0GZd2XvT6jzjNirGo+Eu5aoC5+Q2YZAQcxmax3RHf079mW9NSsmTh6BRTtyex1hHWvwdyu6gJeQfQMolrOm3LWIu9Wt6Ll7btk61n6zt7I+iNXqG04zwr3Zxl0YBp/H7IjACmKxanTb0vC4cOH8fPzY8yYMc7eFCHsZjJpLF74KdNQB0mbKw7nr/u6q9p5uytC9JkCazgA3NasEt//s4/TFyK5ogXg7uFN4C1PMbJjPo0pgH4ta9P+zylEp6Xhvv8SL8anUD5X3b6iSk7LoOL5P8EA6wztuKNWAfVS/MIgrDn4551KlkNhDxQs0ys0dVXeO8j6Omp1VwfyLrnq5h1cBnsWQ62e0O5B+8YUQghH8i04s7mP7ym4Dvu16rSpX4wmGWaWboc2OvG931ydWNy/GsKaFn+80sbVHXq+6OytcBxbWXP3rVBZR+Vr6z9m1wKKupevBS9c1r/8hdFFHRskXVfHA7mzEK+dgF8fBv8qMO1AkYZ4dkB99l+MYdspNTVt2NzNXItPITY53bLM9zvPs/JAJNN61+We9tX4cO1x3l9zjBBi8fdM5DG3n0jq+WmO9YYHefNb+QF0iD5Iz5Q17Dh9nTY17Dse0jSN9Uev8O6qI+y/kFWT+My1RMbO2853E9rj55k3ey0xNZ1vV28maNubTOIfMMBe1yaMCfLOs6zFgV/gz2eh8R1w51fWl/HwVXULU+Jsd9XWmzlQ4ahgU/XO6padZwC0GQ/uPo4Z05qGt4Hvb3DkD/W9I3zVD85tgzG/QI1c02ETdc6WM0tLUv+f6SkqIzM3g1F18U64pl9zO6+801FLhL3JDqBv7dMKdaFapzyZhUE+7jQPD2T32WjiUrL2Y2keQQRoCQQbYtn6/Qs0e/IzS/09R9M0jajENCJikoiISiIqJprUhGgSUlK5RDBJaRnUrehLzwYhhOe3vypDSk1Qb/78+Q5Zb/369dm3b59D1i2Eo3y7bgf3XnkHDPC18TbGjptEkE9mUKmATIzsDAYDHzQ7S7mrT3IssBNB43+2Kzjn6ebCsNbhfPb3SVIzTHy34zwPda9VnJdk8c+RCG5hOwDR1fvj5lLAVXa/UJiwoeAVJxbygMzFDRrcBm5eoJlsr+P2j6w//8phOLpSbZ8QQpQGPlbqNuXSrWltNv3dirTQpjQP1uFk0ry/tHWCkRwD6ckle+Iq9GMrUy+4jro5kyPKX3iXzwrq5WbpyFrEE+Q93+AWsZe53W+l3zUPLsWmcOpqQo5FjAYwaRCXnM6M3w4yZ80xohJVtmuQQdVfM/oE42slyFa543Difv+I6sZLrP3+Vf5uPpHQAE8qBXjRuno5q4E5k0lj8tLdLP8vZ0MwdxcjqRkmDkXE8tDXu/hqbBvcXdXxWmq6iR92nuf9NUdpFb+Bj91Vg8PlWifK9/tf/lP67Smp4uGnssoc4es74PIhuHtpzosMoU3gtg/V2CUluDYMfLfkxjOr2t52Iws9GIygZVj/TLBMgdU5A/PSQfjiFtsBdxdX1cVbT9Zqc9vrzGZV1qBSCxj6WSHHtbODNGT9DfQI6uVTH7dXgxB2n40GwMvNhUndqjO+qRsLvpnAxKi3uSfjZz78tjePjXXQ/3WmmMQ05i/6kioXVlBRu0aY4TrtDdfxNagLQP+ZanBb6muW5ZP/eAFXn3JodfvSqX0XGlbWsZxDCSs1QT0hhHI4IoZKG56gvDGOQ6aqVBv+elZAD+zKxMiuXIb6sKlTszYUIttuZNuq/LFxM/e6rCL53xAyus7Ov/adnY5tW0kvQzzXND/qte1T7PVZFCWlf3iuGhOuHhDaVF2NKogjOu8JIURxWLqF2w7qNe5+J3S/U78x85uClJGeVQher+wIUbIKqm/nCOmp6jPWYCx6AK2ovMur4vfW3s+W5g1FnGZ9eDkcXk658rX45J47GfHZFlLTTXi6GRnQJIxhrcOpVcGXt/88zHc71HQ1c0DPYICH2wXCHjDYOM7p07wWK37vwB2spW7cVu5Zm1XepIvXaZ66726aVM0ZTJm95liOgF6jSv480ace4UHe3PnpJqIT0/jn+FWe+mEvb97ZlGVbDrLu7w2siFUNTFbQjgUZfUmofxd33nYrFf0KCLT6FHARwNHiIiH2Qt59ZGBVaDnaceOmp8DFPep9Xa+f48YpDfL7TGg3QWUIuuoz+ydrzGLWtyvSmNano9olPhKuHi1axqLlf6iA15qaCKnxmc9xbCOWsR2rc/ZaIl7uLkzsVotQ7QrMbswEoxu/aV241bCRQSdf5q+9HejVzHrn6+sJqTy2dDep6SbeuatZoTPoElLSGTt/G3MvvU5FY3Sex9M1IxpZ57FeJHOfy0o8UtJg30L+2duYQxNX0KBS2QzsSVBPiFIkOS2DNQtn8rBxDymaG+saz2RSg1wFVMOaQe3eEGjntClzLbhCZpRVD/ZhRKUrjL/2B5Gp5dh4+Cm6NyxgCmwBUtNNlDv7JwDraMOtdQuZ5aZptuuO6FGno25fdbM2bnqyyuozk6CeEKK0MV+Nj7fvoo8+Y5qzBqycYKRkTeXD08GF4IVjZM/UM5lUDbvv71PTbttNzDrB1NOmObD2FWhxj/Vs+f0/wb7voU5v/Tsr939DZe9bm1ZsqSNYxECjOfCQFEXLquVYMbkzxy8n0Kl2+RxZdG/d2YwRbavy4q/72X8hFh93F+bc3YKeGf/CHmxmOXm5u5DW6XFW/RPLHlPW7Iqhxr95y/QZv8xbh9fEedQOUdloqw9eYs6aY4DKEHznrmYMbl7Z0qnyy3tbM/LzraSkm1i35yjzD8/hbu0PeuLCeuaQiCe9G4bSqd9X1La3i7YlUy+foN6lg6oJQIW6UK66feu1l7edARG9JV6Hr/qobqfPX4Lz2+H0v+r961VO39qQzpZf9ranv2M+C8yfQ+lJkJqQNzP88mE4vw2C6+XfTKMwihNINJ9DFCVj0d5MvewNBPVsxGLlXMzHw5U378yW+RqhAp0G7yAyOr5B5J+DqGWMYOsvz3G99vycySqZXvx1PxuPXaW98SArPl7IwIffzdPM0ZbktAweWLiD3WejmeMyhP5uuzhZrjNJgXVxD6qCd/kqePv44evpxg8erhgMsOPYBebvnUDt6H/pZtxLZ+N+Ptu4lgbDhxT9d+NEEtQTohR5d+VBbk9YCUb40mss44YMyLtQPunPVpk7bRXhynLd7ndz5YcPCTVE8dO6pXRv+Hih15Hdvyeu8krycP411qVa3aZ4uNpZK+X7++DYarj9Q2g02Poynaeq6RquhZyOk56qDuBtTeM5/Dv8MA6qtIGxy7Pul6CeEKK0KShTLyNdTYfVMxBjPoGzlsllvs/NxzEdJYXjWTIsNRWkTY6GAz+p4ET7hxwzpvlzNfeUX7PLB+HIioJr7RZFpRa2H7NMvy1ic5BcNbhqV/SjdkXr0z1bVi3Hrw93ZvfZKKoH+6iaytsKnro4ok9n4rqupFp0Mm1ikoiMSeb8P/9hjNG4U1vNgs8m4/Xox6SkZTDt2z2W5z3drz5DW+a8iNyqWhBz7m7BY19vZpX7U4QQDQY4ZKrKbTU0hvXvSMuqhTwGMgf1Eq9lBYlz27MYNn8IHR+FPq8Wbv0Fjm8jIHJuu3p/hzRyTFkVn2DAoKalvls/a/x1r8LgT6H53fqP6Sy2fseO5O6rglcZqeq9lTuod3I9rHwaGg7WL6hXoxsMWwTlilCb1jxl13zRpDAsNfUKuHiXvZ6eHo1Yrh6DeQPU77mgmqKW1xfE7R0aMXvvk0y99D9Gar/z9qIFTHtwfI7ZX38eiGT5fxGEco15bm/hlZ7KFx/DwEdm5WgIdCUuhR2nrxMS4EnjSgG4uxpJyzDxyJJdbDqh9o/L3PozasLLdArLP5DZqloQ9GpCREwSf717G/0M2/A88ism02DLhY2yRIJ6QpQS/xy7yuf/nmURLzHSbQN3jXkh32YWditiph5Aj4ZVWOzai3szfqLppR85H/WQ3VdNrFm5L5IEvPjN1JG5bawUsrXFlKamcOX3AebuA0E1C7dByybDrgXQawZ0nmJ9GQ9/laUXl7PejOUKpAT1hBClRc3uMHm37Y6kV4/AJx3VvnLybn3GDGsGT59RRd9zS87sNmjtMVE2uHnCXfPV39DNC/YuVfdX6+i4zqS26viZOeuiWjEukgLZMvXsm67nYjTQunq237GdtYP9PN2oF+pGvVAVMIxp8gQffhDP5MQPuTfjJz792J8fPYdaitoPbBLGg12tHz/1bRTKWz3LEfJPNMmaG59X/B9dbh3LG1WL+Lc3b7uWoQLE1t5DjuysbKum38Z3VJ3kW+dAq3v1H9fFTR2Hx0WoYJdHANQfAI2GQB0dS9GUBt75ZG//8566mN78bjXlWS8Ggxo37qIaN/e69ewCa1auWtECepBtH1acTL3rqsOwrWYynoHQ7iH9ao+6easLG0bX/GdOQbbGM0EYDAZG3XM/P7y7liqmCyw6HUDssgO8fHsjDAYDMYlpHPjxdVoaqrNLq8tc43CmaIsYn7aEuR+5MviRt7kcm8K8TadYvjeC1AxVB93D1Ujz8ECMBgObT6r3mre7CwvGtaVBAQG97MICvPg5pC9c2Ub39H/Yecb+JkOlyQ2U6ytE2RWVkMrj3+8BIBkPKvV5rOA5/ekp9q3cchBa+KCeq4sRU8uxmDQDnY0HWLnhn0Kvwyw9w8SqgyrA6O3uQre6hZi+Yk9h5aIwF0Q2H3j8MA7mtFBdwcz8K6mv5uAoQMwFuLhLfV+xob7bJIQQReXhpwJ2Hjamwl05or7q2eHRxU0FYawd4EtQ78bQaIgqMu/qoTLkAOr1d9x4lky9KOuPm4N9RclyKcilg7DpAzXFN7f4bJkvRVHcbpmmdDUboZD/vwFebtz90IvMdR8DwMTU+bS49hsAdSr68tadTfNtbnFbLRU0MJaryqMPT6N5UQN6oPYX5uxPW8d0xc2IzI85IJJ7aqgeJVwKMug9aD8JRv0ITx6HIZ+qki96ZFGVJvl1Z932Bayf6ZgSEflNrTZvi95dd4vKsg8rwoUJ7/Iqi7XXdLVPsKV8LVVOoNf0ImygtXEz/+9N6aozdX4smXrq9VX08yR0xPuMS3+GWHxYtOUMH68/AcCC73/gsYwF/OA+g5E1kxkx5R0+dxsFwITUhcx/90lu/fAfftp1wRLQA0hJN7H11HVLQM/d1cj8EbVoYTqgsgoLoXKb20jUPKhqvMKuzWsL9dzSQoJ6QjiZpmnMW7yI2xJ+xICJzrWDGdfJehFRAKJOw8wq8JYd3WhNpqygXhGnEwzs0o71mpqO4rV3AclpGYV6/pW4FL7duI+5H7zGD+mTmeTyC/3q+BYuC9Gejr/rXoc1L6s6LPYyf5iaP3yiTsP1k+oKlJn595YaD8mZ9aFizqmrgNU6qw9NIYQoC8xBvQr1SmY8Dz+o2x+qdyqZ8YRjJUXDmX/V944M6hXUnMORmXoXd8Gq52HPkryP9XxRTZWs0qZo6y5kpl4etzyn6rH1eK7QT63g58HAh95kkXEwAK+5fkV1j3jmjm6Fj0cBE7cyg2zuATpNS+09A27/2HZwtLjB0/yYSw/kziKzMwuyWOr1h36vQ51e4Jq3ptgNI7AaVO8ClZrnfcz8e3dELc78atw5IlMvIw32/wjbPlelLQojWyZbobm4qmnpnafo33AkP25eKlsPCq4jaKVmYOf6VXj1ztaWn0/99RlvfL+efidewcWgsZwuPDp8AKEBngx4+F2+dB0BwNOGhTQ1qABggJcbYztWZ3DzSlQplzUt19Vo4JNRLWnrehLmD4Qfxxfqpd3StAaLtT7MSR/MbyczyDBpBT+plJHpt0I42a+bDzDiwqtUcruOp7sro+56O/+5/F7lsroJpiXlbN6QmykNOk5WWWZFvOJZ0d+TI5Xv5JaIXXQ3beZ/P+3j3WHN8r2qC3A0MobVS+fQ6Ppqhhj2427IACOMN6xge5NCHpDaUz9ix1fqwLPRUAioYnu57HJ36LJ2pdbdR02TSIlR0yY8/aFqe3h0d8l22RJCCHtseAuiz6gTf3OmsdmVw+prhfr6jvnXDLh2HHq+BMHZGgxUbgUjl+o7lih5pzbC9RPqWMKUrt4/hS13URiW6be2MvUcGNTzyicwUK2DuhV73UUM6pnZmm5XgCrlvOkw4QMufrKJSobLvHuLDzUr2NHgIl7nzLlWY/N/3JGZev6VIahW3inUJZGpd7MIz1WD2iw1UTWyAMf8npuPhJrdILRx3scSHPH3NcAP9wOaqtVXmE7dngHgV8kxgWuzuMykDu/yKhCoB68gSEvMDErmk4BiI0g+tGUVLsWmELf6TZ5y+5bo/V8TaEzgiuZPaq/XLPXzKgd60eehWez66AAtTfvoHniJEd0HM6RFZbzcs/Z/ETFJ/Hc+hhrBPtQN8YM9RQuW+nm6sa32FFYfvATxsO3UdTrUcv6+4HJsMhX97Zs+LUE9IZzozNV43P98gkqG65wyhdBkyGOEBhTwz+vhDy4ekJGiDrTyq+fg6gG9Xir2dvbu1ZdtCxcRaQrkp90XqBPix0Pd889QW/bdVzwR/Y4lH/iIqQr/unfE2GwkY5oWsgZFQd3STKaiHZDlbkefaOPDwD8MrsRA7MWsDBejsXAf4EIIURIO/AKXD0CdvtDwtpyPWTL1dA7qHVsFl/ZD6/tyBvXEjWHzR3A0W1kKR2bpQVawLi1R1d/KndVkzuCzNPHQUe6LfXoKawqTtjg1cFQ7xJ/Uuz8k2WSkVfXWBT8BVMAiuK5jft+5mTKyZXM5IKhXt6+6ZZeRnvWeclSdSJGtG6uHamyht2YjbD9maRqhY6aei6sKziVHq/dsYc4JhnxavLHjL6tzEv9KtoPfq1+E/5bmXze8sLzLQez5gi9MhLeBtASrWc0Tu9Xk48iBxB5aRqAhAYDF5SfzWOcmOVdR3oewxo3R9h1gapcwDO3y1mAMC/DK0UijOBd8BjUNU0E94Pd9F50e1DsUEcv987ez6dmedi0vQT0hnCQ9w8TPC99jimEz6ZqR32rNYHILO06GDAa1A485pz6kilqktRBq16rNijt/ZvJiVUfurT8PU7uiL70bWi8WHRGTRPylU+AG/1GHbc1fo32b9txXyb/ADD+rzEG9eBsdHVNiVNFlKNwBWfaD94w01fks+/1mfmEqwyUuQmUshLct2ZR3IYSwV7UOKqh3ZlPOoF5GusqmA/2n35r3u7lrGdnqbinKluy16zwDoN4Ax47nEQBNR6gTM1M6kCuol5aZ7eOITD3Lxb5cWYJJUXD4d3U8UNu+k6w83H2gYoOib9vSUSro1e91CMonS6agzajXt+CFsvMPUze9RJ+FK0fBLwRCc57Ik3gNNBNgKLngp+VvbSiZwOXNwpRZ/8z8GZD94ntJ1xFMdFBNPe/yKqhX1Cn1RbV8KhxeDgPegbYPWF/GMuVYxwQEe7ONG9+hblYYDAYmDruN97+4yugLM1hLW4aOetjq+aHrre/B4I/tP44w/x2K0ICkZ4MQ/FzTaW/aA3u3kH5rI1xdnHP8sv7IZR5Zspv4FPundcuRlhBOsnTtdsbFfAzAfPfhjBtxl/1PLijIZZZwVdWJS0su2kZmM6BJGNN61wVUybnHlu7mUESs1WV/23sRd1JJ14wYQhoxfnBfGlcOKFpAD9TBZKUWqsuiNeYPF3e/wgXbsgf1zOswGPMWda/WCeoPUoG/hbfBrIa2u/IJIYQzVeuovpprn5lFnVIlGdx8wN/OEgX2spXdtPJpeDUENs7SdzxRssyBjs5T4cmTUNnODK+iMhph6FxV5N3dO+/jjx+G5yIhuI7+Y5sD1CmxKkvQ7NoJ+PVh+O0x/ce014l1mRmTZa/eUw67FsHiO2Dn/LyPuXmruoX939RvymBBzPstr8CSG/NG935zeKW8mrZv5ugpzilxELkfLh3I+9hd8+GOL/XtuAv51/FzJHPGYX7jJjigNmVYM1VPvJjNr1yMBh4bP449w7bS9tEFVC1vZT8PqsRUYS4MFiNTz9fDlYfCz/K5+yweMS1iywkbs8OsSM8wceJKPDGJaYUeN7dFW84wbv72QgX0QDL1hHCKc9cTqfDPC/gbEtlrqknzu1/Gt6BCxdmZU60TCgjq7ZwPa1+B5qPUlY5ierRHLY5fimXZf5EkpmYwfsEOlj3SifK+OQNpv+y+yMGMW/ksYxDrhupQID2oJjy43vbjlq5Whbwy41sR6vRRV+7MV7S8yuWtV9PtSfV17WvqCnLFBo7puieEEMVVNTOoF7lPdZ81H3y7uEHbCSrzSe/sOW8bJxjJMZCerMYWZZelxl106Ql65FdPuDg8A9XFPc2ksj7MzbLMF1GLe4L87xyIvQCdHstb8zI/aclqOhsUKQslh2sn4OxmNb21bp+Cl9+1SH2t20+fsiOWkipW6iR7+ELzu4s/hi2aBp/foupDPrBWXTT2rQi3f5R/J1FReJpJHZ+bg+/mTG5HNMkAOP4XfD8WqnaAcStzPlazu2PGtFzQKkSmXkqceg96lYN7lxetaYqtLs7ZmR/Tc8pxn1fsWy7mvGqU5eFvMyvTxWigdyMdM4DBdhklO9Voexuxv8wk1BDFr1v/pHPd0QAkpKSz51w0MUlpJKSkk5CSTlxyOievJnA4Mo4Tl+OZYZhLoCGBfR3nMKV3PdxdC3ecpWkaM1cc4vONpyz39Wtkf3OiUvLJLMTNQ9M05ny3gpnsIF0z8m+DF5lUs5B1QyyZegW0hDd3vs1dELgoFtyG4cy/vDNsCWeuB7D3fAwXopN4c+Vh3rozK4Pu2KU4DmZm8DWrEkiNEAdMj8nNUn+lkB9c3kEw6nv1feR+dQXK1tWnjHTYnXlg2/q+om2nEEI4mn+YuhBy/SSc2wZ1eqv7y1WHAW85ZkzLiU2uE4zkGPW1mFf1hZMV1I3WEdJTVNaFu68K9JQUo1GdbCdeUzdLUE+n46kdX6oZFI2GFC6oZ55WZnAp/v/T6X/gt8mq7qY9Qb11r6nyIw+s0ymol7m/yC8g4SgGgwo4JFxWX/3D1LFgi3tKfltuZD7BKjs8+2dCg0FQaRvgoKm3jqyHaUt+jXVsSbwOV4+Cq2fRuyBbMvVs/A9pmmMy9ez1cUdVGumRHcXLqL50EDa8qfbJt84uePliNlHq1qgyf/7chiGGDQSc+I11h/vyy54LrDpwiaS0DJvP8ySFuz3XcV3zZcqGw6w7epX3hjejfqh/juWS0zLwcDVanbn23ZbjfL7xJOb/jwndavJ0X/vrH0tQT4gS9sueC3x/2ot9htfo7nOaR4beWviVVGquDjALqqcXF6m++ul0JcSUjntKNJ+N6UavWRuIS07nh53nebBrTWpX9APg1z0XLYvf3ryyPuNmp2l5r/rokdIf2hgm/G378aMr1UGtdzDUL8LfTAghSkq1jiqod+bfrKCeI9k6mZKg3o3BXG/2wM9qClsRu68WypLhcHIdDJmbswD+lSPw13QVuO77mmPGHrZIZQJm7/BrPkEublDLK0gF9QrbAVfPemTmwKQ5UJkfkylb91sdLhBD/pl6lw6qYFuFuupChCP4haqg3vyB0PQulcEc1tQxY92svK0Ebt199K/nmmNMGxnj0WfV1PVy1VV3XF3HzAzqFaamnh7duwvK1EuJhYzM8gF6ZurZIyNNBfSg+FnFaYlw8BcIsHPadKuxUL0ThDUv0nDe7q5EhPeHCxvoqW2h3fytmAqoVudiNNAyyATx4EsSKbhxKCKWpz5YwsC2dXFJjyf9wj58ow5RIT2Cg9VG8ch9Y3HLVq/v3PVE/Fc+yuduqTybNp4pgztxT/vC1cyXoJ4QJehafAov/3YQgMNaVZ4YOrRw027N2oxXt4KYD9j8dDgQy9YpNsTfk4ndavH2n0cwafDOn0f5dHQrNE3j170XAHjJdQF3n3GFmk+qenjFtWQEnN6o6mLkPkltcidU71z0daenqOk2tqaIXToA345S37cYVfQra0IIURKqdoS932YF1UBlIwdWBU9/288rKnPmTZ7mAtHqqwT1yrZGQ1W2RNMRJRPQg6wT3tzvqZhzcGQFhDTJ+xy9VLdSNkSvwFa2Y6lCsQT1dOjOai7hUlBdZlC/f3MjMr0yfixBPSsBib1LYNMH0OERxwVt+86EVc9DxB7Y/bW61e4NA99xXCDxZuNdQCaZQ8bMNhXWlJG1rzq/Q2WmVu2of1Cv2Qio2r5wHeWL0czBwqeArETz/5a7n76lCo6shGWPQGhTGP2T9WWyN54pbqkiW7MAbGk0uHjjAbXaDST6x1eoYIihqeEke7TaBHi5MaBJKNXL++Dt4Yqvhws+7q5UKedNrYo+eFzeB5+BwSeYuoF+nL90lTkus6i+O9eFExfofG4fHy3yZPKYuzEaDZhMGj8ufF81zjQaubeRa6EDeiBBPSEc5khkHF/+cxJfDzdqBHtTI9iXTRtXE5Z0nSiqM7BpGL1sdI/VTVyE+upr/5x8m3IVg72vU3XmbzrNlbgUVh6IZPfZKEyaxrnrqitdb8/DeB47Ax0mFH9sUFecUuOtX9l18yp6J7gFt8Kpv2HEEqg/0PoyPtmmR7e8t2jjCCFESWk0BBoPzTqYN2XAFz1VfbvJe4rVOdOqegPg6TN5g3eSqXdjCK4Nz5wFlxLs+p69jl925p9Luq6t+SKpTyHLpeRW1ML6llpROtQjMwcmEy4X3KHaXLvZq5x+FzTNAZ+k66q0SfY6jfElMGWwRhdVp/ncNtg2F/b/CMdXqwsh3Z923Lg3Ex8rteZ2zofYCNWVPaSR/mNaAt6a2k/kDnw5opZfaJO8HZwLomumno0yTG5e0H6Smt2kJxdXNWZ+FwTMf3PPgOJfBDLv79ISITXReuMknfVoWIXTy6sRmHqQflUzmNilFT3qV8DDNZ/Xkvkec/OrwLLxnfl8+UZidvsAEKt5c0irykljDWqaTtPccJz9R48zc8UhnhvYgKVrtzI26gMwgMlgZFLSp7DPqBJWCkGCekI4QEp6Bvcv2M75qCTLfa6k84f7K0xzj+BJw1SevfWp4g+UlgxuntYf0zSIM2fq6RHUy/kB7e3uymM96/D8L/sBeHPlYepkTsEFKO+SCOnod+Btb8ffwnLL/IBYPg3+fE59CLZ7MOcyvhXg1jmq/kX5WvqOL4QQest94Bt9JrNhhYf+3f9AnUBYywawBPUC9R9TlCx3n5Idz1amnh4nxAU5u0UFfMKaZWX26Dn9Fgo//TY9WR2v6JGpZz6eMqWrOon5rVPP2sxm3kGoulFaZt3CbOs2BxF9ixk8LYjBAFXbqVufV+H0v1Cvv2PHvJnknh6amqAyMK8dV1NwHRHUc3FTgaTkGJXZlbt2o3cJT0O1xbIPCyz6OgKqQMdHbf9f+leCfq8Xff22eNkx3dj8mB77Kg8/cHFXiR2J1/IP6pky4Mwm9dlQsWGRG4K5uxqpW6MGHDnIxNYB0NiOc+hsDTo83Vx4dEh3DrRdx3fHT1GhYiUaVAqgrb8Ha/47yd3f/sYuUx345xQpaRn03v0UgcYE/jPVILROKyqe+EE1MyokCeqJG4rJpBGVmEpCSgbBfu54uzvnLf71lrM5AnoADQ1nqGO8QKzmTdfeQ6joZyMYZ4/rJ+HTLuoKyDNnrS+THA0ZKep7PQ7GrBSDHd4mnM83nuTMtUS2nLzOrrPRAHi4GvFMz6zBo9eBt08+V6W2zlVZiU3uKvyBgvl1xWfWH0xPsr5cK8nQE0KUQaYMuHJUfR9ct+SmT5pMUKOr+ixyZABG3JhsNecoiUy9oyvhn/dUt9dqnVR2Sq/pqhZelTbFW3dRanABNB+pbhk6dGh1dVf/k0lRKmiXb1DPAZlzRhfo/6Y6Yc99km4Zz8FBvez8K6naekI/wXXU/r9CPZVhtWS4Cuh5+Kuar47iXT4zqJctE9Y8ddMRteWSouD4GvU522y4fc9JzAzqFSfo5R2kgtElzZ5M40QdphebGQwqGBt3Uf0dA8PzH3fBIPX9i4Xcv+Y28B0YNMv+zGgrtd0bVQ6kUeWc5ad6NavFtdSh7PpxHwAVd75DV9e9pGhubGryGhPL74YT2M7AzIcE9USZlp5h4o0/DrPl1DWuxKVwNT6VDFNWqrGfpythAZ6EBnjRoWZ57mpdhWBfx04fiU1O48O1xyw/vze8GclpJlyPXYBjkOxfncEdGxdvEM9ANRUVVD04Vxuvqcvj6gDYVjZfYVipA+PmYuTxPvWY/M1uAFLTTQD0rx+I4XhK1rbqIb8aLPu+h/PboXLrwgf1cn+o6vEhJIQQznZ2C6x4Uu27a3ZX91Wo65ixTCb4fZo6sB30njp5Mhph5FLHjCdufM7M1KvRTQX1jq6ERYPhznmqblbV9sVft3m7C5upZ+ai06mbb0hWUK9iA9vLOSJTD6CdjdIslkw9J3TsFPqp11/dUhPhm+GqJra7H9zzkz6zh2xpOwHSEsA/W6M+R2bqxV2CH+9X/9f2BvVc3dX26VEayZb4K6oWpnd52/XCi8J8LpiebHs6rJ6ZeuYx4y5CQgElC5J0nPZbmM7kUKiGjcPbVOVaQio//7mGR11/AeArz9HcN7gf7DqvFpKgnrjZfLz+BF/8c8rm43HJ6cQlx3P0Ujx/H73CrNVH6Nc4jFHtqtKuRpDVltLFNXfDCaIS0wAY3LwSQ1pUUQ+4esIxqBhapfidy7zKgdENTGnqHz+givVler5YvHGyC6wK1buo4qjZDGoSxtwNJzhwMdZy39AGPnAcMLioK7F6yK9bWnG63+Z+jh71aoQQwtk8AyDyP3D1ytqvFaaYd2EYjXBuK1w+COHtoOMjjhlH3DwsQb3onPeXRFCvVg8Y/jX8PFEFIz7rBsMXQeVWxV93oyGqsVdJZqJZ03emOhYNa5b/co3vgJCGJTOF3mTKCsA4+/cjii8tCZberepWu/vCPT9CeDEzXQvSfmLe+yw19RwQ1LNk3kbnbM6Rn06PqVtxxUaoWUaB1fIG0P5+W9WL7DwNer1U/LHM3H2zzj+TrlsP6gXVUvXH9Zpi7VNenU+mxOa/nOWzwQnJGU2HQ2hjCMgnkzCbh7rVwnDlKCf2hXGY6nS65wU83VyyzUorfIMZCeqJMmvfuSjc179MCH24RBAV/TyokHnz8XDlSlwKl2KTiYxJJiUzgywtQ+O3vRfx3L+Ev4MrMmHiFAK8Cr6CkZ5h4t/jVwkP8qZmBV+by0XGJPNlZpDRPTOLzcJyoKLD1UeDQa0n7qKqMWctqKe3qu1g7PI8dxuNBp7qV597v9oGQKC3Gx3CMusYeAUWP4BpZq6vkmClpp4E9YQQIqfgeurgNuk67P9B3VehXv7PKY52E+C3x1Q5hHYT1cmNAy6ciZtEUE3VbTe4Ts77U+PUV0dP6W5wq5quvnQUXDsGn98CA96Btg8Ub70+wUULLvzxDESdgs5T9ckYrN3TvuX8w9RNb9dPqrpRgVWz9ktJ17N12i0l9c9E0R38VXWedfOBUT+o8whnMH8OOeL43rIfytWcoyR8N1rNUhq2SDUfyS7BAdPmIXM6bJDK4E28Zv38s3on6x3Ei2rENyoxpqAaeZZpvzp8Nlw6CLsXqd9fl2kFLx9cW93sZDAYmHhnf7a1bEt9f09qmWML+SWwFECCeqJMSk7L4J+vX+Ehl2VMdFnGtspjaHvrg1Y7EGmaxplriXyz/Szf7ziPW0Ikb7t9BjEw9/eGTLjTRsfTTOeuJzJ34UKmRr3KaS2UD8PG0HngaJpXzbvTeH/NUZLTVADxnvbVCA/KdgUjQeeaDr6ZQT1b//ixESo92i9Mn+m3+ehaJ5jhrcP5Ydd5Hu9TD7eMS+qqip5Xdv0rQaWWeTNNMtKyirEX5Xeb++qWBPWEEDcCo1HVLjqc7WKMozL1QF2pXvMyxJyFI7+rLO1v7lY1yKxcEBIiXyENYejcvPcP/1plAFECAeMK9eCBNSqwd3ojbP+y+EG9ojq7CSL2Qutxzhlfb5s/hu2fQ9cn4Zbn1X2unjBkrjqm03PKoCh5GWmw8V1VImjMr1CtQ8mMmxQN0WdVUxlzkOXub+H3x1XzBL25uIFHAKRk1vEryaCeOQCUaCWry1FBPVCf6UnR6jyvJNjb8TZJh1qFZrEXYcvHENLEvqBeERgMBtrVynXeKkE9URr9vPs8y/dGcFvzStzWrJKuU13n/7KScckLLcd0bS8shAutrAb1DAYD1YN9eLZ/A6b1rsuWjatgg3qs9n/vcr5nD6qUs77DWLb3Is/9tA9DSjke99BoZTxGq0svcOiLz/gweBR1uo+mVmgAVcp5cz4qkW+3nwPAz8OVR27JFbHXewdrnppgqxvspjlqh9RxMvR5RZ8xIas9era/p8Fg4I07mvDGHU0y/87V4MVrmQfeOglpBA+uy3u/+cqMwaimmxVWYDWo2QNOZq5brxoQQgjhbNmDeq3uU9lPjuLmpQIOf7+tTtjbT1QXlkwZjhtT3JysdVp2FM8AGLMMDi1TmXvFlZoAmz5UDUDMU2DtYemuqFPQ4PpJOP2PqjNWf4Dt5XYuUNtYb4C+2XPWTl49fKHZCP3GEM7j4gajf1EZ246soZfbroWw+gV1kWnoZ+o+d28Y8onjxvQOUkE9e5vfLL5LBcVunV28KarmfYG1WnN6J5JkN2Jx/o/HX1F1Az38SzZbP0nHTD17GoJkt/9HwKDqFxfnPNKngiqZ4uFn/3TuTBLUE7rLMGnMXHHIMg11zeHLrD54idcGNyHAu/hX3rYej6TTvv/hYUxjg6k5zRvWI+Dwt3ZFtT1cXeiWrfZlT+NOPvjlex69L2dn04SUdD78fiWf7AcVOfTlaY/n6Za+idszVtHAeI4G199g7w/f0Tv1ZTSMeLoZMffomNi9FkE+7jkHbzUWwtuqekN6yG86KkBcZjdXPT9MZzVSXWYf26OmTGSTJ2hrMNh/daU4zDtcr3JFK4wa1hTumgcLb1dXeYoSGBRCiNLI3GXQIwAGvuv4zrdtxsM/s+HclqwpdbJPFUWVlqyCXz4VSq5rszVGIzQarN/61s9UX3v8z/66w4k6F58/uxWWPaouauYX1Fv3mppqN6GZzkE9c0Ci8LWjRBkRULngZfRmDnSd3wEb3lYNAwuatlnsMYPU1Hh7A0AXdlnPriss8/9jSWfqFeS70XB2M9w1X9UQLa4zm2Drp+qiijmr1xo9a+pl/91qWsHByT+fU+fHD24oZlfj8vB8ZJGe6uB3ubjZJKSkM2HRDktAz2z5fxH0f/9vNp+4Rmq6iZ1novh0wwkmLNrBUz/s5UhknF3rv56QyoGlL9DEeJpozYfzXd4kIDgzSldQVxyz2r2IfWgvW1EdaDudmsPBCzGWh6MSUvl8zgymHRvDcJf1ANzWrBLvTBvPnf+bz8o+f/Gl291kaAaaGU9SkWgAy7TbEH8PxnWqkXfc6p3UtI2wpnkfK4pKLaBOX5VpZo0jgnpahrrZ+8HlKOZsQShePT0zr3Iw4W+Yss+5Jw5CCKGnkCaq42BKjGpi4Wh+oaqwPsCuBeqrBPVEUWgavBEO79ZTU6HM9y0dpRpY5G6gUVa4eYOLh/re3g64aUmqoyfoVwTet4DZHqAyRczBAb2731rL1Nv0Aez/KecxnhCFYT4XuH4C1r0K/84uuTHt+X/WNP2a/XjbaKpgynae5oygnt5ZxfGXVX3GUxvzX65WTxX0q9un+GOaf7cZqZBSQIxC0/Q5F4ViZTZKpp7QTURMEvfP38HBCNWdxsVoYEyHavy48zyxyelcjElm5Bdb8HA1WgJgZpG7VtCufjhDb7+DsIC80yqS0zJYuOkk5dY9w1htDRhgXrnJTO7ZDrbuVAvZO//cxQ3/kOoc7zyLJhtvJV7z4rM/dvLp+Fu4Gp/CNx+9xJSkT8AAHVyP0GboFO5oWdmSiXZnpyakt/+YtLf/wiX5CkPrebAlKZBz15MwaRpvDG2Kl3sJBIbaPpB/bZf4zKCeni3TvcurKxEFfXDt/hqO/AENb4emw/Qbf8GtcH4njPo+qwhreDuYvEfV7SiOtGQ1XUCCekKIG4WLq9oPZ6SoEgUlocMkNV7iNdX1UIJ6oigMBlWXN+GyytYjXAW3zNPJB7ztxI0rBnOh+bgINV2snI0Ls9lZyoy46Pf/ZA7SxV/KZ9xroJkAQ9ZJrl4sQb3MgMSlA/DXdDClq1rQJVWDTdxYsmeTBtWC1vc5fsyOk1W310rNC142JS6rGUxxg3q2MvUSrwOZgXFH1Anf/DH8M0tNce77Wt7HLTOodLoAYQmaFpDdqGeDDndvNQ02PUmN6+lve9nUeBX8y76tTiBBPaGLY5GxrPr8WSalHmcaD+Hh6cXHo1rSpU4FHuhSk2nf7WHLyetoGnkCeh6kMtPtC6qcvMqf7y5gWeNHCa7WAB9vH/y83LkQncT7fx3jQnQS77klYXTRWEo/7hwzGRejochFJe/s3ppR295nR6w/HE/i1z0XuPDHLB5N+QKAJcZBtHnwE+qE5v1HdnUx4jr6W3Dz4emgGuDqkf9gmqbqsfhUgCpt1YmWo8VlHqjpmaln/gAqKKh3cY868K7YQL+xAdJT1dXq7H9rV3cIspIZWRizGkHseXh4m2O7QwohREkb/FHJjhfWTE27+f1xCeqJ4vEqp4J65syWSwfUV6MbuPs6b7uKy94LpGZJ2TJf9KpRZQ7qJV5TTQ2sNaYwZ/F5l9f/uDV7lpEpA5ZNVgG9+oMkoCeKLrAauLirpit3L3V8l2yAGl3sX9b8v+zqVfzaoJb/oVyzpwxGaP+wCjY54nxTM6nzMGsXBLJnIupVKsASvCzhWWI+wRBzTu2n86tHbN4uVy99yk6teRlOrocuT+RfGiEXCeqJYtt75hon543nYdaCC6z27s+j999P7YqqTkilQC+WjG/P5xtP8sHa4wR4udG2RhDdQlLplLiaw9EubDnalCHaOvoat8HB0XAQUjRX4vBmY/oALmSoVt3vpA/nQu2R3DE4W0afj430Y1u2fwFRp/FoNJSR/bqw47u9AOz//lWec1sCwEKXIXSZ+CE1KuRz0Fi5lf2/pORo+G6M+v75y+j6r5eWnLe7bUpc1lQNPadMmK9AFFQMVq/U8twsf+vCdwXKV+x59fWXSarTnRBCiOIxdyWXoJ4oKvMxRFI0pCbCrw+rnxsMKtkC7HqzvK4o+5ZPiVfTdvVs5OUdpDL/tAx1/OwflncZ80m73lNvIet4LiUGtnwCF3aoUgFlNQNTlA6+FWD8GvU/Fhju7K3JS8/zo+A6KkuwXPWc9/uUh34zi79+WyxNJKycCybHZMtE1CtTL3NfkRQFGem2A5UXdqrSBuVr6dNMybu8CuoVFF/Qa+qt2fVT6rVEnS7U0ySoJ4ply9GLxCweyxDDVst9r/aqiF/FnIV/jUYDE7rVYkK3Wll3Hv8Lvn6LCiFNiH5qLV+sWEXt/2bR3bALF4OGhyEdD2JxQWX2datbgWf6d6FBWK7MOW8b6ce2HFwGpzZAaFMGN2/B5xtP4Re51RLQm+dyF70mvU94eZ/C/TLyY94hePgXnNVnr6vHYW5Xtb6nc9YwtGTpufuqbmJ6sbcbUHK0+uoZqN/YkK05SLa/9b4f4NJ+qNMnqyh8UV0/WbznCyGEUILrqs+A/KatCJEfr0D1NSkKVr8IV4+oANOAd526WcWW30mxNdU6wHMRaraCXowuavZIfKTKhrQW1LPU03NAXS7PQOj9ipq2tmaGuq/XS+BfKd+nCVEgvWqX2yvmgmoM4eEHdfvmv6yeQb1y1aDPK8VfT2GZg3XWEjzM97l55004KfJ42X5XSdezzgVz++5eFYQbvxaqFCLxxpZhC1TGZ0GlByw1BHVKZCniDEQJ6okiW/PfKdx/GENf43+kaK7859mGpu174VethX0rMKcL+5Qn0NudCXcOIrJ3L34/dZWk+BhS4qNJT4zC3RjAksYN6Fjbxj9V+dow7k/7i4FaOgIFYzQaeLZfPfwXPwbAF25302/SLKqUsyN99sxmNbUorCnU62/3mLrxClTZeGkJeadOuPuotF1Tun7jgf3FYB2WqWdlR3fkD9j/g9rpFjeoZ65BIYQQonjq9FZX7Rvf6ewtEWWV+Rjiv+/gzD/q+8EfZ3VOLavyOynOj6u7vtvhmxnUs9Usw5GZekYjdJqsGp9kpKrSNK3v138cIRzt/Hb48X4Ib19wUM9kAv/K1oPoekm4pv6nfIKtT6svrvwSPBJ17EBr5uKqPguSotSYtoJ6lnPPQH3GzZ0BaYvemXoS1BMlaeeZKD799lcWux4gUfPgk9CXeXj8A3i4FaLJgPnNmi0CHhrgyW3NqwBV7F+PuzdUbV/4cX3UTqFrvYr80HcBm84e546B/Qnxt/PKwumNsH4mtBxTiKCejlc7vfKZOuEfBj1f0G8ss+C6UL1LwTXszF3p9Nqxmll2dJkHoJcPw/HV6vviXN2t3EqlOjcdUbztE0IIoVRqoW5CFJU5298c0Gv3ENTu5bTN0U2XadBuor41j4uiz2uqPlZYM+uPNxkGoU0dV5fs4m5Vf9noCre+rwJ9QpQ19pYmAqjTC6bp2Ik+5rwKygfXzZqZtfVT+PstaDMeBjogq9mS4GGlfIBnALQaq3/NU+9gSI613fU8PVXVEAR9yxTYo3oXGLZIv1kJhS0rlkmCeqJI5m86zfaMOjyqTaZ+ndpMHjMSN5dCfhibp8uWZLttG22+7+zUBDo1Kdy6zDu13AVKrUlwwGs1GtU/fvwl21Mn9NZ0mH3dbB1eU+8qxEbA4jtVJkh4O6hnfzHRPEZ+B8dWqy6RQgghhHC+Kq0haQTUukVdHG0z3tlbpI/AqoVbfvPHqmxM85H6HqfU7Jb/4/5hjj22rNQCxvwKV45ASEPHjSOEI9lbmsgR5vWH6LMwbhVUbafuc0QiSXbmc7vUOBVMy55BHFxbBej19uA6cPOxHfg3n3cajOChUx3fs1vhwM+qgWJ+XZQDKqubXiRTT5SU+JR0Vh+MBGCbZ0c+HNNLBfRSE+DaCZU5Zs/VeUugS4d01f++g6tHVaZVcG3byyVeV1clMRQ/Tdb8T2dPLT/La9Vx+i2obMP4SxCf6x8/6ox6nX5h+tU0sJemQVqi+l7vmnoBVVVWXUA4LLlL1U4oX1t1uCrO6/QJhuZ367edQgghhCieJneq283u4i44ulJlhNxoanZXNyHKKkumXpSaXluSGac+FVRQL/u5qCNKPmXnGQghjVVwLy1R/7IA1nj45f+4OUvSM1C/3/+Vw7D1E6jTN/+gnt6KGNSTPGdRaGv2nSY0/QIAA5uGZWXondkEc7uotvT2MAe6CipAaY8dX8Hfb6uGCfmOaZ7yG1T8Nt+F6cTqqKsmlm3IVQ9l7aswpzlsm6vveGZaPrXnDAZ4LhKePa9/HZYqrVT9xMSrELlP/T5H/VDyqdZCCCGEEEURdQbWvwH/2pnRonfNJst2nIadC1QDOWt2zINdCws9DUyIm4q5fpxmymoUaMtf0+HLPnDwV33G9rYyVdMRs8OyMxrhoX9h7PK8ZZaSotQU2fzOEx3BETPELNOMC8jAPPYX7P9JTYXWg0+wykp0L1zDTgnqiUK7uOVH1ns8zkdus7m9ebZ0U/POw1bB3dz0nH5rb4DNHPzSY0zLjtSOdOumw1Vdg7r9ij9uduZiobl/53ER6qufztMmYi7AW7Xg9QLaxBsM6qqKI65WZaSBi7vqrDTyu4Lr+wkhhBBClBbxl2D967D9C/uWt3RX1Dmod2En/DYZttq4ALz2VVj2aFbDDCFEXq7u4J6ZSVZQI8HLh+DcVtu14QrLfP5rNVOvBMtbma19Fd6sBute03e9h1fAt6Nhy6fWH7fsI3VM8rD2u7Xmn/fgh/vU31UPQTXhuYswaXOhnibTb0WhXItPoc7llWCEK+5V6V81W0TcHGBKuGJf+vHtH0PcRZXCW1zmAFtB0fRqnWHqQUhLKv6Y5n/2lBhITwFXD9vLhrdRN71VaqlqygXmCrI5qmOZh1/Wzi01UTUpKWnu3jB8MVw5BKGFrIMohBBCCOFM5swea4XmrXHECStYGsZZDdplpGWrQW2j26QQQvEup2rMFdQsQ+//5dz13TUNrp/IfMxB02/z46gLEFGn4dAy1VSn/cS8jwfXhVue1zeQaW/yjt6Z1AZDkZ5WpjL1xo4d6+xNuOn9tfMQ3Qx7AUhreAdGY7Y3nvkfScuwrwNQhbqqjoYec/7tnX/u4qqKWeZXd89enoGq+yw4pzgqqB3byG+h8R0574/LPEDTu7Oah5/aoYLtv/HFPbB0FKzV+SpNdi6uEtATQgghRNljPqE3F5oviOWkUeegnvnCr7UZNglXAU0d50qJEyHy1/d1lXBQvoDzS72nieaeqZYSm/WYrwMz9X5/XM3c2rUw5/3mc0MvnfcZBU2FrVAXuj6pOu/qxVzzPzVOJe/Y4qjyCIVUpoJ61hgMBqu3t99+2+Zz5s+fb/U5ycnJOZb7+OOPqVGjBp6enrRq1YqNGzfmeFzTNKZPn06lSpXw8vKie/fuHDhwwCGvs7SI2vEDboYMDpqq0blj55wPurhl/RPbOwVXL0UsKlksRiPc9wc8vK3gKwOHf4fT/+a/U9BLWpLKHgT9M/UM2RqM2EoxjzoNh5fDqb/1HVsIIYQQoqzzDAAyL4onFZCtl5YEaQnqe71PGs0zbFJi8s5gsZSrCQaji77jCnGjaTBI3QoKgOsd1DNnk5mbVXgGQIvR0GmKvvXlcktPVjO3cp93Oyyr2M76dnqyJ3lH07Ie0zOQufJ/8FmPQp1Ll/qg3tWrV7n33nupWrUq33zzDbVr12bYsGGkpqorWxERETluX331FQaDgTvuuCPf9fr7++d5rqdnVvfMb7/9lilTpvDcc8+xe/duunTpQv/+/Tl79qxlmbfeeotZs2bx4Ycfsn37dkJDQ+nduzdxcXGO+WU42bnriTSP+QuAf717UD/USicay1W/AupvJF5XjS12f63Pxpn/2Qsq5rtrIfz5HJzbps+4VdupVtcubraXyUhXmWvzB6ipso6Qli0gHac6E+PqmXngqDPLtBEbOzhzkVhHfpgIIYQQQpRFRpesAvMFzWxJjlFF042u4OGv73Z4BoBLZumY3BfjzT/7ytRbIXShadmCejoFgCo1BwzQ/uGs+27/EHrP0Gf9tljOBXPtvxIdlalnpSFIdlePQ+R+fc+zsyey2Bo3OUbNUAR9A5lXj6qu59FnC142U6kP6k2dOpWtW7eyaNEiBgwYwOeff06NGjUwmUwAhIaG5rj9+uuv9OjRg5o1a+a7XoPBkOe52c2aNYv777+f8ePH06BBA2bPnk14eDiffPIJoLL0Zs+ezXPPPcfQoUNp3LgxCxYsIDExkSVLljjml+Fka7fuoq3hMADuze7CYG3Ot6+dGXPRZ1UxzTWv6LNxlky9AoJ6h1fA5g/h8kF9xrVH0nVAAwz67+SuHIHXKsHsbHUJs9fTK+K8/HxZWrfbOBC1fGAF6j+2EEIIIURZZ+ukODe/UFU0/dkL+h/TGQy2p+Caf5Z6ekIU7MpR2PcDnM2nWUJKbFYASK9zpJBGMHU/BFTRZ3328rax/0pyUKZe9um31jrrrpkOn3aC/77Td9xxK+Hxo+r3bI05wcXNB9y89Bu3CDMQS31Qb/fu3YwePZpu3boREBBAjx49ePPNN3Nk1ZldunSJ33//nfvvv7/A9cbHx1OtWjWqVKnCoEGD2L17t+Wx1NRUdu7cSZ8+fXI8p0+fPmzatAmAU6dOERkZmWMZDw8PunXrZlnGmpSUFGJjY3PcyoqUvT9iNGhsNdXnlnYtrS/U/B7oNb3gemeWzrc6FfEMawbj/oSRS/NfTs/utwAn1sL6N/JPjzX/Q3oHqVpwevIMVNMyEq+BKfODwrcidHkCWt2r71hm3pkZeLYORM0dnTwDHTO+EEIIIURZZj7ptacGNYBb3vMeXVia3OUO6jmo4ZoQN6KDv8CP98OefGagpSaAf2UV0NczABRQBTx1zuItiG9mMpS5KQeoWWNpiep73affZsYLTGk56waamc899Z4lVr4W+IXYLkFgTmTRuzSCTwGZiVaU+u63nTp1Yt68eTRr1qzAZRcsWICfnx9Dhw7Nd7n69eszf/58mjRpQmxsLO+//z6dOnVi79691KlTh6tXr5KRkUFISM4PspCQECIj1dRG81dry5w5c8bm2K+//jozZjg4JdYBDkfG8m50V3YZ/alUMZgXg2x0PW023L4VmjvJ6BXU8wyAqu3tGFfnNt9H/oBtn0GXx6FG1/zHdEQXIu/ygAE0kwrs+VSAwGrQ8wX9xzILbap2nrb+djL9VgghhBDCtts+AIMRAsKdux29Z4ApXR3bZdfsbqjcUo7lhLCHrcy17PwrwbQSnCnmSNU7qa/nt6vAllc5FXBrNVb9rHepADcvlQ2XkaLWn7u8lN61Cu0VVBOGf209e7A4ipCpV+qDerNmzWLmzJlMnTqVEydOsGfPHiZOnMjEiXnbGX/11VeMGjXKahZfdu3bt6d9+6wAUKdOnWjZsiUffPABc+bMsdyfe3qppml57rNnmeyeffZZpk2bZvk5NjaW8HAnf6BbYTJpXIhOYs+pS0Qe2cr68xop+LPS1JaX29lIQS0MRwa68h3XnCGoU1CvoDn+jhgzOxdXFdhLvApf9VO/1x7/g/YP6T+WWben1M0WmX4rhBBCCGFbxQb2LXdwmao/XbsntJug/3ZU72z9fv8wdRNCFMw8nT76LJxYp7LJqnZ0bAdaZwqsChUawJVDcHwNNLkTPPzg1vcdN+bjh1Sw0FqcxVENOo79BcdXq8ShRkPyPu4dBA1u1XdMuDGDej4+Prz22mu89tprDB48mP79+zN16lSMRiMPPvigZbmNGzdy5MgRvv3220KPYTQaadOmDceOHQMgODgYFxcXSzae2eXLly2ZeeYafJGRkYSFhVldxhoPDw88PDwKvY2OFJecxuLlq3E7vQ7XlBg80qLxzoilkuEafQyn8DCkkZw+mH8ZhqvRwIAm+XzIpybAtePqql/lVraX03v6LcCuRRB9Rl0lsFZbICU+Ky1YrwCbefvz68aT4IDXml352ur3aU6BjrKdKVoiUjIbxcjVXSGEEEKIort8CI79qWrrCSFKJ/P0y8j/YNFg9f09P0LtXk7bJIdrNAQiajgmacWa/Jo/OipT7/x22PoppKdYD+o5imX67Q0U1MsuMDCQCRMmsGrVKjZu3JgjqPfll1/SqlUru6bp5qZpGnv27KFJE1UHzt3dnVatWrF69WqGDMn6A65evZrbb78dgBo1ahAaGsrq1atp0aIFoGrxbdiwgTfffLM4L7NE7TobxWNLd/Nk3ByaGE5Sw5hZQyPb1PGrmj8aBuqH+vFAl5oE++YTlDy7Gb6+A0Iaw0P/2l7OHOjSM1Nv80fqikH1ztaDeuZ/DFcvcPfRZ0x7/un0nvKb25BPVU0//0rqyklJTeXQNOtXS+75CVLjwZhPR2AhhBBCiJvVhV1w9E91YbbpXbaXsxSe17lmk1nUGTi5Tp0wZz9p3f6FOo6rPwh8HDS2EDeKKq0hvB3EXlTZZB5+4JatVFVyLBxbBVvnQr1+qmxTWdf96Zw/pyZARpralziiUaMtaUmQnqS+17shpSV5x8aMvHPbIOacKl8QXEffcd19c76HClDqg3pTp05l8ODBNG/enIyMDNatW8eGDRt4/vnnLcvExsby/fff8+6779q1zhkzZtC+fXvq1KlDbGwsc+bMYc+ePXz00UeWZaZNm8bo0aNp3bo1HTp04LPPPuPs2bOWab8Gg4EpU6Ywc+ZM6tSpQ506dZg5cybe3t6MHDlS31+CA2SYND7dcIJZq4+SYdKYzKP0Nu6gl/t+kt0CSHcPIN2rAm5V21C7XhPGVivHZE87gjTmLlm5u2jl5ojsNZ9guILtqbDZg2t67WzsmX7bYJCawlChvj5j5hZUQ91KyqmN8P29UK46PLA27+MGg/owE0IIIYQQeUXsgQ1vQL2B+Qf1zDNBHBXUi/wPfnsMqrTNGdRb84qqkRzeVoJ6QhTE3QfuX2X9sZPr4bt7VRPJ89vsn3pf1uxdCr9PUxcCRizWf/17lqha9g1vV9N9zcxZekZX/c8/LV13bdRK3L0Idi2EHs9Dtyf1GzesOfzvQqGeUuqDelWrVmXatGkcO3aMhIQE1q9fz7hx43j00UctyyxduhRN07j77rutrmPs2LGcPn2a9evXAxAdHc2DDz5IZGQkAQEBtGjRgr///pu2bdtanjN8+HCuXbvGyy+/TEREBI0bN2bFihVUq1bNssxTTz1FUlISkyZNIioqinbt2rFq1Sr8/Ep3QONKXAqTv9nN5pNZU0ZbVg3kxRFPEm6rAYa9zF2yEq+qbqy2usX0fU3VfStfu3jjZVdQp5hKLWHaIXUlQe8xbUXwASq1ULcbhZuXOsh00ynbUQghhBDiZuJlZ/dbRwf1zMft5m63oKaamZueSfdbIYrnv+/U/9Ppjepnveu+OVvUaXXubd6XOaqm+qUDcGgZlKuWM6jn6gm3vKD2W3pnCJr3u7ZiC5ZafjpP+y3C6yj1Qb2pU6cydepUQAXn5s+fn2eZBx98MMdU3NxOnz5N9+7dLT+/9957vPfeewWOPWnSJCZNmmTzcYPBwPTp05k+fXqB6yotDkXEMn7BDi5EqzRVgwEe6VGbx3rWwdXFWPwBcndj9a1ofbnytdRNTwUVlXRxVVNUHTFmcgykp4Kru77rL43M9Qqs1RE0ZcB3Y9Qy/d/Ub5qzEEIIIcSNwnIsVVBQz0EF4M3Mx7Hxl7PKqpiPo42u4BnomHGFuFnc+r76nzqWmcl3I9UcP/CLmr0V1gyqZTbd0XsKrJmtrDnvIOj6hGPGLCh5x9EXXQpBhyhO6RYXF8eJEyd44gkH/bHLkLWHL3HnJ5ssAb1Qf0++eaA9j9e5jOvshvDt6OIPYu7GCgVPwdVbETrFFJtnINz3Bzy8XR38WHN0FZz+R98MQWcy/33TEiAtOedjyTFweLlKR3a5CQKcQgghhBCF5W1vpp6Da+qZL76nJ2U1OjMfv/tUBOMNf6oohGO5uMFdC7KCXiGNnLs9eqrWUX2N2Kvq2oMDL0DYUfJKb+YyW4nXVeJKbo4M6i2bDJ91t3vxUp+pl521LL2C+Pn5ce7cOf03pgzRNI15/57m1d8PYtLUfc2qBPD5mNZU9PeEfZcgLqLgq4X28g1REe34S0DjvI+np8C/c1SNjhZjVCBQDwWlyO5dCpH7oN4AqN5JnzGNxqwdmi0/joeUGHhkh75FNJ3FMwAMLqBlqINRt2zZj+a6Bu6+6kNMCCGEEELkZJl+G2W78RhARor66qgTZXcfcPeD1DgVzPP0h2sn1GO+JdTVUogbnbs33LtMTVXVe6aaM/lWVOWtLu6CE5l11h11AcLbRtZcbIS6zy9M31r9kG2/q6l9de71O/Kiy5XDcHG33YuXqaDejSwyJokLCRCdmIanm5FmVQIxGos+LzwuOY1tp66z6cQ1/j1+lcORcZbHBjQJ5d27muPlnlnvzvyG1KsQrm8FuIztjLmEK7DuVZXZ1uo+fcaErEw9WymyR/+EAz+Bf2X9gnoFSU9RAT3Qf0fjLAaD2sklXFHvnexTmpOi1dcbKbVcCCGEEEJP5pNFUzqkxIKLBxz5HSL+g4a3QeVW6vEnjqpZEY6c/eBbEa7HqYvxCZfh58ySRgHhjhtTiJuN0eXGCuiZ1emjgnpmDp9+m6v8095vYM0MaDYShnyi75gubjBpq9pf535dJpNju5P7FO6iigT1Soles/7G6OHNZJef6O+ylfnuHUht+wiD29UjNMAz3+fGJKXx3/lojlyIwnRiHcYrB7kWn8Lc9IGYcs2wfqRHbab1rpszYGgOgun1hmx+D9Tsodo7W2POpPMO1regZfXOcP9q8Au1MW627rd6Opx5EFa3T9ZBmGXMzNd6o9Ul8TIH9XLtWJMzM/VupNcqhBBCCKEnNy9w9VLTXhOvw5/PqaAewL/vQ8sx0PMldcHdLf/zgGLzDYHrJ1RAr/6tqoldcF3V1E4IIfJTt4/q5G3m8Om3uc49zbPEHDVuxfrW70+OVj0EwDGBzEImA0lQr5S5x/UvKhqiKZ8WR8e1g3hr3XluqV+Ru9tWpVvdCjmaWVyLT2Hu2sMs2HaRlHQTLmRwwOM5PA1p4AqHTFVYb2qBwQCNKvkzsVstBjW10ijCMh9cp0yypnfl/7g5iOiIFFnvtrYfNwf19J5OsO97OPCzmpaaJ6iXOabeAUxnq9IG/ELUQWl2lky9wJLeIiGEEEKIsmPs7+Dhq2aQNBoCEXsgpDEc+xN2LYCDv0LPF9SsFqOL47aj54uQkarGdnGFB9ap7RJCiIKEtVDnuYlXVXDLv7JjxjEnH6UnqZlwrh7qZ3NQr6TPPd28YPhiFdxzRKNMydQrm3o1qEhIUADB+9U02TnpQ0jHFTRYeyiSR048yBLXZqQ3u4dO7drxz7btVNg5i97aJT5LfwkwkIELq02taGc8TEVDNHfU0hjRrhXtawYR6J3Pmy1B50y9giQ4qVOMozL18pv2a/7d6j2msw3+yPr9ztqxCiGEEEKUJVWyXQhuNAQaD1XBuzObYcWTcGkf/P44XDsJ/WY6bjuqdcj5swT0hBD2MhqhTm81Dbb5SChXzTHjeAbAM2fBwz9noozl3NNBmXr7f4JzW6Fef6jZPet+Ny9oMMgxY4IE9cqq2SNa4O+aDvtVZ5UJU2YQtDuS73acp378FpobT9LcdBJ2/8zenTUZYziDmyEDDNDa9TR1W3WjRXggNcK+I2j787BnIbfWcofGNqaiZmfO1NMrcy41Aa4eU11iqrTK+7ijgmsA2z6HuEhoPylnjcCM9Gy1A3Ue1zufbjyW13qD1NMrSHJm/UCZfiuEEEIIYZ/sTeOqdYAH18POebDxXdXMQgghSqs2D6hGlNmDXnozGFRgLzdLUM9B9dxPrIXdi9T5viNfX26FTH6SoF5pYg4AeQYSXiGAaX0CmNyzDn8frsdHG3xoFPkzXQ17aWY8CcDfpqbsrz+ZjwbdSoh/tnobR0Jyrq8gAeFQvo7tWnSFdW4rLBoCFRvCpM15H3fU9FtQBz9xESpynj2ol3Qd0ACD/hmCPjYKd4JjA5ilUZfHod1E1RlXCCGEEEIUnosrtH1A3YQQojSr0gqwkshTEiwdaB1cyy/3jLzLh+HSfgiuA2HNHDBuBXC3P2tagnqliZUAkKuLkVsaVeGWRlO4FDuRRf9sI/7ASqjQgFtvvYOu5b3zrsf8/ITL9o2rd6cYn4rqa7yN8bM3ytCbd7AK6uXOmjNvi3d5/euSWH7fVjL1avdUV1jLVdd3TGfbtRD+mq46Hg35NOt+g0GmbQghhBBCCCGE0M/mj+DMJmgzHmr1UPc5OlPP1oy8oyvhr5eg6QgYOlf/cWt0hf9dsHtxCeqVJknRgMFmVleIvyf3DugKA7rmvx5zZNfgwKK6+fHNzBRMvKamvbrkept1ewqaDoOAKvqP7WPjH69iA5h2GFJi9R/T8s9uJTMypJG63WgMLurva282qBBCCCGEEEIIURTntsHh5VC9c1ZQr8PDEH/JcQ06LJl6uWbkJTq4R0AhG2xKUK80aTAIXrymatIVh7nYrrN4B4HBqNo8J17NO603sKq6OYIlay5XsMnoAv5hQJgDxrSRlnsjM6c4597BrX1V1TRsNwFCm5T8dgkhhBBCCCGEuLGYz7njIrLu6zTZsWN62zjPT3LwtN9CMjp7A0QuRhfw9C/eOgoT2b1+Et6tD1/2Kd6Y2RldsoJr8Zf0W689bAX1HCmwGty3EsavyfvYibVwaiMkOyBD0JnMVyXMdQzMDq9QxUQlg08IIYQQQgghhB7MJb7+fR/eawI/PgA75zt2THPQLiF3pp45qOegTD1Qr89OEtS72SVcVdHuuEh912upq2cluPPPe7Djq+JnJFod10bTiv0/wsr/wcn1+o/p5qk6lQXXyfvYssdgwSC4elT/cZ3J3DY8d1DP0XUNhBBCCCGEEELcXBrfAVXaqBmBMWdh33cqpuBI2WfkaVrW/Y6efgsQuc/uRWX6bWmy7nUV/Gn7AFTrWLx1fTMS4i7C3d+CX4jt5RIc1InWtwJcIm+zjvQU1WAB1DRhfPQd11am3vG1sOdrFW0vqXbUmpat+YkDmoI4k/mqRWocpKeCq7v6OTlaffUMdMZWCSGEEEIIIYS40QTXhvF/QUocnN8BZ7c4/hzbrxJM2pI3eFcSQb1CvDYJ6pUmpzbA2c3Q8Pbir+vCDjX1Nf5S/kE98/xwvd+QzUdBzR4Q1jzn/eYgotHVMYGfOn3h/tXgXynXuHk7C+tq3w8qINv4DqhQT92XmgDpSY4d11k8A7PqJiZdV3UT01MgLVE97hXozK0TQgghhBBCCHGj8fBTjTLMzTIcycVVNdzMrSSCerV72b2oBPVKEz0DTz4VVEAvd6ZcbpY3pM5R7iZ32hjPHEQMLnRXF7v4hVgPYpp/D74V9R8T1Hz+0xshuG5WUM/893T1AnedMxKdzWgEvzCo0xvcvNV9SdGZDxrAI8BZWyaEEEIIIYQQQjjG7R+rc/0AB3XdBeg8xe5FJahXmugd1IOszDibY5qDbCXUucVZ01Et04wdlDFnfj3Zp/2aA6Y3Wpae2UP/5qydZ5l6G6CCfkIIIYQQQgghRFm1a6Gqb9d8FFRqru6rP8Cpm5SbnHmXFumpkByjvtcj4GVvF1hzowO9g2ypCXBxN5zbnvN+c+cYRwX1TCbY8gmseRlSM6eClkRtO3OmY/Yg6o1aT88sdzMMc6aeTL0VQgghhBBCCFHWHfwVtn0Glw44e0tskky90sIcXDO46FNrzhzUiy9g+q1vBQiuB/5Vij9mdue3w8LboUJ9eHhr1v3Zp986gsEAf81QtexajIagGpAaD+nJ6nFHZ+olWgvq3aCZemaXDsLWT2HA2/Dshay6ekIIIYQQQgghRFlljlv8857K2os6BQ1ugyZ3QdV2zt22TBLUKy0Ss3Wh1WPqoq+d0297v6xuevPJrF2XO6jo6Ow1g0EF0WLOqtceVCNrG9x8HFfbzsdKpl7VDjDoPfANdcyYpUFGOnx9h+q0HNIY2j0IHr7O3iohhBBCCCGEEKJ4gmqor9eOqRvA9s9VXEOCeiKHpGjVTVSvrC6fCuDup9bpDL6ZzSqSrkNGGri4qZ/bPgi1ejo2e80nWAX1zIHSctXh8SPZGjk4gLXpt8F11O1G5uIKXR+H3x+Hv9+C5iMlqCeEEEIIIYQQouzr8DAEVgPNpM5z3X3Bwz+rvl4pYNA0TXP2RtzMYmNjCQgIICYmBn9fHzVV1FOHzqGa5pjusvYymeCVYNAyYNoh8K9UcmMvvguOrQIXD/APg4a3OyYbMbvT/8D8gVC+Njy6U9VIdHV37JilRUYafNhGpSIDDFsEDW9z7jYJIYQQQgghhBA3OGmUUZoYXfQJ6IF9Ab30FHinHnzcUTW20JPRCL7mKbiX9F13QeoPUrUJM1Ig6rRjM/TMQpvCfSth5HdwfgfMbgI7Fzh+3NLAxQ1ueT7r5xNrnLctQgghhBBCCCHETUKm397MEq9BfKSapurmrf/6fSpAXATEZ+vAu3UuGF2h0RDwDtJ/TIBW90KzERAXqcbXK1CaH09/qNZBZUjOH6h+r+e2qW25GTQaCj/er74PquncbRFCCCGEEEIIIW4CEtQrLbZ8AjGHocU9ULtn8ddnyoAlw1UBx3uXWQ9smeu/eZd3zFRda5l6a1+FlFio0dVxQT0AVw8oV03dStKxVXDmX3D1hB7PluzYzmQ0wmN7Ye9SaDXW2VsjhBBCCCGEEELc8CSoV1qc2QIX1kKtHvqsz+gCZ7dAapwK3lkL6iVeU1+9y+szZm7NR0LN7lC5lfo5PUUF9Bw5pjPtXAC/TVbft5sAAVWcuz0lrVx16P6Ms7dCCCGEEEIIIYS4KUhQr7Qwd2rVsyusT7AK6sVfhvK1rIzp4KBe4zty/nz5kPpqdAXPQMeM6UzmgB5A56nO2w4hhBBCCCGEEELc8KRRRmlhDrDpGdQzT39NuGL98ezTbx3t8mH4qm/WeMYb8K1Xp4/62v5h8Crn3G0RQgghhBBCCCHEDU0y9UqLhKvqr6FngM0cILQV1HN0pl52h5dDenLmeMGOH88ZBrwD57erphFCCCGEEEIIIYQQDiRBvdIiIxlcDfpPvwXbQT3PAAiuVzLNJLo+AcF14a+XoOldjh/PGZzRmEMIIYQQQgghhBA3JQnqlSauXuDuo9/6fAqYftvxEXUrKQ1vUzchhBBCCCGEEEIIUSw3YGGzssqosvQMBv1W6VMBPPwBHdcphBBCCCGEEEIIIZzOoGma5uyNuJnFxsYSEBBATFQU/h6AV6B+K9c0fYOEQgghhBBCCCGEEKJUkEy90sJo1DegBwUH9D7pBB93hOsn9R1XCCGEEEIIIYQQQjiU1NS7WZlMcPkQaBng6unsrRFCCCGEEEIIIYQQhSCZeqXFTxPg8O/6rjM1Eb6+A+Z2hfTUnI8lR6uAHoB3eX3HFUIIIYQQQgghhBAOJUG90uLI73D9lL7rdPWEE+sgYi8kXs35WOJ19dXdD1w99B1XCCGEEEIIIYQQQjiUBPVKE58K+q7PaASfYPV9wpWcj5mDfD6SpSeEEEIIIYQQQghR1pSpoN7YsWOdvQmOZQ7A6brOiuprfO6g3jX1VabeCiGEEEIIIYQQQpQ5ZSqoZ83YsWMxGAw5bu3bty/weT/++CMNGzbEw8ODhg0b8vPPP+dZ5uOPP6ZGjRp4enrSqlUrNm7cmONxTdOYPn06lSpVwsvLi+7du3PgwIGivxiHBPVsZOolZGbqeTtgTCGEEEIIIYQQQgjhUKU+qHf16lXuvfdeqlatyjfffEPt2rUZNmwYqalZjR/69etHRESE5bZixYp817l582aGDx/O6NGj2bt3L6NHj2bYsGFs3brVssy3337LlClTeO6559i9ezddunShf//+nD171rLMW2+9xaxZs/jwww/Zvn07oaGh9O7dm7i4uKK9WL2n32ZfZ+6gnqsnVKgPQTX1H1MIIYQQQgghhBBCOJRB0zTN2RuRn9GjR7N9+3bmzp3L7NmzmTx5MitXrmTGjBl4enoyduxYoqOj+eWXX+xe5/Dhw4mNjeWPP/6w3NevXz/KlSvHN998A0C7du1o2bIln3zyiWWZBg0aMHjwYF5//XU0TaNSpUpMmTKFp59+GoCUlBRCQkJ48803mTBhgtWxU1JSSElJsfwcGxtLeHg4Mc/44f/KVXB1L8yvp2B/PgebP4SOj0KfV/VdtxBCCCGEEEIIIYRwilKfqbd7925Gjx5Nt27dCAgIoEePHrz55pt4enpallm/fj0VK1akbt26PPDAA1y+fDnfdW7evJk+ffrkuK9v375s2rQJgNTUVHbu3JlnmT59+liWOXXqFJGRkTmW8fDwoFu3bpZlrHn99dcJCAiw3MLDwzOf7K9/QA/U9FuPAP3XK4QQQgghhBBCCCGcptQH9Tp16sS8efNYvny51cf79+/P4sWLWbt2Le+++y7bt2/nlltuyZENl1tkZCQhISE57gsJCSEyMhJQU34zMjLyXcb8Nb9lrHn22WeJiYmx3M6dO6ceeMh2ILBYOk2BZ89Klp4QQgghhBBCCCHEDcTV2RtQkFmzZjFz5kymTp3KiRMn2LNnDxMnTmTixImAmkpr1rhxY1q3bk21atX4/fffGTp0qM31GgyGHD9rmpbnPr2Wyc7DwwMPD4+8D3gF2nxOsdjalq/vhNgLMGg2VG3nmLGFEEIIIYQQQgghhEOU+kw9Hx8fXnvtNY4dO8Ztt93GQw89xLRp0/jss8+sLh8WFka1atU4duyYzXWGhobmyaa7fPmyJesuODgYFxeXfJcJDQ0FyHeZUu3qEbh80HbQTwghhBBCCCGEEEKUWqU+qJddYGAgEyZMoH///mzcuNHqMteuXePcuXOEhYXZXE+HDh1YvXp1jvtWrVpFx44dAXB3d6dVq1Z5llm9erVlmRo1ahAaGppjmdTUVDZs2GBZplAO/FL459gj8Tp8fQd81gOy90RJuKa+epd3zLhCCCGEEEIIIYQQwmFKfVBv6tSpbNiwgZiYGDIyMli3bh0bNmygVatWxMfH88QTT7B582ZOnz7N+vXrufXWWwkODmbIkCE21/nYY4+xatUq3nzzTQ4fPsybb77JX3/9xZQpUyzLTJs2jS+++IKvvvqKQ4cOMXXqVM6ePWuZ9mswGJgyZQozZ87k559/Zv/+/YwdOxZvb29GjhxZ+Bd67Xjhn2MPNy84/hdc3AXJMeq+tCRIS1DfS1BPCCGEEEIIIYQQoswp9TX1qlatyrRp0zh27BgJCQmsX7+ecePG8eijj5Kamsq+fftYuHAh0dHRhIWF0aNHD7799lv8/Pws6xg7dqwl6AfQsWNHli5dyvPPP88LL7xArVq1+Pbbb2nXLqu23PDhw7l27Rovv/wyERERNG7cmBUrVlCtWjXLMk899RRJSUlMmjSJqKgo2rVrx6pVq3KMbTfv4CL/jvLl5gXufpAaBwlXVe2+xMwsPaMreEpnXCGEEEIIIYQQQoiyxqBp2edklm5jx45l/vz5hX5e9+7d6d69O9OnT9d9m4orNjaWgIAAYrZ8jX+7UY4ZZE4LuH4S7vsDqnWEPd/ALxPBNwSeOOqYMYUQQgghhBBCCCGEw5T6TL3iiouL48SJEyxfvtzZm5I/LwdOg/WpoIJ6CVcg7hIsn6Lur9ffcWMKIYQQQgghhBBCCIcpU0G9omTp+fn5ce7cOf03Rm8+Dpp+CyqoByqo5xcCt74P57ZB/7ccN6YQQgghhBBCCCGEcJgyFdS7oTmqph6Ad5D6mnBVfW02Qt2EEEIIIYQQQgghRJlU6rvf3jS8Ah237oCqqllGpZaOG0MIIYQQQgghhBBClBjJ1CstjA6Mr7a6FzJSIKiG48YQQgghhBBCCCGEECWmTHW/vRFZut/GxODv7+/szRFCCCGEEEIIIYQQZYBMvxVCCCGEEEIIIYQQooyRoJ4QQgghhBBCCCGEEGWMBPWEEEIIIYQQQgghhChjJKgnhBBCCCGEEEIIIUQZI0E9IYQQQgghhBBCCCHKGAnqCSGEEEIIIYQQQghRxkhQTwghhBBCCCGEEEKIMkaCekIIIYQQQgghhBBClDES1BNCCCGEEEIIIYQQooyRoJ4QQgghhBBCCCGEEGWMBPWEEEIIIYQQQgghhChjJKgnhBBCCCGEEEIIIUQZI0E9IYQQQgghhBBCCCHKGAnqCSGEEEIIIYQQQghRxkhQTwghhBBCCCGEEEKIMsbV2Rtws9M0DYDY2Fgnb4kQQgghhBBCCCGEKA38/PwwGAz5LiNBPSe7du0aAOHh4U7eEiGEEEIIIYQQQghRGly+fJkKFSrku4wE9ZwsKCgIgLNnzxIQEABAmzZt2L59e4lvi4zr/HFjY2MJDw/n3Llz+Pv7l9i4jiTjFk5R3gNl9bXKuAVz5D6hIDfT77ksjavXe6KsvN6yPK78bWXc4rL2nriRX6+Mm3PckjwGKA2v90YeU49xi/p+KKuv92Yf1/z3dnd3L3BZCeo5mdGoyhoGBARY/jldXFxK/ORNxi1d4/r7++u+baX59cq4eRXmPVDWX6uMWzBH7BMKcrP9nsvauMV9T5S111sWx5W/rYyrl+zviZvh9cq4OcctiWOA0vR6b8Qx9Ry3sO+Hsv56b/ZxC5p6C9Ioo1R6+OGHZVwZV8aVcUv1mDLuje9m+z3LuDLujTCmjCvjyrgyroxb+saUcWVcRzJo5k4NwiliY2MJCAggJibGaRkgovSQ94OQ94DITt4PIjd5T9y45G8rcpP3xM1N/v4iO3k/3FwK8/eWTD0n8/Dw4KWXXsLDw8PZmyJKAXk/CHkPiOzk/SByk/fEjUv+tiI3eU/c3OTvL7KT98PNpTB/b8nUE0IIIYQQQgghhBCijJFMPSGEEEIIIYQQQgghyhgJ6gkhhBBCCCGEEEIIUcZIUE8IIYQQQgghhBBCiDJGgnpCCCGEEGWYwWDgl19+cfZmCCGEEEKIEiZBPQc7d+4c999/P5UqVcLd3Z1q1arx2GOPce3aNbuev379egwGA9HR0Y7dUOEwY8eOxWAw8MYbb+S4/5dffsFgMDhpq0RJMr8HDAYDbm5uhISE0Lt3b7766itMJpOzN0840dixYxk8eLCzN0OUAtn3E9lvx48fd/amiWIw/10nTpyY57FJkyZhMBgYO3ZsyW+YKBU2bdqEi4sL/fr1c/amiBIg+wORHzkmFEUlQT0HOnnyJK1bt+bo0aN88803HD9+nE8//ZQ1a9bQoUMHrl+/7uxNFCXE09OTN998k6ioKGdvinCSfv36ERERwenTp/njjz/o0aMHjz32GIMGDSI9Pd3ZmyeEKAXM+4nstxo1ajh7s0QxhYeHs3TpUpKSkiz3JScn880331C1atVirTstLa24myec6KuvvuLRRx/ln3/+4ezZs8VaV0ZGhlwoLAMcuT8QQtycJKjnQA8//DDu7u6sWrWKbt26UbVqVfr3789ff/3FhQsXeO655wBISUnhqaeeIjw8HA8PD+rUqcOXX37J6dOn6dGjBwDlypWTqzdlWK9evQgNDeX111+3ucyPP/5Io0aN8PDwoHr16rz77ruWx5599lnat2+f5zlNmzblpZdecsg2C315eHgQGhpK5cqVadmyJf/73//49ddf+eOPP5g/fz4AMTExPPjgg1SsWBF/f39uueUW9u7dm2M9y5Yto3Xr1nh6ehIcHMzQoUOd8GqEI6xcuZLOnTsTGBhI+fLlGTRoECdOnLA8fvr0aQwGAz/99BM9evTA29ubZs2asXnzZidutdCTeT+R/ebi4sJvv/1Gq1at8PT0pGbNmsyYMSPPxYCIiAj69++Pl5cXNWrU4Pvvv3fSqxC5tWzZkqpVq/LTTz9Z7vvpp58IDw+nRYsWlvvs3Qd89913dO/eHU9PT77++usSfS1CPwkJCXz33Xc89NBDDBo0yHIsAFkzdX7//XeaNWuGp6cn7dq1Y9++fZZl5s+fT2BgIMuXL6dhw4Z4eHhw5swZJ7wSURh67Q9uueUWHnnkkRzrvnbtGh4eHqxdu9bxL0Q4VPXq1Zk9e3aO+5o3b8706dMtPxsMBr744guGDBmCt7c3derUYdmyZSW7oaJUkKCeg1y/fp0///yTSZMm4eXlleOx0NBQRo0axbfffoumaYwZM4alS5cyZ84cDh06xKeffoqvry/h4eH8+OOPABw5coSIiAjef/99Z7wcUUwuLi7MnDmTDz74gPPnz+d5fOfOnQwbNowRI0awb98+pk+fzgsvvGA5wBs1ahRbt27N8WF+4MAB9u3bx6hRo0rqZQid3XLLLTRr1oyffvoJTdMYOHAgkZGRrFixgp07d9KyZUt69uxpyer9/fffGTp0KAMHDmT37t2sWbOG1q1bO/lVCL0kJCQwbdo0tm/fzpo1azAajQwZMiRP5sVzzz3HE088wZ49e6hbty533323ZHvewP7880/uueceJk+ezMGDB5k7dy7z58/ntddey7HcCy+8wB133MHevXu55557uPvuuzl06JCTtlrkdt999zFv3jzLz1999RXjxo3LsYy9+4Cnn36ayZMnc+jQIfr27Vsi2y/09+2331KvXj3q1avHPffcw7x589A0LccyTz75JO+88w7bt2+nYsWK3HbbbTmyMxMTE3n99df54osvOHDgABUrVizplyGKQI/9wfjx41myZAkpKSmW5yxevJhKlSpZkkLEjW/GjBkMGzaM//77jwEDBjBq1CiZDXgz0oRDbNmyRQO0n3/+2erjs2bN0gBt69atGqCtXr3a6nLr1q3TAC0qKspxGysc6t5779Vuv/12TdM0rX379tq4ceM0TdO0n3/+WTP/C44cOVLr3bt3juc9+eSTWsOGDS0/N23aVHv55ZctPz/77LNamzZtHLz1Qg/Z3wO5DR8+XGvQoIG2Zs0azd/fX0tOTs7xeK1atbS5c+dqmqZpHTp00EaNGuXozRUlKL/3xuXLlzVA27dvn6Zpmnbq1CkN0L744gvLMgcOHNAA7dChQyWxucKB7r33Xs3FxUXz8fGx3O68806tS5cu2syZM3Msu2jRIi0sLMzyM6BNnDgxxzLt2rXTHnrooRLZdmGb+X/8ypUrmoeHh3bq1Cnt9OnTmqenp3blyhXt9ttv1+69916rz7W1D5g9e3YJvgLhKB07drT8LdPS0rTg4GDL+YD5+H/p0qWW5a9du6Z5eXlp3377raZpmjZv3jwN0Pbs2VPyGy+KRM/9QXJyshYUFGR5P2iapjVv3lybPn16SbwU4QDZjwmrVaumvffeezkeb9asmfbSSy9Zfga0559/3vJzfHy8ZjAYtD/++KMEtlaUJpKp5yRa5pW4U6dO4eLiQrdu3Zy8RaIkvPnmmyxYsICDBw/muP/QoUN06tQpx32dOnXi2LFjZGRkACpbb/HixYB6/3zzzTeSpXcD0DQNg8HAzp07iY+Pp3z58vj6+lpup06dsmRo7tmzh549ezp5i4WjnDhxgpEjR1KzZk38/f0ttdRy11lq2rSp5fuwsDAALl++XHIbKhymR48e7Nmzx3KbM2cOO3fu5OWXX86xX3jggQeIiIggMTHR8twOHTrkWFeHDh0kU68UCQ4OZuDAgSxYsIB58+YxcOBAgoODcyxj7z5AMrTLviNHjrBt2zZGjBgBgKurK8OHD+err77KsVz2/+ugoCDq1auX4//a3d09x2eCKBv02B94eHhwzz33WN4ze/bsYe/evVKq6SaT/f/fx8cHPz8/OSa8Cbk6ewNuVLVr18ZgMHDw4EGrXWwOHz5MuXLl8Pb2LvmNE07TtWtX+vbty//+978cH7rmwE52Wq4pGCNHjuSZZ55h165dJCUlce7cOcvBoCi7Dh06RI0aNTCZTISFhbF+/fo8ywQGBgLkmcovbiy33nor4eHhfP7551SqVAmTyUTjxo1JTU3NsZybm5vle/N+Q4qj3xh8fHyoXbt2jvtMJhMzZsywWj/T09Mz3/VJh/XSZdy4cZYaWB999FGex+3dB/j4+JTI9grH+fLLL0lPT6dy5cqW+zRNw83NrcCmatn/r728vOT/vIzSY38wfvx4mjdvzvnz5/nqq6/o2bMn1apVK7HXIBzHaDTmORe01hgp+zEhqP2DHBPefCSo5yDly5end+/efPzxx0ydOjXHyXhkZCSLFy9mzJgxNGnSBJPJxIYNG+jVq1ee9bi7uwNYsrVE2ffGG2/QvHlz6tata7mvYcOG/PPPPzmW27RpE3Xr1sXFxQWAKlWq0LVrVxYvXkxSUhK9evUiJCSkRLdd6Gvt2rXs27ePqVOnUqVKFSIjI3F1daV69epWl2/atClr1qzhvvvuK9kNFQ537do1Dh06xNy5c+nSpQtAnn2CuDm1bNmSI0eO5An25bZlyxbGjBmT4+fsRdeF8/Xr189yQp67Fp7sA24e6enpLFy4kHfffZc+ffrkeOyOO+5g8eLFNG7cGFD/x+aOqFFRURw9epT69euX+DYL/emxP2jSpAmtW7fm888/Z8mSJXzwwQeO33BRIipUqEBERITl59jYWE6dOuXELRKlmQT1HOjDDz+kY8eO9O3bl1dffZUaNWpw4MABnnzySSpXrsxrr71GUFAQ9957L+PGjWPOnDk0a9aMM2fOcPnyZYYNG0a1atUwGAwsX76cAQMG4OXlha+vr7NfmiiGJk2aMGrUqBwfvI8//jht2rThlVdeYfjw4WzevJkPP/yQjz/+OMdzR40axfTp00lNTeW9994r6U0XxZCSkkJkZCQZGRlcunSJlStX8vrrrzNo0CDGjBmD0WikQ4cODB48mDfffJN69epx8eJFVqxYweDBg2ndujUvvfQSPXv2pFatWowYMYL09HT++OMPnnrqKWe/PFFM5cqVo3z58nz22WeEhYVx9uxZnnnmGWdvligFXnzxRQYNGkR4eDh33XUXRqOR//77j3379vHqq69alvv+++9p3bo1nTt3ZvHixWzbto0vv/zSiVsucnNxcbFMnTRfsDOTfcDNY/ny5URFRXH//fcTEBCQ47E777yTL7/80nKM9/LLL1O+fHlCQkJ47rnnCA4OtjoDSJQ9eu0Pxo8fzyOPPIK3tzdDhgxx+HaLknHLLbcwf/58br31VsqVK8cLL7yQ530ihJnU1HOgOnXqsGPHDmrVqsXw4cOpVasWDz74ID169GDz5s0EBQUB8Mknn3DnnXcyadIk6tevzwMPPEBCQgIAlStXZsaMGTzzzDOEhITkaV0uyqZXXnklR0p1y5Yt+e6771i6dCmNGzfmxRdf5OWXX85TF+Ouu+7i2rVrJCYmykFdGbNy5UrCwsKoXr06/fr1Y926dcyZM4dff/0VFxcXDAYDK1asoGvXrowbN466desyYsQITp8+bcnI7N69O99//z3Lli2jefPm3HLLLWzdutXJr0wUh8lkwtXVFaPRyNKlS9m5cyeNGzdm6tSpvP32287ePFEK9O3bl+XLl7N69WratGlD+/btmTVrVp4pVjNmzGDp0qU0bdqUBQsWsHjxYho2bOikrRa2+Pv74+/vn+d+2QfcPL788kt69eqVJ6AHKlNvz5497Nq1C1CzOx577DFatWpFREQEy5Yts8ziEWWfHvuDu+++G1dXV0aOHFlgSQZRupmPCQGeffZZunbtyqBBgxgwYACDBw+mVq1aTt5CUVoZtNyTtYUQQghRIvr160ft2rX58MMPnb0pQgghSon169fTo0cPoqKiLHV1hbDm3LlzVK9ene3bt9OyZUtnb44oBjkmFEUlmXpCCCFECYuKiuL3339n/fr1VuupCiGEEELYkpaWxtmzZ3n66adp3769BPTKMDkmFMUlNfWEEEKIEjZu3Di2b9/O448/zu233+7szRFCCCFEGfLvv//So0cP6tatyw8//ODszRHFIMeEorhk+q0QQgghhBBCCCGEEGWMTL8VQgghhBBCCCGEEKKMkaCeEEIIIYQQQgghhBBljAT1hBBCCAd5/fXXadOmDX5+flSsWJHBgwdz5MiRHMtomsb06dOpVKkSXl5edO/enQMHDuRY5rPPPqN79+74+/tjMBiIjo7OM9bRo0e5/fbbCQ4Oxt/fn06dOrFu3TpHvjwhhBBCCCGEE0lQTwghhHCQDRs28PDDD7NlyxZWr15Neno6ffr0ISEhwbLMW2+9xaxZs/jwww/Zvn07oaGh9O7dm7i4OMsyiYmJ9OvXj//97382xxo4cCDp6emsXbuWnTt30rx5cwYNGkRkZKRDX6MQQgghhBDCOaRRhhBCCFFCrly5QsWKFdmwYQNdu3ZF0zQqVarElClTePrppwFISUkhJCSEN998kwkTJuR4/vr16+nRowdRUVEEBgZa7r969SoVKlTg77//pkuXLgDExcXh7+/PX3/9Rc+ePUvsNQohhBBCCCFKhmTqCSGEECUkJiYGgKCgIABOnTpFZGQkffr0sSzj4eFBt27d2LRpk93rLV++PA0aNGDhwoUkJCSQnp7O3LlzCQkJoVWrVvq+CCGEEEIIIUSp4OrsDRBCCCFuBpqmMW3aNDp37kzjxo0BLFNjQ0JCciwbEhLCmTNn7F63wWBg9erV3H777fj5+WE0GgkJCWHlypU5MvqEEEIIIYQQNw7J1BNCCCFKwCOPPMJ///3HN998k+cxg8GQ42dN0/Lclx9N05g0aRIVK1Zk48aNbNu2jdtvv51BgwYRERFR7G0XQgghhBBClD4S1BNCCCEc7NFHH2XZsmWsW7eOKlWqWO4PDQ0FyNPM4vLly3my9/Kzdu1ali9fztKlS+nUqRMtW7bk448/xsvLiwULFujzIoQQQgghhBCligT1hBBCCAfRNI1HHnmEn376ibVr11KjRo0cj9eoUYPQ0FBWr15tuS81NZUNGzbQsWNHu8dJTEwEwGjM+bFuNBoxmUzFeAVCCCGEEEKI0kpq6gkhhBAO8vDDD7NkyRJ+/fVX/Pz8LBl5AQEBeHl5YTAYmDJlCjNnzqROnTrUqVOHmTNn4u3tzciRIy3riYyMJDIykuPHjwOwb98+/Pz8qFq1KkFBQXTo0IFy5cpx77338uKLL+Ll5cXnn3/OqVOnGDhwoFNeuxBCCCGEEMKxDJqmac7eCCGEEOJGZKsu3rx58xg7diygsvlmzJjB3LlziYqKol27dnz00UeWZhoA06dPZ8aMGfmuZ8eOHTz33HPs2LGDtLQ0GjVqxIsvvkj//v11f11CCCGEEEII55OgnhBCCCGEEEIIIYQQZYzU1BNCCCGEEEIIIYQQooyRoJ4QQgghhBBCCCGEEGWMBPWEEEIIIYQQQgghhChjJKgnhBBCCCGEEEIIIUQZI0E9IYQQQgghhBBCCCHKGAnqCSGEEEIIIYQQQghRxkhQTwghhBBCCCGEEEKIMkaCekIIIYQQQgghhBBClDES1BNCCCGEEA7XvXt3pkyZ4uzNEEIIIYS4YUhQTwghhBBClCrr16/HYDAQHR3t7E0RQgghhCi1JKgnhBBCCCGEEEIIIUQZI0E9IYQQQgihq4SEBMaMGYOvry9hYWG8++67OR7/+uuvad26NX5+foSGhjJy5EguX74MwOnTp+nRowcA5cqVw2AwMHbsWAA0TeOtt96iZs2aeHl50axZM3744YcSfW1CCCGEEKWFBPWEEEIIIYSunnzySdatW8fPP//MqlWrWL9+PTt37rQ8npqayiuvvMLevXv55ZdfOHXqlCVwFx4ezo8//gjAkSNHiIiI4P333wfg+eefZ968eXzyySccOHCAqVOncs8997Bhw4YSf41CCCGEEM5m0DRNc/ZGCCGEEEKIG0N8fDzly5dn4cKFDB8+HIDr169TpUoVHnzwQWbPnp3nOdu3b6dt27bExcXh6+vL+vXr6dGjB1FRUQQGBgIq+y84OJi1a9fSoUMHy3PHjx9PYmIiS5YsKYmXJ4QQQghRarg6ewOEEEIIIcSN48SJE6SmpuYIvAUFBVGvXj3Lz7t372b69Ons2bOH69evYzKZADh79iwNGza0ut6DBw+SnJxM7969c9yfmppKixYtHPBKhBBCCCFKNwnqCSGEEEII3RQ0CSQhIYE+ffrQp08fvv76aypUqMDZs2fp27cvqampNp9nDvz9/vvvVK5cOcdjHh4exd9wIYQQQogyRoJ6QgghhBBCN7Vr18bNzY0tW7ZQtWpVAKKiojh69CjdunXj8OHDXL16lTfeeIPw8HAAduzYkWMd7u7uAGRkZFjua9iwIR4eHpw9e5Zu3bqV0KsRQgghhCi9JKgnhBBCCCF04+vry/3338+TTz5J+fLlCQkJ4bnnnsNoVP3Zqlatiru7Ox988AETJ05k//79vPLKKznWUa1aNQwGA8uXL2fAgAF4eXnh5+fHE088wdSpUzGZTHTu3JnY2Fg2bdqEr68v9957rzNerhBCCCGE00j3WyGEEEIIoau3336brl27ctttt9GrVy86d+5Mq1atAKhQoQLz58/n+++/p2HDhrzxxhu88847OZ5fuXJlZsyYwTPPPENISAiPPPIIAK+88govvvgir7/+Og0aNKBv37789ttv1KhRo8RfoxBCCCGEs0n3WyGEEEIIIYQQQgghyhjJ1BNCCCGEEEIIIYQQooyRoJ4QQgghhBBCCCGEEGWMBPWEEEIIIYQQQgghhChjJKgnhBBCCCGEEEIIIUQZI0E9IYQQQgghhBBCCCHKGAnqCSGEEEIIIYQQQghRxkhQTwghhBBCCCGEEEKIMkaCekIIIYQQQgghhBBClDES1BNCCCGEEEIIIYQQooyRoJ4QQgghhBBCCCGEEGWMBPWEEEIIIYQQQgghhChj/g/sALlstAt/NwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# every day's closing price = S&P 500 close adjusted for market closure + Bitcoin close (same for other metrics)\n",
    "fixed_portfolio = sp_reindexed + bitcoin\n",
    "\n",
    "# plot the reindexed portfolio's closing price from Q4 2017 through Q2 2018\n",
    "ax = fixed_portfolio['2017-Q4':'2018-Q2'].plot(\n",
    "    y='close', label='reindexed portfolio of S&P 500 + Bitcoin', figsize=(15, 5), linewidth=2, \n",
    "    title='Reindexed portfolio vs. portfolio with mismatched indices'\n",
    ")\n",
    "\n",
    "# add line for original portfolio for comparison\n",
    "portfolio['2017-Q4':'2018-Q2'].plot(\n",
    "    y='close', ax=ax, linestyle='--', label='portfolio of S&P 500 + Bitcoin w/o reindexing'\n",
    ")\n",
    "\n",
    "# formatting\n",
    "ax.set_ylabel('price')\n",
    "ax.yaxis.set_major_formatter(StrMethodFormatter('${x:,.0f}'))\n",
    "for spine in ['top', 'right']:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}