final_tutorial.ipynb

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   "source": [
    "<h1 align=center>Final Tutorial - Relationship and Impacts of COVID on the Movie Industry</h1>\n",
    "<h2 align=center>Venkat Gokaraju, Sean Tan, Tony Chen, Jahanzeb Hassan</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "COVID-19 has impacted the ways that many of us live our daily lives. In every aspect from the workplace to our home lives, many aspects have changed. However, one of the industry that was most heavily impacted by the coronavirus was the film and movie industry. As a result of many movies closing and the reliance that the film industry has on in-person \"normal\"-looking interaction for filming, they struggled to succeed during the pandemic. [ABC News](https://abcnews.go.com/US/covid-19-changed-movie-industry/story?id=78901700) wrote in July that \"global theatric revenue in the U.S. fell from \\$42.3 billion in 2019 to just \\$12 billion in 2020\". On the other hand, popular movie streaming platforms grew at a fast rate during the pandemic as many Americans yearned for entertainment as they were in lockdown. In a report by Fortune Business Insights, \"a PWC analysis [found that] viewers' time spent streaming has grown by 75% by 2020.\" It goes on to say that \"the number of subscriptions in the U.S. has grown to an astounding 125 million, and a similar trend is being observed internationally\" (https://www.globenewswire.com/news-release/2021/12/15/2352238/0/en/Video-Streaming-Market-Share-to-Touch-USD-932-29-Billion-by-2028-Video-Streaming-Market-Size-2021-to-2028.html).  \n",
    "\n",
    "However, we wanted to explore this further. How large of an impact did COVID have on the success of films? What did the growth of the movie industry look like before COVID, compared to during COVID? We wanted to find out more about the interactions between the movie industry and COVID."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <h2> Setup and Data Collection </h2>\n",
    "</center>\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "Of course, we will start importing some libraries that would allow us to do analysis on our data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3 as sql, pandas as pd, numpy as np, matplotlib as mpl, matplotlib.pyplot as pyplot, math\n",
    "from matplotlib.axes._axes import _log as matplotlib_axes_logger\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "import matplotlib.dates as mdates\n",
    "from scipy.stats import pearsonr\n",
    "import statsmodels.formula.api as smf\n",
    "import seaborn as sns\n",
    "import datetime\n",
    "import warnings\n",
    "import statistics\n",
    "\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "#Ignore warning labels\n",
    "warnings.filterwarnings('ignore')\n",
    "matplotlib_axes_logger.setLevel('ERROR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to begin analysis, we must first obtain data on the movie industry. We decided to focus on a very specific portion of data related to the movie industry - \n",
    "\n",
    "This data comes from [themoviedb.org](https://themoviedb.org). We selected movies fits the following criteria:\n",
    "\n",
    "- Released between 2018 to 2021\n",
    "- Released in United States, European Union, China, or Japan\n",
    "- More than 100 people rated the movie\n",
    "- Received a rating of more than 5\n",
    "- Is at least 1 hour long\n",
    "\n",
    "We got 550 movies for 2018, 604 movies for 2019, 463 movies for 2020, and 311 movies for 2021.\n",
    "\n",
    "```sql\n",
    "CREATE TABLE movies(\n",
    "    id INTEGER PRIMARY KEY,\n",
    "    title TEXT,\n",
    "    release_date TEXT,\n",
    "    adult BOOLEAN,\n",
    "    popularity INTEGER,\n",
    "    vote_average FLOAT,\n",
    "    vote_count INTEGER,\n",
    "    revenue INTEGER,\n",
    "    budget INTEGER\n",
    ")\n",
    "```"
   ]
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  {
   "cell_type": "code",
   "execution_count": 369,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>popularity</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>revenue</th>\n",
       "      <th>budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bad Boys for Life</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>73.253</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6645</td>\n",
       "      <td>426505244</td>\n",
       "      <td>90000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Super Troopers 2</td>\n",
       "      <td>2018-04-20</td>\n",
       "      <td>12.451</td>\n",
       "      <td>5.8</td>\n",
       "      <td>322</td>\n",
       "      <td>18850674</td>\n",
       "      <td>13500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pet Sematary</td>\n",
       "      <td>2019-04-05</td>\n",
       "      <td>24.863</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2559</td>\n",
       "      <td>112399944</td>\n",
       "      <td>21000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>How to Train Your Dragon: The Hidden World</td>\n",
       "      <td>2019-02-22</td>\n",
       "      <td>61.609</td>\n",
       "      <td>7.8</td>\n",
       "      <td>4810</td>\n",
       "      <td>517526875</td>\n",
       "      <td>129000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Star Wars: The Rise of Skywalker</td>\n",
       "      <td>2019-12-20</td>\n",
       "      <td>62.079</td>\n",
       "      <td>6.5</td>\n",
       "      <td>7223</td>\n",
       "      <td>1074144248</td>\n",
       "      <td>250000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                                        title release_date  popularity  \\\n",
       "0                           Bad Boys for Life   2020-01-17      73.253   \n",
       "1                            Super Troopers 2   2018-04-20      12.451   \n",
       "2                                Pet Sematary   2019-04-05      24.863   \n",
       "3  How to Train Your Dragon: The Hidden World   2019-02-22      61.609   \n",
       "4            Star Wars: The Rise of Skywalker   2019-12-20      62.079   \n",
       "\n",
       "   vote_average  vote_count     revenue     budget  \n",
       "0           7.2        6645   426505244   90000000  \n",
       "1           5.8         322    18850674   13500000  \n",
       "2           5.7        2559   112399944   21000000  \n",
       "3           7.8        4810   517526875  129000000  \n",
       "4           6.5        7223  1074144248  250000000  "
      ]
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   "source": [
    "movieDB = 'movie.db'\n",
    "conn = sql.connect(movieDB)\n",
    "\n",
    "# Queries movie.db for movie title, release date, popularity, vote average, vote count, revenue, and budget\n",
    "sql_query = ''' \\\n",
    "    SELECT movies.title, movies.release_date, movies.popularity, movies.vote_average, movies.vote_count, movies.revenue, movies.budget\n",
    "    FROM movies\n",
    "'''\n",
    "\n",
    "moviesDF = pd.read_sql(sql_query, conn)\n",
    "moviesDF.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parameters of interest to us include the movie title, its release date, voting average, voting count, revenue, and budget. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> \n",
    "<h2> Preparing/Tidying Data </h2>\n",
    "</center>\n",
    "\n",
    "To prepare the data for analysis, we will do the following: \n",
    "\n",
    "<ul>\n",
    " <li> Removing movies with 0's in any columns, as this generally indicates that the movies are unreleased. </li>\n",
    "\n",
    " <li> Creating a datetime column for the release date. </li>\n",
    "\n",
    " <li> Sorting by datetime in increasing order. </li>\n",
    " <ul>"
   ]
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  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>popularity</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>revenue</th>\n",
       "      <th>budget</th>\n",
       "      <th>datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Insidious: The Last Key</td>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>34.260</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2125</td>\n",
       "      <td>167184112</td>\n",
       "      <td>10000000</td>\n",
       "      <td>2018-01-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>Batman: Gotham by Gaslight</td>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>18.401</td>\n",
       "      <td>6.7</td>\n",
       "      <td>509</td>\n",
       "      <td>3469151</td>\n",
       "      <td>1000000</td>\n",
       "      <td>2018-01-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>Proud Mary</td>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>12.737</td>\n",
       "      <td>5.4</td>\n",
       "      <td>407</td>\n",
       "      <td>21753365</td>\n",
       "      <td>14000000</td>\n",
       "      <td>2018-01-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>The Commuter</td>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>27.599</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3554</td>\n",
       "      <td>119942387</td>\n",
       "      <td>30000000</td>\n",
       "      <td>2018-01-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>The Vegetable</td>\n",
       "      <td>2018-01-18</td>\n",
       "      <td>6.246</td>\n",
       "      <td>5.8</td>\n",
       "      <td>236</td>\n",
       "      <td>8475827</td>\n",
       "      <td>8166084</td>\n",
       "      <td>2018-01-18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          title release_date  popularity  vote_average  \\\n",
       "152     Insidious: The Last Key   2018-01-05      34.260           6.2   \n",
       "484  Batman: Gotham by Gaslight   2018-01-12      18.401           6.7   \n",
       "293                  Proud Mary   2018-01-12      12.737           5.4   \n",
       "120                The Commuter   2018-01-12      27.599           6.3   \n",
       "582               The Vegetable   2018-01-18       6.246           5.8   \n",
       "\n",
       "     vote_count    revenue    budget   datetime  \n",
       "152        2125  167184112  10000000 2018-01-05  \n",
       "484         509    3469151   1000000 2018-01-12  \n",
       "293         407   21753365  14000000 2018-01-12  \n",
       "120        3554  119942387  30000000 2018-01-12  \n",
       "582         236    8475827   8166084 2018-01-18  "
      ]
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   ],
   "source": [
    "# Minor cleaning, remove movies with any 0s (generally these are unreleased movies)\n",
    "moviesDF = moviesDF.loc[(moviesDF!=0).all(axis=1)]\n",
    "\n",
    "# Adds datetime column.\n",
    "datetimes = [datetime.datetime.strptime(x,'%Y-%m-%d') for x in moviesDF['release_date']]\n",
    "moviesDF['datetime'] = datetimes\n",
    "\n",
    "# Sorts by date in increasing order.\n",
    "moviesDF.sort_values(by='datetime', inplace=True)\n",
    "\n",
    "moviesDF.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> <h2> Exploratory Data Analysis on Movie Data </h2> </center>\n",
    "\n",
    "Now that our data is ready for analysis, we will perform exploratory data analysis to understand basic characteristics of the data. We will do some exploratory analysis of the information on movies in general. We'll first do a linear regression model looking at the relationship between movie popularity and movie revenue. We will look at every movie in the movies (moviesDF) DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Movie Popularity and Revenue </h3>\n",
    "\n",
    "First, we will explore how the popularity and revenue of a movie are related. To do that, we will create a scatter plot showing Revenue vs. Popularity and superimpose a regression line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot popularity against revenue\n",
    "ax1 = moviesDF.plot.scatter(x='popularity', y='revenue')\n",
    "ax1.set_title(\"Revenue vs Popularity\")\n",
    "\n",
    "# Linear regression on popularity and revenue\n",
    "popularityCol = moviesDF['popularity']\n",
    "revenueCol = moviesDF['revenue']\n",
    "\n",
    "m, b = np.polyfit(popularityCol, revenueCol, deg=1)\n",
    "\n",
    "# Plot regression line\n",
    "ax1.plot(popularityCol, m*popularityCol + b, color='red')\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is clearly not a good fit! The data here is very skewed towards low revenue, low popularity so we'll need to do a log transformation on the data to get a better visualization. We will fix this next."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> Log Transformation </h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = 0.7983247133427974x + 14.35708373041807\n"
     ]
    }
   ],
   "source": [
    "pd.options.mode.chained_assignment = None #Removes warning output text\n",
    "\n",
    "popularityColLog = [np.log(x) for x in popularityCol]\n",
    "revenueColLog = [np.log(x) for x in revenueCol]\n",
    "\n",
    "moviesDF.loc[:, 'log popularity'] = popularityColLog\n",
    "moviesDF.loc[:, 'log revenue'] = revenueColLog\n",
    "\n",
    "#Puts popularity and revenue into scatter plot\n",
    "ax2 = moviesDF.plot.scatter(x='log popularity', y='log revenue', color='blue')\n",
    "ax2.set_title(\"Log Revenue vs Log Popularity\")\n",
    "\n",
    "popularityColLog = moviesDF.loc[:, 'log popularity']\n",
    "revenueColLog = moviesDF.loc[:, 'log revenue']\n",
    "\n",
    "#Gets regression line for popularity and revenue and plots it on to scatter plot\n",
    "m, b = np.polyfit(popularityColLog, revenueColLog, deg=1)\n",
    "ax2.plot(popularityColLog, m*popularityColLog + b, color='red')\n",
    "\n",
    "pyplot.show()\n",
    "print(f'y = {m}x + {b}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the scatter plot, we see a positive correlation between popularity and revenue. This means as popularity of a movie rises then its revenue will also rise. More clearly, a 1% increase in popularity is, on average, associated with a .798% increase in revenue for a movie. In addition, we notice that most of the data is centered at around 2 - 5 log popularity with most revenue being above 12.5 log revenue.\n",
    "\n",
    "This result was expected since if a movie was popular then that means a lot of people wanted to go see it and so they bought tickets to go see it at the movie theatre or streamed it on one of the movie streaming platforms that had the movie. This, in turn, would generate revenue for the movie."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> <h3> Movie Rating and Revenue </h3> </center>\n",
    "\n",
    "Now, we will analyze the relationship between a movie's rating and its revenue. We hypothesize that movies with higher ratings will tend to generate more revenue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gets x and y variables.\n",
    "x,y = moviesDF['vote_average'], moviesDF['revenue']\n",
    "\n",
    "# Creates scatter plot.\n",
    "pyplot.scatter(x, y)\n",
    "\n",
    "# Sets plot labels.\n",
    "pyplot.xlabel(\"Average Vote Rating\")\n",
    "pyplot.ylabel(\"Movie Revenue\")\n",
    "pyplot.title(\"Average Vote Rating vs. Revenue\")\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot, we can see that movies with low ratings (<6) have low variance in their revenues and typically generate little revenue. Gradually, as the rating increases, the variation starts to increase. While most of the data points are concentrated near the bottom of the graph, there are several data points spread out near mear the middle of the graph. Finally, we see that the movies with the highest ratings have many outliers, with some movies generating $2B+ while others generating close to nothing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> <h3> Movie Budget and Revenue </h3> </center>\n",
    "\n",
    "We suspect that the budget of the movie might have a heavier influence on the total revenue than the movie's rating, so we are going to plot Budget vs. Movie Revenue to investigate this relationship. Because we have already noted that this data is very skewed, we are instead going to plot Log Budget vs. Log Movie Revenue to help us see the relationship better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Takes log of budget.\n",
    "log_budget = [np.log(x) for x in moviesDF['budget']]\n",
    "moviesDF['log budget'] = log_budget\n",
    "\n",
    "# Filters data to exclude outlier.\n",
    "x, y = moviesDF[moviesDF['log budget'] >= 10]['log budget'], moviesDF[moviesDF['log budget'] >= 10]['log revenue']\n",
    "\n",
    "# Creates scatter plot.\n",
    "pyplot.scatter(x, y)\n",
    "\n",
    "# Plots linear regression line.\n",
    "m, b = np.polyfit(x, y, deg=1)\n",
    "pyplot.plot(x, m*x + b, color=\"red\")\n",
    "\n",
    "# Sets plot labels.\n",
    "pyplot.xlabel(\"Log Budget\")\n",
    "pyplot.ylabel(\"Log Movie Revenue\")\n",
    "pyplot.title(\"Log Budget vs. Log Revenue\")\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson correlation coefficient: 0.6723790834387935\n"
     ]
    }
   ],
   "source": [
    "# Calculates Pearson correlation coefficient. \n",
    "corr, _ = pearsonr(x, y)\n",
    "print(f\"Pearson correlation coefficient: {corr}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the plot, we can see a linear relationship between the budget and the movie revenue. \n",
    "\n",
    "The Pearson correlation coefficent results in a number between -1 <= p <= 1 where p is the corrleation coefficient. A number closer to 1 means that there is a strong positive relationship between the variables being looked at. While a number closer to -1 means that there is a strong negative relationship between the variables. A correlation of 0 means that there is no relationship. You can learn more about the Pearson correlation coefficient here: https://en.wikipedia.org/wiki/Pearson_correlation_coefficient.\n",
    "\n",
    "The Pearson correlation coefficient tells us that the budget and the movie revenue are positively correlated and that the linear association between the two variables is somewhat strong. This makes sense since if you spend/invest more then your return on investment (ROI) should theoretically be higher. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> <h3> Movie Data Over Time </h3> </center>\n",
    "\n",
    "Now, we are going to investigate how movie data has changed from 2018 to 2021 by plotting some violin plots.\n",
    "\n",
    "First, we are going to create a year column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "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>title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>popularity</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>revenue</th>\n",
       "      <th>budget</th>\n",
       "      <th>datetime</th>\n",
       "      <th>log popularity</th>\n",
       "      <th>log revenue</th>\n",
       "      <th>log budget</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Insidious: The Last Key</td>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>34.260</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2125</td>\n",
       "      <td>167184112</td>\n",
       "      <td>10000000</td>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>3.533978</td>\n",
       "      <td>18.934606</td>\n",
       "      <td>16.118096</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>Batman: Gotham by Gaslight</td>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>18.401</td>\n",
       "      <td>6.7</td>\n",
       "      <td>509</td>\n",
       "      <td>3469151</td>\n",
       "      <td>1000000</td>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>2.912405</td>\n",
       "      <td>15.059420</td>\n",
       "      <td>13.815511</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>Proud Mary</td>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>12.737</td>\n",
       "      <td>5.4</td>\n",
       "      <td>407</td>\n",
       "      <td>21753365</td>\n",
       "      <td>14000000</td>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>2.544511</td>\n",
       "      <td>16.895279</td>\n",
       "      <td>16.454568</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>The Commuter</td>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>27.599</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3554</td>\n",
       "      <td>119942387</td>\n",
       "      <td>30000000</td>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>3.317780</td>\n",
       "      <td>18.602522</td>\n",
       "      <td>17.216708</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>The Vegetable</td>\n",
       "      <td>2018-01-18</td>\n",
       "      <td>6.246</td>\n",
       "      <td>5.8</td>\n",
       "      <td>236</td>\n",
       "      <td>8475827</td>\n",
       "      <td>8166084</td>\n",
       "      <td>2018-01-18</td>\n",
       "      <td>1.831941</td>\n",
       "      <td>15.952729</td>\n",
       "      <td>15.915500</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          title release_date  popularity  vote_average  \\\n",
       "152     Insidious: The Last Key   2018-01-05      34.260           6.2   \n",
       "484  Batman: Gotham by Gaslight   2018-01-12      18.401           6.7   \n",
       "293                  Proud Mary   2018-01-12      12.737           5.4   \n",
       "120                The Commuter   2018-01-12      27.599           6.3   \n",
       "582               The Vegetable   2018-01-18       6.246           5.8   \n",
       "\n",
       "     vote_count    revenue    budget   datetime  log popularity  log revenue  \\\n",
       "152        2125  167184112  10000000 2018-01-05        3.533978    18.934606   \n",
       "484         509    3469151   1000000 2018-01-12        2.912405    15.059420   \n",
       "293         407   21753365  14000000 2018-01-12        2.544511    16.895279   \n",
       "120        3554  119942387  30000000 2018-01-12        3.317780    18.602522   \n",
       "582         236    8475827   8166084 2018-01-18        1.831941    15.952729   \n",
       "\n",
       "     log budget  year  \n",
       "152   16.118096  2018  \n",
       "484   13.815511  2018  \n",
       "293   16.454568  2018  \n",
       "120   17.216708  2018  \n",
       "582   15.915500  2018  "
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracts year from release date and creates a seperate 'year' column in the dataframe.\n",
    "year = [x.split('-')[0] for x in moviesDF['release_date']]\n",
    "moviesDF['year'] = year\n",
    "\n",
    "moviesDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FF/9XFOK6WMOKulR/r5xezwBbOAVZWfz9jb/JYvZXoFHLYBNMRkY+ilLmXtZlnT17hilTvuTIkUPowypiqnAbWsMfP93YT/0EgLVa98seR1UV3BkH8aTvxWazMXr0WNq163jFXzARug4c2Mf/ffwhbqeDEQ0iqB1tuuLPfrW7cDu3+5vGXPFnAJJy3Xy3PxdVZ+DhRx6nefNWfq1ZBD+n08mLLz1LnlJARNdKaDQaFJePnOVnaFivEePH/1XuJ7e4pKRTfPbpvzmXfI42ZiutzJZiXRM5Ph9L7Hlke70MGDiUgQOHotfrS6Hi4KHVaoiNvfxqAhLkQpTdXsDChXMLt0zSGjDENcUQWfOKvxTXCnIX+ZxZuM5vxefIoG7d+owZcw9Vq1b3d/kiABRFYenShcye/T3lLHpGNIggznr1m2FxgxxAltPHzIM5nMvzMGDAEAYPvgOdTnfN54my4euvv2D16pVEdK6EodyvYy0dR3Ow787gvvsepnPnrgGsUASKx+Nm0aL5LFw4F5NGQ3eLjSoG4/UdQ1X5xZ7PYbeLhMoJ3P/Ao9SoUbOEKg4+EuTKEK/Xy6pVPzF37izs9gIMkTUwxjdHq79yqwoUP8hB4fgFT/YxPOl7ULxuOnbsxNChw4mJifXLaxClLzMzgy8+/5iDhw7QsJyJwXUiMF1hiZHfup4gB+BRVBYfzWVHipOaNWry8COPU758xZuqXQS/1atX8vXXX2CuG4mt8aX3CVVVyVt7HiXLwwt/fZlateoEqEpR2lRVZceOrXw3fSrpGenUNZroaLFh1t74qK4TbhdrnHbsPh+3d+rCHXeMJCIi0o9VBycJcmXAxUHEc+fOJjMzHZ2tPKb45ujM0cV6/vUEuYtUnxtX+n68WYfR6rR079aTfv0GExERcUOvQZQ+VVXZuHEd30z9Lz6Pmz41w2hR3lzsLq7rDXIX7U1zsvBYHgo6Ro2+h86du6K9iZu3CF6bN2/g008no4+3EN6+PBrtH68txeUjb3UyBkXPX59/SVr5bwFHjx5m5oxvOXL0MDF6PR3NVhKusxXuSlyqwjaHnT1uFwaDgT59B9KrVz8slj8OKyorJMiFMEVR2Lx5I/PmzSYlJRmdJQZjuSbobBWua7zJjQS5oho8BbjS9uLNOYnRaKRnz9707t2fsLDw6z6WKD1paalMnfIle/ftISHCyLC64cRYrm9cyY0GOYAcl495h3M5nu2mTp263Hvvw1SqVPm6jyOC16pVPzF16n/Rx5oI71ABzVVaeX0FHvLWnMeg6nj6qeeoV69BKVYqSsuxY0eZP28We/buxqrT0cpkpoHRjK4Exkdm+bxsdtg57nETZrPRp+9AunXrhdlsvvaTQ4wEuRD0hwBnjsIQ2wh9eMINDRi+mSB3kc+Vizt9L97cJIxGEz179qF3734S6IKM1+tl+fLFzJs3G43io3s1G20qWdDewHVzM0EOQFFVdqY4+fFkAW4F+vUbRP/+gzEa/fPJXASG1+tlxoxprFixDGN5K2Ft468a4i7y2b3krzuPYvdx9133kZjYTSZAlAGqqnLo0AEWLZzHvv17MGt1NDOZaGKyYCiF/78pXg9bnA5Oe9zYrDZ69upL9+69sNkuH3xCkQS5EKIoClu2bGTevDmcP38OnSkSQ7lG6MOr3NQNzx9B7iKfKwd32l68eacl0AURVVXZvn0rM2dMIzUtlfqxJvrWCifSdOMTDm42yF2U71ZYfiKP3alOoqOjGT58DG3bdpA38RCUmprCp5/+mxMnjmOuHYG1cexlu1OvRHH7yN+ShifFTtu2HRg79gGs1htb/kYElqIo7NixjSWL53P8xDEsOh3NjCYal1KA+70Ur4dtTgenPG5MRiNduvakZ88+ZWJ8twS5EHDxTXjOnBkkJ5/1W4C7yJ9B7iKfMxt3+j68eacxmcz07t2P3r37YbHITbm0nTp1gu+++4ZDhw4QZzPQq7qNOjFXnwBTHP4KchedzHaz7EQ+yfkeataoyajR91C7dl2/HFuULEVRWL16Jd9//w1e1Ye1RSymhBtr8VBVFcfBbBwHs4mKiuLBBx6lUaMmfq5YlBSPx826dWtYtnQhKakpROj0NDOaqG8yo7+J96tDF3aIqWe6ua7RDK+XHS4HR90uNFot7dp1pE+f/iQkVL2p4wZSUAe5yZMns2RJ4ca4iYmJPP/886xfv563334bl8tF3759GT9+/HUdM9SC3P79e5kx41uSkk6iM0UUdqFGVPVra0VJBLmLCgPdXrx5Z7BYbQwcMJju3Xth8NPAVnFl586dZe7cmWzduhmrQUeXqlZaVbT4bTyKv4McFHa37kp1svJUAXkuH82aNmfosBEyAD6InT17hq+//oKjRw9jiLNgaxWH7hpL1xSHJ9OJfVs63jw3HTp0YsSIMbfEDMRQlZeXy88/r2DFj0vJL8gnTm+guclMTYPxhoZu/N68vGwABodH3fSxAHJ9Pna7HBxwu/GqCo0aNaFPnwE0bNg45HoDgjbIrV+/no8++ogpU6ag0Wh46KGHGD58OJMmTWLq1KlUrFiRcePGcc8995CYmFjs44ZKkDt79gzffz+NvXt3oTPaCgNcZHU0Gv/P7ivJIHeRz5GJK203voLzRMfEMvzOUdJ9VkJSU1OYP39O4XZIWg3tKllon2DFUoxxStejJILcRS6fwuZzDtadteP0KLRufRtDhgyXCRFBxG63s2DBD/z44xLQa7A0jsFU7cor8d8I1afgOJiN83AOJrOZO4aNoEuXHrIGYRBJTU1h2bLFrF2zCo/XQzWDkWYmM5X0Br9eC/4Ochc5FYX9Lid7PC7sPh9VEqrSp+8A2rRpFzILCwdtkDty5AgFBQU0b94cgNdff52YmBi2bNnC119/DcDcuXPZtGkTb7/9drGPG+xBzuGwM2/ebH78cRkarR5DbAMM0XXRaEvuxlUaQe4ib8F53Km78DmzqF2nHmPvvp8qVUK3STuYJCefZdGi+WzcuBYtcFtFCx0TbNiMJbO0R0kGuYscXoUNZ+xsPOfA41No3aYt/fsPoWrVaiV2TnF1Pp+PNWtWMXvO9xTk52OqHo61UQzamxhvec1z5rkp2JWBJ9VBhYqVGD1qLE2aNCux84lrS0o6yaJF89m6dRMaoK7BSDOzhRhdyYSfkgpyF/lUlcNuF7vcTrK8XmKjY+jTbyCdOnUN+glYQRvkfuvkyZOMHj2au+++mxMnTjBp0iSgsNXuiy++4L///W+xjxXMQW779i1MmfJfcnNzMETVwhjX9JqL+fpDaQY5uLio8HE86btRfW569+7PkCF3Bv0vS7BKSjrFokVz2bplE3qdhlYVLHSobCWiBN9YoXSC3EUFHoWNZ+1sTnbg8io0a9aCAQOGUqtW7Ws/WfiFqqrs3r2T72dM43zyOQzlzFibxKK/yjZul+M6Vbhfs6na9U2AUlUVT7Idx94svPluGjZszIgRY6TbvZQdP36U+fPmsHvPToxaLQ0NJpqazdhKsLEBSj7IXaSqKqc8bna4nJz3egizhdG330C6du0ZtEuXXC3IBUWb4pEjRxg3bhzPP/88Op2OkydPFn1PVdXrbrq90osNpNzcXP7v/z5h7do16MzRWKv3RGcJ/Zk0V6LRaDBG18IQkYArdRdLly5k585t/OUv42nQQNaPKq59+/Yxa+ZMtm7bhkmvpWMVK+0rlVwLXCDZDFq6Vw+jQ2Urm5PtbNy/mzd37aBpkybcOXw4zZs3l276EnTo0CG++uor9u3bhz7MSFjb8hgrWW/o39x5g0FOo9FgrGTDUN6K80Quhw4d5NXXXqRLYiJ333035cuXv+5aRPEdPXqUqVOnsn37dsw6HbeZrTQ2mTGVscW8NRoN1Y0mqhmMJHu9bHc5mDlzOkuXLOCOO++kf//+QRvoLifgQW7btm089dRTTJw4kf79+7N582bS0tKKvp+WlkZ8fPx1HTPYWuT27dvD559/TF5eHsa4JhhjG5TIOLhgpNGZMFe8DX1EVdLOb+Wvf/0r/fsPZtCgYSEzNqG0KYrC7t07WbxoHkePHcFq0NG1mo3bKlqxGMr+dWMxaEmsGka7Sla2nnew8fB+/va3v1G1ajX69x9Mq1a3yS4RfpScfJbZs2ewffsWdGY9tmaxmGpEXNeSIv6m0Wmw1I7EVDUMx+EcVq/5hTVr1tC1a08GDBgiu8v4WUrKeWbN+o5t2zZj1upoZ7HSyGTGWMbfpzQaDZUMBioZDJz3etjqtPO///2PH+bMYfCQ4UG1I03Qdq0mJyczdOhQPvjgA9q3bw+Ay+WiV69eTJkyhYSEBMaNG8cdd9xB3759i33cYAlyXq+XOXNmsHTpQnSmSEyV2hV7Sy1/K+2u1ctRfR6cKdvx5pygRo1aPProk8TFXV9IL8t8Ph9btmxk0aJ5nD17hiiznvaVLbQob8GoC8ybaml2rV6JV1HZlepg/RkHGQ4v8fHx9O8/hPbtb5cPAzchKyuTuXNnsXbtajR6LabaEVhqR6Lxw4eFnF/OARDZudJNHwsKFxJ2HMzCdSoPk9FE3wtbMoVSq0kwcjgcLFw4l+XLFqNFpanRRHOzJWABrrS6Vq/mnMfDJqed814PCZUTGHPXfdSv3zBg9VwUtEHu73//O7Nnz6Zq1V8Hwo8aNYrq1asXLT+SmJjIhAkTrqt5PxiCXErKeT75ZDKnTh3HEFUbU/nmaLSBe9MJhiB3kSc3Cff5rRj0Wu677yHatu0Q6JICyuPxsG7dLyxZPI+09HTibAZur2yhcZwZXQBbRSA4gtxFiqpyIN3F2jN2kvM9REdF0afvQDp37orpJtedupXY7XaWLFnAsuWL8fm8mGqEY6kf7deJDP4Ochf58tzY92XhPldAeHg4Q4YMp1OnLhLob8DOnduZOuVLsrKzqGc00dZiwxbg1qdgCHJQOKTruMfNBqeDPJ+Xjh07M3LkXQFd9D5og1xJCWSQU1WVtWtXM23a13gVFWP5NhgiqgSklt8KpiAHoLjzcZ7biM+RTocOnRgz5t5bbnV3r9fLmjU/s2DBD2RnZ1Mp3ECnBCv1Yk1+WZPJH4IpyF2kqipHs9ysOWMnKcdNeFgY/QcMoUuXHjKZ5iq8Xi+rV69k7rxZFOTnY6wShrVhNDqbwe/nKqkgd5En04ljbyaedCflK1RgxPC7aN68pYyhLAan08m3337N2rWridHrSbTYqKD3/zVwI4IlyF3kVVW2Ou3sdDqIiIjg4UeeoGHDxgGpRYJcKcnKyuR///ucPXt2obfGY6rUFq3BVup1XE6wBTkAVVVwp+/Dnb6fqKgoHnjgERo3LvvLDfh8PtavX8P8+bPJyMigSoSRLlWt1IwyBt0bUTAGud86leNmdVIBx7PdREVGMmDgMDp16oLBEBxvTMFi9+4dTJ8+lZSU8xjiLFgbx1z3TNTrUdJBDn4zw3VfFt48N/XrN2T06HtkqaOrOH8+mY/+9R7nU87TwmyhjdlaIpvZ36hgC3IXpXm9/OTIJ9vrZdDgOxg4cGipj52TIFfCfD4fP/+8gtmzv8ft8WKMa4ohuk5QvSkHY5C7yOfIwJW8GZ8rh3btOjJy5F1ERkYFuqwSsXfvLr6d9jXnU85TKdxA16o2akcHX4C7KNiD3EUnst38fKqApFw3sbGxjBp1Dy1btg7af9fScv58Mt9++zV79+5GH2bE0iQaQ4Ubm4l6PUojyF2kKirOE7k4D2SjehQSE7szbNhw2fv5dw4dOsBH/5qE6nbT02ojIQh33gnWIAfgUVV+sedz2O2iTZt2PPzwn0q1S1+CXAk6dOgA0779mjOnk9DbKmCq0AqtMfhuIMEc5ABUxYc7Yz+ejAMYjUaGDh1Ot249y8zYl7S0VL77bio7dmwj1qqnRzUb9WNNQR00VFXl31vTcflUulQLo3UFS9DXeyzbzY8nCkgp8NCoURPGjLmXihVLPkwEG5fLxcKFP7Bk6SLQgrl+JOZakaU2E7U0g9xFituH40AWzuN5WC0Whg8fQ6dOXYJm1mEg7d+/l399+B5hqPSzhhMRpLtmBHOQg8J7zE6Xg40OO02bNOeJJ/9Sau9REuRKwPnzycycOZ0dO7aiM1gxxLdAH54QlG90qqpScGwRKB6McU0wRNUKyjoBFHcervPb8BacJy4unhEjxtCyZZugrfdaVFVlxYqlzJo5HVSFzlWstK9sRR/gSQzFseWcnUXH8ooe968VTptKwT+O0aeqbD3n4OekAjyqhkGDhtG//+Bb5g199+6dTJn6JZkZGRirhmFrHIPWXLofiAIR5C7y5rix70rHk+6kZq3a3HfvwyQkBH6ccqCcOnWSd95+FZuiMNAWgTVIfw9UVWV6bhZuVaWNxUpDozlo7/v7XA5+sRfQrm0HHn7k8VKpU4KcH2VlZTF//mx++WUVaHUYYupjjKkX0Bmp1+LOOoLr/Laix6YKrTBG1wlgRdfmzU/GnbYTnzOHGjVqMWLEGOrVC62FhAsK8vnvfz9lx45t1I0x0b92OJElvBODP32zN4ujWe6ix7WjjdzdODDL59yIfLfC0uO57E1z0bBhYx555PEyvSF7bm4u307/ms2bNqAPN2JtHoshzhKQWgIZ5KAwFLiS8nHszQKPQr9+gxg4cAiGIOxOLEkFBfm8+rcXcOXmMDQsgrAS3pnhZux1OVhjLyh63Mlqo7EpMNdvcWxz2NnstDNq1N306tWvxM8nQc4P7PaCC1P2l+Dz+tBH1cJYrhFaffAve2BPWo2vILnosc5WEWvVxABWVDyqquDJOYE3fR8+j53GjZtx552jQmIPztTUFCa99yaZmRn0qG6jfeWSH5fkb1/tzuRUjqfocbVIQ9CPlfs9VVXZft7BkuP52MIieObZiWWudUZVVTZt2sA3077C4bBjrheFpW4UmgCtPQiBD3IXKS4f9j0ZuJLyqVChIg8++Ci1agX3h1h/+vzzj9m0cR1DwiIoHyQzU69kUV4OSd5f7zdV9Qb6hwfvBy9VVVlWkEeSz8err71N5coJJXq+qwW54GxjDSJer5dlyxbx3HNPs2jRfLBUwlqzH+YKrUIixAGgeq/+OEhpNFqMUbWw1OyHKb45+w8e4NVXJ/LZZ/8hIyM90OVdUU5ONv+c9CYFuVnc3zSKDgm2kAtxZYVGo6FVRSsPNYtGcRXwz0lvkZaWGuiy/CY3N4fJ//mAzz6bjNvoI6JbZawNogMa4oKJ1qQjrHU84R0rkJabzltvvcrMmdPxeNzXfnKIO3z4IBs2rKW5yRz0IQ7Ai3rVx8FGo9HQ2RqGHpVp0/4X0FokyF3F7t07eOml5/n++2l4tBFYa/TGUrk9WmPw7eValmm0eoyx9bHWHIAxtj6bNm9kwsRnmDdvNi6XK9DlXcLjcfP+P98mJyuTuxpGUiXi1urKCVYVwgzc3SgStyOff056C4fDHuiSbtq2bVt48aXn2LlrG9bGMUR0qYherrfLMpa3EtG9MsZqYSxZsoBXX5vIqVMnA11WiZozZwZWnY6W5uAf1xqqrFotrU0WDh7cz4ED+wJWhwS5y8jPz+eTT/7Nhx++R0aOHUuVzliqdgnY9lqikEZnxBTfDGvNfmCuwLx5s/nb3/7K0aOHA11akTVrVnP6zGmG1QsnISL4PwXfSsrb9IxqEEFqWiorViwPdDk3zOGw88UX/8d//vMBLr2HiK6VC7tSpdX3qrQGLWEt4wjvUIHUrDTe+PvLLFo0D0VRAl2a3508eZzDhw/S3GjGINdFiWpkMmPV6Vi2dFHAapAg9zvHjh3lpZeeY/OWTRjjmmCp0Rt92K23fEEw0xpsWBI6YqnalcycAt5++zUWLZpHoId7er1eFi+aS5UII/ViSm6xVXHjqkUaqRNjYvmyhbhczkCXc92OHj3My3/7K+s3rMFSL4qILpWkFe46GStYieheCX0FM7Nnf88777xOenpaoMvyqzVrVqHXaKhvlPtQSdNpNDQwGNmzdxdZWVkBqUGC3G8cOLCP9957kwKnD2v1npjKNUKjCd5ZPrc6va08lup90IVXYfbs7/nuu28CGuYyMzPIzMqiRfngnTYvoEV5MwV2O+fOnQ10KcWmKArz58/h7bdfI8eZS0TnSlgbxZTaunBljdaoI+y2eMJax3H81DH+9re/snnzxkCX5ReKorBl80aq6Q2YgnSpkbKmrtGMqqps3bopIOeX/8sX5ObmMnnyB/i0ZsxVu0s3aojQ6AyYK7XHEF2XH39cwsaN6wJWi/3C1HmrQd5cg5nVUHjbs9tDY5xcVlYW7773JnPnzsKQYCOiW2UMsSEy0SqIaTQaTFXDiehWCZ8VPvnkI/73v89xu0N7IsSpUyfIL8inhrTGlZoonY4ovZ49u3cE5PwS5C5YtGgeTqcTU6UOaA3Bu3aN+CONRoOpfAt0lli++35awMa8WCyF1022s+yNuSlLcpw+4Nf/X8HswIF9vPLqCxw9doiwVnGEt4lHawju27aqqigOL748N87juQEf8nAtOpuB8M4VsdSN4pdffubvf3+Z1NSUQJd1ww4fPghA5RCYqVqWVNbpOXLkUEDef4L7jlCKkpJOorXEoDMF77o1N0r1eTCbzQwaNAiz2Yzq81z7SSFGo9Ggj6hOXm4OeXm5AamhfPmKVK1Sld2pzqB/8yoOl1e55LpxectGQN2V6iQ2NpYaNWoFupQrUlWVH39cwqR/voVT4yK8SyVM1YJv67/LcZ3IQynworoUCnam4zqRd+0nBZhGq8HaOIbwDhU4l3qOV1+byN69uwNd1g1JSjqFTacL2h0cyqo4nR6X2x2Q5Y3k//QFer0efK4y8Qb8e6rioVevXjz88MP07NkTVSl7QQ5A9TnRaDRoNIG7rBO7dOdcvodt5x0Bq8FfnF71kuvG6Q393419aU6OZ7vp3Llb0I5jVBSFb775iunTp2IobyUiMbQmNLiTC676OJgZK1iJ6FoJr1Hhgw//wapVPwW6pOuWkZFGRADvgbeqiAvBORBrnAbvvlKlrF27juzbtwdPznGMUcH7Sf1GaLQGli9ffuFT/o9otGVvfI3iKcCbfYyGDRsTERERsDoSE7uzY/tWlh7YS6UwA5XCQ7d7w6zXXHLdROmDM/gUV7rdy/yjedSsWYu+fQcGupzL8nq9fP75x2zZshFz3cjCCQ1BGjivRPWpV30c7HQ2AxGdK5K3OZUpU77Ebi+gX79BgS6r2JwOJ8bQumTKBOOF8ByI2fAS2y9o164jDRo2xn1+G9680JnNVhwanQGn08mCBQtwOp1odKEbLi5H8dhxnlmDQQdjxtwT0Fq0Wi0PPfw44ZFRTNmXw8ns0B04bdJrL7luTPrQvV2czfPw1Z5sDCYLf/rTnwtb4IOMqqr873+fs2XLRqyNY7A1jg25EFdWaAxawtuXx5hgY9as71ixYlmgSyo2o8mEJ7SyMwBuVb1kKIc7xHrHPBfqNQZgkkno3pn9TKfT8dijT1GlSlUcZ9bgSj9QJrtZyxqfIx3HqR/RKQ6eeGI8FStWDnRJREREMGHCq0TFxjF1XzZ7UkO/mzWUHc508fWebMzhUUyY+CoxMbGBLumyli1bxPr1a7A0iMZSNyrQ5dzyNFoNYa3jMVa0Mn36lICu3H89ypevQJaqhNz7l0u9dCiHK8Tqz/QVbn0ZH1++1M8tQe43wsLCmDDhFdq0aYc7bReOpJUorsAMnBdXpypenCk7sZ/8iahwGy+++BqNGjUJdFlFypWLY+LE16lZqw6zD+Uy93AOzjIyWSBUuH0qi4/l8u2+bCpUrsJLL70RFEH/clJSzjNr9vcYK9mw1I8KdDniAo1WQ1ibeHRhBr748pOQ2KO1YcPGOHw+kr2hsaf2RSZN4VCOzz77jB9//BFTiLVGH/e4KRdbjnLl4kr93BLkfsdkMvHoo0/y4IOPYlALsJ9chit1V5mc6RmKVFXFk5uE48RSPJkHSUzsyuuvv0OVKlUDXdofhIWF8dxzLzFgwBB2pbr4vx1ZHA/hrtZQcjrXzac7s9h8zkGPHr2ZMOFVIiOjAl3WFS1ePB80KrZm0p0abDR6LdZmsWRlZrBu3ZpAl3NNLVu2IcxmY6vTHlKtckaN5pKhHMYQ+j1I9no46/XQtVvPgPz+Bt9AkSCg0Wjo2LEzjRo1YebM6WzYsBZv7gkMMY0wRNeU3R4CxGtPw522G589jUqVE7hrzNM0aNAo0GVdlV6vZ9iwETRr1oLPP/8PU/ak0jjOTM8aYUSa5Dryt3y3wk8n89iZ4iQqOprnHv9T0F8jANu2b0Ff0YrWIrfkYKSPM6MPN7Jjxza6dOke6HKuymQyMXjIcKZN+x/73U4amYJ/vcRQ5lYVVjkKiImOoUuXHgGpQe4aVxEVFc3DD/+JHj16M/27bzh6ZBverIMYYhuhj6we0GUubiU+RybutD14C5IJC4/gjnsfolOnLmhDaJ2kWrXq8Npr/2DJkgUsXjyfQ5mZdEqw0D7BhkG2WbppPkVlc7KdVUl2PAr06t2fQYOGYrFYA13aNblcTuwFBVirxwS6FL9QPYXrD/bq1Yvly5fj8YT+kAKNRoMmTE9aemgsFNy1aw927tjK2gP7iNDqqGIIneVrQolPVVlekE+uz8czDz0WsEXGJcgVQ40atZjwwt/Yt283s2fP4NSpzegy9qOPbYAhsoYEuhLic2TgTt+LNz8Zq9XG0OGj6datFyZTaG49YzKZGDLkTjp27Mz3309j5fYtbEtx0bWqlabxZrQh1JUQLFRVZV+6i5WnCsh0eGncqAmjx9wTtGPhLsdoNKHX6/E5QmtM05WoHoVevfrw8MMPo6oqi35aEuiS/EJ1+oiIC9zSRtdDq9Xy2J/+zNtvvcqS5LP0soZRXbbs8iuPqrKsII/THjf33fdwQFv+JcgVk0ajoXHjZjRq1JSdO7czb95skpK24M3Yjz7mQqDTSleZP3jtaXjS9+EtOI/VamPQsBF0794rJFpXiiMuLp4nnhjPgQP7mDljGnMPn2TDWQfdq9uoE22UMVLFdCLbzYqT+ZzN81C5UmWefmQMTZs2D7l/v4v3lj0HdqE0UoJ+C65r0Ri0l65baQrt1wPgzXHhzXLRpFvzQJdSbFarlb++8DL//OfbLD11knaKj2YmS8j9fgSjXJ+PpfZ8Mn1e7r//ETp16hLQejRqKI2GLKaMjHwUpWRflqqq7N69k/nz53DixDF0Biv6mPoYomoFXaCzn/oJnz2t6LHOGoe1WvCN8/AWpODJ2Ie3IBVbWDh9+/Sna9eeIbEn5o1SFIWtWzcze/Z00tLSqBZppEd1G1WCYCX/b/ZmcTTr18kZtaON3N04OoAVFUrO9/DTyQKOZrmIjopi6LCRdOjQKaS62n/v+PGj/P3Nv2GqEoatVVxIv9nm/HIOb/qvi6Lqy5mJ7FwpgBXdHNWrkLsmGZPHwNtvvU9YWFigS7ouLpeTL774hG3bNlPDYKSLNQxzEP6uzMvL5txvZtpW0usZHB4VuIKu4LjbxSpHAVqjkUcfe5omTZqVynm1Wg2xsZe/9iTI3SRVVdm/fy/z5s/h6JFDaA0WDEWBLjgaPO1Jq/EVJBc91tkqYq2aGMCKfqWqKj57Ku70ffjsqURERNKv3yASE7uFbBfqjfB6vaxevZIF82eTm5dHvVgT3auFEW8L3DW05ZydRcd+3Sezf61w2lQKXKtohsPLz6cK2JvmxGqxMmDgELp164XRGPjQ6w9z585i/vw5mOtEYm0cejs6XFSWgpzqVcjblIIn1cFTTz5D8+atAl3SDVFVlWXLFjN71nQsGg1dLLagGzcX7EHOrSqstxdwwO2iWtXqPPrYU5QvX6HUzi9BrpQcPLifuXNncfjwwQuBrmFQzHJ1Zx3BdX5b0WNThVYYo+sEsKJChbNQ9xQGuMgoBg4YTOfOXTEE2Q2mNDmdTn78cQlLFs/H5XbRsryFrtVshBlL/xpSVZV/b03H5VPpUi2M1hUC0y1j9yj8klTA5mQHOr2eXr360bfvAKxWW6nXUpJUVeWbb77i559XYKxsw9YyLiS7WctKkPMVeMjfnIov28299z5E585dA13STTtx4jiffzaZ8ynnaWg0095qLdpaKtCCOcid8bhZ7bCTp/jo02cAQ4cOL/XdYSTIlbKDB/cze/YMjh07jM4YhqFcY/QR1QL2CVtVVQqOLQLFgzGuSWFrYQA/7fuc2bjTduHNL5yFOnDAELp06XZLB7jfy8vLZcGCH1i58kf0GuhUxUq7ytZSn+H61e5MAO5vWvozKr2KytZkB6tP23F6FW6/PZGhQ4cTFRX47t2SoqoqS5cuZNas79DZDFhbxmIoF1pDC0I9yKmqijspH/vuTIw6A4888njItsRdjtvtZu7cmSxbthibVktni41qQXDvDcYg51IU1jsKOOh2UT6+PA8+9Bi1a9cNSC0S5AJAVVX27t3NrFnfcfr0KXSWGIxxzdHb4gNSj/3UTwABHRuneBy40/bgyTmB2WJh4IDBIT0LtTScP5/MjBnfsnPnNqLMevrXCqNOTOn9ewUqyJ3MdrPwWD7pdg8NGzRi5KixQbnoc0k5fPggn3/+MRkZ6Ziqh2NtFIM2RNYdDOUg5811Y9+dgSfVQa3adXjk4ceJiwvMPbukHT9+lP9++Qnnks9R22DidqsNSwDHzgVbkDvudrHGacepKPTpO5DBg4cFtLFBglwAKYrCpk3rmTXre7KyMtBHVMUU3xytoXTHGgUyyKmqgifzMJ6MfaAq9OjRmwEDhoTcoOFAOnBgH99M/S/J55NpWM5En5rhRJTCG3tpB7kCj8KPJwoX9C0XW4677r4/JGei+oPL5eSHH2axYsVS0Gkw14vEXCsCjS44usKuJBSDnOL04TiUhet4HiaziTuGjaRr154hPYGmOLxeL4sXz2fB/B8wAB0tFuoYTAH5fVuUl0OS99cdlKrqDfQPjyz1OuyKwhp7Psc9bqokVOWBB8dRrVqNUq/j9yTIBQG3283ixfNZtHg+igLGuKYYouuU2i9MoIKcz5GB6/wWfM5smjRpxpgx95bqANGyxOv1smTJAhYs+AEdCv1rhdE0vmS73UozyB3McDL/aD5Or0rfvgMYMGCotNYC586d5bvvprJ37250FgPmepGYqoejCdKFpEMpyCluH84jObiO5aIqKp07dWXo0BFERITGenH+cvbsGf775SecOHmcGgYjna1hWEs5xO51OVhjLyh63Mlqo3Ep7kqhqipHPW7WOux4NRoGD7mD3r37l/pYuCuRIBdEUlNTmDr1v+zbtwedtRzmim3RGsNL/LylHeRUxYcrbQ+ezENERkZxzz3307x5q1uyZcXfUlKS+fKLTzh67AhN4830qxWOWV8yN93SCHJun8ryE3lsTXZQtUpVHnr4TyQk3DrdqMV16NABZs6azvFjR9FZDZjrRBQGuiBroQuFIKe4fDiP5uA6nofi8dG69W0MHTqCihWDq87SpCgKy5cvZs7sGehVlUSLlZqluIiwqqpMz83Craq0sVhpaDSX2vuFU1H4xZ7PMY+bGjVq8uCDj1GpUnAtKl5qQS43NzcoPskEc5CDwgt2/fo1TPt2Ci6XG2P5Fhgia5boRVuaQc7nysF1bgM+ZzaJid0YPnwMVmvZWMw3WPh8PhYunMv8+XOIMusY1SCS8iWwVElJB7lMh5fvDuSSWuChT5/+DB06AoPBUCLnKgtUVWXfvj3MnTerMNCZ9ZhqR2CuEYEmSGa45q5LxpPiKHpsKG8homPFAFb0K5/dWxjgTuah+hRatbyNwYOHyQeH3zh37iyffTqZpNOnaGA00dEahqGUAtW8vGyAUh0bd87jYYUjH4eqMmTInfTtOxCdLvjGo5Z4kDt+/DhPPPEEeXl5zJo1i/vuu4/JkydTq1atmz30DQn2IHdRZmYGn3/+fxw6tB99RDXMFduU2NpzpRXkPNnHcaVsw2a18tBDj9K0aYsSPd+t7siRQ3z8nw9wFORzZ/0I6vp5IkRJBrmT2W6+P5iL1mBi3KNP0bhxU7+fo6xSVZWDB/ezYOEPHDywH61Rh6lGOObakQGfFOE8nkvBzvSix7bm5TDXDOwHfF+eG8fhHNyn8wFo17Yj/fsPDrpWl2Dh9XqZO3cWixfPJ0avp5c1jGhdyXcxlmaQU1WV7U4HW5x24uLiefSxp6hevWaJn/dGlXiQe+CBB3jwwQd57733mDt3LtOmTWPx4sVMmzatWM/Pz89n1KhRfPLJJyQkJDBhwgS2bdtWtKL/E088Qc+ePYtdT6gEOShszl60aB4//DALnTkCc+XbS6SrtaSDnKr6cJ3fjif7GPXqNeDRR58kMjKqRM4lLpWZmcFHH03idNIp+tQKp60fF+0tqSC3K8XBvCN5xJcvz9NPPy/jJm/CiRPHWLRoPtt3bEGj1WKsFoalTiQ6W2BaNlVVJXv5aVSvgrVBDKYa4QEbUuHNdOI4nI37nB29Xk/nzl3p02cA5crFBaSeULN3724++/TfuB0Oulls1CjhrtbSCnJuVeGngnxOety0va0999z7UNDvIHS1IOeXiJ2dnU3Hjh157733ALjrrruYMWNGsZ67a9cuXnrpJU6ePFn0tb179/LNN98QH182p33/llarZeDAodSsWZuP/+8jHKd+xFSpA3pb6LyxKV4nzrPr8NnT6Nt3IMOGjQjKpumyKiYmlgkTXuHTT//Dkp3bcPtUOlUJ3sVyL+4YUb9+Q554YnyZW9i3tNWoUYsnnhhPcvI5li5dyLr1a3CdyMNYxYalbhT6Ut7uTaPRoLUUvrUEoiVOVVW86U4ch7LxpDowWywMGDCEHj16ExFR+rMgQ1njxk159bV3mPzv91l66gRtFR8tQny/1lyfjyX2PLJ9PkaPvocePXqH9OsB8NugCpfLVfSPkZaWhqIoxXrejBkzeOWVV4pCm8Ph4Ny5c0ycOJGBAwfy0UcfFftYoaxRoya88re/Uz4uDsfpX/BkHw90ScWiuHJxnlqBxp3NI488wfDhoyXEBYDJZObxx/9Mu3Yd+elkPitP5ge6pMvacLaARcfyaNasBePHPy8hzo8qVqzE/fc/wrv/+JBePfugnneRs+IMeZtS8Oa4r32AEKeqKu4UO3m/JJO7Jhmz08CIEWP456TJDBs2QkLcDYqJieWFCa9w223t2OSws9qejxKicyRTvR7mFOTi0Bv4yzMT6NmzT8iHOPBT1+qsWbOYO3cuSUlJDB48mEWLFvHQQw8xZsyYYh+jW7duTJkyBVVVeeedd3jllVcIDw9n3LhxDBgwgBEjRtxsmSHBbrfz9ttvs3PnToyxDTHGNfHLhVYSXateexqus2uxWUz87W8vU79+fb8dW9wYRVH4z3/+w/Lly+lePeymW+b82bV6sSWuQ4cOPPfcc0Ezrb+sysnJYf78+cxfMB+nw4mxsg1Lg+hSaaHL+eUcQKnNVvWkOnAcyMKT4SQmNpYRw4fTs2fPMrMPbzBQVZWpU6cyc+ZMqhuM9LSFo/dzCCrJrtUzHjdL7flExcTw2uuvU6VKFb+fI1D8Nmt18+bNrF69GkVRuP322+nYseN1Pf9ikEtISLjk6z/++CNz587lP//5T7GPFUpj5C7H6/UyZcqXrF27Gn1kjcJJEDe5H56/g5wn9wyu5A3ElYvjL3/5K/Hx5f1yXHHzFEXhiy/+j40b19G3ZjhtK9/4mDl/BbldqQ5+OJRLs2YtePzx8RLiSlFBQT7Lli1m+Y9LcLtdmKqGY2kQjc5acv8PSivIebNd2Pdm4kl1EBUVzcCBQ+nUqYtcXyXop5+WMW3a1yQYDPSxRfh1RmtJBblTHjfLCvIoX7ESzz47MSS3+SvxMXIAt912G7fddttNH+fQoUOcPHmS3r17A4WfAm61X0q9Xs/99z9CTEws8+fPwelzYq7cscRmtF4vd9ZRXOe3UaNGTf785+cIDw/8kjPiV1qtlgcffBSXy8nSHdsIM2ppFGcOWD3HslzMO5xH/XoN+NOfnr7lfp8DzWYLY9iwEfTs2YdFi+bx00/LyT1bgKlOJJa6kUG3Dl1xKE4v9n1ZuE7lYbXZuGPU3XTt2kP2ay4F3bv3xmy28N8vP2FJQR79SqBlzp8uhriEKtV45tkJhIWV/Lqtpc0vd9QWLVpctvtv+/bt130sVVV56623aNeuHVarle+//56hQ4f6o8yQotFoGDLkTqKiopk69b84T6/CnNAJjS5wK92rqoo7fR/u9L00adqcPz32tKy8H6R0Oh3jxj3JpPfe5IfDRwkzaqkWWfpvcufzPcw4mEulSpV54sln5I02gMLDIxg1aizdu/dmxoxv2bZtM57TBViaxWAsHxrrPKqqiutEHo59WaBA7979GThwqKxTWco6duyMRqPhyy/+j2UFefSxhaMLwjB39kKIq5xQlWefm4jNVja3hfRLkFu4cGHR391uN4sWLbrhqbz169fnkUceYfTo0Xi9Xnr16sWAAQP8UWZI6tKlO+Hh4XzyyWQcSSsxJySW+j6tcOEGmrIdT9YROnToxH33PSwtK0HOaDTy1NPP8ubf/8b3B9N5pFk0UebSm4iS7/Yx/UAu1rAIxv/lBXmzDRJxcfE8/vifOXBgH19P+YLUdecxVQvH2jQWbZAsKnw5vgIPBdvT8aQ5qF+/IWPHPnBL78QQaB06dMLtdjNlypessufTzRoWVBMH0r1eltrzKV+hYpkOcVCCW3QNHz6cmTNnlsShrynUx8hdzoED+/jXvybhw4C5SuJ1rzV3M2PkVFXBeW4T3txT9OrVjxEjxpT5zaTLkvPnk3nj9ReJ0Pl4sFk0Rl3xb7Y3OkbOq6h8vSeb8w6ViRNfDYpNp8UfeTxu5s6dzdKlC9HZDNhui0MfdfOt7P4eI+c6W4B9ezoGrZ7Ro++hU6cuQRUabmULFvzADz/MpI3ZSmvLzX1Y89cYuQLFx+z8XIzhEbz40uvExMTe1PGCwdXGyJXIu/GxY8fIyMgoiUPfsho0aMRf//oSJr2KI2klPldOqZxXVXw4z6zDm3uKYcNGMHLkXRLiQkyFChV57E9Pk1rgYfGx3FI5508n8zmd6+bBBx+VEBfEDAYjw4eP5q9/fRmb3kru6mRcZ4Jn6RpVVbHvyyR/UwpVKlfl9df/QefOXSXEBZEBA4bQoUMntjjtnHC7Al0OPlVlWUE+Hp2OP4//a5kIcdfil3fkFi1a0LJlS1q2bEmLFi0YPHgw9957rz8OLX6jRo1aTJjwCmEWI86klficWSV6PlXx4jyzBm/+We666z4GDBgiN9AQ1bhxM/oPGMLOFCe7Uh3XfsJNOJzpYsNZO9269eS229qX6LmEf9StW5/XXn2HWjVrk785FcfR0vmgeDWqopK/LQ3HoWw6derCixNfIy6u7C8SH2o0Gg333vsQ1avVYKWjgFyfL6D1bHAUkOL18NBDj1Glyq2xh65fgtzChQtZsGABCxYsYNGiRWzcuFGCXAmpXDmBCRNeISLMivP0qhILc4UtcWvxFqRw330P0717rxI5jyg9gwffQZ3adVlyLJ88d8ncbB1ehflH86iSUIWRI+8qkXOIkhEREcFzz75Iq1ZtsO/OCGiYU9XCEOdOymfo0OEyJjfIGQwG/vT4n9EZjfwUwAWDkzxu9ricdO/em9at2wakhkDwS5CrXLkyZ8+eZf/+/ezdu5f169ezfPlyfxxaXEb58hV44YW/XQhzq/3ezaqqPpxn1+EtOM8DDzxC585d/Xp8ERg6nY77HxiHFy1Lj+WVyDlWnMinwK3wwIOPygzVEGQwGHj00aeKwlygulnt+zJxn87njjtGMnDgUOkJCAHlysVx99gHOO/1sMflLPXzu1WF1Y4CKlaoyIgRo0v9/IHklyD30ksv8eyzzzJlyhSmTp3K1KlT+eabb/xxaHEF8fHl+etfX8JqMeI6vRrFY/fLcVVVxXluM978c9xzz4PcfnuiX44rgkOFChUZMGAI+9JdnPLztk0pBR62n3fQo0cfGRcXwnQ6HY888gQ1a9XGvj0dX76nVM/vPleA83AOXbp0p1+/QaV6bnFz2rXrSJMmzdjiclCglG4X61aHg3yfj/sfGHfLfYj0S5DbsGEDixcvLgpxU6dOZcqUKf44tLiK8uUr8uwzE9BrFZxnfkH13fwN1522G2/uKe64YyRduvhvOy8RPHr37k9EeDg/JxX49birThVgMpsZOPDWW/exrDEYDDz+pz9jNJgo2J5OCS1u8AeK24d9ZwZVqlRlzJh7pSUuxGg0Gu666z4UjYZNDv80LhRHjs/HHreT229PpHbtuqV23mDhlyBXsWJFwsLK7hotwaxq1Wo8+eRfCjevP7fhpm64npyTuDMOkJjYTT4Jl2Emk4l+/YdwMtvNmVz/tLak270cyHDRq1c/uReUEdHRMYwaeTeedAfuc/4N/VfiOJSNz+XlgQcelTFxISo+vjw9e/blkNtFhtdbKufc5ChAbzAwbNitsSf77/klyLVs2ZLx48czf/58li9fXvSfKB0NGzZmzJixePPP4c44cEPH8LlycJ3fSp069bjrrvvkk3AZ16lTIiajka3n/fOpedt5B1qtlq5de/jleCI43H57IuUrVMB5KKfEW+UUtw/3iTzate1ItWrVS/RcomT16zcIi9nMZmfJt8qle70c87jp1atfSO6h6g9+CXI7duwgPT2dmTNnyhi5AOnWrRdt2rTDnb4HnyPzup6rqj5c5zZgtVp49NGn5JPwLcBisdK2XUf2pbtw+27uDdqnquxKc9GiRWsiI6P8U6AIClqtll49++LNduHL9u+Yyt9znylA8Sr06tW3RM8jSl5YWBi9+wzgpMdNqrdkx1hucdqxmC306dO/RM8TzPzyjj116lR/HEbchMK1fB7k8OFD5J/fhKV6bzSa4uV0d/oBfM5sHnjyGaKjb81PNLeidu068ssvP3Mk00WjOPNlf6ZF+WtvtXcq243d7aNdu47+LlEEgTZt2vHNN//DnVyAPrrk9lZ2nysgLj5eJsqUET179uHH5YvZ4nTQP8xQIudI9Xo46XEzpP+dWK22EjlHKPBLi1xaWhqPPPIIvXv3JiMjgwcffJC0tDR/HFpcB6vVxr33PoDPmYMn83CxnqO48/FkHuC229rTokWrEq5QBJO6desTHhbGgYwrLxXQvLyF5tcIcwcyXBgNBho3burvEkUQCAsLp2rV6ngzSm7VflVV8WW6aNyomQzrKCMsFit9+g4iyeMmuYRa5TY7HdisNnr27FMixw8Vfglyr732Gj169MBkMhEREUH9+vV58cUX/XFocZ2aNWtJ48ZN8WTsR/X92hViiKyJIbLmH37elbYHnU7LiBFjSrNMEQS0Wi1Nm7XkaLYH3w2Of1JVlcNZHho2aorJVHKtNSKwatSoiS/HXWLj5JQCL4pXoXp1aY0rS3r06EVEeAQbHXa/XzunPW5Oe9z0HzAEy03u8Rrq/BLkzp49y4gRI9BqtRgMBp577jmSk5P9cWhxnTQaDXfcMQrF58b9m1Y5Q1QNDFGX3iQVVy7e3FP07NHnltiPTvxRs2YtcHqUG569mmr3keP00qxZCz9XJoJJfHx5FLcP1aOUyPF9BYXXX/nyFUrk+CIwTCYzQ4eN4LzXwzGP/8ZYKqrKBqed2JhY2XUIPwU5jUaDovz6C56fn3/JY1G6qlWrTpMmzfBmH0W9yqKM7szD6PR6evXqV4rViWDSqFETtFothzNvrNvs4vOaNm3ux6pEsImKigFAcRZvkVdztXDM1cKLffyLx71VZx2WZZ06daFKQlU2OO14/NQqt8/lJMPrZeSosRgMJTP+LpT4Jcj16tWLZ599lry8PL777jvuvfde+vaVmUeB1LNnHxSvE2/+2ct+X1U8ePNO0fa29kRGRpZydSJYWCxW6tVrwKHMG2uRO5zpomqVqkRHx/i5MhFMwsMLQ5lazD16TdXCMV1HkLt4XFmDsOzRarWMvecB8n0+Njtufj3CfMXHZpeDRo2a0KpVGz9UGPr8EuQeffRROnfuTJMmTVi/fj0jR47k8ccf98ehxQ1q2LAJ0dGxeLNPXPb73ryzqD6P7KMqaNmyDel2D6kF17d4Z67Lx+lcDy1b3VZClYlgYbMVzghU3SXT06K6FTRa7S0/1qmsql27Ll26dGePy0nKTUx8UFWVNfYCVK2OsWMfkIkxF/glyH333Xf06NGDDz/8kI8++ogRI0bIP3CAabVa2rZtj9eegur7Y7eZNzeJqOiYW3I7E3Gpli3boAH2p1/fRtcHLsxibN26bQlUJYJJWFhh65pSzBa566W4fNisVnnfKMOGDx9NVFQUqxwFV5xcVd9opr7x8kshARzxuDjpcTPsjhHEx5cvqVJDjl+C3KZNm+jRowcTJ05k586d/jik8IPWrW8DVcGbf+nEE1Xx4LOn0LrVbWi1frkERAiLjo6mbt367E2/vlmJe9JcVK6cQKVKlUuwOhEMIiOjQKNBcZRQkHN4iZLu+TLNYrFy3/3jyPR62XKFHR/qmczUM10+yNkVhXUOB7Vq1qZnTxm69Vt+eRf/4IMPWLZsGY0aNeLvf/87AwYM4Ouvv/bHocVNqF69JuHhkXjzz13ydV9BKqrio3nzlgGqTASb9h06kW73cC6/eN2rGQ4vZ3LddOjQqYQrE8HAYDAQHRWFL79k1gNTC3xUkBmrZV6TJs3o2DGRnU4Hade5D+sv9ny8Gg0PPDhOGiB+x2//GpGRkYwcOZJx48ZhtVr5/PPP/XVocYO0Wi1NmzZDsaegqr+ObfHmJ2M0mqhbt34AqxPBpHXrthj0enamOIr18ztTnGg0GtnN4RZSrVoNlBLYpktx+/Dmu6lSpbrfjy2Cz+jRdxMREcEqRwFKMXsAjrtdnPC4GTL0TipWlB6A3/NLkNu/fz9vvPEGiYmJzJgxg4ceeohVq1b549DiJjVq1ATF60JxZhd9TXGkUL9+A9lTVRSxWq20at2WPWkuPNfYe1VRVXalumjcuKnMVr2F1K/fEG+eu2jNN3/xpDouHL+BX48rgpPVauOuux8g3ethr+va43I9qso6p52EygmyVNYV+CXI/elPfyI6OpqZM2fy+eef06tXLwkJQaJBg0YA+OypACgeOz5XXtHXhbioU6cuOL0KB6+yZRfA8Ww3uS4vnTp1KZ3CRFBo0aI1AK7T+X49rut0PuHh4dSqVcevxxXBq1WrNjRq2IStLgfOa6w5u9PpIN/n4+6xD0iuuAK/BLmVK1fy0EMPkZeXh6qqOBzF654RJS8yMor4+Ap4LwQ5n71wD9z69RsGsiwRhOrVa0BsbCw7Uq4e5Hacd2Cz2WjeXPbmvZXExcVTr34D3CfzURX/LOzqK/DgOW+nU6euMu7pFqLRaBg56i7cisJO15XzglNR2O120qpVGxkKdBV++c3Zs2cPPXr0YNy4caSkpNClSxe2b9/uj0MLP6hXrz6qM6NwY2pHGkajiYSEqoEuSwQZrVZLx46JnMh2k+O6/OxEh1fhUKabdu1ul0/Ht6C+fQbis3twnczzy/EcB7PR6fR0797bL8cToSMhoSqt27Rln9uFW718q9w+lxO3ojB48J2lXF1o8UuQ+8c//sH//vc/oqKiqFChAu+++y5vvvmmPw4t/KBWrTooXheqOw/FmUnNmrXQ6XSBLksEofbtb0cF9qRevlVuf5oTr6LKbNVbVJMmzahduy7Og9k3vaacN8uF61Qe3bv1Ijpatua6FfXq1R+3onDU/cdJNIqqst/jomHDxiQkVAlAdaHDL0HO6XRSu3btoseJiYn4fCWz3pC4fjVq1ALA50jH58wueizE75UvX4GaNWqyL/3ye6/uTXcRHx9P9eo1SrkyEQw0Gg133XUvisuHfW/mDR9HVVQKdqYTHh7OoEFD/VihCCU1a9aiYoVKHHH/8X5z3usl3+eT3YeKwS9BTq/Xk5OTU7Qq9/Hjx/1xWOEnFStWQqfT48lNAlWhWrXqgS5JBLHWbdqTnO8h03HpOk8FHoVTOW7atGkvK/DfwqpVq0GvXv1wnczDnXL5hV2vxXE4G2+Wi7vvvh+r1ebnCkWo0Gg0tGp9G8leD67fTXo45XGj0+lo0qR5YIoLIX4Jco899hh3330358+f5y9/+QujR4/mscce88ehhR/o9XrKl6+Ar+A8AJUqJQS4IhHMLm5EfTjz0u6OI5kuFLVwSy9xaxs6dDgVKlTEvj0D5QrjKa/Em+XCeSCbNm3a0aZNuxKqUISKBg0aoQLnfZd+cEz2ealZsxYWiyUwhYUQvwS5rl27MnnyZJ588klatmzJt99+S+/eMng1mFSsWLHo7+XLyx514sri4uKpUL4CR7Iu7e44muUiPCxcWnQFRqORceOeRHUrFOxIL/bWbqpXoWBrGpGRUYwd+0AJVylCQfXqNQFI/81OD4qqkuHzUrOmLElTHDcd5Ox2O16vl2rVqjFmzBjuvvtuKleuzAcffOCP+oSfxMbGARAeHoHBYAxwNSLYNW7SjKRcL94Ly0yoqsqJHC+NmzSTZSIEANWqVefOO0biPldQ7FmsBbsz8Oa7eeSRxwkLCyvhCkUosFgsxERFk6382rKbryh4VZWKFSsFsLLQcVN35Llz59K2bVs6duzIwYMHAVixYgV9+vRh2bJlfilQ+EdUVOGsMLP58hsSC/Fb9eo1wONTSL6wt2a6w0eB20e9erL6vvhVr179qF+/IY49mdfc8cGdbMd1Mo++fQbKOpbiEnHx5cn9zRi53AuhLi4uPlAlhZSbCnKffPIJ06dP55VXXuHzzz/nvffe49lnn2XUqFHMnz/fXzUKPwgPDweQ1hRRLHXq1AMgKbfwzfn0hT8vfl0IKLyfPPTQYxj0Rgq2X7mLVXH7sO9Mp1LlBIYMkTXBxKWiomOw8+u1Y78Q6qKiogJUUWi5qXd1g8FA48aN6devHxs3bmTz5s0sWLCARx99FKNRuu+CidksA0ZF8UVERBIbE8O5vMIAdy7Pg9lspnz5CgGuTASbmJhYRo64C0+aA3fS5bfvsu/PQnH6eOjBRzEYDKVcoQh24eERl2zV5bjwgSA8PDJQJYWUmwpyv13Z3WQy8dlnn1GlyvUv3Jefn8+AAQM4c+YMAOvXr2fgwIH06tVLxtr5iQRrcb2qVa/FeXvhzTW5wEu1qtWlRVdcVufOXalRoxaOfVmo3kuXkfDmuHGdyKVbt55FA9uF+K3w8HDcioLvQoBzqgoajQar1RrgykKD3+7KkZGRN7Q6965duxg9ejQnT54EChcXnjhxIh9//DGLFy9m7969rF692l9l3rLkDVhcr4SEKmTaPXh8Kml2HwlVZFs3cXlarZbRo8fic3pxHsu95HuOA1mYTGYGD74jQNWJYBcWVjj0x3lhqy6nohJmtcn7VjHd1GaJubm5/Pjjj6iqSl5eHsuXL7/k+7169brmMWbMmMErr7zC888/D8Du3bupVq1aUcvewIEDWbp0KYmJiTdT6i1PUS6/l50QV1KpUgIqcDzbjdunyPqD4qpq165Lo0ZNOHjsAObaEWh0Wnx5btznChg4cGjRm7UQvxceHgGAQ1GxacGhKoSFy/VSXDcV5CpVqsSUKVOAwnXKpk6dWvQ9jUZTrCD3+z1ZU1NTiYuLK3ocHx9PSkrKzZQpAI+ncHHX4q73JMTF8XBHL6wnV6FCxav9uBD07t2ffe/vwX3OjqlKGM4TeWh1Orp3v/Z7gbh1RURcCHIXWuQcqlq00oK4tpsKcr8Nbv6iKMol2/+oqnrd2wHFxsr6RL93cXyxTqclLk4+6Yhrs9kK9+Q9llX4IaB+/Zpy7YirSkxsz/++jiXvdD7GBBueswW0ve02atWS1lxxZV5v4fVRcKHnyI5KvfJxcr8pppsKciWhQoUKpKWlFT1OS0sjPv761pLJyMhHUaTl6beSk9MB8Hp9pKUVb/FOIawWC5kOBxqNBkUxyrUjrqlli9b89PNyvBlOfA4vTZq0lOtGXJWqFk7Gy1cUVFWlwOfDao2Q6+Y3tFrNFRupgm4kYbNmzThx4gSnTp3C5/OxcOFCOnfuHOiyQl5OTjYAbrf76j8oxG9Ex8QAEBkRgU6nC3A1IhQ0bNgE1adi35914XHjAFckgp3BYCQiLJx8xYddLZy9GhtbLtBlhYygC3Imk4l33nmHJ598kn79+lGzZk369OkT6LJCXkZGYYtcbm4OXq/3Gj8tRKHIyMJxKjJeRRRX7dp10ev1eNOdVKxUiYgIWQtMXFu5uHhyFaVoh4dy5eKu8QxxkV+6Vs+dO3fJY41Gg9lsvq7lSFauXFn09/bt28vOEH6Wmlo4YURVVTIzM4iPLx/gikQoiIyMAiAiUoKcKJ6wsDDefvt98vPzpFVFFFtcfHkOnD5Fnq9wey4JcsXnlyA3evRoUlNTsdkK133Jy8tDp9MRHR3Nv/71L1q2bOmP04gbpKoq588nozVFobiyOX8+WYKcKBabrXBMhmxwLq5HbGw5CXHiusTFxbPZ6yVbf3GfVQlyxeWXINehQwfatm3LkCFDAFi2bBnr1q1j1KhRvPLKK8ycOdMfpxE3KDc3F6fTgTG2Bm5XNufPn6Np0+aBLkuEAJvNBoDZbA5wJUKIsqxcuThUINnrJTIiEoNBdiMqLr+MkTt48GBRiAPo3bs3e/fupWHDhng8Hn+cQtyE8+cLu7511ji0ehPJyeeu8QwhCl0McBpN0A2nFUKUIRe7Us95PZSLu76VKm51frk7e71eDh8+XPT48OHDKIqCy+WSgfVB4Pz5ZAC0pgg0hnAJcqLYft0iR5bzEUKUnNjY2N/8Xbrlr4dfulafffZZxo4dS506dVAUhVOnTjFp0iQ++ugjevTo4Y9TiJuQmpqCRqNFo7egMdhITUsNdEki5FzfotxCCHE9oqJiiv4eExNzlZ8Uv+eXIJeYmMiyZcvYunUrer2eFi1aEBkZSZMmTWSQdBDIyspAa7Si0WjRGmzkZCahKIpsSCyug7TICSFKjslkKvp7RERU4AoJQX4JcoqiMHPmTH755Re8Xi8dO3bk0UcflRAXJHJzc0Fb+Eui0ZsLV84uyC/aqFiIa5MWOSFE6bi496ooHr80yfzzn/9k48aN3Hvvvdx///3s2LGDd9991x+HFn7gdLlAW5jZNRf+dLlcgSxJhBxpkRNClA5pBLo+fmmRW7NmDbNnz8ZwYWf2Ll26MGjQICZOnOiPw4ubpPh8/NqiUvincmH1bCGKQ6ORFjkhRMlq3LgZe/fuIjZW1pC7Hn4JcqqqFoU4AKPReMljEVgGgwHUwkUW1Qt/yv8fcT1UVVrkhBAl68kn/0J+fh7R0TLZ4Xr4pWu1fv36vPXWWyQlJXH69Gnefvtt6tat649DCz8ICwsDpXA9P9XnBsBqtQWyJCGEEOISBoNBQtwN8EuQe+WVV8jNzWXUqFGMGDGCzMxMXn75ZX8cWvhBZGQUqtcBgOp1YDZbLpkhJMS1SdeqEEIEI790rYaFhfHOO+9c8rUjR44QFRXlj8OLmxQbG4fidaH6PCieAuJiYq/9JCGEEEIEvRJbSGzkyJEldWhxncqXrwCA4s4DTz4VK1YMcEVCCCGE8IcSC3IyODp4VKhQGNwUVzY+dz4VKlQKcEUidFzsUpXfZyGECEYlFuRkuYLgUb58BTQaDd78ZFAVKlaUICeul/w+CyFEMJI9mm4BBoOB2Ng4vPlnASTIiRsgLXJCCBGMbmqyQ4sWLS7b8qaqKk6n82YOLfysQoUKpKenAhAfXz7A1YjQIy1yQggRjG4qyC1cuNBfdYgSFhcXD4DZbMFmk+1PhBBCiLLgpoJc5cqV/VWHKGExF5YcCQsLD3AlQgghhPAXGSN3i4iMjAJAq5UuMiGEEKKskCB3iwgLK+xOla25hBBCiLLDLzs7iOBXt259EhO70a5dx0CXIoQQQgg/kSB3i7Babdx770OBLkMIIYQQfiRdq0IIIYQQIUqCnBBCCCFEiJIgJ4QQQggRoiTICSGEEEKEKAlyQgghhBAhSoKcEEIIIUSIkiAnhBBCCBGiJMgJIYQQQoQoCXJCCCGEECFKgpwQQgghRIgK2i26xo4dS2ZmJnp9YYmvv/46zZo1C3BVQgghhBDBIyiDnKqqnDx5kp9//rkoyAkhhBBCiEsFZdfq8ePHAXjggQcYNGgQ33zzTYArEkIIIYQIPkHZ3JWbm0v79u15+eWX8Xg83HPPPdSoUYOOHTsGujQhhBBCiKARlEGuRYsWtGjRoujxnXfeyerVq4sd5GJjw0qqNCFuKWFhJgAsFgNxceEBrkYIIcTvBWWQ27p1Kx6Ph/bt2wOFY+auZ6xcRkY+iqKWVHlC3DLy810AOBwe0tLyAlyNEELcmrRazRUbqYJyjFxeXh7vvvsuLpeL/Px8fvjhB3r27BnosoQQQgghgkpQtsh17dqVXbt2MWTIEBRFYcyYMZd0tQohSpu0cAshRDAKyiAH8Oc//5k///nPgS5DCAGAJtAFCCGEuIyg7FoVQgQLaYkTQohgJkFOCHFNGmmQE0KIoCRBTghxTao0zAkhRFCSICeEuAppihNCiGAmQU4IUQzSJCeEEMFIgpwQ4iouBjhpmRNCiGAkQU4IIYQQIkRJkBNCCCGECFES5IQQV3GxS1XGyAkhRDCSICeEuAoZIyeEEMFMgpwQQgghRIiSICeEuCJZCFgIIYKbBDkhRDFIohNCiGAkQU4IcUWKolz4m4yRE0KIYCRBTghxRW63CwBVVa7xk0IIIQJBgpwQ4oocDgcAbrcnwJUIIYS4HAlyQogrcjjsl/wphBAiuEiQE0Jckd0uQU4IIYKZBDkhxBVdDHAF+fkBrkQIIcTlSJATQlyRvaAAkBY5IYQIVhLkhBBX5HBIkBNCiGAmQU4IcUVOp7PwT5crwJUIIYS4HAlyQogrcl0IcB6P5zeLAwshhAgWEuSEEFfk8Xp//btH1pITQohgI0FOCHFZqqri8/kw6gq35/J6JcgJIUSwkSAnhLgiVVW5kONQVTWwxQghhPgDCXJCCCGEECFKgpwQ4qp8FxritFq5XQghRLCRO7MQ4rI0Gg16vR73hSSn1xsCXJEQQojfkyAnhLgio+HX8KbX6wNYiRBCiMuRICeEuCKTyVz4p9EoXatCCBGE5M4shLgis/lCkDOZAlyJEEKIy5EgJ4S4IovVCoD1wp9CCCGCiwQ5IcQVWa02ACwWW4ArEUIIcTlBG+QWLFhAv3796NWrF9OmTQt0OULcki62xFltEuSEECIYBeU0tJSUFD744APmzJmD0Whk1KhRtG3bltq1awe6NCFuKb+2yFkCXIkQQojLCcoWufXr19OuXTuioqKwWq307t2bpUuXBrosIW45ZnNhgLNYZIycEEIEo6BskUtNTSUuLq7ocXx8PLt37y7282Njw0qiLCFuOeXKRQEQGRlGXFx4YIsRQgjxB0EZ5BRFQaPRFD1WVfWSx9eSkZGPosgG30LcLK+38E+Hw0NaWl5gixFCiFuUVqu5YiNVUHatVqhQgbS0tKLHaWlpxMfHB7AiIW5NGk1Q3iKEEEJcEJR36Q4dOrBhwwYyMzNxOBwsX76czp07B7osIW5BhS3b19EgLoQQohQFZddq+fLlGT9+PPfccw8ej4c777yTpk2bBrosIW45kZHRAMTFSYu4EEIEI42qqmVuMJmMkRPCP3w+H8eOHaFmzdro9UH5uU8IIcq8q42RkyAnhBBCCBHEQm6ygxBCCCGEuDYJckIIIYQQIUqCnBBCCCFEiJIgJ4QQQggRoiTICSGEEEKEKAlyQgghhBAhqkwuDKXVyjL0QgghhCgbrpZryuQ6ckIIIYQQtwLpWhVCCCGECFES5IQQQgghQpQEOSGEEEKIECVBTgghhBAiREmQE0IIIYQIURLkhBBCCCFClAQ5IYQQQogQJUFOCCGEECJESZATQgghhAhREuRC3OTJk+nfvz/9+/fn3XffBWD9+vUMHDiQXr168cEHH/zhOc8//zxz5swpenzmzBnuuusuBg8ezNixYzl79myp1S8Cwx/Xze7du7njjjsYOHAg48aNIy0trdTqF4FxPdfNihUrGDx4MIMGDeJPf/oTOTk5AJw7d4677rqLPn368Nhjj1FQUBCQ1yJKjz+um4s+/PBD/v3vf5dq/UFPFSFr3bp16siRI1WXy6W63W71nnvuURcsWKAmJiaqSUlJqsfjUR944AF11apVqqqq6vnz59Vx48apTZs2VWfPnl10nGeffVadNm2aqqqqOmXKFPWZZ54JyOsRpcMf142iKGpiYqK6YcMGVVVVddGiReq4ceMC9ppEybue6yYvL0/t2LGjev78eVVVVfXDDz9U33jjDVVVVfWRRx5RFy5cqKqqqk6ePFl99913A/aaRMnz13WTm5urTpgwQW3atKn60UcfBfIlBR1pkQthcXFxvPDCCxiNRgwGA7Vq1eLkyZNUq1aNKlWqoNfrGThwIEuXLgVgwYIFdO/enb59+15yHEVRyM/PB8DhcGA2m0v9tYjS44/rJisrC6fTSbt27QDo2rUra9euxe12B+Q1iZJ3PdeNx+PhlVdeoXz58gDUq1eP5ORkPB4PW7ZsoXfv3gAMGzas6DoTZZM/rhuAn376ierVq3P//fcH8uUEJQlyIaxOnTo0b94cgJMnT7JkyRI0Gg1xcXFFPxMfH09KSgoADz30EMOHD//DcZ5++mn+97//0alTJ/773//y8MMPl0r9IjD8cd1ER0djtVpZu3YtAIsWLcLj8ZCVlVU6L0KUuuu5bqKjo+nZsycATqeTzz77jB49epCVlUVYWBh6vR4ofJO/eJ2Jsskf1w3AkCFDeOSRR9DpdKX+GoKdBLky4MiRIzzwwAM8//zzVKlSBY1GU/Q9VVUveXw5f/3rX3n99ddZs2YNr732Gk888QSqqpZ02SLAbua60Wg0fPTRR3z66acMGTKEvLw8oqKiMBgMpVG6CKDruW7y8vJ45JFHqF+/PkOHDr3sdXWt+5MoG27muhFXJ0EuxG3bto377ruPZ555hqFDh1KhQoVLBp2npaURHx9/xednZmZy/Pjxok89vXv3Ji0tTVpWyribvW4A9Ho9U6dOZe7cuQwaNAhFUYiKiirhykUgXc91k5qaypgxY6hXrx5vvvkmADExMeTl5eHz+f7w86LsutnrRlydBLkQlpyczOOPP86kSZPo378/AM2aNePEiROcOnUKn8/HwoUL6dy58xWPER0djclkYuvWrUDhL5zNZiMmJqZUXoMoff64bgAmTpzI7t27Afjqq6/o06cPWq3cUsqq67lufD4fjz76KH379uXFF18sam0xGAy0bt2axYsXAzB37txrXmcitPnjuhFXpw90AeLGffnll7hcLt55552ir40aNYp33nmHJ598EpfLRWJiIn369LniMTQaDZMnT+aNN97A6XRis9lkancZ54/rBuDVV1/llVdeweFwyKfnW8D1XDcrVqxg//79+Hw+li1bBkDjxo158803eeWVV3jhhRf4v//7PypWrMj7778fqJckSoG/rhtxZRpVBkMJIYQQQoQk6QcRQgghhAhREuSEEEIIIUKUBDkhhBBCiBAlQU4IIYQQIkRJkBNCCCGECFES5IQQ4oLXXnuNESNGFC1YC+Dz+Rg1ahQffPBBACsTQojLkyAnhBAXvPDCCzgcDj799NOir3366afodDqeeuqpAFYmhBCXJ+vICSHEbxw6dIjRo0czbdo0VFXlkUceYfbs2fzyyy9Mnz69aCuyl19+mVq1anHixAlef/11CgoKSEtLo379+nz44YeYTCYaN25M9+7dOXjwIJMmTaJJkyaBfnlCiDJGdnYQQojfqFevHuPHj+fFF19EURTefPNNTp06xdy5c5k2bRoWi4W1a9fyxBNPsGTJEmbMmMGQIUMYPHgwHo+HYcOGsWrVKnr37o3H46Fr167861//CvTLEkKUURLkhBDid8aOHcuyZcuoVasWiYmJvPvuu5w6dYpRo0YV/Uxubi7Z2dk899xzrFu3js8//5yTJ0+SmpqK3W4v+rnWrVsH4iUIIW4REuSEEOIyEhISqFq1KgCKojB48GCee+65osepqalERkYyfvx4fD4fffv2pUuXLiQnJ/PbEStWqzUg9Qshbg0y2UEIIa7h9ttvZ9GiRaSmpgIwffp07r33XgDWrl3L448/Tr9+/QDYtWvXJbNehRCiJEmLnBBCXMPtt9/Oww8/zAMPPIBGoyEsLIzJkyej0WgYP348jz/+OFarlbCwMNq0aUNSUlKgSxZC3CJk1qoQQgghRIiSrlUhhBBCiBAlQU4IIYQQIkRJkBNCCCGECFES5IQQQgghQpQEOSGEEEKIECVBTgghhBAiREmQE0IIIYQIURLkhBBCCCFClAQ5IYQQQogQJUFOCCGEECJESZATQgghhAhREuSEEEIIIUKUBDkhhBBCiBAlQU4IIYQQIkRJkBNCCCGECFES5IQQQgghQpQEOSGEEEKIECVBTgghhBAiREmQE0IIIYQIURLkhBBCCCFClAQ5IYQQQogQJUFOCCGEECJESZATQgghhAhREuSEEEIIIUKUBDkhhBBCiBAlQU4IIYQQIkRJkBNCCCGECFES5IQQQgghQpQEOSGEEEKIECVBTgghhBAiREmQE0IIIYQIURLkhBBCCCFClAQ5IYQQQogQJUFOCCGEECJESZATQgghhAhREuSEEEIIIUKUBDkhhBBCiBAlQU4IUeJee+01RowYgc/nK/qaz+dj1KhRfPDBBwGs7NrGjh3L0qVLS+TY//73v2nXrh2DBw9m0KBB9O3bl2eeeYb8/PzrPlaLFi04c+bMDdcyc+ZMpk2bdsPPF0IEhgQ5IUSJe+GFF3A4HHz66adFX/v000/R6XQ89dRTAaws8Pr168e8efOYP38+CxcupKCggKlTp5Z6Hdu2bcPpdJb6eYUQN0cf6AKEEGWfyWRi0qRJjB49mq5du6KqKt9++y2zZ89Gp9Mxc+ZMpk+fjqIoREVF8fLLL1OrVi1OnDjB66+/TkFBAWlpadSvX58PP/wQk8lE48aN6d69OwcPHmTSpEk0adIEgLy8PBITE1m2bBlxcXEADB8+nCeeeAKbzcY777yDoigAjBs3jt69e9/w6/r++++ZOnUqWq2WcuXK8fLLL1OjRg0yMzOZMGECSUlJREVFERcXR506dXjyySevejyXy4Xdbi+q+4UXXqBOnTo8+OCDf3i8detW3njjDTQaDU2aNCl6TQCfffYZs2bNwmaz0bp1a3766SdWrlyJ2+1m0qRJbNmyBZ/PR8OGDXnppZfYsGEDK1euZN26dZjNZu66664b/jcRQpQuaZETQpSKevXqMX78eF588UUmTpzIm2++Sfny5dm8eTNz585l2rRpzJ07l4ceeognnngCgBkzZjBkyBBmzJjB8uXLOXPmDKtWrQLA4/HQtWtXli1bVhTiAMLDw+nZsyfz588H4NixY6Snp9OpUyf+/e9/c//99zNnzhzeeustNm7ceMOvZ8OGDXzxxRdMmTKF+fPnM2DAAB5//HFUVeXvf/87tWvXZsmSJfzrX/9i+/btVzzO4sWLGTx4MAMHDqRTp05kZWXRq1evq57b7Xbz9NNP88ILLzB37lzatm1b1Jq2Zs0a5syZw6xZs5gzZw4FBQVFz/vss8/Q6XTMmTOH+fPnEx8fz6RJk+jZsyfdunXjvvvukxAnRIiRICeEKDVjx47FarXSrFkzEhMTAVi1ahWnTp1i1KhRDB48mPfee4/c3Fyys7N57rnniImJ4fPPP+fVV18lNTUVu91edLzWrVtf9jzDhw9n7ty5AMyePZs77rgDrVZL3759ef3113nmmWfYt28ff/nLX274taxZs4Z+/foRExMDwLBhw0hJSeHMmTOsXr2akSNHAhAfH0+fPn2ueJyLXasLFixg48aNNG/enPHjx1/13IcPH0av19O+fXsABgwYgM1mA2D16tX06dOHiIgINBrNJcFs1apVrFy5kiFDhjB48GBWrFjBsWPHbvjfQAgReNK1KoQoVQkJCVStWrXosaIoDB48mOeee67ocWpqKpGRkYwfPx6fz0ffvn3p0qULycnJqKpa9Fyr1XrZc7Ru3Rqv18vu3btZuHAh33//PQCjRo2ia9eurFu3jjVr1jB58mSWLl2KyWS67tfx267Mi1RVxev1otfrL6lTqy3eZ2aDwcCYMWOKQqBGo7nkOB6P55Jz/ZZery/687ff0+l0l9Q8ceLEohBdUFCAy+UqVm1CiOAkLXJCiIC6/fbbWbRoEampqQBMnz6de++9F4C1a9fy+OOP069fPwB27dp1yczXqxk+fDhvvPEG9erVo2LFikBhkDtw4ADDhg3jjTfeIDc3l7S0tBuqu1OnTixevJjMzEygsOUvKiqKatWqkZiYyKxZswDIyspixYoVaDSaYh131apVNG3aFIDo6Gj27t0LQEpKCps3bwYKu6lVVWX16tUA/PTTT+Tk5ACQmJjI8uXLycvLAyiqAwr/radNm4bb7UZRFF5++WXef/99oDDweb3eG/q3EEIEjrTICSEC6vbbb+fhhx/mgQceQKPREBYWxuTJk9FoNIwfP57HH38cq9VKWFgYbdq0ISkpqVjHHTJkCO+//35RUAF49tlneeutt/jwww/RaDQ88cQTJCQksGfPHl566SXmzZt32WM9//zzTJgwoejxmDFjeO6557jvvvu49957URSFmJgYPv30U7RaLRMmTOCll15i4MCBREVFUalSJcxm82WPvXjxYrZt24ZGo8HlclGlShX+8Y9/AIVd0c8++yy9e/cmISGBdu3aAYUtd//5z3949dVXef/992nQoAGxsbEAtG/fnhEjRjBy5EjMZjN16tTBYrEA8Kc//Yl//OMfDB06FJ/PR4MGDXjhhRcA6Ny5M++88w5QOAlECBEaNOrv2+eFEELclGnTptGwYUNatGiB2+1mzJgxPPnkk0VdmiVpz5497Nixg3vuuQeAr776il27dvHhhx+W+LmFEKVPWuSEEMLPateuzRtvvIGiKHg8Hvr06VMqIQ6gRo0afP7558yYMQONRkPFihV54403SuXcQojSJy1yQgghhBAhSiY7CCGEEEKEKAlyQgghhBAhSoKcEEIIIUSIkiAnhBBCCBGiyuSs1aysAhRF5nAIIYQQIvRptRqio22X/V6ZDHKKokqQE0IIIUSZJ12rQgghhBAhSoKcEEIIIUSIkiAnhBBCCBGiJMgJIYQQQoQoCXJCCCGEECFKgpwQQgghRIiSICeEuCKfz0dS0im8Xm+gSxFCCHEZEuSEEFe0fPliXn11AnPnzgp0KUIIIS5DgpwQ4opOnz51yZ9CCCGCiwQ5IcQVnTt75sKfpwNciRBCiMuRICeEuKyCgnxOn0nCrNeQkZlJenpaoEsSQgjxOxLkhBCXtXfvblQVulcPA2D37p2BLUgIIcQfSJC7RSQnn+Xdd//OqlU/BboUESJ+WrGMaIueVhUsVAgzsPKnZaiqGuiyhBBlkMfjJi0tlbS0VLKzswJdTkjRB7oAUTp27tzOwYP7yczMoEuX7oEuRwS5gwf3c/TYEXrXDEOr0dCukoW5h8+xfftWWrVqE+jyRBBLSjrJnj27AdBqNbRvfztRUdEBrkoEM6fTyauvvEBqWmrR18aOvZ+uXXsGsKrQUaJBbvLkySxZsgSAxMREnn/+edavX8/bb7+Ny+Wib9++jB8//g/PO3fuHM899xwZGRnUqFGDSZMmYbPZSrLUMu/YsSMApKWl4XA4sFgsAa5IBCun08l/v/yEaIuelhUKr5PGcWY2nHUwdcoX1KtXn7Cw8ABXKYKR1+vl35PfJyM9vehrJ04c409/+nPgihJBTVVVpkz5ktS0VDpYbJg0Gg65XXz/3TfUrFmHatWqB7rEoFdiXavr169n7dq1/PDDD8ydO5d9+/axcOFCJk6cyMcff8zixYvZu3cvq1ev/sNzX3vtNcaMGcPSpUtp3LgxH3/8cUmVeUtwOBzs2bMbrSkSVVXYuXNboEsSQUpRFL7++nMyMtIZUicck67wFqHXahhaL5yC/Hw+//xjWSBYXNaPPy4hIz2d8HbliRlcHUu9KLZu3cyBA/sCXZoIQoqiMHXqf9m4cR1tzFaamS3UN5npaQvHqCj8c9JbnDp1ItBlBr0SC3JxcXG88MILGI1GDAYDtWrV4uTJk1SrVo0qVaqg1+sZOHAgS5cuveR5Ho+HLVu20Lt3bwCGDRv2h58R12flyuV4PG7MFW9DZ4pgyZKFKIoS6LJEkFEUha+++oxNmzbQrXoY1SKNl3y/gs1A31ph7Nmzi0/+7yMJc+ISx44dYc6cGRgr2jBUtKLRabHUi0IfZuSzz/9DTk52oEsUQSQzM4P3LozbbmGy0Mr8ay+RVatloC0CjdPJW2++yqpVP8n43KsosSBXp04dmjdvDsDJkydZsmQJGo2GuLi4op+Jj48nJSXlkudlZWURFhaGXl/Y6xsXF/eHnxHFl5x8jnnz56APq4zOEouhXGPOnEli2bJFgS5NBBGXy8nnn3/MunW/0KWqjU5VLj+UoXVFK31qhrN9x1b+M/kD7PaCUq5UBKMzZ07z4YfvojFrsbUqh0ajAUCj12K7LY68/Fz++f475OfnB7hSEWgej4elSxfx4sRnOHbkMF2tYbSz2oqumYuidDqGhUVQXgNTpnzJP/7xBqdPJwWo6uBW4pMdjhw5wrhx43j++efR6XScPHmy6Huqqv7hf97lvvb7x9cSGxt2w/WWJRkZGXz070ko6LBUaAWAPrwK+vAEZs36jtq1q9OhQ4cAVykC7fTp07z99lucOX2G7tXDuD3BetWfb1fZilYDS/fs5I3XX2TCxInUqlWrlKoVwWb//v28++4bOBU34Z0qoDXqLvm+PsqErW08Zzec5t33Xue1V18jPj4+QNWKQHG5XKxcuZLvv/uOjMxMqhmM3B4eSYROd8XnWLVaBtgiOOB2senoEV555QVu79iRESNHUqNGjVKsPriVaJDbtm0bTz31FBMnTqR///5s3ryZtLRfFxVNS0v7wy90TEwMeXl5+Hw+dDrdZX/mWjIy8lGUW7sZ9syZ0/zrX++RlZ2DOSERraHwzVmj0WCu1A5H0s+8884/uPvu++jSpft1h2UR+hRF4Zdffub776aiw8fdjaOoFW0q1nNvq2Slgk3PrEOZPPvss9xxx0h69OiD7io3ZVG2qKrKqlU/8e23X6Ox6AjvVAFdmOGyP2ssbyW8QwXObUrm6T//mccefYoGDRqVcsUiELKyMlm9eiUrf1pOfkE+5fUGBoZFUFlvKNb7jkajoaHJTC2DkZ0uB5s2bGDtunU0bNiYnj370qRJM7Tasr+SmlaruWIjlUYtoY7n5ORkhg4dygcffED79u2BwkTeq1cvpkyZQkJCAuPGjeOOO+6gb9++lzz3kUceYeDAgQwcOJD/+7//IzU1lVdeeaXY576Vg5yiKKxevZLp301FQY+58u3oLLF/+DlV8eA8ux5vfjJt23bgrrvulZmIt5CkpFNMmfIFx48fo3qUkWF1I4gw/TGE7UxxANC8/OVnORe4FeYdyeVwposqCVW4596HqFWrTonWLgIvNzeXqVO/ZNu2LRjKWwlrHYf2MtfP73lz3RRsTsWX56Fv34EMHnwHBsPlw58IXV6vl717d7Nmzc/s2rkdRVWpZjDSzGSmUjED3JU4FYX9bid73S4KfD5iomPonNiNDh06Ua5c3LUPEKICEuT+/ve/M3v2bKpWrVr0tVGjRlG9evWi5UcSExOZMGECGo2GF198kW7dutG9e3fOnj3LCy+8QEZGBhUrVuT9998nMjKy2Oe+VYPcqVMnmfbt1xw9cgi9rTymSu3Q6q+8zIiqKrjT9+PO2E+Yzcbw4aPp2LHzLfHp5laVn5/H/Pk/sHLlMix6Lb2q22gab77ijfWr3ZkA3N805orHVFWVAxkulh4vIM/l5fZOXRg6dLisHVYGqarKli2b+GbaVxQU5GNpGI25TuR1vTGrXoWCXRm4TuVRqXICD9z/CDVr1i7BqkVpUFWVpKRTbNiwhg3r15KXn4dFp6O+wUhDo/mqXag3wqeqnPS42e92csbjAaB+vQZ06NiZVq3aYLFcfYhIqAlIkAukWy3IpaScZ8GCH1i/fi1avRFjXDP0kTUuubl6sguncBui/jiuwOfMxnV+Cz5HBglVqjJs6AiaNWsh3a1liNvt5scfl7J40VycLicty1voXj0Mq+Hqob04Qe4il1fh56QCNp9zoNfr6dN3IL1795c1C8uI9PQ0vvnmK3bv3ok+2oStZTn0kcXrir8cd3IB9p2ZKE4v3bv3YujQ4WXuzfdWkJ6exqZN61m/bg3J58+h1WiopjdQ32iiisGIrhTeR/J8Pg65XRzyuMn1eTHo9TRv0Zr27TvSuHGzosmToUyCXBl19uwZlixZwIYN60CjQR9VB1O5hmh0xj/8rP1U4dZc1mqX39VBVVW8uUl40vfgc+dTrVoN+vcfTMuWraWFLoT5fD7Wr1/D3B9mkJWdTd0YEz2qhxFvK96N7XqC3EUZDi8/ncxnf7qL8LBwBg2+g8TEbmXiZnor8nq9LFu2iHnz56CoPswNojDXikSjvfk3aMWjYN+Xiet4LhGRkYwaOZa2bdvLh8ggV1CQz5Ytm9iwfg1Hjh4GoKLeQB2jkVoGE+YAvWeoqkqKz8sRt4ujHg9OxYfNauO2tu3p0KETNWvWDtlrS4JcGaKqKgcP7mf58iXs2rUdjVaPPqomxtgGV+1GvVaQ+/X4Ct6ck3gyD+Bz5REfX4HevfvRoUMnTKYb//QtSpeiKGzduokf5swgJTWFyuEGelQPo0bUH0P+1dxIkLvoTK6HH0/mcyrHTWxsLIMH30n79rfLhIgQsn//XqZM/S+pKecxVrJhbRqLzur/QO7NdFKwKwNvlov69Rsyduz9VKxY2e/nETdOURT279/LmjU/s2P7Nrw+L9F6PXX0RuoYTX7pOj3kcgJQz2S+6WP5VJUzXg+HXU5Oej14VZX4uHg6de5Kx46dQ27ohwS5MsDjcbNp0waW/7iEM6eT0OrN6KNqYYiui1Z/7YBV3CB3kaoqePPO4Mk8iM+RicVqo2uX7nTr1pOYmD9OnhDBQVVV9uzZxexZ0zl95jTxNgNdq1qpH2u6oU+iNxPkLtZzLNvNylMFnMvzUKFCBYYNG0WrVm1C9pPxrSAnJ5vp06ewefNG9GFGLE1jMFYo2W5PVVVxncjDsT8LfNC3T38GDhyG0Xh9Hz6Ef+Xl5fLLL6v4eeVyMrMyMWt11DEYqGc0U06n8+vv8by8bAAGh0f57ZgAblXhmNvNYbeLc14PWo2GZs1b0qNHH+rXbxgS9yIJciEsOzuLn39ewcqfV1CQn4fOHIk+qi6GyGpotMX/ZHy9Qe4iVVXxOdLwZB7Gm3cWjUZD69Zt6NmzL7Vq1QmJX4BbxfHjR5k5czqHDh0g2qKna1UrjePMaG/i/9HNBrmLLk6I+DnJTlqBh5o1ajJ8xF3Uq9fgpo4r/EtVVdasWcV333+Dy+XEXDcSS70oNLrr6ypzncoDwFTt+mfCK04f9r0ZuJLyKRcXx/33PSJLlQRARkY6ixcvYO2an/F4vVTWG2hoMlOjBMe9lVSQ+61sn48DLicHPW6cio/KlRIYOGgorVu3DephRBLkQtCpUydYtmwxmzdvRFF86MMqYYipi85a/obC040Gud9S3Pm4s47iyzmO4nNTrVpNevfuR+vWt8n4pwBKS0tl5sxv2bp1MzajjsQqVlpWsKD3wxgmfwW5ixRVZWeKk1VJdnJdXpo1bc6IkXdJN1oQyMhI57///ZQDB/ZhKGfB1iIWXfiNtYbl/HIOgMjOlW64Hk+qA/vODLz5bhITuzFy5N2YzTff5Sauzm4vYP78H/hpxTJUVaGuwUhTs4VYXcnf40sjyF3kVVWOuF3scjnJ8nmpVLESo8fcS6NGTUr83DdCglyIuNgttnjJAg4fOoBWZ0AXUR1jTF20xptb480fQa6oTsWDJ/sk3uwj+Fy5REVF07t3Pzp37iYzFEuR2+1myZIFLF40D1SFDpUtdKhsxaT336dKfwe5i9w+lc3n7Kw9Y8etQK9e/Rg4cKhcPwGyefMG/vf157g9biyNYzDVCL+p1nZ/BDkoXKrEvj8L59Ec4uLiefTRJ6lRQ3YRKSm7d+/kv1/+H7l5edQ3mmhtthJeimNaSzPIXaSoKsc9bjY5HeT6vLRr15G7774fqzW4ZlBLkAtyhQPTN7Nw4VzOnElCZ7Cij66DIarWZWeg3gh/BrmLVFXFl38Od+YhfPZUzBYrPXv0pmfPvoSFyTZpJWn37h18M/Ur0jPSaVTORK+a4UQWY0HW61VSQe6ifLfCipN57ExxEhUZyajR99KmTVvpsi8lHo+bb7+dwurVKzHEmLG1jrvi7gzXw19B7iJPuoOCremoLoWRI8bQo0cfuUb8SFVVFi6cyw8/zCRGr6ebJYy4APSyBCLIXeRVVXY47WxzOoiLi+cvz0wgPr58qddxJRLkgpSiKGzevJF582aTkpKMzhSBIaY++shqaDT+fVMuiSD3Wz5HBu6MA3jzzmA0mujRozd9+vSX3SL8zOGwM336VNauXU2c1UDfWmHUvM6ZqNejpIPcRadz3Sw+lk9yvoc2bdoyduwDcu2UsKysLCZP/icnThzHXDcSa8MYvywpAv4PcgCK20fBtjTcyXbatevIffc9LBMh/GTOnBksXDiXukYTidYw9AEKyYEMchclez0sLcjHFGbjxZfeCJrdIiTIBRlVVdm2bQtzfpjB+eRz6MxRGGIbog9PQKMpmcGWJR3kLvI5s3Fn7Mebm4TJZKZXr7707t0/6JqpQ9GhQwf44vP/kJmVye0JNhKr2vwyDu5qSivIQeFyAetO21mdVIAtPJwHHniUpk2bl/h5b0VJSaf44MN/kJefi7VVHKbKNr8evySCHBTeOx0Hs3EcyKJmrdo89eSzRERE+PUct5pNm9bz6aeTaXAhxAWypTMYghxAps/L3PxcylWoxMt/+3tQfGCQIBckVFVl377dzJr1PUlJJwtb4Mo1Rh9epcR/eUoryF3kc+XgTtuLN+80FouVAQMG061bL1mL7gaoqsrSpQuZNes7os06htYNp0pE6dxYSjPIXZSc7+GHw3mkFngYMGAIQ4bcGdSzyULNgQP7+OijSXi1CmHt49FH+f93sqSC3EWus/kUbE2nXEw5ngmyLrBQkpmZwUsvPkekz8fgsIhS2YXhaoIlyAGc8rhZnJ9Lr179GDXq7kCXc9UgJ1MNS8mhQweYM2cGR44cQme0Ya7Y9kIXatl8g9KZIrEkdMTnyMSVtoeZM6ezdOliBg0aQufO3WSj7GJyOBx8+eUnbN++hYblTAyuE+HXyQzBqGKYgYebR7P4aC4LF87lxIljjBv3hHS1+sG2bZv55JN/o7HpCe9QsUQW9y0NpsphaM16Mjak8OZbr/DcsxNJSKh67SeKIqqqMnXqf/G6XXQPjwp4iAs21S7sEfvjj0to06YdtWoF737A0iJXglRV5fDhg8ybP4eDB/ahNVgwxDTEEFUTjbZ0V7cv7Ra53/Pa03Cn7cFnTyXq/9m77/im6v2P46+TnXS3tOwlypCNoCB7gwgyHDhQ8Tqu815+iltxXMf1Ku6BWxEVlCWyRJANsvfe0EL3TrPP74/SSmW0pWlO0n6ej4eP9jTJOZ9imrzzndExDBlyHT169MJo1L7JOlilpaXy7jtvcPJkIv0ahdOlri3g3R5atMidaeNJO/MO5REbW4N/j3tclimpgFWrlvPll5MwxJgJv7omOlPlvQZVdotcEU+Oi7xVyZgUI/837gmaNLmsUq9XlaxYsZSvvvqUq61htLUEx2zxYGqRg8KFhKfmZhMWV4MXXnwNsx92nLhY0rUaYD6fj23bNjNv3hwOHNiHzmDBGNsCY0yTci3i609aBzk4PcvVnowrbQdeexoREVEMGnQNPXv2lTF0f3PgwD7ef+9N3E47NzaL5JIYbbqktQ5yUDgR4sfdOfh0Ru5/4N+0atVGs1pC1aJF8/nhh8kYE6xEdK6JUsmtuoEKcgDefDd5q5LRueDhhx8N2nXAgsnBgwd4478vkaDoGBpWsaVm/EVVVX7IycSlqnSy2rjcZAmKuhLdLn7Jy6FDh0488MC/NBvmIUEuQBwOB2vWrOS33+YXzkI1hWGIaXa6BU7bLoxgCHJFCgNdCq70XXjzkzGZzPTs2Yc+ffpTs2YtrcvT3OrVK/j6q0+JMuu4uUUkNTTs/gqGIAeQ5fDyw65sUgs83HzzHfTtO0DTekKFqqrMmDGNuXNnY6oTRninBBR95b85BjLIAfgKPOSuTkbN83DPPQ9y5ZWdA3LdUHTw4H4mvvUaRreHkeGRWINk/OkOZwEr7PnFx91tYbQyB0dL4VZHAasL8uncuSt33XWfJgvgyxi5SpaUlMjSpYtZuXIZDkcBemssljpdMETWr7Jj4CpCURQMYTUxhNXE68jElb6HRb8vZNGi+bRq1ZY+ffrRpk37ajfA3efzMXPmNObO/YVGUSZubBGFzVi9/g3OJ9qi56420Uzfm8OUKV+TlHSCm2++XXYUuQC3283XX3/GmjUrMTeKIKx9jaBo4agMOquBiO61yFubzCefvEdGRhoDBw6psr/vxVBVldWrV/DN159jA4aFRQRNiAM46nKddRwsQa6txYpHVVm7dhUZGencf/8jREVFa11WMWmRu0hut4sNG9axdOli9u/fi6Lo0EfUwxTTFJ01LuheQIKpRe5cfO4C3FkH8WQfxOcuIDo6lp49e9O9ey9iY+O0Lq/S2e12PvvsQ7Zu3cwVtaxc0yQCfSUvLVIaVVV5f0MaTq9Kr4bhdKxl1fx57VNVFh/JY9UJO82bteCf9/9Llp84h6ysTD786B0OHtiP9fKYwv1SA/j/LtAtckVUr4+8Dam4EvPp2rUnt98+VsbhAjk52UyZ8g3r16+ljsHIgCALcVA4Pi7J4yk+rmMwBM1YuSL7nA6WFeRjsdkYc/s/6NgxcIuXS9eqHyUmnmDZsiWsWrWcggI7elM4+ugmGKMaozME5z6AqqqSf3Au+NyY4lsX7hgRZEGziKr68OQm4sk6iCf/FIqi0Lp1W3r27EubNu3QB3C7mEA5eTKR9997i5SUZAZeEs6VtbUPTADrk+zMPZhbfDykSQSd6gTHWMatyQXMOZBLZFQMDz/yKA0bNta6pKCxZ88uPv7kPfLtedg61MBcL/C7rGgV5OD0WnO7MynYk0X9Bg158IF/V9vlSbxeL8uWLWHG9B9xOBx0NFtpb7GiC4LXl78LhSAHkOn1sNieT6rHTetWbRh985iATMLSLMjl5eUxevRoPvnkEw4ePMjEiROLb0tOTqZt27ZMmjSpxGNmzpzJW2+9RVxcYStMr169GDduXLmu6+8g5/F42LBhHUuW/MaBA/uKW9+M0Zdc9Cb2geTK3I/z1MbiY3OtKzDFBP/sLp8rD3fWITw5h/G5C4iKiqFXrz707NmH6OgYrcvzi40b1/HF5x+jVz3c0DySRlHB03rw3Y5MDmT+1d1xaYyJ21oFz797Uq6bqXtysHvg9jvupmvXHlqXpCmPx8Mvv8zg17mzMISZCLsqAYNGzyctg1wRV1I++ZvSMCoGbrttLFdf3T3oX6v9RVVVNm3awPSff+BU8inqGI30sIYRE4CN7y9WqAQ5KOwZ2O50sMFZgEdV6dmrL0OHjqjU9yVNgtzWrVt59tlnOXz4MAsWLKBevXrFt6WmpnLzzTfz+eef06hRoxKPe/nll2nfvj3XXnvtRV/bX0EuLy+PJUt+4/fFv5GXm4PeHIEhqgmGqEZB2/p2LvZjy/Dmnyw+1ofVxtagp4YVlY+q+vDkJeHOPIg3/yQ6nY5Ona5i8OChNGjQSOvyLorX62XGjGnMnz+HuhFGbmwRVSl7pVbEV9syOJrtLj5uGGXUfNLD3+W5fPy8N5sjWS569erHzTePqZZrFB49eoQvvviYEyeOY24QTli7GpU+M/V8VFUl67fjqB4fthaxmBtrNyvSa/eQvz4Fd7qDdu2uYMyYu4iJCZ4PI/6mqirbt29h5syfOHr0CNF6A50tVhoZTUEfYkMpyBWx+3xscNjZ7XKi1xvo228AgwZdS2RklN+vpUmQe+aZZxgxYgSPP/443377bYkgN378eJo1a8bdd9991uNuvPFGIiIiSElJoVmzZjz33HNERZXvH6WiQa6goIB582azaNFCXC4nhvDaGGOaog+rFfR/DOdiP7oYrz21+Fhviw/asXKl8blycWUewJt9CJ/XTevWbbnhhptDajHQ/Pw8Pv74PXbt2sEVtawMbhJR6VttXYxQCHJQuLXXktPj5ppccikPPfxouV8zQlVBQQG//DKD3xbNQ2fSY2sXh6mOf7fbKi/HoRzyt6QVH4e1q4HlEu3GMaqqiuNANgW7sjAZTVw/ajS9e/erUpOpCgPcVmbP+pnDRw4RqTdwhdlCU5M5KLtRzyUUg1yRbK+XDQ47+11OjEYjffr6P9BpOkauT58+JYLckSNHuOOOO1i0aNE59y978MEHueuuu+jQoQMTJ04kKSmJt956qzJLLGHNmjV8+OFHZGdnYYhsgCnucvSW6IBdvzJUpSBXRPW6cGXux5O5D9XrYujQodx5551B3xqTmJjISy++SHLyKYY0iaBDreCYlXUuoRLkiuxMdTBrfy5RMTE8//wEGjeuuuPmVFVl+fLlfP7FF2RlZmJuFIGtVWylLvJbVjmrTuJOLig+Nta0Etm1toYVFfLmucnfkoY7pYDGjRtz//3306JFC63LqrCtW7fy7TffsG//fiL0BjqYLTQzmUNup4ZQDnJFMr0eNjkKCgOdycSwYcMYNWoU4eGVO0414EHuv//9L9HR0dx3332lPjY7O5v+/fuzbt26cl3zYlvkZs+ezuzZ09FbYzHXvAK9tWrMlqyKQa6I6nHiTNuOO/MAjRpdwmOPPYXNpm2LxPkcPnyQt958FbwubmoeScMgGg93LqEW5ACS8tz8uCsHp6rjkX+Np0WLllqX5HcHDx7ghx+/5dDBAxhizNjaxmGMDZ6hHtnLk/CkOYqPDTUsmo6VO5OqqrgS8ynYkYnX7qZTp87ccMPN1KgRr3Vp5XbyZCLff/8tO3duJ1yv5wqzNSQDXJGqEOSKZHo9bCgo4IDbidViZfiIG+jTp3+FJutdqEUu4G3Lixcv5pprrjnnbbm5uXz99dfFx6qqBmyW4saN65k9ezqGqEZYG/StMiGuqlMMZiy1OmKp25UjR4/w1VefaV3SOR0+fJA3//cKZtzc0zYm6ENcqKoTbuSedtFEGVXeeecNdu/eqXVJfpOWlsonn7zHK688z9HEI4R1iCeyV52gCnHBTlEUzPXCiepXF2vzaDZsWsdTTz/KTz/9gN1u17q8MvH5fMyZM4vnnnuCA7t3crU1jJsjorncbAnZEFfVxOgN9A+P4IaIaOK8Hn744VtefPFpEhNPVMr1AhrkMjIycDgc1K9f/5y322w2Pv/8c7Zu3QrAd999R//+/QNS24oVf6A3hWOpfWXA90EVFWeMrI8pthkbN67Dfsbq4MEgOfkkb735KhbFwx2toomxyPOrMkWY9NzeKppoE7zzzhscPXpE65IqxG63M23a9zz11P+xfuM6rM2iiepfD0uj4NhaKRQpBh22y2OJ6l8PQx0L8+fP4Ykn/83ixb/hOaNVKNi43S7ee/dNZs6cxiV6A6MjomlrsWKQ50FQqmEwcG1YJAPDIsg4mcRLLz7Njh1b/X6dgAa5EydOUKvW2VswPfPMMyxevBi9Xs8777zDCy+8wODBg9m5cyfjx48PSG0ejwcUPVD1/iBUrxuLxcKwYcOwWCyoXnfpDwpFSmFA8ni8GhfyF5/Px5dfTMLncRaGCwlxARFu0nFHqygsOh+ff/5RUL85n4/P52Pp0sU88eS/WbDgVwx1rUT1r4etZSyK7PjhF3qbgfCOCUT1rovL6mXKlK95/vkn2L7d/2+2/vDDD9+xbfsWutvC6B8eia0KTdhwqWqJ9ylXFVniVlEULjGZuSE8iijgvXffIjU1xa/XqPRFZZYsWVL8fZs2bZg2bdpZ93nllVeKv+/YsSMzZ86s7LLOctVVV7Nr16e4MvZgjgv9AbBnUn1uBgwewD333IOqqvy6YEnpDwoxXmc27sy9NG/eMqhW+l+2bAn7D+xjeNNIaYkLsHCTnmubRPDDrhMsWPAr1147XOuSyuzgwQN8++3nHD9+DGMNC1Gd6mKIMWtdVpVliDET0a0W7pN20nak8/bb/6Vduyu49dY7iIuroXV5ADidTpYvX8LlJkvQbF3lT05VZcCAv96nls6dq3VJfmXT6RgcFsGUnCyWL1/CqFGj/Xbu4F0dMMC6devJtm1b2LhxHXjdmOJbVZl9UhWdkd9++w1VVVm0aBGKrmqNqfHkJ+NMWk2Yzco//lH6JJpAWvfnamqGGWmbEHr/5k6PD4vFwoABA/jtt99wekKvJbdZnJlGUSbW/bk6JIKc0+lkxoxpLPp9PnqLkfArEzDVDZMu1ABQFAVTnTCMNW04DmSzbcdmdj6zjZtuvI1evfpqvlyJTqcDFXxUjZaqvzMrSon3qfAq+Jz3qYVj//2dLapGUvEDRVG4776H6NatJ670XRQcW4rPlad1WX6h6I04HA7mzJmDw+FA0Qf3Eh1lpfq8OFO2UXDsD2rExvDsMy8GzadnKBzPcvDQAS6JNobkG7HD89cn5P79++PwhOYbyCXRRk4kniAvL7f0O2vo5MlEXnzxaRYtmo+5cSSR/epirhceks+dUKboFazNoonsVw9ijHz33Ve8884b5OVp+35gNBrp138ge1xOVtvz8VaRrsciJkUp8T5lqmLP+zSPhzn5uZhMZnr27OPXc0uL3BkMBgNjx95Ls2Yt+O67r7EfXoAxriWmuKYoinSLBRNPfjKu5I14nTl07dqTW2+9A4sluFq9vF4fHo8HiyE0u8QshpKfkKMNofnCajm9w4HbHbwtinv27OLdd/+HBy8R3WphSgiOPW2rM73NQMTVNXEezmXntu28+NLTPPH4c5ouVXL99TfjcrlZuvR3jnjcXGmxconRFDKL/lZHuT4vGwvs7HE5iYyM4rGH/8/vDQ4S5P5GURS6du1BixYtmTLlGzZv3oA35zCm+Hbow2vLp2ON+Vx5OFO24sk9TlxcDe548ElatWqjdVnnZLFYqFGjBsn5wd0SdD5mgw5HfuEnZABzVGi25Cbne7BZrUG7P+/hwweZ+PZ/waoj4uo66G2h/bKsukt2ybvdPq1LumiKomC5JBJ9lInMNcm89vpLTHj+Fc3G4RoMBm6//S7atevAtGlTWJSUSITeQCuTiaYmS5Wa/BDKVFUlyeNmp9PBIbcLnU5H336DGDp0OBER/n/uhPYrRiWKjY3j4Yf/j+3btzJlyjeknFiOIawWpoR2Ib/TQyhSvS5c6btxZ+5Dr9cxfPj1DBp07Tl3BwkmLVq0Ys2qZWQ6vDLZQQN5Li870py0bHNFUH4I83g8fPLJ+6hGiOxWC10VeI6obh8DBgwqHrQ+d/F8rUuqMGOchYira5K1/CTfffclDzzwb03radOmHa1atWHz5o0sWjSfNfv28KejgAYGI01NZhoaTSG3JInhbytG/P04FGR6Pex3OdnvdpPj9WC1WBnY91r69OlfqS25EuRK0bp1W/7znzf444/fmTXrZ+yHF2CIaow5vjU6o3R/VDZV9eLOPIg7fRc+j4MuXboxatRNxMaGxoLNw4dfz59rV7HwUC43tYgKyjBRlf1+JA+3D0aNuknrUs5p27bNpKamENG5ZpUIcQCKUVdycpW5arQSGWItWC6LYsPGdWRkpGv+GqTT6bjiik5ccUUnEhNPsGrVclavWs5vuTkYdToaGYw0MZqoHyKhrqHJxLEzJlQ1DPIP6UWyvV4Oupwc8LhJ97hRgBaXt6Jr1x506NAJs7nyh9ZIkCsDg8FA//6DuPrqbvz662x+/30h9txjGGOaYoprgaIPjSdcKFFVFU/ucdyp2/C68mjW7HJGj76Vhg1Da//MmJhYrht+PT/99ANLjubTt1Hl7rkn/rLqRD5bkh1cc80watcOji2i/i4pKREo3I+0qlCMOhzZf3XJG8KDa+xqRRgTrBTszeLkySTNg9yZ6tatx4033sKoUTexd+9u1q1bw8YNf7I/PxejTkdDvZEmJhMNgjjUtTRZ2OYowKWqdLLauNwUvM+brNPh7eDp8AZwSeMmDOh8NR07diYmJrDDOCTIlUNYWDg33XQrffsOYMaMaaxduxpP9kGMsZdjjLkURSf/nP7gyT+FK3Ub3oIM6tStx403PEDr1m1DtjVr0KBrSUlJZtmyJZj1Ct3qB+desFXJhpN2Fh3Oo1OnzowceaPW5ZxXVFQ0AJ5sl2y1FQI8OS6AoB1vqdfrufzyVlx+eStuu20se/bsYsOGP9m0cR0HikOdgUtNZhoYTUG1pZeiKITpdIQBLYNwnbxsr5cDLicHPS7STy8w3qTJpfTv1IUrruik6YoJkjwuQo0a8dx774MMGjSEn3/+kR07tuDJ3IchriXG6MbBt/6cYrjwcZDwFqTjTN2GNz+ZmNg4Rt16P507d9V8/aaKUhSFMWPuwm638/v6tWQ6vAxuEoFBFzwvolWFV1X5/XAeaxLttGrVhnvueSConz9XXHElU6d+h31LOpE9aqMYgrfW6s6b78axO4tLLrmUunXraV1OqQwGA61ataFVqzaMGXNXcajbuOFPDuTnYtbpaGww0sxkobbBELIflCtTgc93esybi5TTLW+XNrmMAVd25oorrgyaVllFVavYYjRAenoePl/gfq09e3bx008/cvjwAfTmCIxxrTBENgiaPwxX5n6cpzYWH5trXYEp5jINKyrJ68jClbodT14iYeERDBs6gl69+mI0huYsyfPx+XzMnDmNuXN/oX6kiRuaRxJpDt5xUd/tyORApqv4+NIYE7e1Cs6WCIB8l4/pe7M5lOWiT5/+jB49BoMhOD+0nGnr1s28996b6KPNRHROQGcN/povJHt5Ep40R/GxoYaFqB7B2bVdVp5MJ3lrkjEqRl6Y8CoJCTW1LumieTwe9uzZydq1q9m0cR0Op5NIvYHmRhMtzNrOfJ2dmwXAdRHRmtWgqirHPW52OR0c9bjxqSoN6jegc5dudOrUWbOWN51OIS7u3ENzJMj5iaqqbN26iZ+nTyUp8QR6SzTGGq0whNfVPNCpqkr+wbngc2OKb40xuonmNQH4XLk4U7fjyTmG2WzhmmuG0q/fIKzW4GtW96d169by5Rcfo6heBjQOo0NNa1D8//i79Ul25h78a+mUIU0i6FQn+Cb4qKrKjlQn8w/n4fLCbWPuokeP3lqXVS5btmzk40/ew4sPa+tYzA1CdyHgqhTkVK+Pgr1ZOPZmEx0Tw/+NezIkWuPKyul0smnTelauXMbu3TvRKQqXGIy0s9iI1+BDkJZBzq2q7HE62O5yku31EB4WTtduPenatQf16tUPeD1/J0EugHw+H+vXr2XGjJ9ITU1Gb43FVKM1+rBamr4w248uBsDWsK9mNRTxufJwpe3EnXMEo8FI//6DGDToWsLDq89EgOTkU3z99Wfs3bubxtEmhl4aQWyQtcSoqsr7G9JwelV6NQynY63gC5zZTi/zDuayN91J40aXcNc//hmyb7SnTp3kiy8/4eCB/RhiLNhaxmCItwTdv3lpqkKQU1UV17E8CnZn4bW76dKlGzfffHuVfo06deokS5f+zvJlf+BwOqhnNNLJYqOWIXA9I1oEObeqss1RwDaXE4fPyyWNm9Cv/2CuuKJTUPUKSZDTgNfrZfXqFcyaPZ3MjHT0tnhMNVpjCEvQpJ5gCHI+dwGu9J14sg6h0+no3bsfQ4ZcR1RUlGY1acnn87F8+R9Mm/odbpeLTrWt9GgQhs0YPOOkvtqWAcDYNrEaV1KSw+Nj1Qk7axLtKDo9I0fdRP/+g4N6PFxZ+Hw+1qxZyfQZU8nKzMQYa8HSNApjbVvIBLqcVSdxJxcUHxtrWonsWlvDispO9fpwHs3DeSAHT56LBg0bcdONt9KiRUutSwsYu93OsmWLWTD/V3LzcmloNNHFaiNGX/kfNAMZ5Hyqyi6Xgw0OBwU+L23btGPItcO59NKmlX7tiyFBTkMej4cVK/5g9i8zycnOwhBWE1N8G/TWwA6S1DLI+TwOXOm78WQdQEGle/deDB06ImgGimotKyuTmTN/YuXKZZj1Ct3r27iytg2jXvs37mALch6fyqZTBSw7biff5aVz566MHHmjptsmVQaXy8XKlcuYN/8XMtLTMYSbMDUOx9wwAp0peMdVAjgO5ZC/Ja34OKxdDSyXaLMTQll58904DuXgOpqHz+WlUaNLGDJkGO3bdwz5DwcXy+l08PvvC5n76yxcLhdtTBY6Wm0YK/EDRaCC3EmPmxUF+aR7PDRt2pwbbriFJk0urdRrVpQEuSDgcrlYunQxc36dRX5eLobwOoWBLkC7RGgR5Ap3Y9iDO3MfqF66dOnGddeNIj5em1bJYJeYeIJp06awfftWwk16Otex0qm2FbOGMxmDJci5vYUBblViATlOD82ateDGG2+lceNLNK2rsnm9XjZuXMfvvy/kwIF9KHoFY50wLA0jgrbbVVVVsn47jurxYWsRi7lxRHDW6fXhSrLjPJqLO6UARaejQ/sr6NdvEE2bNg/KmrWQk5PN9OlTWbFiKRF6Az2tNuobK2ft1MoOci7Vxxq7nV0uB7Exsdx8y+106NApJP5fS5ALIg6Hg0WLFjB//hwcjgIMkQ0xx7dCZ4qo1OsGMsipPg+uzP14Mnbj87jo2PEqRoy4ntq161b6tauCffv2MGfOTHbu3I7VqOeq2haurGPTpMtV6yDn8PjYcLKANUkF5Lu8XHZZU4YOHUHLlm1C4sXXn44fP8ayZYtZvWYljoIC9DYjpvphmBuEo48IrkXJs5cnAQTd2DhVVfFkOHEey8WdaMfn8hITG0eP7r3o3r2X9BJcwL59e/j6q085lXyK5iYzV1vDMPu5tbIyg9wxt4tlBfnk+3z07z+YESOux2wOnbUbNQtyeXl5jB49mk8++YR69erx1FNPsXHjxuJZiQ899BD9+/cv8ZikpCTGjx9Peno6jRs35s033yQsrHwLqAZzkCuSn5/HggW/svC3+Xg8XoxRjTHVaIXOWDkzNgMR5FTVhzvrEO70nfjcBbRq1ZZRo26iYcNGlXbNquzw4YPMmTOLLVs2YtTraJdg5qo6NmoEcFN1rYJclsPLn0l2NiU7cHp8tGzZmmuvHU6zZi0CWkcwcrlchTMNVy1n964dqKqKMdaCsX4Y5rrhQbHVV7AFOW+euzC8nbDjyXNhMBq5okMnunbtweWXt6q23afl5Xa7+OWXGcybNwebTkdPaxgN/dg6VxlBzqn6WG3PZ4/LSe1atbnrH/cHfTfquWgS5LZu3cqzzz7L4cOHWbBgAfXq1WPo0KF88cUXJCScv2vtvvvuY9iwYQwZMoQPP/wQu93O+PHjy3XtUAhyRbKyMpkzZxbLli1GRYcx5rJK2farMoNc8XZaadvxOnNp0qQpN9wwmqZNm/v9WtXRiRPHWLhwPn+uXYnX6+WyWDNd6tpoFGWs9FapQAe54zku1iTa2Z3uRFEUOnXqzIABQ6p8F+rFyszMZO3alaxZs5ITJ46DomBMsGKuH4apdhiKRhNngiHI+RwenCfycR3Px5PpAAWaN7ucLl260bHjlVitwbeUTqg4fPggX3z+MUknk2hqMtPVGobFD2HY30HuiMvFckc+dp+PwYOHct11IzFWUrdwZdMkyD3zzDOMGDGCxx9/nG+//Za4uDi6detG+/btSU5Opn///jz00EMlPgm53W6uuuoq1q1bh8Fg4OTJk9x2220sXry4XNcOpSBXJCUlmZkzp/Hnn2vQGcwY405v+6X459N1ZQU5jz0FV8pWvAXp1K5dlxtuGE3bth2qXbdXIGRnZ7FkySL+WPIbefn51Ao3cmVtK63jLZU2MSIQQc7jU9mV5uDPpAISc93YrFZ69upH374DpKurHBITT7B27UpWr1lJZkYGil6HsbYVc/0IjDWtKAHcSUSrIKe6fTiT8nEdz8OdWgAq1K/fgC5dunPVVV2IiQmOSTtVgdvtZs6cmcybOxuLTkc3i41LjKYKvfb7K8gV+HystOdzwO2kTp26/OMf/6Rx4yYVOqfWNB0j16dPH7799ltUVeX1119nwoQJREREcN9993Httddy441/7YOYkpLC9ddfz/Lly4HCGZ/t2rVjx44d5bpmKAa5IkePHmbatB/YvXtH4S4RNdpgiKhX4WDk7yDnc+XiTN6CJy+RqKgYRo68ga5de0gXRQC4XC7Wrl3Fot/mkZiUiM2o54paFjrWthLl550iKjPI5bm8bDhZwIZkB3lOLzUTatKv/yC6du2JxRI6Y1eCjc/n48CBfaxdu4p169Zgt9vRmw0Y69oKx9PFmCv9g1Ygg5zqU3GnFBR2nZ4sQPX6iIurQZcu3ejcuSt16sjY3Mp07NgRvvxiEseOH6WR0UR3Wxjhuot7HapokFNVlb0uJ2scBbhRuXboCIYMuS4kdngpzYWCXMB+u/r16/Phhx8WH48ZM4ZZs2aVCHKqqp71AnMxLzjn+2VDQXx8G664ojWbNm3i8y++4MTxVehtCZhrdgjYDNcLUb0unGk78WTux2QyccvttzNs2DDMZrPWpVUro0YNY+TIoWzfvp05v/zCynXrWHXCTssaZrrUs1EnPHgWsvy7lHwPaxLz2ZbqxOtT6dChA8OGDaN9+/byQcBPatbsRNeunXC7H2Tz5s388ccfrF27FsehHAwRJkwNwjA1iEAfZItQl4cn21U44/REPl6Hh7DwMPoOGEjv3r1p3lxmnQZKfHxr2rV7l9mzZzPlu+/4MTebq8xWWpot6AL4/yDb62V5QT4n3C6aN2vGw488QoMGDQJ2fS0F7K947969HDlyhIEDBwKFoe3vKTk2Npbc3Fy8Xi96vZ7U1NQLjqc7n1BukSvSoEFTJjz/KsuX/8GMGdPIP7wQY8xlmONbo+gD/yatqiqenGO4Urfgczvo3r0nI0feSFRUNDk5LsBV6jmE/9Wu3Zh77/sXI0amsHjxQpYtW8L2zRk0ijJxdT0bl8aYAvpiej6qqnI4y8XqRDsHMl0YjUa69+hD//6Dimczp6fna1xl1dS4cQsaN27B6NF3sGHDOlatWs7+nXux78rEmGDD3CgCU21bQLteL5bq9uE8kYfrSB7uTAc6vZ62bdrRtWsP2rRpX/yekpaWp3Gl1U/37v1p3rwt3377BSt3bme/20UvWxixlbyQsE9V2eosYIOjAIPJzJgxY+nZsy86nY7U1NzSTxAigqJFTlVVXn31VTp37ozNZmPq1KmMGDGixH2MRiMdO3Zk3rx5DB06lFmzZtGjR49AlRh09Ho9vXv3o1OnzsycOY0//liMLz8RU82OGMIDt1K6z12A89R6PHlJNGx4CbfffpcMPg8y8fEJjB49hmHDRrF8+RIW/TaP73dmkRBmpFcDG83jzJoEOlVV2Z/pYumxfJJy3USERzB8+DB69+5HRERwLxJb1dhsYfTo0ZsePXqTnHyKVauWs2LlUrL/TEZvNWBqGI65cWRQttJ5sl04DmXjPp6Pz+OjTt169BjYm86duxIZKc+jYBEfn8D//d+TrF27iu+nfMPPudlcYbbS3mKtlNefDK+HP+z5pHjctGt3BWPG3EVMTIzfrxPsAjZGrl69ekyZMoUpU6bg8XgYMGAAjz32GFA4MaJPnz707duXxMREnnzySdLT06lduzYTJ04s9xZOVaFF7lwOHNjHl19+yqlTSRhjm2JOaFvmyRAXO0bOk5uI89Q69IrKqFE30a/fQOn+CgEej4f169cy55cZnEo+Rc0zAl15upwudoycqqocOB3gEnPdxMXFMXToSLp06Rqys8aqIq/Xy7ZtW1i69He279iKgoKxrg3LpVEYYy9+nKI/xsipqor7pB3HwRzcqQUYDAauuupqevXqyyWXXCpdp0EuJyeb7777ig0b1pFgMNLXFk60/sLvV2UdI6eqKtucDv502LHabNw25h906nRVlX5OyILAVYjL5eKnn75n8eLf0NvisdbthmIofXxaeYOcqqq40nfhSt1O/QaN+Od9D1G7dnCsCSXKzufz8eefq5k9+2dSUlKoG2HkmiYR1I0oW/f8xQS55HwP8w7mcjTbRWxMLMOuG8XVV3evEgOOq7KUlGSWLFnE8hVLcBQ4MNawFu7zWtNa7jfIigQ51afiPJaLc38OnlwXMbGx9Os7kO7dexEeXrkLpwv/W7duDd9+8wVup4PuFhvNLrAIb1mCnN3nY4k9j+NuF+3aXcGdd95NZGTV369bglwVtHbtar748hMwhGOp3wud4cKfnssT5FRVxZmyBXfGXjp37srYsfdIK0qI83q9rF27ip9++p7cnByuqG2lb8NwrKWsM1aeIOf0+lh2NJ+1SXasVhsjR91E9+69JcCFmIKCAlas+IMFC+aSlZWJIcaCtUV0uQLdxQQ51afiPJqLY1823nw39es34JprhtGx41XoS2nJEcEtIyOdTyd9wL79e7ncZKGbLQz9OZ5Le50OgPOGvWSPm4X2PFyKwuibb6dXr75VuhXuTBLkqqhdu3bwzrv/QzVEYm3QB+UCU77LE+Sc6btxpWylb9+B3HLL7dXmD6U6sNvtzJr1M4sXL8Rm1DGiaQSXxpy/RbesQe5Yjouf9+SS4/TQvXsvrr9+tIyBC3Eej4fVq1cw+5cZZGakY4y3YmsVi+ECz5ci5QlyRV2oBTsz8eS6aNioMSOGX0/r1u3ktacK8Xq9zJgxjfnz51DHYGRQWES5tvg64HKyxJ5HTGwcDz38fzRo0Kjyig1CEuSqsI0b1/Phh29jjG2KpWaH896vrEHOa0/DfnQxHTtdyT/ve1jGw1VRx44d4bNPPyQpKZHeDcPpVt92zsHIpQU5VVVZd7KAhYfyiKtRg3vueZBLL21aqbWLwPJ4PCxduphZs3/Gnp+PuXEEtpax6Ezn/+DoPFo4W9Dc8MJdod48N/lb03En26lZsxY33HAz7dt3lABXha1Zs5Ivv/iEaJ2Oa8MisZXhPWans4Dl9nwubXIZDz/yaLX8kChBror79tsvWbp0MbbGA8+71lxZgpyq+ig4sogIq8Ir/3lDtrCp4pxOB19//Rl//rmGy2tYGNUsEv3flqC4UJDzqSq/Hshl06kC2rZpxz33PojNVr59kUXosNvtzJ49nd9/X4DOrMfWPg5T7Yv7/62qKo4D2RTsysJkMDJy5I307t1fuuGriZ07t/Peu28SCVxXSsvcbqeDpfY82rRuxwMP/huTqXoO87lQkJPmlipg1KgbsVqtuFK3V+g8ntwTeB2Z3HjDzRLiqgGz2cK99z7EDTfczK40B7P35+Ar4+c6VVVZcLAwxA0ZMoyHH3lMQlwVZ7PZuPnmMTz//CvUqlGL3DXJ5G1JQ/WW70Ozz+Eld9Up7NszaNOqLa++OpH+/QdLiKtGWrZszSP/eows1ccCey7e87zuHHe7WGbPo2XL1jz40LhqG+JKI0GuCggLC6d//0F48hLxOrMv6hyqquJO301CzVpceWUXP1cogpWiKAwePJSRI29kW4qDhYfKtpDq8uP5rDtZwKBBQxg58ibpgq9GGjZsxPPPvcKAAdfgPJRD7oqT+JzeMj3Wk+0iZ2kSvgw3d9xxN4888li1XPdLFIa5sWPvJcntZr3Dftbt+T4fv9vzqV2nLg8+OA6jMXh3q9Faqa++ycnJZ/3swIEDlVKMuHh9+w7AYDTiSt9zUY/32pPxOjK5ZvBQeVOuhoYMuY5+/QbyZ5Kd/RnOC973eE7h+nCdO3flhhtukfFM1ZDRaGT06Nt44IF/oeZ4yF12Em+B54KPcac7yF1+knBTGM8+8xI9e/aR5041d/XV3enevRdbHAWkeko+f1ba8/DqFB58cJzsvVyK875jZ2VlkZWVxT333EN2dnbxcVpaGg899FAgaxRlEBERSc8effDkHMXnKt+2JKqq4krbSWRUNF26dKukCkUwUxSFG264mTq16zDnQB4Oj++c9/P4VGbtzyMmOpYxY8bKG3E117HjVYwf/wx6t0LeylPnbZnzZDnJW51MXEwczz37Mg0bNgpsoSJo3XTTbYSFhfHnGa1ypzxuDrldXDt0hKxfWgbnDXKPPvoonTt3Zt++fVx11VV07tyZzp0706tXL1q2bBnIGkUZXXPNMAwGPc6UbWfdZoy6BGPUubfV8uQl4rWnct2wEdJ8XY0ZjSbG3vVPcpwe1iUVANC+ppX2Na3F99mcXEC63c3td9wt4ygFAJdd1oxx455AtXvJW5fC3+fP+Zxe8tamEBkWyROPP0dsbJxGlYpgZLPZGDhoKMfdLjK9ha1y2x0FWC1W+vcfrHF1oeG8Qe6LL75gz549jBgxgj179hT/t2PHDt56661A1ijKKCYmhmuHXIcn9zievKQStxmjG2OMbnzWY1SvG3fKZmrXqUePHn0CVaoIUk2aXErrVm1Ye7IAl1elXU0r7U4HOa9PZdWJAi65pAmtW7fVuFIRTJo2bc7tt/8Dd2oBjv0lx+nmb0lDdfp45JFHJcSJc+rWrScKcNDlwquqHPF4uKrz1dKlWkalDoZ67bXX2LZtGz/++CMul4vNmzcHoi5xkQYPHkqt2nVwndqA6nWVen9nyha8Ljtj77xbVk8XAAy+Zhh2l5c96Y4SPz+U5SLL4WHw4KHSpSrO0q1bT9q2bY9jTxY+R2HLiju1AFdiPtcNG0mjRufuERAiKiqKuvXqc9LjJtXrwaP6aNmytdZlhYxSg9yMGTN46qmn+Pzzz8nNzeWBBx5g2rRpgahNXASj0cg9d9+Pz+PAkbzpgvf15CXhzjrIoEFDZBFXUaxp0+bEREezI7XkpIcdqQ5sVitt2rTXqDIRzBRFYfTo21C9KgUHcgAo2JNFVHQ0gwYN0bg6Eezq1q1PDio53sJxlnXq1NO4otBRapCbPHkyU6dOJTw8nLi4OGbMmME333wTiNrERWrcuAnXXnsdnuwjeHITz3kf1evCeWoDtWvXZcSI6wNcoQhmOp2OKzp25lCWC/fphbV9qsr+TDdt210h4yjFedWsWZt27TrgPpaHN9eFO7WAfn0Hyl7NolRhYWE4fSrO02Msw8LOvfitOFupQU6n0xEe/tc/aO3ataULLgQMHTqCOnXr4UrZhOo7e1kAZ+p2VE8Bd999v7zIirO0aHE5Hp9KYq4bgFS7F7vbS4sWMtFJXFjnzl3xOjzYd2YCcNVVV2tckQgFqqqiKKAUH5975rw4W6lBLjo6mt27dxePifnll1+Iioqq9MJExRgMBm4fcxdeVz6ujH0lbvO5cnFnHaBXr740bizjVsTZLrusGQAncgqD3IlcV4mfC3E+LVq0AsCVlE+N+Hhq1IjXuCIRCvLz8zArSvF2XXl5ZVucXECpe6I8/fTT/Otf/+LYsWN069YNs9nMRx99FIjaRAU1bdqcVq3asGvvfkxxzVCUwpZUV8Y+9Do9Q4eO0LhCEazCwyOIjoomxV444SEl34PZZCI+PkHjykSwCw8PJyYmlszMDBo1lA+KomzS09MIQyFMKQxymZnp1K0r4+TKotQg16RJE2bPns2RI0fwer00btxYxsiEkD59+rNjxza8+ckYwuugqj68ucfoeEUnoqNlaxxxfnXq1iPt+F4A0uxeatWuI7t+iDKJT0ggMzNDgr8os5TkU9TV6YjSF77GJCcn06qVxkWFiFKD3AcffFDiWFEUrFYrl112Gd27d7/gY/Py8hg9ejSffPIJ9erVY+rUqUyePBlFUWjVqhUvvvjiWZvgzpw5k7feeou4uML1hnr16sW4cePK+3uJ01q2bI3eYMBzOsj5HFn4PE7ateugdWkiyNWoEc/RA7sByHapNJI3ZVFGJqMZgIiICI0rEaGgoMBObl4e0VYbNkWHQdGRnHxK67JCRqlBbt++fWzevJmBAwei1+tZtGgRdevWZf78+Wzbto0HH3zwnI/bunUrzz77LEeOHAHg8OHDfPHFF8yYMYOwsDCefPJJvv/+e+68884Sj9uxYwdPPvkk1157bYV/OVG4Wn/dOvVITC9cpNPnLPwqazqJ0sTF1SDf5cXtU8l2emUxV1FuMpFKlEVqaioAkTo9iqIQqdeTlpaicVWho9R+kvT0dGbMmMGzzz7LU089xfTp01EUhSlTprBgwYLzPm7atGlMmDCBhITCT/Emk4kJEyYQHh6Ooig0bdqUpKSksx63fft2Zs6cydChQ3nsscfIzs4+6z6ifOLj41G8hVsu+TyF+9kVtXgKcT5RUdEAZBZ4cXt9xcdCCOFPWVkZAISdHroRBmRmZGhYUWgptUUuKyuL+Pi/Zh3FxMSQlZWFyWTCYDj/w1955ZUSx3Xr1qVu3boAZGRkMGXKFF577bWzHhcfH89dd91Fhw4dmDhxIi+99FK5twSLi5P1Z84UExOF6iucfYjPjcFgoE4dCXLiwurVqwlAir1w+Zo6dRKIj5euMlE6k6lwYpXZrJPnjCiVXl+41Ij59OoYFp2OHIddnjtlVGqQq1+/Pm+99RY33ngjAD///DMNGjRg69atFzXwOTk5mbvvvptRo0Zx1VVXnXX7hx9+WPz93XffTf/+/ct9jfT0PHw+tfQ7VhMejwqn1+RRfT70egOpqbkaVyWCnddb+GacXlAY5FTVKM8bUSYFBYW7gqSkZMhzRpQqKysfAN3pVeQUwO32yHPnDDqdct5GqlKT2KuvvkpiYiIjRozg+uuvJzk5mf/85z/s3LmTJ554olyFHDx4kNGjRzNixIhzjq3Lzc3l66+/Lj5WVVUWH/YDRVFQ1ZLHQpSmaCHw9AJviWMhSpOdk1X4NTtL0zpEaChaCcNL4RuVV1UxGmR1jLIqtUUuNjaWiRMnnvXzW265pVwXysvL4x//+Af//ve/GT58+DnvY7PZ+Pzzz2nfvj1t27blu+++u6gWOfF3ClCU5FQJcqJMwsIKuzUyTgc52TJHlIWqqqSnpwOQlp6mcTUiFISHF77WFPh8oIcCVSU8MlLjqkLHeYPcmDFjLviG/+2335brQj///DNpaWl89dVXfPXVVwD06dOHf/3rXzzzzDP06dOHvn378s477/DCCy/gcDho1KgRb7zxRrmuI86m1+sobpJTVRRZC0yUQVhYGAAZDk+JYyEuJCcnB6ejcCHpUyfPntAmxN/FxdUAINdXOAQoD5X6siNImZ03yN12220ALFq0iLy8PEaNGoVer2f27NlEliMpL1myBIA777zzrKVGipw5MaJjx47MnDmzzOcXpTMYjKhqYauKqnoxXmCSihBF9Ho9ZpMJu6twey6bTYKcKF1S0gkADDUsZKZlUFBQgNVq1bgqEcxq1IhHr9eT6fXiVlVyPB5q166jdVkh47zv6AMHDgTgiy++4Mcffyye2NCrVy9uuummwFQn/MJisaD6vIWbEPs8WGzyoirKxmYLw+lyYS5llroQRY4fPwaAuX44njQHiYnHufTSphpXJYKZXq+nTu26pJ86SYa3sAegXr36GlcVOkrtY8vMzMTpdBYf5+fny9puIaZo/IHqdaJ6nbLauigzm80GIC0qosyOHz+K3mLAWLPwuXPs2FGNKxKhoGGjxqSpPtJOB7kGDRppW1AIKfUj9rXXXsuNN95I//79UVWVBQsWFC9FIkJDVFQUAKrHAT6nLOwqysxqLXwztlgsGlciQsWxY0fQRRrRWfXoTHqOH5cgJ0pXv35DVq5cxjG3G6vFWjxuTpSu1CD3r3/9i5YtW7J27VoAnnzySXr27FnphQn/iY0t/IPwufPxufJlVwdRZiaz+fRXCXKidD6fj6STSRgbh6EoCvpIIydOHNe6LBEC6tdvAMARt4umjZvJ6grlUKadHTp27EjHjh1L/Cw6Oroy6xJ+VKPG6SBXkI7q81Cjhmx+LsqmaH0nk+yZKcogPT0Nr8eDJbzweaMLN3LqlMxcFaWrU6fuGd/X07CS0FNqkOvcufNZyTg+Pp7ly5dXWlHCv8LDI7BYrLjyTgIU738rRGn0+sKXCL1MdBBlkJFRuH6czmYo/pqfn4nL5cJkkg8D4vwiIv5aDSMhoaaGlYSeUl+d9+zZU/y9y+Xi119/5fDhw5ValPAvRVFISKjJsWNHAEhIqKVtQSJkFH2Ik24OURa5uYVbKunM+hJf8/JyiY2VIR3i/M58jSkaDiTKplwrw5pMJkaOHMmqVasqqx5RSWrWLAxviqJQQxZaFGXkO71AZ9FXIS7E7S5cc1DRKyW+Fv1ciAvp3r0XRqOxeLycKJsyjZEroqoqO3bsICcnpzJrEpWgKLxFRkbJemCizLzewoWkPR63xpWIUFC0N/YZG8kAoNPJntmidGPH3svYsfdqXUbIKfMYOfX0X2RcXBzPPPNMpRcm/Cs2NhYAowxaF+VQ1JLicUuQE6UrWq5GdftKfC36uRDC/8o1Rk6ErqK143Q6Geskys7lcpb4KsSFFL3O+Ao8xV/1BoPs0ytEJbpgkEtOTsZmsxEREcG8efNYv349bdu2Zfjw4QEqT/hLWFi41iWIEFS0+fmZu7sIcT7x8YUz4n35hS243jw38fHxMllGiEp03skO06ZNY8CAAQwYMIB3332XiRMnYjQa+eabb3j//fcDWaPwg6IX2M6du2pciQglRQFOgpwoC6vVSlR0NJ6cwiCn5nmoW0f2zBSiMp23RW7y5MksXLiQrKwsRo0axW+//UbdunWx2+2MHDmShx9+OJB1igqqUSOejz76EvPplfqFKIvirlUZIyfKqGGDRuw6uhvV48OT65IZiEJUsvO2yCmKQq1atWjevDkNGjSgbt3CVZdtNptsoB2iLBaLdHGIcnGfDnAej6d4wpMQF9KgQSM8OS48mYUfAurXb6hxRUJUbecNckXTyEE2zBaiuvJ4POf8XojzqV+/AagqzuN5fx0LISrNeYOcw+Fg165d7Ny5s8T3RcdlkZeXx7XXXsuJEycAWL16NUOHDmXAgAG8/fbb53xMUlISt956K4MGDeL+++8nPz//In4tIYQ/nNkKJy1yoizq1SscE+c8kYfZYiEuTlbpF6IynXeMnNPp5KGHHio+PvP7snTPbd26lWeffZYjR44AhcHw6aefZvLkydSuXZv77ruPZcuW0bNnzxKPe/HFF7nlllsYMmQIH374IR999BHjx48v7+8lhPAzCXKiLBISaqHT6fB5fNSpX1eGcwhRyc4b5JYsWVKhE0+bNo0JEybw+OOPA7Bt2zYaNmxI/fqFn9aGDh3KggULSgQ5t9vN+vXr+fDDDwEYOXIkt912mwQ5ITSi0/3VaC87goiy0Ov1xMbVIC01hZo1ZfNzISpbpb0yv/LKKyWOU1JSiI//a4/PhIQEkpOTS9wnMzOT8PDw4jeM+Pj4s+4jhAgcg8GA0+VCUZQSoU6IC6lxOshJt6oQlS9gH7F9Pl+JJnZVVc9qcj/Xzy6mWT4uTha/FcIfbDYr+XY7FrOZhIRIrcsRIaJOnVrs2bOLxo0bEB8foXU5QlRpAQtytWrVIjU1tfg4NTWVhISEEveJjY0lNzcXr9eLXq8/533KIj09D59PxvMIUVFGY+G6gyaTidTUXI2rEaFixIjRtG9/FU2bNpfnjRB+oNMp522kKrWvJCkpqcR/J0+eJDMzs9xFtG3blsOHD3P06FG8Xi+//vorPXr0KHEfo9FIx44dmTdvHgCzZs066z5CiMCxnd4j02aTvTJF2YWFhdOyZWuMRqPWpQhR5ZXaInfzzTeTkpJCWFgYOp2O3Nxc9Ho9MTExvPvuu3To0KFMFzKbzbz++us8/PDDOJ1OevbsyaBBgwB45pln6NOnD3379mXChAk8+eSTfPzxx9SuXZuJEydW7DcUQlw0m63wE6DVZtO4EiGEEOeiqKWsKfDUU09x1VVXMXz4cAAWLlzIqlWrGD16NBMmTOCnn34KRJ3lIl2rQvjHp59+yNq1q2jdui3jxj2hdTlCCFEtVahrdc+ePcUhDmDgwIHs2LGDyy+/vHj7HiFE1WQ73RInXatCCBGcSg1yHo+Hffv2FR/v27cPn8+H0+mULXuEqOKsVtvpr7K/shBCBKNSx8g99thjjBkzhssuuwyfz8fRo0d58803ee+99+jXr18gahRCaKSoRe7MvZeFEEIEj1KDXM+ePVm4cCEbNmzAYDDQvn17oqKiaN26NeHhsl6bEFXZX7s5yDZLQggRjEoNcj6fj59++only5fj8Xjo2rUr//znPyXECVEtFAU4mTwkhBDBqNQxcm+99RZr167ljjvuYOzYsWzevJk33ngjELUJITRXFOCkRU4IIYJRqS1yK1asYPr06cULO/bq1Ythw4bx9NNPV3pxQohgIS1yQggRjEptkVNVtcTq3CaTSVbrFkIIIYQIAqUGuebNm/Pqq69y7Ngxjh8/zmuvvUbTpk0DUZsQQnPSpSqEEMGs1CA3YcIEcnJyGD16NDfeeCMZGRk899xzgahNCKE5GSMnhBDBrNQxcuHh4bz++uslfrZ//36io6MrqyYhhBBCCFEGpbbInctNN93k7zqEEEFMkQY5IYQIShcV5FRVZrAJUZ3In7wQQgSniwpyinw8F0IIIYTQ3EUFOSFE9SKf3YQQIjidd7JD+/btz9nypqoqDoejUosSQgQX6VoVQojgdN4g9+uvvwayDiFEEJMWOSGECE7nDXJ169atlAv+9NNPfPfdd8XHJ06c4LrrruP5558v/tkHH3zA9OnTiYyMBODGG2/k1ltvrZR6hBClkxY5IYQITqWuI+dvN9xwAzfccANQuB7dgw8+yEMPPVTiPjt27GDixIm0b98+0OUJIUqQpjghhAhmAQ9yZ3rhhRcYN24csbGxJX6+Y8cOJk2aRGJiIp06deKJJ57AbDZrVKUQQgghRHDSLMitXr0ah8PB4MGDS/w8Pz+fFi1aMH78eBo2bMiTTz7JRx99xLhx48p87ri4cH+XK0S1FB5e+AHKajUSHx+hcTVCCCH+TrMg9+OPPzJ27Nizfh4WFsZnn31WfHzXXXfx9NNPlyvIpafn4fPJoB4hKiovzwlAQYGb1NRcjasRQojqSadTzttIpck6ci6Xi/Xr19OnT5+zbktKSuLnn38uPlZVFYNB0x5gIYQQQoigpEmQ27t3L40aNcJms511m8Vi4X//+x/Hjx9HVVWmTJlC//79NahSCCGEECK4aRLkjh8/Tq1atUr87J577mH79u3Exsby0ksvcf/99zNo0CBUVT1nF6wQQgghRHWnqGrVWyFKxsgJ4R+LFi3ghx++pW/fgdx66x1alyOEENVS0I2RE0IIIYQQFSdBTgghhBAiREmQE0IIIYQIURLkhBBCCCFClAQ5IYQQQogQJUFOCCGEECJESZATQgghhAhREuSEEEIIIUKUBDkhhBBCiBAlQU4IIYQQIkRJkBNCCCGECFES5IQQQgghQpQEOSGEEEKIECVBTgghhBAiREmQE0IIIYQIURLkhBBCCCFClEGLi44ZM4aMjAwMhsLLv/TSS7Rt27b49t27d/PMM8+Qn59Px44defHFF4vvK4QQQgghCgU8HamqypEjR/jjjz/OG87Gjx/Pf/7zH9q1a8fTTz/NtGnTuOWWWwJcqRBCCCFEcAt41+qhQ4cAuOuuuxg2bBjfffddidsTExNxOBy0a9cOgJEjR7JgwYJAlymEEEIIEfQC3iKXk5NDly5deO6553C73dx+++00btyYrl27ApCSkkJ8fHzx/ePj40lOTg50mUIIIYQQQS/gQa59+/a0b9+++Pj6669n2bJlxUHO5/OhKErx7aqqljgui7i4cP8UK0Q1Fx5uBsBqNRIfH6FxNUIIIf4u4EFuw4YNuN1uunTpAhQGtTPHytWqVYvU1NTi47S0NBISEsp1jfT0PHw+1T8FC1GN5eU5ASgocJOamqtxNUIIUT3pdMp5G6kCPkYuNzeXN954A6fTSV5eHjNnzqR///7Ft9etWxez2czGjRsBmD17Nj169Ah0mUIIIYQQQS/gLXK9e/dm69atDB8+HJ/Pxy233EL79u255557eOSRR2jdujVvvvkmzz77LHl5ebRs2ZLbb7890GUKIYQQQgQ9RVXVKtcHKV2rQvjHokUL+OGHb+nbdwC33nqn1uUIIUS1FFRdq0KIUFL0gah8E46EEEIEhgQ5IYQQQogQJUFOCFGqcq4AJIQQIkAkyAkhSlX1RtIKIUTVIEFOCFEqaZETQojgJEFOCHEBhQlOWuSEECI4SZATQgghhAhREuSEEGUgTXJCCBGMJMgJIcpABskJIUQwkiAnhCgDaZETQohgJEFOCHEBsrODEEIEMwlyQojz+mu2qrTICSFEMJIgJ4Q4L6fTqXUJQgghLkCCnBDivAoK7AC4XC6NKxFCCHEuEuSEEOdVFOSKvgohhAguEuSEEOeVn59f4qsQQojgIkFOCHFednthgCuwS5ATQohgZNDioh988AHz588HoGfPnjz++ONn3T59+nQiIyMBuPHGG7n11lsDXqcQ1V1RkJMWOSGECE4BD3KrV69m5cqVzJw5E0VRuPvuu1m0aBH9+/cvvs+OHTuYOHEi7du3D3R5QogzOE6PjXM4CjSuRAghxLkEPMjFx8fz5JNPYjKZAGjSpAlJSUkl7rNjxw4mTZpEYmIinTp14oknnsBsNge6VCGqPYfDUeKrEEKI4BLwIHfZZZcVf3/kyBHmz5/PDz/8UPyz/Px8WrRowfjx42nYsCFPPvkkH330EePGjSvzNeLiwv1asxDVlcfjAcDt8VCjRjiKIjs8CCFEMNFkjBzA/v37ue+++3j88cdp1KhR8c/DwsL47LPPio/vuusunn766XIFufT0PHw+WYleiIpyu93F3586lYXBoNlLhhBCVFs6nXLeRipNZq1u3LiRO++8k0cffZQRI0aUuC0pKYmff/65+FhVVXnzEEIjvr/26MLn82lYiRBCiHMJeJA7efIkDz74IG+++SZDhgw563aLxcL//vc/jh8/jqqqTJkypcRECCFE4OjO6ErV6WS1IiGECDYBb+r64osvcDqdvP7668U/Gz16NEuWLOGRRx6hdevWvPTSS9x///243W46dOjA2LFjA12mEIKS4U2CnBBCBB9FVdUqN5hMxsgJ4R///tc/ycnNwWAw8Omn32pdjhBCVEtBN0ZOCBEaipb9MZ9eLkgIIURwkSAnhDgvq81W+NVq1bgSIYQQ5yJBTghxXjZbGABWq03jSoQQQpyLBDkhxHkVBTlbmCyyLYQQwUiCnBDivIqDnE1a5IQQIhhJkBNCnFdRgCsKdEIIIYKLBDkhxHkVBTmLxaJxJUIIIc5FgpwQ4rxMJvPp75QL3k8IIYQ2JMgJIc7rr90cZIFtIYQIRhLkhBBlIC1yQggRjCTICSHOKzo6BoCEhJoaVyKEEOJcZK9VIcR5+Xw+DhzYxyWXXIrBYNC6HCGEqJYutNeqBDkhhBBCiCB2oSAnXatCCCGEECFKgpwQQgghRIiSICeEEEIIEaI0CXJz5szhmmuuYcCAAUyZMuWs23fv3s3IkSMZOHAgzzzzDB6PR4MqhRBCCCGCW8CDXHJyMm+//Tbff/89s2bNYurUqRw4cKDEfcaPH8/zzz/PwoULUVWVadOmBbpMIYQQQoigF/Agt3r1ajp37kx0dDQ2m42BAweyYMGC4tsTExNxOBy0a9cOgJEjR5a4XQghhBBCFAr4wlApKSnEx8cXHyckJLBt27bz3h4fH09ycnK5rqHTySr0QgghhKgaLpRrAh7kfD4fivJXQaqqljgu7fayiIkJq3ihQgghhBBBLuBdq7Vq1SI1NbX4ODU1lYSEhPPenpaWVuJ2IYQQQghRKOBB7uqrr2bNmjVkZGRQUFDAb7/9Ro8ePYpvr1u3LmazmY0bNwIwe/bsErcLIYQQQohCmmzRNWfOHCZNmoTb7eb666/nnnvu4Z577uGRRx6hdevW7Nmzh2effZa8vDxatmzJa6+9hslkCnSZQgghhBBBrUrutSqEEEIIUR3Izg5CCCGEECFKgpwQQgghRIiSICeEEEIIEaIkyAkhhBBChCgJckIIIYQQIUqCXIj74IMPGDJkCEOGDOGNN94ACvezHTp0KAMGDODtt98+6zGPP/44M2bMKD4+ceIEt956K9dddx1jxowhMTExYPULbfjjebNt2zZGjRrF0KFDue+++0os5C2qpvI8b37//Xeuu+46hg0bxgMPPEB2djYASUlJ3HrrrQwaNIj777+f/Px8TX4XETj+eN4Ueeedd3j//fcDWn/QU0XIWrVqlXrTTTepTqdTdblc6u23367OmTNH7dmzp3rs2DHV7Xard911l7p06VJVVVX11KlT6n333ae2adNGnT59evF5HnvsMXXKlCmqqqrqt99+qz766KOa/D4iMPzxvPH5fGrPnj3VNWvWqKqqqnPnzlXvu+8+zX4nUfnK87zJzc1Vu3btqp46dUpVVVV955131JdffllVVVW999571V9//VVVVVX94IMP1DfeeEOz30lUPn89b3JyctSnnnpKbdOmjfree+9p+SsFHWmRC2Hx8fE8+eSTmEwmjEYjTZo04ciRIzRs2JD69etjMBgYOnQoCxYsAAoXYu7bty+DBw8ucR6fz0deXh4ABQUFWCyWgP8uInD88bzJzMzE4XDQuXNnAHr37s3KlStxuVya/E6i8pXneeN2u5kwYQI1a9YEoFmzZpw8eRK328369esZOHAgACNHjix+nomqyR/PG4DFixfTqFEjxo4dq+WvE5QkyIWwyy67jHbt2gFw5MgR5s+fj6IoxMfHF98nISGB5ORkAO6++25uuOGGs87zr3/9i6+//pru3bvz5Zdfcs899wSkfqENfzxvYmJisNlsrFy5EoC5c+fidrvJzMwMzC8hAq48z5uYmBj69+8PgMPh4NNPP6Vfv35kZmYSHh6OwWAACt/ki55nomryx/MGYPjw4dx7773o9fqA/w7BToJcFbB//37uuusuHn/8cerXr4+iKMW3qapa4vhcnnjiCV566SVWrFjBiy++yEMPPYQqG35UeRV53iiKwnvvvcekSZMYPnw4ubm5REdHYzQaA1G60FB5nje5ubnce++9NG/enBEjRpzzeVXa65OoGiryvBEXJkEuxG3cuJE777yTRx99lBEjRlCrVq0Sg85TU1NJSEg47+MzMjI4dOhQ8aeegQMHkpqaKi0rVVxFnzcABoOByZMnM2vWLIYNG4bP5yM6OrqSKxdaKs/zJiUlhVtuuYVmzZrxyiuvABAbG0tubi5er/es+4uqq6LPG3FhEuRC2MmTJ3nwwQd58803GTJkCABt27bl8OHDHD16FK/Xy6+//kqPHj3Oe46YmBjMZjMbNmwACv/gwsLCiI2NDcjvIALPH88bgKeffppt27YB8NVXXzFo0CB0OnlJqarK87zxer3885//ZPDgwTzzzDPFrS1Go5GOHTsyb948AGbNmlXq80yENn88b8SFGbQuQFy8L774AqfTyeuvv178s9GjR/P666/z8MMP43Q66dmzJ4MGDTrvORRF4YMPPuDll1/G4XAQFhYmU7urOH88bwBeeOEFJkyYQEFBgXx6rgbK87z5/fff2bVrF16vl4ULFwLQqlUrXnnlFSZMmMCTTz7Jxx9/TO3atZk4caJWv5IIAH89b8T5KaoMhhJCCCGECEnSDyKEEEIIEaIkyAkhhBBChCgJckIIIYQQIUqCnBBCCCFEiJIgJ4QQQggRoiTICSHEaS+++CI33nhj8YK1AF6vl9GjR/P2229rWJkQQpybBDkhhDjtySefpKCggEmTJhX/bNKkSej1eh555BENKxNCiHOTdeSEEOIMe/fu5eabb2bKlCmoqsq9997L9OnTWb58OT/88EPxVmTPPfccTZo04fDhw7z00kvk5+eTmppK8+bNeeeddzCbzbRq1Yq+ffuyZ88e3nzzTVq3bq31ryeEqGJkZwchhDhDs2bNGDduHM888ww+n49XXnmFo0ePMmvWLKZMmYLVamXlypU89NBDzJ8/n2nTpjF8+HCuu+463G43I0eOZOnSpQwcOBC3203v3r159913tf61hBBVlAQ5IYT4mzFjxrBw4UKaNGlCz549eeONNzh69CijR48uvk9OTg5ZWVmMHz+eVatW8dlnn3HkyBFSUlKw2+3F9+vYsaMWv4IQopqQICeEEOdQr149GjRoAIDP5+O6665j/PjxxccpKSlERUUxbtw4vF4vgwcPplevXpw8eZIzR6zYbDZN6hdCVA8y2UEIIUrRrVs35s6dS0pKCgA//PADd9xxBwArV67kwQcf5JprrgFg69atJWa9CiFEZZIWOSGEKEW3bt245557uOuuu1AUhfDwcD744AMURWHcuHE8+OCD2Gw2wsPD6dSpE8eOHdO6ZCFENSGzVoUQQgghQpR0rQohhBBChCgJckIIIYQQIUqCnBBCCCFEiJIgJ4QQQggRoiTICSGEEEKEKAlyQgghhBAhSoKcEEIIIUSIkiAnhBBCCBGiJMgJIYQQQoQoCXJCCCGEECFKgpwQQgghRIiSICeEEEIIEaIkyAkhhBBChCgJckIIIYQQIUqCnBBCCCFEiJIgJ4QQQggRoiTICSGEEEKEKAlyQgghhBAhSoKcEEIIIUSIkiAnhBBCCBGiJMgJIYQQQoQoCXJCCCGEECFKgpwQQgghRIiSICeEEEIIEaIkyAkhhBBChCgJckIIIYQQIUqCnBBCCCFEiJIgJ4QQQggRoiTICSGEEEKEKAlyQgghhBAhSoKcEEIIIUSIkiAnhBBCCBGiJMgJIYQQQoQoCXJCCCGEECFKgpwQQgghRIiSICeEEEIIEaIkyAkhhBBChCgJckIIv3jxxRe58cYb8Xq9xT/zer2MHj2at99+W8PKSjdmzBgWLFhQKed+//336dy5M9dddx3Dhw9n6NCh3HnnnRw+fLhSrncxv8sPP/zAp59+CsBPP/3ElClTKqM0IUQlkCAnhPCLJ598koKCAiZNmlT8s0mTJqHX63nkkUc0rEx711xzDbNnz2bWrFnMmTOHvn378uijj2pdVrGbb76Ze++9F4CNGzficDg0rkgIUVYGrQsQQlQNZrOZN998k5tvvpnevXujqirff/8906dPR6/X89NPP/HDDz/g8/mIjo7mueeeo0mTJhw+fJiXXnqJ/Px8UlNTad68Oe+88w5ms5lWrVrRt29f9uzZw5tvvknr1q0ByM3NpWfPnixcuJD4+HgAbrjhBh566CHCwsJ4/fXX8fl8ANx3330MHDjwon+vqVOnMnnyZHQ6HTVq1OC5556jcePGZGRk8NRTT3Hs2DGio6OJj4/nsssu4+GHHy71nF26dGHixIkAnDp1ihdeeIHExERUVWX48OHcfffdnDhxgjFjxtC9e3e2bt2Kqqo8//zzdOzYkffff5/MzEyef/55gLOOi3zyyScsXrwYh8NBQUEBTzzxBP379+f9999ny5YtpKSk0KxZMxo2bEhmZiZdunRhyZIlrFq1CovFwrfffsvzzz9P165dAXjmmWdo2rQpd9xxx0X/ewoh/EuCnBDCb5o1a8a4ceN45pln8Pl8vPLKK9SsWZN169Yxa9YspkyZgtVqZeXKlTz00EPMnz+fadOmMXz4cK677jrcbjcjR45k6dKlDBw4ELfbTe/evXn33XdLXCciIoL+/fvzyy+/8I9//IODBw+SlpZG9+7dGTt2LGPHjmXIkCHs2bOHqVOnXnSQW7NmDZ9//jlTp04lNjaWGTNm8OCDDzJ37lz+85//cOmllzJp0iRSUlIYOXIkl112Wann9Hg8/Pzzz1x11VUAPPbYY/Tt25exY8eSm5vLrbfeSu3atWnbti1JSUl06tSJl156iWXLlvHvf/+bP/74o0y1JyYmsnr1aiZPnozFYmHu3Lm899579O/fv/j2X3/9FYPBwPvvvw9A//79Wbx4MZdddhm33norbrebadOm0bVrV/Ly8liyZAlPPPHERf1bCiEqhwQ5IYRfjRkzhoULF9KkSRN69uwJwNKlSzl69CijR48uvl9OTg5ZWVmMHz+eVatW8dlnn3HkyBFSUlKw2+3F9+vYseM5r3PDDTfw4osv8o9//IPp06czatQodDodgwcP5qWXXmLJkiVcffXV/N///d9F/y4rVqzgmmuuITY2FoCRI0fyyiuvcOLECZYtW8bMmTMBSEhIYNCgQec9z7x589i4cSMAbrebli1b8vLLL2O329m0aRNffvklUBhQR44cyfLly2nbti1RUVEMHToUgJ49e6LX69m7d2+Zaq9bty5vvPEGc+bM4ejRo2zdupX8/Pzi29u1a4fBcOG3gJEjR/Lhhx+SkZHBggUL6NWrF5GRkWW6vhAiMCTICSH8rl69ejRo0KD42Ofzcd111zF+/Pji45SUFKKiohg3bhxer5fBgwfTq1cvTp48iaqqxY+12WznvEbHjh3xeDxs27aNX3/9lalTpwIwevRoevfuzapVq1ixYgUffPABCxYswGw2l/v3KOqePZOqqng8HgwGQ4k6dbrzDzm+5pprzur2BMjLyytxjqJrejweAPR6/Vm36fV6FEUp8Ti3233WuXfu3MkDDzzAnXfeSdeuXenUqRMvvvhi8e3n+3c9U2RkJIMGDeKXX35hzpw5TJgwodTHCCECSyY7CCEqXbdu3Zg7dy4pKSlA4SzJonFWK1eu5MEHH+Saa64BYOvWrSVmvl7IDTfcwMsvv0yzZs2oXbs2UBjkdu/ezciRI3n55ZfJyckhNTX1ouru3r078+bNIyMjA4Dp06cTHR1Nw4YN6dmzJz///DMAmZmZ/P777yiKUq7zh4eH07Zt2+JZorm5ucyaNYurr74agIyMDJYvXw7AkiVLMBqNNG3alJiYGHbu3ImqquTl5Z2zu3X9+vW0atWKsWPHcuWVV7J48eIy/bvq9friIAlw66238u2336KqKm3atCnX7yeEqHzSIieEqHTdunXjnnvu4a677kJRFMLDw/nggw9QFIVx48bx4IMPYrPZCA8Pp1OnThw7dqxM5x0+fDgTJ04snjgAhWPOXn31Vd555x0UReGhhx6iXr16bN++nWeffZbZs2ef81yPP/44Tz31VPHxLbfcwvjx47nzzju544478Pl8xMbGMmnSJHQ6HU899RTPPvssQ4cOJTo6mjp16mCxWMr9b/Pmm2/y0ksvMWPGDFwuF0OHDmXkyJEkJiZiNpuZPXs2b775JhaLhQ8//BC9Xs+wYcNYsWIFAwYMoGbNmlx55ZVntexde+21/PbbbwwePBifz0fv3r3Jzs4mLy/vgvX06NGD119/HSicKNK8eXOioqJKdIsLIYKHov79r18IIUSppkyZwuWXX0779u1xuVzccsstPPzww8XjAivqxIkTDB06lM2bN/vlfBfr2LFjxWvTWa1WTWsRQpxNWuSEEOIiXHrppbz88sv4fD7cbjeDBg3yW4gLFu+++y7Tpk3jxRdflBAnRJCSFjkhhBBCiBAlkx2EEEIIIUKUBDkhhBBCiBAlQU4IIYQQIkRJkBNCCCGECFFVctZqZmY+Pp/M4RBCCCFE6NPpFGJiws55W5UMcj6fKkFOCCGEEFWedK0KIYQQQoQoCXJCCCGEECFKgpwQQgghRIiSICeEEEIIEaIkyAkhhBBChCgJckIIIYQQIUqCnBBCCCE053a7SUtL1bqMkCNBTgghhBCa++KLT3j88X+Rk5OjdSkhRYKcEEIIITS3bt0aAOz2fI0rCS0S5IQQQggRNDwej9YlhJSgCHJ5eXlce+21nDhxAoDVq1czdOhQBgwYwNtvv61xdUIIIYQIFK9Xglx5aB7ktm7dys0338yRI0cAcDgcPP3003z00UfMmzePHTt2sGzZMm2LFEIIIURAuN1urUsIKZoHuWnTpjFhwgQSEhIA2LZtGw0bNqR+/foYDAaGDh3KggULNK5SCCGEEJVJURRAglx5GbQu4JVXXilxnJKSQnx8fPFxQkICycnJgS5LCCGEEBpwuVxalxBSNA9yf+fz+YpTOYCqqiWOyyIuLtzfZQkhhBCiEqmqCoDVqic+PkLjakJH0AW5WrVqkZr614KAqampxd2uZZWenofPp/q7NCGEEEJUstTULFJTc7UuI6jodMp5G6k0HyP3d23btuXw4cMcPXoUr9fLr7/+So8ePbQuSwghhBAB4HA4tC4hpARdi5zZbOb111/n4Ycfxul00rNnTwYNGqR1WUIIIYQIAAly5RM0QW7JkiXF33fp0oVffvlFw2qEEEIIEShnLgLscBRoWEnoCbquVSGEEEJUL2eGt4ICu4aVhB4JckIIIYTQlN3+V3grKJAWufKQICeEEEIITdnt+cXf5+fnX+Ce4u8kyAkhhBBCU0XhTQfk5+dpW0yIkSAnhBBCCE0VBbkonZ78PFlDrjwkyAkhhBBCU3mnw1u0Xk9ergS58pAgJ4QQQghNnRnk8gvs+Hw+jSsKHRLkhBBCCKGp3NwczDodNkWHqqrk5ck4ubKSICeEEEIITeXm5mDV6bHqdMXHomwkyAkhhBBCUzk5OVgAq6IAEuTKQ4KcEEIIITSVnZWJVVGKW+RycrI1rih0SJATQgghhKZyc3KwKoVj5ACysyXIlZUEOSGEEEJoxuPxkGfPx6bTYVEUFKRFrjwkyAkhhBBCM0Xj4Ww6BUVRsOkN0iJXDhLkhBBCCKGZ7OwsgOJuVauiSItcORi0LkAIIYQQ1VdR61uq14PLqWIDsjIztC0qhEiQE0IIIYRmilrkjrvdnFTcROr0nDz9M1E66VoVQgghhGaKgpz+9LFNpyM3N1e26SojCXJCCCGE0Ex2dhZmnZ7TawFjU3T4VJ9s01VGEuSEEEIIoZns7CxsOqX42KbTFf9clE6CnBBCCCE0k5WVhRUJchdLgpwQQgghNJOdlUmY7q848tfuDlkaVRRaJMgJIYQQQhOqqpKdnV0c3kBa5MpLgpwQQgghNFFQUIDb4y4xRs6oKBh1OrKysrQrLIRIkBNCCCGEJrKzMwFKtMgBhOl00iJXRhLkhBBCCKGJolY3m65kHLGikJWVqUFFoUeCnBBCCCE0UdTqFva3IBemKLJNVxlJkBNCCCGEJjJPh7W/t8iF6fRkZWWhqqoWZYUUCXJCCCGE0ERmZiZGnQ7TGevIQWELndvjxm7P16iy0CFBTgghhBCayMzMIFynR1FKBrnw0y10GRnSvVoaCXJCCCGE0ER6Wirh5/h50Zi59PS0wBYUgiTICSGEEEIT6empxa1vZ4rQ6U/fLkGuNBLkhBBCCBFwDoeD3Ly84tB2JpuiYFAUUlNTNKgstEiQE0IIIUTAFYW0KP3ZQU5RFCL1BlJTkwNdVsgJ2iA3e/ZshgwZwpAhQ/jvf/+rdTlCCCGE8KPk5FMARJ2jRQ4gUlE4dTIpkCWFpKAMcgUFBbzyyitMnjyZ2bNns2HDBlavXq11WUIIIYTwk5OnQ1r0OVrkAGJ0elJSUvB6vYEsK+QEZZDzer34fD4KCgrweDx4PB7MZrPWZQkhhBDCTxITjxOhN2D829IjRWL0erw+b3HLnTg3g9YFnEt4eDj/+te/GDx4MFarlU6dOtGhQ4cyPz4u7lyTmYUQQggRLJISjxF3jhmrReL0hRElOzuFtm2bB6qskBOUQW7Pnj1Mnz6dP/74g4iICB577DG++OIL7r777jI9Pj09D59PtvUQQgghgpHD4SAxKYkOZst57xOj16NXFLZv302LFu0DWF3w0emU8zZSBWXX6sqVK+nSpQtxcXGYTCZGjhzJunXrtC5LCCGEEH5w9OhhVFWlpsF43vvoFYU4vYHDhw8GsLLQE5RBrnnz5qxevRq73Y6qqixZsoTWrVtrXZYQQggh/ODAgf0AJOgv3DFYU6/n8KEDeDyeQJQVkoKya7Vbt27s2rWLkSNHYjQaad26Nffee6/WZQkhhBDCD/bu3UWswYD1AmPkAGobjGzPd3D06GGaNLksQNWFlqAMcgD33nuvhDchhBCiivF4POzft4dLS2mNg8IgB7Br104JcucRlF2rQgghhKia9u/fi9Plov4FxscVsel0xBuM7Ni+pfILC1ES5IQQQggRMFu3bkKvKNQzmsp0/wYGAwcP7icvL7eSKwtNEuSEEEIIERCqqrJh/Z/UMxjPuxDw3zU2mvGpKps3b6zk6kKTBDkhhBBCBMShQwfJyMzgkjK2xgHU0OuJ1BtYv25NJVYWuiTICSGEECIg1qxZgUFRaGwqe5BTFIVLjUZ27tpBVlZmJVYXmiTICSGEEKLSud0u/ly7ikYGI2alfPGjqcmMqqqsWbOykqoLXRLkqomcnBz+/e/7WbFiqcaVCCGEqI42bFhHvt1Oiwtsy3U+MXoDtQ1Glv7xOz6frxKqC10S5KqJtLQUcnKymTdvjtaliBCSmprCW2+9xokTx7QuRQgR4hb/vpAovYG6ZVh25Fxami2kpqWyY8c2P1cW2iTIVRNOpxMAVZVPMqLstmzZxM6d21mxYpnWpQghQtiBA/s4dPggrU1mlDLOVv27S4wmwvR6Fi6c6+fqQpsEuWrC4XBoXYIIQaqqAuDzeTWuRAgRyhbM/xWzTkfzi+hWLaJXFFqZzOzevZNjx474r7gQ55cgl5kps0iCnd2er3UJIgRJC64QoqISE0+wafMGWpnMZV477nxamiyYdDrmzJnln+KqAL8EuSFDhvDoo4+yYcMGf5xOVIL8/DwAVI3rEKHFbrcDyOBiIcRFmzNnJkadjjZma4XPZdbpaGUys2njOhITT/ihutDnlyC3ZMkSrr76at544w2GDh3KlClTyMvL88ephZ8U/f/weqSLTJRd0QeAggK7xpUIIULRiRPHWL9uDa1MZiw6/4zmamu2YtTpmDXrJ7+cL9T55V/VYrEwatQopk2bxrPPPsuXX35J9+7defHFF6XbNUgU7VEnXayiPIo+AOTmyh6HQojymzHjJ0w6He390BpXxKLT0cZkZuPG9Rw5cshv5w1VfpvssHz5ch5++GHGjRtHv379+PHHH6lduzYPPPCAvy4hKqDoDdnhKMDrlVY5UTZFHwByc3M0rkQIEWr279/Lli0baWuyYPZTa1yRNhYrFp2en6Z9Xzwpq7oy+OMkvXv3Jjo6mltuuYX//e9/WCyFs1KaNWvG1KlT/XEJUUFntsTZ7flERERqWI0IFXmnA1yeBDkhRDmoqsq0aVMI0+tpY/Ffa1wRs6LjCrOFVXt2sWPHNlq3buv3a4QKvwS5N954g06dOpX42YEDB7j00ktZvHixPy4hKqigoKDE9xLkRFkUtcjl50uXvBCi7DZuXMfBgwfoaQuv8EzV82lptrDd5WTq1O9o2bI1Oj+3+oWKCv3WWVlZZGVl8fLLL5OdnV18nJaWxkMPPeSvGoUfFC0IDOByuTSsRISSog8ATpdLZq6KMnO73Rw/fkyeM9WU2+1m2tQpxBkMNDeZK+06ekWhs8VKUlJitd5+skItco8++iirVq0C4KqrrvrrpAYDAwcOrFhlwq88Hg8oOlB9eL0ercsRIcLpcqFXwKsWLipts9m0LkmEgGnTvmfx4oU89NA4OnToVPoDRJWyePFC0tLTuDY8El0ltcYVucRoopbByMzpU7nyyi5Yrf7vxg12FQpyX3zxBQBPPfUUr732ml8KEpVDRQUq9w9KVC1erxefz0eYUUe+24fH49a6JBEikpNPAZCZmaFxJSLQcnNzmPPLDBoYTdQ3mir9eoqicLXVxozcbObNm82oUaMr/ZrBpkJB7uDBgzRp0oTbbruNnTt3nnV7y5YtK3J64Uc6nR5Or9J/sfvcieqlqFvMoCt5LETpCmcRytjK6mf27Ok4HA66REYH7Jo1DUYuM5lZuGAePXv2pUaN+IBdOxhUKMi98cYbTJo0iYcffvis2xRFkYkOQcRgMFD04mowGLUtRoSEosDvU0seC1GaogAn6w9WL0lJiSz943cuN1uI1ftlLmWZXWW1cTgni+nTf+S++87OJFVZhf6lJ02aBMDTTz9Nv379/FKQqBxm019N3CZT5Td3i9Cn1+sBcHqLPgAE9oVZhK6s7MKF4LOzs7QtRATUzz//gEFR6GQJ/FjaCJ2eNmYLf/65hgEDhtC48SUBr0Erfpmr+/bbb/vjNKISnRnejAEYtyBCn6IomE0mXKeDnNls0bgiEQp8Ph/Z2dkAZGSma1yNCJS9e3ezZcsm2pssWDVaBqS9xYpVp2fa1O+q1SLBfvmI3bRpUz7++GM6duxYYlabjJELHkaj8YzvpWVFlI3VasXpcmE0GqVFTpRJVlYmvtO7x6SlpWpcjQgEVVX5+ecfCNPraV0Ji/+Wlen0IsEr9+1h587ttGrVRrNaAskvr8xbt25l69at/PTTXxvYyhi54HLmm3BRl5kQpbHZwsjKziZMlh0RZVQ0Y9VQw0JOWjZOp0Nac6u47du3cPDgAXrYwipt8d+yutxsYZvLyfSff6Rly9bVYmyvX4LckiVL/HEaETBV/4kt/CM8IhJOJhEWHq51KSJEnDyZBICpThieNAcnTybRqFH1Ga9U3aiqysyZPxGpN9DcpH1g1ysKV5gt/HHsCFu3bqJduyu0LqnS+SXIZWRk8Msvv5Cfn4+qqvh8Po4ePcpbb73lj9MLP/B4/loE2Hu620OI0kRERAAQHh6hcSUiVJw4cQydSY+pphU7cPz4MQlyVdiOHds4evQIvWzh6IOk9esyk5mNTge/zJ5B27YdqnyrnF9GJP773/9m9erVTJ8+nVOnTjFr1qxqu+dZsDpziy6n06FhJSKU2GxhgAQ5UXaHDh9EH2VCF25EZ9Rz5MghrUsSlWju3NmE6/U0rcStuMpLryi0N1s4cvQwe/bs0rqcSueXtJWUlMSnn35Kjx49uO222/jhhx84dEj+eINJbl5e8fd5Z3wvxIWEhRUGOas1TONKRChwOh0cP34MQ4wZRVHQR5vYf2Cf1mWJSnLkyCH27dtDa5O5wq1xqqqS7/OR6fWy01lQ4VmnTU1mrDo9CxfOrdB5QoFfglyNGjUAaNSoEfv27aNmzZoluvKE9rKyMtGZowBZ20mUneX0DDSzWZasEaU7cGA/qs+HoUbhWClDDQuJJ46Tny8fHquixYt/w6jT0cIPk1l2uhxk+3wUqCrL7fnsdFWs58igKLQ0mdm2bUvxBJyqyi9BLi4ujs8//5xWrVoxffp0lixZgsMh3XfBwul0UGDPR28tDNwZGbK2kyibM5etEaI0u3btQFEUjHGFb+zGeAuqqlaL7q3qxm7PZ92fa7jMYMKsVDxKHHW5Lnh8MVqYzSjAihVLK3yuYOaXIPfSSy9hMpno2LEjrVq14r333uOxxx7zx6mFH6SlpQGgt9UARZG1nUSZ6U9vs1PVBwsL/9i2fTOGODOKsfCtxRBrQWfUs337Vo0rE/72559rcHvcXO6npWU8qBc8vhjhOj0NjCZWrlhapfeK9luL3O233w7A+PHjmTVrFv3796/QOZcsWcLIkSMZPHgw//nPf/xRZrVVFNx0xgj0pjDS0lI0rkiEmmq0SLq4SGlpqSSeOIGx9l9rDio6BUOChc1bNlbpN9Lq6M8/VxNjMFAjyNclbWYyk5Obw969u7UupdJUaPmR9u3bX/CT+qZNmy7qvMePH2fChAn89NNPxMXFcccdd7Bs2TJ69ux5saVWa0VBTjGGgd5Gaqq0yImy8fmKlqqRJCcubOPGdQCYapecGGOqE0bu+hQOHNhH06bNtShN+Fl2djb79+3hCost6FvrGxhNGHU6Nmz4kxYtquZuUxUKcr/++qu/6ihh0aJFXHPNNdSqVQso3MvVbA6eqc2hJisrA0XRoRgsKEYb6TJGTpSRyw/jVET18Oe6NRiizejDS46rNNWyoegV1q9fK0GuitixYysq0CgE9u02Kgr19Aa2btnEbbeNDfrgeTEq1LVat25d6tatS1ZW1jn/u1hHjx7F6/Xyz3/+k+uuu47vv/+eqKioipRareXk5KAzWFAUBUVvJi8vV+uSRIiw2+1AyXUIhfi75ORTHDl8CFO9wtY459FcnEcLX2cUow5jLRtr/1wtqxlUEdu3b8Wm1wd9t2qRBkYTGZkZnDqVpHUplcIvOzs8/PDDxd+73W5SU1Np1aoVP//880Wdz+v1smHDBiZPnozNZuP+++9n5syZjBw5skyPj4uT7YTO5PO5UfSFn5IVvQm32010tEVmJIpSud0FADgc+cTHy6LA4twWLpwNCpjqF772Ok6HOHPDwueMuX44uYnJHD++nyuvvFKzOoV/HNy/lzo6fci0btUxFL7XnTx5lDZtql6rcKXstfrnn38yZ86ciz5fjRo16NKlC7GxsQD069ePbdu2lTnIpafn4fPJmJ4ieXl21NONr4pS+Anq5MlMrFarlmWJEHDyZDIAycnJpKZKS644m8/n47dFizDGW9Fbz/2WYqxlQ282MHfuAho3bhHgCoU/ZWSkk56ZQfMQWiQ8SqfDqtezZct2OnS4WutyLopOp5y3kapS9tG66qqr2Llz50U/vnfv3qxcuZKcnBy8Xi8rVqygZcuqOUgxYELkk5MILqkphQtppqamVnildVE17dq1g8yMDMyNzt9iq+gUjPXD2LJ1Ezk52QGsTvjb8ePHAIg3+KUdKCAURSFO0XH82FGtS6kUfvk/cWZoU1WVHTt2VGhB4LZt23L33Xdzyy234Ha76dq1K6NGjfJHqdWSwWAAtXD2oXr6qyGE/giFNpxOJxmZGdgMCnaHg5ycbKKiorUuSwSZZcuWoDPpz5qt+nfmRhE4DmSzevUKBg26NkDVCX9LSjoBQKwuNMbHFYnV69mdlIjP56tye8H7fYycoijExsbywgsvVOic119/Pddff30FKxMAJpMZipaR8HlRdDoJcqJUSUmJqCq0jLew/mQBJ04clyAnSsjJyWbzlo2YGoej6C/c6m+INGGMs7B02WIGDhwSMuOrRElpaalYdHrMIRaGInV63E4Hubk5Ve51rFLGyIngYrVaUX1uAFSfG7PJLC+iolRHjx4GoH1NK+tPFnDkyGFatmytcVUimKxatRyf13vBbtUzmRpFkLIxmb17d9O8+eWVXJ2oDBkZ6YSFWIgDCD9dc0ZGepULcn75v5Gfn88bb7zBsGHDGDFiBO+//76sPxVErFYrPq8bVVVRfW6sVlvpDxLV3qFDB7AZ9dQONxBnM3Lw4H6tSxJBxOfzsXTpYow1LBgiy7aemLleGDqjnmXLFldydaKy5OXmEorT5Kyng1x+fp7GlfifX4Lcs88+S3JyMk899RTjx4/n4MGDsq1WELFYrKD6Cv/zebDIbFVRBvv27aZ+hAFFUagXrufA/j0y4UEU27t3N6mpKWVujQNQ9DpM9cPYsGGdrGcZouz2fIwh2KNjOl2z3V6gcSX+55eu1V27drFw4cLi486dOzNkyBB/nFr4gbFo9W3Vi+rzYjJJkBMXlpGRTkpKCu0aF053bxRlYmtKDomJx6lXr4HG1YlgsHz56UkOdcu3DIW5cQSOQzmsXr2SAQMGV1J1orJ4PB4MIRjk9BTW7PG4Na7E//zSIpeQkEBGRkbxsd1uJyYmxh+nFn5QNEOnsDXFhz5EVuMW2tmxYxsAl8SYSnwt+rmo3vLz89iwcT2memEo+vK9jRiizBhjLCxfvkRaeIXwA7+0yNWqVYtRo0YxaNAg9Ho9ixcvpkaNGsXdq88++6w/LiMuUtHG54UTHHR4ZZscUYotWzYRZTFQ01b4EhFl1lMzzMjmzRtl6QjB2rWr8Xo8hJejW/VMpobhJG1J5MiRQzRu3MTP1YnKZNAb8IZgAPdRWHNVbMjwS5Br2LAhDRs2LD6WbtXgUrymn86AojNQUFD1xggI/7Hb7ezYsZUrEkwlZje3iDOxbP9eMjMziImJ1bBCobUVK5diiDZjiDZf1ONN9cOxb89g5crlEuRCjNlqwV0JQc6lqlgsFgYMGMBvv/2Gy+3fLlDX6Zotlqo3tMgvQe6hhx4iPz+fnTt34vF4aNOmDeHhst9psMjJyUZnMKEoOhS9mZzcdK1LEkFs/fq1eDweWsdHsiW5MPS3q2mldYKFpcfyWbt2FYMHD9W4SqGVxMQTHDt6BFubuIs+h86ow1jHxpq1Kxk9+jbZ9zmEhIWFk1oJ53WqKgMGDOCee+5BVVWWzp3r9/MDhIWFztZiZeWXILdt2zYeeOABatSogdfrJTk5mU8++YQOHTr44/SiglJSklEMhU9enSkMR5ad/Pw8wsIkbIuSVFVlyZLfSAgzUjfCwKIjhTML29W0Emc10DDKxB9LFjFw4JAqtzq6KJvVq1egKArm+hV7/TA3CCf3+Cm2bt1Ex45X+ak6Udmio2M4WgktcmZF4bfffkNVVRYtWkS4nydU2H0+gCq3hhz4abLDf//7X958801mzZrFnDlzePfdd3n99df9cWpRQaqqcvToURRzFAA6czTw1355Qpxp164dHD9+jM51rOdcNPqqOlbS0tPYsOFPDaoTWvP5fKxavRxDTSs6c8XGGhkTrOitBlavXuGn6kQgxMTEku/1+H2cnElRcDgczJkzB4fDUbxciL/k+XwoQHR0tF/PGwz8tiBw586di4+7dOki47CCRHLyKXJzs9FbC7tB9NbCsU179+7WsiwRhFRVZcb0qURZDLRJsJzzPs3jzMSHGZk16ye8Xm+AKxRa27NnFznZ2ZgbVLw1X1EUjPXC2LZ9q6wpF0ISEmqiArmnW7hCRbbPS0x0zF/LcVUhfglyiqKQmJhYfHzixIkqOTMkFG3ZsgkAQ1gtABS9Gb01jk2bN2pZlghCa9eu4vCRQ/Ssb8OgO/enYZ2i0KeBjVOnTvHHH78HuEKhtbVrV6Ez6jHV9s/uMOb64fi8XtavlxbeUFGrVm0AMr2htfpBps9Hrdp1tS6jUvhljNyDDz7ITTfdRJcuXVAUhZUrVzJhwgR/nFpUgKqqrFy1DL01Dp3pr2UCDBENOH5sMydOHKdevfoaViiCRV5eHlN/nEydCCPtap67Na5I8zgzl0SbmDFjKldc0UlmsFYTLpeL9Rv+xFjbWu61485HH2XCEGlizZqV9O7dzy/nFJWrbt3C94wMr5fGGtdSVj5VJcProX39qrmYuV/+Gvv168e3335L+/btadOmDZMnT2bgwIH+OLWogN27d5KUeAJjdMnp/YaoRig6A7/9Nk+jykSw+f77b8jLy2PopRHoShmboigKQy6NwOt28fXXn8mirtXEtm1bcDocmCo4yeFMRd2rBw7sIz09zW/nFZXHarWSUCOe1BBqkcv0efGqKg0bNtK6lEpR4SA3ZcoU/vOf/3Do0CFuueUWbrvtNpo0kXWBtKaqKjNm/oTOaMMQ2bDEbTqDGUNUY1atWkFy8kmNKhTBYu3aVaxdu4oe9W3UDi/bMhBxVgP9GoaxfftWlixZVMkVimDw55+r0FsMGOP9uw5X0ezXdevW+PW8ovI0uawZyT5vyHyISz69CP4ll1yqcSWVo0JB7rXXXmPOnDmYzWYmTpzI119/7aeyREWtW7eGQwf3Y4y7HEV39nhFU43LQdExdeoUDaoTwSI5+STffPM59SNNdG9QvvWVrqxj5dIYE1OnfsfRo0cqp0ARFAoK7GzduhljHRvKecZPXix9mBFDjIW1a1f59byi8jRpchl2rzdkJjyc9LgJDwsjIaGm1qVUigoFudWrV/Pdd98xfvx4vvrqK+bMmeOvukQF5OXl8cMPk9FbYzFGX3LO++gMVow1WrJlyyY2blwf4ApFMHA6nXz4wdvofB6ubx6JvpzT/RVFYUTTKGx6+OjDidjt+ZVUqdDali2b8Hg8fu1WPZOpno3jx49x6pT0EISCZs1aAJAYAhvQq6pKktdLs+Ytz7mkUlVQoSBnMBgwGArnS9SsWRO3n7fUEBdnypSvycnNxVyzI4pS+L/YnXUYd9bhEvczxTZDb4nhm28+JycnW4tShUZUVWXy5C9JTDzByKYRRF3kmmBhJh3XN48gPT2dzz77CF+IfEIX5bNu3Rr0NiOG2Ivbkqs0prqFAXH9+rWVcn7hX3Xq1CUyIpITbpfWpZQq2+cjz+uhRYuWWpdSafy6NLssOaK91atX8OefqzHFXV68ZhyAO/sQ7uxDJe6rKDrMta8i327n888/ljfhamTp0t9ZvXoFPRuEcVkF35wbRJoY2DicrVs3M3fuL36qUASLwr13t2E8zyLR/qC3GTDGWVi3XsbJhQJFUWjdph0nvF58QT5O7tjpsNmqVRuNK6k8FQpyDoeDXbt2sXPnTnbu3HnWsQisEyeO8803X6C3xReOgSsDvSUaU0I7duzYxvz50jVeHRw4sI/vv/+Wy2LN9CjnuLjzubKOldbxFmbNnMaOHVv9ck4RHLZt24zX6y1uNassxrphJJ44IROwQkTr1u1w+LwkB/ns1aNuFzUTalbZ8XFQwXXknE4nDz30UImfFR0risLixYsrcnpRDnZ7Pu+/PxEveqx1ri7uUi0LY/SleO2pzJgxjYYNG1fpTy7VXXZ2Fh99+DaRJoWRTSNLXWqkrBRFYehlkaQUePnk4/eZ8MKrxMcn+OXcQlsbN65DbzVUWrdqEVOdMOzb0tm0aQODBw+t1GuJimvdug16nZ7DLhe1DWWb7R5oTp+PJK+HgVdcqXUplapCQW7JkiX+qkNUgM/n45NPPiA1LRVrg17ojOVbHkBRFCy1O1HgyuHjj9/j+ef/Q82atSqpWqEVj8fDRx+9Q35eLv9oG43V6N9N7016hZuaR/Lp1iw+eP8tnn7mJczmyn3zF5XL7XaxfftWDHUrr1u1iN5mwBBtZuOm9RLkQoDVaqPF5S05vHsXXVQ1KCcSHHW78KkqHTp01LqUSuXfV3KhiWnTvmfHjq2Ya3bAYLu4VhBFZ8RStxtOt5d33vmfzECsgn788Tv279/H0EvDqRVWOZ+gY60GRjaNON3NL4sFh7rdu3fhcrkw1fZPF3xpjLVtHDp0QCZfhYhOnTqT4/UE7eLAB9wuYqJjaNy4aq9tK0EuxC1btoTffpuHMeYyTDEVW+xQZwrHXKcrKSnJfPTRu7IpehWyfPkfLFnyG13q2miT4N8FXf+uaayZXg3DWLt2NQsXzq3Ua4nKtXXrZhSDDmP8hbdt8xdTLRuosH27jLMMBVdc0Qm9Ts9+l1PrUs7i8Pk47nFz5VVXo9NV7ahTtX+7Km737p1MnvwlhrBamGu298s5DWEJmGt1ZNeuHUyZ8o20qFQBe/bsYvLkL7kk2kS/xpU7YL1Ij/phXF7DzE/Tvmfz5o0BuabwL1VV2bJ1E4Z4i9/2Vi2NPtqE3mpg27bNAbmeqBibLYw2bdtxwOMOutmrB91OfKpK585dtS6l0vnlrzMpKanEfydPniQzM9MfpxbnkZx8kg8+eBvFFIGlbvkmN5TGGH0JprjmLF36O4sX/+a384rAS0w8wQfvTyTWrOOGFlHlXvT3YimKwvCmUdSOMDLpk/c4fPhgQK4r/OfkySQyM9Ix1bQF7JqKomBIsLJ9xzbpEQgRXbp0x+71ciLIFgfe63JRp3YdGjRoWPqdQ5xf3v1vvvlm+vbty7Bhwxg+fDh9+vShe/fudO/enU2bNvnjEuIMdrudd955E6fbi6VudxS9ye/XMMW3xRBelx9+mMzOndv9fn5R+dLT05j41qvofE5uaRmF1RDYBniTXuHmy6MIM6i8PfF1Tp5MCuj1RcUULSNjrFW5XfF/Z6xlxVFQwKFDBwJ6XXFx2rZtT5jNxl6nQ+tSimV6PSR73HTr3isoJ2H4m19e2a+++mpee+01NmzYwLp163jnnXcYOXIkkyZN4rXXXvPHJcRpPp+Pzz7/iOSUZMx1uqIzVU5XmaIoWOp0RmeO5KOP3yMtLbVSriMqR2ZmJv974z8U5OVw6+VRxFi0Waw7wqTntpZRqG4H/3vjP6SkJGtShyi/bdu3YogwobcFdmkJY7wVFNixY1tArysujtFopHOX7hz2uHEEyaLye5xOdIpCly7dtC4lIPwS5Pbs2cPw4cOLjwcOHMiOHTu4/PLLZdsuP1uw4Fe2btmEOaEdhrDKXadL0Rux1O2K0+nmgw/exuMJzplJoqSMjHT++98XycpI49aWUdQO13aNpzirgdtbReG05/Lf11+UBV9DgNPpYO/e3RhqBrY1DkBn0mOMtbBVxsmFjO7de+FVVfYFwaQHr6qy1+OiTdsOREVFa11OQPglyHk8Hvbt21d8vG/fPnw+H06nU978/ejgwQNMnz4NQ0R9jDGXBeSaOlMEplpXcuzYEaZPnxqQa4qLl5x8itdfe5HsjDRuaxVF/Uj/d7tfjJphRu44HeZee+1FTpw4pnVJ4gJ2796J1+PBqEGQAzDUtHLs6BGygvCakwAAtq9JREFUs7M0ub4onwYNGtKoYWN2u52aT5A76nZR4PXSs2cfTesIJL8Euccee4wxY8Zw2223ccsttzB27FieeOIJ3nvvPfr16+ePS1R7TqeTzz77CJ3RiqV2p4D2+xsj62GMvpSFC+exd+/ugF1XlM+xY0d47dUJ2HMyub1VNA2CJMQVqRVu5M5W0ajOfF5/7UX279+rdUniPLZs2YTOoMNYQ5sgZ6pVOMFi61ZplQsVPXv1JcPj0XzLrl0uBzHR0dVqhyK/BLmePXuycOFC7rzzTu69917mzZtHly5duP/++/n3v//tj0tUe7/8MoOUlFOYal1ZKZMbSmOu2Q69OZwvv5yEy+UK+PXFhe3cuZ3XX3sRxV3A2DbR1I0Izi1zEsIM3NUmBqvi4c3/vcLGjeu1Lkn8jc/nY/PmDRhqWlH02gwU10eZ0IcZ2bRJnh+h4soru2A2mdh1kZMeDCgXPC6LHK+X42433Xv0Qa/XZlywFvwS5Hw+Hz/99BPffPMNkyZNYvLkyXg8HsLDA7NmVVV3/PgxFiyYizH6Egxh2mz8q+gMmGpeQWpqCr/+OkuTGsS5rVq1nLff/i9RBh//aBNNvK1CO+9VuhiLnrvaRFPTpuOjD9/m998Xal2SOMO+fXvIzc3FVDcwuzmci6IoGGvb2LFzO/n5eZrVIcrOarVyVeeuHHS7cKrln/TQ0GS64HFZ7HE5UCgcs1ed+CXIvfXWW6xdu5Y77riDsWPHsnnzZt544w1/nLra8/l8fPvtlyh6E+aEtprWYgirhSGyIfPnz+HUKRmwrjVVVZkzZxZffPEJDSMNjG0TTaQ5ND6Fhhl13N4qmqZxZr7//humTZuCL0hmvFV3a9euQmfQFXdvasVcLwyf18uGDes0rUOUXc+effCoKvud5Z/00NJkIUqnw6oo9LCF0dJUvt1EfKrKHreLVq3aEBdXo9zXD2V+CXIrVqzgk08+oV+/fgwYMICPP/6Y5cuX++PU/Pe//+XJJ5/0y7lC0Zo1Kzl4cB/G+NYoeu03IDfXbIeKju+++1rzQa3Vmc/n47vvvmLmzGm0jrdwa8toLAFeJ66iTHqFm1pE0am2lQUL5vL55x/L5CiNORwO/vxzNcY6NhSNn0/6GDOGSDPLly/RtA5Rdo0aXUK9evXZ4y7/8BtFUQjT6YjR62lptpZ7HPhxj5t8r5cePfuW+9qhzi9/qaqqYjT+NSbHZDKVOL5Ya9asYebMmRU+T6jKycnhhx8mo7fWwBh1idblAKAzWDHWaM2uXdv588/VWpdTLXk8Hj777CP++ON3rq5rY0SzSAy60Fz0UqcoXNMkgj4Nw1m7dhUffvg27ot4ExD+sWbNCpxOJ+ZLIi/6HKqq4ivw4M114TiUc9Ef+BRFwdQonMOHD3HkyKGLrkcEjqIo9OjRh1SPm7QAfyjb7XQQER5B27b+2a4ylPglyDVv3pxXX32VY8eOcfz4cV577TWaNm1aoXNmZWXx9ttv889//tMfJYYcVVWZPPlLCgoKMAd4lmppjDGXorfG8d13X5OVJVuxBZLX6+XTTz/gzz9X07dROAMuiUAXRM+Ni6EoCj0ahDGkSQTbtm7m/fcnSpjTgM/nY/6CXzHGWDDEXHzrv/NwLr58D6rTR/6WNJyHcy/6XOaGEeiMeubP//WizyECq3Pnruj1eva6ArfTQ4HPx1GPm6u79sBgCO4xwpXBL0FuwoQJ5OTkMHr0aG688UYyMjJ47rnnKnTO559/nnHjxhEZefGfDEPZsmVL2LhxHcYardCbo7QupwRF0WGufSUFDieffvaR7IkYIKqq8tVXn7JhwzoGNA6ne/3KHYyuqiq5Ti9pdg/rT9orvSu9Ux0bQy+LZMeObXz88fvyvAqwNWtWkpaairlpVIU+OLpO5l/wuDx0Rh2mxhGs37CWEyeOX/R5ROCEh4fTrl0H9rvdeAM0/Oagy4lPVenatXtArhds/BJdw8PDef3110v8bP/+/URHR1/U+X766Sdq165Nly5dmDFjRrkfHxcX2rNlt2/fznfffYUhvBamuOZal3NOenMU5pod2LN7HTNn/sA///nPoGo1rIomT57M6tUr6NUgjKvrVf6Mwg0nC8hwFE5AmHsgF9TCsFWZOtSy4vapzN+ykRkzvueBBx6o1OuJQg6Hg1mzf8IYY8ZUwf/Hqle94HF5WS+LwnU4l9mzp/HCCy9U6FwiMAYNGsDGjetJ9LhpYKz85bL2u100bNiQ9u1bVvq1glGltUHedNNNbNq06aIeO2/ePFJTU7nuuuvIzs7Gbrfz6quv8vTTT5fp8enpefh8oTkQf//+vbz11usoxggsda5GUYJ3ALsx+hK8zmzmzZsHGBg16iYJc5Vk/fq1TJs2jQ61rPRsEJhlIfZmOM86ruwgB3BVHRvZTi/z588nPr4OvXpVv8HLgTZ9+lTS09KJ7FE76P6GdWY9luZRbNy4kd9++4P27TtqXZIoRYMGTf+fvfsOj6JcGzj8m9m+2U2vQGih996RJk2kKohiL+gR9RzrUY9+Yu8FxAKCeETFBtIREAi9995bIJBet+/O90eAI0oJkM3sbt77unLBbnZnnk0mM8+85XkxGY0ccDn9nsgVeL2c9ri5pVV7MjOvvRs/0MmydMlGKr8lctfTDTN58uTz/58+fTrr168vdRIXzDZt2sCECZ/hk40Yk7uqUvj3ahnim4HPw7x5sygqKuTOO++rkGMU/CkzM4PJX4+nSriem1Ks5Xahdf/lZuivj/3pxuoWzhR7+eGH/1KrVm2qVKlabvuuaI4dO8K8+bMwVLWotpLDlRhTInAdL+a/306idu16okZpgNPpdLRo2YaNa1biVRQ0fjxnHXaX3HC2bt3Wb/sIdH5r7gm0u7pA5vF4mD79Zz777GN8WivGqt2QdYF5Qv0rSZIwJLZCH1Of5cuX8v77b5KdnaV2WCHD5/Mx8avPUbxubg3i2alXS5YkBtcJxygrTBg/TpQl8ROHw8EXX45FNmgxN4lRO5xLkmSJsBaxFBYWMHnyeFH6KAi0aNEap8/HKY/br/s54naTXCWZ+Hh1iuUHgsDttztryJAhfxt/F0pOnkzjzTdHM2fODLQRNTBV7Y6sDY4k7hxJkjDEN8VYqR2HDh/m5Zf/zapVy8XJtgwsXbqIAwf306emhUhjcBT7LSsWvczNtSyknUxj3rxZaocTcnw+H5MmfUFGxhnCWsUi6wP7+NJGGTA1jGLLlk3Mnz9b7XCEK2jYsDF6nY6jfpyBbvf5OO1x06JlG7/tIxhcVx9Y8+bNL9rypigKDkf5TT0ORjabjVmzprFo0QIkjQ5j5Y7owpP9si9FUfC57eBz48o9iC4yxS8tprqI6mhMsTjS1zFp0pcsW7aEO++8j6pVq5X5viqCnJxsfv31R1KiDDSLv7oq56GiXoyRRnEO5sz+jVat2lKpUmW1QwoZM2b8yqZNGzA3jkYXFxw3j8ZaEXhynfw67UcSEpJo2bK12iEJl6DX66lXvyFHd+1EURS/XHNOnE0SmzRpVubbDibXlcjNmSNq+1wtp9PBkiWLmDN3FnZbMbrIFAxxTZC0/lu1wZ13EMVdsl6h8/RGQEEfVdsv+5L1FkxVu+POP8zhozsYPfpF2rXrwMCBQ0hISPLLPkORoih8M3kCPo+bm2tFV+ihCn1qhnMoL4fJX4/nhRdHI8sB35EQ8BYvXsicOTMwVLNirBVY5Y0uR5IkLC3iKLR5GT/+U55++gXq1q2vdljCJTRu3Izt27dS4PMR4YdF7E943FjCwqhWrUaZbzuYXFciV7myuDsuLZvNRmrqH/y+YB5FhQVoLUmYa3RCY4zy+749haf+9thfiRycrcgemYLOmowrew/r1q9j3bo1tG/fkT59bqZKFf+0PIaS1NTF7Ny1g74pVqIqWJfqX1n0Mn1rhjF930HmzZvFzTcPUjukoLZ8+VK+//4b9ElmwprHBt1NgqSVsbRPoHB5Oh9/8h7PPP0CtWpdXwF6wT8aNCgpB3LS4y7zRE5RFE55PdRr0qzC39yJ6YV+lpWVyZIlC1m6dDFOpwNtWCKmaq3RmuPKLwjFc/nHfiJp9Bjim6KLroMrew9r1q5h9eoVNG7SjN69bqJ+/YZBdxEpD8eOHWXq1G9JiTLQOik4urz8rXGckX3ZTmbM+IXateuKVphrtHTpH0yZ8jW6BDOWNglIQTp5RjZosHZKpHDFaT748G2eevLf1KkTmDU3K7LExEqEW8M55XTSwFC2w0MKfT6KvF7q1WtQptsNRiKR8wNFUdizZxd//LGAbds2oyigDU/GXKl+ubTABRpZa8KY0AIlpiGu3APs2r2XHdu3kphUiZ439qZ9+84YjRVzDNhf5eRkM+aTdwnTwuA64UG//FZZkSSJ/rXDObMtj3GffshLL78uuuqv0vz5s/nll6noEs1Y28YjaYL72JJNWqydEylcWZLMPTbqXzRpUvHW2QxkkiRRu049Dmy9tpqyl5N+djZsnTp1y3zbwaZit0eWscLCAn7/fS4vvPA0H3zwFtt37kIXXY+wWjdjqtyhQiZxfyZpDRjiGmFO6Y8xqS2ZeQ6mTJnMk08+yrffTuL48aNqh6iqvLxc3n/vDezFhdzeIByLXvx5/plRK3NHg3AUj5P33nuDjIwzaocUFHw+Hz/99D2//DIVfZUwrO0SkDShcWyVJHNJECYzduyHrF69Qu2QhL+oXbsuBV4Pxb6yXXLvtMeD0WCkUqUqZbrdYCRa5K6Toijs37+X1NTFbNy0Hq/Hg8YcizGpLdrwqkhyxR7fdDGSrEEXWQNtRHV8jmxcuQdZtjyV1NTFVK9ek65de9CmTfsK1UqXkXGGjz58m7ycLO5sGEFimE7tkAJStEnLXQ0j+HZnPu++8ypPPf0ilSuLE/mluN1uJk36gvXr12JMCcfcJCbkhjPIRg3WzkkUrT3DxIlfkJuby0039Q+5zxmsatSoCUCGx0ONMixxk+nzUqN2nQo/Pg5EInfNCgsLWLVqOampS8jIOI2s0aMJr4EhMgWNMVLt8IKCJEloTLGYTLEo3ha4849w4vRhvvnmK6ZOnUL79h3p2rUHVatWVztUvzp8+CBjx7yP22HjrkYRJIcH/ooeakqy6LinUQTf787n7bdeYdRjT1G/fsVcY/FyiouL+PTTj9i/fy/mRtEYa0eEbHIj62SsHRMp2pTJtGk/kp2dyYgR96Lxw0xJ4epUrVodWZLI8HqoQdlUZ/AqCtkeD61rpJTJ9oKdSOSugqIoHDy4n6VL/2DDxnVnW9/izra+JSPJ4sd5rSSNHn10XXRRdfDZs3HlHWTZ8mWkpi6mRo0UunW7kTZt2qPXh1aSs2bNSiZPnoBVB3c1iSTOLI6h0ki06Li/SRQ/7M7nww/f5o477qFbtxtDNlG5Wjk52Xzw4ducOZOOpXU8huTQX9JKkiUsreKwmbSkpi4mLy+Xhx9+HIPBf6WdhCszGAwkJlUiKyOjzLaZ4/XiQ6F69YpdduQccdUoBY/Hw/r1a1iwcD4njh9F1uhE65ufSJKExhyLyRyL4nXhzj/C8VOH+Prr8fz443d0796Tbt16EhUV3OMNvV4vv/wylYUL51EtQs+w+hGE6UQXwdWIMmp4oEkk0/YV8N13kzl27Ah33nkfOl3F7pY+deok73/wJoXFhVg7JKKLrzgznyVJIqxRNBqThq1bN/PBB2/yr389R1hY6CeygaxatRpsP3O6zLaX6S2pvBDqvTWlJRK5y3C5XKSm/sHcubMpLMxHY4jAkNgKXUQ1JLliXyzKw59b6by2DFw5+5kzZwbz5s2iffvO9O8/KCjX17PZivn88zHs3r2TNpVM9K5hRROAZSCcHh9Go5FevXqxcOFCnH5eM/FaGLUytzeIYOmxYlasSOXUqTQef/xpwsODp8htWTp27Cjvf/AmTq8Ta+dEtJEVszXKmBKBZNRweMMh3nn3NZ595j8V9pgIBFWrVmPNmpXYfT5MZTCmLdvrwWgwEBtbjmW8AphI5C7C5/ORmrqYmTOnlyRwYfGYkrugCUsUXTcqkCQJbVgC2rAEfK4iXDn7WL1mJWvWrKBjxxu45ZbbguYknZ2dxYcfvkXmmTMMqB1Oi8TAbS1xeBR69e7FQw89hKIorFg4V+2QLkqWJHpUt5AYpmXGgcO8/tp/eOrpF0lKqqR2aOXq+PGjvPf+G7hwY70hCY2lYt9sGipbkLQyp9ae4r333uC5514mPDxc7bAqpHMtZ9leD1Xk6x8ek+31UqVadTHR4SzxU/iLtLTjvPnmK3z33WRsXgOmat0xV+2O1pIUtEmc4nVjNBoZMGAARqMRxRt4LSulJestGBNbYq55M5qIWqxcuZwXXniaFStSURRF7fAuKyPjDG+/NZq8rAzuahQZ0EkcgFErsXDhQiZMmMCiRYswagP7+G8YZ+S+xpE4ivJ55+1XSUs7oXZI5SYzM4MPPny7JInrnKhqEqe4fReeb9w+1WLRJ5ixtk8g/Uw6n3zyHk6nU7VYKrJzq/lkea+/GL2iKGT7vCSLbtXzRCL3J7t27eC111/m2ImTGCu1w1S1G1pzvNphXTfF56ZXr5KWlZ49e6L4gjeRO0fWmTAmtsBUozcuKYzJkyfwww/fBmwy53A4GDvmfexF+dzdOJLqkYE/acOglXE4HMyePRuHw4FBG/ini0pWHfc2jgS3jTFj3qOoqFDtkPzO5XIxZsz72Jw2rB0T0KhcukZx+y4836iYyAHo4k1YWsdx9GjJjPhAPUeEMqs1nIjwCLK9119LrtDnw+XziaUe/yTwz8zlJC3tOJ+MeR9FE4apRh90EdWDtgXuryRZd0HLSiiN79MYIjBV7Y4uui6LFy9g1qzpaod0UTNnTiP99ClurWulUgXv8vK3OLOW2+pFkJebw08//aB2OH73yy9TOXXqJGGt49BY1b9BkHTyheebAJjEo68UhqlBFOvWrWbt2lVqh1MhJVetRnYZFAXOPj/Rodp1bytUqP8XFiD++GMBPh8Yk7sia0OrEK2k0V3QsiJpQiuRkCQJQ3wztJbKLFz0O263S+2QLuByuVi2bDENY42kRFXMweflrUq4jhYJRtatXUlRUZHa4fhNevpJlixdiKFmOPoEs9rhACWJ3AXnmwBI5ABMdSPRRhn56efvRRerCpKTq5Hr9eK9zhbRbK8XCUSL3J8Exl9YAMjMzETWhYVcEldRSJKEbIrGbivGbrerHc4FiouLcDgcVI8IrQQ60FWP0OPxesnLy1U7FL9ZsmQRSBLm+sFdjqc8SJKEuWEUBfn5bN68Qe1wKpyqVavhUxRyr7N7NcvrIT4+AYNBXKvPEYncWY0bN8XrzMeVe0jtUIRr4HMW4Mk7RNWq1QNuBqvRaEKr0ZBedP0DfYXSSy92I0mEdA2xLVs3oY03IhvECgaloY0zojHp2OqHRdyFyzs3c/V6JzxkKz6qVhOFgP9MJHJn9ejRk0aNmuI8vQHHmS1BPbOzIlEUBXfBcewnlmIyaHjwwUfUDulvTCYTHTt1YWuGg8O5okunPKQVuFl/ykGLFq2Dvnj0pXi9XnJzctCKJd1KTZIkZKuWzMwzaodS4SQkJKLX668rkXP6fBR4PKIQ8F+IRO4snU7P448/RZcu3XHn7Md2ZB6u3AMoPtGKEogURcFTfAb78aU4Tq6mcmI8Lzz/f1SpUlXt0C7qlluGk5hYial7Ctid5VA7nFLR/aVI8V8fB6oDOU6+251HRFQ0d9xxr9rh+I0sy2g0GhSvmIV5NRSvgl4s21XuZFmmanI1Mq+ja/Xcig5iaa4LiUTuT3Q6Hffc8yAvv/wa1ZMr4zy9Cduh2TgztuFzF6sd3rWTtJd/HEQUnwd33mHsRxdiP74Uo2Tj7rsf4JVX3qRSpcpqh3dJFouFZ559icrJ1fh5Tz4z9udTrHJZhiupG2247ONA4/D4mHewgO935REdm8izz70csq1xUNK6VKVKVTxBcmMQCBS3D2+ei+QAveELddVr1CTL68V3jRMeziVy1apVL8Oogl/wXtH9qEaNFP7zn1c5cGAfixbNZ/Pmjbiy96A1x6OJqIbOmoykCZ7uDK21Et7i9AseBxNF8eG1ZeLOP4qvKA2f101SUmV6976Vdu06otcHx+8iIiKCF198lVmzpjN37kz2ZrvonGymTSVzQLZ2tUoyseZkMU6vQtdqFloFaAFjr09h02k7qSds2FxeevTozdChtwfNcXE92rfvyNSpU3Bn2tHFBebvJ5A4DhegeH20a9dR7VAqpOrVa+JRfOT6vMRorj79yPR4iI2OwWKx+iG64CUSuUuQJIk6depRp049srIyWb16BatWrSAzfQOuM5vRmBPRhiejtVQK+KROF1kLV/Y+8LnRxzVGF5midkhXVJK8ZeEpPIGv6CRetw2DwUi79h3p0KETdevWD8o6f1qtliFDhtG2bQd++eUHFm3fyppTdtolmWiVZMIYQEV3JUnCatBgBVonBUZpiz9zeRU2n7az5pSdfIeHenXrM+y2EVSvXlPt0MpNly49WLBwHgVbs7F2rYQcIKU+ApGnwIVjXx7NmrUgJaW22uFUSDVqlPxtZno815bI+XzUqVmrrMMKeiKRK4XY2DgGDBhC//6DOXLkEGvXrmLDhvXkn1qLJMlozAlorFXQWishawPvrliSJGSdCTChjwrcPwLF58VrO4On8CTe4pP43A60Wh1NGjelbdsONGvWImRaWSpXrsK//vUce/fuZs6cGfyxeycr02w0TzDSppKZKKOYhXgp+U4vG9PtbDrjwObyUrtWHe67eRCNGzcNyuT+euj1eh568FHee/9NitZnYG2fgKRy666kkS77WA0+h4fitRmYTWHceed9aodTYSUkJGE0GDnj8VDvKkdq2H0+CrweatYM/IaI8iYSuasgSRI1a9aiZs1aDB9+F0eOHGLjxvVs2LCOnNMbcJ4GjSkWjaUyOmtlZINYoPlKFK8LT9EpPIUn8dlO4/O60esNtGzWjFat2tK4cTOMxtCtF1SvXgPq1WvA0aOHmT9/Nus2rWftSRu1ow20TjKREqVHrmDJycUoisLRfDcb0m3szXaiKNC0WQv69LmZOnXqqR2equrWrc/dd93Pf/87kaK1Z7C0jUfSqNcyp08Kw33GfsFjNfnsHgpXnkZyKTzxzNNER8eoGk9FJssyNWqmkHlg/1W/94ynZHxcjRoikfsrkchdI1mWSUmpTUpKbYYNu4O0tBNs2bKRTZs3cuL4NlyZ29AYwtFYKqG1VEE2xVS41oJL8bmL8RSexFN0Eq8tExQfFms4LTt1pnnzltSv3xCdLjRa3kqrevWa/OMf/yQ3N4fU1MWkLv2D73flEWnU0iLBQLMEE+EVsFZYscvH1gw7m884yLZ5MJvM9Op9M9279yQ2Nk7t8AJGly7d8fl8TJnyNYUrTmNpF49sVOf0bqhhxX4gD8Xjw1w/GkMN9cYzefKcFK3JQOOTefLJf1OrVh3VYhFK1KyZwr49u/EoCtqruCZmeN3IkiRmrF6ESOTKgCRJJCdXJTm5KgMGDCE7O4utWzexefMm9u3bjSt7L7LOhCasElprMpqweCSpYo1l8ToL8BSewFt0Eq89B4DExEq06HIzzZu3pEaNFGS5Yv1MLiYqKprBg4fSv/9gNm/eSGrqHyzZu5vU48XUjjLQItFErWg9mhC+KfApCkfyXGw6bWdftguvolArpTaDu91Iq1ZtQ6Z7vax163Yj4eERTPhqHAVL0wlrHYsutvyHepSsslJyaTHWVKdXQlEUnMcKsW3LITI8kn/96zmSk8VM1UBQs2ZtfChkej0kaUu/2s0Zj4dKlauIFR0uQiRyfhATE0uPHr3p0aM3Nlsx27dvY/PmDWzfvhV73iFkrQE5LAldeDU0YQkhm9R5nfl4Co7jLUzD68wHoEaNWrRq1ZvmzVuRmJikcoSBS6vV0qZNO9q0acfp0+msWJHKqpWpTN2dh9WgoVm8kRaJppAaS1fg9LLljJ0tZ5zkOTyEmc10v7EPnTt3FesqllLLlq35T9xrfPb5x2SuSMdULwpT3UjVx82VJ5/LS/HWLFxpxdSr34CHRz5ORERgrfZSkdU8O1nhjMdd6kROURQyfF46iBbVixKJnJ+ZzWG0a9eBdu064HK52LVrO5s2bWDz5o3YTxxF1hrRWKqgjaiGxhQb9N2vPncx7vzjeAuP43XkIkkStevUo3WrQWer7EerHWLQSUxMYujQ2xk8eCjbt29h2bIlrNy5nZUniqkRqadVkom60QY0QXix9ikKB3JKWt8O5JaMfatXrwG3delOixatKlwXe1moWrUao195iylTJrN27So8p+2YW8ZWiBUgXGds2DZn43N6GTToVm6+eZBo6Q8wERERxEbHcKawsNTvyfV5cfl855NA4UIikStHer2e5s1b0bx5K9xuNzt3bmPt2tVs2boJe97BkjF14TXQRVYPyNmvl6L4vHiKTuLOO4y3+DRQMiC1Xbv+tGnTjoiISHUDDBFarZYWLVrTokVrcnKyWbEileXLl/DznlwsBg0tzrbSRQZBK12hy8vm03Y2n3GS7/AQHh7OTTf1pnPnbsTHJ6gdXtAzmcyMHDmK5s1b8t//TqRgyUlM9SMx1g7N1jmfy4ttRw7OY4UkJCYy8qFRYlB8AEupXZddG9eX+vXnJjqIsjEXJxI5leh0uvNJncPhYNOm9SxbvpSDB7bhytqO1lIFfXRdNOZYtUO9JJ/HjjvnAJ78w/g8DiIjo+jcfzAdO94gLsZ+Fh0dw8CBt9C//2B27NhG6tI/WLFjCyvSiqkXbaBtZTPVwnUB18KbVuhm3Ukbu7OceBWFBg0acWe3G2natAVarTgdlbXWrdtRp059vvtuMps2rceVZiOsRSzaqMBepaO0FEXBdaoY+7YcfC4vN900gIEDh4iW3ACXklKbdetWU+TzYpGvfON5xuPGZDSRkJBYDtEFH3HmDABGo5GOHW+gY8cbOH06neXLl5C6bCm2Y3+gMcWgi6mP1lI5YC7KPmcBzuzdeAuOoygKzZo1p1u3njRs2Fh0Y5QzWZZp2rQ5TZs2Jysrk9TUxSxL/YM923NJtOjoWNlEgzijqpMjfIrCvmwnq07aSStwYTQa6X5jH7p3v5GEBDFO0t8iIiIYNepfbNq0gSnffU1+6kmMKeGYG0QjBVAB6qvltXmwbcvClW4jObkq9903skIVgw5mKSnnxsl5sOhLkcj5fKTUqSuuL5cgErkAk5iYxLBhIxgw4BZWrVrOggXzyEpbicYUjT6mERpLkmoJnc9VhDNrJ578Y+h0Orp3v5Ebb+wtLsYBIjY2jltvHc6AAUNYu3YVC36fw7R96Sw5bqNDZRPNE0xoy7Fbzaso7MhwsDLNTpbNTVxsLCNG9KNDhxswmYJn6ECoaNmyNfXrN+TXX38kddkfuE/ZMTeNQR+Aq3ZcjqIoOA4V4Nidi0bWMGzYHfTs2ReNJvCHFAglkpOrodNqOe1xk6K/fOuwS/GR43HTWXSrXpJI5AKU0WikR49edO3ag7VrVzFj5jSy05ajDUvEkNCiXIsNKz4PrqzduHP2otFo6NWrL3379hczwQKUXq/nhhu60alTF7Zt28y8ubOYe/Agq9LsdEk20yTBvy10PkVhV6aT1BM2sm1ukpOr8sjdA2nZso242KrMbDZz993306FDJyZ/M4H0NafQVw4jrGmManXnroYn30nx5mw8uQ4aNWrC3Xc/IOoJBiGtVku1ajXIOHb0iq/NEOPjrihg/3LHjRvH/PnzAejSpQvPPfecyhGpQ6PR0LHjDbRt24GlSxcxffov2I78ji6mAfrYBn4vXeIpPoPr9Hq8rmLatevI0KG3i5mnQUKWZZo3b0WzZi3ZtWsH06f/xMwDR1h7yk7fmhaqR5b9OKK0QjfzDxVystBN5UqVeez+YTRv3ipghgUIJWrVqsOro99h/vw5zJo9nfw/TmJqHI2hqiUgf1eKV8G+LxfHvnzMYWHcP/Ix2rZtH5CxCqWTUqsOfxw+iFdRLntjeW6ig1ia69ICMpFbvXo1K1eu5LfffkOSJB588EEWLVpEz5491Q5NNVqtlp49+9K2bQemTp3CunWr8RafwlipPbK+7CunK4oX55ltuHP3Ex+fyP33P13hl0IKVpIk0ahRExo2bMymTev5ceoUvtmRQ+M4I31TrJjLYKF1p8fHoiNFbDxtJzw8nAceeID27TuJMS0BTKvV0r//IFq1as3XkydwaNMBXGnFhDWPRWMOnEuDJ8dR0gpX4KRDh84MH34nFot6q0UIZSMlpRYLFIVsr4f4y9STO+N1k5SYhNms7lJvgSxw/lr/JC4ujueff/58BfeUlBROnTqlclSBITw8gocffoyWLVszefJX2I8uwpDUFq21cpntw+e24Ti5Cq89mx49enHrrbdjMITGLLeKTJKk8+vXzps3i7lzZ3K0IIeBta3Uuo5ZjMfzXUzfX0i+w0OvXjcxcOAtYgxcEElKqswLz7/CkiWL+OXXHyhYfBJz02j0yeq2zik+BfueXOz784mIiOC+f/2TJk2aqRaPULbO1YQ77bl0IqcoCme8PlqJQsCXFZCJXO3a/+sLP3r0KPPnz2fq1Kmlfn9MjMUfYQWUvn1vpHnzRrz11lscObICQ0Jz9NF1r3u7XkcuzrQVaGUfzz7/PB07diyDaIXAYmXkyPvp3v0GPv7oQ77fmUb36hY6VTFf9YV7wykb8w8XERcXxzuvPk2DBg38FLPgb7fffitdu3bk448/Zs/GPejTbYQ1j0UuxazCsuYtclO8IRN3roPu3bvz0EMPYbGE/nm9IomLsxIVGUmGzX7+uXr6C5ffKvD5cPi8NG3aiLg40Qp7KQGZyJ1z4MABHn74YZ577jmqV69e6vdlZxfh8yn+CyxAaDRh/PvfrzB+/Di2bNmIz23HEN/0mu+iPbYMnGkrCLdaeOqpf1OlSlUyM0tffVsILhERCbzw4mt8/fV4Fm9YR77Ty00pVuRSHD+KorD4WBErT9ho3LgpDz/8GGZzmDhegpxWa+Hpp//D/Plz+O23nynIO0VYqzh0MeW3vqXzeCG2rdkY9UYeevRftGrVBrtdwW4Xx1aoqVGzFge3bT3/uO5f1lE943UDEBdXpcKfW2RZumQjVcAOYNm0aRP33nsvTz/9NIMHD1Y7nICl1+sZNepfdOt2I+6cvTjPbEZRrj6J9RSfxnFiOfFxcbz88utUqSIWmK4IDAYjjzzyBH379mdjup25BwtLdfycS+K6dOnOP//5rBi/EkJkWaZfvwG88MJoIozhFC5Px34o/5rOK1dD8SoUbcmkaGMmNWvU4rXX3qVVqzZ+3aegrpo1a1Hg9WD3+S76/QyPB71OR+XKVco5suASkC1y6enpjBo1io8//pj27durHU7Ak2WZO++8D73ewIIFc0GSMcQ3K3XLnMeWgSNtJZWSknj22f8QHl5+pU0E9UmSxK23DkeSJObNm0W0SUPHKpdOzDaftp9P4u6++wExczBEpaTU4rVX32HChM/Yvm0r3lwnYc3jkDRl//v2OTwUrc3AneOgb9/+DBkyTJSqqQDOjZPL8HqoJv99Fn2G10u16jXEsXAFAZnITZo0CafTyTvvvHP+ueHDh3P77berGFVgkySJYcPuwO12s2TJQiStEUNM/fPf10VcvOK515GHM20F8XFxPPvsiyKJq6AkSWLIkGFkZJxm8ab1VAvXUyVcR/OECyctZBR7mHeosGRprTvvE0lciDObw3jiiWeYNWs6s2ZNx1fswdI+oUzHzXkKXBStPoPkhkcf/SetWrUts20Lga1atRpIlLS8VfvLsmpeRSHL66Hp2WRPuLSATOReeuklXnrpJbXDCDqSJHHHHXdTWFjAhg1rkXUWdOHJAOgia/zt9T6PA+fJFVgsYTzzzAuEh4sCvxWZLMvce+9IDh86yMyDhTzSPIpmf0rkFEVh1sFCjCYzDz00StwlVxCyLDNo0K1UqlSZr776nMLl6Vg6JF62RImxWukGpruzHRStOUOYMYwnn3lOLLFVwZhMJhITk8jMzvrb93K8XryKQo0a4pi4koAdIydcG1mWefDBR6hRI6WkkK+z4KKvUxQfzlOrkXwunvzXs8TExJZzpEIgMpvN3DHiHjKL3WxOt1/wvV1ZTtIKXAwdNkKs6lEBtWnTnqeffgGNS6ZoxWm8Ns8lX2uoZsVwhWTOnWWnaNVpYiJjeOk/r4kkroKqXiOFzIuMkcv0lhxfNWqIQsBXIhK5EKTT6XnssScxGQ0409eiKN6/vcaVvRdPcQZ3330/1ar9vbVOqLiaN29FSs1arDppx3t29reiKKxIs1EpqRIdO96gcoSCWurVa8Czz7yI1leSzPnsl07mLsed7aBw9RniYxN48YXRxMXFl3GkQrCoXr0GNq+XYt+F16lMjweT0SSOjVIQiVyIioqK5r77HsJrz8GVve+C7/mcBbizdtKqVVtxURb+RpIk+t7UnzyHhwO5TgCOF7g5U+Smd5+bxWoNFVzNmrV45ukXkd1QuPoMPvfFZxxeirfQRdGaM8RGxfLvf79MRESkfwIVgsK5hoQs74WJXJbPS7Vq1cU43FIQZ+QQ1qJFa5o3b4U7ezc+z/+6yZwZW9Hr9YwYcY/4IxEuqkmT5oRbrWzPcACwPcOBQa+nTZt2KkcmBIKaNWvx+ONP4y10U7Q+o9SlSXwuL0VrMjAbzDzzzIsiiRNITi4pdZXl+V/rrk9RyPF6qCp6i0pFJHIhbtiw20Hxnm+V89pz8BSd4qabBoiTqHBJWq2WFi3bcDDXjdunsC/XRZOmzTEYyq8wrBDYGjZszJ0j7sF9xoZ9b94VX68oCkUbM1HsXp54/GnRZSYAYDKZiYuNI8v7v0Qu3+fFoyjnkzzh8kQiF+ISEpJo1aoN3oLDKD4v7tyD6PUGevTopXZoQoBr1KgJLq+PrWfsFDm9NGrUVO2QhADTteuNtGvXEfveXNw5jsu+1nm4APdpG7fddie1xNqZwp8kV61GjvK/Lvrss92sIpErHZHIVQCdO3fD53HhKTqJt+gErVu3xWw2qx2WEODq1KkHwNqTtgseC8I5kiRx1133ERkZhW1zFsollkb02jzYd+XSsGFjcRMp/E2VKlXJ93jwnO2iz/F6kCSJpKRKKkcWHEQiVwHUq9cAg8GIK3MnPq+b5s1bqh2SEAQsFivRUVFk270YjUbi4xPUDkkIQCaTmbvvegBPgQvHofyLvsa2MxsZmXvueVCMyxX+plKlyihA3tmWuByvl/i4eHS6v6/2IPydSOQqAK1WS+3adfC5SmrK1alT/wrvEIQSlc6ucZiYmCQuwMIlNW3anAYNG+PYX4Dyl1msnjwnrrRi+vS5mdjYOJUiFAJZUlJlAPLOliDJVxSSKon1VUtLJHIVRNWq1QEIj4jEYrGoG4wQNGJjSwakx8WJ1jjh0iRJYsjgoficHhxHLyxCbt+Xh9FopE+ffipFJwS6c639eV4vPkUh3+sR3apXQSRyFcS5RK5q1WrqBiIElcjIKACs1tItuSRUXDVr1qJW7Tq4DhedL0fis3twnbLRpUsPzOYwlSMUApXBYCAqIpJ8n5cinw+vopCQkKh2WEEjINdaFcpe69ZtqVbtQyIjo9UORQgi57rCxBJuQml07dKDiRO/wJPtQBdrwplWBIpCly7d1Q5NCHDxCYkUHD5EwdnuVVGepvREIldBSJJEQkKS2mEIQaZdu45UrVpddHMIpdKiRWs0mgm4TtnQxZpwn7KRnFyVxERx7hEuLzYunpOHDlJ4dt1VMZ6y9ETXqiAIlyTLMlWqJKPRaNQORQgCRqORevUb4Mmw43N5cec4aNZMzJIXriw6OoZir4cCnxeJkmUmhdIRiZwgCIJQZurXa4inwIU73QZKSfkjQbiS6OgYFCDD48FqDUerFR2GpSUSOUEQBKHM1KiRAoDjSAFIEtWr11Q5IiEYnFsyMsvrOT/JSigdkcgJgiAIZebcskqeHCcxMTGYTCaVIxKCQWRkJAAORSEySiRyV0MkcoIgCEKZsVismM4uAZiUKCbJCKVjtYZf9P/ClYlEThAEQShTVaokX/CvIFzJhYmcqFt5NcRoQkEQBKFM/eufz5KdnUWiaJETSkmv/9+6qqJ49NURiZwgCIJQpkwmM1WqVFU7DCFIiUTu6oiuVUEQBEEQAoaYIHN1RCInCIIgCILqEs+uryomO1wdSTm3unEIyc4uwucLuY8lCIIgCCErOzuLM2dOU69eA2RZtDP9mSxLxMRYLvo9kcgJgiAIgiAEsMslciLlFQRBEARBCFIikRMEQRAEQQhSIpETBEEQBEEIUiKREwRBEARBCFIikRMEQRAEQQhSIpETBEEQBEEIUiG5RJcsS2qHIAiCIAiCUCYul9eEZB05QRAEQRCEikB0rQqCIAiCIAQpkcgJgiAIgiAEKZHICYIgCIIgBCmRyAmCIAiCIAQpkcgJgiAIgiAEKZHICYIgCIIgBCmRyAmCIAiCIAQpkcgJgiAIgiAEKZHICYIgCIIgBCmRyAW5cePG0a9fP/r168d7770HwOrVq+nfvz+9evXi448//tt7nnvuOaZPn37+cVpaGiNGjGDgwIHcddddnDx5stziF9RRFsfN9u3bueWWW+jfvz8PP/wwmZmZ5Ra/oI6rOW7++OMPBg4cyIABA3j00UfJz88H4NSpU4wYMYI+ffrwj3/8g+LiYlU+i1B+yuK4OeeTTz7h008/Ldf4A54iBK1Vq1Ypt912m+J0OhWXy6XcfffdyuzZs5UuXboox48fV9xut3L//fcrqampiqIoyunTp5WHH35YadKkiTJt2rTz23nmmWeU77//XlEURfn222+Vp59+WpXPI5SPsjhufD6f0qVLF2XNmjWKoijK3LlzlYcffli1zyT439UcN4WFhUrHjh2V06dPK4qiKJ988ony+uuvK4qiKCNHjlTmzJmjKIqijBs3TnnvvfdU+0yC/5XVcVNQUKC88MILSpMmTZSxY8eq+ZECjmiRC2JxcXE8//zz6PV6dDodKSkpHD16lGrVqpGcnIxWq6V///78/vvvAMyePZsePXrQt2/fC7bj8/koKioCwG63YzQay/2zCOWnLI6b3NxcHA4H7dq1A6Bbt26sXLkSl8ulymcS/O9qjhu3280rr7xCQkICAHXr1iU9PR23282GDRvo3bs3AEOGDDl/nAmhqSyOG4DFixdTvXp17rvvPjU/TkASiVwQq127Ns2aNQPg6NGjzJ8/H0mSiIuLO/+a+Ph4zpw5A8CDDz7I0KFD/7adf/7zn3zzzTd07tyZr7/+moceeqhc4hfUURbHTVRUFGazmZUrVwIwd+5c3G43ubm55fMhhHJ3NcdNVFQUPXv2BMDhcDBhwgRuvPFGcnNzsVgsaLVaoOQif+44E0JTWRw3AIMGDWLkyJFoNJpy/wyBTiRyIeDAgQPcf//9PPfccyQnJyNJ0vnvKYpyweOL+fe//81rr73GihUrePXVV3nsscdQFMXfYQsqu57jRpIkxo4dy/jx4xk0aBCFhYVERkai0+nKI3RBRVdz3BQWFjJy5Ejq1avH4MGDL3pcXen8JISG6zluhMsTiVyQ27RpE/feey9PP/00gwcPJjEx8YJB55mZmcTHx1/y/Tk5ORw+fPj8XU/v3r3JzMwULSsh7nqPGwCtVsuUKVOYMWMGAwYMwOfzERkZ6efIBTVdzXGTkZHBHXfcQd26dXnzzTcBiI6OprCwEK/X+7fXC6Hreo8b4fJEIhfE0tPTGTVqFB988AH9+vUDoGnTphw5coRjx47h9XqZM2cON9xwwyW3ERUVhcFgYOPGjUDJH1xYWBjR0dHl8hmE8lcWxw3Aiy++yPbt2wGYPHkyffr0QZbFKSVUXc1x4/V6eeSRR+jbty//+c9/zre26HQ6WrVqxbx58wCYMWPGFY8zIbiVxXEjXJ5W7QCEazdp0iScTifvvPPO+eeGDx/OO++8w+OPP47T6aRLly706dPnktuQJIlx48bx+uuv43A4CAsLE1O7Q1xZHDcAo0eP5pVXXsFut4u75wrgao6bP/74g927d+P1elmwYAEAjRo14s033+SVV17h+eef54svviApKYmPPvpIrY8klIOyOm6ES5MUMRhKEARBEAQhKIl+EEEQBEEQhCAlEjlBEARBEIQgJRI5QRAEQRCEICUSOUEQBEEQhCAlEjlBEARBEIQgJRI5QRCEs1599VWGDRt2vmAtgNfrZfjw4Xz88ccqRiYIgnBxIpETBEE46/nnn8dutzN+/Pjzz40fPx6NRsMTTzyhYmSCIAgXJ+rICYIg/Mm+ffu4/fbb+f7771EUhZEjRzJt2jSWL1/O1KlTzy9F9vLLL5OSksKRI0d47bXXKC4uJjMzk3r16vHJJ59gMBho1KgRPXr0YO/evXzwwQc0btxY7Y8nCEKIESs7CIIg/EndunV58skn+c9//oPP5+PNN9/k2LFjzJgxg++//x6TycTKlSt57LHHmD9/Pj///DODBg1i4MCBuN1uhgwZQmpqKr1798btdtOtWzfGjBmj9scSBCFEiUROEAThL+666y4WLFhASkoKXbp04b333uPYsWMMHz78/GsKCgrIy8vj2WefZdWqVXz11VccPXqUjIwMbDbb+de1atVKjY8gCEIFIRI5QRCEi6hSpQpVq1YFwOfzMXDgQJ599tnzjzMyMoiIiODJJ5/E6/XSt29funbtSnp6On8esWI2m1WJXxCEikFMdhAEQbiCTp06MXfuXDIyMgCYOnUq99xzDwArV65k1KhR3HTTTQBs27btglmvgiAI/iRa5ARBEK6gU6dOPPTQQ9x///1IkoTFYmHcuHFIksSTTz7JqFGjMJvNWCwWWrduzfHjx9UOWRCECkLMWhUEQRAEQQhSomtVEARBEAQhSIlEThAEQRAEIUiJRE4QBEEQBCFIiUROEARBEAQhSIlEThAEQRAEIUiJRE4QBEEQBCFIiUROEARBEAQhSIlEThAEQRAEIUiJRE4QBEEQBCFIiUROEARBEAQhSIlEThAEQRAEIUiJRE4QBEEQBCFIiUROEARBEAQhSIlEThAEQRAEIUiJRE4QBEEQBCFIiUROEARBEAQhSIlEThAEQRAEIUiJRE4QBEEQBCFIiUROEARBEAQhSIlEThAEQRAEIUiJRE4QBEEQBCFIiUROEARBEAQhSIlEThAEQRAEIUiJRE4QBEEQBCFIiUROEARBEAQhSIlEThAEQRAEIUiJRE4QBEEQBCFIiUROEARBEAQhSIlEThAEQRAEIUiJRE4QBEEQBCFIiUROEARBEAQhSIlEThAEQRAEIUiJRE4QBEEQBCFIiUROEARBEAQhSIlEThAEQRAEIUiJRE4QBEEQBCFIiUROEARBEAQhSIlEThAEXn31VYYNG4bX6z3/nNfrZfjw4Xz88ccqRlY6+/bto27dukyYMEHtUErtxIkTNGrUiDNnzvzte/3792fRokWXfO/27dv5v//7v6veZ/fu3enduzcDBw5k0KBB3HTTTdx8880sX778iu9NTU1lzJgxACxevJg33njjqvcvCELZE4mcIAg8//zz2O12xo8ff/658ePHo9FoeOKJJ1SMrHR++OEH+vfvz/fff4/H41E7nFJJTk6mQ4cOTJ8+/YLnt2zZQmFhId27d7/kew8ePHjRBLA0PvjgA2bOnMmMGTOYN28ejz32GC+++OIV37djxw7y8/MB6NGjBy+99NI17V8QhLKlVTsAQRDUZzAY+OCDD7j99tvp1q0biqLwww8/MG3aNDQaDb/88gtTp07F5/MRGRnJyy+/TEpKCkeOHOG1116juLiYzMxM6tWrxyeffILBYKBRo0b06NGDvXv38sEHH9C4cWMACgsL6dKlCwsWLCAuLg6AoUOH8thjjxEWFsY777yDz+cD4OGHH6Z3796Xjb2oqIjZs2fzyy+/sHfvXhYsWEC/fv04cuQIw4cPZ8WKFej1erxeL127duWbb74hPj6eN998k/379+N2u2nfvj3PPfccWq32b3Hv27ePn376CbfbTX5+Pg899BB33HEHXq+X9957jyVLlmC1WmnSpAmHDh1iypQpFBYWXnL7fzZixAjeeOMNHnnkESRJAuDnn39m+PDhaDQafvrpJ6ZMmYIsy8TGxvLyyy9jNBoZO3YshYWFvPDCC7z99tssWbKEL774ArfbjdFo5N///jfNmze/4u9dURTS0tKIiIgAwGazMXr0aI4dO0ZeXh5hYWF88MEHFBYW8uOPP+L1erFarVSrVo0FCxYwfvx47rrrLpo1a8bmzZtJT0+nffv2vP7668iyzPTp05kwYQJGo5F27drx7bffsnv37qs7OAVBuDxFEAThrG+//VYZPHiwMnDgQCU1NVVRFEVZt26dcscddyg2m01RFEVZsWKF0qdPH0VRFOWdd95RZsyYoSiKorhcLuXmm29Wfv/9d0VRFKVOnTrKb7/9dtH9PPfcc8rEiRMVRVGUgwcPKl27dlW8Xq9y9913K3PmzFEURVH27NmjjB49+ooxf/fdd8rgwYMVRVGUr776Srn11lvPf2/EiBHK/PnzFUVRlNTUVGX48OGKoijK888/r3z77beKoiiKx+NRnnnmGWXChAl/i7uoqEgZNmyYkpOToyiKomzZskVp1qyZoiiKMnXqVGXEiBGKw+FQnE6ncv/99yt33nnnFbf/Z16vV+nRo4eydu1aRVEUpaCgQGndurWSlZWlrF69WrnxxhuV7OxsRVEUZdq0aUrfvn0Vn8+nTJs2TRk5cqSiKIpy5MgR5eabbz4f4/79+5WOHTsqxcXFf9tft27dlF69ein9+/dXOnfurHTu3Fl54YUXlOPHjyuKoijz589XXn/99fOvf/nll5XXXntNURRFGTt2rPLqq6+ej+Xc/u+8807liSeeULxer1JYWKh06tRJWbNmjXLgwAGlffv2Snp6uqIoivLpp58qderUudyvUhCEayBa5ARBOO+uu+5iwYIFpKSk0KVLF6BkbNSxY8cYPnz4+dcVFBSQl5fHs88+y6pVq/jqq684evQoGRkZ2Gy2869r1arVRfczdOhQXn31VR544AGmTZvGLbfcgizL9O3bl9dee40lS5bQoUMHnnrqqSvG/OOPPzJs2DAABgwYwEcffcSWLVto3rw5t956K7/99ht9+vRh+vTp51+XmprKjh07+PXXXwFwOBwXbPNc3GFhYXz55ZcsW7aMo0ePsnfv3vOfb9myZQwcOBCDwQDAbbfdxpQpU0q1/XNkWWb48OFMmzaNtm3bMmvWLLp06UJMTAwrVqzgpptuIjo6GoAhQ4bw5ptvkpaWdsE2Vq1aRUZGBvfee+/55yRJ4vjx49SrV+9v+zzXOnrixAnuu+8+6tevT3JyMgB9+vQhOTmZKVOmcOzYMdavX1+qlr1u3bohyzIWi4Vq1aqRn5/P3r176dixI4mJiQDceeedfPrpp1fcliAIV0ckcoIgXKBKlSpUrVr1/GOfz8fAgQN59tlnzz/OyMggIiKCJ598Eq/XS9++fenatSvp6ekoinL+vWaz+aL7aNWqFR6Ph+3btzNnzhx++uknAIYPH063bt1YtWoVK1asYNy4cfz+++/nk6W/2rhxIwcOHGDixIlMnjwZAJ1OxzfffEPz5s3p27cv77zzDocOHWLDhg2888475z/DmDFjSElJAUoS03Ndm3+O+/Tp09x2220MGzaMli1b0qdPH5YuXQrwt25SWf7fkOMrbf/PbrnlFvr06UNRURE///wzr7766vlt/JWiKH8bA+jz+Wjfvj2ffPLJ+efS09OJj4+/6P7OSU5O5r333uPuu++madOmNGnShB9++IGff/6ZESNG0L9/fyIjI/+WOF6M0Wg8/39JklAUBY1Gc8GxoNForrgdQRCunpjsIAjCZXXq1Im5c+eSkZEBwNSpU7nnnnsAWLlyJaNGjeKmm24CYNu2bRfMfL2coUOH8vrrr1O3bl2SkpKAkkRuz549DBkyhNdff52CggIyMzMvuY2pU6cycOBAli1bxpIlS1iyZAlffvklixYt4tSpUxgMBvr168fzzz9Pr169MJlM5z/TN998g6IouFwu/vGPf/Ddd9/9bfs7d+4kOjqaRx99lE6dOp1P4rxeL126dGHWrFm4XC48Hg+//fbbBT+z0mwfICoqim7dujF27Fg0Gg3NmjUDoHPnzsybN4+cnBwApk2bRmRkJNWqVUOj0ZxP6Nq3b8+qVas4dOgQUNJSOGDAgEu2Av5ZixYtGDRoEKNHj8bn87Fy5UoGDx7M0KFDqVGjBkuWLDn/+/zzPkujU6dOrFmz5vykjF9++aXU7xUEofREIicIwmV16tSJhx56iPvvv5/+/fszZ84cxo0bhyRJPPnkk4waNYr+/fvzf//3f7Ru3Zrjx4+XaruDBg1iz549DB069PxzzzzzDGPHjmXQoEHcddddPPbYY1SpUoUdO3YwcODAC96fk5PDwoULeeCBBy54vn379jRr1ux8N+fQoUPZvn37Bfv5z3/+g81mo3///vTv3586derw4IMP/i3Gjh07kpCQQJ8+fejbty/p6elER0dz7NgxhgwZQpMmTRg0aBDDhw9Hp9OdTxRLu/1z7rjjDr799ltGjBhxwb7vvfde7rnnHvr168eMGTMYP348sizTrFkzTpw4wWOPPUatWrV47bXXeOqppxgwYABjxozhiy++ICwsrFS/h6eeeoqTJ0/y888/c//99/PTTz/Rv39/RowYQcOGDc//Ptu1a8fKlSt5/fXXS7XdGjVq8MILL/DAAw8wZMgQDh06dP7nIwhC2ZGUP7d9C4IgCKWycuVKsrOzzyeYb7zxBgaD4XwXdEV34sQJZs6cyaOPPoosyyxcuJCvvvpKtMwJQhkTY+QEQRCuQe3atZk0aRITJ07E5/NRr149Ro8erXZYASMxMZGMjAz69++PRqPBarXy1ltvqR2WIIQc0SInCIIgCIIQpMQYOUEQBEEQhCAlEjlBEARBEIQgJRI5QRAEQRCEICUSOUEQBEEQhCAVkrNWc3OL8fnEHA5BEARBEIKfLEtERV28NmRIJnI+nyISOUEQBEEQQp7oWhUEQRAEQQhSIpETBEEQBEEIUiKREwRBEARBCFIikRMEQRAEQQhSIpETBEEQBEEIUiKREwRBEARBCFIikRMEQRDKlM1WzLFjR3C73WqHIgghLyTryAmCIAjqGfvph+zft5e+ffszdOjtaocjCCEtYFvkJkyYQO/evenfvz9ffPGF2uEIgiAIpZSRcQaAzMwMlSMRhNAXkInc6tWrmT17NtOmTWPGjBls27aNhQsXqh2WIAiCcAWKolBcXARAQWG+ytEIQugLyERu9+7ddOrUCYvFgkajoXPnzvzxxx9qhyUIgiBcgd1ux+0qGRuXm5urcjSCEPoCMpFr2LAhK1euJC8vD6fTyZIlS8jKylI7LEGocPbv38uHH77Njh3b1A5FCBK5uTkASAYN+Xm5KIpY91oQ/CkgJzu0b9+eIUOGcNdddxEZGUn79u3Ztq30F5KYGIsfoxOEimPmzG3s2rWDxMR4unfvpHY4QhA4dqwYAF2sEdfJYoxGCA+3qhyVIISugEzkioqK6NWrF/fddx8AEydOJDk5udTvz84uwucTd4GCcL0yM0taV7Kzc8nMLFQ5GiEYHDx4DABdnAnXyWL27j1MjRopKkclCMFNlqVLNlIFZNdqWloajz76KB6Ph8LCQn799Vf69u2rdlhBLTMzg//+dyIbN65XOxQhiBQUlAxWz8/LUzcQIWicOZOOrJXRxhoBOH06XeWIBCG0BWSLXL169ejVqxcDBgzA6/Vy77330rJlS7XDCmobN65j2bIl7N23h1at2qgdjhAk8s6Od8rLy1E5EiFYpKefQrbo0ITpQCp5LAiC/wRkIgcwatQoRo0apXYYIaOwsKRbzGG3qxyJECwURSEru2SSUV5+Ph6PB602YE8ZQoA4kXYcTYQOSSOhtRg4eTJN7ZCEIFBUVMQvv/yAy+WiSZNmtG8vxuSWVkB2rQplLz8/D4DCokJ8Pp+6wQhBobi4GIfDQbxZi6IoZGeLmePC5RUWFlCQn48mXA+AHK7l2PGj6gYlBIUdO7ayYkUqm9avYcZvv6gdTlARiVwFca6ek8/rpahIDFoXruz06ZIusToxJRdl0UUmXMmxY0cA0EYZSv6NNJCTnUVRUZGaYQlBIC3tBLIk0dRgJCsrE7voPSo1kchVEFlZmSBp/vd/QbiCc11iDc8OWj91SnSRCZd35MhhADSRJYmcJqrkJuDo0cOqxSQEh8OHDxKr0ZKg1aEgjpmrIRK5CsDj8ZCdk4U2LBGAjAyx/qFwZceOHcGolUkI0xJp1HLs2FG1QxIC3KFDB9Ba9ci6kkuLNqrkJuDw4YNqhiUEOLfbxeFDB0jQaEjUlIzD3b9/r8pRBQ+RyFUAmZlnUHw+NJYkQDrfZSYIl3P40AGSLFpkSaKSRcPhQwfUDkkIYD6fjwMH96OJNpx/TtbJaCMMHDiwT8XIhEC3f/8+3B4PVbQ6DLJMvFbHTrGaTKmJRK4CSEs7AYDGGI3GYCUt7bjKEQmBzmazcSLtBMlWHQDJ4Tqyc7LJyclWOTIhUKWnn8Jus6GNMV7wvCbawMGD+/F6vSpFJgS6LVs2opUkKutKuuKraXUcPnzw/CQ94fJEIlcBHDt2FCQZ2RCBpI/gyJEjaockBLgDB/ahKArVI0tOrNUjSv7dt2+PmmEJAezcsaGLuzCR08UacTqdHD9+TI2whADn8XhYv24NVbU6dJIEQE29HgVYv36NusEFCZHIVQCHDx9EY4xEkjVoTDHk5maTn5+vdlhCANu5cxs6jXy+RS4hTEuYXsMO0d0hXMKePbvQmHXI5gtrDerOTpbZt2+3GmEJAW779i0UFRdRR/+/LvlojZY4rY7ly5aiKGK5zSsRiVyI83g8HD58CNkYA4DGFAvAwYNizIpwcYqisG3rZqpHaNFpSu6QZUkiJVLHjh1bRReZ8Dc+n4+9e3ehjTUgnW1VOUc2adFa9ezes0ul6IRAtnjxQiwaDdXOdque00Bv4OSpNDHpoRREIhfijh49jMvlRGOOB0A2RSHJWvbuFXfHwsWdOHGMrOws6v5p0DpA3WgDxcXF4sQq/E1a2gmKi4vRxpku+n1NrJH9+/fi8XjKOTIhkB05cpg9e3bRSG9A/ssNQG29AZNGw7x5s1SKLniIRC7E7d69E+B8IidJGmRTHDt2blczLCGAbdy4HkmC+n8ZtF472oBOI7Nx43qVIhMC1d69Ja1tukskcrp4Iy6nkyNHDpVnWEKAmzXzVwyyTEOD8W/f00kSjXUGduzYJo6bKxCJXIjbtm0rGlMMsvZ/rStaSyIZZ06TkXFGxciEQKQoCuvWrqRGhJ4w/YWnB71GonaUjg3rV4uWFeECu/fsQmvRozFffC1eXWxJgid6AoRz9u/fy7btW2mmN6KXLp6KNDYaMckafvllqhgrdxkikQth+fn5HDlyCE1Y0gXPay2VANi2bbMaYQkB7NChA2RmZdE4/u93yABN4o0UFRezU7ToCmd5vV727d2NJs5wydfIBg3aSAO79+wsx8iEQOXz+Zj6w7eEaTQ0Nl68FRdAL8m0NBjZu3c3W7duKscIg4tI5ELYli0bAQWttcoFz8t6KxpjBJs2bVAnMCFgrVy5DJ1GpkHMxS/KtaIMmPUaVq1aXs6RCYHq+PGjOJ3OS3arnqONNXLw4AHcblc5RSYEquXLl3Ls+FHaGUznS45cSgODkWitlh++/y9Op7OcIgwuIpELYRs3rkNjsCIbIv72PY2lCgcO7BNlSITznE4H69evoUGMHoP24qcGrSzRJNbA1q2bKCwsKOcIhUB0rrv0XJmRS9HFGvGenUUvVFx5ebn88vMPVNbpqK2/dCvuORpJorMxjOycbGbNml4OEQYfkciFqMLCAvbs2Y3GUuVv5QAAtNZkFEVh82YxcF0osXHjehwOBy0SL9+y0jzRiNfrZc2aVeUUmRDI9u/fW7K+qvHi4+PO0Z6vJyeKSldUiqIwZcrXuJ0ObjBZLnptuphKOh319AZ+/32OmPhwESKRC1GbNm1AUXxow6te9PuyIQKNIYJ160TlbKHEstTFxJh1VA3XnX9u6xk7W8/YL3hdQpiOKlY9y5ctFgOQKzifz8f+A/suWF8VwHmsEOexwguek/WaknVXRQ3LCmvNmpVs2bKJVkYTkRrNVb23gykMsywz8avPcblE9/yfiUQuRK1btwaNIRzZEHnR70uShMaazP79+8jNzSnf4ISAc/JkGgcPHaBFwoUFXbecsbPlL4kcQItEI6fST3Hw4P7yDFMIMKdPp59dX/XCRM5xrBDHXxI5AE20noMHD+Dz+corRCFAZGVl8t2UySRpdTQ1XL7V/2IMskw3Uxjpp9P59depfogweIlELgTl5eWyb98eNNbkyzZd68KrAgobNqwrv+CEgLRs2WI0kkSz+NKdYBvFGTFoZVJTF/s5MiGQHT58EABt9OXHx52jjTbidDg4fTrdn2EJAcbr9TJh/Dh8bhfdzZa/Ff8trWSdnsYGI3/8sYDt27eUcZTBSyRyIagkMVMu2a16jmwIR2OMYt261eUTmBCQXC4Xq1ctp37s32vHXYpeI9E4zsCGDWspKiryc4RCoDp8+CCyToPGqrvyiwHd2S7YQ4cO+DMsIcDMnDmNg4cO0NloJvwqu1T/qp0pjBitlolffS56k84SiVwIWr9+LRpjJJo/zVZ15x3BnXfkb6/VWJM5cuQQWVmZ5RmiEEA2bFiLzW6nZaL5qt7XKtGEx+NhzZoVfopMCHRHjhxGE6kv9aB12aJD1mk4duzv5yIhNO3cuY25c2ZQT2+gzkVWcPizfU4H+5yOy75GK0n0NFtw2u18+cVYUZwckciFnNzcXA4d2o/GmnzB8+78w7jzD//t9brwktdt2iRmr1ZUy5ctIcaspXpE6VpVzkm06Khs1bEsVUx6qIg8Hg9pacfRROqv/OKzJElCE6nnsJh5WCFkZWUy/stxRGu1dDJbrvj6vS4He12XT+QAojRabjCZOXBwP9Om/VQWoQY1kciFmK1bNwL8rQjwpZQUB45k8+aN/gxLCFAnT6Zx4OB+mscbS92q8mctE02cSj/FgQNiJmJFc/JkGl6vF23UlWuB/ZkmQk/aieN4vV4/RSYEApfLxbhPP8LtsNPLbL1i4d+rVUdvpJHByIIFc1m/vmJXXxCJXIjZsmVTSRFgfXip36OxVObgwf0UFIgCrxXN8uVLSyY5JFz9LDKAhnEGDFqZ5cuXlnFkQqA71z2qjby6RE4bqcfj8XDq1El/hCUEAEVR+GbyBI6fOEYPk+WqS42UVgdTGElaHZMmflmhu+tFIhdCnE4ne/buRg6rdFWtK1pLZRRFYdcusX5mReJ2u1i9ahl1Y/RYSjnJ4a8MGplGsQY2rF9DcbGY9FCRHDt2BFmnQQ67fCHgvzrXgleRL7yhbu7cmaxdt5o2RjPV9aXver9aGkmiV5gVg+JjzCfvk5ub67d9BTKRyIWQffv24PV40IYlXtX7ZGMUstYoFkKvYDZuXE+xzUarK6zkcCWtkky4PR6x0kMFc+To1U10OEe26JC1MkeP/n3MrhD81q5dzfTpP1Nbb6CF8frOLaVhlmX6mq0UFxYw5pN3cTiuPMYu1IhELoTs3r0TSZLRmOOu6n2SJCGb49m1a4cYtF6BpC79g2iTlupXMVj9YpLOTnpIXbpIHD8VhNvt5sTxY2iirv7YkSQJTZRBTHgIQbt27WDSxM+ppNXRzVz6JbiuV6xWS0+zhRMnjjNu3Ee43e5y2W+gEIlcCNmzZxeyKRZJvrquDgCNOZ6CgnxRqLOCSEs7zoGD+2mVaLzm4px/1urspIf9+/eWQXRCoDt+/Og1TXQ4RxOl5/jxY7jdYqmlUHHw4H4+/fRDImWZPmFWNOWUxJ1TTaenq9nC7t07mTDhswo1mUYkciHCbreVlAK4yta4c7TmeAAx+7CCWLJkEVr52ic5/FXDOCMmnYY//lhQJtsTAtu5hF0XU7oVHf5KF2PE5/Vy+LBolQsFhw8f5KMP38Hk9dEvLByDrE5qUc9gpIMpjE2b1jPxq88rTDInErkQcejQQRRFQWO6tkRO0luRtUaRyFUARUWFrFq1nCbxRsy6sjkF6DUSLRIMbN68QRSXrgD27t2N1qpHNl596z+A9mwCuGfPrrIMS1DB/v17+eD9N9F7PAywWAlTKYk7p6nRRFuTmXXr1/Dll59WiILBIpELEeeWvNGYYq7p/ZIkIRtjOHBALIIe6pYuXYzb7aZtpatbyeFKWieZkVD444/fy3S7QmBxuVzs2bsbbfy1tcYByHoNumgjO3ZsLbvAhHK3detmPvzgbYxeLwMtViyyf8qMXK0WRvP5lrmxYz8I+QkQIpELEQcPHkBjjETSXF11/j+TTTFkZJymqKiwDCMTAonT6eSPRfOoHW0g4SrLRlxJpFFDw1gjy1IXi/VXQ9ju3TvxuN3oEq7vRkCbYOLI0cPk5+eXUWRCeUpNXVwyJg4YGBYeMEncOU2NJrqaLezauZ133n6VvLzQLU0iErkQ4PP5OHToALIx+rq2ozHFAiXdtEJoWrZsCYVFRXSqUratced0SjbjdLlYtGi+X7YvqG/9+jUlLWrx1ze+Ul85DBQqfFX+YOPxePj++2/49ttJJGt1DLSEY1a5O/VS6huM9LWEk37yBK+9+h+OHAnNkjeB+dMXrkp6+ikcDvs1j487R2OKBkni4EExTi4UOZ0O5s6ZQY1IPdUi/FOkMyFMR/0YA4sWzhMtuyHIbrexefMGdJXMSPL1zUrUhuvRRhhYtXp5GUUn+Ft+fh4ffvAWixcvpKnBRN+wsl96q6xV0+kZFBaOt6iId94ezYoVqSpHVPZEIhcC9u3bA4DGHHtd25FkLRpjNPv2iRISoWjhwvkUFhXSrVqYX/fTrZoFp9PJ7Nkz/LofofytWrUCl8uFobq1TLanr27h+LGjohcgCOzdu5tX/u95Dh3YR3ezhQ7msDIpXVQeYrVabrGEkyBJTJ48gUmTxuN0hs64uYBN5GbOnEm/fv3o168f7777rtrhBLQ9e3ah0Ych6SzXvS2NOY7DRw6F1EEuQF5eLvPmzqR+jIGq4f5bMgcgPkxLswQjS5Ys5MwZUZcwVHi9XhYtmo8u2ogu+tonOvyZsaoVWadh4cK5ZbI9oex5PB5+/fVH3n/vDWS7jSHWCOoayub3X55Msky/sHBaGk2sWrWM0a+8GDLLxAVkIme323nzzTeZMmUKM2fOZOPGjaxevVrtsAKSx+Nh164dSKb4MqmirQlLxOf1irIAIebnn3/A43ZzY43rT/ZLo3t1CxoUfvjhW7HaQ4hYvXoFmZkZGGpHlNk2JZ2MvoaVDRvXkZZ2vMy2K5SNkyfTeOP1l5k3bxb19AZutUQQoynbSVLlSZYk2pjCGGAJpyg7kzdef5nZs2cEfb25gEzkvF4vPp8Pu92Ox+PB4/FgMFxbBfFQd+DAPhwOO1pL5TLZnsYch6zRsXXr5jLZnqC+PXt2sXbtKjpWMRNjKp+TsFWvoWtVMzt2bGPTpg3lsk/Bf1wuFzNmTkMbZURfxmVrTHUikLUafp32U5luV7h2Xq+XefNmMXr0C2SeSqN3mJWuQTAerrQq6/QMs0RQQ6Plt99+5s03/o+TJ9PUDuuaBWQiZ7FY+Oc//0nfvn3p0qULlStXpkWLFmqHFZDWrVuNJGvRWhLLZHuSpEEOS2LDxvUVbr26UOR0Ovhm8gSiTFo6J/t3bNxfta1sJtGi47vvvhblSILcnDkzyM3JxtwoqszXz5T1Gox1I9i+bQtbtmwq020LV+/48WO88frL/Prrj1SVNQyzRFBTH3oNKUZZpqclnF5hVk6fOM7oV15g1qzpQVlA2O+35xs2XHg3LkkSRqOR6tWrY7FcvJtn7969TJs2jaVLl2K1WnnmmWeYNGkSDz74YKn2GRNTPt1HanM6nWzYsA6Ntco1ra96KbqIGthPHOfIkT107NixzLYrlL8JE6aSmZXJvY2j0GnK925aI0kMrG3lq625TJv2Pc8880y57l8oG8ePH2f+/Nnoq1rQxZXNkm5/ZawVget4MT9M/YZOndpgNvunPI5waQ6Hgx9//JEZv/2GQZLoFWalpk5fbgvfqyVFb6CSVsdKWzEzZvzK5k3reOzxx6lfv77aoZWa3xO5t99+m71791KrVi00Gg379+8nLi7u/Di4G2+88W/vWblyJe3btycmpmSVgiFDhvDDDz+UOpHLzi7C5wv9cTmpqYux222Y4mqW6XY1YQlo9GFMnz6DOnWalOm2hfKzc+d2Zs+eTZtKJqpH+neCw6UkWXR0TjazbNky6tdvQps27VWJQ7g2brebt99+B7QSYY2ubdWY0pBkCXPzGLKXn2Ls2M944IFH/LYv4e927drBf7/5iqzsLOrpDbQ3hWEM0Npw/mCSZXparNR2GVhx6hT/fu45unTtwa23DsdsLt+ejEuRZemSjVR+/01VqlSJyZMnM2vWLH777TemTp1KixYtmDlzJp999tlF31OvXj1Wr16NzWZDURSWLFlC48aN/R1qUPF6vcz/fQ4aUzQa8/XVj/srSZLRRtZm//69HD4sygIEo4KCfCZ+9RnxYTp6llGpiGt1Q3IYVcL1JRcKsQ5rUPn11x9JSzuBuUUMstG/lft1MUZMdSNZtWo569aJyW3lIT8/n/FffsqHH76NOy+PAZZwuoVZK1QS92fV9XqGWyNpbChZoebFF55m/fo1AT9hy++/rRMnTtC2bdvzj5s0acLRo0dJTLz0mK5OnTrRr18/hgwZwoABA/B4PIwcOdLfoQaVVauWk5lxBl10fb80fesiU5C1Rn799ceAP4iFC/l8PiaMH4etuIhb6lrLvUv1rzSyxC11w/F5XHzx+ZigHINSEa1du4pFi+ZjrBmOPql8WiVM9aLQxRj5evJ4TpwQs1j9xefzkZq6mBdfeIqNG9bSymhimDWCyjp1Wu4DiU6S6Gi2cIs1EoPdzpdffspHH71DRsYZtUO7JL8nclqtlpUrV55/vHLlSnQ6HTk5OZc9oY8cOZLff/+d2bNn89Zbb4lZq39it9uZPv1nNKZYtNYqftmHpNGhi2nA3r272bZti1/2IfjHrFnT2b1nF31TLCSEXfvau2UpyqhhYG0rR44e5scfv1M7HOEKjh49zNeTJ6CLNWJu4r8u1b+SZAlLm3h8Ghg79gMKCgrKbd8VRVraCd5+azTffjuJKK+HodYIWpvC0Ib4WLirFafVMsQSTkdTGPv37OLll55l7tyZAXkj6vcxcq+88gpPPPEEkiTh8/kwGAyMHTuWiRMnMnz4cH/vPiRNm/YTBQX5mKvf6NeBqLqoFDx5h5gyZTJ169bHZPLPQGeh7GzdupnZs6bTNN5Ii4TA+n01iDXSrrKbJUsWkpJSi/btO6kdknARmZkZfPzxuyg6sLaJv+6luK6WbNIS1jaOnBWnGTPmPZ577mVxI18G3G4Xs2fPYN68WRiQ6G62UEdvCPnJDNdDliSaGE3U1OtZaStm2rSfWLtmFffeN5KUlFpqh3ee3xO5Jk2asHjxYvbv349GoyElJQWNRkO9evX8veuQtH//XpYsWYQuqs75Re79RZI0GBJbk3vsD3799Ufuuus+v+5PuD6nT6czYcI4Ei06bq4VHpAn6J7VLaQXefhm8gQqVapMtWo11A5J+JOCggI+/Ohtip02rDckIRvVKf6qizZiaR3HkXWH+eKLMTz22FNotcFbiFZtBw/uZ9KkLzlz5jR19AY6mMIwVdBxcNfCImvoYwnniMvJijPpvPXWK/TqdRODBt0aEDcZfv/LyMrK4scffyQvL++C51966SV/7zrkFBUV8uWXn6IxWDDEl8/kD405Fl10XZYuXUSjRo1p3rxVuexXuDrFxUWM+eQ9ZJ+b2+qXf6mR0tLIEkPrRfDVtlzGjnmf/3vlLSIiItUOS6DkGPrgw7fIzMrE2jEBrZ+XcrsSfaUwwprGsn3rViZO+oKRD41CFsnHVXG7XUyf/gsLF8zFotFysyWcZDEO7prV0BuorNOxxmZjwYK5bNm8gQcfepRateqoGpff/yqeffZZdu3aRUREBJGRkee/hKvj8/mYNGk8+fn5GJLaI8nlN/bJENcEjSmaiRO/FLMOA5DX6+XLLz8lKzOD2+qFE+nn2YXXy6KXGV4/nKLCAj799EPcbpfaIVV4druNjz56h5MnT2BpF48uNjC65Y01wzE3imb9ujVMnvwVPp9P7ZCCxvHjx3j11f+wYMFc6uuNDLOKJK4s6CWZLmEWBljCsefk8PbbrzJ9+s+qjp3ze4vc6dOnmT9/vr93E/JmzpzGtm2bMSS0QGOKLtd9S7IGY6X22I8uYsyYD/jPf17FaAy+RZND1dSpU9i1awf9a1upFhEcJ+oki47Bdaz8vOcQX389gZEjRwVkV3BFYLMV8+GHb3P06BEsbePRJwRWMV5TnUgUj49Vq5YBCvfdN1K0zF2GoigsXfoHP06dgh6FfpZwqooErsxV1ukZptWy0lbMnDkz2LN7J4/84wliYvw75OliyqWOnM1m8/duQtr69WuZPfs3dBE10EXVViUGWW/FUKk9J0+mMXHiF+LOOEAsXryAJUsW0qGymZaJgXUBvpIGsUa6V7Owbt1qZs/+Te1wKqTi4iLef/8tjh47m8RVCozip39lbhCNqX4Uq1YtZ9KkL4N+kXN/cTodfPnlp3z33WSSZIlhlgiRxPmRXpLpHmalZ5iVE0cP88r/Pc/OndvLPQ6/t8jFx8czaNAg2rRpc0ErjhgjVzoHDuzjq4mfozHHYUhspWqrhdaShCG+GZs3b+DXX6cybNgI1WIRYNu2Lfzww7fUjTZwY43gXJauc7KZbLuHGTN+JT4+gXbtxJJw5SU/P4/3P3iL9PSTJUlcOdWKu1bm+lEgwZo1K3E6nTz88GPodIFRXicQ5ORkM+aT9zmRdpy2RjPNjaagauVWFIVinw+XorDLaaeB3hg08dfSG4jTaFlgK+Tjj9/ltttG0LNn33KL3++JXOXKlalcubK/dxOSTp9OZ8yYD0Bjwli5E5Ks/tgnXXQdfO4ifv99LjExcfTo0UvtkCqkY8eO8uUXY0gM03JLvQjkIDnh/ZUkSfSvHU6e08fXk74kKiqaunWDZ43DYJWVlcn7779JVm4Wlg4J6OODozXXXC8KSSuzefMGxoz9gMcfexKDQQzzOHkyjY8+fIviggJusoRTLQhb4Xa5HOSf7elZbitGARoZAmOsZmlEaDQMtkSwuLiQH3/8jpycbIYNG1EuwwAkJQTL9ofCWqv5+Xm8/vr/kVdQhKlaD2T99S2zZDu2GABztR7XHZui+HCkrcRTlM6oUf+kZcs2171NofSys7N44/WXwGXjgSaRhBv8l+BP3p4DwH1N/Dsu0+b28fX2PGyKjv+89CpJSeLmz19Onkzjgw/fotBWiKV9ArqYsk+E8pefAiDihkplvm0Ax9ECirdkUbNmLf71z+ewWIKzRbosHD9+jPfffR1cTvqZrcQEaZmWuYX5HPe4zz+uqtXRzxqhYkTXRlEUVtmL2eF00LFjF+6776EySeZUWWv19ttvB6B58+a0aNHib1/Cpdntdj766F1y8/IwVul83UlcWZMkGWPlDmhMMXw5fhz79u1RO6QKw2Yr5pOP38VhK+KOBhF+TeLKk1knc0eDcCSvk48/epf8/Hy1QwpJhw4d4K23R1PkLMLaOdEvSVx5MFYPx9ImgSNHDvHW26PJyclWOyRVnDp1kg/efwPJ5WRQWHjQJnEAHpTLPg4WkiTR0RRGS6OJVauW8cMP//X7Mpd+S+TGjBkDwJw5c5g9e/YFX7NmzfLXboOex+Nh3LiPOZF2/HyyFIgkWYupSmfQmBkz9kNOnkxTO6SQ53a7+XTsh5xOP8Vt9cJJCAvek/bFRJu03N4ggvy8nJJk1eFQO6SQsn37Ft597w3csgfrDUloI9QvZHo9DJXDsHZMJCPrDG++9Qrp6SfVDqlcFRYW8MnH7+JzOOgfFk64JjRu6kKBJEm0NpppajCxZMkiFi3yb+UOvyVy8fHxQMkSXefGyZ37euqpp/y126CmKAqTJ09gz56dGBNbo7X4p1uirEhaA8bkLrg8Ch9++Da5uTlqhxSyfD4fEyd+wb79exlYx0rNqOC+CF9KFauOW+uGc/zEUT7//JOAXNcwGK1atZwxYz+AMBnrDYloAmQN3uulizNh7ZRIga2AN998hYMH96sdUrnw+XxMGP8ZuTnZ9DFbiBRJXMCRJIn2JjM1dXp++ul7v/Zc+S2Re+KJJ+jfvz8bN26kf//+57/69u2L0+n0126D2syZ01izZiX62EboImuqHU6pyLowjFVuoKCwiE8+eV+0oviBoihMnfotGzas5cbqFprEB88A4GtRN8bAzSlWdu7czuTJE0Spm+ugKApz585i0qQv0cYaCe+s3rJb/qKNMmC9IQmX7OG9999k27Ytaofkd0uX/sGu3TvoYDKToA2NpDwUSZJE9zAr4bKGryZ8ht1u98t+/PYX/dxzz3Hy5ElefvllXn755fPPazQaatUKnMVmA8WaNSuZNWs62oga6GMbqh3OVdEYozBUas+JEysYP/5THn/8aVGwswzNnTuTxYsX0q6ymY5VgmN24fVqmWSmyO1j6ZqVhIdHMGzYHUFTiiBQ+Hw+pk6dwuLFC9BXCcPSKh5JDs2focaiw3pDEoVrTjP20w+4956H6Ny5q9ph+UVBQQHTfv2RZJ2ehvrgHONYkegkie7mMH7LzWHevFnccsttZb4PvyVyVapUoUqVKvz+++9/u6iLAsEXOnLkEF9PnoDWHI8xSd1acddKa6mEIaEF27Zt4rfffvHLwVoRLV36B9On/0zjOCO9aliC8ti4Vjckh1Hk8rFgwVwsFgv9+g1UO6Sg4Xa7mTjxCzZsWIuxVgTmxtEhf+zIRg3hnZIoXHeGyZMnUFCQz003DQi5zz1//mycTgcdwyND7rOFqkStjjp6Awt+n8uNN/YhIqJsZ+P6vY19yZIljB07FpvNhqIo+Hw+8vLy2LIl9Ju/S6OgoICxYz8C2YChckckKXjHOuiiauFz5jJ37kyqVatBq1aiLMn1WLt2Fd9N+Zo60QYG1QkP2lpx10qSJPqmWHF4FKZN+wmTyUz37j3VDivg2e02Ph33EXv37MbcKBpj7YgKc8GXdDLWDokUbcpk2rSfyMvL5fbb7w6ZHgK73c6y1D9I0RmI0oRWF3moa2k0s78gl6VLFzFo0K1lum2/H93vvfcejzzyCElJSbzyyit07tyZ4cOH+3u3QaFkAPvnFBQWYKjcCVkb3APYJUnCkNASjSmGSZO+JCPjjNohBa2NG9czceLnVIvQM7ReBJoQ7RK7ElmSGFQnnDrRBr77bjIrVqSqHFFgKyjI5513X2fv3j1YWsZhqlPxWm0kWcLSKg5jrQgWL17IhAnjQmbSzNatm3A4nTQSa10HnUiNhipaHWtWryjzciR+T+RMJhM33XQTzZo1w2AwMHr0aFJTU/2926CwcOE8du7cjj6+ORpjlNrhlAlJ1mCs1B63x8cXX4wNmRNoedq6dRPjx4+lskXH7Q0j0Gkq1oX4rzSyxND6EaRE6flm8gTWrFmpdkgBKTMzgzfffIW0kyewtk/AUC2w6k+WJ0mSMDeOxtwomvXr1/LJmNCYiLV16ybCNBoSRWtcUErRG8jMyuTUqbItleP3RM5gMOByuahatSp79uxBluUKd4d4MWlpJ5g27Se01iroIlPUDqdMyXoL+sTWHDt2hHnzRM3Aq7FlyyY+++wTEs0aRjSMwKAJjS6h66WTJW6rH0n1SD0TJ34hkrm/OHkyjTffeoXs/GysnRLRJ1aMSTGXI0kSpjqRhLWIY/fuHbz//hsUFRWpHdZ1Obh/H0myVlxDg1SlszOMy7pMjt+vEt27d2fkyJHccMMNfPPNNzz++ONERYVG69O18nq9fDXxCxRJhyExOCc3XIkuPBlteDVmzZrO8eNH1Q4nKGzatIHPP/+ERLPMnY0iMWpFEvdneo3E7Q0iqRauY+JXn7Nq1XK1QwoIR44c4u23R1PsKsbaOSloV2vwF2N1K9a2CRw9doR33nmVvLxctUO6Jna7jdz8PGK1wTuOuqKLkGV0slzmBfT9fqV45JFHeOutt0hISOCzzz6jVatWfPrpp/7ebUD744/fOXH8KPqElsja0D3pGhNbgEbPN99MFLXArmDt2tV88cUnJIVpuLNRJCaRxF2UXiNxR8NIakTqmTTpS5Yu/UPtkFS1b98e3n3vDZyS++xqDcG3WHp50FcKw9ohkdMZ6bz19miysjLVDumq5eSUFFy3yCKRC1aSJGGVNWW+pJxfrxZHjhwhIyODSpVKViho2LAhffr04c033/TnbgNaTk42v/32K1pLJbTWKmqH41eSxoA+rhlHjx5m2bLFaocTsJYvX8pXE8aRbNVxV8MIkcRdgV4jcXvDSOpEG5gy5Wt+/32O2iGpYufObXz40Tv4DArhNySFzGoN/qKLN2HplEhOfg5vvT2aM2dOqx3SVbHbS8p26UOwB6ci0aHgcJRtYWC/XTEmTpzIkCFD6N27Nxs2bADgm2++4aabbiIzM/juhsrKL79Mxe3xYEhoEZJdqn+lDa+GJiyeX6f9HPTjU/xh/vzZfPPNV6REGRjRMBKDSOJKRSdLDKsfQcNYAz///APTpv3o94WpA8m2bVsYM+bskludk5BNYvB7aeiijVg7JVJoK+Ttt18lPf2U2iFdtdC/aoQ2CSl4Zq3+9NNPzJs3j0mTJvHf//6Xp556iokTJzJ69Gi+/fZbf+02oB08uJ9161aji6qLrLeoHU65kCQJQ3wL7DYbM2f+qnY4AUNRFH799Ud++WUqDWMNDG8Qgb6Cz069WlpZ4pZ6EbRMNDF37iymTPm6QnThb926mU/HfYQUrsPaKRHZILraroY20oClcyLFrmLeefc10tPLdgahv+h0Jd3mngp0wxKKPIBeX7ZDIPyWyJlMJpKSkmjRogUbN27EZrMxb948+vfv769dBjSfz8cPP3yLrDOjj22gdjjlSmOMRBeZwpIlfwTNSdOfvF4v33zzFfPmzaJloolb6kWgDcA6cYqiUOj0kmXzsCHdFpAtXrIkcXMtKx2rmElNXcyXX47F7XarHZbfbN++lXGffYwcrsPaMQFZL5K4a6EN12PplIjNZeOdd17j9Ol0tUO6ovDwktUA7Ero36yEMpviw2ot25Ud/JbIaTT/O8FYLBY++eQTwsPD/bW7gLd+/VqOHj2MPrYxklzxukH0cY2QZA0//vS92qGoyu128cUXY1mxIpUbksO4uZY1YFds2JhuJ8fho9itMPdgIRvT/bPg8/WSJImeNaz0qmFh48b1jAmRmmF/tWfPLsaN+wjZqhVJXBn4XzJn57333gj4CRARERFoNBoKvKGZyLkUBaPRyIABAzAajbgC8MbxerkVBZvXS2xsbJlut1wG5FitVowVuBK1y+Xi519+QGOMQhtRXe1wVCFrjehiGrBj+1Z27dqhdjiqsNvtfPzxe2zevIE+Na10rx7Ya6fuy3Fe9nGg6VAljEF1wtm7ZycfvP8GRUWFaodUZo4cOcSYMe+DWYO1Y6JI4sqINlyPpWMCBbYC3nv/DfLz89UO6ZJkWSYhPpEcb2gWWXcqCr169eKhhx6iZ8+eOEMwkcvzegGoVKlymW7Xb01D2dnZTJ48+W//P+e+++7z164Dzu+/zyEvNwdTte4BfeH2N11UHTx5h/jhh2957bV3Lmi1DXVFRUV8/NE7HDt2mMF1wmmaYFI7pCty+5TLPg5EzRJMGLUSv+49wjtvv8ozz/6HyMjgrluZkXGGjz5+F69OIbyjGBNX1rSRBiztE8heeZpPPnmXf//7/wK24aF6jZpszTiNoighdy0xSBILFy5EURQWLVqEJcQ+H0CGt2TYR9Wq1ct0u35rkevYsSP79+9n//79F/z/3FdFkZWVyZy5M9Fak9Ga49UOR1WSrEEX14z09JMVqv5XQUE+7737GsePH2FY/YigSOKCWb0YIyMaRpKVcZp33nm1zGs2laeioiI+/PBt7C471g4JQTE7VVEUfHYP3kIXjsMFATm28q90MUYsbeI4dvwoX3w5NmAnzdSqVQeb10t+gMZ3PfSShMPhYPbs2TgcjpAss5Lu8RBusRIXV7a5gN/OCm+//ba/Nh1UfvzxO7xeBXNCM7VDCQhaa2W0YYlMn/4zrVu3IyKibAd9BpqCggLee/d1MjNOc0eDCFKiDGqHVCHUiNRzV6MIvtuVxdtvj+aFF0YTHR2jdlhXxev18sUXY8jKzsTaOQmNNTiK/TqPFOIrLun+K96aBYCxZuCPj9YnhRHWNJYdW7cyffrP3HrrcLVD+pv69UsmyqW5XURqxA1hMFEUhZNeDw3rNy/z1lRRtMqPtmzZxObNG9DFNEDWhakWh6Io+Nx2fM4CXLkHVb1DliQJQ0ILnE4nU6eGdhmaoqIiPvzgTZHEqSQ5XM/djSIpys/lvXdfD7qlmaZN+4k9e3ZhbhYbVMtuudKLL/s4kBlrhmOoYWXevFls2LBW7XD+Jj4+kbjYOI55QndmdqjK9Hqweb00adKszLctEjk/sdlsTJnyNRpjJPqYeqrG4s47iOIuQvE6cZ7eiDvvoKrxyIZwdDENWL9+Ddu2bVE1Fn9xuVyM+eQ9Tp1K47b64dSIDI7WlFBT2apjRMMI8nKy+PCDt7DZbGqHVCo7d27n99/nYKhhxVjdqnY4V0XxKpd9HOjCmsaiizYy+ZsJATeTVZIkWrZqQ5rHjTMEu1dD2WGXC1mWadq0RZlvWyRyfvLTT9+Rl5eHIbE1kqTuj9lTeOqyj9Wgj62PxhjJ5G++org4tFZ88Pl8TJz4OYcPH+SWuuHUEi1xqqoarue2+uGkp5/iyy/G4D07cyxQFRUV8dXEz9GGGwhrElzdwaFAkiXCWsfh8rj5auLnATdernXr9vgUhYPuwJ5FLvyPT1E44HHRsGFjLJayXwygXDKM7du38+OPP+JyudiyJTRbYP5s+/YtrFiRij6mHhpTAJyIFc/lH6tAkjQYEttQUFDA99//V+1wytS8ebPYuHE9N9aw0CA2eLrEQllKlIGba1nZuWsHv/76o9rhXNb06T9RWFhIWKtYJI2411aDJkyHqXEUB/bvY/XqFWqHc4Hq1WtQqVJl9rpcaocilNIJj5sir5dOnbr4Zft+P0tMnz6dF154gYkTJ1JYWMijjz7Kzz//7O/dqqagIJ+JE78s6VKNbaR2OAFNY4pGH9OAtWtXsW7darXDKROHDh1kxoxfaBRnoENls9rhCH/SItFEqyQTCxbMDdhahseOHSF12WKMNa1oI0VLrpoM1azoYoz89PP3AdUlL0kS3br1JMPj5rQYKxcUdjodhFvDad68lV+27/dEbsqUKfz0009YLBZiYmKYPn06//3v5VtgfvnlFwYOHHj+q2XLlrz22mv+DvW6KYrCpK/HU2yzYUhqhySLek9Xoo9tgMYUy3//O4ns7Cy1w7kuHo+HSZM+J1yvoV+t8JCr8xQKetWwEmfW8fWkL3A6A69ratas6cg6DaYG0WqHUuFJkoS5SQzFRUUBVy6pQ4fOmIwmtjoCc7UV4X+yPR6Ou11079ELrdY/hUL8nsjJsnxBn3BSUtIVC8EOHTqUmTNnMnPmTD744ANiYmJ47LHH/B3qdVu8eCE7tm9FH9cUjTFS7XCCgiTJGCu1w+X2MH78uIAfv3Q5K1Ys5fTp0/SpGYZJK7rEApFeI3FzLQu5eXksWjRf7XAukJ5+ki1bNmGoaUXWieMnEGijDOgSzCxYOBe3O3C6Mk0mEzf27MMRt4ssj/pDZYRL2+iwYdAb6NGjl9/24fezRWRkJHv27DnfOjFr1qyrqh02evRonnzySaKjA/sO9cSJ4/z08/doLZXQRdVWO5ygIust6BNacvDgfubMmaF2ONfE6/Uya+Z0qkboqRstusQCWbUIPXVjDMybNyugWuVWrVoBkoSxZmjXVgw2xlrhFBUWsmPHNrVDuUCvXn0xGU2sdQRPeZeK5ozHzWG3i959+hEWVvaTHM7xeyL34osv8uyzz3Lo0CE6derEmDFjeOmll0r13tWrV+NwOOjbt6+fo7w+TqeTL74ciyLpMCS1EV1q10AXUR1tRHVmzZrOgQP71A7nqu3atYP8gnzaVzKJ338QaF/JjMPhYPPmDWqHApQMy9iwcS26OCOyUQzJCCS6OBOyXsPGjevVDuUCYWEW+g8YzAm3m2MB1FoolFAUhVV2G+FWK7179/Prvvy+3kvNmjWZOXMmR48exev1UqNGjVIPHP3xxx+vaU3WmBj/Zb4X88UX33E6/RSm5K7IWjFL8VoZE1pid2Tx1Vef89ln4zCbg2eywM6dmzHpNNQWrXFBoWqEjkijlu3bNzFggPo3ijk5OWRmZGAW5UYCjiRLaOON7Nu/h7i4wKrpN3z4raxamcrKMxlU0urQiZvIgLHH5eSMx82TDz5G1ar+XZ7T74nckCFD+O2330hJSTn/3IgRI5gzZ85l3+dyudiwYQPvvPPOVe8zO7sIXzkt8L1r1w7mzZuHLrouWktiuewzVEkaHYakdmQdXcynn37O/fc/rHZIpbZ3z14qWzRoZXEiDQayJJFs1bJ//34yMwvVDoedO3cDoI0QhaMDkTbCQG5aDkePpvu1i+xa3HnXA7z77uussxfTyRxYsVVUhT4vaxw26tdrQKNGrcrkHCPL0iUbqfzWtXrPPffQokUL9u3bR4sWLc5/NWvWjKioqCu+f9++fVSvXj2gW2XsdjuTJn2JxhCOIa6x2uGEBI0pFn1MPVauXMb27cFTczD9dDrx5sBf0Fz4n/gwLbm5uTgcDrVDISurZMa2HKZTORLhYmRLyd92IM6sr1u3Pj169GKH08EJ0cWqOp+isKS4CEmr5d77RpbLUBu/JXKfffYZs2bNolWrVsyePfv814IFC65YfgTgxIkTJCYGdgvXb7/9Ql5ebsm4OFlcxMuKPrYRGkME3347OaAGo1+Koih4vV70mtBqjXN6fBiNRgYMGIDRaMTpCawK99dLd7b11BMAs/7OxSCFwDGkuC88bhR38B83klxyqQyEY+Vihg69g6TEJJbYiykOsJUoKppNDjunPG5G3HkfcXH+7VI9x2+JnMVioUqVKnz7bcnC6OvXr2f16tU4nU5k+cq7vemmm/j444/9Fd51O378KIsXL0AXWQuNKVbtcEKKJGvQJ7YkJyeLOXN+UzucK1IUBUmSCLIlJa/I4VHo1asXDz30ED179sThCa0P6FNKPk8gDCs6f9euBP/PWHH7LjhuQiGR4/yxEphlYfR6Pf949F+4JZk/igvxhsBxFIxOuF1sdNho374THTveUG779ftRuWLFCm655RYWLVrE4sWLueWWW/jjj8AqrngtfvllKpKswxDfRO1QQpLWHI82vBoLFswnNzdH7XAuS5ZloqOiyHMEbw28izFqJRYuXMiECRNYtGgRRm0AZDxlKM/hxWg0YjaHqR0K0dElkxy8tsBs8bkakk6+4LiRQqAmntde8ns593sKRFWqJHPvfQ9xyuNmtV2UJClv+V4vi2xFVK5Uhbvvvr9cqxf4/S9szJgxfPfdd3z++ed8+eWX/PDDD4wbN87fu/WrvXt3s2vXDnQxDZA0YnCyvxjiGpfUZ5s1Xe1QriipUmXO2EIrkTNoZRwOB7Nnz8bhcGAIsSLHp4u9JCVWCohyMfHxCQB4C4N/ySVJd+FxExKJXIEbvcFAeHi42qFcVvv2nejdux87nQ52OcWqD+XF6fMx31aI1mjkiX8+g8FQvtUr/P4X5na7qVWr1vnHtWvXDurq/QDz589B1hrRRdW68ouFaybrLWgjarJy1XIKCvLVDuey6tdvREaxm3xncB/bFYXD4yOt0EWDhoGxHnJSUiXMYWF4MsXFNxB5sx3UqV03IJL+Kxk69HaaNG7GCltxUE1+0CJd9nGg8ioKC22FFPh8PPb40+U2Lu7P/J7IGY1Gduz43wLVO3bswGQy+Xu3fnPmTDo7dmxFG5kiJjiUA310HbweD6mpi9UO5bKaNGkOwJ6swJ+cIcC+bCc+5X+/N7XJskz9eg3xZDhQxPimgOItduMpcFG/fkO1QykVWZZ5+JHHqVwlmYW2oqBZwquaXn/Zx4FIURSW24pIc7u5976R1K1bX5U4/J7IPfvsszzyyCPccccdjBgxgpEjR/LMM8/4e7d+s2bNKgB0kaI1rjzIhnA0YfGsXr0yoC9wlStXoWaNmmw6LS7EwWDjaQcJ8QnUqlVH7VDOa9u2A16HB3eGaJULJM7jRQC0bt1O5UhKz2Qy8a9/PYfZGs48WyFFvsDvKWioNxIhy5gkiRvMYTTUB35x/Y0OG3tdTgYMGFKukxv+ym+J3FtvvcWxY8do1aoVc+fO5eGHH+bBBx9k3rx5tGnTxl+79bt169agCYtH1gVvq2Kw0VqrkZFxmuPHj6kdymV1696LTJubg7nB051REaUVujlR4KJrtxsDqqusadPmmMxmnEfUL1AslFB8Cq7jRdSpW5/Y2Di1w7kq0dExPPX083i1OuYWF+IM8LIkkiQRJstEaTQ0NAT+Uod7nA42Oux07HgDAwfeomosfkvkvF4vw4YN44EHHmDLli3ccMMNdOvWrVTFgANVdnYWZ86ko7VUVjuUCkVrLfl579694wqvVFfbth2IiY5m2XGbaJULYKnHigkLC6NLlx5qh3IBnU5Ht6434kovxlsU/JMeQoHrVDHeYjc9b+yjdijXpEqVqjz+xNPk+3z8bhNlScrKcbeLZbYiGjZozD33PKh60um3RO7ll19m+fLl3HzzzUyYMIEbb7yRr776iry8PH/t0u/27i1ZRkdjLv/BjNdD8bovLNDpDa6LhKw1ojFEsGfPLrVDuSytVkv/AbeQVuhiT7YYKxeIDue5OJjrpG/f/hiNgdd1c+ONvdHIGuz789QOpcJTFAXHvnzi4uNp3ryl2uFcs/r1G3L/A49wyu0m1VYkbjKvU5bHw0JbEVWqVOXRUf9Cq1V/rLxfx8gZDAYGDx7M1KlTGT9+PJmZmQwZMoQXXnjBn7v1m2PHjiLJWmRDhNqhXBXF576wQKcvuBI5AMkYzdGjR9QO44o6dryBypWrsPBIMe5QqxAc5LyKwu+Hi4iNiaVnz8BsYYmMjKJr1xtxHisSrXIqc50sxpPvZNDAW0tVxD6QtW/ficGDh7Hf5WSDw6Z2OEGryOdlnq0QS3g4/3ry3wEzcbPcjs5atWrRtWtXWrZsybJly8prt2UqLe0EsiE8YKt7X4ok6y4s0CkH33qOGmMkRUWFAV+GRKPRMGLEveQ5PKSeHSQtBIY1aTYyit3cNvwudLrAnRF3880D0em02HYFdiHsUKb4FBy780iqVIm2bTuoHU6ZuPnmgXTs2IVNDjv7neqvLxxs3IrC78VFeDQa/vXk8wE1TMzvGUl6ejrjxo2je/fufPzxx3Ts2JHU1FR/79YvMjLOIOmsaodx1SSN7sICnZrgS+Tksz/3zMwMlSO5snr1GtCpUxfWnLRxMgQKvIaCLJuH1OPFtGjeihYtWqkdzmVFRETSt09/XCeLcWeLC64aHIcL8BS5uG3YiKBvjTtHkiTuuecB6tSpR6q9mDMecW4qLUVRWFJcSJbHzSP/+CfJyVXVDukCfjtC582bx/33389NN93EyZMnGTNmDL/88guDBg1CHwT1Yf7K5/ORl5eDrDOrHUqFJJ39uWdnZ6scSekMH34n4eER/La/EJfoYlWVx6cwfX8BeoOJO++6T/WByaXRp8/NhEdEYNueI8Y0lTOf04tjbx716zekceNmaodTprRaLaNG/YvIqGgW2IqwBfhM1kCx2WHnsNvF0GF30LRpYNSe/DO/JXIff/wxnTp1YunSpbz99ts0btzYX7sqFzZbMT6fD0kbeAOkK4JzP/fCwsDuWj3HbA7jwYdGkW1zs+CwKCehptRjRZwqLCnYGRkZON0hl2M0Ghl66+14ch3n65gJ5cO2OxfF4+P22+8OiqT/almt4Tzxz2dxyzILi8VM1is57nax3mGjXdsO9O7dT+1wLspvidzChQu5//77iYyM9NcuylVxccnJVNIYVI6kYjq3pm1RUfBc1Bo0aESfvv3ZdNrOdlHkVRX7c5ysTLPRuXNXWrUKrvqV7dt3okbNFBy7cvG5RctJefDkOXEeLaBH915UqZKsdjh+k5xclXvve5h0j5t19mK1wwlYhV4vi23FVKmSzD33PhSwib3fErlA/cDXyuE4O1ZFLMulCkmSkWQtziAbpDt48FBq167D7INFnCkWY1LKU47dw/T9BSQnV2XEiHvVDueqybLMnSPuw+v0YN+bq3Y4IU9RFGzbswkLs6he4LU8tGvXge7de7HN6eCwS5RL+quSNVSLQKdj1KgnMRgCtxEnNEZxlgOns+RAlySNypFUXCWJXHCdcLRaLf/4xz8xh1n4cU8BxaJlpVw4PD5+3FOArDUwatSTQTkuF6BGjZp07tQV58ECvIVixRB/cqUV485ycOstwwkLs6gdTrkYPvxOqlerQaq9mAJv4C/jVZ7W2IvJ8Li5/4FHSEhIVDucyxKJXCn5zg0KDbLSI6FEkiS8QXiyiYyM4vEnnqHIDT/tycfjE2NS/MmrKEzbV0CW3cOjo54kPj5B7ZCuyy233IbeYKB4e7aY+OAniseHfVcuyclV6dy5q9rhlButVss/Hv0nst7AQluRGC931mGXkx1OBzfe2DsohmT4PSvJzMxk5MiR9O7dm6ysLB544AEyMgK/hMRf/e8EGlpdxsEleH/2NWvW4v4HHuF4vosZ+/PxiROmXyiKwvyDhRzIcTJixH00aNBI7ZCuW3h4BIMG3oL7jB33aTHW0h/s+/Pw2tyMGHFvyJQbKa24uHgeePARMj1uVovxchR4vaTai6lWrTrDho1QO5xS8fsR++qrr3LjjTdiMBiIiIigXr16vPTSS/7ebZnTaM51qYquMbUoiu9Pv4fg07ZtB269dTg7M50sOiKWyvGHFSeK2XjaTt++/enW7Ua1wykzPXr0Jj4hAfvOHBTRolumvDYPjgMFtG7djjp16qkdjipatGhNr1592el0cLACj5fznB0XJ+sNPPpoYCy/VRp+T+ROnjzJsGHDkGUZnU7Hs88+S3p6ur93W+bOV4L3BV/XXqhQfB50uuArZvxnffv2p3v3Xqw5aWNlmlgqpyxtOGVjybFi2rXryC233KZ2OGVKq9Vy27A78RS6cBwpUDuckGLblYMsyQwdervaoajq1ltvJyWlFqn2YrK9HrXDKXeKorDCVkSmx81DIx8lLi541lT3eyInSdL/xpdRUj7CF4RFCMPCwgBQvBX3bkVNis+L4vME/SBkSZK44467adu2A4uPFrH+lEjmysL2DDvzDhXStEkz7r//4ZDsHmvWrAX16jXAsTdflCMpI55cJ64TRfTudROxsXFqh6MqrVbLo48+iSksjAXFRTiC8Dp9PXY6Hex1Obn55kE0a9ZS7XCuit/Pdr169eKZZ56hsLCQH3/8kXvuuYe+ffv6e7dlLiIiEgCfJ7jKX4QKxVvycw8Pj1A5kusnyzIPPPAIzZq1YN6hQjYH6LgnnSxd9nGg2JXpYMb+AurUrc8/gqg75GpJksRtt92Jz+XBvk+UI7leiqJg25lDmMVCv34D1A4nIERFRfHY409ThMJCW8UpFnzc7WKVvZimTZszaNCtaodz1fyeyD3yyCPccMMNNG7cmNWrV3PbbbcxatQof++2zBkMBqzhEfhcoltDDT5nyc890KeBl9a5siSNGjVh9oECtp4JvGSubrThso8DwZ4sB9P2FVCzZm3++c9ng7bMSGlVq1adDu074zxUgNdW8bq/ypL7tA13pp1BA2/BZBJLL55Tq1Yd7r//YU663aTaQn8sb6bHw0JbEZUrV2HkyMeCsjW/XG5dBw0axKBBg8pjV36VXCWZfYdPqh1GheRz5gFQuXIVdQMpQzqdjscee4qxYz9g5u6dADRLMKkc1f+0SjKx5mQxTq9C12oWWiUGTmxQksT9sreA6tVr8ORT/8ZorBjL5w0ePJR169dg252DtVXwjOMJJIpPwb4rl7j4eLp06aF2OAGnfftOZGdnMX36zxgkiY6msJAr8g+Q6/Uwz1aIJTyCJ596HpMpsM5xpeX31LNhw4bUr1//gq/mzZtz2223cejQIX/vvkzVrl0XryMXn0eMkytvnuIzxCckYrWGqx1KmdLr9TzxxDPUr9+QmfsL2BJA3aySJGE1aIg1a2mdZA6oE/nuc0lcjZo8/cyLFapFJSYmll49++I6XoQnT5yLroXzWCGeAhfDht4Rsl3x16tfv4H06tWXHU4H6+y2kGuZy/d6mVNciMZk5tnnXiIqKlrtkK6Z3xO5u+++m1GjRrF27VrWr1/Ps88+S79+/XjwwQcZPXq0v3dfpho3bgqAt+iUypFULIrXjc+eSdMmzdUOxS/0ej1P/PNZ6jdoxMwDBWxKFxMgLmdXpoNf9+ZTo0YKTz/9QoVK4s7p128A5rAwbDtyQu4C62+Kx4djTx4ptWrTokVrtcMJWOfGZHbt2oMtTjtrQyiZy/N6mVlcgGIw8vQzL5KYmKR2SNfF74ncunXreOyxx4iIiMBqtXL//fezd+9eevbsSUFBcI03q169JrGx8bjzD6sdSoXizj+K4vPSpk17tUPxG71ezz//+QyNGzVh9sFCNojZrBe1I8POtH35pKTU4akKmsQBmM1hDBp4K+5MO+7TgXWsSBrpso/VZt+Xh9fh4bZhIwKqlTkQSZLEXXfdT/fuPdnqtLPcVhz0xcwzPR5mFBUgGU089++XqVq1mtohXTe/J3J2u/2ClRwyMjLOr5cZbMstybJM9+498doy8dpz1A6nQlAUH568A1StWp2aNVPUDsevdDo9jz3+NE2bNmfuoULWnQysC7Tatp6xM31/AbVr1wvq8SxlpWvXHiQkJGLfmRtQRYL1SWGXfawmr82D42ABbdq2p1atOmqHExQkSWLEiHvp128gu10OFhYX4gnSZC7N7WJWcQGmiAheeHE0yclV1Q6pTPg9kXvooYcYPHgw//73v3n22WcZMmQIjzzyCOPGjaNFixb+3n2Zu+GGboRZrDgztoZMM3Mgc+cdxuss4OabB1WIu2edTseoUU/Sonkr5h8uZHWaWDIHYPNpOzP3F1CvXgP+9WTFmdhwOVqtlttvv6ukSPDBfLXDOc9Qw4ocpkUyyIQ1i8VQw6p2SOfZdmSjkTUMvbViF/+9WpIkccstt3H77Xdx1O1iVlEBtiCrM7fX6WBucSGxiUm8+J9XSUqqpHZIZcbvozyHDBlC06ZNWbFiBVqtllGjRlG9enXS0tJITAy+UhJms5khg4cyZcrXeAqOoYuornZIIcvnceDO2knt2nVp2bLijGXRarU88o8nGD9+HAs3rUcBOlYJnFaN8rYp3cbsg4U0atiYxx5/OuRLjFyNJk2a07Rpc3bs2oY+2YLGpP7AfUmSkM/GYawZOJOT3Bl2XCeLGTx4KDExsWqHE5R69uxLdHQME8Z/xvSiAvqaLcQE+GQRn6Kwzm5jq9NO/foNGTXqSczm0BqSUS4FU5xOJ40aNaJu3bqcPn2an3/+mSpVqgTtbKEbbuhGzZq1cJ3ZhM9VpHY4VyZpL/84ACmKgvPUOmS83HXX/RWiNe7PtFotDz/8GK1bt2XRkSJWVdCWufNJXKMmPP6ESOIu5vbb70ZGxrYtW+1QApbi9WHbmk1sXBx9+vRTO5yg1rJlG55/4RXksDB+Ky7gcACvzepUfPxeXMhWp52uXXvw5JP/Drkkjv9v786jo6qy/YF/q1KVsVKZAwkZgEAShgSiAQIJJCEhIUQS5tGoBAjIKDLIpAjq+/Xy+Vqezetetus9fW37w4VL5SfYSjuCKCqignRkEhIgM5lqvnWH8/sjEJuWKVBVt27V/qzVy65K1b279FTdfc+wD1yQyG3ZsgULFy5EVVUVnnzySVRWVmLfvn3OPq1T+fj4YOnSldBqfGCr/wpMcu/CnJrg2Fs+dkf21moI5gbMnfsg4uLi5Q5HFhqNBlVVKzBiRBY+umDyumHW7xut2HfOiLSh6Vi58vFf9zsm14mO7oXy8hmw15vB1XlXG7lT1p87IJjseOThxdSOHKBfv/54attziEvoiwNmI761mt1uqlG7KOAdkwGXRAEPPrgAFRWViu08uh2nJ3JHjhzBJ598ggkTJuDPf/4zXn31VY+Y3xIZGYWqquUQre2w1R0BY+47X0AbOgAqrQ4qHz/49c6ENnSA3CHdEt9ZA3vLTxg1agzy8grlDkdWPj4+qKpajszMkfj7BRO+9pIFED82WbHvrAFDh6ZjBSVxt1VcPAlxcfGwHm+FxClrEZmz8W02WM92Ijs7F4MHD5U7HI8RFhaOjZueQnb2OByzWfGB2QjOTa6DF+wc3jEZIPj5Y/36LRg/foJHj+o4PZGLiopCYGAg+vfvjzNnzmDUqFFobGx09mldIiPjfsyb9xAEUx24xu/c7o7kGpVKBbU2AGo/PXzDBrh1gxZM9eAavkVycioqK5e4dayu0pXMrcB9943Ah+eN+M7D68ydbLHh/501YNCgIVixgpK4O6HRaLB48XIwnsH8Q4vb/ha5GhMkWI5dQWhoKObOrZA7HI+j1fqisnIJ5s9/BJdEAe8YDWgX5RuhYozhW6sZH5qNiIlPwNPb/w9SUgbJFo+rOD2R02q1OHr0KJKSknDo0CEYjUZYLJ5zISosLMYDD0wB33EeXONR+gG9B4KpHrbLhxEXn3B1KE0rd0huQ6PRYOnSlUgbmo73zxlxotl9doBwpNOtHN45bcCApGSaE9dD8fEJmD5tFuz1FnA1RrnDcQvm460QTHYsXrTMI+dGuQOVSoWCgiJs2LAVYkAA3jYZcEGGeXN2JuEDsxHHbFaMGTMWmzY9jfDwCJfHIQenJ3Lr16/Hm2++idzcXJw6dQpZWVkoKytz9mldaurUmd3JnK3hG7ceZnVXvOFSdxK3ft1mBAXp5A7J7Wg0Gixf8TiSUwZh7xkDzrS57yTju1HTacdbpzqRkJCIx9ZsgJ+f8qdguFpxcSkGD0mD5USr12/fxdUawdUa8UDpFAwaNETucDxecnIqtj39b+gTn4gPzUZ8dxc7QaT6+iPVt+ff+w5R7J4PN3/+w1i4cKlX3QQ6PZE7efIk/uM//gNarRZ79uzB119/jdWrV9/2fZ9++immTZuGkpISPPvss84O856oVCpMnToTU6bMgNBZA9vlL9x+AYQ7sbefg63uS/Tr1x8b1m+GTkdJ3M107c26FvHxfbHn505c7LTLHZJDNJp47K7uRGR0r6vFfqn35G6o1WpULV6OYJ0epm+avXa+nNDBwfxjK5KTU1FePl3ucLxGeHgENm3ehtGjc3DUZsFHPSwenOLnj5Qe3sBd5u14x9QJ3s8P69ZtRkFBsddNyXF6Ird79+7rHgcH37445KVLl7Bt2zb88Y9/xHvvvYfq6mocPHjQWSE6hEqlQlnZNDz00EKI5kZYL34GSbDJHZZbY4yBaz4BrvE7pKdnYP36LdQTdwcCAgKx5vGNiIiMxu6fDWixKPumod0m4o3qTgTq9Fi3bguCg92n9pgS6fV6rFyxFrBJMH3b7Fa7PriCxIkwfd2MEH0Ili1bDR8fH7lD8iparS8WLXoUM2fOxXknFw++VuQ3PLo3nnzqOaSmDnbKedyd0xO5fv36YevWrdi7dy/+/ve/d//vVj766CNMmjQJvXv3hlarxYsvvohhw4Y5O1SHyMsrwIoVa6ASDLDVfgSRc5+K6+6ESQJs9Udgb63G2LF5WLnycfj5+ckdlmLo9XqseXwjtP6B+Os/OmG0K7PnxcpLeOMfnRBUWqx5fJPXzGlxtqSkAXjkkcXgW6ww/3jFa+buMlGC6UgTVHaG1avWQa8PkTskr6RSqVBSMhnLV6xBG4C9JgM6HbglJ2MM31kt+MxiQuqgIdiydQeioqIddnylcXpRlY6ODnR0dKC2trb7OZVKhaKiopu+p7a2FlqtFkuXLkVDQwPy8vLw2GOP3fE5IyLk7dUpKspHv35x2L59B4y1n8AvdjQ0uhhZY3InkmADV3cYguUKFixYgKlTp3pdV7gjREUFY/uOHdj4xBN4s9qAh9NC4etmG5TfiiAx7DnViXZOwjPPbEdamnfeTTvLlCmlMBha8dZbb0EdqEFgapjcITkVYwymoy3g2zls3rQJmZnpcofk9YqLxyMxMRbbn96OvWYDHggKRoTPvaUdjDF8aTXjJ86G/Px8rFq1ymPrw90pFXPDW7WtW7fihx9+wOuvv47AwEA8+uijmDx5MqZNm3ZH729tNUFyg+GE1tYr2Lnz31FXfxl+0ffBN3ygbLFYaj8BAAQmFsgWAwCItg5wdV9AxeyoWtxVH43cmx9++A67/vB7DIr0w8zUEIcmxa+eaAMALEgPd9gxga4f433njPi+0YqFC5ciO3ucQ49PukiShFde+SO++eYrBN0XCf++rhm27jxUDwAIGeea4uOMMZh/vALughFz5lSgqKjEJecld6au7jJe+PdnwZnMKAsKvuttvRhjOGQxo9puw4QJJZgz50Gv6QRQq1U37aRy+tBqS0sLqqqqUFxcjNbWVixcuBAtLS23fE9kZCRGjx6N8PBw+Pv7o7CwECdOnHB2qA4XERGJzZufRnracHBNx2BrPObVK1oFUz2sFz9BUIAWmzdtoyTOQTIyMjFz1jxUX+Fw6JIyKvsfbbDi+0YrSkvLKIlzIrVajcrKJRg8JA3mH66Aq1PAloJ3wVrdDu6CESUlkymJc0N9+sRh0+bt8A8Oxj6z8a5qzTHG8JW1K4krKZnsVUnc7Tg9kdu+fTsKCwvh5+cHvV6P1NRUbNmy5Zbvyc/Px+HDh2EwGCCKIr744gsMGaLM5eMBAQFYtWotiotLwbefhe3yYTCRlzssl7O3nYX10heIi43FtqeeRd++/eUOyaMUF5ciKysbn9WacbrVvctO1Hba8eF5E4alD8fUqbPkDsfjabVarFyxBv37D4D5aAvsjZ5TxxMArKfbYT3dgdzc8ZgxY47c4ZCbiI7uhfUbtkITGIi/mU09XgBxnLPiBGdDYeFEzJgxh5K4f+L0RK6urg6zZs2CWq2GVqvF+vXr0dDQcMv3DBs2DIsWLcK8efMwadIkxMbGYvp05S4hV6vVmD17PioqKq+uaP0UEu+ZBV3/FWMMtqYfwDUdw7Bhw7Fp0zaEhTl2mI50zTt95JHFSEhIxLtnDWizuudKVqNdxFunDYiMisLiqhVQq53+E0QA+Pn54/E1TyAuLgGmb5pgb/KMZM56tgOWf7RjVNYYVFRU0sXdzcXExOKxNU/AplbhQ7MR4h3O7KqxczhitWBE5ijqibsBp/+KqlQqSP+UeZtMpuse38yMGTOwf/9+HDhwAE899ZRH/ODn5xfiscfWw0eywHbxY0icQe6QnIpJImz1R8C3nUZBQRFWrlzrEfvsuitfX18sX74Gao0f3jplgOAG80T/mcQY3j5lgF1SY8WKtVRp38UCA4Owft1mxMb0genrZvAK3x3EerYDlp/akJk5EosWPuoR1whv0K9ffyxavAxNAo8j1ttPBTGIIj61mpEQn4hFi+m/8404/d9IUVER1q1bB6PRiDfffBMPP/wwSkq8dw5DWtowPPHEkwjwVcN68ROI1ja5Q3IKJgmwXf4CguEiZs6ci3nzHqYvoAtERUVj4aJlaDDx+OiCe82HOnTRjJpOOyoeWoi4uHi5w/FKOl0w1q/bipjesTAeaYK9WZk9c9eSuPvvH4GqqhVUK05hMjNHYcKEEvzE2XCJv3lRc8YYPrOYoNL6YvmKNbTv8k04/cq6dOlSjBs3Dmlpafjqq68we/ZsLF++3NmndWv9+vXH1q07EKLXwXbpc4iWK3KH5FBM5GG9dBCipQmVlUtQUjKZusJdKCPjfhQUFOGbegvOusk2XhcNdhy8ZEZWVjbGjBkrdzheTa/XY8P6rYiNiYXpiPKGWa1nfu2JW7JkpdeXnlCqGTNmo3ev3jhotdx094dquw31Ao+58x7y6jpxt+P0RO7NN99EYWEhdu7ciZdeegmzZs2iizq6Jn5u3rQNEeFhsF7ynGSuK4n7HMzWiiVLViInJ1fukLzSrFnzEBsTi/fOGWHh5V0pzYkS9p4xIjw8AhUVC+j77wb0ej2e2PAk+sTGdyVzClkAYTnVDsvJNowYkYWqqhWUxCmYVuuLhx5eBKMo4Ljtt8P8HJNw1GZD8sAUuo7chtMTuW+++QaFhYXYvHkzfvzxR2efTlEiIiKxadNTiIyIgO3yIcUPs14bTpVs7Vi2bDVGjsySOySvpdX6YnHVClgEhg9+Mcoay8cXTGi3Cli0aBntoepGdLpgbFi/FfHxiTB93QR7vXuXrrH83A5rdTuysrJRVbWckjgPkJo6GMOH34/jdhvs/1Ka66TNBqskYs7cCrr5uw2nJ3IvvvgiDhw4gCFDhuDZZ5/FAw88gP/93/919mkVIzQ0DBs2bEGIPhi2y4cg2eW96N4txiTY6o5AsLRg8eJHcd99I+QOyeslJvbFAw9MxU8tNpyRaYi1ttOOow1WFBRORErKIFliIDen0+mwYf0WJCb0g+nbZnB17pfMMcZgqW6D9ed2jBkzFosWPUpz4jxIWdlUcJKEau7XvclFxvCTnUNa2jAqVXUHXDL7PCQkBLNnz8aSJUsQGBiIV155xRWnVYyIiEhs2LAZfr4+sF3+Aky8+eRPd8U1/wjBVIf58x9GVla23OGQq0pLy9Entg/2/2ICJ7p2iFWQGPadMyEiIgLTplG9OHcVGBiEdes2o2/f/jC7WTLHGIO1uh3WUx3IyclFZeUSWjTlYfr27Y+kpAH42W7v3hP4Am+HVRIxYYL3LozsCad/I6qrq/HMM88gNzcXe/bsweLFi3Hw4EFnn1ZxevWKwaqVj4PxZtjqvlLUDhB8x3nwbWdQWDgRBQU330OXuJ5Go8HDjyyGwSbg4EXXXqCP1FlwxcKjomIhlZ1xc4GBgVi3dhP69U+C+dtmtxlmtZ7qgPV0VxL3yCOLKYnzUGPH5qNDFHBFFAEAZ+0cwkJDMXjwUJkjUwanfyuWLVuGsLAwvPXWW/jTn/4EjuMwZw5V376RlJRBqKhYAMHcCPuVarnDuSOirR1c0zEMGjQUc+Y8KHc45AYGDEjG2LF5+LrOihaLawoFd3IiDl2y4L77RiA9fbhLzknuTUBAIB5fsxGJfbuGWeVeAGE93Q7rz+3Izh5HSZyHy8i4HyqVCjU8B4ExXBZ43Hf/KPpvfoec/m/p008/RUVFBfbv34+CggJs374dOTk5zj6tYo0bl4/Ro3Ngv3ISgqVZ7nBuiUkCuPojCNbpsGTJcvrSubHp0+fAz88PB867Zg7mxxdMgEpNyb3CXOuZ69oBohl8izxFg62/dMLyj66FDQsWVNFvi4cLDtYjISERdYKAJoGHwBiGDEmTOyzFcOq34/z589ixYwfy8vLw3nvvwWaz4dNPP8Xq1audeVpFU6lUqKioRERkFOwN34JJ7rsvK9fyE0TOgKqq5dDrQ+QOh9yCXq9HWfl0nGu3O7223GUDj59abCie+AAiI6Ocei7ieIGBQVj7+Cb0iuoF05EmCO2uXSjDXTTCcrwVGRn3Y+HCpZTEeYmUlEFoEQU0CF2jBgMHJssckXI47RtSVVWFBx98EBqNBn/5y1+wf/9+BAUFITg42Fmn9Bj+/v5YtHApRLsJXPNPcodzQ6L1Cvi208jPL6R5DAoxfnwRIiMi8XGNGdId7nHYU4wxfFRjQrAuGCUlk51yDuJ8er0e69dvQYg+FKYjTRDNrrmhtDdZYD52BSmpg7B06SpanepFEhP7QWAM5+wcIiMiERSkkzskxXBaIlddXY0hQ4Zg4MCBSExMBACqBdMDKSmDkJs7HnzHWYi2DrnDuQ5jErim76EPCcWMGXPlDofcIa1Wi2nT56DJzONki+32b7gL59rtqO20o3zKDAQEBDjlHMQ1wsLCsfbxTfBV+8L0VRMku+jU8wmddpi/bUFsnz5YuWIttFqtU89H3EtsbBwAoF0S0ScuQeZolMVpidznn3+OqVOnYv/+/cjJycGqVavAce6xXZBSTJ8+G4EBgbA3f9+9LNsd8B0XIFrbMHfOg3SxVpiRI7MQ1ycOBy9ZIDq4TTHG8NlFMyIjIjFuXL5Dj03kERvbB6tXrYNkEWH6thlMcs7vkMSJMH3dBF2gDo+v2YjAQCoc7W3i4uKRl1eAjIxMqn7QQ05L5DQaDSZNmoTXX38d77zzDqKjo8FxHIqKirB7925nndaj6HTBmDp1BgRzM0Rzo9zhAOha4MC3nkT/pIEYOXK03OGQHlKr1Zg6bRZaLQJONDu2V+50G4d6I4+y8ulUdd+DJCenouLBBeCbrbBUO373GcYYTN82AxzDqpVrERYW7vBzEPfn4+ODhx5aiJUrH8fQoelyh6MoLplFOmDAAGzduhWHDh3CwoULsWfPHlec1iPk5hZ0LXxoOeEWvXL29rOQeCtmzZxLQ+UKNXz4/UiIT8DhyxaHzZVjjOHQRQuiIqMwejStSvc0ubnjkZdXANuZToeXJbGe6gDfYsVDFZXo33+AQ49NiDdw6XKggIAAzJ49G++++64rT6toGo0GU8qnQ7S1QzDVyRoLkwQIbacxZEgakpNTZY2F3D2VSoXJZdPQahHwDwfNlfulw456E4/SB6bQBHUPNXduBfrExcFy7Aokm2PqEfJXbLCeasfo0TkYOzbPIcckxNvQum4FyMrKRmRkNPjWall75fj2c5AEG8rLp8sWA3GMjIxM9O7dG1/WWR3Spg5fsiA0NBRjxox1QHTEHWm1vnh06WpABMw/tt7z8ZgowfL9FUSER+LBBxc4IEJCvBMlcgrg4+OD0tIyiNY2iJYmWWJgTITQfgbJyYMwYADV91E6tVqNkpIyNJp4/NJxb3v71hl51HTaUVRUSnPjPFxsbB9MKZ8Oe735nvdktfzcAcFkx4IFVbRoipB7QImcQowZk4Pg4BDwradkOb/QWQuRt2DSJKoN5imysrKh1+vxdd29zXk6UmeBv58fcnNppao3KC4uRZ+4OFhPtoGJd7cntGjiwZ3rRHb2OKpDScg9okROIbRaX0yYUAzB3OjyunKMMfDtZxAbG4e0tGEuPTdxHq1Wi4KCYpxrt9/1HqwGTkT1FQ7jcscjIIBKRngDjUaDuXMegmjmYfvFcFfHsJxsg1ajxfTpsx0cHSHehxI5BcnLK4RW6wt722mXnle0NEG0daC4eBKtVPUwubnjofHxwdH6u+uVO9bYNcdu/Hiq++RNBg8eirS0YeDOGsCEnvXKCR0c7PVmTJz4AEJDw5wUISHegxI5BdHpdMjJGQfReBGS4JzK/DfCt52BLliPrKxsl52TuIZeH4IRI0fjeAsHrofDZKLEcKzJhrT04YiO7uWkCIm7KiubBpETYLvQs1456+kO+Pn5YcKEiU6KjBDvQomcwhQWTgSTRPDt51xyPokzQDDVo2D8BNoyx0Pl5xeCE6Qeb9t1uo2DiRORl1fopMiIO0tKGojk5FRwvxjveOWzaBFgrzcjP38C7aVJiINQIqcwMTGxSEsbBqHjHJjk3L0PAcDedgY+Gg3y8+li7amSkgYiNiYWPzT2bAu97xutCA0NpXmTXmz8+CKIFh58k/WOXs/VGgEG5OUVODkyQrwHJXIKVFQ0CZJgg2Codep5mMBBMNRgdFY29PoQp56LyEelUiFnbB4uG+980YOBE/FLhx3Z2blUANiL3XdfJgKDgsBdNN72tYwx8BdNGDRoCA3FE+JAlMgp0ODBQ9GnTzz49tNOLRBs7zgHJgkoKprktHMQ9zB6dA5UKhWON1/fs5LRKwAZvX5b4+unFhsYA7Kzx7kqROKGNBoNRmSOgtBovW0pErHDDsHM01xbQhyMEjkFUqlUmDixFKKtE6K50SnnYJIIoeMchgxJQ1xcvFPOQdxHSEgoBg8eipMt9utuDob3CsDwGyRyJ1s49O3bD717x7gyTOKGMjNHQRIk8M23Hl6115uhUqmQkXG/iyIjxDtQIqdQo0aNQUhIGOxtzikQzBtqIPFWlJRQAWBvkZWVjQ6bgDrjrYdXW60CGkw8srJyXBQZcWfJySnQ+mpvO09OaLahf/8k6HTBLoqMEO9AiZxCaTQaFBeXQDQ3QbS2OfTYjEkQ2k4jPj4RgwYNceixifsaPvw+qNVqVLfeevVq9ZWuRRGZmSNdERZxc1qtL1JSBkO4cvPFMhIvgW+3YciQdBdGRoh3oEROwXJzx8PfPwD21mqHHlcw1kHkDCgtLacCwF4kKEiHwYOG4FSr/ZZzL0+1cujXtz/CwyNcGB1xZynJqRAMHCTuxivphas3Bykpg1wZFiFegRI5BQsICERBQREE42WI3N1tlfOvGGPg235GVFQ09bh4oeEZmWizCrhivfEF2WgXUWfkMTwj08WREXc2YEAyAEBou3GvnNBmg0qlQr9+Sa4MixCvQImcwk2YUAKNVuuwXjnR3AjR2obS0nKo1dQ8vM2wYRkAgLM3uSCfa7Nf9zpCACAhoS8AQOi8SSLXYUfvmBj4+/u7MCpCvANdqRVOr9cjL7cAgqEWkt10z8ezt/6M0NAwjBkz1gHREaWJiIhETEwMzrXbb/j3c+0cQvR6xMcnuDgy4s4CAgIQFR0NsePGiZzUyaNvYn8XR0WId6BEzgNMnFgKH7UP7K33toJVsDRDtDRj0qQyaDQaB0VHlGbo0OG4aODBS9fPk5MYwwWDgMFD0mnuJPmNhPhESDdY8SzZRYhWnsoYEeIklMh5gPDwCIwdmwuh8zwk3nLXx+GvVEOnC8a4cfkOjI4oTWrqYAgSQ52Bv+75FosAi12klczkhmJj4yCY7GCiBP/EYPgndpUZEY18998JIY7ntolcRUUFSktLUV5ejvLychw/flzukNzatXpvd1tXTrS2QjA3oqTkAfj6+joyNKIwyckpUKmAWsP1w6u1nV0X5NTUwXKERdxcTEwswADRLMAvMRh+3Ymc/de/E0Iczi3HzxhjqKmpwWeffUZDfHcoKioao0dn48jXRyBFDIZa07NJxfYr/0BAQCDy8gqdFCFRiqAgHWJj+uCioeW65y8aeISGhCAiIlKmyIg7u7bLh2i0Q6P/9WZQNPHw8fFBZGSUXKER4tHcskfu/PnzAIDKykqUlZXhr3/9q8wRKUNpaRmYJIJvO9Oj94m2DgimehQVlSAg4LfbMRHvM2BgCupM4nX15OpMApIGJNP8OHJDvXr1BgBIpuvnyUkmHpFRUbQKnhAncctvlsFgwOjRo/Ff//VfeO211/Dmm2/iyy+/lDsstxcT0wf33z8CQsc5MJG//Ruusrf+DF9fPxQUFDsxOqIk/folwcaLaLN11ZMz8xLarQL69x8gc2TEXQUEBEIXrINovv63RzKL6BXdW6aoCPF8bjlumZGRgYyMX+tUzZgxAwcPHkR2dvYdvT8iQues0Nze/PlzcOzYt+A7foFvRGr389qQGy/9l3gzBONFlJeVoV8/2gCddMnIGIrXXgMaTAIiAjRoMHVdnIcNG4KoKNork9xYn9g+ON9W2/2YMQbJzKNv3wRqN4Q4iVsmct999x14nsfo0aMBdP0Y9GSuXGurCZJ08y2GPFloaG+kpAzCufNnoA1PhkrV1emqDe13w9fb285ABRVycgrQ0mJ0ZajEjfn7h0KtVqPBxGNolD+azF3DZXp9NLUTclPh4VH45dL57seMEyEJEoKCQqndEHIP1GrVTTup3HJo1Wg04vnnnwfHcTCZTHj33XcxYcIEucNSjOLiSRB5CwTj5Vu+jok8xM7zGDFiFE1gJ9fRarWI6R2DFktXAtdsFhAaEgKdznt7u8ntRUZGQbDyYFdvpMWrNwDR0b3kDIsQj+aWPXL5+fk4fvw4pkyZAkmSMG/evOuGWsmtpadnIDIyGu3tZ6HV37wCP99ZA0nkMWHCRBdGR5Qitk88zp1sAgC0WEXExlNBV3JrkZFRAAMkiwAfnRbS1RsBWrFKiPO4ZSIHAI899hgee+wxucNQJLVajfHjC7Fnz/+FyHXCxy/kN69hjEHo/AXxCX2RlDRQhiiJu4uJicV3RwUIEkOrVcQgqgNGbiMqKhoAIFp4+Oi03QsfKJEjxHnccmiV3Lvs7HHw8fEB3/7LDf8u2dog2jqQn1fg4siIUvTq1RsMwGUDD06QEE0rD8ltXEvYpKtDqpJFQJBOBz8/PznDIsSjUSLnoYKD9Rg27D6IpotgTPrN3/nOGvhoNBg5MkuG6IgSXOtdudBpv+4xITcTFhYOlVrdPaQqWQREUW8cIU5FiZwHGzMmBxJvg2huuu55xiSIxkvIGH4/AgODZIqOuLtrC2BqryZykZG0IIbcmo+PD0JCQiBauxI5ZhVpWJUQJ6NEzoOlpQ2Dn5//b1avipYWSIINI0eOlikyogQhIaFQqVS4fHXT8/DwCJkjIkoQGRkFySKAMQbRIiAighI5QpyJEjkPptX6Ij19OERz3XVbLQnGOmg0WqSlpcsYHXF3arUaIXo9BKmrHElAQKDcIREFiAiPBKwSmF0CEyWEh4fLHRIhHo0SOQ+Xnj4cEm+DxHV0PydZGpGaOgh+fv7yBUYUQa/vWvGsD9bTHqvkjoSFhUO08ZCuDq+GhVFPLiHORImchxs6tKvXTTA3AujakkvkDN3PE3IrwVcTuWC9XuZIiFKEhYWBiQyiwd79mBDiPJTIebiQkFBE9+oN0dICABAtVwAAKSmD5AyLKIROF3z1n5TIkTsTGtqVuAntHICu3yBCiPNQIucFkgemgtlauyYfW6/A19cPcXE33/GBkGuurWoODKT5ceTOXEvchA77dY8JIc5BiZwX6Nu3HySBAxMskLh2xCckwsfHR+6wiAIEBARc909CbufavErRYId/QAC0Wq3MERHi2SiR8wIJCYkAAMnWAcZ1IvHqY0Jux9+/a0GMRuO2u/kRN6O/Op+S8RKCg4NljoYQz0eJnBeIubpHpmBphiTyiI2NkzkiohS+vte2VqIVq+TOBAQEQq3uurQEB9PcSkKcjRI5LxAUpENQkA6CqR5A1x6ahNyJa0PwNDxG7pRKpULA1TmVwTrqkSPE2Wi8xEtERkWjtuZ81/+nLXPIHcrMHIm2tlbk5xfKHQpRkKCgIJhNJtoCkBAXoB45LxEZ8es+mVRpndypkJBQzJw5l5J/0iO02pkQ16FEzkuEhHStJAsICIRW6ytzNIQQTxZ4dTs32taNEOejRM5LXJt0HBSkkzkSQoinU6u75lZeW/VMCHEeSuS8xLUE7tpqMkIIcZbExK4SR9dWzBNCnIcWO3iJa9vmREZG3uaVhBByb6ZPn4Oysum02pkQF1AxxpjcQThaa6sJkuRxH+ueCIKA06d/RlxcPG2ZQwghhCiIWq1CRMSNp0ZRIkcIIYQQ4sZulcjRhClCCCGEEIWiRI4QQgghRKEokSOEEEIIUShK5AghhBBCFIoSOUIIIYQQhaJEjhBCCCFEoTyyILBarZI7BEIIIYQQh7hVXuORdeQIIYQQQrwBDa0SQgghhCgUJXKEEEIIIQpFiRwhhBBCiEJRIkcIIYQQolCUyBFCCCGEKBQlcoQQQgghCkWJHCGEEEKIQlEiRwghhBCiUJTIEUIIIYQoFCVyCrdr1y6UlpaitLQUzz//PADgq6++wuTJk1FUVIQXX3zxN+/ZsGED3nnnne7Hly9fxvz581FeXo6KigrU1dW5LH4iD0e0mxMnTmD69OmYPHkylixZgpaWFpfFT+TRk3bz8ccfo7y8HGVlZVi2bBk6OzsBAPX19Zg/fz4mTpyIRx99FGazWZbPQlzHEe3mmp07d+IPf/iDS+N3e4wo1pdffslmz57NOI5jdrudPfTQQ2zfvn0sNzeXXbx4kfE8zyorK9nnn3/OGGOssbGRLVmyhKWnp7O33367+zjr1q1jb7zxBmOMsb/85S9s7dq1snwe4hqOaDeSJLHc3Fx25MgRxhhj77//PluyZIlsn4k4X0/ajdFoZNnZ2ayxsZExxtjOnTvZM888wxhjrKqqiu3fv58xxtiuXbvY888/L9tnIs7nqHZjMBjYpk2bWHp6OnvppZfk/Ehuh3rkFCwqKgobN26Er68vtFotkpKSUFNTg8TERMTHx0Oj0WDy5Mn48MMPAQD79u1DQUEBSkpKrjuOJEkwmUwAAKvVCn9/f5d/FuI6jmg37e3tsNlsyMrKAgDk5+fj8OHDsNvtsnwm4nw9aTc8z2Pbtm3o1asXACAlJQUNDQ3geR5Hjx5FcXExAGDatGnd7Yx4Jke0GwD45JNP0LdvXyxYsEDOj+OWKJFTsIEDB2L48OEAgJqaGnzwwQdQqVSIiorqfk10dDSampoAAIsWLcLMmTN/c5zVq1fjtddew9ixY/E///M/WLx4sUviJ/JwRLsJCwtDYGAgDh8+DAB4//33wfM82tvbXfMhiMv1pN2EhYVhwoQJAACbzYY///nPKCwsRHt7O3Q6HTQaDYCui/y1dkY8kyPaDQBMmTIFVVVV8PHxcflncHeUyHmAs2fPorKyEhs2bEB8fDxUKlX33xhj1z2+kSeeeAI7duzAF198ge3bt2PFihVgjDk7bCKze2k3KpUKL730El5++WVMmTIFRqMRoaGh0Gq1rgidyKgn7cZoNKKqqgqpqamYOnXqDdvV7X6fiGe4l3ZDbo0SOYU7duwYHnnkEaxduxZTp05F7969r5t03tLSgujo6Ju+v62tDefPn+++6ykuLkZLSwv1rHi4e203AKDRaPD6669j7969KCsrgyRJCA0NdXLkRE49aTfNzc2YN28eUlJS8NxzzwEAwsPDYTQaIYrib15PPNe9thtya5TIKVhDQwOWL1+OF154AaWlpQCAYcOG4cKFC6itrYUoiti/fz/GjRt302OEhYXBz88P3333HYCuL1xQUBDCw8Nd8hmI6zmi3QDA5s2bceLECQDAq6++iokTJ0Ktpp8UT9WTdiOKIpYuXYqSkhJs2bKlu7dFq9UiMzMTf/vb3wAAe/fuvW07I8rmiHZDbk0jdwDk7v33f/83OI7D7373u+7n5syZg9/97ndYuXIlOI5Dbm4uJk6ceNNjqFQq7Nq1C8888wxsNhuCgoJoabeHc0S7AYCnn34a27Ztg9VqpbtnL9CTdvPxxx+juroaoijiwIEDAIChQ4fiueeew7Zt27Bx40b86U9/QkxMDH7/+9/L9ZGICziq3ZCbUzGaDEUIIYQQokg0DkIIIYQQolCUyBFCCCGEKBQlcoQQQgghCkWJHCGEEEKIQlEiRwghhBCiUJTIEULIVdu3b8esWbO6C9YCgCiKmDNnDl588UUZIyOEkBujRI4QQq7auHEjrFYrXn755e7nXn75Zfj4+GDVqlUyRkYIITdGdeQIIeSfnD59GnPnzsUbb7wBxhiqqqrw9ttv49ChQ9i9e3f3VmRPPvkkkpKScOHCBezYsQNmsxktLS1ITU3Fzp074efnh6FDh6KgoACnTp3CCy+8gLS0NLk/HiHEw9DODoQQ8k9SUlKwZs0abNmyBZIk4bnnnkNtbS327t2LN954AwEBATh8+DBWrFiBDz74AHv27MGUKVNQXl4Onucxbdo0fP755yguLgbP88jPz8d//ud/yv2xCCEeihI5Qgj5FxUVFThw4ACSkpKQm5uL559/HrW1tZgzZ073awwGAzo6OrB+/Xp8+eWXeOWVV1BTU4Pm5mZYLJbu12VmZsrxEQghXoISOUIIuYG4uDgkJCQAACRJQnl5OdavX9/9uLm5GSEhIVizZg1EUURJSQny8vLQ0NCAf56xEhgYKEv8hBDvQIsdCCHkNnJycvD++++jubkZALB79248/PDDAIDDhw9j+fLlmDRpEgDg+PHj1616JYQQZ6IeOUIIuY2cnBwsXrwYlZWVUKlU0Ol02LVrF1QqFdasWYPly5cjMDAQOp0OI0aMwMWLF+UOmRDiJWjVKiGEEEKIQtHQKiGEEEKIQlEiRwghhBCiUJTIEUIIIYQoFCVyhBBCCCEKRYkcIYQQQohCUSJHCCGEEKJQlMgRQgghhCgUJXKEEEIIIQr1/wEadiB+Xa9legAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 720x1296 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = pyplot.subplots(4, 1, figsize=(10,18))\n",
    "fig.tight_layout(pad=5)\n",
    "\n",
    "# -- Revenue --\n",
    "# Creates violin plot.\n",
    "sns.violinplot(x=\"year\", y=\"log revenue\", ax=axes[0], data=moviesDF)\n",
    "# Sets plot labels.\n",
    "axes[0].set_xlabel(\"Year\")\n",
    "axes[0].set_ylabel(\"Log Revenue\")\n",
    "axes[0].set_title(\"Year vs. Log Revenue\")\n",
    "\n",
    "# -- Budget --\n",
    "# Creates violin plot.\n",
    "sns.violinplot(x=\"year\", y=\"log budget\", ax=axes[1], data=moviesDF)\n",
    "# Sets plot labels.\n",
    "axes[1].set_xlabel(\"Year\")\n",
    "axes[1].set_ylabel(\"Log Budget\")\n",
    "axes[1].set_title(\"Year vs. Log Budget\")\n",
    "\n",
    "# -- Popularity --\n",
    "# Creates violin plot.\n",
    "sns.violinplot(x=\"year\", y=\"log popularity\", ax=axes[2], data=moviesDF)\n",
    "# Sets plot labels.\n",
    "axes[2].set_xlabel(\"Year\")\n",
    "axes[2].set_ylabel(\"Log Popularity\")\n",
    "axes[2].set_title(\"Year vs. Log Popularity\")\n",
    "\n",
    "# -- Average Vote Rating --\n",
    "# Creates violin plot.\n",
    "sns.violinplot(x=\"year\", y=\"vote_average\", ax=axes[3], data=moviesDF)\n",
    "# Sets plot labels.\n",
    "axes[3].set_xlabel(\"Year\")\n",
    "axes[3].set_ylabel(\"Average Vote Rating\")\n",
    "axes[3].set_title(\"Year vs. Average Vote Rating\")\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the plots, there are not any noticeable changes over the years for the movie budget and the revenue. However, the movies' popularity has increased from 2020 to 2021. Also, we see that the ratings for the movies have increased slightly over the years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> <h2> Relating Movie Data to COVID-19 Data </h2> </center>\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "<center> <h3> Covid Dataset </h3> </center>\n",
    "\n",
    "Before we can analyze the COVID-19 data with respect to the movie data, we have to fetch the COVID-19 data and then integrate it into the movies data frame.\n",
    "\n",
    "We will use the [Covid-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University](https://github.com/CSSEGISandData/COVID-19) which contains Covid case numbers and deaths around the world between 2019 and the present day (as of writing this, that would be December 2021). \n",
    "\n",
    "The parameters of interest to us include cumulative cases (or total number of covid cases since that day), daily new cases (number of cases found on that day), two week cases, cumulative deaths, and daily new deaths. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> <h3> Fetching Data </h3> </center>\n",
    "\n",
    "Here, we are simply fetching data from our source. In order to make advanced analyses later, we will also calculate the daily new cases, daily new deaths, and 14-day case increase for each date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "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>cumulative_cases</th>\n",
       "      <th>daily_new_cases</th>\n",
       "      <th>two_week_cases</th>\n",
       "      <th>cumulative_deaths</th>\n",
       "      <th>daily_new_deaths</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</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>1/22/20</th>\n",
       "      <td>557</td>\n",
       "      <td>557</td>\n",
       "      <td>557</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/23/20</th>\n",
       "      <td>655</td>\n",
       "      <td>98</td>\n",
       "      <td>655</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/24/20</th>\n",
       "      <td>941</td>\n",
       "      <td>286</td>\n",
       "      <td>941</td>\n",
       "      <td>26</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/25/20</th>\n",
       "      <td>1434</td>\n",
       "      <td>493</td>\n",
       "      <td>1434</td>\n",
       "      <td>42</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/26/20</th>\n",
       "      <td>2118</td>\n",
       "      <td>684</td>\n",
       "      <td>2118</td>\n",
       "      <td>56</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         cumulative_cases  daily_new_cases  two_week_cases  cumulative_deaths  \\\n",
       "index                                                                           \n",
       "1/22/20               557              557             557                 17   \n",
       "1/23/20               655               98             655                 18   \n",
       "1/24/20               941              286             941                 26   \n",
       "1/25/20              1434              493            1434                 42   \n",
       "1/26/20              2118              684            2118                 56   \n",
       "\n",
       "         daily_new_deaths  \n",
       "index                      \n",
       "1/22/20                17  \n",
       "1/23/20                 1  \n",
       "1/24/20                 8  \n",
       "1/25/20                16  \n",
       "1/26/20                14  "
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reads covid19 worldwide cases csv file\n",
    "cases_COVID = pd.read_csv(\"COVID-19/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\")\n",
    "deaths_COVID = pd.read_csv(\"COVID-19/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv\")\n",
    "\n",
    "# Aggregating data as we will focus on just aggregate information by date\n",
    "cases_COVID.drop(columns=['Province/State','Country/Region', 'Lat', 'Long'], inplace=True)\n",
    "deaths_COVID.drop(columns=['Province/State','Country/Region', 'Lat', 'Long'], inplace=True)\n",
    "\n",
    "cumulative_cases = cases_COVID.sum(axis=0)\n",
    "cumulative_deaths = deaths_COVID.sum(axis=0)\n",
    "\n",
    "# Calculate daily new cases & deaths\n",
    "daily_new_cases = cumulative_cases.copy()\n",
    "daily_new_deaths = cumulative_deaths.copy()\n",
    "two_week_cases = cumulative_cases.copy()\n",
    "\n",
    "covid_DF = pd.concat([cumulative_cases, daily_new_cases, two_week_cases, cumulative_deaths, daily_new_deaths], axis=1)\n",
    "covid_DF.columns = ['cumulative_cases', 'daily_new_cases', 'two_week_cases', 'cumulative_deaths', 'daily_new_deaths']\n",
    "\n",
    "# Reset index for calculations\n",
    "covid_DF.reset_index(inplace=True)\n",
    "\n",
    "# Daily Cases/Deaths\n",
    "for i in range(1, len(covid_DF)):\n",
    "    covid_DF.loc[i,'daily_new_cases'] = covid_DF.loc[i,'cumulative_cases'] - covid_DF.loc[i-1,'cumulative_cases']\n",
    "    covid_DF.loc[i,'daily_new_deaths'] = covid_DF.loc[i,'cumulative_deaths'] - covid_DF.loc[i-1,'cumulative_deaths']\n",
    "covid_DF.loc[0,'daily_new_cases'] = covid_DF.loc[0,'cumulative_cases']\n",
    "covid_DF.loc[0,'daily_new_deaths'] = covid_DF.loc[0,'cumulative_deaths']\n",
    "\n",
    "# Two Week Cases\n",
    "running_sum = 0\n",
    "for i in range(0, 14):\n",
    "    running_sum = running_sum + covid_DF.loc[i,'daily_new_cases']\n",
    "    covid_DF.loc[i, 'two_week_cases'] = running_sum\n",
    "for i in range(14, len(covid_DF)):\n",
    "    running_sum = running_sum - covid_DF.loc[i-14, 'daily_new_cases'] + covid_DF.loc[i,'daily_new_cases']\n",
    "    covid_DF.loc[i, 'two_week_cases'] = running_sum\n",
    "\n",
    "covid_DF.set_index('index', inplace=True)\n",
    "covid_DF.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> <h3> Combining COVID-19 and Movie Data </h3> </center>\n",
    "\n",
    "Here, we will extract relevant information from the COVID-19 table and add it to the movie data frame. We simply append the rows from the COVID dataframe based on matching release date, and then add a 'during_covid' indicator variable for whether or not the movie was released during the pandemic or not. This will allow us to analyze this data more easily moving forward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "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>title</th>\n",
       "      <th>release_date</th>\n",
       "      <th>popularity</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>revenue</th>\n",
       "      <th>budget</th>\n",
       "      <th>datetime</th>\n",
       "      <th>log popularity</th>\n",
       "      <th>log revenue</th>\n",
       "      <th>log budget</th>\n",
       "      <th>year</th>\n",
       "      <th>cumulative_cases</th>\n",
       "      <th>daily_new_cases</th>\n",
       "      <th>cumulative_deaths</th>\n",
       "      <th>daily_new_deaths</th>\n",
       "      <th>two_week_cases</th>\n",
       "      <th>during_covid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Insidious: The Last Key</td>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>34.260</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2125</td>\n",
       "      <td>167184112</td>\n",
       "      <td>10000000</td>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>3.533978</td>\n",
       "      <td>18.934606</td>\n",
       "      <td>16.118096</td>\n",
       "      <td>2018</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>Batman: Gotham by Gaslight</td>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>18.401</td>\n",
       "      <td>6.7</td>\n",
       "      <td>509</td>\n",
       "      <td>3469151</td>\n",
       "      <td>1000000</td>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>2.912405</td>\n",
       "      <td>15.059420</td>\n",
       "      <td>13.815511</td>\n",
       "      <td>2018</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>Proud Mary</td>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>12.737</td>\n",
       "      <td>5.4</td>\n",
       "      <td>407</td>\n",
       "      <td>21753365</td>\n",
       "      <td>14000000</td>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>2.544511</td>\n",
       "      <td>16.895279</td>\n",
       "      <td>16.454568</td>\n",
       "      <td>2018</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>The Commuter</td>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>27.599</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3554</td>\n",
       "      <td>119942387</td>\n",
       "      <td>30000000</td>\n",
       "      <td>2018-01-12</td>\n",
       "      <td>3.317780</td>\n",
       "      <td>18.602522</td>\n",
       "      <td>17.216708</td>\n",
       "      <td>2018</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>The Vegetable</td>\n",
       "      <td>2018-01-18</td>\n",
       "      <td>6.246</td>\n",
       "      <td>5.8</td>\n",
       "      <td>236</td>\n",
       "      <td>8475827</td>\n",
       "      <td>8166084</td>\n",
       "      <td>2018-01-18</td>\n",
       "      <td>1.831941</td>\n",
       "      <td>15.952729</td>\n",
       "      <td>15.915500</td>\n",
       "      <td>2018</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          title release_date  popularity  vote_average  \\\n",
       "152     Insidious: The Last Key   2018-01-05      34.260           6.2   \n",
       "484  Batman: Gotham by Gaslight   2018-01-12      18.401           6.7   \n",
       "293                  Proud Mary   2018-01-12      12.737           5.4   \n",
       "120                The Commuter   2018-01-12      27.599           6.3   \n",
       "582               The Vegetable   2018-01-18       6.246           5.8   \n",
       "\n",
       "     vote_count    revenue    budget   datetime  log popularity  log revenue  \\\n",
       "152        2125  167184112  10000000 2018-01-05        3.533978    18.934606   \n",
       "484         509    3469151   1000000 2018-01-12        2.912405    15.059420   \n",
       "293         407   21753365  14000000 2018-01-12        2.544511    16.895279   \n",
       "120        3554  119942387  30000000 2018-01-12        3.317780    18.602522   \n",
       "582         236    8475827   8166084 2018-01-18        1.831941    15.952729   \n",
       "\n",
       "     log budget  year  cumulative_cases  daily_new_cases  cumulative_deaths  \\\n",
       "152   16.118096  2018                 0                0                  0   \n",
       "484   13.815511  2018                 0                0                  0   \n",
       "293   16.454568  2018                 0                0                  0   \n",
       "120   17.216708  2018                 0                0                  0   \n",
       "582   15.915500  2018                 0                0                  0   \n",
       "\n",
       "     daily_new_deaths  two_week_cases  during_covid  \n",
       "152                 0               0         False  \n",
       "484                 0               0         False  \n",
       "293                 0               0         False  \n",
       "120                 0               0         False  \n",
       "582                 0               0         False  "
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moviesDF.loc[:,'cumulative_cases'] = [0] * len(moviesDF)\n",
    "moviesDF.loc[:,'daily_new_cases'] = [0] * len(moviesDF)\n",
    "moviesDF.loc[:,'cumulative_deaths'] = [0] * len(moviesDF)\n",
    "moviesDF.loc[:,'daily_new_deaths'] = [0] * len(moviesDF)\n",
    "moviesDF.loc[:,'two_week_cases'] = [0] * len(moviesDF)\n",
    "moviesDF.loc[:,'during_covid'] = [False] * len(moviesDF)\n",
    "\n",
    "for i, row in moviesDF.iterrows():\n",
    "    curr_date = row['release_date'].split('-')\n",
    "    curr_date[0] = curr_date[0][len(curr_date[0])//2:]\n",
    "    date = str(int(curr_date[1])) + '/' + curr_date[2] + '/' + curr_date[0]\n",
    "    try:\n",
    "        covid_row = covid_DF.loc[date]\n",
    "        moviesDF.loc[i, 'cumulative_cases'] = covid_row.loc['cumulative_cases']\n",
    "        moviesDF.loc[i, 'daily_new_cases'] = covid_row.loc['daily_new_cases']\n",
    "        moviesDF.loc[i, 'cumulative_deaths'] = covid_row.loc['cumulative_deaths']\n",
    "        moviesDF.loc[i,'two_week_cases'] = covid_row.loc['two_week_cases']\n",
    "        moviesDF.loc[i, 'daily_new_deaths'] = covid_row.loc['daily_new_deaths']\n",
    "        moviesDF.loc[i, 'during_covid'] = True\n",
    "    except KeyError:\n",
    "        pass\n",
    "\n",
    "moviesDF.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> <h3> Popularity v Cumulative Cases </h3> </center>\n",
    "\n",
    "To start, we will do a least squares regression to ensure that there is a relationship between population and cumulative cases. This is important as it serves as a basis for our further comparative analysis between the movie industry and COVID.\n",
    "\n",
    "To do this, we will create a least squares regression and then perform a t-test to test the significance of cumulative cases in predicting the popularity of a movie. We will test $H_0: \\beta_1 = 0$ and $H_a: \\beta_1 \\neq 0$, using a significance level of $\\alpha = 0.05$.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>popularity</td>    <th>  R-squared:         </th> <td>   0.047</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   21.84</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 20 Dec 2021</td> <th>  Prob (F-statistic):</th> <td>3.92e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:09:07</td>     <th>  Log-Likelihood:    </th> <td> -3431.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   449</td>      <th>  AIC:               </th> <td>   6867.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   447</td>      <th>  BIC:               </th> <td>   6875.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>        <td>   84.8673</td> <td>   25.585</td> <td>    3.317</td> <td> 0.001</td> <td>   34.585</td> <td>  135.149</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cumulative_cases</th> <td> 1.877e-06</td> <td> 4.02e-07</td> <td>    4.674</td> <td> 0.000</td> <td> 1.09e-06</td> <td> 2.67e-06</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>835.123</td> <th>  Durbin-Watson:     </th>  <td>   1.944</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>562099.474</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>11.964</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>174.677</td> <th>  Cond. No.          </th>  <td>6.83e+07</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 6.83e+07. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             popularity   R-squared:                       0.047\n",
       "Model:                            OLS   Adj. R-squared:                  0.044\n",
       "Method:                 Least Squares   F-statistic:                     21.84\n",
       "Date:                Mon, 20 Dec 2021   Prob (F-statistic):           3.92e-06\n",
       "Time:                        13:09:07   Log-Likelihood:                -3431.3\n",
       "No. Observations:                 449   AIC:                             6867.\n",
       "Df Residuals:                     447   BIC:                             6875.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "Intercept           84.8673     25.585      3.317      0.001      34.585     135.149\n",
       "cumulative_cases  1.877e-06   4.02e-07      4.674      0.000    1.09e-06    2.67e-06\n",
       "==============================================================================\n",
       "Omnibus:                      835.123   Durbin-Watson:                   1.944\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           562099.474\n",
       "Skew:                          11.964   Prob(JB):                         0.00\n",
       "Kurtosis:                     174.677   Cond. No.                     6.83e+07\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 6.83e+07. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get model for population by case\n",
    "pop_by_case = smf.ols('popularity~cumulative_cases', data=moviesDF).fit()\n",
    "pop_by_case.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> <h2> Hypthosis Testing </h2> </center>\n",
    "\n",
    "Our results here are quite interesting, as they contradict our original hypothesis. Our results show that cases and popularity are actually positively correlated. That is, an increase in cases by 1 is correlated with an increase in popularity of 0.000001877. While this may seem small at first glance, this is quite significant when considering the number of cases of COVID that occurred internationally.\n",
    "\n",
    "Our hypothesis test also resulted in a t-statistic of 4.674 and a corresponding p-value of 0.000, as shown in the above output, further clarifying that cases are significant in explaining the variability of popularity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> <h3> Revenue and Budget Before and After COVID-19 </h3> </center>\n",
    "\n",
    "Of course, popularity is not the only metric by which the success of an industry is determined. So, we will next look at how the relationship between revenue and budget of movies was affected by whether or not the movie was released during the pandemic. That is, we will compare the revenue and budget of movies before and after COVID-19."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KAIQQIlCUAAghRKDMmgA+++wzDBkyBEOGDMHKlSsBlD2fOzw8HCEhIfjkk0/MuXtCCCFVMNtF4JMnT+LEiRPYvXs3OI7DxIkTkZiYiISEBGzatAmNGjXC5MmTkZycXOufH0Nqv1u3biA9/QyKi4vg4uKKLl264fnn9S/2QmxT6qUH2JV8E7kFcni5O+CVIB/0aqd7XQh7cSR1B+7/ngKpgodSJkLjF/rg5V6mea6S2c4AvL29MX/+fMhkMkilUvj4+CAjIwMtWrRAs2bNIJFIEB4ejoMHD5orBCIQt27dQGrqcRQXl02RKy4uQmrqcdy6dcPKkRFTSr30ABsOXEVOgRwMQE6BHBsOXEXqpQfWDs1sjqTuQNZvxyFT8OAAyBQ8sn47jiOpOwz+bHWYLQH4+vpqlv/LyMjAgQMHwHGc5lnmQNkyguULaBBSU+npZ6BWay8wo1arkJ5+xkoREXPYlXwTChWvVaZQ8diVfNNKEZnf/d9TIK4081PMyspNwez3Ady4cQOTJ0/G3LlzIRaLkZGRoXmPMWb0o4vr1nWtcSze3lUvSWiPhNDm8iN/XeVCaD8gjO85t0Cut9xe2y9V8HrLTdFmsyaA33//HTNmzMCHH36IIUOG4PTp08jOzta8n52dXe31S8vl5BTV6GYIb283ZGcXGv1ztkwobXZxcdWZBFxcXAXRfqF8z17uDsjRkQS83B3stv1KmQgyHUlAKRNVu80iEaf3wNlsQ0CZmZmYNm0aEhISMGTIEABAp06dcPv2bdy5cwdqtRqJiYno27evuUIgAtGlSzeIxdrHMmKxBF266F82ktieV4J8IJNod1kyiQivBPlYKSLza/xCH6grDZKoubJyUzDbGcD//vc/yOVyLF++XFMWGRmJ5cuXY/r06ZDL5QgKCkJoaKi5QiACUT7bh2YB2bfy2T5CmgX0cq9ROAKYbRaQza0IRkNA1UdtFgZqszDUtM1WGQIihBBSu1ECIIQQgaIEQAghAkUJgBBCBIoSACGECBQlAEIIEShKAIQQIlCUAAghRKAoARBCiEBRAiCEEIGiBEAIIQJFCYAQQgSKEgAhhAgUJQBCCBEosy8JSQgxj6LcC8i/fxR3lfkQSz3g0TgYrl4drB2WWW3aloy0u2rkqwEPMdC1uRhRrwZZOyyz+jp1I84XXoFSrIZULUZHtzaY0OsNk9RNZwCE2KCi3AvIu5sItTIfAKBW5iPvbiKKci9YOTLz2bQtGScyyjp/AMhXAycy1Ni0Ldm6gZnR16kbkV58CUqJGuAApUSN9OJL+Dp1o0nqpwRAiA3Kv38UjCm1yhhTIv/+UStFZH5pd9VQVloLSsnKyu3V+cIr4EXajeZFDOcLr5ikfkoAhNig8iP/6pbbg3w9/by+cnugFOtunL5yY1ECIMQGiaUeRpXbAw+xceX2QKrW3Th95caiBECIDfJoHAyOk2qVcZwUHo2DrRSR+XVtLoaU0y6TcmXl9qqjWxuIeO1Gi3gOHd3amKR+SgCE2CBXrw6o0zxMc8QvlnqgTvMwu54FFPVqEAKeE2uO+D3EQMBz9j0LaEKvN9DFpR2kKjHAAKlKjC4u7Uw2C4hjjDHDm9UeOTlF4HnjQ/b2dkN2dqEZIqq9qM3CQG0Whpq2WSTiULeuq+73njUoQgghtokSACGECBQlAEIIEShKAIQQUkup1WrUr++O+vXdcfbsWZPXT88CIoSQWuTx4zz4+bV4qrxbt254+LDApPuiMwBCCLGyq1evaI70dXX+AGCOCZt0BkAIIVbw44+7MXHiuCq3MfURf2WUAAghxEIWL16Izz//VO/7AQF9sWtXosXioQRACCFmFBIShD/+SNf7/pw50ZgzJ9qCEf2DEgAhhJhY/fruVb6/adM2DBw4yELR6EcJgBBCTMBQp3/y5O9o1crXQtFUDyUAQgipIUOd/q1bf8PV1c1C0RiPEgAhhBjBUKf/4MFjiES2McPerFEWFRUhLCwMf/31FwDgxIkTGDp0KMLCwjB37lwoFApz7p4QQp4Zz/OaOfr6Ov+HDws0/2yl8wfMeAZw7tw5xMTEICMjQ1O2YMECfP311/Dx8cGMGTOwd+9ejBo1ylwhCNahQ4eReeEwZKwECs4ZjTr0x4AB/a0dlln9suMb3Cv554CimbMM/Ua9acWIzO+bnStxwS0PcrEaDmoxOhTWwZsRc60dllkdStqBrD/TIJEXQ+XgggatumLAQNP3IYWFBfDxaVrlNuaeo1/u+sn9OPfnbZQyCZw4FTq1agm/3oNNUrfZUtX27dsRGxuL+vXra8rUajWKioqgVqshl8vh4OBgrt0L1qFDh5F9/ic4oAQcBzigBNnnf8KhQ4etHZrZaDp/jtP8u1eiwC87vrF2aGbzzc6VSPPIgVyiBjhALlEjzSMH3+xcae3QzOZQ0g48upIKqbwYHACpvBiPrqTiUNIOk9R/48Z1zVG+vs6/4pG+JVw/uR9nbtxBKaQAx6EUUpy5cQfXT+43Sf1mSwDx8fF48cUXtco++ugjREVFITAwEHl5eQgNDTXX7gUr88JhiDntBaPFnBqZF+w3AWg6/4r+PwnYqwtueeBF2o8G4EUMF9zyrBSR+WX9mQYRr9IqE/EqZP2ZVuM6Dxz4SdPp9+nzos5tLN3pV3Tuz9tQc9pLXqo5Mc79edsk9VvsInB2djYSEhKQmJiIpk2bYtmyZVi2bBliY2ONqkffyjbV4e1de6/Gm4qMlQCc7nIhtL8ye22zXKzWW26vbZbIi/WWG9PmRYsWYenSpXrf79mzJ1JTU42OzxxKmUTn33Mpk5jke7ZYAjh79iz8/PzQvHlzAMDo0aMxc+ZMo+uhJSGrpuCc4YASneVCaH9l9tpmB7W4bPhHR7m9tlnl4AKpjiSgcnAx2OahQ0Px228n9b4/Y8ZsxMR8pHldWz5DJ05VNvyjo7y6MdaKJSH9/Pxw/vx5PHr0CABw5MgRdOhgvwtYW0ujDv2hZpVOGZkYjTrY70XgZs4yoPKTEhkrK7dTHQrrQMRrHxqKeA4dCutYKSLza9CqK3iR9jErL5KgQauuOrevOHNHV+f/v/9t0gztVOz8a5NOrVpCzCoN6TI1OrVqaZL6LZYAfHx88N577+GNN95AeHg4Ll68iLlz7XvGgjUMGNAf3h2HQA5nMAbI4QzvjkPsehZQv1Fv/pME/v+fvc8CejNiLrrm14WDSgwwwEElRtf8unY9C2jAwFGo16YXlA4uYACUDi6o16aX1iwgQ9M1T5w4o+n0w8OHWSjymvPrPRjdfFvACUqAMThBiW6+LUw2C4hj5njItBnREFD1UZuFQehtNnRj1p9/3oO7u4clwjKrmn7PVQ0B0Z3AhBCbw1We9VWJLd2Na02UAAghtR7P82jY0LPKbawxTdPWUQIghNRKWVkP0KGDX5XbUKf/bOgciRBSaxw6dFBzEVdf5//wYQEYY9T5mwCdARBCrOqDD2Zi48avq9yGOnvzoARACLE4QzN3AOr0LYESACHEIqjTr30oARBCzMZQpx8Y+BJ27vzRQtGQyigBEEJMylCnv2rVWowdO85C0ZCqUAIghDwzQ51+SspZ+PpWPaWTWB4lAEJIjRjq9P/66xFkMvt9IJ89oARACKkWxhgaNKj6mTp0Ede2UAIghOiVk5ODNm2qfvQwdfq2ixIAIUTL8ePJiIgIr3Ib6vTtAyUAO7Rv3wEU3kiGiJWA55zh5huE8PBB1g7LrPZ/vQmP8y5ChFLwcIJnnfYYPCHK2mGZ1dF136DOpXQ4FxegxMUdee26IHhSzdZAiI1dgP/8Z22V29SGTj/79E6UKC7gjjMHlDA4yzrAu3uEtcMyq6LcC8i/fxR3lfkQSz3g0TgYrl6mWUyL1gOwM/v2HUDx9Z/BVVgYnjExXPxC7DYJ7P96Ewry0p5qs3udrnabBI6u+wYN01IgUf2zSLpKIsGDrn2qnQRatmyM4uKiKrepDZ1+uezTO1GCC+Ck/zzCjCl5OMN+k0BR7gXk3U0EY0pNGcdJUad5WLWTQK1YEpJYRuGNZK2OEAA4To3CG8lWisj8Hudd1Nnmx3kXrRSR+dW5lK7V+QOARKVCnUvpVf5cxRWz9HX+5Stm1abOHwBKFNqdPwBwUhFKFBesFJH55d8/qtX5AwBjSuTfP2qS+qs9BFRQUAB3d8O3chPrErESQMdaGSL29ELx9kKEUqPK7YFzse7OWVe5oemaL7zwIg4cME2HYlbOehaB0VduB9TKfKPKjWXwDODWrVsYPHgwhgwZgqysLAwaNAg3b940yc6J6fGcs1Hl9oCHk1Hl9qDERXenXl5uaG3c+PgVmqN8m+j8AaBEz9CvvnI7IJbqnnarr9xYBhNAXFwcFixYgLp166JBgwYYO3YsFi1aZJKdE9Nz8w0CY2KtMsbEcPMNslJE5udZp73ONnvWaW+liMwvr10XqCTaJ/ABP+7CwK3f6u30f/nlpKbTf/vtqZYI06ScZR3AlLxWGVPycJaZ5oJobeTROBgcJ9Uq4zgpPBoHm6R+gwng8ePH6NOnj+b166+/jqKiqi8cEesJDx8EF78QqOEMxgA1nO36AjAADJ4QBfc6XaFmTmVtZk52fQEYAIInvYkHXfsg4Mddmn+6ZGQ80HT67drZdkL07h4BZ3QAK+bBGAMrtu8LwADg6tUBdZqHaY74xVIPoy4AG1KtawByuVyzCHN2djZ4njfwE8Sayjr7QYKZ+QRA09nbe5uFfjduWWcfYfffc0WuXh3g6tXBLG02mABee+01vPXWW8jJycHHH3+Mn376CRMnTjRpEIQQ/ehuXGIuBhPAyJEj0bx5cyQnJ0OlUmHp0qVaQ0KEENM7evQwIiNfqXKb8k5fSEfDxLSqNQTUvXt3dO/e3dyxECJos2dPx+bNG6rcho70iSkZTABdunTRjP9XlJaWZpaACBESWiaRWJPBBJCYmKj5v0KhwE8//QQnJ/udX02IuVGnT2oLgwmgSZMmWq/fffddjBo1Cm+99ZbZgiLE3lCnT2ojo58FdPPmTeTk5JgjFkLsiqG7cd9+e0qtfe4OEQajrgEwxqBUKjFnzhyzB0aILTJ0pL9rVyICAvpaKBpCqmbUNQCO4+Du7g5XV92PFiVEiAx1+jdu3IWHh6dlgiHECNW6BnD69Gnk5+ej4tIBISEhZg2MkNrMUKdPQzrEFhhMADExMTh27BhatGihKeM4jhIAEZSCgny0atWsym2o0ye2xmACSE1Nxf79+2nYhwhOamoKhg2r+iF61OkTW2YwATRq1Ig6fyIYCxfOx5dfflHlNtTpE3thMAF07doVs2bNQr9+/eDo6Kgpr84QUFFRESIjI/Hf//4XTZs2RXp6OpYtW4bi4mK0bt0ay5cvh0wme7YWEPKMGjTwgKGlsanTJ/bIYAJITy9bY3THjh2asupcAzh37hxiYmKQkZEBoCwZTJ8+HevXr4e/vz9mz56NH374Aa+99tozhG/YiV/O4dLvj8CrxBBJ1Gj3Qj0E9Otk1n1a2+ktX0F65jyciwpR4uoGZbeO6P7629YOy6zSv0xCA2k95InEKOXVyFI+QpfJA/Vubw83Zh3ZvxWtH6SiQFWAAok7rjXshZcHj7F2WGZ16c5piHKP4w4rRgnnAt4rEO1a0HPKaopjhg59amjBggUYMWIE5s6di40bN+LSpUvYt28fPvvsMwBAbm4u1Go1vL29jao3J6cIPF+9kE/8cg4XTucCrML9bhyPDt297DYJnN7yFVyPn9JaMFwlkaAosIfdJoH0L5PQTNYAEtE/z6xS8Qz3FFlaScAeOv1yR/ZvRZe/j0DK/vmelZwE6U1ettskcOnOaTjmHIKUU2vKlEyMJ3UHCCIJ1PSpryIRh7p1dQ/jG7wTODs7G5MmTcLAgQORk5ODt956C9nZ2QZ3Gh8fjxdffFHz+s6dO3B2dsasWbMwbNgwrF271uyLzF/6/ZF25w8ATFRWbqekZ85rdf4AIFGpID1z3koRmV8DaT2tzh8AJCIODaT1DN6NO2bMWJu8G7f1g1Stzh8ApEyF1g9SrRSR+Ylyj2t1/gAg5dQQ5R63UkS2z+AQ0OLFi9G/f39s3rwZ7u7u8Pf3x4IFC7Bu3TqjdqRWq3HixAls27YNjRs31tQxffp0o+rRl8l04VViveXe3m5G7ddWOBfpPkJwLiq02zbnibS/Z785gVVuv3//fgwaZNtLZBaodCcrd1WB3X7Pd1gx8PSDieHMiu22zZWZup0GE8Dff/+N0aNH47vvvoNUKsWcOXMQHh5u9I7q1auHTp06oVmzsrnUgwYNwubNm42ux5ghIJFEDV71dBNFErXdLqBR4uoGFx1JoMTVfhcNKeXV6Dy/X5XbXL58C/Xq1dO8tvXPokDiDg8dSaBA4m7zbdOnhHOBC4p1lttrmyuyyhAQx3FaawAXFRXVaE3ggIAAXLp0CZmZmQCAX375Be3atTO6HmO0e6EewFWKlePLyu2UsltHqCTaSU8lkUDZraOVIjKf8qEdfZ1/0tIdmqGdip2/PbjWsBeUnPb3rOQkuNawl5UiMj/eKxBKpn22p2Ri8F5Vn/ER/QyeAYSEhOCDDz5AYWEhvv/+e+zYsaNGp8+NGjXCkiVLMGXKFMjlcrRp0wbz5s2rUdDVVXahV1izgLq//jZOA3Y5C6i0tBQtWjSocptrK49VaxaQrXt58Bgc2V92LcBdILOA2rXojksAFLnH4Vw+C6guzQJ6FtWaBbRnzx78+uuv4HkeAQEBGDVqlM5VwizBmCGgioS4bqo9tPnq1Svo27dHldtUvHhrD202FrVZGMwxBGTwDOD7779HWFgYhg8fbvSOCamJTZu+xfvvz6hyG1uasUNIbWUwAZw6dQqrV69GcHAwRo8ejc6dO1sgLCI0o0cPx6+/Hq1yG+r0CTEtgwngk08+QX5+PhITExEXF4cnT55g1KhRGDdunCXiI3bMnm7MIsQWVWtJSA8PD7z66quYPHkynJ2d8dVXX5k7LmKnDN2Y1ahRY5u8MYsQW2TwDODy5cvYuXMnDh48iLZt22LixIkIDg62RGzEThg60p858wN8+OEiC0VDCClnMAG88847GDlyJHbs2IHGjRtbIiZiBwx1+rt3/4Q+fWj+NiHWZDABHD16FAqFAnfu3AFjDE+ePIGTk5MlYiM2xlCnf+XKbdStW9dC0RBCDDF4DeDChQvo378/Jk+ejKysLLz00ktIS0uzRGzEBhga08/KyteM51PnT0jtYjABrFixAt9++y08PT3RsGFDrFy5EvHx8ZaIjdRCcrncYKdf8SKutW4YJIQYZjABPHnyBK1atdK8DgoKglqtruIniL25detPTYffrJnu9Rto5g4htsfgNQCJRIL8/HzNkdytW7fMHhSxvu3bt+LddydXuQ119oTYNoMJYOrUqRg7diwePXqE2bNnIyUlBUuWLLFEbMTC3nxzLH766ccqt6FOnxD7YTAB9OvXD88//zxSUlLA8zymTZsGHx8fS8RGLIDuxiVEuKpMACUlJZDJZGjRogVatGgBoOyawCeffIJZs2ZZJEBieoY6fTc3d9y8+ZeFoiGEWIveBLBnzx4sXLgQzs7O2LBhA/z9/XH48GHExcXB0dHRZhKA/FYunqQ9wONiJTgXKRy7NoTD817WDsviDHX6kyZNRVzcCgtFY3qnEtejTr0HuCPjIVeIkPeoIXqETbR2WGZ19vJ9HM4vQoEYcFcD/T1c8WJbulmTVJ/e9QBCQ0ORkJCAu3fv4siRI2jYsCG2bNmCKVOmYMKECZDJZJaOFYBx6wHIb+Wi9ORfgLrC9mIOTr2bCiIJGOr0v/9+F4KD+1soGvM5lbge9Rvdh7jCnDY1DzzMbGy3SeDs5fv4sbAIKtE/02wlPMNQN2EkAVoPoPpqtB6AVCpF+/bt0b59e8THx6Nx48bYt2+fZk1fW/Ak7YF25w8AaoYnaQ/sNgEY6vQvXboJb2/dUzltVZ16D7Q6fwAQi8rK7dXh/CKoJNr3WKhEHA7nF+FFK8VEbI/eBCCpsK6sg4MD1q1bhzp16lgkKFNhxUqjym2VoU7/wYPHEImq9eBXm+Qg071Gtb5ye1AgNq6cEF0MzgICyh4HbWudPwBwLlKdnT3nIrVCNKbD8zwaNvSscpuHDwsEc5osV4jg6PB0Zy9X2G/Sc1cDBTr+et3pHk1iBL0JoKCgAIcOHQJjDIWFhfj555+13g8JCTF7cM/KsWtDndcAHLs2tF5QNVRSUoLnnqs6bqFO18x71FDnNYC8R7b3PVdXfw9XndcA+nvoHuslRBe9F4GjoqL0/xDHYePGjWYLqirGLgpfPguI2eAsoMzM++jUyV/v+02aNEV6+mW97wvlDAD4ZxaQA80CsnZYFiGk3+1y5rgIrDcB1FbGJoBytvILc+5cOgYMCNL7/ogREfjyy2+qVZettNmUqM3CQG2uvhrNAiKWs2/fXrz1lv4zrpiYjzBjxmwLRkQIEQJKAFby8ccrsGKF/sdqf/vtdxg8OMyCERFChIYSgAWNG/caDhxI1Pv+kSPH0aFDJwtGRAgRMoMJ4P79+1qvOY6Do6OjTU4LtYb27X3x8GGW3vcvXLiOBg3sd7YKIaT2MpgAxowZg4cPH8LFxQUikQiFhYUQi8WoU6cOPv30U3Tt2tUScdoUQzdm3bmTResqE0KszmAC6N27N3r06IHhw4cDAJKSkpCSkoLIyEjExsZix44d5o7RJhjq9LOy8ml5REJIrWLwVsmrV69qOn8AGDhwIC5evIi2bdtCqbSvRyoYgzFGa+MSQmyawTMAlUqF69evw8/PDwBw/fp18DwPuVwOlUpl9gBrk9LSUrRo0aDKbYR6Ny4hxPYYTAAffPABoqKi4OvrC57ncefOHSQkJGDNmjXo39/2HyVsSFZWFjp08NX7fr169XD5Mq2TTAixPQYTQFBQEJKSknD27FlIJBJ06dIFHh4e6NChA1xd7fO5IxcunMfLLwfofT80dAg2btxqwYgIIcT0DCYAnuexY8cOHDt2DCqVCn369MGUKVPsrvM/cOAnjBs3Ru/78+YtwPvvz7NgRIQQYl4GE8DHH3+Mq1evYty4ceB5Htu2bcPKlSvx4YcfWiI+s/rss0+xZMlCve+vX78BQ4eOsGBEhBBiOQYTwPHjx7Fz505IpWXP0H/ppZcwdOhQm00AkyaNx549u/S+n5T0C7p0ecGCERFCiHUYTACMMU3nDwAymUzrdVWKiooQGRmJ//73v2jatKmmfPPmzUhKSsKmTZtqELLxUlNTMGzYIL3v//HHFTRu3MQisVhCyfENUF9NRiHjAU4EsX8QnAPHWTsss0q99AC7km8it0AOL3cHvBLkg17t6A5rQqpi8D4Af39//Otf/8Ldu3dx7949LFu2TDMltCrnzp3DmDFjkJGRoVX+559/Yt26dTUOuCZ0df63b2dq5ujbXed/5ReA/f8KWYyH+sovKDm+wbqBmVHqpQfYcOAqcgrkYAByCuTYcOAqUi/Z75rAhJiCwQQQGxuL/Px8REZGYtSoUcjJycHChfrHzctt374dsbGxqF+/vqZMoVBg0aJFmDFjxrNFbaTDh48BKFsbt7zTd3FxsWgMlqK+mmxUuT3YlXwTCpX2kpAKFY9dyTetFBEhtsHgEJCrqytWrFhhdMXx8U8/6vjjjz9GRESE1nCQsfQtbFCVl18OhI2te1NjhUzPQuiMh7e3m2WDsZDcArnecnttc2VCaWdF1OZnpzcBhIeHV/mD+/btM2pHKSkpyMzMRHR0NE6dOmXUz1Zk7yuCPTNO9M/wT6Vye22/l7sDcnQkAS93B7ttc0WC+d2ugNpcfTVaEaw6wzzGSExMxI0bNzBs2DCUlJTg0aNHmDlzJlavXm3S/Qid2D+o7BqAjnJ79UqQDzYcuKo1DCSTiPBKkI8VoyKk9tObALp3727SHS1btkzz/1OnTuGzzz6jzt8MnAPHoQT/P+YvkFlA5bN9aBYQIcahFcHskHPgOCBwnKBOk3u1a4he7RoKqs2EPCuzJ4CjR48+VdajRw/06NHD3LsmhBBSBYPTQAkhhNgnSgCEECJQlAAIIUSgKAEQQohAUQIghBCBogRACCECRQmAEEIEihIAIYQIFCUAQggRKEoAhBAiUJQACCFEoCgBEEKIQFECIIQQgbL7x0EX/HYSj3btxPW8XEjqeKHeKxFw79nb2mGZ1fo9e/H7NTHkzAkOXCleaK3GxOHDrB0WMTH5rVw8SXuAx8VKcC5SOHZtCIfnvawdFrEhdn0GUPDbSWRt/Baq3ByAMahyc5C18VsU/HbS2qGZzfo9e/HbVSfImTMADnLmjN+uOmH9nr3WDo2YkPxWLkpP/gVWrAQAsGIlSk/+BfmtXCtHRmyJXSeAR7t2gikUWmVMocCjXTutFJH5/X5NDL7SiR0PCX6/JrZSRMQcnqQ9ANSV1sZWs7JyQqrJrhOAKjfHqHJ7IGdORpUT21R+5F/dckJ0sesEIPGqa1S5PXDgSo0qJ7aJc5EaVU6ILnadAOq9EgFOJtMq42Qy1HslwkoRmd8LrdUQQaVVJoIKL7RWWykiYg6OXRsCYk67UMyVlRNSTXadANx79kaDN8aXHfFzHCReddHgjfF2PQto4vBh6OlfCgeuBACDA1eCnv6lNAvIzjg87wWn3k01R/ycixROvZvSLCBiFI4xxgxvVnvk5BSB540P2dvbDdnZhWaIqPaiNgsDtVkYatpmkYhD3bquut971qAIIYTYJkoAhBAiUJQACCFEoCgBEEKIQFECIIQQgaIEQAghAkUJgBBCBIoSACGECBQlAEIIEShKAIQQIlCUAAghRKAoARBCiEBRAiCEEIGiBEAIIQIlMbxJzRUVFSEyMhL//e9/0bRpU2zbtg2bNm0Cx3Fo3749Fi9eDFmlBVtM7datG0hPP4Pi4iK4uLiiS5dueP55X7Pu09pS1n8F97OpkKl4KCQiFLzYC30mvm3tsMxKceMkFGd2orAoF5yrF2TdIiDztd91HwgxBbOdAZw7dw5jxoxBRkYGAOD27dv43//+h++//x4//vgjeJ7Hd999Z67dAyjr/FNTj6O4uAgAUFxchNTU47h164ZZ92tNKeu/gtepFDioeHAAHFQ8vE6lIGX9V9YOzWwUN05CfvxbsKIcAAysKAfy499CceOktUMjpFYzWwLYvn07YmNjUb9+fQCATCZDbGwsXF1dwXEc/Pz8cP/+fXPtHgCQnn4GarX28ohqtQrp6WfMul9rcj+bCnGl9XLErKzcXinO7ARUCu1ClaKsnBCil9mGgOLj47VeN2nSBE2aNAEA5ObmYsuWLVi2bJnR9epb2UaX8iN/XeXe3m5G79sWyFS83nJ7bXNhUa7OclaUa7dtrkwo7ayI2vzszHoNQJesrCxMnDgRERER6NGjh9E/b8ySkC4urjqTgIuLq90uJ6eQiOCgIwkoJCK7bTPn6vX/wz9Pl9trmyui5RGFweaXhLx58yYiIyMxYsQITJs2zez769KlG8Ri7RwnFkvQpUs3s+/bWgpe7AU1p12m5srK7ZWsWwQgqTSZQCIrKyeE6GWxM4CioiK89dZbmDlzJoYPH26RfZbP9hHSLKA+E99GCiCoWUDls30UZ3aC0SwgQqqNY4xVbzylhoKDg7Fx40YcPnwYCQkJ8PHx0XrvvffeM6o+Y4aAKqJTRmGgNgsDtbn6qhoCMnsCMDVKANVHbRYGarMw2Pw1AEIIIbUHJQBCCBEoSgCEECJQlAAIIUSgKAEQQohAUQIghBCBogRACCECRQmAEEIEihIAIYQIFCUAQggRKEoAhBAiUJQACCFEoCgBEEKIQFl8RTBCzOH0gzT8ePMgHssfw9PBE0N9QtG9YVdrh2VWiT+n4MAf+SjlHeAkkmNQZw+EhfSxdljEhtAZALF5px+k4burO5EnfwwGIE/+GN9d3YnTD9KsHZrZJP6cgj1pxSjlHQFwKOUdsSetGIk/p1g7NGJDKAEQm/fjzYNQ8kqtMiWvxI83D1opIvM78Ec++Eon8DwkOPBHvpUiIraIEgCxeXnyx0aV24NS3sGockJ0oQRAbF4dB0+jyu2Bk0huVDkhulACIDZvqE8opCKpVplUJMVQn1ArRWR+gzp7QASVVpkIKgzq7GGliIgtollAxOaVz/YR0iygstk+NAuIPBtaFN6OUZuFgdosDLQoPCGEEJOhBEAIIQJFCYAQQgSKEgAhhAiUzc0CEok4q/ysraI2CwO1WRhq0uaqfsbmZgERQggxDRoCIoQQgaIEQAghAkUJgBBCBIoSACGECBQlAEIIEShKAIQQIlCUAAghRKAoARBCiEBRAiCEEIGy2wRQVFSEsLAw/PXXX1rlmzdvRlRUlJWiMq/KbY6OjkZISAiGDRuGYcOG4dChQ1aO0PQqtzk9PR2jR4/GkCFDMHv2bCgUCitHaHoV25ycnKz5focNG4aePXti8uTJ1g7R5Cp/zydOnMDQoUMRFhaGuXPn2v33DAC7du3C4MGDER4ejri4OKhUKgM1VAOzQ3/88QcLCwtj7dq1Y/fu3dOU37hxgwUGBrKxY8daMTrz0NXmsLAwlpWVZeXIzKdymwsLC1mfPn3YlStXGGOMzZo1i23ZssXKUZqWvt9txhh7+PAhe/nll9nt27etE5yZ6Gpz37592Z9//skYY2z69Ols+/bt1gzR5Cq3+ebNmywwMFDz9xwbG8u+/vrrZ96PXZ4BbN++HbGxsahfv76mTKFQYNGiRZgxY4YVIzOfym0uLS3F/fv38eGHHyI8PBxr1qwBz/NWjtK0Krc5JSUFnTt3hr+/PwAgJiYGAwYMsGaIJqfrd7vcypUrERkZieeee87ygZmRrjar1WoUFRVBrVZDLpfDwcHBihGaXuU2X7t2DZ07d9a87tevHw4fPvzM+7G5p4FWR3x8/FNlH3/8MSIiItC0aVMrRGR+ldv86NEj9OzZE7GxsXBzc8PkyZPxww8/YPTo0VaK0PQqt/nOnTtwdnbGrFmzcOvWLXTt2hXz58+3UnTmoet3GwAyMjJw+vRpve/bMl1t+uijjxAVFQVXV1c0bdoUoaGhVojMfCq32d/fH8uXL0dmZibq16+PgwcP4tGjR8+8H7s8A6gsJSUFmZmZiIiIsHYoFtOsWTN8/vnnqF+/PpycnBAVFYXk5GRrh2VWarUaJ06cwOzZs7Fr1y6UlpZi3bp11g7LIrZt24bXXnsNMpnM2qGYXXZ2NhISEpCYmIgTJ06gU6dOWLZsmbXDMquWLVvi/fffx9SpU/H666+jdevWkEqlz1yvIBJAYmIibty4gWHDhiEmJgYXL17EzJkzrR2WWV27dg1JSUma14wxSCR2ecKnUa9ePXTq1AnNmjWDWCzGoEGDcP78eWuHZRFHjhzB4MGDrR2GRZw9exZ+fn5o3rw5RCIRRo8ejdOnT1s7LLOSy+Xo2LEj9uzZg++//x4NGjRAs2bNnrleQSSAZcuW4cCBA9i7dy/i4uLQvn17rF692tphmRVjDP/617+Qn58PpVKJbdu22d14eGUBAQG4dOkSMjMzAQC//PIL2rVrZ+WozC83NxdPnjwxSYdgC/z8/HD+/HnNEMiRI0fQoUMHK0dlXiUlJRg/fjyKioqgUCiwefNmkyR8+z4kFDB/f39MmjQJY8aMgUqlQkhICMLCwqwdllk1atQIS5YswZQpUyCXy9GmTRvMmzfP2mGZ3V9//YWGDRtaOwyL8fHxwXvvvYc33ngDYrEYLVq0wJIlS6wdllnVqVMH06ZNw6uvvgqVSoWwsDCEh4c/c720IhghhAiUIIaACCGEPI0SACGECBQlAEIIEShKAIQQIlCUAAghRKAoARC7dOrUKZNOew0ODsbAgQMxbNgwhIeHY8iQIdi2bZvR9Rw8ePCZn0Y7YcIE5ObmPlMdhAB0HwAh1ZaQkKC54SgzMxMDBw5E37590ahRI4vGkZKSYtH9EftFCYDYvcLCQixevBhXr14Fx3EIDAzE7NmzIZFIkJycjISEBIhEIrRp0wYnT57Ed999Z/Chgfn5+XBycoKzszMAoHXr1khNTYWXl9dTrz/99FPs27cPnp6eaNGihaaO3NxcREdH4+7du/D09IS3tzd8fX0xffp03Lx5E/Hx8Xj8+DHUajWioqIwcuRIREdHAwDGjRuHdevWWTz5EPtCCYDYvbi4OHh6emLfvn1QKpWYOnUqvv76a4waNQpz587Fhg0b4O/vj927d2P37t166/nggw/g6OgIuVyOO3fuYNKkSfDw8Khy34cPH8bPP/+MPXv2wNHREdOmTdOKq1WrVvjyyy/x8OFDvPLKK/D19YVKpcKMGTOwcuVKtGvXDoWFhXj11VfRqlUrLFu2DLt27cKGDRs0yYaQmqIEQOzesWPHsHXrVnAcB5lMhsjISGzYsAEtW7aEj4+PZv2AESNGIC4uTm89FYeA7t27h/Hjx8PX17fKaw2pqakYMGAAXF1dAQARERHYtGkTACA5OVmTcOrXr695pHFGRgbu3r2LDz/8UFPPkydPcPnyZXTu3LnmHwQhlVACIHaP53lwHKf1WqVSQSwWo/KTUESi6s2LaNasGYKDg3HmzJmnEkDl5Qkr7kMsFmv+L5FItN4r37darYabmxv27t2ree/Ro0dwc3OrVmyEVBfNAiJ2LyAgAJs3bwZjDAqFAtu3b0fv3r3RtWtXZGRk4OrVqwCApKQkFBQUaCULfUpKSnDmzBl07NgRAODl5YULFy4AKHv8eLm+ffvi4MGDKCgoAM/zWp16UFAQfvjhBwBAXl4eDh8+DI7j0LJlSzg6Omq2zczMRFhYGC5evAigLImYZD1YInh0BkDsXkxMDOLi4hAeHg6lUonAwEBMmTIFMpkMq1atwrx58yASidC+fXtIJBI4OTnprKf8GgDHcSgtLcWgQYM0iwzFxMRgyZIlcHd3R+/eveHt7Q2grJO/du0aIiIi4O7uDn9/f+Tl5QEAoqOjERMTg/DwcHh6eqJx48ZwdHSETCbDF198gfj4eKxfvx4qlQrvvfceXnjhBQBAaGgooqKisHbtWvj5+VngEyT2ip4GSgSrqKgIX3zxBaZPnw4nJydcunQJkydPxvHjx6t1FvCstmzZgrZt26JLly5QKBR47bXXMH36dAQFBZl934QAdAZABMzV1RVSqRQjR46ERCKBRCLB6tWrLdL5A0CrVq2wdOlS8DwPpVKJ0NBQ6vyJRdEZACGECBRdBCaEEIGiBEAIIQJFCYAQQgSKEgAhhAgUJQBCCBEoSgCEECJQ/wf6iM5YucGdngAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "revenueDuringCovid = []\n",
    "revenueBeforeCovid = []\n",
    "budgetDuringCovid = []\n",
    "budgetBeforeCovid = []\n",
    "\n",
    "fig1, ax1 = pyplot.subplots() # revenue vs budget before covid scatter plot\n",
    "fig2, ax2 = pyplot.subplots() # revenue vs budget during covoid scatter plot\n",
    "\n",
    "#Plots revenue and budget data on to either the revenue vs budget before covid scatter plot or revenue vs budget\n",
    "#during covid scatter plot. \n",
    "for i, row in moviesDF.iterrows():\n",
    "    revenue = int(moviesDF.at[i, 'log revenue'])\n",
    "    budget = int(moviesDF.at[i, 'log budget'])\n",
    "    if moviesDF.at[i, 'during_covid']:\n",
    "        revenueDuringCovid.append(revenue)\n",
    "        budgetDuringCovid.append(budget)\n",
    "        ax2.scatter(budget, revenue)\n",
    "    else:\n",
    "        revenueBeforeCovid.append(revenue)\n",
    "        budgetBeforeCovid.append(budget)\n",
    "        ax1.scatter(budget, revenue)\n",
    "\n",
    "ax1.set_title(\"log Budget vs log Revenue Before Covid\")\n",
    "ax1.set_xlabel(\"log Budget\")\n",
    "ax1.set_ylabel(\"log Revenue\")\n",
    "\n",
    "ax2.set_title(\"log Budget vs log Revenue During Covid\")\n",
    "ax2.set_xlabel(\"log Budget\")\n",
    "ax2.set_ylabel(\"log Revenue\")\n",
    "\n",
    "budgetBeforeCovid, budgetDuringCovid = pd.Series(budgetBeforeCovid), pd.Series(budgetDuringCovid)\n",
    "\n",
    "m1, b1 = np.polyfit(budgetBeforeCovid, revenueBeforeCovid, deg=1)\n",
    "#Plot regression line for budget before covid vs revenue before Covid scatter plot\n",
    "ax1.plot(budgetBeforeCovid, m1*budgetBeforeCovid + b1, color=\"black\");\n",
    "\n",
    "m2, b2 = np.polyfit(budgetDuringCovid, revenueDuringCovid, deg=1)\n",
    "#Plot regression line for budget after covid vs revenue after Covid scatter plot\n",
    "ax2.plot(budgetDuringCovid, m2*budgetDuringCovid + b2, color=\"black\");\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot titled, \"Budget vs Revenue Before Covid\" we see a positive correlation between budget and revenue. This means that before Covid appeared, movies with larger budgets resulted in larger revenue. \n",
    "\n",
    "On the other hand, from the plot titled, \"Budget vs Revenue During Covid\" we don't see much of a correlation. Multiple data points are scattered throughout the plot and so it doesn't show a clear relationship between budget and revenue during Covid. This could indicate that Covid disrupted the movie industry and their revenue streams. \n",
    "\n",
    "We also see that budget and revenue is lower for movies during Covid in comparison to movies released before covid. This makes sense as lockdowns forced movie theatres to close down which negatively affects the sales of movies being released at that time. However, we do see some movies who have high revenues and this could possibly be due to the movies being released through movie streaming platforms which exploded in popularity during the pandemic. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> <h3> Movie Popularity and Daily New Cases </h3> </center>\n",
    "\n",
    "Here, we will investigate the relationship between movie popularity and daily new COVID-19 cases during the pandemic. We want to check out if there's a parallel or inverse relationship between popularity and daily new Covid-19 cases. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filters out data points that did not occur during the pandemic.\n",
    "df = moviesDF[moviesDF['during_covid'] == True]\n",
    "\n",
    "x, movie_y, covid_y = df['datetime'], df['popularity'], df['daily_new_cases']\n",
    "\n",
    "fig, ax1 = pyplot.subplots(figsize=(10, 6))\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "# Creates scatter plot.\n",
    "p1 = ax1.plot(x, movie_y, color='red', label='Movie Popularity')\n",
    "p2 = ax2.plot(x, covid_y, label='Daily New Cases')\n",
    "\n",
    "# Creates legend.\n",
    "plot1, label1 = ax1.get_legend_handles_labels()\n",
    "plot2, label2 = ax2.get_legend_handles_labels()\n",
    "ax2.legend(plot1 + plot2, label1 + label2, loc=\"upper left\")\n",
    "\n",
    "# Adjusts plot axes.\n",
    "ax1.xaxis.set_major_formatter(mdates.DateFormatter('%b-%Y'))\n",
    "\n",
    "for label in ax1.get_xticklabels(which='major'):\n",
    "    label.set(rotation=30, horizontalalignment='right')\n",
    "    \n",
    "ax1.set_title(\"Date vs. Movie Popularity and Daily New COVID-19 Cases\")\n",
    "ax1.set_xlabel(\"Date\")\n",
    "\n",
    "ax1.set_ylabel(\"Movie Popularity\")\n",
    "ax2.set_ylabel(\"Daily New COVID-19 Cases\")\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above scatter plot shows Date vs. Popularity and COVID-19 Cases using two y-axes. From the plot, we can see several spikes of daily new cases. The movie popularity seems to match the spikes slightly, but overall the movie popularity has a positive trend over time. The reason why there is not a significant relationship between COVID-19 cases and movie popularity is because the increase in movie popularity during the pandemic through streaming services may have offset the decrease in movie popularity as a result of movie theatres shutting down. Though, because there is a postive trend in the plot, the increase in movie popularity due to streaming services must have been more significant than the decrease in movie popularity due to movie theatres shutting down."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> <h3> Log Standardized Popularity vs. Log Standardized Daily New Cases </h3> </center>\n",
    "\n",
    "Now, we will plot Log Standardized Popuarity vs. Log Standardized Daily New Cases to help us see if there is a relationship between the two variables. We first standardize the variables using the Z-score to make the variables more comparable. Next, we take the log of these variables to make the data less skewed. Because the Z-score can have values <= 0, we will add a constant 'c' before taking the log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extracts appropriate columns.\n",
    "# popularity, daily_new_cases = moviesDF['popularity'], moviesDF['daily_new_cases']\n",
    "\n",
    "daily_new_cases = []\n",
    "popularity = []\n",
    "for i, row in moviesDF.iterrows():\n",
    "    if moviesDF.at[i, 'during_covid'] == True:\n",
    "        daily_new_cases.append(moviesDF.at[i, 'daily_new_cases'])\n",
    "        popularity.append(moviesDF.at[i, 'popularity'])\n",
    "\n",
    "# Standardizes columns using Z-score.\n",
    "standardized_pop = (popularity - statistics.mean(popularity)) / statistics.stdev(popularity)\n",
    "standardized_new_cases = (daily_new_cases - statistics.mean(daily_new_cases)) / statistics.stdev(daily_new_cases)\n",
    "\n",
    "# Takes the log of Z-score.\n",
    "c = 1\n",
    "popularity_log = [np.log(x + c) for x in standardized_pop]\n",
    "cases_log = [np.log(x + c) for x in standardized_new_cases]\n",
    "\n",
    "cases_log_without_nan = []\n",
    "popularity_log_without_nan = []\n",
    "\n",
    "#Removes nan values from both cases log and popularity log\n",
    "for i in range(0, len(cases_log)):\n",
    "    if not math.isnan(cases_log[i]):\n",
    "        cases_log_without_nan.append(cases_log[i])\n",
    "        popularity_log_without_nan.append(popularity_log[i])\n",
    "\n",
    "# Gets x and y variables to plot.\n",
    "x, y = pd.Series(cases_log_without_nan), pd.Series(popularity_log_without_nan)\n",
    "\n",
    "# # Creates scatter plot.\n",
    "fig1, ax1 = pyplot.subplots()\n",
    "ax1.scatter(x, y)\n",
    "\n",
    "# # # Creates linear regression line.\n",
    "m, b = np.polyfit(x, y, deg=1)\n",
    "pyplot.plot(x, m*x + b, color=\"red\")\n",
    "\n",
    "# # Adjusts plot axes.\n",
    "ax1.set_title(\"Log Standardized Daily New Cases vs. Log Standardized Popularity\")\n",
    "ax1.set_xlabel(\"Log Standardized Popularity\")\n",
    "ax1.set_ylabel(\"Log Standardized Daily New Cases\")\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson correlation coefficient: 0.2305347883361427\n"
     ]
    }
   ],
   "source": [
    "# Calculates Pearson correlation coefficient. \n",
    "corr, _ = pearsonr(x, y)\n",
    "print(f\"Pearson correlation coefficient: {corr}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The popularity data in the plot is only for movies during Covid. We had to do this because our data would get really ugly and skewed towards the left as movies released before covid had data with zero covid cases. We also standardized both popularity and daily new cases. Standardization is a useful technique used in statistics to put the the variables onto the same scale or units. You can learn more about the usefulness of standardization here: https://www.sisense.com/glossary/data-standardization/.\n",
    "\n",
    "Looking at the plot, there is a slight positive correlation between popularity and daily new cases, which we also observed in the previous plot. As we stated earlier, the reason why the correlation is positive and fairly weak is because the increase in movie popularity during the pandemic through streaming services which may have offset the decrease in movie popularity as a result of movie theatres shutting down. However, the increase in movie popularity during the pandemic through streaming services may have been more significant, which is why the correlation is positive. The Pearson correlation coefficient backs this weak relationship as it provided a low correlation coefficient of 0.33. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> <h2> Conclusion and Summary </h2> </center>\n",
    "\n",
    "In summary, before looking at Covid data we looked at average movie rating, budget, and popularity and analyzed each of them to see if there was any relationship between it and movie revenue. We used linear regression to look at the relationship between movie budget and movie revenue and at the relationship between movie popularity and movie revenue. For both of these parameters we did find a positive correlation which suggests that there is a relationship. For average movie rating, we used a violin plot to analyze the relationship between it and movie revenue. The relationship presented by the plot wasn't as clear as the linear regression plots. We do see an increase in revenue as average movie rating increases. However, at the same time, the variance increases as average movie rating increases and we see some outliers. \n",
    "\n",
    "We then took a look at the effects Covid-19 had on the movie industry and the results were more or less expected. We first compared the relationship between budget and revenue before and after covid. We found a positive correlation between budget and revenue before Covid but not for one after Covid. This could suggest that the movie industry was disrupted by the pandemic. Throughout most of 2020, countries ordered lockdowns and closed commerical businesses that weren't considered non-essential and this included movie theatres. This is most likely the reason our regression plot for revenue vs budget after Covid had data points that didn't show uniformity like the regression plot for revenue vs budget before Covid. \n",
    "\n",
    "After that, we compared the trend between movie popularity with Covid-19 cases using a line plot. What's interesting here is that movie popularity increased at the beginning of 2021. This could be due to the rising popularity of watching movies on movie streaming services like Netfix, Disney+, Hulu, etc. Since people couldn't go to movie theatres due to lockdowns and/or avoiding crowds of people due to the ongoing pandemic, they chose to subscribe to movie streaming services to watch new movie releases and to stay entertained. There could also be other confounding factors that could be affecting the trend presented in the line plot and this is something we will look at in our future research. \n",
    "\n",
    "Lastly, we looked at the relationship between daily new cases and standardized popularity. We calculated the Pearson correlation coefficient to do this and the coefficient we got was very low (~0.34) which suggests that the relationship was very weak. \n",
    "\n",
    "After all that analysis, we found that the movie industy was in fact impacted by Covid 19. \n",
    "\n",
    "\n",
    "<center> <h2> Future Research </h2> </center>\n",
    "\n",
    "We were very happy at the results we came to in our research and we would like to expand our research to other industries to see how their revenues were affected. We are interested to see if there were other industries that were resillient against the effects of the pandemic and which industries were not so lucky. \n",
    "\n",
    "\n"
   ]
  }
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